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==== Front
J Neurol Sci
J Neurol Sci
Journal of the Neurological Sciences
0022-510X
1878-5883
Elsevier B.V.
S0022-510X(22)00380-X
10.1016/j.jns.2022.120518
120518
Clinical Short Communication
Effect of multiple sclerosis disease-modifying therapies on the real-world effectiveness of two doses of BBIBP-CorV (Sinopharm) vaccine
Etemadifar Masoud a
Abhari Amir Parsa bc
Nouri Hosein bc
Eighani Naghme b
Salari Mehri d
Sedaghat Nahad bc⁎
a Department of Neurosurgery, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
b Alzahra Research Institute, Alzahra University Hospital, Isfahan University of Medical Sciences, Isfahan, Iran
c Network of Immunity in Infection Malignancy and Autoimmunity (NIIMA), Universal Scientific, Education, and Research Network (USERN), Isfahan, Iran
d Functional Neurosurgery Research Center, Shohada Tajrish Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
⁎ Corresponding author at: Alzahra Research Institute, Alzahra University Hospital, Isfahan University of Medical Sciences, Isfahan, Iran.
9 12 2022
15 1 2023
9 12 2022
444 120518120518
16 10 2022
1 12 2022
6 12 2022
© 2022 Elsevier B.V. All rights reserved.
2022
Elsevier B.V.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Background
Immunogenicity data shows blunted responses to COVID-19 vaccination among people with MS (pwMS) on certain disease-modifying therapies (DMTs). Still, it is uncertain how this data translates into the clinic.
Objective
To assess the effect of DMTs and other factors on the effectiveness of inactivated vaccination in pwMS.
Methods
This cohort study was conducted in a period in which Iran experienced two COVID-19 peaks caused by the Delta variant. We used multivariable cox regression to compare COVID-19-free survivals, and an ordinal logistic model to compare COVID-19 severity between vaccinated pwMS on different DMTs.
Results
A total of 617 pwMS were included in the final analysis, with a mean [SD] follow-up of 25.59 weeks [5.48] after their second dose. Laboratory/imaging-confirmed breakthrough COVID-19 occurred in 15/277 (5.41%) of injectable-treated (reference), 10/61 (16.39%) of fingolimod-treated (adjusted hazard ratio (aHR) [95% confidence interval (CI)]: 2.80 [1.24, 6.29]; P = 0.01), 9/128 (7.03%) of other oral-treated (aHR [95%CI]: 1.16 [0.50, 2.68]; P = 0.73), 19/145 (13.10%) of anti-CD20-treated (aHR [95%CI]: 2.11 [1.05, 4.22]; P = 0.04), and 6/56 (10.71%) of non-treated pwMS (aHR [95%CI]: 1.52 [0.57, 4.04]; P = 0.40). Age (adjusted Odds Ratio [aOR] [95%CI]: 1.05 [1.00, 1.10], P = 0.05) number of comorbidities (aOR [95%CI]: 2.05 [1.06, 3.96], P = 0.03), fingolimod therapy (aOR [95%CI]: 10.39 [2.47, 43.62], P < 0.01), and anti-CD20 therapy (aOR [95%CI]: 4.44 [1.49, 13.23], P < 0.01) were independently associated with a more severe COVID-19 course.
Conclusion
The observed results stress the importance of developing personalized vaccination schedules and reservation of COVID-19 treatment resources for older pwMS with comorbidities receiving fingolimod or anti-CD20 therapies.
Keywords
SARS-CoV-2
COVID-19 vaccines
Multiple sclerosis
Disease-modifying therapies
==== Body
pmc1 Introduction
Owing to the development of several effective vaccines, many hope that the coronavirus disease 2019 (COVID-19) pandemic will reach its final days soon; although herd immunity is still far from reach in many countries and the emergence of the vaccine-escape variants of the severe acute respiratory coronavirus 2 (SARS-CoV-2) has again raised several concerns, particularly in people with multiple sclerosis (pwMS), who are still thought to be vulnerable to severe outcomes of COVID-19 [1]. Prioritizing their vaccination has been among the adopted strategies to protect pwMS from severe COVID-19 outcomes, although immunogenicity data indicating antibody and/or cellular response blunts in pwMS on specific disease-modifying therapies (DMTs) i.e., sphingosine 1-phosphate receptor (S1PR) modulators and anti-CD20 therapies (aCD20) [[2], [3], [4], [5], [6], [7], [8], [9]] have added to their concerns and their physicians'. It was still unclear when we conducted this study how the immunogenicity evidence translates into the clinic. While some data pertaining to mRNA vaccines is available in this regard, no data is available on the real-world effectiveness of inactivated vaccines – which were used predominantly in our setting and many other geographical regions – for the pwMS who receive immunomodulatory DMTs. Hence, we aimed to investigate the factors associated with the effectiveness of COVID-19 inactivated vaccination among pwMS on DMTs in reducing COVID-19 incidence and severity.
2 Methods
2.1 Design and setting
Adhering to the Strengthening the reporting of observational studies in epidemiology (STROBE) statement (available from https://www.strobe-statement.org/), we designed and conducted an observational prospective cohort study in Isfahan, Iran, from March until December 2021 – when Iran experienced two COVID-19 peaks with the Delta variant. The pwMS were recruited from the people referring to the Isfahan MS clinic for routine post-vaccination follow-up visits. After obtaining general characteristics, vaccination dates, and telephone numbers, the participants were followed up until receiving their third dose, or 26 December 2021, whichever came first. They were visited at least twice either remotely or in-person during the follow-up period. Furthermore, the participants who received only one vaccine dose upon recruitment were contacted again 40 days after the documented date of their first dose to ensure they completed vaccination and to document the date they received their second dose. Although no limit/goal was set for the sample size, we aimed to maintain a relative balance between the cohorts on each DMT.
2.2 Participants
The eligibility criteria for the pwMS along with their rationales are presented below:1. Definitive diagnosis of MS made by a neurologist based on the McDonald criteria [10] at least a year before the study: to include definitive MS cases; and
2. Receiving two doses of BBIBP-CorV vaccine, either during or before the study period: to include the fully-vaccinated pwMS.
The below exclusion criteria were defined for all of the participants based on the following rationale:1. Contraction of COVID-19 between the first and 14 days after the second vaccine dose: to exclude the COVID-19 cases before complete vaccination;
2. Absence in the subsequent follow-up visit or no response to follow-up calls: as data regarding outcomes could not be gathered from these participants.
It should be emphasized that contraction of COVID-19 prior to the first vaccine dose was not among the exclusion criteria.
2.3 Variables and measurements
The list of study variables, their role – including baseline characteristics, potential confounders, and outcomes – and their source can be interpreted from eTable 1 along with their measurements. Data collection was done by two trained clinical research nurses of the clinic. Participants were stratified into five cohorts based on their DMT: i) Injectable therapies (interferons and glatiramer acetate), ii) Oral therapies except fingolimod (dimethyl fumarate and teriflunomide), iii) Fingolimod (FNG), iv) Infused aCD20, and v) no DMTs. Other DMTs e.g., natalizumab (NTZ), cladribine, and alemtuzumab are of limited use in our setting and were therefore, not considered in our classification. The primary outcome was contraction of laboratory/imaging-confirmed COVID-19, defined as having typical clinical presentations and course of COVID-19 along with either chest high-resolution computer tomography (HRCT) findings typical of COVID-19 and/or positive serological tests or positive result of reverse transcriptase polymerase chain reaction (RT-PCR) test.
Secondary outcomes included:• All possible COVID-19, including the ones described above, along with the ones having typical clinical presentations and course, but confirmed with neither imaging nor laboratory tests;
• Severity of COVID-19, measured by a 4-point ordinal scale (1: having a typical clinical presentation and course but confirmed neither with HRCT nor RT-PCR; 2: having a typical clinical presentation and course confirmed with either HRCT, serological tests, or RT-PCR, but without requirement of hospitalization; 3: requirement of hospitalization/supplementary oxygenation; and 4: ICU admission/death).
To reduce bias and to ensure privacy protection of the participants, the collected data were anonymized after the initial enrollment. To ensure the integrity of the recruitment process and the data collected by our nurses, the participants were deanonymized, reidentified, and visited in the week ending in December 26th and the correctness of the collected measures were confirmed. The participants who could not be reidentified were excluded from analyses. The data was reanonymized after validation.
2.4 Statistical analysis
The normality assumptions regarding all of the variables were tested using the Kolmogorov–Smirnov method. If the normality assumptions were rejected by alpha <0.05, non-parametric statistics, and otherwise, parametric statistics were used for a brief descriptive analysis of the study cohorts. Then, a multivariable Poisson model offset for follow-up was used to estimate the effect of age, sex, number of comorbidities, MS subtype and duration, Expanded Disability Status Scale (EDSS) category, history of COVID-19 before vaccination, and DMTs on the incidence rate of COVID-19 among participants. A multivariable cox regression model was used to assess the effect of the same variables on COVID-19-free survivals. In the cox model, follow-up time variable was considered from the date of second dose, until the date of COVID-19 diagnosis, receiving a third dose, or the last follow-up visit, whichever came first. A multivariable ordinal logistic model was used to assess the effect of mentioned variables on COVID-19 severity. Results were reported as incidence rate ratio (IRR), adjusted hazard ratio (aHR), and adjusted odds ratio (aOR) all along with a 95% confidence interval (CI). Effect measures with P-values below 0.05 were referred to as statistically significant.
In the comparative analyses addressing DMTs, the pwMS on injectable therapies were considered as reference. We also conducted a sensitivity analysis taking the group of pwMS on no DMTs as reference instead of pwMS on injectable DMTs. The IBM SPSS v.23 and GraphPad Prism 9 software for MacOS were used for analyses and graphing.
2.5 Approval and data availability
All of the participants were enrolled in this study consensually. This study was conducted in accordance with the World Medical Association Declaration of Helsinki and was approved by the ethics committee of the Shahid Beheshti University of Medical Sciences. Anonymized data will be shared with any qualified investigator upon reasonable request.
3 Results
3.1 Descriptive data
A total of 617 participants (mean age [SD]: 40.06 [9.96]; number [%] of females: 465 [75.36]) with a mean follow up duration (SD) of 25.59 (5.48) weeks were included in the final analysis (eFig. 1); the descriptive characteristics of the cohort can be interpreted in Table 1 . None of the participants reported switching DMTs in the prior two years. The cumulative incidence of all possible COVID-19 cases in each DMT cohort could be interpreted from Fig. 1 . One participant on aCD20 contracted PCR-confirmed COVID-19 two times in our follow-up period with complete resolution of symptoms in between. One pwMS on FNG died of COVID-19-associated pneumonia. She was a 46-year-old woman with a comorbid pulmonary condition, was living with relapsing-remitting MS for over 12 years, and her EDSS score in her last visit was 2. All other infected participants managed to recover without complications; six of whom required hospitalization but were not admitted to ICU (Table 1). One participant on NTZ who also contracted COVID-19 was excluded from analysis as no other participants on NTZ were included.Table 1 Descriptive characteristics of the pwMS included in analyses.
Table 1Variable Whole cohort (n = 617) No COVID-19 (n = 503) Suspected COVID-19 (n = 55) Laboratory/imaging-confirmed COVID-19 without hospitalization (n = 52) Hospitalized/required supplementary oxygenation (n = 7) P value
Mean age (SD) 40.06 (9.96) 40.02 (10.03) 38.13 (8.56) 42.31 (10.83) 41.86 (4.10) 0.27*
Sex (female:male) 465:152 374:129 45:10 40:12 6:1 0.72**
Number of comorbidities (n, %) None: 413 (66.94)
One: 153 (24.80)
Two: 47 (7.62)
Three: 4 (0.65) None: 336 (66.80)
One: 124 (24.65)
Two: 39 (7.75)
Three: 4 (0.79) None: 40 (72.73)
One: 12 (21.82)
Two: 3 (5.45) None: 35 (67.31)
One: 12 (23.08)
Two: 5 (9.62) None: 2 (28.57)
One: 5 (71.43) 0.54**
Prior COVID-19 (n, %) 181 (29.34) 160 (31.81) 12 (21.82) 8 (15.38) 1 (14.29) 0.07**
Subtype of MS (n, %) RR: 525 (85.09)
SP: 54 (8.75)
PP: 38 (6.16) RR: 427 (84.89)
SP: 43 (8.55)
PP: 33 (6.56) RR: 47 (85.45)
SP: 6 (10.91)
PP: 2 (3.64) RR: 44 (84.62)
SP: 5 (9.62)
PP: 3 (5.77) RR: 7 (100) 0.97**
Median duration of MS (range) [years] 9 (34) 10 (34) 7 (29) 8 (24) 10.5 (21) 0.24***
EDSS category (n, %) 0–3.5: 557 (90.27)
4–6: 45 (7.29)
>6: 15 (2.43) 0–3.5: 454 (90.26)
4–6: 38 (7.55)
>6: 11 (2.19) 0–3.5: 52 (94.55)
4–6: 1 (1.82)
>6: 2 (3.64) 0–3.5: 45 (86.54)
4–6: 5 (9.62)
>6: 2 (3.85) 0–3.5: 6 (85.71)
4–6: 1 (14.29) 0.25**
DMT (n, %) 0.15**
Injectable IFN: 147 (23.82)
GA: 80 (12.97) IFN: 122 (24.25)
GA: 68 (13.52) IFN: 12 (21.82)
GA: 10 (18.18) IFN: 13 (25.00)
GA: 2 (3.77) 0
Oral DMF: 79 (12.80)
TFN: 49 (7.94)
FNG: 58 (9.40) DMF: 66 (13.12)
TFN: 40 (7.95)
FNG: 47 (9.34) DMF: 9 (16.36)
TFN: 4 (7.27)
FNG: 4 (7.27) DMF: 4 (7.55)
TFN: 5 (9.43)
FNG: 7 (13.21) FNG: 3 (42.86)
Infused aCD20: 145 (23.50) aCD20: 116 (23.06) aCD20: 10 (18.18) aCD20: 15 (28.30) aCD20: 4 (57.14)
No DMT 56 (9.08) 44 (8.75) 6 (1.19) 6 (11.32) 0
Mean follow-up after second dose (SD) [weeks] 25.59 (5.48) 25.06 (5.66) 27.85 (4.59) 28.04 (3.11) 27.57 (1.90) <0.01*
*One-way ANOVA; **Pearson Chi-Square; ***Kruskal-Wallis. Abbreviations: pwMS, people with multiple sclerosis; SD, standard deviation; MS, multiple sclerosis; RR, relapsing-remitting; EDSS, expanded disability status scale; DMT, disease-modifying therapy.
Fig. 1 COVID-19 cumulative incidence and severity stratified by DMT groups. Abbreviations: DMT, disease-modifying therapy; FNG, fingolimod; aCD20, anti-CD20 therapies.
Fig. 1
3.2 Incidence of COVID-19
In the Poisson models, FNG therapy (IRR [95%CI]: 2.82 [1.25, 6.36], P = 0.01), and aCD20 therapy (IRR [95%CI]: 2.28 [1.15, 4.53], P = 0.02), were independently associated with an increase, while prior COVID-19 was associated with a decrease (IRR [95%CI]: 0.43 [0.21, 0.88], P = 0.02) in incidence rate of COVID-19. Also, a marginally-insignificant trend for age (IRR per year [95%CI]: 1.03 (1.00, 1.06), P = 0.05) was observed in this model (Table 2 ). No other measurable association was established between the incidence of COVID-19 and other variables (Table 2).Table 2 Results of multivariable Poisson regression.
Table 2Variable Modelled outcome:
Laboratory/imaging-confirmed COVID-19 Modelled outcome:
All possible COVID-19
IRR (95% CI) P value IRR (95% CI) P value
Age (per year) 1.03 (1.00, 1.06) 0.05 1.01 (0.99, 1.03) 0.35
Sex
- Male
0.84 (0.46, 1.55) 0.59 0.75 (0.47, 1.18) 0.22
- Female
(ref) (ref)
Number of comorbidities (per one increase) 1.14 (0.77, 1.68) 0.51 0.97 (0.72, 1.31) 0.85
COVID-19 history before vaccination
- Yes
0.43 (0.21, 0.88) 0.02 0.53 (0.33, 0.86) 0.01
- No
(ref) (ref)
MS subtype
- PP
0.48 (0.13, 1.73) 0.26 0.63 (0.24, 1.70) 0.37
- SP
0.66 (0.24, 1.77) 0.40 1.05 (0.53, 2.06) 0.89
- RR
(ref) (ref)
EDSS category (per one increase) 1.37 (0.69, 2.70) 0.36 1.09 (0.63, 1.90) 0.76
MS duration (per year) 0.96 (0.92, 1.01) 0.16 0.97 (0.93, 1.00) 0.08
DMTs
- Injectable*
(ref) (ref)
- FNG
2.82 (1.25, 6.36) 0.01 1.46 (0.78, 2.71) 0.24
- Other oral**
1.16 (0.50, 2.68) 0.73 1.04 (0.61, 1.77) 0.88
- aCD20
2.28 (1.15, 4.53) 0.02 1.28 (0.78, 2.09) 0.32
- No DMT
1.53 (0.57, 4.10) 0.39 1.36 (0.69, 2.66) 0.37
*Injectable DMTs: glatiramer acetate and interferons. **Other oral DMTs: teriflunomide and dimethyl fumarate. Abbreviations: IRR, incidence rate ratio; CI, confidence interval; ref, reference; MS, multiple sclerosis; RR, relapsing-remitting; SP, secondary progressive; PP, primary progressive; EDSS, expanded disability status scale; DMT, disease-modifying therapy; FNG, fingolimod; aCD20, anti-CD20 therapies; PCR, polymerase chain reaction; CT, computer tomography.
After addition of clinically-suspected cases not confirmed with imaging/laboratory testing, only the association of prior COVID-19 was maintained with incidence of all possible COVID-19 (IRR [95%CI]: 0.53 [0.33, 0.86], P = 0.01), while other mentioned associations lost their statistical significance (Table 2).
3.3 COVID-19-free survival
In the cox model, the COVID-19-free survival was positively associated with prior COVID-19 history (aHR [95%CI]: 0.42 [0.21, 0.86], P = 0.02), while negatively associated with increasing age (aHR per year [95%CI]: 1.03 [1.00, 1.06], P = 0.04), FNG therapy (aHR [95%CI]: 2.80 [1.24, 6.29], P = 0.01), and aCD20 therapy (aHR [95%CI]: 2.11 [1.05, 4.22], P = 0.04) (Fig. 2 ). No other associations were established in the cox model (Table 3 ).Fig. 2 Survival curves indicating lower laboratory/imaging-confirmed COVID-19-free survivals among COVID-19-naïve, FNG-treated, and aCD20-treated pwMS after inactivated vaccination. The plotted whiskers correspond to the 95% confidence limits. The presented aHR's and CI's were retrieved from the multivariable cox regression analysis. Abbreviations: DMT, disease-modifying therapy; FNG, fingolimod; aCD20, anti-CD20 therapies, aHR, adjusted hazard ratio; CI, confidence interval.
Fig. 2
Table 3 Results of multivariable cox regression.
Table 3Variable Modelled outcome:
Laboratory/imaging-confirmed COVID-19 Modelled outcome:
All possible COVID-19
aHR (95% CI) P value aHR (95% CI) P value
Age (per year) 1.03 (1.00, 1.06) 0.04 1.01 (0.99, 1.03) 0.35
Sex
- Male
0.76 (0.40, 1.42) 0.39 0.71 (0.45, 1.14) 0.16
- Female
(ref) (ref)
Number of comorbidities (per one increase) 1.10 (0.74, 1.64) 0.63 0.96 (0.71, 1.30) 0.79
COVID-19 history before vaccination
- Yes
0.42 (0.21, 0.87) 0.02 0.53 (0.33, 0.85) <0.01
- No
(ref) (ref)
MS subtype
- PP
0.46 (0.13, 1.69) 0.24 0.63 (0.24, 1.70) 0.36
- SP
0.67 (0.25, 1.84) 0.44 1.12 (0.56, 2.22) 0.75
- RR
(ref) (ref)
EDSS category (per one increase) 1.35 (0.68, 2.67) 0.39 1.09 (0.62, 1.92) 0.76
MS duration (per year) 0.97 (0.92, 1.01) 0.18 0.97 (0.93, 1.00) 0.07
DMTs
- Injectable*
(ref) (ref)
- FNG
2.80 (1.24, 6.29) 0.01 1.51 (0.81, 2.82) 0.19
- Other oral**
1.17 (0.51, 2.70) 0.71 1.70 (0.63, 1.82) 0.80
- aCD20
2.11 (1.05, 4.22) 0.04 1.22 (0.74, 2.01) 0.43
- No DMT
1.52 (0.57, 4.04) 0.40 1.38 (0.70, 2.70) 0.35
*Injectable DMTs: glatiramer acetate and interferons. **Other oral DMTs: teriflunomide and dimethyl fumarate. Abbreviations: aHR, adjusted hazard ratio; CI, confidence interval; ref, reference; MS, multiple sclerosis; RR, relapsing-remitting; SP, secondary progressive; PP, primary progressive; EDSS, expanded disability status scale; DMT, disease-modifying therapy; FNG, fingolimod; aCD20, anti-CD20 therapies.
After addition of clinically-suspected cases not confirmed with imaging/laboratory testing to the model, statistical significance was maintained regarding prior COVID-19 history (aHR [95%CI]: 0.53 [0.32, 0.85], P < 0.01), but not regarding other mentioned variables (Table 3). Also, a marginally-insignificant association was observed between MS duration and all possible COVID-19-free survival (aHR [95%CI]: 0.97 [0.93, 1.00], P = 0.07) (Table 3).
3.4 Severity of COVID-19
Among the modelled variables in the logistic regression model (Table 4 ), age (aOR [95%CI]: 1.05 [1.00, 1.10], P = 0.05) and number of comorbidities (aOR [95%CI]: 2.05 [1.06, 3.96], P = 0.03), FNG therapy (aOR [95%CI]: 10.39 [2.47, 43.62], P < 0.01), and aCD20 therapy (aOR [95%CI]: 4.44 [1.49, 13.23], P < 0.01) were independently associated with a more severe COVID-19 course. Also, a marginally-insignificant trend was observed suggesting a possible association between EDSS category and severe COVID-19 (aOR [95%CI]: 3.23 [0.91, 11.47], P = 0.07) (Table 4).Table 4 Results of multivariable ordinal logistic regression.
Table 4Variable Outcome:
4-point COVID-19 severity scale
aOR (95% CI) P value
Age (per year) 1.05 (1.00, 1.10) 0.05
Sex
- Male
1.15 (0.43, 3.06) 0.77
- Female
(ref)
Number of comorbidities (per one increase) 2.05 (1.06, 3.96) 0.03
COVID-19 history before vaccination
- Yes
0.58 (0.20, 1.67) 0.31
- No
(ref)
MS subtype
- PP
0.27 (0.02, 3.43) 0.31
- SP
0.40 (0.09, 1.73) 0.22
- RR
(ref)
EDSS category (per one increase) 3.23 (0.91, 11.47) 0.07
MS duration (per year) 0.96 (0.89, 1.04) 0.35
DMTs
- injectable*
(ref)
- FNG
10.39 (2.47, 43.62) <0.01
- Other oral**
0.77 (0.24, 2.49) 0.67
- aCD20
4.44 (1.49, 13.23) <0.01
- No DMT
1.22 (0.26, 5.65) 0.80
*Injectable DMTs: glatiramer acetate and interferons. **Other oral DMTs: teriflunomide and dimethyl fumarate. Abbreviations: aOR, adjusted odds ratio; CI, confidence interval; ref, reference; MS, multiple sclerosis; RR, relapsing-remitting; SP, secondary progressive; PP, primary progressive; EDSS, expanded disability status scale; DMT, disease-modifying therapy; FNG, fingolimod; aCD20, anti-CD20 therapies.
3.5 Sensitivity analysis
Taking the pwMS on no DMTs as reference instead of pwMS on injectable DMTs when measuring the effect of different DMTs resulted similar measurements in all models, although statistical significance was not achieved due to the limited number of pwMS on no DMTs (results not shown).
4 Discussion
Similar to the studies among the populations receiving mRNA or viral vector vaccines [11,12], our study confirmed that the blunted humoral response to BBIBP-CorV vaccination observed in pwMS on FNG and aCD20 [9] translates into higher incidence rates and severity of COVID-19 among them. A notable finding is that no COVID-19-related hospitalization was reported among the pwMS on injectable, oral other than FNG, and no therapies, indicating a considerable drop compared to the previous studies on unvaccinated pwMS [[13], [14], [15], [16], [17], [18], [19], [20], [21], [22]]; however, the hospitalization rate among pwMS on FNG (21.43%) and aCD20 (13.79%) does not seem to have dropped. This finding – along with the previously observed susceptibility of pwMS to unfavorable COVID-19 outcomes [1] – further validates the vaccination of pwMS on injectable, other-than-FNG oral, and no DMTs, but also stresses the importance of crafting personalized vaccination schedules, screening of the serological responses to the vaccines, and reserving COVID-19 treatment resources for pwMS on S1PR modulators and aCD20, especially the ones with older age and more disabilities.
Regarding the role of DMTs, previous studies on unvaccinated pwMS showed that aCD20 therapies render pwMS more susceptible to worse COVID-19 outcomes, but they did not confirm any effect for S1PR modulators [1]. It should be noted that the observed effect of FNG on the COVID-19 outcome in the present study was in comparison with other vaccinated pwMS and (most probably) stemmed from failure of response to vaccination, rather than the mechanistic effects of FNG itself, otherwise, like aCD20 therapies, this effect would have been also observed in the multiple previous studies among unvaccinated pwMS with COVID-19. An interesting point in our study is that the post-vaccination COVID-19 severity was comparable between FNG- and aCD20-treated pwMS; this finding is inconsistent with pre-vaccination studies which showed that S1PR modulators do not affect, but aCD20 worsen the course of COVID-19 [1]. Although the post-vaccination humoral responses are affected similarly by both of these DMTs [9], our observation may be explained by the blunted T cell responses in S1PR-treated [4] in contrast to the robust T cell responses in aCD20-treated pwMS [[4], [5], [6], [7]].
5 Conclusion
It was demonstrated in this study that the blunted humoral responses to COVID-19 inactivated vaccination in pwMS on FNG and aCD20 translates into higher incidence and severity of breakthrough COVID-19 among them. While further replicative studies confirm our piece of evidence, the importance of personalized vaccination schedules, post-vaccination humoral screening, and reservation of COVID-19 treatment resources for the pwMS on S1PR modulators and aCD20 should be stressed among the policy makers and physicians.
6 Limitations
The most important limitations of our study included lack of unvaccinated cohorts and possible missing of asymptomatic COVID-19 cases. As mentioned, we considered adding to our sample a cohort of healthy participants to serve as controls. However, although they were successfully identified in our initial enrollment, we failed in their follow-up re-identification and validation of their data. Furthermore, we considered identifying a new healthy cohort, however, this was not implemented as (i) the overall epidemiological characteristics of COVID-19 was shifted significantly in Iran since the initial cohorts were recruited, and if we added to our sample, the data would have not been comparable to the data we collected initially when Iran was experiencing a peak in covid cases; (ii) adding participants in a retrospective manner would have presented bias as the initial cohorts were recruited prospectively, not to mention the challenge in retrieving reliable data from several months before; and (iii) the Iranian population was already vaccinated significantly, and hence, prospective recruiting of new participants i.e. identifying unvaccinated people willing to be vaccinated would have required a significant effort. Therefore, we encourage further replicative studies to account for our limitations and further confirm our results.
Funding
None.
Author contributions
ME provided the resources required for the study and supervised its conduction. APA, HN, and MS critically edited and reviewed the initial draft, and conducted literature reviews to provide the presented discussions. NE managed and supervised all of the data collection processes and holds responsibility of the integrity, correctness, and replicability of the collected data. NS designed the study, created the presented figures, and provided the initial draft. NS analyzed the raw data and holds responsibility of the integrity and correctness of the presented analyses. All of the authors have read and approved the final version of the manuscript.
Declaration of Competing Interest
None.
==== Refs
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| 36521195 | PMC9731817 | NO-CC CODE | 2022-12-14 23:45:34 | no | J Neurol Sci. 2023 Jan 15; 444:120518 | utf-8 | J Neurol Sci | 2,022 | 10.1016/j.jns.2022.120518 | oa_other |
==== Front
Trends Analyt Chem
Trends Analyt Chem
Trends in Analytical Chemistry
0165-9936
1879-3142
Elsevier B.V.
S0165-9936(22)00363-6
10.1016/j.trac.2022.116880
116880
Article
On-site bioaerosol sampling and detection in microfluidic platforms
Lee Inae ab1
Jeon Eunyoung a1
Lee Joonseok abc∗
a Department of Chemistry, Hanyang University, Seoul, 04763, South Korea
b Research Institute for Convergence of Basic Sciences, Hanyang University, 222 Wangsimni-Ro, Seongdong-Gu, Seoul, 04763, South Korea
c Research Institute for Natural Sciences, Hanyang University, Seoul, 04763, South Korea
∗ Corresponding author. Department of Chemistry, Hanyang University, Seoul, 04763, South Korea.
1 I.L. and E.J. contributed equally to this work.
9 12 2022
1 2023
9 12 2022
158 116880116880
5 8 2022
7 12 2022
7 12 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.
As the recent coronavirus disease (COVID-19) pandemic and several severe illnesses such as Middle East respiratory syndrome coronavirus (MERS-CoV), Influenza A virus (IAV) flu, and severe acute respiratory syndrome (SARS) have been found to be airborne, the importance of monitoring bioaerosols for the control and prevention of airborne epidemic diseases outbreaks is increasing. However, current aerosol collection and detection technologies may be limited to on-field use for real-time monitoring because of the relatively low concentrations of targeted bioaerosols in air samples. Microfluidic devices have been used as lab-on-a-chip platforms and exhibit outstanding capabilities in airborne particulate collection, sample processing, and target molecule analysis, thereby highlighting their potential for on-site bioaerosol monitoring. This review discusses the measurement of airborne microorganisms from air samples, including sources and transmission of bioaerosols, sampling strategies, and analytical methodologies. Recent advancements in microfluidic platforms have focused on bioaerosol sample preparation strategies, such as sorting, concentrating, and extracting, as well as rapid and field-deployable detection methods for analytes on microfluidic chips. Furthermore, we discuss an integrated platform for on-site bioaerosol analyses. We believe that our review significantly contributes to the literature as it assists in bridging the knowledge gaps in bioaerosol monitoring using microfluidic platforms.
Keywords
Airborne pathogens
Bioaerosols monitoring
Microfluidics
Lab-on-a-chip
Collection
Concentration
Biosensor
Integration
==== Body
pmcAbbreviations
ATP Adenosine triphosphate
COVID-19 coronavirus disease
DLD deterministic lateral displacement
ESP electrostatic precipitation
EWOD Electrowetting on a dielectric
FET field-effect transistor
IAV influenza A virus
LAMP Loop-mediated isothermal amplification
LFIA lateral flow immunoassay
MERS-CoV Middle East respiratory syndrome coronavirus
NIR near infrared
PCR polymerase chain reaction
PDMS polydimethylsiloxane
PM particulate matter
SARS severe acute respiratory syndrome
SARS-CoV-2 severe acute respiratory syndrome coronavirus 2
SiNW silicon nanowire
SPR surface plasmon resonance
TCID50 50% tissue culture infective dose
1 Introduction
In recent decades, several deadly infections, such as Middle East respiratory syndrome coronavirus (MERS-CoV), Influenza A virus (IAV) flu, and severe acute respiratory syndrome (SARS), have been reported to spread via bioaerosols, thereby causing massive economic losses and human and animal casualties [1,2]. The most recent outbreak of coronavirus disease (COVID-19) caused more than 583 million confirmed cases and 6.43 million deaths as of August 2022 and has spread worldwide with mutations. The causative agent of COVID-19 has also been shown to transmit through the air [3]. Therefore, it is critical to precisely detect bioaerosols for the early prediction and real-time alerts of airborne infectious illnesses.
Bioaerosols contain various pathogenic and/or non-pathogenic components (viruses, bacteria, fungi, spores, byproducts of microbial metabolism, biological warfare agents, etc.) [4,5] and are ubiquitously dispersed in ambient air. Air samples must be handled carefully and reliably because of the different aerodynamic diameters and forms of each airborne particulate and the low quantities of the targeted component in the air sample [6]. Additionally, they are suspended in a mixture of dust, droplets, salts, and other particles [7]. Therefore, to monitor these airborne microorganisms, especially infectious pathogens or hazardous biological particles, considerations prior to bioanalysis are to effectively separate, collect, or capture the target bioaerosols from the background environment [8]. Currently, most approaches involve on-site sampling and subsequent laboratory tests, with complex operational processes and lengthy detection times [9,10].
The lab-on-a-chip platform based on microfluidic technology demonstrates remarkable characteristics such as high-resolution separation and concentration of samples, purification of analytes, and feasibility of fabricating integrated analytical devices [[11], [12], [13], [14], [15], [16], [17], [18]], thereby highlighting its potential for on-site bioaerosol monitoring. Research on potential bioaerosol monitoring based on microfluidic platforms has been published in recent years [[19], [20], [21]]. However, a limited number of published review articles have described recent advances in bioaerosol monitoring using microfluidic platforms. Recent reviews have discussed conventional and advanced bioaerosol detection methods and introduced microfluidic platforms as part of detection strategies [9,[22], [23], [24]]. Our review provides 1) a broad overview of the bioaerosol collection principles and analytical techniques and 2) the recent progress in microfluidic platforms for sampling and detecting airborne pathogens (Fig. 1 ). We focused on describing bioaerosol sample preparation strategies, such as sorting, concentrating, and extracting, as well as rapid and field-deployable detection methods for analytes on a microfluidic chip. Furthermore, we discuss an integrated platform that can collect, prepare, and detect airborne pathogens using a single device for on-site bioaerosol analysis.Fig. 1 Schematic illustration of an integrated microfluidic platform for on-site bioaerosol sampling and detection. Two main procedures are required for bioaerosol analysis: sampling based on sorting, concentration, and lysis; and detection based on electrochemical, optic, and fluorescence signals. The microfluidic platform with integrated sampling and detection enables on-site bioaerosol monitoring.
Fig. 1
2 Analysis of airborne microorganisms from air samples
2.1 Sources and transmission of bioaerosols
Bioaerosols consist of various components, such as viruses, bacteria, fungi, spores, and pollen [25] and can be dead or alive pathogenic and non-pathogenic microorganisms [5]. Their cell number in the atmosphere is between 104 and 106 m−3 [26], and they are small airborne particles that typically range from 0.001 to 100 μm in size [27]. Bioaerosols can be generated from natural and anthropogenic sources and are easily translocated by wind and water [25,26]. The World Health Organization describes airborne transmission as “the spread of an infectious agent caused by the dissemination of droplet nuclei (aerosols evaporated from larger droplets) that remain infectious when suspended in air over long distances and time” [28]. Various daily activities, such as chatting, coughing, sniffling, walking, showering, taking whirlpool baths, and flushing the toilet can also contribute to the dispersion of bioaerosols [29]. Depending on their size, fine bioaerosols typically float in the atmosphere for several minutes to hours. Both organic and inorganic components of aerosols can influence the size and infectivity of aerosolized particles. The airborne virus infectivity can be affected by temperature, relative humidity, exposure time, chemical composition of the air, aerosolization medium, and sampling technique [30].
Airborne microorganisms are present in the atmosphere in both indoor and outdoor environments [31]. Molds and fungi are mostly discovered in indoor settings with humid conditions on walls and corners. Airborne microorganisms can be released by infected people and animals through their daily activities and rapidly spread through the air in confined spaces. Therefore, personal (houses) and public spaces (offices, schools, and transportation terminals) shared by many people and occupational outdoor places (animal and plant farms, and waste treatment plants) are critical sites for bioaerosol monitoring. In medical facilities such as hospitals and nursing facilities, many people are infected or vulnerable to infection. When a pathogen-containing particle (droplet) from one person is produced and then comes into direct or indirect contact with another person, respiratory illnesses can spread [32]. Human exhaled breath contains disease biomarkers such as respiratory pathogens that can cause infection and transmission. Coughing and sneezing produce bioaerosols with large diameters and high concentrations, whereas simple breathing produces bioaerosols with smaller diameters and lower concentrations.
2.2 Bioaerosol sampling
2.2.1 Air sample collection
The collection of air samples is a crucial step in bioaerosol monitoring that is key for bioaerosol detection (Fig. 2 A). Bioaerosol collection methods mostly depend on the physical characteristics of airborne particles, such as size and weight [33], Brownian motion adhesion properties, thermal gradients, and the particles’ inertia [30]. Filtration, centrifugation, impaction, impingement, and electrostatic precipitation (ESP) are examples of current bioaerosol collection techniques. Filtration is frequently used to collect bioaerosols owing to its convenience and ease of use [34]. Bioaerosol particles are attached to the surfaces of fibrous and porous membranes by interception, impaction, diffusion, gravitational settling, and electrostatic attraction [30]. They are then eluted into liquid for subsequent analysis or examined directly using microscopes. The centrifugation method involves injecting bioaerosols into a specially designed chamber that creates swirling air, which causes the bioaerosols to be separated into the collection liquid or wall based on their disparate masses [21,[35], [36], [37]]. The bioaerosol-containing liquid was drained from the bottom and transferred for further analysis, whereas the clean air was exhausted from the top [38] (Fig. 2B). The impaction method involves collecting bioaerosols onto a solid medium based on inertia. The bioaerosols are first pulled through a nozzle connecting with a vacuum pump, followed by deposition onto a collection media, while those with lesser inertia flow with the air [33]. The Anderson cascade impactor is commercially available for bioaerosol sampling [[39], [40], [41]]. Impingement deposits bioaerosols in a liquid collection medium that assists in maintaining the viability of the virus. Bioaerosol-laden medium can be used directly as an analytical sample without having to extract microbes from a surface or filter. A liquid impinge was used to examine potential viral aerosol transmission through hospital indoor air [42]. ESP is another sampling method that has recently increased in popularity and employs electrostatic attraction to move particles to a collecting electrode [[43], [44], [45]]. Bioaerosols are charged by metal needles at the inlet of the ESP, causing a corona discharge that compels them to travel in the direction of electrodes with opposing charges (Fig. 2C) [46]. The sampling methods and devices are optimized by considering sampling efficiency, flow rate scalability, sampling duration, and sample viability according to the bioaerosol exposure conditions, such as the type and concentration of pathogens and environmental parameters at the sampling location.Fig. 2 (A) Illustration of methods by sampling, pre-treatment, and analysis steps to monitor bioaerosols. (B) Schematic diagram of cyclone sampler named as the Yao-CSpler for bioaerosol collection. Reproduced with permission from Ref. [38]. (C) Schematic of an electrostatic air sampler to collect airborne virus. Reproduced with permission from Ref. [46]. (D) Schematic illustration of a nanoscale sensor design and virus sensing mechanism. Reproduced with permission from Ref. [54]. (E) Schematic of elution of the virus from the wearable collector with antibody-based sensing. Reproduced with permission from Ref. [58]. (F) Schematic of an integrated bioaerosol monitoring platform through a swab-based sampling and bioluminescence detection. Reproduced with permission from Ref. [60].
Fig. 2
2.2.2 Sample treatment
Bioaerosols are suspended with other environmental substances and have various types and a wide range of aerodynamic diameters. Therefore, it is necessary to separate other components prior to analyzing specific microorganisms for accurate results. Moreover, airborne infectious microorganisms are a part of these bioaerosols, and because they are diffused in indoor spaces and the atmosphere, it may not be possible to attain detectable levels with small air sample volumes [6]. Proteins, nucleic acids, and chemicals in microorganisms may be required depending on the analysis method; therefore, the process of thermally or physically extracting these substances from the collected microorganisms is also used. This requires a pre-treatment process, such as separation and concentration of specific airborne microorganisms, as well as extraction and purification of the target analyte between the air sampling and analysis processes (Fig. 2A). Although some of these functions are included in air samplers and analytical instruments, continuously integrated systems have not been commercially developed and are at a research level.
2.3 Analytical methods for collected bioaerosol
2.3.1 Culturing
Microbial culture is the most widespread method for detecting airborne bacteria and fungi [40,[47], [48], [49]] owing to operational simplicity, low cost, and low equipment investment. Bioaerosols collected in a buffer solution or on a surface of agar plates in samplers are incubated at appropriate temperature for sufficient time (over 24 h) for growth and proliferation. The colonies formed on the plate are counted to determine bioaerosol concentrations. Bioaerosols can be identified using specific culture media or further biochemical analysis. For airborne viral assays, 50% tissue culture infective dose (TCID50) and culture-based plague are traditional techniques for enumerating the concentration of infectious viruses. The TCID50 assay is based on the infectious dosage of viruses necessary to kill 50% of implanted tissue culture cells, whereas the plaque assay is based on the viral plaques produced by the cytopathic effect in host cells. The culturing method is known as the gold standard for microbial assays but has some limitations, including significant workload requirement, long detection time, and unsuitability for non-cultural pathogens and on-site applications.
2.3.2 Immunological assay
Immunological detection methods are based on the binding reaction of antibodies to their specific target analytes [[50], [51], [52]]. Approaches for immunosensing include lateral flow immunoassay (LFIA), surface plasmon resonance (SPR), and enzyme-linked immunosorbent assay [53]. The immunological binding for each technique is on a paper strip, on a metal surface at the interface of a glass and liquid media, and on a surface of a well plate, resulting in color, fluorescence, and electrical signals (Fig. 2D and E). Their outstanding features of quick response, miniature size, low cost, and easy integration have contributed to the development of rapid on-site bioaerosol collection and detection technologies [43,54]. The disadvantages of immunological assays are possible false-positive results, complex measurement steps, and environmentally sensitive components.
2.3.3 Nucleic acid amplification
Nucleic acid amplification method is known as polymerase chain reaction (PCR), which can quantitatively and qualitatively detect various airborne microorganisms for monitoring of indoor air quality. The collected bioaerosols are lysed to release nucleic acids. DNA or RNA sequences from the target microorganisms are denatured, annealed, and extended to achieve amplification. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) aerosols in hospitals and healthcare facilities are identified by PCR method [37,42,55]. Loop-mediated isothermal amplification (LAMP) is an alternative to PCR and is less costly and less specialized because it amplifies nucleic acid fragments at constant temperature [53]. Forward and backward primers are employed in LAMP to produce looped DNA segments that can be identified using fluorescence or other techniques [56]. LAMP typically requires 100 min or less to complete and maintains a temperature of 60–65 °C [57]. Soto et al. detected SARS-CoV-2 in exhaled breath using qualitative colorimetric LAMP, quantitative reverse transcription PCR, and antibody-based dot blot assays (Fig. 2E) [58]. Compared to other detection methods, nucleic acid amplification is rapid and highly sensitive, has good specificity, and has been widely used for the detection of airborne viruses and bacteria. However, the method also has some limitations in that it requires high technical skill due to the complexity of nucleic acid extraction and professional equipment used and an inability to distinguish living and dead microorganisms.
2.3.4 Adenosine triphosphate (ATP) bioluminescence assay
ATP bioluminescence has been used to rapidly quantify the concentration of airborne microorganisms [35,59,60]. Within microbial cells, ATP performs a crucial role as an energy carrier, connecting catabolism to biosynthesis [61]. The measurement of the light produced by the reaction between luciferin and luciferase is the basis of an ATP bioluminescence test. Proven correlation between light intensity and ATP concentration may be used to estimate target microorganism concentration in bioaerosol samples (Fig. 2F) [60].
3 Microfluidic-based sampling and analysis of bioaerosols
3.1 Bioaerosol sampling in microfluidic device
Various pathogens and particulate matter (PM) are present in collected aerosol samples. Detecting target materials in collected bioaerosol sample requires sample treatment steps, such as separation, concentration, and extraction. For microfluidic platforms, channels of various designs, such as pillars, herringbones, centrifugals and spirals, can be designed to control the flow of fluids and particles for separation and concentration. Additionally, extraction can be performed within microfluidic channels using a small amount of sample. With this microfluidic platform, single or multiple sample preparation steps can be performed on a single miniaturized chip or developed as a platform integrated with other samplers or analysis equipment.
3.1.1 Separation and sorting
Separation accounts for the distinction of particulate matter by size and is important in the understanding of the health effects of PM. Technologies that separate PM according to particle size have existed in the past, but studies using microfluidic platforms are being conducted for miniaturization, a simple structure, and high separation efficiency. Yin et al. designed a microfluidic deterministic lateral displacement (DLD) separation method using I-shaped pillars to separate airborne particulates according to their uniform size (Fig. 3 A). For the DLD arrays, small particles follow their initial flow and larger particles are displaced along the pillar gradient, then separated to the “bottom-out” outlet [62]. Bertke et al. proposed a single-chip differential mobility particle sizer that integrated a microfluidic channel and a piezoresistive microcantilever to monitor airborne particles (Fig. 3B). With this technique, the positively charged particles are separated from the air flow according to their aerodynamics and trajectories of size bins [63].Fig. 3 (A) Schematic illustration of size fractionation with an I-shape pillar-based deterministic lateral displacement (DLD) method through a microfluidic device. Reproduced with permission from Ref. [62]. (B) Schematic and scanning electron micrograph (SEM) images of a micro cantilever with released piezoresistive struts located in a microfluidic channel (μFC) guiding a particle-laden air flow. Reproduced with permission from Ref. [63]. (C) Design of a microfluidic chip with the staggered herringbone mixer (SHM) and fluorescence image of microfluidic channels with bacteria. Reproduced with permission from Ref. [64]. (D) Schematic showing the principle of aerosol collection using a uniform and stable liquid film via optimization of two-phase flow conditions and hydrophilic surface treatment. Reproduced with permission from Ref. [19]. (E) Schematic of the bacteria enrichment and lysis using gold-nanoparticle-embedded microfluidic chips. Reproduced with permission from Ref. [67].
Fig. 3
3.1.2 Trapping and concentration
The concentrations of pathogenic microorganisms in bioaerosols are relatively low. To monitor bioaerosols rapidly, it is essential to develop an enrichment technique for concentrating the bioaerosols [19,64]. Recent research has developed specific sampling equipment for efficient collection of environmental bioaerosols [44,65]. Aerosolized bacteria (Staphylococcus aureus, Bacillus cereus, Escherichia coli, and Acinetobacter baumannii) [65], coronavirus, and influenza virus [44] were collected via an air sampler and then transferred to a fluidic channel for analysis. Jing et al. fabricated a microfluidic chip with a herringbone structure and showed high efficiency of capture and enrichment (Fig. 3C) [64]. Heo et al. developed a device that can transfer aerosol samples into concentrated hydrosol samples. Bioaerosols were captured into a homogeneous and stable liquid layer using optimized flow conditions and a hydrophilic surface without additional post-treatment (Fig. 3D) [19].
3.1.3 Extraction and purification of analytes
It is necessary to separate analytes from aerosol samples containing various pathogens and PM and to extract and purify the nucleic acids of samples [66,67]. Kwon et al. fabricated a gold nanoparticle-embedded polydimethylsiloxane (PDMS) microfluidic device for the capture, lysis, and detection of airborne bacteria. Airborne bacteria (S. aureus, Pseudomonas aeruginosa, E. coli, and B. cereus) were collected in a hydrosol using an electrostatic sampler. Hydrosolized airborne bacteria were captured and enriched in the herringbone channel, and bacterial lysis was conducted by effective photothermal lysis (Fig. 3E) [67].
3.2 Bioaerosol detection in microfluidic device
For the traditional microbiological detection methods mentioned above, airborne microbes are transferred to a solid or liquid medium for analysis by culturing, immunoassay, or PCR. They are identified and quantified using multistep procedures, including sample acquisition and the separation of analytes from other substances. It is not easy to meet the needs of fast detection due to extensive time and complicated operations required by professionals. Therefore, developing detection methods based on sensor and biosensor technologies is required for rapid detection of airborne microbes.
3.2.1 Electrical-based detection
Electrical-based sensing technologies, such as electrochemistry, field-effect transistors (FET), and piezoelectric sensors, are commonly used technologies that utilize electrical signals for detection [23,68]. The electrical signals are acquired from antigen-antibody reactions or nucleic acid hybridization on the surface of the electrode or between electrodes in the biosensor device. The magnitude of the signal is proportional to the number of airborne microbes [69,70] and can be used to differentiate the type and size [21]. The electrical sensing platform can become smaller and cheaper, require a less complicated setup, and can be easily integrated into a microfluidic chip. Several studies on electrical signal-based quantification of airborne microbes in microfluidic devices have been published [21,[69], [70], [71], [72]]. The airborne fungal pathogen Sclerotinia scleritiorum was collected and quantified on a microfluidic chip integrated with microelectrodes and microwells for dielectrophoresis-driven capture and impedimetric sensing [69]. Lee et al. detected bacterial aerosols and their viability by integrating a wet cyclone air sampler with a DC impedance microfluidic cytometer (Fig. 4 A) [21]. The wet cyclone air sampler aspirated the air and concentrated it with a solution, which was detected using a microfluidic cytometer. In this system, live E. coli, dead E. coli, and dust were effectively differentiated [21]. Ma et al. developed a multichannel microfluidic chip packed with an ultrasensitive silicon nanowire (SiNW)–FET biosensor. Mycobacterium tuberculosis from tuberculosis patients was collected using exhaled breath condensate and automatically transferred to the SiNW-EFT biosensor detection system through a peristaltic pump and a switch valve. The system reduces the number of samples, decreases the error caused by operators, and enables detection to be carried out without the aid of experts (Fig. 4B) [71].Fig. 4 (A) Schematic illustration of two-channel microfluidic cytometer and magnified part of the microfluidic sensing channel. Reproduced with permission from Ref. [21]. (B) Design of a multichannel microfluidic chip and detection principle. Reproduced with permission from Ref. [71]. (C) Design and reaction image of portable microfluidic chip for multiplex nucleic acid detection. Reproduced with permission from Ref. [74]. (D) Schematic and images of a two-dimensional gold nanoisland (AuNI) microfluidic sensor chip under a plasmonic photothermal (PPT) heating system. Reproduced with permission from Ref. [78]. (E) Schematic of a wax-patterned μPAD (microfluidic paper-based analytical device) and a lateral flow assay (LFA) strip. Reproduced with permission from Ref. [84]. (F) Schematic illustration of an integrated sampling/monitoring platform and working principle of an airborne pathogen kit. Reproduced with permission from Ref. [86].
Fig. 4
3.2.2 Optical-based detection
In the optical-based detection method, the signal form the detected target is converted into detectable optical signals such as fluorescence [73], color, and SPR. Additionally, real-time detection of an object can be achieved through chemical or biological luminescence sensing without changing the target's label. Fluorescence biosensors employ the distinctive photophysical features of fluorescent nanomaterials for the tagging and detection of airborne microorganisms [[74], [75], [76], [77]] The colorimetric biosensors detect target microbes by changing the color of the detection solution and rely on changes in their concentration. Lu et al. used a microfluidic chip as a rapid multiplex nucleic acid detection system for airborne fungi (Fig. 4C) [74]. Simulated spore aerosols of Aspergillus fumigatus were accumulated using a wet cyclone sampler. After DNA was released using the Lyticase-Motor-Chemical reagent nucleic acid-releasing method, LAMP amplification reactions were conducted on microfluidic chips. To assess the specificity and sensitivity of the airborne fungus LAMP assays, two readout techniques (fluorescence and colorimetric LAMPs) were utilized. A practical SPR apparatus combines an optical detector component, usually measuring the intensity shift, which is coupled with a fluidic system, enabling a flow-through operation for the detection of SARS-CoV-2 (Fig. 4D) [78] and airborne bacteria [79]. As an affordable method, the ATP-based bioluminescence assay in a microfluidic device has been applied in real time and continuous monitoring of bioaerosols [19,80].
3.2.3 Paper-based detection
Microfluidic paper-based analytical devices are suitable for use in confined environments because they are inexpensive, lightweight, portable, and provide fast detection with small sample volumes without requiring a pump [[81], [82], [83]]. Owing to these advantages of paper-based microfluidic devices, studies on detection of airborne microbes in the atmosphere or respiration have recently been reported [35,43,77,[84], [85], [86]]. Nguyen et al. designed a face-mask sensor for SARS-CoV-2 detection in exhaled aerosols as shown in Fig. 4E. The presence of a specific molecular target is indicated via colorimetric changes after activating the device upon rehydration from aqueous exposure [84]. SARS-CoV-2 droplets and aerosols in the air were directly captured on the paper microfluidic chips without any sampling equipment [77]. Antibody-conjugated fluorescent particles on the chip were aggregated by antibody-antigen binding in the presence of the SARS-CoV-2, and a fluorescence image from immunoagglutinated particles was obtained using a smartphone-based fluorescence microscope. Lee et al. used a LFIA to detect airborne viruses, MS2 bacteriophages, and avian influenza viruses (Fig. 4F). The aerosol particles were collected directly from the sampling pads, which were made of air-filter materials. Through capillary force, the collected airborne viral particles were transferred for conjugation with near infrared (NIR)-to-NIR nanoprobes, followed by binding with detector antibodies on the test line. NIR signals from nanoprobes were detected using a portable reader [86]. An electrochemical paper immunosensor based on a vertical-flow assay for rapid quantification of airborne influenza H1N1 viruses was fabricated by Bhardwaj et al. After collecting the viruses with an electrostatic particle concentrator, redox signals and charge transfer resistance (RCT) were obtained depending on the virus concentration [43].
3.3 Integrated sampling/detection platform for bioaerosol monitoring
To date, microfluidic device-based bioaerosol collection, processing, and detection technology has been individually investigated as a lab-on-a-chip platform. Therefore, there is a demand for an integrated platform of capture, enrichment, and detection technologies that can monitor bioaerosols quickly and conveniently in the field [87]. With these demands and the excellent compatibility of microfluidic chips with other sampling and analysis components, research on integrated platforms with microfluidic devices has been reported recently (Fig. 5 A) [19,20,[74], [75], [76],79,88,89]. A single microfluidic device or a series of microfluidic devices to collect and investigate bioaerosols from the air has been designed. Choi et al. developed an optofluidic platform consisting of a microfluidic-based aerosol sampler and Raman spectrometer to continuously monitor airborne microorganisms in real time (Fig. 5B). The air passed through a serpentine mixing channel of a 99.6% collection efficiency and was detected in real time using surface-enhanced Raman spectroscopy. The Raman peak of airborne Staphylococcus epidermidis corresponded to 732.5 cm−1, and the detection limit was approximately 102 CFU/mL [79]. Electrowetting on a dielectric-based (EWOD) digital microfluidic platform was developed for the automated immunoassay of airborne pathogens (Fig. 5C) [88]. The sample concentration, immunomagnetic reaction, and luminescence were detected using the EWOD chip. This produced microliter-concentrated droplet samples as a result of the recovery process. In the biodetection test, antigens are first selectively captured by antibody-coated magnetic beads, a secondary detection antibody is then added, and finally, a chemiluminescent reaction results in the quantifiable emission of light. The EWOD then transfers the luminescent droplet to the photodetector for light collection. Liu et al. integrated a microfilter for enrichment and concentration of airborne pathogens, P. aeruginosa, and LAMP for in-situ immunofluorescence detection on a portable PDMS chip [76]. A smartphone-based integrated microsystem was developed for real-time detection of indoor airborne microparticles [89]. The integrated system consisted of an air sample collection device and microfluidic biochip. The collection of airborne microorganisms was inspired by the Venturi effect and trapped in the biochip. A CMOS photodetector was placed below the biochip to measure the optical density change induced by the airborne particles, followed by transmitting the acquired data to a smartphone via a wireless communication module. Heo et al. presented a novel bioaerosol sampler called enriched, rapid, and continuous aerosol-to-hydrosol transfer, using a superhydrophilic surface and centrifugal flow (Fig. 5D). The number of airborne microorganisms in the hydrosol samples was estimated by measuring ATP bioluminescence using an optofluidic bioluminescence detector [19]. A microfluidic system consisting of an enrichment chip for airborne fungal spores from Aspergillus niger sampling and a detection chip for immunofluorescence analysis was constructed (Fig. 5E) [75]. The sampling chip adopted the staged herringbone mixer structure to control the amount of eluent from the sampling chip to dozens of microliters, which is feasible for transferring the enriched sample into the downstream analysis chip directly. A column-structured analysis chip combines nonspecific physical interpretation with target immunocapture to enrich and recognize target spores in the air. Xiong et al. reported an aerosol sampling system integrated with a small-volume rotating chip to meet the demand for rapid and on-site sample collection and SARS-CoV-2 detection. The SARS-CoV-2 aerosols were collected and enriched in a filter membrane. The nucleic acids of SARS-CoV-2 were extracted and introduced into a microfluidic chip for real-time fluorescence acquisition [20].Fig. 5 (A) Rapid and on-site bioaerosol monitoring using microfluidic platform integrated with sampling and detection. (B) Schematic of a continuous optofluidic surface-enhanced Raman spectroscopy (SERS) system for detecting airborne biological particles. Impacted particles were continuously collected in the μ-sampler and analyzed using Raman spectroscopy in the optofluidic platform. Reproduced with permission from Ref. [79]. (C) Schematic diagram of Electrowetting on Dielectric (EWOD) chip and a parallel-plate EWOD chip in operation. Reproduced with permission from Ref. [88]. (D) Schematic showing aerosol collection, enrichment, and detection using enriched, rapid, and continuous aerosol-to-hydrosol transfer (ERC-ATHT) collector and optofluidic bioluminescence detector for rapid and continuous monitoring of airborne microorganisms. Reproduced with permission from Ref. [19]. (E) Images and schematic illustrations of the microfluidic chip for the airborne spore enrichment chip and detection chip. Reproduced with permission from Ref. [75].
Fig. 5
Compared to conventional bioaerosol monitoring procedures, the microfluidic chip-based integrated monitoring platform has several apparent advantages: 1) It is economical to collect, pre-treat, and detect bioaerosols within a single chip, since the microfluidic chip-based integrated platform can be miniaturized and made portable. 2) The concentration and viability of bioaerosols, which might be reduced during recovery or transport, can be mitigated by a series of bioaerosol sampling analysis within the integrated platform. 3) It can sensitively detect a small number of airborne pathogens because the microfluidic chip can concentrate a large volume of air sample into a small volume of liquid sample to be analyzed. 4) Separation and purification of bioaerosols from particulates are possible through the sophisticated design of the microfluidic chip, which can improve the detection selectivity of specific bioaerosols. These features of the microfluidic chip-based integrated platform allow the real-time and continuous monitoring of bioaerosols in the field, thus making it possible to respond to sudden risks quickly.
4 Conclusions and future perspectives
The level of bioaerosols containing airborne pathogens becomes important during a respiratory infectious disease epidemic. Our review addressed conventional methods for sampling and detecting bioaerosols and advances made on on-site bioaerosol monitoring platforms based on microfluidic devices. Bioaerosols sampled from indoor/outdoor spaces or human respiration are subjected to separation and purification and then tested for target harmful microorganisms. Distinguishing bioaerosols based on their type, activity, or pathogenicity is also a consideration that requires more time and effort. In the microfluidic device, pre-treatment of collected bioaerosols and identification of the target analyte can be performed after collecting air samples, overcoming temporal and spatial limitations. The procedures have been integrated into a single device or developed on a platform in which devices designed for each function were connected, demonstrating the potential for on-site bioaerosol monitoring. Along with these technological advances, many review papers related to bioaerosol monitoring also mention the need for an integrated platform for on-site, continuous, and automated tracking of bioaerosols [53,90,91]. The microfluidic-based integrated platform simplifies these cumbersome processes and enables sensitive and rapid in-situ diagnosis of bioaerosols through highly efficient collection, concentration, and separation strategies.
Despite the progress in developing microfluidic-based bioaerosol sampling and detection, it is still in the laboratory-based research stage, and there are some areas to be improved in the field-deployable platform to overcome the limitations of existing bioaerosol monitoring methodologies. 1) Bioaerosol collection still relies on separated commercial air samplers compared to the sample preparation or analysis parts. When transferring a sample to a microfluidic instrument, it is necessary to minimize sample loss and microbe viability. Alternatively, miniaturized air collectors may be desired to couple with microfluidic devices. The collection efficiency of bioaerosol sampling should be equal to or better than that of commercial samplers. 2) Bioaerosol detection based on enzyme and immune reactions in the field may encounter decreased detection accuracy and reliability caused by degradable reagents, enzymes, fluorescent probes, etc., which are vulnerable to the field environment. In addition, highly sensitive analytical tools are required to detect trace amounts of infectious airborne microorganisms. Nanomaterials or nanotechnologies that are stable in the field environment and are capable of electrical, optical, and visual signal amplification can be combined with the platform for further improvement of detection performance. 3) Platform validation studies using single or complex bioaerosols have been conducted; however, field sample validation is still lacking. There are more suspended particles in the field; therefore, it is necessary to verify whether they are effectively removed during the pre-treatment process and confirm that interference does not occur during the detection operation. It is essential to compare the performance of a microfluidics-based platform to that of various bioaerosol monitoring techniques to assess its performance in either a mimic environment or an actual field with multiple airborne particulates. A rationally designed microfluidics-based bioaerosol sampling and detection platform will enable timely on-site air quality monitoring, which will help ensure safety from airborne pathogens that threaten public health.
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
This study was supported by the 10.13039/100006180 Bio & Medical Technology Development Program (Grant number: 2021M3E5E3080381), National R&D Program (Grant number: 2021M3C1C3097695), and the 10.13039/501100003725 National Research Foundation of Korea (NRF) grant funded by 10.13039/501100014188 Ministry of Science and ICT (MSIT) (Grant number: 2020R1A2C2101111, 2020R1A6A1A06046728).
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| 36514783 | PMC9731818 | NO-CC CODE | 2022-12-15 23:17:50 | no | Trends Analyt Chem. 2023 Jan 9; 158:116880 | utf-8 | Trends Analyt Chem | 2,022 | 10.1016/j.trac.2022.116880 | oa_other |
==== Front
Process Biochem
Process Biochem
Process Biochemistry (Barking, London, England)
1359-5113
1873-3298
Elsevier Ltd.
S1359-5113(22)00441-X
10.1016/j.procbio.2022.12.002
Article
Lessons learned from COVID-19 pandemic: Vaccine platform is a key player
Hossaini Alhashemi Samira a
Ahmadi Fatemeh b⁎
Dehshahri Ali ac⁎
a Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
b Department of Pharmaceutics, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
c Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
⁎ Correspondence to: School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran.
9 12 2022
1 2023
9 12 2022
124 269279
21 6 2022
15 10 2022
2 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 SARS-CoV-2 outbreak and emergence of COVID-19 resulted in the development of different vaccines based on various platforms to combat the disease. While the conventional platforms of inactivated/live attenuated, subunit proteins and virus-like particles (VLPs) have provided efficient and safe vaccines, novel platforms of viral vector- and nucleic acid-based vaccines opened up new horizons for vaccine development. The emergence of COVID-19 pandemic showed that the availability of platforms with high possibility of quick translation from bench to bedside is a prerequisite step in vaccine development in pandemics. Moreover, parallel development of different platforms as well as considering the shipping, storage condition, distribution infrastructure and route of administration are key players for successful and robust response. This review highlights the lessons learned from the current COVID-19 pandemic in terms of vaccine development to provide quick response to future outbreaks of infectious diseases and the importance of vaccine platform in its storage condition and shipping. Finally, the potential application of current COVID-19 vaccine platforms in the treatment of non-infectious diseases has been discussed.
Graphical Abstract
ga1
Keywords
Vaccine platform
COVID-19
Vaccine development
Pandemic
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pmc1 Introduction
The breakthrough of virus propagation in cell culture during the 1950 s, provided great opportunity for vaccine manufacturers to produce vaccine for human application in large-scale. The global vaccination against life-threatening infectious diseases such as smallpox, polio and measles have saved million lives and have shown undeniable impact on the improvement of global health and economy [1]. Vaccine development is a time-consuming laborious process. According to previous experiences in the design, development and formulation of vaccines for various infectious diseases, the average time for vaccine development is usually around 10–15 years. For example, the development of vaccines against polio and Ebola took more than four decades while the effective vaccine against measles was developed in a decade. Before development of COVID-19 vaccine, the mumps vaccine was the fastest one to have ever been developed which took around four years [2].
Generally, there are four main platforms for vaccines against viral infectious diseases including inactivated/live attenuated, recombinant protein, viral vector and nucleic acid-based vaccines. These platforms are able to induce different immune responses with different duration of protection. While attenuated vaccines results in the induction of immune responses for several years, recombinant protein vaccines have shown the shortest protection [3]. Moreover, the industrial manufacturing, distribution condition, route of administration and public perception and confidence is dependent on the selected platform for a specific vaccine. Furthermore, the regulations for vaccine approval are different based on the platform used for the development and production of a vaccine. The first approved COVID-19 vaccine for human application entered the market less than one year from the beginning of the pandemic. After around 2.5 years of starting the pandemic by SARS-CoV-2, there are several approved vaccines in the market with various platforms, different efficacies and prices [4]. This shows that COVID-19 pandemic led to the investment and investigation on novel vaccine platforms as well as conventional ones. One of the biggest and most challenging decisions for companies and health policymakers at the beginning of the pandemic was to choose the most efficient platform with the highest safety profile and affordable price. Since there were no general formulae to answer this question, almost all vaccine platforms were being developed. While conventional platforms of attenuated or recombinant protein had a long history of successful application with high safety, novel platforms of mRNA or adenoviral vectors were also used for vaccine development due to great advantages which had been shown in previous studies. The focus of this review is on the lessons learned from the pandemic in terms of vaccine platform. These lessons are necessary in the management of future pandemics and potential use of these platforms for the treatment of other non-viral diseases and may help policymakers, investors and scientists to know how vaccine platform can change the game [5].
2 Necessity of considering different vaccine platforms
The progress toward vaccine development may encounter failure or setbacks. The examples from vaccine candidates investigated by Merck, Sanofi and GlaxoSmithKline shows that investment in parallel in multiple technologies can be a solution to overcome the potential failure during the development of vaccine [6]. No scientist or health policymaker was confident that vaccine would be ready for large-scale production and entering the market. One reason for pessimism was the experience with the original SARS-COV virus in 2003, when vaccines failed in the field. This led to the investment on various platforms at the beginning of COVID-19 pandemic [7]. In the United States, parallel investments on multiple technologies including mRNA and viral based- vaccines as well as recombinant protein guaranteed the success of their efforts toward the production of efficient vaccines. Since there is no unique platform to fulfil all requirements needed for development of safe and efficient vaccine, advantages and disadvantages of available platforms must be carefully considered.
Inactivated vaccines have been used for human application for decades and their high safety profile particularly in children, elderly and immunocompromised individuals have made them suitable platforms for vaccine development. This platform has been applied for the production of various viral vaccines including polio, hepatitis A and rabies as well as SARS-CoV-2 [8]. Moreover, the application of continuous cell lines for their large-scale production reduces the cost of their scale up and makes them as an attractive candidate for vaccine manufacturers. Furthermore, the long history of their effectiveness and safety facilitates their acceptance by regulatory authorities as well as public [9]. However, inactivated vaccines are less immunogenic than their attenuated counterparts. This is because the viral particles cannot replicate in the host cells and therefore are not able to induce efficient activation of dendritic cells. Hence, multiple doses are required to maintain the immune protection against infection. In pandemics such as COVID-19, this means the administration of millions excess doses. The repeated dose needs the production of higher number of viral particles and subsequently larger quantities of production and formulation materials as well as facilities [10]. Moreover, inactivated vaccines lead to the production of strain-specific neutralizing antibodies limiting their cross-protection responses. This is a matter of concern particularly in the emergence of new variant of concerns (VOCs) during pandemics [11].
Live attenuated vaccines have been used for several decades against various viral infections including mumps, measles and rubella as well as influenza, varicella and polio. Since the viral particles in these vaccine formulations are alive, they can mimic the natural infection and are suitable platforms to induce robust immune responses particularly in comparison with their inactivated counterparts [12]. Generally single doses of these vaccines are sufficient to induce long lasting protection due to their ability to replicate inside the cells. The replication of attenuated vaccines reduces the administration of large quantities of viral particles in repeated doses. In other words, the administration of small quantities in single shot is sufficient to induce protective immune responses. This significantly reduces the cost of mass vaccination in pandemics. Also, viral components such as RNA can be recognized by innate immune cell receptors such as toll-like receptor (TLR) leading to the induction of cytokine secretion [13], [14]. Despite these advantages, there are serious concerns to use this platform for vaccine production in viral pandemics such as COVID-19. The recovery of virulence and contamination with other viruses during the industrial production are two major concerns for their application in viral pandemics [15]. Since the downstream processing of these vaccines does not include inactivation steps, other viruses such as simian virus 40 (SV40) may be present in the final formulation. This platform is not recommended for immunocompromised individuals and elderly due to these concerns [15], [16]. Also, large-scale culture of highly contagious viruses such as SARS-CoV-2 and extensive quality control tests need facilities which are not easily available in several countries. Moreover, maintaining cold chain for their distribution is another limiting factor hampering their wide distribution for mass vaccination programs especially in pandemics [17], [18]. Hence, very few number of COVID-19 vaccines have been developed using live attenuated platform [19].
In order to overcome the barriers hampering the application of live attenuated viruses for vaccine development against highly contagious or pathogenic viral infections such as SARS-CoV-2, Zika, and Ebola, an alternative strategy has been suggested and employed based on engineered viral vectors [20]. In this strategy, different viruses including adenovirus and cytomegalovirus can be used. The viral vectors contain all necessary genes for transmission and amplification inside the hosts’ cells while the replication-competent virus is produced by the deletion of the genes such as E1 transcriptional unit of adenoviruses. The desired antigen is cloned into the viral host and the engineered viruses are propagated in various mammalian cell lines using suspension cell culture bioreactor systems [21], [22]. Following nuclease-treatment of the product, a two-step purification is carried out including sequential chromatography or chromatography-tandem ultracentrifugation/filtration [23], [24]. This vaccine platform has shown substantial ability in triggering humoral and cellular immune responses by the induction of neutralizing antibodies as well as cytotoxic T lymphocyte (CTL) responses. The latter is not generally induced by subunit protein or inactivated vaccines. It could be considered as the main advantage of this platform since it could present the epitopes in their native three dimensional structure to the immune system resulting in robust immune responses [25], [26]. Since the viral vectors used in this platform are avirulant and replication deficit, there is no risk of reversion to virulent state or replication in the host body. The recent advances in the large scale-production of viral particles have enabled researchers and developers to produce high titer of viruses in bioreactors. Moreover, the fast development of viral vector-based vaccines during several months makes them suitable candidates for vaccine production in pandemics with low cost [27], [28]. However, there are some concerns regarding their wide application. For example, the expression of viral vector genes which is necessary for its transmission or amplification may alter the responses of host immune system to desired antigens. Since various types of adenoviruses are circulating in populations, the pre-existing antibodies against these viruses may neutralize the viral-vector based vaccine before starting immune response to desired antigens [29]. Even if the pre-existing antibodies against the vector do not exist, the administration of repeated doses can elevate the titer of such antibodies limiting the application of this platform as booster shot [30]. Considering the major advantages of this platform, some COVID-19 candidate vaccines could obtain regulatory approval for human application including ChAdOx1-S and Ad26. COV2-S [31].
Recombinant protein vaccines are produced in the forms of subunit or virus-like particles (VLP). While subunit vaccines contain recombinant immunogenic viral proteins with the ability to induce immune response, VLPs are composed of viral antigens with the ability to be self-assembled into supramolecular structures to mimic native viruses in terms of geometry and particle size [32], [33]. The process of large-scale production of recombinant vaccines is laborious, expensive and time-consuming. However, some categories of this platform including VLPs are able to induce strong humoral and cellular immune responses making them suitable candidates for vaccine development in pandemics. The production of subunit protein vaccine starts with finding the appropriate antigens with the ability to elicit immune response and expression of desired recombinant proteins in suitable organisms offering high protein yields [34]. Hepatitis B surface antigen (HBsAg) is one of the successful examples of this vaccine platform. On the other hand, VLPs can either be formed via self-assembly by expressing antigenic proteins in a eukaryotic or prokaryotic system or the expression of blank proteins and subsequent conjugation of these peptides on pre-formed scaffolds [35]. Subunit vaccines only contain desired antigens resulting in the highest safety profile. Also, the low risk of antibody-dependent enhancement (ADE) and induction of eosinophilic immunopathology makes them promising candidates for immunocompromised persons [36], [37], [38]. VLPs can mimic the characteristics of natural viruses and enter the target cells via specific receptors. They can be degraded in the cells resulting in promising biocompatibility for human application. Moreover, there is no risk of reversion to virulence state for this vaccine platform. Despite promising safety and biocompatibility of recombinant vaccines, their immunogenicity is generally lower than the other platforms and it is necessary to add adjuvants into their formulation for the induction of robust protection and long-term responses. Another challenge in their large-scale production is finding a suitable expression system providing high yields of recombinant protein with high stability. Unlike subunit vaccines, VLPs have shown higher stability. However, the lack of viral genome makes them susceptible to disintegration during purification and formulation [39]. Although recombinant protein platform has shown substantial ability to induce immune responses with high safety, the challenges of downstream process result in slowing down their development. This feature led to the fact that there is only one FDA-approved COVID-19 vaccine using recombinant subunit or VLP technology so far (Novavax® (NVX-CoV2373)) [40].
Nucleic acid vaccines are the most recent platform for vaccine development that showed promising results for quick and robust response to viral infections [41], [42]. This platform contains two major subcategories of DNA- and mRNA-based vaccines. The similarity of DNA and mRNA based vaccines is that they use translational machinery of the cells to produce the antigens. In other word, they use host cells as the factory of antigen production [43]. Therefore, the production of final antigen occurs inside the cells and does not need the formulation of protein antigen or whole viral particles [44], [45]. DNA vaccines are plasmid structures containing the desired antigen under control of viral promoters. Considering the stability of DNA in room temperature and no requirement for freezing the product for distribution, it seems that this vaccine platform can be used in pandemics [46], [47]. However, there are several drawbacks which led to limited application of this platform for human vaccines. During COVID-19 pandemic, only one plasmid DNA vaccine was approved. ZyCoV-D is a plasmid DNA vaccine for intradermal injection via jet injector approved by Indian regulator for restricted use [48]. One of the major concerns of DNA vaccines is the potential of their integration into the host genome which may lead to insertional mutagenesis or chromosomal instability [49], [50]. Also, various studies have shown low induced immunogenicity of plasmid based vaccines compared with conventional vaccines such as inactivated platform [51]. Another limitation of plasmid DNA vaccines is their need for efficient delivery system [52], [53]. Plasmid must enter the cells, bypass degrading endo-lysosomal compartments, move inside cytosol and reach the cell nucleus [54]. Overcoming these barriers needs sophisticated methods or materials which in turn increases the final cost of the product [55]. Furthermore, strict regulation and rules of regulatory authorities to approve plasmid DNA vaccines increases the cost and reduces the development speed [56]. Hence, it seems that this platform is more appropriate for animal application or in human when the quick response is not necessary [57].
The idea of mRNA application as therapeutic agent was shown by Wolf and colleagues in the 1990 s when the direct injection of mRNA encoding chloramphenicol acetyltransferase, luciferase, and beta-galactosidase into mouse skeletal muscle resulted in the expression of the protein [58]. The immunostimulatory effects of mRNA have been considered as the major obstacle for further development of this macromolecule as a therapeutic agent [59]. It has been demonstrated that mRNA acts as an endogenous ligand for Toll-like receptor 3 (TLR 3) [60]. In order to modulate this effect, modified nucleosides were used to prepare mRNA through in-vitro transcription (IVT). Kariko and colleagues replaced pseudouridine instead of uridine and showed that this modification led to the less immunogenicity of mRNA. Also, the replacement of 5-methylcytidine for cytidine was carried out to modify the mRNA properties. These nucleoside replacements not only lead to the lower immunogenic effects of mRNA, but also increased the biological stability and translational capacity [61]. These experimental and lab-scale investigations led to the introduction of mRNA as a new macromolecule suitable for vaccine development. mRNA molecule requires to contain key elements to act as a vaccine. In addition to the coding sequence and its stop codon, a 5 ´ cap and a 5 ´ untranslated region (UTR) combined with 3 ´ UTR and a poly adenylated tail (poly A) are crucial for antigen production [62]. mRNA vaccine is produced by in vitro transcription (IVT) of the desired antigen. This process starts from a linearized DNA followed by the chemical synthesis of mRNA and finally the treatment with DNases to digest the DNA template and mRNA purification by tangential flow filtration (TFF). mRNA is purified from the reaction mixture by various filter membranes including polysulfone, and polyethersulfone based on the size differences between the impurities and mRNA [63], [64]. There are several advantages to use this platform for vaccine production particularly in pandemics. First, production of desired protein antigen occurs inside the host cells. In other words, the technology uses the human cells as the facility of vaccine production. Unlike viral vector or inactivated/live attenuated vaccines, the production of bulk materials does not rely on biological systems such as chicken eggs or mammalian cell culture in bioreactor. The chemical synthesis instead of biological production of a vaccine can be considered a significant achievement in industrial production [65], [66]. Second, rapid scale-up with low cost is possible due to high yields of mRNA synthesis by IVT. Moreover, non-infectious and non-integrating nature of mRNA results in a negligible risk of infection and insertional mutagenesis in patients [67]. Third, the strong humoral and cellular immune responses in mRNA vaccines are not only associated with the production of the desired antigen in the cells with native folding and post-translational modifications (PTM), but also is the result of intrinsic adjuvant activity of mRNA and its lipid nanoparticle (LNP) delivery system. Hence, no added adjuvant is required for the formulation which in turn leads to the faster scale-up and simpler formulation as well as low cost [68], [69], [70]. Fourth, the risk of pre-existing antibodies that limit the application of viral vector vaccines for mass vaccination programs in pandemics or the production of post-exposing antibodies that hamper the application of such vaccines as booster shots is significantly lower in mRNA platform. The approved mRNA vaccines contain lower required doses of mRNA compared with the conventional platforms. For instance, Moderna and BioNTech/Pfizer mRNA vaccines contain mRNA in the scale of microgram while the conventional platforms require milligram amount of antigen per dose [71], [72]. Moreover, mRNA technology can also be applied for the manufacturing of vaccine for a single infection in which different antigens needed for inducing robust immune responses. For example, commercially available influenza vaccines are produced based on two main antigens (i.e.; neuraminidase and hemagglutinin). These antigens can be coded by one or two mRNA constructs formulated in the same LNP carrier [73].
Despite these significant advantages of mRNA platform for vaccine development, there are some concerns regarding their application in future pandemics. The first mRNA vaccines obtained emergency used authorization in 2020. The little regulatory precedents for mRNA vaccines are the main obstacle for manufacturers to produce vaccines based on this platform for global application. The different regulations in different countries may act as a barrier to obtain approval. Furthermore, double-stranded RNA (dsRNA) is a main contaminant of mRNA formulations and has shown pathogen-associated molecular patterns (PAMPs) activation of innate immunity. The efficient removal of dsRNA by high pressure liquid chromatography (HPLC) or filter-binding technology not only reduces the risk of innate immunity activation but also increases translation efficiency [74], [75]. Another concern for the use of mRNA vaccine in different populations is the risk of type I interferon (IFN) responses which may interfere with the capacity of mRNA vaccines to elicit CTL responses [76]. Therefore, identification of individuals with higher probability of induction of such responses or prevention of type I IFN induction at the site of injection could be a promising strategy to enhance the efficiency of mRNA vaccine. However, these solutions cannot be easily applicable in pandemics in which billions of people may need extra-interventions [77], [78]. Moreover, naked mRNA may induce thrombosis. Therefore, the efficient loading and packaging of mRNA molecules by LNP to prevent mRNA leakage from its carrier is a crucial step for successful formulation of mRNA vaccines [79], [80].
For future pandemics, it is necessary not to put all eggs in one basket. The second important lesson for scientists and policymakers is that “diversity of vaccine platforms is critical for their success”.
Table 1 summarizes platform and manufacturing company of all the COVID-19 vaccines authorized at least by one regulatory authority.Table 1 Covid-19 vaccines in the market authorized by at least one regulatory authority.
Table 1Vaccine platform Name Antigen/Mechanism Company Authorized by
Inactivated Sinopharm / BIBP
Coronavac
Covaxin
CoviVac
VLA2001
QazCovid-in
Mihai
COVIran Barekat
Fakhravac
Turkovac
Sinopharm / WIBP
Inactivated, produced in Vero cells
Inactivated, produced in Vero cells
Whole-Virion Inactivated Vero Cell
Inactivated
Inactivated
Inactivated
Inactivated
Inactivated
Inactivated
Inactivated
Inactivated, produced in Vero cells Beijing Institute of Biological
Products Co., Ltd.
Sinovac Life Sciences Co., Ltd.
Bharat Biotech, India
Russian Academy of Sciences
Valneva SE and Dynavax Technologies
Research Institute for Biological Safety Problems in Kazakhstan
Minhai Biotechnology Co. and Shenzhen Kangtai Biological Products
Shifa Pharmed Industrial
Organization of Defensive Innovation and Research
Health Institutes of Turkey
Wuhan Institute of Biological Products Co Ltd WHO1
Bahrain, China, UAE
WHO, China
WHO, India
Russia, Cambodia, Belarus
UK
Kazakhstan,
Kyrgyzstan
China, Indonesia
Iran
Iran
Turkey
North Macedonia
Peru
Philippines
UAE
Venezuela
Protein (recombinant subunit/conjugated) NVX-CoV2373/Covovax/Nuvaxovid
Abdala/CIGB-66
Soberana
02/FINLAY-FR-2/PastoCovac
MVC-COV1901/MVC
COVAX-19/SpikoGen
Razi Cov Pars
Corbevax
CoVLP / Covifenz
Noora
ZF2001/ Zifivax /ZF-UZ-VAC-2001
SKYCovione Recombinant nanoparticle
prefusion spike protein formulated
with Matrix-M™ adjuvant
Monomeric RBD subunit, residues 331–530 of the Spike protein of SARS-CoV-2 strain 156 Wuhan-Hu-1, expressed in the yeast Pichia pastoris
RBD of the SARS-CoV-2 spike protein conjugated to tetanus toxoid
S-2 P spike protein adjuvanted with CpG
Recombinant prefusion-stabilized SARS-CoV-2 spike protein combined with the Advax-CpG55.2™ adjuvant
SARS-CoV-2 spike protein
RBD of the SARS-CoV-2 spike protein with the adjuvants aluminium hydroxide gel and CpG 1018
Recombinant spike protein (VLP)
Recombinant RBD Protein
Adjuvanted dimeric form of the RBD
self-assembled nanoparticle vaccine targeting the RBD Novovax/Serum institute of India
Center for Genetic Engineering and Biotechnology,Cuba
Finlay Institute, Cuba and Pasteur Institute of Iran
Medigen Vaccine Biologics Corporation in Taiwan and Dynavax Technologies
Vaxine and CinnaGen
Razi Vaccine and Serum Research Institute
Baylor College of Medicine / Indian biopharmacutical firm Biological E. Limited (BioE)
Medicago /GlaxoSmithKline (GSK) /Nicotiana benthamiana
Baqiyatallah University of Medical Sciences
Anhui Zhifei Longcom Biopharmaceutical
SK Bioscience/ University of Washington WHO, EMA2
Cuba
Mexico
Nicaragua
Venezuela
Vietnam
Cuba
Iran
Nicaragua
Venezuela
Taiwan
Paraguay
Somaliland
Iran
Iran
India
Botswana
Canada
Iran
China
Colombia
Indonesia
Pakistan
Uzbekistan
South Korea
Viral vector AZD1222/ Vaxzevria/ Covishield
Janssen/ Ad26. COV
2. SConvidecia/ Ad5-nCoV
Sputnik V Recombinant ChAdOx1 adenoviral
vector encoding the Spike protein
antigen of the SARS-CoV-2.
Recombinant, replication-
incompetent adenovirus type 26
(Ad26) vectored vaccine encoding
the (SARS-CoV-2) Spike (S) protein
Recombinant Novel Coronavirus
Vaccine (Adenovirus Type 5 Vector)
Two recombinant replication-defective human adenoviruses: Ad26 (serotype 26) and Ad5 (serotype 5) encoding the full-length spike protein (S) of SARS-CoV-2 University of Oxford/AstraZeneca/ Serum Institute of India
Janssen Pharmaceuticals
CanSino Biologics
Gamaleya Research Institute of Epidemiology and Microbiology WHO,EMA, FDA3
WHO,EMA, FDA
WHO, China
Russia, Turkmenistan
Uzbekistan
Nucleic acid vaccine BNT162b2/COMIRNATY
Tozinameran
mRNA-1273/ Spikevax
ZyCoV-D Nucleoside modified mRNA encoding a mutated form of the full-length spike protein
Nucleoside modified mRNA encoding a version of the spike protein with a modification called 2 P
DNA plasmid vector encoding the spike protein of SARS-CoV-2 adjuvanted with CpG BioNTech Manufacturing
GmbH/Pfizer
Moderna
Cadila Healthcare WHO,EMA, FDA
WHO,EMA, FDA
India
1 World Health Organization
2 European Medicines Agency
3 US Food and Drug Administration
3 How vs. how fast
The average daily death toll of COVID-19 from May 2020 to February 2022 was around 4000–10,000 people per day, globally. While the confirmed number of deaths by COVID-19 is around 6 million, the latest estimate from World Health Organization (WHO) suggests that the full death toll associated with the pandemic is approximately 14.9 million [81]. This shows that the quick response to pandemic is a prerequisite step in the reduction of deaths associated directly or indirectly with the virus. In other words, each single day without vaccine means higher number of deaths. The first approved COVID-19 vaccine obtained approval from regulatory authorities less than one year after beginning of the pandemic. Thanks to the mRNA technology, the fastest vaccine development in the human history emerged. mRNA technology does not need biological safety measurements used for the development of inactivated or live attenuated vaccines. In vitro transcription (IVT) process provides a quick and robust production of mRNA molecules used for vaccine formulation. Since IVT is a chemical process and does not need any cellular components, the final costs and safety considerations are really lower than conventional platforms and the process can be approved based on the GMP of conventional pharmaceuticals rather than the biologicals [82], [83]. In other words, for the first time in the history of vaccine development, the process is more chemical rather than biological. The development of such vaccines is based on the sequence of the gene encoding viral proteins which is necessary for the stimulation of immune responses. Therefore, the process of vaccine developments starts at the point in which the coding sequence information is available while the process of vaccine development for conventional platforms starts with the virus culture. This significant difference increases the speed of production and reduces the safety considerations [45].
Another major concern in pandemics is the generation of new mutations in specific antigens. Such mutations in viruses may lead to escape of virus from immune system and inefficiency of available vaccines in the market. In the COVID-19 pandemic, the generation of such variant of concerns (VOC) resulted from several mutations in their spike protein receptor binding domain (RBD) led to significant increase in their binding affinity as well as rapid spread in human populations. In such situations, quick response to these variants is a prerequisite step for disease control. Using conventional platforms, it may take years to develop a variant-specific vaccine for effective prevention of new variants. Thanks to the novel technologies including mRNA or DNA vaccines, the manufacturing platform can be easily adopted to these new variants. This is the results of the application of same chemical components used for the formulation of mRNA vaccines as well as the same technology for large-scale production of mRNA encoding antigen. In other words, various genetic codes can be designed in the same way and formulated with the same lipid nanoparticle (LNP) carrier. This flexibility in manufacturing and scale-up is a substantially attractive feature for the development of variant-specific boosters [84].
Another challenge of future is the quick production of vaccines for the viruses which may spread simultaneously. For example, the co-emergence of SARS-CoV-2 and seasonal influenza virus leads to overwhelming pressure on the health system and results in increased number of deaths. On the other hand, overlapping epidemics with previously-known infections (e.g.; Dengue, COVID-19 and influenza) is a matter of concern particularly in some regions including Latin America, Asia and Africa [85]. In order to address these concerns, different antigens can either be coded by a single long mRNA or several small short ones. Using this strategy, the administration of one shot of vaccine can prevent two or more infections accelerating the effective response to viruses.
Regarding the speed of response to pandemic, viral-based vaccines can also be considered as a suitable choice. The whole process of identification of protective antigens to large-scale production in bioreactors, clinical-grade purification process and packaging takes less than a year. Moreover, the availability of quality-controlled production cell lines as well as standard regulatory approaches have accelerated fast production of viral-based vaccines [7], [86]. One of the major factors accelerating the bench to bedside translation of viral-based vaccines is the use of bioinformatics approach in their production. The process of viral-based vaccine development starts with sequencing and construction of antigen library according to bioinformatics approaches. The application of bioinformatics in the vaccine development is a robust approach shortening the whole process of vaccine production. This strategy not only expedites the vaccine development but also enable researchers to prepare variant-specific vaccines in a short time. By finding the differences of nucleotide sequence between various variants and the impact of such differences in the induction of immune responses, researchers are able to update their vaccines in a short time [43].
Although the other platforms can lead to a product in the market, the quick response is the most important factor in the management of such human disasters [73]. Overall, it seems that nucleic acid and viral based platforms are the most suitable vaccines to provide quick response to future pandemics. Hence, selecting platform with the ability to provide “quick response (and not just response)” is a key step for vaccine development in future pandemics.
4 Vaccine is not vaccination
An efficient and safe vaccine does not necessarily mean a successful vaccination would be achieved. A successful vaccination needs several factors including availability of stable and safe vaccine, shipment and distribution facility as well as expert healthcare worker for administration. Vaccine platform can determine its stability, storage and shipment condition as well as its route of administration [87]. Storage and shipping condition have shown significant impact on vaccination success in pandemics. One of the examples comes from the Ebola epidemics in Africa. Since the storage condition for the live attenuated vaccine (rVSV-ZEBOV; Ervebo®) was − 60 °C, shipping and distribution of the vaccine encountered big challenges in African countries [44]. While inactivated vaccines presented the highest stability among conventional platforms, live attenuated and viral-vector vaccines are more sensitive to high temperatures due to the presence of live viral particles. Recombinant protein vaccines and VLPs also need cold-chain shipping and storage condition. The three dimensional structure and correct folding of the protein antigen or VLPs is essential to induce desired immune response [88], [89]. The novel platforms of plasmid DNA and mRNA exhibited different stability profiles [90]. While plasmid based vaccines are more stable, and do not need freezing condition for shipping and distribution, mRNA vaccines require storage conditions between − 20 °C to − 70 °C due to the instability of mRNA and its formulation [73]. This different storage and shipping condition may act as a determining factor for the success of a specific vaccine in pandemics. Successful global vaccination relies on the distribution of the vaccine to various countries with different health infrastructure in different seasons and weather conditions. As an alternative approach, freeze-drying of such formulation can be considered. However, maintaining the activity of mRNA and its LNP formulation during freezing and dehydration steps of lyophilization is another challenge [91]. Regarding the stability of mRNA-LNP formulation, several studies have investigated the role of different factors in the stability of final formulation. It has been shown that mRNA and ionizable lipids are positioned in the core of LNP formulation. Since water is also present in the core, it is estimated that mRNA degradation might be a key factor in the instability of mRNA vaccine formulations [92]. There are some reports indicating the mRNA hydrolysis in aqueous solution. However, mRNA in LNP formulation is located in the core of LNP structure and coated with ionizable lipids. Therefore, this formulation may increase the stability of mRNA compared with naked mRNA dissolved in aqueous environment [93]. Another factor is determination of the exact mechanism(s) of mRNA degradation in the final formulation. The determination of mechanism(s) of degradation may help researchers to find out whether the optimization of mRNA nucleotide composition may help the mRNA stability. Also, there are some other factors determining the stability of mRNA-LNP formulation including pH inside the LNP as well as the formation of secondary and tertiary mRNA structures which may increase the stability of mRNA strands [94].
Availability of different vaccine platforms in pandemics may provide another opportunity to mix various platforms for vaccination programs. There are several studies suggesting that mix-and-match vaccines result in potent immune responses with side effects at least at the same level of standard regimens. The administration of different vaccine platform was not only used for the second dose of COVID-19 vaccine, but also was recommended for the booster jabs [95], [96]. Generally, there are two strategies for mix-and-match including heterologous prime-boost vaccination regimen as well as a different booster shot after two dose of homologous prime-boost vaccination. For example, several heterologous prime-boost vaccination regimens have been reported including the combination of ChAd and BNT in UK [97] and mRNA-1273,ChAd, and BNT in the US [98]. Also, the combination of Sputnik V with rAd26 and rAd5 was investigated in Russia [99]. The results of these studies showed that this strategy led to the induction of comparable or higher antibody titers and a similar reactogenicity profile to the homologous prime-boost regimen. The application of different vaccines as booster job is another approach in mix-and-match strategy. For example, one of the largest studies involved more than 4 million patients in Chile showed that protection of patients against severe disease and death is higher in the patients who received a booster shot of mRNA or viral vector vaccine after two doses of an inactivated virus vaccine than those who received three doses of inactivated virus vaccine [100]. The three-dose homologous inactivated vaccine effect against hospitalization, intensive care unit admission, and death was 86.3%,92.2%, and 86.7%, respectively. By comparison, the mRNA booster was more effective at 96.1%, 96.2%, and 96.8%, respectively, while the viral vector vaccine booster was the most effective at 97.7%, 98.9%, and 98.1%, respectively [100]. Besides the advantages such as potent immune responses and low side effects, the mix-and-match strategy may help mass vaccination programs in low-income countries where shortage of a vaccine platform is a big problem [101].
Another important factor for successful vaccination in pandemic is the route of administration which can be determined by various factors including the platform [102]. Almost all of COVID-19 vaccines are used via intramuscular (i.m.) injection. This route has been widely used for several parenteral pharmaceuticals and vaccines and needs little training program for healthcare workers to be eligible to use it. Although this administration route is the mostly-used route for vaccination against viral infections, its application for billions of individuals in a short period of time to combat pandemics is another major challenge. In this situation, the platforms with the ability to provide alternative routes of administration may be preferred [103]. For example, nasal administration of viral vector, mRNA and subunit proteins is under investigation [104], [105]. Nasal delivery of vaccines not only induces humoral and cellular immunity, but also provides the protection against respiratory viruses at their entrance site inside the nasal cavity by IgA secretion [106]. Indeed, nasal administration is less painful and more friendly to children and can also be used as booster shot for adults.
Another challenge for recently-approved platforms is the fact that their conventional platforms (e.g., live attenuated or inactivated) have been used for decades and enormous data regarding their safety and efficacies are available. On the other hand, viral vector or nucleic acid based vaccines are in their infancy. They received emergency use authorization (EUA) based on the pandemic situation according to limited clinical trials conducted on thousands of volunteers while the conventional platforms had been tested on billions of individuals. The assessment of the risks and adverse reactions must be carried out in real world setting. For example, vaccine-induced thrombotic thrombocytopenia and myocarditis were observed after the injection of million doses of viral vector and mRNA based COVID-19 vaccines; respectively. According to such reports, some countries stopped Vaxzevria and Ad26. COV2. S vaccination or limited its injection to specific age groups [107]. These rare events may make great impact on perception, confidence, hesitancy and acceptance of vaccines during a pandemic [48]. In such situation, some countries may halt the application of these platforms not based on scientific data but according to their socio-political interests [108]. The next lesson is that to know the “the possibility of vaccination in real world is as important as its development”.
5 Future application vs. today demands
Millions of people have died during pandemics in the human history. Optimistically, we have no ever-lasting pandemic. Even, the worst pandemics such as plague, smallpox, cholera and flu finally ended [109]. It means that scientists and policymakers must focus on the day after the pandemic, too. The winners of vaccine development are not those who are able to produce a robust, efficient and safe vaccine to save lives. Real winners of this breathtaking game are those who use the opportunity for introducing a novel technology that can be used for future applications. This application may be the vaccine for infectious diseases, as well as the technology for efficient treatment of diseases and novel diagnostics. For example, mRNA platform can also be used as vaccine or immunotherapy for cancer treatment. One of these approaches is the development of mRNA vaccines for patient-specific neoantigens. Somatic mutations in cancer cells lead to the expression of specific proteins on cancer cells which are not present on the normal cells. The processing of these proteins to peptides occurs in proteasomes and the peptides are presented by MHC I and consequently recognized by T cells [110]. These neoantigens can be considered as targets for personalized cancer therapy by mRNA. Following the identification of patient-specific neoantigens by next-generation sequencing (NGS), the mRNA encoding specific neoantigen is prepared. The injection of this mRNA is expected to result in the generation of patient-specific anti-tumor response [111]. There are several examples of such approach for cancer treatment, tested by BioNTech and Moderna companies. For instance, BNT122 and mRNA-4157 are two mRNA formulations based on targeting patient-specific antigens for pancreatic cancer, non-small cell lung cancer and solid tumors [112], [113].
Another possibility of mRNA modality is its potential to produce therapeutic levels of antibodies or cytokines in desired organs. The production of antibodies from mRNA rather than conventional manufacturing process via recombinant technology may have some benefits including lower required doses and longer intervals between the injections as well as the pattern of antibody glycosylation [114]. This approach has been examined to produce monoclonal antibodies against Chikunguya and SARS-CoV-2 viruses as well as malignancies [115], [116]. mRNA technology can also be used for the generation of immunostimulatory or immunomodulatory fusion proteins. Various mRNA based formulations are under investigation to produce a variety of proteins including OX40 ligand (OX40L), CD40L, CD70, caTLR4, IL-23 and IL-36γ, IL-12sc, IL-15sushi, IFN-α and granulocyte–macrophage colony-stimulating factor (GM-CSF) [117], [118]. One of the recent applications of mRNA technology is its combination with chimeric antigen receptor (CAR) T-cell therapy. Conventionally, CAR T-cells are produced by retroviral gene transfer. Using mRNA formulations, patients’ T cells are transfected with desired mRNA to induce specific immune responses against tumor tissues. Using this approach, mRNAs encoding mesothelin- or c-Met- directed CAR have been applied for the treatment of pancreatic and breast cancers; respectively [119], [120]. Since mRNA has been formulated for various routes of administration, the future application of this technology is not limited to vaccines or cancer therapy. In other words, mRNA can be used via different routes of administration for the treatment or prevention of a variety of diseases [73]. Table 2 briefly reports mRNA- and viral vector-based therapeutics approved by FDA or in clinical trials for other applications.Table2 Examples of mRNA- and viral vector-based therapeutics approved by FDA or in clinical trial.
Table2Drug candidate Platform Mechanism Target/indication Clinical trial phase Company
Onasemnogene abeparvovec Adeno-associated virus vector A recombinant AAV9-based gene therapy designed to deliver a copy of the gene
357 encoding the human SMN protein Spinal muscular atrophy (SMA FDA- Approved Novartis Gene Therapies, Inc.
Voretigene neparvovec Adeno-associated virus vector To deliver a normal copy of the gene encoding the human retinoid isomerohydrolase
RPE65 (RPE65) to cells of the retina in persons with reduced or absent
10
levels of biologically active RPE65 Biallelic RPE65 mutation-associated retinal dystrophy FDA- Approved Spark Therapeutics, Inc.
Talimogene laherparepvec HSV-1 virus Live, attenuated HSV-1 that has been genetically modified to express huGM-CSF Melanoma FDA- Approved Amgen Inc.
Betibeglogene autotemcel (Zynteglo) Lentiviral vector Transplantation of autologous CD34+ stem cells transduced ex vivo with a Lentiviral βA-T87Q-globin vector β-Thalassemia FDA-approved Bluebird Bio
Lumevoq, lenadogene
Nolparvovec, GS010 Adeno-associated virus vector Recombinant adeno-associated viral vector, serotype 2 (rAAV2/2), encoding the human wild-type mitochondrial NADH dehydrogenase 4 protein (ND4), for the treatment of LHON associated with mutation in the ND4 gene Leber hereditary optic neuropathy Phase III GenSight Biologics
Valoctocogene roxaparvovec Adeno-associated virus vector AAV5-based gene-therapy vector that expresses a B-domain–deleted human factor VIII coding sequence from a hepatocyte-selective promoter Haemophilia A Phase III BioMarin
Pharmaceutical
Etranacogene dezaparvovec Adeno-associated virus vector Adeno-associated virus (AAV) 5 vector with a liver-specific promoter and a hyperactive Factor IX transgene Haemophilia B Phase III uniQure
Olenasufligene
relduparvovec Adeno-associated virus vector Adeno-associated virus carrier, AAVrh.10, which has a particular tropism for cells of the CNS to transfer SGSH gene mucopolysaccharidosis type IIIA (Sanfilippo A syndrome) Phase III Lysogene
NSR-RPGR Adeno-associated virus vector An AAV8 vector–based gene therapy that uses codon optimization to express the full-length, correctly sequenced retinitis pigmentosa GTPase regulator (RPGR) protein X-linked retinitis pigmentosa Phase III Biogen/
Nightstar Therapeutics
Timrepigene emparvovec Adeno-associated virus vector Recombinant AAV2 vector designed to deliver a functional version of the human choroideremia gene into the retinal pigment epithelium and photoreceptor cells Choroideremia Phase III Biogen/Nightstar
Therapeutics
CG7870 adenovirus replication-selective oncolytic adenovirus genetically engineered to replicate preferentially in prostate tissue Metastatic hormone-refractory prostate cancer Phase I/II Cell Genesys
OBP-301 adenovirus gene-modified oncolytic adenovirus in which selectively replicate in cancer cells by introducing human telomerase reverse transcriptase (hTERT) promotor Metastatic melanoma Phase II Syneos Health /Oncolys BioPharma Inc
Elivaldogene autotemcel (eli-cel) Lentiviral vector Transplantation of autologous CD34+ hematopoietic stem cells, transduced ex-vivo with Lenti-D lentiviral vector cerebral adrenoleukodystrophy (CALD) Phase III Bluebird Bio
BNT111 mRNA
FixVac (fixed combination of
shared cancer antigens) a fixed combination of mRNA-encoded, tumor-associated antigens aiming to trigger a strong and precise immune response against cancer. Advanced melanoma Phase II BioNTech
BNT112 mRNA
FixVac (fixed combination of
shared cancer antigens) Targeted antigens of BNT112 are 5 prostate cancer specific antigens (PAP, PSA
and 3 undisclosed antigens Prostate cancer Phase I BioNTech
BNT113 mRNA
FixVac(fixed combination of
shared cancer antigens) Targeting a fixed combination of non-mutated shared antigens shared across patients HPV16 + head and neck cancer Phase II BioNTech
BNT115 mRNA
FixVac(fixed combination of
shared cancer antigens) Targeting a fixed combination of non-mutated shared antigens shared across patients Ovarian cancer Phase I BioNTech
Autogene
cevumeran
(BNT122) mRNA
iNeST(Tailored Treatment to Exploit Individual Targets) Fully individualized mRNA immunotherapy
Targeting 20 neo-antigens unique to each patient 1 L melanoma, solid tumors, Adjuvant colorectal cancer Phase I BioNTech /Genentech
SAR441000 (BNT131) mRNA mRNA mixture encoding IL-12sc, interferon alpha2b, GM-CSF and IL-15sushi as monotherapy and in combination with cemiplimab Advanced solid tumors Phase I BioNTech /Sanofi
BNT151 mRNA
(mRNA-encoded
Cytokines) mRNA encoding sequence-modified IL-2,
Modification that weakens binding to IL-2Rα (CD25)
Designed to stimulate naïve and effector T cells with low to no expression of IL-2Rα Solid tumors
Phase I BioNTech
BNT152
BNT153 mRNA
(mRNA-encoded
Cytokines) mRNAs encoding IL-2 and IL-7
BNT153 (IL-2):
Stimulates recently activated anti-tumor T cells and regulatory T cells
BNT152 (IL-7)
ensitizes effector T cells to IL2
Controls fraction of immunosuppressive regulatory T cells Solid tumors Phase I BioNTech
mRNA-6231 mRNA mRNA encoding human IL-2, fused to human serum albumin IL-2 autoimmune disorders Phase I Moderna
mRNA-6981 mRNA mRNA-encoding PD-L1 to send a tolerizing signal to immune cells to influence myeloid cells to provide additional co-inhibitory signal PD-L1 autoimmune hepatitis Phase II Moderna
mRNA-4157 mRNA mRNA based personalized cancer vaccine encoding neoantigens selected using a proprietary algorithm to induce neoantigen specific T cells and associated anti-tumor responses Personalized cancer vaccine (PCV) Phase II Moderna/Merck
mRNA-5671 mRNA mRNA tetravalent vaccine that targets G12D, G12V, G13D or G12C driver mutations in the KRAS gene KRAS vaccine Phase I Moderna
mRNA-2752 mRNA mRNAs encoding human OX40L, IL-23, and IL-36γ, for intratumoral injection alone and in combination with durvalumab Solid tumors/lymphoma Phase I Moderna
MEDI1191 mRNA mRNA encoding IL-12, followed by durvalumab (anti-PD-L1) in patients with advanced solid tumors Solid tumors Phase I Moderna/AstraZeneca
AZD8601 mRNA mRNA encoding vascular endothelial growth factor (VEGF-A) for direct injection into the myocardium of patients undergoing elective coronary artery bypass surgery (CABG) surgery Myocardial ischemia Phase II Moderna/AstraZeneca
mRNA-3927 mRNA mRNA encoding two proteins that form the
deficient enzyme (PCCA (PA Type I) and
PCCB (PA Type II)) PCCA/PCCB Propionic acidemia (PA) Phase I Moderna
mRNA-3705 mRNA mRNA encoding human MUT, the mitochondrial enzyme commonly deficient in methylmalonic acidemia (MMA) MUT Methylmalonic acidemia (MMA) Phase I Moderna
Although mRNA modality was firstly introduced in COVID-19 era, viral vector based therapeutics had been applied previously. This modality is the mostly-investigated platform for gene delivery and there are some approved products in the market based on this modality. Despite the approval of some gene therapy therapeutics based on adeno-associated viruses (e.g. Luxturna and Zolgensma), the clinical translation of this approach has encountered several difficulties [121]. One of the major reasons that the approved COVID-19 vaccines used novel technologies such as viral-based or mRNA based technologies is that the companies can collect tremendous data on the application of such new formulations in the patients as well as healthy individuals [122]. This can provide massive data facilitating the translation of these technologies into the other applications as well as modification and improvement of the currently-used therapeutics. The last but not the least lesson for policymakers and scientists is to “focus on the future potential applications of vaccine platforms beside their application to control a pandemic”.
6 Concluding remarks
One of the biggest challenges in the emergence of pandemics is to choose the appropriate platform for vaccine development. While the conventional platforms of live attenuated/inactivated and subunit protein have a long history of efficacy and safety, the novel modalities of mRNA and viral vector- based systems have shown substantial advantages. Since there is no guarantee over the selection of platforms to achieve success, it seems that parallel investment on various platforms may provide a better choice. The failure of Sanofi-GSK and CureVac in the development of subunit and mRNA vaccines showed that the selection of a platform is not sufficient to achieve success. Safety and efficacy are prerequisites for vaccine production. However, the speed of vaccine development can change the game. The capacity of novel platforms such as mRNA and viral vector based vaccine for the quick response to pandemics makes them suitable platforms for future outbreaks. This fast response also provides great opportunity for production of variant-specific vaccines. The selection of the most appropriate platform for vaccine development in a pandemic is not only based on safety, efficacy or development speed, but also is dependent on the shipping and storage condition as well as route of administration. The distribution difficulties for the mRNA vaccine showed that the improvement of storage condition and finding novel routes of administration (e.g.; nasal delivery) may accelerate the robust response to control pandemics. Regardless of the infectious diseases, a suitable platform for vaccine development may open up new horizons for various different diseases and facilitate the bench-to-bedside translation of novel approaches for prevention and treatment.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or that personal relationships could have appeared to influence the work reported in this paper.
Data availability
No data was used for the research described in the article.
Acknowledgements
We would like to thank 10.13039/501100004320 Shiraz University of Medical Sciences for financial support (grant number #26240).
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| 36514356 | PMC9731819 | NO-CC CODE | 2022-12-14 23:43:09 | no | Process Biochem. 2023 Jan 9; 124:269-279 | utf-8 | Process Biochem | 2,022 | 10.1016/j.procbio.2022.12.002 | oa_other |
==== Front
Rob Auton Syst
Rob Auton Syst
Robotics and Autonomous Systems
0921-8890
0921-8890
Elsevier B.V.
S0921-8890(22)00221-4
10.1016/j.robot.2022.104332
104332
Article
UV disinfection robots: A review
Mehta Ishaan a1
Hsueh Hao-Ya a⁎1
Taghipour Sharareh a
Li Wenbin b
Saeedi Sajad a
a Toronto Metropolitan University, Canada
b University of Bath, United Kingdom
⁎ Corresponding author.
1 These authors contributed equally to this work.
9 12 2022
9 12 2022
104332© 2022 Elsevier B.V. All rights reserved.
2022
Elsevier B.V.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
The novel coronavirus (COVID-19) pandemic has completely changed our lives and how we interact with the world. The pandemic has brought about a pressing need to have effective disinfection practices that can be incorporated into daily life. They are needed to limit the spread of infections through surfaces and air, particularly in public settings. Most of the current methods utilize chemical disinfectants, which can be laborious and time-consuming. Ultraviolet (UV) irradiation is a proven and powerful means of disinfection. There has been a rising interest in the implementation of UV disinfection robots by various public institutions, such as hospitals, long-term care homes, airports, and shopping malls. The use of UV-based disinfection robots could make the disinfection process faster and more efficient. The objective of this review is to equip readers with the necessary background on UV disinfection and provide relevant discussion on various aspects of UV robots.
Keywords
UV disinfection
Autonomous robots
UVC
COVID-19
Disinfection robots
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pmc1 Introduction
The COVID-19 outbreak has been classified as a global public health emergency. According to a recent report from WHO [1], more than 624 million cases and over 6.57 million deaths have been reported since the start of the pandemic. COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2, SARS-CoV-2. Enormous efforts have been devoted by nations worldwide to contain this disease. The virus can either be transmitted directly through respiratory droplets and aerosol particles in the air or indirectly through infectious droplets that are deposited onto surfaces [2], [3], [4]. Studies have shown that the virus can remain active and contagious on surfaces from hours to days depending on the surface material [5].
Although vaccines are being used to tackle the current pandemic, there is a need to develop more efficient decontamination procedures for the current pandemic and the post-pandemic world. Cimolai [6] presents a systematic review of environmental issues and decontamination strategies for COVID-19. The conventional method of decontamination is through manual cleaning followed by disinfection with chemicals. These procedures are labor-intensive, error-prone, could increase exposure risk for cleaning personnel, and do not provide consistent and effective results. Chemical disinfectants, including household bleach and quaternary ammonium compounds, can also be harmful to humans, leave unwanted residue, and be resisted by certain pathogens over time [7], [8].
On the other hand, ultraviolet radiation exposure is used as a no-contact decontamination method. There is a wealth of evidence indicating the effectiveness of ultraviolet (UV) germicidal irradiation as a disinfection and sterilization approach for the prevention of various infectious diseases, including COVID-19, influenza, and tuberculosis [9]. Short-wavelength ultraviolet light, known as UVC (200–280 nm, Fig. 1), disrupts the DNA/RNA of micro-organisms and terminates their cellular activities and reproductions. UVC disinfection can be achieved by mounting UVC lights on the ceiling or using portable UVC lamps to disinfect various surfaces and the air [10]. However, these modes are not very efficient in practice, as they could require long operating times and a lot of manual supervision.
There is a rising interest in using autonomous disinfection systems like UV robots, due to their potential for more efficient disinfection [12] These robots have been deployed to disinfect hospital rooms against COVID-19 through the use of intense radiation [13]. Although the routine application of UV robots in public places could significantly limit the spread of infections, the intense UV radiation from these devices is hazardous to human skin, and thus human presence should be avoided. This may limit the operating time of such devices, and modifications to existing UV systems may be required to further improve the safety and performance of disinfection.Fig. 1 The ultraviolet spectrum and its applications: UVC and UVB light (200–320 nm) are known to have germicidal capabilities. UVA radiation (320–400 nm) is not germicidal, and vacuum UV (100–200 nm) is absorbed in air rapidly and is not used for surface disinfection [11].
With the onset of the COVID-19 pandemic, there has been an increased focus on UV disinfection technology. Abajo et al. [14] and Raeiszadeh and Adeli [24] present critical reviews on UV disinfection, and their discussions cover a broad range of UV disinfection methods and the efficacy and safety of UV devices. Chiappa et al. [17] provided a review that demonstrates the efficacy of a variety of UV disinfection systems against different strains of coronavirus, while Martins et al. [22] explored the validity of different disinfection methods for SARS-CoV-2 in various settings. Some works have also covered the response of the robotics community [19], [25], [26], [27]. They gave an overview of robots, including disinfection, cleaning, and COVID-19 testing robots, that are being used to address problems faced during the pandemic. Kumar et al. [21] gave a brief overview of the basics of UV disinfection, and Guettari et al. [28] provided a discussion on the efficacy of UV robots and devices. However, most of the existing reviews focus solely on classical ultraviolet germicidal irradiation (UVGI) systems, and none of these works provide discussion regarding the autonomy systems of UV robots. Recently published reviews on UV disinfection systems and robots have been compared in Table 1.Table 1 List of review papers on ultraviolet germicidal irradiation (UVGI).
Author/Year Disinfection fundamentals UVGI systems UVGI robots Efficacy analysis Limitations of UVGI Irradiance modeling Open problems
García de Abajo et al./2020 [14]
Raeiszadeh et al./2020 [15]
Ramos et al./2020 [16]
Chiappa et al./2021 [17]
Dancer et al./2021 [18]
Holland et al./2021 [19]
Kwok et al./2021 [20]
Kumar et al./2022 [21]
Martins et al./2022 [22]
Scott et al./2022 [23]
This paper
Although the COVID-19 pandemic has led to significant growth in UV disinfection and robotics research, their uses are not limited to the current pandemic. Other existing and future infectious diseases could also be combated with the derived research. The contagious nature of pathogens poses a problem in various public spaces. For example, Hospital-Acquired Infection (HAI) has been a major concern. HAIs may occur in hospitals, clinics, surgical centers, and long-term care facilities. It is estimated that there are more than 440,000 HAIs per year in the US [29]. UV robots can be used as additional tools to combat HAIs.
In this work, we aim to provide a complete guide for UV robots that covers relevant background on UV disinfection and classical UVGI systems. We also point out the open problems and potential directions of research to address the operational issues of UV robots. This paper is organized as follows: Section 2 presents all the relevant background for UV disinfection. Section 3 presents a discussion on commercially available UV robots and autonomous UVGI systems under research, while Section 4 explores non-UVGI disinfection robots. Finally, future directions and the conclusion are included in Sections 5, 6, respectively.
2 Background on UV disinfection
Decontamination is the process of removal, inactivation, and destruction of pathogens, including bacteria, viruses, prions, fungi, and other microorganisms, to prevent the spread of infections [30]. It consists of three steps: cleaning, disinfection, and sterilization.
Cleaning involves the physical removal of infectious material through mopping, vacuuming, and scrubbing. Disinfection is the process of eliminating pathogens (except for bacterial spores), while sterilization eliminates all microorganisms. Both chemical disinfectants, such as hydrogen peroxide and bleach, and UV radiation are utilized for disinfection, and sterilization [30]. There are stringent sets of guidelines and policies that are typically used to ensure sufficient decontamination [31]. This paper focuses primarily on methods of disinfection and sterilization through UV-based strategies.
Ultraviolet germicidal irradiation (UVGI) is a proven way of disinfection. In this section, we provide relevant background on UV disinfection and various essential factors associated with designing and developing the large variety of available UV disinfection devices.
2.1 UV disinfection mechanism
UVGI can induce photochemical effects in cells to have a germicidal impact. Ultraviolet light in the wavelength range of 200–320 nm possesses such germicidal properties and is further categorized into germicidal wavelengths of UVC (200−280nm) and UVB (280−320nm). UVB and UVC lights are absorbed by the DNA, RNA, and proteins of the cells, which prevent them from replicating and surviving. UVA (320−400nm), around 95% of what sunlight on earth is composed of, does not have germicidal properties [11]. In this paper, “UVGI” and “UV” will be used interchangeably for simplicity, with both terms referring to UV light with a germicidal wavelength of 200–320 nm.
UVGI systems have been widely used in the disinfection of air, surfaces, water, and food. Some of these systems are enlisted in the following:
• Air Purification: For air disinfection, in-duct UV systems are commonly adopted. They consist of arrays of UV bulbs installed in air ventilation ducts to inactivate pathogens in moving air streams [32]. In addition, there are upper room UV systems that primarily create a zone of germicidal irradiation in the upper region of rooms to disinfect bio-aerosols [33].
• Surface Disinfection: Surface disinfection can be achieved by exposing the target surface to an appropriate UV dose. Typically, it is used for the sterilization of equipment and rooms using portable disinfection UV devices or overhead UV fixtures that encompass certain areas [34].
• Water Disinfection: UV chambers in which the water is irradiated are integrated into many water purification systems [35], [36].
• Food Safety: UV irradiation has been approved for inactivation of pathogens in various food products [37]. It is typically used for inactivating microbes to enhance the shelf life of liquid foods, fruits, and vegetables [38].
2.2 UV Safety consideration
UV exposure from sunlight and artificial sources, such as UVGI systems, could be hazardous. It is important to consider the probable phototoxic effects of these systems to avoid accidental harm prior to implementation.
• UV Exposure: UV wavelengths ranging from 200−320nm have the most severe side effects to humans. Although UV light is undetectable to the human eye, exposure to intense UV rays could lead to cataracts and vision loss [24]. In addition, potential effects of UV exposure on the skin include erythema, photoaging, immuno-suppression, and skin cancer [39]. In some recent works, it was observed that far-UVC light (207−222nm) effectively kills various strains of human coronaviruses without any harmful effects on exposed human skin [40], [41]. This is primarily due to the limited penetration distance of far-UVC from the outer layer of the mammalian skin [42]. On the other hand, UVB (280−320nm) rays, which are often also present in UVGI systems, can penetrate deeper into the skin and eye to potentially cause cancer and DNA damage similar to UVC [43]. According to the International Commission on Non-Ionizing Radiation Protection (ICNIRP), the effective UV exposure dose on human eyes and skin should not exceed 30[J/m2] in a period of 8 hours [44]. This recommendation has been adopted as a regulation by the US Food and Drug Administration (FDA) and the European Union [24].
• Ozone Production: Another risk associated with UV disinfection is the production of ozone. UV radiation of wavelength less than 240nm can produce ozone and other oxides [11]. As a corrosive gas, ozone could lead to fire hazards. Ozone is also a strong oxidant and toxic air pollutant. Health Canada limits ozone exposure to no more than 20 ppb (parts per billion) in the span of 8 h [45]. Similarly, the FDA limits the amount of ozone from indoor medical devices to 50 ppb [24]. Manufacturers of UV lamps usually mention the amount of ozone the lamp may produce. Depending on the spectral range and the lamp type of the UVC light source, it may or may not produce ozone. Many such lamps have doped glasses to block ozone-generating wavelengths [46]. It is worth mentioning that ozone gas also has a germicidal effect. For instance, ozone generators, which use UV lamps to generate ozone, are used in the ozonization of water for its disinfection [47].
• Presence of Mercury: Mercury-based UV lamps are of concern due to the heavy metal’s known harm to the environment and human health. Inhalation of mercury vapor via UV lamp breaks can lead to accumulation within the body, and damage to the neurological and renal systems [48]. In addition, improper disposal of mercury lamps pollutes the environment and adversely affects aquatic life and the soil quality. Other UV light sources mentioned in Section 2.1, including UVC LEDs, PXL, and excimer lamps, do not experience safety concerns for mercury presence.
Various countries have regulations to limit the adverse effects of UV devices. For example, in the US [49] and Canada [50], UV devices must comply with the laws on radiation-emitting devices. International Organisation for Standardization (ISO) provides guidelines on minimum requirements of human safety of UV devices as well [51]. Further, they elaborate on the use of protective equipment, such as UV shields and reflectors, that should be used when operating such devices. Nenova et al. [52] presents a systematic approach to improve the safety of UV devices.
2.3 UV light sources
UVGI systems utilize ultraviolet radiation of varying wavelengths produced from different sources. Each light source poses distinct advantages and disadvantages. It is essential to select a suitable light source to ensure desired disinfection performance. UV light sources currently used are classified into four categories, described below:
• Mercury Gas Discharge Lamps: These lamps are made of electrodes containing plasma sealed within a glass body. They are filled with inert gas (e.g. argon) and mercury. When a high voltage is applied, electrons get excited and emit UV light on returning to ground state [53]. These lamps can be classified depending on the pressure of the gas. Low-pressure (LPM) and medium-pressure mercury (MPM) lamps are most commonly used for germicidal applications. MPMs produce a broad spectrum of UV radiation, while LPMs have a narrow emission spectrum band centered at 254nm.
• UVC light-emitting diodes (LEDs): These are compact semiconductor devices. Typically the semiconductor material for UVC LEDs is Aluminium Gallium Nitride [54]. They produce monochromatic light and generally are available in the range of 255−285nm. Unlike conventional mercury UV lamps, UV-LEDs can selectively combine multiple UV wavelengths for potentially more effective disinfection [55].
• Pulsed Xenon Arc Lamps (PXL): In these lamps, a broad spectrum UV light (with a greater UVC component) is released in the form of short duration high intensity pulses [56]. Xenon gas flash bulbs used in the lamps can emit a UV spectrum of 200 to 280 nm and light in the visible light spectrum [57]. They are a nontoxic alternative to mercury lamps.
• Excimer Lamps: These lamps produce UV radiation via the decomposition of a complex of excited gases [38]. Recently proposed far-UVC lamps use krypton-chlorine (Kr-Cl) gas mixture and are examples of excimer lamps [40], [41]. Excimer lamps produce a monochromatic spectrum [38].
The emission spectrum of different light sources is depicted in Fig. 2. Most of these light sources have a monochromatic spectrum except for the PXL. There are a limited amount of studies comparing the effectiveness of these different light sources; as a result, it is hard to present a fair comparison.Fig. 2 Plot of Normalized Intensity (arbitrary units) Vs Wavelength (nm) [58]. Spectral Distribution of UV Light sources. LPM and UV-LEDs have narrow emission spectrum. MPMs have a wider spectrum which extends in ozone producing wavelenghts as well.
Table 2 Comparison of UVGI light sources.
Property UVC-LED LPM PXL Excimer lamp
Instant on/off Yes No Yes Yes
Small footprint Yes No No No
Mercury free Yes No Yes Yes
Ozone generation No No Yes Yes
DC operation Yes No No No
Tentative life span (h) 10K 10K 2K 6K
Wattage rangea [W] 0.5 8–335 10–8000 5-500
Price range per unita (USD) 1–10 20–1200 50–750 600–5000
Wall plug efficiency (%) <5 [59] 15–35 [59] 12–17 [60] 40 [61]
a Based on data from [59], [62].
Gas discharge lamps are the most commonly used. A large variety (in terms of wattage, UV output, size, fixtures, etc.) of these lamps are commercially available. These lamps have greater wall plug efficiency than PXLs and LEDs; however, they suffer from some limitations [60], [63]. The main concerns are health and environmental risks due to mercury contamination, and long warm-up times of the lamp that lead to excessive power consumption [60]. Some studies suggest that disinfection by PXLs lead to more significant inactivation of microbes in shorter exposure times [56], making them a popular choice in surface disinfection.
UVC-LEDs are relatively new to the UV market. It is mainly used in water disinfection applications [64]. They offer many advantages, including a smaller footprint, unlimited on-off cycles, no warm-up time, environmentally friendly, and longer lifetimes [59] when compared to the traditionally used mercury lamps. However, UVC-LEDs have low wall plug efficiency and lower power outputs. More UVC-LED bulbs are required to achieve similar power outputs as systems that utilize mercury UVC lamps, making UVC-LED systems more expensive [63]. UVC-LEDs still hold great potential due to the flexibility in the design of these LED-based reactors [59].
Far-UVC light generated from excimer lamps has been demonstrated to eliminate methicillin-resistant S. aureus (MRSA) bacteria while avoiding skin-related damage, such as erythema, skin cancer, and UV-associated premutagenic DNA lesions, unlike regular UVC, UVB and UVA radiation [42], [65]. A comparison on some properties for these light sources is given in Table 2.
2.4 UV Irradiance dosage and modelling
In order to determine the effectiveness of UVGI systems quantitatively, UV irradiance dosage and modeling are required. The inactivation of pathogens depends on the supplied UV dosage and duration of light exposure. The UV dosage, D [J/m2], for microbes subjected to UV radiation is given by [10]: (1) D=Et⋅Ir,
where Et is the exposure time in seconds and Ir is the radiant flux received by a surface per unit area (a.k.a irradiance) with SI units [W/m2]. The effectiveness of a dose is classified in terms of log reduction of the microorganism population: (2) log10(r)=log10(N0N),
where N0 and N represent the pathogen population before and after UV exposure and r represents the reduction factor. Reduction factor can take values 1,2,3 and 4 which corresponds to pathogen population reduction rate of 90%, 99%, 99.9% and 99.99% respectively. Different pathogens have varied susceptibility to UV radiation; hence, the required UV dose for an application should be selected based on the targeted microbes. Detailed data on exposure dosages for different reduction rates (e.g., D90, D99.9) for various bacteria and viruses are provided in [10]. A few doses are included for reference in Table 3.
Irradiance Models: The modeling of irradiance fields for UV light sources is essential for determining the UV doses delivered by a system to a target surface or airborne microbes. These models can be utilized to simulate the dosage distribution by a UV-robot, which in turn can be used for path planning of the robot [67], [68].Table 3 Required D90 UV doses for surface disinfection.
Pathogen Dose [J/m2]
Bacteria (Average UV dose) [10] 54
Viruses (Average UV dose) [10] 73
SARS-COV-2 (COVID-19 virus) [66] 27
The inverse square law (ISL) model is a commonly used method, where irradiance at a point is inversely proportional to the distance ρ from a point light source with radiant power ϕ [W]: (3) I∝ϕρ2
ISL can be easily extended to non-point light sources. Jacobm and Dranoff [69] used ISL for cylindrical lamps by approximating it as a line source, where each line source can be represented as an aggregation of multiple point sources. ISL is a simple model, but it is only valid in cases where the irradiated surface is far from the light source [70]. Further, ISL doesn’t accurately account for the geometry or the type of light source. Many models have been proposed for cylindrical gas discharge lamps. Kowalski and Bahnfleth used thermal view factors to model the near fields for cylindrical light sources [71]. These view factors account for radiation transmitted from emitting surface to the receiving surface. Detailed calculations of view factors for different light sources are given in [71]. The irradiance is computed using: (4) I=ϕ2π⋅r⋅l⋅Ftotal
where r is the radius of the cylindrical lamp, l is the length of the lamp, and Ftotal is the view factor. They demonstrated superior accuracy compared to ISL for short and far distances. The view factor model can easily replace the ISL for dosage simulation in a robotics application. In many cases, UV lamps are mounted in reflective housing. This changes the irradiance field and should be accounted for while designing a system. Various reflectivity models based on the type of reflectivity are presented in [10].
A UV-LED can be treated as a point light source for far-off distances; hence, ISL model, e.g. Eq. (3), can be used in such cases. The irradiance field due to a complete LED module can be generated by aggregating the contribution of irradiance from each LED in the module. As mentioned before, ISL does not hold [72] for closer distances. An exact expression for irradiance is deduced for rectangular LED in [73]. This method can be used to model the dosage by UV-LEDs for closer distances accurately.
There are models that more accurately account for additional factors, including target surface geometry, the reflectance of surfaces and fixtures, and refractivity. These models are typically more complicated than the simple models presented above. For instance, irradiance models for UV-LEDs for water treatment were derived and incorporated in computational fluid dynamics packages to generate irradiance fields [74], [75]. . However, these models are more applicable to small-scale systems and are likely to be computationally expensive for modeling a 3D environment of the world. Ahmed et al. [76] used ray tracing software to accurately model UV irradiance in a UV photo-reactor. Ray tracing software simulates the trajectories of electromagnetic rays emitted from a light source while accounting for laws of optics, refraction, reflection, and shadowing. Ray tracing tools can accurately model the irradiance, but these models are highly computationally expensive.
Table 4 Non-exhaustive list of recent efficacy studies on UVGI systems.
Author/Year Study location UV Light type Pathogen type Disinfection method Results
Astrid et al./2021 [77] Outpatient hospital clinic (Austria) Clean Room Solutions’ UVD Robot (254 nm) Four strains of Candida auris UVC irradiation was done on high-touch surfaces after the standard cleaning and disinfection procedure. Reduction in target pathogen concentration in irradiated areas and negative effects in shadowed regions were observed.
Buonanno et al./2020 [78] Laboratory (Israel) Far-UVC light (222 nm) Aerosolized alpha HCoV-229E and beta HCoV-OC43 Target pathogens inactivated in an aerosol irradiation chamber. 99.9% inactivation observed for UV doses of 1.2–1.7 [mJ/cm2]
Gerchman et al./2020 [79] Laboratory (USA) UVC-LED system (267–297 nm) HCoV-OC43 virus Irradiance dosages were up to 60 s for 267 and 279 nm and up to 90 s for 286 and 297 nm UV. Shorter UV wavelengths were more effective at inactivating the virus (3-log inactivation at irradiation 6–7 mJ/cm2).
Heilingloh et al./2020 [80] Laboratory (Germany) UVC (254 nm) and/or UVA (365 nm) SARS-CoV-2 The emitted light was at a distance of 3 cm and viral samples were taken at 3-minute intervals for 30 min. The emitted dose required for a complete inactivation of SARS-CoV-2 was 1048 mJ/cm2 after 9 min of exposure.
Morikane et al./2020 [81] Hospital ICU (Japan) PX-UV device (200–280 nm) MRSA Effects of adding PX-UV device to manual terminal cleaning in the hospital was evaluated for 2.5 yrs Cases of newly acquired MRSA and Antinobector reduced significantly.
Yıldırım et al./2015 [57] Acute-care hospitals (USA) PX-UV device (200–280 nm) C. difficile, MRSA, and VRE Pathogen contamination on high-touch surfaces was assessed before and after 10 min of PX-UV irradiation. PX-UV device lowered the recovery of C. difficile spores, MRSA, and VRE.
2.5 Efficacy of UVGI systems
The effectiveness of UVGI systems has been compared to manual cleaning and disinfection. Justification for redesigned decontamination strategies with UV-based systems can be acquired with such studies. The disinfection performance of UVGI systems with varying light sources and UV wavelengths has also been validated under research and real-world environments. This sub-section discusses the capability of UVGI systems to disinfect various pathogens.
Table 4 displays some recent studies that have analyzed various UV disinfection setups against commonly encountered pathogens. Buonanno et al. [78] observed the efficacy of far-UVC light (222 nm) against aerosolized alpha HCoV-229E and beta HCoV-OC43 in a laboratory setting. They found that there was 99.9% inactivation (3-log reduction) for the pathogens at low UV light doses of 1.2 to 1.7 mJ/cm2. Gerchman et al. [79] and Heilingloh et al. [80] examined UVC-LED (267–297 nm) and UVC lamp (254 nm) respectively in a laboratory setting instead. The UVC-LED system achieved 3-log inactivation of HCoV-OC43 at irradiation of 6–7 mJ/cm2 after 60 s. In addition, the UVC lamp was able to completely inactivate SARS-CoV-2 at 1047mJ/cm2 after 9 min.
Unlike the previously discussed laboratory studies, Morikane et al. [81] inspected the performance of a PX-UV device (200–280 nm) in a Japanese hospital’s intensive care unit (ICU) over 2.5 years. They discovered a significant decline in incidences of newly acquired methicillin-resistant Staphylococcus aureus (MRSA) and drug-resistant infections. Nerandzic et al. [57] also conducted their PX-UV device analysis in a hospital. A reduction of 0.55-log to 1.85-log was demonstrated for C. difficile spores, MRSA, and VRE on high-touch surfaces within the hospital after 10 min of disinfection. Newer efficacy studies, such as [77], that feature UV robots in real-world settings have also begun to emerge. Detailed reviews of published studies evaluating the efficacy of UVGI systems are presented in [16], [22].
2.6 Limitation of classical UVGI systems
Classical UVGI systems, such as the previously introduced fixed UV lamps, upper room UV germicidal systems, and portable UV lights, are effective in complementing and improving existing decontamination procedures; however, there are certain limitations:
• Shadowing: Pathogens are protected when engulfed by sha-dows from objects (e.g. equipment, chairs, beds), since the UV radiation cannot reach such pathogens. The issue of shadowed regions can be addressed by reflecting UV radiation for indirect disinfection [82]. Another solution could be to manually maneuver the UV fixture and lamp around the room to increase the surface exposure to the UV source.
• Pathogen Coating: Pathogens within respiratory droplets and aerosol particles, such as sputum and blood, have been proven to block the effects of UV radiation partially. The shie-lding of the medium encompassing the infectious microbe can hinder disinfection capabilities. The shielding effect increases as the particle size increases and can be significantly reduced when targeted particles are illuminated with UV light equally from multiple directions [83]. This can be achieved by utilizing multiple UV sources, mobile UV devices, or reflective materials and surfaces for uniform illumination on coated pathogens.
• Logistical Challenges and Cost: Logistical issues that include the operation, scheduling, and moving of UV fixtures also limit the adoption of these disinfection systems. Unlike when using a traditional chemical disinfectant, UV disinfection systems cannot be used when humans are nearby. This requires additional safety measures, such as emptying rooms, displaying safety signs, and active monitoring. Staff members dedicated to manually moving and monitoring the devices are needed. The increased complexity of the new disinfection approach creates logistical challenges and added costs.
Table 5 List of commercially available UVC disinfection devices.
Robot Cost (USD) Light source Autonomy Human safety Efficacy study
Xenex Lightstrike [84] 125,000 PXL × × ✓
Tru D Smart UVC [85] 125,000 LPM × × ✓
Sterilray Far-UV Robot [86] – Excimer lamp ✓ ✓ ✓
UVD Robot by Blue-Ocean robotics [87] 90,000 LPM ✓ × ✓
Surfacide’s Helios UVC Disinfection System [88] – Amalgam UVC lamps × × ✓
Honeywell’s UV System [89] – LPM ✓ × ✓
Ava Robotics UV Disinfection robot [90] – – ✓ × ×
Blue Shift UV’s R-Zero Arc [91] 45,000 – × × ✓
BooCax UV1500 [92] – Quartz lamps ✓ × ✓
Safe Space Technology’s RoverUV [93] – – ✓ × ×
BlueBotics’s mini UVC [94] – – ✓ × ×
Pudu’s Puductor 2 [95] – LPM ✓ × ×
Prescientx’s Violet [96] – – ✓ ✓ ✓
GlobalDWS’s DSR [97] – – ✓ × ×
TMiRob’s Intelligent Disinfection Robot [98] – – ✓ × ✓
Aitheon’s UVD Robots [99] – LPM ✓ × ✓
Lumnicleanse’s UV-C robot [100] – – ✓ × ✓
The Badger UV Disinfect Robot [101] – LPM ✓ × ✓
3 UV Disinfection robots
Significant progress has been made in the speed of adoption for robotic technologies in recent years. Robotics can be incorporated to improve multiple facets of infectious disease management in the future, including disinfection [27], [102]. Classical UVGI systems discussed in Section 2.1 integrate fixed devices that often require manual supervision and maneuvering. Limitations of such devices, mentioned in Section 2.6, can be addressed with UV robots that are mobile or autonomous. The autonomy offered by UV robotic disinfection systems can result in less labor-intensive and more efficient decontamination procedures. They can also be monitored remotely via tablets and apps for disinfection status and troubleshooting. This section provides an overview of UV robots and explores the effectiveness of these robots. Lastly, limitations of the current generation of robots are identified.
Fig. 3 Area disinfection units (a-b), and disinfection robots (c-d).
3.1 UV disinfection robot mechanism
In recent years, there has been a growing interest in using autonomous area disinfection UV devices and robots. These devices are used to supplement manual cleaning and are comprised of a mobile base, high-power pulsed xenon lamps or an array of LPM lamps, and motion detectors [103]. These high-powered lamps have a significant irradiance envelope and can disinfect a volume of space in all directions. In health care settings, they are used to disinfect rooms after manual cleaning is completed by staff. Some of these devices have sensors that monitor environmental conditions, such as humidity and temperature. This data is used by the devices to modify the UV dose accordingly. Since UV is harmful to human skin, the robot is usually operated in empty rooms. Measures that include using curtains to cover the windows are taken to prevent undesired exposure to UV radiation. In addition, motion detection sensors are used, so that the UV lights can be cut off immediately if any human presence is detected. Classical UV disinfection devices are usually either placed in a single position in the room for a complete disinfection cycle or are moved manually by the designated operator to different parts of the rooms [103].
UV robots can provide automatic and consistent disinfection. Such robots extend classical UVGI devices in which UV lamps are mounted on mobile platforms that offer potential autonomy [104]. The UVGI robots can perform autonomous decision-making by using inputs they receive from sensors. They rely on simultaneous localization and mapping (SLAM) [105] to build the map of the environment and use the map to deliver high-powered UV dose [13].
Multiple studies demonstrate the effectiveness of UV devices and robots. A randomized cluster trial conducted in the US across nine hospitals for over two years demonstrated that adding UV-C robots to quaternary ammonium disinfection decreased the risk of subsequent acquisition of infection [106]. Guettari et al. [28] developed a mobile UVC disinfection robot that is able to eliminate bacteria. However, Dancer and King [18] reviewed efficacy studies of automated decontamination devices that utilized UV light and recommended that more efficacy research of UV autonomous robots with control groups should be done before the overhaul of existing cleaning and disinfection procedures.
3.2 Existing UV disinfection robots
With the rising interest in using service robots in the healthcare systems and the onset of the COVID-19 pandemic, the research on the development of autonomous UVGI robots has accelerated. Research institutes and companies around the world have been creating, implementing, and testing autonomous UV disinfection. Commercially available UV robots and recent research contributions on UV robots are discussed and assessed in this sub-section.
3.2.1 Commercially available UVC robots
Companies and research labs have been developing disinfection robots before the COVID-19 pandemic, aiming to improve the efficiency of cleaning and disinfection in areas with a high risk of infections.
Xenex Disinfection Services has shown that their Lightstrike robot can significantly reduce SARS-CoV-2 on hard surfaces and N95 respirators with PX-UV [107]. Tru D Smart UVC robots were examined in a tertiary acute care hospital, and they are able to effectively reduce C. difficle and Acinetobacter in patient rooms [108]. Autonomous Disinfection Vehicle Robot (ADV) from Far-UV Sterilray are being advertised, but their production has not begun. They utilize excimer lamps that produce far-UVC [86]. Hospitals in Romania, Croatia, and Italy have begun using Blue Ocean Robotics’ autonomous disinfection robots. They claim to be able to disinfect rooms with LPM within 10 min [87]. Surfacide’s Helios UV-C Disinfection System coordinates multiple robots to overcome shadowed regions of rooms and improve disinfection time. They have also been implemented by multiple hospitals in the USA [88]. Honeywell’s UV Treatment System is designed specifically to disinfect aircraft cabins. It is able to sufficiently disinfect 30 rows of seating in 8 to 10 min. They have been implemented into Qatar Airways’ disinfection practice [89]. Additional UV disinfection robots are included in Table 5 and shown in Fig. 3, and more are under development.
As seen in Table 5, robots that are currently available mostly support autonomy. Although some claim to be able to disinfect within a reasonable time, experiments and set-ups were not standardized; thus, numbers do not offer a fair comparison. They are also expensive, and a cost–benefit analysis should be conducted by users prior to purchase and adoption. With autonomous UV disinfection being a relatively new mode of disinfection, experimentation and analysis on commercially available robots from sources without conflict of interest are greatly needed to verify the claimed effectiveness and safety of available devices. Regulations and standards for autonomous systems should also be introduced as robots inevitably become increasingly intertwined in our daily lives(see Fig. 4).
Fig. 4 UVGI robotic features in development.
Table 6 A non-exhaustive list of current UVC robots in research.
Robot Type Light source Human safety Efficacy study Human operator requirements
ADAMMS UV Robot [109], [110] Mobile Manipulator LPM and UV Wand × × Select targets to disinfect through teleoperation.
Fetch UV Robot [111] Mobile Manipulator UV Flashlight × × Select targets to disinfect through teleoperation.
Ultrabot [112] Mobile Base LPM ✓ ✓ –
G-Robot [113] Mobile Manipulator Far-UVC ✓ × –
UV-Robot by [114] Mobile Manipulator LPM and UV lamp × × Select targets to disinfect through teleoperation.
UV-PURGE [115] Mobile Base LPM × ✓ Drive the robot via teleoperation.
UV-Robot by [116] Mobile Manipulator UVC-LED × ✓ Select targets to disinfect through teleoperation.
UV-Robot by [117] Mobile Base LPM × ✓ Select targets to disinfect through teleoperation.
UV-Robot by [118] Mobile Base LPM × × Select targets to disinfect through teleoperation.
UV-Robot by [119] Mobile Base LPM ✓ ✓ Drive the robot via teleoperation.
3.2.2 UV-robots and algorithms in development
Current research contributions for UV robots focus on hardware development and creating autonomy algorithms to maximize the performance and efficiency of disinfection. The UV robots proposed in the literature either use a mobile manipulator configuration or a typical mobile base with UV lamps mounted on it. Most of these robots use prior maps and target locations specified by a human operator to generate disinfection plans. Furthermore, some works use customized planning algorithms to ensure all the areas in the environment receive desired UV dosage. A detailed discussion of various contributions is described next. A non-exhaustive list of novel designs proposed in the literature and their key features are summarized in Table 6.
Many proposed disinfection systems adopt a mobile manipulator design. ADDAMS UV robot [109], [110] is a teleoperated semi-autonomous mobile manipulator equipped with an LPM and a UV wand. The LPM is used for generic environment disinfection, and the manipulator uses the UV wand to disinfect target surfaces. A human operator can either drive via teleoperation or specify the desired location to navigate the robot. When the robot is at the desired location, the operator uses a GUI to select target surfaces for the robot to disinfect. Similarly, Conte et al. [114] proposed a teleoperated mobile manipulator where UV lamps are mounted on the mobile base and the manipulator’s end effector. The manipulator disinfects regions that are beyond line of sight of the lamps on the mobile base. The robot operates in two stages. First, a coarse 3D map of the environment is created by teleoperating the robot in the environment. Next, the trajectories for the mobile base are generated before disinfection, based on the waypoints specified by the human operator. During the execution of these trajectories, a fine 3D dosage map is continuously updated to monitor the disinfection levels. Upon approaching objects, such as chairs, tables, and cabinets, the mobile base is stopped, and the arm moves to disinfect the object.
A Fetch mobile manipulator equipped with a UV flashlight to disinfect target surfaces in the environment is proposed in [111], [120]. The planner generates the path for the manipulator based on waypoints specified by the human operator. Further, their planner generates the speed of the manipulator based on desired disinfection level and an empirical UV dosage model. Ma et al. [116] proposed a mobile manipulator where the robotic arm is equipped with an array of UVC LEDs. A prior point cloud map of the environment and a graphical interface are used by the operator to select the targets for disinfection. The planning module generates the path for the mobile base and the arm to disinfect the target surface. The quality of disinfection by the robot was evaluated using UVC fast check strips in testing locations that included bookshelves, card readers, and toolboxes.
A limitation of the works mentioned above is the requirement for an operator to specify disinfection targets. Hence, the operator should have additional skills to interact with the software to select the target locations. Furthermore, manually setting targets in large spaces may be time-consuming and error-prone. This issue was partly addressed through G-Robot [113], an autonomous mobile manipulator equipped with human-safe Far-UVC. It uses a plane segmentation algorithm to detect target surfaces like tables, chairs, and shelves. The coverage planner uses the map of the environment and detected planar surfaces to generate waypoints for the robot. The disinfection of the surfaces is then done by executing appropriate joint motions determined by the planning module. However, currently G-Robot cannot detect complex non-planar surface in the environment.
Some works have focused on improving the classical UVC robot configuration of a mobile base mounted with UVC lamps. Conroy et al. [118] proposed using a UVC lamp on a low-cost mobile base made of off-the-shelf parts. A human operator would drive this robot via teleoperation to create the floor plans of the environment. In the offline planning stage, linear programming is used to determine waypoints and disinfection time for the robot to ensure that sufficient dosage is received by all the locations in the given floor plan. Finally, the robot disinfects the environment based on waypoints and disinfection time generated in the offline planning stage. Pierson et al. [117] proposed using a mobile base mounted with UVC lamps. Their robot uses a map with target locations specified by an operator. They proposed two path planning modules. First, an augmented A∗ algorithm is used for planning paths in non-changing environments. Second, Voronoi-based coverage control is used to plan paths in cases where the environment may have new static obstacles that are not present on the map. In both cases, the planner uses a dosage map based on irradiance models to ensure that all the locations in the environment receive sufficient UVC dosage. They tested their robot at a food bank facility and were able to deliver desired disinfection dosage. In [115], a cost-efficient mobile robot for UVC disinfection is proposed. The robot was made of off-the-shelf components. It is teleoperated using a mobile app and has no autonomy features. Further, it was tested in an office facility and reported a reduction in bacteria count after using the robot. The downside of using a mobile base mounted with UVC lamps is the lack of human safety due to exposure to UV radiation. Consequently, these robots can only be used when there is no human presence.
Some works have focused on improving safety of classical UVC robots through augmented physical design. McGinn et al. [119] proposed a novel design of the UV robot that controls its UV irradiation through a physical barrier to shield bystanders and equipment from UV rays. Their robotic base uses an open-source robotic mobile base that supports autonomous navigation. An operator drives the robot in radiology labs via teleportation to evaluate disinfection quality. Perminov et al. [112] proposed Ultrabot, an autonomous mobile UV disinfection robot. The UV lamps on Ultrabot are shield- ed from one side, which effectively limits the field of view of lamps to 180°. This design choice enables the operation of the robot alongside humans. Furthermore, Ultrabot has an ozone-based air purification mechanism in which all the UVC lamps are fully shielded. The ozone generated by UVC lights is redistributed in the environment using a set of fans. Ozone is a toxic gas, so there is a need to ensure that the ozone produced during disinfection does not exceed safety limits for humans. In both cases, for the physical shielding to be effective, the robot must plan its pose so that the bystanders are not exposed to UV rays. This might be a challenging task in a busy environment like a shopping mall.
Some contributions have focused purely on algorithmic development for efficient distribution of UV dosage. Tiseni et al. [121] proposed a novel motion planner that consists of a genetic algorithm for UV disinfection robots and showed in simulation that it improved disinfection performance, time, and energy requirement. Similarly, an optimized coverage planning algorithm for UV surface disinfection is proposed in [67]. Tazrin et al. [122] proposed schedulers for efficient disinfection by a team of drones equipped with UVC band panels. They developed schedulers based on heuristics, such as the randomized approach, a greedy approach, and genetic algorithm-based scheduling for energy-efficient path scheduling for disinfection drones.
Many proposed autonomous disinfection robots use alternative disinfection mediums like chemical sprays. The autonomy components of such robots can potentially benefit the development of autonomous UV disinfection robots. Some of these robots are discussed in Section 4.
3.3 Limitations of UV disinfection robots
Similarly to classical UVGI systems, UV disinfection robots also possess limitations and drawbacks that require further improvements and research.
• UV Safety: One of the biggest issues with current disinfection robots is that they cannot be used in dynamic environments with human presence due to UV’s harmful effect on human skin and eyes. As a result, these devices cannot be used in shared patient rooms and public spaces while there is human traffic. Some existing works [112], [113], [119] have addressed this issue. However, stronger safety guarantees should be established to avoid any undesirable exposure.
• Navigation Difficulties: Spaces with a heavy human presence or areas occupied with cluttered, dynamic, and large objects, such as hospital receptions, hallways, MRI rooms, and the ICU, could be difficult for autonomous robots to navigate and disinfect efficiently while avoiding any collisions. Furniture and machinery arrangement in the disinfection space could lead to areas that are inaccessible for the mobile robot, causing the issues mentioned in Section 2.6. Areas that cannot be reached by the most commonly used ground mobile robots could also include surfaces that are too high and far away or blocked by objects such as tables, chairs, and beds. Reachability issues might be partly resolved by using a mobile manipulator configuration.
• Logistical Issues and Cost: Studies have demonstrated that the use of UV technology could be limited in hospitals currently due to required logistics and operational times [123]. These UV robotic systems are more expensive than classical UVGI devices. Additional training and education of staff members and the general public for the safe deployment of UV robots are also time-consuming and resource-intensive.
These potential issues for the integration of UV robots must be considered and examined further for the development of future autonomous UV disinfection robots.
4 Other types of disinfection robots
Disinfection robots that are currently available and in development are not limited to using UVGI as the disinfection medium. Another novel mode for no-touch environmental cleaning and disinfection is to use hydrogen peroxide (H2O2).
Aerosolized hydrogen peroxide (AHP) and vapour hydrogen peroxide (H2O2) are two forms of hydrogen peroxide used for disinfection. AHP devices have been proven to achieve 4-log reduction of MRSA; however, vapor H2O2 systems have been shown to be safer, faster, and better at inactivating pathogens than AHP [124]. Falagas et al. [125] presented a review of multiple studies on the efficacy of vaporized H2O2. They conclude that vaporized H2O2 can be used as an effective supplement to manual cleaning. Some of the robots, such as XDbot [126], is comprised of a robotic arm equipped with a spray nozzle that is used to spray vaporized H2O2. A semi-autonomous quadruped robot for performing disinfection is proposed in [127]. The robot is equipped with a spray-based disinfection system on the robot’s back. The system includes an image processing capability to verify disinfected regions. The authors argue that quadruped’s control of body orientation results in better accessibility in more complex environments. Additionally, this work uses CNN to verify the disinfection quality. If the disinfection quality is not sufficient, then the robot can spray that particular area again. Thakar et al. [128] used mobile manipulators mounted with a spray nozzle to disinfect surfaces. A branch and bound-based area coverage algorithm is presented to determine spray paths on a point cloud of the surface being disinfected. A spline-based representation of the robot’s degrees of freedom and successive refinement-based optimization is used for generating robot motion. Another interesting robot that utilizes spray-based disinfection, from Peanut Robotics, incorporates the ability for both cleaning and disinfection [129]. It houses the required disinfectant and mops on a mobile platform where a 7 DoF gripper can swap and apply each tool for autonomous cleaning.
Robots that use a combination of UV and vaporized H2O2 are also in development. For instance, TMI’s air disinfection robot is equipped with UVC, an overhead spray nozzle for H2O2, and a HEPA filter [98]. Similarly to UV disinfection systems, H2O2 devices and robots also require further research to validate their applicability. Problems that include the inability to use H2O2 disinfection in human presence, long disinfection time, and the requirement for more safety training make efficient implementations of H2O2 systems difficult. Both UVGI and H2O2 robots will lead to a need to overhaul existing cleaning and disinfection procedures. Further research that addresses the logistical and safety issues of UVGI and H2O2 robots should be explored to aid large-scale implementations of future disinfection approaches.
5 Future directions and open problems for UV robots
While current issues limit the widespread use of UV robots, there is much ongoing work and scope for improvement in the future. The areas open for improvement include (i) efficient resource management, (ii) accurate dosage modelling, (iii) human safety, and (iv) benchmarking, described next.
5.1 Resource management
UV disinfection is an energy-intensive process; therefore, a more informed usage of these resources can lead to better resource management. For instance, the use of semantic segmentation of the environment to identify high-touch surfaces can be a significant source of the spread of infection [130], [131]. Different surface material can have varied disinfection requirements, and can be identified using deep learning models [132]. Additionally, learning the interaction of humans with various objects in the environment through object affordance [133] can enable the robot to identify critical points to disinfect on a target object. Besides this, planning algorithms should be designed for optimized utilization of resources while maximizing the coverage of disinfection. Different environmental regions can be prioritized based on the concentration of high-touch surfaces. For example, high-traffic and high-risk areas within a shopping mall, including food courts and washrooms, would hold higher priority for disinfection. The planning algorithms can incorporate these priorities and factors like battery charge and environmental traffic to generate robot paths for maximized disinfection coverage. Further, deploying a team of disinfection robots can ensure efficient disinfection operation. Such multi-robot systems can sufficiently disinfect large spaces while being energy efficient. Autonomous UAVs that can emit UV for disinfection can also be utilized to make navigation around cluttered rooms and disinfecting obstructed and more difficult-to-reach areas easier. UAVs could possibly attach themselves to ceilings or other surfaces during fixed point disinfection to conserve energy use. In addition
5.2 Dosage modeling
Many recently proposed UV robots (see Section 3.2.2) have adopted dosage maps in their planning modules. These maps help generate optimal coverage routes and track the dosage received by different environmental regions. For computational efficiency, most of the existing dosage maps are 2D, i.e., the dosage footprint on the floor is used as a cue to measure the disinfection quality of the 3D environment. Besides this, approximate irradiance models for different UV lamps are used. These approximations in dosage representation may lead to insufficient or excessive disinfection by the robot. A more accurate 3D representation of the dosage map and accurate irradiation simulation models would be a valuable contributions. An accurate dosage map could be used for auditing the quality of disinfection. Such audits can be used as feedback by the robot for covering any missed or insufficiently disinfected spots in subsequent operations.
5.3 Human safety
There is also an opportunity to use UVGI in human presence. Some UV robots [113] have adopted human-safe far-UVC. However, commercial far-UVC lights are still limited and have not been tested thoroughly to ensure safety. They are also a more expensive light source. On the other hand, Yildirim et al. [134] tried shielding UVC-LEDs in a busy CT scan room with human presence and found that the proposed robot could eliminate microbial air contamination. However, the effect of UV shielding on the skin and eye protection was not thoroughly analyzed. Another way could be to use mobile manipulators equipped with UV modules (mounted on the end-effector) to disinfect various objects and surfaces as presented by Hu et al. [130]. Their system uses a CNN-based detector to identify objects that humans interact with frequently. Human safety in UVGI systems can be ensured by incorporating the information on the irradiance envelope of UV light in the planning module, such that configurations that lead to direct exposure to humans can be avoided. Such a planner can be inspired by the existing literature on the next best view problem [135]. The position of humans in the environment can be gathered using LIDAR data [136].
5.4 Benchmarking
It would be worthwhile to conduct additional studies to aid the development of UV robots. Standardised benchmarks should be established to evaluate the performance of UV robots The design requirement analysis based on perspectives from experts in infection prevention and control, and environmental health and safety would help develop practical UV robots. A usability analysis should also be conducted to ensure that UV robots seamlessly integrate with the existing infrastructure of healthcare and other public institutions. Besides this, unbiased efficacy studies should be conducted to make a strong case for UV robots. Lastly, studies on the perception of humans on robots, and GBA+, gender, and cultural-based analysis, should also be conduct-ed since the adoption of autonomous disinfection robots would impact existing decontamination procedures and personnel significan-tly across different cultures and ethnicity. GBA+ studies that examine the impact of UVC light on; for instance, different ethnicity’s skin types and decontamination practices could provide valuable insights.
6 Conclusions
In this work, we reviewed the basics of UV germicidal systems and presented various necessary details associated with the development of the technology. A discussion on the effectiveness and current state of classical UVGI systems and UVGI robots was presented. As of now, the use of these robots is limited to healthcare settings where they are used after standard cleaning procedures. Due to the risks associated with UV light to humans, these robots cannot currently be operated in human presence without more examination and development; however, the implementation of such devices is expected to increase in the post-pandemic world.
The future generation of UV disinfection robots needs to possess improved designs that could enable their use in the presence of humans. There are many open research problems in the use of UV robots for disinfection, such as (i) path planning to maximize UV irradiance to the surfaces, (ii) disinfecting objects and surfaces which are frequently used and touched in hospitals and public places, (iii) designing efficient disinfection strategies and devices to minimize UV exposure risks, (iv) training robots to identify frequently used spaces, surfaces, and objects for timely disinfection, (v) coordinating a team of robots to perform disinfection faster and more reliably , (vi) using manipulators and drones to disinfect surfaces that are difficult for UV light to reach, (vii) designing disinfection robots that can be used in the presence of humans, and (viii) designing robots that can perform both manual cleaning and UV disinfection would be beneficial to develop.
Uncited References
Jinadatha et al. [137], Zhao et al. [138], Muzslay et al. [139], Roelofs et al. [140]
Ishaan Mehta is a research assistant at RCVL lab, Ryerson University. He has completed his MEng from UTIAS with emphasis in robotics.
Hao-Ya Hsueh is a student at the Department of Mechanical and Industrial Engineering, Ryerson University. His research interests include robotics and path planning.
Wenbin Li is Assistant Professor at University of Bath. His research interests lie in the development of general autonomous systems and their applications in manufacturing and professional capture.
Sajad Saeedi is an assistant professor at Ryerson University. His research interests include robotics and computer vision.
Data availability
Data will be made available on request.
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| 36514383 | PMC9731820 | NO-CC CODE | 2022-12-14 23:31:53 | no | Rob Auton Syst. 2022 Dec 9;:104332 | utf-8 | Rob Auton Syst | 2,022 | 10.1016/j.robot.2022.104332 | oa_other |
==== Front
Int J Med Inform
Int J Med Inform
International Journal of Medical Informatics
1386-5056
1872-8243
The Authors. Published by Elsevier B.V.
S1386-5056(22)00270-2
10.1016/j.ijmedinf.2022.104956
104956
Article
Predicting medical specialty from text based on a domain-specific pre-trained BERT
Kim Yoojoong a1
Kim Jong-Ho bc1
Kim Young-Min d⁎
Song Sanghoun e⁎
Joo Hyung Joon bcf⁎
a School of Computer Science and Information Engineering, The Catholic University of Korea, Bucheon 14662, Republic of Korea
b Korea University Research Institute for Medical Bigdata Science, Korea University, Seoul 02841, Republic of Korea
c Department of Cardiology, Cardiovascular Center, Korea University College of Medicine, Seoul 02841, Republic of Korea
d School of Interdisciplinary Industrial Studies, Hanyang University, Seoul 04763, Republic of Korea
e Department of Linguistics, Korea University, Seoul 02841, Republic of Korea
f Department of Medical Informatics, Korea University College of Medicine, Seoul 02841, Republic of Korea
⁎ Corresponding authors.
1 Yoojoong Kim and Jong-Ho Kim are co-first authors.
9 12 2022
2 2023
9 12 2022
170 104956104956
2 7 2022
15 11 2022
3 12 2022
© 2022 The Authors
2022
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Background
Owing to the prevalence of the coronavirus disease (COVID-19), coping with clinical issues at the individual level has become important to the healthcare system. Accordingly, precise initiation of treatment after a hospital visit is required for expedited processes and effective diagnoses of outpatients. To achieve this, artificial intelligence in medical natural language processing (NLP), such as a healthcare chatbot or a clinical decision support system, can be suitable tools for an advanced clinical system. Furthermore, support for decisions on the medical specialty from the initial visit can be helpful.
Materials and methods
In this study, we propose a medical specialty prediction model from patient-side medical question text based on pre-trained bidirectional encoder representations from transformers (BERT). The dataset comprised pairs of medical question texts and labeled specialties scraped from a website for the medical question-and-answer service. The model was fine-tuned for predicting the required medical specialty labels among 27 labels from medical question texts. To demonstrate the feasibility, we conducted experiments on a real-world dataset and elaborately evaluated the predictive performance compared with four deep learning NLP models through cross-validation and test set evaluation.
Results
The proposed model showed improved performance compared with competitive models in terms of overall specialties. In addition, we demonstrate the usefulness of the proposed model by performing case studies for visualization applications.
Conclusion
The proposed model can benefit hospital patient management and reasonable recommendations for specialties for patients.
Keywords
Bidirectional encoder representations from transformers
Deep learning
Medical specialty prediction
Medical question-and-answer post
Natural language processing
==== Body
pmc1 Introduction
The COVID-19 pandemic has disturbed medical care systems worldwide by affecting trends in hospital admissions [1], [2]. This has led to increased patient management requirements at the initial visit stage and a precise clinical diagnosis [3]. However, discrepancies between symptoms and the selected medical department frequently occur in the clinical process, resulting in various costs [4]. To relieve these unnecessary costs, when selecting an appropriate medical specialty, the symptoms of patients should be thoroughly considered.
The rapid progress of machine learning and deep learning has enabled the development of various implementations of artificial intelligence (AI) in the real world [5]. In terms of text data analysis, natural language processing (NLP) has provided word embeddings for numerical representations of text [6] and has been developed for various applications in medicine [7]. Accordingly, studies on medical AI dealing with text data have been proposed for clinical support applications, such as healthcare chatbots [8], [9], automated clinical diagnosis [10], and text-based report recognition [11].
In recent deep-learning-based NLP, bidirectional encoder representations from transformers (BERT) [12] has been considered state-of-the-art models. By employing a self-attention mechanism and transfer learning, BERT outperformed contemporary models in several language-understanding evaluation benchmarks for NLP downstream tasks. Although several post-BERT models [13], [14] have been developed, BERT is still widely used in various fields [15], [16].
Medical specialty prediction and recommendation methods have been studied in several ways. Habib et al. proposed a medical recommendation method using text generation and evaluated the method for a dataset across five specialties [17]. Weng et al. developed a medical subdomain classification model for clinical notes based on feature-based machine learning as well as convolutional neural networks (CNN) [18]. Lee et al. [19] presented a medical specialty recommendation method using sentences for a conversational AI chatbot [19]. These studies covered NLP problems for specialty prediction but considered partial specialties only, used hospital text data, or conducted sentence-level analysis. Therefore, we focus on medical specialty predictions by leveraging text data scraped from a medical question-and-answer portal website.
This study proposes a medical specialty prediction model based on a domain-specific pre-trained BERT. To demonstrate the validity of the proposed model, we evaluated its predictive performance and compared it with that of four deep learning NLP models. The proposed model showed outstanding performance for the comprehensive and individual aspects of medical specialties.
2 Materials and methods
2.1 Dataset
In this study, we exploit healthcare counsel posts in the NAVER portal, which provides medical questions and answers to services to general users in Korea. The posts were composed of a medical question, a medical specialty label, and an answer from doctors corresponding to the labeled specialty for the question. We employ the question and the corresponding specialty by extracting text through web crawling. Medical questions generally include explanations of symptoms and requests for medical guidance to obtain relevant solutions. The medical questions were represented in colloquial rather than written expressions. To preserve the properties of the patient-side text data and avoid contrived manipulation, we applied minimal preprocessing to remove tags and figures from the text.
2.2 Bidirectional encoder representations from transformers
BERT is a deep learning model that has shown remarkable performance in various natural language processing tasks compared to contemporary models. BERT has been evaluated on the General Language Understanding Evaluation (GLUE) benchmark, Stanford Question Answering Dataset (SQuAD), Situation with Adversarial Generations (SWAG), and CoNLL-2003 named entity recognition (NER). BERT employs transfer learning to improve language understanding, where the model is initially trained using a large-scale unlabeled corpus and is then fine-tuned for a specific downstream task. In fine-tuning, the last layer is replaced with an appropriate fully-connected layer for the targeted task.
BERT is composed of the encoders of the transformer model that employs stacked self-attention layers [17]. The attention layer performs the multiplication and softmax of the inputs as follows:AttentionQ,K,V=softmaxQKTdkV,
where Q denotes queries, K denotes keys, V denotes values, and dk denotes the dimensions of the keys. Multi-head attention performs parallel computation for separated attention heads and merges them as follows:MultiHeadQ,K,V=Concathead1,⋯,headhWO,
where W is the fully-connected layer. In the self-attention layer of BERT, each head is calculated as follows:headi=AttentionXWiQ,XWiK,XWiV,
where X denotes the output of the previous layer. In the first encoder layer, X is a tensor derived from the positional encoding of the embedded inputs. The inputs consist of tokens converted by a tokenizer, which relieves the out-of-vocabulary problem by splitting a word into smaller pieces. The first element of all the inputs is a special token ‘[CLS]’ for classification.
2.3 Pre-training of BERT
Before BERT is applied to various NLP tasks, the model undergoes a pre-training process by leveraging an unlabeled corpus. At this stage, the model performs two types of supervised learning for prediction with an unlabeled corpus. BERT employs the masked language model (MLM) and next-sentence prediction (NSP) to learn natural language concerning the relation between sentences and the meaning of words. In both tasks, the model uses an identical input constructed using a pair of sentences and concealing a part of the tokens. In MLM, the model is trained to predict certain masked tokens within token indices in the vocabulary dimension of a tokenizer for a categorical cross-entropy loss. Simultaneously, the model trains for the binary classification of the next sentence relationship for the inputs concerning the binary cross-entropy loss in the NSP. Subsequently, the model weights are updated by optimizing to minimize the summation of both losses.
2.4 Predicting the required medical specialty based on a pre-trained BERT
We exploited a pre-trained BERT and fine-tune the model for predicting the required medical specialty from colloquial narrative symptom text, as shown in Fig. 1 . We used the entire question text as input to consider multiple sentences. If the number of tokens exceeded the maximum length of the model, the input was pruned from the front to the maximum length.Fig. 1 A framework for medical specialty prediction from question text using a pre-trained BERT.
In this work, we use a pre-trained Korean Medical BERT (KM-BERT) model to address the domain-specific and language-specific datasets elaborately. KM-BERT was trained using the Korean medical corpus with a bidirectional wordpiece tokenizer for Korean [18]. In fine-tuning, the last layer of the pre-trained model for the MLM and NLP is replaced with a fully-connected layer. The output size of the layer is equal to the number of predictive specialties and is considered only in the first token tensor. Then, the model is fine-tuned to predict a single specialty using softmax activation in the last layer.
3 Results
3.1 Data collection
We retrospectively collected pairs of medical question text and specialty by scraping text from a website. The website provides an answering service regarding medical questions requested by users. Each user can upload a medical question when they need. Medical experts certified by the website only can examine the uploaded questions and answer them as a text reply. The experts consist of medical professionals such as physicians and dentists. The website provides the medical specialty of each expert.
We considered 27 specialty labels in this experiment, including anesthesiology and urology. Table 1 lists the number of questions that were collected for each specialty. We divided the dataset into training and test sets according to the collection date. The training set consisted of 50,454 questions and specialty pairs uploaded from 13/7/2020 to 13/7/2021. The test set comprised 31,858 pairs uploaded from 13/7/2021 to 13/9/2021. There was no overlap between the training and test sets.Table 1 The number of collected medical consultation question texts for 27 medical specialty labels.
Specialty N
Training set Test set
Anesthesiology 1,980 1,980
Cardiac and Thoracic Surgery 636 46
Cardiology 1,090 184
Dentistry 1,980 1,980
Dermatology 1,980 1,980
Emergency Medicine 764 591
Endocrinology 718 169
Family Medicine 1,980 1,980
Gastroenterology and Hepatology 1,980 306
General Surgery 3,960 3,268
Hematology and Oncology 2,838 532
Infectious Diseases 716 146
Nephrology 481 67
Neurology 1,980 558
Neurosurgery 1,980 1,980
Obstetrics and Gynecology 5,537 2,644
Ophthalmology 1,980 1,980
Orthopedic Surgery 1,980 1,980
Otolaryngology 1,980 1,980
Pediatrics 1,979 389
Plastic Surgery 1,980 1,980
Psychiatry 1,980 500
Pulmonology 457 43
Radiology 1,980 422
Rehabilitation Medicine 1,980 1,980
Rheumatology 1,578 213
Urology 1,980 1,980
3.2 Experiment
To demonstrate the validity of the proposed model, we investigated the performance of the proposed model for medical specialty prediction using the dataset. We evaluated the performance in three ways: (1) validation in the training set, (2) evaluation in the test set, and (3) evaluation for each specialty. In the first evaluation, we performed 5-fold cross-validation to derive unbiased performance in the training set. We randomly split the training set into training and validation sets. The second evaluation was performed by separating the entire dataset according to the uploading date. The test set had a more skewed distribution than the training set in terms of the labeled specialty because of the relatively shorter collection period. Finally, we estimated the predictive performance of the models for each specialty.
In this experiment, we compared the proposed model with four competitive deep-learning-based NLP models for medical specialty prediction. We considered KoRean-BERT (KR-BERT) [18], multilingual BERT (M-BERT) [19], the CNN model, and the bidirectional long short-term memory (LSTM) model as competitors. KR-BERT is a language-specific pre-trained BERT model on the Korean corpus. M-BERT is a pre-trained model for 104 languages from Wikipedia. Lastly, two conventional deep learning models were evaluated for comparison.
In the evaluation process, we assessed each model for the prediction of specialty based on the question text. To assess each model precisely, we considered the average top-k accuracy, precision, recall, and F1 score as performance factors. The top-k accuracy denotes the accuracy when considering the top-k specialties as predictive positives by sorting the softmax output of the model. In other words, the performance was estimated by considering whether multiple specialty candidates contained the labeled answer, where k is an integer larger than one. The top-1 accuracy corresponds to the general classification accuracy. In addition, we evaluated the precision, recall, and F1 scores of the predictive performance of the models. We investigated the comprehensive performance of the targeted specialties by implementing a macro-averaged assessment asPrecisionMacro=1L∑l=1LTruepositivelPredictivepositivel,
RecallMacro=1L∑l=1LTruepositivelConditionalpositivel,
F1Macro=1L∑l=1L2precisionl×recalllprecisionl+recalll,
where l denotes a specialty label and L is 27 in this experiment. We employed macro-averaging because the prediction performance for the minority of specialties was also considered essential.
3.3 Experiment results
This section reports the three types of evaluation results for medical specialty prediction. We used training epochs of 2, 3, 4, and 5; learning rates of 2e-5, 3e-5, and 5e-5; and batch sizes of 16 and 32 as hyperparameters for the fine-tuning of the BERT-based models. For the conventional deep learning models, the learning rates of 2e-3, 3e-3, and 5e-3 were used. The maximum length of the input text was 128.
Table 2 summarizes the 5-fold cross-validation results of the training set. For this evaluation, each model was trained five times for each fold. After completing the training, each model was evaluated using a validation set. Accordingly, Table 2 lists the average performance of the five folds. Each listed performance was measured using the best combination of hyperparameters for top-1 accuracy.Table 2 Summary of performance of medical specialty prediction via 5-fold cross-validation.
Model Top-1 Accuracy Top-2 Accuracy Top-3 Accuracy Precision Recall F1
KM-BERT 0.706 0.830 0.885 0.664 0.661 0.658
KR-BERT 0.698 0.825 0.878 0.657 0.656 0.653
M-BERT 0.686 0.813 0.868 0.642* 0.641 0.638*
CNN 0.545 0.666 0.739 0.517* 0.457 0.528*
LSTM 0.601 0.729 0.795 0.557 0.535 0.541
The star (*) indicates at least one specialty with zero predictive positives.
Overall, the proposed model outperformed the competitive models regarding reported performance. In terms of a single prediction (top-1), the proposed model showed an accuracy of 0.706. The other four competitive models did not achieve an accuracy of >0.7. For the top-3 accuracy, the proposed model showed an accuracy of 0.885, closest to 0.9 among the compared models. This tendency was also observed in the precision, recall, and F1 scores, which were 0.664, 0.661, and 0.658, respectively, for the proposed model. The KR-BERT and M-BERT models followed in order with a slight performance gap. The precision of M-BERT and CNN was calculated by omitting specialties with zero predictive positives. Accordingly, F1 scores were derived equally.
Table 3 presents the evaluation results of the test set. Before the evaluation, each model was trained using the entire training set, with the best hyperparameters selected by cross-validation. After training, each model was evaluated using the test set. The Top-1 accuracy was relatively decreased compared with the cross-validation performance for the models, except for the CNN. Meanwhile, the top-3 accuracy increased for all the models. In terms of precision, KR-BERT surpassed KM-BERT with a gap of 0.008, and a lower performance was observed for the overall models. Accordingly, the F1 score was estimated to be lower, but there was no significant difference in the recall.Table 3 Summarized evaluation results for medical specialty prediction on the test set with the best hyperparameters obtained by cross-validation.
Model Top-1 Accuracy Top-2 Accuracy Top-3 Accuracy Precision Recall F1
KM-BERT 0.685 0.830 0.891 0.551 0.660 0.579
KR-BERT 0.678 0.823 0.889 0.559 0.644 0.577
M-BERT 0.666 0.814 0.877 0.540 0.641 0.556
CNN 0.567 0.712 0.790 0.489* 0.503 0.505*
LSTM 0.595 0.736 0.814 0.473 0.542 0.468
The star (*) indicates at least one specialty with zero predictive positives.
Fig. 2 shows the accuracy of medical specialty prediction in more detail. We investigated the differences in the predictive performance of the three BERT-based models in the test set according to the specialty. Regarding top-2 accuracy, the proposed model achieved the best performance in 14 specialties, and KR-BERT and M-BERT showed the best prediction in 11 and 2 specialties, respectively.Fig. 2 Top-k accuracy of three BERT-based models for each medical specialty in the test set evaluation with best hyperparameters. Specialties are grouped according to the top-1 accuracies (k=1) of the proposed model being (A) over 0.75, (B) between 0.5 and 0.75, and (C) 0.5 or less.
As shown in Fig. 2A, these three models showed significantly outstanding performance for several specialties, such as ophthalmology, obstetrics and gynecology, and dentistry. These specialties tend to deal with relatively specific body parts or symptoms compared to other specialties. For ophthalmology, KM-BERT and M-BERT achieved a top-1 accuracy of 0.94, and KR-BERT achieved a value of 0.92. The top-3 accuracy of KM-BERT was 0.98, whereas that of the others was 0.97. Among these specialties, the largest performance gaps in KM-BERT, KR-BERT, and M-BERT were observed in the top-1 accuracy in pediatrics, with 0.77, 0.70, and 0.64, respectively.
Fig. 2B shows the specialties in which the top-1 accuracy of the proposed model is between 0.5 and 0.75. Regarding the top-1 accuracy, several specialties showed a significant performance gap between the models. Notable gaps between the highest and lowest performances were observed in infectious disease (0.21), anesthesiology (0.18), and neurology (0.11). Nonetheless, the gap decreased for the top-2 and top-3 accuracies.
Several specialties showed inferior accuracy, including emergency medicine, cardiac and thoracic surgery, and family medicine (Fig. 2C). This deterioration is attributed to the association of specialties with comprehensive treatment for similar symptoms or multidisciplinary care. For instance, cardiac and thoracic surgery tended to be predicted with the next priority of prediction for pulmonology. Among the specialties, emergency medicine showed the lowest predictive performance among the three models. Despite its worse performance, the proposed model showed notable performance compared to the other models, with a top-3 accuracy of 0.57.
4 Applications and case studies
In this section, we demonstrate the application of the proposed framework based on pre-trained BERT through case studies. The application contains a visualization of the self-attention of the model to provide evidence for the feasibility of medical specialty prediction. We employed an average heatmap for all the attention heads and each token to interpret the model prediction. In this application, we consider the last self-attention layer for interpretability.
We provide four examples of medical specialty prediction from colloquial question texts translated from the original in Korean. Fig. 3 presents four examples of the implementation. Fig. 3A depicts the ophthalmological question. According to attention, the proposed model was likely to concentrate on ‘LASIK’ rather than other words. The proposed model appropriately predicts ophthalmology from a term related to the eye. Fig. 3B presents a description of pain in the finger for neurology. The model mainly focused on ‘do not’ and ‘numb’. As a result, neurology was adequately predicted along with a high predictive probability for neurosurgery. Fig. 3C shows the question regarding the symptoms while breathing. The model predicted the specialty to be pulmonology due to ‘middle ribs pain’ and ‘cough’, but the label was cardiac and thoracic surgery. Although the model did not provide an answer label, pulmonology could be a considerable candidate, and the model properly predicted it in the top three. Fig. 3D shows the question regarding renal disease with a creatinine condition. The model considered ‘renal’ as the most important word even though the word was near the end. This case was regarded as an incorrect prediction from the question, but the predicted result was more acceptable than the labeled specialty.Fig. 3 Case studies with four examples of applying medical specialty prediction based on the pre-trained BERT. The question translated from the original in Korean, the corresponding specialty label, and the top-3 specialties predicted by the proposed model are listed. The heatmap under the text denotes the average attention for each token. (A) Correctly predicted case with high predictive probability. (B) Case of correct prediction with uncertain prediction. (C) Incorrect prediction, but the label is in the top-3 predicted specialties. (D) Wrong prediction for the label.
5 Discussion
This study proposes a framework for medical specialty prediction based on pre-trained BERT from medical question text. To demonstrate the feasibility of the medical specialty prediction, we exploited medical questions and their corresponding specialty label datasets. The dataset was collected from the medical question-and-answer portal. Using this dataset, we evaluated the proposed model through cross-validation, a test set, and predictive performance for each specialty. To demonstrate the superiority of the proposed model, we compared it with two BERT-based models and two conventional deep learning models. The results were measured in terms of accuracy, precision, recall, and F1 score. In addition, case studies for the application of the proposed model were performed by visualizing the interpretable attention in the model. The results comprehensively demonstrated the usefulness of the proposed medical specialty prediction model.
The main contributions of this study can be summarized as follows: (1) comprehensive specialty prediction, (2) prediction from patient-side and question-level text, and (3) using BERT pre-trained for the medical domain. First, the proposed model considers the overall medical specialties commonly covered by most general hospitals for prediction. The medical recommendation model proposed by Habib yields a response through natural language generation for five of the most active specialties [20]. Unlike that study, our study covers 27 specialties independent of demand. In addition, our work reflects the properties of spoken words close to those of unrefined text because we used minimal preprocessing and multiple sentences as inputs. Consequently, the proposed model is more suitable for implementing medical AI for real-life patients than models trained using regular clinical notes [21], [22]. Finally, we employed a pre-trained BERT model using the medical corpus. The BERT model pre-trained on the medical corpus showed higher performance on medical downstream tasks than the base BERT model [16], [23]. Furthermore, the proposed model achieved higher performance than M-BERT by pre-training in a non-English language.
However, our study has several limitations. In general, the performance of predictive models significantly depends on the quality of the data. The data used in this study treated the medical specialty of the experts who answered the question with the correct label instead of confirming the actual clinic where the questioner received treatment. Accordingly, the quality cannot be fully guaranteed. Nevertheless, it can perform a more thorough evaluation of actual applications and a more explicit reflection of reality. In addition, the difficulty of prediction for each medical specialty is different. Medical specialties with prominent features in the question text, such as ophthalmology and obstetrics and gynecology, showed high predictive performance. However, medical specialties that deal with a wide range of organ systems and symptoms, such as family medicine and general surgery, showed low predictive performance. This performance difference seems to be affected by the medical specificity of the text and the number of data. In particular, emergency medicine showed significantly low performance compared to its importance. A cautious approach to the problem of predicting emergency medicine will be required. Therefore, a considerably careful interpretation of the prediction results is required for the practical application of the proposed model. In a future study, we will perform a further study to overcome these problems and improve the predictive performance for overall medical specialties.
6 Conclusion
The discrepancies between the diagnostic range of the medical specialty and the symptoms of patients may be associated with enormous costs for both the patient and the hospital. This work proposes a medical specialty prediction model based on domain-specific pre-trained BERT from health question texts. The proposed model showed higher predictive performance in the experiments compared with four competitive deep-learning-based NLP models. We expect that the proposed model can relieve the discrepancy problem by suggesting suitable specialties from the text and can be utilized by hospitals or healthcare institutions.
Summary Table.What was already known on the topic
· Artificial intelligence in medical natural language processing can be a suitable tool for an advanced clinical system.
· In the medical process aspect, patients' symptoms should be thoroughly considered when selecting an appropriate medical specialty to reduce unnecessary costs.
· BERT is one of the outperforming NLP models and is widely used for various text-based downstream tasks using fine-tuning.
What this study added to our knowledge
· A medical specialty prediction model from patient-side health question text can be developed based on a pre-trained BERT.
· The pre-training of BERT for domain-specific and language-specific corpus enhances the accuracy of the medical specialty prediction.
CRediT authorship contribution statement
Yoojoong Kim: Conceptualization, Methodology, Software, Validation, Investigation, Writing – original draft, Visualization, Funding acquisition. Jong-Ho Kim: Resources. Young-Min Kim: Writing – review & editing, Supervision. Sanghoun Song: Writing – review & editing, Supervision. Hyung Joon Joo: Conceptualization, Writing – review & editing, Supervision.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This research was supported by a grant of the Basic Science Research Program through the National 10.13039/100005930 Research Foundation of Korea (NRF), funded by the Ministry of Education (No. 2021R1I1A1A01044255).
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| 36512987 | PMC9731829 | NO-CC CODE | 2022-12-14 23:30:05 | no | Int J Med Inform. 2023 Feb 9; 170:104956 | utf-8 | Int J Med Inform | 2,022 | 10.1016/j.ijmedinf.2022.104956 | oa_other |
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Am J Cardiol
Am J Cardiol
The American Journal of Cardiology
0002-9149
1879-1913
Elsevier Inc.
S0002-9149(22)01233-4
10.1016/j.amjcard.2022.11.040
Article
Mortality and Major Adverse Cardiovascular Events in Hospitalized Patients With Atrial Fibrillation With COVID-19
Wang Lucas MD a⁎
Hoang Lawrence MD a
Aten Kristopher DO a
Abualfoul Mujahed DO a
Canela Victor DO a
Prathivada Sri MD c
Vu Michael DO a
Zhao Yi MD a
Sidhu Manavjot MD b
a Department of Internal Medicine
b Methodist Dallas Cardiovascular Consultants, Methodist Medical Group, Division of Cardiology
c Clinical Research Institute, Methodist Dallas Medical Center, Dallas, Texas
⁎ Corresponding author: Tel: 254-716-0273; fax: (214) 947-8181.
9 12 2022
15 2 2023
9 12 2022
189 4148
© 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.
COVID-19 results in increased incidence of cardiac arrhythmias, including atrial fibrillation (AF). However, little is known about the combined effect of AF and COVID-19 on patient outcomes. This study aimed to determine if AF, specifically new-onset AF (NOAF), is associated with increased risk of mortality and major adverse cardiovascular events (MACEs) in hospitalized patients with COVID-19. This multicenter retrospective analysis identified 2,732 patients with COVID-19 admitted between March and December 2020. Data points were manually reviewed in the patients’ electronic health records. Multivariate logistic regression was used to assess if AF was associated with death or MACE. Patients with AF (6.4%) had an increased risk of mortality (risk ratio 2.249, 95% confidence interval [CI] 1.766 to 2.864, p <0.001) and MACE (risk ratio 1.753, 95% CI 1.473 to 2.085, p <0.001) compared with those with sinus rhythm. Patients with NOAF had an increased risk of mortality compared with those with existing AF (odds ratio 19.30, 95% CI 5.39 to 69.30, p <0.001); the risk of MACE was comparable between NOAF and patients with existing AF (p = 1). AF during hospitalization with COVID-19 is associated with a higher risk of mortality and MACE. NOAF in patients with COVID-19 is associated with a higher risk of mortality but a similar risk of MACE compared with patients with existing AF.
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pmcAs of September 2022, there have been over 600 million cases of COVID-19, with over 6 million deaths worldwide.1 COVID-19 is understood to primarily affect the pulmonary system, with the potential to cause severe medical conditions, such as pneumonia and acute respiratory distress syndrome. These medical conditions frequently necessitate mechanical ventilation and often lead to death.2 As more information is captured on this disease, we began to see that the effects of COVID-19 extend far beyond the lungs.3, 4, 5 Recent studies have shown that COVID-19 increases the incidence of complications in most major organ systems, especially the cardiovascular system.6 Elevated cardiac markers,6 prothrombotic states,7 and newly diagnosed arrhythmias8 have been observed in some patients with COVID-19. Among the plethora of sequelae of COVID-19, recent studies have reported an increased incidence of atrial fibrillation (AF) associated with COVID-19.9 , 10 Both AF and COVID-19 have been shown to be independent predictors of increased incidence of mortality and major adverse cardiovascular events (MACEs).11, 12, 13 Further clarification is needed on the effects of AF and, in particular, new-onset AF (NOAF) induced by COVID-19 on mortality and MACE. Our study aimed to better understand and describe the relations between COVID-19 and AF, namely how AF affects mortality and MACE rates in hospitalized patients with COVID-19. Our objectives were to determine if AF significantly increases the risk of MACE or mortality in hospitalized patients with COVID-19 compared with those presenting with sinus rhythm (SR) and to determine whether patients with a known history of AF (AF1) have increased rates of MACE or all-cause mortality compared with those with NOAF.
Methods
This multicenter retrospective cohort study included unvaccinated adults (aged ≥18 years) with polymerase chain reaction-confirmed COVID-19, admitted at 4 hospitals within the Methodist Health System from March 2020 to December 2020. Patient data were abstracted from the electronic medical records. All hospitalized patients who tested polymerase chain reaction-positive for COVID-19, regardless of the reason for admission, were included (Figure 1 ). Patients were excluded if there was no electrocardiogram (ECG) on admission or telemetry during hospitalization, if their initial heart rhythm was not SR or AF, or if their COVID-19 test was positive at an outside hospital. All patient data were deidentified before analysis, and data abstraction was approved by WCG/Aspire Institutional Review Board (institutional review board number 20201424).Figure 1 Flowchart of the patients who participated in our study.
Figure 1
Data manually collected from the electronic medical records included baseline demographics, symptoms and vital signs on arrival, co-morbidities (i.e., history of congestive heart failure, stroke, diabetes, hypertension, chronic obstructive pulmonary disease [COPD], asthma, chronic kidney disease [CKD], end-stage renal disease [ESRD], cirrhosis, human immunodeficiency virus, coronary artery bypass graft, and cancer), laboratory measurements, inpatient medications, and outcomes (i.e., mortality and MACE). Our study defined MACE as either heart failure exacerbation, cardiac tamponade, pericardial effusion, myocarditis, pericarditis, myocardial infarction, stroke, pulmonary embolism, deep venous thrombosis, or shock. Mortality was defined as either in-hospital death or discharge to hospice. A patient was determined to have AF by either ECG or telemetry findings. ECG and telemetry strips were manually read by 2 or more trained medical physicians. Patients were considered to have NOAF if they presented with SR and developed AF at any point after the initial ECG or telemetry reading and if they did not have AF1.
Continuous variables were characterized by mean and SD or median and interquartile range, depending on whether they were normally distributed. For multiple means or medians, 1-way analysis of variance and/or Kruskal–Wallis test was used based on normality. Bonferroni correction was used to adjust the α when multiple comparisons were performed. A p ≤0.05 was considered significant.
Multivariate logistic regression was used to assess whether AF or NOAF was independently associated with death or MACE. Logistic regression was used to assess if AF was independently associated with MACE events. Regression was constructed to adjust for co-morbidities, demographics, and laboratory values (Supplementary Table 1). The base regression only included variables with data available for >90% of patients. Additional subsets included patients with NOAF versus AF1. We did not perform imputation for missing laboratory values because they were likely nonrandom. C-reactive protein, d-dimer, initial troponin, and peak troponin levels were inputted sequentially as appropriate, in addition to the base regression noted before because of missing data. Statistical analysis was performed in R version 4.1.2, using the EZR package version 1.55.
Results
In total, 3,335 patients with COVID-19 were admitted at our health system during the study period. Among 2,732 patients with initial ECG and/or telemetry data, 174 were confirmed to have AF (6.4%), including 82 with rapid ventricular rate (RVR; 47.1%) and 28 with NOAF(16%; Figure 1, Table 1 ). The mean age of patients presenting with SR was significantly lower than patients with AF (61.37 ± 16.3 years vs 76.9 ± 11.3 years, p <0.001). Patients with AF had lower median body mass indexes than those with SR (28.9 [24.8 to 33.3] vs 30.2 [25.6 to 35.7], p = 0.014).Table 1 Demographics of the study cohort
Table 1Factor Group SR
(n = 2558) AF
(n = 174) p Value AF Without RVR
(n = 65) AF With RVR
(n = 109) p Value AF1
(n = 146) NOAF
(n = 28) p Value
Age, mean (SD) 61.37 (16.3) 76.52 (11.3) <0.001 79 (9.96)
75.04 (11.77) 0.024 77.03 (11.30) 73.86 (10.93) 0.173
BMI, median [IQR] 30.2 [25.6, 35.7] 28.9 [24.8, 33.3] 0.014 27.70 [25.1, 31.47]
29.21[24.80, 35.4] 0.109 28.85 [24.83, 33.27] 28.94 [24.80, 32.90] 0.873
Gender, n (%) Female 1225 (47.9) 64 (36.8) 0.005 20 (30.8)
44 (40.4) 0.256 53 (36.3) 11 (39.3) 0.832
Male 1333 (52.1) 110 (63.2) 45 (69.2)
65 (59.6) 93 (63.7) 17 (60.7)
Race/Ethnicity, n (%) White 764 (29.9) 104 (59.8) <0.001 44 (67.7)
60 (55) 0.421 92 (63.0) 12 (42.9) 0.217
Black 902 (35.3) 36 (20.7) 9 (13.8)
27 (24.8) 26 (17.8) 10 (35.7)
Hispanic 631 (24.7) 22 (12.6) 7 (10.8)
15 (13.8) 18 (12.3) 4 (14.3)
Asian 81 (3.2) 4 (2.3) 2 (3.1) 2 (1.8) 3 (2.1) 1 (3.6)
Native American 4 (0.2) 1 (0.6) 0 (0) 1 (0.9) 1 (0.7) 0 (0.0)
Other 172 (6.7) 7 (4.0) 3 (4.6) 4 (3.7) 6 (4.1) 1 (3.6)
O2 Requirements, n (%) No O2 819(32) 55 (31.6) 0.079 22 (33.8)
33 (30.3) 0.588 49 (33.6) 6 (21.4) 0.501
1 – 6L 1294 (50.6) 76 (43.7) 24 (36.9)
52 (47.7) 63 (43.2) 13 (46.4)
7 – 20 L 264 (10.3) 28 (16.1) 13 (20)
15 (13.8) 21 (14.4) 7 (25)
21 – 100 L 74 (2.9) 4 (2.3) 1 (1.5)
3 (2.8) 4 (2.7) 0 (0)
Intubated 107 (4.2) 11 (6.3) 5 (7.7)
6 (5.5) 9 (6.2) 2 (7.1)
Co-morbidities, median [IQR] 2 [1, 3] 2 [1, 4] <0.001 2 [1, 4]
3 [1, 3] 0.835 2.00 [1.00, 4.00] 3.00 [1.75, 3.25] 0.669
Co-morbidities, n (%) Hypertension 1625 (63.5) 133 (76.4) 0.001 52 (80) 81 (74.3) 0.462 112 (76.7) 21 (75.0) 0.812
Diabetes 1061 (41.5) 68 (39.1) 0.578 23 (35.4) 45 (41.3) 0.521 51 (34.9) 17 (60.7) 0.019
CAD 365 (14.3) 61 (35.1) <0.001 28 (43.1) 33 (30.3) 0.102 54 (37) 7 (25.0) 0.282
Cirrhosis 61 (2.4) 6 (3.4) 0.317 3 (4.6) 3 (2.8) 0.672 5 (3.4) 1 (3.6) 1
CKD/ESRD 354 (13.8) 43 (24.7) < 0.001 18 (27.7) 25 (22.9) 0.586 34 (23.3) 9 (32.1) 0.343
COPD/Asthma 390 (15.2) 43 (24.7) 0.002 14 (21.5) 29 (26.6) 0.475 38 (26) 5 (17.9) 0.475
Heart Failure 320 (12.5) 49 (28.2) <0.001 19 (29.2) 30 (27.5) 0.862 43 (29.5) 6 (21.4) 0.494
HIV 18 (0.7) 0 (0.0) 0.624 0 (0.0) 0 (0.0) N/A 0 (0.0) 0 (0.0) 1
MAP < 65 mm Hg 61 (2.4) 6 (3.4) 0.317 3 (4.6) 3 (2.8) 0.672 5 (3.4) 1 (3.6) 1
History of Stroke 229 (9.0) 25 (14.4) 0.022 5 (7.7) 20 (18.3) 0.073 45 (30.8) 6 (21.4) 0.372
Laboratory values, median [IQR]
Potassium (mmol/L) 4 [3.7, 4.4] 4.2 [3.80, 4.70] 0.005 4.3 [3.8, 4.8]
4.2 [3.7, 4.6] 0.315 4.20 [3.80, 4.60] 4.10 [3.80, 4.70] 0.915
Creatinine (mg/dL) 0.90 [0.7, 1.4] 1.3 [0.9, 2.1] <0.001 1.34 [0.9, 2.28]
1.2 [0.88, 1.95] 0.233 1.27[0.90, 2.10] 1.37 [0.78, 3.37] 0.77
CRP (mg/L) 71 [34, 184.2] 69 [27, 190] 0.863 69 [22, 191]
69 [33 186.25] 0.456 64 [24, 173.0] 128 [48.25, 225.00] 0.154
D-Dimer (µ/mL) 1.13 [0.62, 2.25] 1.83 [0.94, 3.49] <0.001 1.88 [1.24, 3.85]
1.57 [0.75, 2.68] 0.062 1.93 [0.87, 3.58] 1.82 [1.57, 2.67] 0.585
Initial Troponin (ng/mL) 0.011 [0.01, 0.03] 0.03 [0.01, 0.07] <0.001 0.03 [0.01, 0.07] 0.03 [0.01, 0.08] 0.92 0.03 [0.01, 0.07] 0.04 [0.02, 0.09] 0.114
Peak Troponin (ng/mL) 0.01 [0.01, 0.04] 0.03 [0.01, 0.09] <0.001 0.03 [0.01, 0.08] 0.03 [0.01, 0.12] 0.934 0.03 [0.01, 0.08] 0.09 [0.02, 0.21] 0.02
Values are n (%) or median [IQR] with Wilcoxon rank sum test P.
AF = atrial fibrillation, AF1 = known history of atrial fibrillation, BMI = body mass index, CAD = coronary artery disease, CKD = chronic kidney disease, COPD = chronic obstructive pulmonary disease, CRP = C-reactive protein, ESRD = end-stage renal disease, HIV = human immunodeficiency virus, MAP = mean arterial pressure, NOAF = new-onset atrial fibrillation, O2 = oxygen, RVR = rapid ventricular rate.
In our population, patients with AF had higher rates of hypertension (76.4% vs 63.5%, p = 0.001), coronary artery disease (CAD; 35.1% vs 14.3%, p <0.001), CKD/ESRD (24.7% vs 13.8%, p = 0.01), COPD/asthma (24.7% vs 15.2%, p = 0.002), and congestive heart failure (28.2% vs 12.5%, p <0.001) than patients with SR (Table 1). Our AF patient population also demonstrated higher levels of potassium (4.2 [3.8 to 4.7] vs 4.0 [3.7 to 4.4] milliequivalents/L, p = 0.005), d-dimer (1.83 [0.94 to 3.49] vs 1.13 [0.62 to 2.25] ng/ml; p <0.001), initial troponin (0.03 [0.011 to 0.07] vs 0.011 [0.011 to 0.033] ng/ml, p <0.001), and peak troponin (0.030 [0.011 to 0.09] vs 0.011 [0.011 to 0.041] ng/ml, p <0.001) than patients with SR.
The demographics and co-morbidities in patients with AF that presented with RVR were examined (Table 1). Patients with AF and RVR tended to be younger than patients with AF without RVR (75.04 ± 11.77 years vs 79 ± 9.96 years, p = 0.024). Otherwise, the “with RVR” and “without RVR” demographics and co-morbidities data were comparable with each other. Overall, these data indicate that RVR was not a contributing factor in the outcomes in our study cohort.
Comparing AF1 and NOAF, the only significant differences between the 2 groups were that patients with NOAF tended to have a higher incidence of diabetes (34.9% vs 60.7%, p = 0.019) and higher peak troponin levels (0.03 [0.01 to 0.08] vs 0.09 [0.02 to 0.21] ng/ml, p = 0.02; Table 1).
The mortality rate of patients with AF was significantly higher than that of patients with SR (31% vs 13.8%, risk ratio [RR] 12.249, 95% confidence interval [CI] 1.766 to 2.864, p <0.001; Figure 2 ). Of note, patients with AF presenting with RVR did not have an increased risk of mortality compared with patients with AF without RVR (p = 1). The logistic regression model for mortality showed that AF was not independently associated with increased risk of mortality (odds ratio [OR] 1.45, 95% CI 0.97 to 2.17, p = 0.073; Figure 3 ). The significant factors associated with increased incidence of mortality included male gender (OR 1.41, 95% CI 1.10 to 1.81, p = 0.007), mean arterial pressure <65 mm Hg (OR 2.33, 95% CI 1.24 to 4.38, p = 0.009), age (OR 1.05, 95% CI 1.04 to 1.07, p <0.001), and peak troponin level (OR 1.02, 95% CI 1.01 to 1.04, p = 0.01). Also, patients requiring any level of oxygenation, including 1 to 6 liters (OR 2.33, 95% CI 1.68 to 3.24), 7 to 20 liters (OR 6.59, 95% CI 4.44 to 9.80), 21 to 100 liters (OR 11.6, 95% CI 6.57 to 20.5), and intubated (OR 20.7, 95% CI 12.5 to 34.2) all showed an increased risk of mortality (p <0.001; Supplementary Table 2).Figure 2 In-hospital mortality rates categorized by heart rhythm. Error bars represent 95% CI.
Figure 2
Figure 3 Adjusted ORs of the associations between AF and all-cause mortality and major adverse cardiac events. Boxes indicate ORs, and the lines indicate the 95% CIs.
Figure 3
The presence of AF was significantly associated with MACE incidence (46.0% vs 26.2%; RR 1.753, 95% CI 1.473 to 2.085, p <0.001; Figure 4 , Table 2 ). Individual MACE outcomes that were significantly associated with AF included heart failure exacerbation (RR 3.915, 95% CI 2.343 to 6.542, p <0.001), myocardial infarction (RR 1.947, 95% CI 1.602 to 2.367, p <0.001), and shock (RR 1.604, 95% CI 1.061 to 2.425, p = 0.038) (Figure 5 ). Significant factors associated with increased incidence of MACE were older age (p <0.001), male gender (p = 0.013), African-American race (p <0.001), any amount of O2 requirement on arrival, history of CAD (p <0.001), history of CKD/ESRD (p <0.001), history of heart failure (p <0.001), history of stroke (p = 0.018), mean arterial pressure <65 mm Hg on arrival (p = 0.006), and elevated d-Dimer (p <0.001) (Supplementary Table 3). There was no significant difference in the rates of MACE between the AF and AF with RVR cohorts (p = 1).Figure 4 In-hospital major adverse cardiac event rates characterized by heart rhythm. Error bars represent 95% CI.
Figure 4
Table 2 Major adverse cardiac events associated with atrial fibrillation with and without rapid ventricular rate
Table 2Factor Group SR
(n = 2558) AF
(n = 174) p Value RR Without RVR
(n = 65) With RVR
(n = 109) p Value RR
MACE No 1887 (73.8) 94 (54.0) 37 (56.9) 57 (52.3)
Yes 671 (26.2) 80 (46.0) < 0.001 1.753 (1.473–2.085) 28 (43.1) 52 (47.7) 0.638
Heart failure exacerbation (%) No 2484 (97.1) 158 (90.8) 62 (95.4) 96 (88.1)
Yes 74 (2.9) 16 (9.2) <0.001 3.087 (1.839 – 5.182) 3 (4.6) 13 (11.9) 0.173
Cardiac tamponade (%) No 2589 (100.0) 174 (100.0) 65 (100.0) 109 (100.0)
Yes 0 (0) 0 (0) 0 (0) 0 (0)
Pericardial effusion (%) No 2544 (99.5) 171 (98.3) 0.089 63 (96.9) 108 (99.1)
Yes 14 (0.5) 3 (1.7) 2 (3.1) 1 (0.9) 0.557
Myocarditis (%) No 2551 (99.7) 174(100.0) 1 65 (100.0) 109(100.0)
Yes 7 (0.3) 0 (0.0) 0 (0.0) 0 (0.0)
Pericarditis (%) No 2555 (99.9) 174 (100.0) 1 65 (100.0) 109 (100.0)
Yes 3 (0.1) 0 (0.0) 0 (0.0) 0 (0.0)
Myocardial infarction (%) No 1976 (77.2) 97 (55.7) 36 (55.4) 61 (56.0)
Yes 582 (22.8) 77 (44.3) <0.001 1.945 (1.622–2.332) 29 (44.6) 48 (44.0) 1
Stroke (%) No 2454 (95.9) 166 (95.4) 0.692 61 (93.8) 105 (96.3)
Yes 104 (4.1) 8 (4.6) 4 (6.2) 4 (3.7) 0.474
PE/DVT (%) No 2448 (95.7) 166 (95.4) 0.846 63 (96.9) 103 (94.5)
Yes 110 (4.3) 8 (4.6) 2 (3.1) 6 (5.5) 0.712
Shock (%) No 2332 (91.2) 142 (81.6) < 0.001 2.082 (1.486–2.915) 53 (81.5) 89 (81.7)
Yes 226 (8.8) 32 (18.4) 12 (18.5) 20 (18.3) 1
Values are n (%) or median [IQR] with Wilcoxon rank sum test P.
AF = atrial fibrillation, AF1 = known history of atrial fibrillation, BMI = body mass index, CAD = coronary artery disease, CKD = chronic kidney disease, COPD = chronic obstructive pulmonary disease, CRP = C-reactive protein, ESRD = end-stage renal disease, HIV = human immunodeficiency virus, MAP = mean arterial pressure, NOAF = new-onset atrial fibrillation, O2 = oxygen, RVR = rapid ventricular rate.
Figure 5 Frequency of major adverse cardiac events in patients with sinus rhythm or atrial fibrillation. PE = pulmonary embolism, DVT = deep venous thrombosis.
Figure 5
Patients with NOAF were, on average, older than patients with SR or AF1 (73.86 ± 10.93 vs 62.2 ± 16.41, p <0.001); had more co-morbidities (3 [1.75 to 3.25] vs 2 [1–3], p = 0.004); and had higher levels of creatinine (1.37 [0.78 to 3.37] mg/100 ml vs 0.95 [0.70 to 1.46], p = 0.031), d-dimer (1.82 [1.57 to 2.67] vs 1.15 [0.62 to 2.32] µg/ml, p = 0.013), initial troponin (0.04 [0.02 to 0.09] vs 0.01 [0.01 to 0.04] ng/ml, p <0.001), and peak troponin (0.09 [0.02 to 0.21] vs 0.01 [0.01 to 0.04], p <0.001; Supplementary Table 4).
After accounting for co-morbidities and demographics, NOAF was associated with substantially higher mortality risk than the rest of the patients (OR 7.84, 95% CI 3.27 to 18.80, p <0.001; Figure 3, Supplemental Table 5) and those with AF1 (64.3% vs 24.7%, OR 19.30, 95% CI 5.39 to 69.30, p <0.001; Supplementary Table 6).
NOAF was also associated with increased incidence of MACE (46.4% vs 27.3%, RR 1.701, 95% CI 1.137 to 2.544, p = 0.032), specifically, the incidence of myocardial infarction (42.9% vs 23.9%, RR 1.791, 95% CI 1.162 to 2.762, p = 0.026) and shock (35.7% vs 9.2%, RR 3.894, 95% CI 2.336 to 6.491, p <0.001), compared with SR (Figure 4, Table 3 ). Compared with AF1, NOAF was associated with increased incidence of shock (35.7% vs 15.1%, RR 2.37, 95% CI 1.26 to 4.44, p = 0.016) but not the MACE total (p = 1) or myocardial infarction (p = 1; Supplementary Table 7).Table 3 Major adverse cardiac events associated with new-onset atrial fibrillation
Table 3MACE, n (%) SR and AF1
(n = 2704) NOAF
(n= 28) p Value RR (95% CI)
Any MACE 738 (27.3) 13 (46.4) 0.032 1.701 (1.137–2.544)
Heart failure exacerbation 90 (3.3) 0 (0.0) 1
Cardiac tamponade 0 (0.0) 0 (0.0)
Pericardial effusion 17 (0.6) 0 (0.0) 1
Myocarditis 7 (0.3) 0 (0.0) 1
Pericarditis 3 (0.1) 0 (0.0) 1
Myocardial infarction 647 (23.9) 12 (42.9) 0.026 1.791 (1.162–2.762)
Stroke 110 (4.1) 2 (7.1) 0.32
PE/DVT 118 (4.4) 0 (0.0) 0.631
Shock 248 (9.2) 10 (35.7) <0.001 3.894 (2.336–6.491)
Values are n (%). Analysis was done with Χ2 or Fisher's exact test.
CI = confidence interval, ECG = electrocardiogram, DVT = deep vein thrombosis, MACE = major adverse cardiovascular event, NOAF = new-onset atrial fibrillation, PE = pulmonary embolism, RR = relative risk, SR = sinus rhythm.
Discussion
In this study, we analyzed the outcomes of hospitalized patients with COVID-19 across 4 hospitals in Dallas, Texas. Here, we demonstrated that AF was an individual predictor for mortality, and that patients with NOAF were more susceptible to mortality than patients with AF1. NOAF was associated with significantly higher rates of mortality and MACE. These data suggest that COVID-19 or its sequelae is associated with a higher rate of AF, which can lead to increased incidence of mortality and MACE. The strengths unique to this study include: (1) a large, diverse patient population across 4 different hospitals, with significant numbers of known AF risk factors, (2) a comprehensive validation of data, with a thorough chart review of each individual measurement, (3) a direct comparison of both mortality and MACE in the same patient population, with a focused comparison of patients with NOAF versus AF1, and (4) patient data from a unique point in the history of COVID-19 because patients in this study were all not immunized, given the vaccine was not yet widely distributed.
The data from this study replicate the trend observed in the study by Musikantow et al14 in that in-hospital AF in patients with COVID-19 occurred more often in those with pre-existing co-morbidities, in particular CAD, CKD/ESRD, COPD, asthma, and congestive heart failure. Interestingly, for NOAF, the number of co-morbidities likely matters more than the presence of any single factor, except for age and CKD.
To the best our knowledge, this is the first study involving a large cohort of patients to address the effect of in-hospital AF in its various forms in patients with COVID-19 in terms of mortality and MACE. A previous analysis involving a cohort of 1,053 patients in 2 healthcare centers showed atrial arrhythmias were independently associated with increased mortality.15 Another study analyzed a smaller sample of 160 hospitalized patients with COVID-19 and demonstrated that NOAF is related to worse cardiovascular outcomes and increased mortality.16 In the studies on critical care, NOAF has been linked to increased mortality in non-COVID-19 acute respiratory distress syndrome.17
Here, we demonstrated that patients with COVID-19 diagnosed with AF showed a significantly higher rate of mortality, particularly in those with NOAF. Interestingly, NOAF was not associated with an increase in any category of MACE, except for shock, compared with AF1. Previous studies have demonstrated an association with COVID-19 and a prothrombotic state18 and increased incidence of heart failure exacerbation17 or other cardiovascular injury.19 However, none seemed to observe a significant difference in the mortality rates in patients with NOAF compared with patients with AF1.
This study is limited to a specific time during the height of the Alpha variant, with some crossing over to the rising prominence of the Delta variant. Unfortunately, we do not have data specific to the Omicron variant because of the time of our study and therefore, we cannot speak to the current COVID-19 climate. Given the nature of retrospective studies based on chart reviews, we cannot say with absolute certainty the exact timing of AF onset and if it was because of COVID-19. Furthermore, this study did not directly compare patients with similar cardiovascular risk profiles. However, we propose that our comprehensive set of values accounted for in our multivariate linear regression model to determine AF and NOAF as independent predictors of mortality and MACE encompasses the cardiac and noncardiac co-morbidities used in previous studies.20, 21, 22 Another limitation to this study is that the analysis of both mortality and MACE was not adjusted for the severity of COVID-19 disease during the hospitalization.
The results of this study will help practitioners better triage and treat patients with COVID-19 with multiple co-morbidities presenting with AF. By understanding the risk factors for major outcomes, such as mortality and MACE, physicians cannot only gain insight over the clinical course of these patients but also prepare better to manage and anticipate complications associated with this process.
Disclosures
The authors have no conflicts of interest to declare.
Appendix Supplementary materials
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Acknowledgment
The authors thank Anne Murray, PhD, MWC of the Clinical Research Institute at Methodist Health System for providing editorial support.
Funding: none.
Supplementary material associated with this article can be found in the online version at https://doi.org/10.1016/j.amjcard.2022.11.040.
==== Refs
References
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4 Su H Yang M Wan C Yi LX Tang F Zhu HY Yi F Yang HC Fogo AB Nie X Zhang C. Renal histopathological analysis of 26 postmortem findings of patients with COVID-19 in China Kidney Int 98 2020 219 227 32327202
5 Xiao F Tang M Zheng X Liu Y Li X Shan H. Evidence for gastrointestinal infection of SARS-CoV-2 Gastroenterology 158 2020 1831 1833 e3 32142773
6 Shi S Qin M Shen B Cai Y Liu T Yang F Gong W Liu X Liang J Zhao Q Huang H Yang B Huang C. Association of cardiac injury with mortality in hospitalized patients with COVID-19 in Wuhan, China JAMA Cardiol 5 2020 802 810 32211816
7 Giannis D Ziogas IA Gianni P. Coagulation disorders in coronavirus infected patients: COVID-19, SARS-CoV-1, MERS-CoV and lessons from the past J Clin Virol 127 2020 104362
8 Clerkin KJ Fried JA Raikhelkar J Sayer G Griffin JM Masoumi A Jain SS Burkhoff D Kumaraiah D Rabbani L Schwartz A Uriel N. COVID-19 and cardiovascular disease Circulation 141 2020 1648 1655 32200663
9 Wu N Xu B Xiang Y Wu L Zhang Y Ma X Tong S Shu M Song Z Li Y Zhong L. Association of inflammatory factors with occurrence and recurrence of atrial fibrillation: a meta-analysis Int J Cardiol 169 2013 62 72 24095158
10 Bhatla A Mayer MM Adusumalli S Hyman MC Oh E Tierney A Moss J Chahal AA Anesi G Denduluri S Domenico CM Arkles J Abella BS Bullinga JR Callans DJ Dixit S Epstein AE Frankel DS Garcia FC Kumareswaram R Nazarian S Riley MP Santangeli P Schaller RD Supple GE Lin D Marchlinski F Deo R. COVID-19 and cardiac arrhythmias Heart Rhythm 17 2020 1439 1444 32585191
11 Dherange P Lang J Qian P Oberfeld B Sauer WH Koplan B Tedrow U. Arrhythmias and COVID-19: a review JACC Clin Electrophysiol 6 2020 1193 1204 32972561
12 Karamchandani K Quintili A Landis T Bose S. Cardiac arrhythmias in critically ill patients with COVID-19: a brief review J Cardiothorac Vasc Anesth 35 2021 3789 3796 32888796
13 Klok FA Kruip MJHA van der Meer NJM Arbous MS Gommers DAMPJ Kant KM Kaptein FHJ van Paassen J Stals MAM Huisman MV Endeman H. Incidence of thrombotic complications in critically ill ICU patients with COVID-19 Thromb Res 191 2020 145 147 32291094
14 Musikantow DR Turagam MK Sartori S Chu E Kawamura I Shivamurthy P Bokhari M Oates C Zhang C Pumill C Malick W Hashemi H Ruiz-Maya T Hadley MB Gandhi J Sperling D Whang W Koruth JS Langan MN Sofi A Gomes A Harcum S Cammack S Ellsworth B Dukkipati SR Bassily-Marcus A Kohli-Seth R Goldman ME Halperin JL Fuster V Reddy VY. Atrial fibrillation in patients hospitalized with COVID-19: incidence, predictors, outcomes, and comparison to influenza JACC Clin Electrophysiol 7 2021 1120 1130 33895107
15 Peltzer B Manocha KK Ying X Kirzner J Ip JE Thomas G Liu CF Markowitz SM Lerman BB Safford MM Goyal P Cheung JW. Outcomes and mortality associated with atrial arrhythmias among patients hospitalized with COVID-19 J Cardiovasc Electrophysiol 31 2020 3077 3085 33017083
16 Pardo Sanz A Salido Tahoces L Ortega Pérez R González Ferrer E Sánchez Recalde Á Zamorano Gómez JL New-onset atrial fibrillation during COVID-19 infection predicts poor prognosis Cardiol J 28 2021 34 40 33140386
17 Omidi F Hajikhani B Kazemi SN Tajbakhsh A Riazi S Mirsaeidi M Ansari A Ghanbari Boroujeni M Khalili F Hadadi S Nasiri MJ COVID-19 and cardiomyopathy: a systematic review Front Cardiovasc Med 8 2021 695206
18 Abou-Ismail MY Diamond A Kapoor S Arafah Y Nayak L. The hypercoagulable state in COVID-19: incidence, pathophysiology, and management Thromb Res 194 2020 101 115 32788101
19 Moayed MS Rahimi-Bashar F Vahedian-Azimi A Sathyapalan T Guest PC Jamialahmadi T Sahebkar A. Cardiac injury in COVID-19: a systematic review Adv Exp Med Biol 1321 2021 325 333 33656737
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22 Aydemir S Aksakal E Aydınyılmaz F Gülcü O Saraç İ Aydın SŞ Doğan R Lazoğlu M Kalkan K. Does new onset and pre-existing atrial fibrillation predict mortality in COVID-19 patients? Egypt Heart J 74 2022 53 35796916
| 36502570 | PMC9731831 | NO-CC CODE | 2022-12-15 23:18:12 | no | Am J Cardiol. 2023 Feb 15; 189:41-48 | utf-8 | Am J Cardiol | 2,022 | 10.1016/j.amjcard.2022.11.040 | oa_other |
==== Front
J Virus Erad
J Virus Erad
Journal of Virus Eradication
2055-6640
2055-6659
The Authors. Published by Elsevier Ltd.
S2055-6640(22)00243-6
10.1016/j.jve.2022.100305
100305
Original Research
Host-directed therapy with 2-Deoxy-D-glucose inhibits human rhinoviruses, endemic coronaviruses, and SARS-CoV-2
Wali Laxmikant a1
Karbiener Michael b1
Chou Scharon a
Kovtunyk Vitalii a
Adonyi Adam a
Gösler Irene c
Contreras Ximena a
Stoeva Delyana a
Blaas Dieter c
Stöckl Johannes d
Kreil Thomas R. b
Gualdoni Guido A. a
Gorki Anna-Dorothea a∗
a G.ST Antivirals GmbH, Austria
b Global Pathogen Safety, Takeda Manufacturing Austria AG, Austria
c Center of Medical Biochemistry, Max Perutz Labs, Vienna Biocenter, Medical University of Vienna, Austria
d Institute of Immunology, Center of Pathophysiology, Immunology & Infectiology, Medical University of Vienna, Austria
∗ Corresponding author. G.ST Antivirals GmbH, Doktor-Bohr-Gasse 7 (VBC6), 1030, Vienna, Austria.
1 Contributed equally.
9 12 2022
9 12 2022
1003057 9 2022
30 11 2022
1 12 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.
Rhinoviruses (RVs) and coronaviruses (CoVs) upregulate host cell metabolic pathways such as glycolysis to meet their bioenergetic demands for rapid multiplication. Using the glycolysis inhibitor 2-deoxy-D-glucose (2-DG), we assessed the dose-dependent inhibition of viral replication of minor- and major-receptor group RVs in epithelial cells. 2-DG disrupted RV infection cycle by inhibiting template negative-strand as well as genomic positive-strand RNA synthesis, resulting in less progeny virus and RV-mediated cell death. Assessment of 2-DG´s intracellular kinetics revealed that after a short-exposure to 2-DG, the active intermediate, 2-DG6P, is stored intracellularly for several hours. Finally, we confirmed the antiviral effect of 2-DG on pandemic SARS-CoV-2 and showed for the first time that 2-DG also reduces replication of endemic human coronaviruses (HCoVs). These results provide further evidence that 2-DG could be utilized as a broad-spectrum antiviral.
Keywords
Antiviral
Broad-spectrum antiviral therapy
2-DG
Rhinovirus
Coronavirus
SARS-CoV-2
==== Body
pmc1 Introduction
Rhinoviruses (RVs) and endemic human coronaviruses (HCoVs) are the major cause of acute respiratory tract (RT) infections in humans1,2. These are largely self-limiting in healthy adults, where they usually remain confined to the upper respiratory tract. However, as the viruses spread rapidly and circulate seasonally, they lead to high incidence rates on an annual basis. These can cause severe morbidity in elderly, children, and immune-compromised patients.3, 4, 5, 6 Along with human suffering, these viral infections lead to high economic losses and healthcare costs7,8. While global efforts are underway to develop an effective therapy, the current lack of FDA-approved antivirals has limited the treatment of RT infections to supportive and symptomatic care.
As Picornaviridae, RVs are non-enveloped and contain a positive-sense single-stranded RNA genome ((+)ssRNA).9 They are divided into three species, RV-A, RV-B and RV-C. RV-A and RV-B are further classified as minor- and major-group based on the cognate host cell receptors they use for cell entry.10, 11, 12 Coronaviruses (CoVs) are enveloped viruses, belong to the Coronaviridae family and also contain a (+)ssRNA genome.13 They are classified into four major genera: alpha, beta, gamma, and delta, targeting a variety of host species. In humans, strains from the alpha14, 15, 16 and beta genera17 are known to induce common colds similar to the ones caused by RVs18,19. However, three strains from the beta genus, including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) were found to be more pathogenic with high fatality rates.20
Viruses are dependent on the host cell metabolism and machinery to ensure their replication. RVs and CoVs in particular are known to hijack and reprogram the host cell metabolic pathways for rapid multiplication, causing an increase in bioenergetic demand21,22. This leads to an elevated anabolic state, forcing the host cell to synthesize more lipids and nucleotides using glucose and glutamine as substrates.23 In addition, there is an increased demand for energy in the form of adenosine triphosphate (ATP) for viral replication and assembly, which is predominantly provided by glycolysis.23, 24, 25 As an essential metabolic pathway, this involves breakdown of hexoses like glucose into pyruvate for ATP production. This dependency of RVs and CoVs, and presumably of other viruses, on host glucose metabolism for replication presents a promising target for the development of effective antiviral therapies.
2-Deoxy-D-glucose (2-DG), a stable analogue of glucose, is taken up by cells via glucose transporters and subsequently phosphorylated to 2-deoxy-D-glucose-6-phosphate (2-DG6P) by hexokinase26,27. Unlike in glucose metabolism, 2-DG6P cannot be further metabolized by phosphoglucose isomerase.28 This leads to intracellular accumulation of 2-DG6P and arrest of glycolysis at the initial stage, causing depletion of glucose derivatives and substrates crucial for viral replication.29 Previously, it has been demonstrated that 2-DG affects viral replication by reverting virus-induced metabolic reprogramming of host cells24,25,30,31.
The present study explores the broad-spectrum antiviral activity of 2-DG. In this process, we investigated the antiviral activity of 2-DG against minor- and major-group RVs in epithelial cells including primary human nasal epithelial cells (HNECs), the main site of RV replication. In concurrent experiments, we characterized 2-DG´s intracellular kinetics. Finally, to better understand the inhibitory activity of 2-DG on the RV infection cycle, we quantified the template (−)ssRNA as well as the genomic (+)ssRNA and analyzed 2-DG's effect on RV-mediated cell death. Finally, we assessed the antiviral activity of 2-DG against endemic HCoVs as well as the pandemic SARS-CoV-2 strain. In summary, our study provides further evidence that reverting virus-induced metabolic reprogramming by 2-DG treatment critically affects viral RNA replication and thus holds great potential in combating respiratory viral infections.
2 Methods
Details including supplier and catalogue number of all materials used are listed in Supplement table 1.
Cell culture. Cells were seeded in either 24-well tissue culture plates or T25 flasks and incubated at 37 °C in media and densities (cells per well or cells per flask) for the given times as indicated below; human nasal epithelial cells (HNECs) in HNEC medium (Pneumacult-ex plus basal medium supplemented with 1x Pneumacult-ex plus supplement, 0.1% Hydrocortisone stock solution and 1% Penicillin/Streptomycin (100 Units/mL) at 4.5 × 104 cells/well (72 h) and HeLa Ohio cells in HeLa Ohio medium (RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS), 1% Penicillin/Streptomycin (100 Units/mL) and 2 mM l-glutamine) at 2 × 105 cells/well (16–20 h). LLC-MK2 and MRC-5 cells were cultured in T25 cell culture flasks in LLC-MK2 medium (Eagle-MEM supplemented with 10% FBS, 1x non-essential amino acids solution (NEAS), 100 mg/mL Gentamycin sulfate and 25 mM HEPES) and MRC-5 medium (Eagle-MEM supplemented with 10% FBS, 2 mM l-Glutamine, 1x NEAS, 1 mM sodium pyruvate, 100 mg/mL gentamycin sulfate, 0.15% sodium bicarbonate) at densities of 8 × 105 and 9 × 105 cells/flask, respectively. Vero cells were cultivated in TC Vero medium (supplemented with 5% FBS, 2 mM l-Glutamine, 1x NEAS, 100 mg/mL gentamycin sulfate, and 0.075% sodium bicarbonate).
Viral infection and 2-DG treatment. HeLa Ohio cells and HNECs were infected for 1 h at 37 °C or 34 °C with RV at 0.005 to 0.5 TCID50/cell and 4.5 × 104 TCID50/well, followed by treatment with 2-DG for 6 h, 24 h or 48 h. The supernatant from the cells was then subjected to virus titer analysis or the cells were treated with cell lysis buffer for RNA extraction. LLC-MK2 cells and MRC-5 cells were infected with SARS-CoV-2 (Beta-CoV/Germany/BavPat1/2020) (MOI of 0.001) at 36 °C and HCoV-229E (MOI of 0.01) at 36 °C or HCoV-NL63 (MOI of 0.01) at 33 °C, respectively. Cells were treated with 2-DG 1 h post-infection and samples were collected at the indicated times for virus titer analysis.
RNA isolation and cDNA synthesis. Intra- and extra-cellular RNA was isolated according to the ExtractMe Total RNA Kit instructions. To avoid bias in extracellular RNA isolation, an internal spike-in RNA control was added to each sample. RNA concentration and purity was assessed using a nanophotometer. cDNA was synthesized according to the First strand cDNA synthesis kit using the program: 37 °C for 60 min and 70 °C for 5 min. Measurement of viral negative-sense single-strand RNA ((−)ssRNA) was performed as previously described32 except that the synthesized cDNA wasn't RNase treated and purified. The cDNA from (−)ssRNA was synthesized using a mix of strand-specific, chimeric sequence-containing primer chimHRV-b14_RT and control primer HPRT_R (Supplement table 1) instead of oligo(dT).
qPCR. qPCR was performed using SYBR green mix and primers as specified in Supplement table 1. For measuring intracellular viral RNA, gene expression was normalized to HPRT using the Livak method33 and expressed as fold change to control (infected, but untreated). Primers HRV-B14_R and chimHRV-b14_R1 were used for measurement of viral (−)ssRNA. For extracellular viral RNA, synthetic oligo standard (HRV-B14_F, HRV-B14_R and HRV-B14 primer amplicon, Supplement table 1) was used to generate a standard curve for the calculation of viral copy number by interpolation. Based on the qPCR data, the IC50 was calculated using least squares regression on Prism 9.0.2.
Virus titration. Samples from SARS-CoV-2, HCoV-229E and HCoV-NL63 were titrated on Vero cells, MRC-5 cells, and LLC-MK2 cells, respectively. Samples from RV-B14 were titrated on HeLa Ohio cells. Titration was performed using eightfold replicates of serial half-log10 (for SARS-CoV-2, HCoV-229E and HCoV-NL63) or log10 (for RV-B14) dilutions of virus-containing samples followed by incubation at 36 °C (SARS-CoV-2, HCoV-229E), 33 °C (HCoV-NL63) and 34 °C (RV-B14) for 5–7 days (SARS-CoV-2, HCoV-229 EE, RV-B14) or 9–11 days (HCoV-NL63). Wells were inspected under a microscope for cytopathic effect (CPE). For RV-B14, CPE was visualized by crystal violet staining. Recognizable CPE at each tested dilution was used to determine the dose according to Reed and Muench34 and reported as log10-transformed median tissue culture infectious dose per milliliter (log10 [TCID50/mL]).
Virus-induced cytopathic effect. HeLa Ohio cells were infected for 1 h at 37 °C with RV-B14 (0.5 TCID50/cell) followed by 2-DG treatment for 24 h or 48 h at 37 °C. CPE was visualized by crystal violet staining. The effect of 2-DG on virus-induced cell death was assessed by calculating the ratio of the average of treated, uninfected to each treated, infected sample value.
Cell viability. HNECs were treated with 2-DG for 7 h at 37 °C. Cell viability was assessed by crystal violet staining. The effect of 2-DG on cell viability was calculated relative to untreated cells.
Crystal violet staining. Cells were incubated with crystal violet solution (0.05% crystal violet in 20% methanol) for 30–60 min, washed with ddH2O, air-dried, followed by 25% glacial acetic acid. The absorbance was recorded at 450 nm.
Glucose-uptake assay. Cells were treated with 2-DG in the absence of glucose for 10 min at 37 °C, followed by washing with PBS and incubation for up to 270 min in glucose-free medium. 2-DG uptake was assessed using the Glucose-Uptake Glo™ Assay kit. Luminescence was recorded on a microplate reader. 2-DG6P levels were calculated as percentage of signal upon exposure to 2-DG after subtracting the background value obtained from control samples (not treated with 2-DG).
Statistical analysis. The graphs show pooled results of independent experiments with each experiment containing two to four cell culture wells per condition with the standard error of the mean (SEM). Analysis of statistical significance was performed using Student's t-test (unpaired analysis) or ordinary one-way ANOVA with Dunnett's correction or 2-way ANOVA with Bonferroni's correction and considered significant when p < 0.05 (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001).
3 Results
3.1 2-DG inhibits RV replication in HeLa Ohio cells and HNECs
2-Deoxyglucose (2-DG) treatment has been shown to inhibit rhinovirus (RV) infection by reverting RV-induced anabolic reprogramming of host cell metabolism.25 While the effect of 2-DG on RV-B1425 and RV-C1535 was shown before, its effect on additional serotypes belonging to minor- and major-group RVs10, 11, 12 remains to be investigated. For this, HeLa Ohio cells were infected with minor-group (RV-A1B, RV-A2) and major-group (RV-A89, RV-A16, RV-A54) RVs. 2-DG treatment led to a dose-dependent reduction in intracellular viral RNA levels of all major- and minor-group RVs tested (Supplement Figure 1). As 2-DG is transported into cells utilizing the same transporters as glucose, this results in a competition for the uptake of 2-DG26,27. The glucose concentration in conventional cell culture media ranges from 2 g/L to 4.5 g/L and is much higher compared to in vivo glucose levels (e.g., in the blood it ranges from 3.9 to 5.6 mmol/L i.e., 0.7–1 g/L). Therefore, we tested the antiviral effect of 2-DG under physiological glucose levels (Fig. 1 ). We reduced the glucose concentration in the cell culture medium to 1 g/L to mimic a setting corresponding to human plasma. 2-DG treatment at physiological glucose levels showed an even stronger inhibitory effect on intracellular viral RNA levels of all major- and minor-group RVs (Fig. 1A). With the highest tested concentration of 2-DG (30 mM) we observed a complete abolishment of viral RNA replication (Fig. 1A). In line with these results, the absolute half-maximal inhibitory concentration (IC50) of 2-DG was lower under the physiological glucose setting: The IC50 ranged from 1.92 mM to 2.67 mM as compared to 3.44 mM–9.22 mM for cells infected and treated under conventional culture conditions (Fig. 1B, Supplement table 2).Fig. 1 Inhibition of RV replication by 2-DG in HeLa Ohio cells and HNECs. Intracellular viral RNA was assessed by qPCR 7 h post-infection at 0.005 TCID50/cell for the indicated RV strains in HeLa Ohio cells in medium containing 1 g/L glucose (A). Comparison of IC50 of 2-DG on the indicated RV strains under physiological versus conventional culture conditions (B). Intracellular viral RNA was assessed by qPCR 7 h post-infection at 4.5 × 104 TCID50/well for the indicated RV strains in HNECs (C). In (A) and (C) cells were treated with the indicated concentrations of 2-DG (represented on a log10 scale) 1 h post-infection until samples were collected. The viability of HNECs was assessed at 7 h post-treatment with indicated concentrations of 2-DG (D). Graphs show pooled result ± SEM of 3–4 independent experiments. HNEC: human nasal epithelial cells, RV: rhinovirus.
Fig. 1
Further, we evaluated the effect of 2-DG on RV-B14 and RV-A16 replication in human nasal epithelial cells (HNECs), the natural replication site for RVs. In line with the previous findings, 10 mM and 30 mM 2-DG treatment strongly inhibited RV-B14 and RV-A16 replication (Fig. 1C). To be noted, unlike in HeLa Ohio cell culture medium, where the glucose level is known, glucose levels in HNECs culture medium (STEMCELL Technologies) are not disclosed.
As 2-DG inhibits glycolysis, a major energy generating pathway, we assessed whether it has an impact on cell viability in our setting. We did not measure a significant reduction in cell viability after 7 h 2-DG treatment (Fig. 1D). Taken together, the data suggests that 2-DG inhibits RV replication in a dose-dependent manner, independent of the RV strain and cell type used. No toxic effects on the cells were recorded at concentrations those employed in the virus inhibition experiments. Furthermore, we observed better uptake and enhanced antiviral activity of 2-DG at physiological glucose levels.
A short exposure to 2-DG leads to extended intracellular storage of 2-DG6P.
Once 2-DG is taken up by the cell, it is phosphorylated to 2-deoxy-D-glucose-6-phosphate (2-DG6P), which leads to the arrest of glycolysis and altering of viral replication.25 Thus, the kinetics of cellular uptake and intracellular storage are crucial for the antiviral activity of 2-DG. Therefore, we investigated the intracellular concentration kinetics of 2-DG6P in HeLa Ohio cells and HNECs. The experimental setup was designed to mimic treatment setting of 2-DG in vivo, e.g., a local application to the nasal cavity. Cells were treated with 1 mM and 10 mM 2-DG for 10 min, followed by washing to remove extracellular 2-DG and subsequent incubation up to 270 min and quantification of 2-DG6P levels (Fig. 2 A). At time zero (immediately after the 10 min 2-DG treatment), higher 2-DG6P levels were observed in 10 mM 2-DG treatment compared to 1 mM 2-DG treatment, in both HeLa Ohio cells and HNECs (Fig. 2B and C, left graph). The intracellular 2-DG6P level measured at time zero was then set to 100%, and the percentage decay of 2-DG6P over time was calculated. In HeLa Ohio cells 3.5% ± 0.6% (mean ± SEM) and 18.5% ± 3.4% 2-DG6P were measured in 1 mM and 10 mM 2-DG treated cells after 270 min (Fig. 2B). In the case of HNECs, higher levels of 2-DG6P retention were observed after 270 min; 10.1% ± 1.5% and 42.6% ± 7.2% 2-DG6P being detected in cells pre-treated with 1 mM and 10 mM 2-DG (Fig. 2C), respectively. Collectively, the data suggest that short exposure of the cells to 2-DG leads to an intracellular accumulation of the active intermediate 2-DG6P for several hours.Fig. 2 Intracellular storage of 2-DG6P after short-term exposure to 2-DG. 2-DG uptake experimental setup (A). Luminescence-based measurements of intracellular 2-DG6P at the indicated times after HeLa Ohio cells (B) or HNECs (C) were exposed to 2-DG for 10 min. In (B) and (C), the left graphs show the 2-DG6P levels (in RLU) at time 0 min (i.e., immediately after 10 min 2-DG treatment), and the right graphs show percentage decay of 2-DG6P over time in HeLa Ohio and HNECs, respectively. Data show pooled result ± SEM of 2–3 independent experiments. RLU: relative luminescence units, HNEC: human nasal epithelial cells.
Fig. 2
3.2 2-DG disrupts RNA template strand synthesis and inhibits RV-mediated cell death
In our initial investigation of 2-DG mediated inhibition of RV replication, we measured the (+)ssRNA copies because of its abundance (10,000-fold higher than (−)ssRNA)36 and the ease of quantification. However, the RV replication cycle involves generation of (−)ssRNA which is used as template for the replication of positive strand genomes.37 Thus, the determination of (−)ssRNA serves as a means to quantify double stranded RNA (dsRNA), which is an intermediate of viral replication36,38.
Therefore, we analyzed the influence of 2-DG on synthesis of (−)ssRNA and of (+)ssRNA at 24 h post-infection. 10 mM 2-DG treatment led to a significant decrease in template (−)ssRNA levels of RV-B14 at 24 h post-infection (Fig. 3 A). This result was closely mirrored by decrease in the (+)ssRNA strand upon 2-DG treatment (Fig. 3A). Simultaneously, we found that 2-DG treatment led to a significant decrease in the number of viral RNA copies in the supernatant (Fig. 3B), implying an impairment of the amount of released virus. Next, we assessed 2-DG´s impact on viral load by means of median tissue culture infectious dose (TCID50) assays. RV-B14 infected HeLa Ohio cells were treated with 2-DG at 3.57 mM, corresponding to IC90, up to 48 h and the supernatants containing progeny virus were collected every 24 h and analyzed by virus infectivity assay. The above IC90 concentration of 2-DG was calculated from the previously derived dose-response curve in HeLa Ohio cells (Fig. 1A, RV-B14). In comparison to the untreated cells, 2-DG treated cells showed a significant reduction in viral load 48 h post-infection (Fig. 3C).Fig. 3 2-DG disrupts RNA template strand synthesis and inhibits RV-mediated cell death. HeLa Ohio cells were infected with RV-B14 (0.5 TCID50/cell) and treated with 10 mM 2-DG for 24 h to measure intracellular negative and positive viral RNA strand (A) or released extracellular viral RNA (B). Cells infected with RV-B14 (0.005 TCID50/cell) were treated with 3.57 mM 2-DG (IC90 for RV-B14) for up to 48 h at 34 °C to measure viral load (C). Cells infected with RV-B14 (0.5 TCID50/cell) and treated with the indicated concentrations of 2-DG for 24h or 48 h at 37 °C for measurement of virus-induced cytopathic effect (D). Graphs show pooled results ± SEM of 2–4 independent experiments (A,B,D) or one experiment (C). ns: non-significant; p < 0.05 (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001). RV: rhinovirus. AU: Arbitrary units.
Fig. 3
A characteristic of RV infection of tissue culture cells is the cytopathic effect (CPE).39 The impact of increasing concentrations of 2-DG on RV-induced cell death was assessed in HeLa Ohio cells at 24 h and 48 h post-infection. A significant reduction in CPE was seen in cells treated with 2-DG at 0.33 mM and higher after 24 h (Fig. 3D). At 48 h post-infection, a stronger CPE could be observed in infected but untreated cells (‘Virus only’) and cell death was significantly reduced upon treatment with 2-DG at 0.33 mM or higher (Fig. 3D). Together, these results suggest that 2-DG affects the RV life cycle by suppressing viral RNA replication and viral load and reduces RV-mediated cell death.
3.3 2-DG decreases CoV viral load
Similar to RVs, SARS-CoV-2 was recently shown to exploit the host glucose metabolism for replication and can potentially be targeted by 2-DG24,35. However, 2-DG´s effect on endemic HCoVs hasn't been investigated so far. With this rationale we investigated the effect of 2-DG on the viral load of the pandemic strain, SARS-CoV-2 as well as the two endemic human CoV stains, HCoV-229 EE and HCoV-NL63. Cells with known susceptibility to these coronaviruses were treated with increasing concentrations of 2-DG for 24 h–48 h. The supernatant containing released virus was sampled every 24 h and viral load was assessed as TCID50. We observed a significant reduction in SARS-CoV-2 at 24 h post-infection at the highest tested 2-DG concentration (10 mM), and further, lower 2-DG concentrations led to significant effects 48h post-infection (Fig. 4 A). A similar behavior was observed for HCoV-229E, where 24 h and 48 h post-infection, a significant reduction in viral load was observed in cells treated with 0.32 mM and 1 mM 2-DG (Fig. 4B). The use of lower 2-DG concentrations was based on decreased viability of MRC5 cells at 2-DG concentrations above 1 mM (data not shown). In the case of HCoV-NL63, there was no significant decrease in viral load at 24 h, however, at 48 h post-infection 2-DG concentrations above 1 mM suppressed viral load significantly (Fig. 4C). These results suggest that 2-DG exerts a dose-dependent reduction in viral load of pandemic as well as endemic CoV strains.Fig. 4 2-DG shows a dose-dependent antiviral effect on human coronaviruses. Viral load was measured from cell culture supernatants 24 h–48 h post-infection. 2-DG treatment with the indicated concentrations was started 1 h post-infection. Viral load of SARS-CoV-2 (MOI 0.001) released from LLC-MK2 cells (A), HCoV-229E (MOI 0.01) released from MRC5 cells (B) and HCoV-NL-63 (MOI 0.01) released from LLC-MK2 cells (C). Graphs show pooled results ±SEM of 3 independent experiments. ns: non-significant; p < 0.05 (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001). SARS-CoV-2: severe acute respiratory syndrome coronavirus 2, HCoV: human coronavirus.
Fig. 4
4 Discussion
In this study we investigated a host-directed approach to combat rhinovirus (RV) and coronavirus (CoV) infection by using 2-Deoxy-D-glucose (2-DG). This approach is based on the understanding that virus-induced metabolic reprogramming of the host cell plays a crucial role in viral replication21,22,.25 Previously, Gualdoni et al.25 demonstrated that 2-DG reverts RV-induced metabolic reprogramming of host cells and inhibits RV-B14 replication. Consequently, in the present study, we investigated the antiviral activity of 2-DG against additional minor- and major-group RVs, where 2-DG showed a dose-dependent inhibition of RV replication in epithelial cells including primary human nasal epithelial cells (HNECs). Simultaneously, we showed that treatment with 2-DG does not induce cytotoxic effects in this setting. Further, we sought to elucidate the implications of 2-DG on the RV replication cycle, intracellular kinetics of 2-DG and its impact on RV viral load. We found that 2-DG treatment led to a marked inhibition of template negative strand as well as genomic positive strand RNA replication. 2-DG treatment caused a significant reduction in the extracellular viral RNA level, RV viral load and in the RV-mediated cytopathic effect. At a physiological glucose concentration, 2-DG treatment led to enhanced inhibition of RV replication as compared to conventional high-glucose culture conditions. Assessment of 2-DG´s intracellular kinetics showed accumulation of the active intermediate, 2-DG6P, for several hours. Our concurrent study of 2-DG´s impact on CoVs also showed a significant reduction in viral load. Taken together, the results suggest 2-DG to be a potential broad-spectrum antiviral.
In our study, treatment with 2-DG inhibited replication of all tested minor- and major-receptor group strains of RV in HeLa Ohio cells under conventional culture condition (i.e., 2 g/L glucose) (Supplement Figure 1) and in primary human nasal epithelial cells (HNECs) (Fig. 1C). As 2-DG competes with glucose for cellular uptake26,27, we lowered the glucose concentration to 1 g/L glucose – mimicking the human plasma glucose concentration – to assess the efficacy of 2-DG in a physiological context. We found that lower glucose concentrations potentiated 2-DG-mediated inhibition of RV replication, pointing to a higher efficacy of 2-DG in physiological settings (Fig. 1A, Supplement table 2). It should be noted that the glucose concentration in fluid lining the nose and lung epithelium in humans is around 12.5 times lower than in plasma.40 Therefore, it can be anticipated that 2-DG exhibits even higher antiviral efficacy in therapeutic target tissues. However, additional studies in models closer to the physiologic conditions are warranted to test this hypothesis. Further, as exposure to 2-DG has been shown to induce cytotoxic effects41, 42, 43 we specifically tested the effect of 2-DG on HNECs and found no significant reduction in cell viability after 7 h 2-DG treatment (Fig. 1D). Based on the experimental evidence and toxicology studies, the safety and pharmacokinetics of local (intranasal) 2-DG administration is currently being investigated in a Phase I clinical trial in Austria (NCT05314933).44
In the next step, we characterized the intracellular kinetics of 2-DG6P after a short exposure to 2-DG (Fig. 2A). In the cell, 2-DG is phosphorylated to 2-DG6P, leading to its intracellular accumulation. Cytochalasin B, an inhibitor of the glucose transporter, was used as a control to ensure 2-DG6P specificity in our set-up (data not shown). Overall, we found that 2-DG6P was detectable up to several hours in HeLa Ohio cells and HNEC after a short incubation of the cells with 2-DG. The setup in this experiment mimics the in vivo setting where local treatment, e.g., in the nose, would only lead to a short exposure of epithelial cells to 2-DG. Our results suggest that even a brief exposure time is sufficient for extended inhibition of glycolysis via 2-DG6P and thereby to exhibit an antiviral effect.
During the RV replication cycle, the viral polyprotein is first generated via translation from the (+)ssRNA genome, which is then processed by viral proteases to generate viral proteins including the viral RNA polymerase.45 Next, RNA polymerase generates (−)ssRNA strand copies, which in turn serve as a template for the multifold replication of the positive stand viral genome to be packaged in viral capsids, finally leading to release of the mature virions.46 As conventional qPCR holds limitations to detect the negative strand in excess of positive strand copies, we employed a recently published strategy by Wiehler and Proud32 to analyze the negative strand level. We observed that 2-DG significantly reduced the template (−)ssRNA as well as the genomic (+)ssRNA, a likely cause for the measured significant reduction in detectable extracellular viral RNA (Fig. 4A&B). These findings point at a 2-DG-mediated impairment in viral RNA replication, resulting in a reduced amount of released virus. In line with this, TCID50 titration of the released virus on HeLa Ohio cells showed a reduction in viral load (Fig. 4C). To be noted, HeLa Ohio cells used in this experimental setup, due to their cancerous origin, have a high glucose demand and are especially sensitive to glucose starvation and 2-DG treatment. Therefore, low amounts of 2-DG were used, and the cells were treated only once after the start of the RV infection. This could explain the relatively small difference in viral load (Fig. 3C) in contrast to the significant difference in released extracellular viral RNA (Fig. 3B).
In our subsequent analysis, we found that 2-DG exerted a protective effect by significantly reducing virus-induced cell death in HeLa Ohio cells (Fig. 4D). In contrast, RV infection does not cause cell lysis in cultures of healthy bronchial epithelial cells.47 Interestingly, the same study reported increased viral replication and cell lysis after RV infection in asthmatic bronchial epithelial cells.47 Based on these findings, we could envision protection of RV-infected bronchial epithelial cells of asthma patients by 2-DG.
The host metabolic dependency of CoVs is similar to that of RVs and studies suggest that 2-DG alters SARS-CoV-2 replication24,26,.48 These results prompted us to further investigate the effect of 2-DG on CoVs infection. In our study, 2-DG treatment of pandemic SARS-CoV-2 resulted in a dose-dependent reduction of viral load. In line, 2-DG has been approved for use in patients with moderate to severe SARS-CoV-2 infection in India by Drug Controller General India (DCGI) after performance of Phase II and Phase III clinical trials conducted by the Defense Research and Development Organization (DRDO), India in collaboration with Dr Reddy's Laboratories, India.49 However, the peer reviewed data of the trials are still unpublished. Further, in our study, we show for the first time the antiviral effect of 2-DG on endemic HCoVs 229 EE and NL63. As in the case of SARS-CoV-2, 2-DG caused a dose-dependent reduction in viral load in both endemic HCoV strains.
Comparing our data from RV viral load, lower concentrations of 2-DG are sufficient to cause a long-term significant reduction in viral load in both endemic and pandemic CoVs. The difference between RV and CoV with respect to the required 2-DG concentrations can be attributed to differences in cell culture models. Another possible explanation is that CoVs are enveloped13 and contain glycosylated envelope proteins responsible for host cell interaction and infection. Along with CoVs dependence on host glucose metabolism for replication,24 they are also dependent on the host cell machinery for glycosylation of viral proteins.50 Thus, the reduction in CoV viral load could originate from 2-DG not only inhibiting glycolysis but also affecting protein and lipid glycosylation.51 However, further studies are required to decipher a possible role of 2-DG in the production of defective virions in enveloped viruses.
In conclusion, we present further in vitro data that support a host-directed approach to tackle RV and CoV infections. The dependency of these viruses on the host cell metabolism and cell machinery reveals a therapeutic opportunity to target them with host-directed antivirals such as 2-DG. The low cytotoxicity of 2-DG and the long half-life of the active metabolite 2-DG6P advocates its short-time topic application at comparably high concentrations, e.g., as a spray to be employed early in infection, which might safely block viral spreading.
Funding
This study was supported by a 10.13039/501100004955 FFG Basisprogramm, grant number 36734898 (to G. ST Antivirals).
Author contribution
L.W., M.K., S.C., V.K., A.A., X.C., D.S. and A.-D.G. performed experiments and analyzed data. D.B. and I.G. provided virus strains, reagents, and valuable input. A.-D.G., J.S., T.R.K., M.K. and G.G. were in charge of planning and directing the study. L.W and A.-D.G. wrote the manuscript with input from co-authors. All authors read and approved the final 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 are the Supplementary data to this article:Multimedia component 1
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Data availability
No data was used for the research described in the article.
Acknowledgments
We thank Melanie Graf and the Global Pathogen Safety Team (Takeda), most notably Jasmin de Silva, Elisabeth List and Effie Oindo (experiments), Veronika Sulzer (cell culture), Eva Ha, Simone Knotzer and Alexandra Schlapschy-Danzinger (virus propagation). SARS-CoV-2 was sourced via EVAg (supported by the European Community) and kindly provided by Christian Drosten and Victor Corman (Charité Universitätsmedizin, Institute of Virology, Berlin, Germany). HCoV-NL63 was kindly provided by Lia van der Hoek (Medical Microbiology, Academisch Medisch Centrum, Amsterdam, Netherlands).
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jve.2022.100305.
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| 36514716 | PMC9731833 | NO-CC CODE | 2022-12-14 23:45:33 | no | J Virus Erad. 2022 Dec 9; 8(4):100305 | utf-8 | J Virus Erad | 2,022 | 10.1016/j.jve.2022.100305 | oa_other |
==== Front
Cell Host Microbe
Cell Host Microbe
Cell Host & Microbe
1931-3128
1934-6069
Elsevier Inc.
S1931-3128(22)00577-7
10.1016/j.chom.2022.12.005
Article
An epithelial-immune circuit amplifies inflammasome and IL-6 responses to SARS-CoV-2
Barnett Katherine C. 12313
Xie Yuying 4513
Asakura Takanori 613
Song Dingka 127
Liang Kaixin 128
Taft-Benz Sharon A. 1
Guo Haitao 12
Yang Shuangshuang 12
Okuda Kenichi 6
Gilmore Rodney C. 6
Loome Jennifer F. 3
Oguin Thomas H. III 9
Sempowski Gregory D. 910
Randell Scott H. 5
Heise Mark T. 13
Lei Yu Leo 111214
Boucher Richard C. 614
Ting Jenny P.-Y. 12314∗
1 Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
2 Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
3 Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
4 Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, 48824, USA
5 Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, 48824, USA
6 Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
8 Oral and Craniofacial Biomedicine Program, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
9 Duke Human Vaccine Institute, Durham, NC, 27701, USA
11 Department of Periodontics and Oral Medicine, Rogel Cancer Center, University of Michigan, Ann Arbor, MI, 48104, USA
12 Department of Otolaryngology-Head and Neck Surgery, Rogel Cancer Center, University of Michigan, Ann Arbor, MI, 48109, USA
∗ Lead contact and corresponding author email:
7 Present address: State Key Laboratory of Oncogenes and Related Genes, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
10 Present address: RTI International, Research Triangle Park, NC, 27709, USA
13 These authors contributed equally.
14 Senior author
9 12 2022
9 12 2022
29 6 2022
12 10 2022
3 12 2022
© 2022 Elsevier Inc.
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.
Elevated levels of the cytokines IL-1β and IL-6 are associated with severe COVID-19. Investigating the underlying mechanisms, we find that while primary human airway epithelia (HAE) have functional inflammasomes and support SARS-CoV-2 replication, they are not the source of IL-1β released upon infection. In leukocytes, the SARS-CoV-2 E protein upregulates inflammasome gene transcription via TLR2 to prime, but not activate, inflammasomes. SARS-CoV-2-infected HAE supply a second signal, which includes genomic and mitochondrial DNA, to stimulate leukocyte IL-1β release. Nuclease treatment, STING and caspase-1 inhibition but not NLRP3 inhibition blocked leukocyte IL-1β release. After release, IL-1β stimulates IL-6 secretion from HAE. Therefore, infection alone does not increase IL-1β secretion by either cell type. Rather, bi-directional interactions between the SARS-CoV-2-infected epithelium and immune bystanders stimulates both IL-1β and IL-6, creating a pro-inflammatory cytokine circuit. Consistent with these observations, patient autopsy lungs show elevated myeloid inflammasome gene signatures in severe COVID-19.
Graphical abstract
IL-1β and IL-6 are increased in severe COVID-19. Examining the underlying mechanisms, Barnett et al. show that SARS-CoV-2 E protein primes, and DNA from infected epithelial cells activates, inflammasome-dependent IL-1β secretion from leukocytes, which in turn stimulates IL-6 release from epithelial cells.
Key Words
Inflammasome
SARS-CoV-2
IL-6
IL-1β
NLRP3
NLRP1
AIM2
STING
TLR2
Published: February 8, 2023
==== Body
pmc
| 0 | PMC9731922 | NO-CC CODE | 2022-12-14 23:31:56 | no | Cell Host Microbe. 2022 Dec 9; doi: 10.1016/j.chom.2022.12.005 | utf-8 | Cell Host Microbe | 2,022 | 10.1016/j.chom.2022.12.005 | oa_other |
==== Front
Cell Host Microbe
Cell Host Microbe
Cell Host & Microbe
1931-3128
1934-6069
Elsevier Inc.
S1931-3128(22)00577-7
10.1016/j.chom.2022.12.005
Article
An epithelial-immune circuit amplifies inflammasome and IL-6 responses to SARS-CoV-2
Barnett Katherine C. 12313
Xie Yuying 4513
Asakura Takanori 613
Song Dingka 127
Liang Kaixin 128
Taft-Benz Sharon A. 1
Guo Haitao 12
Yang Shuangshuang 12
Okuda Kenichi 6
Gilmore Rodney C. 6
Loome Jennifer F. 3
Oguin Thomas H. III 9
Sempowski Gregory D. 910
Randell Scott H. 5
Heise Mark T. 13
Lei Yu Leo 111214
Boucher Richard C. 614
Ting Jenny P.-Y. 12314∗
1 Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
2 Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
3 Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
4 Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, 48824, USA
5 Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, 48824, USA
6 Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
8 Oral and Craniofacial Biomedicine Program, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
9 Duke Human Vaccine Institute, Durham, NC, 27701, USA
11 Department of Periodontics and Oral Medicine, Rogel Cancer Center, University of Michigan, Ann Arbor, MI, 48104, USA
12 Department of Otolaryngology-Head and Neck Surgery, Rogel Cancer Center, University of Michigan, Ann Arbor, MI, 48109, USA
∗ Lead contact and corresponding author email:
7 Present address: State Key Laboratory of Oncogenes and Related Genes, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
10 Present address: RTI International, Research Triangle Park, NC, 27709, USA
13 These authors contributed equally.
14 Senior author
9 12 2022
9 12 2022
29 6 2022
12 10 2022
3 12 2022
© 2022 Elsevier Inc.
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.
Elevated levels of the cytokines IL-1β and IL-6 are associated with severe COVID-19. Investigating the underlying mechanisms, we find that while primary human airway epithelia (HAE) have functional inflammasomes and support SARS-CoV-2 replication, they are not the source of IL-1β released upon infection. In leukocytes, the SARS-CoV-2 E protein upregulates inflammasome gene transcription via TLR2 to prime, but not activate, inflammasomes. SARS-CoV-2-infected HAE supply a second signal, which includes genomic and mitochondrial DNA, to stimulate leukocyte IL-1β release. Nuclease treatment, STING and caspase-1 inhibition but not NLRP3 inhibition blocked leukocyte IL-1β release. After release, IL-1β stimulates IL-6 secretion from HAE. Therefore, infection alone does not increase IL-1β secretion by either cell type. Rather, bi-directional interactions between the SARS-CoV-2-infected epithelium and immune bystanders stimulates both IL-1β and IL-6, creating a pro-inflammatory cytokine circuit. Consistent with these observations, patient autopsy lungs show elevated myeloid inflammasome gene signatures in severe COVID-19.
Graphical abstract
IL-1β and IL-6 are increased in severe COVID-19. Examining the underlying mechanisms, Barnett et al. show that SARS-CoV-2 E protein primes, and DNA from infected epithelial cells activates, inflammasome-dependent IL-1β secretion from leukocytes, which in turn stimulates IL-6 release from epithelial cells.
Key Words
Inflammasome
SARS-CoV-2
IL-6
IL-1β
NLRP3
NLRP1
AIM2
STING
TLR2
Published: February 8, 2023
==== Body
pmc
| 36514442 | PMC9731923 | NO-CC CODE | 2022-12-14 23:31:56 | no | Clin Nutr Open Sci. 2022 Dec 9; doi: 10.1016/j.nutos.2022.11.008 | latin-1 | Clin Nutr Open Sci | 2,022 | 10.1016/j.nutos.2022.11.008 | oa_other |
==== Front
Int Immunopharmacol
Int Immunopharmacol
International Immunopharmacology
1567-5769
1878-1705
Elsevier B.V.
S1567-5769(22)01037-2
10.1016/j.intimp.2022.109552
109552
Article
Effect of inactivated COVID-19 vaccination on pregnancy outcomes following frozen-thawed embryo transfer: A retrospective cohort study
Huang Jialyu a1
Liu Yiqi b1
Zeng Han a1
Tian Lifeng a
Hu Yina a
He Jinxia a
Nie Ling a
Li You a
Fang Zheng c
Deng Weiping a
Chen Mengyi a
Zhao Xia a
Ouyang Dongxiang a
Fu Yuqing a
Lin Jiaying d⁎
Xia Leizhen a⁎
Wu Qiongfang a⁎
a Center for Reproductive Medicine, Jiangxi Maternal and Child Health Hospital, Maternal and Child Health Hospital of Nanchang Medical College, Nanchang University, Nanchang, China
b Department of Clinical Medicine, School of Queen Mary, Nanchang University, Nanchang, China
c Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Tangdu Hospital, Air Force Medical University, Xi'an, China
d Department of Assisted Reproduction, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
⁎ Corresponding authors at: Department of Assisted Reproduction, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai 200011, China (J. Lin). Center for Reproductive Medicine, Jiangxi Maternal and Child Health Hospital, Nanchang University School of Medicine, 318 Bayi Avenue, Nanchang 330006, China (L. Xia, Q. Wu).
1 These authors contributed equally to this work.
9 12 2022
1 2023
9 12 2022
114 109552109552
3 9 2022
16 11 2022
2 12 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.
Objective
To investigate the effect of inactivated coronavirus disease 2019 (COVID-19) vaccination on frozen-thawed embryo transfer (FET) outcomes.
Methods
This retrospective cohort study enrolled 1,210 patients undergoing FET cycles in a single university-affiliated hospital between July 1, 2021, and May 1, 2022. Of them, 387 women with two full doses of inactivated SARS-CoV-2 vaccines (CoronaVac or BBIBP-CorV) after oocyte retrieval were assigned to the vaccinated group, while 823 were unvaccinated as controls. Propensity score matching and multiple regression analysis were applied to control for baseline and cycle characteristics (19 covariates in total).
Results
There were 265 patients in each group after matching. The rates of clinical pregnancy (58.5% vs. 60.8%; P = 0.595) and live birth (44.4% vs. 48.8%; P = 0.693) were similar between vaccinated and unvaccinated patients, with adjusted odds ratios of 0.89 (95% confidence interval [CI] 0.61–1.29) and 1.31 (95% CI 0.37–4.56), respectively. Consistently, no significant differences were found in serum human chorionic gonadotropin levels as well as biochemical pregnancy, biochemical pregnancy loss, and embryo implantation rates. Based on the time interval from vaccination to FET, vaccinated patients were further subdivided into two categories of ≤2 months and >2 months, and the outcomes remained comparable.
Conclusion
Our study showed that inactivated COVID-19 vaccination in women did not have measurable detrimental impact on implantation performance and live birth outcome during FET treatment cycles. This finding denies the impairment of endometrial receptivity and trophoblast function by vaccine-induced antibodies at the clinical level.
Keywords
COVID-19
SARS-CoV-2
Vaccination
Frozen-thawed embryo transfer
Live birth
==== Body
pmc1 Introduction
Since the first case was discovered in December 2019, coronavirus disease 2019 (COVID-19) has spread rapidly and become a global pandemic. To date, over 560 million confirmed cases have been reported worldwide and more than 5 million in China [1]. Among women of reproductive age, pregnant women are more susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, with increased risks of adverse outcomes including premature delivery and stillbirth [2], [3].
To effectively reduce morbidity and mortality from the pandemic, a number of vaccines have been developed in an urgent rush. Consistent with other countries, China has launched a large-scale vaccination campaign against COVID-19, which includes inactivated vaccines, adenovirus vector vaccines and recombinant subunit vaccines [4]. As of July 20, 2022, the total number of COVID-19 vaccine doses in China exceeded 3.4 billion, with inactivated vaccines accounting for the largest proportion [4], [5], [6]. However, the promotion of vaccines has been hampered by widespread concerns regarding their possible detrimental effect on reproductive health. In a survey of unvaccinated individuals, 23.9% were convinced that there were negative fertility impacts, and 21.8% thought there may be [7]. The initial argument is that the placental syncytin-1 protein is similar to the spike protein of SARS-CoV-2 and therefore, vaccine-induced antibodies may result in cross-reactivity and infertility [8], [9]. Despite the lack of experimental evidence, such conjectures persist on various social media platforms and undermine vaccination coverage.
In an effort to address this issue, emerging cohort studies have been performed and concluded that COVID-19 vaccination had no adverse effects on ovarian reserve, oocyte quality, as well as embryonic development during in vitro fertilization (IVF) treatment [6], [10], [11], [12], [13], [14]. By taking a specific focus on frozen-thawed embryo transfer (FET), four studies also demonstrated a neutral influence of mRNA vaccines on endometrial receptivity and embryo implantation [15], [16], [17], [18]. Nonetheless, the association of inactivated COVID-19 vaccines with FET outcomes has been unclear and remains clarification. In addition, none of these studies reported the key outcome of live birth rate, indicating the need of longer follow-up for corroboration.
The aim of this study was to comprehensively evaluate the effect of inactivated SARS-CoV-2 vaccination on pregnancy outcomes in a large cohort of 1210 FET patients.
2 Materials and methods
2.1 Study design and participants
This was a single-center retrospective cohort study, in which all patients were recruited from the Center for Reproductive Medicine, Jiangxi Maternal and Child Health Hospital between July 1, 2021, and May 1, 2022. All procedures performed in this work were in accordance with the standards of Reproductive Medicine Ethics Committee of Jiangxi Maternal and Child Health Hospital (approval No. 2022-03) and all patient information were used anonymously.
Prior to the initiation of FET, participants were screened for COVID-19 using epidemiological questionnaire, temperature measurement, and pharyngeal swab polymerase chain reaction test for SARS-CoV-2 RNA. Vaccination status, including vaccine type, date, dose, and manufacturer, were also obtained and verified via access to immunization records. Patients were assigned to the vaccinated group if they had completed two doses of inactivated COVID-19 vaccine (CoronaVac or BBIBP-CorV) and to the control group if they had not been vaccinated (Figure S1). To eliminate the confounding effect of vaccination on gamete and embryo, we excluded cycles with paternal vaccination before ovarian stimulation from analysis. Other exclusion criteria were: 1) history of SARS-CoV-2 infection by self-report; 2) receipt of other types of SARS-CoV-2 vaccines, including recombinant vaccine (ZF2001) and adenovirus type-5 vector-based vaccine (CanSino) [19]; 3) partial vaccination before FET; 4) donor sperm or oocyte cycle; 5) preimplantation genetic testing cycle; 6) second or higher cycle in the same patient during the timeframe; and 7) lost to follow-up or missing data in the electronic health record.
2.2 Endometrial preparation and embryo transfer
The implementation of three different endometrial preparation protocols was jointly determined by patient preference and physician discretion. During the natural cycle, follicular development was closely monitored by transvaginal ultrasonography and blood sampling, starting from day 10 of the menstrual cycle and every one or two days thereafter. In occurrence of ovulation, intramuscular injection of progesterone (40 mg/d; Xianju Pharma, China) was initiated for endometrial transformation, followed by cleavage-stage embryo transfer on the fourth day or blastocyst transfer on the sixth day.
In the artificial cycle, oral estradiol valerate (4–6 mg/d; Progynova, Bayer, Germany) was given from the second or third day of menstruation. After 7 days, the absence of dominant follicle was confirmed by ultrasound, while the estradiol dose was adjusted according to endometrial thickness. When oral estradiol was used for 12–14 days with the thickness reaching 7 mm, intramuscular progesterone (80 mg/d; Xianju Pharma, China) was administered daily and FET was scheduled for cleavage-stage embryos after 4 days or blastocysts after 6 days. For patients with endometriosis, adenomyosis, hyperandrogenic polycystic ovary syndrome and history of cesarean section, a long-acting gonadotropin-releasing hormone (GnRH) agonist of 3.75 mg leuprorelin acetate (Lizhu Pharmaceutical Trading Co, China) was given on day 2 or 3 of the preceding menstrual cycle. On 28 days post-injection, patients were required to return to the hospital for the same procedure as the artificial cycle described above.
With the assistance of abdominal ultrasound, up to two embryos per patient were transferred in one FET cycle. After thawing, top-quality embryos on day 3 were defined as those with 7–10 cells and grade I–II based on the Cummins’s morphological criteria. Blastocysts were graded according to the Gardner and Schoolcraft system, and the top-quality blastocysts were those with an expansion score ≥4, an inner cell mass ≥B, and a trophectoderm score ≥B. Luteal support was carried out from the day of transfer via oral (20 mg/d; Duphaston, Abbott Biologicals, USA) and vaginal (90 mg/d; Crinone, Merck Serono, Switzerland) routes, and maintained until 10 weeks of gestation when a pregnancy was established.
2.3 Outcome measures
The primary outcome was the rate of clinical pregnancy per cycle, defined as the presence of one or more gestational sacs detected under transvaginal ultrasound one month after embryo transfer. Secondary outcomes included biochemical pregnancy, biochemical pregnancy loss, embryo implantation, and live birth rates. Biochemical pregnancy was defined as a serum β-human chorionic gonadotropin (hCG) level exceeding 5 IU/L on day 12 after cleavage-stage embryo transfer or day 10 after blastocyst transfer. Biochemical pregnancy loss was defined as the loss of hCG positivity prior to clinical pregnancy in patients with biochemical pregnancy. The implantation rate was calculated as the ratio between the number of gestational sacs detected and the number of embryos transferred. Live birth was defined as a viable infant delivered after a complete gestational period of 24 weeks or more, and was available for 318 women with embryo transfer before October 3, 2021.
2.4 Sample size calculation
Sample size calculation was performed with an online cost-free power and sample size calculator at https://powerandsamplesize.com/ (HyLown Consulting LLC, USA). The category chosen was “Compare 2 Proportions: 2-Sample, 2-Sided Equality”. According to prior data from our center, the primary outcome of clinical pregnancy rate per FET cycle was 60%. Assuming α = 0.05 and 80% power, it was calculated that 384 patients were required for each group to detect an absolute difference of 10% between vaccinated and unvaccinated patients.
2.5 Statistical analyses
In this study, the SAS version 9.4 (SAS Institute Inc., USA) was employed for all statistical analyses. The Shapiro-Wilk test was applied to evaluate the normality of quantitative data, with values presented as means ± standard deviations. Student's t-test or Mann-Whitney U test was performed to compare differences between groups as appropriate. Categorical variables were summarized as numbers and percentages, and analyzed using χ2 test or Fisher's exact test. All tests were two-tailed and P < 0.05 was considered statistically significant.
Propensity score matching (PSM) was applied to balance the baseline and cycle characteristics of the vaccinated group with those of the control group at a ratio of 1:1 via the nearest-neighbor matching algorithm. A caliper width of 0.2 standard deviations was determined. The following variables were selected as potential confounders for matching, including maternal age at retrieval, maternal age at transfer, body mass index, infertility type (primary or secondary), infertility duration, infertility diseases (tubal factor, male factor, ovulatory dysfunction, diminished ovarian reserve, endometriosis, or uterine factor), ovarian stimulation protocol (agonist, antagonist, or others), fertilization method (IVF or intracytoplasmic sperm injection), prior embryo transfer failure (0, 1–2, or ≥3), endometrial preparation regimen (natural cycle, artificial cycle, or artificial cycle with GnRH agonist), endometrial thickness, number of embryos transferred (1 or 2), embryo developmental stage (cleavage or blastocyst), and whether top-quality embryo was transferred (yes or no).
To further assess the independent association between inactivated SARS-CoV-2 vaccination and FET pregnancy outcomes, multiple logistic regression analyses were performed using both pre- and post-matching data. Adjusted odds ratios (aORs) with 95% confidence interval (CIs) were computed after controlling for the same covariates in PSM. For subgroup analysis, the time interval between the last vaccine dose and embryo transfer was classified as ≤2 and >2 months in accordance with the guideline of the European Society of Human Reproduction and Embryology (ESHRE) [20].
3 Results
Initially, a total of 2,251 FET cycles were screened for eligibility and 1,210 patients were included for the final analysis. Depending on the status of inactivated SARS-CoV-2 vaccination, the participants were further divided into the vaccinated group (n = 387) and control group (n = 823). The detailed study flowchart is depicted in Figure S2.
Table 1 presents the baseline demographics and cycle characteristics before (left) and after (right) matching. Prior to PSM processing, the two groups differed significantly in maternal age at retrieval and transfer, infertility duration and type, proportion of diminished ovarian reserve and endometriosis, ovarian stimulation and endometrial preparation protocol, as well as number, stage, and quality of transferred embryos. After subsequent PSM, 265 individuals remained in each group, and all parameters were comparably adjusted. The distribution of propensity scores before and after PSM are demonstrated in Figure S3.Table 1 Baseline and cycle characteristics grouped by the vaccination status.
Before matching After matching
Vaccinated
(n = 387) Unvaccinated
(n = 823) P value Vaccinated
(n = 265) Unvaccinated
(n = 265) P value
Age at OPU (years) 30.3 ± 4.7 31.3 ± 5.4 0.002 31.0 ± 4.8 30.9 ± 4.7 0.824
Age at ET (years) 32.8 ± 4.5 32.1 ± 5.4 0.010 32.6 ± 4.7 32.3 ± 4.8 0.557
Body mass index (kg/m2) 22.1 ± 2.5 22.0 ± 3.2 0.256 22.2 ± 2.6 22.2 ± 3.3 0.474
Infertility duration (years) 6.3 ± 3.5 4.8 ± 3.4 <0.001 5.4 ± 3.2 5.0 ± 3.1 0.056
Infertility type, n (%) <0.001 0.489
Primary 72 (18.6) 295 (35.8) 66 (24.9) 73 (27.6)
Secondary 315 (81.4) 528 (64.2) 199 (75.1) 192 (72.5)
Infertility diseases
Tubal factor, n (%) 266 (68.7) 588 (71.5) 0.334 179 (67.6) 198 (74.7) 0.069
Male factor, n (%) 98 (25.3) 199 (24.2) 0.667 52 (19.6) 68 (25.7) 0.097
Ovulatory dysfunction, n (%) 64 (16.5) 156 (19.0) 0.309 41 (15.5) 40 (15.1) 0.904
Diminished ovarian reserve, n (%) 47 (12.1) 157 (19.1) 0.003 41 (15.5) 38 (14.3) 0.714
Endometriosis, n (%) 35 (9.0) 43 (5.2) 0.012 24 (9.1) 20 (7.6) 0.529
Uterine factor, n (%) 47 (12.1) 84 (10.2) 0.312 35 (13.2) 28 (10.6) 0.348
Ovarian stimulation protocol, n (%) <0.001 0.493
GnRH agonist 316 (81.7) 548 (66.6) 201 (75.9) 211 (79.6)
GnRH antagonist 33 (8.5) 136 (16.5) 31 (11.7) 29 (10.9)
Others 38 (9.8) 139 (16.9) 33 (12.5) 25 (9.4)
Fertilization method, n (%) 0.205 0.760
IVF 299 (77.3) 608 (73.9) 203 (76.6) 200 (75.5)
ICSI 88 (22.7) 215 (26.1) 62 (23.4) 65 (24.5)
Prior ET failure, n (%) 0.525 0.797
0 212 (54.8) 477 (58.0) 132 (49.8) 131 (49.4)
1–2 167 (43.2) 327 (39.7) 128 (48.3) 131 (49.4)
≥3 8 (2.1) 19 (2.3) 5 (1.9) 3 (1.1)
Endometrial preparation, n (%) 0.011 0.649
Artificial cycle with GnRH agonist 215 (55.6) 531 (64.5) 150 (56.6) 160 (60.4)
Artificial cycle 130 (33.6) 224 (27.2) 89 (33.6) 83 (31.3)
Natural cycle 42 (10.9) 68 (8.3) 26 (9.8) 22 (8.3)
Endometrial thickness (mm) 9.3 ± 1.7 9.5 ± 1.8 0.477 9.3 ± 1.6 9.3 ± 1.8 0.521
No. of embryos transferred, n (%) <0.001 0.223
1 199 (51.4) 318 (38.6) 130 (49.1) 116 (43.8)
2 188 (48.6) 505 (61.4) 135 (50.9) 149 (56.2)
Embryo developmental stage, n (%) <0.001 0.927
Cleavage 121 (31.3) 371 (45.1) 89 (33.6) 88 (33.2)
Blastocyst 266 (68.7) 452 (54.9) 176 (66.4) 177 (66.8)
Transfer of ≥1 top-quality embryo, n (%) 206 (53.2) 522 (63.4) 0.001 145 (54.7) 148 (55.9) 0.793
Notes: Data are presented as mean ± standard deviation or number (percentage).
Abbreviations: OPU, oocyte pick-up; ET, embryo transfer; IVF, in vitro fertilization; ICSI, intracytoplasmic sperm injection; GnRH, gonadotropin-releasing hormone.
Pregnancy outcomes grouped by the vaccination status are shown in Table 2 . After matching, the rates of clinical pregnancy (58.5% vs. 60.8%; P = 0.595) and live birth (44.4% vs. 48.8%; P = 0.693) were similar between vaccinated and unvaccinated patients. Consistently, there were no statistically significant differences in biochemical pregnancy, biochemical pregnancy loss, and embryo implantation rates. In addition to pregnancy outcomes, serum β-hCG levels were also compared among patients with biochemical pregnancy after cleavage-stage embryo or blastocyst transfer (Fig. 1 ). The lack of a discernible difference before and after PSM suggested that inactivated COVID-19 vaccines had no measurable impact on hCG production.Table 2 Pregnancy outcomes grouped by the vaccination status.
Before matching After matching
Vaccinated (n = 387) Unvaccinated (n = 823) P value Vaccinated (n = 265) Unvaccinated (n = 265) P value
Biochemical pregnancy, n (%) 285 (73.6) 625 (75.9) 0.388 191 (72.1) 200 (75.5) 0.374
Biochemical pregnancy loss, n/N (%) 51/285 (17.9) 126/625 (20.2) 0.423 36/191 (18.9) 39/200 (19.5) 0.870
Clinical pregnancy, n (%) 234 (60.5) 499 (60.6) 0.956 155 (58.5) 161 (60.8) 0.595
Embryo implantation, n/N (%) 274/575 (47.7) 610/1328 (45.9) 0.490 186/400 (46.5) 192/414 (46.4) 0.972
Live birth a, n/N (%) 17/37 (46.0) 151/281 (53.7) 0.372 12/27 (44.4) 41/84 (48.8) 0.693
Notes: Data are presented as number (percentage).
a Live birth outcomes were completely followed-up for 318 women with embryo transfer before October 3, 2021.
Fig. 1 Comparison of serum human chorionic gonadotropin (hCG) levels in vaccinated and unvaccinated patients with biochemical pregnancy (A) before and (B) after matching. The measurement of hCG was performed on day 12 after cleavage-stage embryo transfer or day 10 after blastocyst transfer. Abbreviation: NA, not available, as the unvaccinated group included only one patient with single cleavage-stage embryo transfer.
On further adjusted analysis, there was still no significant association between female vaccination and the odds of clinical pregnancy (aOR 0.89, 95% CI 0.61–1.29) or any of the secondary outcomes assessed: biochemical pregnancy (aOR 0.82, 95% CI 0.55–1.25), biochemical pregnancy loss (aOR 1.08, 95% CI 0.62–1.89), or live birth (aOR 1.31, 95% CI 0.37–4.56) (Table 3 ). Multivariable logistic regression was also performed on the full cohort before matching, with no associations observed in all four outcomes.Table 3 Association between vaccination and pregnancy outcomes on crude and adjusted analysis.
Before matching After matching
cOR (95% CI) aOR (95% CI) a cOR (95% CI) aOR (95% CI) a
Biochemical pregnancy 0.89 (0.67–1.17) 0.74 (0.53–1.03) 0.84 (0.57–1.24) 0.82 (0.55–1.25)
Biochemical pregnancy loss 0.86 (0.60–1.24) 1.01 (0.66–1.55) 0.96 (0.58–1.59) 1.08 (0.62–1.89)
Clinical pregnancy 0.99 (0.78–1.27) 0.84 (0.63–1.14) 0.91 (0.64–1.29) 0.89 (0.61–1.29)
Live birthb 0.73 (0.37–1.46) 0.68 (0.29–1.62) 0.84 (0.35–2.01) 1.31 (0.37–4.56)
Abbreviations: cOR, crude odds ratio; aOR, adjusted odds ratio; CI, confidence interval.
a Analyses were adjusted for age at retrieval, age at transfer, body mass index, type of infertility, infertility duration, infertility diseases, ovarian stimulation protocol, fertilization method, prior embryo transfer failure, endometrial preparation regimen, endometrial thickness, number of embryos transferred, embryo developmental stage, and embryo quality.
b Odds ratios for live birth were calculated on the basis of 318 and 111 women before and after matching, respectively.
Based on the vaccination interval to embryo transfer, vaccinated participants were further divided into two categories of ≤2 months and >2 months. As displayed in Table 4 and Figure S4, comparison between the subgroups revealed similar pregnancy outcomes as well as early serum β-hCG levels.Table 4 Comparison of pregnancy outcomes based on the vaccination interval to embryo transfer.
≤2 months (n = 59) >2 months (n = 328) P value cOR (95% CI) aOR (95% CI) a
Biochemical pregnancy, n (%) 44 (74.6) 241 (73.5) 0.860 1.06 (0.56–2.00) 1.66 (0.77–3.58)
Biochemical pregnancy loss, n/N (%) 10/44 (22.7) 41/241 (17.0) 0.363 1.44 (0.66–3.13) 1.53 (0.60–3.93)
Clinical pregnancy, n (%) 34 (57.6) 200 (61.0) 0.628 0.87 (0.50–1.53) 1.15 (0.58–2.27)
Notes: Data are presented as number (percentage).
Abbreviations: cOR, crude odds ratio; aOR, adjusted odds ratio; CI, confidence interval.
a Analyses were adjusted for age at retrieval, age at transfer, body mass index, type of infertility, infertility duration, infertility diseases, ovarian stimulation protocol, fertilization method, prior embryo transfer failure, endometrial preparation regimen, endometrial thickness, number of embryos transferred, embryo developmental stage, and embryo quality.
4 Discussion
In this retrospective cohort study, the analysis of 1,210 patients revealed that inactivated SARS-CoV-2 vaccination after oocyte retrieval had no detrimental impact on subsequent FET outcomes. Furthermore, the results demonstrated that pregnancy rates were not significantly affected by the time interval between vaccination and embryo transfer. This updated evidence can be helpful for clinicians to promote vaccination coverage in unvaccinated patients, to reduce psychological burden of vaccinated patients, and to arrange FET cycles at their earliest convenience.
A number of studies have focused on the effects of COVID-19 vaccination on ovarian stimulation during IVF treatment, and found no significant association with oocyte yield or embryo quality [6], [10], [11], [12], [13], [14]. Consistently, three studies reported that the perlecan level as well as hormonal, lipid and metabolic profiles in follicular fluid were comparable between vaccinated and unvaccinated participants, suggesting the unaltered growth and development microenvironment of follicles [21], [22], [23]. In terms of pregnancy outcomes after fresh embryo transfer, Avraham et al. [10] enrolled 200 vaccinated and 200 age-matched unvaccinated patients, demonstrating no adverse impact of BNT162b2 on clinical pregnancy rate (32.8% vs. 33.1%, P = 0.96). In another study by Jacobs et al. [11], 142 vaccinated (mRNA or adenovirus vector) women were compared with 138 unvaccinated women. Using the logistic regression model adjusted for age and body mass index, it also revealed no significant difference in ongoing pregnancy (aOR 0.79, 95% CI 0.48–1.29). Similar outcomes have been observed concerning inactivated vaccines [6], [13], [14], which imply their safety in female reproduction as other vaccine types among fresh IVF cycles.
By separating the procedures of ovarian stimulation and embryo transfer, FET offers an excellent model to evaluate the independent influence of infection and/or vaccination on endometrial receptivity for embryo implantation. Prior to our study, there has been a paucity of literatures addressing the impact of COVID-19 itself on FET outcomes. The first study by Morris et al. [18] included 143 women undergoing their first single FET, and found that seropositivity for the SARS-CoV-2 spike protein due to prior infection or vaccination did not alter implantation rates compared with seronegative women. Similarly, by retrospectively analyzing 672 FET cycles, Aizer et al. [16] demonstrated no significant differences in biochemical, clinical and ongoing pregnancy rates among infected, vaccinated and control patients. In contrast, Youngster et al. [10] showed that past SARS-CoV-2 infection was associated with decreased odds of clinical pregnancy (aOR 0.325, 95% CI 0.106–0.998; P = 0.05), which was more evident in the subgroup of women transferred within 60 days after infection (OR 0.072, 95% CI 0.012–0.450; P = 0.005). Mechanistically, a recent study found no SARS-CoV-2 RNA in the infected endometrial tissue, suggesting that COVID-19 may not directly invade the female reproductive tract [24]. Instead, transcriptomic profiling indicated altered endometrial gene expression in 75% of women, which were mainly enriched in immunological, inflammatory, and metabolic processes [24]. These indirect changes may thus pose potential detrimental effects on endometrial receptivity and consequently FET outcomes. However, current studies are still limited by small sample sizes of infected patients (ranging from 20 to 41), and larger cohorts are warranted for a more decisive conclusion.
To our knowledge, only several prior studies have addressed FET outcomes following COVID-19 vaccination. Aharon et al. included 947 patients undergoing single euploid FET, and the adjusted analysis demonstrated no association between vaccination and clinical pregnancy (aOR 0.79, 95% CI 0.54–1.16) or any other pregnancy outcomes [15]. More recently, Brandão et al. [17] compared cycle outcomes in women who underwent euploid FET one year before the pandemic (n = 3272) with those who had received at least one dose of BNT162b2 or mRNA-1273 (n = 890). Based on the large caseloads, they concluded that injection of vaccines against COVID-19 had no measurable effect on clinical pregnancy and sustained implantation rates, regardless of the number of doses and the time interval from vaccination to transfer. Similar findings have been observed in the other two studies [16], [18], while all studies included only mRNA vaccines, which should not be directly extrapolated to other types of vaccine. In addition, these studies did not differentiate the vaccination timepoints before or after oocyte retrieval to rule out its confounding effects on gametes and embryos. In this regard, our study was conducted and clearly proved that inactivated SARS-CoV-2 vaccines did not affect the treatment outcomes of FET.
According to a meta-analysis conducted in 2021, there was a declining tendency in the willingness to be vaccinated across countries when COVID-19 vaccines became available [25]. Women showed lower intentions to be vaccinated than other populations, especially among those who were currently or planning to become pregnant. Such hesitancy has been thought to be derived from fertility concerns [7], and exaggerated by the widespread dissemination of unproven claims. One of them is the similarity between the SARS-CoV-2 spike protein and the human placental protein syncytin-1. Syncytin-1 is encoded by the human endogenous retrovirus W (HERV-W) gene in trophoblast cells and plays an important role in trophoblast fusion during placenta formation [26]. Nonetheless, recent studies have shown limited homology and there is no cross-reactivity of antibody against SARS-CoV-2 spike protein to syncytin-1 [8], [9], [27]. Moreover, compared with unvaccinated adults, COVID-19 vaccinated participants did not present elevated levels of circulating anti-syncytin-1 antibodies by enzyme linked immunosorbent assay [28]. In our study, we found that inactivated vaccine administration had no significant effect on serum β-hCG levels during the earliest stage of pregnancy, which further denies the damage of trophoblast cells by vaccine-induced antibodies at the clinical level.
The consensus on the optimal time interval between vaccination completion and assisted reproductive treatment has been varied in different scientific societies. Due to the lack of evidence on the reproductive effects of COVID-19 vaccines, experts at the Beijing Human Assisted Reproductive Technology Center for Quality Control and Improvement recommend starting treatment after one month of vaccination for the stabilization of immune response [29]. Instead, the American Society for Reproductive Medicine recommends avoiding COVID-19 vaccination for at least three days only before and after a planned surgical procedure (e.g., oocyte retrieval) or outpatient treatment (e.g., embryo transfer) [30]. Holding a more cautious attitude, the ESHRE guideline suggests a two-month postponement to allow sufficient time for antibody development [20]. In the present study, we showed no significant difference in biochemical and clinical pregnancy rates between ≤2 and >2 months, suggesting a neutral impact of intervals between vaccination and embryo transfer. This finding is also supported by other studies using different intervals of 1, 1.8 or 3 months for categorization [6], [14], [17], which could be reassuring for women to prepare for FET promptly following vaccination.
To date, this is the first study to examine the effect of inactivated COVID-19 vaccines on FET live birth outcomes. One of the main strengths is that we assessed the rate of live birth, which is the key IVF outcome but has not been covered in previous studies due to limited follow-up duration. In addition, PSM was employed to control for confounding variables and minimize bias, which remains among the best approaches for drawing conclusions about causality from observational data [31]. The high consistency and reproductivity in multiple regression models further add to the robustness and reliability of our finding.
It should be emphasized that the current work also has some limitations to be resolved in the future. First, there were inherent bias and residual confounding associated with data from a retrospective cohort study [32]. For instance, although we considered embryo quality for adjustment, we did not screen euploid embryos for transfer, which ought to be verified in preimplantation genetic testing cycles. In addition, the type of inactivated vaccines (i.e. CoronaVac or BBIBP-CorV) was not recorded for subgroup analysis, whose potential confounding effect should be controlled in future prospective cohort studies. Second, the determination of COVID-19 history was based on self-report by patients without objective seropositivity test. In this regard, a misclassification risk may be present since the infected patients should be deemed immunized as those vaccinated. It also remains to be clarified whether vaccine-induced SARS-CoV-2 neutralizing antibody affects endometrial receptivity and embryo implantation in a concentration-dependent manner. Third, the study did not assess the status of newborns, which limits the determination of vaccine safety in the long-term period. Also, we did not complete the live birth follow-up of the entire cohort, resulting in a reduced sample size and potentially decreased statistical power. Finally, since this was a single-center cohort, the findings need to be corroborated by further multicenter studies for generalizability.
5 Conclusion
In conclusion, our work provides evidence that inactivated SARS-CoV-2 vaccination has no detrimental impact on endometrial receptivity and embryo implantation during FET treatment cycles. This finding further dispels the misconception that COVID-19 vaccines impair female fertility, which is reassuring for vaccinated women planning on pregnancy and could be informative for physicians in clinical counseling. Larger prospective cohort studies with continuous follow-up are needed to validate our conclusion.
6 Ethics approval and consent to participate
The study was approved by the Reproductive Medicine Ethics Committee of Jiangxi Maternal and Child Health Hospital (No. 2022-03). Informed contents were obtained from all patients for de-identified data use in scientific research.
7 Consent for publication
Not applicable.
8 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
The study was funded by the National Natural Science Foundation of China (82260315, 81960288) and Key Research and Development Program of Jiangxi Province (20203BBGL73159).
CRediT authorship contribution statement
Jialyu Huang: Conceptualization, Methodology, Investigation, Writing – original draft, Funding acquisition. Yiqi Liu: Formal analysis, Investigation, Writing – original draft, Visualization. Han Zeng: Formal analysis, Investigation, Data curation, Project administration. Lifeng Tian: Data curation, Writing – review & editing, Funding acquisition. Yina Hu: Data curation, Writing – review & editing. Jinxia He: Data curation, Writing – review & editing. Ling Nie: Data curation, Writing – review & editing. You Li: Data curation, Writing – review & editing. Zheng Fang: Formal analysis, Data curation. Weiping Deng: Data curation. Mengyi Chen: Data curation. Xia Zhao: Data curation. Dongxiang Ouyang: Data curation. Yuqing Fu: Data curation. Jiaying Lin: Conceptualization, Methodology, Writing – review & editing, Supervision. Leizhen Xia: Methodology, Formal analysis, Investigation, Visualization, Project administration. Qiongfang Wu: Conceptualization, Investigation, Writing – review & editing, Supervision, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A Supplementary data
The following are the Supplementary data to this article:Supplementary Fig. 1
Supplementary Fig. 2
Supplementary Fig. 3
Supplementary Fig. 4
Data availability
Data will be made available on request.
Acknowledgements
None.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.intimp.2022.109552.
==== Refs
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| 0 | PMC9731924 | NO-CC CODE | 2022-12-15 23:17:46 | no | Int Immunopharmacol. 2023 Jan 9; 114:109552 | utf-8 | Int Immunopharmacol | 2,022 | 10.1016/j.intimp.2022.109552 | oa_other |
==== Front
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)00165-5
10.1016/j.accpm.2022.101184
101184
Original Article
Epidemiology, risk factors and prognosis of ventilator-associated pneumonia during severe COVID-19: multicenter observational study across 149 European intensive care units
Garnier Marc a⁎
Constantin Jean-Michel b
Heming Nicholas cde
Camous Laurent f
Ferré Alexis g
Razazi Keyvan hi
Lapidus Nathanaël j
on behalf of the COVID-ICU Investigators
1
a Sorbonne University, GRC29, Assistance Publique-Hôpitaux de Paris (APHP), DMU DREAM, Anesthesiology and Critical Care Medicine Department, Tenon Hospital, Paris, France
b Sorbonne University, GRC29, Assistance Publique-Hôpitaux de Paris (APHP), DMU DREAM, Anesthesiology and Critical Care Medicine Department, Pitié-Salpêtrière Hospital, Paris, France
c Department of Intensive Care, Hôpital Raymond Poincaré, APHP University Versailles Saint Quentin — University Paris Saclay, France
d Laboratory of Infection & Inflammation — U1173, School of Medicine Simone Veil, University Versailles Saint Quentin — University Paris Saclay, INSERM, Garches, France
e FHU SEPSIS (Saclay and Paris Seine Nord Endeavour to PerSonalize Interventions for Sepsis) & RHU RECORDS (Rapid rEcognition of CORticosteroiD resistant or sensitive Sepsis), Garches, France
f Antilles-Guyane University, Medical and Surgical Intensive Care Unit, Guadeloupe Teaching Hospital, Les Abymes, France
g Intensive Care Unit, Versailles Hospital, Le Chesnay, France
h AP-HP, Hôpitaux Universitaires Henri-Mondor, Service de Médecine Intensive Réanimation, F-94010 Créteil, France
i Université Paris Est Créteil, Faculté de Médecine de Créteil, IMRB, GRC CARMAS, Créteil 94010, France
j Sorbonne University, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, AP-HP, Saint-Antoine Hospital, Public Health Department, F75012 Paris, France
⁎ Corresponding author at: Anesthesiology and Critical Care Department, Tenon University Hospital, 4 rue de la Chine, 75020 Paris, France.
1 COVID-ICU Investigators are listed in Appendix A.
9 12 2022
9 12 2022
101184© 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.
Background
COVID-19 patients requiring mechanical ventilation are particularly at risk of developing ventilator-associated pneumonia (VAP). Risk factors and the prognostic impact of developing VAP during critical COVID-19 have not been fully documented.
Methods
Patients invasively ventilated for at least 48 h from the prospective multicentre COVID-ICU database were included in the analyses. Cause-specific Cox regression models were used to determine factors associated with the occurrence of VAP. Cox-regression multivariable models were used to determine VAP prognosis. Risk factors and the prognostic impact of early vs. late VAP, and Pseudomonas-related vs. non-Pseudomonas-related VAP were also determined.
Main findings
3388 patients were analysed (63 [55-70] years, 75.8% males). VAP occurred in 1523/3388 (45.5%) patients after 7 [5-9] days of ventilation. Identified bacteria were mainly Enterobacteriaceae followed by Staphylococcus aureus and Pseudomonas aeruginosa. VAP risk factors were male gender (Hazard Ratio (HR) 1.26, 95% Confidence Interval [1.09-1.46]), concomitant bacterial pneumonia at ICU admission (HR 1.36 [1.10-1.67]), PaO2/FiO2 ratio at intubation (HR 0.99 [0.98-0.99] per 10 mmHg increase), neuromuscular-blocking agents (HR 0.89 [0.76-0.998]), and corticosteroids (HR 1.27 [1.09-1.47]). VAP was associated with 90-mortality (HR 1.34 [1.16-1.55]), predominantly due to late VAP (HR 1.51 [1.26-1.81]). The impact of Pseudomonas-related and non-Pseudomonas-related VAP on mortality was similar.
Conclusion
VAP affected almost half of mechanically ventilated COVID-19 patients. Several risk factors have been identified, among which modifiable risk factors deserve further investigation. VAP had a specific negative impact on 90-day mortality, particularly when it occurred between the end of the first week and the third week of ventilation.
Keywords
COVID-19
intensive care unit
invasive mechanical ventilation
mortality
risk factors
ventilator-associated pneumonia
Abbreviations
ARDS, Acute Respiratory Distress Syndrome
C-VAP, COVID-19-related VAP
COVID-19, COronaVIrus(SARS-CoV-2)-related Disease
HR, Hazard Ratio
ICU, Intensive Care Unit
IL, Interleukin
IMV, Invasive Mechanical Ventilation
NC-VAP, non COVID-19-related VAP
NMBA, Neuro Muscular Blocking Agents
VAP, Ventilator-Associated Pneumonia
==== Body
pmcIntroduction
“Coronavirus disease 2019” (COVID-19), due to SARS-CoV-2 infection, primarily affects the lungs. From the beginning of the pandemic to early September 2022, approximately 615 million people have been diagnosed with COVID-19, among whom approximately 6.5 million died, worldwide [1]. Severe pneumonia and acute respiratory distress syndrome (ARDS) are the two most severe forms of COVID-19. High-flow oxygen therapy is the first-line treatment of COVID-19-related severe hypoxemia [2], however, more than half of critically ill COVID-19 patients will require invasive mechanical ventilation (IMV). Ventilator-associated pneumonia (VAP) affects between 29% and 64% of COVID-19 patients [3], [4], [5], [6], [7]. VAP incidences of up to 84% have even been reported in SARS-CoV-2 infected patients requiring ECMO [8]. To date, several pathophysiological hypotheses have been proposed to explain the particular vulnerability of COVID-19 patients to bacterial lung superinfection. However, the identification of risk factors of VAP, as well as the impact of VAP on the prognosis of severely SARS-CoV-2 infected patients is not fully documented. Previous reports based on small cohorts suggested that the male gender and the need for vasopressors are risk factors of VAP in COVID-19 [3], [9]. Additionally, the occurrence of VAP in ventilated COVID-19 patients did not seem to affect mortality [9], mainly driven by VAP-induced septic shock and ARDS [4].
Based on the analysis of the largest multicenter prospective cohort including 4929 critically-ill COVID-19 patients admitted into the ICU, the primary objectives of this study were to describe the incidence, characteristics, risk factors, and the prognosis of VAP in severe COVID-19 pneumonia; and secondly to describe the risk factors and the prognosis of early vs. late VAP, and Pseudomonas-related vs. non-Pseudomonas-related VAP.
Patients and Methods
Study design
The multicenter prospective COVID-ICU cohort has previously been described [6]. Briefly, the study was conducted in 149 ICUs from 138 centers in France, Switzerland, and Belgium. Between February 25, 2020, and May 2, 2020, all consecutive patients over 16 years of age suffering from respiratory failure with laboratory-confirmed SARS-CoV-2 infection admitted to a participating ICU were prospectively included.
Inclusion and exclusion criteria
Were included in the current analysis all invasively mechanically ventilated (>48 h) patients of the COVID-ICU cohort. Were excluded patients who had been invasively ventilated for more than 24 hours prior to a transfer into one of the participating centers.
Data collection
Full data collection has been previously described [6]. Briefly, data were retrieved via an electronic form, completed daily. A particular focus on patients’ respiratory support was made, regarding both the type of support and its settings. Records of additional treatments such as neuromuscular blocking agents (NMBA), corticosteroids, etc. were collected. Recorded outcomes included time of weaning from IMV, time of ICU and hospital discharge, vital status at ICU and at hospital discharge, and vital status 28 and 90 days after ICU admission.
VAP definition
Only the first episode of bacterial VAP was considered when addressing the primary objectives. Regarding subgroup analyses, only the first episode of early and/or late VAP, and Pseudomonas-related and/or non-Pseudomonas-related VAP were considered.
VAP diagnosis was based on: 1) clinical and radiological suspicion based on the European Center of Disease Control criteria [10]; 2) confirmed by one positive microbiological sample defined when culture recovered ≥106 CFU.mL-1 for tracheal aspirate, ≥104 CFU.mL-1 for broncho-alveolar lavage, and ≥103 CFU.mL-1 for distal protected brush or aspirate [10]; 3) leading the attending physician to initiate an antimicrobial therapy. In addition, VAP must have occurred at least 48 h after the onset of IMV. Finally, pneumonia only related to L. pneumophila, mycoplasma, and chlamydia species, anaerobes, or isolated oro-pharyngeal flora were excluded from the analyses. Early and late VAP were defined as occurring < and ≥ 5 days respectively after the onset of IMV [11].
Statistical analyses
Baseline characteristics of patients are described as counts (proportions) for categorical variables and median [1st-3rd quartiles] for quantitative variables.
Factors associated with the occurrence of VAP were identified using cause-specific Cox regression models, after checking the proportional-odds hypothesis and controlling for the competing risk of death. The at-risk period started 48 h after the onset of IMV. Factors associated with early and late VAP were identified in independent analyses using the same models. For early VAP, all patients were censored on day 5 following the onset of invasive mechanical ventilation. For late VAP, the at-risk period started 5 days after the onset of IMV in all patients still requiring IMV, and the occurrence of an early VAP was considered as a covariate. In another analysis, only Pseudomonas-related VAPs were considered, using the same statistical approach, with the occurrence of earlier non-Pseudomonas-related VAP considered as a covariate.
To determine whether the occurrence of VAP was associated with patients’ prognosis, Cox regression multivariable models were used to study the time to death or hospital/ICU discharge alive, considering the occurrence of a VAP as a time-dependent variable. For length-of-stay outcomes, death was considered as a competing event with the use of cause-specific models. For all regression analyses, a univariable analysis was conducted and a multivariable model for variables deemed clinically relevant was built, regardless of the univariable significant associations [12].
All tests were two-tailed and interpreted at the 0.05 significance threshold. All analyses were performed using the R statistical software version 4.0.3 (R Foundation for Statistical Computing, Vienna, Austria).
IRB approval
The study was approved by the French Intensive Care Society ethical committee (CE-SRLF 20-23). Due to the observational design and in accordance with French law [13], patients or next-of-kin of ICU non-survivors were informed that anonymized data regarding their hospital stay were collected in the database.
Reporting guidelines
This observational study follows the STROBE guidelines. STROBE checklist is provided as a Supplementary file.
Results
Patients
Over the study period, 4929 patients were included in the COVID-ICU database. Complete follow-up data were available for 4676 out of the 4929 patients, including 473 (10.1%) patients exclusively treated with standard oxygen therapy, 381 (8.1%) with high-flow oxygen therapy, and 156 (3.3%) with non-invasive ventilation. Among the remaining 3666 patients, 278 were ventilated for more than 24 h prior to admission into a participating ICU. Eventually, 3388 invasively ventilated patients were analyzed. According to the protocol, episodes only due to Legionella pneumophila (n = 1), mycoplasma and chlamydia species (n = 1), anaerobes (n = 8), or isolated oro-pharyngeal flora (n = 116) were not considered as VAP. Finally, 1523 (45.0%) patients fully analyzable presented at least one episode of VAP (Fig. 1 ).Fig. 1 Flow chart of the selection of patients included into the COVID-ICU database for analyses.
Fig. 1
Characteristics of patients and their clinical and biological data at ICU admission are available in Table 1 . The median age was 63 [55-70] years. There were 2545/3388 (75.1%) male patients. The most frequent comorbidities were hypertension (48.9%) and diabetes (27.5%). There were 352 (10.4%) immunocompromised patients. Median time between the first COVID-19 symptoms and ICU admission was 7 [4], [5], [6], [7], [8], [9], [10] days. At admission, SOFA and SAPS-II scores were 10 [8], [9], [10], [11], [12] and 39 [30-52], respectively. Concomitant bacterial pneumonia has been diagnosed in 245 (7.2%) SARS-CoV-2 infected patients. There was no relevant difference regarding co-morbidities, and characteristics at admission and during the first 24 h following tracheal intubation between patients with and without subsequent VAP, except for the prevalence of diabetes and values of PaO2/FiO2 ratio (Table 1).Table 1 Demographic, clinical, biological, and ventilatory support characteristics of the 3388 patients according to the occurrence of ventilator-associated pneumonia (VAP).
Table 1 No. All patients (n = 3388) VAP (n = 1523) No VAP (n = 1865)
Age, years 3387 63 [55-70] 63 [55-70] 63 [55-71]
Men, number (%) 3358 2545 (75.8%) 1195 (79%) 1350 (73.2%)
Body mass index, kg.m-² 3182 28.6 [25.6-32.5] 28.7 [25.7-32.7] 28.4 [25.6-32.4]
<18 18 (0.6%) 5 (0.3%) 13 (0.7%)
18-25 649 (20.4%) 292 (20.2%) 357 (20.5%)
25-30 1268 (39.8%) 565 (39.1%) 703 (40.4%)
>30 1247 (39.2%) 582 (40.3%) 665 (38.3%)
Active smoker, number (%) 3388 141 (4.2%) 58 (3.8%) 83 (4.5%)
Cardiovascular comorbidities
Treated hypertension 3388 1656 (48.9%) 898 (48.2%) 758 (49.8%)
Coronary artery disease 3388 373 (11.0%) 173 (11.4%) 200 (10.7%)
Chronic heart failure 3388 115 (3.4%) 50 (3.3%) 65 (3.5%)
Respiratory comorbidities
COPD 3388 206 (6.1%) 98 (6.4%) 108 (5.8%)
Asthma 3388 221 (6.5%) 97 (6.4%) 124 (6.6%)
Diabetes 3388 932 (27.5%) 450 (29.5%) 482 (25.8%)
Chronic renal failure 3388 310 (9.1%) 147 (9.7%) 163 (8.7%)
Chronic liver failure 3388 22 (0.6%) 10 (0.7%) 12 (0.6%)
Immunodeficiency 352 (10.4%) 160 (10.5%) 192 (10.3%)
Hematological malignancies 3388 97 (2.9%) 46 (3.0%) 51 (2.7%)
Active solid tumor 3388 50 (1.5%) 16 (1.1%) 34 (1.8%)
Solid organ transplant 3388 74 (2.2%) 40 (2.6%) 34 (1.8%)
Human Immunodeficiency Virus 3388 56 (1.7%) 24 (1.6%) 32 (1.7%)
Immunosuppressive therapya 3388 145 (4.3%) 72 (4.7%) 73 (3.9%)
Long-term corticosteroidsb 3388 135 (4.0%) 62 (4.1%) 73 (3.9%)
Clinical frailty scorec 3028 2 [2–3] 2 [2–3] 2 [2–3]
Time between first symptoms and ICU admission, days 3387 7 [4–10] 7 [4–10] 7 [4–10]
NSAID intake before ICU admission 2919 207 (7.1%) 96 (7.3%) 111 (7.0%)
At ICU admission
SAPS II score 3107 39 [30-52] 39 [30-52] 40 [31-52]
SOFA score at ICU admission 2567 10 [8–12] 10 [8–12] 10 [7–12]
Patient origin 3373
Direct admission from home/emergency medical ambulance 511 (15.1%) 237 (15.6) 274 (14.8%)
Emergency room 1521 (45.1%) 693 (45.6%) 828 (44.7%)
Medical wards 1078 (32%) 463 (30.5%) 615 (33.2%)
Other ICU 259 (7.7%) 125 (8.2%) 134 (7.2%)
Operating theatre 4 (0.1%) 2 (0.1%) 2 (0.1%)
Concomitant bacterial pneumonia 3387 245 (7.2%) 140 (9.2%) 105 (5.6%)
Respiratory support, number (%) 3383
Standard oxygen therapy 448 (13.2%) 193 (12.7%) 255 (13.7%)
High-flow oxygen 264 (7.8%) 106 (7%) 158 (8.5%)
Non-invasive ventilation 88 (2.3%) 34 (2.2%) 54 (2.9%)
Invasive mechanical ventilation 2583 (76.4%) 1190 (78.1%) 1393 (74.9%)
At intubation
Ventilator settingsd
Vt, mL. kg-1 PBW 2997 6.2 [5.9-6.7] 6.1 [5.8-6.7] 6.2 [5.9-6.8]
Set PEEP, cmH2O 1306 12 [10–14] 12 [10–14] 12 [10–13]
Plateau pressure, cmH2O 2049 24 [21–27] 24 [21–27] 24 [21–27]
Driving pressure, cmH2O 1318 12 [10–14] 12 [9–14] 12 [10–14]
Set FiO2 2823 0.50 [0.40-0.60] 0.50 [0.40-0.60] 0.50 [0.40-0.60]
PaO2/FiO2, mmHg 2652 186 [146-243] 176 [140-225] 193 [150-247]
Biologyd
White blood cells, x109.L-1 3108 9.0 [6.8-11.9] 9.2 [6.9-12.3] 8.8 [6.7-11.7]
Lymphocytes, x109.L-1 2745 0.8 [0.5-1.1] 0.8 [0.5-1.1] 0.8 [0.5-1.1]
Hemoglobin, g.dL-1 3126 11.4 [10.1-12.6] 11.4 [10.1-12.6] 11.4 [10.2-12.6]
Platelets, x109.L-1 3122 240 [183-311] 240 [182-309] 240 [184-313]
Creatinine, µmol.L-1 3073 82 [61-125] 83 [63-124] 80 [60-125]
Bicarbonates, mmol.L-1 3107 25 [23–28] 26 [23–28] 25 [23–28]
C-reactive protein, mg.L-1 2067 195 [126-283] 201 [130-290] 190 [123-276]
Procalcitonine, ng.mL-1 1571 0.58 [0.25-1.44] 0.60 [0.28-1.54] 0.54 [0.22-1.40]
Fibrinogen, g.L-1 2138 6.9 [5.7-7.9] 6.9 [5.8-7.9] 6.8 [5.6-7.9]
D-dimers, µg.L-1 1483 1860 [1047-4000] 1893 [1022-4099] 1830 [1060-3818]
Lactate, mmol.L-1 3065 1.30 [1.00-1.70] 1.30 [1.00-1.70] 1.30 [1.00-1.70]
Results are expressed as n (%) or median [25th–75th percentiles].
No.: number of available data; NSAID: non-steroidal anti-inflammatory drug; PBW: predicted body weight.
a Except corticosteroids.
b Daily intake above 20 mg of prednisone equivalent.
c Clinical Frailty Score (CFS) as described by Rockwood et al. (A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005;173:489–95). The validated French translation of the CFS was used in this study (Abraham et al. Validation of the clinical frailty score (CFS) in French language. BMC Geriatr. 2019 Nov 21;19(1):322).
d Worst value during the 24 h following tracheal intubation.
First VAP episode description and predictors
At least one episode of VAP occurred in 1523 (45.0%) patients, after a median of 7 [5], [6], [7], [8], [9] days of IMV. The cumulative probability of developing VAP is presented in Fig. A.1. Distribution of the bacteria isolated during the first episode of VAP is presented in Table 2 . VAP was monomicrobial in 1263/1523 (82.9%) of cases. Among the 1814 bacterial species isolated, Enterobacteriaceae were found the most frequently, followed by Staphylococcus aureus and Pseudomonas aeruginosa.Table 2 Treatments, VAP characteristics and outcomes of the 3388 patients according to the occurrence of ventilator-associated pneumonia (VAP).
Table 2 No. All patients (n = 3388) VAP (n = 1523) No VAP (n = 1865)
Treatments
Neuromuscular blockers usea 3076 2125 (69.1%) 1008 (70.8%) 1117 (67.6%)
Prone positioning usea 3187 1209 (37.9%) 602 (41.8%) 607 (34.7%)
Systemic antibiotic usea 3242 2080 (64.2%) 934 (63.9%) 1146 (64.4%)
Including beta-lactam antibiotics 3249 2049 (63.1%) 935 (63.8%) 1114 (62.4%)
Corticosteroid treatment 3388 815 (24.1%) 363 (23.8%) 452 (24.2%)
VAP characteristics
Early VAP 1523 - 491 (32.2%) -
Late VAP 1523 - 1313 (86.2%) -
Monomicrobial VAP 1523 - 1263 (82.9%) -
Polymicrobial VAP 1523 - 260 (17.1%) -
Isolated pathogenb
Pseudomonas aeruginosa - 286 (15.8%) -
Acinetobacter baumanii - 19 (1%) -
Haemophilus influenzae - 48 (2.6%) -
Enterobacteriaceae and other GNB - 1087 (59.9%) -
Staphylococcus aureus - 242 (13.3%)c -
Streptococcus pneumoniae - 35 (1.9%) -
Enterococci - 61 (3.4%) -
Other streptococci - 36 (2%) -
Outcomes
Subsequent VAP
Second episode 1523 - 736 (48.3%) -
Third episode 1523 - 174 (11.4%) -
>3 episodes 1523 - 145 (9.5%) -
28-day mortalityd 3388 874 (25.8%) 329 (21.6%) 545 (29.2%)
90-day mortalityd 3388 1074 (31.7%) 456 (29.9%) 618 (33.1%)
Length of mechanical ventilatione, days 2389 17 [8–28] 25 [16–41] 10 [5–19]
ICU length-of-staye, days 2389 19 [12–32] 28 [19–43] 14 [8–21]
Hospital length-of-staye, days 2344 31 [19–49] 41 [28-60] 23 [15–37]
a At least during 48 h hours following intubation and before VAP diagnosis.
b Total number of bacterial isolates, n = 1814 in 1523 first VAP episodes.
c Including 37 (2%) methicillin-resistant Staphylococcus aureus.
d Crude mortality at 28 and 90 days could not be directly compared between patients with and without VAP due to immortal time bias. See Table 4 for multivariable analysis and text for interpretation.
e Among survivors.
Univariable and multivariable analyses of COVID-19-related VAP (C-VAP) predictors are reported in Table 3 . After adjustment, variables independently associated with C-VAP were male gender (HR 1.26 95%CI[1.09-1.46], p = 0.002), concomitant bacterial pneumonia at ICU admission (HR 1.36 [1.10-1.67], p = 0.004), the severity of respiratory failure at the time of IMV initiation (HR 0.99 [0.98-0.99] per 10 mmHg increase of the PaO2/FiO2 ratio, p = 0.001), NMBA use (HR 0.89 [0.76-0.998], p = 0.046), and corticosteroid use (HR 1.27 [1.09-1.47], p = 0.002).Table 3 Factors associated with ventilator-associated pneumonia (VAP) in mechanically ventilated adults with COVID-19.
Table 3 Univariable HR [95%CI] p-value Multivariable HR [95%CI] p-value
Age, per 10 years 0.99 [0.95-1.04] 0.72 0.96 [0.91-1.02] 0.18
Male gender 1.22 [1.07-1.38] 0.002 1.26 [1.09-1.46] 0.002
Active smoking 0.90 [0.69-1.17] 0.44 0.83 [0.61-1.12] 0.22
Body mass index ≥ 30 kg.m-² 1.04 [0.91-1.20] 0.57 1.05 [0.92-1.20] 0.45
Treated hypertension 1.08 [0.98-1.20] 0.12 1.00 [0.88-1.13] 0.97
Chronic heart failure 1.02 [0.77-1.35] 0.89 1.07 [0.78-1.49] 0.67
COPD 1.09 [0.83-1.44] 0.54 1.18 [0.92-1.50] 0.20
Diabetes 1.19 [1.07-1.33] 0.002 1.08 [0.94-1.23] 0.27
Chronic renal failure 1.19 [1.00-1.41] 0.05 1.10 [0.89-1.36] 0.37
Immunodeficiency 1.17 [1.00-1.38] 0.06 1.09 [0.89-1.34] 0.39
Concomitant bacterial pneumonia at admission 1.51 [1.28-1.78] <0.001 1.36 [1.10-1.67] 0.004
Non-respiratory SOFA score at intubation, per point 1.04 [1.02-1.06] <0.001 1.02 [1.00-1.05] 0.09
PaO2/FiO2 at intubation, per 10 mmHg 0.99 [0.98-0.995] 0.002 0.99 [0.98-0.99] 0.001
Leucopenia or hyperleucocytosis at intubation 1.10 [0.99-1.22] 0.08 1.07 [0.95-1.21] 0.24
Neuromuscular blockersa 0.95 [0.85-1.07] 0.39 0.89 [0.76-0.998] 0.046
Prone positioninga 1.14 [1.03-1.27] 0.02 1.11 [0.98-1.27] 0.10
Corticosteroids useb 1.39 [1.23-1.57] <0.001 1.27 [1.09-1.47] 0.002
a At least during 48 h hours following intubation and before VAP diagnosis
b At least during 48 h hours following intubation and before VAP diagnosis at a dose greater than or equal to 40 mg prednisone equivalent
Early and late VAP description and predictors
Among the 1523 patients that presented C-VAP, 491 (32.2%) patients presented an early VAP, and 1313 (86.2%) had at least one episode of late VAP. Distribution of the bacterial species isolated during early and late VAP is presented in Table A.1. S. aureus, S. pneumoniae, and H. influenzae were isolated more frequently in early than in late VAP, while P. aeruginosa was isolated more frequently in late than in early VAP.
After adjustment, the only variable independently associated with early VAP was concomitant bacterial pneumonia at ICU admission (HR 1.51 [1.03-2.22], p = 0.04) (Table A.2), while variables independently associated with late VAP were male gender (HR 1.21 [1.03-1.44], p = 0.02), the severity of respiratory failure at the time of intubation (HR 0.99 [0.98-0.99] per 10 mmHg increase of the PaO2/FiO2 ratio, p = 0.002), NMBA use (HR 0.84 [0.72-0.97], p = 0.02), and a previous episode of early VAP (HR 3.11 [2.64-3.67], p < 0.001) (Table A.3).
Pseudomonas-related VAP description and predictors
Among the 1523 patients that presented C-VAP, 656 (43.1%) presented at least one episode of Pseudomonas-related VAP. After adjustment, variables independently associated with Pseudomonas-related VAP occurrence were the use of prone positioning (HR 1.25 [1.02-1.53], p = 0.03) and a previous non-Pseudomonas-related VAP (HR 5.87 [4.74-7.27], p < 0.001) (Table A.4).
Patient outcomes and impact of VAP on prognosis
All VAP
Overall 28-day and 90-day mortality were 25.8% and 31.7%, respectively. Among survivors, the median length of IMV, ICU, and hospital stay were 17 [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], 19 [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], and 31 [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49] days, respectively (Table 2). After adjustment for the other predictors of 90-day mortality of critically ill COVID-19 patients [6], the occurrence of at least one episode of VAP was significantly associated with poor outcome (HR 1.34 [1.16-1.55], p < 0.001) (Table 4 and Fig. 2 ). VAP was also associated with higher ICU and hospital length-of-stay among survivors (Table 2 and Fig. A.2).Table 4 Factors associated with 90-day mortality in mechanically ventilated adults with COVID-19.
Table 4 Univariable HR [95%CI] p-value Multivariable HR [95%CI] p-value
Age, per 10 years 1.49 [1.40-1.58] <0.001 1.47 [1.37-1.58] <0.001
Body mass index >30 kg.m-2 0.82 [0.72-0.94] 0.004 0.94 [0.81-1.09] 0.39
Diabetes 1.50 [1.32-1.70] <0.001 1.30 [1.13-1.50] <0.001
Immunodeficiency 1.74 [1.47-2.06] <0.001 1.60 [1.33-1.96] <0.001
Time between first symptoms and ICU admission, per day 0.96 [0.95-0.87] <0.001 0.96 [0.95-0.98] <0.001
Non-respiratory SOFA score at intubation, per one point 1.10 [1.07-1.13] <0.001 1.09 [1.06-1.12] <0.001
PaO2/FiO2 at intubation, per 10 mmHg 0.99 [0.98-0.998] 0.02 0.98 [0.98-0.99] 0.003
At least one episode of VAP 1.38 [1.20-1.58] <0.001 1.34 [1.16-1.55] <0.001
Fig. 2 Landmark analyses of the cumulative probability of death for patients still alive and mechanically ventilated at day 5, day 10, day 15 and day 20, depending on the earlier occurrence of VAP.
Fig. 2
Early and late VAP
After adjustment, late VAP was independently associated with 90-day mortality (HR 1.51 [1.26-1.81], p < 0.001), while early VAP was not (HR 1.10 [0.91-1.32], p = 0.34) (Table A.5).
Pseudomonas-related VAP
The impact of Pseudomonas-related and non-Pseudomonas-related VAP on 90-day mortality was similar (HR 1.18 [0.99-1.40] and HR 1.18 [1.02-1.37], respectively) (Tables A.6A&B).
Discussion
VAP incidence and microbial ecology
In this prospective multicenter study, 45% of mechanically ventilated COVID-19 patients presented at least one episode of VAP. This result is in keeping with previous smaller reports, in which the incidence of C-VAP ranged from 30% to 60% [14]. However, this incidence is higher than reported in mixed non-COVID ICU patients, usually ranging from 10% to 25% [15], [16], and reaches similar incidences to those reported in trauma or brain injury [17]. Taking into consideration the severity of lung injury, this incidence remains higher than reported in patients with severe ARDS due to other causes [18], [19], including other viral ARDS [8], [20], [21]. This suggests the involvement of a specific mechanism related to COVID-19. It cannot be ruled out that the surge of severe patients, may have led to a decrease in vigilance and incomplete compliance with infection prevention measures [22]. Indeed, the surge of ICU patients led to work overload and understaffing (notably due to infection of healthcare workers), and consequently to hiring non-ICU nurses and increasing the patient-to-nurse ratio [23]. Moreover, nurses had to take care of more severe patients requiring more frequent invasive ventilation than before the first epidemic waves. In addition, closing the doors of patients’ rooms, the need to wear highly protective equipment to get in (and their possible shortage), and the possible fear of staff contamination contributed to decreasing the number of daily visits to patients [24]. Finally, ICU workers experienced high levels of stress and psychological burden, decreasing their vigilance and compliance with bundles of care for VAP prevention [25].
However, other pathophysiological features related to SARS-CoV-2 itself or to the excessive host inflammatory response may contribute to this increased susceptibility to bacterial superinfection, such as a large production of Interleukin (IL)-6, IL-1β, and IL-10 [26], [27], [28], lymphopenia [26], [29], decreased B and dendritic cells activation [30], decreased HLA-DR expression on lung macrophages [31], or expansion of myeloid-derived suppressor cells [32].
Despite this increased incidence, the bacterial ecology reported in C-VAP appears close to that of non-COVID-19-related VAP (NC-VAP) [15]. Indeed, Enterobacteriaceae, Pseudomonas aeruginosa, and Staphylococcus aureus were the three main isolates, similar to the findings of a large European multicenter study [33]. Our results are also in keeping with those of previous smaller studies comparing bacterial ecology in C-VAP and NC-VAP [7], [34], [35]. Finally, we confirmed previous observations made both in NC-VAP [33] and C-VAP [9], reporting a greater proportion of S. aureus, S. pneumoniae, and H. influenzae, and less P. aeruginosa in early compared to late VAP.
VAP predictors
Among patient-related risk factors, only the male gender was independently associated with C-VAP in our study. This is in keeping with previous reports about NC-VAP [36], [37], [38] and C-VAP [3], [9]. Conversely, chronic renal and heart failures, COPD, diabetes, and immunodeficiency, frequently cited as NC-VAP risk factors [15], [39], were not associated with C-VAP. Regarding severity factors, we observed that the severity of hypoxemia at intubation was associated with a moderate additional risk of C-VAP, which is consistent with the previous identification of ARDS as a risk factor of NC-VAP [15], [39]. In contrast with several small-size studies [40], [41] and randomized controlled trials [42], steroids use was associated with C-VAP in this study, as previously suggested in NC-VAP [15]. However, due to the design of the study, we cannot rule out the participation of non-respiratory organ failure in this result, notably of associated septic shock, which is both a potential indication for hydrocortisone therapy and a risk factor of VAP [37], [43]. In addition, the effect of steroids on the incidence of VAP may vary during ICU stay, with an increased risk being reported for patients hospitalized for at least 14 days in the ICU [44]. Conversely, NMBA use was associated with a reduced incidence of C-VAP in this study. This is an unexpected result as a large recent study showed a 2.5-fold increased risk of NC-VAP with NMBA use, even after adjustment for the severity of the ARDS [38]. Thus, further studies are needed to determine if early administration of NMBA could be a protective factor in C-VAP. NMBA may prevent patient self-induced lung injury (P-SILI) by removing the strong inspiratory efforts arising from the high respiratory drive frequently observed in COVID-19 patients [45], which may favor VAP by worsening lung injuries and prolonging the duration of IMV.
Finally, the parameter associated with VAP with the highest hazard ratio was concomitant bacterial pneumonia at ICU admission, which occurred in 7.2% of patients. This incidence, lower than for other viral ARDS such as influenza-related ARDS, is consistent with previous reports [46], [47]. The risk of VAP may in part be explained by a more dysregulated inflammation in case of bacterial co-infection, favoring subsequent superinfection. This is in keeping with the higher mortality observed in COVID-19 intubated patients suffering from concomitant bacterial infection [47], similar to that reported in other respiratory viral-bacterial co-infections [48].
VAP impact on the outcome
The negative impact of VAP on mortality is established in general ICU patients [16], [49] and in patients suffering from influenza pneumonia [50]. However, the impact of C-VAP on mortality is still a matter of debate. Nseir et al. reported a 1.7-fold increase in 28-day mortality due to C-VAP [50], while Gamberini et al. did not observe any significant excess in mortality either for early or late C-VAP [51]. The crude mortality rates of patients with and without VAP in our study must not be directly compared due to the immortal time bias leading to underestimating mortality in patients with VAP. The Cox regression analysis accounting for competing risk of death and time-varying history of VAP controlled this bias and confirmed that the occurrence of C-VAP was associated with higher mortality (HR 1.34 [1.16-1.55]). These estimates are somewhat smaller than those reported by Nseir et al. Moreover, we observed different results for early and late C-VAP, only the latter being significantly associated with increased 90-day mortality. Gamberini et al. previously reported a detrimental impact of late rather than early C-VAP on weaning from IMV, although no effect on mortality was observed, possibly due to a lack of power. In addition, landmark analyses suggested that VAP-related excess in mortality is of particular concern during the first 15 days following intubation. Indeed, the cumulative probability of death at day 90 no longer differed between patients with and without C-VAP as soon as VAP occurred 15 days or more after intubation, suggesting that beyond this time point the adverse effect of VAP may be overcome by other factors affecting patients with a prolonged ICU stay. Finally, we observed a similar impact on mortality of Pseudomonas-related and non-Pseudomonas-related C-VAP, confirming in C-VAP the absence of specific risk due to Pseudomonas recently reported in NC-VAP [49].
Strengths and limitations
To our knowledge, this study is the largest cohort of ICU COVID-19 patients with VAP. Thanks to the detailed data available in the COVID-ICU database, we were able to perform a comprehensive risk factor analysis and adjust prognostic analyses on a large number of variables already known to impact COVID-19 patients’ mortality. Moreover, the Cox regression models used for prognostic analyses allowed us to estimate the specific association of VAP with mortality independently of the ventilation time already elapsed at the time of VAP onset. Thus, our study adds to current knowledge the identification of C-VAP risk factors and definitely confirmed the link between developing VAP and over-mortality during critical COVID-19.
We acknowledge several limitations to our study. First, all patients were included during the first epidemic wave of SARS-CoV-2 affecting Europe in the spring of 2020. Thus, it cannot be excluded that recent features including the acquisition of immunity following successive epidemic waves or vaccination, or the emergence of SARS-CoV-2 variants, may change some of our results. Second, collected data did not allow us to precisely describe the bacterial ecology of C-VAP, for instance regarding the distribution of species within the Enterobacteriaceae family, or to provide antibiotic susceptibility and resistance profiles. Finally, although this study was conducted in 149 ICUs from 138 centers, across three countries, our results were obtained from a west European population. Since genetic predispositions to VAP and ethnical quantitative and qualitative variations in immune function have been described [39], further large studies are needed to confirm our results in different populations.
Conclusion
Patients with critical COVID-19 requiring IMV are especially exposed to the risk of VAP, in particular if they are males, present a bacterial pneumonia concomitantly with COVID-19, suffer from severe hypoxemic respiratory failure, and are treated with corticosteroids. C-VAP is independently associated with 90-day mortality and ICU morbidity, mainly due to the effect of late-VAP occurring before the 3rd week of ICU stay. A potential protective effect of NMBA use at the early stage of ventilation of critical COVID-19 patients requiring IMV deserves further investigations.
Author contributions
MG, NH, LC, AF, KR and JMC contributed to conceptualization and design of the protocol. Data were analyzed by MG and NL who have accessed and verified the data. NL performed the statistical analyses. MG wrote the first draft of this submission. All authors revised the report critically for important intellectual content and approved the final version of the manuscript. All authors confirmed that they had full access to all the data in the study and accepted responsibility to submit for publication.
Financial and non-financial disclosures
MG reports personal fees as a speaker received from Medtronic outside the submitted work.
JMC reports personal fees and non-financial support from Drager, GE Healthcare, Sedana Medical, Baxter, and AOP Health; personal fees from Fisher and Paykel Healthcare, GSK, Guilead, Orion, Philips Medical, and Fresenius Medical Care; and non-financial support from LFB and Bird Corporation, outside of the submitted work.
KR, NH, LC, AF and NL declare no competing interests.
Data sharing statement
The data analyzed and presented in this study are available from the corresponding author on reasonable request, providing the request meets local ethical and research governance criteria after publication. Patient-level data will be anonymized and study documents will be redacted to protect the privacy of trial participants.
Funding information
This ancillary study of the COVID-ICU database has not been funded by any external source.
The COVID-ICU database was funded by the Foundation APHP and its donators through the program “Alliance Tous Unis Contre le Virus”, the “Direction de la Recherche Clinique et du Développement”, the French Ministry of Health, and the foundation of the University hospitals of Geneva, Geneva, Switzerland.
Appendix A Participating Sites and COVID–ICU Investigators
CHU Angers, Angers, France (Alain Mercat, Pierre Asfar, François Beloncle, Julien Demiselle), APHP - Hôpital Bicêtre, Le Kremlin-Bicêtre, France (Tài Pham, Arthur Pavot, Xavier Monnet, Christian Richard), APHP - Hôpital Pitié Salpêtrière, Paris, France (Alexandre Demoule, Martin Dres, Julien Mayaux, Alexandra Beurton), CHU Caen Normandie - Hôpital Côte de Nacre, Caen, France, (Cédric Daubin, Richard Descamps, Aurélie Joret, Damien Du Cheyron), APHP - Hôpital Cochin, Paris, France (Frédéric Pene, Jean-Daniel Chiche, Mathieu Jozwiak, Paul Jaubert), APHP - Hôpital Tenon, Paris (France, Guillaume Voiriot, Muriel Fartoukh, Marion Teulier, Clarisse Blayau), CHRU de Brest – La Cavale Blanche, Brest, France (Erwen L'Her, Cécile Aubron, Laetitia Bodenes, Nicolas Ferriere), Centre Hospitalier de Cholet, Cholet, France (Johann Auchabie, Anthony Le Meur, Sylvain Pignal, Thierry Mazzoni), CHU Dijon Bourgogne, Dijon, France (Jean-Pierre Quenot, Pascal Andreu, Jean-Baptiste Roudau, Marie Labruyère), CHU Lille - Hôpital Roger Salengero, Lille, France (Saad Nseir, Sébastien Preau, Julien Poissy, Daniel Mathieu), Groupe Hospitalier Nord Essonne, Longjumeau, France (Sarah Benhamida, Rémi Paulet, Nicolas Roucaud, Martial Thyrault), APHM - Hopital Nord, Marseille, France (Florence Daviet, Sami Hraiech, Gabriel Parzy, Aude Sylvestre), Hôpital de Melun-Sénart, Melun, France (Sébastien Jochmans, Anne-Laure Bouilland, Mehran Monchi), Élément Militaire de Réanimation du SSA, Mulhouse, France (Marc Danguy des Déserts, Quentin Mathais, Gwendoline Rager, Pierre Pasquier), CHU Nantes - Hôpital Hotel Dieu, Nantes, France (Jean Reignier, Amélie Seguin, Charlotte Garret, Emmanuel Canet), CHU Nice - Hôpital Archet, Nice, France (Jean Dellamonica, Clément Saccheri, Romain Lombardi, Yanis Kouchit), Centre Hospitalier d'Orléans, Orléans, France (Sophie Jacquier, Armelle Mathonnet, Mai-Ahn Nay, Isabelle Runge), Centre Hospitalier Universitaire de la Guadeloupe, Pointe-à-Pitre, France (Frédéric Martino, Laure Flurin, Amélie Rolle, Michel Carles), Hôpital de la Milétrie, Poitiers, France (Rémi Coudroy, Arnaud W Thille, Jean-Pierre Frat, Maeva Rodriguez), Centre Hospitalier Roanne, Roanne, France (Pascal Beuret, Audrey Tientcheu, Arthur Vincent, Florian Michelin), CHU Rouen - Hôpital Charles Nicolle, Rouen, France (Fabienne Tamion, Dorothée Carpentier, Déborah Boyer, Christophe Girault ), CHRU Tours - Hôpital Bretonneau, Tours, France (Valérie Gissot, Stéphan Ehrmann, Charlotte Salmon Gandonniere, Djlali Elaroussi), Centre Hospitalier Bretagne Atlantique, Vannes, France (Agathe Delbove, Yannick Fedun, Julien Huntzinger, Eddy Lebas), CHU Liège, Liège, Belgique (Grâce Kisoka, Céline Grégoire, Stella Marchetta, Bernard Lambermont), Hospices Civils de Lyon - Hôpital Edouard Herriot, Lyon, France (Laurent Argaud, Thomas Baudry, Pierre-Jean Bertrand, Auguste Dargent), Centre Hospitalier Du Mans, Le Mans, France (Christophe Guitton, Nicolas Chudeau, Mickaël Landais, Cédric Darreau), Centre Hospitalier de Versailles, Le Chesnay, France (Alexis Ferre, Antoine Gros, Guillaume Lacave, Fabrice Bruneel), Hôpital Foch, Suresnes, France (Mathilde Neuville, JérômeDevaquet, Guillaume Tachon, Richard Gallot), Hôpital Claude Galien, Quincy sous Senart, France (Riad Chelha, Arnaud Galbois, Anne Jallot, Ludivine Chalumeau Lemoine), GHR Mulhouse Sud-Alsace, Mulhouse, France (Khaldoun Kuteifan, Valentin Pointurier, Louise-Marie Jandeaux, Joy Mootien), APHP - Hôpital Antoine Béclère, Clamart, France (Charles Damoisel, Benjamin Sztrymf), APHP - Hôpital Pitié-Salpêtrière, Paris, France (Matthieu Schmidt, Alain Combes, Juliette Chommeloux, Charles Edouard Luyt), Hôpital Intercommunal de Créteil, Créteil, France (Frédérique Schortgen, Leon Rusel, Camille Jung), Hospices Civils de Lyon - Hôpital Neurologique, Lyon, France (Florent Gobert), APHP - Hôpital Necker, Paris, France (Damien Vimpere, Lionel Lamhaut), Centre Hospitalier Public du Cotentin - Hôpital Pasteur, Cherbourg-en-cotentin, France (Bertrand Sauneuf, Liliane Charrrier, Julien Calus, Isabelle Desmeules), CHU Rennes - Hôpital du Pontchaillou, Rennes, France (Benoît Painvin, Jean-Marc Tadie), CHU Strasbourg - Hôpital Hautepierre, Strasbourg, France (Vincent Castelain, Baptiste Michard, Jean-Etienne Herbrecht, Mathieu Baldacini), APHP - Hôpital Pitié Salpêtrière, Paris, France (Nicolas Weiss, Sophie Demeret, Clémence Marois, Benjamin Rohaut), Centre Hospitalier Territorial Gaston-Bourret, Nouméa, France (Pierre-Henri Moury, Anne-Charlotte Savida, Emmanuel Couadau, Mathieu Série), Centre Hospitalier Compiègne-Noyon, Compiègne, France (Nica Alexandru), Groupe Hospitalier Saint-Joseph, Paris, France (Cédric Bruel, Candice Fontaine, Sonia Garrigou, Juliette Courtiade Mahler), Centre hospitalier mémorial de Saint-Lô, Saint-Lô, France (Maxime Leclerc, Michel Ramakers), Grand Hôpital de l'Est Francilien, Jossigny, France (Pierre Garçon, Nicole Massou, Ly Van Vong, Juliane Sen), Gustave Roussy, Villejuif, France (Nolwenn Lucas, Franck Chemouni, Annabelle Stoclin), Centre Hospitalier Intercommunal Robert Ballanger, Aulnay-sous-Bois, France (Alexandre Avenel, Henri Faure, Angélie Gentilhomme, Sylvie Ricome), Hospices Civiles de Lyon - Hôpital Edouard Herriot, Lyon, France (Paul Abraham, Céline Monard, Julien Textoris, Thomas Rimmele), Centre Hospitalier d'Avignon, Avignon, France (Florent Montini), Groupe Hospitalier Diaconesses - Croix Saint Simon, Paris, France (Gabriel Lejour, Thierry Lazard, Isabelle Etienney, Younes Kerroumi), CHU Clermont-Ferrand - Hôpital Gabriel Montpied, Clermont Ferrand, France (Claire Dupuis, Marine Bereiziat, Elisabeth Coupez, François Thouy), Hôpital d'Instruction des Armées Percy, Clamart, France (Clément Hoffmann, Nicolas Donat, Anne Chrisment, Rose-Marie Blot), CHU Nancy - Hôpital Brabois, Vandoeuvre-les-Nancy, France (Antoine Kimmoun, Audrey Jacquot, Matthieu Mattei, Bruno Levy), Centre Hospitalier de Vichy, Vichy, France (Ramin Ravan, Loïc Dopeux, Jean-Mathias Liteaudon, Delphine Roux), Hopital Pierre Bérégovoy, Nevers, France (Brice Rey, Radu Anghel, Deborah Schenesse, Vincent Gevrey), Centre Hospitalier de Tarbes, Tarbes, France (Jermy Castanera, Philippe Petua, Benjamin Madeux), Hôpitaux Civils de Colmar - Hôpital Louis pasteur, Colmar, France (Otto Hartman), CHU Charleroi - Hôpital Marie Curie, Bruxelles, Belgique (Michael Piagnerelli, Anne Joosten,Cinderella Noel, Patrick Biston), Centre hospitalier de Verdun Saint Mihiel, Saint Mihiel, France (Thibaut Noel), CH Eure-Seine - Hôpital d'Evreux-Vernon, Evreux, France (Gurvan LE Bouar, Messabi Boukhanza, Elsa Demarest, Marie-France Bajolet), Hôpital René Dubos, Pontoise, France (Nathanaël Charrier, Audrey Quenet, Cécile Zylberfajn, Nicolas Dufour), APHP - Hôpital Lariboisière, Paris, France (Buno Mégarbane, Sébastian Voicu, Nicolas Deye, Isabelle Malissin), Centre Hospitalier de Saint-Brieuc, Saint-Brieuc, France (François Legay, Matthieu Debarre, Nicolas Barbarot, Pierre Fillatre), Polyclinique Bordeaux Nord Aquitaine, Bordeaux, France (Bertrand Delord, Thomas Laterrade, Tahar Saghi, Wilfried Pujol), HIA Sainte Anne, Toulon, France (Pierre Julien Cungi, Pierre Esnault, Mickael Cardinale), Grand Hôpital de l'Est Francilien, Meaux, France (Vivien Hong Tuan Ha, Grégory Fleury, Marie-Ange Brou, Daniel Zafimahazo), HIA Robert Picqué, Villenave d'Ornon, France (David Tran-Van, Patrick Avargues, Lisa Carenco), Centre Hospitalier Fontainebleau, Fontainebleau, France (Nicolas Robin, Alexandre Ouali, Lucie Houdou), Hôpital Universitaire de Genève, Genève, Suisse (Christophe Le Terrier, Noémie Suh, Steve Primmaz, Jérome Pugin), APHP - Hôpital Beaujon, Clichy, France (Emmanuel Weiss, Tobias Gauss, Jean-Denis Moyer, Catherine Paugam Burtz), Groupe Hospitalier Bretage Sud, Lorient, France (Béatrice La Combe, Rolland Smonig, Jade Violleau, Pauline Cailliez), Centre Hospitalier Intercommunal Toulon, La Seyne sur Mer, France (Jonathan Chelly), Centre Hospitalier de Dieppe, Dieppe, France (Antoine Marchalot, Cécile Saladin, Christelle Bigot), CHU de Martinique, Fort-de-France, France (Pierre-Marie Fayolle, Jules Fatséas, Amr Ibrahim, Dabor Resiere), Hôpital Fondation Adolphe de Rothchild, Paris, France (Rabih Hage, Clémentine Cholet, Marie Cantier, Pierre Trouiler), APHP - Bichat Claude Bernard, Paris, France (Philippe Montravers, Brice Lortat-Jacob, Sebastien Tanaka, Alexy Tran Dinh), APHP - Hôpital Universitaire Paris Sud, Bicêtre, France (Jacques Duranteau, Anatole Harrois, Guillaume Dubreuil, Marie Werner), APHP - Hôpital Européen Georges Pompidou, Paris, France (Anne Godier, Sophie Hamada, Diane Zlotnik, Hélène Nougue), APHP, GHU Henri Mondor, Créteil, France (Armand Mekontso-Dessap, Guillaume Carteaux, Keyvan Razazi, Nicolas De Prost), APHP - Hôpitaux Universitaires Henri Mondor, Créteil, France (Nicolas Mongardon, Nicolas Mongardon, Meriam Lamraoui, Claire Alessandri, Quentin de Roux), APHP - Hôpital Lariboisière, Paris, France (Charles de Roquetaillade, Benjamin G. Chousterman, Alexandre Mebazaa, Etienne Gayat), APHP - Hôpital Saint-Antoine, Paris, France (Marc Garnier, Emmanuel Pardo, LeaSatre-Buisson, Christophe Gutton), APHP Hôpital Saint-Louis, Paris, France (Elise Yvin, Clémence Marcault, Elie Azoulay, Michael Darmon), APHP - Hôpital Saint-Antoine, Paris, France (Hafid Ait Oufella, Geoffroy Hariri, Tomas Urbina, Sandie Mazerand), APHP - Hôpital Raymond Pointcarré, Garches, France (Nicholas Heming, Francesca Santi, Pierre Moine, Djillali Annane), APHP - Hôpital Pitié Salpêtrière, Paris, France (Adrien Bouglé, Edris Omar, Aymeric Lancelot, Emmanuelle Begot), Centre Hospitalier Victor Dupouy, Argenteuil, France (Gaétan Plantefeve, Damien Contou, Hervé Mentec, Olivier Pajot), CHU Toulouse - Hôpital Rangueil, Toulouse, France (Stanislas Faguer, Olivier Cointault, Laurence Lavayssiere, Marie-Béatrice Nogier), Centre Hospitalier de Poissy, Poissy, France (Matthieu Jamme, Claire Pichereau, Jan Hayon, Hervé Outin), APHP - Hôpital Saint-Louis, Paris, France (François Dépret, Maxime Coutrot, Maité Chaussard, Lucie Guillemet), Clinique du MontLégia, CHC Groupe-Santé, Liège, Belgique (Pierre Goffin, Romain Thouny, Julien Guntz, Laurent Jadot), CHU Saint-Denis, La Réunion, France (Romain Persichini), Centre Hospitalier de Tourcoing, Tourcoing, France (Vanessa Jean-Michel, Hugues Georges, Thomas Caulier), Centre Hospitalier Henri Mondor d'Aurillac, Aurillac, France (Gaël Pradel, Marie-Hélène Hausermann, Thi My Hue Nguyen-Valat, Michel Boudinaud), Centre Hospitalier Saint Joseph Saint Luc, Lyon, France (Emmanuel Vivier, SylvèneRosseli, Gaël Bourdin, Christian Pommier) Centre Hospitalier de Polynésie Française, Polynésie, France (Marc Vinclair, Simon Poignant, Sandrine Mons), Ramsay Générale de Santé, Hôpital Privé Jacques Cartier, Massy, France (Wulfran Bougouin), Centre Hospitalier Alpes Léman, Contamine sur Arve, France (Franklin Bruna, Quentin Maestraggi, Christian Roth), Hospices Civils de Lyon - Hôpital de la Croix Rousse, Lyon, France (Laurent Bitker, François Dhelft, Justine Bonnet-Chateau, Mathilde Filippelli), Centre Cardiologique du Nord, Saint-Denis, France (Tristan Morichau-Beauchant, Stéphane Thierry, Charlotte Le Roy, Mélanie Saint Jouan), GHU - Hôpital Saint-Anne, Paris, France (Bruno Goncalves, Aurélien Mazeraud, Matthieu Daniel, Tarek Sharshar) CHR Metz - Hôpital Mercy, Metz, France (Cyril Cadoz, RostaneGaci, Sébastien Gette, Guillaune Louis), APHP - Hôpital Paul Brousse, Villejuif, France (Sophe-Caroline Sacleux, Marie-Amélie Ordan), CHRU Nancy - Hôpital Central, Nancy, France (Aurélie Cravoisy, Marie Conrad, Guilhem Courte, Sébastien Gibot), Centre Hospitalier d’Ajaccio, Ajaccio, France (Younès Benzidi, Claudia Casella, Laurent Serpin, Jean-Lou Setti), Centre Hospitalier de Bourges, Bourges, France (Marie-Catherine Besse, Anna Bourreau), Centre hospitalier de la Côte Basque, Bayonne, France (Jérôme Pillot, Caroline Rivera, Camille Vinclair, Marie-Aline Robaux), Hospices Civils de Lyon - Hôpital de la Croix Rousse, Lyon, France (Chloé Achino, Marie-Charlotte Delignette, Tessa Mazard, Frédéric Aubrun), CH Saint-Malo, Saint-Malo, France (Bruno Bouchet, Aurélien Frérou, Laura Muller, Charlotte Quentin), Centre Hospitalier de Mulhouse, Mulhouse, France (Samuel Degoul), Centre Hospitalier de Briançon, Briançon, France (Xavier Stihle, Claude Sumian, Nicoletta Bergero, Bernard Lanaspre), CHU Nice, Hôpital Pasteur 2, Nice, France (Hervé Quintard, Eve Marie Maiziere), Centre Hospitalier des Pays de Morlaix, Morlaix, France (Pierre-Yves Egreteau, Guillaume Leloup, Florin Berteau, Marjolaine Cottrel), Centre Hospitalier Valence, Valence, France (Marie Bouteloup, Matthieu Jeannot, Quentin Blanc, Julien Saison), Centre Hospitalier Niort, Niort, France (Isabelle Geneau, Romaric Grenot, Abdel Ouchike, Pascal Hazera), APHP - Hôpital Pitié Salpêtrière, Paris, France (Anne-Lyse Masse, Suela Demiri, Corinne Vezinet, Elodie Baron, Deborah Benchetrit, Antoine Monsel), Clinique du Val d'Or, Saint Cloud, France (Grégoire Trebbia, Emmanuelle Schaack, Raphaël Lepecq, Mathieu Bobet), Centre Hospitalier de Béthune, Béthune, France (Christophe Vinsonneau, Thibault Dekeyser, Quentin Delforge, Imen Rahmani), Groupe Hospitalier Intercommunal de la Haute-Saône, Vesoul, France (Bérengère Vivet, Jonathan Paillot, Lucie Hierle, Claire Chaignat, Sarah Valette), Clinique Saint-Martin, Caen, France (Benoït Her, Jennifier Brunet), Ramsay Générale de Santé, Clinique Convert, Bourg en Bresse, France (Mathieu Page, Fabienne Boiste, Anthony Collin), Hôpital Victor Jousselin, Dreux, France(Florent Bavozet, Aude Garin, Mohamed Dlala, KaisMhamdi), Centre Hospitalier de Troye, Troye, France, (Bassem Beilouny, Alexandra Lavalard, Severine Perez), CHU de ROUEN-Hôpital Charles Nicolle, Rouen, France (Benoit Veber, Pierre-Gildas Guitard, Philippe Gouin, Anna Lamacz), Centre Hospitalier Agen-Nérac, Agen, France (Fabienne Plouvier, Bertrand P Delaborde, Aïssa Kherchache, Amina Chaalal), APHP - Hôpital Louis Mourier, Colombes, France (Jean-Damien Ricard, Marc Amouretti, Santiago Freita-Ramos, Damien Roux), APHP - Hôpital Pitié-Salpêtrière, Paris, France (Jean-Michel Constantin, Mona Assefi, Marine Lecore, Agathe Selves), Institut Mutualiste Montsouris, Paris, France (Florian Prevost, Christian Lamer, Ruiying Shi, Lyes Knani), CHU Besançon – Hôpital Jean Minjoz, Besançon, France, (Sébastien Pili Floury, Lucie Vettoretti), APHP - Hôpital Universitaire Robert-Debré, Paris, France (Michael Levy, Lucile Marsac, Stéphane Dauger, Sophie Guilmin-Crépon), CHU Besançon – Hôpital Jean Minjoz, Besançon, France, (Hadrien Winiszewski, Gael Piton, Thibaud Soumagne, Gilles Capellier) ; Médipôle Lyon-Villeurbanne, Vileurbanne, France, (Jean-Baptiste Putegnat, Frédérique Bayle, Maya Perrou, Ghyslaine Thao), APHP - Ambroise Paré, Boulogne-Billancourt, France (Guillaume Géri, Cyril Charron, Xavier Repessé, Antoine Vieillard-Baron), CHU Amiens Picardie, Amiens, France (Mathieu Guilbart, Pierre-Alexandre Roger, Sébastien Hinard, Pierre-Yves Macq), Hôpital Nord-Ouest, Villefranche-sur-Saône, France (Kevin Chaulier, Sylvie Goutte), CH de Châlons en Champagne, Châlons en Champagne, France (Patrick Chillet, Anaïs Pitta, Barbara Darjent, Amandine Bruneau), CHU Angers, Angers, France (Sigismond Lasocki, Maxime Leger, Soizic Gergaud, Pierre Lemarie), CHU Grenoble Alpes, Grenoble, France (Nicolas Terzi, Carole Schwebel, Anaïs Dartevel, Louis-Marie Galerneau), APHP - Hôpital Européen Georges Pompidou, Paris, France (Jean-Luc Diehl, Caroline Hauw-Berlemont, Nicolas Péron, Emmanuel Guérot), Hôpital Privé d'Antony, Antony, France (Abolfazl Mohebbi Amoli, Michel Benhamou, Jean-Pierre Deyme, Olivier Andremont), Institut Arnault Tzanck,Saint Laurent du Var, France (Diane Lena, Julien Cady, Arnaud Causeret, Arnaud De La Chapelle) ; Centre Hospitalier d’ Angoulême, Angoulême, France (Christophe Cracco, Stéphane Rouleau, David Schnell) ; Centre Hospitalier de Cahors, Cahors, France (Camille Foucault), Centre hospitalier de Carcassonne, Carcassonne, France (Cécile Lory) ; CHU Nice – Hôpital L’Archet 2, Nice, France (Thibault Chapelle, Vincent Bruckert, Julie Garcia, Abdlazize Sahraoui) ; Hôpital Privé du Vert Galant, Tremblay-en-France, France (Nathalie Abbosh, Caroline Bornstain, Pierre Pernet) ; Centre Hospitalier de Rambouillet, Rambouillet, France (Florent Poirson, Ahmed Pasem, Philippe Karoubi) ; Hopitaux du Léman, Thonon les Bains, France (Virginie Poupinel, Caroline Gauthier, François Bouniol, Philippe Feuchere), Centre Hospitalier Victor Jousselin, Dreux, France (Florent Bavozet, Anne Heron), Hôpital Sainte Camille, Brie sur Marne, France (Serge Carreira, Malo Emery, Anne Sophie Le Floch, Luana Giovannangeli), Hôpital d’instruction des armées Clermont-Tonnerre, Brest, France (Nicolas Herzog, Christophe Giacardi, Thibaut Baudic, Chloé Thill), APHP - Hôpital Pitié Salpêtrière, Paris, France (Said Lebbah, Jessica Palmyre, Florence Tubach, David Hajage) ; APHP - Hôpital Avicenne, Bobigny, France (Nicolas Bonnet, Nathan Ebstein, Stéphane Gaudry, Yves Cohen) ; Groupement Hospitalier la Rochelle Ré Amis, La Rochelle, France (Julie Noublanche, Olivier Lesieur) ; Centre Hospitalier Intercommunal de Mont de Marsan et du Pays des Sources, Mont de Marsan, France (Arnaud Sément, Isabel Roca-Cerezo, Michel Pascal, Nesrine Sma) ; Centre Hospitalier Départemental de Vendée, La-Roche-Sur-Yon, France (Gwenhaël Colin, Jean-Claude Lacherade, Gauthier Bionz, Natacha Maquigneau) ; Pôle Anesthésie-Réanimation, CHU Grenoble (Pierre Bouzat, Michel Durand, Marie-Christine Hérault, Jean-Francois Payen).
Appendix A Supplementary data
The following are Supplementary data to this article:Fig. A.1
Cumulative probability of VAP occurrence across the duration of mechanical of ventilation. The at-risk period starts after the initial 48h gray area.
Fig. A.2
Landmark analyses of the cumulative probability of discharge from the ICU for patients still alive and mechanically ventilated at day 5, day 10, day 15 and day 20, depending on the earlier occurrence of VAP.
Acknowledgments
The authors gratefully acknowledge all the French, Belgian, and Swiss clinical research centers, COVID-ICU investigators, medical students, Polytechnic University students, and patients involved in the study, without whom we would not have been able to perform this work.
Appendix B Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.accpm.2022.101184.
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| 36509387 | PMC9731925 | NO-CC CODE | 2022-12-14 23:31:56 | no | Anaesth Crit Care Pain Med. 2022 Dec 9;:101184 | utf-8 | Anaesth Crit Care Pain Med | 2,022 | 10.1016/j.accpm.2022.101184 | oa_other |
==== Front
Auris Nasus Larynx
Auris Nasus Larynx
Auris, Nasus, Larynx
0385-8146
1879-1476
Japanese Society of Otorhinolaryngology-Head and Neck Surgery, Inc. Published by Elsevier B.V.
S0385-8146(22)00230-9
10.1016/j.anl.2022.12.002
Article
Insight into the mechanisms of olfactory dysfunction by COVID-19
Koyama Sachiko 1⁎
Mori Eri 2
Ueha Rumi 34
1 Indiana University, School of Medicine, Department of Medicine
2 Department of Otorhinolaryngology, Jikei University, School of Medicine
3 Swallowing Center, The University of Tokyo Hospital
4 Department of Otolaryngology, Head and Neck Surgery, Faculty of Medicine, the University of Tokyo
⁎ Correspondence author: Sachiko Koyama, Indiana University, Richard L. Roudebush VA Medical Center, 1481 W Tenth St., Indianapolis, IN, 46202
9 12 2022
9 12 2022
25 9 2022
6 12 2022
© 2022 Japanese Society of Otorhinolaryngology-Head and Neck Surgery, Inc. Published by 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.
One of the unique symptoms of COVID-19 is chemosensory dysfunction. Almost three years since the beginning of the pandemic of COVID-19, there have been many studies on the symptoms, progress, and possible causes, and also studies on methods that may facilitate recovery of the senses. Studies have shown that some people recover their senses even within a couple of weeks whereas there are other patients that fail to recover chemosensory functions fully for several months and some never fully recover. Here we summarize the symptoms and the progress, and then review the papers on the causation as well as the treatments that may help facilitate the recovery of the symptoms. Depending on the differences in the levels of severity and the locations where the main pathological venues are, what is most effective in facilitating recovery can vary largely across patients and thus may require individualized strategies for each patient. The goal of this paper is to provide some thoughts on these choices depending on the differences in the causes and severity.
Key words
COVID-19
olfactory dysfunction
multiple causes and severity
treatments
precision medicine for chemosensory dysfunction
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pmc1 Introduction
It has been almost three years since the beginning of the outbreak of COVID-19. The world has gone through a tragic pandemic that has killed over 6.49 million people worldwide with an official record of over 604 million cases as of September 2022. In the United States alone, the number of deaths surpassed one million, which is more than 15% of the overall deaths worldwide. The symptoms are broad; from coughs, fever, headaches, shortness of breaths to the unique symptoms of chemosensory dysfunction (losing the sense of smell, i.e., anosmia, and/or the sense of taste, i.e., ageusia) [1], [2], [3].
The unique symptoms of chemosensory dysfunctions and the mechanisms with which the virus enters host cells and travels along the body system have been studied extensively during these almost three years by scientists around the world. There is evidence for several possible causes of chemosensory dysfunction due to COVID-19, which suggests there can be multiple differences across patients. Here, we summarize the symptoms, the progress of the symptoms, the possible causes, the chemical compounds that often trigger sensing altered smell, and the studies on the various treatments that may help facilitate the recovery from chemosensory dysfunction.
2 Epidemiology of COVID-19 induced chemosensory dysfunction
2.1 Olfactory dysfunction due to COVID-19: the numbers and symptoms
Typical course of COVID-19 starts with common cold-like symptoms (for example, fever, cough, sputum, sore throat, and nasal discharge) and malaise, and abnormal sense of smell and taste appear approximately 5 days on average (range 1-14 days) after infection. These cold-like symptoms last about one week [4]. Some patients have gastrointestinal symptoms such as nausea and diarrhea as well, and some patients remain asymptomatic [[1], [2], [3],5].Among the various clinical manifestations of COVID-19, sensory dysfunction is particularly characterized with a higher prevalence compared to other viral infections [1,[5], [6], [7]]; new onset of smell and/or taste disorders were significantly more frequent among COVID-19 patients (39%) than influenza patients (13%) [8].
The COVID-19-induced olfactory and taste dysfunctions are so unique that they are considered to be one of the diagnostic markers of COVID-19. This is based on 1) the high percentage of the patients that exhibit the symptoms [8], [9], [10], [11], 2) the early onset of these symptoms compared to other symptoms [11], and also 3) because for some patients they are the only symptom of COVID-19 [1,12]. The incidence of olfactory dysfunction and taste dysfunction ranges 32-87% and 35-89%, respectively, with a concomitant incidence of olfactory and taste dysfunctions reported to be about 35% [9,10] (As the number of cases of taste dysfunction, especially of the self-reported ones, highly likely include impaired flavor perception due to olfactory impairment, the number of cases of actual taste dysfunction is most likely less than the numbers reported). About 10% of the patients had olfactory and taste dysfunctions preceding the onset of other symptoms, and there have been many patients not even showing other symptoms. The asymptomatic carriers of the virus may have contributed to the spread of COVID-19 without knowing they were infected [1,12].
Olfactory dysfunction is more prevalent in females compared to males and is more common in the younger age groups between 20 to 40 years old [1,2,8,12]. There are differences in the incidences of olfactory dysfunction depending on geographic areas as well. In Western countries, the prevalence of olfactory dysfunction is over 50%, while in Asian countries it is only about 30%. This suggests the possibility of genetic differences in the vulnerability to contract the virus [13,14], cultural differences in accepting to wear masks and other face coverings in public [15], or other factors like cultural differences in the eating and drinking habits of materials that contain phytochemicals with beneficial effects against the virus [16].
Symptoms of olfactory dysfunction include anosmia (complete lack of olfactory sense), hyposmia (reduced olfactory sense), parosmia (distorted olfactory sense), and phantosmia (sensing odors that don't exist). Anosmia seems to be more common than hyposmia in COVID-19 patients [9,10]. Patients with olfactory dysfunction due to COVID-19 often experience parosmia and the distorted smell is often “smoky” or “burnt odor” [1,7]. Parosmia is often observed in patients who had olfactory dysfunction, for example, in conventional post-viral olfactory dysfunction (PVOD) and it is not unique to COVID-19-induced olfactory dysfunction [16]. We will discuss parosmia in more detail later in this review.
SARS-CoV-2 showed rapid evolution, leading to the emergence of various variants, most notably the rampant Delta and Omicron variants, which differ in infectivity and clinical symptoms compared to the original lineages [17,18]. The Omicron variant shows less tissue damage to the olfactory neuroepithelium [19] and a lower incidence of olfactory dysfunction compared to the original lineages [20], although it still ranges from 5.8 to 32.5% [20,21].
2.2 Recovering from COVID-19 induced chemosensory dysfunction
Like other post-viral olfactory dysfunctions, the symptoms of chemosensory dysfunction caused by COVID-19 persist for a long term in some patients. In a study in the UK that conducted two surveys to the same participants, 86.4% of the COVID-19 patients had anosmia and 11.5% had hyposmia at the first survey, which was completed 1 to 2 weeks after the onset of COVID-19 [22]. At the second survey one week later, 80.1% of the patients showed improvement, whereas 17.3% of the patients showed no change [22]. In a longer-term follow-up study by Ferreli et al. (2022) [23], more than 80% of the patients with COVID-19-induced olfactory dysfunction reported complete recovery of olfactory within the first three months. In the same study, 87% of the patients reported complete recovery of the smell function after 18 months. The severity of chemosensory impairment at the onset was reported to negatively correlate with recovery, i.e., the more severe the initial symptoms, the longer it took to regain the olfactory sense [23]. The patients who showed improvements in the olfactory function within the 7 days after contracting COVID-19 also showed early recovery [23].
However, there are studies reporting higher incidences of persisting olfactory dysfunction (5-60%) 2 to 6 months after the onset of the symptom [24], [25], [26], [27], [28]. A high percentage of healthcare workers who had mild COVID-19 still had olfactory dysfunction (52%) 5 months post-COVID-19 [29]. Niklassen. et al (2021) [28] reported that 26% of the patients still had some olfactory dysfunction 2 months post-COVID-19, although others recovered within one month of the onset. While many patients with long-lasting (long-haul) COVID-19 have persistent olfactory dysfunction, very few studies provide a prognosis for possible time to recovery. Even after 6 months to a year post-COVID-19, approximately 25 to 30% of patients still suffer from persistent subjective olfactory dysfunction [29], [30], [31]. Ohla et al. (2022) [32] reported that half of the patients felt their olfactory sense was less than 80% of their pre-COVID-19 status [32].
Some studies have measured and compared the levels of severity in the olfactory dysfunction of COVID-19 patients with negative controls and found a higher prevalence of olfactory dysfunction in the former group one year after contracting COVID-19 [33,34]. The Global Consortium for Chemosensory Research (GCCR) investigated parosmia/phantosmia and reported that, less than 10% of patients had parosmia/phantosmia during the early stages of infection, which rose to 47% having parosmia and 25% having phantosmia after 2-10 months [32]. In addition, 56.7% of the patients who still had olfactory dysfunction 11 months post-COVID had parosmia and 28.0% of them had phantosmia [29]. During the infection, the frequency is 16.9% and 22.9%, respectively, both of which are more frequent [29].
3 Possible causes of olfactory dysfunction
During the two years since the COVID-19 pandemic started, many papers on the possible causes of COVID-19-induced chemosensory dysfunction have been published. As early as in spring 2020, papers showing that angiotensin converting enzyme-2 (ACE2), which has been known as the entry receptor of SARS-CoV-2 [35,36], is not expressed in the mouse olfactory sensory neurons but is expressed in the supporting cells in the olfactory epithelium were published [37,38]. Transmembrane protease serine 2 (TMPRSS2), which primes the spike protein of SARS-CoV-2, and furin, which facilitates spike protein cleavage, were both missing in the olfactory sensory neurons [38]. This first suggested that the chemosensory dysfunction is not due to the damage in the olfactory sensory neurons themselves as the virus won't enter them without the ACE2, TMPRSS2, and furin. However, later studies using hamsters have shown that infection can cause complete morphological damage to the olfactory epithelium, not only to the supporting cells but including the olfactory sensory neurons [39]. They have also shown that, in the olfactory mucosa samples from COVID-19 patients and olfactory mucosa tissue samples from hamsters, cells positive for olfactory marker proteins (OMP) overlapped with immunostaining of SARS-CoV-2 antigens [39]. Later on, several other proteins have been found to have binding affinity with the receptor binding domain (RBD) of the spike protein (S-protein) of SARS-CoV-2, and facilitate the entry of the virus into the cells. For example, neuropilin-1 (NRP-1) is known to bind to the S-protein of SARS-CoV-2, and NRP-1 is expressed profoundly in the olfactory epithelium [36,40]. Sialic acid is also known to mediate binding of the virus to host cells, and facilitate entry of the virus [41,42].
Other than the multiple types of proteins that serve as receptors or those that facilitate the entries, there are also other factors that can become involved in worsening the symptoms. Studies using brain organoids have shown that the SARS-CoV-2 negative cells around the SARS-CoV-2 positive cells show upregulation of pathways related to the cellular responses to decreased oxygen levels (hypoxia), whereas the infected cells showed gene expressions typical to excess supply of oxygen (hyperoxia) [43]. These studies suggest that even if the olfactory sensory neurons were not infected by SARS-CoV-2, the hypoxic environment may weaken them and may cause apoptosis, which can cause damage in the olfactory membrane.
Infection can cause inflammation, i.e., the release of proinflammatory cytokines, and this can weaken the signaling from the olfactory epithelium to the olfactory bulb, and from olfactory bulb to regions in the brain related to olfactory sense. A new paper published in June 2022 has shown that inflammation can be the key factor in the lingering symptoms of long COVID, including long term chemosensory dysfunction [44]. They have shown using hamsters and humans that SARS-CoV-2 causes long-term injury to the tissues and organs with persisting activation of myeloid, T cells, proinflammatory cytokines and interferons even after the acute stage and without detectable virus [44]. De Melo et al. (2021) [39] have also shown that even after over 100 days post-infection, some post-COVID-19 patients still had viral load in the olfactory mucosa [39], suggesting that persisting virus or particles of virus might be involved in causing inflammation, which can weaken the olfactory function.
Recent studies have proposed new hypotheses on the mechanisms of COVID-19-induced chemosensory dysfunction. Hernandez-Clavijo et al. (2022) [45] have shown that the supporting cells of olfactory epithelium co-expressed ACE2 and transmembrane protein 16F (TMEM16F), which is a membrane protein involved in translocation of phosphatidylserine and is involved in syncytia formation. They suggested that large syncytia induced by cell-to-cell fusion can be involved in causing olfactory dysfunction. Syncytia, i.e., fusion of neighboring cells, formation has had been observed in the lungs of COVID-19 patients. Buchrieser et al. (2020) [46] have shown that cells infected by SARS-CoV-2 can ‘express the Spike protein at their surface’ and bind to the ACE2 receptors of neighboring cells, causing cell-to-cell fusion and form large multinucleated syncytia [46], [47], [48]. TMEM16F is a calcium-activated scramblase involved in the fusion of the cells, and drugs that inhibit TMEM16F can suppress the fusion, thus suppress the syncytia formation [47]. This abnormal fusion, compared to normal fusion like fertilization, can facilitate the spread of infection to neighboring cells and damage the function of the cells. The expression of TMEM16F in the olfactory epithelium suggests that large syncytia induced by cell-to-cell can be involved in causing or facilitating olfactory dysfunction.
Changes in gene expression in the olfactory system have also been proposed to be involved in causing anosmia/hyposmia. Studies with humans and hamsters have shown that genes involved in olfactory signaling and olfactory receptor genes were downregulated [49], which can cause less functional olfactory sense because of the reduced expression of olfactory receptors and less functional olfactory signaling. In addition, recent studies have suggested the possible role of UDP-glucuronosyltransferase (UGT) [50]. UGT2A1 is expressed in the sustentacular cells and the cilia of sensory neurons in the olfactory epithelium and they are involved in odorant metabolization [51]. This odorant metabolization can suppress the olfactory sense by modulating the odorant chemical compounds into glucuronidated odorant metabolites, which don't activate olfactory receptors [51]. Thus, an upregulation of UGT2As could suppress the olfactory sensitivity. Shelton et al. (2022) [50] found from saliva samples that a genetic locus containing UGT2A1 and UGT2A2 on chromosome 4 (chr4q13.3) could be involved in the loss of the sense of smell and taste because of the significantly upregulated expression [50]. Although the mechanisms that they are upregulated are unknown, the higher expression of them could function in suppressing the olfactory system at peripheral level.
In addition to causes at the peripheral level, there are also reports on pathologies in the brain [52], [53], [54]. In a study with human subjects, paranasal sinus CT scanning and MRI of patients with persistent COVID-19-induced olfactory dysfunction revealed that 43.5% of the patients had a significantly lower volume in the olfactory bulb [52]. In addition, in 54.2% of the patients, there were changes in the shape of the olfactory bulb, and the signal intensity was abnormal in 91.3% of the patients [52].
SARS-CoV-2 can travel from peripheral locations to the brain [55]. Ueha et al. (2022) [55] demonstrated that inoculation of SARS-CoV-2 in the oral cavity of hamsters spread to the central nervous system through the nasal cavity. Studies which inoculated SARS-CoV-2 into the nostrils of mice have also found that the virus reached to the olfactory bulb and other regions in the brain, as well as lung, eye, kidney, spleen, pancreas, heart, and liver tissues in a day [43,53]. Importantly, TUNEL staining of the brain tissues have revealed that there were a significantly high number of apoptotic cells in the brain [43,53]. Similarly, in a study using rhesus monkeys, exposure to SARS-CoV-2 by aerosol inhalation or a multi-route mucosal infection (through conjunctival, nasal, pharyngeal, intratracheal routes) caused neuroinflammation, microhemorrhages, brain hypoxia, neuropathology, neuronal degeneration and apoptosis [56].
What these results suggest is that COVID-19-induced olfactory dysfunction can be caused by weakened signaling at the olfactory bulb and at the olfactory cortex. Further, the causation of COVID-19-induced olfactory dysfunction may have multiple sources, depending on the patient. Thus, which factor is the major cause of dysfunction could vary in different patients, resulting in large differences in the time length it takes to recover.
4 Chemical compounds involved in parosmia
Olfactory sense is a sensory system that detects and recognizes chemical compounds. The large number of different chemical compounds are differentially sensed by the specialized dedicated receptors which become activated only by certain types of chemical compounds. These receptors form the mechanisms by which we identify and distinguish smells and the varied receptors are the reason why chemical senses involve a large number of different receptor genes.
When patients lose their senses of olfaction (anosmia or hyposmia), they sometimes experience distorted smells/tastes and the smells/tastes that don't exist. The former is called parosmia and the latter is called phantosmia. Many patients who experience the distorted smell, and smells that don't exist, describe the distorted smells as containing ‘ashes’, ‘smoke’, or ‘metallic’ elements. A new study showed that there are specific types of chemical compounds that trigger these types of parosmia [57].
Parker and colleagues, the authors of the paper [57] mentioned above, noticed the possibility that some common chemical compounds may trigger parosmia when they saw the list of food/beverage items that caused parosmia (Parker, personal communication). They hypothesized that these chemical compounds could trigger parosmia. Prior to that study, there were several hypotheses about parosmia. For example, 1) damage in the olfactory epithelium caused a reduction in the number of functioning olfactory sensory neurons so that the inputs are ‘incomplete’ and the smell seems distorted [58]. 2) The regeneration process includes rewiring of the axons, which become misguided causing the distorted smell [59]. These early hypotheses did not consider that there are some more specific chemical compounds that may trigger parosmia. This hypothesis was a very innovative one that only a chemist might notice.
The chemical compounds found to trigger parosmia (see Table 1 ) the most were structurally grouped into four types: thiols, trisubstituted pyrazines, methoxypyrazines, and disulfides [57]. The ones that highly triggered parosmia were the chemical compounds with lower threshold concentration. 2-Furanmethanethiol, which showed the highest score in triggering parosmia, is known to have the smell of coffee and roasted meat, and it is insoluble in water (PubChem 7363; CAS 98-02-2; FEMA 2493; C5H6OS). An interesting description in PubChem is that it is an “extremely powerful and diffusive odor which on dilution becomes agreeable, coffee-like, caramellic-burnt, sweet”, and insoluble in water. An intriguing part of this description is that this chemical compound has a ‘burnt’ smell. The second from the top chemical compound that triggered parosmia was 3-methyl-2-butene-1-thiol [57]. An intriguing part of the report on this chemical compound is that it is found to be the source of the ‘skunk-like’ smell of cannabis(60).Table 1 Chemical compounds reported to cause parosmia in Parker et al. (2022) [57].
Table 1 PubChem CID CAS FEMA Molecular formula Synonyms Smell/flavor MW solubility
2-Furanmethanethiol 7363 98-02-2 2493 C5H6OS furan-2-ylmethanethiol; furfuryl mercaptan; 2-furylmethanethiol; furfuryl thiol; and others coffee-like, caramellic-burnt, sweet (PubChem); disagreeable unpleasant at high concentration (Acros Organics, ACC#34573); coffee roasted meat (FEMA 2493) 114.17 Insoluble in water
3-Methyl-2-butene-1-thiol 146586 5287-45-6 3896 C5H10S Prenylthiol; 3-methyl-2-butene-1-thiol; prenyl mercaptan; 3-methyl-2-buten-1-thiol; and others almond, coffee, foxy, spice (FEMA 3896); skunk-like smell (Oswald et al. 2021)(60) 102.20 Insoluble in water
2,3-diethyl-5-methylpyrazine 28905 18138-04-0 3336 C9H14N2 2,3-diethyl-5-methyl-pyrazine; 2-methyl-5,6-diethylpyrazine Nutty, roasted, vegetable odor (PubChem); earth, meat, potato, roast (FEMA 3336) 150.22 6.7 ug/mL
2-methyl-3-furanthiol 34286 28588-74-1 3188 C5H6OS 2-methylfuran-3-thiol; 3-furanthiol; 2-methyl-3-furanethiol; and other Roasted meat (PubChem); fried, nut, potato, roasted meat (FEMA 3188) 114.17 Insoluble in water
2-ethyl-3,5-dimethylpyrazine 26334 13925-07-0 3150; 3149 C8H12N2 3,5-dimethyl-2-ethylpyrazine; 3-ethyl-2,6-dimethylpyrazine, 2,6-dimethyl-3-ethylpyrazine Toasted nut, chocolaty, sweet woody odor (PubChem); burnt type odor; roasted cocoa or nuts (Burdock and Carabin 2008); earth, nut, potato, roast (FEMA 3150); broth, earth, potato, roast (FEMA 3149) 136.19 Soluble in water, oils, organic solvents
2-isobutyl-3-methoxypyrazine 32594 24683-00-9 3132 C9H14N2O 3-isobutyl-2-methoxypyrazine; 2-methoxy-3-(2-methylpropyl)pyrazine; 2-methoxy-3-isobutylpyrazine Green bell pepper, green pea odor (PubChem); bell pepper, earth, green pepper, spice (FEMA 3132) 166.22 Soluble in water, organic solvents, oils
Table 1 shows the chemical compounds listed in Parker et al. (2022) [57] that are also included in PubChem. Most of the smells of these chemicals have the description frequently reported in parosmia, such as “toasted”, “burnt”, and “coffee”. This suggests a possibility that parosmia is caused by recognizing specific chemical compound(s) of the smell of, for example, coffee or roasted meat. It is sensed distorted but actually it may not be ‘distorted’ and instead it could be partial or incomplete smell of the food/beverages. Smells/flavors of foods and beverages contain hundreds of chemical constituents. It is possible that, as Leopold (2002) [58] hypothesized, it is caused by the limited number of functioning olfactory sensory neurons, and this also explains why it happens a few weeks to months after the damage in the olfactory epithelium occurred [61,62]. That is, it is maybe caused by the differences in the pace of recovery of the olfactory sensory neurons and such differences are causing sensing of a limited number of the types of odorants, and becomes recognized ‘distorted’.
If parosmia is an ‘incomplete’ perception of the smell of the foods by sensing some chemical constituents among the whole odor profile, how can we explain phantosmia, which is sensing smell that doesn't exist. Is it a peripheral phenomenon, in which olfactory receptors become activated without the ligands? Is it caused by wrongly activated central neural system? Or, are the olfactory receptors activated by chemical compounds that are not their original ligands? We still don't have the answers to these questions yet.
5 Treatments
5.1 Olfactory training
Since 2009, a series of studies showing the positive influences of inhaling chemical constituents of four different types of odors on facilitating recovery from ano/hyposmia have been published. The first group of studies focused on the major chemical constituent of the four different odors, i.e., phenyl ethyl alcohol (PEA) representing rose, eugenol representing clove, citronellal representing lemon, and eucalyptol representing eucalyptus [63] (for review, see Koyama and Heinbockel, 2022 [64]). These four types of odors were selected based on the odor prism hypothesis proposed by Henning (1916) [65] and each odor represented flowery smells, (rose), fruity (lemon), aromatic (clove), and resinous (eucalyptus). Basically, the participants smelled each of the multiple types of odors twice daily and 15 to 20 sec for each odorant. This means that olfactory training is not just sniffing for a second to test whether they can sense the smell but it is more like thoroughly exposing the olfactory system to these chemical compounds. Higher concentration of the smell was found to have stronger effects, in particular with patients who started olfactory training within 12 months after the onset of the disorder [66], and longer periods of olfactory training resulted in better effects [67]. Using fMRI, it was found that olfactory training can facilitate the recovery of the volume of grey matter at the limbic system and the thalamus of the brain [68]. It was also found that increasing the variety of the types of odorants had stronger effects on improving the olfactory sense [69]. Patel et al. [70] reported that using the essential oils of rose, lemon, eucalyptus, and clove instead of the single types of odorants for the olfactory training was similarly effective. Using essential oils for olfactory training, and not using single types of odorants is now very common in the era that large number of COVID-19 patients have lost their sense of smell completely or partially.
In addition to the four types of odors commonly used in the olfactory training, some studies have shown the possibility that stimulating trigeminal nerves could be important in the recovery of olfactory sense [71]. Frasnelli et al. (2007) [71] have shown that patients who are showing recovery of olfactory sense showed an increase in the responses to irritants (CO2 was used as stimulant) as well. Bensafi et al. (2007) [72] also reported that the presence of a trigeminal stimulus (CO2) during odor encoding alters the neural representation of the pure odor. Oleszkiewicz et al. (2018) [73] has reported that trigeminal training using CO2 increases the self-rated nasal patency, which suggests that this can help if olfactory dysfunction is associated with nasal patency.
This olfactory training has been used not only in patients with olfactory dysfunction but also in healthy elderly people and children. In the elderly, it has the potential to delay the decrease in the sense of smell due to ageing, although it will not prevent a gradual loss [74]. In the children, not only the ability to identify the odor types was increased but also the threshold concentration to detect the odors that were not used in training became lower, indicating the higher sensitivity to odors in general [75]. This suggests that olfactory training has benefits in all age groups and not only for recovery from ano/hyposmia caused by COVID-19 and other post-viral olfactory dysfunction but also for healthy people, in enhancing olfactory sensitivity.
5.2 Supplements/medicines/herbal medicines
During over three years since the outbreak of COVID-19, an extensive amount of clinical data has been accumulated because of the uniquely high occurrence of chemosensory dysfunction. Despite such increase in the data, the treatment strategies for post-COVID-19 olfactory dysfunction (PCOD) are still limited, and current evidence supports only olfactory training as a first-line intervention [76], [77], [78]. A variety of drugs and supplements have been proposed for the treatment of non-conductive smell disorders, including post-viral olfactory dysfunction, such as, corticosteroids (systemic, topical), caroverine [79], theophylline [80], minocycline [81], insulin [82], tokishakuyakusan (herbal medicine) [83], sodium citrate [84], alpha-lipoic acid [85], vitamin A [86], and zinc [87], [88], [89], [90], as some of the examples. Nevertheless, the effectiveness of most of these treatments remains uncertain.
Steroid therapy was initially considered negative due to concerns about the promotion of viral rebound and association with adverse events including acute respiratory distress syndrome [91]. However, at present, corticosteroids are likely the most effective drugs in reducing immunopathological damage [92]. Corticosteroids come in a variety of forms, including injectable steroids, oral steroids, nasal steroids, and nasal sprays. According to two consensuses [76,93], limited intranasal or oral corticosteroid course may be effective for patients with PCOD. Due to the multi-system nature of SARS-CoV2, multidimensional risk benefit analysis should occur before initiation of oral steroid therapy. Topical steroids include nasal drops, nasal irrigation, and intranasal corticosteroid sprays (ICS). Topical steroids may ameliorate olfactory impairment in patients with COVID-19, but it is unclear whether they contribute to full olfactory recovery [94]. Posture is also important when using nasal steroid drops, and the Kaiteki position [95] (Figure 1 ) may help the steroids to reach the olfactory cleft, facilitating the drug's effect. While nasal irrigation, rather than ICS, may be more effective at treating PCOD due to increased penetration to the olfactory cleft [96], the current consensus is that ICS should be used for patients with PCOD symptoms lasting longer than 2 weeks [76,93].Figure 1 Kaiteki position (a kind gift from Dr. Yuko Yamanaka, drawn based on the figure in [95]). To administer nasal drops to the right nostril, let the patient lie down with the left side down and turn the neck and the head to the right about 20 to 30° (A), and then tilt the head down making the jaw up about 20 to 40° (B) so that the right nostril slightly faces up. For the left nostril, let the patient lie down with the right side down, turn the neck and the head to the left 30°, and tilt the head down 30° so that the left nostril slightly faces up. ‘Kaiteki‘ means comfortable in Japanese language.
Figure 1
Some studies suggest that experiencing a variety of food textures [97] may have indirect effects on facilitating recovery from olfactory dysfunction. The senses of smell and taste are involved in the pleasure of eating and cooking. Patients with chemosensory dysfunction often lose appetite and decrease nutritional intake which can affect their recovery. The process of eating foods with different textures may stimulate a variety of different sensory modality, and such stimulation may have positive influences on patients with anosmia [97,98]. Harder foods require longer time to chew and ingest, and the hard textures may stimulate trigeminal nerves [97].
In summary, the most effective treatment for PCOD at present is olfactory training, and no other treatments as effective as olfactory treatment have been identified so far. Combinations of olfactory training and ICD may be more therapeutic than olfactory training alone. Additional study is required to define specific treatment recommendations and expected outcomes for PVOD in the setting of COVID-19.
6 Conclusion: What is next
Although post-viral chemosensory dysfunction has been known for decades, COVID-19 has extremely increased the number of patients with chemosensory dysfunction. What we can see by reviewing the studies published during these several years is that the causation and the level of severity can be different depending on the patient. Such differences suggest that the strategy of treatment would require adjustments depending on these differences in order to expect it to bring improvements effectively. This could be the time to develop a treatment strategy based on such differences to provide a ‘precision medicine’ that matches the needs of each patient.
Uncited References
[60]
Acknowledgements
We would like to express our appreciation to Mr. Gary Lucas for editing the paper. We are also grateful to Dr. Yuko Yamanaka for the kind gift of picture she drew, which we used in Figure 1.
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| 0 | PMC9731926 | NO-CC CODE | 2022-12-14 23:35:53 | no | Auris Nasus Larynx. 2022 Dec 9; doi: 10.1016/j.anl.2022.12.002 | utf-8 | Auris Nasus Larynx | 2,022 | 10.1016/j.anl.2022.12.002 | oa_other |
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J Infect
J Infect
The Journal of Infection
0163-4453
1532-2742
The British Infection Association. Published by Elsevier Ltd.
S0163-4453(22)00693-4
10.1016/j.jinf.2022.12.003
Letter to the Editor
Safety and immunogenicity of a bivalent SARS-CoV-2 protein booster vaccine, SCTV01C in adults previously vaccinated with inactivated vaccine: A randomized, double-blind, placebo-controlled phase 1/2 clinical trial
Hannawi Suad a
Saifeldin Linda b
Abuquta Alaa a
Alamadi Ahmad c
Mahmoud Sally A. d
Li Jian e
Chen Yuanxin e
Xie Liangzhi ef⁎
a Internal Medicine Department, Al Kuwait-Dubai (ALBaraha) Hospital, Dubai, United Arab Emirates
b General Surgery Department, Al Kuwait-Dubai (ALBaraha) Hospital, Dubai, United Arab Emirates
c Ear, Nose and Throat Department (ENT), Al Kuwait-Dubai (ALBaraha) Hospital, Dubai, United Arab Emirates
d Biogenix labs, G42 Healthcare, Dubai, United Arab Emirates
e Beijing Engineering Research Center of Protein and Antibody, Sinocelltech Ltd., Beijing, China
f Cell Culture Engineering Center, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing China
⁎ Corresponding author at: Beijing Engineering Research Center of Protein and Antibody, Sinocelltech Ltd., No.31 Kechuang 7th Street, BDA, Beijing, China; Cell Culture Engineering Center, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
9 12 2022
9 12 2022
6 12 2022
© 2022 The British Infection Association. Published by Elsevier Ltd. All rights reserved.
2022
The British Infection 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.
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pmcTo the editor,
In this journal, Liu and colleagues evaluated the immunogenicity of seven COVID-19 vaccines given as third dose boosters following two doses of primary series of ChAdOx1 nCov-19 or BNT162b2.1 However, with the emergence of the SARS-CoV-2 variants, existing evidence shows the waning protection of COVID-19 primary/booster vaccination and the reduced effectiveness of the monovalent vaccines developed based on the original SARS-CoV-2 strain against COVID-19.2 Recent evidences indicated that multivalent booster vaccination could provide significant additional protection against symptomatic SARS-CoV-2 infection in persons who had previously received monovalent vaccine doses.3 , 4 Herein, with this Phase 1/2 safety and immunogenicity clinical trial, we assessed a novel SARS-CoV-2 bivalent vaccine, SCTV01C, given as a heterologous booster for people who had previously received the primary series of an inactivated vaccine. The data showed that SCTV01C was well tolerated with reactogenicity profile that was comparable to that of inactivated vaccines, and induced substantial neutralizing antibody responses to Delta and Omicron variants.
SCTV01C is a recombinant protein vaccine composed of the spike protein extracellular domain (S-ECD) of Alpha (B.1.1.7) and Beta (B.1.351) variants, and adjuvanted with a squalene-based oil-in-water emulsion SCT-VA02B. The bivalent design increases the coverage of epitope mutations and may provide improved cross-strain protection. Preclinical studies showed that SCTV01C remained stable at 25 °C for six months and at 2–8 °C for over 24 months, and induced potent T-helper-1-biased T-cell responses and broad-spectrum neutralizing antibodies against a panel of genetically distinct lineages of SARS-CoV-2 variants, including D614G, Alpha, Beta, Delta, Gamma, Omicron, Lambda, Mu, Iota, Kappa, Epsilon, C.36.3 B1.618 and 20I/484Q.5 , 6
Between January 18, 2022, and April 5, 2022, 234 adults who had previously received primary series of BBIBP-CorV (Sinopharm inactivated vaccine) 3–24 months earlier and had no history of infections with SARS-CoV-2 were randomly assigned to receive placebo (normal saline, n = 75), 20 µg SCTV01C (n = 79) or 40 µg SCTV01C (n = 80) and completed at least a 4-week follow-up (Supplementary Methods). No deaths or hospitalizations, serious adverse events (SAEs) and AEs of special interest (AESIs) were reported. The overall occurrence of treatment related AE (TRAE) was 27.7%. 25.3%, 30.4%, and 25.0% participants in the placebo, 20 μg SCTV01C and 40 μg SCTV01C groups experienced at least one TRAE within 28 days. 20 μg and 40 μg SCTV01C showed similar occurrence of solicited AEs (24.1% vs. 17.5%). The most common solicited AEs with SCTV01C were injection-site pain (11.9%) and pyrexia (6.3%). There were 3 reports of Grade 3 pyrexias (1.9%) in SCTV01C groups (Table 1 and Supplementary Fig. 1). All AEs resolved within 7 days without intervention. The overall reactogenicity profile of SCTV01C was similar to that of reported primary and booster vaccination with the inactivated vaccines (CoronaVac showed 6–18% of solicited ARs and 1%−16% of injection-site pain. BBIBP-CorV showed 12.72% of solicited ARs, 3.98% of injection-site pain and 4.2% of headaches)4 , 7, and also consistent with that of reported recombinant protein vaccines, NVSI-06–084 given as a heterologous booster primed with the BBIBP-CorV series.Table 1 Adverse events and reactions after the booster vaccination.
Table 1AE SCTV01C
Saline 20 μg 40 μg Total
(N = 75) (N = 79) (N = 80) (N = 159)
n (%) n (%) n (%) n (%)
TEAEs 21 (28.0) 29 (36.7) 23 (28.8) 52 (32.7)
Vaccine-related TEAEs 19 (25.3) 24 (30.4) 20 (25.0) 44 (27.7)
AEs within 0–7 days 13 (17.3) 18 (22.8) 15 (18.8) 33 (20.8)
AEs within 0–28 days 19 (25.3) 23 (29.1) 20 (25.0) 43 (27.0)
Grade 3 of above AEs 0 0 3 (3.8) 3 (1.9)
Grade 3 of Vaccine-related AEs 0 0 3 (3.8) 3 (1.9)
Solicited AEs
Any 12 (16.0) 19 (24.1) 14 (17.5) 33 (20.8)
Grade ≥3 0 0 3 (3.8) 3 (1.9)
Solicited Local AEs
Any 1 (1.3) 13 (16.5) 9 (11.3) 22 (13.8)
Grade ≥3 0 0 0 0
Injection site pain 1 (1.3) 10 (12.7) 9 (11.3) 19 (11.9)
Injection site pruritus 0 2 (2.5) 1 (1.3) 3 (1.9)
Injection site swelling 0 2 (2.5) 0 2 (1.3)
Injection site erythema 0 1 (1.3) 0 1 (0.6)
Solicited Systemic AEs
Any 11 (14.7) 8 (10.1) 6 (7.5) 14 (8.8)
Grade ≥3 0 0 3 (3.8) 3 (1.9)
Pyrexia 6 (8.0) 6 (7.6) 4 (5.0) 10 (6.3)
Headache 4 (5.3) 1 (1.3) 1 (1.3) 2 (1.3)
Fatigue 0 1 (1.3) 0 1 (0.6)
Insomnia 0 0 1 (1.3) 1 (0.6)
Myalgia 2 (2.7) 1 (1.3) 0 1 (0.6)
Pruritus 1 (1.3) 1 (1.3) 0 1 (0.6)
Vaccine-related Solicited AEs 12 (16.0) 18 (22.8) 14 (17.5) 32 (20.1)
Grade ≥3 0 0 3 (3.8) 3 (1.9)
Pyrexia 0 0 3 (3.8) 3 (1.9)
Unsolicited AEs
Any 12 (16.0) 16 (20.3) 15 (18.8) 31 (19.5)
Grade ≥3 0 0 0 0
TEAE= treatment-emergent adverse event.
Viral neutralizing antibody responses are highly predictive of immune protection from symptomatic SARS-CoV-2 and have been used to infer COVID-19 vaccine effectiveness.8 , 9 In this study, the primary analyses evaluated the geometric mean concentration (GMC) of the specific anti-spike protein and the geometric mean titer (GMT) of neutralizing antibody against Delta (B.1.617.2) and Omicron (B.1.1.529).
At day 28 post injection, the GMCs of the specific spike binding IgG (converted to WHO International Binding Antibody Units, BAU) were 322 (95% CI: 245–424), 4736 (95% CI: 3905–5745), and 5852 (95% CI: 4942- 6930) BAU/ml, with 0.8, 12.7 and 12.2-fold over baseline (D0), for the placebo, 20 µg SCTV01C and 40 µg SCTV01C groups, respectively (Fig. 1 A and Supplementary Table 1). The GMCs of SCTV01C were comparable to those of heterologous BNT162b2 booster in participants who had received primary series of inactivated vaccines (4349 BAU/ml), and higher than those of homologous prime/booster with CoronaVac (312 BAU/ml), and heterologous booster with the adenovirus vaccines (ChAdOx1: 2173 BAU/ml and Ad26.COV2: 2184 BAU/ml).10 Fig. 1 A: GMC (BAU/mL) of anti- spike protein IgG. IgG were measured using Enzyme-linked Immunosorbent Assay (ELISA) and converted to geometric mean concentration (GMC) using WHO assigned International Binding Antibody Units (BAU); B: GMTs of neutralizing antibodies against live SARS-CoV-2 Delta and Omicron. GMTs were measured using 50% plaque reduction neutralization test (PRNT50); C: Fold increase of neutralizing antibodies with SCTV01C against live SARS-CoV-2 Delta Omicron in groups with high, medium and low baseline titers; D: Th1 (IFN-γ release) and Th2 (IL-4 release) responses. The peripheral blood mononuclear cells were collected before and on day 14 after booster vaccination. The number of specific T cells with secretion of IFN-γ (Th1) and IL-4 (Th2) were measured with spot per 10⁶ PBMC using enzyme-linked immunospot (ELISpot) assay. Note: *p<0.05; *** p<0.0001.
Fig. 1
The Day 28 GMTs of neutralizing antibody against live Delta variant were 296 (95% CI: 221–398), 3830 (95% CI: 3144- 4664), and 3953 (95% CI: 3364–4644), with 0.9, 12.9 and 11.5-fold over baseline, and GMTs against Omicron BA.1 were 58 (95% CI: 40–85), 840 (95% CI: 665–1060), and 901 (95% CI: 751–1081), with 0.8, 12.2 and 11.1-fold over baseline for the placebo, 20 μg SCTV01C and 40 μg SCTV01C, respectively (Fig. 1B and Supplementary Table 1). The GMT levels with SCTV01C compared favorably to those reported in the literatures (The peak GMTs against Delta and Omicron variants were 1653 (95% CI:1118–2443) and 223 (95% CI:108–458) after a booster dose of BNT162b2), although cautions should be taken in interpreting data from different labs due to significant assay variabilities. 7 , 10
Post hoc analyses evaluated the impact of the pre-existing SARS-COV-2 immunity on the neutralizing antibody responses. The participants were assigned to different groups based on their baseline titers. Participants with the GMTs at baseline below the lower limit of quantitation (LLOQ: 20) was considered as low baseline titer, GMTs in the range of 1 to 4-fold over LLOQ (GMT: 20–80) were considered as medium baseline titer, and GMTs of 4-fold over LLOQ (GMT > 80) for Omicron or 8-fold over LLOQ (GMT > 160) for Delta were considered as high baseline titer. The Day 28 GMTs against Delta with SCTV01C were 4854, 3538 and 3848 with 512.0, 61.1, and 5.2-fold over baseline, and GMTs against Omicron were 851, 918 and 846 with a fold of 144.7, 18.5 and 2.3 over baseline for the low, medium and high baseline titer groups, respectively (Fig. 1C). Notably, SCTV01C elicited consistently high GMTs to Delta and Omicron, irrespective of baseline GMTs levels of the participants.
The peripheral blood mononuclear cells were collected to assess specific Th1 (IFN-γ release) and T2 (IL-4 release) responses before and 14 days after injection. The number of specific IFN-γ secreting T-cells (Th1) increased by 0.7, 10.7 (p<0.0001) and 3.4- (p<0.05) fold from the baseline, and the number of IL-4 secreting T-cells (Th2) increased by 0.8, 4.6 (p<0.05) and 2.3- fold from the baseline for saline, 20 µg SCTV01C and 40 µg SCTV01C, respectively (Fig. 1D and Supplementary Table 2). The results suggested that SCTV01C booster induced T-helper-1 biased CD4+T cell responses.
In summary, the current data showed that SCTV01C booster was safe with reactogenicity profile that was comparable to that of the inactivated vaccines, and induced consistently high neutralizing antibody responses to Delta and Omicron variants. SCTV01C may be a new tool against emerging variants of SARS-CoV-2.
Declaration of Competing Interests
Dr. Liangzhi Xie, Dr. Jian Li, and Dr. Yuanxin Chen are employees of Sinocelltech Ltd. and have ownership or potential stock option interests in the company. All authors declare no other conflicts of interest.
Appendix Supplementary materials
Image, application 1
Data availability
Anonymized participant data will be made available when the trials are complete, upon requests directed to the corresponding author.
Funding
This study was sponsored by 10.13039/501100012401 Beijing Science and Technology Plan Project (Z221100007922012) and the 10.13039/501100013290 National Key Research and Development Program of China (2022YFC0870600).
Acknowledgments
This Article and clinical trial were funded by Sinocelltech Ltd. We thank the contribution from the CRO team of PDC FZ-LLC, for their hard work, support, and guidance of the trial; Mr. Bo Zhong, the project manager of SCTV01C-01-1 trial operation teams, for his committed dedication to managing and running of the trial; Mr. Adham Rezk and Revonbio B.V for their vaccine consultancy, strategy and coordination; and Dr. Dongfang Liu for his critical and timely review of the trial data. We also acknowledge medical writing and editorial support by Dr. Adam Abdul Hakeem Baidoo and Dr. Miaomiao Zhang.
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jinf.2022.12.003.
==== Refs
References
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2 Andrews N. Stowe J. Kirsebom F. Toffa S. Rickeard T. Gallagher E. Covid-19 vaccine effectiveness against the Omicron (B.1.1.529) variant N Engl J Med 386 2022 1532 1546 35249272
3 Chalkias S. Harper C. Vrbicky K. Walsh S.R. Essink B. Brosz A. A bivalent Omicron-containing booster vaccine against Covid-19 N Engl J Med 387 2022 1279 1291 36112399
4 Kaabi N.A. Yang Y.K. Du L.F. Xu K. Shao S. Liang Y. Safety and immunogenicity of a hybrid-type vaccine booster in BBIBP-CorV recipients in a randomized phase 2 trial Nat Commun 13 2022 3654 35760812
5 Wang R. Huang X. Cao T. Sun C. Luo D. Qiu H. Development of a thermostable SARS-CoV-2 variant-based bivalent protein vaccine with cross-neutralizing potency against Omicron subvariants Virology 576 2022 61 68 36174448
6 Wang R. Sun C. Ma J. Yu C. Kong D. Chen M. A bivalent COVID-19 vaccine based on alpha and beta variants elicits potent and broad immune responses in mice against SARS-CoV-2 variants Vaccines 10 2022 702 35632456
7 Zeng G. Wu Q. Pan H. Li M. Yang J. Wang L. Immunogenicity and safety of a third dose of CoronaVac, and immune persistence of a two-dose schedule, in healthy adults: interim results from two single-centre, double-blind, randomised, placebo-controlled phase 2 clinical trials Lancet Infect Dis 22 2022 483 495 34890537
8 Zeng G. Wu Q. Pan H. Li M. Yang J. Wang L. Neutralizing antibody levels are highly predictive of immune protection from symptomatic SARS-CoV-2 infection Nat Med 27 2021 1205 1211 34002089
9 Khoury D.S. Cromer D. Reynaldi A. Schlub T.E. Wheatley A.K. Juno J.A. Neutralising antibody titres as predictors of protection against SARS-CoV-2 variants and the impact of boosting: a meta-analysis Nat Med 3 2022 e52 e61
10 Costa Clemens S.A. Weckx L. Clemens R. Almeida Mendes A.V. Ramos Souza A. Silveira M.B.V. Heterologous versus homologous COVID-19 booster vaccination in previous recipients of two doses of CoronaVac COVID-19 vaccine in Brazil (RHH-001): a phase 4, non-inferiority, single blind, randomised study Lancet 399 2022 521 529 35074136
| 36509358 | PMC9731927 | NO-CC CODE | 2022-12-15 23:16:04 | no | J Infect. 2022 Dec 9; doi: 10.1016/j.jinf.2022.12.003 | utf-8 | J Infect | 2,022 | 10.1016/j.jinf.2022.12.003 | oa_other |
==== Front
J Transp Geogr
J Transp Geogr
Journal of Transport Geography
0966-6923
1873-1236
Elsevier Ltd.
S0966-6923(22)00230-7
10.1016/j.jtrangeo.2022.103507
103507
Article
Jobs-housing relationships before and amid COVID-19: An excess-commuting approach
Chen Ruoyu a
Zhang Min b
Zhou Jiangping c⁎
a School of Urban Planning and Design, Peking University Shenzhen Graduate School, China
b Cities Research Institute, Griffith University
c Department of Urban Planning and Design, Faculty of Architecture, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
⁎ Corresponding author.
9 12 2022
9 12 2022
1035076 7 2022
13 11 2022
4 12 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.
The outbreak of COVID-19 and subsequent pandemic containment measures have significantly affected our daily life, which has been extensively examined in the existing scholarship. However, the existing scholarship has done little on the jobs/housing relationship impacts of COVID-19. We attempted to fill this gap by looking into this using an excess-commuting approach. The approach allows us to analyse a series of jobs-housing matrices based on the location-based service big data of around fifty million individuals in the Pearl River Delta (PRD), China before and amid COVID-19. In the PRD, a zero-COVID policy was implemented, which presents a distinct and interesting context for our study. We found that after the COVID-19 outbreak: (1) residences and employment became more centrally located in downtowns, which is opposite to the trend of suburbanization elsewhere; (2) in the whole PRD, the minimum and maximum commutes became smaller while the actual commute became larger, indicating the simultaneous presences of some paradoxical phenomena: a better spatial juxtaposition of jobs and housing, more compressed distribution of jobs and housing, and longer average actual commutes; (3) inter-city commutes from and to large cities were significantly confined and decreased, while new inter-city commuters in smaller cities emerged; (4) it is more likely for the less-educated and female workers to observe smaller minimum commutes amid COVID-19. This paper illustrates the potential of big data in the longitudinal study of jobs-housing relationships and excess commuting. It also produces new insights into such relationships in a unique context where stringent anti-COVID-19 policies have been continuously in place.
Keywords
Jobs-housing relationship
Change
COVID-19
Big data
China
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pmc1 Introduction
Coronavirus disease 2019 (COVID-19) has significantly threatened the health of human beings and affected multiple dimensions of our daily life. To mitigate the spread of COVID-19, governments have introduced and implemented different non-pharmaceutical interventions (e.g., stay-at-home orders, travel restrictions, and lockdown), which are proved to be effective but costly (Dehning et al., 2020; Gostin and Wiley, 2020). Most of the interventions affect mobility patterns and then mitigate the infection risk on social contact networks (Eubank et al., 2004; Schlosser et al., 2020). Apart from the disease moderation effects, those measures have also brought impacts on transportation systems, e.g., a decreased transit ridership (Parker et al., 2021; Zhao and Gao, 2022), commuting trips (Currie et al., 2021), and non-essential trips (Abdullah et al., 2021). Thanks to the effective treatments and emerging vaccines, the world is gradually recovering from a mobility perspective (Dai et al., 2021; Dube et al., 2021). However, those interventions will bring attitudinal changes and long-lasting impacts on people's behavior of residence choice, daily visitation, and commutes.
Commutes are regular trips between residences and workplaces, which have a lot to do with urban spatial structure, transportation system performance, and social equity (Murphy and Killen, 2011; Shen, 2000; Zhang et al., 2019). Commuting patterns can directly represent the spatial relationships between jobs and housing, which in return have impacts on commuting choices (Yang, 2008). Because it is far more difficult and costly to relocate than to adapt daily travel behavior, the jobs-housing relationships are therefore more statable than daily mobility patterns. Mobility responses might happen in a few days (e.g., Kraemer et al., 2020; Parker et al., 2021; Zhou et al., 2021), but it can take decades for the jobs-housing relationship, especially the spatial distribution of jobs and housing, to change (e.g., Hu and Wang, 2016; Yang et al., 2012). Besides spatiality, social determinants of commuting patterns are worth attention (Shen, 2000). Specifically, the static and dynamic commuting patterns of subpopulations are a function of race, social status, education, and income level (Li et al., 2021; Yang, 2008). Knowing this function would enable us to better consider disparity in commuting and possible equity implications. Prior to the advent of big and/or open data that are passively and continuously produced and updated and that cover a large and even full set of the population, it has been challenging if not impossible for people to trace the evolution of the function and to see its time-sensitive variance across different subpopulations.
To fill this gap, we tried to characterize the jobs-housing relationship changes before and amid COVID-19 with a location-based service (LBS hereafter) dataset, which is acquirable in the market. We did not challenge existing knowledge concerning changes in daily mobility and travel behavior but added a new dimension: internal structural changes in the locational choice of residences and workplaces and their relative relationships before and amid COVID-19. Such changes can be resulted from pandemic mitigation policies, perceived health risks, and subsequent adaptations. We took the Pearl River Delta (“PRD” hereafter for shorthand) in southern China as our case, where the zero-COVID policy was implemented and the number of confirmed cases was relatively low (Chen and Chen, 2022). The case study, we hope, can provide findings from a populous country that carried out different measurements to deal with the pandemic from the west. These findings can supplement what we have known from the existing scholarship, the regional focuses of which were predominantly the west (e.g., Beck and Hensher, 2020; Irawan et al., 2021; Parker et al., 2021).
In this study, we investigated two research questions: (a) What were the overall trends of jobs-housing relationships in PRD after the outbreak of COVID-19, were they identical to the major findings in other contexts? (b) Were the changes in jobs-housing relationships comparable across different subpopulations? If not, how much was the divergence? We hypothesize that (a) residences and workplaces became more centrally located to avoid inter-region or inter-city travel, which was severely restricted during the pandemic, leading to a better juxtaposition of jobs and employment; (b) the jobs-housing changes varied across subpopulations with different socio-economic statuses and spatial characteristics.
To empirically examine the above hypotheses, we acquired a series of commuting trip matrices for the PRD, China, which were retrieved from the LBS big data. The matrices cover the three fourth quarters of 2019, 2020, and 2021, respectively. Approximately 50 million commuters were captured in each of the matrices.
In the remainder of the article, Section 2 reviews the relevant existing literature. Section 3 describes the empirical case and methods. Section 4 presents the results. Section 5 concludes.
2 Literature review
Jobs-housing relationships, i.e., the numbers and spatial distribution of employment and housing opportunities have been intensively studied in the existing scholarship. This is because many uphold the perspective that if there is a jobs-housing balance, i.e., a reasonable ratio of employment and housing opportunities across subdivisions of a city or region, most workers would enjoy a shorter commute and many negative consequences of long commutes such as traffic congestion, wasteful energy consumption, and air pollution can be decreased (Cervero, 1989). The excess commuting approach is frequently adopted for people to measure and compare jobs-housing relationships in a city across times or cross cities for a fixed time or across time. In the ensuing subsections, we first review existing scholarship on jobs-housing relationships based on the excess commuting approach. Then we focus on such scholarship in the Chinese context that is different significantly from that of the west, in which one can find the most literature on jobs-housing relationships based on the excess commuting approach.
2.1 Jobs-housing relationships and the excess-commuting approach
Commuting trips encapsulate a notable portion of daily travel demand and contribute to peak hours' congestion (Hu and Wang, 2016). The study of these trips, therefore, has been recurrent in fields such as urban planning, transportation, and geography (Ma and Banister, 2006a, Ma and Banister, 2006b). To mitigate commute-related issues such as traffic congestion and excessive travel time, the jobs-housing balance, i.e., the quantitative and/or qualitative match between jobs and housing opportunities, has been advocated (Cervero, 1989; Gordon et al., 1989; Kim, 2008). The efficacy of the jobs-housing balance in reducing commute-related issues, however, can be limited (Giuliano and Small, 1993; Wachs et al., 1993). Nevertheless, multiple frameworks and metrics for measuring the relationships between residences and workplaces have been proposed (Yang and Ferreira, 2005), e.g., the ratio of jobs to housing, which can be both measured at the local (Cervero, 1989) and regional levels (Levinson, 1998). From a systematic perspective, the excess-commuting approach can benchmark and evaluate the jobs-housing relationships with less bias (Yang and Ferreira, 2005). Such an approach is also more suitable for cross-city and longitudinal comparisons (Kanaroglou et al., 2015). Our study is a case in point.
The approach of benchmarking jobs-housing relationships using commuting costs was pioneered by Hamilton and Röell (1982). He used the optimum commute from the monocentric urban model to gauge the surplus (namely the wasteful commuting) of the actual commute (Tact). White (1988) improved the calculation of minimum commute (Tmin, namely optimum commute in Hamilton's paper) by adopting the linear optimization method. This method has since then dominated the excess-commuting scholarship. Tmin is achieved when (a) residences and jobs are considered homogeneous, (b) workers can exchange jobs and/or residences without losing any utilities, and (c) each worker is assigned to the nearest possible job from his/her residence given a fixed jobs/housing distribution in the study area. The differences between the Tmin and Tact are considered “excess commuting”. After Hamilton and Röell (1982) and White (1988), the scholarship on excess commuting has mushroomed, providing new lenses for us to examine issues such as commuting efficiency/economy (Horner, 2002; Murphy and Killen, 2011), jobs/housing balance (Giuliano and Small, 1993; Zhou et al., 2016), and urban spatial structure evolution (Schleith et al., 2016; Yang, 2008).
In a nutshell, the excess-commuting scholarship problematizes jobs-housing relationships into three sets of concepts and develops corresponding indicators: first, the (appropriate) lower or upper bounds for the average/total commute costs given known jobs-housing distribution— Tmin (White, 1988), Tmax (Horner, 2002), and random commute (Trand) (Charron, 2007) are cases in point; second, various actual or predicted commutes, e.g., actual commute by distance (Frost et al., 1998), time (Giuliano and Small, 1993), or modes of travel (Murphy and Killen, 2011); third, the differences between the first two or some ratios based on them, e.g., commuting capacity used (Cu) (Horner and Murray, 2002), commuting economy (Ce) (Murphy and Killen, 2011), and commuting efficiency gains (Zhou et al., 2018). More details are as follows.
As the earliest commuting benchmark in the excess-commuting scholarship, Tmin measures the absolute degree of the spatial juxtaposition of residences and employment, i.e., the existing or planned jobs/housing distribution. After this, more commuting benchmarks were developed. Tmax was proposed by Horner (2002), which forms the duality of the same optimization problem together with Tmin. It measures the maximum commute cost if workers prefer the furthest residence possible. Because Tmin and Tmax are extreme and seldom exist in the real world, Charron (2007) proposed Trand. Trand measures the average cost of many possible commuting trip matrices. Compared to Tmax, Trand is a more reasonable upper bound for us to gauge excess commuting. Trand can be calculated using Yang (2005)’s approach of proportionally matched commute (Tpro), according to Kanaroglou et al. (2015).
Tact is relatively straightforward. It is the average commuting time or distance in the real world or simulation based on hypothetical data. The commuting time can be self-reported (Yang, 2008) and predicted by travel demand models (Giuliano and Small, 1993) or from online map services (Zhang et al., 2021a, Zhang et al., 2021b). The commuting distance can be the Euclidian distance (Zhou et al., 2014a, Zhou et al., 2014b) and network distance (Zhou et al., 2020a, Zhou et al., 2020b).
Commuting efficiency can be measured using metrics such as EC, Cu, and (normalized) Ce. As shown in Eq. (1), EC measures how much Tact deviates from Tmin (Hamilton and Röell, 1982; White, 1988), which is the simplest and most long-standing measure of commuting efficiency. By introducing Tmax, Horner (2002) proposed Cu and argued that Cu and EC together can paint a more accurate picture of commuting efficiency. The calculation of Cu is given in Eq. (2). Murphy and Killen (2011) proposed a commuting economy metric (Ce) and normalized commuting economy (NCe) using Trand instead of Tmax as the upper bound. Eqs. (3), (4) show how Ce and NCe can be calculated, respectively.(1) EC=Tact−TminTact∗100
(2) Cu=Tact−TminTmax−Tmin∗100
(3) Ce=Trand−TactTrand∗100
(4) NCe=Trand−TactTrand−Tmin∗100
Because the original excess-commuting approach oversimplifies the reality, more and more scholars have extended the framework by accounting for more influencing factors of commuting costs, for instance, congestion (Zhou et al., 2020a, Zhou et al., 2020b), trip-chain (Hu and Li, 2021), and time of the day (Niedzielski et al., 2020) to improve its policy relevance. Meanwhile, the excess-commuting metric calculation inevitably faces the modifiable areal unit problem (MAUP) when workplaces or residences are aggregated into some unit of analysis. According to Horner and Murray (2002) and Niedzielski et al. (2013), the newly developed indicators (e.g., Tmax, Cu) are less likely to be subject to MAUP than the traditional indicators (e.g., Tmin, EC).
It is because of all the aforementioned efforts, the excess commuting approach has gradually become a widely accepted framework to evaluate and compare the jobs-housing relationships, commuting patterns, and urban structure across different subpopulations and times. Plus, consideration of mode choice, subpopulation, and time can greatly increase the approach's applicability and relevance. Last but not least, we need to look at several excess-commuting metrics (e.g., EC, Tact, and Tmin) simultaneously to draw reliable conclusions concerning jobs-housing relationships.
An essential dimension of jobs-housing relationships is these relationships' changes over time. Despite its great significance, the literature on the temporal variation of jobs-housing relationships based on the excess commuting approach is rare (see the summary in Table A.1). The lack of comparable datasets across time is one of the reasons. In the case of the U.S., the Census Transportation Planning Package (CTPP) and Longitudinal Employer-Household Dynamics (LEHD) datasets generated more than half of the studies in Table A.1 (e.g., Horner and Schleith, 2012; Hu and Wang, 2016; Schleith et al., 2016; Yang, 2005). These studies have analyzed the temporal changes in the performance of commuting and corresponding jobs-housing relationships in multiple US cities. Some developed cities in Asia and Europe, e.g., Dublin (Murphy and Killen, 2011), Seoul (Ma and Banister, 2006a, Ma and Banister, 2006b), and London (Frost et al., 1998) have also been the subjects of the existing scholarship. In these cities, scholars were able to access multi-year survey or simulation- or prediction-based datasets to examine the temporal changes of different excess-commuting metrics.
The studies mentioned above examined the temporal changes across years with traditional datasets, which might or might not cover the periods before and after some important mega events such as COVID-19. The only exception is Zhou and Murphy (2019), which calculated the day-to-day excess-commuting patterns with smartcard big data and found that the patterns were stable except on some special public holidays in Brisbane, Australia. Nevertheless, Zhou and Murphy (2019) did not consider abrupt/M events' impacts. To our best knowledge, there is still no research concerning the temporal changes in excess-commuting metrics in developing countries like China, especially given abrupt/M events such as COVID-19.
2.2 The literature in the Chinese context
Chinese cities have witnessed intensive spatial and institutional transformations in the past few decades (Yang, 2006; Zhou et al., 2014a, Zhou et al., 2014b). As for the spatial transformation, the rapid urbanization and suburbanization processes significantly changed the supply and spatial configuration of land use and transportation. This has profoundly shaped and reshaped jobs-housing relationships and commuting patterns (e.g., Gao et al., 2019; Hu et al., 2018; Wang and Zhou, 2017). Institutional factors like Danwei (working units) and Hukou (household registration)1 still had significant but weakening effects on jobs-housing relationships, because they shaped the preference of and imposed constraints on people's choices of residences and workplaces (e.g., Li et al., 2021). In the process of transition, both the government and market forces had important impacts on transportation and land use and thus jobs-housing relationships and commuting patterns (Hu et al., 2019).
The jobs-housing relationship literature using the excess-commuting approach in China is rather limited. Liu et al. (2008) were among the first cohorts in estimating the Tmin, Tact, and EC for the city of Guangzhou, China. In 2016, they replicated a similar study by considering more time spans and more social groups (Liu and Hou, 2016). By integrating institutional factors, Zhou et al., 2016, Zhou et al., 2014a, Zhou et al., 2014b uncovered the excess-commuting impacts of Danwei in Xi'an, China and the industry park in Suzhou, China. They found that Danwei had positive impacts on reducing excess commuting while industry park, which was also planned to be self-contained, did not. Using the excess-commuting indicators, Xu et al. (2019) evaluated the effects of different transportation policies on excess-commuting metrics in Xiamen, China. However, the aforementioned studies were all based on small samples of survey data (n ~ 1500 in Guangzhou, 3800 in Suzhou, 50,000 in Xi'an, and 96,000 in Xiamen). It is difficult to evaluate the representativeness of the samples. Plus, all the surveys were one-off and did not produce several waves of data for one to trace the changes in excess-commuting metrics.
The availability of big data has the potential to help us address some of these issues. Based on the smartcard and/or survey data, Zhou and Long, 2014, Zhou and Long, 2015 studied the excess-commuting issue of >200,000 transit riders in Beijing, China. They found that the excess-commuting patterns and internal modal differences were different from the western studies. With the smartcard data in Shanghai, Zhang et al. (2019) studied the efficiency and equity of commuting patterns simultaneously, whereas the large dataset enabled them to conduct complex simulations. To compare the commuting efficiency across cities, Zhang et al., 2021a, Zhang et al., 2021b proposed a framework using a publicly available point of interest dataset. They simulated the commuting flows based on the data and compared the excess-commuting indicators across cities, but it was difficult to ascertain what contributed to the differences between the simulated commuting patterns and the actual ones.
Most excess-commuting studies in the Chinese context have adopted a cross-sectional perspective, even though some of them required a temporal examination to draw a robust conclusion (Hu et al., 2019). This is because it is challenging to obtain comparable datasets across time. Multi-year surveys are the common data source to conduct longitudinal analyses of commuting (e.g., Li et al., 2021; Liu and Hou, 2016). Data from these surveys, however, are subject to two disadvantages when compared to data from emerging sources such as smartcard swipes and mobile phone records: small sample sizes and discrete episodes. However, few studies have taken advantage of the data from emerging sources. One of the few exceptions is Yang (2020) work: he compared the basic commuting patterns in Shanghai between 2011 and 2014 with the mobile phone data, but he did not adopt the excess-commuting framework. To the best knowledge of the authors, analyzing (excess) commuting patterns across times and cities in China is still rare.
2.3 COVID-19 impacts on commuting and jobs-housing relationships
The impacts of COVID-19 on commuting and jobs-housing relationships can be temporary and permanent. The temporary impacts can be short-term adaptations resulting from anti-pandemic countermeasures. The permanent impacts are caused by people's changing attitudes toward and usage of the transportation-land use system—or more specifically, their choice of residences and workplaces and subsequent commuting patterns. The travel restriction measures could lead to decreasing commuting volume (Kissler et al., 2020; Shamshiripour et al., 2020), changing commuting mode choice (Abdullah et al., 2021; Harrington and Hadjiconstantinou, 2022), and increasing telecommuting population (Currie et al., 2021). These is people's instant responses to governments' measures, which help prevent them from being infected by the virus. These responses were short-lived and came and went quickly as the pandemic situation changed. In this paper, we focus on the impacts of COVID-19 on commuting and jobs-housing relationships in the longer term. In particular, we are interested in the numbers and spatial distribution of workplaces and residences before and amid COVID-19 and related excess-commuting metrics.
Fueled by the work-from-home requirement in many cities amid COVID-19, telecommuting is one of the major trends. This can challenge our existing notion of the jobs-housing relationships—for instance, more and more workers and employers might no longer be as sensitive to the physical separation of workplaces and homes as ever before and homes can be simultaneously workplaces (c.f., de Palma et al., 2022). Evidence in Australia showed that the work-from-home population significantly increased during the pandemic by >300% (Currie et al., 2021). Hensher et al. (2022) explored the determinants of telecommuting in Australia to provide more insights for policy making. In a developing country context, Irawan et al. (2021) found that high-income people were more likely to telecommute. In China, Pan and He (2022) hinted that there might be a new lifestyle concerning telecommuting though the evidence in early 2020 was not strong enough. Another significant issue is people's attitudes toward working from home. Irawan et al. (2021) found that attitudes toward telecommuting and COVID-19 directly affected the travel response of individuals. In the long run, Currie et al. (2021) and Beck et al. (2020) suggested that there might be an ongoing increase in working from home in the post-pandemic era, whose volume would be above the pre-COVID level because some people became accustomed to working from home and the corresponding attitudes changed. The evidence concerning working from home in China amid COVID-19 is still very limited, and our study attempts to provide some indirect evidence.
The discussion concerning whether the suburbanization trend will be boosted as people would leave downtowns with high infectious risk is also popular (Hamidi et al., 2020; Jasiński, 2022). Some surveys revealed the rationale of suburbanization by looking into the changing preference of people's residential location choices: better open space accessibility and mental health benefits (Zarrabi et al., 2021), and home workability (Shamshiripour et al., 2020). Empirically, Liang et al. (2021) found that tourists preferred to rent places in suburbs during the pandemic, indirectly suggesting the potential trend of residential suburbanization. de Palma et al. (2022) summarized the potential popularity of a suburb lifestyle. Regarding the changes in the residence place, Stawarz et al. (2022) revealed a preliminary suburbanization process of jobs during the pandemic in Germany. Many scholars discussed the home as a workplace (e.g., Reuschke and Felstead, 2020), which could greatly blur the boundaries of homes and workplaces. But there is little empirical knowledge about the workplaces' quantity and spatial distribution changes after the outbreak of COVID-19. Concerning the jobs-housing relationships, we only found that Loo and Huang (2022) mentioned the potential impacts of working from home on the jobs-housing relationships based on empirical traffic data from Hong Kong.
Another issue related to the commuting impacts of COVID-19 is the different responses of inter-city and intra-city commuters. As long-distance and inter-city travel is regarded as a critical pathway of disease transmission (Kraemer et al., 2020; Schlosser et al., 2020), inter-city travel was at times restricted amid COVID-19 in most countries including China (An et al., 2021; Tian et al., 2020). The indirect evidence showed that inter-city travel probably decreased due to such restrictions compared to its intra-city counterpart (Huang et al., 2020). The year-to-year comparison of metro ridership showed that transfer stations for inter-city trips saw a declining ridership while other stations did not (Zhang and Yang, 2022), implying that the inter-city travel demand may decline amid COVID-19. Apart from the demand impacts, survey results from Pakistan suggested that the changes in modal split may be caused by the restrictions on inter-city travel (Abdullah et al., 2021). As a result, the COVID-19 impacts on commuting and jobs-housing relationships will probably be different between intra-city and inter-city commuters; however, little existing scholarship has examined this issue.
2.4 Summary
Given the above backdrop, we can conclude that studies simultaneously examining jobs-housing relationships and excess-commuting metrics amid abrupt events, e.g., COVID-19, are essentially non-existent. There are gaps to be filled because mega/abrupt events such as COVID-19 had brought many significant and unintended impacts to transportation-land use systems (Zhou et al., 2021). To best monitor and operate these systems, one cannot circumvent the topics such as the jobs-housing balance and (excess) commuting. Our empirical study below attempts to fill some of the gaps by (a) illustrating the potential of big data in the longitudinal and simultaneous study of jobs-housing relationships and excess commuting and (b) providing a novel method to evaluate the jobs-housing relationship and excess-commuting metric changes amid COVID-19, which can be transferable.
3 The case and methods
3.1 The site
Megaregions are a rising part of the global economy (Ross, 2009). There are many inter-city commutes in megaregions. Overlooking these commutes can produce misleading results (Zhang et al., 2020). In China, the PRD is one of megaregions envisioned by the central government (see Fig. 1 -a). Its GDP and population are US$ 1300+ billion (similar to Australia) and 78+ million (similar to Germany), respectively in 2020. The PRD consists of 9 different cities, including Guangzhou and Shenzhen, i.e., two of the four most developed cities in China. We took the PRD and the corresponding jobs-housing relationships as our case.Fig. 1 Case description: (a) A map of the PRD and the spatial distribution of the jobs-housing linkage in 2019; (b) The COVID-19 trend in Guangdong and its corresponding policy.
Fig. 1
To provide the analysis at a finer scale, we used Jiedao as our basic unit of analysis at the sub-city scale (see Fig. 1-a). Jiedao serves as the basic unit of social management in China, so it seldom separates spatial locations with similar and connected functions. It is also the basic unit of the population census dataset. From the perspective of function and scale, it plays a similar role as the census tract in the U.S. (Yang and Ferreira, 2008) or Dong in South Korea (Jun, 2020). Also, it has been widely used as a substitution for the traffic analysis zone in China (e.g., Hu et al., 2018; Wu and Hong, 2017; Zhao et al., 2011).
The COVID-19 situation, policy making, and mobility outcomes in China and the PRD are quite different from the western world. As shown in Fig. 1-b, the relative number of confirmed cases is fairly low after March 2020. To sustain this situation, the government introduced the normalized COVID-19 prevention and control measures and policies under the background of the zero-COVID policy (Chen and Chen, 2022). This policy can provide most of the residents with a back-to-normal lifestyle by occasionally lifting swift and strict local lockdown. Without a threatening risk of being affected by COVID-19, working from home has not been strongly encouraged in China, and most people returned to their office after May 2020.2
3.2 Data
To measure the jobs-housing relationships before and amid COVID-19, we selected the dataset in the fourth quarter of 2019 as the control group before COVID-19 and the datasets in the fourth quarter of 2020 and 2021 as the short- and mid/long-term representation of the jobs-housing relationships amid COVID-19, respectively. The number of confirmed cases was very low during those periods (see Fig. 1-b). The LBS dataset provides the aggregated jobs-housing linkage of millions of mobile phone users in the format of origin-destination matrices at the scale of Geohash6 (~1.2 KM * 0.6 KM grids). Similar datasets like Safegraph in the U.S. (Benzell et al., 2020; Brough et al., 2020) have been widely used to study urban mobility. Datasets from the same company have been used to analyse the commuting patterns of the Greater Bay Area, China as well (Chen and Zhou, 2022). We aggregated the original data at the Jiedao scale and visualized the baseline commuting pattern in 2019 (Fig. 1-a).
The datasets use rules to single out workplaces and residences of millions of smartphone users. There were 50 million such users in the PRD as of 2019. The most frequently visited places of each mobile phone user during the day and night, respectively, are treated as the residence and workplace. The datasets were created based on the mobile phone users' high-resolution (~100 m) GPS records from 100+ smartphone apps. More details about the datasets are as follows. First, a server collected and stored all the GPS records because of mobile phone users' usage of the different smartphone apps. Second, using queries, the server administrator identified the places where mobile phone users visited most frequently during 9:00 p.m. ~ 6:00 a.m. and 10:00 a.m. ~ 5:00 p.m. on every weekday and regarded them as the probable daily residences and workplaces. Third, the administrator created a list for each mobile phone user's probable residences and workplaces on each weekday in three consecutive months. Fourth, an administrator counted each distinct residence and workplace by mobile phone user in the list. The probable residence and workplace that a mobile phone user frequented the most were treated as his/her final residence and workplace. Fifth, using the final residences and workplaces, the administrator created a series of origin-destination matrices concerning jobs-housing linkage and commuting trips.
The dataset is one of the few datasets that can cover all the 9 cities in the PRD with comparability, which has rarely been exploited in the existing studies. The quality and representativeness of the dataset were validated. We checked the data quality by fitting a simple linear regression between the LBS- and census-based residential population density (see Fig. A.1). The result suggests that the LBS dataset can well represent the population (R2 > 0.85) though systematical underestimation exists, which is understandable as not all the people used a smartphone or allowed their exact geolocation to be captured while using different smartphone apps. By comparing the relationships at the city scale, we can find that the proportion of the total residential population in most cities exceeds 50% (see Table A.2), indicating a much larger sample size than traditional survey data (normally <5%). Assuming that the smartphones' market penetration rate is high across all walks of life, a large sample size might also imply a good representation of the population.
Apart from the jobs-housing matrices, the 7th National Population Census data in 2020 was also used to provide evidence of socio-economic statuses, which covers all the cities mentioned above at the Jiedao scale with comparable formats. The price of second-hand housing from an online platform (fang.com) was also collected to enrich the profile of each Jiedao. We also used the GIS dataset from the Chinese Ministry of Natural Resources, which contains the shapefile of the local administrative boundaries and transportation networks.
3.3 Methodology
Fig. 2 describes the analytical framework. We considered three steps in the evaluation of jobs-housing relationship dynamics. Step 1: Uncovering the basic trends by data visualization. We explored the dataset by visualizing it spatially and quantitatively. We focused on three major issues identified in the literature review, namely the trends of suburbanization and working from home and the divergent response between inter-city and intra-city commutes. The results can uncover the basic patterns simply and obviously. Step 2: Understanding the temporal changes with an excess-commuting approach. The rationale underneath of the temporal changes is still unknown but important, thus we used the excess-commuting approach to examining it from the lens of commuting benchmark, commuting choice, and commuting efficiency, which imply the spatial distribution of jobs and housing, the preference/choice of commuters, and the overall performance/efficiency of transportation-land use system, respectively. Step 3: Examining the heterogenous impacts by regression models. As the profile of each Jiedao varies, we used the regression models to examine the heterogeneous impacts of socio-economic and spatial factors on the excess-commuting metrics. The results of Steps 1, 2, and 3 are detailed in 4.1, 4.2, and 4.3, respectively. As for the excess-commuting approach and regression models, more details are as follows.Fig. 2 Analytical framework of this paper.
Fig. 2
3.3.1 The excess-commuting approach
The excess-commuting approach uses three categories of indicators: commuting benchmarks (e.g., Tmin, Tmax, and Trand), actual commuting choice (e.g., Tact), and commuting efficiency (e.g., Cu), which can be used to fathom jobs-housing relationships. For the commuting benchmarks, we chose the two most widely used benchmarks: the Tmin and Tmax. Tmin indicates the relative balance of jobs with respect to housing in a city or region (Small and Song, 1992). Tmax reflects the amount of dispersion of jobs relative to residences, that is, the extreme quantitative imbalance of jobs relative to housing opportunities in a city or region (Horner, 2002). Following White (1988), Tmin can be determined by solving a linear optimization problem:(5) Minimize:Tmin=1W∑i=1n∑j=1mCijxij
(6) Subject to:∑i=1nxij=Dj,∀j=1,2,…,m
(7) ∑j=1mxij=Oj,∀i=1,2,…,n
(8) xij≥0,∀i,j
where, n and m are the numbers of origin and destination unit of analysis respectively; Oi and Dj are the total number of residents living in unit i and employees working in unit j respectively; Cij is the Euclidian distance between units i and j; xij is the number of commuting linkage from unit i to j; W is the total number of commuters.
Likewise, Tmax can be calculated by maximizing the same objective function with identical constraints as Horner (2002) indicated, and the objective function is in Eq. (9):(9) Maximize:Tmax=1W∑i=1n∑j=1mCijxij
We used Tact as the commuting choice indicator. It is calculated based on the Euclidian distance between the centroids of Jiedaos. Even though it might oversimplify the real world travel cost, it is still proved to be effective enough (Frost et al., 1998; Murphy and Killen, 2011; Zhou et al., 2014a, Zhou et al., 2014b). If a pair of residence and workplace were in the same unit, we used the radius of the circle with an identical area to the unit as the travel cost, following Zhou and Long (2014).
Combining the two types of indicators mentioned above, the two commuting efficiency indicators: EC and Cu by White (1988) and Horner (2002), respectively, can be calculated. EC and Cu jointly can allow us to measure the commuting efficiency of a given city or region (Horner, 2002). If there are EC and Cu across times, we can even see how the quantitative (im)balance between workplaces and residences evolves over time (Zhou et al., 2014a, Zhou et al., 2014b). Together, Tmin, Tmax, Tact, EC, and Cu enable us to portray the jobs-housing relationships and their changes before and amid COVID-19 in the PRD based on the excess-commuting framework.
3.3.2 The regression analysis
We developed linear regression models to further evaluate the heterogeneous impacts of COVID-19 on different subpopulations' jobs-housing relationships, which we measured by different excess-commuting metrics. As for the dependent variables, we used the absolute changes of all the excess-commuting metrics (See Table 1 ). According to Horner (2002), EC and Cu should be examined simultaneously, therefore the product of changes in EC and Cu was developed as a combined variable. We used all the socio-economic characteristics available in the census data as the independent variables, i.e., the sex ratio, years of education, and proportions of juniors or seniors. Those variables can directly examine the impacts of the sex, educational level, and age composition on jobs-housing relationships. We collected the housing prices data by Jiedao averaged them to serve as a proxy of the average income level by Jiedao. Specifically, the average price per square meter of on-sale housing units in each Jiedao was calculated, and it can show the living cost of each Jiedao which is highly related to the corresponding income level of its residents. To examine the divergence among inter-city and intra-city commuters, we used the proportion of the inter-city commuters at the Jiedao level as an independent variable. As for spatial variables, we calculated the densities of residents and workers retrieved from the LBS dataset. Distance to the nearest city center was calculated to measure the degree of spatial centrality of each Jiedao. Besides, the density of the road, distance to the nearest metro station, and density of bus stations were calculated with the GIS dataset to measure the accessibility of different mode choices in the PRD.3 Some Jiedao's EC or Cu was negative, but this was not strange. According to Zhou et al. (2015), some units' Tmin might be larger than Tact after the global optimization, then they would possess a negative EC or Cu.Table 1 Summary statistics of the variables in the regression analysis.
Table 1Dependent Variables Description Mean S.D. Median Min Max Source
Tmin_Change Absolute changes in Tmin, Tact, Tmax, EC, and Cu from 2019Q4 to 2021Q4. 0.01 0.48 0.00 −3.20 4.11 The LBS dataset
Tmax_Change 0.14 14.60 0.00 −98.91 123.09
Tact_Change 0.10 1.51 0.00 −3.75 19.43
EC_Change 0.13 7.06 0.35 −57.47 29.44
Cu_Change 0.11 1.80 0.03 −4.52 19.34
EC*Cu_Change 6.80 28.27 0.74 −8.09 387.97
Independent Variables
Socio-economic
Year_Edu Average year of education for residents over 15 years old. 11.00 1.39 10.89 8.25 15.03 The 7th national census of China
Ratio_Sex Number of male/female residents * 100 115.45 16.17 113.59 86.25 271.63
≤14% Percentage of residents under 14 years old. 15.63 3.82 15.25 1.76 26.18
≥ 60% Percentage of residents over 60 years old. 11.86 6.18 10.27 2.00 28.28
Price_Housing Average price of the on-sale housing units in each Jiedao (CNY/m2). 25,145.64 19,356.35 18,726.17 2984.00 106,632.90 Online platform (fang.com)
Inter_City% Proportion of the inter-city commuters in Jiedao. 4.75 2.62 4.00 1.67 27.19 The LBS data
Spatial
Dens_Worker Density of residential/working population retrieved from the LBS data in the Jiedao (per km2). 7313.48 11,516.29 2685.50 9.04 63,946.75 The LBS data
Dens_Resident 6750.71 9944.15 2594.93 7.89 83,062.13
Dis_Center Distance to the nearest city center from the Jiedao centroid (km). 20.44 17.35 17.02 0.55 106.80 The GIS dataset
Dens_Road Density of the major road network length in the Jiedao (km/km2). 0.72 0.60 0.57 0.00 4.17
Dis_Metro Distance to the nearest metro station from the Jiedao centroid (km). 24.54 17.35 17.02 0.03 168.77
Dens_Bus Density of the bus stations in the Jiedao (/km2). 3.18 3.43 2.11 0.00 18.57
N = 414
Due to the data availability, 414 Jiedaos were included in the regression analysis, consisting of 93.75% and 93.69% of the residential population in the LBS and census datasets, respectively, indicating nice representativeness of the sample. All the cities in the PRD were covered.
4 Results
4.1 The overall trends in jobs and housing
We were interested in the numbers and spatial distribution of jobs and housing. As shown in Fig. 3 -a, the total numbers of jobs and housing, i.e., the jobs-housing linkage, did not vary greatly across the years, implying that COVID-19 did not significantly reduce the numbers of active workers and occupied residences in the PRD. We further estimated the Pearson's correlation coefficient to see how the jobs or housing in different units of analysis varied across years. Specifically, the numbers of workers or residents at the Geohash6 level in two years were regarded as a pair of variables. As the correlation coefficient ranges between 0 and 1, the larger it is, the more two variables are correlated. The coefficients between the 2021Q4 and 2020/2019Q4 variables were the lowest (~0.8) whereas the coefficients between 2019Q4 and 2020Q4 were much higher (i.e., > 0.97) (Fig. 3-b). This indicates that there existed notable differences in the numbers of workers and residents between 2021Q4 (amid COVID-19) and 2019Q4 (before COVID-19). Therefore, we focused on the changes in jobs and housing between 2019Q4 and 2021Q4 hereafter.Fig. 3 Quantitative changes of jobs-housing characteristics: (a) Number of total commuters in each year; (b) Correlation coefficients at the Geohash6 level; (c) The proportional changes of inter/intra-Geohash6/Jiedao/city commuting trips.
Fig. 3
A dominant structural change in the jobs-housing linkage in the western world amid COVID-19 is the growing trend of working from home, where there could be fewer commuting trips as compared to the pre-COVID-19 time (e.g., Hensher et al., 2022; de Palma et al., 2022). In our datasets, we could not single out the telecommuters or those working from home. But comparing internal commuting trips within different units of analysis such as Geohash6, Jiedao, and city is helpful (see Fig. 3-c). It is highly likely that the percentage of internal commuting trips would increase if there existed a growing number of telecommuters. However, we did not find a higher percentage of internal commuting trips across three units of analysis: Geohash6, Jiedao, and city. It seemed that working from home had not become popular in China amid COVID-19.
The spatial distribution of residences and workplaces are worth mentioning (Fig. 4 -a). Jiedaos near the city centers possessed more jobs than housing. In contrast, Jiedaos in suburbs owned more housing than jobs. These indicate a monocentric megaregion (e.g., Yang et al., 2012; Zhang et al., 2020). Further, we classified the Jiedao into four categories by looking at the signs of changes in the proportion of residences and workplaces compared with the total population. Fig. 4-b shows the spatial distribution of these four categories of Jiedao. The figure partially illustrates a suburbanization of workplaces, which has been observed in the west (e.g., Liang et al., 2021; Stawarz et al., 2022). Jobs and housing in most of the exurbs decreased simultaneously while increased in or around most city centers and some of the suburbs. In other words, city centers, their adjacencies, and some suburbs have a higher concentration of residences and workplaces after the outbreak of COVID-19. This could have significantly changed the jobs-housing relationships, which can be measured using various excess-commuting metrics.Fig. 4 Spatial changes in the distribution of residences and workplaces: (a) Spatial distribution of the ratio of jobs to housing (averaged from 2019 to 2021); (b) Changes in jobs and housing from 2019 to 2021.
Fig. 4
Because inter-city and intra- city commuters possibly faced two different sets of policies and mobility restrictions amid COVID-19, we differentiated inter-city and intra-city commuters and consider their respective changes in jobs and housing. Following the symbology of Fig. 4-b, the patterns in Fig. 5 indicate that inter-city and intra-city commuters responded differently to the pandemic. The residences and workplaces of intra-city commuters were concentrated in most city centers and some of the suburbs following the major trend of the population in Fig. 4-b. In contrast, the numbers of inter-city residents and workers declined in the same place, especially in the suburbs of the two largest cities in PRD, i.e., Shenzhen and Guangzhou. Meanwhile, the numbers of inter-city workers and residents increased in the city centers and suburbs of smaller cities in PRD, respectively. This phenomenon implies that the inter-city commutes from and to larger cities were significantly confined while new inter-city commuters emerged in smaller cities amid COVID-19.Fig. 5 Changes in jobs and housing of (a) inter-city commuters and (b) intra-city commuters from 2019 to 2021.
Fig. 5
As a result of the spatial structure changes in jobs and housing distribution, the jobs-housing linkage changed accordingly. At the linkage level, we calculated the intensity of the weakened and enhanced jobs-housing linkage from 2019 to 2021 and visualized them separately in Fig. 6 . The intensity was calculated based on the ratio of the number of commuters at the specific linkage from 2021 to 2019, where a value smaller than 1 implies more commuters on that linkage, and vice versa. As shown in Fig. 6-a, the weakened linkage was mainly long-distance and inter-city linkage, especially the cross-city linkage from and to larger cities like Guangzhou and Shenzhen. In contrast, the enhanced linkage was mainly short-distance and intra-city linkage, especially the intra-city linkage in Guangzhou.Fig. 6 Spatial distribution and intensity of (a) weakened and (b) enhanced jobs-housing linkage from 2019 to 2021.
Notes: Only the linkage with >50 commuters in 2019 is visualized.
Fig. 6
4.2 Jobs-housing relationship changes from an excess-commuting perspective
After examining the overall trends in the numbers and spatial distribution of residences and workplaces, we calculated, visualized, and compared excess-commuting metrics such as Tmin, Tmax, Tact, EC, and Cu for the whole PRD region, each city, and every Jiedao. As mentioned above, these metrics allow us to better measure jobs and housing relationships. In all the calculations, we used Jiedao as the basic unit. This is because it is the most disaggregate unit that we can have statistics across all the datasets (i.e., commuting trip matrices, censuses, and GIS datasets) that we accumulated. Besides, a uniform unit is proved to be the best in terms of minimizing MAUP (Horner and Murray, 2002). Though MAUPs are unavoidable in any spatial analysis, Niedzielski et al. (2013) proved that those MAUP-related errors are systematical rather than random with the excess-commuting approach, thus comparing the indicators across time with the same spatial units is recommended.
Table 2 presents the region- and city-level excess-commuting metrics. Judged from the changes in Tmin and Tmax between 2019 and 2021, we can see that the PRD and most cities therein had seen a better spatial juxtaposition and a shrinking distribution of residences and employment. The decreasing trend of Tmin and Tmax also revealed an improved level of jobs-housing balance at the PRD level concerning the spatial distribution of residences and workplaces. However, such a configuration was not translated into a shorter average actual commute for workers. Tact for the whole region and most cities therein had become larger in the same period. Because of the above, it is not surprising that commuting efficiency in the whole region and most cities, which can be measured simultaneously by Cu and EC, worsened. Concerning the response of inter-city and intra-city commuters, their changes in the excess-commuting metrics were consistent with those of all the commuters. However, the inter-city commuters saw larger Tmin, indicating that they possessed a better spatial juxtaposition of residences and employment and a more balanced jobs-housing relationship. This can be explained by the spatial pattern in Fig. 5-a that inter-city commuters declined in larger cities and concentrated in smaller cities thus leading to a more compact distribution. Given this, the commuting efficiency of the inter-city commuters declined more.Table 2 The temporal changes of the excess-commuting metrics.
Table 2Unit of Analysis 2019Q4 2020Q4 2021Q4
Tmin Tmax Tact EC Cu Tmin Tmax Tact EC Cu Tmin Tmax Tact EC Cu
PRD 4.69 108.60 8.95 47.63 4.10 4.68 108.55 9.14 48.77 4.29 4.61↓ 106.91↓ 9.22↑ 50.03↑ 4.51↑
PRD (Inter-city) 7.75 109.92 57.41 86.50 48.61 6.56 109.24 55.91 88.27 48.06 6.28↓ 107.62↓ 58.00↑ 89.17↑ 51.04↑
PRD (Intra-city) 4.60 108.51 6.44 28.57 1.77 4.63 108.50 6.58 29.64 1.88 4.57↓ 106.80↓ 6.47↑ 29.37↑ 1.86↑
Guangzhou 3.52 93.77 8.05 56.35 5.03 3.53 92.57 8.35 57.79 5.42 3.56↑ 93.48↓ 8.79↑ 59.48↑ 5.82↑
Shenzhen 3.96 107.83 8.42 52.92 4.29 3.95 109.32 8.64 54.32 4.46 3.93↓ 106.56↓ 8.31↓ 52.65↓ 4.26↓
Dongguan 5.27 90.83 8.01 34.18 3.20 5.28 88.00 8.20 35.54 3.52 5.26↓ 88.57↓ 8.14↑ 35.33↑ 3.45↑
Foshan 6.01 115.06 9.63 37.62 3.32 6.00 117.66 9.93 39.55 3.52 5.96↓ 113.78↓ 10.11↑ 41.06↑ 3.85↑
Huizhou 5.95 150.34 11.47 48.19 3.83 5.89 147.93 11.53 48.90 3.97 5.85↓ 147.40↓ 11.73↑ 50.14↑ 4.15↑
Zhongshan 4.91 80.38 8.12 39.53 4.25 4.92 81.87 8.40 41.45 4.52 4.91↑ 80.74↑ 8.69↑ 43.52↑ 4.99↑
Jiangmen 5.17 140.63 10.31 49.88 3.80 5.23 141.37 10.11 48.29 3.58 5.09↓ 139.58↓ 10.16↓ 49.86↓ 3.77↓
Zhaoqing 6.12 194.73 13.57 54.87 3.95 6.06 197.46 13.09 53.72 3.67 5.98↓ 199.08↑ 13.58↑ 55.98↑ 3.94↓
Zhuhai 5.00 117.73 10.64 53.01 5.00 5.01 116.09 10.96 54.26 5.35 5.06↑ 116.03↓ 11.73↑ 56.90↑ 6.01↑
Notes: All the city-level results are based on the residences of commuters.
At the Jiedao scale, we used visuals to help us to better detect changes in the excess-commuting metrics and their spatial distribution given that there were 400+ Jiedaos in the PRD region. As shown in Fig. 7 -a, the spatial distribution of Jiedao-level changes in Tmin seemed random, implying that the jobs-housing distribution/juxtaposition might not follow a specific spatial pattern. In contrast, the changes in Tmax displayed another pattern: it increased significantly in most suburbs. These results suggest that the relocation of residences and workplaces brought about a worsened jobs-housing balance in most suburbs. The changes in Tact also had a clear spatial pattern and had more clusters of comparable changes (see Fig. 7-c). The presence of the clusters indicated that it was more likely for residents from certain Jiedaos to change their commuting patterns, e.g., residents in suburbs might increase their actual commutes after the outbreak of COVID-19. From a Jiedao perspective, the EC significantly increased in suburbs and decreased in exurbs (see Fig. 7-d), indicating a more efficient combined pattern of jobs-housing relationships in exurbs but a less efficient one in suburbs. The pattern of Cu in Fig. 7-e was very similar, while the extent was smaller than that of EC. The results present very consistent spatial patterns with the changes in commuting choice above.Fig. 7 Spatial distribution of changes in (a) Tmin, (b) Tmax, (c) Tact, (d) EC, and (e) Cu from 2019 to 2021.
Notes: All the Jiedao-level results are based on the residences of commuters.
Fig. 7
4.3 Heterogeneous jobs-housing relationship changes across social groups
We tried to explain the underlying mechanism of spatial heterogeneity with a set of linear regression models concerning socio-economic and spatial factors. Table 3 presents the results of the models. We found that Tact and EC were better predicted than the other indicators according to their larger R2, this is consistent with their clearer spatial patterns. The value of R2 was satisfying for models explaining changes. The detailed result of the models is as follows.Table 3 Linear regression results.
Table 3 Commuting benchmark Commuting choice Commuting efficiency
Tmin_Change Tmax_Change Tact_Change EC_Change Cu_Change EC*Cu_Change
Estimate p-value Estimate p-value Estimate p-value Estimate p-value Estimate p-value Estimate p-value
(Intercept) (1.636) 0.007 ** 48.170 0.009 ** (2.111) 0.262 0.145 0.095 # (0.023) 0.312 (0.007) 0.064 #
Year_Edu 0.109 0.000 *** (1.503) 0.113 0.092 0.343 (0.013) 0.004 ** (0.000) 0.727 0.000 0.053 #
Ratio_Sex 0.004 0.077 # (0.175) 0.013 * 0.010 0.168 (0.000) 0.908 0.000 0.051 # 0.000 0.060 #
≤14% 0.005 0.538 (0.481) 0.053 # 0.034 0.185 0.001 0.611 0.000 0.152 0.000 0.949
≥ 60% 0.001 0.901 (0.280) 0.146 0.014 0.489 (0.000) 0.787 0.000 0.086 # 0.000 0.019 *
Price_Housing (0.000) 0.025 * 0.000 0.668 (0.000) 0.254 0.000 0.430 0.000 0.916 0.000 0.972
Inter_City% (0.017) 0.078 # 0.106 0.713 (0.054) 0.070 # 0.000 0.757 (0.000) 0.359 (0.000) 0.698
Dens_Worker 0.000 0.536 0.000 0.924 0.000 0.069 # 0.000 0.608 0.000 0.103 0.000 0.672
Dens_Resident (0.000) 0.538 (0.000) 0.693 (0.000) 0.143 (0.000) 0.887 (0.000) 0.221 (0.000) 0.538
Dis_Center 0.002 0.217 (0.045) 0.435 (0.018) 0.003 ** (0.001) 0.000 *** (0.000) 0.023 * 0.000 0.717
Dens_Road (0.009) 0.842 0.107 0.935 (0.154) 0.255 (0.000) 0.941 (0.000) 0.897 (0.000) 0.838
Dis_Metro 0.000 0.870 0.026 0.512 0.005 0.172 0.000 0.084 # 0.000 0.490 (0.000) 0.347
Dens_Bus 0.015 0.166 (0.070) 0.833 0.010 0.760 (0.001) 0.381 (0.000) 0.663 (0.000) 0.167
R-square 0.048 0.026 0.052 0.078 0.040 0.036
Notes: N = 414; ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05, ‘#’ 0.1; values in the brackets are negative.
Socio-economic factors proved to be effective and important in predicting and explaining commuting patterns (Shen, 2000) and particularly excess-commuting metrics (Yang, 2008). In the western context, race, sex, education, and income-related factors are usually included. Because there does not exist significant racial issues in the Chinese context, we included sex, education, and income-related factors in our socio-economic factors. Also, as inter-city and intra-city commuters faced different policies and mobility restrictions amid COVID-19, we included the proportion of inter-city commuters to consider this. As the results imply, at least one socio-economic factor significantly impacted the dependent variables in all models, indicating that the changes in jobs-housing relationships were not uniform among populations with different socio-economic statuses. Well-educated residents witnessed an increasing Tmin and a decreasing EC. This means that corresponding jobs and housing opportunities became more spatially dispersed as compared to the pre-COVID-19 period; however, it was likely that more commuters chose to telecommute or chose workplaces closer to homes or vice versa. Female workers in general suffered from a larger Tmin and enjoyed a smaller Tmax after COVID-19. It was likely that for these workers, the number of jobs near their homes on average increased (or vice versa) after COVID-19; however, there were also probably slightly more jobs emerged further away from homes. As a result, female residents were less likely to possess an increased Cu. The above-mentioned trends suggested that the female residents, the subpopulations who possessed less mobility and capacity to adapt, were less likely to follow the flow under a spatial reconstruction. Besides, the impacts of the junior population proportion also suggested that they were more likely to possess a shortened Tmax, indicating a smaller shed to search for residences and workplaces. There are similar findings in other studies (e.g., Liu et al., 2020; Shamshiripour et al., 2020; Zhang et al., 2021a, Zhang et al., 2021b): the female, senior, and junior student had poorer access to media and information and thus were less likely to make optimal decisions, also, their mobility was more limited that they cannot easily adapted to the abrupt changes. As for the divergence between intra-city and inter-city commuters, the model suggested that inter-city commuters were more likely to witness a larger decrease in Tmin, indicating a better juxtaposition residences and workplaces therein. Also, they were more likely to see a larger Tact, implying that they were probably more capable to adapt their commuting patterns amid COVID-19. These results are consistent with the findings revealed by the visualization and estimation in the former sections.
Spatial factors were intuitively and empirically related to commuting patterns (Shen, 2000). As scholars suggested, locations (Alonso, 1964), jobs/housing densities (Cervero, 1996), and accessibility (Levinson, 1998) played an important role in determining commuting patterns. Given this, we chose the independent variables in response to those factors, respectively. In our results, spatial factors such as the density of workers and distance to city centers and the nearest metro station significantly predicted the changes in excess-commuting metrics, validating the spatial heterogeneity revealed by the geo-visualization above. Residents who lived in Jiedaos with higher job densities were more likely to possess a lengthened commute after the outbreak of COVID-19 compared to that before. The results are consistent with the increasing trend of Tact in the suburbs (see Fig. 7-c), where the jobs-housing ratio is relatively small. Concerning the distance to the city center, residents who lived closer to the city center were more likely to possess an increasing Tact and thus more excess commuting indicated by EC and Cu after the outbreak of COVID-19. Residents who lived closer to metro stations had a smaller chance to see larger EC.
5 Discussion and conclusions
COVID-19 has changed the daily life of every individual. In a world with the presence of the pandemic, it is vital to understand the pandemic's long-lasting impacts. Using millions of LBS records and based on the excess commuting approach, we empirically examined the probable impacts of COVID-19 on jobs-housing relationships in the PRD, China.
Our empirical work generates at least the following findings, some of which can be transferable to other contexts: (1) residence and employment became more centrally located in downtowns, which is opposite to the trend of suburbanization found elsewhere in the western context; (2) in the whole PRD, the overall Tmin and Tmax became smaller while the Tact became larger, indicating the co-presences of a better spatial juxtaposition of jobs and housing, more compressed distribution of jobs and housing, and longer average actual commutes, and those trends led to larger EC and Cu simultaneously; (3) compared with the centralization of intra-city commuters, the inter-city commutes from and to large cities were significantly confined and decreased, while new inter-city commuters who lived or worked in smaller cities emerged, leading to a larger decrease in Tmin; (4) divergent changes in jobs-housing relationships among subpopulations, most notably, the less-educated and females were more likely to observe smaller Tmin amid COVID-19.
These findings help advance existing knowledge related to the mobility implications of COVID-19. We found that the jobs and housing opportunities moved from suburbs to downtowns in the PRD, which conflicts with the existing knowledge from the survey in Germany and Iran (Stawarz et al., 2022; Zarrabi et al., 2021). We speculate that the differences can be due to these reasons. Firstly, a group of people may change their jobs proactively or become unfortunately unemployed. In this case, people would be more willing to work further away from homes. A similar phenomenon was witnessed after the Great Recession in the U.S. as well (Kim and Horner, 2021). Secondly, a group of people started moving to residences in city centers, because most people in China have suffered from the inconvenience of living in suburbs during the multiple waves of the lockdown and the difficulties of inter-city commutes (An et al., 2021; Kraemer et al., 2020; Zhou et al., 2020a, Zhou et al., 2020b). However, the costs of moving is too large that only a small group of people can afford it in the study period. As a combined effect, residences and workplaces concentrated in the city center simultaneously but a longer actual commute was observed.
Our study examined temporal changes in excess commuting metrics amid an abrupt change, i.e., COVID-19. By using passively collected big data, we illustrated the usefulness of big data in the longitudinal, cross-sectional, and continuous study of jobs-housing dynamics based on the excess commuting approach. The only comparable study is to examine the impacts of the Great Recession on commuting dynamics (Kim and Horner, 2021). The findings in our study in general echo to some of theirs. First, both their and our findings revealed that different subpopulations, for instance, public versus private workers and inter-city versus intra-city commuters responded differently amid an abrupt change. Second, an improved jobs-housing balance in the absolute manner (reflected by Tmin) but a longer commute in reality. It is likely that more were willing to commuter longer to earn a living in difficult times. Kim and Horner (2021) suggested that there was a temporal delay in response in certain subpopulations. As our input data were aggregated, we could not verify this in this PRD case.
Despite the above progress, this study does face some limitations. Firstly, as we mentioned before, the changes in jobs-housing relationships reflect the variations in attitudes toward COVID-19 and various COVID-19-related policies. However, the relationships between COVID-19, attitudinal changes, and jobs-housing relationships cannot be uncovered without a combination of big LBS and small survey-based datasets containing detailed socio-economic attributes, e.g., job type and income level. Secondly, the excess-commuting matrices are aggregated by Jiedao in our study, thus we cannot say much about what happened at smaller scales than Jiedao. To know more about those changes at smaller scales, more personal-level data should be collected and analyzed. Thirdly, the excess-commuting matrices used in this study can only represent the population with smartphone access and frequently using apps provided by specific companies. We could have overlooked many who did not use smartphones. Fourthly, there exist inevitable methodological shortcomings due to the nature of the geographical analysis. Besides the MAUP, the modifiable temporal unit problem and the uncertain geographic context problem can also exist. These issues will never be fully resolved but can be minimized as much as possible by acquiring datasets at finer scales and applying more sophisticated methods in the future.
Author statement
The views and any omissions in the paper are solely responsibilities of the authors.
Declaration of Competing Interest
None.
Appendix A Appendix
Table A.1 Literature considering temporal variation in excess-commuting metrics.
Table A.1Source Objectives Case Data and time Spatial unit Benchmarks Major findings
Frost et al., 1998 To calculate changes in excess commuting for two time periods. Sample of British cities Census worktravel data in 1981 and 1991 Census wards Tmin The changing form of urban areas is exerting the strongest influence on the increasing length of work journeys.
Yang, 2005 To compare commuting and urban spatial structure across space and over time. Boston and Atlanta, US CTPP in 1980, 1990, and 2000 Census tracts Tmin and Tpro Alternative decentralization pathways can result in very different transportation outcomes.
Ma and Banister, 2006a, Ma and Banister, 2006b To measure the quantitative and qualitative imbalance of jobs and housing. Seoul, Korea Census of population and housing in 1995, 2000, and 2005 Zones Tmin and Tmax Measured in distance, Tact and Tmin are relatively stable and Tmax increases significantly. Measured in time, all indicators decrease.
Yang, 2008 To evaluate the impacts of a spatial decentralization process on excess commuting. Boston and Atlanta, US CTPP in 1980, 1990, and 2000 Census tracts Tmin and Tpro The transport-land use connection appears weaker over the decades as the dispersion of jobs changes the dynamics of commuting and the selection of residential location varied.
Horner, 2008 To find the theoretical ‘optimal’ urban jobs–housing balance. Tallahassee, US CTPP in 1990 and 2000 TAZs Tmin Tmin of two years are not significantly different; but the actual commutes are.
Murphy, 2009 To investigate the divergence of excess commuting between different mode. Dublin, Ireland Traffic simulation model data in 1991 and 2001 Zonal sub-division Tmin and Tmax Excess commuting is considerably greater for users of private transport implying the greater inefficiency of commuting associated with that mode.
Murphy and Killen, 2011 To provide an alternative method of benchmarking commuting efficiency based on how individuals are economising commuting costs. Dublin, Ireland Traffic simulation model data in 1991 and 2001 Zonal sub-division Tmin, Trand, and Tmax The Tact has moved further away from the Trand, implying that greater intermixing of residential and employment functions has led to more efficient commuting behavior.
Horner and Schleith, 2012 To explore the relationships between land use and commuting outcomes over time. Leon County, US LEHD 2002– 2010 Census blocks Tmin and Tmax Estimation of several commuting and jobs-housing metrics lends insights into growth and decline that have occurred in the recent housing boom and bust.
Schleith et al., 2016 To measure how commuting travel has changed. Sample of 26 US metro regions LEHD 2003 and 2013 Block group Tmin, Trand, and Tmax Commutes are generally increasing although Columbus, OH is the notable exception.
Hu and Wang, 2016 To analyse the temporal trends of commuting patterns in both time and distance. Baton Rouge, US CTPP in 1990, 2000, and 2006– 2010 Census tracts Tmin The percentage of excess commuting increased significantly between 1990 and 2000 and stabilized afterward.
Zhou and Murphy, 2019 To quantify the temporal variation in excess-commuting indicators over short time periods Brisbane, Australia Smartcard data Nov, 2012 ~ Apr, 2013 SA2s in Australia Tmin, Trand, and Tmax Excess-commuting indicators vary considerably from one day to the next especially around public holidays.
Kim and Horner, 2021 To explore the impacts of major economic changes on commuting dynamics. Atlanta, US LEHD 2005– 2015 Census blocks Tmin and Tmax The Great Recession worsened the commuting situation, the effect was more significant for public workers in terms of increasing their travel burdens.
This study To explore the impacts of COVID-19 on jobs-housing relationships Pearl River Delta, China LBS big data 2019– 2021 Jiedao Tmin and Tmax The Tmin and Tmax became smaller while the Tact became larger, indicating the co-presences of better spatial juxtaposition of jobs and housing, more compressed distribution of jobs and housing, and longer average actual commutes.
Fig. A.1 Relationship between the residential density derived from the LBS and census datasets in 2020.
Fig. A.1
Table A.2 Summary statistics of the LBS-based population and census-based population.
Table A.2Unit of statistics LBS data residential population Census data Ratio: LBS/census
2019Q4 2020Q4 2021Q4 2020 2019Q4 2020Q4 2021Q4
PRD 47,273,911 50,007,166 48,745,558 86,091,977 54.91% 58.09% 56.62%
Guangzhou 11,772,366 12,232,005 13,013,193 18,676,605 63.03% 65.49% 69.68%
Shenzhen 10,218,569 11,101,839 11,459,449 17,560,061 58.19% 63.22% 65.26%
Dongguan 7,861,322 8,293,806 7,474,515 10,466,625 75.11% 79.24% 71.41%
Foshan 5,665,886 6,085,205 5,675,712 9,498,863 59.65% 64.06% 59.75%
Huizhou 3,621,321 3,859,413 3,492,087 6,042,852 59.93% 63.87% 57.79%
Zhongshan 3,052,065 3,121,176 2,821,441 4,418,060 69.08% 70.65% 63.86%
Jiangmen 2,018,305 2,141,669 1,910,729 4,798,090 42.06% 44.64% 39.82%
Zhaoqing 1,661,182 1,707,679 1,451,590 4,113,594 40.38% 41.51% 35.29%
Zhuhai 1,402,895 1,464,374 1,446,842 2,439,585 57.51% 60.03% 59.31%
Data availability
The authors do not have permission to share data.
1 Danwei is a self-contained and jobs-housing-integrated unit mainly set up in the centrally-planned economy era in China. Hukou is a household registration system in China that classifies people into agricultural and nonagricultural statuses, while a local Hukou can provide people with a few local rights such as access to education.
2 According to the Ministry of Industry and Information Technology of China (https://news.cctv.com/2020/05/20/ARTI7Kby06rLsqXVGts2dGEP200520.shtml, accessed on May 17, 2022), there are twenty provinces possessing a return to office ratio over 90% in May 2020.
3 Without a regional-level travel survey, we took the travel survey data in Shenzhen, the second largest city in the study area, to draw a figure. In Shenzhen, the shares of bus, metro, taxi, and private cars among motorized modes are 18%, 20%, 5%, and 53% in 2020, respectively (https://mp.weixin.qq.com/s/Rk97owcnWv66B_mlOngTMQ, accessed on Sep 14, 2022, in Chinese).
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| 36514556 | PMC9731928 | NO-CC CODE | 2022-12-14 23:31:56 | no | J Transp Geogr. 2022 Dec 9;:103507 | utf-8 | J Transp Geogr | 2,022 | 10.1016/j.jtrangeo.2022.103507 | oa_other |
==== Front
Vaccine
Vaccine
Vaccine
0264-410X
1873-2518
Elsevier Ltd.
S0264-410X(22)01516-X
10.1016/j.vaccine.2022.12.003
Article
Prior Immunization Status of COVID-19 Patients and Disease Severity: A Multicenter Retrospective Cohort Study Assessing the Different Types of Immunity
Aslam Javaria ab1⁎
Shehzad Faisal b1
Talha Haris Muhammad b1
Hewadmal Hewad c1
Khalid Maryam d2
Alshahrani Mohammad Y. e2
Aslam Qurrat-ul-ain b1
Aneela Irrum g2
Zafar Urooj f2
a Department of Medicine, Qauid e Azam Medical College Bahawalpur, 63100, Pakistan
b Department of Medicine, Sir, Sadiq Abbasi Hospital Bahwalpur, 63100, Pakistan
c Department of Cardiology, Sheikh Zayed Medical College, Rahim Yar Khan, 64200, Pakistan
d Internal Medicine Unit, Dammam Medical Complex, Dammam, Eastern Province, 32210, Saudi Arabia
e Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, P.O. Box 61413, Abha 9088, Saudi Arabia
f Department of Psychiatry Sheikh Zayed Medical College, Rahim Yar Khan, 64200, Pakistan
g Department of Rehabilitation Medicine, Astley Ainslie Hospital, Edinburg, Scotland, EH92HL United Kingdom
⁎ Corresponding author.
1 Fisrt set of equal contributors.
2 second set of equal contributors.
9 12 2022
9 12 2022
20 9 2022
21 11 2022
3 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.
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pmc1 Introduction:
COVID-19 pandemic caused significant economic and societal disruptions as a result of the social and physical distancing constraints put to slow SARS-CoV-2 transmission in light of its lethality. When it comes to the pandemic, it's important to know probability of mortality among diagnosed cases. COVID-19 hospitalization rates ranged from 13 to 21 cases per 1,000 confirmed infections and primarily depended on the affected population's age and co-morbid conditions[1]. As of May 2022, there were over 528 million (almost 3 percent of the world's population) confirmed cases of COVID-19 and nearly 63 million deaths related to COVID-19 [2]. WHO has already pointed out the excess deaths of 14.9 million in two years of pandemic from January 1st, 2020 to December 31st, 2021, 81% of which belong to low to middle income countries[3]. Furthermore, not all patients admitted to COVID-19 designated wards and ICU were PCR confirmed cases; they were simply diagnosed based on high-resolution computed tomography (HRCT) chest findings and an acute clinical presentation similar to COVID in a pandemic setting [4]. Since mass testing was cost prohibitive in low- and middle-income countries, hospital burden served as a true predictor of disease severity in the population[5], [6].Fig 1 .Fig 4.Figure 1 shows the flow through which participants were included and excluded in the study
After two years, 11 billion vaccine doses, and 528 million confirmed cases, most immune systems are no longer empty vessels for SARS-CoV-2 [2]. If we only evaluate the vaccine's role in preventing SARS-COV-2 infection, it is undervalued in terms of its effects on disease progression and severity in breakthrough cases [7]. Moreover, the natural immunity conferred on 3% of the world's population by infection cannot be overlooked. Despite the fact that prospective cohort studies have the ability to evaluate outcomes between immunized and non-immunized individuals, their enormous size and complex longitudinal follow-up may make such research logistically impractical. Consequently, retrospective investigations comparing past immunization status among patients with known clinical outcomes of disease or absence of disease offer an effective alternative[8], [9]. Because delta variant B.1.17 was the most lethal of all variants and waves in Pakistan, as well as other parts of the world, many people were vaccinated before it arrived. During the delta wave of SARS-CoV-2, there were a substantial number of fully vaccinated breakthrough COVID-19 cases[12], [10], [11].
Despite the fact that fully vaccinated individuals were admitted during both the delta and pre-delta periods, only a negligible number of patients had a history of re-infection. Comparing the pre-delta and delta periods of SARS-COV-2 in Pakistan, we have designed a study to determine COVID-19 admissions in relation to their previous immunization status and to compare the baseline characteristics and outcomes of immunity-naive, vaccinated, and patients with previous infection exposure admitted to the hospital for COVID-19.
2 Material and Methods:
A multicenter retrospective cohort study based on the two largest tertiary care hospitals in Bahawalpur Division, Pakistan, was performed from December 1st, 2020, to December 15th, 2021. The study included all symptomatic, COVID-19-confirmed patients who sought hospital care for their illness. Confirmation was done by molecular testing with real-time PCR or with HRCT chest findings typical of COVID pneumonia with acute-onset respiratory symptoms. Data collection was done by a responsible person from each hospital after approval by the ethical review board vide No. 1264 DME/QAMC Bahawalpur, Pakistan.
All adults aged 17 and above, of both genders, were included. Participants who had previously been immunized (infected with SARS-CoV-2 or vaccinated) were considered the “exposed group,” while immunity-nave (unvaccinated and infection-nave) were considered the “unexposed group.” Further, dividing exposed group of participants into previously infected with SARS-CoV-2, fully vaccinated and partially vaccinated subgroups. Immunization status (previous infection or vaccination exposure) of each patient was confirmed from electronic record of hospital and the national database of COVID-19. Previous vaccination status was confirmed by putting the national identity number of each individual in the database of the vaccination programme by the National Command and Operation Center (NCOC), which gives the information on the vaccination status of each Pakistani national along with the type, date, and dose of vaccine each individual has received. The duration between onset of symptoms and dose of vaccination was also noted to label the participants as unvaccinated, partially vaccinated or fully vaccinated breakthrough infections. Participants who had received both doses of m-RNA vaccine (mRNA 1273 Moderna, Pfizer BioNTech) or inactivated vaccine (Sino Pharm BBIBP vaccine and Sinovac) at least two weeks before the onset of symptoms were considered fully vaccinated; those who had completed two doses of vaccine less than two weeks before the onset of symptoms or received their first dose of vaccine more than two weeks before the onset of symptoms were considered partially vaccinated; and those who had never received any dose of vaccine or received their first dose of vaccine less than two weeks before the onset of symptoms were considered unvaccinated. Previous infection exposure was also confirmed from the COVID-19 national data base, available at covid-19.pshealthpunjab.gov.pk, which gives a track record of each patient regarding molecular testing for SARS-CoV-2 by real-time PCR and admission for severe COVID-19 at any hospital around the country. Previous infection exposure of each patient was confirmed by checking the registration number of each patient from hospital records for their re-entry in COVID-19 wards and later by entering their national ID card number at the national database website, which was accessed via the dashboard of COVID-19 designated wards. Patients who were positive for SARS-CoV-2 by PCR or remained admitted within 3 months of having confirmed COVID-19 were excluded. Re-infected cases who received one or two vaccine doses were also excluded. Patients who were admitted within one week of the closing date without having a desirable outcome were also excluded.
The electronic record and medical log of hospitals were used to collect demographic information such as age, gender, and comorbid conditions (DM, cardiovascular disease, pulmonary disease, immunosuppressive drugs, autoimmunity conditions, organ transplant). The primary outcome measure was 28-day hospital mortality or survival, and secondary outcome measures included the presence or absence of hypoxemia at admission, maximum severity of illness experienced by the patient during the hospital stay, the length of hospital stay in days, and the need for ICU care or organ support. These were also noted from the electronic record and medical log of COVID-19-designated wards of each hospital. We utilized a modified version of the World Health Organization COVID-19 Clinical Progression Scale to classify COVID-19 severity, which ranges from uninfected (level 0) through infected but asymptomatic (level 1) to death (level 9). The highest ordinal level that the patient encountered during the first 28 days of hospitalization was used to classify severity. The highest severity level experienced in this analysis of hospitalized patients could range from level 3 to 9, including hospitalized without supplemental oxygen (level 4), with standard supplemental oxygen (level 5), with high-flow nasal cannula or noninvasive ventilation (level 6), with invasive mechanical ventilation (level 7), with mechanical ventilation and additional organ support (extracorporeal membrane oxygenation, vasopressors, or new Renal Replacement Therapy) (level 8), or with hospital death (level 9)(13)(14). According to scale level 3 was categorized as mild disease, 4 as moderate, level 5 as severe disease and level 6, 7 and 8 as critical illness. Disease severity, hospital stay in days and need for ICU care were taken as secondary outcome.
Because of retrospective nature of study in which both outcome and exposure have already occurred, consent waivers can be applied to study participants. Collection of data was done by a responsible person at each Centre. Patients admitted sequentially to participating COVID dedicated wards, high dependency units, and ICUs during the study period were included in this retrospective analysis without an a priori calculation of sample size. In the main analysis, for the occurrence of outcome measures, the exposed group (vaccinated or with previous infection) was compared to the immunity-naive group. In the subgroup analysis, fully vaccinated participants were compared with the immunity-naive group in both the pre-delta and delta periods (i), re-infected were compared with matched immunity-naive patients during both periods (ii), and re-infected patients with matched fully vaccinated patients during the delta period (iii). Matching was done on the basis of the 5-year age group, gender, and time duration in a 3:1 ratio.
After data collection, data analysis was done through IBM SPSS version 26. We presented continuous variables as median (IQR) values and compared them using the Mann-Whitney test for more than two groups. Categorical variables were presented as frequencies and percentages, as appropriate. The risk of COVID-19 illness severity, hypoxemia at time of hospitalization, need for ICU care after hospitalization, and death outcome among exposed and unexposed groups was calculated in both the pre-delta and delta periods in the primary analysis and all three subgroup analyses. The Chi Square test of independence was used in conjunction with 2*2 tables to determine the relative risk (RR). A post-hoc sensitivity test was done to compare the subgroups and calculate the p values. A relative risk of more than one indicates increased risk, whereas a risk of one indicates decreased risk, with a p value of <.05 considered significant. Risk estimation was calculated with (1-RR)*100. We used a Cox proportional hazards regression model for 28-day survival that included immunization status. Relevant results were presented as hazard ratios (HRs) and 95% CIs, and the corresponding Cox-generated estimated survival curves.
3 Results:
During the study period, a total of 6110 patients were admitted at both tertiary care hospitals; among them, 525 patients were excluded from the study, and the most common reason for exclusion was an unknown previous immunization status or outcome.
The total participants included in the main analysis were 5585, with a median age of 55, an IQR (43, 65), a predominant male gender of 57.6%; 29.3% had no comorbid condition, 25.7% had cardiovascular diseases, 20.9% had diabetes mellitus, and 37.3% had at least one risk factor for severe COVID-19.
Two thousand eight hundred sixty-six (51.3%) patients were admitted between December 1, 2020, and July 15, 2021 (the pre-delta period in Pakistan), and 2719 (48.7%) patients were admitted between July 16, 2021, and November 30, 2021 (the delta period in Pakistan). The unexposed group (immunity naive group) comprised 4695 (84.1%) of the cohort, while the exposed group (immunity naive group) comprised 903 (15.9%) participants of the cohort [445 (8%) were partially vaccinated, 419 (7.5%) were fully vaccinated, and 26 (0.5%) were re-infected]. The characteristics of exposed and unexposed groups are given in Table 1 . Since vaccination began in Pakistan in March 2021 and was phased in over time, it was open to all age groups by May 2021. So, 2777 (97% of patients admitted during the pre-delta period) were immune-naive, with only 63 (2.2%) partially vaccinated, 20 (0.7%) completely vaccinated, and 6 (0.2%) re-infected cases. During the delta period, 1918 (70.5%) of the participants were immune-naive, with 382 (14%) partially vaccinated, 399 (14.7%) fully vaccinated, and 20 (0.7%) re-infected.Table 1 Characteristics of Immunity Naïve, fully vaccinated, partially vaccinated and previously infected admitted patients
Characteristics Unexposed . n (%) Exposed Group n (%)
Partially vaccinated Fully vaccinated Re infected
Age, Median IQR 55(43, 65) 55(45, 65) 55(41, 66) 66(53, 70)
17-45 yrs. 1375(29.3) 113(25.4) 132(31.5) 4(15.4)
46-65 yrs. 2319(49.4) 224(50.3) 184(43.9) 9(34.6)
66-80 yrs. 788(16.8) 86(19.3) 81(19.3) 12(46.2)
>81 yrs. 213(4.5) 22(4.9) 22(5.3) 1(3.8)
No. of Comorbid conditionsMedian (IQR) 1(0, 2) 1(1, 2) 2(1,3) 2(1, 2)
No risk factor 1454(31) 79(17.8) 96(22.9) 6(23.1)
Diabetes Mellitus 944(20.1) 113(25.4) 104(24.8) 5(19.2)
CVD 1169(24.9) 139(31.2) 121(28.9) 7(26.9)
Pulmonary disease 471(10) 52(11.7) 36(8.6) 3(11.5)
CKD 65(1.4) 5(1.1) 3(0.7) 0
CLD 14(0.3) 1(0.2) 0 0
Pregnancy 37(0.8) 2(0.4) 4(1.0) 0
Obesity 103(2.2) 14(3.1) 8(1.9) 0
Old age 397(8.5) 39(8.8) 44(10.5) 5(19.2)
Immunocompromised 41(0.9) 1(0.2) 3(0.7) 0
Hospital stay in daysMedian (IQR) 6 (3, 11) 6 (3, 9) 4 (2, 7) 1 (1, 2)
Admission hypoxemia 4037(86) 350(78.7) 267(63.7) 1(3.8)
No hypoxemia 658(14) 95(21.3) 152(36.3) 25(96.2)
Two hundred and fifty one (28.2%) exposed vs. 1724 (36.7%) immunity-naive were died [absolute difference, 8.5%; 95%CI, (2.22 to 14.19); RR, .768; 95%CI, (.687, .858), p<.001]. So, COVID-19 admitted patients who had prior immunization exposure had 23.2% lower risk of mortality than immunity-naive patients. When the results from pre-delta and delta periods were compared, the risk reduction was more significant in the delta period than in the pre-delta period as shown in Table.2 . Total in-hospital deaths were also significantly lower during the pre-delta period than during the delta period [869 (30.3%) pre-delta period vs. 1106 (40.7%); RR, .745; 95%CI (.694, .801); p.001].Table 3 .Table 2 Comparison of exposed vs immunity-naïve groups for outcome measures in both periods.
Outcome measures. Exposed group n(%) Immunity naïve n(%) Absolute difference95%CI Relative Risk95%CI P value
Pre-delta period
Deaths 17(19.3) 852(30.7) 11.4%(1.7 to 18.5) .629(.409, .968) .013
Admission Hypoxemia 61(69.3) 2322(83.6) 14.3%(5.5 to24.6) .535(.387, .740) .001
Need of ICU care 29(33) 1190(42.8) 9.8%(-.7 to18.9) .853(.734, .991) .04
Mild to moderate disease 27(30.7) 455(16.4) 14.3%(5.5 to 24.6) 1.673(1.35, 2.59) .006
Delta period
Deaths 234(29.2) 872(45.5) 16.3%(12.3 to20.1) .642(.571, .723) <.001
Admission hypoxemia 557(69.5) 1715(89.4) 19.9%(16.4 to23.4) .347(.294, .411) <.001
Need of ICU Care 311(38.8) 1115(58.1) 19.3%(15.2 to23.2) .684(.634, .739) <.001
Mild to moderate disease 247(30.9) 201(10.5) 20.4%(16.9 to23.9) 2.94(2.49, 3.47) <.001
Table 3 Comparison of COVID-19 disease severity and death progression among re-infected with matched fully vaccinated subgroup.
Outcome Measures Re-infected Fully vaccinated RR 95%CI P value
Deaths 0/20 17/60 .082 (.005, 1.32) .07
Severity of COVID-19
Mild to moderate 20/20 6/60 10 (4.68, 21.36) <.001
Admission hypoxemia 0/20 54/60 .100(.047, .214) <.001
Need of ICU care 0/20 22/60 .633(.522, .768) .001
Patients with prior immunization exposure were less likely to be hospitalized with hypoxemia at the time of admission [618(69.5%) exposed vs. 4037(80%) immunity-naïve, absolute difference 16.5%, 95%CI (13.39% to 19.75%); RR, .460; 95%CI (.408, .520); P<.001]. Three hundred and forty (38.2%) exposed vs. 2305(49.1%) needed ICU care after hospitalization [absolute difference, 10.9%, 95%CI (5.25% to 16.31%); RR, .778; 95%CI (.712, 0.850); p <.001]. Five hundred and thirteen (57.6%) exposed vs. 2268(48.3%) had hospital stay of 0-5 days, absolute difference of 9.3%, 95%CI (4.52 to 13.98), p<.001.
For time-to-event analysis from admission to all-cause mortality, we used a Cox proportional-hazards regression model including immunization status and we plotted the corresponding Cox generated estimated survival curves as shown in Figure 2 . Cox regression survival analysis shows that the exposed group, as opposed to the immunity naïve group, had a 39.1% lesser chance of in-hospital 28-day mortality in pre-delta period and a 41.3% lesser chance in delta period, as shown by Hazard Ratio (HR) [HR, .609; 95%CI (.377, .985); p=.043, pre-delta period] and [HR, .587; 95%CI (.508, .678); p<.001, delta period], respectively.Figure 2 shows the Kaplan-Meier (survival) curves done in cox-regression analysis by comparing the exposed vs. immunity naïve groups in both pre-delta and delta periods in the main analysis and then separating the exposed group into partially vaccinated, fully vaccinated and re-infected subgroups in subgroup analyses.
In a subgroup analysis (i), when fully vaccinated patients were compared with immunity naive patients, 21.5 % vs. 36.7 % were died, [absolute difference, 15.2%; 95% CI(10.1% to19.65%); RR, .585; 95% CI(.485,.705), p.001], suggesting that the fully vaccinated group has a 41.5 percent lower risk of death than the immunity-naive group. Risk reduction was significant in delta period [86(21.6%) vs. 872(45.5%) died, RR, .474; 95%CI (.391, .575); p<.001] whereas, insignificant in pre-delta period owing to lesser number of admissions with fully vaccinated status [4(20%) vs. 852(30.7%); RR, .651; 95%CI (.27, 1.56) p=.33]. The risk reduction for being fully vaccinated was greatest among those aged 16 to 45 years old and those without a risk factor, with RR, .442; 95%CI(.248, .787) and RR, .402; 95%CI(.195, .831), respectively, as shown in figure 3 .Fig 4. Figure 3 Compares the disease severity and death progression for being previously vaccinated vs. immunity naïve group. DM (Diabetes Mellitus), CVD (Cardio Vascular Diseases). It shows association between disease severity and its progression to death with previous vaccination status in subgroups of age and comorbid conditions. A Relative Risk of less than 1 indicates a lower risk of death and disease severity among fully vaccinated against Immunity naïve group, however the 95% CI should not cross null.
Figure 4 Compares the disease severity and death outcome among re-infected patients with matched immunity-naïve group in term of absolute difference and Relative risk. RR less than 1 shows significant risk reduction among re infected cases. The forest plot provides a visual representation of relative risk, with a crossing of 1 being deemed insignificant. The findings suggest that the re-infected group has 91.5 percent lesser likelihood of death than the immunity-naive group.
In subgroup analysis (ii), when re-infected patients were compared with matched immunity naïve, 0(0%) vs. 29(48.3%) were died during delta period, RR, .049; 95%CI (.003, .77); p=.03. The results were insignificant during pre-delta period as shown in figure 3. Only one re-infected patient was hypoxemic at the time of admission, and he too had a history of interstitial lung disease, rheumatoid arthritis, and post COVID cryptogenic organizing pneumonia with long-term oxygen reliance. He was the only patient in the re-infected group who died because of complications. Twenty five (96.1%) re-infected vs. four (5.1%) experienced mild to moderate illness, RR, 18.75; 95% CI (7.19, 48.85), p<.001. So, COVID-19 patients who had previous SARS-CoV-2 infection exposure were 18 times more likely to experience mild to moderate disease after hospitalization.
In subgroup analysis (iii), during the delta period, 20 re-infected patients were compared with 60 matched, fully vaccinated patients; 0 re-infected vs. 17 (28.1%) fully vaccinated patients died [absolute difference, 28.3%; 95%CI (9.43% to 40.73%); p=.007; RR, .082, 95%CI (.005, 1.32); p=.07]. Re-infected patients had decreased hazard of 28-day mortality when compared to fully vaccinated admitted COVID-19 patients [HR, .030; 95%CI (.00, 2.64), Chi-square p=.001] as shown in Figure.2. Re-infected COVID-19 patients were 10 times more likely to experience mild to moderate disease during hospital stay when compared with fully vaccinated patients, as shown in table 3. Median hospital stay in days was 1, IQR (1, 1) among re-infected and 5(3, 8) among fully vaccinated patients, p<.001. All 20(100%) re-infected vs. 43 (71.7%) improved and discharged home, RR 1.395; 95%CI (1.19, 1.636), p<.01. Re-infected patients were 1.3 times more likely to improve and be discharged home after hospitalization than fully vaccinated patients.
4 Discussions
In this multicenter study of hospitalizations for COVID-19 from December 2020 to December 2021 (including pre-delta and delta period in Pakistan), among adults ≥17 years, whose immunity status was either “naïve” or “immunized” against SARS-COV-2 (vaccination or previous infection exposure occurred >90 days earlier), previous immunization exposure was associated with a 54 percent reduction in the risk of having hypoxemia at time of hospitalization, a 22.2 percent reduction in the risk of ICU admission for organ support, and almost 40 percent risk reduction in 28-day mortality. Results were more significant in delta period than in pre-delta period. The immunized group had shorter hospital stay than the immunity-naive group.
In terms of mortality and the need for ICU care, the risk reduction increased to more than 40 percent when comparing the completely vaccinated group to the immunity-naive group. Because of the lower rate of vaccination prior to the pre-delta period, the results of risk reduction were more significant in the delta period than in the pre-delta period. Even though the severity of the disease and the likelihood of death were greatly reduced in young patients and those without a risk factor, there was also a significant risk reduction among patients with diabetes, cardiovascular disease, and pulmonary illness too. Studies show that presence of comorbid conditions such as diabetes mellitus and cardiovascular disease are known to contribute the progression of disease in COVID-19 patients and accounts for their high mortality rate [15], [16], [17]. This study showed that fully vaccinated patients with these comorbidities were less likely to die or experience severe disease after hospitalization for COVID-19.
Among patients with previous infection exposure, only one patient had admission hypoxemia, required ICU care and died. This 75-year-old male had a previous history of COVID pneumonia with long COVID's clinical features, became positive by PCR, and was re-admitted to the hospital after 4 months since his previous admission. 96.2% of re-infected COVID-19 cases had mild to moderate illness after being hospitalized when compared to the immunity-naive group. Comparing re-infected cases to the immunity-naive group, there was a 91.5% reduction in the risk of death, a 96% reduction in admission hypoxemia, and a 93.2% reduction in ICU hospitalizations. Compared to fully vaccinated individuals, re-infected patients were more likely to experience mild disease, a shorter hospital stay, and survival leading to discharge from the hospital.
For protection against severe COVID-19, natural immunity appears to be both more effective and lasting than vaccine-induced immunity, as mounting evidence suggests[18], [19]. With one year under its belt since the outbreak, there should have been admissions of re-infection, as there had been with previous vaccination. A one-year retrospective study found that only 0.5% vs. 7.5% (fully vaccinated) of total hospitalized patients had previously been exposed to the pandemic infection; these too were not sick enough to require oxygen or ICU admission. According to studies, patients with natural infection exposure are protected against reinfection for at least 10 months[20], [21], [22]
The vaccination process was completed quickly after the vaccine was introduced to the market, as it was in Pakistan. As all symptomatic and asymptomatic patients were asked to get vaccinated after 2 weeks of recovery from COVID-19[23], it was hard to know which immunity was effective in terms of prevention against hospitalization for COVID-19, severe disease, and death outcomes among breakthrough cases. Emerging evidence suggests waning of vaccine-induced immunity with time, particularly against the variants of concern, it is suggested that all vaccinated individuals should receive a booster dose at least every six months[12]. Keeping past infection exposure to confer strong and long-lasting protection, people with hybrid immunity (natural and vaccine induced immunity) may be able to forego a booster shot[18], [24], [25]. However, this must be proven by randomized controlled trials.
5 Conclusion
Prior immunization exposure was associated with reduced disease severity and risk of mortality among COVID-19 patients compared to immunity naïve patients. Full vaccination was related with a substantial reduction in the likelihood of the worst outcome and the results are more significant in delta period of SARS-COV-2. Among the hospitalized patients, re-infection was shown to be extremely unlikely, and those who had re-infection were more likely to develop mild disease, with an even lower risk of death and disease severity compared to the completely vaccinated and immunity-naive groups.
5.1 Limitation
Retrospective observational nature of study is a limitation of study. Moreover, patients who are admitted with immunity naïve status cannot be ruled out for subclinical previous infections exposure. Prospective longitudinal studies are needed among vaccinated population and infected patients for occurrence of breakthrough cases and reinfections with respect to severity and outcome. Although these studies cannot be carried in an effective way as almost majority population has been vaccinated now. Observational studies include additional problems, such as the larger possibility for bias.
Funding Support
There is no funding support to declare or to acknowledge in this investigation.
Uncited references
[13], [14].
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:
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2 World Health Organization. Weekly epidemiological update on COVID-19 [Internet]. [cited 2022 Jun 3]. Available from: https://covid19.who.int
3 14.9 million excess deaths associated with the COVID-19 pandemic in 2020 and 2021 [Internet]. [cited 2022 Jun 3]. Available from: https://www.who.int/news/item/05-05-2022-14.9-million-excess-deaths-were-associated-with-the-covid-19-pandemic-in-2020-and-2021
4 Gaipov A. Gusmanov A. Abbay A. Sakko Y. Issanov A. Kadyrzhanuly K. SARS-CoV-2 PCR-positive and PCR-negative cases of pneumonia admitted to the hospital during the peak of COVID-19 pandemic: analysis of in-hospital and post-hospital mortality BMC Infect Dis. 2021 Dec 1;21(1
5 Nyberg T. Twohig K.A. Harris R.J. Seaman S.R. Flannagan J. Allen H. Risk of hospital admission for patients with SARS-CoV-2 variant B.1.1.7: Cohort analysis BMJ. 373 2021 1 10
6 Harris J.E. COVID-19 Incidence and hospitalization during the delta surge were inversely related to vaccination coverage among the most populous U.S Counties. Heal policy Technol. 11 2 2022 Jun 100583
7 Aslam J, Rauf ul Hassan M, Fatima Q, Bashir Hashmi H, Alshahrani MY, Alkhathami AG, et al. Association of Disease Severity and Death Outcome with Vaccination Status of Admitted COVID-19 Patients in Delta Period of SARS-COV-2 in Mixed Variety of Vaccine Background. Saudi J Biol Sci [Internet]. 2022 May;103329. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1319562X22002455
8 Lewnard J.A. Patel M.M. Jewell N.P. Verani J.R. Kobayashi M. Tenforde M.W. Theoretical Framework for Retrospective Studies of the Effectiveness of SARS-CoV-2 Vaccines Epidemiology. 32 4 2021 508 517 34001753
9 Vandenbroucke Jan P PN. Test-Negative Designs: Differences and Commonalities with Other Case-Control Studies with “Other Patient” Controls. Epidemiology. 30(6):p 838-844 doi: 10.1097/EDE.0000000000001088.
10 Gazit S. Shlezinger R. Perez G. Lotan R. Peretz A. Ben-Tov A. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Naturally Acquired Immunity versus Vaccine-induced Immunity, Reinfections versus Breakthrough Infections: A Retrospective Cohort Study Clin Infect Dis. 75 1 2022 e545 e551 35380632
11 Johnson AG, Amin AB, Ali AR, Hoots B, Cadwell BL, Arora S. COVID-19 Incidence and Death Rates Among Unvaccinated and Fully Vaccinated Adults with and Without Booster Doses During Periods of Delta and Omicron. MMWR Morb Mortal Wkly Rep. 2022;71(January 21, 2022):1–7.
12 Paz-Bailey G. Sternberg M. Kugeler K. Hoots B. Amin A.B. Johnson A.G. Covid-19 Rates by Time since Vaccination during Delta Variant Predominance NEJM Evid. 1 3 2022 1 13
13 Tenforde M.W. Self W.H. Adams K. Gaglani M. Ginde A.A. McNeal T. Association Between mRNA Vaccination and COVID-19 Hospitalization and Disease Severity Jama. 30329 2021
14 Ramaswamy P. Gong J.J. Saleh S.N. McDonald S.A. Blumberg S. Medford R.J. Developing a COVID-19 WHO Clinical Progression Scale inpatient database from electronic health record data J Am Med Informatics Assoc. 29 7 2022 1279 1285
15 Hebbard C. Lee B. Katare R. Garikipati V.N.S. Diabetes, Heart Failure, and COVID-19: An Update Front Physiol. 12 October 2021 1 12
16 Ku C.R. Jung K.Y. Ahn C.H. Moon J.S. Lee J.H. Kim E.H. Covid-19 vaccination for endocrine patients: A position statement from the korean endocrine society Endocrinol Metab. 36 4 2021 757 765
17 Pal R. Kumar S. Misra A. COVID-19 vaccination in patients with diabetes mellitus: Current concepts, uncertainties and challenges Diabetes Metab Syndr Clin Res Rev. 15 2 2021 19 23
18 Pilz S, Theiler-Schwetz V, Trummer C, Krause R, Ioannidis JPA. SARS-CoV-2 reinfections: Overview of efficacy and duration of natural and hybrid immunity. Vol. 209, Environmental Research. Academic Press Inc.; 2022.
19 Abu-Raddad L.J. Chemaitelly H. Bertollini R. Severity of SARS-CoV-2 Reinfections as Compared with Primary Infections N Engl J Med. 385 26 2021 Dec 23 2487 2489 34818474
20 O Murchu E, Byrne P, Carty PG, De Gascun C, Keogan M, O’Neill M, et al. Quantifying the risk of SARS-CoV-2 reinfection over time. Vol. 32, Reviews in Medical Virology. John Wiley and Sons Ltd; 2022.
21 Pilz S. Chakeri A. Ioannidis J.P.A. Richter L. Theiler-Schwetz V. Trummer C. SARS-CoV-2 re-infection risk in Austria Eur J Clin Invest. 2021 Apr 1;51(4
22 Shenai MB, Rahme R, Noorchashm H. Equivalency of Protection from Natural Immunity in COVID-19 Recovered Versus Fully Vaccinated Persons: A Systematic Review and Pooled Analysis. Available from: https://doi.org/10.1101/2021.09.12.21263461
23 Bozio CH, Grannis SJ, Naleway AL, Ong TC, Butterfield KA, DeSilva MB, et al. Morbidity and Mortality Weekly Report Laboratory-Confirmed COVID-19 Among Adults Hospitalized with COVID-19-Like Illness with Infection-Induced or mRNA Vaccine-Induced SARS-CoV-2 Immunity-Nine States, January-September 2021. Available from: https://www.medrxiv.org/content/10.1101/2021.08.24.21262415v1
24 Gazit S. Shlezinger R. Perez G. Lotan R. Peretz A. Ben-Tov A. The Incidence of SARS-CoV-2 Reinfection in Persons With Naturally Acquired Immunity With and Without Subsequent Receipt of a Single Dose of BNT162b2 Vaccine Ann Intern Med 2022 May 15
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| 36517324 | PMC9731929 | NO-CC CODE | 2022-12-14 23:31:56 | no | Vaccine. 2022 Dec 9; doi: 10.1016/j.vaccine.2022.12.003 | utf-8 | Vaccine | 2,022 | 10.1016/j.vaccine.2022.12.003 | oa_other |
==== Front
Br J Anaesth
Br J Anaesth
BJA: British Journal of Anaesthesia
0007-0912
1471-6771
British Journal of Anaesthesia. Published by Elsevier Ltd.
S0007-0912(22)00663-8
10.1016/j.bja.2022.12.002
Correspondence
Checking in on residents who commenced anaesthesia training during the Covid-19 pandemic to mitigate mental health impacts of their experience
Lurie Jacob M. 1
Brumberger Eric 1
Pryor Kane O. 1
Gotian Ruth 1∗1
1 Weill Cornell Medicine, Department of Anesthesiology, New York, United States of America
∗ Corresponding author. , Weill Cornell Medicine, Department of Anesthesiology, 525 East 68th Street, New York, New York 10065
1 Twitter@RuthGotian.
9 12 2022
9 12 2022
9 11 2022
4 12 2022
4 12 2022
© 2022 British Journal of Anaesthesia. 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.
Keywords
Covid-19
burnout
residency
mentorship
wellness
==== Body
pmcBurnout has been a significant occupational concern since the term was first coined by Herbert Freudenberger in 1974.1 Known to disproportionately impact physicians, burnout can be exacerbated by work-home conflicts and a lack of control over schedules and priorities. In 2019, a survey of 15,000 American physicians determined that 44% felt “burned out,” while 15% were either “colloquially depressed” or “clinically depressed.”2 In 2019, more than two-in-five anaesthesiologists reported feeling burned out. The subsequent coronavirus disease 2019 (Covid-19) pandemic worsened burnout for most physicians.3 A 2021 survey found that more than one-in-five physicians reported new burnout symptoms and 47% reported their burnout had a “strong/severe impact” on their wellbeing.4 As explained by Afonso et al., the inherent stressors of anaesthesiology “have been amplified during the Covid-19 pandemic, with anaesthesiologists often performing on the front lines.”5
We would like to call attention to the mental health and wellbeing of anaesthesia trainees who, quite early in their medical careers, were faced with the daunting task of caring for patients during a pandemic. In regions such as the United Kingdom, Ireland, and Australasia, anaesthesia trainees had only a few years of medical experience prior to the pandemic. In the United States, many anaesthesia residents graduated from medical school in the midst of Covid-19, with no pre-pandemic experience. In addition, some of these residents elected to enter the workforce early, having their medical training abruptly halted to fight the pandemic.6 Even though many feel that the virus has been neutralized with the advent of vaccinations and appearance of more contagious but less morbid strains, the early impact of the pandemic on the mental health of trainees should be addressed. As much of the world retreated and came to a standstill, these trainees marched forward to combat Covid-19. Residents who started their training around the time of the pandemic have been under a unique pressure, and a failure to recognize this stress and appropriately support these trainees could lead to burnout sooner rather than later. And now, as we enter the fourth year of the pandemic, we need to take a moment to check in with these residents.
A recent multi-national survey of residents and fellows determined that residents caring for patients with Covid-19 are indeed burned out, and the primary predictor of burnout was the number of patients on a trainee’s roster with Covid-19.7 Preventing burnout among residents is imperative for residency programs seeking to foster the next generation of anaesthesiologists, and perhaps more so now than ever before, we should take tangible steps to ensure that burnout is mitigated and these doctors are protected. Strategies utilizing a multifaceted approach are the most effective at reducing burnout in physicians,8 and thus we offer several suggestions for anaesthesiology residency programs to help combat resident-physician burnout stemming from the Covid-19 pandemic.
Meet with residents often, one-on-one and in groups
Regular meetings between program directors and residents are good practice, and these debriefs should prioritize resident wellbeing. As recognized by the Association of American Medical Colleges, the Covid-19 pandemic has disrupted all aspects of life, and residency is no exception.9 Academic leaders should question residents about their work during the pandemic and allow trainees to speak freely about their experiences. At Weill Cornell Medicine, for example, we orchestrated regular virtual check-ins with residents to ensure opportunities for debriefing as the pandemic unfolded, in addition to the regularly scheduled resident meetings already in place. In an American Medical Association (AMA) survey, about one-in-three resident-physicians reported “work overload” as well as “high stress,” of which over half was associated with fear of exposure to the virus.9 Utilizing one-on-one and group meetings reassured residents and contextualized their experiences with those of others in their cohort. Many residents, despite having treated patients with Covid-19 for multiple years, have not been given a platform to speak with program leadership to reflect upon and synthesize their experiences. We suggest encouraging residents to share, if they are willing, but nonetheless remind them that they are always welcome to talk and that they are not alone.
Reaffirm trainees’ purpose and dedication, and thank them
Less than half of the residents surveyed by the AMA in its 2021 Covid-19 Survey felt that they mattered to their organization.10 During residency, positive feedback can be elusive, and trainees who are at baseline high-achieving and successful may be hard on themselves. We recommend offering verbal support to residents and consider establishing a system to celebrate their success. A regular email that is sent to the department with “shout-outs” to specific residents for their hard work and achievements, for instance, could serve to boost morale and allow trainees to celebrate both themselves and one another.
Cultivate comradery, and actively encourage resident bonding
Residency in general, augmented by quarantine, can at times be monotonous, with long working days that seem to blend together. Even if they need to be virtual, it is vital to have items on the calendar to look forward to, to be excited about, and to break up the day-in, day-out routines. It is one thing to suggest residents gather socially to decompress, but it is quite another to provide funding for such events and to encourage resident support committees, if they exist, to organize gatherings among the residency. Residents are each other’s best support system,11 offering ideas, perspective, and empathy. As Covid-19 has sporadically made in-person gatherings difficult, we suggest promoting resident hangouts (virtual, if necessary), such as creative or sporting events.
Create space for sharing experiences
Too often do our experiences with Covid-19 go unspoken. When confronted with the pandemic each day, for what has now been years, we often gravitate toward discussing something – anything - other than Covid-19. While such discussions can be a valuable escape, we should also encourage residents to give voice to their concerns and worries. Little of what residents have experienced since Covid-19 began has been normal. We recommend promoting a culture that supports residents in sharing their thoughts and allows them to be vulnerable.
Continue to seek proven solutions, and where there are few, strive toward your own
It is well known that burnout is a problem in medicine, but we still have little data to guide us in preventing burnout among trainees. Brainstorming with your faculty and engaging residents in conversations about how they can best be supported may be useful. They will tell you, and if they are not sure, they will still appreciate your asking. Be there for your residents and keep lines of communication open. A little effort on this front can go a long way. In addition, resources exist that can help guide programs looking to improve their approach to burnout, such as the National Academy of Medicine’s Resource Toolkit for Clinician Well-Being.12
Conclusion
While we are still learning about the effects on residents’ mental wellbeing associated with the pandemic, there are concrete actions that residency programs can take to help alleviate Covid-19 burnout. We encourage anaesthesia residency programs to check in with their trainees and keep lines of communication open to ensure residents are supported and are not burning out.
Funding
None.
Conflict of Interest Disclosure
The authors report no conflicts of interest.
Author Contributions
JML: Conception of article, drafting and critical revisions.
EB: Critical revisions and input on intellectual content.
KOP: Review of draft and input on intellectual content.
RG: Conception of article, critical revisions and input on intellectual content.
==== Refs
References
1 Freudenberger H.J. Staff Burn-Out Journal of Social Issues 30 1 1974 159 165
2 Kane L. Medscape National Physician Burnout, Depression & Suicide Report 2019. Available from: https://www.medscape.com/slideshow/2019-lifestyle-burnout-depression-6011056?faf=1#5. Date accessed: November 2, 2022.
3 Berg S. Half of health workers report burnout amid COVID-19. 2021. Available from: https://www.ama-assn.org/practice-management/physician-health/half-health-workers-report-burnout-amid-covid-19. Date accessed: November 2, 2022.
4 Kane L. Medscape National Physician Burnout & Suicide Report 2021. 2021. Available from: https://www.medscape.com/slideshow/2021-lifestyle-burnout-6013456#1. Date accessed: November 2, 2022.
5 Afonso A.M. Cadwell J.B. Staffa S.J. Zurakowski D. Vinson A.E. Burnout Rate and Risk Factors Among Anesthesiologists in the United States. 2021 Anesthesiology 134 5 2021 683 696 33667293
6 Abrams A., Ducharme J. Meet the Medical Students Becoming Doctors in the Middle of a Pandemic. 2020. Available from: https://time.com/5820046/medical-students-covid-19/. Date accessed: November 2, 2022.
7 Cravero A.L. Kim N.J. Feld L.D. Impact of exposure to patients with COVID-19 on residents and fellows: an international survey of 1420 trainees Postgraduate Medical Journal 97 2021 706 715 33087533
8 Panagioti M. Panagopoulou E. Bower E. Controlled Interventions to Reduce Burnout in Physicians: A Systematic Review and Meta-analysis JAMA Internal Medicine 177 2 2017 195 205 27918798
9 Transition in a Time of Disruption: Practical Guidance to Support Learners in the Transition to Graduate Medical Education. 2022. Available from: https://www.ecfmg.org/annc/ume-gme.pdf. Date accessed: November 2, 2022.
10 American Medical Association Coping with COVID-19 for Caregivers Survey. 2021. Available from: https://www.ama-assn.org/system/files/coping-with-covid-19-caregivers-survey-national-comparison-report.pdf. Date accessed: November 2, 2022.
11 Gotian R. Why You Need a Support Team Nature 568 2019 425 426 30976108
12 National Academy of Medicine: Resource Toolkit for the Clinician Well-Being Knowledge Hub. 2022. Available from: https://nam.edu/resource-toolkit-clinician-well-being-knowledge-hub/. Date accessed: November 23, 2022.
| 0 | PMC9731930 | NO-CC CODE | 2022-12-15 23:16:09 | no | Br J Anaesth. 2022 Dec 9; doi: 10.1016/j.bja.2022.12.002 | utf-8 | Br J Anaesth | 2,022 | 10.1016/j.bja.2022.12.002 | oa_other |
==== Front
Vaccine
Vaccine
Vaccine
0264-410X
1873-2518
The Authors. Published by Elsevier Ltd.
S0264-410X(22)01520-1
10.1016/j.vaccine.2022.11.077
Article
Willingness of Brazilian caregivers in having their children and adolescents vaccinated against Covid-19
Fernandes Nehab Marcio am
Gonçalves Camacho Karla b
Teixeira Reis Adriana c
de Fátima Junqueira-Marinho Maria d
Marques Abramov Dimitri e
Maria Almeida de Azevedo Zina f
dos Santos Salú Margarida g
Farias Meira de Vasconcelos Zilton h
Clair dos Santos Gomes Junior Saint i
Carvalho da Silva Filho Orli j
Tuani Candida de Oliveira Salvador Petala k
Yasmin Andrade Alves Kisna l
Roseli Silva de Carvalho Katiuscia m
Campelo Batalha Cox Moore Daniella n⁎
a National Institute of Womeńs, Childreńs and Adolescentś Health Fernandes Figueira, FIOCRUZ; adress: Avenue Rui Barbosa, 716, Flamengo, Rio de Janeio-RJ, Brazil, zip code 22250-020
b National Institute of Womeńs, Childreńs and Adolescentś Health Fernandes Figueira, FIOCRUZ; adress: Avenue Rui Barbosa, 716, Flamengo, Rio de Janeio-RJ, Brazil, zip code 22250-020
c National Institute of Womeńs, Childreńs and Adolescentś Health Fernandes Figueira, FIOCRUZ; adress: Avenue Rui Barbosa, 716, Flamengo, Rio de Janeio-RJ, Brazil, zip code 22250-020
d National Institute of Womeńs, Childreńs and Adolescentś Health Fernandes Figueira, FIOCRUZ; adress: Avenue Rui Barbosa, 716, Flamengo, Rio de Janeio-RJ, Brazil, zip code 22250-020
e National Institute of Womeńs, Childreńs and Adolescentś Health Fernandes Figueira, FIOCRUZ; adress: Avenue Rui Barbosa, 716, Flamengo, Rio de Janeio-RJ, Brazil, zip code 22250-020
f National Institute of Womeńs, Childreńs and Adolescentś Health Fernandes Figueira, FIOCRUZ; adress: Avenue Rui Barbosa, 716, Flamengo, Rio de Janeio-RJ, Brazil, zip code 22250-020
g National Institute of Womeńs, Childreńs and Adolescentś Health Fernandes Figueira, FIOCRUZ; adress: Avenue Rui Barbosa, 716, Flamengo, Rio de Janeio-RJ, Brazil, zip code 22250-020
h National Institute of Womeńs, Childreńs and Adolescentś Health Fernandes Figueira, FIOCRUZ; adress: Avenue Rui Barbosa, 716, Flamengo, Rio de Janeio-RJ, Brazil, zip code 22250-020
i National Institute of Womeńs, Childreńs and Adolescentś Health Fernandes Figueira, FIOCRUZ; adress: Avenue Rui Barbosa, 716, Flamengo, Rio de Janeio-RJ, Brazil, zip code 22250-020
j National Institute of Womeńs, Childreńs and Adolescentś Health Fernandes Figueira, FIOCRUZ; adress: Avenue Rui Barbosa, 716, Flamengo, Rio de Janeio-RJ, Brazil, zip code 22250-020
k School of Health at the Federal University of Rio Grande do Norte. Adress: Avenue Senador Salgado Filho, s/n - Lagoa Nova, Natal-RN, Brazil, zip code: 59078-970
l School of Health at the Federal University of Rio Grande do Norte. Adress: Avenue Senador Salgado Filho, s/n - Lagoa Nova, Natal - RN, Brazil, zip code: 59078-970
m Secretary of State for Public Health of Rio Grande do Norte; adress: Avenue Deodoro da Fonseca, 730 - Cidade Alta, Natal - RN, 59025-600
n National Institute of Womeńs, Childreńs and Adolescentś Health Fernandes Figueira, FIOCRUZ, Avenue Rui Barbosa, 716, Flamengo, Rio de Janeio-RJ, Brazil, zip code 22250-020
⁎ Corresponding author at: Daniella Campelo Batalha Cox Moore. National Institute of Womeńs, Childreńs and Adolescentś Health Fernandes Figueira, FIOCRUZ; adress: Avenue Rui Barbosa, 716, Flamengo, Rio de Janeio-RJ, Brazil.
9 12 2022
9 12 2022
9 5 2022
10 11 2022
27 11 2022
© 2022 The Authors. Published by Elsevier Ltd.
2022
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Background
The vaccination of children and adolescents for the prevention of Covid-19 is important to:decrease in deaths and hospitalizations, prevent multisystem inflammatory syndrome, avoid long-term complications and decrease the suspension of on-site classes. Despite of these benefits, some studies have shown that some caregivers are still hesitancy.
Methods
This is a voluntary and anonymous online survey conducted from November 17 to December 14, 2021, in Brazil, through a free-of-charge platform with a link provided on social networks. A bivariate analysis was conducted with the independent variables, with vaccine hesitancy as the outcome variable, and a multivariate logistic model was used to calculated adjusted odds ratios.
Results
The sample included 15,297 respondents. Approximately 13.3% (2,028) of the caregivers were hesitant to vaccinate their children and adolescents against Covid-19 in at least one age group. The vaccination hesitanty rate of caregivers of children aged 0-4 years, 5-11 years and adolescents were 16%, 13%, 15%, respectively. The principal factors associated with vaccine hesitancy were the following: belief that they need to wait longer, belief that children that had natural infection doesn’t need to vaccinate and belief that vaccine has long term adverse effects.
Interpretation
The present study showed that the willingness of caregivers to have their children and adolescents vaccinated in Brazil is high compared to data from adult and pediatric international studies. This study provides a profile of the hesitant caregivers considering their perspectives and beliefs regarding vaccines that can help the elaboration of strategies to increase vaccine adherence.
Keywords
vaccine hesitancy
survey
Brazil
Covid-19 vaccine
children
adolescents
==== Body
pmc1 Introduction
Since March 11, 2020, when WHO declared Covid-19 a pandemic, global efforts have been made to control the dissemination of the new Coronavirus[1]. In this setting, vaccines against Covid-19 are critical in reducing the epidemic curve. However, although vaccines have historically been responsible for the control of a large part of the epidemics that humankind has experienced so far, representing the triumph of modern Medicine, a study conducted in emergency departments in the USA, Canada, and Israel, has shown that the number of caregivers willing to vaccinate their children against Covid-19 has been decreasing even though the pandemic still inspires concern[2]. A systematic review has found that caregivers’ willingness to vaccinate children against Covid-19 is approximately 59.3%, which rises concern, perplexity, and the need to have a more in-depth understanding of this hesitancy[3].Table 1. Table 1 Socio-demographic and vaccine characteristics of the study participants and the children/adolescents they care for stratified by vaccine hesitancy
Variables Hesitant Not Hesitant p-value
Age group18-39 years old40-59 years old60-74 years old≥ 75 years old 789 (11.7%)1,181 (14.3%)58 (21.7%)0 (0%) 5,959 (88.3%)7,090 (85.7%)209 (78.3%)11 (100%) <0.001
EthnicityWhiteBlackBrownYellowIndigenous 1,515 (13.6%)63 (8.1%)413 (13.4%)29 (12.6%)2 (8%) 9,622 (86.4%)716 (91.9%)2,671 (86.6%)202 (87.4%)23 (92%) <0.001
GenderFemaleMaleOther/I'd rather not inform 1,535 (12%)479 (19.9%)10 (21,7%) 11,284 (88%)1927 (80.1%)36 (78.3%) <0.001
SchoolingIncomplete Fundamental EducationCompleted Fundamental EducationCompleted High SchoolCompleted Higher Education 10 (24.4%)22 (14.28%)248 (12.42%)1,740 (13.3%) 31 (75.6%)132 (85.7%)1,748 (87.57%)11,313 (86.6%) <0.001
Monthly IncomeNo incomeUp to US$ 641US$ 642-855US$ 856-2,137> 2,138 41 (14.2%)176 (9.8%)275 (11%)491 (11.70%)936 (16.3%) 248 (85.8%)1,615 (90.2%)2,222 (89.0%)3,705 (88.3%)4,791 (83.7%) <0.001
Change of income during the pandemicNo income changeIncreased incomeDecreased income 876 (12.2%)89 (12.3%)1,048 (14.4%) 6,323 (87.8%)632 (87.7%)6,244 (85.6%) <0.001
Complete vaccination of caregivers against Covid-19YesNo, only the 1st doseNot vaccinated 1,476 (10.1%)154 (64.7%)392 (96.8%) 13,163 (89.9%)84 (36.3%)13 (3.2%) <0.001
Routine vaccinesVaccinates their children with all vaccinesRefuses some vaccinesDoes not vaccinate 1,878 (12.29%)135 (44.7%)11 (84.6%) 13,080 (85.5%)167 (55.3%)2 (15.4%) <0.001
Any child with deficiency or chronic diseaseYesNo 212 (11.5%)1,813 (13.5%) 1,625 (88.5%)11,618 (86.5%) 0.01
Children attending on-site educationYes (some/all of them)No 1,787 (11.68%)116 (6.8%) 10,832 (70.81%)1600 (93.2%) <0.001
Any child feeling lonely, sadness, depression, anxiety due to social distancingYesNo 668 (10.1%)1,355 (15.7%) 5,936 (89.9%)7,299 (84.3%) <0.001
Any children who have had CovidYesNo 502 (19.6%)1,523 (12%) 2,057 (80.4%)11,195 (88%) <0.001
Vaccination of children and adolescents is especially important due to many factors, and the major ones are the decrease in deaths and hospitalizations. The estimated effectiveness of 2 doses of Pfizer-BioNTech vaccine against severe conditions such as the multisystem inflammatory syndrome in children (MIS-C) from 12 to 18 years old was 91% in a control case study in the USA[4]. Another potential target is to avoid long-term complications, such as long Covid, in this age group [5]. Aside from protecting children, the vaccine can reduce the number of school outbreaks, and therefore, decrease the suspension of on-site classes, avoiding disruption to in-person learning[5]. Distancing measures, particularly school closures for in-person education, which have protected children so far, have already shown damaging effects on children’s mental health, learning, and psychosocial development[6].
A study conducted to evaluate the willingness of Brazilian adults to vaccinate themselves against Covid-19 has shown vaccine hesitancy of only 10.5%[7], suggesting that Brazilians have higher adhesion to vaccination than that observed in other countries [8], [9]. Considering that the perspectives and beliefs that caregivers have regarding vaccines are important factors to predict children immunization status[10], the aim of the present study is to evaluate the willingness of Brazilian caregivers to vaccinate their children against Covid-19 and factors associated to vaccine hesitancy.
2 Methodology
This is a voluntary and anonymous online survey conducted from November 17 to December 14, 2021, in Brazil, through a free-of-charge platform (https://www.google.com/forms/about/) with a link provided on the following social networks: WhatsApp, Telegram, Facebook, Instagram, Twitter, and LinkedIn. All participants were encouraged to share the study form through their own social media. The form link was also disclosed on the official page of Instituto Nacional de Saúde da Mulher, da Criança e do Adolescente Fernandes Figueira, FIOCRUZ (National Institute of Women, Children and Adolescents Health) (www.iff.fiocruz.br). The form was comprised of 33 closed questions and 2 open questions, elaborated after a literature review and a discussion with the group of experts from the research team.
2.1 Inclusion and exclusion criteria
The sample was comprised of all the questionnaires answered by participants who declared they were 18+ years old, Brazilians, residing in Brazil at the time of the survey, and caregivers of children and/or adolescents below 18 years old. The sample would exclude one of the records when all items completed in two questionnaires were identical, which reflected duplicated answers, or when the forms were sent with all items in blank. Duplicated records were excluded using SPSS, which compared the level of similarity between answers in closed variable fields, with two open fields that should have a higher level of heterogeneous answers.
2.1.1 Outcome
Study outcome is vaccine hesitancy, defined according to the criteria of the SAGE Working Group on Vaccine Hesitancy, which is delayed acceptance or complete refusal of vaccination. Caregivers were asked about their willingness to vaccinate their children in a stratified manner, according to age group: 0-4 years old, 5-11 years old, and 12+ years old. The format of the answer followed a Likert scale: very likely, likely, unlikely, very unlikely, not sure. Therefore, the study defined as vaccine hesitant those caregivers who answered the question concerning their willingness to vaccinate their children as “unlikely”, “very unlikely”, or “not sure”.
2.1.2 Variables
The following variables about the respondents were considered: 1) demographic: gender (male, female, others), age group (18-39 years old, 40-59 years old, ≥ 60 years old), self-declared ethnicity (white, brown, black, yellow, indigenous), education (completed nine-year fundamental education, High School, or Higher education), monthly income, information about respondents' vaccination against Covid-19, information about whether there was change in income during the pandemic (increased, decreased, no change), information about compliance with vaccination against other diseases other than Covid-19. Variables related to children and adolescents were: whether the child or adolescent had any physical, mental, sensory deficiency or a chronic disease that needs follow-up, whether they were attending on-site education, the perception of children and adolescents concerning loneliness, whether they felt sad or depressed due to limited social contact during the pandemic, private health insurance, information about whether the child or adolescent had had Covid-19, whether hospitalization had been required to treat Covid-19 or its complications, perception of the caregivers on the potential severity of Covid-19 in children and adolescents. Additionally, a set of sentences with options for answers was provided in likert format regarding the perception of caregivers about Covid-19 and vaccination.
2.1.3 Context/Background
Until the collection of data, Brazil had had a total of 616,251 accumulated deaths since the beginning of the pandemic. The number of new cases was 10,055. Vaccination against Covid-19 in adolescents aged 12 years or older had been authorized by ANVISA and the Pfizer vaccine (Cominarty) had been provided by the Ministry of Health of Brazil on August 23, 2021. Study recruitment was completed on December 14, the authorization was extended to Comirnarty at a dosage of 10mcg against Covid-19 for children aged 5-11 years on December 16, 2021, and Coronavac was approved for children aged 6-11 years on January 20, 2022. Authorization for vaccination against Covid-19 had not been provided for children aged 0-4 years until April 2022.
2.1.4 Statistical analysis
Data were coded and analyzed using the SPSS software. All variables were analyzed according to their absolute and relative frequencies. Bivariate analysis was performed with vaccine hesitancy as the outcome. This analysis used the chi-square test, and statistical significance of differences was set at a p-value < 0.05. A multivariate logistic model was used to calculate adjusted odds ratios with the respective 95% confidence intervals for the set of statistically significant variables in the bivariate analysis.
3 Results
After the exclusions (268 reports), a total of 15,297 respondents were included in the analysis. Approximately 13.3% (2,028) of the caregivers were hesitant to vaccinate their children and adolescents against Covid-19 in at least one age group. The caregivers of children aged 0-4 years had the highest vaccination hesitancy rate, 16%; The vaccination hesitancy rate of caregivers of children aged 5-11 years and caregivers of adolescents younger than 18 years old were 13% and 15%, respectively (Figure 1 ).Figure 1 The graphs show the distribution of answers about willingness to vaccinate children aged 0-4 years (graph a) and children aged 5-11 years (graph b) with the following options: very likely, somewhat likely, not sure, unlikely, very unlikely. Graph c shows the distribution of answers about the intention of vaccinating adolescents (vaccines already available) with the options: vaccinated, not vaccinated, not sure.
All five macro-regions in Brazil were represented in this study although there was predominance of answers from the southeastern region (northern region, n=368; northeastern region, n=1,266; mid-western region, n=1,167; southern region, n=1,703; southeastern region, n=10,793).
Socio-demographic variables and variables related to vaccination were stratified according to vaccine hesitancy and are available in table 1. Higher vaccination hesitancy rates against Covid-19 in children and adolescents were observed among caregivers who had not vaccinated against Covid-19. However, it was also observed among caregivers that had only received the first dose, which means this reflects the standpoint of caregivers, either those who did not complete or who are delaying their own vaccination. Approximately 46.7% of caregivers believed that children have little or no chance of developing the severe form when infected by SARS-CoV-2.
Table 2 shows some beliefs, fears, and perceptions related to Covid-19 and vaccination, stratified by vaccine hesitancy. Most participants (89.1%) do not believe that natural immunity is a better protection measure than vaccination, disagree that natural products are more valuable than vaccines (90%), and believe in the severity of the pandemic (91.3%). The fear of long-term adverse reactions resulting from vaccination was only reported by approximately 12.9% of caregivers. The vaccine is also seen as important for a safer return to school by 88.1% of the caregivers. Higher vaccination hesitancy rates were observed among those who believe that vaccines are safer for adults than for children, who believe vaccines have long-term adverse effects, who feel they need more time to feel safer to have their children vaccinated, who believe that those who have already had Covid-19 do not need vaccination, who believe natural immunity is better than vaccination, that natural products are preferable rather than vaccines, and that the pandemic is not as severe as the press has claimed. Those who disagree that vaccines might ensure a safer return to school also showed higher vaccine hesitancy rates.Table 3. Table 2 Beliefs, fears, and perceptions related to Covid-19 and vaccination, stratified by vaccine hesitancy.
Variables Hesitancyyes Hesitancyno p-value
Afraid that the son/daughter has an adverse reaction resulting from the vaccineVery afraidSomewhat afraidIndifferentSlightly afraidNot afraid 1,399 (75.3%)451 (13%)31 (9%)103 (1.9%)41 (1%) 460 (24.7%)3,022 (87%)314 (91%)5,437 (98.1%)4,022 (99%) <0.001
What is the chance of the child/adolescent having serious Covid-19?High chanceSome chanceNot sureLow chanceNo chance 44 (4.3%)244 (5.2%)187 (7.8%)1,345 (19.6%)207 (70.6%) 990 (95.7%)4,465 (94.8%)2,197 (92.2%)5,514 (80.4%)86 (29.4%) <0.001
Vaccines are safer for adults than for children and adolescentsCompletely agreeAgreeNot sureDisagreeCompletely disagree 523 (61.3%)585 (41.8%)513 (11.1%)223 (4.4%)178 (5.4%) 330 (38.7%)814 (58.2%)4,115 (88.9%)4,894 (95.6%)3,104 (94.6%) <0.001
Vaccines have long-term adverse effectsCompletely agreeAgreeNot sureDisagreeCompletely disagree 1,054 (93.2%)465 (55.4%)424 (7.7%)66 (1.4%)18 (0.6%) 77 (6.8%)375 (44.6%)5091 (92.3%)4,570 (98.6%)3,140 (99.4%) <0.001
I need more time to feel safeCompletely agreeAgreeNot sureDisagreeCompletely disagree 1334 (86.9%)422 (26.2%)107 (9.3%)110 (1.7%)55 (1.2%) 201 (13.1%)1,186 (73.8%)1,040 (90.7%)6,235 (98.3%)4,590 (98.8%) <0.001
Vaccines ensure a safer return to on-site educationCompletely agreeAgreeNot sureDisagreeCompletely disagree 176 (1.7%)283 (8.8%)263 (70.7%)540 (82.2%)764 (95.6%) 10,060 (98.3%)2,934 (91.2%)109 (29.3%)117 (17.8%)35 (4.8%) <0.001
People who have had Covid-19 do not need to vaccinateCompletely agreeAgreeNot sureDisagreeCompletely disagree 832 (91%)427 (75.4%)319 (43.2%)305 (6.5%)144 (1.7%) 82 (9%)139 (24.6%)419 (56.8%)4,366 (93.5%)8,249 (98.3%) <0.001
Natural immunity is a better solution than vaccinationCompletely agreeAgreeNot sureDisagreeCompletely disagree 484 (90.5%)408 (85.4%)369 (56.3%)496 (16.5%)266 (13.2%) 51 (9.5%)70 (14.6%)286 (43.7%)2,514 (83.5%)10,329 (97.5%) <0.001
The pandemic is not as severe as the media claimsCompletely agreeAgreeNot sureDisagreeCompletely disagree 326 (82.3%)424 (71.5%)216 (60.7%)612 (25.1%)446 (3.9%) 70 (17.7%)169 (28.5%)140 (39.3%)1,827 (74.9%)11,045 (96.1%) <0.001
I prefer to use natural products than vaccineCompletely agreeAgreeNot sureDisagreeCompletely disagree 495 (95.7%)488 (80.7%)237 (57.1%)503 (14.2%)299 (2.9%) 33 (4.3%)117 (19.3%)178 (42.9%)3,037 (85.8%)9,896 (97.1%) <0.001
Table 3 Multivariate logistic regression to evaluate factors associated to vaccine hesitancy of those responsible for vaccinating children and adolescents.
Logistic regression for overall hesitancy
variables β Wald p-value AOR (95%CI)
Sex (male x others) 0.315 6.802 0.009 1.37 (1.08-1.73)
Children attending on-site education 0.397 5.634 0.018 1.48 (1.07-2.06)
Fear of adverse reactions 1.303 138.28 <0.001 3.68 (2.96-4.57)
Belief that children might develop the severe form of Covid-19 -0.596 34.166 <0.001 0.55 (0.45-0.67)
Belief that vaccines are safer for adults than for children 0.655 40.182 <0.001 1.92 (1.57-2.35)
Belief that the vaccine has long-term adverse effects 1.622 219.20 <0.001 5.06 (4.08-6.27)
Belief that they need to wait longer to vaccinate 1.918 361.06 <0.001 6.80 (5.58-8.29)
Belief that the child who has had Covid-19 does not need vaccination 1.682 148.69 <0.001 5.37 (4.10-7.04)
Belief that natural immunity is better than vaccination 0.749 15.879 <0.001 2.11 (1.46-3.05)
Belief that the pandemic is not as severe 0.433 6.201 0.013 1.54 (1.09-2.17)
Preference for using natural products to increase immunity rather than the vaccine 1.313 67.290 <0.001 3.71 (2.71-5.08)
Constant -4.800 358.63 <0.001
A logistic regression was performed to evaluate the variables that were more related to vaccine hesitancy and it is available in table 3. Variables with the highest value were the perception that more time is required (OR=6.80), the perception that it is not necessary to vaccinate those who have already had the infection (OR=5.37), and the belief that vaccines might have long-term adverse reactions (OR=5.06).
4 Discussion
The present study gathered the opinion of 15,297 caregivers from all regions of the country and shows that 13.3% of caregivers are hesitant to vaccinate children and adolescents against Covid-19, a number higher than that observed in the study on the willingness of Brazilian adults to vaccinate[7]. In Brazil, a study with 501 caregivers of children and adolescents residing in São Paulo, conducted between May and June, 2021, showed vaccine hesitancy of only 2.5%[11]. International studies have shown a higher hesitancy profile. In December to march 2021, a study from USA, Canada and Israel showed a drop in the willingness of caregivers to have their children vaccinated against Covid-19 [2]. In Saudi Arabia, the parental acceptability of vaccination was 53.7%[12]. A Chinese study recruited 2,026 caregivers between May 1 and 19, 2020, and showed that 22.3% were hesitant to have their children vaccinated[10]. A systematic review found a median rate of only 59.3% of caregivers willing to have their children vaccinated against Covid-19 [3]. These differences in willingness to vaccinate between Brazilians and individuals from other countries shows the importance of understanding the beliefs that support the higher adhesion in Brazil. The present study helps build this knowledge that might provide a better understanding about vaccine hesitancy, which is important both at a national level and as a key factor in devising potential vaccine adhesion strategies in other countries. The strong will of Brazilian parents to vaccinate their children against covid-19 is even more valuable if we understand that they are part of a population that went through the pandemic under the aegis of a president with science-denying behavior, discrediting vaccines and encouraging ineffective treatments such as use of hydroxychloroquine[13]. Furthermore, a large amount of disinformation was spread through social media as showed in a study that evaluated messages from more than 500 public political groups in Brazil finding a conection between disinformation on WhatsApp and a far-right political discourse[14].
The data in the present study clearly show higher hesitancy in having children aged 0-4 years vaccinated, which might be associated to the absence of available studies that ensure the efficacy and safety for this age group so far. Although the vaccine against Covid-19 for children aged 5-11 years has not been approved, yet, and was not available in Brazil at the time of this study, it has already been approved in the USA and in other countries since November 2021. This was the group with the lowest vaccine hesitancy, even lower than the willingness to have adolescents vaccinated, for whom Comirnaty has been already available and approved for use in Brazil since mid-September 2021.
Another finding was the association between the belief that children and adolescents are low risk for Covid-19 and higher vaccine hesitancy. It is important to understand that although this age group shows asymptomatic or light infection compared to adults, this does not mean there are no cases that evolve to more severe conditions, including death. Underestimating the severity in children and adolescents is considered a cognitive illusion created by the direct comparison with the much more significant number of cases of hospitalization and deaths in adults[15]. In Brazil, data reported until December 2021 showed 19,900 confirmed Covid-19 cases in people below 19 years old, who were hospitalized due to severe acute respiratory syndrome (SARS), resulting in 1,422 deaths[16]. In Brazil, 1,412 cases of multisystemic inflammatory syndrome in children have been notified, with 85 deaths[17], while 6,431 MIS-C cases have been notified in the United States, resulting in 55 deaths. Hence, although the severity of Covid-19 in children is a fact regardless of the country, a higher risk profile has been observed in continents with high burden of infectious diseases, such as Latin America and Africa, due to comorbidities and to the poor healthcare system in these regions[18]. This shows that conditions of socioeconomic vulnerability might be a risk factor for the severity of Covid-19[19], which renders vaccination in children even more important in Brazil.
Underestimating Covid-19 severity seems to be as harmful as overestimating the ability of children and adolescents who are deprived of social contact to maintain their mental health. Approximately 43.1% of caregivers mentioned feelings of loneliness, sadness, depression, or anxiety by their children caused by social distancing; among these caregivers, vaccine hesitancy was lower. Over 13% of boys and girls aged between 10 and 19 years were estimated to have some type of mental disorder in 2019, prior to the Covid-19 pandemic, with an incidence of 6/100,000 suicide cases in the population between 15 to 19 years old, i.e. the fourth cause of death in this age group[20]. UNICEF has alerted to the fact that the Covid-19 pandemic has worsened the mental health of children and adolescents[20]. A systematic review gathered 36 studies from 11 countries involving a total of 79,781 children and adolescents and showed that 18 to 60% of the children and adolescents scored above risk thresholds for distress, particularly anxiety and depressive symptoms [21]. Therefore, it is already clear that the taboo mentioned previously must be overcome regarding mental health issues in childhood and adolescence[22] and measures must be taken to remediate knowledge gaps related to how to approach this issue by teachers, healthcare professionals, or caregivers. What is more, it is time to stop the damage, prioritizing the reopening of schools, which can only be safely performed by having children and adolescents vaccinated.
Hesitancy to have children and adolescents vaccinated has also been associated with beliefs related to concerns with vaccine safety. The belief that vaccines against Covid-19 are safer for adults than for children has also been associated with vaccine hesitancy and this might explain why less than 5% of the caregivers has not received at least two doses of vaccine, while 13.3% showed vaccine hesitancy for children and adolescents on average. This also explains why a lower rate was found in a previous study evaluating the willingness of Brazilian adults to vaccinate, only 10.5%[7]. Apprehension with both short- and long-term adverse reactions are at the forefront of their concerns. However, Cominarty has been approved at all clinical trial phases, for children aged 5 to 11 years[23] and 12 to 17 years[24], which ensures its efficacy and safety for these age groups. Most of the post-vaccine adverse reactions observed in both age groups were light to moderate, similar to those caused by other vaccines with which the public are quite familiar, and with which caregivers usually deal quite confidently, such as fever, headache, myalgia, and pain at the injection site. No cases of myocarditis (a rare adverse effect feared by caregivers) were observed in the clinical trials. This rare event was observed only as the vaccine started being used at a large scale. The U.S. Vaccine Adverse Event Reporting System (VAERS) reported 11 cases of myocarditis in 8.7 million doses of Pfizer vaccine in children aged 5-11 years [25] and 397 cases of myocarditis were reported in 8.9 million doses of Pfizer vaccine in adolescents aged 12 to 17 years[26]. The occurrence of myocarditis after the vaccination with Pfizer-BioNTech vaccine is a rare post-vaccine event; it was also a complication with mild course and possible to treat with only anti-inflammatory medication and rest. No deaths from post-vaccine myocarditis were observed[25], [26]. The fear of myocarditis after the vaccination with Pfizer-BioNTech vaccine should not be a reason for delayed vaccination, since it is an extremely rare event, which is self-limited and treatable with no after-effects. On the other hand, myocarditis caused by SARS-CoV-2 infection, either isolated or within the spectrum of multisystemic inflammatory syndrome, is much more frequent and usually has a more severe course, and might even cause death. Up until late January 2022, 6,581 cases of multisystemic inflammatory syndrome in children had been reported in the United States[27] and 1,412 cases had been reported in Brazil until December 2021, with 85 deaths [17]. A study evaluated the incidence of myocarditis based on notifications by the vaccine adverse event reporting system (VAERS) in the period ranging from December 2020 to August 2021 in the USA and it showed that 1,626 reports of myocarditis were identified among 354,100,845 doses applied with RNA-base vaccines, which means a rate of 0.0000045%, thus confirming that it is a rare event [34]. Myocarditis rates were higher after the second dose of vaccine in male adolescents aged 12-15 years (70.7 per million doses of Pfizer vaccine) and in adolescents aged 16-17 years (105.9 per million doses of Pfizer vaccine)[28]. A study conducted in England with people older than 16 years old from December 2020 to August 2021 showed that the extra risk of myocarditis was 1 event per 1 million vaccinated and one extra risk of 40 events per 1 million people infected by SARS-CoV-2, which shows that vaccination could be a beneficial option particularly to those people concerned with the risk of having this pathology[29].
Believing that vaccines might have long-term adverse effects was also identified as a factor associated with higher vaccine hesitancy in the logistic regression. This highlights the important fact that there is an information gap about how these new vaccination platforms actually work. Educational activities that explain that RNA-messenger vaccines constitute temporary fragments that allow the reproduction of viral proteins as a training strategy for the immune system that will be responsible for our defense against diseases might help in the process to convince caregivers[30]. The vaccine against Covid-19 that uses this platform does not blend into our genetic material or remain in the organisms of people who take it. After the body produces an immune response triggered by the RNA messenger vaccine, it eliminates even other cells that might occasionally have viral proteins being produced, either via natural infection or by additional doses that some people might receive [31]. Therefore, vaccines are unlikely to have long-term adverse reactions. On the other hand, there is evidence that long Covid-19 has already been observed in adults and in children and adolescents who had MIS-C, with a mean recovery period of 3-6 months of cardiological, neurological, and laboratory changes, although some changes persist for a longer period of time[32], [33], [34] . Data from the Centers for Disease Control (CDC) show that the chance of children and adolescents being diagnosed with diabetes is 2.5 times higher 30 days or more after being infected by Covid-19 [35].
Failure to understand clearly how vaccines actually work might be at the root of another belief associated to higher vaccine hesitancy, which is that natural immunity is better than vaccines. Building immunity using vaccination provides several benefits compared with natural immunity, since it is possible to avoid becoming sick, risk their lives, and even having long Covid-19. A study evaluated that individuals that had recovered from Covid-19 previously had increased immune responses after vaccination (hybrid or heterologous immunity) compared to their vaccinated peers who had not been infected[36]. This emphasizes the value of vaccination for those who already had the disease. It is important to bolster an important notion with caregivers, that natural immunity cannot be separated from vaccine immunity; in fact, the vaccine is created to enhance natural immunity and not to replace it, as already observed in an American study[30]. Brazil has one of the highest rates of cases and deaths attributed to Covid-19 in the world[13]. One of the worst moments of the pandemic in Brazil occurred during the second wave of infection in Manaus in late 2020 and early 2021, driven by the spread of the Gamma variant. The Brazilian population watched perplexed the news of the collapse of the health system with dramatic reports of people dying of asphyxia in the “Earths lungs” due to lack of oxygen in hospitals [37]. The experience of Manaus was a clear example of how assumptions of reaching herd immunity in the absence of vaccination can turn into a tragedy[37].
A study that evaluated beliefs associated with vaccine hesitancy showed that some people understand that good health is the absence of disease, and thus, they infer that this is the standard human condition what make the effect of the vaccine imperceptible[30]. This study also showed that the understanding of the American public of natural immunity of the human body lies across a spectrum of strength and weaknesses, which leads to the conclusion that not everybody needs vaccination [30]. This type of belief can also explain another belief shown by Brazilian caregivers with high vaccine hesitancy rate: that it is preferable to use natural products to increase immunity than to be vaccinated.
Vaccination of children and adolescents is important because although Covid-19 affects adults more severely than children, it cannot be considered a benign disease as the pandemic has had a significant impact on the health of this age group.[38]. We identified 5 key points to recommend vaccination for children and adolescents: 1) Reduction of hospitalization for Covid-19. 2) Reduction of MIS-c cases. 3) Reduce deaths. 4) Reduction of the risk of post-acute sequelae. 5) It is a right of children and adolescents to be healthy. Access to vaccination for protection against disease is in line with the WHO concept of health, which defines it as a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.[39].
The present study is the most comprehensive survey regarding the willingness of caregivers to have their children and adolescents vaccinated in Brazil so far, and it provides important information on which factors are related to vaccine hesitancy. It is also aligned with the five-year strategy (“Gavi 5.0”) developed by GAVI which has as its vision of “Leaving no one behind with immunisation”, in which one of the principles focus on community owned that should ensure community trust and confidence in vaccines by engaging communities[40]. This study aimed not to label parents pejoratively as anti-vaxx but to understand the concerns since according to anthropologist Heidi Larson to build trust in vaccines this must be done through listening and engagement rather than creating a polarized debate[41]. According to the Vaccine messaging guide of Unicef, we see what we believe instead of believing what we see[42]. Thus, knowing the beliefs and feelings of Brazilian parents may help design campaigns and strategies to immunize against disinformation through critical thinking that enable better decisions by families. This study has some limitations that must be addressed.The most important limitations are those already reported in other similar websurvey studies, such as higher recruitment of women, individuals with higher socio-economic status and higher education, which comprise the subgroups that most frequently answer online surveys. This means that hesitancy in the general population may be slightly higher, since those with low monthly income and low education were underrepresented as in most of websurveys[7]. In the present study, there was also a preferential recruitment from the southeastern regions. The choice of the sample for this study by convenience (not probabilistic) was made due to the difficulties imposed by the pandemic, and because it is an urgent context, this fact has demonstrated acceptability among the population. The fact that the form was made online restricts the participation of people who do not have internet. Access to the internet grows every day, in Brazil, in 2019, three out of four Brazilians had access to the internet, which is equivalent to 134 million internet users [43]. In view of these limitations future campaign validation studies should focus especially on the portion of the population with low education and low income that are usually underrepresented in websurvey-type studies.
5 Conclusion
The present study showed that the willingness of caregivers to have their children and adolescents vaccinated in Brazil is high compared to data from international studies. Underestimating the risk of death and complications due to Covid-19 infection in children and adolescents was one of the major factors associated to hesitancy, as well as the fear of adverse reactions. Believing that natural immunity is better than vaccination might lead to vaccine hesitancy and to risky behaviors with unpredictable outcomes. . It is important to understand that it is not about changing the campaigns that are being done, as there is a lot of good work being done and the result of this is that most choose to vaccinate their children but understand the challenges we are facing. In the age of the internet and post modernity, fake news has to be seen as a virus and every effort to stop them must be made. But to defend against the virus of misinformation, it will be necessary to immunize hesitant people against them through critical thinking tools.
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 authors do not have permission to share data.
==== Refs
References
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| 0 | PMC9731931 | NO-CC CODE | 2022-12-14 23:36:06 | no | Vaccine. 2022 Dec 9; doi: 10.1016/j.vaccine.2022.11.077 | utf-8 | Vaccine | 2,022 | 10.1016/j.vaccine.2022.11.077 | oa_other |
==== Front
Acad Radiol
Acad Radiol
Academic Radiology
1076-6332
1878-4046
Published by Elsevier Inc. on behalf of The Association of University Radiologists.
S1076-6332(22)00651-1
10.1016/j.acra.2022.12.011
Original Investigation
Imaging spectrum of Coronavirus Disease- 2019 associated Rhino-Orbital-Cerebral Mucormycosis; from sinonasal inflammation to intracranial involvement
Khademi Behzad a
Dehghan Alireza b⁎
Zia Zahra a
Dehghan Yasamin b
a Department of Ophthalmology, Poostchi Ophthalmology Research Center, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
b Medical Imaging Research Center, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
⁎ Corresponding author: Alireza Dehghan M.D., Iran- Shiraz- Zand Street- Namazi hospital. Phone number: +989177126866
9 12 2022
9 12 2022
27 10 2022
27 11 2022
5 12 2022
© 2022 Published by Elsevier Inc. on behalf of The Association of University Radiologists.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Objectives
Rhino‑Orbital‑Cerebral Mucormycosis (ROCM) is a life-threatening opportunistic fungal infection, which mostly affects immunocompromised patients. There has been a notable rise in the incidence of ROCM during the COVID-19 outbreak. In this study we described imaging characteristics of ROCM in detail, from early sinonasal inflammation to late intracranial involvement.
Materials and Methods
In this retrospective study, Computed Tomography (CT) scan and Magnetic Resonance Imaging (MRI) of 48 patients with proven ROCM in biopsy or culture were evaluated. All the patients had a history of COVID-19 infection within the previous three months. The imaging findings were described and the frequency of different parameters was reported.
Results
Paranasal inflammation was detected in all the patients on imaging. The most common involved paranasal sinuses were ethmoid sinuses (97.9%). On diffusionweighted images, restricted diffusion was seen in the paranasal sinuses of 81.1% of the patients. In addition, sinus wall bone involvement was observed in 87.5% of the cases. The most common anatomical sites for extrasinus involvement were the retroantral soft tissue (89.6%) and orbital cavity (87.5%). Dacryocystitis in 50%, optic nerve inflammation in 43.2%, globe involvement in 18.9%, and trigeminal nerve involvement in 16% of the patients were detected. There was extension of inflammation through the cavernous sinuses and alongside the internal carotid arteries in 24% of the patients.
Conclusion
Characteristic imaging findings of ROCM not only play a vital role in the early diagnosis of this infection, but they also contribute to the assessment of the extension of inflammation, which is vitally important in surgical planning.
Keywords
Mucormycosis
Rhino-Orbital-Cerebral Mucormycosis
Coronavirus Disease- 2019
Magnetic resonance imaging
Computed tomography scan
==== Body
pmcIntroduction
Coronavirus Disease 2019 (COVID-19) infection has been a pandemic threat since March 2020.1 Although most patients with COVID-19 experience mild to moderate symptoms and fully recover after a few weeks, some others, particularly those with underlying medical conditions, are prone to the severe form of the disease.2 The severe form of COVID-19 pneumonia is accompanied by an excessive cytokine release as well as the over activity of the immune system in response to the Coronavirus. Therefore, immune modulators such as corticosteroids play an essential role in the treatment of these patients.3
Secondary fungal infections are common among critically ill patients with COVID-19, especially the patients with uncontrolled diabetes mellitus and those treated with high doses of corticosteroids.3 , 4 Rhino‑Orbital‑Cerebral Mucormycosis (ROCM) is a rare and life-threatening opportunistic fungal infection, which mostly affects immunocompromised patients.5 Recently; there have been multiple case reports on mucormycosis among COVID-19 patients.1 Clinically, the early stage of ROCM usually presents with nonspecific signs and symptoms of sinusitis such as fever, headache, and nasal obstruction. Sometimes, black nasal crust may be detected as a more specific sign. The infection will extend rapidly and may lead to intra-orbital and intra-cranial involvement. In this stage, some more specific signs and symptoms such as ophtalmoplegia, proptosis, facial edema, and cranial nerve palsies may be present.6
Early diagnosis and treatment of acute ROCM can significantly improve patients’ outcomes. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) play an important role in the early diagnosis of ROCM.5 In this regard, it is essential for radiologists to be familiar with different imaging characteristics of ROCM and to be aware of the common anatomical areas that ROCM may extend to.
The present study aims to describe different CT scan and MRI features of ROCM and its complications among COVID-19 patients to help radiologists and clinicians diagnose this life-threatening infection in a timely manner, thereby improving the patients’ outcomes.
Material and methods
In this retrospective study, CT scans and MRIs of 48 patients with proven ROCM in biopsy or culture were evaluated. All the patients had a history of COVID-19 infection confirmed by Polymerase Chain Reaction (PCR) within the previous three months. The patients without available cross-sectional images and those who had a history of a previous sinus surgery were excluded from the study.
Routine Para Nasal Sinus (PNS) CT scans with 120 Kilovoltage peak (kVp) and 200 milliamperes (mAs) tube current and slice thickness of 0.5 mm were taken by a 16-slice General Electric light speed scan machine (GE Medical Systems, Milwaukee, WI, USA) with image reconstruction in soft-tissue and bone windows. Orbital MRI scans (including paranasal sinuses) were performed using a 1.5 Tesla Siemens Magnetum Avanto scanner (Siemens healthcare, Erlangen, Germany) with dedicated head coil. Axial images were taken from the top of the frontal sinuses up to the chin, while coronal images were acquired from the tip of the nose anteriorly up to the level of the brainstem posteriorly. Pre-contrast sequences included axial T1-Weighted (T1W) images, axial and coronal T2-Weighted (T2W) images, axial and coronal Short Tau Inversion Recovery (STIR) images, and axial Diffusion-Weighted Images (DWI; b-values of 50 and 1000). Axial, sagittal, and coronal fat-suppressed pre-contrast and post-contrast T1W images were taken, as well. Standard brain MRI sequences with axial, coronal, and sagittal T1W, axial and coronal T2W, axial Fluid Attenuation Inversion Recovery (FLAIR), axial DWI, axial Susceptibility-Weighted Imaging (SWI), and post-contrast axial T1W images were also obtained to determine intra-cranial complications (Table 1 ). Then, detailed findings on CT scans and MRIs of paranasal sinuses, orbits, and the brain were compiled into a checklist. This information was gathered from the archived medical reports and the Picture Archiving and Communication System (PACS). All CT scans and MRIs were assessed separately by two experienced radiologists to look for imaging characteristics, anatomical sites of involvement, extent of infection, and complications. Both radiologists were blinded to the patients' clinical information. In case of disagreement, a final consensus was achieved during one session.Table 1 MRI sequence parameters
Table 1 Weighting and planes TE(ms) TR(ms) TI(ms) Matrix(mm) FOV(mm) Slice thickness(mm) Slice gap (mm)
Orbital MRI T1W axial 17 400 - 256 × 218 220 × 200 2 0.25
T2W (axial, coronal) 86 3460 - 256 × 218 220 × 200 2 0.25
STIR (axial and coronal) 211 3000 160 256 × 218 220 × 200 1 0
DWI (axial) 100 4500 - 256 × 182 240 × 240 2 0.2
Pre contrast fat saturated T1W (axial, coronal, sagittal) 17 500 - 256 × 174 200 × 180 2 0.25
Post contrast fat saturated T1W (axial, coronal, sagittal) 17 500 - 256 × 174 200 × 180 2 0.25
Brain MRI T1W (axial, coronal, sagittal) 9 400 - 256 × 218 230 × 210 3 0.3
T2W (axial and coronal) 93 3300 - 256 × 218 230 × 230 3 0.3
FLAIR (axial) 93 7000 2215 256 × 182 230 × 210 3 0.3
DWI (axial) 100 4500 - 256 × 182 240 × 240 3 0.3
SWI (axial) 20 28 - 256 × 182 230 × 210 3 0.3
Post contrast T1W (axial, coronal, sagittal) 9 400 - 256 × 218 230 × 210 3 0.3
TE, echo time; TR, repetition time; TI, inversion time; FOV, field of view; ms, milliseconds; T1W, T1-weighted; T2W, T2-weighted; STIR, Short Tau Inversion Recovery; DWI, Diffusion-Weighted Images; FLAIR, Fluid Attenuation Inversion Recovery; SWI, Susceptibility-Weighted Imaging
The radiological findings of CT scans and MRIs were analyzed and reported descriptively. Besides, the frequency of different parameters was presented as mean, standard deviation, or percentage. All analyses were done using the SPSS 26 software (IBM Corporation, Armonk, NY, USA).
This study was approved by the institutional Ethics Committee and informed consent was taken from all the patients recruited in the study.
Results
A total of 55 patients with histopathology-confirmed ROCM were evaluated in this study. All of them had orbital signs and symptoms (including periorbital pain, periorbital edema, vision loss, ophtalmoplegia, and proptosis) and a recent history of COVID-19 infection. Seven patients were excluded from the study, since their CT scans and MRIs were performed prior to admission to the medical center. Out of the remaining 48 patients, 27 (56.2%) were male and 21 (43.8%) were female. The patients’ ages ranged from 34 to 85 years, with the mean age of 56.6±11.5 years. Among the patients, 44 (91.6%) had underlying diabetes mellitus and were on anti-diabetic medications. Additionally, 45 patients (93.7%) had received high doses of steroids during the treatment course of COVID-19 infection. Moreover, two patients (4.1%) had a history of solid organ transplantation.
All the patients had non-contrast orbital CT scans. Besides, orbital MRI (including paranasal sinuses) and brain MRI was performed in 37 participants (77.1%), 25 of whom (67.6%) underwent post-contrast MRI, as well.
Sinonasal imaging features
Paranasal inflammation was detected in all the patients on CT scan (and MRI, if available). The most common involved paranasal sinuses were ethmoid sinuses (n=47, 97.9%) followed by maxillary sinuses (n=44, 91.6%), sphenoid sinuses (n=38, 79.1%), and frontal sinuses (n=28, 58.3%) (Table 2 ). The predominant involvement of the maxillary sinuses and ethmoid sinuses was also observed in 31 (64.5%) and 19 (39.5%) patients, respectively. Additionally, sphenoid sinuses and frontal sinuses were the predominantly involved sinuses in 11 (22.9%) and 6 (12.5%) patients, respectively. Predominant right-sided paranasal sinuses involvement was observed in 28 patients (58.3%), while the predominant left-sided involvement was seen in 20 participants (41.7%). In 47 patients (97.9%), more than one paranasal sinus showed mucosal thickening/inflammation. Moreover, air-fluid level was identified within at least one paranasal sinus in 21 patients (43.7%), and hyperdense nasal or paranasal content on CT-scan was detected in 25 patients (52.1%).Table 2 The sinuses involved in rhino‑orbital‑cerebral mucormycosis
Table 2Involved sinus Side Number (%)
Maxillary sinus
Right 40 (83.3)
Left 36 (75)
Both 32 (66.6)
Right or left 44 (91.6)
Ethmoid sinus Right 43 (89.6)
Left 33 (68.8)
Both 29 (60.4)
Right or left 47 (97.9)
Sphenoid sinus Right 37 (77.1)
Left 29 (60.4)
Both 28 (58.3)
Right or left 38 (79.1)
Frontal sinus Right 22 (45.8)
Left 21 (43.8)
Both 14 (29.1)
Right or left 28 (58.3)
On non-contrast CT scans, hyperdense sinus content was detected within at least one of the inflamed sinuses in 25 patients (52.1%). The hyperdensity could be identified in mucosa or within paranasal sinus cavity (Fig. 1 ).Figure 1 Paranasal sinus involvement in Rhino‑Orbital‑Cerebral Mucormycosis (ROCM). Axial CT-scan of a patient with ROCM shows heterogeneous left maxillary sinus contents with some characteristic patchy hyperdensities (arrows). Axial T1-weighted (B) and T2-weighted (C) MRI of another patient illustrate irregular mucosal thickening of the right maxillary sinus. There is T1 and T2-weighted hypersignal content within the involved maxillary sinus (asterisk in B and C). Note extension of inflammation to right retro-antral fat and infra-temporal space (arrows in B and C). Axial Diffusion-Weighted Image (DWI) (D) and corresponding Apparent Diffusion Coefficient (ADC) image (E) show restricted diffusion in the right maxillary sinus mucosa (thin arrows in D and E) and right retro-antral fat (thick arrows in D and E)
Figure 1
Simple inflammatory paranasal mucosal thickening and secretions are predominantly hyposignal on T1W and hypersignal on T2W images. In the current research, however, MRI showed variable signal intensities in the affected sinuses by mucormycosis on T1W and T2W images. On non-contrast MRIs, the sinus mucosa and content were iso to hypersignal in at least one of the involved sinuses on T1W images in 32 cases (86.5%). On T2W images, iso to hyposignal mucosa/content was observed within at least one sinus in 32 patients (86.5%). On DWI, restricted diffusion was seen in paranasal sinuses in 30 patients (81.1%) either in the sinus mucosa (n=24, 64.9%) or in the sinus content (n=16, 43.2%). In 16 patients (43.2%), restricted diffusion was found in both sinus mucosa and the sinus content (Fig. 1).
In the current investigation, different patterns of mucosal enhancement were observed on post-contrast MRI. The first pattern was the linear mucosal hyperenhancement (n=9, 36%), which is not a specific pattern and has been reported in a variety of other sinonasal inflammatory processes. The second type was irregular and heterogeneous mucosal enhancement (n=7, 28%). The third pattern was the lack of mucosal enhancement in the affected sinuses (n=5, 20%). Another pattern was the presence of two parallel enhancing lines on the deep and superficial layers of the thickened mucosa along with the central non-enhancing part (n=5, 20%) (Fig. 2 ). Nasal turbinate non-enhancement (black turbinate sign) was also detected in seven patients (28%).Figure 2 Sinonasal mucosal enhancement in Rhino‑Orbital‑Cerebral Mucormycosis (ROCM). Post-contrast axial T1-weighted MRI of two different patients with ROCM reveal linear mucosal hyperenhancement in both maxillary sinuses in A and two parallel enhancing lines in the right maxillary sinus in B.
Figure 2
Bone erosion
Sinus wall bone involvement was detected on the CT scan images of 42 patients (87.5%). The most common anatomic site of bone involvement was maxillary sinus walls (n=38, 79.2%) followed by lamina papyracea (n=16, 33.3%), nasal septum (n=6, 12.5%), and sphenoid sinus walls (n=3, 6.2%). Erosion of nasal bones, pterygoid bones, and cribriform plate was also detected in different patients. In some patients, more than one anatomic site of bone involvement was detected.
The current study's findings indicated bone involvement in 91.3% of the patients who had the imaging features of extrasinus involvement on their CT scans or MRIs. Different patterns of bone involvement were identified in this study (Fig. 3 ). In 31 patients (64.6%), apparent focal or diffuse bone erosion was detected on non-contrast CT scans. In 26 patients (54.2%), irregular thinning of the sinus wall with a permeative appearance was observed. Eight patients (16.7%) showed the irregular thickening of the sinus wall with decreased bone density and rarefaction. In nine patients (18.8%), mottled air foci were detected within the bone.Figure 3 Different patterns of bone involvement in Rhino‑Orbital‑Cerebral Mucormycosis (ROCM). Axial CT-scans of four different patients with ROCM demonstrate: irregular thinning of sinus wall with permeative appearance (thin arrow in A); irregular thinning of sinus with no apparent wall erosion (arrow in B); irregular thickening of sinus wall with decreased bone density and rarefaction (thin arrow in C); focal bone erosion (thin arrow in D). Note fat stranding in retroantral fat adjacent to posterior wall of maxillary sinus (thick arrow in D)
Figure 3
Sinus wall bone marrow edema was clearly seen on MRI of the nine patients (24.3%).
Extra sinus soft tissue extension
The study results demonstrated the imaging features of the extrasinus extension of inflammation on either CT scans or MRIs of 46 patients (95.8%). The most common anatomical sites for extrasinus involvement were the retroantral soft tissue (n=43, 89.6%) and the orbital cavity (n=42, 87.5%). As mentioned earlier, all the participants had orbital symptoms at the time of initial imaging. One of the present study patients presented the further spread of the infection to the temporal fossa and parotid space. The details of different anatomical sites of extrasinus involvement have been presented in Table 3 .Table 3 Extrasinus involvement in Rhino‑Orbital‑Cerebral Mucormycosis
Table 3Involved part Number (%) Side Number (%)
Orbit 42 (87.5) Right 25 (52.1)
Left 17 (35.4)
Prefrontal soft tissue 4 (8.3) Right 1 (2.1)
Left 3 (6.2)
Premaxillary soft tissue 30 (62.5) Right 19 (39.6)
Left 9 (18.8)
Both 2 (4.2)
Infratemporal space 43 (89.6) Right 21 (43.8)
Left 13 (27.1)
Both 9 (18.8)
Parotid space 4 (8.3) Right 3 (6.2)
Left 1 (2.1)
Pterygopalatine fossa 10 (20.8) Right 7 (14.6)
Left 3 (6.2)
Cavernous sinus† 6 (24) Right 5 (20)
Left 1 (4)
Internal carotid artery† 6 (24) Right 4 (16)
Left 2 (8)
Trigeminal nerve and its branches† 4 (16) Right 3 (12)
Left 1 (4)
Intra cranial extension‡ 6 (16.2)
Brain infarction‡ 4 (10.8)
† In the patients who had available post contrast MRI
‡ In the patients who had available Brain MRI
Orbital involvement
Different parts of the orbital cavity and the periorbital soft tissue could be involved by either mucormycosis infection or superimposed inflammation. The details regarding the anatomical sites of orbital involvement have been provided in Table 4 . The present study revealed dacryocystitis in half of the patients, which is presented as the enlargement of the lacrimal sac with adjacent fat stranding on CT scan and MRI. There was also peripheral enhancement at the lacrimal sac in six patients on post-contrast MRI. Mucosal thickening and increased signal intensity at STIR images as well as mucosal hyperehancement at the course of lacrimal duct in post contrast images were the other common findings in dacrocystitis (Fig. 4 ).Periorbital and orbital cellulitis/inflammation may both occur in patients with ROCM (Table 4). On imaging, periorbital cellulitis is characterized by fat stranding limited to the soft tissue anterior to the orbital septum (Fig 4). ROCM can also extend to the retroseptal orbit. In this case, focal or diffuse orbital inflammation may occur, which can spread to the intraconal fat and orbital apex (Figs 4 and 5 ). Nonetheless, orbital involvement is not always accompanied by adjacent sinus wall erosion. In the four out of 42 patients (9.5%) with orbital inflammation, no adjacent sinus wall erosion or irregularity was detected. The present study results demonstrated localized retroseptal orbital involvement more commonly at the inferior and medial parts of the orbital cavity.Table 4 Orbital involvement in Rhino‑Orbital‑Cerebral Mucormycosis
Table 4Involved part of orbit Number (%) Side Number (%)
Pre-septal 34 (70.8) Right 19 (39.6)
Left 14 (29.2)
Both 1 (2.1)
Post-septal 42 (87.5) Right 25 (52.1)
Left 17 (35.4)
Diffuse 17 (35.4)
Localized apex 10 (20.8)
Localized medial 13 (27.1)
Localized lateral 0 (0)
Localized superior 5 (10.4)
Localized inferior 14 (29.2)
Extraconal 17 (35.4)
Intraconal and extraconal 25 (52.1)
Extra-ocular muscles 23 (47.9) Medial 16 (33.3)
Lateral 8 (16.6)
Superior 8 (16.6)
Inferior 16 (33.3)
Proptosis 26 (54.2) Right 13 (27.1)
Left 13 (27.1)
Globe† 7 (18.9) Right 3 (8.1)
Left 4 (10.8)
Optic nerve† 16 (43.2) Right 7 (18.9)
Left 9 (24.3)
Lacrimal sac 24 (50) Right 14 (29.2)
Left 10 (20.8)
† In the patients who had available orbital MRI
Figure 4 Orbital involvement by Rhino‑Orbital‑Cerebral Mucormycosis (ROCM) in four different patients. Coronal CT-scan of a patient with ROCM (A) shows focal inflammation of extraconal orbital fat at the inferior and lateral aspects of right orbit (arrows). Axial CT-scan of another patient with ROCM (B) reveals intraconal orbital fat inflammation on the right side extending to the orbital apex. Axial CT-scan of another patient (C) demonstrates diffuse orbital fat inflammation of the right orbit evident as faint diffuse increased orbital fat density compared to the left side. Note the periorbital cellulitis at the medial aspect of the right orbit (arrow). Axial Short Tau Inversion Recovery (STIR) image of another patient (D) reveals diffuse orbital fat inflammation of the right orbit associated with increased signal intensity of the optic nerve suggestive of optic nerve inflammation (thin arrow). There is also periorbital cellulitis at the medial aspect of the right orbit associated with increased signal intensity of lacrimal duct mucosa suggestive of dacryocystitis (thick arrow).
Figure 4
Figure 5 Diffuse orbital inflammation involving Extra Ocular Muscles (EOM). Coronal CT-scan (A) and coronal T2 weighted MRI (B) of a patient with Rhino‑Orbital‑Cerebral Mucormycosis (ROCM) demonstrate diffuse orbital fat inflammation on CT-scan and MRI associated with significant enlargement of the right side EOM. Note the hazy borders of inflamed EOM on CT-scan and MRI. Axial Short Tau Inversion Recovery (STIR) (C) and post-contrast axial T1-weighted MRI (D) of another patient with ROCM reveal diffuse orbital inflammation on the right side associated with orbital proptosis. Extra ocular muscles are inflamed and enlarged with hyperenhancement on the post-contrast images. There is also thickening and hyperenhancement in ocular coats, particularly alongside the posterior wall of the globe suggestive of scleritis (thin arrow in D). Note thickening and hyperenhancement of the optic nerve sheath on post-contrast images suggestive of optic neuritis (thick arrow in D).
Figure 5
In five cases (13.5%) under the present study, restricted diffusion was detected on the DWI sequence in the retro orbital fat that regarding the angioinvasive nature of mucormycosis, might be related to ischemic change and necrosis (Fig. 6 ).7 In 6 patients (24%), a heterogeneously enhancing inflammatory tissue extended from the lamina papyracea was identified on post-contrast MRIs. In one of the patients, an abscess with irregular peripheral enhancement and central restricted diffusion on DWI was detected in the medial orbital cavity. Orbital inflammation may involve Extra Ocular Muscles (EOM). In the current research, inferior and medial EOM that were closer to maxillary and ethmoid sinuses were more commonly involved. On non-contrast CT scan, inflamed EOM were enlarged with hazy and inflamed borders (Fig. 5). On MRI, they were prominent and bulky with increased signal intensity on T2W and STIR images and showed hyper-enhancement after contrast administration (Fig. 5). By extension of inflammation from maxillary and ethmoid sinuses, medial and inferior EOM could displace laterally and superiorly, respectively. Proptosis was quite common in the patients with orbital involvement. Orbital inflammation may extend to the intraconal space around the orbital apex, thereby putting the optic nerve at risk and leading to sudden onset blindness.8 Different MRI characteristics were identified in the patients with optic nerve involvement; i.e., thickening of the optic nerve sheath, enlargement of the optic nerve itself, increased signal intensity in the nerve and/or its sheath on STIR and FLAIR sequences, and hyper-enhancement of the optic nerve and/or its sheath on post-contrast images (Figs 4 and 6). Hypointensity of optic nerve on T2W and STIR sequences representing involvement of optic nerve by Mucormycosis and perineural spread of infection (Fig. 6). Optic nerve stretching was also detected in nine patients. Additionally, ocular globe conical deformity with posterior tenting (guitar-pick sign) was observed in four patients (Fig. 6). These patterns could be the consequences of significant proptosis or severely increased intra-orbital pressure, which could put the optic nerve and the globe under stretching pressure.9 , 10 Furthermore, extension of inflammation to the globe was detected in six patients as thickening, increased signal on T2W, STIR, and FLAIR sequences, and hyper-enhancement in ocular coats, particularly alongside the posterior wall of the globe (Fig. 6). ROCM may also involve the ophthalmic artery and the central retinal artery, resulting in ischemia and infarction in the optic nerve and retina.8 , 10 Optic nerve ischemia could be identified as a short or long segment of increased signal on DWI alongside the optic nerve (Fig. 6). Restricted diffusion on DWI sequences (representing ischemia) was detected alongside the optic nerve and in ocular globe coats in eight (21.6%) and five (13.5%) patients, respectively.Figure 6 Globe and optic nerve involvement in Rhino‑Orbital‑Cerebral Mucormycosis (ROCM). Axial Short Tau Inversion Recovery (STIR) (A) and post-contrast axial T1-weighted MRI (B) of a patient with ROCM demonstrate ocular globe conical deformity with posterior tenting (guitar-pick sign) and significant proptosis on the left side. There is also thickening and hyper-enhancement in ocular coats, particularly alongside the posterior wall of the globe (thick arrow in B). Hypointensity of the optic nerve on STIR sequence (thin arrow in A) is a sign of the optic nerve involvement by Mucormycosis. Thickening and hyper-enhancement of the anterior part of the optic nerve sheath are visualized on the post-contrast image (thin arrow in B) but note the large non-enhancing area in the posterior part of the left orbital cavity involving the posterior part of the optic nerve (asterisk in B) representing necrosis. Axial Diffusion-Weighted Images (DWI) of two different patients with Rhino‑Orbital‑Cerebral Mucormycosis (C and D) illustrates focal (C) and diffuse (D) restricted diffusion alongside the optic nerves (arrows) representing optic nerve ischemia.
Figure 6
Pterygopalatine fossa and neural infiltration
In the current research, extension of the inflammation into the PPF was found in ten patients (20.8%). PPF involvement was presented as fat stranding and relative expansion of this fossa on CT scans and increased signal intensity on STIR images. After contrast administration, heterogeneous enhancement of the PPF was seen. Infra-orbital nerve inflammation (presented as thickening and increased signal intensity on STIR images and relative enhancement after contrast administration) was observed on the MRI of two of the patients.
Mucormycosis can spread alongside the branches of the trigeminal nerve and lead to the involvement of the intracranial parts of the trigeminal nerve. On MRI, nerve inflammation was presented as nerve thickening with increased signal on T2W and FLAIR sequences. After contrast administration, hyperenhancement mainly in perineural region was identified at the Meckel's cave (Fig. 7 ). In one of the patients, dural thickening and enhancement were detected at the lateral aspect of Meckel's cave.Figure 7 Trigeminal nerve involvement in Rhino‑Orbital‑Cerebral Mucormycosis (ROCM). Coronal post-contrast axial T1-weighted (T1W) MRI of a patient with ROCM demonstrates enlargement and peripheral semilunar enhancement of both side trigeminal nerves at the Meckel's caves (arrows in A). Axial post-contrast T1W image of another patient with ROCM shows perineural hyperenhancement of the right side trigeminal nerves at the Meckel's cave (arrow in B).
Figure 7
Mucormycosis may also extend to the PPF via the direct erosion of the posteromedial wall of the maxillary sinus. In the present study, apparent sinus wall bone erosion next to the PPF was identified in three out of the ten patients (30%) with PPF involvement.
Vascular complications
The present study results showed the cavernous sinus involvement in six out of the 25 patients (24%) who had undergone MRI with contrast. Four patients showed the involvement of the orbital apex at the same time. In addition, one patient had the simultaneous inflammation of the pterygopalatine fossa. Another patient also had focal erosion in the sphenoid sinus wall with direct invasion to the ipsilateral cavernous sinus. Moreover, MRI showed the loss of normal signal void in the involved cavernous sinus on T1W and T2W sequences. Bulging and convexity of the lateral wall of the cavernous sinus were observed, as well. On STIR and FLAIR sequences, increased signal intensity was identified. After the contrast administration, an asymmetric enhancement of the cavernous sinuses was visualized. On the other hand, hyperenhancement of the lateral wall of cavernous sinus may be visualized indicating inflammation (Fig. 8 ).Figure 8 Vascular involvement in Rhino‑Orbital‑Cerebral Mucormycosis (ROCM). Axial T2-weighted (T2W) (A) image reveals loss of normal signal void in the right cavernous sinus (thin arrow in A) indicating cavernous sinus thrombosis. On the Short Tau Inversion Recovery (STIR) sequence (B), there is increased signal intensity of the involved cavernous sinus (thin arrow in B). Post-contrast axial T1-weighted MRI (C) shows loss of enhancement at the right cavernous sinus suggestive of thrombosis (thin arrow in C). There is also hyperenhancement of the lateral wall of the cavernous sinus. Look at the bulging of the lateral wall of the right cavernous sinus on T2W, STIR, and the post-contrast images. Note thickening and hyper-enhancement of the optic nerve and its sheath on post-contrast images suggestive of optic neuritis (thick arrow in C). Coronal (D) and axial (E) T2-weighted images of another patient illustrate the loss of normal signal void in the cavernous portion of the right ICA suggestive of thrombosis (arrows in D and E). Note hypointensity of ICA wall indicating the perivascular spread of Mucormycosis alongside the arterial wall. Axial Diffusion-Weighted Image (DWI) (F) shows some areas of restricted diffusion in the watershed zone of the right cerebral hemisphere suggestive of acute infarction.
Figure 8
The present study findings indicated the decreased signal intensity of the ICA wall on T2W sequence (indicating perivascular spread of Mucormycosis alongside arterial wall) which is associated with arterial wall thickening and increased wall enhancement after the administration of contrast agents. ICA thrombosis is characterized by the loss of normal signal void in the lumen of ICA on T1W and T2W images and the loss of normal luminal enhancement on post-contrast images. Hypointensity of ICA wall was seen in 3 patients indicating perivascular spread of Mucormycosis alongside arterial wall (Fig. 8). In the current study, ICA involvement was detected in five out of the six patients who concurrently had cavernous sinus involvement. Nevertheless, one of the patients had the imaging features of early ICA involvement without any signal change in the surrounding cavernous sinus. However, there was evidence of ischemia in the optic nerve in that patient, which could represent the extension of infection to ICA through the ophthalmic artery. Narrowing or occlusion of ICA can cause a significant reduction in brain perfusion, eventually resulting in brain ischemia. The current study showed the evidence of brain ischemia (restricted diffusion on the DWI sequence) in four out of the six patients with ICA involvement mainly in the watershed zones of the ipsilateral cerebral hemisphere (Fig. 8). Besides, two other patients who had no evidence of brain ischemia at the time of initial imaging showed restricted diffusion on DWI with the same pattern in the follow-up imaging. However, none of the patients without imaging features of ICA involvement had evidence of brain ischemia.
Intracranial involvement
In the present research, the evidence of brain parenchymal inflammation/cerebritis was observed in three out of the 37 patients (8.1%) who had brain MRIs. In these three patients, rectus gyrus in the inferomedial aspect of the frontal lobes was involved. On MRI, there was increased signal intensity on T2W and FLAIR sequences in gyrus rectus with increased enhancement after contrast injection, which was suggestive of cerebritis. Adjacent meningeal thickening and enhancement were also identified in these patients. In three other patients, the imaging features of meningitis were observed, but without any evidence of cerebritis. Brain abscess was detected in one of the patients under the present study.
Discussion
Imaging modalities play an important role in assessing the disease extension and are helpful in identifying different complications of ROCM. They also play a decisive role in surgical planning.6 The present study aimed to describe different imaging characteristics of ROCM in detail, from early sinonasal inflammation to late intracranial involvement specifically focusing on orbital complications.
ROCM is an invasive fungal infection developing from the inoculation of inhaled spores onto the nasal mucosa, which can lead to a severe and life-threatening infection mainly among immunocompromised patients and those with poorly controlled diabetes mellitus.11 During the COVID-19 outbreak, there has been a notable rise in the incidence of ROCM worldwide.12 A significant increase in the number of this fungal infection has also been reported in Iran, particularly among patients with COVID-19.13 Uncontrolled diabetes mellitus, immunosuppression, and long-term use of corticosteroids are the major predisposing factors for the development of ROCM.11 Nevertheless, several factors can explain the rapid rise in mucormycosis infection amongst patients with COVID-19. This disease is accompanied by an excessive cytokine release and impaired cell mediated immunity.14 High doses of corticosteroids that are commonly used in the treatment course of COVID-19 infection can lead to a decrease in phagocytic activity, resulting in susceptibility to superimposed fungal infections.15 Besides, excessive use of oxygen humidifiers can play a role in the rise of ROCM during the COVID-19 outbreak by increasing the chance of fungal transmission via aerosol particles.16 No significant difference in neurological manifestations, imaging classification, treatment success and mortality rates in ROCM has been found between COVID-19 positive and negative groups in previous studies.17, 18, 19 In the present study, 91.6% of the participants had underlying diabetes mellitus and 93.7% had received high doses of corticosteroids in the treatment course of COVID-19.
Sinonasal imaging features
Mucormycosis involves nasal mucosa initially but it rapidly spreads to paranasal sinuses. Mucosal thickening itself is not a definite sign of fungal involvement of a sinus; it can be secondary to reactive inflammation. Simple sinonasal mucosal thickening and inflammatory secretion are usually hypoattenuated on non-contrast CT scan. Nevertheless, the presence of hyperdense areas within the lumen of a paranasal sinus or nasal cavity is a relatively specific but not sensitive sign of fungal infection. This hyperdense material can be related to deposited fungal calcium and metal containing metabolites and waste products.9 , 20 , 21 It seems that as mucormycosis involves the paranasal mucosa, hyperdensity is identified only in paranasal sinus mucosa. However, by progression of infection throughout all parts of the sinus cavity, diffuse hyperdense content appears. Patchy hyperdense content may be sloughed hyperdense mucosa or the remnant of fungus elements floating in the inflammatory fluid and cell debris. Yet, this issue is suggested to be confirmed in further prospective studies.
MRI appearance of paranasal sinus mucosa and secretions could be different and is related to their viscosity, variable compositions of the sinus fluid from different amounts of protein content, inflammatory fluid, fungal elements and presence of calcifications. (Fig. 1). T2W hypointensity could be due to the presence of manganese, magnesium, and iron in the fungal elements. On T1W images, variable patterns from low signal intensity to high signal intensity were identified within the paranasal sinuses.These findings were in agreement with those of several previous studies.1 , 5 , 6 , 8 , 9 , 21 , 22
DWI can be a very helpful sequence in the diagnosis of RCOM. Hada et al. also identified restricted diffusion on the DWI sequence in 36% of their patients with mucormycosis.7 Considering the angioinvasive nature of mucormycosis, mucosal restricted diffusion could represent mucosal ischemia and necrosis. Restricted diffusion in sinus secretions could be associated with high protein content or a necrotic fungal tissue. Up to now, controversial results have been obtained regarding the DWI sequence in invasive fungal sinusitis. For instance, Sasaki et al. stated that the ADC values were significantly lower in fungal sinusitis compared to simple inflammatory sinusitis.23 Moreover, Mazzai et al. reported that necrotic sinonasal tissues in fungal sinusitis might show restricted diffusion on DWI.5 On the other hand, Choi et al. found two cases of acute invasive fungal rhinosinusitis with increased diffusion at necrotic areas.22
Different patterns of mucosal enhancement were identified on post-contrast MRI. Non-specific linear mucosal hyperenhancement has been reported in many other sinonasal inflammatory processes. This type of enhancement can result from the accumulation of inflammatory cells, granulation tissue formation, and neovascularization in sinus mucosa.22 Another pattern was irregular and heterogeneous mucosal enhancement, which could represent the combination of inflammatory cells, granulation tissue, fungal hyphae, and necrotic tissue in the sinus mucosa.1 The affected sinus mucosa is prone to necrosis secondary to mycotic microvascular invasion and the necrotic tissue can give a non-enhancing appearance on post-contrast T1W images.10 Lack of mucosal enhancement is a characteristic imaging feature of invasive fungal sinusitis. However, heterogeneous, focal, and irregular mucosal enhancement is not that specific and may be seen in different inflammatory processes as well as in normal individuals as a physiological variety.8 , 22 The above-mentioned three patterns have been well described in several previous studies.1 , 5 , 6 , 10 , 21 , 22 Another pattern of paranasal sinus mucosal enhancement was detected among the patients under the present study, in which two parallel enhancing lines were identified on the deep and superficial layers of the thickened mucosa along with the central non-enhancing part (Fig. 2). This pattern might result from the central fungal elements and necrotic tissue surrounded by the granulation tissue and inflammatory cells.
Bone erosion
Mucormycosis is an invasive infective process, which can extend to extrasinus structures. There are different routes for the spread of the infection. The fungus mainly spreads by direct tissue invasion and bone destruction. It can also spread through natural bony defects as well as anatomical pathways such as neurovascular bundles, lymphatic vessels, and nasolacrimal ducts.8 We identified different patterns of sinus wall bone involvement in ROCM. The most common patterns were focal/ diffuse bone erosion followed by sinus wall irregularity with permeative appearance. In some patients, irregular thickening of the sinus wall with decreased bone density and rarefaction was seen, which could be suggestive of osteomyelitis.24 Nevertheless, it was not clear whether this pattern was created by a recent infection or it was the manifestation of a previous chronic sinusitis. and bone rarefaction. In some other patients, particularly those with the involvement of the inferior maxillary sinus wall (alveolar process of maxilla); mottled air foci were detected within the bone. This pattern might be secondary to the extension of bone erosion to oral mucosa and the contamination of the underlying fungal infection by oral microflora.
Extra sinus soft tissue extension
By spread of mucormycosis beyond the sinus walls, extrasinus structures such as orbit, subcutaneous region of face, infratemporal fossa, pterygopalatine fossa, cavernous sinus, and anterior cranial fossa may be involved. Mucormycosis can pass through the anterior walls of maxillary sinuses and frontal sinuses, causing inflammation within the subcutaneous region of cheek and forehead. Infection may also extend to the infratemporal fossa mainly from the posterolateral wall of maxillary sinuses. Initially, focal inflammation may be observed adjacent to the posterolateral wall of the maxillary sinus (at the site of bone erosion or near the entrance of the posterior superior alveolar neurovascular bundle). Then, inflammation may extend alongside the posterolateral wall of the maxillary sinus within the loose retroantral fat. After that, diffuse inflammation of the infratemporal space with enlargement of pterygoid muscles may occur (Figs 1 and 3).
In appropriate clinical settings, the extrasinus extension of inflammation in form of fat stranding on imaging is highly suggestive of invasive fungal infection. Hence, careful attention to subtle fat stranding in paranasal structures is very important in the early diagnosis of mucormycosis.6
Orbital involvement
Orbital cavities are in close proximity to frontal, ethmoid, and maxillary sinuses. Therefore, an invasive process such as mucormycosis may extend through these cavities. Lamina papyracea, which forms the medial wall of orbits and the lateral wall of ethmoid sinuses, is a very thin bone and is usually perforated by valveless ethmoidal veins. Besides, it commonly has the focal areas of congenital dehiscence. Thus, mucormycosis can easily pass through it. On the other hand, lacrimal ducts that connect lacrimal sacs to nasal cavities are natural anatomical pathways that permit the spread of sinonasal infections through the lacrimal sac and periorbital structures.19 , 25
Generally, MRI is more sensitive than CT scan in detecting orbital inflammation. STIR and fat saturated T2W images are the best MRI sequences for evaluating the orbital fat (Figs 4 and 5).5 , 8 It seems that there is a spectrum of orbital inflammation in the course of the disease. It commonly begins from the inferior/medial part of the orbital cavity usually adjacent to the involved paranasal sinus, extending around and within EOM. As the fungal infection progresses, diffuse orbital involvement may happen and the infection may spread to the intraconal region and lead to the involvement of the orbital apex, orbital fissures, optic nerves, and the globe. In more advanced stages of the disease, orbital tissue ischemia/necrosis with the loss of enhancement may be observed in post-contrast images and restricted diffusion could be seen in DWI. Optic nerve ischemia could also be identified as an increased signal on DWI alongside the optic nerve. Orbital apex involvement may lead to the extension of infection through the optic canal, superior and inferior orbital fissure into the intracranial fossa, cavernous sinus, and pterygopalatine fossa.
Vascular complications
Mucormycosis may invade vascular structures such as cavernous sinuses and blood vessels’ walls such as the Internal Carotid Artery (ICA) through either direct invasion or remote draining/arising vessel walls, eventually leading to intra-luminal thrombosis followed by brain infarction.26 Non-contrast CT scan is not sensitive for detection of cavernous sinus pathologies, but MRI including post-contrast images is a sensitive and specific modality for the evaluation of the cavernous sinus.27 High resolution intracranial vessel wall MRI (VW-MRI) using black blood techniques can improve visualization of blood vessel walls.28 The loss of normal signal void in the involved cavernous sinus on T1W and T2W sequences, Bulging and convexity of the lateral wall of the cavernous sinus, increased signal intensity on STIR and FLAIR sequences and asymmetric enhancement of the cavernous sinuses after contrast administration are the most important signs of cavernous sinus thrombosis on MRI. Acute thrombosis was not enhanced. However, organized thrombosis and fungal tissue were enhanced, albeit less than the normal cavernous sinus. Almost similar findings were obtained in the previous studies.1 , 5 , 8 , 9 , 29
ICA passes through the cavernous sinus and can be involved by ROCM. Detection of ICA invasion by ROCM is very important, but can be challenging, particularly in early stages.5 Thickening of arterial wall, decreased signal intensity of the ICA wall on T2W sequence (indicating perivascular spread of Mucormycosis alongside arterial wall) and arterial wall hyperenhancement on post contrast images could be found in the early stages of ICA involvement. As the fungal infection progresses, luminal narrowing and wall irregularity may occur, leading to ICA thrombosis and luminal occlusion. Narrowing or occlusion of ICA can cause a significant reduction in brain perfusion, eventually resulting in brain ischemia mainly in the watershed zones of the ipsilateral cerebral hemisphere. Hence, it seems that the main cause of brain ischemia in ROCM is narrowing/occlusion of the lumen of ICA (rather than other etiologies such as thromboembolism). In other words, once the ICA is involved by Mucormycosis, brain ischemia could probably occur. Yet, this hypothesis is required to be confirmed in further prospective studies.
Intracranial involvement
Mucormycosis may extend into the anterior cranial fossa by either direct invasion across the cribriform plate, sphenoid sinus walls, and frontal bones or natural cranial orifices such as superior orbital fissures, foramina ovale, and vidian canals.8,25Meningeal enhancement is the first and most common imaging finding that indicates the intracranial extension of ROCM.1 , 8 Cerebritis is another common finding which can lead to intracranial abscess formation.1 , 5 , 8 , 9
The main limitation of the present study was its retrospective nature, which led to the non-availability of MRI in some of the participants. The study's cross-sectional design, on the other hand, limited the assessment of the temporal relationships between the imaging findings. Additionally, we have used conventional post-contrast T1W sequence for evaluation of intracranial vessels in this study but high resolution intracranial vessel wall MRI (VW-MRI) can improve visualization of blood vessel walls and could be more helpful in the assessment of intracranial extension of mucormycosis. Finally, MRI might be accompanied by some artifacts such as “susceptibility artifacts,” particularly in the soft tissues surrounding the air-containing structures, which might decrease the quality of images.
In conclusion, early diagnosis of ROCM is very important in post-COVID-19 patients, immunocompromised patients and those with poorly controlled diabetes mellitus. Characteristic imaging findings on CT scans and MRIs not only play a vital role in the early diagnosis of ROCM, but they also contribute to the assessment of the extension of inflammation to extrasinonasal tissues, orbit, pterygopalatine fossa, cavernous sinus, and intracranial fossa, which is vitally important in surgical planning. Furthermore, MRI is very helpful in the evaluation of neurovascular complications, which are the major cause of morbidity and mortality in ROCM.
Acknowledgement
The authors would like to thank Ms. A. Keivanshekouh at the Research Consultation Center (RCC) of Shiraz University of Medical Sciences for her invaluable assistance in editing the manuscript.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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References
1 Desai SM Gujarathi-Saraf A Agarwal EA. Imaging findings using a combined MRI/CT protocol to identify the "entire iceberg" in post-COVID-19 mucormycosis presenting clinically as only "the tip" Clin Radiol 76 10 2021 10.1016/j.crad.2021.07.002 784.e27-784.e33
2 Radmard AR Gholamrezanezhad A Montazeri SA A Multicenter Survey on the Trend of Chest CT Scan Utilization: Tracing the First Footsteps of COVID-19 in Iran Arch Iran Med 23 11 2020 787 793 10.34172/aim.2020.105 33220698
3 Devnath P Dhama K Tareq AM Emran TB. Mucormycosis coinfection in the context of global COVID-19 outbreak: A fatal addition to the pandemic spectrum Int J Surg 92 2021 106031 10.1016/j.ijsu.2021.106031
4 Al-Tawfiq JA Alhumaid S Alshukairi AN COVID-19 and mucormycosis superinfection: the perfect storm Infection 49 2021 833 853 10.1007/s15010-021-01670-1 34302291
5 Mazzai L Anglani M Giraudo C Martucci M Cester G Causin F. Imaging features of rhinocerebral mucormycosis: from onset to vascular complications Acta Radiol 63 2 2022 232 244 10.1177/0284185120988828 33615823
6 Therakathu J Prabhu S Irodi A Sudhakar SV Yadav VK Rupa V. Imaging features of rhinocerebral mucormycosis: a study of 43 patients Egypt J Radiol Nucl Med 49 2 2018 447 452
7 Hada M Gupta P Bagarhatta M Orbital magnetic resonance imaging profile and clinicoradiological correlation in COVID-19-associated rhino-orbital-cerebral mucormycosis: A single-center study of 270 patients from North India Indian J Ophthalmol 70 2 2022 641 648 10.4103/ijo.IJO_1652_21 35086254
8 Sreshta K Dave TV Varma DR Magnetic resonance imaging in rhino-orbital-cerebral mucormycosis Indian J Ophthalmol 69 7 2021 1915 1927 10.4103/ijo.IJO_1439_21 34146057
9 Kondapavuluri SK Anchala VKR Bandlapalli S Spectrum of MR imaging findings of sinonasal mucormycosis in post COVID-19 patients Br J Radiol 94 1127 2021 20210648 10.1259/bjr.20210648
10 Yadav T Tiwari S Gupta A Magnetic Resonance Imaging in Coronavirus Disease - 2019 Associated Rhino-Orbital-Cerebral Mucormycosis (CA-ROCM) - Imaging Analysis of 50 Consecutive Patients Curr Probl Diagn Radiol 51 1 2022 112 120 10.1067/j.cpradiol.2021.09.004 34802841
11 Petrikkos G Skiada A Lortholary O Roilides E Walsh TJ Kontoyiannis DP. Epidemiology and clinical manifestations of mucormycosis Clin Infect Dis 54 Suppl 1 2012 S23 S34 10.1093/cid/cir866 22247442
12 Sharma S Grover M Bhargava S Samdani S Kataria T. Post coronavirus disease mucormycosis: a deadly addition to the pandemic spectrum J Laryngol Otol 135 5 2021 442 447 10.1017/S0022215121000992 33827722
13 Avatef Fazeli M Rezaei L Javadirad E Increased incidence of rhino-orbital mucormycosis in an educational therapeutic hospital during the COVID-19 pandemic in western Iran: An observational study Mycoses 64 11 2021 1366 1377 10.1111/myc.13351 34252988
14 Song G Liang G Liu W. Fungal Co-infections Associated with Global COVID-19 Pandemic: A Clinical and Diagnostic Perspective from China Mycopathologia 185 2020 599 606 10.1007/s11046-020-00462-9 32737747
15 Singh AK Singh R Joshi SR Misra A. Mucormycosis in COVID-19: A systematic review of cases reported worldwide and in India Diabetes Metab Syndr 15 2021 102146 10.1016/j.dsx.2021.05.019
16 Passi N, Wadhwa AC, Naik S. Radiological spectrum of invasive mucormycosis in COVID-19. BJR Case Rep. 2022;7:20210111. doi:10.1259/bjrcr.20210111
17 Singh G Vishnu VY. Neurological manifestations of rhino-oculo-cerebral mucormycosis in the COVID-19 era Nat Rev Neurol 17 11 2021 657 658 10.1038/s41582-021-00560-2 34480144
18 Meher R Wadhwa V Kumar V COVID associated mucormycosis: A preliminary study from a dedicated COVID Hospital in Delhi Am J Otolaryngol 43 1 2022 103220 10.1016/j.amjoto.2021.103220
19 Dave TV Gopinathan Nair A Hegde R Clinical Presentations, Management and Outcomes of Rhino-Orbital-Cerebral Mucormycosis (ROCM) Following COVID-19: A Multi-Centric Study Ophthalmic Plast Reconstr Surg 37 5 2021 488 495 10.1097/IOP.0000000000002030 34314399
20 Cha H Song Y Bae YJ Clinical Characteristics Other Than Intralesional Hyperdensity May Increase the Preoperative Diagnostic Accuracy of Maxillary Sinus Fungal Ball Clin Exp Otorhinolaryngol 13 2 2020 157 163 10.21053/ceo.2019.00836 31674170
21 Raz E Win W Hagiwara M Lui YW Cohen B Fatterpekar GM. Fungal Sinusitis Neuroimaging Clin N Am 25 4 2015 569 576 10.1016/j.nic.2015.07.004 26476380
22 Choi YR Kim JH Min HS Acute invasive fungal rhinosinusitis: MR imaging features and their impact on prognosis Neuroradiology 60 7 2018 715 723 10.1007/s00234-018-2034-0 29774383
23 Sasaki M Eida S Sumi M Nakamura T. Apparent diffusion coefficient mapping for sinonasal diseases: differentiation of benign and malignant lesions AJNR Am J Neuroradiol 32 6 2011 1100 1106 10.3174/ajnr.A2434 21393402
24 Eustis HS Mafee MF Walton C Mondonca J MR imaging and CT of orbital infections and complications in acute rhinosinusitis Radiol Clin North Am 36 6 1998 10.1016/s0033-8389(05)70238-4 1165-xi
25 Herrera DA Dublin AB Ormsby EL Aminpour S Howell LP Imaging findings of rhinocerebral mucormycosis Skull Base 19 2 2009 117 125 10.1055/s-0028-1096209 19721767
26 Thajeb P Thajeb T Dai D. Fatal strokes in patients with rhino-orbito-cerebral mucormycosis and associated vasculopathy Scand J Infect Dis 36 9 2004 643 648 10.1080/00365540410020794 15370650
27 Nadarajah J Madhusudhan KS Yadav AK Chandrashekhara SH Kumar A Gupta AK. MR imaging of cavernous sinus lesions: Pictorial review J Neuroradiol 42 6 2015 305 319 10.1016/j.neurad.2015.04.010 26421483
28 Mandell DM Mossa-Basha M Qiao Y Intracranial Vessel Wall MRI: Principles and Expert Consensus Recommendations of the American Society of Neuroradiology AJNR Am J Neuroradiol 38 2 2017 218 229 10.3174/ajnr.A4893 27469212
29 Metwally MI Mobashir M Sweed AH Post COVID-19 Head and Neck Mucormycosis: MR Imaging Spectrum and Staging Acad Radiol 29 5 2022 674 684 10.1016/j.acra.2021.12.007 34998684
| 0 | PMC9731934 | NO-CC CODE | 2022-12-14 23:31:56 | no | Acad Radiol. 2022 Dec 9; doi: 10.1016/j.acra.2022.12.011 | utf-8 | Acad Radiol | 2,022 | 10.1016/j.acra.2022.12.011 | oa_other |
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Perfusion
Perfusion
spprf
PRF
Perfusion
0267-6591
1477-111X
SAGE Publications Sage UK: London, England
36475516
10.1177_02676591221141323
10.1177/02676591221141323
The incidental detection of CAD related to COVID-19 during CABG
Incidentally discovered cold hemagglutinins within autologous blood bag and cardioplegia line in a patient with a recent history of COVID-19 undergoing coronary artery surgery
Şimşek Erdal 1
Karaca Okay G 1
https://orcid.org/0000-0003-1162-5383
Çetinkaya Ferit 1
Can Ferda 2
Günaydın Serdar 1
1 Department of Cardiovascular Surgery, 574949 Ankara City Hospital , Ankara, Turkiye
2 Department of Hematology, 536164 Ankara City Hospital , Ankara, Turkiye
Ferit Çetinkaya, Department of Cardiovascular Surgery, Ankara City Hospital, Bilkent Mah. Bilkent 1 camlik sitesi d2 blok kat3 no8, Cankaya 06800, Turkey. Email: [email protected]
7 12 2022
7 12 2022
02676591221141323© The Author(s) 2022
2022
SAGE Publications
This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
Introduction
Cold agglutinin disease (CAD) is a rare autoimmune disorder characterized by destruction (hemolysis) of erythrocytes. In CAD, autoantibodies that cause agglutination at temperature of optimum +3-+4 ℃ degree cause symptoms. It is known that CAD often occurs after viral infections. Also, it has been reported in case reports that COVID-19 disease can cause CAD.
Case Report
46-year-old male patient with a history of diabetes mellitus and hypertension presented to outpatient clinic in our department to have CABG surgery. He recovered from COVID-19 disease 1.5 months ago. Cardiopulmonary bypass was initiated and the cross-clamp was placed and antegrade Delnido cardioplegia solution was started to be given at +4 ℃. It was observed that the cardioplegia line was agglutinated. On the other hand, it was seen that the autologous blood taken by the anesthesiologist was also agglutinated and formed air bubbles and became unusable. X-clamp was removed and the heart rhythm recovered. The patient was consulted to hematology during postoperative intensive care follow-ups. The cold agglutinin test performed at of +4 ℃ was reported as positive. In this case, we associated the CAD with covid-19 for three main reasons. First one, the patient’s complaints about CAD started after COVID-19 disease. Secondly, in the national health archive, the patient’s pre-COVID-19 blood tests were completely normal but it was seen that LDH increased and RBC-HCT incompatibility started after COVID-19. As the third, when we search the literature, we have seen the COVID-19 related CAD in many case reports published by hematologists.
Conclusion
With the rare cold agglutinin disease, it seems that we will encounter it more often after the COVID-19 pandemic. Except for deep hypothermia, the most important problem is seen during cardioplegia administration. Therefore, non-blood cardioplegia can be lifesaving.
COVID-19
cold agglutinin disease
cabg;cardiac surgery
cardiopulmonary bypass
coronavirus
hemolysis
autoimmune disease
coronary artery
celsius
CAD
edited-statecorrected-proof
typesetterts10
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pmcIntroduction
Cold Agglutinin Disease (CAD) can be detected as idiopathic, or it can be developed due to secondary causes. Although, it is generally said that M. pneumoniae and Epstein-Barr virus pathogens are cause of cold agglutinin, the published case reports have shown that COVID-19 is also cause of CAD.1 CAD should be suspected in patients with cold-related complaints such as pain and bruising in the extremities, especially in cold weather or in the case of cold food and drinks are taken.2 In addition, since cell counters in laboratories cannot count agglutinated erythrocytes, low erythrocyte counts (RBC) and low hematocrit incompatible with hemoglobin suggest CAD.3 Findings of hemolysis, direct antiglobulin test positively for C3d, cold agglutinin titer >64 at +4°C are main criteria for diagnosis.2
Case report
46-year-old male patient with a history of diabetes mellitus and hypertension presented to outpatient clinic in our department to have CABG surgery. He recovered from COVID-19 disease 1.5 months ago. Preoperative RBC (red blood cell) and HCT (hematocrit) values were found to be incompatible with hemoglobin level and LDH value was measured 313 U/L (120–246 reference range). Preoperative electrocardiography was normal and left ventricular ejection fraction (LVEF) was %55. He was taken to the operating room where the temperature was +19°C with the MIDCAB (minimally invasive direct coronary bypass surgery) plan. The patient was cooled to temperature of +32°C. Cardiopulmonary bypass was initiated with right femoral artery/vein cannulation. Cross-clamp was placed and antegrade Delnido cardioplegia solution (prepared 20% blood) was started to be given from the pump at +4°C. It was observed that the cardioplegia line was agglutinated. Administration of Delnido solution was stopped urgently. The current was cut off quickly with the clamp, but some agglutinated solution went to the heart. Cardiac arrest was achieved with antegrade plegisol solution. On the other hand, it was seen that the autologous blood taken by the anesthesiologist was also agglutinated and formed air bubbles and became unusable (Figure 1).Figure 1. The patient’s autologous blood was agglutinated at temperature of +15°C (operation room temperature). Air bubbles (yellow arrow) seen in the cardioplegia line were also seen in the autologous blood bag. Macroscopic view of agglutinated erythrocytes indicated by the black arrow.
Operation was completed and after cessation of anesthesia, he was extubated without any complication. When the patient was questioned about whether he had a cold-related symptom or not, he stated that he had aches and bruises on the tip of the nose, auricle and fingertips in the cold that started after the COVID disease, and this bruising and pain were disappeared when warmed up. It was seen that the blood tests before he had COVID-19 disease, his hemogram and LDH were completely normal. The patient was consulted to hematology during follow-ups. Among the requested tests, reticulocyte was normal, C3d and direct/indirect Coombs tests were negative, and haptoglobin was normal. The cold agglutinin test performed at +22°C was negative, but the test performed at of +4°C was reported as positive.
Discussion
In this case, we associated the CAD with covid-19 for three main reasons. First one, the patient’s complaints about CAD started after COVID-19 disease. Secondly, in the national health archive, the patient’s pre-COVID-19 blood tests were completely normal but it was seen that LDH increased and RBC-HCT incompatibility started after COVID-19. As the third, when we search the literature, we have seen the COVID-19 related CAD in many case reports published by hematologists.
E. Zagorski published the first known case of COVID-19-related CAD exitus in May 20201(1). In August 2020, V. Maslov published the report of a patient with CAD associated with COVID-19 presenting with fulminant hemolytic anemia.4 PA Patel et al., during on-pump CABG, noticed cold agglutination in the blood cardioplegia solution given anterograde at +4°C, similar to our case, and took some precautions such as warming the patient and giving warm cardioplegia from the retrograde coronary sinus.5 In September 2020, Hematologist E. Jensen from North Carolina published the post-COVID-19 CAD report of 2 cases.6 Our case is also a remarkable first case in cardiac surgery that demonstrated the potential of COVID-19 disease causing the development of autoimmune disease.
We noticed was that in the cardioplegia solution received from the closed-circuit pump system, air bubbles were developed besides agglutination. It was thought that it could be oxygen released from agglutinated erythrocytes. A small amount of the agglutinated blood cardioplegia solution reached the heart until we stop the flow of cardioplegia solution. Also, the ST elevations were absent in the previous ECGs and began to improve from the 8th hour postoperatively. It suggests that the precipitate which could be obstructing the coronary arteries, might dissolve spontaneously after a while when the patient warms up sufficiently. As a matter of fact, studies show that cold agglutination are reversible and agglutination dissolves when heat is increased.7
Conclusion
With the rare CAD, it seems that we will encounter it more often after the COVID-19 pandemic. More detailed examination before the operation has recently become necessary. Except for deep hypothermia, the most important problem is seen during cardioplegia administration. Therefore, non-blood cardioplegia can be lifesaving.
ORCID iD
Ferit Çetinkaya https://orcid.org/0000-0003-1162-5383
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
==== Refs
References
1 Zagorski E Pawar T Rahimian S , et al. Cold agglutinin autoimmune hemolytic anemia associated with novel coronavirus (COVID‐19). Br J Haematolo 2020; 190 : e183–e184.
2 Brugnara C Berentsen S . Cold agglutinin disease. ■■■: UptoDate com, p. 22. UptoDate com Updated April. 2021.
3 Milevoj Kopcinovic L Bronic A Pavic M . Effect of cold agglutinins on red blood cell parameters in a trauma patient: a case report. Biochemia Medica 2018; 28 (3 ): 528–534.
4 Maslov DV Simenson V Jain S , et al. COVID-19 and cold agglutinin hemolytic anemia. TH Open 2020; 4 (03 ): e175–e177.32844144
5 Patel PA Ghadimi K Coetzee E , et al. Incidental cold agglutinins in cardiac surgery: intraoperative surprises and team-based problem-solving strategies during cardiopulmonary bypass. J Cardiothoracic Vascular Anesthesia 2017; 31 (3 ): 1109–1118.
6 Jensen CE Wilson S Thombare A , et al . Cold agglutinin syndrome as a complication of Covid-19 in two cases. Clin Infect Pract 2020; 7 : 100041.32924007
7 Swiecicki PL Hegerova LT Gertz MA . Cold agglutinin disease. Blood. J Am Soc Hematol 2013; 122 (7 ): 1114–1121.
| 36475516 | PMC9732487 | NO-CC CODE | 2022-12-14 23:35:50 | no | Perfusion. 2022 Dec 7;:02676591221141323 | utf-8 | Perfusion | 2,022 | 10.1177/02676591221141323 | oa_other |
==== Front
Int Relat (David Davies Mem Inst Int Stud)
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International Relations (David Davies Memorial Institute of International Studies)
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1741-2862
SAGE Publications Sage UK: London, England
10.1177/00471178221140084
10.1177_00471178221140084
Original Research Article
Conflict and Covid-19: exploring the effects on women
https://orcid.org/0000-0003-2118-6177
Donovan Outi
Griffith University
Outi Donovan, School of Government and International Relations, Griffith University, Glyn Davis Building N72, Nathan, QLD 4111, Australia. Email: [email protected]
7 12 2022
7 12 2022
00471178221140084© The Author(s) 2022
2022
SAGE Publications
This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
What happens when conflicts collide with major disease pandemics? Weak or fragmented institutions, contested legitimacy of authorities, overstretched or destroyed health sector, crowded refugee camps and population flows are but some of the characteristics of conflict-affected societies rendering them vulnerable to pandemics such as Covid-19. Importantly, societal crises are deeply gendered; women and men experience conflicts and are affected by them in profoundly different ways. Focusing on the ‘first wave’ of Covid-19 in 2020, I map out the gendered impacts of Covid-19 in conflict-affected societies with particular focus on women. I situate their experience of the dual crisis in the context of ‘vulnerability multipliers’ that limit the ability of individuals to manage societal crises. I find that the pandemic and policy responses to it exacerbated multiple forms of gendered insecurity. They include physical, economic and health insecurities as well as increasing political marginalisation. All this points to multiple, overlapping insecurities operating simultaneously, leading to ‘layered violence’ whereby the pre-existing violence against women and girls escalates, subjecting them to more intense forms of harm.
conflict
covid-19
gender
insecurity
women
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pmcIntroduction
While the coronavirus outbreak caused world-wide economic, political and social disruption, it did little to quell armed conflicts around the world. Despite the call for a global ceasefire by the UN Secretary-General and the Security Council in 2020, wars have continued unabated in conflict-affected societies. The effects of the pandemic on conflicts has varied. In some cases, such as Tigray in Ethiopia, coronavirus-related delay in elections triggered violence between government forces and militias,1 while in other cases, armed groups, such as Boko Haram in Nigeria,2 were able to capitalise on the government’s preoccupation with Covid-19 response and launch attacks to gain further territory. These and other impacts of the pandemic on conflicts have not only affected the dynamics of violence but have had implications on civilians, caught up in increasing levels of insecurity. Importantly, these impacts are gendered; women, girls, men and boys experience conflicts and are affected by them in profoundly different ways. While societal upheavals can provide opportunities for marginalised groups as traditional gender structures and roles shift, the dual crisis of armed conflict and Covid-19 has become a humanitarian crisis as noted by the UN Secretary General, with ‘disproportionate negative impact on women and girls’.3
Against this backdrop, this paper maps out the gendered impact of Covid-19 in conflict-affected societies in the first few months of the pandemic, with particular focus on women. What were the effects of the dual crisis on women? While gender is not synonymous to women, I focus on women who are disproportionally affected by both armed conflicts4 and public health crises,5 making them some of the most vulnerable groups in societies undergoing multiple crises. Importantly, I situate their experiences in the context of vulnerability multipliers (restrictive gender norms, gendered division of labour and lack of access to resources) that limit the ability of individuals to manage societal crises. I find that multiple and overlapping forms of gendered insecurity and marginalisation – physical, economic, health, political – emerge from the intersection of conflict and the Covid-19 pandemic.
This line of enquiry is important for a number of reasons. Firstly, the existing research on violence, gender and Covid-19 has largely focused on ‘private’ gendered violence.6 Various analyses of the ‘shadow pandemic’ have identified a rise in domestic violence around the world as a consequence of Covid-19.7 Intimate partner violence, however, sits in a broader spectrum of violence spanning across private, public, physical and economic modes of violence and insecurity.8 The intention of this analysis is to go beyond the focus on domestic violence and identify other types of gendered insecurity that emerged from the dual crisis of armed conflict and Covid-19. In doing so, the analysis extends recent research exploring gender, collective violence and Covid-19.9 Such knowledge will further our understanding of the intersections of gender, security and disease pandemics, a line of inquiry pioneered by Colleen O’Manique and others.10 In adopting feminist and gender-based approaches to interrogating global health governance and international security, they have not only exposed the ‘invisibility’ of women in efforts to address pandemics11 but have also challenged traditional assumptions underwriting global health security.12 My analysis speaks to this body of scholarship by seeking to identify variety of gendered violences and insecurities that stem from the conflict/pandemic nexus across cases. It will also provide a framework (vulnerability multipliers) for exploring gendered effects of multiple crises that may facilitate further analyses of the gender, conflict and pandemics nexus.
Secondly, conflict-affected societies represent some of the most vulnerable contexts for pandemics such as Covid-19. Health systems are at best overwhelmed and at worst destroyed in the course of conflict. Previous research on pandemics such as Ebola and Zika13 in humanitarian settings provide us important insights on the gendered effects of pandemics but in contrast to local or regional outbreaks, the Covid-19 pandemic represented a public health crisis of a global scale that undermined the provision of humanitarian aid to conflict-affected societies as well as diplomatic efforts to end violence. This provides an opportunity to interrogate the intersection of conflict, gender and pandemic in the context of an outbreak with a global reach and in doing so, contribute to discussions about appropriate policy measures.
The discussion is meant as a first reading of patterns and trends across conflict-affected societies in the first few months of the pandemic rather than as an exhaustive analysis. It represents a snapshot in time, focusing on the ‘first wave’ in 2020 and is based on evidence reported by civil society groups. It is important to acknowledge that all societies, including those not considered conflict-affected, experience forms of conflict and violence, often manifested in ‘slow’14 or structural violence. Feminist scholars have produced important insights into the continuities between different modalities of violence and conflict that manifest in most modern societies.15 For the purposes of this analysis – investigating the intersection between health pandemic and war – conflict-affected society is used as a shorthand for a country that is experiencing collective armed violence between two or more governmental and/or non-governmental actors or transitioning from collective armed violence to post-settlement phase. According to estimates, during the outbreak of the Covid-19 pandemic in 2020, there were around 50 armed conflicts around the world.16 As an initial analysis, and one constrained by data limitations, my discussion focuses on cases where research material is accessible online.17
The analysis draws on country-specific surveys and reports by human rights and women’s rights organisations operating in conflict settings, such as UN Women, Oxfam and CARE. This corpus of research material, consisting of over 30 reports, offers a glimpse of the gendered experience of living through the dual crisis of conflict and pandemic by documenting the experiences of individuals, families and civil society organisations in the early stages of the pandemic in 2020. Majority of the reports are based on survey and interview data,18 some conducted via telephone19 and others in-person, through local partner organisations.20 While some reports have a broader focus on the humanitarian impact of Covid-19, the vast majority of the reports are gender specific surveys on the impact of Covid-19. The material was analysed inductively by coding the key themes emerging from it. The initial round of open coding entailed establishing nodes from the research material and looking for patterns among them. The next round of analysis entailed ‘theoretical sampling’, where further research material is collected on the basis of the leads generated by the initial round of coding.21 A careful analysis of the nodes pointed towards a reoccurring theme emerging from the dual crisis of armed conflict and Covid-19; a set of interconnected gendered insecurities ranging from increasing physical violence to economic and health insecurities, alongside increasing political marginalisation. In the final round of analysis, the interrelations between the core nodes were analysed and the nodes were organised into a narrative.
The remainder of the paper proceeds as follows. The first section provides a brief overview of the intersection between armed conflict and pandemics, alongside with key findings from existing research. The second section turns to the gendered context within which the dual crisis of conflict and pandemics occur. I argue that the effects on women should be understood in the context of vulnerability multipliers that limit the ability of many women to manage the effects of the interlocking crises. I do not suggest that women should be viewed as victims with no agency but instead, seek to foreground the structural and normative environment within which many, if not all, women seek to negotiate the crises. In the subsequent sections, I move onto tracing the various forms of gendered insecurity that flow from the intersection of Covid-19 and conflict. This review points towards significant physical, economic and health insecurities, alongside increasing political marginalisation. The following section suggests that the above insecurities overlap and intersect in important ways, compelling those subjected to them to adopt coping mechanisms that further increase their vulnerability.
The pandemic-conflict nexus
What happens when conflicts collide with major disease pandemics?22 Weak or fragmented institutions, contested legitimacy of and deep-seated mistrust in the authorities, overstretched or destroyed health sector, crowded refugee camps and population flows are but some of the characteristics of conflict-affected societies rendering them vulnerable to pandemics such as Covid-19. At the same time, health policy is deeply embedded in the politics of armed conflicts. In Iraq, for example, armed groups have placed strategic priority on medical facilities as controlling health and social services is seen as means for controlling the population.23 In many conflict zones, parts of the territory are controlled by armed groups or other rival political institutions which further complicates policy responses in health crises. Despite these challenges, Covid-19 responses by governments in conflict-affected societies by and large mirrored those deployed by governments around the world in 2020. According to the University of Oxford’s Coronavirus Government Response Tracker, authorities in Iraq, Ukraine, Libya, Sudan, the DRC, Syria, Central African Republic, Myanmar, South Sudan opted for varying levels of school and workplace closures, restrictions on public gatherings, stay-at-home orders, restrictions on internal movement and controls on international travel ranging from border closures to quarantines of travellers from high-risk regions. As argued later, the gender-blind nature of these policies contributed to the emergence of multiple, interconnected insecurities.
Existing research provides important clues for thinking about the conflict-pandemic nexus. Literature on health security, for one, has outlined the impact of health crises on state capacity and security.24 While some of its more alarmist arguments have not withstood empirical scrutiny,25 it is commonly accepted that pandemics are likely to shape conflict dynamics in complex ways.26 A body of research that has studied this complexity in detail is the scholarship on natural disasters.27 This line of enquiry has identified multiple ways in which biologic, geophysical and climate-related disasters affect armed conflicts. Studies on the impact of disasters on armed conflicts have identified a range of effects; in some cases, disasters have increased the likelihood of armed conflict28 while in other conflict settings disasters have reduced the risk of conflict.29 In summarising the above and related empirical findings, Katie Harris, David Keen and Tom Mitchell30 suggest that natural disasters intensify existing conflicts by contributing to the key conflict drivers, including economic opportunities, grievances and conflict feasibility. They argue that disasters may create economic opportunities for conflict actors and incentivise individuals to join armed groups. At the same time, where disasters exacerbate resource scarcity or marginalisation of communities, grievances are likely to increase. Finally, disasters may shape the feasibility of conflict and political opportunities for violence by, for example, providing a pretext for deploying troops in a politically sensitive area.
Emerging research on Covid-19 and armed conflicts aligns with the above findings.31 More specifically, it suggests that while the coronavirus pandemic did not trigger conflicts,32 it shaped the feasibility of conflicts in important ways. Tobias Ide, in one of the first analyses of the effects of Covid-19 on armed conflict, argues that the Covid-19 shifted the strategic calculus of conflict actors by providing opportunities for violence and, in some cases, by shoring up the legitimacy of non-state actors as service providers.33 The pandemic also provided a justification for using force against political opponents. In this regard, Dorothea Hilhorst and Rodrigo Mena’s34 analysis suggests that governments securitised the pandemic, providing a pretext for suppressing social protest. Importantly for this analysis, these changing conflict dynamics have implications that extend well beyond the conflict actors. Being attentive to the implications of the changing conflict dynamics to those not bearing arms brings to the fore an array of insecurities that go beyond physical violence.
Vulnerability multipliers
While the above research provides an important entry point into thinking about the dynamics between armed conflict and disease pandemics, locating women in the dual crisis of conflict and Covid-19 requires readjusting the analytical lens to the bottom-up experience of Covid-19 in conflict zones. In more concrete terms, this means broadening the analytical focus beyond the experiences and interests of conflict actors and narrow conceptions of insecurity. It requires shifting the focus away from how the pandemic affects opportunities and grievances of warring parties to the implications of the changing conflict dynamics on women. How do escalating or de-escalating conflict levels (as a result of the pandemic) impact women? To reflect on the above questions, it is necessary to situate the effects of the pandemic-conflict nexus on women within broader socio-economic structures that shape their ability to manage compounding crises. These structures, or ‘vulnerability multipliers’, are grounded in deeply embedded gender hierarchies and inequalities35 whereby the feminine is devalued. This is not to argue that women have no agency in addressing the dual crisis. In many conflict settings, such as South Sudan, Libya and Central African Republic, women’s civil society organisations quickly transformed into first responders to the pandemic, disseminating information and medical supplies. Yet, ‘people’s ability to recover from shocks’, as Judy El Bushra36 notes, ‘is at least partly determined by their position in the evolving power structures’. Given the gendered hierarchies that underwrite structures of power, where women and those considered ‘feminine’ are seen as subordinate to men and context-specific masculinities, ‘women are more likely than men to have to struggle to survive’, in the face of societal shocks.37 While not all women are equally affected by the vulnerability multipliers, they are critical in shaping the gendered experience of the pandemic in conflict zones and critically, affect individuals’ ability to manage the increasing violence and insecurities generated by the collision of conflict with Covid-19. The primary vulnerability multipliers in conflict-contexts are restrictive gender norms,38 gendered division of labour39 and lack of access to resources.40
When it comes to restrictive gender norms, many conflict-affected societies are characterised by the prevalence of hypermasculinity. This type of masculinity prioritises aggressiveness and physical strength, while devalorising the feminine and homosexuality.41 Hypermasculinity is not exclusively associated with conflict-affected societies nor do all men in conflict settings subscribe to it. Yet, in the context of war the traditional gender roles that depict men as protectors ready to use violence to protect ‘their’ women and women as vulnerable subjects of protection, become heightened. If anything, the coronavirus pandemic intensified the masculine protector logic as the virus was frequently framed as an ‘enemy’ and efforts to contain it as ‘war’. These securitised framings of the pandemic were also deployed by political actors in countries not affected by collective armed conflict, but in conflict-affected societies the securitisation of the virus tapped on a pre-existing hypermasculinity. This reinforced a protection bargain whereby women, seen as weak and vulnerable, exchange their autonomy for protection.42 The ability of men to live up to the role of the protector in the context of the dual crisis has been limited, however, resulting in frustration and in some cases, violence.
One of the key manifestations of this masculine protector dynamic is restricting women’s mobility. While framed as an act of protection, mobility limitations undermine the ability of women to engage in paid employment or education outside the home, thus reproducing dependency on men. During the initial stages of the coronavirus pandemic, mobility restrictions limited women’s access to healthcare as they needed male relatives to accompany them to medical facilities. Similarly, civil society groups in Iraq43 and Palestine44 reported of cases where women were not allowed to stay in quarantine facilities unaccompanied, raising concerns about increasing familial and community spread of the virus. Moreover, the combined restrictions on women’s movement stemming from the hypermasculine gender norms and Covid-19 prevention measures further reduced women’s ability to engage in income generating activities or employment outside home, thus reducing their independence and financial capability to weather the dual crisis of armed conflict and Covid-19.
An upshot of hierarchical gender norms is the gendered division of labour. Women undertake the majority of unpaid domestic labour and if they do enter the workforce outside the home, their remuneration is lower than men in aggregate. While armed conflicts provide opportunities for women to enter paid employment because of the shortage of male labour, the type of employment available to them is often in ‘unprotected’ sectors where they have few state-mandated protections of their rights. Lack of access to productive resources such as land, jobs, education, credit and technology45 is another key vulnerability multiplier. In many conflict-affected societies women are systematically excluded from owning or accessing such assets. According to estimates, in conflict-affected societies girls are two and half times more likely than boys to not attend primary school and adolescent girls are 90% more likely not to be attending secondary school compared to boys.46 The resulting lack of human capital renders women dependent upon men and further ‘feminisation of poverty’, leaving girls and women vulnerable to exploitation.47 Gendered barriers to owning or inhering land is of equal importance in conflict-affected societies. Women are not allowed to own or inherit land in some societies, based on the assumption that women are unable to manage land or that such assets will be ‘lost to another family’ when women marry.48 This compounds the lack of autonomy and economic insecurity. Access to another important resource, communications technology, is deeply gendered in conflict contexts. According to estimates, boys are one and a half times more likely to own a phone than girls and women,49 young people and those living in urban areas are more likely to have a mobile phone and access to social media.50 These inequalities not only sustain women’s economic dependency but became particularly pronounced during the Covid-19 pandemic as education, work and politics moved online.
Exploring the impact of the dual crisis of armed conflict and disease pandemic on women through the above framing provides an analytical tool for tracing the effects of the sudden social, political and economic disruption that the coronavirus pandemic and its related policies inflicted on conflict-affected societies. Critically, situating the effects of the dual crisis and attempts to manage the subsequent insecurities in the context of the vulnerability multipliers outlined above can yield a better understanding of the intersections of gender, armed conflict and disease pandemic. The next section explores the impact of the coronavirus pandemic on conflict dynamics and its consequences to women in conflict-settings in the early stages of the pandemic. It outlines the various gendered insecurities- physical, economic, health and political – intensified by the dual crisis of global health and pandemic and conflict. While some of these insecurities also manifest in societies not affected by collective armed conflict, they are multiplied in conflict settings where the ability of governments to address them in gender-sensitive way is often low or non-existent.51 The analysis commences with a discussion of changing conflict dynamics and the ensuing changes in physical violence from a gender perspective, followed by economic and health insecurities and political marginalisation.
Physical insecurities
Commentaries on Covid-19 attentive to gender have documented the rise of domestic violence during the Covid-19 pandemic across the globe. Conflict-affected societies represent no exception to this trend.52 The pandemic exacerbated the already high rates of domestic violence in conflict-zones caused by militarisation, the prevalence of hyper-masculinity, mental health problems and substance abuse. In conflict contexts the ‘shadow pandemic’ of domestic violence has been compounded by exposure to or threat of violence in the public sphere. Indeed, whereas some types of violent disorder decreased after the onset of the pandemic,53 violence against civilians, communal violence and state repression increased globally during the pandemic.54 For example, fighting escalated in Afghanistan,55 Yemen56 and the DRC57 in 2020. In Western Africa violent acts perpetrated by militias were up by 50% from the average monthly number of incidents58 and in Myanmar the military increased attacks on minority groups in Karen, Kachin, Shan and Rakhine states. In April 2020, 32 civilians – most of whom were women and children – were killed in the fighting between the Myanmar military and the Arakan Army, Rakhine rebel group.59 In Libya, armed groups shelled residential areas where civilians were in Covid-lockdown and shelled hospitals treating Covid-19 patients.60 While increasing levels of collective violence generally results in more men and boys killed in fighting, it also results in further displacement of women, forced to flee to camps where they are vulnerable to sexual abuse and unhygienic conditions. The Global Protection Cluster observed a rise in sex and gender-based violence in 90% of humanitarian contexts where it operated in 2020.61 Increase in physical insecurity was highly uneven, however. Socio-economic status, age, geographical location, community ties, among other factors, shaped the physical insecurities women, girls, men and boys.
Militarisation of government responses to the pandemic further compounded the physical insecurity. Not only did the framing of the pandemic as a war run the risk of legitimising deaths from the virus as ‘heroic sacrifices’62 and reinforcing hypermasculinity, but such measures went hand in hand with the deployment of security forces to monitor lockdowns and curfews. Police used fatal violence in enforcing coronavirus measures in Nepal, Mali, Colombia, Burundi, Kenya, Central African Republic, Uganda and Nigeria.63 People with non-binary identities and the LGBTQ+ communities were particularly vulnerable to abuse of power by security forces. The police in Uganda, for example, raided an LGBT shelter and made arrests on the grounds of failure to adhere to social distancing rules.64
At the same time, armed non-state groups violently enforced Covid-19 prevention measures. In Colombia, the National Liberation Army reportedly killed and abused civilians violating such measures.65 Excessive use of force had particular implications to the most marginalised in the society. In South Sudan, for example, security forces assaulted IDPs seeking to leave their camp to obtain food and other basic supplies.66 Civil society groups in Mozambique and east Africa were reporting sexual and gender-based violence by security forces enforcing Covid-19 prevention measures.67
The gendered implications of the increasing violence were far-reaching. Attacks on hospitals and schools, in Mozambique,68 Libya and Afghanistan, directly placed women, as the majority of health care providers and teachers, in the ‘front line’ of the conflict. The effects of violence were uneven and shaped by intersecting inequalities; in Colombia, for example, violent attacks on Afro-Colombians and Indigenous populations increased sharply in the early stages of the pandemic.69 As noted earlier, increase in conflict results in further displacement70 which is deeply gendered. In Myanmar, for example, 77% of internally displaced populations (IDPs) are women and children.71 According to estimates, more than three out of four displaced people in conflict zones lost incomes due to the pandemic.72 At the same time, lack of personal identification documents hindered IDPs access to health care in Libya, Iraq and Myanmar73 and the delivery of humanitarian aid to camps was severely restricted by the pandemic-related travel restrictions. Furthermore, border closures and tightened migration policies by host states complicated efforts to migrate or seek asylum by those residing in IDP and refugee camps.
The above trends suggest that in many conflict-affected societies, Covid-19 contributed to escalation of armed violence by providing new opportunities for use of force. This translated into further displacement and intensification of physical insecurities. ‘Militarisation’ of coronavirus measures further reinforced hypermasculinity and restrictive gender roles and undermined women’s ability to manage the insecurities stemming from the dual crisis.
Economic insecurities
Major societal crises have varied impacts on women’s economic activities. Conflicts, for example, often increase women’s paid employment where men are conscripted to armies or recruited to militias to fight.74 In Libya many women have started small businesses operating from home as a way to support their families. Similarly, prior to the US withdrawal in 2021, in certain areas of Afghanistan many women had jobs outside home or engaged in activities such as selling goods at markets. Although women’s paid employment may not directly translate into greater gender equality, it marks a shift away from traditional gender roles that depict women as homemakers rather than breadwinners. Employment or income generating activities outside the home provide a degree of independence.
Responses to the pandemic, however, severely undermined women’s livelihoods. Many households in conflict areas are led by women and loss of livelihoods, coupled with limited access to resources such as land or credit, posed a major risk of deepening poverty. A survey found that in humanitarian contexts 52% of women reported loss of livelihood due to the pandemic, in comparison to 34% of men.75 Owing to one of the key vulnerability multipliers, the gendered division of labour, the pandemic hit jobs dominated by women particularly hard. Women are overrepresented in insecure and informal jobs that have few social protections.76 In Myanmar, an estimated 90% of women who work outside the home are employed in informal jobs, such as street vendors, farm workers and sex workers.77 There were reports that Myanmar garment factories, where the vast majority of employees are migrant women and girls, used the pandemic as a pretext for dismissing employees belonging to unions.78 At the same time, many jobs and income generating activities dominated by women offer no opportunities to save money as families rely on the daily wage. While women’s lack of access to resources further intensified their vulnerability to the economic disruption caused by the pandemic, remittances from overseas were down by 20% by some estimates in 2020.79 Although these dynamics are not unique to conflict-affected societies, armed conflicts limit the state’s capacity to respond to the economic shock in a gender-sensitive way.80
As elsewhere in the world, women in conflict contexts took on additional care duties at home in line with the gendered division of labour, limiting their ability to earn an income outside the home. For some, stay-at-home orders or disruption to public transport (in Syria and Libya, for example) due to the pandemic translated into a significant loss of income. In many cases men were subject to the same Covid-19 travel restrictions but were reportedly more able to flout such restrictions. In Iraq, for example, men defied travel restrictions by using backroads, an option not available to many women due to personal safety concerns.81 Similar trends were evident in Palestine where a survey found that 91% of women indicated that mobility restrictions, since the onset of the pandemic, ‘prevented access to goods and resources’ while 57% of men found movement restrictions rendering goods and sources inaccessible.82
To summarise, armed violence, compounded with Covid-lockdowns, had disastrous effects on livelihoods as people were unable to leave their houses to earn an income. This affected women in particular as conflict zones are characterised by significant number of female-led households whose vulnerability is multiplied by lack of access to credit. Importantly, economic insecurities are linked to women’s physical insecurity in conflict-zones as loss of livelihoods leaves women susceptible to sexual exploitation. For example, women who are dependent upon humanitarian aid may face demands for sexual favours in return for aid.83 There are also reports that the diminishing economic opportunities were driving recruitment into non-state armed groups. Anecdotal evidence suggests that men and boys were enlisting into armed groups as a coping mechanism in Syria, Colombia, Yemen and Burkina Faso.84 Other coping strategies, namely child marriage and child labour, were also on the increase in 2020.85 Marrying girls off at a young age is seen as a way to protect girls from sexual violence, while simultaneously reducing household consumption. At the same time, child labour around the world increased in 2020 for the first time since 2000. In Myanmar and Syria, boys reportedly dropped out of school in order to engage in casual labour to boost family incomes.86
Health insecurity
While the multifaceted impacts of disease pandemics on the gendered health needs are well-documented,87 those living in conflict zones face additional obstacles. Health sectors are destroyed by the fighting, overstretched beyond their capacities or harnessed for strategic purposes. Moreover, in some cases, health officials in conflict settings are dealing with other disease outbreaks directly related to the conflict, as the cholera epidemic in Yemen and Ebola in the DRC suggest. Services such as maternal, reproductive and post-natal care critical to the wellbeing of women and girls are often severely limited; major disease pandemics reduce further the availability and access to such services as resources are directed to the pandemic at hand.
A related and reoccurring theme in the review of the gendered effects of Covid-19 on conflict dynamics is the exacerbation of food insecurity. For example, prior to the pandemic, 2.4 million Afghans had no secure access to food; Covid-19 lockdowns further exacerbated the problem by restricting the delivery of humanitarian aid, increasing the number of Afghans affected by food insecurity to 3.3 million.88 Similarly, in the Sahel, the number of food insecure people rose by 1 million since the start of the pandemic in early 2020.89 While food prices increased sharply in many conflict-affected societies,90 in Somalia the Covid-19-related spikes in food insecurity were compounded by a locust infestation that destroyed crops. Not only is food insecurity closely linked to physical violence,91 it also has distinctly gendered effects. Women and girls in particular are at risk because of the expectation in some cultures that women ‘eat last and least’.92
The rise in food insecurity in conflict affected societies in 2020 forced many families to turn to coping strategies that exacerbate health insecurities. According to a survey of displaced and conflict-affected populations in 14 countries, 73% of respondents reported having reduced their meal intake during the first wave of the pandemic.93 Another survey found that in Afghanistan 81% of survey respondents resorted to limiting household food consumption.94 In Iraq, in turn, 74% of respondents indicated reduced food intake as a strategy for coping with the hardship caused by the intersection of conflict and the pandemic response.95 Surveys in Libya, Palestine and Iraq found similar patterns.96 Another way to cope with lack of access to food was enlisting to an armed group, as mentioned previously. In Colombia, civil society groups reported an increase in the recruitment of child soldiers (majority of whom are boys) by non-state armed groups.97 They note that while some children were forcefully recruited, others joined voluntarily to get access to regular meals. Similar dynamics were evident in Mali and Mozambique.98
Political marginalisation
Compounding the above gendered insecurities was the increasing political marginalisation of women, that can be viewed as ‘violence of exclusion’, ‘indivisible from women’s political, social, cultural and economic disempowerment’.99 Women in conflict-affected societies were significantly under-represented in decision-making structures relating to the coronavirus. Ad hoc committees tasked with developing pandemic strategies were by and large replicating the unequal gender balance in governance structures, evident in the exclusion of women and marginalised communities. In Ukraine, for example, less than 20% of members of committees developing the country’s Covid-19 response in 2020 were women.100 Similar patterns are evident in the Middle East.101 This is despite the fact that the majority of healthcare workers in most conflict zones are women. Even where women were included in the pandemic response, additional care duties at home due to school closures limited women’s ability to participate in decision-making.
Beyond the under-representation of women in formulating coronavirus policies, the pandemic has further increased women’s exclusion from peace processes that, in many cases, continued despite the pandemic.102 While women’s mobility is often limited in conflict settings, the coronavirus related restrictions further hindered women’s ability to travel to peace talks. Where peace processes moved to virtual spaces, women’s participation was largely limited to individuals and groups who have access to the internet, reinforcing inequalities among women.
Another trend emerging from the intersection between conflict and pandemic is the shrinking space for civil society. This has significant gendered implications as in most conflict-affected societies, women’s political action is channelled through civil society activism as their access to the formal political structures is limited. While civil society organisations took an active role in disseminating health information and protective gear, governments and other conflict actors used the pandemic as a pretext to limit civil society activism. Civil society groups in Yemen, Nigeria and South Sudan, for example, reported increasing restrictions, justified as a response to the coronavirus. These restrictions limited the right to protest and freedom of speech and enabled detentions of civil society activists on tenuous charges.103 In Colombia, armed groups intensified violent attacks against human rights defenders, many of whom are women from the indigenous or Afro-Colombian communities.104 In Iraq, civil society activists were subjected to increasing levels of kidnappings and assassinations in the early stages of the pandemic.105
Overlapping insecurities
This initial overview on the effects of the pandemic in conflict zones points towards a set of overlapping and compounding insecurities – physical, economic and political – that have particularly devastating impact on women and girls. The insecurities emerging out of this initial analysis overlap and intersect in multiple ways. An increase in physical insecurity results in further displacement which can, in turn, intensify health insecurity as a consequence of living in a refugee or IDP camp. Increasing physical insecurities tend to also result in further mobility restrictions, reducing access to economic opportunities. The nexus between economic and food insecurity is equally important to understanding the overlapping and intersecting insecurities. As noted previously, food insecurity has particular effects on women and girls due to gendered societal expectations and is likely to be felt most acutely among the most marginalised in the society. While armed violence, disease pandemic and economic disruption drive food insecurity, food shortages often fuel physical violence. At the same time, marginalisation of women from decision-making, as Agnieszka Fal-Dutra Santos and Panthea Pourmalek note, ‘not only aggravates other forms of violence. . .but can be experienced as a distinct form of violence itself’.106 All this points to multiple, overlapping insecurities operating simultaneously, leading to ‘layered violence’ whereby the pre-existing violence against women and girls escalates, subjecting them to more intense forms of harm.107
It is notable that gender-blind government policies in conflict affected societies exacerbated the above insecurities. For example, in central and southern Asia, only 15% of economic measures addressed the gendered effects of the pandemic.108 Other regions of the world reflect similar patterns. In northern Africa, Western Asia and sub-Saharan Africa less than 20% of economic measures were gender sensitive, according to an assessment by the UN Women.109 Some conflict-affected countries fared slightly better in addressing the increase in sexual and gender-based violence (SGBV). Setting up helplines (such as South Sudan, Nepal, Haiti, Lebanon) and shelters (Afghanistan, Nepal, Colombia) to assist survivors of SGBV represent common government responses. Yet, on the whole, gender-sensitive Covid-19 measures made up a mere ‘a fraction’ of policies, according to the UN Women.110 These policies exacerbated gendered insecurities. Use of security forces to enforce Covid-19 measures went hand in hand with increasing reports of sexual violence and harassment of civil society groups.111 Lockdowns and travel restrictions without gender-sensitive economic support measures, in turn, significantly reduced economic opportunities for women in conflict settings, many of whom are the breadwinners in their families.
While foregrounding the above insecurities, it is important to be attentive to women’s efforts to negotiate the intersection of armed conflict and disease pandemic. Far from succumbing to essentialised gender roles depicting women as weak and defenceless, individual women and women’s groups across conflict-affected societies engaged in service provision, information campaigns and other activities to support communities. For example, in Afghanistan women’s associations raised funds to re-employ women who lost their jobs due to the pandemic to sew masks.112 In Yemen, women’s groups provided legal support for girls forced to early marriages,113 while in Libya women’s groups raised awareness of the virus and distributed masks.114 Yet, while women have sought to negotiate the range of insecurities flowing from the dual crisis of conflict and Covid-19, their ability to successfully do so is often undermined by the vulnerability multipliers. Restrictive gender norms have subjected women and girls to malnourishment and limited their access to healthcare, while gendered division of labour rendered women responsible for additional coronavirus-related care duties at home. Lack of access to resources in the face of severe economic disruption have compelled many female-led households to turn to coping strategies that further increase their vulnerability. These coping strategies may offer immediate physical or economic security but have adverse long-term effects on peoples’ security, health and economic prospects.
Conclusion
This paper set out to cast an initial eye on the effects of the collision between armed conflict and the Covid-19 pandemic on women. While most countries are emerging out of Covid-19 related restrictions, the patterns of insecurity documented in this discussion provide insights for responding to future crises, whether pandemics or environmental disasters. The analysis highlights the need to address the vulnerability multipliers that have exposed women and girls to the double crises of conflict and disease pandemic. Short-term measures could include, for example, supporting income generating activities by female-led households through small grants. This may help to reduce economic insecurity and the associated coping strategies such as early marriage and child labour. The focus should be specifically on home-based activities that can be undertaken during lockdowns. Another measure could be to provide increased support for women’s groups who have access to, and provide services to, the most vulnerable populations in conflict-affected societies.
As noted in the introduction, this analysis marks the beginning rather than the end of the inquiry. There are a number of important issues that merit further research. For example, it has been shown that responses to disease pandemics result in worse public health outcomes as resources are redirected away from the treatment of other health conditions.115 What were the broader health impacts of Covid-19 in conflict affected societies and how did they intersect with gender, class, race and sexuality? How were LGBTQ+ communities in conflict settings affected by the pandemic? At the same time, more research is required to document the experience of Covid-19 in areas controlled by non-government armed groups. What were the gendered dynamics of conflict and access to health in contested territories? In what way can external actors, such as the WHO, provide aid and build capacity in areas controlled by non-state groups without undermining the de jure sovereignty of the country’s government?116 These are but some lines of research that are needed to generate a more refined understanding of the interplay between conflict, pandemics and gender and facilitate the process of ‘building back better’.
The author would like to thank Sara Davies for her comments on the paper.
Author biography
Outi Donovan is a lecturer in International Relations at Griffith University, Queensland, Australia. Her research investigates peacebuilding and peace processes in conflict-affected societies. Among other themes, her work has explored interactions between local and UN-led peacebuilding processes, the responsibility to protect principle and the gendered dynamics of civilian protection in peacebuilding contexts. Her current research explores the intersection of pragmatist philosophy, feminism and peacebuilding.
Funding: The author received no financial support for the research, authorship, and/or publication of this article.
ORCID iD: Outi Donovan https://orcid.org/0000-0003-2118-6177
1. Laura Hammond, ‘Ethnic Violence in Tigray has Echoes of Ethiopia’s Tragic Past’, The Conversation, 20 November 2020, available at: https://theconversation.com/ethnic-violence-in-tigray-has-echoes-of-ethiopias-tragic-past-150403 (accessed 21 November 2020).
2. Britt Koehnlein and Ore Koren, ‘COVID-19, State Capacity, and Political Violence by Non-State Actors’, Journal of Peace Research, 59(1), 2022, pp. 90–104.
3. United Nations, ‘COVID-19 Fast Becoming Protection Crisis, Guterres Warns Security Council’, 2 July 2020, available at: https://news.un.org/en/story/2020/07/1067632; United Nations (UN), ‘Security Council, Resolution 2532’, 1 July 2020, available at: http://www.unscr.com/en/resolutions/doc/2532 (accessed 2 August 2020).
4. Patricia H. Hynes, ‘On the Battlefield of Women’s Bodies: An Overview of the Harm of War to Women’, Women’s Studies International Forum, 27(5–6), 2004, pp. 431–45; Thomas Plümper and Eric Neumayer, ‘The Unequal Burden of War: The Effect of Armed Conflict on the Gender Gap in Life Expectancy’, International Organization, 60(3), 2006, pp. 723–54.
5. ‘Protecting Humanity From Future Health Crises’, Report of the High-Level Panel on the Global Responses to Health Crises. United Nations General Assembly, A/70/723, 9 February 2016, p. 13; Clare Wenham, Julia Smith, Sara E. Davies, et al., ‘Women are Most Affected by Pandemics—Lessons From Past Outbreaks’, Nature, 8 July 2020, pp. 194–198.
6. E.g. Lindsay Stark, Melissa Meinhart, Luissa Vahedi, et al., ‘The Syndemic of COVID-19 and Gender-Based Violence in Humanitarian Settings: Leveraging Lessons From Ebola in the Democratic Republic of Congo’, BMJ Global Health, 5(11), 2020, e004194;UNICEF, ‘Responding to the Shadow Pandemic: Taking Stock of Gender-Based Violence Risks and Responses During COVID-19’, 20 August 2020, available at: https://www.unicef.org/documents/responding-shadow-pandemic-taking-stock-gender-based-violence-risks-and-responses-during (accessed 15 September); UN Women, ‘The COVID-19 Shadow Pandemic: Domestic Violence in the World of Work’, May 2020, available at: https://www.unwomen.org/en/digital-library/publications/2020/06/brief-domestic-violence-in-the-world-of-work#view (accessed 15 September 2020).
7. See for example, UNICEF, ‘Responding to the Shadow Pandemic’.
8. Cynthia Cockburn, ‘The Continuum of Violence – A Gender Perspective on War and Peace’, in Wenona Giles and Jennifer Hyndman (eds), Sites of Violence – Gender and Conflict Zones (Berkeley, Los Angeles, CA: University of California Press, 2004), pp. 24–44; Aisling Swaine, ‘Beyond Strategic Rape and Between the Public and Private: Violence Against Women in Armed Conflict’, Human Rights Quarterly, 37(3), 2015, pp. 755–86.
9. Sophia N. Nesamoney, Gary L. Darmstadt and Paul H. Wise, ‘Addressing the Impacts of COVID-19 on Gender Equality and Global Health Security in Regions of Violent Conflict’, Journal of Global Health, 11, 2021, p. 03074; Agniezska Fal-Dutra Santos, Nikou Salamat, Sena Bölükoğlu, et al., ‘Lockdown on Peace? COVID-19’s Impact on Women Peacebuilders’, Social Politics: International Studies in Gender, State and Society, 2022, online first.
10. Colleen O’Manique and Pieter Fourie (eds), Global Health and Security: Critical Feminist Perspectives (Abingdon: Routledge, 2018); Fal-Dutra Santos et al., ‘Lockdown on Peace?’. See also Sophie Harman, ‘Ebola, Gender and Conspicuously Invisible Women in Global Health Governance’, Third World Quarterly, 37(3), 2016, pp. 524–41.
11. Harman, ‘Ebola, Gender and Conspicuously Invisible Women’.
12. O’Manique and Fourie, Global Health and Security.
13. Sara E. Davies and Belinda Bennett, ‘A Gendered Human Rights Analysis of Ebola and Zika: Locating Gender in Global Health Emergencies’, International Affairs, 92(5), 2016, pp. 1041–60; Jennifer Diggins and Elizabeth Mills, ‘The Pathology of Inequality: Gender and Ebola in West Africa’, Institute of Development Studies Practice Paper 23 (Brighton: IDS, 2015); Barbara Ribeiro, Sarah Hartley, Brigitte Nerlich, et al., ‘Media Coverage of the Zika Crisis in Brazil: The Construction of a ‘War’ Frame That Masked Social and Gender Inequalities’, Social Science & Medicine, 200, 2018, pp. 137–44.
14. Nahid Rezwana and Rachel Pain, ‘Gender Based Violence Before, During and After Cyclones: Slow Violence and Layered Disasters’, Disasters, 45(4), 2021, pp. 741–61.
15. Cockburn, ‘The Continuum of Violence’.
16. PRIO Conflict Trends, ‘Trends in Armed Conflict, 1946–2020’, 2021, available at: https://reliefweb.int/sites/reliefweb.int/files/resources/Strand%20and%20Hegre%20-%20Trends%20in%20Armed%20Conflict%2C%201946-2020%20-%20Conflict%20Trends%203-2021.pdf (accessed 5 January 2022).
17. Primarily, but not exclusively, Afghanistan, Libya, Syria, Myanmar, Iraq and Yemen.
18. Such as: CARE, ‘Rapid Gender Analysis – Middle East North Africa (MENA)’, June 2020, available at: http://careevaluations.org/evaluation/rapid-gender-analysis-middle-east-north-africa-mena/ (accessed 13 April 2022); CARE Palestine, ‘Rapid Gender Assessment – An Summary of Early Gender Impacts of the COVID-19 Pandemic’, March 2020, available at: https://healthclusteropt.org/admin/file_manager/uploads/files/1/5eaebacaa7de5.pdf (accessed 13 April 2022); CARE Afghanistan, ‘Rapid Gender Analysis: COVID-19’, July 2020, available at: https://reliefweb.int/report/afghanistan/afghanistan-rapid-gender-analysis-covid-19 (accessed 13 April 2022); CARE, ‘Rapid Gender Analysis: Myanmar, Rakhine State’, August 2020, available at: https://www.careevaluations.org/evaluation/rapid-gender-analysis-myanmar-rakhine-state-covid-19/ (accessed 13 April 2022); OXFAM, ‘A New Scourge to Afghan Women: COVID-19’, Oxfam Afghanistan, Briefing Note, April 2020, p. 6, available at: https://oi-files-cng-prod.s3.amazonaws.com/asia.oxfam.org/s3fs-public/file_attachments/COVID%2019.%20A%20New%20Scourge%20to%20Afghan%20Women_OXFAM.pdf (accessed 13 April 2022); Oxfam, ‘Gender Analysis of the COVID-19 Pandemic in Iraq’, June 2020, available at: https://policy-practice.oxfam.org/resources/gender-analysis-of-the-covid-19-pandemic-in-iraq-conducted-in-kirkuk-diyala-and-621007/ (accessed 13 April 2022); Daniel Gorevan, ‘Downward Spiral: The Economic Impact of Covid-19 on Refugees and Displaced People’, Norwegian Refugee Council, Oslo, 21 September 2020, p. 7, available at: https://www.nrc.no/resources/reports/downward-spiral-the-economic-impact-of-covid-19-on-refugees-and-displaced-people/ (accessed 6 January 2022); UN Women, ‘Gender Sensitive Prevention, Response and Management of COVID-19 Outbreak in Libya’, UN Women, n.d., available at: https://www2.unwomen.org/-/media/field%20office%20arab%20states/attachments/publications/2020/04/covid-19%20in%20libya/survey%20gendersensetive%20prevention%20response%20and%20management%20of%20covid19%20outbreak%20in%20libyabrief%20design.pdf?la=en&vs=908 (accessed 13 April 2022); UN Women, ‘Rapid Gender Assessment of the Situation and Needs of Women in the Context of COVID-19 in Ukraine’, May 2020, available at: https://reliefweb.int/report/ukraine/rapid-gender-assessment-situation-and-needs-women-context-covid-19-ukraine-enuk (accessed 13 April 2022).
19. UN Women, ‘Rapid Gender Assessment of the Situation and Needs of Women in the Context of COVID-19 in Ukraine’.
20. CARE, ‘Rapid Gender Analysis: Myanmar, Rakhine State’; Oxfam, ‘Gender Analysis of the COVID-19 Pandemic in Iraq’.
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22. Paraphrased from Katie Harris, David Keen and Tom Mitchell, When Disasters and Conflicts Collide: Improving Links between Disaster Resilience and Conflict Prevention. Overseas Development Institute. February 2013, p.vii.
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27. E.g. Philip Nel and Marjolein Righarts, ‘Natural Disasters and the Risk of Violent Civil Conflict’, International Studies Quarterly, 52(1), 2008, pp. 159–85; Robert A. Stallings, ‘Conflict in Natural Disasters: A Codification of Consensus and Conflict Theories’, Social Science Quarterly, 69(3), 1988, pp. 569–86; Cooper A. Drury and Richard Stuart Olson, ‘Disasters and Political Unrest: An Empirical Investigation’, Journal of Contingencies and Crisis Management, 6(3), 1998, pp. 153–61; Dawn Brancati, ‘Political Aftershocks: The Impact of Earthquakes on Intrastate Conflict’, Journal of Conflict Resolution, 51(5), 2007, pp. 715–43.
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32. Tigray can be considered an exception to this general trend as noted in the opening paragraph.
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85. Global Protection Cluster, ‘The Coping Crisis’.
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87. Davies and Bennett, ‘A Gendered Human Rights Analysis of Ebola and Zika’; Harman, ‘Ebola, Gender and Conspicuously Invisible Women’; O’Manique and Fourie, Global Health and Security; Julia Smith, ‘Overcoming the “Tyranny of the Urgent”: Integrating Gender Into Disease Outbreak Preparedness and Response’, Gender & Development, 27(2), 2019, pp. 355–69.
88. ‘Afghanistan, FAO and Belgium Assist Food-Insecure Farming Households Impacted by the Shock of COVID-19’, Food and Agriculture Organization of the United Nations, 3 September 2020, available at: http://www.fao.org/resilience/news-events/detail/en/c/1305946/ (accessed 10 September 2020).
89. Will Brown, ‘No Time for Complacency: The COVID-19 Pandemic in West Africa’s Sahel Region’, Centre for Strategic and International Studies, 8 July 2020, available at: https://www.csis.org/analysis/no-time-complacency-covid-19-pandemic-west-africas-sahel-region (accessed 10 September 2020).
90. OXFAM, ‘A New Scourge to Afghan Women: COVID-19’, p. 6.
91. In May 2020, armed clashes ensued between crowds and security forces in the Ghor province in Afghanistan over distribution of food aid that was seen as unfair, favouring those with political connections. Violent clashed triggered by food insecurity have also been reported in Kenya.
92. ‘As Million Face Famine, Women at Risk as they Eat Last and Least’, Reuters, 19 November 2020, available at: https://in.reuters.com/article/global-women-hunger/as-millions-face-famine-women-at-risk-as-they-eat-last-and-least-idINL8N2I44G7 (accessed 12 September 2020).
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94. Gorevan, ‘Downward Spiral’, p. 16.
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| 0 | PMC9732488 | NO-CC CODE | 2022-12-14 23:35:51 | no | Int Relat (David Davies Mem Inst Int Stud). 2022 Dec 7;:00471178221140084 | utf-8 | Int Relat (David Davies Mem Inst Int Stud) | 2,022 | 10.1177/00471178221140084 | oa_other |
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Original Manuscript
Coercive and mimetic isomorphic mechanisms for service provision: The creation of nonprofit organizations in Mexico before and during the COVID-19 pandemic
https://orcid.org/0000-0002-8046-6024
Hernandez Ortiz Tania L
129334 Invest in Open Infrastructure , Portland, OR, USA
Appe Susan
Nelson A. Rockefeller College of Public Affairs & Policy, University at Albany, 129334 State University of New York (SUNY) , Albany, NY, USA
Tania L Hernandez Ortiz, Research Data Analyst, Invest in Open Infrastructure, Code for Science & Society, 3439 SE Hawthorne Blvd, #247 Portland, OR 97214, USA. Email: [email protected]
7 12 2022
7 12 2022
09520767221142186© The Author(s) 2022
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Nonprofit organizations represent diverse efforts of collective action and service provision, and have for some time been collaborators to public governance systems in developed and developing economies. In this article, we contribute to the limited empirical and analytical study in the field of public policy and administration about the operational environments that enable or constrain nonprofits in the provision of public goods and services. The operational environment for organized civil society, namely for nonprofits, includes the combination of their regulatory, political, and funding contexts. By analyzing the purposes of 296 new nonprofits registered between 2016 to 2020 in Mexico, the empirical context of our inquiry, we find that as resources have declined, new nonprofits adopt isomorphic mechanisms by resembling their purposes to the services the government intends to support. Nonprofits have also responded to the pandemic by focusing more than before on areas related to health, social assistance, and funeral services. The study contributes to bigger questions about the relationships between and balance across the responsibilities of governments and nonprofits, including during the COVID pandemic, and to the understanding of nonprofits as service providers in public governance.
Coercive isomorphism
mimetic isomorphism
operational environment
nonprofits in Mexico
Covid-19 pandemic
edited-statecorrected-proof
typesetterts10
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pmcIntroduction
Nonprofit organizations1 represent diverse efforts of collective action and service provision and have for some time been collaborators to public governance systems in developed and developing economies. The growth of nonprofits has been well documented, what Lester Salamon (1994) called decades ago a clear “global associational revolution.” Indeed, nonprofits are relied on by governments at various levels to provide a wide range of public goods and services. In their service provision, nonprofits tend to complement and supplement government goods and services that are collective in nature (Rees, 2014; Weisbrod, 1975, 1988), from workforce development training to shelter for the homeless and healthcare services, among others.
The reach of nonprofits in service provision is increasingly recognized and debated in various countries, however, there is surprisingly limited empirical and analytical study in the field of public policy and administration in particular about the operational environments that enable or constrain nonprofits. Such operational environments result from the combination of the regulatory, political, and funding contexts for organized civil society. We address this limitation by focusing on how the operational environments shape the activities and behaviors of organized civil society, namely nonprofit organizations and the services they provide. In this paper, we assess how nonprofits grow and fare in what have become complex operational environments put forth by governments and, given the demands of, at this writing, the current global pandemic.
Mexico is the empirical context of our inquiry. Nonprofits are a relatively newly recognized sector for the provision of public goods and services in Mexico. Like in many developed and developing economies, in Mexico nonprofits started to be more visible and needed in the 1990s. This was heightened in the mid-90s when several neighborhood organizations in Mexico City responded to the public demands associated with the 1985 earthquake. Nevertheless, it was not until almost two decades later that the Mexican government’s formal public recognition of nonprofits become evident with the publication of the Federal Law for the Promotion of the Activities Conducted by Civil Society Organizations2 (FLPACCSO) in 2004. This law has further shaped nonprofits as a sector in Mexico and established the relationships between government and nonprofits as including “supplementary, cooptive, and adversarial” dimensions (Appe and Layton, 2016: 126).
Those nonprofits that adhere to the FLPACCSO are eligible to receive support from the federal government, and this funding accelerated in the years after the democratization process in Mexico in the early 2000s. In fact, the funding to nonprofits from the federal government steadily increased from 2005 to 2015. However, this close relationship between nonprofits and the federal government began to erode in 2016, the year in which earmarked resources for nonprofits started to decline. The number of new nonprofits registered has also declined. The relationship between nonprofits and the federal government was further damaged in 2019 when the federal government decided to stop all together federal resources to nonprofits. Then 2020 debilitated even more nonprofits in Mexico. Nonprofits needed to respond to the pressing demands of the public affected by the COVID-19 pandemic without the allocation of federal resources to support their programs and activities.
This context for nonprofits provides a window in which to explore the influence of operational environments and shifts within them on the activities and behaviors of nonprofits. It also allows us to consider bigger questions about the relationships between and balance across the responsibilities of governments and nonprofits as contributors to public governance, particularly in the context of the ongoing global pandemic (see Dunlop, Ongaro, and Baker, 2020). Research has shown that countries have outdated, shifting, and/or hostile legal frameworks governing nonprofits. At the same time, collaborative and networked governance has become reliant on nonprofit organizations and other civil society actors. This is all coupled still with lingering distrust, misunderstanding, and even animosity between government and nonprofits in many contexts that hamper administrative capacity and effective service delivery. The result of these dynamics includes truncated or reduced public goods and services by nonprofits to their publics.
To better understand the operational environments and how nonprofits cope, we use the lens of institutional theory (DiMaggio and Powell, 1983), outlined in the next section. Our research asks: To what extent and in what ways do nonprofits respond and adapt to the complex demands of their operational environment? Through the case of Mexico, we study a period with key institutional changes for nonprofits using a unique dataset. Our study focuses on understanding the responses and adaptations of new nonprofits created between 2016 to 2020 through the analysis of their articles of incorporation. By examining articles of incorporation, we focus our analysis on the coercive and mimetic isomorphisms that nonprofits in Mexico activate to respond to government regulations and expectations as well as to public demands.
Institutional forces shaping nonprofits: an institutional perspective
We follow the foundational proposition of an institutional perspective: institutions shape organizations. This means that the institutional environment shapes the organizational field to which organizations need to conform. This is what Zucker (1987) identifies as the environment as the institution perspective where the environment presents the rules and expectations that organizations need to resemble. We follow this environment as the institution perspective by emphasizing two elements: the power and influence of the environment to shape organizations, and the subsequent responses of organizations to their environment.
Organizations respond to the demands of their environment through diverse structural processes. One of these processes is homogenization or isomorphism. DiMaggio and Powell (1983) have proposed three types of isomorphic processes: coercive, mimetic, and normative. We focus on coercive and mimetic isomorphisms to explain how nonprofits respond to the demands of their environment. DiMaggio and Powell (1983) define coercive isomorphism in terms of formal and informal pressures that organizations receive from the organizations or entities they depend upon. Organizations responding by coercive isomorphism change or adapt based on, for example, government mandates, regulations, or state standards which usually come from authorities (DiMaggio and Powell, 1983). Mimetic isomorphism is different; it is a mechanism that organizations use to respond to uncertainty. Organizations imitate others when they have problems understanding organizational technologies, the goals of the field, or when there is uncertainty in the environment (DiMaggio and Powell, 1983).
In this study, we draw on the basic premises on coercive and mimetic isomorphisms (DiMaggio and Powell, 1983) for the study of nonprofits and their responses to government and public demands. A common example of coercive isomorphism for nonprofits is the need to comply with financial and annual reporting to maintain their legal status. Nonprofits are required by authorities to provide information about their activities in unified formats that meet accepted standards (Verbruggen et al., 2011), often taking on forms and practices of these authorities (see Buckingham, 2009; Rees, 2014). On the contrary, mimetic isomorphism processes are harder to pinpoint because nonprofits respond to uncertainty through diverse mechanisms. For example, organizations imitate best practices from other organizations and replicate innovations (AbouAssi and Bies, 2018), put forward coping strategies that include new organizational configurations (Appe, Barragán, and Telch, 2019; Herrold, 2016; Herrold and Atia, 2016), form collective responses using deliberative and discursive power (Appe, 2019) and adopt and adapt alternative institutional logics, such as hybrid organizations (Skelcher and Smith, 2015; see also Knutsen, 2012).
Studies on isomorphism and nonprofits have also elaborated on the characteristics of the coping mechanisms of organizations to respond to the demands of the environment. Studies have criticized the assumption of linear trajectories on organizational change. For instance, Arvidson (2018) has proposed that organizations not only have multiple and intermittent sources of tension but also experience evolving and episodic changes. This means that the isomorphic processes may follow a discontinuous trajectory. Indeed, these practices can be complex to explain as the degree of coercion and imitation varies from organization to organization. In addition, Gestel et al. (2020) observe that some organizations respond to uncertainty and institutional pressures without predetermined outcomes that may affect the core of nonprofits. For instance, as proposed by Hersberger-Langloh et al. (2021), some nonprofits adopt business-like practices to respond to institutional pressures, in turn, these practices may create a problem of mission drift. Specifically, at the board level, nonprofits may experience pressures to comply with corporate practices such as minimizing overhead costs which may be detrimental to the organizational performance of nonprofits (Krause et al., 2019).
In other instances when organizations conform to the demands of their environment by isomorphic mechanisms, they can receive in return benefits or outcomes, such as legitimacy and access to resources. Compliance with the demands of the environment determines the legitimacy of organizations as they present themselves as needed, as well as their projects and activities (Suchman, 1995; Appe 2019). Organizational conformity also increases their chances to access resources and receive positive evaluations, which help them increase their survival opportunities (Scott and Meyer, 1983). This is particularly evident for nonprofits that depend on government funding, whether through grants, contracting, procurement, or commissioning processes (Rees, 2014). Organizations are more likely to comply with financial reports and other administrative procedures when they depend on governmental subsidies or when the government is an important source of funding (Appe, 2019; Verbruggen et al., 2011). As we discuss in the next section, this is familiar to the case of nonprofits in Mexico: they adapt to government regulations and expectations with the hope to receive support and funding from the federal government.
The case of nonprofits in Mexico: A brief introduction
To understand the responses and adaptations of nonprofits to the demands of their environment, we study the Mexican case. Nonprofits in Mexico have a unique relationship with the government characterized by reliance on public funding. We first identify the sources of power and influence that shape nonprofits in the Mexican context. At the national level, we identify three major forces: federal government regulations, funding and support from the federal entities, and public demands associated with the COVID-19 pandemic.
The institutional context for nonprofits in Mexico has been characterized as an “institutional entrapment” (Muñoz, 2014). This means that the regulations and government entities instead of facilitating the functioning of nonprofits, hinder and impede their activities. Scholars have suggested that nonprofits are over-regulated (e.g., Muñoz, 2014). The multiple levels of oversight refer to formal incorporation, financial reporting, and reporting on programs and activities to federal authorities. Moreover, requirements vary by type of association and are overseen by different agencies.
Financial co-responsibility by the state is another challenge to the regulatory environment of nonprofits (Muñoz, 2014). In the context of government and nonprofit relationships in Mexico, financial co-responsibility is a desired cooperation between the federal government and nonprofits to provide social goods. Based on this relationship, in theory, the federal government commits to financially support nonprofits if they contribute to any of the thematic areas outlined by the federal law. Such federal financial support is conditional on the nonprofits maintaining good standing status with the federal authorities. They are required by federal regulations to submit annual reports on their activities. While reporting activities can be seen as an incentive, that is, nonprofits may receive financial support for informing the government about their activities. This is problematic when this is not operationalized in practice, as the availability of federal funding is scant, and thus the incentive for reporting activities to federal authorities thereby declines.
We examine the over-regulating and financial co-responsibility logics in a context like Mexico by analyzing the responses of nonprofits to the Federal Law for the Promotion of the Activities Conducted by Civil Society Organizations (FLPACCSO) (Ley Federal De Fomento a las Actividades Realizadas por OSC, 2004). As its name reads, this law has the purpose to regulate and support the activities of nonprofits in Mexico. The law was signed into effect in 2004 during the presidential period of Vicente Fox, the first president that was not a member of the long-time majoritarian party “Institutional Revolutionary Party” (PRI). President Fox was a member of the National Action Party (PAN), which is a party with a long-standing tradition of civic participation. In this context, expectations were high that the government would promote the activities of nonprofits, and that the demands from nonprofits would be recognized (Vargas González, 2012).
The FLPACCSO establishes that organizations that want to be recognized as nonprofits need to register in the Federal Registry of nonprofits (Art. 6, FLPACCSO). If they also aim to receive funding from the federal government, they need to present annual reports of their activities to the Commission for the Promotion of Activities of Civil Society Organizations (Art. 7, FLPACCSO). The Commission is not a tax authority nor an incorporation authority, which means that nonprofits still need to register and inform financial activities to tax authorities (namely the Servicio de Administración Tributaria). Additionally, reporting activities to the Commission does not guarantee that nonprofits will receive support from the federal government. Thus, it adds another layer of federal government oversight.
Going into effect in 2004, the FLPACCSO established a financial co-responsibility of the federal government to support the activities of nonprofits. The law identifies 19 activities that are subject to be supported with federal funding (Art. 5, FLPACCSO; see next section for the list of activities). This means that nonprofits that fall outside these activities are not well-positioned to receive funding from the federal government, as the federal government cannot support nonprofits that do not focus on the activities stated in the law (Art. 13, FLPACCSO). Even though the law does not require nonprofits to incorporate these activities in their articles of incorporation, in practice as our analysis will show, they do. We argue that nonprofits include the activities stated in the FLPACCSO in their articles of incorporation as isomorphic mechanisms to adapt to the demands of their environment.
Since the FLPACCSO establishes a financial co-responsibility of the government and nonprofits, the operational environment for nonprofits is closely associated with the availability of resources that the federal government offers. In other words, the federal government provides resources to the nonprofits that meet the requirements established in the FLPACCSO. Based on the law, the federal government needs to compile an Annual Report of the actions and the financial support provided to nonprofits (Art. 14, FLPACCSO).
Before the law, there were scant and fragmented reports on the federal resources granted to nonprofits. The Annual Report represented at that time (in 2004, when the law was signed into effect), and continues to be the primary source to know about the federal funding transferred to nonprofits. Using the descriptive data available on the Annual Reports from 2005 to 2020, we observe a decrease in the resources granted to nonprofits, note that the data is reported in Mexican pesos3 (see Graph 1).Graph 1. Federal resources granted to nonprofits in Mexico 2005-2020.
Except for the period 2009-2010 linked to the economic crisis in Mexico, the resources granted to nonprofits increased steadily for the period 2005-2015. Excluding 2018, starting 2016 began the period where fewer resources were granted to nonprofits, this decrease was slow during the 2016-2017 period and more evident for the 2019-2020 period. The federal elections help to explain the peaks of funding availability for nonprofits; presidential elections occurred in 2012 and 2018, and a midterm election in 2015. However, in 2019 the Executive Order 1 (Circular Uno) of President Andrés Manuel López Obrador established that “no federal resources should be transferred to social organizations, unions, civil organizations or citizen movements.” The President justified this order as the way “to definitively end with the intermediation that has created discretion, opacity, and corruption” (Circular Uno, 2019: 1). There is suggestive evidence that the decline in federal funding is associated with the decline in new nonprofits in Mexico. Although we cannot affirm a causal relationship, the decrease in both federal funding and the creation of new nonprofits seem to coincide around the same years (2014 to 2016) (see Graph 2).Graph 2. Number of new nonprofits created and registered in Mexico 2005-2020.
Thus, with data available in the Federal Registry of nonprofits, we observe a decreasing trend of newly formed nonprofits. The data of nonprofits “created” corresponds to the year in which they were incorporated as legal entities. Data for registered nonprofits is based on the year they were registered in the Federal Registry. Both the new nonprofits created, and the new nonprofits registered in Mexico began to decrease in 2014. This trend started in 2014 and became more evident in 2016, the same year that the resources granted to nonprofits began to decline. The relationship between less federal funding for nonprofits and the declining number of new nonprofits is further evident in 2019. This trend continued for 2020, the year that the pandemic hit worldwide.
Indeed, the force of the COVID-19 pandemic represents a major challenge for nonprofits. While often governments turn to nonprofits in times of disasters and emergencies (e.g., Nolte and Boenigk, 2011; Kapucu, 2006; Cheng, Yu, Shen and Huang, 2020), this did not occur in the case of the pandemic in Mexico. Unlike in other comparable countries (e.g., Colombia, and even those federal governments with adversarial relations with nonprofits like Brazil, India, among others; see Escudero, 2020; Kant, 2020; Semana, 2020), the federal government in Mexico did not request the help of nonprofits to mitigate the effects of the pandemic. The year before the pandemic (2019), the federal government had effectively stopped granting federal resources to nonprofits and this did not change during the pandemic. Nonprofits criticized the role of the federal government that left them without financial resources to support programs for vulnerable populations in need during the pandemic. Human service and human rights organizations were among the nonprofits particularly impacted by the lack of federal funding (Garcia and Ortega, 2020). Still from the beginning of the pandemic, without financial resources from the government, nonprofits ran programs to aid vulnerable populations. For instance, programs supported victims of domestic violence and people in financial need due to the pandemic (Torres, 2020). Without federal resources, nonprofits that ran these programs relied more on private or corporate contributions than on funds from federal government agencies.
Following the institutional perspective, due to the demands of their environment, nonprofits responded and adapted through coping mechanisms. We center our attention on the isomorphic mechanisms that nonprofits in Mexico used to respond to over-regulation and limited funding. Added to this is the financial co-responsibility that the federal government assumes to support nonprofits. This means that the operational environment regulates nonprofits with the failed promise of providing financial resources to them. In addition, nonprofits are impacted by the effects of the COVID-19 pandemic and needed to respond to the associated public demands, even with the limited funding from the government.
Methodological approach
Research aim
This study aims to understand the organizational structural adaptations and processes that nonprofits in Mexico used to respond to and adapt to their contextual constraints (e.g. government regulations) and emerging demands from the public. Our analysis focuses on the new nonprofits created and registered from 2016 to 2020. The period under analysis starts in 2016, the year that federal funding for nonprofits started to decline, and goes until 2020 to identify the potential differences before and during the COVID-19 pandemic. The guiding question of this study is, to what extent and in what ways do nonprofits respond and adapt to the complex demands of their operational environment?
We propose that nonprofits in Mexico adapt to the demands of their environment by coercive and mimetic isomorphisms. Although these responses are theoretically distinctive (DiMaggio and Powell, 1983), we observe these isomorphisms are interrelated because of the financial co-responsibility that the federal government assumed due to the law that attempts to regulate the activities of nonprofits (FLPACCSO). Additionally, new nonprofits also responded to the greater demand for their services due to the pandemic. With the COVID-19 pandemic, we anticipated that new nonprofits in Mexico created during the pandemic likely ignited isomorphic mechanisms, in this instance focusing on distinct services areas in comparison to prior to the pandemic.
Data
To achieve out research objective, we analyzed the purposes stated in the articles of incorporation of new nonprofits created (incorporated) and registered as nonprofits between 2016 to 2020. The purposes stated on the articles of incorporation are available to download on the webpage of the Federal Registry of nonprofits. These purposes are written as articles (in Spanish called “objeto social”) and may include several statements as well as specific actions to achieve them. The length of the purposes varies from one short paragraph to two pages. There was no massive download available, thus we needed to download the information of each organization under analysis. We selected a random sample of 50 nonprofits per each year of analysis from 2016 to 2019. For 2020, we analyzed the universe of nonprofits created and registered. We grouped the nonprofits of 2020 into a: “before the pandemic” group which included 35 nonprofits created before April 1st, 2020, and the second group “during the pandemic” which included 61 nonprofits created between April 1st and December 31 of 2020. We analyzed a total of 296 purposes stated in the articles of incorporation of new nonprofits.
Method
We used a focused coding method. This method is optimal for research using existing categories and analyzing large sets of data (Charmaz, 1983). It is commonly used to identify recurrent patterns and multiple layers of meaning in the data (Charmaz, 2008). The key component of this method is a consistent codification process. Once data is codified, codes may be grouped into categories that in turn help to create or refine concepts or theories (Saldaña, 2016). For this purpose, we created a codebook to codify the articles of incorporation of new nonprofits. The codebook has three groups of codes.
The first group includes 19 codes that correspond to textual references to each of the 19 activities that the FLPACCSO establishes as subjects to be supported by the federal government (Art. 5, FLPACCSO) (see Table 1 for the 19 activities on the law). For instance, if a nonprofit under analysis included in its article of incorporation the textual statement “Social assistance, in accordance with the provisions of the Law of Social Assistance and the General Law of Health,” we codified this section with the code AR01. The first two letters of the code stand for activity real (meaning equal segments) and the numbers for the activity as listed in the law.Table 1. Activities supported by federal government established on the FLPACCSO (Art. 5).
Activity Description
01 Social assistance, in accordance with the provisions of the Law of Social Assistance and the General Law of Health
02 Support and supply of food to towns
03 Civic organizations focused on promoting citizen participation in matters of public interest
04 Legal assistance
05 Support for the development of indigenous communities
06 Promotion of gender equity
07 Services for groups with disabilities
08 Cooperation for community development in urban and rural areas
09 Support in the defense and promotion of human rights
10 Sports
11 Promotion and provision for health care and health issues
12 Support in the use of natural resources, the protection of the environment, flora and fauna, the preservation and restoration of ecological balance, as well as the promotion of sustainable development at the regional and community levels in urban and rural areas
13 Foment and promote educational, cultural, artistic, scientific and technological matters
14 Promotion of actions to improve the economy of towns
15 Participation in emergency and relief activities
16 Support services for the creation and strengthening of organizations that carry out activities promoted by this Law (FLPACCSO)
17 Promotion and defense of consumer rights
18 Actions that promote the strengthening of the social capital and safety
19 Those determined by other laws
Translation by the authors.
The second group contains 18 codes that focus on identifying similar but not equal statements to the activities established on the FLPACCSO (Art. 5). We searched for statements that paraphrased the activities established on the law, used some words or fragments that refer to these, or have a similar orientation. For this group of codes, we assigned the letters AS that refer to activity similar and a number from 1 to 18 that correspond to each of the activities listed on the law, except for the last one. Table 2 presents examples of three codified segments using the second group of codes.Table 2. Examples of coded segments using the second group of codes.
Activity as established in the law Code Example of coded segment
Activity 2: Support and supply of food to towns AS02 “Attention to basic subsistence requirements in terms of food.”
Activity 6: Promotion of gender equity AS06 “Promote human development and equity and gender parity, as well as the inclusion of indigenous areas.”
Activity 8: Promotion of actions to improve the economy of towns AS08 “Support and promotion of the economy of families; through learning projects of job skills which an economic benefit can be obtained.”
Translation by the authors.
The third group contains 8 thematic codes that resemble the major groups on the National Taxonomy of Exempt Entities (NTEE) in the United States (excluding religion). Although Mexico does not follow this system to classify nonprofits, the NTEE classification informed how we thematically organize the emergent activities of nonprofits that did not fit the pre-determined categories in the Mexican context. Table 3 presents three examples of segments coded using this group of codes.Table 3. Examples of coded segments using the third group of codes.
Major group of the taxonomy of exempt entities Code Example of coded segment
Arts, culture and humanities AA01 “The realization of artistic and recreational activities in all media, whether in theater, shows, television, radio, cinema, digital media and the Internet, for people from vulnerable groups due to age, sex, or disability problems and indigenous communities.”
Health EF01 “Medical assistance or rehabilitation or care in specialized establishments”
Human services IJ01 “Foment and promote among Oaxacans their participation in civil protection actions and participate in them.”
Translation by the authors.
Our coding process was comprised of four rounds. In the first round, we use the first group of codes and analyzed all the articles of incorporation under analysis. Once this process was completed, we proceeded with the second round using the codes of the second group. Again, once we completed the second round, we used the codes of the third group. Finally, in the fourth round, we only focused on identifying any remaining segments that needed to be coded. We used the software Dedoose to code the texts. The software allowed us to collaboratively work online to conduct a consistent and collective review of the coding and any discrepancies. Discrepancies in coding were thoroughly discussed and resolved collectively among the authors.
Findings: How nonprofits respond and adapt to Mexico’s current operational environment
From the analysis of data, we observe that new nonprofits adapt to the demands of the federal government and the public in various ways. We present results in four areas. First, we present the categorization process derived from the analysis of codes. Second, we report the results regarding the use of textual references from the law in the articles of incorporation of nonprofits. Third, we present the results of the data analysis from the second group of codes which are those used to analyze paraphrased or similar statements to the law. In the fourth subsection, we present the results of the comparison of nonprofits before and during the COVID-19 pandemic in terms of the adaptation mechanisms used. For this last analysis, we use all codes in the three groups.
Categorization process
Following the coding process proposed by Saldaña (2016) that goes from data to codes to categories, from the analysis of codes, we derived four emergent categories that characterize nonprofits based on their intensity of adaptation to the existing regulations. These four categories have the purpose to represent the behavior of nonprofits regarding their refusal or acceptance of the demands of their environment. To create these categories, we searched for regularities in the data codified. We found two things. First, several organizations do not include any references to the law. Second, among those that include or resemble some elements of the law, they usually include two or five references. Therefore, we used such regularities as thresholds to categorize organizations. The four resulting categories are independent, observer, aligned, and embracer. Definitions of these categories as well as the number of textual or similar references are presented in Table 4.Table 4. Categories from the use of textual and paraphrased references to the FLPACCSO.
Category Description of a nonprofit in this group Number of textual references to the Law Number of paraphrased references to the law
Independent Crafts its articles of incorporation not to conform to the demands of its institutional environment but outlines explicitly its own purpose(s) 0 0
Observer Recognizes the existing rules that surround its environment 1 to 2 1 to 2
Aligned Not only recognizes the existing rules surrounding its environment, but it also shapes its purpose(s) accordingly 3 to 5 3 to 5
Embracer Adopts existing rules and aims to conform to the demands of its institutional environment 6 or more 6 or more
Adopting the law using textual references
In Graph 3, we present the results of the categorization of nonprofits for the period 2016 to 2020. We present the percentage of the use of textual references per year. The first finding is that independent nonprofits have a downward trend over the 2016-2020 period. This means that fewer nonprofits have decided not to include any textual references to the FLPACCSO. While in 2016 more than half of the new nonprofits did not include any textual references to the law, in 2020 before and during the pandemic, the independent category only accounted for around a quarter of nonprofits. This behavior is likely linked to the resources available to nonprofits. As the resources have decreased, it seems that nonprofits try to reduce the uncertainty of not receiving resources by incorporating textual references to the law to increase their opportunities to receive federal funding.Graph 3. Use of textual references of the FLPACCSO in the articles of incorporation of new nonprofits.
Other than the abrupt decline of 2019, we do not observe any obvious differences in the period under analysis for the observer category. It is a different case for the aligned category, as this category flourished. In 2016 only eight percent of new nonprofits included three to five textual references in their articles of incorporation while in 2020 (during the pandemic), this percentage tripled (to 26%). The embracer nonprofits suggest notable behavior. While we expected this category to grow, it only grew in 2018 and 2019. The data for 2020 show a decline in this category and could reflect a lack of hope on the part of nonprofits to receive funding from the federal government, as a result, they did not identify any advantage to including more references to the law in their articles of incorporation. As mentioned, in 2019, Executive Order 1 by President Andres Manuel Lopez Obrador instructed that federal resources were not to be provided to nonprofits, including social organizations, unions, civil organizations, or citizen movements.
Adopting the law using paraphrased or similar statements
Graph 4 presents the behavior of nonprofits within the four categories for the period 2016 to 2020. The independent category represents a smaller percentage of nonprofits in this new composition of cases. Although we expected this category to decline (as we observed in the direct inclusion of textual references grouping), this did not happen. On the contrary, it has a slight upward trend. For the observer category, excluding 2016, we see a minor increment of nonprofits from 2017 to 2020. In the analysis of textual references, we noticed no trend. For the aligned category, we expected this category to grow as we observed the use of textual references. This again was not evident. The aligned category presents an erratic behavior in the years under analysis. Finally, for the embracers, we expected this category to grow as the resources for nonprofits have declined. This only is true for the period 2016 to 2019, but not for 2020.Graph 4. Use of paraphrased references of the FLPACCSO in the articles of incorporation of new nonprofits.
Nonprofits pre and during the COVID-19 pandemic
To analyze the responses of new nonprofits to the demands of their operational environment before and during the COVID-19 pandemic, we analyzed the differences between the articles of incorporation for the period 2016 to 2020. For this analysis, we used the three groups of codes. We included an additional code that emerged from the data that refers to funeral services provided by nonprofits, for a total of 46 codes. We counted excerpts that were coded. This means that one organization can be counted more than once. We coded a total of 2815 segments/excerpts.
Using the first group of codes (direct textual references, see Table A in Appendix), in the years before the pandemic, we observe nonprofits had a particular interest in Activity 5. This activity focuses on “support for the development of indigenous communities” (Art. 5, FLPACCSO). This interest grew particularly among the new nonprofits created during the pandemic (April to December 2020). The attention to Activity 14 “promotion of actions to improve the economy of towns” also grew. Although federal resources to nonprofits declined, particularly in 2020, these two activities corresponded with two service areas that the federal government has prioritized. For instance, in the National Development Plan (Plan Nacional De Desarrollo) 2019-2024, the support for indigenous communities and the economy for well-being are central in the design and implementation of federal government policies.
From the analysis using the second group of codes (similar not equal), we found evidence of new nonprofits seeking to mitigate the effects of the COVID-19 pandemic among vulnerable groups. Although the first activity established on the FLPACCSO, “social assistance,” has been the most popular among new nonprofits during the years of analysis, this activity has received more attention from the new nonprofits created during the pandemic (April to December 2020, see Table B in Appendix). New nonprofits created during the pandemic recognized the need to focus attention on basic needs and they included these goals in their articles of incorporation. For instance, a nonprofit included “attention to basic subsistence requirements in terms of food, clothing or housing” (Fun Fraternidad Ciudadana Internacional, 2020: 1), and another one used “assistance activities carried out to people, sectors, and regions with resources” (Fundacion Juntos para Mejorar y Contribuir, 2020: 1).
The interest of new nonprofits created during the pandemic in providing health care also grew. New nonprofits included more statements in their articles of incorporation paraphrasing Activity 11 “promotion and provision for health care and health issues.” We documented nonprofits that included statements that specifically mentioned a focus on medical assistance or rehabilitation for people affected by the COVID-19 pandemic. For instance, a nonprofit created in June 2020, included in its articles of incorporation: health services to provide “diagnoses and treatments derived from the coronavirus disease (COVID-19), caused by the coronavirus 2, a virus of the severe acute respiratory syndrome” (AMFER AM, 2020: 1). However, the inclusion of textual references associated with the COVID-19 pandemic was not as common as we originally anticipated. Out of the 61 nonprofits created during the pandemic (April to December 2020), we only identified three organizations that mentioned measures to mitigate the effects of COVID-19.
Using the third group of codes (thematic codes of NTEE), we observe that new nonprofits created during the pandemic reported less interest in arts, culture, and humanities as well as in education matters (see Table C in Appendix). This was evident when we compared nonprofits created before and nonprofits created during the pandemic.
Finally, a topic that emerged from the data was funeral services. Nonprofits created during the pandemic included more statements than before indicating they constituted themselves to provide financial support for funeral services. As an example, a nonprofit reported proving funeral services to underserved populations as follows “provide help for funeral services to people with limited resources, indigenous communities or marginalized areas to support them in the vulnerable situation in which they find themselves” (CIBADH, 2020: 1).
Discussion
We studied the responses of nonprofits to an operational environment characterized in Mexico by a decline in public support from the federal government. In the early 2000s, nonprofits were recognized as providers of public services and the financial support from the federal government steadily grew from 2005 until 2015. Starting in 2016, the resources for nonprofits declined and this was even more evident when, in 2019, the federal government effectively decided to stop granting resources to nonprofits. A year later, the COVID-19 pandemic represented a new challenge for nonprofits that needed to respond to the emerging and pressing demands of the public. However, as observed above, the federal government did not publicly request their help nor were resources made available to nonprofits to respond to these emerging needs. To understand how nonprofits respond and adapt to the complex demands of their operational environment, we analyzed the purposes stated in the articles of incorporation of new nonprofits created and registered between 2016 and 2020.
New nonprofits in Mexico adapt their articles of incorporation based on the existing rules that regulate their activities and the expectations outlined in these rules. As federal resources to nonprofits have declined, nonprofits have included more textual references to the activities established by the FLPACCSO (see particularly Graph 1). With the decline of funding, nonprofits have tried to reduce the uncertainty by mimicking the activities established on the FLPACCSO. DiMaggio and Powell (1983) propose that organizations use mimetic isomorphism to respond to uncertainty. For the operational environment of nonprofits, it seems that this uncertainty is not only caused by the decrease in the resources provided by the federal government (material resources) but also by a larger political narrative pointing to the lack of government support (discursive resources) for their activities. Returning to our previous example, Executive Order 1 by President Andres Manuel Lopez Obrador not only mandates federal agencies to stop the resources to nonprofits but also justifies this to stop the “intermediation that has created discretion, opacity, and corruption” (Circular Uno, 2019: 1). We see suggestive evidence that a political narrative in Mexico, which makes up the political component of an operational environment for nonprofits, seems to effectively intersect with regulatory and funding components (see also, Appe and Barragán, 2017).
Because nonprofits in Mexico face an operational environment characterized as an “institutional entrapment” (Muñoz, 2014), they need to comply with multiple regulations dictated by local, state, and federal authorities that often over-regulate their activities. At the federal level, nonprofits are required by the FLPACCSO to register in the Federal Registry of nonprofits and to provide annual reports of their activities to be eligible to receive federal resources. Nonprofits also need to report financial activities to other governmental agencies. And, even though nonprofits are not required to include the activities supported by the FLPACCSO, we find that they include textual and paraphrased references of the law in their articles of incorporation (specifically in their purposes, “objeto social”). This inclusion responds to coercive isomorphism. As DiMaggio and Powell (1983) propose, organizations receive formal and informal pressures from the organizations or entities they depend upon, and they respond to these pressures by coercive isomorphism. We observe that as resources for nonprofits have eroded, the number of nonprofits in the independent category (those nonprofits not adopting any textual reference to the FLPACCSO) has also declined. This means that nonprofits in Mexico have formally or informally been shaped by the FLPACCSO and we have suggestive evidence that this is associated with the declining federal funding available for nonprofits (See Graph 1).
In addition, nonprofits responded to the demands of their operational environment by incorporating in their articles of incorporation direct and indirect references to aid people affected by the COVID-19 pandemic. New nonprofits created during the pandemic focus more of their attention on social assistance activities, which provide support for basic needs. Not surprisingly, new nonprofits in 2020 also focus their attention more on health care issues. We found, however, only a few of them included references to provide medical assistance for people affected by the coronavirus in their articles of incorporation.
Finally, new nonprofits created during the pandemic emphasized their support to provide help for funeral services, something that was distinctive from nonprofits’ activities before the 2020 pandemic. Based on social assistance regulations, the people in need that require financial support for funeral services can request them from the National System for the Integral Development of the Family (SNDIF) which is a federal agency proving social assistance. During the pandemic, state and local governments also provided help for funeral services. However, this help had some institutional limitations, for instance, Mexico City’s government provided help for family members of the deceased in public hospitals, whereas some local governments (e.g., Iztapalapa) started to provide help for family members of deceased that passed away in their homes (Gaceta Oficial De la Ciudad De México, 2021). However, governments usually required a death certificate to grant financial resources to the families. These certificates were hard to obtain due to the high mortality associated with the COVID-19 pandemic. As Weisbrod (1988) and others have proposed, nonprofits provide goods and services that would otherwise be underprovided by the government or the market. We observe in our analysis that nonprofits responded and adapted: in 2020 they filled a gap by assisting with funeral services when governments at different levels were unable to immediately address those needs.
Limitations
While our inquiry uses a unique data set that helps to answer our research question, we recognize the limitations of our study. For example, the minimal analysis of the financial information of nonprofits in Mexico is a limitation. As Verbruggen et al. (2011) have proposed, coercive isomorphism is likely associated with the dependence organizations have to access funding sources. Understanding the connection between resource dependence and coercive and mimetic isomorphisms deserves further attention. To study this in Mexico in particular, the financial data of nonprofits will need to be improved to assess the connections (or lack thereof) between compliance and the availability of federal funding. At this moment, we only have the aggregated data about the resources granted from the federal government to nonprofits on an annual basis (provided in Graph 1).
We outline clear regulatory and funding forces that shape nonprofits in Mexico as well as the 2020 global pandemic which at this writing continues to affect the country. We would argue, as we have inferred, that political dimensions such as elections, mandates, and the political narrative by the President are related to the regulation and funding of nonprofits. Future studies should examine in-depth political narratives and forces in Mexico (and beyond) as well as the further dimensions that contribute to the operational environments of nonprofits. The COVID-19 pandemic is an example of the heightened demands from society for public services. Other demands could result from new policy designs and instruments that target nonprofits, availability of funding from major multilateral, bilateral, and private donors as well as innovations put forth by nonprofits, all of which can influence and shape the services they provide. Furthermore, we confined our analysis to the relationship between nonprofits and the federal government, but we recognize nonprofits interface with government agencies at transcalar levels, including the local, regional, and international. The responses and isomorphic mechanisms they use to navigate these influences and forces deserve attention.
Finally, while the analysis of organizational texts is a common and valid mode of inquiry in the field of public administration as well as nonprofit management (e.g., Koch, Galaskiewicz, and Pierson, 2015; Marberg, Korzilius and Van Kranenburg, 2019), we recognize its limitations. Future research might further triangulate by assessing through observation and interviews the provision of services by nonprofits in Mexico to continue to understand how government regulation and political narratives, shifting funding patterns, and COVID-19 are shaping the service provision of these organizations.
Conclusions
This article presents an analysis of how nonprofits respond and adapt to the demands of their operational environment using an institutional perspective. We frame our study around coercive and mimetic isomorphisms that new nonprofits in Mexico use to respond to environmental demands (DiMaggio and Powell, 1983). This context is characterized as over-regulated because of the multiple requirements that the federal government mandates to nonprofits. The Federal Law for the Promotion of the Activities Conducted by Civil Society Organizations (FLPACCSO) requires nonprofits to register in the Federal Registry of nonprofits and submit annual reports of their activities. The law also assumes a financial co-responsibility of the federal government that, while it regulates the activity of nonprofits, implies that if nonprofits adopt federal requirements, they will be eligible to receive federal funding. As a result, nonprofits respond by complying with the law (coercive isomorphism) and adapt by directly including elements of the law in their articles of incorporation to minimize the uncertainty of not receiving federal resources (mimetic isomorphism). Nonprofits have also responded to the demands of their environment associated with the COVID-19 pandemic, as they have focused more on providing social assistance, health, and funeral services.
Decidedly, the role of nonprofits in service provision during and eventually in the aftermath of the global pandemic will garner continued empirical attention in Mexico and other settings, especially given its influence on public governance. Public policy and administration scholarship can contribute to understanding these dynamics by not only exploring the efficiency and effectiveness of service provision and administrative capacities (albeit important) but also by examining the institutional environments that shape such service delivery. In particular, the study of the operational environment for nonprofits, including its regulatory, political, and funding components, can be further illuminated to improve public governance, to understand the extent of nonprofits’ relationships with government agencies and to consider how organized civil society is responsive and adaptable to public demands.
Acknowledgements
Melissa Martínez Riojas for her help coding and compiling information for analysis. The authors did not receive funding for this research.
ORCID iD
Tania L Hernandez Ortiz https://orcid.org/0000-0002-8046-6024
Notes
Appendix Table A. Code application percentage with codes of Group 1.
Code Code name 2016 (%) 2017 (%) 2018 (%) 2019 (%) 2020 B (%) 2020 D (%)
AR01 Statements equal to the activity 1 of the FLPACCSO 0 1 0 1 3 2
AR02 Statements equal to the activity 2 of the FLPACCSO 0 1 1 0 0 1
AR03 Statements equal to the activity 3 of the FLPACCSO 6 6 8 9 7 7
AR04 Statements equal to the activity 4 of the FLPACCSO 2 0 1 1 0 0
AR05 Statements equal to the activity 5 of the FLPACCSO 11 9 9 7 8 10
AR06 Statements equal to the activity 6 of the FLPACCSO 8 11 9 11 10 10
AR07 Statements equal to the activity 7 of the FLPACCSO 10 10 8 8 11 8
AR08 Statements equal to the activity 8 of the FLPACCSO 2 0 2 0 1 0
AR09 Statements equal to the activity 9 of the FLPACCSO 6 9 8 7 10 8
AR10 Statements equal to the activity 10 of the FLPACCSO 1 0 0 0 1 2
AR11 Statements equal to the activity 11 of the FLPACCSO 0 3 1 0 0 1
AR12 Statements equal to the activity 12 of the FLPACCSO 13 11 10 10 10 5
AR13 Statements equal to the activity 13 of the FLPACCSO 13 11 10 10 12 11
AR14 Statements equal to the activity 14 of the FLPACCSO 11 10 9 9 12 14
AR15 Statements equal to the activity 15 of the FLPACCSO 5 6 6 9 9 7
AR16 Statements equal to the activity 16 of the FLPACCSO 7 6 7 7 4 5
AR17 Statements equal to the activity 17 of the FLPACCSO 5 4 6 6 4 4
AR18 Statements equal to the activity 18 of the FLPACCSO 0 1 1 0 1 1
AR19 Statements equal to the activity 19 of the FLPACCSO 0 0 1 0 0 0
Total 100 100 100 100 100 100
Table B. Code application percentage with codes of Group 2.
Code Code name 2016 (%) 2017 (%) 2018 (%) 2019 (%) 2020 B (%) 2020 D (%)
AS01 Sentences paraphrasing activity 1 of the FLPACCSO 38 31 35 35 27 40
AS02 Sentences paraphrasing activity 2 of the FLPACCSO 1 1 1 0 0 0
AS03 Sentences paraphrasing activity 3 of the FLPACCSO 6 10 5 4 5 3
AS04 Sentences paraphrasing activity 4 of the FLPACCSO 9 7 11 10 9 7
AS05 Sentences paraphrasing activity 5 of the FLPACCSO 3 5 2 5 9 3
AS06 Sentences paraphrasing activity 6 of the FLPACCSO 4 4 1 1 2 3
AS07 Sentences paraphrasing activity 7 of the FLPACCSO 3 2 1 4 2 2
AS08 Sentences paraphrasing activity 8 of the FLPACCSO 1 3 3 0 0 0
AS09 Sentences paraphrasing activity 9 of the FLPACCSO 6 9 4 6 5 5
AS10 Sentences paraphrasing activity 10 of the FLPACCSO 1 2 1 0 2 1
AS11 Sentences paraphrasing activity 11 of the FLPACCSO 8 5 10 10 10 14
AS12 Sentences paraphrasing activity 12 of the FLPACCSO 7 7 8 6 6 3
AS13 Sentences paraphrasing activity 13 of the FLPACCSO 5 3 5 5 4 5
AS14 Sentences paraphrasing activity 14 of the FLPACCSO 3 2 3 3 4 3
AS15 Sentences paraphrasing activity 15 of the FLPACCSO 0 0 1 0 0 0
AS16 Sentences paraphrasing activity 16 of the FLPACCSO 1 2 1 1 2 1
AS17 Sentences paraphrasing activity 17 of the FLPACCSO 0 0 1 0 0 0
AS18 Sentences paraphrasing activity 18 of the FLPACCSO 6 8 8 10 13 10
Total 100 100 100 100 100 100
Table C. Code application percentage with codes of Group 3.
Code Code name 2016 (%) 2017 (%) 2018 (%) 2019 (%) 2020 B (%) 2020 D (%)
AA01 Arts, culture & humanities 30 27 27 21 25 14
BB01 Education 23 23 26 29 33 23
CD01 Environment and animals 11 8 5 6 6 5
EF01 Health 18 13 6 9 17 14
IJ01 Human services 3 6 5 1 0 3
QQ01 International, Foreign Affairs 3 4 5 4 2 3
RS01 Public, Societal Benefit 0 10 13 14 7 15
YY01 Mutual/Membership Benefit 1 2 2 0 0 0
FF01 Funeral services 11 8 12 15 10 24
Total 100 100 100 100 100 100
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
1. We use the term nonprofit organizations, this term and the term civil society organization are widely used across contexts, including in Mexico.
2. This is a direct translation of the law which refers to nonprofit organizations using the broad term: civil society organizations.
3. A billion Mexican pesos is equivalent to 48,177,000 US dollars (November 18, 2021).
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Original Articles
The impact of COVID-19 on labour markets and living standards in Mauritius
Ranzani Marco World Bank Group, USA
Kern Andreas Georgetown University, USA
Marco Ranzani, Poverty and Equity Global Practice, World Bank Group, 1818 H Street NW, Washington, DC 20433, USA. Email: [email protected]
12 2022
12 2022
12 2022
33 4 806828
© The Author(s) 2022
2022
University of New South Wales
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.
Understanding the distributional impact of the COVID-19 crisis on the labour market and ultimately on the living standards of the population is key to designing adequate policy responses to shield individuals’ and families’ livelihoods. This article illustrates the impact of COVID-19 on the labour market as well as on living standards in the case of a small open economy: Mauritius. We present descriptive evidence based on a unique set of telephone household surveys, representative of the Mauritian population, conducted between May 2020 and March 2021. We find that women had a higher risk of losing their job and leaving the labour force, reversing a decade-long trend of increasing labour force participation. Low-skill workers in sectors that depend on global demand – and even more so if employed informally – together with women were more likely to be affected by the crisis. One in three households reported a loss in income since the start of the pandemic, and the probability of experiencing this shock increases with the number of household members who lost their job and who were employed informally. From a policy perspective, our findings underscore the negative distributional consequences of the pandemic and provide substantive evidence for the viability of a further proactive policy stance to shield the livelihoods of vulnerable households during the economic recovery phase.
JEL Codes: J21, J24, J33
Coronavirus
inequality
labour market
Mauritius
recessions
typesetterts1
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pmcIntroduction
The COVID-19 pandemic, a relentless health emergency, has quickly evolved into one of the most severe economic crises in history. As of 16 July 2022, over 560 million cases had been confirmed worldwide, with over 6.3 million people losing their lives. The toll continues to rise as many countries face a new wave of infections.1 Since the first cases were reported on March 18, Mauritius has recorded a total number of 233,000 cases, with about 1000 deaths.2 The International Monetary Fund (2021) estimates a contraction in global growth by −3.2% in 2020 and projects a rebound in global economic growth to 6% in 2021 and 4.9% in 2022. According to Statistics Mauritius, the Mauritian economy contracted by 14.9% in 2020 with the sharpest declines in the industrial sector (−16.6%) and the services sectors (−15.4%), followed by agriculture which posted a 2.5% contraction.
The economic shock triggered by the pandemic in 2020 laid bare structural issues in the long-term growth model of many countries, including Mauritius. Even before COVID-19, the long-term growth model of the small island state had started to show its fragility: declining investments and stagnating productivity of capital, loss in export competitiveness and declining market shares, an ageing population and frictions in the labour market, in addition to fiscal deficits and rising levels of public debts (World Bank, 2021a). This paper focuses on the issue of sustainable development of small island states by looking at the case of Mauritius, a success story of growth and rising living standards based on economic diversification and fiscal redistribution. It indicates how economic shocks such as the one generated by the pandemic could be the springboard to push forward with a new generation of policy reforms. In particular, understanding the distributional impact of the COVID-19 crisis on the labour market and ultimately on the living standards of the population is key to designing adequate policy responses to shield the livelihoods of both individuals and families, and limit further the adverse effects of the ongoing crisis. Following the outbreak of COVID-19 and lockdown measures introduced in Mauritius on 20 March, Statistics Mauritius suspended all field activities involving face-to-face data collection. To monitor the socioeconomic effects of the pandemic on Mauritian households, Statistics Mauritius and the World Bank launched household telephone surveys that provide a distinctive window into studying the impact of COVID-19 pandemic on households.3 Relying on this unique dataset, we document the impact of the pandemic on the Mauritian labour market and provide a preliminary assessment of key policy measures implemented in response to the crisis.
Despite a growing literature on the socio-economic impact of the COVID-19 pandemic on small island nations (Guerra-Marrero et al., 2021; Hakim, 2020; Rashid et al., 2020; United Nations [UN], 2021; World Economic Forum, 2020), our study is arguably the first attempt to document the negative effects of the COVID-19 pandemic and consequent economic crisis on the labour market on the labour market in such a country context.4 The study contributes to the literature on the impact of economic downturns on labour market outcomes (among others, Christiano et al., 2015; Hoynes et al., 2012) and the importance of short-time work, also known as short-time compensation schemes, to help firms and workers during the first phase of economic downturns. Among others, Cahuc et al. (2018) show that short-term work schemes can save jobs in French firms severely affected by the demand shock cause by the Great recession and the cost per saved job is limited relative to other employment policies. Second, it contributes to the rapidly growing literature on the effects of the COVID-19 pandemic, lockdown and subsequent economic crisis on labour markets using data collected before the crisis, as well as mobile phone and real-time survey data. Recent research indicates that the magnitude of the COVID-19 shock differs across countries, depending on the institutional context, economic structure and availability of short-time work schemes. It particularly affects sectors and occupations in which tasks cannot be carried out from home, as well as workers who are less educated, youth, women and the self-employed (among others, see Adams-Prassl et al., 2020; Blundell and Machin, 2020; Montenovo et al., 2020). Looking at job vacancies, Campello et al. (2020) and Forsythe et al. (2020) found that the demand for labour had decreased drastically in the United States (US). Similarly, our results confirm a sharp decline in labour demand in Mauritius. Insofar, our analysis supports the notion that observed negative effects on labour market outcomes are even more pronounced for small-island economies that heavily rely on in-person services such as tourism. From a policy perspective, a key insight is that, due to their greater vulnerability to these type of adverse shocks, small island economies can reap substantial benefits and enhance their socio-economic resilience when intensifying their investments into blue economic activities and diversifying their economies towards more sustainable sources of economic activity.
The rest of the paper is structured as follows. Section ‘A snapshot of labour market trends throughout the pandemic’ discusses broad labour market trends. Section ‘The determinants of labour market outcomes during the pandemic’ provides an analysis of the correlates of labour force participation, employment and displacement in Mauritius. Section ‘Protecting jobs and living standards in the short and medium-term’ illustrates the key features of the government’s main support programmes with a focus on the Government Wage Assistance Scheme (GWAS) and the Self-Employed Assistance Scheme (SEAS). Section ‘Measuring the effects of the pandemic on the labour market and on living standards’ features a preliminary assessment concerning the effectiveness of these programmes in shielding household incomes. Section ‘Conclusions’ indicates that coupled with structural challenges pre-dating the pandemic, the unequal impact of the crisis on the economy, the labour market and living standards and the uncertainty about the timing and intensity of the economic recovery calls for a recalibration of the economic and social model to achieve an equitable and sustainable recovery from the pandemic. An economic model aimed at enhancing the resilience of key economic sectors to climate change impacts by investing in the blue economy from tourism to fishing and taking advantage of new opportunities from decarbonisation.
A snapshot of labour market trends throughout the pandemic
The Mauritian economy has realised an unprecedented structural transformation since independence. Today, the agricultural sector accounts for about 3% of GDP, the industrial sector contributes about 20%, while the remaining 77% of GDP is produced by the services sector. Trade, financial and insurance activities and tourism services together account for almost 40% of the country’s GDP, followed by manufacturing, public administration, health and education services, real estate activities and administrative and support services activities. With the outbreak of COVID-19 and border closures, there were virtually no tourist arrivals between April and October 2020.5 Hospitality and food services, tour operators, taxis and shops selling to tourists have been heavily affected. In comparison with 2019, accommodation and food services activities contracted by 65.8% in 2020. Manufacturing exports, wholesale trade and construction activity are expected to partially rebound in 2021.6
Before the pandemic struck, labour force participation had increased over time, reaching 59.3% in 2019 while unemployment had declined from 7.3% in 2009 to 6.7% in 2019. Gender gaps had narrowed over the past decade, with women’s labour force participation increasing from 42.9% to 46.2% between 2011 and 2019. In 2019, over 80% of the employed population worked for a wage, about 15% were self-employed, while the remaining 5% were distributed between employers (about 3%) and contributing family workers (about 2%). The services sector, led by trade, accommodation and food services activities and in third position transport, contributed over 70% of total employment. The secondary sector followed with 24% of total employment, with manufacturing and construction constituting the lion’s share, while agriculture contributed about 6% in 2019.7
The outbreak of the pandemic and the subsequent economic crisis have taken a toll on employment. The number of employed declined by 13.9%, approximately 74,000 people, from the first quarter of 2020 to the first quarter of 2021 (Table 1).
Table 1. Employment, unemployment and inactivity, from first quarter 2020 to first quarter 2021.
Q1 2020 May June July September October December Q1 2021
Employed 534,802 405,387 473,062 498,036 506,363 510,239 523,725 460,729
Unemployed 38,307 45,771 65,997 57,246 62,220 60,682 60,973 49,847
Inactive 208,087 336,897 230,612 215,806 203,621 201,977 188,045 299,794
Activity rate, % 73.4 57.2 70 72 73.6 73.9 75.7 63
Source: Based on data from the first quarter of 2020, 2021 of the Continuous Multi-Purpose Household Survey and the Rapid Continuous Multi-Purpose Household Survey, Statistics Mauritius and World Bank.
Activity rate: labour force over population; Unemployment rate: unemployed over labour force.
In the first quarter of 2020, the employed population aged 15–64 not in full-time education was estimated at 534,802 persons. In May, when the country was still in lockdown, total employment declined to 405,387 (−24%). Although employment bounced back to 523,725 employed persons in December 2020, the resurgence of cases and the re-introduction of pandemic-related measures led to a severe drop in employment to 460,729 (−12%) during the first quarter of 2021. The loss in employment has been accompanied both by an increase in the number of unemployed and in the number of inactive individuals. Whereas the unemployment rate increased from 7.2% in the first quarter of 2020 to 10.8% during the first quarter of 2021, the inactivity rate dropped from 73.4% to 63% during the same time span, indicating that displaced workers stopped looking for new work and left the labour force in greater numbers.
Analysing labour demand during this time, measured as the number of advertised vacancies, several patterns emerge. In line with the observed employment outcomes (Figure 1), job postings sharply contracted from a monthly average of over 3000 over the period October 2019 to February 2020 to less than 800 between March and May 2020. Although labour demand picked up in June (2125) and August 2020 (2292), generally, demand continued to trend downward from September 2020 (824) into the first quarter of 2021 (quarter mean: 617).
Figure 1. Trends in number of advertised vacancies, Q4/2019–Q1/2021.
Source: Based on data from Employment Service Monthly Bulletins of the Ministry of Labour, Human Resource Development and Training of the Republic of Mauritius.
The number of vacancies represents the aggregate number of public and press vacancy announcements from the last quarter of 2019 to the first quarter of 2021.
Numbers of vacancies for Q4-2019, March–May 2020 and Q1-2021 are a monthly average over the period.
Women too, have suffered negative labour market effects as a consequence of the pandemic. The percentage decline in employment has been similar for women and men during the initial phase of the pandemic. Between the first quarter and July 2020, about 6.5% of women and 7.2% of men lost their jobs. Although the labour market was seemingly recovering in the latter half of the year, a total of 559,636 individuals reported losing their employment between September 2020 and the first quarter of 2021. Analysing these specific displacement patterns, several observations stand out. First, in absolute numbers, the total amount of jobs lost due to the pandemic and the ensuing economic crisis was higher among men given that the pre-crisis employment rate was higher for men. However, while most men who had lost their jobs started to look for new work, and thus remained engaged in the labour market, most women exited the labour market and became inactive after displacement.
The number of unemployed men rose by 83% compared with a growth of about 6% among women until July 2020. Even though these growth effects levelled off in the second part of the year, this trend was reversed at the beginning of 2021. Between December 2020 and the first quarter of 2021, a total of 41,830 fewer men and 21,166 fewer women reported being employed (Table 2). Given that unemployment rates remained stable in comparison to December 2020, increased job losses translated into a drop in the activity rates for both men (−15%) and women (−10%). Although the female activity rate was estimated at 58.3% in July 2020 and recovered to 61.4% – outpacing its pre-pandemic level (61.0% in the first quarter 2020) – female activity rate saw a sharp decline in the first quarter of 2021 reaching 51.5%.
Table 2. Employment, unemployment and inactivity, by sex and age group, first quarter 2020 to first quarter 2021.
Men
Q1 2020 May June July September October December Q1 2021
Employed 319,740 248,436 273,166 296,811 301,627 304,590 317,329 275,499
Unemployed 16,931 28,606 36,327 32,171 31,985 28,783 29,798 25,309
Inactive 53,172 111,563 74,109 53,781 50,451 50,020 38,706 102,514
Activity rate, % 86.4 71.3 80.7 85.9 86.9 87.0 90.0 74.6
Unemployment rate, % 5.3 11.5 13.3 10.8 10.6 9.4 9.4 9.2
Women
Q1 2020 May June July September October December Q1 2021
Employed 215,062 156,952 199,896 201,225 204,735 205,649 206,396 185,230
Unemployed 21,376 17,164 29,670 25,076 30,236 31,899 31,175 24,538
Inactive 154,915 225,334 156,503 162,025 153,170 151,957 149,339 197,279
Activity rate, % 60.4 43.6 59.5 58.3 60.5 61.0 61.4 51.5
Unemployment rate, % 9.9 10.9 14.8 12.5 14.8 15.5 15.1 13.2
Youth (ages 16–24)
Q1 2020 May June July September October December Q1 2021
Employed 54,002 40,591 44,290 45,925 44,079 46,399 55,080 36,354
Unemployed 17,454 9802 16,469 20,642 20,517 20,441 18,210 17,791
Inactive 29,572 53,667 28,888 25,263 25,393 27,082 17,580 88,822
Activity rate, % 70.7 48.4 67.8 72.5 71.8 71.2 80.7 37.9
Unemployment rate, % 32.3 24.1 37.2 44.9 46.5 44.1 33.1 48.9
Source: Based on data from the first quarter of 2020, 2021 of the Continuous Multi-Purpose Household Survey and the Rapid Continuous Multi-Purpose Household Survey, Statistics Mauritius and World Bank.
Activity rate: labour force over population; Unemployment rate: unemployed over labour force.
At a disadvantage in terms of access to jobs before the pandemic, Mauritian youth aged 16–24 years were not spared by the crisis. Youth employment declined by about 33% between the first quarter of 2020 and the first quarter of 2021, compared with a drop of 10% among workers in other age groups. In 2020, the activity rate among youth declined substantially as a result of COVID lockdown measure until May and gradually bounced back to 80.7% in December. The number of youth looking for a job increased by about 756, and the unemployment rate rose from 24.1% in May to 33.1% in December. Again, although the recovery throughout 2020 positively impacted the Mauritian labour market, these positive trends have been entirely reversed during the first quarter of 2021. Youth were not spared from these developments, their employment decreased by 18,726 between December 2020 and the first quarter of 2021 leading to an increase in youth unemployment from 33.1% to 48.9% during the same time. As unemployment and inactivity heavily influence future outcomes at a young age and youth inactivity can permanently impair productive potential and influence lifetime patterns of employment, pay and job tenure, the sharp decline in youth labour force participation to 37.9% warrants further policy attention.
In terms of sectoral distribution, the reduction in employment during the first 3 months since the start of the pandemic was largely attributable to the dynamics of informal employment (Statistics Mauritius and World Bank, 2020). Whereas formal employment dropped by about 10% from 384,000 in the first quarter of 2020 to 343,817 in May 2020, informal employment sharply contracted from 150,800 to 61,570 during the same time (−59%). By September 2020, formal employment had recovered and in the first quarter of 2021 was above pre-crisis levels. By contrast, informal employment increased between June and December and then contracted again and fell to 53,184 in the first quarter of 2021 (Figure 2).
Figure 2. Trends in employment levels by formality status, Q1/2010–Q1/2021.
Source: Based on data from the first quarter of 2020 and of 2021 of the Continuous Multi-Purpose Household Survey and six rounds of the Rapid Continuous Multi-Purpose Household Survey, Statistics Mauritius and World Bank.
The determinants of labour market outcomes during the pandemic
To substantiate our initial findings, we analysed the correlates of labour force participation and corresponding employment and displacement patterns through a series of regressions.8 To structure our analysis, we performed regressions on four selected time windows. Starting with the widest possible time frame of all available survey months in 2020, we separately analysed the first quarter of 2020 (i.e. Pre-pandemic time window); the first period after the initial lockdown (i.e. May until July 2020); the second period after the lockdown had been (partially) lifted in August (i.e. September until December 2020); and the first quarter of 2021. This way of analysing the data allowed us to study the responsiveness of labour market outcomes and derive insights into the effectiveness of the implemented policy measures to the ongoing pandemic (e.g. the labour market effect of lockdowns). Given existing sex differences in the Mauritian labour market, we also provide the results of our analysis for subgroups of men and women. Due to data limitations, our analysis of displacement patterns is restricted to the time window between September and the first quarter of 2021.
We report our findings in Figure 3 and the full results in the Supplemental Table A2. In line with prior findings, men were more likely to participate in the labour force. For instance, men across all groups were 12.6% more likely than women to be in the labour force at the outset of the pandemic (p < 0.01). According to our estimation results, slightly more women than men left the labour force during the pandemic, with this effect most pronounced in the aftermath of the first lockdown (i.e. between May and July 2020). This can be attributed to women’s greater prevalence of displacement and household-related factors. Interestingly, these sex differences in labour force participation level off for divorced, separated and single women during the pandemic.
Figure 3. Correlates of labour force participation by sex and time period.
Source: Based on data from the Continuous Multi-Purpose Household Survey and the Rapid Continuous Multi-Purpose Household Survey, Statistics Mauritius and World Bank.
The point estimates are based on OLS regression including month and district fixed effects. The dependent variable in all model specifications is a respondent’s answer to the question of whether they have lost their employment due to business closures between September 2020 and the first quarter of 2021. At the outset of the pandemic no significant differences existed between women based on marital status, however by the September–December period women that were either divorced, separated and/or single were between 18.9% and 21.1% more likely to be active participants in the labour market than their married counterparts. We cannot detect such a pattern among men. With respect to education, higher education seems to be a robust predictor of labour force participation, even when considering the pandemic. For instance, in comparison to their peers without a completed primary education and/or no schooling, women with post-secondary or tertiary education were almost 30% more likely to be active participants in the labour force (see Supplemental Table A2). Although this advantage varies across educational attainment it varies little during the course of the pandemic, and in comparison to men, the effect is approximately double in size. The observed education advantage for men ranged between 8.9% and 21.7% between the first quarter of 2020 and corresponding first quarter in 2021. As expected, those aged 25–44 years were more likely to be active in the labour force than their peers aged 16–24 and 45–64, even though this effect is subject to moderation once the pandemic progressed. Also, these age cohort effects seem not to differ between men and women. In line with this finding, across all model specifications, household size seems to be a robust predictor of labour force participation. Again, no significant differences existed between women and men.
With respect to our analysis on employment, we report our findings in Figure 4 and the full results in Supplemental Table A3. Although men were more likely to participate in the labour force, these differences did not translate into more employment opportunities after the onset of the pandemic. While men were 3.6% more likely to be employed during the first quarter of 2020, this effect disappeared in the direct aftermath of the first lockdown between May and July 2020. Interestingly, the gains in employment between September and December 2020 led to the re-emergence of existing gender biases with respect to employment. Although at the pandemic’s outset divorced, separated and single women were less likely to be employed in comparison to their married peers, they were just as likely to be employed as the pandemic progressed. A notable exception were single women, who were 16.7% more likely to be employed in comparison to their married peers, a remarkable difference compared to the first quarter of 2020, during which single women were 14.8% less likely to be employed. In sharp contrast, we can detect a somewhat opposite pattern among single men who were 7.1% less likely to be employed than their married peers between September and December 2020 but showed little difference during the first quarter of 2020.
Figure 4. Correlates of employment by sex and time period.
Source: Based on data from the Continuous Multi-Purpose Household Survey and the Rapid Continuous Multi-Purpose Household Survey, Statistics Mauritius and World Bank.
The point estimates are based on OLS regression including month and district fixed effects. The dependent variable in all model specifications is a respondent’s answer to the question of whether they have lost their employment due to business closures between September 2020 and the first quarter of 2021.With respect to education, higher education was a more robust predictor of employment, especially during the pandemic. Interestingly, the education effect on employment was equally pronounced between men and women across all levels of education whereas these differences did not seem to matter at the outset of the pandemic. For instance, in comparison to their peers without a completed primary education and/or no schooling, women with post-secondary or tertiary education were almost 10% more likely to be employed between September and December 2020. Although this advantage varied across level of educational attainment, a lower or upper secondary education was more conducive for employment during the pandemic. At the outset of the pandemic, in the first quarter of 2020, those aged 25–44 and 45–64 were more likely to be employed than youth. However, this effect was subject to modest moderation once the pandemic progressed. These findings are in line with recent research on youth employment dynamics in Mauritius (Castaneda et al., 2020). In addition, there were no differences in these age cohort effects between men and women. In line with this finding, across all model specifications, household size was not a robust predictor of employment and again, no significant differences existed between women and men.
The observed drops in employment during the pandemic warrant further investigation. For this reason, we analyse specific displacement patterns in this section. Due to data limitations, this analysis is limited to between September 2020 and March 2021.
Again, our findings confirm the notion of an asymmetric displacement pattern between women and men (Figure 5). For example, we find that women were 12.1% more likely to lose their job when compared to their male peers. Importantly, women with less education were most susceptible to displacement during the pandemic. For example, women who had attained an upper secondary education or higher were up to 10% less likely to be displaced compared to women without any or an incomplete primary education. Furthermore, we find that married women were more likely to lose their employment compared to their divorced (−24.3%), separated (−24.1%) and single peers − (−23.3%).
Figure 5. Correlates of displacement by sex, May–March 2021.
Source: Based on data from the Continuous Multi-Purpose Household Survey and the Rapid Continuous Multi-Purpose Household Survey, Statistics Mauritius and World Bank.
The point estimates are based on OLS regression including month and district fixed effects. The dependent variable in all model specifications is a respondent’s answer to the question of whether they have lost their employment due to business closures between September 2020 and the first quarter of 2021.
Displacement was most likely to occur for those aged 45–64 years (11.0%), with this effect more pronounced for men (11.5%) and less so for women (8.7%). These findings are notable to the extent that re-employment and re-training for this age cohort might prove to be more challenging (Bednarzik et al., 2021). When looking at the sectoral composition of displacement, the effect was most pronounced in the services sector. As expected, most displaced workers were employees and again, women were disproportionally represented among those workers who were displaced (i.e. approximately 65% of displaced employees). Synthesising these insights, displacement appears to be concentrated in the services sector and among women, with women who are the least educated the most vulnerable.
Further analysis of displacement patterns between September 2020 and the first quarter of 2021 revealed that women were more likely to be displaced than men, indicating the presence of a potential gender bias in firms’ layoff decisions. As expected, men and women with higher education were significantly less likely to be laid off during this period, underscoring the importance of education for increased job security. Interestingly, we find that older employees were better protected from being laid-off in comparison to their younger peers, reinforcing the importance of tenure/work experience for increased job security. This effect holds for both men and women. The upshot in unemployment corresponds to intensifying displacement in the secondary (or manufacturing) sector. According to estimation results, being employed in the secondary sector increased the likelihood of being displaced by 9%.
Against this background, a key insight of our analysis is the stark differences between men and women in their displacement and employment. Although initial differences in gender patterns can be attributed to gender roles, where women are the primary providers of childcare and other unpaid work, the pandemic has proven to be a powerful catalyst in undermining any advances in levelling the labour market playing field.9
Protecting jobs and living standards in the short and medium-term
Following the outbreak of COVID-19 in Mauritius, the Government implemented several measures to contain the spread of the virus, including adopting a sanitary curfew; the cessation of public transport; international border closures; the closure of schools, universities and shopping malls; suspension of onsite work among public and private sector workers except for essential activities; and mass testing.10
To contain the socioeconomic impact of the pandemic, the government intervened promptly with fiscal support measures.11 These included a moratorium on capital and interest payments on existing and new loans; a loan facility benefiting micro, small and medium enterprises; the distribution of food packages to the most vulnerable households; and compensation schemes for workers. This section focuses on the compensation schemes, namely, the GWAS and the SEAS, which were introduced to protect jobs and support incomes.12
Temporary wage subsidies are an adequate instrument for providing support for worker incomes during crises, such as the one triggered by the COVID-19 pandemic, that affect both labour supply – in this case, the ability of workers to go to work – and labour demand – because firms are obliged to close. Under the GWAS, private sector firms are entitled to cash transfers to pay their wage bills. In particular, private-sector employees with basic salaries up to MUR 25,000 were entitled to receive their full salaries, employees taking in basic monthly salaries between MUR 25,000 and MUR 50,000 received a fixed amount of MUR 25,000, while employees with basic salaries above MUR 50,000 a month were not eligible to receive wage subsidies from the government. Under the SEAS, the self-employed aged 18 years and over (i.e. including those aged 18) and in business during the 3 months prior to the outbreak were entitled to receive monthly financial support of MUR 5100 as long as they were not: beneficiaries of government assistance, dependent spouses, belonging to a household with a monthly income above MUR 50,000, fishermen, full-time university students or formal private-sector employees.
Based on pre-crisis labour force survey data, virtually all workers employed in low-skill jobs and most workers performing medium-skill jobs received a monthly basic salary of up to MUR 25,000 and were therefore eligible to receive full subsidies under the GWAS.
Since July 2020, the schemes have only covered workers in the tourism sector. During the second lockdown in March 2021, the support was provided to other sectors as a reduced payment at half the original monthly payment to wage workers, as well as a one-off grant of MUR 10,000 to the self-employed. With the start of the reopening in April 2021, a full month assistance was provided under both GWAS and SEAS for all sectors and the schemes were extended until September 2021 for tourism-related companies.
Between 20% and 50% of workers in high-skill jobs typically received a basic monthly salary between MUR 25,000 and MUR 50,000 and were therefore eligible to partial salary replacement. These workers were in households from the middle and top of the household income distribution. The GWAS, therefore, appears to be well-targeted toward the workers most in need. Workers in high-skill jobs were also more likely to be able to work from home as about 79% used computers at work, compared to only 30% of workers in medium-skilled jobs and less than 6% in low-skilled jobs (based on the Continuous Multi-Purpose Household Survey, first quarter, 2020).
According to data from the Mauritius Revenue Authority published by Statistics Mauritius, about 270,000 private sector workers benefited from the GWAS in March 2020, about 256,000 in April and about 220,000 in May. Based on 2019 labour force survey data, this corresponds to 84% coverage of private-sector employees with a basic salary below MUR 50,000 (in March), 80% (in April) and 69% (in May) (Statistics Mauritius, 2020).13
The SEAS provided minimum income replacement to both formal and informal self-employed workers. The monthly transfers of MUR 5100 corresponded to about 50% of the minimum wage; this is considerably less than the median (MUR 14,630) and average monthly (MUR 18,390) business income (in 2020 prices) reported by the self-employed in 2019. Nonetheless, providing the self-employed with a cash transfer in a time of crisis is critical to ensuring these people receive income, as the self-employed cannot operate their businesses during lockdowns and face considerable uncertainty over both their ability to reopen and the availability of customers while demand remains weak.
About 203,000 SEAS applications were processed in March, over 186,000 in April and about 185,000 in May. These numbers are considerably higher than the 97,900 employers and own-account workers who were estimated to have been operating in Mauritius in 2019 (Statistics Mauritius, 2020) and still considerably higher than the 108,200 if contributing family workers were considered as potential applicants. This gap may be attributable to informal employees applying for income support under SEAS rather than the GWAS. Informal employees are private-sector workers whose employers do not pay contributions to the National Pension Fund. Such workers are not registered with the Mauritius Revenue Authority and are therefore not eligible to benefit from the wage subsidies provided through the GWAS. Instead, informal employees are likely to have applied for financial assistance under the SEAS.14 Based on the 2019 Continuous Multi-Purpose Household Survey data, about 91,000 private sector workers can be categorised as informal according to the criterion explained above. Adding these informal employees to the number of self-employed leads to a total number of potential applicants that is closer to the number of applications processed under the SEAS.
It is extremely important that financial assistance schemes reach all workers, particularly those at the bottom of the income distribution, including informal wage workers. However, it is worth noting that the amount provided under the SEAS to informal private sector employees is equivalent to 50% of the minimum wage and therefore only a fraction of what these workers usually make per month and of what was provided to formal employees.
In October 2020, the government announced several funding initiatives to be implemented over the following 8 months to reduce unemployment. As a result, from 1 November 2020, until 30 June 2021 five initiatives were funded. First, the Human Resource Development Council increased the National Training and Reskilling Intake by some 9000 unemployed in construction, manufacturing, logistics, ICT-BPO, agro-industry, renewable energy and the circular economy. These individuals received a monthly benefit of MUR 10,200 for the duration of a 6-month training programme.15 Second, the Employment Support Scheme for Small and Medium Enterprises was to support 11,000 employees through a monthly benefit of MUR 10,200. Third, Landscope (Mauritius) Ltd was to hire about 2000 unemployed to work with the National Clean-Up Campaign. Fourth, the Air Freight Scheme – incorporated into the Economic Recovery Plan — had two components: namely supervision of the national airline which was under voluntary administration, and support for the export sector. Finally, to support the most vulnerable following a new lockdown, electricity was made free for March and April for individuals on the Social Register of Mauritius or under the National Empowerment Foundation, as well as for low-consuming SMEs, and at a 46% discount for the following 4 months. The Government also established the COVID-19 Solidarity Fund aimed at funding COVID-19 related projects such as financial support to Mauritian residents, and the financing of projects related to the COVID-19 virus and other related health issues. The fund primarily relies on donations from the public and enterprises, however at roughly MUR 500 million it is rather small.
Measuring the effects of the pandemic on the labour market and on living standards
Despite the fast roll-out of government programmes aimed at shielding labour incomes, the question arises as to whether these programmes were effective in mitigating income losses. To provide an answer to this question, we analysed data on self-reported income losses among Mauritian households between May and December 2020 (Figure 6). According to our estimates, on average more than one in three Mauritian households reported an income loss in comparison to their pre pandemic income (in the May, September, October and December round), or in comparison to their previous monthly income (in the July and June round). These income losses were most pronounced at the outset of the COVID-19 pandemic and gradually reduced towards the end of 2020. Whereas 41.7% of households reported experiencing an income loss during May 2020, 30.1% or almost one in three households saw their incomes fall below pre pandemic levels in December 2020 and living standards have continued to worsen for a non-negligible share of the population. First, although employment bounced back in December from the May decline, the sharp contraction in January 2021 hints at the temporary nature of this labour market recovery. Second, for many workers, even individuals who have gone back to work after an interruption, labour income is below pre-pandemic levels. Both individuals who have lost their jobs and displaced workers will need continuing support. In this way the income support schemes, such as SEAS and GWAS, have been useful in maintaining job attachment and protecting some workers from the scarring effects associated with prolonged periods of unemployment, particularly worrisome among youth.
Figure 6. Changes in household income, relative to pre-lockdown (May, September, October and December round), to previous month (June and July round).
Source: Based on data from the Rapid Continuous Multi-Purpose Household Survey, Statistics Mauritius and World Bank.
In the May round, the question regarding the change in household income has a reference period the time since the start of the lockdown; in the June and July round, the reference period is the previous month; in the September–December rounds, the reference period is the first months of 2020 before CVOID-19.
To further disentangle these findings, we performed a regression analysis to study the correlates of the probability of reporting an income loss at the household level between May and December 2020. The results are reported in Figure 7.
Figure 7. Correlates of household income loss by time period.Source: Based on data from the Continuous Multi-Purpose Household Survey and the Rapid Continuous Multi-Purpose Household Survey, Statistics Mauritius and World Bank.
The point estimates are based on OLS regression including month and district fixed effects. The dependent variable in all model specifications is a respondents answer to the question whether they have lost their employment due to business closures between September 2020 and the first quarter of 2021.
In line with the informal sector’s observed decline in activity and limited coverage from government programmes (see Supplemental Table A5), it is those households with a larger share of household members working informally that were more likely to experience income loss. Similarly, those households in which one or more members lost their employment during the reference period were at greater risk of experiencing lost income. Households with more members engaged in financial, health services and science/technology sectors were less likely to report a negative income shock, possibly due to the relatively easy switch to a remote working environment. Relying on previous surveys, our results show that workers in the services sector, especially in modern services (such as information and communication, professional, financial, insurance and real estate activities, scientific and technical activities, administrative and support service activities) are more likely to access computers and therefore potentially perform their work from home (Figure 8a). The skill level of jobs also has an affect with the share of workers able to use computers employed in high (96.3%) and medium (59.4%) skill jobs considerably larger than the 30.8% of workers in low skill jobs (Figure 8b).
Figure 8. Share of the employed able to use computer by sector and skill level first quarter 2020: (a) by sector and (b) by skill level.
Source: Based on data from the 2020 Continuous Multi-Purpose Household Survey, Statistics Mauritius.
Interestingly, while female-headed households represented 9.4% of all households in our sample, they were less likely to be affected by income loss. This effect cannot be attributed to better coping strategies but is likely due to such households being largely dependent on government transfers rather than income from labour, prior to the outbreak of the pandemic (World Bank, 2017).
Although we do not have direct information on the distributional consequences of these income losses, our results indicate that the crisis has impacted those sectors, occupations and informal jobs in which workers from poor and low-income households are concentrated. For example, according to prior estimates, the manufacturing, trade and construction sectors combined employed 39% of all workers but 50% of the poor. Additionally, 66% of the poor worked informally, disproportionately more than in the informal share of the workforce overall (31%). At the same time, it is households exposed to these sectors that are experiencing the largest reported income losses.
The limited availability of home-based work among workers in traditional sectors such as agriculture and manufacturing and in low skill jobs, and the limited access to the internet among the most disadvantaged households, have likely aggravated these adverse economic impact on vulnerable households. Synthesising our results, our findings support the notion that the pandemic has undermined some of the progress achieved in poverty reduction and shared prosperity in recent years.
Conclusions
The COVID-19 crisis has had sizable negative effects on economies around the globe. In particular, small island economies with significant reliance on tourism have proven to be vulnerable to the adverse consequences towards lockdown measures and a sudden stop in international travel. In this paper, we study the case of such a small island economy: Mauritius. Despite a promising development track record over the past decades (lifting the country to a high-income country status in July 2020) and the government’s policy measures to diversify economic activity, Mauritius was hit hard by the pandemic. Importantly, we show that these adverse effects have not been distributed evenly. Low-skilled and informal workers employed in traditional sectors such as agriculture and manufacturing and in tourism and trade have suffered more job losses and will likely endure the negative effects for a longer time. Furthermore, women were more severely affected by the crisis as job losses were more concentrated among women and displaced women stopped looking for work to take care of their families during the pandemic, effectively undoing some of the progress on greater female labour force participation achieved over the past decade.
The immediate response of the government, including compensation schemes for private sector employees and an income support programme for own account workers, has played an important role in limiting job destruction and income loss. However, these measures are costly and are to be replaced in the medium term with social protection schemes that can help sustain the incomes of the most vulnerable households, while potentially providing retraining and upskilling among individuals who have lost their jobs. Retraining could be relatively easy for workers previously employed in the tourism sector who are largely young and well educated. Two examples of such schemes in Mauritius are the Social Aid Program and the Marshall Plan Social Contract. The first appears to be particularly well suited to providing income relief to individuals who have temporarily lost their jobs and to their families. The second may represent an alternative in the event some individuals are unable to find new jobs in the medium term because of, for instance, a slow recovery in the tourism sector or a persistent pandemic crisis.
From a policy perspective, the unequal impact of the crisis on the economy, the labour market and living standards, together with the uncertainty about the timing and intensity of the economic recovery, calls for a strengthening of existing social protection programmes in the medium term to support individuals and households affected by the crisis (World Bank, 2021b). Beyond these immediate socio-economic concerns, both for Mauritius and many other small island economies (Rasheed, 2021; UN, 2021; United Nations World Tourism Organization [UNWTO], 2021), the COVID shock represents an opportunity to recalibrate existing economic development and growth-models to achieve an equitable and sustainable recovery from the pandemic. For Mauritius and small island economies, recovering from the global economic setback of the pandemic, the recent experience provides a unique window of opportunity to transition to an economic growth model that bolsters the resilience of key economic sectors to climate change impacts. Particularly, investments in the blue economy, ranging from tourism to fishing, and taking advantage of new opportunities from decarbonisation, represent unique opportunities to create more resilient and sustainable economic foundations.
Supplemental Material
sj-pdf-1-elr-10.1177_10353046221135076 – Supplemental material for The impact of COVID-19 on labour markets and living standards in Mauritius
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Supplemental material, sj-pdf-1-elr-10.1177_10353046221135076 for The impact of COVID-19 on labour markets and living standards in Mauritius by Marco Ranzani and Andreas Kern in The Economic and Labour Relations Review
The authors would like to thank Mario Negre, and Saagarika Tanvi for helpful comments. All errors remain the authors.
Author biographies
Marco Ranzani is a senior economist in the Middle East and North Africa region of the Poverty and Equity Global Practice, at the World Bank. His research focuses on labour market, poverty and inequality issues. Previously, he was consultant for the Poverty Reduction Anchor of the PREM network, researcher at Understanding Children Work, an inter-agency research cooperation initiative involving the International Labour Organization, UNICEF and the World Bank and a post-doctoral fellow at the University of Bergamo, Italy. He holds a BA in Economics and a PhD in Public Economics from the Catholic University of Milan, Italy.
Andreas Kern is teaching professor at the McCourt School of Public Policy at Georgetown University. Dr. Kern currently works with leading researchers and policymakers on designing programmes and initiatives to address global policy challenges. He is a member of the World Economic Forum’s Expert Network on the Future of Financial and Monetary Systems, Advanced Manufacturing and Production and Global Governance. Dr. Kern earned his PhD in Political Economy from Freie Universität Berlin and holds an MSc in Economics from Ludwig Maximilians Universität in Munich.
Authors’ note: The findings, interpretations and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organisations, or those of the Executive Directors of the World Bank or the governments they represent.
The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
Supplemental material: Supplemental material for this article is available online at https://journals.sagepub.com/doi/suppl/10.1177/10353046221135076
1. Based on data collected by Johns Hopkins University, coronavirus.jhu.edu/map.html accessed on 16 July 2022 at 9.10 am EST.
2. Further details are available at https://covid19.mu/ and at coronavirus.jhu.edu/map.html.
3. An in-depth description of the survey instrument is provided in Supplemental Table A1.
4. A notable exception is Rashid et al. (2020), who provide an overview of the pandemic’s impact on selected macroeconomic outcome variables for the entire universe of small island developing nations. More recent analyses of the World Economic Forum (2020), the United Nations (UN) High Level Political Forum on Sustainable Development (UN, 2021) and United Nations Conference on Trade and Development (UNCTAD, 2021) provide further evidence on the adverse economic and social effects of the pandemic on small-island economies and emphasise the need for concerted international action to support these nations to achieve a sustainable recovery. Importantly, these contributions highlight the need to account for the vulnerability of these economies to the impacts of climate change and opportunities arising from policy reform resulting from the pandemic.
5. According to Statistics Mauritius, between April and October 2020, only 740 arrivals were recorded, which can be attributed to repatriations of nationals. In the same period in 2019, Mauritius recorded 621,337 arrivals. In addition to the lockdown response to the ongoing pandemic, an oil spill from a stranded freighter in July 2020 might have further contributed to the steep decline in tourist arrivals, even after travel restrictions were lifted in October.
6. The financial sector is expected to pick up in 2022 only if some progress is achieved in anti-money laundering following the October addition by the European Union of Mauritius to the list of High Risk Third Countries for Money Laundering. For further information, see: https://ec.europa.eu/info/business-economy-euro/banking-and-finance/financial-supervision-and-risk-management/anti-money-laundering-and-counter-terrorist-financing/eu-policy-high-risk-third-countries_en.
7. The figures reported refer to Mauritian employment. In addition, about 44,000 valid work permits were issued to foreign workers as of December 2019, largely employed in manufacturing and construction and for low-skill occupations. Foreign labour represented about 7% of total employment in 2019.
8. To this end, we construct several models centred on three key dependent variables. First, we construct a variable that captures whether a person within the reference population is in the labour force. This variable takes the value of 1 if a person is in the labour force and 0 otherwise. Second, we construct a similar variable for employment, which takes the value of 1 if a person is employed and 0 if unemployed during the reference period. Finally, we construct a displacement variable that takes the value of 1 if a respondent has reported to have lost their employment due to a COVID-19 related business closure or workforce reduction. To account for confounding factors, we included a series of control variables in our regressions. Besides a variable for sex, we incorporated variables capturing the level of education, age, marital status and relationship to the head of the household. Furthermore, we included district fixed effects to account for location-specific differences and month fixed effects to control for unobserved temporal effects impacting the Mauritian labour market. We relied on non-linear probability models and cluster standard errors at the household level in all model specifications.
9. Data collected in 2018 indicated that women spent an average of about 5 hours a day on activities outside the system of national accounts production boundary, which includes both care work and unpaid domestic work. Men devoted an average of less than 2 hours per day. Moreover, most inactive women aged 25 or more mention household responsibilities as the main reason for not engaging in the labour market (Gaddis and Ranzani, 2020).
10. Between March 25 and April 1, the authorities closed all supermarkets, bakeries and shops and distributed food to vulnerable households. A curfew was instituted on March 20 which lasted until May 30. A gradual reopening was adopted on May 15. Initially, employees could return to work after obtaining a work access permit. From 1 June, work permits were no longer required, but employers must comply with social distancing rules, while working from home was encouraged. Borders reopened on 2 October 2020, with all arriving passengers quarantining for 2 weeks. Following new cases of domestic transmission after almost a year, a second lockdown was introduced on 11 March 2021 with a phased reopening from 1 April. A partial lockdown remained in place until the end of April, when only some specific economic activities could operate under strict sanitary conditions.
11. A summary of the measures adopted by the government has been compiled by the International Monetary Fund and is available at https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19.
12. The schemes were introduced in March and maintained during April and May. The schemes have remained in place for workers and the self-employed who had licenses or permits to operate in the tourism sector or related activities.
13. This estimate is based on a total of 394,000 private sector employees, which excludes workers in public administration and defence sector (48,100).
14. While precise eligibility criteria were identified in order to apply for financial assistance under both the SEAS and GWAS (see https://www.mra.mu/index.php/media1/self-employed-assistance-scheme), the design of the SEAS scheme was intended to cover informal self-employed, which are not registered with the tax authority. This implies that informal private sector employees and the unemployed/inactive could have applied to the scheme and received assistance.
15. A circular economy is an industrial system that promotes a gradual delinking of growth and development from the consumption of finite resources by relying on renewable energy and seeking to eliminate waste through the superior design of materials, products, systems and business models.
==== Refs
References
Adams-Prassl A Boneva T Goldin M , et al . (2020) Inequality in the impact of the coronavirus shock: Evidence from real time surveys. Journal of Public Economics 189 : 104245.
Bednarzik R Kern A Hisnanick J (2021) Displacement and debt—The role of debt in returning to work after displacement. Journal of Financial Economic Policy 13 (5 ): 600–650.
Blundell R Machin S (2020) Self-employment in the Covid-19 crisis. CEP COVID-19 Analyses cepcovid-19-003. London: Centre for Economic Performance, London School of Economics.
Cahuc P Kramarz F Nevoux S (2018) When short-time work works. Discussion Paper 11673. Bonn, Germany: Institute for the Study of Labour (IZA).
Campello M Kankanhalli G Muthukrishnan P (2020) Corporate hiring under COVID-19: Labor market concentration, downskilling, and income inequality. NBER Working Papers 27208. Cambridge, MA: National Bureau of Economic Research.
Castaneda JL Gaddis I Ranzani M , et al . (2020) Supporting Mauritian Youth With Little Education in Their Job Search. Preliminary Evidence From a Behavioral Intervention. Washington, DC: World Bank.
Christiano LJ Eichenbaum MS Trabandt M (2015) Understanding the great recession. American Economic Journal: Macroeconomics 7 (1 ): 110–167.
Forsythe E Kahn LB Lange F , et al . (2020) Labor demand in the time of COVID-19: Evidence from vacancy postings and UI claims. Journal of Public Economics 189 : 104238.32834178
Gaddis I Ranzani M (2020) Fostering Labor Force Participation Among Mauritian Women: Quantitative and Qualitative Evidence. Washington, DC: World Bank.
Guerra-Marrero A Couce-Montero L Jiménez-Alvarado D , et al . (2021) Preliminary assessment of the impact of Covid-19 pandemic in the small-scale and recreational fisheries of the Canary Islands. Marine Policy 133 : 104712.34608348
Hakim L (2020) COVID-19, tourism, and small islands in Indonesia: Protecting fragile communities in the global coronavirus pandemic. Journal of Marine and Island Cultures 9 (1 ): 130–141.
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International Monetary Fund (2021) World Economic Outlook Update. Fault lines widen in the global recovery, July. Washington, DC: IMF.
Montenovo L Jiang X Rojas FL , et al . (2020) Determinants of disparities in Covid-19 job losses. NBER Working Paper 27132. Cambridge, MA: National Bureau of Economic Research.
Rasheed A (2021) Small island developing states drive a green post-COVID-19 recovery agenda. Policy Brief 2021/8. Canberra: ANU College of Asia and the Pacific. The Department of Pacific Affairs (DPA).
Rashid H Ng P Cheng H , et al . (2020) The COVID-19 pandemic puts small island developing economies in dire straits. Policy Brief No 64. New York, NY: United Nations Department of Economic and Social Affairs (UN/DESA).
Statistics Mauritius (2020) Social support. Financial support given to workers during the lockdown period and extended to specific activities after lockdown. September. Available at: https://statsmauritius.govmu.org/Documents/Homepage/Covid19/Covid_doc_Social_support.pdf (accessed 15 June 2022).
Statistics Mauritius and World Bank (2020) Monitoring the socio-economic effects of COVID-19 on Mauritian households—May to July 2020. October 2020. Available at: https://statsmauritius.govmu.org/Slider/SitePages/CMPHS_May-July2020.aspx (accessed 15 June 2022).
United Nations (UN) (2021) Coming together to help Small Island Developing States to get on a path to realize the SDGs. Available at: https://sustainabledevelopment.un.org/index.php?page=view&type=20000&nr=7185&menu=2993 (accessed 15 June 2022).
United Nations Conference on Trade and Development (UNCTAD) (2021) Small island developing states face uphill battle in COVID-19 recovery. Available at: https://unctad.org/news/small-island-developing-states-face-uphill-battle-covid-19-recovery (accessed 15 June 2022).
United Nations World Tourism Organization (UNWTO) (2021) Small Islands Developing States (SIDs). Available at: https://www.unwto.org/sustainable-development/small-islands-developing-states (accessed 15 June 2022).
World Bank (2017) Mauritius: Addressing Inequality Through More Equitable Labor Markets. Washington, DC: World Bank.
World Bank (2021a) Mauritius Country Economic Memorandum: Through the Eyes of a Perfect Storm. Washington, DC: World Bank.
World Bank (2021b) Small States: Fighting the pandemic, focusing on solutions. Available at: https://www.worldbank.org/en/news/feature/2021/09/23/small-states-fighting-the-pandemic-focusing-on-solutions (accessed 15 June 2022).
World Economic Forum (2020) Small developing countries face difficult Covid-19 recovery. Forbes, 19 May 2020. Available at: https://www.forbes.com/sites/worldeconomicforum/2020/05/19/small-developing-countries-face-difficult-covid-19-recovery/?sh=4394d206fbfa (accessed 15 June 2022).
| 0 | PMC9732493 | NO-CC CODE | 2022-12-14 23:35:51 | no | 2022 Dec; 33(4):806-828 | utf-8 | null | null | null | oa_other |
==== Front
J Health Serv Res Policy
J Health Serv Res Policy
sphsr
HSR
Journal of Health Services Research & Policy
1355-8196
1758-1060
SAGE Publications Sage UK: London, England
36475326
10.1177_13558196221135119
10.1177/13558196221135119
Original Research
Locked down or locked out? Trends in psychiatric emergency services utilization during the COVID-19 pandemic
https://orcid.org/0000-0002-7809-1454
Duncan Alison 12
Herrera Carolina-Nicole 3
Okobi Margaret 45
Nandi Shurobhi 6
Oblath Rachel 7
1 Director, Psychiatric Emergency Services, Boston Medical Center, Massachusetts, USA
2 Assistant Professor, 1836 Boston Univeristy Chobanian and Avedisian School of Medicine , Massachusetts, USA
3 Doctoral Candidate, Department of Health Law , Policy, and Management, 27118 Boston University School of Public Health , Massachusetts, USA
4 Medical Student, 1811 Harvard School of Medicine , Boston, Massachusetts, USA
5 Candidate, Masters of Public Health, 1848 TH Chan School of Public Health , Harvard University , Cambridge, Massachusetts, USA
6 Undergraduate, 1848 Northeastern University , Boston, Massachusetts, USA
7 Postdoctoral Associate, Department of Psychiatry , 1836 Boston Medical Center , Massachusetts, USA
Alison Duncan, Psychiatric Emergency Services, Boston Medical Center, 85 E Newton St, Boston, MA 02118, USA. Email: [email protected]
6 12 2022
6 12 2022
13558196221135119© The Author(s) 2022
2022
SAGE Publications
This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
Objective
To estimate changes in Boston Emergency Services Team (BEST) psychiatric emergency services (PES) encounter volume (total and by care team) and inpatient disposition during the first 8 months of the COVID-19 pandemic.
Methods
Data on 30,657 PES encounters was extracted from the four-county, BEST reporting system. The study period consisted of the first 34 weeks of 2019 and 2020. This period corresponded to the first five stages of Massachusetts’s COVID-19 public health restrictions: pre-lockdown, lockdown, Phase I, II and III reopenings. Descriptive and regression analyses were performed to estimate changes in encounter volume by care team and disposition.
Results
Compared to the same period in 2019, covariate-adjusted, weekly PES encounters decreased by 39% (β = −0.40, 95% Confidence Interval (CI) = [−0.51, −0.28], p < 0.00) during the lockdown. PES volume remained significantly lower during Phase I reopening compared to the previous year but returned to 2019 levels during Phase II. The covariate-adjusted proportion of weekly encounters that led to inpatient admission significantly increased by 16% (CI = [0.11, 0.21], p < 0.00) for mobile crisis teams (MCTs) and significantly declined by 13% (CI = [−0.19, −0.07], p < 0.00) for BEST-designated emergency departments during the lockdown period compared to the prior year.
Conclusions
The overall drop in PES utilization and the rise in inpatient admissions for MCT encounters suggests that during the early phases of the pandemic, patients delayed psychiatric care until they had a psychiatric crisis. Public health messaging about the lockdowns and absent equivalent messaging about the availability of telehealth services may have made patients more reluctant to seek psychiatric care.
psychiatry
emergency services
community care
edited-statecorrected-proof
typesetterts10
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pmcIntroduction
A month after the Commonwealth of Massachusetts’s first reported case of the novel coronavirus (COVID-19), the governor declared a state of emergency (10 March 2020) to combat the pandemic.1 This state of emergency was quickly followed with public safety orders including limiting the size of gatherings, closing schools, restaurants, and non-essential businesses, and a stay-at-home advisory (a lockdown beginning 23 March 2020). Massachusetts loosened public health restrictions in late May 2020 and preceded to move through phased ‘reopening’ throughout the summer of 2020.
During spring and summer 2020, US COVID-19 restrictions occurred in parallel with the European response, although European mitigation efforts were typically more severe. Most European states and the US underwent a lockdown period in March and April 2020. The number of US and European patients seeking non-pandemic–related emergency treatment during the early pandemic lockdown periods significantly declined.2–6 US and European hospitals conducted significantly fewer psychiatric consultations in the emergency department (ED) and saw fewer patients with psychiatric disorders in the months of March, April and May 2020.2,3,5–17 Some studies reported rising European psychiatric ED volumes towards the end of lockdown periods, but visit rates remained lower than pre-pandemic volumes.16,17
As COVID-19 cases declined in May 2020, non-essential businesses began to reopen. One US study found ED visits for psychiatric conditions rose in late April 2020 and reached pre-lockdown levels by the end of June 2020.3 Another found that psychiatric emergency services (PES) volumes remained below pre-pandemic volumes through August, leaving the effect of reopenings on PES volume unclear.18 One Massachusetts study found evidence suggesting non-PES mental health utilization rose during the pandemic lockdown period and began to return to pre-pandemic levels shortly after reopening.19
Some European studies found a significantly higher percentage of PES patients were admitted to hospitals during lockdown, whereas other studies found no significant differences in rates of hospital admission in 2020 compared to 2019.2,6,9–11,16 Studies of European PES encounters during COVID-19 found a trend towards a greater proportion of visits for suicidal behavior and psychotic disorders, and a decrease in the proportion of visits for mood disorders and anxiety disorders.13,15–17,20
There has been speculation that precautions taken to combat COVID-19, such as social distancing, weaken typical social support systems and increase loneliness. This, in turn, may increase the risk of anxious and depressive symptoms.21 One study found increased rates of suicidal ideation at the height of the Spring 2020 lockdown.20 A small study found significant increases in outpatient care for anxiety disorders and bipolar/schizophrenic disorders during the lockdown phase in Massachusetts.19 Fear of contracting COVID-19 may have been a barrier to accessing both emergency and non-emergency care. These factors increase the importance of tracking PES visits to determine how the pandemic and related stressors affected visit volume and acuity.
While many patients access PES through EDs, many cities have begun using mobile crisis teams (MCTs) to respond to psychiatric emergencies in the community.22 MCTs have been shown to have lower rates of hospitalization and, as a community-based program, can reach more diverse populations.23,24 Despite there being MCTs in over 17 states in the US, there are currently no studies detailing the relationship between COVID-19 and MCT volume.25
This retrospective cross-sectional study characterized how the COVID-19 lockdown and reopening of Massachusetts were associated with the encounter volume and acuity of PES provided by the Boston Emergency Services Team (BEST). We examined differences in utilization outcomes for two distinct types of PES: teams dedicated to ED and community-based MCTs. To the best of our knowledge, this study is the first to compare EDs and MCTs during the COVID-19 pandemic.
Methods
Study site
Every region in Massachusetts is served by one designated emergency services provider. The BEST, a multi-channel PES program operated by Boston Medical Center, is the designated PES program in metropolitan Boston and Fall River. BEST ED teams provide emergency psychiatric evaluation and management at three designated EDs. In addition, BEST has three MCTs that perform evaluations and interventions in a wide array of community settings (including three designated BEST urgent care centers (UCC), homes, schools, shelters, non-designated ED, and other health care settings). BEST services can be accessed through multiple pathways. A patient may walk into a designated ED or UCC. Any member of the community may contact the call center to ask for MCT evaluation, including patients, friends or family members of patients, health care providers, police, school staff, shelter staff, non-designated EDs and employers. The call center will dispatch an MCT or arrange transportation to a UCC whenever safe or will access emergency services to transport a patient to a designated ED when needed. MCT teams staff the UCC and respond to calls from the community.
BEST is designed and structured to meet the needs of patients who are publicly insured or uninsured; however, BEST serves all youth regardless of insurance status and provides services for commercially insured adults in certain circumstances (when facilities contract for BEST services regardless of insurance status or when emergency care is required before insurance status can be established). In 2020, about 6% of the commonwealth was uninsured, and 40% were publicly insured, whereas BEST patients were overwhelmingly either publicly insured (85%) or uninsured (12%).26 While the population of Massachusetts was approximately 78% non-Latino White, 6% non-Latino Black and 8% Latino, the population served by BEST is approximately 41% non-Latino White, 27% non-Latino Black, 25% Latino and 7% another race/ethnicity.26 As well, almost one-fifth of BEST patients were homeless.
BEST maintained in-person services – including walk-in urgent care center access, ED services and home visits – across all sites throughout the lockdown and phased reopening. Staff conducted COVID-19 screening surveys and arranged for evaluation; no patients were turned away. Starting on the second day of lockdown (11 March 2020), the MCTs also offered telehealth for clients in the community who were in quarantine for COVID-19 or who had active COVID-19 symptoms. Clients who declined an in-person evaluation due to fear of COVID-19 exposure could also opt for a telemedicine encounter. Patients were seen in the urgent care centers and EDs using the PPE protocols for each individual site. Of note, non-BEST services in the community (e.g. outpatient providers, partial hospital programs and in-home therapists) moved to telemedicine during the initial lockdown phase and slowly reopened as the public health restrictions eased. At the time of this writing, most non-BEST community behavioral health services are a hybrid of virtual and in-person delivery.
Data sources
We extracted aggregate weekly reports of PES encounters from the BEST electronic health record system. We analyzed 34 weeks of data from 2020 (January 1–August 31) and compared it to the corresponding 34 weeks of data from 2019 (January 1–August 31). We excluded encounters that were for scheduled urgent medication management services or were part of a jail diversion program. Our final sample, 30,657 total encounters, was 98.9% of all BEST encounters during the study period. We drew on other two other data sources for covariates. Given the positive relationship between ambient temperature and emergency psychiatric visits we controlled for weekly temperature using data from the National Oceanographic and Atmospheric Agency.27 We extracted weekly, county level data on COVID-19 cases in the four counties served by BEST (Suffolk, Middlesex, Norfolk and Bristol) from the New York Times COVID-19 database.28 All study procedures were approved by the Boston University Medical Campus and BMC Institutional Review Board.
Variables
Care team
Weekly visit counts were categorized by which PES team (ED or MCT) performed the encounter. Care Team was included to evaluate differences between the teams in the context of COVID public safety measures.
Disposition
To assess for changes in the acuity of psychiatric presentations, weekly visit counts were also categorized by discharge to: (1) locked inpatient units, (2) outpatient care (outpatient appointments, partial hospital program, intensive outpatient treatments, and natural supports) or (3) unlocked units with 24-h supervision and treatment (e.g. community crisis stabilization units or acute substance use disorder treatment programs).
COVID-19 phases
To evaluate volume and acuity in the context of different levels of public health response to COVID-19, we organized weekly visit counts into five distinct phases corresponding to Massachusetts’s COVID-19 closures and subsequent phased reopening: (1) Pre-lockdown – January 1 to March 9, (2) COVID-19 lockdown – March 10 to May 17, (3) Phase I reopening – May 18 to June 21 (outdoor recreation, high priority preventative services and some personal service businesses reopened), (4) Phase II reopening – June 22 to July 12 (retail businesses and restaurants reopened at limited capacity) and (5) Phase III reopening – July 13 to August 31 (museums, gyms, and casinos reopened).29
Temperature and Seasonality
Independent of the pandemic, seasonality and weather are known to affect emergency service use.30–32 Boston and Fall River weekly average temperatures were used to control for changes in temperature. The week of service (1–34) was used to control for seasonality. We defined a week as belonging to the month it started in. All regression models included these two covariates.
Analysis
All analyses were performed in Excel and Stata 14.2. Weekly patterns in encounter volume and disposition acuity were examined first with a series of graphs. Each graph compared new weekly COVID-19 cases in the BEST catchment area and PES volume changes.
Regression analysis was used to estimate the association between COVID-19 volume and BEST volumes overall. To account for pre-pandemic visit trends, we performed statistical and regression analyses comparing week-on-week encounter volumes and admission testing comparing before the pandemic to the pandemic period. Using t-testing, we examined volumes and changes in volume for all BEST encounters; volumes and changes in volumes stratified by type of care team (MCT and ED). To estimate the percentage changes in outcomes, we ran log-linear, fixed effects regressions separately for each pandemic phase.
Results
During the first 34 weeks of 2019 and 2020, the BEST program managed 30,657 PES encounters (16,586 and 14,071 respectively). ED visits accounted for 32.0% and 33.4% of PES encounters in 2019 and 2020, respectively; MCT visits accounted for 68.0% and 66.6% of encounters. Inpatient dispositions accounted for 40% of cases in both years. Table 1 reports descriptive statistics for encounters by year, type of visit, disposition and COVID-19 phase.Table 1. Patient volume, by care team and disposition.
Pre-lockdown Lockdown Phase I reopening Phase II reopening Phase III reopening
Total encounters (n)
2019 4549 5155 2417 1318 3147
2020 4495 3616 1802 1174 2984
Care team (%)
Emergency department (ED) team
2019 29.5% 30.9% 33.8% 36.5% 34.3%
2020 31.4% 33.3% 35.5% 35.4% 34.5%
Mobile crisis team (MCT)
2019 70.5% 69.1% 66.2% 63.5% 65.7%
2020 68.6% 66.7% 64.5% 64.6% 65.5%
Disposition (%)
Inpatient (IP)
2019 40.1% 37.4% 39.8% 43.8% 44.3%
2020 37.4% 38.4% 41.9% 43.9% 42.7%
Outpatient (OP)
2019 46.8% 49.2% 46.8% 42.4% 42.3%
2020 50.1% 48.8% 45.9% 43.0% 46.7%
24-h supervision
2019 13.1% 13.4% 13.4% 13.8% 13.4%
2020 12.5% 12.8% 12.2% 13.1% 10.6%
Figure 1 shows the visits graphically, with the five time periods (‘phases’) each shaded differently, starting with white. Weekly volume in 2019 and 2020 was comparable during the first 10 weeks of each year (Figure 1(a)). The greatest difference in volume between 2019 and 2020 occurred in during lockdown, when there were 242 fewer encounters in 2020 than in the corresponding week of 2019. Even as new COVID-19 cases decreased during lockdown, PES volume remained lower than in 2019. During the first reopening phase, 2020 weekly volume gradually approached that of 2019, and reached parity with 2019 volume by the start of Phase III. Weekly volume for EDs and MCTs both showed similar trends, as shown in Figures 1(b) and (c). A 1000-case increase in COVID-19 cases within a week in the Boston area was associated with a 0.8% reduction in weekly BEST encounters overall, and a 1% decrease in weekly BEST ED care team encounters.Figure 1. Weekly encounter volume with COVID-19 case counts.
Table 1 shows 2019 and 2020 encounter rates broken down by care team and disposition for each phase in the pandemic timeline. Encounter volumes in each phase were lower in 2020 than in 2019. In all phases and in both years, more than 63% of encounters were performed by the MCT. ED teams accounted for the less than 37% of encounters in any phase. A disposition of inpatient admission suggests that an encounter was of high acuity. During the 2020 lockdown, Phase 1 reopening and Phase II reopening, the share of encounters that resulted in inpatient disposition were slightly higher in 2020 than in 2019.
Table 2 shows the weekly descriptive statistics for the sample. On average, each location averaged 81.30 encounters per week in 2019 compared to 60.98 encounters per week in 2020 (p < 0.00). For the MCT teams, average weekly encounters decreased significantly from 110.51 in 2019 to 91.77 in 2020 (p < 0.00; Table 2). For the ED teams, the average weekly encounters did not significantly change between 2019 and 2020. Estimates of the average temperature, the share of weekly inpatient dispositions in each year were insignificantly different.Table 2. Mean numbers of weekly encounters and percentages of encounters that resulted in inpatient disposition (IP).
2019 2020
Mean (SE) Mean (SE) p-value
Weekly encounters, All 81.30 (42.42) 68.98 (36.06) 0.00
Weekly encounters, MCT 110.51 (37.36) 91.77 (31.81) <0.00
Weekly encounters, ED 52.10 (22.33) 46.18 (23.48) 0.06
Average temperature (Fahrenheit) 53.57 (17.68) 54.28 (15.65) 0.67
IP disposition, All 44% 43% 0.57
IP disposition, MCT 38% 40% 0.23
IP disposition, ED 49% 45% 0.14
Observations = 204 week-care team-service location observations per year.
Table 3 presents covariate-adjusted encounter rates, overall and by care team. Overall, BEST encounter rates for the first 10 weeks of 2019 and 2020 were similar. MCT encounters declined by 5% (β = −0.05, 95% CI = [−0.07, −0.04], p < 0.00), whereas ED encounter rates were insignificantly lower. Compared to the prior year, the lockdown phase of 2020 saw total encounters significantly decline by 39% (CI = [−0.51, −0.28], p < 0.00). MCT encounters declined by 40% (CI = [−0.45, −0.35], p < 0.00) and ED encounters declined by 39% (CI = [−0.67, −0.11], p < 0.01). During Phase I reopening compared to the same period in 2019, total encounters were 32% lower (CI = [−0.45, −0.19], p < 0.00), MCT encounters were 30% lower (CI = [−0.43, −0.16], p < 0.00) and ED encounters were 35% lower (CI = [−0.53, −0.16], p < 0.01). During Phase II and Phase III encounter volumes were not significantly lower than the comparable periods in 2019 for either ED or MCT teams.Table 3. Regression analysis – change in encounters between 2019 and 2020.
All encounters MCT encounters ED encounters
β/95% CI β/95% CI β/95% CI
Pre-lockdown −0.04 −0.05*** −0.03
[−0.11,0.030] [−0.07,−0.04] [−0.15,0.09]
Lockdown −0.39*** −0.40*** −0.39**
[−0.51,−0.28] [−0.45,−0.35] [−0.67,−0.11]
Phase I reopening −0.32*** −0.30*** −0.35***
[−0.45,−0.19] [−0.43,−0.16] [−0.54,−0.16]
Phase II reopening −0.13* −0.06 −0.20
[−0.24,−0.02] [−0.16,0.03] [−0.43,0.04]
Phase III reopening −0.06* −0.04 −0.09
[−0.12,−0.00] [−0.17,0.08] [−0.20,0.02]
Change log of weekly encounters with 95% confidence intervals in brackets. Covariates include: fixed effects by service location and week and average temperature in the metropolitan statistical area. Statistical significance: *p < 0.05, **p < 0.01, ***p < 0.001.
Table 4 shows that, after adjusting for covariates, during lockdown the proportion of MCT encounters that ended in an inpatient disposition increased by 16% (CI = [0.11, 0.21], p < 0.00) and the proportion of ED encounters decreased by 13% (CI = [−0.20, −0.07], p < 0.00) compared to the same period in 2019. During Phase II reopening, MCT inpatient dispositions increased by 9% (CI = [0.08, 0.10], p < 0.00) over 2019. No other changes in inpatient dispositions during reopening were significantly different.Table 4. Regression analysis – Change in inpatient disposition.
All encounters MCT encounters ED encounters
β/95% CI β/95% CI β/95% CI
Pre-lockdown −0.010*** −0.08*** −0.11***
[−0.14,−0.06] [−0.20,−0.07] [−0.18,−0.05]
Lockdown 0.01 0.16*** −0.13***
[−0.11,0.14] [0.11,0.21] [−0.19,-0.07]
Phase I reopening 0.07 0.104 0.04
[−0.04,0.19] [−0.04,0.25] [−0.15,0.23]
Phase II reopening 0.02 0.09*** −0.05
[−0.05,0.10] [0.08,0.10] [−0.15,0.06]
Phase III reopening −0.01 0.00 −0.03
[−0.11,0.09] [−0.06,0.07] [−0.16,0.10]
Change in the proportion of weekly encounters that resulted in inpatient admissions, with 95% confidence intervals in brackets. Covariates include: fixed effects by service location and week, and average temperature in the metropolitan statistical area. Statistical significance: *p < 0.05, **p < 0.01, ***p < 0.001.
Discussion
To the best of our knowledge, this is the first paper to describe trends in US PES volume and acuity across different care settings during the COVID-19 pandemic’s first 6 months, in a predominantly publicly insured and uninsured population. The initial lockdown and first reopening phases were associated with significantly lower rates of encounters than the prior year. PES volume increased through Phase I and returned to 2019 levels in Phase II. The proportion of encounters that led to inpatient psychiatric admission significantly increased for MCT in the lockdown phase and Phase II and declined for EDs in lockdown.
The mission of PES is to supply timely, high quality, specialized psychiatric care to patients who would otherwise be seen by general practitioners in emergency room settings. The public health response to COVID-19 temporarily disrupted these efforts. As occurred in Europe, the initial lockdown phases of the COVID-19 pandemic significantly decreased use of PES.
Typically, emergency services become the source of care when other services are unavailable. This is not what happened in Massachusetts during March through July. During the lockdown and phased reopening, patients had no access, limited access or remote-only access to outpatient psychiatrists, therapists, day programs, partial hospital programs, support groups, drop-in centers, community centers, libraries and retail businesses. They did not, however, have an equivalent increase in use of PES. Domestic and international studies of local psychiatric care during COVID-19 may need to estimate pandemic-related shortages in service availability. Such information may be invaluable to future efforts to continue psychiatric care during a local health shock.
The increase in inpatient admissions following an MCT encounter suggests that patients treated by MCTs may have had higher acuity during the pandemic than the year prior. Another explanation is the introduction of telehealth. Prior to the COVID-19 pandemic, BEST PES services were provided in person when a patient or patient advocate reached out. The BEST MCTs introduced telehealth visit at the beginning of the lockdown as a safer option for accessing care. Telemedicine has been used previously for non-routine care, with the provider reaching out to the patient with instructions on how to access care. In contrast, emergency telemedicine services require the patient or patient advocate to reach out to the provider for those services. The decrease in overall MCT admissions suggests that many potential patients or referral sources (such as schools and families) may not have been aware that emergency telehealth was available. The increase in inpatient admissions during the early pandemic could reflect greater underlying need or, alternatively, telehealth evaluations may have made assessment of acuity more difficult and MCTs could have erred towards safety.
One universal concern in health care systems during the pandemic was that patients would delay care until it was an emergency. Increased acuity in psychiatric visits reported nationally would suggest this fear was well founded.19 In Massachusetts, this concern was confirmed by the drop in total volume during the lockdown and increased rate of inpatient admissions for urgent care centers during the lockdown. Patients with serious mental illness are at higher risk of adverse social determinants of health and faced higher rates of unemployment, housing insecurity and food insecurity during the pandemic.33 Emergency services often act as the last safety net for patients – providing safe shelter, food, and 24-h medical care. The adverse effects on social determinants of health for patients with serious mental illness would predict an increase in PES volume if patient access to care was not hindered by pandemic-related factors. The drop in PES utilization during the pandemic is of concern because it may also reflect a worsening deficiency in meeting basic needs.
Limitations
This study has three main limitations. First, it only examined one PES program that serves primarily publicly insured or uninsured individuals. These findings therefore may not generalize to other programs and location. Given that patients of color, a population heavily represented in BEST, have been disproportionately affected by the pandemic, it may be particularly important to understand how the pandemic changed health care utilization by this population.34,35 At the time of this analysis, patient-level diagnostic and demographic information about this population during the pandemic was unavailable, precluding person-level trend analysis.
Second, in response to public health messaging about lockdown, patients may have been afraid to leave the house or shelter for fear of exposure to COVID-19. It is difficult to measure this factor because it is difficult to gauge how lockdown restrictions were advertised and enforced. To address this limitation, we did assess how increases in new COVID-19 cases were associated with the weekly rate of BEST encounters (Figure 1), as a proxy for the relative burden of COVID-19 at any given time.
Third, there is no weekly data available on how the COVID-19 pandemic affected availability of inpatient and 24-h beds, and different outpatient levels of care. As such, this study could not consider those issues.
Conclusions
This study raises a series of questions about the reaction of PES systems to disasters such as the COVID-19 pandemic. The disproportionate effect of the pandemic on the health of communities of color makes investigation of trends, diagnoses, and emergency psychiatry evaluations by racial/ethnic group vital to understanding the resilience of PES. The development of telemedical services created a series of unexpected innovations in care, namely, a PES structure for handling telemedicine patients throughout the BEST system and new forms of billing by insurance to support telemedicine. Local health care organizations remain committed to reducing ED boarding. However, increases in inpatient admissions from BEST and COVID-19 restrictions on inpatient units may have affected ED boarding times for patients, leading to lower quality of care and a hidden burden on ED personnel. To address such questions, quantitative analysis of PES data and qualitative studies involving policy makers, patients and front-line providers are needed.
An intervention that could be supported by this study is better messaging to the community about the availability of telehealth for emergency evaluations. To address the concern that many patients may have had limited access to telehealth for both routine and emergency care, health care systems and municipalities should explore options for public access to phones or computers that can be sanitized and used with appropriate privacy.
ORCID iD
Alison Duncan https://orcid.org/0000-0002-7809-1454
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
Ethics approval: The Authors declare that all the research meets the ethical guidelines.
==== Refs
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| 36475326 | PMC9732494 | NO-CC CODE | 2022-12-14 23:35:51 | no | J Health Serv Res Policy. 2022 Dec 6;:13558196221135119 | utf-8 | J Health Serv Res Policy | 2,022 | 10.1177/13558196221135119 | oa_other |
==== Front
Eur Stroke J
Eur Stroke J
ESO
speso
European Stroke Journal
2396-9873
2396-9881
SAGE Publications Sage UK: London, England
10.1177/23969873221139695
10.1177_23969873221139695
Original Research Article
Quality in stroke care during the early phases of the COVID-19 pandemic: A nationwide study
https://orcid.org/0000-0002-4846-9516
Blauenfeldt Rolf A 12
Hedegaard Jakob N 3
https://orcid.org/0000-0002-4210-0523
Kruuse Christina 4
Gaist David 56
Wienecke Troels 7
Modrau Boris 8
Damgaard Dorte 12
Johnsen Søren P 3
Andersen Grethe 12
Simonsen Claus Z 12
1 Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
2 Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
3 Danish Center for Clinical Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
4 Department of Neurology, Copenhagen University Hospital-Herlev Gentofte, Copenhagen, Denmark
5 Research Unit for Neurology, Odense University Hospital, Odense, Denmark
6 University of Southern Denmark, Odense, Denmark
7 Department of Neurology, Zealand University Hospital, Roskilde, Denmark
8 Department of Neurology, Aalborg University Hospital, Aalborg, Denmark
Rolf A Blauenfeldt Department of Neurology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, Aarhus 8200, Denmark. Email: [email protected]
7 12 2022
7 12 2022
239698732211396954 10 2022
1 11 2022
© European Stroke Organisation 2022
2022
European Stroke Organisation
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.
Introduction:
Evidence-based early stroke care as reflected by fulfillment of process performance measures, is strongly related to better patient outcomes after stroke and transient ischemic attack (TIA). Detailed data on the resilience of stroke care services during the COVID-19 pandemic are limited. We aimed to examine the quality of early stroke care at Danish hospitals during the early phases of the COVID-19 pandemic.
Materials and methods:
We extracted data from Danish national health registries in five time periods (11 March, 2020–27 January, 2021) and compared these to a baseline pre-pandemic period (13 March, 2019–10 March, 2020). Quality of early stroke care was assessed as fulfilment of individual process performance measures and as a composite measure (opportunity-based score).
Results:
A total of 23,054 patients were admitted with stroke and 8153 with a TIA diagnosis in the entire period. On a national level, the opportunity-based score (95% confidence interval [CI]) at baseline for ischemic patients was 81.1% (80.8–81.4), for intracerebral hemorrhage (ICH) 85.5% (84.3–86.6), and for TIA 96.0% (95.3–96.1). An increase of 1.1% (0.1–2.2) and 1.5% (0.3–2.7) in the opportunity-based score was observed during the first national lockdown period for AIS and TIA followed by a decline of −1.3% (−2.2 to −0.4) in the gradual reopening phase for AIS indicators. We found a significant negative association between regional incidence rates and quality-of-care in ischemic stroke patients implying that quality decreases when admission rates increase.
Conclusion:
The quality of acute stroke/TIA care in Denmark remained high during the early phases of the pandemic and only minor fluctuations occurred.
Graphical abstract
COVID-19
stroke
transient ischemic attack
incidence
quality
Lundbeckfonden https://doi.org/10.13039/501100003554 349-2020-907 edited-statecorrected-proof
typesetterts1
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pmcBackground
Since COVID-19 was declared a pandemic in March 2020 evidence suggests substantial changes in access to and quality-of-care for other major serious and life-threatening conditions, such as stroke.1,2
Stroke center admission and multidisciplinary investigations and treatment are the hallmarks of modern stroke care. Fulfillment of quality performance measures is associated with lower stroke recurrence, lower mortality, and improved functional outcome.3 Therefore, monitoring quality-of-care using performance measures is an important part of health-care. In Denmark, it is mandatory for all stroke units providing acute stroke care to monitor the quality of the provided early care by reporting to a national clinical quality database, the Danish Stroke Registry (DSR).4 Studies on quality of stroke care during the pandemic have been sparse and have mainly focused on changes in reperfusion therapy rates.5–7 However, in a global perspective, treatment with intravenous thrombolysis and thrombectomy are only used in 7.3% and 1.9% of cases with ischemic stroke, respectively.8 Information about the quality of other key elements of early stroke care during the pandemic is limited and may provide insight on the resilience of every link in the “stroke chain” and help improve stroke care in the future.
We undertook a nationwide study where we examined quality-of-care for patients with acute stroke and transient ischemic attack (TIA) as reflected by care performance measures before and at different time periods during the early stages of the pandemic.
Methods
Setting and study population
In Denmark, there is equal, unrestricted, and tax-funded access to acute care. All acute stroke and TIA patients are evaluated at public hospitals and data on each event is reported to the Danish Stroke Registry (DSR). The DSR contains structured data that is collected prospectively and nationwide. It is estimated that more than 80% of all acute strokes are hospitalized at stroke units and the sensitivity and positive predictive value of registration in the DSR at the stroke units has been found to be >90%.9
We included acute ischemic stroke (AIS), intracerebral hemorrhage (ICH), as well as TIA events. Events with missing information on residency (healthcare region) were excluded.
Stroke care performance measures
The stroke care performance measures cover acute treatment (admission to stroke unit <24 h from onset, scan <6 h from admission, revascularization offered, timing of thrombolysis, and groin puncture in case of thrombectomy), prophylactic treatment, and the timing thereof (start of antiplatelet within the second day of admission, start of oral anticoagulant <14 days for patients eligible), carotid vessel imaging, and early rehabilitation (physio- and occupational therapy evaluation, out-of-bed orders, nutrition, and swallow screening). The indicators were reported as an opportunity-based score which was calculated as the number of indicators fulfilled for a patient divided by the number of indicators estimated to be relevant for that patient.10 In the case of missing single indicators, the opportunity based score was calculated as all relevant available and fulfilled indicators divided by all relevant available indicators. In Table 2, the indicators relevant for the subgroups are listed.
The presented performance measures were used by the DSR to monitor quality of Danish stroke and TIA treatment.
The quality indicators were evaluated in different time periods during the pandemic: “Baseline” was defined as 13 March, 2019–10 March, 2020 (the year prior to the lockdown.) “first national lockdown” was 11 March–15 April, 2020. “Gradual reopening” 16 April–8 June, 2020. “Few restrictions” 9 June–30 September, 2020. “Regional lockdown” 1 October–15 December, 2020. And finally, “second national lockdown” was 16 December, 2020–27 January, 2021. The first vaccine arrived in Denmark on 27th of December 2020 and did not affect admission numbers for the period studied.11
Stroke severity was measured by the Scandinavian Stroke Scale (SSS). A mild stroke was defined as an SSS score between 45–58, moderate stroke 30–44, severe stroke as 15–29, and very severe stroke as 0–14.12
Statistical analyses
We first compared opportunity-based scores in five time periods during the early phases of the pandemic to the baseline pre-pandemic period on a national level and stratified by diagnosis. The same analyses were then performed for each healthcare region. The Danish national health care system is divided into five regions. We extracted data for the five different regions to examine if fulfillment of quality indicators differed between regions. To account for an effect of incidence rates on quality-of-care in each region, we calculated (1) an aggregated opportunity-based scores for each healthcare region and (2) logarithmic transformed regional incidence rates. We then used random effects meta-regression analysis to investigate whether quality-of-care was affected by regional incidence rates. Regional incidence rates were calculated as number of cases in each region during the interrogated time period divided by person-time experienced for all people in Denmark in the time period. The incidence rate was measured as rate of cases per 1000 person-years. To make comparison between the five regions possible we adjusted for the regional effects by adding “region” as a covariate in the meta regression. To visualize the association graphically, we centered the regional opportunity-based scores and the log-transformed regional incidence rates and used these variables as the axes in a bubble plot with a regression line obtained from meta regression of the two variables and intercept. Logarithmic transformation of data was performed as we aimed to investigate relative differences between stroke units with different size and admission rates.
Data from OurWorldinData.org was used for national COVID-19 hospitalization rates in different countries during the pandemic (Supplemental Figure 2).13
Data are reported by mean (95% confidence interval [CI]), median (interquartile range [IQR]), numbers, and percentages, as appropriate. The opportunity-based score is reported as means with 95% CI. All analyses were conducted using Stata version 16 (StataCorp LLC).
Research ethical approval is not required for register-based studies in Denmark. Upon approval from the Danish Data Protection Agency, pseudonymized data can be accessed through the Danish Health Data Authority and Statistics Denmark for researchers at authorized institutions.
Results
In the study period, a total of 31,499 stroke and TIA events were registered, corresponding to 23,054 cases of AIS or ICH, and 8153 cases of TIA (Figure 1). The mean age of the patients were 74.1 years and 56% were male (Table 1). Type of stroke, stroke severity, and prevalence of comorbidities (diabetes, hypertension, atrial fibrillation, prior myocardial infarction, and peripheral arterial disease) expressed as proportions was unchanged during the study period (Table 1). The number of COVID-19 hospitalizations per million inhabitants in Denmark compared to Australia, Norway, USA, and Italy during the period March 1, 2020 to January 27, 2021 is visualized in Supplement Figure 1.13
Figure 1. Study flowchart describing included events.
TIA: transient ischemic attack.
Stroke with-out specification (n = 292) is considered an ischemic stroke.
Table 1. Study population characteristics, stratified by period.
Baseline First national lockdown Gradual reopening Few restrictions Regional lockdown Second national lockdown
N = 16,519 N = 1475 N = 2585 N = 5485 N = 3580 N = 1855
Age, median (IQR) 74.1 (64.5–81.7) 73.8 (65.2–81.2) 74.3 (64.7–81.4) 74 (64.1–81.1) 74.4 (65.3–81.6) 75.1 (65.8–82.4)
Male, % (n) 55.8 (9212) 56.3 (830) 56.6 (1463) 56.1 (3079) 55.5 (1987) 57.8 (1072)
Very severe stroke, % (n) 5.3 (852) 6.1 (89) 4.3 (109) 5.0 (266) 5.1 (178) 5.1 (92)
Severe stroke, % (n) 6.6 (1063) 6.9 (100) 6.2 (157) 5.8 (309) 6.9 (240) 6.6 (121)
Moderate stroke, % (n) 16.0 (2593) 16.5 (239) 14.1 (357) 15.7 (842) 14.8 (519) 17.0 (309)
Mild stroke, % (n) 72.1 (11655) 70.4 (1020) 75.3 (1903) 73.6 (3952) 73.2 (2558) 71.3 (1298)
SSS score, median (IQR) 53 (43–58) 52.5 (42–58) 54 (45–58) 54 (44–58) 54 (43–58) 53 (43–58)
AIS, % (n) 64.6 (10672) 63.9 (943) 65.4 (1691) 65.7 (3605) 63.0 (2254) 65.7 (1219)
ICH, % (n) 8.5 (1397) 10.0 (147) 7.5 (193) 7.7 (422) 10.1 (362) 8.0 (149)
TIA, % (n) 25.9 (4279) 25.3 (373) 26.2 (676) 25.6 (1405) 26.3 (941) 25.8 (479)
Diabetes (known/newly diagnosed), % (n) 15.8 (2556) 17.2 (249) 15.8 (401) 16.2 (867) 15.7 (553) 17.4 (319)
Hypertension (known/newly diagnosed), % (n) 58.7 (9659) 60.7 (892) 58.1 (1498) 58.7 (3203) 59.8 (2133) 59.7 (1102)
Atrial fibrillation (known/newly diagnosed), % (n) 18.2 (3008) 18.4 (271) 18.5 (478) 17.6 (959) 19.2 (682) 18.5 (343)
Acute myopcardial infarction (known/newly diagnosed), % (n) 7.2 (1183) 5.9 (87) 7.6 (196) 7.2 (392) 6.5 (232) 6.7 (123)
Peripheral artery disease (known/newly diagnosed), % (n) 4.5 (731) 3.3 (48) 4.3 (108) 4.4 (236) 4.2 (146) 4.9 (90)
SSS: Scandinavian Stroke Scale.
Process performance measures for AIS, ICH, and TIA are listed in Table 2. On a national level, the opportunity-based score (95% CI) at baseline for ischemic stroke patients was 81.1% (CI: 80.8–81.4), for ICH 85.5% (84.3%–86.6%), and for TIA 96.0% (95.3%–96.12%; Table 3). A slight increase of 1.1% (CI: 0.1%–2.2%) and 1.5% (0.3%–2.7%) in the opportunity-based score was observed during the first national lockdown period for AIS and TIA compared to the baseline period. This was followed by a decline of −1.3% (−2.2% to −0.4%) for AIS indicators in the gradual reopening phase and −1.1% (−2.1% to −0.1%) during the second national lockdown. (Table 3). Regional performance measures in different time periods during the pandemic compared to the baseline period demonstrated only minor changes and followed the national trend (Supplemental Table 2).
Table 2. Quality-of-care indicators for acute ischemic stroke (AIS), intracerebral hemorrhage (ICH), and transient ischemic attack (TIA) in the Danish stroke registry.
AIS TIA ICH
Reperfusion therapy X
IVT treatment X
EVT treatment X
Door to needle time <45 min (IVT) X
Door to groin puncture <180 min (EVT) X
Admission to stroke unit admission <24 h X X
Platelet inhibitors (AIS, TIA w/o AFIB) <48 h X X
Anticoagulation <14 days (AIS, TIA + AFIB) X X
CT/MRI <6 h from admission X X X
Physiotherapy assessment <48 h of admission X X
Occupational therapy assessment <48 h admission X X
Out of bed orders on day of admission X X
Nutritional screening <48 h X X
Swallowing screen (indirect) on day of admission X X
Swallowing screen (direct) on day of admission X X
Carotid vessel imaging <96 h (AIS)/48 h (TIA) of admission X X
Carotid surgery (AIS, TIA w/symptomatic stenosis) <14 days of admission X X
AFIB: atrial fibrillation; AIS: acute ischemic stroke; EVT: endovascular therapy; IVT: intravenous thrombolysis; TIA: transient ischemic attack.
Table 3. National opportunity score on quality of stroke and TIA care indicators stratified by period and compared baseline (pre-pandemic period).
Baseline (Ref), % First national lockdown, change in % p-Value Gradual reopening, change in % p-Value Few restrictions, change in % p-Value Regional lockdown, change in % p-Value Second national lockdown, change in % p-Value
AIS 81.11 (80.79–81.43) 1.14 (0.11 to 2.18) 0.030 −1.31 (−2.20 to −0.41) 0.004 −0.45 (−1.07 to 0.18) 0.159 −0.18 (−0.92 to 0.56) 0.640 −1.09 (−2.09 to −0.10) 0.030
TIA 95.73 (95.33–96.12) 1.46 (0.28 to 2.65) 0.016 −0.05 (−1.07 to 0.96) 0.919 0.16 (−0.59 to 0.90) 0.675 −0.19 (−1.12 to 0.73) 0.682 0.10 (−1.14 to 1.33) 0.879
ICH 85.46 (84.29–86.64) 1.62 (−2.04 to 5.28) 0.386 0.73 (−2.53 to 3.98) 0.661 −2.15 (−4.69 to 0.39) 0.097 −1.33 (−4.02 to 1.36) 0.331 −0.89 (−4.66 to 2.89) 0.645
Significant changes (p < 0.05) are marked in bold.
Regional changes in opportunity-based scores are visualized in Figure 2.
Figure 2. Margins-plot on process performance measures for acute ischemic stroke, transient ischemic attack, and intracerebral hemorrhage (opportunity score) by health care region.
All process performance measures for AIS patients before and during the pandemic are available in Supplemental Table 1. Performance measures stratified by whether patients with ischemic stroke were admitted directly to a stroke unit or not are available in Supplemental Table 3. Overall, quality-of-care were lower in patients not directly admitted to a stroke unit.
The proportion of patients with at least one missing process performance indicator for acute ischemic stroke (0%–2%) was low in all healthcare regions (Supplemental Figure 2).
To account for an effect of different regional incidence rates of quality of stroke care, we calculated an aggregated opportunity-based scores per healthcare region and used logarithmic transformed regional incidence rates. We found a significant negative association between regional incidence rates and opportunity-based scores in patients with AIS implying that quality decreases when admission rates increase (Figure 3), Coef. = −15.3% (−22.8 to −7.8), p < 0.001. No significant effect of admission rates on the opportunity score were found for ICH and TIA patients (data not shown).
Figure 3. Association of opportunity scores to regional incidence rates (log) for ischemic stroke.
Bubble-plot. Centered opportunity scores and centered logarithmically transformed incidence rates stratified by region and time period are plotted against each other with a regression line obtained from fixed-effects meta regression. The sizes of the circles reflect the number of observations, with larger circles corresponding to greater precision of the estimates and greater weight in the analysis.
Discussion
We found that the quality of acute stroke (AIS, ICH) and TIA care in Denmark during the start of the pandemic remained high with only minor fluctuations. During the first national lockdown a slight increase in stroke care quality was observed. This was followed by a decline during reopening phase and second national lockdown, however, the magnitudes of all changes were minimal. The observed change in acute stroke care quality and regional differences may be explained by increased regional incidence rates/admission rates of stroke following the first national lockdown. Our study provides in depth details on fulfillment of different acute stroke and TIA care quality indicators during the pandemic.
Early in the pandemic, direct and indirect consequences on the quality of global stroke care were observed, as lower stroke admission rates, increased prehospital delay, and declining rates of reperfusion therapy were observed.14 In one of the first published studies, massive decreases in stroke admissions and thrombolysis and thrombectomy rates were reported among 280 surveyed Chinese hospitals in February 2020.15 Later reports on the overall incidence and admission rates for stroke have reported more varied results.7,16–20 In a recent nationwide study we found only relatively small changes of admission rates for stroke and TIA with a decrease of 7% during the first lockdown and an increase of 5%–7% in the following periods of the pandemic.11 Prehospital and in-hospital workflow metrics have been reported to be affected during the pandemic in previous studies, resulting in decreased utilization of reperfusion treatment.5–7 In this study, 24.6% received IVT and/or EVT before the pandemic and 21.7%–27.1% during different phases of the pandemic. Only minor changes in in-hospital workflow occurred measured as treatment with IVT within 45 min of arrival (84.1% before and 80.3%–84.8% during) and neuroimaging performed within 6 h of stroke center admission (91.6% before and 90.4%–93.9% during) were observed (Supplemental Table 1). Door to groin puncture and rate of stroke center admission <24 h from onset was also largely unchanged in this study. By comparison, in a study from Australia using a national stroke registry, a decreased stroke unit access as well as fewer stroke patients admitted in stroke center beds was observed. This translated into a decline in quality-of-care, and prolonged door-to-needle times during the first months of the pandemic.21 In a French study, carotid endarterectomy procedures for symptomatic carotid stenosis decreased during the first peak (March to May 2020) of the pandemic with a later increase.22 Finally, a large registry-based cohort study from United Kingdom found preserved stroke quality-of-care measures and improvement in some (direct access to stroke unit care, 1-h brain imaging, and swallow screening) during the lockdown. Only the period before and during the first lockdown (March 23–April 30, 2020) was investigated.23
Stroke care is multidisciplinary and includes assessment of physiotherapy, mobilization, occupational therapy needs as well as nutritional, and dysphagia screening.3 We found only smaller changes on several quality-of-care indicators, without obvious large declines in a single factor. The impact of the pandemic on stroke care is likely to affect multiple areas of care and thus an aggregated opportunity-based score was selected.2,23 Despite some changes being significant in this study, it is important to emphasize that the magnitude of these changes was minimal (e.g. for AIS, −2.2% to −0.4%). These results are line with results from UK during the first lockdown.23 Provision of stroke care has likely been heterogenous in different countries, depending on pandemic control/hospitalization burden, differences in health care systems and pre-pandemic organization of stroke care.24 Further, there may have been regional changes in transportation protocols/triage processes, reallocation of neurology and stroke beds and staff to COVID-19 patients or departments.15 We found regional differences in quality care following the first national lock down which may be a reflection of regional increases in incidence/admission rates and increased patient flow at the stroke wards. A similar picture with increased admission rates following the first lockdown has recently been reported.11,25
From an organizational point of view, stroke care remained unchanged in Denmark and only a small proportion of stroke personnel was reallocated to staff COVID-19 departments. Denmark enacted strict and early government regulations, including stay-at-home orders, and early mass scale up testing. The healthcare system was never overwhelmed and experienced lower COVID-19 hospitalization rates compared to many other countries.26 During the first national lockdown outpatients clinics were closed, freeing healthcare workers to man the wards, which may explain the minor increase in quality-of-care during the first national lockdown.
The main strength of this study is the use of a compulsory, nationwide stroke registry with individual patient data on quality-of-care indicators from all Danish stroke units, both comprehensive and non-comprehensive centers. Further, we were able to perform aggregated quality-of-care scores for each healthcare region and compare it to the regional stroke/TIA incidence rates, highlighting the association of stroke incidence rates, and quality-of-care. There are, however, limitations: Not all stroke patients are admitted to a stroke unit and no stroke care performance measures are available for these. The proportion of patients not admitted to a stroke unit at all and thus not registered in the DSR have been estimated to be 6% in 2020 (6%) and 8% in 2021.27,28 These patients are often elderly nursing home residents for whom there are no treatment or rehabilitation consequences. Quality-of-care were lower in patients not directly admitted to a stroke unit. The quality-of-care indicators for stroke/TIA do not include information on COVID-19 infection status and we do not have information on whether quality-of-care remained high from a patient/relative perspective (support, level of information, and involvement of the relatives).
Conclusion
The quality of acute stroke/TIA care in Denmark remained high during the early stages of the pandemic and only minor fluctuations occurred. During the first national lockdown, a small increase in stroke care performance measures was observed, followed by a decline in the gradual reopening phase and second national lockdown. Denmark experienced lower COVID-19 hospitalization rates compared to many other countries.
Supplemental Material
sj-docx-1-eso-10.1177_23969873221139695 – Supplemental material for Quality in stroke care during the early phases of the COVID-19 pandemic: A nationwide study
Click here for additional data file.
Supplemental material, sj-docx-1-eso-10.1177_23969873221139695 for Quality in stroke care during the early phases of the COVID-19 pandemic: A nationwide study by Rolf A Blauenfeldt, Jakob N Hedegaard, Christina Kruuse, David Gaist, Troels Wienecke, Boris Modrau, Dorte Damgaard, Søren P Johnsen, Grethe Andersen and Claus Z Simonsen in European Stroke Journal
The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: RAB is supported by National Institute of Health (1R01NS112511-01A1). CK is supported from Novo Nordisk Foundation (NNF18OC0031840). DG is supported by Novo Nordisk Foundation (NNF20OC0064637). GA is supported by Novo Nordisk Foundation (NNF18OC0052924, NNF20OC0060998) Trygfoundation (120636), Lundbeck Foundation (349-2020-907), and National Institute of Health (1R01NS112511-01A1). CZS is supported by a research grant from Novo Nordisk Foundation (NNF17OC0029520) and Health Research Foundation of Central Denmark Region. Authors DD, TWS, JNH, BM, and SPJ reports no conflict of interest.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study is supported by a research grant from Lundbeck foundation (349-2020-907).
Ethical approval: Ethical approval is not required for register-based studies in Denmark. Data can be accessed through the Danish Health Data Authority and Statistics Denmark for researchers at authorized institutions.
Guarantor: CZS.
Author contributions: RAB, CZS, GA, and SPJ researched literature and conceived the study. JNH, SPJ, and RAB performed the data analysis, and all authors were involved in critical interpretation of data. RAB wrote the first draft of the manuscript. All authors reviewed and edited the manuscript and approved the final version of the manuscript.
ORCID iDs: Rolf A Blauenfeldt https://orcid.org/0000-0002-4846-9516
Christina Kruuse https://orcid.org/0000-0002-4210-0523
Supplemental material: Supplemental material for this article is available online.
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References
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22 Crespy V Benzenine E Mariet AS , et al Impact of the first COVID-19 pandemic peak and lockdown on the interventional management of carotid artery stenosis in France. J Vasc Surg 2022; 75 (5 ): 1670–1678.e2.
23 Douiri A Muruet W Bhalla A , et al Stroke care in the United Kingdom during the COVID-19 pandemic. Stroke 2021; 52 : 2125–2133.33896223
24 Burns SP Fleming TK Webb SS , et al Stroke recovery during the COVID-19 pandemic: a position paper on recommendations for rehabilitation. Arch Phys Med Rehabil 2022; 103 (9 ): 1874–1882.35533736
25 Drenck N Grundtvig J Christensen T , et al Stroke admissions and revascularization treatments in Denmark during COVID-19. Acta Neurol Scand 2022; 145 (2 ): 160–170.34605006
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| 0 | PMC9732497 | NO-CC CODE | 2022-12-14 23:35:51 | no | Eur Stroke J. 2022 Dec 7;:23969873221139695 | utf-8 | Eur Stroke J | 2,022 | 10.1177/23969873221139695 | oa_other |
==== Front
eBioMedicine
EBioMedicine
eBioMedicine
2352-3964
The Authors. Published by Elsevier B.V.
S2352-3964(22)00583-7
10.1016/j.ebiom.2022.104401
104401
Articles
Identification of an immunogenic epitope and protective antibody against the furin cleavage site of SARS-CoV-2
Li Lili abi
Gao Meiling abi
Li Jie ci
Xie Xuping di
Zhao Hui ei
Wang Yanan fi
Xu Xin ab
Zu Shulong ab
Chen Chunfeng f
Wan Dingyi g
Duan Jing g
Wang Jingfeng abh
Aliyari Saba R. h
Gold Sarah h
Zhang Jicai c
Qin Cheng-Feng e∗∗∗∗
Shi Pei-Yong d∗∗∗
Yang Heng ab∗∗
Cheng Genhong h∗
a Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
b Suzhou Institute of Systems Medicine, Suzhou, China
c Department of Laboratory Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, China
d Institute for Human Infections and Immunity, University of Texas Medical Branch, Galveston, TX, USA
e Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing, China
f Suzhou Func Biotech Inc, Suzhou, China
g AtaGenix Laboratories (Wuhan) Co., Ltd., Wuhan, China
h Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA, USA
∗ Corresponding author.
∗∗ Corresponding author.
∗∗∗ Corresponding author.
∗∗∗∗ Corresponding author.
i L.L., M.G., J.L., X.X., H.Z., and Y.W. share the co-first authorship.
9 12 2022
1 2023
9 12 2022
87 104401104401
26 4 2022
16 11 2022
21 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
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the global coronavirus disease 2019 (COVID-19) pandemic, contains a unique, four amino acid (aa) “PRRA” insertion in the spike (S) protein that creates a transmembrane protease serine 2 (TMPRSS2)/furin cleavage site and enhances viral infectivity. More research into immunogenic epitopes and protective antibodies against this SARS-CoV-2 furin cleavage site is needed.
Methods
Combining computational and experimental methods, we identified and characterized an immunogenic epitope overlapping the furin cleavage site that detects antibodies in COVID-19 patients and elicits strong antibody responses in immunized mice. We also identified a high-affinity monoclonal antibody from COVID-19 patient peripheral blood mononuclear cells; the antibody directly binds the furin cleavage site and protects against SARS-CoV-2 infection in a mouse model.
Findings
The presence of “PRRA” amino acids in the S protein of SARS-CoV-2 not only creates a furin cleavage site but also generates an immunogenic epitope that elicits an antibody response in COVID-19 patients. An antibody against this epitope protected against SARS-CoV-2 infection in mice.
Interpretation
The immunogenic epitope and protective antibody we have identified may augment our strategy in handling COVID-19 epidemic.
Funding
The 10.13039/501100001809 National Natural Science Foundation of China (82102371, 91542201, 81925025, 82073181, and 81802870), the 10.13039/501100019018 Chinese Academy of Medical Sciences Initiative for Innovative Medicine (2021-I2M-1-047 and 2022-I2M-2-004), the Non-profit Central Research Institute Fund of the 10.13039/501100005150 Chinese Academy of Medical Sciences (2020-PT310-006, 2019XK310002, and 2018TX31001), the 10.13039/501100012166 National Key Research and Development Project of China (2020YFC0841700), 10.13039/100000002 US National Institute of Health (NIH) funds grant AI158154, 10.13039/100007185 University of California Los Angeles (UCLA) AI and Charity Treks, and 10.13039/100007185 UCLA DGSOM BSCRC COVID-19 Award Program. H.Y. is supported by 10.13039/501100004608 Natural Science Foundation of Jiangsu Province (BK20211554 andBE2022728).
Keywords
SARS-CoV-2
COVID-19
Immunogenic epitope
Furin site
S protein
==== Body
pmc Research in context
Evidence before this study
SARS-CoV-2 contains a unique, four aa “PRRA” insertion in the S protein, creating a TMPRSS2/furin cleavage site. The furin site of SARS-CoV-2 has been found to enhance viral infectivity,1 to be required for transmission in ferrets,2 and to mediate membrane fusion in either the presence or absence of trypsin.3 However, relevant immunogenic epitopes and human antibody screening against this site are rarely reported.
Added value of this study
By bioinformatic prediction and experimental confirmation, we identified the S672-691 peptide as an immunogenic epitope that can be used for COVID-19 diagnosis with high sensitivity and specificity. Further, mouse immunization experiments proved that the S672-691 peptide stimulated antibody generation in mice, and a monoclonal antibody identified from COVID-19 patient peripheral blood mononuclear cells using an scFv phage display library protected against SARS-CoV-2 infection.
Implications of all the available evidence
Our identification of an immunogenic epitope and protective antibody against the furin cleavage site indicates the ability of our immune system to react to the unique evolution of SARS-CoV-2 and also provides potential applications in the diagnosis and treatment of COVID-19.
Introduction
Since the initial outbreak in December 2019, coronavirus disease 2019 (COVID-19) has quickly developed into a full-blown global pandemic.4, 5, 6 New severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are continuously emerging, posing sustained threats to global health and economic stability.7, 8, 9, 10 COVID-19 patients generate polyclonal antibodies that recognize various epitopes across the SARS-CoV-2 proteome.11 , 12 These polyclonal antibodies can neutralize and provide humoral immunity, whereas CD8 T cells can provide cellular immunity against SARS-CoV-2 infection. Many previous efforts have tried to identify and isolate immunogenic T cell and B cell epitopes using single-cell sequencing, activation-induced marker (AIM) assay, degranulation, proliferation, ELISA, ELISpot, intracellular cytokine staining (ICS), cytotoxicity, and multimer-based assays.13, 14, 15, 16, 17, 18, 19 However, many new epitopes exist among SARS-CoV-2 viral proteins, and which among these are immunogenic and have potential value in COVID-19 treatment requires investigation.
The SARS-CoV-2 viral genome consists of a 29.8 kb, positive-strand RNA molecule encoding proteins including 16 nonstructural proteins (NSPs), 4 structural proteins (Spike (S), Envelope (E), Membrane (M), and Nucleocapsid (N)) and several accessory proteins (ORF3a, ORF6, ORF7a, ORF7b, ORF8, and ORF10).20 , 21 SARS-CoV-2 infects host cells through its S protein, which contains the S1 domain, which binds to cellular angiotensin-converting enzyme 2 (ACE2) receptors, and the S2 domain, which accomplishes membrane fusion.22, 23, 24 As a member of the large coronavirus family that includes Alphacoronavirus (i.e., Human coronavirus 229E, Human coronavirus NL63), Betacoronavirus (SARS-CoV, SARS-CoV-2, Middle East respiratory syndrome-related coronavirus (MERS-CoV), human coronavirus OC43 and HKU1), Gammacoronavirus (Avian coronavirus (IBV)), and Deltacoronavirus (Bulbul coronavirus HKU11 (Bulbul-CoV HKU11)),25 , 26 SARS-CoV-2 has a unique four amino acid (aa) insertion between the S1 and S2 domains of the S protein, which creates a transmembrane protease serine 2 (TMPRSS2)/furin cleavage site and has been shown to increase viral infectivity.1
Most of the neutralization antibodies identified thus far bind to the receptor binding domain (RBD), while a few bind to the amino-terminal domain (NTD) or the S2 domain of the S protein. In this study, we have identified an immunogenic epitope at the furin cleavage site that can specifically and sensitively detect IgM and IgG in COVID-19 patients and can generate strong immune response in immunized mice. Most importantly, we have identified monoclonal antibody targeted to this furin cleavage site in COVID-19 patient's sera and shown to have immuno-protective activity against SARS-CoV-2 infection in a mouse model.
Methods
Viruses, cells, plasmids, and reagents
SARS-CoV-2 virus and SARS-CoV-2 original and Omicron strain pseudovirus with Luciferase coding sequence were described in our previous paper.27 Huh7.5 (RRID: CVCL_U443), HEK293T (RRID: CVCL_4U22), and Vero (RRID: CVCL_0059) cell lines were purchased from American Type Culture Collection (ATCC) and cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 U/mL penicillin and 50 μg/mL streptomycin (37 °C, 5% CO2). Authentication of cell lines (Huh7.5, HEK293T, and Vero) with short tandem repeat DNA profiles were performed every year with Procell Life Science & Technology Co., Ltd. (Wuhan, China). Mycoplasma contamination was tested by PCR with supernatant of cell culture (Beyotime, Cat No.: C0301S), and only cell lines which were negative for mycoplasma testing were used in this study. hACE2 expression plasmid was purchased from GenScript (Cat No.: OHU20260D). hTMPRSS2 expression plasmid was purchased from Neobioscience (Cat No.: puno1-htps2a). 2-(N-Morpholino) ethanesulfonic acid (MES) was purchased from Sigma (Cat No.: S908908), 1-(3-Dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDC) and 3,3′,5,5′-Tetramethylbenzidine (TMB) were purchased from ThermoFisher Scientific (Cat No.: 22981 and 002023). Adjuvant MF59 was prepared according to the protocol described by Ott G.28 S monomer and trimer proteins were purified and provided by AtaGenix Laboratories (Wuhan).
Clinical investigations and data analysis
The clinical information on human serum samples was self-reported by participants and described in our previous work.27 Clinical investigations of patients with SARS-CoV-2 infection and healthy individuals were approved by the Ethics Committee of Taihe hospital (2021XM001), and informed consent was obtained from the patients. All serum samples were collected from patients admitted to Taihe hospital (Shiyan, China) for COVID-19 diagnosis from December 01, 2019 to March 11, 2020. The patients' venous blood was collected and serum samples were obtained by centrifugation. Briefly, 43 COVID-19 patients' samples were collected from the patients with obvious SARS-CoV-2 infection symptoms (high temperature, radiologic evidence of pneumonia, low or normal white-cell or lymphocyte count), and PCR+; 117 COVID-19 suspected patients' samples were collected from patients who were PCR-at the time the sera were collected, but were admitted in hospital because of obvious SARS-CoV-2 infection symptoms like high temperature, radiologic evidence of pneumonia, low or normal white-cell or lymphocyte count, and probable SARS-CoV-2 infection; 46 healthy individuals’ samples were collected from who were PCR- and without SARS-CoV-2 infection symptoms.
The participants’ clinical information (sex, age, whether high temperature and cough, and CT scan results) was collected, statistically compared and analyzed to avoid potential confounders before serum ELISA data was analyzed.
Mouse experiments
The animal study was carried out following the recommendations for the care and use of animals by the Office of Laboratory Animal Welfare, NIH. The Institutional Animal Care and Use Committee (IACUC) of the University of Texas Medical Branch (UTMB) approved the animal studies (2103023). Sample sizes of mice utilized in each experiment are calculated based on the pre-experiment results. The calculation formula is n = 2 (Zα + Z1−β)2δ2/Δ2. Zα (1.96) and Z1−β (0.8416) are constants set by convention according to the accepted α error and power of the study, δ is the standard deviation and Δ is the difference in effect of two interventions which are calculated based on the results of pre-experiments. We referenced the calculation with animal ethnicity and animal welfare considered to evaluate how many mice should be used in each experiment. For R4P1-C2 prophylactic assay, when the mean of three mice per group in pre-experiments is used, the sample size n is 2∗(1.96 + 0.8416)2∗(0.5915)2/(1.12)2 = 4.69 mice/group. So, it would need approximately 5 mice/group for this assay.
Ten 8-week-old C57Bl/6 (For the immunization assay, S672-691 peptide group and control group, n = 5/group) and 10 10 to 12-week-old female Balb/c mice (For R4P1-C2 prophylactic assay, R4P1-C2 antibody group and IgG control group, n = 5/group) were purchased from Charles River Laboratories and maintained in Sealsafe HEPA-filtered air in/out units.
For the immunization assay, C57Bl/6 mice were randomly divided into two groups. Under sterile conditions, 50 μg BSA-conjugated peptide dissolved in 50 μL DNase/RNase-free H2O was mixed with 50 μL adjuvant MF59. Each C57Bl/6 mouse was immunized with 50 μg BSA-conjugated S672-691 peptide or control peptide. Eight days post immunization, 100 μL orbital blood was collected. The sera were used for ELISA and SARS-CoV-2 pseudovirus neutralization assay.
For R4P1-C2 prophylactic assay, Balb/c mice were randomly divided into two groups, and administrated intraperitoneally with antibody R4P1-C2 (1.7 mg/kg) or IgG control. 6 h later, animals were challenged intranasally with 104 PFU of mouse-adapted SARS-CoV-2 (CMA3p20 strain).29 Two days after infection, lung samples of infected mice were harvested and homogenized in 1 mL DPBS using the MagNA Lyser (Roche Diagnostics). The homogenates were clarified by centrifugation at 15,000 rpm for 5 min. The supernatants were collected for measuring infectious virus titers by standard plaque assay.
B-cell epitope prediction and analysis
SARS-CoV-2 sequence data were obtained from NCBI GenBank (NC_045512). The SARS-CoV-2 S protein sequence was extracted based on the whole genome for subsequent analysis. We used Bepipred Linear Epitope Prediction 2.0 method (http://tools.immuneepitope.org/bcell/) at the immune epitope database and analysis resource (IEDB, https://www.iedb.org/home_v3.php) to predict the potential B cell epitopes in SARS-CoV-2 S protein. Based on the computer-guided homology modeling method, the structural models were constructed by SWISS-MODEL online server as described.30 , 31 The model of SARS-CoV-2 S protein was based on PDB ID: 6M0J, and the epitope sequences are marked in PYMOL.32
Viral sequence analysis
We obtained S protein sequences of representative human CoVs, bat SARS-like CoVs, and MERS from NCBI GenBank (The corresponding accession numbers are marked in Fig. 1 d). Then the SARS-CoV-2 S protein sequences were extracted based on the whole genome for subsequent analysis. The multiple sequence alignment was conducted using the MegAlign Pro software in DNASTAR Lasergene package with default parameters.Fig. 1 Prediction and identification of B cell epitopes in the SARS-CoV-2 S protein. (a) Prediction of specific B cell epitope sequences and sites of SARS-CoV-2 S protein. (b) The structure of SARS-CoV-2 S protein (Modelled by SWISS-MODEL). The aa 25–39 region is marked with blue, aa 672–691 is marked with green, aa 764–778 is marked with red, and aa 907–921 is marked with purple. (c) Antibody profile of randomly chosen patients with clinical SARS-CoV-2 infection (2, 3, 7, 17, n = 4) and healthy individuals (H1, H2, n = 2). Each row represents a predicted B cell epitope peptide, each column represents a serum sample. The color intensity of each cell indicates the ELISA OD450. (d) Coronavirus S protein structure and potential furin or TMPRSS2 cleavage sites at the S1/S2 junction. Insertion mutation at aa position 672 of S protein and sequence comparison with human and bat SARS-like CoVs and MERS.
Enzyme-Linked ImmunoSorbent Assay
Enzyme-Linked ImmunoSorbent Assay (ELISA) was described in our previous paper.27 Briefly, we synthesized peptides from GenScript (Nanjing, China) and diluted in MES buffer (0.1 M, PH = 6.0) before adding 10 μL EDC and 50 μL peptide to each well and incubating the plates either at 4 °C overnight, or at room temperature (RT) for more than 2 h. After washing three times and blocking the plates, 100 μL/well diluted sera (1:500) was added to the plates and incubated at 37 °C for 2 h. The plates were washed three times before addition of 100 μL/well diluted secondary antibody (HRP-goat anti human IgG) and incubation at 37 °C for 50 min. These plates were then washed 5 times before addition of 100 μL/well TMB and incubation for 5–10 min with protection from light. The reactions were terminated by adding 50 μL/well 2 M H2SO4 before measuring absorbance at 450 nm with SpectraMax i3 (Molecular Devices) plate reader.
Net OD450 calculation
We used the net OD450 values in statistical analysis of ELISA results and in sensitivity and specificity calculations for receiver operating characteristics (ROC) curves. The net OD450 is equal to OD450 in the experimental samples minus the background OD450, for which serum was added to wells without peptide.
Virus neutralization assay
The neutralizing ability of S672-691 immunized mouse sera were determined in Huh7.5 cells via Luciferase reporter assay. Briefly, serially diluted sera were mixed with SARS-CoV-2 pseudovirus and incubated at 37 °C for 2 h. The mixtures were then added to Huh7.5 cells and incubated for 24 h. Luciferase activity was measured as described previously,33 with the average activity in the control group set as 0, representing no neutralization.
S672-691 peptide inhibition assay
Huh7.5 cells were seeded in 48-well plates and cultured overnight. BSA-conjugated 672–691 peptide was diluted as indicated and added to Huh7.5 cells. Then Huh7.5 cells were incubated for 2 h before infection with SARS-CoV-2 original or Omicron strain pseudovirus. Quantity of pseudovirus was measured at approximately 24 h post infection by Luciferase reporter assay.
In vitro enzyme cleavage assay
Approximately 2 μg of full-length S protein was cleaved in vitro by 0.2 μL furin enzyme in 10 μL reaction system supplemented with PBS, 2 μg BSA, BSA-S25-39, BSA-S672-691, BSA-S672-691 sequence scrambled (S672-691 (S), QTQAARSYTVASRSQSNSPR) peptide or titrated BSA-672-691 peptide as indicated at 25 °C for 1 h. Then, the cleaved S protein was immunoblotted with anti-S2 antibody (MP Biomedicals, Cat No.: S201123, RRID: AB_2920626), which detected both full-length S and cleaved S2.
COVID-19 scFv phage display library construction, biopanning, and purification
COVID-19 scFv phage display library was used in this study as previously described.27 Specific phages against the S672-691 were affinity-enriched by 4 rounds of biopanning which cross-used biotin and BSA-S672-691 peptides as the capture antigens. When analyzing for S672-691 naked peptide-specific binding by phage ELISA, 2 unique positive antibodies were obtained by validated ELISA and sequencing analysis. R3P1-B9 and R4P1-C2 expression plasmids were transformed into cells and the expressed scFv were purified as described.27
Surface plasmon resonance
The binding of R4P1-C2 antibody to S672-691 peptide and S protein under laminar flow was analyzed by surface plasmon resonance (SPR) using a BIAcore T200 system (GE Healthcare). The surface of a carboxymethylated dextran (CM5) sensor chip (GE Healthcare) was activated with 0.4 M 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (ThermoFisher Scientific, Cat No.: E2247) and 0.1 M N-hydroxysuccinimide (ThermoFisher Scientific, Cat No.: 24500). R4P1-C2 antibody was immobilized by amine coupling to one flow cell. All free reactive surface groups were blocked using 1 M ethanolamine (Merck, Cat No.: 398136). Different concentrations of antigen in HBS buffer containing 0.005% Tween-20 were injected over the flow cells at 30 μL/min (contact time, 2 min). After each injection, any bound protein was stripped with 10 mM glycine (15 s). Data analysis was performed using the BIAcore T200 evaluation software 3.1 (GE Healthcare).
Quantitative reverse transcription PCR
Vero cells were treated for 1 h with either ddH2O (vehicle control) or 1 μg/mL BSA-conjugated S672-691, and then infected with 100 μL TCID50 SARS-CoV-2. The SARS-CoV-2 RNA copies in supernatant were measured by quantitative reverse transcription-PCR (qRT-PCR).
Statistics
Statistical analysis was performed with GraphPad Prism 8 software or R Studio version 3.6.3. For serum samples collected from individuals with normal distribution (Whether the data followed a normal distribution or not was analyzed by Normality and Lognormality analysis with D'Agostino & Pearson test method). Before the unpaired and paired Student's t test for continuous variables were used to compare IgM/IgG levels among different peptides or human serum samples, the demographics of healthy individuals, COVID-19 suspected and COVID-19 patients were statistically compared with Chi-square test or t-test, and the correlation of every confounder (age, sex, whether high temperature and cough, and CT scan results) and IgG/IgM levels against S25-39 and S672-691 peptides were respectively analyzed with Pearson correlation analysis to adjust for potential confounders. ELISA data from all the serum samples was used in t-test analysis. For some clinical information was not completely recorded, only recorded data was used in multiple variable analysis. For mice experiments and other detection results without supposed distribution model, Mann–Whitney U test was used for other analysis. The data were presented as mean ± SD. P values were indicated by ns, not significant, ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, and ∗∗∗∗P < 0.0001. ROC curve analysis and their corresponding areas under the curve (AUC) were calculated using the predicted values estimated by supervised machine learning random forest (RF) algorithm models, and AUC were calculated using MedCalc statistic software.
Ethics
The clinical samples used in the study were approved by the Ethics Committee of Taihe hospital and informed written consent was obtained from the patients (2021XM001). The IACUC of the UTMB approved animal studies (2103023).
Role of funders
The funders have no role in study design, data collection, data analyses, interpretation and writing of the manuscript or decision to publish it. Study, writing and review of the manuscript was completed by the authors.
Results
Prediction and identification of B cell epitopes in the SARS-CoV-2 S protein
Among the surface proteins shared by the coronavirus family, the S protein is the most divergent and consequently the best candidate for strain-specific epitope screening. To identify epitopes specific to SARS-CoV-2, we used IEDB prediction to select seven peptides,34 which structural modeling indicated are exposed, from the S protein of SARS-CoV-2 (Fig. 1a and b). Based on the computer-guided homology modeling method, structural models were constructed by SWISS-MODEL online server30 , 31 (Fig. 1b). These peptides were chemically synthesized and used as bait in an ELISA to measure peptide-specific antibody levels in serum samples from healthy donors and COVID-19 patients. We found that the peptide located at the 672–691 aa position of the S protein (S672-691) had the strongest antibody reactivity in sera from COVID-19 patients, suggesting that this peptide contains at least one immunoreactive epitope (Fig. 1c). We then compared the aa sequence of the SARS-CoV-2 S672-691 peptide to the corresponding region of other human coronaviruses. We found that S672-691 peptide of SARS-CoV-2 possessed sequence differing from that of other coronaviruses (Fig. 1d). Interestingly, the S672-691 peptide has a unique, four aa “PRRA” insertion between the S1 and S2 domains of the S protein that does not exist in other coronaviruses. We therefore hypothesized that the S672-691 peptide contained an immunoreactive epitope specific for SARS-CoV-2 that could specifically detect COVID-19 patients.
Antibodies against the S672-691 peptide were generated in both COVID-19 patients and suspected patients
To investigate the antibody profiles of different COVID-19 patients, we used serum samples from COVID-19 patients and suspected patients to measure both the IgM and IgG levels against the S672-691 peptide and a control peptide, S25-39. As shown in Fig. 2 a–f, the IgM and IgG antibody levels specific for the S672-691 peptide were significantly higher than those for the S25-39 peptide and negative control (P < 0.0001). When compared among different individuals, IgG and IgM levels against S672-691 peptide in both COVID-19 patients (SARS-CoV-2 PCR test positive) were much higher than those in healthy controls. We also tested a group of COVID-19 suspected patients who were SARS-CoV-2 PCR test negative but showed COVID-19 like symptoms and/or abnormal lung CT scans at the time of serum sample collection. Interestingly, they also had higher IgG and IgM levels against the S672-691 peptide than those in healthy controls. Our heat map analysis presented similar results (Fig. S1a and b). These results showed that antibodies against the S672-691 peptide were generated in both COVID-19 patients and suspected patients.Fig. 2 Antibodies against the S672-691 peptide were generated in both COVID-19 patients and suspected patients. (a–f) S25-39 and S672-691 epitope-specific IgM/IgG antibody levels were detected by peptide-specific ELISA. Data are shown for serum samples from COVID-19 patients, suspected patients, and healthy individuals (n = 43:119:46) (Paired Student t test, ns, not significant, P < 0.0001 for panel a, c–f, P = 0.000285 for panel b).
S672-691 peptide-based ELISA for COVID-19 diagnosis showed high sensitivity and specificity
To further explore the possibility of using the S672-691 peptide as an immunogenic antigen for the diagnosis of COVID-19 patients, IgM and IgG levels against the S672-691 peptide were compared among COVID-19 patients, suspected patients, and healthy individuals (Table S1). Before serum ELISA data was analyzed, the participants’ clinical information was collected and compared with Chi-square test or t-test to avoid unnecessary variance of samples. Age and sex showed no obvious difference among healthy individuals, COVID-19 patients, and suspected patients. Although whether cough and lung CT scan results showed no difference between COVID-19 patients and suspected patients, more COVID-19 patients were high temperatures than COVID-19 suspected patients, which may result from viral replication in COVID-19 patients (Table S2). Pearson correlation analysis was also performed to check the correlation between IgG/IgM levels and potential confounders (sex, age, whether high temperature and cough, and CT scan results). The results suggested that age and sex did not correlate with IgG and IgM levels against S25-39 or S672-691 peptides among healthy individuals, COVID-19 patients and suspected patients, and whether high temperature and cough and lung CT scan results also did not correlate with IgG and IgM levels between COVID-19 patients and suspected patients (Table S3). These results showed that the above demographics do not interfere with the serum ELISA detection results.
Presence of a furin site in the S protein is not uncommon in human coronaviruses; about half of human seasonal coronaviruses as well as MERS-CoV contain a furin site. A furin site is also present in H5N1 avian influenza virus. The healthy individuals in Fig. 2a and b were not infected by SAR-CoV-2, but they may have been infected by some other virus which also contained a furin site, resulting in the relatively higher IgG and IgM level against the S672-691 peptide than the S25-39 peptide or a negative control. However, when compared with COVID-19 patients and suspected patients, the IgG and IgM levels against the S672-691 peptide in healthy individuals were significantly lower (Fig. 3 a and b, P < 0.0001). The Fig. 3c and d represent the ROC curves for measuring both the sensitivity and specificity of the S672-691 peptide. The AUC for the S672-691 peptide-specific IgM was 0.918 and 0.8198 among COVID-19 patients and suspected patients, respectively. More strikingly, the AUC for the S672-691 peptide specific IgG was 0.9855 and 0.9588 among COVID-19 patients and suspected patients, respectively (Fig. 3c and d). These results suggest a possible use of the levels of antibodies (particularly IgG) specific for the S672-691 peptide as biomarkers for COVID-19 diagnosis.Fig. 3 S672-691 peptide-based ELISA for COVID-19 diagnosis showed high sensitivity and specificity. (a and b) Scatter plot of S672-691 peptide IgG/IgM OD450 readings detected in COVID-19 patients, COVID-19 suspected patients, and healthy individuals (n = 43:119:46) (Unpaired Student t test, P < 0.0001). (c and d) ROC curves of (a) and (b).
S672-691 peptide and immunized mouse sera inhibit SARS-CoV-2 infection
To further determine the function of the S672-691 peptide in SARS-CoV-2 infection, we added BSA-conjugated synthetic S672-691 peptide to cultured cells and found that the S672-691 peptide significantly blocked the original (P = 0.0002, P < 0.0001) and Omicron (P < 0.0001) strain SARS-CoV-2 pseudovirus infection and SARS-CoV-2 RNA replication (Fig. 4 a and b, P = 0.0039). More importantly, when hTMPRSS2 was co-transfected with hACE2 into HEK293T cells, it strongly enhanced SARS-CoV-2 pseudovirus infection, which could be inhibited in turn by the S672-691 peptide (Fig. 4c, P = 0.0035). Overall, these results indicate that the synthetic S672-691 peptide inhibited SARS-CoV-2 infection.Fig. 4 S672-691 peptide and immunized mouse sera inhibit SARS-CoV-2 infection. (a) The infection of original (left) and Omicron (right) strains of SARS-CoV-2 pseudovirus in control (1 μg/mL) and BSA-S672-691 peptide treated Huh7.5 cells was analyzed by Luciferase reporter assay (P = 0.0002 and P < 0.0001). (b) The replication of SARS-CoV-2 in control (2 μg/mL) and BSA-conjugated S672-691 peptide (2 μg/mL) treated Vero was analyzed by qRT-PCR (P = 0.0039). (c) HEK293T cells were transfected with ACE2 and TMPRSS2 plasmids as indicated. Transfected cells were pre-treated with BSA-conjugated S672-691 peptide (2 μg/mL) before SARS-CoV-2 pseudovirus infection. The infection of SARS-CoV-2 pseudovirus was analyzed by Luciferase reporter assay (P = 0.0035). (d) S672-691 specific IgG levels were analyzed in S672-691, and control peptide immunized mice sera (n = 5:5). (e) The SARS-CoV-2 pseudovirus neutralization activities of S672-691 immunized mouse sera were analyzed by Luciferase reporter assay (n = 5:5). (f) Diagram of S672-691 deletion mutations. The red arrow indicates the furin cleavage site. (g and h) Specific antibodies against S672-691 and its deletion mutations in S672-691 immunized mouse sera were detected by ELISA. Data are shown in both heatmap (g) and scatter plot (h) (ns, not significant, P < 0.0001). (i and j) Specific antibodies against S672-691 and its deletion mutations were detected by ELISA in COVID-19 patients' sera. Data are shown in both heatmap (i) and scatter plot (j) (ns, not significant, P < 0.0001). (k) Full-length S protein was cleaved by furin enzyme in a reaction system supplemented with PBS, BSA, BSA-S25-39, BSA-S672-691 or BSA-S672-691 sequence scrambled (S672-691 (S), QTQAARSYTVASRSQSNSPR) peptide (2 μg) at 25 °C for 1 h. The cleaved S protein was detected by immunoblotting with anti-S2 antibody (left panel), and the immunoblot result was quantitated and normalized (right panel, P = 0.0035 and P = 0.0184). (l) Full-length S protein was cleaved by furin enzyme in reaction system supplemented with titrated BSA-S672-691 peptide at 25 °C for 1 h. The cleaved S protein was detected by immunoblotting with S2 antibody (left panel), and the immunoblot result was quantitated and normalized (right panel, ns, not significant, P = 0.043 and P = 0.0066). The statistical analysis was performed with Mann–Whitney U test, and P values were indicated. Data represent the cumulative results from two or three independent experiments.
To further determine if antibodies binding to the S672-691 peptide have a neutralization effect against SARS-CoV-2 infection, we immunized mice with the S672-691 peptide in the presence of MF59 adjuvant. We found high levels of S672-691 specific antibody presented in the sera of immunized mice, which again indicated that the S672-691 peptide contains an immunogenic epitope (Fig. 4d). To test neutralization activity against SARS-CoV-2, serum samples from the immunized mice were used for neutralizing pseudovirus. As shown in Fig. 4e, sera from independently immunized mice, but not control peptide immunized mice, inhibited the infection of SARS-CoV-2 pseudovirus at IC50 = 10−2.55 (Sera inhibited SARS-CoV-2 infection by half at the dilution of 102.55). These results suggest that antibodies binding SARS-CoV-2 outside the RBD also had antiviral activity. The S672-691 peptide contains a unique four aa “PRRA” insertion, which creates a potential TMPRSS2 and/or furin cleavage site. In order to further understand the mechanism responsible for the humoral immunity against this region, we generated deletion mutants to map the antibody recognition epitope within the S672-691 peptide (Fig. 4f). The S681-691 peptide, but not the S672-691 peptide with deleted PRRA sequence (ΔPRRA), strongly bound to antibodies in sera both from mice immunized with the S672-691 peptide (Fig. 4g and h, P < 0.0001) and from multiple COVID-19 patients (Fig. 4i and j, P < 0.0001). To prove the direct furin enzyme competition activity of the S672-691 peptide, we performed an in vitro S protein cleavage assay and found that the S672-691 peptide modestly but significantly inhibited S protein cleavage mediated by furin (Fig. 4k and l, P = 0.0035 and P = 0.0184 for panel k, P = 0.043 and P = 0.0066 for panel l). These data suggest that the immunogenic epitope of the S672-691 peptide overlaps with the four aa “PRRA” insertion, unique to SARS-CoV-2, that creates a TMPRSS2/furin cleavage site.
Monoclonal antibody against the S672-691 peptide inhibits SARS-CoV-2 infection
We then used a scFv phage display library with surface expression of Ig heavy and light chain variable region pairs from 15 COVID-19 patients’ peripheral blood mononuclear cell (PBMC) samples to screen for human monoclonal antibodies against the S672-691 peptide (Fig. 5 a). From the library containing 8.7 × 109 scFv phages, we obtained 192 phage clones after four rounds of enrichment for binding to the BSA/biotin-labeled S672-691 peptide. The four strongest clones were identified and two independent clones were revealed by sequencing analysis: R3P1-B9 and R4P1-C2, which represented three clones with identical sequences (Fig. 5b and Table S4). S672-691 peptide-dependent ELISA showed that R4P1-C2 scFv protein fragments were able to bind strongly to BSA-S672-691 peptide, while R3P1-B9 scFv bound weakly to BSA-S672-691 peptide (Fig. 5c). We subsequently cloned the R3P1-B9 and R4P1-C2 scFv cDNA fragments into expression constructs containing the human IgG1 backbone and analyzed the binding of R3P1-B9 and R4P1-C2 IgG1 antibodies with the S672-691 peptides of both SARS-CoV-2 original and Omicron strains. The results showed that R4P1-C2, but not R3P1-B9, bound to the S672-691 peptides from both SARS-CoV-2 original and Omicron strains (Fig. 5d). We further detected the binding affinity with S monomer and trimer proteins, finding that only R4P1-C2 bound well to both S monomer and trimer proteins (Fig. 5e). Surface plasmon resonance (SPR) assay also confirmed binding of the R4P1-C2 antibody to both the S672-691 peptide and S protein (Fig. 5f and g). Phage ELISA assay used S672-691 peptide as bait. The screened R3P1-B9 and R4P1-C2 scFv thus bound to S672-691 peptide. Full-length S protein may display folding structure that disabled the binding of R3P1-B9 scFv with S protein. To observe the role of “PRRA” aa on the binding with R4P1-C2 antibody, S672-691 mutant peptide ELISA was performed. We found that while the S681-691 peptide had weak binding activity, the S672-691 peptide lacking the four aa “PRRA” had no binding activity to this monoclonal antibody, suggesting that the R4P1-C2 antibody binds at the furin cleavage site (Fig. 5h). More importantly, when we tested the antiviral effect of R4P1-C2 antibody in SARS-CoV-2 infected mice, we found that the R4P1-C2 antibody effectively decreased the viral titer in the lung of SARS-CoV-2 infected mice (Fig. 5i, P = 0.032). These studies have therefore demonstrated that a monoclonal antibody against the S672-691 peptide, identified from COVID-19 patients' PBMC, inhibited SARS-CoV-2 infection.Fig. 5 Monoclonal antibody against the S672-691 peptide inhibits SARS-CoV-2 infection. (a) Schematic diagram of monoclonal antibody screening. (b) Phage verified binding ELISA identified four strongest clones. (c) ELISA verified the binding of S672-691 peptide with R3P1-B9 and R4P1-C2 scFv monoclonal antibodies. (d) ELISA verified the binding of R3P1-B9 and R4P1-C2 antibodies with S672-691 peptide from SARS-CoV-2 original or Omicron strain. S672-691 peptide from SARS-CoV-2 original or Omicron strain were coated on the plates and serially diluted R3P1-B9 and R4P1-C2 antibodies were added into the plates to detect the binding. (e) ELISA verified the binding of R3P1-B9 and R4P1-C2 antibodies with S monomer and trimer protein. (f and g) Sensorgrams of the binding of R4P1-C2 IgG1 antibody with S672-691 peptide (f) and S protein (g). The antibody concentrations were used as indicated. (h) The binding of R4P1-C2 scFv monoclonal antibody with S672-691 and its deletion mutations in COVID-19 patient sera was detected by ELISA. (i) Mice were administrated intraperitoneally with R3P1-C2 Ab (1.7 mg/kg) or IgG control (n = 5:5) 6 h before intranasal challenged with SARS-CoV-2 mouse-adapted strain CMA3p20 (104 PFU). Lung viral loads were tested by plaque assay on day 2 post infection (Mann–Whitney U test, P = 0.032). Data (b–e) represent the cumulative results from two or three independent experiments.
Discussion
COVID-19 is a worldwide pandemic disease caused by SARS-CoV-2 infection, with hundreds of millions of confirmed cases and several million deaths.7 , 35 Through studying a unique peptide in the S protein of SARS-CoV-2, we have demonstrated that while SARS-CoV-2 has evolved to possess a four aa “PRRA” insertion to create a TMPRSS2/furin cleavage site that makes the virus more infectious, the host immune system recognizes this inserted sequence as an immunogenic epitope and generates antibodies against it to inhibit viral infection. Furthermore, our results suggest that a monoclonal antibody binding to this peptide may have therapeutic potential to neutralize SARS-CoV-2 infection.
S protein epitopes have been extensively screened by both computational and experimental methods.36, 37, 38 Among these studies, Li et al.37 used a microarray of 12 aa peptides with 6-aa overlap between adjacent peptides to generate a linear epitope landscape of the S protein by analyzing the serum IgG of COVID-19 patients. Wang et al.36 used a proteome microarray of 15 aa peptides with 5-aa overlap to analyze IgG and IgM of COVID-19 patients. In this study, through bioinformatic analysis and structure-based prediction, we have tested several peptides in the S protein for their ability to detect antibodies in COVID-19 patients and suspected patients, and have identified the S672-691 peptide as a potential antigen (Fig. 1c). Though short peptides would be nicely presented, conformational epitopes may be lost. S672-691 peptide, which is 20 aa and located on the surface of S protein (based on the structural prediction), contains a four aa, positively charged peptide insertion that is unique to SARS-CoV-2. Upon the outbreak of the COVID-19 epidemic, some researchers have predicted the epitopes of SARS-CoV-2 with computational methods provided by the IEDB server.39, 40, 41 However, those predictions were dependent on literature mining and lacked experimental confirmation.
Our study clearly identified the S672-691 peptide with complementary use of both computational and experimental methods. The S672-691 peptide is longer than peptides used in previously reported microarrays, which may be missed in those screens. When comparing the S672-691 peptide sequences of SARS-CoV-2 with the corresponding aa sequences of SARS, MERS and other human coronaviruses, we did not find any coronavirus that shared five or more consecutive aa with the S672-691 peptide sequences of SARS-CoV-2 (Fig. 1d). We therefore believe the S672-691 peptide can specifically detect COVID-19 patients, although more patients’ sample studies are needed to confirm this conclusion. More importantly, the S672-691 peptide proved useful both for the inhibition of SARS-CoV-2 infection and for immunogenicity, triggering protective antibodies in mice that could suppress SARS-CoV-2 infection (Fig. 5i). The emergence and rapid spread of SARS-CoV-2 Omicron strain have challenged current SARS-CoV-2 vaccines and monoclonal antibodies. The serum samples from COVID-19 patients, COVID-19 suspected patients infected with SARS-CoV-2 original strain we used in this study may not be representative of the wider population infected with SARS-CoV-2 Omicron strain. However, our S672-691 peptide also showed suppression on Omicron strain (Fig. 4a), indicating the potential of S672-691 peptide for COVID-19 diagnosis of patients infected with SARS-CoV-2 Omicron strain and more work is needed to confirm this.
The immunodominant S672-691 peptide is located in the middle of the S protein between the S1 domain, responsible for ACE2 receptor binding, and the S2 domain, responsible for fusion between viral and cell membranes.3 , 42 , 43 Interestingly, this peptide contains a unique, four aa “PRRA” insertion as the result of a 12 nucleotides insertion in the genome of SARS-CoV-2 during its evolution. This insertion creates a potential cleavage site for proteases such as TMPRSS2 and furin.1 , 44 , 45 TMPRSS2 is a transmembrane protease that has been shown to play an essential role in facilitating cellular entry of many viruses, including influenza viruses and coronaviruses.46, 47, 48 We found that the S672-691 peptide may inhibit TMPRSS2-mediated cleavage of the S protein and suppress SARS-CoV-2 infection. Furthermore, sera from mice immunized with the S672-691 peptide strongly inhibited SARS-CoV-2 infection. More importantly, the monoclonal antibody we isolated from the COVID-19 patients directly targeted the four aa “PRRA” insertion and inhibited SARS-CoV-2 infection. Our studies therefore indicate that while SARS-CoV-2 creates a TMPRSS2 protease cleavage site through four aa insertion to enhance its infectivity, the immune system recognizes this inserted sequence as an immunogenic epitope and generates antibodies to protect cells against SARS-CoV-2 infection. ACE2 has a high affinity for binding the RBD domain, and antibodies against the RBD domain with higher affinity than ACE2 may inhibit SARS-CoV-2 viral entry.49 , 50 We speculate that the antibody we identified may suppress SARS-CoV-2 by affecting the binding of TMPRSS2/furin with the S protein. Further structure analysis is needed to provide more details.
Our current studies have limitations because of a small number of samples without samples from asymptomatic infected patients or SARS-CoV-2 vaccinated individuals. The efficiency of this antigen in detecting recovered COVID-19 patients and vaccinated individuals who may gain protective immunity against SARS-CoV-2 infection will be needed to determine in future study. Meanwhile, part of the clinical information of human serum samples is not completely recorded, and the missing data in Table S1 is not missed at random. When performing statistical comparisons in Tables S2 and S3, we just used the available information and excluded the missing data. While analyzing ELISA data in Figs. 2 and 3, we used data from all the serum samples (Although some clinical information such as sex and age et al. is missed, the clinical symptoms and subsequent SARS-CoV-2 PCR test results suggest the probable infection of COVID-19 suspected patients). Potential bias exists in our conducting a complete-case analysis for data that cannot be assumed to be missing at random. Thus, the conclusion is conserved based on our existing data, other possible confounders are not excluded. More research work about the efficiency, safety, and potential of clinical application of S672-691 peptide and isolated R4P1-C2 antibody is also needed.
COVID-19 is still spreading, with continuously emerging mutant variants causing sustained global health and economic impacts. Identification of an immunogenic epitope that overlaps with the critical furin cleavage site may help our future understanding of virus-host interactions affecting viral infectivity and host immunity. As most of the currently available monoclonal neutralization antibodies bind to the RBD region, there remains further opportunity to develop monoclonal antibodies as potential therapeutic agents against COVID-19 by targeting them to an alternative yet critical site for SARS-CoV-2 infection.
Contributors
G.C., H.Y., P.-Y.S. and C.-F.Q. jointly designed this study. M.G., Y.W. and C.C. performed experiments concerning serum ELISA. L.L., H.Z., J.W., X.X., S.R.A. and S.Z. performed virus infection experiments. J.L. and J.Z. collected patient serum samples and clinical data analysis. L.L. and X.X. performed in vitro S protein cleavage assay. D.W. and J.D. performed the scFv phage library construction and panning experiments. X.X. (Xuping Xie) performed the mouse experiments. G.C. and L.L. wrote the manuscript. S.G. revised and edited the manuscript. All authors read and approved the final version of the manuscript, and ensured it was the case. G.C. and H.Y. have verified the underlying data.
Data sharing statement
Datasets and additional documents generated, analyzed, or used during the study are available upon reasonable request to the corresponding author.
Declaration of interests
H.Y. have filed a patent related to the antiviral activity of R3P1-B9 and R4P1-C2 antibodies. The other authors declared that no conflict of interest exists.
Appendix A Supplementary data
Supplementary Figures S1–S4 and Table S1
Full Western Blots
Acknowledgments
This project is supported by the 10.13039/501100001809 National Natural Science Foundation of China (82102371, 91542201, 81925025, 82073181, and 81802870), the 10.13039/501100019018 Chinese Academy of Medical Sciences Initiative for Innovative Medicine (2021-I2M-1-047 and 2022-I2M-2-004), the Non-profit Central Research Institute Fund of the 10.13039/501100005150 Chinese Academy of Medical Sciences (2020-PT310-006, 2019XK310002, and 2018TX31001), the National Key Research and Development Project (2020YFC0841700) of China, US 10.13039/100000002 NIH funds grant AI158154, 10.13039/100007185 University of California Los Angeles (UCLA) AI and Charity Treks, and 10.13039/100007185 UCLA DGSOM BSCRC COVID-19 Award Program. H.Y. is supported by 10.13039/501100004608 Natural Science Foundation of Jiangsu Province (BK20211554 and BE2022728). Grateful acknowledgement is made to all the patients who participated in this study. We would also like to thank Drs. Shanlu Liu and Lishan Su for validating some of the experimental results.
Appendix A Supplementary data related to this article can be found at https://doi.org/10.1016/j.ebiom.2022.104401.
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| 36508877 | PMC9732504 | NO-CC CODE | 2022-12-14 23:35:51 | no | eBioMedicine. 2023 Jan 9; 87:104401 | utf-8 | eBioMedicine | 2,022 | 10.1016/j.ebiom.2022.104401 | oa_other |
==== Front
Biomed Pharmacother
Biomed Pharmacother
Biomedicine & Pharmacotherapy
0753-3322
1950-6007
The Author(s). Published by Elsevier Masson SAS.
S0753-3322(22)01471-8
10.1016/j.biopha.2022.114082
114082
Article
Humulus lupus extract rich in xanthohumol improves the clinical course in critically ill COVID-19 patients
Dabrowski Wojciech a⁎
Gagos Mariusz b
Siwicka-Gieroba Dorota a
Piechota Mariusz c
Siwiec Jan d
Bielacz Magdalena a
Kotfis Katarzyna e
Stepulak Andrzej f
Grzycka-Kowalczyk Luiza h
Jaroszynski Andrzej g
Malbrain Manu LNG a
a First Department of Anesthesiology and Intensive Therapy Medical University of Lublin, Lublin, Poland,
b Department of Cell Biology, Institute of Biological Sciences, Maria Curie-Sklodowska University, Lublin, Poland,
c Department of Anesthesiology and Intensive Therapy, Centre for Artificial Extracorporeal Kidney and Liver Support, Dr. W. Bieganski Regional Specialist Hospital, Łódź, Poland,
d Department of Pneumonology, Oncology and Allergology Medical University of Lublin, Poland,
e Department of Anesthesiology, Intensive Therapy and Acute Intoxications, Pomeranian Medical University, Szczecin, Poland,
f Department of Biochemistry and Molecular Biology, Medical University of Lublin, Poland,
g Collegium Medicum, Jan Kochanowski University of Kielce, Poland,
h First Department of Medical Radiology, Medical University of Lublin, Poland
⁎ Correspondence to: First Department of Anesthesiology and Intensive Therapy Medical University of Lublin, Jaczewskiego Street 8, 20-954 Lublin, Poland.
9 12 2022
2 2023
9 12 2022
158 114082114082
3 9 2022
22 11 2022
2 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.
Background
The systemic inflammatory response following severe COVID-19 is associated with poor outcomes. Several anti-inflammatory medications have been studied in COVID-19 patients. Xanthohumol (Xn), a natural extract from hop cones, possesses strong anti-inflammatory and antioxidative properties. The aim of this study was to analyze the effect of Xn on the inflammatory response and the clinical outcome of COVID-19 patients.
Methods
Adult patients treated for acute respiratory failure (PaO2/FiO2 less than 150) were studied. Patients were randomized into two groups: Xn – patients receiving adjuvant treatment with Xn at a daily dose of 4.5 mg/kg body weight for 7 days, and C – controls. Observations were performed at four time points: immediately after admission to the ICU and on the 3rd, 5th, and 7th days of treatment. The inflammatory response was assessed based on the plasma IL-6 concentration, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), C-reactive protein (CRP) and D-dimer levels. The mortality rate was determined 28 days after admission to the ICU.
Results
Seventy-two patients were eligible for the study, and 50 were included in the final analysis. The mortality rate was significantly lower and the clinical course was shorter in the Xn group than in the control group (20% vs. 48%, p < 0.05, and 9 ± 3 days vs. 22 ± 8 days, p < 0.001). Treatment with Xn decreased the plasma IL-6 concentration (p < 0.01), D-dimer levels (p < 0.05) and NLR (p < 0.01) more significantly than standard treatment alone.
Conclusion
Adjuvant therapy with Xn appears to be a promising anti-inflammatory treatment in COVID-19 patients.
Graphical Abstract
ga1
Abbreviations
ACE-2, Angiotensin converting enzyme 2ALC, Absolute lymphocyte count
APACHE II, Acute Physiology and Chronic Health Evaluation II
APTT, Activated partial thromboplastin time
CoV-2, Coronavirus-2
CI, Cardiac index
CO, Cardiac output
CRP, C-reactive protein
CT, Computed tomography
DGAT, Diacylglycerol acyltransferase
FXR, Farnesoid X receptor
HMGB1, High-mobility group box 1 protein
HR, Heart rate
ICU, Intensive care unit
iNOS, Inducible nitric oxide synthase
IXn, Isoxanthohumol
MAP, Mean arterial pressure
NADPH, Nicotinamide adenine dinucleotide phosphate
NLR, Neutrophil-to-lymphocyte ratio
ORI, Oxygen reserve index
PLR, Platelet-to-lymphocyte ratio
PRRSV, Porcine reproductive and respiratory syndrome virus
ROS, Reactive oxygen species
SAPS, Simplified Acute Physiology Score
SARS-CoV-2, Severe Acute Respiratory Syndrome (induced by) Coronavirus-2
SOFA, Sequential Organ Failure Assessment Score
SpO2, Peripheral saturation
SrO2, Cerebral tissue oxygen saturation
SVV, Stroke volume variation
WBC, White blood cells
Xn, Xanthohumol
Keywords
Xanthohumol
COVID-19
Coronavirus
Critically ill
Inflammatory response
Neutrophil-to-lymphocyte ratio
Mortality
==== Body
pmc1 Introduction
Since December 2019, when a new type of severe acute respiratory syndrome coronavirus (SARS-CoV-2) was described, many studies have been performed to identify an effective treatment to alleviate its signs and symptoms. This infection is caused by β-coronavirus 2 (CoV-2) and is associated with multiorgan dysfunction resulting mainly from endothelial damage complicated by massive inflammatory response syndrome [1], [2], [3], [4]. Coronavirus-induced symptoms are referred to as COVID-19.
Several studies on COVID-19 documented a strong relationship between the severity of the clinical condition and the degree of inflammatory response to viral infection [5], [6], [7], [8]. Immune dysregulation is a trigger for cytokine storms. An increase in the release of inflammatory cytokines, especially interleukin-6 (IL-6), associated with T-cell lymphopenia is correlated with poor outcomes and death [5], [6], [8]. A rapid increase in the inflammatory response is associated with the uncontrolled production of reactive oxygen species and free radicals, which significantly impair cellular metabolism [9], [10]. Several authors have studied the efficiency of anti-inflammatory and antioxidant treatment to reduce COVID-19-related complications and death [5], [9], [10], [11], [12], [13], [14], [15], [16], [17]. Interestingly, natural compounds have also been suggested as effective adjuvants in COVID-19 therapy [18], [19]. These compounds possess different anti-COVID-19 activities. Some of these compounds block IL-6 release, which could reduce the need for mechanical ventilation and thus also admission to the intensive care unit (ICU) [13], [14], [15]. Other compounds directly inhibit viral replication by binding to specific receptors [20], [21]. Interestingly, some of these compounds have similar chemical structures. In silico studies suggested a high efficiency of naturally occurring prenylated chalcones for treating coronavirus infection [15], [22], [23].
Xanthohumol (Xn) is a prenylated chalcone ( Fig. 1) that can be extracted from female inflorescences of hop cones (Humulus lupus). Increasing numbers of studies have documented the immunomodulatory properties of Xn [24], [25], [26]. Xanthohumol inhibits proinflammatory pathways via inhibition of farnesoid X receptor (FXR) activity and NF-κB-dependent inhibition of proinflammatory gene expression, such as IL-1β, IL-6, IL-8, IL-12p70, TNFα, and interferon γ [23], [24], [25], [26]. Experimental studies have documented a strong effect of Xn against many DNA and RNA viruses, such as herpes simplex virus types 1 and 2, cytomegaloviruses, and porcine reproductive and respiratory syndrome virus (PRRSV) [22], [25], [26], [27], [28].Fig. 1 Chemical structure of xanthohumol (2’,4’,4-trihydroxy-6’-methoxy-3-(3methyl-but-2-en-1-yl) with atom numbering.
Fig. 1
Interestingly, PRRSV infection is similar to the course of coronavirus infection, with the main symptoms consisting of high fever, significant morbidity, and severe respiratory disease resulting in high mortality. In vivo experiments showed that PRRSV-infected piglets treated with Xn at a dose of 20 mg/kg exhibited only moderate clinical signs and low viral loads, whereas 25 mg/kg Xn practically eliminated all clinical symptoms [28]. Another experimental study documented that Xn inhibited many viral diseases, including SARS-CoV-2 and other fatal diseases caused by alpha- or beta-coronavirus [29]. Notably, Xn is safe and well tolerated in healthy humans and is available as a dietary supplement. Based on its antiviral and anti-inflammatory properties, we hypothesized that the administration of Xn could improve the clinical course and outcome in critically ill COVID-19 patients requiring mechanical ventilation.
Therefore, the aim of this study was to analyze the effect of Xn supplementation on the clinical course, inflammatory response, and outcome in patients admitted to the ICU due to COVID-related acute respiratory failure with an oxygenation index (PaO2/FiO2) less than 150.
2 Patients and methods
2.1 Ethical considerations
This prospective, observational study was conducted in accordance with the Declaration of Helsinki and applicable regulatory requirements. The local Institutional Review Board and the Bioethics Committee of the Medical University in Lublin, Poland approved the protocol (KE-0254/201/2020). Written informed consent was obtained from all patients just after admission to the ICU prior to randomization. Additionally, the patient’s legal representatives were informed about the main purpose of this study. The present study is registered at ClinicalTrial.gov (http://www.clinicaltrials.gov) with the unique identifier NCT05463393.
2.2 Study drug treatment
All patients were treated following current guidelines at the time of admission. After admission to the hospital, remdesivir (Veclury, Ireland) was administered at an initial dose of 200 mg/day followed by 100 mg/day for 5–7 days. Additionally, vitamin D3 at a dose of 4000 U per day was supplemented in all patients. Corticosteroid therapy with dexamethasone (Dexaven, GmbH Arzneimittel, Germany) at a dose of 8 mg per day for 10 days and anticoagulant therapy with enoxaparin (Clexane, Sanofi-Aventis, France) were started upon admission to the ICU. All patients received a continuous infusion of insulin to maintain plasma glucose concentrations between 100 and 160 mg/dL.
Patients who were included in the present study received additional treatment with Xn or normal saline. Patients were randomized in a double-blind, placebo-controlled fashion into two groups using sealed envelopes. Group Xn included patients who received an extract from Humulus lupus L rich in xanthohumol (Hop-RXn™, BioActive-Tech Ltd., Lublin, Poland; http://xanthohumol.com.pl/) as adjuvant therapy, and Group C included patients who received 0.9% NaCl and formed the control group. Based on its pharmacokinetics and bioactivity, Xn was administered enterally three times a day every 8 h at a dose of 1.5 mg/kg body weight (4.5 mg/kg body weight/day) for 7 days [30]. The first dose of Xn was administered within 4 h after admission to the ICU. In the control group, 3 mL of 0.9% NaCl was administered enterally three times a day.
2.3 Monitoring
In all patients, systolic diastolic and mean arterial blood pressure (MAP), heart rate (HR), and expiratory CO2 tension were monitored continuously. Additionally, hemodynamic variables, such as cardiac output/index (CO/CI), stroke volume variation (SVV), systemic vascular resistance index (SVRI), and central venous pressure (CVP), were monitored using the EV 1000 platform (Edwards Lifescience, Irvine, CA, USA). Masimo Root monitor (USA) with SEDLine was used for continuous measurement of regional cerebral oxygen saturation (SrO2), frontotemporal electroencephalography, peripheral oxygen saturation (SpO2) with hemoglobin level, and oxygen reserve index (ORI). Fluid administration with balanced crystalloids and vasopressors (norepinephrine) was titrated to obtain a MAP higher than 65 mmHg.
2.4 Patient selection and inclusion criteria
This study was performed between October 2020 and January 2021. Adult patients aged 18 years or older admitted to the ICU who were treated for severe COVID-19 with acute respiratory failure (PaO2/FiO2 below 150) due to bilateral and multifocal ground-glass opacities involving greater than half of the lungs were included in this study. Quantitative computed tomography (CT) with thoracic VCAR software and the parenchymal analysis option were used to assess the degree of parenchymal impairment. Patients who were treated for COVID-19 for more than one week were excluded. Other exclusion criteria were chronic renal failure, history of illness affecting the human immune system (modulated immune system, such as transplant patients), and/or diseases causing prolonged inflammatory responses such as malignancies, rheumatologic diseases, and chronic inflammatory disease. Pregnant or lactating women were also excluded. Patients who did not respond to the prone ventilation strategy were screened for eligibility for extracorporeal oxygenation (ECMO) and were excluded from this study. Patients who died within 7 days were also excluded due to incomplete data.
2.5 Biochemical analysis
Routine biochemical examination with full blood count and morphology, including erythrocyte, platelet, leukocyte, neutrophil and lymphocyte counts, serum interleukin 6 (IL-6) concentration, C-reactive protein (CRP) and D-dimers, were performed at the laboratory of University Hospital No. 4 in Lublin, Poland using commercial reagents. Arterial blood gas analysis was performed a minimum of 4–6 times per day using GEM 5000 (Werfen, Barcelona, Spain).
2.6 Pulmonary disease evaluation
The ventilator settings were determined in accordance with the results of the blood gas examination, and respiratory insufficiency was assessed by calculating the PaO2-to-FiO2 ratio (PaO2/FiO2). Patients with PaO2/FiO2 less than 100 were placed into the prone position in accordance with the local protocol [31]. A high-resolution computed tomography (CT) technique with artificial intelligence software (Thoracic VCAR software with Parenchymal Analysis, GE Healthcare, USA) was used to assess the severity and quantitatively measure lung injury. In all participants, CT was performed immediately before admission to the ICU. A control CT was performed 2–3 days after extubation or immediately after discharge from the COVID zone in the ICU.
2.7 Study protocol, measured variables and outcomes
Observations were performed at four time points: 1) immediately after admission to the ICU (baseline), 2) 3 days after admission to the ICU, 3) on the 5th day of treatment and 4) on the 7th day of treatment. The degree of the inflammatory response was measured by NLR, PLR, D-dimer, and plasma IL-6 concentration. The following formulas were used for the calculation of NLR and PLR [32], [33]:- NLR: the number of neutrophils divided by the number of lymphocytes,
- PLR: the number of platelets divided by the number of lymphocytes.
The PaO2/FiO2 ratio was calculated as the ratio between the oxygen tension obtained from routine blood gas analysis and the fraction of inhaled oxygen (FiO2). The primary outcome was mortality rates, which were determined at 7 and 28 days after admission to the ICU. The secondary outcomes were the dynamics and evolution of the inflammatory parameters and the evolution of CT imaging.
2.8 Statistical analysis
Statistical analysis was performed using Statistica 13.1 software (StatSoft, USA). Means and standard deviations (SD) were calculated for normally distributed variables, whereas non-Gaussian distributed variables were presented as medians and interquartile ranges. The Kolmogorov–Smirnov test was used to analyze the normality of the data distribution. Categorical variables were compared using the χ2 and Fisher exact tests, and Yates correction was applied. The value at ICU admission was regarded as the baseline. Unpaired Student’s t-test was used to analyze variables with a normal distribution. Nonparametric data were statistically analyzed using the Wilcoxon signed-rank test and the Kruskal–Wallis test. Additionally, the Pearson test was used for the analysis of any correlations among normally distributed variables, whereas Spearman’s rank test was used for interpoint and intergroup comparisons for variables with a non-Gaussian distribution. Kaplan–Meier estimation with a log-rank test was performed for survival probability analysis. A value of p < 0.05 was considered significant, and the power of the statistical test (1 – β) was calculated using G*Power 3.1 software.
3 Results
3.1 Study population
Seventy-two critically ill adult patients treated for COVID-19 with severe respiratory failure were included in the present study. A total of 22 patients were excluded from the final analysis: 11 were excluded because informed consent could not be obtained, and 4 died within 7 days with incomplete data. Additionally, seven patients were excluded due to incomplete data or consent withdrawal after recovery. Finally, fifty patients (18 women and 32 men) aged 22–83 years (mean 58 ± 17) were studied. Twenty-five patients were randomly assigned to the Xn group and were treated with Xn, and 25 received 0.9% NaCl and were allocated to the control group. The relevant demographic data and comorbidities are presented in Table 1.Table 1 Baseline demographic data and comorbidities. NS – not statistically significant. * p < 0.05 – differences in the SOFA score from the baseline, S * * p < 0.01 – differences in the SOFA score after the exclusion of patients who died between Days 7 and 28 (Student’s t-test).
Table 1 Xn group Control group p value
Female/Male 8/17 11/14 NS
Mean BMI (kg/m2) 29.95 ± 5,34 31.09 ± 6.81 NS
Comorbidities Diabetes 16 13 NS
Hypertension 21 23 NS
Coronary disease 12 9 NS
Cardiac arrhythmia 6 4 NS
Asthma 2 0 NS
Thyroid disease 0 1 NS
Gout 3 0 NS
Immunology disorders 1 0 NS
Duration of mechanical ventilation (days) 9 ± 3 days 22 ± 8 days p < 0.001
Septic complications 0 9 p < 0.01
Baseline APACHE II score 13.6 ± 3.7 12.8 ± 4.1 NS
Baseline SAPS II score 35.7 ± 5.4 34.6 ± 4.8 NS
SOFA Baseline 5.5 ± 3 5.3 ± 3.7 NS
3rd day 4.9 ± 2.4 5.3 ± 2 NS
5th day 4.7 ± 3.1 5.2 ± 3.1 NS
7th day 3.5 ± 2.8 * 4.4 ± 3.1 NS (S **)
3.2 Primary endpoints
Overall mortality was 34%. Seventeen patients died between Days 7 and 28 of treatment: 5 (20%) in the Xn group and 12 (48%) in the control group (χ2 = 5.56, p < 0.05 and χ2 with Yates correction = 4.25 and p < 0.05, Fig. 2). All patients treated with Xn who survived were discharged from the ICU to the pulmonology or rehabilitation ward and then discharged home in good clinical condition. In the control group that did not receive Xn, none of the patients were discharged directly home. These patients were discharged to another pulmonology hospital followed by another hospital, and their outcomes could not be determined.Fig. 2 The Kaplan–Meier estimation with a log-rank test for the 28-day probability of survival in patients receiving adjuvant therapy with xanthohumol at a daily dose of 4.5 mg per kg body weight (Image 1 Xn group) and patients who did not receive adjuvant therapy (Image 2 Control group).
Fig. 2
3.3 Changes in inflammatory markers
The mean baseline value of NLR was 20.8 ± 16 in all participants and was comparable in both groups (21.5 ± 14.2 vs. 20.2 ± 17.6 in the Xn treated and control groups, respectively). Treatment with Xn resulted in a nearly 5-fold significant reduction in NLR at Day 7 compared with baseline. In contrast, no significant NLR decrease was observed in the control group ( Table 2). In patients who survived, the NLR decreased on the 3rd and 7th days of treatment with Xn and on Day 7 in the control group ( Fig. 3).Table 2 Changes in the selected biochemical variables and the PaO2/FiO2 ratio in the two study populations (neutrophil-to-lymphocyte ratio (NLR), plasma IL-6 concentration, C-reactive protein (CRP), platelet-to-lymphocyte ratio (PLR), absolute lymphocyte count (ALC)), and white blood cell count (WBC). Data are presented as medians and quartiles 1 and 3. * p < 0.05, p < 0.01, * ** p < 0.001 – difference compared to baseline. ‡ p < 0.05, ‡‡‡ - p < 0.01 – the difference between the Xn and control groups.
Table 2 Xn group Control group
Baseline Day 3 Day 5 Day 7 Baseline Day 3 Day 5 Day 7
WBC 10.2
[7.9; 14.12] 13.8 * *
[10.13; 17.9] 12.5 * *
[9.86; 15.46] 10
[9.75; 14.5] 10.42
[6.9; 14.62] 11.4
[8.92; 13.98] 12.85 *
[9.44; 15.85] 12.4
[8.55; 16.13]
ALC 0.4
[0.33; 0.64] 0.85 * **‡
[0.72; 1.38] 1.1 * *
[0.4; 1.6] 1.91 * *
[0.79; 1.96] 0.54
[0.4; 0.79] 0.7
[0.57; 0.93] 1.05
[0.89; 1.24] 1.2 * *
[0.95; 1.4]
NLR 23.64
[13.45; 27.71] 12.61
[6.49; 21.29] 7.75
[5.44; 31.34] 4.28 *
[3.96; 22.41] 16.44
[8.08; 23.75] 12.30
[9.4; 19.07] 10.48
[7.6; 19.9] 7.03
[5.67; 14.79]
IL-6 105.5
[40.4; 406.8] 33.8 * *
[13.52; 129.1] 8.4 * ‡‡
[4.81; 56] 29.1 * *‡‡
[5.65; 40.41] 165.1
[70.48; 287.17] 146.2
[34.1; 283.7] 124.9 *
[53.8; 200.1] 109.6 *
[52.2; 168.4]
CRP 98.2
[18.95; 151.14] 58.96 * *
[9.13; 77.71] 38.2 *
[4.8; 51.19 38.32
[5.49; 69.96] 92.4
[27.25; 154.55] 57.83 *
[38.8; 94.7] 48.09
[24.8; 94.4] 41.3
21.8; 58.3
D-dimers 4907
[782.5; 23549] 2429 * *
[794.7; 4102] 1802.5 * *
[910; 2237] 723 * **‡
[505.25; 1894] 4675.5
[1325.8; 11385] 3459.5 *
[1765; 8289.3] 1955 *
[916.8; 4509] 2147 *
[1021; 3846]
PLR 762
[409; 800.5] 316.5 *
[220.45; 356.4] 290
[197.77; 513.5] 178.7 *
[155.3; 307.4] 457.5
[350.9; 741.67] 374.4
[283.5; 556.6] 340
[219.8; 445] 267.4 * *
[204.1; 373.5]
PaO2/FiO2 58
[52.25; 98.16] 95.12 * *
[82.18; 142.42] 98.75 * *
[78.68; 185.4] 148.5 * **‡
[119.6; 295.27] 59.9
[50.9; 87.8] 112.8 * **
[89.2; 128.9] 119.1 * **
[88.7; 177.8] 125.4 * **
[111; 146.45]
Fig. 3 Evolution of the neutrophil-to-lymphocyte ratio (NLR) in survivors receiving xanthohumol (Image 3 - Xn group, n = 20) as an adjuvant therapy compared to those treated with placebo (Image 4 - control group, n = 13). * p < 0.05, * * p < 0.01 – differences from the baseline in the Xn group, †† - p < 0.01 – differences from the baseline in the control group. The power of the statistical analysis was > 0.8.
Fig. 3
The platelet-to-lymphocyte ratio decreased on the 3rd and 7th days of treatment in the Xn group and on the 5th and 7th days in the control group (Table 2). Changes in both groups were comparable, and no significant differences were noted.
The mean baseline value of the plasma IL-6 concentration was 279.1 ± 380.1 pg/mL in all patients. IL-6 levels were comparable in both groups (298.4 ± 453.5 pg/mL vs. 256.7 ± 337.5 pg/mL in the Xn-treated and control groups, respectively). Treatment with Xn reduced plasma IL-6 concentrations on the 3rd and 7th days, whereas these values were reduced on the 5th and 7th days in the control group (Table 2). In patients who survived, the plasma IL-6 concentration decreased on Days 3, 5, and 7 in both groups. The decrease in plasma IL-6 concentration was more pronounced in the Xn group ( Fig. 4).Fig. 4 Evolution of the plasma IL-6 concentration in survivors receiving xanthohumol (Image 3 - Xn group, n = 20) as adjuvant therapy and those treated with placebo (Image 4 - control group, n = 13). * * p < 0.01, * ** p < 0.001 – differences from the baseline in the Xn group, †† - p < 0.01 – differences from the baseline in the control group. ‡ p < 0.05, ‡‡ p < 0.01, ‡‡‡ p < 0.001 – difference between the Xn and control groups. The power of the statistical analysis was > 0.8.
Fig. 4
In both groups, D-dimer levels decreased on the 3rd, 5th, and 7th days; however, treatment with Xn resulted in a more pronounced reduction compared to the control group (Table 2). In patients who survived, D-dimers decreased in both studied groups, but their concentrations were significantly lower in patients treated with Xn on the 3rd and 7th days (p < 0.05).
3.4 Changes in CT lung scans and gas exchanges (PaO2/FiO2)
In all patients, a CT examination of the lung showed massive bilateral and multifocal ground-glass opacities ( Table 3). A significant improvement in CT lung scans was noted in patients treated with Xn ( Fig. 5), whereas only a slight improvement was noted in patients treated with placebo ( Fig. 6). Additionally, mechanical ventilation was completed within 7 days in 14 Xn patients, and 6 patients required mechanical ventilation/support for more than 7 days. None of these patients required a tracheostomy. In the control group, mechanical ventilation was completed within 7 days in only 4 patients, and 8 of them required percutaneous tracheostomy due to the necessity of prolonged mechanical ventilation for up to 14 days. PaO2/FiO2 decreased in both groups; however, the changes were more pronounced in the Xn group (Table 2).Table 3 Evolution of lung injury measured with high-resolution computed tomography (CT) combined with artificial intelligence software with the percentage of the pulmonary parenchyma and affected automatic detection of pathology (emphysema, normal parenchyma, ground glass opacity, and consolidation). Percentages are expressed as the mean with standard deviation (SD). Baseline – CT performed immediately before admission to the ICU. * * p < 0.01, * ** p < 0.001 – changes between the baseline and control lung pathologies assessed by artificial intelligence software, † p < 0.05, ††† p < 0.001 – differences between lung pathologies observed in the Xn and control groups.
Table 3 Xn group Control group
Baseline CT Control CT Baseline CT Control CT
Emphysema (%) 0.1 ± 0.13 0.7 ± 1.2 0.2 ± 0.3 2 ± 3.6
Normal pulmonary paremchyma (%) 35 ± 11.8 65 ± 15 * ** 34 ± 13 41 ± 10†††
Ground glass opacity (%) 49 ± 11 25 ± 10 * ** 46 ± 7 42 ± 6.7†††
Consolidation (%) 4.4 ± 2.7 3.3 ± 2.6 5.6 ± 4.6 5 ± 2.9
Other (%) 11 ± 4.5 5 ± 4.5 * * 14.8 ± 10.8 11.6 ± 2.9†
Fig. 5 Examples of quantitative computed tomography (CT) of the lung with thoracic VCAR analysis from 2 patients successfully treated with Xn at a daily dose of 4.5 mg/kg body weight for severe COVID-19. Both were mechanically ventilated with FO2 1.0 in the prone position. Panel A - patient A: CT examination at baseline (A-0) was performed a few hours before the start of mechanical ventilation. The first dose of Xn 1.5 mg/kg body weight (158 mg of Xn) was administered before intubation. The baseline PaO2/FiO2 immediately after intubation was 58. After 6 days, the patient was extubated, and a control CT (A-1) was performed on Day 8. His PaO2/FiO2 was 232 on the 7th treatment day (end of the study period). This patient was transferred to the pulmonology ward and discharged to home after 32 days of treatment in good clinical condition. Panel B - patient B: CT examination at baseline (B-0) was performed just before admission to the ICU. The first dose of Xn was administered immediately before admission. The patient was intubated within 3 h after admission to the ICU, and mechanical ventilation in the prone position was started after intubation. His baseline PaO2/FiO2 was 52 just after intubation. The patient was extubated on the 7th day of treatment, and a control CT (B-1) was performed on the 9th day. The patient was discharged to the pulmonology ward and then discharged to home on Day 35.
Fig. 5
Fig. 6 Example of quantitative computed tomography (CT) of the lung with thoracic VCAR analysis in a patient treated with placebo (NaCl 0.9% at a dose of 3 mL administered 3 times per day). The patient was mechanically ventilated with FO2 1.0 in the prone position. Quantitative CT was performed a few hours before the start of mechanical ventilation (C). The baseline PaO2/FiO2 just after intubation was 55. After 13 days, the patient was extubated and placed on noninvasive ventilation (NIV). The patient was transferred to another hospital and was finally discharged to home on Day 98.
Fig. 6
4 Discussion
In the present study, we documented that treatment with Xn significantly reduced the severity of the inflammatory response, as reflected by the plasma IL-6 concentration and NLCR, improved patient outcomes and reduced the mortality rate. Additionally, Xn at a daily dose of 4.5 mg/kg body weight improved the oxygenation index and reduced the length of mechanical ventilation. The mechanism responsible for these phenomena seems to be complex and pleiotropic.
4.1 Presumed pathophysiologic mechanisms
Xanthohumol is the most abundant prenylated flavonoid in hops. Beer is the most important dietary source of Xn and other related prenylflavonoids. Admittedly, the brewing process induces thermal isomerization of Xn to isoxanthohumol (IXn), but Xn can be converted into IXn in the stomach [34], [35]. An in vitro study showed that both forms could be biotransformed by human liver microsomes into glucuronides, hydroxylated metabolites, and cyclic dehydrometabolites [30], [35], [36]. The bioavailability of Xn is dose-dependent and it increases linearly with an increasing oral dose [30]. Xn has strong antioxidant and anti-inflammatory properties and protects cells from injury induced by upregulated angiotensin-2 activation [23], [24], [25], [26], [37], [38].
An experimental study showed that angiotensin-2 stimulates the production of reactive oxygen species (ROS) via the activation of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, and Xn and its major bioactive metabolite IXn strongly inhibit this process, preventing oxidative-related endothelial injury [37], [39]. It was previously shown that Xn inhibits the viral-encoded cysteine protease (the main protease of CoV-2) in a dose-dependent manner [29]. Importantly, this protease is necessary for viral replication. Another reported pathomechanism is based on a reduction in the intensity of viral replication related to the inhibition of diacylglycerol acyltransferase (DGAT) [20]. Massive viral replication is associated with metabolic cell damage, and the rapid upregulation of lipid biosynthesis, particularly triacyglycerol, plays a crucial role in this process. The last step in triacyglycerol synthesis is catalyzed by DGAT, the inhibition of which reduces the availability of fuel for viral replication. Xanthohumol strongly inhibits DGAT activity in a dose-dependent manner, and its antiviral effect has been noted in cardiomyocytes and type II alveolar epithelial cells – the major portal of CoV-2 infection [20]. Interestingly, Xn has the most potent activity among all chalcones extracted from Humulus lupus [38]. In the present study, we observed relatively quick improvements in blood oxygenation and CT-lung imaging after only 7 days of treatment with Xn. Therefore, we can speculate that the use of Xn at a dose of 4.5 mg/kg body weight may be a safe and effective adjuvant therapy in severe COVID-19 patients.
4.2 Anti-inflammatory properties
The therapeutic effect of Xn can also be explained by its anti-inflammatory properties. We noted a significantly lower IL-6 concentration and NLR in patients treated with Xn. Rapid and massive production of proinflammatory cytokines, particularly IL-6, is associated with the severity of COVID-19. In the present study, the plasma IL-6 concentration upon admission was 100-fold higher than normal, and the addition of Xn to the treatment regimen resulted in a rapid and significant decrease in its concentration. Several studies documented the strong anti-inflammatory properties of Xn with a reduction in proinflammatory cytokines and the number of macrophages in injured tissues [24], [40], [41]. Administration of Xn reduced plasma IL-6 levels by approximately 80% [42]. An animal study showed that Xn effectively reduced tumor necrosis factor-alpha (TNF-α), IL-6, and IL-1β secretion and suppressed high-mobility group box 1 protein (HMGB1) and inducible nitric oxide synthase (iNOS) expression [40]. A decrease in the production and release of proinflammatory cytokines is associated with the suppression of nuclear factor-kappa B (NF-κB), which inhibits T-cell proliferation [29], [40], [41]. Interestingly, anatomopathological examination of animal lungs revealed significantly lower neutrophil infiltration in injured lungs, and lung damage was markedly reduced in animals treated with Xn compared to those treated with remdesivir [20], [41]. In the present study, CT scans also showed markedly fewer consolidations and bilateral diffuse mixed densities of the lung in patients treated with Xn compared to controls. All of our patients tolerated Xn well, and none of them had adverse effects. Therefore, we suggest adding Xn as an adjuvant to standard therapy in COVID-19 patients.
The neutrophil-lymphocyte count ratio is frequently used as a marker of the severity of inflammation and outcome [43], [44], [45], [46], [47]. Elevated NLR values predict poor outcomes in patients treated for traumatic brain injury [43], mesenteric ischemia [44], or sepsis [45]. Importantly, NLR has also been proposed as a sensitive marker of endothelial dysfunction following viral infection [46]. Progressive endothelial damage following viral infection, including CoV-2, induces massive glycocalyx injury, leading to endothelial inflammation with uncontrolled neutrophil activation, vasoconstriction, and coagulation disorders [3], [4], [47]. Anatomopathological examination of lungs from patients with COVID-19 showed the presence of viral inclusions and massive inflammation in endothelial cells [48], [49], [50]. The virus binds to the angiotensin-converting enzyme 2 (ACE-2) receptor, displaying profound tropism for the human vascular endothelium and the lungs [49], [50]. Inflamed endothelial cells induce proinflammatory cytokine production, leading to general hyperinflammation with the subsequent influx of activated monocytes, neutrophils, and other immune cells. An increase in blood neutrophils with a low lymphocyte count may predict poor outcomes. It has been shown that an increase in NLR above 10 is a strong predictor of fatal outcomes in critically ill COVID-19 patients [50], [51].
4.3 Endotheliopathy
Severe COVID-19 has been linked to endotheliopathy and vasculitis, which has been documented in several studies [1], [2], [3], [4], [52], [53]. Elevated plasma D-dimer concentrations, which are fibrin degradation fragments, are associated with an increased risk for morbidity and mortality in COVID-19 patients [54], [55]. The virus possesses a strong affinity for the vascular endothelium, leading to lymphocytic endotheliitis with infiltration of inflammatory cells around the vessels and endothelial apoptotic cell death [56]. A rapid increase in the concentration of proinflammatory cytokines, such as IL-6 and TNF-α, reduces the physiological antithrombotic and anti-inflammatory functions of endothelial cells and triggers a procoagulopathy cascade [57]. Hence, extensive inflammation may disturb the crosstalk between the endothelium, platelets, and the coagulation system, leading to the formation of clots in the microvascular circulation of several organs, especially the lungs. Xanthohumol inhibits inflammatory-induced endothelial dysregulation, exerting antiangiogenic and anti-inflammatory effects via the reduction of NF-κB activity, a well-established angiogenic and inflammatory factor [39], [40], [58]. Interestingly, an experimental study documented that Xn at a dose of 10 mg/kg body weight administered twice daily for 7 days improves blood velocity and reduces the risk of arterial thrombosis, decreasing the incidence of pulmonary embolism [59]. Additionally, treatment with Xn does not affect other coagulation factors, such as prothrombin time (PT), activated partial thrombin time (APTT), or thrombin time (TT), but it insignificantly inhibits platelet activation and adhesion on collagen-coated surfaces [59]. In the present study, we noted a much more profound decrease in D-dimers in the Xn group than in the control group. Additionally, changes in lung CT were also more pronounced in the Xn group. Therefore, we can speculate that Xn reduces vascular damage and the formation of microarterial thrombosis; however, this hypothesis should be confirmed in additional studies.
4.4 Limitations
Despite its promising findings, our study has several limitations. First, because of the small number of patients treated with Xn, the power of our analysis was significantly reduced. Second, our analysis of Xn-related anti-inflammatory effects was based on only a few commonly assessed variables. Several experimental studies have documented that Xn reduces many circulating proinflammatory cytokines in different diseases [8], [23], [24], [25], [26], [40], [60], [61]. Third, we did not analyze blood Xn levels or its metabolite concentrations. Previous studies have shown that Xn is a safe and nontoxic supplementary product; however, its interaction with other anti-inflammatory medications has not been well documented.
Additionally, we did not analyze the effect of Xn on blood glucose levels. Experimental studies have shown that Xn may be favorable for glucose metabolism, and treatment with Xn at a dose of 60 mg/kg body weight per day effectively reduced plasma glucose, total cholesterol, and LDL-cholesterol concentrations [42], [62]. A reduction in plasma glucose concentration following Xn administration was only noted in male mice, whereas higher liver concentrations of Xn and its metabolites were found in female mice [62]. It has been well established that IL-6 affects glucose homeostasis. Increased IL-6 levels impair insulin action, whereas inhibiting IL-6 improves hepatic insulin sensitivity [63], [64]. In the present study, the blood glucose concentration was maintained with continuous insulin administration, and the dose of insulin was not analyzed. Therefore, we hypothesize that Xn affects glucose metabolism via a decrease in IL-6 concentration; however, this hypothesis should be confirmed in additional studies.
Fifth, estrogen and other sex hormone activity was not monitored. Importantly, Xn exhibits estrogen activity by increasing the level of 8-prenylnarigenin, which strongly reduces the inflammatory response and proinflammatory cytokine release [62], [65], [66]. Additionally, 8-prenylnarigenin also shows anti-inflammatory and vascular-protective properties, which could have had a significant impact on our patients [64]. Sex steroids are potent immune modulators and suppress the production of proinflammatory cytokines, such as IL-6, IL-1β, and TNF-α [67]. An experimental study showed a reduction in IL-6 production following estrogen administration, and clinical observations documented a negative correlation between plasma estrogen concentration and lung functionality in COVID-19 patients [68], [69]. Another clinical observation documented a significantly increased mortality rate and worse respiratory failure in men relative to women [70]. Estrogen supplementation was also associated with a decreased risk of death in postmenopausal women [71]. In the present study, the numbers of men and women were comparable in the studied groups. However, only one woman died in the Xn group, and two died in the control group. Therefore, we hypothesize that the Xn-related increase in the estrogen concentration might play a role in the outcome but the limited number of patients and lack of hormone control preclude drawing such a conclusion.
In the present study, we confirmed the beneficial effects of adjuvant therapy with Xn in critically ill COVID-19 patients requiring mechanical ventilation. Based on our findings, we hypothesize that Xn may also improve the clinical course of COVID-19 in patients with only slight symptoms and may reduce the risk of developing severe respiratory failure and the need for mechanical ventilation; however, this hypothesis must be confirmed in additional studies.
5 Conclusions
Xanthohumol is a promising adjuvant treatment for COVID-19 patients with severe respiratory failure who require mechanical ventilation. Treatment with Xn improved the clinical course and reduced the severity of the inflammatory response and mortality rate. Additional studies in a large cohort of patients are needed to confirm these findings.
Funding
This study was supported by a grant from the 10.13039/501100010621 Medical University of Lublin , Poland (DS 352/2019). The funder had no role in the study design, data collection, analysis, decision to publish, or preparation of the manuscript.
CRediT authorship contribution statement
Wojciech Dabrowski – conceptualisation, methodology, writing – original draft preparation, software. Mariusz Gagos - conceptualisation, methodology, statistical analysis. Dorota Siwicka-Gieroba – data collection, data interpretation. Mariusz Piechota – data colletion, visualization. Jan Siwiec – data collection, data interpretation. Andrzej Stepulak – supervision, writing – original draft preparation. Luiza Grzycka-Kowalczyk – CT scans preparation, data collection. Magdalena Bielacz – writing – original draft preparation, visualization. Katarzyna Kotfis – conceptualisation, methodology, writing – original draft preparation, statistical analysis. Andrzej Jaroszynski – supervision, original draft preparation. Manu LNG Malbrain – conceptualisation, supervision, data analysis, language correction.
Ethical approval and consent to participate
This study was conducted in accordance with the Declaration of Helsinki and applicable regulatory requirements and was approved by the Institutional Review Board and the Bioethics Committee of the Medical University at Lublin, Poland (KE-0254/201/2020). Informed consent was obtained from all patients. Additionally, their legal representatives were informed about the main purpose of this study.
Consent for publication
Written informed consent for publication was obtained from all patients.
Conflict of interests
All authors declare no conflicts of interest. Additionally, none of the authors received money in connection with this research.
Data Availability
Data will be made available on request.
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71 Sound M. Fonseca-Rodriguez O. Josefsson A. Welen K. Fors Connolly A.M. Association between pharmaceutical modulation of oestrogen in postmenopausal women in Sweden and death due to COVID-19: a cohort study BMJ Open 12 2022 e053032 10.1136/bmjopen-2021-053032
| 36508996 | PMC9732508 | NO-CC CODE | 2022-12-14 23:35:51 | no | Biomed Pharmacother. 2023 Feb 9; 158:114082 | utf-8 | Biomed Pharmacother | 2,022 | 10.1016/j.biopha.2022.114082 | oa_other |
==== Front
Transportation Research Procedia
2352-1465
2352-1465
The Author(s). Published by Elsevier B.V.
S2352-1465(22)00749-9
10.1016/j.trpro.2022.12.029
Article
Spacing-Assistant for Leipzig and Munich Approach
Haugg Eliana
Konopka Jens
DFS Deutsche Flugsicherung, Am DFS Campus 10, 63225 Langen
9 12 2022
2022
9 12 2022
66 292303
© 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.
Before the COVID-19 pandemic and as the ATM industry recovers again, there is a growing demand for airport capacity. A possibility to meet this demand is to introduce revised wake turbulence categories and reduced separation minima according to RECAT. To enable air traffic controllers to memorize and apply these, DFS developed a Spacing-Assistant which shows the optimal turning point from the downwind to the final approach, the minimum separation between aircraft, and their optimal spacing to achieve the separation target. It automatically detects gaps on the final and calculates turning points for aircraft on the downwind to fill these gaps. In mixed mode operations, it can also be actively used to plan gaps for departures between arrivals. Objective and subjective data were collected in a Real Time Simulation with four air traffic controllers from Leipzig and Munich Approach. Results indicate that the Spacing-Assistant supports the controllers’ situational awareness and planning. It shows the potential to save capacity when taking over a working position by helping to quickly familiarize with the traffic situation and to stabilize runway throughput. When working with the Spacing-Assistant the final was shorter which implies a reduced environmental and noise impact.
Keywords
Spacing-Assistant
Final Approach
RECAT
Controller Assistance Tools
Automation
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pmc
==== Refs
References
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==== Front
Transportation Research Procedia
2352-1465
2352-1465
The Authors. Published by Elsevier B.V.
S2352-1465(22)00736-0
10.1016/j.trpro.2022.12.018
Article
Exploring the Relationship between the Fear of Covid-19, Job Insecurity, Employee Well-Being, and Job Involvement in Flight Personnel
Saruhan Neşe a
Yıldız Ezgi b
Anuk Dilek c
Ünsal Pınar d
a Psychology Department, Istanbul Gedik University, Istanbul, Turkey
b Institute for Aviation Psychology Research, Istanbul University, Istanbul, Turkey
c Department of Consultation-Liaison Psychiatry, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
d Institute for Aviation Psychology Research, Istanbul University, Istanbul, Turkey
9 12 2022
2022
9 12 2022
66 167178
© 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 aviation industry has recently experienced several difficulties due to Covid-19. Especially flight personnel (pilots and cabin crews), who worked under the health-threatening effects of Covid-19, either lost their jobs or were faced with a dramatic decrease in their incomes. The aim of this research is to understand the impact of the fear of Covid-19 on the well-being of flight personnel, to reveal the mediating role of job insecurity on this relationship, and to observe the moderating role of job involvement on the relationship between job insecurity and well-being. The study was conducted with pilots (N=111) and cabin crew members (N=45) who worked for different airlines in Turkey. The total sample size was 154 participants. The results showed that job insecurity played a mediating role as fear of Covid-19 began to have a less significant impact on employee well-being. In addition, the analysis of the moderating role of job involvement showed that high job involvement exacerbates the negative effect of job insecurity on flight personnel's well-being. The results are discussed in relation to the organizational and human resources management practices in airline companies.
Keywords
Fear of Covid-19
Job insecurity
Well-being
Job involvement
Flight personnel
==== Body
pmc
==== Refs
References
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| 0 | PMC9732712 | NO-CC CODE | 2022-12-14 23:29:58 | no | 2022 Dec 9; 66:167-178 | utf-8 | null | null | null | oa_other |
==== Front
Transportation Research Procedia
2352-1465
2352-1465
The Author(s). Published by Elsevier B.V.
S2352-1465(22)00737-2
10.1016/j.trpro.2022.12.019
Article
The Psychological Impact of COVID-19 on Pilot Mental Health and Wellbeing – Quarantine Experiences
Stadler Karien
AeroAssess, PO Box 342001, Dubai, United Arab Emirates
9 12 2022
2022
9 12 2022
66 179186
© 2022 The Author(s). 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.
Introduction. The COVID-19 pandemic proved to be a global challenge and the worst crisis in the aviation industry's history. Pilots were exposed to particularly high psychological stressors due to new preventative measures, stringent quarantine experiences, loss of income and unemployment. Research question. The purpose of the study was to assess the immediate psychological impact of COVID-19 on pilot mental health and wellbeing - with specific reference to quarantine experiences. Method. Research was conducted over the period May-July 2020 and data was gathered via a snowball sampling as well as a voluntary response technique. A total of N=1324 pilots completed a bespoke online biodata survey, the General Anxiety Disorder-7 Questionnaire (GAD-7) and the Patient Health Questionnaire-9 (PHQ-9). Results. The analysis revealed that there was no statistically significant relationship between quarantine context or quarantine duration and pilot mental health. However, a fairly high percentage of respondents reported moderate depression (10,9%) and moderately severe to severe depression (8,8%) when quarantine extended to 10 weeks and beyond. When considering anxiety, 28,3% of the respondents reported moderate symptoms and 12,5% reported severe symptoms of anxiety with extended quarantine. Discussion. The psychological impact of COVID-19 causes serious concerns from a risk management perspective. Screening processes to identify or mitigate risks associated with the psychological impact should be considered. Conclusion. The findings of the study may be useful to formulate interventions that can minimise psychological stressors and improve mental health and resilience as pilots return to normal operations. It can also serve as historical reference or baseline for evaluating prevention, control and treatment programmes in the coming years.
Keywords
COVID-19
quarantine
mental health
pilots
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References
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| 0 | PMC9732713 | NO-CC CODE | 2022-12-14 23:35:56 | no | 2022 Dec 9; 66:179-186 | utf-8 | null | null | null | oa_other |
==== Front
Transportation Research Procedia
2352-1465
2352-1465
Published by Elsevier B.V.
S2352-1465(22)00724-4
10.1016/j.trpro.2022.12.006
Article
Applying psychophysiological coherence training based on HRV-biofeedback to enhance pilots’ resilience and wellbeing
Zhang Jingyi a
Li Wen-Chin a
Andrews Gavin b
a Safety and Accident Investigation Centre, Cranfield University, Bedfordshire, United Kingdom
b HeartMath UK+IRL, United Kingdom
9 12 2022
2022
9 12 2022
66 4956
© 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.
Introduction. The COVID-19 pandemic not only limited pilots’ proficiency in performing routine tasks, but also increased stress levels and operational risk due to new procedures in flight operations related to safety and health regulations. There is, therefore, an increasing need to improve pilots’ mental and physical health to maintain aviation safety
Research question. (1) Does the practice of psychophysiological coherence using heart rate variability (HRV) biofeedback and the Quick Coherence Technique (QCT) improve pilots’ resilience? (2) What effects does psychophysiological coherence practice have on pilots’ resilience and wellbeing?
Method. Eighteen commercial pilots’ perceived stress and wellness were evaluated subjectively by the Perceived Stress Scale (PSS) and Ardell Wellness Self-Assessment (AWSA). They were taught the QCT for facilitating psychophysiological coherence, and their HRV data reflecting automatic nervous system (ANS) activities were collected as they practiced QCT via Inner Balance HRV sensors.
Results. The QCT training improved pilots’ AWSA scores (t = -3.55, p = .002) and decreased PSS scores (t = 6.37, p < .001). Pilots’ post-training HRV were improved with SDNNs higher than pre-training, t = -4.88, p < .001; normalized low frequency (LF) power increased (t = -10.91, p < .001) and low-frequency to high-frequency (LF/HF) ratios increased (t = -3.92, p = .001). Additionally, pilots’ post-training respiration rates were lower than pre-training, t = -2,45, p = .025.
Discussion. Based on the empirical data analysis, HRV-biofeedback QCT can improve psychophysiological coherence and thereby increase pilots’ resilience and wellbeing. Increased post-training SDNNs, normalized LF power, and LF/HF ratio indicate the improvement of ANS control and balance, and stress management capacity. These findings demonstrate the effectiveness of HRV-biofeedback QCT training in improving psychophysiological coherence, which confers real-time and post-practice benefits of optimal energy utility and self-regulation in challenging situations on flight operations and everyday life.
Conclusion. This research demonstrates significant benefits of a short session of HRV-biofeedback QCT on pilots’ resilience and cognitive process by improving psychophysiological coherence. HRV-biofeedback QCT training can be an effective intervention for aviation authorities and airline operators to develop peer support programs for pilots to increase psychological resilience and wellbeing. This may be particularly beneficial given the various challenges presented to pilots in their preparation for return to normal operations.
Keywords
Biofeedback Breathing
Heart Rate Variability
Quick Coherence Technique
Resilience
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References
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Boutcher S.H. Stocker D. Cardiovascular response of young and older males to mental challenge The Journals of Gerontology Series B: Psychological Sciences and Social Sciences 51 5 1996 261 267 10.1093/geronb/51B.5.P261
Bradley R.T. McCraty R. Atkinson M. Tomasino D. Daugherty A. Arguelles L. Emotion self-regulation, psychophysiological coherence, and test anxiety: Results from an experiment using electrophysiological measures Applied Psychophysiology Biofeedback 35 4 2010 261 283 10.1007/s10484-010-9134-x 20559707
CAA The effect on aviation mental health from the covid-19 pandemic and return to re-defined ‘normal’ flight operations (Safety Notice No. SN–2020/014) Civil Aviation Authority 2020 https://publicapps.caa.co.uk/docs/33/SN%20-%20The%20Effect%20on%20Mental%20Health%20From%20Return%20To%20Work%20Due%20to%20Covid%2019.pdf
Cahill J. Cullen P. Anwer S. Wilson S. Gaynor K. Pilot work related stress (WRS), effects on wellbeing and mental health, and coping methods International Journal of Aerospace Psychology 31 2 2021 87 109 10.1080/24721840.2020.1858714
Causse M. Dehais F. Arexis M. Pastor J. Cognitive aging and flight performances in general aviation pilots. Aging Neuropsychology, and Cognition 18 5 2011 544 561 10.1080/13825585.2011.586018
Chrousos G.P. Kino T. Interactive functional specificity of the stress and immune responses: the ying, the yang, and the defense against 2 major classes of bacteria The Journal of Infectious Diseases 192 4 2005 551 555 10.1086/432135 16028122
Gevirtz R. The promise of heart rate variability biofeedback: evidence based applications Biofeedback 41 2013 110 120 10.5298/1081-5937-41.3.01
Goldstein D.S. Bentho O. Park M.Y. Sharabi Y. Low-frequency power of heart rate variability is not a measure of cardiac sympathetic tone but may be a measure of modulation of cardiac autonomic outflows by baroreflexes Experimental physiology 96 12 2011 1255 1261 10.1113/expphysiol.2010.056259 21890520
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Miani P. Kille T. Lee S.Y. Zhang Y. Bates P.R. The impact of the COVID-19 pandemic on current tertiary aviation education and future careers: Students’ perspective Journal of Air Transport Management 94 2021 1 8 10.1016/j.jairtraman.2021.102081
Otzenberger H. Gronfier C. Simon C. Charloux A. Ehrhart J. Piquard F. Brandenberger G. Dynamic heart rate variability: a tool for exploring sympathovagal balance continuously during sleep in men American Journal of Physiology-Heart and Circulatory Physiology 275 3 1998 946 950 10.1152/ajpheart.1998.275.3.H946
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==== Front
J Am Soc Mass Spectrom
J Am Soc Mass Spectrom
js
jamsef
Journal of the American Society for Mass Spectrometry
1044-0305
1879-1123
American Chemical Society
36371691
10.1021/jasms.2c00082
Research Article
Large-Scale Interlaboratory DI-FT-ICR MS Comparability Study Employing Various Systems
https://orcid.org/0000-0003-1976-7976
Forcisi Sara *12
https://orcid.org/0000-0002-1167-6191
Moritz Franco *1
https://orcid.org/0000-0002-3022-3710
Thompson Christopher J. *3
Kanawati Basem 1
Uhl Jenny 1
https://orcid.org/0000-0002-2406-5664
Afonso Carlos 4
Bader Chantal D. 5
Barsch Aiko 6
https://orcid.org/0000-0001-6342-9814
Boughton Berin A. 7†
Chu Rosalie K. 8
Ferey Justine 4‡
https://orcid.org/0000-0002-1283-4390
Fernandez-Lima Francisco 910
Guéguen Céline 11§
Heintz Dimitri 12
Gomez-Hernandez Mario 910
Jang Kyoung-Soon 13
Kessler Nikolas 6
Mangal Vaughn 11
https://orcid.org/0000-0002-1042-5665
Müller Rolf 5
https://orcid.org/0000-0002-8674-0928
Nakabayashi Ryo 14
https://orcid.org/0000-0001-8791-9949
Nicol Edith 15
https://orcid.org/0000-0001-8393-1625
Nicolardi Simone 16
https://orcid.org/0000-0002-5865-8994
Palmblad Magnus 16
Paša-Tolić Ljiljana 8
Porter Jacob 910
Schmitz-Afonso Isabelle 4
Seo Jong Bok 17
Sommella Eduardo 18
https://orcid.org/0000-0003-0556-5564
van der Burgt Yuri E. M. 16
Villette Claire 12
Witt Matthias 6
https://orcid.org/0000-0002-2712-2003
Wittrig Ashley 20#
Wolff Jeremy J. 3
Easterling Michael L. 3
Laukien Frank H. 321
Schmitt-Kopplin Philippe *1222
1 Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany
2 German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
3 Bruker Daltonics Inc., Billerica, Massachusetts 01821, United States
4 COBRA, UMR 6014 et FR 3038, INSA de Rouen, CNRS, IRCOF, Normandie Université, Université de Rouen, 76130 Cedex Mont Saint Aignan, France
5 Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, Saarland University Campus, 66123 Saarbrücken, Germany and Department of Pharmacy, Saarland University, 66123 Saarbrücken, Germany
6 Bruker Daltonik GmbH, Fahrenheitstrasse 4, 28359 Bremen, Germany
7 Metabolomics Australia, School of BioSciences, University of Melbourne, Melbourne, Victoria 3010, Australia
8 Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
9 Department of Chemistry and Biochemistry, Florida International University, 11200 SW Eighth Street, AHC4-233, Miami, Florida 33199, United States
10 Biomolecular Sciences Institute, Florida International University, 11200 Eighth Street, AHC4-211, Miami, Florida 33199, United States
11 Chemistry Department, Trent University, 1600 West Bank Drive, Peterborough, ON K9J 7B8, Canada
12 Plant Imaging and Mass Spectrometry (PIMS), Institut de Biologie Moléculaire des Plantes, CNRS, Université de Strasbourg, 12 rue du Général Zimmer, 67084 Strasbourg, France
13 Bio-Chemical Analysis Team, Korea Basic Science Institute, Cheongju 28119, South Korea
14 Metabolomics Research Group, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
15 Laboratoire de Chimie Moléculaire (LCM), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau, France
16 Center for Proteomics and Metabolomics, Leiden University Medical Center Leiden, 2333 ZC Leiden, The Netherlands
17 Seoul Center, Korea Basic Science Institute, 145, Anam-Ro, Seongbuk-Gu 02841, Seoul, South Korea
18 Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano (SA), Italy
20 ExxonMobil Research and Engineering Company, 1545 Route 22 East, Clinton, New Jersey 08869, United States
21 Department of Chemistry & Chemical Biology, Cambridge, Harvard University, Cambridge, Massachusetts 02138, United States
22 Analytical Food Chemistry, Technical University of Munich, 85354 Freising, Germany
* Email: [email protected].
* Email: [email protected].
* Email: [email protected].
* Email: [email protected].
13 11 2022
07 12 2022
33 12 22032214
18 03 2022
23 08 2022
12 08 2022
© 2022 The Authors. Published by American Chemical Society
2022
The Authors
https://pubs.acs.org/page/policy/authorchoice_ccby_termsofuse.html This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Ultrahigh resolution mass spectrometry (UHR-MS) coupled with direct infusion (DI) electrospray ionization offers a fast solution for accurate untargeted profiling. Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometers have been shown to produce a wealth of insights into complex chemical systems because they enable unambiguous molecular formula assignment even if the vast majority of signals is of unknown identity. Interlaboratory comparisons are required to apply this type of instrumentation in quality control (for food industry or pharmaceuticals), large-scale environmental studies, or clinical diagnostics. Extended comparisons employing different FT-ICR MS instruments with qualitative direct infusion analysis are scarce since the majority of detected compounds cannot be quantified. The extent to which observations can be reproduced by different laboratories remains unknown. We set up a preliminary study which encompassed a set of 17 laboratories around the globe, diverse in instrumental characteristics and applications, to analyze the same sets of extracts from commercially available standard human blood plasma and Standard Reference Material (SRM) for blood plasma (SRM1950), which were delivered at different dilutions or spiked with different concentrations of pesticides. The aim of this study was to assess the extent to which the outputs of differently tuned FT-ICR mass spectrometers, with different technical specifications, are comparable for setting the frames of a future DI-FT-ICR MS ring trial. We concluded that a cluster of five laboratories, with diverse instrumental characteristics, showed comparable and representative performance across all experiments, setting a reference to be used in a future ring trial on blood plasma.
H2020 Research Infrastructures 10.13039/100010666 731077 German Center for Diabetes Research NA G-501901-020 German Center for Diabetes Research NA G-501900-482 Région Normandie 10.13039/501100018696 NA European Regional Development Fund 10.13039/501100008530 HN0001343 Centre National de la Recherche Scientifique 10.13039/501100004794 FR 3624 Agence Nationale de la Recherche 10.13039/501100001665 ANR-11-LABX-0029 document-id-old-9js2c00082
document-id-new-14js2c00082
ccc-price
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pmcIntroduction
The study of complex chemical systems requires instrumentation that captures their chemical space.1 Mass spectrometry is probably the most versatile among the techniques at the disposal of an analytical chemist. It provides high sensitivity and a multitude of means for molecular characterization.
There are two poles toward chemical characterization in mass spectrometry. At one pole there is quantitative hyphenation of separation techniques to tandem mass spectrometry, which provides structural information on the most abundant ions produced from a sample. The chemical information on the less abundant proportion of ions is lost oftentimes as these signals do not produce abundances of fragment ions that suffice for annotation. On the other end, fast profiling using direct infusion electrospray ionization with ultrahigh resolution mass spectrometry (DI-ESI-UHR) allows for qualitative and nonquantitative analyses and unambiguous assignment of molecular formulas to those MS signals that escape annotation by tandem MS.
Fourier transform mass spectrometers such as FT-ICR MS and Orbitrap offer highly resolved, accurate, and precise determination of mass-to-charge ratios (m/z), state of the art prerequisites for the characterization of complex mixtures.2 FT mass spectrometers, in general, have a capacity for very sensitive characterizations of MS features, as analyte ions can be trapped for prolonged periods of time. Within the family of FT instruments, FT ion cyclotron resonance (FT-ICR) MS enables the detection of thousands of peaks in complex matrices at a time, with lower parts-per-billion mass accuracy and even a resolution as high as 2,400,000 at 400 m/z (21T instrumentation).3 Its power to characterize the compositional space of a multitude of complex chemical systems is well acknowledged within disciplines such as metabolomics, petroleomics, foodomics, lipidomics, microbiome analysis, natural organic matter (NOM), and dissolved organic matter (DOM) analysis.3−8
Several works describe the application of DI-MS in answering specific questions by looking at the whole MS profile, eventually integrating orthogonal techniques such as LC-MS or GC-MS for deeper isomeric elucidation.8−11 However, broad applications require strict interlaboratory comparability.12,13 To date, interlaboratory comparability in untargeted metabolomics was mainly tested considering LC-MS or GC-MS, DI-stable isotope dilution MS, and NMR techniques.14−17 Each laboratory employing FT-ICR MS has an interest to achieve results that are the most representative for the analytical matrix under inspection (IUPAC project 2016-015-2-60015,14). To observe the same distinctive features, other laboratories must be able to observe identical and unique analytical patterns, as the same sample set is analyzed.
FT-ICR MS data were investigated by Kirwan et al.16 to study the experimental reproducibility of a large multibatch metabolomics study of mammalian cardiac tissue extracts acquired by means of nanoinfusion FT-ICR MS in one laboratory. They developed a batch correction algorithm based on cubic spline interpolation across quality control (QC) samples.17 Intralab reproducibility was also examined in NOM14,18 and DOM19 investigations. Although it was proven that batch effects and systematic errors within DI-MS data can be controlled using appropriate study designs on one instrument,16 other resources indicate that the overlap of detected signals between two laboratories can be lower than 25%.20 Assessments on the comparability of untargeted DI-MS data produced by different laboratories are under-represented in the present literature.
How can data generated by different laboratories be compared when they used nonquantitative DI-FT-ICR MS?
We set up a preliminary study which encompassed a set of laboratories with a high variability in terms of instrumental characteristics and routine applications. The same sample set and a standard operating procedure (see Supporting Information) were sent to 17 different laboratories worldwide, which have expertise in diverse application areas. The aim of this study was to assess the extent to which the outputs of differently tuned FT-ICR mass spectrometers, with different technical specifications, are comparable for setting the frames of a future DI-MS ring trial.
Three experiments were set up to evaluate typical effects observed when acquiring DI-ESI-FT-ICR mass spectra of standard human blood plasma SPE eluates.
Experiment X is intended to capture how different instruments data (generated by FT-ICR MS of different build) are comparable as matrix effects vary due to variation of whole matrix concentration. We found that matrix effects can generally be reproduced across different laboratories and that appropriate data normalization can produce interlaboratory coefficients of variation below 20% for those laboratories that co-detected the most signals.
Experiment Y simulates strong concentration changes in specified sets of analytes at constant matrix concentration. This is particularly interesting, as it is not clear how many signals native to an analytical matrix are sacrificed due to suppression with internal standards. We found that at least 48% of blood plasma signals get repressed by pesticide spiking and that the effects were very comparable between laboratories with coefficients of variation below 20% across laboratories.
Experiment Z intended to capture how many FT-ICR MS scans need to be accumulated in order to provide a sufficient description of the analytical matrix. This point is of high relevance for routine analysis (e.g., clinical cohort analysis21) and planning of future ring trials. The National Institute of Standards and Technology (NIST) Standard Reference Material (SRM) for blood plasma (SRM 1950)22 was used for this experiment as several hundreds of individual metabolites are quantified and certified for this reference matrix. We found that the accumulation of merely 50 scans, which is performed within 60–90 s, provides 75% of the information acquired after 300 scans with respect to those metabolites certified for this reference material.
A cluster of five laboratories, with diverse instrumental characteristics, achieved the highest comparability across all experiments. Participants of a future ring trial on blood plasma profiling could tune their instruments against the median spectral intensities produced by this cluster of laboratories within this preliminary study.
Experimental Section
Aim of Study
We developed a study design that could be completed by a single person within 8 working hours, at any partner laboratory.
C18-SPE eluates of standard human plasma were prepared at different dilution levels (experiment X) to capture the matrix effect, a major concern in DI-MS analysis. A second batch of the same C18-SPE standard human plasma eluate was spiked with different concentrations of pesticides (experiment Y) to compare both matrix effects derived from internal standards and the upper bounds of dynamic ranges. C18-SPE eluate of a second human plasma standard recognized as reference material (SRM 1950)22 was acquired at different scan numbers. Concentration levels of several hundreds of metabolites are available for SRM 1950. Following the presence of MS signals potentially related to the set of quantified compounds over the course of different scanning times was scheduled to obtain a rough estimate of the minimum number of scans required to produce meaningful data.
Study Design
Seventeen partner laboratories across four continents were recruited for the comparability study (the laboratories identity is kept anonymous, they are classified by alphabetic letters). A kit for the analysis, standard operating procedure (Supporting Information, SI-10) were sent to each participant. Three main experiments (X,Y,Z) were set up:(X) A C18-SPE eluate of standard human blood plasma (P9523, Sigma-Aldrich; HIV, hepatitis B, and hepatitis C, none detected) was delivered in triplicates of five different dilutions with dilution ratios ranging from 1/25 to 1/200 (v/v). Number of scans accumulated: 100 purpose: comparison of matrix effects derived from matrix dilution.
(Y) A C18-SPE eluate of standard human blood plasma (P9523, Sigma-Aldrich; HIV, hepatitis B, and hepatitis C, none detected) was delivered in triplicates at a constant concentration (1/50), spiked with five different concentrations of a mixture of 85 pesticides (LC/MS pesticide standard kit, mix 5, 6 and 7; Agilent Technologies) ranging from 1 to 20 ppb for all 85 compounds. Number of scans accumulated: 100 purpose: comparison of matrix effects derived from internal standards.
(Z) A C18-SPE eluate of standard human blood plasma (NIST SRM 1950 plasma, Sigma-Aldrich; HIV, hepatitis B, and hepatitis C, none detected) delivered as single sample at a constant concentration (1/50) and acquired in triplicate accumulating 50 scans, 100 scans, and 300 scans. Purpose: comparison to the P9523 standard (experiment X) and investigation of the sufficient number of scans for routine profiling.
Sample Preparation and Analysis
Sample Preparation
A pool of citrated plasma sample (P9523, Sigma-Aldrich) was extracted by OMIX C18 solid-phase extraction (SPE) pipet tips (Agilent Technologies, USA) in combination with a liquid-handling system epMotion (Eppendorf, Germany). Five milliliters of plasma was diluted (1:1) with 2% phosphoric acid, vortex mixed for 30 s, and transferred into a 96 well-plate reservoir. 96 Omix C18 100 μL tips (Agilent, A57003100) were placed in epMotion 96 trays and loaded with 100 μL of the acidified plasma. The extraction followed the protocol described in Forcisi et al.7 The same procedure was applied for the preparation of SRM 1950 blood plasma (Sigma-Aldrich). All of the eluates were pooled, vortex-mixed, and split into aliquots following the study design.
Sample Analysis
The 17 laboratories have different FT-ICR MS instruments varying in their magnetic field strength (encompassing 7, 9.4, 12, and 15 T systems) and their ICR cell designs (Infinity or ParaCell). Their field of expertise and application was also very diverse, ranging from the fields of small molecules or metabolomics on different body fluids and biological matrices to proteomics, environmental chemistry or petroleomics, and additionally different combinations of ion source usage (ESI versus MALDI). Each laboratory received a kit containing the same samples to be measured in triplicate. A standard operating procedure (SOP) was established in Munich and distributed to each laboratory participant (see Supporting Information, SI-10). A centralized collection of the acquired data was organized to study the interlaboratory comparability. Each participant in the interlaboratory study was kept anonymous. The part of the study presented here consisted of 10 samples analyzed in triplicate with additional quality controls and blanks resulting in 713 spectra to be calibrated and processed accordingly in files of 4 Mega word (MW; measure of time domain transient length in FT-ICR MS) size containing on average 5094 ± 1168 m/z peaks over a mass range from 150 to 1000 m/z.
Spectral Calibration and Alignment
The authors used a two-step calibration scheme starting by external calibration on-site to remove the influence of local mass error drifts prior measurement, followed by internal calibration using any suitable tool to correct sample-specific space-charge effects. All instruments were calibrated on arginine clusters externally prior measurement. Data from all laboratories were peak-picked using Bruker Compass Data Analysis 4.4 at a signal-to-noise threshold (S/N) ≥ 4. Peak-picked MS lists (m/z, intensity, resolving power) were exported to tab delimited text files. All spectra were calibrated internally against a list of calibration m/z values designed for the study. The calibration list contained 285 m/z peaks composed of 153 theoretical pesticide m/z values as well as 132 human plasma m/z values whose formulas were assigned through an in-house mass-difference-based algorithm (NetCalc)23 and further validated by isotopic fine structure. All m/z values, known from blood plasma and the Agilent pesticide mix, were excluded if they were within 5 ppm proximity to another calibrant. This way, the same calibration list could be used for the spectra generated within experiments X, Y, and Z. Mass spectra were calibrated using kernel-based calibration24 (available upon request). Mass spectra calibrated with a standard deviation of mass measurement error exceeding 300 ppb were excluded. Gibbs peaks were removed on the basis of resolution following Kanawati et al.25 Multiply charged features were removed on the basis of mass defect regions (width = 20 ppm) that were covered by the Pubchem database. Absolute feature intensity cutoffs were adjusted manually using absolute mass defect (AMD) plots.1 Intensity thresholds were adjusted to be the minimum intensity that kept the region 0.1 < AMD < 0.9 at 150 < m/z < 200 empty. Spectra were aligned into an MS feature versus observation matrix using a moving alignment error window of 0.5 ppm width.
Data Processing and Statistics
All computations were performed excluding missing data marked as “NaN” (not a number), except stated otherwise. MS features were kept for further analyses if they met the following criteria after locating triplicate measurements with at least two nonzero detections: (1) Keep triplicates that occur at least twice in at least three laboratories. (2) Count frequency of missing features (missingness [%]) per dilution (across laboratories) and keep features whose missingness is <40% in at least one dilution. The resulting data distributions can be viewed in Figure 1. Details on scaling, imputation, visualization, principal component analysis (PCA), linear regression models, and comparison of signal magnitudes are detailed in Supporting Information, SI-1.
Figure 1 (a) Multivariate dynamic ranges, experiment X. Median-centered PCA on 5th to 95th percentiles of spectral intensities involving 150 spectra from 10 laboratories. The first positive component (ordinate) covers approximately 99.9% of median-centered covariance and is proportional to each laboratory’s spectral signal magnitudes. The second PC (abscissa) covers approximately 0.1% of median-centered covariance and covers the experimental effect. (b) Univariate dynamic ranges, experiment X. Box plots showing the spread of distribution and magnitude of spectral intensities. Note the correspondence of box plot magnitudes to the above PC1. Both the magnitude of differences between laboratories and the outlying behavior of laboratory D are confirmed. The PCA plot is more informative in terms of lab-to-lab comparisons of intensity distributions.
Normalization
Here, no normalization, probabilistic quotient normalization26 (PQN), and smoothed quotient correction (SQC) were compared. PQN is commonly used for the correction of median linear shifts of spectral intensities. SQC was devised here because mass spectrometers from different generations can be equipped with varying ion optics and hardware combined with diverse instrumental settings that cause instrument specific biases. As an example, the instrument of laboratory A has poor ion transmission at m/z < 200, and varying time-of-flight in the hexapole or ion varying accumulation time in the ICR cell will change sensitivity toward lower or higher m/z. Such differences in instrumental characteristics cannot be adjusted with a monoparametric data correction method such as PQN. SQC uses smoothed quotients for data correction: compute a median spectrum R across all spectra T (excluding NaN’s) and compute Q = R/T as performed in PQN. Smooth the quotients Q of each spectrum t in T with an appropriate smoother. Here, smoothed quotients SQ were computed following the matlab function SQ = smooth data (Q, “sgolay”, “Window”, “omitnan”), with “sgolay” being the Savitzky-Golay smoother, “Window” specifying the number of features passed to the smoother at each spectral position, and “omitnan” specifying that NaN’s are to be omitted by the smoother. To avoid smoothing experimental characteristics, a window size of 50% of each spectrum’s nonzero entries was used.
Co-presence Analysis (CPA)
The filtered MS feature matrix was transformed into a binary data matrix with nonzero entries and NaN’s replaced by ones and zeroes, respectively. PCAs were performed at different levels of missingness to locate co-presence clusters (CPCs). CPCs were determined visually in X, Y, and Z experiments. Here, CPA means co-presence PCA on binary data. Mean positions of CPCs on the first and second PCs were computed, and each laboratories’ Euclidean distances to the CPC’s center were computed (jointly normalized to the range [0,1]). Diverging and converging behavior as a function of missingness was visualized (Figure 2 and Figure S11).
Figure 2 Summary of co-presence analysis at different degrees of missingness. See the formation of CPCs (laboratories that remain tightly associated despite increasing overall missingness) in the Supporting Information, SI-4. Vertical lines at 40% missingness indicate the order of laboratory removal from top to bottom as performed in Figure 3. (a) Normalized Euclidean distance of every laboratory’s scores computed on CPA relative to the mean scores of the CPC {A,B,C,D,E} of experiment X. PCAs were computed per missingness level (expressed in %) and dilution. (b) Normalized Euclidean distance of every laboratory’s scores computed on CPA relative to the mean scores of the CPC {A,B,C,D,E,F,J} of experiment Y. PCAs were computed per missingness level (expressed in %) and dilution. (c) Normalized Euclidean distance of every laboratory’s scores computed on CPA relative to the mean scores of the CPC {A,B,D,E} of experiment Z. PCAs were computed per missingness level (expressed in %) and dilution.
Univariate Reproducibility
Coefficients of Variance (CV)
Intra-CVs were computed on triplicates of MS features. Inter-CVs were computed on the features’ triplicate means across all laboratories within the same level in terms of spiking or dilution. As such, inter-CVs report the relative standard error of the means across laboratories.
Multivariate Comparability
Each laboratory’s dilution/spiking data (mean centered per laboratory) were used as generators for PCA models (scores and loadings of the first PC were stored for each laboratory). Loadings of each model generator were applied to compute “pseudoscores” given the data of the nongenerator laboratories. Scores and pseudoscores were transformed to the domain of dilution/spiking levels. That is, scores were normalized to the Euclidean length of the series {−2,–2,–2,–1,–1,–1,0,0,0,1,1,1,2,2,2} of centered dilution/spiking levels. Addition of the constant “3” shifted the resulting scores into the same domain as the sequence of dilution levels {1,1,1,2,2,2,3,3,3,4,4,4,5,5,5}. Normalized scores and pseudoscores were subjected to linear regression. Further, linear regression models of each laboratory’s generator scores against dilution/spiking levels were generated. All models were visualized in a heatmap of laboratories versus laboratories and dilution/spiking levels.
Results and Discussion
Comparability of Laboratory Performances
Raw Data
The data of seven out of 17 laboratories had to be excluded due to asymmetric or split peak shapes, strong contamination or absence of matrix-specific MS feature patterns (Supporting Information, SI-2). The only criterion for the inclusion/exclusion of a laboratory was that the spectra were calibratable (standard deviation of mass measurement error <300 ppb). The criteria were not known to participating laboratories in advance. MS peak asymmetry and splitting are usually caused by the gas pressure in the ICR cell increasing from e–10 mbar toward e–9 mbar. Drifts of excitation powers in the ICR cell as well as ICR-cell overloading all result in aberrant shapes and trajectories of ion clouds within the ICR cell and the magnetic field. The new dynamically harmonized cell provided in new instrumentation enables better control over some of the underlying phenomena.27 Strong peak shifts (several ppm) can occur at unstable electrospray as fluctuating ionization efficiency leads to different ion densities in the ICR cell distorting the mass-error distribution. Data matrices of plasma dilution series (experiment X), spiked plasma series (experiment Y), and varying depth of acquisition (experiment Z) were computed (see Experimental Section). The resulting matrices X and Y comprised 7974, 8918, and 8163 features over 150, 150, and 90 samples, respectively. A first assessment of laboratory comparability was performed using the filtered raw data. Figure 1 visualizes a PCA on a matrix composed of the 5th to 95th percentiles of the intensities in each spectrum of experiment X (see Experimental Section).
The first PC’s magnitude is proportional to spectral signal strength, while the second PC reflects the general effect of the dilution. PCs 1 and 2 covered 99.9 and 0.10% of covariance. The scatter plot of both scores shows how overall signal magnitudes decrease as a function of dilution level (increasing matrix concentration). Therefore, increasing matrix concentrations lead to increasing signal magnitudes in all laboratories except for laboratory D. Laboratories F, G, I, and J do not show strong responses to matrix concentration in overall signal magnitudes in Figure 1a,b. Laboratory H shows a flat but monotone increase on Figure 1a and no response in Figure 1b. The behavior of these laboratories allows for two different hypotheses: (1) the analytical systems are in suppression already in the lowest matrix concentration; (2) the analytical system has strong contaminations or background that are suppressed with increasing matrix concentration. The Multivariate Comparability section will deliver which of the two hypotheses is correct. Figure 1 displays the high diversity of dynamic ranges in terms of signal magnitudes and responses to experimental intervention. The PCA’s on matrices Y and Z are not shown since the results are similar.
Univariate Comparability
Different laboratories may detect diverse sets of MS features and produce different MS traces, depending on instrument parametrization, purpose of instrument use and contaminations. Here, two strategies are followed to select the laboratories for comparison: (1) analysis of CPCs on binary data and (2) removal of laboratory-specific biases by means of smoothed quotient correction (detailed rationale in the Supporting Information, SI-3).
Co-presence Analysis
While it is common to exclude features at more than 10% missingness, it is important to first identify what spectra detect the same features. The data from all experiments were transformed into binary data matrices with ones and zeros indicating feature presence and absence to compute co-detection tables (here, “co-detection” means the joint detection of a feature in at least two laboratories). Table S1 shows that laboratories A–E co-detected 4952, 5155, and 5522 MS features on average in experiments X, Y, and Z, respectively. Correspondingly, laboratories F–J co-detected 2761, 3556, and 3225 MS features on average. The overlap of laboratories A–E among each other computes to 84, 83, and 91% in X, Y, and Z, respectively. These laboratories’ average overlap with laboratories F–J amounts to 62, 69, and 69%. Globally, laboratory E co-detected most features with the other laboratories followed by laboratory B. Laboratories F, H, and I have the least feature counts, sharing the least number of features with the other laboratories. More detailed insights on aberrant co-detection can be obtained by performing a PCA on binarized data. The scores of a principal component analysis on such a binary data matrix indicate feature frequencies within CPCs. Here, CPAs were performed within the dilution levels for experiment X, within spiking levels for experiment Y and within levels of scan number for experiment Z.
CPA scores were generated at varying levels of missingness, and their normalized Euclidean distance to the most robust CPC was computed (Figures S6, S7, and S8). Figure 2 shows the convolutions of co-presence structure along with increasing missingness for experiments X, Y, and Z. Laboratory I had the most aberrant co-presence structure across all levels of missingness, closely followed by laboratories H and F. Laboratories A, B, C, D, and E showed consistently similar peak detection across all experiments and levels of missingness. Those laboratories are expected to show the best intercomparability. Any clustering was independent of magnetic field strength or type of analyzer cell.
Global Co-detection between Experiments X, Y, and Z
Table S1 contains a comparison of the overlap of MS features between all three experiments computed at 40% missingness. Two thirds of all MS features detected within experiments X and Z overlapped, while one-third was found to be specific for either X or Z. Comparing the spiking experiment Y to the matrices of X and Z revealed that 48% (3815 features) and 57% (4668) of all MS features originally contained in X and Z cannot be detected when spiking with pesticides. Spiking a mixture of 85 pesticides produced 4759 (versus X) and 5423 (versus Z) features that were never detected in blood plasma extracts. Future ring trials for routine analyses will have to assess the type and concentration of internal standards thoroughly as was done in Chekmeneva et al.28
Normalization and Univariate Comparability
Here, the major measure to assess interlaboratory comparability is the coefficient of variation computed on co-detected feature intensities of the laboratories to be compared. Figure 3 shows the results of this computation at different levels of laboratory exclusion on the abscissa. Inter-CVs in all plots of Figure 3 decrease as the most dissimilar laboratories are removed from computation following the vertical dashed lines of Figure 2 from top to bottom. This result indicates that CPA captures the similarity between detection patterns well. The exception to that observation was the convolution of inter-CVs on raw data in experiment Y. Pesticide spiking emphasizes the differences in the instruments’ dynamic ranges and this difference appears to be stronger than the effect of MS feature co-detection. Different strategies of spectral normalization (PQN and SQC) and feature scaling (UVC and L2) were tested on the data available, knowing that there are a multitude of effects influencing the comparability of mass spectra between different batches and laboratories. Normalization methods act on the distribution of signal intensities in a spectrum, while scaling methods act on the magnitude of an individual MS feature across spectra. We found that feature scaling across all laboratories at once distorts intensity distributions. Here, scaling was used on each laboratory’s data individually. Figure 3 shows that the application of the L2 norm (Euclidean norm) was always superior to UV scaling. L2 exercises the greatest impact in terms of inter-CV improvement relative to raw data among individual correction methods tested. At the same time, any combination involving SQC was superior to PQN except for experiment Z. Here, PQN-L2 and SQC-L2 performed the best resulting in exactly the same results while both PQN-UV and SQC-UV performed worse than raw data. A likely reason for this behavior is that the denominator in the computation of standard deviation (itself the denominator in UV scaling) is corrected for degrees of freedom, which exercises a large effect in small sample sizes. The removal of aberrant laboratories had the least noticeable effect on inter-CVs in terms of absolute removal of error. The improvement of inter-CVs was the weakest in experiment Z when data correction was used. No matrix composition was modified in experiment Z and the single sources of variation were instrument specific biases and number of scans. While Figure 3 displays inter-CVs only, it is to be noted that any combination of scaling and normalization methods did not alter intra-CV significantly (Supporting Information, SI-5).
Figure 3 Convolution of CVs as a function of increasing co-detection of MS features on experiments X, Y, and Z. Inter- and intra-CVs were computed across the entire data set iteratively removing laboratories following Figure 2 at 40% missingness, beginning with the uppermost (most aberrant) laboratory, with the members of CPCs being the last to be removed. Inter- and intra-CVs of the following data treatments are compared: raw (Raw), raw with laboratory Euclidean (L2), raw with laboratory UV scaling (UV), PQN on raw data (PQN), SQC on raw data (SQC), PQN followed by L2 (PQN_L2), PQN followed by UV (PQN_UV), SQC followed by L2 (SQC_L2), and SQC followed by UV (SQC_UV). The effects of both SQC and Euclidean-norm affect inter-CVs majorly, while intra-CVs do not change due to Euclidean normalization (of features) and improve only slightly as SQC is applied (a zoom-in toward intra-CVs is provided in the Supporting Information, SI-6).
The results substantiate that L2 normalization should be used for feature scaling and that SQC corrects interlaboratory variation majorly and that intra-CV magnitude is not an artifact of data treatment.
Multivariate Comparability
Multivariate comparability was assessed in a cross validation scheme for experiments X and Y. A PCA model was built for each laboratory. Each laboratory’s original model was then applied on the data of all other laboratories, generating validation scores that were scaled to same Euclidean length. R2 values of regression analyses between a laboratory’s original model score (rows in Figure 4) and the validation scores plus dilution or spiking levels (columns in Figure 4) were visualized in a heat map. Figure 4 shows the cross-validation map computed on the experiment X (matrix effect) at 40% missingness per dilution level and laboratory L2 norm applied on features. The interesting case of laboratory H shows that applying its model to the data other laboratories generated high R2 values (R2 > 0.8). A linear regression of the H-scores against dilution levels showed good linearity in the scores (R2 = 0.94). At the same time, the data of H did not perform well using the other laboratories’ PCA models. H is one of the two laboratories with a strongly aberrant missingness structure in experiments X, Y, and Z (Figure 2). The result can be interpreted as follows: Those features that follow matrix dilution levels in H show the same trends in the other laboratories. However, these features receive small loadings in the PCAs of other laboratories. This insight is supported by the scatter plots corresponding to Figure 4 (Figure S14). Recall the case of laboratory D, which showed decreasing signal magnitudes with increasing sample concentration. Figure 4 implies that the dependency of signal magnitudes on matrix concentration was a sign of increasing suppression strong background ions (Figure S16). The models of laboratory D produced an average R2 ≈ 0.7 when applied to the data of other laboratories. Likewise, the loadings of other laboratories produced an average R2 ≈ 0.75 when applied on the data of D. The cross-validation map of experiment Y (Figure S13) shows perfect R2 values for all combinations reflecting excellent reproduction the effects pesticide spiking exercised on the analytical matrix. The scatter-plot map for the cross-validation of experiment X shows that the FT-ICR analyzers were challenged by large ion abundances at spiking levels 15 and 20 ppb (Figure S15).
Figure 4 PCA cross-validation map for experiment X.
Spectral normalization did not have significant effects on the cross-validation experiment. Univariate comparability as measured by inter-CVs did not appear to be of major importance for multivariate comparability. One possible explanation would derive from the central limit theorem: PCA scores represent a weighted mean of the hundreds or thousands of features’ univariate signal magnitudes.
Evaluation of Scanning Depth
High-throughput routine analyses are required to be fast and robust and need to detect features of relevance. Experiment Z was performed using the NIST SRM 1950 standard for which quantified metabolite identities and concentrations are available. We manually extracted the names and identifiers for 682 small molecules, amounting to 430 individual compounds and 319 unique molecular formulas from Simón-Manso et al. All 319 molecular formulas transformed into seven fundamental ion types where possible: [M + H]+, [M + CH4O + H]+, [M + Na]+, [M + H2O + H]+, [M + CHO2Na + H]+, [M – NH3 + H]+, and [M – H2O + H]+. All ion types were combined combinatorically to build homodimers and homotrimers of different adduct types. The expanded list encompassed 26,908 positive ionization mode m/z values to be searched in the Z experiment. The raw data matrices of experiments X and Z were fused at 1 ppm error and matched against the built SRM 1950 metabolic feature collection. Mass matching at a 0.5 and 1 ppm search window size resulted in 1111 and 1345 hits against the fused XZ matrix.
While laboratory B showed significantly more hits than all other laboratories, only laboratories F and I detected significantly fewer putatively annotated MS features (Figure 5a). Setting the number of detected features after 300 scans to 100% for each laboratory, 76 and 89% of all SRM 1950 features are detected after 50 and 100 scans, respectively. SRM 1950 annotated MS features showed significantly higher detection frequencies compared to detection frequencies in the corresponding data sets (Figure 5b). Experiment Z showed consistently higher detection frequencies compared to experiment X, which is partially due to longer scanning times and no variation of matrix composition in experiment Z. Figure 5 and experiment Z imply that (i) features detected within this study are representative for the analytical matrix and that it is more likely to detect metabolic features that are already known; (ii) the accumulation of merely 50 scans, which is performed within 60–90 s, provides a read out that is already 75% complete (compared to acquisition of 300 scans, which takes approximately 10 min depending on instrumental settings). The detection of isotopic fine structures naturally requires longer scanning times.27
Figure 5 Putatively annotated signals in SRM 1950 blood plasma per scanning number. (a) Number of MS features putatively annotated against SRM 1950 metabolites per number of accumulated scans for each participant. (b) Comparison of the MS feature frequency (percentage of valid triplicate detection across all laboratories) between the MS features co-detected in X and Z alone against the frequency of those features with SRM 1950 annotations within either experiment.
Conclusion
This preliminary interlaboratory study was set up to comprehend whether different FT-ICR mass spectrometers across the globe, at their routine performance, have the aptitude to detect the same signals generated on the same sample sets. The subsequent question was whether feature intensities were comparable and what data correction technique could be applied to minimize interlaboratory CVs. The global aim was to collect the adequate experience and knowledge for setting up a future DI-FT-ICR MS ring trial. The variability in laboratory performance across the FT-ICR community around the world was found to be significant, likely due to diverse scientific scopes, some of which may not fall into the field of analyzing small molecules.
The data provided by seven out of 17 laboratories had to be excluded because either MS-peak deformation, lack of signals to be calibrated or strong contaminations hindered calibration to below 300 ppb of mass error standard deviation. The remaining laboratories exhibited strong variability in terms of signal magnitudes at smaller or larger m/z values and number of valid triplicate features detected. Co-detection analyses across three different experiments showed that laboratories A, B, C, D, and E co-detect up to 90% their valid triplicate signals. Laboratories B and E were found to be the most representative for all laboratories. Globally, 67 and 69% of the MS features detected in experiments X and Z overlapped, indicating a good representation of MS features typical for blood plasma SPE extracts. In turn, pesticide spiking caused the loss of almost half of the MS features detected in X, substantiating that the type and concentration of authentic standards have to be evaluated carefully when standard addition is performed in DI-MS. Comparisons of interlaboratory CVs consistently showed that smoothed quotient correction followed by scaling MS features on the L2 norm within each laboratory individually resulted in median CVs between 10 and 20%. Multivariate cross-validation on experiments X and Y showed that multivariate comparability of experimental effects was acceptable not necessarily depending on univariate comparability.
In effect, an appropriate strategy toward interlaboratory comparability for untargeted DI-FT-ICR MS would encompass the following steps: (1) Optimize instrumental parameters to meet the detection pattern of the laboratories that showed best co-detection (e.g., B and E). (2) Define a study design that is representative of the analytical task and maintain a constant batch size. (3) Always use the exact same amount of quality control samples that are randomly distributed in the batch so that L2 normalization can be performed. (4) Perform SQC toward the median of QCs and use the L2 norm of the QCs to scale the samples in question. We suggest the above points as one step toward applications of untargeted diagnostic UHR-MS profiling for fields of application such as quality control in food industries, pharmaceuticals, or clinical phenome centers.
Finally, experiment Z, using the SRM 1950 blood plasma standard, showed that 50 scans (60–90 s scanning time) were found to be sufficient to detect 75% of all potential SRM 1950 metabolites detected at 300 scans (8–10 min scanning time). These results suggest that FT-ICR mass spectrometers can be used for routine high-throughput measurements. Follow-up studies could encompass ring trials with cloned instruments (7Ts, 9Ts, 12Ts, Infinity versus ParaCell), clusters of laboratories of similar scope and expertise (proteomics, lipidomics, petroleomics, etc.), and more elaborate study designs (e.g., including a clinical study).
Supporting Information Available
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/jasms.2c00082.Experimental information, supporting concepts and figures (PDF)
Table S1 (XLSX)
Supplementary Material
js2c00082_si_001.pdf
js2c00082_si_002.xlsx
Author Present Address
† Australian National Phenome Centre, Murdoch University, Harry Perkins Institute of Medical Research, 5 Robin Warren Drive, 6150 Murdoch, Western Australia
Author Present Address
‡ Metatoul-AXIOM Platform, MetaboHUB, Toxalim, INRAE, Toulouse, France. Toxalim, Toulouse University, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
Author Present Address
§ Département de chimie, Université de Sherbrooke, 2500 Boulevard de l’université, Sherbrooke, Québec, J1K 2R1, Canada.
Author Present Address
# ExxonMobil Chemical Company, 22777 Springwoods Village Parkway, Spring, TX 77389.
Author Contributions
Conception, study design and logistics: S.F., F.M., C.J.T., B.K., J.U., A.B., M.L.E., F.L., P.S.-K. On-site coordination of the experiments: S.F., F.M., B.K., J.U., C.A., B.A.B., F.F.-L., C.G., D.H., K.-S.J., R.M., R.N., E.N., M.P., L.P.-T., J.B.S., E.S., M.W., A.W., J.W., P.S.-K. Preparation and distribution of samples: S.F., F.M., J.U. Development, distribution and revision of standard operating procedure (SOP): S.F., F.M., C.J.T., B.K., J.U., S.N., M.P., Y.E.M.vdB., M.W. Data acquisition: S.F., F.M., B.K., C.D.B., B.A.B., R.K.C., J.F., F.F.-L., M.G.-H., K.-S.J., V.M., R.N., E.N., S.N., M.P., J.P., I.S.-A., J.B.S., E.S., Y.E.M.vdB., C.V., M.W., A.W., J.W. Data analysis, interpretation and drafting of the manuscript: S.F., F.M., N.K., C.J.T. Critical revision of the manuscript: S.F., F.M., C.J.T., B.K., J.U., C.D.B., B.A.B., F.F.-L., C.G., K.-S.J., E.N., E.S., C.V., M.W., P.S.-K. S.F., F.M., and C.J.T. contributed equally.
The authors declare the following competing financial interest(s): A.B., N.K., and M.W. are employees of Bruker Daltonik GmbH, F.H.L. is President and CEO of the Bruker Corporation, and C.J.T. and J.W. were employees of Bruker Daltonics Inc. which manufactures and sells mass spectrometers and software used in this study.
Acknowledgments
This research was supported by the German Center for Diabetes Research (DZD; Grants G-501900-482 and G-501901-020), the European Regional Development Fund (ERDF) No. HN0001343, the European Union’s Horizon 2020 Research Infrastructures program (Grant Agreement 731077), the Région Normandie, the Laboratoire d’Excellence (LabEx) SynOrg (ANR-11-LABX-0029), and the national FT-ICR network (FR 3624 CNRS). A portion of the research was performed using EMSL (grid.436923.9), a DOE Office of Science User Facility sponsored by the Biological and Environmental Research program.
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Laber S. ; Forcisi S. ; Bentley L. ; Petzold J. ; Moritz F. ; Smirnov K. S. ; Al Sadat L. ; Williamson I. ; Strobel S. ; Agnew T. ; Sengupta S. ; Nicol T. ; Grallert H. ; Heier M. ; Honecker J. ; Mianne J. ; Teboul L. ; Dumbell R. ; Long H. ; Simon M. ; Lindgren C. ; Bickmore W. A. ; Hauner H. ; Schmitt-Kopplin P. ; Claussnitzer M. ; Cox R. D. Linking the FTO Obesity rs1421085 Variant Circuitry to Cellular, Metabolic, and Organismal Phenotypes in Vivo. Sci. Adv. 2021, 7 , eabg0108 10.1126/sciadv.abg0108.34290091
Simon-Manso Y. ; Lowenthal M. S. ; Kilpatrick L. E. ; Sampson M. L. ; Telu K. H. ; Rudnick P. A. ; Mallard W. G. ; Bearden D. W. ; Schock T. B. ; Tchekhovskoi D. V. ; Blonder N. ; Yan X. ; Liang Y. ; Zheng Y. ; Wallace W. E. ; Neta P. ; Phinney K. W. ; Remaley A. T. ; Stein S. E. Metabolite Profiling of a NIST Standard Reference Material for Human Plasma (SRM 1950): GC-MSLC-MSNMR, and Clinical Laboratory Analyses, Libraries, and Web-Based Resources. Anal. Chem. 2013, 85 , 11725–11731. 10.1021/ac402503m.24147600
Tziotis D. ; Hertkorn N. ; Schmitt-Kopplin P. Kendrick-Analogous Network Visualisation of Ion Cyclotron Resonance Fourier Transform Mass Spectra: Improved Options for the Assignment of Elemental Compositions and the Classification of Organic Molecular Complexity. Eur. J. Mass Spectrom. 2011, 17 (4 ), 415–421. 10.1255/ejms.1135.
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| 36371691 | PMC9732881 | NO-CC CODE | 2022-12-14 23:35:59 | no | J Am Soc Mass Spectrom. 2022 Dec 7; 33(12):2203-2214 | utf-8 | J Am Soc Mass Spectrom | 2,022 | 10.1021/jasms.2c00082 | oa_other |
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J Am Soc Mass Spectrom
J Am Soc Mass Spectrom
js
jamsef
Journal of the American Society for Mass Spectrometry
1044-0305
1879-1123
American Chemical Society
36371691
10.1021/jasms.2c00082
Research Article
Large-Scale Interlaboratory DI-FT-ICR MS Comparability Study Employing Various Systems
https://orcid.org/0000-0003-1976-7976
Forcisi Sara *12
https://orcid.org/0000-0002-1167-6191
Moritz Franco *1
https://orcid.org/0000-0002-3022-3710
Thompson Christopher J. *3
Kanawati Basem 1
Uhl Jenny 1
https://orcid.org/0000-0002-2406-5664
Afonso Carlos 4
Bader Chantal D. 5
Barsch Aiko 6
https://orcid.org/0000-0001-6342-9814
Boughton Berin A. 7†
Chu Rosalie K. 8
Ferey Justine 4‡
https://orcid.org/0000-0002-1283-4390
Fernandez-Lima Francisco 910
Guéguen Céline 11§
Heintz Dimitri 12
Gomez-Hernandez Mario 910
Jang Kyoung-Soon 13
Kessler Nikolas 6
Mangal Vaughn 11
https://orcid.org/0000-0002-1042-5665
Müller Rolf 5
https://orcid.org/0000-0002-8674-0928
Nakabayashi Ryo 14
https://orcid.org/0000-0001-8791-9949
Nicol Edith 15
https://orcid.org/0000-0001-8393-1625
Nicolardi Simone 16
https://orcid.org/0000-0002-5865-8994
Palmblad Magnus 16
Paša-Tolić Ljiljana 8
Porter Jacob 910
Schmitz-Afonso Isabelle 4
Seo Jong Bok 17
Sommella Eduardo 18
https://orcid.org/0000-0003-0556-5564
van der Burgt Yuri E. M. 16
Villette Claire 12
Witt Matthias 6
https://orcid.org/0000-0002-2712-2003
Wittrig Ashley 20#
Wolff Jeremy J. 3
Easterling Michael L. 3
Laukien Frank H. 321
Schmitt-Kopplin Philippe *1222
1 Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany
2 German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
3 Bruker Daltonics Inc., Billerica, Massachusetts 01821, United States
4 COBRA, UMR 6014 et FR 3038, INSA de Rouen, CNRS, IRCOF, Normandie Université, Université de Rouen, 76130 Cedex Mont Saint Aignan, France
5 Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, Saarland University Campus, 66123 Saarbrücken, Germany and Department of Pharmacy, Saarland University, 66123 Saarbrücken, Germany
6 Bruker Daltonik GmbH, Fahrenheitstrasse 4, 28359 Bremen, Germany
7 Metabolomics Australia, School of BioSciences, University of Melbourne, Melbourne, Victoria 3010, Australia
8 Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
9 Department of Chemistry and Biochemistry, Florida International University, 11200 SW Eighth Street, AHC4-233, Miami, Florida 33199, United States
10 Biomolecular Sciences Institute, Florida International University, 11200 Eighth Street, AHC4-211, Miami, Florida 33199, United States
11 Chemistry Department, Trent University, 1600 West Bank Drive, Peterborough, ON K9J 7B8, Canada
12 Plant Imaging and Mass Spectrometry (PIMS), Institut de Biologie Moléculaire des Plantes, CNRS, Université de Strasbourg, 12 rue du Général Zimmer, 67084 Strasbourg, France
13 Bio-Chemical Analysis Team, Korea Basic Science Institute, Cheongju 28119, South Korea
14 Metabolomics Research Group, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
15 Laboratoire de Chimie Moléculaire (LCM), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau, France
16 Center for Proteomics and Metabolomics, Leiden University Medical Center Leiden, 2333 ZC Leiden, The Netherlands
17 Seoul Center, Korea Basic Science Institute, 145, Anam-Ro, Seongbuk-Gu 02841, Seoul, South Korea
18 Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano (SA), Italy
20 ExxonMobil Research and Engineering Company, 1545 Route 22 East, Clinton, New Jersey 08869, United States
21 Department of Chemistry & Chemical Biology, Cambridge, Harvard University, Cambridge, Massachusetts 02138, United States
22 Analytical Food Chemistry, Technical University of Munich, 85354 Freising, Germany
* Email: [email protected].
* Email: [email protected].
* Email: [email protected].
* Email: [email protected].
13 11 2022
07 12 2022
33 12 22032214
18 03 2022
23 08 2022
12 08 2022
© 2022 The Authors. Published by American Chemical Society
2022
The Authors
https://pubs.acs.org/page/policy/authorchoice_ccby_termsofuse.html This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Ultrahigh resolution mass spectrometry (UHR-MS) coupled with direct infusion (DI) electrospray ionization offers a fast solution for accurate untargeted profiling. Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometers have been shown to produce a wealth of insights into complex chemical systems because they enable unambiguous molecular formula assignment even if the vast majority of signals is of unknown identity. Interlaboratory comparisons are required to apply this type of instrumentation in quality control (for food industry or pharmaceuticals), large-scale environmental studies, or clinical diagnostics. Extended comparisons employing different FT-ICR MS instruments with qualitative direct infusion analysis are scarce since the majority of detected compounds cannot be quantified. The extent to which observations can be reproduced by different laboratories remains unknown. We set up a preliminary study which encompassed a set of 17 laboratories around the globe, diverse in instrumental characteristics and applications, to analyze the same sets of extracts from commercially available standard human blood plasma and Standard Reference Material (SRM) for blood plasma (SRM1950), which were delivered at different dilutions or spiked with different concentrations of pesticides. The aim of this study was to assess the extent to which the outputs of differently tuned FT-ICR mass spectrometers, with different technical specifications, are comparable for setting the frames of a future DI-FT-ICR MS ring trial. We concluded that a cluster of five laboratories, with diverse instrumental characteristics, showed comparable and representative performance across all experiments, setting a reference to be used in a future ring trial on blood plasma.
H2020 Research Infrastructures 10.13039/100010666 731077 German Center for Diabetes Research NA G-501901-020 German Center for Diabetes Research NA G-501900-482 Région Normandie 10.13039/501100018696 NA European Regional Development Fund 10.13039/501100008530 HN0001343 Centre National de la Recherche Scientifique 10.13039/501100004794 FR 3624 Agence Nationale de la Recherche 10.13039/501100001665 ANR-11-LABX-0029 document-id-old-9js2c00082
document-id-new-14js2c00082
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pmcIntroduction
The study of complex chemical systems requires instrumentation that captures their chemical space.1 Mass spectrometry is probably the most versatile among the techniques at the disposal of an analytical chemist. It provides high sensitivity and a multitude of means for molecular characterization.
There are two poles toward chemical characterization in mass spectrometry. At one pole there is quantitative hyphenation of separation techniques to tandem mass spectrometry, which provides structural information on the most abundant ions produced from a sample. The chemical information on the less abundant proportion of ions is lost oftentimes as these signals do not produce abundances of fragment ions that suffice for annotation. On the other end, fast profiling using direct infusion electrospray ionization with ultrahigh resolution mass spectrometry (DI-ESI-UHR) allows for qualitative and nonquantitative analyses and unambiguous assignment of molecular formulas to those MS signals that escape annotation by tandem MS.
Fourier transform mass spectrometers such as FT-ICR MS and Orbitrap offer highly resolved, accurate, and precise determination of mass-to-charge ratios (m/z), state of the art prerequisites for the characterization of complex mixtures.2 FT mass spectrometers, in general, have a capacity for very sensitive characterizations of MS features, as analyte ions can be trapped for prolonged periods of time. Within the family of FT instruments, FT ion cyclotron resonance (FT-ICR) MS enables the detection of thousands of peaks in complex matrices at a time, with lower parts-per-billion mass accuracy and even a resolution as high as 2,400,000 at 400 m/z (21T instrumentation).3 Its power to characterize the compositional space of a multitude of complex chemical systems is well acknowledged within disciplines such as metabolomics, petroleomics, foodomics, lipidomics, microbiome analysis, natural organic matter (NOM), and dissolved organic matter (DOM) analysis.3−8
Several works describe the application of DI-MS in answering specific questions by looking at the whole MS profile, eventually integrating orthogonal techniques such as LC-MS or GC-MS for deeper isomeric elucidation.8−11 However, broad applications require strict interlaboratory comparability.12,13 To date, interlaboratory comparability in untargeted metabolomics was mainly tested considering LC-MS or GC-MS, DI-stable isotope dilution MS, and NMR techniques.14−17 Each laboratory employing FT-ICR MS has an interest to achieve results that are the most representative for the analytical matrix under inspection (IUPAC project 2016-015-2-60015,14). To observe the same distinctive features, other laboratories must be able to observe identical and unique analytical patterns, as the same sample set is analyzed.
FT-ICR MS data were investigated by Kirwan et al.16 to study the experimental reproducibility of a large multibatch metabolomics study of mammalian cardiac tissue extracts acquired by means of nanoinfusion FT-ICR MS in one laboratory. They developed a batch correction algorithm based on cubic spline interpolation across quality control (QC) samples.17 Intralab reproducibility was also examined in NOM14,18 and DOM19 investigations. Although it was proven that batch effects and systematic errors within DI-MS data can be controlled using appropriate study designs on one instrument,16 other resources indicate that the overlap of detected signals between two laboratories can be lower than 25%.20 Assessments on the comparability of untargeted DI-MS data produced by different laboratories are under-represented in the present literature.
How can data generated by different laboratories be compared when they used nonquantitative DI-FT-ICR MS?
We set up a preliminary study which encompassed a set of laboratories with a high variability in terms of instrumental characteristics and routine applications. The same sample set and a standard operating procedure (see Supporting Information) were sent to 17 different laboratories worldwide, which have expertise in diverse application areas. The aim of this study was to assess the extent to which the outputs of differently tuned FT-ICR mass spectrometers, with different technical specifications, are comparable for setting the frames of a future DI-MS ring trial.
Three experiments were set up to evaluate typical effects observed when acquiring DI-ESI-FT-ICR mass spectra of standard human blood plasma SPE eluates.
Experiment X is intended to capture how different instruments data (generated by FT-ICR MS of different build) are comparable as matrix effects vary due to variation of whole matrix concentration. We found that matrix effects can generally be reproduced across different laboratories and that appropriate data normalization can produce interlaboratory coefficients of variation below 20% for those laboratories that co-detected the most signals.
Experiment Y simulates strong concentration changes in specified sets of analytes at constant matrix concentration. This is particularly interesting, as it is not clear how many signals native to an analytical matrix are sacrificed due to suppression with internal standards. We found that at least 48% of blood plasma signals get repressed by pesticide spiking and that the effects were very comparable between laboratories with coefficients of variation below 20% across laboratories.
Experiment Z intended to capture how many FT-ICR MS scans need to be accumulated in order to provide a sufficient description of the analytical matrix. This point is of high relevance for routine analysis (e.g., clinical cohort analysis21) and planning of future ring trials. The National Institute of Standards and Technology (NIST) Standard Reference Material (SRM) for blood plasma (SRM 1950)22 was used for this experiment as several hundreds of individual metabolites are quantified and certified for this reference matrix. We found that the accumulation of merely 50 scans, which is performed within 60–90 s, provides 75% of the information acquired after 300 scans with respect to those metabolites certified for this reference material.
A cluster of five laboratories, with diverse instrumental characteristics, achieved the highest comparability across all experiments. Participants of a future ring trial on blood plasma profiling could tune their instruments against the median spectral intensities produced by this cluster of laboratories within this preliminary study.
Experimental Section
Aim of Study
We developed a study design that could be completed by a single person within 8 working hours, at any partner laboratory.
C18-SPE eluates of standard human plasma were prepared at different dilution levels (experiment X) to capture the matrix effect, a major concern in DI-MS analysis. A second batch of the same C18-SPE standard human plasma eluate was spiked with different concentrations of pesticides (experiment Y) to compare both matrix effects derived from internal standards and the upper bounds of dynamic ranges. C18-SPE eluate of a second human plasma standard recognized as reference material (SRM 1950)22 was acquired at different scan numbers. Concentration levels of several hundreds of metabolites are available for SRM 1950. Following the presence of MS signals potentially related to the set of quantified compounds over the course of different scanning times was scheduled to obtain a rough estimate of the minimum number of scans required to produce meaningful data.
Study Design
Seventeen partner laboratories across four continents were recruited for the comparability study (the laboratories identity is kept anonymous, they are classified by alphabetic letters). A kit for the analysis, standard operating procedure (Supporting Information, SI-10) were sent to each participant. Three main experiments (X,Y,Z) were set up:(X) A C18-SPE eluate of standard human blood plasma (P9523, Sigma-Aldrich; HIV, hepatitis B, and hepatitis C, none detected) was delivered in triplicates of five different dilutions with dilution ratios ranging from 1/25 to 1/200 (v/v). Number of scans accumulated: 100 purpose: comparison of matrix effects derived from matrix dilution.
(Y) A C18-SPE eluate of standard human blood plasma (P9523, Sigma-Aldrich; HIV, hepatitis B, and hepatitis C, none detected) was delivered in triplicates at a constant concentration (1/50), spiked with five different concentrations of a mixture of 85 pesticides (LC/MS pesticide standard kit, mix 5, 6 and 7; Agilent Technologies) ranging from 1 to 20 ppb for all 85 compounds. Number of scans accumulated: 100 purpose: comparison of matrix effects derived from internal standards.
(Z) A C18-SPE eluate of standard human blood plasma (NIST SRM 1950 plasma, Sigma-Aldrich; HIV, hepatitis B, and hepatitis C, none detected) delivered as single sample at a constant concentration (1/50) and acquired in triplicate accumulating 50 scans, 100 scans, and 300 scans. Purpose: comparison to the P9523 standard (experiment X) and investigation of the sufficient number of scans for routine profiling.
Sample Preparation and Analysis
Sample Preparation
A pool of citrated plasma sample (P9523, Sigma-Aldrich) was extracted by OMIX C18 solid-phase extraction (SPE) pipet tips (Agilent Technologies, USA) in combination with a liquid-handling system epMotion (Eppendorf, Germany). Five milliliters of plasma was diluted (1:1) with 2% phosphoric acid, vortex mixed for 30 s, and transferred into a 96 well-plate reservoir. 96 Omix C18 100 μL tips (Agilent, A57003100) were placed in epMotion 96 trays and loaded with 100 μL of the acidified plasma. The extraction followed the protocol described in Forcisi et al.7 The same procedure was applied for the preparation of SRM 1950 blood plasma (Sigma-Aldrich). All of the eluates were pooled, vortex-mixed, and split into aliquots following the study design.
Sample Analysis
The 17 laboratories have different FT-ICR MS instruments varying in their magnetic field strength (encompassing 7, 9.4, 12, and 15 T systems) and their ICR cell designs (Infinity or ParaCell). Their field of expertise and application was also very diverse, ranging from the fields of small molecules or metabolomics on different body fluids and biological matrices to proteomics, environmental chemistry or petroleomics, and additionally different combinations of ion source usage (ESI versus MALDI). Each laboratory received a kit containing the same samples to be measured in triplicate. A standard operating procedure (SOP) was established in Munich and distributed to each laboratory participant (see Supporting Information, SI-10). A centralized collection of the acquired data was organized to study the interlaboratory comparability. Each participant in the interlaboratory study was kept anonymous. The part of the study presented here consisted of 10 samples analyzed in triplicate with additional quality controls and blanks resulting in 713 spectra to be calibrated and processed accordingly in files of 4 Mega word (MW; measure of time domain transient length in FT-ICR MS) size containing on average 5094 ± 1168 m/z peaks over a mass range from 150 to 1000 m/z.
Spectral Calibration and Alignment
The authors used a two-step calibration scheme starting by external calibration on-site to remove the influence of local mass error drifts prior measurement, followed by internal calibration using any suitable tool to correct sample-specific space-charge effects. All instruments were calibrated on arginine clusters externally prior measurement. Data from all laboratories were peak-picked using Bruker Compass Data Analysis 4.4 at a signal-to-noise threshold (S/N) ≥ 4. Peak-picked MS lists (m/z, intensity, resolving power) were exported to tab delimited text files. All spectra were calibrated internally against a list of calibration m/z values designed for the study. The calibration list contained 285 m/z peaks composed of 153 theoretical pesticide m/z values as well as 132 human plasma m/z values whose formulas were assigned through an in-house mass-difference-based algorithm (NetCalc)23 and further validated by isotopic fine structure. All m/z values, known from blood plasma and the Agilent pesticide mix, were excluded if they were within 5 ppm proximity to another calibrant. This way, the same calibration list could be used for the spectra generated within experiments X, Y, and Z. Mass spectra were calibrated using kernel-based calibration24 (available upon request). Mass spectra calibrated with a standard deviation of mass measurement error exceeding 300 ppb were excluded. Gibbs peaks were removed on the basis of resolution following Kanawati et al.25 Multiply charged features were removed on the basis of mass defect regions (width = 20 ppm) that were covered by the Pubchem database. Absolute feature intensity cutoffs were adjusted manually using absolute mass defect (AMD) plots.1 Intensity thresholds were adjusted to be the minimum intensity that kept the region 0.1 < AMD < 0.9 at 150 < m/z < 200 empty. Spectra were aligned into an MS feature versus observation matrix using a moving alignment error window of 0.5 ppm width.
Data Processing and Statistics
All computations were performed excluding missing data marked as “NaN” (not a number), except stated otherwise. MS features were kept for further analyses if they met the following criteria after locating triplicate measurements with at least two nonzero detections: (1) Keep triplicates that occur at least twice in at least three laboratories. (2) Count frequency of missing features (missingness [%]) per dilution (across laboratories) and keep features whose missingness is <40% in at least one dilution. The resulting data distributions can be viewed in Figure 1. Details on scaling, imputation, visualization, principal component analysis (PCA), linear regression models, and comparison of signal magnitudes are detailed in Supporting Information, SI-1.
Figure 1 (a) Multivariate dynamic ranges, experiment X. Median-centered PCA on 5th to 95th percentiles of spectral intensities involving 150 spectra from 10 laboratories. The first positive component (ordinate) covers approximately 99.9% of median-centered covariance and is proportional to each laboratory’s spectral signal magnitudes. The second PC (abscissa) covers approximately 0.1% of median-centered covariance and covers the experimental effect. (b) Univariate dynamic ranges, experiment X. Box plots showing the spread of distribution and magnitude of spectral intensities. Note the correspondence of box plot magnitudes to the above PC1. Both the magnitude of differences between laboratories and the outlying behavior of laboratory D are confirmed. The PCA plot is more informative in terms of lab-to-lab comparisons of intensity distributions.
Normalization
Here, no normalization, probabilistic quotient normalization26 (PQN), and smoothed quotient correction (SQC) were compared. PQN is commonly used for the correction of median linear shifts of spectral intensities. SQC was devised here because mass spectrometers from different generations can be equipped with varying ion optics and hardware combined with diverse instrumental settings that cause instrument specific biases. As an example, the instrument of laboratory A has poor ion transmission at m/z < 200, and varying time-of-flight in the hexapole or ion varying accumulation time in the ICR cell will change sensitivity toward lower or higher m/z. Such differences in instrumental characteristics cannot be adjusted with a monoparametric data correction method such as PQN. SQC uses smoothed quotients for data correction: compute a median spectrum R across all spectra T (excluding NaN’s) and compute Q = R/T as performed in PQN. Smooth the quotients Q of each spectrum t in T with an appropriate smoother. Here, smoothed quotients SQ were computed following the matlab function SQ = smooth data (Q, “sgolay”, “Window”, “omitnan”), with “sgolay” being the Savitzky-Golay smoother, “Window” specifying the number of features passed to the smoother at each spectral position, and “omitnan” specifying that NaN’s are to be omitted by the smoother. To avoid smoothing experimental characteristics, a window size of 50% of each spectrum’s nonzero entries was used.
Co-presence Analysis (CPA)
The filtered MS feature matrix was transformed into a binary data matrix with nonzero entries and NaN’s replaced by ones and zeroes, respectively. PCAs were performed at different levels of missingness to locate co-presence clusters (CPCs). CPCs were determined visually in X, Y, and Z experiments. Here, CPA means co-presence PCA on binary data. Mean positions of CPCs on the first and second PCs were computed, and each laboratories’ Euclidean distances to the CPC’s center were computed (jointly normalized to the range [0,1]). Diverging and converging behavior as a function of missingness was visualized (Figure 2 and Figure S11).
Figure 2 Summary of co-presence analysis at different degrees of missingness. See the formation of CPCs (laboratories that remain tightly associated despite increasing overall missingness) in the Supporting Information, SI-4. Vertical lines at 40% missingness indicate the order of laboratory removal from top to bottom as performed in Figure 3. (a) Normalized Euclidean distance of every laboratory’s scores computed on CPA relative to the mean scores of the CPC {A,B,C,D,E} of experiment X. PCAs were computed per missingness level (expressed in %) and dilution. (b) Normalized Euclidean distance of every laboratory’s scores computed on CPA relative to the mean scores of the CPC {A,B,C,D,E,F,J} of experiment Y. PCAs were computed per missingness level (expressed in %) and dilution. (c) Normalized Euclidean distance of every laboratory’s scores computed on CPA relative to the mean scores of the CPC {A,B,D,E} of experiment Z. PCAs were computed per missingness level (expressed in %) and dilution.
Univariate Reproducibility
Coefficients of Variance (CV)
Intra-CVs were computed on triplicates of MS features. Inter-CVs were computed on the features’ triplicate means across all laboratories within the same level in terms of spiking or dilution. As such, inter-CVs report the relative standard error of the means across laboratories.
Multivariate Comparability
Each laboratory’s dilution/spiking data (mean centered per laboratory) were used as generators for PCA models (scores and loadings of the first PC were stored for each laboratory). Loadings of each model generator were applied to compute “pseudoscores” given the data of the nongenerator laboratories. Scores and pseudoscores were transformed to the domain of dilution/spiking levels. That is, scores were normalized to the Euclidean length of the series {−2,–2,–2,–1,–1,–1,0,0,0,1,1,1,2,2,2} of centered dilution/spiking levels. Addition of the constant “3” shifted the resulting scores into the same domain as the sequence of dilution levels {1,1,1,2,2,2,3,3,3,4,4,4,5,5,5}. Normalized scores and pseudoscores were subjected to linear regression. Further, linear regression models of each laboratory’s generator scores against dilution/spiking levels were generated. All models were visualized in a heatmap of laboratories versus laboratories and dilution/spiking levels.
Results and Discussion
Comparability of Laboratory Performances
Raw Data
The data of seven out of 17 laboratories had to be excluded due to asymmetric or split peak shapes, strong contamination or absence of matrix-specific MS feature patterns (Supporting Information, SI-2). The only criterion for the inclusion/exclusion of a laboratory was that the spectra were calibratable (standard deviation of mass measurement error <300 ppb). The criteria were not known to participating laboratories in advance. MS peak asymmetry and splitting are usually caused by the gas pressure in the ICR cell increasing from e–10 mbar toward e–9 mbar. Drifts of excitation powers in the ICR cell as well as ICR-cell overloading all result in aberrant shapes and trajectories of ion clouds within the ICR cell and the magnetic field. The new dynamically harmonized cell provided in new instrumentation enables better control over some of the underlying phenomena.27 Strong peak shifts (several ppm) can occur at unstable electrospray as fluctuating ionization efficiency leads to different ion densities in the ICR cell distorting the mass-error distribution. Data matrices of plasma dilution series (experiment X), spiked plasma series (experiment Y), and varying depth of acquisition (experiment Z) were computed (see Experimental Section). The resulting matrices X and Y comprised 7974, 8918, and 8163 features over 150, 150, and 90 samples, respectively. A first assessment of laboratory comparability was performed using the filtered raw data. Figure 1 visualizes a PCA on a matrix composed of the 5th to 95th percentiles of the intensities in each spectrum of experiment X (see Experimental Section).
The first PC’s magnitude is proportional to spectral signal strength, while the second PC reflects the general effect of the dilution. PCs 1 and 2 covered 99.9 and 0.10% of covariance. The scatter plot of both scores shows how overall signal magnitudes decrease as a function of dilution level (increasing matrix concentration). Therefore, increasing matrix concentrations lead to increasing signal magnitudes in all laboratories except for laboratory D. Laboratories F, G, I, and J do not show strong responses to matrix concentration in overall signal magnitudes in Figure 1a,b. Laboratory H shows a flat but monotone increase on Figure 1a and no response in Figure 1b. The behavior of these laboratories allows for two different hypotheses: (1) the analytical systems are in suppression already in the lowest matrix concentration; (2) the analytical system has strong contaminations or background that are suppressed with increasing matrix concentration. The Multivariate Comparability section will deliver which of the two hypotheses is correct. Figure 1 displays the high diversity of dynamic ranges in terms of signal magnitudes and responses to experimental intervention. The PCA’s on matrices Y and Z are not shown since the results are similar.
Univariate Comparability
Different laboratories may detect diverse sets of MS features and produce different MS traces, depending on instrument parametrization, purpose of instrument use and contaminations. Here, two strategies are followed to select the laboratories for comparison: (1) analysis of CPCs on binary data and (2) removal of laboratory-specific biases by means of smoothed quotient correction (detailed rationale in the Supporting Information, SI-3).
Co-presence Analysis
While it is common to exclude features at more than 10% missingness, it is important to first identify what spectra detect the same features. The data from all experiments were transformed into binary data matrices with ones and zeros indicating feature presence and absence to compute co-detection tables (here, “co-detection” means the joint detection of a feature in at least two laboratories). Table S1 shows that laboratories A–E co-detected 4952, 5155, and 5522 MS features on average in experiments X, Y, and Z, respectively. Correspondingly, laboratories F–J co-detected 2761, 3556, and 3225 MS features on average. The overlap of laboratories A–E among each other computes to 84, 83, and 91% in X, Y, and Z, respectively. These laboratories’ average overlap with laboratories F–J amounts to 62, 69, and 69%. Globally, laboratory E co-detected most features with the other laboratories followed by laboratory B. Laboratories F, H, and I have the least feature counts, sharing the least number of features with the other laboratories. More detailed insights on aberrant co-detection can be obtained by performing a PCA on binarized data. The scores of a principal component analysis on such a binary data matrix indicate feature frequencies within CPCs. Here, CPAs were performed within the dilution levels for experiment X, within spiking levels for experiment Y and within levels of scan number for experiment Z.
CPA scores were generated at varying levels of missingness, and their normalized Euclidean distance to the most robust CPC was computed (Figures S6, S7, and S8). Figure 2 shows the convolutions of co-presence structure along with increasing missingness for experiments X, Y, and Z. Laboratory I had the most aberrant co-presence structure across all levels of missingness, closely followed by laboratories H and F. Laboratories A, B, C, D, and E showed consistently similar peak detection across all experiments and levels of missingness. Those laboratories are expected to show the best intercomparability. Any clustering was independent of magnetic field strength or type of analyzer cell.
Global Co-detection between Experiments X, Y, and Z
Table S1 contains a comparison of the overlap of MS features between all three experiments computed at 40% missingness. Two thirds of all MS features detected within experiments X and Z overlapped, while one-third was found to be specific for either X or Z. Comparing the spiking experiment Y to the matrices of X and Z revealed that 48% (3815 features) and 57% (4668) of all MS features originally contained in X and Z cannot be detected when spiking with pesticides. Spiking a mixture of 85 pesticides produced 4759 (versus X) and 5423 (versus Z) features that were never detected in blood plasma extracts. Future ring trials for routine analyses will have to assess the type and concentration of internal standards thoroughly as was done in Chekmeneva et al.28
Normalization and Univariate Comparability
Here, the major measure to assess interlaboratory comparability is the coefficient of variation computed on co-detected feature intensities of the laboratories to be compared. Figure 3 shows the results of this computation at different levels of laboratory exclusion on the abscissa. Inter-CVs in all plots of Figure 3 decrease as the most dissimilar laboratories are removed from computation following the vertical dashed lines of Figure 2 from top to bottom. This result indicates that CPA captures the similarity between detection patterns well. The exception to that observation was the convolution of inter-CVs on raw data in experiment Y. Pesticide spiking emphasizes the differences in the instruments’ dynamic ranges and this difference appears to be stronger than the effect of MS feature co-detection. Different strategies of spectral normalization (PQN and SQC) and feature scaling (UVC and L2) were tested on the data available, knowing that there are a multitude of effects influencing the comparability of mass spectra between different batches and laboratories. Normalization methods act on the distribution of signal intensities in a spectrum, while scaling methods act on the magnitude of an individual MS feature across spectra. We found that feature scaling across all laboratories at once distorts intensity distributions. Here, scaling was used on each laboratory’s data individually. Figure 3 shows that the application of the L2 norm (Euclidean norm) was always superior to UV scaling. L2 exercises the greatest impact in terms of inter-CV improvement relative to raw data among individual correction methods tested. At the same time, any combination involving SQC was superior to PQN except for experiment Z. Here, PQN-L2 and SQC-L2 performed the best resulting in exactly the same results while both PQN-UV and SQC-UV performed worse than raw data. A likely reason for this behavior is that the denominator in the computation of standard deviation (itself the denominator in UV scaling) is corrected for degrees of freedom, which exercises a large effect in small sample sizes. The removal of aberrant laboratories had the least noticeable effect on inter-CVs in terms of absolute removal of error. The improvement of inter-CVs was the weakest in experiment Z when data correction was used. No matrix composition was modified in experiment Z and the single sources of variation were instrument specific biases and number of scans. While Figure 3 displays inter-CVs only, it is to be noted that any combination of scaling and normalization methods did not alter intra-CV significantly (Supporting Information, SI-5).
Figure 3 Convolution of CVs as a function of increasing co-detection of MS features on experiments X, Y, and Z. Inter- and intra-CVs were computed across the entire data set iteratively removing laboratories following Figure 2 at 40% missingness, beginning with the uppermost (most aberrant) laboratory, with the members of CPCs being the last to be removed. Inter- and intra-CVs of the following data treatments are compared: raw (Raw), raw with laboratory Euclidean (L2), raw with laboratory UV scaling (UV), PQN on raw data (PQN), SQC on raw data (SQC), PQN followed by L2 (PQN_L2), PQN followed by UV (PQN_UV), SQC followed by L2 (SQC_L2), and SQC followed by UV (SQC_UV). The effects of both SQC and Euclidean-norm affect inter-CVs majorly, while intra-CVs do not change due to Euclidean normalization (of features) and improve only slightly as SQC is applied (a zoom-in toward intra-CVs is provided in the Supporting Information, SI-6).
The results substantiate that L2 normalization should be used for feature scaling and that SQC corrects interlaboratory variation majorly and that intra-CV magnitude is not an artifact of data treatment.
Multivariate Comparability
Multivariate comparability was assessed in a cross validation scheme for experiments X and Y. A PCA model was built for each laboratory. Each laboratory’s original model was then applied on the data of all other laboratories, generating validation scores that were scaled to same Euclidean length. R2 values of regression analyses between a laboratory’s original model score (rows in Figure 4) and the validation scores plus dilution or spiking levels (columns in Figure 4) were visualized in a heat map. Figure 4 shows the cross-validation map computed on the experiment X (matrix effect) at 40% missingness per dilution level and laboratory L2 norm applied on features. The interesting case of laboratory H shows that applying its model to the data other laboratories generated high R2 values (R2 > 0.8). A linear regression of the H-scores against dilution levels showed good linearity in the scores (R2 = 0.94). At the same time, the data of H did not perform well using the other laboratories’ PCA models. H is one of the two laboratories with a strongly aberrant missingness structure in experiments X, Y, and Z (Figure 2). The result can be interpreted as follows: Those features that follow matrix dilution levels in H show the same trends in the other laboratories. However, these features receive small loadings in the PCAs of other laboratories. This insight is supported by the scatter plots corresponding to Figure 4 (Figure S14). Recall the case of laboratory D, which showed decreasing signal magnitudes with increasing sample concentration. Figure 4 implies that the dependency of signal magnitudes on matrix concentration was a sign of increasing suppression strong background ions (Figure S16). The models of laboratory D produced an average R2 ≈ 0.7 when applied to the data of other laboratories. Likewise, the loadings of other laboratories produced an average R2 ≈ 0.75 when applied on the data of D. The cross-validation map of experiment Y (Figure S13) shows perfect R2 values for all combinations reflecting excellent reproduction the effects pesticide spiking exercised on the analytical matrix. The scatter-plot map for the cross-validation of experiment X shows that the FT-ICR analyzers were challenged by large ion abundances at spiking levels 15 and 20 ppb (Figure S15).
Figure 4 PCA cross-validation map for experiment X.
Spectral normalization did not have significant effects on the cross-validation experiment. Univariate comparability as measured by inter-CVs did not appear to be of major importance for multivariate comparability. One possible explanation would derive from the central limit theorem: PCA scores represent a weighted mean of the hundreds or thousands of features’ univariate signal magnitudes.
Evaluation of Scanning Depth
High-throughput routine analyses are required to be fast and robust and need to detect features of relevance. Experiment Z was performed using the NIST SRM 1950 standard for which quantified metabolite identities and concentrations are available. We manually extracted the names and identifiers for 682 small molecules, amounting to 430 individual compounds and 319 unique molecular formulas from Simón-Manso et al. All 319 molecular formulas transformed into seven fundamental ion types where possible: [M + H]+, [M + CH4O + H]+, [M + Na]+, [M + H2O + H]+, [M + CHO2Na + H]+, [M – NH3 + H]+, and [M – H2O + H]+. All ion types were combined combinatorically to build homodimers and homotrimers of different adduct types. The expanded list encompassed 26,908 positive ionization mode m/z values to be searched in the Z experiment. The raw data matrices of experiments X and Z were fused at 1 ppm error and matched against the built SRM 1950 metabolic feature collection. Mass matching at a 0.5 and 1 ppm search window size resulted in 1111 and 1345 hits against the fused XZ matrix.
While laboratory B showed significantly more hits than all other laboratories, only laboratories F and I detected significantly fewer putatively annotated MS features (Figure 5a). Setting the number of detected features after 300 scans to 100% for each laboratory, 76 and 89% of all SRM 1950 features are detected after 50 and 100 scans, respectively. SRM 1950 annotated MS features showed significantly higher detection frequencies compared to detection frequencies in the corresponding data sets (Figure 5b). Experiment Z showed consistently higher detection frequencies compared to experiment X, which is partially due to longer scanning times and no variation of matrix composition in experiment Z. Figure 5 and experiment Z imply that (i) features detected within this study are representative for the analytical matrix and that it is more likely to detect metabolic features that are already known; (ii) the accumulation of merely 50 scans, which is performed within 60–90 s, provides a read out that is already 75% complete (compared to acquisition of 300 scans, which takes approximately 10 min depending on instrumental settings). The detection of isotopic fine structures naturally requires longer scanning times.27
Figure 5 Putatively annotated signals in SRM 1950 blood plasma per scanning number. (a) Number of MS features putatively annotated against SRM 1950 metabolites per number of accumulated scans for each participant. (b) Comparison of the MS feature frequency (percentage of valid triplicate detection across all laboratories) between the MS features co-detected in X and Z alone against the frequency of those features with SRM 1950 annotations within either experiment.
Conclusion
This preliminary interlaboratory study was set up to comprehend whether different FT-ICR mass spectrometers across the globe, at their routine performance, have the aptitude to detect the same signals generated on the same sample sets. The subsequent question was whether feature intensities were comparable and what data correction technique could be applied to minimize interlaboratory CVs. The global aim was to collect the adequate experience and knowledge for setting up a future DI-FT-ICR MS ring trial. The variability in laboratory performance across the FT-ICR community around the world was found to be significant, likely due to diverse scientific scopes, some of which may not fall into the field of analyzing small molecules.
The data provided by seven out of 17 laboratories had to be excluded because either MS-peak deformation, lack of signals to be calibrated or strong contaminations hindered calibration to below 300 ppb of mass error standard deviation. The remaining laboratories exhibited strong variability in terms of signal magnitudes at smaller or larger m/z values and number of valid triplicate features detected. Co-detection analyses across three different experiments showed that laboratories A, B, C, D, and E co-detect up to 90% their valid triplicate signals. Laboratories B and E were found to be the most representative for all laboratories. Globally, 67 and 69% of the MS features detected in experiments X and Z overlapped, indicating a good representation of MS features typical for blood plasma SPE extracts. In turn, pesticide spiking caused the loss of almost half of the MS features detected in X, substantiating that the type and concentration of authentic standards have to be evaluated carefully when standard addition is performed in DI-MS. Comparisons of interlaboratory CVs consistently showed that smoothed quotient correction followed by scaling MS features on the L2 norm within each laboratory individually resulted in median CVs between 10 and 20%. Multivariate cross-validation on experiments X and Y showed that multivariate comparability of experimental effects was acceptable not necessarily depending on univariate comparability.
In effect, an appropriate strategy toward interlaboratory comparability for untargeted DI-FT-ICR MS would encompass the following steps: (1) Optimize instrumental parameters to meet the detection pattern of the laboratories that showed best co-detection (e.g., B and E). (2) Define a study design that is representative of the analytical task and maintain a constant batch size. (3) Always use the exact same amount of quality control samples that are randomly distributed in the batch so that L2 normalization can be performed. (4) Perform SQC toward the median of QCs and use the L2 norm of the QCs to scale the samples in question. We suggest the above points as one step toward applications of untargeted diagnostic UHR-MS profiling for fields of application such as quality control in food industries, pharmaceuticals, or clinical phenome centers.
Finally, experiment Z, using the SRM 1950 blood plasma standard, showed that 50 scans (60–90 s scanning time) were found to be sufficient to detect 75% of all potential SRM 1950 metabolites detected at 300 scans (8–10 min scanning time). These results suggest that FT-ICR mass spectrometers can be used for routine high-throughput measurements. Follow-up studies could encompass ring trials with cloned instruments (7Ts, 9Ts, 12Ts, Infinity versus ParaCell), clusters of laboratories of similar scope and expertise (proteomics, lipidomics, petroleomics, etc.), and more elaborate study designs (e.g., including a clinical study).
Supporting Information Available
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/jasms.2c00082.Experimental information, supporting concepts and figures (PDF)
Table S1 (XLSX)
Supplementary Material
js2c00082_si_001.pdf
js2c00082_si_002.xlsx
Author Present Address
† Australian National Phenome Centre, Murdoch University, Harry Perkins Institute of Medical Research, 5 Robin Warren Drive, 6150 Murdoch, Western Australia
Author Present Address
‡ Metatoul-AXIOM Platform, MetaboHUB, Toxalim, INRAE, Toulouse, France. Toxalim, Toulouse University, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
Author Present Address
§ Département de chimie, Université de Sherbrooke, 2500 Boulevard de l’université, Sherbrooke, Québec, J1K 2R1, Canada.
Author Present Address
# ExxonMobil Chemical Company, 22777 Springwoods Village Parkway, Spring, TX 77389.
Author Contributions
Conception, study design and logistics: S.F., F.M., C.J.T., B.K., J.U., A.B., M.L.E., F.L., P.S.-K. On-site coordination of the experiments: S.F., F.M., B.K., J.U., C.A., B.A.B., F.F.-L., C.G., D.H., K.-S.J., R.M., R.N., E.N., M.P., L.P.-T., J.B.S., E.S., M.W., A.W., J.W., P.S.-K. Preparation and distribution of samples: S.F., F.M., J.U. Development, distribution and revision of standard operating procedure (SOP): S.F., F.M., C.J.T., B.K., J.U., S.N., M.P., Y.E.M.vdB., M.W. Data acquisition: S.F., F.M., B.K., C.D.B., B.A.B., R.K.C., J.F., F.F.-L., M.G.-H., K.-S.J., V.M., R.N., E.N., S.N., M.P., J.P., I.S.-A., J.B.S., E.S., Y.E.M.vdB., C.V., M.W., A.W., J.W. Data analysis, interpretation and drafting of the manuscript: S.F., F.M., N.K., C.J.T. Critical revision of the manuscript: S.F., F.M., C.J.T., B.K., J.U., C.D.B., B.A.B., F.F.-L., C.G., K.-S.J., E.N., E.S., C.V., M.W., P.S.-K. S.F., F.M., and C.J.T. contributed equally.
The authors declare the following competing financial interest(s): A.B., N.K., and M.W. are employees of Bruker Daltonik GmbH, F.H.L. is President and CEO of the Bruker Corporation, and C.J.T. and J.W. were employees of Bruker Daltonics Inc. which manufactures and sells mass spectrometers and software used in this study.
Acknowledgments
This research was supported by the German Center for Diabetes Research (DZD; Grants G-501900-482 and G-501901-020), the European Regional Development Fund (ERDF) No. HN0001343, the European Union’s Horizon 2020 Research Infrastructures program (Grant Agreement 731077), the Région Normandie, the Laboratoire d’Excellence (LabEx) SynOrg (ANR-11-LABX-0029), and the national FT-ICR network (FR 3624 CNRS). A portion of the research was performed using EMSL (grid.436923.9), a DOE Office of Science User Facility sponsored by the Biological and Environmental Research program.
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| 0 | PMC9732918 | NO-CC CODE | 2022-12-14 23:36:00 | no | Immunooncol Technol. 2022 Dec 9; 16:100226 | latin-1 | Immunooncol Technol | 2,022 | 10.1016/j.iotech.2022.100226 | oa_other |
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J Am Soc Mass Spectrom
J Am Soc Mass Spectrom
js
jamsef
Journal of the American Society for Mass Spectrometry
1044-0305
1879-1123
American Chemical Society
36371691
10.1021/jasms.2c00082
Research Article
Large-Scale Interlaboratory DI-FT-ICR MS Comparability Study Employing Various Systems
https://orcid.org/0000-0003-1976-7976
Forcisi Sara *12
https://orcid.org/0000-0002-1167-6191
Moritz Franco *1
https://orcid.org/0000-0002-3022-3710
Thompson Christopher J. *3
Kanawati Basem 1
Uhl Jenny 1
https://orcid.org/0000-0002-2406-5664
Afonso Carlos 4
Bader Chantal D. 5
Barsch Aiko 6
https://orcid.org/0000-0001-6342-9814
Boughton Berin A. 7†
Chu Rosalie K. 8
Ferey Justine 4‡
https://orcid.org/0000-0002-1283-4390
Fernandez-Lima Francisco 910
Guéguen Céline 11§
Heintz Dimitri 12
Gomez-Hernandez Mario 910
Jang Kyoung-Soon 13
Kessler Nikolas 6
Mangal Vaughn 11
https://orcid.org/0000-0002-1042-5665
Müller Rolf 5
https://orcid.org/0000-0002-8674-0928
Nakabayashi Ryo 14
https://orcid.org/0000-0001-8791-9949
Nicol Edith 15
https://orcid.org/0000-0001-8393-1625
Nicolardi Simone 16
https://orcid.org/0000-0002-5865-8994
Palmblad Magnus 16
Paša-Tolić Ljiljana 8
Porter Jacob 910
Schmitz-Afonso Isabelle 4
Seo Jong Bok 17
Sommella Eduardo 18
https://orcid.org/0000-0003-0556-5564
van der Burgt Yuri E. M. 16
Villette Claire 12
Witt Matthias 6
https://orcid.org/0000-0002-2712-2003
Wittrig Ashley 20#
Wolff Jeremy J. 3
Easterling Michael L. 3
Laukien Frank H. 321
Schmitt-Kopplin Philippe *1222
1 Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany
2 German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
3 Bruker Daltonics Inc., Billerica, Massachusetts 01821, United States
4 COBRA, UMR 6014 et FR 3038, INSA de Rouen, CNRS, IRCOF, Normandie Université, Université de Rouen, 76130 Cedex Mont Saint Aignan, France
5 Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, Saarland University Campus, 66123 Saarbrücken, Germany and Department of Pharmacy, Saarland University, 66123 Saarbrücken, Germany
6 Bruker Daltonik GmbH, Fahrenheitstrasse 4, 28359 Bremen, Germany
7 Metabolomics Australia, School of BioSciences, University of Melbourne, Melbourne, Victoria 3010, Australia
8 Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
9 Department of Chemistry and Biochemistry, Florida International University, 11200 SW Eighth Street, AHC4-233, Miami, Florida 33199, United States
10 Biomolecular Sciences Institute, Florida International University, 11200 Eighth Street, AHC4-211, Miami, Florida 33199, United States
11 Chemistry Department, Trent University, 1600 West Bank Drive, Peterborough, ON K9J 7B8, Canada
12 Plant Imaging and Mass Spectrometry (PIMS), Institut de Biologie Moléculaire des Plantes, CNRS, Université de Strasbourg, 12 rue du Général Zimmer, 67084 Strasbourg, France
13 Bio-Chemical Analysis Team, Korea Basic Science Institute, Cheongju 28119, South Korea
14 Metabolomics Research Group, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
15 Laboratoire de Chimie Moléculaire (LCM), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau, France
16 Center for Proteomics and Metabolomics, Leiden University Medical Center Leiden, 2333 ZC Leiden, The Netherlands
17 Seoul Center, Korea Basic Science Institute, 145, Anam-Ro, Seongbuk-Gu 02841, Seoul, South Korea
18 Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano (SA), Italy
20 ExxonMobil Research and Engineering Company, 1545 Route 22 East, Clinton, New Jersey 08869, United States
21 Department of Chemistry & Chemical Biology, Cambridge, Harvard University, Cambridge, Massachusetts 02138, United States
22 Analytical Food Chemistry, Technical University of Munich, 85354 Freising, Germany
* Email: [email protected].
* Email: [email protected].
* Email: [email protected].
* Email: [email protected].
13 11 2022
07 12 2022
33 12 22032214
18 03 2022
23 08 2022
12 08 2022
© 2022 The Authors. Published by American Chemical Society
2022
The Authors
https://pubs.acs.org/page/policy/authorchoice_ccby_termsofuse.html This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Ultrahigh resolution mass spectrometry (UHR-MS) coupled with direct infusion (DI) electrospray ionization offers a fast solution for accurate untargeted profiling. Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometers have been shown to produce a wealth of insights into complex chemical systems because they enable unambiguous molecular formula assignment even if the vast majority of signals is of unknown identity. Interlaboratory comparisons are required to apply this type of instrumentation in quality control (for food industry or pharmaceuticals), large-scale environmental studies, or clinical diagnostics. Extended comparisons employing different FT-ICR MS instruments with qualitative direct infusion analysis are scarce since the majority of detected compounds cannot be quantified. The extent to which observations can be reproduced by different laboratories remains unknown. We set up a preliminary study which encompassed a set of 17 laboratories around the globe, diverse in instrumental characteristics and applications, to analyze the same sets of extracts from commercially available standard human blood plasma and Standard Reference Material (SRM) for blood plasma (SRM1950), which were delivered at different dilutions or spiked with different concentrations of pesticides. The aim of this study was to assess the extent to which the outputs of differently tuned FT-ICR mass spectrometers, with different technical specifications, are comparable for setting the frames of a future DI-FT-ICR MS ring trial. We concluded that a cluster of five laboratories, with diverse instrumental characteristics, showed comparable and representative performance across all experiments, setting a reference to be used in a future ring trial on blood plasma.
H2020 Research Infrastructures 10.13039/100010666 731077 German Center for Diabetes Research NA G-501901-020 German Center for Diabetes Research NA G-501900-482 Région Normandie 10.13039/501100018696 NA European Regional Development Fund 10.13039/501100008530 HN0001343 Centre National de la Recherche Scientifique 10.13039/501100004794 FR 3624 Agence Nationale de la Recherche 10.13039/501100001665 ANR-11-LABX-0029 document-id-old-9js2c00082
document-id-new-14js2c00082
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pmcIntroduction
The study of complex chemical systems requires instrumentation that captures their chemical space.1 Mass spectrometry is probably the most versatile among the techniques at the disposal of an analytical chemist. It provides high sensitivity and a multitude of means for molecular characterization.
There are two poles toward chemical characterization in mass spectrometry. At one pole there is quantitative hyphenation of separation techniques to tandem mass spectrometry, which provides structural information on the most abundant ions produced from a sample. The chemical information on the less abundant proportion of ions is lost oftentimes as these signals do not produce abundances of fragment ions that suffice for annotation. On the other end, fast profiling using direct infusion electrospray ionization with ultrahigh resolution mass spectrometry (DI-ESI-UHR) allows for qualitative and nonquantitative analyses and unambiguous assignment of molecular formulas to those MS signals that escape annotation by tandem MS.
Fourier transform mass spectrometers such as FT-ICR MS and Orbitrap offer highly resolved, accurate, and precise determination of mass-to-charge ratios (m/z), state of the art prerequisites for the characterization of complex mixtures.2 FT mass spectrometers, in general, have a capacity for very sensitive characterizations of MS features, as analyte ions can be trapped for prolonged periods of time. Within the family of FT instruments, FT ion cyclotron resonance (FT-ICR) MS enables the detection of thousands of peaks in complex matrices at a time, with lower parts-per-billion mass accuracy and even a resolution as high as 2,400,000 at 400 m/z (21T instrumentation).3 Its power to characterize the compositional space of a multitude of complex chemical systems is well acknowledged within disciplines such as metabolomics, petroleomics, foodomics, lipidomics, microbiome analysis, natural organic matter (NOM), and dissolved organic matter (DOM) analysis.3−8
Several works describe the application of DI-MS in answering specific questions by looking at the whole MS profile, eventually integrating orthogonal techniques such as LC-MS or GC-MS for deeper isomeric elucidation.8−11 However, broad applications require strict interlaboratory comparability.12,13 To date, interlaboratory comparability in untargeted metabolomics was mainly tested considering LC-MS or GC-MS, DI-stable isotope dilution MS, and NMR techniques.14−17 Each laboratory employing FT-ICR MS has an interest to achieve results that are the most representative for the analytical matrix under inspection (IUPAC project 2016-015-2-60015,14). To observe the same distinctive features, other laboratories must be able to observe identical and unique analytical patterns, as the same sample set is analyzed.
FT-ICR MS data were investigated by Kirwan et al.16 to study the experimental reproducibility of a large multibatch metabolomics study of mammalian cardiac tissue extracts acquired by means of nanoinfusion FT-ICR MS in one laboratory. They developed a batch correction algorithm based on cubic spline interpolation across quality control (QC) samples.17 Intralab reproducibility was also examined in NOM14,18 and DOM19 investigations. Although it was proven that batch effects and systematic errors within DI-MS data can be controlled using appropriate study designs on one instrument,16 other resources indicate that the overlap of detected signals between two laboratories can be lower than 25%.20 Assessments on the comparability of untargeted DI-MS data produced by different laboratories are under-represented in the present literature.
How can data generated by different laboratories be compared when they used nonquantitative DI-FT-ICR MS?
We set up a preliminary study which encompassed a set of laboratories with a high variability in terms of instrumental characteristics and routine applications. The same sample set and a standard operating procedure (see Supporting Information) were sent to 17 different laboratories worldwide, which have expertise in diverse application areas. The aim of this study was to assess the extent to which the outputs of differently tuned FT-ICR mass spectrometers, with different technical specifications, are comparable for setting the frames of a future DI-MS ring trial.
Three experiments were set up to evaluate typical effects observed when acquiring DI-ESI-FT-ICR mass spectra of standard human blood plasma SPE eluates.
Experiment X is intended to capture how different instruments data (generated by FT-ICR MS of different build) are comparable as matrix effects vary due to variation of whole matrix concentration. We found that matrix effects can generally be reproduced across different laboratories and that appropriate data normalization can produce interlaboratory coefficients of variation below 20% for those laboratories that co-detected the most signals.
Experiment Y simulates strong concentration changes in specified sets of analytes at constant matrix concentration. This is particularly interesting, as it is not clear how many signals native to an analytical matrix are sacrificed due to suppression with internal standards. We found that at least 48% of blood plasma signals get repressed by pesticide spiking and that the effects were very comparable between laboratories with coefficients of variation below 20% across laboratories.
Experiment Z intended to capture how many FT-ICR MS scans need to be accumulated in order to provide a sufficient description of the analytical matrix. This point is of high relevance for routine analysis (e.g., clinical cohort analysis21) and planning of future ring trials. The National Institute of Standards and Technology (NIST) Standard Reference Material (SRM) for blood plasma (SRM 1950)22 was used for this experiment as several hundreds of individual metabolites are quantified and certified for this reference matrix. We found that the accumulation of merely 50 scans, which is performed within 60–90 s, provides 75% of the information acquired after 300 scans with respect to those metabolites certified for this reference material.
A cluster of five laboratories, with diverse instrumental characteristics, achieved the highest comparability across all experiments. Participants of a future ring trial on blood plasma profiling could tune their instruments against the median spectral intensities produced by this cluster of laboratories within this preliminary study.
Experimental Section
Aim of Study
We developed a study design that could be completed by a single person within 8 working hours, at any partner laboratory.
C18-SPE eluates of standard human plasma were prepared at different dilution levels (experiment X) to capture the matrix effect, a major concern in DI-MS analysis. A second batch of the same C18-SPE standard human plasma eluate was spiked with different concentrations of pesticides (experiment Y) to compare both matrix effects derived from internal standards and the upper bounds of dynamic ranges. C18-SPE eluate of a second human plasma standard recognized as reference material (SRM 1950)22 was acquired at different scan numbers. Concentration levels of several hundreds of metabolites are available for SRM 1950. Following the presence of MS signals potentially related to the set of quantified compounds over the course of different scanning times was scheduled to obtain a rough estimate of the minimum number of scans required to produce meaningful data.
Study Design
Seventeen partner laboratories across four continents were recruited for the comparability study (the laboratories identity is kept anonymous, they are classified by alphabetic letters). A kit for the analysis, standard operating procedure (Supporting Information, SI-10) were sent to each participant. Three main experiments (X,Y,Z) were set up:(X) A C18-SPE eluate of standard human blood plasma (P9523, Sigma-Aldrich; HIV, hepatitis B, and hepatitis C, none detected) was delivered in triplicates of five different dilutions with dilution ratios ranging from 1/25 to 1/200 (v/v). Number of scans accumulated: 100 purpose: comparison of matrix effects derived from matrix dilution.
(Y) A C18-SPE eluate of standard human blood plasma (P9523, Sigma-Aldrich; HIV, hepatitis B, and hepatitis C, none detected) was delivered in triplicates at a constant concentration (1/50), spiked with five different concentrations of a mixture of 85 pesticides (LC/MS pesticide standard kit, mix 5, 6 and 7; Agilent Technologies) ranging from 1 to 20 ppb for all 85 compounds. Number of scans accumulated: 100 purpose: comparison of matrix effects derived from internal standards.
(Z) A C18-SPE eluate of standard human blood plasma (NIST SRM 1950 plasma, Sigma-Aldrich; HIV, hepatitis B, and hepatitis C, none detected) delivered as single sample at a constant concentration (1/50) and acquired in triplicate accumulating 50 scans, 100 scans, and 300 scans. Purpose: comparison to the P9523 standard (experiment X) and investigation of the sufficient number of scans for routine profiling.
Sample Preparation and Analysis
Sample Preparation
A pool of citrated plasma sample (P9523, Sigma-Aldrich) was extracted by OMIX C18 solid-phase extraction (SPE) pipet tips (Agilent Technologies, USA) in combination with a liquid-handling system epMotion (Eppendorf, Germany). Five milliliters of plasma was diluted (1:1) with 2% phosphoric acid, vortex mixed for 30 s, and transferred into a 96 well-plate reservoir. 96 Omix C18 100 μL tips (Agilent, A57003100) were placed in epMotion 96 trays and loaded with 100 μL of the acidified plasma. The extraction followed the protocol described in Forcisi et al.7 The same procedure was applied for the preparation of SRM 1950 blood plasma (Sigma-Aldrich). All of the eluates were pooled, vortex-mixed, and split into aliquots following the study design.
Sample Analysis
The 17 laboratories have different FT-ICR MS instruments varying in their magnetic field strength (encompassing 7, 9.4, 12, and 15 T systems) and their ICR cell designs (Infinity or ParaCell). Their field of expertise and application was also very diverse, ranging from the fields of small molecules or metabolomics on different body fluids and biological matrices to proteomics, environmental chemistry or petroleomics, and additionally different combinations of ion source usage (ESI versus MALDI). Each laboratory received a kit containing the same samples to be measured in triplicate. A standard operating procedure (SOP) was established in Munich and distributed to each laboratory participant (see Supporting Information, SI-10). A centralized collection of the acquired data was organized to study the interlaboratory comparability. Each participant in the interlaboratory study was kept anonymous. The part of the study presented here consisted of 10 samples analyzed in triplicate with additional quality controls and blanks resulting in 713 spectra to be calibrated and processed accordingly in files of 4 Mega word (MW; measure of time domain transient length in FT-ICR MS) size containing on average 5094 ± 1168 m/z peaks over a mass range from 150 to 1000 m/z.
Spectral Calibration and Alignment
The authors used a two-step calibration scheme starting by external calibration on-site to remove the influence of local mass error drifts prior measurement, followed by internal calibration using any suitable tool to correct sample-specific space-charge effects. All instruments were calibrated on arginine clusters externally prior measurement. Data from all laboratories were peak-picked using Bruker Compass Data Analysis 4.4 at a signal-to-noise threshold (S/N) ≥ 4. Peak-picked MS lists (m/z, intensity, resolving power) were exported to tab delimited text files. All spectra were calibrated internally against a list of calibration m/z values designed for the study. The calibration list contained 285 m/z peaks composed of 153 theoretical pesticide m/z values as well as 132 human plasma m/z values whose formulas were assigned through an in-house mass-difference-based algorithm (NetCalc)23 and further validated by isotopic fine structure. All m/z values, known from blood plasma and the Agilent pesticide mix, were excluded if they were within 5 ppm proximity to another calibrant. This way, the same calibration list could be used for the spectra generated within experiments X, Y, and Z. Mass spectra were calibrated using kernel-based calibration24 (available upon request). Mass spectra calibrated with a standard deviation of mass measurement error exceeding 300 ppb were excluded. Gibbs peaks were removed on the basis of resolution following Kanawati et al.25 Multiply charged features were removed on the basis of mass defect regions (width = 20 ppm) that were covered by the Pubchem database. Absolute feature intensity cutoffs were adjusted manually using absolute mass defect (AMD) plots.1 Intensity thresholds were adjusted to be the minimum intensity that kept the region 0.1 < AMD < 0.9 at 150 < m/z < 200 empty. Spectra were aligned into an MS feature versus observation matrix using a moving alignment error window of 0.5 ppm width.
Data Processing and Statistics
All computations were performed excluding missing data marked as “NaN” (not a number), except stated otherwise. MS features were kept for further analyses if they met the following criteria after locating triplicate measurements with at least two nonzero detections: (1) Keep triplicates that occur at least twice in at least three laboratories. (2) Count frequency of missing features (missingness [%]) per dilution (across laboratories) and keep features whose missingness is <40% in at least one dilution. The resulting data distributions can be viewed in Figure 1. Details on scaling, imputation, visualization, principal component analysis (PCA), linear regression models, and comparison of signal magnitudes are detailed in Supporting Information, SI-1.
Figure 1 (a) Multivariate dynamic ranges, experiment X. Median-centered PCA on 5th to 95th percentiles of spectral intensities involving 150 spectra from 10 laboratories. The first positive component (ordinate) covers approximately 99.9% of median-centered covariance and is proportional to each laboratory’s spectral signal magnitudes. The second PC (abscissa) covers approximately 0.1% of median-centered covariance and covers the experimental effect. (b) Univariate dynamic ranges, experiment X. Box plots showing the spread of distribution and magnitude of spectral intensities. Note the correspondence of box plot magnitudes to the above PC1. Both the magnitude of differences between laboratories and the outlying behavior of laboratory D are confirmed. The PCA plot is more informative in terms of lab-to-lab comparisons of intensity distributions.
Normalization
Here, no normalization, probabilistic quotient normalization26 (PQN), and smoothed quotient correction (SQC) were compared. PQN is commonly used for the correction of median linear shifts of spectral intensities. SQC was devised here because mass spectrometers from different generations can be equipped with varying ion optics and hardware combined with diverse instrumental settings that cause instrument specific biases. As an example, the instrument of laboratory A has poor ion transmission at m/z < 200, and varying time-of-flight in the hexapole or ion varying accumulation time in the ICR cell will change sensitivity toward lower or higher m/z. Such differences in instrumental characteristics cannot be adjusted with a monoparametric data correction method such as PQN. SQC uses smoothed quotients for data correction: compute a median spectrum R across all spectra T (excluding NaN’s) and compute Q = R/T as performed in PQN. Smooth the quotients Q of each spectrum t in T with an appropriate smoother. Here, smoothed quotients SQ were computed following the matlab function SQ = smooth data (Q, “sgolay”, “Window”, “omitnan”), with “sgolay” being the Savitzky-Golay smoother, “Window” specifying the number of features passed to the smoother at each spectral position, and “omitnan” specifying that NaN’s are to be omitted by the smoother. To avoid smoothing experimental characteristics, a window size of 50% of each spectrum’s nonzero entries was used.
Co-presence Analysis (CPA)
The filtered MS feature matrix was transformed into a binary data matrix with nonzero entries and NaN’s replaced by ones and zeroes, respectively. PCAs were performed at different levels of missingness to locate co-presence clusters (CPCs). CPCs were determined visually in X, Y, and Z experiments. Here, CPA means co-presence PCA on binary data. Mean positions of CPCs on the first and second PCs were computed, and each laboratories’ Euclidean distances to the CPC’s center were computed (jointly normalized to the range [0,1]). Diverging and converging behavior as a function of missingness was visualized (Figure 2 and Figure S11).
Figure 2 Summary of co-presence analysis at different degrees of missingness. See the formation of CPCs (laboratories that remain tightly associated despite increasing overall missingness) in the Supporting Information, SI-4. Vertical lines at 40% missingness indicate the order of laboratory removal from top to bottom as performed in Figure 3. (a) Normalized Euclidean distance of every laboratory’s scores computed on CPA relative to the mean scores of the CPC {A,B,C,D,E} of experiment X. PCAs were computed per missingness level (expressed in %) and dilution. (b) Normalized Euclidean distance of every laboratory’s scores computed on CPA relative to the mean scores of the CPC {A,B,C,D,E,F,J} of experiment Y. PCAs were computed per missingness level (expressed in %) and dilution. (c) Normalized Euclidean distance of every laboratory’s scores computed on CPA relative to the mean scores of the CPC {A,B,D,E} of experiment Z. PCAs were computed per missingness level (expressed in %) and dilution.
Univariate Reproducibility
Coefficients of Variance (CV)
Intra-CVs were computed on triplicates of MS features. Inter-CVs were computed on the features’ triplicate means across all laboratories within the same level in terms of spiking or dilution. As such, inter-CVs report the relative standard error of the means across laboratories.
Multivariate Comparability
Each laboratory’s dilution/spiking data (mean centered per laboratory) were used as generators for PCA models (scores and loadings of the first PC were stored for each laboratory). Loadings of each model generator were applied to compute “pseudoscores” given the data of the nongenerator laboratories. Scores and pseudoscores were transformed to the domain of dilution/spiking levels. That is, scores were normalized to the Euclidean length of the series {−2,–2,–2,–1,–1,–1,0,0,0,1,1,1,2,2,2} of centered dilution/spiking levels. Addition of the constant “3” shifted the resulting scores into the same domain as the sequence of dilution levels {1,1,1,2,2,2,3,3,3,4,4,4,5,5,5}. Normalized scores and pseudoscores were subjected to linear regression. Further, linear regression models of each laboratory’s generator scores against dilution/spiking levels were generated. All models were visualized in a heatmap of laboratories versus laboratories and dilution/spiking levels.
Results and Discussion
Comparability of Laboratory Performances
Raw Data
The data of seven out of 17 laboratories had to be excluded due to asymmetric or split peak shapes, strong contamination or absence of matrix-specific MS feature patterns (Supporting Information, SI-2). The only criterion for the inclusion/exclusion of a laboratory was that the spectra were calibratable (standard deviation of mass measurement error <300 ppb). The criteria were not known to participating laboratories in advance. MS peak asymmetry and splitting are usually caused by the gas pressure in the ICR cell increasing from e–10 mbar toward e–9 mbar. Drifts of excitation powers in the ICR cell as well as ICR-cell overloading all result in aberrant shapes and trajectories of ion clouds within the ICR cell and the magnetic field. The new dynamically harmonized cell provided in new instrumentation enables better control over some of the underlying phenomena.27 Strong peak shifts (several ppm) can occur at unstable electrospray as fluctuating ionization efficiency leads to different ion densities in the ICR cell distorting the mass-error distribution. Data matrices of plasma dilution series (experiment X), spiked plasma series (experiment Y), and varying depth of acquisition (experiment Z) were computed (see Experimental Section). The resulting matrices X and Y comprised 7974, 8918, and 8163 features over 150, 150, and 90 samples, respectively. A first assessment of laboratory comparability was performed using the filtered raw data. Figure 1 visualizes a PCA on a matrix composed of the 5th to 95th percentiles of the intensities in each spectrum of experiment X (see Experimental Section).
The first PC’s magnitude is proportional to spectral signal strength, while the second PC reflects the general effect of the dilution. PCs 1 and 2 covered 99.9 and 0.10% of covariance. The scatter plot of both scores shows how overall signal magnitudes decrease as a function of dilution level (increasing matrix concentration). Therefore, increasing matrix concentrations lead to increasing signal magnitudes in all laboratories except for laboratory D. Laboratories F, G, I, and J do not show strong responses to matrix concentration in overall signal magnitudes in Figure 1a,b. Laboratory H shows a flat but monotone increase on Figure 1a and no response in Figure 1b. The behavior of these laboratories allows for two different hypotheses: (1) the analytical systems are in suppression already in the lowest matrix concentration; (2) the analytical system has strong contaminations or background that are suppressed with increasing matrix concentration. The Multivariate Comparability section will deliver which of the two hypotheses is correct. Figure 1 displays the high diversity of dynamic ranges in terms of signal magnitudes and responses to experimental intervention. The PCA’s on matrices Y and Z are not shown since the results are similar.
Univariate Comparability
Different laboratories may detect diverse sets of MS features and produce different MS traces, depending on instrument parametrization, purpose of instrument use and contaminations. Here, two strategies are followed to select the laboratories for comparison: (1) analysis of CPCs on binary data and (2) removal of laboratory-specific biases by means of smoothed quotient correction (detailed rationale in the Supporting Information, SI-3).
Co-presence Analysis
While it is common to exclude features at more than 10% missingness, it is important to first identify what spectra detect the same features. The data from all experiments were transformed into binary data matrices with ones and zeros indicating feature presence and absence to compute co-detection tables (here, “co-detection” means the joint detection of a feature in at least two laboratories). Table S1 shows that laboratories A–E co-detected 4952, 5155, and 5522 MS features on average in experiments X, Y, and Z, respectively. Correspondingly, laboratories F–J co-detected 2761, 3556, and 3225 MS features on average. The overlap of laboratories A–E among each other computes to 84, 83, and 91% in X, Y, and Z, respectively. These laboratories’ average overlap with laboratories F–J amounts to 62, 69, and 69%. Globally, laboratory E co-detected most features with the other laboratories followed by laboratory B. Laboratories F, H, and I have the least feature counts, sharing the least number of features with the other laboratories. More detailed insights on aberrant co-detection can be obtained by performing a PCA on binarized data. The scores of a principal component analysis on such a binary data matrix indicate feature frequencies within CPCs. Here, CPAs were performed within the dilution levels for experiment X, within spiking levels for experiment Y and within levels of scan number for experiment Z.
CPA scores were generated at varying levels of missingness, and their normalized Euclidean distance to the most robust CPC was computed (Figures S6, S7, and S8). Figure 2 shows the convolutions of co-presence structure along with increasing missingness for experiments X, Y, and Z. Laboratory I had the most aberrant co-presence structure across all levels of missingness, closely followed by laboratories H and F. Laboratories A, B, C, D, and E showed consistently similar peak detection across all experiments and levels of missingness. Those laboratories are expected to show the best intercomparability. Any clustering was independent of magnetic field strength or type of analyzer cell.
Global Co-detection between Experiments X, Y, and Z
Table S1 contains a comparison of the overlap of MS features between all three experiments computed at 40% missingness. Two thirds of all MS features detected within experiments X and Z overlapped, while one-third was found to be specific for either X or Z. Comparing the spiking experiment Y to the matrices of X and Z revealed that 48% (3815 features) and 57% (4668) of all MS features originally contained in X and Z cannot be detected when spiking with pesticides. Spiking a mixture of 85 pesticides produced 4759 (versus X) and 5423 (versus Z) features that were never detected in blood plasma extracts. Future ring trials for routine analyses will have to assess the type and concentration of internal standards thoroughly as was done in Chekmeneva et al.28
Normalization and Univariate Comparability
Here, the major measure to assess interlaboratory comparability is the coefficient of variation computed on co-detected feature intensities of the laboratories to be compared. Figure 3 shows the results of this computation at different levels of laboratory exclusion on the abscissa. Inter-CVs in all plots of Figure 3 decrease as the most dissimilar laboratories are removed from computation following the vertical dashed lines of Figure 2 from top to bottom. This result indicates that CPA captures the similarity between detection patterns well. The exception to that observation was the convolution of inter-CVs on raw data in experiment Y. Pesticide spiking emphasizes the differences in the instruments’ dynamic ranges and this difference appears to be stronger than the effect of MS feature co-detection. Different strategies of spectral normalization (PQN and SQC) and feature scaling (UVC and L2) were tested on the data available, knowing that there are a multitude of effects influencing the comparability of mass spectra between different batches and laboratories. Normalization methods act on the distribution of signal intensities in a spectrum, while scaling methods act on the magnitude of an individual MS feature across spectra. We found that feature scaling across all laboratories at once distorts intensity distributions. Here, scaling was used on each laboratory’s data individually. Figure 3 shows that the application of the L2 norm (Euclidean norm) was always superior to UV scaling. L2 exercises the greatest impact in terms of inter-CV improvement relative to raw data among individual correction methods tested. At the same time, any combination involving SQC was superior to PQN except for experiment Z. Here, PQN-L2 and SQC-L2 performed the best resulting in exactly the same results while both PQN-UV and SQC-UV performed worse than raw data. A likely reason for this behavior is that the denominator in the computation of standard deviation (itself the denominator in UV scaling) is corrected for degrees of freedom, which exercises a large effect in small sample sizes. The removal of aberrant laboratories had the least noticeable effect on inter-CVs in terms of absolute removal of error. The improvement of inter-CVs was the weakest in experiment Z when data correction was used. No matrix composition was modified in experiment Z and the single sources of variation were instrument specific biases and number of scans. While Figure 3 displays inter-CVs only, it is to be noted that any combination of scaling and normalization methods did not alter intra-CV significantly (Supporting Information, SI-5).
Figure 3 Convolution of CVs as a function of increasing co-detection of MS features on experiments X, Y, and Z. Inter- and intra-CVs were computed across the entire data set iteratively removing laboratories following Figure 2 at 40% missingness, beginning with the uppermost (most aberrant) laboratory, with the members of CPCs being the last to be removed. Inter- and intra-CVs of the following data treatments are compared: raw (Raw), raw with laboratory Euclidean (L2), raw with laboratory UV scaling (UV), PQN on raw data (PQN), SQC on raw data (SQC), PQN followed by L2 (PQN_L2), PQN followed by UV (PQN_UV), SQC followed by L2 (SQC_L2), and SQC followed by UV (SQC_UV). The effects of both SQC and Euclidean-norm affect inter-CVs majorly, while intra-CVs do not change due to Euclidean normalization (of features) and improve only slightly as SQC is applied (a zoom-in toward intra-CVs is provided in the Supporting Information, SI-6).
The results substantiate that L2 normalization should be used for feature scaling and that SQC corrects interlaboratory variation majorly and that intra-CV magnitude is not an artifact of data treatment.
Multivariate Comparability
Multivariate comparability was assessed in a cross validation scheme for experiments X and Y. A PCA model was built for each laboratory. Each laboratory’s original model was then applied on the data of all other laboratories, generating validation scores that were scaled to same Euclidean length. R2 values of regression analyses between a laboratory’s original model score (rows in Figure 4) and the validation scores plus dilution or spiking levels (columns in Figure 4) were visualized in a heat map. Figure 4 shows the cross-validation map computed on the experiment X (matrix effect) at 40% missingness per dilution level and laboratory L2 norm applied on features. The interesting case of laboratory H shows that applying its model to the data other laboratories generated high R2 values (R2 > 0.8). A linear regression of the H-scores against dilution levels showed good linearity in the scores (R2 = 0.94). At the same time, the data of H did not perform well using the other laboratories’ PCA models. H is one of the two laboratories with a strongly aberrant missingness structure in experiments X, Y, and Z (Figure 2). The result can be interpreted as follows: Those features that follow matrix dilution levels in H show the same trends in the other laboratories. However, these features receive small loadings in the PCAs of other laboratories. This insight is supported by the scatter plots corresponding to Figure 4 (Figure S14). Recall the case of laboratory D, which showed decreasing signal magnitudes with increasing sample concentration. Figure 4 implies that the dependency of signal magnitudes on matrix concentration was a sign of increasing suppression strong background ions (Figure S16). The models of laboratory D produced an average R2 ≈ 0.7 when applied to the data of other laboratories. Likewise, the loadings of other laboratories produced an average R2 ≈ 0.75 when applied on the data of D. The cross-validation map of experiment Y (Figure S13) shows perfect R2 values for all combinations reflecting excellent reproduction the effects pesticide spiking exercised on the analytical matrix. The scatter-plot map for the cross-validation of experiment X shows that the FT-ICR analyzers were challenged by large ion abundances at spiking levels 15 and 20 ppb (Figure S15).
Figure 4 PCA cross-validation map for experiment X.
Spectral normalization did not have significant effects on the cross-validation experiment. Univariate comparability as measured by inter-CVs did not appear to be of major importance for multivariate comparability. One possible explanation would derive from the central limit theorem: PCA scores represent a weighted mean of the hundreds or thousands of features’ univariate signal magnitudes.
Evaluation of Scanning Depth
High-throughput routine analyses are required to be fast and robust and need to detect features of relevance. Experiment Z was performed using the NIST SRM 1950 standard for which quantified metabolite identities and concentrations are available. We manually extracted the names and identifiers for 682 small molecules, amounting to 430 individual compounds and 319 unique molecular formulas from Simón-Manso et al. All 319 molecular formulas transformed into seven fundamental ion types where possible: [M + H]+, [M + CH4O + H]+, [M + Na]+, [M + H2O + H]+, [M + CHO2Na + H]+, [M – NH3 + H]+, and [M – H2O + H]+. All ion types were combined combinatorically to build homodimers and homotrimers of different adduct types. The expanded list encompassed 26,908 positive ionization mode m/z values to be searched in the Z experiment. The raw data matrices of experiments X and Z were fused at 1 ppm error and matched against the built SRM 1950 metabolic feature collection. Mass matching at a 0.5 and 1 ppm search window size resulted in 1111 and 1345 hits against the fused XZ matrix.
While laboratory B showed significantly more hits than all other laboratories, only laboratories F and I detected significantly fewer putatively annotated MS features (Figure 5a). Setting the number of detected features after 300 scans to 100% for each laboratory, 76 and 89% of all SRM 1950 features are detected after 50 and 100 scans, respectively. SRM 1950 annotated MS features showed significantly higher detection frequencies compared to detection frequencies in the corresponding data sets (Figure 5b). Experiment Z showed consistently higher detection frequencies compared to experiment X, which is partially due to longer scanning times and no variation of matrix composition in experiment Z. Figure 5 and experiment Z imply that (i) features detected within this study are representative for the analytical matrix and that it is more likely to detect metabolic features that are already known; (ii) the accumulation of merely 50 scans, which is performed within 60–90 s, provides a read out that is already 75% complete (compared to acquisition of 300 scans, which takes approximately 10 min depending on instrumental settings). The detection of isotopic fine structures naturally requires longer scanning times.27
Figure 5 Putatively annotated signals in SRM 1950 blood plasma per scanning number. (a) Number of MS features putatively annotated against SRM 1950 metabolites per number of accumulated scans for each participant. (b) Comparison of the MS feature frequency (percentage of valid triplicate detection across all laboratories) between the MS features co-detected in X and Z alone against the frequency of those features with SRM 1950 annotations within either experiment.
Conclusion
This preliminary interlaboratory study was set up to comprehend whether different FT-ICR mass spectrometers across the globe, at their routine performance, have the aptitude to detect the same signals generated on the same sample sets. The subsequent question was whether feature intensities were comparable and what data correction technique could be applied to minimize interlaboratory CVs. The global aim was to collect the adequate experience and knowledge for setting up a future DI-FT-ICR MS ring trial. The variability in laboratory performance across the FT-ICR community around the world was found to be significant, likely due to diverse scientific scopes, some of which may not fall into the field of analyzing small molecules.
The data provided by seven out of 17 laboratories had to be excluded because either MS-peak deformation, lack of signals to be calibrated or strong contaminations hindered calibration to below 300 ppb of mass error standard deviation. The remaining laboratories exhibited strong variability in terms of signal magnitudes at smaller or larger m/z values and number of valid triplicate features detected. Co-detection analyses across three different experiments showed that laboratories A, B, C, D, and E co-detect up to 90% their valid triplicate signals. Laboratories B and E were found to be the most representative for all laboratories. Globally, 67 and 69% of the MS features detected in experiments X and Z overlapped, indicating a good representation of MS features typical for blood plasma SPE extracts. In turn, pesticide spiking caused the loss of almost half of the MS features detected in X, substantiating that the type and concentration of authentic standards have to be evaluated carefully when standard addition is performed in DI-MS. Comparisons of interlaboratory CVs consistently showed that smoothed quotient correction followed by scaling MS features on the L2 norm within each laboratory individually resulted in median CVs between 10 and 20%. Multivariate cross-validation on experiments X and Y showed that multivariate comparability of experimental effects was acceptable not necessarily depending on univariate comparability.
In effect, an appropriate strategy toward interlaboratory comparability for untargeted DI-FT-ICR MS would encompass the following steps: (1) Optimize instrumental parameters to meet the detection pattern of the laboratories that showed best co-detection (e.g., B and E). (2) Define a study design that is representative of the analytical task and maintain a constant batch size. (3) Always use the exact same amount of quality control samples that are randomly distributed in the batch so that L2 normalization can be performed. (4) Perform SQC toward the median of QCs and use the L2 norm of the QCs to scale the samples in question. We suggest the above points as one step toward applications of untargeted diagnostic UHR-MS profiling for fields of application such as quality control in food industries, pharmaceuticals, or clinical phenome centers.
Finally, experiment Z, using the SRM 1950 blood plasma standard, showed that 50 scans (60–90 s scanning time) were found to be sufficient to detect 75% of all potential SRM 1950 metabolites detected at 300 scans (8–10 min scanning time). These results suggest that FT-ICR mass spectrometers can be used for routine high-throughput measurements. Follow-up studies could encompass ring trials with cloned instruments (7Ts, 9Ts, 12Ts, Infinity versus ParaCell), clusters of laboratories of similar scope and expertise (proteomics, lipidomics, petroleomics, etc.), and more elaborate study designs (e.g., including a clinical study).
Supporting Information Available
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/jasms.2c00082.Experimental information, supporting concepts and figures (PDF)
Table S1 (XLSX)
Supplementary Material
js2c00082_si_001.pdf
js2c00082_si_002.xlsx
Author Present Address
† Australian National Phenome Centre, Murdoch University, Harry Perkins Institute of Medical Research, 5 Robin Warren Drive, 6150 Murdoch, Western Australia
Author Present Address
‡ Metatoul-AXIOM Platform, MetaboHUB, Toxalim, INRAE, Toulouse, France. Toxalim, Toulouse University, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
Author Present Address
§ Département de chimie, Université de Sherbrooke, 2500 Boulevard de l’université, Sherbrooke, Québec, J1K 2R1, Canada.
Author Present Address
# ExxonMobil Chemical Company, 22777 Springwoods Village Parkway, Spring, TX 77389.
Author Contributions
Conception, study design and logistics: S.F., F.M., C.J.T., B.K., J.U., A.B., M.L.E., F.L., P.S.-K. On-site coordination of the experiments: S.F., F.M., B.K., J.U., C.A., B.A.B., F.F.-L., C.G., D.H., K.-S.J., R.M., R.N., E.N., M.P., L.P.-T., J.B.S., E.S., M.W., A.W., J.W., P.S.-K. Preparation and distribution of samples: S.F., F.M., J.U. Development, distribution and revision of standard operating procedure (SOP): S.F., F.M., C.J.T., B.K., J.U., S.N., M.P., Y.E.M.vdB., M.W. Data acquisition: S.F., F.M., B.K., C.D.B., B.A.B., R.K.C., J.F., F.F.-L., M.G.-H., K.-S.J., V.M., R.N., E.N., S.N., M.P., J.P., I.S.-A., J.B.S., E.S., Y.E.M.vdB., C.V., M.W., A.W., J.W. Data analysis, interpretation and drafting of the manuscript: S.F., F.M., N.K., C.J.T. Critical revision of the manuscript: S.F., F.M., C.J.T., B.K., J.U., C.D.B., B.A.B., F.F.-L., C.G., K.-S.J., E.N., E.S., C.V., M.W., P.S.-K. S.F., F.M., and C.J.T. contributed equally.
The authors declare the following competing financial interest(s): A.B., N.K., and M.W. are employees of Bruker Daltonik GmbH, F.H.L. is President and CEO of the Bruker Corporation, and C.J.T. and J.W. were employees of Bruker Daltonics Inc. which manufactures and sells mass spectrometers and software used in this study.
Acknowledgments
This research was supported by the German Center for Diabetes Research (DZD; Grants G-501900-482 and G-501901-020), the European Regional Development Fund (ERDF) No. HN0001343, the European Union’s Horizon 2020 Research Infrastructures program (Grant Agreement 731077), the Région Normandie, the Laboratoire d’Excellence (LabEx) SynOrg (ANR-11-LABX-0029), and the national FT-ICR network (FR 3624 CNRS). A portion of the research was performed using EMSL (grid.436923.9), a DOE Office of Science User Facility sponsored by the Biological and Environmental Research program.
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| 0 | PMC9732919 | NO-CC CODE | 2022-12-14 23:36:00 | no | Immunooncol Technol. 2022 Dec 9; 16:100340 | latin-1 | Immunooncol Technol | 2,022 | 10.1016/j.iotech.2022.100340 | oa_other |
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J Virus Erad
J Virus Erad
Journal of Virus Eradication
2055-6640
2055-6659
Elsevier
S2055-6640(22)00245-X
10.1016/j.jve.2022.100307
100307
Original Research
Identification of a pharmacological approach to reduce ACE2 expression and development of an in vitro COVID-19 viral entry model
Endo Yukinori a
Hickerson Brady T. b
Ilyushina Natalia A. b
Mohan Nishant a
Peng Hanjing a
Takeda Kazuyo c
Donnelly Raymond P. b
Wu Wen Jin a∗
a Division of Biotechnology Review and Research 1 (DBRR1), Office of Biotechnology Products (OBP), Office of Pharmaceutical Quality (OPQ), Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA
b Division of Biotechnology Review and Research 2 (DBRR2), Office of Biotechnology Products (OBP), Office of Pharmaceutical Quality (OPQ), Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA
c Microscopy and Imaging Core Facility, Center for Biologics Evaluation and Research (CBER), U.S. Food and Drug Administration (FDA), 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA
∗ Corresponding author.
9 12 2022
12 2022
9 12 2022
8 4 100307100307
22 6 2022
18 11 2022
8 12 2022
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Because of rapid emergence and circulation of the SARS-CoV-2 variants, especially Omicron which shows increased transmissibility and resistant to antibodies, there is an urgent need to develop novel therapeutic drugs to treat COVID-19. In this study we developed an in vitro cellular model to explore the regulation of ACE2 expression and its correlation with ACE2-mediated viral entry. We examined ACE2 expression in a variety of human cell lines, some of which are commonly used to study SARS-CoV-2. Using the developed model, we identified a number of inhibitors which reduced ACE2 protein expression. The greatest reduction of ACE2 expression was observed when CK869, an inhibitor of the actin-related protein 2/3 (ARP2/3) complex, was combined with 5-(N-ethyl-N-isopropyl)-amiloride (EIPA), an inhibitor of sodium-hydrogen exchangers (NHEs), after treatment for 24 h. Using pseudotyped lentivirus expressing the SARS-CoV-2 full-length spike protein, we found that ACE2-dependent viral entry was inhibited in CK869 + EIPA-treated Calu-3 and MDA-MB-468 cells. This study provides an in vitro model that can be used for the screening of novel therapeutic candidates that may be warranted for further pre-clinical and clinical studies on COVID-19 countermeasures.
Keywords
SARS-CoV-2
ACE2
Calu-3
Spike protein
ARP2/3 complex inhibitors
Sodium-hydrogen exchangers (NHEs) inhibitors
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pmc1 Introduction
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged as a global pandemic at the end of 2019.1, 2, 3 Since the Pfizer-BioNTech COVID-19 vaccine was authorized for emergency use by the FDA on Dec. 11, 2020, a total of three COVID-19 vaccines have been authorized or approved for use in the U.S.A.4 However, subsequent emergence of SARS-CoV-2 variants, e.g., Alpha, Beta, Gamma, Delta, and Omicron, gave rise to additional public health concerns.5, 6, 7 Furthermore, it has been reported that waning efficacy of COVID-19 vaccines was most notable against the Omicron variant.8 Thus, there is still an urgent need to develop novel and effective therapeutics to treat COVID-19.
Angiotensin-converting enzyme 2 (ACE2) is a dimeric, type 1 membrane protein expressed in a wide variety of human tissues, including lungs, heart, kidneys, and intestines.9 The SARS-CoV-2 spike protein binds to ACE2 to facilitate viral entry. The expression pattern of ACE2 suggests that in addition to playing important roles in the regulation of the biological functions in those tissues and organs, ACE2 also serves as the receptor for SARS-CoV-2 to infect other tissues and organs apart from the lungs.10 , 11 A correlation has been shown between a high level of ACE2 expression and increased SARS-CoV-2 infection,12 , 13 and downregulating ACE2 expression may reduce SARS-CoV-2 infection.12 , 14
In this study, in order to establish an in vitro model of SARS-CoV-2 cellular entry, we first examined ACE2 expression in a variety of mammalian cell lines, some of which are commonly used to study SARS-CoV-2. Based on ACE2 expression levels, Calu-3, Vero, and MDA-MB-468 cells were selected for further study to develop the in vitro model. As the second step, a variety of compounds or inhibitors were screened based on ACE2 expression changes, and a treatment combination of CK869, an inhibitor of the actin-related protein 2/3 (ARP2/3) complex plus 5-(N-ethyl-N-isopropyl)-amiloride (EIPA), an inhibitor of sodium-hydrogen exchangers (NHEs) was selected as this combination efficiently decreased ACE2 protein expression in the cell lines tested. Next, we used pseudotyped lentiviruses expressing the SARS-CoV-2 full-length spike protein from the Wuhan-Hu-1 strain, Delta and Omicron variants to study viral entry. Finally, we confirmed that the combination of CK869 plus EIPA inhibited viral entry in MDA-MB-468 and Calu-3 cells.
2 Materials & methods
2.1 Cells
Calu-3, Vero, Caco-2, and BT-20 cells were purchased from the American Type Culture Colelction (ATCC) and were maintained in Minimum Essential Medium (MEM) containing 10% foetal bovine serum (FBS). MDA-MB-468 (ATCC) and BT-474 cells (ATCC) were maintained in RPMI-1640 medium containing 10% FBS. SKBR-3 cells (ATCC) were maintained in DMEM/F12 (1:1) containing 10% FBS. JIMT1 (DSMZ), MCF-7 (ATCC), HT-1080 (ATCC), and HFF (kindly provided by Susan Yamada, NIH, Bethesda) cells were maintained in Dulbecco's modified Eagle Medium (DMEM) containing 10% FBS.
2.2 Ligand, chemical compounds, and inhibitors
All ligands, chemical compounds, and inhibitors used in this manuscript were cell biological grade: EGF (Sigma-Aldrich, cat# E9644), NH4Cl (Sigma-Aldrich, cat# A0171), NSC23766 (TOCRIS, cat# 2161), Casin (TOCRIS, cat# 5050), ZCL278 (TOCRIS, cat# 4794), ML141 (TOCRIS, cat# 4266), PBP10 (Calbiochem, cat# 529625), Rapamycin (Sigma-Aldrich, cat# S-015), U0126 (Sigma-Aldrich, cat# 19–147), Quercetagetin (Calbiochem, cat# 551590), Afatinib (Selleckchem, cat# S1011), 5-(N,N-Dimethyl)-amiloride hydrochloride (DMA, Sigma-Aldrich, cat# A4562), 5-(N-ethyl-N-isopropyl)-amiloride (EIPA, Cayman Chemical Company, cat# 14406), Wiskostatin (TOCRIS, cat# 4434), 187-1, N-WASP inhibitor (TOCRIS, cat# 2067), CK869 (TOCRIS, cat# 4984), CK666 (TOCRIS, cat# 3950), Cytochalasin D (TOCRIS, cat# 1233), and LY294002 (Sigma-Aldrich, cat# L9908).
2.3 Cell culture on Matrigel
Matrigel matrix (Corning, cat# 354234) preparation was described previously.15 Briefly, vials of aliquoted Matrigel stored at -20°C were thawed on ice and the liquid Matrigel was immediately applied to 6-well plates (≈600μl per well). Dishes were incubated at 37°C for 30 min until the Matrigel was polymerized. 5 x 106 cells were seeded on top of the polymerized Matrigel in 6-well plates and incubated for 4 days. Cells cultured on 6-well plates were subjected to Western blotting analysis.
2.4 Viral entry assay
Lentiviral particles pseudotyped with the SARS-CoV-2 spike protein were produced in 293T cells by transfection of a lentiviral backbone encoding CMV-Luciferase-IRES-ZsGreen as well as lentiviral helper plasmids and Wuhan-Hu-1, Delta (B.1.617.2), and Omicron (BA.1) spike expression plasmid as previously described.16 To measure viral entry, 2.0 x 105 Calu-3, Vero and MDA-MB-468 cells were seeded in 96-well plates and incubated overnight at 37°C. Cells were pre-treated with a combination of 50 μM CK869 and 40 μM EIPA or left untreated for 24 h at 37°C. Prior to the addition of lentiviral pseudovirus, cells were pretreated with SARS-CoV-2 pseudovirus infection enhancer (101Bio, cat# CoV2) at a volume of 1/10 of the cell culture media in each well and incubated for 30 min at 37°C. Lentiviral pseudovirus with a titer of approximately 1 x 106 relative luminescence units (RLU)/mL of luciferase activity was then added, and Calu-3, Vero, and MDA-MB-468 cells were incubated for 72 h at 37°C. Cell extracts were harvested, lysed, and luciferase levels were assayed using a luciferase-based assay system (Promega, Madison, WI). The experiment was performed in at least triplicate.
2.5 Western blotting
The 5 x 105 Calu-3, Vero, and MDA-MB-468 cells were seeded in 6-well plates one day prior to cell treatment, the next day cells were treated with a series of drugs as described in the manuscript. Twenty-four or 48 hours after drug treatment, cells were washed with PBS twice and then lysed with a NP40 lysis buffer on ice for 30 min. After centrifugation, whole cell lysates (WCL) were subjected to Western blot analysis. Western blotting panels shown in the figures are a representative of three independent experiments. Image J software (NIH, Bethesda) was used for densitometry of Western blotting. The following primary antibodies were used for Western Blot analysis: ACE2 (Abcam, cat# ab15348), TMPRSS2 (Abcam, cat# ab92323), EGFR (BD Biosciences, cat# 610016), phospho-EGFR (Y1045) (Cell Signaling Technology, cat# 22371), Actin (Sigma-Aldrich, cat# A1978). For detecting secreted form of ACE2 in cell culture media, 1.5 x 106 of Vero and Calu-3 cells were seeded in 10-cm dishes one day prior to change to serum-free media. The next day the cell culture media containing 10% FBS was changed to FBS-free media, and cells were then cultured for 4 days. After 4 days, the conditioned cell culture media was collected, and cell debris was removed by centrifugation. The cell culture medium was concentrated using Amicon Ultracel 10k centrifugal filters (Millipore, cat# UFC901024) and the 50-times concentrated cell culture media was subjected to Western blot analysis for detection of ACE2 expression (Abcam, cat# ab15348).
2.6 Flow cytometry
Cell surface ACE2 expression level was evaluated using flow cytometry. Briefly, after harvesting cells using 0.05% Trypsin-EDTA (Thermo Fisher Scientific, cat# 25300-054), cells were washed with PBS twice and fixed in 4% paraformaldehyde (PFA) for 30 min. Then, cells were washed with PBS twice and incubated with anti-ACE2 antibody (Thermo Fisher Scientific, cat# MA5-32307) in FACS buffer (1% FBS in PBS) on ice for 1 h. After washing with PBS twice, cells were incubated with FITC-conjugated secondary antibody for 30 min at room temperature (RT). The rabbit IgG was used as isotype control. Subsequently, cells were washed with FACS buffer (1% FBS in PBS) and analyzed using a LSR Fortessa flow cytometer (BD Bioscience, San Jose, CA, USA).
2.7 Statistical analysis
GraphPad Prism was used for statistical studies. Statistical significance was determined by Student's t-test (*, p-value <0.05; **, p-value <0.01; ***, p-value <0.0001). Data is expressed as mean ± SD.
3 Results & discussion
To develop an in vitro model to screen potential drugs that can inhibit ACE2-dependent viral entry into mammalian cells, we examined expression levels of ACE2 and TMPRSS2 in cells using Western blot analysis. As shown in Fig. 1 a and b, two ACE2 bands with different molecular weights (120 kDa and 85 kDa) were detected. According to published literature, the high molecular weight version of ACE2 is a glycosylated form, which exists on the cell surface, whereas its lower molecular weight version is the enzymatically deglycosylated form or a secreted form that is not located on the cell surface.17 Among the cell lines that we have tested, Calu-3 cells (human respiratory epithelial cell line), which are commonly used in COVID-19 studies, expressed the highest level of ACE2 (Fig. 1b), while MDA-MB-468 cells, a triple-negative breast cancer cell line, expressed the highest level of EGFR (Fig. 1a and b). TMPRSS2 is a co-receptor for SARS-CoV-2 and supports ACE2 binding and entry.18 , 19 It was detectable in all cell lines (Fig. 1a and b), but levels of TMPRSS2 expression did not correlate with that of ACE2. Furthermore, cell surface ACE2 expression was evaluated in MDA-MB-468, Vero, and Calu-3 cells using flow cytometry analysis. As shown in Fig. 1c, cell surface ACE2 expression level was very similar in Vero and Calu-3 cells, but not detected in MDA-MB-468 cells. The secreted form of ACE2 was also examined in conditioned cell culture media from Vero and Calu-3 cells after the collected conditioned cell culture media was concentrated 50 times using centrifugal filters. Only a 120 kDa band of ACE2, but not a 75 kDa one was detected in Calu-3 cells (Fig. 1d). None of the 120 kDa and 75 kDa bands were detected in Vero cells (Fig. 1d). These results suggest that the lower band of ACE2 shown in Fig. 1a, b and 1d is likely a deglycosylated form of ACE2, but not the secreted form. Based on the results shown in Fig. 1a, b, and 1c, Calu-3, Vero, and MDA-MB-468 cells with different levels of ACE2 expression were selected for further studies.Fig. 1 ACE2 is highly expressed in Calu-3, Vero and MDA-MB-468 cells. (a) The levels of ACE2, TMPRSS2, and EGFR expression were evaluated by Western blotting in whole cell lysate (WCL) of SKBR-3, BT-474, JIMT1, BT-20, MDA-MB-468, MCF-7, HFF, and HT-1080 cells. (b) The levels of ACE2, TMPRSS2, and EGFR expression were evaluated by Western blotting in WCL of Calu-3, Vero, Caco-2, and MDA-MB-468 cells. (c) The levels of cell surface ACE2 were evaluated using flow cytometry analysis in non-permeabilized MDA-MB-468, Vero, and Calu-3 cells. (d) The levels of secreted form of ACE2 were examined by Western blotting in concentrated conditioned cell culture medium (CM) (50x) of Vero and Calu-3 cells.
Fig. 1
We recently found that cell extrinsic factors from Matrigel activate epidermal growth factor receptor (EGFR), resulting in primary resistance of HER2-positive breast cancer cells to T-DM1 which is an FDA-approved antibody-drug conjugate (ADC) for the treatment of HER2-positive breast cancers.15 Because MDA-MB-468 cells express high levels of EGFR (Fig. 1a and b), we examined if ACE2 expression can be affected by cell extrinsic factors. MDA-MB-468 cells were seeded on a Matrigel and cultured for 4 days. ACE2 expression was then examined in the WCLs using Western blot analysis. MDA-MB-468 cells formed spheroid-like clusters on the Matrigel matrix after 2–3 days (data not shown). An 8.2-fold increase in ACE2 expression was observed in MDA-MB-468 cells grown on Matrigel as compared to that of cells cultured on regular tissue culture dishes (2D system), while a 1.4-fold increase in EGFR expression was observed (Fig. 2 a). In contrast, a decrease in TMPRSS2 expression was observed in the MDA-MB-468 cells grown on Matrigel (Fig. 2a). An 18.9-fold increase of ACE2 expression was also observed in Vero cells grown on Matrigel (Fig. 2b).Fig. 2 ACE2 expression is increased when MDA-MB-468 and Vero cells grow on a Matrigel matrix and decreased after MDA-MB-468 cells are treated by EGF. (a) The levels of ACE2, TMPRSS2 and EGF expression were evaluated by Western blotting in WCL of MDA-MB-468 cells cultured either on 2D (lanes 1, 2, 3) or grown on Matrigel matrix for 4 days. Lanes 1, 2, and 3 shows different WCLs harvested from three different cell densities of MDA-MB-468 cells on 2D. (b) The level of ACE2 expression was evaluated by Western blotting in WCL of Vero cells cultured either on 2D (WCLs harvested from three different cell densities of Vero cells) or grown on Matrigel matrix for 4 days. Lanes 1, 2, and 3 shows different WCLs harvested from three different cell densities of Vero cells on 2D. (c) The levels of ACE2, TMPRSS2, EGFR, and phosphorylated EGFR (Y1045) were evaluated by Western blotting in WCL of MDA-MB-468 cells in the absence or presence of 100 ng/ml EGF for 2 days. It should be noted that actin amount shown in the lower panel was used as the reference to calculate the fold changes in ACE2, TMPRSS2, EGFR, and phospho-EGFR (Y1045).
Fig. 2
Since EGF-dependent signaling causes EGFR activation and downregulation,20 we next examined if EGF treatment can affect ACE2 expression in MDA-MB-468 cells. Cells were cultured in 2D dishes in the absence or presence of 100 ng/ml EGF for 2 days. As shown in Fig. 2c, EGF ligand treatment enhanced EGFR phosphorylation (the second panel from the bottom) and decreased EGFR expression. Interestingly, the EGF-mediated downregulation of EGFR was accompanied by a 67.5% decrease in ACE2 expression compared with that of non-EGF-treated cells (Fig. 2c). Taken together, the results shown in Fig. 2a and c suggest that extrinsic factors and EGF-mediated signaling can alter ACE2 expression in MDA-MB-468 cells.
We then tested a variety of compounds and inhibitors, which are either directly or indirectly involved in the downstream effectors of growth factor receptors (e.g., EGFR and cytoskeleton rearrangement), to further determine if they can regulate ACE2 expression in MDA-MB-468 cells treated with EGF (Fig. 3 a, b, 3c). The compounds and inhibitors used in this study included 20 mM ammonium chloride (NH4Cl, autophagy inhibitor), 50 μM NSC23766 (a Rac1 GTPase inhibitor), 5 μM Casin and 20 μM ZCL278 (Cdc42 GTPase inhibitors), 20 μM ML141 (a Rac1/Cdc42 inhibitor), 10 μM PBP10 (FPR2 antagonist), 1 μg/ml rapamycin (a mTORC1 inhibitor), 10 μM U0126 (a MAPK kinase inhibitor), 50 μM quercetagetin (a flavonol that inhibits proto-oncogene serine/threonine-protein kinases, Pim-1), 200 μM afatinib (a kinase inhibitor of HER2 and EGFR), 20 μM DMA (5-(N,N-Dimethyl)-amiloride hydrochloride) and 40 μM EIPA (5-(N-ethyl-N-isopropyl)-amiloride) (inhibitors of the Na+/H+ exchanger (NHE)), 5 μM wiskostatin and 5 μM 187-1 (N-WASP inhibitors), 50 μM CK666 and 50 μM CK869 (Arp2/3 inhibitors), 10 μM cytochalasin D (an inhibitor for actin polymerization), and 10 μM LY294002 (an inhibitor of PI3K). While ACE2 reduction was observed in a range of 30% to 50% in the indicated cells treated with EGF alone, a 74% reduction of ACE2 expression was observed in PBP10 + EGF-treated cells, a 73% reduction in quercetagetin + EGF-treated cells, a 57% reduction in 187-1 + EGF-treated cells, and a 58% reduction in CK869 + EGF-treated cells (Fig. 3a, b, 3c). The results from this pilot screen suggested that PBP10, quercetagetin, 187-1, and CK869 are promising candidates to downregulate ACE2 expression in cells. The reduction of ACE2 expression was also detectable when Vero cells were treated with those compounds or inhibitors with or without EGF (Fig. 3d, e and 3f).Fig. 3 Chemical compounds and inhibitors can downregulate ACE2 expression in MDA-MB-468 and Vero cells. The level of ACE2 expression was evaluated by Western blotting in WCLs of MDA-MB-468 cells (a, b, c) and Vero cells (d, e, f) after the cells were treated with indicated compounds and inhibitors for 24 h in the absence or presence of EGF. It should be noted that actin amount shown in the lower panel was used as the reference to calculate the fold changes in ACE2 and TMPRSS2.
Fig. 3
We next examined kinetics of ACE2 expression in Vero cells after exposure to different concentrations of cytochalasin D. ACE2 expression was decreased in a dose-dependent manner (Fig. 4 a). Moreover, a greater reduction in ACE2 expression (50%) was observed when Vero and Calu-3 cells were co-treated with CK869 plus EIPA as compared to a single inhibitor treatment (Fig. 3, Fig. 4c). Then, cytotoxicity of CK869 plus EIPA was evaluated in Calu-3 cells. From the results shown in Fig. 4d, the 50% cytotoxic concentration (CC50) was 150 ± 3.0 μM CK869 + 120.3 ± 2.4 μM EIPA. We next tested whether the ACE2-mediated viral entry was inhibited in cells treated with 50 μM CK869 + 40 μM EIPA using pseudotyped lentivirus expressing the SARS-CoV-2 full-length spike protein from the Wuhan-Hu-1, Delta, and Omicron variants. Calu-3 and MDA-MB-468 cells were selected for this experiment because luciferase activity was not detectable in Vero cells (data not shown). As shown in Fig. 4e, luciferase activity was dramatically diminished in CK869 + EIPA-treated Calu-3 cells compared with mock-treated Calu-3 cells. Interestingly, even though cell surface ACE2 was not detected in MDA-MB-468 cells (Fig. 1c), similar levels of luciferase activity to those in Wuhan-Hu-1, Delta and Omicron-infected Calu-3 cells were detected in MDA-MB-468 cells, and these were significantly diminished in CK869 plus EIPA-treated cells (Fig. 4f). These results showed at least two possible mechanisms for SARS-CoV-2 viral entry. One is that the reduction of ACE2 expression correlates with the inhibition of the ACE2-mediated entry of pseudotyped lentivirus expressing the SARS-CoV-2 full-length spike protein into cells and the other is that the entry of pseudotyped lentivirus may occur independently of ACE2.Fig. 4 A combination of CK869 and EIPA reduces ACE2 expression in Calu-3 and inhibits ACE2-mediated viral entry in Calu-3 and MDA-MB-468 cells. (a) The levels of ACE expression were evaluated by Western blotting in WCLs of Vero cells after the cells were treated with different concentrations of cytochalasin D for 24 h. (b) Kinetics of ACE2 expression in cytochalasin D-treated Vero cells were obtained from results of Fig. 4a. (c) The level of ACE2 expression was evaluated by Western blotting in WCLs of Calu-3 cells after the cells were treated with indicated compounds and inhibitors for 24 h. Noted that actin amount shown in the lower panel was used as the reference to calculate the fold changes in ACE2. (d) Cytotoxicity of CK869 plus EIPA in Calu-3 cells. (e) Luciferase activity is proportional to the number of Calu-3 cells infected with pseudotyped lentivirus particles expressing SARS-CoV-2 full-length spike protein from Wuhan-Hu-1, Delta and Omicron variants. Luciferase expression (RLU) was quantified. ***: p < 0.0001. Viral entry assay using Wuhan-Hu-1, Delta, and Omicron variants shown in this figure are a representative of four and two independent experiments, respectively. (f) Luciferase activity is proportional to the number of MDA-MB-468 cells infected with pseudotyped lentivirus particles expressing SARS-CoV-2 full-length spike protein from Wuhan-Hu-1, Delta and Omicron variants. Luciferase expression (RLU) was quantified. ***: p < 0.0001. Viral entry assay using Wuhan-Hu-1, Delta, and Omicron variants shown in this figure are a representative of four and two independent experiments, respectively.
Fig. 4
The ACE2 gene and protein expression is controlled by many factors, including both extrinsic and intrinsic cellular factors.21, 22, 23, 24 In this study, we have defined a pharmacological approach to reduce ACE2 expression levels in a variety of mammalian cell lines which should facilitate the discovery of novel drugs capable of either blocking or reducing ACE2-mediated viral entry. These may help to alleviate the multi-organ complications and severity of COVID-19.
We first took advantage of our previous findings that protein levels of cell surface receptors such as HER2 and EGFR can be regulated by cell extrinsic factors from the Matrigel system that activates EGF-mediated receptor activation and downregulation.15 Surprisingly, we found that Matrigel can modulate ACE2 expression in cells, which suggests that EGF-coupled signaling may also play a role in regulation of ACE2 protein expression on the cell surface. Similar to EGFR, ACE2 expressio was also downregulated when cells were treated with EGF, which correlated with EGFR activation and downregulation. Our findings provide an important clue that compounds or inhibitors that are functionally directly or indirectly linked to the EGFR-couple signaling and/or other important biological activity such as cytoskeleton rearrangement may modulate ACE2 protein expression. However, further study is needed on how ACE2 protein expression is regulated by extrinsic factors from the Matrigel matrix and EGF-mediated signaling. Several drug candidates, i.e., 187-1, quercetagetin, EIPA and K869, were evaluated using our model system for their capability to reduce ACE2 protein expression. The combination of two inhibitors, i.e., CK869 + EIPA, exhibited high potency in reducing ACE2 protein levels in Calu-3 cells than did either agent alone. This finding indicates that this combination potently inhibits ACE2-mediated SARS-CoV-2 viral entry into Calu-3 cells. Calu-3 cells were originally isolated from the lung epithelial tissue of a male patient with lung adenocarcinoma.25 Since Calu-3 cells naturally express relatively high levels of ACE2, these cells are therefore a highly relevant in vitro system for studying ACE2-mediated viral entry. Interestingly in our study, we found that pseudotyped lentivirus particles bearing the SARS-CoV-2 spike were unable to infect Vero cells that had similar levels of ACE2 expression to that in Calu-3 cells. These results suggest that the expression of ACE2 alone may not be sufficient to mediate SARS-CoV-2 cellular entry or that in Vero cells there is a mechanism, yet unidentified, that might inhibit SARS-CoV-2 cellular entry. Recently, ACE2-independent viral entry mechanisms have been suggested.26, 27, 28, 29 We found that pseudotyped lentivirus particles bearing the SARS-CoV-2 spike can infect MDA-MB-468 cells that do not express ACE2 on the cell surface. This finding suggests that SARS-CoV-2 can infect ACE2-negative cells via ACE2-independent mechanism(s). The combination treatment of CK869 plus EIPA blocks viral entry in both Calu-3 and MDA-MB-468 cells, indicating that this combination treatment has an impact on both ACE2-mediated and ACE2-independent SARS-CoV-2 cellular entry pathways.
4 Conclusions
The results from our study provide a proof of concept that the ACE2 protein expression levels can be downregulated by a number of inhibitory compounds that target EGF-mediated signaling and that the downregulation of ACE2 expression reduces ACE2-mediated viral entry into human cells. This study also found that SARS-CoV-2 may infect human cells via an ACE2-independent pathway. Taken together, our results suggest that further pre-clinical and clinical studies are warranted to develop drugs that could reduce ACE2 protein expression and inhibit ACE2-mediated and ACE2-independent viral entry to treat COVID-19.
Author contributions
Conceptualization, Y.E. and W.J.W.; methodology, Y.E., B.H., N.I., H.P., and K.T.; software, Y.E., B.H., and N.I.; validation, Y.E. and W.J.W; formal analysis, Y.E., and W.J.W.; investigation, Y.E. and W.J.W.; resources, W.J.W.; data curation, Y.E. and W.J.W.; writing-original draft, Y.E. and W.J.W.; editing, Y.E., B.H., N.I., N.M., H.P., R.P.D., and W.J.W.; visualization, Y.E. and W.J.W.; supervision, W.J.W.; project administration, W.J.W.; funding acquisition, Y.E. and W.J.W. All authors have read and agreed to the published version of the manuscript.
Disclaimer
This article reflects the views of the authors and should not be construed to represent the U.S. FDA's views or policies.
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
We thank Drs. Tao Xie and Nozomi Sakakibara of FDA for critical internal review of this manuscript.
Brady T. Hickerson and Hanjing Peng are supported in part by an appointment to the Research Participation Program in the Office of Biotechnology Products, Center for Drug Evaluation and Research at the U.S. Food and Drug Administration (FDA) administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the FDA and the U.S. Department of Energy.
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| 36514715 | PMC9733118 | NO-CC CODE | 2022-12-16 23:16:00 | no | J Virus Erad. 2022 Dec 9; 8(4):100307 | utf-8 | J Virus Erad | 2,022 | 10.1016/j.jve.2022.100307 | oa_other |
==== Front
SSM Popul Health
SSM Popul Health
SSM - Population Health
2352-8273
The Authors. Published by Elsevier Ltd.
S2352-8273(22)00293-2
10.1016/j.ssmph.2022.101314
101314
Article
Health disparities in past influenza pandemics: A scoping review of the literature
D'Adamo Angela ab
Schnake-Mahl Alina cd
Mullachery Pricila H. c
Lazo Mariana ce
Diez Roux Ana V. cf
Bilal Usama cf∗
a Edward J. Bloustein School of Planning and Public Policy, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
b Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
c Urban Health Collaborative, Drexel Dornsife School of Public Health, Philadelphia, PA, USA
d Department of Health Management and Policy, Drexel Dornsife School of Public Health, Philadelphia, PA, USA
e Department of Community Health and Prevention, Drexel Dornsife School of Public Health, Philadelphia, PA, USA
f Department of Epidemiology and Biostatistics, Drexel Dornsife School of Public Health, Philadelphia, PA, USA
∗ Corresponding author. Urban Health Collaborative, Drexel Dornsife School of Public Health, 3600 Market St. Suite 730, Philadelphia, PA, USA.
9 12 2022
9 12 2022
10131422 7 2022
14 11 2022
8 12 2022
© 2022 The Authors. Published by Elsevier Ltd.
2022
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Objective
The COVID-19 pandemic has exacerbated existing health disparities. To provide a historical perspective on health disparities for pandemic acute respiratory viruses, we conducted a scoping review of the public health literature of health disparities in influenza outcomes during the 1918, 1957, 1968, and 2009 influenza pandemics.
Methods
We searched for articles examining socioeconomic or racial/ethnic disparities in any population, examining any influenza-related outcome (e.g., incidence, hospitalizations, mortality), during the 1918, 1957, 1968, and 2009 influenza pandemics. We conducted a structured search of English-written articles in PubMed supplemented by a snowball of articles meeting inclusion criteria.
Results
A total of 29 articles met inclusion criteria, all but one focusing exclusively on the 1918 or 2009 pandemics. Individuals of low socioeconomic status, or living in low socioeconomic status areas, experienced higher incidence, hospitalizations, and mortality in the 1918 and 2009 pandemics. There were conflicting results regarding racial/ethnic disparities during the 1918 pandemic, with differences in magnitude and direction by outcome, potentially due to issues in data quality by race/ethnicity. Racial/ethnic minorities had generally higher incidence, mortality, and hospitalization rates in the 1957 and 2009 pandemics.
Conclusion
Individuals of low socioeconomic status and racial/ethnic minorities have historically experienced worse influenza outcomes during pandemics. These historical patterns can inform current research to understand disparities in the ongoing COVID-19 pandemic and future pandemics.
Keywords
Health inequities
Influenza
Pandemic
Social class
Ethnic groups
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pmc1 Introduction
COVID-19 caused more than 18 million deaths worldwide in 2020 and 2021 (Johns Hopkins University of Medicine Coronavirus Resource Center; Wang et al., 2022). There have been wide spatial, socioeconomic, and racial/ethnic disparities in COVID-19 incidence, hospitalizations, and mortality during the COVID-19 pandemic (Bassett et al., 2020; Bilal et al., 2021; American Public Media Research Lab Staff; Chen & Krieger, 2020; Mackey, Ayers, Kondo, & et al., 2020a, 2020b; Riou et al., 2021). Although caused by different pathogens, COVID-19 and influenza are both contagious respiratory illnesses caused by viruses, transmitted by respiratory droplets and aerosols (Cowling et al., 2013), and share symptoms including fever, chills, headache, myalgias, cough, fatigue, and more severe manifestations leading to hospitalization and mortality (Solomon et al., 2020). Though influenza has killed 12,000 to 61,000 people annually in the US since 2010 (Centers for Disease Control and Prevention, 2020), influenza virulence and infectivity changes frequently, leading to annual differences in transmission and mortality. Influenza pandemics emerge when a large mutation in the virus occurs, leading to a strain for which there is little to no existing immunity (Kilbourne, 2006). With a lack of immunity, new virus strains can easily infect humans, efficiently transmit to other people, and because of these features, have the potential to cause pandemics.
According to the Centers for Disease Control and Prevention (CDC), there have been, so far, four confirmed influenza pandemics: 1918, 1957–1958, 1968, and 2009 (Monto & Fukuda, 2020; CfDCa). These four remain the only pandemics with molecular confirmation of influenza virus spread. Controversy remains about the origin of previous pandemics, such as the 1889 “Russian flu” (Valleron et al., 2010), which despite its name, may have been caused by coronaviruses (Brüssow & Brüssow, 2021; Vijgen et al., 2005), or whether other epidemics, such as the 1977 one, reached pandemic status (Kilbourne, 2006; Monto & Fukuda, 2020). The 1918 influenza pandemic resulted from an H1N1 influenza A virus. The pandemic featured high mortality among otherwise healthy young adults (ages 20 to 39), and a large proportion of influenza-related deaths were associated with secondary bacterial infections (Monto & Fukuda, 2020). An estimated one-third of the world's population became infected with the virus and at least 50 million died globally (Centers for Disease Control and Prevention, 1918). The influenza pandemics of 1957–58 (henceforth referred as 1957) and 1968 originated in Hong Kong, involved the H2N2 and H3N2 ƒinfluenza A strains, respectively (Monto & Fukuda, 2020), and each caused around 1 million deaths (Centers for Disease Control and Prevention, 19571958–; Centers for Disease Control and Prevention, 1968). Both strains caused high infection and mortality rates among the very young and very old (Monto & Fukuda, 2020). The 2009 pandemic was caused by an H1N1 influenza A virus that originated in Mexico (Monto & Fukuda, 2020). Adults older than 65 experienced lower morbidity and mortality than during usual flu seasons, while children and young and middle aged adults accounted for the majority of deaths, hospitalizations, and infections (Centers for Disease Control and Prevention, 2009). During the first year of infection, an estimated 151,700 to 575,400 people died worldwide (Centers for Disease Control and Prevention, 2009).
Prior reviews have summarized a single dimension of health disparities within pandemics, especially during the 1918 and 2009 pandemics (Mamelund, 2017; Roberts & Tehrani, 2020; Økland & Mamelund, 2019); in this paper, we scope the public health literature examining health disparities across multiple dimensions, including geographic, socioeconomic, and racial/ethnic disparities, during the 1918, 1957, 1968, and 2009 influenza pandemics, worldwide. Health disparities are unjust and preventable differences in health based on place or social group (e.g. race-ethnicity, socioeconomic status (SES), immigration status, or other social status) (Braveman, 2014). The examination of documented health disparities in previous influenza pandemics can help us better understand patterns and predictors of disparities in past pandemics and inform the understanding of disparities during the COVID-19 and future pandemics.
2 Methods
2.1 Search strategy
We conducted a scoping review (Peters et al., 2015) of the public health literature assessing health disparities during the 1918, 1957, 1968, and 2009 influenza pandemics. We selected scoping reviews as we anticipated highly heterogenous nature of the evidence, not amenable to a more quantitatively focus systematic review of the evidence. Scoping reviews aid summarizing complex research findings and identifying research gaps (Peters et al., 2015). We employed the PICOT (population, intervention/exposure, comparison, outcome, and time) framework to guide inclusion criteria (Schardt et al., 2007). We did not limit the review to studies examining specific populations. We focused on disparities by individual and contextual SES (i.e., individual-level or neighborhood/area income, wealth, education, occupation, unemployment) and race/ethnicity. Comparison groups were either population subgroups (e.g., different racial/ethnic or SES groups), or geographic units. Outcomes of interest included any health-related outcome linked to influenza, including infection and severity (mortality, hospitalizations, etc.). We did not include studies that studied preventive interventions (e.g., vaccination, non-pharmaceutical interventions, etc.)
2.2 Identification and inclusion of articles
We included articles that met inclusion criteria within our PICOT framework, specifically: 1) publication in English, 2) assessment of short-term health disparities in any influenza-related health outcome, 3) set in one (or more) of the influenza pandemics of 1918, 1957, 1968, and 2009, and 4) empirical studies or reviews of empirical studies.
We employed three complementary search strategies (Fig. 1 ). First, a snowball search that was initiated by searching in PubMed on July 1, 2020 using the following terms, without language or date restrictions: “influenza pandemics” AND “health disparities”. This resulted in 75 articles, 5 of which fit our PICOT criteria (Blumenshine et al., 2008; Dee et al., 2011; Grantz et al., 2016; Krishnan et al., 2020; Quinn et al., 2011) as screened by AD and UB. Using these “seed” articles, we then initiated a three step snowball search by: (1) reviewing the reference lists of included articles (backwards search); (2) looking for articles citing the included articles (forwards search), using Google Scholar; and (3) searching for articles concurrently cited with included articles (co-citation search) using Co-Cites (Janssens & Gwinn, 2015). We repeated the snowball search until further backwards, forwards, and co-citation searches did not render any more included articles. From the snowball search, 15 additional articles met inclusion criteria.Fig. 1 Search strategy.
Fig. 1
Second, in October 2021 we employed a more detailed structure term search in PubMed (((influenza OR flu) AND pandemic) AND (health AND (disparities OR inequalities OR inequities))), netting 159 articles which were screened for inclusion by AD and ASM. Third, we further reviewed three reviews of the literature that we found during the previous searches and that focused on specific disparities in specific influenza pandemics (Mamelund, 2017; Mamelund & Dimka, 2021; Økland & Mamelund, 2019), and screened their respective reference lists for inclusion of new papers.
These search strategies resulted in 18, 14, and 8 articles respectively, which following the removal of duplicates between searches resulted in 29 final included articles. Two of the five original “seed” articles were commentaries/frameworks (Blumenshine et al., 2008; Krishnan et al., 2020), which we excluded from the review, but retained for the snowball search (Fig. 1).
2.3 Data extraction
From each included article, one reviewer (AD) extracted information on publication date, geographic and temporal setting, health equity angle (SES or race/ethnicity), comparison groups, outcomes, measures of disparities, adjustment covariates, and main results. ASM and UB reviewed all extraction information and reconciled any differences in interpretation.
We categorized articles based on the comparison groups reported into articles looking at population subgroups (e.g., by SES or race/ethnicity) or articles comparing geographical units (e.g., neighborhoods, counties, etc.).
3 Results
In total, this review is based on 29 articles: 12 articles focused solely on the 1918 pandemic, 16 on only the 2009 pandemic, and one on the 1918, 1957, and 2009 pandemics. No articles examined the 1968 pandemic. Of the 29 articles, ten focused on socioeconomic disparities, thirteen on racial/ethnic disparities, and six examined both (see Table 1 for study characteristics and Table 2 for a summary of results; please note that a study may be includesssd in multiple rows of Table 2 if it reported more than one type of disparity or result). The seventeen articles that examined outcomes with respect to socioeconomic factors examined nine different outcomes: hospitalizations (6), influenza/pneumonia mortality (6), excess mortality (2), incidence (4), severity of disease (2), and speed of transmission, ICU admissions, onset, and duration (1 each). Nineteen articles examined 11 different outcomes in relation to race or ethnicity: hospitalizations (7), influenza/pneumonia mortality (5), incidence (6), intensive care unit admissions (3), and risk of exposure, susceptibility, access to health care, pediatric deaths, onset, duration, and severity of disease, and characteristics of severe cases (1 each). Very few studies reported disparities outside the US (10 studies out of 29). The data was very sparse for some racial/ethnic groups; few articles (5) examined American Indian/Alaska Native populations and only two looked at Native Hawaiian/Pacific Islanders. Most studies (21) used a relative measure of inequalities (hazard, rate, odds, prevalence, or probability ratios), while others (6) only compared rates and proportions. All studies but four adjusted for age and most (18) adjusted or stratified by sex or gender.Table 1 Study characteristics for the 29 included articles.
Table 1Citation Equity angle Pandemic Context Unit of Analysis Outcomes Measure of Association Covariates/Adjustments
Økland, 2019 (Økland & Mamelund, 2019) Racial-ethnic 1918 US a a a a
Gamble, 2010 (Gamble, 2010) Racial-ethnic 1918 US a a a a
Frankel, 1919 (Frankel & Dublin, 1919) Racial-ethnic 1918 US Individual Influenza-pneumonia incidence, mortality Comparison of rates Age, sex, race/ethnicity
Brewer, 1918 (Brewer, 1918) Racial-ethnic 1918 Army Camp (US) Regiment Influenza incidence Comparison of rates Number of weeks
Mamelund, 2006 (Mamelund, 2006) SES 1918 City (Norway) Individual Influenza mortality Hazard ratio Age, gender, marital status, social class, apartment size
Mamelund, 2018 (Mamelund, 1918) SES 1918 City (Norway) Individual ILI incidence Probability differences, probability ratio Gender, SES (number of rooms), pandemic wave
Sydenstricker, 1931 (Sydenstricker, 1931) SES 1918 Nine cities in the US Individual Influenza mortality, incidence Comparison of rates, rate ratio Household size, crowding, sex, age
Bengtsson, 2018 (Bengtsson et al., 2018) SES 1918 Norway Individual Excess influenza mortality Comparison of rates, relative risk Age, marital status, number of children, migrant status, urban status, and county of residence
Clay, 2019 (Clay et al., 2019) SES 1918 US Ecological (cities) Excess mortality Comparison of rates Percentage of urban residents in 1910, proximity to WWI base, coal capacity
Wilson, 2018 (Wilson et al., 2018) SES 1918 City (New Zealand) Ecological (suburbs) Influenza mortality Rate ratio NA
Grantz, 2016 (Grantz et al., 2016) SES 1918 City (US) Ecological (Census tracts) Influenza & pneumonia mortality, transmissibility Risk ratio Population density, homeownership, unemployment, age
Britten, 1932 (Britten, 1932) Racial-ethnic, SES 1918 Cities, County (US) Individual Influenza-pneumonia incidence; onset, duration, severity of disease; mortality Comparison of rates Age, sex
Rutter, 2012 (Rutter et al., 2012) SES 2009 UK Ecological (LSOAs)* Influenza mortality Rate ratio Age, gender
Balter, 2010 (Balter et al., 2010) SES 2009 City (US) Ecological (neighborhoods) Hospitalizations Comparison of rates Age
Lowcock, 2012 (Lowcock et al., 2012) SES 2009 Province (Canada) Individual Hospitalizations Comparison of proportions, odds ratio Age, gender
Navaranjan, 2014 (Navaranjan et al., 2014) Racial-ethnic 2009 Province (Canada) Individual Incidence Odds ratio Age, sex, children in household, material deprivation, chronic conditions, receipt of 2008 seasonal vaccine, tested prior to June 11th, 2009, length of stay in Canada, total deprivation, high proportion of neighborhood with low income, material deprivation, post-secondary education, chronic condition, residence in Toronto
Quinn, 2011 (Quinn et al., 2011) Racial-ethnic 2009 US Individual Risk of exposure, susceptibility, access to care Odds ratio Age, gender, presence of children in household, income, education
Dee, 2011 (Dee et al., 2011) Racial-ethnic 2009 US Individual ILI, hospitalization, pediatric deaths Comparison of proportions Age
Soyemi, 2014 (Soyemi et al., 2014) Racial-ethnic 2009 State (US) Individual Hospitalizations, influenza mortality Comparison of proportions, rate ratio Age, gender
Truelove, 2011 (Truelove et al., 2011) Racial-ethnic 2009 State (US) Individual Hospitalizations, mortality, ICU admissions Rate ratio Age, clinical condition, hospital course
Castrodale, 2009 (Castrodale, 2009) Racial-ethnic 2009 States (US) Individual Influenza mortality Comparison of rates, rate ratio Age
Placzek, 2014 (Placzek & Madoff, 2014) Racial-ethnic; SES 2009 State (US) Individual Influenza-related ICU stay Comparison of rates, odds ratio Age, gender, admission type, interactions between race and gender
Kumar, 2012 (Kumar et al., 2012) Racial-ethnic; SES 2009 US Individual ILI incidence Comparison of means, odds ratio Income, education, age, gender
Nyland, 2015 (Nyland et al., 2015) Racial-ethnic; SES 2009 UK Individual Clinical outcomes, primary healthcare access, secondary healthcare access, in-patient care Odds ratio Age, sex, English Index of Multiple Deprivation score, recorded obesity, smoking and chronic obstructive pulmonary disease, admission delay > or equal to 4 days and severity at presentation for admission
Mayoral, 2013 (Mayoral et al., 2013) Racial-ethnic; SES 2009 Spain Individual Hospitalizations Odds ratio Age, gender, unfavorable medical factors, vaccination, type of encounter
Thompson, 2011 (Thompson et al., 2011) Racial-ethnic; SES 2009 State (US) Individual Hospitalizations Rate ratio, odds ratio, risk ratio Age, gender, obesity status, high-risk medical conditions, antiviral treatment
Levy, 2013 (Levy et al., 2013) Racial-ethnic; SES 2009 City (US) Individual Hospitalizations Odds ratio Age, access to care, medical conditions, area of residence
Esteban-Vasallo, 2012 (Esteban-Vasallo et al., 2012) Racial-ethnic 2009 City (Spain) Individual H1N1 infection, severe cases (hospital admission, chronic pathology, pregnancy, delay in hospital admission, and ICU admission) Comparison of rates, odds ratio, prevalence ratio Age, chronic pathology, pregnancy, delay in hospital admission
Wilson, 2012 (Wilson et al., 2012) Racial-ethnic 1918, 1957, 2009 New Zealand Ecological (country) Mortality, hospitalizations Rate ratio Age
a multiple units of analysis or outcomes. SES = socioeconomic status. US=United States. UK=United Kingdom. ILI=Influenza-Like Illness. ICU=Intensive Care Unit. LSOA = lower super-output areas.
Table 2 Summary of results by level/type of disparity and pandemic.
Table 2Main results Influenza pandemic Citation
Less affluent neighborhoods had worse outcomes compared to more affluent neighborhoods 1918 Clay, 2019 (Clay et al., 2019), Mamelund, 2006 (Mamelund, 2006), Grantz, 2016 (Grantz et al., 2016)
2009 Rutter, 2012 (Rutter et al., 2012), Thompson, 2011 (Thompson et al., 2011), Levy (Levy et al., 2013), Balter, 2010 (Balter et al., 2010), Lowcock, 2012 (Lowcock et al., 2012)
Individuals of low SES had worse outcomes compared to individuals of high SES 1918 Sydenstricker, 1931 (Sydenstricker, 1931), Bengtsson, 2018 (Bengtsson et al., 2018), Wilson, 2018 (Wilson et al., 2018), Mamelund, 2018 (Mamelund, 1918)
2009 Lowcock, 2012 (Lowcock et al., 2012), Mayoral, 2013 (Mayoral et al., 2013), Levy, 2013 (Levy et al., 2013)
Racial/ethnic minority groups had worse outcomes than whites 1918 Økland, 2019 (Økland & Mamelund, 2019), Gamble, 2010 (Gamble, 2010), Frankel, 1919 (Frankel & Dublin, 1919)
2009 Quinn, 2011 (Quinn et al., 2011), Dee, 2011 (Dee et al., 2011), Placzek, 2014 (Placzek & Madoff, 2014), Soyemi, 2014 (Soyemi et al., 2014), Mayoral, 2013 (Mayoral et al., 2013), Truelove, 2011 (Truelove et al., 2011), Thompson, 2011 (Thompson et al., 2011), Levy, 2013 (Levy et al., 2013), Navaranjan, 2014 (Navaranjan et al., 2014), Castrodale, 2009 (Castrodale, 2009), Brewer, 1918 (Brewer, 1918), Nyland, 2015 (Nyland et al., 2015), Esteban-Vasallo, 2012 (Esteban-Vasallo et al., 2012)
1918, 1957, 2009 Wilson, 2012 (Wilson et al., 2012)
Racial/ethnic minority groups had increased risk of exposure compared to whites 2009 Kumar, 2012 (Kumar et al., 2012), Quinn, 2011 (Quinn et al., 2011)
Individuals of low SES/living in less affluent neighborhoods/racial/ethnic minorities had better outcomes 1918 Gamble, 2010 (Gamble, 2010), Grantz, 2016 (Grantz et al., 2016), Mamelund, 2018 (Mamelund, 1918), Frankel, 1919 (Frankel & Dublin, 1919), Britten, 1932, Brewer, 1918 (Brewer, 1918)
2009 Placzek, 2014 (Placzek & Madoff, 2014), Nyland, 2015 (Nyland et al., 2015), Esteban-Vasallo, 2012 (Esteban-Vasallo et al., 2012)
Footnote: SES = socioeconomic status; a study may be included in multiple rows if it includes multiple results or disparities.
We found eight articles that assessed outcomes in the 1918 pandemic in relation to SES, specifically illiteracy, homeownership, occupation, apartment size, and unemployment (Clay et al., 2019; Grantz et al., 2016; Mamelund, 2006), two at the neighborhood level and six at the individual level. Grantz et al. looked at neighborhood-level factors in Chicago and found that cumulative incidence of influenza and pneumonia mortality was 34% and 20% lower per 10% higher census tract homeownership and unemployment rate (Grantz et al., 2016), respectively. Clay et al. examined socioeconomic factors across U.S. cities and found that, compared to cities with the lowest percentage of illiterate residents, cities with the highest percentage of illiterate residents had 21.3 additional excess deaths per 10,000 residents during the pandemic (Clay et al., 2019). Mamelund examined individual- and household-level socioeconomic factors in Norway and found that influenza mortality rates were 49% higher in individuals in the deprived social class compared to the class considered advantaged in terms of income, education, and employment (Mamelund, 2006). Additionally, Bengtsson et al. examined differences in excess mortality in Sweden by individual-level social class and found that among occupational groups (white-collar, skilled, low-skilled, unskilled, farmers), low-skilled workers had the highest excess mortality rate and skilled workers had a significantly lower death rate than unskilled and low-skilled workers (Bengtsson et al., 2018).
We also found eight studies that examined socioeconomic factors in the 2009 pandemic (Balter et al., 2010; Kumar et al., 2012; Levy et al., 2013; Lowcock et al., 2012; Mayoral et al., 2013; Placzek & Madoff, 2014; Rutter et al., 2012; Thompson et al., 2011). Balter et al. reported hospitalization rates two times higher in high-poverty neighborhoods than in low-poverty neighborhoods in New York City (Balter et al., 2010). Similarly, Rutter et al., found that influenza mortality rates were three times higher in the least affluent compared to the most affluent areas of England (Rutter et al., 2012). One study examining neighborhood-level factors in Ontario found that during the first and second 2009 pandemic waves, hospitalized individuals were more likely to have a lower education level and live in deprived neighborhoods than those who were not hospitalized (high school or less; Odds Ratio (OR) = 2.28 and 1.77 in first and second pandemic phases, respectively) and live in deprived neighborhoods (OR = 3.46 and 1.54 in first and second phases, respectively) (Lowcock et al., 2012). Similarly, in a study examining the association between education level and hospitalizations in New York City, adults with no education beyond high school had 4.5 times higher hospitalization odds than adults with some college education, and those without a high school education had 32 times higher odds of hospitalization than those with some college education (Levy et al., 2013).
We found five articles exploring racial/ethnic disparities in the 1918 pandemic in the US (Brewer, 1918; Britten, 1932; Frankel & Dublin, 1919; Gamble, 2010; Økland & Mamelund, 2019). Økland reviewed military, survey, and insurance data in the US and found heterogeneous disparities. Black soldiers had 2.3 times lower influenza incidence than white soldiers (Økland & Mamelund, 2019), but higher excess mortality and case fatality than white soldiers (Økland & Mamelund, 2019). Using insurance data for the general population, the same article also reported that during the 2nd pandemic wave, excess mortality was higher for Blacks than whites aged 1–14 years, there were no differences in mortality by race for those aged 15 to 19, and mortality was higher among whites than Blacks for those aged 20 to early 50s (Økland & Mamelund, 2019). During the third pandemic wave, mortality rates were higher in Blacks than in whites in all age groups (Økland & Mamelund, 2019). Brewer examined incidence rates at the racially segregated Camp AA (a military camp) in Humphreys, Virginia during the 2nd pandemic wave and found that white regiments experienced higher incidence rates than most Black regiments (Brewer, 1918). Frankel examined incidence rates among life insurance policy holders in the US population and discovered a similar pattern, with higher rates for whites than Blacks during the first three months of the pandemic (Frankel & Dublin, 1919). For instance, during the height of the pandemic, white women experienced rates of 1723 compared with 1522 per 10,000 among Black women (Frankel & Dublin, 1919). Lastly, Britten examined outcomes among US localities during the 1918 pandemic and found that across almost all cities, white populations experienced higher rates of influenza than Black populations, after adjustment for age and sex (Britten, 1932).
We also found thirteen studies exploring racial/ethnic disparities in the 2009 pandemic (Castrodale, 2009; Dee et al., 2011; Esteban-Vasallo et al., 2012; Kumar et al., 2012; Levy et al., 2013; Mayoral et al., 2013; Navaranjan et al., 2014; Nyland et al., 2015; Placzek & Madoff, 2014; Quinn et al., 2011; Soyemi et al., 2014; Thompson et al., 2011; Truelove et al., 2011). Black and Hispanic populations in the US had generally worse outcomes in the 2009 pandemic as compared to the white population. One study based in Illinois reported that hospitalization rates were 2–3 times greater for Black (36/100,000) and Hispanic (35/100,000) populations than white populations (13/100,000) (Soyemi et al., 2014). During the first wave of the 2009 pandemic in Wisconsin, hospitalization rates were highest among Black, Hispanic, and Asian residents, especially in Milwaukee. Black residents had lower hospitalization rates in the second wave as compared to other minoritized groups, but still higher rates than the white population (Truelove et al., 2011). Two nationwide studies in the US also found that the Hispanic population most often lacked access to health care and were at higher risk of exposure (Kumar et al., 2012; Quinn et al., 2011). Despite greater exposure, the Hispanic and American Indian/Alaska Native population was less likely to self-report Influenza-like illness than the white population in the US (Dee et al., 2011). Pediatric deaths were also highest among Hispanic residents and lowest among white residents in the US (Dee et al., 2011). In contrast, another study based in Massachusetts reported that the Hispanic population was at a significantly lower risk of ICU influenza admission than whites (Placzek & Madoff, 2014). Castrodale examined mortality rates among 10 US states, and found that during the pandemic, American Indian/Alaska Native individuals experienced a mortality rate 4 times higher than those in all other racial/ethnic groups combined (3.7/100,000 vs. 0.9/100,000) (Castrodale, 2009). A study by Quinn et al. of the US population found that Black individuals had the highest susceptibility to complications based on prevalence of chronic conditions compared to white and Hispanic individuals (Quinn et al., 2011). The study by Navaranjan et al., based in Canada, found that those who tested positive for influenza were more likely to be of East/Southeast Asian, South Asian, and Black ethnicity compared to test-negative controls (Navaranjan et al., 2014). An article based in Spain by Esteban-Vasallo that examined outcomes for native individuals and immigrants (born in Europe, North Africa, sub-Saharan Africa, Latin America, North America) during the 2009 pandemic, found that infection rates during the pandemic were lower for the immigrant population than Spanish-born individuals (2396/100,000 vs. 2796/100,000), however, the differences in severe infection rates were not significant. Additionally, ICU admission of severe cases was slightly higher for immigrants than the Spain-born population (23.8% vs. 20.4%) (Esteban-Vasallo et al., 2012). Finally, a study by Nyland et al. that examined outcomes in the United Kingdom (UK), found no significant differences between white vs. non-white individuals in length of hospital stay, severity at admission, admission delay, or mortality among admitted patients, after adjusting for confounders (Nyland et al., 2015).
Last, we found only one study comparing multiple pandemics, specifically the 1918, 1957, and 2009 pandemics in New Zealand, which analyzed mortality rates for indigenous peoples (Māori), as compared to people of European ancestry (Wilson et al., 2012). The Māori had mortality rates 7.3, 6.2, and 2.6 times higher than persons of European ancestry during the 1918, 1957, and 2009 pandemics, respectively (Wilson et al., 2012).
4 Discussion
In this scoping review of the literature on the evidence of health disparities in the influenza pandemics of 1918, 1957, 1968, and 2009, we describe the persistence of socioeconomic and racial/ethnic disparities throughout the four prior confirmed influenza pandemics. All reviewed articles focused on the 1918 or 2009 pandemics, except for one that also included 1957 data. Given the fact that the 1918 pandemic was one of the deadliest recorded health events in history (Centers for Disease Control and Prevention, 1918), and the recency of the 2009 pandemic, this finding is not surprising. Far fewer people died during the 1957 and 1968 pandemics compared to 1918, with 1.1 and 1 million deaths vs 50 million deaths worldwide (Centers for Disease Control and Prevention, 1918; Centers for Disease Control and Prevention, 19571958–;Centers for Disease Control and Prevention, 1968). There was also less media coverage of these pandemics, as healthcare professionals at the time worried about causing public panic and fear (Honigsbaum, 2020). While the impact of the 2009 pandemic was small compared to 1918, at around 151,700–575,400 deaths globally, its recency and resulting increased data availability likely facilitated research on health disparities (Centers for Disease Control and Prevention, 2009).
Across included studies on the 2009 pandemic in the United States, minoritized individuals generally had higher incidence, mortality, or hospitalization rates than whites (Dee et al., 2011; Levy et al., 2013; Soyemi et al., 2014; Thompson et al., 2011; Truelove et al., 2011). Hispanic populations also experienced elevated risk of exposure to pandemic influenza in 2009 compared to all other groups (Kumar et al., 2012; Quinn et al., 2011). Individuals of low SES, or living in areas of low SES, also experienced worse outcomes, including higher mortality in the 1918 (Clay et al., 2019; Grantz et al., 2016; Mamelund, 2006) and 2009 pandemics (Balter et al., 2010; Kumar et al., 2012; Levy et al., 2013; Lowcock et al., 2012; Mayoral et al., 2013; Placzek & Madoff, 2014; Rutter et al., 2012; Thompson et al., 2011). One study of the 1918 pandemic did find lower mortality rates in census tracts with higher unemployment rates in Chicago. However, people of higher SES who were able to not work were considered unemployed in the 1918 data (Grantz et al., 2016) and the highest 1918 mortality rates in Chicago were among those 21–44 years old, who made up most of the working-age population. The higher SES of some unemployed people and high mortality rates in the 21–44 age group may help explain the counterintuitive finding of higher mortality among the employed vs. unemployed (Grantz et al., 2016).
In contrast with the 2009 findings, and the recent COVID-19 pandemic (Bilal et al., 2022; Mackey et al., 2020a, 2020b; Shiels et al., 2021), during the 1918 pandemic, the Black population experienced lower influenza incidence and morbidity; a surprising finding given the large racial mortality disparities in the early 20th century and in subsequent pandemics. Black populations did experience higher case fatality rates than whites, which may be because of increased baseline lung disease, malnutrition, inadequate access to care, lower SES, and poor housing conditions, all stemming from structural racism (Krishnan et al., 2020). There are also several potential explanations for the lower incidence and mortality among the Black population. First, the lower incidence among Black individuals may be due lower detection and reporting of influenza cases (Krishnan et al., 2020). A recent study examining current socioeconomic biases in seasonal influenza surveillance found that measures used for both surveillance and research may be biased against low SES individuals and racial/ethnic minorities, as they are less likely to be part of the most commonly used surveillance networks (Scarpino et al., 2020). This type of bias may have been even more pronounced in 1918, when surveillance and vital registration systems were far less developed. Second, according to Krishnan, segregation may have also prevented Black individuals from being in close contact with whites during the 1918 pandemic, reducing their risk of contracting influenza (Krishnan et al., 2020). Krishnan also hypothesizes that protective immunity from greater exposure to a less virulent early wave, as a result of living in overcrowded high exposure risk areas, potentially led to lower susceptibility among Black populations to the deadlier fall/winter wave (Krishnan et al., 2020). Brewer also suggests that the worse living conditions in tents for Black military members may have created conditions for more limited spread because of potential for less overcrowding in tents compared to buildings (Brewer, 1918). In a very recent study, Eirmann et al. tests these hypotheses (Eiermann et al., 2022), as well as potential reduced disparities from non-pharmaceutical interventions, greater implementation of protective behaviors among the Black population, and differential racial urban-rural migration and prior exposure to the 1890 flu. They find evidence that provides suggestive support for a combination of two hypothesis: 1) that race-specific migration patterns created a differential exposure to the 1890 flu, producing greater vulnerability among the white population, and 2) that Black population's exclusion from health care and public health systems may have encouraged greater community-based education and prevention, encouraging behaviors that reduced exposure to infections (Eiermann et al., 2022). Last, we found no studies examining disparities among the Hispanic population during the 1918 pandemic, potentially due to the small Hispanic population in the US at that time (around 1.3 million by 1920) (Contributors, 2021) and, maybe more importantly, the lack of measurement of Hispanic ethnicity. In general, the scope of 1918 studies was smaller in size, with a number of them including single cities (or smaller samples of cities) or military populations.
During the 2009 pandemic, both SES and race/ethnicity predicted influenza incidence, hospitalization, and ICU stay (Kumar et al., 2012; Levy et al., 2013; Mayoral et al., 2013; Placzek & Madoff, 2014; Thompson et al., 2011). For example, factors that low-income minoritized populations tend to disproportionately experience such as household overcrowding, inability to engage in social distancing because of work, lack of sick leave and paid time off, and job insecurity predict influenza-like-illness incidence (Kumar et al., 2012). These same factors, as well as more limited access to health care and greater susceptibility to severe disease, because of underlying structural inequities, likely contribute to the persistent socioeconomic and racial disparities identified in the included articles. For example, neighborhood disinvestment has led to a range of structural housing conditions and employment with inadequate protections and crowded working conditions, which potentially increase the risk of exposure to the viruses that cause pandemic influenza. Additionally, non-white populations who experience overcrowding, lower household income, and lower educational attainment than their white counterparts tend to lack information on preventative measures and are at an increased risk of hospitalization (Levy et al., 2013; Mayoral et al., 2013; Thompson et al., 2011).
This scoping review has some limitations. We used a three-pronged search strategy to scope the public health field, but may have missed some articles that examined disparities (or inequalities or inequities) but did not label them as such, or articles written in languages other than English as well as those not available via standard indexing platforms. Additionally, our search was limited to the public health literature, and our initial search strategy was limited to PubMed (though our secondary searches included articles that may have been indexed elsewhere). This scoping approach may miss research indexed in other search engines, including articles from economics and history, that may examine the effects of the prior pandemics on disparities from other disciplinary perspectives or on related outcomes such as economic impacts (Clay et al., 2018; Fourie & Jayes, 2021; Garrett, 2007; Johnson & Mueller, 2002; Mourits et al., 2021; Tuckel et al., 2006). Moreover, our although the choice of a scoping review was intentional given the research question and the anticipated heterogeneity and complexity of the evidence, we are unable to provide quantitative summary measures of the association between SES and health outcomes across all pandemics. Pooling results of the studies included in this review would be inappropriate given the differences in the definitions of outcomes and other methods. (Peters et al., 2015). Future research should consider literature published in other disciplines and new literature search tools, such as elicit.org, which employ language models to automate literature searches.
Finally, we focused on health outcomes (e.g., incidence, hospitalization, ED use) and did not analyze outcomes related to vaccination or testing rates, though vaccination was not available during the 1918 pandemic. The included studies generally employed mean comparisons of health between social groups within countries, but other measures of disparities compare health outcomes across individuals, by examining the range or variance of a given measure across a population (Arcaya et al., 2015), and inferences about the direction and magnitude of disparities may depend on the disparity measure employed (Harper et al., 2010). Additionally, because our study is a scoping review rather than a meta-analysis, we qualitatively describe the direction of disparities for each pandemic, but do not present the direct estimates from each study. The SES and racial categories employed by studies varied substantially within and between pandemics (e.g., enumerator impression of household economic conditions (Sydenstricker, 1931), versus education level (Lowcock et al., 2012)) precluding direct comparisons between disparity estimates. Furthermore, we did not include formal assessment of methodological quality (Peters et al., 2015), including assessment of confounding factors, representativeness or the data, or other issues around article quality.
Since the beginning of the COVID-19 pandemic, racial/ethnic minorities and people of low SES have been disproportionately impacted by COVID-19 (Bassett et al., 2020; Chen & Krieger, 2020; Mackey et al., 2020a, 2020b; Bilal et al., 2021; American Public Media Research Lab Staff; Bilal et al., 2020; The COVID Racial Data Tracker). Structural factors, including structural racism, economic inequality, and segregation place Black, Latino, and Indigenous people as well as those of lower SES at greater risk of being exposed to the virus, developing severe disease if infected, and dying from the disease (Bassett et al., 2020; Chen & Krieger, 2020; Mackey et al., 2020a, 2020b; Bilal et al., 2021; American Public Media Research Lab Staff). Studies also show spatial clusters of high rates of COVID-19 in areas disinvested in due to decades of segregation (Bilal et al., 2021; Chen & Krieger, 2020).
In this study, we have shown that these patterns are not unique to COVID-19 and represent a continuation of a century of health disparities in influenza burden during pandemics. The medical and public health fields have both changed dramatically. The last century was marked by vast improvement in therapeutics, diagnostics, expansion of healthcare coverage to large groups of populations, including the creation of the Medicaid and Medicare programs in the US, and various other public health interventions (Bhatia et al., 2019; Centers for Disease Control and Prevention, 1999; Colgrove, 2002). The contribution of these various structural factors to disparities may have varied by pandemic. For example, in the 1918 pandemic there were few medical interventions to prevent hospitalization or death, and in fact some medical interventions may have exacerbated illness (Barry, 2005; Eiermann et al., 2022), so disparities in health care access may not have played an important role in the 1918 socioeconomic disparities but may have been critical to 2009 disparities. For example, one study found that Hispanic individuals and those of lower SES had lower risk of ILI-related ICU stay compared to their affluent white counterparts (Placzek & Madoff, 2014), which the authors partially attributed to lower access to care (Placzek & Madoff, 2014).
The repeat of disparities in the current COVID-19 pandemic suggests that part of the failed policy response to this pandemic may be a consequence of not fully comprehending the history of health disparities from prior pandemics, and failure to address social determinants of health (McCarthy et al., 2022). While the extent to which policy recommendations are appropriate based on scoping reviews is limited (Peters et al., 2015), the findings of this review suggest that public health must grapple with the likelihood that without action to address structural racism and economic inequities, disparities will likely persist in current and future pandemics. The public health field can directly act to integrate the recognition of differential risks and potential emerging inequities into pandemic preparedness and planning. However, reducing the upstream structural determinants of disparities requires change enacted through political systems and policy actors, with whom public health can influence and work.
5 Conclusion
The century-long patterns we described here highlight the long-lasting effects of structural racism and economic inequality that prevent the achievement of health equity. Prior literature has limited coverage of some populations, making it difficult to measure and address these problems. The current COVID-19 pandemic, in an era with better data, has both enabled better characterization and tracking of disparities but also a clearer understanding of how much more work we must do. Understanding the history of disparities in past pandemics should have informed initial and continued mitigation efforts to focus on vulnerable populations, as some researchers called for very early in the pandemic (Bailey et al., 2020), but a lack of understanding the persistence of disparities in past pandemic may have hindered public health's preparation for emerging disparities in the COVID-19 pandemic. Reducing COVID-19 disparities, and avoiding repetition of disparities in future pandemics requires a thorough grappling with the structural determinants of these disparities.
Contributorship statement
AD and UB conceived the study. AD conducted the literature search and extracted results, supported by UB and ASM. AD wrote the first version of the manuscript, supported by ASM and UB. All authors reviewed the manuscript, provided critical content, and approved the final version.
Funding
UB, PHM, ASM and ML were supported by the 10.13039/100000179 Office of the Director of the 10.13039/100000002 National Institutes of Health (NIH) under award number DP5OD26429. ML is supported by NIH/10.13039/100006545 National Institute on Minority Health and Health Disparities R21MD012352-02S1. ASM was supported by 10.13039/100000060 National Institute of Allergy and Infectious Diseases K01AI168579. The funding sources had no role in the analysis, writing or decision to submit the manuscript.
Research and ethics approval
This study is a literature review and does not involve human subjects or animal participants.
Declaration of competing interest
The authors declare no conflict of interest.
Data availability
No data was used for the research described in the article.
==== Refs
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| 36514788 | PMC9733119 | NO-CC CODE | 2022-12-14 23:38:10 | no | SSM Popul Health. 2022 Dec 9;:101314 | utf-8 | SSM Popul Health | 2,022 | 10.1016/j.ssmph.2022.101314 | oa_other |
==== Front
New Microbes New Infect
New Microbes New Infect
New Microbes and New Infections
2052-2975
Published by Elsevier Ltd.
S2052-2975(22)00115-9
10.1016/j.nmni.2022.101063
101063
Original Article
Frequency, subtypes distribution, and risk factors of Blastocystis sp. in COVID-19 patients in Tehran, capital of Iran: A case-control study
Taghipour Ali a
Pirestani Majid b
Hamidi Farahani Ramin a
Barati Mohammad a∗
Asadipoor Esfandiar a
a Infectious Diseases Research Center, AJA University of Medical Sciences, Tehran, Iran
b Department of Parasitology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
∗ Corresponding author. Infectious Diseases Research Center, AJA University of Medical Sciences, Tehran, Iran.
9 12 2022
9 12 2022
10106322 9 2022
5 12 2022
6 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.
Background
Recent theories on the possible interactions between the intestinal parasites and COVID-19 have stated that these co-infections may cause immune imbalance and further complications in the affected patients. Until now, there is no data about Blastocystis subtypes as an intestinal parasite in COVID-19 patients. Therefore, the present work was done to evaluate the molecular prevalence of Blastocystis sp. and related risk factors in Iranian patients with COVID-19.
Method
ology: Stool samples were gathered from 200 COVID-19 patients and 200 control, being matched regarding age, gender and residence. Then, stool samples were surveyed by parasitological methods, including direct slide smear and formalin-ether concentration. In the following, PCR and sequencing were used to detect Blastocystis sp. and their subtypes.
Results
The frequency of Blastocystis sp. in patients with COVID-19 (7.5%; 15/200 by molecular method vs. 6%; 12/200 by microscopy method) was slightly higher than in individuals without COVID-19 (4.5%; 9/200 by molecular method vs. 4%; 8/200 by microscopy method), this difference was not statistically significant (P value = 0.57 for molecular method vs. P value = 0.81 for microscopy method). Regarding associated factors for Blastocystis sp., we found significant differences regarding the residence (rural), loose and watery stool with diarrhea, and duration of treatment (6 weeks <) in the COVID-19 group. Blastocystis ST3 was the most common subtype in the patients with COVID-19 and control group.
Conclusions
Based on this results, health education, improved sanitation and good personal hygiene are highly recommended to prevent Blastocystis in COVID-19 patients.
Keywords
Blastocystis
COVID-19
Iran
Subtyping
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pmc1 Introduction
In late December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic was first reported in Wuhan, China [[1], [2], [3]]. COVID-19 is frequently asymptomatic or it is slightly manifested in immunocompetent individuals, while multi-organ dysfunction mediated by endothelial damage and a severe acute respiratory distress syndrome (ARDS) are the major consequences in at-risk groups [[3], [4], [5], [6]]. A meta-analysis study showed that the case fatality rate due to COVID-19 in general population, hospitalized patients, patients older than 50 years, and patients admitted in intensive care unit (ICU) was 1.0% (95% confidence interval (CI): 1.0-3.0), 13.0% (95% CI: 9.0-17.0), 19.0% (95% CI: 13.0-24.0), and 37.0% (95% CI: 24.0-51.0), respectively [7]. Multiple risk factors for mortality due to COVID-19 include older age, diabetes, asthma or chronic lung disease, sickle cell disease, hypertension, cardiovascular disease, and immune compromise [[8], [9], [10], [11], [12]]. Moreover, a meta-analysis study showed that 19% of patients with COVID-19 have co-infections and 24% have superinfections, leading to poor outcomes and increased mortality [13].
Blastocystis sp. is an anaerobic protozoan that is often isolated in the digestive tract of humans and a wide range of animals [[14], [15], [16], [17], [18], [19]]. Until now, 17 different subtypes of Blastocystis sp. have been characterized in human and animal hosts [20,21]. Nevertheless, the pathogenic potential of Blastocystis sp. subtypes is still debatable, since some researchers have demonstrated its association in gastrointestinal disorders, while others have rejected such involvement [[22], [23], [24]]. Therefore, there are no substantial evidences for association between presence of Blastocystis subtype and clinical manifestations.
Currently, it was recommended that the status of public health and education affect the incidence rates of Blastocystis and COVID-19 [[25], [26], [27]]. Therefore, the prevalence rates of Blastocystis and COVID-19 shows that these infections is most common in those areas that suffer from undernourishment, poverty, and low level of sanitation [[26], [27], [28], [29], [30]]. Until now, there is no data about Blastocystis subtypes in COVID-19 patients. Therefore, the present study was done to evaluate the frequency of Blastocystis sp. and related risk factors in the COVID-19 patients and control groups in Iran.
2 Methodology
2.1 Study population
This study was obtained ethical approval from the Ethics Committee of the Aja University of Medical Science (AJAUMS), Tehran, Iran (no. IR.AJAUMS.REC.1401.044). Stool samples were collected from April 2021 to May 2022 in health care centers, Tehran, Iran. The inclusion criteria were a confirmed COVID-19 history and consent for participation, while the presence of immunodeficiency and administration of anti-parasitic drugs during last three months before sampling were set as exclusion criteria. Considering WHO recommendations for the diagnosis of COVID-19, nasopharyngeal/oropharyngeal swabs samples were gathered from each individual and transferred to sterile bottles of viral transport medium, which were sent immediately to the laboratory of COVID-19. The RNA was extracted from the samples using Viral Nucleic Acid Kit and the viral RNA was detected using the real time reverse-transcription polymerase chain reaction (rRT-PCR) assay [31]. Stool samples were taken from 200 confirmed patients with COVID-19 undergoing treatment. The control group comprised 200 non-COVID-19 individuals (negative for COVID-19 test) and without any history of COVID-19, as approved by a clinician. To improve the accuracy of the case-control design of the study, age, gender and the place of residence were matched between both groups. A standard questionnaire was filled by each participant, which included sociodemographic conditions and risk factors related to Blastocystis sp., upon giving a consent.
2.2 Fecal sample collection and parasitological analysis
Stool samples were gathered in sterile containers tagged with patient's name and identification number, then immediately sent to the parasitology laboratory at the Tarbiat Modares University, and Jahrom University of Medical sciences under aseptic conditions. Then, stool samples were examined using parasitological methods, including direct slide smear and formalin-ether concentration under light microscopy (Zeiss, Germany) with 10X, 40X and 100X objective magnification. Also, stool samples were preserved in 70% alcohol at 4°C for the DNA extraction.
2.3 DNA extraction PCR and subtyping
The genomic DNA was extracted from 200 mg of stool samples in both case and control groups using a DNA purification mini kit (Yekta Tajhiz Azma Co., Iran), based on the manufacturer's instructions. The extracted DNA was preserved at -20°C until PCR.
A PCR assay was done using primer pairs RD5 (5′-ATCTGGTTGATCCTGCCAGT-3′) and BhRDr (5′-GAGCTTTTTAACTGCAACAACG-3′) [23,32]. For this purpose, the following condition was performed for amplification of targeted fragment: denaturation at 95 °C for 5 minutes, 35 cycles at 94 °C for 30 seconds, 59 °C for 30 seconds, and 72 °C for 30 seconds, followed by a final extension step at 72 °C for 5 minutes [23].
Subsequently, PCR products were observed by ultraviolet illumination after electrophoresis on 1.5% agarose gel stained with ethidium bromide. The PCR products of the positive samples were sequenced using Applied Biosystems 3730/3730xl DNA Analyzers (Bioneer, Korea) and the results were compared with those deposited in the GenBank using BLAST software.
2.4 Statistical analysis
The data analysis was used to evaluate the molecular prevalence of Blastocystis sp., while other descriptive data were estimated by binomial distribution. The Chi-square and Fisher's exact tests were applied to compare the prevalence of this parasite among cases and controls using SPSS software version 16 (SPSS, Chicago, IL, USA). P values < 0.05 were considered statistically significant.
3 Results
Among 400 included participants, 200 subjects were confirmed patients with COVID-19 (53.5% male; mean age of 47.14 ± 12.29 years) and 200 participants were subjects without COVID-19 (51% male; mean age 47.77 ± 11.57 years). The frequency of Blastocystis sp. in patients with COVID-19 (7.5%; 15/200 by molecular method vs. 6%; 12/200 by microscopy method) was slightly higher than in individuals without COVID-19 (4.5%; 9/200 by molecular method vs. 4%; 8/200 by microscopy method), this difference was not statistically significant (P value = 0.57 for molecular method vs. P value = 0.81 for microscopy method). Regarding associated factors for Blastocystis sp., we found significant differences regarding the residence (rural), loose and watery stool with diarrhea, and duration of treatment (6 weeks <) in the patients with COVID-19 group (Table 1 ).Table 1 Age, gender, type of stool, diarrhea status, residence, type of patients, lung complications, and duration of treatment of patients with COVID-19 and healthy group, according to the presence or absence of Blastocystis sp. using PCR
Table 1Variables COVID-19 patients (n = 200) Healthy group (n = 200)
No. Examined No. Positive (%) No. Negative (%) P-value No. Examined No. Positive (%) No. Negative (%) P-value
Age group (year)
20-30 20 0 (0%) 20 (100%) 0.44 18 0 (0%) 18 (100%) 0.98
31-40 44 2 (4.55%) 42 (95.45%) 44 2 (4.54%) 42 (95.46%)
41-50 52 3 (5.77%) 49 (94.23%) 48 2 (4.16%) 46 (95.84%)
51-60 51 7 (13.73%) 44 (86.27%) 64 4 (6.25%) 60 (93.75%)
61-70 33 3 (9.09%) 30 (90.91%) 26 1 (3.84%) 25 (96.16%)
Sex
Male 107 10 (9.35%) 97 (90.65%) 0.28 102 5 (4.90%) 97 (95.10%) 0.77
Female 93 5 (5.38%) 88 (94.62%) 98 4 (4.08%) 94 (95.92%)
Residence
Urban 151 8 (5.30%) 143 (94.70%) 0.03* 145 7 (4.82%) 138 (95.18%) 0.85
Rural 49 7 (14.29%) 42 (85.71%) 55 2 (3.63%) 53 (96.37%)
Type of stool
Formed 61 0 (0%) 61 (100%) < 0.00005* 71 2 (2.82%) 69 (97.18%) 0.82
Soft 101 5 (4.95%) 96 (95.05%) 99 5 (5.05%) 94 (94.95%)
Loose 18 5 (27.78%) 13 (72.22%) 17 1 (5.88%) 16 (94.12%)
Watery 20 5 (25%) 15 (75%) 13 1 (7.69%) 12 (92.31%)
Diarrhea
Yes 38 9 (23.69%) 29 (76.31%) < 0.00002* 30 2 (6.66%) 28 (93.34%) 0.53
No 162 6 (3.70%) 156 (96.30%) 170 7 (4.12%) 163 (95.88%)
Duration of treatment of patients with COVID-19
2 weeks 61 2 (3.27%) 59 (96.73%) 0.007* — — — —
2-4 weeks 43 1 (2.33%) 42 (97.67%) — — — —
4-6 weeks 25 0 (0%) 25 (100%) — — — —
6 weeks< 71 12 (16.90%) 59 (83.10%) — — — —
Type of patients
Inpatients 109 10 (9.17%) 99 (90.83%) 0.32 — — — —
Outpatients 91 5 (5.50%) 86 (94.50%) — — — —
Lung complications
25%> 49 3 (6.12%) 46 (93.88%) 0.24 — — — —
25-49% 42 1 (2.38%) 41 (97.62%) — — — —
50%≤ 109 11 (10.10%) 98 (89.90%) — — — —
*p-value<0.05; Statistically significant.
All 24 positive samples were successfully sequenced in both case and control groups. In COVID-19 group, the results of sequences in BLAST presented that subtype 3 (9/15; 60% with accession numbers OP456400– OP456408) was the most common followed by subtype 2 (4/15; 26.66% with accession numbers OP456409-OP456412) and subtype 1 (2/15, 13.33% with accession numbers OP456413-OP456414) (Fig. 1 ), while in control group, subtype 3 (5/9; 55.55% with accession numbers OP456433-OP456437) was the most common followed by subtype 2 (3/9; 33.33% with accession numbers OP456438-OP456440) and subtype 1 (1/9, 11.11% with accession number OP456441) (Fig. 2 ).Fig. 1 Distribution of Blastocystis sp. subtypes in patients with COVID-19.
Fig. 1
Fig. 2 Distribution of Blastocystis sp. subtypes in control groups.
Fig. 2
4 Discussion
The prevalence of Blastocystis differs from county to county, different counties of a country as well as studied hosts. In this regard, it appears that the frequency of this protozoan is high in developing region with low-level of public health [33]. However, high prevalence of Blastocystis sp. has been reported in different host in some European countries [[34], [35], [36], [37], [38]]. Internationally, very few studies have been performed on the prevalence of Blastocystis sp. among patients with COVID-19. In this regard, a prospective cohort study in Ethiopia found that 37.81% (284 of 751) patients with COVID-19 were infected with intestinal parasites [39], although they do not have a specific report on the abundance of Blastocystis sp. Different prevalence has been reported for intestinal parasites in both developing and developed countries and numerous parameters may influence such distribution, including the Human Development Index (HDI), geographical, and demographic factors may increase prevalence of intestinal parasites [[40], [41], [42], [43], [44], [45]].
Although the results of molecular and microscopy methods were approximately in the same range, but these methods have its own features. As the current study has shown, the results of the microscopy prevalence are slightly lower than the molecular method, which may be due to misdiagnosis with other elements and microorganisms. In this regard, Blastocystis sp. life-cycle is not fully understood and highly polymorphic, this forms include cystic forms, granular, multivacuolar, vacuolar, avacuolar and amoeboid structures [46]. While PCR has higher sensitivity and easier interpretation than the microscopy method [23,47]. Also, PCR is capable of directly differentiating the derived subtypes of Blastocystis sp. between different hosts and reservoirs [23].
In this study, we surveyed frequency and Blastocystis subtype among COVID-19 patients. Also, ST3 was the most common subtype, while ST1 was only reported from two patients with COVID-19. In Iran, some studies surveyed Blastocystis sp. subtypes in healthy groups (without clinical manifestations) [[48], [49], [50]], individuals who suffer from inflammatory bowel syndrome (IBS) [51] and patients with inflammatory bowel diseases (IBD) [52]. Nevertheless, in Iran, it suggests that ST3 is the most common subtype, followed by ST2 and ST1 [25]. Some studies have showed that ST1 in diarrhea, IBS, and tuberculosis patients is higher than other subtypes and therefore, this subtype was considered that as a pathogenic subtype [23,53]. More recently, a meta-analysis study showed the association of Blastocystis sp. with colorectal cancer, and subtypes 1 and 3 had the highest rates in these patients [54]. However, a strong association between Blastocystis subtypes and clinical manifestations has not still been recognized. In the current study, Blastocystis ST1 was isolated from two diarrhea subjects. Considering the diarrhea status, we found a significant association between Blastocystis sp. and diarrhea in COVID-19 patients, which may be associated to the high prevalence of Blastocystis sp. in the participants. Based on the literature, intestinal protozoa can potentially polarize the helper T cells towards type 1 (Th1) [44,[55], [56], [57], [58]]. Also, co-infections of intestinal protozoa and some intracellular pathogens, such as Mycobacterium tuberculosis and human immunodeficiency virus (HIV) may substantially cause imbalances in the host and further pathological consequences [[55], [56], [57], [58]]. Since the emergence of the SARS-CoV-2, there have been some hypotheses on the likely interaction between the intestinal parasite and COVID-19 [59,60]. Nevertheless, diarrhea reported in the present study cannot be reliably attributed to the Blastocystis sp., because other infectious (bacterial, fungal and viral agents) and/or non-infectious diseases may have had a role in the initiation and progression of diarrhea. Thus, the likely association between the Blastocystis sp. and diarrhea cannot be reliably surmised and requires future extensive studies. Regarding the risk factors, we found that the living in the rural and duration of treatment (6 weeks <) were more likely to be infected with Blastocystis sp. These risk factors could be explained by the fact that poor hygiene conditions and low levels of socio-economic factor in living areas of carriers as a potential risk factor of Blastocystis sp., therefore, they are more exposed to Blastocystis sp.
There were some limitations to the present study. At first, stool samples were gathered only once; it would have been better to collect three samples to increase the sensitivity and accuracy of detection. Second, due to our limited facilities and funds, we did not examine some clinical features such as hematological parameters in the study population. Also, recent studies have shown the potential ability of Blastocystis sp. and COVID-19 to alter the gut microbiota ecosystem, which may lead to beneficial or harmful functions in the digestive system [[61], [62], [63], [64]]. Therefore, it is suggested to measure the status of Blastocystis sp. in COVID-19 patients by considering the gut microbiota in future studies.
5 Conclusions
Blastocystis ST3 was the most common subtype in both groups. Based on this, health education, improved sanitation and good personal hygiene are highly recommended to prevent Blastocystis sp. in COVID-19 patients. Moreover, it is highly suggested to perform more comprehensive epidemiological studies to better recognize the interaction between Blastocystis sp. and COVID-19 in Iran and other countries.
Funding
This study was supported by Infectious Diseases Research Center, AJA University of Medical Sciences, Tehran, Iran.
Availability of data and materials
Not applicable.
Ethics approval and consent to participate
This study received ethical approval from the Ethics Committee of AJA University of Medical Sciences, Tehran, Iran (Code: IR.AJAUMS.REC.1401.044).
Consent for publication
Not applicable.
CRediT authorship contribution statement
Ali Taghipour: Formal analysis, Writing – review & editing, contributed to all parts of the study, contributed to study implementation, collaborated in the analysis and interpretation of data. collaborated in the manuscript writing and revision. All the authors commented on the drafts of the manuscript and approved the final version of the article, All authors contributed to study design and. Majid Pirestani: contributed to study implementation, All the authors commented on the drafts of the manuscript, and, approved the final version of the article, All authors contributed to study design, and. Ramin Hamidi Farahani: Formal analysis, collaborated in the analysis and interpretation of data. All the authors commented on the drafts of the manuscript, and, approved the final version of the article, All authors contributed to study design, and. Mohammad Barati: Writing – original draft, Writing – review & editing, contributed to all parts of the study, collaborated in the manuscript writing and revision. All the authors commented on the drafts of the manuscript and approved the final version of the article, All authors contributed to study design, and. Esfandiar Asadipoor: Writing – review & editing, Writing – original draft, collaborated in the manuscript writing and revision. All the authors commented on the drafts of the manuscript and approved the final version of the article, All authors contributed to study design.
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.
Acknowledgements
We thank the scientists and personnel of the Infectious Diseases Research Center, AJA University of Medical Sciences, Tehran, Iran.
==== Refs
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| 36514342 | PMC9733120 | NO-CC CODE | 2022-12-14 23:36:08 | no | New Microbes New Infect. 2022 Dec 9;:101063 | utf-8 | New Microbes New Infect | 2,022 | 10.1016/j.nmni.2022.101063 | oa_other |
==== Front
Build Environ
Build Environ
Building and Environment
0360-1323
1873-684X
Elsevier Ltd.
S0360-1323(22)01123-4
10.1016/j.buildenv.2022.109893
109893
Article
Experimental investigation of the effect of surgical masks on outdoor thermal comfort in Xiamen, China
Zhou Zhiqiang
Dong Liang ∗
School of Architecture, Huaqiao University, Xiamen, 361021, China
∗ Corresponding author.
9 12 2022
9 12 2022
10989313 9 2022
11 11 2022
1 12 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.
The COVID-19 pandemic has significantly changed people's lifestyles, and wearing surgical masks in outdoor public spaces has become commonplace. However, few studies have explored the impact of wearing masks on outdoor thermal comfort in different seasons. From May 2021 to February 2022, a series of longitudinal experiments were conducted in Xiamen, China to examine the effect of wearing surgical masks on outdoor thermal comfort. Forty-two participants took part in the experiments with and without masks. During the experiments, the thermal perceptions of the subjects and environmental thermal parameters were collected. Differences in outdoor thermal comfort between subjects wearing masks and those not wearing masks were determined in summer, autumn, and winter. Results showed that 1) the subjects wearing masks had lower neutral temperatures, and this difference was particularly pronounced in summer and exacerbated by walking; 2) in warm environments, masks reduced thermal comfort, and discomfort associated with masks was worse when walking than when sitting; 3) wearing masks significantly worsened facial comfort and increased chest discomfort, as summer turned to winter, the impact of masks on facial comfort decreased; 4) radiation and air temperature were the environmental parameters with the greatest impact on outdoor thermal sensation. Subjects who wore masks preferred lower temperatures, radiation, and humidity, and higher wind speeds.
Graphical abstract
Image 1
Keywords
Outdoor thermal comfort
Surgical masks
Human body
Thermal sensation
Abbreviations
Thermal sensation vote, TSV
Mean thermal sensation vote, MTSV
Thermal comfort vote, TCV
Mean thermal comfort vote, MTCV
Physiologically equivalent temperature, PET
Air temperature, Ta
Wind speed, Ws
Relative humidity, RH
Global radiation, G
==== Body
pmc1 Introduction
Thermal comfort in outdoor environments is an important factor affecting quality of life. In recent years, an increasing number of studies on outdoor thermal comfort have been performed [1]. Previous studies on outdoor thermal comfort have been conducted in different climatic regions to investigate types of human thermal sensation and activities in various outdoor spaces [[2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15]]. These studies have shown that people's thermal comfort in outdoor environments is influenced by several factors, including combinations of thermal environment factors, regional climate, seasonal variations, individual clothing status, metabolic rate, and various psychological factors. However, the COVID-19 spread in late 2019 has brought new issues to the study of outdoor thermal comfort.
COVID-19 has had an unprecedented impact on the world and has significantly changed the way people live their lives. Masks effectively prevent COVID-19 infection and have thus become essential items in people's daily activities, such as travel, work, and study [16]. However, studies have shown that masks can cause discomfort. For example, long-term mask use can cause breathing problems [17], and the temperature inside a mask has significant effects on the thermal sensation of the human body [18]. Wearing a mask has been shown to cause slight changes in some physiological indicators but does not produce excessive heat load on a wearer [19].
Numerous studies have examined the impact of masks on thermal comfort. Nielsen et al. [18] conducted an experiment on subjects during exercise and found that masks had a significant effect on respiratory heat loss in humans, and air temperature primarily determined thermal sensation. Roberge et al. [19] discovered that wearing a surgical mask at a low-to-moderate work rate slightly increased heart rate, facial skin temperature, core temperature, fatigue, and thermal perception. Through indoor experiments, Lin et al. [20] discovered that wearing a mask in a humid atmosphere increases heat stress and impacts metabolic heat loss. Tang et al. [21] conducted a thermal comfort study enrolling mask-wearing and mask-free borrowers in a university library in Guangzhou during summer and discovered that mask-wearers had higher environmental comfort requirements and slightly lower neutral temperature. Li et al. [22] compared surgical masks and N95 masks in terms of thermal, and the results showed significant difference between temperature and humidity inside surgical and N95 masks, and the degree of discomfort caused by wearing N95 masks was higher than that of surgical masks. Zhang et al. [23] examined the thermal comfort of mask-wearers in a climate chamber. The results indicated that wearing surgical masks would decrease summer neutral temperature by 1.5 °C and optimal temperature by 1.4 °C. In addition, some studies [20,24,25] have demonstrated that masks with breathing valves provide higher degree of thermal comfort than masks without breathing valves. However, masks with breathing valves protect the wearer but not the surrounding population, so these masks were not recommended during the COVID-19 pandemic.
However, current studies on the effects of masks on human thermal comfort have some limitations. First, most existing studies have been conducted indoors. Few studies have examined the effects of masks on thermal comfort in outdoor environments despite that the number of people wearing masks in outdoor public spaces has increased dramatically. Second, the durations of previous studies were limited, and seasonal variations in the effects of masks on thermal comfort were not analyzed. Third, the vast majority of previous studies have only examined the effect of masks on thermal comfort in a single state, such as sitting or working, whereas the effects of wearing masks in different activity states on neutral temperature, thermal comfort, and preference for thermal environmental factors are rarely discussed.
Our study aims to fill this gap by conducting a comprehensive investigation of the effects of masks on outdoor human thermal sensation and thermal comfort in different seasons and at different activity levels. This investigation was conducted at a university in Xiamen, China to collect thermal comfort questionnaires from subjects and measure environmental parameters. Analyses were performed on the effect of surgical masks on thermal comfort in outdoor thermal environments. The main goals of this study are as follows:(1) To compare the difference in neutral temperature between wearing masks and not wearing masks during summer, autumn, and winter.
(2) To evaluate the impact of masks on thermal comfort in outdoor environments.
(3) To compare the effects of masks on the thermal comfort of various body parts in different seasons.
(4) To quantify the effects of temperature, radiation, wind speed, and relative humidity on the thermal perception of groups with and without masks.
2 Methods
2.1 Experimental location
This research was carried out in Xiamen, China (N24° 43′, E118° 10’). According to the Chinese standard of climatic regionalization for architecture, this area belongs to the hot summer and warm winter climate regions (Fig. 1 (a)), with a humid and hot climate. In the meteorological records of Xiamen (Fig. 1 (b)), the average monthly outdoor temperature ranges from 10.7 °C to 32.7 °C for over a year. The hottest month of the year is July (28.5 °C), and the coldest month is January (13.3 °C). High relative humidity persists throughout the year, fluctuating between 66% and 84%. This study lasted from May 2021 to February 2022, spanning the hottest and coldest months.Fig. 1 (a) Climate classification for building design in China and the location of Xiamen. Source: [26]. (b) Monthly variation in temperature and relative humidity in Xiamen (2000–2021). Source: China Meteorological Data Sharing Service System.
Fig. 1
The experimental site was in the rooftop garden of the Architecture Experimental Building of Huaqiao University. This is a purpose-built thermal comfort experiment site for outdoor use. It contains ponds, small structures, shrubs, and ground cover plants, and has a weather station that monitors exterior thermal ambient factors and a room where temperature and humidity can be controlled. Experiments conducted in this location can reduce the impact of outdoor traffic, changes in scenery, changes in acoustic environment, and other perturbing factors on experimental outcomes.
2.2 Subjects
A total of 42 subjects participated in this study, all of whom were daily users of the rooftop garden at the experimental site and were familiar with the experimental site. All subjects were acclimatized to the climate of Xiamen, having lived there for at least 1 year. During the experiment, all subjects were in good health and did not take any prescription medication, so they can represent the general healthy adult population. The subjects' physical characteristics are listed in Table 1 . All subjects were informed of the experiment's purpose and significance. Before participating in the experiment, volunteers were required to eat a balanced diet and get adequate rest 24 h before the experiment. Subjects participating in the same experiment were asked to wear the same clothes. The subjects' dressing and thermal insulation values for clothing ensembles are shown in Table 2 , and the insulation values are based on ISO 9920 (2007).Table 1 Basic information of subjects.
Table 1Gender Amount Age, years Height, cm Weight, kg BMI
Male 20 25.5 ± 3.5 175 ± 8 67.5 ± 9.5 22.05 ± 3.12
Female 22 24.5 ± 3.5 165.5 ± 5.5 53 ± 7 19.94 ± 2.35
Table 2 The subjects’ dressing and thermal insulation values for clothing ensembles.
Table 2Season Condition Garment Thermal insulation value（clo）
Summer – Underpants, shirt with short sleeves, light trousers, light socks, shoes 0.5
Autumn Ta≥24 °C Underpants, shirt, light trousers, socks, shoes 0.6
Ta＜24 °C Panties, shirt, trousers, jacket, socks, shoes 1
Winter – Underwear with long sleeves and legs, shirt, trousers, sweater, jacket, socks, shoes 1.3
2.3 Surgical masks
The mask used in this study was a disposable surgical mask. According to the standards provided by the manufacturer, the inner dimensions of the mask are 17.5 cm × 9.5 cm (±3%). The mask facepiece is divided into three layers: inner, middle, and outer, of which the inner and outer layers are made of polypropylene spunbonded nonwoven fabrics and the middle layer filter material is polypropylene melt-blown nonwoven web [27]. The rubber bands at both ends of the mask are suspended from both ears, and then the mask nasal bar is pressed to better fit the nasal bridge and cover the nose, mouth, and jaw. Related studies have shown that surgical masks can effectively block the transmission of viruses and bacteria [28,29].Experimental process.
In each experiment, two or four participants were evenly divided into experimental and control groups. The subjects in the experimental group were required to wear masks, whereas the subjects of the control group were not. During each experiment, the subjects of both groups wore identical clothing. Every experiment lasted for 1 h. Each subject was instructed to first sit in a controlled indoor environment for 30 min (with an indoor air temperature of 24.5 °C–26.5 °C and a relative humidity of 45%–55%). The purpose was to stabilize the subject's physical state and reduce errors due to difference in thermal environment experience among subjects. The subjects then entered the outdoor space for the experiment, which lasted 30 min (Fig. 3). During the experiment, the subjects were asked to fill out a thermal comfort questionnaire: one questionnaire every 5 min and six questionnaires per experiment. The outdoor experiment was divided into two exercise states, sitting and walking. Throughout the day, each subject was only permitted to take part in one sitting experiment or one walking experiment. Walking was performed on a treadmill at a speed of 1.2 m/s.The same subject was instructed to participate in the experimental and control groups an equal number of times per season. When a subject decided to withdraw from the experiment while it was in progress, the data obtained from the experiment were invalidated. We conducted the experiment on a non-rainy day from 9:30 to 11:30 or 14:30 to 16:30 to prevent the effects of eating breakfast and lunch on the subjects' thermal sensation. Each month, 12–20 experiments were conducted. The experiment was approved by the Biomedical Experimental Ethics Review Committee of Huaqiao University.
2.4 Questionnaire survey
The questionnaire (i.e., electronic questionnaire) was distributed to the subjects during the experiment through a cellular phone. The e-questionnaire automatically recorded the time of submission. The questionnaire was divided into two sections: personal information and subjective perception survey. The first part of the questionnaire recorded the subject's name. Given that the subjects' physiological parameters were pre-recorded, they were not required to provide information on gender, age, height, and weight. The second part included the subjects' thermal sensation vote (TSV), thermal comfort vote (TCV), discomfortable body parts vote, radiation preference vote, temperature preference vote, wind speed preference vote, and humidity preference vote. The discomfortable body parts vote is multiple choice, whereas others are single choices. The thermal comfort questionnaire was designed by ISO 105511 (2019), and the specific options are shown in Table 3 .Table 3 Subjective voting scale.
Table 3Thermal Sensation Thermal Comfort Discomfort body parts Radiation preference Temperature preference Air speed preference Humidity preference
·-4 Very cold ·0 Comfortable ·Face ·Higher ·Higher ·Higher ·Higher
·-3 Cold ·1 Slightly uncomfortable ·Chest ·No change ·No change ·No change ·No change
·-2 Cool ·2 Uncomfortable ·Abdomen ·Lower ·Lower ·Lower ·Lower
·-1 Slightly cool ·3 Very uncomfortable ·Back
·0 Neutral ·4 Extremely uncomfortable ·Upper limbs
·+1 Slightly warm ·Lower limbs
·+2 Warm ·No discomfort
·+3 Hot
·+4 Very hot
2.5 Data collection
A weather station equipped with a global temperature sensor, a wind speed recorder, and an air temperature and humidity recorder was installed at the experiment site (Fig. 2 , Table 4 ). All sensors uploaded the collected data to a cloud server. The sensors were placed at a distance of 1.1 m from the ground and recorded environmental parameters around the clock with a recording interval of 1min.Fig. 2 Experimental site and thermal meteorological parameter instruments.
Fig. 2
Fig. 3 (a) Experimental procedure. (b) Preparation before the experiment. (c) Sitting experiment. (d) Walking experiment.
Fig. 3
Table 4 Specifications of measurement devices.
Table 4Equipment Model Measurement parameter Range Accuracy
Air temperature and humidity recorder PTS-3 Ta（°C）; −40-80 °C; ±0.1 °C;
RH（%） 0-100%RH ±2%RH
Global radiation sensor TBQ-2 G（W/m2） 0–2000W/m2 ＜5%
Wind speed recorder EC-9S Ws（m/s） 0–70 m/s ±0.3 m/s
2.6 Thermal comfort indices
In this study, the physiologically equivalent temperature (PET) [30] was selected as an objective thermal comfort index. PET is widely used in outdoor thermal comfort studies and provides a convenient way to make comparisons among studies. In this study, the software Rayman Pro [31,32] was used to calculate PET. The six parameters required in calculating PET were air temperature (Ta), relative humidity (RH), wind speed (Ws), global radiation (G), clothing thermal resistance value, and metabolic rate. Ta, RH, Ws, and G were measured directly from the weather station. The thermal resistance of the garment is shown in Table 2. Metabolic rate was estimated based on ISO 8996 (2004). The mean PET within 5 min prior to the submission of the questionnaire was used as the PET corresponding to the questionnaire results.
3 Results
3.1 Thermal parameters
Based on temperature fluctuations in Xiamen and the seasonal division of similar climate zones [33,34], the period from May 1, 2021 to September 30, 2021 was categorized as summer, October 1, 2021 to November 30, 2021 as autumn, and December 1, 2021 to February 28, 2022 as winter. We collected 2184 valid questionnaires in total; 864 were collected in summer, 648 in autumn, and 672 in winter. The thermal environment parameters recorded during the experiment in the three seasons are shown in Table 5 .Table 5 Outdoor thermal parameters.
Table 5Season Meteorological factors Min Max Mean Standard deviation
Summer Ta（°C） 22.3 35.6 30.77 2.86
G（W/m2） 54 935 284.22 311.75
Ws（m/s） 0 3.5 0.59 0.85
RH（%） 37.4 92.5 68.65 12.54
PET（°C） 20.2 51.2 33.9 7.0
Autumn Ta（°C） 18.5 31.7 25.56 3.24
G（W/m2） 35 858 314.32 302.45
Ws（m/s） 0 3.1 0.51 0.78
RH（%） 25.3 79.5 54.78 12.1
PET（°C） 14.5 48.3 28.51 8.96
Winter Ta（°C） 10.1 26.4 18.04 3.99
G（W/m2） 28 782 323.07 298.1
Ws（m/s） 0 2.2 0.38 0.56
RH（%） 20.6 81.4 45.6 10.16
PET（°C） 7.2 39.9 21.4 8.1
3.2 Thermal sensation and neutral temperature
The TSV scatter plots for the subjects while sitting and walking are shown in Fig. 4, Fig. 5 , respectively. Linear regressions were performed separately. PET at TSV = 0 was defined as the neutral temperature, and PET within the interval (−1,1) for TSV was the neutral range. The results are shown in Table 6 . In the sitting position, the neutral temperatures were 25.7 °C (summer), 24.3 °C (autumn), and 22.2 °C (winter) for subjects wearing masks and 24.5 °C (summer), 23.2 °C (autumn), and 21.6 °C (winter) for subjects without masks. In the walking condition, the neutral temperatures were 23.8 °C (summer), 22.1 °C (autumn), and 19.5 °C (winter) for subjects with mask and 22.1 °C (summer), 20.8 °C (autumn), and 18.7 °C (winter) without masks. In the sitting position, differences in neutral temperature between subjects with masks and those without masks were 1.2 °C in summer, 1.1 °C in autumn, and 0.6 °C in winter. In the walking condition, the differences in neutral temperature between subjects with masks and those without masks were 1.7 °C in summer, 1.3 °C in autumn, and 0.8 °C in winter.Fig. 4 Correlation between TSV and PET in different seasons while sitting: (a) Summer - Without Masks; (b) Summer - With Masks; (c) Autumn - Without Masks; (d) Autumn - With Masks; (e) Winter - Without Masks; (f) Winter - With Masks.
Fig. 4
Fig. 5 Correlation between TSV and PET in different seasons while walking: (a) Summer - Without Masks; (b) Summer - With Masks; (c) Autumn - Without Masks; (d) Autumn - With Masks; (e) Winter - Without Masks; (f) Winter - With Masks.
Fig. 5
Table 6 Neutral temperature and neutral temperature range while sitting.
Table 6Condition Season Without Masks With Masks
Neutral temperature(°C) Comfort temperature range (°C) Neutral temperature(°C) Comfort temperature range (°C)
Sitting Summer 25.7 ＜31.2 24.5 ＜29.6
Autumn 24.3 18.4–30.2 23.2 17.7–28.8
Winter 22.2 17.6–26.8 21.6 16.9–26.5
Walking Summer 23.8 ＜29.5 22.1 ＜27.4
Autumn 22.1 15.9–28.2 20.8 15.1–26.4
Winter 19.5 14.9–24.2 18.7 13.9–23.5
3.3 Effect of masks on thermal comfort
Fig. 6 shows change in mean TCV (MTCV) with TSV in summer, autumn, and winter with and without masks. In summer, difference in MTCV between the groups with and without masks was the most obvious. When TSV >0, the MTCV of the mask-wearing group was higher than that of the mask-free group under the same thermal sensation, and the difference gradually increased as thermal environment deteriorated. In the walking condition, difference in MTCV between the groups under the same thermal sensation increased, and significant differences were found at TSV of 1, 2, or 3（p＜0.05）. In autumn and winter, when TSV <1, difference in MTCV between the mask-wearing and mask-free groups was generally small. When TSV >1, the MTCV of the mask-wearing group was greater.Fig. 6 Correlation between TSV and PMV in different seasons: (a) Summer-Sitting; (b) Summer-Walking; (c) Autumn-Sitting; (d) Autumn-Walking; (e) Winter-Sitting; (f) Winter-Walking. T-Test significative difference with: *, p < 0.05; **, p < 0.01.
Fig. 6
3.4 Discomfort in different parts of the body
A higher proportion of subjects felt discomfort in the chest and abdomen than in other parts of the body in the mask-free group (Fig. 7 ). The face was the most unpleasant portion in the mask-wearing group, and the percentage of the subjects who experienced discomfort in the face was higher than that in the group without masks (p＜0.001). Approximately 75.9%, 68.5%, and 53% of mask wearers experienced facial discomfort in the summer, autumn, and winter, respectively, when they were seated; and 92.8%, 79.6%, and 66.7%, respectively, when they were walking. The percentage of facial discomfort decreased as the season changed from warm to cool, but the facial discomfort was exacerbated by walking. In addition, the group that wore masks had a slightly higher proportion of chest discomfort than the group that did not, but no significant difference was found in winter (p＞0.05). Differences in the degrees of discomfort in the abdomen, back, and upper and lower extremities were found between the groups.Fig. 7 Percentages of participants who voted that they experienced discomfort in various body parts: (a) Summer-Sitting; (b) Summer-Walking; (c) Autumn-Sitting; (d) Autumn-Walking; (e) Winter-Sitting; (f) Winter-Walking. Chi Square Test significative difference with: *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Fig. 7
3.5 Meteorological factors
Random forest was used in analyzing the relative importance of thermal environment parameters in TSV. Random forest refers to a classifier that uses multiple trees to train and predict samples [35]. The classifier was first proposed by Leo Breiman and Adele Cutler. A decision tree is a primary classifier that generally classifies features into two classes. A forest with a series of decision trees is created. The constructed decision tree has a tree-like structure and can be considered a collection of if then rules, and the main advantages are readability of the model and fast classification [36].
Two random forests were created for each season to predict the relative importance of Ta, RH, Ws, and G in TSV without and with masks. The results are shown in Table 7 . The TSV for each season was mainly predicted by G and Ta. In winter, G was slightly more important than Ta, and the opposite was true in summer and autumn. The influence of RH on thermal sensation was relatively low. The effect of Ws on thermal sensation was lower than the effects of G and Ta but higher than the effect of RH. The importance of Ws in thermal sensation increased gradually from summer to winter. The temperature had a greater effect on the thermal sensation of mask-wearing subjects than those on subjects without masks, whereas Ws had a smaller effect on thermal sensation. This difference was pronounced in summer.Table 7 Random forest modeling results for summer, autumn, and winter.
Table 7Season Condition Ta (°C) RH (%) Ws(m/s) G (W/m2)
Summer Without Masks 0.501 0.045 0.106 0.348
With Masks 0.613 0.031 0.051 0.305
Autumn Without Masks 0.467 0.051 0.085 0.397
With Masks 0.54 0.033 0.047 0.38
Winter Without Masks 0.307 0.059 0.152 0.482
With Masks 0.354 0.073 0.123 0.45
Fig. 8 depicts the seasonal thermal preferences of subjects wearing masks and those without masks for environmental parameters. Overall, subjects wearing masks preferred higher Ws and lower G, Ta, and RH. In summer, more than half of the subjects hoped to reduce G, Ta, and Ws, especially those who were wearing masks. The highest proportion of subjects who expected no change in G and Ta was observed in autumn. In winter, difference in perception of meteorological parameters between the groups was the smallest, suggesting that masks have less effect on thermal comfort in winter than in summer or autumn. G, Ta, and Ws were the most important meteorological parameters affecting thermal sensation. In summer and autumn, the proportion of subjects who wore masks and preferred G, Ta, and Ws to remain constant was lower than that of subjects who did not wear masks, and the opposite result was obtained in winter. This finding indicated that subjects wearing masks were more likely to be satisfied with outdoor thermal environment factors in winter. Among the four meteorological factors, the proportion of preferred RH to remain unchanged was always the highest. The main reason was that the subjects had strong adaptability to high humidity in the south, and the satisfaction of subjects wearing masks or not wearing masks with humidity was the highest in winter.Fig. 8 Percentage distribution of thermal preference for environmental parameters with or without masks：（a）Summer; （b）Autumn; （c）Winter.
Fig. 8
4 Discussion
4.1 Neutral temperature
Table 8 shows the results of several outdoor thermal comfort studies on neutral PET. Most studies were conducted before the COVID-19 epidemic; thus, the subjects can be assumed to wear masks rarely at that time. Previous studies have shown that neutral PET is generally higher in summer than in winter [5,37,40,[47], [48], [49]]. The neutral PET in different seasons in Taichung [7], Hong Kong [5], and Guangzhou [10,40], which belong to the hot-summer-warm-winter climate region as Xiamen, were close to the neutral PET under the mask-free condition. The winter neutral PET in Harbin [49], Mianyang [41], Tianjin, and Chengdu [46] were lower than that in this study, because of regional climate differences.Table 8 Neutral PET in different outdoor thermal comfort studies.
Table 8Authors Place Season Condition Neutral PET Linear regression equation
This study Xiamen Summer Sitting-Without Masks 25.7 TSV = 0.1807·PET-4.6452
Sitting-With Masks 24.5 TSV = 0.1945·PET-4.7628
Walking-Without Masks 23.8 TSV = 0.1765·PET-4.3692
Walking-With Masks 22.2 TSV = 0.1867·PET-4.2997
Autumn Sitting-Without Masks 24.3 TSV = 0.1697·PET-4.1321
Sitting-With Masks 23.2 TSV = 0.1783·PET-4.1425
Walking-Without Masks 23.2 TSV = 0.1733·PET-4.029
Walking-With Masks 21.8 TSV = 0.1785·PET-3.8982
Winter Sitting-Without Masks 22.2 TSV = 0.2194·PET-4.8746
Sitting-With Masks 21.6 TSV = 0.2092·PET-4.5389
Walking-Without Masks 20.5 TSV = 0.2129·PET-4.3727
Walking-With Masks 19.7 TSV = 0.2064·PET-4.067
Lin [7] Taichung Cool Season – 23.7 TSV = 0.1991·PET-4.7229
Hot Season – 25.6 TSV = 0.1184·PET-3.0253
Cheng [5] Hong Kong Summer – 25.0 TSV = 0.1372·PET-3.4335
Winter – 21.0 TSV = 0.119·PET-2.4638
Lai [37] Tianjin Summer – 15.6 MTSV = 0.101·PET-1.571
Winter – 9.2 MTSV = 0.188·PET-1.73
Zeng [38] Chengdu Summer – 24.8 MTSV = 0.105·PET-2.6
Chen [39] Shanghai Autumn – 24.5 MTSV = 0.037·PET-0.98
Winter – 22.4 MTSV = 0.071·PET-1.59
Li [40] Guangzhou Spring – 25.6 TSV = 0.25·PET - 6.40
Summer – – TSV = 0.07·PET - 0.36
Winter – 15.6 TSV = 0.18·PET - 2.81
Cheng [41] Mianyang Summer – 30.0 TSV = 0.1114·PET - 3.0032
Autumn – 23.2 TSV = 0.1048·PET - 2.4274
Winter – 17.3 TSV = 0.0735·PET - 1.2726
Fang [12] Guangzhou Summer and autumn 1∼2Met 21.9 MTSV = 4.11ln(PET)-12.65
2.6Met 19.4 MTSV = 4.79 ln(PET)- 14.247
Leng [42] Harbin Winter Static activities 21 MTSV = 0.127·PET-2.667
Dynamic activities 18.2 MTSV = 0.112·PET-2.037
Niu [43] Xian Summer light intensity activities 26.1 TSV = 0.0822·PET-2.142
moderate intensity 22.1 TSV = 0.0661·PET-1.4632
vigorous intensity 11.9 TSV = 0.0571·PET-0.679
Nasrin [44] Kuala Lumpur April to June – 25.6 TSV = 0.248·PET-6.341
Tang [45] Guangzhou Summer Before physical training 19.3 TSV = 0.15·PET-2.9
After physical training 4.6 TSV = 0.09·PET-0.41
Wei [46] Chengdu Summer – 15.1 TSV = 0.1046·PET-1.5816
Winter – 12.8 TSV = 0.0828·PET-1.062
The effect of different activity metabolic rates on outdoor neutral temperature was significant. Studies in Harbin [42], Xi'an [43], and Guangzhou [12,45] showed that outdoor neutral temperature decreases with increasing activity intensity (Table 7). Our research indicated that the neutral temperature in the sitting condition was higher than that in the walking condition. The differences between the two in the absence of masks were 1.9 °C, 2.2 °C, and 2.7 °C in summer, autumn, and winter, respectively. The differences between the two were 2.3 °C, 2.4 °C, and 2.9 °C in summer, autumn, and winter, respectively, in the mask-wearing group. Wearing masks increased the neutral temperature difference between sitting and walking. During exercise, the body's metabolic rate and oxygen demand increase, a higher amount of heat needs to be removed through breathing, and the frequency of breathing through the mouth increases. Oral breathing expels more heat than nasal breathing [50], promotes heat accumulation in masks and results in a heightened perioral skin thermal sensation. In addition, some studies found that breathing through the nose cools the venous blood leading to the skull and reduces brain heat sensation, whereas breathing through the mouth diminishes this cooling mechanism [51]. Owing to breathing resistance caused by the mask, people who wear masks during exercise breathe more frequently through the mouth than those who do not wear a mask. The neutral temperature gap between mask-wearing subjects who walked and those who sit increased.
Zhang et al. [23] discovered that masks lowered the neutral temperature of indoor workers by 1.5 °C. According to the study conducted by Tang et al. [21] in a library in Guangzhou, indoor borrowers with masks had a 0.5 °C higher neutral operative temperature and 0.3 °C higher neutral standard effective temperature than those without masks. The indoor studies did not identify seasonal differences in the effects of masks on thermal comfort. The results of our study in an outdoor environment showed that neutral PET differences between subjects wearing masks and those without masks were 1.2 °C, 1.1 °C, and 0.6 °C while sitting in summer, autumn, and winter, respectively. The differences were 1.7 °C, 1.3 °C, and 0.8 °C in summer, autumn, and winter, respectively, during walking.
Under normal conditions, facial skin temperature is influenced by the temperature and humidity of the surrounding air [18]. Wearing a mask generates a microenvironment in the vicinity of the perioral region, creating a mask dead space that functions as the mask wearer's breathing environment. This microenvironment has a substantial effect on heat exchange in the face. In hot outdoor environments during summer, the temperature gradient between the surrounding environment and the environment inside the mask decreases, and the heat in the dead space of the mask is less likely to dissipate [22], which can worsen the microenvironment inside the mask and increase the thermal sensation of a mask wearer. The reason for this seasonal difference may be related to the local thermal sensitivity of the human skin. Perception of thermal changes in the environment varies by human surface part [52]. The face has the highest sensitivity to thermal changes in a warm environment [53]. In a cool environment, the thermal sensitivity of the face is relatively low, and the thermal sensitivity of the chest and abdomen increases [54,55]. In a warm environment in summer, a mask increases thermal stress on the overall thermal sensation of the human body. In a cool environment in winter, the effect of wearing a mask on the overall thermal sensation decreases due to the decreased thermal sensitivity of the face.
4.2 Thermal comfort votes
As the outdoor thermal environment deviates from the neutral temperature, people's discomfort and TCV increase. A survey conducted by Huang et al. [56] in Mianyang, China showed that the percentage of people who felt comfortable outdoors was lower in summer (36.5%) than in winter (44.1%). In humid and hot subtropical regions, such as Taichung [7], Guangzhou [40,57], and Belo Horizonte [48], surveys showed that summer was the least comfortable season, whereas winter was mild; outdoor respondents were more likely to feel comfortable in the latter season. In winter, the degree of comfort was significantly higher in warm environments than it was in summer. In the present study, when TSV = 1, 2, 3, MTCV was lower in winter than in summer, similar to the results of studies in the aforementioned regions.
Previous studies have shown that wearing a mask increases discomfort. Zhang et al. [23] discovered that as the indoor air temperature increased, the thermal comfort of mask wearers decreased at an accelerated rate. Tang et al. [21] found that 73.9% of the indoor population wearing masks felt uncomfortable during summer, and facial discomfort accounted for 71% of cases of discomfort. These results were similar to the present study. In a warm environment, the MTCV of mask wearers was always higher than that of subjects without masks for the same thermal sensation, and this difference was more pronounced under the walking condition. The face was the most uncomfortable area for mask wearers, followed by the chest. As temperatures changed from warm to cool from summer to winter, the proportion of individuals experiencing facial discomfort while sitting or walking decreased. In a cool environment, this difference was nonsignificant. This result is due to the fact that the contribution of facial thermal comfort to overall thermal comfort is greater in warm environments and less in cool environments [54]. Therefore, difference in MTCV between subjects with and without masks under cold stress was small. The chest and overall comfort can be affected by a mask's restrictive effect on breathing. A portion of the exhaled air remains between the mask and a wearer's face [61]. Several studies have demonstrated that the level of carbon dioxide in a mask's dead space is significantly higher than the normal value [61]. The high concentration of CO2 and low concentration of O2 inhaled by mask wearers stimulate the sympathetic nervous system and increase the body's cardiopulmonary burden [58], increasing the degrees of increased chest and general discomfort. In an exercise state, the body's O2 demand and breathing rate increase, increasing discomfort.
4.3 Meteorological factors
A number of investigations [37,49,59,60] have demonstrated that Ta is the most essential meteorological parameter for outdoor thermal comfort and has the highest association with thermal sensation. G and Ws are regarded as significant factors influencing outdoor thermal sensation [[61], [62], [63], [64], [65]]. Our study showed that Ta and G were the most important thermal environment parameters affecting thermal perception, in subjects with or without masks. This result is similar to the results of previous studies. The effect of Ta on thermal sensation was greater for mask-wearing subjects than subjects without masks. This result should be related to the fact that the temperature gradient inside and outside a mask affects heat dissipation in the mask [22].In winter, the effect of Ws on thermal sensation was greater than that in summer. The effect of Ws on thermal perception in the masked group was lower than that in the mask-free group. However, the mask-wearing subjects preferred increase in Ws and decrease in RH. This finding can be explained by the fact that wearing a mask for an extended period of time causes an increase in Ta and RH inside the mask, which hinders people's respiratory heat dissipation and facial skin heat dissipation [18,66]. Consequently, individuals with masks prefer to increase the level of heat dissipation by increasing Ws and decreasing RH.
4.4 Limitations
In this study, we reported the effect of masks on thermal perception during the first 30 min of outdoor exposure, but did not examine the situation after 30 min. Future research should investigate the effect of masks on outdoor thermal perception over longer time periods. The subjects selected for this study were all college students in good physical condition, and investigations involving other age groups or people with underlying diseases were lacking. This is a longitudinal experimental study and no cross-sectional investigation was conducted, but different types of outdoor spaces may also lead to differences in the effects of masks on thermal comfort.In addition, only surgical masks were used. The thermal comfort effects of other types of masks may be different from those of surgical masks, and thus such potential differences should be explored in future studies.
5 Conclusions
This study aimed to assess the impact of surgical masks on outdoor thermal comfort in Xiamen, China. The following conclusions were drawn.(1) Surgical masks reduced the neutral temperature of individuals outdoors. In the sitting position, surgical masks decreased the neutral PET by 1.2 °C, 1.1 °C, and 0.6 °C in summer, autumn, and winter, respectively. In the state of walking, wearing masks decreased the neutral PET by 1.7 °C, 1.3 °C, and 0.8 °C in summer, autumn, and winter, respectively. Masks had the greatest impact on neutral temperature in summer and the least in winter and had a greater impact on walking neutral temperature than on sitting neutral temperature.
(2) Wearing masks in a warm environment decreased the subjective thermal comfort of individuals. When TSV >0, the MTCV of mask-wearing subjects was higher than that of subjects without masks for the same thermal sensation. In the walking condition, this difference was more pronounced. In cool environments, the effect of masks on thermal comfort was nonsignificant.
(3) Surgical masks significantly increased facial discomfort. The discomfort under the walking condition was more intense. As the season changed from summer to winter, discomfort caused by masks tended to diminish.
(4) Radiation and air temperature were the primary environmental parameters that influenced the outdoor thermal sensation in hot and humid regions, regardless of the use of masks. The influence of air temperature on thermal sensation was higher in subjects with masks than in those without masks and was more pronounced in summer. Subjects with masks preferred lower air temperature, radiation, and humidity and higher wind speeds.
CRediT authorship contribution statement
Zhiqiang Zhou: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Conceptualization. Liang Dong: Supervision, Resources, Project administration, Funding acquisition.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
The data that has been used is confidential.
Acknowledgments
The work was supported by the 10.13039/501100001809 National Natural Science Foundation of China (No.51678253), the Scientific Research Funds of 10.13039/501100003815 Huaqiao University (No.15BS302).
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| 36514557 | PMC9733126 | NO-CC CODE | 2022-12-14 23:38:30 | no | Build Environ. 2022 Dec 9;:109893 | utf-8 | Build Environ | 2,022 | 10.1016/j.buildenv.2022.109893 | oa_other |
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Med (N Y)
Med (N Y)
Med (New York, N.y.)
2666-6359
2666-6340
Elsevier Inc.
S2666-6340(22)00484-6
10.1016/j.medj.2022.11.003
Letter
Recombination shapes the 2022 monkeypox (mpox) outbreak
Yeh Ting-Yu 1∗
Hsieh Zih-Yu 27
Feehley Michael C. 17
Feehley Patrick J. 17
Contreras Gregory P. 17
Su Ying-Chieh 34
Hsieh Shang-Lin 5
Lewis Dylan A. 6
1 Auxergen Inc., Columbus Center, Baltimore, MD 21202, USA
2 Lake Washington High School, Kirkland, WA 98033, USA
3 Department of Thoracic Surgery, Chi-Mei Medical Center, Tainan City, 710, Taiwan
4 Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan City, 710, Taiwan
5 MacKay Memorial Hospital, Taipei City, 104, Taiwan
6 Monte Vista High School, Danville, CA 94526, USA
∗ Corresponding author
7 These authors contributed equally
9 12 2022
9 12 2022
9 12 2022
3 12 824826
© 2022 Elsevier Inc.
2022
Elsevier Inc.
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.
Monkeypox (Mpox) is a global health emergency. Yeh et al. analyze tandem repeats and linkage disequilibrium in monkeypox virus (MPXV) sequences from the 2022 pandemic to determine the virus evolution, showing that these are useful tools to monitor and track phylogenetic dynamics and recombination of MPXV.
Monkeypox (Mpox) is a global health emergency. Yeh et al. analyze tandem repeats and linkage disequilibrium in monkeypox virus (MPXV) sequences from the 2022 pandemic to determine the virus evolution, showing that these are useful tools to monitor and track phylogenetic dynamics and recombination of MPXV.
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pmcMain text
The 2022 monkeypox outbreak represents the first time this disease has spread widely beyond Central and West Africa. Initially identified in the UK in May 2022, monkeypox case numbers quickly increased in Europe, North and South America, Asia, Africa, and Oceania. On July 23, 2022, the WHO declared the monkeypox outbreak a global health emergency. On August 12, 2022, a total of 35,032 cases were confirmed in nearly 80 countries (Monkeypox data explorer. Our World in Data).
Most monkeypox virus (MPXV) sequences from the 2022 outbreak belong to the B.1 clade (except two cases from the US). In this study, we analyze MPXV sequences during 2022 pandemic to investigate whether the virus is adapting for better survival and transmission among the human population.
In a rapidly evolving poxvirus, adaptation is simultaneously driven by two mechanisms: recombination (gene copy number variation, fast) and single nucleotide variants (SNVs, slow) at the same loci.1 Recombination generates new phenotypes with greatly altered disease potential that are better suited to viral survival. It has been shown that vaccinia viral DNA is swapped back and forth ∼18 times per genome in a single round of infection to make recombinant phenotypes. Recombination in poxvirus genomes has been commonly detected by selection or screening in laboratory animals or cell culture for more than 60 years2 , 3 Gershon et al. described recombination of four capripoxvirus isolates during natural virus transmission by analyzing physical maps of the viral genome.4 However, very little is known about poxvirus recombination in nature due to its relatively large genome size and lack of genomic surveillance data, which is now becoming available.
Our efforts focused on discovering the variability of the MPXV genome via recombination to determine the potential risk of new viral strains. Tandem repeats (TRs) were first identified within the inverted terminal repeat of vaccinia virus DNA with a 70-base-pair sequence arranged in two blocks of 13 and 17 copies, respectively.5 Other poxvirus TRs are small pieces of DNA sequences (3–25 nucleotides) and their sequences and copy numbers vary among different poxvirus family members or isolates.6 The insertions and deletions of TRs are common events among these poxviruses.7 Therefore, these TRs exhibit high rates of variation and they represent a target for poxvirus gene truncation and variation.6 , 8 Here, we report the first evidence of natural recombination of monkeypox virus by analyzing TRs. We also use linkage disequilibrium, a well-known SNVs-based analysis, to detect new lineages and recombination in MPXV genome of 2022 pandemics.
To determine the genomic diversity in monkeypox genomes in the 2022 outbreak, we first searched TRs in 415 available MPXV sequences (B.1 clade) worldwide from January 1 to July 20, using the Tandem Repeat Finder algorithm. The advantage of this method is that it eliminates the bias caused by sequence alignment error, especially in low complexity sequences like TRs. Based on the criteria of the alignment score (>100) and length (>7 base pair, bp), we identified 6 TRs with variations in their copy numbers (Figures S1A and S1B). TR A/E have identical 16 bp sequences of inverted repeats (5′-TAACTCTAACTTATGACT-3′ and 5′-AGTCATAAGTTAGTTA-3′) at both ends of the MPXV genome (Figure S1B). The viral populations were further categorized into six groups based on TR numbers (TRNs) of TRA/E (Figure S1C). Three hundered and seventy-eight cases (90.6%) were associated with TRN = 7.9 versus 14 cases of TRN = 15.9 (3.4%), which were collected in the US (ON959133, ON959134, ON959135, ON959136, ON954773, ON959131, ON959132), Belgium (ONON622712, ON622713, ON880419, ON880420, ON880421, ON880422) and the Czech Republic (ON983168). One case of TRN = 5.6, TRN = 3.6, and TRN = 2.6 was found in the UK (ON 619837, ON619835, ON022171). There were 21 cases with different TR numbers between TR A and E (“mismatch” in Figure S1C). This result shows that genome diversity can be grouped by TR polymorphism among MPXV populations in the 2022 pandemic.
TR B, C, D, and F are direct repeats and located at either the intergenic regions (TR B, C, D) or 3′ inverted terminal repeat (TR F) (Figure S1C, detail information of TR B, C, D in Mendeley data: 10.17632/txgdw36vxc.1). Each TR C and F contain one and three copies of 9 bp sequence (5′-TATGATGGA-3′), respectively. While the majority of viral sequences contain TR F (TRN = 3.5, 97.1%), 50.8 and 48.2% of total samples either have TR C with TRN = 9.7 and TRN = 7.7, respectively (Figure S1D). Based on TR C/F pattern, the viral populations can be classified into 4 lineages (M, 210 cases; U, 193 cases; I, 7 cases; and one uncategorized, Figure S1D). Further, in combination with TRNs of TR C/F and TR A/E, we are able to categorize viral populations into 11 subgroups. TR D is a 9 base-pair sequence (5′-ATATCATT-3′) with more various copy numbers, ranging from TRN = 2 to 54.6 (Figure S1E). Interestingly, the sequences with high TRNs of TR D (TRN>30) also contain higher TRNs of TR A/E (TRN>15.9, 20 cases) in the group U, indicating that lineage U has more TR diversity than the others.
Taken together, our data demonstrate that TRs diverged frequently during natural transmission within the B.1 clade and that the virus is evolving as its population expands (Zeng’s E = −1.65, Achaz’s Y = −2.52, p < 0.001).
Poxvirus recombination between two co-infecting parental viruses generates genetic diversity.1 , 2 MPXV recombination in natural infection has not been reported to date. Using TR polymorphism, we identified eight genomes with recombinant crossovers (Figure S1F). Case FVG-ITA-01 (ON755039) in Italy may be generated from parental sequences from the group I and M. Case VIDRL01 (ON631963) in Australia comes from parental sequence of the group M and U (Figure S1D), as well as six cases in Slovenia (ON838178, ON631241, ON609725, ON754985, ON754986, ON754987). This is the first report of recombination of MPXV in natural transmission to our knowledge. Our results also suggests that six Slovenian cases may have evolved into a new lineage.
We then employed single nucleotide polymorphism (SNPs) analysis using the DNASP v6 algorithm to detect the occurrence of recombination (Rozas’s Za = 0.0005, p < 0.05). We also used Haploview algorithm to visualize the patterns of linkage disequilibrium (LD) between variants with minor alleles in at least two MPXV isolates and to detect the possible recombination (Figure S1G). In the absence of evolutionary forces or natural selection, D′, the normalized coefficient of LD, converges to zero along the time axis at a rate depending on the magnitude of the recombination rate between the two loci. Since most SNPs were at very low frequencies, many SNP pairs had low values of squared coefficient of correlation (r 2) and the log of the odds (LOD) (Figures S1J and S1K).
The LD analysis reveals five SNP pairs located at C22736T/G74357A, G34305A/G148421A, G34305A/G189246A, G148421A/G189246A, and G186153A/C188379T (8 cases) with the high log of odds (>10) and strong evidence of LD (χ2 test, p < 0.0001), in which the upper 95% confidence bound of D′ is above 0.98 and the lower bound is above 0.7 (Figures S1H and S1I). C22736T/G74357A SNPs are present in 28 cases, including 25 in Germany, one in Austria, one in the UK, and one in Portugal. G186153A/C188379T SNP pairs has eight German cases. There are 14 Canadian cases containing G34305A/G148421A/G189246A SNPs (Mendeley data: https://doi.org/10.17632/txgdw36vxc.1). Our results suggest that virus has evolved into at least three new lineages.
Moreover, the upper 95% confidence bound of D′ for SNP pairs C25641T/C70777T and G5592A/G78031A was 0.34 and 0.87, respectively, showing strong evidence of recombination. This result suggests that two Germany cases (ON959149 and ON637939) and one Spain case (ON720849) already gained their mutations via recombination (Figure S1I).
In this study, we show the first report of natural recombination of MPXV genome based on TR (SNP-independent analysis) and LD (SNP-dependent analysis). Kugelman et al. have investigated MPXV genome diversity from 60 human samples collected in the Democratic Republic of the Congo from 2005 through 2007 based on four regions with TRs.6 MPXV populations in 2022 pandemics can be categorized into 4 lineages with 11 different subgroups in clade B.1. Like Kugelman et al.’s studies, we found that none of the TR of MPXV strains were located at the protein coding region. However, all of their 60 samples (2005–2007) had identical right and left TRs. In contrast, we have detected 21 genomes (5.1%) with mismatch TR A/E during the 2022 pandemic to date (Figure S1C).
LD analysis also detected three new lineages (G22736T/G74357A, G186153A/G188379T, G34305A/G148421A/G189246A), suggesting that MPXV has diverged during the 2022 pandemic. Based on a neutrality test, directional selection appears to not yet be significant (normalized Wu and Fay’s DH = −0.74, p > 0.05), consistent with the idea that SNVs’ mutation rates are generally slower than recombination in poxvirus evolution.7
It has been shown TRs with the diverse length (54, 70, 125 bp) near the ends of vaccinia virus genome can provide a novel marker to detect unequal crossing over and compare the relatedness of poxviruses.9 However, the TR diversity has been underappreciated in poxvirus study. To prevent the power of TR annotation being compromised by sequencing-assembly error, we also validated TR A to F using multiple TR detection TRAL algorithms combined with evolutionary and statistical analyses (Mendeley data: 10.17632/txgdw36vxc.1). We have not detected ambiguous sequences at the boundary of TRs in MPXV recombinants; therefore, it is very unlikely that their sequences resulted from mixed infection of different MPXV isolates. Taken together, these data confirmed that recombination did occur in 2022 monkeypox outbreak. Our results indicate that TR analysis is well-suited for detecting poxvirus recombination events. These data also demonstrated that TR and LD analysis can detect different recombination events (Figures S1F versus S1I). In combination with genomic surveillance, TR and LD analysis are both useful tools to monitor and track phylogenetic dynamics and genetic epidemiology of monkeypox transmission.
We speculate that the MPXV genomes in the 2022 outbreak emerged most likely from a single origin, gained mutations or TRs, and then evolved into different lineages and subgroups. Then, co-infection of viruses from two parental lineages occurred, followed by homologous recombination via multiple possible mechanisms.2 Therefore, the progeny MPXV recombinants have mosaic pattern of TRs or mutations. So far, we have not detected any defective MPXV virus arising from a single infection.
TR insertions in the promoter and 3′-untranslated regions of MPXV may also have influence on gene expression and regulation (Mendeley data: 10.17632/txgdw36vxc.1). It has been reported that the 3′-to-5′ exonuclease activity of viral DNA polymerase plays an essential role in promoting extraordinarily high levels of genetic recombination in vaccinia infection.2 Recombination is involved in vaccinia virus adaptation to counteract the interferon-induced antiviral host cell response mediated by the double-stranded RNA-dependent protein kinase (PKR). One of the weak PKR inhibitors, vaccinia virus K3L, undergoes recurrent gene amplification with a beneficial SNV (His47Arg) via active recombination driven by selection.1 , 10 This event is mediated by vaccinia RNA polymerase and leads to rapid homogenization of K3L gene arrays.10 It is worth noting that natural MPXV C3L (vaccinia K3L homologue) is truncated at amino acid 43 by the stop codon and loss of binding site to PKRs and eIF2B (Mendeley data: 10.17632/txgdw36vxc.1). It is unclear whether MPXV virulence is changed due to the loss of the anti-interferon activity.
Due to the limitation of available demographic information, our study does not imply that MPXV recombination occurred in any specific populations of sex, gender, age, ethnicity, or socioeconomic status.
Supplemental information
Document S1. Figure S1
Document S2. Article plus supplemental information
Acknowledgments
We sincerely thank scientists worldwide for providing sequence information during 2022 monkeypox outbreak. This paper is made possible by their collective work on MPXV genomic surveillance. T.-Y. Yeh designed the overall experiments. T.-Y Yeh and G. P. Contreras had unrestricted access to all data. T.-Y Yeh, Z.-Y. Hsieh, G. P. Contreras performed the TR analysis, T.-Y Yeh, M. C. Feehley and P.J. Feehley performed phylogenetic analysis and summarized the sequence information. T.-Y Yeh, Y.-C. Su, S.-L. Hsieh, and D. A. Lewis performed the LD analysis. T.-Y Yeh, and Z.-Y. Hsieh performed statistical analyses. T.-Y Yeh and G. P. Contreras wrote the article. All authors read and approved the final article and take responsibility for its content.
Declaration of interests
T.Y.Y. and G.P.C. are founders of Auxergen, Inc. Y.C.S. and S.L.H. are stockholders of Auxergen, Inc. All authors and Auxergen, Inc., declare no competing or financial interests.
Supplemental information can be found online at https://doi.org/10.1016/j.medj.2022.11.003.
==== Refs
References
1 Sasani T.A. Cone K.R. Quinlan A.R. Elde N.C. Long read sequencing reveals poxvirus evolution through rapid homogenization of gene arrays Elife 7 2018 e35453 10.7554/eLife.35453 30156554
2 Evans D.H. Poxvirus recombination Pathogens 11 2022 896 10.3390/pathogens11080896 36015016
3 Fenner F. Comben B.M. Genetic studies with mammalian poxviruses. I. Demonstration of recombination between two strains of vaccinia virus Virology 5 1958 530 548 10.1016/0042-6822(58)90043-6 13557735
4 Gershon P.D. Paul Kitching R. Hammond J.M. Black D.N. Poxvirus genetic recombination during natural virus transmission J. Gen. Virol. 70 1989 485 489 10.1099/0022-1317-70-2-485 2543750
5 Wittek R. Moss B. Tandem repeats within the inverted terminal repetition of vaccinia virus DNA Cell 21 1980 277 284 10.1016/0092-8674(80)90135-X 6250716
6 Kugelman J.R. Johnston S.C. Mulembakani P.M. Kisalu N. Lee M.S. Koroleva G. McCarthy S.E. Gestole M.C. Wolfe N.D. Fair J.N. Genomic variability of monkeypox virus among humans, Democratic Republic of the Congo Emerg. Infect. Dis. 20 2014 232 239 10.3201/eid2002.130118 24457084
7 Coulson D. Upton C. Characterization of indels in poxvirus genomes Virus Gene. 42 2011 171 177 10.1007/s11262-010-0560-x
8 Hatcher E. Wang C. Lefkowitz E. Genome variability and gene content in Chordopoxviruses: dependence on microsatellites Viruses 7 2015 2126 2146 10.3390/v7042126 25912716
9 Baroudy B.M. Moss B. Sequence homologies of diverse length tandem repetitions near ends of vaccinia virus genome suggest unequal crossing over Nucleic Acids Res. 10 1982 5673 5679 10.1093/nar/10.18.5673 6292846
10 Elde N. Child S. Eickbush M. Kitzman J. Rogers K. Shendure J. Geballe A. Malik H. Poxviruses deploy genomic accordions to adapt rapidly against host antiviral defenses Cell 150 2012 831 841 10.1016/j.cell.2012.05.049 22901812
| 36495863 | PMC9733179 | NO-CC CODE | 2022-12-15 23:21:51 | no | Med (N Y). 2022 Dec 9; 3(12):824-826 | utf-8 | Med (N Y) | 2,022 | 10.1016/j.medj.2022.11.003 | oa_other |
==== Front
Clin Dermatol
Clin Dermatol
Clinics in Dermatology
0738-081X
1879-1131
Elsevier Inc.
S0738-081X(22)00195-X
10.1016/j.clindermatol.2022.12.001
Graduate Medical Education Rounds
The Indelible Marks on Dermatology: Impacts of COVID-19 on Dermatology Residency Match using the Texas STAR Database
Williams Georgia MArch 1
Turner Jessica BS 1
Wiggins Claire MD 1
Seervai Riyad MD, PhD 2
Mialic Angela MD 3
Ahmed Ammar M MD 1⁎
1 Dell Medical School at the University of Texas at Austin
2 Baylor College of Medicine
3 University of Texas Southwestern Medical Center
⁎ Corresponding author: Ammar M Ahmed, MD, Division of Dermatology, 1601 Trinity Street, Suite 7.802, Austin, TX 78712, (512) 495-5650
9 12 2022
9 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.
With changes to interview format and away rotations, the COVID-19 pandemic has reshaped the residency application process. In this retrospective cohort study of data from the nationwide Texas Seeking Transparency in Applications to Residency (STAR) survey, we sought to understand how the pandemic has impacted applicants in the 2021 dermatology Match. We compared applicants in the “post-COVID-19” Match year (2021) to “pre-COVID-19” Match years (2018-2020) regarding match rates, interview costs, residency geographic connections, and number of interviews attended. A total of 439 dermatology applicants who completed the Texas STAR survey were included. There was no difference in percentage of applicants with a geographic connection to their matched program (43.88% vs. 47.20%). Compared to prior cycles, applicants in the 2021 Match attended a higher percentage of interview offers (96% vs 90%, P < 0.0001) and more applicants attended 16 or more interviews (p=0.0489). Applicants in the 2021 Match reported an average savings of $5000 compared to prior cycles. Virtual interviews offer savings for applicants but may encourage interview hoarding. Though applicants did not perform away rotations, there was no increase in geographic connection for matched applicants. Stakeholders should consider these data when evaluating the pros and cons of virtual interviewing post-pandemic.
Edited by Min Deng, MD
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pmcIntroduction
The 2019 coronavirus disease (COVID-19) pandemic has transformed the residency application process. With in-person interviews moved to a virtual format, financial and logistical barriers to attending interviews are reduced. This raises a concern for “interview hoarding,” a phenomenon in which applicants attend a disproportionately large number of interviews. Without away rotations, students’ ability to establish meaningful relationships with faculty at other programs is limited.1 Away rotations have been cited as a crucial opportunity for students to “audition” at programs outside their home institution and for programs to determine a candidate's “fit” for the residency.2,3 Without this critical component, one might expect increased geographic connections between applicant and matched residency. Though this has not been shown in recent otolaryngology Match data, the implications of the pandemic on the dermatology Match are still unknown.4
In light of multiple changes to the application process during recent interview cycles, we sought to evaluate whether there were significant differences in interview distribution, geographic clustering, and application-related costs in the “post-COVID-19” Match (applicants matching in March 2021) compared to previous, “pre-COVID-19” cycles.
Methods
Sample
Data was collected from the Texas Seeking Transparency in Applications to Residency (STAR) database, a collection of nationwide self-reported information from residency applicants who applied to any residencies in the United States. Student affairs deans opt in to participate and invite fourth year medical students to complete the survey between Match day and April 10th of each application cycle. The only applicants unable to take the survey are those whose medical schools do not opt-in to participate.
Applicants included in the present study applied to dermatology residency programs and completed the Texas STAR survey between 2018 and 2021. The overall response rate for all applicants, regardless of specialty was 47% in 2018, 38% in 2019, 47% in 2020, and 40% in 2021.5 The number of participating schools has increased from 78 in 2018 to 123 medical schools in 2021.5 Exclusion criteria for different analyses included respondents who indicated matching but reported applying to 0 programs or attending 0 interviews as well as applicants who did not report any data in question, such as cost.
Texas STAR survey
The Texas STAR survey asks applicants to report information as it appeared on their residency applications, including board scores, academic achievements and extracurricular work. Respondents are also asked the number of interviews offered, interviews attended, matching status, geographic connection to programs, whether they completed an away rotation, virtual seminar attendance (2021 applicants only), as well as total application costs.
Data Analysis
Data were analyzed using Prism (GraphPad, version 9.3.1). The following statistical tests were used: Figure 1 , Fisher's exact test; Figure 2 , Fisher's exact test; Figure 3 , Fisher's exact test.Figure 1 Total and matched survey respondents in 2021 Match year vs 2018-2020 Match years (p=0.0296, Fisher's exact test). 2021 data exclude 3 matched applicants who reported attending 0 interviews. 2018-2020 data exclude 3 matched applicants who reported attending 0 interviews.
Figure 1:
Figure 2 Reported geographic connection for matched survey respondents in 2021 Match year vs 2018-2020 Match years (p=0.6390, Fisher's exact test)
Figure 2:
Figure 3 Distribution of reported interviews attended in pre- vs post- COVID Match cycles. All tests of significance were calculated using absolute values, shown as percentages. Combined interview attendance distribution by percentage, p=0.0494 (Fisher's exact test)
Figure 3:
Results
A total of 439 dermatology applicants responding to the Texas STAR survey met inclusion criteria. This total represents 98.6% of all survey respondents applying to dermatology and includes 332 applicants in 2018-2020 and 107 applicants in 2021.
Matched applicants
In the Texas STAR data sample, there were significantly more applicants who successfully matched into dermatology in 2021 compared to the three previous application cycles (93.46% vs. 85.24%, p=0.03) (Fig 1). Importantly, this data only reflects the applicants who self-selected to complete the survey and does not reflect national match statistics.
Geographic connection
Respondents were asked to report if they have a geographic connection, via family or institution, to the program to which they matched. There was no significant difference in percentage of applicants with a geographic connection to their matched program in the 2021 Match compared to the 2018-2020 Matches. (43.88% vs. 47.20%, p=0.6390) (Fig 2).
Distribution of interviews
Applicants reported the number of interviews they attended. When considering the total number of interviews attended, regardless of matched status, there was a significant difference in the distribution of number of interviews attended in pre- vs post-COVID-19 cycles (p=0.0494) (Fig 3). In 2019-2020, 9.7% of applicants interviewed at 16 or more programs, whereas in the 2021 Match, 18% of applicants interviewed at 16 or more programs (p=0.0489). Additionally, in 2021 applicants attended a larger percentage of interviews offered compared to 2019-2020 (96.4% vs. 90.0%, P < 0.0001).
Interview costs
Applicants were asked to report the total interview costs (within a $500 range). Cost data is available for 2019, 2020 and 2021. Of the 346 dermatology responses for these years, 338 reported total costs associated with the application cycle. The cumulative costs for 2019-2020 applicants were much lower than for 2021 applicants (P < 0.0001). The median cost interval range in the 2021 Match was $2000-2499, whereas for the 2019-2020 cycles it was $7000-7499.
Discussion
With a shift towards virtual interviews and away from in-person experiences, various stakeholders are concerned about how residency applications and matching opportunities may be affected by the COVID-19 pandemic. Examining the changing Match data can help guide residency application policies, as discussions are ongoing regarding interview caps, preference signaling, and away rotation recommendations for the 2022-2023 application cycle and beyond.6 Despite COVID-19-related travel and away rotation limitations, there was no significant change in the rate of reported geographic connections between applicants and their matched programs in the present data. There was, however, a greater percentage of interview offers attended and a higher number of applicants attending 16 or more interviews in the 2021 Match, indicating that “interview hoarding” in the virtual landscape may limit interview opportunities for less traditionally competitive applicants.
It comes as no surprise that virtual interviews are associated with decreased costs compared to in-person interviews; our data shows an average savings of $5,000 for per applicant upon switching to virtual interviews, potentially eliminating a barrier for those with limited resources. These data depict the potential advantages and disadvantages of virtual versus in-person interviews. Virtual interviews provide substantial cost savings, but introduce a maldistribution in interview attendance, with both of those factors potentially leading to inequities amongst applicants. Stakeholders will need to balance these advantages and disadvantages in future guidance on the format of interviews.
Limitations to our study include the self-reported nature of the Texas STAR data, with only 38-47% of all residency applicants completing the survey during the years included in this study. However, our findings have been congruent with that of other studies; an orthopedic residency study found that virtual interviews reduce applicant costs by an average of $6,311.7 Additionally, studies using data from the Association of American Medical College Residency Explorer tool and online platforms such as Reddit have not found a significant increase in geographic connections within the dermatology Match.8,9
Conclusions
This retrospective cohort study quantifies the substantial cost savings for applicants with the use of virtual interviews for the dermatology Match, but suggests that these changes may encourage “interview hoarding” and introduce new inequities for applicants. As discussions on further application reforms are ongoing, we hope that our data and that of future studies will inform evidence-based decision making to improve the application process for applicants and programs alike.
References
1 Hammoud, MM, Standiford, T, Carmody, JB.: Potential Implications of COVID-19 for the 2020-2021 Residency Application Cycle. JAMA. 2020; 324:29-30.
2 Cao, SZ, Nambudiri, VE.: A national cross-sectional analysis of dermatology away rotations using the Visiting Student Application Service database. Dermatol Online J. 2017; 23:13030.
3 Adusumilli, NC, Kalen, J, Hausmann, K, et al.: Dermatology applicant perspectives of a virtual visiting rotation in the era of COVID-19. J Am Acad Dermatol. 2021; 84:1699-1701.
4 Lenze, NR, Mihalic, AP, Kovatch, KJ, et al.: Impact of the COVID-19 Pandemic on the 2021 Otolaryngology Residency Match: Analysis of the Texas STAR Database. Laryngoscope. 2021; 132:1177-1183.
5 Texas STAR. Houston, TX: UT Southwestern Medical Center; 2021. https://www.utsouthwestern.edu/education/medical-school/about-the-school/student-affairs/texas-star.html. Accessed February 12, 2022.
6 Brumfiel, CM, Jefferson, IS, Rinderknecht, FA, et al.: Current perspectives of and potential reforms to the dermatology residency application process: A nationwide survey of program directors and applicants. Clin Dermatol. 2022.
7 Gordon, AM, Malik, AT, Scharschmidt, TJ, et al.: Cost Analysis of Medical Students Applying to Orthopaedic Surgery Residency: Implications for the 2020 to 2021 Application Cycle During COVID-19. JB JS Open Access. 2021; 6.
8 Dowdle, TS, Ryan, MP, Wagner, RF.: Internal and geographic dermatology match trends in the age of COVID-19. J Am Acad Dermatol. 2021; 85:1364-1366.
9 Mulligan, KM, Zheng, DX, Narang, J, et al.: The effect of COVID-19-related changes on geographical outcomes in the 2021 dermatology residency match. Clin Exp Dermatol. 2022; 47:445-447.
| 36509341 | PMC9733282 | NO-CC CODE | 2022-12-14 23:36:14 | no | Clin Dermatol. 2022 Dec 9; doi: 10.1016/j.clindermatol.2022.12.001 | utf-8 | Clin Dermatol | 2,022 | 10.1016/j.clindermatol.2022.12.001 | oa_other |
==== Front
iScience
iScience
iScience
2589-0042
The Author(s).
S2589-0042(22)02056-9
10.1016/j.isci.2022.105783
105783
Article
Molecular basis for antiviral activity of two pediatric neutralizing antibodies targeting SARS-CoV-2 Spike RBD
Chen Yaozong 116
Prévost Jérémie 2316
Ullah Irfan 4
Romero Hugo 5
Lisi Veronique 5
Tolbert William D. 1
Grover Jonathan R. 6
Ding Shilei 2
Gong Shang Yu 27
Beaudoin-Bussières Guillaume 23
Gasser Romain 23
Benlarbi Mehdi 2
Vézina Dani 2
Anand Sai Priya 27
Chatterjee Debashree 2
Goyette Guillaume 2
Grunst Michael W. 6
Yang Ziwei 6
Bo Yuxia 8
Zhou Fei 9
Béland Kathie 5
Bai Xiaoyun 10
Zeher Allison R. 910
Huang Rick K. 910
Nguyen Dung N. 1
Sherburn Rebekah 1
Wu Di 11
Piszczek Grzegorz 11
Paré Bastien 12
Matthies Doreen 9
Xia Di 10
Richard Jonathan 23
Kumar Priti 4
Mothes Walther 6
Côté Marceline 8
Uchil Pradeep D. 6
Lavallée Vincent-Philippe 1314
Smith Martin A. 512
Pazgier Marzena 1∗
Haddad Elie 3515∗
Finzi Andrés 23717∗
1 Infectious Disease Division, Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD 20814-4712, USA
2 Centre de Recherche du CHUM (CRCHUM), Montreal, QC H2X 0A9, Canada
3 Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montreal, QC H2X 0A9, Canada
4 Department of Internal Medicine, Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT 06520
5 Centre de Recherche du CHU Ste-Justine, Montreal, QC H3T 1C5, Canada
6 Department of Microbial Pathogenesis, Yale University School of Medicine, New Haven, CT 06510, USA
7 Department of Microbiology and Immunology, McGill University, Montreal, QC H3A 2B4, Canada
8 Department of Biochemistry, Microbiology and Immunology, Center for Infection, Immunity, and Inflammation, University of Ottawa, Ottawa, ON K1H 8M5, Canada
9 Unit on Structural Biology, Division of Basic and Translational Biophysics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA
10 Laboratory of Cell Biology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
11 Biophysics Core Facility, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, 20892
12 Département de Biochimie et Médecine Moléculaire, Université de Montréal, Montreal, QC H3T 1C5, Canada
13 Department of Pediatrics, Division of Pediatric Hematology-Oncology, Charles-Bruneau Cancer Center, Centre Hospitalier Universitaire (CHU) Sainte-Justine, Montréal, QC, Canada
14 Immune Diseases and Cancers Axis, CHU Sainte-Justine Research Center, Montréal, QC, Canada
15 Département de Pédiatrie, Université de Montréal, Montreal, QC H3T 1C5, Canada
∗ These authors share the seniorship of the manuscript and are co-corresponding authors: , ,
16 These authors contributed equally.
17 Lead contact
9 12 2022
9 12 2022
1057839 8 2022
7 11 2022
7 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.
Neutralizing antibodies (NAbs) hold great promise for clinical interventions against SARS-CoV-2 variants of concern (VOCs). Understanding NAb epitope-dependent antiviral mechanisms is crucial for developing vaccines and therapeutics against VOCs. Here we characterized two potent NAbs, EH3 and EH8, isolated from an unvaccinated pediatric patient with exceptional plasma neutralization activity. EH3 and EH8 cross-neutralize the early VOCs and mediate strong Fc-dependent effector activity in vitro. Structural analyses of EH3 and EH8 in complex with the receptor-binding domain (RBD) revealed the molecular determinants of the epitope-driven protection and VOC-evasion. While EH3 represents the prevalent IGHV3-53 NAb whose epitope substantially overlaps with the ACE2 binding site, EH8 recognizes a narrow epitope exposed in both RBD-up and RBD-down conformations. When tested in vivo, a single-dose prophylactic administration of EH3 fully protected stringent K18-hACE2 mice from lethal challenge with Delta VOC. Our study demonstrates that protective NAbs responses converge in pediatric and adult SARS-CoV-2 patients.
Graphical abstract
Published: ▪▪ ▪▪, ▪▪
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pmc
| 36514310 | PMC9733284 | NO-CC CODE | 2022-12-14 23:36:15 | no | iScience. 2022 Dec 9;:105783 | utf-8 | iScience | 2,022 | 10.1016/j.isci.2022.105783 | oa_other |
==== Front
Respir Med
Respir Med
Respiratory Medicine
0954-6111
1532-3064
Elsevier Ltd.
S0954-6111(22)00349-3
10.1016/j.rmed.2022.107084
107084
Original Research
Multisystem inflammatory syndrome in adults: Comparison with other inflammatory conditions during the Covid-19 pandemic
Auger Nathalie abcd∗
Bégin Philippe e
Kang Harb f
Lo Ernest bd
Brousseau Émilie ab
Healy-Profitós Jessica ab
Potter Brian J. ag
a University of Montreal Hospital Research Centre, Montreal, Quebec, Canada
b Institut national de santé publique du Québec, Montreal, Quebec, Canada
c Department of Social and Preventive Medicine, School of Public Health, University of Montreal, Montreal, Quebec, Canada
d Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
e Sainte-Justine Hospital Research Centre, University of Montreal, Montreal, Quebec, Canada
f Department of Rheumatology, Cité-de-la-Santé Hospital, Laval, Quebec, Canada
g Division of Cardiology, Department of Medicine, University of Montreal Hospital Center, Montreal, Quebec, Canada
∗ Corresponding author. 190 Cremazie Blvd. E., Montreal, Quebec, H2P 1E2, Canada, ,
9 12 2022
9 12 2022
10708431 8 2022
18 11 2022
2 12 2022
© 2022 Elsevier Ltd. All rights reserved.
2022
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Background
Multisystem inflammatory syndrome in adults (MIS-A) is an increasingly recognized complication of Covid-19. We assessed risk factors, clinical characteristics, and outcomes of patients with MIS-A compared with other inflammatory conditions.
Methods
We analyzed a cohort of patients ≥21 years hospitalized with MIS-A in Quebec, Canada between February 2020 and March 2021. We included comparison groups that share symptomatology or pathophysiology with MIS-A, including Kawasaki disease, toxic shock syndrome, and other Covid-19 complications. We examined characteristics of men and women at admission, and identified preexisting factors associated with MIS-A through odds ratios (OR) and 95% confidence intervals (CI) from adjusted logistic regression models.
Results
Among 22,251 patients in this study, 52 had MIS-A, 90 Kawasaki disease, 500 toxic shock syndrome, and 21,609 other Covid-19 complications. MIS-A was associated with an elevated risk of respiratory failure compared with Kawasaki disease (OR 7.22, 95% CI 1.26–41.24), toxic shock syndrome (OR 4.41, 95% CI 1.73–11.23), and other Covid-19 complications (OR 3.03, 95% CI 1.67–5.50). Patients with MIS-A had a greater risk of cardiac involvement, renal failure, and mortality. The data pointed towards sex-specific differences in presentation, with more respiratory involvement in women and cardiac involvement in men compared with patients that had other Covid-19 complications. Except for allergic disorders and cancer, prior medical risk factors were not associated with a greater likelihood of MIS-A.
Conclusions
Patients with MIS-A have an elevated risk of mortality compared with other inflammatory conditions, with women having a predominance of respiratory complications and men cardiovascular complications.
Keywords
COVID-19
Cytokine release syndrome
Mucocutaneous lymph node syndrome
Systemic inflammatory response syndrome
Toxic shock syndrome
Abbreviations
CI, confidence interval
ICD, International Classification of Diseases
MIS-A, multisystem inflammatory syndrome in adults
OR, odds ratio
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pmc1 Introduction
Multisystem inflammatory syndrome was initially documented in children (MIS-C) at the start of the pandemic, but has since been found in adults (MIS-A) as well [1,2]. MIS-C is defined as fever accompanied by a combination of rash, conjunctivitis, hypotension, shock, myocardial dysfunction, coagulopathy, gastrointestinal manifestations, and markers of inflammation manifesting between two to six weeks after a SARS-CoV-2 infection among persons under 20 years of age [3]. Apart from obesity and chronic respiratory disorders, children with MIS-C tend to have few predisposing morbidities [4]. However, risk factors for MIS-A remain poorly characterized. While MIS-A is an increasingly recognized complication of SARS-CoV-2 infection in older patients [1,2], the clinical profile of these patients is less clearly understood.
The Centers for Disease Control and Prevention first issued a working case definition for MIS-A in October 2020 [5]. A few studies have since been published [2,[6], [7], [8]], with some suggesting that MIS-A has features of inflammatory diseases such as Kawasaki disease and toxic shock syndrome [9]. Kawasaki disease is a systemic vasculitis characterized by coronary artery aneurysms and mucocutaneous complications in children [9,10], but adults may also present with this condition [11]. Toxic shock syndrome is an acute inflammatory reaction triggered by pathogenic toxins acting as superantigens that lead to shock and multiorgan failure [9,12]. MIS-A has signs and symptoms similar to Kawasaki disease and toxic shock syndrome [6,9,12]. These conditions have been comparison groups for pediatric MIS-C in previous research [13]. However, comparisons of MIS-A with these inflammatory conditions are lacking. In this exploratory study, we contrasted the risk factors and clinical characteristics of adults hospitalized for MIS-A against Kawasaki disease, toxic shock syndrome, and other Covid-19 complications in a Canadian population heavily affected by the pandemic.
2 Materials and methods
2.1 Population
We analyzed a cohort of 22,251 adults admitted to hospital for MIS-A, Kawasaki disease, toxic shock syndrome, or other Covid-19 complications between April 1, 2006 and March 31, 2021 in Quebec, Canada. We identified patients with these conditions in the Maintenance and Use of Data for the Study of Hospital Clientele repository, which contains discharge abstracts for all admissions and hospital-based procedures, excluding ambulatory or emergency care [14]. The data include patients with conditions severe enough to require in-hospital care anywhere in the province. We used encrypted health insurance numbers to trace patients back in time for their clinical history.
We restricted the analysis to individuals age 21 years and over, following the current case definition for MIS-A [15]. We used diagnostic codes from the 9th and 10th revision of the International Classification of Diseases (ICD-9 and ICD-10) to identify patient characteristics. We used codes from the Canadian Classification of Health Interventions to identify interventions during admission.
2.2 MIS-A
Cases included all admissions for MIS-A after February 25, 2020 (ICD-10 U07.3) [16]. The first case of acute Covid-19 infection was identified in Quebec on that date [17]. The Centers for Disease Control and Prevention currently defines MIS-A as the presence of 1) elevated inflammatory biomarkers, 2) positive test for SARS-CoV-2 infection at or prior to symptom onset, and 3) fever lasting at least 24 h or emerging in the first 72 h of admission [15]. Patients must have at least three of the following symptoms: severe cardiac illness; rash or conjunctivitis; neurological signs; shock or hypotension; gastrointestinal symptoms; and thrombocytopenia [15].
2.3 Comparison groups
We included patients hospitalized for Kawasaki disease (ICD-10 M30.3) and toxic shock syndrome (ICD-10 A48.3) as comparison groups. As these inflammatory conditions are rare, we identified patients with these conditions any time between April 2006 and March 2021. In addition, we included a comparison group of patients hospitalized for other Covid-19 complications during the pandemic (ICD-10 U07.1, U07.2, U07.4, U07.5) [16]. Patients with MIS-A, Kawasaki disease, toxic shock syndrome, and other Covid-19 complications were mutually exclusive and included only once in the analysis.
2.4 Clinical characteristics
We assessed the clinical presentation, management, and outcomes of patients during admission using diagnostic and intervention codes (Table S1). Physicians used criteria in the literature to diagnose clinical complications. Each patient could have up to 41 diagnoses and 35 treatments during a given admission [14]. We identified outcomes such as respiratory failure, respiratory distress syndrome, pleural effusion, pneumothorax, pulmonary embolism, myocardial infarction, heart failure, carditis, coronary aneurysm, cardiac arrhythmia, cardiogenic shock, hypotension, renal failure, electrolyte imbalance, septic shock, and death. For management, we identified patients who required intubation, dialysis, blood transfusion, admission to an intensive care unit, lengthy hospital stays (≥14 days), or had an adverse drug reaction or other medical and surgical complication during admission.
2.5 Risk factors
To investigate potential risk factors, we examined prior hospitalization records between 1989 and the date of admission for MIS-A or other inflammatory conditions. Risk factors included any Charlson morbidity [18], metabolic disorder (diabetes, obesity, hypertension, dyslipidemia), cardiovascular disease (myocardial infarction, heart failure, cerebrovascular disease), pulmonary disease (chronic obstructive, pneumonia), asthma and allergic disorder, other infection, autoimmune disease, cancer, renal disease, digestive/liver disease, anemia/blood disorder, skin disorder, nervous system disorder, sensory organ disorder, mental illness, and tobacco or other substance use disorder. We identified these conditions using diagnostic codes (Table S2).
2.6 Covariates
We considered the following covariates as potential confounders: age (21–54, 55–64, 65–74, ≥75 years), sex, location prior to admission (home; hospital transfer; long-term care, seniors home, other), rural residence (rural, urban, unknown), and socioeconomic deprivation (yes, no, unknown). Socioeconomic deprivation was defined as the lowest quintile of a population index measuring average income, education level, and employment rates within neighborhoods [19].
2.7 Data analysis
We calculated descriptive statistics using frequencies and percentages and applied multivariable logistic regression models to estimate odds ratios (OR) and 95% confidence intervals (CI). For the analysis of clinical presentation, we considered MIS-A the exposure and computed the odds of adverse outcomes for MIS-A relative to each inflammatory condition separately. For the analysis of risk factors for MIS-A, we considered prior medical conditions the exposure and computed the odds of developing of MIS-A as opposed to Kawasaki disease, toxic shock syndrome, or other Covid-19 complications. We stratified the analysis by sex to determine if the pattern of associations differed between men and women. In sensitivity analyses, we stratified toxic shock syndrome by type (streptococcal, non-streptococcal). We also stratified other Covid-19 complications by symptom severity.
All statistical models were adjusted for age, location prior to admission, rural residence, and material deprivation. We conducted the analysis in SAS version 9.4 (SAS Institute Inc., Cary, NC). The institutional review board of our hospital research centre issued an ethics waiver for this study, since the data were anonymized and informed consent was not required.
3 Results
Among 22,251 patients who were hospitalized, 52 (0.2%) were admissions for MIS-A, 90 (0.4%) for Kawasaki disease, 500 (2.2%) for toxic shock syndrome, and 21,609 (97.1%) for other Covid-19 complications (Table 1 ). The majority of MIS-A patients were men, age 75 years and older, from urban areas, and hospitalized during the second wave of the pandemic. In contrast, patients with Kawasaki disease and toxic shock syndrome were mainly between 21 and 54 years of age. Most patients with Kawasaki disease were male, whereas patients with toxic shock syndrome were predominantly female.Table 1 Demographic characteristics of patients with MIS-A, Kawasaki, toxic shock syndrome, and other Covid-19 complications.
Table 1 No. patients (%)
MIS-A Kawasaki Toxic shock syndrome Other Covid-19 complications
Covid-19 wavea
Prepandemic – 80 (88.9) 470 (94.0) –
Wave 1 8 (15.4) <5 19 (3.8) 7,509 (34.8)
Wave 2 44 (84.6) 6 (6.7) 11 (2.2) 14,100 (65.3)
Age at first admission, years
21-54 5 (9.6) 64 (71.1) 370 (74.0) 3,922 (18.2)
55-64 10 (19.2) 7 (7.8) 58 (11.6) 2,850 (13.2)
65-74 14 (26.9) 10 (11.1) 43 (8.6) 3,839 (17.8)
≥75 23 (44.2) 9 (10.0) 29 (5.8) 10,998 (50.9)
Sex
Male 37 (71.2) 56 (62.2) 197 (39.4) 10,690 (49.5)
Female 15 (28.9) 34 (37.8) 303 (60.6) 10,919 (50.5)
Location prior to admission
Home 28 (53.9) 75 (83.3) 457 (91.4) 13,094 (60.6)
Hospital transfer 14 (26.9) 12 (13.3) 31 (6.2) 2,682 (12.4)
Long-term care, seniors home, other 10 (19.2) <5 12 (2.4) 5,833 (27.0)
Rural residence
Rural <5 15 (16.7) 101 (20.2) 2,039 (9.4)
Urban 48 (92.3) 57 (63.3) 395 (79.0) 19,316 (89.4)
Socioeconomic deprivation
Yes 11 (21.2) 18 (20.0) 92 (18.4) 4,360 (20.2)
No 35 (67.3) 63 (70.0) 381 (76.2) 12,312 (57.0)
Total 52 (100) 90 (100) 500 (100) 21,609 (100)
a Prepandemic: April 1, 2006 to February 24, 2020; Wave 1: February 25, 2020 to August 22, 2020; Wave 2: August 23, 2020 to March 31, 2021.
3.1 Clinical presentation of MIS-A
Patients with MIS-A had a considerably elevated risk of mortality compared with Kawasaki disease (OR 31.08, 95% CI 3.01–321.21), toxic shock syndrome (OR 2.96, 95% CI 1.31–6.66), and other Covid-19 complications (OR 4.03, 95% CI 2.21–7.34) (Table 2 ). These patients were at risk of respiratory failure compared with Kawasaki (OR 7.22, 95% CI 1.26–41.24), toxic shock syndrome (OR 4.41, 95% CI 1.73–11.23), and other Covid-19 complications (OR 3.03, 95% CI 1.67–5.50). MIS-A was associated with a generally higher risk of respiratory distress syndrome, pulmonary embolism, carditis, renal failure, electrolyte imbalance, admission to an intensive care unit, intubation, and blood transfusion. Patients with MIS-A were also more likely to require a hospital stay ≥14 days.Table 2 Clinical presentation of MIS-A compared with Kawasaki, toxic shock syndrome, and other Covid-19 complications.
Table 2Outcome No. MIS-A (%) No. Kawasaki (%) No. Toxic shock syndrome (%) No. Covid-19 (%) Odds ratio (95% confidence interval)a
MIS-A vs Kawasaki MIS-A vs Toxic shock syndrome MIS-A vs Covid-19
Clinical
Respiratory failure 16 (30.8) <5 26 (5.2) 2,556 (11.8) 7.22 (1.26–41.24) 4.41 (1.73–11.23) 3.03 (1.67–5.50)
Adult respiratory distress syndrome 16 (30.8) 0 32 (6.4) 900 (4.2) – 7.00 (2.68–18.25) 8.52 (4.60–15.75)
Pleural effusion <5 0 40 (8.0) 651 (3.0) – 0.77 (0.18–3.36) 1.86 (0.57–6.01)
Pneumothorax <5 0 0 131 (0.6) – – 7.25 (2.19–24.02)
Pulmonary embolism 6 (11.5) 0 <5 761 (3.5) – 24.37 (3.65–162.91) 2.98 (1.26–7.06)
Myocardial infarction 8 (15.4) 17 (18.9) 24 (4.8) 1,283 (5.9) 0.30 (0.08–1.16) 2.25 (0.72–7.04) 2.64 (1.23–5.71)
No ST elevation 7 (13.5) 11 (12.2) 23 (4.6) 1,162 (5.4) 0.51 (0.12–2.19) 2.10 (0.63–6.98) 2.52 (1.12–5.68)
Heart failure 9 (17.3) 9 (10.0) 30 (6.0) 2,494 (11.5) 1.14 (0.25–5.21) 0.97 (0.34–2.77) 1.56 (0.74–3.27)
Carditis <5 <5 8 (1.6) 175 (0.8) – 41.20 (5.34–317.84) 6.58 (2.02–21.47)
Coronary aneurysm 0 27 (30.0) 0 <5 – – –
Cardiac arrhythmia 17 (32.7) 9 (10.0) 56 (11.2) 4,709 (21.8) 4.02 (1.08–14.89) 0.92 (0.41–2.09) 1.71 (0.92–3.16)
Cardiogenic shock <5 0 <5 83 (0.4) – – 14.91 (5.13–43.30)
Hypotension 5 (9.6) <5 30 (6.0) 1,801 (8.3) 3.65 (0.28–47.01) 1.30 (0.35–4.76) 1.08 (0.43–2.74)
Renal failure 31 (59.6) 7 (7.8) 217 (43.4) 7,262 (33.6) 13.94 (3.56–54.61) 0.90 (0.44–1.82) 2.93 (1.62–5.30)
Septic shock 8 (15.4) 0 73 (14.6) 359 (1.7) – 0.86 (0.34–2.21) 8.50 (3.93–18.38)
Electrolyte imbalance 22 (42.3) 5 (5.6) 150 (30.0) 5,075 (23.5) 9.42 (2.25–39.46) 1.54 (0.77–3.10) 2.30 (1.32–4.01)
Death 26 (50.0) <5 40 (8.0) 4,847 (22.4) 31.08 (3.01–321.21) 2.96 (1.31–6.66) 4.03 (2.21–7.34)
Management
ICU admission 36 (69.2) 29 (32.2) 363 (72.6) 3,931 (18.2) 5.77 (1.93–17.31) 1.32 (0.60–2.88) 9.65 (5.18–17.97)
Intubation 22 (42.3) 13 (14.4) 118 (23.6) 1,541 (7.1) 3.99 (1.27–12.49) 1.63 (0.78–3.42) 8.50 (4.69–15.40)
Dialysis 7 (13.5) 0 30 (6.0) 597 (2.8) – 1.09 (0.36–3.31) 4.23 (1.87–9.60)
Blood transfusion 14 (26.9) 7 (7.8) 89 (17.8) 1,690 (7.8) 7.97 (1.68–37.92) 0.68 (0.30–1.55) 3.81 (2.04–7.12)
Medical complication 15 (28.9) 17 (18.9) 129 (25.8) 3,443 (15.9) 2.02 (0.60–6.79) 0.68 (0.31–1.47) 1.85 (1.01–3.40)
Length of stay, ≥14 days 29 (55.8) 23 (25.6) 110 (22.0) 8,531 (39.5) 3.68 (1.32–10.32) 2.38 (1.17–4.84) 1.73 (0.98–3.05)
a Odds ratio for patients exposed to MIS-A vs. other inflammatory conditions, adjusted for sex, age at first admission, location prior to admission, rural residence, and socioeconomic deprivation.
Clinical profiles appeared to be sex-dependent (Table 3 ). Men and women with MIS-A both had increased odds of respiratory failure. Among women, MIS-A was strongly associated with adult respiratory distress syndrome (OR 8.06, 95% CI 1.71–37.98), intubation (OR 6.76, 95% CI 1.70–26.99), and hospital stays of 14 days or more (OR 4.66, 95% CI 1.26–17.26) compared with toxic shock syndrome. Men with MIS-A had a greater prevalence of adverse cardiovascular events such as pulmonary embolism and cardiogenic shock than men with Kawasaki disease, whereas women with MIS-A did not appear to be at risk of cardiovascular events. Among men, MIS-A was associated with 7.62 times the odds of renal failure (95% CI 1.73–33.54), 25.03 times the odds of electrolyte imbalance (95% CI 3.61–173.38), and 4.56 times the odds of admission to an intensive care unit (95% CI 1.24–16.86).Table 3 Clinical presentation of MIS-A in men and women, compared with Kawasaki and toxic shock syndrome.
Table 3Outcome Men Women
No.
MIS-A (%) No. Kawasaki (%) Odds ratio (95% confidence interval)a No.
MIS-A (%) No. Toxic shock syndrome (%) Odds ratio (95% confidence interval)a
Clinical
Respiratory failure 12 (32.4) <5 12.09 (1.12–130.31) <5 13 (4.3) 10.22 (1.63–64.13)
Adult respiratory distress syndrome 11 (29.7) 0 – 5 (33.3) 22 (7.3) 8.06 (1.71–37.98)
Pleural effusion <5 0 – <5 30 (9.9) 0.53 (0.04–6.56)
Pneumothorax <5 0 – <5 0 –
Pulmonary embolism 6 (16.2) 0 – 0 <5 –
Myocardial infarction 7 (18.9) 15 (26.8) 0.32 (0.07–1.48) <5 10 (3.3) 0.80 (0.05–12.32)
No ST elevation 6 (16.2) 9 (16.1) 0.61 (0.11–3.53) <5 10 (3.3) 0.80 (0.05–12.32)
Heart failure 8 (21.6) 8 (14.3) 1.26 (0.25–6.31) <5 16 (5.3) 0.16 (0.01–2.35)
Carditis <5 <5 – 0 5 (1.7) –
Coronary aneurysm 0 21 (37.5) – 0 0 –
Cardiac arrhythmia 12 (32.4) 7 (12.5) 2.40 (0.52–11.14) 5 (33.3) 22 (7.3) 1.51 (0.37–6.12)
Cardiogenic shock <5 0 – 0 0 –
Hypotension <5 <5 – <5 18 (5.9) 1.54 (0.22–10.93)
Renal failure 23 (62.2) 7 (12.5) 7.62 (1.73–33.54) 8 (53.3) 104 (34.3) 1.32 (0.40–4.40)
Septic shock 5 (13.5) 0 – <5 37 (12.2) 1.46 (0.33–6.48)
Electrolyte imbalance 18 (48.7) <5 25.03 (3.61–173.38) <5 88 (29.0) 0.58 (0.16–2.14)
Death 17 (46.0) 0 – 9 (60.0) 15 (5.0) 8.81 (1.99–39.03)
Management
ICU admission 26 (70.3) 23 (41.1) 4.56 (1.24–16.86) 10 (66.7) 220 (72.6) 1.27 (0.34–4.72)
Intubation 13 (35.1) 11 (19.6) 1.78 (0.45–7.11) 9 (60.0) 59 (19.5) 6.76 (1.70–26.99)
Dialysis 6 (16.2) 0 – <5 11 (3.6) 1.50 (0.14–16.45)
Blood transfusion 10 (27.0) <5 7.02 (0.88–56.05) <5 47 (15.5) 1.10 (0.27–4.41)
Medical complication 9 (24.3) 12 (21.4) 2.20 (0.50–9.65) 6 (40.0) 77 (25.4) 1.03 (0.30–3.55)
Length of stay, ≥14 days 18 (48.7) 16 (28.6) 2.80 (0.80–9.87) 11 (73.3) 55 (18.2) 4.66 (1.26–17.26)
a Odds ratio for patients exposed to MIS-A vs. other inflammatory conditions, adjusted for age at first admission, location prior to admission, rural residence, and socioeconomic deprivation.
The tendency for cardiovascular events in men and respiratory events in women persisted when we compared MIS-A with other Covid-19 complications (Table 4 ). Men with MIS-A had increased odds of cardiovascular complications such as myocardial infarction (OR 3.14, 95% CI 1.35–7.31), carditis (OR 9.15, 95% CI 2.72–30.83), cardiogenic shock (OR 18.14, 95% CI 6.11–53.91), and pulmonary embolism (OR 4.42, 95% CI 1.80–10.86) compared with other Covid-19 complications. Men with MIS-A were also more likely to require dialysis (OR 4.71, 95% CI 1.92–11.55). Although men and women both had an increased risk of respiratory complications, women with MIS-A had greater odds of respiratory distress syndrome (OR 13.26, 95% CI 4.35–40.45), pneumothorax (OR 15.51, 95% CI 1.92–125.13), intubation (OR 23.97, 95% CI 7.79–73.74), and death (OR 9.19, 95% CI 3.02–28.03). Women with MIS-A were also more likely to experience complications during admission (OR 3.42, 95% CI 1.21–9.69) and remain in hospital for 14 days or longer (OR 4.89, 95% CI 1.47–16.25).Table 4 Clinical presentation of MIS-A in men and women, compared with other Covid-19 complications.
Table 4Outcome Men Women
No.
MIS-A (%) No. Covid-19 (%) Odds ratio (95% confidence interval)a No.
MIS-A (%) No. Covid-19 (%) Odds ratio (95% confidence interval)a
Clinical
Respiratory failure 12 (32.4) 1,405 (13.1) 3.12 (1.56–6.23) <5 1,151 (10.5) 2.75 (0.87–8.74)
Respiratory distress syndrome 11 (29.7) 623 (5.8) 7.16 (3.43–14.96) 5 (33.3) 277 (2.5) 13.26 (4.35–40.45)
Pleural effusion <5 335 (3.1) 1.77 (0.42–7.42) <5 316 (2.9) 2.22 (0.29–17.23)
Pneumothorax <5 102 (1.0) 5.55 (1.29–23.85) <5 29 (0.3) 15.51 (1.92–125.13)
Pulmonary embolism 6 (16.2) 471 (4.4) 4.42 (1.80–10.86) 0 290 (2.7) –
Myocardial infarction 7 (18.9) 700 (6.6) 3.14 (1.35–7.31) <5 583 (5.3) 1.29 (0.17–9.96)
No ST elevation 6 (16.2) 628 (5.9) 2.88 (1.17–7.06) <5 534 (4.9) 1.45 (0.19–11.26)
Heart failure 8 (21.6) 1,230 (11.5) 1.94 (0.86–4.35) <5 1,264 (11.6) 0.62 (0.08–4.88)
Carditis <5 99 (0.9) 9.15 (2.72–30.83) 0 76 (0.7) –
Coronary aneurysm 0 <5 – 0 0 –
Cardiac arrhythmia 12 (32.4) 2,454 (23.0) 1.48 (0.72–3.05) 5 (33.3) 2,255 (20.7) 2.64 (0.83–8.39)
Cardiogenic shock <5 64 (0.6) 18.14 (6.11–53.91) 0 19 (0.2) –
Hypotension <5 935 (8.8) 0.53 (0.13–2.21) <5 866 (7.9) 3.21 (0.88–11.64)
Renal failure 23 (62.2) 4,026 (37.7) 2.65 (1.32–5.33) 8 (53.3) 3,236 (29.6) 3.67 (1.25–10.76)
Septic shock 5 (13.5) 225 (2.1) 6.58 (2.51–17.25) <5 134 (1.2) 14.92 (4.04–55.09)
Electrolyte imbalance 18 (48.7) 2,482 (23.2) 3.02 (1.57–5.79) <5 2,593 (23.8) 1.10 (0.35–3.48)
Death 17 (46.0) 2,583 (24.2) 2.83 (1.39–5.77) 9 (60.0) 2,264 (20.7) 9.19 (3.02–28.03)
Management
ICU admission 26 (70.3) 2,465 (23.1) 9.78 (4.63–20.65) 10 (66.7) 1,466 (13.4) 9.79 (3.18–30.15)
Intubation 13 (35.1) 1,036 (9.7) 5.60 (2.72–11.52) 9 (60.0) 505 (4.6) 23.97 (7.79–73.74)
Dialysis 6 (16.2) 405 (3.8) 4.71 (1.92–11.55) <5 192 (1.8) 2.79 (0.35–22.11)
Blood transfusion 10 (27.0) 942 (8.8) 3.82 (1.82–8.03) <5 748 (6.9) 4.01 (1.25–12.80)
Medical complication 9 (24.3) 1,869 (17.5) 1.42 (0.66–3.02) 6 (40.0) 1,574 (14.4) 3.42 (1.21–9.69)
Length of stay, ≥14 days 18 (48.7) 4,255 (39.8) 1.23 (0.64–2.39) 11 (73.3) 4,276 (39.2) 4.89 (1.47–16.25)
a Odds ratio for patients exposed to MIS-A vs. other inflammatory conditions, adjusted for age at first admission, location prior to admission, rural residence, and socioeconomic deprivation.
In sensitivity analyses, MIS-A remained associated with adverse outcomes compared with both streptococcal and non-streptococcal toxic shock syndrome (Table S3). Adverse outcomes were greater when MIS-A was compared with nonsevere than severe Covid-19 (Table S4).
3.2 Risk factors
We did not find strong associations between prior medical history and MIS-A (Table 5 ). MIS-A patients were more likely to have a prior history of asthma, allergic disorders, and cancer relative to Kawasaki disease, toxic shock syndrome, and other Covid-19 complications, although the difference was not statistically significant.Table 5 Association of prior medical history with MIS-A, compared with Kawasaki, toxic shock syndrome, and other Covid-19 complications.
Table 5Exposure No. MIS-A (%) No. Kawasaki (%) Odds ratio (95% confidence interval)a No. Toxic shock syndrome (%) Odds ratio (95% confidence interval)a No. Covid-19 (%) Odds ratio (95% confidence interval)a
Charlson morbidity 33 (63.5) 38 (42.2) 0.50 (0.16–1.59) 144 (28.8) 0.49 (0.20–1.19) 13,867 (64.2) 0.85 (0.46–1.60)
Metabolic disorder 41 (78.9) 43 (47.8) 0.18 (0.03–1.19) 154 (30.8) 0.94 (0.35–2.53) 17,122 (79.2) 0.77 (0.37–1.58)
Diabetes 21 (40.4) 11 (12.2) 1.22 (0.37–4.05) 67 (13.4) 0.61 (0.27–1.40) 7,684 (35.6) 1.04 (0.60–1.83)
Obesity 10 (19.2) 13 (14.4) 0.61 (0.18–2.14) 54 (10.8) 1.00 (0.40–2.52) 4,308 (19.9) 0.95 (0.47–1.91)
Hypertension 35 (67.3) 34 (37.8) 0.27 (0.06–1.18) 107 (21.4) 0.99 (0.41–2.38) 14,694 (68.0) 0.83 (0.44–1.56)
Dyslipidemia 35 (67.3) 29 (32.2) 0.50 (0.13–1.87) 80 (16.0) 1.63 (0.72–3.67) 11,485 (53.2) 1.58 (0.85–2.94)
Cardiovascular disease 29 (55.8) 49 (54.4) 0.09 (0.02–0.40) 119 (23.8) 0.27 (0.10–0.70) 13,188 (61.0) 0.66 (0.36–1.23)
Myocardial infarction 13 (25.0) 17 (18.9) 0.64 (0.18–2.27) 21 (4.2) 1.52 (0.59–3.94) 3,268 (15.1) 1.67 (0.87–3.21)
Heart failure 8 (15.4) 6 (6.7) 0.49 (0.11–2.29) 24 (4.8) 0.76 (0.25–2.31) 3,108 (14.4) 1.03 (0.48–2.24)
Cerebrovascular disease 6 (11.5) 5 (5.6) 1.02 (0.17–6.17) 20 (4.0) 0.49 (0.14–1.71) 2,781 (12.9) 0.80 (0.34–1.92)
Pulmonary disease 25 (48.1) 26 (28.9) 1.41 (0.49–4.10) 139 (27.8) 0.85 (0.39–1.84) 9,454 (43.8) 1.16 (0.66–2.04)
Pneumonia 10 (19.2) 5 (5.6) 2.36 (0.42–13.33) 32 (6.4) 0.99 (0.37–2.67) 3,928 (18.2) 1.03 (0.51–2.08)
Chronic obstructive 15 (28.9) <5 1.54 (0.33–7.24) 36 (7.2) 1.10 (0.46–2.67) 4,224 (19.6) 1.59 (0.86–2.97)
Asthma 11 (21.2) 6 (6.7) 1.79 (0.34–9.37) 43 (8.6) 1.12 (0.42–3.01) 2,811 (13.0) 1.98 (1.01–3.87)
Allergic disorder (except asthma) <5 <5 3.13 (0.24–41.72) 24 (4.8) 1.06 (0.27–4.16) 1,308 (6.1) 1.39 (0.50–3.88)
Common allergiesb <5 <5 3.13 (0.24–41.72) 16 (3.2) 1.79 (0.41–7.85) 1,052 (4.9) 1.78 (0.64–4.97)
Severe and rare allergiesc <5 <5 – 10 (2.0) 0.79 (0.11–5.59) 318 (1.5) 2.88 (0.69–12.00)
Infectiond 24 (46.2) 30 (33.3) 0.88 (0.30–2.56) 158 (31.6) 0.60 (0.27–1.35) 9,328 (43.2) 1.14 (0.65–2.00)
Autoimmune disease <5 38 (42.2) 0.15 (0.04–0.60) 44 (8.8) 0.46 (0.14–1.58) 2,271 (10.5) 0.74 (0.26–2.05)
Cancer 13 (25.0) <5 4.72 (0.79–28.32) 35 (7.0) 1.65 (0.67–4.06) 3,833 (17.7) 1.45 (0.76–2.76)
Renal disease 16 (30.8) 6 (6.7) 1.89 (0.42–8.61) 49 (9.8) 0.83 (0.34–2.03) 5,970 (27.6) 1.08 (0.58–2.00)
Digestive/liver disease 29 (55.8) 45 (50.0) 0.17 (0.05–0.66) 162 (32.4) 0.95 (0.44–2.04) 12,227 (56.6) 0.90 (0.51–1.58)
Anemia/blood disorder 21 (40.4) 17 (18.9) 1.86 (0.59–5.86) 94 (18.8) 1.19 (0.53–2.64) 8,300 (38.4) 1.09 (0.61–1.94)
Skin disorder 7 (13.5) 12 (13.3) 0.54 (0.12–2.40) 95 (19.0) 0.29 (0.11–0.80) 4,779 (22.1) 0.52 (0.23–1.16)
Nervous system disorder 18 (34.6) 19 (21.1) 0.39 (0.11–1.39) 117 (23.4) 0.43 (0.19–1.01) 9,032 (41.8) 0.68 (0.38–1.24)
Sensory organ disorder 25 (48.1) 21 (23.3) 0.92 (0.29–2.90) 83 (16.6) 1.50 (0.67–3.33) 10,492 (48.6) 1.11 (0.59–2.07)
Mental illness 8 (15.4) 15 (16.7) 0.52 (0.13–2.07) 66 (13.2) 0.77 (0.26–2.31) 5,204 (24.1) 0.59 (0.27–1.27)
Substance use disorder 11 (21.2) 13 (14.4) 1.05 (0.25–4.37) 73 (14.6) 0.51 (0.21–1.27) 4,643 (21.5) 0.81 (0.41–1.60)
a Odds ratio for a prior medical risk factor vs. no risk factor, adjusted for sex, age at first admission, location prior to admission, rural residence, and socioeconomic deprivation. Results are for three separate models comparing the outcome MIS-A with Kawasaki disease, MIS-A with toxic shock syndrome, and MIS-A with other Covid-19 complications.
b Allergic conjunctivitis, rhinitis, urticaria, dermatitis.
c Anaphylaxis, allergic purpura, allergic otitis media, alveolitis, pulmonary eosinophilia, allergic gastroenteritis and colitis, allergic arthritis.
d Excludes pneumonia and Covid-19 infection.
4 Discussion
In this preliminary study of severe inflammatory disorders, adults hospitalized for MIS-A had an elevated risk of morbidity and mortality compared with Kawasaki disease, toxic shock syndrome, and other Covid-19 complications. Relative to toxic shock syndrome, women with MIS-A were more likely to develop respiratory complications, including respiratory failure and adult respiratory distress syndrome. Men with MIS-A presented more frequently with respiratory and renal failure than men with Kawasaki disease. When compared with other Covid-19 complications, MIS-A was associated with an elevated risk of respiratory complications among women and cardiovascular complications among men. The findings suggest that the clinical outcomes of MIS-A differ from other inflammatory disorders, with men potentially at greater risk of cardiac complications than women. Risk factors for MIS-A did not appear to differ significantly from that of Kawasaki disease, toxic shock syndrome, or other Covid-19 complications.
Previous studies of the demographic and clinical presentation of patients with MIS-A are conflicting [2,[5], [6], [7], [8]]. In existing studies, MIS-A patients were young with a median age of 21–45 years [2,7,8]. However, an American study of nationwide hospital data reported that MIS-A patients had a median age of 62 years [6]. Most studies noted that MIS-A was more frequent among men [2,[6], [7], [8]]. In our study, over 70% of MIS-A patients were men and most were 65 years and older. Earlier studies reported that MIS-A rarely led to death or the need for invasive treatment [2,5,7,8], but a recent investigation found that 43% of patients died in hospital and 57% needed mechanical ventilation [6]. In our data, half of MIS-A patients died and 42% required intubation. Life threatening outcomes were also more frequent for MIS-A than toxic shock syndrome, Kawasaki disease, and other Covid-19 complications.
MIS-A is considered a severe complication of Covid-19 [1,2], yet comparisons with other inflammatory disorders are lacking. The only study that contrasted MIS-A against other disorders assessed demographic characteristics of acute Covid-19 infection [7]. The study found that patients with MIS-A were younger than patients with other acute Covid-19 complications (median age 45.1 vs. 56.5 years) [7]. There was no difference in race, ethnicity, underlying health conditions, or length of stay [7]. In our data, patients with MIS-A also tended to be younger, but we were unable to study race or ethnicity. However, patients with MIS-A had greater odds of death, admission to an intensive care unit, intubation, carditis, myocardial infarction, shock, and renal and respiratory failure than patients with other Covid-19 complications.
Studies have yet to contrast MIS-A with Kawasaki disease. Kawasaki is more frequent in males [20], but is less prevalent in adults than children [21]. At the beginning of the pandemic, children with MIS-C were found to have a Kawasaki-like syndrome due to an elevated prevalence of mucocutaneous and cardiac manifestations [9,10]. In our study, men with MIS-A had a greater frequency of cardiogenic shock, pulmonary embolism, respiratory and renal failure, and admission to an intensive care unit than men with Kawasaki disease. However, men with MIS-A were not at risk of coronary aneurysm. Vasculitis leading to coronary aneurysm is a central hallmark of Kawasaki disease, resulting from interleukin-1 mediated coronary endothelial cell inflammation [10,22]. In multisystem inflammatory syndrome, cardiac dysfunction instead appears to be caused by interleukin-6, which acts as a myocardial depressor in the context of a cytokine storm [22,23].
Some have proposed that MIS-A has clinical features closer to toxic shock syndrome [9]. Toxic shock syndrome is a systemic inflammatory reaction caused by superantigenic toxins released by certain strains of Staphylococcus aureus and Streptococcus pyogenes [12,24]. Superantigens trigger cytokine production through widespread activation of T cells [25]. Toxic shock syndrome is more prevalent in females, likely due to extraneous sources of infection [24]. Compared with toxic shock syndrome, women with MIS-A in our study had a greater risk of respiratory complications, intubation, and death, but had a similar risk of shock, renal failure, and heart failure. Because MIS-A and toxic shock syndrome both generate a cytokine storm, it has been suggested that the SARS-CoV-2 spike protein possesses superantigen properties [12,25,26]. The common presence of a cytokine storm could explain why MIS-A and toxic shock syndrome are both associated with shock and multiorgan failure [9,24], although our data suggest that MIS-A is more life-threatening.
Less is known about prior medical risk factors for MIS-A compared with other inflammatory conditions. One study found that MIS-A and acute Covid-19 infection did not differ significantly in the prevalence of comorbidities at admission, including cancer, hypertension, diabetes mellitus, obesity, chronic obstructive pulmonary disease, chronic kidney disease, and heart failure [7]. In our data, prior medical history was not more strongly associated with MIS-A than Kawasaki disease, toxic shock syndrome, or other Covid-19 complications, suggesting that risk factors for these conditions may be similar. Nevertheless, patients with a history of asthma, allergic disorders, and cancer had a slightly higher risk of MIS-A than other inflammatory disorders. Patients with allergies have a tendency for inadequate immune regulation [27]. Allergic disorders such as asthma are usually associated with a T-helper 2 response, but can be exacerbated by interleukin-6 [28]. In cancer, tumor cells often express interleukin-6, and patients with elevated concentrations of this cytokine typically have a poorer prognosis [29]. Interleukin-6 levels are frequently elevated in patients with multisystem inflammatory syndrome [22].
This study has a number of limitations. Medical administrative data are subject to coding errors. A working definition for MIS-A was not published until later in the pandemic [5], thus misclassification may have been greater early on. Nevertheless, validation data suggested that sensitivity and specificity for MIS-C was high even at the start of the pandemic [3]. The dataset does not include covariates such as ethnicity, hence there may be residual confounding. Data on previous history of Covid-19 infection were not available, but reinfections were rare. We were able to identify prior medical hospitalizations, not underlying medical conditions that never resulted in hospitalization. We could not account for individuals treated in outpatient settings. We did not examine the long-term outcomes of MIS-A, Kawasaki disease, toxic shock syndrome, and other Covid-19 complications. MIS-A is a heterogeneous condition that shares inflammatory features with other conditions in this study. How MIS-A compared with infectious disorders such as sepsis or other acute respiratory distress syndrome remains to be investigated. As the sample size was small, we were limited by low statistical power for some comparisons. We did not have access to laboratory data, and could not examine biomarker profiles or confirm that patients with MIS-A had a positive Covid-19 test prior to diagnosis. Generalizability of our findings requires further investigation, although the data encompassed a culturally diverse population that was covered by universal health insurance. The elderly were eligible for vaccination towards the end of the study.
5 Conclusions
In this observational study, adult patients hospitalized with MIS-A had a greater likelihood of mortality compared with Kawasaki disease, toxic shock syndrome, and other Covid-19 complications. Women with MIS-A were more likely to experience respiratory complications, while men had a greater prevalence of cardiovascular complications. Prior medical history was not disproportionately associated with the development of MIS-A compared with other inflammatory diseases, although patients with MIS-A had a slightly higher prevalence of allergy and cancer. More research will be needed to confirm if there are sex-based differences in presentation and identify biomarker profiles that can accurately detect MIS-A. Overall, this preliminary study suggests that MIS-A has a different clinical presentation than Kawasaki disease, toxic shock syndrome, and other Covid-19 complications, but a better understanding of risk factors for MIS-A is needed.
Author contributions:
Nathalie Auger: Conceptualization, Methodology, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Funding acquisition. Philippe Bégin: Conceptualization, Writing - Review & Editing. Harb Kang: Conceptualization, Writing - Review & Editing. Ernest Lo: Conceptualization, Methodology, Writing - Review & Editing. Émilie Brousseau: Conceptualization, Formal analysis, Visualization, Writing - Original Draft. Jessica Healy-Profitós: Conceptualization, Writing - Review & Editing. Brian J. Potter: Conceptualization, Writing - Review & Editing.
Funding
This work was supported by the 10.13039/501100000024 Canadian Institutes of Health Research [grant number PUU-177957]; and the 10.13039/100008240 Fonds de recherche du Québec-Santé [grant number 296785], both awarded to N.A.
Declaration of competing interest
N.A. received funding from the Canadian Institutes of Health Research and the Fonds de recherche du Québec-Santé for this work. The remaining authors have no conflicts of interest to declare.
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.rmed.2022.107084.
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13 Godfred-Cato S. Abrams J.Y. Balachandran N. Jaggi P. Jones K. Rostad C.A. Distinguishing multisystem inflammatory syndrome in children from COVID-19, Kawasaki disease and toxic shock syndrome Pediatr. Infect. Dis. J. 41 2022 315 323 35093995
14 Ministry of Health and Social Services Med-Echo System Normative Framework - Maintenance and Use of Data for the Study of Hospital Clientele 2021 [Internet]. Quebec: Government of Quebec https://publications.msss.gouv.qc.ca/msss/fichiers/2000/00-601.pdf
15 Centers for Disease Control and Prevention Multisystem inflammatory syndrome in adults (MIS-A) case definition information for healthcare providers [Internet]. Centers for Disease Control and Prevention [cited 2022 Mar 2];Available from: https://www.cdc.gov/mis/mis-a/hcp.html 2021
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| 0 | PMC9733296 | NO-CC CODE | 2022-12-15 23:18:07 | no | Respir Med. 2023 Jan 9; 206:107084 | utf-8 | Respir Med | 2,022 | 10.1016/j.rmed.2022.107084 | oa_other |
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Diabet Epidemiol Manag
Diabet Epidemiol Manag
Diabetes Epidemiology and Management
2666-9706
The Authors. Published by Elsevier Masson SAS.
S2666-9706(22)00073-7
10.1016/j.deman.2022.100123
100123
Original Article
Dexamethasone Use and Insulin Requirements in Coronovirus-19 (COVID-19) Infection Stratified by Hemoglobin A1c
Gordon Caitlyn 1⁎
Barsoum Barbara 1
McKeon Lauren 1
Brooks Danielle 2
Schulman-Rosenbaum Rifka 2
1 Department of Pharmacy, Long Island Jewish Medical Center; 270-05 76th Ave, New Hyde Park, NY 11040
2 Division of Endocrinology, Long Island Jewish Medical Center; 270-05 76th Ave, New Hyde Park, NY 11040
⁎ Corresponding author: Postal Address: Long Island Jewish Medical Center – Department of Pharmacy, 270-05 76th Ave, New Hyde Park, NY 11040, Telephone Number: 718-470-5005 ext.69247, Fax Number: 718-470-5641
9 12 2022
9 12 2022
1001231 12 2022
7 12 2022
© 2022 The Authors. 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.
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pmc1 Introduction
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19), has led to over 600 million confirmed cases and over 6 million deaths worldwide.[1,2] Dexamethasone 6 mG daily is the mainstay of therapy for patients hospitalized with COVID-19 infection requiring supplemental oxygen or invasive mechanical ventilation. The duration of treatment is 10 days or until hospital discharge, whichever is sooner.[3], [4], [5].
Steroid-induced hyperglycemia occurs in 50-70% of hospitalized patients without known diabetes.[6], [7], [8] Most of these patients experience hyperglycemia within 48 hours of starting high potency steroids.[6] Rhou et al. reports dexamethasone-induced hyperglycemia occurring at a rate of 47.6% in COVID-19 patients without diabetes peaking 7-9 hours after exposure.[9] High-risk individuals for steroid-induced hyperglycemia are recommended to undergo BG monitoring for at least 24 to 48 hours after initiating steroids. If BG remains > 140 mG/dL, then continued monitoring and therapy should be considered.[10] Besides the potency and duration of steroid therapy, other risk factors for steroid-induced hyperglycemia include pre-diabetes, family history of diabetes, gestational diabetes, older age, length of hospital stay, and elevated c-reactive protein.[6,8,9,11] Inpatient hyperglycemia in patients with and without diabetes can increase mortality in both critically ill and non-critically ill patients, as well as lead to increased length of stay or admission to the intensive care unit.[12] Inpatient steroid-induced hyperglycemia regardless of diabetes has shown increased mortality, cardiovascular events, and infections, as well as adverse effects from hypoglycemia.[10,13]
Elevated BG in COVID-19 is associated with disease progression and increased mortality and can occur in patients with and without pre-existing diabetes.[14], [15], [16], [17] Besides dexamethasone-induced impaired glucose metabolism, characteristics unique to COVID-19, including effects on the pancreatic beta cells, cytokine storm, impaired insulin secretion, and insulin resistance, distinguish hyperglycemia in COVID-19.[17] One retrospective study reported higher mortality rates with worse glycemic control from dexamethasone-induced hyperglycemia in COVID-19.[18] Additionally, Farzadfar et al. reported higher mortality and higher cumulative insulin requirements to resolve DKA in type 2 diabetes in COVID-19 patients versus non-COVID-19 patients.[19]
Clinicians should initiate insulin in hospitalized patients with steroid-induced hyperglycemia though data regarding ideal insulin dosing in COVID-19 infection is largely based on expert opinion and reactionary to blood glucose values [10,[20], [21], [22], [23], [24], [25], [26]]; only one algorithm to our knowledge has been tested and published in Saudi Arabia.[18] While randomized controlled trials have studied steroid hyperglycemia protocols in other patient populations, such data for COVID-19 is lacking.[22] Furthermore, this study aims to report weight-based daily insulin requirements and mean BG levels stratified by HbA1c in patients treated with dexamethasone for COVID-19 infection.
2 Materials and Methods
A retrospective, single-arm study was performed evaluating patients being treated with dexamethasone 6 mG daily for COVID-19 infection during November 2020 at a tertiary care teaching hospital located in Queens, NY; this period was the beginning of the second COVID-19 wave with the original strain. Expedited approval was granted by the Northwell Health® Institutional Review Board. Patients were included if they were 18 years of age or older, had a diagnosis of COVID-19 infection confirmed by positive polymerase chain reaction, received at least one dose of dexamethasone 6 mG, and were admitted to the hospital. Patients were excluded if they were on chronic steroids at home for any indication or if they did not have a HbA1c. Insulin prescribing was based on standard inpatient diabetes practice and varied by provider (no protocol was in place to follow). After patients had been identified through a generated list from the electronic medical record, a retrospective chart review was conducted to collect pertinent data. Data collected included pertinent baseline demographics, baseline HbA1c, diabetes classification, home medications for diabetes, and oxygen status prior to dexamethasone use. Descriptive statistics were computed for all variables.
During inpatient dexamethasone therapy, mean daily BG and median and mean daily insulin (units and units/kG/day) were collected and stratified by HbA1c. Dexamethasone's effects on BG were evaluated including time to hyperglycemia (BG > 180 mG/dL) and time to first insulin adjustment. The safety endpoint included hypoglycemia upon discontinuation of dexamethasone, defined as BG < 70 mG/dL.
3 Results
A total of 75 patients were screened and 30 were excluded due to a lack of HbA1c. The total prevalence of hyperglycemia for all COVID-19 patients on dexamethasone during this time frame was 54.7% (41/75). Forty-five patients were included who received dexamethasone 6 mG daily for COVID-19 infection during the inclusion period. Twenty-two patients (48.9%) did not have a history of diabetes and the remaining 23 patients had type 2 diabetes. For patients with pre-existing diabetes, 39.1% were using insulin therapies prior to admission (with or without non-insulin medications). Eighty-four percent of patients were on oxygen therapy at the time of dexamethasone initiation. Fourteen patients (31.1%) eventually required mechanical ventilation and thirteen patients (28.9%) expired. Median length of inpatient stay was 10 days (8, 21). Additional patient demographics and baseline characteristics are listed in Table 1 .Table 1 Demographics and Baseline Characteristics
Table 1:Variable Total (N = 45)
Mean age, years (SD) 63.6 ± 14.9
Male sex, n (%) 33 (73.3)
Race, n (%)
White
Black
Asian
Other or Multiracial
Unavailable, Unknown
11 (24.4)
5 (11.1)
13 (28.9)
15 (33.3)
1 (2.2)
Ethnicity, n (%)
Hispanic or Latino
8 (17.8)
Mean weight, kG (SD) 82.2 ± 21.6
Median length of stay, days (IQR) 10 (8, 21)
Diabetes (DM), n (%)
Type 1 DM
Type 2 DM
No DM history
0 (0)
23 (51.1)
22 (48.9)
Mean HbA1c, % (SD) 7.0 ± 1.4
Home medication(s) for DM, n (%)
Insulin
Non-insulin medication(s)
Both (insulin and non-insulin medications)
No medications
4 (17.4)
11 (47.8)
5 (21.7)
3 (13.0)
Respiratory support prior to dexamethasone, n (%)
No oxygen
Nasal cannula
Non-rebreather
High flow nasal cannula
BIPAP
Mechanical ventilation
7 (15.6)
24 (53.3)
11 (24.4)
1 (2.2)
1 (2.2)
1 (2.2)
Route of dexamethasone, n (%)
IV
Oral
41 (91.1)
4 (8.9)
Mean duration of dexamethasone, days (SD) 10 ± 4.7
Abbreviations: BIPAP, bilevel positive airway pressure; DM, diabetes; HbA1c, hemoglobin A1c; IQR, interquartile range; IV, intravenous; kG, kilogram; SD, standard deviation
Thirty-seven out of 45 patients (82.2%) with a HbA1c experienced inpatient hyperglycemia; eight patients without hyperglycemia had a mean HbA1c of 5.96%. Ninety-six percent (22/23) of patients with a history of diabetes had inpatient hyperglycemia; one patient with diabetes without hyperglycemia had a HbA1c of 5.1%. All patients with a HbA1c ≥ 7% (n = 16) and 72.4% of patients with a HbA1c between 5 and 6.9% (n = 21) developed hyperglycemia. Mean number of days until hyperglycemia was 0.6 ± 0.8 for patients with an HbA1c ≥ 7% and 2.14 ± 2.9 for patients with a HbA1c < 7%. The mean number of days to new insulin or change in insulin dosing was 2.6 ± 3.0 for patients with a HbA1c ≥ 7% and 4.1 ± 3.0 for patients with a HbA1c < 7%. Higher HbA1c was associated with faster onset and longer duration of hyperglycemia (Figure 1 ).Figure 1 Mean Blood Glucose While on Dexamethasone Abbreviations: HbA1c, hemoglobin A1c
Figure 1:
One out of five patients with a HbA1c between 5 and 5.9% experienced hyperglycemia starting on day 3 and lasting 2 days. This patient required an average of 2.5 units of insulin daily. Eighty-three percent of patients with an HbA1c 6 to 6.9% experienced hyperglycemia only on day 1 of dexamethasone therapy (Figure 1). Median daily insulin was 0 (0, 15.6) or 0.03 unit/kG/day (0, 0.32). On day 10 of therapy (n = 11), median insulin required was 0.07 unit/kG/day (0.01, 0.31) (Figures 1, 2 , and 3 ).Figure 2 Mean Daily Insulin Requirements Abbreviations: HbA1c, hemoglobin A1c
Figure 2:
Figure 3 Median Weight-Based Insulin Requirements an = 11; bn = 5; cn = 2; dn = 1 Abbreviations: HbA1c, hemoglobin A1c; IQR, interquartile range; kG, kilogram; SD, standard deviation
Figure 3:
For 9 patients with an HbA1c between 7 to 7.9%, hyperglycemia on average began on day 2 of dexamethasone treatment and BG remained above goal through day 10. Median daily insulin was 8 units (5, 28) or 0.1 units/kG/day (0.06, 0.36). On day 10 of dexamethasone therapy, when BG was closest to goal, median insulin required was 0.59 units/kG/day (0.11, 0.75) (n = 5).
In four patients with an HbA1c 8 to 8.9%, hyperglycemia started prior to dexamethasone initiation and remained elevated until day 10 of dexamethasone treatment. Median daily insulin required was 34 units (24, 44) or 0.66 units/kG/day (0.39, 0.69). On day 10 of therapy, median insulin required was 1.15 units/kG/day (0.95, 1.35) (n=2). Lastly, for three patients with a HbA1c ≥ 9%, BG was above goal for 8 out of 10 days of dexamethasone treatment. Median daily insulin required was 90 units (62, 105) or 0.72 units/kG/day (0.63, 0.78). On day 10 of therapy, when BG was at goal, median insulin required was 1.14 unit/kG/day (n = 1) (Figures 1, 2, and 3).
Of the 24 patients that completed 10 days of inpatient dexamethasone treatment, 6 patients (25%) experienced hypoglycemia upon discontinuation of dexamethasone. The median time to hypoglycemia was 96 hours after dexamethasone discontinuation (range: 1 – 216 hours).
4 Discussion
This study contains novel information related to BG trends and insulin requirements in relation to HbA1c for patients being treated with dexamethasone for COVID-19 infection. HbA1c is an important lab value for anyone started on dexamethasone for COVID-19 given the prevalence of hyperglycemia in patients with and without a history of diabetes (54.7% of the total screened population). Rhou et al. had similar findings where 47.6% of patients without diabetes experienced dexamethasone-induced hyperglycemia.[9] This study showed a clear trend in increasing BG and insulin requirements for higher HbA1c.
Insulin requirements are reported as median (IQR) (days 1 through 10) and median (IQR) on day 10 due to wide variability, particularly at lower HbA1c values (Figure 3). Day 10 requirements may be more accurate than days 1 – 10 combined since BG control was achieved in most of the cohorts by day 10, meaning earlier days of therapy may have underdosed patients leading to hyperglycemia. One limitation of day 10 requirements was that some patients were discharged prior to day 10 of therapy so it includes a smaller sample of patients.
In patients with a HbA1c < 6%, monitoring of BG via point-of-care testing (POCT) and utilization of correction scale insulin should be considered initially until hyperglycemia can be ruled out. Most of these patients did not have diabetes or hyperglycemia; one patient with hyperglycemia was managed with minimal insulin via correction scale. In patients considered to have pre-diabetes or controlled diabetes (HbA1c between 6 and 6.9%), insulin requirements varied greatly as evidenced by the large interquartile range around the median. Median insulin required on day 10 for these patients was 0.07 units/kG/day (0.01, 0.31) (n = 11) meaning some patients required correction scale alone whereas others required basal ± bolus insulin. This patient population should be monitored closely inpatient as they may require insulin support beyond rapid-acting correction scale (e.g., basal insulin plus correction scale) to treat hyperglycemia. One patient on insulin prior to admission had insulin requirements above 1 unit/kG/day; future studies should calculate the percent increase from home to inpatient doses of insulin while on dexamethasone to achieve normoglycemia.
Similarly, patients with a HbA1c between 7 and 7.9% had variable insulin requirements as evidenced by the large interquartile range (Figure 3). Since BG control was not achieved until dexamethasone was completed, median day 10 requirements may be a more accurate representation of how to dose this cohort which was 0.59 units/kG/day (0.11, 0.75) (n = 5) though a large interquartile range is present.
The greatest daily insulin requirements were in patients with an HbA1c ≥ 8%. For patients with an HbA1c between 8 to 8.9%, median daily weight-based insulin was 0.66 units/kG/day (0.39, 0.69), while requirements on day 10 were 1.15 unit/kG (0.95, 1.35) (n = 2). For patients with an HbA1c ≥ 9%, median daily weight-based insulin was 0.72 units/kG/day (0.63, 0.78) and control of hyperglycemia was only achieved on days 8 and 10 of therapy. Only one patient completed 10 days of therapy with an HbA1c ≥ 9% and their day 10 insulin requirement was 1.14 unit/kG/day. In Figure 3, the trend was increasing weight-based requirements during the 10-day course, more pronounced the higher the HbA1c; on day 8, for HbA1c 8 – 8.9% and ≥ 9%, there is a significant decline in the weight-based requirement which can be explained by small sample size and early discharges (meaning the calculated weight-based dose only accounted for part of the day's insulin). Three out of seven patients with an HbA1c ≥ 8% were on non-insulin therapies at home and one was not on any diabetes medication at home. Although this study did not look at home insulin requirements, this could be an area for future studies.
While this sample size is small, our study highlights that the combination of uncontrolled diabetes, COVID-19, and dexamethasone can temporarily cause high BG and insulin requirements. In patients with a HbA1c ≥ 7%, more than a week was needed to achieve glucose control, which may have been due to a lack of provider familiarity with insulin requirements to achieve normoglycemia while treating COVID-19 with dexamethasone. As a result, our health system has since adopted a hyperglycemia management guideline for patients receiving dexamethasone for COVID-19 with directions on how to start and titrate insulin both while on the medication and when it is completed. Currently, the only published insulin dosing protocol for COVID-19 that was tested against a control group reported a decrease in mortality with better glycemic control; however, they did not disclose patient HbA1c or insulin requirements to achieve normoglycemia.[18] This information is vital given our study's findings. Additionally, since it was limited to one healthcare center in Saudi Arabia, the generalizability may be limited. To our knowledge, ours is the first study to present weight-based insulin requirements stratified by HbA1c. This data could be used to develop future studies and inpatient protocols given the limited information available regarding ideal insulin dosing in this patient population.[20], [21], [22], [23], [24], [25], [26].
Hypoglycemia was a common side effect seen in patients after discontinuing dexamethasone. Five of the six patients with hypoglycemia required insulin for hyperglycemia associated with dexamethasone. In Figure 1, average BG was well-controlled as soon as one day after discontinuing dexamethasone in all patients, regardless of HbA1c level at initiation. It could be that providers did not decrease insulin requirements soon enough after discontinuing dexamethasone. Additionally, the long duration of dexamethasone means the medication may still have an effect after discontinuation which could also be why the median time to hypoglycemia was 96 hours.[24] Some outliers exist with hypoglycemia occurring 6 and 9 days after dexamethasone discontinuation; it is likely, though unclear, that insulin dosing and other factors contributed to these hypoglycemia events.
Being that this study was retrospective and observational in nature, limitations included lack of documentation, single-arm, small sample size (based on feasibility of the study authors), and other potential confounders (e.g., other hyperglycemia-causing medications). Thirty patients did not have a HbA1c or POCT, so serum glucose values were only available on morning labs. Since steroids have a post-prandial effect on blood glucose, hyperglycemia may have been underrecognized for these patients. Baseline HbA1c and POCT for the first 48 hours should be implemented in all patients on high-potency steroids.[10] This study did not break down insulin requirements based on home insulin regimen. Patients who are more insulin resistant at baseline would therefore require higher inpatient insulin doses. It is also possible for patients with controlled HbA1c to have high insulin requirements at baseline which could skew our inpatient insulin data. This study also did not account for abnormalities in renal function which can affect insulin clearance and dosing. Variability in COVID-19 management could have occurred with the addition of travel nurses and redeployed team members unfamiliar with the institution's inpatient glycemic management standard of care, waivers in documentation, and lack of a standardized guideline for insulin titration with dexamethasone. Additionally, this study took place prior to vaccinations and other COVID-19 variants.
5 Conclusion
Dexamethasone 6 mG daily can cause hyperglycemia in patients with COVID-19 and higher HbA1c is associated with increased hyperglycemia and insulin requirements. It is important to start and titrate insulin accordingly to prevent prolonged hyperglycemia. This is the first study to our knowledge that provides insulin requirements based on HbA1c for COVID-19-infected patients on dexamethasone treatment. Application of these results may aid COVID-19 protocols as well as help achieve BG control while patients are treated with dexamethasone, though more large-scale, prospective studies are needed to corroborate this data.
Author Contributions
B.B. collected data; B.B., C.G., L.M., D.B., and R.S.R. designed the study, analyzed data, and reviewed the manuscript.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Declaration of interests
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.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix Supplementary materials
Image, application 1
Acknowledgements
The authors would like to acknowledge Thien-Ly Doan, PharmD for assistance in formulating the study question
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.deman.2022.100123.
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3 The RECOVERY Collaborative Group Dexamethasone in hospitalized patients with covid-19 N Engl J Med 384 8 2021 693 704 10.1056/NEJMoa2021436 32678530
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5 COVID-19 Treatment Guidelines Panel Coronavirus Disease 2019 (COVID-19) Treatment Guidelines National Institutes of Health. Available at June 16, 2021 https://www.covid19treatmentguidelines.nih.gov/ Accessed
6 Fong AC NWah Cheung The high incidence of steroid-induced hyperglycaemia in hospital Diabetes Research and Clinical Practice 99 3 2013 277 280 10.1016/j.diabres.2012.12.023 23298665
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8 Tamez-Pérez HE Quintanilla-Flores DL Rodríguez-Gutiérrez R González-González JG Tamez-Peña AL. Steroid hyperglycemia: Prevalence, early detection and therapeutic recommendations: A narrative review World J Diabetes 6 8 2015 1073 1081 10.4239/wjd.v6.i8.1073 26240704
9 Rhou YJJ Hor A Wang M Dexamethasone-induced hyperglycaemia in COVID-19: Glycaemic profile in patients without diabetes and factors associated with hyperglycaemia Diabetes Research and Clinical Practice 194 2022 110151 10.1016/j.diabres.2022.110151
10 Korytkowski MT Muniyappa R Antinori-Lent K Management of Hyperglycemia in Hospitalized Adult Patients in Non-Critical Care Settings: An Endocrine Society Clinical Practice Guideline The Journal of Clinical Endocrinology & Metabolism 107 2022 2101 2128 10.1210/clinem/dgac278 35690958
11 Burt MG Roberts GW Aguilar-Loza NR Frith P Stranks SN. Continuous monitoring of circadian glycemic patterns in patients receiving prednisolone for copd The Journal of Clinical Endocrinology & Metabolism 96 6 2011 1789 1796 10.1210/jc.2010-2729 21411550
12 Umpierrez G.E. Isaacs S.D. Bazargan N. Hyperglycemia: an independent marker of in-hospital mortality in patients with undiagnosed diabetes J Clin Endocrinol Metab 87 2002 978 982 10.1210/jcem.87.3.8341 11889147
13 Delfs N Struja T Gafner S Outcomes of hospitalized patients with glucocorticoid-induced hyperglycemia—a retrospective analysis JCM 9 12 2020 4079 33348743
14 Wang W Shen M Tao Y Elevated glucose level leads to rapid COVID-19 progression and high fatality BMC Pulm Med 21 1 2021 64 10.1186/s12890-021-01413-w 33627118
15 Singh AK Gupta R Ghosh A Misra A. Diabetes in COVID-19: Prevalence, pathophysiology, prognosis and practical considerations Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14 4 2020 303 310 10.1016/j.dsx.2020.04.004
16 Bode B Garrett V Messler J Glycemic Characteristics and Clinical Outcomes of COVID-19 Patients Hospitalized in the United States J Diabetes Sci Technol 14 4 2020 813 821 10.1177/1932296820924469 32389027
17 Rayman G Lumb AN Kennon B Dexamethasone therapy in COVID-19 patients: implications and guidance for the management of blood glucose in people with and without diabetes Diabet Med 38 1 2021 10.1111/dme.14378
18 Asiri AA Alguwaihes AM Jammah AA Assessment of the effectiveness of a protocol to manage dexamethasone-induced hyperglycemia among hospitalized patients with covid-19 Endocrine Practice 27 12 2021 1232 1241 10.1016/j.eprac.2021.07.016 34358694
19 Farzadfar D Gordon CA Falsetta KP Assessment of insulin infusion requirements in covid-19-infected patients with diabetic ketoacidosis Endocrine Practice 28 8 2022 787 794 10.1016/j.eprac.2022.05.006 35623591
20 Brooks D Levy CJ. Overview and Management of Glucocorticoid-Induced Hyperglycemia in Pulmonary Diseases: Insight into the COVID-19 Pandemic Int J Diabetes Metabolic Synd 1 1 2021 1 13
21 Gianchandani R Esfandiari NH Ang L Managing Hyperglycemia in the COVID-19 Inflammatory Storm Diabetes 69 10 2020 2048 2053 10.2337/dbi20-0022 32778570
22 Brooks D Schulman-Rosenbaum R Griff M Lester J Low Wang CC Glucocorticoid-induced hyperglycemia including dexamethasone-associated hyperglycemia in covid-19 infection: a systematic review Endocrine Practice 28 11 2022 1166 1177 10.1016/j.eprac.2022.07.014 35940469
23 Rayman G Lumb A Kennon B New Guidance on Managing Inpatient Hyperglycaemia during the COVID-19 Pandemic Diabetic Medicine 37 7 2020 1210 1213 10.1111/dme.14327 32418245
24 Pasquel FJ Umpierrez GE. Individualizing Inpatient Diabetes Management During the Coronavirus Disease 2019 Pandemic J Diabetes Sci Technol 14 4 2020 705 707 10.1177/1932296820923045 32370606
25 Bellido V Pérez A. Inpatient hyperglycemia management and covid-19 Diabetes Ther 12 1 2021 121 132 10.1007/s13300-020-00966-z 33278017
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| 36514311 | PMC9733297 | NO-CC CODE | 2022-12-14 23:36:15 | no | Diabet Epidemiol Manag. 2022 Dec 9;:100123 | utf-8 | Diabet Epidemiol Manag | 2,022 | 10.1016/j.deman.2022.100123 | oa_other |
==== Front
Clin Microbiol Infect
Clin Microbiol Infect
Clinical Microbiology and Infection
1198-743X
1469-0691
Published by Elsevier Ltd on behalf of European Society of Clinical Microbiology and Infectious Diseases.
S1198-743X(22)00610-3
10.1016/j.cmi.2022.12.006
Systematic Review
Antibiotic resistance associated with the COVID-19 pandemic: a systematic review and meta-analysis
Langford B.J. 1∗
Soucy J.-P.R. 2
Leung V. 13
So M. 4
Kwan A.T.H. MSc 5
Portnoff J.S. 6
Bertagnolio S. 7
Raybardhan S. 8
MacFadden D. 9
Daneman N. 10
1 Public Health Ontario
2 Dalla Lana School of Public Health, University of Toronto
3 Public Health Ontario, Toronto East Health Network
4 University Health Network, University of Toronto
5 Faculty of Medicine, University of Ottawa
6 HBSc, Faculty of Medicine, University of Queensland
7 Antimicrobial Resistance Division, World Health Organization, Geneva, Switzerland
8 North York General Hospital
9 The Ottawa Hospital, University of Ottawa
10 Public Health Ontario, Sunnybrook Health Sciences Centre, University of Toronto
∗ Corresponding author:
9 12 2022
9 12 2022
1 9 2022
29 11 2022
2 12 2022
© 2022 Published by Elsevier Ltd on behalf of European Society of Clinical Microbiology and Infectious Diseases.
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
COVID-19 and antimicrobial resistance (AMR) are two intersecting global public health crises.
Objective
We aim to describe the impact of the COVID-19 pandemic on AMR across healthcare settings.
Data Source
A search was conducted in December 2021 in World Health Organization’s COVID-19 Research Database with forward citation searching up to June 2022.
Study Eligibility
Studies evaluating the impact of COVID-19 on AMR in any population were included and influencing factors were extracted. Reporting of enhanced infection prevention and control (IPAC) and/or antimicrobial stewardship programs (ASPs) were noted.
Methods
Pooling was done separately for Gram-negative and Gram-positive organisms. Random effects meta-analysis was performed.
Results
Of 6036 studies screened, 28 were included and 23 provided sufficient data for meta-analysis. The majority of studies focused on hospital settings (n=25, 89%). The COVID-19 pandemic was not associated with a change in the incidence density (IRR 0.99, 95% CI: 0.67 to 1.47) or proportion (RR 0.91, 95% CI: 0.55 to 1.49) of MRSA or VRE cases. A non-statistically significant increase was noted for resistant Gram-negatives (i.e., ESBL, CRE, MDR or carbapenem-resistant Pseudomonas aeruginosa or Acinetobacter baumannii, IRR 1.64, 95% CI: 0.92 to 2.92; RR 1.08, 95% CI: 0.91 to 1.29). The absence of reported enhanced IPAC and/or ASP initiatives was associated with an increase in Gram-negative AMR (RR 1.11, 95%CI: 1.03 to 1.20). However, a test for subgroup differences showed no statistically significant difference between the presence and absence of these initiatives (P=0.40).
Conclusion
The COVID-19 pandemic could have hastened the emergence and transmission of AMR, particularly for Gram-negative organisms in hospital settings. But there is considerable heterogeneity in both the AMR metrics utilized and the rate of resistance reported across studies. These findings reinforce the need for strengthened infection prevention, antimicrobial stewardship, and AMR surveillance in the context of the COVID-19 pandemic.
PROSPERO registration
CRD42022325831.
Graphical abstract
Image 1
Editor: Mical Paul
==== Body
pmcData and code are available at: https://github.com/jeanpaulrsoucy/covid-19-amr-meta-analysis;
Background
High antibiotic use in patients with COVID-19 threatens to contribute to the antimicrobial resistance (AMR) crisis. Although antibiotics do not treat COVID-19, they are commonly used because of initial diagnostic uncertainty in patients presenting with respiratory illness, and of concern for bacterial co-infection or secondary infection in those with confirmed COVID-19. In previous rapid reviews, we found high antibiotic prescribing (approximately 75%) to patients with COVID-19 despite the relatively low bacterial infection rates, particularly in patients outside of the ICU setting (<10%).1, 2, 3
Our most recent systematic review identified COVID-19 patients as a potential important reservoir for antimicrobial resistance. Over 60% of patients with COVID-19 who had a bacterial infection carried a highly resistant organism.4 Due to person-to-person transmission of organisms, particularly in healthcare settings, this presents a threat to the broader population beyond those with COVID-19.
While substantial inappropriate antibiotic prescribing has occurred in patients with COVID-19, antibiotic use for other infectious syndromes has declined early in the pandemic, particularly in community settings.5 , 6 This could potentially be due to the attenuation of transmission of other viral and bacterial pathogens due to public health measures to contain COVID-19, including physical distancing and masking. Enhanced infection prevention and control activities in healthcare settings could further mitigate the impact on AMR.7 Given potentially opposing effects, it is unclear how selection of AMR in bacteria has occurred across populations during the pandemic. Emerging data from the United States Centers for Disease Control and Prevention suggests the pandemic has resulted in rising rates of AMR, including carbapenem-resistant Acinetobacter and extended spectrum beta-lactamase producing Enterobacterales.8
While we have reported that antimicrobial resistance is high in individual patients with COVID-19 and bacterial infection, the ecological impact of the pandemic on AMR at the population level is not yet well-described. In this analysis, we present the findings of a systematic review and meta-analysis describing the impact of the COVID-19 pandemic on AMR across healthcare settings.
Methodology
Searches
We performed a comprehensive search of the World Health Organization (WHO) COVID-19 Research Database for published literature in any language from January 1, 2019 to December 1, 2021. The WHO COVID-19 Research Database is a comprehensive multilingual source of COVID-19 literature updated weekly that includes citations from Medline, Scopus, CINAHL, ProQuest Central, Embase, and Global Index Medicus.9 The search strategy was structured to include co-infection or secondary infection terms and bacterial infection terms which were applied to the COVID-19 literature in the database. The full search strategy is available in the supplement. Forward citation searching was performed in Google Scholar to capture more recent publications up to June 2022.10
Study Eligibility
All studies in inpatient and outpatient settings were eligible for inclusion. The following inclusion and exclusion criteria were applied:
Inclusion Criteria
1. Study provides data on AMR before (before January 2020, or as identified by authors) vs. during the COVID-19 pandemic (January 2020 or later, or as identified by authors) in a specific healthcare setting.
2. AMR is reported as 1) incidence density rate (e.g., rate per 1000 patient days or per patient population), and/or 2) effect measure (e.g., risk, odds, rate ratio) of antimicrobial resistance, and/or 3) prevalence of antimicrobial resistant organisms (e.g., methicillin-resistant Staphylococcus aureus (MRSA) out of all Staphylococcus aureus).
AMR includes any of the following pathogens and resistance phenotypes, as defined by study authors: MRSA, vancomycin-resistant enterococci (VRE), carbapenem or multi-drug resistant (MDR) Pseudomonas aeruginosa., carbapenem or MDR Acinetobacter baumannii., extended-spectrum beta-lactamase (ESBL)-producing (or third generation cephalosporin resistant) Enterobacterales, carbapenem-resistant Enterobacterales (CRE).
Exclusion Criteria
1. Reviews, editorials, case studies, case series, letters, pre-print publications, dissertations, poster presentations.
2. Studies including <100 patients.
3. Studies combining bacterial and non-bacterial co-infection as a single metric.
Population
Individuals receiving care in any healthcare setting and in any age group.
Main Outcomes
The main outcome is the incidence of AMR in the population associated with COVID-19, either expressed as an incidence density rate (antibiotic resistant infections per 1000 patient days) or proportion (e.g. proportion of S. aureus that were MRSA, proportion of patient admissions with resistant infection).
Data Screening and Extraction
Records were managed using Covidence bibliographic software. All titles and abstracts were screened by a single author (in our previous review,4 there was substantial reviewer agreement, kappa: 0.66). Full text screening was performed by at least a single author (in the previous review, we determined kappa to be substantial at 0.62 to 0.68). A single review author extracted study characteristics and data according to a pre-defined list of study elements, with a second check by another review author. Study characteristics including design, patient population, and AMR metrics were extracted. We also extracted whether the authors indicated infection prevention and control (IPAC) measures were strengthened during the pandemic and/or whether there was an antimicrobial stewardship program (ASP) in place. This was categorized into two groups: 1) reporting of enhanced IPAC or ASP or 2) reported no enhanced IPAC/ASP OR did not report enhanced IPAC/ASP. These variables were extracted in order to stratify changes in AMR based on potential AMR-mitigating factors.
Risk of Bias Assessment
We used a 10-item validated risk of bias in prevalence studies tool incorporated into data extraction.11
Data Analysis
Findings were summarized descriptively. In studies providing complete numerator and denominator data, incidence rate ratios (IRR) were pooled using a GLMM random-effects meta-analysis and risk ratios (RR) were pooled using Mantel-Haenszel random effects meta-analysis with between-study variance estimated using the Paule-Mandel estimator. Results were presented in forest plots and pooled across Gram-positive and Gram-negative organisms, stratified by the reporting of enhanced IPAC measures and/or ASP. All analyses were carried out using R version 4.1.2 with the packages metafor and meta.
Heterogeneity was assessed using the I2 statistic, with <40% considered low heterogeneity, 30–60% considered moderate heterogeneity, 50–90% considered substantial heterogeneity, and 75–100% considered considerable heterogeneity.12
Results
Of 6036 studies identified via literature search, 28 were eligible for inclusion (18 via full-text screening, 9 via forward citation screening, and 1 expert-identified; Figure 1 ).13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 The most common countries of origin were the United States (n=4), Italy (n=4), and Brazil (n=3). Patient populations studied included all hospitalized patients (n=17), those hospitalized in intensive care units only (n=5), special populations (e.g., oncology, surgery) (n=3), mixed hospitalized and community-dwelling patients (n=2), and community-dwelling patients only (n=1). Studies evaluated a range of both community- and healthcare-acquired infection. Combined healthcare and community-acquired infection or setting of acquisition commonly undistinguished (n=15), followed by only healthcare-associated (n=11), and only community-acquired (n=2). The majority of studies derived resistance data from clinical specimens (n=20), six included both clinical and screening specimens or did not specify the type of specimen, followed by two studies using screening specimens only. Most studies had moderate risk of bias (n=18), followed by low (n=5), and high (n=5) risk of bias (Table 1 ). The most common reasons for downgraded risk of bias included inconsistent or lack of reporting on mode of data collection across study subjects, lack of reporting of case definitions, and lack of reporting of complete numerator and denominator data (Supplementary Table 1).Figure 1 PRISMA Flow Diagram.
Figure 1
Table 1 AMR Associated with COVID-19 Study Characteristics
Table 1Author, Year Country Healthcare Setting Acquisition AMR measure Directionalitya Risk of Bias
MRSA VRE PsA Abau ESBL CRE
Baker MA, 2021 United States All Hospitalized Healthcare-Acquired Incidence per COVID rate ↑ ↑ moderate
Belvisi V, 2021 Italy ICU Only Not specified Point prevalence, incidence density ↑ high
Bentivegna E, 2021 Italy All Hospitalized Healthcare-Acquired Incidence ↓ ↓ high
Castro MG, 2022 Argentina All Hospitalized Both Incidence density ↑ moderate
Chamieh A, 2021 Lebanon All Hospitalized Both Incidence density ↓ ↑ ↓ ↓ moderate
Despotovic A, 2021 Serbia ICU Only Healthcare-Acquired Proportion ↑ ↑ ↑ ↓ ↑ moderate
Evans ME, 2022 United States All Hospitalized Healthcare-Acquired Incidence density ↑ Moderate
Gaspar GG, 2021 Brazil ICU Only Not specified Incidence density ↑ moderate
Gisselo KL, 2022 Denmark All Hospitalized Healthcare-Acquired Incidence ↓ low
Guven DC, 2021 Turkey Palliative oncology Healthcare-Acquired Proportion ↑ ↑ ↑ moderate
Hirabayashi A, 2021 Japan All Hospitalized Both Proportion ↑ ↑ ↑ moderate
Jeon K, 2022 South Korea ICU Only Both Incidence density ↓ ↑ ↓ ↓ ↑ moderate
La Vecchia A, 2022 Italy All Hospitalized Both Proportion ↓ moderate
Lemenand O, 2021 France Community Community-Acquired Proportion ↓ low
Lo S-H, 2020 Taiwan All Hospitalized Not specified Incidence density ↓ ↓ ↑ ↓ moderate
Mares C, 2022 Romania Hospitalized/non-Hosp Both Proportion ↑ ↑ ↑ ↑ moderate
McNeil MJ, 2021 United States All Hospitalized Community-Acquired Incidence ↓ moderate
Micozzi A, 2021 Italy Malignant hematology Both Proportion ↓ low
O'Riordan F, 2022 Ireland All Hospitalized Both Proportion ↓ ↓ ↑ high
Ochoa-Hein E, 2021 Mexico All Hospitalized Healthcare-Acquired Incidence density ↓ ↑ ↓ ↑ ↑ moderate
Polemis M, 2021 Greece All Hospitalized Both Proportion ↑ ↑ ↓ ↑ ↑ moderate
Polly M, 2022 Brazil All Hospitalized Healthcare-Acquired Incidence density ↑ ↓ ↓ ↑ ↑ moderate
Porto APM, 2022 Brazil ICU Only Healthcare-Acquired Incidence density ↑ ↑ ↑ low
Tham N, 2022 Australia Surgical Healthcare-Acquired Incidence ↑ ↑ ↑ moderate
Tizkam HH, 2020 Iraq All Hospitalized Not specified Proportion ↑ ↑ ↑ high
Wardoyo EH, 2021 Indonesia Hospitalized/non-Hosp Not specified Proportion ↓ high
Wee LEI, 2021 Singapore All Hospitalized Both Incidence density ↓ ↑ ↓ low
Weiner-Lastinger LM, 2022 United States All Hospitalized Healthcare-Acquired standardized infection ratio ↑ moderate
a Directionality refers to numerical change in AMR during COVID-19 compared to before the pandemic, MRSA: methicillin-resistant S. aureus, VRE: Vancomycin-resistant Enterococcus, PsA: MDR or carbapenem-resistant Pseudomonas aeruginosa, Abau: MDR or carbapenem-resistant Acinetobacter baumannii, ESBL: extended-spectrum beta-lactamase producing organism, CRE: carbapenem-resistant Enterobacterales.
Measures of Antimicrobial Resistance
Incidence density (e.g., cases of resistant infections per 1000 patient days) was most commonly used to measure a change in AMR associated with COVID-19 (n=11) or proportion of isolates or infections (e.g., percentage of S. aureus cases that were MRSA, n=11), followed by incidence (e.g., cases per admission or discharges, n=5), and other (standardized infection ratio, point prevalence n=2). Study details and AMR metric directionality are provided in Table 1. Of the 28 eligible studies, 23 (82%) provided raw numerator and denominator data to facilitate meta-analysis.
Resistance in Gram Positive Organisms
MRSA
Over 6,848,357 patient days of follow-up, our meta-analysis found that the COVID-19 pandemic was not associated with a change in incidence rate of MRSA (IRR 1.03, 95%CI: 0.65 to 1.62, I2=95%, n=5). Similarly the COVID-19 pandemic was not associated with a change in the proportion of cases that were MRSA (RR 0.91, 95%CI: 0.60 to 1.36, I2=93%, n=7).
VRE
Over 356,056 patient days, meta-analysis shows that the COVID-19 pandemic was not associated with a change in the incidence of VRE (IRR 0.75, 95%CI: 0.49 to 1.15, I2=56%, n=3). Similarly there was no change in the proportion of VRE cases (RR 0.91, 95%CI: 0.30 to 2.79, I2=94%, n=5).
Overall Gram-Positive Resistance and Association with Infection Prevention and Antimicrobial Stewardship Initiatives
When pooling both MRSA and VRE, no association was found between COVID-19 pandemic and the incidence (IRR 0.99, 95%CI: 0.67 to 1.47, I2=91%, n=8) or proportion (RR 0.91, 95%CI: 0.55 to 1.49, I2=92%, n=12) of resistant Gram-positive cases. The reported presence of IPAC or ASP interventions was not associated with a statistically significant difference resistance rates (reporting IPAC/ASP: RR 0.59, 95%CI 0.15 to 2.42, I2=89%, n=4; not reporting IPAC/ASP: RR: 1.15, 95%CI: 0.94 to 1.41, I2=89% n=8, test of subgroup difference P=0.36). See Figure 2 and 3 .Figure 2 COVID-19 Pandemic and Gram-Positive AMR Incidence Rate Ratio
*All included studies reported IPAC/ASP initiatives.
Figure 2
Figure 3 COVID-19 Pandemic and Gram-Positive AMR Risk Ratio and Reported Presence vs. Absence of IPAC/ASP*
*represents proportions of patients (e.g., visits) or organisms in which AMR was identified.
Figure 3
Resistance in Gram-Negative Organisms
Resistant Acinetobacter baumannii. (n=8)
Across 325,847 patient days, there was no association between COVID-19 and the incidence of carbapenem- or multi-drug resistant Acinetobacter spp. (IRR 0.79, 95%CI: 0.30 to 2.07, I2=77%, n=4). However, there was a small increase in the proportion of infections that were resistant Acinetobacter spp. (RR 1.02, 95%CI 1.01 to 1.03, I2=0%, n=2).
Resistant Pseudomonas aeruginosa
Across 1,609,923 patient days, there was no association between COVID-19 and the incidence of resistant Pseudomonas (IRR 1.10, 95%CI 0.91 to 1.30, I2=0%, n=4). Similarly there was no association with the proportion of cases that were resistant (RR 1.02, 95%CI: 0.85 to 1.23, I2=58%, n=6).
(ESBL)-producing (or third generation cephalosporin resistant) Enterobacterales
One study with 87,204 patient days of follow-up found an increased IRR associated with the COVID-19 pandemic (IRR 15.20, 95%CI: 4.90 to 47.14). However the proportion of cases with an ESBL-producing organism was not significantly altered with COVID-19 (RR: 1.10, 95%CI: 0.91 to 1.33, I2=94%, n=8).
CRE
Across 587,047 patient days, there was no significant change detected in the incidence of carbapenem-resistant Enterobacterales (E. coli and Klebsiella spp.) (IRR 2.05, 95%CI: 0.77 to 5.44, I2=95%, n=5). Similarly, there was no identified increase in proportion of cases that were CRE (RR 1.10, 95%CI: 0.61 to 1.99, I2=88%, n=6).
Overall Gram-Negative Resistance and Association with Infection Prevention and Antimicrobial Stewardship
When pooling all resistant Gram-negative organisms, there was a non-statistically significant association between COVID-19 pandemic and the incidence rate (IRR 1.64, 95%CI: 0.92 to 2.92, I2=93%, n=14) as well as the proportion of cases that were resistant (RR 1.08, 95%CI: 0.91 to 1.29, I2=92%, n=22). The lack of reporting of enhanced IPAC and/or ASP was significantly associated with an increase in Gram-negative AMR (RR 1.11, 95%CI: 1.03 to 1.20, I2=88%, n=5), whereas no significant association with AMR was seen in studies that did report such initiatives (RR 0.80, 95%CI: 0.38 to 1.70, I2=90%, n=17). A test for subgroup differences showed no statistically significant difference between the presence and absence of reported enhanced IPAC/ASP interventions when evaluating changes in AMR (P=0.40). Figure 4 and 5 .Figure 4 COVID-19 Pandemic and Gram-Negative AMR Incidence Rate Ratio. *All included studies reported IPAC/ASP initiatives.
Figure 4
Figure 5 COVID-19 Pandemic and Gram-Negative AMR Risk Ratio and Reported Presence vs. Absence of IPAC/ASP*. *represents proportions of patients (e.g., visits) or organisms in which AMR was identified.
Figure 5
Discussion
AMR definitions and reporting in the context of the COVID-19 pandemic is variable with substantial heterogeneity in reported outcomes among studies. We found that although the COVID-19 pandemic was not associated with a change in Gram-positive AMR, Gram-negative resistance appears to have increased (MDR or carbapenem-resistant P. aeruginosa or Acinetobacter spp., ESBL and CRE), particularly in settings where enhanced IPAC and ASP initiatives were not reported.
A recent special report from the US Centers for Disease Control and Prevention (CDC) found a 15% increase in the rate (per discharge or admission) of resistant organisms including carbapenem-resistant Acinetobacter, MRSA, CRE, and ESBL.8 Study location, burden of COVID-19, burden of non-COVID-19 respiratory infections, background epidemiology, and antimicrobial prescribing practices may partially explain the difference between the CDC data and our findings. The apparent increase in incidence of Gram-negative AMR but not Gram-positive AMR suggests antibiotic prescribing may play an important role, given the high use of beta-lactam/beta-lactamase inhibitors and third generation cephalosporins in patients with COVID-19.3 Nevertheless, both the CDC report and this systematic review present a concern that the COVID-19 pandemic may play a role in increasing rates of AMR in the population.
The concern for increasing AMR in the context of COVID-19 has been previously highlighted.41 , 42 Inappropriately high usage of antibiotics in patients with COVID-19 selects for resistant organisms which can potentially be transmitted to the broader population.42 We have previously shown that in the context of COVID-19 co-infection and secondary infections, 38% of organisms and 61% of patients harbour AMR.4 This analysis extends the concern for drug resistance beyond COVID-19 infected patients themselves to document an ecologic impact of the pandemic on AMR.
The relationship between COVID-19 and AMR is complex, as several factors such as improved hand hygiene, personal protective equipment use, and physical distancing may help to reduce the transmission of AMR organisms, at least temporarily while such enhanced measures are in place.7 On the other hand, shortage of medical personnel and personal protective equipment during the pandemic could thwart this effect. Our findings reinforce that infection prevention and control activities are important mitigating factors limiting the growth of AMR associated with COVID-19.
While this study provides a broad global view of AMR in the context of COVID-19 from an ecological perspective, there are several important limitations. There is significant heterogeneity in methodology and AMR outcome measures reported across studies. At least five different AMR metrics were provided (incidence density, incidence per admission/discharge, proportion of infections, standardized infection ratio, point prevalence), which prevents direct comparison and makes meta-analysis challenging. A lack of adjustment for confounding factors raises the possibility that changes in AMR over time may be due to changes in patient populations or other underlying factors. And lack of longitudinal data also limits the ability to account for existing temporal trends in AMR incidence and prevalence, as well as more distal changes that continue to evolve after the pandemic. Differences in regional baseline rates of AMR or epidemiology may also account for the heterogeneity seen, and as such individualized assessment of regional AMR surveillance data is needed. Many studies did not comment on other confounding factors such as the presence or intensity of their infection prevention and control or antimicrobial stewardship program, so in studies reporting these interventions, the association with reduced AMR may represent correlation rather than causation. Several studies only reported a small number of pathogens with AMR, hence there is a risk of selective outcome reporting. It is also important to note that less than 20% of studies had low risk of bias, suggesting that higher quality studies are needed to better understand the impact of COVID-19 on AMR.
Conclusion
The COVID-19 pandemic could play an important role in the emergence and transmission of resistant pathogens, particularly for Gram-negative organisms in hospital settings. There is considerable heterogeneity in both the AMR metrics utilized and the rate of resistance reported across studies. Our findings reinforce not only the need for strengthened infection prevention and antimicrobial stewardship, but also robust and consistent AMR surveillance as part of the pandemic response and recovery.
Transparency Declaration
This study was supported by funding from the World Health Organization.
Conflict of Interest Disclosure
The authors have no conflicts of interest to declare.
SB is staff at WHO. This paper solely reflects the view of the authors and do not necessarily reflect the view of the Organization.
Author Contributions
Concept and design: All authors.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Langford.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Soucy, Langford.
Administrative, technical, or materialsupport: All authors.
This work was previously published as a pre-print:
https://doi.org/10.1101/2022.09.01.22279488.
Appendix A Supplementary data
The following is the Supplementary data to this article:
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.cmi.2022.12.006.
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| 36509377 | PMC9733301 | NO-CC CODE | 2022-12-14 23:36:15 | no | Clin Microbiol Infect. 2022 Dec 9; doi: 10.1016/j.cmi.2022.12.006 | utf-8 | Clin Microbiol Infect | 2,022 | 10.1016/j.cmi.2022.12.006 | oa_other |
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Clin Microbiol Infect
Clin Microbiol Infect
Clinical Microbiology and Infection
1198-743X
1469-0691
Published by Elsevier Ltd on behalf of European Society of Clinical Microbiology and Infectious Diseases.
S1198-743X(22)00607-3
10.1016/j.cmi.2022.12.004
Systematic Review
Immunogenicity of COVID-19 vaccines in solid organ transplant recipients: a systematic review and meta-analysis
Chen Xinpei 13#
Luo De 24#
Mei Bingjie 5
Du Juan 6
Liu Xiangdong 7
Xie Hui 1
Liu Lin 1
Su Song 3∗
Mai Gang 1∗∗
1 Department of Hepatobiliary Surgery, People's Hospital of Deyang City, Deyang, China
2 Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
3 Department of General, Visceral and Vascular Surgery, Jena University Hospital, Jena, Germany
4 Department of Nephrology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
5 Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
6 Department of Clinical Medicine, Southwest Medical University, Luzhou, China
7 Department of Hepatobiliary Surgery, The 4th People's Hospital of Zigong City, Zigong, China
∗ Corresponding author. Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China..
∗∗ Corresponding author. Department of Hepatobiliary Surgery, People's Hospital of Deyang City, Deyang, China..
# These authors have contributed equally to this work.
9 12 2022
9 12 2022
13 8 2022
17 11 2022
1 12 2022
© 2022 Published by Elsevier Ltd on behalf of European Society of Clinical Microbiology and Infectious Diseases.
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
Solid organ transplant (SOT) recipients have increased morbidity and mortality risk from coronavirus disease 2019 (COVID-19).
Objectives
This study aimed to evaluate the immunogenicity of COVID-19 vaccines in SOT recipients.
Data sources
Electronic databases were searched for eligible reports published from December 1, 2019 to May 31, 2022.
Study eligibility criteria
We included reports evaluating the humoral immune response (HIR) or cellular immune response (CIR) rate in SOT recipients after COVID-19 vaccines.
Participants
SOT recipients who received COVID-19 vaccines.
Assessment of risk of bias
We used the Newcastle-Ottawa Scale to assess bias in case-control and cohort studies. For the randomized controlled trials, the Jadad Scale was used.
Methods of data synthesis
We used a random-effects model to calculate the pooled rates of immune response with 95% confidence intervals (CI). We used a risk ratio (RR) with 95% CI for a comparison of immune responses between SOT and healthy controls.
Results
A total of 91 reports involving 11,886 transplant recipients (lung: 655, heart: 539, liver: 1,946 and kidney: 8,746) and 2,125 healthy controls revealed pooled HIR rates after the 1st, 2nd, and 3rd COVID-19 vaccine doses in SOT recipients were 9.5% (95% CI: 7%-11.9%), 43.6% (95% CI: 39.3%-47.8%) and 55.1% (95% CI: 44.7%-65.6%), respectively. For specific organs, the HIR rates were still low after 1st dose vaccination (lung: 4.4%; kidney: 9.4%; heart: 13.2%; liver: 29.5%) and 2nd dose(lung: 28.4%; kidney: 37.6%; heart: 50.3%; liver: 64.5%).
Conclusion
A booster vaccination enhances the immunogenicity of COVID-19 vaccines in SOT, however, a significant share of the recipients still has not built a detectable HIR after the 3rd dose. This finding calls for alternative approaches, including the use of monoclonal antibodies. In addition, lung transplant recipients need urgent booster vaccination to improve the immune response.
Graphical abstract
Image 1
Keywords
COVID-19 vaccines
Immune response
Immunogenicity
meta-analysis
Solid organ transplant
Editor: Dr Andre Kalil
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pmcIntroduction
COVID-19 significantly increases the risk of severe diseases and death in solid organ transplant (SOT) recipients attributed to underlying immunosuppression and concomitant comorbidities[1]. The hospitalization rate and mortality rate of COVID-19 vary 26%∼63%[2, 3] and 13%∼30%[4] in SOT recipients, respectively.
It has been determined that COVID-19 vaccines are the most effective way to control the pandemic[5, 6]. By July 2022, over 10 billion vaccination doses have been administered worldwide. COVID-19 vaccines’ immunogenicity has been well demonstrated in the general population in large-scale phase III trials[[7], [8], [9]]. Unfortunately, initial trials for these vaccines did not include SOT recipients. Recent studies have demonstrated reduced immune response in transplant recipients, however, results among studies vary widely[[10], [11], [12], [13]]. Therefore, it is necessary to integrate these findings to better understand the immunogenicity of the COVID-19 vaccine in these populations. The purpose of this meta-analysis was to assess the immunogenicity of the COVID-19 vaccine in recipients of SOT.
Methods
Overview
This systematic review and meta-analysis was conducted using the Meta-analyses of Observational Studies in Epidemiology[14] guidelines. This report was preregistered and submitted to the PROSPERO (CRD42022311886).
Data Sources and Searches
We performed a search in electronic databases (PubMed, Web of Science, Cochrane Library, and Embase) from December 1, 2019 to May 31, 2022. The search strategy details were shown in Table S1. The following terms were identified: “COVID-19”, “SARS-CoV-2”, “vaccine”, “vaccination”, “solid organ transplant”, “transplantation”, “liver transplant (LIT)”, “kidney transplant (KT)”, “heart transplant (HT)”, “lung transplant (LUT)”, “pancreas transplant”, “immunogenicity”, “seroconversion”, “humoral immune response (HIR)”, and “cellular immune response (CIR)”. Two reviewers independently performed the search, and a third reviewer resolved disagreements.
Study Selection
Articles were included irrespective of the publication format (original papers, comments, abstracts, and letters). There were no language restrictions. We included reports evaluating at least one of the key outcomes. Reports with less than 10 patients were excluded.
Outcomes of interest
The primary outcomes of this study included HIR rates among SOT recipients and the comparison of HIR between SOT recipients and healthy controls. The secondary outcomes included CIR rates and comparisons of CIR between individuals in SOT and healthy controls.
Data extraction & Quality assessment
We screened all eligible studies to extract details of the first author, publication year, study design, gender, age, sample size, vaccine types, organ types, number of doses administered, the time post-transplant, the measure of immunogenicity, the time interval between the 2nd and 3rd dose, and the outcomes of interest. We contacted the authors for the missing data.
The Newcastle-Ottawa Scale (NOS)[15] was used in case-control studies, which was also suitable for the cohort studies. For the randomized controlled trials (RCTs), the Jadad Scale[16] was used. The NOS scale ranges from 0 to 9 and the Jadad scale ranges from 0 to 5, with higher scores indicating a lower risk of bias. Grades of Recommendation, Assessment, Development and Evaluation (GRADE)[17] was conducted to assess the reliability of evidence for the outcomes of interest.
Data Synthesis and Analysis
Stata (v.12 Stata Corp) statistical software was conducted in this meta-analysis. The random-effects model was used to estimate the pooled rates with 95% confidence intervals (CI) of immune response after the COVID-19 vaccine in SOT recipients. We used a risk ratio (RR) with 95% CI for a comparison of immune responses between SOT and healthy controls. We calculated pooled rates and RR using the ‘metaprop’ and ‘metan’ commands in Stata software, respectively. Heterogeneity was defined as mild (I2 = 0–25%), moderate (I2 = 26–75%), or considerable (I2 >75%). Sensitivity analysis by removing one study at a time was conducted to confirm the robustness of outcomes[18]. Subgroup analyses were conducted according to the organ type, type of vaccine, time post-transplant, the time interval between the 2nd and 3rd dose and vaccine dosage (one-, two-, or three-dose). We did not perform subgroup analysis if the number of included reports was less than five.
A mixed-effects meta-regression analysis was conducted to assess the source and magnitude of heterogeneity. The dependent variable may be moderated by the following variables: type of vaccine, time post-transplant, quality of studies, and study sample size. If the number of included studies was more than ten, an assessment of publication bias was performed using Egger's test and funnel plots.
Results
Characteristics of included studies
Our electronic database search identified 2,073 articles. 151 potentially relevant articles were identified for full-text evaluation after removing duplicate articles and critically evaluating abstracts and titles (Figure 1 ). After applying the eligibility criteria, 91[5, 6, [10], [11], [12], [13], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88], [89], [90], [91], [92], [93], [94], [95], [96], [97], [98], [99], [100], [101], [102], [103]] articles were included in the quantitative synthesis, of which two were RCTs. This meta-analysis involved a total of 11,886 transplant recipients (LUT: 655, HT: 539, LIT: 1,946 and, KT: 8,746) and 2,125 healthy controls. The details of the included studies are shown in Table 1 .Figure 1 Flow diagram of the study selection process.
Figure 1
Table 1 Characteristics of the included studies in systematic review and meta-analysis of immune response to COVID-19 vaccines in SOT recipients
Table 1Study Population Sample size Male/Female Age, years mean±SD/median (IQR/Range#) Time post-transplant, years mean±SD/median (IQR/Range#) Type of vaccine Immunogenicity detection method Cutoff value for positivity Detection time mean±SD/median (IQR/Range#) Vaccine
Dose Time interval between 2-3 dose, days mean±SD/median§ (IQR) Immunotherapy drugs/regimen NOS/Jadad
Alejo 2021[20] SOT KT 18 9/9 54(42-73) 4(0.5-21)# BNT162B2 mRNA-1273
JNJ-78436735 ECLIA
ELISA 50 U/mL (ECLIA)
4 AU/mL (ELISA) 2-6 weeks# 2/3/4 67(54-81) NA 4
LIT
HT
Azzi 2021[21] KT 76 45/31 62(52-70) 3.9(1.6-8.7) BNT162B2 mRNA-1273
JNJ-78436738 CLIA Signal-to-cutoff ratios >1 NA 2 NA CNI (98.7%), MPA/MMF (80.2%), Prednisone (100%) 5
Ben-Dov 2022[22] KT 252 168/84 53.5±14.4 4(0.3-49)# BNT162b2 CLIA 59 AU/ml 10-20 d/2-6 weeks# 1/2 NA Tacrolimus(83%), Everolimus (9%), CyA (7%), Sirolimus (1%) 7
HC 71 25/46 43.6±14.3 NA 8-117 days# NA
Ben-Dov 2022[23] KT 118 NA NA NA BNT162b2 CLIA 59 AU/ml NA 3 180§ NA 5
HC 82
Benotmane 2021[19] KT 159 98/61 57.6(49.6-66.1) 5.3(1.9-11.1) mRNA-1273 CLIA 50 AU/mL 28(27-33)days 3 51(48-59) Tacrolimus + MMF/MPA + steroids (84%) 6
Benotmane 2021[24] KT 241 156/87 57.7(49.3-67.6) 6.4(2.9–13) mRNA-1273 CLIA 59 AU/ml 4weeks 1 NA CNI (89.2%), MPA/MMF (79.3%), AZA (2.9%), Belatacept (3.8%), mTORi(14.5%), Steroids (58.9%) 6
Benotmane 2021[25] KT 204 130/74 57.7(49.4–67.5) 6.2(3–12.8) mRNA-1273 CLIA 59 AU/ml 4weeks 2 NA CNI(89.2%), MPA/MMF(78.9%), AZA(2.9%), Belatacept(2.5%), mTORi (13.2%), Steroids (59.8%) 6
Bergman 2021[26] SOT 89 45/44 NA NA BNT162b2 ECLIA 0.80 U/mL 2weeks 2 NA NA 8
HC 90 39/51 35 day
Bertrand 2021[27] KT 45 23/22 63.5±16.3 6.9(0.22-30.2)# BNT162b2 CLIA 59 AU/ml 3/4 weeks 1/2 NA Tacrolimus (53.3%), CyA (17.8%), MMF (82.2%), AZA (8.9%), Everolimus(6.7%), Belatacept (22.2%), Steroids (46.7%) 5
Boyarsky 2021[10] SOT KT 658 267/391 NA NA BNT162B2 mRNA-1273 ECLIA;
ELISA 0.80 U/mL;
1.1 AU/mL NA 2 NA Antimetabolite (71.9%) 6
LIT
HT
LUT
Pan
Boyarsky 2021[28] SOT KT 436 160/266 55.9(41.3-67.4) 6.2(2.7-12.7) BNT162B2 mRNA-1273 ECLIA;
ELISA 0.80 U/mL;
1.1 AU/mL 20(17-24)days 1 NA Antimetabolite (73.4%) 6
LIT
HT
LUT
Bruminhent 2021[29] KT 35 21/14 50(42-54) 4.5(2-9.5) CoronaVac CLIA 50 AU/mL 4/2 weeks 1/2 NA Tacrolimus (63%), CyA (31%), MMF (60%), MPS (37%), Sirolimus (3%), Everolimus (3%), Prednisolone (97%) 7
HC 38 7/31 39(34-42) NA NA
Buchwinkler 2021[30] KT 216 147/69 59.9 (50.7–68.5) NA BNT162B2 mRNA-1273 CLIA 13 AU/ml 4weeks 1/2 NA Tacrolimus (70%), CyA (18%), AZA (15%), MPA (69%), Belatacept (6%), Glucocorticoids (82%), mTORi (5%) 7
Cao 2021[31] SOT LUT 37 10/27 64(50-69) 36(1-365)# BNT162B2 mRNA-1273 CLIA 50 AU/ml 21(19-25)days 2 NA Tacrolimus (81.1%), CyA (13.5%), Everolimus (2.7%), Sirolimus (2.7%) 5
HT
Chavarot 2021[32] KT 62 36/26 63.5(51-72) 3.9(2.1-6.6) BNT162b2 CLIA 50 AU/mL 28(28-33)days 3 NA CNI (3%), Belatacept (100%), Steroids (100%), mTORi (Everolimus) (13%), MPA (71%), AZA (5%) 7
Chavarot 2021[33] KT 101 68/33 64(53-73) 4.9(2.4-8.7) BNT162B2 mRNA-1273 CLIA 50 AU/mL 28 days 1/2 NA CNI (7.9%), Belatacept +MPA(78.2%), Steroids (96%), mTORi(11.9%), AZA (2%) 5
Cholankeril 2021[34] LIT 69 48/21 63(51-68) 3.3(1.7-8.3) BNT162b2 ELISA Titer>20 30-70 days# 2 NA Tacrolimus (90%), MMF(36%), Prednisone(37%) 6
Correia 2022[35] KT 131 85/46 59.3±9.6 9.2±7.2 BNT162b2 mRNA-1273
AZD1222
JNJ-78436735 CLIA 50 AU/mL 20.3±6.2 days 2 NA Antimetabolite(64.1%) 6
Cotugno 2022[36] SOT 34 NA 14.6±7.2 14.7±7.2 BNT162B2 CLIA NA 1weeks 2 NA NA 5
HC 36 NA NA NA NA NA
Crane 2021[37] KT 25 14/11 19(18-20) 5(4-9) mRNA-1273
BNT162b2 CLIA 50 AU/mL 4weeks 2 NA MMF(84%), AZA (12%) 5
Crespo 2021[38] KT 90 55/35 59.7±12.5 NA mRNA-1273
BNT162b2 CLIA 13 AU/ml 4weeks 2 NA Prednisone(91.1%), MPA derivatives(54.4%), Tacrolimus(91.1%), CyA(3.3%), mTORi(24.4%) 7
HC 32 5/27 52.7±10.7 NA
Cucchiari 2021[39] KT 117 79/38 57.62±14.32 1.7(0.8-4.9) mRNA-1273 ELISA Titers>1 2weeks 2 NA Anti-thymocyte Globulins(11.5%), Rituximab(2%), Tacrolimus(84.5%), CyA(3.4%), MPA(62.8%), mTORi(32.4%), Prednisone(79.7%), AZA (2.7%), Belatacept(8.1%), Eculizumab(1.4%) 7
Danthu 2021[40] KT 74 44/30 64.8±11.5 5.5±7.1 BNT162b2 CLIA 13 AU/ml 4weeks 1 NA Antithymocyte serum(35.1%), Basiliximab(64.9%), CNI(91.8%), Belatacept(2.6%),Everolimus(10.8%), Antimetabolite(82.4%), Steroids(45.9%) 7
HC 7 4/3 51.6±6.8 NA NA
Davidov 2021[41] LIT 76 43/33 59±15 7(4-16) BNT162b2 ELISA Titers>1.1 38±24 days 2 NA Prednisone(16%), MMF(21.3%), Everolimus(14.7%) 6
HC 174 86/88 59±13 NA 36±22 days NA
Debska-Slizien 2022[42] KT 142 83/59 54 (43–63) 8 (3.5–15) mRNA-1273
BNT162b2 CLIA 12 AU/mL 2-3 weeks# 2 NA Steroids(8.5%), MMF/MPS(21.1%) 7
HC 36 21/15 48 (45–62) NA NA
Del Bello 2022[43] SOT 396 257/139 59 ± 15 NA BNT162b2 ELISA Titers >1.1 NA 3 59(25-75) NA 5
Devresse 2021[44] KT 90 47/43 60(38-79) 8.5(0.8-36.7) BNT162b2 ECLIA 0.8 U/mL 4weeks 2 NA Tacrolimus+MPA+Steroids(41%),Antimetabolite(76%) 5
Eren Sadioglu 2021[45] KT 85 38/47 46.4 ± 12.5 6.8±5.7 Sinovac ELISA >10 IU/ml 4weeks 2 NA Tacrolimus(88.2%), CyA(5.9%), MMF-MPA(63.6%), AZA(32.9%), mTORi(1.1%), Steroids(95.3%) 7
Erol 2021[46] SOT KT 48 32/16 36.5(1-62)# 8(1-21) Sinovac
BNT162b2 CLIA 50 AU/mL 4/4-6 weeks# 1/2 NA NA 7
LIT
HC 56 NA 37.5(22-52) NA Sinovac
Fernández-Ruiz 2021[47] SOT KT 44 27/17 52.4±11.5 2.3 (1.3–4.8) BNT162b2 ELISA Titers>1.1 4/2 weeks 1/2 NA Tacrolimus(80%), MMF/MPS(80%), Prednisone(60%), mTORi(20%) 7
LIT NA
HC without transplant 28 7/21 43.1±15.1 NA NA
Firket 2021[48] KT 10 5/5 49.7±13.8 10.1±8.8 BNT162b2 CLIA 1 U/mL 2weeks 2 NA anti-thymocyte globulin(21%), Rituximab (50%) 7
HC 10 7/7 51.5±10.5 NA NA
Georgery 2021[49] KT 78 40/36 62 (18–84) 9.7(0.3-50.7) BNT162b2 ECLIA 1 U/mL 4weeks 1 NA Tacrolimus/MPA/Steroids(50%), Tacrolimus/Steroids(24%), Anti-metabolite(31%) 5
Georgery 2021[11] KT 79 38/41 61(18-88) 105(5-609) BNT162b2 ECLIA 0.8 U/mL 4weeks 2 NA Tacrolimus/MPA/Steroids(59.5%), Anti-metabolite(29%) 4
Grupper 2021[50] KT 136 111/25 58.6±12.7 3.3(1.5-5.2) BNT162b2 CLIA 15 AU/mL 16.5±6.2 days 2 NA High-dose steroids(23.5%), Antithymocyte globulin(7.4%), Rituximab(2.9%), CNI(90.4%), mTORi(7.4%), MMF(76.5%) 6
HC 25 8/17 52.7±11.5 NA 16.8±2.9 days NA
Guarino 2022[51] LIT 492 371/121 64.9(57.2-70.1) 14.1 (5.7-20.1) BNT162b2 CLIA 1 4weeks 2 NA CNI(80.3%), Antimetabolite(34.2%), mTORi(27.9%), Steroids(7.1%) 8
HC 307 NA NA NA NA NA NA NA
Haidar 2021[52] SOT 183 110/73 61.2±13.4 NA BNT162B2 mRNA-1273
JNJ-78436735 CLIA signal/cutoff) ratios>1 2weeks 2 NA NA 7
HC 107 30/77 43.7±13.7
Hall 2021[53] KT 127 88/39 66.2 (63.4–70.6) 2.9(1.6-6.3) mRNA-1273 ECLIA 0.8 U/ml 4/4-6 weeks# 1/2 NA Prednisone(69.3%), CNI(84.3%), MMF/MPS(64.6%), AZA(8.7%), Sirolimus(7.9%) 6
Hall 2021[54] KT 60 37/23 66.9 (64.0 – 71.8) 3.6 (2.0 – 6.8) mRNA-1273 ECLIA 100 U/ml 4±1 weeks 3 60§ Prednisone(83.3%), CNI(98.3%), MMF/MPS(73.3%), AZA(13.3%), Sirolimus(10%) 4*
Hallett 2021[55] SOT HT 237 110/127 62 (46-69) 5.1 (2.5-11.0) mRNA-1273
BNT162b2 ELISA;
ECLIA Titers>1.1 21 (19-26) days 1/2 NA Tacrolimus(86%), MPA(62%), Corticosteroids(57%), Sirolimus(14%), CyA(8%), AZA(8%), Everolimus(7%), Belatacept(1%) 6
LUT 134 67/67 60 (44-69) 5.5 (2.6-12.4) NA NA
Haskin 2021[56] KT 38 25/13 18.6 ± 2.8 7.3 ± 5.6 BNT162b2 CLIA 50 AU/mL 37 (20.5–53)days 2 NA Rituximab(23.7%), anti-thymocyte globulin(13.2%) 7
Havlin 2021[57] LUT 48 29/19 52.1±14.3 4.3(0.3-20.1) BNT162b2 ELISA NA 1/4-6 weeks# 1/2 NA CNI(100%), Tacrolimus(97.9%), CyA(2.1%), MPA(91.7%) 5
HC 10 NA NA NA NA
Herrera 2021[58] SOT LIT 104 NA NA 5.4(0.3-27) mRNA-1273 ELISA Titers>1.1 4/4 weeks 1/2 NA CNI(60.5%), MPA(29.6%), Prednisone(31.5%), mTORi(17.3%) 7
HT 58 40/18 61.5(18-88)# 4.6(0.-26.8)
Hod 2021[59] KT 120 96/24 59.7 ± 13 5.8 ± 6.3 BNT162b2 ELISA Titers>1.1 2-4 weeks# 2 NA Tacrolimus(85.8%), MPA(77.5%), Prednisone(79.2%), CyA(6.7%), AZA(2.5%), mTORi(5.8%) 7
HC 202 61/141 57.0 ± 13.6 NA NA
Hoffman 2022[12] LUT 91 NA NA 5.4(1.9-9.7) BNT162b2 mRNA-1273 CLIA 59 AU/ml 4/6 weeks 1/2 NA NA 5
Holden 2021[60] SOT KT 80 44/36 58.9(47.9-66.8) 9.6(4.8-16.0) BNT162b2 ELISA Titers>1.1 5.6(5.1-6.3)days 2 NA Prednisolone(26.3%), CNI(93.8%), Proliferation inhibitor(93.8%), mTORi(2.5%) 5
LIT
HT
LUT
Huang 2022[61] SOT KT 1163 706/457 62.0 (52.0-68.0) 3.2 (1.1- 6.8) mRNA-1273
BNT162b2 ELISA Titers>0.02 2weeks-3 months# 2 NA Antithymocyte globulin(0.3%), Antimetabolites(63.5%), Belatacept(0.6%), CNI(78.5%), mTORi(15.3%), Rituximab(0.2%) 7
LIT
HT
LUT
Husain 2021[62] KT 28 17/11 66 (42–87) 8.0 (1.0–15.8) mRNA-1273
BNT162b2 CLIA 59 AU/ml 29 (12–59)days 2 NA Tacrolimus(75%), Belatacept(21%), Prednisone(32%), MMF/MPA(61%), AZA(11%), Leflunomide(4%), Sirolimus/everolimus(14%) 5
Itzhaki Ben Zadok 2022[63] HT 42 35/7 61 (44–69) 9.2(2.7-13.8) BNT162b2 CLIA 50 AU/mL 21-26 days# 1/2 NA CNI(81%), mTORi(57%), Steroids(69%), Anti-metabolites(55%) 5
Kamar 2021[64] SOT KT 101 70/41 58±2 8.1±0.7 BNT162b2 ELISA Titers> 1.1 4weeks 1/2/3 61±1 CNI(72.3%), Anti-metabolite(64.4%), mTORi(28.7%), Steroids(85.1%), Belatacept(11.9%) 8
LIT
Kamar 2021[65] SOT KT 37 20/17 60±14 9.1±7.0 BNT162b2 ELISA Titers> 1.1 4weeks 4 NA CNI(86.5%), MPA (86.5%), mTORi(24.3%), Steroids(91.9%) 5
LIT
HT
Kantauskaite 2022[66] KT 225 148/77 62(54-70) 6.8(2.6-12.3) mRNA-1273
BNT162B2 ELISA >35.2 BAU/ml 14 ± 2 days 2 NA CNI(96.4%), MPA (83.1%), mTORi(3.1%), Steroids(94.2%), AZA(1.8%) 5
HC 176 NA 60(54-69) NA 17 days NA
Karaba 2022[67] SOT 58 27/31 NA NA mRNA-1273
BNT162B2 ELISA Titers> 1.1 2weeks 2 NA Prednisone(66%), CNI(90%), mTORi(14%), Antimetabolites(69%) 6
35 16/19 NA NA BNT162b2 mRNA-1273
AZD1222 3 83§ Prednisone(49%), CNI(80%), mTORi(9%), Antimetabolites(66%)
HC 16 11/5 NA NA BNT162b2 2 NA NA
Korth 2021[68] KT 23 11/12 57.7±13.5 11.4±9.2 BNT162b2 CLIA 13 AU/ml 2weeks 2 NA MPA(78%), Corticosteroids(60%), Tacrolimus(60%), CyA(17%), Sirolimus(22%), Everolimus(4%), belatacept (4%), AZA(4%) 7
HC 23 9/14 44.4±9.2 NA NA
Kumar 2021[69] SOT KT 60 37/23 66.9(64.0–71.8) 3.6 (2.0–6.8) mRNA-1273 ELISA >0.33 4-6 weeks# 2/3 60§ Prednisone(83%), CNI(98%), MMF/MPS(73%), AZA(13%), Sirolimus(10%) 4*
LIT
HT
LUT
SOT without vaccination 57 39/18 66.1 (63.0–70.6) 2.3 (1.5-5.8) 3 NA
Marinaki 2021[70] SOT KT 34 27/7 60(49.1-68.4) 11.1(7.3-15.8) BNT162b2 CLIA 50 AU/ml. 10(9-10)days 2 NA Antimetabolite (33.3%) 5
HT
HC 116 NA NA NA NA NA
Marion 2021[71] SOT KT 367 232/135 59±1 9±0.4 BNT162b2 ELISA Titers> 1.1 4weeks 2 NA Anticalcineurins(85%), Tacrolimus(78.2%), CyA(7.1%), MPA(68%), mTORi(25%), Steroids(82%), Belatacept(9%) 5
LIT
Marlet 2021[72] KT 97 58/39 NA NA mRNA-1273
BNT162b2 CLIA 7.1 BAU/mL 3weeks 2 NA NA 6
160 103/57 3 43(33-63)
Massa 2021[73] KT 61 44/17 58.0 (47.1-66.1) 4.5(1.8-11.3) BNT162b2 ELISA 50 AU/mL 4/4 weeks 2/3 28§ Corticosteroïds(88.5%), Antimetabolites(62.3%), CNI(93.4%), mTORi(9.8%), Belatacept(1.6%) 7
Masset 2021[74] KT 456 NA 61.4±12.1 10.5±8.5 BNT162b2 ECLIA 50 U/mL NA/4 weeks 1/2 NA CNI(84.6%), mTORi (15.6%), Antimetabolite(74.5%), Steroid (34.4%) 5
KT 136 63.7±11.7 9.4±8.1 4weeks 3 50§
Mazzola 2021[13] SOT KT 133 92/41 NA 3.8(1.8-8.8) BNT162b2 CLIA 50 AU/mL 4/4 weeks 1/2 NA Corticoids(60.1%), CNI(81.9%), MMF(71.4%), mTORi(19.5%) 6
LIT
HT
HC 25 7/18 55(38-62) NA 4weeks 2 NA NA
Medina-Pestana 2021[75] KT 942 592/350 40(32-46) 7(3-12) CoronaVac CLIA 50 AU/mL 4weeks 1/2 NA Tacrolimus+prednisone+AZA(32%%), Tacrolimus+prednisone+ MPA(43%), prednisone+AZA+ CyA (10%), Tacrolimus+prednisone+ mTORi(11%) 7
KT without vaccination 6510 3971/2539 45(37-53) 6(2.8-11.1) 2 NA
Middleton 2022[76] KT 70 NA NA NA BNT162b2
AZD1222 Siemens immunoassay NA 40(12-79)days 1 NA NA 5
Midtvedt 2021[77] KT 141 79/62 67.4±17.2 11.7±9.8 BNT162b2 Flow cytometric assay 1.0 BAU/mL 25-89 days# 2 NA CNI+MPA+prednisolone(74%), CNI+prednisolone(13%), MPA(82%) 5
Miele 2021[78] SOT 16 13/3 57±15.7 9±7.5 BNT162b2 CLIA 59 AU/mL 2weeks 2 NA Tacrolimus(93.7%), Everolimus(6.3%), MMF(62.5%), Corticosteroids(56.3%) 5
Narasimhan 2021[79] LUT 73 54/19 65(53.5-69.5) 3.3(1.6-5.3) mRNA-1273
BNT162b2 CLIA 50 AU/mL 2weeks 2 NA anti-metabolite(99%) 6
Noble 2021[80] KT 57 NA NA 14.5±28.8 mRNA-1273
BNT162b2 ELISA Titers> 1.1 4/4 weeks 2/3 NA Belatacept(72%), Tacrolimus(28%) 4
Ou 2021[81] KT 609 244/365 58 (45–68) 7 (3–15) mRNA-1273
BNT162b2 ELISA Titers> 1.1 1/1 weeks 1/2 NA Prednisone(68.5%), MPA(71.9%), Tacrolimus(77.2%), AZA(9.7%), Sirolimus(8.4%) 7
Pedersen 2021[82] KT 58 NA NA NA BNT162b2 CLIA 34.8 AU/mL 4weeks 2 NA Tacrolimus(77.6%), Everolimus(1.7%), CyA(15.5%), mTORi(87%), MMF/MPA(93.1%), AZA(6.9%), Steroids(12.1%) 5
HC 20 4/16 NA
Peled 2021[83] HT 77 50/27 62(49- 68) 7.4 (3.3-15.1) BNT162b2 ELISA Titers> 1.1 3weeks 2 NA MPA(75.3%), MPS(53.2%), MMF, (22.1%), Everolimus(26%) 6
HC 136 86/50 63±13 NA 13.3±1.4 days NA
Rabinowich 2021[84] LIT 80 56/24 60.1±12.8 6.4±6.2 mRNA-1273
BNT162b2 CLIA 15 AU/mL 14.8±3.2 days 2 NA Prednisone(30%), CNI(93.7%), Everolimus(22.5%), AZA(5%), MMF(50%) 6
HC 25 8/17 NA NA 15.8±2.9 days NA
Rashidi-Alavijeh 2021[5] LIT 43 26/17 47(36-54) 8 (4–12) BNT162b2 CLIA 13 AU/mL 2weeks 2 NA Tacrolimus use(93%), Tacrolimus + everolimus(55%), Tacrolimus + MMF(28%), Tacrolimus monotherapy(18%), CyA(5%), Everolimus(2%) 6
HC 20 9/11 NA NA NA
Rincon-Arevalo 2021[85] KT 40 28/12 62.4(51.3–69.5) 5(2-10) BNT162b2 ELISA >0.3 1weeks 2 NA Steroid+Tacrolimus+MMF(22%), Steroid+ CyA +AZA(1%), Steroid +CyA +MMF(13%), mTORi + MMF + Steroid(3%), mTORi + CyA + MMF(1%) 8
HC 35 20/15 51(34–80) NA 3-4 weeks# NA
Rozen-Zvi 2021[86] KT 308 197/111 57.5 ± 13.8 7.1±7.5 BNT162b2 CLIA 50 AU/mL 2-4 weeks# 2 NA Tacrolimus(92.5%), CyA(7.5%), mTORi(8.4%), CNI(58.8%), rituximab(1.9%), antithymocyte globulin(4.5%) 7
Ruether 2021[87] LIT 138 79/59 55.0±13.2 7(2-17) mRNA-1273
BNT162B2
AZD1222 ECLIA 33.8 AU/mL 29(25–39) days 2 NA Prednisone(31.2%), CNI(92.8%) 7
HC 52 19/33 50.9±11.6 NA 36(22–63) days NA
Russo 2021[88] KT 82 47/35 58.5(50.3-65.0) 5.8(2.9-11.9) BNT162B2 CLIA 12 AU/mL 43 (23-63)days 1/2 NA Steroid(91.5%), Everolimus(12.2%), CNI(97.6%), CyA(18.3%), Tacrolimus(79.3%), Anti-metabolite(69.5%) 7
Sattler 2021[89] KT 39 28/11 57.4±14.0 8.2±6.1 BNT162B2 ELISA Titer> 1.1 1/3 weeks 2 NA Corticosteroids+ tacrolimus+MMF(56.4%), Corticosteroids+ CyA+MMF(33.3%), mTORi+MMF(7.7%), mTORi+MMF+ CyA(2.6%) 7
HC 39 20/19 53.0±17.6 NA 8±1 days NA
Schmidt 2021[90] SOT KT 40 22/18 54.5 ± 12.7 6.5±9.9 BNT162b2 mRNA-1273
AZD1222 ELISA 35.2 BAU/ml 15±6/14±1 days 1/2 NA CNI+antimetabolite+glucocorticoids(70%), CNI+antimetabolite(12.5%), CNI+glucocorticoids(5%), CNI only(5%), mTORi+antimetabolite+glucocorticoids(5%), mTORi +glucocorticoids(2.5%) 7
LIT
HT
LUT
LIT+KT
Schramm 2021[91] SOT HT 50 32/18 55±10 1.9(1.4-2.4) BNT162b2 ECLIA 0.8 U/ml 21/21 day 1/2 NA Tacrolimus/MMF(82%), CyA/MMF(10%), Tacrolimus/mTORi(8%) 6
LUT
HT+LUT
HC 50 17/33 47±10 NA NA
Shostak 2021[92] LUT 168 112/56 60.5(49.3–67.8) NA BNT162b2 CLIA 50 AU/mL 1/2 weeks 1/2 NA mTORi(17%), antimetabolite/Prednisone(92%) 6
Spinner 2022[93] HT 40 27/13 17.1(15.7-18.4) NA BNT162B2 mRNA-1273
JNJ-78436735 CLIA signal-to-cutoff ratio>1 118(57-152)days 2 NA MPA(42.5%), Prednisone(35%), Sirolimus(40%), Tacrolimus(85%), anti−thymocyte globulin(7.5%), rituximab(7.5%) 5
Strauss 2021[94] LIT 161 69/72 64(48-69) 6.9(2.9-15) BNT162b2 mRNA-1273 ELISA
ECLIA Titers> 1.1 (ELISA);
0.8 U/mL (ECLIA) 4weeks 2 NA Tacrolimus(81%), MPA(35%), Corticosteroids(22%), Sirolimus(11%), CyA(8%), AZA(6%), Everolimus(3%) 5
Stumpf 2021[95] KT 368 241/127 57.3 ± 13.7 9.9 ± 6.8 mRNA-1273
BNT162b2 ELISA Titers> 1.1 4/3-4 weeks# 1/2 NA Corticosteroids(48.4%), CNI(87.5%), MMF/MPA(76.1%), mTOR-Inhibitor(16%), Belatacept/(4.6%) 7
HC 144 34/110 48.0 ± 11.9 NA NA
Stumpf 2021[96] KT 71 45/26 57.0±14.4 NA BNT162b2 ELISA Titers> 1.1 4/4 weeks 2/3 68±1 CNI(87%), MMF/MPA(73%), mTORi(24%) 5
Thuluvath 2021[97] LIT 62 41/21 65.7±8.7 NA mRNA-1273
BNT162B2
JNJ-78436735 ECLIA 0.4 U/ml 4weeks 2 NA AZA(12%), Prednisone(12%), Tacrolimus(18%) 6
Timmermann 2021[98] LIT 118 75/43 66.1(28-89)# 14.4(0-37) BNT162b2 mRNA-1273
JNJ-78436735 ELISA Titers> 1.1 3weeks 2 NA Tacrolimus(69.1%), MMF(36.1%), Everolimus(14.4%), Ciclosporin(5.2%) 6
Tylicki 2021[99] KT 83 54/29 55 (42–63) 8 (3.5–15) BNT162b2 mRNA-1273 CLIA 33.8 BAU/mL 2/3 weeks 2/3 90§ Steroids(89.2%), MMF/MPS(78.1%) 6
Vaiciuniene 2021[100] KT 136 84/52 NA 5.8 (0.9–21.8)# BNT162b2 ELISA 35.2 BAU/mL 3-6 weeks# 2 NA Steroids(88.2%), MMF(83.1%), CNI(90.4%), CyA(34.6%), Tacrolimus(58.8%), Sirolimus(9.6%) 6
Werbel 2021[101] SOT KT 30 13/17 57(44-62) 4.5(2.3-10.5) mRNA-1273
BNT162B2
JNJ-78436735 ELISA
ECLIA Titers> 1.1 (ELISA)
0.8U/ml (ECLIA) 14(14-17)days 3 67(54-81) Tacrolimus/CyA+ MPA(83.3%), Corticosteroids(80%), Sirolimus(3.3%), Belatacept(3.3%) 5
LIT
HT
LUT
Westhoff 2021[102] KT 10 8/2 54(41-74) 4.3(0.8-15.8) BNT162b2 ECLIA 0.8 U/ml 4/2 weeks 2/3 70§ NA 5
Yanis 2022[6] KT 54 33/21 72.1±3.6 7.0 (2.7–13.0) BNT162b2 ELISA NA 21–42 days# 2 NA CNI(100%), Corticosteroids(50.1%), MPA (38.9%), AZA(13%), mTORi(5.6%) 6
Yi 2021[103] KT 145 NA NA 5(3-10) BNT162B2 mRNA-1273 NA NA NA 1 NA NA 5
SOT: Solid organ transplant; KT: Kidney transplant; LIT: Liver transplant; LUT: Lung transplant; HT: Heart transplant; Pan: Pancreas transplant; CLIA: chemiluminescence analysis; ECLIA: electrochemiluminescence immunoassay analyzer; ELISA: enzyme-linked immunosorbent assay; HC: heaLUThy controls; IQR: interquartile range; CNI: calcineurin inhibitor; MMF: mycophenolate mofetil; mTOR: mammalian target of rapamycin; MPA: mycophenolic acid; MPS: Mycophenolate sodium; CyA: Cyclosporine A; AZA: azathioprine; mTOR: mammalian target of rapamycin; NA: not available; NOS: Newcastle-Ottawa scale; SD: standard deviation. #: indicates range; §: indicates median; *:The Jadad scale was performed. The NOS was performed for the rest of unmarked stuidies. Newcastle-Ottawa scale is out ot 9 and the Jadad scale rates studies from 1 to 5, with higher scores indicating lower risk of bias.
HIR in SOT recipients
Twenty-four studies[13, 24, 27, 28, 33, 40, 43, 46, 47, 49, 53, 55, 58, 63, 64, [74], [75], [76], 81, [90], [91], [92], 95, 103] assessed the HIR after the 1st dose vaccine in SOT recipients. The pooled response rate was 9.5% (95% CI=7%–11.9%, Figure 2 A) with considerable heterogeneity (I2 = 89.1%, P<0.001). Sensitivity analysis indicated that the result was not changed markedly (Figure S1A).Figure 2 Meta-analysis of the HIR after 1st, 2nd, and 3rd doses COVID-19 vaccine in SOT recipients and comparison of HIR. (A) HIR after 1st dose vaccine in SOT (9.5%); (B) HIR after 2nd dose vaccine in SOT (43.6%); (C) HIR after 3rd dose vaccine in SOT (55.1%); (D) Comparison of HIR after 1st dose vaccine (SOT vs. healthy controls, RR=0.036); (E) Comparison of HIR after 2nd dose vaccine (SOT vs. healthy controls, RR=0.382). HIR, humoral immune response; SOT, solid organ transplant; RR, risk ratio.
Figure 2
Seventy-five studies[5, 6, [10], [11], [12], [13], 20, 21, 23, [25], [26], [27], [29], [30], [31], [34], [35], [36], [37], [38], [39], [41], [42], [43], [44], [45], [46], [47], [48], [50], [51], [52], [53], 55, 56, [58], [59], [60], [61], [62], [63], [64], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [77], [78], [79], [80], [81], [82], [83], [84], [86], [87], [88], [89], [90], [91], [92], [93], [94], [95], [97], [98], [99], [100]] assessed the HIR after the 2nd dose vaccine in SOT recipients. The pooled response rate was 43.6% (95% CI=39.3%–47.8%, Figure 2B) with considerable heterogeneity (I2 = 95.4%, P<0.001). Sensitivity analysis did not change the result (Figure S1B).
Seventeen studies[19, 20, 23, 32, 43, 54, 64, 67, 69, [72], [73], [74], 80, 96, 99, 101, 102] assessed the HIR after the 3rd dose vaccine in SOT recipients, of whom 55.1% (95% CI=44.7%–65.6%, Figure 2C) exhibited a humoral response, with considerable heterogeneity (I2=95.2%, P<0.001). Sensitivity analysis revealed that the HIR rate was 60% (95% CI=55.1%-64.9%; I2=69.9%, P<0.001, Figure S1C) after removing the study by Chavarot et al.[32].
Comparison of HIR (SOT recipients vs. healthy controls)
Four studies[22, 40, 91, 95] compared the HIR between SOT recipients and healthy controls after the 1st dose vaccine. All included studies used the mRNA vaccine. The result demonstrated that SOT recipients had a significantly lower rate of HIR than healthy controls (RR=0.036; 95% CI=0.014-0.091; I2 = 55.9%, P =0.078, Figure 2D). Sensitivity analysis did not change the result (Figure S1D).
Twenty-nine studies[1, 4, 5, 17, 21, 25, 32, 34, 37, 38, 42, 44, [47], [48], [49], 54, 56, 65, 66, 68, 73, [84], [85], [86], 88, 90, 92, 94, 98] compared the HIR among SOT recipients and healthy controls after the 2nd dose vaccine. Only twenty-five studies[1, 4, 5, 17, 21, 32, 34, 37, 38, 44, 47, 48, 54, 56, 65, 66, 68, 73, [84], [85], [86], 88, 92, 94, 98] used the mRNA vaccine, one study[25] used only inactivated vaccines, two [49, 90] used mRNA or adenovirus vector vaccines, and one [42] used mRNA or inactivated vaccines. The result demonstrated that SOT recipients exhibited a significantly lower HIR rate than healthy controls (RR=0.382; 95% CI=0.313-0.468; I2 = 96.1%, P <0.001, Figure 2E). Sensitivity analysis did not change the result (Figure S1E).
CIR in SOT recipients
Six studies[27, 33, 47, 53, 90, 95] assessed the CIR after the 1st dose vaccine in SOT recipients. Five studies[27, 33, 47, 53, 95] used mRNA vaccines, and one study[90] used mRNA or adenovirus vector vaccines. The overall proportion of SOT recipients who exhibited CIR was 12.2% (95% CI=6.5%–17.8%, Figure 3 A), with moderate heterogeneity (I2=60.5%, P=0.027). Sensitivity analysis did not change the result (Figure S2A).Figure 3 Meta-analysis of the CIR after 1st, 2nd, and 3rd doses COVID-19 vaccine in SOT recipients. (A) CIR after 1st dose in SOT (12.2%); (B) CIR after 2nd dose in SOT (48.3%); (C) CIR after 3rd dose in SOT (57.6%); (D) Comparison of CIR after 2nd dose (SOT vs. healthy controls, RR=0.477). CIR, cellular immune response; SOT, solid organ transplant; RR, risk ratio.
Figure 3
Eighteen studies[6, 27, 29, 33, 38, 39, 44, 47, 53, 57, 58, 78, 79, 87, 89, 90, 95, 102] assessed the CIR after the 2nd dose vaccine in SOT recipients. Fifteen studies[6, 27, 33, 38, 39, 44, 47, 53, 57, 58, 78, 79, 89, 95, 102] used mRNA vaccine, one [30] used inactivated vaccine, and two [87, 90] used mRNA or adenovirus vector vaccine. The overall proportion of SOT recipients who exhibited CIR was 48.3% (95% CI=34.2%–62.4%, Figure 3B) with considerable heterogeneity (I2 = 96.5%, P<0.01). Sensitivity analysis did not change the result (Figure S2B).
Only two studies[96, 102] reported the CIR after the 3rd dose vaccine in SOT recipients (Figure 3C). The overall proportion of SOT recipients who exhibited CIR with considerable heterogeneity (I2 = 96.5%, P<0.001) was 57.6% (95% CI=-5.4%–120.6%).
Comparison of CIR (SOT recipients vs. healthy controls)
Five studies[29, 38, 47, 87, 89] compared the SOT recipients and healthy controls after the 2nd dose vaccine (Figure 3D). The meta-analysis result revealed that the SOT recipients had a significantly lower rate of CIR than the healthy controls (RR=0.477; 95% CI=0.257-0.885, I2=96.9%, P<0.001). Sensitivity analysis did not change the result (Figure S2C).
Subgroup analysis based on the organ type for HIR
As shown in Figure 4 A and Table 2 , sixteen studies[13, 24, 27, 28, 33, 40, 46, 49, [74], [75], [76], 81, 95, 103] assessed the HIR after the 1st dose vaccine in KT recipients. The pooled rate was 9.4% (95% CI=6%–12.7%), with considerable heterogeneity (I2= 90.9%, P<0.001). Sensitivity analysis did not change the result (Figure S3A). Forty-two studies[5, 10, 11, 13, 21, 23, 25, 27, 29, 30, 35, [37], [38], [39], 42, [44], [45], [46], [47], [48], 50, 53, 56, [59], [60], [61], [62], 66, 68, [70], [71], [72], [73], [74], [75], 77, [80], [81], [82], 86, 88, 89, 95, 99, 100] assessed the HIR after the 2nd dose in KT recipients. The overall proportion of KT recipients who exhibited HIR was 37.6% (95% CI=33.5%–41.6%; I2 = 90.5%, P<0.001). Sensitivity analysis did not change the result (Figure S3B). Only 13 studies[19, 23, 32, 43, 64, [72], [73], [74], 80, 96, 99, 101, 102] assessed the HIR after the 3rd dose vaccine in KT recipients, of whom 54.4% (95% CI=40.8%–68.1%) exhibited a humoral response, with considerable heterogeneity (I2 = 96.3%, P<0.001). Sensitivity analysis did not change the result (Figure S3C).Figure 4 Meta-analysis of the HIR and CIR after 1st, 2nd, and 3rd COVID-19 vaccine doses in different types of transplant recipients. (A) HIR in LUT (1st dose: 4.4%, 2nd dose:28.4%), HT (1st dose: 13.2%, 2nd dose: 50.3%), LIT (1st dose: 29.5%, 2nd dose: 64.5%) and KT (1st dose: 9.4%, 2nd dose: 37.6%, 3rd dose: 54.4%); (B) CIR in LIT (2nd dose: 66.3%) and KT (1st dose: 6.9%, 2nd dose: 42.6%, 3rd dose: 57.6%). HIR humoral immune response; CIR, cellular immune response; KT, kidney transplant; LIT, liver transplant; HT, heart transplant; LUT, lung transplant.
Figure 4
Table 2 Subgroup analysis based on the type of vaccine, type of organ, and the time post-transplant.
Table 2Subgroup No. of included article Pooled rate (%) 95%CI (%) I2 p
HIR 1st Type of vaccine mRNA 20 7.9 5.7-10.1 85.2 <0.01
Type of organ KT 16 9.4 6-12.7 90.9 <0.01
KT vs. HCs 3 3.0* 0.7-12.4 75 0.018
LIT 4 29.5 12.2-46.7 86.5 <0.01
HT 4 13.2 8.1-18.2 0 0.934
LUT 2 4.4 0.9-7.9 17.7 <0.01
Time post-transplant ≤5 year 6 5.2 2.5-7.8 53.2 0.058
>5 year 15 11.2 7.9-14.4 89.1 <0.01
2nd Type of vaccine mRNA 59 42.2 37.6-46.9 95.2 <0.01
inactivated 3 24.8 3.5-46.1 96.3 <0.01
Type of organ KT 42 37.6 33.5-41.6 90.5 <0.01
KT vs. HCs 12 32.2* 24.2-42.9 90.7 <0.01
LIT 17 64.5 57.4-71.6 89.5 <0.01
LIT vs. HCs 7 67.6* 58.9-77.6 83.7 <0.01
HT 11 50.3 37.6-63 89.1 <0.01
LUT 8 28.4 22.3-34.5 64.5 <0.01
Time post-transplant ≤5 year 17 33.3 28.1-38.5 80.6 <0.01
>5 year 46 45.0 39.4-50.5 95.9 <0.01
3rd Type of vaccine mRNA 14 56.2 44.5-67.9 95.8 <0.01
Type of organ KT 13 54.4 40.8-68.1 96.3 <0.01
Time post-transplant ≤5 year 8 51.2 30.6-71.8 95.7 <0.01
>5 year 6 58.6 48.6-68.7 85.0 <0.01
CIR 1st Type of vaccine mRNA 5 12.2 5.7-18.8 67.4 <0.01
Type of organ KT 2 6.9 3.1-10.7 0 0.51
Time post-transplant ≤5 year 3 11.5 2.0-21.1 68.1 0.043
>5 year 3 13.7 4.4-23.0 68.3 0.027
2nd Type of vaccine mRNA 15 51.6 39-64.2 93.8 <0.01
Type of organ KT 11 42.6 23.5-61.7 97 <0.01
KT vs. HCs 3 41.6* 41.6-122 98.1 <0.01
LIT 3 66.3 30.1-102.5 96.1 <0.01
LUT 2 56.9 14.5-99.2 88.9 <0.01
Time post-transplant ≤5 year 8 44.5 21.1-68.0 96.6 <0.01
>5 year 9 51.2 32.0-70.4 96.2 <0.01
3rd Type of organ KT 2 57.6 -5.4-120.6 96.5 <0.01
SOT vs. HCs 2nd Type of vaccine mRNA 25 36.6* 29.1-46.1 96.0 <0.01
HIR: Humoral immune response; CIR: Cellular immune response; SOT: Solid organ transplant; KT: Kidney transplant; LIT: Liver transplant; LUT: Lung transplant; HT: Heart transplant; HCs: Healthy controls; CI: confidence interval; *: risk ratio.
The overall proportion of LIT recipients who exhibited HIR after the 1st vaccine dose was 29.5% (95% CI=12.2%–46.7%), with considerable heterogeneity (I2 = 86.5%, P<0.001). Sensitivity analysis indicated that the pooled rate of response was 37% (95% CI=29.1%-44.8%) without heterogeneity (I2=0, P=0.951), after removing the study by Mazzola et al.[13] (Figure S3D). Seventeen studies[5, 10, 13, 34, 41, 46, 47, 51, 53, 58, 60, 61, 71, 84, 87, 94, 97, 98] assessed the HIR after the 2nd dose vaccine in LIT. The response rate was 64.5% (95% CI=57.4%–71.6%; I2 = 89.5%, P<0.01). Sensitivity analysis did not change the result (Figure S3E).
Four studies[13, 28, 58, 63] assessed the HIR after the 1st dose vaccine in HT. All the studies used the RNA vaccine. The pooled response rate was 13.2% (95% CI=8.1%-18.2%) without heterogeneity (I2=0, P=0.934). Eleven studies[10, 13, 31, 53, 55, 58, 61, 63, 70, 83, 93] assessed the HIR after the 2nd dose vaccine in HT recipients, of whom 50.3% exhibited a humoral response (95% CI=37.6%-63%) with considerable heterogeneity (I2=89.1%, P<0.001). Sensitivity analysis did not change the result (Figure S3F).
Only two studies[28, 92] detected the HIR after the 1st dose vaccine in LUT patients. The pooled rate of response was 4.4% (95% CI=0.9%-7.9%, I2=17.7%). Eight studies[10, 12, 31, 53, 55, 61, 79, 92] assessed the HIR after the 2nd dose vaccine in LUT recipients of whom 28.4% (95% CI=22.3%-34.5%), exhibited a response with considerable heterogeneity (I2=64.5%, P<0.001). Sensitivity analysis revealed that the overall proportion of HIR was 30.8% (95% CI=26.2%-35.3%, I2=12.5, P=0.335, Figure S3G), after removing the study by Shostak et al.[92].
Subgroup analysis based on the organ type for CIR
As shown in Figure 4B and Table 2, only 2 included studies[33, 95] reported the CIR after 1st vaccine dose in KT recipients. The overall proportion of KT recipients who exhibited CIR was 6.9% (95% CI=3.1%–10.7%) without heterogeneity (I2 = 0, P=0.51). Eleven studies[6, 27, 29, 33, 38, 39, 44, 47, 89, 95, 102] assessed the CIR after the 2nd dose vaccine in KT recipients. Ten studies[6, 27, 33, 38, 39, 44, 47, 89, 95, 102] used mRNA vaccine and one [29] used inactivated vaccine. The overall proportion of KT recipients who exhibited CIR was 42.6% (95% CI=23.5%–61.7%) with considerable heterogeneity (I2 = 97.0%, P<0.001). Sensitivity analysis did not change the result (Figure S3H).
Three studies[47, 58, 87] assessed the CIR after the 2nd dose vaccine in LIT recipients. Two studies[47, 58] used mRNA vaccines, and one [87] used mRNA or adenovirus vector vaccines. The overall proportion of LIT recipients who revealed CIR rate was 66.3% (95% CI=30.1%–102.5%) with considerable heterogeneity (I2 = 96.1%, P<0.001). Sensitivity analysis showed that the pooled response rate was 85% (95% CI=76.7%-93.3%) without heterogeneity (I2=0, P=0.459) after removing the study by Ruether et al.[87].
Only two studies[57, 79] assessed the CIR after the 2nd dose vaccine in LUT recipients. The overall proportion of LUT recipients with high heterogeneity (I2=88.9%, P=0.003) was 56.9% (95 CI%=14.5%-99.2%).
Subgroup analysis based on organ type: comparison of HIR (transplant recipients vs. healthy controls)
As shown in Table 2, only 3 studies[22, 40, 95] compared the HIR among KT recipients and healthy controls after the 1st dose vaccine. The result revealed that compared with healthy controls, KT recipients had a significantly lower rate of HIR (RR=0.030; 95% CI=0.007-0.124; I2 = 75%, P =0.018). Sensitivity analysis did not change the result markedly (Figure S4A).
After the 2nd vaccine dose, twelve studies[29, 38, 42, 48, 50, 59, 66, 68, 82, 85, 89, 95] showed that compared with healthy controls, KT recipients had a significantly lower rate of HIR (RR=0.322; 95% CI=0.242-0.429; I2 = 90.7%, P <0.001). Sensitivity analysis did not change the result markedly (Figure S4B). Seven studies[5, 13, 41, 46, 51, 84, 87] showed that LIT recipients exhibited significantly lower HIR rates than the healthy controls (RR=0.676; 95% CI=0.589-0.776; I2 = 83.7%, P <0.001). Sensitivity analysis did not change the result markedly (Figure S4C).
Subgroup analysis based on organ type: comparison of CIR (transplant recipients vs. healthy controls)
As shown in Table 2, three studies[29, 38, 89] compared the CIR between KT recipients and healthy controls after the 2nd dose vaccine. The results showed that KT recipients exhibited a significantly lower CIR rate than the healthy controls (RR=0.416; 95% CI=0.416-1.220; I2 = 98.1%, P <0.001). Sensitivity analysis did not change the result markedly (Figure S4D).
Subgroup analysis based on vaccines type in SOT recipients
As shown in Table 2, subgroup analysis revealed that the mRNA vaccine’s pooled HIR rates were 7.9% (95% CI=5.7%–10.1%, I2=85.2%), 42.2% (95% CI=37.6%–46.9%, I2=95.2%) and 56.2% (95% CI=44.5%–67.9%, I2=95.8%) after the 1st, 2nd, and 3rd vaccine dose, respectively. However, the inactivated vaccine’s pooled HIR rate was only 24.8% (95% CI=3.5%–46.1%, I2=96.3%) after the 2nd vaccine dose in SOT recipients.
Subgroup analysis revealed that the mRNA vaccine’s pooled CIR rates were 12.2% (95% CI=5.7%–18.8%, I2=67.4%) and 51.6% (95% CI=39%–64.2%, I2=93.8%) after the 1st and 2nd vaccine dose, respectively.
Subgroup analysis revealed that SOT recipients who received only mRNA vaccine had a significantly lower rate of HIR than the healthy controls (RR=0.366; 95% CI=0.291-0.461; I2 = 96.0%). Sensitivity analysis showed that all the results in this subgroup analysis were not changed markedly.
Subgroup analysis of the time post-transplant in SOT recipients
As shown in Table 2, subgroup analysis revealed that the pooled HIR rates of time post-transplant less than 5 years were 5.2% (95% CI=2.5%–7.8%, I2=53.2%), 33.3% (95% CI=28.1%–38.5%, I2=80.6%) and 51.2% (95% CI=30.6%–71.8%, I2=95.7%) after the 1st, 2nd, and 3rd vaccine dose, respectively. In contrast, the pooled HIR rates of time post-transplant more than 5 years were 11.2% (95% CI=7.9%-14.4%, I2=89.1%), 45.0% (95% CI=39.4%–50.5%, I2=95.9%) and 58.6% (95% CI=48.6%–68.7%, I2=85%) after the 1st, 2nd, and 3rd vaccine dose, respectively.
Subgroup analysis revealed that the pooled CIR rates of time post-transplant less than 5 years were 11.5% (95% CI=2.0%–21.1%, I2=68.1%) and 44.5% (95% CI=21.1%–68.0%, I2=93.8%) after the 1st and 2nd vaccine dose, respectively. The rates of time post-transplant more than 5 years were 13.7% (95% CI=4.4%–23.0%, I2=68.1%) and 51.2% (95% CI=32.0%–70.4%, I2=93.8%) after the 1st and 2nd vaccine dose, respectively. Sensitivity analysis did not change the result in the subgroup analysis of time post-transplant.
Subgroup analysis based on vaccine type in different types of transplant for HIR
As shown in Table 3 , the mRNA vaccine’s pooled HIR rates were 6.6% (95% CI=3.8%–9.4%, I2=85.3%), 37% (95% CI=29.1%–44.8%, I2=89.8%) and 55.5% (95% CI=41.2%–69.8%, I2=96.6%) after the 1st, 2nd, and 3rd vaccine dose in KT recipients, respectively. Besides, the inactivated vaccines’ pooled HIR rate was 24.8% (95% CI=3.5%–46.1%, I2=96.3) after the 2nd vaccine dose in KT recipients. The mRNA vaccine’s pooled HIR rates were 37% (95% CI=29.1%–44.8%, I2=0%) and 60.7% (95% CI=51.9%–69.5%, I2=90.9%) after the 1st and 2nd dose vaccine in LIT recipients, respectively. The mRNA vaccine’s pooled HIR rate was 48.1% (95% CI=34.7%–61.4%, I2=89.1%) after the 2nd dose vaccine respectively in HT recipients.Table 3 Subgroup analysis based on vaccine type and the time post-transplant in different transplant types.
Table 3Subgroup No. of included article Pooled rate (%) 95%CI (%) I2 p
KT HIR 1st Type of vaccine mRNA 11 6.6 3.8-9.4 85.3 <0.001
Time post-transplant ≤5 years 3 2.9 0.7-5.1 24.2 0.267
>5 years 10 8.6 6.7-14.0 89.5 <0.001
2nd Type of vaccine mRNA 35 37.6 33.2-42.0 89.8 <0.001
inactivated 3 24.8 3.5-46.1 96.3 <0.001
Time post-transplant ≤5 years 11 30.3 23.5-37.1 78.8 <0.001
>5 years 29 37.8 33.0-42.6 91.2 <0.001
3rd Type of vaccine mRNA 11 55.5 41.2-69.8 96.6 <0.001
Time post-transplant ≤5 years 5 44.7 15.8-73.6 96.8 <0.001
>5 years 6 58.5 48.4-68.6 84.6 <0.001
CIR 2nd Type of vaccine mRNA 10 46.7 30.3-63.2 94.6 <0.001
Time post-transplant ≤5 years 5 37.4 9.3-65.4 96.0 <0.001
>5 years 5 45.5 15.0-76.1 97.5 <0.001
LIT HIR 1st Type of vaccine mRNA 3 37 29.1-44.8 0 0.951
2nd Type of vaccine mRNA 13 60.7 51.9-69.5 90.9 <0.001
Time post-transplant ≤5 years 5 45.7 36.5-54.9 45.1 0.122
>5 years 11 70.4 64.5-76.3 81.0 <0.001
CIR 2nd Type of vaccine mRNA 2 85 76.7-93.3 0 0.459
HT HIR 2nd Type of vaccine mRNA 9 48.1 34.7-61.4 89.1 <0.001
Time post-transplant ≤5 years 4 30.6 16.3-44.9 54.5 0.086
>5 years 6 52.4 35.0-69.8 92.9 <0.001
LUT HIR 2nd Time post-transplant ≤5 years 4 28.8 22.9-34.8 0 0.434
>5 years 3 33.8 26.9-40.7 32.6 0.227
HIR: Humoral immune response; CIR: Cellular immune response; KT: Kidney transplant; LIT: Liver transplant; HT: Heart transplant; LUT: Lung transplant; CI: confidence interval.
Subgroup analysis based on vaccine type in different types of transplant for CIR
The mRNA vaccine’s pooled CIR rates were 46.7% (95% CI=30.3%–63.2%, I2=94.6%) and 85% (76.7%-93.3%, I2=0%) after the 2nd vaccine dose in KT and LIT recipients, respectively (Table 3).
Subgroup analysis based on time post-transplant in different types of transplant
As shown in Table 3, with the time post-transplant increasing, the pooled rates of HIR and CIR were higher in different organ transplant recipients after the 1st, 2nd, and 3rd vaccine doses.
Subgroup analysis based on the time interval between the 2nd and 3rd dose vaccination in SOT and KT recipients
The subgroup analysis was performed based on the time interval between the 2nd and 3rd doses vaccination. And the result showed the pooled HIR rates did not change significantly both in SOT and KT recipients (Figure S5).
Grading the Quality of Evidence
The GRADE approach indicated that the overall quality of evidence was low because most of the data were from observational studies (Table S2).
Meta-regression analysis
Meta-regression analysis indicated that the heterogeneity of HIR after the 1st dose and 2nd dose vaccine originated from the vaccine type and the time post-transplant, respectively (Table S3). Sample size and risk-of-bias scores at the study level did not show significant effect modifiers in the meta-regression analysis.
Publication bias
Publication bias was determined via funnel plot effect sizes. No publication bias was found in the main outcomes (Figure S6).
Discussion
This systematic review and meta-analysis summarized the cumulative evidence of COVID-19 vaccines’ immunogenicity in SOT recipients. We demonstrated that the vaccine’s immunogenicity was poor in SOT especially for LUT recipients, varied among target organs, and was significantly lower than healthy controls. A booster vaccination could induce a stronger immune response. Besides, the longer the time post-transplant was, the higher the probability of a detectable HIR and CIR after the vaccination in SOT recipients. Moreover, the immunogenicity of mRNA-based vaccines was stronger than the inactivated vaccines in SOT recipients.
Our meta-analysis showed that COVID-19 vaccines’ immunogenicity in cellular or humoral immune responses was significantly impaired in SOT patients. However, with an increased vaccination dose, SOT patients’ immune capacity can be enhanced, consistent with previous studies[64, 67, 69, 74]. In our analysis, we found that, after receiving the 2nd dose, SOT patients’ immune response rate significantly increased from 9.5% to 43.6% in HIR, and from 12.2% to 48.3% in CIR. In addition, the rates of HIR and CIR reached 56.2% and 57.6% after the 3rd dose, respectively. The same trend was observed in different transplant recipients. The HIR rates after the second dose of vaccine increased from 4.4% to 28.4% in LUT recipients and from 13.2% to 50.3% in HT recipients. A higher proportion of LIT and KT recipients exhibited a humoral response after the 2nd vaccine dose.
Although vaccine immunogenicity in SOT patients improves with the increased dose, immune response varies among different transplant recipients. Therefore, we performed a subgroup analysis of different organ transplants. Our results revealed that vaccination appeared to induce a relatively poor HIR in LUT recipients (1st dose: 4.4%, 2nd dose: 28.4%) but a relatively strong one in LIT recipients (1st dose: 29.5%, 2nd dose: 64.5%). We found that the high proportion of elderly patients in LUT patients may be responsible for the low immune response. Narasimhan et al.[79] and Hallett et al.[55] included 63% and 58% of LUT patients aged more than 60 years, respectively. However, less than 50% of elderly recipients were included in LIT. In addition, the proportion of recipients receiving antimetabolite therapy was different.[57, 92]. Antimetabolites were used in a high proportion of LUT recipients, a high proportion of 99% reported by Narasimhan et al.[79], but less than 40% in LIT patients. Furthermore, Patients who have a shorter time post-transplant have a stronger immunosuppressive state[51, 55, 94]. 34.9% of LUT recipients had a time post-transplant of fewer than 3 years[55], but only 28.4% in HT recipients[94]. Our subgroup analysis results also indicated that with the longer time post-transplant, the immunogenicity was stronger in SOT recipients after vaccination. We speculate that this may be due to the longer time since immunosuppression induction.
The immunogenicity appears to vary with the vaccine type. Therefore, the subgroup analysis based on vaccine type was performed. We found that the mRNA vaccine may cause a stronger HIR than the inactivated vaccines (56.2% vs. 24.8%), consistent with previous studies [[104], [105], [106]]. Saure et al. compared the seroconversion rates of CoronaVac and BNT162b2 by recruiting 56,261 individuals. They found that after the first dose of CoronaVac in week 4, 28.1% of individuals had positive IgG antibodies for SARS-CoV-2. The peak was 77.4% after the second dose in week 3. However, the seroconversion was 79.4% during week 4 after receiving the first dose of BNT162b2, which increased to 96.5% during week 3 after the second dose[106]. In addition, compared with Sinovac, vaccination with BNT162b2 resulted in a higher antibody titer in immunocompromised populations[104]. The underlying mechanism might be that the mRNA vaccine utilized host cells to synthesize antigens for SARS-CoV-2 and triggered a strong immune response[107, 108].
There are some limitations in this study. Firstly, most articles were observational studies and many healthy controls in the included studies were healthcare workers. Secondly, most of included studies assessed the HIR to COVID-19 vaccines. However, there is a lack of data on the CIR. Thirdly, the majority of studies used mRNA vaccines, but the other types are still lacking. Lastly, different testing methods for HIR and CIR were performed in different individual studies. The cutoff value to define a positive immune response was different across studies, which may lead to potential bias.
Conclusion
A booster vaccination enhances the immunogenicity of COVID-19 vaccines in SOT, however, a significant share of the SOT recipients still has not built a detectable humoral immune response after the third dose. This finding calls for alternative approaches, including the use of monoclonal antibodies. In addition, lung transplant patients need urgent booster vaccination to improve immune response. However, a lack of a serological cutoff value correlated with protective immunity and multiple-site mutated SARS-CoV-2 variants may trigger immune escape against existing humoral and cellular immune responses, leading to breakthrough COVID-19 infection.
Author contributions
XPC and DL: designing study, writing original draft, and editing the manuscript; BJM, JD, and XDL: search and data extraction; HX and LL: the risk of bias assessment and the statistical analysis; SS and GM: oversight, critical evaluation, and verification of the manuscript. All authors contributed to the data interpretation and approved the final manuscript.
Transparency declaration
All data were derived from sources available in the public domain, as referred to in the reference list.
Declaration of Competing Interest
No conflicts of interest to declare.
Role of funding source
No external funding was received for this study.
Funding
None.
Appendix A Supplementary data
The following is/are the supplementary data to this article:
Acknowledgments
None.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.cmi.2022.12.004.
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| 36509376 | PMC9733302 | NO-CC CODE | 2022-12-14 23:36:15 | no | Clin Microbiol Infect. 2022 Dec 9; doi: 10.1016/j.cmi.2022.12.004 | utf-8 | Clin Microbiol Infect | 2,022 | 10.1016/j.cmi.2022.12.004 | oa_other |
==== Front
J Biol Chem
J Biol Chem
The Journal of Biological Chemistry
0021-9258
1083-351X
THE AUTHORS. Published by Elsevier Inc on behalf of American Society for Biochemistry and Molecular Biology.
S0021-9258(22)01233-9
10.1016/j.jbc.2022.102790
102790
Research Article
pH profiles of 3-chymotrypsin-like protease (3CLpro) from SARS-CoV-2 elucidate its catalytic mechanism and a histidine residue critical for activity
Al Adem Kenana 1†
Ferreira Juliana C. 1†
Fadl Samar 1
Rabeh Wael M. 1∗
1 Science Division, New York University Abu Dhabi, PO Box 129188, Abu Dhabi, United Arab Emirates
∗ Corresponding author:
† These authors contributed equally to this work.
9 12 2022
9 12 2022
10279013 10 2022
10 11 2022
6 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.
3CLpro is a promising drug target for COVID-19 and related coronavirus diseases due to the essential role of this protease in processing viral polyproteins after infection. Understanding the detailed catalytic mechanism of 3CLpro is essential for designing effective inhibitors of infection by SARS-CoV-2. Molecular dynamics studies have suggested pH-dependent conformational changes of 3CLpro, but experimental pH profiles of SARS-CoV-2 3CLpro and analyses of the conserved active site histidine residues have not been reported. In this work, pH dependence studies of the kinetic parameters of SARS-CoV-2 3CLpro revealed a bell-shaped pH profile with two pKa values (6.9 ± 0.1 and 9.4 ± 0.1) attributable to ionization of the catalytic dyad His41 and Cys145, respectively. Our investigation of the roles of conserved active site histidines showed that different amino acid substitutions of His163 produced inactive enzymes, indicating a key role of His163 in maintaining catalytically active SARS-CoV-2 3CLpro. By contrast, the H164A and H172A mutants retained 75% and 26% of the activity of wild type (WT), respectively. The alternative amino acid substitutions H172K and H172R did not recover the enzymatic activity, whereas H172Y restored activity to a level similar to that of the WT enzyme. The pH profiles of H164A, H172A, and H172Y were similar to those of the WT enzyme, with comparable pKa values for the catalytic dyad. Taken together, the experimental data support a general-base mechanism of SARS-CoV-2 3CLpro and indicate that the neutral states of the catalytic dyad and active site histidine residues are required for maximum enzyme activity.
Keywords
COVID-19
SARS-CoV-2
3-chymotrypsin-like protease (3CLpro)
catalytic dyad
conserved histidine
thermodynamic stability
initial velocity studies
and pH studies
Abbreviations
3-chymotrypsin-like cysteine protease, 3CLpro
calorimetric enthalpy, ΔHcal
coronavirus disease 2019, COVID-19
differential scanning calorimetry, DSC
emission wavelength, λem
excitation wavelength, λex
melting temperature, Tm
Middle East respiratory syndrome coronavirus, MERS-CoV
non-structural proteins, nsps
papain-like protease, PLpro
open reading frames, ORFs
severe acute respiratory syndrome coronavirus, SARS-CoV
severe acute respiratory syndrome coronavirus 2, SARS-CoV-2
==== Body
pmcIntroduction
Since the advent of the 21st century, the globe has experienced three epidemics caused by coronaviruses, the most recent of which is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 first emerged among the Chinese population in the city of Wuhan in December 2019 before spreading worldwide at an exceptionally high rate. SARS-CoV-2 is responsible for coronavirus disease 2019 (COVID-19), which is characterized by influenza-like symptoms such as fever, fatigue, diarrhea, dry cough, and shortness of breath. According to the World Health Organization (WHO), the global COVID-19 pandemic has resulted in over 600 million cases and 6 million reported deaths thus far (1,2). COVID-19 is considered one of the most challenging viral outbreaks in contemporary times. Fortunately, the development of effective vaccines against SARS-CoV-2 has contributed to reducing viral transmission and preserving public health (3). However, as we continue into the third year of COVID-19 pandemic, additional effective antiviral treatments are urgently needed to combat current and newly emerging SARS-CoV-2 variants as well as future coronavirus outbreaks.
A coronavirus is a small spherical assembly with club-shaped protrusions composed of structural spike proteins that enable host cell entry (4). Once inside host cells, the coronavirus releases a single-stranded positive-sense RNA genome with 14 open reading frames (ORFs) that encode 27 structural and nonstructural proteins (nsps) (2,5). The two largest ORFs (ORF1a/b) are immediately translated by the host cell machinery into two overlapping polyproteins, pp1a and pp1ab, which encode nonstructural proteins (nsps) that are essential for the viral replication/transcription cycles (6,7). These polyproteins are then cleaved by the highly conserved viral cysteine proteases, 3CLpro and papain-like protease (PLpro). First, 3CLpro (nsp5) catalyzes its own cleavage at its N- and C-termini before liberating the other eleven nsps (nsp4–11/16) from the polyproteins. The remaining nsps (nsp1–4) are cleaved by PLpro (nsp3), which first autoprocesses its own cleavage (5,8, 9, 10). The two cysteine proteases, 3CLpro and PLpro, are highly conserved among coronaviruses, including severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV), which emerged in 2002 and 2012, respectively. Given that 3CLpro catalyzes the release of the majority of the nsps, this enzyme represents an attractive drug target for the development of effective, safe antivirals against COVID-19 and other coronavirus diseases (2,11).
Previous studies have shown that homodimer formation is required for 3CLpro catalytic activity (12, 13, 14, 15, 16, 17, 18). However, we recently showed that dimerization does not necessarily guarantee a functional 3CLpro enzyme, as some mutations lead to complete inactivation of the enzyme without disrupting the dimer conformation (19). The crystal structure of 3CLpro revealed that the monomeric subunit comprises three domains, where domain I (residues 10–96) and domain II (residues 102–180) form a five-stranded antiparallel β-barrel structure with a chymotrypsin-like folding scaffold (20). The C-terminal domain III (residues 200–303) is a cluster of five α-helices linked to domain II by a long loop (residues 181–199). In the 3CLpro of SARS-CoV, domain III reportedly controls the dimerization and formation of the active enzyme (21).
The 3CLpro active site is located at the interface between domains I and II (22). In contrast to the Ser–His–Asp triad of chymotrypsin, SARS-CoV-2 3CLpro contains a catalytic His–Cys dyad in which the catalytic residue, Cys145, is located 2.5 Å from the carbonyl carbon of the conserved glutamine of the peptide substrate. The side chains of His41 and Cys145 of the catalytic dyad, which are part of domains I and II, respectively, form H-bond at 3.6 Å. The enzyme catalyzes the cleavage of the 11 nsps by targeting a highly conserved sequence comprising glutamine at P1 and leucine/phenylalanine/valine at P2 (i.e., (L/F/V)Q↓(S, A or G), where ↓ defines the cleavage site) (12). Substrate binding to the active site of 3CLpro is highly specific, with a well-defined binding site consisting of 4 pockets (S1 – S4) (23). Recent high-resolution crystal structures of 3CLpro complexed with nine substrate peptides and six cleavage products revealed a network of conserved hydrogen bonding interactions between the active site residues of 3CLpro and the peptide substrate (24). Specifically, the side chain of the conserved glutamine at P1 of the peptide substrate forms hydrogen bonds with the side chains of His163 and Glu166 in the active site of 3CLpro and the backbone of Phe140. This network of hydrogen bonds is further stabilized by Asn142 (both side and main chains) and Ser1 from one monomer, which interact with Phe140 and Glu166 of the other monomer. The substrate-binding pocket in 3CLpro has three conserved histidine residues that affect enzyme activity depending on their protonation states, as demonstrated by molecular dynamics (MD) studies (Fig. 1 ) (25, 26, 27).Figure 1 Surface representation of the active site of SARS-CoV-2 3CLpro. PDB 7T70 was used to generate the figure. The catalytic residue Cys145 was mutated to Ala to prevent cleavage of the peptide substrate (24). The side chains of the catalytic dyad residues (His41 and Cys145) and the conserved active site histidine residues (H163, H164 and H172) are depicted as dark grey sticks. The 12-mer substrate peptide (TSAVLQ↓SGFRKM) is colored in green. The conserved glutamine at P1 of the peptide substrate forms a hydrogen bond at 2.4 Å with the side chain of His163. The figure was generated using PyMol (Schrodinger LLC).
Crystallographic, biochemical and MD studies have shown that the conformational flexibility and stability of 3CLpro are pH dependent. The potential effect of pH on SARS-CoV 3CLpro activity was first suggested by the significant differences in conformation between 3CLpro crystals grown at pH 6.0 and those grown at pH 7.6 or pH 8.0 (25). SARS-CoV 3CLpro exhibits a bell-shaped pH profile of proteolytic activity, with a peak at pH 7.0–7.4 (21,25). However, no study has reported experimental pH profile data on SARS-CoV-2 3CLpro or examined the effects of the conserved histidine residues near the catalytic dyad on the pH dependence of its catalytic activity. Here, we performed site-directed mutagenesis to assess the effects of the conserved histidine residues in the substrate-binding sites (His163, His162 and His172) on the catalytic activity of SARS-COV-2 3CLpro. Importantly, elucidation of the pH profile of SARS-CoV-2 3CLpro enabled the proposal of a chemical mechanism for this protease.
Results
pH profile of WT SARS-CoV-2 3CLpro
The pH dependence of the kinetic parameters of WT 3CLpro from SARS-CoV-2, including the turnover number (k cat) and catalytic efficiency (k cat/K m), was determined over the pH range of 5.5–10.0 (Fig. 2 ). The proteolytic activity of WT 3CLpro was assayed continuously by monitoring the cleavage of the fluorescent peptide substrate using a highly sensitive FRET-based enzymatic assay (12,22,28, 29, 30). Initial velocity studies were performed at 30 °C in 20 mM Hepes pH 7.0, 100 mM NaCl, 1 mM EDTA, 1 mM TCEP, and 20% (v/v) DMSO to acquire the kinetic parameters of the WT enzyme. The peptide substrate was varied from 20 μM to 500 μM at a fixed enzyme concentration, and the proteolytic cleavage rate were fit to Michaelis–Menten equation.Figure 2 pH profiles of WT SARS-CoV-2 3CLpro. (A) pH profile of kcat showing dependence on a single ionizable group with a pKa of 6.7 ± 0.1. (B) pH profile of kcat/Km showing dependence on two ionizable groups with pKa values of 6.9 ± 0.1 and 9.4 ± 0.2. The lines represent the best fit of the experimental data to half-bell (kcat) and bell-shaped (kcat/Km) models using GraFit 5.0 software (Erithacus Software Ltd.). All reactions were performed at 30 °C with (DABCYL)KTSAVLQ↓SGFRKME(EDANS)-NH2 as the substrate. Data points are means ± SD of triplicate measurements.
The pH profile of k cat had a half-bell shape with maximum k cat values at pH >7.5 as shown in Figure 2A. Fitting the k cat pH profile using equation 1 resulted in a single pK a value of 6.7 ± 0.1. By contrast, the pH profile of k cat/K m was bell shaped, with a maximum k cat/K m value at pH 8.0 (Fig. 2B). Fitting the data to a double-titration bell-shaped model (equation 2) resulted in pK a values of 6.9 ± 0.1 and 9.4 ± 0.1. Despite similar k cat values, k cat/K m values were lower at pH 9.0–9.5 than at pH 7.5–8.5. This observation is due to an increase in K m values (Table S1), which implies that substrate binding is reduced at high pH. Experimentally, we also observed aggregation and precipitation of the peptide substrate at pH ≥10.
Enzymatic activity and initial velocity studies of histidine mutants of SARS-CoV-2 3CLpro
Crystal structure analysis revealed network of bonding interactions with the peptide substrate in the active site of 3CLpro including three histidine residues (Fig. 1). To determine the roles of the conserved histidine residues in the substrate-binding site of SARS-CoV-2 3CLpro, we introduced alanine mutations at H163, H164 and H172. The proteolytic activity of the histidine mutants was compared to the WT 3CLpro, where the rate was measured at a fixed peptide substrate concentration of 60 μM while the enzyme concentration was varied from 0.5 to 5.0 μM (Fig. 3 ). Increasing the enzyme concentration was important for activity detection, as the mutants were expected to have low activity.Figure 3 Effects of 3CLpro histidine (H163, H164 and H172) mutants on activity relative to WT. (A – C) Relative activities of the active site histidine mutants of 3CLpro at increasing enzyme concentrations (0.0–3.5 μM) and a fixed peptide substrate concentration of 60 μM. The proteolytic cleavage rates of each mutant were normalized to the rate of 3CLpro WT to obtain the percent relative enzymatic activity. Enzymatically active mutants are represented by filled colored circles, whereas enzymatically inactive mutants are represented by open black circles. (D) Bar plot of the relative activity of WT 3CLpro and the enzymatically active mutants (H164A, H172A and H172Y). Data are presented as the mean ± SD, n = 3.
Alanine substitution at His163 (H163A) completely inactivated the enzymatic activity of 3CLpro (Fig. 3A). By contrast, H164A and H172A retained 75% and 26% of the enzymatic activity of WT 3CLpro, respectively (Fig. 3D). These results suggested that H163 and H172 are important for maintaining the catalytic activity of SARS-CoV-2 3CLpro. In addition to alanine substitutions, we introduced lysine, arginine and tyrosine at H163 and H172. None of the amino acid substitutions of His163, i.e., H163K, H163R and H163Y, recovered the enzymatic activity of 3CLpro, further supporting the importance of His163 in the catalytic mechanism of 3CLpro (Fig. 3B). However, tyrosine substitution at His172 resulted in full recovery of enzymatic activity to WT levels (Fig. 3C and 3D).
Next, initial velocity studies were performed to acquire the kinetic parameters of the H164A, H172A and H172Y mutants, which had partial or full catalytic activity compared with the WT enzyme (Fig. 4 ). Compared with WT 3CLpro, H164A and H172A exhibited 22% and 80% reductions in k cat, respectively, while the k cat of H172Y was nearly identical to the WT enzyme (Fig. 4A). All tested mutations increased the K m of 3CLpro compared with WT, which had a K m of 67 ± 3 mM; the K m values of H164A, H172A and H172Y were 103 ± 6 mM, 89 ± 1 mM, and 80 ± 2 mM, respectively (Fig. 4B). These effects indicate that the histidine residues play important roles in peptide substrate binding. Overall, the WT and H172Y enzymes had the highest and similar catalytic efficiencies (Fig. 4C), whereas H164A and H172A exhibited decreases in k cat/K m of 49% and 85%, respectively.Figure 4 Effects of 3CLpro histidine mutants on the kinetic parameters (A) kcat, (B) Km, and (C) kcat / Km of 3CLpro compared with WT enzyme. Data points are the means ± SD of triplicate measurements.
Thermodynamic stability of histidine mutants of SARS-CoV-2 3CLpro
The effects of the histidine mutations (H163A, H164A, H172A and H172Y) on the thermodynamic stability of 3CLpro were examined using differential scanning calorimetry (DSC). DSC thermograms of each enzyme were acquired in 20 mM HEPES pH 7.4, 100 mM NaCl and 0.5 mM TCEP, and the temperature was ramped from 15 °C to 75 °C at a scan rate of 1 °C/min to acquire the thermal unfolding transitions (Fig. 5 ). The WT and mutant enzymes exhibited a single transition with an early shoulder peak (Fig. 5A). The melting temperature (T m) was determined from the apex of the thermogram peak, where the WT enzyme had a T m of 52.9 ± 0.1°C, consistent with previously reported values (22,31). The alanine substitutions at H163 and H172 decreased the T m slightly to 50.3 ± 0.1 °C and 50.8 ± 0.6 °C, respectively, while H172Y had a T m of 53.0 ± 0.2 °C, similar to the WT enzyme (Fig. 5B). The calorimetric enthalpy (ΔH cal) values determined from the area under the thermographic peak were similar for all variants: 185 ± 3 kJ/mol for WT, 194 ± 17 kJ/mol for H163A, 186 ± 8 kJ/mol for H164A, 209 ± 10 kJ/mol for H172A, and 187 ± 16 kJ/mol for H172Y (Fig. 5C).Figure 5 DSC thermal scans of WT and mutant 3CLpro. (A) DSC thermal scans of WT and mutant 3CLpro (H163A, H164A, H172A and H172Y) were acquired at a heating rate of 1.0 °C/min. (B) Bar plot of Tm calculated at the apex of the thermographic peak. (C) Bar plot of ΔHcal calculated from the area under the DSC thermal peak. Data are presented as the mean ± SD, n=3.
pH profiles of the histidine mutants of SARS-CoV-2 3CLpro
Given its complete loss of enzymatic activity, the pH profiles of H163 mutants of 3CLpro could not be determined. The pH profiles of the catalytically active histidine mutants H164A, H172A and H172Y were similar to those of the WT enzyme (Fig. 6 ). The k cat pH profiles exhibited a half-bell shape with one ionizable residue with a pK a value of 6.8 ± 0.1, 6.2 ± 0.1 or 6.1 ± 0.2 for H164A, H172A and H172Y, respectively (Fig. 6A, 6C and 6E). The k cat/K m pH profiles exhibited a bell shape with two ionizable groups with pK a values similar to those of the WT enzyme (6.9 ± 0.1 and 9.4 ± 0.2): H164A (7.1 ± 0.1 and 9.4 ± 0.2), H172A (6.5 ± 0.1 and 9.6 ± 0.2) and H172Y (6.4 ± 0.1 and 9.4 ± 0.3) (Fig. 6B, 6D and 6F). For H172A and H172Y, the first pK a was slightly lower than that of WT. Importantly, the overall kinetic parameters of H172A were lower than those of WT at all tested pH values.Figure 6 pH profiles of SARS-CoV-2 3CLpro histidine mutants. (A, C and E) pH profiles of kcat for H164A, H172A, and H172Y showing dependence on a single ionizable group with one pKa value. (B, D and F) pH profiles of kcat/Km for H164A, H172A and H172Y showing dependence on two ionizable groups with two pKa values. The lines represent the best fit of the experimental data to half-bell (kcat) and bell-shaped (kcat/Km) models using GraFit 5.0 software (Erithacus Software Ltd.). Data points are means ± SD of triplicate measurements.
Discussion
The cysteine protease 3CLpro is highly conserved in all coronaviruses due to its essential role in processing viral polyproteins (5,8, 9, 10). MD and crystallographic studies have indicated that coronavirus 3CLpro enzymes undergo pH-dependent conformational changes (23,25,32). This conformational dependence on pH is due to the high flexibility of 3CLpro enzymes and is physiologically relevant: 3CLpro is assembled in late endosomes, where the low pH environment maintains the enzyme in an inactive state to prevent autoproteolysis of the viral polyproteins (33). Crystal structures of SARS-CoV 3CLpro have been determined in different pH environments (34). The dimeric structures of 3CLpro at pH 7.6 and pH 8.0 are in the fully active conformations, whereas at pH 6.0, 3CLpro undergoes substantial conformational changes that lead to complete inactivation of one protomer. These initial crystallographic data were later supported by several pH profiles of SARS-CoV 3CLpro, which revealed a bell-shaped curve with maximum activity at pH 7.0–8.5 (21,25,34).
In the present study, we identified key residues in the active site of SARS-CoV-2 3CLpro that interact with the substrate, in addition to the catalytic dyad His41 and Cys145. Among these residues, His163, His164, and His172 play important roles in binding the peptide substrate and ensuring its proper orientation for catalysis. Within the peptide substrate, the Gln residue has the largest number of binding interactions with the protease. The S1 pocket of the active site of 3CLpro is mainly formed by residues Phe140, Leu141, Asn142, Glu166, His163, and His172 from one monomer and Ser1 of the other monomer (35). The ionizable groups in the active site of an enzyme must adopt the proper orientation to ensure substrate binding and functional catalysis. Although previous MD and crystallographic studies have discussed the pH-dependent conformational changes of 3CLpro of SARS-CoV and SARS-CoV-2, this study is the first to report experimental pH profiles of the kinetic parameters of the WT enzyme and variants with mutations of key histidine residues at the substrate binding site of SARS-CoV-2 3CLpro.
The pH profile of k cat/K m of SARS-CoV-2 3CLpro is bell-shaped with pK a values of 6.9 ± 0.1 and 9.4 ± 0.1 (Fig. 2), which are likely attributable to the ionizable side chains of the catalytic dyad His41 and Cys145, respectively. The pH profiles of k cat and k cat/K m of H164A, H172A and H172Y are similar in shape to those of the WT enzyme, with comparable pK a values (Fig. 6). The similar shapes of the pH profiles and calculated pK a values of the mutants and the WT enzyme further indicate that the estimated pK a values correspond to the catalytic dyad His41 and Cys145. Our findings are in agreement with previous MD studies proposing a general base mechanism of 3CLpro in which the catalytic residues are neutral at physiological pH (i.e. non-ion-pair mechanism), with pK a values of 6.4 and 8.3 for the catalytic dyad His41 and Cys145, respectively (21,26,27,35, 36, 37). A comprehensive MD study of the neutral and zwitterionic states of the catalytic dyad of SARS-CoV-2 3CLpro found enhanced binding and stability of the peptide substrate in the proper mode for catalysis when the catalytic residues were in the neutral form (27). By contrast, the zwitterionic state of 3CLpro disturbed domain I of the active site and impaired substrate binding (27). Our experimental pH profiles of k cat/K m support the need for a neutral state of the catalytic dyad to ensure proper substrate binding (i.e., low K m values) and maximal enzyme catalysis (high k cat values). However, the pH profile of k cat yielded only one titratable group, the imidazole side chain of His41, is needed for optimal enzyme catalysis, with a pK a of 6.7 ± 0.1.
In addition to the active site residues, computational studies of the protonation states of conserved histidine residues in the substrate-binding site have reported that H163 and H172 have pK a values of <5.0 and 6.6, respectively, and thus exist in their neutral states under physiological conditions (26,36). At physiological pH, the neutral (singly protonated Nε) state of His163 facilitates polar and non-polar contacts to maintain a stable S1 binding pocket. In fact, a structural analysis of the 3CLpro substrate-binding site found that a H-bond forms between His163 (at Nε) and the highly conserved Gln substrate residue (at its side chain carbonyl oxygen); this interaction requires a neutral His163 to ensure the absolute specificity of 3CLpro for Gln at P1 of the peptide substrate (34).
Our work demonstrates that hydrogen bonding interactions between His163 (at Nε) with the side chain carbonyl oxygen of P1 Gln of the peptide substrate at a 2.4Å is crucial for the activity of 3CLpro of SARS-CoV-2 (Fig. 1). Hence, different amino acid substitutions of H163 including substitution of arginine (H163R), lysine (H164K), and tyrosine (H163Y) could not recover the activity of 3CLpro. Even though, arginine, lysine, and tyrosine are able to form hydrogen bonding interaction; however, the large size of their side chains did not facilitate the required H-bonding distances with the peptide substrate. Overall, H-bond with specific distance between His163 and P1 of the peptide substrate is crucial to facilitate proper peptide substrate orientation for optimum activities of 3CLpro of SARS-CoV-2.
Additionally, His163 participates in aromatic stacking with Phe140 to support the neutral state of His163 and ensure its optimal interaction with the substrate Gln (26,34). The neutral side chain of His163 (at Nδ) also acts as a H-bond acceptor in a H-bond interaction with the side chain of the donor Tyr161 (38). MD simulations further revealed that full protonation of H163 results in the spontaneous collapse of the binding pocket and inactivation of 3CLpro (25,38).
The estimated pK a of H172 was 6.6; thus, this residue is also neutral in the optimum pH range (7.5–8.5) of WT 3CLpro (26,36). In fact, MD simulations have indicated that protonation of His172 at pH 6.0 results in collapse of the oxyanion hole, leading to conformational deactivation of the S1 pocket (23,26). The imidazole side chain of the neutral H172 forms a conserved H-bond with the side chain of Glu166, a key interaction that is lost upon H172 protonation (19,25,34). Additionally, a computational analysis demonstrated that protonation of His172 abolishes its interaction with Ser1 in the N-finger domain of the opposite monomer, whereas this interaction is maintained when H172 is in the neutral state (26).
Consequently, our results provide experimental evidence of the important roles of H163 and H172 in maintaining the catalytic activity of 3CLpro. Alanine substitution of H163 resulted in complete loss of enzymatic activity, and substitution with other amino acids did not recover 3CLpro activity (Fig. 3A and 3B). Our data provide experimental proof of the reported interaction between the imidazole of His163 (at Nε2) and the highly conserved Gln residue of the peptide substrate (38). Alanine substitution of H172 reduced the catalytic activity and kinetic parameters of 3CLpro by 80% compared with WT (Fig. 3 and 4). These reductions may be due to the loss of the H-bond interaction between the side chains of His172 and Glu166, which was previously reported to be critical for stabilizing the oxyanion hole and hence activating enzyme function (19,23). We experimentally verified the neutral state of His172 by introducing lysine, arginine and tyrosine mutations and assessing the activity and pH profiles of the resulting mutants (Fig. 3, 4 and 6). H172K and H172R were inactive, and only H172Y resulted in full recovery of activity similar to that of WT 3CLpro (Fig. 3D).
In light of these findings, a catalytic mechanism of 3CLpro can be proposed (Fig. 7 ). Catalysis begins with the deprotonation of the thiol side chain of Cys145 by His41 to facilitate nucleophilic attack of Cys145 on the carbonyl carbon of glutamine in the polyprotein backbone and the formation of a covalent thioester bond. The resulting tetrahedral thiohemiacetal intermediate contains an oxyanion group that is stabilized by hydrogen bonding with the amides of the main chain residues Ser139-Leu141. Subsequent collapse of the thiohemiacetal complex releases the C-terminal segment of the polypeptide substrate (20,21,39), and hydrolysis of the thioester linkage by a water molecule displaces Cys145 and releases the N-terminal part of the polypeptide substrate.Figure 7 Proposed chemical mechanism of 3CLpro SARS-CoV-2.
In summary, our experimental data and previous MD studies support a general base mechanism of SARS-CoV-2 3CLpro in which the neutral states of the catalytic dyad residues and conserved active site histidine residues are required for catalysis. In addition, we highlight the importance of the neutral states of His163 and His172 for achieving a fully active enzyme state.
Experimental procedures
Expression and purification of WT and mutant 3CLpro
The recombinant genes for WT and mutant 3CLpro were introduced into pET28b(+) bacterial expression vectors by GenScript Inc. (Piscataway, NJ). The vectors were used to transform E. coli BL21-CodonPlus-RIL (Stratagene) for protein expression as previously described (31). The inoculated culture (1 L) was grown in terrific broth medium at 30 °C in the presence of 100 mg/L kanamycin and 50 mg/L chloramphenicol until the absorbance at 600 nm reached 0.8. The temperature was then lowered to 15 °C, and protein expression was induced overnight with 0.5 mM IPTG. The cells were harvested by centrifugation at 8000 rpm and 4 °C for 10 min in an Avanti J26-XPI centrifuge (Beckman Coulter Inc.). The cells were homogenized in lysis buffer containing 20 mM Tris pH 7.5, 150 mM NaCl, 5 mM imidazole, 3 mM β-mercaptoethanol (β-ME), and 0.1% protease inhibitor cocktail (Sigma–Aldrich: P8849). The cell lysates were sonicated on ice before centrifugation at 40,000 xg for 45 min at 4 °C.
The supernatant was loaded onto a ProBond nickel-chelating column (Life Technologies) previously equilibrated with binding buffer containing 20 mM Tris pH 7.5, 150 mM NaCl, 5 mM imidazole, and 3 mM β-mercaptoethanol (β-ME) at 4 °C. The column was washed with binding buffer followed by washing buffer containing 20 mM Tris pH 7.5, 150 mM NaCl, 25 mM imidazole, and 3 mM β-ME. The Hisx6-tagged 3CLpro enzyme was eluted with 20 mM Tris pH 7.5, 150 mM NaCl, 300 mM imidazole, and 3 mM βME. Finally, the fractions containing 3CLpro were loaded onto a HiLoad 16/600 Superdex 200 size-exclusion column (GE Healthcare) on an ÄKTA pure 25 chromatography system (Cytiva, USA). The gel filtration column was pre-equilibrated with 20 mM HEPES pH 7.5, 100 mM NaCl, and 0.5 mM TCEP. The final protein was collected and concentrated to 55 μM as determined by the Bradford assay, and the protein purity was assessed via SDS-PAGE.
Enzymatic activity analysis
The enzymatic activities of WT 3CLpro and the histidine mutants were assessed by a FRET-based assay using the 14-amino-acid fluorogenic peptide substrate (DABCYL)KTSAVLQ↓SGFRKME(EDANS)-NH2 (GenScript, Inc) as described previously (12,19,28,29,40, 41, 42). The reaction was initiated by adding WT or mutant 3CLpro to the peptide substrate in 20 mM HEPES, pH 7.0, 100 mM NaCl, 1 mM EDTA, and 1 mM TCEP. The assay buffer contained 20% (v/v) DMSO to reduce the aggregation of the peptide substrate and enhance its stability (22). The reaction rate was measured for 10 min at 30 °C in a thermostatically controlled cell compartment. The catalytic rates were determined from the cleavage of the fluorogenic substrate, which was monitored by the increase in the fluorescence signal upon release of the EDANS group in a 96-well plate assay format in a Cytation 5 multimode microplate reader (Biotek Instruments). The fluorescence signal was monitored at λex of 360 nm and λem of 500 nm.
To account for the inner filter effect in the FRET enzymatic assay, first, the excitation coefficient of free EDANS was determined in the absence of the peptide substrate by varying the concentration of free EDANS, f0(EDANS). Next, the correction factor (Corr%) required to correct for the decrease in the emission signal of the fluorogenic substrate in the presence of the quencher (DABCYL) was calculated (22,40,41,43,44). To calculate Corr%, the fluorescence of a fixed concentration (50 μM) of free EDANS was measured in the absence, f(S), and presence, f(S+EDANS), of various concentrations of the peptide substrate (from 20 to 500 μM):fs(EDANS)=f(S+EDANS)−f(S)
To determine Corr%, the emission reduction of free EDANS at a specific substrate concentration, fs (EDANS), was compared with that of EDANS in the absence of peptide substrate, f0 (EDANS).Corr=fs(EDANS)f0(EDANS)
The values of Corr% calculated at different peptide substrate concentrations were taken into consideration when measuring the cleavage rate of 3CLpro. The effect of histidine mutations on the catalytic rate of 3CLpro was determined by measuring enzymatic activity at different enzyme concentrations ranging from 0.5 μM to 5.0 μM and a fixed peptide substrate concentration of 60 μM. The relative activity of each mutant was obtained from the slope of the straight line for each mutant.
Initial velocity studies and pH dependence of kinetic parameters
Next, initial velocity studies were performed to determine the kinetic parameters k cat and K m for the WT enzyme and the histidines’ catalytically active histidine mutants. The concentration of the peptide substrate was varied from 20 μM to 500 μM at a fixed enzyme concentration. Three independent experiments were performed with triplicate measurement each to obtain the cleavage rate data that were fitted to the Michaelis–Menten equation using the global fitting analysis function in the kinetics module of SigmaPlot (Systat Software, Inc). The kinetic parameters, k cat and K m were obtained and the standard error bars were calculated from triplicate measurements of each reaction using GraphPad Prism 9.0 software. The results are presented as the mean ± SD.
The pH profiles of WT 3CLpro and the catalytically active mutants H164A, H172A, and H172Y were measured using the FRET enzymatic assay by varying the concentration of the peptide substrate from 20 to 500 μM at a fixed enzyme concentration. The reaction buffers were prepared over a pH range of 5.5–10 using 20 mM MES for pH 5.5–6.5, 20 mM HEPES for pH 7.0–8.0, and 20 mM CHES for pH 8.5–10.0. All reaction buffers contained 100 mM NaCl, 1 mM EDTA, 1 mM TCEP and 20% (v/v) DMSO. Initial velocity studies were performed to determine the kinetic parameters k cat and k cat/K m as a function of pH. GraFit 5.0 software (Erithacus Software Ltd.) was used to determine the pK a values. Equation 1 was used to fit the pH profile data of k cat with a single ionizable group resulting in a half-bell curve with zero activity at low pH and an activity plateau at high pH. Equation 2 was used to fit the pH profile data of k cat/K m with two ionizable groups resulting in a bell-shaped curve with zero activity at low and high pH.eq 1 k=k(limit)10pH−pKa10pH−pKa+1
eq 2 k=k(limit)[11+10pK1−pH+10pH−pK2]
In equation 1, k is k cat, k (limit) corresponds to the maximum limit of k cat, and pK a is the dissociation constant of the single ionizable group. In equation 2, k is k cat/K m, k (limit) corresponds to the maximum limit of k cat/K m, and pK a1 and pK a2 are the dissociation constants of the first and second ionizable groups. The three independent pH profile measurements were analyzed using GraFit 5.0 software that provided the pK a values and standard errors.
Differential scanning calorimetry (DSC)
The effects of mutations on the thermodynamic stability of 3CLpro were assessed by DSC in a Nano-DSC instrument (TA Instruments, New Castle, DE, United States). A fixed enzyme concentration of 25 μM was used in buffer containing 20 mM HEPES pH 7.4, 100 mM NaCl and 0.5 mM TCEP. All samples were scanned from 15 to 75 °C at a temperature ramp rate of 1 °C/min. The buffer was used as a reference, and the protein samples were degassed for 10 min prior to the start of each analysis run. The DSC scans were acquired by ramping up the temperature twice to obtain two thermograms; the second scan was used as the buffer background for each sample. The lack of signal in the second ramp-up temperature scan confirmed that the melting transitions of all 3CLpro variants were irreversible. The DSC scans were normalized for protein concentration and baseline corrected by subtracting the corresponding buffer baseline. The data were then converted to plots of excess heat capacity (Cp) as a function of temperature. The Tm of 3CLpro was determined from the temperature at the apex of the thermal transition, and the calorimetric enthalpy (ΔH cal) of the transition was estimated from the area under the thermal transition curve using NanoAnalyze Software v3.11.0 (TA Instruments).
Data Availability
The authors declare that all data that support the findings of this study are available within this paper and its accompanying files.
Supporting information
This article contains supporting information.
Competing Interests
The authors declare that they have no interests that conflict with the content of this article.
Supplementary Data
Acknowledgments
This research was partially carried out using the Core Technology Platforms resources at New York University Abu Dhabi.
Funding
This work was supported by New York University Abu Dhabi through research funds provided to the lab of Prof. Rabeh in addition to the COVID-19 Facilitator Research Fund (grant number: ADC05).
CRediT author statement
K. Al Adem, J.C. Ferreira and S. Fadl: expressed 3CLpro, conducted the biochemical analysis, and analyzed the data. K. Al Adem and W.M. Rabeh: designed the biochemical experiments, supervised the project, and wrote the manuscript.
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| 36509143 | PMC9733303 | NO-CC CODE | 2022-12-14 23:36:15 | no | J Biol Chem. 2022 Dec 9;:102790 | utf-8 | J Biol Chem | 2,022 | 10.1016/j.jbc.2022.102790 | oa_other |
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Mol Immunol
Mol Immunol
Molecular Immunology
0161-5890
1872-9142
Elsevier Ltd.
S0161-5890(22)00492-8
10.1016/j.molimm.2022.12.002
Article
Synergism of TNF-α and IFN-β triggers human airway epithelial cells death by apoptosis and pyroptosis
Sun Rui a
Jiang Kaimin a
Zeng Chengyue a
Zhu Rui b
Chu Hanyu a
Liu Huiyong a
Du Jingchun ac1⁎
a Department of Clinical Immunology, Kingmed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, Guangdong 510182, China
b Department of Obstetrics and Gynecology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China
c Guangzhou Key Laboratory of Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, Guangzhou Medical University, Guangzhou, Guangdong 510182, China
⁎ Corresponding author at: Department of Clinical Immunology, Kingmed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, Guangdong 510182, China.
1 ORCID ID: http://orcid.org/0000-0002-0571-7444
9 12 2022
1 2023
9 12 2022
153 160169
21 6 2022
19 10 2022
3 12 2022
© 2022 Elsevier Ltd. All rights reserved.
2022
Elsevier Ltd
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Cytokine release syndrome, also called cytokine storm, could cause lung tissue damage, acute respiratory distress syndrome (ARDS) and even death during SARS-CoV-2 infection. However, the underlying mechanisms of cytokine storm still remain unknown. Among these cytokines, the function of TNF-α and type I IFNs especially deserved further investigation. Here, we first found that TNF-α and IFN-β synergistically induced human airway epithelial cells BEAS-2B death. Mechanistically, the combination of TNF-α and IFN-β led to the activation of caspase-8 and caspase-3, which initiated BEAS-2B apoptosis. The activated caspase-8 and caspase-3 could further induce the cleavage and activation of gasdermin D (GSDMD) and gasdermin E (GSDME), which finally resulted in pro-inflammatory pyroptosis. The knock-down of caspase-8 and caspase-3 could effectively block the activation of GSDMD and GSDME, and then the death of BEAS-2B induced by TNF-α and IFN-β. In addition, pan-caspase inhibitor Z-VAD-FMK (ZVAD) and necrosulfonamide (NSA) could inhibit BEAS-2B death induced by TNF-α and IFN-β. Overall, our work revealed one possible mechanism that cytokine storm causes airway epithelial cells (AECs) damage and ARDS. These results indicated that blocking TNF-α and IFN-β-mediated AECs death may be a potential target to treat related viral infectious diseases, such as COVID-19.
Keywords
TNF-α
IFN-β
Airway epithelial cells
Apoptosis
Pyroptosis
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pmc1 Introduction
The ongoing global pandemic of coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused 535,248,141 confirmed cases, including 6,313,229 deaths (WHO, 2022). Characterization of histopathology and cellular localization of SARS-CoV-2 in the tissues of patients with fatal COVID-19 confirmed that SARS-CoV-2 mainly infected upper and lower airways epithelial cells, type 2 pneumocytes, and endothelial cells, which resulted in tracheobronchitis, diffuse alveolar damage (DAD) and vascular injury. As the first defense line, airway epithelial cells were first infected and attacked by SARS-CoV-2. So, respiratory mucosal ulceration and pathologic injury mixed with inflammatory cell infiltration were the classic traits of COVID-19 (Borczuk et al., 2020, Martines et al., 2020). However, the molecular mechanism employed by SARS-CoV-2 to destroy respiratory mucosal epithelium still remains unclarified.
At present, plenty of evidence indicated that COVID-19 mainly was fatal to elderly adults, which may be related to severe systemic elevation of several pro-inflammatory cytokines and then induced cytokine storm (Meftahi et al., 2020). Several studies reported that serum levels of pro-inflammatory cytokines, such as IFN-α, IFN-γ, IL-1β, IL-6, TNF-α, GM-CSF, IP10, C-reactive protein (CRP) et al., were markedly increased in patients with severe COVID-19 (Huang et al., 2020, Karki et al., 2021, Meftahi et al., 2020, Wang et al., 2020). Among these cytokines, the function of TNF-α and type I IFNs especially deserved further investigation (Lee and Shin, 2020). TNF-α is an important inflammatory cytokine, which promotes homeostasis by regulating inflammation, cell proliferation, differentiation, survival, and death in response to infection (Chen and Goeddel, 2002, Walczak, 2011). In general, TNF-α induces the formation of Complex I through its membrane receptor TNFR1, which activates NF-κB signaling and benefits cell survival (Silke, 2011). However, under certain conditions Complex I is dissociated to form Complex II, which could lead to apoptosis by activating caspase-8, or necroptosis mediated by RIPK3 (receptor-interacting protein kinase 3) and MLKL (mixed lineage kinase-like protein) (Pasparakis and Vandenabeele, 2015). Excessive or continuous involvement of TNF-α during infection, ischemia, or trauma could cause inflammatory disease, such as SIRS (systematic inflammatory response syndrome), and during which type I IFNs act as essential mediators in TNF-induced lethal inflammatory shock, possibly by enhancing cell death and inducing chemokines and WBCs (white blood cells) infiltration in tissues (Huys et al., 2009, Tracey et al., 1986). Based on present studies, we hypothesized that TNF-α and type I IFNs might mediate the destroy of respiratory mucosal epithelium during COVID-19.
Here, we modeled the effect of pro-inflammatory cytokines on human airway epithelial cells by culturing human airway epithelial cells BEAS-2B under different combinations of cytokines and first reported that TNF-α and IFN-β synergistically induced BEAS-2B death. Mechanistically, the co-treatment of TNF-α and IFN-β triggered BEAS-2B death by apoptotic and pyroptotic, not necroptotic pathway. In addition, pan-caspase inhibitor Z-VAD-FMK (ZVAD) and necrosulfonamide (NSA) could inhibit BEAS-2B death induced by TNF-α and IFN-β. Our findings may provide new insight to treat related viral infection and diseases, such as COVID-19.
2 Materials and methods
2.1 Cell culture
BEAS-2B, 16HBE, H1975, and HT29 cells were obtained from the ATCC and routinely cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), penicillin (100 U/ml) and streptomycin (100 μg/ml) in a 37 °C humidified incubator containing 5% CO2. All the cell lines were validated by short tandem repeat profiling analysis and were free of mycoplasma contamination.
2.2 Reagents
The following reagents were used: TNF-α (T, Novoprotein, Fremont, CA), IFN-β (Peprotech, Rocky Hill, NJ), IFN-γ (Novoprotein, Fremont, CA), Z-VAD-FMK (Z, ZVAD), Z-DEVD-FMK, Z-IETD-FMK, Smac-mimetic (S), GSK-872 and necrosulfonamide (NSA). All small molecular chemical compounds were provided by MCE, Shanghai, China. The compouds ZVAD, Z-DEVD-FMK, Z-IETD-FMK, S, GSK-872 and NSA were used at concentration of 20 μM, 20 μM, 10 μM, 100 nM, 10 μM and 5 μM, respectively. Antibodies against Caspase-8, Caspase-3, PARP1, RIPK3, pMLKL, MLKL, β-Actin, and GAPDH were purchased from Cell Signaling Technology, and antibodies against Caspase-1, GSDMD and GSDME were obtained from Abcam.
2.3 Cellular viability and death assays
Cells were plated in a 96-well plate (4 ×104 cells/ml) and treated with different combinations of TNF-α and type I/II IFNs with or without compounds for 72 h or TS for 3 h as indicated. Next, cell viability was analyzed by the CellTiter-Glo Luminescent Cell Viability Assay kit (Promega, Madison, WI), and cell death was determined by the Cytotoxicity LDH Assay kit (Dojindo, Kumamoto, Japan) according to the manufacturer’s instructions. Luminescence and absorbance were measured using a Microplate Reader (Bio-Rad, Hercules, CA). The results were shown as the ratio against the untreated control group, respectively.
To directly observe cell death, cells plated in 96-well plates (4 ×104 cells/ml) were treated as mentioned above, and then stained with Annexin V-FITC and PI (Sungene Biotech, Tianjin China), or nucleic acid dye SYTOX Green (Promega, Madison, WI) and DAPI (Sigma-Aldrich, St. Louis, MO) according to the manufacturer's instructions. To quantify the difference between each group, the ratio of positive cells were analyzed based on 10 high power fields (HPF, 200x).
2.4 Western blot analysis
After being treated as indicated, the cells were washed twice with cold PBS buffer, scraped, and lysed with lysis buffer (100 µl RIPA lysis buffer supplemented with protease and phosphatase inhibitors) to extract the whole protein. Protein concentration was determined by BCA Protein Assay Kit (Bio-rad) according to the manufacturer’s protocol. Cell lysates were separated by electrophoresis on SDS- PAGE gel and then transferred to PVDF (Millipore, Billerica, MA). The membranes were blocked with 5% skimmed milk in phosphate buffer containing 0.1% Tween-20 (PBST) for 30 min at room temperature, and incubated with primary antibodies specific for PARP1 (1:1000), Caspase-1 (1:1000), Caspase-8 (1:1000), Caspase-3 (1:1000), GSDME (1:1000), GSDMD (1:1000), RIPK3 (1:1000), pMLKL (1:1000), MLKL (1:1000), β-Actin(1:2000) or GAPDH (1:2000) at 4 °C overnight. After washed, the membranes were incubated with HRP-conjugated secondary antibodies at room temperature for 2 h, then detected using a chemiluminescence signaling detection kit (CST, Beverly, MA).
2.5 Immunofluorescence
Cells were fixed with 4% paraformaldehyde and then permeated with 0.3% Triton X-100. After blocking with 5% normal goat serum for 1 h, the cells were incubated with the primary antibody of active-Caspase3 (Promega, Madison, WI) diluted at 1:300 overnight at 4 °C, and then incubated with the Cy3-coupled secondary antibody (Abcam) diluted at 1:500 for 1 h at room temperature and protected from light. Cells were stained with DAPI according to the manufacturer's instructions, and the staining results were recorded and analyzed with Cell Imaging Multi-Mode Reader (BioTek, Winooski, VT).
2.6 Knock-down of caspase-8 and caspase-3
Gene-specific targeting oligos were cloned into the LentiCRISPR V2 vector (Addgene 52961), which was co-transfected with pMD2.G (Addgene 12259) and psPAX2 (Addgene 12260) into 293 T cells to produce lentiviruses. BEAS-2B were transduced with the viruses, and then were selected with 0.5 μg/ml puromycin and/or 50 μg/ml hygromycin. After two turns of selection, the pool cells were used for the followed assay. The following targeting sequences were used. Caspase-8, ATGATCAGACAGTATCCCCG and CAAATGAAAAGCAAACCTCG; Caspase-3,
CAAGGAATGACATCTCGGTC and ATGTCGATGCAGCAAACCTC.
2.7 ELISA
The supernatant media derived from the untreated control, BEAS-2B treated with TNF-α and IFN-β for 24 h and 48 h, were collected, and the concentration of IL-1β in which was measured using a human IL-1β ELISA kit (Neobioscience, Shenzhen, China), according to the instructions of the manufacturer.
2.8 Statistical analysis
All experiments were representative of three or more experiments. GraphPad Prism version 9.0 software was used for graph formation and data analysis. All quantitative data were shown as mean ± SEM. Statistical significance was determined by t test (two-tailed) for two groups or one-way ANOVA for three or more groups. Statistical significance was determined with: *P < 0.05; * *P < 0.01; * **P < 0.001; * ** *P < 0.0001; ns, not significant.
3 Results
3.1 TNF-α and IFN-β synergistically affect airway epithelial cells growth and death
To investigate which kind of cytokine affect airway epithelial cells growth, we first treated human airway epithelial cells BEAS-2B with different concentration of TNF-α, IFN-β, and IFN-γ, respectively, and found that all three kinds of cytokine had some degree of inhibitory effect on cell growth, while the effect of IFN-β was the most significant among the detected cytokines ( Fig. 1A). Next, the different combinations of three cytokines were used to treat BEAS-2B and we found that the combination of TNF-α and IFN-β significantly reduced cell viability and it seemed that IFN-γ lack the synergic effect with TNF-α and IFN-β (Fig. 1B). These results suggested that TNF-α and IFN-β had a synergistic effect on inhibiting cell growth. To further make sure the effect of cytokine concentration on cell survival, BEAS-2B were then treated with different concentration of TNF-α and IFN-β as indicated. The results indicated that the concentration of TNF-α in 20 ng/ml & 40 ng/ml had little difference on effect of cell survival, while the concentration of IFN-β had dramatically effect on cell survival and the inhibitory effect was concentration- dependent (Fig. 1C). So the concentration of all cytokines we used in the following experiments was 20 ng/ml. Moreover, the inhibitory effect of TNF-α and IFN-β was also time-dependent (Fig. 1D).Fig. 1 TNF-α and IFN-β synergistically inhibit human airway epithelial cells growth. The cell viability of BEAS-2B was detected after cells were treated with different concentration of TNF-α, IFN-β, and IFN-γ as indicated for 72 h (A), different combinations of TNF-α, IFN-β and IFN-γ (20 ng/ml of each cytokine) for 72 h (B), different concentrations of IFN-β as indicated for 72 h under two concentrations of TNF-α (C), TNF-α and IFN-β (20 ng/ml of each cytokine) for 24, 48 and 72 h, respectively (D). The results are representative of at least three separate experiments. All data are shown as mean ± SEM. * p < 0.05, * * p < 0.01, * ** p < 0.001, * ** * p < 0.0001, ns, not significant. All analysis was performed using the one-way ANOVA.
Fig. 1
On the other hand, the morphology change of BEAS-2B treated with TNF-α and IFN-β was observed and the characteristics of cell death phenomena, including a reduction in the extent of cell-cell adherence and the appearance of cellular debris, were very obvious ( Fig. 2A). To further understand the cell death, BEAS-2B treated with TNF-α and IFN-β were stained with SYTOX Green/DAPI and a large number of fluorescein-binding dying cells were observed when IFN-β was used alone, and more dying cells were observed when IFN-β was used in combination with TNF-α (Fig. 2B). As shown in Fig. 2C, the percentage of SYTOX Green positive cells were significantly higher in cytokines-treated BEAS-2B than those in control. In addition, the LDH levels in supernatant media were significantly increased after BEAS-2B was treated with TNF-α and IFN-β (Fig. 2D), which further confirmed the cytotoxic effect of TNF-α and IFN-β. These results indicated that cell membrane damage and cell death had occurred in TNF-α and IFN-β co-treated BEAS-2B. At the same time, TNF-α combined with IFN-β also significantly reduced the survival of another two lung tissue-derived cell lines 16HBE and H1975 cells (Fig. 2E). So it may be a common phenomenon that TNF-α and IFN-β synergistically damage airway epithelial cells. Taken together, these findings revealed that TNF-α enhances IFN-β -induced airway epithelial cells death.Fig. 2 TNF-α and IFN-β synergistically induce human airway epithelial cells death. (A) Representative light microscopy images of BEAS-2B after cells were treated with IFN-β alone or combined with TNF-α for 72 h. The typical dead cells were marked with arrow. (B) Representative fluorescence microscope images of BEAS-2B after cells were treated with IFN-β alone or combined with TNF-α for 72 h, and TS for 3 h. Green fluorescence represented dead cells and blue fluorescence represented total cells. Scale bar, 100 µm. (C) The difference of SYTOX Green-positive cells between control and treated groups were quantified. (D) The LDH released from cells was measured after BEAS-2B cells were treated as indicated for 72 h. (E) The cell viability was detected after 16HBE and H1975 cells were treated with TNF-α and IFN-β for 72 h, respectively. The results are representative of at least three separate experiments. Data are shown as mean ± SEM (C-E). * *p < 0.01, * ** p < 0.001, ns, not significant. Analysis was performed using the one-way ANOVA (C&D) or the t test (E).
Fig. 2
3.2 TNF-α and IFN-β induced apoptosis of BEAS-2B
To understand how TNF-α and IFN-β induce cell death in BEAS-2B, special compound inhibitors including broad-spectrum caspase inhibitor ZVAD, RIPK3 inhibitor GSK-872, and MLKL inhibitor NSA, were first used to identify possible programmed cell death pathway. The results indicated that both ZVAD and NSA, not GSK-872, could partially inhibit TNF-α/IFN-β-induced cell death ( Fig. 3A), which prompted that BEAS-2B might happen apoptosis and necroptosis after treatment with TNF-α and IFN-β. The appearance of Annexin V-FITC/PI double-positive cells after co-treatment with TNF-α and IFN-β further intensified our hypothesis (Fig. 3B). To confirm the apoptosis of BEAS-2B treated with TNF-α and IFN-β, the activation of caspase-3 was first detected by immunofluorescence staining and the activated caspase-3 was observed when IFN-β was used alone, and the activation of caspase-3 was more pronounced when TNF-α was combined with IFN-β (Fig. 3C&D), which indicated that TNF-α and IFN-β synergistically induced apoptosis in BEAS-2B. Then, the western blot was used to further confirm the occurrence of apoptosis in BEAS-2B, the results indicated that caspase-8, caspase-3, and PARP1 were activated in both IFN-β and TNF-α/IFN-β treated cells and the activation of these effector molecules showed dramatically time-dependent. Moreover, IFN-β combined with TNF-α could more effectively activate caspase-8, caspase-3, and PARP1 compared to IFN-β alone, and ZVAD could inhibit the activation of these effector molecules induced by TNF-α and IFN-β (Fig. 3E). So, it could be concluded that apoptosis was truly occurred in BEAS-2B after treatment with TNF-α and IFN-β.Fig. 3 TNF-α and IFN-β synergistically induce apoptosis of BEAS-2B. (A) The inhibitory effect of specific compounds on BEAS-2B death induced by TNF-α and IFN-β were detected by cell viability assay. (B) BEAS-2B cells were treated with TNF-α and IFN-β for 72 h followed by Annexin V-FITC and PI staining. (C) The active-caspase-3 in BEAS-2B were detected by IF staining after cells were treated with TS for 3 h, and IFN-β alone or combined with TNF-α for 72 h. (D) The difference of activated caspase-3 between control and treated groups were quantified. (E) The cleavage and activation of caspase-8, caspase-3, and PARP1 were analyzed by western blot after BEAS-2B was treated as indicated for 24 and 48 h, respectively. (F) The cell death pathways of BEAS-2B induced by TS and TNF-α/IFN-β were compared using specific compound inhibitors as indicated. The results are representative of at least three separate experiments. Data are shown as mean ± SEM. * p < 0.05, * * p < 0.01, * ** p < 0.001, * ** * p < 0.0001, ns, not significant. Analysis was performed using the one-way ANOVA.
Fig. 3
In addition, the broad-spectrum caspase inhibitor ZVAD, caspase-3 inhibitor Z-DEVD-FMK and caspase-8 inhibitor Z-IETD-FMK were used to explore whether TNF-α and IFN-β induced other forms of cell death pathways besides apoptosis by comparing cell survival of BEAS-2B after treatment with TNF-α/IFN-β for 72 h and TNF-α/Smac-mimetic (T/S) for 24 h, respectively. The results indicated that caspase inhibitors could almost completely inhibit T/S-induced cell death, but not TNF-α/IFN-β-induced cell death, suggesting that the cell death pathways induced by T/S and TNF-α/IFN-β were not completely same, and TNF-α/IFN-β should also induce other form of cell death besides apoptosis (Fig. 3F).
3.3 TNF-α and IFN-β could not induce necroptosis of BEAS-2B
To investigate whether TNF-α and IFN-β could induce necroptosis of BEAS-2B, GSK-872 (RIPK3 inhibitor), and NSA (MLKL inhibitor) were used to inhibit TNF-α/IFN-β-induced cell death under the condition that caspase-8 activity was blocked with ZVAD. The results indicated that NSA, not GSK-872, could partially inhibit TNF-α/IFN-β-induced cell death when caspase-8 activity was deficient, which was also intensified by the immunofluorescence staining results ( Fig. 4A-C). But because GSK-872 and NSA did not consistently function inhibitory effect on cell death compared to control treated with TNF-α and IFN-β (Fig. 4A), whether TNF-α and IFN-β synergistically induced necroptosis of BEAS-2B deserved further investigation. First, the phosphorylated and total MLKL (executor of necroptosis) were detected and the results indicated that IFN-β and TNF-α/IFN-β just increased the expression of MLKL, and all combinational treatment did not induce phosphorylated MLKL in BEAS-2B (Fig. 4D). Secondly, the expression of RIPK3 was then detected, and the result indicated that RIPK3 was not expressed in BEAS-2B though it was expressed in HT29 (Fig. 4E). In general, the phosphorylation and activation of MLKL occur downstream of activation of protein kinases RIPK3, one key effect molecule of necroptosis, during happening of necroptosis (Pasparakis and Vandenabeele, 2015). Together, these findings explained why activated MLKL was deficient in TNF-α/IFN-β-treated BEAS-2B and demonstrated that TNF-α and IFN-β did not induce necroptosis of BEAS-2B.Fig. 4 TNF-α and IFN-β could not cause necroptosis of BEAS-2B. (A) The blocking effect of inhibitor GSK-872 and NSA on BEAS-2B death induced by TNF-α and IFN-β were detected under condition that enzymatic activity of caspase-8 was inhibited by ZVAD. (B) Representative fluorescence microscope images of BEAS-2B after treated with cytokines and inhibitors as indicated for 72 h. Green fluorescence represented dead cells and blue fluorescence represented total cells. Scale bar, 100 µm. (C) The difference of SYTOX Green-positive cells between control and treated groups were quantified. (D) The phosphorylated MLKL and total MLKL were analyzed by western blot after BEAS-2B were treated for 24 and 48 h with different combinations of cytokines and inhibitors as indicated. TSZ-treated HT29 was as a control. (E) The expression of RIPK3 in BEAS-2B and HT29 were analyzed by western blot. The results are representative of at least three separate experiments. Data are shown as mean ± SEM. * * p < 0.01, * ** p < 0.001. Analysis was performed using the one-way ANOVA.
Fig. 4
3.4 TNF-α and IFN-β induced pyroptosis of BEAS-2B by activating GSDMD and GSDME
Pyroptosis is one form of pro-inflammatory cell death pathway and depends on caspase-mediated cleavage and activation of gasdermin family proteins (GSDMs), which eventually leads to cell membrane rupture and the release of cytoplasmic contents, such as IL-1β. During the processing of pyroptosis, cells commonly show typical morphological changes, including plasma membrane swelling and bubble-like bulging. It was amazing that the typical morphological characteristics of pyroptotic cells also appeared in TNF-α/IFN-β-treated BEAS-2B ( Fig. 5A&B). At the same time, there existed the release of IL-1β after BEAS-2B were treated with TNF-α and IFN-β (Fig. 5C). It was reported that activated caspase-1, caspase-8 and caspase-3 could initiate pyroptosis by cleaving GSDMD and GSDME, respectively (Sarhan et al., 2018, Wang et al., 2017). So the activation of caspase-1 was first detected, the results indicated that TNF-α and IFN-β did not induce the activation of caspase-1 in BEAS-2B (Fig. 5D). To confirm whether TNF-α and IFN-β could induce pyroptosis of BEAS-2B by caspase-3/8-mediated pathway, caspase-3 inhibitor Z-DEVD-FMK and caspase-8 inhibitor Z-IETD-FMK with or without NSA were used to block cell death induced by TNF-α and IFN-β, the results indicated that combination of caspase-3 inhibitor and caspase-8 inhibitor could partially improve cell survival and reduce LDH release (Fig. 5E&F). Because of the possible off-target effect of small molecular inhibitor, and to further identify the activation role of caspase-8 and caspase-3 on GSDMD and GSDME, the expression of caspase-8 and caspase-3 were separately or simultaneously knocked down ( Fig. 6A&B). The expression decline of caspase-8 and caspase-3 obviously relieved the cytotoxic effect of TNF-α and IFN-β on BEAS-2B (Fig. 6C), and correspondingly caused the decline of activated GSDMD and GSDME (Fig. 6D&E).Fig. 5 TNF-α and IFN-β synergistically induce BEAS-2B death in a manner similar to pyroptosis. (A&B) Representative light and fluorescence microscopy images of BEAS-2B after treated with TNF-α and IFN-β for 72 h. The green fluorescence indicates Annexin V-FITC positive. Scale bar, 100 µm. (C) The concentration of IL-1β in supernatant media was assayed by ELISA, after BEAS-2B were treated with TNF-α and IFN-β. (D) The cleaved and full length fragment of caspase-1 was assayed by western blot, after BEAS-2B were treated as indicated. The cell viability (E) and LDH release (F) of BEAS-2B were determined after cells were treated as indicated for 72 h. The results are representative of at least three separate experiments. Data are shown as mean ± SEM. * p < 0.05, * * p < 0.01, * ** p < 0.001, * ** * p < 0.0001, ns, not significant. Analysis was performed using the one-way ANOVA.
Fig. 5
Fig. 6 Knock-down of caspase-8 and caspase-3 relieve the cytotoxic effect of TNF-α and IFN-β on BEAS-2B, and correspondingly cause the decline of activated GSDMD and GSDME. (A&B) The expression of caspase-8 and caspase-3 was knocked down by CRISPR- CAS9. (C) The cell viability of BEAS-2B were compared between control and caspase-8/3-KD cells after treatment with TNF-α and IFN-β for 72 h. (D&E) The activation of GSDMD and GSDME were compared between control and caspase-8/3-KD cells after treatment with TNF-α and IFN-β for 24 h and 48 h. The results are representative of at least three separate experiments. Data are shown as mean ± SEM. * p < 0.05, * * p < 0.01, * ** p < 0.001, * ** * p < 0.0001. Analysis was performed using the one-way ANOVA.
Fig. 6
To further investigate whether ZVAD and NSA could co-operate to block the cytotoxic effect of TNF-α and IFN-β on BEAS-2B, the expression and activation of GSDMD and GSDME were first examined, and the results indicated that ZVAD could obviously decline the cytotoxic cutting-fragment of GSDMD and GSDME induced by TNF-α and IFN-β ( Fig. 7A). NSA could co-operate with ZVAD to inhibit the activation of GSDMD and GSDME by non-directly blocking the activation of caspase-8 and caspase-3 (Fig. 7B-D). On the other hand, when the activation of caspase-8 and caspase-3 induced by TNF-α and IFN-β were inhibited by ZVAD, the activation of GSDMD and GSDME were correspondingly attenuated (Fig. 7D). Taken together, these results indicated that TNF-α and IFN-β induced the cleavage and activation of GSDMD and GSDME in BEAS-2B cells by activating caspase-8 and caspase-3, which finally resulted in the happening of pyroptosis, and ZVAD and NSA could co-operate to block this cytotoxic effect of TNF-α and IFN-β.Fig. 7 TNF-α and IFN-β synergistically induce pyroptosis of BEAS-2B by activating both GSDMD and GSDME. (A&D) The cleavage and activation of caspase-8, caspase-3, PARP1, GSDMD and GSDME were analyzed by western blot after BEAS2B were treated as indicated for 24 and 48 h, respectively. (B) The fluorescence microscope images of BEAS-2B after cells were treated as indicated for 72 h. The red fluorescence indicates activated caspase-3 and the blue fluorescence indicates total cells. (C) The difference of activated caspase-3 between control and treated groups were quantified. Scale bar, 100 µm. The results are representative of at least three separate experiments. Data are shown as mean ± SEM. * * p < 0.01. Analysis was performed using the one-way ANOVA.
Fig. 7
4 Discussion
Present reports have shown that cytokine storm rather than a direct damage effect of the virus itself plays a key important role in pneumonia, ARDS and multi-organ dysfunction caused by SARS-CoV-2 infection (Lucas et al., 2020, Mehta et al., 2020). However, the concrete pro-inflammatory cytokines those played a determinant role in COVID-19 still remain incompletely clear. At the same time, different cytokines may function different role in inducing different forms of cell death and tissue damage. For example, the combination of TNF-α and IFN-γ could induce inflammatory cell macrophage death, and then inflammation, tissue and organ damage (Karki et al., 2021). Type I IFNs could restrict SARS-CoV-2 infection of human airway epithelial cultures, but it seems that type I IFNs also exacerbated TNF-α and IL-1-drived inflammation in the progression to severe COVID-19 (Lee et al., 2020, Vanderheiden et al., 2020). Furthermore, type I IFNs could disrupt lung epithelial repair during recovery from influenza viral infection by directly reducing p53-mediated epithelial proliferation and differentiation (Major et al., 2020). These reports indicated that the effect of type I IFNs on airway epithelial cells was complex and the related molecular mechanism was not completely clear.
Our study first suggested that IFN-β could inhibit proliferation and induce death of human airway epithelial cells BEAS-2B, and TNF-α exacerbated this effect (Fig. 1&2). The morphological change of cells and release of a large amount of LDH indicated that BEAS-2B treated with TNF-α and IFN-β might undergo not only non-proinflammatory cell death but also pro-inflammatory cell death (Fig. 2). It was reported that the combinations of TNF-α and type I IFNs signaling pathways mediated the co-occurrence of apoptosis and necroptosis, leading to perinatal death in RIPK1-deficient mice (Kaiser et al., 2014). Next, we made sure whether the combination of TNF-α and IFN-β could induce apoptosis and necroptosis in BEAS-2B and found that broad-spectrum caspase inhibitor ZVAD and NSA, not RIPK3 inhibitor GSK-872, could block cell death induced by TNF-α and IFN-β, respectively (Fig. 3A). The Annexin V-FITC was positive and a large amount of activated caspase-3 was detected in BEAS-2B treated with TNF-α and IFN-β, which confirmed the occurrence of apoptosis, but could not confirm whether there was an occurrence of necroptosis in this progression (Fig. 3B-D). Moreover, as we traced further, the activated fragments of caspase-8, caspase-3, and PARP1 were detected, and the specific compound inhibitors blocked the activation of these effector molecules, all these results proved the occurrence of apoptosis in BEAS-2B treated with TNF-α and IFN-β (Fig. 3E). Interestingly, the death of BEAS-2B induced by TNF-α and IFN-β could not be completely blocked by caspase inhibitors, compared to the cell death induced by TS, which almost were completely blocked by these inhibitors (Fig. 3F). These results prompted that TNF-α and IFN-β might induce other forms of cell death pathways besides apoptosis.
The cascade of necroptotic signals initiated by TNF-α is usually activated in the absence of caspase-8 activity and executed by RIPK3-phosphorylated MLKL (He et al., 2009, Sun et al., 2012). In our study, specific inhibitors were used to confirm whether TNF-α and IFN-β could synergistically trigger BEAS-2B necroptosis in the condition that caspase-8 activity was blocked by ZVAD. The results indicated that ZVAD did not intensify BEAS-2B death induced by TNF-α and IFN-β, as expected, and NSA, not RIPK-3 inhibitor GSK-872, blocked the cytotoxic effect of TNF-α and IFN-β when the enzymatic activity of caspase-8 was blocked with ZVAD, which prompted that it seemed that there was no necroptosis happening in TNF-α and IFN-β treated BEAS-2B (Fig. 4A-C). The western blot results further confirmed that co-treatment of TNF-α and IFN-β could not activate MLKL by phosphorylation, but promote the expression of MLKL (Fig. 4D). The function of increased MLKL in BEAS-2B induced by TNF-α and IFN-β deserved further investigation in the future. Given that the activation of MLKL is dependent on the expression and phosphorylation of RIPK3 (Pasparakis and Vandenabeele, 2015), the expression of RIPK3 in BEAS-2B was detected by western blot. The results indicated that BEAS-2B did not express RIPK3 (Fig. 4E), which might explain why TNF-α and IFN-β could not induce BEAS-2B to undergo necroptosis. Generally speaking, cancer cells do not express RIPK3 due to genomic methylation near its transcriptional start site, thus RIPK3-dependent activation of MLKL and downstream necroptosis are largely blocked in cancer cells (Fukasawa et al., 2006, Koo et al., 2015). According to our knowledge, it was seldom reported that normal tissue-derived cells did not express RIPK3. So the physiological function and related mechanism of RIPK3 silencing in normal cells, such as airway epithelial cells, deserve further investigation.
Pyroptosis is another form of proinflammatory cell death pathway initiated by activated caspases and characterized by increased release of LDH and IL-1β, and the presence of PI-positive cells and bubbles in the cell membrane of dying cells (Li et al., 2018, Wang et al., 2017). All these features of pyroptosis were found in TNF-α and IFN-β treated BEAS-2B in our experiment, except activation of caspase-1 (Figs. 2D, 3B, 5A-D). The combination of special inhibitors of caspase-8 and caspase-3 could partially improve the cells survival and reduce the release of LDH from BEAS-2B treated with TNF-α and IFN-β (Fig. 5E&F), which might be related to that caspase-8 and caspase-3 could initiate pyroptosis by activating GSDMD and GSDME (Sarhan et al., 2018, Wang et al., 2017). The decrease of activeated GSDMD and GSDME induced by TNF-α and IFN-β, and of the inhibitory effect of TNF-α and IFN-β on cell viability provided more direct support about this view, after the expression of caspase-8 and caspase-3 were knocked down in BEAS-2B (Fig. 6). The western blot results further confirmed that GSDMD and GSDME were cleaved into active fragments with the elongation of treatment time (Fig. 7A). At the same time, ZVAD could inhibit the activation of caspase-8 and caspase-3, and then the activation of GSDMD and GSDME, NSA had the inhibitory co-effect on the activation of GSDMD and GSDME with ZVAD (Fig. 7B-D). All these data indicated that there existed pyroptosis in TNF-α and IFN-β-treated BEAS-2B. In addition, NSA could not obviously block the activation of GSDMD and GSDME, but could inhibit TNF-α and IFN-β-induced BEAS-2B death alone or in combination with ZVAD (Figs. 3A, 4A, 7 D), which might be related to that NSA could directly bind Cys191 of activated GSDMD to inhibit its oligomerization and the occurrence of pyroptosis (Rathkey et al., 2018).
Generally speaking, pyroptosis is triggered by the assembly and activation of inflammasomes and induced by inflammatory caspases, such as caspase-1/4/5 (Van Opdenbosch and Lamkanfi, 2019). Recently, it was reported that apoptotic casapse-8 and casapse-3 could also induce pyroptosis by cleaving GSDMD and GSDME, respectively (Sarhan et al., 2018, Wang et al., 2017) and our results also confirmed this conclusion. During pyroptosis, the cytokines and pathogens released from infected cells could recruit and activate immune cells to the site of infection, ultimately helping the host defend against invading pathogens. But the rapidly increased cytokines might also contribute to tissue damage and inflammatory disease (Man et al., 2017, Mandal et al., 2018). The function of IFN-β, which inhibited the replication of SARS-CoV-2 in airway epithelial cells on the one hand, and combined with TNF-α to induce airway epithelial cells death on the other hand, might reflect the double-edged sword feature of pyroptosis.
In conclusion, we first reported that the combination of TNF-α and IFN-β could induce airway epithelial cells BEAS-2B death. Furthermore, TNF-α and IFN-β mainly activated caspases-mediated apoptosis and GSDMs-mediated pyroptosis. At the same time, inhibiting activation of caspases and activity of GSDMs could block BEAS-2B death induced by TNF-α and IFN-β. These would provide insight for further elucidating the pathogenesis of COVID-19, and the development of targeted therapy for related viral infectious diseases.
Funding
This research was financially supported by the 10.13039/501100001809 National Natural Science Foundation of China (81101730), the Basic and Applied Basic Research Fund of Guangdong Province (2020A1515110562), the Science and Technology Project of Guangzhou City (202102100003, 202102020092), the University Innovation and Entrepreneurship Education Project of Guangzhou (2020PT102) and the Student Innovation Promotion Program of 10.13039/100009659 Guangzhou Medical University (202266J056).
CRediT authorship contribution statement
R.S. and J.D. conceived and designed the study, performed experiments, and wrote the article. K.J., C.Z., R.Z., H.C., and H.L. performed experiments and analysis. All authors read and approved the final manuscript.
Declarations of interest
None.
Data Availability
Data will be made available on request.
==== Refs
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| 36508750 | PMC9733417 | NO-CC CODE | 2022-12-14 23:36:19 | no | Mol Immunol. 2023 Jan 9; 153:160-169 | utf-8 | Mol Immunol | 2,022 | 10.1016/j.molimm.2022.12.002 | oa_other |
==== Front
Discourse Context Media
Discourse Context Media
Discourse, Context & Media
2211-6958
2211-6966
The Author. Published by Elsevier Ltd.
S2211-6958(22)00017-4
10.1016/j.dcm.2022.100594
100594
Article
‘My countrymen have never disappointed me’: Politics of service in Modi’s speeches during Covid-19
Sambaraju Rahul
School of Law, Trinity College Dublin, University of Dublin, Ireland
28 3 2022
6 2022
28 3 2022
47 100594100594
4 11 2021
25 2 2022
15 3 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.
In this paper I study discursive practices of Indian Prime Minister Narendra Modi during the COVID-19 pandemic. In response to the pandemic, political leadership across the globe had to take tough decisions such as restrictions on the social and personal lives of individuals. This meant addressing concerns over ensuring compliance with these restrictions. I examine how Modi managed these concerns in his communication with the Indian polity over TV and radio broadcasts. I do so in instances where Modi gave specific instructions about following restrictions or other COVID appropriate behaviours. Using discourse analysis, I analyse data from two prominent ways of communicating in the pandemic, Mann Ki Baat and addresses to the nation. Analyses show that Modi developed two sets of non-electoral relations across his communication, which treated compliance as normatively expected: a) between Modi and Indians and b) among Indians themselves. These relations made way for treating audiences as those who are in specific social roles where duty and service were normative. Instructions and their compliance were embedded in these roles and treated as expected and consequently moral acts. Modi’s discursive practices worked to perform a politics of service and duty, where compliance is ultimately treated as expected service.
Keywords
Compliance
COVID-19
Modi
India
Political discourse
Service
Duty
==== Body
pmc1 Introduction
The first few cases of COVID-19 were reported to the World Health Organization by China on December 31, 2019. Although identified at that time as pneumonia of unknown viral etiology, the virus causing the illness has since been identified as SARS-CoV-2. Soon after the first few cases, countries across the globe reported cases of COVID-19 leading the World Health Organization to declare COVID-19 as a pandemic on March 11, 2020 (WHO, 2020). Similar to initial responses to other pandemics or epidemics, initial responses to COVID-19 were primarily epidemiological, in so far as these addressed concerns over spread. For individuals and communities this implied complying with a range of restrictions on their movements and other tasks. Implementing these restrictions required able leadership. Indeed, the WHO Director-General Dr Ghebreyesus, in the speech where he declared COVID-19 as a pandemic, also nominated political leadership as key to safeguarding public health (WHO, 2020). In this paper, I examine one aspect of political leadership that was at the core of many actions taken in response to the COVID-19 pandemic – addressing compliance. I examine how political leadership engaged with compliance in the case of Union of India.
India reported its first case on January 30, 2020. Increasing numbers of cases were being reported from mid-March. To date in India, the reported deaths due to COVID-19 are 5,12,281 and excess deaths are estimated to be around 1,285,240, with 41,952,712 total cases since the pandemic began. On March 24, 2020, the Union Government of India declared a nation-wide lockdown in response to rising cases of COVID-19. From mid-June 2020, the Government treated this ‘first wave’ as abating, with gradual lifting of restrictions. In response to a ‘second wave’ in 2021 however, the Union Government did not impose a nation-wide lockdown. There were other forms of restrictions on social and personal lives. Alongside lockdowns, the Union and State Governments implemented a range of measures in the dedicated program of ‘#IndiaFightsCorona - COVID-19′.
Political leadership was particularly important in managing the COVID-19 pandemic since social measures, under the rubric of ‘COVID appropriate behaviours’, were the first line of defence until any medical interventions, such as vaccinations were available. In response to a pandemic such as this, political leadership is faced with two challenges: first is to take decisions that imposed restrictions on the polity in ways that could well be detrimental to many individuals and groups. Researchers have identified how trust in the national political leadership can promote compliance (Ezeibe et al., 2020, Faulkner, 2021). However, protests and anti-mask movements in several nations suggest that securing compliance is not an easy task (Faulkner, 2021, Favero and Pedersen, 2020, France 24, 2021). Second, an equally significant task is to communicate these decisions. In particular, decisions that involved compliance with the state-imposed restrictions required careful framing (Hunt, 2021). For instance, Wodak (2021) shows that political leaders in Europe framed the COVID-19 pandemic in ways that resonated with long cherished regional ideals in legitimizing these restrictive measures. In the present paper then, I examine similar discursive practices in the case of Union of India. I examine practices of India’s Prime Minister Narendra Damodardas Modi (Modi from hereon) in speeches that addressed concerns over compliance with COVID-appropriate behaviours1 .
2 The pandemic and political discourse
Discursive researchers show that politicians use several linguistic, discursive, and rhetorical practices to legitimize various forms of actions (Chilton et al., 2002, Van Dijk, 1998). Wodak’s (2021) study on European political leaders’ framing of the COVID-19 pandemic, mentioned above, focuses on discursive practices of framing the pandemic and responses to it. Wodak (2021) draws attention to the use of four discursive frames: religious resurrection, dialogue, trust, and war. For Wodak (2021) these frames are ‘typical patterns and characteristics’ (p. 336) of political discourses and significantly, were embedded in ‘nativist and nationalistic rhetoric’. It is noteworthy then that those restrictions were not merely framed in terms of health and illness, but implicated specific relations between individual themselves or individuals and a nation. Wodak argues that frames such as this are ways of dealing with anxieties about death. At the same time, these also develop specific forms of biopolitics.
In other contexts, researchers similarly show that other ways of discursively making sense of the pandemic involve political threads. Rohela et al (2020) show the use of ‘war metaphor’ in political and media discourse in India. Metaphors are powerful linguistic and rhetorical devices that define what is real and at the same time exclude what is not (Charteris-Black, 2014, Lakoff and Johnson, 2002). The use of metaphors can then promote and hide different ideologies (Van Leeuwan, 2018). Rohela et al (2020) do identify several problems with the use of the war metaphor, prominent among which is the short-sightedness of responses to COVID-19. Martinez-Brawley and Gualda (2020) show the pervasive use of war metaphor in Spain and the US. Other researchers identify the use of ‘nation as a family’ metaphor during the COVID-19 pandemic. Hunt (2021) examines the use of the ‘family metaphor’ by South African President Cyril Ramphosa during the COVID-19 pandemic to legitimize the imposition of lockdowns and other measures. Wodak (2021) shows similar uses of the family metaphor in the Austrian context during the COVID-19 pandemic in ways to legitimize the rules and restrictions imposed. Frames and metaphors identified above then are not merely opportune linguistic devices, but carry with them inferences about leadership, coordination, appropriateness of responses, and rejections of alternatives. The political significance of these comes from their use by political spokespersons in political contexts, like addressing the nation, but also through the ideologies effected through these frames and metaphors.
For researchers, who examine discourse of politics, the practices of communicating are central to the ideological effects of the use of metaphors and frames (Cammaerts, 2012). Researchers show that political discourse is mediated in ways that shape and frame the message and the audiences (see research collections in: Ekström and Firmstone, 2017, Partington and Taylor, 2018). A relevant finding from such research studies is that political discourse cannot be studied in isolation to the medium of its communication. The importance of media is primarily for the possibility for the communication to reach large and specific groups of audiences. In addition, different media themselves promote distinct forms of discursive practices (Bennett & Entman, 2001). An outcome then is that political discourse is not merely that which is designed for reception by public, but also shaped by and shapes media (Fetzer, 2013). Features of contemporary media add another layer of complexity, since distinctions between these platforms can become blurred – since content generated in one medium is readily made available and accessed via other media. Indeed Rai (2019) shows that Modi’s speeches are designed to be consumed across different media. For communication that purportedly address a wider polity then, a more relevant concern is around features of those being addressed.
3 Political discourse and audiences
It is by no means news that a substantial aspect of mediated political discourse is about the ‘people’, ‘polity’, or ‘citizens’. On the one hand, research shows that references to such vague groups or entities allows for justifying various political actions (Peters & Witschge, 2015; van Dijk, 2007). On the other hand, researchers make the argument that such entities themselves do not pre-exist the very political discourse (Chakravartty and Roy, 2015, Coleman and Ross, 2010). Constructions of these entities are then to be examined for how these are accomplished and their uses in specific occasions.
Examination of constructions of the polity or audience point to an important practice for politics, namely constructions that develop relations between the political leader and the wider polity (Mazzoleni, 2017). Political speakers might construct common group membership with those ostensibly being addressed or construct them as agents of action seeking to mobilize them (Condor, Tileagă & Billig, 2013). A common practice is the use of pronouns that show alignment or distancing of political spokespersons from these groups. De Fina (1995) argues that pronoun usage by political leaders can offer distinct inferences about how inclusive the leader is in relation to those being referred. Deriving from the work of Goffman (1981) on footing and the implications of taking differing stances with respect to what is being said, De Fina (1995) shows that the use of different pronouns works to flexibly align the speaker with the interests of different groups. She notes that the use of self-inclusive pronouns, such as ‘we’ and ‘us’ along with the absence of the singular ‘I’, in speeches of an Indian peasant organization leader in Mexico, work to legitimize the group, instead of the political leader, as political actor(s). In contrast, the use of singular ‘I’ is routinely used to offer evaluations and opinions, rather than to indicate inclusion or solidarity with groups. Wodak and van Dijk (2000) show that pronouns such as ‘we’ and ‘they’ can work to construct a distinct ‘other’ who is unrelated to the speaker.
Another notable practice is the use of membership categories. Sacks (1995) demonstrated that the use of membership categories, like ‘leader’, ‘citizen’, or ‘young’, are means of sense-making of and accomplishing social action. This is because the use of these categories offers inferences about known-in-common features associated with these categories. The features can be about normatively associated activities, rights and entitlements, or relations. A prominent feature of membership categories then is that these derive from membership categorization devices (Housley & Fitzgerald, 2015). Categories such as ‘mother’, ‘brother’, and ‘daughter’, are collected together by the device ‘family’, where mutual expectations of activities and entitlements are informed by culturally salient kinship relations. In political discourse, it is expected that devices of governance and democracy can collect categories of ‘Prime Minister’, ‘elected leader’, and ‘citizen’ in ways so that normative relations of accountability and responsibility can be mobilized (Housley & Fitzgerald, 2009). The normativity also makes way for moral character of the relations and expectations, since a breach of these normative duties is open to complaints (Jayyusi, 1984).
For political spokespersons these categorizations provide unique possibilities for implicating differing relations with social groups in ways that serve their own political projects. Again, pronouns – ‘we’, ‘they’, and ‘our’ – provide useful resources (Housley & Fitzgerald, 2009). LeCouteur et al. (2001) demonstrate that membership categories used by political actors in combination with pronouns enable flexible alignment with different groups to accomplish specific political ends. Their examination showed that Australian parliamentarians developed categorizations of themselves and Aboriginal peoples of Australia in ways to align themselves with the former or the wider national group of Australians in managing their support for controversial land policies. In taking-up these positions vis-à-vis audiences or others, political leaders present themselves as those who are in specific forms of leadership positions.
A similar note is voiced by political scientists. Chakravartty & Roy (2015) make a pertinent observation that the followers or enemies of political leaders or indeed other such groups do not exist a priori (p. 312). Rather these are constructed by political leaders in and through their discursive practices. The role of such sets of individuals either as intended or unintended audiences, or the ultimate targets of leaders’ discourses is of core concern in analyses of populism (Mazzoleni, 2007). In India political leaders have routinely made references to ‘aam admi’ (common man) as facing up to political elite in regional populist politics (Roy, 2014). This allows leaders to position themselves as aligned with the ‘common man’ in their resistance to elite politics, rather than as a politician doing politics. Discursive practices of alignment with ‘the people’ and opposition to ‘others’ were central to Modi’s politics and continued to inform his political discourse in the pandemic. Indeed, major announcements about the lockdown, such as its imposition, extension, and removal were all communicated by public announcements broadcast on TV and radio. These preceded official communications, which outlined specific rules and conditions. Modi’s political communication via media is then central to the management of the COVID-19 pandemic.
4 Modi, populism, and the pandemic
Modi was twice elected as India’s Prime Minister and both times with a notable majority. His political party, the Bharatiya Janata Party (Indian People’s Party), have over the last 8 years secured majorities in various states in India. Recent surveys suggest that Modi continues to enjoy strong approval ratings in India (Ghosh, 2021). Alongside explanations based in ethno-religious populism (Sinha, 2021; Vajpeyi, 2020), scholars identify the role of performative mediated populism in the form of Modi’s use of various media platforms in developing and marketing his image (Rai, 2019; Rodriguez & Niemann, 2019; Ward, 2014).
Rai (2019) shows that Modi was the first political leader in India to use media in combination with marketing and branding at a national level. For instance, Modi would use holographic projections of himself during political campaigns. Alongside this, he purportedly used hired ‘trolls’ to manage his brand image on social media (Rai, 2019). Rodriguez and Niemann (2019) show that Modi used social media to bypass broadcast news media, which might involve questions around policy, implementation, and outcomes. The authors argue that this demonstrates Modi’s abilities to prioritize political gains over constraints and affordances of broadcast media.
Simultaneously, broadcast news media have conceded to Modi’s politicking. Ward (2015) shows that Modi received disproportionately more coverage than other political leaders in the 2014 general elections. Chakravartty and Roy (2015) show how media and political institutions in India come together in developing novel networks that Modi has been able to exploit. They argue that the 2014 general elections could well be named ‘mediated elections’ where the political theatre was continuously conveyed to nation-wide audiences via broadcast news media. Bal (2020) makes the provocative argument that increasingly broadcast news media is a loyal echo-chamber for the BJP Government and Modi, with little by way of questions or critical reporting (also see: Rai, 2019).
Notwithstanding the acquiescence of mainstream broadcast news media, a notable element in Modi’s oeuvre is his personal communication via nationwide broadcast programs over the radio or India’s national television network, Doordarshan. Modi started a monthly program, Mann Ki Baat (loosely, ‘Speaking my Heart’) in October 2014 on All India Radio, where he speaks about policies, visions for the country, opposition, and a range of other matters. Further, Modi addresses recipients as ‘Friends’ or ‘countrymen’, a practice among others, which Bhaishya (2015) argues allows Modi to execute soft power (van Dijk, 1993). Bajpai (2021) makes two pertinent observations about Mann Ki Baat: first, she shows that the revival of a radio program in the contemporary times allows for synchronous listening and a distal asynchronous participation through ‘forwarding’ or ‘liking’ on other media platforms. Second, she demonstrates that Modi’s Mann Ki Baat establishes Modi as a sentimental leader who deeply cares about the Indian public. Together, Modi establishes a ‘new public intimacy’ (p.124) in and through a ritualistic performance. Several research papers from within India are exceedingly favourable of Mann Ki Baat (Gandhi and Balamurugan, 2017, Sharma and Dubey, 2021). Kaur and Mishra (2020) in a corpus analyses of Mann Ki Baat programs during the COVID-19 pandemic, offer a highly praiseworthy evaluation of Modi’s communication. This is interesting to note in the face of severe criticisms of how Modi managed the pandemic.
While many of these criticisms have come in the context of the severity of the second wave (March 2021-August 2021), initial criticisms pointed to the delayed response to the pandemic and ignoring scientific input (Ghoshal and Das, 2021). More severe criticisms were about India’s structural preparedness in dealing with a lockdown (Krishnan, 2020). For instance, the first lockdown (25 March 2020) was announced with a few hours warning leaving the public unprepared. India’s internal migrant workers were the worst affected with over 10 million workers having to leave urban places with no public transportation, which resulted in around 200 deaths during their travel (Yadav, 2020). In contrast, Modi was praised for the handling of the first wave, through imposing an early lockdown for example (Sikander, 2021). While it is unclear if this is the reason, but India did report fewer cases and deaths than what was anticipated (Frayer, 2021).
However, extreme death and suffering in the second wave, with an estimated 300,000 deaths between May and September 2021 (Rukmini, 2021), is widely attributed to Modi government’s mismanagement. This has involved references to the lack of preparation after the first wave, which resulted in severe shortage of supply of oxygen and other medical facilities during the second wave (Choudhary, 2021), easing of restrictions, holding outdoor political campaigning and religious festivals, and exporting vaccines to other countries (Ganguly, 2021). The BJP government routinely denied any mismanagement (John, 2021). Government spokespersons dismissed opposition parties’ claims and instead held them responsible for spreading misinformation (Joy, 2021). Critics on social media and elsewhere were targeted legally, through changing internet regulation laws that restricted platforms like Twitter from hosting critical content, or by trolls (Ghaffary, 2021). The Modi government launched a ‘positivity drive’ to address these criticisms and offer a more favourable view of the pandemic situation (John, 2021). It is in this rhetorical space that Modi’s own communication must be studied. For Modi then, the issues are not merely about communicating COVID-19 related instructions, but also managing concerns over his own political leadership and wider politics. Modi’s communication about compliance becomes important in this light, since the discourses that are part of this communication can offer specific roles to Modi himself, the government, wider polity, and how Modi relates to the Indian polity.
5 The present study
The present study the examines how compliance with COVID-appropriate behaviours was a concern for Modi in his political discourse. Rather than examine which factors promote or suppress compliance (Ezeibe et al., 2020, Liekefett and Becker, 2021), the present analysis focuses on how Modi oriented to and dealt with compliance as a concern in managing the COVID-19 pandemic, in his communication with the Indian audiences. To do so, it focuses on Modi’s communication about instructions around the COVID appropriate behaviours, vaccination, and other forms of activities proposed to manage the pandemic. Constructions of Modi and the audiences in terms of electoral relations will bring up expectations of activities and entitlements normatively associated with a government and its citizens/residents. These assumptions can be about the role of the government, such as to provide for the public, and the rights and entitlements of citizens/constituents, such as those of freedom and recourse to due processes of law. Similarly, giving and following instructions are expected aspects of relations between a government and its citizens.
Alternative constructions of political leaders and other groups can develop forms of relations that are not based on social arrangements other than electoral relations. Hunt (2021), in this special issue, shows that the President of South Africa Cyril Ramaphosa uses metaphors of family in treating the relations between himself and the public as one that is based on a kinship than a political relation. Hunt (2021) argues that this allows him to maintain legitimacy and credibility in dealing with COVID-19 pandemic. In the present paper, I extend current research knowledge on discursive practices of political leaders through a dedicated focus on how relations between the political leader and the audience are constructed to effect specific outcomes in the COVID-19 pandemic. As researchers discussed above note, Modi is known to develop alternative forms of communication with the audiences, which embed various forms of relations between himself and his audiences. I then specifically ask what forms of relations were developed and used by Modi in managing concerns over compliance with COVID-19 appropriate behaviours.
6 Method
This paper takes a discourse analytic approach to examine broadcast speeches of Modi that focus on addressing issues of compliance with COVID appropriate behaviours in India during the COVID-19 pandemic.
6.1 Data and participants
The data for this paper come from naturally occurring political discourse, in line with much of discourse analytic work. Political communication in the form of broadcast speeches of Prime Minister Modi were retrieved online for the duration of the COVID-19 pandemic where restrictions were relevant: from 01 March 2020 to 31 August 2021. Speeches were accessed from official sources of the Indian government, namely the website primeminister.gov.in. Modi’s public communication was in two forms: one is the speeches and addresses to the nation broadcast on national and other TV networks, and the second is the monthly radio program Mann Ki Baat through which he addresses the Indian public. Both sets of data for within the above stated period constituted the corpus for this study: 35 media speeches and 18 Mann Ki Baat episodes. Speeches lasted between 20 and 48 min and were in Hindi-English, with one speech fully in English. Mann Ki Baat episodes lasted between 38 and 50 min and were all in Hindi-English.
6.2 Coding
In line with the focus of the study, I focused on those instances where Modi offered instructions and guidance on the COVID-19 pandemic. To do this, I thoroughly read and watched the official transcripts and the videos several times. The discursive practices of giving instructions also involved talk about the recipients of these instructions. Together then, instances in these data where Modi was giving instructions and references to Indian people in the context of the receiving instructions about the COVID-19 pandemic were coded in the corpus. Coding was done at the utterance level. In the absence of recipients’ orientation that treats whether an utterance was instruction-giving or merely advising, I coded all instances where action taking was the focus. As a result, a data set comprising of 78 instances was developed. This set involved instances where instructions were directed at ‘Indians’, a broader ‘we’, and specific groups of Indians, such as ‘youth’ or ‘farmers’. These instances were either accessed through the official transcripts provided (62) or where unclear were transcribed verbatim (16) to be prepared for analysis. As official transcripts these are unique social objects that are produced and edited for consumption by the public as part of institutional communication. However, the present purposes are to examine discursive practices of constructing Modi, Indian polity, and their relationship that made way for addressing compliance with actions during the pandemic. As such, the analysis did not differentiate between institutional and author-generated transcripts.
6.3 Analytic procedure
The data were analyzed using techniques of constructionist discourse analysis (Billig, 1991, Condor et al., 2013, McKinlay and McVittie, 2008) as relevant for mediated political discourse. This approach is derived from a social constructionist turn in social sciences (Billig, 1991; Edwards & Potter, 1995; Hepburn, 2005; Potter, 1996), according to which our social world is variously constructed in our social interactions. These constructions are made in and through discourse to address ongoing social action. Researchers undertake a close and systematic analysis of discourses to identify how and what social actions are accomplished. This approach admits both, a broader appreciation of social and discursive context and more micro-approaches that focus on membership categories and talk-in-interaction (Sacks, 1995). For the present paper, the approach has three procedural elements.
First, the descriptions are examined for constructions. I identify and examine descriptions where specific versions of Modi, the Indian polity, and the relations between them were constructed. I focused on the use of pronouns, descriptions of the type of agents Modi and Indians are, and the forms of relations between them. I concentrate on the use of specific pronouns (‘I’, ‘we’, ‘our’ and so on) for the footing-shifts (De Fina, 1995, Goffman, 1981) and the outcomes of these for the relations between Modi and the groups being referred to. I examine these relations in terms of reciprocity of duties and obligations in terms of the categorizations made of the Indian polity as those who are the recipients of these instructions. For this, I use techniques of membership categorization analysis (Housley & Fitzgerald, 2015), where the focus is on categorization of individuals as members in categories to mobilize the known-in-common features of these categories (Sacks, 1995). I examine forms of relations that were constructed by Modi through categorizing himself, the audience members, and the wider Indian polity. I then examine how compliance was managed through inferences arising from the normative relations between these categories of individuals.
Second, I examine these discursive features and their outcomes as ultimately rhetorical in focusing on the role of generic overhearers of these speeches. I treat these speeches as made not only for any one section of the Indian audience, but for any direct or indirect (such as this researcher and those who will read this paper) potential audiences. This means, I consider the rhetorical political space of criticisms from the opposition parties, scholars and commentators, and some sections of the media. I treat Modi’s discursive practices as rhetorical actions (Billig, 1991, Condor et al., 2013) that offer competing versions of agents, events, and actions to other versions, in addressing the issue of compliance.
Third, I consider the mediated aspect of these speeches. I consider how the speeches and Mann Ki Baat are routinized aspects of Modi’s communication with the Indian public, in treating these as direct communications with the Indian public and bypassing news media (Bajpai, 2021). A significant focus of the analysis is on the forms of leadership accomplished in and through these acts of political communication (Bajpai, 2021; Chakravartty and Roy, 2015, Van Dijk, 2006). Overall then, the speeches were analyzed for how specific versions of Modi, audience-citizens, and the relations between them were developed and used in addressing issues of compliance with the instructions given by Modi, alongside Modi’s own politics.
7 Results
The analysis examined how the issue of compliance was addressed in instances where Modi’s speeches were giving instructions to the Indian public. Results show that Modi addressed issues of compliance by constructing two types of relations: first, are non-electoral forms of relations between himself and the audience. These relations offered the inference that compliance was presumed and expected. Second, Modi offers these instructions as arising from specific forms of normative relations between groups of Indians as members of a nation. Below, I examine extracts that demonstrate these two aspects.
7.1 Modi and audiences
In Extracts 1, 2, and 3, Modi develops relations between himself and audiences, in ways that are different to that of between an elected representative and the electorate. These relations are developed in the process of making specific requests from the public in broadcast speeches.
Extract 1 comes from Modi’s first public speech about restrictions in response to the COVID-19 pandemic, transcribed verbatim by the author (a non-verbatim transcription follows below). This was broadcast live on March 19, 2020. In this speech Modi had imposed a 14-hour curfew in India from 7am to 9 pm on March 22, called ‘janata curfew’, characterized as a curfew of the people and by the people.
7.2 Extract 1
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7 Friends whenever I asked anything of you my countrymen have never disappointed me (.) the strength of your blessings is that all of us together us are moving forward towards chosen targets within us (.) our efforts are reaching success too (.) today to all you countrymen a total 130 crore countrymen I want to ask something I have come I want a few of your coming weeks (.) I want some of your coming time (.) my dear countrymen to date science has not been able to develop a certain solution to the Corona pandemic nor is there any vaccine yet (.) in this situation everyone is concerned which is quite natural
Extract 1 – non-verbatim transcript. Friends, whenever I asked you for something, you have never let me down. Our efforts succeed, only on the strength of your blessings. Today, I am here to ask you, all my fellow citizens, for something. I want your coming few weeks from you, your time in the near future. Friends, till now, science has not been able to find a definite solution to save us from the Corona pandemic, neither has a vaccine been developed. In such a situation, it is very natural to get worried.
Above, Modi develops relations between himself and audiences, in ways that treat his instructions as requests of service. First, Modi uses the address terms (Jaworski & Galasiński, 2000) ‘Friends’ and ‘countrymen’ (line 1), which do not readily suggest any electoral relation. While the latter is perhaps routine for politicians, it’s use along with the second-person pronoun ‘you’ and the categorization ‘my countrymen’ (line 1) indicate an active categorization by Modi of the audiences as co-members with himself in the national group. The address term ‘Friends’ is distinctly informal but is routine for Modi in his speeches (Chakravartty & Roy. 2015). It is notable that the content is designed in ways to set-up a probable acceptance of his request from recipients who might be familiar. There is then a tension between Modi as the prime minister addressing the electorate and the informality of his speech.
Second, a relation between Modi and these ‘countrymen’, is made available in the following description: ‘whenever I asked anything of you my countrymen have never disappointed me’. The pronouns ‘I’ and ‘you/your’ make it clear that the present matters concern Modi as an individual and the audiences as recipients of this request. Irrespective of possibilities for the truthfulness of this claim, the extreme case formulations (Pomerantz, 1986) ‘asked anything of you’ and ‘never disappointed me’ (line 1), treat it as routine that Indians willfully oblige Modi’s requests. This is noteworthy since it removes the necessity for the use of State resources in ensuring that the instructions of the government are indeed observed. Modi treats compliance as assumed and as an outcome of the unspecified relation between himself and audience-citizens. This is furthered through claims that Indians’ ‘blessings’ on Modi have contributed to favourable outcomes.
Third, Modi frames his request as about taking over their ‘time’. The request is proceeded by a pre-request (Fox, 2015) – ‘I want to ask something’ – that presents him as in a position where he cannot readily give orders (Clift, 2016). While this form of pre-request is routine for interactions where the requesting party does not see themselves as possessing rights to make such requests (such as children asking their parents; or those in junior positions asking their immediate seniors), in doing this here, Modi acts as if there is a gradient between himself and the audience-citizens where he cannot directly make claims on them, despite his position as the Prime Minister of the country. In presenting his instructions in this way, Modi frames the act of following instructions as an act of service that Indians have to grant him. This is a notably distinct form of framing instructions about following restrictions.
Together then, Modi’s earlier framing of the relation between himself and Indians, in non-institutional ways, and requesting the audience from a downgraded position make way for treating the instructions and possibly following these instructions as occurring in contexts of sacrifice and service. Notably, it minimizes possibilities for seeing Modi as ‘imposing’ restrictions on Indians. This way of characterizing instructions at the beginning of the pandemic then sets-up a frame for possible oncoming instructions in the future. In the extracts below, similar features are seen to varying degrees. In Extract 2, Modi develops a kinship relation with Indians in offering specific instructions about following lockdown restrictions.
7.3 Extract 2
Extract 2 comes from the transcript of Mann Ki Baat broadcast on April 24, 2020. By the end of April 2020, India reported a total of approximately 34,000 cases and 1,200 deaths (Worldometer, 2021).1
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6 My dear countrymen, in the midst of this pandemic, as a member of your family, and all of you happen to be my family members, it is also my responsibility to touch upon certain points and offer some suggestions. To my countrymen, I urge, let us not at all get caught in the trap of over-confidence, let us not harbor a feeling that if corona has not yet reached our city, our village, our street or our office, it is not going to reach now. See! Never make such a mistake!
Above, Modi presents himself as a ‘family member’ in delivering specific instructions framed as requests. Modi at lines 1–3, develops a reciprocal relation between himself and the audience, which is that of being family members. The use of extreme case formulation (Pomerantz, 1986) – ‘all of you’ – in developing this claim of kinship relations, addresses how it is that any of the audience members could be his family members. Even if a few of the audience members are his family, others are unlikely to be. The extreme case formulation offers an alternative, which is that the relation between Modi and the audiences, irrespective who they are, is one of kinship. This is notably distinct to normative understandings of family where individuals are related to each other, through kinship relations of ‘daughter’, ‘aunt’, ‘sister-in-law’ and so on (Sacks, 1995).
Throughout, Modi develops his affiliation with audiences through pronouns ‘I’, ‘us’ and ‘our’. These pronouns not only indicate his inclusion in the group along with the audiences, but also that the issues and concerns for them are shared by him. It is noteworthy that Modi treats this to mean that he normatively owes a responsibility to the audience in the form of ‘certain points’ and ‘some suggestions’, which are to be given. While being in a relation of kinship might offer several implications, Modi takes up the one about ‘being responsible’ for the audience. Again, this is an alternative form of responsibility to that of being in a governing position that Modi is normatively expected to fulfil as an elected member (see Hunt, 2021). In so doing, Modi treats his position vis-à-vis audience-citizens as that of a ‘caretaker’ with responsibilities to his kin and who will instruct them about appropriate behaviours. While the recipients may not receive these in this way, this serves as a means to rhetorically set-up future claims about how Modi has acted in the spirit of a responsible family member (Billig, 1991).
It is noteworthy that these suggestions are not problematic in so far as these refer to assumptions of ‘over-confidence’. Rather these suggestions deal with taking precautions about the possible spread of COVID-19. In framing these suggestions as those that are being given among family members and the act of making these suggestions as a ‘responsibility’, Modi eschews the view that his oncoming instructions are government mandates. Rather, he forwards the inference that these are acts of care that family members can mutually expect. In this way, Modi presents himself as acting in the interests of Indians as not an elected official, but as someone who cares in ways similar to that of a family member.
7.4 Extract 3
In Extract 3, Modi employs a similar frame of being a family member with audiences. Extract 3 comes from the transcript of a video titled ‘PM Modi addresses the nation’ broadcast on April 14, 2020.1
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4 If we cannot handle the next 21 days then the country and your family will go back 21 years if these 21 days we cannot manage them your family forever and ever can get destroyed and I say this not as a Prime Minister but as a member of your family (.) so forget what it means to go out for these days
In Extract 3, Modi similarly foregrounds an alternative relation to that of between an elected official and resident/citizen. Here, this is done to make an appeal towards effective adherence to the lockdown. Modi describes the possible consequences of not adhering to the lockdown in severe terms: ‘country and your family will go back 21 years’; ‘family forever and ever can get destroyed’. Both these descriptions indicate severe problems in case the lockdown is not adhered to. Notably, these treat family as the unit for which problems and concerns apply.
Rather than point to possible issues for individuals, Modi frames the consequences in terms of broader units: family and country. In so doing, he makes relevant that family is a meaningful frame for conveying the seriousness of the lockdown and by extension the pandemic. In that, his instructions are framed in ways to treat individuals as responsible agents for their own kinship relations. Further, the inclusion pronoun ‘we’ treats these consequences as applicable to audiences and himself. In that, he is just as responsible as audience members might be towards their own relations.
It is here that Modi introduces himself in ways alternative to that of an elected official: ‘not as a Prime Minister but as a family member’ (line 3). Specifically, Modi presents his use of the descriptions of problems with not adhering to the lockdown as arising from his position as a fellow family member. Doing this downgrades the possible inference that this is a threat to audiences and confers a sense of concern arising from being a fellow family member. Modi uses this positioning to mirror the responsibility that he takes-up as a fellow family member. Below, Modi similarly treats individual audience members as members in specific categories who are in normatively expected relations with other audience members to manage issues with compliance.
7.5 Relations among audiences
In the extracts in this section, Modi frames the instructions for COVID-appropriate behaviours in terms of normative relations that exist among audiences themselves. A notable aspect of the extracts in this section is that the pronoun use imposes a distance between Modi and audiences. Here, Modi directs audiences to undertake specific tasks and follow instructions.
7.6 Extract 4
Extract 4 was taken from the Mann Ki Baat episode broadcast on June 28, 2020. At this time, India reported approximately, 585, 792 total cases and 17,000 deaths (Worldometer, 2021).1
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6 Friends, during the unlock period, we have to stay more vigilant compared to the lockdown period. Only your alertness can save you from corona. Always, remember, if you do not wear a mask, do not observe the two-yard social distancing norms or do not take other precautions, you are putting others at risk besides yourselves, especially the elderly and children at home. Hence, I urge all countrymen…and I repeatedly do so…do not be negligent…take care of yourselves and others too.
In Extract 4, Modi frames adherence to COVID-appropriate behaviours as morally valuable. Here, Modi does not instruct audiences directly. Rather, these are framed in ‘if you do not do X, then…’ formulations which treat problematic outcomes as inevitable if these behaviours are not adhered to. Notably, the outcomes are described in terms of problems not just for individual audience members, but for ‘elderly and ‘children at home’ (line 5). These categories are noteworthy since these are readily hearable as those who are vulnerable. By implication, Modi positions the audiences as those who are less vulnerable and are possibly in breach of following those behaviours. In so doing, Modi develops a particular position for those who are to directly receive these instructions or primary audiences: those who occupy positions of less vulnerability and can take charge – ‘you are putting’ (line 4). Modi does not treat himself as part of those who are receiving instructions, since these are delivered to others: ‘you’ and ‘yourselves’. This distancing is notable given Extracts 2 and 3, where Modi works to develop commonality of membership. Here, it works to present Modi as giving instructions to achieve specific outcomes.
Identifying these types of agents as primary recipients frames the instructions as duties or service taken by the able for or on behalf of those who are perhaps less so. Modi’s subsequent appeal directed to ‘all countrymen’, treats similar caution as more broadly applicable. Overall, Modi treats following the COVID-appropriate behaviours as a moral responsibility arising from their relations with other family members or Indians. In Extract 5, Modi frames these behaviours as based on normative relations between farmers and other citizens.
7.7 Extract 5
Extract 5 comes from a video titled ‘PM’s speech at release of 8th instalment of financial benefit under PM-KISAN’ broadcast on May 14, 2021. By this time, India was going through the second wave of the COVID-19 pandemic with a rapid rise in cases. By the end of May 2021, India reported 200,000 new cases per day and around 3000 new deaths per day and a total of 30 million cases and 350,000 deaths (Worldmeters, 2021).1
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9 Friends in today’s program I want to country’s every farmer (.) brothers and sisters who live in villages again alert about corona (0.5) this infection has now reached villages very rapidly (.) country’s all governments are working to deal with this in all possible ways (.) in this (.) the awareness of people in villages is just as important as whatever our panchayat raj related systems that are there (.) their cooperation and consultation are just as necessary for our country (.) you have never disappointed the country this time also we have the same expectation (.) to be safe from corona you have to (.) at a personal level at a family level at a community level whatever compulsory steps (.) necessities there are we have to absolutely take them
In Extract 5, Modi develops specific relations between the wider Indian citizenry, and those who are the more direct audiences to this part of his speech, variously described as ‘farmer’ (line 1), ‘brothers and sisters who live in villages’ (lines 1–2), and ‘people in villages’ (line 4). Modi also describes the efforts and tasks taken by various levels of the Indian government in favourable ways. Described using extreme case formulations (Pomerantz, 1986) – ‘all governments’ and ‘all possible ways’ (line 1) – these efforts are constructed as fully directed at dealing with the pandemic. At the same time, Modi outlines a role for India’s residents too. First, Modi presents himself as one among other Indian residents who are in a specific relation with farmers and people in villages, through the pronoun ‘we’. This relation is developed through claims about how the latter have ‘never disappointed the country’ (line 6) and have to meet similar ‘expectations’ (line 7). These descriptions indicate that Modi and Indians owe a debt of gratitude to farmers and people in villages for their unspecified services, thus far. While the normative relation is not spelled out, Modi treats farmers and people in villages as helping and working for the rest of the country. Notably this again is not a relation embedded in the civic constitution of India. Rather, this mutuality is based on normative understandings of ‘farmers’ and ‘people in villages’, such as that they provide for the sustenance of ‘rest of the country’. This frames the current instructions for following COVID-appropriate behaviours as similar normative duties that famers and people in villages will take-up in relation to fellow Indian citizens. The selection and use of ‘farmers’ in this way, as those who provide for Indians rather than any other possible attributes of farmers, renders the instructions given to them as based in service.
7.8 Extract 6
In Extract 6, Modi instructs youth to take-up a range of tasks ostensibly in the interests of themselves and the nation. However, these tasks coincide with issues of vaccine availability. This extract comes from a speech broadcast live on April 8, 2021, where Modi is instructing Chief Ministers of various states in India about the pandemic. At this time, there were concerns with vaccine availability in India and especially for those between the ages of 18–45. This speech then potentially offers alternative means of dealing with vaccine shortages (PTI, 2021).1
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10 I will also exhort the country’s youth that that they should help those who are over 45 years around them to get their vaccine (.) and I have a unique appeal to the youth that you are healthy you are capable and you can do a lot of things (.) but if my country’s young man follows the SOPs of corona and protocols like maintaining distance or wearing masks (.) if my country’s youth follows them then corona has no power that it can reach near my country’s youth (.) first we should impress upon the young people to take on these precautions (.) how much we want to pressurize them to take vaccines if we do more convincing about following the protocols if our youth take up this gauntlet then they themselves will follow this protocol and get others to follow as well then see just like we were at the peak and came down we can do the same again
In Extract 6, Modi gives a series of tasks for ‘youth’ in India. These are given in the present tense on a broadcast speech and in so doing accomplish the act of performative leadership (Bajpayi, 2021; Chakravartty and Roy, 2015, Van Dijk, 2006). This performance involves, first constructing these tasks as exhortations, appeals, and requests. Second, while these tasks are framed towards ‘young persons’, these are also part of a broadcast that is seen/heard by several other types of individuals. In doing so, Modi makes it seeable and/or hearable to others the type of relation he has and performs with the ‘youth’, which relation (see Extract 1) positions him as a requester and the youth as those who are in a position to grant his requests. Third, Modi positions other audiences as those who are to ensure that the youth participate in these actions – ‘we should impress upon’ (line 6) – including himself. The act of requesting actions from a specific group (‘youth’), while giving roles to others, points to possible measures by which to ensure that these actions are indeed carried out. These elements together then, accomplish a performance of leadership for the audiences.
The categorization ‘youth’ is of interest for two reasons. First, it is used in contrast to another age-related categorization ‘over 45 years’, in offering a specific task. Modi’s description of task is around vaccination for those who are ‘over 45′. He characterizes this as an exhortation and that the task involves ‘help’, which together treat the task as favourable. The categorization ‘youth’ then treats its members as in a more capable position than those who are ‘over 45 years’. In so doing, Modi renders the instructions being given as those that are undertaken in the spirit of service for a wider ‘public good’. Second, Modi ascribes to this category, features that are normatively associated with age-related aspects of ‘youth’ (Sacks, 1995): ‘healthy’, ‘capable’ and ‘can do a lot of things’ (line 3). This set of features are based on age-related differences that are less likely to be negated. Modi’s instructions then can be seen as grounded in normative social relations.
Modi’s second task is phrased as an ‘appeal’ to observe COVID appropriate behaviors. While this can be mandated as part of the government’s handling of the pandemic, here Modi offers an alternative framing that turns on these features of the ‘youth’ in ways to suggest that they have ‘health on their side’. Modi orients to the broadcasting aspect of this speech in specifically developing a distinction between the youth who will have to be persuaded to take-up these behaviors and others ‘we’ (line 6). He instructs to this ‘we’, tasks of persuading and convincing the youth to practice COVID appropriate behaviors.
Modi specifies that these features of the ‘youth’ when applied to following COVID appropriate behaviors can prevent being infected by the Corona virus, without having to take a vaccine. One rhetorical outcome of this set of instructions is to minimize the importance of vaccinations for those who are under 45 years of age, in a context where India was facing vaccine shortages. Modi ascribes a favourable outcome to these forms of activities, helping those over 45 years of age to get vaccinated, following COVID appropriate behaviors, and motivating others to do the latter, in terms of lessening the numbers of COVID-19 cases. Again, the specific categorization used in framing relations with fellow Indians, treats the instructions being given to them as those which are undertaken in a spirit of service. For Indians, the instructions being given to Indians are then framed as duties or service that they will have to do rather than obligations routinely expected of citizens of a country.
8 Discussion
In this paper, I examined Modi’s political discourse about compliance with COVID appropriate and other actions in the context of the COVID-19 pandemic in India. The analysis focused on instances where instructions in dealing with the pandemic were the topics. The analysis examined two sets of practices by which Modi addressed issues of compliance: first, Modi developed relations between himself and the audiences that are different to that between an elected representative and the electorate, and second, developed relations of duty and service among audiences themselves, which offered a normative framework for undertaking the instructions. Together, these provide alternative relations to what can be expected to operate between an elected Prime Minister and the electorate. The alternative is to ground the instructions being given and the expectations to undertake them in a sense of duty or service.
First, Modi gives instructions based on non-electoral relations between himself and Indians. This involves presenting himself as a co-member in family categories, or as in a position that allows for acting with moral responsibility. This of course is notably distinct to the forms of responsibility that an elected representative might enact, such as that of claiming a sense of political duty. While lockdowns were legally enforced by the Union Government’s penal and health provision laws, the framing and justification of these in informal ways accomplishes important rhetorical functions. The present practice allows for presenting Modi as having a non-electoral direct relation with the Indian public. This achieves a similar political effect to that where Modi had side stepped broadcast news media and used alternative practices of communicating with the Indian polity (Chakravartty & Roy, 2015). Rhetorical and communicative practices developed relations where Modi is one with the public.
In the present data, using inclusive pronouns, Modi presents himself as part of the polity, which is facing the crisis, and shares their worries or responsibilities for acting. Consider Extracts 3 and 4, where instructions are offered in a framework of family. Modi presents himself as a family member and the audiences themselves as those who have families. Modi then treats the act of giving instructions as arising from a sense of family-bound responsibility or care and frames those receiving instructions in similar terms. For audiences then, the instructions are hearable as acts of care or duty that one normatively expects from family members rather than as acts owed because of shared citizenship. Again, irrespective of how the audience receive it, these discursive practices present Modi as performing specific forms of politics (Chakravartty and Roy, 2015, Rai, 2019; Sinha, 2021).
Second, Modi also offers specific versions of relations among audiences in ways that bring-up normative relations between various groups of Indians. It is normatively expected that ‘youth’ are healthy and can look out for others (Extract 6), just as the ‘elderly and children’ (Extract 4) are known to be vulnerable, or that ‘farmers’ provide sustenance for others (Extract 5). The use of these categories then brings-up normative relations that treat taking up COVID-appropriate actions as unproblematic.
Significantly, the use of these categories implies relations between groups of Indians that make way for mutual entitlement of duties or service. The relations then are not always bearing among citizens. It is this that confers a sense of morality in the following of these instructions (Jayyusi, 1984). Again, in Extracts 4, 5, and 6, Modi’s use of pronouns here distance himself from specific groups of audiences, such as the ‘youth’ or ‘farmers’, while aligning himself with the broader Indian polity. In so doing, Modi accomplishes voicing concerns and making requests on behalf of the wider polity. Modi’s instructions to these other groups then gain legitimacy, since he is now acting as a leader in bringing-up relations between groups of Indians to enable mutual actions.
Research on political discourse shows that persuasion is a core aspect of this discourse (Billig, 1991, Van Leeuwen, 2007). Researchers identify a range of practices such as the use of metaphors (Charteris-Black, 2014), frames (Wodak, 2021), or selective pronouns (De Fina, 1995; LeCouter et al., 2001) in accomplishing various forms of persuasion in the audience. The present findings show that another core aspect of political communication can be found in how relations between the political leader and the polity, and among sections of the polity themselves, are constructed. Within these relations certain actions are expected and legitimate, whereas the absence of these can give cause for complaints. The constructions of these relations then serve as vehicles for political actions and outcomes.
Audiences here are not constructed as mere listeners or individual citizens of a democracy. Rather, they are flexibly constructed as those who are in particular relation to Modi or fellow audiences, which normatively admits service, duty, and care. Modi’s use of pronouns works to flexibly align himself with those being requested, as one of those needing aid of other citizens, or as one among those who will enable the successful accomplishment of these actions. Through these discursive practices, Modi also positions himself, in relation to Indians, as the agent who is promoting and organizing service or dutiful actions by, among, and for Indians. This construction of himself is then less of an overbearing manager or an imposing figurehead and more of an orchestrator of relations among Indians that make way for service towards each other. Modi then enacts a form of politics where audiences and the wider Indian polity is related to him and each other, in ways other than those based on administrative grammar (cf. Mazzoleni 2007). Modi’s obligation to Indian citizens and their return obligations to Modi and fellow citizens are constructed in a grammar of service, duty, and care. The mutuality in operation is then less subject to checks and balances that a constitution might allow. Modi’s development of these relations and their situated use contributes to doing politics of service and duty (Bhaishya, 2015).
These constructions of Modi himself, others, and relations are used to develop the view that compliance is unproblematic (cf. Hunt, 2021). Instead, the compliance is attributed to the relation that Modi has with Indian peoples and mutual relations among Indians themselves. This then marks persuasion or compliance itself as the topic than as an outcome of the discourse (Condor et al., 2013, Van Leeuwen, 2007). It is important to consider the rhetorical outcomes in constructing compliance as easily achieved in this context.
It is here that the broadcast nature of this type of political discourse and the rhetorical space of the pandemic become relevant. First, these discourses are broadcast live over radio and TV news channels to reach a vast number of Indian audiences. Given the likelihood that, in India, majority of consumption of TV and radio broadcasts happen in family spaces, these discursive practices make way for audiences to identify themselves as family members or individuals in these groups (youth or farmers), or as those who are in other normative relations with members in those groups within India. Instructions given and to be followed come to be seen as extensions of already present normative relations among Indians, rather than novel actions that are framed as restrictions. The use of normative relations that are available for use by audiences to understand actions achieves the effect of normalizing following the instructions being given (Billig, 1991).
Overall, these constructions and the relations speak to constituting a polity that does not exist as result of constitutional guarantees, but as a national collective bound by mutual expectations and entitlements of service and duty. Modi includes himself in this network of relations as both a co-participant and organized. In his discursive practices, Modi builds a relational nationalism and national polity, within which are embedded instructions and compliance with these instructions.
These findings then speak to wider understandings of how political discourse functions to effect political and rhetorical outcomes. Future research can examine how audiences respond to these categorizations and roles etched out for them by political leaders in contexts where specific actions are expected of them. Researchers can also examine the role of similar or other forms of relations that are used by political leaders to address other pressing issues.
To conclude, the present findings show that Modi frames instructions and possibilities for complying with them in terms of expected national service. Constructions of the leader, the relations between the leader and the audiences, and among audiences themselves developed a sense of mutually owed activities and entitlements based less in civic terms and more in terms of duty and service to each other. Compliance with COVID-appropriate behaviours and government instructions was then embedded in the grammar of social relations grounded in duty and service.
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 used here were obtained from the public domain and are freely available
1 In this paper, this will refer to a range of social and personal behaviours that individuals were asked to adopt: personal hygiene, social distancing, mask wearing, staying at home, restrictions of mobility and public gatherings, and so on.
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| 36514379 | PMC9733435 | NO-CC CODE | 2022-12-14 23:36:19 | no | Discourse Context Media. 2022 Jun 28; 47:100594 | utf-8 | Discourse Context Media | 2,022 | 10.1016/j.dcm.2022.100594 | oa_other |
==== Front
Discourse Context Media
Discourse Context Media
Discourse, Context & Media
2211-6958
2211-6966
Published by Elsevier Ltd.
S2211-6958(22)00018-6
10.1016/j.dcm.2022.100595
100595
Article
“In these pandemic times”: The role of temporal meanings in ambient affiliation about COVID-19 on Twitter
Zappavigna Michele a⁎
Dreyfus Shoshana b
a School of the Arts and Media, Faculty of Arts, Architecture & Design, The University of New South Wales, Sydney, NSW 2052, Australia
b English Language and Linguistics, School of Humanities and Social Inquiry, Faculty of Arts, Social Sciences and Humanities, University of Wollongong, Northfields Avenue, Keiraville, NSW 2522, Australia
⁎ Corresponding author.
27 4 2022
6 2022
27 4 2022
47 100595100595
9 12 2021
2 2 2022
15 3 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.
This paper explores the role of a particular set of commonly occurring temporal meanings relating to the shared experience of being in a pandemic (e.g., in these unprecedented times) and how these foster ambient affiliation on Twitter. Temporal meanings can be realised as a range of grammatical structures in texts and are linguistic resources that add meaning – in terms of dimensions such as manner, time, or place – to the main activities, entities or events in a clause. While often viewed in terms of their role in how experience is represented, we suggest they play a pivotal interpersonal role in how values are positioned and how social bonds are offered to ambient audiences. The paper also draws on communing affiliation, a system in the ambient affiliation framework for understanding how people share and contest values in social media environments, to show how these temporal meanings are functioning. Corpus-based discourse analysis of the contribution of temporal meanings to communing affiliation in a large of corpus of COVID-19 tweets was undertaken. Three major affiliation strategies that these temporal meanings were involved in were observed: centring in the service of convoking affiliation, contrasting in the service of finessing affiliation, and accentuating in the service of promoting affiliation.
Keywords
Covid-19 discourse
Social media discourse
Ambient affiliation
Circumstances
Twitter
==== Body
pmc1 Introduction: “during a deadly pandemic”
The social distancing and lockdowns 1 necessitated by COVID-19 pandemic have meant that people have developed an acute concern with our collective experience of the pandemic and the ‘times’ that we are living in. One small way in which this has been visible is in idiomatic email signoffs such as:“Stay safe in theseunprecedented/strange/unusual times”
“Take care in these difficult/tough/scary/troubling times.”
The phrases underlined in these signoffs are all circumstances of time, that is, linguistic resources for adding contextual meanings about the main activities taking place in a clause (in these instances the acts of staying safe and taking care). Their presence indicates the importance of the times, or time more generally, as part of the lived experience of pandemics, as people frequently make mention of these in their discourse practices. The phrases in these difficult times or even, in these pandemic times are temporal meanings about the experience of being ‘within’ an ongoing pandemic which have proliferated in social media discourse. This paper is focused on the role that choices in temporal meanings of this kind play in how people foster certain kinds of social connection – what can be referred to technically as ‘ambient affiliation’ (Zappavigna and Martin, 2018a). Ambient affiliation refers to how social connection is forged in social media discourse, even in the absence of direct interaction between users. It can be seen in the ways that people commune around shared experience and values on any given topic, often through particular digital affordances such as hashtags. Ambient affiliation has been applied across a range of social media contexts such as deceptive communication (Inwood and Zappavigna, 2021), political discourse (Zappavigna, 2019), YouTube discourse (Zappavigna, 2021), and sporting discourse (Tovares, 2020), and is closely related to ideas of axiological affiliation (solidarity through shared attitude) (Zhao, 2020), social bonding through shared values (Xie, Tong, & Yus, 2020), and alignment with putative readers/audiences (White, 2021).
Qualitative studies have sought to understand how COVID-19 has impacted the subjective experience of time in people’s daily lives (Bolander & Smith, 2020). Some studies have suggested that time is experienced differently during pandemics: as cycles of recurring phases which lack the transformative element that occurs from knowing when something will come to an end (De Pascale, 2021). Velasco, Perroy, and Casati (2021, p. 449) argue that pandemic time is disorienting because there are unpredictable changes that structure people’s lives as outbreaks wax and wane in severity: “As a hallmark of disorientation, the epidemic’s temporal frame of reference can simply and suddenly supersede in a matter of weeks (or even days for local outbreaks) the frames on which the public life is structured.” Cellini, Canale, and Mioni (2020) studied the effects of the COVID19 pandemic on people’s sense of time, sleep patterns and use of digital media in Italy during the 2020 “first wave”; they found that people’s relationship to time did change, with the result that their sleep was negatively impacted. Constructions of temporality have also been noted as important in political and governmental discourse about COVID-19, with “specific framings of temporality saturated executive discourse … playing a vital role in situating the virus historically, and in charting its likely development” (Jarvis, 2021, p. 15). The presence of time within these studies points to the need for more research to interpret our experience of time within the pandemic, and we argue, within discourse produced in COVID-19 pandemic times.
Circumstantial meanings are integral to how meaning is developed in a text at multiple levels of linguistic stratification and in multiple grammatical structures including but not limited to circumstances (Dreyfus & Hao, 2020, p. 16). Due to circumstantial meanings being part of the experiential metafunction (Halliday & Matthiessen, 2014), they might easily be overlooked as incidental to the ways in which solidarity can be negotiated in language. Here we argue that circumstantial meaning can provide useful insights into interpersonal meaning – specifically how attitudes are modulated and experiences are grounded in shared contexts. The particular shared context considered in this paper is the collective experience of the COVID-19 pandemic, a health disaster occurring across the world which continues to generate a large volume of social media communication.
In terms of affiliation, we argue that temporal circumstantial meaning can play an important role in positioning and contextualising the attitudinal stances that underly social bonds. This is due to the way in which circumstantial meanings create a shared discursive frame which grounds affiliation – for instance, in a particular temporal location such as in these pandemic times, which we explore in this paper. In mass communicative contexts such as social media, this kind of temporal grounding enables communities to be called together or “convoked” around shared values and experiences, particularly where the temporal meaning features in a hashtag (Zappavigna & Martin, 2018a, p. 9). This kind of temporal grounding afforded by temporal circumstantial meanings is important for fostering a sense of shared humanity during mass crises such as the current pandemic. In short, we argue that the temporal meanings that are the focus of this paper act as linguistic resources that support ambient affiliation – the kind of social bonding that occurs on social media platforms (Zappavigna, 2011).
Temporal meanings in circumstantial phrases such as in these unprecedented times have proliferated across social media and other forms of digital communication (e.g. in email signoffs) as people attempt to bond with their audiences or with interlocutors over the impact that the pandemic has had on their lives. Using social media datasets to understand how people experience epidemics and pandemics, such as the ‘Swine Flu’ pandemic of 2009, has been a focus of social media research across both communication studies and health-related disciplines (Lampos and Cristianini, 2010, Ritterman et al., 2009). Since the current COVID-19 pandemic is one of the most discussed topics on social media at the present time, it has also become the focus of discourse analysis. Attention has been given to the linguistic choices made in discussing the pandemic on social media platforms, for example, how the pandemic is conceptualised on Twitter (Wicke & Bolognesi, 2020) where COVID-19 neologisms such as covidiot and coronacation have proliferated (Khalfan, Batool, & Shehzad, 2020), and on other platforms such as Reddit (Aggarwal, Rabinovich, & Stevenson, 2020).
While there does not appear to have been any work in social media discourse analysis focused in particular on temporal meaning, the grammatical concept of a circumstance has been used across a range of linguistic traditions that recognise “some kind of syntagmatic cline from the process nucleus of a clause via different kinds of participant to a circumstantial periphery or margin” and use terms such as case frames and grid or argument structure (Halliday & Matthiessen, 2014, p. 221). An early approach in work on syntax is Tesniere’s distinction between actants (arguments) and circonstants (circumstances) in relation to the “little drama” of a verb, where the circumstance might be thought of as a kind of stage direction (Fillmore, 1994, p. 158). The approach adopted in this paper draws on the social semiotic tradition encapsulated in Systemic Functional Linguistics. Within this tradition Halliday and Matthiessen (2014, p. 221) consider the grammatical circumstance as the element which augments the central process in a clause “temporally, spatially, causally, and so on” but which has a more peripheral “status in the configuration [since] unlike participants, they are not directly involved in the process”. In other words, circumstances can express meanings about how the activity or happening is occurring. However, Dreyfus and Bennett (2017) showed that circumstantial meanings are not only realised in circumstances but in a variety of other grammatical structures, from clauses to clause constituents such as participants as well as within participants and noun groups. They also showed that if you do not count the circumstantial meanings occurring in locations other than the constituent of circumstance, you miss half of the circumstantial meaning. We thus talk about circumstantial meaning, rather than circumstances. Circumstantial meaning might involve information about when, where, how, why things are happening, as shown in the examples in Table 1 ; these examples are drawn from the corpus of posts about COVID-19 to be analysed in the paper.Table 1 Circumstance functions in tweets about the pandemic, based on (Halliday & Matthiessen, 2004, p. 291).
Type & subtype Probe Example
[grammatical structure]
Extent:
duration, frequency (temporal), distance (spatial) How long/often/ far? Mf pandemic for two yearssssssssss
[Circumstance]
Location:
(temporal), place (spatial) When? Where? i was crying n my doctor said “do u need a hug? in the middle of a pandemic?”
[Circumstance]
Manner:
means, quality, comparison, degree How? How quickly people forget that we are in the midst of a deadly pandemic
[Circumstance]
Cause:
reason, purpose, behalf What for? Why?
On whose behalf? Vaccines are the nuclear option for ending this pandemic
[Circumstance]
Some private hospitals are creating fake demand of beds in order to plunder money from the people.
[dependent clause]
Contingency:
condition, concession, default In case of? I’m sure they’ll be catalogued, and stored in case of another pandemic.
[Circumstance]
Accompaniment:
comitation, addition With whom/ what? 2021 is the year I been waiting for (besides all the burnout from working through a pandemic)
[Circumstance]
Role:
guise, product What as? Pandaspicious When a company or person tries to use the pandemic as a way to up charge or charge without cause
[Circumstance]
Matter What about? New rules about eating, exercising and sleeping as you age and prepare for the next pandemic:)
[Qualifier of a nominal group]
Angle:
source, viewpoint According to whom? Pandemic fatalities close to 6.8 million, according to University of Washington’s Institute for Health Metrics
[Circumstance]
While Table 1 shows all the different semantic types of circumstantial meaning, it also shows that while the circumstance is the most frequent way of instantiating these kinds of meaning, other grammatical structures may do it too. For instance, the examples for Cause and Matter in Table 1 include realisations as dependent clauses (e.g., Some private hospitals are creating fake demand of beds in order to plunder money from people) and Qualifiers in nominal groups (e.g., New rules about eating, exercising and sleeping as you age and prepare for the next pandemic), and there are other grammatical structures that are possible. Particular types of circumstantial meanings that have been explored using Halliday and Matthiessen’s framework are circumstantiation of place (Dreyfus & Jones, 2011), circumstance of projection, focusing on angle (e.g., according to prepositions) (Chen, 2016), Cause circumstances (Hao, 2018), and a the use of circumstances in contexts such as EAP instruction (Walsh Marr & Martin, 2021). While circumstantial meaning is usually understood to construe ideational meanings such as time, place, manner (etc.), we argue here that it can also work to build affiliation, and it is this work that we explore in this paper. The evaluative function of circumstances was first proposed by Bennett (2016), who found that within the introductions of journal articles, circumstances have a persuasive role in arguing for research gaps.
2 Dataset and sampling
The dataset explored in this study is a corpus of tweets containing any of the following case-insensitive search terms commonly used to refer to the COVID-19 pandemic: Coronavirus, SARS-CoV-2, corona, covid, covid19, covid, rona, and pandemic. While the focus of the data analysis was on circumstantial meaning, broad selection criteria were used in the hope that this would gather a wide assortment of COVID-19-related discourse. The software ‘Social Feed Manager’ was used to harvest these tweets, querying Twitter every 30 mins from the 6th-7th May 2021. The harvest produced a set of 1,217,825 tweets which resulted in a corpus of 331,008 posts (7,810,129 words) following removal of duplicate tweets, tweets that appeared to be made by bots, and tweets in languages other than English (since the detailed discourse analysis required high levels of understanding that were not possible for other languages due to the researchers’ language proficiency and lack of descriptions of functional grammar for some of these languages).
Circumstantial meaning was very frequent in the corpus, as evidenced by the fact that seven out of the ten most frequent 3-grams functioned for the most part as circumstances (shown in bold in Table 2 , noting that even the unbolded 3-grams might be involved in circumstantial meaning if a broader window was selected (e.g., ‘during the covid pandemic’). This suggests that circumstantial meaning is very important within Twitter discourse about COVID-19.Table 2 Most frequent 3-grams in the corpus3.
N 3-gram Frequency
1 due to covid 2952
2 during the pandemic 1870
3 of the pandemic 1721
4 the covid vaccine 1579
5 the covid pandemic 1527
6 in this pandemic 1207
7 of the covid 1156
8 for covid vaccines 1150
9 on covid vaccines 950
10 during a pandemic 886
3 ‘Fuzzy duplicate’ posts (that were not removed in the automated deduplication during the data cleaning stage as they were not exact duplicates) were excluded.
In order to sample a workable dataset for close discourse analysis of temporal meanings from this large corpus, a purposive sampling approach was adopted, focused on identifying patterns of temporal circumstances in terms of how they seemed to enact communing affiliation. The first step was determining some of the most frequent types of time entities in the corpus (Table 3 ) to isolate examples. This included lexical items that directly indicate time (e.g., year) as well as those that don’t mention time directly but instead package it into episodes (e.g., wave). We return to this point at the end of this section in relation to historical discourse.Table 3 Most frequent Time entities in the corpus.
N time entity Frequency
1 time 8095
2 year 6253
3 crisis 5876
4 day 5641
5 today 5570
6 lockdown 4633
7 wave 4254
8 life 3633
9 days 3441
10 times 3084
While the singular ‘time’ was the most frequent time entity in the corpus, it referred to a variety of temporal meanings, such as ‘time of day’. In contrast the plural ‘times’ construed meanings about an era. Thus, a decision was made to focus on ‘times’ (shown in bold in Table 3), which likely refers to any shared experience of a pandemic and thus underscores our collective experience and possible trauma.
The most frequent 4-grams for times are shown in Table 4 . As this table shows in bold, seven of the ten most frequent 4-grams have the pattern in these ___ times. This pattern was chosen as a potentially fruitful starting point for closer discourse analysis using the analytical methods described in the next section. There were 251 tweets containing this pattern, and each was analysed using the methods described in the next section. Table 5. .Table 4 Most 4-grams containing ‘times’ in the corpus.
N 4-gram Frequency
1 helps in corona times 34
2 in these tough times 33
3 in these covid times 28
4 in these difficult times 26
5 be safe in times 25
6 in these testing times 23
7 in these hard times 17
8 strain at least 15 times 16
9 in these pandemic times 14
10 in these trying times 14
Table 5 Semantic categories of temporal resources.
Type Function Key resources Examples
[grammatical structure]
sequencing time temporally linking unfolding events Temporal conjunction & dependent clause (simultaneous) They are the ones who sufferwhen people get covid.
[dependent clause]
Temporal conjunction & dependent clause (successive) @UserAfter I got bannedsome of my friends told me they did too …
[dependent clause]
external conjunctive Adjunct Last year I went to a restaurant and got pasta for the first time.And then the pandemic happened.
[independent clause]
Process Itwas precededby a sharp warning [process]
Ordinative Covid really stole the holidays from uslast year
[Circumstance]
segmenting time dividing of time into segments nominal group,
nominalization,
specialized lexis The extraordinary circumstances ofthe COVID-19 pandemiccall for extraordinary measures
[in nominal group in Qualifier]
setting in time locating events at a particular point in time Circumstance of time: location Most people in UK did not work from homein 2020
[Circumstance]
duration in time specifying how long an event lasts Circumstance of extent: duration the vaccine will be neededfor many years to come.
[Circumstance]
phasing time indicating the beginning, continuation and end phases of an event or
activity Process
nominal group Theonsetof the pandemic had a far-reaching impact
[noun/entity]
Circumstance I will WFH full timeby the end of the year.
[Circumstance]
external conjunctive Adjunct finallyin 2015 i decided my next job would be at home.
[Circumstance]
organizing through time structuring texts Internal conjunctive Adjunct Firstly, the sooner we enter the tunnel the sooner we'll emerge from it.
[conjunction]
Internal Ordinative (seepreviouspoint on likelihood of that happening)
[Classifier]
3 Method of analysis
The following types of analysis were conducted on the sample of in these ___ times tweets described in the previous section. The aim was to understand the role that the circumstances were playing in affiliation within this dataset. The analysis involved a combination of corpus-based techniques such as inspection of frequency lists, n-grams and concordance lines – in conjunction with close discourse analysis. This discourse analysis incorporated analysis of temporal circumstances and their function in terms of affiliation, by which we mean alignments around stances, as detailed in Section 3.2.
3.1 Analysing temporal circumstances
Halliday and Matthiessen (2014) distinguish two key dimensions of time: location, answering the semantic probe “when?” and duration, answering the probe “for how long?”, as was exemplified in examples in Table 1. The present study also draws on Coffin’s (2009, p. 102) configuration of semantic resources for construing time. These resources include sequencing time, setting in time, duration in time, phasing time, and organizing through time. Of most relevance to the “in these ___ times” pattern is ‘setting in time’ realised through a variety of grammatical structures including circumstances of location in time.
Martin, Maton, and Matruglio (2010, p. 441) note the role of episodic time in history discourse for “interpreting the past in uncommonsense ways which involve packaging up sequences of actions by individuals into episodes”. This packaging allows episodes to be named (e.g., the Industrial Revolution, the Great Depression etc.) and thus positioned or framed in various ways, for example, via evaluation resources (e.g., the catastrophic Great Depression). We argue here that similarly to the packaging of time in history discourse, social media discourse on the pandemic packages the pandemic as serial time that is loosely bounded (Bennett, 2016), being only during the pandemic, but also unbounded as no one knows when (and if) the pandemic will end. The approach to understanding the complexity of temporal circumstances adopted in this paper draws on Bennett’s (2016) study of the persuasive function of temporal qualities in academic discourse.
3.2 Affiliation analysis
The affiliation strategies (or functions) in the sampled dataset were explored using the system of communing affiliation (Fig. 1 ), which forms part of the ambient affiliation framework. This system was originally developed to understand how hashtags are used in Twitter discourse to create ambient attitudinal alignments even in the absence of direct interaction between social media users (Zappavigna and Martin, 2018a).Fig. 1 The system of communing affiliation, based on (Zappavigna and Martin, 2018a).
3.2.1 Analysis of communing affiliation strategies
Three systems of communing affiliation were used to analyse the text samples from the corpus:• convoke – Mustering a community around a bond, for instance, via resources such as vocatives, which foster collectivity and togetherness.
• promote – Adjusting the prominence of a bond, for instance, by raising or lowering its stakes, such as through the upscaling or downscaling of graduation resources.
• finesse – Positioning a bond amongst networks of other potential bonds, for instance via resources of heteroglossia, e.g., engagement choices such as contraction.
As the brace in the communing affiliation system network indicates, these systems represent simultaneous rather than mutually exclusive choices and can potentially co-occur within a single post.
The convoking system involves tendering a coupling to a particular community by ‘pitching’ or ‘calling together’ a group to bond around the coupling or by suggesting the parameters of the community to which the bond appeals. We can further delineate resources that marshal a community from those that designate a community, although these two options can co-occur in a text. For example, vocatives or other resources of address can be used to marshal a group to align with a coupling (e.g., guys). Alternatively, the relevant community may be designated through an explicit reference to a group (e.g., nurses). Finessing is concerned with modulating the coupling in relation to other potential stances that may be present in the social stream through resources that parallel or oppose bonds such as choices in engagement. For example, a particular coupling might be affirmed through embellishing it via entertaining possibility (e.g., must) or by setting it in contrast to other potential couplings, contracting the potential of other voices and thus distilling the coupling (e.g., never). Finally, promoting, raises or lowers the interpersonal stakes of a coupling by raising or lowering the strength or scope of the bond and hence raising or lowering its stakes. For example, fostering might be achieved via upscaling the graduation through intensification (e.g., really). The scope of a bond can also be adjusted by modulating the prototypicality of the coupling against some standard (e.g., true). It should also be noted that while choices from the Appraisal framework (Martin & White, 2005), are the most obvious resources involved in the affiliation strategies at work on a given coupling, there are a wide range of resources that can be deployed at any stratum of language, and indeed, as burgeoning work on paralanguage is suggesting, across different semiotic modes (Martin and Zappavigna, 2019, Ngo et al., 2021).
3.2.2 Ideation-attitude coupling analysis
Critical to understanding how affiliation works is the concept of an ideation-attitude ‘coupling’, which refers to how social bonds are construed in language as a combination of content-meanings and value-meanings. In other words, analysis of attitude alone is insufficient for understanding communing affiliation since we “don't after all simply affiliate with feelings; we affiliate with feelings about people, places and things, and the activities they participate in, however abstract or concrete” (Martin, 2008, p. 58). Thus the affiliation systems used in this paper are concerned with what Han (2015, p. 30) has termed “alignment to the coupling”, that is, how a text works to cultivate a particular interpersonal orientation to an ideation-attitude coupling where this coupling is the value construed in the text as an evaluation about an entity or process:What is of central concern in relation to strategies of affiliation is … ‘alignment/disalignment’ towards value positions, as this determines whether the writer is introducing the coupling to commune around a bond or to reject it. (Han, 2015, p. 57).
This kind of concern with alignment/disalignment is also seen in the body of work on stance and positioning amongst speakers/writers (Du Bois, 2007, Du Bois and Kärkkäinen, 2012, Ochs, 1996).
The concept of coupling is grounded in Knight’s (2013) work on affiliation in casual conversation, where a coupling is the linguistic realisation of a social bond between people and/or communities. Couplings have been used as an analytical unit to explore affiliation in a diverse range of areas such as restorative justice (Zappavigna and Martin, 2018b), business discourse (Szenes, 2021), internet hoaxes (Inwood and Zappavigna, 2021), terrorist discourse (Etaye and Zappavigna, 2021) and stand-up comedy (Logi and Zappavigna, 2019, Logi and Zappavigna, 2021).
The evaluative dimension of the coupling was analysed using the appraisal framework (Martin & White, 2005), specifically the system of attitude 2. Appraisal is a framework for analysing evaluative language and has been applied to a vast range of domains of communication, including work on social media, “since sharing and contesting opinion and sentiment is central to social media discourse” (Zappavigna, 2017, p.435). The system of attitude maps evaluation as a choice between affect (expressing emotion, e.g., love, disgust, fear etc.), judgement (assessing people and their behaviour, e.g., evil, ethical, trustworthy etc.) and appreciation (estimating the value of phenomena and situations, e.g., beautiful, treasured, noteworthy etc.) (Fig. 2 ). All of these systems can be realised by specific attitudinal lexis or through phrases or longer stretches of discourse. In additional, attitudinal meaning can be hinted at or implied, and this is termed invoked attitude in the appraisal framework, including attitude that is realised through metaphors or shared cultural understandings about what is valued.Fig. 2 The Appraisal framework with examples from the corpus (adapted from (Martin & White, 2005)).
The construal of evaluative meaning is very complex, particularly in social media environments which often tend to involve layers of complicated intertextual references and span multiple contexts that can be difficult to untangle (sometimes referred to as context collapse (Marwick and boyd, d. , 2011, Wesch, 2009)). Most social media texts are tinged in some way with evaluative meaning and implicate particular value positions, (cf. Voloshinov’s (1929/1973/1986, p. 103) idea of an ‘evaluative accent’). The appraisal framework attempts to factor in this complexity by distinguishing attitude which is inscribed (realised via explicit evaluative choices) from attitude which is invoked (hinted at or suggested by particular linguistic choices that imply evaluative meaning). Appraisal distinguishes between three systems of implied evaluation: provoke, flag, and afford. Attitude may be provoked via lexical metaphors, flagged via graduation resources (upscaling or downscaling a meaning), or afforded by ideation that has accrued a positive or negative association.
The ideational dimension of the coupling was analysed using Hao’s (2020, p. 64) discourse semantic systems for describing entity types (Fig. 3 ) and figure types (Fig. 4 ). Entities are the ideational discourse semantic units construing items in a field of experience. They can be categorised into source entities (for instance, participants projecting verbiage e.g., doctors, experts, reporters etc), thing entities (a person, place or object e.g., mask, vaccine, needle, hand sanitiser), activity entities (an activity or sequence of activities e.g., vaccination, elimination), semiotic entities (verbiage or ideas, e.g., reports), place entities (e.g., hospital, vaccination clinic) and time entities (e.g., three hours, 2020).Fig. 3 Entity categorisation system, adapted from Hao (2020, p. 64) with examples from the corpus.
Fig. 4 System for figure types (Hao, 2020, p. 94).
Figures can involve one or more entities in a process of change (occurrence figures), or in relations (state figures). For example, the top example in Fig. 4 is an occurrence figure where an occurrence is construed by an activity (dying), whereas the bottom example is a state figure where an entity (COVID-19) is evaluated by a quality (deadly in the elderly). For example,
3.3 Data annotation
The format for integrating the coupling analysis and analysis of circumstantial meaning in a single annotation is the following: the ideation in the coupling is in italics and underlined, the attitude is in bold, and the circumstance is shown without italics but still underlined. For example, consider the tweet:Weekends are the only positivesin these Pandemic times.
Ideation-attitude couplings were annotated using the following convention, together with associated circumstantial meaning:[ideation: ≪ideation ≫ / evaluation: ≪attitude≫] x Circumstance.
The square brackets in this annotation indicate the fusion of ideational and attitudinal meaning into a single value that is open for negotiation. The ‘x’ is used to indicate that this coupling is inflected or framed by the circumstance. For example:Weekends are the only positivesin these Pandemic times.
The following annotation strategy is used to represent the coupling’s inflection or modulation by the circumstance:[ideation: entity / evaluation: positive appreciation] x location in time.
4 Analysis: The affiliative functions of temporal circumstances
The examples used to illustrate the system of communing affiliation in Fig. 1 in the method section show that each affiliation strategy can co-occur with a circumstantial meaning of Extent or Location (e.g., during a global pandemic, during this horrible pandemic, during a pandemic, during COVID, during the pandemic, during a pandemic). Zooming into “in these ___ times” in particular, three major affiliative functions that these circumstantial meanings were involved in were observed:• Centring in the service of convoking affiliation, where a key bond is foregrounded or put at the deictic centre by the temporal circumstance.
• Contrasting in the service of finessing affiliation, where a key bond is set in opposition with another bond.
• accentuating in the service of promoting affiliation, where a key bond is upscaled.
The sections which follow explore each of these patterns. Since the aim was to explore the function of circumstantial meanings in terms of how they contribute to ambient affiliation, rather than to assess quantitatively how frequently they occurred, statistical information is not included.
4.1 Centring values on “these times” through convoking affiliation
Temporal meanings foregrounding the shared nature of time through deictic resources (i.e., pointing to the shared ‘times’ e.g., these times) operated in the services of convoking affiliation. These resources include place/spatial deixis used to locate the relative position of the time entity in the circumstance. We describe the function of these kinds of deictic in terms of affiliation as centring, drawing inspiration from the idea of a deictic centre (Lenz, 2003) but considering this concept through a more social expansive lens, in order to think about how the deixis can operate to coordinate mass sharing of a collective pandemic experience. In other words, the deixis does not only function to coordinate a speaker and listener around a single shared instance in their localised discursive environment (e.g., in their local co-text or context) but operates on a larger scale to coordinate discourse about the collective times that the large-scale ambient audience are co-experiencing (See also Lemke (2000) for work on time scaled and discourse). For example, consider the “these times” in the following concordance lines where the deictic ‘these’ centres the shared experiences in the temporal location of the unfolding pandemic:Maybe if there wasn't a pandemic they could but even selling 50 k + during these times is great.
…can you please guide how to NOT feel Anxious during these times, when there is so much negativity because of the pandemic.
Any research intention will just self-combust in these times!
Playing petty politics in these times of pandemic. Shame!!!
@User You are ray of light in these times.
Further evidence that this kind of centring is operating on a mass scale is the use of #thesetimes as a hashtag to coordinate shared feeling about the experience. For example, the following instances were retrieved via a Twitter search:This was a good weekend. The best you can expect in #thesetimes.
I need a mask so I can go in a shop, to buy a mask. #thesetimes
Sad news!!! If this doesn't keep the people home and healthy! #theseTimes.
Working from home. #TheseTimes.
This search also uncovered the following post which indicates that ‘these times’ is considered a significant enough concept to warrant a museum project:I've been documenting my daily walks during #COVIDー19 as a part of @MuseumOrdinary's #TheseTimes project. Really excited to get all of this film developed. When that will be is another whole question [selfie of the photographer’s hand holding a digital camera showing the image being taken on the screen].
The elevation of this temporal meaning to a hashtag signals its traction in convoking people around particular ideas or stances. Hashtags are a form of social metadata that can both enact particular linguistic functions when co-occurring with clauses as well as having an aggregation affordance (linking a post to other posts containing the same tag). They have also been identified as a resource for enacting communing affiliation (Zappavigna and Martin, 2018a).
The pandemic itself was often a Classifier for the kind of times, for example, in circumstances such as in these covid times (36), in these pandemic times (14), in these #covid19times (3), and in these corona times (2). These examples invoke rather than inscribe negativity: a pandemic can function as a token of negative appreciation of a period of time (i.e., bad times when disease is spreading). These kinds of temporal meanings occurred with a range of affiliation strategies, for example, in posts promoting an ‘angry reaction’ bond featuring negative affect:I want a dislike button or may be angry one to be added by Twitter esp in these Covid-19 times… There are so many posts on which I just want to click the angry emoji….
[ideation: semiotic entity (posts) / evaluation: negative affect]x location in time.
but also in calls for benevolent action:@User In these pandemic times, please release all political prisoners and show humanity.
[ideation: occurrence figure (release… show…) / evaluation: positive propriety]x location in time.
They are also implicated in ‘laughing-off’ (Knight, 2013) a bond about the unusual social interactions that covid has generated, for example:@User I would think that greeting someone at the door with hand sanitizer could actually be considered to be the epitome of hospitalityin these Covid times.
[ideation: occurrence figure (greeting…) / evaluation: positive propriety]x location in time.
The centring function of ‘In these times’ also seems to be part of how these temporal circumstances invoke blame and casuality, that is, the stance that blame for the bad things can be laid squarely on COVID-19.
4.2 contrasting shared values with “tough times” through finessing affiliation
A prominent pattern in the dataset was establishing an evaluative disparity between the current pandemic times and other points in time, through what we refer to as contrasting. In terms of affiliation, this involved shared positive experiences that were set in contrast to the negative situation indicated in the circumstance. contrasting is part of a broader choice in tenor relations in terms of how interpersonal relations can be organised: likening where meanings are associated (e.g., via resources such as similarity conjunctions) and opposing where meanings are disassociated (e.g., via resources such as negation) (Doran, 2019, Doran et al., 2023).
A frequent pattern involving contrasting in the corpus were circumstances in the format “in these [negative attitude] times” set in opposition to shared positive feelings and experiences. The attitude in these temporal circumstances was most often either an instance of negative appreciation (reaction), for example, in these tough times, or a Classifier, for example, in these pandemic times. The most frequent 4-grams for times, all of which are circumstances of the pattern “in these _____ times”, are shown in Table 6 . The most frequent evaluation in this pattern was negative attitude: tough (34), difficult (26), testing (23), hard (17), and trying (14). One way in which contrasting was involved in affiliation was through the coordination of temporal meanings that include negative attitude such as in these tough times, with finessing affiliation strategies where tough times are contrasted with positively evaluated relationships with family or with highly valued people such as health workers, as in the following examples:In these tough times you needyour family members near you (I'm saying this because I have experienced it).
[ideation: occurrence figure (you need…) / evaluation: invoked positive affect]x location in time.
Salute volunteers as well as corona warriors who are serving the people in these tough times.
[ideation: occurrence figure (salute…) / evaluation: positive judgement]x location in time.
Table 6 Most frequent 4-grams for ‘times’.
N ‘times’ 4-gram Freq. example
1 in these tough times 34 Salute volunteers as well as corona warriors who are serving the people in these tough times
2 in these covid times 33 In April I raised $2800 for a local shelter because I feel it’s important in these covid times. I did it by phone and school. I’ll go back to raising money for animals when more people are good. I see and hear too many people on brink of total breakdown.
3 in these difficult times 26 This post for all the healthcare workers out there who prove them as frontliner in these difficult times of covid and taking care of nation since forever #covid19 #HealthcareHeroes #FrontlineWarriors #COVIDSecondWave #SaluteToCoronaWarriors
4 in these testing times 23 In these testing times let us fight this pandemic by following all the COVID protocols & make self care a priority! We will fight this together #StayHomeStaySafe
5 in these hard times 17 “ Hope is the only weapon which makes us hold a bit longer, don't lose hope in these hard times. We are together in this and we will fight it back as one”
#pandemic #CleanYourHands #AMillionLittleThings #COVIDSecondWave
If we consider the evaluation inside these temporal meanings, they themselves incorporate a coupling [ideation: Time entity/ attitude: negative attitude], where the time entity targeted by the evaluation is a plural noun ‘times’ suggesting an extended period of time, historical period, or the span of a particular world-view or perspective. For example:We're all going thru toughtimes. We're all doing our bit to help others. In these hardtimes just wanted to share a Covid related story that gives uscourage hope and liftourspirit. Have a good day! [embedded image of a newspaper article about a 102 year old man who recovered from covid-19 in 20 days].
The Time entity (times) seems to contribute to the convoking as it flags a shared experience of an ongoing situation rather than an individual experience of a particular point in time. This also functions to enact convoking affiliation by marshalling shared awareness of shared feeling (us, our, shown in underlining above). The ‘bad times’ bond can be set in contrast with other bonds about positive feelings and experience realised, as we saw in the examples explored earlier and as is visible in the positive attitude (shown in bold italics above) in this example.
As the most frequent 4-grams for ‘times’ (in these tough times) in Table 6 indicates, while the times are characterised as negative in this pattern, the rest of the post typically was saturated with positive attitude rallying the ambient audience around positive stances or behaviours they might adopt in reaction to the negative times. The examples in Table 6 involve negative evaluation instantiated in the main circumstantial meaning in the post, together with activities and entities that are positively evaluated in couplings such as [ideation: entity (healthcare workers)/attitude: positive tenacity (prove..as frontliner)]. For example, the third instance in the table tables a ‘Good healthcare worker’ bond realised as positive evaluation of healthcare workers both in the verbiage and in emoji such as . It enacts convoking affiliation by designating (healthcare workers, nation, HealthcareHeroes and FrontlineWarriors) and fosters through accentuating (all, forever). Similarly, the fourth instance in the table convokes the ambient audience through marshalling them around activities which they should undertake in order to fight the pandemic. The marshalling is construed via reference to shared collectivities (us, we, together) and fostered by accentuating (all, a priority,!) and the emoji intensifying the imperatives in the body of the post and in the hashtag (#StayHomeStaySafe).
The kind of constrasting identified in the examples above is part of how circumstantial meanings can be involved in the ways bonds are arranged or organised within a particular bond cluster (i.e., configuration of values) that might be held by a particular community. The discursive patterns identified above have become established enough that they are themselves the target of metadiscourse critiquing the use of certain evaluative phrases such as ‘unprecedented’ in temporal meanings. For example, posts such as the following which mock the kinds of negative appraisal that regularly occur inside COVID-19 circumstantial meanings:If I see one more email about grad students being 'resilient' in these 'unprecedented' 'extraordinary' pandemic times I will flip a table. I've aged so much in the past months, my hair is turning white more frequently, and every grad student I know is stressed, treading water.
[ideation: occurrence figure (If I see…) / evaluation: invoked negative affect]x location in time.
Commenting on annoyingly frequent turns of phrase is a common practice on social media platforms once certain trends in meaning have become prevalent. Thus, the kind of evaluative metadiscourse in the above tweet suggests that circumstances (of the kind explored in the analysis in his section) are important rather than peripheral to the kinds of discourse about COVID-19 circulating on social media platforms at the time of writing.
4.3 Accentuating shared hardship through promoting affiliation
In terms of their role in ambient affiliation, circumstantial meanings could be used to raise the interpersonal stakes of the bond realised by an ideation-attitude coupling in a post through promoting affiliation. Temporal circumstances were often involved in accentuating prosodies of negative attitude targeted at state and occurrence figures about activities and situations relating to the pandemic involved in construing ‘bad situation’ and ‘bad activity’ bonds. An example is the following tweet realising a ‘bad behaviour’ bond that was posted in response to a tweet about someone spitting at a rape victim outside a court:That'sdisgusting anytime.. more so now in these covid times. Absolute filth.
[ideation: activity entity (That (spitting)) / evaluation: negative judgement]x location in time.
[ideation: source entity (elided spitter) / evaluation: negative judgement].
In this example the temporal circumstance coordinates with two couplings negatively judging the spitter and their behaviour. In combination with the accentuating (more so, absolute), the circumstance fosters the ‘bad behaviour’ bond construed via these couplings. In addition, the first temporal circumstance (anytime), is set in contrast to the second temporal circumstance (in these covid times) and is an example of the way that contrasting can support accentuating, since creating an opposition can also act as a form of emphasis. Another example is the following:Yes, in these trying times, we definitely seeHumanity winning the race against this Pandemic #COVID19 #CovidHelp #COVIDEmergencyIndia.
[ideation: occurrence figure( Humanity winning... Pandemic) / evaluation: invoked positive judgement] × location in time.
In this example a ‘good people’ bond is contrasted with a ‘bad pandemic’ bond at the same time as contributing to the accentuating of the ‘good people’ bond. Other resources such as ‘definitely’ and the ‘raising hands’ emoji also contribute to the interpersonal emphasis. The general function of the accentuating is to cultivate alignments with the ambient audience through underscoring the extent of the shared hardship that so many people have been experienced during the pandemic.
5 Conclusion
While there are so many ways humans can construe separation from each other – by nation, region, political affiliation, and a vast array of other dimensions of variation – the COVID-19 pandemic offers a unique opportunity to explore global shared experience. This paper has explored temporal meanings as a resource for building solidarity in social media posts about the COVID-19 pandemic. Circumstantial meanings are often backgrounded in interpersonal analysis since they are part of experiential meaning. However, as this paper has shown, temporal circumstantial meanings are critical to the way interpersonal meaning is construed and affiliation is enacted in posts about COVID-19.
This paper has detailed three major patterns in terms of how temporal meanings, most frequently instantiated as circumstances, coordinate with communing affiliation strategies: centring in the service of convoking affiliation, contrasting in the service of finessing affiliation, and accentuating in the service of promoting affiliation. The analysis focused on the temporal circumstantial meaning “in these ___ times” in the dataset of tweets about the pandemic, where the open slot was filled with either a Classifier e.g., in these COVID times, or evaluation, e.g., in these tough times. In this particular set of temporal meanings, the Deictic ‘these’ was important in terms of how the temporal meaning coordinated with affiliation strategies as it offers a centring the shared context within which to bond. It also offered a means of contrasting the current time with other times, by accentuating positive and negative oppositions in terms of other moral evaluations (e.g., how health workers were being treated or how people were generally treating each other now compared to at other times). This opened up the possibility creating social alignments as shared stances (or bonds) regarding the state of the world and how we treat each other at times of crisis. Such sharing of opinion is important since the COVID-19 pandemic brings into stark relief our shared humanity.
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.
1 A term used during the pandemic to describe stay at home orders issued by governments when COVID-19 cases needed to be controlled by restricting citizens’ movement. Possibly derived from prison discourse.
2 Technical terms drawn from the Appraisal and Affiliation frameworks are shown in small caps to avoid confusion with everyday terms.
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| 36514380 | PMC9733436 | NO-CC CODE | 2022-12-14 23:36:19 | no | Discourse Context Media. 2022 Jun 27; 47:100595 | utf-8 | Discourse Context Media | 2,022 | 10.1016/j.dcm.2022.100595 | oa_other |
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J Cell Commun Signal
J Cell Commun Signal
Journal of Cell Communication and Signaling
1873-9601
1873-961X
Springer Netherlands Dordrecht
36474111
718
10.1007/s12079-022-00718-7
Editorial
2022, a year of the water tiger
Perbal Bernard [email protected]
International CCN Society, Nice, France
6 12 2022
12 2022
16 4 485486
© The International CCN Society 2022
According to Chinese astrology, the Tiger is considered as the king of all animals. The Year of the Tiger 2022 was meant to symbolize determinism, vitality, strength, spontaneity and novelty, with the water element making it wiser and thoughtful. Often associated with the defeat of evil, a Water Tiger year occurs only every 60 years. The 2022 version was indeed a year of resilience, even in the time of conflict and the struggles that we have faced both in personal and professional realms. It was a good time to reflect in order to overcome all challenges and difficulties. The revamping of both the International CCN society (ICCNS) and the Journal of Cell Communication and Signaling (JCCS) that I had previously initiated and officially announced in 2019, are on track to becoming a successful reality and will be pursued over the coming years, thanks to the strong support of our colleagues, members of the JCCS Editorial board and representatives of other scientific societies who support our efforts to broaden the scope of the ICCNS and its communication organ. I will schematically draw below the guidelines that we intend to follow in the near future.
Keywords
Lunar year
Water Tiger
Water rabbit
ICCNS Workshops
CCN Society
issue-copyright-statement© The International CCN Society 2022
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pmcThe 11th workshop on the CCN family of genes
Our 11th workshop was held in the city of Nice, at the Hôtel Aston La Scala. The workshop celebrated, with a slight delay due to the COVID-19 pandemic, the 20th anniversary of both the International workshop on the CCN family of genes and the birth of the International CCN society, the details of its inception being presented in a commentary within the present issue1.
This workshop was a very special and important event for a variety of reasons.
First of all, it was the first face-to-face ICCNS meeting that we could organize since we had to postpone our workshop series because of the COVID-19 pandemic. Second, the workshop welcomed, for the first time, colleagues working in broader communication and signaling fields that are close to, but distinct from, the specific CCN focus of the Society. Last but not least, the workshop was also a unique opportunity for JCCS Editorial Board members to convene and critically address various aspects of manuscript evaluation and publication, and discuss the aims and objectives of the Journal.
1) The reunion
Since our last meeting in 2019, many colleagues kept asking Annick to organize a special venue to celebrate 20 years of friendship and collaboration from which stemmed an ever-increasing quality of the science produced by all those who constitute the core of our closely-knit family-type reunions.
The 11th workshop was a genuine success, from both personal and scientific perspectives. Some of our colleagues did not hesitate to fly for many hours from very remote places to attend this meeting, even for a few days.
We are extremely grateful to all those who made the venue a great human adventure and a wonderful moment of scientific information sharing.
We will shortly announce details of the next workshop which is scheduled to be held in Oslo, Norway under the responsibility of Professor Havard Attramadal. All information will be available on the ICCNS society website https://ccnsociety.com.
2) The expanded topics
I have had the opportunity in previous editorials to provide details about my plans to broaden the scope of JCCS and include new research areas in our topics of competence.
We deliberately invited colleagues from a broader community of interest and expertise. We welcomed scientific societies, outside of our specific CCN realm who could share common ways of approaching the biological functions of regulatory elements that might be cofactors or inhibitors of the action of CCN proteins in various biological models.
Several researchers responded positively to our invitation. Their presentations triggered a real interest among the CCN community and built the foundation for more extensive collaborative projects to be pursued in the near future.
These are important steps in the direction to share more views, reagents, expertise and complex biological platforms.
A detailed report of the presented communications will appear in a forthcoming issue of JCCS.
Along the same vein, JCCS welcomes submissions from Middle East countries and China, regions which previously might have been underrepresented. I have clearly mentioned my firm opposition to the unprofessional practice of immediate manuscript rejection based on geographical origin. JCCS is putting particular emphasis on ensuring that all of the manuscripts from these regions are fully and respectfully reviewed.
This reminds me of the time when researchers from Eastern bloc countries belonging to the USSR2 bloc would have a real hard time publishing their work in international journals3.
Fortunately, this time is over now and colleagues from the Middle East, central Europe, and South America, for example, are most welcome to submit manuscripts that reach the required standard and threshold for publication.
We are currently positively impressed by the high quality of submissions that would have probably not made their way to international audiences.
3) The editorial board members appreciation of JCCS
The escalation of JCCS popularity is very satisfactory, as shown by the regular monthly increase of article downloads (27,6% over the first 8 months of 2022)4. The expansion of our readership occurred without any compromise on the quality of the work that is published in JCCS. It required scientific integrity, faithfulness, probity, responsibility, loyalty, and respect.
I also wish to mention and acknowledge the altruism and dedication of all the members of the JCCS editorial board who commit their precious time to deliver a constant high quality level of critical analysis.
Too many editors of publishing houses and open access mills, eager to earn the most financial profit from their journals, instead of prioritizing the highest quality of published science, should carefully consider this point. We can see more and more of a disagreement growing up among reviewers who are questioning the financial recognition of their input without which the publication world will collapse.
A summary of the conclusions reached during these very positive discussions will be presented in the near future.
This year of a Water Tiger will end on January 22, 2023 to make way for a year 2023 under the sign of a Water Rabbit. The water element in its Yin form is said to be the main source of energy in the Chinese calendar throughout 2023.
The year of a Water Rabbit 2023 is described as a prelude to a gentleness and serenity rhythm that should certainly be appreciated by all of us. It should therefore be an ideal period of time to regain a positive inner balance and prepare for the first ARBIOCOM meeting that is presently in the early stages of preparation.
All information will be available on the ICCNS web page.
We wish you all a very quiet and fruitful end of the year, away from conflict, uncertainty, disruptive thoughts and full of mutual respect, humanity and peace.
Acknowledgements
I am grateful to the various colleagues who accepted to proofread this Editorial prior to submission. Particular thanks are due to Annick, S. Gabaron, A. Mobasheri and H. Yeger for their thoughtful comments and suggestions regarding the last version.
Note
The previous Editor in Chief had the intention to publish a special issue of the journal on “Matrigel and microenvironment”. Because of an insufficient number of manuscripts received and the absence of a mandatory corresponding thematic section in the Editorial Manager, Annick and I could not publish these articles as stand alone. They have now been included at the end of this issue, along with its editorial.
1 PERBAL B. Inception and establishment of the International CCN Society (ICCNS) and of the Journal of Cell Communication and Signaling (JCCS). Current JCCS issue.
2 Union of Soviet Socialist Republics created in 1922.
3 See for example the very complete work of Pecenka V., Dvorak M., Travnicek M. Avian nephroblastomas induced by a retrovirus (MAV-2) lacking oncogene. I. Construction of MAV-1 and MAV-2 proviral restriction maps and preparation of specific proviral molecular subclones. Folia Biol (Praha). 1988 34:129 − 46.
4 Calculated from the numbers of downloads provided by Springer.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
| 36474111 | PMC9733734 | NO-CC CODE | 2022-12-14 23:36:27 | no | J Cell Commun Signal. 2022 Dec 6; 16(4):485-486 | utf-8 | J Cell Commun Signal | 2,022 | 10.1007/s12079-022-00718-7 | oa_other |
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Clin Neurophysiol Pract
Clin Neurophysiol Pract
Clinical Neurophysiology Practice
2467-981X
Published by Elsevier B.V. on behalf of International Federation of Clinical Neurophysiology.
S2467-981X(22)00040-3
10.1016/j.cnp.2022.10.002
Editorial
Clinical neurophysiological tests as objective measures for acute and long-term COVID-19
Seeck Margitta ⁎
Dept of Clinical Neurosciences, University Hospitals of Geneva, 1211 Geneva, Switzerland
Tankisi Hatice
b Department of Clinical Neurophysiology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 165, 8200 Aarhus N, Denmark
⁎ Corresponding author.
17 10 2022
2023
17 10 2022
8 12
2 10 2022
6 10 2022
© 2022 Published by Elsevier B.V. on behalf of International Federation of Clinical Neurophysiology.
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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pmcThe world suffered tremendously from the worldwide SARS-CoV-2 pandemic, officially declared as such the 11.3.2020 by the WHO, and its toll affected our economy, our socio-professional life and our physical and mental well-being. Sadly, the burden still endures, including the medical consequences. At the time of writing of this editorial, fortunately, subsequent mutations of the original virus appear to be related to less severe diseases. However, this may not last and they were and probably will always be severe clinical cases, requiring medical interventions. Most importantly, an increasing number of long COVID cases particularly after mild-moderate acute COVID-19 suggests that less severe acute disease as a consequence of less pathogenic new variants or vaccination could still have the ability to cause serious long-term disabilities. While initial descriptions stemmed mainly from internal medicine and intensive care due to predominant suffering of the respiratory system, it became soon evident that the disease affects also both the central and peripheral system, which are best probed with neurophysiological methods.
In the current volume of Clinical Neurophysiology Practice, Menkes and Haykal summarized the scope of diagnostic studies and their results, which were requested to determine the acute and long-term consequences of COVID-19. They provide us with an excellent survey of which body part is affected most in the acute or chronic phase, or also in relation to complication of SARS-CoV-2 -vaccination (Menkes and Haykal, 2022).
Regarding the acute phase, most of the evidence points to critical illness myopathy in intensive care unit patients (Tankisi et al., 2020, Rodriguez et al., 2022), Guillain-Barré syndrome (Uncini et al., 2021) and encephalopathy (Canham et al., 2020), while in patients with long COVID, small fiber neuropathies are often at the origin of peripheral dysesthesia and may result in dysautonomic alterations (Shouman et al., 2021). Thus, neurophysiologists may add electroencephalography (EEG), quantitative sensory and autonomic testing, standard nerve conduction studies (NCS) and electromyography (EMG) to the diagnostic work-up of acute or long-term COVID-19 manifestations.
In the central nervous system, it appears that COVID-19 leads to two major pathologies: an endothelitis due to a major tropism of the virus for endothelial cells (Varga et al., 2020) and meningoencephalitis. Endothelial affection is not limited to the brain, associated to intravascular coagulopathy (Wichmann et al., 2020) and in some cases antiphospholipid antibodies (Zhang et al., 2020). Viral infections in general increase the risk of stroke, which includes the SARS-CoV-virus (Bahouth et al., 2021), resulting in slowing of blood flow as visualized with transcranial doppler ultrasound (TCD). However, the formation of autoantibodies to the CNS may turn out as the bigger – diagnostic and therapeutic – problem of long COVID as well as some cases of post-vaccination complications.
It appears that long COVID may be an equally difficult challenge for our health care system as the acute COVID-19 disease. “Brain fog” is a frequent complaint of patients suffering from long COVID. Predominantly extensive small vessel emboli could be a possible mechanism, but there is little evidence from autopsy or TCD studies. In contrast, breakdown of the blood–brain barrier and as a consequence, infection of the astrocytes, has been reported as significant mechanism of neuronal distress (Crunfli et al., 2022), through changes in the energy metabolism and indirectly to neuronal dysfunction of death. These insults are not seen in the MRI, but in positron-emission tomography (PET) or EEG. In the acute phase, PET showed that the fronto-parietal and temporal cortex is affected predominantly (Hosp et al., 2021). The recovery process in longitudinal PET-studies shows a more complex picture: while the hypometabolism recovers by 5–6 months, hypermetabolic areas in the hippocampus, amygdala, cerebellum and brainstem persists (Martini et al., 2022) and would explain subjective feelings such as “brain fog”. Additionally, transcranial magnetic stimulation (TMS) studies showed altered GABAergic and cholinergic neurotransmission in long COVID patients with cognitive deficit and brain fog (Versace et al., 2021, Ortelli et al., 2022). Fatigue is among the most prevalent described long COVID symptoms (Nasserie et al., 2021). While fatigue has a central origin also described as brain fog, its close association with myalgia and muscle weakness suggests an additional peripheral origin. The myopathic EMG changes that were reported as a cause of fatigue (Agergaard et al., 2021) has recently been confirmed with histopathological evidence of fiber atrophy, mitochondrial changes, inflammation, and capillary injury in muscle biopsies (Hejbol et al., 2022). Recent literature highlights the similarities between long COVID and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) (Sukocheva et al., 2022), and gives hope to turning a corner in ME/CFS research which has been a conundrum for decades.
Regarding temporal lobe structures, EEG could be helpful in this regard, while the brainstem is best evaluated with evoked potentials (EP). Regarding the latter, not enough or large enough studies have been done to define the role of EPs in the acute and chronic work-up of COVID-19, as identified by the authors. However, EPs deserve a second look, also beyond the “traditional” EPs. Despite the fact that smell and taste were impaired in the majority of patients, and the deficits were long lasting, there is no study on olfactory EP.
EEG studies showed pattern of encephalopathy with focal or multifocal slowing. Interestingly a study of patients before and after COVID-19 infection and initial olfactory symptoms showed grey matter damage in the orbitofrontal cortex and parahippocampal gyrus, indicating degeneration through spread of the disease through olfactory pathways (Douaud et al., 2022). With 25 scalp electrode EEGs (Seeck et al., 2017), temporal alterations should be reliably picked up and monitored over time (Bernard-Valnet et al., 2021). Such studies, including high-density EEG studies for better spatial precision, are missing so far.
Minor or major infectious diseases are known to elicit autoantibodies in a small number of patients, including COVID-19, which can lead to dramatic neurological and psychiatric symptoms in adults and younger patients. In the CSF of three teenagers with new onset neuropsychiatric symptoms like psychosis, suicidal ideation and personality changes, no signs of significant inflammation but anti–SARS-CoV-2 antibodies with positive immunostaining of cortical and cerebellar cells were found. An EEG montage covering the temporal lobes might have noted abnormalities or even epileptogenic discharges in the left temporal lobe of patient 1 who presented an enlarged hippocampus in the MRI (figure 1 in Cabral-Marques et al., 2022). EEG can be helpful to identify focal abnormalities, which justify follow-up exams and extend pathological findings in COVID-19, like microbleeds, which would go otherwise unnoticed (De Stefano et al., 2020).
The question is not so much if a neurophysiological test is pathognomonic for COVID-19, this is best obtained with immunological and other blood tests as well as accessory exams. However, with the increase of long COVID cases, we need cost-effective algorithms to objectivate the patients’ complaints and monitor their evolution. These should include neurophysiological tools, perhaps using more and longer measurements or more sensors to avoid misdiagnosis of “unrelated” neurological and psychiatric dysfunctions. In that sense, the review of Menkes and Haykal is stimulating, in that it shows us where the areas of future neurophysiological research in COVID-19 could be.
Conflict of interest statement
None.
==== Refs
References
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Bahouth M.N. Venkatesan A. Acute Viral Illnesses and Ischemic Stroke: Pathophysiological Considerations in the Era of the COVID-19 Pandemic Stroke 52 2021 1885 1894 33794653
Bernard-Valnet R. Perriot S. Encephalopathies associated with severe COVID-19 present neurovascular unit Alterations Without Evidence for Strong Neuroinflammation Neurol. Neuroimmunol. Neuroinflamm. 8 2021 e1029 34135107
Cabral-Marques O. Halpert G. Schimke L.F. Autoantibodies targeting GPCRs and RAS-related molecules associate with COVID-19 severity Nat. Commun. 13 2022 1220 35264564
Canham LJW, Staniaszek LE, Mortimer AM, Nouri LF, Kane NM. Electroencephalographic (EEG) features of encephalopathy in the setting of Covid-19: A case series. Clin Neurophysiol Pract 2020;5:199-205. doi: 10.1016/j.cnp.2020.06.001. Epub 2020 Jul 2. PMID: 32838076; PMCID: PMC7329683.
Crunfli F. Carregari V.C. Veras F.P. Morphological, cellular, and molecular basis of brain infection in COVID-19 patients Proc. Natl. Acad. Sci. USA 119 2022
De Stefano P. Nencha U. De Stefano L. Mégevand P. Seeck M. Focal EEG changes indicating critical illness associated cerebral microbleeds in a Covid-19 patient Clin. Neurophysiol. Pract. 5 2020 125 129 32607454
Douaud G. Lee S. Alfaro-Almagro F. SARS-CoV-2 is associated with changes in brain structure in UK Biobank Nature 604 2022 697 707 35255491
Hejbol E.K. Harbo T. Agergaard J. Madsen L.B. Pedersen T.H. Ostergaard L.J. Myopathy as a cause of fatigue in long-term post-COVID-19 symptoms: Evidence of skeletal muscle histopathology Eur. J. Neurol. 29 2022 2832 2841 35661354
Hosp J.A. Dressing A. Blazhenets G. Bormann T. Rau A. Schwabenland M. Thurow J. Wagner D. Waller C. Niesen W.D. Frings L. Urbach H. Prinz M. Weiller C. Schroeter N. Meyer P.T. Cognitive impairment and altered cerebral glucose metabolism in the subacute stage of COVID-19 Brain 144 2021 1263 1276 33822001
Martini A.L. Carli G. Kiferle L. Piersanti P. Palumbo P. Morbelli S. Calcagni M.L. Perani D. Sestini S. Time-dependent recovery of brain hypometabolism in neuro-COVID-19 patients Eur. J. Nucl. Med. Mol. Imaging 19 2022 1 13
Menkes D.L. Haykal M.A. The Clinical Neurophysiology of COVID-19- Direct Infection, Long-Term Sequelae and Para-Immunization responses: A literature review Clin. Neurophysiol. Pract. This Volume 2022
Nasserie T. Hittle M. Goodman S.N. Assessment of the Frequency and Variety of Persistent Symptoms Among Patients With COVID-19: A Systematic Review JAMA Netw. Open 4 2021 e2111417 34037731
Ortelli P. Ferrazzoli D. Sebastianelli L. Maestri R. Dezi S. Spampinato D. Altered motor cortex physiology and dysexecutive syndrome in patients with fatigue and cognitive difficulties after mild COVID-19 Eur. J. Neurol. 29 2022 1652 1662 35138693
Rodriguez B. Branca M. Gutt-Will M. Roth M. Soll N. Nansoz S. Development and early diagnosis of critical illness myopathy in COVID-19 associated acute respiratory distress syndrome J. Cachexia Sarcopenia Muscle 13 2022 1883 1895 35384375
Seeck M. Koessler L. Bast T. Leijten F. Michel C. Baumgartner C. He B. Beniczky S. The standardized EEG electrode array of the IFCN Clin. Neurophysiol. 128 2017 2070 2077 28778476
Shouman K. Vanichkachorn G. Cheshire W.P. Suarez M.D. Shelly S. Lamotte G.J. Autonomic dysfunction following COVID-19 infection: an early experience Clin. Auton. Res. 31 2021 385 394 33860871
Sukocheva OA, Maksoud R, Beeraka NM, Madhunapantula SV, Sinelnikov M, Nikolenko VN, et al. Analysis of post COVID-19 condition and its overlap with myalgic encephalomyelitis/chronic fatigue syndrome. J Adv Res. 2022;40:179-196. doi: 10.1016/j.jare.2021.11.013. Epub 2021 Nov 26. PMID: 36100326; PMCID: PMC8619886.
Tankisi H. Tankisi A. Harbo T. Markvardsen L.K. Andersen H. Pedersen T.H. Critical illness myopathy as a consequence of Covid-19 infection Clin. Neurophysiol. 131 2020 1931 1932 32619798
Uncini A. Foresti C. Frigeni B. Storti B. Servalli M.C. Gazzina S. Electrophysiological features of acute inflammatory demyelinating polyneuropathy associated with SARS-CoV-2 infection Neurophysiol. Clin. 51 2021 183 191 33685769
Varga Z. Flammer A.J. Steiger P. Haberecker M. Andermatt R. Zinkernagel A.S. Mehra M.R. Schuepbach R.A. Ruschitzka F. Moch H. Endothelial cell infection and endotheliitis in COVID-19 Lancet 395 2020 1417 1418 32325026
Versace V. Sebastianelli L. Ferrazzoli D. Romanello R. Ortelli P. Saltuari L. Intracortical GABAergic dysfunction in patients with fatigue and dysexecutive syndrome after COVID-19 Clin. Neurophysiol. 132 2021 1138 1143 33774378
Wichmann D. Sperhake J.P. Lütgehetmann M. Autopsy Findings and Venous Thromboembolism in Patients With COVID-19: A Prospective Cohort Study Ann. Intern. Med. 173 2020 268 277 32374815
Zhang Y. Xiao M. Zhang S. Coagulopathy and Antiphospholipid Antibodies in Patients with Covid-19 N. Engl. J. Med. 382 2020 e38 32268022
| 0 | PMC9733941 | NO-CC CODE | 2022-12-14 23:28:27 | no | Clin Neurophysiol Pract. 2023 Oct 17; 8:1-2 | utf-8 | Clin Neurophysiol Pract | 2,022 | 10.1016/j.cnp.2022.10.002 | oa_other |
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Lancet Microbe
Lancet Microbe
The Lancet. Microbe
2666-5247
The Author(s). Published by Elsevier Ltd.
S2666-5247(22)00357-3
10.1016/S2666-5247(22)00357-3
Correspondence
Mpox screening in high-risk populations finds no asymptomatic cases
Van Dijck Christophe ab
De Baetselier Irith a
Kenyon Chris ac
Liesenborghs Laurens a
ITM Monkeypox Study GroupVan Dijck Christophe MD
De Baetselier Irith PhD
Kenyon Chris PhD MD
Brosius Isabel MD
Liesenborghs Laurens PhD MD
Van den Bossche Dorien MD
Florence Eric PhD MD
van Griensven Johan PhD MD
Bottieau Emmanuel PhD MD
Soentjens Patrick PhD MD
Berens-Riha Nicole PhD MD
Vanbaelen Thibaut MD
Van Frankenhuijsen Maartje MD
Vandenbruaene Marc MD
Huyst Veerle MD
Wouters Kristien MD
Apers Ludwig MD
Kint Ilse MD
Caluwaerts Séverine MD
Coppens Jasmine PhD
Van Esbroeck Marjan MD
Vercauteren Koen PhD
Vercauteren Koen a
Van Esbroeck Marjan a
a Department of Clinical Sciences, Institute of Tropical Medicine, 2000 Antwerp, Belgium
b Laboratory of Medical Microbiology, University of Antwerp, Antwerp, Belgium
c Division of Infectious Diseases and HIV Medicine, University of Cape Town, Cape Town, South Africa
9 12 2022
9 12 2022
© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license
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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pmcIn their Correspondence, Abdullah Reda and colleagues emphasised the importance of undiagnosed mpox (formally known as monkeypox) cases and called for surveillance with rapid diagnostic or home-based tests.1 We agree that the threshold for testing or screening at-risk populations should be low at the beginning of an epidemic with a new pathogen, to ascertain the nature and prevalence of atypical presentations.
Since we discovered three asymptomatic mpox cases by retrospectively testing stored samples taken for chlamydia and gonorrhoea testing in May, 2022,2 we were interested to assess whether in the following months, more cases occurred among men who have sex with men (MSM) who did not fulfil the WHO case definition of a suspected mpox case. We decided to expand the retrospective testing programme to samples collected in June, 2022, and to offer mpox testing to HIV-positive MSM and HIV-negative MSM using HIV pre-exposure prophylaxis, who attended our sexual health clinic for anorectal chlamydia or gonorrhoea testing or screening from July to September, 2022. Our analysis detected seven previously undiagnosed mpox cases among the samples from 146 MSM in June, and seven additional cases among 181 prospectively screened MSM between July and September (table ). Among these 14 mpox cases, five (35·7%) were HIV-positive, the remaining cases used HIV pre-exposure prophylaxis. The median age of participants was 41 years (IQR 38·0–43·0). All mpox cases were from before Aug 25, 2022, and only two fulfilled the WHO definitions of a suspected mpox case or European Centre for Disease Prevention and Control (ECDC) defined probable mpox case that were in effect at the time. These two people with mpox had subtle macular skin lesions and lymphadenopathy. The WHO and ECDC updated their mpox case definitions on Aug 25, and Sept 3, 2022, respectively, to include cases without skin lesions but with lymphadenopathy, mucosal lesions (including proctitis), or prodromal symptoms after contact with aanother person with mpox. Among our 12 remaining people with mpox, eight fulfilled these updated case definitions at the time of sampling due to proctitis (n=6) or lymphadenopathy (n=2), and three did so within the next 7 days because they developed typical febrile mpox skin lesions (n=2) or proctitis (n=1). One man did not meet any mpox case definitions as he only had prodromal symptoms without exposure to another person with mpox.Table Mpox patient characteristics and outcomes
Diagnosis Overall (n=14)
Retrospective (n=7) Prospective (n=7)
Age, median (IQR) 41 (39·0–42·0) 40 (38·0–47·5) 41 (38·0–43·0)
Sexual behaviour
Male-to-male sexual intercourse 7 (100%) 7 (100%) 14 (100%)
Multiple or anonymous sex partners in the previous 21 days 7 (100%) 6 (85·7%) 13 (92·9 %)
HIV status
Positive 4 (57·1%) 1 (14·3%) 5 (35·7%)
Negative and on PrEP 3 (42·9 %) 6 (85·7 %) 9 (64·3 %)
Coinfection
Gonorrhoea 3 (42·9%) 1 (14·3%) 4 (28·6%)
Chlamydia 1 (14·3%) 1 (14·3%) 2 (14·3%)
Mpox symptoms
Subtle skin lesions and lymphadenopathy 2 (28·6%) 0 2 (14·3%)
Proctitis with or without fever 4 (57·1%) 3 (42·9%)* 7 (50%)*
Lymphadenopathy 1 (14·3%) 1 (14·3%) 2 (14·3%)
Typical monkeypox skin lesions with fever 0 2 (28·6%)† 2 (14·3%)†
Prodromal symptoms only 0 1 (14·3%) 1 (7·1%)
Case definition
WHO suspected case (May 21, 2022) 2 (28·6%) 2 (28·6%)† 4 (28·6%)†
ECDC probable case (before Sept 8, 2022) 2 (28·6%) 2 (28·6%)† 4 (28·6%)†
WHO (Aug 25, 2022) and ECDC (Sept 3, 2022) revised definition of suspected case 7 (100%) 6 (85·7%)‡ 13 (92·9%)‡
Sample type
Anorectal swab 3 (42·9%) 4 (57·1%) 7 (50%)
Pooled sample§ 4 (57·1%) 3 (42·9%) 7 (50%)
Monkeypox virus-PCR, ct value (IQR) 21 (20·0–23·4) 28 (25·5–30·3) 28 (21·5–27·5)
Data is n/N (%). ct=cycle threshold. ECDC=European Centre for Disease Prevention and Control. PrEP=HIV pre-exposure prophylaxis.
§ Anorectal swab, oropharyngeal swab, and urine sample.
* One man was presymptomatic at the time of sampling.
† Two men were presymptomatic at the time of sampling.
‡ Three men were presymptomatic at the time of sampling.
Screening for mpox among individuals at high-risk, diagnosed 12 cases that did not fulfil the preliminary mpox case definition, of whom three individuals were presymptomatic and one individual only had prodromal symptoms. Our findings underscore the accuracy of the updated mpox case definition and suggest that the prevalence of asymptomatic mpox cases should not be overestimated, as we found no asymptomatic mpox cases.
This research was approved by the Institutional Review Board of the Institute of Tropical Medicine Antwerp, Antwerp, Belgium, on Sept 30, 2022, (ref 1629/22) and the Ethical Committee of the University Hospital Antwerp, Antwerp, Belgium, on Oct 17, 2022 (ref 3863). All authors declare no competing interests. CVD, IDB, KV, and MVE contributed equally.
==== Refs
References
1 Reda A El-Qushayri AE Shah J Asymptomatic monkeypox infection : a call for greater control of infection and transmission Lancet Microbe 2022 published online Oct 6. 10.1016/S2666-5247(22)00259-2
2 De Baetselier I Van Dijck C Kenyon C Retrospective detection of asymptomatic monkeypox virus infections among male sexual health clinic attendees in Belgium Nat Med 28 2022 2288 2292 35961373
| 36509096 | PMC9733947 | NO-CC CODE | 2022-12-15 23:21:52 | no | Lancet Microbe. 2022 Dec 9; doi: 10.1016/S2666-5247(22)00357-3 | utf-8 | Lancet Microbe | 2,022 | 10.1016/S2666-5247(22)00357-3 | oa_other |
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Free Radic Biol Med
Free Radic Biol Med
Free Radical Biology & Medicine
0891-5849
1873-4596
The Authors. Published by Elsevier Inc.
S0891-5849(22)01032-2
10.1016/j.freeradbiomed.2022.12.006
Article
Decreased oxidative stress and altered urinary oxylipidome by intravenous omega-3 fatty acid emulsion in a randomized controlled trial of older subjects hospitalized for COVID-19
Pawelzik Sven-Christian a1
Arnardottir Hildur a1
Sarajlic Philip a
Mahdi Ali a
Vigor Claire b
Zurita Javier c
Zhou Bingqing b
Kolmert Johan cd
Galano Jean-Marie b
Religa Dorota e
Durand Thierry b
Wheelock Craig E. df
Bäck Magnus a∗
a Department of Medicine, Karolinska Institutet, Theme Heart, Vessels, and Neuro, Karolinska University Hospital, Stockholm, Sweden
b Institut des Biomolécules Max Mousseron, IBMM, UMR 5247, Université de Montpellier, CNRS, ENSCM, Pôle Recherche Chimie Balard, 34293, Cedex 5, Montpellier, France
c Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
d The Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
e Department of Neurobiology, Karolinska Institutet and Theme Ageing, Karolinska University Hospital, Stockholm, Sweden
f Department of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm, Sweden
∗ Corresponding author. Karolinska University Hospital, M85, 14186, Stockholm, Sweden.
1 Shared first author.
10 12 2022
10 12 2022
7 11 2022
7 12 2022
7 12 2022
© 2022 The Authors. Published by Elsevier Inc.
2022
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Bioactive lipid proinflammatory mediators and oxidative stress are increased in coronavirus disease 2019 (COVID-19). The randomized controlled single-blind trial COVID-Omega-F showed that intravenous omega-3 polyunsaturated fatty acids (n-3 PUFA) shifted the plasma lipid signature of COVID-19 towards increased proresolving precursor levels and decreased leukotoxin diols, associated with a beneficial immunodulatory response. The present study aimed to determine the effects of n-3 PUFA on the urinary oxylipidome and oxidative stress in COVID-19. From the COVID-Omega-F trial, 20 patients hospitalized for COVID-19 had available serial urinary samples collected at baseline, after 48 h, and after completing 5 days treatment with one daily intravenous infusion (2 mL/kg) of either placebo (NaCl; n = 10) or a lipid emulsion containing 10 g of n-3 PUFA per 100 mL (n = 10). Urinary eicosanoids and isoprostanes were analysed by liquid chromatography tandem mass spectrometry (LC-MS/MS). Erythrocytes obtained at the different time-points from n = 10 patients (n = 5 placebo and n = 5 n-3 PUFA) were used for determination of reactive oxygen species. Intravenous n-3 PUFA emulsion administration altered eicosanoid metabolites towards decreased levels for mediators of inflammation and thrombosis, and increased levels of the endothelial function mediator prostacyklin. Furthermore, non-enzymatic metabolism was skewed towards n-3 PUFA-derived metabolites with potential anti-inflammatory and pro-resolving effects. The oxidative stress marker 15-F2t-isoprostane was significantly lower in patients receiving n-3 PUFA treatment, who also exhibited significantly decreased erythrocyte oxidative stress compared with placebo-treated patients. These findings point to additional beneficial effects of intravenous n-3 PUFA emulsion treatment through a beneficial oxylipid profile and decreased oxidative stress in COVID-19.
Graphical abstract
Image 1
Keywords
Coronavirus disease 2019 (COVID-19)
Eicosanoids
Erythrocyte oxidative stress
Resolution of inflammation
Isoprostanes
Inflammation
==== Body
pmcAbbreviations
acute respiratory distress syndrome (ARDS)
arachidonic acid (AA)
eicosapentaenoic acid (EPA)
docosahexaenoic acid (DHA)
coronavirus disease 2019 (COVID-19)
isoprostane (IsoP)
liquid chromatography tandem mass spectrometry (LC-MS/MS)
omega-3 polyunsaturated fatty acids (n-3 PUFA)
omega-6 polyunsaturated fatty acid (n-6 PUFA)
oxidative stress (OS)
polyunsaturated fatty acids (PUFA)
prostacyclin (PGI2)
Resolving inflammatory storm in COVID-19 patients by Omega-3 Polyunsaturated fatty acids trial (COVID-Omega-F)
reactive oxygen species (ROS)
specialized proresolving mediators (SPMs)
1 Introduction
Coronavirus disease 2019 (COVID-19) is characterized by high levels of inflammatory mediators and oxidative stress (OS), which are associated with more severe disease. In addition to cytokines, also lipid mediators of inflammation, e.g. prostaglandins and leukotrienes are increased in the uncontrolled inflammatory response during a SARS-CoV2 infection with acute respiratory distress syndrome (ARDS). Fatty acids are hence crucial in controlling excessive inflammation that is part of COVID-19 through a balance between proinflammatory and proresolving lipid mediators [1,2]. The formation of toxic oxidation products may contribute to the uncontrolled systemic inflammation in acute, as well as the sustained pathological findings, including endothelial dysfunction [3]. Anti-oxidants have been evaluated for their therapeutic potential to improve prognosis in acute COVID-19 [4,5] and to alleviate symptoms in long-haul COVID-19 [6].
Oxylipids are bioactive lipids generated from polyunsaturated fatty acids (PUFAs) through both enzymatic and non-enzymatic lipid peroxidation, which may contribute to the inflammatory and OS response. The most studied non-enzymatic lipid oxidation products are isoprostanes derived from the n-6 polyunsaturated fatty acid (PUFA) arachidonic acid and exerting pro-inflammatory and prothrombotic effects. In contrast, isoprostanes and neuroprostanes derived from non-enzymatic oxidation of n-3 PUFA are also associated with anti-inflammatory and pro-resolving responses [7]. n-3 PUFA may in addition act directly as enhancers of antioxidant defense against reactive oxygen species (ROS) [8]. However, the notion has been raised that n-3 PUFA make cell membranes more susceptible to non-enzymatic oxidation and to the formation of potentially toxic oxidation products and increasing the oxidative stress [9].
The n-3 PUFA eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are precursors for an enzymatic biosynthesis of lipid mediators stimulating the resolution of inflammation. Referred to as specialized proresolving mediators (SPMs), these bioactive lipids are endogenously formed to dampen systemic inflammation while also improving healing and microbial elimination [10]. Importantly, also oxidative non-enzymatically formed n-3 metabolites may exert beneficial effects in analogy with the SPMs.
The IMPRECOVID study revealed specific plasma oxylipid profiles to distinguish COVID-19-associated pneumonia requiring intensive care [11]. The randomized controlled single-blind COVID-Omega-F trial showed that intravenous n-3 PUFA administration shifted the plasma lipid signature of COVID-19 towards increased proresolving precursor levels and decreased leukotoxin diols [12]. While these plasma analyses provide a snapshot of lipid metabolism in COVID-19, the urinary oxylipidome gives a more complete picture of time-integrated chemically stable metabolites that reflects systemic biosynthesis [13,14]. The full urinary oxilipid profile in COVID-19, and the effects of PUFA supplementation have not been previously reported.
The beneficial immune responses after n-3 PUFA administration in the COVID-Omega-F trial included decreased neutrophil to lymphocyte ratio, preserved leukocyte phagocytic capacity, decreased immunothrombosis and preserved interferon-response [12]. The recent VASCEPA-COVID-19 CardioLink-9 trial showed that oral n-3 PUFA treatment with the EPA ethyl ester icosapent ethyl for 14 days improves symptoms and reduces C-reactive protein (CRP) in ambulatory COVID-19 patients [15]. Similar results have been reported in a smaller studies of n-3 PUFA added to hydroxychloroquine-treatment of COVID-19 (performed at the time it was used) [16] and in critically ill COVID-19 patients [17]. However, no previous study has examined the relation of beneficial effects of n-3 in relation to OS in patients with COVID-19.
The aim of the present study was to determine the effects of n-3 PUFA treatment in patients with COVID-19 on (a) the complete urinary enzymatic and oxidative oxylipidome, and (b) the effects on oxidative stress responses in patients with COVID-19.
2 Materials and methods
2.1 Patients
The trial “Resolving inflammatory storm in COVID-19 patients by Omega-3 Polyunsaturated fatty acids” (COVID-Omega-F) was approved by Swedish Ethical Review Authority (Dnr 2020–02592 and Dnr 2020–06137) and by the Medical Product Agency (Dnr 5.1-2020-42861 and Dnr 5.1-2020-96391). Inclusion criteria were a diagnosis of COVID-19 and a clinical status requiring hospitalization. After signed informed consent, participants were randomized 1:1 to a once daily i.v. infusion (2 mL/kg) of either placebo (0.9% NaCl) or n-3 poyunsaturated fatty acid (PUFA) emulsion containing 0.1 g/mL of fish oil (Omegaven®; ApoEX, Stockholm, Sweden), including 12.5–28.2 mg/mL DHA and 14.4–30.9 mg/mL EPA for 5 days. The study protocol is registered at clinicaltrials.gov with reference NCT04647604 has been published [18]. The primary endpoint measures have been reported [12].
2.2 Blood and urine sample collection
Samples were collected at study start before administration of the first dose (baseline), 24–48 h after the first administered dose (early), and within 24 h after the last administered dose (end) as illustrated in the Graphical Abstract. Laboratory measures of blood cell counts and Hb were performed by the Karolinska University Laboratory in accordance with ISO15189. Blood samples were collected by venipuncture into 8 ml sodium heparinized CPT™ Vacutainer® tubes (Becton Dickinson AB, Stockholm, Sweden) for erythrocyte isolation and processed within 2 h from collection. Urine samples for analysis of urinary lipid metabolites were collected in the morning into 10 mL tubes, immediately transferred to 4 °C, and aliquots were prepared within 2 h from collection and stored at −80 °C.
2.3 Erythrocyte isolation and incubation for ROS measurements
For each time point, erythrocytes were isolated. Whole blood was centrifuged at 1800 x g for 15 min at room temperature to separate peripheral blood mononuclear cells from erythrocytes and neutrophils. Erythrocytes were further isolated by sedimentation on 3% dextran at 1 x g for 20 min at 4 °C and washed 3 times with PBS−/- followed each time by centrifugation at 1000 x g for 10 min at 4 °C. In a protocol initiated after inclusion of the first 8 patients, erythrocytes derived from 10 of the study participants (n = 5 placebo and n = 5 n-3 PUFA treated subjects) were used to evaluate the production of ROS. Erythrocytes were incubated with the spin probe 1-hydroxy-3-methoxycarbonyl-2,2,5,5-tetramethylpyrrolidine (CMH; Noxygen Science Transfer & Diagnostics GmbH, Elzach, Germany). In brief, 5 μl of washed erythrocytes were added to 1 mL 200 μM CMH:KREBS/HEPES solution, (1:1, v/v), mixed well, and incubated for 30 min at 37 °C with gentle shaking. Incubations were stopped by freezing the samples on dry ice. Samples were stored at −80 °C until ROS production was quantified using electron paramagnetic resonance (EPR) spectroscopy on a Bruker E-Scan M system (Bruker, Billerica MA, USA) as previously described [3,19,20]. The following settings were used: center field 1.99 g, microwave power 1 mW, modulation amplitude 9 G, sweep time 10 s, number of scans 10, field sweep 60 G. The EPR spectrums are expressed as fold change from baseline.
2.4 Urinary eicosanoid analysis
300 μL urine were spiked with 10 μL of deuterated internal standard mix and acidified with 1500 μL of acetic acid (0.12%) in H2O. For solid-phase extraction, a mixed polymer phase (30 mg, ABN Evolute, Biotage Uppsala, Sweden) was conditioned with 1 mL methanol and equilibrated with 0.1% acetic acid in H2O, the samples were loaded, and the SPE phase was washed with 1 mL 0.1% acetic acid in H2O, followed by 1 mL 8% methanol in H2O. Analytes were eluted using a 3:1 mix of methanol and acetonitrile and dried under nitrogen. The dry extracts were reconstituted in 100 μL 1:1 mix of methanol and H2O and filtered using a filter plate + (0.22 μM, Biotage, Uppsala, Sweden). Urinary eicosanoids were analyzed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) on an Acquity UPLC and Xevo TQ-XS system (Waters, Milford, MA, USA) and quantified using two separate LC-MS/MS methods as described before [21]. 7.5 μL of the reconstituted extracts were separated on a HSS T3 column (2.1 × 100 mm, 1.8 μm, Waters, Milford, MA, USA) equipped with a HSS T3 VanGuard column. The remaining extract was incubated with 10 μL methoxyamine, and derivatized 2,3-dinor TXB2 and 2,3-dinor-6-keto PGF1α were quantified as described before [21]. In both methods, mobile phases were 0.1% acetic acid in H2O (A) and a 9:1 mix of acetonitrile and isopropanol (B). Analytes were quantified in both methods using an external 10-point calibration curve, and three low and high spiked quality control urine samples per plate were measured to assess method performance.
2.5 Urinary isoprostane analysis
Urinary isoprostane profiling was performed using a micro-LC-MS/MS equipment after extraction of lipids from the biological matrix. 1 mL of urine was added to 1 mL of 40 mM formic acid (pH 4.5) including 4 μL internal standard mixture (1 ng/μL of each IS) and vortexed for 30 s. The samples were then extracted by Solid Phase Extraction (SPE) using Oasis Max cartridges (30 μm, 60 mg, Waters, Milford, MA, USA). Cartridges were conditioned with 2 mL of MeOH followed by 2 mL of 20 mM formic acid. After loading of the samples, they were washed with 2 × 1 mL of four successive solvent mixtures: 2% NH3, methanol/20 mM formic acid (30:70, v/v), hexane, and hexane/ethyl acetate (70:30, v/v). The metabolites were eluted with hexane/ethanol/acetic acid (70:29.4:0.6, v/v/v), concentrated to dryness under a nitrogen stream, and reconstituted in 100 μL of mobile phase (water/acetonitrile, 83:17, v/v, with each containing 0.1% formic acid). The metabolites were analyzed by microLC-MS/MS on an Eksigent chromatographic system (Sciex Applied Biosystems, Flamingham, MA, USA) coupled to a QTRAP 5500 mass system (Sciex, Concord, ON, Canada) as previously described [22]. The MS/MS was operated in electrospray ionization (ESI) negative mode. Detection of the fragmentation ion products from each deprotonated molecule [M-H]- was performed in the multiple reaction monitoring mode (MRM). Concentration of the analytes was obtained by calibration curves calculated by the area ratio of analytes and IS. Data was processed using MultiQuant 3.0 software (Sciex Applied Biosystems). The obtained IsoP levels were normalized to urinary creatinine concentrations, which were determined using a standard colorimetric assay (Cayman Chemicals, Ann Arbor, MI, USA).
2.6 Statistical analysis
To determine the effect of time and the n-3 PUFA intervention, a mixed analysis of variance model was implemented in which time was defined as a within-subjects effect and the intervention as a between-subjects effect. Moreover, the interaction between these effects was measured. All significance tests were two-sided and findings with p < 0.05 were regarded as statistically significant. The above-mentioned analyses were performed in Prisma 8, version 8.4.3 (erythrocyte ROS) or R version 4.1.1 (R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/) [23].
3 Results
3.1 Patient characteristics
3.1.1 Patient characteristics
All subjects were included from June to December 2020, before COVID-19 vaccination was available, at a first COVID-19 infection. The COVID-Omega-F trial was completed for 22 older subjects (mean age 81 ± 6.1 years). Of these, serial urine samples were available from 20 participants, which did not exhibit any significant differences in baseline characteristics between the groups (Table 1 ). The severity of COVID-19 was evaluated at admission before initiation of treatments by CRP, which was 71.6 ± 46.5 pg/mL (n = 20).Table 1 Baseline characteristics.
Table 1 All Placebo n-3 PUFA P
n 20 10 10
Age - years 80.7 ± 6.2 80.7 ± 5.8 80.7 ± 7.0 1.0
Female sex – no. (%) 11 (55%) 6 (60%) 5 (50%) 1.0
Body Mass Index - kg/m2 25.6 ± 4.1 24.9 ± 3.9 26.3 ± 4.4 0.47
Current smoker – no. (%) 2 (10%) 0 (0%) 2 (20%) 0.47
Days since symptom start 7.4 ± 3.7 7.5 ± 3.6 7.0 ± 3.1 0.87
Days since COVID-19 diagnosis 3.9 ± 2.2 4.2 ± 2.7 3.7 ± 1.3 0.83
White Blood Cells
Leukocytes (x109/L) 7.3 (5.9–9.6) 6.1 (4.1–8.0) 8.1 (6.1–11) 0.06
Monocytes (x109/L) 0.60 (0.46–0.74) 0.52 (0.27–0.77) 0.65 (0.50–0.85) 0.25
Neutrophils (x109/L) 5.4 (4.2–7.0) 4.4 (2.8–6.0) 5.6 (4.1–9.2) 0.09
Lymphocytes (x109/L) 1.1 (0.9–1.4) 0.98 (0.71–1.2) 1.15 (0.78–1.6) 0.20
Red Blood Cells
Erythrocytes (x1012/L) 3.9 (3.6–4.3) 4.1 (3.5–4.6) 4.0 (3.4–4.3) 0.45
EVF 0.36 (0.33–0.38) 0.35 (0.31–0.40) 0.36 (0.34–0.39) 0.89
Ery-MCH 29 (28–31) 29 (26–31) 31 (29–32) 0.13
Ery-MCV 90 (86–94) 87 (80–94) 93 (90–96) 0.09
Hb (g/L) 116 (102–129) 114 (99–130) 120 (106–129) 0.80
Demographic data are expressed as either mean ± standard deviation or numbers and per cent. Laboratory measures are expressed as mean (95% CI). Statistical analyses were performed by either a Student's t-test (continuous variables) or a Fisher Exact Test (categorical variables) Abbreviations: EVF, erythrocyte volume fraction; Ery-MCH, erythrocyte mean corpuscular hemoglobin, Ery-MCV mean corpuscular volume.
3.1.2 n-3 PUFA administration alters the urinary eicosanoid profile in COVID-19 patients
The urinary profile of enzymatically synthesized eicosanoids and their metabolites (Table 1) revealed a significant increase in the prostacyclin (PGI2) metabolite, 2,3-dinor-6-keto PGF1α, at the early time point in the n-3 PUFA group compared with the placebo group (Fig. 1 , left panel; Table 1). The increase in the thromboxane (TX) A2 metabolite, TXB2, observed in the placebo-group at the end of the treatment was not observed in the n-3 PUFA treated group, but failed to reach significance (Fig. 1, middle panel). There was also a trend towards decreased levels of the urinary leukotriene metabolite LTE4 in the n-3 PUFA treated compared with the placebo group (Fig. 1, right panel). The urinary concentrations of the urinary metabolites of PGE2, PGD2, and PGF2α were not significantly altered over time or between the groups (Table 2 ).Fig. 1 Mass spectrometry analysis of urinary eicosanoids. Urinary levels of the prostacyclin urinary metabolite 2,3-dinor-6-keto-PGF1α (left panel), the thromboxane A2 metabolite thromboxane B2 (middle panel), and the cysteinyl-leukotriene E4 (right panel). Urine was collected from patients at baseline, 48 h (Early) and after treatment (End) following intravenous infusion (2 mL/kg) of either placebo (NaCl; black symbols, n = 12) or n-3 PUFA emulsion containing 10 g of n-3 polyunsaturated fatty acid emulsion per 100 mL (blue symbols, n = 10) for 5 days. Results (mean ± SEM) are expressed as mg/mg creatinine. Statistical analyses were performed with 2-way ANOVA for repeated measures and post hoc testing. *P < 0.05 compared to baseline and #P < 0.05 between placebo and n-3 PUFA treatment.
Fig. 1
Table 2 Urinary eicosanoids.
Table 2 Baseline Early End Baseline Early End Interv Progress Interv:Progr
Leukotriene E4
LTE4 0.17 ± 0.03 0.41 ± 0.28 0.45 ± 0.27 0.27 ± 0.09 0.3 ± 0.09 0.15 ± 0.04 0.450 0.332 0.072
Prostaglandin D2metabolites 3.65 ± 1.13 3.62 ± 0.89 3.84 ± 0.71 3.35 ± 0.55 4.65 ± 1.09 4.36 ± 1.16 0.561 0.942 0.640
TetranorPGDM 2.75 ± 1.01 3.04 ± 0.78 3.3 ± 0.6 2.83 ± 0.54 4.12 ± 1.02 3.75 ± 1.13 0.543 0.911 0.504
TetranorPGJM 0.79 ± 0.43 0.42 ± 0.13 0.35 ± 0.05 0.41 ± 0.07 0.41 ± 0.08 0.51 ± 0.17 0.509 0.917 0.509
2,3-dinor-11-B-PGF2a 0.11 ± 0.05 0.17 ± 0.09 0.19 ± 0.08 0.12 ± 0.04 0.12 ± 0.04 0.11 ± 0.02 0.467 0.957 0.359
Prostaglandin E2metabolites 59.7 ± 20.5 40.1 ± 9.81 55.0 ± 13.2 82.7 ± 24.3 75.0 ± 14.5 82.6 ± 17.6 0.107 0.213 0.678
PGE2 0.21 ± 0.06 0.27 ± 0.09 0.52 ± 0.16 1.28 ± 0.59 0.81 ± 0.22 0.87 ± 0.45 0.203 0.448 0.638
TetranorPGAM 1.93 ± 0.61 1.29 ± 0.29 1.48 ± 0.3 2.71 ± 0.89 2.55 ± 0.54 2.4 ± 0.69 0.090 0.960 0.667
TetranorPGEM 57.4 ± 20.0 38.3 ± 9.7 52.6 ± 12.9 77.9 ± 23.4 69.9 ± 13.6 78.8 ± 17.1 0.123 0.178 0.745
TetranorPGE1 0.11 ± 0.04 0.18 ± 0.09 0.38 ± 0.2 0.73 ± 0.25 1.7 ± 1.07 0.52 ± 0.16 0.163 0.419 0.257
Prostaglandin F2ametabolites 3.52 ± 0.77 2.64 ± 0.70 3.01 ± 0.55 3.61 ± 0.47 2.95 ± 0.47 2.76 ± 0.34 0.955 0.850 0.569
PGF2a 1.06 ± 0.09 1.25 ± 0.18 1.8 ± 0.41 1.66 ± 0.21 1.45 ± 0.34 1.17 ± 0.16 0.481 0.643 0.158
13,14-dihydro-15-ketoPGF2a 1.42 ± 0.15 1.79 ± 0.23 2.24 ± 0.31 1.87 ± 0.39 1.54 ± 0.44 1.24 ± 0.26 0.141 0.713 0.095
TetranorPGFM 2.46 ± 0.74 1.39 ± 0.65 1.2 ± 0.31 1.95 ± 0.4 1.5 ± 0.28 1.59 ± 0.26 0.612 0.887 0.636
Prostacyclin metabolite
2,3-dinor-6-keto-PGF1a 0.83 ± 0.19 0.36 ± 0.17 1.02 ± 0.22 0.81 ± 0.22 1.89 ± 0.76 1.16 ± 0.78 0.310 0.882 0.009*
Thromboxane A2metabolites 4.69 ± 1.02 4.16 ± 0.86 18.9 ± 14.5 4.45 ± 1.25 6.19 ± 2.53 4.89 ± 1.59 0.404 0.356 0.273
TXB2 0.15 ± 0.04 0.11 ± 0.06 0.61 ± 0.43 0.34 ± 0.21 0.28 ± 0.11 0.15 ± 0.06 0.511 0.375 0.139
2,3-dinor-TXB2 1.07 ± 0.2 1 ± 0.27 4.16 ± 3.18 0.85 ± 0.25 1.27 ± 0.53 0.63 ± 0.15 0.306 0.424 0.232
11-dehydroTXB2 2.32 ± 0.52 2.29 ± 0.46 8.25 ± 5.99 2.32 ± 0.76 3.01 ± 1.31 3.28 ± 1.53 0.488 0.326 0.367
11-dehydro-2,3-dinor-TXB2 1.15 ± 0.41 0.76 ± 0.17 5.91 ± 4.91 0.94 ± 0.24 1.63 ± 0.69 0.85 ± 0.16 0.384 0.367 0.225
Results are presented as mean ± SD.
3.1.3 Altered urine autooxidation lipid profile by n-3 PUFA administration in COVID-19
A targeted urinary lipid metabolite analysis was performed for a panel of non-enzymatically oxidized PUFA metabolites. The urinary concentrations of 15-F2t-isoprostane (15(RS)-15-F2t IsoP) were increased in the placebo-group, whereas n-3 PUFA-treated patients exhibited a time-dependent decrease of 15(RS)-15-F2t IsoP, resulting in significantly lower urinary 15(RS)-15-F2t-IsoP levels in n-3 PUFA-treated patients compared to placebo treated patients at end of study (Fig. 1, left panel). In contrast, the n-3/n-6 ratio for all oxidative urinary metabolites was significantly increased by n-3 PUFA treatment, which reflected the increased n-3 PUFA metabolite F3t-IsoP observed for the n-3 PUFA compared with placebo treatment (Fig. 2 ). The complete results of the oxidative PUFA metabolite profiling are shown in Table 3 .Fig. 2 Mass spectrometry analysis of urinary isoprostane (IsoP) derived from polyunsaturated fatty acids (PUFA). Left panel shows the n-6 PUFA-derived isoprostane 15(RS)-15-F2t IsoP and the middle panel shows the n-3 PUFA-derived isoprostane 5(R)-5-F3t IsoP. Results (mean ± SEM) are expressed as mg/mg creatinine.The right panel shows the ratio (mean ± SEM) between total urinary IsoP derived from n-3 and n-6 PUFA. Urine was collected from patients at baseline, 48 h (Early) and after treatment (End) following intravenous infusion (2 mL/kg) of either placebo (NaCl; black symbols, n = 10) or n-3 PUFA emulsion containing 10 g of n-3 PUFA emulsion per 100 mL 100 mL (blue symbols, n = 10) for 5 days. Statistical analyses were performed with 2-way ANOVA for repeated measures and post hoc testing. *P < 0.05 compared to baseline and #P < 0.05 between placebo and n-3 PUFA treatment.
Fig. 2
Table 3 Urinary oxidation metabolites.
Table 3 Baseline Early End Baseline Early End Interv Progress Interv:Progr
AA oxidation 21.10 ± 3.790 20.30 ± 5.190 16.90 ± 2.720 18.50 ± 1.78 22.40 ± 2.890 25.60 ± 4.45 0.308 0.984 0.144
5(RS)-5-F2t-IsoP 1.50 ± 0.270 1.33 ± 0.280 1.19 ± 0.230 1.42 ± 0.190 1.68 ± 0.220 2.02 ± 0.310 0.114 0.404 0.052
5-F2c-IsoP 13.80 ± 2.780 13.70 ± 4.32 11.00 ± 2.370 12.30 ± 1.100 16.30 ± 2.210 17.60 ± 3.170 0.284 0.675 0.221
15(RS)-F2t-IsoP 0.65 ± 0.120 0.62 ± 0.180 0.46 ± 0.120 0.66 ± 0.062 0.68 ± 0.081 0.93 ± 0.130 0.134 0.627 0.042
15-F2t-IsoP 0.24 ± 0.047 0.22 ± 0.074 0.17 ± 0.047 0.24 ± 0.031 0.22 ± 0.026 0.31 ± 0.045 0.282 0.542 0.046
15-epi-15-F2t-IsoP 0.41 ± 0.081 0.40 ± 0.110 0.29 ± 0.086 0.42 ± 0.041 0.46 ± 0.060 0.61 ± 0.087 0.101 0.717 0.046
15-F2t-IsoP Metabolites 4.60 ± 0.761 4.16 ± 0.730 3.77 ± 0.339 3.81 ± 0.649 3.38 ± 0.518 4.45 ± 1.013 0.957 0.469 0.127
2,3-dinor-15-F2t-IsoP 2.26 ± 0.340 2.08 ± 0.336 1.91 ± 0.197 1.94 ± 0.329 1.78 ± 0.299 2.29 ± 0.504 0.926 0.476 0.163
2,3-dinor-15-epi-15-F2t-IsoP 1.94 ± 0.367 1.66 ± 0.329 1.53 ± 0.153 1.46 ± 0.298 1.25 ± 0.199 1.71 ± 0.400 0.745 0.383 0.125
ent-2,3-dinor-5,6-dihydro-15F2t-IsoP 0.41 ± 0.069 0.42 ± 0.083 0.33 ± 0.051 0.41 ± 0.065 0.35 ± 0.056 0.46 ± 0.136 0.786 0.903 0.103
15-A2-IsoP 0.52 ± 0.100 0.43 ± 0.064 0.46 ± 0.056 0.27 ± 0.039 0.34 ± 0.062 0.61 ± 0.132 0.770 0.014 0.046
EPA oxidation 3.05 ± 0.497 4.71 ± 0.962 4.91 ± 1.161 3.29 ± 0.739 2.85 ± 0.320 5.10 ± 1.667 0.529 0.208 0.288
5(R)-5-F3t-IsoP 0.818 ± 0.130 1.78 ± 0.707 1.90 ± 0.581 0.82 ± 0.220 0.84 ± 0.086 1.17 ± 0.144 0.187 0.391 0.687
5(S)-5-F3t-IsoP 1.71 ± 0.388 2.48 ± 0.513 2.63 ± 0.716 2.18 ± 0.470 1.62 ± 0.258 3.52 ± 1.550 0.992 0.219 0.291
DHA oxidation 1.37 ± 0.503 1.63 ± 0.542 2.81 ± 1.660 1.03 ± 0.182 1.49 ± 0.316 1.36 ± 0.206 0.488 0.378 0.270
4(RS)-4-F4t-NeuroP 0.80 ± 0.011 0.10 ± 0.010 0.10 ± 0.020 0.09 ± 0.005 0.12 ± 0.015 0.11 ± 0.019 0.648 0.645 0.856
14(RS)-14-F4t-NeuroP 1.28 ± 0.500 1.53 ± 0.550 2.71 ± 1.660 0.941 ± 0.170 1.38 ± 0.309 1.25 ± 0.200 0.483 0.374 0.271
ALA oxidation 1.11 ± 0.129 1.49 ± 0.320 1.25 ± 0.330 1.87 ± 0.690 1.34 ± 0.170 2.81 ± 1.120 0.318 0.262 0.128
ent-16-F1t-PhytoP 0.35 ± 0.052 0.41 ± 0.130 0.31 ± 0.130 0.98 ± 0.560 0.45 ± 0.078 1.29 ± 0.606 0.151 0.230 0.133
ent-16-epi-16-F1t-PhytoP 0.32 ± 0.043 0.34 ± 0.420 0.35 ± 0.100 0.39 ± 0.077 0.37 ± 0.061 0.76 ± 0.230 0.130 0.134 0.145
9-F1t-PhytoP 0.14 ± 0.032 0.22 ± 0.035 0.16 ± 0.032 0.13 ± 0.018 0.20 ± 0.023 0.32 ± 0.160 0.431 0.665 0.293
9-epi-9-F1t-PhytoP 0.12 ± 0.021 0.19 ± 0.056 0.15 ± 0.052 0.14 ± 0.036 0.16 ± 0.033 0.24 ± 0.110 0.736 0.616 0.169
ent-16-B1-PhytoP 0.15 ± 0.043 0.28 ± 0.102 0.25 ± 0.098 0.20 ± 0.049 0.14 ± 0.040 0.17 ± 0.043 0.299 0.902 0.410
ent-9-L1-PhytoP 0.03 ± 0.005 0.05 ± 0.002 0.04 ± 0.001 0.03 ± 0.005 0.03 ± 0.007 0.03 ± 0.005 0.472 0.375 0.396
18(S)-18-F3t-IsoP 0.52 ± 0.130 0.45 ± 0.089 0.38 ± 0.110 0.29 ± 0.098 0.39 ± 0.052 0.42 ± 0.089 0.892 0.783 0.532
Results are presented as mean ± SD.
3.1.4 Reduced oxidative stress in erythrocytes by n-3 PUFA administration in COVID-19
Urinary 15-F2t-IsoP is an oxidative stress marker. To examine if the significant 15-F2t-IsoP decrease observed after n-3 PUFA treatment (Fig. 2) reflected a beneficial effect on OS burden, EPR spectroscopy using the spin probe CMH was performed on erythrocytes. The results demonstrated significantly lower levels of ROS in erythrocytes derived from n-3 PUFA-compared with placebo-treated COVID-19 patients (Fig. 3 , n = 5 in each group).Fig. 3 Erythrocyte oxidative stress. Upper panel shows illustration for oxidation of CMH-hydrochloride probe by ROS into EPR visible species (left) and representative curves obtained by EPR spectroscopy from the measurement of ROS produced by red blood cells derived from patients randomized to either placebo (black) or i.v. n-3 PUFA treatment (blue). Lower panel shows the absorbance (mean ± SEM) for measurement of ROS production following CMH-hydrochloride incubation of red blood cell derived from patients at baseline, at 48 h (Early), and after treatment (End) with intravenous infusion (2 mL/kg) of either placebo (black symbols NaCl; n = 5) or n-3 PUFA emulsion containing 10 g of fish oil per 100 mL (blue symbols, n = 5). Statistical analyses were performed with 2-way ANOVA for repeated measures and post hoc testing. **P < 0.01compared to baseline and #P < 0.05 between placebo and n-3 PUFA treatment.
Fig. 3
4 Discussion
The urinary oxylipidome after i.v. n-3 PUFA treatment of patients hospitalized with COVID-19 revealed altered PUFA metabolism, affecting eicosanoids involved in inflammation, thrombosis, and endothelial function. Furthermore, non-enzymatic metabolism was skewed towards n-3 PUFA-derived metabolites with potential anti-inflammatory and pro-resolving effects. In line with those findings, the OS marker 15-F2t-isoprostane was significantly lower in patients receiving n-3 PUFA treatment, which was also detected through decreased erythrocyte OS. These findings point to additional beneficial effects of n-3 PUFA treatment through on reduced OS response in COVID-19.
Most lipid mediators are, in line with their biological role as local autacoids, rapidly inactivated through enzymatic and non-enzymatic degradation to several metabolites. Therefore, the plasma metabolome of these molecules is dynamic, and its plasma analysis provides only a snapshot of the metabolic situation in vivo. In contrast to the plasma metabolome of PUFA metabolites, the analysis of the urinary oxylipidome yields a more complete picture of time-integrated chemically stable metabolites that reflects systemic biosynthesis [13,14]. In addition to the separately reported plasma analyses [12], we here analyzed the urinary eicosanoid metabolites.
The urinary eicosanoid profiling revealed a significant transient increase in the urinary prostacyclin metabolite by n-3 PUFA treatment. Endothelial dysfunction is a hallmark of COVID-19 [24], which potentially can be improved by increased prostacyclin formation. In contrast to the increase in the urinary prostacyclin metabolite, a trend towards lower thromboxane formation was accompanied by significantly decreased urinary concentrations of the isoprostane 15-F2t-IsoP, which transduces prothrombotic responses by means of the thromboxane prostanoid (TP) receptor. Taken together, these findings reinforced that the thrombotic balance is tipped in a beneficial direction through a decrease in ligands for the pro-aggregatory TP receptor and increased anti-aggregatory prostacylin.
Importantly, eicosanoids and isoprostanes may contribute to the uncontrolled inflammatory response in COVID-19 [18]. The trend towards decreased urinary LTE4 by iv n-3 PUFA emulsion to the possible therapeutic anti-inflammatory effects of leukotriene receptor antagonists have been evoked for treatment of COVID-19 [25] in addition to specific cytokine antibody treatment [26,27].
The isoprostane 15-F2t-IsoP, which is an established OS marker formed through non-enzymatic oxidative metabolism of the n-6 PUFA arachidonic acid (AA), was significantly lower compared with placebo after n-3 PUFA treatment. Previous results from this trial revealed that plasma levels of EPA and DHA were significantly increased by n-3 PUFA treatment, whereas AA levels were not significantly altered [12]. The increased n-3/n-3 PUFA may however skew the enzymatic and non-enzymatic metabolism.
Although few of the individual metabolites were significantly altered by n3-PUFA treatment, a skewing of the oxidative PUFA metabolism was reflected by the significantly increased n-3/n-6 ratio for the complete non-enzymatic urinary oxylipidome after i.v. n-3 treatment of COVID-19. Within the n-3 metabolome, the n-3 isoprostane (F3t-IsoP) is part of the neuroprostane family of proresolving mediators [7]. EPA and DHA are enzymatically metabolized into SPMs, which directly can contribute to the observed anti-oxidative effects [28]. N-3 PUFA are also subjected to non-enzymatical oxidative metabolism yielding additional mediators of the resolution of inflammation [7].
Results using EPR technique with the spin probe CMH for high precision quantification revealed a significantly lower ROS production in erythrocytes derived from n-3 PUFA-compared with placebo-treated COVID-19 patients in the present study, which may reflect established direct antioxidant effects of n-3 PUFA, and the skewed lipid metabolism away from n-6 isoprostanes. Furthermore, elevated ROS production in erythrocytes from patients with COVID-19 induce endothelial dysfunction ex vivo [3]. Therefore, treatment with n-3 PUFA might improve vascular function in patients with COVID-19 by means of increasing prostacyclin and restoring the redox balance in erythrocytes. A high neutrophil to lymphocyte ratio (NLR) reflects neutrophil-induced oxidative stress in COVID-19, which contributes to disease severity in terms of involvement in tissue damage, thrombosis, and red blood cell dysfunction. Importantly, iv n-3 PUFA emulsion treatment significantly reduces the NLR in COVID-19 [12]. The present study hence extends the potential therapeutic effects of anti-oxidative treatments in COVID-19 [29] by showing reduced OS by n-3 PUFA.
This is the first report of effects of iv n-3 PUFA emulsion on the oxidative PUFA metabolism in COVID-19. There are however limitations that should be acknowledged. Since patients randomized to placebo received the same volume of NaCl, it cannot be determined which of the constituents of the n-3 PUFA emulsion were active. It should be pointed out that the emulsion contains the anti-oxidant dl-α-tocopherol (0.02–0.03 g/100 mL). However, the altered oxylipidome being dependent of rapid increase in DHA and EPA [12], was further reinforced by the present study showing an increased ratio between the urinary isoprostanes derived from n-3 and n-6 PUFA. The PUFA substrate availability in cells and tissues was not determined in this study. The low number of participants is also a limitation, and larger studies are needed to determine the relation of the observed beneficial effects of iv n-3 PUFA emulsion on oxidative stress to clinical outcomes in COVID‐19. Finally, the older study population may limit the extrapolation of the results to younger subjects.
5 Conclusions
In summary, the present study shows beneficial effects on OS exerted by iv n-3 PUFA emulsion treatment of COVID-19 through (a) altered levels of eicosanoids and isoprostanes towards a beneficial profile for inflammation, thrombosis, and endothelial function, (b) reduced urinary levels of the oxidative stress marker 15-F2t-isoprostane and decreased erythrocyte OS, and (c) a skewed non-enzymatic metabolism towards n-3 PUFA-derived oxy-lipid metabolites with potential anti-inflammatory and pro-resolving effects. Meta-analyses have shown significantly shorter length of intensive care hospital stay and recued infections by n-PUFA lipid emusion in parental nutrition [30,31]. The use of n-3 PUFA emulsions as treatment of COVID-19 [32] induced beneficial immunomodulation [12]. In conclusion, the present trial identified additional beneficial effects of n-3 PUFA treatment on the OS response in COVID-19.
Funding sources
The study received a research grant from King Gustaf V and Queen Victoria Freemason Foundation. The investigators were supported by the 10.13039/501100004359 Swedish Research Council [Grant number 2019-01486] and the Swedish Heart and Lung Foundation [grant numbers 20180571, 20190625; 20190196; 20200693; 20210519]. The sources of funding had no access to the study data and no role in the design, implementation, or reporting.
Registration in trial registries
This trial “Resolving Inflammatory Storm in COVID-19 Patients by Omega-3 Polyunsaturated Fatty Acids - A single-blind, randomized, placebo-controlled feasibility study” (COVID-Omega-F) is registered in the European Union Drug Regulating Authorities Clinical Trials (EudraCT) database with number 2020-002293-28 and at Clinical Trials.gov with number NCT04647604.
Data availability
Individual participant data that underlie the results reported will be shared, after deidentification, with researchers who provide a methodologically sound proposal.
Time frame
Beginning 9 months following article publication and finishing 36 months following article publication.
Access criteria
Investigators interested in data should contact the corresponding author.
Declaration of competing interest
None.
==== Refs
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| 36509313 | PMC9733960 | NO-CC CODE | 2022-12-14 23:28:27 | no | Free Radic Biol Med. 2023 Jan 10; 194:308-315 | utf-8 | Free Radic Biol Med | 2,022 | 10.1016/j.freeradbiomed.2022.12.006 | oa_other |
==== Front
Respir Med
Respir Med
Respiratory Medicine
0954-6111
1532-3064
Elsevier Ltd.
S0954-6111(22)00353-5
10.1016/j.rmed.2022.107088
107088
Original Research
Health trajectories in older patients hospitalized for COVID-19: Results from the GeroCovid multicenter study
Trevisan Caterina ab
Tonarelli Francesco c
Zucchelli Alberto d
Parrotta Ilaria e∗
Calvani Riccardo f
Malara Alba k
Monzani Fabio h
Gareri Pietro i
Zia Gianluca j
Antonelli Incalzi Raffaele g
the GeroCovid Acute Wards Working Group
a Department of Medical Science, University of Ferrara, Ferrara, Italy
b Geriatrics Division, Department of Medicine (DIMED), University of Padua, Italy
c Geriatric Intensive Care Unit, Department of Experimental and Clinical Medicine, University of Florence, Italy
d Department of Information Engineering, University of Brescia, Brescia, Italy
e Movement Control and Neuroplasticity Research Group, Tervuursevest 101, 3001, Leuven, Belgium
f Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
g Unit of Geriatrics, Department of Medicine, Campus Bio-Medico University and Teaching Hospital, Rome, Italy
h Scuola di Specializzazione in Geriatria e Gerontologia Università di Pisa UOC Geriatria, Universitaria Azienda Ospedaliero-Universitaria Pisana, Italy
i Geriatra ASP Catanzaro CDCD Catanzaro Lido, Italy
j Bluecomapanion, LTD, Italy
k Presidente Fondazione ANASTE-HUMANITAS, Responsabile Scientifico European Confederation of Care-Home Organisations (E.C.H.O.), Associazione Nazionale Strutture Territoriali (ANASTE) Calabria A full list of the working group members is provided in Supplementary material – Appendix 1, Italy
∗ Corresponding author.
10 12 2022
10 12 2022
10708811 4 2022
3 12 2022
6 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.
Background
COVID-19 has disproportionately affected older adults. Yet, healthcare trajectories experienced by older persons hospitalized for COVID-19 have not been investigated. This study aimed at estimating the probabilities of transitions between severity states in older adults admitted in COVID-19 acute wards and at identifying the factors associated with such dynamics.
Methods
COVID-19 patients aged ≥60 years hospitalized between March and December 2020 were involved in the multicentre GeroCovid project–acute wards substudy. Sociodemographic and health data were obtained from medical records. Clinical states during hospitalization were categorized on a seven-category scale, ranging from hospital discharge to death. Based on the transitions between these states, first, we defined patients’ clinical course as positive (only improvements), negative (only worsening), or fluctuating (both improvements and worsening). Second, we focused on the single transitions between clinical states and estimated their probability (through multistage Markov modeling) and associated factors (with proportional intensity models).
Results
Of the 1024 included patients (mean age 78.1 years, 51.1% women), 637 (62.2%) had a positive, 66 (6.4%) had a fluctuating, and 321 (31.3%) had a negative clinical course. Patients with a fluctuating clinical course were younger, had better mobility and cognitive levels, fewer diseases, but a higher prevalence of cardiovascular disease and obesity. Considering the single transitions, the probability that older COVID-19 patients experienced clinical changes was higher within a 10-day timeframe, especially for milder clinical states. Older age, male sex, lower mobility level, multimorbidity, and hospitalization during the COVID-19 first wave (compared with the second one) were associated with an increased probability of progressing towards worse clinical states or with a lower recovery.
Conclusion
COVID-19 in older inpatients has a complex and dynamic clinical course. Identifying individuals more likely to experience a fluctuating clinical course and sudden worsening may help organize healthcare resources and clinical management across settings at different care intensity levels.
Keywords
Aged
Health transitions
Trajectories
COVID-19
==== Body
pmc1 Introduction
COVID-19 pandemic is responsible for over 300 million infections worldwide and over five million deaths to date [1]. The spectrum of COVID-19 clinical severity is highly heterogeneous and ranges from mild upper respiratory tract symptoms to severe respiratory insufficiency and multiorgan failure [2]. Negative health outcomes, including hospitalization and death, have disproportionately affected older people [2,3]. Rising evidence suggests that, besides old age, several risk factors are associated with both COVID-19 severity and progression [4]. In particular, COVID-19 patients with lower functional status, multimorbidity, and those affected by chronic conditions, such as obesity, type 2 diabetes, hypertension, often experienced worse clinical courses, increased length of hospital stay, and mortality [4,5].
Hospitalized COVID-19 patients show varying disease dynamics [6]. While most persons show rapid clinical improvements and require brief stays, others progress towards more severe disease and need admission to intensive care unit (ICU) with or without invasive mechanical ventilation [6,7].
The risk factors associated with stable/unstable disease courses, as well as worsening or improving transitions, have not been clearly established, because, to date, most of the studies on COVID-19 hospitalization are cross-sectional and focused on critically ill patients, mainly from single clinical centers [[8], [9], [10]]. Indeed, data on transitions between clinical states in COVID-19 hospitalized patients from multiple clinical centers over time are limited [6,7].
In the present study, we sought to determine the health trajectories of older COVID-19 patients admitted to a network of Italian hospitals. Afterward, we characterized individuals more likely to experience a fluctuating clinical course or health worsening during the hospital stay, who would require closer monitoring in settings at a high intensity of care. This approach may lend important insights into geriatric care needs during the COVID-19 pandemic and provide essential information for guiding clinical decision-making.
2 Methods
GeroCovid is a multicenter retrospective-prospective study promoted by the Italian Society of Gerontology and Geriatrics, in collaboration with the Norwegian Society of Gerontology and Geriatrics. GeroCovid was designed to investigate characteristics and clinical outcomes of COVID-19 in persons aged ≥60 years across different healthcare settings. Details on the GeroCovid study protocol can be found elsewhere [11].
In the present study, we included all patients from the Italian GeroCovid Observational – acute wards cohort, admitted with laboratory-confirmed COVID-19 in 19 clinical centers across Italy from March 1 through December 31, 2020.
The study cohort consisted of 1276 patients. Of these, 252 patients were excluded from the analysis because were younger than <60 years (n = 4) or had incomplete information on clinical status at admission (n = 248). The final study sample included 1024 older patients. Compared with the analytical sample, those excluded due to incomplete data were more likely to be older (80.6 vs 78.1 years, p < 0.001) and not autonomous in living at home (35.9% vs 50.9%, p < 0.001) and in walking (44.8% vs 60.5%, p < 0.001). Instead, no significant differences between groups were found regarding age, sex, and the number and frequency of chronic diseases (data not shown).
The protocol of the Gerocovid Observational study was reviewed and approved by the Ethics Committee of the Campus Bio-Medico University (Rome, Italy) and was subsequently ratified by the Ethics Committees at all participating centers. Written or dematerialized informed consent was obtained from all patients or their next of kin when applicable before enrolment. In case of impossibility in obtaining the patient's consent, a written declaration was collected by the local investigator, responding to applicable derogations during the COVID-19 pandemic. The study was registered in ClinicalTrials.gov (NCT04379440).
2.1 Data collection
Data were collected by trained physicians from hospital and medical records, and, when possible, through interviews with patients or their caregivers. Data were recorded anonymously in an e-Registry developed by Bluecompanion Ltd (London, UK). For the present study, the following information was considered: sociodemographic (i.e., age, sex, ethnicity, and living arrangements) and lifestyle data, functional parameters, medical and medication history, and clinical data (including physical examination, standard blood biochemistry, radiological reports, and oxygen requirement) at hospital admission, during hospital stay until discharge, transfer to other wards, or death.
Based on smoking habits, participants were classified as current, former, and never smokers. As a proxy of premorbid functional status, information on mobility level before hospitalization, categorized as high (i.e., walking independently or with a single cane) or low (i.e., using a walker, wheelchaired, or bedridden), were considered.
The presence of chronic diseases was ascertained based on medical records and medication history (for the complete list of chronic diseases please see Appendix 2). The number of chronic diseases was used as an indicator of multimorbidity.
White blood cell (WBC), lymphocyte (LCT) and platelet (PLT) counts, International Normalized Ratio (INR), Activated Partial Thromboplastin Time (APTT), and serum levels of D-dimer (DD), C-reactive protein (CRP), hemoglobin (Hb) and creatinine were measured using standard biochemistry methods on fully automated testing systems.
The clinical status of the study participants was recorded in the e-Registry at hospital admission and during the hospitalization in case of changes in the clinical conditions, categorized through the World Health Organization classification [12] considering the need for low- or high-flow oxygen therapy and organ support. Data on patients' hospital discharge, transfer to ICU or lower-intensity care settings (e.g. long-term care unit, rehabilitation unit), and date of death were also collected at the end of the observation period. Based on this information, we distinguished the following seven states: hospital discharge (state 1), transfer to unspecified or lower-intensity care setting (state 2), mild disease with no need of oxygen therapy (state 3), mild disease with the need for low-flow oxygen therapy (state 4), severe disease with the need for high-flow oxygen therapy or noninvasive ventilation [NIV] (state 5), severe disease with the need for mechanical ventilation and/or organ support or transfer to ICU (state 6), and death (state 7). In order to synthesize the clinical course of the study participants over the hospitalization, from the collected information, we derived the patients’ status at admission (time 0) and after at 3, 5, 10, and 30 days.
In this study, patients’ health trajectories were explored both by considering all the single transitions between the above-listed states and categorizing the latter into three main clinical courses, i.e., positive, fluctuating, and negative course. The positive clinical course included patients who experienced only transitions from worse to better clinical states, hospital discharge, transfer to low-intensity care settings, or who had a stable mild COVID-19 disease over the observation period. The fluctuating clinical course included patients who experienced both transitions from worse to better, and from better to worse states. The negative clinical course included patients who experienced only transitions from better to worse clinical states or death, or who had a stable severe COVID-19 disease over the observation period.
2.2 Statistical analysis
Participant characteristics are described as mean (standard deviation) or median (interquartile range) values for quantitative variables and as counts and percentages for qualitative variables, according to the main clinical courses. Comparisons were performed through ANOVA and Chi-square statistics, as appropriate.
Transitions between different COVID-19 severity states and outcomes at pre-specified time points are illustrated through an alluvial plot, and the probability of these transitions was estimated using non-hidden continuous-time Markov models. For this analysis, all transitions over the observation period were considered, while death, transfer to other settings, and end of observation were set as absorbing states.
Sociodemographic (age, sex, living arrangements), time- (COVID-19 wave), and health-related factors (mobility level, number of chronic diseases, CVD, obesity, diabetes, CKD, cognitive disorders, respiratory diseases, depressive mood, and inflammatory and coagulation markers) that were potentially associated with the probability of experiencing transitions were identified using proportional intensity models and expressed as hazard ratio (HR) with 95% confidence intervals (95%CIs) [3]. For this analysis, we performed a logarithmic transformation of the inflammatory and coagulation markers due to their non-normal distribution. The model convergence was optimized using a quasi-Newton optimization algorithm (the Broyden – Fletcher – Goldfarb – Shanno, BFGS) and a discrete-time model. Factors were tested first in univariable analyses, and then, in analyses adjusted for age and sex. The chance of all possible transitions was evaluated, and participants stable in a given status at each time-point were considered as the reference category.
In all analyses, a p-value <0.05 was considered statistically significant. Analyses were performed using ggalluvial and msm packages in R.
3 Results
The mean age of the 1024 participants was 78.1 (SD 9.3) years, 51.1% were women. More than 80% of the study population was enrolled during the first COVID-19 wave (Table 1 ). One out of five lived in a nursing home, and more than one-third had a low mobility level. The mean number of chronic diseases was four, with cardiovascular disease (CVD), musculoskeletal disorders, and diabetes being the most common conditions. During the observation period, 637 (62.2%) participants experienced a positive clinical course, while 66 (6.4%) had a fluctuating and 321 (31.3%) had a negative course. Most of the patients with a fluctuating COVID-19 course experienced a transition toward a worse clinical status, followed by an improvement; only for one patient, we recorded three transitions, i.e. worsening, improvement and worsening again. Participants in this group were more likely to be younger, to have better mobility and cognitive levels, fewer diseases, but a high prevalence of CVD and obesity (Table 1). These participants were also more often admitted with a mild disease requiring no or low-flow oxygen therapy. Individuals who experienced a negative clinical course were more likely to be older, men, with low mobility level, and one out of four lived in a nursing home. These participants had the highest number of chronic diseases, especially CVD, cerebrovascular and cognitive disorders, and most of them had low- (43%) or high-flow (37.3%) oxygen requirements. As for COVID-19 symptoms and biochemical parameters at ward admission, participants with a negative clinical course presented more frequently with typical (e.g. fever, cough, dyspnea) and some atypical symptoms (delirium), and had higher serum creatinine values (Supplementary Table 1).Table 1 Characteristics of the study population according to the main clinical course during the observation period.
Table 1N All (n = 1024) Clinical course p-value
Positive (n = 637) Fluctuating (n = 66) Negative (n = 321)
Age (years) 78.07 (9.32) 77.25 (9.36) 72.29 (8.55) 80.88 (8.51) <0.001
Women 513 (50.1) 342 (53.7) 28 (42.4) 143 (44.5) 0.012
Living arrangementa <0.001
At home, alone 521 (50.9) 355 (55.7) 52 (78.8) 114 (35.5)
At home, assisted 196 (19.1) 113 (17.7) 3 (4.5) 80 (24.9)
Institutionalized 189 (18.5) 111 (17.4) 3 (4.5) 75 (23.4)
Low mobility levela 373 (36.4) 206 (32.3) 4 (6.1) 163 (50.8) <0.001
Smoking habitsa 0.027
Never 367 (35.8) 222 (34.9) 31 (47.0) 114 (35.5)
Former 149 (14.6) 83 (13.0) 14 (21.2) 52 (16.2)
Current 26 (2.5) 14 (2.2) 3 (4.5) 9 (2.8)
Covid-19 wave II 126 (12.3) 76 (11.9) 16 (24.2) 34 (10.6) 0.008
N. chronic diseases 4.06 (2.66) 3.88 (2.65) 3.50 (2.38) 4.53 (2.67) <0.001
Diabetes mellitus 258 (25.2) 163 (25.6) 17 (25.8) 78 (24.3) 0.905
Chronic liver diseases 24 (2.3) 14 (2.2) 3 (4.5) 7 (2.2) 0.474
Osteoporosis 277 (27.1) 181 (28.4) 8 (12.1) 88 (27.4) 0.018
Osteoarthrosis 219 (21.4) 141 (22.1) 7 (10.6) 71 (22.1) 0.087
Hypertension 694 (67.8) 425 (66.7) 45 (68.2) 224 (69.8) 0.631
CVD 590 (57.6) 338 (53.1) 42 (63.6) 210 (65.4) 0.001
Cerebrovascular diseases 111 (10.8) 59 (9.3) 4 (6.1) 48 (15.0) 0.012
Chronic respiratory diseases 146 (14.3) 87 (13.7) 8 (12.1) 51 (15.9) 0.568
CKD 135 (13.2) 79 (12.4) 3 (4.5) 53 (16.5) 0.021
Depressive disorders 181 (17.7) 114 (17.9) 11 (16.7) 56 (17.4) 0.961
Cognitive disorders 164 (16.0) 100 (15.7) 1 (1.5) 63 (19.6) 0.001
Obesity 153 (14.9) 82 (12.9) 17 (25.8) 54 (16.8) 0.01
WHO status at ward admission <0.001
Mild disease, no O2 303 (29.6) 232 (36.4) 23 (34.8) 48 (15.0)
Mild disease, low-flow O2 445 (43.5) 270 (42.4) 38 (57.6) 137 (42.7)
Severe disease, high-flow O2/NIV 248 (24.2) 117 (18.4) 5 (7.6) 126 (39.3)
Severe disease, organ support 28 (2.7) 18 (2.8) 0 (0.0) 10 (3.1)
Abbreviations: CVD, cardiovascular diseases; CKD, chronic kidney disease; WHO, World Health Organization.
a Missing values in living arrangements (n = 118), mobility level (n = 31), smoking habits (n = 482). P-values refer to the comparison between different categories of clinical trajectory.
When looking at all the transitions between the single COVID-19 states (Fig. 1 and Supplementary Table 2), we found that the mean estimated permanence time in each state was consistently around 3 days (Supplementary Table 3). Table 2 shows the probabilities of experiencing such transitions estimated through multistate Markov models. As reported, the probability of experiencing clinical transitions during the hospitalization was higher within a 10-day time frame, especially for the less severe states.Fig. 1 Alluvial plot for the transitions of older patients with COVID-19 between different clinical states since hospital admission (time 0).
Fig. 1
Table 2 Transition's probabilities at 3, 5, 10, and 30 days according to participants' clinical status.
Table 2From 3-day transition's probability (%) to
State 1 State 2 State 3 State 4 State 5 State 6 State 7
State 3 34.4 9.5 42.2 3.7 2.9 1.3 6.1
State 4 18.9 6.8 11.7 39.9 9.2 2.3 11.3
State 5 15.0 8.6 5.9 3.3 34.9 3.4 28.8
State 6 20.6 7.3 3.4 6.4 1.5 36.1 24.7
5-day transition's probability (%) to
State 3 46.2 12.9 24.2 3.5 2.8 1.3 9
State 4 28.5 10.2 11.2 22.3 8.2 2.3 17.3
State 5 21.3 11.6 5.6 3.2 17.7 2.9 37.6
State 6 28.1 9.9 3.7 5.7 1.8 18.5 32.4
10-day transition's probability (%) to
State 3 59.4 16.9 6.5 1.8 1.5 0.7 13.3
State 4 42.4 15.1 5.7 5.7 3.6 1.3 26
State 5 29.3 15.1 2.8 1.6 3.6 1.2 46.3
State 6 36.9 13 2.3 2.5 1.2 3.6 40.4
30-day transition's probability (%) to
State 3 65.2 18.7 0.1 0 0 0 15.9
State 4 50.8 17.9 0.1 0.1 0 0 31.1
State 5 33.7 16.6 0 0 0 0 49.6
State 6 41.6 14.6 0.1 0 0 0 43.7
Notes. State 1, hospital discharge with clinical improvement/stability; state 2, transfer to unspecified or low-intensity of care setting; state 3, mild disease – no O2-therapy; state 4, mild disease – low-flow O2-therapy; state 5, severe disease – high-flow O2-therapy or NIV; state 6, severe disease – intubation/organ support/ICU transfer; state 7, death.
In particular, mild COVID-19 patients with no oxygen therapy had a chance of requiring low- or high-flow oxygen therapy at 3 days of 3.7% and 2.9%, respectively, while the probability of transition towards ICU admission was 1.3%. Considering the health trajectories of patients with some oxygen requirements, we found that those with mild COVID-19 and low-flow oxygen therapy had a 9.2% chance at 3 days of worsening towards the need for higher oxygen support or NIV. For these patients, as well as for those with severe COVID-19 disease and higher oxygen requirements, the probability of ICU admission over the entire observation period remained below 4%. Concerning mortality, the 30-day probability of death ranged from 16% for those who did not require oxygen therapy to 49.6% for those who needed high-flow oxygen or NIV, and 43.7% for those who were mechanically ventilated or required organ support. Conversely, the 30-day chance of being discharged was 65.2% for those with mild disease and no oxygen requirements, and 33.7% and 41.6% for those in the most severe COVID-19 categories (state 5 and 6).
When identifying the factors related to the above-described transitions, we found that older age, low mobility level, living in a nursing home, former smoking, a greater number of chronic diseases, CVD, cognitive disorders, and depressive mood made the individuals less likely to reverse from worse to milder clinical states (Table 3, Table 4 ; for the univariable analyses, please see Supplementary Tables 4 and 5). Female sex decreased the probability of worsening from mild COVID-19 states, while the opposite trend was observed for those who had fever at admission and for current smokers. The probability of death increased with age, male sex, low mobility level, living at home with the need of assistance, being institutionalized, and having a greater number of chronic diseases. Individuals who were hospitalized in the COVID-19 wave II compared with wave I had an increased probability of improving and being discharged from mild or severe COVID-19 status requiring oxygen therapy in wave II than in wave I. An opposite trend was observed for the transition from mild COVID-19 no requiring oxygen to mild COVID-19 requiring low-flow oxygen, which was experienced by only a few patients (n = 22, Supplementary Table 2). Instead, after adjusting for age and sex, no significant results were found concerning some chronic conditions, such as obesity and respiratory and cognitive disorders. Finally, among biochemical parameters (Supplementary Tables 4–7), higher lymphocytes were linked to higher chances of improvements from mild COVID-19 states, while higher platelet, D-dimer (only for severe COVID-19 states), CRP, and creatinine levels were associated with a lower probability of reversing to milder clinical states or with increased mortality.Table 3 Factors associated with transitions from mild disease – no O2-therapy and COVID-19 status (age- and sex-adjusted model).
Table 3 Hazard Ratios (95% Confidence Intervals) of Transition
From state 3 (mild disease no O2-therapy), to From state 4 (mild disease low-flow O2-therapy), to
State 1 State 2 State 4 State 5 State 6 State 7 State 1 State 2 State 3 State 5 State 6 State 7
Age (years) 0.98 (0.96– 0.99) 1.02 (0.98–1.06) 0.97 (0.92–1.02) 0.96 (0.9–1.02) 0.94 (0.85–1.03) 1.06 (1.01–1.13) 0.98 (0.96–1.00) 1.00 (0.96–1.04) 0.97 (0.94–0.99) 0.97 (0.95–1.00) 0.97 (0.91–1.03) 1.05 (1.02–1.09)
Sex (F vs M) 0.72 (0.51–1.01) 0.85 (0.44–1.66) 0.35 (0.13–0.91) 0.38 (0.12–1.25) 0.37 (0.06–2.32) 0.99 (0.38–2.59) 0.89 (0.59–1.35) 1.51 (0.72–3.17) 1.17 (0.76–1.82) 0.56 (0.35–0.91) 0.40 (0.12–1.32) 1.27 (0.68–2.37)
Not walk independently 0.76 (0.50–1.15) 2.74 (1.26–5.97) 0.97 (0.33–2.82) – 0.72 (0.07–7.22) 1.49 (0.58–3.79) 0.64 (0.41–0.99) 1.77 (0.89–3.55) 0.60 (0.36–0.99) 0.68 (0.39–1.17) 0.10 (0.01–0.79) 4.52 (2.34–8.77)
Lives at home autonomous [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref]
Lives at home dependent 0.66 (0.39–1.09) 1.70 (0.69–4.22) 1.08 (0.32–3.63) 0.21 (0.03–1.69) 1.52 (0.14–16.1) 0.97 (0.23–4.17) 0.63 (0.36–1.08) 1.17 (0.45–3.08) 0.84 (0.49–1.44) 0.84 (0.46–1.56) 0.35 (0.07–1.67) 2.73 (1.25–5.95)
Lives in NH 0.69 (0.41–1.16) 3.05 (1.31–7.09) 0.31 (0.04–2.56) – – 4.28 (1.40–13.2) 0.78 (0.45–1.37) 3.04 (1.32–7.02) 0.34 (0.15–0.75) 0.40 (0.16–0.99) 0.23 (0.03–1.94) 4.12 (1.87–9.06)
Smoking habit (never) [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref]
Former 1.78 (1.04–3.04) 0.45 (0.10–2.00) 0.78 (0.23–2.68) 0.39 (0.08–1.91) 1.82 (0.32–10.5) 0.80 (0.16–3.85) 0.59 (0.30–1.14) 1.84 (0.75–4.51) 0.96 (0.44–2.07) 1.09 (0.59–2.02) 2.21 (0.61–7.99) 1.28 (0.58–2.82)
Current 2.12 (0.65–6.89) 1.61 (0.20–12.9) 1.75 (0.21–14.6) – – – 0.40 (0.05–3.0) – 2.79 (0.96–8.1) – 8.13 (1.49–44.4) 1.78 (0.23–13.5)
N. diseases 0.94 (0.88–1.01) 1.00 (0.89–1.12) 1.01 (0.86–1.18) 0.96 (0.79–1.16) 0.90 (0.65–1.23) 0.99 (0.85–1.15) 0.97 (0.90–1.05) 0.96 (0.85–1.09) 0.92 (0.84–1.00) 1.02 (0.94–1.12) 0.87 (0.69–1.1) 1.07 (0.98–1.16)
Obesity 1.07 (0.69–1.65) 0.75 (0.22–2.56) 0.66 (0.15–2.95) 6.05 (1.33–27.6) 0.65 (0.23–1.83) 0.58 (0.13–2.47) 1.11 (0.68–1.81) 1.08 (0.63–1.87) 2.45 (1.48–4.06) 2.11 (0.67–6.70) 0.26 (0.06–1.07) 0.59 (0.25–1.38)
CVD 0.71 (0.51–0.98) 0.58 (0.31–1.1) 0.99 (0.42–2.35) 1.52 (0.53–4.31) 6.3 (0.76–52.3) 1.75 (0.72–4.26) 1.30 (0.89–1.90) 0.71 (0.38–1.30) 0.63 (0.41–0.96) 1.23 (0.77–1.97) 0.96 (0.35–2.61) 1.36 (0.81–2.29)
CKD 0.92 (0.58–1.45) 0.62 (0.24–1.6) 0.71 (0.2–2.46) 2.37 (0.78–7.21) – 2.41 (1.05–5.57) 0.94 (0.53–1.67) 2.63 (1.32–5.24) 0.51 (0.22–1.18) 0.50 (0.20–1.24) – 0.97 (0.50–1.85)
Respiratory diseases 1.23 (0.78–1.95) 0.32 (0.04–2.60) 1.05 (0.24–4.62) – 1.06 (0.42–2.69) 0.69 (0.17–2.90) 0.69 (0.40–1.21) 0.96 (0.54–1.70) 1.29 (0.73–2.26) 0.78 (0.18–3.40) 0.38 (0.11–1.25) 1.07 (0.57–1.98)
Cognitive disorders 0.77 (0.46–1.29) 1.23 (0.57–2.67) 0.63 (0.14–2.88) – – 0.66 (0.24–1.85) 0.85 (0.50–1.46) 0.96 (0.43–2.13) 0.47 (0.22–0.98) 0.47 (0.20–1.11) 0.93 (0.20–4.37) 1.32 (0.77–2.26)
Depressive mood 0.77 (0.50–1.20) 0.86 (0.4–1.85) 1.06 (0.35–3.21) 0.69 (0.15–3.13) 0.94 (0.11–7.96) 0.83 (0.32–2.14) 1.05 (0.65–1.7) 1.86 (0.95–3.64) 0.49 (0.24–0.98) 0.73 (0.36–1.49) 0.89 (0.19–4.04) 1.45 (0.85–2.49)
Fevera 1.07 (0.73–1.58) 0.97 (0.46–2.07) 1.02 (0.39–2.7) 2.57 (0.57–11.7) – 0.63 (0.25–1.61) 0.68 (0.44–1.05) 1.91 (0.66–5.50) 1.01 (0.54–1.86) 3.84 (1.39–10.6) 3.17 (0.40–25.3) 0.83 (0.47–1.47)
Deliriuma – – 14.64 (1.66–129) – – – 0.69 (0.33–1.45) 0.91 (0.26–3.15) 0.72 (0.25–2.06) 0.88 (0.37–2.1) 0.66 (0.08–5.44) 1.54 (0.77–3.09)
Wave II vs I 1.23 (0.73–2.06) 0.53 (0.13–2.21) 3.11 (1.14–8.46) 2.39 (0.68–8.42) – 0.93 (0.22–3.96) 0.79 (0.47–1.33) 0.24 (0.06–0.99) 1.84 (1.16–2.92) 1.34 (0.78–2.30) 0.32 (0.04–2.38) 0.87 (0.46–1.65)
Abbreviations: CVD, cardiovascular diseases; CKD, chronic kidney disease; NH, nursing home Notes. State 1, hospital discharge with clinical improvement/stability; state 2, transfer to unspecified or low-intensity of care setting; state 3, mild disease – no O2-therapy; state 4, mild disease – low-flow O2-therapy; state 5, severe disease – high-flow O2-therapy or NIV; state 6, severe disease – intubation/organ support/ICU transfer; state 7, death.
a Symptoms at ward admission.
Table 4 Factors associated with transitions from severe COVID-19 (age- and sex-adjusted model).
Table 4 Hazard Ratios (95% Confidence Intervals) of Transition
From state 5 (severe disease – high-flow O2-therapy or NIV), to From state 6 (severe disease – intubation/organ support/ICU), to
State 1 State 2 State 3 State 4 State 6 State 7 State 1 State 2 State 3 State 4 State 5 State 7
Age (years) 0.97 (0.94–1.00) 0.99 (0.95–1.03) 0.94 (0.90–0.98) 0.91 (0.85–0.97) 0.94 (0.89–1.00) 1.09 (1.06–1.11) 1.01 (0.93–1.09) 0.98 (0.86–1.11) 1.01 (0.87–1.16) 0.94 (0.84–1.06) 1.13 (0.88–1.45) 1.05 (0.99–1.12)
Sex (F vs M) 1.70 (0.96–3.01) 0.81 (0.39–1.68) 1.08 (0.50–2.32) 1.04 (0.36–2.97) 0.63 (0.22–1.84) 0.56 (0.38–0.82) 1.98 (0.64–6.09) 0.59 (0.07–4.97) 0.02 (0–882.3) 1.37 (0.30–6.22) – 0.23 (0.05–1.07)
Not walk independently 0.49 (0.23–1.07) 1.28 (0.56–2.92) 0.26 (0.06–1.14) 2.47 (0.82–7.45) – 1.90 (1.25–2.89) – – – – – 3.17 (0.66–15.1)
Lives at home autonomous [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref]
Lives at home with assistance 0.39 (0.14–1.09) 0.74 (0.25–2.20) 0.62 (0.18–2.10) 1.76 (0.48–6.49) 0.32 (0.04–2.42) 2.60 (1.63–4.16) 0.34 (0.04–2.81) – – 4.05 (0.21–77.2) – 6.18 (0.97–39.3)
Lives in NH 0.88 (0.36–2.14) 1.08 (0.33–3.51) 0.34 (0.04–2.65) 1.23 (0.15–10.1) – 1.86 (1.03–3.34) – – – – – 12.27 (0.91–166.2)
Smoking habit (never) [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref] [ref]
Former 0.71 (0.34–1.48) 1.04 (0.46–2.35) 1.20 (0.46–3.14) 1.11 (0.32–3.84) 1.14 (0.27–4.83) 0.91 (0.53–1.55) 1.47 (0.29–7.45) 5.01 (0.31–81.9) – 1.26 (0.17–9.46) – 0.72 (0.18–2.89)
Current – 0.76 (0.10–5.83) 1.18 (0.15–9.37) – 9.69 (2.47–38.1) 1.14 (0.35–3.74) 4.05 (1.03–15.9) – – – – 0.39 (0.05–3.14)
N. chronic diseases 0.94 (0.84–1.05) 1.07 (0.93–1.22) 1.04 (0.89–1.22) 0.85 (0.67–1.09) 1.04 (0.85–1.28) 1.10 (1.03–1.18) 1.15 (0.90–1.46) 0.85 (0.52–1.38) 0.66 (0.33–1.36) 1.20 (0.82–1.78) 1.68 (0.48–5.84) 0.93 (0.74–1.16)
Obesity 1.19 (0.64–2.21) 0.47 (0.14–1.55) 1.25 (0.41–3.82) 2.74 (1.07–7.01) 0.28 (0.07–1.18) 1.43 (0.91–2.25) 1.35 (0.36–5.01) – 5.13 (0.94–28.0) – – 1.94 (0.71–5.27)
CVD 1.10 (0.65–1.85) 1.28 (0.64–2.55) 1.66 (0.78–3.51) 0.42 (0.14–1.22) 1.20 (0.47–3.07) 1.19 (0.81–1.75) 0.91 (0.30–2.72) 1.95 (0.23–17.0) – 3.22 (0.38–26.9) – 0.72 (0.27–1.90)
CKD 0.38 (0.09–1.59) 2.08 (0.84–5.20) 1.61 (0.47–5.58) 1.11 (0.14–9.09) – 1.36 (0.85–2.20) 1.67 (0.18–15.2) – – – – –
Respiratory diseases 1.20 (0.59–2.46) 2.01 (0.84–4.80) 0.36 (0.04–3.07) 1.67 (0.54–5.23) 1.11 (0.46–2.69) 0.86 (0.51–1.45) 1.87 (0.59–5.98) – – – – 1.28 (0.42–3.88)
Cognitive disorders 1.20 (0.49–2.94) 0.54 (0.12–2.38) – 2.64 (0.58–12.0) – 1.32 (0.81–2.15) 0.74 (0.09–6.36) – – – – 1.14 (0.13–9.90)
Depressive mood 0.86 (0.41–1.77) 1.81 (0.83–3.94) 0.65 (0.20–2.14) 1.35 (0.38–4.79) – 1.04 (0.65–1.68) 2.87 (0.37–22.3) – – 5.40 (0.62–46.8) – –
Fevera 5.17 (1.23–21.7) 1.88 (0.57–6.19) 1.70 (0.52–5.54) 0.61 (0.17–2.21) 0.48 (0.17–1.36) 0.68 (0.44–1.05) 0.72 (0.17–3.08) 0.60 (0.04–8.11) – 0.84 (0.08–9.42) – 1.99 (0.39–10.33)
Deliriuma 0.89 (0.38–2.13) 0.62 (0.14–2.69) 0.89 (0.27–3.00) 0.79 (0.10–6.15) – 1.10 (0.68–1.76) 0.41 (0.05–3.33) – 4.15 (0.25–70.2) – – 1.38 (0.36–5.31)
Wave II vs I 2.79 (1.56–4.97) 0.49 (0.12–2.03) 0.77 (0.23–2.55) 1.95 (0.63–6.00) – 0.67 (0.33–1.37) 5.78 (0.66–50.9) – – – – –
Abbreviations: CVD, cardiovascular diseases; CKD, chronic kidney disease; NH, nursing home. Notes. State 1, hospital discharge with clinical improvement/stability; state 2, transfer to unspecified or low-intensity of care setting; state 3, mild disease – no O2-therapy; state 4, mild disease – low-flow O2-therapy; state 5, severe disease – high-flow O2-therapy or NIV; state 6, severe disease – intubation/organ support/ICU transfer; state 7, death.
a Symptoms at ward admission.
4 Discussion
The present study found that COVID-19 has a heterogeneous and dynamic clinical course in hospitalized older patients. Health transitions occurred over a short timeframe, especially during the first 10 days of admission and even the mildest clinical states showed to be stable for no longer than 3 days before progressing toward better or worse conditions.
The above findings are in keeping with those obtained from a large cohort of patients hospitalized with COVID-19 in a US hospital network, where both the risk of decompensation and discharge peaked in the first 3–5 days after admission [6]. These data underline the importance of closely monitoring older patients with COVID-19 during the first days of hospitalization, also in cases with mild disease.
Notably, in our study population, around one out of ten individuals who required no or low-flow oxygen therapy worsened towards more severe clinical states. However, the probability of progressing towards intensive care unit admission or organ support was low and did not reach 4% even among patients who presented with severe disease needing high-flow oxygen therapy or NIV. This result differs from previous studies in younger patients, in whom greater COVID-19 severity at admission was associated with a higher probability of ICU transfer [[13], [14], [15], [16]]. Other studies and meta-analyses showed that the progressive age-related increase in in-hospital mortality was not associated with a similar trend in ICU admission, which, instead, tended to decrease in the oldest old [[17], [18], [19], [20]]. Collectively, these results suggest that older adults might have been excluded from more intensive care, especially during the first pandemic waves when there was limited healthcare resource availability and a shortage of ICU beds [7,21]. Accordingly, patients who were hospitalized during pandemic wave II were more likely to transition towards milder COVID-19 and had a higher probability of recovering from more severe disease states [6].
Concerning the COVID-19 course, we found that half of the sample experienced a positive course with a progressive improvement in clinical status, while almost one-third showed a gradual worsening. In keeping with previous studies [[5], [6], [7],15,16,22], patients with a worse clinical course were more likely to be older, to have lower mobility levels and a higher number of chronic conditions. Interestingly, 6.4% of the sample showed a fluctuating course with both worsening and improving clinical changes during hospitalization. These individuals had a generally healthier profile, in terms of sociodemographic data, mobility, cognitive status, and chronic conditions, and were more likely to present with no or low oxygen requirements at ward admission. We can therefore speculate that the fluctuating trend of these patients was due to a more intensive care approach adopted in light of their healthier pre-admission status, or the onset of concurrent acute conditions during the hospital stay. Although we had no available data to verify these hypotheses, the identification of individuals more likely to experience an unstable clinical course is highly relevant to appropriately address patients to care settings with different possibilities of monitoring and intensity of care. In this regard, our study, using advanced statistical analyses, allowed us to characterize the older individuals who had a higher chance of worsening or improving from a certain level of COVID-19 severity. These insights may help to better predict patients’ health trajectories and anticipate their assistance and care needs. In particular, among the factors associated with either a higher probability of experiencing worsening transitions or a lower chance of recovering from COVID-19, we identified older age, male sex, lower mobility level, being institutionalized, and reporting former or current smoking habits. Concerning chronic diseases, multimorbidity and specific chronic conditions were associated with worsening transitions, while fever at admission was identified as a negative prognostic factor. Some other diseases, such as obesity, cognitive and respiratory disorders were not associated with clinical changes irrespective of age and sex. Instead, biochemical parameters indicating higher inflammation, lower lymphocytes count, and worse kidney function were linked to clinical worsening.
Most of the factors listed above were also associated with a higher risk of in-hospital mortality and corroborate the results of previous studies. Indeed, in addition to demographic factors, laboratory parameters, and vital signs, specific conditions at admission, such as fever and delirium, have previously been related to greater mortality in COVID-19 patients. Poor functional status, which was defined as self-reported low mobility or nursing-home residency, seemed to reduce the chance of COVID-19 recovery, and increase in-hospital mortality in our sample. This point also confirmed studies that considered the impact of dependency in activities of daily living and frailty on COVID-19 outcomes [[22], [23], [24]]. Consistent with existing literature, a higher number of chronic diseases and some specific conditions, such as cerebrovascular and cardiovascular diseases, were found to be negative prognostic factors in our study [15,25]. In addition, depressive mood was associated with a lower chance of reverting from worse to better clinical states, in line with the pooled results of a recent meta-analysis that showed higher mortality among COVID-19 patients with mental disorders, including depression [26]. ì Similar findings emerged for cognitive disorders both in the hospital and in nursing home settings, which may predispose individuals to COVID-19 clinical complications [23,27].
This study has limitations and strengths. First, most of the data were collected retrospectively during the most burdensome phases of the pandemic. This, together with the limited human resources dedicated to data collection, may have affected the completeness of the information recorded, especially biochemical and radiologic data. Second, no information was available on the clinical course of patients transferred to institutions outside the GeroCovid network. Third, to explore the probability of different clinical transitions, we derived the patients’ status at specific time points, which allowed us to synthesize the studied phenomenon and facilitated the multistate analysis but could have partly limited our evaluation of the disease course variability. Finally, data for this study refer to the COVID-19 course during the first pandemic waves; therefore, similar analyses should be performed considering the disease caused by the most recent virus variants. Concerning the study strengths, the multicenter nature of the GeroCovid allowed collecting information on older patients hospitalized across Italy. Moreover, the large cohort involved and the number of health-related parameters evaluated allowed obtaining a robust and comprehensive picture of the clinical course of COVID-19 in older patients. Finally, to our knowledge, this is the first study investigating the health trajectories of older patients with COVID-19 using both classical and advanced statistical approaches.
In conclusion, COVID-19 in hospitalized older adults may present a complex and dynamic clinical course characterized by worsenings and improvements within short timeframes, i.e. 3-10 days. The identification of individuals with a more fluctuating clinical course and higher probability of worsening may help predict outcomes to better organize hospital pathways and healthcare resources allocation for older patients with COVID-19.
Author agreement
We declare that the manuscript is original, has not been published before and is not currently being considered for publication elsewhere.
E confirm that the manuscript has been read and approves by all named authors and that there are no other person who satisfied the criteria for authorship but are not listed. We further confirm that the order of author listed in the manuscript has been approved by all of the authors.
We understand that the Corresponding Author is the contact for Editorial process. He/She is responsible for communicating the authors about progress, submission of revision and final approval of proofs.
Funding sources
This research did not receive any funding from agencies in the public, commercial, or not-for-profit sectors.
Declaration of competing interest
Authors declare no conflict of interests for this article.
Appendix A Supplementary data
The following is the Supplementary data to this article:Multimedia component 1
Multimedia component 1
Acknowledgements
We are thankful to our colleagues who are collaborating in data collection for their valuable contribution, and to all the study participants. We thank Gilda Borselli for her precious support for the organization of the GeroCovid initiative.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.rmed.2022.107088.
==== Refs
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| 0 | PMC9733961 | NO-CC CODE | 2022-12-14 23:28:27 | no | Respir Med. 2022 Dec 10;:107088 | utf-8 | Respir Med | 2,022 | 10.1016/j.rmed.2022.107088 | oa_other |
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J Allergy Clin Immunol
J Allergy Clin Immunol
The Journal of Allergy and Clinical Immunology
0091-6749
1097-6825
Published by Elsevier Inc. on behalf of the American Academy of Allergy, Asthma & Immunology.
S0091-6749(22)01655-4
10.1016/j.jaci.2022.11.020
Article
NFKB2 haploinsufficiency identified via screening for IFNα2 autoantibodies in children and adolescents hospitalized with SARS-CoV-2-related complications
Bodansky Aaron MD a∗
Vazquez Sara E. PhD bc∗
Chou Janet MD de#∗
Novak Tanya PhD fg
Al-Musa Amer MD d
Young Cameron BS f
Newhams Margaret MPH f
Kucukak Suden MD f
Zambrano Laura D. PhD h
Mitchell Anthea BS bi
Wang Chung-Yu BA i
Moffitt Kristin MD ej
Halasa Natasha B. MD, MPH k
Loftis Laura L. MD l
Schwartz Stephanie P. MD m
Walker Tracie C. MD m
Mack Elizabeth H. MD, MS n
Fitzgerald Julie C. MD, PhD o
Gertz Shira J. MD p
Rowan Courtney M. MD, MScr q
Irby Katherine MD r
Sanders Ronald C. Jr. MD, MS r
Kong Michele MD s
Schuster Jennifer E. MD t
Staat Mary A. MD, MPH u
Zinter Matt S. MD v
Cvijanovich Natalie Z. MD w
Tarquinio Keiko M. MD x
Coates Bria M. MD y
Flori Heidi R. MD, FAAP z
Dahmer Mary K. PhD z
Crandall Hillary MD, PhD aa
Cullimore Melissa L. MD, PhD ab
Levy Emily R. MD, FAAP ac
Chatani Brandon MD ad
Nofziger Ryan MD, MBA ae
Overcoming COVID-19 Network Study Group Investigators
Geha Raif S. MD d
DeRisi Joseph PhD bi
Campbell Angela P. MD, MPH h
Anderson Mark MD, PhD c†
Randolph Adrienne G. MD, MSc fge†
a Department of Pediatric Critical Care Medicine, University of California, San Francisco, San Francisco, CA
b Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA
c Diabetes Center, School of Medicine, University of California San Francisco, San Francisco, CA
d Division of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, MA
e Department of Pediatrics, Boston Children’s Hospital and Harvard Medical School, Boston, MA
f Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA
g Department of Anesthesia, Harvard Medical School, Boston, MA
h COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA
i Chan Zuckerberg Biohub, San Francisco, CA
j Division of Infectious Diseases, Boston Children's Hospital, Boston, MA
k Department of Pediatrics, Division of Pediatric Infectious Diseases, Vanderbilt University Medical Center, Nashville, TN
l Section of Critical Care Medicine, Department of Pediatrics, Baylor College of Medicine, Houston, TX
m Department of Pediatrics, University of North Carolina at Chapel Hill Children’s Hospital, Chapel Hill, NC
n Division of Pediatric Critical Care Medicine, Medical University of South Carolina, Charleston, SC
o Department of Anesthesiology and Critical Care, Division of Critical Care, The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
p Department of Pediatrics, Division of Pediatric Critical Care, Cooperman Barnabas Medical Center, Livingston, NJ
q Department of Pediatrics, Division of Pediatric Critical Care Medicine, Indiana University School of Medicine, Riley Hospital for Children, Indianapolis, IN
r Section of Pediatric Critical Care, Department of Pediatrics, Arkansas Children's Hospital, Little Rock, AR
s Department of Pediatrics, Division of Pediatric Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL
t Department of Pediatrics, Division of Pediatric Infectious Diseases, Children’s Mercy Kansas City, Kansas City, MO
u Department of Pediatrics, Division of Infectious Diseases, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
v Department of Pediatrics, Divisions of Critical Care and Bone Marrow Transplantation, University of California, San Francisco, San Francisco, CA
w Division of Critical Care Medicine, UCSF Benioff Children's Hospital, Oakland, CA
x Department of Pediatrics, Division of Critical Care Medicine, Emory University School of Medicine, Children's Healthcare of Atlanta, Atlanta, GA
y Department of Pediatrics, Division of Critical Care Medicine, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
z Department of Pediatrics, Division of Pediatric Critical Care Medicine, Mott Children’s Hospital and University of Michigan, Ann Arbor, MI
aa Department of Pediatrics, Division of Pediatric Critical Care, Primary Children’s Hospital and University of Utah, Salt Lake City, UT
ab Department of Pediatrics, University of Nebraska Medical Center, College of Medicine, Children's Hospital and Medical Center, Omaha, NE
ac Department of Pediatric and Adolescent Medicine, Division of Pediatric Infectious Diseases, Division of Pediatric Critical Care Medicine, Mayo Clinic, Rochester, MN
ad Department of Pediatrics, Division of Pediatric Critical Care Medicine, Holtz Children’s Hospital, University of Miami Miller School of Medicine, Miami, FL
ae Department of Pediatrics, Division of Critical Care Medicine, Akron Children’s Hospital, Akron, OH
# Corresponding author: Janet Chou, MD. 1 Blackfan Circle, Karp Building #10007D. Boston, MA 02115.
∗ Equal contribution
† Equal contribution
9 12 2022
9 12 2022
8 9 2022
21 11 2022
29 11 2022
© 2022 Published by Elsevier Inc. on behalf of the American Academy of Allergy, Asthma & Immunology.
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
Autoantibodies against type I interferons (IFNs) occur in approximately 10% of adults with life-threatening COVID-19. The frequency of anti-IFN autoantibodies in children with severe sequelae of SARS-CoV-2 infection is unknown.
Objective
To quantify anti-Type I IFN autoantibodies in a multi-center cohort of children with severe COVID-19, Multisystem Inflammatory Syndrome in Children (MIS-C), and mild SARS-CoV-2 infections.
Methods
Circulating anti-IFNa2 antibodies were measured by a radioligand binding assay. Whole exome sequencing (WES), RNA-sequencing, and functional studies of peripheral blood mononuclear cells were used to study any patients with levels of anti-IFNα2 autoantibodies exceeding the assay’s positive control.
Results
Among 168 patients with severe COVID-19, 199 with MIS-C, and 45 with mild SARS-CoV-2 infections, only one had high levels of anti-IFNα2 antibodies. Anti-IFNα2 autoantibodies were not detected in patients treated with intravenous immunoglobulin prior to sample collection. WES identified a missense variant in the ankyrin domain of NFKB2, encoding the p100 subunit of NF-kB essential for non-canonical NF-kB signaling. Her peripheral blood mononuclear cells exhibited impaired cleavage of p100 characteristic of NFKB2 haploinsufficiency, an inborn error of immunity with a high prevalence of autoimmunity.
Conclusions
High levels of anti-IFNα2 autoantibodies in children and adolescents with MIS-C, severe COVID-19, and mild SARS-CoV-2 infections are rare, but can occur in patients with inborn errors of immunity.
Clinical implications
Anti-IFNα2 autoantibodies should prompt diagnostic evaluation for inborn errors of immunity if identified in children or adolescents.
Key words
anti-interferon autoantibody
COVID-19
MIS-C
NFKB2
inborn errors of immunity
Abbreviations
IFN, interferons
COVID-19, coronavirus disease 2019
SARS-CoV-2, severe acute respiratory syndrome coronavirus 2
MIS-C, Multisystem Inflammatory Syndrome in Children
WES, whole exome sequencing
NF-κB, nuclear factor kappa B
APS-1, autoimmune polyendocrine syndrome type 1
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pmcDisclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the U.S. Centers for Disease Control and Prevention.
All authors declare no conflict of interest relevant to this manuscript.
Funding: This study was supported by contracts 75D30120C07725 and 75D30121C10297 from the Centers for Disease Control and Prevention (A.G.R.). Additional support was from the National Institute of Allergy and Infectious Diseases (R01AI154470 to A.G.R., 5P01AI118688 for M. Anderson, and R01AI139633-04S1 to R.S.G. and J.C.), National Institute of Diabetes and Digestive and Kidney Diseases (1F30DK123915 to S. Vazquez; R01DK130465 to J.C.), Pediatric Scientist Development Program and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (K12-HD000850 to A. Bodansky), Chan Zuckerberg Biohub for J. DeRisi.
Capsule Summary. In contrast to studies of adults with COVID-19, this multicenter study of 412 pediatric patients with severe COVID-19, MIS-C, or mild SARS-CoV-2 infections shows that anti-IFNα2 autoantibodies are unlikely to cause severe COVID-19 in the general pediatric population, but can be associated with an inborn error of immunity.
| 36509151 | PMC9733962 | NO-CC CODE | 2022-12-14 23:28:27 | no | J Allergy Clin Immunol. 2022 Dec 9; doi: 10.1016/j.jaci.2022.11.020 | utf-8 | J Allergy Clin Immunol | 2,022 | 10.1016/j.jaci.2022.11.020 | oa_other |
==== Front
Int J Infect Dis
Int J Infect Dis
International Journal of Infectious Diseases
1201-9712
1878-3511
The Authors. Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.
S1201-9712(22)00642-7
10.1016/j.ijid.2022.12.008
Article
Long-term outcomes of COVID-19 convalescents: An 18.5-month longitudinal study in Wuhan
Guo Yi a#
Wang Hao a#
Xiao Mingzhong bc#
Guan Xin a#
Lei Yanshou a
Diao Tingyue a
Long Pinpin a
Zeng Rui a
Lai Xuefeng a
Cai Hao a
You Yutong a
Wen Yuying a
Li Wenhui a
Wang Xi a
Wang Yufei a
Chen Qinlin a
Yang Yuchan a
Qiu Yutong a
Chen Jishuai a
Zeng Huidan a
Ni Wei bc
Zhao Youyun bc
Ouyang Kani bc
Wang Jingzhi bc
Wang Qi a
Liu Li a
Song Lulu a
Wang Youjie a
Guo Huan a
Li Xiaodong bc##
Wu Tangchun a⁎⁎
Yuan Yu a⁎
a Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
b Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, 430061, China.
c Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, 430061, China.
⁎ Corresponding author: Yu Yuan, 13 Hangkong Rd, Wuhan, 430030, Hubei, China; Phone: +86-27-83692347; Fax: +86-27-83692560.
⁎⁎ Corresponding author: Tangchun Wu, 13 Hangkong Rd, Wuhan, 430030, Hubei, China; Phone: +86-27-83692347; Fax: +86-27-83692560.
## Corresponding author: Xiaodong Li, Hubei Provincial Hospital of Traditional Chinese Medicine, 4 Garden Hill, Wuhan, 430061, Hubei, China; Phone: +86-27-88920956.
# These authors contributed equally to this article.
9 12 2022
9 12 2022
10 8 2022
17 11 2022
6 12 2022
© 2022 The Authors. Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Objective
To describe the full scope of long-term outcomes and the ongoing pathophysiological alterations among the COVID-19 survivor.
Methods
We established a longitudinal cohort of 208 COVID-19 convalescents and followed them at 3.3 (IQR: 1.3, 4.4, visit 1), 9.2 (IQR: 9.0, 9.6, visit 2), and 18.5 (IQR: 18.2, 19.1, visit 3) months after infection, respectively. Serial changes in multiple physical and psychological outcomes were comprehensively characterized. We additionally explored the potential risk factors of SARS-CoV-2 antibody response and sequelae symptoms.
Results
We observed continuous improvement of sequelae symptoms, lung function, chest CT, 6-minute walk test, and the Borg dyspnoea scale, whereas sequelae symptoms (at least one) and abnormal chest CT patterns still existed in 45.2% and about 30% of the patients at 18.5 months, respectively. Both anxiety and depression disorders were alleviated for the convalescents, although the depression status was sustained for a longer duration.
Conclusions
Most COVID-19 convalescents had an overall improved physical and psychological health status, whereas sequelae symptoms, residual lesions on lung function, exercise impairment, and mental health disorders were still observed in a small proportion of the participants at 18.5 months after infection. Implementing appropriate preventive and management strategies for the ever-growing COVID-19 population is warranted.
Keywords
COVID-19
longitudinal cohort
sequelae
lung function
CT abnormalities
depression and anxiety
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pmcIntroduction
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with high covertness and high transmissibility has caused more than 0.5 billion confirmed cases and 6.3 million deaths globally as of July 13, 2022 (2022, Hao et al., 2020). Huge burdens for the health care system and the whole society may occur due to the overwhelming COVID-19 pandemic and the rapidly growing population of post-COVID-19 patients worldwide (Pan An et al., 2020). Emerging evidence has suggested that many COVID-19 survivors suffered from a higher rate of long-term complications and limited day-to-day activities (Ayoubkhani, 2021, Evans et al., 2021, Han et al., 2022), as well as showing a relatively lower physical and mental health status than the general population (Ayoubkhani et al., 2021, Dennis et al., 2021, Huang et al., 2022). Previous studies primarily focused on sequelae symptoms or respiratory outcomes within 1-year after infection (Fernández-de-Las-Peñas et al., 2021, Huang L. et al., 2021, Logue et al., 2021, Seessle et al., 2022, Wynberg et al., 2021, Zhang et al., 2021), and the majority of studies were limited to the cross-sectional design (Fernández-de-Las-Peñas et al., 2021). Few prospective investigations depicted the overall health outcomes of discharged COVID-19 patients with repeated assessments (Huang et al., 2022). Moreover, despite the reported possible persisting myocarditis and inflammation (Kotecha et al., 2021, Puntmann et al., 2020), less attention is paid to the recovery condition of the myocardial injury. To date, little is known about the natural history of long-term COVID-19. There is still an immediate need for studies to explore the longer health outcomes and the ongoing pathophysiology alterations among COVID-19 survivors.
In this study, we established a longitudinal cohort of COVID-19 convalescents with different disease severity with over 18.5 months of follow-up. We comprehensively characterized the serial changes of multiple indicators, including sequelae symptoms, respiratory outcomes, computed tomography scans, physical function, a biomarker of myocardial injury, SARS-CoV-2 antibody response, and mental health disorders. We further explored the potential risk factors of the SARS-CoV-2 antibody and sequelae symptoms.
Methods
Study design
In this prospective cohort study, we invited discharged COVID-19 patients from different hospitals in multiple districts of Wuhan, China. There were 289 convalescents who agreed to participate in this study and attend designated follow-up at Hubei Provincial Hospital of Traditional Chinese Medicine on February 17, 2020. A total of 81 non-consecutive patients were lost to follow-up due to declining participation (64 patients), leaving Wuhan (15 patients), and dying before visiting 1 (2 patients). There were 208 participants who were included in the final analysis and participated in three visits. The median duration of three visits was 3.3 (IQR: 1.3, 4.4, visit 1), 9.2 (IQR: 9.0, 9.6, visit 2), and 18.5 (IQR: 18.2, 19.1, visit 3) months after infection, respectively. This study was approved by the Ethics Committee of the School of Public Health, Tongji Medical College, Huazhong University of Science and Technology (approval number: 202001). All participants provided written informed consent.
Procedure
At each visit, the participants underwent a detailed questionnaire interview, physical examination, routine blood test, pulmonary function tests, high-resolution chest CT (HRCT) scan, and 6-min walking test (6MWT). Self-reported sequelae symptoms include cough, fatigue or muscle weakness, sleep difficulties, decreased appetite, diarrhoea or vomiting, smell or taste disorder, dizziness or headache, sore throat, and chest pain.
Lung function tests were performed before and after 6MWT according to guidelines of the American Thoracic Society (ATS) (1995). Parameters consisted of forced expiratory volume in one second (FEV1), forced vital capacity (FVC), FEV1/FVC ratio, and forced expiratory flow between 25% and 75% of vital capacity (FEF25-75). All measurements were performed with a spirometer (SP80B, CONTEC Medicine, Qinhuangdao, China) and expressed as predicted percentages of normal values. Measurements assessed after 6MWT were shown in the results.
Patients went through the unenhanced chest CT examinations using a 40-section CT scanner with breath holding at the end of inspiration (uCT 530, United Imaging Healthcare, Shanghai, China). Images were reconstructed at 0.55 mm slice thickness, with 513 mm × 768 mm matrix size. The collimation and rotation time was 22 mm and 0.7 seconds, respectively. Other parameters were set according to the manufacturer's standard routine. CT abnormalities were demonstrated according to the terms of the international standards defined by the Fleischner Society glossary and peer-reviewed COVID-19 literature (Hansell et al., 2008, Pan et al., 2022, Pan Feng et al., 2020). In our study, two professional radiologists and one experienced pulmonologist constituted an evaluation group, and we invited two groups with the same specialist configuration to read CT reports separately and further assess ground-glass opacity (GGO) scores and reticular pattern (RP) scores. The average score of the two groups was considered the final score for statistical analysis. Details of GGO scoring system are: 0, no involvement; 1, involvement <25%; 2, 25-50% involvement; 3, 50-75% involvement; 4, involvement >75%. The unilateral lung is divided into three sections: upper (above the level of the carina); middle; lower (below the level of the right lower pulmonary vein). There are six sections on both sides, including right upper, right middle, right lower, left upper, left middle, and left lower. Each lobe is scored separately and then calculate the total GGO score of six lobes (0-24). Details of the RP scoring systems are: 0, no lines; 1, 1-2 short line/grid shadows; 2, multiple line/grid shadows; 3, <1 lung segment's aggregated line/grid-like shadows in the subpleural area; 4, 1-3 lung segments' large grid shadows; 5, >3 lung segments' diffuse grid shadows and/or deformed lung structure. The right upper lobe contains 3 segments, the left upper lobe usually contains 2 segments, the right middle, and the left lingual lung contain 2 segments, and the highest score is 4 points; the lower lobe of both lungs has more than 2 segments, and the highest score is 5 points; thus, the maximum score is 26 points. Each lobe was scored separately and then we calculated the total RP score of the six lobes (0-26). All patients were scored excluding pre-morbid lung nodules or scars.
6MWT test was performed on a walk monitoring and analysis system (YK2020A, Wocaring Medical Equipment, Wuhan, China), following the standardized protocol of the ATS (2002). Patients walk as far as possible on a flat and hard surface indoors within 6 minutes. Distance and predicted percentage of walking distance were calculated based on Jay SJ's method (Jay, 2000). Also, the Borg dyspnoea scale was assessed after 6MWD, as patients were asked to grade their level of breath shortness from 0 (no dyspnoea at all) to 10 (excessive dyspnoea) (Just et al., 2010).
For biomarkers of cardiac injury, we measured cardiac troponin T using a highly sensitive reagent of TNT‑HS on the Cobas E601 immune analyser (Roche Diagnostics), following the manufacturer's instruction. The measuring range was 3-10000 ng/L, and the intermediate precision coefficient of variation was <10%. A 99th percentile value of 14 ng/L in the general reference population had been reported previously (Giannitsis et al., 2010, Saenger et al., 2011). Myocardial injury is defined as circulating cardiac troponin levels higher than the 99th percentile upper reference limit, regardless of new abnormalities of electrocardiography and echocardiography (Shi et al., 2020, Thygesen et al., 2018).
Additionally, we assessed the SARS-CoV-2 neutralizing antibody (NAb) level by a pseudotyped virus-based assay and calculated the half-maximal inhibitory concentration (NT50) according to the inhibition rate of each dilution using nonlinear regression. The details have been described in a previous study (Wang et al., 2021).
To further evaluate patients’ mental health, the validated Chinese versions of the Patient Health Questionnaire-9 (PHQ-9) contained a 9-item depression module ranging from 0 to 27 points was used to evaluate post-discharge depression status (Kroenke et al., 2001). The depression level was grouped by minimal (0-4), mild (5-9), moderate, (10-14), moderately severe (15-19), and severe (20-27) (Kroenke et al., 2001). Meanwhile, the Generalized Anxiety Disorder-7 (GAD-7) included a 7-item anxiety module ranging from 0 to 21 points was applied to assess post-discharge anxiety conditions (Spitzer et al., 2006). The anxiety severity was levelled as minimal (0-4), mild (5-9), moderate (10-14), and severe (15-21) (Spitzer et al., 2006). Each above item scored from 0 (not at all) to 3 (nearly every day).
Results
The demographics and comorbidities were shown in Table 1 . Among 208 COVID-19 convalescents, there were 146 mild cases and 62 severe cases. The median age of patients was 58.0 years (IQR: 50.0, 64.3), and 100 (48.1%) were men. 69 (32.7%) participants had college or higher education attainment. There were 101 (48.6%) participants who had more than 50000 RMB per year of household income. The majority of participants were never-smokers (n=183, 88.0%) and never-drinkers (n=183, 88.0%). There were 75 (36.1%), 23 (11.1%), and 14 (6.7%) participants who had pre-existing hypertension, diabetes, and cardiovascular disease. The median BMI and waist circumferences were 24.3 and 90 cm. There were no significant differences in most variables between mild and severe cases except for BMI, with severe cases having a significantly lower median BMI of 23.4 (IQR: 22.0, 25.5) (P<0.01, eTable 1).Table 1 Basic characteristics of COVID-19 convalescents.
Table 1Variables Total population (N=208)
Age, years 58 (50.0, 64.3)
Gender, n (%)
Male 100 (48.1)
Female 108 (51.9)
Education, n (%)
Middle school or lower 140 (67.3)
College or higher 68 (32.7)
Household income, n (%)
<50000 RMB/year 107 (51.4)
≥50000 RMB/year 101 (48.6)
Cigarette smoking, n (%)
Never-smoker 183 (88.0)
Ever-smoker 25 (12.0)
Alcohol consumption, n (%)
Never-drinker 183 (88.0)
Ever-drinker 25 (12.0)
Comorbidity, n (%)
Hypertension 75 (36.1)
Diabetes 23 (11.1)
CVD 14 (6.7)
Body mass index 24.3 (22.6, 26.5)
Waist circumference 90 (83, 97)
Duration from symptom onset to the last follow-up, months 18.5 (18.2, 19.1)
Table 2 illustrated the longitudinal trend in sequelae symptoms, lung function, chest CT, 6-minute walk test, hsTnT, NT50 for plasma SARS-CoV-2 NAb, and psychological condition of the COVID-19 survivors during the follow-up. The percentage of convalescents with at least one sequelae symptom decreased from 62.0% at 3.3 months to 50.0% and 45.2% at 9.2 months and 18.5 months, respectively. The most commonly reported symptoms in each visit were fatigue or muscle weakness and sleep difficulties (35.1% and 33.2%, respectively at visit 1), with the frequency gradually declining during the follow-up. A significant decrease was observed for most sequelae symptoms between visit 1 and visit 2, whereas no significant decrease was observed for sequelae between visit 2 and visit 3. The indicators for lung function were slightly increased during the convalescence, although only a significant increase was observed for FEV1/FVC ratio. The FEV1/FVC was 83.0% at visit 1 and increased to 85.6% at visit 3. FEF25-75% was 81.2 (IQR: 71.9, 97.0) % at visit 1, while 91.8 (IQR: 75.6, 110.0) at visit 3. 95 participants attended chest CT during each visit, and the frequency of GGO dropped from 69 (72.6%) at visit 1 to 28 (29.5%) at visit 3. The CT scores of GGO and RP also experienced a drastic decline, which was 6 and 7, respective at visit 1, while 1 and 3, respectively, at visit 3. The median distance of the 6-minute walk test significantly improved, from 514.9 m (IQR: 480.2, 556.0) at 3.3 months to 535 m (IQR: 509.0, 570.0) at 18.5 months. Similarly, significant improvements were found for the percentage of predicted value and the Borg dyspnoea scale after 6MWT between visit 1 and visit 3. Only four patients (4.7%) experienced dyspnoea (Borg dyspnoea scale ≥1) after 6MWT at 18.5 months, whereas 30 people had a Borg scale equal to or greater than 1 (50.8%) at 3.3 months. The levels of hsTnT experienced a slight fall over time, from 4.5 ng/L (IQR: 3.0, 7.5) at visit 1 to 4.1 ng/L (IQR: 3.0, 6.6) at visit 3, and the myocardial injury frequency (hsTnT ≥14 ng/L) declined to 3.8% at visit 3 compared to 7.0% at visit 1, although no significant difference was found between each visit. NT50 for plasma SARS-CoV-2 NAb experienced a continuous decline in the unvaccinated group, from 1153.0 (IQR: 473.5, 2095.8) at visit 1 to 281.0 (IQR: 128.2, 499.8) at visit 3. Meanwhile, the trend in the vaccinated group showed a rapid decline between visit 1 and visit 2, with 765.5 (IQR: 343.5, 1510.5) and 287.5 (IQR: 140.2, 585.0) respectively, and then mounted to 910.0 (IQR: 497.0, 1460.8) due to the COVID-19 vaccination after 1 year of pandemic. For mental health disorders, 96 and 97 participants provided information on depression and anxiety disorders during each visit. Both depression and anxiety scores showed decreasing trend during the follow-up, with a slight difference among the three visits. The depression score gradually descended from 5 at 3.3 months to 4 at 9.2 months, and then rapidly dropped to 1 at 18.5 months. Meanwhile, the anxiety disorder scores decreased rapidly during the first 2 visits (median score=4 and 1, respectively, at visit 1 and visit 2) and remained relatively stable during the last visit (median score=0). Over 20% of our participants were suffering from at least mild depression or anxiety status at 18.5 months. The overall trends of the above parameters were similar in mild and severe convalescents (eTable 2). Compared with the mild, severe convalescents had a relatively higher frequency of any sequelae symptoms within 18.5 months, and higher CT scores of GGO and RP during the early follow-up period, as shown in eTable 3 . Meanwhile, we compared the variation of lung function, CT, 6MWT, and Borg dyspnoea scale during each visit between the mild and the severe, and found that the mild convalescents had a substantial better improvement in Borg dyspnoea scale between visit 3 and visit 1 (Δ=-1.000 [-1.000, 0.000], P=0.025) comparing to the severe (eTable 4).Table 2 Physical and psychological health status of convalescents during follow-up.
Table 2Variables Total population (N=208)
Visit 1 (3.3 months) Visit 2 (9.2 months) Visit 3 (18.5 months)
Sequelae symptoms
Any sequelae symptoms, n (%) 129 (62.0) *# 104 (50.0) 94 (45.2)
Cough, n (%) 41 (19.7) *# 22 (10.6) 22 (10.6)
Fatigue or muscle weakness, n (%) 73 (35.1) *# 45 (21.6) 37 (17.8)
Sleep difficulties, n (%) 69 (33.2) *# 40 (19.2) 34 (16.3)
Decreased appetite, n (%) 21 (10.1) # 10 (4.8) 7 (3.4)
Diarrhoea or vomiting, n (%) 21 (10.1) 9 (4.3) 10 (4.8)
Smell or taste disorder, n (%) 10 (4.8) 9 (4.3) 8 (3.8)
Dizziness or headache, n (%) 9 (4.3) 7 (3.4) 8 (3.8)
Sore throat, n (%) 10 (4.8) 11 (5.3) 6 (2.9)
Chest pain, n (%) 12 (5.8) 12 (5.8) 13 (6.2)
Lung function
FEV1% 94.3 (82.1, 106.8) 96.8 (86.1, 104.5) 96.6 (88.6, 107.6)
FVC% 92.6 (83.5, 108.5) 93.4 (84.9, 102.5) 92.9 (83.7, 103.7)
FEV1/FVC 83.0 (80.2, 85.4) # 83.3 (81.0, 87.5) 85.7 (81.1, 89.1)
FEF25-75% 81.2 (71.9, 97.0) 88.3 (69.8, 103.7) 91.8 (75.6, 111.0)
Chest CT
CT abnormal of GGO, n (%) 69 (72.6) *# 38 (40.0) 28 (29.5)
CT scores of GGO 6.0 (3.0, 10.5) *# 3.0 (1.0, 5.5) † 1.0 (0.0, 4.0)
CT abnormal of RP, n (%) 61 (64.2) # 47 (49.5) 34 (35.8)
CT scores of RP 7.0 (4.0, 14.0) *# 4.0 (2.0, 9.0) † 3.0 (1.0, 7.0)
6-minute walk test
Distance, m 514.9 (480.2, 556.0) *# 565.2 (522.2, 610.0) † 535.0 (509.0, 570.0)
Predicted distance% 92.0 (86.0, 99.0) *# 100.0 (94.0, 106.0) † 96.0 (89.0, 103.0)
Borg dyspnoea scale ≥1, n (%) 30 (50.8) *# 15 (16.5) † 4 (4.7)
HsTnT (ng/L) 4.5 (3.0, 7.5) 4.3 (3.0, 6.8) 4.1 (3.0, 6.6)
HsTnT ≥14 ng/L, n (%) 10 (7.0) 5 (3.3) 6 (3.8)
NT50 for plasma SARS-CoV-2 NAb 815.0 (396.0, 1804.5) *# 293.5 (147.8, 595.0) † 678.0 (309.5, 1279.5)
Unvaccinated group 1153.0 (473.5, 2095.8) *# 346.0 (162.5, 712.2) 281.0 (128.2, 499.8)
Vaccinated group 765.5 (343.5, 1510.5) * 287.5 (140.2, 585.0) † 910.0 (497.0, 1460.8)
Mental health disorders
Depression, score 5.0 (2.0, 9.0) # 4.0 (1.0, 8.0) † 1.0 (0.0, 4.0)
Depression score ≥5, n (%) 53 (55.2) # 43 (44.8) † 23 (24)
Anxiety disorder, score 4.0 (0.0, 7.0) # 1.0 (0.0, 5.0) 0.0 (0.0, 4.0)
Anxiety score ≥5, n (%) 36 (37.1) 29 (29.9) 22 (22.7)
Table 3 Potential risk factors associated with NT50 for plasma SARS-CoV-2 NAb during follow-up.
Table 3Variables β (SE) P value
Number of any symptom 0.126 (0.045) 0.005
CT scores of GGO 0.045 (0.017) 0.009
CT scores of RP 0.045 (0.015) 0.004
Depression score -0.002 (0.015) 0.901
Anxiety score 0.019 (0.018) 0.276
Table 3 provided a summary of the potential risk factors of NT50 for plasma SARS-CoV-2 NAb during follow-up. We established a linear mixed model to calculate the effect size of the number of any symptoms, CT scores of GGO and RP, and depression and anxiety score on NT 50 for plasma SARS-CoV-2 NAb. The number of any symptoms, CT scores of GGO, and RP were positively associated with a higher level of NT50 for plasma SARS-CoV-2 NAb, and the β (SE) were 0.126 (0.045), 0.045 (0.017), and 0.045 (0.015), respectively (all P<0.01). No significant associations were observed for depression and anxiety scores with NT50 though (both P>0.05). Figure 1 illustrated the potential risk factors of any sequelae symptoms. Generalized linear mixed models were established to explore the associations of any sequelae symptoms with age group, gender, education, and comorbidities including hypertension, diabetes and CVD, and disease severity, with multivariable adjustment conducted. Compared with participants without hypertension, participants with hypertension had an OR of 0.44 (95% CI 0.24-0.80) for any sequelae symptoms. Meanwhile, severe participants had an OR of 3.60 (95% CI 1.97-6.58) compared to mild participants.Figure 1 The potential risk factors of sequelae symptoms. Generalized linear mixed models were established to explore the associations of any sequelae symptoms (categorical) with fixed effects. Each patient was included in random effect. For the age group (<60 years/≥60 years), we adjusted gender (male/female), education (middle school or lower/college or higher), and comorbidities including hypertension (no/yes), diabetes (no/yes), and CVD (no/yes). Disease severity (mild/severe) was excluded due to the potential mediating effects. For gender, we adjusted age group, education, comorbidities, and disease severity. For education, we adjusted age group, gender, and disease severity. Comorbidities were excluded due to the potential mediating effects. For comorbidities (hypertension, diabetes, and CVD), we adjusted each for age group, gender, education, and comorbidities except itself. Disease severity (mild/severe) was excluded due to the potential mediating effects. For disease severity, we adjusted age group, gender, education, and comorbidities. Abbreviation: OR, odds ratio.
Figure 1
Additionally, all participants in this study were hospitalized and discharged after recovery (Supplementary eTable 5). The median days of hospitalization were 27 (IQR 17.0, 35.0) for the total population, and 25.0 (IQR 15.2, 32.0) and 29.0 (IQR 22.0, 37.0), respectively for the mild and severe patients. A total of 9 (4.3%) patients were admitted to ICU. Only one patient received treatment with invasive mechanical ventilation, whereas no one underwent a tracheostomy or developed renal failure that needed renal function replacement treatment. Compared with the mild patients, severe patients had a significantly higher frequency of ICU admission, more intensive treatments, and longer hospital stays. As shown in eTable 6, the obvious improvement of CT abnormalities, 6MWD, Borg dyspnoea scale, and mental health disorders was observed in both groups (hospitalization <27 days/≥27 days). Substantial improvement of sequelae symptoms was further observed in patients with a stay of more than or equal to 27 days. The NT50 levels underwent a fall and a rise due to the vaccination after visit 2 in both groups. In eTable 7, we observed an obvious recovery trend in any sequelae symptoms, CT abnormalities, 6MWD, Borg dyspnoea scale, and mental health disorders in the non-ICU group, while no substantial improvement was observed in the ICU group during three visits.
Discussion
With the pandemic of COVID-19, there has been growing concern that survivors might be at higher risk of multiple complications. Large-scale, longitudinal measurements and longer-term data are urgently needed to comprehensively characterize the trend of health consequences of COVID-19 convalescents. In this study, we comprehensively assessed the sequential pathophysiology changes of COVID-19 convalescents based on a considerably long follow-up cohort in Wuhan (over 18.5 months). We observed that most patients had substantial improvement in their general health status during the follow-up visits. Meanwhile, some convalescents still have the sequelae symptoms, including fatigue, muscle weakness, sleep difficulties, abnormal CT patterns, and depression and anxiety disorder at 18.5 months after diagnosis. Our finding provides novel evidence for the personalized prevention and intervention of long-term outcomes among COVID-19 convalescents.
Prior studies have reported that fatigue, muscle weakness, and sleep difficulties were the most frequent post-discharge sequelae symptoms and could last at least 12 months (Seessle et al., 2022). We confirmed this finding and advanced the evidence to 18.5 months after the COVID-19 diagnosis. The potential pathogenesis of fatigue and muscle weakness may include viral-induced myositis, long periods of bed rest during convalescence, and the use of systemic corticosteroid therapy (Hui et al., 2005, Peiris et al., 2003). We found continuous improvement in chest CT in these patients. In the meantime, the CT appearance of the abnormal radiographic pattern was still detected in around 30% of the convalescents at 18.5 months. The CT abnormalities could even be sustained beyond this duration. The improvement with concurrent residual lesions of lung CT was also observed in previous studies with 1 to 2 years of follow-up (Barini et al., 2022, Chen et al., 2021, Huang et al., 2022). Meanwhile, we observed some fluctuations in the indicators of lung function and the 6-minute walk test. The convalescents experienced fluctuations of FEV1%, FVC%, 6-minute walking distance, and predicted distance during the three visits, with an overall recovery trend. The possible mechanisms of this phenomenon include the recovery of early injury and the increase of pulmonary ventilation from 3 to 9 months after symptom onset, and then pulmonary atelectasis and parenchymal fibrosis occurred due to the persistent effect of inflammation in the later stages of the disease course (Hui et al., 2005, Nicholls et al., 2003). This hypothesis is supported by the CT results in our study, as 35.8% of convalescents still had reticular patterns at 18.5 months. Previous studies of acute respiratory distress syndrome also reported similar trends in lung function and 6-minute walk tests among SARS survivors (Herridge et al., 2011). Further studies are still warranted to confirm our findings and unravel the underlying mechanisms.
Notably, the results demonstrated that both depression and anxiety disorders were alleviated for the convalescents, although at least mild depression and anxiety status were sustained in over 20% of our participants at 18.5 months. A cohort study suggested that some COVID-19 survivors had psychiatric sequelae (mood, anxiety, or psychotic disorder) and more frequent substance use at the 6-months post-COVID-19, although the longitudinal assessment of the outcomes was not available in the study (Taguet et al., 2021). Moreover, Huang et al. (Huang L. et al., 2021) reported that more patients had anxiety or depression at 12 months than at 6 months (26% vs 23%). In addition, another study reported that after two years post-symptom onset, there were still 12% of COVID-19 survivors who had anxiety or depression, compared to 5% of participants in matched non-COVID-19 controls (Huang et al., 2022). Long-term and multi-centre investigations with larger sample sizes and standard assessment methods are warranted to confirm the conclusion.
Long-COVID symptoms (García-Abellán et al., 2021), pulmonary involvement (Başaran et al., 2021, He et al., 2020), and mental status (Madison et al., 2021) was associated with antibody immune response, and inflammation mechanisms including the number of CD4+ T cells declining and the IL-6 level elevation may partially explain the associations. We included the afore-mentioned three indices in multivariable analysis using a linear mixed model and observed that the number of any symptoms, CT scores of GGO, and CT scores of RP were positively associated with higher NT50 for plasma SARS-CoV-2 NAb. Higher NAb titre of SARS-CoV-2 indicates a potential persistent inflammation and immune response in our participants during 18.5 months. Prior studies demonstrated that negative psychological experiences such as less social cohesion were associated with lower vaccine efficacy (Madison et al., 2021) and lower antibody titre (β=-0.10, P=0.01) (Stephen et al., 2022). However, no significant associations were observed for depression and anxiety scores with the NT50 in our participants due to the relatively small sample size. Hence, further psychological and random behavioural intervention studies with a larger sample size are needed to explore the potential mechanism.
Hypertension was associated with lower risks of any sequelae symptoms among our COVID-19 survivors, with adjusted OR=0.44 (95% CI: 0.24-0.80). Some studies reported that hypertension was associated with higher risks of adverse outcomes in COVID-19 patients (Guan et al., 2020, Wu et al., 2020). However, Huang et al. (Huang et al., 2020) reported that hypertension was not an independent risk factor for increasing COVID-19 severity or mortality. Tadic et al. (Tadic et al., 2020) also suggested that hypertension was not an independent predictor of the lethal outcome in COVID-19 patients. A large cohort study included 153,760 COVID-19 individuals and over 10 million controls suggested that the incident risk of cardiovascular outcome was lower in the hypertension group (HR=1.57, 95% CI:1.51-1.64) than in the normotensive group (HR=1.66, 95% CI: 1.61-1.72) (Xie et al., 2022). The angiotensin-converting enzyme 2 (ACE2) has been suggested to be a coreceptor for SARS-CoV-2 to enter epithelial cells (Zhou et al., 2020). Previous studies have indicated that the angiotensin-converting-enzyme inhibitor (ACEI)/angiotensin Ⅱ receptor blocker (ARB) medication played a protective role in the COVID-19 prognosis (Meng et al., 2020, Zhang et al., 2020). A retrospective study involving 1128 adult patients observed that compared with ACEI/ARB non-users, the use of ACEI/ARB among hospitalized patients with COVID-19 and hypertension was associated with a lower risk of all-cause mortality (HR=0.42, 95% CI: 0.19-0.92) (Zhang et al., 2020). Meng et al. (Meng et al., 2020) also reported that COVID-19 patients with hypertension receiving ACEI/ARB therapy had a lower rate of severe diseases and better clinical outcomes compared with non-ACEI/ARB users. Despite the above evidence, the underlying mechanisms for the observed association remain largely unclear. The findings should be interpreted cautiously, and future studies are warranted to confirm our finding and unravel the potential mechanism.
The severe patients had a higher risk for any sequelae symptoms compared to the mild patients, with adjusted OR=3.60 (95% CI: 1.97-6.58). This is in line with Cao and his colleagues’ studies (Huang Chaolin et al., 2021, Huang et al., 2022, Huang L. et al., 2021). They provided consistent findings that compared with mild patients, severe or critically ill patients had a significantly poor recovery and more post-COVID symptoms at 6, 12, and 24 months after discharge (Huang Chaolin et al., 2021, Huang et al., 2022, Huang L. et al., 2021). A meta-analysis including 18 follow-up studies (N=8591) also supported this conclusion (Han et al., 2022). Survivors with severe initial illness were more likely to have a higher burden of sequelae symptoms after a year since infection (Han et al., 2022).
Compared to the wild-type strain, the current dominant variant omicron differs in the transmissibility, pathogenicity, and immune escape due to the deletions and many mutations in the spike (S) protein (Antonelli et al., 2022, Maslo et al., 2022, Rössler et al., 2022, Torjesen, 2021). Patients infected with omicron had milder clinical manifestations and decreased disease severity and mortality, compared with those who were infected with the wild-type strain (Maslo et al., 2022). Meanwhile, a previous study reported that omicron-infected patients had a largely reduced virus-neutralizing activity (Carreño et al., 2022) and an increased risk of reinfection (Pulliam et al., 2022). A recent study reported that the prevalence of any sequelae symptoms was 4.5% in omicron cases at 4 weeks infection (Antonelli et al., 2022), while 45.2% of our participants (infected with wild-type strain) still experienced any sequelae symptoms at 18.5 months after symptom onset. Therefore, the generalizability of our findings should be tested in other populations infected with the new variants.
A longitudinal and comprehensive profile of the disease was observed at 18.5 months of follow-up in our study. Nevertheless, this investigation has several limitations. First, participants in this study were recruited during the early period of the COVID-19 pandemic, thus the generalizability to other new SARS-CoV-2 variants is limited (Antonelli et al., 2022). Second, more than 70% of our participants were mild patients at the initial stage, further investigations that include participants of various disease severity and larger sample size are needed. Third, not all participants attended three visits, thus selection bias could be introduced during this process. Although there were 81 non-consecutive patients who lost to follow-up, we performed the additional analysis to compare the basic characteristics between included (N=208) and declined (N=81) participants (Supplementary eTable 8). No significant difference in basic characteristics was observed for the two groups, which confirmed the robustness of our finding. The selection bias could be minimal. Fourth, we did not assess the transfer factor for carbon monoxide (TLCO) in the pulmonary function test. We intended to conduct a comprehensive study including multiple physical and mental health indices, and only assessed the key indicators for each category. The TLCO should be tested in future studies to distinguish diffusion deficit.
In summary, this longitudinal study suggested that most COVID-19 convalescents had an improved physical and psychological health status in general, whereas post-discharge sequelae symptoms, residual lesions on lung function and exercise impairment, and mental health disorders were still observed in a small proportion of our participants during 18.5 months. Clinicians and policymakers should be aware of the risk of physical and mental complications in the ever-growing COVID-19 convalescents. The development of targeted strategies for the early prevention of post-discharge sequelae symptoms, CT abnormalities, and mental health problems is warranted.
Declaration of competing interest
No potential conflict of interest was reported by the author(s).
Acknowledgements
We thank all the study participants and project staff from Tongji Medical College, Huazhong University of Science and Technology, and Hubei Provincial Hospital of Traditional Chinese Medicine for the work they have done.
This work was supported by the Emergency Key Program of Guangzhou Laboratory (EKPG21-30), the National Science Foundation of China (82204113 and 72061137006), and the Fellowship of China Postdoctoral Science Foundation (2020T130034ZX).
Ethical approval statement
This study was approved by the Ethics Committee of the School of Public Health, Tongji Medical College, Huazhong University of Science and Technology (approval number: 202001). All participants provided written informed consent.
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Data were expressed as median (IQR) or frequency (percentage).
Data were expressed as median (IQR) or frequency (percentage). Kruskal-Wallis test was applied for group comparisons of continuous variables, and χ² test or Fisher Exact tests were performed to analyze the categorical variables. * Significant difference between visit 1 and visit 2 groups (P<0.05). # Significant difference between visit 1 and visit 3 groups (P<0.05). † Significant difference between visit 2 and visit 3 groups (P<0.05). Abbreviations: FEV1: forced expiratory volume in one second; FVC: forced vital capacity; FEF25-75: forced expiratory flow between 25% and 75% of vital capacity; GGO, ground-glass opacity; RP, reticular pattern; HsTnT, highly-sensitive troponin T; NT50, the half-maximal inhibitory concentration; NAb, neutralizing antibody.
Linear mixed model of repeated measures was conducted to estimate fixed effects of any symptom, CT scores, and mental health disorders on log-transformed NT50 of SARS-CoV-2 neutralizing antibody, adjusted for age, gender, and vaccination. Random effects consisted of days of the neutralizing antibody test after symptom onset and days of the first shot of the COVID-19 vaccine since symptom onset of each participant. The number of any symptoms refers to the number of sequelae a participant has. Abbreviations: NT50, the half-maximal inhibitory concentration; NAb, neutralizing antibody; GGO, ground-glass opacity; RP, reticular pattern; SE, standard error of the beta coefficient.
Appendix Supplementary materials
Image, application 1
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ijid.2022.12.008.
| 36509334 | PMC9733963 | NO-CC CODE | 2022-12-14 23:45:33 | no | Int J Infect Dis. 2022 Dec 9; doi: 10.1016/j.ijid.2022.12.008 | utf-8 | Int J Infect Dis | 2,022 | 10.1016/j.ijid.2022.12.008 | oa_other |
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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)00643-9
10.1016/j.ijid.2022.12.007
Article
Continued demographic shifts in hospitalised COVID-19 patients from migrant workers to a vulnerable and more elderly local population at risk of severe disease
Ngiam Jinghao Nicholas MBBS 1
Chhabra Srishti MBBS 2
Goh Wilson MBBS 2
Sim Meng Ying MBBS 2⁎
Chew Nicholas WS MBBS 3
Sia Ching-Hui MBBS 3
Cross Gail Brenda MBBS 14
Tambyah Paul Anantharajah MBBS 345
1 Department of Infectious Diseases, National University Health System, Singapore
2 Department of Medicine, National University Health System, Singapore
3 Department of Cardiology, National University Heart Centre Singapore, Singapore
4 Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
5 Infectious Diseases Translational Research Programme, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore
⁎ Corresponding author: Dr Jinghao Nicholas Ngiam, Division of Infectious Diseases, National University Health System Singapore, 1E Kent Ridge Rd, NUHS Tower Block, Level 10, Singapore 119228, Fax: (65) 67794112, Telephone: (65) 67795555
9 12 2022
9 12 2022
24 10 2022
22 11 2022
6 12 2022
© 2022 The Author(s)
2022
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Objectives
In the early months of the COVID-19 pandemic in Singapore, the vast majority of infected persons were migrant workers living in dormitories who had few medical co-morbidities. In 2021, with the Delta and Omicron waves, this shifted to the more vulnerable, elderly population within the local community. We examined evolving trends amongst the hospitalised cases of COVID-19.
Methods
All patients with PCR-positive SARS-CoV-2 admitted from February 2020 to October 2021 were included, and subsequently stratified by their year of admission (2020 or 2021). We compared the baseline clinical characteristics, clinical course and outcomes.
Results
A majority of cases were seen in 2020 (n=1359), compared with 2021 (n=422), due to the large outbreaks in migrant worker dormitories. Nevertheless, the greater proportion of locally-transmitted cases outside of dormitories in 2021 (78.7% vs 12.3%) meant a significantly older population with more medical co-morbidities had COVID-19. This led to an observably higher proportion of patients with severe disease, presenting with raised inflammatory markers, need for therapeutics, supplemental oxygenation and higher mortality.
Conclusions
Changing demographics and the characteristics of the exposed populations are associated with distinct differences in clinical presentation and outcomes. Older age remained consistently associated with adverse outcomes.
Keywords
COVID-19
demographics
Singapore
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pmcIntroduction
In a prior study, we had examined the first 3 months of the pandemic and the shifting demographics of hospitalised patients with COVID-19 in Singapore (Ngiam, et al. 2021a). In summary, the first cases had been observed were labelled as ‘imported cases’ as they were predominantly returning travellers (Lim, et al. 2012). This subsequently led to a small outbreak within the local community, which was rapidly curbed by aggressive case-finding and quarantining of the affected cases and their contacts (MOH, 2020; Lee, et al. 2020)., Similar approaches had been adopted in several other countries to effectively control the cases of COVID-19 in the early stages of the pandemic (Peck, 2020).
However, although these measures had been effective in limiting the cases within the local community, migrant workers in dormitories with crowded living conditions were not spared (Yi, et al. 2021; Government of Singapore, 2020; Chew, et al. 2020a). This large and sustained outbreak in the migrant worker dormitories accounted for the vast majority of the hospitalised patients with COVID-19 in Singapore in 2020 (Ngiam, et al. 2021a). These migrant workers tended to be young and fit males, with few medical co-morbidities. As such, they were at low risk for progression to severe COVID-19 or developing life-threatening complications of the disease (Ngiam, et al. 2021a; Ngiam et al. 2021b, Kim, et al. 2020). Indeed, at that time, due to the active case-finding (a “zero-covid” approach) and the relatively young and low-risk affected population, Singapore reported among the lowest mortality rates for COVID-19 in the world (Our World in Data 2020; Lim, et al. 2022).
Singapore was able to quickly vaccinate the majority of its population against COVID-19 (Griva, et al. 2021; Chew, et al. 2021b), with 80% of the country's population having completed two doses by August 2021 (MOH, 2021a). In addition, the rapid spread of the delta variant in the third quarter of 2021 which raised concern of overcrowding in healthcare facilities, thus in comparison to 2020, in 2021 persons with COVID-19 were no longer routinely quarantined in a hospital or care facilities, but were allowed to recover at home. People with COVID-19 were however required to be hospitalised if they had risk factors for disease progression, such as if they had been unvaccinated, and/or were more vulnerable to severe disease despite vaccination on account of comorbidities or age. In this study, we described the changing trends in the hospitalised patients with COVID-19 as the pandemic progressed in Singapore from 2020 to 2021.
Methods
We retrospectively examined patients consecutively admitted between February 2020 to October 2021 at a single tertiary academic institution in Singapore who had polymerase chain reaction (PCR) proven COVID-19. Patients who were less than 21 years of age were excluded from this study, as were patients who did not require hospital admission, or who were cared for only in community care facilities.
The study population was divided into based on their admission year (2020 or 2021), and subsequently also stratified by age category (<40 years of age, or ≥40 years of age). For each patient, we collected information on their demographic background, clinical presentation, laboratory findings and other investigations conducted within 24 hours of hospital admission. The progress of each patient was followed, including the use of COVID-19 specific therapeutics (such as remdesivir, dexamethasone, baricitinib or tocilizumab), as well as clinical outcomes such as the presence of pneumonia, need for supplemental oxygen, transfer to intensive care and in-hospital mortality.
To compare differences in characteristics between those admitted in 2020 compared with 2021, t-tests were used for continuous variables, while chi-squared tests were used for categorical variables. Data for continuous variables were expressed as means (± 1 standard deviation), while data for categorical variables were expressed as frequencies (and percentages). A p-value of less than 0.05 was considered significant in this study. All data analyses were performed on SPSS version 20.0 (SPSS, Inc., Chicago, Illinois). This study was approved by the National Healthcare Group Domain Specific Review Board (DSRB 2020/00545). The study was conducted in line with the principles laid out by the Declaration of Helsinki. All data was anonymised and a waiver of written informed consent was obtained prior to the conduct of the study.
Results
A total of 1781 patients required hospital admission for COVID-19 between February 2020 to October 2021. 1359 patients with COVID-19 were admitted in 2020 and 422 in 2021. The mean age of those admitted in 2021 was 60.0 years compared with 39.4 years in 2020 (p<0.001). Dormitory workers accounted for the majority of the outbreak in 2020, compared with 2021 (83.7% vs 4.0%, p<0.001), with males correspondingly accounting for a much larger proportion in 2020 compared with 2021 (90.8% vs 46.2%, p<0.001) (Table 1 ). The outbreak in Singapore began with a few cases amongst returning travellers from February to March 2020, followed by small clusters within the community in April 2020. Alongside the outbreak in the community outside the dormitories which was brought under control quickly, there was a large, uncontrolled outbreak in migrant workers dormitories, which peaked in May 2020, and only subsided substantially in August 2020. Between Aug 2020 and the new year there was little transmission within the dormitories or within the community at large in Singapore despite the loosening of social distancing restrictions. However once again in early 2021 with the advent of the delta variant of concern, a few cases in returning travellers gradually built to a relatively large outbreak of cases amongst the local population in Singapore which initially peaked around mid-September 2021 (Figure 1 ).Table 1 Demographic shifts in the clinical characteristics of patients admitted with COVID-19 from 2020 to 2021 in a tertiary academic hospital in Singapore
Table 1:Parameter Admission for COVID-19 in 2020 (n=1359) Admission for COVID-19 in 2021 (n=422) p-value
Demographics
Age (years) 39.4 (±11.3) 60.0 (±19.7) <0.001
Body mass index (kg/m2) 25.8 (±5.1) 26.0 (±5.8) 0.742
Sex (male) 1231 (90.8%) 195 (46.2%) <0.001
Ethnicity <0.001
Chinese 220 (16.2%) 252 (59.7%)
Malay 59 (4.3%) 67 (15.9%)
Indian 496 (36.5%) 63 (14.9%)
Others 584 (42.9%) 40 (9.5%)
Transmission <0.001
Overseas 56 (4.1%) 73 (17.3%)
Local 166 (12.3%) 332 (78.7%)
Dormitory 1137 (83.7%) 17 (4.0%)
Smoking history 99 (7.7%) 31 (7.4%) 0.816
Hypertension 133 (9.8%) 121 (28.7%) <0.001
Hyperlipidaemia 74 (5.4%) 99 (23.5%) <0.001
Diabetes mellitus 75 (5.5%) 55 (13.0%) <0.001
Asthma 12 (0.9%) 19 (4.5%) <0.001
Chronic kidney disease 4 (0.3%) 17 (4.0%) <0.001
Cancer 13 (1.0%) 19 (4.5%) <0.001
No prior medical conditions 1148 (84.5%) 251 (59.5%) <0.001
Vaccinated against COVID-19 0 (0.0%) 273 (64.7%) <0.001
Clinical presentation
Acute respiratory symptoms 827 (60.9%) 339 (80.3%) <0.001
Anosmia 80 (5.9%) 14 (3.3%) 0.045
Asymptomatic illness 252 (18.5%) 55 (13.0%) 0.009
Admission temperature (degC) 37.3 (±0.8) 37.1 (±0.8) 0.002
Persistent fever >72h 149 (11.0%) 55 (13.0%) 0.244
Length of time with fever (days) 1.2 (±2.3) 1.0 (±1.6) 0.096
Systolic blood pressure (mmHg) 129.7 (±16.7) 131.6 (±21.5) 0.060
Diastolic blood pressure (mmHg) 80.7 (±17.7) 76.2 (±13.1) <0.001
Pulse rate (per minute) 90.3 (±17.7) 85.7 (±14.9) <0.001
Oxygen saturation (%) 98.0 (±2.0) 97.6 (±3.0) 0.003
Initial laboratory findings
Total white cell count (x109/L) 6.88 (±2.28) 6.22 (±2.28) <0.001
Haemoglobin count (g/dL) 14.9 (±1.5) 13.3 (±1.8) <0.001
Platelet count (x109/L) 240 (±65) 231 (±77) 0.025
Absolute lymphocyte count (x109/L) 2.00 (±1.45) 1.48 (±0.74) <0.001
Serum Creatinine (µmol/L) 78.7 (±24.4) 81.9 (±102.5) 0.292
Estimated glomerular filtration rate (ml/min/1.73m2) 104.5 (±17.2) 94.9 (±27.8) <0.001
Serum C-reactive protein (mg/dL) 12.5 (±26.4) 23.1 (±38.0) <0.001
Serum ferritin (µg/L) 196.1 (±233.5) 254.1 (±444.7) 0.001
Serum lactate dehydrogenase (U/L) 420.6 (±283.3) 418.2 (±168.1) 0.881
Therapeutics
Dexamethasone 10 (0.7%) 47 (11.1%) <0.001
Remdesivir 39 (2.9%) 47 (11.1%) <0.001
Sotrovimab 0 (0.0%) 16 (3.8%) <0.001
Baricitinib 4 (0.3%) 5 (1.2%) 0.039
Tocilizumab 1 (0.1%) 2 (0.5%) 0.142
Clinical outcomes
Length of hospital stay (days) 7.8 (±9.1) 7.4 (±7.9) 0.356
Requiring supplemental oxygenation 40 (2.9%) 46 (10.9%) <0.001
Pneumonia 180 (13.2%) 105 (24.9%) <0.001
Requiring intensive care 37 (2.7%) 18 (4.2%) 0.110
High flow nasal cannula 2 (0.1%) 9 (2.1%) <0.001
Myocarditis 8 (0.6%) 4 (0.9%) 0.494
Stroke 4 (0.3%) 1 (0.2%) 0.999
Acute kidney injury 60 (4.4%) 15 (3.6%) 0.491
Death 5 (0.4%) 8 (1.9%) 0.004
Figure 1 Transmission of COVID-19 cases in Singapore over time, comparing local (community) cases, with returning travellers (overseas) and migrant worker (dormitory) cases
Figure 1:
Patients admitted in 2021 had a greater likelihood of having medical co-morbidities such as hypertension, diabetes and chronic kidney disease. 59.5% had no prior medical conditions in 2021, compared with 84.5% in 2020. Asymptomatic disease was more common amongst hospitalised patients in 2020 (18.5%) compared with 2021 (13.0%) (Table 1). Those above and below 40 years of age had comparable prevalence of asymptomatic disease (Table 2 ). Despite good uptake of the COVID-19 vaccines in the community in Singapore since early 2021 (64.7% of the admitted COVID-19 cases in 2021 had received at least 1 dose of the vaccination), symptoms of acute respiratory illness (e.g., cough, rhinorrhoea, sore throat) were far more common in 2021 (80.3% vs 60.9%, p<0.001) probably related to the public health policies which did not mandate admission to a hospital or community care facility for all patients who were SARS-CoV-2 positive by PCR in 2021. Anosmia was slightly more common in 2020 compared with 2021 (5.9% vs 3.3%, p=0.045) (Table 1). Older patients (>40 years of age) had similar prevalence of anosmia compared with their younger counterparts (Table 2). Oxygen saturations at presentations in 2021 were marginally lower compared with 2020 (97.6±3.0 vs 98.0±2.0%, p=0.003), but no significant differences in the duration of fever was found (Table 1). When stratified by age, older patients (> 40 years of age) tended to have more persistent fever and marginally lower oxygen saturations at presentation (Table 2).Table 2 Clinical characteristics of patients admitted with COVID-19 from 2020 to 2021 in a tertiary academic hospital in Singapore, stratified by year of admission and age category
Table 2:Parameter Admission for COVID-19 in 2020 (n=1359)
Admission for COVID-19 in 2021 (n=422) p-value
Age<40 years
(n=756) Age≥40 years
(n=603) Age<40 years
(n=164) Age>40 years
(n=258)
Demographics
Age (years) 30.8 (±4.8) 50.1 (±7.1) 32.3 (±4.5) 62.8 (±16.1) <0.001
Body mass index (kg/m2) 24.8 (±4.7) 26.5 (±5.3) 27.1 (±6.5) 25.5 (±5.4) 0.062
Sex (male) 690 (91.8%) 541 (89.7%) 57 (34.8%) 138 (53.5%) <0.001
Ethnicity <0.001
Chinese 42 (5.6%) 178 (29.5%) 66 (40.2%) 186 (72.1%)
Malay 29 (3.8%) 30 (5.0%) 35 (21.3%) 32 (12.4%)
Indian 297 (39.3%) 199 (33.0%) 33 (20.1%) 30 (11.6%)
Others 388 (51.3%) 196 (32.5%) 32 (19.5%) 103 (3.9%)
Transmission <0.001
Overseas 33 (4.4%) 23 (3.8%) 43 (26.2%) 30 (11.6%)
Local 55 (7.3%) 111 (18.4%) 108 (65.8%) 224 (86.8%)
Dormitory 668 (88.4%) 469 (77.8%) 13 (7.9%) 4 (1.6%)
Smoking history 41 (5.7%) 58 (10.3%) 4 (2.5%) 27 (10.6%) <0.001
Hypertension 15 (2.0%) 118 (19.6%) 4 (2.4%) 117 (45.3%) <0.001
Hyperlipidaemia 6 (0.8%) 68 (11.3%) 3 (1.8%) 96 (37.2%) <0.001
Diabetes mellitus 9 (1.2%) 66 (10.9%) 1 (0.6%) 54 (20.9%) <0.001
Asthma 7 (0.9%) 5 (0.8%) 3 (1.8%) 16 (6.2%) <0.001
Chronic kidney disease 2 (0.3%) 2 (0.4%) 0 (0.0%) 3 (1.2%) <0.001
Cancer 2 (0.3%) 11 (2.0%) 0 (0.0%) 13 (5.1%) <0.001
No prior medical conditions 722 (95.5%) 426 (70.6%) 151 (92.1%) 100 (38.8%) <0.001
Vaccinated against COVID-19 0 (0.0%) 0 (0.0%) 84 (51.2%) 189 (73.3%) <0.001
Clinical presentation
Acute respiratory symptoms 475 (62.8%) 352 (58.4%) 129 (78.7%) 210 (81.4%) <0.001
Anosmia 40 (5.3%) 40 (6.6%) 7 (4.3%) 7 (2.7%) 0.114
Asymptomatic illness 93 (12.3%) 159 (26.4%) 29 (17.7%) 26 (10.1%) <0.001
Admission temperature (degC) 37.4 (±0.8) 37.1 (±0.7) 37.0 (±0.6) 37.3 (±0.9) <0.001
Persistent fever >72h 74 (9.8%) 75 (12.4%) 14 (8.5%) 41 (15.9%) 0.029
Length of time with fever (days) 1.2 (±2.3) 1.2 (±2.3) 0.7 (±1.3) 1.1 (±1.7) 0.061
Systolic blood pressure (mmHg) 127.2 (±15.1) 132.9 (±18.1) 120.1 (±14.8) 138.9 (±22.0) <0.001
Diastolic blood pressure (mmHg) 80.0 (±11.4) 81.5 (±12.5) 74.5 (±12.3) 77.3 (±13.4) <0.001
Pulse rate (per minute) 92.0 (±18.3) 88.1 (±16.6) 87.6 (±15.2) 84.6 (±14.7) <0.001
Oxygen saturation (%) 98.2 (±1.4) 97.7 (±2.6) 98.4 (±1.3) 97.1 (±3.6) <0.001
Initial laboratory findings
Total white cell count (x109/L) 6.89 (±2.18) 6.87 (±2.39) 6.56 (±2.20) 6.03 (±2.30) <0.001
Haemoglobin count (g/dL) 15.2 (±1.4) 14.4 (±1.5) 13.5 (±1.8) 13.2 (±1.8) <0.001
Platelet count (x109/L) 238 (±62) 242 (±68) 253 (±75) 219 (±75) <0.001
Absolute lymphocyte count (x109/L) 2.07 (±1.79) 1.91 (±0.86) 1.60 (±0.74) 1.41 (±0.73) <0.001
Serum Creatinine (µmol/L) 77.9 (±14.8) 79.7 (±32.5) 62.2 (±17.9) 92.8 (±126.0) <0.001
Estimated glomerular filtration rate (ml/min/1.73m2) 111.0 (±15.4) 96.4 (±15.9) 115.0 (±17.6) 83.9 (±26.4) <0.001
Serum C-reactive protein (mg/dL) 28.2 (±1.1) 23.9 (±1.0) 12.9 (±1.2) 45.1 (±2.9) <0.001
Serum ferritin (µg/L) 149.9 (±132.5) 257.3 (±311.6) 105.4 (±145.5) 330.7 (±521.4) <0.001
Serum lactate dehydrogenase (U/L) 409.4 (±298.6) 434.3 (±262.7) 368.3 (±123.4) 444.7 (±182.3) 0.019
Therapeutics
Dexamethasone 3 (0.4%) 7 (1.2%) 2 (1.2%) 45 (17.4%) <0.001
Remdesivir 4 (0.5%) 35 (5.8%) 3 (1.8%) 44 (17.1%) <0.001
Sotrovimab 0 (0.0%) 0 (0.0%) 0 (0.0%) 16 (6.2%) <0.001
Baricitinib 0 (0.0%) 4 (0.7%) 0 (0.0%) 5 (1.9%) 0.001
Tocilizumab 0 (0.0%) 1 (0.2%0 0 (0.0%) 2 (0.8%) 0.066
Clinical outcomes
Length of hospital stay (days) 7.7 (±9.1) 8.0 (±9.1) 6.9 (±7.8) 7.7 (±7.9) 0.519
Requiring supplemental oxygenation 9 (1.2%) 31 (5.1%) 1 (0.6%) 45 (17.4%) <0.001
Pneumonia 49 (6.5%) 131 (21.7%) 13 (7.9%) 92 (35.7%) <0.001
Requiring intensive care 8 (1.1%) 29 (4.8%) 3 (1.8%) 15 (5.8%) <0.001
High flow nasal cannula 1 (0.1%) 1 (0.2%) 1 (0.6%) 8 (3.1%) <0.001
Myocarditis 3 (0.4%) 5 (0.8%) 1 (0.6%) 3 (1.2%) 0.569
Stroke 1 (0.1%) 3 (0.5%) 0 (0.0%) 1 (0.4%) 0.537
Acute kidney injury 27 (3.6%) 33 (5.5%) 1 (0.6%) 14 (5.4%) 0.025
Death 2 (0.3%) 3 (0.5%) 0 (0.0%) 8 (3.1%) <0.001
Patients in 2021 were more likely to present with a lower lymphocyte count (1.48±0.74 vs 2.00±1.45 × 109/L, p<0.001), a higher C-reactive protein concentrations (23.1±38.0 vs 12.5±26.4 mg/dL, p<0.001), and higher serum ferritin concentrations (254.1±444.7 vs 196.1±233.5 µg/L, p=0.001) (Table 1). Older patients (>40 years of age) across both the years of the study consistently had higher levels of C-reactive protein and serum ferritin levels compared with their younger counterparts (Table 2). In 2021, a greater proportion of patients received COVID-19 specific drugs than in 2020; including dexamethasone (11.1% vs 0.7%, p<0.001), remdesivir (11.1% vs 2.9%, p<0.001), and baricitinib (1.2% vs 0.3%, p=0.039). Sotrovimab, a neutralising monoclonal antibody was also only available in Singapore in 2021, and 16 patients (3.8%) of the cohort in 2021 received this medication (Table 1). Similarly across both the years of study, older patients (>40 years of age) were more likely to receive COVID-19 specific therapeutics compared with those that were younger (Table 2).
No difference in the length of hospital stay was found between the two study periods (7.8±9.1 days in 2020 and 7.4±7.9 days in 2021, p=0.356) mainly because discharge policies changed in tandem with admission policies meaning that in 2021, patients were not kept in hospital until they had tested negative for COVID-19 on PCR. As shown in Figure 2, Figure 3 , the proportion of cases who had pneumonia and required intensive care fell dramatically in 2020 and then rose again in June 2021 when the vulnerable, elderly population were once again exposed to COVID-19 (Figures 2 and 3) A higher proportion of patients required supplemental oxygenation in 2021 (10.9%) compared with 2020 (2.9%, p<0.001) and there was higher mortality amongst those admitted in 2021 (1.9% vs 0.4%, p=0.004). Across both the years of study, older patients consistently had a higher proportion of patients with adverse outcomes, such as requiring supplemental oxygenation, intensive care and death (Table 2).Figure 2 Percentage of hospitalised COVID-19 patients with pneumonia over time
Figure 2:
Figure 3 Percentage of hospitalised COVID-19 patients requiring intensive care over time
Figure 3:
Discussion
In this retrospective analysis of the first 1781 patients admitted with COVID-19 to our hospital between February 2020 to October 2021, we examined the differences in the patient profile between those admitted in the year 2020 compared with those admitted in 2021. The key findings were that i) the outbreak in 2020 encompassed predominantly a young, migrant worker population whereas in 2021, the patient profile was largely elderly patients from the community with comorbidities ii) patients were significantly more unwell in 2021 with a higher proportion of pneumonia, severe disease at presentation, higher inflammatory markers and the greater prevalence in use of COVID-19 specific drugs; and iii) that despite the availability of effective COVID-19 therapeutics and effective vaccination in 2021 (where 64% of the hospitalised cohort in 2021 had received at least one dose of the vaccine, and 54.5% had two or more doses of vaccination), there remained a higher proportion of patients with adverse outcomes, including mortality amongst the hospitalised cohort. Furthermore, another consistent finding was that despite the changes in the circulating virus strains, and introduction of COVID-19 specific therapeutics and effective vaccination across the years, we consistently found that iv) older age was uniformly associated with more severe illness as well as adverse clinical outcomes amongst those hospitalised with COVID-19.
A broad overview of the changes to the community measures implemented and COVID-19 management in response to the pandemic from 2020 to 2021 is summarized in Table 3 . Our study demonstrated that COVID-19 patients who were younger and had fewer medical co-morbidities appeared to be protected against the development of moderate to severe disease, even if infected with the original Wuhan strain, or in the absence of vaccination. This original Wuhan strain had been associated with much higher overall excess mortality in Europe and North America (Bonanad, et al. 2020; Ho, et al. 2020).Table 3 Overview of community measure and changes to COVID-19 management in Singapore from 2020 to 2021
Table 3:Date Community Measuresa COVID-19 Management/Therapeuticsb
Jan – Mar 2020 23 Jan 20: First case of COVID-19 diagnosed in Singapore
4 Feb 20: First case of local transmission in Singapore
7 Feb 20: DORSCON Orange
20 Mar 20: Launch of contact-tracing app All COVID-19 patients admitted to tertiary hospitals for initial evaluation and monitoring, then subsequently discharged to community facilities for further isolation.
Apr – Jun 2020 4 Apr 20: Several clusters identified in migrant worker dormitories
7 Apr 20: “Circuit-breaker” measure start in the community
9 Apr 20: “Stay-home notices” for returning travellers from all countries
1 Jun 20: Phase 1 of Singapore's reopening (end of Circuit-breaker measures)
19 Jun 20: Phase 2 of Singapore's reopening 2 Apr 20: National Centre of Infectious Diseases (NCID) Singapore releases Interim Treatment Guidelines for COVID-19 (version 1.0)
10 Apr 20: Large community isolation facility opens in the Expo to isolate COVID-19 patients during the recovery period
10 Jun 20: Remdesivir approved for use in COVID-19 treatment in Singapore by the Health Sciences Authority
16 Jun 20: Preliminary results of RECOVERY trial announced - Dexamethasone for the use in COVID-19
Jun – Dec 2020 28 Dec 20: Phase 3 of Singapore's reopening 30 Dec 20: Start of the COVID-19 vaccination compaign
Jan – Jun 2021 8 May 21: Back to Phase 2 measures due to rise in cases from the Delta-variant
16 May 21: Back to Phase 2 heightened alert measures
14 Jun 21: Phase 3 heightened alert measures 4 Jan 21: Baricitinib recommended for severe COVID-19 as part of the NCID Interim Treatment Guidelines (version 5.0)
14 Jun 21: Tocilizumab recommended for severe COVID-19 as part of the NCID Interim Treatment Guidelines (version 6.0)
30 Jun 21: Sotrovimab receives interim authorisation in Singapore by the Health Sciences Authority
Jul – Dec 2021 22 Jul 21: Return to Phase 2 (Heightened Alert) measures to curb COVID-19
13 Oct 21: Vaccination-differentiated measures implemented (e.g. only fully vaccinated individuals may enter shopping malls) 29 Aug 21: 80% of the population is vaccinated against COVID-19
14 Sep 21: COVID-19 booster vaccination for senior citizens above 60 years of age
10 Oct 21: Home recovery programme the default for most of the population
a Ministry of Health Singapore. Updates on COVID-19 Situation. https://www.moh.gov.sg/covid-19 (Date accessed 2 April 22)
b National Centre for Infectious Diseases Singapore. Treatment Guidelines for COVID-19. https://www.ncid.sg/Health-Professionals/Diseases-and-Conditions/Pages/COVID-19.aspx (Date Accessed 2 Apr 2022)
Besides patient-related factors, differences seen between the two study periods were also likely to be due to a change in public health policy: in 2020 all COVID-19 patients were required to be isolated in a care facility or hospital, whilst in 2021 patients with COVID-19 were instead encouraged to recover at home (MOH, 2021b). Only those deemed to be at greater risk of moderate to severe disease were hospitalised; namely those who were not vaccinated, partially vaccinated, who were in an older age category or who had medical comorbidities, reflective of the demographic change we saw in our inpatient cohort in 2021. This meant that the hospitalised cohort was more selected and reflected only those at risk of more severe disease and progression, placing a significant burden on intensive care facilities in 2021 (Cai, et al. 2022).
Additionally, the circulating viral strain was also likely to have differed across these two periods: the delta-variant was the predominant strain in Singapore in 2021 compared with the original Wuhan strain which affected the majority of the migrant worker population in Singapore in 2020 (Chia, et al. 2021). The delta-variant was associated with increased transmissibility and was reported to have more pneumonia compared with the original Wuhan strain, which may have impacted our findings (Choi and Smith, 2021; Lin, et al. 2021). It was difficult to tease out the relative contribution of vaccination uptake, the changes in strain and the changes in demographics on the outcomes of the hospitalised patients with COVID-19. All these factors were likely to have been contributory to the observed changes in the clinical profile and patient outcomes. However, the consistent age related differences in mortality in both years in our cohort suggests that demographics played at least a major contributing role in outcomes.
Finally, the increase in use of COVID-19 specific therapeutics would likely also have been related to better-established international guidelines on COVID-19 management and increasing global experience in the efficacy and safety of these therapeutic agents (NIH, 2021; Nicola et al. 2020; Andrews, et al. 2022). Furthermore, the National Centre of Infectious Disease (NCID) in Singapore also released and updated interim treatment guidelines for COVID-19 (NCID, 2021). These guidelines were first released on 2 April 2020 and updated over the years to guide the use and improve the uptake of COVID-19 specific therapeutics in Singapore, such as Remdesivir, dexamethasone, sotrovimab, tocilizumab and baricitinib, where clinically indicated. The higher uptake of COVID-19 specific therapeutics within the hospitalised cohort in 2021 compared with 2020 may reflect the more severe disease in this population, but improved knowledge and confidence in their use together with the established treatment guidelines may also have been contributory.
Several countries in Asia had also adopted a strict containment strategy for COVID-19 early in the pandemic. For example, South Korea and China both rapidly enforced strategies early in the pandemic in 2020 to test, contact trace and treat cases of COVID-19 (Chen, et al. 2021) together with quarantine for contacts and various social distancing measures. Other similar strategies included enforcing border controls and restricting entry into the country in 2020 (Olufadewa, et al. 2021). Despite efforts in containment, these countries still saw relatively large outbreaks of COVID-19 cases, but with very low levels of mortality (Chen, et al. 2021).
However, containment strategies were resource-intensive and had significant socio-economic impacts on countries that adopted their use (Jiao, et al. 2022). Several countries, including Singapore, shifted towards aiming to effectively vaccinate and protect their vulnerable population against COVID-19, whilst easing other restrictions as economies and global trade began to recover (Tsou, et al. 2022). In most countries, similar “waves” of cases of COVID-19 were observed, which each new circulating variant of the virus. With its highly vaccinated population, although the hospitalised cohort appeared more ill in 2021 compared with 2020, Singapore continued to observe relatively low mortality rates amongst patients infected with COVID-19. Indeed, these patterns have also been seen in other countries with high vaccination rates (Ziakas, et al. 2022).
As the pandemic situation continues to evolve in Singapore, as it does around the world, accurate interpretation of clinical case data is critically important. With changes in public health policies at every jurisdiction and the discovery of new variants, against which vaccines, monoclonal antibodies and antiviral agents may have reduced efficacy, we will no doubt continue to field new challenges in managing COVID-19 and discovering how it impacts various sectors in our communities.
Limitations
This was a retrospective single-centre cohort of patients hospitalised with COVID-19. It was a heterogenous cohort of patients given that not all patients required hospital admission because they had been clinically unwell or were at significant risk of deterioration. In the clinical context, early in the pandemic, patients had also been admitted for the purpose of isolation and triage, before subsequent transfer to isolation facilities within the community. Our study was observational and retrospective in design. We did not capture or examine the cases that were managed directly in these community facilities or who recovered at home, which formed the bulk of the COVID-19 cases in Singapore from 2020 to 2021. The observed differences in trends and outcomes amongst hospitalised patients with COVID-19 may be a result of a combination of factors, including the changes in circulating viral strain, changes in public health policy (for example, only admitting severely ill patients to the hospital) in addition to the changes in the demographics of the affected population which we highlighted. Our study had not been designed to estimate the effect size of each factor on the changes in the observed trends and outcomes. Nevertheless, we believe that this snapshot of hospitalised patients gives valuable insight into the profile of hospitalised patients over time, and their evolving needs.
Conclusions
In conclusion, the demographics of individuals affected by COVID-19 in Singapore shifted dramatically from 2020 to 2021. Despite high vaccination uptake rates against COVID-19, the disease has shifted from a predominantly young and low-risk migrant worker population to affect the more vulnerable local community in 2021, with hospitalised patients consequently being more ill, and having a greater need for intensive care and higher mortality. With the benefit of hindsight, it is possible to argue that the zero covid strategy which reduced transmission outside the migrant worker dormitories in 2020 was not sustainable in an open society where there is the constant risk of the introduction of new more transmissible variants of concern. In spite of the changes in the circulating viral strains over the study period, as well as the introduction of effective vaccination against COVID-19 and COVID-19 specific therapeutics, older age remained an important risk factor for more severe disease and adverse clinical outcomes amongst the hospitalised patients with COVID-19 in our centre and elsewhere. Further prospective study is warranted to monitor the shifting demographic trends with new and emerging variants-of-concerns.
Conflict of Interest
All authors have no conflicts of interest to declare.
Funding Source
There was no funding for this study. CHS was supported by the National University of Singapore Yong Loo Lin School of Medicine's Junior Academic Faculty Scheme.
Ethical Approval
This study was approved by the hospital's institutional review board (National Healthcare Group (NHG) Domain Specific Review Board (DSRB) 2020/00545)
Author Contribution Statement
JNN, SC, WG, MYS contributed to the conception, data collection, analysis and writing of the manuscript. NWSC, CHS, GBC and PAT contributed to the conception, data analysis, writing and critical review of the manuscript.
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Financial Disclosure
None reported. The authors declare no conflict of interest.
Funding/Support
None reported. CHS was supported by the National University of Singapore Yong Loo Lin School of Medicine's Junior Academic Faculty Scheme.
| 36509335 | PMC9733964 | NO-CC CODE | 2022-12-14 23:28:27 | no | Int J Infect Dis. 2022 Dec 9; doi: 10.1016/j.ijid.2022.12.007 | utf-8 | Int J Infect Dis | 2,022 | 10.1016/j.ijid.2022.12.007 | oa_other |
==== Front
J Clin Virol
J Clin Virol
Journal of Clinical Virology
1386-6532
1873-5967
The Authors. Published by Elsevier B.V.
S1386-6532(22)00284-0
10.1016/j.jcv.2022.105352
105352
Article
Converting to an international unit system improves harmonization of results for SARS-CoV-2 quantification: Results from multiple external quality assessments
Buchta Christoph a
Kollros Dominik a
Jovanovic Jovana a
Huf Wolfgang b
Delatour Vincent c
Puchhammer-Stöckl Elisabeth d
Mayerhofer Maximilian e
Müller Mathias M. a
Shenoy Santosh f
Griesmacher Andrea a
Aberle Stephan W. d
Görzer Irene d
Camp Jeremy V. d⁎
a Austrian Association for Quality Assurance and Standardization of Medical and Diagnostic Tests (ÖQUASTA), Vienna, Austria
b Karl Landsteiner Institute for Clinical Risk Management, Vienna, Austria
c Laboratoire National de Métrologie et d'Essais (LNE), Paris, France
d Center for Virology, Medical University of Vienna, Vienna, Austria
e Armament and Defence Technology Agency, NBC & Environmental Protection Technology Division, Vienna, Austria
f Department of Surgery, Kansas City Veterans Medical Center, University of Missouri at Kansas City, Kansas City, MO, USA
⁎ Corresponding author at: Center for Virology, Medizinische Universitat Wien, Kinderspitalgasse 15, 1095 Vienna, Austria
10 12 2022
1 2023
10 12 2022
158 105352105352
12 10 2022
30 11 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.
Background
The detection of SARS-CoV-2 vRNA in clinical samples has relied almost exclusively on RT-qPCR as the gold standard test. Published results from various external quality assessments (“ring trials”) worldwide have shown that there is still a large variability in results reported for the same samples. As reference standards of SARS-CoV-2 RNA are available, we tested whether using standard curves to convert Ct values into copies/mL (cp/mL) improved harmonization.
Methods
Nine laboratories using 23 test systems (15 of which were unique) prepared standard dilution curves to convert Ct values of 13 SARS-CoV-2 positive samples to cp/mL (hereafter IU/mL). The samples were provided in three rounds of a virus genome detection external quality assessment (EQA) scheme. We tested the precision and accuracy of results reported in IU/mL, and attempted to identify the sources of variability.
Results
Reporting results as IU/mL improved the precision of the estimated concentrations of all samples compared to reporting Ct values, although some inaccuracy remained. Variance analysis showed that nearly all variability in data was explained by individual test systems within individual laboratories. When controlling for this effect, there was no significant difference between all other factors tested (test systems, EQA rounds, sample material).
Conclusions
Converting results to copies/mL improved precision across laboratory test systems. However, it seems the results are still very specific to test systems within laboratories. Further efforts could be made to improve accuracy and achieve full harmonization across diagnostic laboratories.
Keywords
SARS-CoV-2
RT-qPCR
Harmonization
Ring trials
International units
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pmc1 Introduction
The rapid establishment and dissemination of reliable RT-qPCR assays for the diagnosis of SARS-CoV-2 infections had an immeasurable benefit in the early pandemic phase. This diagnostic assay provides the gold standard assessment of whether a person is vRNA-negative or vRNA-positive, and its success is surely measured by the large number of commercially available test kits and test systems. As a highly sensitive test, the Ct value can also be used to estimate the viral concentration in a sample by converting to copy number based on a standard dilution curve. Given the absence of a proper standard at the beginning of the SARS-CoV-2 pandemic, it was understandable that the Ct value was reported and used as an approximate indicator for disease status [1,2]. However, Ct values have become firmly anchored in SARS-CoV-2-associated laboratory diagnostics.
Relatively early in the pandemic, several scientific organizations discouraged using Ct values in management of patients with COVID-19, and the dangers of using Ct values as a quantitative measure for SARS-CoV-2 RNA burden have been reported [3], [4], [5]. Among them, the most important are that Ct values are assay-specific, highly variable, and are mistaken for quantitative and harmonized values [6], [7], [8], [9], [10]. It has been well documented that there exists large variability in Ct values reported by different test systems from the same samples [1,2,[11], [12], [13]].
Thus, to date, it is advisable to compare results only from assays of the same type (from same test kit and/or for the same target), if not only from the same individual instrument [8,12]. This complicates recommendations for patients based on specific viral load cut-offs to make clinical decisions and determine infectivity [5,6,9,14,15], given the variability not only between test systems and between laboratories, but also given the demonstrated variability between sample type and sampling method [15], [16], [17], [18], [19]. There been only tentative efforts to harmonize SARS-CoV-2 RT-qPCR assays and the reporting of quantitative results [12,20].
The goal of the study was to test whether the use of standard curves to convert Ct values to copies/mL (cp/mL, hereafter IU/mL) improved harmonization across laboratories. We used clinical samples and reference standards over a range of concentrations distributed to blinded laboratories in three rounds of a dedicated external quality assessment (EQA) scheme. In such schemes, samples meeting requirements for homogeneity and stability are used and the same samples are analyzed by all participant laboratories almost simultaneously with their routinely used test systems [21]. This makes EQA schemes particularly useful for evaluating and comparing the performance of a wide range of assays, in addition to assessing the performance of individual laboratories and test systems.
2 Materials and methods
2.1 Test material
The data for this study came from three recent rounds of the SARS-CoV-2 virus genome detection EQA scheme: November 2021 (three positive and one negative samples); February 2022 (six positive and one negative samples); and May 2022 (four positive and one negative samples). Each round had one positive clinical sample, one negative sample, and the rest were dilutions of a reference standard based on the WHO reference sequence (SeraCare AccuPlex SARS-CoV-2 Molecular Controls Kit Full Genome). The three positive clinical samples were determined to be a Delta variant (lineage B.1.617.2) and two Omicron variants (lineages BA.2 and BA.5) by whole genome sequencing. The reference standard was prepared from two lots from the manufacturer as follows: 1000 copies/mL supplied five times in two lots; 5000 copies per mL supplied twice from one lot; and 50, 100 and 500 copies/mL supplied once each from the same lot (Table 4).
2.2 Data collection and organization of the EQAs
Preparation and characterization of samples followed a procedure described earlier [11]. Namely, sample stability was verified by replicating shipping conditions (shipping at room temperature and storage for up to one week at 4°C). Characterization also included determination of copy number by digital PCR (dPCR). Details of sample preparation for the February 2022 EQA round were included in another study [22].
2.3 Dilution curve and conversion of Ct to IU/mL
Participant laboratories were asked to establish a standard dilution curve with the first WHO International Standard for SARS-CoV-2 RNA for each of their test systems [1,2]. As five laboratories reported only single values for each dilution (Lab3, Lab5, Lab7, Lab8, Lab9), we therefore used the average of duplicates (Lab1, Lab2, Lab4, Lab6) reported by some laboratories to calculate the conversion equation (Table 1 ).Table 1 Reaction efficiency and conversion of Ct-value to IU/mL based on a standard dilution series of a SARS-CoV-2 reference
Table 1 All values Optimized
IDa Deviceb Reagentb Slope Y-int Efficiency Slope Y-int Efficiency
Lab1 Abbott Alinity (1) Alinity m SARS-CoV-2 AMP Kit (1) -3.71 43.4 86% -3.69 43.5 87%
Lab2 Abbott Alinity (2) Alinity m SARS-CoV-2 AMP Kit (2) -3.79 44.5 83% -3.84 44.9 82%
Lab3 Bio Molecular Systems, Magnetic Induction Cycler New England Biolabs Luna Probe One-Step RT-qPCR Kit (No ROX) -3.27 45.7 102% -3.34 45.9 99%
Lab4 BioRad CFX96 (1) Anchor SARS-CoV-2 PCR Kit 2.0 -3.65 49.0 88% -3.65 49.0 88%
Lab4 BioRad CFX96 (2) Shimadzu 2019 Novel Coronavirus Detection Kit -3.39 45.2 97% -3.39 45.2 97%
Lab5 Cobas 6800 (1) cobas SARS-CoV-2 Test (1) -2.85 40.9 124% -3.16 42.6 107%
Lab6 Cobas 6800 (2) cobas SARS-CoV-2 Test (2) -3.07 42.0 112% -3.21 42.8 105%
Lab7 Cobas 6800 (3) cobas SARS-CoV-2 Test (3) -3.14 41.9 108% -3.40 43.1 97%
Lab2 Cobas 6800 (4) cobas SARS-CoV-2 Test (4) -3.18 43.2 106% -3.36 44.2 98%
Lab6 cobas Liat cobas Liat Assay Tube -3.50 42.6 93% -3.50 42.6 93%
Lab8 GeneXpert (1) Xpert Xpress SARS-CoV-2 (1) -3.45 45.0 95% -3.55 45.6 91%
Lab5 GeneXpert (2) Xpert Xpress SARS-CoV-2 (2) -3.24 43.1 104% -3.24 43.1 104%
Lab2 GeneXpert (3) Xpert Xpress SARS-CoV-2 (3) -3.45 46.2 95% -3.82 48.1 83%
Lab8 Liaison MDX (1) Simplexa COVID-19 Detection Kit (1) -3.41 42.0 97% -3.84 44.3 82%
Lab7 Liaison MDX (2) Simplexa COVID-19 Detection Kit (2) -3.28 41.5 102% -4.22 45.9 73%
Lab9 Liaison MDX (3) Simplexa COVID-19 Detection Kit (3) -3.25 40.7 103% -3.25 40.7 103%
Lab2 Light Cycler TIB MOLBIOL SARS-Cov-2 N gene -3.06 42.3 112% -3.33 43.6 100%
Lab5 LightCycler 480 II RIDA GENE SARS-CoV-2 -3.72 47.2 86% -3.72 47.2 86%
Lab4 LightCycler 480 TIB MOLBIOL Sarbeco E gene -3.31 46.0 101% -3.31 46.0 101%
Lab9 LineGene 9600 (1) artus SARS-CoV-2 Prep&Amp UM Kit -3.42 44.3 96% -3.42 44.3 96%
Lab9 LineGene 9600 (2) PerkinElmer SARS-CoV-2 Real-time RT-PCR Assay -3.31 44.5 100% -3.31 44.5 100%
Lab2 NeuMoDx 288 NeuMoDx SARS-CoV-2 Assay -3.08 41.1 111% -3.28 42.7 102%
Lab8 Panther Fusion Aptima SARS-CoV-2-Assay -2.86 42.9 124% -3.17 44.5 107%
a nine participant laboratories were given unique anonymized identifiers, and some laboratories reported multiple test systems (e.g., Lab2 used five test systems)
b each row contains an individual test system where numbers in brackets indicate the same Device/Reagent was used multiple times.
2.4 Statistical analysis
For each standard dilution curve (i.e., each within-laboratory test system), the efficiency was estimated by the following equation: Efficiency=100%×(−1+10−1slope). The equation c=10Ct−ab was used for conversion of Ct values into log10 IU/ml, where c is the concentration, a is the intercept, and b is the slope.
Precision was analyzed by subtracting the sample mean value from all measurements (“mean centered” differences). Accuracy was analyzed by subtracting the target value from all measurements (“target centered” differences). For values converted to IU/mL (log10 copy number / mL), the concentration measured by dPCR was used as the target value; for Ct values, the mean of the validated sample material from the reference laboratory was used as a target Ct value. Validation of the prepared reference samples showed a 1:1 linear correspondence between the target dilutions of the standard and results measured by dPCR, however the actual dilutions contained approximately 10−0 . 26 (=1.8-fold) fewer copies of viral RNA than expected (Figure S1). In order to compare between the two units (difference in Ct value and difference in log10 IU/mL) we transformed difference in Ct values (i.e., difference from the mean or target) into the log10 scale, assuming perfect efficiency of a RT-qPCR reaction.
For precision, variances of mean-centered differences were compared with the variance test. To determine accuracy, a one-way repeated measures ANOVA of target-centered differences was used, followed by one sample T-tests. Type I error was controlled at alpha = 0.05, adjusting of p-values to account for multiple comparisons using Bonferoni's method. Multiple factor repeated measures ANOVAs were used to identify sources of variance.
3 Results
The standard curves, expressing Ct as a function of log10 cp/mL, were mostly linear (Figure S2), as seen by the variation in efficiencies (Table 1). In some cases, the individual standard curves could be optimized by excluding some values, based on identifying departures from a least squares line (typically by excluding the lowest and/or highest values; Figure S2). However, this did not result in a significant improvement of the results (Figure S3)
3.1 Clinical samples
In comparing the accuracy based on difference from target, there was a significant difference due to units (F 1,112=64.86, p < 0.001), but not due to sample (F 2,112 = 0.052, p = 0.950), controlling for repeated measures (Fig. 1 , Figure S4). Using post-hoc one sample T-tests, Ct values were significantly different from the target for one of three samples. After converting the measurements IU/mL, the means were different from the target value for two of the three samples.Fig. 1 The accuracy of measuring the concentration of SARS-CoV-2 RNA in three clinical samples (S1, S11, and S5) expressed as Ct values and converted into IU/mL based on an international standard. The values are displayed as differences in observed Ct values from target Ct values, adjusted to log10 copies (assuming perfect efficiency, left) and the difference in observed IU/mL from target log10 copies/mL, determined by digital droplet PCR (right). The individual observations from 15 unique test systems from nine laboratories are shown as black dots, with boxes indicating the inter-quartile range and a median-value horizontal bar. Asterisks indicate sample means that were significantly different from the target (one sample T-test, adjusted p-values < 0.05).
Fig 1
Using mean-centered differences to test precision, the variance of the Ct values was 2.68 times higher than the variance of the log10 IU/mL values (variance test, F 68,68 = 2.686, p < 0.001, Table 2 ), when both were expressed on the log10 scale. The Ct values were different from the mean by a range spanning 5.8-6.3 Ct values over all three samples (1.74-1.90 log10 copies, Table 2, Fig. 1). The concentrations converted to IU/mL were different from the target value by a range spanning 1.19-1.39 log10 copies (Fig. 1). This corresponds to Ct values varying by up to ∼10-fold above and ∼10-fold below a given target value, whereas converting to IU/mL reduced this variability to ∼2-fold differences above/below a given target value.Table 2 Variance in measuring SARS-CoV-2 RNA in three clinical samples (S1, S11, S5) using two measurement systems – IU/mL and Ct values – where values were set on the same scale by mean-centering (Δ) the IU/mL (expressed as log10 copies/mL) and converting ΔCt values to log10 scale (middle columns). The range of Ct values is provided in the far right column.
Table 2Sample Var(ΔIU) Var(ΔCt) Range of Ct (Min∼Max)
S1 0.086 0.261 5.8 (20.2∼26.0)
S11 0.103 0.266 6.3 (23.0∼29.3)
S5 0.124 0.314 5.8 (20.0∼25.8)
All 0.101 0.272*
*ratio of Var(ΔCt):Var(ΔIU) = 2.69; variance test, F68,68 = 2.686, p < 0.001.
3.2 Measurement of reference samples
The variance of scaled mean-centered Ct values was 1.8 times higher than the variance of scaled mean-centered IU values (variance test, F 220,220=1.851, p < 0.001) (Table 3 ); however the largest variances were due to the sample with 50 copies/mL (target Ct value = 38.2, with a range spanning 9.7 Ct units from Ct 32.3∼42.0). Ignoring that sample, the variance in Ct values ranged from 0.25 to 0.30 per sample (log10 scale), while the variance of the mean-centered differences in log10 IU/mL were significantly lower (0.10 to 0.19, variance test, F 203,203=1.81, p > 0.001). The corresponding Ct values had differences in ranges from 5.5 to 6.9 units, corresponding to a range of 45- to 119-fold differences between measurements of the same sample, or approximately 5- to 15-fold differences above and below the sample mean. The variability in log10 IU/mL values (range of 101 . 22 to 101 . 84 copies) corresponds to approximately 16- to 70-fold differences between measurements of the same sample, or approximately 3.7- to 13.5-fold differences from the sample mean.Table 3 Ten test samples made from dilutions of SARS-CoV-2 reference material (Accuplex) were supplied to labs in a series of ring tests diluted to various concentrations and then reported in Ct units and converted to IU/mL based on a standard curve (WHO standard). The target concentrations of test samples were checked with digital drop PCR. The differences from the measured target concentration were used to compare the accuracy of each reporting method.
Table 3Sample Lot Target (copies/mL) dPCRa (copies/mL) Ct Range (Min.∼Max.) ΔCt (95% C.I.)b ΔIU/mL (95% C.I.)c
S8d 10593976 50 57 9.7 (32.3∼42.0) -2.16 (-3.42 ∼ -0.90)* 0.71 (0.45 ∼ 0.97)*
S4e 10593976 100 230 5.5 (32.8∼38.3) -1.97 (-2.77 ∼ -1.17)* 0.30 (0.10 ∼ 0.49)*
S6e 10593976 500 1400 6.0 (29.3∼35.3) -1.99 (-2.81 ∼ -1.18)* 0.18 (0.002 ∼ 0.37)
S3e 10576151 1000 1200 6.9 (30.1∼37.0) -0.51 (-1.26 ∼ 0.25) 0.14 (-0.03 ∼ 0.31)
S7 10593976 1000 2400 5.6 (29.1∼34.7) -1.68 (-2.46 ∼ -0.91)* 0.15 (-0.02 ∼ 0.33)
S9 10593976 1000 2700 6.5 (28.2∼34.7) -1.78 (-2.55 ∼ -0.10)* 0.13 (-0.04 ∼ 0.30)
S10 10576151 1000 1400 5.6 (31.0∼36.6) -0.44 (-1.17 ∼ 0.29) 0.04 (-0.15 ∼ 0.22)
S13 10593976 1000 1700 6.3 (29.6∼35.9) -1.18 (-1.97 ∼ -0.40)* 0.16 (-0.002 ∼ 0.32)
S2 10576151 5000 3300 6.3 (28.0∼34.3) -0.16 (-0.98 ∼ 0.56) 0.25 (0.11 ∼ 0.39)*
S12 10576151 5000 5200 6.5 (28.0∼34.5) 0.03 (-0.68 ∼ 0.75) 0.004 (-0.15 ∼ 0.16)
a dPCR = digital PCR
b mean difference in Ct value (ΔCt) from target value measured and validated by the national reference laboratory, converted to log10 copies, reported with 95% confidence intervals
c mean difference in log10 IU/mL (ΔIU/mL) from target value measured by ddPCR, reported with 95% confidence intervals
d this sample was reported negative from six of 15 unique test systems
e these samples were reported negative from one test system each
⁎ indicates Padj < 0.05 by a one-sample T-test with H0 true mean = 0, adjusting for multiple comparisons by Bonferroni method
In assessing the accuracy, there was a clear influence of sample (Fig. 2 ) and there was a statistically significant difference in target-centered differences due to reporting unit (F(unit)1,409 = 155.79, p < 0.001; F(sample)9,409 = 2.31, p = 0.015). Laboratories significantly overestimated the target value when reporting IU/mL by 5.2-fold and 2.0-fold copies for the two samples with the lowest concentration (Table 4). Of the eight remaining samples whose measured concentration was between 1200 and 5200 cp/mL, only one of the tests samples (S2) was measured incorrectly by IU/mL (Fig. 2, Table 4). Six of 15 unique test systems could not detect the sample with the lowest concentration (“S8”, 57 copies/mL), and one test system each could not detect S4 (230 copies/mL), S6 (1400 copeis/mL), and S3 (1200 copies/mL)Fig. 2 Accuracy of measuring SARS-CoV-2 vRNA in various dilutions of a standard test sample, and reporting results by either Ct value or by converting Ct values to IU/mL based on laboratory-specific standard curves. The values are displayed as differences in observed Ct values from target Ct values (red-filled boxplots), adjusted to log10 copies (assuming perfect efficiency) and the difference in observed log10 IU/mL from target log10 copies/mL (blue), determined by digital droplet PCR. The individual observations from 15 unique test systems from nine laboratories are shown as black dots, with filled boxes indicating the inter-quartile range and a median-value horizontal bar. The sample target values are ordered left-to-right from lowest target concentrations (S8 = 50 copies/mL, S4 = 100 copies/mL, S6 = 100 copies/mL) to highest concentrations (S3, S7, S9, S10, S13 = 1000 copies/mL; S2, S12 = 5000 copies/mL).
Fig 2
Overall, the Ct values and converted IU/mL clustered by lab independently of sample (Figure S5). Focusing on a subset of six laboratories, wherein three combinations of assays on specific platforms were used (cobas SARS-CoV-2 test/Cobas 6800; Xpert Xpress SARS-CoV-2 test/GeneXpert, and Simplexa COVID-19 Detection kit/Liaison MDX), with five of these labs using more than one of the three systems (Table 1, Fig. 3 ), we tested the factors explaining the variance in the data. While the test system was not significant (F 2,3=0.462, p=0.67), the within-laboratory effect of test system was significant (F 2,81=8.425, p < 0.001) (Fig. 3). This was true when also adding the lot number used to prepare the standard and EQA round as independent predictors, as well as controlling for variance due to specific samples: the within-laboratory effects were significant, but not the overall main effects.Fig. 3 Points and boxplots of the difference in measured SARS-CoV-2 vRNA in nine standard test samples of various concentrations (from 230 to 5200 copies/mL) from the target concentration in log10 copies/mL (expressed in IU/mL) over three test systems (unique combinations of platform and specific assay) for six laboratories (colors) in three rounds of an external quality assessment scheme. The boxes show the interquartile range and an internal horizontal median line with statistical outliers not connected by a vertical line. The within-laboratory variance was statistically significant while the test system did not explain a significant amount of the variance in mean difference from target. A gray background was used to aid in visualization of the three test systems, used by four, three, and three laboratories (respectively, left to right).
Fig 3
4 Discussion
The goal of this study was to improve the harmonization of measuring the concentration of SARS-CoV-2 vRNA by RT-qPCR. To this end, we tested whether using laboratory-specific standard dilution curves to convert Ct values to IU/mL would improve the accuracy and precision of results across laboratories and test systems compared to the now-traditional practice of reporting Ct values, alone.
Comparison of standard curves clearly supported the ability of all laboratories to implement the reporting of IU/mL rather than Ct values. The efficiencies were within acceptable ranges (90-110%), exhibited an appropriate dynamic range (detections from ∼106-101 cp/mL), and seemed to be highly reproducible. While we noted that we could optimize some standard curves by removing visual “outliers,” we confirmed that this did not bias the outcomes of our analysis (Figure S3). However, there are important caveats and considerations to using standard curves to report IU/mL rather than Ct values. For example, here we based the conversion equation on single values from each dilution series, as some laboratories did not provide replicates. We assume that, in practice, laboratories would base their conversion on replicated dilution series within test systems, and that laboratories would include within- and between-run replicates to ensure a confident conversion of Ct to IU/mL. At the very least, once a conversion equation is established, laboratories should include known standard(s) that give expected results (target +/- s.d.) in order to validate each test run.
Laboratories should remain vigilant in monitoring the emergence of variants, which may contain mutations that affect the efficiency of their chosen RT-qPCR assay(s). The use of multiple targets is recommended by many regulatory agencies, and is less likely to fail to detect novel variants. In the event that a new test system is needed, laboratories would need to reestablish conversion equations as part of their validation procedure. With a standard curve, laboratories can also determine the dynamic range of their test system, paying particular attention to limits of detection. Fortunately, there now exist commercially available reference materials of both the “wildtype” and variant strains for the purposes of establishing standard curves and validating test systems.
We and others have observed through EQAs that there exists relatively high variability when reporting Ct values, and we presume this is also true outside of ring trials, for example, when measuring virus concentration in patient samples [5,8,9,11,12,20,22]. Our data suggested that converting Ct values to copies per mL (and reporting them as IU/mL) statistically improved precision for all samples. Moreover, reporting IU/mL provides comparability between laboratories, as the units are adjusted to a practical and biologically relevant unit scale (cp/mL) rather than “Ct value units.” Our data showed that converting to IU/mL based on within-laboratory standard curves reduced the variability of Ct values (which vary in a range of approximately 6 Ct units ∼ 100-fold differences) by an order of magnitude, with laboratories reporting approximately 10-fold differences for a given sample.
However, it is clear that the accuracy of the tests, and the ability of estimating a “true” concentration, is still limited. As we noted, our estimation of accuracy for Ct values was based on a target value from the reference laboratory. This may have biased the analysis of the accuracy of Ct values results, given that we also determined that Ct values are specific to each laboratory test-system (Fig. 3). We used dPCR to validate the “true” concentration when evaluating the accuracy, and although we noted that the measured concentration was different than the target for some samples, we did not evaluate the accuracy of dPCR, but note that it was different than we expected (Figure S1). We therefore stress that, for the purposes of our analysis, it was not critical to conclude whether one test system consistently over- or under-estimates the true value. Nonetheless, our results demonstrated that the average value of all laboratories was different from the true concentration in 2 of 3 clinical samples, and 3 of 10 reference samples (Fig. 1 and Fig. 2). Despite this demonstration of inaccuracy, we found that the overall accuracy – in a practical sense – was actually relatively high (no more than 5-fold difference from the true cp/mL on average), and accuracy mostly improved when reporting IU/mL versus Ct (Fig. 1 and Fig. 2).
Fundamentally, the issue of accuracy – and the degree to which accurate results are required – depends on the desired goal of knowing the specific concentration. Certainly, reporting results in a biologically meaningful way makes it possible to compare these to in vitro results, for example, on the limit of isolation from patient samples (reported in terms of concentration of virus particles, e.g., TCID50/mL or pfu/mL). As this was the basis of some health regulations to set target viral loads, below which patients are considered noncontagious [14, 15, 23, 24], perhaps there is some demand in achieving high accuracy. However, the use of “viral loads” to make clinical decisions – particularly when concentrations are estimated based on Ct values – has received a fair amount of criticism in the scientific literature [[3], [4], [5], [6],9, 10].
We were able to investigate the sources of variability underlying the reporting of results. Our data suggest that the overall variability – and thus the limitation on accuracy – does not depend on the test system, per se, but is intrinsic to each within-laboratory test system. This accounted for both within-laboratory variance when two or more test systems were used, as well as between-laboratory variance for a given test system (Fig. 3). This agrees with the results of others, who have attempted to calibrate the Ct values across multiple laboratories using reference samples with a specific target value, and concluded that Ct values are essentially unique to a given lab [12]. The fact that the variability was not necessarily attributed to a given test system is fortunate, given the large number of commercially-available kits to identify SARS-CoV-2 by RT-qPCR. We hypothesize that variations in sample preparation (e.g., efficiency of nucleic acid extraction, total volume used as template) contribute to variations within and between laboratories and/or within and between test systems (Fig. 3), however this remains to be tested. If these are truly the underlying reasons for the strong within-laboratory within-test system effect, improving the accuracy across all laboratories could come from implementing strict “universal” standard operating procedures or improving external quality assessments to providing laboratories the opportunity to adjustment their protocols to achieve higher accuracy [7,12,20,21].
While we assessed analytical variability within well-characterized samples, variability also depends on pre-examination processes, e.g., quality of sampling [[16], [17], [18], [19],25]. Although there have been promising approaches to normalize virus quantitation results from swab specimens by relating them to quantitation results of host cell housekeeping genes, similar to the delta-delta Ct method used in quantifying relative gene expression, these have not been adequately pursued nor have they received wide acceptance [26].
5 Conclusions
We demonstrated that results reported by RT-qPCR can be delivered as quantitative results in a biologically meaningful unit system, as is performed with many other viruses in clinical diagnostics (e.g., HIV, hCMV). The high precision of results across samples and across laboratories when utilizing standard curves to report results contributes to harmonization and thus allows a more reliable comparison of SARS-CoV-2 RT-qPCR results obtained by different assays. We note that accuracy could be improved, but the reported results mostly reached target values. Importantly, our results demonstrate the value of EQAs as tools for within- and between-laboratory comparisons; and we believe this is the key to improve the accuracy of measurements for all laboratories.
Author contributions
CB: Conceptualized, conducted and analysed this EQA study, wrote and edited the manuscript draft; DK: Analysed data and provided technical EQA support, visualized data, critically reviewed manuscript; JJ: Analysed data and provided technical EQA support, critically reviewed manuscript; WH: supervised statistical analysis, critically reviewed manuscript; VD: Provided metrological advice, critically reviewed the manuscript; EPS: Provided scientific advice, critically reviewed the manuscript; MM: Performed and analysed dPCR of samples, critically reviewed manuscript; MMM: Provided scientific advice to the overall study, reviewed and edited the manuscript; SS: critically reviewed the manuscript; AG: Provided scientific advice to the overall study, reviewed and edited the manuscript; SWA: Provided sample material, conceptualized, conducted and supervised this EQA study, provided scientific advice to the study, reviewed and edited the manuscript; IG: Conceptualized, conducted and analysed this EQA study, critically reviewed the manuscript; JVC: Conducted and analysed this EQA study, wrote and edited the manuscript.
Funding
The authors declare no funding sources.
Ethical approval
Ethical approval was not applicable for this study.
Consent for publication
Not applicable
Availability of data and materials
The datasets generated and/or analysed during the current study are not publicly available as they may be used to identify specific diagnostic laboratories. However, de-identified data are available from the corresponding author on reasonable request.
Declaration of Competing Interest
The authors declare no competing financial nor non-financial interests.
Appendix Supplementary materials
Image, application 1
Acknowledgements
We gratefully acknowledge all laboratories that participated in this study and made special efforts to report more data than was required to participate in this EQA round; we particularly acknowledge Gerda Dorfinger, Christine Gränitz-Trisko, Lorin Loacker, Thomas Löffelmann, Lisa Mustafa, Robert Strassl, Sabine Sussitz-Rack, René Zadnikar, and Katharina Grohs. We thank Thomas Urbanek and Andreas Rohorzka for providing excellent technical assistance.
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jcv.2022.105352.
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17 Richard-Greenblatt M Ziegler MJ Bromberg V Huang E Abdallah H Tolomeo P Quantifying the impact of nasopharyngeal specimen quality on severe acute respiratory syndrome coronavirus 2 test performance Open Forum Infect. Dis. 8 2021 ofab235 34095340
18 Tsang NNY So HC Ng KY Cowling BJ Leung GM Ip DKM. Diagnostic performance of different sampling approaches for SARS-CoV-2 RT-PCR testing: a systematic review and meta-analysis Lancet Infect. Dis. 21 2021 1233 1245 33857405
19 Wang H Liu Q Hu J Zhou M Yu MQ Li KY Nasopharyngeal swabs are more sensitive than oropharyngeal swabs for COVID-19 diagnosis and monitoring the SARS-CoV-2 load Front. Med. 7 2020 334
20 Cuypers L Bode J Beuselinck K Laenen L Dewaele K Janssen R Nationwide harmonization effort for semi-quantitative reporting of SARS-CoV-2 PCR test results in Belgium Viruses 2022 14
21 Buchta C Muller MM Griesmacher A. The importance of external quality assessment data in evaluating SARS-CoV-2 virus genome detection assays Lancet Microbe 3 2022 e168 35098178
22 Buchta C Camp JV Jovanovic J Puchhammer-Stockl E Strassl R Muller MM Results of a SARS-CoV-2 virus genome detection external quality assessment round focusing on sensitivity of assays and pooling of samples Clin. Chem. Lab Med. 60 2022 1308 1312 35599330
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| 0 | PMC9733965 | NO-CC CODE | 2022-12-14 23:45:35 | no | J Clin Virol. 2023 Jan 10; 158:105352 | utf-8 | J Clin Virol | 2,022 | 10.1016/j.jcv.2022.105352 | oa_other |
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Can J Cardiol
Can J Cardiol
The Canadian Journal of Cardiology
0828-282X
1916-7075
Canadian Cardiovascular Society. Published by Elsevier Inc.
S0828-282X(22)01091-1
10.1016/j.cjca.2022.12.002
Clinical Research
Objective Hemodynamic Cardiovascular Autonomic Abnormalities in Post-Acute Sequelae of COVID-19
Hira Rashmin BSc BA 1a
Baker Jacquie R. PhD 1a
Siddiqui Tanya MBBS MPhil 1
Ranada Shaun I. BSc 1
Soroush Ateyeh BSc 2
Karalasingham Kavithra 1
Ahmad Hyeqa 1
Mavai Vibhuti 1
Ayala Valani Luciano Martin 1
Ambreen Sakina 1
Bourne Kate M. BSc 1
Lloyd Matthew G. PhD 1
Morillo Carlos A. MD 1
Sheldon Robert S. MD PhD 1
Raj Satish R. MD MSCI 13∗
on behalf of the
Canadian Long COVID Autonomic Network (CanLoCAN)1
1 Department of Cardiac Sciences, Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
2 Department of Neuroscience, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta
3 Vanderbilt Autonomic Dysfunction Center, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
∗ Corresponding Author: Dr Satish R. Raj, GAC70 HRIC Building, University of Calgary, 3280 Hospital Dr NW, Calgary, Alberta T2N 4Z6, 1-403-210-6152; fax: 1-403-210-9444.
a RH and JRB contributed equally to this work and are co-first authors
9 12 2022
9 12 2022
19 9 2022
4 12 2022
6 12 2022
© 2022 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.
2022
Canadian Cardiovascular 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.
Background
Many COVID-19 patients are left with symptoms several months after resolution of the acute illness (“Post-Acute Sequalae of COVID-19” [PASC]). We aimed to determine the prevalence of objective hemodynamic cardiovascular autonomic abnormalities (CAA), explore sex differences, and assess the prevalence of CAA among hospitalized vs non-hospitalized PASC patients.
Methods
Patients with PASC (n=70; F=56; 42 years 95% CI [40,48]) completed standard autonomic tests, including an active stand test 399 days [338,455] after their COVID-19 infection. Clinical autonomic abnormalities were evaluated.
Results
Most patients with PASC met the criteria for at least one CAA (51; 73%; F=43). The Postural Orthostatic Tachycardia Syndrome hemodynamic criterion (POTSHR) of a heart rate increase of >30bpm within 5-10mins of standing was seen in 21 patients (30%; F=20; p=0.037 [by sex]). The Initial Orthostatic Hypotension hemodynamic criterion (IOH40) of a transient SBP change of >40mmHg in the first 15s of standing was seen in 43 (61%) patients and equally among females and males (63% vs. 57%; p=0.7). Only 9 (13%) patients were hospitalized; hospitalized vs. non-hospitalized patients had similar frequencies of abnormalities (67% vs. 74%; p=0.7).
Conclusions
Patients with PASC have evidence of CAA, most commonly IOH40, which will be missed unless an active stand test is used. Females have increased frequency of POTSHR, but IOH40 is equally prevalent between sexes. Finally, even non-hospitalized “mild” infections can result in long-term CAA.
Graphical abstract
KEYWORDS
Post-Acute Sequalae of COVID-19
Autonomic nervous system
Cardiovascular hemodynamics
Initial Orthostatic Hypotension
Postural Orthostatic Tachycardia Syndrome
Active Stand Test
ABBREVIATIONS
BP, Blood Pressure
CASS, Composite Autonomic Severity Score
COMPASS-31, Composite Autonomic Symptoms Scale – 31-items
CAA, Cardiovascular Autonomic Abnormalities
HR, Heart Rate
IOH, Initial Orthostatic Hypotension
IOH40, Initial Orthostatic Hypotension Hemodynamic Criterion (transient SBP drop ≥40mmHg within 15 seconds of standing with recovery within 45 seconds)
IST, Inappropriate Sinus Tachycardia
IST100, Inappropriate Sinus Tachycardia Hemodynamic Criterion (resting supine HR >100bpm)
OH, Orthostatic Hypotension
OH20, Orthostatic Hypotension Hemodynamic Criterion (systolic BP (SBP) drop ≥20mmHg within 3 minutes of standing)
PASC, Post-Acute Sequelae of COVID-19
POTS, Postural Orthostatic Tachycardia Syndrome
POTSHR, Postural Orthostatic Tachycardia Syndrome Hemodynamic Criteria (HR increase ≥30 bpm within 10 minutes of standing in the absence of OH)
QSART, Quantitative Sudomotor Axon Reflex Testing
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pmcINTRODUCTION
The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has impacted the health and economies of Canada and the world. Worldwide, there have been over 560 million cases and 6 million deaths attributed to COVID-19 (SARS-CoV-2 disease)1. While most patients recover, many are left with residual and sometimes disabling symptoms, several months after resolution of the acute illness.
These ongoing symptoms have been termed "post-acute sequelae of COVID-19 syndrome" (PASC), defined as symptoms that develop during or after an infection consistent with COVID-19, continue for >12 weeks and are not explained by an alternative diagnosis2. Common symptoms include exercise intolerance, dyspnea, fatigue, light-headedness, and tachycardia/palpitation2 , 3, which are common in cardiovascular autonomic abnormalities (CAA).
Although studies have reported CAA in patients with PASC, including reports of new-onset postural orthostatic tachycardia syndrome (POTS)4, 5, 6, 7, 8, 9, 10, inappropriate sinus tachycardia (IST)9 , 11, and orthostatic hypotension (OH)12, these studies have been retrospective7, had small patient sample sizes5, 6, 7, and/or did not perform autonomic evaluations using standard tests of autonomic function13. Further, most studies used a tilt table test8 , 14, and not an active stand test15; which, in conjunction with beat-to-beat hemodynamic monitoring, is required to diagnose initial orthostatic hypotension (IOH) 15 , 16. As such, we currently do not know the prevalence of objective CAA in patients with PASC based on standard tests of autonomic function17.
We determined the frequency of objective CAA in a cohort of patients with PASC and examined whether there was a sex dimorphism in the frequency of these abnormalities, and whether they were more frequent among patients hospitalized for their COVID-19 infections.
METHODS
Participants
Patients with PASC between 18-80 years were recruited from the Calgary Autonomic Investigation and Management Clinic, the Alberta Health Services Calgary COVID Clinics, and through local advertising. All patients met the consensus statement criteria for PASC9, including a positive SARS-CoV-2 polymerase chain reaction test and symptoms consistent with COVID-19 lasting >12 weeks following a COVID-19 infection18. In our cohort, 80% of patients were not vaccinated before their initial COVID-19 infection. Participants were excluded if they were unable to provide informed consent or could not safely withdraw from medications affecting heart rate and blood pressure. The Conjoint Health Research Ethics Board at the University of Calgary (REB21-1188) provided ethical oversight and approval for this study. All participants provided written informed consent prior to study participation. Participants also completed the Composite Autonomic Symptom Score (COMPASS-31) questionnaire and a general autonomic symptoms questionnaire via RedCap19.
Instrumentation
Studies were conducted in a post-void and post-absorptive state at least 2h after a meal. Continuous heart rate (HR) and blood pressure (BP) were recorded using a 5-lead ECG (IVY Biomedical Model 450C, Connecticut, USA) and non-invasive beat-to-beat finger cuff (BMEYE, Amsterdam, The Netherlands). Brachial BP measurements were obtained before, during, and after the tests to verify finger cuff BP recordings. Advanced hemodynamics, including stroke volume (SV), cardiac output (CO), and systemic vascular resistance (SVR), as well as indices corrected for body surface area (BSA) (i.e., SV index [SVI], cardiac index [CI], and SVR index [SVRI]) were calculated using waveform based Modelflow software (BMEYE, Amsterdam, The Netherlands). All analogue signals were sampled at 500 Hz (WinDaq, DATAQ Corporation) and stored digitally for off-line analysis using custom software written in MATLAB r2021b (December 2021, Mathworks, Natick, MA, USA).
Assessment of Hemodynamic Criteria for Cardiovascular Autonomic Abnormalities
Following a 10-minute baseline in the supine position, participants completed an active stand test15 where they were instructed to stand as quickly as possible, and remain standing for 10 minutes.
Hemodynamic criteria for cardiovascular autonomic disorders were determined using HR and BP changes during the active stand test. The hemodynamic criterion for orthostatic hypotension (OH) was defined as a systolic BP (SBP) drop ≥20mmHg within 3 minutes of standing (OH20)20, 21, 22. The hemodynamic criterion for postural orthostatic tachycardia syndrome (POTS) was defined as a HR increase ≥30 bpm within 10 minutes of standing in the absence of OH (POTSHR)21 , 23. Initial orthostatic hypotension (IOH) was defined as a transient SBP drop ≥40mmHg within 15 seconds of standing with recovery within 45 seconds (IOH40)24. Inappropriate sinus tachycardia (IST) was assessed as a resting supine HR >100bpm (IST100)25.
Autonomic Function Testing
Participants completed the following standard tests of autonomic function: quantitative sudomotor axon reflex testing (QSART), deep breathing, and Valsalva maneuver. QSART (QSWEAT, WR Medical Electronics Co., Stillwater, Minnesota, USA) was used to evaluate post-ganglionic peripheral sympathetic nerve integrity. Axon terminals were transdermally iontophoresed at a constant current (2mA) for 5-minutes with 10% acetylcholine at four standard sites (left forearm, left proximal leg, left distal leg, and left foot)26, followed by a 5-minute post-stimulation period. Total sweat volumes were calculated as the area under the curve over 10 minutes.
Cardiovagal function was assessed using deep breathing and the Valsalva maneuver. During deep breathing17, participants were instructed to breathe at a rate of 6 breaths/min for 90s. The peak to trough difference over five consecutive breaths was averaged to provide an average HR response to deep breathing (ΔHRDB). For the Valsalva maneuver17, participants were instructed to blow into a tube with an air leak (to ensure an open glottis) and to maintain an expiratory pressure of 40mmHg for 15s. A Valsalva ratio was calculated by dividing the maximum HR obtained during the maneuver by the minimum HR in the 30s immediately following release.
The CASS was calculated for each participant to quantify the severity and distribution of autonomic dysfunction normalized for age and sex17. This 10-point scale evaluates autonomic dysfunction across three functional domains: sudomotor, adrenergic, and cardiovagal17.
Autonomic Symptom Assessment
Prior to their visit, all participants completed the COMPASS-31 Autonomic Symptom Score questionnaire27. The COMPASS-31, comprised of 31 self-reported questions with a total score of 100, quantifies symptoms associated with autonomic dysfunction in the following domains: orthostatic intolerance (max. score of 40), vasomotor (max. score of 5), secretomotor (max. score of 15), gastrointestinal (max. score of 25), bladder (max. score of 10), and pupillomotor (max. score of 5).
Participants also completed a questionnaire pertaining to common clinical symptoms in PASC9 (Supplemental Table S1).
Active Stand Symptom Assessment
Participants were asked to report symptoms they experienced during the active stand test. Specifically, participants were asked to report if they experienced symptoms within the first 60-seconds of the active stand. At the end of each stand, orthostatic symptoms were also assessed using the Vanderbilt Orthostatic Symptoms Score (VOSS)28, 29, 30. In brief, the VOSS assesses nine orthostatic symptoms, with scores ranging from 0 (no symptoms) to 10 (worst symptoms), with higher scores indicating worse orthostatic intolerance.
Statistical Analyses
Continuous data are presented as median [95% Confidence Interval]. Categorical data are presented as number (%). A Fisher’s exact test was used to compare categorical variables, while continuous variables were compared using Mann-Whitney U tests. A 2-tailed p-value of <0.05 was deemed to be statistically significant.
Sub-group analyses were conducted to evaluate (1) sex (male vs. female) differences; and (2) hospitalization (hospitalized vs. non-hospitalized) differences. Statistical analyses were performed using SPSS statistical software for Windows version 28 (SPSS, Inc., IBM, Armonk, NY). Figures were created using GraphPad Prism (version 8.0.0 for Windows, GraphPad Software, San Diego, California USA, www.graphpad.com).
RESULTS
Demographics
Patients with PASC (n=70; 42 years [40,48]) were evaluated for CAA a median of 399 days (338,455) following an acute infection with COVID-19 (Table 1 ).Table 1 Participant characteristics, hemodynamics, and symptoms in patients with PASC with and without hemodynamic autonomic abnormalities
Characteristic Patients with PASC: Overall (n=70) Patients with PASC with CAA (n=51) Patients with PASC without CAA (n=19) p-value
Age, years 42 (40, 48) 44 (40, 49) 41 (36, 49) 0.409
Female 56 (80%) 43 (84%) 13 (68%) 0.139
Height, cm 168 (165, 170) 167 (165, 170) 170 (161, 173) 0.979
Weight, kg 77 (71, 88) 78 (71, 90) 73 (60, 88) 0.222
BMI, kg/m2 27.2 (24.7, 28.7) 27.8 (25.2, 31.5) 26.1 (22.7, 28.5) 0.237
BSA, m2 1.92 (1.82, 2.02) 1.92 (1.83, 2.07) 1.85 (1.69, 2.08) 0.202
Race 0.727
Caucasians 64 (91.4%) 46 (90.2%) 18 (94.7%)
South Asians 2 (2.9%) 2 (3.8%) 0 (0%)
Indigenous Canadians 1 (1.4%) 1 (2.0%) 0 (0%)
West Asians 1 (1.4%) 1 (2.0%) 0 (0%)
≥1 race 2 (2.9%) 1 (2.0%) 1 (5.3%)
Duration since Initial COVID infection, days 399 (338, 455) 418 (341, 456) 344 (319, 477) 0.989
Hospitalized with COVID-19 infection 9 (13%) 6 (12%) 3 (16%) 0.655
Autonomic Function Testing
Sympathetic Nerve Integrity (Total Sweat Volumes)
Forearm, μL 0.51 (0.39, 0.74) 0.48 (0.35, 0.64) 0.86 (0.39, 1.47) 0.093
Proximal leg, μL 0.39 (0.32, 0.66) 0.38 (0.30, 0.54) 0.70 (0.12, 1.23) 0.276
Distal leg, μL 0.49 (0.40, 0.67) 0.46 (0.36, 0.54) 0.74 (0.20, 0.167) 0.063
Foot, μL 0.45 (0.30, 0.55) 0.44 (0.29, 0.55) 0.48 (0.22, 0.81) 0.663
Cardiovagal function
ΔHRDB (bpm) 15 (12, 18) 15 (12, 18) 15 (10, 20) 0.769
VR 1.75 (1.67, 1.91) 1.74 (1.64, 1.92) 1.82 (1.55, 2.11) 0.840
Composite Autonomic Severity Score (CASS)
Overall CASS 1 (1, 2) 1 (1, 2) 1 (0, 2) 0.163
Sudomotor 0 (0, 0) 0 (0, 0) 0 (0, 1) 0.701
Cardiovagal 0 (0, 0) 0 (0, 0) 0 (0, 1) 0.702
Adrenergic 1 (1, 1) 1 (1, 1) 0 (0, 1) 0.06
Autonomic Symptom Assessment
COMPASS-31 Domains
Orthostatic Intolerance 20 (16, 20) 20 (16, 24) 16 (12, 20) 0.038
Vasomotor 0 (0, 0) 0 (0, 0) 0 (0, 0.33) 0.578
Secretomotor 6.43 (4.29, 6.43) 6.43 (4.29, 6.43) 6.43 (4.29, 8.57) 0.757
Gastrointestinal 7.14 (6.25, 8.92) 8.04 (6.25, 9.82) 7.14 (4.46, 10.7) 0.736
Bladder 1.11 (0, 1.11) 1.11 (0, 1.11) 1.11 (0, 2.22) 0.693
Pupillomotor 2.33 (2.0, 2.67) 2.33 (2.0, 2.67) 2.33 (1.67, 3.0) 0.873
Total 36 (31, 40) 37 (31, 40) 32 (23, 40) 0.288
PASC Symptoms
Light-headedness 55 (79%) 40 (78%) 15 (79%) 0.963
Shortness of breath 51 (74%) 37 (74%) 14 (74%) 0.979
Palpitations 49 (70%) 37 (73%) 12 (63%) 0.446
Fatigue 64 (91%) 48 (94%) 16 (84%) 0.188
Headache 41 (59%) 30 (60%) 11 (58%) 0.874
Loss/change in taste 21 (31%) 16 (33%) 5 (26%) 0.612
Constipation 20 (29%) 16 (32%) 4 (21%) 0.371
Problems with sleeping 51 (75%) 38 (76%) 13 (72%) 0.751
Values are expressed as Median (95% Confidence Interval [CI]). P-values were generated from Mann-Whitney U test for continuous variables and Fischer’s Exact test for categorical variables. BMI – Body Mass Index; BSA – Body Surface Area; CASS – Composite Autonomic Severity Score; COMPASS-31 - Composite Autonomic Symptom Score; ΔHRDB – Delta HR in Deep Breathing; PASC – Post-Acute Sequelae of COVID-19; VR – Valsalva Ratio; HR – Heart Rate.
Sex-based analysis: Fifty-six female (80%; 42 years [40,47]), and fourteen male (20%; 50 years [31,63]) patients were evaluated a median of 427 days (357,461) and 324 days (285,475) after a COVID-19 infection (Table 2).Table 2 Participant characteristics, hemodynamics, and symptoms in male vs. female patients with PASC
Characteristic Female Patients with PASC (n=56) Male Patients with PASC (n=14) p-value
Age, years 42 (40, 47) 50 (31, 63) 0.577
Height, cm 167 (163, 168) 179 (172, 183) <0.001
Weight, kg 72 (67, 83) 89 (78, 109) 0.015
BMI, kg/m2 26.3 (23.7, 29.1) 27.9 (26.1, 33.6) 0.322
BSA, m2 1.84 (1.77, 1.92) 2.1 (2.0, 2.33) 0.004
Race 0.060
Caucasians 52 (92.8%) 12 (85.8%)
South Asians 2 (3.6%) 0 (0%)
West Asians 0 (0%) 1 (7.1%)
Indigenous Canadian 0 (0%) 1 (7.1%)
≥1 race 2 (3.6%) 0 (0%)
Duration since Initial COVID infection, days 427 (357, 461) 324 (285, 475) 0.083
Hospitalized with COVID-19 infection 6 (10.7%) 3 (4.3%) 0.284
Autonomic Function Testing
Sympathetic Nerve Integrity (Total Sweat Volumes)
Forearm, μL 0.47 (0.37, 0.59) 1.08 (0.28, 1.84) 0.01
Proximal leg, μL 0.38 (0.27, 0.55) 0.72 (0.10, 1.41) 0.093
Distal leg, μL 0.48 (0.38, 0.57) 0.75 (0, 1.77) 0.277
Foot, μL 0.39 (0.28, 0.50) 1.18 (0, 2.04) 0.028
Cardiovagal function
ΔHRDB (bpm) 15 (12, 18) 14 (11, 24) 0.489
VR 1.82 (1.67, 1.94) 1.73 (1.38, 2.11) 0.146
Composite Autonomic Severity Score (CASS)
Overall CASS 1 (1, 2) 2 (1, 3) 0.468
Sudomotor 0 (0, 0) 0 (0, 2) 0.245
Cardiovagal 0 (0, 0) 0 (0, 1) 0.972
Adrenergic 1 (1, 1) 1 (0, 1) 0.960
Autonomic Symptom Assessment
COMPASS-31 Domains
Orthostatic Intolerance 20 (16, 24) 16 (0, 20) 0.163
Vasomotor 0 (0, 0) 0 (0, 0) 0.172
Secretomotor 6.43 (4.29, 6.43) 5.36 (2.14, 6.43) 0.468
Gastrointestinal 8.04 (7.14, 9.82) 4.91 (3.57, 10.7) 0.032
Bladder 1.11 (0, 1.11) 0 (0, 2.22) 0.742
Pupillomotor 2.33 (2.0, 2.67) 1.83 (0.67, 2.67) 0.065
Total 37 (32, 40) 28 (12, 40) 0.083
PASC Symptoms
Light-headedness 45 (80%) 10 (71%) 0.466
Shortness of breath 40 (73%) 11 (79%) 0.657
Palpitations 43 (77%) 6 (43%) 0.013
Fatigue 53 (95%) 11 (79%) 0.055
Headache 37 (67%) 4 (29%) 0.008
Loss/change in taste 20 (37%) 1 (7.1%) 0.031
Constipation 19 (35%) 1 (7.1%) 0.044
Problems with sleeping 43 (78%) 8 (62%) 0.213
Values are expressed as Median (95% Confidence Interval [CI]). P-values were generated from Mann-Whitney U test for continuous variables and Fischer’s Exact test for categorical variables. BMI – Body Mass Index; BSA – Body Surface Area; CASS – Composite Autonomic Severity Score; COMPASS-31 - Composite Autonomic Symptom Score; ΔHRDB – Delta HR in Deep Breathing; PASC – Post-Acute Sequelae of COVID-19; VR – Valsalva Ratio; HR – Heart Rate.
Hospitalization-based analysis: A minority of patients were hospitalized during their initial COVID-19 infection (13%; Supplemental Table S2). Hospitalized patients trended older (53 years [41,59] vs. 41 years [39,46]; p=0.051). Hospitalization frequency did not differ between sexes (p=0.3).
Prevalence of Hemodynamic Criteria for Cardiovascular Autonomic Disorders
Most patients with PASC (73%) met the criteria for at least one CAA (Figure 1 ). Among the CAA group, 16 (31%) patients met the hemodynamic criteria for >1 abnormality. The POTSHR criterion was met in 21 (30%) patients with PASC (40 years [35,45]) with a median ΔHR of +41bpm (+34,+59) from supine to standing. IST100 was seen in one (1.4%; 18-year-old) patient (supine HR: 136bpm). Sustained OH20 was evident in two (2.9%) patients with PASC (ΔSBP: -21mmHg [-21,-35]) with an average age of 52 years [46,58]. In contrast, the IOH40 criterion was evident in 43 (61%) patients (45 years [39,51]) with PASC with a median ΔSBP of -52mmHg (-61,-46) in the first 15 seconds of an active stand.Figure 1 Proportions of Hemodynamic Cardiovascular Autonomic Abnormalities (CAA) in Patients with Post-Acute Sequelae of COVID-19 (PASC), including sex-based and hospitalization status-based differences. Caption: (a) CAA were evaluated using hemodynamic criteria from an active stand test, which assessed for hemodynamic criteria for Postural Orthostatic Tachycardia Syndrome (POTS), Inappropriate Sinus Tachycardia (IST), Orthostatic Hypotension (OH), and Initial Orthostatic Hypotension (IOH); (b) Proportions of PASC patients meeting hemodynamic criteria for a CAA, broken down by patient sex. (c) Proportions of PASC patients meeting hemodynamic criteria for a CAA, broken down by patient hospitalization status during initial COVID-19 infection.
Sex-based analysis: The POTSHR criterion was evident in 36% females vs. 7.1% males (p=0.037; Figure 1). In contrast, IOH40 criterion was equally prevalent in females and males (63% vs. 57%; p=0.7). The OH20 (p=0.5) and IST100 criterion (p=0.6) were only met in females. Overall, the increased estimates of females vs. males meeting any CAA criteria were not significant (77% vs. 51%; p=0.14).
Hospitalization-based analysis: The overall prevalence of CAA was not different (67% vs. 74%; p=0.7). IOH40 was similar between the groups (67% vs. 61%; p=0.7), There was not a significant difference in the prevalence of patients with POTSHR who were hospitalized (11% vs. 33%; p=0.18). The OH20 and IST100 patients were not hospitalized (Figure 1).
Autonomic Function Testing
Post-ganglionic sympathetic nerve integrity: Reduced nerve integrity was evident in 15 patients with CAA and in 4 patients without (p=0.5). There was a trend for sympathetic nerve function to be attenuated in the forearm and distal leg for patients with CAA (Table 1). Between sexes, reduced nerve integrity was present in 13 females and 6 males (p=0.14). Sympathetic nerve function was reduced in females at the forearm and foot (Table 2). Abnormal nerve function was observed in both hospitalized and non-hospitalized patients (p=0.2). The forearm had worse sympathetic nerve integrity in non-hospitalized patients (Supplemental Table S2).
Cardiovagal: HR responses to deep breathing and Valsalva maneuver were not different between patient groups (Table 1; Table 2; Supplemental Table S2).
CASS: Hospitalized patients had higher cardiovagal scores (Supplemental Table S2; p=0.022).
Autonomic Symptom Assessment
All patients reported at least one symptom on the COMPASS-31 with an overall median score of 36/100 (31,40). Patients with CAA had higher COMPASS-31 scores in the orthostatic intolerance domain vs. patients without CAA (p=0.038; Table 1). Among all patients, females had higher gastrointestinal scores compared to males (p=0.032; Table 2) on the COMPASS-31. On the PASC-symptom questionnaire, more females than males reported a loss/change in taste (p=0.031), constipation (p=0.044), palpitations (p=0.013), and headache (p=0.008), and a trend toward more fatigue (p=0.055) (Table 2). Supplemental Table S1 provides an assessment of all symptoms in the general PASC symptom questionnaire.
Active Stand Symptom Assessment
In our cohort, 71% of patients reported symptoms in the first 60s of standing and 100% of patients reported a score of ≥1 on the VOSS. Among patients with symptom assessments, 74% of IOH patients reported symptoms within the first 60s of standing and 84% reported symptoms of new onset orthostatic intolerance (OI), including light-headedness or palpitations. Additionally, 100% of patients meeting POTS criteria reported a score of ≥ 1 on the VOSS and 91% reported new onset OI symptoms of light-headedness or palpitations following their COVID-19 infection in the PASC questionnaire. Finally, 50% of patients meeting OH criterion reported symptoms while standing, and the single IST patients (100%) did not complete a symptom assessment during the active stand. Notably, with the IOH sub-group, the change in BP in the first 60s of stand was negatively correlated with higher OI scores (rs=-0.328; p=0.006). Similarly, patients with higher orthostatic tachycardia also reported higher OI symptoms (rs=0.284; p=0.017).
DISCUSSION
In this study we report one of the largest PASC cohorts that have undergone autonomic testing. Here, we report that: 1) many patients with PASC have objective evidence of CAA; 2) the most common abnormality is IOH, followed by POTS; 3) both males and females with PASC meet the IOH criterion with a similar frequency, but POTS skewed heavily toward female patients; and 4) hospitalized patients with PASC did not have increased rates of CAA compared to non-hospitalized patients, suggesting even mild, non-hospitalized COVID-19 infections can result in long-term CAA.
Prevalence of cardiovascular autonomic abnormalities (CAA)
Within our cohort, IOH was the most prevalent hemodynamic abnormality (61%). Previous studies have observed IOH ranging from 15-30% in the general population31, suggesting IOH may be more prevalent among patients with PASC. IOH requires an active stand with beat-to-beat hemodynamics for accurate detection15 , 24 , 32. Few studies have employed these methods, suggesting underdiagnosis of IOH is likely in patients with PASC. This is important as there are targeted non-pharmacological treatments that can help patients with IOH14 , 24 , 33. The POTS hemodynamic criterion was met in 30% of patients with PASC, which is consistent with prior reports14 , 34, but higher than the prevalence seen in the general population (0.2%)25, and specifically within females aged 20-40 years. For example, in a Croatian cohort, the prevalence of POTS in females 20-40 years ranged from 3-21%35. Finally, IST and OH were relatively uncommon in our PASC cohort.
In addition to abnormal orthostatic hemodynamics, CAA patients also had higher OI scores, and these scores were related to orthostatic hemodynamic abnormalities. For example, IOH patients with larger BP drops and POTS patients with higher HRs both reported higher OI scores. These findings further emphasize the need to incorporate an active stand and beat-to-beat hemodynamics when evaluating patients presenting with chronic PASC symptoms, especially symptoms of OI (e.g., light-headedness or palpitations).
Sex-based differences
There was a trend toward increased frequency of a CAA among females compared to males. These findings are consistent with many disorders of the ANS, including POTS23, which predominantly affects pre-menopausal Caucasian females (5-fold female predominance)36. It is important, however, to acknowledge that our PASC cohort was predominantly comprised of Caucasian females. It is hard to know if this represents the broader PASC population or if this reflects our sampling and recruitment. Despite a strong overall female predominance, we found that individuals meeting the cardiovascular criteria for IOH was equally prevalent among males and females. IOH can be a common cause of syncope in individuals with unexplained syncope33 , 37, suggesting both males and females are at an equal risk of experiencing a syncopal episode related to PASC.
From a symptom perspective, there were several symptoms that were more prevalent among females, including reports of headache, loss/change in taste, gastrointestinal (e.g., constipation), fatigue, and palpitations. Many of these symptoms are reported among patients with POTS, which was also female dominated in our PASC cohort. Palpitations is a common cardiac symptoms of POTS38, however, what might be less appreciated is that many POTS patients also experience several non-cardiac symptoms including significant, debilitating fatigue38, 39, 40, which has important implications in all facets of life, including increased absences from school, work and loss of productivity41. Other non-cardiac symptoms reported in patients with POTS include headache and GI issues38, which are also consistent with our findings. Conversely, shortness of breath, light-headedness and sleep difficulties were not different between females and males within our cohort. Light-headedness is commonly reported in patients with autonomic disorders24 , 38. Given that IOH was equally prevalent among males and females and was frequently reported within the first 60s of standing, it is not surprising that light-headedness was not different between sexes.
Hospitalization Status
Contrary to our hypothesis, patients with PASC hospitalized due to their COVID-19 infections did not have increased rates of CAA compared to non-hospitalized patients. Similarly, reported symptoms did not differ between groups. These findings suggest that even “mild” COVID-19 infections may result in CAA with significant symptomatology.
Although we cannot infer causation from the current study, viral infections are commonly reported triggers of altered autonomic control, and have been reported in various disorders of orthostatic intolerance36 , 42. Additionally, autoantibodies have been reported in patients with OH43 and POTS44 , 45. Recently, Blitshteyn et al., reported that ∼20% of their PASC patient cohort had abnormal autoimmune or inflammatory biomarkers7. These findings lend to the possibility that the observed CAA may result from an underlying autoimmune/inflammatory response. However, further studies including inclusion of a healthy control group are needed to explore whether these findings are related to PASC, and to explore the relationship between autoimmune/inflammatory biomarkers and severity of autonomic dysfunction and/or symptoms.
Limitations
Our study highlighted the importance of testing autonomic function in patients with PASC, but there were some limitations. First, this was a descriptive study and lacked a proper control group to determine what is truly abnormal with PASC physiology. Our initial focus was to recruit and study patients with PASC, and there are plans to study a matched control group going forward. Despite this limitation, due to the societal impact of the COVID-19 related health crisis, we felt it was important to report these data quickly. Second, our cohort may not represent the full PASC patient population. We recruited through local advertising and “Long-COVID” clinics, but it is possible that some patients found our study based on knowledge of our interest and expertise in CAA. This could lead to an overestimation of the prevalence of CAA in the broader PASC population. Additionally, we acknowledge that the low number of male patients and hospitalized patients may have an impact on statistical power. Finally, we focussed on the hemodynamic criteria that could be identified with autonomic testing. Some of these disorders (such as POTS and IOH) also require a specific symptoms constellation during a clinical evaluation. Therefore, it is more correct for us to speak about the hemodynamics of these disorders being present, rather than the disorders being present. Finally, a 24-hour Holter monitor can also be used to diagnose IST. As a single-day study, we opted to assess IST as a resting supine HR >100bpm25. However, the use of a 24-h Holter may provide additional insights that may not have been captured with the timeframe of our study potentially result in a underdiagnosis within our cohort.
CONCLUSION
Given the high prevalence of CAA among patients with PASC, these patients should have cardiovascular autonomic function testing, especially if they report symptoms of orthostatic intolerance. Ideally, this evaluation will include beat-to-beat BP and HR monitoring with an active stand test to evaluate for criteria consistent with IOH, in addition to POTS, OH & IST. While IOH was common in both sexes, POTS was seen primarily in females. Hemodynamic cardiovascular autonomic abnormalities were common even in patients who were not hospitalized with their COVID-19 infections, suggesting even ‘mild’ infections can results in CAA.
Supplementary Material
Acknowledgements
The authors would like to acknowledge the patients who took the time to participate in our study.
Twitters: @satish_r_raj & @TeamSRRaj
Brief Summary: There is a high prevalence of hemodynamic cardiovascular autonomic abnormalities in PASC (also called Long-COVID). The most common abnormality is Initial Orthostatic Hypotension (IOH), followed by Postural Orthostatic Tachycardia Syndrome (POTS), with female predominance. Autonomic abnormalities are equally common in patients who were and were not initially hospitalized with COVID-19.
Funding: This work was supported by the Canadian Institutes of Health Research (CIHR; Ottawa, ON, Canada) grant G4A- 177741, Dysautonomia International Grant-in-Aid (2019), and the Vanderbilt Institute for Clinical and Translational Research (NIH UL1-TR000445).
Disclosures: SRR – Consultant to Lundbeck LLC, Theravance Biopharma, Amneal Pharma, Servier Affaires Medicales, Regeneron, and argenx BV.
CAM – Consultant to Abbott, Medtronic, Novartis, and Boston Scientific. Chair of the BETTY Trial DSMB, Drugs for Neglected Disease Advisory Board, and Global Chagas Platform Advisory Board.
Data Availability Statement: The data underlying this article will be shared on reasonable request to the corresponding author.
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| 36509178 | PMC9733966 | NO-CC CODE | 2022-12-14 23:28:27 | no | Can J Cardiol. 2022 Dec 9; doi: 10.1016/j.cjca.2022.12.002 | utf-8 | Can J Cardiol | 2,022 | 10.1016/j.cjca.2022.12.002 | oa_other |
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Int J Infect Dis
Int J Infect Dis
International Journal of Infectious Diseases
1201-9712
1878-3511
The Authors. Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.
S1201-9712(22)00641-5
10.1016/j.ijid.2022.12.004
Article
Identifying age- and sex-specific COVID-19 mortality trends over time in six countries
Torres Catalina ab⁎
García Jenny a
Meslé France a
Barbieri Magali ac
Bonnet Florian a
Camarda Carlo Giovanni a
Cambois Emmanuelle a
Caporali Arianna a
Couppié Étienne a
Poniakina Svitlana a
Robine Jean-Marie ad
a Institut national d’études démographiques (INED), Campus Condorcet, 9 cour des Humanités, 93300 Aubervilliers, France
b Eco-anthropologie (EA), Muséum national d'Histoire naturelle, CNRS, Université Paris Cité, Musée de l'Homme, 17 place du Trocadéro, 75016 Paris, France
c University of California, Berkeley, United States
d INSERM, France
⁎ Corresponding author.
10 12 2022
10 12 2022
13 9 2022
14 11 2022
5 12 2022
© 2022 The Authors. Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.
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
The COVID-19 pandemic is characterized by successive waves that each developed differently over time and through space. We aim to provide an in-depth analysis of the evolution of COVID-19 mortality during 2020 and 2021 in a selection of countries.
Methods
We focus on five European countries and the United States. Using standardised and age-specific mortality rates, we address variations in COVID-19 mortality within and between countries, as well as demographic characteristics and seasonality patterns.
Results
Our results highlight periods of acceleration and deceleration in the pace of COVID-19 mortality, with substantial differences across countries. Periods of stabilization were identified during summer (especially in 2020) among the European countries analysed, but not in the United States. The latter stands out as the study population with the highest COVID-19 mortality at young ages. In general, COVID-19 mortality is highest at old ages, particularly during winter. Compared to women, men have higher COVID-19 mortality rates at most ages and in most seasons.
Conclusions
There is seasonality in COVID-19 mortality for both sexes at all ages, characterized by higher rates during winter. In 2021, the highest COVID-19 mortality rates continued to be observed at ages 75+, despite vaccinations having specifically targeted those ages.
Keywords
COVID-19 mortality
seasonal mortality
epidemic waves
cross-country comparisons
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pmcIntroduction
The COVID-19 pandemic is characterized by a sequence of waves that exhibit different features over time and through space. For example, in some European countries and in the north-eastern United States, COVID-19 mortality rates substantially declined between the first and the second wave (James et al., 2021). Likewise, Italian provinces with the most severe initial COVID-19 outbreaks faced milder second waves (Minnai et al., 2022; Vinceti et al., 2021). However, the experience of successive COVID-19 waves in other countries remains relatively unexplored, as previous studies tend to focus on the cumulative number of deaths by a given date, or use other approaches that make it difficult to clearly distinguish variations in the age and sex-specific structure of COVID-19 mortality across pandemic waves (Dudel et al., 2020; Gallo et al., 2021; Leong et al., 2021; Bauer et al., 2021).
International comparisons of the demographic characteristics of people who died during these different waves provide valuable insights into the efficiency of health measures implemented by governments, from basic prevention—such as regular hand washing, facemask use, and lockdowns—to testing and immunization. To document the possible impact of those measures, it is crucial to carefully analyse the pace at which the lethal impact of COVID-19 progressed over time, while simultaneously disaggregating COVID-19 mortality by age and sex. However, such comparisons must be conducted cautiously, given the heterogeneity of the available data across countries. More precisely, data collected through specific observation and registration systems could potentially lead to differences in data coverage and representativeness, thus introducing bias when carrying out undocumented international comparisons (García et al. 2021).
Here, we aim to provide an in-depth descriptive analysis of the evolution of COVID-19 mortality in a selection of countries throughout the pandemic seasons and waves during the years 2020 and 2021, including changes in the age- and sex-structure of the deceased.
Since COVID-19 waves were driven by different variants of the virus and public health interventions, we hypothesize that the intensity of epidemic waves in each country may show distinct patterns according to calendar, overall lethality, and sex- and age-specific mortality. For example, we expect cross-country variations in the lethal impact of the first COVID-19 waves in 2020, which may partly reflect the strictness of public health protocols in the use of non-pharmaceutical interventions. Furthermore, considering that COVID-19 vaccination was introduced during the first months of 2021, we expect the 2020 waves to be more lethal than those of 2021, especially among the oldest age groups, who were the first to be targeted by vaccination campaigns and received booster shots in priority.
Data
We used national-level data on COVID-19 death counts by age and sex from “The Demography of COVID-19 Deaths” database (2022), with a specific focus on six countries: Belgium, England and Wales, France, Scotland, Sweden, and the United States (see Supplements: Table S1 and Figure S1). We chose these countries because their official data sources use comparable definitions in the attribution of a death to COVID-19 (Caporali et al., 2022), providing comprehensive COVID-19 death counts, i.e., “statistics from the vital registration system, where COVID‑19 is mentioned on the death certificate, or surveillance systems or health agencies that report both laboratory‑confirmed and suspected COVID-19 deaths” (García et al., 2021, p. 49). Most data sources used in this study provide COVID-19 death counts based on data from the civil registration system, i.e., death certificates were COVID-19 is mentioned. The only exception is Belgium, for which data come from the epidemiological surveillance system where both confirmed and suspected COVID-19 deaths are reported. Thus, even though we use comparable data from sources offering comprehensive death counts, our results refer to deaths associated to (and not due to) COVID-19, as not all data sources allow a clear distinction of the role of COVID-19 in the death, i.e., whether underlying or contributory cause of death.
We focus on the period beginning with the pandemic in March 2020 and ending with the 2021–2022 winter season (i.e., until the end of February 2022)—with the exception of France, who, at the moment of writing this article, has no available COVID-19 data after December 24 2021.
For calculating COVID-19 mortality rates, we also used population counts by age and sex. Population estimates and projections for 2019, 2020, and 2021 were retrieved from the official national statistics websites pertaining to our study populations (see Supplements: Box S1).
Finally, to compare the structure of COVID-19 mortality with that of all-cause mortality in a pre-pandemic year, we also used 2019 death counts by age and sex for each study country, retrieved from the Human Mortality Database (HMD, 2022).
Methods
In order to highlight overall differences and similarities between countries and across waves, we first show how total COVID-19 mortality rates have evolved over time. For international comparisons, we estimate weekly, standardized COVID-19 death rates (SDR) for each country, by applying the classic direct standardization method (Preston et al., 2001). In order to remove the influence of different age structures across populations, we use Eurostat's European Standard Population (Eurostat, 2013).
Weekly and seasonal COVID-19 mortality rates by age are calculated as the number of COVID-19 deaths occurring (or registered) during a specific week or season, divided by the corresponding estimated population (see Supplements: Table S2 and Figure S2). For the analyses by season, we used the meteorological definitions: winter (December 1 – February 28), spring (March 1 – May 31), summer (June 1 – August 31), and autumn (September 1 – November 30).
Finally, sex differences are explored using the ratio of male to female COVID-19 mortality rates, that is, the age-specific COVID-19 mortality rates for men divided by those for women. We also used the sex- and age-distribution of COVID-19 deaths within each season in each country.
Results
Timing and intensity of COVID-19 waves
Figure 1 shows the evolution of the COVID-19 SDR by country. Within each country can be identified at least three waves that unequally affect the population.Figure 1 Weekly standardized death rate (SDR, per 1 million) associated with COVID-19, by country.
Note: Labels for the dates on the x-axis correspond to the first day of the indicated months. The grey vertical lines indicate the start of each season.
Figure 1
The first COVID-19 wave began abruptly in March 2020 and peaked in April, reaching 164 deaths per 1 million in Belgium; 149 in England and Wales; 127 in Scotland; 90 in France; 70 in Sweden; and 62 in the United States. Then, the SDR declined in all countries, albeit slightly faster in Belgium and France. In the five European countries included in Figure 1, few COVID-19 deaths occurred during the summer and beginning of autumn 2020. In contrast, in the United States there was a new period of increase beginning in June 2020, which reflects the geographic spread of the pandemic from east to west (Oster et al., 2020; McMahon, et al., 2022).
During the second wave, mortality increased rapidly in Belgium, with an SDR rising from about 8 at the end of September to 120 at the beginning of November 2020. However, England and Wales had the highest COVID-19 mortality rates during the second wave, as the SDR reached 162 in the third week of January 2021. France had the lowest SDR at the peak of the second wave (60.1 in the second week of November 2020), followed by renewed periods of increased mortality between which the SDR did not substantially decline.
During the spring and summer of 2021, a new stabilization phase began, first in England and Wales and in Scotland (March), and then in Belgium, France, and Sweden (June). Unlike the stability observed during the summer of 2020, the SDR increased slightly in England and Wales, Scotland, and France during the summer of 2021.
Compared to the same seasons one year earlier, markedly lower COVID-19 mortality rates were observed in autumn 2021 and in winter 2021–2022. This difference is striking in all study countries except the United States, where COVID-19 mortality was higher than in the other countries during the indicated period. Although the weekly SDR did not exceed 100 in the United States at any moment during the study period, it is the only country where it did not decline close to zero, as occurred in the European countries during the summer months. By the end of the study period, the United States had the highest cumulative COVID-19 mortality rate since the beginning of the pandemic, followed by England and Wales (see Supplements: Figure S3).
Variations in COVID-19 mortality by age group
Figure 2 illustrates the differential impact of COVID-19 on mortality by age, as younger age groups were less affected than older ones in all countries and waves. Below age 45, marked wave patterns in the death rates are observed only in England and Wales and in the United States. From age 45, each wave has a distinct impact in all countries. At the peak of the first wave, England and Wales had the highest mortality rates under age 75 while Belgium had the highest mortality at ages 75+. Comparing the age patterns with all-age COVID-19 mortality (Figure 1), the mortality peaks at ages 75+ clearly drive each country's trends in COVID-19 mortality and the intensity of each wave.Figure 2 COVID-19 mortality rates (weekly deaths) by age group, for both sexes combined.
Note: Scales for each age group are different. Due to different age groups in the original data sources, data for Sweden correspond to ages 0-49 and 50-74 (instead of 0-44 and 45-74, respectively). See also the cumulative mortality rates by age (Supplements: Figure S4).
Figure 2
A rapid mortality decline following the second peak is observed at all ages in England and Wales and in Scotland, as well as above age 75 in the United States; whereas a slight mortality increase from March to May 2021 is observed in France and Belgium at all ages, as well as below age 75 in the United States. After spring 2021, the mortality rates at ages 75+ significantly diminished in comparison to previous levels in all countries.
Unlike the summer of 2020, more cross-country variation in COVID-19 mortality rates is observed during the summer of 2021, with most countries (except Belgium and Sweden) having an increase towards the end of the season. This increase—which is more pronounced under age 75—was modest in England and Wales, Scotland, and France, but sharp in the United States.
Figure 3 summarizes variations between seasons in the age-structure of COVID-19 mortality. In general, mortality is highest during winter (especially the winter of 2020–2021) and at old ages. In all countries except the United States, the spring of 2020 (which represents the first wave) also has among the highest mortality rates at most ages compared to the other periods.Figure 3 Age-specific COVID-19 mortality rates by sex and season.
Note: For Sweden, the point at the youngest age corresponds to age group 0–49. Overall differences between seasons are further explored in the Supplements (Table S3). In Scotland (spring 2020 and summer and autumn of both years) and Belgium (summer 2021), no COVID-19 deaths were registered at the youngest age group (i.e., ages 0 to 14 in Scotland and ages 0 to 24 in Belgium). Therefore, we dropped those points from Figure 3, as the y-axis shows the mortality rate in log scale (and log(0) = - inf).
Figure 3
With a few exceptions, the COVID-19 mortality rates in 2020 increased or decreased proportionally at most ages across seasons, as shown by the solid lines shifting up and down. In 2021 there was a crossover, as COVID-19 mortality rates among the oldest age groups did not increase as much as in 2020 (except for France during the summer of 2021). However, at younger ages, COVID-19 mortality rates in 2021 were close to (or even higher than) those observed in 2020 in most countries and seasons.
In all countries except Sweden, COVID-19 mortality rates at all ages during summer 2020 were among the lowest observed, but they were higher in summer 2021, especially at the youngest ages. In Sweden, mortality rates in summer 2020 were relatively high, as they were even higher at most ages than those observed in the 2021–2022 winter season. Swedish mortality rates during the summer of 2020 were also higher in comparison with the other European study countries (see Supplements: Figure S5).
Figure 3 shows that COVID-19 mortality rates were lower in the 2021–2022 winter season than in the previous one. However, while the difference is substantial in England and Wales, Scotland, and Sweden, it is only modest and concerns mainly the oldest age groups in Belgium and in the United States. In the latter country, young age mortality was even slightly higher in the 2020–2021 winter season than in the previous one.
Variations in COVID-19 mortality by sex
Sex differences in COVID-19 mortality are explored in Figure 4 . In general, COVID-19 mortality rates are higher among men than women. The largest differences are observed at ages 50 to 75. The only instances where women displayed higher COVID-19 mortality rates compared to men (shown in orange-red colours in Figure 4) concern the youngest age groups, but they involve very few deaths.Figure 4 Sex differences in age-specific COVID-19 mortality rates (coloured boxes) and total female COVID-19 death counts (numbers), by season.
Note: Relative differences illustrated with the coloured boxes are calculated as the ratio of COVID-19 mortality rates for men to those for women. Similar mortality levels for men and women are represented in beige. Higher mortality among men compared to women is represented in blue, while higher mortality among women compared to men is represented in red (the darker the colour, the greater the differences). Darker rows indicate higher male or female mortality during a specific season at various ages, while darker columns indicate higher male or female mortality for a specific age group across various seasons. Inside each coloured box is indicated the total number of COVID-19 deaths among females observed in each country, season and age group. Higher or lower COVID-19 mortality among men – as illustrated by the relative differences – is therefore calculated with respect to the shown number of female deaths.
Figure 4
Sweden is the country with the largest sex differences in COVID-19 mortality, as the rates for men in most age groups and in most seasons were at least 1.75 times higher compared to their female counterparts. In contrast, such large sex differences have been rare in the United States throughout the pandemic. Furthermore, the age gradient of sex differences in COVID-19 mortality has been smaller in the United States than in the other countries, where the rates at older ages have been much higher than at younger ages.
Finally, Figure 5 illustrates how the age and sex distribution of COVID-19 mortality changed across seasons in 2020 and 2021. We also compare these distributions with those of all-cause mortality in 2019. We chose 2019 because it is the most recent pre-pandemic year with available data for all study countries in the HMD. Furthermore, we do not distinguish between seasons in 2019, because the age and sex distribution of deaths from all causes did not vary much by season (see Supplements: Figures S6 and S7). While there were indeed more winter deaths, their age and sex distribution varied little compared to the other seasons in 2019.Figure 5 Age and sex distribution of COVID-19 deaths by season, and of all-cause mortality in 2019.
Note: The coloured lines (red and blue) show the distribution of COVID-19 deaths, whereas the black line shows the distribution of all-cause mortality in 2019. The age groups for Scotland and Sweden are different than those for the other countries due to different age groupings in the original data sources. For each season, the proportions by age and sex (i.e., the two characteristics simultaneously) add up to 100%.
Figure 5
In most seasons and countries, the age and sex structure of COVID-19 deaths in 2020 was older than that of all-cause mortality in 2019. By contrast, the age and sex distribution in 2021 tells of a younger structure of COVID-19 deaths, driven mainly by a reduction in the proportional weight of female mortality at the oldest ages. In all countries, there is a marked decline in the proportions of female COVID-19 deaths at ages 85+ in the 2021 seasons compared to those of 2020. The younger structure of COVID-19 deaths in 2021 is however more visible among men, as there are higher proportions of male COVID-19 deaths in the 45 to 74 age range. This pattern is most visible in Sweden and Belgium during the summer of 2021, as well as in the United States during the summer and autumn of 2021. It should be kept in mind, however, that the data used for these analyses do not specify the role of COVID-19 in the death, which can contribute to increase the number of deaths (with or due to COVID-19) at young ages in periods with more contagious variants, such as Delta and Omicron in 2021.
Conclusions and Discussion
This study shows that the timing and intensity of the waves of COVID-19 mortality experienced during 2020 and 2021 varied across study countries. Pronounced periods of stabilization were identified during the summer (especially in 2020) among the European countries included in our analyses, but not in the United States. Possibly, this is related to stricter and more uniform public health measures within the European countries; for example, lockdowns were implemented almost simultaneously across the national territories of the European countries analysed here, except for Sweden (Glass, 2020). In contrast, the United States was more heterogeneous in terms of each wave's beginning as well as the types and timing of public health strategies implemented by local governments to contain the spread of the virus (Oster et al., 2020; Pei et al., 2020; McMahon et al., 2022). Between the European countries analysed here, we found calendar differences, with France and Belgium experiencing an earlier start of the stabilization period in 2020 and Sweden doing so later. An analysis of the differential effect of public health policies could be refined in light of these findings, considering that Sweden distinguished itself from most other European countries during the first wave by not imposing mandatory lockdowns (Born et al., 2021; Cho, 2020). According to the Containment and Health Index (Ritchie et al., 2020), Sweden had the least strict policy responses to COVID-19 throughout periods in 2020 and 2021, compared with the other European study countries. Seasonal variations in policy responses are most evident in France, where there is a relaxation of government measures during the summer months. Despite variations in policy responses to the COVID-19 crisis, our results show that there are certain similarities between the European countries in terms of the general trends in COVID-19 mortality, as noted above.
In the European countries studied here, the first two COVID-19 waves were the most lethal while the waves that followed during the year 2021 and the first months of 2022 were milder in comparison. Previous studies have observed a decrease in COVID-19 lethality over time, as a result of a combination of factors, notably mass vaccination, improved medical management of the disease, immunity from natural infection, and new variants of the virus (Marziano et al., 2022; Stepanova et al., 2022). Nevertheless, we also observed a sharp increase in COVID-19 mortality in the United States towards the end of summer 2021, which lasted through the autumn and winter. This finding suggests higher mortality during the Delta and Omicron variants (predominant from July until November 2021 and from December 2021 until February 2022, respectively) in the United States than in the European countries studied here. This finding requires further investigation and should be interpreted with caution, however, as the data used here do not allow identifying the precise role of COVID-19 in the death. The Delta and Omicron strains, while highly contagious, were initially thought to be less fatal compared to previous variants. Nevertheless, in Massachusetts (a State with high vaccination coverage) Omicron caused a substantial number of excess deaths, even more than Delta (Faust et al. 2022).
By the end of the study period, the highest cumulative COVID-19 mortality rates from the beginning of the pandemic were observed in the United States and then in England and Wales. However, we found that the age-distribution of deaths in those two countries differs substantially, as the United States displayed the highest COVID-19 mortality rates at young ages. In addition to higher COVID-19 mortality, all-cause mortality was already higher in the United States than in Europe even before the pandemic. The pandemic may have “accentuated the pre-existing mid-life mortality crisis” in the United States, as non-COVID mortality (mainly from external causes of death) also increased in 2020 and 2021 among working age adults, especially among men, possibly reflecting the lethal impact of drug overdose and homicides (Schöley et al., 2022). Previous studies have shown that COVID-19 hit the United States more severely than other countries; substantial losses in life expectancy in that country in 2020 are attributed to the pandemic (Aburto et al., 2022; Ahmad and Anderson, 2021; Woolf et al., 2021).
We also found that in general, COVID-19 mortality is highest during winter (especially the winter of 2020–2021) and at old ages. The seasonality of COVID-19 mortality is hence similar to that of influenza, which is one of the causes of death associated with higher winter mortality – a pattern already observed before the pandemic. For instance, in the United States, ‘winter life expectancy’ is about 1 year lower than ‘summer life expectancy’ (Ho and Noymer, 2017).
In 2020, the age and sex structure of COVID-19 deaths was older compared to that for all causes of death in 2019. Lower COVID-19 mortality rates and changes in the sex and age distribution of those deaths were observed in 2021, which could be linked to country-specific health policies. A marked reduction in COVID-19 mortality rates at the oldest ages was observed in England and Wales, Scotland, and the United States in the spring of 2021. This could possibly reflect (at least in part) the effects of vaccination in those populations (i.e., lower COVID-19 mortality rates with increasing vaccination coverage): by the beginning of April 2021, the share of people who had received at least one dose of COVID-19 vaccine was about 46% in the United Kingdom and 34% in the United States, but only 14% in France and Belgium and 13% in Sweden (Mathieu et al., 2021). COVID-19 mortality at the oldest ages declined in France, Belgium, and Sweden during the summer of 2021, a period when these countries considerably increased their share of fully vaccinated people (70.5% in Belgium, 60% in France and 56% in Sweden by the end of August 2021). Increased vaccination coverage during the summer of 2021 in these three countries is most likely related to reductions in mortality at ages 40 to 75 during the following autumn. In contrast, the reduction in COVID-19 mortality at ages 40 to 75 continued to be a challenge for the United States, which could possibly be related to the slower pace in vaccination during the second half of 2021. After being one of the forerunners in COVID-19 vaccination, the share of the fully vaccinated people in the United States fell behind all other study countries; only 65% of the United States population was fully vaccinated by the end of February 2022. This proportion was higher at the oldest ages (92% and 86% at ages 65–74 and 75+, respectively), but considerably lower among the youngest (between 62% and 74% in the 18 to 49 age group) (CDC 2022). It should be noted, however, that other factors may also have contributed to changes in the structure and intensity of COVID-19 mortality during 2021 in the study countries, as vaccinations alone are insufficient to contain the pandemic (Faust et al. 2022; Olivera Mesa et al. 2022; Moore et al. 2021). Even when a large share of the population has been fully vaccinated, widespread COVID-19 testing among both vaccinated and unvaccinated individuals remains important, as the efficacy of the vaccine wanes over time (Di Lego et al., 2022). Furthermore, fully vaccinated individuals who later get infected with SARS-CoV-2 may carry high viral loads (even if they do not develop serious symptoms).
Finally, the pattern observed for sex differentials in COVID-19 mortality also shows consistency with that of all-cause mortality, with men experiencing higher risks of dying compared to women at any age (Pison and Meslé, 2022; Ahrenfeldt et al., 2021). Other studies have found that excess mortality – i.e., higher mortality levels from all causes combined, compared to previous years – has been greater among men than women during the COVID-19 pandemic (Modig et al., 2021; Nielsen et al., 2021). We found that sex differences were largest at ages 50 to 75 years. However, the magnitude of those differences varies by country. While comorbidities and, more specifically, respiratory diseases are major risk factors for COVID-19 death, gender differences in the prevalence of diseases might be at play. Another possible explanation could be differential exposure to the virus associated with sex differences in domestic, social and economic activities. Further examinations are required for better understanding such variations across countries.
Strengths and limitations of this study
This paper compares the variability in cross-seasonal COVID-19 mortality by age and sex in six countries, covering the years 2020 and 2021. Our analyses add to current evidence on the lethality of COVID-19, by showing the changing age- and sex-structure of COVID-19 mortality between populations and over time within the same population.
Furthermore, unlike previous studies, our analyses are based exclusively on the type of data sources that currently offer the most complete information on COVID-19 deaths. These results provide solid material to be confronted with country-specific health policies implemented by governments. Nevertheless, the real burden of COVID-19 will be more accurately determined once vital statistics for 2020 and 2021 become available, by assessing the precise role played by COVID-19 in the death, whether immediate, contributory, or underlying cause of death.
Conflict of Interest
None declared.
Funding source
None.
Ethics approval statement
This study only uses aggregate data available online.
Data availability
Data and codes to reproduce the analyses presented here will be available in an open-access repository. The datasets were derived from sources in the public domain: French Institute for Demographic Studies (INED) (distributor). The Demography of COVID-19 Deaths. https://dc-covid.site.ined.fr/en/
Author contributions
Conceptualisation, methodology, writing original draft: CT, JG and FM. Review and editing: MB, EC, FB. Data collection and metadata curation: AC, EC, JG, CT, FM, MB, SP, CGC, J-MR.
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Appendix Supplementary materials
Image, application 1
Image, application 2
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ijid.2022.12.004.
| 36509336 | PMC9733967 | NO-CC CODE | 2022-12-15 23:17:59 | no | Int J Infect Dis. 2022 Dec 10; doi: 10.1016/j.ijid.2022.12.004 | utf-8 | Int J Infect Dis | 2,022 | 10.1016/j.ijid.2022.12.004 | oa_other |
==== Front
Talanta
Talanta
Talanta
0039-9140
1873-3573
Elsevier B.V.
S0039-9140(22)00986-9
10.1016/j.talanta.2022.124190
124190
Article
Electromechanical RT-LAMP device for portable SARS-CoV-2 detection
Tarim E. Alperay a
Oksuz Cemre a
Karakuzu Betul a
Appak Ozgur b
Sayiner Ayca Arzu b
Tekin H. Cumhur ac∗
a Department of Bioengineering, Izmir Institute of Technology, Izmir 35430, Turkey
b Department of Medical Microbiology, Dokuz Eylul University, Faculty of Medicine, Izmir 35330, Turkey
c METU MEMS Center, Ankara 06520, Turkey
∗ Corresponding author. Department of Bioengineering, Izmir Institute of Technology, Izmir 35430, Turkey.
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© 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.
Rapid point-of-care tests for infectious diseases are essential, especially in pandemic conditions. We have developed a point-of-care electromechanical device to detect SARS-CoV-2 viral RNA using the reverse-transcription loop-mediated isothermal amplification (RT-LAMP) principle. The developed device can detect SARS-CoV-2 viral RNA down to 103 copies/mL and from a low amount of sample volumes (2 μL) in less than an hour of standalone operation without the need for professional labor and equipment. Integrated Peltier elements in the device keep the sample at a constant temperature, and an integrated camera allows automated monitoring of LAMP reaction in a stirring sample by using colorimetric analysis of unfocused sample images in the hue/saturation/value color space. This palm-fitting, portable and low-cost device does not require a fully focused sample image for analysis, and the operation could be stopped automatically through image analysis when the positive test results are obtained. Hence, viral infections can be detected with the portable device produced without the need for long, expensive, and labor-intensive tests and equipment, which can make the viral tests disseminated at the point-of-care.
Graphical abstract
Image 1
Keywords
SARS-CoV-2
Loop-mediated isothermal amplification (LAMP)
Point-of-care testing
Electromechanical systems
Colorimetric detection
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pmc1 Introduction
RNA viruses are pathogenic organisms that cause important diseases in humans, and diagnostic tests are of great importance for the early diagnosis and treatment process of these viruses [1]. Immuno-based methods are performed based on capturing antibodies produced against the virus and are widely used but are not effective in the early detection of the virus [2]. With the spread of COVID-19, early detection methods have gained great importance in order to take timely preventive measures [3]. Although RT-qPCR has a high accuracy that is accepted as the gold standard method for COVID-19 detection, its usage is limited because it is a non-portable, laboratory-dependent, and high-cost test, and it also needs technical expertise for its operation [4]. On the other hand, LAMP tests are performed at a constant temperature, they eliminate the need for a thermal cycler and can offer a cost-effective analysis, unlike the RT-qPCR method [5]. The LAMP method, which has been widely used in studies for COVID-19 detection, has revealed sensitive and easy-to-use analysis [6]. Colorimetric detection can be used for LAMP tests, where the color change is appeared for a positive sample with an altered pH value due to the reaction [7]. For instance, the viral genome of SARS-CoV-2 could be detected in real-time by the RT-LAMP method [4]. With the use of this diagnostic method, by targeting ORF1ab and N regions of SARS-CoV-2 RNA performed on the heating block, 103 copies/mL limit of detection was reached using naked eye inspection in 25 min [8]. In another LAMP-based diagnostic system, an artificial SARS-CoV-2 genome was detected in real-time using microelectrodes [9]. In this portable platform supported by Arduino to control temperature and measure electrical potential, the presence of viral genome was studied in the 10-104 copies in a 50 μL sample by monitoring the pH change through the electrical potential that could increase the cost per test. RT-LAMP-based SARS-CoV-2 detection can also be performed using electrochemical sensors. For instance, disposable electrochemical test strips containing screen-printed electrodes were used for the detection of N and ORF1ab genes of the SARS-CoV-2 [10]. This sensor with a detection limit of 38 × 10−6 ng/μL was successfully tested in wastewater samples. Furthermore, a potentiometric device monitoring pH changes of the LAMP process was developed for point-of-care detection of SARS-CoV-2 [11]. The device could detect SARS-CoV-2 in 25 min with a limit of detection of 105 copies in 25 μL sample. Based on RT-LAMP, the Palm Germ-Radar (PaGeR) platform can detect the COVID-19 viral genome down to 1-5 ×103 copies/mL within 1 h with a naked-eye using three detection methods, which were colorimetric, fluorimetric, and lateral dipstick [12]. However, the platform could not enable real-time in situ quantitative monitoring. Furthermore, the handheld portable platform contains a single-use microfluidic cartridge integrated with ion-sensitive field-effect transistors (ISFET) to identify voltage changes due to pH variations in LAMP reaction [13]. Although the detection limit of 10 copies/reaction has been reached with 20 min of reaction, the high cost of cartridge due to ISFET could be a limiting issue. The detection limit is important for the diagnosis of COVID-19, since the viral load of the collected sample could be down to ≤103 copies/mL in the early-stage of the infection [14]. This may result in tests that do not have a low detection limit giving false results in the first days of COVID-19. Although some methods were developed for COVID-19 detection, a complete solution for automated LAMP testing at the point-of-care is limited. Here, we present a new COVID-19 diagnostic device using the LAMP method with automatic image analysis to monitor color change in the sample for real-time detection of a low amount of viral RNA. This low-cost device offers portable, rapid, and automated viral RNA detection without the need for expensive instruments, and technical expertise.
2 Materials and methods
2.1 Template and primer design
The LAMP primers used in the device were designed for the N gene region of SARS-CoV-2 (GenBank Accession no: NC_045512, positions 28,285–28,529), where designed primers are highly conserved for different variants of SARS-CoV-2 (e.g., Delta B.1.617.2 (GenBank Accession no: OX000604.1), and Omicron BA.5.2 (GenBank Accession no: OP136952.1)) [15]. Forward outer primer (F3: TGGACCCCAAAATCAGCG), backward outer primer (B3: AGCCAATTTGGTCATCTGGA), forward inner primer (FIP: CGTTGTTTTGATCGCGCCCCATTACGTTTGGTGGACCCTC), backward inner primer (BIP: TACTGCGTCTTGGTTCACCGCATTGGAACGCCTTGTCCTC), forward loop primer (LF: TCCATTCTGGTTACTGCCAG), and backward loop primer (LB: GCAAGGAAGACCTTAAATTCCCTC) were initially designed using New England Biolabs (NEB) LAMP Primer Design Tool Version 1.0.1. All primers were synthesized (Triogen Biyoteknoloji, Turkey). As a positive control, a SARS-CoV-2 RNA positive sample of QCMD SARS-CoV-2 EQA, 2020 panel was used, while a sample carrying human cytomegalovirus DNA (CMV, QCMD Human Cytomegalovirus DNA EQA panel, 2019) and hepatitis C virus RNA (HCV, QCMD Hepatitis C Virus RNA EQA Programme, 2020) were chosen as negative controls. Nucleic acid extraction of both positive and negative controls was done by an EZ-1 virus mini kit (Qiagen, Germany). The specific amount of template RNA and negative controls carrying CMV DNA, and HCV RNA were obtained from QCMD standards sample code as SCV2_101S-04, CMVDNA19S-02, and HCVRNA20C1-01, respectively. SARS-CoV-2 RNA concentrations were adjusted by dilution of the known SCV2_101S-04 sample (104.29 copies/mL) in distilled water (dH2O). The LAMP reaction mixture was prepared using 25 μL Warmstart Colorimetric LAMP 2X Master Mix (New England Biolabs, USA), 2.5 μL of primer mix containing 6 LAMP primers, 8 μL of dH2O, and 2 μL of the sample. 2 μL of dH2O was used instead of RNA sample as a blank group.
2.2 Fabrication of electromechanical device
The fabricated automated electromechanical device allows the amplification of the SARS-CoV-2 viral gene. The device's dimensions are kept small for portable operation (50 mm × 50 mm × 39.3 mm) (Fig. 1 ). SARS-CoV-2 viral gene detection is conducted on this device, which automatically analyses color change due to LAMP reaction at a constant temperature of 65 °C. As the LAMP reaction takes place, there is a color change from pink to yellow in the reaction solution. The temperature required for the LAMP reaction is supplied with four Peltier heaters (TEC1-04905, Thermonamic, China) surrounding the disposable PCR tube, where LAMP amplification takes place. It is hard to couple temperature sensors on the rotating PCR tube containing the sample. Here, a low-cost thermocouple temperature sensor (WRN-02B–NiCr–Ni Thermocouple, ISISO, Turkey) was used to monitor the temperature nearby the rotating PCR tube during operation. Temperature is controlled with an Arduino microprocessor (Arduino Mega 2560 R3, Italy) using the temperature sensor and it is maintained at 65 ± 2 °C by powering on/off Peltier heaters automatically. The wiring schematic and control algorithm for temperature control were shown in Fig. S1 and Fig. S2, respectively. A DC motor (Micro Metal Gearmotor HPCB 3061, Pololu, USA), which is driven with 1.2 V, rotates the PCR tube at 300 rpm to mix the sample in order to accelerate LAMP amplification [16,17] and obtain a homogeneous color in the reaction tube [18]. The observations of LAMP amplification are done by taking the photos with the camera (Raspberry Pi Camera Module V2, Raspberry Pi, USA) from the bottom of the PCR tube. The camera is mounted 2.5 cm away from the bottom of the PCR tube, but it does not need to focus well on the tube which makes the device size minimum. Camera control and analysis of the obtained photos are made on a microcomputer (Raspberry Pi 3 B+, Raspberry Pi, USA). The lighting of the platform is maintained with two white LEDs (12383, Robotistan, Turkey), which are located at the opposite top corners of the platform and are operated with 5 V supplied from the microcomputer (Fig. S1). The technical drawing of the complete device is shown in Fig. S3.Fig. 1 Illustrations and photographs of automated LAMP analysis device for COVID-19 diagnosis. The device is composed of LEDs, Peltier heaters, a mixing apparatus, a DC motor, a temperature sensor, a camera, and a 3D-printed frame. The scale bar is 10 mm.
Fig. 1
2.3 Detection procedure
For the LAMP amplification, the device is first heated for 2 min to reach 65 °C and then the PCR tube containing the LAMP reaction mixture is mounted on the mixing apparatus found on the lid of the device (Video S1). After the lid is closed, sample mixing and LAMP reactions are initiated. LAMP uses forward outer primer (F3), backward outer primer (B3), forward inner primer (FIP), backward inner primer (BIP), backward loop primer (LB), and forward loop primer (LF) to recognize target RNA (Fig. 2 ). The amplification is initiated with the annealing of the inner primer to the target region which is then extended by RNA polymerase. The outer primer anneals to the original backbone to displace the product then reverse complementary sequences on the product anneals to each other and forms a self-hybridizing loop structure. This process goes with the displacement-annealing cycles to form a dumbbell structure. The dumbbell structure is a seed for the exponential LAMP amplification containing multiple initiations and annealing sites for primers. Thus, the amplification takes place in these multiple regions, and the products increase and form yellow color in the PCR tube for the detection [19]. The camera on the device starts to take photos of the PCR tube three times every 5 min for the entire amplification process without stopping the tube rotation.Fig. 2 The illustration of the RT-LAMP protocol conducted in the presented device. RT-LAMP protocol was realized in the PCR tube with F3/B3 (forward/backward outer primers), FIP/BIP (forward/backward inner primers), and LF/LB (forward/backward loop primers) for the exponential amplification. The color change in the PCR tube indicating the presence of SARS-CoV-2 RNA is monitored with a camera. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2
The following is the supplementary data related to this article:Multimedia component 2
Multimedia component 2
Color analysis of the captured PCR tube photos is made in hue/saturation/value (HSV) color space by masking specific color intensity at a certain threshold [20,21]. As explained in Fig. S4 and Fig. S5, the captured images are first converted from the red-green-blue (RGB) color space matrix to the HSV color matrix with the color gamut conversion algorithm [22], and then they are processed to measure the yellow color intensity value indicating the presence of SARS-CoV-2. For this purpose, the HSV representation of the captured images are masked with the hue value (hue: >0.982 and < 0.257) for specifying the specific hue value of the yellow color, the brightness value (brightness: >1.0 and < 0.528) for masking unwanted dark and bright fields, and the saturation value (saturation: >1.0 and < 0.287) for determining the color change from pink to yellow due to the amplification. By doing so, positive samples can be identified with increased saturation value depending on the yellow color intensity. Each experiment was repeated 3 times and saturation values of experiments every 5 min were calculated as mean ± standard deviation (SD) by using the photo giving maximum saturation values from three consecutive photos of the PCR tube in each experiment.
3 Results and discussion
The device was tested at different rotational speeds to ensure optimum mixing for amplification without affecting HSV analysis on captured photos. 3 repeated photographs of yellow food dye in PCR tubes were captured with continuous rotation at 0, 100, 300, and 600 revolutions per minute (rpm). The observations show that rotational speeds up to 300 rpm did not affect the HSV analysis (Fig. 3 ). The HSV saturation value decreases due to the change in a blur of moving objects at 600 rpm rotational speed. Therefore, 300 rpm was chosen as the optimal mixing speed having a negligible effect on the HSV analysis that can allow real-time analysis without stopping the rotation. The reason for taking photos without stopping the mixing process is to model the real-time RNA detection process which could inform users immediately about the positive results without waiting for the whole pre-set duration of the LAMP process. The effect of the mixing procedure on the LAMP process was also examined. It was shown that the mixing process improved the difference between the detection signals of positive samples (Fig. S6).Fig. 3 HSV saturation values at different rotational speeds. HSV saturation values of the masked photographs taken at 0, 100, 300, and 600 rpm rotational speeds were measured. Data were calculated as the mean ± SD of 3 replicates of experiments. Statistical differences were obtained using a one-way ANOVA test at each rotational speed. The symbols (*), (**), and (***) represent p < 0.05, p < 0.01, and p < 0.001, respectively. ns means non-significant.
Fig. 3
Furthermore, camera to PCR tube distance was examined to show the effect of the camera focusing on HSV analyses. The HSV saturation value decreases as the PCR tube moves away from the camera in order to focus the photos. (Fig. S7). The camera to PCR tube distance should be set to 10 cm to get focused images that can enlarge the device size. Although a macro lens could be used to get focused photos at a shorter distance, this lens could increase the cost and the size of the device. On the other hand, the whole PCR tube is not visible at distances below 2.5 cm. Because of that camera to PCR tube distance is set to 2.5 cm in the device to get maximum HSV values.
The device was tested with different concentrations of SARS-CoV-2 RNA, negative controls carrying CMV DNA and HCV RNA, and the blank group without any nucleic acids. The color change started to be seen at 103 copies/mL (∼15 fg/mL) and 104 copies/mL of SARS-CoV-2 RNA concentrations on PCR tube photos after 55 min (Fig. 4 a). HSV masked images were also obtained from captured photos (Fig. 4b). The saturation values of HSV masked images were analyzed (Fig. 4c). A significant difference was observed for ≥103 copies/mL of SARS-CoV-2 RNA after 50 min compared to blank and control groups. However, there was no significant difference at 102 copies/mL SARS-CoV-2 during 70 min of the whole amplification process. Due to used low sample volume of 2 μL, which is in the range of advised sample volume (2–10 μL) in the LAMP 2X Master Mix datasheet, the probability of RNA copies available inside the sample is low at this concentration level. This indicates that the detection limit of the platform is ∼103 copies/mL. To decrease the detection limit further, the sample volume and also the LAMP reaction mixture could be increased but this would increase the cost per test. Moreover, no statistical differences were obtained for the 2.5 × 106 copies/mL (∼10 pg/mL) CMV sample and 1.98 × 103 copies/mL HCV sample (∼10 fg/mL) used as negative controls with DNA and RNA viruses compared to the blank. Thus, used LAMP protocol allows high specificity for detecting SARS-CoV-2 RNA.Fig. 4 Detection results obtained on the platform. (a) Unmasked raw photos of PCR tubes taken at 5th, 30th, and 55th min of the incubation time for different SARS-CoV-2 RNA concentrations (104 copies/mL, 103 copies/mL and 102 copies/mL), 2.5 × 106 copies/mL CMV sample (negative control), 1.98 × 103 copies/mL HCV sample (negative control), and blank group (0 copies/mL). (b) HSV masked images of the PCR tube having 104 copies/mL of SARS-CoV-2 RNA. The inset images show the unmasked raw photos. The scale bars are 2 mm. (c) Time-dependent graph of saturation values of HSV masked images. Data were shown as the mean ± SD of 3 replicates of experiments. Statistical differences were compared with the blank using a 2-way ANOVA test in each time set. The symbols (*), (**), and (***) represent p < 0.05, p < 0.01, and p < 0.001, respectively.
Fig. 4
After 70 min of LAMP amplification for different samples, spectrometric measurements, which are also gold standards to analyze the color changes [23], were conducted. For this purpose, 12.5 μL of dH2O was added to 37.5 μL of amplification solution to make up to 50 μL final solution to conduct absorbance measurements on a 96-well plate using a spectrophotometer (Multiscan Go, Thermo Fisher Scientific, USA). The spectrometric absorbance measurements between 300 and 800 nm wavelengths were shown in Fig. 5 a. The amplifications gave peak values at around 432 and 560 nm wavelengths, since the wavelength of 432 nm, and 560 nm represent the intensity of the yellow and pinkish colors, respectively. The ratio of these two values (432 nm/560 nm) can measure how much color change occurs from pink to yellow in the amplification that can be used to quantify the RNA sample [24]. As shown in Fig. 5b, there were significant differences for ≥103 copies/mL SARS-CoV-2 compared to control groups as in HSV analysis in the presented device. Moreover, 104 copies/mL of SARS-CoV-2 showed a higher detection signal, which was also observed in HSV analysis. Hence, instead of using an expensive spectrophotometer, a simple camera module combined with HSV analysis can be utilized for sensitive analysis of SARS-CoV-2 samples.Fig. 5 Spectrometric measurements of LAMP mixture after amplification. (a) Absorbance graphs for different SARS-CoV-2 RNA concentrations (102-104 copies/mL), 2.5 × 106 copies/mL CMV sample (negative control), 1.98 × 103 copies/mL HCV sample (negative control), and blank group (0 copies/mL). The inset images show PCR tube photographs for different samples captured outside the device. Absorbance values are shown as the average of 3 replicates of experiments. (b) Absorbance ratio at the wavelengths of 432 nm and 560 nm. Data were shown as the mean ± SD of 3 replicates of experiments. Statistical differences were compared with the controls using one-way ANOVA by Holm-Sidak statistical hypothesis testing. The symbols (*) and (**) represent p < 0.05 and p < 0.01, respectively.
Fig. 5
In addition, the device was tested with different SARS-CoV-2 concentrations to analyze the performance of the device for quantitative detection (Fig. 6 ). The analyses were conducted on images taken after 70 min of amplification. The limit of quantification (LOQ) signal was calculated as mean + 10 × SD of the mean of the HCV negative control group, which gives the highest detection signal among the control groups [25]. The HSV values for the concentration of ≥103 copies/mL are bigger than LOQ, so these concentrations (≥103 copies/mL) could be sensed in the device. Although high standard deviation values were observed for different SARS-CoV-2 RNA concentrations, a linearity between the measured HSV saturation values and spiked RNA concentrations was seen (Fig. 6).Fig. 6 The analyses in the device for different SARS-CoV-2 RNA concentrations at 70th minute of amplification. Data were shown as the mean ± SD of 3 replicates of experiments. The coefficient of determination (R2) value calculated using semi-log regression analysis of the data was shown on the graph. R2 was evaluated on the mean HSV saturation values of each concentration.
Fig. 6
The comparison of the LAMP-based devices to detect SARS-CoV-2 was shown in Table S1. The presented device has solid features and qualifications for detecting SARS-CoV-2 RNA in terms of limit-of-detection, cost, and real-time monitoring. The limit-of-detection value of our device (103 copies/mL) is sufficient for diagnosis from swab samples at the early stage of infection [9]. The real-time detection feature of this device could terminate the protocol earlier depending on viral loads. Although electrochemical detection methods could improve the detection time further, these methods need special electrodes that should be disposed of after each test. On the other hand, colorimetric detection methods, which monitor only the color changes in the sample, could reduce the cost of tests by eliminating the need to dispose of high-cost detection sensors. Moreover, the integrated heater on the presented device could maintain the required temperature for LAMP process without external components. Automated readout could also be used to quantify SARS-CoV-2. These features ensure user-friendly and plug-and-play operation of the device.
4 Conclusion
The proposed device was applied to detect SARS-CoV-2 RNA using LAMP protocols at a constant temperature with automatic and real-time colorimetric analysis. Real-time detection could reduce viral detection assay time of qualitative tests by terminating the LAMP amplification procedure when positive test results were observed. The device allows sensitive detection of viral RNA down to 103 copies/mL in ∼55 min with low-cost components. In this way, it could permit early-stage COVID-19 detection with low detection limits. This affordable device costs less than $200 with low-cost components like Peltier heaters, DC motor, thermocouple temperature sensor, microcomputer, and camera and it requires only ∼$2 per virus detection test. It can be 3D printed, assembled, and installed easily with accessible components even in remote rural regions. The device can detect viral RNA at point-of-care settings without the need for any professional equipment or personnel and is portable due to its small dimensions. Hence the device and the developed assay can provide rapid and point-of-care detection of COVID-19 and improve on-site monitoring of the infection. It provides a stable temperature for amplification and automated SARS-CoV-2 RNA detection with a mounted camera module at the bottom of the device using unfocused PCR tube photos. Although primers specific to the N gene of the SARS-COV-2 Wuhan-Hu-1 virus were used in the platform, primer sequences can be designed for other gene regions or different variants so that genomic changes can be tested, and other regions of the virus can be identified to obtain more precise results. The primers can also be designed for different viruses. Thus, this automated electromechanical device can be easily updated to detect viral RNA/DNA of interest, making it useable in future epidemics and pandemics.
Author contributions
E. Alperay Tarim: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization; Cemre Oksuz: Conceptualization, Methodology, Investigation, Writing – original draft; Betul Karakuzu: Conceptualization, Methodology, Investigation, Writing – original draft; Ozgur Appak: Methodology, Validation, Resources, Writing – review & editing; Ayca Arzu Sayiner: Methodology, Validation, Resources, Writing – review & editing; H. Cumhur Tekin: Conceptualization, Methodology, Resources, Formal analysis, Writing – original draft, Writing – review & editing, Supervision, Project administration, Funding acquisition.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A Supplementary data
The following are the supplementary data to this article:Multimedia component 1
Multimedia component 1
Data availability
Data will be made available on request.
Acknowledgements
H.C.T. would like to thank the Outstanding Young Scientists Award funding (TUBA GEBIP 2020) from the Turkish Academy of Science, Young Scientist Awards (BAGEP 2022) from 10.13039/501100008967 Science Academy (10.13039/501100008967 Bilim Akademisi ) and the scientific research project (2020IYTE0042) funded by 10.13039/501100003984 Izmir Institute of Technology (10.13039/501100003984 IZTECH ). B.K. and E.A.T. acknowledge the support of The 10.13039/501100004410 Scientific and Technological Research Council of Turkey for the 2211-A BIDEB doctoral scholarship and the support of the Turkish 10.13039/501100007246 Council of Higher Education for the 100/2000 CoHE doctoral scholarship. The authors would like to thank Engin Ozcivici, Ph.D. and Humeyra Taskent Sezgin, Ph.D. from the Department of Bioengineering, IZTECH, and Meltem Elitas, Ph.D. from the Department of Mechatronics Engineering, Sabanci University for helpful discussions.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.talanta.2022.124190.
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| 36521325 | PMC9733968 | NO-CC CODE | 2022-12-14 23:45:34 | no | Talanta. 2023 Mar 1; 254:124190 | utf-8 | Talanta | 2,022 | 10.1016/j.talanta.2022.124190 | oa_other |
==== Front
J Behav Exp Econ
J Behav Exp Econ
Journal of Behavioral and Experimental Economics
2214-8043
2214-8051
Elsevier Inc.
S2214-8043(22)00138-0
10.1016/j.socec.2022.101968
101968
Article
The Effect of Disinformation about COVID-19 on Consumer Confidence: Insights from a Survey Experiment
Balcaen Pieter a⁎
Buts Caroline b
Bois Cind Du c
Tkacheva Olesya d
a Dr. Pieter Balcaen is a researcher at the Royal Military Academy Hobbemastraat 184, 1000 Brussels, Belgium
b Prof. Dr. Caroline Buts is Professor at the Vrije Universiteit Brussel Pleinlaan 2, 1050 Elsene, Belgium
c Prof. Dr. Cind Du Bois is Professor at the Royal Military Academy Hobbemastraat 184, 1000 Brussels, Belgium
d Prof. Dr. Olesya Tkacheva is Assistant Professor at the Brussels School of Governance Pleinlaan 2, 1050 Elsene, Belgium
⁎ Corresponding author, Royal Military Academy Belgium
9 12 2022
9 12 2022
10196815 3 2022
26 11 2022
7 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.
Although the COVID-19 pandemic was accompanied by an infodemic about the origin of the virus and effectiveness of vaccines, little is known about the causal effect of this disinformation on the economy. This article fills in this void by examining the effects of disinformation about COVID-19 vaccines on consumer confidence by means of an original survey experiment in Dutch speaking communities of Belgium. Our findings show that the information set that impacts consumer confidence is much broader than previously assumed. We show that disinformation changes the perception of the effectiveness of vaccines which in turn indirectly impacts the future economic outlook, measured by the metric consumer confidence. Moreover, we find that the above effects are larger for respondents exposed to disinformation that is framed as containing ‘scientific evidence’ compared to ‘conspiracy frames’.
Keywords
Disinformation
COVID-19
consumer confidence
fake news
==== Body
pmc1 Introduction
The COVID-19 pandemic has revived scholarly interest in factors affecting consumer sentiments by providing an unprecedented opportunity to examine the relevance of both macro and micro determinants. The pandemic allowed to study the effect of information on consumer sentiments in a new environment, while at the same time identifying health anxiety as an important, but previously overlooked factor affecting consumer economic outlook. This paper contributes to this rapidly growing literature by examining how consumers reacted to disinformation about the effectiveness of anti-COVID-19 vaccines in the midst of the lockdown in Belgium. Unlike the Great Recession or other economic downturns, the COVID-19 pandemic was accompanied by an infodemic that first spread confusion about the origins and contagiousness of the virus and later questioned the effectiveness and safeness of vaccines. Surprisingly however, very little is known about the impact of this infodemic on consumer sentiments. Our study fills in this void by conducting an original online survey-embedded experiment involving a sample of Belgian consumers who were randomly exposed to a scientifically accurate assessment of vaccine effectiveness and two different treatments containing false information about the effectiveness of anti-COVID vaccines. The first treatment uses a scientific frame that mimics an academic jargon to question the effectiveness of the vaccine, whereas the second is framed in terms of a conspiracy theory, portraying vaccines as the means to covertly extend state control over the population. We find that both of these frames negatively affect respondents’ confidence in the effectiveness of the vaccine which subsequently negatively impacts their economic outlook.
Our study is the first of its kind to bridge public health studies that focus on the impact of disinformation on vaccine acceptance, with the economics literature focusing on the determinants of consumer expectations. In doing so, we generate important policy implications by showing that during times of crises (characterized by increased consumer uncertainty), disinformation can create an additional impediment towards economic recovery. Our analysis shows that the information set relevant for economic expectations encompasses factors outside the economic domain. By increasing uncertainty and anxiety related to public and personal health, disinformation un-anchors economic expectations from the traditional macroeconomic indicators that can be targeted by government policies and/or central bank communication, which subsequently weakens the effectiveness of targeted interventions.
Our second contribution consists in comparing how framing changes the effectiveness of disinformation on vaccine hesitancy. The infodemic about COVID-19 flooded the information environment with both conspiracy theories about the man-made origin of the virus and false statements full of references to “scientific” evidence. Although our study falls short of providing causal mechanisms that discuss the cognitive processes triggered by these two manipulations the contribution is important. To the best of our knowledge, there's only one other study that measures the effect of framing (Loomba et al., 2021), showing that disinformation that mimics scientific objectivity has a greater negative impact on vaccine hesitancy than other frames.
Throughout this study we will refer to information that distorts scientific knowledge about the vaccine as “disinformation.” The political communication literature differentiates between “fake news”, “misinformation” and “disinformation”, with the latter having a deliberate deception component with the intent to harm (Lazer et al., 2018). We use the term “disinformation” throughout the paper to capture the fact that the infodemic surrounding the pandemic had a malign intent.
The article is organized as follows: we begin by reviewing the literature on the effect of the pandemic on consumer confidence and the literature on disinformation (including a focus on the recent use of disinformation during the COVID-19 pandemic) in Section 2, followed by developing hypotheses about the effects of disinformation about vaccine effectiveness on consumer confidence in Section 3. We present our methodology and data collection in Section 4. Section 5 provides an overview of our main findings, followed by a discussion of the implications of our study in Section 6. Section 7 concludes and provides direction for follow-up research.
2 What influences consumer confidence?
Consumer confidence is an important measure of the health of the economy because it captures individual prospective and retrospective evaluations of the state of the national economy and one's personal financial situation (Katona; 1951, 1975). The literature on consumer confidence generally reveals that it is strongly correlated with the evolution of GDP by acting as a driver of consumer spending (Ludvigson, 2004; Perić & Sorić, 2017) and with other economic and financial variables (Nowzohour and Stracca, 2017).1 A decrease in consumer confidence can exert a substantial negative effect on the economy because it functions as an intervening variable between numerous pieces of information and the final expenditure decision (Katona 1951, 1975). It therefore conveys information about future economic behavior (Vuchelen, 2004). A decline in consumer confidence might subsequently lead to a decrease in consumption and the associated increase in savings (Hetsroni et al., 2014).2
The pre-pandemic literature has focused on how economic news affects consumer confidence (Vliegenthart et al., 2021). Both observational (Blood & Phillips, 1995; Doms & Morin, 2004; Hollanders & Vliegenthart, 2011; Casey & Owen, 2013) and experimental studies (Damstra, 2019, Vliegenthart et al., 2021) find that negative news about economic downturns undermine consumer confidence and that a negative tone has a greater impact on consumer confidence than positive reporting (Soroka 2006). The literature explains this negativity bias either by priming theory (Vliegenthart et al., 2021) or by attention bias, which emerges due to the fact that individuals are generally risk-averse and as such read news about economic downturns with greater interest than news about economic growth. This subsequently increases the negative impact of bad news (Damstra, 2019).
The COVID-19 pandemic reinvigorated scholarly interest in the factors affecting consumer sentiments by providing an opportunity to examine the link between public and personal health concerns and economic expectations (see Table A1 in online annex for summary). Methodological approaches used to study this issue have encompassed big data analysis of Google search trends and Twitter feeds, survey-based observational studies, and survey-embedded experiments. The studies based on big data (van der Wielen & Barrios, 2021) compared Google search trends across the European Union countries and found that the growth of COVID-19 cases and COVID-19 related deaths contributed to the anxiety about the economy and jobs, while content analysis of Twitter feeds related to the post-lockdown plans exhibited cross-cultural differences in the preferred types of activities with individual-centric activities more frequently mentioned by users from individualistic cultures and collective activities by those from community-centric cultures (Pantano et al., 2021). Observational studies based on time series analysis of data from U.S. consumers show that the pandemic contributed to the surge in uncertainty about short-term inflation and greater polarization of consumer expectations between those who perceive pandemics as deflationary and those perceiving it as inflationary without at the same time affecting the expected level of inflation (Apergis & Apergis, 2021; Detmers et al., 2022).
Others point to the differences in correlation pattern between COVID-19 cases and death vis-a-vis the consumer confidence index and producers’ economic expectations with the former being more pessimistic about the economy than the latter, particularly in Europe as compared to the United States or China (Teresiene et al. 2021). Others still note that the correlation between consumer sentiments and consumer spending was weaker than before the pandemic (Abosedra et al. 2021).
Two experimental studies conducted respectively by Binder (2020) and Fetzer et al. (2020) in the United States during the first months of the pandemic are particularly relevant to our analysis because they focus on how consumers react to the information about the severity of the pandemic and the risk of contagion. Binder (2020), using a sample of Mechanical Turk workers, tested how the exposure to the WHO declaration of COVID-19 as a global pandemic affected both the economic outlook and COVID-19 related anxiety and found that the treatment triggered higher health anxiety among participants who do not follow the news frequently, albeit she found no effects of this treatment on the expectations for the economy. Fetzer et al. (2020) test how U.S. consumers respond to the choice of frame about the mortality rates by exposure to different frames about mortality rates of the virus: “5 times lower than for SARS” vs. “20 time higher than for the flu” and find that high mortality frames increased anxiety about both the aggregate and personal economic situations. They also show that consumer sentiments are correlated with different mental models about the vector of contagion. Those consumers who understand the exponential nature of virus diffusion adjusted their economic expectations downward by a greater amount than those who assumed a linear trajectory for the spread of the virus.
Our study extends the above literature by shifting the attention from the effects of accurate information to those of disinformation that deliberately distorts the facts with an intent to harm. Our analysis tests whether and how consumers react to disinformation that was presented using a frame that mimics an academic journal vis-a-vis a more politicized frame of conspiracy theory that has been used by populist parties. To the best of our knowledge this is the first study to examine the link between disinformation and consumer sentiments. Our analysis is based on a Belgian sample that allows us to evaluate generalizability of studies conducted in the United States. Unlike the previous two experiments that were conducted during the first months of the pandemic, our study took place almost a year after the first cases were reported in China, resulting in the loss of thousands of lives.
3 Disinformation and Consumer Confidence: A Conceptual Model
The importance of information for determining consumer sentiment has received attention from two strands of the literature: the media effect theory that focused on the link between the content of economic news and economic confidence (Blood & Phillips, 1995; Damstra, 2019; Doms & Morin, 2004; Hollanders & Vliegenthart, 2011; Vliegenthart et al., 2021) and the more recent literature on the inflation expectation that examined how consumers respond to different frames used in official communication about changes in interest rates (Coibion et al., 2022), economic news (Buckman et al., 2020; Deitrich et al., 2022) or perceptions of other consumers (Bui et al. 2021). Both of these camps implicitly assume that there are no challenges to the accuracy of information received by consumers. However, the current information landscape is loaded with fake news, misinformation, and disinformation which requires paying more close attention to the ways inaccurate information may affect consumer confidence. As noted by the World Health Organization's official during the early phase of the pandemic it was going to “be accompanied by a kind of tsunami of information, but also within this information you always have misinformation, rumors” (Zarocostas, 2020) because an incomplete information environment is particularly conducive to the proliferation of rumors that seek to compensate for the shortage of factual information (Larson, 2020).
The misinformation “tsunami” about COVID-19 has received extensive attention by the rapidly sprawling literature in communication, health sciences, and psychology that focused on (1) which cognitive processes may increase individual susceptibility to false information (Bastick, 2021; Greifeneder et al., 2020; Pennycook & Rand, 2019, 2021; Pennycook et al., 2018, Vegetti & Mancosu, 2020), (2) how the social media ecosystem can facilitate the spread of misleading content (Bridgman et al., 2020; Caldarelli et al., 2021; Papakyriakopoulos et al., 2021; Yang et al. 2021) and (3) how disinformation impacts individual compliance with government COVID-19 guidelines and willingness to get vaccinated (Dib et al., 2021; Kreps & Kriner, 2020, Roozenbeek et al., 2020; Loomba et al. 2021; Wilson & Wiysonge, 2021). A comprehensive discussion of this vast literature has been provided elsewhere (see Melchior, et al., 2021; Rocha et al., 2021). We single out studies that are most relevant to ours.
The content analysis of social media posts related to COVID-19 conducted by Zeng and Schafer (2021) uncovered two most prominent frames: 1) the conspiracy theory claiming that the pandemic was a man-made phenomenon to tighten control over the population; 2) the scientific expertise frame that portraits the pandemic as a hoax and/or questions the effectiveness of vaccines. Given that both types of narratives were prevalent on social media, our experiment allows comparing the effect of each of these frames on consumer confidence by exposing a subset of participants to the treatment that incorporates the conspiracy theory frame and the other that evokes scientific expertise. A priori, however, it is not possible to predict which of these two frames will have a greater effect on the perceptions of the vaccine effectiveness as the vast literature on this topic (reviewed by Aw et al., 2021) is inconclusive. The two most relevant experimental studies are by Loomba et al. (2021) conducted during the early phase of the COVID-19 and by Jolley and Douglas (2014) conducted well before the outbreak of the pandemic. Both studies seek to test for the causal effect of disinformation about vaccine effects on the willingness to vaccinate by exposing respondents to scientific statements in Loomba et al. (2021) and conspiracy theories in Jolley and Douglas (2014). Loomba et al. (2021) find that misinformation references to “scientists” had a greater negative impact on the willingness to vaccinate than other wordings, whereas Jolley and Douglas (2014) show that conspiracy theories had a negative impact on the intent to vaccinate, compared to the statement that refutes anti-vaccine conspiracies. Neither of these two studies, however, discusses the respective causal mechanisms that could lead to the differences in the effect size of these two frames.
Other relevant studies are Pomerance et al. (2020) and Cavazos (2019), focusing on the effect of misinformation on uncertainty3 and its subsequent effect on consumer behavior. Pomerance et al. (2020) find that the exposure to misinformation regarding COVID-19 increases uncertainty, which subsequently leads to: (1) resource conservation and (2) compensatory consumption, where the effect of uncertainty on spending intentions varies with income.4 Cavazos examines the economic costs stemming from disinformation.5 Our experimental design capitalizes on the above findings, as well as on the growing consensus in the consumer behavior literature that the pandemic increased uncertainty about macroeconomic trends (Apergis & Apergis, 2021; Detmers et al., 2022, Dietrich et al., 2022). Disinformation about the effectiveness of anti-COVID-19 vaccines is hence particularly relevant in this context. If the vaccine is not working properly, the virus prevents people from going to work and depresses consumer-spending intentions. This in turn has an effect on uncertainty surrounding future income and the degree of unemployment, which have been proven to be key determinants of consumer confidence (Vuchelen, 2004).
This causal mechanism is presented in Figure 1 . The effectiveness of informational manipulation depends on the individual cognitive ability and willingness to question the credibility of disinformation. Individuals who accept disinformation as factually correct change their perception of vaccine effectiveness, become more pessimistic about the future, and subsequently adjust downward their economic outlook; whereas the economic outlook remains unchanged if an individual does not find the disinformation credible. This gives rise to the following hypothesis:Figure 1 The effect of disinformation on the perception of vaccine effectiveness and economic outlook
Figure 1:Source: created by the authors.
H1: Disinformation questioning vaccine effectiveness will negatively affect the individual perception of the effectiveness of the vaccine (cognitive filtering phase).
As we will discuss further in Section 4.4, the economic outlook in Figure 1 encompasses both assessments of one's personal (the so-called egotropic evaluation) as well as the aggregate macroeconomic situation (i.e. the sociotropic evaluation). Although there is a convention in the consumer confidence literature to treat the social and personal component as complementary and linear additive, there is a growing empirical evidence that the relation between them is more nuanced. When it comes to the link between consumer confidence and consumption, confidence about personal finances serves as a mitigating factor connecting confidence about aggregate economic situation with consumption. The more empowered consumers feel about their personal financial situation, the stronger the link between confidence in the aggregate economy and their consumption (Hampson et al., 2021). In addition, when it comes to coping with price shocks that increase the relative price of foreign to domestic brands, consumer confidence in the national economy conditions the ways in which they deal with deteriorating personal financial situations (Hapmson et al., 2018). Consumers (and their degree of consumption) respond differently to the changes of the macroeconomic vis-a-vis their personal situation (Scholdra et al., 2022).
In the light of these studies, we separately examine the effect of disinformation on egotropic vs. sociotropic confidence. Consumer expectations depend on prior experiences and the amount of information. The media dependency theory shows that the effects of economic news are greater when the audience lacks first-hand experience with the issue which increases individual dependency on the media as the source of information (Vliegenthart et al., 2019, Jonkman et al., 2020) which leads to an uneven effect of economic news on the ‘sociotropic’ and ‘egotropic’ evaluation of the economy (Svensson et al., 2017; Jonkman et al., 2020; Scholdra et al., 2022). Economic news is more likely to influence sociotropic evaluations because individuals can evaluate the evolution of the personal situation, better than the state of the economy in the country (Boomgaarden et al., 2011; Damstra, 2019).
Following the above literature we expect sociotropic confidence to be more sensitive to disinformation about vaccines because consumers are better informed about their own financial situation and own health risks than about herd immunity and the aggregate economy. They should be able to better anticipate future personal financial situations compared to the expectations about the aggregate economy. As such, we expect that sociotropic expectations will be more sensitive to disinformation:
H2: Disinformation about the effectiveness of the vaccine will undermine consumer confidence, and its effect will be greater for sociotropic than for egotropic measures of consumer confidence (formation of expectations phase).
4 Methodology
4.1 Interventions
We conducted a survey experiment in Dutch speaking communities of Belgium in December 2020 well before the approval and distribution of vaccines.6 Respondents were randomly assigned to two treatments and a control group. All participants were asked to read a short article about anti-COVID-19 vaccines without any attribution to the source (see Online Appendix B for the texts). The control article (treatment A) comes from De Tijd, a respected Belgian journal. It discusses the effectiveness of the vaccine and provides a timeline for its distribution in Belgium.
To find articles with disinformation about COVID-19, we first used the European External Action Services disinformation dashboard (www.euvsdisinfo.eu), which contains an extensive database of fake news and corresponding counter messages. We obtained 98 articles (all in English) pertaining to COVID-19 that targeted the EU audience at large. We then selected those articles with the conspiracy frame that was widely disseminated in online sources. It states that the vaccines contain nano-chips and are used by a so-called ‘deep state’ to create a new world order. The article was translated into Dutch by three different native speakers to ensure that the content is accurately conveyed.
For the scientific frame we selected a Dutch-written article that was circulated shortly before we launched our survey locally in Belgium. The article was distributed in Leuven (a large city in Belgium) by ‘antivaxers’ (De Prikkrant, 2020) and questions the effectiveness of the vaccines, warning that the vaccine might even aggravate the health crisis. The article and its content was afterwards explicitly labeled as disinformation7 by experts in the field. The article contains a mix of facts and erroneous statements, including references to ‘scientists’. The wording of each of the treatments is provided in Annex B.
4.2 Experiment design
We used Qualtrics software and embedded our experiment into an online survey. After asking respondents several background questions, they were asked to read one of the three articles. The software monitored the time respondents dedicated to reading the article to ensure that the treatment was administered properly. After completing the article the question about the effectiveness of the vaccine was presented: “Do you think that vaccines will protect us?”, followed by four questions related to the expectations about the economic situation in Belgium and respondents’ personal financial situation. For ethical reasons, respondents received a notification at the end of the survey, stating that they were potentially exposed to ‘fake news’. They were also allowed to opt out of the survey at any time or skip questions.
4.3 Sample
Our target population consists of Dutch speaking Belgian residents. We used snowball sampling and disseminated the survey via email, Facebook, and other social media platforms. In total, 927 participants started the survey, 9 of them refused to sign the 'consent to participation form’ and 102 did not complete the entire survey and were subsequently dropped from the sample. In addition, to ensure that respondents were actually exposed to the manipulation we measured the time they took to read the article8 , which allows us to evaluate how carefully the respondents read the article. We remove observations for which this reading time was unrealistically short9 , indicating that the respondents had not or only partially read the article. This resulted in a final sample of 705 observations.
There were no significant differences between the three groups according to gender, age, income and level of education.10 Overall, our respondents are relatively well educated11 (secondary degree at most: 26.67%, undergraduate: 35.18% and graduate degree: 38.16%). The Belgian Dutch speaking community is however generally well educated: about 50% of the population holds an undergraduate or higher degree. Details regarding the composition of our sample can be found in Table A2 of Online Appendix C.
4.4 Dependent Variables
Consumer confidence is our ultimate variable of interest to measure the effect of increased uncertainty about the effectiveness of the vaccine on the respondents’ expectations regarding the economy. Our operationalization of this construct mimics the approach used by the National Bank of Belgium12 (see Online Appendix D for more details) that uses four questions13 : (1) What are your perspectives regarding the economic situation in Belgium for the coming 12 months, (2) what are your perspectives regarding the degree of unemployment in Belgium for the coming 12 months, (3) what are your perspectives regarding the financial situation of households for the coming 12 months and (4) what are your perspectives regarding households saving potential for the coming 12 months. Questions 1 and 2 refer to the evolution of the national economy (the before-mentioned ‘sociotropic’ evaluation of the economy), whereas question 3 and 4 refer to the evolution of the personal financial situation (i.e. the ‘egotropic’ evaluation).
Our approach is slightly different from other studies. Whereas the composite measure ‘consumer confidence’ is normally obtained by calculating the arithmetic mean of the seasonally adjusted average group responses to the four questions, we use the four above-mentioned questions as separate dependent variables. The reason is as follows: weighted averages are normally calculated for each question, resulting in an assessment of the economy on a group level. To obtain a sufficiently large dataset, most of the studies are therefore collecting a time series dataset. Our dataset, on the contrary, was collected during a single month, resulting in a cross-sectional dataset. We hence would only be able to calculate the consumer confidence of the three groups (i.e. the control group and the two groups exposed to disinformation), as we also do in Section 6.1. In order to increase our sample size, we were therefore obliged to use each individual's assessment of the four separate questions. This conceptual approach has its benefits. Rather than considering consumer confidence as a unidimensional construct, we contribute to recent literature (Hampson et al., 2021; Scholdra et al., 2022) that strives to study consumer confidence in a more granular way. The internal consistency between the four questions in our sample is low (Cronbach's α=0.6315) which suggests that the perceptions (corresponding with the four separate questions) are influenced by different factors, hence supporting the use of the four separate questions.14
Trust in the vaccine is our second variable of interest. As explained before, the assessment of the effectiveness of the vaccine might mediate the indirect relationship between being exposed to disinformation and a more depressed consumer confidence. Vaccine effectiveness is measured by means of the following question: “Do you think the vaccine will protect us?” with the answers measured using a seven-point Likert scale (from 1 “very unlikely” to 7 “very likely”).
4.5 Control Variables
Credibility of the article. In order to test whether the respondents recognized the deception (see Figure 1), we asked to assess the credibility of the article at the end of the questionnaire, using a seven-point Likert scale (1 “Completely uncredible” to 7 “Completely credible”). Evaluating the extent to which individuals can discern the difference between true and false contents is in line with Pennycook and Rand (2019), Bado al. (2020) and Zimmermann and Kohring (2020).
Concerns about the virus. Before exposing the respondents to the treatment articles, we measured the extent to which they were concerned about the virus by asking them the question: “how worried are you about the virus” (Pennycook, 2020) using a seven-point Likert scale (1 “not at all worried” to 7 “extremely worried”). In addition, we included a question to test the respondents’ objective knowledge regarding the epidemiological situation in the country: “What is the current number of daily deaths due to COVID-19 in Belgium?” This question was transformed into a dummy variable, with people answering “over 500 deaths” being labeled as having a lack of objective knowledge because this number overstated by 10 times the official statistics.
Trust in media, government and science. As recently demonstrated by Zimmermann and Kohring (2020) and Pomerance (2020), trust in media and politics could explain the belief in disinformation. Similarly, Roozenbeek et al. (2020) find that a higher trust in scientists is associated with a lower susceptibility to disinformation about COVID-19. We measure the trust in media, the government, and science, respectively, using seven-point Likert scales (1 “I do not trust them at all” to 7 “I completely trust them”).
We use socio-demographic control variables that have been validated in previous studies focusing on consumer confidence (e.g. the official questionnaire used by the European Commission; Pomerance, 2020) and studies analyzing the effects and credibility of disinformation (Bail et al., 2019; Loomba et al., 2021; Roozenbeek et al., 2020; Zimmermann & Kohring, 2020). These include 1) gender; 2) a categorical age variable; 3) a dummy variable assessing the respondent's level of education (with a value of ‘one’ representing respondents that enjoyed higher education, i.e. a Bachelor degree or higher); 4) the income group in which the respondent is located; 5) the extent to which the respondent has suffered a loss of income as a result of the COVID-19 crisis (Pomerance, 2020); and 6) the household composition (number of household members and number of these members at work).
5 Results
5.1 Descriptive analysis
We visualized the different answers across groups for the questions “credibility of the article”, “will the vaccine protect us” and the four questions on consumer confidence in Figure A2 in Appendix C. We begin by examining how the respondents assessed the article across the three groups, i.e. the variable “Credibility of the article”. More than half of the respondents in the control group assessed the article as “rather credible” or “credible”. The numbers are substantially lower for the conspiracy frame, only 20 percent of the respondents exposed to the conspiracy frame perceived the article as “credible”. Remarkably, 20.9% of the respondents had difficulties in assessing the article as “incredible”, despite the “obvious” presence of erroneous statements. The respondents in the group presented with the scientific frame had difficulties with determining the credibility of the article: about one third of the sample believed the article, another one third selected “neither uncredible, nor credible,” and the other one-third selected “rather credible” or “credible.” Using a Kruskal-Wallis test followed by Dunn's pairwise comparison, we find these differences between the perceived credibility of the article to be significant. 15
We subsequently analyze how the different groups perceived the vaccine effectiveness. The number of respondents assessing the vaccine effectiveness as ‘likely or very likely’ is slightly lower in the group exposed to the conspiracy frame (60.8 %) compared to the control group (62.8%), but substantially lower in the group exposed to the scientific frame (50%). A Kruskal-Wallis test followed by Dunn's pairwise comparison again shows that the differences in terms of perceived vaccine efficacy between the three groups are significant.16 Finally, we compare cross-group differences in consumer confidence, using the official methodology used to calculate the metric ‘consumer-confidence’ (discussed in Online Appendix D). The values for the aggregate measure of consumer confidence are reported in Table 1 . Column 5 indicates that respondents exposed to the treatment based on the conspiracy frame are only slightly more pessimistic (-5.95%)17 than the respondents in the control group. However, the consumer confidence indicator is 21.34% lower for the group exposed to the treatment based on the scientific frame compared to the control group, indicating that the group exposed to the scientific frame is substantially more pessimistic regarding the future evolution of the economy.18 As column 4 reveals, especially the prospects regarding the households’ financial situation (i.e. the egotropic evaluation) seem to be substantially more pessimistic (-43%)19 for those exposed to the scientific frame.Table 1 Aggregated Differences Across Groups in Consumer Confidence
Table 1: Economic situation Belgium(1) Unemployment in Belgium(2) Financial situation households(3) Saving potential households(4) Indicator Consumer Confidence(5)
Control Group (N= 229) -14.19 39.51 -7.86 42.58 15.01
Conspiracy Frame (N=240) -17.91 41.88 -7.92 40.42 14.12
Scientific Frame (N=236) -19.29 44.49 -11.24 33.27 11.81
Note: Columns 1-4 contain the average group responses to the four standard questions (by using the formula in Online Appendix D) used to calculate the variable ‘consumer confidence’, which can be found in column 5 (obtained by taking the arithmetic mean of column 1-4). Source: own calculations based on survey results.
More descriptive statistics (Mean and Standard Deviation of all variables), cross-group comparison of means20 , and a correlation matrix are presented in appendix C. The correlation matrix in Table A3 of the online Annex C indicates that the variable ‘credibility of the article’ is significantly negatively correlated with the variable ‘will the vaccine protect us’. Moreover, there is a significant negative relation between the dummy variable ‘scientific frame’ and the perceived effectiveness of the vaccine. However, the relationship between the variable ‘conspiracy frame’ and the variable ‘will the vaccine protect us’ is, remarkably, not significant. Furthermore, there appears to be a significant negative relation between the variable ‘will the vaccine protect us’ and the four underlying variables of consumer confidence.
5.2 Regression analysis
We start by examining the impact of how disinformation is framed on the perception of vaccine effectiveness (H1). Since our dependent variables are measured on ordered scales we use ordered logistic regressions.21 Table 2 reports odd ratios from the logistic regression with ‘do you think the vaccine will protect us’ as the dependent variable.22 Odds ratios (OR) indicate the change in the probability of observing outcome j, relative to outcome j-1, with respect to one unit change in the explanatory variable. OR between 0 and 1 indicate that the relative probability declines, and OR greater than one, that the probability increases.Table 2 Estimated Odds Ratios of the Effect of Disinformation on the Perception of Vaccine Effectiveness (H1)
Table 2:Dependent variable: ‘Will the vaccine protect us’
OR SD
Risk-patient 1.373 0.267
Correct estimation casualties 0.809 0.156
Trust in scientists 2.313*** 0.131
Male 2.470*** 0.371
Age (base: 18-29)
30-49
50-64
>65
1.112
1.145
1.324
0.216
0.254
0.441
Higher educated 1.486** 0.244
Trust news 1.346*** 0.096
Perceived credibility of article 0.839*** 0.050
Conspiracy Frame 0.605** 0.139
Scientific Frame 0.515*** 0.092
Mc Fadden pseudo R²
Log Likelihood
p (χ2) 0.162
-926
0.000
Observations 699
Note: *** p<0.01, ** p<0.05, * p<0.1. OR values represent the odds ratios from the ordered logistic regression model with the dependent variable “Do you think the vaccine will save us?” and the answer categories ranging from 1 for “very unlikely” to 6 “very likely.” Source: own calculations based on survey results.
The results show that the variable ‘perceived credibility of the article’ has a significant and negative effect on the trust respondents have in the vaccine (OR=0.84, SE=0.05), i.e. the more credible the article is perceived, the less likely the respondent will agree with the statement that the vaccine will protect us. The model contains two dummy variables for the scientific and the conspiracy frames. Both treatments have a significant and negative effect on the perception of vaccine effectiveness, albeit with the conspiracy frame having a smaller effect than the scientific frame. All other factors held constant, exposure to the conspiracy frame leads to a 39.5 % decrease in odds (i.e., 1-OR=1-0.605, SE= 0.1391) of trusting the vaccine. This effect is even stronger for those exposed to the scientific frame, leading to a 48.46 % decrease in odds (i.e., 1-OR= 1-0.5154, SE= 0.0915) to indicate they trust the vaccine, all other factors held constant. As hypothesized, these results confirm our first hypothesis, demonstrating that exposure to disinformation that questions the vaccine generally reduces the likelihood to consider the vaccine as sufficiently effective to protect us, with the scientific frame having a larger effect than the conspiracy frame. Respondents who consider themselves as being a risk patient, that have a higher trust in scientists23 , that are male, that have a higher level of education (Bachelor degree or higher) and have a higher trust in the news are generally more confident that the vaccine will protect them.
Now we test whether the uncertainty about the vaccine also impacts respondents’ perceptions regarding the further evolution of the economy as discussed in H2. As stated before, consumer confidence constitutes a metric that provides an indication of future economic prospects on the group level. As we cannot use the composite variable to examine effects on the individual level, we analyze the impact of exposure to disinformation on each separate question. The results (in odds-ratio) are reported in Table 3 .24 Table 3 The Effect of Exposure to Disinformation on Consumer Confidence (H2)
Table 3: 1.Evolution economy 2.Evolution unemployment 3.Evolution financial situation 4.Evolution saving potential
OR SD OR SD OR SD OR SD
Concern about virus 0.976 0.056 1.034 0.059 1.219*** 0.078 1.108* 0.066
Will the vaccine protect us 0.784*** 0.046 1.167*** 0.068 0.839*** 0.055 0.809*** 0.048
Trust government 0.683*** 0.038 1.292*** 0.071 0.754*** 0.048 0.848*** 0.050
Male 0.661*** 0.101 1.415** 0.216 0.930 0.167 0.853 0.138
Age (base: 18-29)
30-49 1.359 0.269 0.861 0.172 1.099 0.257 2.052*** 0.458
50-65 1.726** 0.381 0.571** 0.127 1.959*** 0.501 3.807*** 0.920
>65 1.178 0.365 0.491** 0.155 1.591 0.590 7.492*** 2.496
Higher educated 1.642*** 0.275 0.689** 0.117 1.117 0.217 0.766 0.131
# Household at work 0.972 0.086 0.967 0.089 0.832* 0.089 0.948 0.086
Income 0.894 0.092 1.108 0.115 0.698*** 0.085 0.620*** 0.066
Income affected 1.255*** 0.098 0.775*** 0.059 2.165*** 0.192 2.007*** 0.159
Credibility article 1.002 0.061 0.983 0.0560 1.008 0.068 1.090 0.067
Conspiracy Frame 1.105 0.264 0.832 0.202 1.204 0.326 1.398 0.340
Scientific Frame 1.007 0.180 0.951 0.171 1.091 0.228 1.237 0.230
Mc Fadden pseudo R² 0.082 0.051 0.140 0.151
Log likelihood -936 -899 -597 -726
p (χ2) 0.000 0.000 0.000 0.000
Observations 685 685 685 685
Note: *** p<0.01, ** p<0.05, * p<0.1. OR values represent the odds ratios from four ordered logistic models with corresponding dependent variables: “How do you think the economic situation in Belgium is and how will it evolve generally” with 1 corresponding to “obviously getting better” and 5 to “obviously getting worse”; “How do you think unemployment in Belgium will develop in the next twelve months? In your opinion, the number of unemployed will” with answers ranging from “clearly increase” (1) to “clearly decreased” (5); “What are your expectations for your household financial situation for the next twelve months? In the next twelve months it will” with answers ranging from “clearly improve” (1) to “obviously deteriorate” (5); Do you expect to save money in the next twelve months?”, with answers ranging from (1) “yes for sure” to “definitely no” (4). Columns 1 and 2 are associated with the sociotropic evaluation of the economy, whereas columns 3 and 4 are associated with the egotropic evaluation of the economy.
Source: own calculations based on survey results
The ‘trust in vaccine’ variable has an OR lower than 1 in the models with the ‘evolution of economy’, ‘evolution of financial situation’ and ‘evolution of saving potential’ as dependent variables. Higher values for these variables correspond to the worsening of the future economic outlook. OR lower than 1 indicated that the odds of observing the most extreme negative outcome relative to less negative outcomes declines as the respondents become more confident in vaccine effectiveness. The magnitude of these ORs is similar for both sociotropic and egotropic variables. The variable ‘unemployment’ is reversely coded (a higher value corresponds with a lower degree of unemployment). An OR above 1 hence implies that a one-unit increase in perceived effectiveness of the vaccine leads to higher odds of respondents expecting unemployment to decrease. As hypothesized, a higher perceived vaccine efficacy thus increases the likelihood to assess the evolution of unemployment more positively. These four sets of correlations point to the indirect negative effect of disinformation on consumer confidence.
When it comes to the direct effects of disinformation (the dummy variables ‘conspiracy frame’ and ‘scientific frame’) on consumer confidence, the direction of the relationship is consistent with H2, but the coefficients are not statistically significant. Hence, after controlling for the perception of the vaccine effectiveness, respondents in either of the two treatment groups do not register a lower confidence in the future course of the economy or their own financial situation. Our ordered logistic regressions do not indicate that exposure to disinformation exerts a significant effect on the way our respondents assess the evolution of the state of the economy, neither for the questions assessing the sociotropic evaluation nor for the egotropic evaluations after accounting for the effects of disinformation about the effectiveness of the vaccine.
This outcome could be due to the fact that the link between the public health domain and the economic domain is indirect. When consumers are forming expectations about the economy they do not integrate public health risks into their economic outlook, unless these risks have clear economic implications. Greater uncertainty about the magnitude of the economic effects of health risks subsequently translates into greater uncertainty or even negative expectations about the economic outcomes. The uncertainty about health risks is captured by the perceptions of the vaccine effectiveness. And thus, after it is accounted for, there is no direct effect from the public health domain to the economic realm.
We conduct two robustness checks. First, we create a composite variable that measures the sociotropic evaluation of the economy, averaging the values for ‘evolution of the economy’ and the reversed coding of ‘evolution of unemployment’. This composite variable did not lead to different results. Second, we jointly estimated the four regressions (a Seemingly Unrelated Regression model). This also confirms our previous results.
Our estimations show an interesting difference in significance of our control variables, depending on whether we are looking at the sociotropic or the egotropic evaluation of the economy. Respondents who perceive themselves as being a risk patient are 20% more likely to have negative outlook about household financial situation and 10% more likely to have a negative outlook about the saving potential. This variable is not significant in the sociotropic model. This might be associated with a fear of losing income due to (future) illness or having to close down one's own business for respondents who perceive themselves in the risk category. Female respondents and those who have higher education have a significant lower confidence in the future evolution of the economy. These variables do however not play a role when assessing one's egotropic evaluation of the economy. The level of income does not play a role in the sociotropic evaluation but has a positive and significant effect on the egotropic evaluation of the economy, i.e. the higher the income the higher the confidence in one's evolution of the personal financial situation. The trust in government and the extent to which one's income has been affected by the crisis have a significant effect on the four variables of consumer confidence. A higher trust in the government leads to an increased confidence in the evolution of the economy, whereas people that suffered income losses during the COVID-19 crisis evaluate the economy in a more pessimistic way.
6 Discussion and limitations
The increasing presence and use of disinformation is a reason for concern, especially in light of the current pandemic. Our study contributes to this discussion in the following ways. First, we demonstrate that disinformation containing a mix of scientific facts and erroneous statements is perceived as more credible than a conspiracy theory frame. Our respondents clearly encounter difficulties in distinguishing between the factually correct information from disinformation when exposed to a scientific-sounding frame. Second, our results confirm our first hypothesis, i.e. being exposed to disinformation questioning the effectiveness of vaccines affects the extent to which respondents believe that the vaccine will protect them, with the scientific frame exerting a greater effect (1-OR=1-0.515) than the conspiracy frame (1-OR=1-0.605). These results are in line with the findings of Loomba et al. (2021) and Iyengar and Massey (2019) who show that the scientific frame is more persuasive. Third, our study provides strong evidence for the link between the anxiety about health and economic expectations suggesting that the factors affecting economic outlook encompass a wider set of factors than economic indicators. It remains to be seen whether this is uniquely due to the COVID-19 pandemic or a permanent feature of the consumer decision-making process.
Fourth, we examine whether the uncertainty created by disinformation has a uniform effect on different measures of consumer confidence. At the aggregate level, i.e. the consumer confidence index computed by means of the official methodology, we found a substantial decline across the four components of the index. The magnitude of the effect is again correlated with the perceived credibility of the article, the effect being the largest in the group exposed to the scientific frame (-21.34%). Ordered logistic regressions, measuring the impact of disinformation on consumer confidence at the individual level (i.e. looking at the effect on the four individual questions used to calculate the composite metric consumer confidence), points to mixed results. Uncertainty about vaccine effectiveness undermines consumer confidence, whereas disinformation narratives are not significant. This points to the limited spillover effects of a particular narrative across issues. Finally, our results show the positive effect of trust in the government, scientists and the news on the dependent variables ‘trust in the vaccine’ and ‘consumer confidence’. Maintaining and increasing trust in these agencies and actors is therefore of great importance and can act as a bulwark against disinformation.
Our research is however not without limitations. First, our respondents are only exposed once to an article containing disinformation. This is however in line with recent research in the domain of mis/disinformation (Pennycook, 2020; Pomerance, 2020; Roozenbeek et al., 2020; Loomba et al., 2021). Moreover, Bastick (2021) recently demonstrated how even short exposures to disinformation have the potential to significantly modify the unconscious behavior of individuals. Follow-up research should find out whether an indirect cognitive effect on other variables can be found, when respondents are repeatedly exposed to disinformation. As indicated by the Persuasion Knowledge Model of Friestad & Wrigt (1994) an individual's knowledge on persuasion and hence also on disinformation develops over time. Hence, it would be interesting to see how these “disinformation knowledge” progresses contingently and how this effects the impact on perception and behaviour. In this context, the impact on a wide range of variables (other than consumer confidence) could also be examined. Second, our sample contains a relatively high number of highly educated respondents. This poses no problem since there is no significant difference between the sample composition of our control group and the two treatment groups. This comment is even worrying, given that highly educated (or people with greater cognitive reflection, measured by analytic tests) are in general found to be less susceptible to disinformation, misinformation and conspiracy theories (Martel et al., 2020; Pennycook, 2020; Van Prooijen, 2016). Future research, in which less educated respondents are well represented may therefore yield even stronger results. Third, our survey was distributed among the Dutch speaking communities in Belgium. Future research, including the French speaking, the German speaking communities and other countries might reveal interesting insights. Finally, our research assumes an information mechanism which hypothesizes that future economic prospects are associated with news about the success of the vaccine. Within the economic literature, future research can examine the effect of other narratives that are frequently the subject of disinformation and that might even more impact the way we make forecasts about the economy. This is of great importance as the literature discussed in Section 2 demonstrates that there is a clear (empirically tested) relationship between consumer confidence and the real economy. Disinformation, or other types of news that succeed in affecting this variable could hence have real economic consequences.
7 Conclusion
Our analysis provides important insights into the interdependencies between public health and the state of the economy by underscoring the multifaceted nature of disinformation, showing that it could trigger reactions about health risks that spill over to the economic domain. We demonstrated that both scientifically sounding disinformation and conspiracy theory frames affect perceptions of vaccine effectiveness, and that this variable subsequently affects consumer confidence.
The interdependence between public health and economic domains, which are usually perceived as orthogonal but brought to the foreground by our analysis, suggests several fruitful venues for future research. First, it underscores the need for greater scrutiny of the assumptions about the relevant information set on which individual decision-making processes are based. Weaponization of information by authoritarian regimes, politicization of the truth by populist governments, and commodification of attention by social media platforms make the current information environment very different from the one when the foundations of the media effects theory were laid. As such, the studies of consumer expectations should pay greater attention to the process unleashed by these transformations and implications these changes have for the decision-making processes.
The second fruitful venue could be to explain interdependencies across issues. The studies of consumer confidence have focused on the link between the economic news and economic outlook, while leaving exposure to information in other policy areas intact. Our study demonstrates that a more comprehensive model of how individuals convert information from multiple domains into exceptions about the future of the economy could be a productive venue for the media effects theory.
A final line of effort could focus on the causal mechanisms that make some frames more persuasive than others and why. This could be caused by the differences in emotional stimulation between the frames which subsequently impacts the negative and positive economic outlook. It could be the case that the readers perceive articles with positive economic forecasts as more emotionally natural than the articles with negative forecasts. This emotional component could consequently be responsible for asymmetric effects.
Uncited References
Armantier et al., 2020, Disinformation database, 2021, Fetzer et al., 2021, He et al., 2020, Bail et al., 2020
Disclosure Statement
No potential conflict of interest was reported by the authors.
Appendix Supplementary materials
Image, application 1
Data Availability
Data will be made available on request.
Acknowledgement
The authors would like to thank the anonymous reviewers for providing helpful feedback on the earlier draft of the manuscript.
The study was approved by the Ethics Committee of the VUB, ECHW_268
1 Most of the correlations are contemporaneous or forward-looking. Other studies even go further. Scholdra et al. (2022) for example study how the evolution of consumer confidence impacts consumer's grocery shopping behavior (both in terms of purchase volume as the shopping basket composition).
2 The variables consumption and financial saving are the most frequent studied. Other original research however demonstrates that consumer confidence can have a broader impact on economic behavior. Dhar and Weinberg (2016) for example show that a depressed consumer sentiment is countercyclical to movie attendance. Hampson et al. (2018) find that decreased perceived financial well-being leads consumers to prefer domestic products compared to foreign products.
3 Based on answering two questions: (1) “How worried are you about the coronavirus” and (2) “How afraid are you that you or someone close to you will get infected with the coronavirus”.
4 Pomerance et al. (2020) did not expose participants to disinformation but showed them an infographic with the title ‘Fake news: more common than ever’. Participants then indicated how uncertain the infographic made them feel about information stemming from 6 news sources. Our study measures the effect of ‘actual’ articles containing disinformation. Moreover, we rely on the variable ‘Consumer confidence’ to measure increased uncertainty stemming from disinformation (whereas Pomerance looks at the impact of fake news on saving and spending behavior). Our approach is hence original.
5 Cavazos (2019) estimates the economic damage stemming from ‘fake news’ globally amounting to $78 billion. He focuses on the impact of ‘fake news’ on stock markets, the loss of faith in democratic institutions which in some cases even leads to violence, reputational costs on big brands and the resources spent to tackle the problem.
6 The study was approved by the Ethics Committee of the VUB, ECHW_268. .
7 We also label this article as disinformation, following the specific intent of the authors to sow confusion and to deceive their target audience.
8 As stated by Bado et al. (2020), more time for ‘deliberation’ improves the extent to which respondents can discern the difference between true and false news.
9 Respondents who spent less than 10 seconds reading the article were dropped.
10 Using Kruskal-Wallis tests, we find no significant differences between our three groups according to age: χ2 (2, 705) = 0.768, p=0.7128; gender: χ2 (2, 705) = 0.370, p=0.8312; level of education: χ2 (2, 705) = 2.121, p=0.3463 and income: χ2 (2, 705) = 0.660, p= 0.7189.
11 A large number of observations stemming from ‘lower’ educated respondents was removed when controlling for reading time.
12 The methodology for measuring consumer confidence in the European Union has been harmonized by the European Commission.
13 Until 2001, consumer confidence was measured based upon 5 questions. This composition was changed by the European Commission.
14 Moreover, the correlation matrix (see appendices, Table A3) shows that the correlation between the four questions used to calculate the metric ‘consumer confidence’ is low.
15 χ2 (2, 705) = 274.468, p=0.0001.
16 χ2 (2, 705) = 10.557, p=0.0034.
17 computed as [100 x (14.12-15.011)/15.011)]
18 (100 x [(11.807-15.011)/15.011])
19 100 x [-7.885+11.235)/(-7.855))
20 Figure A1 in the online Annex C compares the mean values of the individual components of the consumer confidence across the treated and control groups. The respondents who were exposed to the scientific frame on average have more negative economic outlook than those who were exposed to the conspiracy frame and the control group. These differences are not statistically significant because the 95 percent confidence intervals overlap. Thus, without controlling for other variables, the link between the economic outlook and the framing of disinformation cannot be established. Therefore, we carry out the regression analysis in the following section.
21 The conditions for this estimation method are fulfilled, following testing for ‘the proportionality of odds assumption’ (Williams, 2016).
22 Online Appendix E contains two alternative estimations. We first estimated the model by means of OLS, to give a preliminary (and more comprehensible) interpretation of the results (Table A4). Moreover, we also estimated the ordered logistic regression model, presenting the estimation results by means of coefficients (Table A6).
23 Since we find a strong degree of multicollinearity, we only include the variable ‘trust in scientists’ in this model, given that the development of a vaccine and its efficacy is a prime responsibility of scientists. We include the variable ‘trust in the government’ when we look at respondents' perceptions regarding the evolution of the economy, given that the government is mainly responsible for taking measures to sustain our economy.
24 Online Appendix E contains two alternative estimations. We first estimated the model by means of OLS, to give a preliminary (and more comprehensible) interpretation of the results (Table A5). Moreover, we also estimated the ordered logistic regression model, presenting the estimation results by means of coefficients (Table A7).
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.socec.2022.101968.
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| 0 | PMC9733969 | NO-CC CODE | 2022-12-16 23:21:40 | no | J Behav Exp Econ. 2023 Feb 9; 102:101968 | utf-8 | J Behav Exp Econ | 2,022 | 10.1016/j.socec.2022.101968 | oa_other |
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Biosens Bioelectron X
Biosens Bioelectron X
Biosensors & Bioelectronics
2590-1370
The Author(s). Published by Elsevier B.V.
S2590-1370(22)00182-0
10.1016/j.biosx.2022.100289
100289
Article
Nanotechnology-based diagnostic methods for coronavirus: From nucleic acid extraction to amplification
Huang Xucheng a1
Fu Ruijie ab1
Qiao Sai a
Zhang Jun a∗∗
Xianyu Yunlei ab∗
a Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
b State Key Laboratory of Fluid Power and Mechatronic Systems, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
∗ Corresponding author. Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
∗∗ Corresponding author.
1 These authors contribute equally to this work.
10 12 2022
10 12 2022
1002895 10 2022
25 11 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.
The recent emergence of human coronaviruses (CoVs) causing severe acute respiratory syndrome (SARS) is posing a great threat to global public health. Therefore, the rapid and accurate identification of pathogenic viruses plays a vital role in selecting appropriate treatments, saving people's lives and preventing epidemics. Nucleic acids, including deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), are natural biopolymers composed of nucleotides that store, transmit, and express genetic information. Applications of nucleic acid detection range from genotyping and genetic prognostics, to expression profiling and detection of infectious disease. The nucleic acid detection for infectious diseases is widely used, as evidenced by the widespread use of COVID-19 tests for the containment of the pandemic. Nanotechnology influences all medical disciplines and has been considered as an essential tool for novel diagnostics, nanotherapeutics, vaccines, medical imaging, and the utilization of biomaterials for regenerative medicine. In this review, the recent advances in the development of nanotechnology-based diagnostic methods for coronavirus, and their applications in nucleic acid detection are discussed in detail. The techniques for the amplification of nucleic acids are summarized, as well as the use of magnetic nanoparticles for nucleic acid extraction. Besides, current challenges and future prospects are proposed, along with the great potential of nanotechnology for the effective diagnosis of coronavirus.
Keywords
Coronaviruses
Nucleic acid extraction
Nucleic acid amplification
Magnetic nanoparticles
CRISPR-Cas
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pmc1 Introduction
Coronaviruses (CoVs) belongs to the subfamily Coronavirinae in the family of Coronaviridae of the order Nidovirales, which are enveloped and spherical viruses with a single-stranded RNA genome(Haake et al., 2020). The first coronavirus was identified in the 1960s. Coronaviruses are classified into four genera including alpha-coronavirus(α-CoV), beta-coronavirus(β-CoV), gamma-coronavirus(γ-CoV), and delta-coronavirus(δ-CoV)(Chen et al., 2020a, Chen et al., 2020b), of which α-CoV and β-CoV are reported to infect humans(de Wilde et al., 2018). Coronaviruses can cause respiratory and neurological diseases(Corman et al., 2012; Lim et al., 2019; Wang et al., 2019). The emergence of CoVs causing severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) with global spread represents a significant threat to public health(Noh et al., 2017). Recently, the emergence of ribonucleic acid (RNA) enveloped human β-CoV named SARS-CoV-2 which is etiologically related to the well-known severe acute respiratory syndrome coronavirus (SARS-CoV), has been challenging the global public health community to confront a novel infectious disease (coronavirus disease 2019, COVID-19)(Shen et al., 2020). COVID-19 that affects the lower respiratory tract and manifests as pneumonia in humans(Sohrabi et al., 2020), remains a significant issue on the global health. They can infect respiratory, gastrointestinal, hepatic, and central nervous system of human(Ge et al., 2013). The pandemic of COVID-19 has threatened the public health worldwide, with the living and working conditions of billions of people globally severely disrupted due to various forms of social distancing and lockdowns in many cities.
Nucleic acids (DNA and RNA) are natural biopolymers composed of nucleotides which play crucial biological roles in all forms of living organisms, such as storing, encoding, and transmitting genetic information for cellular function maintaining and genetic information relaying. DNA stores genetic information and encodes the amino acid sequences of proteins responsible for cellular function. RNA plays various important roles in the coding, decoding, regulation, and expression of genes. Consequently, nucleic acids are used as important biomarkers for biological studies and medical diagnostics(Breaker, 2004). For example, nucleic acids are used for clinical diagnostics for infectious disease and cancer as well as for monitoring epidemics and outbreaks of new diseases(Hartman et al., 2013). Quantitative and qualitative determination of nucleic acids is of great significance in modern biology and medicine. Detection of DNA/RNA of pathogenic bacteria and viruses is beneficial for making an appropriate strategy for the treatment. During the past several decades, many approaches have been developed for the detection of nucleic acids that hold great promise for clinical translation. The classic nucleic acid test (NAT) process includes nucleic acid extraction, amplification, and detection. Molecular diagnostics is widely adapted in various fields such as disease detection and health monitoring, which evaluates the nucleic acids (e.g., DNA, RNA, or a variation of both) of bacteria or virus in human samples as for diseases diagnosis(Park et al., 2018). The polymerase chain reaction (PCR) has become one of the most important tools in molecular diagnostics, providing exquisite sensitivity and specificity for detection of nucleic acids. Real-time quantitative PCR (RT-qPCR) is a rapid, specific, and sensitive TaqMan PCR method for detection, subgrouping, and quantitation of pathogens. This assay increases the sensitivity of conventional PCR(Zhang et al., 2020). Owing to its broad applicability, high sensitivity, and high sequence specificity, the PCR-based method has become a routine and reliable technique for detecting coronaviruses(Shen et al., 2020).
Nanotechnology is broadly defined as the application of materials and devices where at least one dimension is less than 100 nanometers, which has already been employed in the diagnosis and treatment of viral diseases. Nanoscience and nanotechnology deal with very small particles in many disciplines such as biology, chemistry, materials science, physics, engineering and so on. Materials science is important in all areas of antiviral research, including viral structural and biological studies, detection, treatment and vaccination(Tang et al., 2020, Tang et al., 2020). Nanoparticles are small size particles with a large surface-to-volume ratio(Kaushik, 2020; Saravanan et al., 2021), which offer many applications in a range of fields from chemistry to biology and biomedicine(Zhu et al., 2015). Over the decades, nanoparticles have been widely used and studied for their unique properties, such as small size, surface adaptability, improved solubility and multifunctionality, resulting in the development of better and safer drugs, tissue-targeted treatments, personalized nanomedicine and early diagnosis and prevention of diseases(Fornaguera and García-Celma, 2017; Soares et al., 2018). A variety of nanomaterials, including metallic nanoparticles, magnetic nanoparticles, silica nanoparticles, polymeric nanoparticles, carbon nanotubes, and quantum dots, have already been used for virus detections that may open a new area of potential applications (Draz and Shafiee, 2018; Halfpenny and Wright, 2010; Lee et al., 2013). Nanotechnology has already proven its value through its diagnostic, vaccine, and therapeutic applications that have expanded into clinical applications(Varahachalam et al., 2021). Nanomaterials-based nucleic acid detection of viral infectious diseases has various advantages in the diagnostic field(Cheng et al., 2008). Moreover, nanomaterials are powerful tools for the diagnosis, prevention, and treatment of COVID-19(Tang et al., 2020, Tang et al., 2020).
Molecular diagnostics based on nucleic acid detection follows four basic steps: sample collection, nucleic acid extraction from the collected sample, nucleic acid amplification, and analysis. The process examines the nucleic acids to investigate the pathogen responsible for the infections. Efficient and robust nucleic acid extraction from complex clinical samples is one of the most fundamental steps. The nucleic acid extraction is the first step in the molecular analysis of nucleic acids (Wu et al., 2019). It is crucial for the entire detection and analysis as it directly influences the subsequent steps(Liu et al., 2010).Nucleic acid extraction generally involves cell (or virion) lysis, nucleic acid isolation and purification(Ali et al., 2017). However, traditional nucleic acid extraction methods are time-consuming and labor-intensive(Stray and Zimmermann, 2019). To address this issue, magnetic nanomaterials have offered a promising and efficient platform for the extraction and purification of nucleic acids. For the nucleic acid amplification, different techniques have been developed for the amplification for enhanced sensitivity. In this review, the recent advances in the development of nanotechnology-based diagnostic methods for coronavirus, and their applications in COVID-19 detection are discussed in detail. The use of magnetic nanomaterials for nucleic acid extraction and techniques for the amplification of nucleic acids are summarized (Scheme 1 ). Besides, current challenges and possible solutions are proposed, along with the great potential of nanotechnology for the effective diagnosis of coronavirus.Scheme 1 An overview of nucleic acid extraction and nucleic acid amplification for COVID-19 detection.
Scheme 1
2 Nucleic acid extraction based on magnetic nanomaterials
2.1 Classification and characteristics of MNPs
Magnetic nanoparticles (MNPs) with a diameter of 10–100 nm are preferred for use in vivo, as this reduces particle opsonization and their subsequent clearance, allowing to overcome most of the physiological barriers (Canfarotta and Piletsky, 2014; Martinelli et al., 2019; Shubayev et al., 2009). The properties of MNPs strongly derive from their physicochemical characteristics, mean size, and morphology(Farinha et al., 2021; Manescu et al., 2021). As shown in Fig. 1 , MNPs are usually categorized into three groups: (1) single metal MNPs, (2) metal oxide MNPs, and (3) metal alloy MNPs. Single metal MNPs present a core composed of one single pure metal structure such as iron (Fe), Co, and nickel (Ni). Metal oxides essentially include iron oxides (FexOy) and ferrites, such as CoFe2O4, MgFe2O4 or MnFe2O4. Alloy MNPs consist of a combination of two or more different pure metals, for example, iron cobalt alloys (FeCo) and iron platinum alloys (FePt)(Farinha et al., 2021). MNPs have high surface area-to-volume ratio, high binding affinity to the detection targets, and can be magnetically controlled for aggregation and dispersion, making the pre-concentration, purification and separation of nucleic acids simple and easy. Active substances attached to the surface of MNPs can be combined with specific biomolecules, such as DNA/RNA, enzymes, proteins, and can be separated under the action of an external magnetic field that has rapid separation, high specificity, and good reproducibility. Therefore, MNPs are widely used for the rapid automatic detection of nucleic acids, which is of great significance in the medical field(Ngo et al., 2016; Tang et al., 2018; Tran and Piro, 2021).Fig. 1 The classification and characteristics of MNPs.
Fig. 1
In MNPs-based methods, nucleic acids in lysed samples can be specifically absorbed on MNPs due to various surface-modified functional groups. Therefore, nanomaterials with magnetic properties could be customized with unique virus receptors, allowing viral molecules to attach to MNPs, which would facilitate their magnetic extraction using an external magnetic field. In the presence of magnetic fields, nucleic acids are rapidly separated from most impurities in the supernatant. After quick washing steps to eliminate trace impurities, purified nucleic acids can be further released from the surface of MNPs by elution buffer with altered ionic strength, which is much simpler and faster than spin column-based methods.
2.2 Magnetic beads technology
Magnetic beads technology is one of the emerging strategies for the extraction of genomic and RNA, plasmid, and mitochondrial DNA, and it is one of the common methods to alleviate the centrifugation requirements and has been widely used in the genomics and proteomics(Váradi et al., 2014). During the past few years, specifically functionalized magnetic particles have been developed. Together with an appropriate buffer system, they allow for the rapid and efficient purification directly after extraction from crude cell extracts(Berensmeier, 2006). Furthermore, they allow for simple automation of the entire process and the isolation of nucleic acids from larger sample volumes. Several studies have shown that magnetic particles can isolate as few as tens of copies of target sequences from one milliliter of serum, which could then be amplified and detected by conventional means. The positive aspect of this technique is that it avoids the centrifugation step and provides an alternative way for automation of extraction procedures from a large number of samples. Magnetic particle or beads are the first choice to eliminate centrifuge-dependent steps during the extraction process. Magnetic beads utilize different ligands such as antigens, antibodies, oligonucleotides, or aptamers, which bind specifically to its target in the sample. This extraction technique can be used in batch processes with a large number of samples (blood, tissues, etc.) and is relatively easy to perform, making it one of the best options for automation, high-throughput applications, and high sample processing capabilities(Franzreb et al., 2006). This method is also suitable for resource-limited environments as it is virtually equipment-free(Ali et al., 2017). Magnetic bead separation presents many advantages over centrifuge-dependent extraction process by allowing an equipment-free process. Magnetic bead-based extraction realizes the collection of nucleic acids through the binding of nucleic acids to a silicon-based matrix with moderate operation difficulty, high product purity, and proper automatic level.
2.3 MNPs-based nucleic acid extraction
The inherent properties of MNPs, such as high surface-to-volume ratio, magnetically controlled particle aggregation and dispersion(Ma et al., 2019) and their ability to bind to a large number of different biomolecules (DNA, RNA, enzymes), offer a promising platform for the isolation of DNA and RNA from complex samples as well as enriching nucleic acids that facilitates their detection(Farinha et al., 2021). In recent years, MNPs have been applied for nucleic acid separation and purification, which could bind nucleic acid by the free chemical groups modified on the surface(Bhati et al., 2021; Oberacker et al., 2019; Ota et al., 2006). Using magnetic separation methods, nucleic acids can be directly isolated from crude biological samples without any restrictions with respect to the sample volumes. Typically, MNPs allow the nucleic acids to be gathered by magnets, and the nucleic acid binding on MNPs could be rapidly eluted. The heterogeneous extraction process is greatly simplified and has the potential for the development of “universal” nucleic acid extraction methods for the detection of pathogens in various samples(Shih et al., 2016; Zhang et al., 2019). By developing functional magnetic materials and suitable buffers, it is possible to extract target nucleic acids from crude cell extracts and purify them directly and efficiently(Chen et al., 2021). The centrifugal steps that may lead to degradation of nucleic acids are avoided in the magnetic separation process(Smerkova et al., 2013). The entire separation process does not require centrifugation or column separation, and multiple samples can be processed simultaneously, which is easy to realize automatic operation. Due to its excellent efficiency, this method is particularly suitable for nucleic acid extraction of micro samples.
Fig. 2 illustrates a schematic diagram of the extraction process of nucleic acids based on MNPs. The whole process consists of five major steps: lysis, MNPs-nucleic acids binding, washing, elution, and collection. In this process, MNPs bind to the nucleic acids by functionalizing MNPs with ligands that specifically bind to DNA and RNA, and are then separated from the rest of sample matrix by applying a magnetic field through the magnetophoretic phenomenon. During this step, the MNPs, which are still bound to their target, are collected towards the magnet, making it easy to discard the unwanted material. The MNPs are then washed in elution buffer to facilitate the release of the DNA/RNA molecules from the nanoparticles. Afterwards, the MNPs are separated from the supernatant containing the free nucleic acid molecules using an external magnetic field(Chircov et al., 2019).Fig. 2 The extraction process of nucleic acids based on MNPs including lysis, binding, washing, elution, and collection.
Fig. 2
2.4 Applications of MNPs in DNA/RNA extraction
Over the past few years, MNPs have been widely used in the biomedical fields including magnetic biosensing, magnetic imaging, magnetic separation, drug and gene delivery, and hyperthermia therapy. Ma et al. analyzed and compared genomic DNA extraction based on MNPs from E.coli JM109, yeast, whole blood, and serum respectively, and found that all these genomic nucleic acids extracted using the MNPs-based method from different species can be applicable for the molecular biology research(Ma et al., 2013). Kaur et al. demonstrated a rapid and highly sensitive detection of S. typhi, in which they employed a MNPs-based pathogen enrichment protocol, followed by loop-mediated nucleic acid amplification and simultaneous detection by an in-situ optical system(Kalendar et al., 2018). Won et al. developed a simple and fast bacteria isolation method using magnet nanoparticle-embedded silica nanotube (MNSNT). Under certain ionic conditions, bacteria in the sample were simply bound on the outside wall of MNSNT, which were further collected with a magnet. The bacteria separated with MNSNT were then detected using PCR after heat-induced cell lysis without the need of washing and elution steps(Won et al., 2013). Kang et al. described an approach of conducting MNPs-based nucleic acid extraction procedure in an ordinary plastic Pasteur pipette with no vortex mixer, centrifuge, or dry bath, and applied this approach to extract nucleic acid of various pathogens, including RNA virus, DNA virus, gram-negative bacteria, and gram-positive bacteria from a broad range of samples(Kang et al., 2021). Bhati et al. developed an improved method for DNA extraction from human saliva using bare MNPs, and they found that the yield and purity of DNA was higher compared to other methods. In addition, this method requires no centrifugation step while it is mandatory for the spin column-based techniques (Bhati et al., 2021). MNPs have been used in RT-qPCR diagnosis for the extraction of viral RNA from SARS-CoV-2. Studies have shown that silica-coated MNPs can be used to rapidly extract RNA from the virus in patient samples for the further detection by RT-PCR that avoided the needs for lengthy RNA extraction while also making the method more sensitive(Campos et al., 2020). Zhao et al. developed a carboxyl polymer-coated MNPs, namely pcMNPs, and established a simple but efficient viral RNA extraction system for the sensitive detection of SARS-CoV-2 RNA via RT-PCR. This method merges the lysis and binding steps, and the pcMNPs-RNA complexes can be directly introduced into RT-qPCR reactions(Yoo et al., 2021). As compared with traditional column-based extraction methods, the pcMNPs-based method has several advantages. Firstly, pcMNPs-based method combines the virus lysis and RNA binding into one step, and the pcMNPs-RNA complexes can be directly introduced into subsequent RT- PCR reactions without elution step, which dramatically reduces the operation time and risk of contamination. Secondly, pcMNPs have excellent viral RNA binding properties, which results in high sensitivity and linearity for the detection of SARS-CoV-2 viral RNA using RT-PCR. Thirdly, since no centrifugation steps are required, MNPs-based methods allow fully automated nucleic acid purification, which is highly important in current SARS-CoV-2 diagnosis. Furthermore, the pcMNPs-RNA complexes obtained by this method are also compatible with various isothermal amplification methods, and thus could be used in the development of point-of-care devices. In conclusion, benefitting from its simplicity, robustness, and excellent performances, this new extraction method may provide a promising alternative to solve the time-consuming and laborious viral RNA extraction operations, and thus exhibits a great potential in current molecular diagnosis of COVID-19, especially for the early clinical diagnosis.
One of the greatest advantages of the MNPs-based extraction methods is that their aggregation require neither centrifugation nor column separation, thus they can shorten the time for the separation the components, reduce the risk of cross-contamination, and eliminate the need for costly equipment, such as centrifuges and liquid chromatography systems(Laurent et al., 2008). Besides, MNPs-based methods can also serve for a variety of automated low- to high-throughput procedures that can help save time and money. In addition, magnetic separation allows for recycling of the magnetic beads that is useful for large-scale use. These advantages enable faster, more efficient and cheaper MNPs-based methods for nucleic acid separation and purification.
3 Nucleic acid detection based on CRISPR and amplification techniques
The clustered regularly interspaced short palindromic repeats (CRISPR) system, first discovered in bacteria and capable of removing viral genes incorporated into bacterial genes, has received a great deal of attention in the nucleic acid detection(Bolotin et al., 2005; Mojica et al., 2005). The working mechanism is that the CRISPR-Cas protein can locate and cut out the target nucleic acid sequence with the aid of an RNA called crRNA(Shmakov et al., 2017). Particularly, the CRISPR-Cas system shows outstanding gene-editing capabilities(Cong et al., 2013; Gilbert et al., 2013), and it also exhibits enormous promise for the rapid and sensitive detection of nucleic acid. At present, class 2 CRISPR-Cas system, including type II, type V and type VI, has been widely used to detect nucleic acids(van Dongen et al., 2020). Among the CRISPR-Cas system, the CRISPR-Cas12a has drawn a lot of attention since it not only can cleave the target nucleic acid (cis cleavage) but also exhibit strong collateral cleavage activity (trans-cleavage) for single-stranded DNA (ssDNA). Specifically, when the specific DNA targets are recognized by the Cas12a/crRNA duplex, the collateral cleavage activity of the system is activated to nonspecifically and indiscriminately cleave non-target ssDNA strands(Janice S. Chen et al., 2021). Recently, the trans-cleavage activity of Cas12a has been reported as 3–17 turnovers per second (Janice S. Chen et al., 2021). In CRISPR-Cas12a-based biosensing, a range of amplification techniques have been exploited for SARS-CoV-2 detection that allow for highly sensitive and specific detection of nucleic acids including recombinase polymerase amplification (RPA), loop-mediated isothermal amplification (LAMP), and PCR (Scheme 2 ).Scheme 2 Schematic illustration of the techniques for nucleic acid amplification including RPA, LAMP, and PCR.
Scheme 2
3.1 Recombinase polymerase amplification (RPA)
RPA is an isothermal nucleic acid amplification method that mimics the process of nucleic acid replication in cells(Olaf Piepenburg et al., 2006). RPA is free of thermal cycling process, making it simple to operate without the need of expensive, high-tech equipment. The reaction of RPA can be completed under 37–42 °C in 3–10 minutes, which can well integrate with the CRISPR-Cas12a system in an assay. Several RT-RPA-assisted CRISPR-Cas12a-based detection techniques have been developed for the sensitive detection of SARS-CoV-2. Using RT-RPA and Cas12a, Wang et al. developed a highly efficient detection platform able to detect less than 5 viral copies per reaction for the SARS-CoV-2 genes (Fig. 3 A)(Wang et al., 2021, Wang et al., 2021, Wang et al., 2021). Briefly, the target RNA was extracted from clinical samples and amplified by RT-RPA reaction, and the target amplicons were injected into the CRISPR-Cas12a system. Specifically, the target amplicon was recognized by the Cas12a/crRNA duplex, thus activating the collateral activity of CRISPR-Cas12a to cut the reporter probes effectively, leading to the quencher moving away from the fluorophore. Consequently, the fluorescence intensity increased. Huang et al. also combined RT-RPA with the CRISPR-Cas12a system for the detection of SARS-CoV-2 (Fig. 3B)(Huang et al., 2020). In this work, the trans-cleavage activity of the CRISPR-Cas12a system and a fluorescent signal probe was utilized to detect SARS-CoV-2. The detection process took ∼50 min and the limit of detection was 2 copies per sample. However, it still remains an issue that the target amplification and detection steps are separate from each other where the nucleic acid-rich sample (e.g., RPA amplicons) may expose to the environment that potentially increases the risk of contamination. To meet this challenge, Sun et al. developed a one-pot method based on RT-RPA and CRISPR-Cas12a to detect SARS-CoV-2 (Fig. 3C)(Sun et al., 2021). In this assay, the target RNA was first extracted from the clinical samples and then added to the bottom of the tube that contained the RT-RPA mix. Simultaneously, the CRISPR-Cas12a reagents were placed on the tube lid. During this amplification process, the target amplification regents and other components were physically separated. The CRISPR-Cas12a reagents were centrifuged to transfer them to the tube bottom after the amplification. At this time, the target amplicons served as activators to activate the Cas12a/crRNA duplex to cleave the reporter probe and increase the fluorescence intensity of the solution. The assay had excellent sensitivity and selectivity toward SARS-CoV-2, with a low detection limit of 2.5 copies/μl input (RNA standard), as well as a rapid process within 50 min.Fig. 3 CRISPR and RPA for the fluorescent detection of nucleic acid. (A) Schematic of the detection method based on RPA and CRISPR-Cas12a for the determination of SARS-CoV-2(Wang et al., 2021, Wang et al., 2021, Wang et al., 2021) (Copyright, 2021 American Chemical Society). (B) A CRISPR-fluorescent assay for detection of SARS-CoV-2 RNA in clinical samples(Huang et al., 2020) (Copyright, 2020 Elsevier, reproduced with permission from Elsevier Ltd.). (C) A RT-RPA-based detection method for SARS-CoV-2(Sun et al., 2021) (Copyright, 2021 Springer Nature, reproduced with permission from Springer Nature Ltd.).
Fig. 3
Other than fluorescent reporters, colorimetric probes such as gold nanoparticles (AuNPs) are convenient and cost-effective for the detection of SARS-CoV-2 in clinical samples. Jiang et al. proposed an effective way to visually detect the SARS-CoV-2 genome by utilizing the magnetic pull-down to capture AuNPs (Fig. 4 A)(Jiang et al., 2021). In this assay, a biotinylated ssDNA probe served as a substrate for CRISPR-Cas12a, and AuNPs modified with complementary DNA strands were used to visually detect viral RNA. In the absence of the target RNA, the biotinylated ssDNA probes were kept intact and DNA-AuNPs would bind with biotinylated ssDNA probes through the DNA hybridization reaction. The complex was further immobilized on streptavidin-coated magnetic beads through biotin–streptavidin interaction. Consequently, the red DNA-AuNPs were magnetically enriched at the bottom of the tube and the supernatant presented colorless. When the target RNA was present, the CRISPR-Cas12 system was activated to cleave biotinylated ssDNA. At this point, the DNA-AuNPs would be in the supernatant, so the solution still showed a red color. In this assay, the detection limit is 50 RNA copies per reaction. The colorimetric detection of SARS-CoV-2 can be also achieved by modulating the dispersion state of AuNPs in solution. For instance, Zhang et al. developed a colorimetric assay based on RT-RPA and CRISPR-Cas12a to detect the SARS-CoV-2 genome (Fig. 4B)(Zhang et al., 2021, Zhang et al., 2021, Zhang et al., 2021). When the target was absent, the CRISPR-Cas12a system would not be activated and ssDNA strands modified on AuNPs were kept intact. Thus, AuNPs remained dispersed in the solution. However, when the target was present, the CRISPR-Cas12a system was activated to cleave ssDNA strands modified on AuNPs, leading to the aggregation of AuNPs. As a consequence, a peak shift and color change of the solution was observed. The detection limit of this approach is 1 copy of the viral genome sequence per test.Fig. 4 CRISPR and RPA for the colorimetric detection of nucleic acid. (A) CRISPR-Cas12a-based colorimetric technique for the visual detection of SARS-CoV-2 (Jiang et al., 2021) (Copyright, 2021 American Chemical Society). (B) RT-RPA-coupled CRISPR-Cas12a for the colorimetric detection of SARS-CoV-2(Zhang et al., 2021) (Copyright, 2021 American Chemical Society). (C) A wearable diagnostic approach for the visual detection of SARS-CoV-2 (Nguyen et al., 2021) (Copyright, 2021 Springer Nature, reproduced with permission from Springer Nature Ltd.).
Fig. 4
AuNPs-based lateral flow assays (LFAs) can be also used to detect SARS-CoV-2 with naked-eye readout. Li et al. developed a microfluidic platform based on RT-RPA, CRISPR-Cas12a, and LFA for the contamination-free and visual detection of the SARS-CoV-2 genome(Li et al., 2022). In this method, the viral RNA was first extracted from swab samples. Subsequently, the extracted RNA was amplified by the RT-RPA reaction in a microfluidic chip. The detection results could be directly read by the naked eye against an LFA dipstick. In this LFA dipstick, fluorescein (FAM)-ssDNA-biotin probe acted as the reporter probe, and FAM would bind with anti-FITC antibody-AuNPs to achieve a visible detection. In the absence of the target, the reporter probe would not be cleaved, thus anti-FITC antibody-AuNPs would be anchored on the control band. Conversely, in the presence of the target, the reporter was cleaved by the activated CRISPR-Cas12a. At this time, anti-FITC antibody-AuNPs bound with the FAM and the complex was anchored on the test band. It accomplished the detection of 100 copies of SARS-CoV-2 RNA target. In addition, a novel detection platform was also reported which combined LFA and microfluidic chip with wearable materials (Fig. 4C)(Nguyen et al., 2021). This designed face-mask sensor contained three reaction zones, including the lysis reagents zone, RT-RPA reaction zone, and CRISPR-Cas12a reaction zone. When the SARS-CoV-2-derived amplicons were present, the Cas12a/crRNA duplex was activated to cut the FAM-ssDNA-biotin probe. At this point, the LFA strip was utilized to achieve the visual detection of the target. The whole assay took ∼1.5 h and the limit of detection for this face-mask sensor was 500 copies of the SARS-CoV-2 in vitro transcribed RNA.
3.2 Loop-mediated isothermal amplification (LAMP)
LAMP is an isothermal nucleic acid amplification technique in which a DNA polymerase and four DNA primers are used including two inner primers and two outer ones. LAMP can generally amplify a few copies of DNA targets to detectable amounts under isothermal conditions in less than an hour. LAMP only needs one enzyme for the exponential amplification, thus it holds great promise for point-of-care detections. LAMP can combine with the CRISPR-Cas system for the detection of nucleic acid. Specifically, the Cas12a/crRNA complex can recognize the LAMP product with a large amount of the repeat target-specific dsDNA. The CRISPR-Cas12a system is activated to cut the signal probes that provides an ideal way for the specific detection of dsDNA. At present, a variety of RT-LAMP-assisted CRISPR-Cas12a system-based sensing strategies have been designed for the sensitive detection of SARS-CoV-2.
Alfredo et al. proposed a direct approach based on LAMP and the CRISPR-Cas12a for the rapid detection of SARS-CoV-2 without RNA extraction (Fig. 5 A)(Garcia-Venzor et al., 2021). Firstly, the inactivated clinical sample was amplified through a LAMP reaction. Meanwhile, Cas12a protein and N-gene specific crRNA were preincubated and the reporter probe was added. The CRISPR-Cas12a system was activated by the target amplicons, leading to the cleavage of the reporter probe effectively. Consequently, the fluorophore was released and emitted fluorescence. The limit of detection for this method is 16 copies/μL. Despite the great amplification efficiency of LAMP, false-positive results inevitably affect the detection results. To meet this challenge, Zhang et al. proposed a detection method for the specific detection of SARS-CoV-2(Zhang et al., 2021, Zhang et al., 2021, Zhang et al., 2021). In this assay, a uracil-DNA-glycosylase (UDG) and the reverse transcription-LAMP were utilized to reduce the aerosol contamination. Based on this detection mechanism, the wild-type or spike mutant SARS-CoV-2 spike N501Y could be detected with a limit of detection of 10 copies/μL (wild-type). To reduce the risk of carryover contaminations, researchers developed a one-pot method for SARS-CoV-2 detection (Fig. 5B)(Pang et al., 2020). In this assay, the CRISPR-Cas12a reagents and RT-LAMP reagents were put inside the top and bottom of the tube, respectively. The target RNA was then put in the bottom where it was amplified based on RT-LAMP. The CRISPR-Cas12a reagents were then mixed with the target amplicon produced by RT-LAMP at the bottom of the tube by simply inverting the tube and flicking the wrist. As a consequence, the CRISPR-Cas12a system was activated to cleave the signal probe, leading to the generation of bright green fluorescence. The whole process took 40 min and showed 100% clinical specificity. Wang et al. also accomplished one-pot SARS-CoV-2 detection using a similar approach (Fig. 5C)(Wang et al., 2021). The whole process took 45 min and the diagnostic results accorded with the quantitative method authorized by the Centers for Disease Control and Prevention. To achieve the point-of-care detection of SARS-CoV-2 in clinical samples, Chen et al. utilized a smartphone and portable 3D printing instrument to realize contamination-free and visual SARS-CoV-2 detection(Wang et al., 2020a, Wang et al., 2020b). In this detection platform, the whole process takes 40 min, and the limit detection is 20 copies of RNA of SARS-CoV-2.Fig. 5 CRISPR and LAMP for the fluorescent detection of nucleic acid. (A) A fluorescent method based on LAMP and CRISPR-Cas12 for SARS-CoV-2 detection (Garcia-Venzor et al., 2021) (Copyright, 2021 Frontiers, reproduced with permission from Frontiers Ltd.). (B) A one-pot fluorescent method for COVID-19 detection(Pang et al., 2020) (Copyright, 2020 American Chemical Society). (C) Working principle of one-pot and visual COVID-19 detection based on RT-LAMP-CRISPR(Wang et al., 2021) (Copyright, 2021 Elsevier, reproduced with permission from Elsevier Ltd.).
Fig. 5
Other than fluorescent readouts, colorimetric assays are also developed with a convenient and cost-effective readout for the detection of SARS-CoV-2. Zhang et al. designed a AuNPs-based visual assay that combined CRISPR-Cas12a with RT-LAMP for the rapid and sensitive determination of COVID-19 (Fig. 6 A)(Zhang et al., 2021). When the viral RNA was present, the Cas12a/crRNA duplex was activated to cleave linker-ssDNA. Hence, AuNP probe pairs (AuNP-DNA1 and AuNP-DNA2) could not be cross-linked and the solution displayed a red color. Conversely, when the SARS-CoV-2 RNA was absent, the CRISPR-Cas12a was kept inactivated and the linker-ssDNA was kept intact. Consequently, AuNP probe pairs were cross-linked with linker-ssDNA, accompanied by a color change from red to purple. The whole assay time was 40 min and it could detect down to 4 copies/μL of SARS-CoV-2 RNA. Apart from AuNPs, other colorimetric substrates could also be applied for the visual detection of viral nucleic acids. For instance, Xie et al. evaluated the performance of 16 types of fluorophore-ssDNA-quencher reporters that served as colorimetric substrates in CRISPR-Cas12a-based assays (Fig. 6B)(Xie et al., 2022). Among them, 9 fluorophore-ssDNA-quencher reporters were suitable for colorimetric detection, with an excellent performance using ROX-labeled reporters. Particularly, in this colorimetric method, a convolutional neural network algorithm was developed to standardize and automate the colorimetric analysis of images and integrated this into the MagicEye mobile phone software. The sensitivity of this technique reached 40 total copies.Fig. 6 CRISPR and LAMP for the detection of nucleic acid. (A) Schematic diagram for COVID-19 detection based on CRISPR-Cas12a and the LAMP(Zhang et al., 2021) (Copyright, 2021 Elsevier, reproduced with permission from Elsevier Ltd.). (B) A colorimetric method for the visual detection of SARS-CoV-2 (Xie et al., 2022) (Copyright, 2022 American Chemical Society). (C) A LFA strip for the portable and visual detection of SARS-CoV-2 (Broughton et al., 2020) (Copyright, 2020 Springer Nature, reproduced with permission from Springer Nature Ltd.).
Fig. 6
For the quick and self-inspection of viral nucleic acids, LFA can be utilized as a portable, easy-to-operate tool. Broughton et al. reported a rapid RT-LAMP-assisted CRISPR-Cas12a system-based LFA for the detection of SARS-CoV-2 (Fig. 6C)(Broughton et al., 2020). To achieve the visual detection of SARS-CoV-2, the FAM-biotin reporter was used as a substrate and LFA strips were designed to capture the labeled reporter. FAM-biotin molecules were captured at the control line, but a signal was produced at the test line due to the collateral cleavage activity of CRISPR-Cas12a. Based on this detection mechanism, the whole assay took ∼45 min and the limit of detection is 10 copies per μL. Furthermore, Yi et al. designed a DNA capture probe-based LFA strip for the detection of SARS-CoV-2 (Yi et al., 2021). Compared to conventional LFA strips, streptavidin-modified AuNPs instead of anti-FAM antibody-AuNPs acted as signal probes for the naked-eye readout. When the target RNA was absent, the biotinylated-ssDNA reporter was kept intact and then hybridized with an ssDNA probe immobilized on the test line of the LFA strip. Subsequently, the streptavidin-modified AuNPs were captured by the test line through biotin-streptavidin interaction. The excess streptavidin-modified AuNPs were anchored on the control line by biotinylated-antibody. Conversely, when the target RNA was present, the biotinylated-ssDNA reporter was cleaved by the activated CRISPR-Cas12a system. At this time, streptavidin-modified AuNPs would not aggregate on the test line of the LFA strip due to a lack of biotinylated-ssDNA reporter, thus only showing a visible red signal on the control line. The approach achieved ultra-sensitivity of 1 copy/μL in ∼60 min. To improve the portability, Rezaei et al. proposed a portable and semi-automated device for the detection of COVID-19 (Rezaei et al., 2021). The device contained four parts, including a heater, a cooler fan, a proportional integral derivative controller to regulate the temperature, and designated areas for 0.2 mL Eppendorf® PCR tubes. Notably, up to 500 samples could be processed simultaneously in under 35 min using this multiplexing portable equipment that could increase the number of wells in the reaction zone.
3.3 Polymerase chain reaction (PCR)
For the molecular diagnosis of coronavirus infections, PCR is regarded as the gold standard due to its great sensitivity and specificity (Chan et al., 2015). A variety of biosensors have been proposed based on PCR and CRISPR-Cas12a. Specifically, by combining PCR pre-amplification with the CRISPR-Cas12a system, a nucleic acid detection approach named HOLMES (one-hour low-cost multipurpose highly efficient system) was proposed by Wang et al.(Li et al., 2018). Recently, Liang et al. proposed a RT-PCR-assisted CRISPR-Cas12a-based assay to specifically detect the Omicron variant (Fig. 7 A)(Liang et al., 2022). In this assay, the target RNA was extracted from patient samples and amplified by RT-PCR. Subsequently, the CRISPR-Cas12a system was activated by the target amplicons, resulting in an effective cleavage of the signal probe and an increase in the fluorescence signal. In contrast, no significant fluorescence signal was observed in the absence of the target RNA. Notably, this method had good specificity and sensitivity that could detect as low as 2 copies/μL of target RNA. To further improve portability, Li et al. constructed an automated microfluidic system to distinguish the variant of SARS-CoV-2 (Fig. 7B)(Li et al., 2021). By detecting 30 clinical samples, the practicability and accuracy of the assay were validated. This approach showed 100% consistency with the next-generation sequencing technology, which could act as a potential and portable tool to distinguish the delta variant of SARS-CoV-2.Fig. 7 CRISPR and PCR for the detection of nucleic acid. (A) RT-PCR and CRISPR-Cas12a for the detection of SARS-CoV-2 Omicron variant(Liang et al., 2022) (Copyright, 2022 Elsevier, reproduced with permission from Elsevier Ltd.). (B) Principle of the automated Cas12a-microfluidic assay(Li et al., 2021) (Copyright, 2021 Royal Society of Chemistry, reproduced with permission from Royal Society of Chemistry Ltd.). (C) A visual biosensor for SARS-CoV-2 detection(Ma et al., 2022) (Copyright, 2022 Elsevier, reproduced with permission from Elsevier Ltd.). (D) Schematic illustration of a solid-state nanopore sensor based on the CRISPR-Cas12a system (Nouri et al., 2021) (Copyright, 2021 American Chemical Society).
Fig. 7
Besides fluorescence, colorimetric sensing strategies can be also developed for the detection of SARS-CoV-2. For the ultrasensitive detection of SARS-CoV-2, Ma et al. developed an RT-PCR-assisted CRISPR-Cas12a-powered detection device with a smartphone readout (Fig. 7C)(Ma et al., 2022). In the absence of SARS-CoV-2, the CRISPR-Cas12a system would not be activated and the linker ssDNA would not be cleaved. AuNPs-DNA would aggregate through complementary hybridization between the linker ssDNA and the ssDNA modified on AuNPs. At this point, the color of the solution changed from red to purple. Conversely, in the presence of target RNA, dsDNA amplicons would activate the Cas12a/crRNA complex to cut the linker ssDNA, resulting in the dispersion of AuNPs-DNA and a red color of the solution, achieving a limit of detection of 1 copy/μL.
Apart from the above sensing strategies, Nouri et al. proposed a solid-state CRISPR-Cas12a-assisted nanopore detection method for the specific determination of SARS-CoV-2(Fig. 7D)(Nouri et al., 2021). This work contained three streamlined steps: RT-PCR, CRISPR-Cas12a assay, and nanopore-based molecule classification and counting. The circular M13mp18 ssDNA with an excellent signal-to-noise ratio was selected as the reporter in the nanopore measurement. In this assay, the target RNA was first amplified by one-step RT-PCR. After amplification, the dsDNA amplicons served as activators to activate the CRISPR-Cas12 system to cleave the signal reporters, leading to a signal change. Conversely, in the absence of the target RNA, the CRISPR-Cas12 system would not be activated and the circular ssDNA reporter would not be degraded. The whole process took 65 min with a sensitivity of 13.5 copies/μL of viral RNA.
3.4 Other techniques for nucleic acid amplification
In addition to conventional RPA, LAMP, and PCR amplification, other amplification techniques have been constructed for the detection of nucleic acid. Recombinase-aided amplification (RAA), primer exchange reaction (PER), dual-priming isothermal amplification (DAMP), enzymatic recombinase amplification (ERA), multiple cross displacement amplification (MCDA), exonuclease III cleavage reaction, and entropy-driven reaction system, can combine with CRISPR-Cas12a to detect SARS-CoV-2.
Similar to RPA, RAA is an isothermal amplification technique without the need for a sophisticated thermal cycler in which recombinase uvsX (E. coli), ssDNA binding protein, and DNA polymerase were used. Wang et al. introduced an RT-RAA-assisted detection method to advance the point-of-care diagnosis of COVID-19 (Fig. 8 A)(Wang et al., 2020a, Wang et al., 2020b). Briefly, the target RNA was extracted from clinical samples and amplified by RT-RAA. Then, target amplicons were injected into the CRISPR-Cas12a reaction solution. The RT-RAA products would be recognized by the Cas12a/crRNA duplex, thus activating the trans-cleavage activity of the CRISPR-Cas12a system to cut the reporter probe. Consequently, it generated green fluorescence that could be seen with the naked eye under 485 nm light. The limit of detection for this method was 10 copies of SARS-CoV-2.Fig. 8 CRISPR and other amplification techniques for the detection of nucleic acid. (A) A detection approach based on the CRISPR-Cas12a system for naked-eye readout of COVID-19(Wang et al., 2020a, Wang et al., 2020b) (Copyright, 2020 Elsevier, reproduced with permission from Elsevier Ltd.). (B) Principle of the PER-based assay for the detection of SARS-CoV-2 RNA (Li et al., 2022) (Copyright, 2022 Royal Society of Chemistry, reproduced with permission from Royal Society of Chemistry Ltd.). (C) Overview of the digital warm-start-CRISPR assay(Ding et al., 2021) (Copyright, 2021 Elsevier, reproduced with permission from Elsevier Ltd.).
Fig. 8
Another isothermal nucleic acid amplification technique known as PER was developed by Yin et al.(Kishi et al., 2017), which has received a great deal of interest in the fields of DNA nanotechnology and bio-imaging. Li et al. developed a technique for the detection of SARS-CoV-2 RNA by combining PER with CRISPER-Cas12a (Fig. 8B)(Li et al., 2022). When the target RNA was present, the PER cascade reaction was triggered to produce a significant amount of ssDNAs. The CRISPR-Cas12a system was activated by these ssDNAs to cut the signal reporter and generate a fluorescent signal. This detection approach could achieve a detection limit as low as 1.3 pM in 40 min at 37 °C.
RT-DAMP was proposed by Liu et al.(Ding et al., 2019), which was a variant of RT-LAMP with a new primer design strategy. Ding et al. chose RT-DAMP to develop a digital warm-start CRISPR assay for the quantitative detection of SARS-CoV-2 (Fig. 8C)(Ding et al., 2021). In this work, the detection was established by partitioning the first warm-start CRISPR-Cas12a-based reaction into sub-nanoliter aliquots within a Quant Studio 3D digital chip. In this assay, the target RNA was amplified by the RT-DAMP reaction to generate a lot of target amplicons. Meanwhile, the Cas12a/crRNA duplex was specifically bound to target amplicons to activate the cleavage activity of the CRISPR-Cas12a system. Consequently, the reporter probe was cleaved which generated a fluorescent signal. The assay was able to detect down to 5 copies/μl SARS-CoV-2 RNA in the chip.
MCDA, an isothermal nucleic acid amplification method, allows nucleic acid amplification using a simple instrument for COVID-19 diagnosis. Zhu et al. combined RT-MCDA with CRISPR-Cas12a to design an approach for the detection of SARS-CoV-2 RNA(Zhu et al., 2021). In this assay, the viral RNA was first converted to cDNA by RT reaction. Subsequently, the cDNA served as the template for MCDA amplification. Through MCDA amplification, a lot of amplicons with the TTTT PAM site were generated. Then, the CRISPR-Cas12a system was activated by amplicons to cleave the signal probes effectively. In particular, a LFA strip was applied for the signal readout to achieve visual detection. The detection could be completed within 1 h and the sensitivity of LFA was 7 copies (for each of the target templates) per test.
4 Conclusion and future perspective
The SARS-CoV-2 is a newly emerged virus that causes mild to severe pneumonia. COVID-19 is still a major issue worldwide after its global super-spread, and emerging viral diseases have not got specific and reliable treatments. The pivot of an effective human response to this COVID-19 pandemic is early, rapid and accurate testing of clinical samples of suspected and probable cases. Molecular diagnostics such as nucleic acid detection can achieve early and rapid detection of targets and are considered as an ideal approach for the detection of pathogens in infectious diseases. The nucleic acid detection for infectious diseases are widely used, as evidenced by the widespread use of COVID-19 tests for the containment of the pandemic(Zhou et al., 2020).
The ongoing SARS-CoV-2 pandemic highlights the importance of nanotechnology in providing tools and technologies for diagnostic and antiviral research. Nanotechnology is one of the most promising tools for the development of simple and fast sample preparation methods that require no multiple steps and complex systems for sample preparation because of the increased surface-to-volume ratio of nanostructures (Lundqvist et al., 2008). To date, considerable efforts have been made to improve the detection of coronavirus and a variety of improved or new methods have been developed. Obtaining high-quality nucleic acid is a key factor for pathogen detection since nucleic acid extraction is the first step. Accordingly, various studies have focused on methods to improve the efficiency of gene extraction, including the MNPs-based nucleic acid extraction. Nowadays, the idea of using magnetic separation techniques to purify biologically active compounds (nucleic acids and proteins) from the cells and cell organelles has attracted rapidly growing interest. Comparing with other nucleic acid separation techniques that are generally time-consuming and require expensive equipment, magnetic separation has several advantages. MNPs have exhibited superior properties such as larger surface-to-volume ratio, excellent reactivity and unique magnetic response(Hajba and Guttman, 2016; Haun et al., 2010), which make the pre-concentration, purification and separation of nucleic acids easy and feasible(Tang et al., 2020). MNPs shorten the purification stage and eliminate pre-treatment and pre-enrichment steps. Overall, MNPs are expected to facilitate the development of improved analysis protocols that are faster, cheaper and simpler than currently existing ones.
Accurate and early detection of SARS-CoV-2 infection is critical for minimizing spread and initiating treatment(Espy et al., 2006). Nucleic acid amplification has been considered the gold standard for diagnosis of many viral infections. The nucleic acid detection for infectious diseases are widely used, as evidenced by the widespread use of COVID-19 tests for the containment of the pandemic. In this review, we summarize the nucleic acids detection methods based on the combination of CRISPR-Cas and amplification techniques such as RPA, LAMP, PCR and other nucleic acid amplification methods. The performances of these detection methods based on amplification techniques with CRISPR-Cas12a technology for coronavirus detection are listed in Table 1 , and the comparison in the reaction conditions of different amplification techniques is listed in Table 2 . In summary, technical breakthroughs have been reported in viral detection on molecular levels. Many of these breakthroughs have taken advantage of recent advances in rapidly evolving micro and nanotechnology to bring improvements to the speed, sensitivity, operability, and portability of viral diagnostics. Nanomaterials can provide new opportunities such as more efficient, convenient, and safer applications. However, challenges still remain such as costs, toxicities to the environment and humans, and regulatory issues before being introduced to the market. Although the nanotechnology-based approaches for the nucleic acid extraction and detection of coronavirus have attracted considerable attentions, there are still some challenges that should be addressed in the future works. Firstly, the magnetic separation performance of MNPs should be improved to meet the application requirements in fast response and accurate positioning under external magnetic field(Liu et al., 2018; Wu et al., 2016). Secondly, the stability of MNPs could be improved that directly affects their real-world application (Defaei et al., 2018). Thirdly, the amplification techniques can be simplified and optimized to well integrate with other platforms such as microfluidics to better serve as point-of-care testing tools for the detection of SARS-CoV-2. The fight against infectious diseases caused by SARS-CoV-2 remains challenging despite the tremendous efforts and significant advances in public healthcare. As shown in this review, nanotechnology has already been shown to enhance the diagnostics in coronavirus infections. To tackle future challenges, the collaboration between different scientific fields, clinicians and industry is required. With the rapid development of new technologies and methods, we believe that more excellent and efficient detection methods will be developed in the future.Table 1 A summary of the detection performance based on amplification techniques with CRISPR-Cas12a technology for coronavirus detection.
Table 1Signal amplification Sensitivity Detection time Real sample Signal output Ref
RPA <5 viral copies per reaction – Nasal swab, oropharyngeal swab, anal swab, sputum, stool, and sputum supernatant Fluorescence Wang et al. (2021c)
2 copies per sample ∼50 min Nasal swab Fluorescence (Huang et al., 2020b)
2.5 copies/μl input (RNA standard);
1 copy/μl input (pseudovirus) ∼50 min Pharyngeal swab Fluorescence Sun et al. (2021)
2 copies/μL of full-length COVID-19 genome; 0.5 copy/μL of DNA fragment of N gene – – Fluorescence Malci et al. (2022)
50 RNA copies per reaction – Nasopharyngeal and throat swab Colorimetry (Jiang et al., 2021b)
1 copy of viral genome sequence per test – Clinical standard sample Colorimetry (Zhang et al., 2021d)
100 copies – Nasopharyngeal swab LFA Li et al. (2022b)
500 copies ∼90 min Clinical samples LFA Nguyen et al. (2021)
LAMP 16 copies/μL 40 min Clinical samples Fluorescence (Alfredo Garcia-Venzor et al., 2021)
10 copies/μL (wild-type) – – Fluorescence Zhang et al. (2021b)
30 copies/μL (150 copies) 40 min Respiratory swab Fluorescence (Pang et al., 2020b)
5 copies 45 min Clinical samples Fluorescence (Wang et al., 2021a)
20 copies 40 min Respiratory swab Fluorescence Chen et al. (2020b)
4 copies/μL 40 min – Colorimetry (Zhang et al., 2021e)
58 copies – Clinical samples Colorimetry Xie et al. (2022)
10 copies per μl input <40 min Respiratory swab LFA Broughton et al. (2020)
1 copy/μL ∼60/32 min Nasopharyngeal swab LFA (Yi et al., 2021b)
35 copies/μL 35 min Nasopharyngeal or oropharyngeal swab LFA Rezaei et al. (2021)
PCR 2 copies per reaction – Oropharyngeal swab Fluorescence (Yuanhao Liang et al., 2022)
1 copy/μL – Clinical samples Fluorescence Li et al. (2021)
1 copy/μL ∼90 min Throat swab Colorimetry (Ma et al., 2022a)
13.5 copies/μL 65 min – Electrochemistry (Nouri et al., 2021a)
RAA 10 copies – Clinical samples Fluorescence Wang et al. (2020b)
PER 1.3 pM 40 min Complex biological samples Fluorescence (Li et al., 2022a)
DAMP 5 copies/μL – Swab and saliva samples Fluorescence (Ding et al., 2021a)
ERA 0.25/0.5 copies/μL 40 min Clinical samples LFA Liu et al. (2021)
MCDA ∼60 min 7 copies/test LFA Zhu et al. (2021)
Table 2 A comparison of the reaction conditions of different amplification techniques.
Table 2Methods Target Primers Required enzymes Reaction time (h) Temperature (°C) Amplicon
RPA DNA 2 DNA polymerase and recombinase 0.5–1.5 37–42 DNA
LAMP DNA 4–6 DNA polymerase <1 60–65 DNA
PCR DNA 2 Taq DNA polymerase 1.5–2 95/55/72 DNA
RAA DNA 2 DNA polymerase and recombinase 0.5 37 DNA
PER RNA 1 DNA polymerase 0.5 37 DNA
DAMP DNA 6 DNA polymerase 2 60–65 DNA
ERA DNA 2 DNA polymerase and recombinase 20–30 37–42 DNA
MCDA DNA 6 DNA polymerase 35 63 DNA
Uncited reference
Meysam Rezaei,.
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.
Acknowledgements
The authors are grateful for the financial support from 10.13039/501100012166 National Key Research and Development Program of China (2020YFA0909100), 10.13039/501100001809 National Natural Science Foundation of China (22104128), 10.13039/501100004731 Zhejiang Provincial Natural Science Foundation of China (LR22C200003), and the Fundamental Research Funds for the Central Universities (226-2022-00169).
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| 0 | PMC9733970 | NO-CC CODE | 2022-12-15 23:18:08 | no | Biosens Bioelectron X. 2023 May 10; 13:100289 | utf-8 | Biosens Bioelectron X | 2,022 | 10.1016/j.biosx.2022.100289 | oa_other |
==== Front
Biosens Bioelectron X
Biosens Bioelectron X
Biosensors & Bioelectronics
2590-1370
The Author(s). Published by Elsevier B.V.
S2590-1370(22)00182-0
10.1016/j.biosx.2022.100289
100289
Article
Nanotechnology-based diagnostic methods for coronavirus: From nucleic acid extraction to amplification
Huang Xucheng a1
Fu Ruijie ab1
Qiao Sai a
Zhang Jun a∗∗
Xianyu Yunlei ab∗
a Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
b State Key Laboratory of Fluid Power and Mechatronic Systems, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
∗ Corresponding author. Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
∗∗ Corresponding author.
1 These authors contribute equally to this work.
10 12 2022
10 12 2022
1002895 10 2022
25 11 2022
3 12 2022
© 2022 The Author(s)
2022
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The recent emergence of human coronaviruses (CoVs) causing severe acute respiratory syndrome (SARS) is posing a great threat to global public health. Therefore, the rapid and accurate identification of pathogenic viruses plays a vital role in selecting appropriate treatments, saving people's lives and preventing epidemics. Nucleic acids, including deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), are natural biopolymers composed of nucleotides that store, transmit, and express genetic information. Applications of nucleic acid detection range from genotyping and genetic prognostics, to expression profiling and detection of infectious disease. The nucleic acid detection for infectious diseases is widely used, as evidenced by the widespread use of COVID-19 tests for the containment of the pandemic. Nanotechnology influences all medical disciplines and has been considered as an essential tool for novel diagnostics, nanotherapeutics, vaccines, medical imaging, and the utilization of biomaterials for regenerative medicine. In this review, the recent advances in the development of nanotechnology-based diagnostic methods for coronavirus, and their applications in nucleic acid detection are discussed in detail. The techniques for the amplification of nucleic acids are summarized, as well as the use of magnetic nanoparticles for nucleic acid extraction. Besides, current challenges and future prospects are proposed, along with the great potential of nanotechnology for the effective diagnosis of coronavirus.
Keywords
Coronaviruses
Nucleic acid extraction
Nucleic acid amplification
Magnetic nanoparticles
CRISPR-Cas
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pmc1 Introduction
Coronaviruses (CoVs) belongs to the subfamily Coronavirinae in the family of Coronaviridae of the order Nidovirales, which are enveloped and spherical viruses with a single-stranded RNA genome(Haake et al., 2020). The first coronavirus was identified in the 1960s. Coronaviruses are classified into four genera including alpha-coronavirus(α-CoV), beta-coronavirus(β-CoV), gamma-coronavirus(γ-CoV), and delta-coronavirus(δ-CoV)(Chen et al., 2020a, Chen et al., 2020b), of which α-CoV and β-CoV are reported to infect humans(de Wilde et al., 2018). Coronaviruses can cause respiratory and neurological diseases(Corman et al., 2012; Lim et al., 2019; Wang et al., 2019). The emergence of CoVs causing severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) with global spread represents a significant threat to public health(Noh et al., 2017). Recently, the emergence of ribonucleic acid (RNA) enveloped human β-CoV named SARS-CoV-2 which is etiologically related to the well-known severe acute respiratory syndrome coronavirus (SARS-CoV), has been challenging the global public health community to confront a novel infectious disease (coronavirus disease 2019, COVID-19)(Shen et al., 2020). COVID-19 that affects the lower respiratory tract and manifests as pneumonia in humans(Sohrabi et al., 2020), remains a significant issue on the global health. They can infect respiratory, gastrointestinal, hepatic, and central nervous system of human(Ge et al., 2013). The pandemic of COVID-19 has threatened the public health worldwide, with the living and working conditions of billions of people globally severely disrupted due to various forms of social distancing and lockdowns in many cities.
Nucleic acids (DNA and RNA) are natural biopolymers composed of nucleotides which play crucial biological roles in all forms of living organisms, such as storing, encoding, and transmitting genetic information for cellular function maintaining and genetic information relaying. DNA stores genetic information and encodes the amino acid sequences of proteins responsible for cellular function. RNA plays various important roles in the coding, decoding, regulation, and expression of genes. Consequently, nucleic acids are used as important biomarkers for biological studies and medical diagnostics(Breaker, 2004). For example, nucleic acids are used for clinical diagnostics for infectious disease and cancer as well as for monitoring epidemics and outbreaks of new diseases(Hartman et al., 2013). Quantitative and qualitative determination of nucleic acids is of great significance in modern biology and medicine. Detection of DNA/RNA of pathogenic bacteria and viruses is beneficial for making an appropriate strategy for the treatment. During the past several decades, many approaches have been developed for the detection of nucleic acids that hold great promise for clinical translation. The classic nucleic acid test (NAT) process includes nucleic acid extraction, amplification, and detection. Molecular diagnostics is widely adapted in various fields such as disease detection and health monitoring, which evaluates the nucleic acids (e.g., DNA, RNA, or a variation of both) of bacteria or virus in human samples as for diseases diagnosis(Park et al., 2018). The polymerase chain reaction (PCR) has become one of the most important tools in molecular diagnostics, providing exquisite sensitivity and specificity for detection of nucleic acids. Real-time quantitative PCR (RT-qPCR) is a rapid, specific, and sensitive TaqMan PCR method for detection, subgrouping, and quantitation of pathogens. This assay increases the sensitivity of conventional PCR(Zhang et al., 2020). Owing to its broad applicability, high sensitivity, and high sequence specificity, the PCR-based method has become a routine and reliable technique for detecting coronaviruses(Shen et al., 2020).
Nanotechnology is broadly defined as the application of materials and devices where at least one dimension is less than 100 nanometers, which has already been employed in the diagnosis and treatment of viral diseases. Nanoscience and nanotechnology deal with very small particles in many disciplines such as biology, chemistry, materials science, physics, engineering and so on. Materials science is important in all areas of antiviral research, including viral structural and biological studies, detection, treatment and vaccination(Tang et al., 2020, Tang et al., 2020). Nanoparticles are small size particles with a large surface-to-volume ratio(Kaushik, 2020; Saravanan et al., 2021), which offer many applications in a range of fields from chemistry to biology and biomedicine(Zhu et al., 2015). Over the decades, nanoparticles have been widely used and studied for their unique properties, such as small size, surface adaptability, improved solubility and multifunctionality, resulting in the development of better and safer drugs, tissue-targeted treatments, personalized nanomedicine and early diagnosis and prevention of diseases(Fornaguera and García-Celma, 2017; Soares et al., 2018). A variety of nanomaterials, including metallic nanoparticles, magnetic nanoparticles, silica nanoparticles, polymeric nanoparticles, carbon nanotubes, and quantum dots, have already been used for virus detections that may open a new area of potential applications (Draz and Shafiee, 2018; Halfpenny and Wright, 2010; Lee et al., 2013). Nanotechnology has already proven its value through its diagnostic, vaccine, and therapeutic applications that have expanded into clinical applications(Varahachalam et al., 2021). Nanomaterials-based nucleic acid detection of viral infectious diseases has various advantages in the diagnostic field(Cheng et al., 2008). Moreover, nanomaterials are powerful tools for the diagnosis, prevention, and treatment of COVID-19(Tang et al., 2020, Tang et al., 2020).
Molecular diagnostics based on nucleic acid detection follows four basic steps: sample collection, nucleic acid extraction from the collected sample, nucleic acid amplification, and analysis. The process examines the nucleic acids to investigate the pathogen responsible for the infections. Efficient and robust nucleic acid extraction from complex clinical samples is one of the most fundamental steps. The nucleic acid extraction is the first step in the molecular analysis of nucleic acids (Wu et al., 2019). It is crucial for the entire detection and analysis as it directly influences the subsequent steps(Liu et al., 2010).Nucleic acid extraction generally involves cell (or virion) lysis, nucleic acid isolation and purification(Ali et al., 2017). However, traditional nucleic acid extraction methods are time-consuming and labor-intensive(Stray and Zimmermann, 2019). To address this issue, magnetic nanomaterials have offered a promising and efficient platform for the extraction and purification of nucleic acids. For the nucleic acid amplification, different techniques have been developed for the amplification for enhanced sensitivity. In this review, the recent advances in the development of nanotechnology-based diagnostic methods for coronavirus, and their applications in COVID-19 detection are discussed in detail. The use of magnetic nanomaterials for nucleic acid extraction and techniques for the amplification of nucleic acids are summarized (Scheme 1 ). Besides, current challenges and possible solutions are proposed, along with the great potential of nanotechnology for the effective diagnosis of coronavirus.Scheme 1 An overview of nucleic acid extraction and nucleic acid amplification for COVID-19 detection.
Scheme 1
2 Nucleic acid extraction based on magnetic nanomaterials
2.1 Classification and characteristics of MNPs
Magnetic nanoparticles (MNPs) with a diameter of 10–100 nm are preferred for use in vivo, as this reduces particle opsonization and their subsequent clearance, allowing to overcome most of the physiological barriers (Canfarotta and Piletsky, 2014; Martinelli et al., 2019; Shubayev et al., 2009). The properties of MNPs strongly derive from their physicochemical characteristics, mean size, and morphology(Farinha et al., 2021; Manescu et al., 2021). As shown in Fig. 1 , MNPs are usually categorized into three groups: (1) single metal MNPs, (2) metal oxide MNPs, and (3) metal alloy MNPs. Single metal MNPs present a core composed of one single pure metal structure such as iron (Fe), Co, and nickel (Ni). Metal oxides essentially include iron oxides (FexOy) and ferrites, such as CoFe2O4, MgFe2O4 or MnFe2O4. Alloy MNPs consist of a combination of two or more different pure metals, for example, iron cobalt alloys (FeCo) and iron platinum alloys (FePt)(Farinha et al., 2021). MNPs have high surface area-to-volume ratio, high binding affinity to the detection targets, and can be magnetically controlled for aggregation and dispersion, making the pre-concentration, purification and separation of nucleic acids simple and easy. Active substances attached to the surface of MNPs can be combined with specific biomolecules, such as DNA/RNA, enzymes, proteins, and can be separated under the action of an external magnetic field that has rapid separation, high specificity, and good reproducibility. Therefore, MNPs are widely used for the rapid automatic detection of nucleic acids, which is of great significance in the medical field(Ngo et al., 2016; Tang et al., 2018; Tran and Piro, 2021).Fig. 1 The classification and characteristics of MNPs.
Fig. 1
In MNPs-based methods, nucleic acids in lysed samples can be specifically absorbed on MNPs due to various surface-modified functional groups. Therefore, nanomaterials with magnetic properties could be customized with unique virus receptors, allowing viral molecules to attach to MNPs, which would facilitate their magnetic extraction using an external magnetic field. In the presence of magnetic fields, nucleic acids are rapidly separated from most impurities in the supernatant. After quick washing steps to eliminate trace impurities, purified nucleic acids can be further released from the surface of MNPs by elution buffer with altered ionic strength, which is much simpler and faster than spin column-based methods.
2.2 Magnetic beads technology
Magnetic beads technology is one of the emerging strategies for the extraction of genomic and RNA, plasmid, and mitochondrial DNA, and it is one of the common methods to alleviate the centrifugation requirements and has been widely used in the genomics and proteomics(Váradi et al., 2014). During the past few years, specifically functionalized magnetic particles have been developed. Together with an appropriate buffer system, they allow for the rapid and efficient purification directly after extraction from crude cell extracts(Berensmeier, 2006). Furthermore, they allow for simple automation of the entire process and the isolation of nucleic acids from larger sample volumes. Several studies have shown that magnetic particles can isolate as few as tens of copies of target sequences from one milliliter of serum, which could then be amplified and detected by conventional means. The positive aspect of this technique is that it avoids the centrifugation step and provides an alternative way for automation of extraction procedures from a large number of samples. Magnetic particle or beads are the first choice to eliminate centrifuge-dependent steps during the extraction process. Magnetic beads utilize different ligands such as antigens, antibodies, oligonucleotides, or aptamers, which bind specifically to its target in the sample. This extraction technique can be used in batch processes with a large number of samples (blood, tissues, etc.) and is relatively easy to perform, making it one of the best options for automation, high-throughput applications, and high sample processing capabilities(Franzreb et al., 2006). This method is also suitable for resource-limited environments as it is virtually equipment-free(Ali et al., 2017). Magnetic bead separation presents many advantages over centrifuge-dependent extraction process by allowing an equipment-free process. Magnetic bead-based extraction realizes the collection of nucleic acids through the binding of nucleic acids to a silicon-based matrix with moderate operation difficulty, high product purity, and proper automatic level.
2.3 MNPs-based nucleic acid extraction
The inherent properties of MNPs, such as high surface-to-volume ratio, magnetically controlled particle aggregation and dispersion(Ma et al., 2019) and their ability to bind to a large number of different biomolecules (DNA, RNA, enzymes), offer a promising platform for the isolation of DNA and RNA from complex samples as well as enriching nucleic acids that facilitates their detection(Farinha et al., 2021). In recent years, MNPs have been applied for nucleic acid separation and purification, which could bind nucleic acid by the free chemical groups modified on the surface(Bhati et al., 2021; Oberacker et al., 2019; Ota et al., 2006). Using magnetic separation methods, nucleic acids can be directly isolated from crude biological samples without any restrictions with respect to the sample volumes. Typically, MNPs allow the nucleic acids to be gathered by magnets, and the nucleic acid binding on MNPs could be rapidly eluted. The heterogeneous extraction process is greatly simplified and has the potential for the development of “universal” nucleic acid extraction methods for the detection of pathogens in various samples(Shih et al., 2016; Zhang et al., 2019). By developing functional magnetic materials and suitable buffers, it is possible to extract target nucleic acids from crude cell extracts and purify them directly and efficiently(Chen et al., 2021). The centrifugal steps that may lead to degradation of nucleic acids are avoided in the magnetic separation process(Smerkova et al., 2013). The entire separation process does not require centrifugation or column separation, and multiple samples can be processed simultaneously, which is easy to realize automatic operation. Due to its excellent efficiency, this method is particularly suitable for nucleic acid extraction of micro samples.
Fig. 2 illustrates a schematic diagram of the extraction process of nucleic acids based on MNPs. The whole process consists of five major steps: lysis, MNPs-nucleic acids binding, washing, elution, and collection. In this process, MNPs bind to the nucleic acids by functionalizing MNPs with ligands that specifically bind to DNA and RNA, and are then separated from the rest of sample matrix by applying a magnetic field through the magnetophoretic phenomenon. During this step, the MNPs, which are still bound to their target, are collected towards the magnet, making it easy to discard the unwanted material. The MNPs are then washed in elution buffer to facilitate the release of the DNA/RNA molecules from the nanoparticles. Afterwards, the MNPs are separated from the supernatant containing the free nucleic acid molecules using an external magnetic field(Chircov et al., 2019).Fig. 2 The extraction process of nucleic acids based on MNPs including lysis, binding, washing, elution, and collection.
Fig. 2
2.4 Applications of MNPs in DNA/RNA extraction
Over the past few years, MNPs have been widely used in the biomedical fields including magnetic biosensing, magnetic imaging, magnetic separation, drug and gene delivery, and hyperthermia therapy. Ma et al. analyzed and compared genomic DNA extraction based on MNPs from E.coli JM109, yeast, whole blood, and serum respectively, and found that all these genomic nucleic acids extracted using the MNPs-based method from different species can be applicable for the molecular biology research(Ma et al., 2013). Kaur et al. demonstrated a rapid and highly sensitive detection of S. typhi, in which they employed a MNPs-based pathogen enrichment protocol, followed by loop-mediated nucleic acid amplification and simultaneous detection by an in-situ optical system(Kalendar et al., 2018). Won et al. developed a simple and fast bacteria isolation method using magnet nanoparticle-embedded silica nanotube (MNSNT). Under certain ionic conditions, bacteria in the sample were simply bound on the outside wall of MNSNT, which were further collected with a magnet. The bacteria separated with MNSNT were then detected using PCR after heat-induced cell lysis without the need of washing and elution steps(Won et al., 2013). Kang et al. described an approach of conducting MNPs-based nucleic acid extraction procedure in an ordinary plastic Pasteur pipette with no vortex mixer, centrifuge, or dry bath, and applied this approach to extract nucleic acid of various pathogens, including RNA virus, DNA virus, gram-negative bacteria, and gram-positive bacteria from a broad range of samples(Kang et al., 2021). Bhati et al. developed an improved method for DNA extraction from human saliva using bare MNPs, and they found that the yield and purity of DNA was higher compared to other methods. In addition, this method requires no centrifugation step while it is mandatory for the spin column-based techniques (Bhati et al., 2021). MNPs have been used in RT-qPCR diagnosis for the extraction of viral RNA from SARS-CoV-2. Studies have shown that silica-coated MNPs can be used to rapidly extract RNA from the virus in patient samples for the further detection by RT-PCR that avoided the needs for lengthy RNA extraction while also making the method more sensitive(Campos et al., 2020). Zhao et al. developed a carboxyl polymer-coated MNPs, namely pcMNPs, and established a simple but efficient viral RNA extraction system for the sensitive detection of SARS-CoV-2 RNA via RT-PCR. This method merges the lysis and binding steps, and the pcMNPs-RNA complexes can be directly introduced into RT-qPCR reactions(Yoo et al., 2021). As compared with traditional column-based extraction methods, the pcMNPs-based method has several advantages. Firstly, pcMNPs-based method combines the virus lysis and RNA binding into one step, and the pcMNPs-RNA complexes can be directly introduced into subsequent RT- PCR reactions without elution step, which dramatically reduces the operation time and risk of contamination. Secondly, pcMNPs have excellent viral RNA binding properties, which results in high sensitivity and linearity for the detection of SARS-CoV-2 viral RNA using RT-PCR. Thirdly, since no centrifugation steps are required, MNPs-based methods allow fully automated nucleic acid purification, which is highly important in current SARS-CoV-2 diagnosis. Furthermore, the pcMNPs-RNA complexes obtained by this method are also compatible with various isothermal amplification methods, and thus could be used in the development of point-of-care devices. In conclusion, benefitting from its simplicity, robustness, and excellent performances, this new extraction method may provide a promising alternative to solve the time-consuming and laborious viral RNA extraction operations, and thus exhibits a great potential in current molecular diagnosis of COVID-19, especially for the early clinical diagnosis.
One of the greatest advantages of the MNPs-based extraction methods is that their aggregation require neither centrifugation nor column separation, thus they can shorten the time for the separation the components, reduce the risk of cross-contamination, and eliminate the need for costly equipment, such as centrifuges and liquid chromatography systems(Laurent et al., 2008). Besides, MNPs-based methods can also serve for a variety of automated low- to high-throughput procedures that can help save time and money. In addition, magnetic separation allows for recycling of the magnetic beads that is useful for large-scale use. These advantages enable faster, more efficient and cheaper MNPs-based methods for nucleic acid separation and purification.
3 Nucleic acid detection based on CRISPR and amplification techniques
The clustered regularly interspaced short palindromic repeats (CRISPR) system, first discovered in bacteria and capable of removing viral genes incorporated into bacterial genes, has received a great deal of attention in the nucleic acid detection(Bolotin et al., 2005; Mojica et al., 2005). The working mechanism is that the CRISPR-Cas protein can locate and cut out the target nucleic acid sequence with the aid of an RNA called crRNA(Shmakov et al., 2017). Particularly, the CRISPR-Cas system shows outstanding gene-editing capabilities(Cong et al., 2013; Gilbert et al., 2013), and it also exhibits enormous promise for the rapid and sensitive detection of nucleic acid. At present, class 2 CRISPR-Cas system, including type II, type V and type VI, has been widely used to detect nucleic acids(van Dongen et al., 2020). Among the CRISPR-Cas system, the CRISPR-Cas12a has drawn a lot of attention since it not only can cleave the target nucleic acid (cis cleavage) but also exhibit strong collateral cleavage activity (trans-cleavage) for single-stranded DNA (ssDNA). Specifically, when the specific DNA targets are recognized by the Cas12a/crRNA duplex, the collateral cleavage activity of the system is activated to nonspecifically and indiscriminately cleave non-target ssDNA strands(Janice S. Chen et al., 2021). Recently, the trans-cleavage activity of Cas12a has been reported as 3–17 turnovers per second (Janice S. Chen et al., 2021). In CRISPR-Cas12a-based biosensing, a range of amplification techniques have been exploited for SARS-CoV-2 detection that allow for highly sensitive and specific detection of nucleic acids including recombinase polymerase amplification (RPA), loop-mediated isothermal amplification (LAMP), and PCR (Scheme 2 ).Scheme 2 Schematic illustration of the techniques for nucleic acid amplification including RPA, LAMP, and PCR.
Scheme 2
3.1 Recombinase polymerase amplification (RPA)
RPA is an isothermal nucleic acid amplification method that mimics the process of nucleic acid replication in cells(Olaf Piepenburg et al., 2006). RPA is free of thermal cycling process, making it simple to operate without the need of expensive, high-tech equipment. The reaction of RPA can be completed under 37–42 °C in 3–10 minutes, which can well integrate with the CRISPR-Cas12a system in an assay. Several RT-RPA-assisted CRISPR-Cas12a-based detection techniques have been developed for the sensitive detection of SARS-CoV-2. Using RT-RPA and Cas12a, Wang et al. developed a highly efficient detection platform able to detect less than 5 viral copies per reaction for the SARS-CoV-2 genes (Fig. 3 A)(Wang et al., 2021, Wang et al., 2021, Wang et al., 2021). Briefly, the target RNA was extracted from clinical samples and amplified by RT-RPA reaction, and the target amplicons were injected into the CRISPR-Cas12a system. Specifically, the target amplicon was recognized by the Cas12a/crRNA duplex, thus activating the collateral activity of CRISPR-Cas12a to cut the reporter probes effectively, leading to the quencher moving away from the fluorophore. Consequently, the fluorescence intensity increased. Huang et al. also combined RT-RPA with the CRISPR-Cas12a system for the detection of SARS-CoV-2 (Fig. 3B)(Huang et al., 2020). In this work, the trans-cleavage activity of the CRISPR-Cas12a system and a fluorescent signal probe was utilized to detect SARS-CoV-2. The detection process took ∼50 min and the limit of detection was 2 copies per sample. However, it still remains an issue that the target amplification and detection steps are separate from each other where the nucleic acid-rich sample (e.g., RPA amplicons) may expose to the environment that potentially increases the risk of contamination. To meet this challenge, Sun et al. developed a one-pot method based on RT-RPA and CRISPR-Cas12a to detect SARS-CoV-2 (Fig. 3C)(Sun et al., 2021). In this assay, the target RNA was first extracted from the clinical samples and then added to the bottom of the tube that contained the RT-RPA mix. Simultaneously, the CRISPR-Cas12a reagents were placed on the tube lid. During this amplification process, the target amplification regents and other components were physically separated. The CRISPR-Cas12a reagents were centrifuged to transfer them to the tube bottom after the amplification. At this time, the target amplicons served as activators to activate the Cas12a/crRNA duplex to cleave the reporter probe and increase the fluorescence intensity of the solution. The assay had excellent sensitivity and selectivity toward SARS-CoV-2, with a low detection limit of 2.5 copies/μl input (RNA standard), as well as a rapid process within 50 min.Fig. 3 CRISPR and RPA for the fluorescent detection of nucleic acid. (A) Schematic of the detection method based on RPA and CRISPR-Cas12a for the determination of SARS-CoV-2(Wang et al., 2021, Wang et al., 2021, Wang et al., 2021) (Copyright, 2021 American Chemical Society). (B) A CRISPR-fluorescent assay for detection of SARS-CoV-2 RNA in clinical samples(Huang et al., 2020) (Copyright, 2020 Elsevier, reproduced with permission from Elsevier Ltd.). (C) A RT-RPA-based detection method for SARS-CoV-2(Sun et al., 2021) (Copyright, 2021 Springer Nature, reproduced with permission from Springer Nature Ltd.).
Fig. 3
Other than fluorescent reporters, colorimetric probes such as gold nanoparticles (AuNPs) are convenient and cost-effective for the detection of SARS-CoV-2 in clinical samples. Jiang et al. proposed an effective way to visually detect the SARS-CoV-2 genome by utilizing the magnetic pull-down to capture AuNPs (Fig. 4 A)(Jiang et al., 2021). In this assay, a biotinylated ssDNA probe served as a substrate for CRISPR-Cas12a, and AuNPs modified with complementary DNA strands were used to visually detect viral RNA. In the absence of the target RNA, the biotinylated ssDNA probes were kept intact and DNA-AuNPs would bind with biotinylated ssDNA probes through the DNA hybridization reaction. The complex was further immobilized on streptavidin-coated magnetic beads through biotin–streptavidin interaction. Consequently, the red DNA-AuNPs were magnetically enriched at the bottom of the tube and the supernatant presented colorless. When the target RNA was present, the CRISPR-Cas12 system was activated to cleave biotinylated ssDNA. At this point, the DNA-AuNPs would be in the supernatant, so the solution still showed a red color. In this assay, the detection limit is 50 RNA copies per reaction. The colorimetric detection of SARS-CoV-2 can be also achieved by modulating the dispersion state of AuNPs in solution. For instance, Zhang et al. developed a colorimetric assay based on RT-RPA and CRISPR-Cas12a to detect the SARS-CoV-2 genome (Fig. 4B)(Zhang et al., 2021, Zhang et al., 2021, Zhang et al., 2021). When the target was absent, the CRISPR-Cas12a system would not be activated and ssDNA strands modified on AuNPs were kept intact. Thus, AuNPs remained dispersed in the solution. However, when the target was present, the CRISPR-Cas12a system was activated to cleave ssDNA strands modified on AuNPs, leading to the aggregation of AuNPs. As a consequence, a peak shift and color change of the solution was observed. The detection limit of this approach is 1 copy of the viral genome sequence per test.Fig. 4 CRISPR and RPA for the colorimetric detection of nucleic acid. (A) CRISPR-Cas12a-based colorimetric technique for the visual detection of SARS-CoV-2 (Jiang et al., 2021) (Copyright, 2021 American Chemical Society). (B) RT-RPA-coupled CRISPR-Cas12a for the colorimetric detection of SARS-CoV-2(Zhang et al., 2021) (Copyright, 2021 American Chemical Society). (C) A wearable diagnostic approach for the visual detection of SARS-CoV-2 (Nguyen et al., 2021) (Copyright, 2021 Springer Nature, reproduced with permission from Springer Nature Ltd.).
Fig. 4
AuNPs-based lateral flow assays (LFAs) can be also used to detect SARS-CoV-2 with naked-eye readout. Li et al. developed a microfluidic platform based on RT-RPA, CRISPR-Cas12a, and LFA for the contamination-free and visual detection of the SARS-CoV-2 genome(Li et al., 2022). In this method, the viral RNA was first extracted from swab samples. Subsequently, the extracted RNA was amplified by the RT-RPA reaction in a microfluidic chip. The detection results could be directly read by the naked eye against an LFA dipstick. In this LFA dipstick, fluorescein (FAM)-ssDNA-biotin probe acted as the reporter probe, and FAM would bind with anti-FITC antibody-AuNPs to achieve a visible detection. In the absence of the target, the reporter probe would not be cleaved, thus anti-FITC antibody-AuNPs would be anchored on the control band. Conversely, in the presence of the target, the reporter was cleaved by the activated CRISPR-Cas12a. At this time, anti-FITC antibody-AuNPs bound with the FAM and the complex was anchored on the test band. It accomplished the detection of 100 copies of SARS-CoV-2 RNA target. In addition, a novel detection platform was also reported which combined LFA and microfluidic chip with wearable materials (Fig. 4C)(Nguyen et al., 2021). This designed face-mask sensor contained three reaction zones, including the lysis reagents zone, RT-RPA reaction zone, and CRISPR-Cas12a reaction zone. When the SARS-CoV-2-derived amplicons were present, the Cas12a/crRNA duplex was activated to cut the FAM-ssDNA-biotin probe. At this point, the LFA strip was utilized to achieve the visual detection of the target. The whole assay took ∼1.5 h and the limit of detection for this face-mask sensor was 500 copies of the SARS-CoV-2 in vitro transcribed RNA.
3.2 Loop-mediated isothermal amplification (LAMP)
LAMP is an isothermal nucleic acid amplification technique in which a DNA polymerase and four DNA primers are used including two inner primers and two outer ones. LAMP can generally amplify a few copies of DNA targets to detectable amounts under isothermal conditions in less than an hour. LAMP only needs one enzyme for the exponential amplification, thus it holds great promise for point-of-care detections. LAMP can combine with the CRISPR-Cas system for the detection of nucleic acid. Specifically, the Cas12a/crRNA complex can recognize the LAMP product with a large amount of the repeat target-specific dsDNA. The CRISPR-Cas12a system is activated to cut the signal probes that provides an ideal way for the specific detection of dsDNA. At present, a variety of RT-LAMP-assisted CRISPR-Cas12a system-based sensing strategies have been designed for the sensitive detection of SARS-CoV-2.
Alfredo et al. proposed a direct approach based on LAMP and the CRISPR-Cas12a for the rapid detection of SARS-CoV-2 without RNA extraction (Fig. 5 A)(Garcia-Venzor et al., 2021). Firstly, the inactivated clinical sample was amplified through a LAMP reaction. Meanwhile, Cas12a protein and N-gene specific crRNA were preincubated and the reporter probe was added. The CRISPR-Cas12a system was activated by the target amplicons, leading to the cleavage of the reporter probe effectively. Consequently, the fluorophore was released and emitted fluorescence. The limit of detection for this method is 16 copies/μL. Despite the great amplification efficiency of LAMP, false-positive results inevitably affect the detection results. To meet this challenge, Zhang et al. proposed a detection method for the specific detection of SARS-CoV-2(Zhang et al., 2021, Zhang et al., 2021, Zhang et al., 2021). In this assay, a uracil-DNA-glycosylase (UDG) and the reverse transcription-LAMP were utilized to reduce the aerosol contamination. Based on this detection mechanism, the wild-type or spike mutant SARS-CoV-2 spike N501Y could be detected with a limit of detection of 10 copies/μL (wild-type). To reduce the risk of carryover contaminations, researchers developed a one-pot method for SARS-CoV-2 detection (Fig. 5B)(Pang et al., 2020). In this assay, the CRISPR-Cas12a reagents and RT-LAMP reagents were put inside the top and bottom of the tube, respectively. The target RNA was then put in the bottom where it was amplified based on RT-LAMP. The CRISPR-Cas12a reagents were then mixed with the target amplicon produced by RT-LAMP at the bottom of the tube by simply inverting the tube and flicking the wrist. As a consequence, the CRISPR-Cas12a system was activated to cleave the signal probe, leading to the generation of bright green fluorescence. The whole process took 40 min and showed 100% clinical specificity. Wang et al. also accomplished one-pot SARS-CoV-2 detection using a similar approach (Fig. 5C)(Wang et al., 2021). The whole process took 45 min and the diagnostic results accorded with the quantitative method authorized by the Centers for Disease Control and Prevention. To achieve the point-of-care detection of SARS-CoV-2 in clinical samples, Chen et al. utilized a smartphone and portable 3D printing instrument to realize contamination-free and visual SARS-CoV-2 detection(Wang et al., 2020a, Wang et al., 2020b). In this detection platform, the whole process takes 40 min, and the limit detection is 20 copies of RNA of SARS-CoV-2.Fig. 5 CRISPR and LAMP for the fluorescent detection of nucleic acid. (A) A fluorescent method based on LAMP and CRISPR-Cas12 for SARS-CoV-2 detection (Garcia-Venzor et al., 2021) (Copyright, 2021 Frontiers, reproduced with permission from Frontiers Ltd.). (B) A one-pot fluorescent method for COVID-19 detection(Pang et al., 2020) (Copyright, 2020 American Chemical Society). (C) Working principle of one-pot and visual COVID-19 detection based on RT-LAMP-CRISPR(Wang et al., 2021) (Copyright, 2021 Elsevier, reproduced with permission from Elsevier Ltd.).
Fig. 5
Other than fluorescent readouts, colorimetric assays are also developed with a convenient and cost-effective readout for the detection of SARS-CoV-2. Zhang et al. designed a AuNPs-based visual assay that combined CRISPR-Cas12a with RT-LAMP for the rapid and sensitive determination of COVID-19 (Fig. 6 A)(Zhang et al., 2021). When the viral RNA was present, the Cas12a/crRNA duplex was activated to cleave linker-ssDNA. Hence, AuNP probe pairs (AuNP-DNA1 and AuNP-DNA2) could not be cross-linked and the solution displayed a red color. Conversely, when the SARS-CoV-2 RNA was absent, the CRISPR-Cas12a was kept inactivated and the linker-ssDNA was kept intact. Consequently, AuNP probe pairs were cross-linked with linker-ssDNA, accompanied by a color change from red to purple. The whole assay time was 40 min and it could detect down to 4 copies/μL of SARS-CoV-2 RNA. Apart from AuNPs, other colorimetric substrates could also be applied for the visual detection of viral nucleic acids. For instance, Xie et al. evaluated the performance of 16 types of fluorophore-ssDNA-quencher reporters that served as colorimetric substrates in CRISPR-Cas12a-based assays (Fig. 6B)(Xie et al., 2022). Among them, 9 fluorophore-ssDNA-quencher reporters were suitable for colorimetric detection, with an excellent performance using ROX-labeled reporters. Particularly, in this colorimetric method, a convolutional neural network algorithm was developed to standardize and automate the colorimetric analysis of images and integrated this into the MagicEye mobile phone software. The sensitivity of this technique reached 40 total copies.Fig. 6 CRISPR and LAMP for the detection of nucleic acid. (A) Schematic diagram for COVID-19 detection based on CRISPR-Cas12a and the LAMP(Zhang et al., 2021) (Copyright, 2021 Elsevier, reproduced with permission from Elsevier Ltd.). (B) A colorimetric method for the visual detection of SARS-CoV-2 (Xie et al., 2022) (Copyright, 2022 American Chemical Society). (C) A LFA strip for the portable and visual detection of SARS-CoV-2 (Broughton et al., 2020) (Copyright, 2020 Springer Nature, reproduced with permission from Springer Nature Ltd.).
Fig. 6
For the quick and self-inspection of viral nucleic acids, LFA can be utilized as a portable, easy-to-operate tool. Broughton et al. reported a rapid RT-LAMP-assisted CRISPR-Cas12a system-based LFA for the detection of SARS-CoV-2 (Fig. 6C)(Broughton et al., 2020). To achieve the visual detection of SARS-CoV-2, the FAM-biotin reporter was used as a substrate and LFA strips were designed to capture the labeled reporter. FAM-biotin molecules were captured at the control line, but a signal was produced at the test line due to the collateral cleavage activity of CRISPR-Cas12a. Based on this detection mechanism, the whole assay took ∼45 min and the limit of detection is 10 copies per μL. Furthermore, Yi et al. designed a DNA capture probe-based LFA strip for the detection of SARS-CoV-2 (Yi et al., 2021). Compared to conventional LFA strips, streptavidin-modified AuNPs instead of anti-FAM antibody-AuNPs acted as signal probes for the naked-eye readout. When the target RNA was absent, the biotinylated-ssDNA reporter was kept intact and then hybridized with an ssDNA probe immobilized on the test line of the LFA strip. Subsequently, the streptavidin-modified AuNPs were captured by the test line through biotin-streptavidin interaction. The excess streptavidin-modified AuNPs were anchored on the control line by biotinylated-antibody. Conversely, when the target RNA was present, the biotinylated-ssDNA reporter was cleaved by the activated CRISPR-Cas12a system. At this time, streptavidin-modified AuNPs would not aggregate on the test line of the LFA strip due to a lack of biotinylated-ssDNA reporter, thus only showing a visible red signal on the control line. The approach achieved ultra-sensitivity of 1 copy/μL in ∼60 min. To improve the portability, Rezaei et al. proposed a portable and semi-automated device for the detection of COVID-19 (Rezaei et al., 2021). The device contained four parts, including a heater, a cooler fan, a proportional integral derivative controller to regulate the temperature, and designated areas for 0.2 mL Eppendorf® PCR tubes. Notably, up to 500 samples could be processed simultaneously in under 35 min using this multiplexing portable equipment that could increase the number of wells in the reaction zone.
3.3 Polymerase chain reaction (PCR)
For the molecular diagnosis of coronavirus infections, PCR is regarded as the gold standard due to its great sensitivity and specificity (Chan et al., 2015). A variety of biosensors have been proposed based on PCR and CRISPR-Cas12a. Specifically, by combining PCR pre-amplification with the CRISPR-Cas12a system, a nucleic acid detection approach named HOLMES (one-hour low-cost multipurpose highly efficient system) was proposed by Wang et al.(Li et al., 2018). Recently, Liang et al. proposed a RT-PCR-assisted CRISPR-Cas12a-based assay to specifically detect the Omicron variant (Fig. 7 A)(Liang et al., 2022). In this assay, the target RNA was extracted from patient samples and amplified by RT-PCR. Subsequently, the CRISPR-Cas12a system was activated by the target amplicons, resulting in an effective cleavage of the signal probe and an increase in the fluorescence signal. In contrast, no significant fluorescence signal was observed in the absence of the target RNA. Notably, this method had good specificity and sensitivity that could detect as low as 2 copies/μL of target RNA. To further improve portability, Li et al. constructed an automated microfluidic system to distinguish the variant of SARS-CoV-2 (Fig. 7B)(Li et al., 2021). By detecting 30 clinical samples, the practicability and accuracy of the assay were validated. This approach showed 100% consistency with the next-generation sequencing technology, which could act as a potential and portable tool to distinguish the delta variant of SARS-CoV-2.Fig. 7 CRISPR and PCR for the detection of nucleic acid. (A) RT-PCR and CRISPR-Cas12a for the detection of SARS-CoV-2 Omicron variant(Liang et al., 2022) (Copyright, 2022 Elsevier, reproduced with permission from Elsevier Ltd.). (B) Principle of the automated Cas12a-microfluidic assay(Li et al., 2021) (Copyright, 2021 Royal Society of Chemistry, reproduced with permission from Royal Society of Chemistry Ltd.). (C) A visual biosensor for SARS-CoV-2 detection(Ma et al., 2022) (Copyright, 2022 Elsevier, reproduced with permission from Elsevier Ltd.). (D) Schematic illustration of a solid-state nanopore sensor based on the CRISPR-Cas12a system (Nouri et al., 2021) (Copyright, 2021 American Chemical Society).
Fig. 7
Besides fluorescence, colorimetric sensing strategies can be also developed for the detection of SARS-CoV-2. For the ultrasensitive detection of SARS-CoV-2, Ma et al. developed an RT-PCR-assisted CRISPR-Cas12a-powered detection device with a smartphone readout (Fig. 7C)(Ma et al., 2022). In the absence of SARS-CoV-2, the CRISPR-Cas12a system would not be activated and the linker ssDNA would not be cleaved. AuNPs-DNA would aggregate through complementary hybridization between the linker ssDNA and the ssDNA modified on AuNPs. At this point, the color of the solution changed from red to purple. Conversely, in the presence of target RNA, dsDNA amplicons would activate the Cas12a/crRNA complex to cut the linker ssDNA, resulting in the dispersion of AuNPs-DNA and a red color of the solution, achieving a limit of detection of 1 copy/μL.
Apart from the above sensing strategies, Nouri et al. proposed a solid-state CRISPR-Cas12a-assisted nanopore detection method for the specific determination of SARS-CoV-2(Fig. 7D)(Nouri et al., 2021). This work contained three streamlined steps: RT-PCR, CRISPR-Cas12a assay, and nanopore-based molecule classification and counting. The circular M13mp18 ssDNA with an excellent signal-to-noise ratio was selected as the reporter in the nanopore measurement. In this assay, the target RNA was first amplified by one-step RT-PCR. After amplification, the dsDNA amplicons served as activators to activate the CRISPR-Cas12 system to cleave the signal reporters, leading to a signal change. Conversely, in the absence of the target RNA, the CRISPR-Cas12 system would not be activated and the circular ssDNA reporter would not be degraded. The whole process took 65 min with a sensitivity of 13.5 copies/μL of viral RNA.
3.4 Other techniques for nucleic acid amplification
In addition to conventional RPA, LAMP, and PCR amplification, other amplification techniques have been constructed for the detection of nucleic acid. Recombinase-aided amplification (RAA), primer exchange reaction (PER), dual-priming isothermal amplification (DAMP), enzymatic recombinase amplification (ERA), multiple cross displacement amplification (MCDA), exonuclease III cleavage reaction, and entropy-driven reaction system, can combine with CRISPR-Cas12a to detect SARS-CoV-2.
Similar to RPA, RAA is an isothermal amplification technique without the need for a sophisticated thermal cycler in which recombinase uvsX (E. coli), ssDNA binding protein, and DNA polymerase were used. Wang et al. introduced an RT-RAA-assisted detection method to advance the point-of-care diagnosis of COVID-19 (Fig. 8 A)(Wang et al., 2020a, Wang et al., 2020b). Briefly, the target RNA was extracted from clinical samples and amplified by RT-RAA. Then, target amplicons were injected into the CRISPR-Cas12a reaction solution. The RT-RAA products would be recognized by the Cas12a/crRNA duplex, thus activating the trans-cleavage activity of the CRISPR-Cas12a system to cut the reporter probe. Consequently, it generated green fluorescence that could be seen with the naked eye under 485 nm light. The limit of detection for this method was 10 copies of SARS-CoV-2.Fig. 8 CRISPR and other amplification techniques for the detection of nucleic acid. (A) A detection approach based on the CRISPR-Cas12a system for naked-eye readout of COVID-19(Wang et al., 2020a, Wang et al., 2020b) (Copyright, 2020 Elsevier, reproduced with permission from Elsevier Ltd.). (B) Principle of the PER-based assay for the detection of SARS-CoV-2 RNA (Li et al., 2022) (Copyright, 2022 Royal Society of Chemistry, reproduced with permission from Royal Society of Chemistry Ltd.). (C) Overview of the digital warm-start-CRISPR assay(Ding et al., 2021) (Copyright, 2021 Elsevier, reproduced with permission from Elsevier Ltd.).
Fig. 8
Another isothermal nucleic acid amplification technique known as PER was developed by Yin et al.(Kishi et al., 2017), which has received a great deal of interest in the fields of DNA nanotechnology and bio-imaging. Li et al. developed a technique for the detection of SARS-CoV-2 RNA by combining PER with CRISPER-Cas12a (Fig. 8B)(Li et al., 2022). When the target RNA was present, the PER cascade reaction was triggered to produce a significant amount of ssDNAs. The CRISPR-Cas12a system was activated by these ssDNAs to cut the signal reporter and generate a fluorescent signal. This detection approach could achieve a detection limit as low as 1.3 pM in 40 min at 37 °C.
RT-DAMP was proposed by Liu et al.(Ding et al., 2019), which was a variant of RT-LAMP with a new primer design strategy. Ding et al. chose RT-DAMP to develop a digital warm-start CRISPR assay for the quantitative detection of SARS-CoV-2 (Fig. 8C)(Ding et al., 2021). In this work, the detection was established by partitioning the first warm-start CRISPR-Cas12a-based reaction into sub-nanoliter aliquots within a Quant Studio 3D digital chip. In this assay, the target RNA was amplified by the RT-DAMP reaction to generate a lot of target amplicons. Meanwhile, the Cas12a/crRNA duplex was specifically bound to target amplicons to activate the cleavage activity of the CRISPR-Cas12a system. Consequently, the reporter probe was cleaved which generated a fluorescent signal. The assay was able to detect down to 5 copies/μl SARS-CoV-2 RNA in the chip.
MCDA, an isothermal nucleic acid amplification method, allows nucleic acid amplification using a simple instrument for COVID-19 diagnosis. Zhu et al. combined RT-MCDA with CRISPR-Cas12a to design an approach for the detection of SARS-CoV-2 RNA(Zhu et al., 2021). In this assay, the viral RNA was first converted to cDNA by RT reaction. Subsequently, the cDNA served as the template for MCDA amplification. Through MCDA amplification, a lot of amplicons with the TTTT PAM site were generated. Then, the CRISPR-Cas12a system was activated by amplicons to cleave the signal probes effectively. In particular, a LFA strip was applied for the signal readout to achieve visual detection. The detection could be completed within 1 h and the sensitivity of LFA was 7 copies (for each of the target templates) per test.
4 Conclusion and future perspective
The SARS-CoV-2 is a newly emerged virus that causes mild to severe pneumonia. COVID-19 is still a major issue worldwide after its global super-spread, and emerging viral diseases have not got specific and reliable treatments. The pivot of an effective human response to this COVID-19 pandemic is early, rapid and accurate testing of clinical samples of suspected and probable cases. Molecular diagnostics such as nucleic acid detection can achieve early and rapid detection of targets and are considered as an ideal approach for the detection of pathogens in infectious diseases. The nucleic acid detection for infectious diseases are widely used, as evidenced by the widespread use of COVID-19 tests for the containment of the pandemic(Zhou et al., 2020).
The ongoing SARS-CoV-2 pandemic highlights the importance of nanotechnology in providing tools and technologies for diagnostic and antiviral research. Nanotechnology is one of the most promising tools for the development of simple and fast sample preparation methods that require no multiple steps and complex systems for sample preparation because of the increased surface-to-volume ratio of nanostructures (Lundqvist et al., 2008). To date, considerable efforts have been made to improve the detection of coronavirus and a variety of improved or new methods have been developed. Obtaining high-quality nucleic acid is a key factor for pathogen detection since nucleic acid extraction is the first step. Accordingly, various studies have focused on methods to improve the efficiency of gene extraction, including the MNPs-based nucleic acid extraction. Nowadays, the idea of using magnetic separation techniques to purify biologically active compounds (nucleic acids and proteins) from the cells and cell organelles has attracted rapidly growing interest. Comparing with other nucleic acid separation techniques that are generally time-consuming and require expensive equipment, magnetic separation has several advantages. MNPs have exhibited superior properties such as larger surface-to-volume ratio, excellent reactivity and unique magnetic response(Hajba and Guttman, 2016; Haun et al., 2010), which make the pre-concentration, purification and separation of nucleic acids easy and feasible(Tang et al., 2020). MNPs shorten the purification stage and eliminate pre-treatment and pre-enrichment steps. Overall, MNPs are expected to facilitate the development of improved analysis protocols that are faster, cheaper and simpler than currently existing ones.
Accurate and early detection of SARS-CoV-2 infection is critical for minimizing spread and initiating treatment(Espy et al., 2006). Nucleic acid amplification has been considered the gold standard for diagnosis of many viral infections. The nucleic acid detection for infectious diseases are widely used, as evidenced by the widespread use of COVID-19 tests for the containment of the pandemic. In this review, we summarize the nucleic acids detection methods based on the combination of CRISPR-Cas and amplification techniques such as RPA, LAMP, PCR and other nucleic acid amplification methods. The performances of these detection methods based on amplification techniques with CRISPR-Cas12a technology for coronavirus detection are listed in Table 1 , and the comparison in the reaction conditions of different amplification techniques is listed in Table 2 . In summary, technical breakthroughs have been reported in viral detection on molecular levels. Many of these breakthroughs have taken advantage of recent advances in rapidly evolving micro and nanotechnology to bring improvements to the speed, sensitivity, operability, and portability of viral diagnostics. Nanomaterials can provide new opportunities such as more efficient, convenient, and safer applications. However, challenges still remain such as costs, toxicities to the environment and humans, and regulatory issues before being introduced to the market. Although the nanotechnology-based approaches for the nucleic acid extraction and detection of coronavirus have attracted considerable attentions, there are still some challenges that should be addressed in the future works. Firstly, the magnetic separation performance of MNPs should be improved to meet the application requirements in fast response and accurate positioning under external magnetic field(Liu et al., 2018; Wu et al., 2016). Secondly, the stability of MNPs could be improved that directly affects their real-world application (Defaei et al., 2018). Thirdly, the amplification techniques can be simplified and optimized to well integrate with other platforms such as microfluidics to better serve as point-of-care testing tools for the detection of SARS-CoV-2. The fight against infectious diseases caused by SARS-CoV-2 remains challenging despite the tremendous efforts and significant advances in public healthcare. As shown in this review, nanotechnology has already been shown to enhance the diagnostics in coronavirus infections. To tackle future challenges, the collaboration between different scientific fields, clinicians and industry is required. With the rapid development of new technologies and methods, we believe that more excellent and efficient detection methods will be developed in the future.Table 1 A summary of the detection performance based on amplification techniques with CRISPR-Cas12a technology for coronavirus detection.
Table 1Signal amplification Sensitivity Detection time Real sample Signal output Ref
RPA <5 viral copies per reaction – Nasal swab, oropharyngeal swab, anal swab, sputum, stool, and sputum supernatant Fluorescence Wang et al. (2021c)
2 copies per sample ∼50 min Nasal swab Fluorescence (Huang et al., 2020b)
2.5 copies/μl input (RNA standard);
1 copy/μl input (pseudovirus) ∼50 min Pharyngeal swab Fluorescence Sun et al. (2021)
2 copies/μL of full-length COVID-19 genome; 0.5 copy/μL of DNA fragment of N gene – – Fluorescence Malci et al. (2022)
50 RNA copies per reaction – Nasopharyngeal and throat swab Colorimetry (Jiang et al., 2021b)
1 copy of viral genome sequence per test – Clinical standard sample Colorimetry (Zhang et al., 2021d)
100 copies – Nasopharyngeal swab LFA Li et al. (2022b)
500 copies ∼90 min Clinical samples LFA Nguyen et al. (2021)
LAMP 16 copies/μL 40 min Clinical samples Fluorescence (Alfredo Garcia-Venzor et al., 2021)
10 copies/μL (wild-type) – – Fluorescence Zhang et al. (2021b)
30 copies/μL (150 copies) 40 min Respiratory swab Fluorescence (Pang et al., 2020b)
5 copies 45 min Clinical samples Fluorescence (Wang et al., 2021a)
20 copies 40 min Respiratory swab Fluorescence Chen et al. (2020b)
4 copies/μL 40 min – Colorimetry (Zhang et al., 2021e)
58 copies – Clinical samples Colorimetry Xie et al. (2022)
10 copies per μl input <40 min Respiratory swab LFA Broughton et al. (2020)
1 copy/μL ∼60/32 min Nasopharyngeal swab LFA (Yi et al., 2021b)
35 copies/μL 35 min Nasopharyngeal or oropharyngeal swab LFA Rezaei et al. (2021)
PCR 2 copies per reaction – Oropharyngeal swab Fluorescence (Yuanhao Liang et al., 2022)
1 copy/μL – Clinical samples Fluorescence Li et al. (2021)
1 copy/μL ∼90 min Throat swab Colorimetry (Ma et al., 2022a)
13.5 copies/μL 65 min – Electrochemistry (Nouri et al., 2021a)
RAA 10 copies – Clinical samples Fluorescence Wang et al. (2020b)
PER 1.3 pM 40 min Complex biological samples Fluorescence (Li et al., 2022a)
DAMP 5 copies/μL – Swab and saliva samples Fluorescence (Ding et al., 2021a)
ERA 0.25/0.5 copies/μL 40 min Clinical samples LFA Liu et al. (2021)
MCDA ∼60 min 7 copies/test LFA Zhu et al. (2021)
Table 2 A comparison of the reaction conditions of different amplification techniques.
Table 2Methods Target Primers Required enzymes Reaction time (h) Temperature (°C) Amplicon
RPA DNA 2 DNA polymerase and recombinase 0.5–1.5 37–42 DNA
LAMP DNA 4–6 DNA polymerase <1 60–65 DNA
PCR DNA 2 Taq DNA polymerase 1.5–2 95/55/72 DNA
RAA DNA 2 DNA polymerase and recombinase 0.5 37 DNA
PER RNA 1 DNA polymerase 0.5 37 DNA
DAMP DNA 6 DNA polymerase 2 60–65 DNA
ERA DNA 2 DNA polymerase and recombinase 20–30 37–42 DNA
MCDA DNA 6 DNA polymerase 35 63 DNA
Uncited reference
Meysam Rezaei,.
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.
Acknowledgements
The authors are grateful for the financial support from 10.13039/501100012166 National Key Research and Development Program of China (2020YFA0909100), 10.13039/501100001809 National Natural Science Foundation of China (22104128), 10.13039/501100004731 Zhejiang Provincial Natural Science Foundation of China (LR22C200003), and the Fundamental Research Funds for the Central Universities (226-2022-00169).
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| 36509504 | PMC9733971 | NO-CC CODE | 2022-12-14 23:28:27 | no | J Natl Med Assoc. 2022 Dec 10; 114(6):551-552 | latin-1 | J Natl Med Assoc | 2,022 | 10.1016/j.jnma.2022.12.010 | oa_other |
==== Front
Ann Allergy Asthma Immunol
Ann Allergy Asthma Immunol
Annals of Allergy, Asthma & Immunology
1081-1206
1534-4436
American College of Allergy, Asthma & Immunology. Published by Elsevier Inc.
S1081-1206(22)01988-3
10.1016/j.anai.2022.12.006
Letters
Impact of Vaccination against SARS-CoV-2 on the Atopic Dermatitis Serum Proteome
Kim Madeline BA
Ungar Benjamin MD
Estrada Yeriel BA
Pavel Ana B. PhD
Guttman-Yassky Emma MDPhD ⁎
Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, U.S.A
⁎ Corresponding Author: Emma Guttman-Yassky, MD, PhD, Department of Dermatology, Icahn School of Medicine at Mount Sinai, 5 E. 98th Street, New York, NY 10029, Telephone: 212-241-9728/3288, Fax: 212-876-8961
10 12 2022
10 12 2022
4 10 2022
30 11 2022
1 12 2022
© 2022 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.
2022
American College of Allergy, Asthma & Immunology
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KeyWords
atopic dermatitis
COVID-19
SARS-CoV-2
dupilumab
vaccine
Abbreviations
AD, atopic dermatitis
DEP, differentially expressed protein
FCH, fold-change
FDR, false discovery rate
==== Body
pmcVaccination against SARS-CoV-2 is currently recommended for patients with atopic dermatitis (AD), including those managed with immunomodulatory therapy.1,2 Dupilumab, an IL-4Rα inhibitor, is commonly used in the treatment of moderate-to-severe AD.3 Although available data are limited,2,4 there is no evidence to suggest that dupilumab may interfere with the immune response to COVID-19 vaccination, and dupilumab has, in fact, been associated with reduced symptom severity and mortality in patients hospitalized with COVID-19.5 Conversely, understanding of the effects of COVID-19 vaccines on AD also remains limited, particularly after the 2-week period, with one study reporting transient AD exacerbations in patients taking dupilumab ≤2 weeks after vaccination.6 It is therefore important to characterize the molecular mechanisms underlying the longer-term effects of immunization against SARS-CoV-2 on AD in the context of dupilumab therapy, which we aim to elucidate in this proteomic study in blood.
Patients were enrolled with institutional review board-approved consent, and serum samples were collected before and at least 14 days after full vaccination (2 doses of Pfizer-BioNTech or Moderna, or 1 dose of Janssen/Johnson & Johnson) between June 2020 and February 2022. Inclusion criteria included age ≥12 years and a diagnosis of moderate-to-severe AD (defined as currently or previously on systemic therapy [oral or injected immunomodulatory medications or phototherapy] or candidate for systemic therapy), as previously reported.4
Serum samples were centrifuged and stored at –80°C. Aliquots were subsequently processed with the OLINK Proseek multiplex ultrasensitive platform using 7 custom panels: inflammation, cardiovascular disease (CVD) II, CVD III, and neuroinflammation, cardiometabolic, oncology II, development (644 measured protein products), similar to prior work.7
Analyses were performed using R language (R-project.org) and bioconductor project packages (www.bioconductor.org), as previously described.7 Protein expression profiles were modeled by linear models using the R limma framework.8 P-values were adjusted for multiple hypotheses using the Benjamini-Hochberg procedure to control for false discovery rates (FDRs). Differentially expressed proteins (DEPs) were defined with an absolute fold-change >1.2 and an FDR<0.1.
119 samples were obtained from 68 patients. Treatment groups were defined as follows (n denotes number of samples): limited (topicals/no treatment; pre-vaccine, n=8; post-vaccine, n=9) and dupilumab (pre-vaccine, n=52; post-vaccine, n=50). Patients on dupilumab had been receiving treatment between 3.3 and 45.7 months at the time of vaccine series completion. No significant differences between treatment groups were found in age, time between completion of vaccine series and sample 2 collection, sex, or vaccination type (Pfizer-BioNTech, Moderna, or Janssen/Johnson & Johnson). In patients for whom clinical severity data were available (Investigator's Global Assessment/IGA and Body Surface Area/BSA [%]), average IGA was significantly greater in the limited treatment group (n=7) than in the dupilumab group (n=45), while BSA was similar between groups. Further demographic information is provided in Table 1 .Table 1 Patient demographics. P-values for age, Investigator's Global Assessment/IGA, Body Surface Area/BSA, and time between sample collection determined by unpaired Student's t-tests. P-values for sex, vaccination type, race, and sample size by treatment groups were calculated with Fisher's exact tests. “Other” race indicates unknown race or patient declined to specify.
Table 1 Limited Dupilumab p-value
n=10 n=58
Age (years)
Mean (Standard Deviation) 37.5 (14.4) 43.4 (19.3) 0.28
Range 19-61
Time between Sample Collection (months)
Mean (Standard Deviation) 6.5 (3.7) 7.2 (2.9) 0.58
Range 1-12
Time between Vaccine Series Completion and Sample 2 Collection (months)
Mean (Standard Deviation) 3.8 (1.9) 3 (2.7) 0.31
Range 0.7-6 0.1-11.6
Duration of Treatment at Vaccine Series Completion (months)
Mean (Standard Deviation) N/A 21.6 (9.5)
Range N/A 3.3-45.7
Duration of Treatment at Sample 2 (months)
Mean (Standard Deviation) N/A 26.3 (11.7)
Range N/A 6-57
IGA†
Mean (Standard Deviation) 2.9 (0.7) 1.1 (0.8) 1.73E-04
Range 2-4 0-3
BSA (%)†
Mean (Standard Deviation) 10.9 (11.2) 4.8 (5.2) 0.21
Range 1-35 0-28
Sex n % n %
Female 6 60.0% 25 43.1% 0.49
Male 4 40.0% 33 56.9%
Vaccine Type n % n %
Pfizer 8 80.0% 38 65.5% 0.13
Moderna 1 10.0% 19 32.8%
Janssen/Johnson & Johnson 1 10.0% 1 1.7%
Race n % n %
White 7 70.0% 36 62.1% 0.59
Asian 0 0.0% 3 5.2%
Black 1 10.0% 12 20.7%
Other 2 20.0% 7 12.1%
Sample Size by Treatment Group n % n %
Pre-Vaccination 8 47% 52 51.0% 0.92
Post-Vaccination 9 53% 50 49.0%
† IGA and BSA were available for 7 patients in the limited group and 45 patients in the dupilumab-dupilumab group. Subgroup analysis with patients with clinical severity data adjusting for IGA and BSA was consistent with unadjusted analysis with whole cohort.
A total of 2 DEPs (IL-4R, IL-4) were found in intra- and inter-treatment group comparisons. No markers were significantly modulated with vaccination in either treatment group (Figure 1 ), though direction of change was noted to be positive post-vaccination for more markers in the limited group than in the dupilumab group. There were also a small number of significant pre-vaccination inter-treatment group comparisons: IL-4 and IL-4R were upregulated in patients receiving dupilumab relative to limited therapy (Figure 1), and IL-4R was also found to be upregulated in the dupilumab group relative to the limited group post-vaccination. A subgroup analysis with patients with clinical severity scores, adjusted for IGA and BSA, was consistent with these results.Figure 1 Expression of a subset of previously published immune markers7,10 by treatment group and vaccination status for the limited group (n=10) and dupilumab group (n=58). Protein expression profiles were fitted to linear regression models, and p-values were adjusted for multiple comparisons using the Benjamini-Hochberg procedure. Pre, pre-vaccination; post, post-vaccination. *, FDR<0.05; **, FDR<0.01.
Figure 1
To summarize, patients on dupilumab, relative to patients on topical or no therapeutics, expressed significantly more IL-4R and IL-4 pre-vaccination and more IL-4R post-vaccination, and no markers, including other Th2 markers (e.g. IL-4, IL-13, IL-10, CCL7, CCL18), were significantly modulated relative to baseline in either treatment group after full vaccination. The increased expression of IL-4R in the dupilumab-treated group relative to the limited therapy group may, in fact, be an effect of dupilumab itself as internalization of the IL-4R receptor in immune cells9 and a compensatory upregulation of serum IL-4R with prolonged inhibition. The absence of otherwise significant immune modulation in the serum at least 2 weeks after vaccination suggests that COVID-19 vaccination does not appear to induce proteomic changes relevant to AD pathogenesis, regardless of treatment. Prior work in AD has shown that pronounced inflammatory protein expression in skin is accompanied by more modest modulation in the blood,7 which may, in part, explain the similarity in limited and dupilumab serum proteomic phenotypes despite differences in baseline severity scores. Additional studies examining changes in skin in the context of COVID-19 vaccination, therefore, may capture more proteomic changes. In conclusion, our report provides molecular context supportive of vaccination against SARS-CoV-2 in patients with AD, without discontinuation of dupilumab. Future studies may expand upon our findings with comparisons with more patients, healthy controls and the inclusion of skin samples.
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8. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical applications in genetics and molecular biology. 2004;3(1).
9. Heeb LEM, Boyman O. Comprehensive analysis of human IL-4 receptor subunits shows compartmentalization in steady state and dupilumab treatment. Allergy. 2022.
10. Ewald DA, Malajian D, Krueger JG, Workman CT, Wang T, Tian S, et al. Meta-analysis derived atopic dermatitis (MADAD) transcriptome defines a robust AD signature highlighting the involvement of atherosclerosis and lipid metabolism pathways. BMC Med Genomics. 2015;8:60.
Conflicts of Interest: Dr. Benjamin Ungar is an employee of Mount Sinai and has received research funds (grants paid to the institution) from: Incyte, Rapt Therapeutics, and Pfizer. He is also a consultant for Arcutis Biotherapeutics and Castle Biosciences
Dr. Ana B. Pavel is an employee of the Icahn School of Medicine at Mount Sinai and conducts research sponsored by Pfizer and Regeneron.
Dr. Emma Guttman-Yassky has served as a consultant for AbbVie, Amgen, Allergan, Asana Bioscience, Celgene, Concert, Dermira, DS Biopharma, Escalier, Galderma, Glenmark, Kyowa Kirin, LEO Pharmaceuticals, Lilly, Mitsubishi Tanabe, Novartis, Pfizer, Regeneron, Sanofi, and Union Therapeutics; a member of advisory boards of Allergan, Asana Bioscience, Celgene, DBV, Dermavant, Dermira, Escalier, Galderma, Glenmark, Kyowa Kirin, LEO Pharma, Lilly, Novartis, Pfizer, Regeneron, and Sanofi; and a recipient of research grants from AbbVie, AnaptysBio, AntibioTx, Asana Bioscience, Boehringer-Ingelheim, Celgene, DBV, Dermavant, DS Biopharma, Galderma, Glenmark, Innovaderm, Janssen Biotech, Kiniska Pharma, LEO Pharmaceuticals, Lilly, Medimmune, Sienna Biopharmaceuticals, Novan, Novartis, Ralexar, Regeneron, Pfizer, UCB, and Union Therapeutics.
The other authors do not declare any conflicts of interest.
Funding Sources: This work was supported by the Department of Dermatology at the Icahn School of Medicine at Mount Sinai and a grant from Regeneron and Sanofi. Patients were recruited from within the Department of Dermatology at the Icahn School of Medicine. All funding sources reviewed and accepted the study design and the manuscript, with minimal input from Regeneron and Sanofi. Research reported in this publication was also supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award number U01AI152036. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
| 36509406 | PMC9734064 | NO-CC CODE | 2022-12-14 23:28:27 | no | Ann Allergy Asthma Immunol. 2022 Dec 10; doi: 10.1016/j.anai.2022.12.006 | utf-8 | Ann Allergy Asthma Immunol | 2,022 | 10.1016/j.anai.2022.12.006 | oa_other |
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Arch Phys Med Rehabil
Arch Phys Med Rehabil
Archives of Physical Medicine and Rehabilitation
0003-9993
1532-821X
Published by Elsevier Inc. on behalf of the American Congress of Rehabilitation Medicine
S0003-9993(22)01759-2
10.1016/j.apmr.2022.10.016
Information/Education Page
The COVID-19 Vaccine: Why should I get the Vaccines and the Boosters?
Zanwar Preeti Pushpalata PhD, MPH, MS
Sasson Comilla MD, PhD
Heyn Patricia C. PhD, FGSA, FACRM
Pinto Shanti M. MD
Magasi Susan PhD
Hirsch Mark A. PhD, FACRM
Negm Ahmed MD, MSc, PhD
American Congress of Rehabilitation Medicine (ACRM) Aging Research & Geriatric Rehabilitation Covid-19 & Frailty Task Force
10 12 2022
10 12 2022
18 4 2022
12 8 2022
25 10 2022
© 2022 Published by Elsevier Inc. on behalf of the American Congress of Rehabilitation 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.
==== Body
pmcSince March 2020, the novel coronavirus virus (hereafter coronavirus) has resulted in more than 585 million cases and 6.4 million deaths globally. Not being vaccinated increases your risk for COVID-19, the disease caused by coronavirus.
According to the National Institutes of Health, even if you recover from COVID-19 in the short term, COVID-19 can· damage vital organs (i.e. lungs, heart, brain)
· increase your risk for COVID-19 long-term symptoms (i.e. brain fog, a symptom similar to early dementia) (1).
· make you socially isolated and miss out on activities such as work, school, social activities. Participation in these activities are all important for preserving mental health and overall well-being
· make you end up in the emergency department for care
· require a long hospital stay leading to high hospital and out-of-pocket medical bills
· be costly for you, your family, and your community.
Specific examples that directly relate to cost include
1) public restrictions due to ongoing societal spread result in job loss
2) limited resources for childcare, and
3) increase need for community support to compensate for individual losses result in an overall societal cost
The new sub-variants are more infectious and can spread faster than older variants of the original coronavirus. You may not have symptoms of COVID-19, but you still can carry the virus and spread it to other people and animals (2). People with disabilities (PWD) may be at increased risk of contracting COVID-19 due to several reasons including: limited mobility or inability to avoid coming into close contact with others who may be infected, need for close contact with personal attendants and caregivers, having trouble understanding information or practicing preventive measures, or inability to communicate symptoms of illness (3).
Understanding COVID-19 Vaccine • Three different COVID-19 vaccines produced by Pfizer, Moderna, and Johnson & Johnson, were approved in U.S. by the U.S. Food and Drug Administration (FDA) after evaluation of the vaccine's safety and effectiveness in clinical trials that included diverse population PWD (4).
• These vaccines and their boosters are a result of more than 40 years of biotechnological and virology advancements of global scientists working together (5).
• All three vaccines with boosters are highly effective at preventing severe disease and death in adults all ages, teens, and children 6-months-old and older (6-9).
• Unvaccinated persons were at much higher risk of dying from COVID-19 in comparison to those vaccinated (with or without a booster).
• The COVID-19 vaccines decrease your risk of having severe COVID-19 symptoms, and lowers your chance of being hospitalized (7-9).
• Fully vaccinated or those with up to two doses of vaccine are 14 times less likely to die of COVID-19; while those who receive the additional booster are 97 times less likely to die of COVID-19 (6-7).
• The virus is changing and the vaccine antibodies fade with time. Staying up-to-date with booster is important to help increase your own body's defenses against COVID-19.
• Boosters are recommended 6 months after you have been fully vaccinated.
• The COVID-19 vaccines are safe for all people 6-months-old and older except for those with severe allergies to vaccines or components of the vaccines.
The misinformation about the vaccine on television and social media outlets can make you feel uncertain, scared, or vaccine hesitant.· A big reason for 40 percent of the population to remain unvaccinated is due to false information spread on the internet.
· In 2021 a Kaiser Family Foundation report found about 80 percent of adults who say they will "definitely not" get the vaccine believe or are unsure about at least one prevailing COVID-19 vaccine myth.
· Additionally, a majority of adults (54 percent) either believe some rampant misinformation about the COVID-19 vaccines or are unable to debunk it.
Fact checking (such as checking the source and credentials of the person posting) is a key strategy to verify factual basis of the information.· Our vaccine guide is based on scientific evidence to help you make the right choice regarding staying up-to-date with vaccines and boosters.
Common Fears Related to Vaccine· There is no evidence any vaccines cause fertility problems in women or men.
· However, getting COVID-19 can lead to male fertility problems, cause thyroid problems which can affect your menstrual cycle and fertility.
· Pregnant people who get COVID-19 experience higher risk of preterm birth, low birth weight and stillbirth.
Interconnected benefits of being fully vaccinated for COVID-19 and staying up-to date with boosters
Health/ Social Benefits 1 The benefits of vaccination outweighs potential harms, even during pregnancy (10).
2 Vaccinated pregnant mothers can transfer their antibodies and thus pass immunity to their babies, helping to protect them from COVID-19 (11).
3 The vaccine makes your immune system stronger and helps build resistance to the virus.
4 Getting vaccinated prevents you and others from the risk of severe disease hospitalizations, and deaths (7-9).
5 Since children are at risk for getting COVID-19 and for long-term health problems, the best way to keep your kids, 6-months and older safe is by getting them vaccinated (9).
Historical, Social/ Economic Benefits 1 Vaccines have played a huge role to help eradicate other diseases such as smallpox from the world.
2 You can help stop the virus from spreading to other vulnerable people.
3 You can celebrate family occasions, partake in family holidays, public events with larger crowds, i.e. sporting events and other fun activities safely. Know your community levels of COVID-19 before going to these activities. Some businesses and workplaces require vaccination proof or have mandates on staying up-to-date.
4 Partaking in recreational and social activities are important for overall well-being, and may protect against depression and social isolation.
5 The sooner a greater number of people are vaccinated, the better chance we have of keeping the new number of virus cases low in your community and return to work, school, travel and eat in restaurants safely in-person which in turn can help our economy return to pre-COVID-19 era.
For continuous update and to avoid misinformation, please check the Centers for Disease Control (CDC) site at COVID-19 What's new and updated-19 What's new and updated. The CDC has updated guidance for fully vaccinated people based on new evidence.
Where can I find a place to get my COVID-19 vaccine?
Currently, the vaccine is FREE and available for everyone 6-months old and older. Contact your doctor, and your local health department to find out where to get vaccines. Check the following websites:· Learn how to find a COVID-19 vaccine near you as soon as you can.
· Vaccines.gov
· CVS.com
· Walgreens.com
What should I expect after getting vaccinated? · Getting a vaccine is fast. You may experience one or a combination of side effects such as a sore arm, feeling tired, headache, mild fever/chills after vaccination. Usually, these symptoms last 1-2 days.
· It generally takes 2 weeks after vaccination for the body to build protection against the coronavirus.
· Continue to practice good public health measures after the vaccine shot such as mask wearing, hand washing hygiene with soap and water, social and physical distancing when indoors, and make sure your indoor spaces have good ventilation.
· The virus is always changing and generating new variants in different populations. Keep yourself protected from variants by getting the COVID-19 booster.
· Learn what you can do when you have been fully vaccinated.
Where can I find help after I get my vaccine? • For COVID-19 vaccine aftercare visit
• Use the BC COVID-19 Self-Assessment tool at https://bc.thrive.health/covid19/en if you experience ANY symptoms of COVID-19.
• Serious side effects (e.g., blood clots or inflammation of the heart) after receiving the vaccine are rare. Seek urgent medical attention or call 9-1-1 if you develop any serious side effects or a severe allergic reaction (i.e. hives, swelling of your face, tongue or throat or difficulty breathing). Tell your doctor you have received a COVID-19 vaccine.
Four simple ways to stay safe 1 Wear a mask. Consider a N95/KN95/N94 mask whenever possible as these protect by filtering out more virus (12). Get your free mask at participating pharmacies: https://www.cdc.gov/vaccines/covid-19/retail-pharmacy-program/participating-pharmacies.html
2 Get fresh air!
3 Keep your distance.
4 Test for COVID-19 when you are feeling sick. It is still possible to get COVID-19 and pass it to others even if you are vaccinated. For free COVID-19 tests : https://www.covidtests.gov/
Disclaimers
This information is not meant to replace the advice of a medical professional and should not be interpreted as a clinical practice guideline. Statements or opinions expressed in this document reflect the views of the contributors and do not reflect the official policy of ACRM, unless otherwise noted. Always consult your healthcare provider about your specific health condition. This Information/Education Page may be reproduced for noncommercial use for health care professionals and other service providers to share with their patients or clients. Any other reproduction is subject to approval by the publisher.
1. COVID-19 (coronavirus): Long-term effects. Mayo Foundation for Medical Education and Research (MFMER) Accessed August 12, 2022 from https://www.mayoclinic.org/diseases-conditions/coronavirus/in-depth/coronavirus-long-term-effects/art-20490351
2. Zanwar, Preeti, One Health and Averting the Next Pandemic: Concept, Origin, Evolution, and Challenges (June 12, 2021). Available at SSRN fromhttps://ssrn.com/abstract=3865704 or http://dx.doi.org/10.2139/ssrn.3865704
3. People with disabilities. National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention. Reviewed July 20, 2022. Accessed August 12, 2022 from https://www.cdc.gov/ncbddd/humandevelopment/covid-19/people-with-disabilities.html
4. COVID-19 Vaccines. U.S. Food & Drug Administration. Accessed July 24, 2022 from https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/covid-19-vaccines
5. Understanding mRNA COVID-19 vaccines. Vaccines and immunization. National Center for Immunization and Respiratory Diseases (NCIRD), Division of Viral Diseases, Center for Disease Control. Accessed August 12, 2022 from https://www.cdc.gov/coronavirus/2019-ncov/vaccines/different-vaccines/mRNA.html?s_cid=11344:what%20is%20mrna%20vaccine:sem.ga:p:RG:GM:gen:PTN:FY21
6. Rates of COVID-19 Cases and Deaths by Vaccination Status. COVID Data Tracker. Centers for Disease Control & Prevention. Accessed August 12, 2022 from https://covid.cdc.gov/covid-data-tracker/#rates-by-vaccine-status
7. How do death rates from COVID-19 differ between people who are vaccinated and those who are not? Our world in data. Accessed August 12, 2022 from https://ourworldindata.org/grapher/united-states-rates-of-covid-19-deaths-by-vaccination-status?country=∼All+ages
8. COVID-19 Vaccines for Children and Teens. Centers for Disease Control and Prevention. Accessed August 12, 2022 https://www.cdc.gov/coronavirus/2019-ncov/vaccines/recommendations/children-teens.html
9. Rates of laboratory-confirmed COVID-19 hospitalizations by vaccination status. COVID Data Tracker. Centers for Disease Control & Prevention. Accessed August 12, 2022 from https://covid.cdc.gov/covid-data-tracker/#covidnet-hospitalizations-vaccination
10. COVID-19 Vaccines While Pregnant or Breastfeeding. Centers for Disease Control and Prevention. Accessed August 12, 2022 from https://www.cdc.gov/coronavirus/2019-ncov/vaccines/recommendations/pregnancy.html
11. Lydia L Shook, Caroline G Atyeo, Lael M Yonker, Alessio Fasano, Kathryn J Gray, Galit Alter, Andrea G Edlow. Durability of Anti-Spike Antibodies in Infants After Maternal COVID-19 Vaccination or Natural Infection. JAMA 2022 Feb 7. doi: 10.1001/jama.2022.1206.
12. Andrejko KL, Pry JM, Myers JF, et al. Effectiveness of Face Mask or Respirator Use in Indoor Public Settings for Prevention of SARS-CoV-2 Infection — California, February–December 2021. MMWR Morb Mortal Wkly Rep. ePub: 4 Accessed August 12, 2022 doi: http://dx.doi.org/10.15585/mmwr.mm7106e1
| 36513123 | PMC9734065 | NO-CC CODE | 2022-12-16 23:20:03 | no | Arch Phys Med Rehabil. 2022 Dec 10; doi: 10.1016/j.apmr.2022.10.016 | utf-8 | Arch Phys Med Rehabil | 2,022 | 10.1016/j.apmr.2022.10.016 | oa_other |
==== Front
Urology
Urology
Urology
0090-4295
1527-9995
Elsevier Inc.
S0090-4295(22)01025-1
10.1016/j.urology.2022.11.034
Article
The Impact of Holistic Review of Urology Residency Applications on Selection for Interview During the COVID-19 Pandemic
Schulz Alison E. BS 1
Nussbaum Jeffrey E. BS 1
Loloi Justin MD 2
Sankin Alex MD 2
Abraham Nitya MD 2⁎
1 Albert Einstein College of Medicine, Bronx, NY 10461
2 Department of Urology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY 10461
⁎ Address for Correspondence: Nitya Abraham, MD, Associate Professor, Department of Urology, Montefiore Medical Center, Albert Einstein College of Medicine, 1250 Waters Place, Tower 1 PH, Bronx, NY 10461. Phone: 800 636-6683
10 12 2022
10 12 2022
25 4 2022
8 11 2022
© 2022 Elsevier Inc. All rights reserved.
2022
Elsevier Inc.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Objective
To investigate the impact of a holistic review of urology residency applications on interview selection at our institution during the COVID-19 pandemic.
Methods
In the 2019-2020 cycle, applicants were filtered by a Step 1 score of 230 and whether they applied from selected east coast medical schools. For the 2020-2021 and 2021-2022 cycles, we implemented a scoring system which focused on desirable attributes based on our program training needs and resources. We compared applicant and interviewee demographics and United States Medical Licensing Examination (USMLE) scores using descriptive statistics and one-way Analysis of Variance tests.
Results
A total of 282, 300, and 367 students applied to our residency program with 50, 45, and 52 selected for interviews during the 2019-2020, 2020-2021, and 2021-2022 cycles, respectively. Compared to 2019-2020, the 2020-2021 and 2021-2022 interviewee cohorts comprised of more non-tri-state applicants (36%, 55.6%, 46.2%, respectively). Underrepresented minority representation increased for the 2020-2021 interviewee cohort; however, this was not observed in 2021-2022 (16%, 24.4%, 15.4%, respectively). Additionally, USMLE Step 1 and 2 scores were similar between interviewee cohorts in 2019-2020, 2020-2021 and 2021-2022, respectively (Step 1: 244.2 ± 8.8, 242 ± 12.1, 242.8 ± 12.4, p = 0.624) (Step 2: 249.1 ± 11.5, 251.5 ± 10.5, 254.4 ± 10.8, p = 0.143).
Conclusion
Utilizing a comprehensive review resulted in a geographically diverse interview pool and no significant difference in academic performance amongst interviewees. Holistic review provides an alternative, balanced evaluation of residency applicants which may increase diversity in urology.
Keywords
Urology
Residency
USMLE
Step 1
Pass/Fail
Holistic
==== Body
pmcIntroduction
Matching into urology residency has become increasingly competitive as the number of well-qualified applicants has increased.1 The rapid rise in urology applicants has not been matched by similar expansion in residency training positions (Figure 1 ).2 Unfortunately, this leaves many well-qualified residency applicants unmatched, with the lowest recorded match rate (68%) in the 2021-2022 cycle.1 Applicants have increased the number of applications submitted, casting a wide net to increase odds of matching, which contributes to financial burden, especially for those of lower socioeconomic status and underrepresented minorities (URM), deterring some from pursuing urology.3 Figure 1 Total number of urology residency applicants, available urology residency positions and unmatched applicants over the past 7 years.
Figure 1
Historically, many urology residency programs have used the United States Medical Licensing Examination (USMLE) Step 1 scores to screen applicants.4 USMLE Step 1 was the second most important factor in resident selection per urology program directors, with urologist letters of recommendations being first.5 However, the USMLE Step 1 exam was never intended as a screening metric. Rather, its original purpose was to serve as a licensing exam and assess whether students can apply basic sciences to medical practice.6 With USMLE Step 1 cut offs, well-qualified applicants who did not perform well could be overlooked.7 , 8 In fact, studies have suggested USMLE Step 1 scores do not correlate with residency performance.9 , 10 Medical students may place undue emphasis on scoring highly, thereby ignoring other aspects of their education that contribute to becoming a well-rounded physician.11 Given these concerns, the National Board of Medical Examiners (NBME) announced in January 2022 that the USMLE Step 1 exam will now be scored pass/fail.12
Holistic review is an alternative method for reviewing applications, defined as “a flexible, individualized way of assessing an applicant's capabilities by which balanced consideration is given to experiences, attributes, and academic metrics”.13 Given the need for novel assessment of residency candidacy, we investigated the impact of holistic review of urology residency applications on interview selection during the COVID-19 pandemic.
Methods
This study was approved by the institutional review board at the Albert Einstein College of Medicine (AECOM) (# 2020-12634).
Study Design
We retrospectively reviewed and compared urology residency applicants during the 2019-2020, 2020-2021 and 2021-2022 cycles. During the 2019-2020 cycle, applications were filtered by a USMLE Step 1 score ≥ 230 and connection to the tristate area (New York (NY), New Jersey (NJ), Connecticut (CT)) or selected East Coast states for selection for in-person interviews. For the 2020-2021 and 2021-2022 cycles, a holistic scoring tool was used to screen for selection for virtual interviews due to COVID-19 restrictions. Additionally, applicant pictures were removed during the 2021-2022 cycle and preference was given to applicants who signaled our program. Reviewer training consisted of an annual institution-mandated online unconscious bias module and a grand rounds unconscious bias lecture during the 2020-2021 and 2021-2022 application cycles. No focused unconscious bias training was done prior to application review.
Holistic scoring tool
A holistic scoring tool was created by the program director, associate program director, and application review committee faculty based on a framework created for an internal medicine residency interview selection.13 Applicants were scored on 7 core domains that align with our program's mission and goals: manual dexterity, commitment to underserved populations, leadership experience, academic performance, research, resilience, and connection to NY (Table 1 ). Each domain was scored from 0-2, with 0 being lowest and 2 being highest, and sum total domain score ranging from 0 minimum to maximum 14. There was no minimum score cut-off for receiving an interview, however students scoring in the top 50% were considered for interview. AECOM students or those performing visiting sub-internships were automatically invited to interview with a few exceptions such as interview declined due to conflicting invitations, interview completed during the sub-internship, and student not invited based on unsatisfactory performance.Table 1 Holistic scoring tool used to evaluate urology residency applicants.
Table 1:A: Connection to NY:
0. None
1. Grew up in NY, lived in NY, or attended school in NY
2. Explicitly stated interested in NY program
B: Manual Dexterity:
0. None
1. Sports/sing
2. Plays instrument, paints, wood carver, etc.
C: Commitment to Underserved Populations:
0. None
1. Volunteer experience, mission trips, etc.
2. Explicitly committed to underserved populations
D: Leadership Experience:
0. None
1. Club leader
2. Student council president, developed new program
E: Academic Performance:
0. Step 1 < 230, no honors in clerkships
1. Step 1/2 ≥ 230, some honors in clerkships
2. AOA/SSP, Step 1/2 ≥ 250, all honors in clerkships
F: Research:
0. Minimal
1. Abstracts
2. Publications
G: Resilience:
0. None
1. Overcame a challenge not stated in "2"
2. First generation college graduate, English is second language, had to take care of family member
Data analysis
Demographics and academic metrics were compared using descriptive statistics including race/ethnicity, sex, tri-state, applicants ≥ 30 years old, international medical applicants (IMGs), and Doctor of Osteopathic Medicine (DO) applicants. URM was defined by Association of American Medical Colleges guidelines as those racial/ethnic populations underrepresented in medicine relative to their numbers in the general population.14 Applicants were considered from the tri-state area if their contact state or medical school was in NY, CT, or NJ, or if they had lived in these states for work or education. Lastly, applicants were categorized by age into < 30 and ≥ 30 years old at the time of submitting the application in September of the respective year. USMLE Step 1 and Step 2 scores were compared by one-way Analysis of Variance (ANOVA) tests. Statistical analysis was conducted with Microsoft Excel 2022 (Microsoft Corp, Redmond, WA) with statistical significance level p < 0.05.
Results
A total of 282, 300 and 367 students applied to our program during the 2019-2020, 2020-2021, and 2021-2022 cycles, respectively. Fifty students were interviewed in 2019-2020, 45 in 2020-2021, and 52 in 2021-2022. Five, four and three students declined an interview in the 2019-2020, 2020-2021 and 2021-2022 cycles, respectively. We compared the demographics of applicants, those invited to interview, and interviewee cohorts between the three cycles (Table 2 ) and evaluated academic metrics of the interviewee cohorts before and after implementation of the holistic review (Table 3 ).Table 2 Applicant, Interview Invite, and Interviewee demographics for the 2019-2020, 2020-2021, and 2021-2022 cycles.
Table 2: 2019-2020 (n) (%) 2020-2021 (n) (%) 2021-2022 (n) (%)
Applicants
Total Applications 282 300 367
Male 197 (69.9) 213 (71) 249 (67.8)
Male Invited to Interview 28 (14.2) 27 (12.7) 32 (12.8)
Male Interviewed 25 (12.7) 25 (11.7) 31 (12.5)
Female 85 (30.1) 87 (29) 118 (32.2)
Female Invited to Interview 27 (31.7) 22 (25.3) 23 (19.5)
Female Interviewed 25 (29.4) 20 (23.0) 21 (17.8)
URM 58 (20.6) 79 (26.3) 89 (24.2)
URM Invited to Interview 8 (13.8) 13 (16.5) 10 (11.2)
URM Interviewed 8 (13.8) 11 (13.9) 8 (9.0)
Tri-state 78 (27.7) 66 (22) 80 (21.8)
Tri-state Invited to Interview 34 (43.6) 23 (34.8) 29 (36.3)
Tri-state Interviewed 32 (41) 20 (30.3) 28 (35)
Non-Tri-state 180 (63.8) 194 (64.7) 252 (68.7)
Non-Tri-state Invited to Interview 21 (11.7) 26 (13.4) 26 (10.3)
Non-Tri-state Interviewed 18 (10) 25 (12.9) 24 (9.5)
IMG 24 (8.5) 40 (13.3) 35 (9.5)
IMG Invited to Interview 0 (0) 0 (0) 0 (0)
IMG Interviewed 0 (0) 0 (0) 0 (0)
DO 18 (6.4) 19 (6.3) 31 (8.4)
DO Invited to Interview 1 (5.6) 1 (5.3) 2 (9.7)
DO Interviewed 1 (5.6) 1 (5.3) 2 (9.7)
Applicants ≥ 30 years old 37 (13.1) 50 (16.7) 61 (16.6)
Applicants ≥ 30 years old Invited to Interview 3 (8.1) 4 (8.0) 4 (6.6)
Applicants ≥ 30 years old Interviewed 3 (8.1) 4 (8.0) 4 (6.6)
Total Interviewed 50 (17.7) 45 (15) 52 (14.2)
Invited to Interview
Total Invited to Interview 55 49 55
DO 1 (1.8) 1 (2.0) 2 (3.6)
Male 28 (50.9) 27 (55.1) 32 (58.2)
Female 27 (49.1) 22 (44.9) 23 (41.8)
URM 8 (14.5) 13 (26.5) 10 (18.2)
Tristate 34 (61.8) 23 (46.9) 29 (52.7)
Non-Tristate 21 (38.2) 26 (53.1) 26 (47.3)
Applicants ≥ 30 years old 3 (5.5) 4 (8.1) 4 (7.3)
Interviewees
Total Interviewees 50 45 52
DO 1 (2) 1 (2.2) 2 (5.7)
Male 25 (50) 25 (55.6) 31 (59.6)
Female 25 (50) 20 (44.4) 21 (40.4)
URM 8 (16) 11 (24.4) 8 (15.4)
Tristate 32 (64) 20 (44.4) 28 (53.8)
Non-Tristate 18 (36) 25 (55.6) 24 (46.2)
Applicants ≥ 30 years old 3 (6) 4 (8.9) 4 (7.7)
Table 3 Comparison of interviewee academic performance between the three cycles.
Table 3: 2019-2020 (n = 50) 2020-2021 (n = 45) 2021-2022 (n = 52) P -value
Average USMLE Step 1 score (SD) 244.2 (8.8) 242 (12.1) 242.8 (12.4) 0.624
Average USMLE Step 2 score (SD) 249.1 (11.5) (n = 30) 251.5 (10.5) (n = 31) 254.4 (10.8) (n = 42) 0.143
AOA / SSP Membership (%) 19 (38%) 13 (28.9%) 10 (19.2%) -
Overall, the greatest number of applicants applying to our institution occurred in the 2021-2022 cycle, with a 22% increase from the previous year. Specifically, there was a 12.7% increase in URM applicants, 21.6% increase in applicants ≥ 30 years old, 29.9% increase in non-tri-state applicants, 35.6% increase in female applicants, and a 63.2% increase in DO applicants. The 2020-2021 and 2021-2022 cycles had a slightly higher percentage of URM and applicants ≥ 30 years old compared to the 2019-2020 cohort (20.6% 2019-2020 vs 26.3% 2020-2021 vs 24.2% 2021-2022 and 13.1% 2019-2020 vs 16.7% 2020-2021 vs 16.6% 2021-2022 respectively). The percentage of female applicants was similar between the cohorts (30.1% 2019-2020 vs 29% 2020-2021 vs 32.2% 2021-2022). A similar observation was seen with regards to non-tri-state applicants (63.9% 2019-2020 vs 64.7% 2020-2021 vs 68.7% 2021-2022). The percentage of DO applicants was similar between 2019-2020 and 2020-2021, but higher in the 2021-2022 cycle (6.4% 2019-2020 vs 6.3% 2020-2021 vs 8.4% 2021-2022). For IMG applicants, the percentage was highest in the 2020-2021 cycle and similar in the 2019-2020 and 2021-2022 cycles (8.5% 2019-2020 vs 13.3% 2020-2021 vs 9.5% 2021-2022).
With regards to female applicants, the percent selected to interview had a declining trend (31.7% 2019-2020 vs 25.3% 2020-2021 vs 19.5% 2021-2022) leading to the 2020-2021 and 2021-2022 cohorts comprised of fewer female interviewees (50% 2019-2020 vs 44.4% 2020-2021 vs 40.4% 2021-2022). The percent of female applicants who declined interviews had an uptrend of 40% (2/5), 50% (2/4) and 67% (2/3) for 2019-2020, 2020-2021 and 2021-2022 respectively. URM applicants had a similar trajectory with a declining trend for the percent selected to interview (13.8% 2019-2020 vs 16.5% 2020-2021 vs 10% 2021-2022) and an increasing trend for the percent who declined interviews (0% (0/5) 2019-2020 vs 50% (2/4) 2020-2021 vs 67% (2/3) 2021-2022). The 2020-2021 interviewee cohort had greater URM representation, with comparable percentages in the 2019-2020 and 2021-2022 cycles (16% 2019-2020 vs 24.4% 2020-2021 vs 15.4% 2021-2022). The number of non-tri-state applicants invited to interview was slightly higher in 2020-2021 compared to 2019-2020 and 2021-2022 (11.7% 2019-2020 vs 13.4% 2020-2021 vs 10.3% 2021-2022). In addition, the holistic review process elicited a larger number of non-tri-state interviewees in the 2020-2021 and 2021-2022 cohorts, highest in the 2020-2021 cycle (36% 2019-2020 vs 55.6% 2020-2021 vs 46.2% 2021-2022). The percentage of DO applicants interviewed was greatest in the most recent cycle (5.6% 2019-2020 vs 6.3% 2020-2021 vs 9.7% 2021-2022) and composed of a higher percentage of the interviewee cohort (2% 2019-2020 vs 2.2% 2020-2021 vs 5.7% 2021-2022). Applicants interviewed who were ≥ 30 years old slightly decreased in the most recent cycle compared to 2019-2020 and 2020-2021 (8.1% 2019-2020 vs 8.0% 2020-2021 vs 6.6% 2021-2022). However, their representation in the interviewee cohort was similar in all three years (6% 2019-2020 vs 8.9% 2020-2021 vs 7.7% 2021-2022). No IMG applicants were interviewed in the three cycles.
After applying the traditional Step 1 cutoff and tri-state connection to the 2020-2021 and 2021-2022 cycles, 48.8% (22/45) and 55.7% (29/52) of applicants would have been potentially selected for interview, respectively. With the Step 1 cutoff applied, 13.3% of applicants (6/45) in 2020-2021 and 11.5% of applicants (6/52) in 2021-2022 would not have been interviewed. Additionally, with the tri-state area connection filter applied, 44.4% (20/45) of applicants in 2020-2021 and 42.3% (22/52) in 2021-2022 would not have been interviewed.
Academic Performance
Average USMLE Step 1 scores did not significantly differ between 2019-2020, 2020-2021, and 2021-2022 interviewees (244.2 ± 8.8, 242 ± 12.1, 242.8 ± 12.4, p = 0.624, respectively). Likewise, average USMLE Step 2 scores were comparable between the interviewee cohorts (249.1 ± 11.5, 251.5 ± 10.5, 254.4 ± 10.8, p = 0.143). The percentage of students inducted into Alpha Omega Alpha (AOA) or Sigma Sigma Phi (SSP) honor societies had an overall decreasing trend (38% 2019-2020 vs 19.2% 2021-2022). Table 4 describes the comparison of academic performance between the three cohorts.
Discussion
To enhance recruitment of diverse applicants into competitive fields, the NBME has converted the USMLE Step 1 exam to pass or fail.9 Numerous studies have shown that URM and female applicants historically performed lower on the USMLE Step 1 exam and therefore, are negatively impacted by the overemphasis placed on these scores.15 , 16 By de-emphasizing the USMLE Step 1 score in applicant screening, multiple specialties have shown an increase in URM applicants invited to interview.15 , 17 In this study, we analyzed the utilization of a holistic review process which de-emphasizes USMLE Step scores on urology applicants. By implementing holistic review, we found that more geographically varied applicants were interviewed, thus enhancing the geographic diversity of applicants in our interviewee cohorts without a significant difference in academic performance. Since our program is in the tri-state region, those connected to the region are more likely to be chosen for interviews as they are more likely to stay.18 , 19 Geography was found to be an integral driver in urology applicants’ away rotation and interview invite selection20 and was found to not be affected by the COVID-19 pandemic or virtual interviews21. In a study analyzing geographic diversity for plastic surgery residency applicants, the Northeast had the strongest geographic association between residency program match and medical school location.19
We found an increase in DO applicants interviewed during the 2021-2022 cycle. It is historically difficult for DO applicants to interview and match into competitive specialties.22 Therefore, holistic review may reduce barriers for DO applicants, allowing more to enter urology. Interestingly, we found a decrease in female representation in our interviewee cohorts, despite a 35.6% increase in female applicants during the 2021-2022 cycle. It is important to consider possible gender bias in the interview selection process. For the 2021-2022 cycle, applicant pictures were removed from their application to reduce unconscious bias based on appearance. In a study assessing applicant photographs on dermatology residency selection, Corcimaru et al. found no significant difference in matching and male specific features but found that female applicants features were significant in matching.23 Maxfield et al. analyzed bias in radiology resident selection based on attractiveness and obesity in photographs.24
The percentage of URM interviewees was highest in 2020-2021, but similar between the 2019-2020 and 2021-2022 cycles. Despite an increase in URM applicants nationwide, we did not observe an increase in our interview cohorts. Of note, 50% of applicants in 2020-2021 and 67% of applicants in 2021-2022 who declined interview invitations were URM. Since we gave strong preference to applicants who signaled us in 2021-2022, the number of URM and females interviewed was highly dependent on how many signaled us. Additional strategies are needed to recruit URM applicants, such as increasing faculty diversity and offering sub-internship scholarships for URM applicants.25 A more diverse group of residents would ultimately be the best URM recruitment strategy and begins by improving the interview selection process.
Importantly, there was no significant difference in USMLE Step 1 and 2 scores between cohorts, indicating that a holistic review has no negative impact on academic qualifications for interview selection. However, the holistic review process did elicit an interviewee cohort with lower AOA/SSP memberships. Recently, there has been a shift away from honor societies due to their reinforcement of structural biases and social privilege in medical education and may not be representative of academics for the cohorts.26
To our knowledge, this is the first report of a holistic review of urology residency candidates. Other specialties have explored a holistic analysis of residency applicants and its effects on enhancement of diversity and academic achievements of their cohorts.27, 28, 29 Nehemiah et al. created a holistic algorithm of a diverse group of faculty and residents for the selection committee and blinding of USMLE scores and grades for general surgery applicants. A standardized scoring sheet was completed by interviewers which ranked six domains: academic achievement/potential, quality of researcher/intellectual curiosity, leadership capacity, teamwork and altruism, motivation for surgery, and strengths of LORs. They compared the holistic review process to prior years and found a significantly larger proportion of women (p < 0.01) and URM students (p = 0.046) interviewed after holistic review with no significant difference in USMLE Step 1 scores (p = 0.32) of ranked applicants.28
Gardner et al. evaluated the implementation of a two-step process for surgical residency applicants in 7 residency programs; the USMLE Step 1 cutoff was lowered to 210 and those applicants were invited to take a situational judgement test based on hypothetical but realistic scenarios where applicants indicated the effectiveness of potential responses.27 By lowering the USMLE threshold, 35% more applicants were invited to take the test, who were predominantly non-white and female (p<0.01, p<0.05, respectively), leading to more URM invited to interview (p<0.01).27 Butler et al. implemented a three-step approach for several surgical subspecialties which consisted of a holistic review along with a 4-week visiting clerkship including a stipend for URM 4th year medical students and targeted outreach to URM applicants following the interview. Over 3 years, they found an increase in URM interviewees to 12.1%, 12.5%, and 18.8% respectively compared to 11.2% at baseline.29
There are several limitations to our study. First, we only evaluated use of the holistic review for two cohorts at a single urological residency based in NY. More data is needed from future cohorts at our institution and programs in other regions. However, due to the immediate transition of USMLE Step 1 to pass/fail in February 2022, there is an urgent need to assess alternative scoring metrics for residency candidates. In addition, our scoring system relies on subjectivity of each reviewer; each application may be interpreted in different ways and some ambiguity exists within the scoring system. Dimensions such as manual dexterity, research, commitment to underserved populations and resilience were somewhat open to interpretation and may differ based on the reviewer. Further standardization of scoring is needed to reduce subjectivity. Some domains, such as resilience, were mostly beneficial for those with weaknesses in their applications, such as a lower academic score. Therefore, it may be useful for those to be “bonus” columns to boost an applicant's score. Utilizing a 3 valued scoring system implies that all domains are weighted equally. However, some categories such as manual dexterity may not have equal weight to research or leadership. A more nuanced method could be to weigh some of the categories from 1-5 to distinguish exceptional candidates. More data are needed to determine whether a minimum overall score would be necessary to select applicants for interview. It is important to consider applicant characteristics of those who declined interviews in addition to those who accepted, as both candidates are selected by our committee using the holistic tool. There may be other aspects important in determining urology resident success that were not included in our scoring system. Lastly, it is important to recognize that the 2020-2021 and 2021-2022 cycles occurred during the COVID-19 pandemic and may not be representative of a traditional application cycle.
Our program implemented the holistic review to create an equitable screening process and prepare for the USMLE Step 1 transition to pass/fail, with the expectation of increasing diversity and inclusion among our interviewees and ultimately matched residents. We were pleased to see an increase in geographic diversity among our interviewees but surprised at the decrease in percentage of women and URMs interviewed. First, our findings demonstrate the importance of reflecting on whether changes implemented in the application screening process achieve the desired outcome to continually make improvements. Second, implicit and systemic bias may have significant influence despite attempting to make application screening as objective and equitable as possible. In the most recent cycle, we removed applicant pictures. However, literature has shown the detriment of a “color-blind” approach and that a “multicultural” approach is preferable.30 Third, we gave strong preference to applicants who signaled us in the most recent cycle, which influenced our interviewee demographics. Our holistic scoring tool will be revised for the upcoming cycle. Our application review committee will expand to include more perspectives and we will discuss strategies to address implicit bias with committee members prior to disseminating applications. Applicant pictures will be included to highlight applicant multiculturalism and diversity. Prior to sending interviewee invitations, we will examine the selected pool metrics for opportunities to diversify. This is a work-in-progress and hopefully, we will learn from each other's successes and failures.
Conclusion
As the USMLE Step 1 exam transitions to pass or fail scores, urology residency programs can no longer rely on USMLE Step 1 scores to screen applicants. The results of this study demonstrate that a holistic review process provides a balanced evaluation of residency applicants which retains the high level of academic excellence of urology applicants. We have shared our screening criteria to increase transparency to potential applicants and will continue to improve the holistic screening process in resident selection.
Author Roles: AS performed data analysis, wrote, and edited the manuscript. JN performed data collection and edited the manuscript. JL edited the manuscript. AS edited the manuscript. NA conceived and designed the analysis, edited the manuscript, approved the final version, and is the article guarantor.
Funding: None
Disclosures: AS - no disclosures; JN - no disclosures; JL - no disclosures; AS - no disclosures; NA - no disclosures.
Informed Consent: Informed consent was not needed, as applicant data was secured and de-identified.
Prior Presentations: This project was presented at the AUA annual meeting in September 2021.
==== Refs
References
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16 Williams M Kim EJ Pappas K The impact of United States Medical Licensing Exam (USMLE) step 1 cutoff scores on recruitment of underrepresented minorities in medicine: A retrospective cross-sectional study Health Sci Rep 3 2 2020 e2161 10.1002/hsr2.161 32318628
17 Teherani A Hauer KE Fernandez A How Small Differences in Assessed Clinical Performance Amplify to Large Differences in Grades and Awards: A Cascade With Serious Consequences for Students Underrepresented in Medicine Acad Med 93 9 2018 1286 1292 10.1097/ACM.0000000000002323 29923892
18 Quesada PR Solis RN Ojeaga M Overemphasis of USMLE and Its Potential Impact on Diversity in Otolaryngology OTO Open 5 3 2021 10.1177/2473974X211031470 2473974X211031470
19 Hashmi A Khan FA Policherla R No Place like Home: Is There Selection Bias in Plastic Surgery Residency Match Process? Plast Reconstr Surg Glob Open 5 1 2017 e1207 10.1097/GOX.0000000000001207 28203507
20 Patel S Hamad J Wallen E Geographic Distribution of Away Rotations Impacts the Urology Match Process in the United States Urology. 154 2021 68 76 10.1016/j.urology.2021.01.004 33454359
21 Gabrielson AT Meilchen CK Kohn JR Kohn TP. The COVID-19 Residency Application Cycle Did Not Affect Geographic Dispersal Patterns Among Applicants Entering the Urology Match: A Quantitative Mapping Study Urology. 158 2021 26 32 10.1016/j.urology.2021.05.093 34324912
22 Craig E Brotzman E Farthing B Poor match rates of osteopathic applicants into ACGME dermatology and other competitive specialties JOM 121 3 2021 281 286 10.1515/jom-2020-0202 33635959
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27 Gardner AK Cavanaugh KJ Willis RE Dunkin BJ. Can Better Selection Tools Help Us Achieve Our Diversity Goals in Postgraduate Medical Education? Comparing Use of USMLE Step 1 Scores and Situational Judgment Tests at 7 Surgical Residencies Acad Med 95 5 2020 751 757 10.1097/ACM.0000000000003092 31764083
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| 36513217 | PMC9734066 | NO-CC CODE | 2022-12-14 23:28:27 | no | Urology. 2022 Dec 10; doi: 10.1016/j.urology.2022.11.034 | utf-8 | Urology | 2,022 | 10.1016/j.urology.2022.11.034 | oa_other |
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Heliyon
Heliyon
Heliyon
2405-8440
The Author(s). Published by Elsevier Ltd.
S2405-8440(22)03629-5
10.1016/j.heliyon.2022.e12341
e12341
Case Report
A case of Legionnaires’ disease with severe rhabdomyolysis misdiagnosed as COVID-19
Sayinalp-Arslan Basak a∗
Er Ahmet Gorkem b
Yildirim Mehmet c1
Kilicaslan Banu d
Akinci Seda Banu d
Uzun Omrum b
a Hacettepe University Faculty of Medicine, Department of Internal Medicine, Ankara, Turkey
b Hacettepe University Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Ankara, Turkey
c Hacettepe University Faculty of Medicine, Department of Internal Medicine, Division of Intensive Care, Ankara, Turkey
d Hacettepe University Faculty of Medicine, Department of Anesthesiology and Reanimation, Ankara, Turkey
∗ Corresponding author.
1 (Present Address: Diskapi Yildirim Beyazit Training and Research Hospital, Intensive Care Unit, Ankara, Turkey).
10 12 2022
12 2022
10 12 2022
8 12 e12341e12341
18 3 2022
29 7 2022
6 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.
Background
COVID-19 case numbers have begun to rise with the recently reported Omicron variant. In the last two years, COVID-19 is the first diagnosis that comes to mind when a patient is admitted with respiratory symptoms and pulmonary ground-glass opacities. However, other causes should be kept in mind as well. Here we present a case of Legionnaires’ disease misdiagnosed as COVID-19.
Case presentation
A 48-year-old male was admitted with complaints of dry cough and dyspnea. Chest computed-tomography revealed bilateral ground-glass opacities; therefore, a preliminary diagnosis of COVID-19 was made. However, two consecutive COVID PCR tests were negative and the patient deteriorated rapidly. As severe rhabdomyolysis and acute renal failure were present, Legionnaires’ disease was suspected. Urine antigen test for Legionella and Legionella pneumophila PCR turned out to be positive. The patient responded dramatically to intravenous levofloxacin and was discharged successfully.
Discussion
Legionnaires’ disease and COVID-19 may present with similar signs and symptoms. They also share common risk factors and radiological findings.
Conclusions
Shared clinical and radiological features between COVID-19 and other causes of acute respiratory failure pose a challenge in diagnosis. Other causes such as Legionnaires’ disease must be kept in mind and appropriate diagnostic tests should be performed accordingly.
SARS-CoV-2; COVID-19 pandemic; legionella; rhabdomyolysis.
Keywords
SARS-CoV-2
COVID-19 pandemic
Legionella
Rhabdomyolysis
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pmc1 Introduction
Coronavirus disease-19 (COVID-19) caused by SARS-CoV-2 was declared to be a pandemic on March 11, 2020 by World Health Organization and it has affected millions of lives worldwide since then. Many variants of the virus have emerged; the most recent one -Omicron variant-was reported from South Africa on November, 26 2021 [1]. With this new variant, case numbers have begun to rise once again. In the last two years, COVID-19 is the first diagnosis that comes to mind when a patient is admitted with respiratory symptoms and ground-glass opacities at chest computed-tomography (CT). However, other infectious and non-infectious causes should be kept in mind as well. Legionnaires' disease is still a common cause of community-acquired pneumonia with ground-glass appearance and here we present a case of Legionnaires’ disease misdiagnosed as COVID-19.
2 Case Presentation
A 48-year-old male with a history of glaucoma was admitted to another hospital in December 2020 with complaints of dry cough and dyspnea for four days. He was not receiving any treatment for glaucoma. Chest CT revealed bilateral widespread ground-glass opacities and a preliminary diagnosis of COVID-19 was made despite the fact that the patient did not exhibit any other peculiar symptoms of COVID-19 such as scratchy throat or anosmia. The patient was not vaccinated as no COVID-19 vaccine was available in Turkey at that time. The patient was tachypneic and required supplemental oxygen; he was transferred immediately to our COVID-19 intensive care unit (ICU).
Upon admission his body temperature was 37 °C, heart rate 109 bpm and respirations 30 breaths per min. His oxygen saturation was 90% with 4 L per minute of supplemental oxygen. Laboratory tests revealed mild leukocytosis, lymphopenia, thrombocytopenia, elevated creatinine and blood urea nitrogen levels and elevated liver function tests (Table 1 ). Rhabdomyolysis was present, since total creatine kinase level increased approximately 10-fold, and was accompanied by markedly elevated myoglobin and lactate dehydrogenase levels, acute kidney failure, dark urine and myoglobinuria. After obtaining oropharyngeal and nasopharyngeal swab samples for SARS CoV-2 polymerase-chain-reaction (PCR) test (Coronex® COVID-19 Multiplex Real Time-qPCR Test Kit, DS Bio and Nano Technology, Turkey), oral favipiravir -the antiviral recommended by the Ministry of Health of Turkey for treatment of COVID-19 at that time-was initiated, intravenous piperacillin/tazobactam was also added to the treatment for antibacterial coverage. COVID-19 PCR test turned out to be negative. Chest CT was re-interpreted in our hospital as highly suggestive of diffuse alveolar hemorrhage because of centrally located ground-glass opacities (Figure 1 A-D).Table 1 Important laboratory results at admission.
Table 1Laboratory Parameter Value Normal Range
Leukocyte Count (x103/μL) 11,1 4,3–10,3
Neutrophil Count (x103/μL) 10,2 2,1–6,1
Lymphocyte Count (x103/μL) 1,01 1,3-3,5
Thrombocyte Count (x103/μL) 110 156–373
Creatinine (mg/dL) 2,33 0,67–1,17
Blood Urea Nitrogen (mg/dL) 35,3 6–20
Aspartate Aminotransferase (U/L) 213 <50
Alanine Aminotransferase (U/L) 109 <50
Erythrocyte Sedimentation Rate (mm/h) 24 0–20
C-reactive Protein (CRP) (mg/dL) 42,07 0-0,5
Procalcitonin (ng/mL) 20,5 0-0,1
Total Creatine Kinase (U/L) 10573 <171
Lactate Dehydrogenase (U/L) 827 <248
Myoglobin (μg/L) 1779,8 17,4–105,7
Troponin I (ng/L) 40,9 14-42,9
Creatine Kinase MB (μg/L) 5,8 0,6-6,3
Urinalysis Dark yellow, pH: 5,5, Density: 1028, Protein: +, Glucose: -, Ketone: -, Bilirubin: -, Urobilinogen: -, Blood (Hemoglobin/Myoglobin): +++, Nitrite: -, Leukocyte count: 3, Erythrocyte count: 7
Figure 1 Chest CT findings. Cross-sections of chest CT showing multifocal ground-glass opacities (A, B) and consolidation at right lower lobe (C, D) are shown here.
Figure 1
On follow-up, the patient had fever (38.5 °C maximum) and became desaturated despite supplemental oxygen. Respiratory failure did not improve by non-invasive mechanical ventilation and high flow nasal cannula. Arterial blood gas analysis showed worsening in hypoxemia with a PaO2/FiO2 ratio of 88, and patient was diagnosed with severe acute respiratory distress syndrome (ARDS). He was intubated and lung protective ventilation strategy was performed. His renal functions deteriorated rapidly, and hemodialysis was initiated. Another sample for COVID-19 PCR was taken via the endotracheal tube -24 h after the first negative result-, and was also negative. Procalcitonin levels increased up to 278.5 ng/mL and CRP levels to 64.1 mg/dL. Because the diagnosis of COVID-19 became less likely with two consecutive negative PCR tests, further tests for pulmonary-renal syndromes and other infectious diseases were performed (Table 2 ). Urine antigen test for Legionella (Legionella pneumophila Rapid Test Cassette, Acro Biotech, United States) and Legionella pneumophila PCR (Ezplex® Respiratory Pathogen Real-time PCR Kit, SML Genetree, Republic of Korea) in deep tracheal aspirate sample both turned out to be positive and a diagnosis of Legionnaires’ disease was made. Piperacillin/tazobactam and favipiravir were discontinued after 4 days of treatment and intravenous levofloxacin was initiated. The patient worked as an operator at Ankara Municipal Water and Sewerage Administration and a written notice regarding the diagnosis was made to Turkish Public Health Institution. Further environmental investigations were not performed at the time of this manuscript.Table 2 Laboratory tests for differential diagnosis.
Table 2Laboratory Test Result
Anti-Nuclear Antibody Negative
Extractable Nuclear Antigen Antibodies Panel (RNP, Sm, SS-A, SS-B, Jo-1, Scl-70)a Negative
Anti-Double Stranded DNA Negative
Anti-Neutrophil Cytoplasmic Antibody (IFA and ELISA)b Negative
Anti-Glomerular Basement Membrane Antibody Negative
Myositis Panelc Negative
Mycoplasma Pneumonia IgM/IgG Negative/Negative
Cytomegalovirus IgM/IgG Negative/Positive
Urine Antigen Test for Legionellad Positive
Respiratory Tract Viral and Bacterial Molecular Panele Positive for Legionella pneumophila
Anti-Human Immunodeficiency Virus Non-reactive
Mycobacterial Culturef Negative
a RNP: Ribonucleoprotein, Sm: Smith antibody, SS-A: Ro, SS-B: La, Scl-70: Topoisomerase 1.
b IFA: Immune Fluorescent Antibody, ELISA: Enzyme-Linked Immunosorbent Assay).
c Myositis panel is composed of anti-Ku, anti PL-7, anti PL-12, anti-MDA5, anti-NXP2, anti-EJ, anti-SRP, anti-MI2 alpha, anti-MI2 beta, anti-SAE1, anti-TIF1g, anti-OJ and anti-PM/Scl 75.
d Legionella pneumophila Rapid Test Cassette, Acro Biotech, United States.
e Respiratory tract viral and bacterial molecular panel (Ezplex® Respiratory Pathogen Real-time PCR Kit, SML Genetree, Republic of Korea) detects the following pathogens: Chlamydophila pneumonia, Haemophilus influenzae, Legionella pneumophila, Mycoplasma pneumonia, Streptococcus pneumoniae, Bordetella pertussis, Adenovirus, Bocavirus, Enterovirus, Humanrhinovirus, Influenza A, Influenza B, Parainfluenza 1–4, RSV A, RSV B, Metapneumovirus, Coronavirus OC43, Coronavirus HKU1, Coronavirus 229E, Coronavirus NL63.
f Deep tracheal aspirate sample was cultured for Mycobacterium tuberculosis.
On the following days, acute phase reactant levels, liver function tests, total creatine kinase and myoglobin levels began to decrease. However, on follow-up the patient had recurrent fever and hypoxemia and laboratory tests revealed bacteremia and healthcare-associated pneumonia caused by Acinetobacter baumannii/calcoaceticus complex. Intravenous meropenem and colistin were added to the treatment. Respiratory mechanics and PaO2/FiO2 ratio improved gradually and he was extubated on the 24th day of ICU admission. Levofloxacin was continued for 21 days. Since his neurological examination revealed limb weakness, neurology consultation and electromyography (EMG) was performed which determined critical illness myopathy. Intensive physiotherapy program was applied for critical illness myopathy. He was discharged after 62 days of hospital stay. An informed consent regarding this case report was obtained from the patient upon discharge.
3 Discussion
COVID-19 pandemic changed the diagnostic workup and management of patients presenting with severe respiratory disease. It is imperative to rule out COVID-19 under these conditions, but also keep an open mind to other causes for timely diagnosis and management. Legionnaires' disease may present with acute respiratory failure similar to COVID-19. Both entities share symptoms such as fever, cough, dyspnea, chest pain and myalgia. They also have common risk factors such as older age, compromised immune system and chronic lung disease. Chest CT findings of Legionnaires’ disease are non-specific, including multilobar or multisegmental opacities separated by ground-glass opacities [2], therefore radiological differential diagnosis may be challenging [3].
Rhabdomyolysis is a medical condition caused mainly by direct muscle injury, drugs, toxins or infections leading to skeletal muscle disruption [4]. Although it is a rare complication of pneumonia, Legionella spp. have been reported to be the most common infectious cause of rhabdomyolysis [5]. Kennedy et al. reported elevated levels of creatine kinase in 78% of the patients [6]. In a literature review by Simoni et al., 22 out of 27 Legionella patients suffered from rhabdomyolysis associated kidney injury [7]. It remains unclear how Legionella spp. cause rhabdomyolysis, whether by direct invasion or toxin release. The most widely accepted hypothesis is that it generates an endotoxin with a vasoconstrictive effect on small vessels causing local ischemic changes [8]. A case reported by Brivet et al. supports this hypothesis as muscle biopsy was negative for the organism [9]. In contrast, in a case reported by Warner et al. L. pneumophila was shown to invade skeletal muscle tissue causing myositis [10]. On the other hand, rhabdomyolysis was reported in only 0.2% of COVID-19 patients according to a study of 1099 patients in China [11]. Other than that, there are only a few case reports of rhabdomyolysis associated with COVID-19 [12,13]. Therefore, a triad of acute respiratory failure, rhabdomyolysis and kidney failure should necessitate diagnostic tests for Legionnaires’ disease in this pandemic era.
Diagnostic tests for Legionella spp. include urinary antigen tests (UAT), culture of respiratory specimens and polymerase chain reaction (PCR). While PCR and culture can detect all Legionella spp. and subgroups, most UAT can detect only L. pneumophila serogroup 1 [14-16]. All methods have their own limitations; culture has low sensitivity [17], most UAT only detects L. pneumophila serogroup 1 and PCR may be false-negative due to insufficient sample or false-positive due to specimen contamination with Acinetobacter spp. or Gemella spp. Therefore, confirmation with at least two different methods is recommended [15]. In our patient, diagnosis of Legionnaires’ disease was confirmed by both UAT and PCR and he had a dramatic response to appropriate antibiotic therapy. It is important to note that in our patient, nasopharyngeal swab sample obtained upon admission for Legionella PCR was negative, however subsequent deep tracheal aspirate sample was positive, indicating that type and quality of the sample also have great importance for accurate diagnosis.
We believe our patient contracted the disease occupationally since he had no other risk factors. As lockdown measures are being eased, many buildings are reopened worldwide and there is a potential risk of exposing many people to dormant water in plumbing systems containing Legionella spp. Professional agencies such as Centers for Disease Control and Prevention (CDC) have issued guidelines on how to safely reopen buildings and prevent Legionella spread [18]. Therefore, we may come across more Legionella cases than we did before in the following months.
4 Conclusions
In conclusion, in the COVID-19 era, shared clinical and radiological features between COVID-19 pneumonia and other infectious and non-infectious causes of acute respiratory failure pose a challenge in diagnosis. Other causes such as Legionnaires’ disease must be kept in mind and appropriate diagnostic tests should be performed accordingly.
Declarations
Author contribution statement
All authors listed have significantly contributed to the investigation, development and writing of this article.
Funding statement
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability statement
No data was used for the research described in the article.
Declaration of interest's statement
The authors declare no conflict of interest.
Additional information
No additional information is available for this paper.
Acknowledgements
None.
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References
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3 Hani C. COVID-19 pneumonia: a review of typical CT findings and differential diagnosis Diagn Interv Imaging 101 5 2020 263 268 32291197
4 Torres P.A. Rhabdomyolysis: pathogenesis, diagnosis, and treatment Ochsner J. 15 1 2015 58 69 25829882
5 Soni A.J. Peter A. Established association of legionella with rhabdomyolysis and renal failure: a review of the literature Respir Med Case Rep 28 2019 100962
6 Kennedy D.H. Love W.C. Pinkerton I.W. Rhabdomyolysis and systemic infection Br. Med. J. 286 6376 1983 1517
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10 Warner C.L. Fayad P.B. Heffner R.R. Jr. Legionella myositis Neurology 41 5 1991 750 752 2027497
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15 Peci A. Winter A.L. Gubbay J.B. Evaluation and comparison of multiple test methods, including real-time PCR, for Legionella detection in clinical specimens Front. Public Health 4 2016 175 27630979
16 Murdoch D.R. Diagnosis of Legionella infection Clin. Infect. Dis. 36 1 2003 64 69 12491204
17 Tronel H. Hartemann P. Overview of diagnostic and detection methods for legionellosis and Legionella spp Lett. Appl. Microbiol. 48 6 2009 653 656 19291209
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| 0 | PMC9734067 | NO-CC CODE | 2022-12-14 23:53:51 | no | Heliyon. 2022 Dec 10; 8(12):e12341 | utf-8 | Heliyon | 2,022 | 10.1016/j.heliyon.2022.e12341 | oa_other |
==== Front
Int J Cardiol
Int J Cardiol
International Journal of Cardiology
0167-5273
1874-1754
Elsevier B.V.
S0167-5273(22)01891-5
10.1016/j.ijcard.2022.12.019
Article
Acute pericarditis as a major clinical manifestation of long COVID-19 syndrome
Dini Frank Lloyd ab⁎
Baldini Umberto c
Bytyçi Ibadete bd
Pugliese Nicola Riccardo a
Bajraktari Gani bd
Henein Michael Y. b
a Centro Medico Sant'Agostino, Milano, Italy
b Heart Center, Umeå University, Sweden
c Centro Medico Salus Itinere, Livorno, Italy
d Department of Clinical and Experimental Medicine, University of Pisa, Italy
⁎ Corresponding author at: Centro Medico Sant'Agostino, Via Temperanza 6, 20127 Milano, Italy.
10 12 2022
10 12 2022
13 11 2022
5 12 2022
8 12 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 long COVID-19 syndrome has been recently described and some reports have suggested that acute pericarditis represents important manifestation of long COVID-19 syndrome. The aim of this study was to identify the prevalence and clinical characteristics of patients with long COVID-19, presenting with acute pericarditis.
Methods
We retrospectively included 180 patients (median age 47 years, 62% female) previously diagnosed with COVID-19, exhibiting persistence or new-onset symptoms ≥12 weeks from a negative naso-pharyngeal SARS CoV2 swamp test. The original diagnosis of COVID-19 infection was determined by a positive swab. All patients had undergone a thorough physical examination. Patients with suspected heart involvement were referred to a complete cardiovascular evaluation. Echocardiography was performed based on clinical need and diagnosis of acute pericarditis was achieved according to current guidelines.
Results
Among the study population, shortness of breath/fatigue was reported in 52%, chest pain/discomfort in 34% and heart palpitations/arrhythmias in 37%. Diagnosis of acute pericarditis was made in 39 patients (22%). Mild-to-moderate pericardial effusion was reported in 12, while thickened and bright pericardial layers with small effusions (< 5 mm) with or without comet tails arising from the pericardium (pericardial B-lines) in 27. Heart palpitations/arrhythmias (OR:3.748, p = 0.0030), and autoimmune disease and allergic disorders (OR:4.147, p = 0.0073) were independently related to the diagnosis of acute pericarditis, with a borderline contribution of less likelihood of hospitalization during COVID-19 (OR: 0.100, p = 0.0512).
Conclusion
Our findings suggest a high prevalence of acute pericarditis in patients with long COVID-19 syndrome. Autoimmune and allergic disorders, and palpitations/arrhythmias were frequently associated with pericardial disease.
Keywords
COVID-19
SARS-CoV2
Acute pericarditis
==== Body
pmc1 Introduction
Coronavirus Disease-2019 (COVID-19) is a multi-organ disease whose severity ranges from asymptomatic to very serious illness. A large number of patients, who have had COVID-19, experience persistent symptoms or new onset symptoms, after the initial recovery with a negative test, that are not explained by alternative diagnoses. A wide range of symptoms may be present varying from shortness of breath to chest pain. This very debilitating condition has been termed long COVID-19 syndrome, also known as post-acute COVID syndrome, post-acute sequelae or chronic COVID syndrome [[1], [2], [3], [4]]. The origin of symptoms and number of patients who experience long COVID-19 is unknown and varies according to the definition used and the population studied and it is unclear what cardiovascular disorders may be contributing to these symptoms [5,6]. To further characterize patients with long COVID-19 syndrome, we evaluated the clinical, instrumental and biochemical features of patients who developed persistent or new onset symptoms after the initial recovery from COVID-19 with a negative naso-pharyngeal swab test for acute respiratory syndrome-coronavirus-2 (SARS-CoV2) nucleic acid.
2 Methods
In this retrospective study, we collected data from ambulatory centers specifically devoted to diagnosis and treatment of the long COVID-19 syndrome. We retrospectively included 180 consecutive patients (median age 47 years, 62% female) previously diagnosed with COVID-19, who exhibited persistent symptoms or new onset symptoms after at least 12 weeks from a negative swamp for SARS-CoV2 nucleic acid. All patients included in this study signed an individual informed consent form prior to data collection. The original diagnosis of COVID-19 was confirmed by a positive naso-pharyngeal swab test for SARS-CoV2 nucleic acid. Clinical history of COVID-19 was carefully obtained. In particular, fever was defined as intra-auricular temperature > 38 °C, tachycardia as heart rate ≥ 90 beats/min, and hypoxemia as peripheral finger oxygen saturation ≤ 94%. All patients had undergone a thorough physical examination, and blood samples were sent for a series of hematological and biochemical investigations including, white blood cells, platelets, C reactive protein, erythrocyte sedimentation rate, D-dimer, fibrinogen and high-sensitivity cardiac troponin when needed. Patients with suspected heart involvement were referred to a complete cardiovascular evaluation, including an electrocardiogram and possibly a transthoracic echocardiogram (TTE). Echocardiography was performed based on clinical need, according to the published recommendations on the optimization of echocardiographic examinations during COVID-19 pandemic [7]. Other imaging and functional tests were performed, as clinically indicated.
The diagnosis of pericarditis was made by the presence of at least two of the following criteria: 1) typical chest pain, 2) pericardial rubs, 3) electrocardiographic changes, 4) pericardial effusion [8]. Patients were divided into two groups according to the diagnosis of acute pericarditis: patients who met the criteria, and patients who did not.
2.1 Statistical analysis
Categorical variables were presented as the proportion of valid cases and continuous variables were expressed as mean ± standard deviation. Differences were assessed by Student's t-test for continuous variables or contingency tables for categorical variables. Comparisons between continuous variables were analyzed by the standard parametric methods. Non-parametrical variables were expressed by the median value and interquartile range. The Mann and Whitney test was used for comparison. Significance was set at p < 0.05. Logistic regression was used to explore the determinants of acute pericarditis. All variables showing p value <0.1 at univariable analysis were tested in multivariable models using an “Enter” procedure. Data were analyzed using the Statistical Package for the Social Sciences version 26.0 for Windows statistical software program (SPSS, Chicago, Illinois).
3 Results
The age of the study participants ranged between 14 and 82 years. Among the study population, 40 (22%) had at least one comorbidity and 6 (3%) presented a history of cardiovascular disease.
All patients were clinically symptomatic and diagnosis was confirmed by a positive naso-pharyngeal SARS-CoV2 swamp test. Most patients recovered at home, while the minority required hospitalization. The median time interval between initial recovery with a negative test and the time of assessment at the ambulatory center was 131 days. The most common symptoms were fatigue/ shortness of breath (52%), followed by heart palpitations (37%) and chest pain (34%). Chest pain was mostly described as sharp and postural. Clinical examination was unremarkable in all, with normal heart sounds and non-raised jugular venous pressure. Friction rub was reported only in one patient. There was no clear relationship between cardiovascular manifestations and pre-existing cardiovascular disease.
TTE showed normal myocardial structure and function in all patients with the exception of 10; 8 (5%) of whom exhibited a dysfunctional right ventricle, one was diagnosed as post COVID-19 dilated cardiomyopathy and one had regional left ventricular dysfunction due to a previous myocardial infarction. Thirty-nine (22%) patients fulfilled the criteria for acute pericarditis according to ESC guidelines. Twenty-eight presented two classical criteria; 10 with three criteria and one with four criteria for pericarditis. Mild-to-moderate pericardial effusion (5–12 mm) was reported in 12 (31%) (Fig. 1 ). In 27 (69%) patients, pericardial layers were thickened and bright with small or negligible effusions (< 5 mm) with or without comet tails arising from the pericardium (pericardial B-lines) (Fig. 2 ). In 15 (39%) patients, the electrocardiogram showed concave ST segment elevation in most leads or focal T wave inversion in several leads.Fig. 1 Echocardiographic parasternal long axis view showing a thickened pericardium with an effusion of 10 mm between the layers.
Fig. 1
Fig. 2 Increased echogenity of the pericardium in the left basal segments with thickened layers and numerous comet tails (pericardial B-lines). A. Short-axis view. B. Long-axis view.
Fig. 2
Table 1 shows the characteristics of the study patients. Compared to patients without diagnosis of pericarditis, those with pericarditis were slightly younger and were more often women. Patients with acute pericarditis also had more frequent history of autoimmune disease and allergic disorders (Fig. 3 ). Raised inflammatory markers were more frequent in patients diagnosed with pericarditis; raised erythrocyte sedimentation rate in 26% and high C-reactive protein in 21%. Markers of myocyte injury (high-sensitivity troponin) were normal. Heart palpitations and arrhythmias were more frequent in patients with pericarditis. The most common arrhythmia was sinus tachycardia (69%), but there were patients showing repetitive supraventricular and ventricular premature beats.Table 1 Baseline clinical features, clinical complains and laboratoryfindingsof our study cohort.
Table 1 Whole population (n = 180) Acute pericarditis (n = 39) No acute pericarditis (n = 141) P value
Baseline features
Age (years) 48 ± 16 45 ± 14 48 ± 16 0.320
Sex (female) 112 (62) 31 (80) 81 (57) 0.0120
Hypertension, (%) 30 (17) 4 (10) 26 (18) 0.201
Diabetes 4 (2) 1 (3) 3 (2) 0.870
Dyslipidemia 11 (6) 3 (7) 8 (6) 0.641
Coronary artery disease 4 (2) 1 (3) 3 (2) 0.870
Impaired renal function 4 (2) 0(0) 4 (3) 0.288
Asthma/Chronic obstructive pulmonary disease 10 (6) 3 (8) 7 (5) 0.510
Autoimmune and allergic disorders 29 (16) 15 (38) 14 (10) <0.0001
Duration of positivity at SARS-CoV2 test (days) 21 [14–41] 19 [14–41] 22 [14–32] 0.6997
Duration of symptoms (days) 131 [94–255] 166 [116–255] 122[94–183] 0.0106
Prior COVID-19 vaccination (%) 68 (38) 22 (56) 46 (33) 0.00670
Prior COVID-19 hospitalization 23 (13) 1 (3) 22 (16) 0.0310
Emergency department visits 34 (19) 5 (13) 29 (21) 0.274
Oxygen desaturation during COVID-19 37 (21) 3 (8) 34 (24) 0.0247
Temperature > 38 °C during COVID-19 62 (34) 8 (21) 54 (38) 0.0386
Clinical complaints
Brain fog/lack of concentration (%) 18 (10) 5 (13) 13 (9) 0.507
Chest pain/discomfort 61 (34) 35 (90) 26 (18) <0.0001
Cough 19 (11) 4 (10) 15 (11) 0.945
Headache 7 (4) 1 (3) 6 (4) 0.629
Heart palpitations/arrhythmias 66 (37) 26 (67) 40 (28) <0.0001
Shortness of breath/fatigue 93 (52) 13 (33) 80 (57) 0.00964
Other symptoms 41 (23) 10 (26) 31 (22) 0.573
Laboratory analyses
Abnormal C reactive protein (%) 18 (10) 8 (21) 9 (6) 0.00101
Abnormal erythrocyte sedimentation rate 16 (9) 10 (26) 6 (4) <0.0001
Abnormal D dimer 17 (9) 4 (10) 13 (9) 0.845
Fig. 3 Histograms describing prevalence of autoimmune and allergic disorders in long COVID-19 patients with or without pericarditis.
Fig. 3
Among patients diagnosed with acute pericarditis, 25 were initially treated with nonsteroidal anti inflammatory drugs (NSAIDs), 8 with corticosteroids and 26 with colchicine. Seven non-responders to NSAIDs were thereafter shifted to corticosteroids. Thirty-three patients recoverd from symptoms between one and four weeks of therapy, while 6 had persistent symptoms and were considered non-responders to therapy. Patients in whom symptoms persisted long after the optimization of treatment were especially those in whom too much time elapsed between the onset of symptoms and the diagnosis of pericardial disease. Recurrence of acute pericarditis (after a minimum symptom-free interval of one month) was observed in 10. Relapsing pericarditis was attributed to idiopathic pericarditis in 6, to COVID-19 vaccination-related pericarditis in 5 and to pericarditis due to COVID-19 recurrence in one.
At logistic regression analysis, relations of variables with acute pericarditis, diagnosed according to the ESC criteria, are displayed in Table 2 . Abnormal C-reactive protein, abnormal erythrocyte sedimentation rate, autoimmune and allergic disorders, heart palpitations/arrhythmias, prior COVID-19 hospitalization, previous vaccination and female gender were found to be univariate predictors of acute pericarditis. Heart palpitations/arrhythmias, autoimmune disease and allergic disorders were related to the diagnosis of acute pericarditis on multivariate analysis. Previous COVID-19 hospitalization was less often independently associated with pericarditis.Table 2 Univariate and multivariate analysis of selected variables for the prediction of acute pericarditis.
Table 2 Univariate analysis [OR and CI (95%)] Unadjusted P value Multivariate analysis [OR and CI (95%)] Adjusted P value
Abnormal C reactive protein 3.785 [1.352–10.598] 0.011 2.697 [0.674–10.795] 0.161
Abnormal D dimer 1.125 [0.345–3.667] 0.845
Abnormal erythrocyte sedimentation rate 7.759 [2.612–23.047] <0.001 2.569 [0.637–10.359] 0.185
Age 0.988 [0.966–1.011] 0.319
Autoimmune/allergic disorders 5.670 [2.426–13.252] <0.001 4.147 [1.466–11.728] 0.007
Heart palpitations/arrhythmias 5.050 [2.362–10.796] < 0.001 3.748 [1.565–8.976] 0.003
Persistence of symptoms 2.557 [1.200–5.446] 0.015 1.579 [0.652–3.822] 0.311
Prior COVID-19 vaccination 2.673 [1.295–5.514] 0.008 1.624 [0.695–3.795] 0.262
Prior COVID-19 hospitalization 0.142 [0.019–1.091] 0.061 0.100 [0.010–1.012] 0.051
Sex 0.348 [0.150–0.812] 0.015 0.7773 [0.282–2.119] 0.617
CI, confidence interval; OR, odds ratio.
4 Discussion
The results of this study showed that acute pericarditis was an important manifestation of long COVID-19 syndrome. There was evidence for an association of acute pericarditis presenting with palpitations/arrhythmias, and autoimmune and allergic disorders. Objective assessment of pericardial disease in long COVID-19 patients was mostly characterized at TTE by thickened and bright pericardial layers with small or negligible effusions.
COVID-19 disease is caused by SARS-CoV2 infection, which leads to a wide spectrum of clinical manifestations, ranging from complete lack of symptoms to severe illness, including respiratory failure and multi-organ dysfunction. The lungs are the organs most affected by COVID-19 and this may lead to aggressive bilateral pneumonia with a high fatality rate. While many patients with COVID-19 fully recover, a significant percentage experience long-term health consequences [[9], [10], [11]].
Little is known about cardiovascular manifestations/complications occurring after clinical and virology recovery from SARS-CoV2 infection and there are few studies focusing on a comprehensive evaluation of long COVID-19 patients that aim at establishing the extent of cardiovascular disturbance contribution to the development of symptoms after recovery [[12], [13], [14], [15]].
It is well known that pericarditis is among the most frequent cardiac complications after viral infections [[16], [17], [18]]. Case reports have recently documented pericarditis with or without pericardial effusion as delayed complications of COVID-19, but the real prevalence of pericarditis in long COVID-19 patients is still unknown [19,20]. In our study, diagnosis of delayed cases of acute pericarditis was based on the presence of at least two of the following criteria: typical sharp and positional chest pain, presence of pericardial effusion of any degree, and electrocardiographic changes. Most study patients diagnosed with acute pericarditis showed thickened and bright pericardial layers with small or negligible effusion. Pericarditis was diagnosed in patients with thickened pericardium albeit no signs of effusion, only in the presence of typical chest pain and electrocardiographic alterations. The association with typical chest pain, electrocardiographic changes and eventually the response to anti-inflammatory medications has also been helpful in substantiating the final diagnosis.
De novo palpitations/arrhythmias were common in the study population. The occurrence of either supraventricular or ventricular arrhythmias have previously been documented in post COVID-19 patients and were found to be often associated with right ventricular dysfunction [21]. Other studies have suggested a possible relation between autonomic nervous system disturbances and post COVID-19 sequelae [22]. Our results show that acute pericarditis was often accompanied by concomitant heart rhythm disturbances. It is reasonable to believe that pericarditis could extend to the epicardial layer of the myocardium, with myocardial damage from inflammatory cascade and subsequent fibrosis, remodeling, and arrhythmias [23].
Although echocardiography is considered the standard cardiac imaging technique, inflammation of the pericardial layers may not always be easily detected by TTE.
Cardiovascular magnetic resonance (CMR) can provide comprehensive information on pericardial disease, including assessment of pericardial thickness and small or negligible effusions. In patients recovered from COVID-19, CMR studies performed more than two months after the infection have shown that as many as 78% had cardiac involvement with abnormal findings, while signs of ongoing myocardial inflammation were present in 60% [24]. Twenty percent had pericardial effusion >10 mm versus 7% in case-matched controls. Despite such high prevalence, cardiac involvement was not related to the severity of COVID-19 and persisted well beyond the acute phase [5]. Recently, cardiovascular complications have been also reported from COVID-19 vaccines and this may suggest common pathways shared by the untoward effects of anti-SARS CoV2 vaccination and the manifestations of the long COVID-19 syndrome [25].
The pathogenesis of long COVID-19 syndrome remains not well defined but seems to be different from acute COVID-19. Several mechanisms have been proposed to explain the occurrence of the long COVID-19, including ongoing inflammation activated by the virus and host of factors comprising allergic conditions, autoimmune reactions, and vascular injury caused by hypercoagulability and thrombosis [26]. It has been recognized that inadequate or excessive immune response driven by T and B cell-mediated mechanisms may be implicated in the occurrence of pericarditis and myocarditis after viral infections [27,28]. Our finding of more frequent occurrence of pericarditis in patients presenting an history of autoimmune and allergic disorders suggests that an association might be present between the two conditions and that the immune system continues to over-react after the coronavirus infection and is unable to reset itself to idle.
Limitations.
An important limitation in this study is that the prevalence of pericarditis in long COVID-19 may vary according to the setting. Studied patients were those referred to ambulatory centers mostly dealing with cardiovascular diseases and this might have led to an overestimation of the real prevalence of pericardial disease among patients with long COVID-19 syndrome [29]. Cardiovascular symptoms, such as chest pain and palpitations commonly occur in long COVID-19 syndrome. In our study cohort, chest pain was reported in just above one third of patients while acute pericarditis was diagnosed in 22%, which suggests that pericarditis could be an underdiagnosed disease, and therefore not optimally managed. There was a discrepancy between the relatively high occurrence of pericarditis in our long COVID-19 patients and that reported in a large study performed in post COVID-19 unvaccinated patients, where the incidence was very low [30]. Possible explanations for the latter finding is that post COVID-19 patients are intrinsically different from those with long COVID-19 syndrome. Moreover, patients with pericarditis frequently show thickened pericardial layers with small or negligible effusions and diagnosis of pericarditis may be difficult unless an echocardiographic examination especially focused on the pericardium is performed.
5 Conclusion
To our knowledge this is the first study describing acute pericarditis associated with long COVID-19 in consecutive patients who have had symptomatic COVID-19 and a negative SARS-CoV2 nucleic acid test. Female gender, the presence of chest pain, history of autoimmune and allergic disorders, and palpitations/arrhythmias were risk factors for the development of acute pericarditis.
Authors' contribution
FLD, MH contributed to study conception and design; FLD, UB, NRP contributed to data collection; FLD, GB and IB performed data analysis; FLD, MH drafted the manuscript, FLD, MH, GB critically revised the work. All the authors read and approved the final version of the paper.
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6 Kang Y. Chen T. Mui D. Ferrari V. Jagasia D. Scherrer-Crosbie M. Chen Y. Han Y. Cardiovascular manifestations and treatment considerations in COVID-19 Heart 106 15 2020 1132 1141 10.1136/heartjnl-2020-317056 (Epub 2020 Apr 30. PMID: 32354800; PMCID: PMC7211105) 32354800
7 Cameli M. Pastore M.C. Soliman Aboumarie H. Mandoli G.E. D'Ascenzi F. Cameli P. Bigio E. Franchi F. Mondillo S. Valente S. Usefulness of echocardiography to detect cardiac involvement in COVID-19 patients Echocardiography. 37 8 2020 1278 1286 10.1111/echo.14779 32654210
8 Adler Y. Charron P. Imazio M. Badano L. Barón-Esquivias G. Bogaert J. Brucato A. Gueret P. Klingel K. Lionis C. Maisch B. Mayosi B. Pavie A. Ristic A.D. Sabaté Tenas M. Seferovic P. Swedberg K. Tomkowski W. ESC Scientific Document Group 2015 ESC Guidelines for the diagnosis and management of pericardial diseases: The Task Force for the Diagnosis and Management of Pericardial Diseases of the European Society of Cardiology (ESC)Endorsed by: The European Association for Cardio-Thoracic Surgery (EACTS) Eur Heart J. 36 42 2015 2921 2964 10.1093/eurheartj/ehv318 (Epub 2015 Aug 29. PMID: 26320112; PMCID: PMC7539677) 26320112
9 Nalbandian A. Sehgal K. Gupta A. Post-acute COVID-19 syndrome Nat. Med. 2021 10.1038/s41591-021-01283-z. 2021/03/22
10 Korompoki E. Gavriatopoulou M. Hicklen R.S. Ntanasis-Stathopoulos I. Kastritis E. Fotiou D. Stamatelopoulos K. Terpos E. Kotanidou A. Hagberg C.A. Dimopoulos M.A. Kontoyiannis D.P. Epidemiology and organ specific sequelae of post-acute COVID19: A narrative review J Infect. 83 1 2021 1 16 10.1016/j.jinf.2021.05.004 (Epub 2021 May 14. PMID: 33992686; PMCID: PMC8118709) 33992686
11 Huang C. Huang L. Wang Y. Li X. Ren L. Gu X. Kang L. Guo L. Liu M. Zhou X. Luo J. Huang Z. Tu S. Zhao Y. Chen L. Xu D. Li Y. Li C. Peng L. Li Y. Xie W. Cui D. Shang L. Fan G. Xu J. Wang G. Wang Y. Zhong J. Wang C. Wang J. Zhang D. Cao B. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study Lancet 397 10270 2021 220 232 10.1016/S0140-6736(20)32656-8 (Epub 2021 Jan 8. PMID: 33428867; PMCID: PMC7833295) 33428867
12 Dhakal B.P. Sweitzer N.K. Indik J.H. Acharya D. William P. SARS-CoV-2 infection and cardiovascular disease: COVID-19 heart Heart Lung Circ. 29 7 2020 973 987 10.1016/j.hlc.2020.05.101 32601020
13 Carubbi F. Alunno A. Leone S. Di Gregorio N. Mancini B. Viscido A. Del Pinto R. Cicogna S. Grassi D. Ferri C. Pericarditis after SARS-CoV-2 infection: another pebble in the mosaic of long COVID? Viruses. 13 10 2021 1997 10.3390/v13101997. PMID: 34696427; PMCID: PMC8540566 34696427
14 Proal A.D. VanElzakker M.B. Long COVID or post-acute sequelae of COVID-19 (PASC): an overview of biological factors that may contribute to persistent symptoms Front. Microbiol. 23 12 2021 698169 10.3389/fmicb.2021.698169. PMID: 34248921; PMCID: PMC8260991
15 Dixit N.M. Churchill A. Nsair A. Hsu J.J. Post-acute COVID-19 syndrome and the cardiovascular system: what is known? Am Heart J Plus. 5 2021 100025 10.1016/j.ahjo.2021.100025 (Epub 2021 Jun 24. PMID: 34192289; PMCID: PMC8223036)
16 Chiabrando J.G. Bonaventura A. Vecchié A. Wohlford G.F. Mauro A.G. Jordan J.H. Grizzard J.D. Montecucco F. Berrocal D.H. Brucato A. Imazio M. Abbate A. Management of Acute and Recurrent Pericarditis: JACC state-of-the-art review J. Am. Coll. Cardiol. 75 1 2020 76 92 10.1016/j.jacc.2019.11.021 (PMID: 31918837) 31918837
17 Furqan M.M. Verma B.R. Cremer P.C. Imazio M. Klein A.L. Pericardial diseases in COVID19: a contemporary review Curr. Cardiol. Rep. 23 7 2021 90 10.1007/s11886-021-01519-x (PMID: 34081219; PMCID: PMC8173318) 34081219
18 Diaz-Arocutipa C. Saucedo-Chinchay J. Imazio M. Pericarditis in patients with COVID-19: a systematic review J. Cardiovasc. Med. (Hagerstown) 22 9 2021 693 700 10.2459/JCM.0000000000001202 (PMID: 33927144) 33927144
19 Soewono K.Y. Raney K.C. 3rd Sidhu M.S. Pericarditis with pericardial effusion as a delayed complication of COVID-19 Proc (Bayl Univ Med Cent). 34 5 2021 629 630 10.1080/08998280.2021.1918975. PMID: 34456496; PMCID: PMC8366921 34456496
20 Khasnavis S. Habib M. Kaawar F. Lee S. Capo A. Atoot A. New perspectives on long COVID syndrome: the development of unusually delayed and recurring pericarditis after a primary SARS-CoV-2 infection Cureus 14 6 2022 e25559 10.7759/cureus.25559. PMID: 35784959; PMCID: PMC9247740
21 Ingul C.B. Grimsmo J. Mecinaj A. Trebinjac D. Berger Nossen M. Andrup S. Grenne B. Dalen H. Einvik G. Stavem K. Follestad T. Josefsen T. Omland T. Jensen T. Cardiac dysfunction and arrhythmias 3 months after hospitalization for COVID-19 J Am Heart Assoc. 11 3 2022 e023473 10.1161/JAHA.121.023473 (Epub 2022 Jan 20. PMID: 35048715; PMCID: PMC9238505) 35048715
22 Bisaccia G. Ricci F. Recce V. Serio A. Iannetti G. Chahal A.A. Ståhlberg M. Khanji M.Y. Fedorowski A. Gallina S. Post-acute sequelae of COVID-19 and cardiovascular autonomic dysfunction: what do we know? J Cardiovasc Dev Dis. 8 11 2021 156 10.3390/jcdd8110156 (PMID: 34821709; PMCID: PMC8621226) 34821709
23 Visco V. Vitale C. Rispoli A. Izzo C. Virtuoso N. Ferruzzi G.J. Santopietro M. Melfi A. Rusciano M.R. Maglio A. Di Pietro P. Carrizzo A. Galasso G. Vatrella A. Vecchione C. Ciccarelli M. Post-COVID-19 syndrome: involvement and interactions between respiratory, cardiovascular and nervous systems J. Clin. Med. 11 3 2022 524 10.3390/jcm11030524 (PMID: 35159974; PMCID: PMC8836767) 35159974
24 Puntmann V.O. Carerj M.L. Wieters I. Fahim M. Arendt C. Hoffmann J. Shchendrygina A. Escher F. Vasa-Nicotera M. Zeiher A.M. Vehreschild M. Nagel E. Outcomes of cardiovascular magnetic resonance imaging in patients recently recovered from coronavirus disease 2019 (COVID-19) JAMA Cardiol. 5 11 2020 1265 1273 10.1001/jamacardio.2020.3557 32730619
25 Dini F.L. Franzoni F. Scarfò G. Pugliese N.R. Imazio M. Acute pericarditis in patients receiving coronavirus disease 2019 vaccines: a case series from the community J. Cardiovasc. Med. (Hagerstown) 23 8 2022 551 558 10.2459/JCM.0000000000001342 (PMID: 35904995) 35904995
26 Silva Andrade B. Siqueira S. de Assis Soares W.R. de Souza Rangel F. Santos N.O. Dos Santos Freitas A. Ribeiro da Silveira P. Tiwari S. Alzahrani K.J. Góes-Neto A. Azevedo V. Ghosh P. Barh D. Long-COVID and post-COVID health complications: an up-to-date review on clinical conditions and their possible molecular mechanisms Viruses 13 4 2021 700 10.3390/v13040700 (PMID: 33919537; PMCID: PMC8072585) 33919537
27 Vabret N. Britton G.J. Gruber C. Hegde S. Kim J. Kuksin M. Levantovsky R. Malle L. Moreira A. Park M.D. Pia L. Risson E. Saffern M. Salomé B. Esai Selvan M. Spindler M.P. Tan J. van der Heide V. Gregory J.K. Alexandropoulos K. Bhardwaj N. Brown B.D. Greenbaum B. Gümüş Z.H. Homann D. Horowitz A. Kamphorst A.O. Curotto de Lafaille M.A. Mehandru S. Merad M. Samstein R.M. Sinai immunology review project. Immunology of COVID-19: current state of the science Immunity 52 6 2020 910 941 10.1016/j.immuni.2020.05.002 (Epub 2020 May 6. PMID: 32505227; PMCID: PMC7200337) 32505227
28 Sokolowska M. Lukasik Z.M. Agache I. Akdis C.A. Akdis D. Akdis M. Barcik W. Brough H.A. Eiwegger T. Eljaszewicz A. Eyerich S. Feleszko W. Gomez-Casado C. Hoffmann-Sommergruber K. Janda J. Jiménez-Saiz R. Jutel M. Knol E.F. Kortekaas Krohn I. Kothari A. Makowska J. Moniuszko M. Morita H. O'Mahony L. Nadeau K. Ozdemir C. Pali-Schöll I. Palomares O. Papaleo F. Prunicki M. Schmidt-Weber C.B. Sediva A. Schwarze J. Shamji M.H. Tramper-Stranders G.A. van de Veen W. Untersmayr E. Immunology of COVID-19: mechanisms, clinical outcome, diagnostics, and perspectives-a report of the European academy of allergy and clinical immunology (EAACI) Allergy. 75 10 2020 2445 2476 10.1111/all.14462 (PMID: 32584441; PMCID: PMC7361752) 32584441
29 Raman B. Bluemke D.A. Lüscher T.F. Neubauer S. Long COVID: post-acute sequelae of COVID-19 with a cardiovascular focus Eur. Heart J. 43 11 2022 1157 1172 10.1093/eurheartj/ehac031 (PMID: 35176758; PMCID: PMC8903393) 35176758
30 Tuvali O. Tshori S. Derazne E. Hannuna R.R. Afek A. Haberman D. Sella G. George J. The incidence of myocarditis and pericarditis in post COVID-19 unvaccinated patients-a large population-based study J. Clin. Med. 11 8 2022 2219 10.3390/jcm11082219 (PMID: 35456309; PMCID: PMC9025013) 35456309
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Pharmacol Res
Pharmacol Res
Pharmacological Research
1043-6618
1096-1186
The Authors. Published by Elsevier Ltd.
S1043-6618(22)00547-3
10.1016/j.phrs.2022.106601
106601
Article
The elusive role of proton pump inhibitors in COVID-19: can plasma Chromogranin A levels hold the key?
Sciorati Clara a1⁎
De Lorenzo Rebecca ab1
Lorè Nicola I. ac
Tresoldi Cristina d
Cirillo Daniela M. ac
Ciceri Fabio bd
Corti Angelo be
Manfredi Angelo A. ab
Rovere-Querini Patrizia ab
a Division of Immunology, Transplantation & Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
b Vita-Salute San Raffaele University, Milan, Italy
c Emerging Bacterial Pathogens Unit, IRCCS San Raffaele Scientific Institute
d Hematology & Bone Marrow Transplant, IRCCS San Raffaele Scientific Institute
e Tumor Biology & Vascular Targeting Unit, Division of Experimental Oncology, IRCCS San Raffaele Scientific Institute
⁎ Correspondence to: IRCCS San Raffaele Scientific Institute, via Olgettina 58, DIBIT1, 20132 Milano, Italy,
1 These authors equally contributed
10 12 2022
10 12 2022
10660125 11 2022
6 12 2022
6 12 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.
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pmcTo the Editor:
The influence of routinely used chronic medications during Coronavirus Disease 2019 (COVID-19) is often controversial. This is the case of the role of proton pump inhibitors (PPI) in influencing susceptibility to severe COVID-19 and risk of fatal outcome 1, 2. Here we investigated whether the measurement of chromogranin A (CgA) plasma levels might influence the clinical outcome and unveil PPI impact on disease outcome.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a highly contagious disease, which was responsible for more than 6 million deaths worldwide since 2019. A titanic effort has been made to identify the best therapeutic approach to treat COVID-19 patients. Over the past two years, scientists identified several risk factors and biochemical indicators to predict clinical outcome or mortality in acute COVID-19 [3]. Age, sex and pre-existing comorbidities (i.e. arterial hypertension, chronic kidney disease, diabetes mellitus, cardiovascular disease, active neoplasia, chronic obstructive pulmonary disease, etc.) increased morbidity and mortality. Risk factors include the medication commonly used before the infection.
PPI are widely used to treat diseases of the upper digestive apparatus such as gastroesophageal reflux disease or peptic ulcer. They are well tolerated and often used long-term and at high doses. It has been suggested that PPI users have higher risk of acquiring SARS-CoV-2 infection and/or experiencing severe outcomes in COVID-19 disease [1], but no clear evidence has been obtained yet [2]. The frequent co-morbidities (which themselves influence the outcome of COVID-19) among PPI users might act as confounder, hampering the generalizability and reproducibility of results [4].
It is well known that PPI treatment increases CgA secretion, at least in a subpopulation of patients [5]. CgA and its fragments, secreted mainly by endocrine/neuroendocrine cells, regulate vascular homeostasis, cardiac function and immune responses [6]. We have recently demonstrated that CgA levels increased in the plasma of patients with COVID-19 at the time of admission at the Emergency Department, in particular in those who died from the disease. CgA plasma levels emerged as early independent predictors of mortality [6], pointing to a role of the early generation of CgA in the response against SARS-CoV-2.
In the present study, we compared plasma CgA levels in PPI users and non-users and investigated whether CgA differentially predicts mortality depending on PPI status and plasma CgA may predict fatal outcome in patients on PPI treatment. The study was approved by the Hospital Ethics Committee (protocol no. 34/int/2020) and registered on ClinicalTrials.gov (NCT04318366).
We analyzed plasma level of CgA in 269 patients admitted at the Emergency Department of San Raffaele University Hospital, Milan, from March 18, 2020 to May 5, 2020. Among them, 111 were receiving chronic PPI therapy at admission. Inclusion criteria were: age ≥18 years, positive nasopharyngeal swab for SARS-CoV-2 (confirmed by real-time reverse-transcriptase polymerase chain reaction), and clinical or radiological signs or symptoms of COVID-19. Patients were followed up until death or hospital discharge. Patient and disease characteristics were prospectively collected following blood withdrawal, and reported in Table 1 (supporting information). Outcomes of PPI users and non-users are depicted in Fig. 1, panel A.Fig. 1 (A) Clinical outcomes of the cohort. The flowchart shows the number of patients and the percentage of the original population. (B) CgA levels in PPI users and non-users (left) and in alive or dead patients (right); Data are in log scale. ****= p <0.0001, ** =p<0.01. (C) Multivariate Cox regression analysis (left) and Kaplan-Meier survival curves (right) in PPI users (Log rank test, p=0.013).
Fig. 1
No significant difference was found between PPI users and non-users in the hospitalization rate (87.4% vs. 79.1% respectively, p=0.11) or length of hospital stay (median [interquartile range, IQR] 10.5 [5-28] days vs. 12 [4-22] days, p=0.96). PPI users more frequently had at least one comorbidity (84.6% in PPI users vs. 46% in non-users, p<0.0001) and were older (median [IQR] age 70 [61.51-77.4] years in PPI users vs. 60 [49.2-71.2] years in non-users, p<0.0001). The proportion of PPI users who died was significantly higher than in non-users (Fig. 1 and in Table 1, supporting information).
Plasma CgA levels were measured in blood specimens collected at hospital admission. As expected, we found that CgA levels were higher in PPI users than in non-users (2.5 [1.4-5.9] nM vs. 0.9 [0.4-2.4], respectively), although some patients on PPI therapy showed plasma CgA levels similar to patients not using PPI (Fig. 1, B left panel). In both PPI users and non-users, CgA levels were significantly higher in patients that did not survive (Fig. 1, B right panel).
It is known that CgA plasma levels at the admission correlated with age, degree of hypoxia and plasma creatine phosphokinase [6]. CgA plasma levels correlated with age in both PPI users and not users (R =0.396 and p <0.0001, R= 0.446 and p value <0.0001, respectively, supporting information, Fig. 1). Elderly is often link to multidrug therapy and PPI are usually prescribed. Multivariate Cox regression analyses performed specifically in PPI users (thus excluding non-users) revealed that CgA plasma levels still predicted mortality when adjusting for age, number of comorbidities, degree of respiratory dysfunction (PaO2/FiO2), systemic inflammation as reflected by C-reactive protein (CRP), levels at admission and time from symptom onset to sampling (Fig. 1, panel C). Kaplan Meier survival analysis confirmed that among PPI users, those with levels of CgA above the median value of 2.49 nM at admission had a higher risk of mortality compared to those with plasma levels of CgA below the median (log rank test, p=0.013, Fig. 1, panel C). This result demonstrates that, independent of PPI use, CgA plasma levels predicted fatal outcome.
In addition to the frequent pre-infection use of PPI in the general population, COVID-19 patients may specifically require PPIs in certain conditions. Administration of high doses of non-steroidal anti-inflammatory drugs or corticosteroids to control fever and inflammation could damage the gastro-intestinal tract. Moreover, the use of low molecular weight heparin to control thromboembolic complications of COVID-19 might increase the risk of bleeding in pre-existing mucosal lesions. These conditions frequently prompt physicians to add PPIs to therapy for COVID-19. The cellular source of circulating CgA remains to be defined. Secretion of CgA could be increased in endocrine/neuroendocrine cells by stress-induced activation of sympathetic-adrenal-medullary system during the COVID-19 disease. Dispersed neuroendocrine cells that secrete CgA are also physiologically present in the bronchopulmonary tract. Hyperplasia of these cells could be induced in COVID-19-patients due to hypoxia status similarly to chronic lung diseases such as interstitial pneumonia and obstructive lung disease [7]. CgA is also expressed by myocardial and immune cells [6] and could be upregulated and secreted in patients due to the spread inflammatory state contributing to increase of plasma levels. Although the pathogenic role of CgA in COVID-19 have not yet been elucidated, we provide evidence that CgA plasma levels are a key biomarker of the risk of COVID-19 progression and that this effect is not influenced by PPI therapy. Therefore, measuring CgA plasma levels in PPI users (and non-users) at hospital admission might be clinically relevant to predict the risk of adverse outcome.
Declaration of Competing Interestt
All authors disclose any financial and personal relationships with other people or organizations that could inappropriately influence (bias) their work
Appendix A Supplementary material
Supplementary material
Data availability
Data will be made available on request.
Acknowledgement
This work was supported by a COVID-19 program project grant from the IRCCS San Raffaele Hospital and the grant COVID-2020-12371617 from the Italian Ministero della Salute.
Appendix A Supplementary data associated with this article can be found in the online version at doi:10.1016/j.phrs.2022.106601.
==== Refs
References
1 Lee S.W. Severe clinical outcomes of COVID-19 associated with proton pump inhibitors: a nationwide cohort study with propensity score matching Gut 70 1 2021 76 84 32732368
2 Israelsen S.B. Proton Pump Inhibitor Use Is Not Strongly Associated With SARS-CoV-2 Related Outcomes: A Nationwide Study and Meta-analysis Clinical gastroenterology and hepatology: the official clinical practice journal of the American Gastroenterological Association 19 9 2021 1845 1854 e6 33989790
3 Su M. Xu S. Weng J. A bibliometric study of COVID-19 research in Web of Science Pharmacological research 169 2021 105664
4 Andreotti F. Methodological education in response to the quality of COVID-19 publications Pharmacological research 164 2021 105381
5 Mosli H.H. Effect of short-term proton pump inhibitor treatment and its discontinuation on chromogranin A in healthy subjects The Journal of clinical endocrinology and metabolism 97 9 2012 E1731 E1735 22723311
6 De Lorenzo R. Chromogranin A plasma levels predict mortality in COVID-19 PloS one 17 4 2022 e0267235
7 Maki Y. A case of multiple lung carcinoid tumors localized in the right lower lobe Respiratory medicine case reports 38 2022 101679
| 36513209 | PMC9734069 | NO-CC CODE | 2022-12-16 23:19:49 | no | Pharmacol Res. 2022 Dec 10;:106601 | utf-8 | Pharmacol Res | 2,022 | 10.1016/j.phrs.2022.106601 | oa_other |
==== Front
Soc Sci Med
Soc Sci Med
Social Science & Medicine (1982)
0277-9536
1873-5347
The Authors. Published by Elsevier Ltd.
S0277-9536(22)00915-7
10.1016/j.socscimed.2022.115609
115609
Article
Risk perception, adaptation, and resilience during the COVID-19 pandemic in Southeast Alaska Natives
van Doren Taylor P. a∗
Zajdman Deborah b
Brown Ryan A. b
Gandhi Priya b
Heintz Ron a
Busch Lisa a
Simmons Callie a
Paddock Raymond c
a Sitka Sound Science Center, 834 Lincoln Stree, Sitka, AK, 99835, USA
b RAND Corporation, 1776 Main Street, Santa Monica, CA, 90401, USA
c Central Council of Tlingit & Haida Indian Tribes, PO Box 25500, Juneau, AK, 99802, USA
∗ Corresponding author.
10 12 2022
10 12 2022
1156093 8 2022
3 12 2022
9 12 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.
Indigenous communities worldwide are at higher risk of negative pandemic outcomes, and communities Indigenous to the Arctic are disproportionately affected compared to national majorities. Despite this, their experiences have scarcely been investigated qualitatively and from their own perspectives. We collected and analyzed 22 structured interviews in three Southeast Alaska island communities (Sitka, Hoonah, and Kake) to learn about their perceptions of and experiences with the COVID-19 pandemic. Interviews were analyzed with thematic qualitative analysis in Dedoose. Four primary categories were identified within which to discuss risk and resilience in Southeast Alaska: (1) risk perception, (2) socioeconomic impacts, (3) reactions to public health guidelines, and (4) coping. Primary findings indicate that Southeast Alaska Native communities display considerable resilience and adaptive flexibility despite the significant adversity imposed by the COVID-19 pandemic. Southeast Alaska Native people use historical and traditional knowledge to culturally ground adaptive behaviors to cope with the threat of COVID-19. Interviewees expressed that adaptive, community-centered, and non-individualistic behaviors strongly tied to Native culture minimized the negative epidemiological impacts of the pandemic. Future research can more deeply explore the root causes of the need for adaptiveness and resilience, such as histories of colonialism and marginalization, to emergency situations in Indigenous communities.
Keywords
Alaska native people
COVID-19 pandemic
Rural health
Resilience
Thematic analyses
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pmc1 Background
This study highlights the ways rural Southeast Alaska Native people drew on traditional knowledge and community cohesion in their responses to the COVID-19 pandemic in 2020–21. Such insights emphasize the existing strengths of Alaska Native communities that tend to go unnoticed by research that overlooks and/or homogenize Indigenous experiences, and these insights can help inform communities on how to best prepare for future emergent pandemic stressors. Global Indigenous community disaster responses emphasize the value of Indigenous ecological and cultural knowledge while prioritizing sovereignty to sustain capacity building within communities’ responses (Ellemore, 2005; Howitt et al., 2012; Kelmen et al., 2012). Alaska Native communities in the U.S. share various commonalities in both culture and historical experiences with Indigenous communities internationally, such as tribal sovereignty, a history of colonial hegemony, generational ties to environment, and analogous cultural norms and values. However, their specific histories have led to significant cultural differences as well, making it inappropriate to homogenize individual Native experiences—and those of their hundreds of individual communities—with those of other Indigenous identities worldwide.
Epidemiological and demographic studies of Indigenous communities’ pandemic experiences show that they suffered significantly worse outcomes, particularly during the 1918 influenza pandemic (Mamelund, 2003; Mamelund et al., 2013; Rice, 2018) and the 2009 H1N1 influenza pandemic (La Ruche et al., 2009). Alaska and Labrador experienced much higher mortality than the commonly cited percentage of 2.5–5%; Brevig Mission in Alaska experienced upwards of 90% mortality, Okak, Labrador experienced 79% mortality and abandonment of the settlement (Mamelund et al., 2013), while the Māori of New Zealand and Sami of northern Scandinavia were at 4–7 times the risk of the non-Indigenous populations (Mamelund, 2003; Rice, 2018). In 2009, Native American people and Indigenous groups of Oceania and the Pacific Islands had significantly higher mortality than non-Indigenous populations, which has been attributed to underlying health conditions (Hennessy et al., 2014; La Ruche et al., 2009), many of which can be further attributed to compounding effects of colonization (Paradies, 2016). Despite a century of hindsight establishing the foundation of knowledge that Indigenous groups tend to suffer unequal pandemic outcomes, responses of Indigenous communities worldwide from their own perspectives have yet to be extensively elevated. There is a general lack of quantitative epidemiological data on the experiences of specifically Indigenous populations apart from aggregate national populations during pandemics, and this dearth can lead to volatile and misleading conclusions about variation within and between populations (Alves et al., 2022). Others have pointed out that high-level population research conflates Indigenous data with those of the larger population (Chatwood et al., 2012), which leads to homogenization of racial, ethnic, nationality, and linguistic categories (Dimka et al., 2022).
Despite these observations using quantitative data for demographic and/or epidemiological purposes, research has emphasized the considerable strength of Alaska Native communities and the ability to adapt to myriad solutions, of which many—if not all—are in response to colonial realities, such as: navigating contradictory values while “walking in two worlds” between Native homes and Western universities (Wexler and Burke, 2011), Native healing methods for post-trauma recovery (Bassett et al., 2012), continually reaffirming cultural traditions, well-being, health, and education through wood carving (Johnson et al., 2021), and the ability to navigate gender roles in response to rapid social change (Graves, 2004). Although this body of literature exists, a recent systematic review found there is very little research that focuses on resilience to guide public health promotion, despite the strengths of Indigenous communities to contribute to those public health programs (Teufel-Shone et al., 2018).
To better understand Indigenous perception, coping, and resilience to an emergent pandemic event, especially behaviors that are driven by community-based guidance and social norms, researchers must engage with Indigenous perspectives directly. The ways in which Indigenous groups represent themselves dramatically differ from the ways non-Indigenous people represent them, and this has been shown to misconstrue the impacts of the COVID-19 pandemic on Native people (Azocar et al., 2021). Qualitative, emic, and ethnographic approaches that are driven by knowledge co-production can provide essential nuance to the comprehensive understanding of how acute respiratory pandemics impact Indigenous people. In this paper, we present results of interviews with Southeast Alaska Native people to illuminate their cultural strengths that helped mitigate the threat of the COVID-19 pandemic.
1.1 Risk and resilience in rural communities
“Risk” is a concept that traverses disciplinary boundaries and carries disparate definitions across them. Risk can be a statistical concept that describes the probability that an event will occur (Benichou, 2007) or a social categorization that describes how some groups are more likely to experience a negative outcome due to ambient social conditions and biosocial histories (Panter-Brick, 2014). Whether or not the term is applied as an epidemiological or social paradigm, Panter-Brick (2014) argues that the concept of risk is a useful one, in that understanding how populations perceive risk gives crucial insight into their cultural values and how biology, culture, and behavior interact within distinct contexts. Risk is most clearly defined as a product of hazard (the probability of an event occurring, in this case COVID), exposure (the probability that a person will be exposed to the hazard), and vulnerability (the likelihood of the hazard affecting the individual or community in an adverse way). In this way, risk and risk perception are socially, historically, and culturally grounded (Boholm, 1996). For example, the diverse Alaska Native experiences with the pandemic of 1918 and the dependence of culture on oral transmission and Elder knowledge are likely to affect both real and perceived vulnerability to COVID. In turn, different risk perceptions can influence decision making behavior and the ways people perceive relative severity of competing or compounding adversity. In the pandemic context, current population health, the ability to access medical resources, community cohesion, and political division may all contribute to the overall perception of how “risky” the pandemic is.
“Resilience” refers to the capacity to cope and adapt in the face of various stressors or adversity (Norris et al., 2008; Peters, 2020), and emphasizes the adaptability and processes of individuals and systems that promote recovery (Norris et al., 2008). Models of resilience have been applied to different natural disaster and emergency settings as vital coping mechanisms to sustain communities (Cutter et al., 2008; Gunderson, 2010). Resilience can be traced historically as central to the survival of Indigenous communities (Teufel-Shone et al., 2018; Wexler, 2014). Community resilience is largely rooted in the capacities and resources within social, political, and economic structures, and it is more flexible and adaptive when it is culturally grounded (Norris et al., 2008; Wexler et al., 2014). Cultural resilience is embedded in various norms, family structures, and peer relationships (Clauss-Ehlers, 2008). Elements of Alaska Native culture such as spirituality, oral traditions, healing, reciprocity, and collective responsibility may be leveraged as advantageous coping mechanisms (Bassett et al., 2012; Clauss-Ehlers, 2008). These Alaska Native cultural characteristics help illustrate the fact that resilience is strongly tied to culture and place; therefore, observations of resilience in one population are not necessarily transferable to others. This is particularly true for Western models of response and recovery that are distinct from those necessary in remote Indigenous communities.
It is critical to understand the concepts of risk perception and resilience in communities in which health and socioeconomic inequalities exist, yet rarely receive attention and resources. Circumpolar communities’ health disparities tend to be overlooked or homogenized with those of the countries to which they nominally belong, which are overwhelmingly high-income nations (the U.S., Canada, Nordic countries, Kalaallit Nunaat [Greenland], Faroe Islands, and Russia) (Chatwood et al., 2012; Krümmel, 2009). These health disparities are further exacerbated by the challenges of anthropogenic climate change, which has caused rapid environmental shifts that require similarly rapid cultural adaptations that affect rural Indigenous Arctic populations (Ford et al., 2014; Healey et al., 2011). Rural communities face various existing health disparities and challenges in access to healthcare that may increase their risk of COVID-19 compared to urban areas (Peters, 2020; Summers-Gabr, 2020). The COVID-19 pandemic may serve to exacerbate various health, social, and economic vulnerabilities that disproportionately affect rural communities (Mueller et al., 2021; Peters, 2020; Summers-Gabr, 2020).
Past studies have examined COVID-19 risk, safety behaviors, and community responses largely in urban areas (Schuchat & CDC COVID-19 Response Team, 2020; Sharifi and Khavarian-Garmsir, 2020; van Dorn et al., 2020). Smaller, more rural communities face different COVID-19 challenges due to relative resource scarcity, different scales of governance and infrastructure, and different scales of social networks at which social dynamics like status, connectedness, reputation, and stigma operate (Blumenthal et al., 2020; Melvin et al., 2020; Monteith et al., 2020; Mueller et al., 2021). Existing documentation on the direct impacts of COVID-19 on rural communities is often anecdotal (Godfrey, 2021; Khazan, 2021; Kovich, 2020), and a comprehensive exploration of rural community dynamics and perspectives has yet to be explored.
Alaska Native individuals often face socioeconomic, geographic, and environmental barriers that may impede response efforts and require specific considerations that acknowledge these constraints (Allhoff and Goleman, 2020; Safford et al., 2011). These individuals are often studied as a homogenous population, without regard to the differences in backgrounds, interests, and needs among them and other Indigenous populations (Jaeger, 2004). Systematic investigations of how rural communities, such as those in Southeast Alaska, experience the COVID-19 pandemic can provide robust insights into the unique challenges they face. Further, the ways rural Alaska Native communities seek to solve and overcome those challenges illustrate their specific modes of resilience.
This study explores concerns, contexts, and risk facing Southeast Alaska Native communities given impending vulnerabilities such as age, comorbidities, and limitations to emergency medical care. Lingít Aaní, or Southeast Alaska, is the ancestral home of the Tlingit, Haida, and Tsimshian peoples; therefore, this study is primarily within the context of their perspectives. We recognize, however, that there may be Alaska Native individuals represented here with other ancestral backgrounds and experiences, as there are eight major Alaska Native cultural areas (aside from Tlingit, Haida, and Tsimshain, there are also: Athabascan; Siberian Yup'ik; Yup'ik, Cup'ik, and Yupiak; Iñupiaq; Alutiiq and Sugpiaq; Unangan; and Eyak), and at least 229 tribes that are complex and distinct in their social relationships and kinship with thousands of years of history tied to the land on which they live (Roderick, 2010; Williams, 2009). We examine socioeconomic and subsistence challenges, responses, personal and community health risk, and individual- and community-level behaviors in Alaska Native people across three island communities in response to the COVID-19 pandemic. This research will contribute to the knowledge of how people in remote rural communities, specifically those of Southeast Alaska, modify behaviors and exhibit resilience that is culturally grounded in the face of an acute infectious threat. A broader contribution of this research will be to elevate the importance of focusing in on the idiosyncratic knowledge, responses, and experiences of specific Indigenous populations to highlight their strengths rather than continue to make broad strokes generalizations about how Indigenous populations experience pandemics worldwide.
2 Methods
2.1 Overview & setting
The Sitka Sound Science Center (SSSC) partnered with the Central Council of Tlingit and Haida Indian Tribes of Alaska (CCTHITA) and the RAND Corporation to collect interview data surrounding COVID-19 perceptions in Southeast Alaska. SSSC is a community-based non-profit that is engaged in ecological education and research in Southeast Alaska, CCTHITA is a tribal government, and the RAND Corporation is a research non-profit heavily engaged in policy research and implementation. This paper is part of a larger research effort directed by this partnership to better understand the impacts of COVID-19 in Southeast Alaska; the interview data collected, analyzed, and reported here make up only one piece of the research program that will be discussed in future papers.
The first case of COVID-19 in Alaska was identified in Ketchikan, a town in Southeast Alaska, but the epidemic curve was relatively delayed compared to the continental U.S. and other Arctic nations (Alaska COVID-19 Information Hub; O'Malley, 2020; Petrov et al., 2020, 2021). The interviews collected in this study were performed in January through July of 2021, which was a period between waves of COVID-19 in Southeast Alaska; in July, Sitka experienced increasing cases per day without increasing hospitalizations or deaths (Alaska COVID-19 Information Hub). COVID-19 vaccines arrived in Juneau in mid-December 2020 and were quickly distributed to larger communities within Southeast Alaska (e.g., Sitka), who then helped with reaching smaller and more isolated communities (McKinstry et al., 2020). The state of Alaska, overall, has been praised in its ability to quickly vaccinate a large proportion of its population, especially Alaska Native individuals (Press, 2021), as Alaska had the highest per-capita vaccine rate in the U.S. by late January 2021 (Berman, 2021). Table 1 provides a summary of the new cases, hospitalizations, and deaths during this period, as well as a comparison to the total number of each measure for the entirety of the period for which data have been collected by the Alaska COVID-19 Information Hub (April 2020-present).Table 1 The number of cases, hospitalizations, and deaths for the duration of the pandemic and for the period over which interview data were collected for each census area in this study. The percentage of cases, hospitalizations, and deaths that occurred within the study period are also provided.
Table 1 April 2020 – present January–July 2021
Census Area Cases Hosp. Deaths Cases (%) Hosp. (%) Deaths (%)
Sitka City & Borough 3147 30 9 517 (16) 12 (40) 4 (44)
Hoonah-Angoon Census Area 1728 22 10 153 (9) 8 (36) 5 (50)
Prince of Wales-Hyder Census Area 872 13 5 27 (3) 3 (23) 1 (20)
The region in which the study communities are located extends across 500 miles of coastline in the “panhandle” of Alaska, and includes several small, rural, and isolated island communities across the Alexander Archipelago on Tlingit, Haida, and Tsimshian land (Lingít Aaní) (Fig. 1 ). These communities rely on various traditional practices including some participation in traditional subsistence activities such as hunting (moose, caribou), fishing (salmon, shellfish), and gathering (berries, greens) (Redwood et al., 2008). This connection to the natural and marine environments further enhances the unique mechanisms through which acute and chronic stressors might impact community members and opportunities for equity.Fig. 1 Lingít Aaní (Southeast Alaska) relative to the rest of Alaska and western Canada. The four communities from which interviewees in this study hail and the number of interviews performed in each community are highlighted on the map: Hoonah (purple), Kake (yellow), and Sitka (green). For each locality, the total population and the percent of the total population that self-identified as “American Indian and Alaska Native (AI/AN) alone or in combination) on the 2020 U.S. Census are also reported. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1
2.2 Sample & recruitment
In-person interviews were conducted with 22 Alaska Native individuals across the four communities. One interview included a married couple, but the pair answered questions together and are represented as a single interview. All other interviews were with individuals. Interviews were performed with eleven in Hoonah (Hoonah-Angoon Census Area), five in Sitka (Sitka City and Borough), and six in Kake (Prince of Wales-Hyder Census Area), summarized and identified on the map in Fig. 1. The populations of each borough, with the percentage of residents who identified as “American Indian and Alaska Native (AI/AN) alone or in combination” in the 2020 U.S. Census, are as follows: Sitka: 8458 people (24.4% AI/AN); Hoonah: 850 (46.7% AI/AN); Kake: 496 (44.4% AI/AN) (U.S. Census Bureau, 2020). Sitka, Hoonah, and Kake are all island communities; Sitka is the second largest city in Southeast Alaska after Juneau, and Hoonah and Kake are isolated and can only be accessed by plane or boat dependent on weather. These outlying island communities have seen considerable out-migration in recent decades, resulting in difficulty maintaining health and social services and infrastructure, while Sitka is increasingly gentrified and experience a large influx of tourism every summer (Safford et al., 2011; U.S. Census Bureau, 2010). In the small island communities, local access to food is of high importance since imported foods are limited and expensive; this is likely one of the main reasons why Alaska Native people in these communities are generally more concerned than non-Native people about environmental changes that compromise the natural resources on which they depend (Safford et al., 2011).
The COVID-19 pandemic yielded various restrictions in recruitment and participation, including stay-at-home orders and social distancing guidelines. Smaller, rural Alaska Native villages often operate through face-to-face contact, word-of-mouth, and physical gatherings, creating limitations to the adequate circulation of information about and participation in the study. Given these challenges, identifying participants and gathering interview data relied on a convenience sampling approach. CCTHITA organized interviews via contacts within specific Alaska Native communities (Hoonah, Sitka, and Kake). We recognize the bias introduced by convenience sampling, as the interviews were performed by local community members with other members of the community, and the interviews performed reflect “who knows whom.” This is neither a random sample, nor can it be considered generally reflective of Alaska Native opinions, attitudes, and behaviors. We do, however, consider it a strength that the interviews were conducted by Alaska Native people with their own community members, a fact that draws on the strong rapport already established between interviewer and interviewee.
Individuals were eligible to participate if they were 18 years of age or older and identified as Alaska Native. All interview procedures were approved by CCTHITA, SSSC, and RAND's Human Subjects Protection Committee (Approval #2020–0320). Compensation for participation followed local Tribal protocols and guidelines specific to each village. All methods were developed in consultation with community stakeholders, in which interview protocols stemmed from the immediate concerns of the communities.
2.3 Data collection
CCTHITA representatives conducted structured interviews in English with Alaska Native individuals between January and July 2021. The interview protocol contained open-ended questions examining Alaska Native individuals’ perspectives surrounding traditional knowledge, threat perception, and adaptation in the context of COVID-19. Questions explored traditional, ecological, and historical knowledge, as well as engagement in cultural activities during the pandemic. Threat perception examined individual- and community-level risk perceptions, as well as vaccine trust and willingness to be vaccinated. Adaptation inquired into attitudes surrounding COVID-19 guidelines, trust in institutions, and community unity or division. No demographic or descriptive information is reported on interviewed individuals to avoid identifiability, per the IRB approval. Since these are small and highly connected communities, any descriptive information could easily inadvertently identify them.
2.4 Data analysis
Discussions were held with interviewers periodically during the data collection process to ensure accurate understanding of findings and to assist with further context for data analyses. Interview transcripts were uploaded to Dedoose (2021), a qualitative analysis software program, and were independently coded within Dedoose by two coders [authors’ initials]. The codebook was developed deductively prior to coding, as well as inductively during coding through discussion between coders. After completion of the thematic analyses, the interview material within the codes was reviewed and synthesized for concurring and opposing viewpoints on these topics. In total, 22 interviews were coded and organized into major themes.
Finally, within each of the major themes, we consider differences in responses between Sitka, the larger of the island communities, and Hoonah and Kake, which are small, remote, and have substantially larger Alaska Native populations. We report the number of respondents who addressed each topic presented in the results and provide a summary table that also includes the percent of respondents from each community who addressed each topic. We refrain from performing statistical analyses here given the relatively small sample size, but instead draw attention towards some meaningful patterns observed through this comparison.
3 Results
We identified four major realms through which we can discuss risk, impacts, and resilience as described by the 22 interviewees: (1) risk perception, (2) socioeconomic impacts, (3) reactions to public health guidelines, and (4) coping. These themes are broad, but together provide a foundation of knowledge of how the COVID-19 pandemic affected rural Southeast Alaska Native communities. Each of these themes are discussed in turn. Table 2 provides the summary of the total number of respondents and percent attribution to each community within the major themes.Table 2 Summary of themes and sub-themes presented in results and discussed throughout the text. The number of respondents from each community, as well as the percentage of the total number of respondents from that community, are presented.
Table 2Major themes and topics discussed Respondents by community
Perception of Risk Sitka (%) Hoonah (%) Kake (%)
Heightened concern for own health & vulnerable 4 (80) 7 (64) 5 (83)
Have not heard about 1918 influenza pandemic 2 (40) 5 (45) 1 (17)
Threat of climate change 0 (0) 8 (73) 5 (83)
Socioeconomic, political, & community impacts
Worry about inability to meet essential needs 3 (60) 4 (36) 2 (33)
More trust in local vs. state/federal government 3 (60) 2 (18) 5 (83)
Reacting to public health guidelines
Positive attitudes towards vaccines 4 (80) 8 (73) 6 (100)
Communities felt united 1 (20) 7 (64) 6 (100)
Communities felt divided 2 (40) 2 (18) 0 (0)
Coping
Traditional knowledge essential for coping 5 (100) 11 (100) 6 (100)
Importance of access to subsistence foods 1 (20) 2 (18) 5 (83)
Physical gatherings are essential for coping 4 (80) 5 (45) 6 (100)
3.1 Risk perception
Interviewees generally expressed the feeling that the threat of the COVID-19 pandemic led to prolonged stress. As one person from Sitka reflected:… It was really the first time people became aware of their own mortality, and how fragile, and how precious life is. There’s not a lot of people that had given thought to, ‘well, I could die this year, or in a month or two’ … What have I done with my life?
By the time the interviews in this study were performed (January through July 2021), people had begun to think more critically about how the threat of the pandemic would not only affect themselves, but their communities via risk to Elders. Most (n = 16, 72.7%; four in Sitka, seven in Hoonah, five in Kake) interviewees expressed heightened concern for the health and wellbeing of vulnerable populations such as youth, Elders, unvaccinated, and unhoused individuals, with one respondent from Sitka stating: “If all the Elders die off, literally we're stopped in our tracks.” In fact, interviewees identified Elder loss as a threat to the propagation of traditional knowledge, because this would effectively mean generations of lost knowledge that has not yet been passed to the younger generations. In this way, the threat of losing generations worth of knowledge, impacting how traditional knowledge is shared and experienced long-term, is a substantial ultimate threat to Southeast Alaska Native communities.
One facet of historical knowledge at risk of being lost through Elder loss is that of the last major pandemic: the 1918 influenza pandemic. While some interviewees (n = 8, 36.4%; two from Sitka, five from Hoonah, one from Kake) claim that they have not heard about the 1918 pandemic from Elders, others offered some insights into this event. One interviewee noted that out of 13 children, their great-grandfather was the only one to survive the flu. Another's mother was only three in 1918 but remembers her grandmother burning sulfur to sanitize the house and protect it from the virus. Four interviewees described learning about how travel in Alaska was halted to help try to stop the spread of the pandemic, which worked, but ultimately ended up worsening the existing problems with lack of access to resources on the remote islands.
While there did seem to be a lack of consensus among interviewees about how they learned (or did not learn) about the 1918 influenza pandemic from community Elders, one interviewee from Sitka offered an explanation for why some may not have shared their knowledge: the early 20th century was a time of significant trauma apart from the pandemic, which included scooping of Native children, the persistent risk of deadly tuberculosis infection, war, and colonialism, all of which perpetuate intergenerational trauma. We acknowledge this barrier to sharing historical knowledge; despite this, one interviewee from Kake said people are still learning about the 1918 flu as a “motivator to take this one seriously, as a reminder of our communities’ resiliency.”
Acknowledgement of contemporary threats also help frame the ways Southeast Alaska Native people perceive the threat of the COVID-19 pandemic. Over half of the interviewees (n = 13, 59.1%; zero from Sitka, eight from Hoonah, five from Kake) mentioned the pressing threat of climate change to their communities. As their histories and cultures are intimately tied to the land on which they live and its natural resources, the threat of environmental degradation was identified as one of the primary adversaries to their current livelihoods, not necessarily the COVID-19 pandemic. One interviewee from Kake listed climate change, collapse of wild salmon runs, ocean acidification, loss of access to traditional foods and lands, dispossession of lands, continued clear cutting, and mining as threats that are regularly on their mind more than COVID-19.
3.2 Socioeconomic & political impacts
There was significant concern and fear of economic impacts throughout the Southeast Alaskan communities. Economic impacts include mass unemployment, in which COVID-19 had disproportionately affected certain industries such as tourism and local small businesses. One interviewee from Sitka described impacts on businesses and access to goods and services, especially those that had been interrupted from the lack of tourism:I’m really concerned for our community in general, definitely the economic impact it had. We really rely on small businesses in our town, and it’s just so sad to see some already closed down, or people having to rework what their plan was. And I don’t ever want to turn into a ghost town where we have to find other sources of revenue.
Further, multiple interviewees (n = 9, 40.9%; three in Sitka, four in Hoonah, two in Kake) described the concern for an inability to meet essential needs. One in Sitka described impending fear with regards to affording necessities:That’s what I’m worried about in the next five years, how we’re going to feel it financially. Especially in the rising cost of utilities: heat, electricity … The minute the Governor said there was no state of emergency, and he took it off, all these people that had passed due utilities had to pay it within a day or come up with a payment plan. And mind you, people couldn’t pay that for a whole year. So, all of a sudden you have this $2,000 [payment] or else you have no electricity.
Almost half of the interviewees (n = 10, 45.5%; three in Sitka, two in Hoonah, five in Kake) expressed the inaccessibility of the state government and much deeper trust in local governments, highlighting the benefit of being able to see the inner workings of the decision-making process play out, knowing decision-makers personally, and understanding why some decisions are made regardless of differences in political opinion, which we will discuss further below. One interviewee from Kake mentioned how some state authorities attempted to ensure the Tribes were “on the ball” with mitigating risk, though the governor was trying to take credit for the success. Another interviewee from Kake also expressed frustration for the governor trying to take credit for vaccine rollout in the Native communities, while continuing to maintain “a nonchalant perspective of rural communities.”
Finally, one of the major community impacts were the restrictions on transportation, which resulted in severe limitations to vital resources and travel. Ferry services and airplanes were seen as potential vulnerabilities in COVID-19 transmission. One participant from Sitka described the pivotal role of transportation and capacity to meet essential community needs given the geographic isolation of the Southeast Alaskan islands:Transportation has always been a huge issue for Sitka—our barge service, our ferry service, and airport are a lifeline for the town. From outside medical care to provisions and things coming in, that system has been hit really hard. I’m curious to see how much of it will survive. Will the barges keep coming every week? Can they afford to? And the airlines keep flying like they do; can they afford to?
3.3 Reacting to public health guidelines
Interviewees described extensive efforts to adapt to COVID-19 guidelines for the sake of the greater community. One participant from Kake emphasized the importance of community-centered behaviors, not just those that benefit the individual: “I think everybody did their part, once they realized personal responsibility … need to socially distance, wear a mask, wash hands, quarantine … I'm proud of how responsible the community has been.” However, there was some variance in attitudes regarding stricter and looser guidelines, leading to some community division stemming from inconsistencies in guidelines and little accountability for those who did not obey. For those who provided detailed answers about how they perceived unity and division in their communities, one person in Sitka, seven in Hoonah, and six in Kake said they were united, while two in Sitka, two in Hoonah, and zero in Kake said there was division. Existing political division exacerbated by opposing COVID-19 perspectives furthered discord within the community, as one Sitka interviewee describes: “I still feel that tension between those who still want to wear masks … more judgements and criticism … It's easy to jump to conclusions, and I'm most worried about that division happening in our community post-COVID.”
Most interviewees (n = 18, 81.8%; four in Sitka, eight in Hoonah, six in Kake) expressed endorsement, trust, and willingness to be vaccinated as an essential means to end the pandemic. Vaccination was described as a pivotal adaptation to returning to normalcy. In Sitka, one participant explained that they were no longer “nervous because people are instantly, like, ‘I'm vaccinated. This is a safe space.’ That makes me feel comfortable … I'm no longer in a bubble.” Another participant in Hoonah echoed this statement, explaining that the response to vaccines was very good in their community, and that when they finally hit around 74% vaccination coverage, they were “pretty close to the mark where they say the community should be safe.”
Further, vaccination was described as a means to sustaining Native culture and values by one Sitka resident:We’re so connected, and it’s hard when it’s taken away. I miss hugs … I can’t go to funerals … when somebody passes away the other Tribe takes care of everything, you put out all your at.óow (Tlingit for “prized possessions”) and be there and hold up your family, your friends, or loved ones and you can’t do that. I see people on the Tlingit & Haida Instagram and Facebook tying the vaccine to the culture: keeping each other safe by getting vaccinated … It’s a personal responsibility to keep our people safe.
The major drivers of these perspectives from interviewees were the willingness—and even enthusiasm—to be vaccinated out of concern for Elders, family, and other community members, referring to the fact that their Native traditions have always shown that looking out for one another is paramount to the survival of the group.
Despite the relative willingness to get the COVID-19 vaccine when it became available, a few interviewees (quotes from these individuals here) expressed distrust, skepticism, and reluctance to be vaccinated. One person in Sitka described their various concerns:A lot of them root in conspiracy theories … what’s really in this, what is the goal of it, what are the repercussions of getting it? … There’s just so much unknown with it, which causes a lot of fear. I’m sure it works, but I didn’t want to be one of the ones to get it first. I was also nursing, so I had a lot of fears around that … I was definitely skeptical at first, especially coming from the government. They’ve never been able to do a vaccine rollout so quickly, once I researched how they did do it so quickly, that made me feel better.
There were also mixed perceptions on the vaccine mandates, with one individual in Hoonah stating: “It should be a choice, not a demand.” Distrust of the government given historical discrimination and marginalization of Alaska Native communities was expressed as significant reason for reluctancy, with one interviewee from Kake stating: “The older people have been through times where the government has done medical testing on them and their communities. Unfortunately, they are the largest demographic in Indian Country that doesn't wanna take it.”
3.4 Coping
A pervasive sentiment of unity within the communities was described by interviewees as a primary method of coping with the effects of the COVID-19 pandemic. Individuals expressed consistent concern for the needs and safety of others, as one Hoonah community member described: “I feel we are united … there are so many people out wanting to help one another.” Another interviewee in Hoonah expressed similar sentiments, stating: “Hoonah is doing the better of all communities and keeping this from spreading,” although they also stated that the “tribe should have been more focused on getting supplies to the tribal members instead of … building new buildings.”
All interviewees (n = 22, 100%; five in Sitka, eleven in Hoonah, six in Kake) described increased engagement and reliance on cultural traditions during the pandemic as an effective method of coping. One participant in Sitka described opportunities to engage with cultural traditions to serve the greater community:I feel there’s way more opportunities for us to do something traditionally: hunting and harvesting … I went out and helped the Herring Protectors with the eggs, even though that’s something we do every year … If you think about it, it fed the whole town, and Southeast Alaska towns that can’t go out and get their own branches. I think it’s pretty cool that Tribes and communities are stepping up: How can we help each other?
Interviewees described resilience as embedded in Native culture and essential to the survival of generations, particularly through knowledge of available natural resources. Interviewees, primarily in the smaller communities of Hoonah and Kake (n = 8, 36.4%; one in Sitka, two in Hoonah, five in Kake) emphasize the importance of their knowledge and access to subsistence foods, both in fresh meats and greens, but also plants with medicinal properties like devil's club for flu-like symptoms and spruce needles for arthritis relief.
Traditional gathering and use of medicinal foods promote the sharing of generational knowledge and traditions, as well as connection to the environment. Subsistence and medicinal foods were critical to addressing economic burdens and reduced resources, as described by one interviewee in Hoonah: “We have knowledge about and how to utilize our land and the food. We know that if we do not have access to some foods, we can always have access to Native foods. Most endearing is everyone is willing to share.” Subsistence and medicinal foods are not withheld by a single household but are conserved and shared with the whole group. On participant in Sitka described sharing resources as engrained in Native culture and tradition, stating: “One of our core beliefs is never to take more than we need … Native people have been living for tens of thousands of years in this land … I don't only share food with Native people. I don't only share information with Native people.”
Changes in subsistence were found to have repercussions throughout generations and households given the interconnectedness of many Southeast Alaska Native families. Ability to meet basic needs were threatened, along with one's sense of purpose and identity. One interviewee in Hoonah describes the significant role of subsistence in connecting individuals to their environment, and the sense of responsibility to others:To be honest it’s been really tough … [being] in nature is my religious experience. During the lockdown times not being allowed to be out hunting or traveling weighed heavy on me. As a son to a Native mother and grandson to Native parents it is my duty to provide deer, fish, shellfish, and so on every year, they depend on me for their food and health, this is a duty I take great honor in. Not being able to provide for them like I normally do feels like I let them down, like I am a failure.
Notably, there was a lack of physical gatherings and opportunities to share culture due to COVID-19 restrictions. Physical gatherings were expressed as essential to coping and passing on traditions, which were already threatened by risk of Elder loss (n = 15, 68.2%; four in Sitka, five in Hoonah, six in Kake). One person in Kake discussed how identity is shaped by gathering, and how that had been compromised over the course of the pandemic by saying: “Our connection, whether we're impacted or not, is the way we relate to all our gathering. Whether it's by family, multi-family, or community, we're able to gather and share our knowledge. I know by the way it affected our culture camps that it really impacted us.” Finally, one interviewee in Sitka reflected on the pandemic and their vision of the coming years:It feels like, looking back on this year (2021), a lot of things were stripped away from us … Not being able to do certain things that really make our culture come to life. But I’m hoping that it puts the fire under everyone’s … priorities in life to keep the culture alive in whichever way they can.
4 Discussion
This study was motivated by concern of heightened vulnerability facing small Alaska communities, especially rural communities with relatively many Alaska Native people. Epidemiological research would suggest that COVID-19 could overwhelm such communities and lead to high mortality and cultural disruption, partially due to loss of Elders. However, interview evidence indicated that small Southeast Alaska communities displayed considerable resilience and adaptive flexibility, relying on cultural history, identity, and practices to achieve internal psychological calm, and a sense of perspective, a coherent and (mostly) united sense of community, and protective behaviors.
The ways Southeast Alaska Native people perceived risks associated with the COVID-19 pandemic and for other prescient issues (e.g., the climate change crisis) were varied, especially between Sitka and the smaller towns of Hoonah and Kake. A large proportion of respondents from all three localities expressed concern about the health of themselves, Elders, family members, unvaccinated people, and unhoused people, but only respondents from Hoonah and Kake (Table 2: 73% and 83%, respectively) discussed the threats and effects of climate change. Because of the deep cultural connections to Native land over thousands of years, the rapid rates at which the environment and ecology of Southeast Alaska are changing may reasonably take precedent as the primary threat to Native way of life, especially in these smaller island communities that rely regularly on subsistence hunting and gathering. This speaks to the way individuals may triage crises and perceive relative threats, and more broadly to how the worldview of Southeast Alaska Native people influences how risks of the pandemic compare to other ecological risk. As presented in the results, the COVID-19 pandemic is indeed viewed as an immediate and significant danger, but it may also be true that the noticeably smaller glaciers, smaller salmon runs, and the loss of traditional subsistence opportunities represent similarly large or even greater obstacles.
In terms of the socioeconomic, political, and community impacts of the COVID-19 pandemic, there were slightly more respondents in Sitka who acknowledged that they were worried about their ability to meet basic needs during the pandemic (Table 2: 60% from Sitka compared to 36% and 33% in Hoonah and Kake, respectively). The essential needs discussed with respondents from Sitka were tied to threats to the tourism industry during the pandemic and the subsequent socioeconomic consequences, such as less income to pay rent and other bills. Throughout the pandemic, researchers observed that there are considerable socioeconomic consequences on rural communities (Henning-Smith, 2020; Mueller et al., 2021; Phillipson et al., 2020), and we acknowledge here that, based on these data, this is likely the case to some extent for the island communities of Southeast Alaska.
A key point for the context of rural communities in Southeast Alaska with substantial Alaska Native populations is the point about how the pandemic has caused shifting perspectives in local, state, and federal levels of governance. The mention of the governor of Alaska specifically, and by association the decisions of state-level government, are notable because Alaska Native people have also expressed that the actions of higher levels of governance are at odds with what rural communities want and need (Shearer, 2007). Levels of government above the local (e.g., state and federal) make decisions that impact more people, and the needs of small, remote, rural communities can become overshadowed with those of the larger population. The socioeconomic and political risks to small rural communities, especially those with relatively large Indigenous populations, are driven by macrosocial forces that cannot be divorced from their microsocial impacts, especially those related to health and influence social inequalities (Farmer, 2004). Negative impacts on financial security and community cohesion, particularly as influenced by polarization in attitudes towards public health guidelines, are high-level agents of perpetuating social inequalities (Krieger, 1994), and could have long-lasting socioeconomic and health consequences.
In response to these drivers, interviewees discussed how they organized themselves and worked with community-centered Native organizations to mitigate the negative influences of national- and state-level public health messages. The organizations that were most helpful in connecting with the immediate socioeconomic needs of these rural communities were the Southeast Alaska Regional Health Consortium, CCTHITA, and the Hoonah Indian Association. Leaning on one another and these organizations, which are often made up of members of the communities they serve, was an essential behavior to mitigate the effects of the pandemic.
Most of the interviewees in this study expressed willingness and enthusiasm about access to the COVID-19 vaccines when they became available (Table 2: 80% in Sitka, 73% in Hoonah, 100% in Kake). Alaska Native people have successfully leveraged community and cultural ties to promote resilience, particularly in the context of vaccination. Despite barriers facing their communities such as constraints in healthcare access, remoteness, and higher COVID-19 infection rates, American Indian and Alaska Native peoples have attained leading vaccination rates against COVID-19 in the U.S. (Foxworth et al., 2021; Haroz et al., 2022). The attitudes that frame vaccination against COVID-19 as a social responsibility and a compelling way to approach the end of the pandemic are in stark contrast to the attitudes of non-Native populations, particularly in the lower 48 (Gerretson et al., 2021). Recent studies suggest adults in the U.S. are more likely to perceive public health messages about vaccination positively if it has an individualist versus collectivist message (Borah et al., 2021; Yuan and Chu, 2022).
This contrast helps elevate the importance of community- and culture-centered approaches to navigating crises in remote and rural Southeast Alaska Native groups. Smaller units of self-governance and autonomy in vaccine decision-making among Alaska Native people have proven to be largely effective in influencing whether to be vaccinated or not, per the interview data. In-depth qualitative interviews with remote Alaska residents reveal that opinions, decision-making, and actions surrounding the COVID-19 vaccines are complex and must involve consistent and clear messaging, trust, and community support (Eichelberger et al., 2022). Recent research using data from all regions of Alaska to explain dynamic attitudes about COVID-19 vaccines show that despite initial hesitancy, over half expressed acceptance of vaccines in winter 2020, and eventually over 80% expressed that they would get the booster, as well (Hahn et al., 2022). In Alaska, state public health agencies partnered with Tribal leadership throughout all stages of vaccine distribution to recognize historical and present-day mistrust and marginalization between Tribal communities and states (Chhean et al., 2021; Sanchez and Foxworth, 2021). Messaging about vaccinations and other state or federally imposed guidelines that highlighted cultural protection and concern for well-being of loved ones were found to be more successful (Sanchez and Foxworth, 2021), parallel to the findings in this study.
The public health messaging that comes from these governmental authorities also sowed distrust in those authorities, as well as community division along political lines. While very few interviewees in Hoonah and Kake expressed feelings of division (Table 2: 18% in Hoonah, 0% in Kake), 40% of interviewees in Sitka said they felt strong political division within their communities; conversely, 64% of Hoonah respondents and 100% of Kake respondents reported the perception of community unity, while only 20% of Sitka respondents reported similarly. This strongly echoes the sentiments discussed above regarding state- and federal-level government decisions and their socioeconomic and community impacts, specifically the disconnect between the intention and the reality of political decisions that instill distrust in Native communities, and inadvertently towards each other. It also highlights the important points that although these communities generally have strong methods for community-centered adaptation, biomedical interventions like vaccinations remain contentious issues.
Finally, the results show that connection to traditional knowledge and community were essential for coping with the COVID-19 pandemic. Every respondent from each locality discussed how community-centered, rather than individual-centered, attitudes and care helped keep people connected to ensure sharing of traditional knowledge could continue. As discussed above, the willingness and enthusiasm for the COVID-19 vaccines were not so much in the interest of individual protection, but as a means of returning to gatherings. Throughout the paper, the main through lines of the results before the explicit presentation and discussion of coping—and therefore resilience—have been about traditional knowledge, whether it was about the 1918 influenza pandemic, access to subsistence foods, connections to the environment, or motivations for community health rather than individual benefits. The values that the Alaska Native respondents discussed kept them grounded through the year of the pandemic before widespread vaccine usage allowed them to return to a version of “normal” that allowed them to mitigate the adversity faced while apart.
The successful efforts of community and cultural resilience discussed by Alaska Native people may be applied and tailored to similar communities with regards to attitudes about vaccines and similar COVID-19 impacts. They may further be applied to community-level pandemic preparedness plans, so that adaptation is more seamless. Findings in this study highlight how priorities differ (or do not differ) among different island communities of Southeast Alaska, as roles and influence of social bonds and institutions are distinctly influential. It is critical to remember, however, that the need for resilience in Indigenous communities often stems from the impacts of long colonial histories and marginalization. Future work should continue to challenge the root causes of resilience in Alaska Native communities to better understand risk perception and coping.
4.1 Limitations
This study has several limitations. First, interviews were conducted during a period of the pandemic in which COVID-19-related guidelines and knowledge were rapidly evolving, so sentiments expressed about adaptiveness were in response to ongoing changes. Further, the study was funded by a rapid-response grant, limiting resources and scope of the study. Interview samples were not random samples, limiting the generalizability of findings for Alaska Native people, which we have reflected upon in the Methods section. Despite this, there are 228 federally recognized Alaska Native tribes; therefore, the findings specific to a few towns in Southeast Alaska reported here should not be considered applicable to Alaska as a whole. Additionally, the findings of this research serve to provide a foundation of understanding Alaska Native perspectives and resilience strategies in the face of the COVID-19 pandemic, so small-scale, focused interviews specific to these island communities were appropriate.
5 Conclusion
An emergency response model of resilience prioritizing Indigenous perspectives acknowledges the differentiating contexts and adaptive behaviors that occurred in response to the COVID-19 pandemic. Indigenous knowledge and practices are critical to promoting social cohesion, resilience, and survival of global Indigenous communities (Kirmayer et al., 2011). Past research has explored response capacities of global Indigenous communities in various emergency settings, but these responses have often neglected to incorporate and uphold Indigenous knowledge, resulting in unsustainable responses and limited self-determination of Indigenous people (Howitt et al., 2012). Southeast Alaska Native coping strategies demonstrate culturally relevant coping mechanisms that serve to strengthen internal cultural ties as well as larger scope solidarity.
Credit author statement
Taylor P. van Doren: Formal analysis, Data Curation, Writing – Original Draft, Writing – Review & Editing, Visualization; Deborah Zajdman: Formal analysis, Data curation, Writing – Original Draft; Ryan A. Brown: Conceptualization, Writing – Review & Editing, Supervision, Funding Acquisition; Priya Gandhi: Formal analysis, Data curation, Writing – Original Draft; Ron Heintz: Conceptualization, Writing – Review & Editing, Supervision, Funding Acquisition; Lisa Busch: Conceptualization, Writing – Review & Editing, Supervision, Funding Acquisition; Callie Simmons: Data Curation, Writing – Review & Editing; Raymond Paddock: Conceptualization, Methodology, Investigation, Data Curation, Writing – Review & Editing, Supervision, Project Administration, Funding Acquisition.
Data availability
The data that has been used is confidential.
==== Refs
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| 0 | PMC9734070 | NO-CC CODE | 2022-12-14 23:29:55 | no | Soc Sci Med. 2023 Jan 10; 317:115609 | utf-8 | Soc Sci Med | 2,022 | 10.1016/j.socscimed.2022.115609 | oa_other |
==== Front
Eur J Med Chem
Eur J Med Chem
European Journal of Medicinal Chemistry
0223-5234
1768-3254
Published by Elsevier Masson SAS.
S0223-5234(22)00912-6
10.1016/j.ejmech.2022.115010
115010
Article
Mucormycosis: A hidden mystery of fungal infection, possible diagnosis, treatment and development of new therapeutic agents
Hussain Mohd Kamil ab
Ahmed Shaista c
Khan Andleeb d
Siddiqui Arif Jamal e
Khatoon Shahnaaz f
Jahan Sadaf g∗
a Department of Chemistry, Govt. Raza PG College, Rampur, 244901, India
b M.J.P. Rohil Khand University, Bareilly, India
c Centre for Clinical and Translational Research, Jamia Hamdard, New Delhi, India
d Department of Pharmacology and Toxicology, College of Pharmacy, Jazan University, Saudi Arabia
e Department of Biology, College of Science, University of Hail, Hail, Saudi Arabia
f Department of Botany D.N. College Meerut, U.P., India
g Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Al-Majmaah, 11952, Saudi Arabia
∗ Corresponding author.
10 12 2022
10 12 2022
11501020 2 2022
15 11 2022
5 12 2022
© 2022 Published by Elsevier Masson SAS.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Mucormycosis is a fungal infection which got worsens with time if not diagnosed and treated. The current COVID-19 pandemic has association with fungal infection specifically with mucormycosis. Already immunocompromised patients are easy target for COVID-19 and mucormycosis as well. COVID-19 infection imparts in week immune system so chances of infection is comparatively high in COVID-19 patients. Furthermore, diabetic disease, corticosteroid medicines, and a weakened immune system are the most prevalent risk factors for this infection as we discussed in case studies here. The steroid therapy for COVID-19 patients sometimes have negative impact on the patient health and this state encounters many infections including mucormycosis. There are treatments available but less promising and less effective. So, researchers are focusing on the promising agents against mucormycosis. It is reported that early treatment with liposomal amphotericin B (AmB), manogepix, echinocandins isavuconazole, posacanazole and other promising therapeutic agents to overcome the burden of mucormycosis. Lipid formulations of AmB have become the standard treatment for mucormycosis due to their greater safety and efficacy. In this review article, we have discussed case studies with the infection of mucormycosis in COVID-19 patients. Furthermore, we focused on anti-mucormycosis agents with mechanism of action of various therapeutics, including coverage of new antifungal agents being investigated as part of the urgent global response to control and combat this lethal infection, especially those with established risk factors.
Graphical abstract
Image 1
Keywords
Mucormycosis
COVID-19
Amphotericin B (AmB)
Azoles
Echinocandins
Statins
GPI biosynthetic Pathways
Antifungal agents
==== Body
pmcAbbreviations
ARDS acute respiratory distress syndrome
AIDS acquired immune deficiency syndrome
HIV Human immunodeficiency virus
RCOM Rhino-Orbito-Cerebral Mucormycosis
ICU Intensive care unit
EGD Esophagogastroduodenoscopy
CT Computed Tomography
MRI Magnetic resonance imaging
qPCR quantitative multiplex polymerase chain reaction
LDM Lanosterol 14α‐demethylase, AmB, AmB
GS β-(1,3)-D-glucan synthase, DKA, Diabetic ketoacidotic
LAmB lipid-based AmB
RHPOR Rhizopus orizae
GPIs Glycosylphosphatidylinositols
Gwt1 GPI-anchored wall protein transfer 1
MGX Manogepix
HMG-CoA 3-hydroxy-3-methylglutaryl-CoA
ROS Reactive Oxygen species
MIC minimum inhibitory concentration
FDA Food and drug administration
1 Introduction
In the last 20 years or more, an increase in the incidence of fungal infections has been noted, along with a substantial increase in the population of severely immunocompromised people. These infections are primarily caused by viral infections, particularly the human immunodeficiency virus epidemic [1] and currently SARS Co–V, haematological disorders such as various types of leukaemia, organ transplants, and more intensive and aggressive medical practises. Fungal infections can be caused by surgery, catheter use, injections, radiation, chemotherapy, antibiotics, and steroids resulting in an increase in the incidence of fungal infection. Fungus-induced respiratory tract infections cause 4.3 million fatalities each year. The exact incidence of these fungal infections of the respiratory system is unknown because they are largely ignored. Despite therapy, the majority of invasive fungal infections have significant mortality rates of more than 50%. In general, fungal infections of the respiratory system are regarded synonymous with Aspergillus spp.-caused invasive pulmonary infections [2]. Recently the whole world is facing the pandemic condition induced by SARS-CoV-2 which cause a respiratory life-threatening infection. Recently, there are some associated complications which have been reported in COVID-19 patients according to the clinical diagnosis. These associated infections are acute respiratory failure, acute respiratory distress syndrome (ARDS), and cardiac injury. Acute liver injury, pneumonia, acute kidney injury, neurological disorders, secondary infection, and multisystem inflammatory syndrome in children, disseminated intravascular coagulation, septic shock, rhabdomyolysis and chronic fatigue are also associated complications but, the most common complications are ARDS and acute respiratory failure [3]. Being at low immunity level, COVID-19 patients are easily target for the fungal infection as host with weak immune system is best option for opportunistic microbial infections. Therefore, clinicians are worried for them and researchers are also focusing their research on the therapeutic strategies against fungal infections. Viral-bacterial and viral fungal co-infections are among the most prominent medical concerns in the current scenario, resulting in an increased mortality rate [4,5].
A major infected population with COVID-19 suffered from the fungal infection mucormycosis, often known as “black fungus,” as part of the chain of co-infections linked with COVID-19. Black molds are a diverse collection of darkly coloured (dematiaceous) fungus that are extensively spread in the environment and can infect people. Mycetomas, chromoblastomycosis, sinusitis, and superficial, cutaneous, subcutaneous, and systemic phaeohyphomycosis are all part of the infection's clinical spectrum. Apart with COVID-19 problems, these black fungus infections are easily transmitted to immunocompromised people. A case study from the San Francisco Bay area is highlighted here where the annual incidence of infection caused by black molds was one case per million [6]. Black molds were found to be responsible for two out of every 20 invasive mould infections in organ transplant recipients in a recent prospective multicenter investigation [7]. There have been recent reports of nosocomial acquisition of black fungi, including a case of meningitis caused by Bipolaris spicifera following auditory neuroma surgery. These infections are fairly common these days, but the survival rate is very low. However, there are various unique therapeutic tactics or treatments available to tackle this deadly disease. Combination therapy with liposuction is one of these therapies.
Combination therapy using lipid-based AmB and caspofungin, or clinical azoles such as itraconazole or posaconazole, or a combination of all three therapies are among the options. When compared to available alternatives for treating microbial and other fungal infections, the number of therapy solutions for this condition is quite modest. Polyenes and azole-based compounds are currently being used in clinical trials to treat this fungal infection [8].
Despite a lack of clear clinical evidence, treating this fungal infection in highly immunocompromised patients with a combination of antifungal medications is now standard clinical practise all over the world. Synergistic effects and a broader therapy range are advantages of these treatments, but possible antagonism, toxicity, and medication interactions are negatives. The first-line treatment for this life-threatening fungal infection is liposomal AmB. Due to residuals concerns, isavuconazole and new posaconazole formulations have been recommended for clinical use, but only as a second-line treatment after liposomal AmB [9].
2 Black fungus: brief taxonomy
The Zygomycota are a subclass of lower fungi with nonseptate thalli (coenocytic). After isogamic sex organs (gametangia) fuse, a single black, thick-walled, often decorated sexual spore called zygospore is produced. Their wide, aseptate, hyaline, randomly branching hyphal components can be identified in host tissue. There are two classes and 11 orders of Zygomycota. Only two of them have clinically relevant fungi: the Entomophthorales, which have violently ejected spores, and the Mucorales, whose spores arise from sporangial plasma cleavage and are passively freed. The Mucorales family is the most clinically significant. Its members are found in food, soil, and the air and are extensively spread. Dolatabadi and Guarro have named and depicted eleven genera containing 22 species of medical relevance. Rhizopus and Absidia are the most common and important mucoralean genera in clinical laboratories [10,11]. Mucormycosis is a rare, invasive, fungal opportunist infection that sometimes responsible for fatal disease caused by a group of molds that belongs to the fungal family, “Mucorales”. This fungus is ubiquitous and naturally found in soil, plants, manures, decaying fruits, and vegetables [12].
3 Epidemiology and pathogenesis of black fungus
About 150 fungal species out of about 1.5 million have been linked to human diseases, and only a dozen or so are commonly encountered in clinical settings. In addition to the scarcity of dangerous fungal species, life-threatening fungal diseases are rare in immunologically healthy human populations when compared to other infectious diseases. The lack of human sickness contrasts with a high prevalence of infection among humans who live in areas where fungi are present. As a result, just a few fungus species are pathogenic, and those that do cause disease are uncommon causes of life-threatening illness [13]. The perception that fungi are less likely to be employed as biological weapons may have been influenced by these epidemiological facts. Depending on whether the organism is obtained from a host or the environment, fungal illnesses can be categorised into two types. Candida spp., Malassessia furfur, and Dermatophytes are examples of pathogenic fungi acquired from the host [14]. These organisms are commonly found in the flora of the host and only cause disease when the host-microbe interaction is disrupted. For example Human candidiasis is linked to a damaged integument, the use of antibacterial drugs, corticosteroid use, and immunological suppression. Fungi obtained from other hosts, such as Candida spp, are generally low pathogenicity organisms that rarely cause disease until the host-microbe interaction is disrupted.
Aspergillus spp, the dimorphic fungus, and Cryptococcus neoformans are examples of pathogenic fungi that can be acquired through the environment. Humans who are exposed to these organisms are frequently infected, although sickness is uncommon unless the host's immunity is compromised [15]. For example, whereas the prevalence of cryptococcosis in the general population is less than one case per 100,000, it was as high as 10% in Acquired immune deficiency syndrome (AIDS) patients prior to the development of effective antiretroviral medication. However, with the introduction of antiretroviral medication, the frequency of cryptococcosis in HIV patients was drastically reduced in areas with access to therapy, demonstrating the disease's reliance on the host population's immunological condition. Initial infection with environmentally acquired fungi is mainly acquired through inhalation, and the result is either asymptomatic or minor disease, while many of these organisms are capable of staying in the host in a dormant form. Even with Coccidioides spp., the vast majority of first infections result in mild or no illness [16]. When fungi from the environment infect the host, the sickness is severe, difficult to treat, and often fatal. None of the fungi obtained in the environment are transmissible from host to host, and disease clusters are mainly the result of unique exposures. For example, pulmonary histoplasmosis outbreaks in apparently immunologically normal people have occurred after tree-cutting operations and cave trips. The lack of communicability of environmentally acquired pathogenic fungus limits their weapon potential for indiscriminate deployment, but it also makes them appealing because they are unlikely to harm non-exposed friendly forces [17].
4 Mode of transmission and associated complications
The fungal spores commonly enter through inhalation and affect the sinus and lungs. They can also penetrate through wounds or an open cut and thus infect the skin. Patient with severe COVID-19, such as those in Intensive care unit (ICU) needs more attention and hygiene because they are particularly vulnerable to bacterial and fungal infection. Apart from black fungus, other fungal infections have already been reported within the past few months all over the globe. The most common fungal infection reported in COVID-19 patients other than black fungus is aspergillosis or invasive candidiasis [18]. All these fungal infections with co-infections are reported more frequently and may cause serious illness and death. Awareness of the possibility of fungal and bacterial co-infection is essential for timely diagnosis, treatment ultimately helpful to prevent serious illness and death caused by these infections [19,20] including COVID-19. According to some health specialists, this infection may be because of steroid medications used while treatment of COVID-19 patients to reduce the inflammation in the lungs for COVID-19 patients but unfortunately resulted into and push up sugar levels in both diabetic and non-diabetic patients in COVID-19 [21] hence the immunity of the patient has already been compromised. Like, “Diabetes lowers the body immune system, coronavirus exacerbates it, and steroids that used for COVID-19 treatments act like fuel to fire, also drops in the immunity may trigger this black fungus infection very fast. More information about the sudden outbreak of black fungus, symptoms, and other complications with COVID-19 is explained in coming section.
5 Recent complications of black fungus with COVID-19 patients
Coronavirus has been associated with a wide range of opportunistic bacterial and fungal infections [22]. Aspergillosis or invasive candidiasis has been reported as primary fungal pathogens for causing disease in COVID-19 patients [23]. After December 2019 outbreak in china, various modifications in terms of pathophysiology, diagnosis, and complications may be correlated with a wide range of disease forms. In COVID-19, the disease manifesting as rhino, orbital, cerebral mucormycosis. Several such cases have been reported during COVID-19 illness [24]. Patients can include those with iatrogenic immunosuppression, haematological malignancies, diabetes mellitus, acquired immunodeficiency syndrome. In the COVID-19 condition, hypoxic conditions occurred due to endothelial barrier disruption and impaired oxygen diffusion capacity [25]. Profound lymphopenia with a decreased number of T lymphocytes (CD4+T and CD8+T cells) may amend the immune response of COVID-19 patients enhancing the risk of invasive fungal infections. Symptoms of COVID-19 and fungal infections are quite similar
some of them are listed here: fever, pain, headache, redness and periocular swelling, drooping of eyelids, limitation of ocular movements, and painful loss of vision [26]. The progression is usually quick, taking only two days on average from the outset. Edema of the eyelids and periocular region, complete ptosis, total ophthalmoplegia proptosis, and relative afferent pupillary defect, unilateral facial or orbital pain, double vision or loss of vision, the sign of exposure keratitis, chemosis, sinusitis, nasal discharge, and neurological signs and symptoms. The clinical profile of coronavirus patients suffering from this life-threatening fungal infection differs depending upon the severity of the disease.
The diagrammatic representation is shown in Fig. 1 to depict the connection of immunological and physiological complications along with improper glucose metabolism which possibly provide a conducive environment for mucormycosis initiation and progression.Fig. 1 COVID-19 induced immunological and physiological complications along with improper glucose metabolism may provide a conducive environment for Mucormycosis initiation and progression. The aggravation of COVID-19 and hyperglycemic conditions due to steroid administration in a diabetic individual add further fuels the Mucormycosis development.
Fig. 1
6 Clinical profile and cases of mucormycosis infection in corona patients
In this section, we will discuss the clinical profile of mucormycosis and case studies conducted in different regions of the world. In Rhino-Orbito-Cerebral Mucormycosis (RCOM), fungus mucormycosis affects the nose, eyes, and brain. This fungal infection starts from the nose, where it rapidly spreads and infects the bone cavity, which surrounds the eye and brain. In starting stage, the patient experience nasal discharge. It can be a bloody, nasal blockage, or pain inside the nose. After that, patient experience numbness or swelling on face and develop facial pain [27]. As in the second stage, progression reaches the orbit, and the patient experiences headache, orbital pain, periocular edema, eyelid dropping, vision loss, and double vision with pain. The passage is usually rapid, an average of two days from onset. Furthermore, in the last stage, dysfunction in jaw movement occurs. Tooth of upper jaw starts loosening, chilling, burning or numbness occurs in the skin. Black eschar develops near the eyes or nose. Pulmonary mucormycosis generally occurs in patients with immunocompromised conditions. This infection affects the lungs and respiratory system. Fever, breathlessness, cough, chest pain, some experienced cough with blood are known as haemoptysis. As the infection progresses, it worsens, and the patient and pleural effusion was observed as well, in which a fluid build-up occurs between the tissues that line the lungs and chest [28].
A retrospective study was done in the Department of Infectious Disease in Manipal Hospital, Bangalore, India, from August to December 2020.
10 patients with a mean age of 55.8 years were out there with mucormycosis associated with COVID-19. Eight patients had diabetes, complaining about severe eye pain, nasal blockage, and facial pain; out of 10 patients, six received steroids. One patient had received tocilizumab for the treatment of COVID-19. Nine patients are suffering from diabetes, hypertension, and chronic kidney disease. Only one patient is suffering from severe COVID-19 infection while the remaining patients are suffering from mild and moderate disease. They treated all the patients with local debridement of the infected and necrotic tissue and AmB with COVID-19 treatment [29].
Another highlight study was done at Sawai Man Singh Medical Hospital, Jaipur, India, from August to December 2020, and a total l of 23 patients were there. Fifteen were male, and eight were female. During treatment, out of 23 patients, 4 were still COVID-19 positive at that time, and 19 had been recovered. Twenty-one of the patients were diabetic, 12 of them have uncontrolled blood sugar levels, nine patients had controlled diabetes, 14 patients suffering from hypertension, and one patient suffers from renal failure. All 23 patients used steroids for the treatment of COVID-19. Now, all of them suffering from invasive mucormycosis, all of them operated while keeping in mind complete surgical debridement and amphotericin administered intravenously [30].
Another study includes 66-year-old male patient who was hospitalised to the University Hospital in Sassari, Italy on March 26, 2020 suffered with COVID infection. For the first ten days, patients were given hydroxychloroquine and lopinavir-ritonavir. Respiratory parameters steadily worsened following admission to the ICU: reduced oxygenation, increased radiological infltrates, and the left lower lobe parenchymal thickening was observed. For prolonged mechanical ventilation, a procedure was conducted at the patient's bedside. Aseptate broad hyphae, sporangia carrying sporangiospores, and aseptate broad hyphae were seen on microscopic examination using lactophenol cotton blue preparation. The mould was identified as Rhizopus spp. based on phenotypic characteristics. The treatment with liposomal AmB, 5 mg kg1 IV, was started according to the instructions and after consultation with an infectious disease physician. After 40 days in the ICU, the antifungal treatment was changed to isavuconazole, and the liposomal AmB treatment was discontinued. Due to refractory shock and hepatic failure, the patient died on day 62 following ICU admission [31].
A 86-year-old man with a history of arterial hypertension was admitted to a Brazilian emergency room with acute diarrhoea, cough, dyspnea, and fever that started five days before to admission. Due to abrupt respiratory failure and hemodynamic instability, he was admitted to the critical care unit (ICU). In the intensive care unit, the patient was given ceftriaxone, azithromycin, oseltamivir, and hydrocortisone, as well as vasopressors and mechanical ventilation. He was treated with three units of red blood cells and omeprazole. Esophagogastroduodenoscopy (EGD) revealed two huge stomach ulcers with dirty debris and a deep hemorrhagic base without active bleeding in the greater and lesser curvature. A pathology investigation confirmed the presence of mucormycosis [32]. Also, another case study has done on 32 years old lady with uncontrolled diabetes at the Department of Otorhinolaryngology and Head and Neck Surgery, KS Hegde Medical Academy, NITTE University, Mangalore, Karnataka. She was presented with left facial pain and left eye complete ptosis. Her CT scan of the nose and paranasal sinus showed total opacification of the left ethmoid, and maxillary and frontal sinus suggest fungal sinusitis. Doctor asked for immediate endoscopic surgery; a COVID-19 test was done, which came positive; after surgery Amphotericin-B (25 mg/day) dose had been administered. The patient followed up was done for two months. There was a reduction in facial pain but no improvement in vision [33].
7 Reasons for mucormycosis in COVID-19 patients
There are several reasons reported for the severe black fungal infection. Many research studies have proven that steroids help to reduce mortality in COVID-19 patients with low oxygen saturation levels and reduce the ability to fight against other infections. Corona patients who are already immunocompromised having diabetes, chronic kidney disease, or chronic liver disease take steroids regularly for a prolonged duration, and that too not in defined dose. They put a very high risk of mucormycosis because fungus mucormycosis cause illness in people who are immunocompromised and have high sugar levels. Therefore, this surge can be attributed to the improper use of steroids to treat patients with COVID-19 and poor management of diabetes. Steroids increase blood sugar levels, causing the blood to become acidic [34]. This fungus thrives in high blood glucose and acidic environment. This fungus has the potential to cause damage under unsanitary conditions. But steroids are not only villains in this fungal infection; they are one of the reasons behind this fungal infection [35]. According to Dr. Hegde, “The virus is pathogenic, causing blood sugar levels to skyrocket to dangerously high levels. Surprisingly, the fungus is harming a large number of young people.” [36].
The pathophysiology of mucormycosis has also been correlated with mononuclear and polymorphonuclear phagocytes of normal hosts killing. Mucorales by producing oxidative metabolites and defensins, making neutropenic individuals and those with defective phagocytes susceptible to invasive mucormycosis. There is severe lymphopenia in COVID-19, and viral replication exacerbates the inflammatory response and neutrophil and monocyte influx in the bloodstream in advanced infections [29]. As a result of this imbalance in neutrophil and lymphocyte activity, the patient is more susceptible to systemic fungal infections. When we talk about other factors then moisture in the environment and oxygen cylinder can be a significant source of mucormycosis infection because the oxygen cylinder used was outdated. There are other likely causes for the fungal infection [37]; It may be improper use of oxygen cylinders, unclean masks, or no pure water in the equipment, which has become the norm in most cities that deal with the shortage of hospital beds. Above mentioned reasons are possible key responsible factors to cause mucormycosis in COVID patients. ‘
8 Diagnosis of mucormycosis
Mucormycosis is difficult to diagnose early on and is associated with a high death rate, particularly in immunocompromised patients [38]. Because underlying co-morbid diseases and clinical presentation are often similar, distinguishing this disorder from invasive aspergillosis is critical, as antifungal treatment may differ. A comprehensive history, physical exam, and imaging are all required to identify probable mucormycosis. A common finding on a cranial CT in diabetes patients is bone destruction. A cranial Magnetic resonance imaging (MRI) is recommended for added sensitivity because the results will reveal any involvement of the brain, sinuses, or orbit. The staging will be indicated on imaging in terms of sinus and brain involvement. A biopsy should be planned and sent for direct microscopy, culture in standard media at 30/37° Celsius. After that, susceptibility testing can be requested for further process [39]. In most rural regions, mucormycosis diagnostic techniques are based on insufficient fundamental microbiology, which has resulted in diagnosis delays. Unlike invasive aspergillosis, mucormycosis diagnosis is not aided by the identification of circulating antigens such as galactomannan and D-1, 3-glucan. As a result, in addition to culture and clinical features, samples from the anatomical site of infection are frequently required for the diagnosis of mucormycosis.
Mucormycosis can now be diagnosed non-invasively with the help of the latest molecular biology tools [40]. Million et al. developed a Mucor/Rhizopus, Lichtheimia, and Rhizomucorxvi 18S rRNA-based quantitative multiplex polymerase chain reaction (qPCR) targeting Mucor/Rhizopus, Lichtheimia, and Rhizomucorxvi. The goal of the PCR assay is to detect Mucorales DNA in the bloodstream (serum) [41]. A study looked at the use of Mucorales-targeting real-time PCR on tissue and respiratory samples in patients with haematological malignancies who had proven or suspected mucormycosis. As a result, the value of reverse halo sign on computed tomography scan combined with serum qPCR targeting Mucorales for the early detection of pulmonary Mucormycosis is now required in patients with COVID-19 infections.
9 Antifungal agents against mucormycosis
Researchers are working towards the promising therapeutic invention against mucormycosis. Combinations of echinocandin and lipid polyenes enhanced survival rates in mice with disseminated mucormycosis [42]. AmB is the most widely used drug for the treatment of mucormycosis. Because of the increased risk of nephrotoxicity when using AmB, it is important to pay close attention to kidney function. If the condition is severe, second-line drugs can be considered. Combination therapy of echinocandin and AmB is recommended as a second-line treatment. When echinocandin is coupled with AmB, a polyene skeleton is added, thereby increasing the success rate of treatment. Triazoles, posaconazole, and isoconazole are some other recognized second-line antifungals. Triazoles block 14-demethylation, leading to an increase in the harmful 14-methyl sterol and changing the permeability of fungal membranes. Posaconazole is used for patients who are intolerant to AmB. Isaconazole has a broad-spectrum effect. Therefore, it is the only antifungal drug that can be used to treat invasive mucormycosis [39]. The antifungal treatment should be continued until clinical signs and symptoms are resolved, radiological signals are resolved or stabilized, and underlying immunosuppression is resolved. The ideal dosage for antifungal treatment of mucormycosis is unknown and is mostly determined by the patient's health and laboratory results. Moreover mucormycosis, whether suspected or diagnosed, necessitates consultation with the surgical team. To prevent the spread of the fungal infection, clean margins are essential during surgical debridement. This is used as emergency therapy. Histopathology and microbiological diagnostics can be performed on biopsies taken during surgery. From the above techniques and therapeutic strategies, the fear of such fungal infections can be diminished up to a significant point. Important antifungal drugs for treatment of mucormycosis and their possible mode of actions are presented in Table 1 .Table 1 Important antifungal drugs for treatment of Mucormycosis and their possible mode of actions.
Table 1S.No Antifungal Drug Target* Mode of action References
1 AmB Ergosterol in the fungal cytoplasmic membrane Ion channel formation
Ergosterol sequestration
Induction of reactive oxygen species [43]
[44]
2 Itraconazole lanosterol 14-α-demethylase Inhibits the biosynthesis of ergosterol [45,46]
3 Posaconazole lanosterol 14-α-demethylase Inhibits the biosynthesis of ergosterol [45,46]
4 Isavuconazole lanosterol 14-α-demethylase Inhibits the biosynthesis of ergosterol [45]
[46]
5 Echinocandins β-(1,3)-D-glucan synthase Inhibition of biosynthesis of β-(1,3)-D-glucan [47]
[48]
6 Oteseconazole (VT-1161) lanosterol 14α‐demethylase Inhibits the synthesis of ergosterol [49]
[50]
7 Terbinafine Squalene epoxidase Inhibits the synthesis of ergosterol [51]
8 Rapamycin FKBP12 • FKBP12-dependent inhibition of a TOR kinase
• Inhibits calcineurin phosphatase activity
[52]
9 Tacrolimus (FK506) FKBP12 FKBP12-dependant inhibition of PP2B [53]
6 Manogepix and prodrug fosmanogepix Inositol acyltransferases (Gwt1) Prevents the maturation of GPI-anchored proteins [54]
7 Jawsamycin Catalytic subunit Spt14/Gpi3 of the fungal UDP-glycosyltransferase Inhibits the biosynthesis of GPI-anchored proteins [55]
8 Fluvastatin and other statin Hydroxymethylglutaryl-CoA (HMGCoA) reductase enzyme Inhibits the biosynthetic pathway of sterols [56]
9 Anti-CotH3 antibodies 16-mer peptide region of the CotH3 of Mucorales Bind to glucose-regulated protein 78 (GRP78) on endothelial cells. [57]
9.1 Polyenes
Amphotericin B (1, AmB) is most widely used mycosamine polyene macrolide in antifungal therapy. Mechanistically this polyene binds to ergosterol (2) and forms ion channels in the fungal cell membrane this may cause the fungal cell death due to loss of membrane integrity and leakage of vital cytoplasmic components through these channels. According to a study by Burke et al., mycosamine sugar play a central role to promote direct binding interaction between AmB and hydroxyl group of ergosterol for the formation of ion channels (Fig. 2 ). AmB also binds to the surface of plasma membrane and physically extracts ergosterol (ergosterol sequestration), which leads to depletion of vital constituent of membrane and ultimately dysfunction of cell membrane. AmB may induce reactive oxygen species (ROS) which may cause protein damage, lipid damage and mitochondrial dysfunction (Fig. 3 ) [43,44,58,59]. Various formulations of AmB have allowed its successful use as a fungicide against mucormycosis, and multidrug-resistant. These formulations have demonstrated remarkable and broad-spectrum activity against various fungal species that cause mucormycosis.Fig. 2 Structure of Amphotericin B (AmB): Mycosamine sugar of AmB promote direct binding interaction between AmB and the hydroxyl group of ergosterol for the formation of ion channels which may lead to fungal cell death.
Fig. 2
Fig. 3 Mode of actions of Amphotericin B (AmB): AmB bind to ergosterol, forming ion channels in the cell membrane. The formation of these ion channels leads to increased membrane permeability. Additionally, AmB induces the accumulation of ROS, which have multiple toxic effects on fungal cells.
Fig. 3
The clinically suggested dose of AmB deoxycholate is 1–1.5 mg/kg/day [[60], [61], [62]] which was found to be highly toxic for patients suffering from fungal diseases. Interestingly the various lipid formulations of AmB was found considerably less nephrotoxic as compared to AmB. This lipid formulation of an antifungal drug can be safely used at higher doses for a longer duration. As compared to AmB (1 mg/kg/day) high-dose of liposomal AmB (15 mg/kg/day) was found to be significantly effective in vivo in R. oryzae infected murine mice model with diabetic ketoacidosis. In the case of liposomal AmB survival rate was reported nearly double as compared to AmB deoxycholate [63]. Lipid formulations of AmB is a clinically established therapeutic option for the primary treatment of mucormycosis. The efficacy of this Lipid formulation of AmB has been established in preclinical and clinical investigations. Various lipid formulations of AmB, including lipid complex, colloidal dispersion, and liposomal AmB are widely used therapies for the treatment of mucormycosis [64]. Natamycin (3) is another naturally occurring polyene antifungal agent. Walther et al. investigated antifungal activities of natamycin against 101 mucoralean strains belonging to the genus Mucor, the closely related species Cokeromyces recurvatus), Rhizopus, Lichtheimia, Rhizomucor and compared with another five antifungals (AmB, terbinafine, isavuconazole, itraconazole, and posaconazole). Natamycin was found less active as compared to AmB and posaconazole. against various fungal strains [65].
9.2 Azoles
Azoles are widely used in clinical practice as antifungal agents. Mechanistically these therapeutic molecules inhibit the fungal lanosterol 14α‐demethylase (LDM) which catalyse the formation of ergosterol in fungal cells. Lanosterol 14α‐demethylase (ERG11 gene) exhibits variable response against different triazoles. Inhibition of LDM decreases the concentration of ergosterol in the fungal cell membrane, which causes less fluidity, inhibition of growth and death of fungal pathogen [66] (Fig. 4 ). Among antifungal triazoles, fluconazole, itraconazole, and voriconazole were found to exhibit minor or no activity against mucormycosis causing fungi. However, newly reported azoles, such as posaconazole and isavuconazole have exhibited significantly improved in vitro antifungal activity against Mucorales and clinical data of these azoles proposing their applications for the treatment of mucormycosis (Fig. 5 ).Fig. 4 General mechanism of mode of action of triazole: Ergosterol is a regulator of the fluidity of fungal cell membrane. Inhibition of ergosterol synthesis may cause membrane dysfunction and increase in the permeability of membrane which may lead to the cell lysis and cell death.
Fig. 4
Fig. 5 Structure of important antifungal azoles potentially active against mucormycosis.
Fig. 5
9.2.1 Itraconazole
Itraconazole (4) is the marketed antifungal azole based drug, which exhibits excellent in vitro activity against mucormycosis [67]. Various case reports and investigations have confirmed that itraconazole is successful therapy against mucormycosis [[68], [69], [70]]. Itraconazole showed anti-fungal activity against fungal isolates in in vitro investigations but in in vivo studies, Rhizopus and Mucor spp., were not found to be susceptible to itraconazole [71,72]. In contrast, itraconazole exhibited in in vivo activity against a hyper susceptible fungal strain of Absidia with a MIC of 0.03 μg/mL. These findings suggest that itraconazole should not be considered as a first-line treatment against this life-threatening disease, yet its clinical use might be used as adjunctive therapy in certain cases where extremely susceptible fungal pathogens against itraconazole have been cultured.
9.2.2 Posaconazole
Antifungal agents such as posaconazole (5) and ravuconazole (6) are important investigational triazole based drug molecules. These molecules have demonstrated excellent in vitro activity against the fungal pathogens of mucormycosis [67,73]. Posaconazole was found to be more effective as compared to itraconazole but less effective in comparision to AmB in an in vivo investigation in animal models [74]. There are various expanding case reports for treatment of refractory mucormycosis where posaconazole was found to be effective therapy. Significant results have been observed in the case of a combination of posaconazole with AmB in patients suffering from rhinocerebral mucormycosis [75] in heart and kidney transplant patients who were unresponsive to AmB treatment [76]. Posaconazole has exhibited varied in vitro activity against various species of Mucorales [71]. A study of 131 clinical isolates of Mucorales species against posaconazole confirmed that the median MICs of this azole drug varied between 1.0 and 8.0 μg/mL [77]. In an in vivo investigation, posaconazole was reported most effective against various species of Mucorales but it was found inactive against infection caused by Rhizopus spp [70,78,79]. In another study by Lewis et al., it was noticed that more than 4.00 mg/mL serum concentration of posaconazole was required to check the growth of strains of Rhizopus spp with a MIC of 2.00 μg/mL in immunosuppressed mice murine model of pulmonary mucormycosis [80]. These data raised concerns about the clinical efficacy of this antifungal posaconazole against mucormycosis causing Rhizopus spp at least in the existing standard dose of 0.30 μg/day of extended-release pills (Fig. 5).
9.2.3 Isavuconazole
Isavuconazole (7) is a recently discovered broad-spectrum antifungal pharmaceutically active triazole based drug of the prodrug isavuconazonium sulphate. This antifungal molecule was approved in the US and Europe for treating mucormycosis. In Europe, isavuconazole was approved due to the non-feasibility of AmB. This anti-fungal drug can be administered intravenously (IV) and in oral formulations with a dose of 0.20 μg/three times/day for two days and 0.20 μg/day thereafter. Isavuconazole showed various pharmacokinetic and safety benefits over other antifungal azole drugs against Mucorales [81,82] such as linear pharmacokinetics, fewer interactions with P450 leading to fewer drug-drug interactions, no liver failure, nephrotoxic cyclodextrin free in the intravenous formulation and dose adjustment in kidney was not required [83,84]. Isavuconazole showed variable in vitro activity against Mucorales which was found to be species-dependent and The MIC values of isavuconazole against Mucorales were found two to four folds higher as compared to those of posaconazole, that's why isavuconazole should be considered in clinical practice [85,86]. Isavuconazole exhibited comparable efficacy to high-dose of liposomal AmB for the reduction of tissue fungal burden, in the lungs and the brain in the neutropenic mouse model of mucormycosis. Isavuconazole provided survival benefits for 21 days of treatment [87]. Several other investigations have also disclosed that isavuconazole could be an effective treatment for the mucormycosis in severely immunosuppressed patients, including posaconazole failure. Isavuconazonium sulphate (8) (Fig. 5) is a broad-spectrum antifungal drug that was approved by the FDA for the treatment of invasive aspergillosis and invasive mcormycosis. Isavuconazonium sulphate is a prodrug of isavuconazole, which is readily hydrolyzed by the enzymatic action of butylcholinesterase into the active form isavuconazole (BAL-4815) and a non-active cleavage product (BAL-8728) [88].
9.3 Echinocandins
Echinocandins (Fig. 7) such as caspofungin (9), micafungin (10), and anidulafungin (11) are specific and noncompetitive inhibitors of β-(1,3)-D-glucan synthase (GS), which play important role in the synthesis of the essential component of the fungal cell wall. GS complex is located in the fungal cell membrane and is a key constituent in maintaining the integrity and strength of the cell wall. Echinocandins display their antifungal activity through noncompetitive binding to the Fks p subunit of the GS complex leading to inhibiting the synthesis of β -(1,3)-D-glucan [47,89]. This inhibition causes lysis and death of fungal cell due to loss of cell wall integrity and imbalance in the intracellular osmotic pressure (Fig. 6 ). Caspofungin is the first echinocandins drug to be marketed in the US as an antifungal agent. Caspofungin displayed minimal in vitro activity against the fungal pathogens of mucormycosis [90,91]. The combination therapy of caspofungin (1 mg/kg/day) and AmB lipid complex (5 mg/kg/day) responded synergistically for the treatment of disseminated mucormycosis in diabetic ketoacidotic (DKA) mice [92].Fig. 6 Schematic representation of the mechanism of action of echinocandin. Echinocandins inhibit the synthesis of β-(1,3)-D-glucan of the fungal cell wall at the level of the cell membrane. Fks is the catalytic subunit, and Rho is the regulatory subunit of the GS complex.
Fig. 6
Fig. 7 Structure of various clinically important antifungal echinocandins.
Fig. 7
In another study by Ibrahim et al. Micafungin (1 or 3 mg/kg/day) or anidulafungin (1 or 10 mg/kg/day), monotherapies exhibited no significant improvement for the survival DKA mice as compared to the placebo dose. The combination therapy of lipid-based AmB (LAmB) and micafungin (at 1 mg/kg/day dose) or LAmB and anidulafungin (10-mg/kg/day dose) synergistically improved the survival of DKA mice infected with R. oryzae as compared to the above monotherapies. In combination therapy, the higher dose of caspofungin (3 mg/kg/day) was not found equally synergistic however a paradoxical loss was seen for the efficacy of caspofungin against mucormycosis as compared to low dose combination therapy. Monotherapy with micafungin at 3 mg/kg/day dose exhibited a higher survival rate of DKA mice as compared to the monotherapy with micafungin (1 mg/kg/day dose), but the difference in survival of DKA mice or time duration to death was not significant [93].
9.4 Novel antifungal agents with activity against Mucorales
Oteseconazole (VT-1161) 12 (Fig. 8 ) is a novel metalloenzyme inhibitor which prevent the synthesis of ergosterol through selective inhibition of fungal Cyp51 (lanosterol 14α‐demethylase). VT-1161 was reported as antifungal agent which displayed in vitro activity (0.12–1 μg/mL) against some strains of Mucorales. Gebremariam et al. compared prophylactic or continuous therapy with oteseconazole to that with clinically relevant posaconazole for the treatment of mucormycosis caused by infection of R. arrhizus var. arrhizus. VT-1161 showed lowered tissue fungal burden and improved survival rate of immunosuppressed infected mice in prophylaxis investigations. As compared to the posaconazole drug, VT-1161 resulted in better extending mouse survival time despite its comparable effect in decreasing tissue fungal burden during continuous therapy [94]. Terbinafine (13, Fig. 8) is an allylamine antifungal drug, which is commonly used for the treatment of dermotropic fungal infections. Mechanistically terbinafine inhibits the enzymatic action of fungal squalene epoxidase, which catalyse the biosynthesis of ergosterol in the fungal cell membrane [51]. Terbinafine has exhibited in vitro activity against few species of Mucorales [71,95,96] but showed mild or minimal in vivo efficacy in animal models [97]. Terbinafine has been used as an adjunct therapy in combination with antifungal azoles (posaconazole, and itraconazole), polyenes (AmB) or echinocandins (caspofungin) against the severe drug-resistant or refractory mycosis. All the combinations of this adjunct therapy synergistically reduced the MICs. This synergistic action was probably due to the inhibition of fungal ergosterol. The GM MIC decreased on average, 18-fold for terbinafine and 36-fold for caspofungin. But the MICs of terbinafine and caspofungin were found comparably high [98].Fig. 8 Structures of potential new antifungal agents exhibiting activity against mucormycosis.
Fig. 8
Ibrexafungerp (14, Fig. 8) is a triterpenoid based antifungal agent which inhibits the biosynthesis of β-(1,3)-D-glucan in the cell wall of the fungal pathogen. Ibrexafungerp has exhibited wide and strong in vitro activity against Aspergillus and Candida spp. ibrexafungerp also has displayed potent activity against azole-resistant isolates, including biofilm-forming Candida spp. and echinocandin-resistant isolates. Unfortunately, Ibrexafungerp exhibited no or weak activity or minimal against Mucorales spp [99]. Recently Colley et al. reported novel triazole, PC1244 (15) which displayed significant antifungal activity against Mucor circinelloides and Rhizomucor pusillus with MIC, 2 μg/mL. PC1244 was found more effective as compared to other azoles such as voriconazole and posaconazole (MIC >8 μg/mL) [100].
Colistin (polymyxin E) (16, Fig. 8) is a positively charged cyclic peptide-based antibiotic. Mechanistically colistin bind to and disrupt the anionic outer cell membrane of Gram -ve bacteria and neutralise the bacterial lipopolysaccharides. This antibiotic has shown activity against Saccharomyces cerevisiae. This provided evidence of the extension of its antimicrobial activity against eukaryotic pathogens. Ben-Ami et al. investigated that colistin shows in vitro activity against Mucorales spp and in vivo study in a murine model of pulmonary mucormycosis [101]. Sofosbuvir (17, Fig. 8) is an FDA approved antiviral drug against Hepatitis C (HCV) which target RNA-dependent RNA polymerase (RdRp) virus. Based on in silico study, Elfiky showed that sofosbuvir can bind to R. oryzae with binding energies comparable to that of HCV NS5b RdRp. It was suggested that RdRp may be a potential target protein against the R. oryzae which cause mucormycosis [102].
The TOR (target of rapamycin) signaling pathway is also an attractive target for antifungal therapy due to its central role in regulating fungal growth. Rapamycin (18) exhibits broad-spectrum antifungal property against various fungal pathogens, including Candida albicans and Cryptococcus neoformans. Rapmycin exerts it therapeutic action through conserved FKBP12-dependent inhibition of a TOR kinase homolog in M. circinelloides. Rapamycin significantly inhibits the growth of various fungal pathogens such as zygomycetes P. blakesleeanus, R. oryzae, and M. circinelloides. Rapamycin showed inhibition up to 80% of growth at 6.26 μg/ml, 12.5 μg/ml, and 100 μg/ml concentrations for P. blakesleeanus, R. oryzae, and M. circinelloides, respectively [52]. Tacrolimus (FK506) (19) also displayed significant inhibition of growth of M. circinelloides R7B (−), M. circinelloides NRRL3631 (+), R. oryzae FGSC9543 and P. blakesleeanus NRRL1555. FK506 exerts its antifungal effect through FKBP12-dependant inhibition of PP2B, but not TOR [103].
Montoir et al. have synthesized a library of azole based antifungal agents fused with pyrrolotriazinone moiety (Fig. 9 ). All the synthesized azoles were screened against various fungal pathogens. Synthesized azoles displayed a broad in vitro activity against fluconazole-susceptible and fluconazole-resistant Candida spp. These azole derivatives were found 10-100-fold more active than voriconazole against two C. Albicans isolates. Compounds 23, 24, 25 and 26 significantly inhibited the growth of Rhizopus orizae (RHPOR1, RHPOR2) and Mucor circinelloides (MURI1).Fig. 9 Structure of new antifungal azoles having pyrrolotriazinone scaffold. Compound 26 shows promising antifungal activity against mucormycosis.
Fig. 9
Compound 23 showed inhibition against RHPOR1, RHPOR2 and MURI1 with MIC values of 0.19, 0.125 and 0.19 μg/mL respectively. Compound 22 displayed inhibition with MIC values 0.0625, 0.25 and 0.25 μg/mL against the above three strains. Compound 25 displayed its inhibition with the MIC 0.125, 0.25 and 0.75 μg/mL while MIC values of compound 26 were found as 0.0625, 0.094 and 0.19 μg/mL against RHPOR1, RHPOR2 and MURI1 respectively. Compound 26 of reported library exhibited significant in vitro antifungal activity against A. fumigatus, R. oryzae and M. circinelloides. Compound 26 displayed in vivo efficacy against two murine models of lethal systemic infections caused by C. albicans [104].
Glycosylphosphatidylinositols (GPIs) are a family of complex glycolipids that are covalently attached to the protein C-terminus through an amide bond. This protein is responsible for adhesion to host epithelium. Recently it was discovered that GPI biosynthetic pathway as a promising target for the treatment of life-threatening fungal infections. GPI biosynthesis is a conserved process, required for anchoring proteins to the fungal cell membrane and essential for the integrity of the cell wall in fungi (yeasts and molds). Inhibition of GPI biosynthesis is damaging to fungal cells as GPI anchor maturation impairment disturbs proteostasis in the endoplasmic reticulum (ER) causing potentially lethal cellular stress. Additionally, lack of GPI-anchored proteins at the cell surface hampers the cell wall integrity ultimately leading to lysis and death of fungal cells. One of the targets of the GPI biosynthetic pathway is the GPI-anchored wall protein transfer 1 (Gwt1), which catalyzes the acylation of inositol during GPI-biosynthesis [105,106]. Inhibition of Gwt1 protein compromises fungal cell wall integrity and produces various fungal growth defects leading to the cell death (Fig. 10 ) [[106], [107], [108]].Fig. 10 Various targets and inhibitors of GPI biosynthetic pathways: General structure of GPI-APs. Ethanolamine phosphate (EtNP) is linked to the C-terminus of the protein through amide linkage, this protein is responsible for adhesion of fungal cells to the host epithelium. The lipophilic fatty acids chains attached to the inositol (Ins) interacts with lipid bilayer membranes. Manogepix (MGX) or Fosmanogepix inhibits the conserved fungal Gwt1 enzyme required for acylation of Ins in the GPI biosynthesis pathway. Jawsamycin inhibits the biosynthesis of GPI by targets the catalytic subunit Spt14/Gpi3 of the fungal UDP-glycosyltransferase of GPI.
Fig. 10
Manogepix (MGX) (10, Fig. 11 ) is a biologically active form of prodrug fosmanogepix (11) (formerly APX001 and E1210, Fig. 11) which is a broad-spectrum antifungal drug that prevents the maturation of GPI-anchored proteins by inhibiting the enzymatic action of inositol acyltransferases (Gwt1). This inhibition caused pleiotropic effects on the growth of fungal pathogens and virulence [54]. MGX has displayed a broad range of in vitro activity against various fungal pathogens and few rare molds [[109], [110], [111]]. MGX has also displayed variable and moderate in vitro activity against some Mucorales strains [111]. Gebremariam et al. have evaluated the activity of fosmanogepix in two mice models. Fosmanogepix is available in both intravenous (IV) and oral formulations, this antifungal prodrug is currently under phase-II clinical investigations as a therapy against life-threatening invasive fungal infections against two strains of R. arrhizus with high MEC (4.0 g/mL) and low MEC (0.25 g/mL) values. Treatment of invasive pulmonary infection mice models with 78 mg/kg or 104 mg/kg doses of fosmanogepix prodrug, significantly improved the median survival time and prolonged overall survival of mice as compared to placebo control. Fosmanogepix treatment reduced the fungal burden significantly in both lungs and the brain. Tissue clearance and survival of animals were found comparable to clinically significant doses of FDA approved isavuconazole for the treatment of mucormycosis against 104 mg/kg fosmanogepix dose. These findings suggest sustained development of fosmanogepix as a first-in-class treatment against invasive mucormycosis [112] (see Fig. 12).Fig. 11 Inhibitors of inositol acyltransferases (Gwt1) of GPI biosynthesis pathway.
Fig. 11
Fig. 12 Structure of jawsamycin and various jawsamycin antifungal derivatives.
Fig. 12
Jawsamycin (29) is an unprecedented and highly complex natural product containing five cyclopropyl rings in the fatty acid tail and dihydro uridine moiety in the head region. Recently Fu et al. reported in vitro antifungal activities against various pathogenic fungi including Molds and Mucorales, and in vivo in a mouse model of invasive pulmonary mucormycosis due to Rhyzopus delemar infection. Jawsamycin exhibited fungicidal activity against Mucorales Rhyzopus delemar (MEC = 0.016 μg/ml), Rhyzopus oryzae (MEC = ≤ 0.008 μg/ml), Absidia corymbifera (MEC = ≤ 0.008 μg/ml), Mucor circinelloides (MEC = 0.016 μg/ml) and Mucor indicus (≤0.008 μg/ml). Chemical derivatization of jawsamycin produced a library of 24 jawsamycin derivatives. This derivatization was achieved by creating variations in tail and head region of this natural product through an efficient synthetic strategy. Unfortunately, none of the tested jawsamycin derivatives displayed improved potency or spectrum compared to the jawsamycin derivatives. Jawsamycin was identified as the first inhibitor of the fungal UDP-GlcNAc phosphatidylinositol transfer complex which exhibited selectivity over the mammalian enzyme and exerts antifungal activities against a wide spectrum of pathogenic fungi in vitro, and an intractable pathogen in a murine disease model [55].
Recently Barakat et al. have reported in vitro antifungal activities of spirooxindole derivatives against Syncephalastrum racemosum RCMB 016001 (rarely lead to complications designated mucormycosis) through agar well diffusion method and compared with AmB and fluconazole. Compound 54 bearing a methyl substituent in phenyl ring, showed better antifungal activity (zone of inhibition = 19 mm) as compared to compound 55 (zone of inhibition = 14 mm). Docking studies of the compounds with lanosterol 14α-demethylase, showed that compound 54 showed four hydrogen bonding interactions with the target protein, while compound 55 only showed one. This might explain the higher antifungal activity of compound 54.
Theses spirooxindoles were found less active as compared to AmB (zone of inhibition = 22 mm) and fluconazole ((zone of inhibition = 22 mm) [113]. Hassaneen et al. have also reported in vitro antifungal activity of spirooxindole-spiropiperidinonepyrrolidines (56–61) and spirooxindole-spiropiperidinone-pyrrolizines (62–67) against S. racemosum. Compounds 56 to 67 exhibited good antifungal activity against S. racemosum (39.46–82.63 mm). Compounds 62, 64 and 68 displayed better activity as compared to the control drug (AmB) while compound 57 and 65 showed antifungal activity comparable to AmB. Compounds 57, 62, 64 and 65 showed MIC 1.95, 0.49, 0.98 and 1.95 μg/ml respectively against S. racemosum. These spiro compounds inhibited the growth of against S. racemosum with the IC50 values of 16.37, 10.64, 11.32 and 11.32 μg/ml respectively. Compound 62 and 64 were found more active than AmB (MIC = 1.95 μg/ml and IC50 14.27 μg/mL) [114].
9.5 Statins
Statins are inhibitors of 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA) reductase, which reduce intracellular levels of mevalonate and farnesyl pyrophosphate (FPP) and ultimately affect the synthesis of ergosterol (see Fig. 13). In the mevalonate (MVA) pathway, mevalonate is synthesized from 3-hydroxy-3methylglutaryl-CoA by the action of HMG-CoA reductase which converted to FPP through an enzyme cascade. FPP is then converted to squalene and, subsequently to ergosterol [115] (Fig. 14 ). Statins prevent the synthesis of mevalonate from HMG-CoA by inhibiting HMG-CoA reductase. Hence statins are able to block ergosterol biosynthesis by inhibition of FPP production in fungal cells (Fig. 14). Various in vitro studies have confirmed that statins can decrease the ergosterol content of fungal cells [116]. Statin-induced lowering in ergosterol seems to be dose-dependent [117], and this is believed to be due to the inhibition of HMG-CoA reductase and not related to direct depletion of intracellular ergosterol [118].Fig. 13 Structures of antifungal spirooxindole, spirooxindole-spiropiperidinonepyrrolidines and spirooxindole-spiropiperidinone-pyrrolizines.
Fig. 13
Fig. 14 Mevalonate (MVA) pathway: Statins are competitive inhibitors of HMG-CoA reductase and block the conversion of 3-hydroxy-3-methylglutaryl-CoA to mevalonate and FPP. Synthesis of mevalonate from HMG-CoA is a rate limiting step in the isoprenoid biosynthetic pathway, which is involved in the synthesis of ergosterol in fungal cells.
Fig. 14
Various statin drugs, fluvastatin, lovastatin, hydrolyzed lovastatin, rosuvastatin atorvastatin, simvastatin and hydrolyzed simvastatin have been shown to inhibit a wide variety of opportunistic fungal pathogens such as R. oryzae, R. homothallicus, R. stolonifer, R. microspores, R. pusillus, R. miehei, M. racemosus, M. mucedo, M. circinelloides A. corymbifera, A. glauca, M. Africana and S. racemosum. These fungal species are responsible for causing mucormycosis in human beings. Fluvastatin seems to be the most effective antifungal statin with the widest and significant in vitro inhibitory activity against mucormycosis. In R. oryzae (Clinical isolate) fluvastatin exhibited higher inhibitory activity (MIC = 2–3.25 μg/mL) against R. oryzae [119] and R. pusillus (MIC90 = 0.4 μg/mL) [120] as compared to AmB (MIC = 2–4 μg/mL) and (MIC90 = 1.0 μg/mL) respectively. fluvastatin also displayed significant activity against R. microspores, R. miehei, M. racemosus, M. mucedo, M. circinelloides M. circinelloides, A. glauca and S. racemosum. Antifungal activities of various statins against mucormycosis have been summarised in Table 2 .Table 2 In vitro antifungal activities of various statins against Mucormycosis with reported MICs ≤128 μg/mL.
Table 2S.No Statin Structure Fungus* Strain MIC
μg/mL References
1 Fluvastatin (FLV) Image 1 R. oryzae CBS 112.07 3.6–11 [121]
CBS 109939 2–3.125 [122]
CBS 146.90 12.5 [123]
CI 2–3.125 (<AmB:2–4) [122]
R. microsporus NRRL 514 MIC90 = 33–96 [121]
R. pusillus CN(M) 231 0.4 (<AmB:1) [121]
ETH M4920 6.25 [123]
R. miehei CBS 360.92 3.125 [123]
CBS 360.92 MIC90 = 3.6 [121]
M. racemosus WRL CN(M)304 25 [123]
M. mucedo WRL CN(M)
12034 6.25 [123]
M. circinelloides MS 12 4 [124]
A.corymbifera SZMC 2010 MIC90 = 3.6 [121]
A. glauca SZMC 11,072 6.25 [123]
S. racemosum SZMC 2011 11–33 [121]
2. Atorvastatin (ATO) Image 2 R. oryzae CBS 109939 32 [122]
CI 64–128 [125]
CI 32 [119]
R. stolonifer SZMC 11101 64 [126]
M. circinelloides MS 12 16 [127]
S. racemosum SZMC 2011 32 [126]
M. africana NRRL 2978 8 [126]
3. Lovastatin (LOV) Image 3 R. oryzae CBS 109939 128 [120,122]
CI 32–56 [124]
CI 128 [119]
R. pusillus CN(M) 231 MIC90 = 3.6 [121]
EI and CI 1–2 [128]
R. homothallicus CI 40–56 [124]
R. miehei EI,CI 64–128 [128]
M. circinelloides CI 16–56 [124]
S. racemosum SZMC 2011 16 [126]
C. bertholletiae CI 32–40 [124]
4. Hydrolyzed lovastatin (HLOV) Image 4 R. pusillus CN(M) 231 MIC90 = 11 [121]
R. miehei CBS 360.92 MIC90 = 11 [121]
A. corymbifera SZMC 2010 MIC90 = 11 [121]
5. Rosuvastatin (ROS) Image 5 R. oryzae CBS 112.07 MIC90 = 96 [121]
R. stolonifer SZMC 11101 64 [126]
R. pusillus CN(M) 231 MIC90 = 11 [121]
R. miehei CBS 360.92 MIC90 = 11–33 [121]
A. corymbifera SZMC 2010 MIC90 = 33 [121]
S. racemosum SZMC 2011 32 [126]
M. africana NRRL 2978 8 [126]
M. circinelloides MS 12 32 [127]
6. Simvastatin (SIM) Image 6 R. oryzae CBS 109939 64 [122]
[119]
R. pusillus CN(M) 231 MIC90 = 11–33 [121]
A. corymbifera SZMC 2010 MIC90 = 96 [121]
S. racemosum SZMC 2011 8 [126]
7. Hydrolyzed simvastatin (HSIM) Image 7 R. pusillus CN(M) 231 MIC90 = 1.2–3.6 [121]
A. corymbifera SZMC 2010 MIC90 = 11 [121]
R. microsporus var. oligosporus NRRL 514 MIC90 = 33–96 [121]
CI = Clinical isolates EI = Environmental isolates, Genus Rhizopus (R. oryzae, R. homothallicus, R. stolonifer, R. microspores), Genus Rhizomucor (R. pusillus, R. miehei), Genus Mucor (M. racemosus, M. mucedo, M. circinelloides) Genus Absidia (A. corymbifera, A. glauca), Genus Mycotypha (M. Africana) Genus Syncephalastrum (S. racemosum).
10 Conclusions
Mucormycosis is a lethal fungal infection that commonly affects immunocompromised patients with an incompletely understood pathogenesis. This is more common in those who are on hemodialysis, taking high-dose glucocorticoids, have significant burns, or have uncontrolled diabetes mellitus [129]. The overall mortality rate of this malady is more than 40% and reaches 100% in patients suffering from disseminated illness, brain infection or persistent neutropenia. This article provides a comprehensive review of the urgent global efforts, currently underway, towards the discovery and development of therapeutic agents for the treatment of mucormycosis. This review is specifically focused on mechanism of action of various therapeutics, including coverage of new antifungal agents being investigated as part of the urgent global response to control and combat this lethal infection. Various antifungal agents, as indicated in this review can be promising agents to treat this life threatening disease. Although, large-scale clinical investigations are strongly suggested to these issues.
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
MKH is thankful to Department of Higher Education, Prayagraj, UP. Authors SA, AK, AJS and SJ would like to thank, 10.13039/100008901 College of Pharmacy , 10.13039/100009388 Jazan University , KSA, College of Science, 10.13039/501100008809 University of Hail , Hail KSA and the Deanship of Scientific Research at 10.13039/501100007613 Majmaah University , for supporting this work respectively.
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| 0 | PMC9734071 | NO-CC CODE | 2022-12-14 23:28:27 | no | Eur J Med Chem. 2022 Dec 10;:115010 | utf-8 | Eur J Med Chem | 2,022 | 10.1016/j.ejmech.2022.115010 | oa_other |
==== Front
Tour Manag
Tour Manag
Tourism Management
0261-5177
1879-3193
Elsevier Ltd.
S0261-5177(21)00004-2
10.1016/j.tourman.2021.104285
104285
Article
Travel burnout: Exploring the return journeys of pilgrim-tourists amidst the COVID-19 pandemic
Yousaf Salman ∗
College of Business and Public Management, Wenzhou-Kean University, 88 Daxue Road, Ouhai District, Wenzhou, PR China
∗ College of Business and Public Management, Wenzhou-Kean University, 88 Daxue Road, Ouhai District, Wenzhou, PR China.
22 1 2021
6 2021
22 1 2021
84 104285104285
16 7 2020
26 11 2020
7 1 2021
© 2021 Elsevier Ltd. All rights reserved.
2021
Elsevier Ltd
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This study investigates the timely, yet academically unexplored, topic of travel burnout. The study explores the return journeys of pilgrim-tourists from Iran to Pakistan during COVID-19 pandemic and contextualizes travel burnout as a negative emotional state placed in the existing theoretical streams. The conservation of resources theory (Hobfoll, 1989; 2004) provided theoretical support to guide current study's research agenda. On the basis of a qualitative grounded theory research design, 47 in-depth interviews of pilgrim-tourists were conducted. Travel burnout emerged as a multidimensional concept comprising 3 core dimensions, i.e., low tourist self-efficacy, travel exhaustion and emotional maladaptation. Travel burnout anchors emerged as those factors that facilitated preservation of the tourists' resources when travel circumstances became beyond their regulation. The results pave the way for a more theoretically sound conceptualization of travel burnout. For destination marketing organizations, various avenues are identified that need attention to alleviate the tourist concerns that lead to burnout.
Keywords
COVID-19
Pilgrim-tourists
Travel burnout
Tourist self-efficacy
Travel exhaustion
Travel burnout anchors
Pandemics
==== Body
pmc1 Introduction
The global penetration of COVID-19 appears to be a significant situational factor in studying the strategic aspects associated with the tourism and hospitality industry, which is particularly susceptible to changes in such situational factors (Wen et al., 2020). There is sufficient evidence regarding the adverse effects of pandemics and diseases on tourism prospects in a region/country. For instance, similar tourism crisis patterns occurred in countries affected by the Ebola virus (Novelli et al., 2018), SARS (Kuo et al., 2008), Swine Flu (Page et al., 2012) and influenza pandemics (Page et al., 2006), but the research agenda of the current study moves beyond the obvious crisis induced tourism studies of the past. This study explores and attempt to conceptualize the travel burnout arising out of the long return journeys of pilgrim-tourists who were caught amidst COVID-19 pandemic in Iran when the country was rapidly becoming the global hot spot of COVID-19 in the months of February and March 2020. As Iran was engaged in dealing with its own pandemic, the pilgrims from neighboring countries were involuntarily sent back across the border. Pilgrims have been recognized as superspreader potential carriers of COVID-19 (Ebrahim & Memish, 2020). As increasing numbers of pilgrims travelling during the COVID-19 epidemic returned to their home countries, there was an increasing risk of community transmission of disease, which made the return journeys of the pilgrims extremely challenging, as they faced strict border inspections, health protocols, Standard Operating Procedures (SOPs) and quarantine strategies.
Burnout is conceptualized as a multidimensional concept by Maslach and Jackson (1981) that comprises three core constructs, i.e., emotional exhaustion, depersonalization and feelings of reduced accomplishment. The burnout concept is studied extensively in organizational behavior studies regarding workplace stressors, in service industry studies, with a focus on frontline employees and in the sport science field, regarding the burnout of professional athletes as a result of excessive travelling (Schaufeli, Leiter, & Maslach, 2009; Fowler et al., 2015). However, in the tourism and travel research, the burnout concept is seldom discussed. The lack of research on travel burnout can be attributed to the lack of extraordinary circumstances of tourism crisis situations in the past. With COVID-19 being considered the most impactful event of the 21st century, it has created implausible circumstances that have drawn attention to underlying relationships needing novel theoretical explanations. Moreover, travel and vacations are usually considered as avenues to destress and rejuvenate the mundane life routine, which traditionally led tourism researchers to approach travel as a source of positive emotional states for tourists. Similarly, Fennel (2017) contends that the negative states of tourist psychology and physiology are scantly researched by tourism and travel scholars. This omission is attributed to the inherent demand for scholarly tourism research in traditionally popular domains, such as sustainability, authenticity, and tourist motivations, which are predominantly considered positive emotional states experienced by tourists (Filep & Laing, 2019). This prevalent infatuation with the positive emotional states of tourists has wider implications for the inadequate or lack of the clear operationalization of the negative emotional states experienced by tourists (Sun et al., 2020), which has kept scholars further away from navigating into the relatively uncharted territories of negative psychological states such as travel burnout.
In addition to this, negative psychological states, such as burnout, have conventionally adhered to the restrictive conceptualization of being linked to workplace or occupational settings, which may not have attracted the interest of tourism researchers to consider new lines of inquiry regarding the applicability of the concept in the travel context. However, this context-dependability is a point of ongoing debate and criticism in the burnout literature, as the cross-domain applicability of the burnout concept to life conditions outside workplace setting is sufficiently evidenced (Bianchi et al., 2014; Pines et al., 2011). Another reason that tourism researchers have not investigated the burnout concept among tourists is probably because the connotation of the term burnout is considered too strong in some cultural pretexts, implying severity or end stage and, hence, a remote possibility of recovery; therefore, relatively milder terms, such as ‘exhaustion’ (Schaufeli et al., 2009), tourism fatigue (Sun et al., 2020) and aggregate stress levels (Taylor et al., 2017), are used. However, this is at odds with the original conceptualization of burnout, which was thought to encapsulate the complete continuum from mild to severe cases. The tourism industry has not suffered a crisis of such an epic proportion in recent human history as is engendered by COVID-19, and considering the severity of the existential threats posed by COVID-19, we expect the prevalence of extreme negative emotional states settling in the form of travel burnout among returning pilgrims.
The theoretical support for the current study is drawn from the Conservation of Resources (COR) theory (Hobfoll, 1989, 2001, 2004), which provides a useful explanation of how the well-being of tourists who found themselves stuck in the middle of the pandemic may be severely affected by the situational demands of travel. The COR perspective reiterates that individuals possess a finite supply of resources and continually strive to preserve, retain and attain their resources. This continuous struggle becomes a prevalent phenomenon due to the strenuous and stressful situational demands engendered as a result of COVID-19, such as long hauls, strict health inspections, quarantine protocols, restricted mobility, and unplanned itineraries, along with the existential concerns about their safety and well-being, which play a crucial role in depleting their resources, such as individual energies, self-efficacy and positive mood among others, which could manifest as travel burnout.
The study has the following two main research objectives: i) to explore the return journeys of pilgrim-tourists and understand their emotional responses to various aspects of their long travel back home; and ii) to conceptualize and contextualize travel burnout as a negative emotional state in the existing theoretical streams of the contemporary tourism literature. The rest of the research paper is organized as follows. First, the literature review section builds the theoretical foundation of the research based on the relevant scholarship. This is followed by the methodology section and the iterative thematic analysis utilizing qualitative grounded theory approach. Lastly, the conclusions based on the study results are presented and implications for research scholars and policy makers are discussed followed by limitations and recommendations for the future research directions.
2 Literature review
2.1 Theoretical backdrop
Traditionally, burnout is conceptualized as a multidimensional notion comprising three core components (Maslach & Jackson, 1981). The first, i.e., emotional exhaustion is a constant state of physical and emotional depletion resulting from excessive or continuous activity. The second, i.e., depersonalization, covers the interpersonal dimension of burnout and epitomizes feelings of detachment and decreased involvement in relationships. The third, i.e., reduced accomplishment, is the self-appraisal dimension of burnout and is characterized by feelings of accomplishing nothing worthwhile (Halbesleben & Buckley, 2004). There is an apparent omission of burnout research in the travel and tourism studies for the reasons delineated in the introduction section. However, it was only recently that Sun et al. (2020), conceptualized and developed a scale to gauge tourism fatigue. They described tourism fatigue as a negative state caused by excessive interaction between the destination and tourists that manifested in diverse psychological and physiological aspects. Further, Sun et al. (2020) uphold that tourism fatigue accumulates gradually over a long period of time and it is more prominent in long-term travel. Similarly, Taylor et al. (2017) discuss the concept of aggregate travel stress to signify the accumulated strain experienced during the course of travel. Comparing tourism fatigue and the aggregate travel stress with the concept of burnout reveals that both the concepts bear striking similarity to one of the core constructs of the burnout concept, i.e., emotional exhaustion, which also epitomizes feeling of tiredness and weariness and manifests itself after excessive or continuous activity (Maslach & Jackson, 1981). Moreover, it is believed that prolonged fatigue/aggregate stress and emotional exhaustion essentially conceptualize the same concept (Michielsen et al., 2004). Therefore, tourism fatigue (Sun et al., 2020) and aggregate travel stress (Taylor et al., 2017) are more proximate to the emotional exhaustion construct of burnout. Although exhaustion remains the central component of burnout, Schaufeli & Taris (2005) and Schaufeli et al. (2009) have cautioned against reducing the burnout concept to merely emotional exhaustion. On the same grounds, this study deconstructs the negative experiences of pilgrims to understand their emotional responses to various aspects of their return journey (Shilon & Shamir, 2016) and contextualizes travel burnout as a more holistic concept encapsulating the complete continuum of the negative emotional states of burnout instead of reducing it to its single dimension. The COR framework (Hobfoll, 1989, 2004) suggests that an individual possesses a limited supply of resources in terms of possessions of physical objects, prevalence of personal characteristics, such as self-efficacy, self-esteem and self-mastery, presence of personal conditions, e.g., familial and occupational statuses and permeation of energies in terms of time, focus, knowledge and attention, among others. These resources are valuable to the extent that they assist in attaining and cultivating personal well-being, but when individuals perceive threats to their existing resources, they experience high levels of stress (Hobfoll & Lilly, 1993). This stress drains the individuals’ reservoir of valued resources, consequently debilitating their personal well-being and impairing their ability to adequately respond to the environmental demands to the extent that burnout settles in (Hobfoll, 2001; Halbesleben & Buckley, 2004; Maslach & Leiter, 2006).
2.2 Context: pilgrim-tourists and COVID-19
Religiously motivated travel is considered one of the oldest mobility motives and is recognized among the largest gatherings of tourists in the world (Wu et al., 2019). Traditionally, in tourism studies there is a pervasive notion to perceive tourists and pilgrims as two evidently dichotomous groups due to different travel motivations and destination choices, i.e., pilgrims are driven by religious and spiritual inspirations to sacrosanct sites, while tourists are motivated by secular interests and hedonic pleasures (Shuo et al., 2009). However, the contemporary literature on travel and tourism has somewhat opposed this binary opposition between pilgrims and tourists and does not characterize only those individuals who are motivated by inherent leisure hedonic orientations as tourists (Nyaupane et al., 2015), thereby implying that the motives of travel do not define who a tourist is, and religiously motivated travel represents a form of special interest tourism (Ron, 2009). Even the motivations of tourists to visit sacred sites are complex and multifaceted, as some seek a life changing experiences, some yearn for worship and prayer, while others are motivated by cultural explorations or the natural environment ((Finney et al., 2009). Therefore, Della Dora (2012) concludes that the difference between pilgrims and tourists is highly indistinguishable and the boundary between the two continues to converge; thus, they are commonly being referred to as religious-tourists or pilgrim-tourists. In the rest of the paper, the term pilgrim-tourist will be used for consistency.
Close to 7.8 million tourists visited Iran in the year 2019, with majority of them being pilgrim-tourists hailing from Pakistan, Turkey, Iraq and Azerbaijan (UNWTO, 2019). The spring season from mid-February till late March is traditionally considered a favorable time for pilgrim-tourists to visit Iran (Badshah et al., 2020). Unfortunately, the months of February and March in the year 2020 concurred with the COVID-19 pandemic. When Iran was becoming one of the worst hit countries by the COVID-19 pandemic in the month of February and March, as infections and death tolls escalated, there were thousands of pilgrim-tourists stuck in Iran. As Iran became busy in dealing with its own pandemic crisis, the pilgrim-tourists from the neighboring countries were involuntarily sent back across their borders (Atyani & Khan, 2020). Although the international border between Pakistan and Iran was sealed, the influx of pilgrim-tourists from Iran continued to increase and the pilgrim-tourists remained stranded on the Iranian side of the Pakistan-Iran border for days until they were allowed by the Pakistani government to enter (Badshah et al., 2020). Temporary quarantine camps were setup in the border town of Taftan, situated on the Pakistani side of the border with Iran, to isolate the returning pilgrim-tourists. However, due to the overburden on facilities in the Taftan border town, pilgrim-tourists were shifted to major cities where they were supposed to be tested for COVID-19 and quarantined for 2 weeks at purpose-built centers. Therefore, overextended travel of pilgrim-tourists in negative emotional states, accumulating a series of negative experiences with decreasing control over their travel arrangements, qualify them as information-rich sources to theorize the concept of travel burnout.
2.3 Theorizing the relationship between travel burnout and pilgrim-tourists from COR perspective
For pilgrim-tourists, resources can be defined to encompass all those things that they value to the extent that they are perceived to help them attain their travel goals. Having said that, these resources are finite in nature and possessing an abundance of resources does not necessarily ensure that individuals will thrive, but the appropriate allocation of these resources to maximize the environmental fit is deemed more consequential by Hobfoll (2011). In this milieu, one of the most valued resources that pilgrim-tourists possess is their faith and spirituality. The environment during their pilgrim visitation constitutes the active community participation of prayers and rituals from fellow worshippers (Kim et al., 2016). Encompassed in religious narratives, divinity is perceived to be closer and divine intervention is believed to heal their emotional and physically mundane sufferings (Sharpley, 2009, pp. 237–253). Time is another highly valued resource, being one of the few absolutes that tourists encounter, as they cannot accumulate it to use at a future date (Truong & Hensher, 1985). The time-bound tourism activity is not only related to the absolute time available but also refers to the number of activities tourists have planned during their stay at a destination (McKercher et al., 2006). In the middle of lockdown, when there is limited mobility and the short-span visa limit is approaching, time resources are rapidly depleting for pilgrimage-tourists as the travel goals of pilgrimaging to religious sites remain unfilled. Moreover, the anxiety fomented by pandemics is believed to consume the mental resources of individuals, which incapacitates their daily functioning and quality of life (Scalabrini et al., 2020).
The personal conditions of tourists, such as their social-cultural background, geographical origins and nationality, influence their interactions with travel destinations (Yan, 2003). For instance, cultural proximity between the tourist's home country and destination can be a useful resource (Yousaf et al., 2020) and affect their choices of tourist attractions at the destination, which are markedly different from tourists who source from culturally distance markets (Flognfeldt, 1999). The pilgrim-tourists come from countries that are culturally, religiously and geographically proximate to Iran, but this valued resource reservoir continued to drain as the pilgrim-tourists found themselves in the middle of their journeys surrounded by societal apprehensions. It is also pertinent to mention that the resources deemed valuable by individuals may not necessarily hold value for them in a specific context, i.e., resources are more valued in their idiosyncratic context (Winkel et al., 2011). Therefore, the proximity with Iranian socioreligious and cultural life in normal circumstances could prove to be a very valuable resource for pilgrim-tourists, but in this pandemic situation, its value may not be realized.
As nonpharmaceutical interventions, such as border control, social distancing and quarantine, went into effect, the uncertainty associated with travel is fueled, inhibiting the pilgrim-tourists’ ability to appropriately respond to these unique environmental challenges. Additionally, to contain the spread of the virus, the unprecedented measures of lockdown, restrictions on inbound and outbound travel and limited internal movement drastically transformed the societies and lifestyles of people in the first quarter of 2020 to adapt to this external change (Lee, 2020). Another important aspect is that COVID-19 is perceived to be an existential threat, as one's sense of self and others is existentially threatened by the danger of becoming infected, infecting others or losing a social relation (Scalabrini et al., 2020). The mass tragedies caused by infectious diseases are believed to trigger a heightened sense of fear and anxiety among people, severely affecting their mental and psychological well-being (Balaratnasingam & Janca, 2006). In this milieu, the personal characteristics of pilgrim-tourists that were stuck amidst the COVID-19 pandemic in Iran, including the resources such as morale, self-control and perseverance, were severely threatened.
The COR framework suggests that when individual resources are continuously being drained but environmental demands do not subside and continue to persist, this is the stage where burnout settles in (Hobfoll, 2001). The pilgrim-tourists on return journeys to their home countries in the middle of a pandemic crisis are vulnerable to experiencing travel burnout instead of merely tourism fatigue (Sun et al., 2020) or aggregate travel stress (Taylor et al., 2017), which are appropriate operationalizations of the weariness engendered in general touristic journeys but may fall short of encapsulating the tourism crisis produced by extraordinary emergency situations such as COVID-19.
3 Methodology
The present study adopted a qualitative grounded theory approach. The grounded theory supports establishing the theory following an iterative analysis, allowing researchers to visualize the emerging patterns from raw data on the basis of their conceptual proximity and underlying theoretical underpinnings (Braun & Clarke, 2006; Strauss & Corbin, 1998). The grounded theory approach allows researchers to discover the theory from data through an iterative procedure which produce conceptual nodes of solution and help researchers recognize links between them (Matteucci & Gnoth, 2017). In doing so, the phenomenon under investigation can be understood better through the grounded theory framework. The data was collected from pilgrim-tourists who travelled to Iran in the months of February and March. This study covered a population of 1270 pilgrim-tourists shifted to the South Punjab quarantine center in the city of Multan. These 1270 pilgrim-tourists came in 33 buses travelling a distance of close to 1260 KMs from Taftan border-town to Multan. After the initial screening for COVID-19 was completed and the health protocols of the quarantine time period were fulfilled, no sign of virus was detected in any pilgrim and they were allowed to return to their homes (Badshah et al., 2020).
The snowball sampling technique was used to recruit potential participants (Biernacki & Waldorf, 1981). Few of the pilgrim-tourists were known to the author through personal contacts and invitations to participate in the interviews were sent out to them in the beginning. The purpose of the research was explained to them and they were further requested to nominate other pilgrim-tourists from their social networks who were part of the same return journey from Iran. This cycle was repeated until a sizable number of appropriately informed candidates were identified and recruited. The following qualifying criteria were used in selecting the participants. The first, following the grounded theory design, only information rich participants were selected to provide deeper understanding of their travel experiences and furnish the best data (Strauss and Corbin, 1998). Second, no face to face interviewing was possible to maintain the mandatory social distancing. Therefore, Zoom/WhatsApp/Skype were deemed the preferred mediums to record the interviews. Lastly, all the interviewees had incomplete trips, as the visits to many religious sites in their original plans remained unfulfilled and no such interviewees were made part of the sample who travelled other than by bus (e.g., plane) during their entire journey to ensure the homogeneity of the sample. As a result, 47 pilgrim-tourists were selected to share their experiences. The interviews were recorded in the local Urdu language and were transcribed into a written English script by the author. On average, each interview lasted from 15 to 30 min. The respondents consist of 35 males (74%) and 12 females (26%), with ages ranging from 28 to 64 years old, with the average age being 42 years old; approximately 70% (33) of the respondents had completed intermediate education (high school). The length of the overall journey including the quarantine period of two weeks and border stays lasted from a minimum of 24 days to a maximum of 38 days, with the average length of travel being 32 days.
The data was sorted through NVivo, following the essential grounded theory method of iteration proposed by Braun and Clarke (2006) and delineated by Yousaf and Fan (2020). In the first stage, the data reduction process was instituted by transcribing the interviews into written form and attaining familiarity with the data by comprehensively reading the transcripts of the interviews and interview-notes and listening/watching the recorded interviews. In the second stage, the screening and coding of the transcripts were performed in accordance with the interview questions and the underlying theoretical underpinnings postulated by the study. As a result, a large number of conceptual nodes of solutions were produced. In the third stage, the conceptual nodes of solutions were segregated into subcategories and categories on the basis of their conceptual closeness and theoretical proximity. Following the same principle, thematic similarities were closely examined and the coded information was ordered by associating conceptual nodes with key themes. The transcripts were continuously consulted and themes were alluded repeatedly to determine the presence of coinciding nodes that would later be consolidated to form a singular node. The nodes were further refined on the basis of theoretical and conceptual closeness to ensure that the nodes within the themes, categories and subcategories are connected to each other but were still mutually exclusive. In the penultimate data interpretation stage, the thematic findings were corroborated by referring to the relevant literature to facilitate a more theoretically informed analysis. Last, the iterative process was reviewed again to further purify the formation of the themes, categories and subcategories to establish clear links with the theory and research purpose.
4 Findings
4.1 Travel burnout constitutes
The first theme alludes at the emergence of a multidimensional concept of travel burnout comprising the following three core constitutes: low tourist self-efficacy, tourist exhaustion and emotional maladaptation.
4.1.1 Low tourist self-efficacy
Tourists are likely to experience exasperation when uncertainty regarding their travel plans occurs and there is a likelihood of unfavorable consequences (Larsen et al., 2009), and situations infused with international turmoil are especially likely to increase tourists’ apprehensions (Brun et al., 2011). In this milieu, tourist self-efficacy refers to the confidence tourists have in their capabilities to approach difficult and challenging situations during their travel (Jin et al., 2016). A pivotal part of the COR theory is the doctrine that individuals endeavor to acquire and preserve the resources they fundamentally value, which largely determines how they fit into the larger context (Hobfoll, 2012). Maintaining a positive sense of self is a universally valued resource, but when a cataclysmic situation, such as COVID-19, arises, the pilgrim-tourists faced challenges in conserving the resources they value, which consequently affected their lives. The existential fear, xenophobic response and reduced tourism participation emerged as predominant contributors to this low sense of self-efficacy among pilgrim-tourists.
4.1.1.1 Existential fear
As returning pilgrim-tourists become aware of virus-contracted acquaintances, friends or family members and number of casualties caused by the infection, the fragility of human existence becomes discernible to them, threatening their relationship with the world, which further escalates their apprehensions. The global pandemic situation engendered by COVID-19 casted a strong impression on the fear and anxiety levels of individuals. Scalabrini et al. (2020) believe that COVID-19 has heightened the introspective awareness of people, as the likelihood of being infected or of being a carrier and spreading the infection to friends and family members makes them experience a perpetual state of worry and apprehension. The social disruption caused by the COVID-19 emergency deeply moved pilgrim-tourists as they obtained continuous manifestations of the inadequacy of their intrinsic connection with human life, invalidating their assumptions about the future, resulting in emotional reactions materializing in the form of existential fears.The uncertainty of human life has become very clear to me during this travel. It feels like you are not in control of your life, as it could take only one person to infect all. Imagine avoiding contact or sitting next to a person when eating, whose company you have enjoyed throughout the travel out of fear of contracting the virus. (Female 48)
Death is inevitable but I did not want to die in such a situation so far from my home. I can recall at least 4 long hauls during our return journey. First, on the Iranian side of the border; second, the Pakistani side of the border; third, the quarantine facility on our way from Taftan border town; and last, the longest quarantine stay in the city of Multan. The lengthier the journey got, the more fearful I became. I just wanted to run away to my home. Probably that's why they had strict security throughout the return travel (to stop us from leaving voluntarily). (Male 36)
Although the continuous reminder about the fragility and vulnerability of human life are the most universal experiences, existential fears engendered at the same time deplete the most valued resources of pilgrim-tourists, i.e., emotional and physical well-being, sense of self and morale. The COR theory advocates the same by underscoring that the resource loss is disproportionately more noticeable compared to resource gain, as resource loss affect people more and at an accelerating rate (Hobfoll, 2012). Therefore, the loss salience stimulated by existential fears had a strong impact on the pilgrim-tourists.
4.1.1.2 Xenophobic response
Although, xenophobia is described as a general fear of something strange or unknown (e.g., foreigners or strangers), the emergent body of scholarship on COVID-19 has contextualized xenophobia reflective of discriminatory attitudes toward potential carriers of pandemic even when it is not confirmed (e.g., Mamun & Griffiths, 2020). In a similar vein, as soon as the first COVID-19 cases in Pakistan were traced back to returning pilgrim-tourists from Iran, there was an increase in the xenophobic response towards pilgrim-tourists from Iran, questioning the rationale for their travel and holding them accountable for the spread of the novel COVID-19 pandemic in Pakistan. The reason being that calamities need to have their origins and causes established in the popular mind; thus, other countries or specific groups are credited as a source of the disease (Phillips & Kilingray, 2011). This xenophobic response can substantially affect the perceptions attributed to a place considered as a source of outbreak (Hall, Scott & Gosling, 2020). For instance, Chinese tourists are believed to have faced the brunt of discriminatory practices in their post COVID-19 travel due to the popular narrative in the media blaming China as the source of COVID-19 (Wen et al., 2020). The same happened to pilgrim-tourists in their return journeys to their homeland, as they encountered this popular perception that pilgrims returning from Iran were the cause of the initial spread of COVID-19 in Pakistan. The primary source from where they discovered this popular perception during the course of their travel was social media, when they found out that their fellow countrymen took to social media avenues to blame the returning pilgrim-tourists for exposing the whole population to the virus and potentially endangering the lives (Tohid, 2020). The country having a history of sectarian strife between Shias (the pilgrim-tourists in this study) and Sunnis, the two largest factions of Muslims in Pakistan, did not help the situation, as relief initiatives and pilgrim-handling responses were being viewed from the narrow perspective of the sectarian divide in popular opinion. This further added to the psychological baggage of pilgrim-tourists by affecting their self-efficacy resources.My friends and relatives keep on questioning why we went to Iran when there was an outbreak there. When we were travelling, there was no health warning from either Iran or Pakistan and travel was going on without restrictions. I saw on social media and people in general blaming pilgrims from Iran for spread of this virus in Pakistan. This was extremely frustrating (Female 31).
4.1.1.3 Reduced tourism participation
The pilgrim-tourists experienced a limited liberty of mobility and access to the outside world due to the strict controlled environment during their return journeys from the time they packed their bags in Iran. The positive impetus of leisure or spiritual travel is not expected to last for the entire duration of the journey but when met with the imposing situational demands of the restricted travel environment that pilgrim-tourists encountered during their return journeys, the positive energy is likely to deplete at an accelerated rate. The COR theory posits that individuals who lack adequate resources possess limited reserve capacity to regulate the increased vulnerability to negative emotions and cognitions (Hobfoll, 2012). The bounded SOP's of travel imply that the administration overseeing the travel arrangements was uncompromising and, in some cases, perceived as authoritarian by the pilgrim-tourists, which further amplified their emotional responses regarding the reduced tourism participation.The kind of controls which were levied on us were really frustrating. We literally had no control over our travel decision-making, add to it, the limited mobility and bounded access reduced us to mere spectators waiting for all this to finish. (Male, 36)
Another important aspect that added to the pilgrim-tourists’ woes was incomplete trip itineraries. The pilgrim trips are meticulously planned with scrupulous details to make the most of their limited time when making pilgrim visits. The pilgrim-tourists planned and expected to do so many things during their travel, and when a contrived plan goes so drastically off-target, all the expended energy in deciding, anticipating and planning the trip becomes ineffectual.I was planning for this trip for a very long time. It is an important pillar of faith in Shiite Islam to pay homage to holy personalities but the trip goals remained unfulfilled. I don't know when this pandemic will be over or if I will ever be able to travel again. I think there is so much uncertainty, and I heard that travel may not be possible for people over 50 years old in the near future. (Male 50)
4.1.2 Travel exhaustion
Travel provides a context against which tourists can continuously interact with the destination, and the experiences rendered in these interactions play a pivotal role in re-energizing their depleted internal resources (Chen, Petrick, & Shahvali, 2016). In the same context, travel exhaustion is marked by extensive travelling followed by low motivation for engaging in further travel. As travel ceases to be fun anymore due to the stress accumulated during the course of travelling; this is accompanied by longing for familiar environments, i.e., home. The travel exhaustion coalesced as an intertwined stress response by the pilgrim-tourists to the situational demands rendered by COVID-19.
4.1.2.1 Aggregate travel stress
As stipulated by the COR theory, the stress becomes pronounced when individuals are not capable of adequately dealing with the situational demands (Hobfoll, 1989, 2004). During their return journeys, when negative incidents continued to build one on the top of another, the pilgrim-tourists became overwhelmed with the feeling of being overextended due to an imbalance between resources and demands being made on them. The certain stressful and traumatic events in life of individuals elicit an acute stress response, which makes them vulnerable to showing signs of burnout (Mather et al., 2014). The pilgrim-tourists who were stuck in one of the worst hit countries by COVID-19 and now must make a long journey home peppered with long hauls, strict quarantine, restricted mobility, frequent health protocols and an overall highly uncertain travel environment took an extreme toll of their patience and sanity and made them more susceptible to exhibit mild to severe signs of burnout due to all the stress accumulated during the course of travel.For most of our return journey, we were in survival–mode, which just squeezed energy out of me. (Male 41)
The trip, which commenced from energetically exploring the holy sites in various cities of Iran, concluded with waking up depleted in a quarantine centers with not much to do. It became clear that the travel situations were not conducive to the personal well-being of the pilgrim-tourists as, they were exposed to their deepest fears about life and death. The aggregate travel stress also created frictions among the pilgrim-tourists who were at the tipping point of their composure, resulting in frequent combative argumentative exchanges.When we were in Mashhad (a city in Iran), we received several new reports about deaths of people due to infection. There was a huge disagreement within the tour group between those who wanted to return back to Pakistan and others who wanted to stay and continue the pilgrimage. We eventually stayed until we were sent back by the Iranian government. The disagreement became the bone of contention during the whole return journey, as the people who wanted to leave early kept on asserting that leaving at the right time could have saved us from all the hardships in the return journey. (Male 33)
4.1.2.2 Home sickness
Homesickness is a psychological state of longing for a familiar home environment when individuals find the new environmental demands difficult to cope with (Fisher, 1989). A point came in their journeys when the pilgrim-tourists started to miss their loved ones and felt nostalgic for the life lived at one place. This was the stage when travelling stops being fun and the rewarding aspects of their travel were being outweighed by the negative aspects. The complexity of the tumultuous external environment caused by the pandemic produced a grief-like reaction among the pilgrim-tourists who yearned for familiar people and surroundings. Based on the COR theory, if resources are exhausted in one domain, e.g., time, energy and emotions, it will not be possible for individuals to optimize their fabricated behavior in another domain (Ten Brummelhuis & Bakker, 2012). For instance, dissatisfaction with the new environment is strongly related with the feelings of homesickness (e.g., Archer et al., 1998). Therefore, it could be the strain of the situational demands of travel for the pilgrim-tourists, resulting in negative attitudes towards the current environment, rather than the separation-reaction from the old and familiar environment that manifested itself in the form homesickness.I just wanted to reach my home as soon as possible. I felt totally worn out by the travel experience and started badly missing the comfort of home, my room and the habitual routine-life I was used to. (Female 31)
4.1.3 Emotionally maladaptive
When emotionally intensive situations are encountered, people modulate their emotions using emotional adjustment and regulation strategies (Dixon-Gordon et al., 2015). Emotional intensity is an important context to consider when regulating one's emotions. At high emotional intensities, people are engaged in putatively maladaptive strategies, such as disengaging from their emotions, which indicates an avoidance of the adequate cognitive appraisal that is pivotal in handling stressful situations (Koopman et al., 2000). In the case of pilgrim-tourists, the uncertainty surrounding their travel, lack of control and inadequate information about their travel itineraries all in the presence of a fatal pandemic made them adjust their inner turmoil using the modulation strategy of emotional disengagement.
4.1.3.1 Emotional disengagement
When people feel inadequate in managing their environment, they resort to self-management practices, such as emotional disengagement from their surroundings (Kim & Lee, 2005). Especially, when emotionally strenuous situations are encountered, they are least likely to acknowledge the empathetic connection with confronting aspects encompassing the issue (Battaly, 2011). The pilgrim-tourists during their return journeys become vulnerable to resource replenishment owing to psychological strains produced by harsh travel circumstances. In such a case, emotionally disengaging oneself from the severity of the situation can help moderate the relationship between stressors and burnout,I avoided talking about the pandemic situation with my family who were travelling with me to calm their nerves. They became panicked hearing all kind of news insinuating fears. I figured out that the best way to deal is to avoid discussing this topic (Male 38).
In the burnout literature, Bianchi et al. (2014) referred to this as a disinvestment policy that can be applied to any previously invested-in activity that did not yield the expected returns to an individual. The COR theory posits that people must invest their resources to protect against resource loss, and those with scarce resources are more susceptible to resource loss and less resource gain (Hobfoll, 2012). Navigating against the challenging situational demands of COVID-19 may quickly drain the reservoir of resources of pilgrim-tourists, which limits the extent to which their responsive resource investment could be improvised.The 18 hours journey from Taftan to Multan quarantine was marked with uncertainty and numerous questions in my mind; am I virus positive? Have I infected anyone? Or am I infected by anyone? Will I ever get back to home? There was a visible apprehensiveness in the whole group, which continued until we got our tests cleared in quarantine. It was the time when many people came out of their shell (Male 41)
However, one significant difference noted between travel burnout and the traditional concept of burnout is that the latter refers to depersonalization as a core component of burnout that is directed towards other people, as they are treated in a detached manner and there is a lack of concern or feeling for them (Maslach & Leiter, 2006), while the former concept that emerged in this study refers to both interpersonal and intrapersonal aspects of emotional disengagement. Interpersonal aspects are conceptually similar to depersonalization by showing disinterest in meeting fellow pilgrim-tourists and avoiding talking about a specific topic (i.e., COVID-19 and the vulnerability of the pilgrim-tourists to it) with the hope of suppressing the distressing emotion. The intrapersonal aspects of emotional disengagement involve withdrawing oneself from the severity of the situation to stem the depletion of one's resource reservoir. In such cases, people evade experiencing strenuous emotions with the hope that they will subsequently discontinue to prevail (Eisenberg et al., 2004; Hill, 2015). The pilgrim-tourists envisaged a parallel reality discontinuous from the current reality, where they curtail their thoughts regarding the gravity of the situation caused by COVID-19. However, such withdrawal behavior is believed to further perpetuate one's fears and dysfunctions and that is why suppressing their distressing emotions during the travel deprived the pilgrim-tourists of key communication tools that could have facilitated a shared emotional connection with other travelers in the journey.
4.2 Travel burnout anchors
Burnout anchors, in this research, refer to those environmental or personal factors that facilitate the preservation, retainment, enrichment and protection of the resources of individuals when circumstances are beyond their control. The COR theoretical streams emphasize the motivational elements of an individual's support system, suggesting that they will engage in behaviors that preclude resource losses since loss can have a significant negative impact on the well-being of people (Halbesleben et el., 2014). The COR theory postulates that these resources are intimately tied to one another and are highly intercorrelated (Hobfoll, 2012). The two burnout anchors identified in this study were appropriated by the pilgrim-tourists during different stages of their return journeys. Fresh off from their pilgrimage visits, internal sources such as faith played a pivotal role in the earlier part of the return journeys. The social sources encompassing family members and relatives accompanying the pilgrim-tourists helped them to manage the middle parts of their return journeys in quarantine. While towards the end of their return journeys, internal sources such as future travel cognitions became salient, as they looked to transform their future travel behaviors for better resource preservation keeping current experiences in perspective.
4.2.1 Internal sources
Although the COR theory usually refers to personal energies and positive self-perception, i.e., as internal sources (Hobfoll, 2001), among pilgrim-tourists, their religious faith emerged as a significant anchor that helped their struggle against travel burnout. Faith is a valuable internal resource, as it provides people with explanations and answers to seemingly inexplicable circumstances. Further, by providing meaning to life and death, faith equips people with strength and security during difficult times (Rasool, 2000). In addition to faith, another internal source that provided sufficient support to pilgrim-tourists was their future travel cognitions. People possess the ability to revisit the past and construct potential future scenarios, and in doing so, they anticipate the future needs and find solace in securing future survival (Suddendorf & Busby, 2005). At the time, when the pilgrim-tourists struggled to effectively manage themselves and the people around surrounded by a multitude of stressors, the ability to anticipate future travel needs on the basis of their projections of the current travel misgivings allowed them to preconstruct future travel situations.
4.2.1.1 Faith
Visits to religious sites aroused strong feelings of religious fervor among the pilgrims (Nyaupane et al., 2015). Within the psychology of religion, God is the most secure figure of devotion for believers who seek proximity to God for emotional support and experience companionship (Counted & Zock, 2019). Similarly, sacred religious figures are essentially believed to possess divine powers to facilitate people's connection with God. Specifically, Shia Muslims pilgrimages, the focus of this study, predominantly pay homage to the shrines of Imams, which are holy figures that they consider divine proxies in establishing a connection with God (Moufahim & Lichrou, 2019).
Faith emerged as one of the strongest avenues for pilgrim-tourists to manage their existential concerns. Drawing from their faith, the pilgrim-tourists perceived the pandemic as an instinctive warning about the uncertainty of their existence and human life on earth against the more powerful nature. It was soothing for pilgrim-tourists to draw analogies of the difficulties they encountered during their travel with the sacred personalities of Imams’ (religious guides) tragic lives and incessant struggles to preserve the identity of Islam (Musa, 2013), as they believe that the difficulties faced by them do not count even an iota of what their infallible religious guides had suffered during their times, and it was only befitting that they come across these struggles during their pilgrimage.My faith helped me get through the whole process. The experiences I rendered in this journey have purified me spiritually. This remind me of the atrocities of the journey which our beloved Ahl-e-bait (The family of Prophet Muhammad S.A.W) must have encountered during their time (Male 47)
In Islam, difficult situations and suffering are envisioned as punitive, or a divine test or simply as God's will, causing people to perceive stressful situations as divine interventions and embrace them unquestionably (Rasool, 2000). For the pilgrim-tourists, their faith and key constituents of faith, such as prayers and supplications, became the salvation that provided a calming influence that helped them find composure in tough circumstances.Death is inevitable and the only reality that we must be prepared for. I kept reminding myself about this and seeking forgiveness for my sins. Even if I had contracted the virus or faced serious illness, it must be the will of Allah and His will supersedes everything else (Male 38)
4.2.1.2 Future travel behavior modifications
There is an inherent plausible connection between past travel experiences and future travel behavior modifications (Sönmez & Graefe, 1998). A salient corollary of the COR theory is that when individuals lose resources, they become cautious of how to invest their resources in the future (Hobfoll, 2001). The resource loss spiral that the pilgrim-tourists encountered during the course of their return journeys enabled them to comprehend the probable threat intensity that was expected of this type of travel in the future. There was an apparent change in the perceptions and preferences for the future travel needs among the pilgrim-tourists, causing them to lean towards more protective travel behavior.I will never make the same mistake again in my future travel. If a pandemic or any crisis emerges anywhere in the world, it can reach other corner in no time. The world is so interconnected that we simply cannot ignore its occurrence (Male 33)
The above comment by a pilgrim-tourist reflects a historical pattern, as earlier pandemics and tourism crises were confined to particular geographical locations and the tourism economies in other parts of the world prospered regardless of it, but this has changed with COVID-19, which has penetrated worldwide. COVID-19 in general acted as a catalyst in transforming the future travel behavior of the tourists. This is especially relevant in the destinations where COVID-19 has become naturalized among the general population, as they will be perceived as high-risk destinations with subsequent repercussions on the travel behaviors and patterns towards them (Hall et al., 2020). There needs to be extensive transformations within the tourism and hospitality sector in the post COVID-19 era to integrate and institutionalize updated protocols and operating procedures (Lew et al., 2020). In this milieu, the pilgrim-tourists kept re-experiencing the episodic memories of their return journeys, and as they project themselves back to the return journeys, the explicit representations of their future travel behavior became more eminent.Without a proper sense of safety, I am not engaging in any kind of long term travelling, let alone pilgrimaging (Female 34).
4.2.2 Social resources
The sudden emergence of pandemics and the uncontrollability aspect associated with them has a significant impact on broader social systems. When individuals are experiencing negative events, they tend to conserve and pursue interpersonal relationships as key social resources, as healthy relational outcomes help people alleviate the unpleasantness of negative events (Doane et al., 2012). Having social support in terms of family members or friends enables pilgrim-tourists to neutralize the demands placed upon them by a challenging travel environment.
4.2.2.1 Family and friends
The pilgrim-tourists who were travelling with their immediate families and those who formed friendships during the course of their travel were able to use social support as a means to expand the depleting reservoir of resources that are contained within the self (Hobfoll et al., 1990). At the same time, the pilgrim-tourists who were travelling with children and elderly family members felt vulnerable and overextended by these additional obligations, but the feeling of being valuable to their family members facilitated a stable sense of self.I was travelling with my family, 2 kids, my wife and mother. We took care of each other and helped get through the pressing situations during the travel (Male 40).
I came to know that elderly people are most vulnerable to this virus, with relatively low chances of survival. I became highly conscious and wary for my parent's health during this trip, but at the same time, it was comforting that I am there to take care of them in tough situations (Male 29).
However, it could not be definitively established that the pilgrim-tourists using internal resources are more likely to use social resources and vice versa. Although, COR's stipulation highlights the mutual dependency of resources, as resources tend to enrich other resources, and likewise, the lack of resources leads to resource depletion (Hobfoll et al., 1990), the patterns that emerged from the interviews suggested that the social support system acts as a supportive reserve that may be called upon when battling challenges that supersede the internal resource reserves. In this milieu, social resources played a vital role in anchoring the burnout effects on the pilgrim-tourists.It was a blessing to have found highly empathetic companions during the return journey. The extremely tough situations fostered friendships that I believe are going to last forever. I don't know how I would have travelled if I hadn't had the company of the friends made during the travel (Male 36).
Nevertheless, social resources appear to be finite, especially in a closed travel environment marked by a pandemic crisis and limited mobility. The pilgrim-tourists travelling with their families became busy in attending to them, while other solo travelling pilgrim-tourists with limited past social exchanges greatly relied on the extent of individual differences in their social skills to secure finite social resources to get through stressful travel circumstances. Moreover, these solo travelling pilgrim-tourists left their social support systems (e.g., families and friends) behind in their homes and now they found themselves in conditions where they were required to build their support systems from scratch. Interestingly, those people who are more reliant on social support systems are expected to become more troubled when encountering stressful situations (Hobfoll, 2001). For such pilgrim-tourists, seeking social support is likely to incur costs, as they feel vulnerable to the fears of rejection from fellow travelers, and in doing so, the spiral of internal resource depletion is initiated and burnout is more likely to settle in.I travelled alone and as I am not very good at making social connections, there were situations during the travel that I felt alone and lost and desperately in need of some companionship. I used to call back my home and talk with family members and friends … That helped (Male 29)
5 Conclusions
Using a qualitative grounded theory approach, a two-fold thematic framework, dichotomized to travel burnout constitutes and travel burnout anchors was derived in this study (depicted in Table 1 ). The travel burnout constitutes provide a multi-dimensional understanding of travel burnout concept, from COR perspective, as a negative emotional state settling in as a response to highly uncertain and demanding travel circumstances, categorized into three core dimensions, i.e., low tourist self-efficacy, travel exhaustion and emotional maladaptation. This expanded conceptualization of travel burnout encapsulates the emotional states of tourists enduring crisis situations during their journey, re-configurating their resource valuation and re-negotiating their travel goals. The second theme derived from the findings concentrated on those internal and social factors, termed as burnout anchors, which facilitated the preservation of the tourists’ resources when the travel circumstances became beyond their regulation. The emergence of burnout anchors made sense according to the COR perspective, as people are naturally inclined to preclude resource loss due to its substantial negative impact on their well-being.Table 1 Themes, categories and subcategories identified in the study.
Table 1Themes Categories Subcategories
Travel burnout constitutes Low tourist self-efficacy Existential fears
Xenophobic response
Reduced tourism participation
Travel exhaustion Aggregate travel stress
Home sickness
Emotionally maladaptive Emotional disengagement
Travel burnout anchors Internal sources Faith
Future travel behavior modifications
Social sources Family and friends
The first core dimension of travel burnout identified from the responses of the pilgrim-tourists is low tourist self-efficacy. Tourists with low self-efficacy are not equipped with sufficient resources that aid them in a taxing travel environment and, consequently, help them avoid burnout, and vice versa. Furthermore, the core dimension of low tourist self-efficacy is further divided into sub-categories. For instance, as the effects of the social disruptions caused by the COVID-19 emergency on the fragility of human existence became more discernible, it gave rise to existential fears among the pilgrim-tourists. Consequently, fear of losing human life or causing loss to other people due to the contagious virus creates resource loss salience, which, as stipulated by the COR perspective, is disproportionately more noticeable than the similarly valued gains (Hobfoll, 2012), hence, depleting the efficacy resources of the pilgrim-tourists and paving the way for burnout. This coupled with the xenophobic response towards the pilgrim-tourists, promulgated on the basis of the popular perception that the pilgrims returning from Iran are responsible for spreading the COVID-19 infection in Pakistan further added to the tourists phycological baggage, thus depleting their efficacy resources. In addition to this, the strict controlled environment during the return journeys and the unfulfilled travel goals of the pilgrim-tourists engendered a sense of reduced tourism participation, which stalled their endeavors to preserve the resources they fundamentally value, i.e., having mobility and access to the world outside their immediate travel space and having liberty to have a say in travel decision-making.
The second core dimension of travel burnout is travel exhaustion; a state of high fatigue and low motivation to engage in travel related activities, and it is further subdivided to sub-categories like aggregate travel stress and home sickness. In relation to the COR theoretical premise, the imbalance between resources and travel demands creates an aggregate travel stress (Hobfoll, 2004), which does not allow the pilgrim-tourists to view travelling as fun anymore, and thus, they continuously yearn for the familiar and comfortable environment of home. The third core construct of travel burnout is referred to as emotional maladaptation, which is a negative emotional reaction to highly demanding travel situations emerged in the form of sub-category, emotional disengagement. Emotional disengagement acts as a deliberate distraction, where the tourist socially withdraws from the immediate travel environment, as well as avoids experiencing the inner turmoil caused by the travel demands by suppressing the distressing emotions. This, instead of moderating the stress response to emotionally strenuous situations, further depletes the tourists’ resource reservoir and speeds up the process of travel burnout manifestation. In short, the multi-dimensional concept of travel burnout identified through grounded theory qualitative research design in this study can be synthesized as a state of exhaustion during travel that causes tourists to emotionally disengage from their travel environment and become doubtful about their capabilities to deal with challenging situations during their travel. Travel burnout is most likely to surface when tourists are met with cataclysmic external situations during a large part of their travel journeys.
The second theme, burnout anchors, concentrated on those internal and social factors that aided pilgrim-tourists in confronting travel challenges. However, compared to the traditional internal resources outlined by the proponents of COR (Hobfoll, 1989, 2001), faith emerged as one of the strongest avenues for the pilgrim-tourists to manage their existential concerns, thus causing them to perceive the travel struggles as divine interventions. The planning about future travel behavior surfaced as a protective travel disposition, reflecting the apparent changes in preferences and perceptions in post pandemic travel needs. The tourists attained much solace by connecting future travel with the episodic memories of their travel done amidst COVID-19 with the resolve to respond better in future emergency travel situations. The social factors predominantly comprised of interpersonal relationships that help pilgrim-tourists use social support family members and friends to expand the pool of the depleting reservoir of resources to alleviate the unpleasantness associated the negative travel events. The healthy relational vitality furnished by social resources is reported to equip people to sustain better against stressful events, as advocated in the relevant COR scholarship (i.e., Doane et al., 2012; Hobfoll, 1990).
6 Implications
The findings draw attention to travel burnout as a relevant concept in line with the contemporary tourism environment. Although the concepts of tourism fatigue (Sun et al., 2020) and aggregate travel stress (Taylor et al., 2017) appropriately conceptualize general tourism weariness, identified to some extent by the travel exhaustion construct identified in this study, as established in this study, the former concepts are deemed inadequate to encapsulate the crisis situations engendered by emergency circumstances of colossal magnitude such as COVID-19. For tourism researchers, the occurrence of emergency crisis situations and the uncertainties associated with travel in the post COVID-19 period entail that trips will increasingly not go as planned. Moreover, with drastic changes in the standard operating procedures of the hospitality and tourism industry and affiliated businesses in the post COVID-19 world imply that there will be extraordinary changes in tourists’ journeys in the foreseeable future, and as advocated by the findings of this study, travel burnout is a reality that shall be accommodated in the existing theoretical streams.
For tourism researchers, travel psychology should be an important domain to further their research agendas. Travel psychology as a subdomain can help navigate the relatively underexplored territories of both the transient and enduring emotional states of tourists and emotional responses elicited in tourists by critical incidents as delineated in the current study. Based on the results of this research, travel psychology is set to emerge as an important research agenda from a plethora of hedonistic psychologically oriented tourism studies. While attempting to understanding the travel burnout concept, drawing on the similarities and differences with the organizational burnout concept is inevitable. The three core constructs of travel burnout identified in this study are not expected to necessarily surface at the same time. There is a possibility of the emergence of a single construct or a combination of constructs being experienced by tourists depending upon the intensity of the travel situations people find themselves in and the extent of their resource reservoirs to deal with the situations. The general weariness aroused out of overextended travel may be marked with basic level resource valuation and could manifest itself in the form of travel exhaustion, but the resource loss and gain spiral is so tightly intertwined in individuals who it is difficult to establish the sequence that the travel burnout constitutes. The sequential manifestation of travel burnout is a topic that could interest tourism researchers to debate regarding how the occurrence and sequence of the varying combinations of the travel burnout constructs may prevail.
This research contributed to the overarching literature on the COR theory by contextualizing resources specific to tourists in a travel environment. Taking cues from the resource allocation corollary of COR (Halbesleben & Buckley, 2004), travel burnout settles in when the tourists' reservoir of resources depletes, impeding their ability to adequately respond to environmental demands. In addition to the generally considered internal resources, such as personal energy and positive self-perception (Hobfoll, 2001), this study evidenced an expanded pool of internal resources by showcasing how a person's faith can prove to be a substantial internal resource when confronted with taxing situations. Another, novel implication of the COR theory uncovered in this study is the use of the resource perspective to plan future travel behavior. For instance, the COR perspective postulates that when people lose resources, they become cautious in investing their resources in the future (Hobfoll, 2001, 2012). Taking this resource perspective will help the tourism scholars view the future travel behavior as a function of the continuous management of tourists' finite resources, where an adequate fit of the tourists' resources with the travel environment is more crucial than the mere possession of an abundance of resources.
For practitioners, keeping realistic expectations of travel for tourists is very important, whether it is pilgrimage tourism or other forms of travel. Travel shall not be marketed only as a highlight reel package to tourists where stressful events are omitted from their imagination. The perception of travel being a source of psychological well-being, providing the opportunity for tourists to energize is often overplayed, unnecessarily heightening the expectation level. Therefore, keeping expectations in check will be helpful in managing the tourists’ reactions in cases of emergency situations. Much such as preflight take off briefings of airlines, it is necessary to remind tourists of what could go wrong during their travel.
Studies have hinted at customer incivility as a prevalent phenomenon in contributing to service sector employees' burnout (Han et al., 2016). In the tourism sector, it will be truly problematic if the supply side is not well-equipped to respond to emotionally strenuous situations. Therefore, tourist authorities, tourism companies and travel agencies need to provide support systems for their employees in the case of emotional outbursts from tourists. For instance, imparting training to diffuse emotionally intensive situations and address the concerns of emotionally maladaptive tourists is important. The present study shows that the perception of being authoritative in imposing travel constraints to follow SOPs was a salient contributor in the resulting frustration felt by the tourists. Travel agencies and tour operators need to establish grievance handling systems for both tourists and employees to mitigate the growing frustration during emergency situations. For travel agencies, it is pertinent to note that tourists' readiness to travel will be slow in the post pandemic world. This provides them with a sufficient time window and an opportunity to incorporate features such as social distancing and other health protocols deemed necessary in their service design, along with providing the necessary training to their staff. This will help alleviate increasing levels of anxiety and concerns among tourists, as existential concerns for their health and safety emerged as a major contributing factor to sustain the tourists’ positive energy during travel.
7 Limitations and future research
The findings of this study are qualified by certain limitations in the research design. First, due to social distancing protocols and quarantine prerequisites, the pilgrims were not reachable during their return travel and quarantine period. It was only after the travel that their responses were recorded. The intensity of the perceptions during their travel mode could have been higher as their positive energy was continuously eroding due to extensively challenging travel situations. However, as the data were collected within 1–2 weeks of their arrival to their homes, the respondents travel endeavors were relative fresh in their minds. Second, the average length of the trips of the tourists involved in this study was 32 days. Further exploration regarding whether travel burnout is a relevant concept for short term trips is needed, as well. Third, the focus of the study was pilgrim-tourists, and the results drawn may not hold true for general touristic behaviors and more research is needed in this regard. For instance, faith as a burnout anchor providing much needed support to the pilgrim-tourists during the course of travel may not be perceived as equally conducive for general tourists. Therefore, when an equally challenging travel environment is confronted by other tourist segments, they might possess different anchors to stem the settling of burnout. Finally, the qualitative research design assisted in uncovering the conceptual constructs of travel burnout, but the future research could employ quantitative research techniques to garner empirical support for theoretical themes and constructs identified in this study.
Impact statement
The findings draw attention to travel burnout as a relevant concept in line with the contemporary tourism environment. Although the concepts of tourism fatigue (Sun et al., 2020) and aggregate travel stress (Taylor et al., 2017) appropriately conceptualize general tourism weariness, identified to some extent by the travel exhaustion construct identified in this study, as established in this study, the former concepts are deemed inadequate to encapsulate the crisis situations engendered by emergency circumstances of colossal magnitude such as COVID-19. For tourism researchers, travel psychology should be an important domain which can help navigate the relatively underexplored territories of both the transient and enduring emotional states of tourists. Understanding travel burnout from the COR theory perspective provides valuable insights on linking the travel goals with resource valuation. For practitioners, Travel shall not be marketed only as a highlight reel package to tourists where stressful events are omitted from their imagination.
Credit author statement
Dr. Salman Yousaf is the sole author of this manuscript.
Declaration of competing interest
None.
Salman Yousaf is an Assistant Professor at College of Business and Public Management, Wenzhou Kean University Wenzhou, PRC. Previously he has been affiliated with Bahauddin Zakariya University Multan, Pakistan. He also served as a post-doctoral research fellow at School of Management, Fudan University, Shanghai. His research focuses on the co-subjectivity of the Marketing discipline, particularly the application of social concepts in the nation branding and destination marketing domains, and the contemporariness of religious beliefs and their practicability and relevance in the development of theoretical models of marketing and heritage/ ethnic tourism. His current research interest involves around tourists' emotional health and socio-cultural placement of copysites and national heritage in destination's marketing. His research work has appeared in prestigious international journals.
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| 0 | PMC9734086 | NO-CC CODE | 2022-12-14 23:28:27 | no | Tour Manag. 2021 Jun 22; 84:104285 | utf-8 | Tour Manag | 2,021 | 10.1016/j.tourman.2021.104285 | oa_other |
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Tour Manag
Tour Manag
Tourism Management
0261-5177
1879-3193
Elsevier Ltd.
S0261-5177(20)30207-7
10.1016/j.tourman.2020.104281
104281
Article
How to survive a pandemic: The corporate resiliency of travel and leisure companies to the COVID-19 outbreak
Kaczmarek Tomasz b
Perez Katarzyna b
Demir Ender cd
Zaremba Adam ab∗
a Montpellier Business School, 2300, Avenue des Moulins, 34185, Montpellier, France
b Department of Investment and Financial Markets, Institute of Finance, Poznan University of Economics and Business, Al. Niepodległości 10, 61-875, Poznań, Poland
c University of Social Sciences, Lodz, Poland
d Faculty of Tourism, Istanbul Medeniyet University, Istanbul, Turkey
∗ Corresponding author. Montpellier Business School, 2300 Avenue des Moulins, 34185 Montpellier cedex 4, France.
7 1 2021
6 2021
7 1 2021
84 104281104281
11 8 2020
3 12 2020
21 12 2020
© 2020 Elsevier Ltd. All rights reserved.
2020
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.
What protects travel and leisure companies from a global pandemic, such as COVID-19? To answer this question, we investigate data on over 1200 travel and leisure companies in 52 countries. We consider 80 characteristics, such as company financial ratios, macroeconomic variables, and government policy responses. Using regressions and machine learning tools, we demonstrate that firms with low valuations, limited leverage, and high investments have been more immune to the pandemic-induced crash. We also find a beneficial effect of stringent containment and closure policies. Finally, our results indicate that countries with less individualism may be better positioned to cope with the pandemic. Our findings have implications for regulatory bodies, managers, and investors concerning future pandemic outbreaks.
Keywords
COVID-19
Novel coronavirus
Tourism and leisure
Stock market
Corporate immunity
Policy responses
Containment
Closure
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pmcCredit author statement
Tomasz Kaczmarek: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Katarzyna Perez: Conceptualization, Writing – original draft, Writing – review & editing. Ender Demir: Conceptualization, Writing – original draft, Writing – review & editing. Adam Zaremba: Conceptualization, Writing – original draft, Writing – review & editing; Project administration, Funding acquisition.
1 Introduction
The COVID-19 outbreak, which at the beginning of 2020 seemed to be a local health problem in Wuhan, China, evolved into a full-scale pandemic by the end of March (WHO, 2020). As a precaution, governments worldwide responded with stringent interventions that within days shut down tourism worldwide. The situation was unprecedented, and perhaps no other sector was hit as hard by COVID-19 as tourism and leisure (Gössling et al., 2020). By April 2020, international tourism arrivals dropped by 97%, translating into a loss of more than USD 200 billion in receipts (World Tourism Organization UNWTO, 2020b). This can be considered the worst decline in global tourism history after World War II (World Tourism Organization UNWTO, 2020a).
The disappearance of international tourism translated into a massive stock selloff in the related sector. During the first quarter of 2020, the travel and leisure sector dropped by more than 40% from its high to its low.1 The travel and leisure industry experienced the fourth-highest drop among 38 industry categories as classified by Datastream. Even in this bleak landscape, there was still some heterogeneous behavior of stock returns in this sector. While some of them fell more than 80% (e.g., Carnival PLC, Eldorado Resorts, and Norwegian Cruise Line Holdings), others rose over the same period (e.g., Domino's Pizza, Haidilao International, and Xi'an Tourism). The performance also differed across countries. Tourism stocks from Brazil, Chile, and New Zealand fell by more than 60%, whereas their counterparts from Slovakia, Cyprus, and Bahrain lost less than 10% of their value.
In this paper, we examine what determines the performance of travel and leisure firms during a pandemic. Why do some companies perform better than others during the pandemic? What corporate or macroeconomic variables play a role? Do policy responses matter? The principal aim of this article is to attempt to answer these questions.
To this end, we use data on more than 1200 tourism firms across 52 countries. We investigate the relationship between tourism stock returns and 80 characteristics for the initial outbreak of the coronavirus pandemic: the first quarter of 2020. We use a machine learning tool Elastic net (Zou & Hastie, 2005) and Fama-MacBeth regressions (Fama & MacBeth, 1973). We consider three broad categories of potential predictors of stock returns: (1) firm characteristics, such as valuation, investment, profitability, leverage, and sector affiliation; (2) country characteristics, such as economic data, national culture-specific features, population data, and industry concentration, and (3) government policy responses to COVID-19 outbreaks, such as containment and closure policies, health interventions, and economic stimuli. The selection of variables is backed up by a theoretical basis stemming from the extant literature.
Our paper aims to contribute to the fast-growing body of research on corporate immunity against COVID-19. Ramelli and Wagner (2020) state that U.S. firms with low leverage and high-cash positions could protect themselves against the pandemic. This finding is extended by Fahlenbrach et al. (2020), who define low leverage as financial flexibility and reach similar conclusions supported by credit market observations in the United States. Dechow et al. (2020) highlight the role of equity duration; Albuquerque et al. (2020) investigate how environmental, social, and governance (ESG) policy affects returns and earnings of U.S. stocks; Haroon and Rizvi (2020) research the role of news coverage; Heyden and Heyden (2020) extend the considerations to fiscal and monetary measures; and Mazur et al. (2020, p. 101690) explore the role of data from financial statements and industry classification. Finally, although most papers are concerned with the United States, some studies cover global markets. Zaremba et al. (2020) analyze the cross-sectional variation in country index returns, and Ding et al. (2020), using simple regression, investigate how corporate characteristics affect international companies' returns. Several studies are examining the effect of epidemics and diseases on tourism inflows (Karabulut et al., 2020; Kuo et al., 2008; McAleer et al., 2010; Rosselló et al., 2017; Yang et al., 2020); however, to the best of our knowledge, there is no study focusing on their effect on travel and leisure companies. This article is the first to comprehensively fill this gap and explore the determinants of corporate immunity of travel and leisure companies to the COVID-19 pandemic.
The potential determinants of corporate immunity to the pandemic of travel and leisure companies may be essential to various decision-makers in a global context, including firm managers, policy-makers, investors, and regulatory bodies. They may help managers to shape better company policies, improving their resiliency to extreme risks such as pandemics. Furthermore, this information may be used by investors to adjust optimized exposure to pandemic risk factors. Finally, the knowledge of the tourist sector resiliency sources may help policy-makers undertake more informed decisions regarding relevant pandemic-related regulations and interventions.
Among the three categories of characteristics studied in this article, we find six drivers of travel and leisure companies' immunity to the COVID-19 pandemic. First, we demonstrate a company valuation's role represented by a relationship between a company's EBITDA and enterprise value (EV). The higher the EBITDA/EV ratio before the crisis, the stronger the firm position is. Second, investment policy matters: we find that firms with high asset growth recorded lower losses during the pandemic. We argue that the outbreak mainly affects imminent cash flows, so firms with value sources from long-run cash flows are less affected. Third, company leverage also plays a vital role. Tourism firms with limited levels of debt could handle the first months of the pandemic better. Low leverage may be beneficial, as it allows for greater financial flexibility and the ability to arrange additional financing when operating activity stops. Fourth, we find that the degree of individualism in national culture matters. Culturally loose and more individualistic countries may find it more challenging to cope with the pandemic swiftly, negatively affecting corporate immunity to COVID-19. Finally, we find that stringent policy responses positively affect the overall performance of the travel and leisure companies. Actions such as school closing (SCHOOL) and stay-at-home requirements (STAYATHM) may seem harmful at first sight but eventually proved advantageous for the tourism sector. As observed by Correira et al. (2020), strict policy interventions helped to curb the local outbreak more quickly, eventually benefitting the economy.
The rest of the article proceeds as follows. Section 2 outlines the theoretical basis for selecting the determinants of COVID-19 immunity of travel and leisure companies. Section 3 presents the data used in our study. Section 4 describes the methodology. In Section 5, we discuss the empirical results. Finally, Section 6 concludes the study.
2 Theoretical basis: variable selection
Our study considers 80 different firms’ characteristics from different domains. This section provides the theoretical basis for the inclusion of different variables in our study. Overall, we examine data from three major domains: firm characteristics, country variables, and government policy responses.
Within the first group—the firm characteristics—we begin with a set of common valuation ratios. Fundamental variables represent an important source of firm value (Liu et al., 2009; Loughran & Wellman, 2011) and, furthermore, Baltussen and van Vliet (2020) argue that some investment styles may be preferred or deferred during a pandemic. Next, we take into account firm investment policies. We consider these because they determine the timing of future cash flows. As Hasler and Marfe (2016) noted and Dechow et al. (2020), when a large part of a company's value comes from short-term cash flows, their stock prices may be more affected by a pandemic-type disaster. Also, in this group, we consider profitability ratios that represent firms' ability to generate cash flow. This attribute may prove highly useful in times of liquidity shortages (Kahle & Stulz, 2013), and we assess the role of indebtedness: prior work indicates that leverage can substantially affect a firm's operating performance during a crisis (Opler et al., 1994; Youn & Gu, 2010; Muradoglu & Sivaprasad, 2014) and evidence from the financial crisis shows that external finance affected corporate ability to recover (Duchin et al., 2010; Giroud & Mueller, 2015). Besides, we include a group of asset pricing variables describing different investment styles commonly used in cross-sectional analysis. These variables represent investors' preferences for certain classes of stocks during a crisis (Baltussen & van Vliet, 2020). When considering the firm-level characteristics, we also take into account their sector affiliation.
The second group encompasses country-level and macroeconomic variables. We conjecture that strong economies can better respond to the pandemic. Hence, firms listed in such countries should benefit from more intensive rescue and stimulation packages. We verify how national GDP, unemployment, inflation, credit rating, and interest rates help protect local firms. We also consider the role of a country's economic openness. Firms are connected globally through networks of suppliers and customers that may have had different exposure levels to the COVID-19 pandemic (Acemoglu et al., 2017; Acemoglu & Robinson, 2012).
Furthermore, following Chui et al. (2010) and Docherty and Hurst (2018), we hypothesize that national culture is an essential determinant of stock prices' reaction to the crisis. For instance, we consider that nations characterized by collective thinking may undertake more effective actions against the virus outbreak, which eventually supports the state of the economy. On the other hand, high uncertainty avoidance may not only encourage social distancing but also influence investors’ attitudes and provoke massive stock selloffs. Along with the degree of individualism and uncertainty avoidance, we assess the effects of three other national culture characteristics—power distance, masculinity, and long-term orientation—that we consider may also be important determinants of population behavior in a pandemic period.2
Also, in this group, we include governance indicators that describe a government's ability to manage a crisis and influence the reaction of its residents. Democratic regimes may be sometimes beneficial. Acemoglu et al. (2019) have shown that democratic countries offer better GDP growth opportunities. There is also a correlation between regime type and the health status of its citizens. Democratic countries generally provide better living conditions that support their citizens' health conditions (Bollyky et al., 2019). As well, some countries are not democratic or where democracy is poor or unstable. This may contribute to greater differences between countries in their populations' health status, as there is a strong connection between regime type and a country's ability to compete on the global market (Acemoglu & Robinson, 2012; Besley, 2007). On the other hand, countries like China and Vietnam show that undemocratic regimes may also provide some benefits related to signaling effects or the level of control over their citizens' behavior (Malesky & London, 2014; Weeks, 2008). We hypothesize that differences in regime type and national governance are the determinants of economic reaction to the pandemic and the vulnerability of travel and leisure companies to COVID-19. We test this with six indicators that measure the perception of government trustworthiness and stability.
Within the set of country-level determinants, we also explore how legal system origin affects stocks' reaction to COVID-19. It has been documented empirically that legal systems determine the level of investor protection: common law systems (with origins in English law) give more protection than civil law systems (those that originate from Roman law, best represented by French law) (La Porta et al., 1998). This relationship is strong enough to affect the size of the domestic capital market in relation to the whole economy, but the effect of legal tradition goes beyond finance. The legal origins theory states that it differentiates countries by their social control styles and the institutions supporting them (La Porta et al., 2008). Inspired by this theory, we test how stocks in countries with different legal origins reacted to the COVID-19 pandemic. In our empirical study, we use four variables describing a country's legal origin.
Another country feature that may determine local travel and leisure companies' vulnerability to a pandemic is demographics. Population density and migration patterns may determine the intensity of viral spread, and the average population age may determine disease severity. To test the relationship between stock price reactions to COVID-19 and these characteristics, we use two measures describing the countries’ population data.
Moreover, we also consider healthcare variables' role, as adequate healthcare resources play a crucial role in determining economic outcomes (Ji et al., 2020; Rhodes et al., 2012). We assume that travel and leisure companies from countries with a higher quality healthcare system perform better during a pandemic than those with healthcare resource shortfalls. We use nine indicators of basic medical care that describe the coverage and cost of essential health services, the general health of the society, and the ability of the local healthcare system to deal with the lower respiratory infections that are a significant feature of the current pandemic (Fullman et al., 2018).
Finally, we study the role of sector concentration and its size in relation to the whole economy. We hypothesize that more concentrated industries—those with a higher market share relative to the total market capitalization—will have experienced more difficulties during the COVID-19 pandemic (Hou & Robinson, 2006).
Last but not least, the third group covers government policy responses to the COVID-19 pandemic. Some nonpharmaceutical interventions (NPIs) had a detrimental effect on international tourism. Following Hale et al. (2020), we scrutinize daily changes in government policies to examine the effect on travel and leisure companies of three groups of factors: 1) closure of public life (closing schools, workplaces, and public transport, cancellation of public events, restrictions on gatherings and local or international movement and travel, and stay-at-home requirements), 2) health system action (public information campaigns, testing policy, and contact tracing), and 3) economic stimuli (income support and debt relief for households and companies). We bear in mind that these interventions' short and long-term effects may be ambiguous (Correira et al., 2020; Heyden & Heyden, 2020; Huo & Qiu, 2020; Shanaev et al., 2020). However, we suppose that the more intensive the NPIs are implemented in a country, the less panic there would be about the pandemic, and the more stable travel and leisure companies' performance would be.
3 Data
We study the determinants that make tourism stocks relatively immune to the COVID-19 pandemic with four groups of data: 1) the number of COVID-19 cases reported per week in each country, 2) firm-level characteristics for 1201 international, stock market-listed tourism companies, 3) country-level characteristics for 52 countries, including economic data, national culture, world governance indicators, legal origin, population data, basic medical care data, and tourism sector composition data, and 4) government policy responses. The corporate immunity of travel and leisure firms is determined with weekly stock returns from the most critical period for stock markets starting from the week beginning on January 6, 2020, when the first confirmed death from COVID-19 was reported in Wuhan, until the week ending on March 23, 2020, right after the U.S. Federal Reserve declared comprehensive new measures to assist the economy.3 The Federal Reserve action ended the sudden global market declines and started a global stock market rebound. Our study period focuses on the first and most severe wave of the pandemic that, at this point, was mainly an unknown and unprecedented shock to the global economy. The post-March period, on the other hand, was strongly influenced by government-orchestrated economic stimuli. During the recovery stage that commences, economic indicators and asset prices rebounded, and the state- and corporate-level immunity was no longer in the spotlight.
3.1 Sample of stocks
We retrieve a global selection of travel and leisure companies with Datastream, which provides 2881 equities classified to the travel and leisure industry. We include several filters to concentrate our study on the most representative stocks: 1) we eliminate all instruments other than shares, 2) we eliminate extreme weekly log excess returns of less than −95% and more than 100%, 3) we remove penny stocks (i.e., prices less than USD 1.00), and 4) we discard companies with a market capitalization smaller than USD 100 million. Finally, we define 1201 worldwide tourism stocks classified in the travel and leisure industry. This provides a total of 13,193 weekly observations from our 11-week study period.
3.2 COVID-19
Our research period covers the 11 weeks between January 6 and March 23, 2020. Following Ding et al. (2020), for each country and each week, we compute the ΔCOVID-19 variable as follows:(1) ΔCOVID-19=ln1+confirmedcasesc,t−ln1+confirmedcasesc,t−1
where c and t represent the country and week, respectively, and confirmedcasesc,t is the cumulative number of confirmed cases in country c as of the last day of week t. Thus, ΔCOVID-19 represents the weekly growth rate of the cumulative number of confirmed cases in country c.
3.3 Firm-level characteristics and sector affiliation
We study 19 firm-level variables that may potentially drive tourism stock immunity to COVID-19. We follow the Fama and French (2015) five-factor model and define firm-level characteristics as follows: (1) market risk is measured with the stock market beta (BETA); (2) the size factor is measured with the log-market value (MV); (3) the value factor is represented by six indicators used in cross-country asset pricing studies (Zaremba, 2019); these are: book-to-market ratio (BM), dividend yield (DY), EBITDA-to-EV ratio (EBEV), forecasted earnings-to-price ratio (FEP), cash flow-to-price ratio (CP), and earnings-to-price ratio (EP); (4) the profitability factor is defined by the ratios of the return on assets (ROA), return on equity (ROE), and return on sales (ROS); and (5) the investment factor is tested by the CAPEX-to-assets ratio (CA) and 12-month asset growth ratio (AG).
Additionally, we consider market-related indicators that explain the cross-sectional differences in returns: momentum (MOM) (Jegadeesh & Titman, 1993), long-run reversal (REV) (Balvers et al., 2000), turnover ratio (TURN) (Lee, 2011), and idiosyncratic volatility (IVOL) (Bali & Cakici, 2010). Following Ding et al. (2020), we take into account the firm debt structure as a determinant of stock reaction to COVID-19 market shock, and we investigate the effects of leverage ratio (LEV) and interest coverage ratio (INTCOV) on tourism stocks.
Finally, we also include six dummy variables representing tourism subsectors: airlines (AIRLINES), casinos and gambling (CAS&GAM), hotels and motels (HOT&MOT), recreational services (RECRSERV), restaurants and bars (RES&BAR), and travel and tourism (TR&TOUR). The detailed description of these variables is presented in Table A1 in the Online Appendix A. We use data from Datastream.
3.4 Country-level variables
Our research's international scope requires extending the firm- and sector-level characteristics typical for cross-sectional analysis with country-level characteristics allowing cross-country analysis. For each stock, we consider 40 country-specific factors along with the firm domiciliation. We divide these factors into seven categories: (1) ten economic indicators describing country financial standing; (2) five national culture indicators representing typical social behavior of country citizens; (3) six indicators of governance quality; (4) four different indicators of legal origin; (5) four country population indicators; (6) nine fundamental medical care indicators; and (7) two country-level features of the tourism industry representing its concentration described with the Gini coefficient and the market share of the sector of the total local market value.4
The data is compiled from such sources as the World Bank, OECD national accounts, the International Monetary Fund, the World Tourism Organization, and the World Health Organization (WHO). The detailed list of all country-level variables is presented in Table A2 in the Online Appendix A.
3.5 Government policy responses
Finally, we consider 14 different government policy response indicators from Hale et al. (2020). We consider all the indicators available, classified into three broad categories: containment and closure policies, health system interventions, and economic stimuli. We also explore the composite Stringency Index aggregating different government actions. The details of the policy response variables are provided in Table A3 in the Online Appendix. All the data is sourced from Hale et al. (2020).
The primary statistical properties of all the variables examined in the study and described in Sections 3.1 to 3, 1, 2.5 are reported in Table A4 in the Online Appendix.
4 Methods
The study aims to find determinants of travel and leisure companies’ immunity to the COVID-19 pandemic. Our model assumes that the key factor explaining cross-country differences is the ΔCOVID-19 variable representing the growth rate of the cumulative number of confirmed cases reported per week in each country. Because the dynamics of cases during the research period varied across countries, we need to adjust each of the potential characteristics to the country-specific pandemic situation. We use the following regression specification to evaluate how different characteristics shape stock price movements (Ding et al., 2020):(2) ri,t=δ0+δ1ΔCOVID-19c,t+γ1CARi,t−1T×ΔCOVID-19c,t+γ2CARc,t−1T×ΔCOVID-19c,t+γ3CONi,t−1T+εi,t
where i, c, and t represent index firm, country, and week, respectively. The dependent variable ri,t is the weekly log-return. ΔCOVID-19 is the growth rate of the cumulative number of confirmed cases in country c and week t. CARi,t−1T and CARc,t−1T represent vectors of characteristics at the firm i and country c levels in week t−1, and γ1 and γ2 are vectors of δ regression coefficients at the firm i and country c levels, respectively. Equation (2) includes interactions between vectors of characteristics and ΔCOVID19. Finally, CONi,t−1′ is the vector of the six control variables (with the corresponding vector γ of appropriate δ) that we include in each regression: BM, AG, ROE, MV, BETA, and MOM.5
We examine the statistical importance of particular characteristics with Fama-MacBeth (FM) regressions (1973, FM hereafter). This two-step procedure is commonly used in cross-sectional research and eliminates problems of heteroskedasticity and autocorrelation.6 Because multivariate linear regression is not suitable for a large number of correlated variables, we employ a two-stage procedure that preselects variables before implementing the final FM regression.
In the first step, we run single-interaction FM regressions to define every single feature's statistical importance. We concentrate on features with a 5% significance level.7 Simultaneously, we verify each characteristic's importance from a different perspective and use a machine learning tool called “Elastic net” that eliminates the weights of the least critical features (Zou & Hastie, 2005). We create a specification that covers all interactions simultaneously, along with control variables. In the second step, we consider only these filtered features demonstrated relevant by both single-interaction FM regressions and Elastic net. The detailed model specifications for the FM regressions and Elastic net are presented in the Online Appendix B.
Subsequently, we continue with a detailed analysis of different categories of potential contributors to travel and leisure companies' resiliency. Specifically, we split our characteristics into groups based on the major domains identified previously: 1) firm-level characteristics and sector affiliations, 2) country characteristics, and 3) government policy responses. In each of these subsets, we run multiple-interaction FM regression specifications and investigate combinations of several variables, as well as all at once. This allows us to determine the validity of variables in a multidimensional setting and capture the immunity determinants reflecting similar phenomena. Therefore, toward the final stage, we pass only these variables that remain significant after controlling for each other within different groups. In all our tests, we employ the 5% significance threshold as a default. A detailed discussion concerning feature selection is presented in sections 1, 2, 3, 4.4.
Finally, in the last step, we run the multivariate FM regressions that simultaneously consider all important variables. These specifications demonstrate the determinants of immunity of tourism stock returns to the COVID-19 pandemic and are discussed in Section 5.5.
5 Empirical findings
This section describes the results for the characteristics proven to be a valuable source of information on the immunity of the travel and leisure companies to the pandemic. For the sake of brevity, we report here only the results for the empirically relevant variables that pass our first-state examinations, i.e., are significant in single-interaction FM-regressions and the Elastic net. Also, the detailed list of all the coefficients determined with Elastic net and the coefficient p-values calculated with FM single-interaction regressions for each of the characteristics is presented in Table A4 in the Online Appendix A.
5.1 The COVID-19 pandemic and the stock market
Our first step is to verify whether the ΔCOVID-19 variable, as defined in section 3.2, contains information useful for explaining variation in tourism stock returns during the pandemic. The coefficient on ΔCOVID-19 in regression with no further interactions is significant and negative, amounting to −2.16 (see Table 1, Table 2, Table 3 , column 1). This indicates that the spread of the pandemic adversely impacts the performance of travel and leisure companies. This observation is in line with a common-sense intuition behind the effect of the pandemic on global tourism and the economy.Table 1 Firm-Level Characteristics and Tourism Stock Returns.
Table 1 (1) (2) (3) (4) (5) (6) (7) (8)
ΔCOVID-19 −2.16** −2.740** −2.724** −1.889** −3.426** −2.469** −2.469** −3.125**
(0.437) (0.576) (0.312) (0.362) (0.369) (0.404) (0.253) (0.276)
EBEV * ΔCOVID-19 5.626** 6.741** 5.594** 6.185**
(1.669) (1.232) (1.028) (1.05)
AG * ΔCOVID-19 3.016* 3.085* 3.081** 3.115**
(1.157) (1.154) (0.991) (0.993)
LEV * ΔCOVID-19 −2.447** −2.435** −1.798* −1.778*
(0.744) (0.733) 0.657 (0.612)
Control variables Y Y Y Y Y Y Y Y
Adjusted R2 0.374 0.374 0.383 0.379 0.384 0.379 0.386 0.386
# of firms 1199 1199 1199 1199 1199 1199 1199 1199
This table shows the estimations of cross-sectional regressions of equation (2) for firm characteristics. The table shows the results for the three firm characteristics that were found to be significantly associated with tourism stock returns with single-interaction FM regressions and the machine learning tool, Elastic net: EBITDA-to-EV ratio (EBEV), 12-month asset growth (AG), and net debt-to-equity ratio (LEV). The importance of each characteristic is calculated through its interaction with the weekly growth rate of the cumulative number of confirmed cases (ΔCOVID-19). The dependent variable is weekly log-return. The panel regression includes the following control variables: book-to-market ratio (BM), 12-month asset growth (AG), return on equity (ROE), log-market value (MV), stock market beta (BETA), and momentum (MOM). The descriptions of the variables and how they are calculated are available in Table A1 in the Online Appendix A. In each row, regression coefficients are shown; the numbers in parentheses are the corresponding standard errors. The asterisks ** and * represent statistical significance at the 1% and 5% levels, respectively.
Table 2 Country Characteristics and Stock Returns in Reaction to the COVID-19 Pandemic.
Table 2 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
ΔCOVID-19 −2.164** 3.447** −0.337 −6.332** 0.600 2.353 −0.657 5.37** 0.134 4.507 −4.815 −1.168
(0.437) (0.960) (0.699) (1.784) (0.752) (1.821) (2.599) (1.564) (2.809) (2.099) (3.839) (2.428)
INDIV * ΔCOVID-19 −0.152** −0.07** −0.096** −0.162** −0.06* −0.077** −0.09** −0.064*
(0.034) (0.016) (0.022) (0.039) (0.023) (0.016) (0.023) (0.024)
ACCOUN * ΔCOVID-19 −2.422* −1.79 −1.51 −1.782 −1.468
(1.071) (0.900) (0.870) (0.887) (0.839)
CON * ΔCOVID-19 22.923* 12.146 10.204 29.277 17.327
(9.441) (8.993) (8.416) (15.797) (10.259)
TRAV * ΔCOVID-19 −108.99** −48.918 −78.478 64.054 29.442
(32.268) (33.137) (45.689) (40.839) (20.279)
Control variables Y Y Y Y Y Y Y Y Y Y Y
Adjusted R2 0.380 0.382 0.381 0.381 0.386 0.388 0.386 0.394 0.393 0.393 0.394 0.380
# of firms 1199 1199 1199 1199 1199 1199 1199 1199 1199 1199 1199 1199
We show the estimations of cross-sectional regressions using equation (2) for country characteristics. The table shows the results for the four country characteristics that were found to be significantly associated with tourism stock returns with single-interaction FM regressions and the machine learning tool, Elastic net: individualism (INDIV); voice and accountability (ACCOUN); industry concentration in the local stock market (Gini coefficient) (CON); and the percentage of the local stock market capitalization comprised of travel and leisure industry stocks (TRAV). The importance of each characteristic is calculated through its interaction with the weekly growth rate of the cumulative number of confirmed cases (ΔCOVID-19). The dependent variable is weekly log-return. The panel regression includes the following control variables: book-to-market ratio (BM), 12-month asset growth (AG), return on equity (ROE), log-market value (MV), stock market beta (BETA), and momentum (MOM). The descriptions of the variables and how they are calculated are available in Table A2 in the Online Appendix A. In each row, regression coefficients are shown; the numbers in parentheses are the corresponding standard errors. The asterisks ** and * represent statistical significance at the 1% and 5% levels, respectively.
Table 3 Government Policy Responses and Tourism Stock Returns.
Table 3 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
ΔCOVID-19 −2.164** −3.144** −3.909** −3.522** −4.68** −3.173** −3.235** −4.335** −2.763** −4.592** −3.17** −3.204** −4.139**
(0.437) (0.571) (0.968) (0.809) (1.338) (0.521) (0.633) (1.187) (0.582) (1.255) (0.592) (0.621) (1.013)
STAYATHM * ΔCOVID-19 2.123* 0.996* 2.854* 1.357** 5.385 1.614* 1.108
(0.740) (0.459) (1.289) (0.460) (3.040) (0.646) (0.570)
SCHOOL * ΔCOVID-19 0.937* 1.201* 0.709
(0.393) (0.552) (0.523)
WORK * ΔCOVID-19 1.179* −0.569 1.860
(0.472) (0.467) (1.234)
PUBEVEN * ΔCOVID-19 1.997* 1.513 0.647
(0.869) (0.758) (0.623)
GATHER * ΔCOVID-19 1.102** −1.552 −0.800
(0.313) (1.211) (0.790)
DOMTRAV * ΔCOVID-19 1.46** 0.489 −1.252
(0.498) (0.244) (0.843)
Control variables Y Y Y Y Y Y Y Y Y Y Y Y Y
Adjusted R2 0.381 0.381 0.380 0.381 0.381 0.380 0.379 0.386 0.383 0.385 0.382 0.382 0.389
# of firms 1199 1199 1199 1199 1199 1199 1199 1199 1199 1199 1199 1199 1199
We show the estimations of cross-sectional regressions using equation (2) for government policy responses. The table shows the results for the six government policy responses that were found to be significantly associated with tourism stock returns with single-interaction FM regressions and the machine learning tool, Elastic net: stay-at-home requirements (STAYATHM), school closing (SCHOOL), workplace closing (WORK), cancellation of public events (PUBEVEN), restrictions on gatherings (GATHER), and domestic travel bans (DOMTRAV). The importance of each characteristic is calculated through its interaction with the weekly growth rate of the cumulative number of confirmed cases (ΔCOVID-19). The dependent variable is weekly log-return. The panel regression includes the following control variables: book-to-market ratio (BM), 12-month asset growth (AG), return on equity (ROE), log-market value (MV), stock market beta (BETA), and momentum (MOM). The descriptions of the variables and how they are calculated are available in Table A3 in the Online Appendix A. In each row, regression coefficients are shown; the numbers in parentheses are the corresponding standard errors. The asterisks ** and * represent statistical significance at the 1% and 5% levels, respectively.
5.2 Firm-level characteristics
We begin our search for determinants that limit the negative shock from COVID-19 on tourism stock returns with the firm-level characteristics. Using both the single-interaction FM regression that contains six control variables and the Elastic net specification, we find that the interactions between ΔCOVID-19 and (1) EBITDA-to-EV ratio (EBEV), (2) 12-month asset growth (AG), and (3) net debt-to-equity ratio (LEV) are statistically significant. None of the industry dummies turn out to be important. The results from the single- and multiple-interaction FM regressions are presented in Table 1.
As we can see in columns 2, 5, 6, and 8 of Table 1, the interaction between ΔCOVID-19 and EBEV is positive and significant in both the univariate and multivariate tests. EBEV characterizes the firm valuation: the higher the ratio, the lower the company valuation. Thus, the regression result indicates that tourism stocks with lower valuation, as measured with EBEV, are more resilient to the selloff caused by the COVID-19 pandemic.
The role of EBEV stands out from other valuation ratios, which are less significant. Notably, Gray and Vogel (2012) also demonstrate that this valuation ratio proves to be the most effective of all valuation ratios for cross-sectional return predictions. One reason for this result is that only EBEV considers debt as an ingredient of firm size, thus linking EBEV with firm leverage. Also, because EBEV relies on EBITDA as a measure of earnings, it includes earnings before interest is paid—in contrast to other valuation ratios that capture earnings after interest is paid. All these factors relate to the company's level of debt, and the results align with Loughran and Wellman's (2011) arguments that EBITDA-to-EV is the most useful and reliable valuation ratio that can be compared more easily across firms with differing leverage.
The reason why firms with higher EBEV were more resilient to the COVID-19 pandemic also relates to the q-theory extended by Liu et al. (2009). This theory states that investment return is equal to unlevered investment return or discount rate, namely the weighted average cost of capital (WACC). EBEV is a proxy for WACC; therefore, just as a firm's WACC is positively associated with the leveraged investment return or cost of equity, a firm's EBEV should also be positively associated with the cost of equity (Loughran & Wellman, 2011). Firms with high EBEV are priced low and have a higher cost of capital and, thus, higher expected returns than low EBEV firms.
The second important determinant of tourism stock returns is AG, which also returned a positive coefficient (Table 1, columns 3, 5, 7, and 8). This means that companies with more aggressive investment policies were more resilient to the COVID-19 pandemic. Our finding may be initially counterintuitive, as firms with high investments are characterized by lower expected returns (Cooper et al., 2008). However, it is entirely consistent with the reasoning of Dechow et al. (2020): as operating activity stops, the pandemic affects mostly the imminent, short-term cash flows, whereas deferred cash flow, resulting from future investments, is less affected. Therefore, the larger the part of a firm value that comes from long-term deferred cash flow, the more immune a company should be.
Let us consider two companies: one low-growth and one high-growth. For the high-growth firm, a large part of its value comes from the profits generated on ongoing and future investments that are relatively deferred. On the other hand, in the low-growth company, a more significant part of its value comes from an ongoing operating activity that is less deferred. Now, assume the pandemic affects the cash flow expectation: short-term cash flows will be most reduced, whereas long-term cash flows may be relatively unaffected. With this shift of cash flow expectations, our low-growth firm will lose a large portion of its valuation, driving its stocks to fall deeply.
In contrast, the high-growth firm should be relatively unaffected, as its long-term cash flow expectations remain solid. This specific relationship has already been supported in the economic literature concerning COVID-19. Dechow et al. (2020) empirically demonstrate that value companies characterized by high short-term cash flow and low equity duration underperformed in the broader market during the first months of the pandemic. These results compare well with our finding that firms with higher asset growth are more resilient to the pandemic.
The last microlevel determinant of tourism stock return behavior during the COVID-19 pandemic is LEV (Table 1, columns 4, 6, 7, and 8). In this case, the observed relationship is negative, indicating that the stocks of tourism companies with less debt are more resilient to the pandemic. LEV describes the level of firm financial flexibility. The lower the leverage, the higher the flexibility, and the ease with which a firm can fund a revenue shortfall resulting from a shock such as COVID-19. A firm with substantial financial flexibility can easily finance a cash flow shortfall (Fahlenbrach et al., 2020). The results for LEV also support our earlier discussion concerning the significance of EBEV, where we note that the level of debt is a component that distinguishes EBEV from other valuation ratios. This result is also consistent with the conclusions of Ding et al. (2020).
5.3 Country characteristics
Next, we examine country characteristics that cause cross-sectional variation in tourism stock returns categorized as economic data, national culture determinants, world governance indicators, legal system origin, population data, basic medical care data, as well as two industry characteristics. With the single-interaction FM regression and Elastic net specification, we find four variables that interact with ΔCOVID-19 and are statistically meaningful: 1) individualism (INDIV), 2) voice and accountability (ACCOUNT), 3) industry concentration in the local stock market described with the Gini coefficient (CON), and 4) share of the tourism sector in the whole stock market capitalization (TRAV).
As we can see in Table 2, the only characteristic that is statistically important in combination with other interactions is individualism (INDIV), which returned a negative coefficient (columns 2, 6–12). INDIV is one of the variables representing national culture. It describes the degree to which individuals relate to the group, meaning that in “individualistic societies the ties between individuals are loose and everybody stands on her own and her immediate family” (Hofstede, 1991; after; Kim, 2001, p. 4). Together with its opposite, collectivism, where social relations are tight, individualism is one of the six dimensions of national culture described by Hofstede (1980). Our result confirms the suggestion of Eun et al. (2015) that culture is a crucial omitted variable in the literature that explores cross-country differences in equity returns.
The interaction between ΔCOVID-19 and INDIV in our study is negative. Thus, the regression results indicate that tourism stocks in countries with a lower degree of individualism (equal to a higher degree of collectivism), as measured with INDIV, are more resilient to the selloff caused by the COVID-19 pandemic. This finding is supported by the evidence that individuals in collectivist societies like China are less crash-averse (Weigert, 2016) and take more risk than individualistic Westerners like in the United States (Wang & Fischbeck, 2008). The latter is described by Hsee and Weber (1999) in the “cushion hypothesis” about the relationship between national culture and individual risk-taking. According to this hypothesis, if individuals from collectivist countries fail, they are more likely to be “cushioned”—i.e., financially supported when they are in need—by their closest family and friends. Therefore they are more comfortable in taking a greater risk than those from individualistic countries. Schneider et al. (2017) confirm that social cushioning is associated with the greater propensity to take the risk, and Illiaschenko (2019) finds that the relationship between individualism and risk-taking is negative.
5.4 Policy responses
Finally, we turn to the examination of national policy responses. The results of the single-interaction FM regression and the Elastic net indicate six interventions as significant.8 These are: stay-at-home requirements (STAYATHM), school closing (SCHOOL), workplace closing (WORK), cancellation of public events (PUBEVEN), restrictions on gatherings (GATHER), and domestic travel bans (DOMTRAV). They can be categorized as containment and closure policies. We include them in multiple-interaction FM regressions, as reported in Table 3. Two variables in this framework—STAYATHM and SCHOOL—prove significant after controlling for other variables, as well as after controlling for each other. In other words, these two stringency responses are essential determinants of tourism stocks’ reaction to COVID-19 from both the individual and collateral perspectives. In contrast, other variables are important individually but fail to remain relevant in multi-interaction specifications (see columns 9–12). In other words, their role is captured by the effect of other policies.9
Notably, it is essential to highlight that the policy response category variables are generally strongly correlated. In consequence, once we include all of them jointly in a regression (Table 3, column 13), all of the coefficients lack significance. Nevertheless, even in this framework, the coefficients for STAYATHM and SCHOOL remain positive, consistent with our assertion that tourist stocks in countries with specific closure policies are more resilient to the COVID-19 pandemic.
School closing (SCHOOL) and avoiding crowding related to stay-at-home requirements (STAYATHM) are among six factors necessary for social distancing during the global pandemic underlined in the policy review of Fong et al. (2020) 10 . It is well documented that closing schools are a useful tool in reducing influenza pandemics transmission (Glass et al., 2006; Kawaguchi et al., 2009; Rashid et al., 2015; Sypsa & Hatzakis, 2009). Avoiding crowds can help to reduce the virus death rate. Both types of policy interventions bring less panic concerning the pandemic and, therefore, more certainty about the financial stability and corporate liquidity and solvency underlined by the IMF (2020). Eventually, this turns into more stability of the performance of travel and leisure companies.
An important feature of school closing and stay-at-home recommendations is that they do not directly affect the tourism business. Though children do not attend school, this does not limit the leisure and travel industry's operations. Similarly, the stay-at-home policies—unless they take the most severe form of freezing entirely social interactions—may not necessarily affect the tourist sector. Soft recommendations targeted at local citizens suggesting they limit time spent outside their households may allow, e.g., the international tourist arrivals to remain unaffected. Consequently, while the adverse impact on the travel and leisure companies is limited, these policies still allow curbing the spread of the pandemic. Furthermore, they may also help strengthen a country's image as one that can cope with healthcare crises and extreme situations efficiently.
Intuitively, stay-at-home requirements (STAYATHM) and school closing (SCHOOL) are complementary to individualism (INDIV) in determining tourism stock return behavior. Huynh (2020) shows that cultural differences influence social distancing across countries during the COVID-19 pandemic. Collectivist countries with tight social bonds have more social distancing discipline, whereas individualistic countries with loose social bonds do not. Actually, in the latter countries at the beginning of the COVID-19 pandemic, social distancing was ignored even by their political leaders, which was spotted by both the media and academia (Cohen et al., 2020; Colarossi, 2020; Cottle, 2020). That leads to our finding that a lower degree of individualism and more social distancing are essential characteristics for the immunity of tourist stock returns to the COVID-19 outbreak.
5.5 Final multiple-interaction approach
The last step of our research is to consider jointly the characteristics selected in the single-interaction regressions to determine their overall predictive ability. We include the following characteristics, selected for the reasons described in sections 1, 2, 3, 4.4: EBEV, AG, LEV, INDIV, as well as SCHOOL and STAYATHM. We analyze different combinations of these variables and find all of them significant in the multiple-interaction FM regressions. The results of this analysis are synthesized in Table 4 .Table 4 Joint Tests of Multiple Variables.
Table 4 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
ΔCOVID-19 −2.74** −2.724** −1.889** −3.909** −3.144** 3.447** −3.125** −5.05** −4.009** 2.865** −5.35** −1.919
(0.576) (0.312) (0.362) (0.968) (0.571) (0.96) (0.276) (0.842) (0.345) (0.834) (0.990) (1.749)
EBEV * ΔCOVID-19 5.626** 6.185** 6.946** 4.207** 6.223** 5.544** 3.534*
(1.669) (1.05) (1.618) (1.043) (1.891) (1.205) (1.353)
AG * ΔCOVID-19 3.016* 3.115** 2.54* 3.102** 2.99** 2.685* 2.622*
(1.157) (0.993) (1.16) (0.99) (0.986) (1.111) (1.091)
LEV * ΔCOVID-19 −2.447** −1.778* −1.619* −1.756* −1.175* −1.484* −1.361*
(0.744) (0.612) (0.649) (0.637) (0.614) (0.649) (0.577)
SCHOOL * ΔCOVID-19 0.937* 1.025* 1.210* 1.224*
(0.393) (0.398) (0.536) (0.492)
STAYATHM * ΔCOVID-19 2.123* 2.142** 1.043* 1.03*
(0.74) (0.703) (0.439) (0.444)
INDIV * ΔCOVID-19 −0.152** −0.177** −0.08*
(0.034) (0.038) (0.032)
Control variables Y Y Y Y Y Y Y Y Y Y Y Y
Adjusted R2 0.374 0.383 0.379 0.380 0.381 0.380 0.386 0.392 0.393 0.392 0.397 0.403
# of firms 1199 1199 1199 1199 1199 1199 1199 1199 1199 1199 1199 1199
We show the estimations of cross-sectional regressions using equation (2) for the joint test of multiple variables. The regression includes the variables found to be most significant in the Fama-MacBeth regressions and Elastic net analysis from each of the three domains of characteristics (i.e., firm, country, government policy responses): EBITDA-to-EV ratio (EBEV), 12-month asset growth (AG), net debt-to-equity ratio (LEV), school closing (SCHOOL), stay-at-home requirements (STAYATHM), and individualism (INDIV). The importance of each characteristic is calculated through its interaction with the weekly growth rate of the cumulative number of confirmed cases (ΔCOVID-19). The dependent variable is the stock weekly log-return. The panel regression includes the following control variables: book-to-market ratio (BM), 12-month asset growth (AG), return on equity (ROE), log-market value (MV), stock market beta (BETA), and momentum (MOM). The descriptions of the variables and how they are calculated are available in Tables A1-A3 in the Online Appendix A. In each row, regression coefficients are shown; the numbers in parentheses are the corresponding standard errors. The asterisks ** and * represent statistical significance at the 1% and 5% levels, respectively.
Let us focus on the results of the all-in-one multivariate regression in Table 4, column 12. First, this approach confirms the significance of the three firm-level characteristics: EBEV, LEV, and AG. Furthermore, also observe the impact of individualism in a national culture (INDIV) on the stocks’ performance—the relationship is negative and significant. In other words, the more individualistic (or less collectivistic) the economy, the more the returns in that economy were affected by the pandemic. This result is intuitive, especially when we combine it with the essential role of policy responses. The interactions between ΔCOVID-19 and school closing (SCHOOL) or stay-at-home requirements (STAYATHM) are significant and positive, showing that fast and restrictive countries in implementing social distancing decreased the market panic and created a supportive environment for the performance of the travel and leisure sector. Our results show that tourism stocks from countries with tight bonds in society and restrictive social distancing policies were more immune to the early effects of the COVID-19 pandemic than those from countries with loose social bonds and that did not introduce any such restrictions or did not pay sufficient attention to it.
The positive role of social distancing may seem astonishing, counterintuitive, and unreasonable at first sight. Nonetheless, the policy's effect may be simply to help mitigate the role of the pandemic itself. For example, Correira et al. (2020) demonstrate that during the 1918 flu pandemic, the U.S. cities' economies that undertook more aggressive approaches did not perform worse and even managed to grow faster. Consistent with these results, Zaremba et al. (2020) find that strict government interventions positively affected domestic stock market returns.
To sum up, our final analysis confirmed the essential role of several variables in tourism company stocks' performance. Companies with low valuations, limited leverage, and high asset growth, as well as those located in countries with less individualism in national culture and undertaking strict policies to fight the pandemic, tended to overperform during the early months of the COVID-19 pandemic.
6 Conclusions
This article searches for the characteristics that may protect stock market-listed companies from the tourism sector against the COVID-19 pandemic. We address this question by assessing the relationships between the company and country characteristics and their stocks' reaction to COVID-19 using the machine learning tool Elastic net and Fama-MacBeth regressions (Fama & MacBeth, 1973).
Our findings identify several significant features that helped to protect tourism firms from the pandemic. First, we show that companies with a low enterprise valuation ratio, limited debt, and intensive investment policies are better prepared to cope with a potential epidemic crisis. Second, a low degree of individualism in the national culture may also prove protective. Last but not least, strong government policies and quick policy responses, such as school closure (SCHOOL) and stay-at-home requirements (STAYATHM), may help travel and leisure companies to cope with a pandemic.
Our findings are relevant to a variety of decision-makers in a global context, including investors, managers, governments, and other regulatory bodies. Investment policies, leverage, and enterprise valuation are key variables providing immunity against the pandemic. This may be considered as a road map for managers. The pandemic has a less negative tone for slightly leveraged firms, emphasizing the role of capital structure choice for managers in the travel and leisure companies. Entering the COVID-19 period with a lower leverage ratio brings higher flexibility and helps fund a revenue shortfall. The companies in the industry are, in general, highly leveraged compared to most of the other industries, which raises concerns for the impact of possible next waves of the pandemic.
Investors can follow microeconomic factors, which provide immunity to travel and leisure companies, and, in a pandemic period, can tilt their portfolios towards the companies with better profiles in these factors. Moreover, investors should be aware of the effect of government policy responses implemented to limit the transmission of the virus on stock returns. As the second wave of COVID-19 is highly expected, the investors need to follow the measures mentioned above closely. Such an investment strategy may protect investors in the pandemic period.
In addition to policies on preventing the transmission of the virus, governments and regulatory bodies should also develop financial strategies for supporting travel and leisure companies during outbreak periods. We find that leverage can serve as a mitigating factor against the pandemic. The halting of international tourism is likely to create serious cash flow problems, and firms can face a liquidity crisis and have problems in debt repayment. Therefore, opening new credit channels, providing opportunities to extend debt maturities, and creating refinancing options might help travel and leisure firms be less affected by the pandemic. A proactive strategy might also be followed by providing liquidity options and financial flexibility in advance if a second wave is highly expected. The results may help prepare firms in this industry for disasters similar to COVID-19 that may occur in the future.
Finally, our findings highlight the role of country-specific cultures on the local resilience to the pandemic. This provides essential insights to policy-makers, who design and implement regulations aimed at curbing the pandemic. The containment and closure policies typically bear substantial social and economic costs, so governments must carefully balance the undertaken actions' costs and benefits. Our findings explicitly indicate that collectivistic cultures may boost corporate immunity to the pandemic, allowing for better design and selection of pandemic-related policies.
Our study is limited by the short examination period and the number of characteristics available for analysis. There may be other determinants of COVID-19 resilience not investigated in this study. Research with a longer time horizon, including a second wave and recovery after this crisis, can provide further insights into characteristics that protect travel and leisure companies against a pandemic.
Impact statement
This study contributes to the global travel and leisure industry by exploring which factors provide corporate immunity against COVID-19, and why some firms perform better than others during the pandemic. The results can help regulatory bodies, managers, and investors to adopt effective strategies in a period of overwhelming global panic and fear. We draw a road map for managers in order to mitigate the devastating impact of the pandemic. Investment policies, leverage, and enterprise valuation are key variables providing immunity against the pandemic. The collectivistic character of a national culture also matters. Governments implemented several policy responses in this novel period; however, school closures and stay-at-home requirements primarily helped travel and leisure companies cope with the pandemic. This implies that a timely and appropriate measure against the transmission of COVID-19 can be helpful for the industry. The findings can provide insight for a possible new wave of the pandemic.
Declaration of competing interest
None.
Tomasz Kaczmarek is a Ph.D. student at Poznan University of Economics and Business (Poland). Professionally strategist, portfolio manager, and capital markets analyst, with 17 years' experience in finance. He is a co-founder and Chief Investment Officer in Starfunds – fintech, an independent fund distributor offering robo-advisory solutions. Before joining Starfunds, he worked in a leading Polish brokerage house from a global capital group in various positions, including Chief Innovation and Product Officer, Chairman of Investment Committee, Portfolio Manager, and Equity Research Analyst. His most recent research focuses on empirical asset pricing via machine learning.
Katarzyna Perez is an Associate Professor and Head of Investment and Financial Markets Department at Poznan University of Economics and Business (Poland). Also serves as an independent member of a supervisory board in a mutual fund management company from a global capital group. Dr. Perez has been engaged in research on mutual funds, portfolio management and asset pricing, lately also in the context of robo-advisory. She is an author of few books on financial investments, bestsellers in a local market.
Ender Demir is an Associate Professor at the Istanbul Medeniyet University in Istanbul, Turkey. Dr. Demir has received his Ph.D. in Business from Ca Foscari University, Italy. Dr. Demir is the founder and conference coordinator of Eurasia Business and Economic Society (EBES). He has published his research in peer-reviewed international journals, including Annals of Tourism Research, Emerging Markets Review, Finance Research Letters, and Journal of International Financial Markets, Institutions & Money. His research interest are corporate finance, Cryptocurrencies, and tourism economics
Adam Zaremba serves as Associate Professor of Finance at Montpellier Business School (France) and Poznan University of Economics and Business (Poland). His research interests include asset pricing, investments, and financial markets. His works have been published in top finance journals, such Journal of Banking and Finance, Energy Economics, or Journal of Portfolio Management. He has worked as an economist, adviser, and portfolio manager for investment management companies. Dr. Zaremba has also written numerous research papers and several books on financial markets.
Appendix A Supplementary data
The following is the Supplementary data to this article:Online Appendices
Acknowledgements
Adam Zaremba acknowledges the support of the National Science Center of Poland [Grant no. 2016/23/B/HS4/00731].
1 The World-DS Travel & Leisure Index fell by 44.7%, while the prices of all stocks represented by the World-DS Global Index fell “only” 32.6%.
2 A detailed description of each characteristic is presented in Table A2 in the Online Appendix A.
3 The FED announcement is available at https://www.federalreserve.gov/newsevents/pressreleases/monetary20200323b.htm.
4 We adopt some data preparation techniques that are commonly applied in cross-sectional analysis. In order to deal with outliers for each variable, we estimate the interquartile range score. If a variable's single value is out of the interquartile range, we apply winsorization at the 0.5% level. Furthermore, we apply normalization for all data used in Elastic net regression. Another issue is missing values, which we replace with a cross-sectional median for each variable.
5 Our control variables stem from the return predictors underlying the six-factor model of Fama and French (2018) that nests other major asset pricing models such as the three-factor and four-factor models. Importantly, the Fama-French six-factor model variables, as well as the nested five-factor model (Fama & French, 2015), capture well the multidimensionality of stock returns. The models explain the broad array of different anomalies and return patterns in the stock market, synthesizing all the most important variables that have been demonstrated in the asset pricing literature to influence the cross-section of stock returns (Fama & French, 2016).
6 The methodology is executed in two steps: a cross-sectional step and a time-series step. In the first step, regression coefficients are defined with a typical multivariate regression. In the second step, we define standard errors and p-values for regression coefficients with the Newey and West (1987) procedure that eliminates the heteroskedasticity and autocorrelation typical for stock returns.
7 As we incorporate six control variables and ΔCOVID-19 into every regression, our single-interaction regressions consist in practice of eight independent variables (a single interaction between the ΔCOVID-19 and a characteristic, the ΔCOVID-19 variable, and six controls).
8 Table A5 in the Online Appendix A presents a detailed list of national policy responses with the FM regression coefficients and p-values and the values of the Elastic net coefficients.
9 For the sake of brevity, we report only the relevant regression specifications. Any further results are available upon request.
10 The other four are: isolating ill people, tracing contacts, quarantine of exposed people, and workplace changes (see Fong et al. (2020)).
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.tourman.2020.104281.
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| 0 | PMC9734087 | NO-CC CODE | 2022-12-14 23:28:27 | no | Tour Manag. 2021 Jun 7; 84:104281 | utf-8 | Tour Manag | 2,021 | 10.1016/j.tourman.2020.104281 | oa_other |
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Tour Manag
Tour Manag
Tourism Management
0261-5177
1879-3193
Elsevier Ltd.
S0261-5177(21)00005-4
10.1016/j.tourman.2021.104286
104286
Article
Too afraid to Travel? Development of a Pandemic (COVID-19) Anxiety Travel Scale (PATS)
Zenker Sebastian ∗
Braun Erik
Gyimóthy Szilvia
Copenhagen Business School, Department of Marketing, Solbjerg Plads 3, 2000, Frederiksberg, Denmark
∗ Corresponding author.
11 1 2021
6 2021
11 1 2021
84 104286104286
4 7 2020
18 12 2020
7 1 2021
© 2021 Elsevier Ltd. All rights reserved.
2021
Elsevier Ltd
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Pandemics are affecting tourism in many ways. Being a niche research field before, the coronavirus (COVID-19) pandemic created a strong urgency to develop this topic. For researching pandemic-induced changes in tourist beliefs and travel behaviour, we developed a construct that measures the intra-personal anxiety of travellers (and non-travellers): the Pandemic (COVID-19) Anxiety Travel Scale (PATS), using two large online studies (N = 2180; N = 2062) and including two different cultural contexts (US and Denmark). In Study 1, explorative and confirmative factors analysis confirms a short and easy-to-use 5-item solution, while the presented model adds face validity. Study 2 confirmed the structure (reliability) and tested nomological validity, by putting PATS into the context of different constructs (xenophobia and prevention focus). Although the proposed scale arose from the coronavirus (COVID-19), it is not limited to this specific pandemic and will hopefully prove to be a valuable measurement tool for future pandemics as well.
Keywords
Scale development
Pandemic
COVID-19
Coronavirus
Tourist behaviour
Xenophobia
Risk perception
==== Body
pmc1 Introduction
For most recent history, pandemics were largely a niche topic in tourism (e.g., Novelli, Burgess, Jones, & Ritchie, 2018; Page, Yeoman, Munro, Connell, & Walker, 2006; Ritchie and Jiang, 2019, Rittichainuwat and Chakraborty, 2009; Zeng, Carter, & De Lacy, 2005), but the outbreak of the coronavirus (COVID-19) has dramatically shifted the narrative. The COVID-19 pandemic is one of the most impactful events of the century and has radically disrupted tourism markets and mobility on a global scale. At the time of writing, the COVID-19 pandemic is still in full swing. For months following its onset, tourism and travel in many countries ground to a complete halt. After months of closed national borders and grounded flights, some countries slowly started to re-open for tourism during early summer 2020 (e.g., allowing citizens of EU member states to travel within Europe), while other countries and regions are still under full or partial lockdown and banned from travel (Boffey, 2020, June 29th).
In just these few months, a large number of social scientists and tourism researchers have started to explore the economic, social and psychological consequences of the outbreak—and one particular focal point has been tourist behaviour. If we assume that this pandemic will “create deep marks in the tourist's thinking and feeling, and change how tourists travel” (Zenker & Kock, 2020, p. 2), then we first need to identify and measure this intra-personal cognitive modality of coronavirus anxiety. Therefore, this paper proposes a scale for measuring travellers' (and non-travellerś) anxiety in regards to COVID-19.
Medical researchers have already put forward distinct scales to measure coronaphobia—in particular, the Fear of COVID-19 Scale (Ahorsu, Lin, Imani, Saffari, Griffiths, & Pakpour, 2020) and the CAS: Corona Anxiety Scale (Lee, 2020). However, these clinical instruments are not sufficiently tailored (or tested) for use in tourism. Likewise, existing tourism research has studied health risk perception (e.g., Reisinger & Mavondo, 2005; Rittichainuwat & Chakraborty, 2009) and general travel risk perceptions (e.g., Floyd, Gibson, Pennington-Gray, & Thapa, 2004; Seabra, Dolnicar, Abrantes, & Kastenholz, 2013), but has not specifically measured cognitive health concerns related to pandemics. Thus, this paper develops and tests a simple and easy-to-use 5-item Pandemic (COVID-19) Anxiety Travel Scale (PATS).
To show the reliability and validity of this scale, we followed standard procedures in scale development, and conducted two independent studies to test its reliability across different cultural contexts. We purposefully selected the US and Denmark in order to create a more universal scale, which functions consistently in different countries and at different COVID-19 emergency levels. At the time of this research (mid-June 2020), the US and Denmark were at very different stages of the pandemic curve, with the US facing an alarmingly high infection rate and death toll record (Boffey, 2020, June 29th). Denmark, on the other hand was at a later stage of the pandemic, with a declining number of new infections and one of the lowest COVID-19 related death tolls in Europe. We focused our second study to test the aspect of nomological validity (Kock, Josiassen, & Assaf, 2019a) because a scale also needs to prove its meaningful explanation value (and must be different to other known constructs). Therefore, we distinguish PATS from the concept of xenophobia (Faulkner, Schaller, Park, & Duncan, 2004; Kock, Josiassen, & Assaf, 2019b) and link it to the concept of prevention focus (Zhao & Pechmann, 2007).
While we tested the scale in an empirical context that is wholly consumed by COVID-19, its conceptual foundations and empirical corroboration may render the PATS applicable to research on other (future) pandemic events.
2 Risk perception and travel anxiety
2.1 Pandemics and global mobility
Global tourism – and with it, the intensifying mobility of capital, goods and people around the world—has contributed to the circulation of infectious diseases (Tatem, 2014; Wilson, 1995). Since the beginning of this millennium, international travel has been affected by several epidemic waves, elevating biosecurity to a prioritized policy issue and public concern. For instance, the previous coronavirus-induced respiratory illnesses of SARS (2003) and MERS (2013) originated from China and Saudi Arabia, respectively; both spread from crowded tourism hotspots (Hong Kong and Jeddah) to over 30 countries across six continents in a matter of few weeks (Al-Tawfiq, Zumla, & Memish, 2014). These occurrences not only revealed international travellers' exposure to severe infections, but also the role of tourism in facilitating the spread of diseases through air travel in densely populated urban areas (Hall, 2015; Raptopoulou-Gigi, 2003) via immunologically naive populations (Widmar, Dominick, Ruple, & Tyner, 2017). Even before COVID-19, extensive media coverage of the Ebola outbreak in West Africa (2013), the recurring waves of Avian and swine flu in South East Asia, and the Zikavirus (ZIKV; 2016) in the Caribbean have heightened the public's awareness about the mobility of highly contagious foreign viruses. Health hazard perceptions are also induced by governments' travel guidelines for citizens and legitimate the World Health Organization's declarations of ‘Public Health Emergency of International Concern’ (PHEIC; WHO, 2019). Even though many countries have upgraded their biosecurity measures to prevent disease transmission, a borderless globalized world remains extremely vulnerable to novel types of biohazard (Wilson, 2010). On several occasions in the past few years, epidemiologists (Bruin, Fischhoff, Brilliant, & Caruso, 2006; Osterholm & Olshaker, 2017) and tourism researchers (Hall, 2015; Hall & James, 2011) have anticipated a coming global pandemic: a ‘perfect storm’ that could affect international mobility on an unforeseen scale.
Acknowledging that human mobility is inherently tied to health risks, tourism researchers are increasingly striving to understand the effects of pandemics on travel behaviour (Zenker & Kock, 2020). A growing body of empirical evidence (Joo, Henry, Lee, Berro, & Maskery, 2019; Kuo, Chen, Tseng, Ju, & Huang, 2008; Novelli et al., 2018; Zeng, Carte, & De Lacy, 2005) demonstrates that pandemics have a severe and enduring influence on risk perceptions and related travel decisions to disease-struck regions. In a retrospective study of epidemic-related decline in travel demand to South Korea, Joo et al. (2019) found that destinations hit by SARS in 2003 were associated negatively during subsequent health emergencies; they also faced dwindling visitor numbers during the 2015 MERS epidemic. Singapore and other Asian destinations with a consolidated tourism sector were quick to recover from the SARS crisis and proved remarkably resilient to sudden breakdowns of international travellers (Zeng et al., 2005). However, pandemic outbreaks may have devastating consequences for developing countries with no strong brand image or exposure in global news media, such as Gambia in West Africa (Cahyanto, Wiblishauser, Pennington-Gray, & Schroeder, 2016). Although that country had no reported cases during the most recent Ebola epidemic, incoming tourism arrivals dropped by 50 percent for two years, and triggered the so-called Ebola-induced tourism crisis (Novelli et al., 2018). Evidently, global travellers are sensitive to mediatized images, with perceived health hazards outweighing documented risks in their travel choices.
2.2 Perception of health risks and travel behaviour
On a more general level, overseas travel and exotic destinations are often associated with higher risks and uncertainties regarding personal health and safety levels. One may risk catching contagious infections on public transport, poorly sanitized beaches or through endemic disease vectors (ticks or mosquitoes). As a consequence of such hazards and frequent epidemic outbreaks, 21st century consumer attitudes and risk perceptions toward international travel are fraught with health concerns. One can even argue that global travel patterns are undergoing a paradigm shift (Irwin, 2020, April 16th). Carefree, adventurous and extroverted tourism practices, which characterized international travel in the late 20th century, are giving way to risk-aversive tendencies (Reisinger & Mavondo, 2005). Consequently, people may be deterred from travelling, in order to minimize the risk of disease contraction (Widmar et al., 2017), or compelled to search for technologically safe substitutes (Nanni & Ulqinaku, 2020).
Reisinger and Mavondo (2005) spearheaded research on the risk perceptions and psychographic dimensions of travel concerns. In an extensive empirical study conducted in the aftermath of 9/11, the authors validated a strong relationship between travel risk perceptions, travel anxiety and travel choices. Furthermore, they suggested the inclusion of five different types of perceived risks associated with travel to predict travel anxiety; they found that levels of travel anxiety and perceived safety shaped international travel intentions. In a similar vein, Widmar and colleagues' (2017) -demonstrated that people avoiding travel to destinations potentially contaminated by ZIKV were also more attentive to their general health, were better educated, and often had children in the household.
2.3 Anxiety and COVID-19
In psychopathology, anxiety is defined as a mental disorder captured along cognitive, behavioural, emotional, and physiological dimensions (American Psychiatric Association, 2013). The concept of anxiety is hardly novel to tourism: The complex affective and physiological responses to travel were already described as a nervous condition during the dawn of organized tourism in the middle of the 19th century. Medical experts of the time denoted this ailment as Reisefieber (German) or rejsefeber (Scandinavian), and diagnosed it as an unbalanced restlessness or overstimulation (Löfgren, 2008). Travel fever arises from the simultaneous feelings of anticipation or longing for the unknown and the fear of temporarily abandoning safe home environments. Most healthy individuals experience moderate levels of anxiety combined with positive arousal before and during vacations. Such transient reactions to specific, stressful situations are termed state anxiety, and would normally have little effect on travel intentions and decisions. However, specific conditions of mass travel have also given rise to new phobias producing clinically significant anxiety disorders. Travel-related phobia can be activated by specific spatial or social stimuli, such as vast public spaces (agoraphobia), spatial confinement in a bus or airplane (claustrophobia), crowds and mass gatherings (demophobia/enochlophobia), road travel (hodophobia) air travel (aviophobia), and worries about contracting an infectious disease (nosophobia). Most recently, the nervous condition associated with the COVID-19 pandemic has received a distinct diagnosis labelled coronaphobia or coronavirus anxiety (Asmundson & Taylor, 2020). These phobias can be clinically diagnosed along phobic stimuli (situations triggering the phobia) as well as two types of anxiety symptoms: somatic modality (physiological symptoms and impairment) and cognitive modality (i.e., distressing thoughts; Nousi, van Gerwen, & Spinhoven, 2008). Phobic stimuli are capable of triggering anticipatory anxiety, that is, a persistent fear of stressful or risky situations in the future. Anticipatory anxiety may be evoked prior to stressful situations: for instance, people who are flying phobic (aviophobia) would experience somatic and cognitive distress just by thinking about flying. Enduring somatic symptoms (insomnia, racing pulse levels, sweaty palms, dizziness, etc.) or cognitive symptoms (distress, worries, doubts) may be so severe that they can hinder the accomplishment of planned activities. In the worst case, anticipatory anxiety may develop into acute mental disorders (depression, panic attacks or suicidality) requiring medical treatment (Lee, Mathis, Jobe, & Pappalardo, 2020).
Medical research has developed a range of psychiatric screening tests to effectively diagnose dysfunctional anxiety, such as the Generalised Anxiety Disorder (GAD 7; Spitzer, Kroenke, Williams, & Löwe, 2006) and the Cognitive and Somatic Anxiety (STICSA; van Dam, Gros, Earleywine, & Antony, 2013). These generic scales provided the basis for the development of the Coronavirus Anxiety Scale (CAS; Lee, 2020), a brief 5-item healthcare screener adapted and tested during the beginning of the COVID-19 crisis in the US. Each item of the CAS-scale covers distressing bodily responses (dizziness, sleep disturbance, tonic immobility, appetite loss and nausea); none of them address the cognitive modalities of anxiety. In contrast, Ahorsu et al. (2020) developed the 7-item Fear of COVID-19 (FCV-19S) scale with the help of Iranian healthcare experts and participants. The FCV-19S scale lists items of cognitive anxiety modalities (worries and fears when thinking about COVID-19), but it does not locate symptoms of anticipatory anxiety in any particular situational context.
Anticipating and planning future vacations is primarily a cognitive endeavour; hence, the design of a travel anxiety scale must be informed by context-relevant items that capture both phobic stimuli (spatial or social settings triggering fears before and during travel) and symptoms (modalities) of anticipatory anxiety.
2.4 Identifying the original items
So far, tourism researchers have approached the measurement of travel anxiety on a very simple level, only including the construct as a moderator between risk perceptions and travel intentions. For instance, Reisinger and Mavondo (2005) employed a scale composed of 12 bipolar adjectives to describe emotional states (e.g., calm/worried, relaxed/tense or composed/stressed), which are neither conceptually corroborated nor aligned with clinical measurements of anxiety. Therefore, we consulted the literature in travel medicine and psychopathology (American Psychiatric Association, 2013; Spitzer et al., 2006; van Dam et al., 2013; Widmar et al., 2017), as well as pandemic-related anxiety scales (Ahorsu et al., 2020; Lee, 2020; Lee et al., 2020), to identify relevant item candidates for our study.
As described before, anxiety is defined as a mental disorder captured along cognitive (i.e., worries, repetitive thinking), behavioural (i.e., dysfunctional, compulsive activities, avoidance), emotional (i.e., fear, nervousness), and physiological dimensions (i.e., somatic distress; American Psychiatric Association, 2013). Accordingly, Lee's (2020) coronavirus anxiety scale entirely consists of physiological arousal symptoms associated with fear, such as panic attacks, depression, traumatic situations and general anxiety disorders. However, in the context of travel planning, clinical physiological symptoms (dizziness, sleep disturbance, tonic immobility, appetite loss and nausea) are very rare and more likely manifest themselves in specific travel situations (e.g., flight anxiety; Nousi et al., 2008). In contrast, cognitive and behavioural modalities reflecting anticipatory anxiety towards future travel situations are highly relevant, because the planning of risky purchase and consumption choices generates a fear of unknown consequences and feelings of anxiety (Reisinger & Mavondo, 2005).
Accordingly, we used the Fear of COVID-19 Scale (Ahorsu et al., 2020) as our point of departure and adapted its seven anticipatory items to the tourism context. Driven by the arguments above, we removed the items related to somatic modalities (i.e., sweating hands; sleep disorders; trembling). We kept items depicting cognitive and emotional modalities, but related them to future travels and travel planning (i.e., items related to feelings or perceptions of discomfort; nervousness; anxiety; and fear of death). Furthermore, we also included two items related to behavioural adjustment or avoidance related to travel-specific phobic stimuli (i.e., avoiding crowds; taking precautionary measures—items C and D). This resulted in the identification and translation of eight items for our original Pandemic Anxiety Travel Scale (Table 1 ).Table 1 Original Pandemic (COVID-19) Anxiety Travel Scale (PATS) items.
Table 1A: I am anxious to travel to crowded destinations due to COVID-19. later deleted
B: COVID-19 makes me worry a lot about my normal ways of travelling. final scale
C: COVID-19 makes me think a lot about taking precautionary measures before travelling. later deleted
D: Avoiding people when I travel is frequently on my mind due to COVID-19. later deleted
E: It makes me uncomfortable to think about COVID-19 while planning my vacation. final scale
F: I am afraid to risk my life when I travel, because of COVID-19. final scale
G: When watching news about COVID-19, I become nervous or anxious in regards to travel. final scale
H: I do not feel safe to travel due to COVID-19. final scale
2.5 Scale development and nomological validity
Scale development is a very common procedure and follows an established sequence of steps. First, developing a list of items that cover the construct from theoretical perspectives, then looking for proof in terms of face, construct, content, and predictive validity, as well as reliability (Churchill, 1979; Hinkin, 1995; Peter, 1981).
In tourism, however, this process is often criticized for methodological approaches that are unnecessarily complicated and often lack a short and precise scale development report (Beritelli, Dolnicar, Ermen, & Laesser, 2016). Furthermore, scale development in tourism has been heavily criticized for not taking nomological validity into account (Kock et al., 2019a). For our scale to be usable and conceptually sound, it needs to be put into a meaningful tourism framework (and also given the possibility to differentiate it from other constructs). To meet both criteria, we put PATS into two short tourism frameworks in order to test its face, predictive and nomological validity.
First, for predictive validity, we assume that PATS has a negative impact on the intention to travel (Zenker & Kock, 2020), as a pathogen threat like COVID-19 should make people avoid crowdedness (Wang & Ackerman, 2019) and unknown situations (Faulkner et al., 2004).
Second, travel anxiety in regards to COVID-19 (PATS) should be influenced by the participants' risk propensity in regards to health risks (Hajibaba, Gretzel, Leisch, & Dolnicar, 2015). People with a higher health risk propensity should have a lower pandemic anxiety level—leading to the first simple face validity model (Fig. 1 ).Fig. 1 Research model Study 1.
Fig. 1
For the nomological validity, we tested PATS in a more complex framework by adding the constructs of prevention focus and xenophobia. Xenophobia is described as a negative predisposition towards, or even the denigration of, groups and/or individuals on the basis of perceived differences (Faulkner et al., 2004). People with xenophobia express a lower intention to travel in order to avoid unknown or foreign experiences (Kock et al., 2019b). This logic is very similar to the proposed influence of PATS on the intention to travel, but for a slightly different reason: Xenophobia is focused more on one's negative predispositions towards other people, while pandemic anxiety centres more on the intra-personal disposition against health hazards. Running both constructs in parallel allows us to show that PATS is measuring a similar, but conceptually distinct predisposition. To do so, we also assume that both constructs are positively influenced by a participant's cognitive prevention focus—as regulation focus theory (Higgins, 1997) suggests that some “consumers are motivated to avoid threats to security and safety and are sensitive to occasions of hazard” (prevention focus; Zhao & Pechmann, 2007, p. 672). This creates our final double mediation model (Fig. 2 ).Fig. 2 Research model Study 2.
Fig. 2
Finally, we acknowledge that PATS (and the other used constructs) are highly influenced by (demographic) variables, such as age, gender, education, income, travel companions (e.g., travelling with younger kids), and especially whether one considers him/herself as part of the COVID-19 risk group. Therefore, we added these variables as controls.
3 Study 1
3.1 Study design and sample
We conducted the first study with a US sample acquired via Amazon Mechanical Turk. To ensure high-quality data, we implemented a quality check question. The study ran in week 25–26, 2020, when the pandemic was dominant in the US, with some regions shifting to a full lockdown and others slowly starting to open up again. Most international travel options were still closed off.
A total of 2180 participants finished the survey. Table 2 presents the demographics. Due to the high educational level, we were compelled to keep education as a control variable in our later model. Additionally to people's demographics and their self-reported alignment with the COVID-19 risk group, we measured our original 8 items for PATS, 3 items for intention to travel (Lee, Agarwal, & Kim, 2012), and 1 item for health risk propensity (adopted from Hajibaba et al., 2015). The full survey can be seen in Appendix A.Table 2 Demographics (Study 1).
Table 2Variable Categories n % Variable Categories n %
Gender Male 816 37.4 Income Less or equal to 20,000 $ 129 5.9
Female 1364 62.6 20,001–40,000 $ 486 22.3
Age 18–30 509 23.3 41,001–60,000 $ 458 21
31–40 689 31.6 60,001–80,000 $ 388 17.8
41–50 451 20.7 80,001–100,000 $ 295 13.5
51–60 313 14.4 100,001 $ and more 424 19.4
61+ 218 10 Job situation Employed 1464 67.2
Travel with partner Yes 1468 67.3 Self-employed 256 11.7
No 712 32.7 Unemployed 145 6.7
Travel with friends Yes 528 24.2 Homemaker 104 4.8
No 1652 75.8 Student 64 2.9
Travel with young kids (0–6 years) Yes 263 12.1 Retired 127 5.8
No 1917 67.6 Other 20 0.9
Education Less than high school 4 0.2 Risk group Definitely not 366 16.8
High school 623 28.6 Probably not 893 41
Bachelor 1086 49.8 Probably yes 481 22.1
Master and higher 467 21.4 Definitely yes 440 20.2
3.2 Results
We started to scrutinize the data by putting all eight items into an explorative factor analysis (EFA). Both the varimax rotation and the parallel analysis (Horn, 1965) indicated that there are potentially two factors. Thus, we analysed the items and dropped three items. The first item (item A) was removed for not loading on any factor structure and two others (item C and D) were removed because of being highly correlated and exhibiting substantial cross-loading.
The removal of these items can also be justified conceptually. The low loadings of item A can be attributed to its dual focus on both phobic stimuli and a modality of anxiety. Thus, it mixes behavioural and emotional modalities (avoidance + anxiety) in one statement, and suggests that avoiding a specific social context is conditioned by fear of COVID-19. For items C and D, both are related to behavioural modalities of anxiety (behavioural adjustment and avoidance) and hence they capture coping intentions in the future, rather than present behavioural manifestations of anticipatory anxiety.
Again, we conducted an exploratory factor analysis (EFA) on the five remaining items that were included in the survey, using Stata 16. All items loaded accurately on the factor with good factor loadings, while both the Bartlett test of sphericity (χ2 = 8172.80; df = 10; p = 0.000) and the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO = 0.90) reported satisfactory statistics. The reported eigenvalue of 3.55 and the varimax rotation confirmed the one-factor structure. Likewise, the parallel analysis validated that there is one factor to retain. Next, we followed Nunnally and Bernstein's (1994) advice in examining the item-rest correlation: the correlation between one item and the scale that is formed by the other four items in the PATS construct (see Table 3 ). The item-rest correlations are high and in the same range. Furthermore, the average inter-item correlations are good and in the same range.Table 3 Exploratory factor analysis for scale development (Study 1).
Table 3Construct's items Item Label Mean Item loadings Item-rest correlation Average inter-item correlation α
COVID-19 make me worry a lot about my normal ways of travelling. PATS_1 5.52 0.81 0.78 0.74 0.92
It makes me uncomfortable to think about COVID-19 while planning my vacation. PATS_2 5.30 0.81 0.79 0.73 0.92
I am afraid to risk my life when I travel, because of COVID-19. PATS_3 5.09 0.88 0.85 0.7 0.90
When watching news about COVID-19, I become nervous or anxious in regards to travel. PATS_4 5.02 0.84 0.81 0.72 0.91
I do not feel safe to travel due to COVID-19. PATS_5 5.27 0.87 0.83 0.71 0.91
Cronbach's Alpha (α) 0.93
Composite reliability (ω) 0.93
Average Variance Extracted (AVE) 0.72
Notes: The items were introduced with: ‘Please rate the following statement:’ and the respondents scored on a seven-point Likert scale (1 = ‘strongly disagree’; 7 = ‘strongly agree’). AVE, α and ω are derived from the EFA.
Next, we tested the change in Cronbach's α by removing a good-fitting item, expecting that α would get lower—as Table 3 corroborated. Finally, we can conclude that the five items form a coherent factor given the first-rate values for Cronbach's α, composite reliability (ω) and average variance extracted (AVE).
For testing predictive validity, we had to estimate the conceptual model depicted in Fig. 1. The first step was to incorporate the two constructs in the model in an EFA. The EFA reported two factors with eigenvalues larger than one. The varimax rotation and the parallel analysis evidenced that these are indeed two separate constructs. The second step was to scrutinize the descriptive statistics of the variables and the items of the constructs of interest for this study in Table 4 . According to Finney and DiStefano (2006), the values for skewness and kurtosis reported in Table 4 can be regarded as moderately non-normal, implying that the MLM-estimator should be used, as it is robust to data non-normality (Satorra & Bentler, 1994). Hence, for the remainder of the empirical analysis, we employed confirmatory factor analysis (CFA) and structural equation modeling (SEM), using the lavaan package (lavaan 0.6–6) for R (Rosseel, 2012) that includes this estimation option. We reported robust standard errors and the Satorra-Bentler χ2 statistic (Satorra & Bentler, 1994) in combination with the degrees of freedom (df) and its p-value, even though we anticipated that the χ2-test is significant in cases with larger sample sizes and (moderate) data non-normality (e.g., Bagozzi & Yi, 2012; Bollen, 1989; Browne & Cudeck, 1993; Hair, Black, Babin, & Anderson, 2014; West, Taylor, & Wu, 2012). Additionally, we followed the advice of Kline (2016) and Goodboy and Kline (2017) to examine the correlation residuals of the estimated models and report the CFI, TLI, SRMR, RMSEA, 90% confidence interval (90%CI) for RMSEA (Steiger, 1990) and the associated PCLOSE. We used cut-off values in line with Bagozzi and Yi (2012): CFI ≥ 0.93, TLI ≥ 0.92, SRMR ≤ 0.07, RMSEA ≤ 0.07.Table 4 Descriptive statistics of variables and items of constructs (Study 1).
Table 4Variables/Construct's items Observations Mean SD Min Max Skewness Kurtosis
Health Risk Propensity 2180 3.11 1.68 1 7 0.41 2.19
Risk Group 2180 2.46 0.99 1 4 0.22 1.99
PATS_1 2180 5.52 1.63 1 7 −1.27 3.84
PATS_2 2180 5.30 1.74 1 7 −1.02 3.10
PATS_3 2180 5.09 1.87 1 7 −0.79 2.48
PATS_4 2180 5.02 1.81 1 7 −0.79 2.60
PATS_5 2180 5.27 1.82 1 7 −0.92 2.78
Intention to Travel_1 2180 4.63 1.69 1 7 −0.50 2.31
Intention to Travel_2 2180 5.06 1.47 1 7 −0.86 3.33
Intention to Travel_3 2180 5.60 1.25 1 7 −1.26 4.99
Notes: SD = Standard Deviation.
The third step was to examine the factor structure using confirmatory factor analysis (CFA). The factor loadings, AVE, α and ω were excellent (PATS) and good (intention to travel). Note that Table 5 shows similar factor loadings for PATS as the EFA, as well as similar values for AVE, α and ω. Finally, a separate CFA-model estimated for PATS only produced good fit statistics, thereby supporting the factor structure derived from the EFA: χ2 = 35.72; df = 5; p = 0.00; CFI = 0.99; TLI = 0.99; SRMR = 0.01; RMSEA = 0.053; 90%CI = [0.042–0.065]; PCLOSE = 0.308.Table 5 Factor loadings, Cronbach's α, composite reliability (ω), average variance extracted (AVE) for Study 1.
Table 5Construct Item Label B SE β α ω AVE
PATS 0.93 0.93 0.73
PATS_1 1.32 0.04 0.81
PATS_2 1.42 0.03 0.82
PATS_3 1.67 0.03 0.89
PATS_4 1.52 0.03 0.84
PATS_5 1.61 0.03 0.88
Intention to Travel 0.80 0.82 0.62
Intention to Travel_1 1.28 0.03 0.76
Intention to Travel_2 1.38 0.03 0.94
Intention to Travel_3 0.74 0.04 0.60
Notes: Concerning the reported β's, both the latent and observed variables are standardized. With regard to the B's, only the latent variables are standardized. All the factor loadings, α, ω and AVE originated from the CFA model; SE = Standard Errors.
Following Campbell and Fiske (1959), we confirmed construct validity for the two constructs in the model for Study 1. Table 5 reports the values for Cronbach's α, composite reliability (ω) and the average variance attracted (AVE), which are well above the thresholds for convergent validity. Discriminant validity was also verified, given that the reported AVE's (PATS = 0.73 and intention to travel = 0.62) are much higher than the squared correlation (SC = 0.06) between the two constructs (Fornell & Larcker, 1981).
Subsequently, we verified that the dataset for study 1 is not prone to common method bias as we loaded all items on one common factor in a CFA-framework (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003) producing bad fit statistics: χ2 = 3619.28; df = 119; p = 0.00; CFI = 0.70; TLI = 0.66; SRMR = 0.10; RMSEA = 0.116; 90%CI = [0.113–0.119]; PCLOSE = 0.00. Lastly, we estimated a SEM-model for testing predictive validity. As stated before, the variables of interest are health risk propensity, PATS and intention to travel. Nonetheless, we also needed to control for the demographic characteristics of our sample such as age, gender, education and income. Furthermore, we controlled for respondents' assessment of whether they would be in the risk-group in regards to COVID-19. In the final step, we controlled for travel-related variables. In the survey, respondents indicated their usual travel companions; thus, we included three dummy variables for travel with friends, travel with partner, and travel with a young child. Table 6 displays the standardized estimated coefficients: the middle column presents the estimates for PATS and the right column for intention to travel. The goodness-of-fit statistics of the model are all good (see Table 6) and all correlation residuals (besides one) are in the range of −0.10 to 0.10 and even 95% are in the range of −0.05 to 0.05. There is also no manifest pattern in the residuals (and they are both negative and positive residuals present). Most importantly, the estimated coefficients for health risk propensity on PATS (−0.307***) are negative and significant. Similarly, the influence of PATS on intention to travel is negative (−0.210***) and significant. Both estimates meet the expectations of our theoretical model.Table 6 Estimated research model (Study 1; depicted in Fig. 1).
Table 6Effects of On:
PATS Intention to Travel
Health Risk Propensity −0.307*** 0.140***
(0.021) (0.025)
Risk Group 0.388*** n.s.
(0.020)
Age −0.160*** 0.079***
(0.020) (0.025)
Gender 0.079*** 0.090***
(0.021) (0.022)
Education 0.055** n.s.
(0.021)
Income n.s. n.s
PATS −0.210***
(0.025)
Travel with Friends 0.058**
(0.022)
Travel with Partner n.s.
Travel with Young Child n.s.
Notes:*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; n.s. = not significant; Standardized coefficients are reported and the related standard errors are in parentheses; Model fit: χ2 = 439.82; df = 76; p = 0.00; CFI = 0.97; TLI = 0.96; SRMR = 0.030; RMSEA = 0.047; 90%CI = [0.043–0.051]; PCLOSE = 0.90; R2 = 0.09.
4 Study 2
4.1 Study design and sample
For the second study, we chose a country at a later stage of the pandemic with a declining number of new cases (Denmark). This time, we used a professional panel provider (Respondi AG) and again applied a quality check question to ensure high-quality data. The study was running in week 25–26, 2020, when the pandemic was relatively controlled in Denmark and the government had resumed endorsing domestic travel to select European countries.
A total of 2062 participants finished the survey. Table 7 presents the demographics. This sample featured an overall older population than the US sample, so we controlled for age. Like in Study 1, we asked all the demographic questions, as well as how strongly the participants aligned themselves with the COVID-19 risk group. Furthermore, we measured our 5 items for PATS, 3 items for intention to travel (Lee et al., 2012), 6 items for xenophobia (Kock et al., 2019b), 1 item for health risk propensity (adopted from Hajibaba et al., 2015), and 3 items for prevention focus (adapted from Zhao & Pechmann, 2007). The full survey can be seen in Appendix A.Table 7 Demographics (Study 2).
Table 7Variable Categories n % Variable Categories n %
Gender Male 927 45 Income* Less or equal to 20,000 $ 148 7.2
Female 1135 55 20,001–40,000 $ 376 18.2
Age 18–30 363 17.6 41,001–60,000 $ 473 22.9
31–40 287 13.9 60,001–80,000 $ 310 15
41–50 432 21 80,001–100,000 $ 238 11.5
51–60 410 19.9 100,001 $ and more 517 25.1
61+ 570 27.6 Job situation Employed 972 47.1
Travel with partner Yes 1362 66.1 Self-employed 75 3.6
No 700 33.9 Unemployed 161 7.8
Travel with friends Yes 430 20.9 Homemaker 35 1.7
No 1632 79.1 Student 157 7.6
Travel with young kids (0–6 years) Yes 215 10.4 Retired 587 28.5
No 1847 89.6 Other 75 3.6
Education Less than high school 286 13.8 Risk group Definitely not 673 32.6
High school 943 45.7 Probably not 498 24.2
Bachelor 570 27.6 Probably yes 535 25.9
Master and higher 263 12.7 Definitely yes 356 17.3
Notes: *The income was measured with the equivalent in DKK.
4.2 Results
Seeking to enrich the face validity model of Study 1, Study 2 tests the scale's nomological validity by adopting a different sample (from Denmark) and adding a distinction of PATS with the existing constructs of prevention focus and xenophobia. These four constructs—prevention focus, PATS, xenophobia and intention to travel—were analysed via an EFA. The items loaded nicely on their respective constructs and all four constructs produced eigenvalues larger than one. The factor structure was supported by the varimax rotation. Table 8 depicts the descriptive statistics and the items representing each construct. The skewness and kurtosis values indicate moderate non-normality of the data, implying the need to use the same procedure as in Study 1.Table 8 Descriptive statistics of variables and items of constructs (Study 2).
Table 8Variables/Construct's items Observations Mean SD Min Max Skewness Kurtosis
Health Risk Propensity 2062 3.39 1.43 1 7 0.04 2.59
Risk Group 2062 2.28 1.10 1 4 0.22 1.71
Prevention Focus_1 2062 4.04 1.51 1 7 −0.26 2.77
Prevention Focus_2 2062 3.85 1.54 1 7 −0.06 2.48
Prevention Focus_3 2062 3.42 1.57 1 7 0.12 2.36
PATS_1 2062 4.57 1.78 1 7 −0.46 2.34
PATS_2 2062 4.42 1.75 1 7 −0.33 2.35
PATS_3 2062 4.13 1.93 1 7 −0.10 1.90
PATS_4 2062 4.01 1.84 1 7 −0.09 2.07
PATS_5 2062 4.61 1.85 1 7 −0.41 2.20
Xenophobia_1 2062 3.07 1.39 1 7 0.38 2.61
Xenophobia_2 2062 2.90 1.47 1 7 0.51 2.55
Xenophobia_3 2062 3.14 1.47 1 7 0.30 2.36
Xenophobia_4 2062 3.08 1.36 1 7 0.25 2.54
Xenophobia_5 2062 2.83 1.41 1 7 0.51 2.60
Xenophobia_6 2062 3.04 1.43 1 7 0.34 2.39
Intention to Travel_1 2062 3.55 1.77 1 7 0.08 2.04
Intention to Travel_2 2062 3.33 1.75 1 7 0.20 2.10
Intention to Travel_3 2062 3.85 1.80 1 7 −0.13 2.08
Notes: SD = Standard Deviation.
Like in Study 1, we estimated a CFA-model encompassing the four constructs (Table 9 ) in order to assess construct validity (Campbell & Fiske, 1959). Overall, α and ω are above 0.7 and the AVE is higher than 0.50 for all constructs. For our developed scale PATS, α and ω are above 0.9 and the AVE is 0.69. Next we tested for discriminant validity. All reported AVE's of the four constructs are higher than the squared correlation between the constructs (Fornell & Larcker, 1981); see Appendix B. This is a more rigorous analysis of the AVE, as it implies that the construct explains more of the variance in its items than it shares with the other constructs (Hair et al., 2014). Finally, just like with the US sample, we estimated a separate CFA-model for PATS in the Danish sample, which produced good fit statistics: χ2 = 30.70; df = 5; p = 0.00; CFI = 1.00; TLI = 0.99; SRMR = 0.01; RMSEA = 0.050; 90%CI = [0.038–0.063]; PCLOSE = 0.478.Table 9 Factor loadings, Cronbach's α, composite reliability (ω), average variance extracted (AVE) for Study 2.
Table 9Construct Item Label B SE β α ω AVE
Prevention Focus 0.74 0.77 0.54
Prevention Focus_1 0.68 0.04 0.43
Prevention Focus_2 1.28 0.03 0.80
Prevention Focus_3 1.32 0.03 0.88
PATS 0.92 0.92 0.69
PATS_1 1.32 0.03 0.74
PATS_2 1.38 0.03 0.79
PATS_3 1.67 0.03 0.86
PATS_4 1.66 0.03 0.90
PATS_5 1.55 0.03 0.84
Xenophobia 0.92 0.92 0.66
Xenophobia_1 0.98 0.03 0.71
Xenophobia_2 1.17 0.03 0.80
Xenophobia_3 1.22 0.02 0.83
Xenophobia_4 1.15 0.02 0.85
Xenophobia_5 1.22 0.02 0.86
Xenophobia_6 1.21 0.02 0.84
Intention to Travel 0.85 0.85 0.66
Intention to Travel_1 1.49 0.03 0.84
Intention to Travel_2 1.53 0.03 0.88
Intention to Travel_3 1.27 0.04 0.70
Notes: Concerning the reported β's, both the latent and observed variables are standardized. With regards to the B's, only the latent variables are standardized. All the factor loadings, α, ω and AVE originated from the CFA model. SE = Standard Errors.
Similar to study 1, we established that common method bias is not a problem for study 2 by estimating a CFA model with all items loading on one common factor that produced bad fit statistics: χ2 = 12996.89; df = 299; p = 0.00; CFI = 0.43; TLI = 0.38; SRMR = 0.14; RMSEA = 0.144; 90%CI = [0.142–0.146]; PCLOSE = 0.00 (Podsakoff et al., 2003). Finally, we assessed nomological validity by estimating a SEM-model that included prevention focus, health risk propensity, PATS, xenophobia and intention to travel as the main variables in the proposed model (depicted in Fig. 2). Furthermore, we included the same demographic control variables (age, gender, education and income) as in Study 1. In the same vein, we accounted for the at-risk group in regards to COVID-19 and the equivalent travel companion variables. The second column of Table 10 displays the anticipated positive effect of prevention focus (0.311***) and the negative effect of health risk propensity (−0.124***) on PATS. In the third column, the expected effect of prevention focus (0.384***) on xenophobia is visible. The right-hand column reports the expected negative impact from PATS (−0.087***) and xenophobia (−0.160***) on intention to travel. All of the main relationships report logical and anticipated effects. Moreover, all correlation residuals of the model are in the range of −0.10 to 0.10 besides one (0.11) and 92% of the correlation residuals are even in the range of −0.05 to 0.05. Again, the residuals show negative and positive values and there is no apparent pattern. Thus, the nomological validity model fit can be regarded as good, due to the analysis of the correlations residuals and the goodness-of-fit statistics reported in Table 10.Table 10 Estimated research model (Study 2; depicted in Fig. 2).
Table 10Effects of On:
Prevention Focus PATS Xenophobia Intention to Travel
Health Risk Propensity −0.089*** −0.124*** n.s. 0.157***
(0.025) (0.023) (0.025)
Age −0.343*** n.s. −0.125*** −0.089***
(0.022) (0.022) (0.027)
Gender 0.046* 0.141*** −0.098*** n.s.
(0.023) (0.020) (0.022)
Education n.s. −0.053** −0.140*** n.s.
(0.020) (0.021)
Income −0.136*** n.s. −0.100*** 0.086***
(0.024) (0.022) (0.026)
Risk Group 0.146*** 0.318*** n.s.
(0.025) (0.023)
Travel with Friends −0.080*** 0.069**
(0.019) (0.023)
Travel with Partner n.s. 0.069**
(0.025)
Travel with Young Child n.s. −0.051*
(0.023)
Prevention Focus 0.311*** 0.384*** 0.091**
(0.026) (0.025) (0.034)
PATS −0.087**
(0.031)
Xenophobia −0.160***
(0.029)
Notes: *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; n.s. = not significant; Standardized coefficients are reported and come with their standard errors in parentheses; Model fit: χ2 = 1303.28; df = 237; p = 0.00; CFI = 0.95; TLI = 0.94; SRMR = 0.033; RMSEA = 0.047; 90%CI = [0.044–0.049]; PCLOSE = 0.991; R2 = 0.10.
5 Discussion
The current coronavirus pandemic (and pandemics as such) calls for a short and easy-to-use scale that can measure travel anxiety in regards to it. For researchers and tourism practitioners alike, this scale needs to be tailored to the context of travel and able to precisely capture anticipatory anxiety prior to holiday trips. PATS aims to deliver just that.
Although we began with an 8-item scale, our analysis revealed that a 5-item structure, comprised solely of cognitive elements, was able to consistently capture apprehensive mental states during travel considerations and planning.
Most importantly, our final 5-item scale is tested and proved reliable in two different countries, cultural contexts and two research models. In order to create a universal scale, which is valid in different stages of a pandemic, we deliberately chose two countries that were very differently impacted by COVID-19 at the time of surveying. The goodness-of-fit statistics of the PATS scale in both studies demonstrate, that the scale works consistently in different stages of the pandemic. The face validity of the scale is further strengthened by results showing that the negative impact of PATS on intention to travel was twice as strong in the US sample compared to the Danish sample. This makes intuitive sense, as these countries were at different stages of the pandemic curve at the time of surveying (the US having the highest number of infections worldwide and Denmark was lifting lockdown restrictions).
Another proof of face validity is the control variable risk group, which does not significantly affect the intention to travel once PATS is inserted into the model. While risk group seems to have a very strong influence on PATS in both samples, there is no such effect for intention to travel. When coupling this with the other measurements, we are very confident that our final cognitive elements of pandemic (COVID-19) travel anxiety form a robust scale that will advance future research on this topic.
5.1 Theoretical and practical implications
Scale development papers mostly deliver new options for uncovering theoretical and practical implications, but rarely produce implications by themselves (Kock et al., 2019a). As this pandemic rages on, PATS can deliver explanations for behavioural changes and/or help to explain these changes (Zenker & Kock, 2020). Likewise, the scale opens up a potential research agenda on the drivers (not only demographics, but also other psychological constructs, like the prevention focus used here) and outcomes (e.g., hospitality towards travellers visiting the country or general support for tourism development; Andriotis & Vaughan, 2003) of pandemic anxiety.
In addition, by including the concept of xenophobia (Faulkner et al., 2004; Kock et al., 2019b) our research shows that PATS is distinct from a general fear or a negative predisposition towards foreign groups and/or individuals. However, future research might consider exploring the similarities and differences in this regard, as the pandemic also induces some similar reservations towards foreigners (albeit likely for different reasons).
5.2 Limitations and future research
No research comes without limitations: First, our current research can only measure intentional and self-reported measurements due to current global travel restrictions. Future studies therefore should measure the impact of PATS on real behaviour (Dolnicar, 2018), which could help to improve the scale's predictive validity.
Second, we cannot confirm the scale's applicability and functionality for other pandemics. For obvious reasons, COVID-19 is overshadowing all other pandemic threats and thus constituted our main focus. Future research should seek to test the scale in other pandemic settings once travel is viable again.
Third, a deeper analysis of potential antecedents, mediators and moderators and other outcomes for PATS would be interesting for future research. For instance, it would be relevant to study the effects of media reports, marketers' strategic crisis communication or promotional activities as mediators for the travellers' anxiety levels in a pandemics context. Furthermore, the scale could also be an appropriate mediator between individual coping behaviour (e.g., safety measures to divert risks) and travel intentions or other potential outcomes.
Finally, the scale was only used in two different cultural contexts and at different stages of the pandemic. As a next step, this scale should receive further testing in other cultural environments or exploring more how the different situations might lead to more subjective interpretations of a pandemic anxiety.
Nevertheless, we hope to inspire other researchers to use this scale to probe more deeply tourists' behavioural changes with regards to COVID-19 and other (future) pandemics. At the time of writing, the long-term effects of COVID-19 on people's travel behaviour and attitudes towards tourism-related health risks are still unknown (Zenker & Kock, 2020). Exploring the pandemic's enduring touristic consequences will require longitudinal studies and comparative setups, which have the ability to consider a broader range of psychographic concepts. For instance, the notion of crisis-resistant tourists (Hajibaba et al., 2015) could be re-analysed with the Pandemic (COVID-19) Anxiety Travel Scale. Likewise, scholars could apply PATS to constructs like ethnocentrism (Kock, Josiassen, Assaf, Karpen, & Farrelly, 2019c), risk perception (Rittichainuwat & Chakraborty, 2009), or the issue of trust in government (Nunkoo & Ramkissoon, 2012; Zuo, Gursoy, & Wall, 2017).
6 Conclusion
Measuring pandemic-induced changes in tourists' beliefs and travel behaviours requires a robust and context-specific construct that can effectively capture the intra-personal anxiety of travellers (and non-travellers) in relation to a pandemic. The Pandemic (COVID-19) Anxiety Travel Scale (PATS) delivers just that: a short and easy-to-use 5-item construct that measures the level of pandemic-induced anxiety. PATS has proven its appropriateness and reliability in two different studies and two different cultural contexts. In Study 1, explorative and confirmative factors analysis detected the 5-item structure of PATS, while the presented (SEM) model added face validity for the proposed scale. In Study 2, we further validated the 5-item structure (reliability) and presented a meaningful nomological validation for the scale. By testing PATS against two different constructs (xenophobia and prevention focus), our second study proved that it is a distinct cognitive modality.
Although the proposed scale arose from the coronavirus (COVID-19), it is not limited to this specific pandemic and will hopefully prove to be a valuable measurement tool for future pandemics.
Impact statement
The COVID-19 pandemic one of the most impactful events of the century. Tourism practitioners and researchers are perplexed to understand the wide-ranging consequences of the disruption, especially regarding enduring changes in tourist behaviour and attitudes. Medical scholars have already developed several scales to monitor coronaphobia, however, we need tailored scales to measure travel anxiety. Existing tourism-scales on health risk perception, or general travel risk perceptions, are not specific enough. Therefore, this paper develops a short and easy-to-use 5-item Pandemic (COVID-19) Anxiety Travel Scale (PATS).
With the help of PATS, researchers and practitioners alike could measure how tourists are psychologically affected by pandemic anxiety. The scale will assist tourism marketers to develop coping strategies to mediate the current crisis. While we tested the scale with COVID-19, PATS builds also a relevant basis to use as a travel anxiety scale in the context of other (future) pandemics.
CRediT author statement
Sebastian Zenker: Conceptualization; Project administration; Investigation; Methodology; Data curation; Writing - original draft; Writing - review & editing. Erik Braun: Conceptualization; Investigation; Methodology; Formal analysis; Writing - original draft; Writing - review & editing. Szilvia Gyimothy: Conceptualization; Investigation; Writing - original draft; Writing - review & editing.
Declaration of competing interest
None.
Sebastian Zenker, PhD, is corresponding author and professor (with special responsibilities) of marketing and tourism at the Copenhagen Business School, Denmark. His current research interests are mainly in tourism crises, over-tourism, and place brand management with the special target groups of residents and tourists. His work was presented at various international conferences, book chapters, peer-reviewed journals, for example, Tourism Management, Environment and Planning A, International Journal of Research in Marketing, or in Psychological Science.
Erik Braun, PhD, is an associate professor of marketing and tourism at the Copenhagen Business School, Denmark. His current research interest concern tourism management, crisis management, the application of marketing and branding concepts by cities and regions, and the governance aspects of place marketing. His research is published in books, book chapters and academic journals including Tourism Management, Cities, Environment and Planning C, Public Administration Review, and Urban Studies.
Szilvia Gyimóthy, PhD, is an associate professor of marketing and tourism at the Copenhagen Business School, Denmark. Her research is focused on how global mobility and mediatized travel is shaping places and tourism consumption. More recently, Szilvia has been working with the marketization of moral and social relationships and Nordic place branding. Her publications appear in books and book chapters and peer-reviewed journals including Annals of Tourism Research, Tourist Studies, European Planning Studies, Tourism Geographies, and Tourism Planning and Development.
Appendix A Measures of Model Constructs
Construct Item Label Item (English) Item (Danish) Source
Health Risk Propensity (Study 1 and 2) Health Risk Propensity Some activities involve a health risk, such as travelling overseas (e.g. in countries of low hygienic standards) or particular “lifestyle” behaviour (e.g. long sunbathing, unsafe sex, drugs for pleasure) or smoking – that is, there is a risk of catching a harmful disease. In general, my willingness to accept health risks is... Nogle aktiviteter indebærer en sundhedsrisiko, såsom udlandsrejser (f.eks. til lande med lave hygiejniske standarder) eller letsindig adfærd (f.eks. at tage lange solbad, partydrugs, usikker sex eller rygning) hvor der er risiko for varige skader eller sygdom. Generelt er min vilje til at acceptere sundhedsrisici ... adopted from Hajibaba et al., 2015
PATS (Study 1 and 2) PATS_1 COVID-19 makes me worry a lot about my normal ways of travelling. COVID-19 får mig til at bekymre mig meget om mine normale rejsevaner. own development
PATS_2 It makes me uncomfortable to think about COVID-19 while planning my vacation. Det er ubehageligt at tænke på COVID-19, mens jeg planlægger min ferie.
PATS_3 I am afraid to risk my life when I travel, because of COVID-19. Jeg er bange for at sætte mit liv på spil hvis jeg rejser, på grund af COVID-19.
PATS_4 When watching news about COVID-19, I become nervous or anxious in regards to travel. Når jeg ser nyheder om COVID-19, bliver jeg nervøs for at skulle til at rejse.
PATS_5 I do not feel safe to travel due to COVID-19. Jeg føler ikke at det er sikkert at rejse på grund af COVID-19.
Intention to Travel (Study 1 and 2) Intention to Travel_1 Whenever I have a chance to travel, I will. Hver gang jeg har en chance for at rejse, gør jeg det. Lee et al. (2012)
Intention to Travel_2 I will do my best to improve my ability to travel. Jeg vil gøre alt for at få en mulighed for at rejse.
Intention to Travel_3 I will keep on gathering travel-related information in the future. Jeg vil blive ved med afsøge information om rejser i det nærmeste fremtid.
Prevention Focus (Only
Study 2) Prevention Focus_1 I am often focused on preventing negative events in my life. Jeg er ofte optaget af at forhindre negative begivenheder i mit liv. Zhao and Pechmann (2007)
Prevention Focus_2 I often worry about making mistakes. Jeg bekymrer mig ofte om at begå fejl.
Prevention Focus_3 I frequently think about bad things that could happen to me. Jeg tænker ofte på dårlige ting, der kunne ske med mig.
Xenophobia (Only Study 2) Intro Please rate the following statements: If I travelled to a foreign country... Bedøm følgende udsagn: Hvis jeg rejste til et fremmed land, ville… Kock et al. (2019b)
Xenophobia_1 …I doubt that the locals would be welcoming to tourists like me. …jeg tvivle på, at de lokale ville byde velkommen til mig som turist.
Xenophobia_2 …I would not feel comfortable in the culture. …jeg ikke føle mig tilpas i en fremmed kultur.
Xenophobia_3 …I would probably feel uneasy to engage with locals there. …jeg sandsynligvis føle at det er besværligt til at snakke med de lokale.
Xenophobia_4 …there would be many misunderstandings between me and the locals there. ...der være mange misforståelser mellem mig og de lokale.
Xenophobia_5 …I would be suspicious toward the locals I encounter there. …jeg være mistænksom overfor de lokale, jeg støder på.
Xenophobia_6 …I would be worried that the locals would meet me with reservation. …jeg være bekymret for, at de lokale møder mig med forbehold.
Appendix B Discriminant Validity: Fornell and Larcker criterion (Study 2)
Prevention Focus PATS Xenophobia Intention to Travel
Prevention Focus 0.54 0.12 0.18 0.00
PATS 0.35 0.69 0.05 0.03
Xenophobia 0.43 0.23 0.66 0.02
Intention to Travel −0.03 −0.16 −0.15 0.66
Notes: The estimates of AVE are on the diagonal in bold. The CFA-model produced the correlations in the table. The correlations are below the diagonal. All correlations are significant at p < 0.001, but one: the correlation between prevention focus and intention to travel is insignificant. The squared correlations (SC) are above the diagonal in italics.
Acknowledgements
None.
==== Refs
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| 0 | PMC9734088 | NO-CC CODE | 2022-12-14 23:28:27 | no | Tour Manag. 2021 Jun 11; 84:104286 | utf-8 | Tour Manag | 2,021 | 10.1016/j.tourman.2021.104286 | oa_other |
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Tour Manag
Tour Manag
Tourism Management
0261-5177
1879-3193
Elsevier Ltd.
S0261-5177(21)00009-1
10.1016/j.tourman.2021.104290
104290
Article
Light at the end of the tunnel: Visitors' virtual reality (versus in-person) attraction site tour-related behavioral intentions during and post-COVID-19
Itani Omar S. a∗
Hollebeek Linda D. bc
a Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon
b Montpellier Business School, University of Montpellier, Montpellier Research in Management, Montpellier, France
c Tallinn University of Technology, Ehitajate tee 5, 12616 Tallinn, Estonia
∗ Corresponding author.
23 1 2021
6 2021
23 1 2021
84 104290104290
7 7 2020
17 11 2020
11 1 2021
© 2021 Elsevier Ltd. All rights reserved.
2021
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.
Consumer behavior is changing as a result of the COVID-19 pandemic, thus compelling attraction sites to find new ways of offering safe tours to visitors. Based on protection motivation theory, we develop and test a model that examines key drivers of visitors' COVID-19-induced social distancing behavior and its effect on their intent to use virtual reality-based (vs. in-person) attraction site tours during and post-COVID-19. Our analyses demonstrate that visitor-perceived threat severity, response efficacy, and self-efficacy raise social distancing behavior. In turn, social distancing increases (decreases) visitors' intent to use virtual reality (in-person) tours during the pandemic. We find social distancing to boost visitors' demand for advanced virtual tours and to raise their advocacy intentions. Our results also reveal that social distancing has no effect on potential visitors' intent to use virtual reality vs. in-person tours post-the pandemic. We conclude by discussing vital implications that stem from our analyses.
Keywords
COVID-19 pandemic
Coronavirus
Social distancing
Protection motivation theory
Tours
Attraction sites
Virtual reality
Consumer intentions
==== Body
pmc1 Introduction
The “severe acute respiratory syndrome coronavirus 2” (SARS-CoV-2) virus that produces COVID-19 has instigated a global pandemic with over 54 million confirmed cases across 191 countries, and a death toll of over 1.3 million1 (Dong, Du, & Gardner, 2020). Due to the pandemic's public health risk, many governments have imposed significant mobility restrictions on their citizens (e.g., lockdown, social distancing, travel bans, quarantine), which are slowing down the world economy (Nicola et al., 2020). In this environment, the tourism sector is experiencing a major impact on its business (Zenker & Kock, 2020). For example, canceled flights, vacant hotels, and closed attraction sites are a common sight in recent months (Gössling, Scott, & Hall, 2020), thus putting tourism and travel on hold and yielding substantial employee layoffs and financial loss. Travel restrictions are considered imperative to control the spread of COVID-19 (Niewiadomski, 2020), with many cases being linked to tourist/tour groups (Yang, Zhang, & Chen, 2020). Countries worst-impacted by the pandemic (e.g., the United States, India, Brazil, Spain, France) tend to be those attracting high tourist numbers (Beech, Ribin, Kurmanaev, & Maclean, 2020; Statista, 2020).
Given their typically high-contact nature, travel/tourism services have suffered significant loss as a result of COVID-19, and now face an uncertain future. For example, after being temporarily closed during lockdown, attraction sites in some countries are currently rebuilding their clientele. However, many of their visitors' disposable incomes are considerably affected by the pandemic (e.g., through job loss). That is, the 3–4% global tourism growth predicted for 2020 has dramatically shifted to a 20–30% pandemic-induced decline (UNWTO, 2020), with cumulative tourism/travel-related GDP loss amounting to $2.1 trillion (WTTC, 2020). While tourism is vulnerable to crises and disasters (Cró & Martins, 2017; Rosselló, Becken, & Santana-Gallego, 2020), evidence shows its disruption as never before by COVID-19, which is described as an amalgamation of “a natural disaster, a socio-political crisis, an economic crisis, and a tourism demand crisis” (Zenker & Kock, 2020, p. 2). Consequently, there is a need to examine the pandemic's effect on the travel/tourism sector, and to devise ways to convert this disruption into transformative opportunities (Sigala, 2020). At the same time, consumers' travel/tourism-related mindset is shifting, including by avoiding crowded destinations in favor of more remote, tranquil options (Zenker & Kock, 2020). Research is therefore needed to answer the “questions of how the tourism industry can respond to and recover from the crisis” (Gretzel et al., 2020, p. 188).
Given these issues, we explore how attraction sites are adapting to COVID-19-induced social distancing and its expected effect on consumers' intent to purchase virtual reality (VR)-based (vs. in-person) site tours, both during and post-the pandemic. While VR has been previously viewed as a threat to the travel/tourism sector (Cheong, 1995), today it offers an important opportunity for attraction sites to overcome the pandemic's challenges. VR, defined as “computer-mediated, interactive environments capable of offering sensory feedback to engage consumers …. and drive desired consumer behaviors” (Hollebeek, Clark, Andreassen, Sigurdsson, & Smith, 2020, p. 1), is increasingly deployed to create personalized, convenient virtual site visits (e.g., to landmarks, museums, zoos, theaters; Bright, 2020; Herrmann, 2020), particularly during COVID-19.
This study offers the following contributions. First, based on Rogers' (1983) protection motivation theory, we empirically examine how consumers' appraisal of COVID-19, including (a) the perceived severity of its threat and one's perceived susceptibility to contracting the virus, and (b) their coping appraisal, gauged by response efficacy and self-efficacy, affect consumers' motivation to protect themselves through social distancing. Given its focus on impending health threats and individuals' motivation to self-protect from the threat, protection motivation theory offers a relevant framework in our research context.
Second, we examine the relationship between consumers' social distancing-based protection behavior on their intent to use VR-based (vs. in-person) attraction site tours both during and after the pandemic. Our rationale is that while COVID-19 currently exerts a disruptive effect on attraction sites in many countries, others are planning to reopen soon. Therefore, investigation of the pandemic's present and future effects on attraction sites is required, in particular for VR-based (vs. in-person) tours, as outlined. By examining the role of social distancing as a self-protective behavior against COVID-19, we illuminate its effect on consumers' intent to visit attraction sites, either in-person or virtually, during and post-the pandemic, thus unlocking new insight (Zenker & Kock, 2020).
Third, we explore consumers' VR-based tour needs in terms of VR's technological advancement level, and its effect on their tour-related advocacy intent, or their resolve to recommend a VR-based tour to others (Ozturk & Gogtas, 2016). This is important because consumers' uptake of virtual (vs. in-person) tours is rapidly growing since the pandemic's onset (Debusmann, 2020), which may extend to impact their future tour-related intentions. We therefore explore the role of VR-based tours' technological advancement level on consumers' tour-related advocacy intent, which represents a proximal predictor of behavior (Fishbein & Ajzen, 1975).
In section 2, we review relevant literature on protection motivation theory, social distancing, and VR tours, followed by an overview of the hypothesis development in section 3. In sections 4, 5, we present the methodology and results, respectively. In section 6, we conclude by discussing our results, outlining their implications, and addressing the study's limitations.
2 Theoretical background
2.1 Protection motivation theory
Protection motivation theory, which proposes that individuals' threat- and coping appraisal generate their motivation to protect themselves from perceived health threats (Rippetoe & Rogers, 1987; Rogers, 1983), is widely adopted in the tourism literature (Badu-Baiden, Adu-Boahen, & Otoo, 2016; Chen, Dai, Zhu, & Xu, 2020). First, threat appraisal comprises (a) perceived threat severity, defined as the “beliefs about the significance or magnitude of the threat” (Witte, 1996, p. 320). The higher a perceived threat's severity, the more extensive individuals' self-protection behaviors, and (b) perceived susceptibility, defined as “beliefs about one's risk of experiencing the threat” (e.g., by contracting COVID-19; Witte, 1996, p. 320). More susceptible individuals are predicted to engage in a greater range of self-protective measures (Rogers, 1975), including COVID-19-imposed social distancing. Overall, threat appraisal focuses on the threat's nature, its perceived seriousness, and the propensity of it eventuating to affect the individual (Norman, Boer, & Seydel, 2005).
Second, coping appraisal involves the assessment of health-protective behavioral alternatives and responses to avoid the threat and its consequences. It focuses on the effectiveness of the coping response as well as its implementation to impede the threat. Coping responses that help individuals avert the threat yield perceived response- and self-efficacy (Rogers, 1975). Response efficacy refers to “beliefs about whether the recommended coping response will be effective in reducing the threat to the individual” (Milne, Sheeran, & Orbell, 2000, p. 109). Self-efficacy denotes the “individual's beliefs about whether (s)he is able to perform the recommended coping response” (Milne et al., 2000, p. 109). For example, consumers may consider the degree to which social distancing, a coping behavior recommended by health organizations, can reduce their risk of contracting COVID-19 (i.e., response efficacy) and whether they are capable of maintaining their physical distance from others (i.e., self-efficacy).
Threat- and coping appraisals drive individuals' motivational intentions and course(s) of action to protect themselves from the threat. Protection motivation is “an intervening variable that arouses, sustains, and directs activity to protect the self from danger” (Conner & Norman, 2005, p. 9). Overall, protection motivation theory posits that individuals' motivation to defend themselves from a threat is a function of the threat's perceived severity, one's own susceptibility to being adversely impacted by the threat, one's self-efficacy in overcoming the threat, and one's perceived efficacy of particular responses to the threat (Rogers, 1975). For example, consumers may be motivated to adapt their behavior by practicing social distancing to protect themselves from COVID-19.
Despite its positive role in curbing the pandemic, social distancing is “the very antithesis of our expectations of the experience of hospitality and tourism” (Baum & Hai, 2020, p. 2). While COVID-19 continues to spread, social distancing has rapidly become the new normal that compels consumers globally to stay at home, cancel their planned site visits, and learn about how to stay safe (Chubb, 2020). That is, due to COVID-19, consumers' ability to visit attraction sites has been reduced to an unprecedented degree (Baum & Hai, 2020). Therefore, attraction sites are considering new ways to bring their service to consumers. One such technique is VR technology, which by offering virtual site visits, can instigate the consumer's sense of being there (i.e., telepresence; Hollebeek, Clark, et al., 2020; Loureiro, Guerreiro, & Ali, 2020). VR-based tours therefore exist as an innovative potential means for attraction sites' survival during COVID-19 (Kwok & Koh, 2020). Given the expected lack of medical treatment or remedy for COVID-19 until (mid-) 2021 (Grenfell & Drew, 2020), attraction sites' adoption of new channels to maintain client demand is key. Before reviewing literature on VR-based tours, we synthesize the budding social distancing literature.
2.2 Social distancing
Social (or physical) distancing is a set of non-pharmaceutical precautions to stop the spread of contagious diseases, including COVID-19, by preserving a physical distance of 1.5–2 m between individuals and limiting face-to-face encounters (Li & Li, 2020; Hollebeek, Smith, et al., 2020). It “is designed to reduce interactions between people in a broader community, in which individuals may be infectious, but have not yet been identified” (Wilder-Smith & Freedman, 2020, p. 2). As COVID-19 is primarily transmitted by respiratory droplets that require physical proximity, social distancing has proven its effectiveness in flattening the curve and controlling the epidemic (Wilder-Smith & Freedman, 2020). Likewise, the Center for Disease Control and Prevention posits that social distancing or “limiting face-to-face contact with others is the best way to reduce the spread of … COVID-19.”2 Therefore, in the absence of COVID-19-based medical treatment or vaccine, social distancing remains a major intervention to control its dissemination (Kissler, Tedijanto, Lipsitch, & Grad, 2020), thus impacting tourism and attraction sites.
Social distancing has proven useful during COVID-19, as it has saved critical care units from being overwhelmed with patients (Ferguson et al., 2020). It has also helped reduce mortality rates, thus yielding monetary savings (Greenstone & Nigam, 2020). Social distancing may need to stay in place until the global population has largely reached immunity, or an effective vaccine and treatment are available (Kissler et al., 2020). During the pandemic, interest in VR-based tours has spiked (Debusmann, 2020), given its capacity to overcome social distancing-imposed mobility- and social restrictions.
Social distancing limits human presence and touch, thus complicating consumers' meaningful tourism experiences. Given social distancing's restriction of conventional face-to-face service interactions (Hollebeek, Smith, et al., 2020), tourism businesses globally are rapidly adopting technology-based alternatives (e.g., VR-based tours) to continue their service delivery (Gössling et al., 2020). Given consumers' perceived threat of contracting COVID-19, they are likely to amend their travel plans (Zhang, Yang, Wang, Zhan, & Bian, 2020), yielding their expected willingness to adopt VR-based (vs. in-person) tours during the pandemic, as discussed further in the next section.
2.3 Virtual reality-based site tours
While COVID-19 is restricting consumer mobility, technology-mediated service delivery offers a viable alternative, as discussed (Ke et al., 2020; Singh et al., 2020). For example, VR-based tours enable organizations to abide by government-imposed social distancing or lockdown requirements, while still permitting a value-laden consumer experience (Debusmann, 2020).
Prior research has established VR's benefits for management, sales, marketing, distribution, and heritage preservation, to name a few (Gibson & O'Rawe, 2018; Moorhouse, tom Dieck, & Jung, 2018). In tourism, VR can be used to create “a virtual environment by the provision of synthetic or 360-degree real life captured content with a capable non-, semi-, or fully-immersive VR system, enabling virtual touristic experiences that stimulate the visual sense and potentially [the user's] 'additional [or] other senses … either prior to, during, or after travel” (Beck, Rainoldi, & Egger, 2019, p. 591). Pre-COVID-19, attraction sites (e.g., museums, theme parks) were increasingly adopting VR technology to innovate their offerings (Jung et al., 2018; Lee, Jung, tom Dieck, & Chung, 2020) or to offer an enhanced user experience (Bruno et al., 2010). However, during COVID-19, VR technology has become an important platform for tourism businesses to maintain their revenue stream. For example, attraction sites including The Louvre, Guggenheim Museum, Vatican City, Yosemite National Park, and many others are offering virtual tours to locked-down global audiences (Jones, 2020).
VR technology, which provides “computer-mediated interactive environments capable of offering sensory feedback to engage consumers … and drive desired consumer behaviors” (Hollebeek, Clark, et al., 2020, p. 1), can be used to foster consumer immersion or telepresence in real time (Guttentag, 2010). Telepresence refers to a user's perception of actually being in the computer-mediated environment (Cummings & Bailenson, 2016; Jung & Dieck, 2017), which is facilitated by sensory feedback that reflects the virtual platform's personalized response to the user's actions (Cowan & Ketron, 2019). VR-based tourism offerings can provide a hedonic (e.g., fun), functional (e.g., learning), or social (e.g., communal) visitor experience (Lee et al., 2020; Voss, Spangenberg, & Grohmann, 2003).
Tourism-based VR's benefits are well-documented in the literature (Bogicevic, Seo, Kandampully, Liu, & Rudd, 2019). For example, VR applications have been shown to boost consumer engagement, including for consumers who are unable to physically visit the site (e.g., due to lacking financial means, physical disability, or COVID-19-imposed lockdown; Moorhouse et al., 2018). Moreover, by allowing geographically-dispersed individuals to interact through a virtual platform, VR-based tours support social interactivity and connectivity (Jung et al., 2018). Given these benefits, many companies are investing in developing such platforms. For example, Google's Heritage on the Edge allows tourists to visit UNESCO World Heritage sites and Amazon Explore provides an interactive virtual experience of visiting historic/cultural sites (Bloom, 2020).
Despite these benefits, VR applications differ with respect to their technological capabilities (Beck et al., 2019). Specifically, more advanced VR platforms (e.g., BNEXT VR Headset, Samsung Galaxy Gear, Oculus Rift) typically generate higher user-perceived telepresence (vs. more basic (e.g., Google Cardboard-based) applications; Hollebeek, Clark, et al., 2020; Lee et al., 2020), as discussed further below. We next develop a research model and an associated set of hypotheses for empirical testing.
3 Hypothesis development
Based on our review, we next develop and test a promotion motivation theory-informed model that examines attraction site visitors' threat- and coping appraisal during COVID-19. In particular, we zoom in on consumers' coping response of social distancing and its anticipated effect on their intent to visit an attraction site during- and post-the pandemic (see Fig. 1 ).Fig. 1 Model.
Fig. 1
3.1 Effect of threat- and coping appraisals on social distancing
As discussed, protection motivation theory proposes threat severity and -susceptibility as key threat appraisal facets (Rogers, 1983). While the former represents the seriousness of harm that the threat can cause, the latter addresses one's perceived risk of being affected by the threat. During COVID-19, the pandemic's perceived threat typically correlates positively with the uptake of virus-preventative measures globally (Dryhurst et al., 2020). That is, high perceived threat severity yields elevated self-protection against the impending threat (Floyd, Prentice-Dunn, & Rogers, 2000; Milne et al., 2000). Similarly, high consumer-perceived susceptibility of contracting the virus will see elevated self-protection (Bengel, Belz-Merk, & Farin, 1996). Likewise, Harris, Ali, and Ryu (2018) identify perceived threat severity and -susceptibility as major drivers of consumers' restaurant avoidance (i.e., protection behavior) after a foodborne illness outbreak. During COVID-19, consumer attitudes toward social distancing vary across individuals (Hollebeek, Smith, et al., 2020). For example, those that perceive themselves to be less susceptible to contracting the virus are more likely to adopt looser social distancing practices (Seres et al., 2020). We hypothesize:H1a Consumers' perceived severity of COVID-19's threat positively affects their social distancing behavior.
H1b Consumers' perceived susceptibility to contracting COVID-19 positively affects their social distancing behavior.
Protection motivation theory also identifies the chief coping appraisal dimensions of response efficacy and self-efficacy (Rogers, 1983), as discussed. First, consumers hold personal beliefs about the efficacy of recommended responses against the threat (e.g., social distancing). That is, their perceptions of social distancing's effectiveness as a coping response to combat COVID-19 will vary. Second, self-efficacy reflects consumers' self-perceived ability to effectively perform the recommended coping response of social distancing.
According to meta-analyses conducted by Milne et al. (2000) and Floyd et al. (2000), response efficacy and self-efficacy positively influence individuals' protection behaviors. For example, both response- and self-efficacy are reported as predictors of cancer-related preventive behaviors, including screening and self-examination (Norman et al., 2005). Fisher, Almanza, Behnke, Nelson, and Neal (2018) further corroborate these results by showing that both response- and self-efficacy favorably affect cruise ship passengers' intent to wash their hands during the norovirus. Therefore, the higher consumers' perceived response efficacy of COVID-19-imposed social distancing and the higher their perceived self-efficacy of performing social distancing, the more motivated they are to protect themselves from the virus through social distancing. We posit:H2a Consumers' perceived response efficacy of social distancing positively affects their social distancing behavior.
H2b Consumers' perceived social distancing self-efficacy positively affects their social distancing behavior.
3.2 Social distancing's effects during the pandemic
Social distancing has revolutionized consumers' activities outside the home and consumer perceptions of these activities (De Vos, 2020). To stay connected to others, consumers are therefore increasingly adopting virtual, technology-based interactions during the pandemic (Hollebeek, Smith, et al., 2020). The virus has thus motivated consumers to seek new ways of interacting with businesses to satisfy their needs, thus impacting their consumption patterns.
The tourism value chain is dramatically impacted by COVID-19, as its coping interventions (e.g., social distancing, lockdown) affect the sector's usual operations (Gössling et al., 2020). Therefore, attraction sites are innovating their service delivery modes, including by adopting VR-based site tours, as discussed. VR-based tours allow consumers to virtually visit attraction sites by replicating the site's physical environment (Errichiello, Micera, Atzeni, & Del Chiappa, 2019), while also overcoming traditional site visit-related issues (e.g., queuing, crowding; Jung & Dieck, 2017). During high COVID-19-imposed uncertainty, virtual site visits allow consumers to cope with the situation, satisfy their visitation needs, and fight boredom (Bright, 2020).
Fisher et al. (2018) report that cruise ship passengers sought to avoid personal contact during a simulated norovirus outbreak. To curtail the virus, passengers were found to avoid crowded areas on board and to minimize touching common surfaces (e.g., buffet area; Wang & Ackerman, 2019). COVID-19 is likely to shift consumers' travel-related mindset, including by evading crowded sites or destinations in favor of more tranquil options (Zenker & Kock, 2020). We posit that during COVID-19, consumers practicing higher levels of social distancing will display a reduced intent to visit an attraction site in-person and instead be more inclined to opt for VR-based site tours. We hypothesize:H3a Consumers' adopted social distancing level positively affects their intent to use virtual reality-based attraction site tours during the pandemic.
H3b Consumers' adopted social distancing level negatively affects their intent to use in-person attraction site tours during the pandemic.
VR tours' technological advancement level is also likely to generate consumers' differing tour evaluations (Hollebeek, Clark, et al., 2020). That is, the more advanced the deployed VR technology, the better the consumer's typical tour experience (Wei, Qi, & Zhang, 2019). Tourism-based VR ranges from non-immersive to fully immersive applications, with limited intention being paid to their differences to date (Beck et al., 2019). We expect more advanced VR systems to boast an elevated capacity to immerse consumers in their high-fidelity site visit and generate telepresence.
Consumers who take social distancing more seriously, in particular, are expected to prefer visiting high (vs. low)-fidelity virtual environments (Thurman & Mattoon, 1994), because while their extensive social distancing behavior largely precludes them from physically visiting attraction sites, they still seek to optimize their virtual visit experience (Hollebeek, Smith, et al., 2020). Moreover, consumers practicing high levels of social distancing will also want others to stick to the social distancing protocol, given its optimal outcomes if - and only if - everyone adheres to it. That is, we expect consumers' social distancing level to affect their advocacy intent for social interaction-minimizing, high-fidelity VR tours to others (Itani, Kassar, & Loureiro, 2019; Stokburger-Sauer, 2011). We postulate:H4a Consumers' adopted social distancing level positively affects their intent to use more advanced virtual reality-based site tours during the pandemic.
H4b Consumers' adopted social distancing level positively affects their intent to advocate virtual reality-based site tours to others.
3.3 Social distancing's post-pandemic effects
COVID-19 will be around at least until the development of an effective treatment and/or vaccine, which are expected to arrive by mid- to late-2021 (Grenfell & Drew, 2020). Until then, social distancing is expected to retain its precautionary value in combating the virus (Kissler et al., 2020), including for attraction sites (Baum & Hai, 2020). Given these issues, we investigate whether consumers' intent to visit attraction sites, either in-person or virtually, post-the pandemic will be affected by the current social distancing protocol. That is, after a period of obligatory social distancing, to what extent may consumers have gotten used to limiting their social interactions, thus affecting their future site tour-related behaviors?
The future availability of medical interventions against COVID-19 will render consumers less reliant on social distancing to stay safe. Therefore, while consumers may retain a level of caution vis-à-vis social interactions in the future, they are expected to practice higher levels of social distancing during (vs. post-) the pandemic (i.e., when a cure is available). Consequently, we expect consumers' short- (i.e., during the pandemic) and long-term (i.e., post-pandemic) social distancing behavior to differ (Jang & Feng, 2007). We postulate:H5a The effect of consumers' adopted social distancing level on their intent to use virtual reality-based site tours post- (vs. during) the pandemic will be weaker.
H5b The effect of consumers' adopted social distancing level on their intent to use in-person site tours post- (vs. during) the pandemic will be weaker.
4 Methodology
4.1 Research design and sample
We deployed a self-administered, web-based Qualtrics survey to collect our convenience sampling-based data. The respondents were sourced from an online panel of demographically and geographically diverse consumers in the United States, where the travel/tourism sector makes a major contribution to GDP. Participants resided in different states and were thus not restricted to specific U.S.-based areas. The number of confirmed COVID-19 cases and deaths reported in the U.S. also renders it one of the most affected countries by the virus (Dong et al., 2020), demonstrating its relevance for this research.
The survey link was shared with the panel members, who were compensated for their participation. At the start of the survey, respondents were given a definition of VR-based site tours, examples of such tours, and a brief explanation of the technology behind these tours. We also outlined the research objective. The survey proceeded with relevant screening questions (e.g., the request to name a focal attraction site) to ensure the respondents' awareness of and interest in local/international attraction sites. Those who were unable to specify an attraction site were excluded from further participation. This procedure was important since the personalized survey questions referred back to the participant's identified site (e.g., Burj Khalifa, the Colosseum, Eiffel Tower, French Quarter (New Orleans), Glacier National Park, Independence Hall, The Louvre, Navy Pier, Sydney Opera House, The Zócalo, Walt Disney World Resort, the Vatican Museum).
The respondents also reported on their perceived severity of COVID-19 and their perceived susceptibility to contracting the virus. Further, they were asked to state social distancing's response efficacy and their perceived self-efficacy in implementing social distancing. Moreover, their social distancing behavior during the pandemic, behavioral intentions toward using VR-based (vs. in-person) attraction site tours (during and post-the pandemic), and their desired VR-based tour's technological advancement level were solicited. Finally, we collected the respondents' familiarity with VR-based tours and their demographic information.
Of the 529 informants who accessed the survey, 181 passed the screening questions and agreed to participate in the study. After dropping a further seven incomplete responses, the final sample included 174 complete responses, yielding an effective 32.8% response rate. Respondents' average age is 40.14 (STD = 11.75). Reported average annual household income is $79,279 (STD = $32,982). For our partial least squares (PLS)-based analyses, we followed the guideline that recommends a sample size exceeding: (1) 10 times the number of indicators of the measure with the larger indicator number, or (2) 10 times the greatest number of structural paths linked to a particular modeled latent construct (Hair, Hult, Ringle, & Sarstedt, 2016). Our sample size is also in line with Cohen's power analysis at 80 %statistical power (Hair et al., 2016). The sample characteristics are summarized in Appendix 1.
4.2 Measures
We measured threat severity by adapting Witte's (1996) instrument to capture COVID-19's perceived seriousness. We also gauged consumers' perceived susceptibility to contracting COVID-19 by using a four-item measure (Rippetoe & Rogers, 1987; Witte, 1996), and social distancing-based response efficacy with a three-item scale (Floyd et al., 2000; Rippetoe & Rogers, 1987; Witte, 1996). Moreover, a three-item self-efficacy measure was used to capture respondents' belief about their own ability to apply social distancing (Witte, 1996). Respondents' social distancing level was gauged by deploying an eight-item scale assessing respondents' physical distancing behavior, including the extent of their avoidance of public gatherings and crowded places. For all measures, seven-point Likert scales were used, which ranged from 1 (strongly disagree) to 7 (strongly agree). All of our deployed measures were of a reflective nature (Diamantopoulos & Siguaw, 2006).
Participants were then asked to share their intent to visit their named attraction site, both in person and via a VR-based tour during the pandemic. They were also requested to report on their intent to recommend the VR-based site tour to others. Moreover, participants reported on their likelihood of an in-person (vs. VR-based) visit to their named site after the pandemic (i.e., when an effective pharmaceutical intervention/vaccine is available). Respondents' reported intent to use these tours was gathered on a five-item measure sourced from existing intention scales (Davis & Warshaw, 1992; Miniard & Cohen, 1981). Seven-point Likert scales were again used to rate our intention measure (1 = extremely unlikely to 7 = extremely likely).
Consumers' VR-based visit's technological advancement need was measured as follows: “When visiting [named attraction site], if you are choosing between different VR-based site tours, which would you prefer?” (measured on seven-point Likert scales: 1 = extremely basic to 7 = extremely advanced). We also gauged respondents' familiarity with VR-based site tours by deploying the following single-item measure: “I am familiar with virtual reality-based site tours” (measured on a seven-point Likert scale: 1 = strongly disagree to 7 = strongly agree). Overall, respondents were relatively familiar with VR-based tours (mean = 5.1).
We included respondents' familiarity with VR-based tours, age, and income as covariates, as these factors can affect respondents' intent to use VR-based and in-person site tours (e.g., Khan, Hollebeek, Fatma, Islam, & Riivits-Arkonsuo, 2020). Examination of the skewness and kurtosis statistics indicated that these were within the acceptable range of ±2 (George & Mallery, 2016). An overview of our measures, items/loadings, skewness, and kurtosis values is offered in Appendix 2.
5 Results
5.1 Reliability and validity
To test our hypotheses, we deployed PLS-based structural equation modeling by using SmartPLS (3.3.2). We conducted PLS path analysis with 5000 bootstrapped subsamples, which is suitable for studying relatively small sample sizes (Hair, Risher, Sarstedt, & Ringle, 2019). Before examining the path coefficients, the measures' reliability and validity were checked. The outer model's results suggest the measures' adequate internal consistency, with the lowest Cronbach's alpha equaling 0.77, thus exceeding the minimum threshold of 0.7 (Cronbach, 1951).
We also checked all measures' composite reliability, with the lowest score being (0.85). Further, the items significantly loaded on their respective latent variables (p < 0.01), without any problematic cross-loadings, thus corroborating the measures' convergent validity. We verified discriminant validity by first conducting the heterotrait–monotrait (HTMT) test. The inter-factor HTMT values were below the 0.85 cut-off, offering evidence of discriminant validity (Henseler, Ringle, & Sarstedt, 2015). To further test discriminant validity, we compared the square root of the average variance extracted (AVE) of the multi-item measures with their respective inter-factor correlations. None of the inter-factor correlations exceeded the square root of the AVE, corroborating discriminant validity. Moreover, all variance inflation factors were below 3, specifying that multicollinearity is not a problem in our data (Hair et al., 2016). Cronbach's alpha, composite reliability, mean, standard deviation, and AVE values are presented in Table 1 .Table 1 Correlations, Reliability, AVE, and descriptive statistics.
Table 1 1 2 3 4 5 6 7 8 9 10 11 Mean STD
1 Social Distancing 0.84 5.91 1.04
2 Perceived Severity 0.57 0.90 5.83 1.11
3 Perceived Susceptibility 0.35 0.47 0.80 4.90 1.21
4 Response Efficacy 0.61 0.53 0.56 0.85 5.54 0.96
5 Self-efficacy 0.63 0.41 0.20 0.61 0.81 5.70 0.91
6 VR Tour Intentions(D) 0.45 0.36 0.34 0.32 0.22 0.84 5.35 1.23
7 In-Person Tour Intentions(D) −0.09 −0.02 0.19 −0.08 −0.31 0.33 0.95 4.60 1.94
8 VR Tour Intentions(P) 0.19 0.29 0.33 0.30 0.15 0.52 0.31 0.91 5.18 1.30
9 In-Person Tour Intentions(P) 0.05 0.01 0.20 0.23 −0.05 0.02 0.17 −0.09 0.86 5.51 1.17
10 VR Advancement Needs 0.25 0.24 0.30 0.41 0.27 -.02 −0.24 −0.29 0.33 ° 5.52 1.25
11 Advocacy Intentions toward VR Tour 0.35 0.36 0.29 0.47 0.44 0.51 0.05 0.41 0.18 0.42 0.84 5.49 0.86
Cronbach's Alpha 0.94 0.92 0.81 0.80 0.77 0.90 0.97 0.95 0.92 ° 0.86 ° °
Composite Reliability 0.95 0.94 0.88 0.88 0.85 0.92 0.98 0.96 0.93 ° 0.90 ° °
Average Variance Extracted 0.70 0.81 0.64 0.72 0.66 0.70 0.91 0.82 0.73 ° 0.70 ° °
Notes: Correlations are provided below the diagonal; correlations equal to or greater than 0.15 are significant (p < 0.05); square root of AVE: refer diagonal; STD = standard deviation; D = During pandemic; P = Post-the pandemic; ° not applicable.
5.2 Common method bias
We next conducted common method bias (CMB) testing to ensure this did not undesirably affect our findings. Using Harman's single-factor test, we conducted a one-factor measurement model by using exploratory factor analysis (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). The single-factor model explained significantly less than 50% of the observed variance. We also applied the marker factor criterion by examining the respondent's time taken to complete the survey, which is theoretically unrelated to the other modeled factors. The marker variable's addition to the model did not yield any significant change to the attained results. Consequently, we did not find CMB to be of concern in our data.
5.3 Path analysis
To test the model's hypothesized path coefficients, we deployed nonparametric bootstrapping. As an overall measure of model fit, the standardized root mean squared residual (SRMR) was 0.056, thus remaining below the 0.08 threshold (Hu & Bentler, 1999). Our results also offer support for most of our hypotheses, as shown in Table 2 .Table 2 Results.
Table 2 During COVID-19 Post-COVID-19
Predictor Outcome
Social Distancing VR Tour Intention In-person Tour Intention VR Tour Advancement Need VR Tour Advocacy Intention VR Tour Intention In-person Tour Intention
Perceived Severity 0.35*(0.08)
Perceived Susceptibility 0.04(0.07)
Response Efficacy 0.29*(0.06)
Self-Efficacy 0.33*(0.10)
Social Distancing 0.21*(0.07) −0.33*(0.05) 0.22*(0.08) 0.23*(0.06) 0.03(0.08) 0.13(0.12)
Covariates
VR Tour Familiarity 0.48*(0.07) 0.21*(0.06) 0.10(0.09) 0.21*(0.06) 0.47*(0.08) −0.27*(0.09)
Age −0.18*(0.05) −0.11*(0.05) −0.19*(0.08) −0.36*(0.07) −0.06(0.09) −0.06(0.12)
Income 0.19*(0.04) 0.29*(0.06) −0.21*(0.08) −0.01(0.09) 0.14*(0.06) 0.33*(0.07)
R2 0.56 0.55 0.31 0.15 0.32 0.31 0.19
Notes: * Significance level: p < 0.05; standard deviations are reported in parentheses.
Two-dimensional threat appraisal was hypothesized to raise consumers' social distancing behavior in the face of COVID-19 (H1a-b). The hypothesized positive effect of perceived threat severity on social distancing (H1a) is supported (β = 0.35, p < 0.05). The results however show that the effect of consumers' perceived susceptibility to contracting the virus on their social distancing behavior (H1b) is nonsignificant (β = 0.04, p > 0.1). Therefore, though H1a is supported, H1b remains unsupported. In H2, consumers' coping appraisal, which includes response- and self-efficacy, is suggested to heighten social distancing behavior. H2a suggests that response efficacy increases social distancing behavior, which is supported (β = 0.29, p < 0.05). Likewise, H2b, which predicts that self-efficacy increases social distancing behavior, is also supported (β = 0.33, p < 0.05). Our full support for H2 therefore suggests that consumers' coping appraisal drives their protective social distancing behavior.
In H3, social distancing is hypothesized to increase (decrease) consumers' intent to visit their named attraction site through VR-based (in-person) tours, respectively, during COVID-19. The results reveal that the higher a consumer's exercised social distancing, the greater his/her intent to use VR-based site tours during the pandemic (β = 0.21, p < 0.05), with a corresponding reduced intent to visit the site in-person (β = −0.33, p < 0.05). Thus, H3a-b are supported, suggesting social distancing's important effect on consumers' intent to visit their named attraction site in-person during the pandemic. We also find social distancing to drive the consumer's need for advanced (vs. basic) VR-based site tours (β = 0.22, p < 0.05), supporting H4a. Moreover, the results show that social distancing drives respondents' intent to advocate VR-based site tours to others by nudging them toward these (vs. in-person) tours during the pandemic (β = 0.23, p < 0.05), thus supporting H4b.
H5a suggests that the effect of social distancing on consumers' intent to use VR-based site tours post- (vs. during) the pandemic will be weaker. The results show that social distancing during the pandemic has a nonsignificant effect on respondents' intent to use VR-based site tours post-the pandemic (β = 0.03, p > 0.1) compared to social distancing's significant effect on respondents' intent to use VR-based tours during-the pandemic (β = 0.21, p < 0.05). The difference between the two effect sizes (Δβ = 0.18) is significant (p < 0.05). Thus, social distancing's effect on consumers' intent to use VR-based site tours post-the pandemic is weaker and nonsignificant (vs. its significant effect during the pandemic), supporting H5a.
H5b stipulates that social distancing's effect on consumers' intent to purchase in-person site tours post- (vs. during) the pandemic will be weaker. The results again reveal a nonsignificant effect on consumers' intent to purchase in-person site tours post-the pandemic (β = 0.13, p > 0.1) compared to social distancing's significant effect on respondents' intent to purchase these tours during the pandemic (β = −0.33, p < 0.05). The difference between the two effect sizes (Δβ = 0.46) is significant (p < 0.05). Thus, social distancing's effect on consumers' intent to purchase in-person site tours post-the pandemic is weaker (nonsignificant) compared to its significant effect during the pandemic. Hence, the results support H5b.
The findings also show that social distancing's effect on consumers' adoption of VR-based and in-person site tours post-the pandemic is nonsignificant. Therefore, though consumers are exercising social distancing during the pandemic, their future intent to purchase future VR-based or in-person site tours is unlikely to be affected by their current social distancing precautions, and they are likely to return to in-person site visits (mean during pandemic = 4.6; mean post pandemic = 5.51), as well as to continue taking VR-based site tours (mean during pandemic = 5.35; mean post pandemic = 5.18) post-the pandemic, as the nonsignificant difference in their respective means suggests.
6 Discussion, implications, and further research
6.1 Discussion
COVID-19 has significantly impacted consumption behavior (e.g., by limiting consumer mobility, imposing social distancing; Baum & Hai, 2020), creating new challenges for attraction sites. Consumers are practicing social distancing by staying at home as much as possible, maintaining a physical distance of 1.5–2 m from others in the servicescape, and avoiding crowds, which attraction sites need to consider in their service (re)design.
To overcome these challenges, attraction sites are increasingly introducing VR-based (vs. in-person) tours. While the adoption of VR-based tours during the pandemic has intuitive appeal, empirically derived insight into consumer responses to these initiatives remains scant, thus exposing an important research gap explored in this paper. Using protection motivation theory, we investigated the role of consumers' COVID-19-related perceived threat appraisal, which comprises the perceived severity of the pandemic's health threat and one's perceived susceptibility to contracting the virus, on social distancing behavior, both during and after the pandemic. We also examined the role of consumers' virus-related coping appraisal, which comprises self- and response efficacy during and after the pandemic. Moreover, we investigated social distancing's effect on consumers' intent to purchase a VR-based (vs. in-person) site tour during and after the pandemic, consumers' desired VR tour's technological advancement level, and their intent to engage in VR-based (vs. in-person) tour-related advocacy behavior.
Our results reveal COVID-19's relatively high perceived threat severity, leading consumers to practice high levels of protective social distancing during the pandemic. Consumers' perceived response efficacy of government-imposed social distancing was also found to be comparatively high. Moreover, we found consumer-perceived social distancing-related self-efficacy to positively affect their social distancing behavior. These associations are in line with prior research that posits threat severity to raise protection behaviors against infectious diseases (Dryhurst et al., 2020; Floyd et al., 2000). We therefore identify social distancing as an effective COVID-19-related coping mechanism.
Though COVID-19 is viewed as a threat, consumer-perceived susceptibility to contracting the virus was not found to significantly drive social distancing behavior. That is, perceived susceptibility is not significant in driving participants to adopt social distancing to fend off COVID-19. This nonsignificant result suggests that perceived susceptibility exhibits a conflicting pattern of effects on consumers' social distancing-based protection motivation (Harris et al., 2018; Norman et al., 2005), potentially given individuals' perceived modest risk of contracting the virus (e.g., as they are not in a high-risk (e.g., elderly) group).
We also illuminated the future impact of social distancing during the pandemic on consumers' intent to purchase VR-based (vs. in-person) site tours post-the pandemic. Our findings suggest that social distancing will not have a lasting effect on consumers' future tour purchase intentions, particularly once an effective COVID-19 treatment or vaccine is available. That is, post-the pandemic, consumers will consider both in-person and VR-based site tours, thus countering anecdotal evidence that suggests that social distancing's effect on tourism is here to stay after the pandemic (e.g., Oguz, Gordon, & Cruz, 2020). Based on our findings, we suggest that tourists will switch to alternative, non-social distancing-based protection methods (e.g., vaccine) once available. We therefore envisage that current social distancing-enforced gaps in the tourism sector will largely dissolve post-the pandemic, thus offering good news to attraction site- and broader tourism providers. This again suggests that tourism is vulnerable to pandemics and crises (Cró & Martins, 2017; Rosselló et al., 2020). Moreover, our results suggest that consumers' decision-making for VR-based (vs. in-person) tours remains unaffected by COVID-19-imposed social distancing post-the pandemic. In other words, they are then expected to consider both VR-based and in-person tours, thus retrieving attraction sites' strategic opportunity for on-site visitation. We next discuss important theoretical implications that arise from our analyses.
6.2 Theoretical implications
We derive the following theoretical implications from our analyses. First, our analyses extend existing protection motivation theory-based insight through its application to COVID-19, by deploying social distancing as the focal protective mechanism. Based on our attained insight, protection motivation theory offers a relevant theoretical frame to inform further COVID-19- or pandemic/crisis-related research, thus unlocking a wealth of avenues for further study. For example, to what extent does our identified positive association of consumers' during-pandemic social distancing behavior on their intent to use VR-based (vs. in-person) tours generalize to other protective behaviors (e.g., frequent hand-washing, use of gloves/face-masks)?
Relatedly, our findings show that the higher a consumer's adopted social distancing level, the greater his/her need for technologically advanced (vs. basic) VR-based site visits during the pandemic. Thus, while those practicing high levels of social distancing seek more advanced VR-based visits during the pandemic, those who adhere less to the social distancing protocol are more likely to opt for basic VR-based tours. This finding suggests that those exhibiting lower threat protection behaviors are likely to continue taking in-person tours for as long as possible leading up to government-imposed social distancing. That is, as these consumers primarily use VR-based site visits to bridge the lockdown period, we expect them to reassume their physical visits soon after social distancing restrictions are lifted (Hollebeek, Smith, et al., 2020), thus adding to the existing knowledge stock on protection motivation theory.
Second, though we identify a growing demand for VR-based site tours, our analyses suggest that VR-based visits will not replace on-site visitation in a post-pandemic era. Instead, consumers are predicted to consider both VR-based and in-person tours once an effective medical intervention for COVID-19 is available. Thus, as these treatments enter the market, alternate theoretical frames may gain prominence to investigate consumers' COVID-19-related behavior, including the theory of planned behavior or regulatory focus theory (e.g., Hollebeek, Smith, et al., 2020), thus sparking a plethora of opportunities for further research. Moreover, as VR-based and in-person site visits continue to co-exist post-the pandemic, we advise tourism researchers to contemplate their respective optimal design in attraction sites' strategic portfolios, both under regular market conditions and in the face of crisis (Hollebeek, Smith, et al., 2020).
6.3 Managerial implications
Our findings also offer a wealth of implications for attraction sites. The results first suggest that attraction sites stand to benefit from offering VR-based tours, allowing them to recuperate at least part of their COVID-19-compromised revenue. We also found that attraction sites planning to reopen during the pandemic (i.e., before the advent of an effective treatment/vaccine) will see lower visitor numbers, which is plausible given the widespread social distancing requirement. Therefore, to improve their rate of visitation during the pandemic, attraction sites are advised to develop and offer VR-based site visits.
Second, we reveal that the more prone consumers are to stick to the social distancing protocol, the greater their demand for more technologically advanced, immersive (vs. basic) VR-based tours during the pandemic (Bogicevic et al., 2020). For example, more advanced VR technology typically allows consumers to navigate the virtual environment using fully immersive applications (Beck et al., 2019). While tourism managers are faced with the dilemma of which VR tools to invest in, we recommend the implementation of more advanced, immersive VR technology (Tussyadiah, Kausar, & Soesilo, 2018), which tends to yield more favorable user evaluations and advocacy.
Third, post-the pandemic, VR-based site visits offer continued value to visitors, including to those wishing to have a ‘taste’ of the site prior to visiting it in-person, individuals desiring convenient armchair travel, those lacking the (e.g., financial) means to visit a desired (e.g., international) site, or those suffering from (e.g., physical) disabilities (Lin, Huang, & Ho, 2020; Olya & Han, 2020; Tussyadiah et al., 2018). VR-based tours are thus able to reach a greater target audience at an improved carbon footprint (i.e., through reduced travel-related pollution), while also allowing infinite potential visitor numbers at any given time, removing wait times (e.g., due to queuing, overcrowding), and being less susceptible to counterfeit entry tickets than in-person tours.
In line with these benefits, visitors are likely to consider both in-person and VR-based tours post-the pandemic. Thus, while we do not expect VR-based tours to replace traditional site visits, they have an important and growing role in supporting attraction sites' revenue, both currently and in the future (Kabadayi, O'Connor, & Tuzovic, 2020; Zenker & Kock, 2020). For example, new COVID-19-based VR tour users are likely to continue considering these tours post-the pandemic. Attraction site managers are therefore advised to regularly update and innovate their VR-based tours (e.g., as new technological capabilities become available; Hollebeek & Rather, 2019). Given their outlined benefits, other or related sub-sectors (e.g., events, trade-shows, conferences) are also predicted to profit from expanding their service portfolio to include VR-based offerings. In sum, we identify VR-based tours as a powerful tool for attraction site and other tourism providers, both during (e.g., by allowing them to continue to operate) and after the pandemic (e.g., by expanding their reach, preparing for potential future crises; Martínez-Román, Tamayo, Gamero, & Romero, 2015).
6.4 Limitations and further research
Despite its contributions, this study is also subject to several limitations, from which we derive opportunities for further research. First, we deployed a cross-sectional research design that captures the observed dynamics at a single point in time. It therefore overlooks the development of the modeled associations over time, which could be addressed in future longitudinal research.
Second, our findings are based on convenience sampling-based panel data, thus incurring potential bias and generalizability issues (Malhotra, 2019). Future researchers may therefore wish to adopt probability sampling methods (e.g., simple random sampling) to address this issue. Further, our results are based on a sample size of 174, which, while adequate, would benefit from further expansion in future research (Malhotra, 2019). Moreover, as we only considered VR technology, further researchers may wish to examine other technologies (e.g., augmented/mixed reality) and their potential unique dynamics (Trunfio & Campana, 2020).
Third, we focused on understanding consumers' COVID-19-induced protection behavior to predict their intent to purchase VR-based (vs. in-person) site tours. We therefore did not consider consumers' past behavior, which may correlate with their current/future behavior. Relatedly, we only focused on social distancing as a protective measure against COVID-19, thus overlooking other potential measures (e.g., use of face-masks, sanitization).
Fourth, our data was collected from the United States, thus offering a limited representation of potential COVID-19 dynamics in other parts of the world. We therefore recommend the undertaking of further (empirical) pandemic-related research in/across other countries. Respondents were also requested to provide a focal attraction site that was used in the survey. However, this single-site focus can skew the responses toward site-specific dynamics, which may incur limited cross-site generalizability. Therefore, further researchers are advised to study multiple attraction sites to enable cross-site assessments. Moreover, it would be beneficial to have respondents experience a specific VR-based tour(s) before gauging their tour-related behavioral intentions.
7 Conclusion
Consumer behavior is shifting as a result of COVID-19, thus requiring attraction sites to identify novel ways of offering safe tours to their visitors. In response to the pandemic's mobility restrictions and social distancing protocol, VR-based site visits offer a viable alternative that allows attraction sites to maintain a revenue stream during the pandemic. Our empirical results show that consumers intend to take VR-based site visits during the pandemic, while considering both VR-based and in-person site visits post-the pandemic. Visitors also prefer more advanced (vs. basic) VR-based tours that typically offer a more immersive experience. Based on VR-based tours' manifold outlined benefits, we recommend attraction site managers to offer these during and post-the pandemic.
Author contribution
Both authors contributed equally to the project and the writing of the article.
Impact statement
The study provides empirical evidence on how attraction sites can respond to and recover from the unprecedented crisis caused by COVID-19 pandemic. Findings show that visitor-perceived threat severity, response efficacy, and self-efficacy raise social distancing, which increases (decreases) visitors’ intent to use virtual reality (in-person) tours during the pandemic. For that, it is concluded that a relationship between consumer protection behavior (social distancing) included by COVID-19 pandemic. The study reveals that social distancing has no long-lasting effect that will impact consumers’ intentions toward attraction sites tours after the pandemic taking into consideration that a medical intervention is available. Social distancing has no effect on potential visitors’ intent to use virtual reality vs. in-person attraction tours post-the pandemic. Further, social distancing is found to boost visitor demand for advanced virtual tours and attract predominantly positive word-of-mouth toward this kind of tours. This is said important to examine as several attraction sites are starting to offer VR-based tours during the pandemic.
Declaration of competing interest
None.
Omar S. Itani earned his Ph.D. from the University Texas Arlington. Dr. Itani is an Assistant Professor of at the Lebanese American University. Dr. Itani's research studies the cross-section of technology and consumer behavior. His research interests also include relationship and strategic marketing, and sales management. His research has appeared in multiple journals including the Organizational Behavior and Human Decision Processes, Journal of Business Research, International Journal of Hospitality Management, Industrial Marketing Management, Journal of Business Ethics, among others.
Linda D. Hollebeek, Ph.D. is Senior Associate Professor at Montpellier Business School and Full Professor at Tallinn University of Technology (Adj.). Her research centers on customer/consumer engagement and interactive consumer/brand relationships and value. Her work to date has appeared in the Journal of the Academy of Marketing Science, Journal of Service Research, Journal of Interactive Marketing, Industrial Marketing Management, Journal of Business Research, and European Journal of Marketing, among others. She is Associate Editor of the European Journal of Marketing and is currently guest editing Special Issues/Sections on Customer Engagement for the Journal of Service Research and the International Journal of Research in Marketing. She is also co-editor of The Handbook of Research on Customer Engagement (2019, Edward Elgar).
Appendix 1 Sample characteristics
Age (years) Frequency Percentage
18–27 43 24.71
28–37 35 20.11
38–47 53 30.45
48–57 25 14.37
≥58 18 10.34
Household Income ($/year)
25,000–50,000 25 14.37
50,001–75,000 76 43.67
75,001–100,000 42 24.14
≥100,000 31 17.82
Marital Status
Married 98 56.32
Never Married 42 24.14
Other 34 19.54
Gender
Female 83 47.70
Male 91 52.30
Education
Some college but no degree 12 6.89
College degree 125 71.84
Graduate Degree 37 21.26
Ethnic Background
Asian/Pacific Islander 12 6.89
Black 23 13.21
Hispanic 31 17.82
White 101 58.05
Other 7 4.02
Appendix 2 Measures and loadings
Measure Loading Skewness Kurtosis
Social Distancing
I currently practice social distancing 0.87 −0.33 0.47
I follow social distancing precaution to avoid getting COVID-19 pandemic 0.75 −0.65 0.89
I apply social distancing recommendations in my daily life 0.94 −0.88 1.42
I don't gather in group 0.89 −1.31 0.94
I am avoiding public gatherings 0.63 −0.59 1.3
I try to keep an appropriate physical distance or space from others 0.92 −0.36 0.67
I try to do most of my activities (e.g., shop, work, learn) from home when possible 0.86 −1.07 1.43
I am connecting with other through mobile, digital and virtual options 0.82 −1.06 1.66
Perceived Severity
I think COVID-19 pandemic is serious 0.92 −1.05 1.78
I believe the threat of COVID-19 pandemic is significant 0.94 −0.82 −0.17
I think that COVID-19 pandemic is of high risk 0.91 −0.31 0.82
COVID-19 pandemic is harmful 0.83 −0.81 1.42
Perceived Susceptibility
There is high probability for someone to contract COVID-19 pandemic 0.90 −0.85 0.52
I am at risk of getting COVID-19 pandemic 0.76 −0.71 −0.69
COVID-19 pandemic is highly contagious 0.77 −0.31 −0.83
It is possible that I will contract COVID-19 pandemic 0.76 −0.60 −0.06
Response Efficacy
Recommended response from healthcare authorities works in avoiding COVID-19 pandemic 0.94 −0.41 −0.03
The response of the accountable authorities and organizations toward COVID-19 pandemic is effective 0.68 −0.58 1.02
The use of the recommended precaution by the health authorities, will stop COVID-19 pandemic from spreading 0.90 −0.42 −0.78
Self-efficacy
I can protect myself from being infected by COVID-19 pandemic by following health authorities' recommendations 0.87 −0.59 0.78
I can effectively follow the recommended precaution by the health authorities to avoid getting COVID-19 pandemic 0.74 −0.72 0.09
Personally, I can deal with COVID-19 pandemic by following the recommended response by the government agencies 0.83 −0.41 −0.86
Advocacy Intentions toward Virtual Reality Tours
I would let me friends know about the virtual reality tours offered 0.79 −0.42 −0.40
I will spread the word around the virtual reality tours offered by the attraction site 0.87 -0.41 −0.18
I would recommend the virtual reality tours to potential visitors 0.90 −0.29 -.66
I will share the benefits of virtual reality tours with others 0.79 −0.07 −0.77
Familiarity with Virtual Reality Tours
Overall, I am familiar with virtual reality tours ° −0.52 −0.81
Virtual Reality Tour Intentions
D P D P D P
I intend to try the virtual reality tours provided by the attraction site 0.82 0.92 −0.77 −0.83 −0.07 −0.08
I predict I will use the virtual reality services offered by the attraction site 0.83 0.93 −0.79 −0.67 −0.26 −0.29
I certainly intend to use the virtual reality tours provided by the attraction site 0.90 0.90 −0.91 −0.75 −0.14 0.38
I plan on virtually visiting the attraction site 0.87 0.87 −1.02 −0.69 0.43 −0.31
It is very likely that I will using virtual reality tours to visit the attraction site 0.75 0.92 −0.81 −0.59 0.11 −0.47
In-person Tour Intentions
D P D P D P
I intend to visit the attraction site 0.97 0.90 −0.64 −0.60 −0.78 −0.45
It is very likely that I will visit the attraction site 0.96 0.89 −0.41 −0.84 −0.31 −0.58
I plan to visit the attraction site 0.94 0.82 −0.53 −0.98 −1.05 0.32
I predict I will be visiting the attraction site 0.93 0.82 −0.45 −0.69 −1.09 −0.60
I certainly intend to go to the attraction site 0.97 0.85 −0.68 −0.82 −0.96 −0.61
Notes: D = During pandemic; P = Post-the pandemic; ° not applicable.
Acknowledgements
None.
1 https://coronavirus.jhu.edu/map.html (Accessed November 16, 2020).
2 https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/social-distancing.html (Accessed June 8, 2020).
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| 0 | PMC9734089 | NO-CC CODE | 2022-12-14 23:28:27 | no | Tour Manag. 2021 Jun 23; 84:104290 | utf-8 | Tour Manag | 2,021 | 10.1016/j.tourman.2021.104290 | oa_other |
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Rev Psiquiatr Salud Ment (Engl Ed)
Rev Psiquiatr Salud Ment (Engl Ed)
Revista De Psiquiatria Y Salud Mental
2173-5050
SEP y SEPB. Published by Elsevier España, S.L.U.
S2173-5050(22)00066-8
10.1016/j.rpsmen.2022.06.010
Letter to the Editor
A Covid-19 outbreak in a Spanish long-term psychiatric hospital led to infections in 6 clozapine patients: elevations in their plasma clozapine levels
Un brote por Covid-19 en un hospital psiquiátrico de larga estancia español dio lugar a infecciones en 6 pacientes en tratamiento con clozapina: elevaciones en los niveles plasmáticos de clozapinaArrojo-Romero Manuel a⁎
Codesido-Barcala Maria Rosario a
Leon Jose de bc
a Department of Psychiatry, Complejo Hospitalario Universitario de Santiago, Santiago de Compostela, Spain
b Mental Health Research Center, Eastern State Hospital, Lexington, KY, USA
c Biomedical Research Centre in Mental Health Net (CIBERSAM), Santiago Apóstol Hospital, University of the Basque Country, Vitoria, Spain
⁎ Corresponding author.
10 12 2022
October-December 2022
10 12 2022
15 4 290292
© 2022 SEP y SEPB. Published by Elsevier España, S.L.U. All rights reserved.
2022
SEP y SEPB
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
==== Body
pmcDear Editor,
Clozapine is mainly metabolized by the cytochrome P450 1A2 (CYP1A2) leading to its main metabolite, norclozapine.1 Clozapine metabolism is influenced by 3 levels of complexity: (1) ancestry groups, (2) sex-smoking subgroups and (3) presence/absence of poor metabolizer (PM) status.2 The concentration providing 350 ng/ml, the minimum therapeutic dose, can be used to compare ancestry groups and patients.1, 2, 3 Asians and their descendants, Amerindians, need lower doses than Europeans.3 In European patients the minimum therapeutic dose is 250 mg/day for female non-smokers and 400 mg/day for male smokers. PMs usually have around half the ability to metabolize clozapine, requiring half the dose.2 Non-genetic CYP1A2 PMs are probably much more frequent than genetic PMs and can be explained by use of CYP1A2 inhibitors, obesity or inflammation. Any systemic inflammation (whether or not associated with infection) releases cytokines and increases the C-reactive protein (CRP) inhibiting CYP1A2.1
In a large clozapine cohort of 131 Chinese inpatients, 18 different episodes of infection/inflammation were associated with: (1) lack of clinically relevant effects in 11% of the infection episodes (no leukocytosis or ↑ CRP), (2) required reduction of the clozapine dose to one-half to compensate for elevated levels (61% of the infection episodes), and (3) required reduction of the clozapine dose to one-third to compensate for elevated levels (28% of infection episodes).4
An expert consensus statement on Covid-19 described the risk of clozapine intoxication during severe infections and proposed halving the dose to avoid intoxication.5 In a comprehensive review, Veerman et al.6 identified 8 cases of patients with elevated clozapine levels during Covid-19 infections and 4 deaths. It is possible the cases reviewed by Veerman et al.6 did not include mild cases. According to the Beijing study in other infections, mild infections with no CRP elevations may have no effects on clozapine levels. Transient drops in neutrophil count during Covid-19 infections have been described.7
This is a systematic study of clozapine level elevations during a Covid-19 outbreak at a Spanish psychiatric hospital with long-term admissions including all severity levels of the Covid-19 infection. As the patients had been followed by obtaining clozapine levels for years, the treating psychiatrists made decisions about decreasing clozapine doses based on the increase in clozapine levels during the Covid-19 infection.
The Covid-19 pandemic arrived in Spain in March 2020 but the first case in this Spanish long-term psychiatric hospital was on January 11, 2021. During the 5-week outbreak, a total of 1480 Covid-19 tests using polymerase chain reaction (PCR) led to 27 positive cases. This provided an incidence of 15% (27/178). Among the 35 clozapine patients, six of them became positive, providing a slightly higher incidence of 17% (6/35). This report on Covid-19 infections focuses on dosage changes after obtaining clozapine levels and using each of the 6 patients as their own control (Supplementary Tables S1–S6).
Plasma clozapine and norclozapine concentrations were collected in trough and steady-state conditions. Steady state was defined as at least 5 days without any clozapine dosing changes (5 half-lives of 24 h).1, 2 The concentrations were measured with high-performance liquid chromatography (HPLC) using a previously published method.8 During the Covid-19 outbreak, psychiatrists were aware of the risk of clozapine intoxication during infections; thus a clozapine blood level was collected in any patient who was identified as positive for Covid-19 independently of any symptoms of Covid-19 and/or clozapine intoxication. Due the urgency and possible risk for patients, the laboratory was willing to provide the results of the clozapine levels in 2 days for 5 of 6 cases (and 1 week for Case 3).
The clozapine C/D ratio in ng/ml per mg/day was calculated by dividing the trough serum concentration by the dose. The total clozapine C/D ratio in ng/ml per mg/day was calculated by dividing the total serum concentration (clozapine and norclozapine) by the dose as an additional measure of clozapine clearance.
Table 1 shows that, of the 6 patients, 4 required no dosage changes and 2 had their clozapine dosage reduced. The cases with no dosage change include: Case 1 with mild symptoms and no CRP elevations; Case 3, who was asymptomatic and had a mild CRP elevation; and Cases 5 and 6 with mild symptoms and mild CRP elevations.Table 1 Description of 6 patients with Covid-19 infections and dose correction factors (6 Supplementary Tables provide details).
Table 1 Clozapine C/D ratio Highest C on D
Patient number: Covid-19 symptoms ↑ CRP No Infection Total C on D D adjustment
Age (yr) sex smoking Mean Mean Peak (ng/ml on mg/day)
1:49 ♀ smoker Mild No No
Fever & little respiratory 0.76 0.72 0.72 255 on 350
Symptoms N = 4 N = 1 N = 1 482 on 350
2: 61 ♀ non-smoker Mild Yes 1.19 3.27 3.40 943 on 300 ×0.50
Fever & dry cough N = 3 N = 2 N = 1 1462 on 300
3: 64 ♂ smoker Asymptomatic Mild 0.89 1.68 1.93 581 on 300 No
N = 3 N = 2 N = 1 911 on 300
4: 69 ♂ non-smoker Severe Very high 1.92 3.35 3.35 1006 on 300 ×0.67
Pneumoniaa N = 4 N = 1 N = 1 1535 on 300
5: 54 ♂ non-smoker Mild Mild 2.45 1.81 1.81 360 on 200 No
Fever N = 3 N = 1 N = 1 599 on 200
6: 37 ♂ smoker Mild Mild 0.85 1.36 1.36 409 on 300 No
N = 3 N = 1 N = 1 613 on 300
Prior polytraumab 2.24 2.24 896 on 400
N = 1 N = 1 1233 on 400
C: concentration; C/D, concentration-to-dose in ng/ml per mg/day; CRP, c-reactive protein; D, dose.
a The clinical picture was severe, requiring admission to the Infectious Diseases Department on the tenth day after diagnosis due to high fever with respiratory distress. The patient was diagnosed with left upper lobe pneumonia with hypoxemia and received treatment with ceftriaxone, azithromycin, oxygen therapy and dexamethasone with progressive improvement. After nine days of admission, the patient returned to the psychiatric hospital.
b Initially, the patient was admitted to the Intensive Care Unit and received a dose of 400 mg/day. He suffered polytrauma following a fall from a sixth floor. During this period, he was followed by the consultation-liaison psychiatry team who prescribed 400 mg/day with clozapine levels of 896 ng/ml and norclozapine levels of 337 ng/dl at the time of very high CRP (8.93 mg/dl); he was not smoking. The lack of smoking and the inflammation associated with polytrauma decreased his clozapine metabolism. The daily dose of clozapine was reduced from 400 to 300 mg/day. A few weeks later the patient returned to the psychiatric hospital and a few days later was diagnosed with Covid-19.
Of the two cases with changes, Case 2 had relatively mild symptoms except for systemic inflammation with fever and CRP elevations, so the clozapine dosage was cut in half (from 300 to 150 mg/day) since on 300 mg/day the clozapine levels had increased to 943 ng/ml (total 1462 ng/ml). Case 4 had severe symptoms and very high CRP elevations and required an admission to a medical hospital. The psychiatrist cut the dosage by one-third (from 300 to 200 mg/day) since on 300 mg/day the clozapine levels increased to 1006 ng/ml (total: 1535 ng/ml).
In summary, 66% (4/6) of patients were managed with no dosage changes because the clozapine elevations were mild if present. Dosage corrections occurred in 2 patients, one of which had to be transferred to a medical hospital due to severe pneumonia. Our clozapine prescribers are familiar with the use of CRP and clozapine levels for managing clozapine dosing and have access to prior clozapine levels over the years for all these patients. Similarly, Tio et al.9 described a clozapine intoxication in a patient followed with clozapine levels for years.
In our sample no patient died. We found a US case report of a patient who died during a Covid-19 infection but levels were not measured,10 and 3 deaths from among 8 patients with Covid-19 infections from a university hospital in the United Kingdom (UK).11 In this UK sample, four cases with Covid-19 pneumonia were described in detail in the article: 3 died and no levels were described but, in the patient who survived, two levels were reported.
Our results are limited by their naturalistic nature and may not extrapolate to other settings; our results reflect a long-term psychiatric hospital where patients have been known for many years and a laboratory has been willing to expedite the measures of clozapine levels. Our results suggest that mild Covid-19 infections with mild symptoms and mild CRP elevations can be managed with no dosage reductions, as long as clozapine levels can be measured, promptly received and compared with their baseline. New methods, such as dried blood spot, are simplifying the measuring of clozapine levels.12 When clozapine levels are not available, it may be important to halve the dose when Covid-19 symptoms are severe, including fever or a dramatic increase in CRP. Similarly, the onset or major exacerbation of some clozapine ADRs, including hypersalivation, constipation, sedation or myoclonus which are dose-dependent side effects, may signal that clozapine levels may be increasing and halving clozapine dose may be indicated.
Authors’ contributions
This retrospective review was planned by MAR and JdL. The data was collected by MAR and MRCB. MAR drafted the initial version of the manuscript and JdL rewrote it to fit the style of the journal. All authors reviewed the initial draft and made critical contributions to the interpretation of the data and approved the manuscript.
Funding
None.
Conflict of interest
No commercial organizations had any role in writing this paper for publication. In the past 3 years, the authors had no commercial conflicts of interest.
Appendix A Supplementary data
The following are the supplementary data to this article:
Acknowledgments
The authors acknowledge Lorraine Maw, M.A., at the Mental Health Research Center at Eastern State Hospital, Lexington, KY, who helped in editing this article.
Appendix A Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.rpsmen.2022.06.010.
==== Refs
References
1 de Leon J. Ruan C.J. Schoretsanitis G. De Las Cuevas C. A rational use of clozapine based on adverse drug reactions, pharmacokinetics, and clinical pharmacopsychology Psychother Psychosom 89 2020 200 204 10.1159/000507638 32289791
2 de Leon J. Schoretsanitis G. Smith R.L. Molden E. Solismaa A. Seppälä N. An international adult guideline for making clozapine titration safer by using 6 ancestry-based personalized dosing titrations, CRP and clozapine levels Pharmacopsychiatry 2021 10.1055/a-1625-6388
3 González-Esquivel D.F. Jung-Cook H. Baptista T. de Leon J. Amerindians may need clozapine dosing similar to that of Asians Rev Psiquiatr Salud Ment 14 2021 177 179 10.1016/j.rpsm.2020
4 Ruan C.J. Zang Y.N. Cheng Y.H. Wang C.Y. de Leon J. Around 3% of 1300 levels were elevated during infections in a retrospective review of 131 Beijing hospital in-patients with more than 24,000 days of clozapine treatment Psychother Psychosom 89 2020 255 257 10.1159/000506355 32114581
5 Siskind D. Honer W.G. Clark S. Correll C.U. Hasan A. Howes O. Consensus statement on the use of clozapine during the COVID-19 pandemic J Psychiatry Neurosci 45 2020 222 223 10.1503/jpn.200061 32297722
6 Veerman S.R.T. Bogers J.P.A.M. Cohen D. Schulte P.F.J. COVID-19: risks, complications, and monitoring in patients on clozapine Pharmacopsychiatry 2021 10.1055/a-1562-2521 Epub ahead of print
7 Vallecillo G. Marti-Bonany J. Robles M.J. Fortuny J.R. Lana F. Pérez V. Transient drop in the neutrophil count during COVID-19 regardless of clozapine treatment in patients with mental illness Rev Psiquiatr Salud Ment (Engl Ed) 2021 10.1016/j.rpsm.2021.06.002 S1888-9891(21)00063-X. Epub ahead of print
8 Hermida J. Paz E. Tutor J.C. Clozapine and norclozapine concentrations in serum and plasma samples from schizophrenic patients Ther Drug Monit 30 2008 41 45 10.1097/FTD.0b013e318154e72 18223461
9 Tio N. Schulte P.F.J. Martens H.J.M. Clozapine intoxication in COVID-19 Am J Psychiatry 178 2021 123 127 10.1176/appi.ajp.2020.20071039 33517757
10 Llesuy J.R. Sidelnik S.A. Death from COVID-19 in a patient receiving clozapine: factors involved and prevention strategies to consider Prim Care Companion CNS Disord 22 2020 20l02699 10.4088/PCC.20l02699
11 Butler M. Bano F. Calcia M. McMullen I. Sin Fai Lam C.C. Smith L.J. Clozapine prescribing in COVID-19 positive medical inpatients: a case series Ther Adv Psychopharmacol 10 2020 10.1177/2045125320959560 2045125320959560
12 Bernardo M. Mezquida G. Ferréc P. Cabrera B. Torrac M. Lizana A.M. Dried Blood Spot (DBS) as a useful tool to improve clozapine, aripiprazole and paliperidone treatment: from adherence to efficiency Rev Psiquiatr Salud Ment (Engl Ed) 2022 10.1016/j.rpsm.2022.04.002 Epub ahead of print
| 36513405 | PMC9734288 | NO-CC CODE | 2022-12-14 23:28:27 | no | Rev Psiquiatr Salud Ment (Engl Ed). 2022 Dec 10 October-December; 15(4):290-292 | utf-8 | Rev Psiquiatr Salud Ment (Engl Ed) | 2,022 | 10.1016/j.rpsmen.2022.06.010 | oa_other |
==== Front
Rev Psiquiatr Salud Ment (Engl Ed)
Rev Psiquiatr Salud Ment (Engl Ed)
Revista De Psiquiatria Y Salud Mental
2173-5050
SEP y SEPB. Published by Elsevier España, S.L.U.
S2173-5050(22)00066-8
10.1016/j.rpsmen.2022.06.010
Letter to the Editor
A Covid-19 outbreak in a Spanish long-term psychiatric hospital led to infections in 6 clozapine patients: elevations in their plasma clozapine levels
Un brote por Covid-19 en un hospital psiquiátrico de larga estancia español dio lugar a infecciones en 6 pacientes en tratamiento con clozapina: elevaciones en los niveles plasmáticos de clozapinaArrojo-Romero Manuel a⁎
Codesido-Barcala Maria Rosario a
Leon Jose de bc
a Department of Psychiatry, Complejo Hospitalario Universitario de Santiago, Santiago de Compostela, Spain
b Mental Health Research Center, Eastern State Hospital, Lexington, KY, USA
c Biomedical Research Centre in Mental Health Net (CIBERSAM), Santiago Apóstol Hospital, University of the Basque Country, Vitoria, Spain
⁎ Corresponding author.
10 12 2022
October-December 2022
10 12 2022
15 4 290292
© 2022 SEP y SEPB. Published by Elsevier España, S.L.U. All rights reserved.
2022
SEP y SEPB
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
==== Body
pmcDear Editor,
Clozapine is mainly metabolized by the cytochrome P450 1A2 (CYP1A2) leading to its main metabolite, norclozapine.1 Clozapine metabolism is influenced by 3 levels of complexity: (1) ancestry groups, (2) sex-smoking subgroups and (3) presence/absence of poor metabolizer (PM) status.2 The concentration providing 350 ng/ml, the minimum therapeutic dose, can be used to compare ancestry groups and patients.1, 2, 3 Asians and their descendants, Amerindians, need lower doses than Europeans.3 In European patients the minimum therapeutic dose is 250 mg/day for female non-smokers and 400 mg/day for male smokers. PMs usually have around half the ability to metabolize clozapine, requiring half the dose.2 Non-genetic CYP1A2 PMs are probably much more frequent than genetic PMs and can be explained by use of CYP1A2 inhibitors, obesity or inflammation. Any systemic inflammation (whether or not associated with infection) releases cytokines and increases the C-reactive protein (CRP) inhibiting CYP1A2.1
In a large clozapine cohort of 131 Chinese inpatients, 18 different episodes of infection/inflammation were associated with: (1) lack of clinically relevant effects in 11% of the infection episodes (no leukocytosis or ↑ CRP), (2) required reduction of the clozapine dose to one-half to compensate for elevated levels (61% of the infection episodes), and (3) required reduction of the clozapine dose to one-third to compensate for elevated levels (28% of infection episodes).4
An expert consensus statement on Covid-19 described the risk of clozapine intoxication during severe infections and proposed halving the dose to avoid intoxication.5 In a comprehensive review, Veerman et al.6 identified 8 cases of patients with elevated clozapine levels during Covid-19 infections and 4 deaths. It is possible the cases reviewed by Veerman et al.6 did not include mild cases. According to the Beijing study in other infections, mild infections with no CRP elevations may have no effects on clozapine levels. Transient drops in neutrophil count during Covid-19 infections have been described.7
This is a systematic study of clozapine level elevations during a Covid-19 outbreak at a Spanish psychiatric hospital with long-term admissions including all severity levels of the Covid-19 infection. As the patients had been followed by obtaining clozapine levels for years, the treating psychiatrists made decisions about decreasing clozapine doses based on the increase in clozapine levels during the Covid-19 infection.
The Covid-19 pandemic arrived in Spain in March 2020 but the first case in this Spanish long-term psychiatric hospital was on January 11, 2021. During the 5-week outbreak, a total of 1480 Covid-19 tests using polymerase chain reaction (PCR) led to 27 positive cases. This provided an incidence of 15% (27/178). Among the 35 clozapine patients, six of them became positive, providing a slightly higher incidence of 17% (6/35). This report on Covid-19 infections focuses on dosage changes after obtaining clozapine levels and using each of the 6 patients as their own control (Supplementary Tables S1–S6).
Plasma clozapine and norclozapine concentrations were collected in trough and steady-state conditions. Steady state was defined as at least 5 days without any clozapine dosing changes (5 half-lives of 24 h).1, 2 The concentrations were measured with high-performance liquid chromatography (HPLC) using a previously published method.8 During the Covid-19 outbreak, psychiatrists were aware of the risk of clozapine intoxication during infections; thus a clozapine blood level was collected in any patient who was identified as positive for Covid-19 independently of any symptoms of Covid-19 and/or clozapine intoxication. Due the urgency and possible risk for patients, the laboratory was willing to provide the results of the clozapine levels in 2 days for 5 of 6 cases (and 1 week for Case 3).
The clozapine C/D ratio in ng/ml per mg/day was calculated by dividing the trough serum concentration by the dose. The total clozapine C/D ratio in ng/ml per mg/day was calculated by dividing the total serum concentration (clozapine and norclozapine) by the dose as an additional measure of clozapine clearance.
Table 1 shows that, of the 6 patients, 4 required no dosage changes and 2 had their clozapine dosage reduced. The cases with no dosage change include: Case 1 with mild symptoms and no CRP elevations; Case 3, who was asymptomatic and had a mild CRP elevation; and Cases 5 and 6 with mild symptoms and mild CRP elevations.Table 1 Description of 6 patients with Covid-19 infections and dose correction factors (6 Supplementary Tables provide details).
Table 1 Clozapine C/D ratio Highest C on D
Patient number: Covid-19 symptoms ↑ CRP No Infection Total C on D D adjustment
Age (yr) sex smoking Mean Mean Peak (ng/ml on mg/day)
1:49 ♀ smoker Mild No No
Fever & little respiratory 0.76 0.72 0.72 255 on 350
Symptoms N = 4 N = 1 N = 1 482 on 350
2: 61 ♀ non-smoker Mild Yes 1.19 3.27 3.40 943 on 300 ×0.50
Fever & dry cough N = 3 N = 2 N = 1 1462 on 300
3: 64 ♂ smoker Asymptomatic Mild 0.89 1.68 1.93 581 on 300 No
N = 3 N = 2 N = 1 911 on 300
4: 69 ♂ non-smoker Severe Very high 1.92 3.35 3.35 1006 on 300 ×0.67
Pneumoniaa N = 4 N = 1 N = 1 1535 on 300
5: 54 ♂ non-smoker Mild Mild 2.45 1.81 1.81 360 on 200 No
Fever N = 3 N = 1 N = 1 599 on 200
6: 37 ♂ smoker Mild Mild 0.85 1.36 1.36 409 on 300 No
N = 3 N = 1 N = 1 613 on 300
Prior polytraumab 2.24 2.24 896 on 400
N = 1 N = 1 1233 on 400
C: concentration; C/D, concentration-to-dose in ng/ml per mg/day; CRP, c-reactive protein; D, dose.
a The clinical picture was severe, requiring admission to the Infectious Diseases Department on the tenth day after diagnosis due to high fever with respiratory distress. The patient was diagnosed with left upper lobe pneumonia with hypoxemia and received treatment with ceftriaxone, azithromycin, oxygen therapy and dexamethasone with progressive improvement. After nine days of admission, the patient returned to the psychiatric hospital.
b Initially, the patient was admitted to the Intensive Care Unit and received a dose of 400 mg/day. He suffered polytrauma following a fall from a sixth floor. During this period, he was followed by the consultation-liaison psychiatry team who prescribed 400 mg/day with clozapine levels of 896 ng/ml and norclozapine levels of 337 ng/dl at the time of very high CRP (8.93 mg/dl); he was not smoking. The lack of smoking and the inflammation associated with polytrauma decreased his clozapine metabolism. The daily dose of clozapine was reduced from 400 to 300 mg/day. A few weeks later the patient returned to the psychiatric hospital and a few days later was diagnosed with Covid-19.
Of the two cases with changes, Case 2 had relatively mild symptoms except for systemic inflammation with fever and CRP elevations, so the clozapine dosage was cut in half (from 300 to 150 mg/day) since on 300 mg/day the clozapine levels had increased to 943 ng/ml (total 1462 ng/ml). Case 4 had severe symptoms and very high CRP elevations and required an admission to a medical hospital. The psychiatrist cut the dosage by one-third (from 300 to 200 mg/day) since on 300 mg/day the clozapine levels increased to 1006 ng/ml (total: 1535 ng/ml).
In summary, 66% (4/6) of patients were managed with no dosage changes because the clozapine elevations were mild if present. Dosage corrections occurred in 2 patients, one of which had to be transferred to a medical hospital due to severe pneumonia. Our clozapine prescribers are familiar with the use of CRP and clozapine levels for managing clozapine dosing and have access to prior clozapine levels over the years for all these patients. Similarly, Tio et al.9 described a clozapine intoxication in a patient followed with clozapine levels for years.
In our sample no patient died. We found a US case report of a patient who died during a Covid-19 infection but levels were not measured,10 and 3 deaths from among 8 patients with Covid-19 infections from a university hospital in the United Kingdom (UK).11 In this UK sample, four cases with Covid-19 pneumonia were described in detail in the article: 3 died and no levels were described but, in the patient who survived, two levels were reported.
Our results are limited by their naturalistic nature and may not extrapolate to other settings; our results reflect a long-term psychiatric hospital where patients have been known for many years and a laboratory has been willing to expedite the measures of clozapine levels. Our results suggest that mild Covid-19 infections with mild symptoms and mild CRP elevations can be managed with no dosage reductions, as long as clozapine levels can be measured, promptly received and compared with their baseline. New methods, such as dried blood spot, are simplifying the measuring of clozapine levels.12 When clozapine levels are not available, it may be important to halve the dose when Covid-19 symptoms are severe, including fever or a dramatic increase in CRP. Similarly, the onset or major exacerbation of some clozapine ADRs, including hypersalivation, constipation, sedation or myoclonus which are dose-dependent side effects, may signal that clozapine levels may be increasing and halving clozapine dose may be indicated.
Authors’ contributions
This retrospective review was planned by MAR and JdL. The data was collected by MAR and MRCB. MAR drafted the initial version of the manuscript and JdL rewrote it to fit the style of the journal. All authors reviewed the initial draft and made critical contributions to the interpretation of the data and approved the manuscript.
Funding
None.
Conflict of interest
No commercial organizations had any role in writing this paper for publication. In the past 3 years, the authors had no commercial conflicts of interest.
Appendix A Supplementary data
The following are the supplementary data to this article:
Acknowledgments
The authors acknowledge Lorraine Maw, M.A., at the Mental Health Research Center at Eastern State Hospital, Lexington, KY, who helped in editing this article.
Appendix A Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.rpsmen.2022.06.010.
==== Refs
References
1 de Leon J. Ruan C.J. Schoretsanitis G. De Las Cuevas C. A rational use of clozapine based on adverse drug reactions, pharmacokinetics, and clinical pharmacopsychology Psychother Psychosom 89 2020 200 204 10.1159/000507638 32289791
2 de Leon J. Schoretsanitis G. Smith R.L. Molden E. Solismaa A. Seppälä N. An international adult guideline for making clozapine titration safer by using 6 ancestry-based personalized dosing titrations, CRP and clozapine levels Pharmacopsychiatry 2021 10.1055/a-1625-6388
3 González-Esquivel D.F. Jung-Cook H. Baptista T. de Leon J. Amerindians may need clozapine dosing similar to that of Asians Rev Psiquiatr Salud Ment 14 2021 177 179 10.1016/j.rpsm.2020
4 Ruan C.J. Zang Y.N. Cheng Y.H. Wang C.Y. de Leon J. Around 3% of 1300 levels were elevated during infections in a retrospective review of 131 Beijing hospital in-patients with more than 24,000 days of clozapine treatment Psychother Psychosom 89 2020 255 257 10.1159/000506355 32114581
5 Siskind D. Honer W.G. Clark S. Correll C.U. Hasan A. Howes O. Consensus statement on the use of clozapine during the COVID-19 pandemic J Psychiatry Neurosci 45 2020 222 223 10.1503/jpn.200061 32297722
6 Veerman S.R.T. Bogers J.P.A.M. Cohen D. Schulte P.F.J. COVID-19: risks, complications, and monitoring in patients on clozapine Pharmacopsychiatry 2021 10.1055/a-1562-2521 Epub ahead of print
7 Vallecillo G. Marti-Bonany J. Robles M.J. Fortuny J.R. Lana F. Pérez V. Transient drop in the neutrophil count during COVID-19 regardless of clozapine treatment in patients with mental illness Rev Psiquiatr Salud Ment (Engl Ed) 2021 10.1016/j.rpsm.2021.06.002 S1888-9891(21)00063-X. Epub ahead of print
8 Hermida J. Paz E. Tutor J.C. Clozapine and norclozapine concentrations in serum and plasma samples from schizophrenic patients Ther Drug Monit 30 2008 41 45 10.1097/FTD.0b013e318154e72 18223461
9 Tio N. Schulte P.F.J. Martens H.J.M. Clozapine intoxication in COVID-19 Am J Psychiatry 178 2021 123 127 10.1176/appi.ajp.2020.20071039 33517757
10 Llesuy J.R. Sidelnik S.A. Death from COVID-19 in a patient receiving clozapine: factors involved and prevention strategies to consider Prim Care Companion CNS Disord 22 2020 20l02699 10.4088/PCC.20l02699
11 Butler M. Bano F. Calcia M. McMullen I. Sin Fai Lam C.C. Smith L.J. Clozapine prescribing in COVID-19 positive medical inpatients: a case series Ther Adv Psychopharmacol 10 2020 10.1177/2045125320959560 2045125320959560
12 Bernardo M. Mezquida G. Ferréc P. Cabrera B. Torrac M. Lizana A.M. Dried Blood Spot (DBS) as a useful tool to improve clozapine, aripiprazole and paliperidone treatment: from adherence to efficiency Rev Psiquiatr Salud Ment (Engl Ed) 2022 10.1016/j.rpsm.2022.04.002 Epub ahead of print
| 0 | PMC9734289 | NO-CC CODE | 2022-12-14 23:28:27 | no | psychopraxis. neuropraxis. 2022 Dec 8; 25(6):296-298 | latin-1 | null | null | null | oa_other |
==== Front
Indian J Otolaryngol Head Neck Surg
Indian J Otolaryngol Head Neck Surg
Indian Journal of Otolaryngology and Head & Neck Surgery
2231-3796
0973-7707
Springer India New Delhi
3300
10.1007/s12070-022-03300-0
Original Article
Fast-Tracking of Publication Times of Otolaryngology Papers During the COVID-19 Pandemic
Duek Irit
Muhanna Nidal
Horowitz Gilad
Warshavsky Anton
Oron Yahav
Shraga Yohai
http://orcid.org/0000-0002-5904-1291
Ungar Omer J. [email protected]
grid.12136.37 0000 0004 1937 0546 Department of Otolaryngology Head and Neck Surgery and Maxillofacial Surgery, Tel Aviv Sourasky Medical Center, Sackler School of Medicine, Tel-Aviv University, Tel Aviv, Israel
8 12 2022
17
8 2 2022
21 11 2022
© Association of Otolaryngologists of India 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
To study the impact of the COVID-19 pandemic on journal processing times before publication in the field of otolaryngology-head and neck surgery (ORL-HNS). Online search of original papers published in selected ORL-HNS journals in terms of times from submission to acceptance (S-A), acceptance to first online publication (A-P), and submission to online publication (S-P). Papers were divided into those published in the pre-COVID-19 era and those during the COVID-19 era. The latter were further divided into unrelated to COVID-19 and related to COVID-19. A total of 487 articles from 5 selected ORL-HNS journals were included, of which 236 (48.5%) were published during the pre-COVID-19 era and 251 (51.5%) were published during the COVID-19 era. Among them, 180 (37%) papers were not related to COVID-19, and 71 (14.5%) were related to COVID-19. The S-A duration of COVID-19-related articles was significantly shorter compared to papers submitted in the pre-COVID-19 era and to papers submitted in the COVID-19 era but unrelated to COVID-19 (median 6–34 days compared to 65–125 and 46–127, respectively) in all 5 journals. The most prominent reductions in S-A and S-P times were documented in the laryngology and otology/neurotology disciplines, respectively. Processing times of the included papers were significantly shorter in most of the selected ORL-HNS journals during the COVID-19 era compared to the pre-COVID-19 era. COVID-19-related papers were processed more rapidly than non-COVID-19-related papers. These findings testify to the possibility of markedly expediting S-P times and hopefully set a precedent for post-pandemic publishing schedules.
Keywords
COVID-19
Pandemic
Publishing
Research
Otolaryngology head and neck surgery
ORL-HNS
==== Body
pmcIntroduction
Since 30 January 2020, when COVID-19 was defined by the World Health Organization as a public health emergency of international concern (PHEIC) [1], research regarding many aspects of COVID-19 is being done at an exceptional volume and rate. The outbreak of COVID-19 has produced an unprecedented interest from the medical and non-medical communities, as well as a remarkable response from healthcare practitioners, the scientific community, and support from biomedical publishers. The scientific community responded to the crisis by extensive mobilization of significant research resources with the aim of shedding light on the virus’ characteristics and mechanisms of its transmission, as well as clinical aspects of the disease, prevention and management strategies. The number of clinical trials on COVID-19 currently exceeds 6340 registrations at ClinicalTrials.gov. Trial results are being published quickly once data collection is completed. Scientific journals have reacted to both the epidemiological and information crisis in accordance with their role in the transmission of new scientific information—swiftly, effectively, and responsibly [2].
The number of articles arising from clinical studies and observations continues to grow at an unparalleled pace. In January 2020, a sharp growth in the number of COVID-19 related publications was documented in PubMed. Many publishers have waived publication fees, accelerated review processes, enabled free viewing or downloading journal’s website content, created portals to view specific content related to COVID-19, and provided free access to all pandemic-related articles.
The aim of the current study is to analyze the extent to which the publication process of scientific papers in the field of otolaryngology-head and neck surgery (ORL-HNS) related and unrelated to the pandemic is being expedited in the COVID-19 era.
Materials and Methods
Ethical Consideration
This study did not require approval from the institutional review board nor that of the ethical committee according to local law because it does not use individualized patient data.
Data Collection and Analysis
We conducted an online search to analyze the processing times of original articles published in selected ORL-HNS peer-reviewed journals. We divided the times into those from submission to acceptance (S-A), from acceptance to publication (A-P), and from submission to publication (S-P). The search was limited to ORL-HNS manuscripts that appeared during the COVID-19 era compared to those that were published during the pre-COVID-19 era. We also compared the S-A, A-P, and S-P times between papers submitted during the COVID-19 era that were and were not related to COVID-19.
Five arbitrarily selected ORL-HNS journals in which the dates of submission, acceptance, and publication were available were included in the study. We chose the first 5 articles of each issue according to 5 otolaryngology subdisciplines (rhinology and paranasal sinuses, laryngology, otology/neurotology, comprehensive [general] otolaryngology, and head and neck) from February 2019 until March 2021. We recorded the 3 relevant dates for each included article.
Statistical Methods
Categorical variables were summarized as frequencies and percentages. Continuous variables were evaluated for normal distribution with histograms and Q-Q plots and reported as median and interquartile range (IQR) since none was normally distributed. Pearson’s correlation coefficient was used to evaluate the correlation between the 3 time intervals, and a violin plot was applied for demonstration. A paired samples t-test was applied to evaluate the absolute difference between the time intervals. The Kruskal–Wallis test and Mann–Whitney test were used to compare the differences between the time intervals in the pre-COVID-19 and COVID-19 eras. All statistical tests were 2-sided, and P < 0.05 was considered significant. All statistical analyses were performed by SPSS software (IBM SPSS statistics for Windows version 25, IBM Corporation, Armonk, NY, USA, 2017).
Results
A total of 487 articles were included from 5 arbitrarily selected ORL-HNS journals: Otolaryngology-Head and Neck Surgery (n = 122, 25.1%), Laryngoscope Investigating Otolaryngology (n = 56, 11.5%), European Archives of Otorhinolaryngology (n = 119, 24.4%), Auris Nasus Larynx (n = 70, 14.4%), and Acta Otolaryngologica (n = 120, 24.6%). The distribution of papers between the various ORL-HNS disciplines was as follows: comprehensive (general) otolaryngology (n = 96, 19.7%), head and neck (n = 88, 18.1%), otology/neurotology (n = 131, 26.9%), rhinology and paranasal sinuses (n = 94, 19.3%), and laryngology (n = 78, 16.0%) (Tables 1 and 2).Table 1 Descriptive for the 5 journals selected for comparison in our analyses
Journal Country of origin Number of issues per year Founded Official journal society
Otolaryngology Head and Neck Surgery USA 12 1978 AAO-HNS, the American Academy of Otolaryngology-Head and Neck Surgery Foundation
Laryngoscope Investigative Otolaryngology USA 6 2016 The American Laryngological, Rhinological and Otological Society, Inc
European Archives of Oto-Rhino-Laryngology Germany 12 1864 Confederation of European Oto-Rhino-Laryngology Head and Neck Surgery
Auris Nasus Larynx Japan 6 1973 The Oto-Rhino-Laryngological Society of Japan, Inc
Acta Otolaryngologica 12 1918 European Federation of Oto-Rhino-Laryngological Societies (EUFOS)
Table 2 Distribution of enrolled papers according to journal and discipline
Journal Total
Otolaryngology, Head and Neck Surgery Auris Nasus Lartynx Acta Otolaryngologica European Archives Otolaryngology Laryngoscope Investigative Otolaryngology
Discipline Rhinology and para-nasal sinuses Count 23 16 20 23 12 94
% within Discipline 24.5% 17.0% 21.3% 24.5% 12.8% 100.0%
% within Journal 18.9% 22.9% 16.7% 19.3% 21.4% 19.3%
% of Total 4.7% 3.3% 4.1% 4.7% 2.5% 19.3%
Laryngology Count 20 12 15 22 9 78
% within Discipline 25.6% 15.4% 19.2% 28.2% 11.5% 100.0%
% within Journal 16.4% 17.1% 12.5% 18.5% 16.1% 16.0%
% of Total 4.1% 2.5% 3.1% 4.5% 1.8% 16.0%
Otology/ Neurotology Count 21 16 60 23 11 131
% within Discipline 16.0% 12.2% 45.8% 17.6% 8.4% 100.0%
% within Journal 17.2% 22.9% 50.0% 19.3% 19.6% 26.9%
% of Total 4.3% 3.3% 12.3% 4.7% 2.3% 26.9%
Comprehensive (general) ORL Count 35 11 11 27 12 96
% within Discipline 36.5% 11.5% 11.5% 28.1% 12.5% 100.0%
% within Journal 28.7% 15.7% 9.2% 22.7% 21.4% 19.7%
% of Total 7.2% 2.3% 2.3% 5.5% 2.5% 19.7%
Head and Neck Count 23 15 14 24 12 88
% within Discipline 26.1% 17.0% 15.9% 27.3% 13.6% 100.0%
% within Journal 18.9% 21.4% 11.7% 20.2% 21.4% 18.1%
% of Total 4.7% 3.1% 2.9% 4.9% 2.5% 18.1%
Total Count 122 70 120 119 56 487
% within Discipline 25.1% 14.4% 24.6% 24.4% 11.5% 100.0%
% within Journal 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
% of Total 25.1% 14.4% 24.6% 24.4% 11.5% 100.0%
Almost one-half (n = 236, 48.5%) of the included articles were published during the pre-COVID-19 era, and 251 (51.5%) articles were published during the COVID-19 era, of which 180 (37%) were unrelated to COVID-19 and 71 (14.5%) were related to COVID-19.
The overall median (IQR) S-A duration was 69 (range 39–118) days, and the overall median (IQR) A-P duration was 25 (14–39) days, resulting in an overall median (IQR) S-P duration of 104 (63–159) days. Stratification of the durations according to submission time showed that the median (IQR) S-A, A-P, and S-P durations in the pre-COVID-19 era were 88 (55–133), 27 (17–48), and 130 (86–180) days, respectively. In contrast, the median (IQR) S-A, A-P, and S-P durations during the COVID-19 era were 68 (45–120), 26 (15–40), and 105 (72–157) days for papers unrelated to COVID-19, while papers related to COVID-19 were processed at durations of 22 (5–36), 15 (9–22), and 39 (21–56) days, respectively. The shorter processing durations of the COVID-19-related papers compared to the unrelated papers reached a level of statistical significance (P < 0.001 for all 3 durations). The same applied to the ORL-HNS papers submitted before the COVID-19 era compared to the COVID-19-related ORL-HNS papers (P < 0.001 for all 3 durations). Interestingly, papers that were unrelated to COVID-19 that were processed during the COVID-19 era were also processed more rapidly (S-A and S-P durations) than the papers that had been submitted during the pre-COVID-19 era (Table 2, Figs. 1, 2 and 3).Fig. 1 Violin plot of S-A duration for all included articles. Each dot represents an enrolled article. The width of the plot represents the probability density
Fig. 2 Violin plot of accepted-publication (A-P) duration for all articles included. Each dot represents an enrolled article. The width of the plot represents the probability density
Fig. 3 Violin plot of submission-publication (S-P) duration for all articles included. Each dot represents an enrolled article. The width of the plot represents the probability density
Additionally, stratification of processing durations was performed as a function of journal (Table 3). The S-A duration of COVID-19-related articles was significantly shorter compared to papers submitted in the pre-COVID-19 era and to papers submitted in the COVID-19 era but that were unrelated to COVID-19 (a median of 6–34 days compared to 65–125 and 46–127, respectively) in all selected journals. The A-P duration of papers related to COVID-19 was significantly shorter than of papers unrelated to COVID-19 in 4/5 journals (median 6–19 days compared to 11–51 days). This shorter A-P duration reflects more rapid editing, proofreading, and graphical designing of the editorial and production teams of the individual journals. The resultant S-P duration of papers related to COVID-19 compared to other papers was significantly shorter in 4/5 journals (median 8–51 days compared to 70–184 days) (Table 4).Table 3 Median (IQR), of processing durations (days) for included articles
Table 4 Median (IQR), of processing durations (days) per individual journal
Stratification according to ORL-HNS disciplines demonstrated variability in processing times. The most impressive reduction in S-A times was documented in the laryngology disciplines, where papers submitted in the pre-COVID-19 era had been processed significantly slower than papers submitted during the COVID-19 era, independently of any relation to COVID-19 (median 106 days compared to 61 days for unrelated papers and 34 days for related to COVID-19 papers, respectively). The results in the S-P duration in the otology/neurotology discipline were similar (median 123 days compared to 104 days for unrelated papers and 46 days for related to COVID-19 papers, respectively). Table 5 lists the processing times for the individual disciplines.Table 5 Median (IQR), of processing durations (days) per individual discipline
Discussion
It was our impression that our manuscripts that were related to the COVID-19 pandemic—and those unrelated as well—were being processed by the target journals more quickly than usual. Our colleagues in other fields also shared this observation. We were therefore curious to analyze the impact of the COVID-19 pandemic on journal processing times before publication in our own field of ORL-HNS.
According to our analysis, article processing times for the arbitrarily selected ORL-HNS journals prior to the COVID-19 era were indeed substantially slower compared to the processing times during the COVID-19 era, and COVID-19-related papers were generally processed more quickly than non-COVID-19-related papers. Our analysis results are in accordance with previously published analysis [3].
It has become standard practice for some journals to suggest an option for fast-track publication not necessarily in regard to PHEICs. In the presence of the swift spread of the COVID-19 pandemic and the appearance of new strains of the virus world-wide, high flow of shared published information and evidence enables the scientific and medical community as well as governments and societies to cope better with the disease and the PHEIC.
The substantial world-wide influence of the current COVID-19 pandemic inarguably necessitates expediting research regarding the new virus and publication of the research findings, especially findings that could impact on the virus spreading and the disease management. However, Ioannidis suggested that the exceptional publication pace and volume of research regarding COVID-19 might be concerning in the aspect of maintaining high standard evidence base that is reliable and credible, without spreading wrong information that might be misleading and destructive [4]. Thus, now, more than ever, it is essential to maintain journals’ high standard supervision by the editors and the reviewers in order to preserve the quality and integrity of the published information and evidence, to withhold it from being misleading and confusing, resulting in damageable public and scientific consequences. Dealing with the COVID-19 new challenge, in which much is still unknown, the seek for new information published quickly to the scientific and medical community will probably continue in the near future.
Scientific publishing is naturally evolving in the presence of new challenges and demands. The current challenge is to enable fast publication of contributing research regarding the COVID-19 PHEIC, without damaging the quality, credibility and reliability of the evidence published and the scientific publishing process.
One of the lessons from the current COVID-19 pandemic is that scientific evidence and information should be shared and transferred from researchers to the health-care providers who can implement them as rapidly as possible. This can occur at different rates depending upon the scientific review process, the timeliness of administrative approval, and the will of individuals to share their information with the broad scientific community. It is clear that the medical community and thus the public may benefit by shortening the time interval from discovery to application.
The increased rate and volume of scientific research and the swift dissemination of information through peer review process and publication during the current pandemic have been exceptional. Processes that have usually taken months have been reduced to weeks or even days. Maintaining the favorable changes in the way medical and scientific journals deal with the epidemiological and information COVID-19 crisis during the post-COVID-19 era will be of profound benefit. Apparently, the review process can be accelerated, and publishers can enable larger volume of preprint articles. It seems that journal editorial boards could preserve those expedited publishing rates by publishing free of charge open access articles that provide significant and pivotal evidence and information for international public health.
We would be remiss by not acknowledging our ignorance on the cost incurred by medical publishers to accomplish these feats, be it financial or extraordinary efforts on the part of the publications’ staff members or both. We can only hope that their efforts and motivation will be sustained when the pandemic is finally over.
Conclusions
Processing times of the included papers were significantly shorter in most of the selected ORL-HNS journals and in most of the ORL-HNS disciplines during the COVID-19 era compared to the pre-COVID-19 era. COVID-19-related papers were also processed more rapidly than non-COVID-19-related papers, during the COVID-19 era. Publishers should be congratulated for their dedication to expeditiously disseminate vital data to the medical community and to the public at large. It is hoped that this momentum will be maintained in a COVID-19-free future.
Declarations
Conflict of interest
The authors have no financial conflict of interest relevant to this article to disclose.
Ethical Approval
N/A. This is an original manuscript reviewed by all authors and not under consideration for publication elsewhere.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References
1. World Health Organization. (2005) Statement on the second meeting of the International Health Regulations Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV). 2020.
2. Hamid AR Social responsibility of medical journal: a concern for COVID-19 pandemic Med J Indones 2020 29 1 3 10.13181/mji.ed.204629
3. Horbach SPJM Medical journals drastically speed up their publication process for COVID-19 Quant Sci Stud 2020 1 3 1056 1067 10.1162/qss_a_00076
4. Ioannidis JPA Coronavirus disease 2019: The harms of exaggerated information and non-evidence-based measures Eur J Clin Invest 2020 50 4 e13222 10.1111/eci.13222 32191341
| 0 | PMC9734291 | NO-CC CODE | 2022-12-14 23:28:27 | no | Indian J Otolaryngol Head Neck Surg. 2022 Dec 8;:1-7 | utf-8 | Indian J Otolaryngol Head Neck Surg | 2,022 | 10.1007/s12070-022-03300-0 | oa_other |
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Agric Human Values
Agric Human Values
Agriculture and Human Values
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1572-8366
Springer Netherlands Dordrecht
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10.1007/s10460-022-10400-8
Article
The COVID-19 pandemic and food assistance organizations’ responses in New York’s Capital District
Winkler Lauren 1Lauren Winkler
was born and raised in the California’s Bay Area before being recruited to play soccer at Skidmore College in Saratoga Springs, New York. While at Skidmore she majored in Environmental Studies and is currently pursuing her J.D. candidacy at a top law school. She has also worked on the election campaign of New Hampshire Senator Maggie Hassan and will be joining the Fund for the Public Interest at their Boston office to continue her work on policy and environmental justice.
Goodell Taylor 2Taylor Goodell
was raised in Pittsburgh, PA before attending Skidmore College to study Environmental Science. Combing her innate passion for sustainability, sovereignty, and food, she evaluates agri-food value chains and recommends policy in her current role at New York State Department of Agriculture and Markets. In her free time, you can find Taylor enjoying nature with her dog.
Nizamuddin Siddharth [email protected]
3Siddharth Nizamuddin
was born in Newton, Massachusetts and received a B.A. in Environmental Studies from Skidmore College. His research includes publications on the Environmental Justice Atlas, soil carbon sequestration through regenerative agriculture, and food sovereignty. He currently lives in Long Beach, California.
Blumenthal Sam [email protected]
12345Sam Blumenthal
was born and raised in Carlisle, Pennsylvania. He attended Skidmore College, receiving a degree in Environmental Studies in 2021. He has a broad background in social science and academic research, concentrating on environmental policies and social responsibility as it relates to sustainability. Sam also enjoys playing the guitar, painting, and thrifting sweaters.
http://orcid.org/0000-0002-4365-4907
Atalan-Helicke Nurcan [email protected]
4Nurcan Atalan-Helicke
is Associate Professor of Environmental Studies and Sciences Program at Skidmore College. She is an interdisciplinary social scientist working on sustainable food production and consumption. Her research about conservation of agricultural biodiversity was published in Gastronomica, Journal of Environmental Studies and Sciences. Her research about genetically engineered food in Islam and food consumption habits of secular and devout Muslim women was published in Agriculture and Human Values and Sociology of Islam, as well as in edited volumes.
1 Boston, USA
2 Albany, USA
3 Long Beach, USA
4 grid.60094.3b 0000 0001 2270 6467 Skidmore College Environmental Studies and Sciences Program, 815 North Broadway, Saratoga Springs, NY 12866 USA
5 Montana, USA
3 12 2022
115
7 11 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
This research examines the impact of COVID-19 on food security in New York state and the innovative approaches employed by food assistance organizations to help address the changing and increasing demand for their services from March 2020 to May 2021. We examine the case study of New York’s Capital District region through a qualitative approach. We find that there was a sharp increase in utilization of emergency services during spring of 2020, which tapered off in the summer and fall of 2020 but remained above the levels of need seen the previous year. Food assistance organizations quickly adapted to the increased demand for their services and changing conditions to reduce gaps in local food distribution chains: They reorganized and tapped into new sources for volunteers, networked with public and private organizations, and coordinated work with other regional food pantries for maximum impact. The flexibility of food assistance organizations to address the disruptions brought about by the COVID-19 pandemic highlights their critical roles in the U.S. food security environment. While organizations are aware of their shortcomings, constraints, and overall role in the American food system, the majority also expressed that the pandemic presented an opportunity to treat a complex problem together and to enact change. Several stakeholders also shared their hope that strengthening their networks and innovations may facilitate post-pandemic recovery, bring about systemic changes to address root causes of food insecurity, and better serve the communities most vulnerable to hunger and service disruptions.
Keywords
Food Security
Innovation
Food Pantries
COVID-19
New York Capital District
http://dx.doi.org/10.13039/100011091 Skidmore College
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pmcIntroduction
The COVID-19 pandemic, declared as such in March 2020, exposed “deep inequities and dysfunction” in the American food system (Anderson 2020). Measures taken to contain the virus, including social distancing, lockdowns, and the closure of facilities, businesses and offices, paired with the threat of COVID-19 outbreaks in food harvesting or processing facilities negatively affected food security1 and the food environment,2 particularly for the poorest and most vulnerable families (Bené et al. 2021; Laborde et al. 2020). The United States already had a food insecurity problem before COVID-19: in 2019, about 10.5% of the population faced food insecurity (Coleman-Jensen et al. 2021). In the United States, food insecurity is not a result of food shortage, but presents itself as “income- related lack of access to nutritionally adequate and safe food or the inability to obtain such foods in socially acceptable ways” (McIntyre et al. 2016, p. 845). Moreover, “it is a result of persistent structural and racial inequalities that continue to limit communities of color to access better socio-economic opportunities” (Elsheikh and Barhoum 2013, p. 3).3 Even before the onset of COVID-19, food insecurity and food deserts4 have been “prevalent in areas where other racialized policy outcomes are visible, such as areas impacted by home foreclosures, lack of funding for public schools, lack of adequate public transportation, and high levels of health disparities” (Elsheikh and Barhoum 2013, p. 2). The social and financial inequalities in the United States have been deepened by COVID-19 and exposed the fragility of the food system (King et al. 2022; Oncini 2021; Temitope and Wolfskill 2021): Food insecurity increased among low-income U.S. households by 26% in the months immediately following the onset of the COVID-19 in the U.S. (Ohri-Vachaspati et al. 2021).
The public health crisis accompanied by an economic crisis due to COVID-19 created additional challenges for communities already facing food security. Long lines of people waiting to receive food from food banks made headlines in the early months of the pandemic (Zack et al. 2021). Feeding America (2020) estimates that 45 million Americans (one in seven), including 15 million children (one in five), likely experienced food insecurity in 2020. Around 40% of recipients visited a food bank for the first time in their lives (Morello 2021). Reduction or elimination in income removed or reduced the ability to purchase food: a record level of 3.8 million Americans filed for unemployment benefits for the first-time in April 2020 alone bringing total number of first-time unemployment claims to over 30 million in the first 6 weeks of COVID-19.5 State-specific studies suggest that an increase in unemployment during the COVID-19 pandemic particularly impacted lower-middle income groups, causing higher rates of job loss and lower opportunities for remote employment, affecting mostly Hispanic, non-Hispanic Black, and minority populations (Feingold et al. 2021). Moreover, low-income people who are also racial and ethnic minorities were disproportionately impacted by COVID-19, facing a higher burden of disease and death (Lopez et al. 2021). Thus, COVID-19 created additional trauma along race, class, and health status lines.
With the onset of COVID-19, food assistance organizations6 served a record number of meals in 2020: according to Feeding America (2020), at least 60 million people in America sought food assistance at some point in 2020, a 50% increase than 2019. The number of meals served in food pantries decreased since March 2021 with the rollout of COVID-19 vaccines, and decreasing concerns about the health risks (Khalil 2021). The rapid coordination and response of these organizations to address systemic and government failures regarding food security during the early months of the COVID-19 pandemic demonstrated that they serve critical roles in the U.S. food system. An exogenous shock, such as an economic or public health crisis, can trigger an innovation response to mobilize resources and capabilities to maintain the level and coverage of services (Rey-Garcia et al. 2018).
Social innovation refers to new solutions that meet a social need and lead to new or improved capabilities and a better use of assets and resources (Krlev et al. 2018a). While problem-solving under crisis conditions can differ from designing a long-term approach to solve the problem of food security, we argue that crisis conditions can force a radical rethinking of approaches that can potentially open new innovative trajectories. Similarly, a combination of urgency of need and configuration of appropriate solutions by engaged users can lead to diffusion and potential learning across different actors (Bessant et al. 2015). Because of the variety of approaches, and shared lessons, food assistance organizations had an opportunity to rethink some of the structural issues they faced, structural issues that lead to food insecurity (e.g., lack of adequate income, access to healthy food) and reevaluate their role in the post-recovery efforts.
In this paper, we examine a case study of the Capital District region of New York and the intersection of federal, state, and local level responses to food security during the COVID-19 pandemic and document the role of innovation in the responses of food assistance organizations. While we primarily focus on food pantries, we also highlight the work of other organizations, such as Radix Ecological Sustainability Center, Capital Roots, and Pitney Meadows Community Farm that started or expanded food security initiatives during this period (see Appendix for a full list of community organizations interviewed). We examined the response of food assistance organizations through three research questions: How has the need for food assistance organizations changed in the initial months of COVID-19? How have food assistance organizations reached people at a time of crisis? What type of adjustments have they engaged in to address a shock likely to have long lasting impacts? The findings contribute to the literature on COVID-19 impacts on food insecurity in the United States and provide an extended analysis of the responses of food assistance organizations. After a brief review of food assistance systems in the United States and COVID-19 impacts, we discuss our methods and case study context. We then present our findings, from the perspective of individuals facing food insecurity and food assistance organizations and discuss the changes food assistance organizations implemented in response to the crisis.
Federal programs and food assistance in the United States and COVID-19
Federal programs
The U.S. has attempted to mitigate some of the impacts of systemic inequity and food insecurity by funding federal programs since the 1960s (Martin 2021). The U.S. Department of Agriculture’s Food and Nutrition Service typically administers 15 domestic food and nutrition assistance programs (Coleman-Jensen et al. 2021). Before the onset of COVID-19, 58% of households facing food insecurity participated in at least one of the three largest federal food and nutrition assistance programs: Supplemental Nutrition Assistance Program (SNAP, formerly food stamps); Special Supplemental Nutrition Program for Women, Infants, and Children (WIC); and the National School Lunch Program. Participation rates in SNAP, the most widely used federal program, traditionally responded proportionately to economic downturns (Ohri-Vachaspati et al. 2021). While Women, Infants, and Children (WIC), provides food and education to low-income women and infants, the National School Lunch Program operates in more than 100,000 public and nonprofit private schools and residential childcare institutions and serves free or reduced-price lunches to low- income students (Coleman-Jensen et al. 2021).
Despite the large number of households reached by these programs, many households experiencing food insecurity, particularly households that are “asset limited, income constrained, employed” (ALICE), are unable to fulfill their needs via federal nutrition programs. ALICE households span all demographics and include individuals who are living paycheck to paycheck, may be working multiple jobs, but still struggle to afford the basics of housing, childcare, food, transportation, health care and have limited technology access. Because they are above the Federal Poverty Level, they do not qualify for federal assistance (United for ALICE 2022). SNAP applications are completed at the state level, and eligibility in terms of resource and income limits thus vary. These programs require enrollment, which is done either online or at a government office. Not all households qualify for these federal programs; households that live just above the poverty line or do not meet the number of program specific qualification criteria, must rely on food pantries and local hunger relief organizations to meet their nutritional needs, while others may be intimidated by the bureaucracy or due to their immigration status (Feeding America 2020). Moreover, there is stigma associated with using SNAP and school lunch programs (Gaines-Turner et al. 2019).
The amount provided by SNAP is not adequate to cover the cost of food. In 2019, before the onset of COVID-19 and the following spike in food prices, an average individual in the U.S. spent about $680 a month on food, about 10% of their income (Bureau of Labor Statistics 2020). The same year, SNAP benefits per month was $136 for an individual and $377 for households with children. The federal government raised benefits for SNAP households permanently beginning October 2021. The goal of this raise was to more accurately “reflect the cost of a healthy diet” (Center on Budget and Policy Priorities 2021). This increased the maximum SNAP allotment for a one-person household to $250 per month, $459 for two people, and $658 for three (New York State 2022a).7 Yet, as the income related lack of access to healthy food continues, food pantries serve to address gaps in food access and stability in the U.S.
Local food assistance
A nationwide network of 200 regional food banks, 60,000 food pantries, and meal programs provides food assistance to people in the U.S. each year (Feeding America 2022). Food assistance has been criticized for being Band-Aid solutions to the complex problem of food insecurity. Poppendieck (1998) criticizes the emergency food system and food charities, which emerged as replacements for scaled-back anti-poverty entitlement programs. She argues that broad participation in the charitable food system acts as a “moral safety valve.” While it relieves guilt, participating in food charity does not present any solutions to alleviate long-term poverty and the root causes of food insecurity. Lohnes (2021) furthers this critique by discussing how the food charity system does not address inherit paradoxes in our capitalist food system, specifically high volumes of food waste coupled with immense food poverty. Since their creation, food pantry programs have also been criticized for prioritizing quantity (i.e., pounds or bags of food) over nutritional quality, offering some choice for clients but not fully catering to clients’ dietary and health needs (Wetherill et al. 2018). Other concerns about food assistance programs regard limited operational hours of food pantries, limited access to food (e.g., availability of fresh produce), concerns about expired food, and the challenge of running out of food entirely (Ginsburg et al. 2019).
In recent years, efforts have been made by food assistance organizations to prioritize client choice, healthy food alternatives, culturally appropriate meals, and increasing overall access (Martin 2021). Pantries and other organizations in the food charity system have also become aware of their shortfalls and are now transitioning to a comprehensive approach to better address the root causes of food insecurity and focus on client empowerment (Powers 2016). Before the pandemic, some pantries were operating as community centers and “spaces of care” that are “characterized by acceptance, moral support, generosity, hospitality, and advice” (Oncini 2021: p. 03). Pantries also provide additional services, such as assistance with rent, job training, and skills development (Taylor et al. 2022). They may also hire their members as paid volunteers, provide legal advocacy and training, and offer assistance with SNAP and WIC applications, thereby offering long-term solutions with self-dignity (Martin 2021). However, COVID-19 halted the use of these spaces as community centers, and in-person access often has been curtailed even in 2022.
COVID-19
In the early months after the declaration of COVID-19 as a pandemic, the federal government passed the Federal Families First Coronavirus Response Act (FFCRA) and authorized the issuance of emergency allotment (EA) supplemental benefits to households receiving SNAP. States were given permission to issue the supplements if the federal public health emergency caused by the pandemic remained in effect. To address some of the concerns related to income loss, the government also offered relief through The Coronavirus Aid, Relief, and Economic Security Act (The CARES Act).8 to individuals and families in several forms: unemployment insurance, loans to small businesses, funding for housing assistance and aid for the homeless, and assistance to states. Anyone who had filed tax returns for 2018, 2019, and 2020 and had a Social Security number was eligible to receive an economic impact payment.Three rounds of stimulus checks were circulated to individuals and families who qualified. However, about 14.4 million people who were income eligible were disqualified from receiving the much-needed aid due to the Social Security Number requirement (Gelatt et al. 2021). Overall, the CARES Act provided an additional $25 billion for domestic food assistance programs, including the school breakfast and lunch programs and SNAP (Moss et al. 2020). These payments worked to stimulate the economy (Zack et al. 2021) and kept the overall food insecurity rates from falling further- 2021 food insecurity rates in the U.S. stayed similar to that of 2020 (USDA 2022). However, the payments did not engage with structural racialization that causes widespread food insecurity in the United States or approach access to adequate and nutritious food as a human rights issue.
Social innovation and food assistance programs
Social innovations can include ideas, objects, services, processes, structures, behaviors, and practices with an open and collaborative character (Krlev et al. 2018). The ability to contribute or create solutions to previously inadequately addressed or new social needs depends on the capacities of actors, their relationships among each other and with affected communities, and contextual factors, which provide a laboratory for exploring alternative approaches (Bessant et al. 2015). Studies examining the impact of disease outbreaks (particularly HIV and Ebola) on global health governance suggest that these functions can be served through the creation of new institutions and coordination mechanisms, intra-institutional innovation within existing and new institutions, ideational innovation, and public sector innovation for capacity building (Held et al. 2019). Examining social innovation among organizations addressing the humanitarian crisis, Bessant and colleagues (2015) argue that some of these innovations, such as process innovation that is focused on improving warehousing and consolidation, on transport and logistics, and on distribution management, became mainstream over time. However, what is critical is how these changes adapted in the short term can be codified and replicated, which can lead to further innovation and transformation of the system.
Case study context and methods
Case study context: New York State
New York is a relatively wealthy state; in 2020, “on a per capita basis, New York State’s GDP was 29.3% higher than the national average” (DiNapoli 2020). However, the loss of manufacturing jobs in 1980s and 2000s negatively affected New York’s economy and the well-being of households. By 2000, there was over a 60% decrease of manufacturing jobs in New York, compared to its peak in the mid-1940s (DiNapoli 2010). While there was some negative impact of the Great Recession of 2008, between 2007 and 2018, New York still experienced steady economic improvements—unemployment fell to historic lows and GDP grew. Yet, in 2018, 45% of the households in New York were struggling to make ends meet, and 31% of these struggling households were ALICE as they did not earn enough to provide the household necessities. Even before 2020, the cost of living was increasing for ALICE populations, and the number of ALICE households was on the rise in New York (United for Alice 2022).
New York ranks high in terms of inequality (Swords 2019); while the Capital District region of New York is not above the national average in terms of food insecurity, there are pockets of food insecurity throughout the region. We focus on two counties within New York’s greater Capital District: Albany and Saratoga. In Albany County, approximately 13% of residents live in poverty (based on 2009–2013 period). Within the city of Albany itself, that percentage rises to 25%, making it one of the most impoverished cities in the region. By contrast, Saratoga county is the best off in the region with only 6.5% of its residents living in poverty (Capital District Regional Planning Committee 2015). However, Saratoga County has high income inequality and high housing prices, which exacerbate food insecurity. Initial findings from the first year of COVID-19 demonstrate that overall food security in the region decreased from 71.9% to 59.9%, but the portion of people facing very low food security more than doubled (Feingold et al. 2021). As previously mentioned, the closure of businesses, loss of jobs, increase in food prices, and lack of items at grocery stores particularly affected those with lower-middle income and households with children in New York (Feingold et al. 2021).
During the initial year of COVID-19 in New York State, households that were already receiving SNAP received supplemental emergency allotment benefits in addition to their original SNAP benefits. Individuals received either an additional $95 per month or were able to increase the maximum allotment per household if they had not received the maximum amount previously (New York State 2022a). Nearly 1.6 million households in New York State were on trajectory to receive these supplemental benefits by September 2021 (Colello 2021). The emergency allotment program came into effect in March 2020 and was revised in April 2021. While the program was set to expire beginning October 2021, it continued until the end of 2021.
Methods
We conducted a phenomenological study to examine how the experiences of food insecure populations and food service organizations were altered in the face of the COVID-19 pandemic. To better understand the lived experiences of individuals, we collected online and paper surveys from 40 individuals who faced food insecurity. To do so, an online survey instrument was created using Qualtrics, with 28 questions focusing on various factors relating to respondents’ food security, including questions on food access both prior to and during the COVID-19 pandemic. Questions specifically regarded changes in income, specific challenges (i.e., finding nutritionally adequate foods), and government and food assistance program resource utilization. We distributed printed copies of surveys to be filled out in person at Lifeworks Soup Kitchen in Saratoga County, Franklin Community Center in Saratoga County, and the Salvation Army in Albany, as well as cards with a QR code and link to an online version of our survey. Although there is an element of convenience sampling with the distribution of surveys at these locations, we used purposive sampling to identify and select cases (from regional food assistance organizations) to use our limited resources effectively and to select respondents that are most likely to yield appropriate responses and useful information (Campbell et al. 2020).
We also conducted 18 semi-structured interviews with local food assistance organizations and government organizations. In interviews, we asked about resources that stakeholder organizations provided, how the pandemic affected their ability to provide those resources, how they adapted to specific challenges arising from the COVID-19 pandemic, and how participation in services changed during the course of the pandemic. Moreover, Wilton Food Pantry, St. Vincent Food Pantry, and Salvation Army Saratoga Springs (food pantry) shared specific service data from 2019 to 2020.
Bracketing and content analysis of survey and interview responses was conducted to reveal prevailing themes and quantify redundancy in responses. Although analysis was objective and unbiased, data may be skewed as the survey was only administered in English and sampling was limited to clients already utilizing select food assistance organizations. Because of this, survey respondents included limited geographic, ethnic and socio-economic diversity. Additionally, information collected via stakeholder interviews may be skewed by the interviewee's inherent bias towards their own organization and any tendencies to overstate the services they provide. All stakeholders we spoke welcomed our questions, but it is possible that stakeholders may have withheld details knowing that their response would be publicly reported. Percentages of interview responses were calculated out of the total 18 stakeholders interviewed The tables isolate food pantries, as categorized in Appendix Table 3, Table 2. Food assistance organizations categorized as food pantries include: LifeWorks, Salvation Army Albany, Salvation Army Saratoga Springs, St. Vincent’s, Trinity Alliance, Wilton Food Pantry, and Franklin Community Center.
We also engaged in participant observation; all authors attended an online regional conference for food assistance organizations and policymakers on June 2, 2021 (referred as “Food Summit” from now on) and had an opportunity to learn about the synergies and collaborations among the food assistance organizations, government agencies and local stakeholders. One of the authors, who used to work with the Regional Food Bank as a volunteer before COVID-19, has served as a volunteer driver for one of the local food pantries since January 2021.
Findings
Households facing food insecurity
In the Capital District Region, multiple demographics can be defined as under-resourced when it comes to food security, including individuals under quarantine, people with disabilities, immigrants, people of color, infants, and families. Our research respondents, whose ages ranged from 34 to 80, were mainly families, with nearly three quarters (74.36%) belonging to a household with two or more persons. This corresponds to data collected from food assistance organizations that reported serving a high percentage of families during the pandemic. More than half of household food insecurity survey respondents (61.54%) identified as female. This is similar to data reported by the Salvation Army of Saratoga County, which served more women than men in the same period of time. Before the start of the COVID-19 pandemic, our survey respondents already faced many barriers to access adequate food. One third of respondents (36.84%) reported income as a limiting factor in sufficient food access while 18.42% reported transportation. Additional challenges reported by survey respondents included insufficient SNAP benefits (10.5%) and lack of grocery stores nearby (5%).
By the end of the first year of COVID-19, there was an increase in limiting factors to sufficient food access in addition to limited income and lack of transportation, which included social isolation amid lockdown measures and empty shelves at local grocery stores. The respondents shared the increased challenges of COVID-19, and how mental health impacts of COVID-19 closures, physical health impacts (and fears of it), and lack of fresh produce in grocery store limited their access to food. Three (7.5%) of our survey respondents also reported feeling lost and without help after COVID-19 as regular food assistance organizations closed temporarily or shifted their work patterns and that they had no knowledge of who else to turn to during a food emergency. While nearly 20% of respondents acknowledged that they benefited from the resources of food pantries in their community, three (7.5%) of respondents also expressed concerns about being food insecure as food pantries and other local food assistance organizations alone were often not enough and they wished for more resources to be made available.
Impact of COVID-19 on food assistance organizations
There was a sharp increase in reliance on the services of food assistance organizations in Spring of 2020, followed by a tapering off, yet organizations still reported increased demands in Summer and Fall of 2020 compared to 2019 (Fig. 1). Four (21%) stakeholders specifically reported that they saw an increase in first time users. Franklin Community Center in Saratoga County reported a rise in new middle-income people at the pantry. It is important to note that while the overall need for food assistance rose, demand fluctuated throughout the pandemic: Nine (47%) stakeholders reported that their largest assistance for 2020 was at the onset of the pandemic and during initial stages of lockdown, due to loss of income and food insufficiency at the grocery stores (due to both price and availability). Four (21%) stakeholders reported dips in demand directly after stimulus check distribution or during the summer of 2020.Fig. 1 Meals served at the Saratoga Springs Salvation Army food pantry
The pandemic associated public health safety guidelines and restrictions imposed a variety of challenges on food assistance organizations throughout the Capital District region.
Food pantries who once served the community face-to-face, in often crowded quarters, had to resort to different methods of distribution. Before the pandemic, food pantries utilized a form of client-choice where community members were able to shop similarly to a grocery setting and choose the exact items, they needed or desired. Due to the pandemic, food pantries either had to alter or eliminate client-choice (Table 1), forcing many pantries to resort to a model where pantry staff pre-packaged items in grocery bags for clients. There were several issues with the pre-packaged form of distribution that stakeholders expressed: clients were unable to choose the exact items they desired, leaving people with items they might not necessarily prefer, utilize or find culturally inappropriate items. A representative from the Salvation Army in Saratoga County stated that with the changes people were more likely to receive “less desired items” due to the inability of food pantries “being able to speak to and really get to know these people’s needs.” Representatives from five food pantries expressed a loss of personal connection and communal feel without full client-service. Before the pandemic, it was a common practice for clients to walk through and retrieve their items with a volunteer and interact directly with pantry staff. Now due to contact-less models, a representative from Salvation Army Albany reported a “transactional” feeling and less relationship building with clients.Table 1 Challenges specific to food pantries due to COVID-19
Challenges due to COVID-19 Salvation army Franklin community center Life works Trinity alliance Wilton food pantry St. Vincents
Increased Demand X X X X
Loss of Client-Choice X X X* X X
Loss of Volunteers X* X X* X X
Delivery Transition & Expansion X X X X
Online & Phone SNAP Assistance X X
*Regained during pandemic
An organization’s capacity to deal with high demand is often dependent on their volunteer base. At the onset of the pandemic, pantries had to either limit or eliminate their volunteer base, including the Salvation Army in Saratoga, Franklin Community Center and St. Vincent’s food pantry. One stakeholder reported that majority of their volunteers were seniors, a group that faced extreme vulnerabilities to COVID-19 and were given directions by health officials to isolate themselves. Another food pantry representative reported that they lost elderly and veteran volunteers but were able to mitigate this loss by having fewer volunteers stand scattered throughout the pantry instead of walking directly with each client. The same food pantry representative also emphasized the importance of “listening to the volunteers and their input” to make changes in the food delivery and service options (e.g., decisions to resume in-person operations). During the online regional food conference, some food pantries acknowledged employing their own staff, other organizations’ employees as volunteers for packing and serving meals in the first months of the pandemic, while one food pantry representative suggested “how volunteers and managers can come together to create a safe space during a time of stress to serve people with dignity.” The shift from volunteers meant “increasing professionalism in the food pantry,” as expressed by a second food pantry representative, who added “how bringing in and working with trained professionals can help to provide the best service to clients.”
Pantries who offered home deliveries or SNAP and WIC assistance also saw an additional challenge of now having to give that same assistance via phone or online.9 Five (26%) stakeholders interviewed now have their clients submit food orders via phone or online and volunteers package the grocery bags. A representative from Hunger Solutions New York explained how pantries had to “rebuild their outreach program[s] to be virtual.” However, a representative from Trinity Alliance explained that Zoom or internet is not a service that many clients can access since many lack access to a computer or may have challenges reading and utilizing online resources, especially if English is their second language. Some of the pantries in the Capital District region, such as Capital Roots already had delivery services before COVID-19. However, now almost all pantries have incorporated home deliveries, as the pandemic increased the overall need and demand for contact-less models (Table 1). Four (25%) stakeholders also started a mobile food system (e.g., Pantry on Wheels), delivering food at specific locations and times and coordinating these outreach efforts to “allocate resources where there is the greatest need” to minimize barriers to food access. One food pantry also started to work with volunteer drivers, who drive their own cars, pick up items from the food pantries and deliver items to the clients. One food pantry started giving bus passes to clients, another started drive-through pick up. Depending on the organization’s capacity to deal with this high demand, delivery systems vary from pantry to pantry with some placing more restrictions on who can receive deliveries (e.g., some prioritize clients experiencing lower mobility and higher risk to COVID-19 infection).
Mobile food deliveries were already used effectively to address the fresh produce needs of remote communities by Capital Roots via their “Veggie Mobile” before COVID-19 in addition to food distributions in its retail shop. “Veggie Mobile” deliveries were set up at certain locations throughout the week, but Capital Roots closed its food retail shop to clients temporarily. The “Veggie Mobile” operates year-round, five-days a week, and provides clients with safe, healthy, and affordable retail access to food; clients can use their SNAP, EBT and other coupons at the same time (Capital Roots 2022). The representative from Capital Roots mentioned the importance of “affordable food” for the communities they work with, and how the donations and their partnerships in the initial months of the pandemic has helped them “move more food” through their organization more effectively, providing increased access to fresh produce as well.
Pitney Meadows Community Farm, which did not previously work on food security issues directly, set up a Food Security Working Group and, after conversations with several other stakeholders, decided to direct its produce to food banks and local households who were facing food insecurity.10 The representative mentioned that they used delivery trucks, worked with the food banks, and expanded their partnerships “to reach a diversity of people in need,” particularly in rural areas to provide access to fresh, local and seasonal produce. The organization also expanded the use of its community gardens and used the outdoor space to continue to teach about growing food, building a community, and collecting and redirecting food donations from the community. (see Table 2).Table 2 Services provided by food assistance organizations in Capital District region
Services provided ALICE Capital roots HPNAP Hunger solutions New York Life works Pitney meadows Radix Regional food bank of New York Salvation army St. Vincents Food pantries for the Capital District Trinity Alliance United Way Wilton Food Pantry Franklin Community Center Food As Medicine
Home delivery X X X X
Food pantry X X X X X X
Emergency food X
Soup kitchen X
Household items X X X
Education X X
Food growing materials X X
SNAP/WIC support X X
Other X X X X X X X
Food Assistance Organizations and federal programs.
Federal government assistance programs such as SNAP and WIC were positively viewed by community stakeholders as 13 of 18 stakeholders (72%) specifically described these benefit programs as essential. Without SNAP and other governmental supplements, the emergency food system would not be able to meet food needs: Five stakeholders (27%) reported a decrease in community reliance on their services following an increase in SNAP and unemployment benefits in Spring of 2020. One food pantry representative mentioned that WIC “provides key nutritional education for both parents and soon-to-be parents.” Four respondents (22%) agreed that SNAP and WIC facilitate local economic stimulus. However, eight respondents (44%) also suggested that although SNAP is extremely beneficial, there are several problems. They mentioned problems such as people waiting to be approved, running out of benefits, technical issues with reapplication, being disqualified due to income levels being just above the qualification threshold, and limitations on what clients can purchase. Two organizations (11%) acknowledged their role as supplemental to SNAP and WIC. A representative from the Regional Food Bank of Northeastern New York explained that without SNAP, the emergency food system would be strained to a point that the regional food bank would not have the capacity to mitigate.
In terms of thinking about the structural issues and systemic solutions, the food assistance organization representatives had concrete suggestions. Six of the respondents (33%) recommended an increased accountability of the federal government, through continuing SNAP benefits at higher levels even after the pandemic ends. One food pantry representative suggested increased collaboration with food assistance organizations and listening to their suggestions in terms of developing long term policies for food security. Another representative emphasized the need to think “outside the box,” “listen to the community,” and “support training of board and staff of the organization” for systemic change. Twelve of the food assistance organizations (66%) acknowledged that SNAP and WIC must expand funding to maximize benefits. A representative from Hunger Solutions New York even suggested a 20–30% increase in SNAP benefits. However, two stakeholders (11%) also noted that expanding SNAP and WIC do not offer a “one size fits all solution” to food insecurity, adding that issues of food deserts and grocery store access should also be addressed. Furthermore, a representative from Hunger Prevention and Nutrition Assistance Program explained that with the current structure in many low-income neighborhoods, SNAP benefits support small bodegas and stores that would otherwise be boarded up and closed. While this could be beneficial for small store owners, it also perpetuated a cycle “where many food options technically exist but there is lack of access to sufficient nutrition.” Nine representatives (50%) also expressed willingness to continue some of these programs to address their long-lasting challenges. Three stakeholders (16%) expressed interest in extending partnerships and moving beyond emergency relief to create systemic changes for long-term community recovery.
During the Food Summit, different representatives from the food assistance organizations, government and the business acknowledged the need to rethink about the food security issues, and to engage with structural issues: A food system coalition and outreach representative mentioned the need to “address the root causes of the problem” and larger scale issues in their community. She added “we need well-paying jobs, livable wages, [and] health insurance,” and suggested that food assistance organizations and government should work together to think about solutions that will “benefit farmers and the community mutually.” This stakeholder continued “We grow good food [in New York]. We need public funds to distribute good food. We need to stimulate community gardens and urban food to meaningfully share food with the community.” A government representative suggested that “We cannot go back. We have to build better” systems to address community and farmer needs. A bank representative providing funding for food banks also acknowledged the need to “address the root problem instead of band-aid solutions.” He added that “the donors need to continue to work together” as they did due to COVID-19 and “provide free training and direct employment” to clients using food assistance organizations to address food security. One food pantry representative also shared that they expanded mental health and trauma training for their staff so that they could address those needs in their communities.
The passionate and powerful messages of food assistance organizations, along with the changes they integrated to reduce bureaucracies in access to their services and to increase fresh food distribution suggest that food assistance organizations in the Capital District of New York would like to take steps for building a resilient food system and address the root causes of food security more holistically. The messages during the Food Summit also reflect similar tones in terms of finding long-term solutions to the structural issues causing food insecurity in the U.S.
Statewide impacts and responses
New York State is a leading agricultural state, with agricultural farms employing over 55,000 people and its production of milk products (e.g., yogurt) among the top three states in the nation (New York Farm Bureau 2022). The closure of restaurants and disruptions in food retail created problems for produce and dairy farms. New York State intervened by implementing Nourish New York to help farmers who lost important buyers and to help the food assistance industry to address the large number of food insecure people (New York State 2022b). This state-wide initiative rerouted surplus agricultural products, particularly dairy, eggs, and fresh vegetables, to populations in need through New York’s network of food pantries. The state government dedicated $85 million to this program and now made the program permanent. The online system also provides an inventory of agricultural products, where farmers and regional food pantries can submit and view available products (New York State 2022b). While some of the farms that work with the Nourish NY program donated food to food banks previously, this new outlet gave them the option to share their best produce with the food banks, providing fresh and nutritious food to food-insecure populations in the Capital District region (New York State 2022b).
During the first months of COVID-19, Capital District Physicians’ Health Plan (CDPHP) expanded partnerships with health care providers to supply nutritionally adequate and tailored meals through food pantries. In Spring 2020, CDPHP also expanded its partnerships with food organizations, which had been ongoing since 2018. CDPHP is a physician founded, community-based not-for-profit health plan and provides a spectrum of services and health interventions that recognize and respond to the link between nutrition and chronic illness. CDPHP also started working closely with Medicaid members who are at-risk for food insecurity to provide them with nutritionally tailored meals through grocery stores, Albany County Sheriff’s Office, and Capital Roots. The program aimed to address the social determinants of health, through food security as a first step.11 In extending the partnership, both CDPHP and food pantries staff acknowledged the racial inequalities exacerbating both food insecurity and COVID-19, and how the program could benefit vulnerable individuals as a preventive approach. They also emphasized the role of their program to build trust with the community (CDPHP 2020).
Discussion
Our findings suggest that there was a drastic increase in reliance on community organizations during the initial months of the pandemic in the Capital Region. This supports the findings of Feingold and colleagues (2021), which found an increase in pantry use from 17.2% to 22.31% in the Capital Region and Feeding America (2020) data on the overall use of food pantries in the United States. Examining the impact of COVID-19 on food systems, Parekh and colleagues (2021) found a shortage of certain food items and affordable food; our findings suggest similar challenges in the case study area.
Food pantries not only saw an increased demand for their resources, but also had to drastically alter the ways in which they distributed the resources most in demand by community members. Many food assistance organizations switched to the delivery model after the pandemic which presented new questions about food choice and food access. Restrictions from the COVID-19 pandemic meant organizations were unable to host volunteers, making it extremely difficult to meet increased demand. Food assistance organizations adapted to low staff and high demand by switching to online or phone orders with full time staff working longer hours.
As restrictions began to lift, a few pantries were able to rehire volunteers, but not back to the numbers prior to COVID-19. The restrictions put in place by the pandemic also meant that clients lost the ability to shop in person and to individually select food options, putting food choice at the mercy of food assistance organizations. The implementation of deliveries also decreased the relational experiences that both food insecure individuals and food assistance organizations previously felt. Although new delivery models may provide convenience for those who lack transportation, the lack of choice significantly limits food sovereignty of community members experiencing food insecurity. As discussed by Wetherill and colleagues (2018), client choice was already a concern at food assistance organizations even before the pandemic. That being said, food assistance organizations were able to increase access and transition through new models of service by focusing on coordination of limited resources more effectively, involving more communication and transparency about resources and needs, and rethinking about structural causes more holistically and reflecting on the steps that need to be taken. These measures taken by these organizations exemplify how they respond to the broader lack of functionality in our food system and the need to increase overall availability, access, utilization, and stability of food.
The public health crisis paired with an economic crisis has created additional challenges for food assistance organizations, and conditions for social innovation. Because the food assistance organizations we examined are organized at the local level, they were able to assess the shifting conditions, respond to the increasing demand and adapt their services. Solutions such as delivery methods and pre-ordering cut down some of the bureaucracy of access, and are considered innovative as they provided access at a time when needed most. They increased communication and coordination for increased efficiency of service, which continued their operations while maintaining autonomy for their respective clients. These innovative solutions serve as operational building blocks that the emergency food system will continue to utilize, even as COVID-19 restrictions are lifted. Because of the variety of adopted approaches and lessons shared, food assistance organizations had an opportunity to rethink some of the structural issues they faced and reevaluated their role in the post-pandemic recovery efforts. While Bessant and colleagues (2015) suggest that problem-solving innovations in response to a crisis can differ from long-term solutions, we suggest that the quick adaptations of food assistance organizations, particularly in building and extending partnerships can be important for integrating more innovations, making them mainstream, and transforming the system.
The findings of this research are critical because the social and financial inequalities in wealthier nations and particularly the U.S. have been deepened by the COVID-19 pandemic, demonstrating that the food system is vulnerable. The coupling of the public health and economic crisis have exposed the fragility of the food system in the U.S. (King et al. 2022; Temitope and Wolfskill 2021), making programs like SNAP, and food pantry assistance ever more important. Yet, it also created new challenges as food pantries had to address growing food needs while protecting staff, volunteers, and clients’ health. Historically, food assistance programs have been criticized as they address only the symptoms of poverty, and in doing so, they prevent long-term solutions to short-term needs (Lohnes 2021; Poppendieck 1998). However, the findings of this research suggest that food pantries and food assistance organizations have been long aware of these criticisms as well as the urgency to address critical issues and are willing to change their approaches and engage in a broader systemic change. The flexibility on eligibility to receive service from food assistance organizations during the pandemic and rethinking ways about expanding access and availability allowed more people to receive support at a time of crisis.
Although government programs helped mitigate disparities in poverty exacerbated by the pandemic, federal aid during the pandemic did not actually help to reduce poverty but rather address some of the income related changes due to initial impact of the pandemic and income loss that particularly affected low-income households. As expressed by our respondents, one of the most important factors limiting access to food is income. At a time when food spending and food prices increased, limited income continues to be a particular problem, for both continuing users and first-time users of food pantries. Government involvement and support in multiple sectors is necessary to address issues of food insecurity more effectively. Factors that will generate more political commitment include better networks of nutrition-related organizations, strong leadership, supportive political administrations, efficient and accurate data systems, and focusing events. This may begin with further funding of state and federal innovations such as Nourish New York and CDPHP’s Food as Medicine which aim to address nutritionally inadequate diets due to food insecurity that results from broader systemic gaps in the food system as forementioned. As we are adjusting to the new reality of COVID-19 and its impacts, as one stakeholder mentioned, it is important to reconsider the role of food assistance organizations and pursue innovations that can help us better rebuild.
Appendix
Table 3.
Table 3 List of all organizations interviewed and the dates of interviews
Food Assistance Organizations Category Date of interview
Asset Limited Income Constrained Employed (ALICE) NGO March 31, 2021
Capital roots Education center, NGO, and community garden March 5, 2021
Hunger Prevention and Nutrition Assistance Program (HPNAP) Government assistance April 15, 2021
Hunger solutions New York Government assistance March 19, 2021
Lifeworks Food pantry March 12, 2021
Lifeworks kitchen saratoga springs Soup kitchen March 13, 2021
Pitney meadows community farm Education center, and community farm March 31, 2021
Radix ecological sustainability center Education center, and community farm March 15, 2021
Regional food bank of northeastern New York Food bank March 29, 2021
Salvation army: Albany Food pantry March 8, 2021
Salvation army: Saratoga Springs Food pantry March 11, 2021
St. Vincent's Food pantry March 30, 2021
The food pantries for the Capital District Coalition of 40 + food pantries March 1, 2021
Trinity alliance Food pantry March 16, 2021
United way of the greater capital region NGO March 22, 2021
Wilton food pantry Food pantry March 17, 2021
Franklin community center Food pantry March 9, 2021
Food as medicine NGO April 2, 2021
Acknowledgements
We would like to thank all of our research subjects, particularly the representatives of food assistance organizations in the Capital District region of New York, for sharing their insights and time during a time of public health and economic crisis. We are also grateful to Andrew Schneller, Lowery Parker, Matthew Sanderson, and anonymous reviewers for their helpful comments on earlier versions of this paper. We also thank Skidmore College Student Opportunity Fund that supported data collection.
Declarations
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. (The project has been cleared by Skidmore College IRB 2011-932).
Informed consent
Informed consent was obtained from all individual participants included in the study.
1 We use food security to refer to food availability, access, utilization, and stability (Committee on World Food Security 2012).
2 Following Bené et al. (2021), we use food environment to refer to proximity, convenience, availability, and quality of food items.
3 Rates of food insecurity were significantly higher for those households below the poverty line, households with children headed by a single parent, and Black and Hispanic households (Coleman-Jensen et al. 2021).
4 Food deserts are areas in the United States where people have limited access to a variety of healthy and affordable food. These regions “often feature large proportions of households with low incomes, inadequate access to transportation, and a limited number of food retailers providing fresh produce and healthy groceries for affordable prices” (Dutko et al. 2012).
5 Indeed, unemployment rates have reached record high levels early in the pandemic, increasing from 3.5% in February 2020 to 14.8% in April 2020. While it reduced in the following months, in April 2021, unemployment rates were about 6.1%, higher compared to the previous year (Bureau of Labor Statistics 2021).
6 We use food assistance organization to refer to food banks, food pantries, food charity organizations, food rescue programs, anti-hunger organizations, as well as other organizations who were involved with food security questions before the pandemic but expanded their service to provide new and additional food security assistance during the pandemic.
7 The SNAP benefits were raised again in 2022 adjusting for inflation: In October 2022, SNAP benefits are $281 for one individual a month, $516 for two people, and $740 for three people (OTDA 2022).
8 To complement the existing programs, the federal government also created two new, temporary programs: the Pandemic Electronic Benefit Transfer (P-EBT) and the Farmers to Families Food Box Program. These two programs accounted for 11% of the total spending in the 2020 financial year. Yet, overall, WIC and the Commodity Supplemental Food Program (CSFP), which works to improve the health of seniors, decreased by 6%, and combined spending on child nutrition programs decreased by 9% in 2020 (Food and Nutrition Service 2021). This shift suggests that the creation of new programs took resources from necessary pre-existing programs, rather than from another sector of the national economy.
9 The online application form is often both in Spanish and English and accessed through a shopper ID and Pin code.
10 Pitney Meadows Community Farm donated 22,350 pounds of fresh produce in 2020, and continued these donations well into 2021. In 2021 production season, the farm set up Pop-Up-Produce markets to bring vegetables directly to individuals and families experiencing food insecurity.
11 CDPHP programs offer one of the following four paths: food pantry plus -food pantry package including fresh produce, lean meats, and whole grain items; medically tailored food packages (e.g., diabetes, hypertension, low salt items + nutritional education) and healthy prepared meals (hot and cold options; and food pantry on the go (CDPHP 2020).
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Gaines-Turner T Simmons JC Chilton M Recommendations From SNAP Participants to Improve Wages and End Stigma American Journal of Public Health 2019 109 12 1664 1667 10.2105/AJPH.2019.305362 31622134
Gelatt, J., R. Clapps, and M. Fix. 2021. Nearly 3 Million U.S. Citizens and Legal Immigrants Initially Excluded under the CARES Act Are Covered under the December 2020 COVID19 Stimulus. U.S. Immigration Policy Program, Commentary.https://www.immigrationresearch.org/system/files/Nearly%203%20Million%20U.S.%20Citizens%20and%20Legal%20Immigrants%20Initially%20Excluded%20under%20the%20CARES%20Act%20Are%20Covered%20under%20the%20December%202020%20COVID-19%20Stimulus.pdf . Accessed 14 June 2022.
Ginsburg ZA Bryan AD Rubinstein EB Frankel HJ Maroko AR Schechter CB Cooksey-Stowers K Lucan SC Unreliable and difficult-to-access food for those in need: A qualitative and quantitative study of urban food pantries Journal of Community Health 2019 44 1 16 31 10.1007/s10900-018-0549-2 30019196
Held D Kickbusch I McNally K Piselli D Told M Gridlock, innovation and resilience in global health governance Global Policy 2019 10 2 161 177 10.1111/1758-5899.12654
Khalil, A. 2021. Fewer in US turn to food banks, but millions still in need. Associated Press. October 12. https://apnews.com/article/coronavirus-pandemic-lifestyle-united-states-health-hunger-2c509e7e1ce108c47287b42315e2a0c3. Accessed 14 June 2022.
King S McFarland A Vogelzang J Food sovereignty and sustainability mid-pandemic: How Michigan’s experience of Covid-19 highlights chasms in the food system Agriculture and Human Values 2022 39 2 827 838 10.1007/s10460-021-10270-6 34602742
Krlev G Anheier HK Milderberger G Anheier HK Krlev G Mildenberger G Introduction: Social innovation—what is it and who makes it? Social innovation comparative perspectives 2018 New York and London Routledge 3 35
Laborde D Martin W Vos R Poverty and food insecurity could grow dramatically as COVID-19 spreads 2020 Washington DC International Food Policy Research Institute (IFPRI)
Lohnes JD Regulating surplus: Charity and the legal geographies of food waste enclosure Agriculture and Human Values 2021 38 2 351 363 10.1007/s10460-020-10150-5 32952292
Lopez L Hart H Katz MH Racial and ethnic health disparities related to COVID-19 Journal of the American Medical Association 2021 325 8 719 720 10.1001/jama.2020.26443 33480972
Martin KS Reinventing food banks and pantries: New tools to end hunger 2021 Washington D.C Island Press
McIntyre L Tougas D Rondeau K Mah CL In-sights about food banks from a critical interpretive synthesis of the academic literature Agriculture and Human Values 2016 33 4 843 859 10.1007/s10460-015-9674-z
Morello, P. 2021. The food bank response to COVID, by the numbers. Feeding America.https://www.feedingamerica.org/hunger-blog/food-bank-response-covid-numbers. Accessed 14 June 2022.
Moss, K., A. Wexler, L. Dawson, M. Long, J. Kates, J. Cubanski, M. Musumeci, M. Freed, A. Ramaswamy, U. Ranji, and K. Pollitz. 2020. The Coronavirus Aid, Relief, and Economic Security Act: Summary of Key Health Provisions. Kaiser Family Foundation.https://www.kff.org/coronavirus-covid-19/issue-brief/the-coronavirus-aid-relief-and-economic-security-act-summary-of-key-health-provisions/ . Accessed 3 October 2022.
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New York Farm Bureau. 2022. New York Agriculture. https://www.nyfb.org/about/about-ny-ag. Accessed 14 June 2022.
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OTDA 2022. Office of Temporary and Disability Assistance: SNAP https://otda.ny.gov/programs/snap/ Accessed 5 October 2022.
Ohri-Vachaspati P Acciai F DeWeese RS SNAP participation among low-income US households stays stagnant while food insecurity escalates in the months following the COVID-19 pandemic Preventive Medicine Reports 2021 24 101555 10.1016/j.pmedr.2021.101555 34540570
Oncini F Food support provision in COVID-19 times: a mixed method study based in Greater Manchester Agriculture and Human Values 2021 38 4 1201 1213 10.1007/s10460-021-10212-2 33935352
Parekh N Ali SH O’Connor J Tozan Y Jones AM Capasso A Foreman J DiClemente RJ Food insecurity among households with children during the COVID-19 pandemic: Results from a study among social media users across the United States Nutrition Journal 2021 20 1 1 11 10.1186/s12937-021-00732-2 33388067
Poppendieck J Sweet charity? Emergency food at the end of entitlement 1998 New York Penguin Books
Powers, J. 2016. Special Report: America’s Food Banks say charity won’t end hunger. WhyHunger. https://whyhunger.org/images/publications/Special-Report.pdf. Accessed 14 June 2022.
Rey-Garcia M Felgueiras A Bauer A Anheier HK Krlev G Mildenberger G Social Innovation for Filling the Resource-Needs Gap in Social Services: New Governance Arrangements Social innovation: Comparative perspectives 2018 New York and London Routledge 104 129
Swords A Action research on organizational change with the Food Bank of the Southern Tier: A regional food bank’s effort to move beyond charity Agriculture and Human Values 2019 36 4 849 865 10.1007/s10460-019-09949-8
Taylor D Ortiz I Surdoval A McCoy E Daupan S Rising food insecurity and the impacts of the COVID-19 pandemic on emergency food assistance in Michigan Journal of Agriculture, Food Systems, and Community Development 2022 11 3 1 29 10.5304/jafscd.2022.113.008
Temitope AP Wolfskill LA Food insecurity determinants amidst the COVID-19 pandemic: An insight from Huntsville Texas Journal of Food Security 2021 9 3 106 114 10.12691/jfs-9-3-3
USDA 2022. Economic Research Service: Key Statistics and Graphics. https://www.ers.usda.gov/topics/food-nutrition-assistance/food-security-in-the-u-s/key-statistics-graphics/#:~:text=Food%20Security%20Status%20of%20U.S.%20Households%20in%202021,-Food%20secure%E2%80%94These&text=89.8%20percent%20(118.5%20million)%20of,from%2089.5%20percent%20in%202020. Accessed 6 October 2022.
United for ALICE. 2022. New York State Overview. https://www.unitedforalice.org/new-york.Accessed 3 October 2022.
Wetherill MS Williams MB White KC Li J Vidrine JI Vidrine DJ Food pantries as partners in population health: Assessing organizational and personnel readiness for delivering nutrition-focused charitable food assistance Journal of Hunger & Environmental Nutrition 2018 14 1–2 50 69 10.1080/19320248.2018.1512931
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Article
The COVID-19 pandemic and food assistance organizations’ responses in New York’s Capital District
Winkler Lauren 1Lauren Winkler
was born and raised in the California’s Bay Area before being recruited to play soccer at Skidmore College in Saratoga Springs, New York. While at Skidmore she majored in Environmental Studies and is currently pursuing her J.D. candidacy at a top law school. She has also worked on the election campaign of New Hampshire Senator Maggie Hassan and will be joining the Fund for the Public Interest at their Boston office to continue her work on policy and environmental justice.
Goodell Taylor 2Taylor Goodell
was raised in Pittsburgh, PA before attending Skidmore College to study Environmental Science. Combing her innate passion for sustainability, sovereignty, and food, she evaluates agri-food value chains and recommends policy in her current role at New York State Department of Agriculture and Markets. In her free time, you can find Taylor enjoying nature with her dog.
Nizamuddin Siddharth [email protected]
3Siddharth Nizamuddin
was born in Newton, Massachusetts and received a B.A. in Environmental Studies from Skidmore College. His research includes publications on the Environmental Justice Atlas, soil carbon sequestration through regenerative agriculture, and food sovereignty. He currently lives in Long Beach, California.
Blumenthal Sam [email protected]
12345Sam Blumenthal
was born and raised in Carlisle, Pennsylvania. He attended Skidmore College, receiving a degree in Environmental Studies in 2021. He has a broad background in social science and academic research, concentrating on environmental policies and social responsibility as it relates to sustainability. Sam also enjoys playing the guitar, painting, and thrifting sweaters.
http://orcid.org/0000-0002-4365-4907
Atalan-Helicke Nurcan [email protected]
4Nurcan Atalan-Helicke
is Associate Professor of Environmental Studies and Sciences Program at Skidmore College. She is an interdisciplinary social scientist working on sustainable food production and consumption. Her research about conservation of agricultural biodiversity was published in Gastronomica, Journal of Environmental Studies and Sciences. Her research about genetically engineered food in Islam and food consumption habits of secular and devout Muslim women was published in Agriculture and Human Values and Sociology of Islam, as well as in edited volumes.
1 Boston, USA
2 Albany, USA
3 Long Beach, USA
4 grid.60094.3b 0000 0001 2270 6467 Skidmore College Environmental Studies and Sciences Program, 815 North Broadway, Saratoga Springs, NY 12866 USA
5 Montana, USA
3 12 2022
115
7 11 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
This research examines the impact of COVID-19 on food security in New York state and the innovative approaches employed by food assistance organizations to help address the changing and increasing demand for their services from March 2020 to May 2021. We examine the case study of New York’s Capital District region through a qualitative approach. We find that there was a sharp increase in utilization of emergency services during spring of 2020, which tapered off in the summer and fall of 2020 but remained above the levels of need seen the previous year. Food assistance organizations quickly adapted to the increased demand for their services and changing conditions to reduce gaps in local food distribution chains: They reorganized and tapped into new sources for volunteers, networked with public and private organizations, and coordinated work with other regional food pantries for maximum impact. The flexibility of food assistance organizations to address the disruptions brought about by the COVID-19 pandemic highlights their critical roles in the U.S. food security environment. While organizations are aware of their shortcomings, constraints, and overall role in the American food system, the majority also expressed that the pandemic presented an opportunity to treat a complex problem together and to enact change. Several stakeholders also shared their hope that strengthening their networks and innovations may facilitate post-pandemic recovery, bring about systemic changes to address root causes of food insecurity, and better serve the communities most vulnerable to hunger and service disruptions.
Keywords
Food Security
Innovation
Food Pantries
COVID-19
New York Capital District
http://dx.doi.org/10.13039/100011091 Skidmore College
==== Body
pmcIntroduction
The COVID-19 pandemic, declared as such in March 2020, exposed “deep inequities and dysfunction” in the American food system (Anderson 2020). Measures taken to contain the virus, including social distancing, lockdowns, and the closure of facilities, businesses and offices, paired with the threat of COVID-19 outbreaks in food harvesting or processing facilities negatively affected food security1 and the food environment,2 particularly for the poorest and most vulnerable families (Bené et al. 2021; Laborde et al. 2020). The United States already had a food insecurity problem before COVID-19: in 2019, about 10.5% of the population faced food insecurity (Coleman-Jensen et al. 2021). In the United States, food insecurity is not a result of food shortage, but presents itself as “income- related lack of access to nutritionally adequate and safe food or the inability to obtain such foods in socially acceptable ways” (McIntyre et al. 2016, p. 845). Moreover, “it is a result of persistent structural and racial inequalities that continue to limit communities of color to access better socio-economic opportunities” (Elsheikh and Barhoum 2013, p. 3).3 Even before the onset of COVID-19, food insecurity and food deserts4 have been “prevalent in areas where other racialized policy outcomes are visible, such as areas impacted by home foreclosures, lack of funding for public schools, lack of adequate public transportation, and high levels of health disparities” (Elsheikh and Barhoum 2013, p. 2). The social and financial inequalities in the United States have been deepened by COVID-19 and exposed the fragility of the food system (King et al. 2022; Oncini 2021; Temitope and Wolfskill 2021): Food insecurity increased among low-income U.S. households by 26% in the months immediately following the onset of the COVID-19 in the U.S. (Ohri-Vachaspati et al. 2021).
The public health crisis accompanied by an economic crisis due to COVID-19 created additional challenges for communities already facing food security. Long lines of people waiting to receive food from food banks made headlines in the early months of the pandemic (Zack et al. 2021). Feeding America (2020) estimates that 45 million Americans (one in seven), including 15 million children (one in five), likely experienced food insecurity in 2020. Around 40% of recipients visited a food bank for the first time in their lives (Morello 2021). Reduction or elimination in income removed or reduced the ability to purchase food: a record level of 3.8 million Americans filed for unemployment benefits for the first-time in April 2020 alone bringing total number of first-time unemployment claims to over 30 million in the first 6 weeks of COVID-19.5 State-specific studies suggest that an increase in unemployment during the COVID-19 pandemic particularly impacted lower-middle income groups, causing higher rates of job loss and lower opportunities for remote employment, affecting mostly Hispanic, non-Hispanic Black, and minority populations (Feingold et al. 2021). Moreover, low-income people who are also racial and ethnic minorities were disproportionately impacted by COVID-19, facing a higher burden of disease and death (Lopez et al. 2021). Thus, COVID-19 created additional trauma along race, class, and health status lines.
With the onset of COVID-19, food assistance organizations6 served a record number of meals in 2020: according to Feeding America (2020), at least 60 million people in America sought food assistance at some point in 2020, a 50% increase than 2019. The number of meals served in food pantries decreased since March 2021 with the rollout of COVID-19 vaccines, and decreasing concerns about the health risks (Khalil 2021). The rapid coordination and response of these organizations to address systemic and government failures regarding food security during the early months of the COVID-19 pandemic demonstrated that they serve critical roles in the U.S. food system. An exogenous shock, such as an economic or public health crisis, can trigger an innovation response to mobilize resources and capabilities to maintain the level and coverage of services (Rey-Garcia et al. 2018).
Social innovation refers to new solutions that meet a social need and lead to new or improved capabilities and a better use of assets and resources (Krlev et al. 2018a). While problem-solving under crisis conditions can differ from designing a long-term approach to solve the problem of food security, we argue that crisis conditions can force a radical rethinking of approaches that can potentially open new innovative trajectories. Similarly, a combination of urgency of need and configuration of appropriate solutions by engaged users can lead to diffusion and potential learning across different actors (Bessant et al. 2015). Because of the variety of approaches, and shared lessons, food assistance organizations had an opportunity to rethink some of the structural issues they faced, structural issues that lead to food insecurity (e.g., lack of adequate income, access to healthy food) and reevaluate their role in the post-recovery efforts.
In this paper, we examine a case study of the Capital District region of New York and the intersection of federal, state, and local level responses to food security during the COVID-19 pandemic and document the role of innovation in the responses of food assistance organizations. While we primarily focus on food pantries, we also highlight the work of other organizations, such as Radix Ecological Sustainability Center, Capital Roots, and Pitney Meadows Community Farm that started or expanded food security initiatives during this period (see Appendix for a full list of community organizations interviewed). We examined the response of food assistance organizations through three research questions: How has the need for food assistance organizations changed in the initial months of COVID-19? How have food assistance organizations reached people at a time of crisis? What type of adjustments have they engaged in to address a shock likely to have long lasting impacts? The findings contribute to the literature on COVID-19 impacts on food insecurity in the United States and provide an extended analysis of the responses of food assistance organizations. After a brief review of food assistance systems in the United States and COVID-19 impacts, we discuss our methods and case study context. We then present our findings, from the perspective of individuals facing food insecurity and food assistance organizations and discuss the changes food assistance organizations implemented in response to the crisis.
Federal programs and food assistance in the United States and COVID-19
Federal programs
The U.S. has attempted to mitigate some of the impacts of systemic inequity and food insecurity by funding federal programs since the 1960s (Martin 2021). The U.S. Department of Agriculture’s Food and Nutrition Service typically administers 15 domestic food and nutrition assistance programs (Coleman-Jensen et al. 2021). Before the onset of COVID-19, 58% of households facing food insecurity participated in at least one of the three largest federal food and nutrition assistance programs: Supplemental Nutrition Assistance Program (SNAP, formerly food stamps); Special Supplemental Nutrition Program for Women, Infants, and Children (WIC); and the National School Lunch Program. Participation rates in SNAP, the most widely used federal program, traditionally responded proportionately to economic downturns (Ohri-Vachaspati et al. 2021). While Women, Infants, and Children (WIC), provides food and education to low-income women and infants, the National School Lunch Program operates in more than 100,000 public and nonprofit private schools and residential childcare institutions and serves free or reduced-price lunches to low- income students (Coleman-Jensen et al. 2021).
Despite the large number of households reached by these programs, many households experiencing food insecurity, particularly households that are “asset limited, income constrained, employed” (ALICE), are unable to fulfill their needs via federal nutrition programs. ALICE households span all demographics and include individuals who are living paycheck to paycheck, may be working multiple jobs, but still struggle to afford the basics of housing, childcare, food, transportation, health care and have limited technology access. Because they are above the Federal Poverty Level, they do not qualify for federal assistance (United for ALICE 2022). SNAP applications are completed at the state level, and eligibility in terms of resource and income limits thus vary. These programs require enrollment, which is done either online or at a government office. Not all households qualify for these federal programs; households that live just above the poverty line or do not meet the number of program specific qualification criteria, must rely on food pantries and local hunger relief organizations to meet their nutritional needs, while others may be intimidated by the bureaucracy or due to their immigration status (Feeding America 2020). Moreover, there is stigma associated with using SNAP and school lunch programs (Gaines-Turner et al. 2019).
The amount provided by SNAP is not adequate to cover the cost of food. In 2019, before the onset of COVID-19 and the following spike in food prices, an average individual in the U.S. spent about $680 a month on food, about 10% of their income (Bureau of Labor Statistics 2020). The same year, SNAP benefits per month was $136 for an individual and $377 for households with children. The federal government raised benefits for SNAP households permanently beginning October 2021. The goal of this raise was to more accurately “reflect the cost of a healthy diet” (Center on Budget and Policy Priorities 2021). This increased the maximum SNAP allotment for a one-person household to $250 per month, $459 for two people, and $658 for three (New York State 2022a).7 Yet, as the income related lack of access to healthy food continues, food pantries serve to address gaps in food access and stability in the U.S.
Local food assistance
A nationwide network of 200 regional food banks, 60,000 food pantries, and meal programs provides food assistance to people in the U.S. each year (Feeding America 2022). Food assistance has been criticized for being Band-Aid solutions to the complex problem of food insecurity. Poppendieck (1998) criticizes the emergency food system and food charities, which emerged as replacements for scaled-back anti-poverty entitlement programs. She argues that broad participation in the charitable food system acts as a “moral safety valve.” While it relieves guilt, participating in food charity does not present any solutions to alleviate long-term poverty and the root causes of food insecurity. Lohnes (2021) furthers this critique by discussing how the food charity system does not address inherit paradoxes in our capitalist food system, specifically high volumes of food waste coupled with immense food poverty. Since their creation, food pantry programs have also been criticized for prioritizing quantity (i.e., pounds or bags of food) over nutritional quality, offering some choice for clients but not fully catering to clients’ dietary and health needs (Wetherill et al. 2018). Other concerns about food assistance programs regard limited operational hours of food pantries, limited access to food (e.g., availability of fresh produce), concerns about expired food, and the challenge of running out of food entirely (Ginsburg et al. 2019).
In recent years, efforts have been made by food assistance organizations to prioritize client choice, healthy food alternatives, culturally appropriate meals, and increasing overall access (Martin 2021). Pantries and other organizations in the food charity system have also become aware of their shortfalls and are now transitioning to a comprehensive approach to better address the root causes of food insecurity and focus on client empowerment (Powers 2016). Before the pandemic, some pantries were operating as community centers and “spaces of care” that are “characterized by acceptance, moral support, generosity, hospitality, and advice” (Oncini 2021: p. 03). Pantries also provide additional services, such as assistance with rent, job training, and skills development (Taylor et al. 2022). They may also hire their members as paid volunteers, provide legal advocacy and training, and offer assistance with SNAP and WIC applications, thereby offering long-term solutions with self-dignity (Martin 2021). However, COVID-19 halted the use of these spaces as community centers, and in-person access often has been curtailed even in 2022.
COVID-19
In the early months after the declaration of COVID-19 as a pandemic, the federal government passed the Federal Families First Coronavirus Response Act (FFCRA) and authorized the issuance of emergency allotment (EA) supplemental benefits to households receiving SNAP. States were given permission to issue the supplements if the federal public health emergency caused by the pandemic remained in effect. To address some of the concerns related to income loss, the government also offered relief through The Coronavirus Aid, Relief, and Economic Security Act (The CARES Act).8 to individuals and families in several forms: unemployment insurance, loans to small businesses, funding for housing assistance and aid for the homeless, and assistance to states. Anyone who had filed tax returns for 2018, 2019, and 2020 and had a Social Security number was eligible to receive an economic impact payment.Three rounds of stimulus checks were circulated to individuals and families who qualified. However, about 14.4 million people who were income eligible were disqualified from receiving the much-needed aid due to the Social Security Number requirement (Gelatt et al. 2021). Overall, the CARES Act provided an additional $25 billion for domestic food assistance programs, including the school breakfast and lunch programs and SNAP (Moss et al. 2020). These payments worked to stimulate the economy (Zack et al. 2021) and kept the overall food insecurity rates from falling further- 2021 food insecurity rates in the U.S. stayed similar to that of 2020 (USDA 2022). However, the payments did not engage with structural racialization that causes widespread food insecurity in the United States or approach access to adequate and nutritious food as a human rights issue.
Social innovation and food assistance programs
Social innovations can include ideas, objects, services, processes, structures, behaviors, and practices with an open and collaborative character (Krlev et al. 2018). The ability to contribute or create solutions to previously inadequately addressed or new social needs depends on the capacities of actors, their relationships among each other and with affected communities, and contextual factors, which provide a laboratory for exploring alternative approaches (Bessant et al. 2015). Studies examining the impact of disease outbreaks (particularly HIV and Ebola) on global health governance suggest that these functions can be served through the creation of new institutions and coordination mechanisms, intra-institutional innovation within existing and new institutions, ideational innovation, and public sector innovation for capacity building (Held et al. 2019). Examining social innovation among organizations addressing the humanitarian crisis, Bessant and colleagues (2015) argue that some of these innovations, such as process innovation that is focused on improving warehousing and consolidation, on transport and logistics, and on distribution management, became mainstream over time. However, what is critical is how these changes adapted in the short term can be codified and replicated, which can lead to further innovation and transformation of the system.
Case study context and methods
Case study context: New York State
New York is a relatively wealthy state; in 2020, “on a per capita basis, New York State’s GDP was 29.3% higher than the national average” (DiNapoli 2020). However, the loss of manufacturing jobs in 1980s and 2000s negatively affected New York’s economy and the well-being of households. By 2000, there was over a 60% decrease of manufacturing jobs in New York, compared to its peak in the mid-1940s (DiNapoli 2010). While there was some negative impact of the Great Recession of 2008, between 2007 and 2018, New York still experienced steady economic improvements—unemployment fell to historic lows and GDP grew. Yet, in 2018, 45% of the households in New York were struggling to make ends meet, and 31% of these struggling households were ALICE as they did not earn enough to provide the household necessities. Even before 2020, the cost of living was increasing for ALICE populations, and the number of ALICE households was on the rise in New York (United for Alice 2022).
New York ranks high in terms of inequality (Swords 2019); while the Capital District region of New York is not above the national average in terms of food insecurity, there are pockets of food insecurity throughout the region. We focus on two counties within New York’s greater Capital District: Albany and Saratoga. In Albany County, approximately 13% of residents live in poverty (based on 2009–2013 period). Within the city of Albany itself, that percentage rises to 25%, making it one of the most impoverished cities in the region. By contrast, Saratoga county is the best off in the region with only 6.5% of its residents living in poverty (Capital District Regional Planning Committee 2015). However, Saratoga County has high income inequality and high housing prices, which exacerbate food insecurity. Initial findings from the first year of COVID-19 demonstrate that overall food security in the region decreased from 71.9% to 59.9%, but the portion of people facing very low food security more than doubled (Feingold et al. 2021). As previously mentioned, the closure of businesses, loss of jobs, increase in food prices, and lack of items at grocery stores particularly affected those with lower-middle income and households with children in New York (Feingold et al. 2021).
During the initial year of COVID-19 in New York State, households that were already receiving SNAP received supplemental emergency allotment benefits in addition to their original SNAP benefits. Individuals received either an additional $95 per month or were able to increase the maximum allotment per household if they had not received the maximum amount previously (New York State 2022a). Nearly 1.6 million households in New York State were on trajectory to receive these supplemental benefits by September 2021 (Colello 2021). The emergency allotment program came into effect in March 2020 and was revised in April 2021. While the program was set to expire beginning October 2021, it continued until the end of 2021.
Methods
We conducted a phenomenological study to examine how the experiences of food insecure populations and food service organizations were altered in the face of the COVID-19 pandemic. To better understand the lived experiences of individuals, we collected online and paper surveys from 40 individuals who faced food insecurity. To do so, an online survey instrument was created using Qualtrics, with 28 questions focusing on various factors relating to respondents’ food security, including questions on food access both prior to and during the COVID-19 pandemic. Questions specifically regarded changes in income, specific challenges (i.e., finding nutritionally adequate foods), and government and food assistance program resource utilization. We distributed printed copies of surveys to be filled out in person at Lifeworks Soup Kitchen in Saratoga County, Franklin Community Center in Saratoga County, and the Salvation Army in Albany, as well as cards with a QR code and link to an online version of our survey. Although there is an element of convenience sampling with the distribution of surveys at these locations, we used purposive sampling to identify and select cases (from regional food assistance organizations) to use our limited resources effectively and to select respondents that are most likely to yield appropriate responses and useful information (Campbell et al. 2020).
We also conducted 18 semi-structured interviews with local food assistance organizations and government organizations. In interviews, we asked about resources that stakeholder organizations provided, how the pandemic affected their ability to provide those resources, how they adapted to specific challenges arising from the COVID-19 pandemic, and how participation in services changed during the course of the pandemic. Moreover, Wilton Food Pantry, St. Vincent Food Pantry, and Salvation Army Saratoga Springs (food pantry) shared specific service data from 2019 to 2020.
Bracketing and content analysis of survey and interview responses was conducted to reveal prevailing themes and quantify redundancy in responses. Although analysis was objective and unbiased, data may be skewed as the survey was only administered in English and sampling was limited to clients already utilizing select food assistance organizations. Because of this, survey respondents included limited geographic, ethnic and socio-economic diversity. Additionally, information collected via stakeholder interviews may be skewed by the interviewee's inherent bias towards their own organization and any tendencies to overstate the services they provide. All stakeholders we spoke welcomed our questions, but it is possible that stakeholders may have withheld details knowing that their response would be publicly reported. Percentages of interview responses were calculated out of the total 18 stakeholders interviewed The tables isolate food pantries, as categorized in Appendix Table 3, Table 2. Food assistance organizations categorized as food pantries include: LifeWorks, Salvation Army Albany, Salvation Army Saratoga Springs, St. Vincent’s, Trinity Alliance, Wilton Food Pantry, and Franklin Community Center.
We also engaged in participant observation; all authors attended an online regional conference for food assistance organizations and policymakers on June 2, 2021 (referred as “Food Summit” from now on) and had an opportunity to learn about the synergies and collaborations among the food assistance organizations, government agencies and local stakeholders. One of the authors, who used to work with the Regional Food Bank as a volunteer before COVID-19, has served as a volunteer driver for one of the local food pantries since January 2021.
Findings
Households facing food insecurity
In the Capital District Region, multiple demographics can be defined as under-resourced when it comes to food security, including individuals under quarantine, people with disabilities, immigrants, people of color, infants, and families. Our research respondents, whose ages ranged from 34 to 80, were mainly families, with nearly three quarters (74.36%) belonging to a household with two or more persons. This corresponds to data collected from food assistance organizations that reported serving a high percentage of families during the pandemic. More than half of household food insecurity survey respondents (61.54%) identified as female. This is similar to data reported by the Salvation Army of Saratoga County, which served more women than men in the same period of time. Before the start of the COVID-19 pandemic, our survey respondents already faced many barriers to access adequate food. One third of respondents (36.84%) reported income as a limiting factor in sufficient food access while 18.42% reported transportation. Additional challenges reported by survey respondents included insufficient SNAP benefits (10.5%) and lack of grocery stores nearby (5%).
By the end of the first year of COVID-19, there was an increase in limiting factors to sufficient food access in addition to limited income and lack of transportation, which included social isolation amid lockdown measures and empty shelves at local grocery stores. The respondents shared the increased challenges of COVID-19, and how mental health impacts of COVID-19 closures, physical health impacts (and fears of it), and lack of fresh produce in grocery store limited their access to food. Three (7.5%) of our survey respondents also reported feeling lost and without help after COVID-19 as regular food assistance organizations closed temporarily or shifted their work patterns and that they had no knowledge of who else to turn to during a food emergency. While nearly 20% of respondents acknowledged that they benefited from the resources of food pantries in their community, three (7.5%) of respondents also expressed concerns about being food insecure as food pantries and other local food assistance organizations alone were often not enough and they wished for more resources to be made available.
Impact of COVID-19 on food assistance organizations
There was a sharp increase in reliance on the services of food assistance organizations in Spring of 2020, followed by a tapering off, yet organizations still reported increased demands in Summer and Fall of 2020 compared to 2019 (Fig. 1). Four (21%) stakeholders specifically reported that they saw an increase in first time users. Franklin Community Center in Saratoga County reported a rise in new middle-income people at the pantry. It is important to note that while the overall need for food assistance rose, demand fluctuated throughout the pandemic: Nine (47%) stakeholders reported that their largest assistance for 2020 was at the onset of the pandemic and during initial stages of lockdown, due to loss of income and food insufficiency at the grocery stores (due to both price and availability). Four (21%) stakeholders reported dips in demand directly after stimulus check distribution or during the summer of 2020.Fig. 1 Meals served at the Saratoga Springs Salvation Army food pantry
The pandemic associated public health safety guidelines and restrictions imposed a variety of challenges on food assistance organizations throughout the Capital District region.
Food pantries who once served the community face-to-face, in often crowded quarters, had to resort to different methods of distribution. Before the pandemic, food pantries utilized a form of client-choice where community members were able to shop similarly to a grocery setting and choose the exact items, they needed or desired. Due to the pandemic, food pantries either had to alter or eliminate client-choice (Table 1), forcing many pantries to resort to a model where pantry staff pre-packaged items in grocery bags for clients. There were several issues with the pre-packaged form of distribution that stakeholders expressed: clients were unable to choose the exact items they desired, leaving people with items they might not necessarily prefer, utilize or find culturally inappropriate items. A representative from the Salvation Army in Saratoga County stated that with the changes people were more likely to receive “less desired items” due to the inability of food pantries “being able to speak to and really get to know these people’s needs.” Representatives from five food pantries expressed a loss of personal connection and communal feel without full client-service. Before the pandemic, it was a common practice for clients to walk through and retrieve their items with a volunteer and interact directly with pantry staff. Now due to contact-less models, a representative from Salvation Army Albany reported a “transactional” feeling and less relationship building with clients.Table 1 Challenges specific to food pantries due to COVID-19
Challenges due to COVID-19 Salvation army Franklin community center Life works Trinity alliance Wilton food pantry St. Vincents
Increased Demand X X X X
Loss of Client-Choice X X X* X X
Loss of Volunteers X* X X* X X
Delivery Transition & Expansion X X X X
Online & Phone SNAP Assistance X X
*Regained during pandemic
An organization’s capacity to deal with high demand is often dependent on their volunteer base. At the onset of the pandemic, pantries had to either limit or eliminate their volunteer base, including the Salvation Army in Saratoga, Franklin Community Center and St. Vincent’s food pantry. One stakeholder reported that majority of their volunteers were seniors, a group that faced extreme vulnerabilities to COVID-19 and were given directions by health officials to isolate themselves. Another food pantry representative reported that they lost elderly and veteran volunteers but were able to mitigate this loss by having fewer volunteers stand scattered throughout the pantry instead of walking directly with each client. The same food pantry representative also emphasized the importance of “listening to the volunteers and their input” to make changes in the food delivery and service options (e.g., decisions to resume in-person operations). During the online regional food conference, some food pantries acknowledged employing their own staff, other organizations’ employees as volunteers for packing and serving meals in the first months of the pandemic, while one food pantry representative suggested “how volunteers and managers can come together to create a safe space during a time of stress to serve people with dignity.” The shift from volunteers meant “increasing professionalism in the food pantry,” as expressed by a second food pantry representative, who added “how bringing in and working with trained professionals can help to provide the best service to clients.”
Pantries who offered home deliveries or SNAP and WIC assistance also saw an additional challenge of now having to give that same assistance via phone or online.9 Five (26%) stakeholders interviewed now have their clients submit food orders via phone or online and volunteers package the grocery bags. A representative from Hunger Solutions New York explained how pantries had to “rebuild their outreach program[s] to be virtual.” However, a representative from Trinity Alliance explained that Zoom or internet is not a service that many clients can access since many lack access to a computer or may have challenges reading and utilizing online resources, especially if English is their second language. Some of the pantries in the Capital District region, such as Capital Roots already had delivery services before COVID-19. However, now almost all pantries have incorporated home deliveries, as the pandemic increased the overall need and demand for contact-less models (Table 1). Four (25%) stakeholders also started a mobile food system (e.g., Pantry on Wheels), delivering food at specific locations and times and coordinating these outreach efforts to “allocate resources where there is the greatest need” to minimize barriers to food access. One food pantry also started to work with volunteer drivers, who drive their own cars, pick up items from the food pantries and deliver items to the clients. One food pantry started giving bus passes to clients, another started drive-through pick up. Depending on the organization’s capacity to deal with this high demand, delivery systems vary from pantry to pantry with some placing more restrictions on who can receive deliveries (e.g., some prioritize clients experiencing lower mobility and higher risk to COVID-19 infection).
Mobile food deliveries were already used effectively to address the fresh produce needs of remote communities by Capital Roots via their “Veggie Mobile” before COVID-19 in addition to food distributions in its retail shop. “Veggie Mobile” deliveries were set up at certain locations throughout the week, but Capital Roots closed its food retail shop to clients temporarily. The “Veggie Mobile” operates year-round, five-days a week, and provides clients with safe, healthy, and affordable retail access to food; clients can use their SNAP, EBT and other coupons at the same time (Capital Roots 2022). The representative from Capital Roots mentioned the importance of “affordable food” for the communities they work with, and how the donations and their partnerships in the initial months of the pandemic has helped them “move more food” through their organization more effectively, providing increased access to fresh produce as well.
Pitney Meadows Community Farm, which did not previously work on food security issues directly, set up a Food Security Working Group and, after conversations with several other stakeholders, decided to direct its produce to food banks and local households who were facing food insecurity.10 The representative mentioned that they used delivery trucks, worked with the food banks, and expanded their partnerships “to reach a diversity of people in need,” particularly in rural areas to provide access to fresh, local and seasonal produce. The organization also expanded the use of its community gardens and used the outdoor space to continue to teach about growing food, building a community, and collecting and redirecting food donations from the community. (see Table 2).Table 2 Services provided by food assistance organizations in Capital District region
Services provided ALICE Capital roots HPNAP Hunger solutions New York Life works Pitney meadows Radix Regional food bank of New York Salvation army St. Vincents Food pantries for the Capital District Trinity Alliance United Way Wilton Food Pantry Franklin Community Center Food As Medicine
Home delivery X X X X
Food pantry X X X X X X
Emergency food X
Soup kitchen X
Household items X X X
Education X X
Food growing materials X X
SNAP/WIC support X X
Other X X X X X X X
Food Assistance Organizations and federal programs.
Federal government assistance programs such as SNAP and WIC were positively viewed by community stakeholders as 13 of 18 stakeholders (72%) specifically described these benefit programs as essential. Without SNAP and other governmental supplements, the emergency food system would not be able to meet food needs: Five stakeholders (27%) reported a decrease in community reliance on their services following an increase in SNAP and unemployment benefits in Spring of 2020. One food pantry representative mentioned that WIC “provides key nutritional education for both parents and soon-to-be parents.” Four respondents (22%) agreed that SNAP and WIC facilitate local economic stimulus. However, eight respondents (44%) also suggested that although SNAP is extremely beneficial, there are several problems. They mentioned problems such as people waiting to be approved, running out of benefits, technical issues with reapplication, being disqualified due to income levels being just above the qualification threshold, and limitations on what clients can purchase. Two organizations (11%) acknowledged their role as supplemental to SNAP and WIC. A representative from the Regional Food Bank of Northeastern New York explained that without SNAP, the emergency food system would be strained to a point that the regional food bank would not have the capacity to mitigate.
In terms of thinking about the structural issues and systemic solutions, the food assistance organization representatives had concrete suggestions. Six of the respondents (33%) recommended an increased accountability of the federal government, through continuing SNAP benefits at higher levels even after the pandemic ends. One food pantry representative suggested increased collaboration with food assistance organizations and listening to their suggestions in terms of developing long term policies for food security. Another representative emphasized the need to think “outside the box,” “listen to the community,” and “support training of board and staff of the organization” for systemic change. Twelve of the food assistance organizations (66%) acknowledged that SNAP and WIC must expand funding to maximize benefits. A representative from Hunger Solutions New York even suggested a 20–30% increase in SNAP benefits. However, two stakeholders (11%) also noted that expanding SNAP and WIC do not offer a “one size fits all solution” to food insecurity, adding that issues of food deserts and grocery store access should also be addressed. Furthermore, a representative from Hunger Prevention and Nutrition Assistance Program explained that with the current structure in many low-income neighborhoods, SNAP benefits support small bodegas and stores that would otherwise be boarded up and closed. While this could be beneficial for small store owners, it also perpetuated a cycle “where many food options technically exist but there is lack of access to sufficient nutrition.” Nine representatives (50%) also expressed willingness to continue some of these programs to address their long-lasting challenges. Three stakeholders (16%) expressed interest in extending partnerships and moving beyond emergency relief to create systemic changes for long-term community recovery.
During the Food Summit, different representatives from the food assistance organizations, government and the business acknowledged the need to rethink about the food security issues, and to engage with structural issues: A food system coalition and outreach representative mentioned the need to “address the root causes of the problem” and larger scale issues in their community. She added “we need well-paying jobs, livable wages, [and] health insurance,” and suggested that food assistance organizations and government should work together to think about solutions that will “benefit farmers and the community mutually.” This stakeholder continued “We grow good food [in New York]. We need public funds to distribute good food. We need to stimulate community gardens and urban food to meaningfully share food with the community.” A government representative suggested that “We cannot go back. We have to build better” systems to address community and farmer needs. A bank representative providing funding for food banks also acknowledged the need to “address the root problem instead of band-aid solutions.” He added that “the donors need to continue to work together” as they did due to COVID-19 and “provide free training and direct employment” to clients using food assistance organizations to address food security. One food pantry representative also shared that they expanded mental health and trauma training for their staff so that they could address those needs in their communities.
The passionate and powerful messages of food assistance organizations, along with the changes they integrated to reduce bureaucracies in access to their services and to increase fresh food distribution suggest that food assistance organizations in the Capital District of New York would like to take steps for building a resilient food system and address the root causes of food security more holistically. The messages during the Food Summit also reflect similar tones in terms of finding long-term solutions to the structural issues causing food insecurity in the U.S.
Statewide impacts and responses
New York State is a leading agricultural state, with agricultural farms employing over 55,000 people and its production of milk products (e.g., yogurt) among the top three states in the nation (New York Farm Bureau 2022). The closure of restaurants and disruptions in food retail created problems for produce and dairy farms. New York State intervened by implementing Nourish New York to help farmers who lost important buyers and to help the food assistance industry to address the large number of food insecure people (New York State 2022b). This state-wide initiative rerouted surplus agricultural products, particularly dairy, eggs, and fresh vegetables, to populations in need through New York’s network of food pantries. The state government dedicated $85 million to this program and now made the program permanent. The online system also provides an inventory of agricultural products, where farmers and regional food pantries can submit and view available products (New York State 2022b). While some of the farms that work with the Nourish NY program donated food to food banks previously, this new outlet gave them the option to share their best produce with the food banks, providing fresh and nutritious food to food-insecure populations in the Capital District region (New York State 2022b).
During the first months of COVID-19, Capital District Physicians’ Health Plan (CDPHP) expanded partnerships with health care providers to supply nutritionally adequate and tailored meals through food pantries. In Spring 2020, CDPHP also expanded its partnerships with food organizations, which had been ongoing since 2018. CDPHP is a physician founded, community-based not-for-profit health plan and provides a spectrum of services and health interventions that recognize and respond to the link between nutrition and chronic illness. CDPHP also started working closely with Medicaid members who are at-risk for food insecurity to provide them with nutritionally tailored meals through grocery stores, Albany County Sheriff’s Office, and Capital Roots. The program aimed to address the social determinants of health, through food security as a first step.11 In extending the partnership, both CDPHP and food pantries staff acknowledged the racial inequalities exacerbating both food insecurity and COVID-19, and how the program could benefit vulnerable individuals as a preventive approach. They also emphasized the role of their program to build trust with the community (CDPHP 2020).
Discussion
Our findings suggest that there was a drastic increase in reliance on community organizations during the initial months of the pandemic in the Capital Region. This supports the findings of Feingold and colleagues (2021), which found an increase in pantry use from 17.2% to 22.31% in the Capital Region and Feeding America (2020) data on the overall use of food pantries in the United States. Examining the impact of COVID-19 on food systems, Parekh and colleagues (2021) found a shortage of certain food items and affordable food; our findings suggest similar challenges in the case study area.
Food pantries not only saw an increased demand for their resources, but also had to drastically alter the ways in which they distributed the resources most in demand by community members. Many food assistance organizations switched to the delivery model after the pandemic which presented new questions about food choice and food access. Restrictions from the COVID-19 pandemic meant organizations were unable to host volunteers, making it extremely difficult to meet increased demand. Food assistance organizations adapted to low staff and high demand by switching to online or phone orders with full time staff working longer hours.
As restrictions began to lift, a few pantries were able to rehire volunteers, but not back to the numbers prior to COVID-19. The restrictions put in place by the pandemic also meant that clients lost the ability to shop in person and to individually select food options, putting food choice at the mercy of food assistance organizations. The implementation of deliveries also decreased the relational experiences that both food insecure individuals and food assistance organizations previously felt. Although new delivery models may provide convenience for those who lack transportation, the lack of choice significantly limits food sovereignty of community members experiencing food insecurity. As discussed by Wetherill and colleagues (2018), client choice was already a concern at food assistance organizations even before the pandemic. That being said, food assistance organizations were able to increase access and transition through new models of service by focusing on coordination of limited resources more effectively, involving more communication and transparency about resources and needs, and rethinking about structural causes more holistically and reflecting on the steps that need to be taken. These measures taken by these organizations exemplify how they respond to the broader lack of functionality in our food system and the need to increase overall availability, access, utilization, and stability of food.
The public health crisis paired with an economic crisis has created additional challenges for food assistance organizations, and conditions for social innovation. Because the food assistance organizations we examined are organized at the local level, they were able to assess the shifting conditions, respond to the increasing demand and adapt their services. Solutions such as delivery methods and pre-ordering cut down some of the bureaucracy of access, and are considered innovative as they provided access at a time when needed most. They increased communication and coordination for increased efficiency of service, which continued their operations while maintaining autonomy for their respective clients. These innovative solutions serve as operational building blocks that the emergency food system will continue to utilize, even as COVID-19 restrictions are lifted. Because of the variety of adopted approaches and lessons shared, food assistance organizations had an opportunity to rethink some of the structural issues they faced and reevaluated their role in the post-pandemic recovery efforts. While Bessant and colleagues (2015) suggest that problem-solving innovations in response to a crisis can differ from long-term solutions, we suggest that the quick adaptations of food assistance organizations, particularly in building and extending partnerships can be important for integrating more innovations, making them mainstream, and transforming the system.
The findings of this research are critical because the social and financial inequalities in wealthier nations and particularly the U.S. have been deepened by the COVID-19 pandemic, demonstrating that the food system is vulnerable. The coupling of the public health and economic crisis have exposed the fragility of the food system in the U.S. (King et al. 2022; Temitope and Wolfskill 2021), making programs like SNAP, and food pantry assistance ever more important. Yet, it also created new challenges as food pantries had to address growing food needs while protecting staff, volunteers, and clients’ health. Historically, food assistance programs have been criticized as they address only the symptoms of poverty, and in doing so, they prevent long-term solutions to short-term needs (Lohnes 2021; Poppendieck 1998). However, the findings of this research suggest that food pantries and food assistance organizations have been long aware of these criticisms as well as the urgency to address critical issues and are willing to change their approaches and engage in a broader systemic change. The flexibility on eligibility to receive service from food assistance organizations during the pandemic and rethinking ways about expanding access and availability allowed more people to receive support at a time of crisis.
Although government programs helped mitigate disparities in poverty exacerbated by the pandemic, federal aid during the pandemic did not actually help to reduce poverty but rather address some of the income related changes due to initial impact of the pandemic and income loss that particularly affected low-income households. As expressed by our respondents, one of the most important factors limiting access to food is income. At a time when food spending and food prices increased, limited income continues to be a particular problem, for both continuing users and first-time users of food pantries. Government involvement and support in multiple sectors is necessary to address issues of food insecurity more effectively. Factors that will generate more political commitment include better networks of nutrition-related organizations, strong leadership, supportive political administrations, efficient and accurate data systems, and focusing events. This may begin with further funding of state and federal innovations such as Nourish New York and CDPHP’s Food as Medicine which aim to address nutritionally inadequate diets due to food insecurity that results from broader systemic gaps in the food system as forementioned. As we are adjusting to the new reality of COVID-19 and its impacts, as one stakeholder mentioned, it is important to reconsider the role of food assistance organizations and pursue innovations that can help us better rebuild.
Appendix
Table 3.
Table 3 List of all organizations interviewed and the dates of interviews
Food Assistance Organizations Category Date of interview
Asset Limited Income Constrained Employed (ALICE) NGO March 31, 2021
Capital roots Education center, NGO, and community garden March 5, 2021
Hunger Prevention and Nutrition Assistance Program (HPNAP) Government assistance April 15, 2021
Hunger solutions New York Government assistance March 19, 2021
Lifeworks Food pantry March 12, 2021
Lifeworks kitchen saratoga springs Soup kitchen March 13, 2021
Pitney meadows community farm Education center, and community farm March 31, 2021
Radix ecological sustainability center Education center, and community farm March 15, 2021
Regional food bank of northeastern New York Food bank March 29, 2021
Salvation army: Albany Food pantry March 8, 2021
Salvation army: Saratoga Springs Food pantry March 11, 2021
St. Vincent's Food pantry March 30, 2021
The food pantries for the Capital District Coalition of 40 + food pantries March 1, 2021
Trinity alliance Food pantry March 16, 2021
United way of the greater capital region NGO March 22, 2021
Wilton food pantry Food pantry March 17, 2021
Franklin community center Food pantry March 9, 2021
Food as medicine NGO April 2, 2021
Acknowledgements
We would like to thank all of our research subjects, particularly the representatives of food assistance organizations in the Capital District region of New York, for sharing their insights and time during a time of public health and economic crisis. We are also grateful to Andrew Schneller, Lowery Parker, Matthew Sanderson, and anonymous reviewers for their helpful comments on earlier versions of this paper. We also thank Skidmore College Student Opportunity Fund that supported data collection.
Declarations
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. (The project has been cleared by Skidmore College IRB 2011-932).
Informed consent
Informed consent was obtained from all individual participants included in the study.
1 We use food security to refer to food availability, access, utilization, and stability (Committee on World Food Security 2012).
2 Following Bené et al. (2021), we use food environment to refer to proximity, convenience, availability, and quality of food items.
3 Rates of food insecurity were significantly higher for those households below the poverty line, households with children headed by a single parent, and Black and Hispanic households (Coleman-Jensen et al. 2021).
4 Food deserts are areas in the United States where people have limited access to a variety of healthy and affordable food. These regions “often feature large proportions of households with low incomes, inadequate access to transportation, and a limited number of food retailers providing fresh produce and healthy groceries for affordable prices” (Dutko et al. 2012).
5 Indeed, unemployment rates have reached record high levels early in the pandemic, increasing from 3.5% in February 2020 to 14.8% in April 2020. While it reduced in the following months, in April 2021, unemployment rates were about 6.1%, higher compared to the previous year (Bureau of Labor Statistics 2021).
6 We use food assistance organization to refer to food banks, food pantries, food charity organizations, food rescue programs, anti-hunger organizations, as well as other organizations who were involved with food security questions before the pandemic but expanded their service to provide new and additional food security assistance during the pandemic.
7 The SNAP benefits were raised again in 2022 adjusting for inflation: In October 2022, SNAP benefits are $281 for one individual a month, $516 for two people, and $740 for three people (OTDA 2022).
8 To complement the existing programs, the federal government also created two new, temporary programs: the Pandemic Electronic Benefit Transfer (P-EBT) and the Farmers to Families Food Box Program. These two programs accounted for 11% of the total spending in the 2020 financial year. Yet, overall, WIC and the Commodity Supplemental Food Program (CSFP), which works to improve the health of seniors, decreased by 6%, and combined spending on child nutrition programs decreased by 9% in 2020 (Food and Nutrition Service 2021). This shift suggests that the creation of new programs took resources from necessary pre-existing programs, rather than from another sector of the national economy.
9 The online application form is often both in Spanish and English and accessed through a shopper ID and Pin code.
10 Pitney Meadows Community Farm donated 22,350 pounds of fresh produce in 2020, and continued these donations well into 2021. In 2021 production season, the farm set up Pop-Up-Produce markets to bring vegetables directly to individuals and families experiencing food insecurity.
11 CDPHP programs offer one of the following four paths: food pantry plus -food pantry package including fresh produce, lean meats, and whole grain items; medically tailored food packages (e.g., diabetes, hypertension, low salt items + nutritional education) and healthy prepared meals (hot and cold options; and food pantry on the go (CDPHP 2020).
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Martin KS Reinventing food banks and pantries: New tools to end hunger 2021 Washington D.C Island Press
McIntyre L Tougas D Rondeau K Mah CL In-sights about food banks from a critical interpretive synthesis of the academic literature Agriculture and Human Values 2016 33 4 843 859 10.1007/s10460-015-9674-z
Morello, P. 2021. The food bank response to COVID, by the numbers. Feeding America.https://www.feedingamerica.org/hunger-blog/food-bank-response-covid-numbers. Accessed 14 June 2022.
Moss, K., A. Wexler, L. Dawson, M. Long, J. Kates, J. Cubanski, M. Musumeci, M. Freed, A. Ramaswamy, U. Ranji, and K. Pollitz. 2020. The Coronavirus Aid, Relief, and Economic Security Act: Summary of Key Health Provisions. Kaiser Family Foundation.https://www.kff.org/coronavirus-covid-19/issue-brief/the-coronavirus-aid-relief-and-economic-security-act-summary-of-key-health-provisions/ . Accessed 3 October 2022.
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Ohri-Vachaspati P Acciai F DeWeese RS SNAP participation among low-income US households stays stagnant while food insecurity escalates in the months following the COVID-19 pandemic Preventive Medicine Reports 2021 24 101555 10.1016/j.pmedr.2021.101555 34540570
Oncini F Food support provision in COVID-19 times: a mixed method study based in Greater Manchester Agriculture and Human Values 2021 38 4 1201 1213 10.1007/s10460-021-10212-2 33935352
Parekh N Ali SH O’Connor J Tozan Y Jones AM Capasso A Foreman J DiClemente RJ Food insecurity among households with children during the COVID-19 pandemic: Results from a study among social media users across the United States Nutrition Journal 2021 20 1 1 11 10.1186/s12937-021-00732-2 33388067
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| 36474064 | PMC9734300 | NO-CC CODE | 2022-12-14 23:28:27 | no | Z Gerontol Geriatr. 2022 Dec 6; 55(8):729-735 | latin-1 | Z Gerontol Geriatr | 2,022 | 10.1007/s00391-022-02129-0 | oa_other |
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Doc Ophthalmol
Doc Ophthalmol
Documenta Ophthalmologica. Advances in Ophthalmology
0012-4486
1573-2622
Springer Berlin Heidelberg Berlin/Heidelberg
36478287
9908
10.1007/s10633-022-09908-5
Technical Note
The effect of COVID-19 on referral patterns for clinical electrophysiological testing
Grinton Michael E. [email protected]
12
Yan Peng 12
Wright Tom 12
1 grid.17063.33 0000 0001 2157 2938 Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON M5T 3A9 Canada
2 grid.512690.9 Kensington Vision and Research Centre, 340 College Street, Suite 501, Toronto, ON M5T 3A9 Canada
7 12 2022
14
5 7 2022
19 10 2022
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Purpose
To provide an overview of the effect that the COVID-19 pandemic has had on visual electrophysiology referral patterns and the subsequent effect this may have on patients.
Methods
All electrodiagnostic tests performed at Kensington Vision and Research Centre, Toronto Canada, in a 3-month period prior to the COVID-19 pandemic (1 September 2019 to 30 November 2019) were compared to a 3-month period after the start of the COVID-19 pandemic (1 September 2021 to 30 November 2021).
Results
A total of 502 patients had electrodiagnostic testing carried out in the designated time periods: 292 in the time period prior to the COVID-19 pandemic and 210 patients after. There was a significant change in the reason for referral in patients pre-COVID compared to post-COVID (p = 0.004). There was a 43% reduction in referrals for drug monitoring, 25% reduction for hereditary pathology and a 27% increase in acquired pathology after the start of the COVID-19 pandemic compared to before.
Conclusions
There was a substantial decrease in the total number of patients referred after the start of the COVID-19 pandemic compared to pre-COVID with inherited retinal pathology and drug monitoring patients being 2 populations most affected by the disruption to healthcare services.
Keywords
Electroretinogram
Multifocal electroretinogram
Vision electrophysiology
COVID-19
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pmcIntroduction
Office-based visual electrophysiology (VE) is a practical, noninvasive test used in Ophthalmology to provide a range of electrodiagnostic tests to help guide clinicians in the diagnosis of ocular pathology and assess function of the visual pathway. VE offers objective and quantifiable data [1] and is particularly important in the diagnosis of inherited retinal conditions and in the detection and monitoring of drug toxicity (most commonly hydroxychloroquine) as well as other acquired retinal and optic nerve conditions.
COVID-19 pandemic lockdowns and restrictions have led to major disruptions in Ophthalmic care and a significant reduction in patient clinic attendances and physician activity in the last 2 years [2]. Our work aims to provide an overview of the effect that the COVID-19 pandemic has had on clinicians practice and the referral pattern for clinical electrophysiological testing and the subsequent effect this may have on patients. We aim to do this by evaluating the patients who were referred to our electrodiagnostic unit. The unit is the only adult referral centre in the Greater Toronto area and typically provides testing to around 1000 patients each year.
Methods
A retrospective chart review, analysing electrodiagnostic tests performed at Kensington Vision and Research Centre, Toronto Canada was carried out. Clinical electronic medical records were accessed, and the following information identified: patient sex and date of birth, reason for referral, electrophysiological tests performed and result of the testing (normal or abnormal). Patients in a 3-month period prior to the COVID-19 pandemic (1 September 2019 to 30 November 2019) were compared with patients seen in a 3-month period after the start of the COVID-19 pandemic (1 September 2021 to 30 November 2021). During both periods there were no significant backlog of patients or delay in performing the requested electrophysiology tests.
All tests performed within the period of interest were identified by querying the test system database (Espion E3, Diagnosys Llc, Lowell, MA, USA). All electrophysiology testing was performed according to standards published by the International Society for Clinical Electrophysiology of Vision (ISCEV) [3, 4]. Patients referred for electrophysiological screening for hydroxychloroquine retinopathy usually only received multifocal ERG testing while all other referrals received multifocal and full-field ERG testing. Multifocal ERG testing was performed without dilation. All test results were assessed for abnormality by an experienced electrophysiologist (TW), response waveform amplitudes and peak times were compared to manufacturer supplied control reference thresholds. Patients referred for mfERG screening for possible HCQ retinopathy were assessed using ring average response thresholds and ring ratios. Ring ratios were assessed for abnormality by comparison with published thresholds [5, 6]. Patients with abnormal electrophysiology results were identified by retrospective chart review.
Student t-test was used to assess group differences in patient age, chi-squared test was used to assess the frequency of abnormal electrophysiology results. 2-way analysis of variance (ANOVA) with post-hoc Tukey honest significant difference was used to assess differences in referral reasons.
Ethical approval for this study was received from the Health Sciences Research Ethics Board of the University of Toronto (protocol # 42517).
Results
A total of 502 patients had electrodiagnostic testing carried out in the designated time periods and were included in the analysis. 292 patients were tested in the specified 3 month prior to the COVID-19 pandemic (1 September 2019 to 30 November 2019) and 210 patients in the 3-month period after the start of the COVID-19 pandemic (1 September 2021 to 30 November 2021). Table 1 gives an overview of patient demographics and the proportion of full-field electroretinogram (ERG) and multifocal ERG which were abnormal in these patients.Table 1 Overview of patient demographics and percentage of those patients which had an abnormal full-field electroretinogram (ERG) and/or multifocal ERG
Prior to COVID-19 pandemic
01/09/2019–30/11/2019 After start of COVID-19 pandemic
01/09/2021–30/11/2021 P-value
Number of patients tested 292 210
Age (± SD) 55.75 (± 15.99) 53.24 (± 16.49) 0.088
Female 205 (70.2%) 152 (72.4%) 0.596
Full-field ERG abnormal 73/121 (60%) 74/120 (62%) 0.895
Multifocal ERG abnormal 124/292 (42%) 102/210 (49%) 0.175
The reason for referral by the clinician was noted from the requisition form and classified into either hereditary ocular pathology, drug monitoring or acquired. For hereditary ocular pathology the main indications for referral were rod-cone dystrophy (45%) and macular/cone-rod dystrophy (40%). For drug monitoring, the medication was hydroxychloroquine in 99% of patients. For acquired referrals, indications included inflammatory disorders (35%), non-hereditary retinal pathology such as vascular occlusions and CSR (33%) and visual or field loss or disturbance (32%). Figure 1 summarises the reason for referral in patients pre-COVID and post-COVID; the change in referral pattern being statistically significant (p = 0.004).Fig. 1 Reason for referral for visual electrophysiology obtained from clinician requisition form for patients pre-COVID (1 September to 30 November 2019) and post-COVID (1 September to 30 November 2021)
Discussion
Our study highlights the substantial decrease in the total number of patients seen after the start of the COVID-19 pandemic compared to pre-COVID. During the COVID-19 pandemic, healthcare systems prioritised more urgent treatments and virtual care became a key tool for physicians. There has been difficulty in measuring the impact that this has had on patients whose care has been delayed or condition never diagnosed and there has been concern that the pandemic has highlighted pre-existing health inequalities [7]. Our results show two populations of patients who have seen a change in the care they have received likely because of the pandemic. For those patients with or suspected to have a hereditary ocular disease this may mean they have had a missed or delayed diagnosis of their condition and for those patients who are on medications which can be toxic to the eye the reduced frequency of monitoring or no monitoring at all would be concerning for missed and delayed detection of ocular toxicity.
In contrast, the number of patients being referred for acquired retinal conditions has increased. A considerable proportion of this group is of more acute presentations of patients with visual loss which were more likely to have been prioritised during the pandemic. Care should be taken to make firm conclusions regarding this as the numbers of patients are smaller compared to the other 2 groups; however, within the acquired group there was a specific increase in the number of patients referred with visual symptoms, but no objective examination or investigation abnormality noted on the requisition form and with subsequently normal electrophysiology results (7 patients pre-COVID to 11 patients post-COVID). It is possible that this may represent an increase in functional or non-organic ocular disorders possibly as a consequence of mental health sequelae secondary to the pandemic.
The study has many strengths including the size of the two comparison groups. The two groups are also from a consistent population and were tested in the same unit by the same electrophysiologists. The time periods were chosen carefully in order to leave a clear 2 year separation between pre- and ongoing pandemic but were at the same time of year in order to account for any bias which may have occurred with seasonal variations in referral patterns.
Limitations of the study include the single centred nature of the study which limits the generalisability of the results nationally and internationally. And although the size of the two comparison groups were large the number of patients referred with less common indications were relatively small.
Our work highlights the substantial decrease in the total number of patients referred after the start of the COVID-19 pandemic compared to pre-COVID and identifies inherited retinal pathology and drug monitoring patients to be two populations most affected by the disruption to healthcare services. The work highlights to clinicians those patients at risk of vision loss due to missed or delayed diagnoses because of the pandemic.
Declarations
Conflict of interest
The authors have no conflict of interest.
Ethical approval
Study was approved by the Research Ethics Board of the University of Toronto (Protocol #: 42517). The procedures used adhere to the tenets of the Declaration of Helsinki.
Statement on the welfare of animals
This article does not contain any studies with animals performed by any of the authors.
Statement of human rights
All procedures performed were done so in accordance with the ethical standards of the research ethics unit at the University of Toronto, Ethics approval for the study was obtained - protocol # 42517.
Consent to participate
As the work was a retrospective analysis / overview of a department's activity, we and the Research Ethics Unit at the Universtity of Toronto, deemed that patient consent was not necessary.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
1. Hamilton R Clinical electrophysiology of vision—commentary on current status and future prospects Eye 2021 35 9 2341 2343 10.1038/s41433-021-01592-0 34045684
2. Canadian Institute for Health Information, "COVID-19’s impact on physician services," December 9, 2021.
3. Robson AG, Frishman LJ, Grigg J, Hamilton R, Jeffrey BG, Kondo M, Li S, McCulloch DL (2022) ISCEV Standard for full-field clinical electroretinography (2022 update). Doc Ophthalmol 144(1):165–177
4. Hood DC, Bach M, Brigell MG, Keating D, Kondo M, Lyons JS, Marmor MF, McCulloch DL, Palmowski-Wolfe AM (2012) ISCEV standard for clinical multifocal electroretinography (mfERG) (2011 edition). Documenta Ophthalmol 124(1):1–13.
5. Lyons JS Severns ML Using multifocal ERG ring ratios to detect and follow Plaquenil retinal toxicity: a review Doc Ophthalmol 2009 118 1 29 36 10.1007/s10633-008-9130-0 18465156
6. Habib F Huang H Gupta A Wright T MERCI: a machine learning approach to identifying hydroxychloroquine retinopathy using mfERG Doc Ophthalmol 2022 145 1 53 63 10.1007/s10633-022-09879-7 35732856
7. Canadian Institute for Health Information, "Overview: COVID-19’s impact on health care systems," December 9, 2021
| 36478287 | PMC9734301 | NO-CC CODE | 2022-12-14 23:28:27 | no | Doc Ophthalmol. 2022 Dec 7;:1-4 | utf-8 | Doc Ophthalmol | 2,022 | 10.1007/s10633-022-09908-5 | oa_other |
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Sci China Mater
Sci China Mater
Science China Materials
2095-8226
2199-4501
Science China Press Beijing
2257
10.1007/s40843-022-2257-6
Articles
Periodate-based molecular perovskites as promising energetic biocidal agents
高碘酸根分子钙钛矿作为新型含能杀菌剂Yu Zhi-Hong
Liu De-Xuan
Ling Yu-Yi
Chen Xiao-Xian
Shang Yu
Chen Shao-Li
Ye Zi-Ming
Zhang Wei-Xiong [email protected]
Chen Xiao-Ming
grid.12981.33 0000 0001 2360 039X MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, Sun Yat-Sen University, Guangzhou, 510275 China
2 12 2022
18
18 8 2022
14 9 2022
© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Epidemics caused by pathogens in recent years have created an urgent need for energetic biocidal agents with the capacity of detonation and releasing bactericides. Herein we present a new type of energetic biocidal agents based on a series of iodine-rich molecular perovskites, (H2dabco)M(IO4)3 (dabco = 1,4-diazabicyclo[2.2.2]octane, M = Na+/K+/Rb+/NH4+ for DAI-1/2/3/4) and (H2dabco)Na(H4IO6)3 (DAI-X1). These compounds possess a cubic perovskite structure, and notably have not only high iodine contents (49–54 wt%), but also high performance in detonation velocity (6.331–6.558 km s−1) and detonation pressure (30.69–30.88 GPa). In particular, DAI-4 has a very high iodine content of 54.0 wt% and simultaneously an exceptional detonation velocity up to 6.558 km s−1. As disclosed by laser scanning confocal microscopy observation and a standard micro-broth dilution method, the detonation products of DAI-4 exhibit a broad-spectrum bactericidal effect against bacteria (E. coli, S. aureus, and P. aeruginosa). The advantages of easy scale-up synthesis, low cost, high detonation performance, and high iodine contents enable these periodate-based molecular perovskites to be highly promising candidates for energetic biocidal agents.
Electronic Supplementary Material
Supplementary material is available in the online version of this article at 10.1007/s40843-022-2257-6.
近年来, 病原体引起的流行病频发, 对含能杀菌剂提出了迫切需求; 它们可通过爆炸性反应快速地大面积抛洒以单质碘为主的高效广谱杀菌物质, 但其高碘含量与高爆性能难以同时兼得. 在此, 我们报道了一类新型含能杀菌剂——多碘分子钙钛矿: (H2dabco)M(IO4)3 (dabco= 1,4-二氮杂双环[2.2.2]辛烷, M = Na+/K+/Rb+/NH4+分别对应DAI-1/2/3/4)和(H2dabco)Na(H4IO6)3 (DAI-X1). 这些化合物具有立方钙钛矿结构, 不仅具有高碘含量(4 9–5 4 w t %), 而且具有较高的爆速(6.331–6.558 km s−1)和爆压(30.69–30.88 GPa). 特别地, DAI-4具有高达54.0 wt%的碘含量, 以及6.558 km s−1的爆速. 激光扫描共聚焦显微镜观察和标准微量肉汤稀释法实验表明, DAI-4的爆炸产物对细菌(大肠杆菌、金黄色葡萄球菌和铜绿假单胞菌)具有广谱杀菌性. 这类基于高碘酸根的分子钙钛矿具有易于放大合成、低成本、高爆轰性能和高碘含量的优点, 是极具潜力的新型含能杀菌剂.
Keywords
energetic material
single explosive
energetic biocidal agent
periodate-based molecular perovskite
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pmcSupporting Information
Periodate-based molecular perovskites as promising energetic biocidal agents
Acknowledgements
This work was supported by the National Natural Science Foundation of China (22071273 and 21821003), the Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program (2017BT01C161), and the Fundamental Innovation Team Fund (CXTD-202001). The authors thank Prof. Cai-Ping Tan at Sun Yat-Sen University for her assistance in biocidal experiments.
Author contributions
Zhang WX and Chen XM designed the research. Yu ZH conducted the synthetic experiments and most measurements. Liu DX, Chen XX, and Ye ZM provided assistance in the single-crystal XRD analysis. Shang Y, Chen SL offered help in the detonation parameter calculation. Ling YY conducted biocidal experiments. Yu ZH, Zhang WX, and Chen XM wrote the manuscript. All the authors contributed to the discussion.
Conflict of interest
The authors declare that they have no conflict of interest.
Supplementary information Experimental details and supporting data are available in the online version of the paper.
Zhi-Hong Yu was born in 1996. He is an MSc candidate in inorganic chemistry at Sun Yat-Sen University (SYSU). His research focuses on energetic salts.
Wei-Xiong Zhang obtained his BSc in 2004 and PhD in 2009 at SYSU, and was a JSPS postdoc at Tohoku University from 2010 to 2012. He joined SYSU in 2012, and became a professor in 2018. His current research interest is in crystal engineering of multi-component dense crystals, especially the structural-phase-transition functional crystals and energetic crystals.
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| 0 | PMC9734302 | NO-CC CODE | 2022-12-14 23:28:27 | no | Sci China Mater. 2022 Dec 2;:1-8 | utf-8 | Sci China Mater | 2,022 | 10.1007/s40843-022-2257-6 | oa_other |
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Surg Endosc
Surg Endosc
Surgical Endoscopy
0930-2794
1432-2218
Springer US New York
36471062
9777
10.1007/s00464-022-09777-8
Article
A retrospective analysis of early discharge following minimally invasive colectomy in an enhanced recovery pathway
http://orcid.org/0000-0002-0082-7193
Robitaille Stephan 12
Wang Anna 1
Liberman A. Sender 1
Charlebois Patrick 1
Stein Barry 1
Fiore Julio F. Jr 12
Feldman Liane S. 12
Lee Lawrence [email protected]
12
1 grid.63984.30 0000 0000 9064 4811 Department of Surgery, McGill University Health Centre, Glen Campus – DS1.3310, 1001 Decarie Boulevard, Montreal, QC H3G 1A4 Canada
2 grid.63984.30 0000 0000 9064 4811 Steinberg-Bernstein Centre for Minimally Invasive Surgery and Innovation, McGill University Health Centre, Montreal, QC Canada
5 12 2022
19
18 3 2022
27 11 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Background
There is increasing evidence to support discharge prior to gastrointestinal recovery following colorectal surgery. Furthermore, many patients are discharged early despite being excluded from an ambulatory colectomy pathway. The objective of this study was to determine the outcomes of patients discharged early following laparoscopic colectomy in an enhanced recovery pathway (ERP).
Methods
A retrospective review of all adult patients undergoing elective laparoscopic colectomy at a single university-affiliated colorectal referral center (08/2017–06/2021) was performed. Patients were included if they had undergone elective laparoscopic colectomy or ileostomy closure and excluded if they had been enrolled in an ambulatory colectomy pathway. Patients were then divided into three groups: LOS =1 day, LOS 2–3 days, and LOS 4+ days. The main outcomes were 30-day emergency room (ER) visits and readmissions. Reasons for inpatient stay per post-operative day (POD) were also recorded.
Results
A total of 497 patients were included [LOS1 n = 63 (13%), LOS2–3 n = 284 (57%), and LOS4+ n = 150 (30%)]. There were no differences in patient characteristics, diagnosis, or procedure between the groups. Patients were discharged with gastrointestinal recovery (GI-3) in 54% LOS1 vs. 98% LOS2–3 vs. 100% LOS4+ (p<0.001). Shorter procedure duration, transversus abdominus plane block, and lower opioid requirements were associated with shorter LOS (p<0.001). The absence of flatus was the most common reason to keep patients hospitalized: 61% on POD1, 21% on POD2, and 8% on POD3 (p<0.001). There were no differences in 30-day emergency visits, or readmission between the groups. In the LOS1 group, there were no differences in outcomes between patients with full return of bowel function at discharge compared to those without.
Conclusion
Discharge on POD1 was not associated with increased emergency department use, complications, or readmissions. Importantly, full return of bowel function at discharge did not affect outcomes. There may be potential to expand eligibility criteria for ambulatory colectomy protocol.
Graphical abstract
Keywords
Early discharge
Same-day discharge
Ambulatory colectomy
Laparoscopic colectomy
Ileostomy reversal
Canadian Institute of Health ResearchCGS M Robitaille Stephan
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pmcEnhanced recovery pathways (ERP) have significantly improved outcomes after colorectal surgery, with significantly decreased length of stay (LOS), complications, and readmissions compared to conventional care [1]. However, most ERPs heavily rely on the return of gastrointestinal function as a necessary criteria for discharge [2]. Furthermore, there is emerging evidence that discharge prior to full gastrointestinal recovery within an ERP may be safe in selected patients and has not been associated with increased morbidity [3]. Considering these findings, recent studies have been successful in implementing same day discharge (SDD) protocols for minimally invasive colectomy in highly selected patients [4, 5]. These advanced ERPs and SDD protocols have the potential to bring value and limit resource utilization by allowing patients to complete their recovery at home which may ultimately contribute to increased efficiency of care and availability of hospital beds. This may be especially beneficial in situations or settings where persistently high bed occupancy rates may lead to cancelations of elective surgeries, which was seen during the recent COVID-19 pandemic [6].
Despite the potential benefits of advanced ERPs and SDD protocols, strict inclusion criteria may limit their generalizability and applicability to a wider patient population [3–5]. As a result, there may be a significant number of patients who are excluded but would otherwise be reasonable candidates for early discharge within an established ERP or a SDD protocol.
As the data-supporting discharge prior to the return of gastrointestinal function or SDD increases, there may be potential to significantly increase enrollment in these pathways and ultimately improve value in this context. More widespread implementation of early discharge and potentially SDD would require accurate prediction of patients who are likely to experience uneventful early discharge. Therefore, the objectives of this study were (1) to characterize the reasons for post-operative hospitalization, (2) determine the outcomes of early discharge and discharge prior to the return of full gastrointestinal function, and (3) identify predictors of successful early discharge in patients undergoing elective colorectal surgery within an established ERP.
Materials and methods
Study population and setting
A retrospective review of all adult patients undergoing elective laparoscopic colectomy at a single high-volume university-affiliated colorectal referral center was performed from 08/2017 to 06/2021. Patients were included if they had undergone elective laparoscopic colectomy or ileostomy closure and were admitted after surgery. They were excluded if they had been enrolled in our ambulatory colectomy pathway (SDD protocol), underwent creation of a new stoma, their procedure was open or converted to open, underwent colostomy closure, extraperitoneal rectal resection and anastomosis, multivisceral resection, were already an inpatient, or scheduled as an urgent or emergent procedure. The procedures that met inclusion criteria for this study were the same as in our SDD protocol. All patients were enrolled in our established colorectal ERP (https://www.sages.org/enhanced-recovery/mcgill-colorectal-pathway/) with a 3-day target LOS. Patient charts were examined by two independent reviewers. Demographic data and other clinical characteristics including principal diagnosis, procedure information (procedure, duration, blood loss), use of neuraxial anesthesia, patient controlled analgesia, and route of medication delivery, comorbidity, which was evaluated using the Charlson comorbidity index (CCI) [7], and American society of anesthesiologists score (ASA) [8]. In addition, the reason for continued hospitalization was recorded for each post-operative day (POD) starting at POD1 to POD3 and based on pre-SDD and post-SDD implementation. Included patients were then divided into three groups based on duration of admission: LOS = 1 day (early discharge), LOS 2–3 days (target LOS), and LOS 4+ days (delayed discharge). A secondary exploratory analysis was performed to evaluate patient and procedure-related factors previously associated with more complicated hospitalization. The study protocol was approved by the institutional research ethics board.
Outcomes
The main outcome measures were 30-day emergency visits, complications, and readmissions. Emergency visits were then classified as (1) “not urgent,” (2) “treatable in ambulatory care setting,” (3) “emergent but appropriate for timely out-patient care,” and (4) “emergency care required” using the New-York University Emergency Department Algorithm (NYU-EDA) [9]. According to the NYU-EDA, grades 1 to 3 are considered potentially preventable. Specifically, potentially preventable visits were considered as those that could be appropriately managed by a specialist surgeon remotely or with timely follow-up (e.g.: wound issues, prescription changes, etc.), while unpreventable visits were those that needed emergency assessment and treatment (e.g., anastomotic leak, hemorrhage, etc.). Complications were classified using the Clavien–Dindo classification for severity [10]. Return of gastrointestinal function was defined as fulfillment of GI-3 criteria (tolerance of oral intake with passage of gas or stool) which is a commonly used composite score for return of gastrointestinal function in the literature [11]. Day of the week was also evaluated as a predictor of LOS, readmission, and emergency visits.
Statistical analysis
Continuous variables were reported as mean (SD) or median [IQR], and categorical variables were presented as frequency and percentage where appropriate. Outcome measures were analyzed using ANOVA or Kruskal–Wallis test for comparison of continuous variables and Pearson Chi Square for categorical comparisons where appropriate. A subgroup analysis comparing patients who were discharged on POD1 with versus without return of bowel function was performed. The effect of SDD implementation (03/2020) on LOS was also explored. Our secondary analysis was preformed using multinomial logarithmic regression to evaluate patient and procedure factors associated with length of stay. A p value < 0.05 was considered to be statistically significant. All statistical analyses were performed using Stata software package (Stata v16.0, StataCorp, College Station, Tx).
Results
A total of 497 patients were included out of a total of 937 elective major abdominal resections (168 excluded for a new stoma creation, 137 for open surgery, 73 for an extraperitoneal anastomosis without a stoma, 62 for other exclusion criteria). Of the 497 patients, 122 (25%) were operated on following the implementation of our institutions SDD protocol. There were 63 (13%) patients in the early discharge group (LOS = 1 day), 284 (57%) patients in the target LOS group (LOS = 2–3 days) and 150 (30%) patients in the delayed discharge group (LOS ≥ 4 days). Patient demographics and clinical characteristics are reported in Table 1. Overall, there were no differences in patient characteristics, indication for surgery, or procedure type between the groups. However, shorter operative time (p = 0.003), transverse abdominis plane (TAP) block (p <0.001), analgesic route, and lower ASA score were associated with shorter LOS (p <0.001) (Table 1). Patients in the LOS1 group also had the lowest mean morphine equivalents and were the least likely to require any subcutaneous or intravenous administration of medications (p <0.001). There was a higher proportion of patients in the LOS1 group after SDD implementation (34% vs. 6%, p<0.001).Table 1 Cohort characteristics
Overall cohort (n = 497) LOS 1day (n = 63) LOS 2–3 (n = 284) LOS 4+ (n = 150) p value
Mean age, years (SD) 64 (± 14) 63 (± 14) 63 (± 13) 66 (± 14) 0.062
Male 273 (54%) 37 (59%) 146 (51%) 90 (58%) 0.162
Mean BMI (SD) 26.8 (± 5.1) 27.2 (± 4.8) 26.7 (± 5.3) 26.9 (± 5) 0.839
Mean CCI, points (SD) 0.431
0–2 109 11 (17%) 70 (25%) 28 (18%)
3–4 137 17 (27%) 75 (26%) 45 (29%)
5+ 257 35 (55%) 140 (49%) 82 (53%)
ASA score 0.001
1 32 (6%) 4 (6%) 25 (9%) 3 (2%)
2 265 (53%) 42 (66%) 148 (52%) 73 (47%)
3+ 205 (41%) 17 (27%) 108 (38%) 78 (51%)
Indication for surgery 0.287
Neoplasm 368 (73%) 43 (68%) 211 (74%) 111 (72%)
Diverticular disease 40 (8%) 5 (8%) 16 (6%) 15 (10%)
IBD 33 (7%) 2 (3%) 20 (7%) 14 (9%)
Stoma closure 61 (12%) 13 (20%) 33 (12%) 15 (10%)
Other 5 (1%) 1 (2%) 3 (1%) 1 (1%)
Procedure 0.121
Right/transverse colectomy 218 (43%) 27 (43%) 114 (40%) 75 (49%)
Left/sigmoid colectomy 159 (32%) 17 (27%) 91 (32%) 50 (32%)
Anterior resection 64 (13%) 6 (10%) 43 (15%) 14 (9%)
Stoma closure 61 (12%) 13 (21%) 33 (12%) 15 (10%)
Mean OR time, min (SD) 170 (± 77.8) 145 (± 63) 167 (± 69) 183 (± 94) 0.003
Median EBL, mL [IQR] 50 [100] 50 [100] 50 [100] 50 [150] 0.119
Intraoperative TAP block 163 (32%) 41 (65%) 82 (29%) 36 (23%) <0.001
Post-operative analgesia <0.001
Epidural 16 (3%) 0 (0%) 9 (3%) 7 (4%)
PCA 73 (15%) 0 (0%) 26 (9%) 47 (31%)
Oral/SC 413 (82%) 63 (100%) 250 (88%) 100 (65%)
MME, mg (SD)
POD0 24.3 (± 18.8) 34.1 (± 26.3) 37.6 (± 27.6) 0.003
POD1 5.14 (± 7.6) 22.4 (± 25.3) 31.7 (± 37.6) <0.001
POD2 12.7 (± 19.8) 23.6 (± 36.7) <0.002
POD3 7 (± 11.5) 13.6 (± 23.6) 0.003
Any SC/IV
POD0 45 (71%) 243 (85%) 142 (92%) <0.001
POD1 1 (1%) 59 (21%) 60 (39%) <0.001
POD2 39 (14%) 53 (34%) <0.002
POD3 2 (1%) 41 (27%) <0.001
BMI body mass index, CCI Charlson comorbidity index, ASA American society of anesthesiology, IBD inflammatory bowel disease, OR operating room, EBL estimated blood loss, TAP transversus abdominis plane, PCA patient controlled analgesia, SC subcutaneous, MME mean morphine equivalents, IV Intravenous
Between the three study groups, there was no significant difference with respect to frequency of emergency visits, timing at which patients presented to the emergency department, and potential preventability of the emergency visits according to the NYU-EDA (Table 2) [9]. There was also no difference in outcomes, ED visits (p = 0.478), readmissions (p = 0.218), or preventable visits (p = 0.089) between the LOS1 vs. LOS2–3 groups. The early discharge group had a significantly higher rate tolerating oral intake without nausea or vomiting on POD 0 at 94% (95% CI: 88–100%) compared to 79% (95% CI: 74–84%) of those not discharged early (p = 0.005). Furthermore, when compared specifically to the target LOS group who tolerated oral intake 80% (95% CI: 75–85%) of the time, the results remained significant (p = 0.011). Discharge with full gastrointestinal recovery (attainment of GI-3) occurred in 54% of patients discharged early, 98% of patients who achieved target LOS, and 100% of patients who were discharged beyond the ERP target. Among patients who were discharged early, there was no difference in timing, number, or preventability of emergency visits between those with return of bowel function and those without. There was also no difference in type (e.g., wound issues, pain, ileus, leak or other) or severity of post-discharge complications or 30-day readmission rates between these two groups (Table 3). Subgroup analysis of LOS before and after the implementation of our SDD protocol revealed that prior to introduction of SDD, 6% of patients were LOS1, 60% were LOS2–3, and 34% were LOS4+ whereas, following implementation of SDD, 34% were LOS1, 48% were LOS2–3, and 18% were LOS4+. This difference represents a significant increase in early discharge following the adoption of a SDD protocol (p<0.001). There was also no significant association between day of surgery and LOS (p = 0.386), overall 30-day ED visit (p = 0.627), preventable ED visits (p = 0.267), or readmissions (p = 0.566).Table 2 Primary outcomes
LOS 1 day LOS 2–3 days LOS 4+ days p value
In-hospital complications 0 (0%) 11 (4%) 77 (50%) <0.001
Max Clavien-Dindo score (in hospital) <0.001
Minor (1–2) 0 (0%) 10 (3.5%) 53 (34%)
Major (3–4) 0 (0%) 1 (0.5%) 24 (16%)
30-day ED visit/complication 7 (11%) 24 (8%) 15 (10%) 0.751
Max Clavien-Dindo score (out of hospital) 0.635
Minor (1–2) 5 (8%) 18 (6%) 14 (5%)
Major (3–4) 3 (5%) 3 (1%) 2 (1%)
Median ED timing, POD [IQR] 10.14 [± 4.26] 11.72 [± 7.29] 13.2 [± 6.32] 0.505
Preventable ED visit 3/7 (43%) 15/24 (65%) 6/15 (40%) 0.249
30-day readmission 4 (6%) 9 (3%) 8 (5%) 0.322
LOS length of stay, ED emergency department, POD post-operative day
Table 3 Early discharge cohort
+Bowel function (n = 34) −Bowel function (n = 29) p value
30-day ED visit / complication 4 (12%) 3 (10%) 0.858
Max Clavien-Dindo score (out of hospital) 1.00
Minor(1–2) 3 (9%) 2 (7%)
Major(3–4) 1 (3%) 1 (3%)
Median ED timing, POD [IQR] 10.5 [7–14.5] 7 [6–15] 0.578
Preventable ED visit 3/4 (75%) 0/3 (0%) 0.243
30-day readmission 1 (3%) 3 (10%) 0.326
ED emergency department, POD post-operative day
The reason for continued hospitalization after POD 1, 2, and 3 is reported in Table 4. Awaiting gastrointestinal recovery (absence of flatus or stool) was the most common reason to keep patients hospitalized for the first two days after surgery. Other important reasons for hospitalization beyond POD 1 were nausea, vomiting, or not tolerating diet (18%), mobility issues (13%), and abnormal vital signs (11%). Awaiting return of bowel function was documented as the main issue requiring hospitalization on POD 1 in 73% of patients prior to implementation of the SDD protocol compared 51% of those following its introduction (Table 4). The proportion of patients kept in hospital beyond POD 1 for medical monitoring increased after SDD initiation (p = 0.003).Table 4 Reason for inpatient stay by post op day and by pre/post-SDD
Reason to stay in hospital POD1 POD2 POD3
Pre-SDD (n = 354) Post-SDD (n = 80) Total (n = 434) Pre-SDD (n = 241) Post-SDD (n = 37) Total (n = 278) Pre-SDD (n = 128) Post-SDD (n = 21) Total (n = 149)
Gastrointestinal function
N/V/not tolerating PO 72 (20%) 5 (6%)* 77 (18%) 67 (28%) 7 (19%) 74 (26%) 42 (33%) 6 (29%) 48 (32%)
Not passing gas or stool 257 (73%) 41 (51%)* 298 (67%) 73 (30%) 6 (16%) 79 (28%) 17 (13%) 2 (10%) 19 (13%)
Other reasons
Pain control 17 (5%) 7 (9%) 24 (5%) 15 (6%) 6 (16%)* 21 (7%) 8 (6%) 3 (14%) 11 (7%)
Urinary issues 7 (2%) 3 (4%) 10 (2%) 8 (3%) 2 (5%) 10 (4%) 3 (2%) 0 (0%) 3 (2%)
Mobilization issues 45 (13%) 11 (14%) 56 (13%) 24 (10%) 4 (11%) 28 (10%) 8 (6%) 4 (19%)* 12 (8%)
Social issues 0 (0%) 2 (3%)* 2 (0.5%) 3 (1%) 1 (3%) 4 (1%) 4 (3%) 1 (5%) 5 (3%)
Medical monitoring 44 (12%) 22 (28%)* 66 (15%) 50 (21%) 17 (46%)* 67 (24%) 59 (46%) 11 (52%) 70 (47%)
Abnormal or awaiting investigation 20 (6%) 11 (14%)* 31 (7%) 29 (12%) 8 (22%) 37 (13%) 30 (23%) 6 (29%) 36 (24%)
Abnormal vitals or physical exam 30 (8%) 18 (23%)* 48 (11%) 35 (15%) 12 (32%)* 47 (17%) 42 (33%) 9 (43%) 51 (34%)
Bleeding 13 (4%) 7 (9%) 20 (5%) 25 (10%) 5 (14%) 30 (11%) 9 (7%) 3 (14%) 12 (8%)
“Other” 31 (9%) 9 (11%) 40 (9%) 23 (10%) 1 (3%) 24 (9%) 9 (7%) 0 (0%) 9 (6%)
“Other” is defined as the residual categories such as, “no clear indication for admission”. POD post-operative day, SDD same-day discharge, N/V nausea or vomiting, PO per os
*Denoting p<0.05
Using multinomial logistic regression adjusted for age, sex, ASA score, BMI, and procedure type, operative time beyond the 75th percentile for respective procedure was associated with increased LOS, whereas TAP block and neoplasia as an indication for surgery were associated with decreased LOS. Day of surgery was not associated with LOS (Table 5).Table 5 Multinomial regression
LOS1 LOS2–3 (reference group) LOS 4+
OR time beyond >75th percentile for procedure 0.82 (0.33, 2.04) 1.87 (1.08, 3.26)*
Body mass index >35 1.03 (0.26, 4.09) 1.12 (0.44, 2.89)
Neoplasm 0.39 (0.12, 1.27) 0.18 (0.07, 0.43)*
TAP block 5.54 (2.58, 11.92)* 0.60 (0.34, 1.06)
Thursday/Friday 1.96 (0.96, 4.00) 1.07 (0.65, 1.77)
Multinomial regression using target LOS as the reference category. Further adjusted for age, gender, ASA score and procedure type. Presented as odds ratio (95% CI). LOS length of stay, OR operating room, TAP transversus abdominis plane, ASA American society of anesthesiology
*Denoting p < 0.05
Discussion
Discharge prior to the return of gastrointestinal function or even on the day of surgery may further reduce LOS and improve outcomes after minimally invasive colorectal surgery. In order to determine which patients may be candidates for early discharge, it is important to understand why patients remain admitted after surgery, and also whether outcomes of early discharge are similar to those who remain hospitalized up to the target LOS of our ERP. We found that the main reason that patients remain hospitalized after laparoscopic colorectal resection is to await return of gastrointestinal function. Moreover, patients that were discharged before full gastrointestinal recovery did not experience increased post-discharge complications, emergency room visits, and readmissions.
In our study, patients in the early discharge group had readmission rates of 6%, which is consistent with the literature [5, 12]. These results are also comparable to readmission rates for elective laparoscopic colectomy in standard pathways [13, 14]. Moreover, no significant differences were identified in the frequency of 30-day readmissions or severity of out of hospital complications between patients who were discharged early and patients who were not. Additionally, there were no significant differences in the frequency, timing, and preventability of emergency room visits between the groups. This suggests that there is no shift in burden to the emergency after discharge. Other studies have shown similar findings; however, they included full return of bowel function as part of their discharge criteria [15].
Awaiting the return of bowel function is frequently the major criterion limiting discharge in standard ERPs with up to 47–73% of patients remaining in hospital on POD 1 awaiting the passage of flatus or stool [16]. The same was seen in our study. However, when evaluating our early discharge group, results show no significant difference in outcomes between those who had full return of gastrointestinal function and those who did not. In a recent large multicenter prospective study, discharge prior to return of bowel function was found to be safe in selected patients undergoing elective colorectal surgery [3]. These results suggest that discharge prior to recovery of gastrointestinal function is safe. Interestingly, when comparing the pre-SDD cohort to the post-SDD cohort, there were significant increases in the proportion of patients requiring further hospitalization for medical monitoring or social issues. These findings may suggest that the patients who remain hospitalized beyond POD1 are likely those who require further medical monitoring and are more frequently being identified based on other criteria such as abnormal vital signs, clinical exam, or investigations.
Patients discharged early had significantly lower ASA scores, mean operating room (OR) time, and opioid and parenteral medication use. Previous studies evaluating early discharge in elective colectomy have found that shorter operative time was associated with early discharge and have used low ASA score to evaluate eligibility for early discharge [15, 17]. Another study by Scheer et al. found that operative times greater than 270 minutes for colectomies were associated with increased complications, prolonged ileus, and longer hospital stay [18]. Notably, operative time for laparoscopic colectomy has been shown to be associated with elevated BMI [19]. However, in our secondary analysis, when we adjusted for obesity and procedure type, we found that operative time was still a significant predictor of LOS. We suspect that operative time is a multifactorial indicator of operative difficulty, disease severity, or other technical factors. These results suggest that early discharge will likely have the highest chance of success in healthier patients undergoing uncomplicated resection.
Importantly, the patients included in this study are those that had been excluded from our institution’s SDD cohort or those treated in the years just before implementation of the SDD protocol. The patients that were enrolled in the SDD protocol tended to be healthier with less extensive procedures when compared to the standard ERP [4], although 35% of patients were ASA 3+ and 15% of patients underwent extraperitoneal resections [4]. When we evaluated patients who were discharged early prior to and after the implementation of our SDD protocol, we found that significantly more patients were being discharged early (LOS = 1 day) while there was a simultaneous SDD protocol in place. We hypothesize that this pattern is likely due to increased confidence of the treating team in the safety of early discharge as a result of emerging literature supporting this practice.
Furthermore, patients who were discharged early were more likely to tolerate their diet on POD 0 than those who were not. Early tolerance of feeding has been associated with decreased complications and failure of early tolerance of feeding can serve as an early indicator of more complicated hospital admissions [20]. These findings solidify the inclusion of early oral intake as a discharge criterion in both SDD and early discharge in an ERP. When considering modes of analgesia, previous studies have found TAP blocks to be associated with decreased opioid use, earlier resumption of diet, and shorter hospital admission [21]. Similarly, we found TAP block to be associated with early discharge, whereas use of parenteral medication on POD 0 and increased opioid use were associated with increased LOS. Given these findings, we suspect that patients who require parenteral medications are more likely to have nausea, intolerance to oral intake, or significant pain not sufficiently relieved by oral analgesia.
Interestingly, day of surgery was not associated with LOS or any other primary outcome despite reports of its association with LOS and mortality in other contexts [22, 23]. These findings are likely an indication of a robust and consistent discharge practice throughout the course of the week and supports early discharge on the weekends.
Limitations
This study is limited by its retrospective nature, and findings should be interpreted in this context. Specifically, there is potential for unmeasured confounding such as patient activation or health literacy, which has been associated with post-discharge resource utilization after surgery [24]. There may also have been socioeconomic factors that affected discharge, such as available social support at home or even distance to the hospital, although this was recorded as ‘social reasons’ for delayed discharge. Furthermore, due to the retrospective nature, it was difficult to determine on occasion the exact reason for keeping patients in hospital, especially when there were discrepancies between the nursing and physician notes. In such cases, the physician notes were given priority. We were also limited to variables available in patient charts and, therefore, could not gather data on potentially relevant variables such as pain scores that may affect readiness for discharge. We used opioid consumption as a proxy measure for pain scores, as we hypothesized that pain that was not controllable with opioid-free analgesia would be a contraindication for discharge. Further, we excluded procedures with fewer than n = 5 cases such as reversal of loop colostomy. Therefore, generalizability to less commonly performed procedures is unknown. Lastly, unplanned patient interactions by phone call or e-mail were not recorded. As a result, additional administrative workload could not be determined for these tasks.
Increased emergency visits and 30-day readmission rates are unfavorable outcomes of early discharge as they are costly and can affect patient satisfaction [25, 26]. Conversely, unnecessary inpatient admission is associated with inefficient hospital resource utilization, and in situations with high bed occupancy may lead to limitations or cancelations of elective procedures. In summary, our study found that (1) the main reason to remain hospitalized after elective laparoscopic colorectal resection was to await the return of gastrointestinal function, and that (2) early discharge, even without full recovery of gastrointestinal function, was not associated with worse outcomes. Lastly, (3) there were several factors that were associated with successful early discharge such as, lower ASA scores, shorter operative times, tolerance of oral intake on POD 0, oral route for medications and hydration on POD 0, use of TAP block, and avoidance of PCA or epidural. These results suggest that a greater proportion of patients, especially those who meet these criteria and have no other contraindication for discharge, may be candidates for early discharge even without return of gastrointestinal function. This approach has the potential of further reduce length of stay, costs, and unnecessary use of valuable inpatient resources.
Funding
Canadian Institutes of Health Research-Frederick Banting and Charles Best Canada Graduate Scholarship-Master’s (CGS M).
Declarations
Disclosures
S Robitaille is supported by a Bursary Award for Master’s Training for Applicants with a Professional Degree from the Canadian Institutes of Health Research (Frederick Banting and Charles Best Canada Graduate Scholarship-Master’s (CGS M)), LS Feldman reports an investigator-initiated grant from TheatOR, and speaker fees from Abbott and Merck. JF Fiore Jr reports an investigator-initiated grant from Merck and consulting fees from Shionogi. AS Liberman is on the advisory board for Novadaq, Merck, and Servier, and receives speaker fees from Ippen. L Lee is supported by a Career Development Award from the American Society of Colon & Rectal Surgeons (CDA-019). L Lee also reports an investigator-initiated operating grant from Johnson & Johnson and speaker fees from Stryker. A Wang, P Charlebois, and B Stein have no disclosures to report.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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| 36471062 | PMC9734303 | NO-CC CODE | 2022-12-14 23:28:27 | no | Surg Endosc. 2022 Dec 5;:1-9 | utf-8 | Surg Endosc | 2,022 | 10.1007/s00464-022-09777-8 | oa_other |
==== Front
J Ambient Intell Humaniz Comput
J Ambient Intell Humaniz Comput
Journal of Ambient Intelligence and Humanized Computing
1868-5137
1868-5145
Springer Berlin Heidelberg Berlin/Heidelberg
4480
10.1007/s12652-022-04480-x
Original Research
EMSRtrc: relaxation of booking limits by total revenue control for expected marginal seat revenue
Ertuğrul Aslı Emine [email protected]
1
http://orcid.org/0000-0001-7074-4038
Şahin Ramazan [email protected]
2
1 Türkiye İş Bankası, İstanbul, Turkey
2 grid.25769.3f 0000 0001 2169 7132 Department of Industrial Engineering, Faculty of Engineering, Gazi University, Ankara, Turkey
7 12 2022
111
13 7 2021
18 11 2022
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The Covid-19 pandemic has negatively affected life worldwide and caused catastrophic loss of life. It has also been harming the economic activities of businesses, and airline companies are among the sectors most affected by this situation. One of the goals for survival in such a situation is to make the best in airline revenue management (ARM). The most helpful model for ARM is Expected Marginal Seat Revenue (EMSR), widely used in the literature and industry. In this study, the simple and effective models that simulated EMSRa, EMSRb, and EMSRc were developed, called EMSR Total Revenue Control (EMSRtrc). The proposed three models aim to keep the simplicity of the original EMSR models while creating a new perspective and methodology. The developed EMSRtrc models were tested with numerical examples and compared with EMSRa, EMSRb, and EMSRc models proposed previously in the literature. Numerical examples show that the developed EMSRtrc models perform better than the EMSRa, b, and c. For the minimum revenue category, the developed EMSRtrc models exhibit outstanding performance. The results show that the proposed models guarantee a higher minimum revenue of 9.900, 11.619, and 2.537%, respectively. The EMSRtrc models have generated higher revenue and achieved a higher load factor rate of up to 98% simultaneously. Considering the third numerical example, the approximate number of empty seats is 1.3 for the EMSRtrc-(a), 2.65 for the EMSRtrc-(b), and 7.85 for the EMSRtrc-(c). The overall results demonstrate that the proposed model is an effective tool for ARM.
Keywords
Airline revenue management
Expected marginal seat revenue (EMSR)
Seat inventory management
Fare level distribution
Relaxation of booking limits
Total revenue control
==== Body
pmcIntroduction
Airlines are the companies whose products are perishable, meaning they cannot be stocked and sold at a later date. It is challenging for companies to sell their products as efficiently as possible in a limited period. Therefore, revenue management (RM) methodologies are required to achieve a satisfactory income level. According to the International Air Transport Association (IATA) Annual Review 2018, the average profit per passenger in the airline industry was only 9.27 USD in 2017. Considering the prices of flight tickets, the profit rates are significantly low in comparison with other sectors, making RM much more critical for the airline industry. According to IATA's Airline Industry Financial Forecast report, the losses of airline companies have constantly been increasing since the beginning of 2020 due to the Covid-19 pandemic. The Annual Review 2021 of IATA mentioned that the total operating revenue of airline companies decreased more than 60% in the second quarter of 2021 compared with the same quarter of 2019.
Airline revenue management (ARM) is concerned with managing airplane seat inventory. Airline companies have different fare levels for the same transportation services as hotels in the tourism sector. Airlines prefer to fill their planes with higher-fare customers since demand uncertainty is still present in the market. Accordingly, companies try to fill their planes with lower-fare customers to avoid opportunity costs incurred by empty seats. The airlines must decide how many lower-fare seats to sell while ensuring that they have enough seats left to sell to higher-fare passengers (An et al., 2021). When determining different fare levels in an airplane, the limited seats should be distributed to predetermined fare levels as efficiently as possible, since any unsold seats or opportunity costs resulting from selling too many low-fare seats are undesirable. In the literature, there are many optimization models such as dynamic programming (Kunnumkal and Topaloğlu, 2010; Selçuk and Avşar, 2019), linear programming (Möller et al., 2004; Liu and van Ryzin, 2008), bid price control (Akan and Ata, 2009; Talluri and van Ryzin, 1998), and nested booking limits (van Ryzin and Vulcano, 2008; Luo et al., 2020). Since the problem space is enormous and human-related aspects are involved, algorithms are becoming more complicated to tackle complex problems arising in the ARM. In the literature, there is a severe need to develop simpler, efficient, and effective solution approaches.
EMSR, developed by Belobaba (1987), is one of the excellent examples of simple and effective heuristic methods. It has been widely studied and many applications have been reported in the literature (Razan et al. 2020; Buyruk and Güner 2021). EMSR is easy to understand and apply, and it is computationally less demanding. Additionally, the EMSR algorithm yields better revenue levels in comparison with its counterparts. Researchers generally use EMSR for benchmarking purposes and reported results to show its superior performance (Gosavii et al., 2002; Lawhead and Gosavi, 2019).
Airline companies operate hundreds of flights per day. The aim of these companies is to earn the highest income from these flights. For this, the distribution of the current seat capacity to different fare levels gains importance and this process needs to be done accurately and quickly. In this study, an extended EMSR model, namely Expected Marginal Seat Revenue Total Revenue Control (EMSRtrc), has been developed. The proposed model keeps the simplicity of the original EMSR while producing higher returns. The main rationale behind the proposed model is that after a prespecified revenue target is reached, the model allows available seats to be sold at the lowest price, in order to increase revenue because selling vacant seats at a higher price level might cause the seats to be unsold, which results in lower revenue. The proposed EMSRtrc improves the canonical EMSR and contributes to the ARM literature by providing a practical solution approach.
When the ARM literature is examined, it is seen that the studies focus on four areas: forecasting, overbooking, seat inventory control, and fare pricing. The proposed ARM method is within the scope of the domain of seat inventory control. The studies in this area deal with the determination of seat protection levels for each fare level. To the best of our knowledge, this is the first paper that handles revenue values as a target in the related literature.
The determination of the target revenue value was proposed in this study for the first time. With the proposed target revenue method, airline companies not only earn higher revenues but also allows them to make dynamic decisions regarding ticket prices and seat protection levels during the sales period.
The remainder of the present work is organized as follows: A literature review is given in Sect. 2. In Sect. 3, the methodology is discussed. Section 4 focuses on a numerical example, in which comparisons with different ARM methods have been provided. The results are compared in Sect. 5 and discussions are given in Sect. 6. Finally, concluding remarks are given in Sect. 7.
Literature review
In the airline industry, the studies on ARM were first started in the 1970s with the Airline Deregulation Act in the United States. Since then, many studies have been conducted and optimization models have been developed. The first study was conducted by Littlewood (1972), and after that, Belobaba (1987) developed the EMSR method with the help of Littlewood’s rule. Belobaba (1992) developed a new version of the EMSR method with some new additions and after this work, the new version was named EMSRb, while the old version was renamed as EMSRa. The EMSR method is still the most easily applicable and preferred ARM method by the airline industry. Additionally, scientists often work on EMSR for improvement. Wollmer (1992), for example, made a comparison for the model that he developed with EMSR under different conditions. Boyd and Kallasen (2004) used EMSR to compare priceable and yieldable environment conditions. They used the EMSRb for simulating the yieldable environments. Another paper on this issue belongs to Weatherford (2004), who developed a model called EMSU, aiming to decrease the EMSR model's risk and compared it with EMSR. Fiig et al. (2010) presented the EMSRb-MR model, which was improved based on the EMSRb. This new model could be applied to single leg and network revenue management. Gönsch et al. (2013) proposed a heuristic method for the ARM model based on the single-leg EMSRa. According to the experiments in the paper, the results of the the new method are better than the existing ARM methods. Tavana and Weatherford (2017) proposed a new ARM algorithm called the EMSRc and compared their model with the EMSRa, the EMSRb, and the EMSRb-MR in unrestricted and restricted fare environments. According to the results, the EMSRc outperforms the EMSRa, the EMSRb, and the EMSRb-MR, especially under the condition of an unrestricted fare environment. Kyparisis and Koulamas (2018) addressed the revenue management problem of a single-leg two-cabin airline where business and economy cabins are flexibly divided. Using three random demand distributions, it was tried to determine the most suitable cabin section and the most reasonable fares. One of the other studies on EMSR belongs by Banciu et al. (2019). A dependency between fare levels was considered while the original EMSR calculated booking limits with the assumption of independence between fare levels. Additionally, different distributions for the demand were attempted in their study. Luo et al. (2020) proposed a generalized nesting policy (GNP) that can enrich the family of nesting policies. A mathematical model for the nesting control under GNP was suggested, in which the nesting policy and booking limits were both taken as the decision variables. Bondoux et al. (2020) presented a new airline Revenue Management System based on Reinforcement Learning, which does not require a demand forecaster. Yazdi et al. (2020) published a case study on the revenue management strategy of the airline industry in Iran. A mathematical model was proposed and solved with a binary differential evolution algorithm. Razan et al. (2020) published a bibliometric analysis article on revenue management in the airline industry. Shihab and Wei (2021) developed DRL (Deep Reinforcement Learning), learning customers' behaviour and other important dynamics of ARM, and compared DRL with the EMSRb. It was determined that DRL earns more revenue than the EMSRb. Also, DRL gets high load factors near a hundred percent. Apart from the airline industry, Seo et al. (2021) applied the EMSRb to compare their model developed for sea cargo management. There are other optimization models for ARM in the literature. Venkataraman et al. (2021) sought to determine an optimal reservation policy that considers the dynamics of group behavior regarding cancelations and refunds. They compared their model with the traditional model under various scenarios and demonstrated that they achieved better total revenue. Escovar-Álvarez and Belobaba (2022) explored revenue management strategies for accepting additional bookings in economy fare classes when some premium cabin seats were expected to be vacant. They proposed two heuristics for this situation. They have shown that the recommended methods increase total revenue by up to 1.1%. Duduke and Venkataraman (2022) proposed a new class of products called flexible products with preferences. They focused on the development of capacity control techniques when flexible products were offered along with specific products. They developed a heuristic to determine a booking and seat allocation policy for all forms of flexible products. Lee and Hersh (1993) presented the advantages of using a dynamic approach against nested methods for the demand uncertainty. Bid price control is an approach used for network revenue management, a popular strategy for the ARM. The first study carried out on bid price control was authored by R. W. Simpson in 1989 (Talluri and van Ryzin, 1998). After that, Williamson (1992) inquired in his Ph.D. thesis on bid price control.
Methodology
Expected marginal seat revenue (EMSR)
As mentioned earlier, EMSR was proposed by Belobaba (1987). The model’s logic is based on Littlewood’s (1972) rule. The model is easy to understand and apply. Moreover, it gives satisfactory results on seat inventory management problems. Therefore, EMSR is used very often in the literature and sector. According to the model, the distribution of the seats among fare levels should be nested so that there will not be any rejected highest fare passengers.
EMSR tries to protect the seats against low fare passengers, which facilitates selling more seats to the high fare passengers. While defining the number of seats protected from the low fare level passengers, the following inequality is applied. On the left-hand side of the formula in the inequality (1), the expected marginal revenue of selling Sth seat in fare level i is written. On the right-hand side, the fare of level i is multiplied by the probability of having a demand of S for level i. The inequality explains that it is illogical to protect the Sth seat if the expected marginal revenue does not exceed the fare level of i + 1.1 EMSRi(Si)≥Fi∗P(Si)
where, Si: the number of seats protected from lower levels in level i;i: fare of level i;(Si): the probability of having a demand of S for level i.
Let us explain inequality (1) with a numerical example to make it more tangible. Assume that there are two fare levels: F1 = $50 and F2 = $30. If the probability of having the 10th passenger at fare level 1 is 0.5, then the expected marginal revenue from selling the 10th seat is $25. At this point, the expected marginal revenue is less than F2. Therefore, it does not make sense to protect the 10th seat from the fare level F2. However, assume that the probability that the 9th passenger is at the F1 fare level is 0.6. As a result, the expected marginal revenue of selling the 9th seat from the F1 fare level is $30. Since it is equal to the F2 fare level, it is logical to protect the 9th seat for the F1 fare level from the F2 fare level. In this way, EMSR defines the booking limits of each fare level. These booking limits are nested, meaning that the highest fare level has no limit, while lower fare levels have limitations according to the protected number of seats. Nine seats are covered from the F2 fare level for the same example. If there are 20 seats in the aircraft, just 11 seats can be sold from the F2 fare level. Therefore, the booking limit of the F2 fare level is 11, while the booking limit of the F1 fare level is 20 (which means no limit for the F1 fare level).
EMSR has two popular types called EMSRa and EMSRb. The EMSRa calculates the protection fare levels by comparing each class with the fare levels lower than itself. However, the EMSRb develops an artificial fare level by taking approximate values of lower fare levels. Therefore, the total protection level for one fare level can be calculated. For the application of EMSR, the booking limits determined employing protection levels are used to accept or reject any incoming passenger. In the literature, it is assumed that the EMSRb yields better results than the EMSRa. However, some researchers argue that better results with EMSRb are controversial However, the EMSRb is more commonly used than the EMSRa compared with EMSR.
Expected marginal seat revenue—total revenue control (EMSRtrc)
In this study, a new model based on EMSR is developed. Under this subtitle, this new ARM method will be explained. The new model is called EMSRtrc. Typically, the EMSR method only considers the booking limits of fare levels. If the number of seats sold from any fare level is equal to its booking limit, the model does not accept any more passengers from this fare level. Additionally, the protected seats are unsold if the targeted level’s passenger does not arrive during the sales period. In other words, the protected seats are either sold to customers of higher levels or unsold to any passengers at all. If the same example given above is handled again, just 11 seats are sold from the F2 fare level. After this limit, passengers who want to buy a flight ticket with the F2 fare level will be rejected. Only the passengers willing to pay the F1 fare level will be accepted. If no one buys a seat from the F1 fare level, the remaining 9 seats will not be sold to anyone. As a result, it is risky to protect seats because it may cause empty seats in the aircraft. Belobaba (2015) states that any empty seat in an aircraft is a loss for airline companies. If empty seats can be sold for even $1, it is better than no sales at all.
EMSR calculates the expected revenue of each fare level for each seat sale. Thus, the total expected revenue can be estimated when all booking limits, fares, and probabilities are known. As a result, EMSR promises a total revenue to the airline company when all booking limits are applied. Airline companies earn the promised revenue by using EMSR. According to the probability of selling Sth seat from fare level i, EMSR gives a probabilistic revenue for each seat. Sometimes airlines can reach more revenue than promised, and sometimes less. This depends on the performance of the sales period. At this point, our proposed EMSRtrc comes in handy. When the sales period goes well, and the revenue exceeds the expected level, the EMSRtrc offers a closed lower fare level for sale. For the example given before, a flight with two fare levels has a booking limit of 11 seats for the F2 fare level. Let us assume that the total expected revenue by selling 11 seats from the F2 fare level and 9 seats from the F1 fare level is $550. This means EMSR promises $550 revenue to the airline company if the booking limit of 11 seats is applied to the F2 fare level and all the seats are sold. The formula that gives the $550 is given in formula 2. For each sale, the fare level of i is multiplied by the probability of selling the Sth seat from fare level i. All expected marginal revenue values are summed. As a result, total expected revenue is found.2 ∑i=1mFi∗P(Si)
where, Si: the number of seats protected from lower levels in level i; Fi: fare of fare level i; (Si): the probability of having a demand of S for level i.
However, this goal is reached when 11 seats are sold from the F2 and 5 from the F1 (30*11 + 50*5 = $580). After this point, it is logical to open the F2 fare level again as the objective revenue by applying EMSR is achieved. The sales period goes better than expected when the objective revenue is gained before the booking limit. However, this does not mean that the period will go well in the next sales periods. The closed fare level can be opened, so as not to risk ending up with empty seats in the aircraft. The flowchart of the EMSRtrc, according to the example, is given in Fig. 1. The mathematical explanation of the algorithm is given as follows:3 If∑i=1mFi∗bi≥∑j=1mEMSRji&bi≥nithen,ai>ni
where, m: number of fare levels; Fi: fare of the fare level i; bi: number of seats sold to fare level i; EMSRji: Total expected revenue, ni: total booking limit for fare level i; ai: number of seats sold to fare level i.Fig. 1 The flowchart of the EMSRtrc algorithm
In Formula 3, it is explained that if the total revenue is greater than or equal to EMSR, there can be a relaxation for ni. Therefore, ai can be greater than ni. Note here that EMSRtrc does not calculate the ni value with its algorithm. The model performs according to the booking limits taken from another EMSR model and its objective revenues. In other words, ni is calculated by another EMSR model, such as EMSRb. The model calculates the total revenue limits according to the booking limits of EMSRb and the start sales period. It keeps the accepted passengers under the control of booking limits and objective revenue thresholds. However, these thresholds should not be confused with the bid price control, as the bid price control defines a threshold for each flight ticket. However, EMSRtrc’s revenue thresholds are defined by the summation of EMSR values. Before each sale, the booking limit is controlled, and the passengers are accepted if the booking limit is not exceeded. After the sale, the total revenue is checked. If the total objective revenue is reached, one lower level is opened for sale again until the next booking limit. All these steps are explained in Fig. 1 visually.
In the next section, three examples will be examined to better understand EMSRtrc and the performance of the algorithm will be demonstrated. Accordingly, EMRStrc will be compared with EMRSa and EMSRb. Moreover, the method developed by Tavana and Weatherford (2017), which is called EMSRc, will also be compared with EMSRtrc.
Numerical example
In this section, three examples will be examined along with the discussions. The examples are derived from Tavana and Weatherford (2017). According to their work, the EMSRc performs better in the unrestricted fare environment when the passengers arrive orderly. An unrestricted fare environment means that passengers purchase the cheapest available flight ticket all the time. Additionally, it is an ordered arriving system when the low fare passengers come first, and high fare passengers arrive later.
Compared with EMSRc, the fare environment is unlimited, and passengers come regularly. Additionally, there is a situation in which there are different wage levels in our examples, but there is no service difference between the wage levels. There is no business or economy class where service levels differ. All seats of the flight will receive the same transportation service under the same conditions. Another assumption of the numerical examples concerns the distribution of demand. In all examples, the demand is normally distributed, and they are all independent demands for each wage level. There is no overbooking, raising fare levels, cancelling a sale, or group booking. The total number of seats sold was limited by an authorized reservation limit.
All models used for comparison in this study were coded in C# programming language, and the problems were solved with Microsoft Visual Studio Express 2015. There are six patterns for each problem except the first one, generated, one for the EMSRa, one for the EMSRb, one for the EMSRc, and three for the EMSRtrc. Three different versions of EMSRtrc were used, which are EMSRtrc-(a), EMSRtrc-(b), and EMSRtrc-(c), respectively. This is because each version is built according to different booking limits and total objective revenues of a different EMSR model. These are EMSRtrc-(a) simulation for EMSRa, EMSRtrc-(b) simulation for EMSRb, and EMSRtrc-(c) simulation for EMSRc. For each example, 20 different demand scenarios were randomly generated according to the distribution type of fare level. In each problem, the same 20 scenarios were applied to the original EMSR models to see the actual performances of the models (every problem has different scenarios, but models within the problem use the same 20 scenarios). Each scenario was run, and the results were recorded. There are four models for the first problem with two fare levels because the EMSRa and EMSRb give the same booking limit result when there are two fare levels.
The data required for the first problem are given in Table 1. The total seat capacity of this problem is 50. The scenarios created for each model were run, and the results are given in Table 2. The models named the EMSRtrc-(a) and the EMSRtrc-(b) represent the EMSRtrc with the booking limits and total objective revenues of the EMSRa and EMSRb, respectively. The EMSRtrc-(c) is the EMSRtrc built upon the outputs of the EMSRc. The maximum, minimum, and approximate revenues are shown in Table 2. The values in the table were taken from the simulations built. According to the approximate revenue, the best models are EMSRtrc-(a) and the EMSRtrc-(b), as shown in Table 2. Similarly, this model provides the best value in the minimum return category.Table 1 Demand, standard deviation, and fare data for the first example
Class Mean demand Standard deviation Fare
Y 20 7 500
H 45 20 300
Table 2 Results of the first example ($)
EMSRa EMSRb EMSRc EMSRtrc-(a/b) EMSRtrc-(c) Improvement (%)
Approximate revenue 14750.00 14750.00 14700.00 15100.00 14550.00 2.373
Maximum revenue 18600.00 18600.00 19000.00 18000.00 18200.00 -4.210
Minimum revenue 10100.00 10100.00 10000.00 11100.00 10500.0 9.900
If we consider scenario 17 to clarify the rationality of the EMSRtrc better, for this scenario, the EMSRtrc-(a), the EMSRtrc-(b), and the EMSRtrc-(c) outperformed the others. In this example, the booking limit for the H class is calculated as 32 from the EMSRa, the EMSRb, and 30 by the EMSRc. Therefore the maximum number of sold tickets from the H class is 32 for the EMSRa simulation, the EMSRb simulation, and 30 for the EMSRc simulations. For the 17th scenario, the EMSRa and EMSRb sold 14 Y class tickets and 32 H class tickets. In these two models, 4 seats remain empty. The EMSRc sold 15 tickets from Y class and 30 H class tickets. In the EMSRc model, five seats remained unoccupied. However, the EMSRtrc-(a) and the EMSRtrc-(b) sold 15 Y class tickets and 34 H class tickets. For the EMSRtrc-(c) the number of sold tickets from class Y is 16, and from class, H is 34. The idea behind the EMSRtrc can be seen through this scenario. When the total objective revenue is reached, the EMSRtrc opens class H again, while the other models protect the seats from class H. In this way, the algorithm does not wait for the Y class passengers and the probability of having an empty seat decreases. Additionally, the total objective revenue is reached even if the booking limits are crashed. As more seats are sold, more revenue is gained since the EMSRtrc gives more opportunities to H class passengers.
The second example has six fare levels. The data for the example are given in Table 3. There are six different ARM models to compare in this example. In this example, the capacity was 65 seats, and 20 scenarios were created according to the mean and standard deviation data. For the second example, Tavana and Weatherford (2017) divided the sale period into four sub-periods. The percentage of passengers arriving according to their fare level differs in each sub-period. The demand arrival distribution is given in Table 4. For example, the probability of customers coming from the fare levels 1 and 2 is zero in the first period. In Table 5, the results of the second example are given, where the best performances for approximate, maximum, and minimum revenue belong to the EMSRtrc models, respectively.Table 3 Demand, standard deviation, and fare data for the second example
Class Mean demand Standard deviation Fare
1 10.9 3.5 1333
2 19.9 6.7 920
3 7.9 2.5 725
4 7.2 2.4 602
5 14.1 4.4 504
6 47.9 15.5 416
Table 4 Arrival distribution for the demand—the second example
Booking Period
Class 1 2 3 4
1 0% 0% 20% 80%
2 0% 5% 30% 65%
3 20% 25% 30% 25%
4 25% 25% 25% 25%
5 60% 35% 5% 0%
6 70% 30% 0% 0%
Table 5 Results of the second example ($)
EMSRa EMSRb EMSRc EMSRtrc-(a) EMSRtrc-(b) EMSRtrc-(c) Improvement (%)
Approximate revenue 41052.40 41744.40 41389.00 41883.40 41303.50 41,682.00 0.333
Maximum revenue 48168.00 47987.00 49617.00 46104.00 47477.00 49,630.00 0.026
Minimum revenue 32491.00 32216.00 31687.00 36266.00 31254.00 35,286.00 11.619
In the last example, models were run for eight fare classes. The capacity was determined as 65 seats, as in the second example. The demands were normally distributed, and data about the example are shown in Table 6. The example's demand arrival distribution was determined as given in Table 7. Twenty scenarios were run for comparison, and the results are provided in Table 8. One more time, the best revenue values were seen under the lines of the EMSRtrc models.Table 6 Demand, standard deviation, and fare data for the third example
Class Mean demand Standard deviation Fare
1 10 5 1000
2 8 3 800
3 22 12 550
4 30 12 450
5 40 20 300
6 35 18 200
7 15 6 160
8 10 1 120
Table 7 Arrival distribution for the demand—the third example
Booking Period
Class 1 2 3 4
1 2% 8% 20% 70%
2 10% 20% 30% 40%
3 20% 25% 30% 25%
4 30% 30% 20% 20%
5 40% 35% 15% 10%
6 50% 40% 10% 0%
7 60% 35% 5% 0%
8 70% 30% 0% 0%
Table 8 Results of the third example ($)
EMSRa EMSRb EMSRc EMSRtrc-(a) EMSRtrc-(b) EMSRtrc-(c) Improvement (%)
Approximate revenue 32290.00 31805.00 31017.50 31565.00 32335.00 30925.00 0.139
Maximum revenue 36650.00 37300.00 36650.00 32550.00 34250.00 37650.00 0.938
Minimum revenue 23650.00 20400.00 20600.00 24250.00 22200.00 21450.00 2.537
The numerical example part comprises three different examples solved by different EMSR models. Each EMSR model solves all instances. For all three problems, the proposed EMSRtrc model exhibits superior performance. A more detailed discussion related to the results is provided in the next section.
Results
The EMSRtrc is an ARM model that relaxes the booking limits of the regular EMSR algorithms. While relaxing the booking limits, does not worsen the total objective revenue of the system. Simultaneously, it makes the system avoid the risk of having many empty seats in the aircraft. The EMSRtrc sells more seats from the lower fare levels, but it does not decrease the total revenue in the end. In the preceding section, three examples are handled for the EMSRa, b, c, and EMSRtrc. It is seen that the EMSRtrc has better results than the others in most cases in terms of total revenue. When the suggested versions of the EMSRtrc are evaluated, it is seen that EMSRtrc-(c) provides the highest return in the top revenue category in all three examples compared to with EMSRtrc-(a) and EMSRtrc-(b). This is because EMSRtrc-(c) protects more seats in higher fare classes than EMSRtrc-(a) and EMSRtrc-(b). As can be seen from the results, a lower maximum income was obtained for the first example problem, while a higher maximum income was achieved for the second and third example problems. However, considering the minimum revenue, the new models proposed produce outstanding results. It promises a better minimum revenue of 9900, 11,619, and 2537%, respectively, for the three sample problems solved.
Load factors of each model are given in Table 9. Loading factors are higher than in EMSRa, b, and c, as the EMSRtrc, allows the seats at the high fare level to be sold at lower prices. As can be understood from the load factors, some seats are empty at the end of the sales period. Considering the third numerical example, the approximate number of empty seats is 4.65 for the EMSRa, 7.25 for the EMSRb, and 9.25 for the EMSRc. However, the approximate number of empty seats is 1.3 for the EMSRtrc-(a), 2.65 for the EMSRtrc-(b), and 7.85 for the EMSRtrc-(c). Regarding the approximate number of empty seats, the highest number of emptied seats was observed in the EMSRtrc-(c) version. Although fewer seats were sold in this version, it achieved the highest revenue in all three examples as the maximum revenue compared with the other two versions. As mentioned earlier, EMSRtrc-(c) protects more seats in higher price classes. The results clearly show that the proposed EMSRtrc model outperforms other three EMSR models.Table 9 Load factors of ARM models
Example number EMSRa EMSRb EMSRc EMSRtrc-(a) EMSRtrc-(b) EMSRtrc-(c)
1 84.6% 84.6% 82.8% 86.8% 86.8% 83.0%
2 91.7% 89.4% 87.3% 94.5% 91.5% 89.2%
3 92.8% 88.8% 84.8% 98.0% 95.9% 87.9%
In Table 10, percentages of each model's total objective revenue are provided. For example, the EMSRa has a total objective revenue of $43046.04 for the second example. The EMSRa model managed to exceed target revenue in 5 of 20 scenarios. That means the EMSRa can reach its total objective revenue by 25%. However, the EMSRtrc-(a) exceeds the target revenue in seven of 20 scenarios. As a result, the EMSRtrc-(a)’s percentage is higher than the EMSRa by 35%. Except for three cases, the EMSRtrc has better percentage values in all comparisons.Table 10 Percentage of reaching total objective revenue of the ARM models
Example number EMSRa EMSRb EMSRc EMSRtrc-(a) EMSRtrc-(b) EMSRtrc-(c)
1 30.0% 30.0% 15.0% 35.0% 35.0% 25.0%
2 25.0% 35.0% 20.0% 35.0% 30.0% 20.0%
3 85.0% 55.0% 40.0% 90.0% 85.0% 30.0%
In this study, each sample problem was run 20 times for each EMSR model, and the minimum, approximate and maximum revenues obtained were reported. Note that standard deviations are important measures to assess the robustness of the models. The standard deviations for the problems solved are given in Table 11. When the standard deviation values were examined, it was seen that the proposed EMSRtrc-(a) model had the smallest standard deviation values among the three sample problems. According to these values, the proposed EMSRtrc-(a) model is more stable in producing good results.Table 11 Standard Deviation Results of all Examples for Approximate Revenue of ARM Models
EMSRa EMSRb EMSRc EMSRtrc-(a) EMSRtrc-(b) EMSRtrc-(c)
Example 1 2806.69 2806.69 2502.00 2487.17 2487.17 2665.99
Example 2 3544.65 4192.02 4333.41 3381.34 4389.38 4450.30
Example 3 3477.13 4340.91 3650.90 2054.33 2950.85 4125.64
Discussion
The critical issue for airline companies is that gain high revenue from each flight. The proposed ARM model achieved a higher revenue target than the traditional methods. Simultaneously, a high rate of up to 98% was achieved in the seat load factor rate. The airline companies earn higher revenues with the proposed model, while customers can receive lower prices. It can be challenging to determine the best model among EMSRtrc, EMSRtrc-(a), EMSRtrc-(b), and EMSRtrc-(c). For example, EMSRtrc-(a) is the best model according to approximate revenue outputs in the second example, while EMSRtrc-(b) is the best in the last example. We state that the model results are dependent on the instances and problem parameters. Because the proposed model is highly practical, all the variants of the proposed models can be easily applied and the best model can be chosen.
The proposed ARM model is quite simple, applicable and based on the idea that airline companies set a satisfactory revenue target they expect from any flight and maintain the set seat protection levels throughout the sales period until that target is reached. It proposes to sell the remaining seats, albeit at a low price, in order to increase its revenue when the set target is achieved. In other words, it can be said that while the seat prices are determined by the airline company at the beginning of the sales period, the seat prices are determined by the customers after the determined target is reached. Therefore, the airline companies can earn more revenue than the target revenue it has set. However, this can sometimes cause airline companies to earn less revenue. Namely, after reaching the predetermined revenue target, it could perhaps gain a higher revenue if the set seat protection levels regarding unsold seats are maintained. However, in this case, the seats may not be sold. The proposed ARM model ensures an acceptable revenue for airline companies by balancing the number of empty seats and lower revenue.
One of the decisions that need to be made regarding the proposed method is to determine the target revenue of the airline companies. What will be the airline's target of revenue from the flight? The value of the target revenue is difficult to determine. Different approaches can be employed to determine it. As the first approach, the airline company can calculate the expected revenue using the demand distribution, fare levels, and the number of seats at each fare level and use this value as the target of revenue. In the second approach, the approximate cost of the flight can be determined by the airline company and this value or adding a certain percentage profit to this value can be utilized as the target value.
On the other hand, the present work was compared with state-of-the-art algorithms. In order to perform fair comparisons, some assumptions and settings were adopted from the literature, which might limit the research scope. For further studies, the performance of the EMSRtrc under the conditions of a restricted fare environment, independent demand, and demand distributions different from the normal distribution can be investigated. furthermore, the ability to overbook or cancelation can be further analyzed. These conditions can complicate the problem, but they will bring the problem closer to ARM problems of the actual world. There are studies to make conditions more realistic in the literature to obtain better solutions for ARM problems. Weatherford and Ratliff (2010) mentioned the importance of dependent demand for the success of ARM methods. It was stated that taking the demand dependency of fare levels into account, the result of EMSR booking limits could change. This paper referred that dependent demand revenue management tools gave more than 5% revenue improvements consistently, according to the study of Gallego et al. (2009).
Another discussion is about demand forecasting for ARM. In that study, the demand distribution is accepted as the normal distribution. However, there are studies on demand forecasting in the literature, which may better represent real-life problems. Gautam et al. (2021) mentioned the importance and validity of demand forecasting in their article. They stated the significance of considering the historical demand data of airlines and other dimensions like market size and market share for demand forecasting. As a result, conditions of the ARM problem can be changed to simulate the real-life better. However, the present work aims to make the problem easier to understand, handle and solve. The same algorithm can be evaluated with more realistic problems in future studies.
In today's information age, the rapid developments in technology and the rapid increase in the use of the internet have led to the formation of a large number and variety of data in the digital field. The processing of this data, which is called big data, and its analysis and conversion into useful information has become an important source of information for businesses. Using this information, decision-makers have started to make more accurate decisions. Likewise, the behaviors and consumption preferences of customers who prefer the airline constitute a large data set. The key to accurate and fast airline revenue management depends heavily on the best analysis of this data set and the accurate prediction of future customer behavior and preferences. Among the prediction techniques, the use of artificial intelligence, which has been very successful in recent years, has increased considerably. More accurate and consistent results can be achieved with the use of artificial intelligence in airline revenue management.
Conclusion
In this study, a new EMSR method was developed by relaxing the booking limits by objective revenue values of the EMSRa, b, and c. In the unrestricted fare environment and orderly arriving passengers, the EMSRa, b, c, and the EMSRtrc were simulated through three different examples. According to the results, the EMSRtrc gives better revenue values in all examples. When the results are examined in detail, it is realized that the EMSRtrc performs better according to total revenue and load factor. Besides, it is seen that the EMSRtrc has better rates for the frequency of reaching the objective revenue of EMSR models.
Another important point is that the new ARM model provides convenience in terms of understanding and application. Like other EMSR models, the EMSRtrc has a simple logic. After a ticket is sold, it checks whether the total target revenue has been reached. Through this control, it decides whether or not to sell the unsold seats in the higher fare levels at a lower price. As a result, the proposed model is feasible and realistic for ARM.
Only single-leg flights were considered in this study. However, in reality there just are no single-leg flights. A flight can have both single-leg passengers and continuing passengers. In this case, more complex analyses are required. In this sense, the proposed method may be insufficient.
This study is limited to determining the number of seats for each fare level and increasing the seat sales revenue of the airline company. Other components of the ARM were excluded from this study. The proposed model was applied only to single-leg flights. However, the model can be generalized to multi-legged flights. In this study, we assumed that the demand is normally distributed. By extending this assumption, the behavior of the model can be examined for different distributions. In addition to the airline sector, the proposed models are applicable to other industries with perishable products, such as the hotel and cruise ship industries.
Author contributions
All authors equally contributed to this paper with the conception and design of the study, literature review and analysis, drafting and critical revision and editing, and final approval of the final version.
Funding
This study was not funded by any institutions.
Availability of data and material
The authors confirm that the data supporting the findings of this study are available within the article.
Code availability
The codes used in this article can be obtained by the authors if necessary.
Declarations
Conflict of interest
There is no conflict of interest in this study.
Ethics approval
This study does not contain any studies with human participants or animals performed by any of the authors.
Consent to participate
No individual participants were included in this study.
Consent for publication
There is no data in this study that requires permission to publish.
Publisher's Note
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Buyruk M Güner E Personalization in airline revenue management: an overview and future outlook J Reven Pric Manag 2022 21 129 139 10.1057/s41272-021-00342-x
Duduke AS Venkataraman SV Airline revenue management with preference based flexible products J Ind Prod Eng 2022 39 2 128 145 10.1080/21681015.2021.1964627
Escovar-Álvarez G Belobaba PP Premium cabin capacity sharing strategies: airline RM insights J Reven Pric Manag 2022 21 3 16 10.1057/s41272-021-00326-x
Fiig T Isler K Hopperstad C Belobaba PP Optimization of mixed fare structures: theory and applications J Reven Pric Manag 2010 9 152 170 10.1057/rpm.2009.18
Gallego G Li L Ratliff RM Choice-based EMSR methods for single-leg revenue management with demand dependencies J Reven Pric Manag 2009 8 207 240 10.1057/rpm.2008.53
Gautam N Nayak S Shebalov S Machine learning approach to market behavior estimation with applications in revenue management J Pric Reven Manag 2021 20 344 350 10.1057/s41272-021-00317-y
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Gosavii A Bandla N Das TK A reinforcement learning approach to a single leg airline revenue management problem with multiple fare classes and overbooking IIE Trans 2002 34 9 729 742 10.1080/07408170208928908
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Jindal Global Law Review
Jindal Global Law Review
0975-2498
2364-4869
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10.1007/s41020-022-00182-9
Book Review
Tracy Gendron: Ageism Unmasked: Exploring Age Bias and How to End It
Steerforth Press, 2022, Pp. vii+178, ISBN: 978-1-58642-322-3
http://orcid.org/0000-0002-1709-4184
Ramachandran Mallika [email protected]
Independent Legal Researcher, Gurgaon, India
5 12 2022
111
8 11 2022
© The Author(s), under exclusive licence to O.P. Jindal Global University (JGU) 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
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pmcAgeism Unmasked: Exploring Age Bias and How to End It1 considers the deep-seated presence of ageism in society, whether in laws and policies, work, the health care sector, or everyday functioning, based often on culturally perpetuated, erroneous presumptions and stereotypes. It demonstrates how, particularly for the older segment of society, ageism has become conflated with ableism, resulting in oppression, discrimination, and a denial of rights.
While the term ‘ageism’ in itself covers any discrimination based on age, whether against younger segments of society who are dubbed reckless or immature, or older segments, who are associated with illness and inability to function,2 the book focuses on the latter group due to the more severe consequences they face resulting from ageism, preventing them from living a dignified life with all its possibilities.3
The author, Tracey Gendron, holds master’s degrees in gerontology and psychology and a PhD in developmental psychology and has over 25 years of experience working in the field of gerontology.4 Prompted to re-examine the concept of age and ageism by the realisation that her own approach too, despite vast experience in the field, was coloured by a negative perception of ageing (p. 2), she attempts in this book to identify the misconceptions and stereotypes that have taken root and encourages readers to reflect on them. The book is written in a way as to be accessible to both common and academic readers (in the fields of law, medicine, social sciences, and, of course, gerontology and public health) and is not encumbered by technicalities. This is a well-researched book as evidenced both in its writing and notes, which also benefit from the author’s rich professional and personal experience.
The review that follows is structured in five sections. After a brief overview in the first section, sections 2 and 3 explore two significant themes highlighted in the book, specifically, understanding ageing in all its nuances, and the discrimination, rights-denial, and structural barriers experienced by older segments of society. The fourth section attempts to identify some learnings from the book that would benefit the fields of law and policy-making, in particular, and the concluding section recapitulates major contributions of the book and considers some limitations.
Overview
In an Introduction and eight chapters, the author engages with a range of aspects and materials associated with ageing, ageism, and related concerns. Laying the foundation for the discussion that follows, the Introduction (pp. 1–18) highlights how ‘old’ and related terms like ‘aged’ or ‘elderly’ have become pejoratives or hold negative connotations. It goes on to note how the dominant cultural narrative leads to the internalisation and perpetuation of ageist notions, bringing about oppression and marginalisation. The chapter also provides conceptual clarity, discussing forms of ageism (positive, negative, cultural, and so on), and its damaging effects whether on health, the economy, or equity.
Flowing from the idea that tools to empower ourselves to fight ageism can be developed by learning from the past, Chapter 1 (pp. 19–30) considers historical outlooks on ageing in various cultures from the Judeo-Christian Bible to Islam, the Hindu Dharmasastras, and Confucianism, which revered the aged, to Greece and the Middle Ages, which on the other hand idolised youth and viewed old age as decrepitude, to demonstrate how age has been a complex issue right from the beginning. Ageing is not one stage but a series of processes which may involve experiencing decline, growth, and maintenance all at the same time, and one that can be addressed with the realisation that it encompasses both challenges and opportunities.
Shifting the focus to technology and medicine, fields which have impacted older persons, Chapter 2 (pp. 31–48) discusses how developments in these areas have led to greater longevity, at the same time also leading to loss of value of elders and the perception of them as a burden on society, besides limitations in opportunities, and segregation of those thought unable to lead independent lives. These changes have driven ‘twin’ forms of discrimination against elders, ageism and ableism, with age being perceived as a disease, and ageism manifesting in health care, structurally and in practice.
Chapter 3 (pp. 49–63) argues that the notion of retirement, a social institution, has become equated with a developmental stage, symbolising complete withdrawal from what one used to do. This has led to age erroneously being equated with decline and inability, and consequent ageism and negative stereotypes at the workplace, including in the form of subtle microaggressions. Taking further the discussion on ‘retirement’, Chapter 4 (pp. 64–89) highlights problems of segregation created by the establishment of retirement communities (however ‘active’ these may be), and by theories claiming to define ‘successful’ ageing, which not only take a narrow view of the term, but fail to consider the multiple factors that determine an individual’s ageing process, and shift the ‘blame’ for ‘unsuccessful’ ageing onto the individual.
Chapter 5 (pp. 90–110) sheds light on another phenomenon that has implicated and thereby further entrenched ageism, namely, ‘anti-aging culture’. Media in particular plays on fears and shame over ageing, thus encouraging the multi-billion-dollar anti-ageing consumer industry. Alongside, this creates an additional obstacle to an understanding and appreciation of elderhood, and fosters an environment that marginalises elders. Visual ageism or stereotypical (negative) representations in the media add to this.
Chapter 6 (pp. 111–123) explores the benefits and challenges brought about by the information age, which has introduced unprecedented opportunities to enhance the lives of elders but has also created digital ageism, ‘othering’ groups in the context of digital media use. Various aspects, including older people often being left out of the R&D for applications designed for them, younger persons writing algorithms, and lumping of big data, lead age stereotypes to become embedded in digital platforms. The discussion also addresses the issue of loneliness, which is often enforced by ingrained stereotypical thinking and ageism, and to which proposed technological solutions are an inadequate answer.
Chapter 7 (pp. 124–140) takes up the changed context brought by the COVID-19 pandemic, which ‘propel[led] ageism into mainstream consciousness’ (p. 124). The chapter discusses how ageism became rampant during the pandemic based on misconceptions linking age with health and underestimating elders’ economic contributions, leading to older segments being viewed as ‘dispensable’, age-based bullying, positive ageist practices depriving older persons of agency, and deepened isolation. Not only that, structural ageism also led to devaluing of long-term caregivers, who received less attention and bore severe consequences.
Finally, Chapter 8 (pp. 141–164) considers the path to ‘elderhood’,5 which needs to be normalised as a life-stage with its uniqueness and value. Ageing being an individual experience, this process requires awareness of one’s thought patterns, asking future-oriented questions (which ageist perceptions prevent one from ordinarily doing), and embracing ageing so as to fully participate in meaning-making activities, amongst other steps. Meaningful roles that elders can play in society, bringing inclusivity and fluidity, are also explored, as is the need to address cognitive dissonance, confront systemic ageism, and celebrate milestones so as to welcome rather than fear this natural process.
A central thread running through the chapters is how cultural messaging, often based on incorrect or incomplete understandings of ageing and its implications, pervades different areas of life, causing ageist attitudes to be imbibed by most. This establishes and maintains ageism in society, both against others and oneself. In keeping with the aims of the book, the chapters trace how ageist notions which had historical roots in some threads of traditional thought, became more firmly entrenched with developments like the industrial revolution, the emergence of the notion of retirement, and consumerism and advertising that exploit people’s shame and fear. This process has only deepened with recent advances in digital technology, and has become especially pronounced in the COVID-19 pandemic. While examining these themes, the author adopts a balanced and even-handed approach, acknowledging the benefits of various developments but also underlining how these may act to the detriment of older segments.
A nuanced understanding of ageing
The starting point in addressing ageing is to recognise that ‘[a]ging is not something that just happens to older people. We all age, every moment of our lives, from birth through death’ (p. 4). This is distinct from the notion of ‘senescence’ which refers to ‘biological aging that leads to the gradual deterioration of function in cells and/or organisms’ (p. 5). Not understanding this difference, or the related fact that age is simply a condition of life, not of itself ‘good’ or ‘bad’,6 results in the term ‘ageing’ being associated with only certain segments of society, who are also seen as ‘not useful’ and are necessarily associated with illness, decrepitude, and disability, resulting in their being segregated, in a sense, from mainstream society.7
More shocking is the fact, as the book points out (p. 93), that the World Health Organization itself had classified ageing, a natural, universal phenomenon, as a disease under the International Classification of Diseases (ICD)-10 in 1992 as ‘senility’, and again as ‘old age’ in the category of ‘symptoms, signs, or clinical findings not elsewhere classified’ under ICD-11 in 2019.8 In fact, the Biomedical Model (of medicine),9 which became the dominant framework for understandings of ageing and disability, medicalised these conditions which in Gendron’s view enabled the proliferation of both ageist and ableist practices (p. 40).
Discrimination, denial of rights, and creation of barriers
The consequence of such attitudes is the perpetuation of ‘widely prevalent’ ageism, or ‘discrimination, marginalization, and/or oppression based on age’ against older segments of the population, as well as ‘ableism’, which refers to discrimination based on (perceived) ‘physical, intellectual, and/or cognitive ability’, even though not all older persons have a disability (pp. 8–9). Such outlooks have an impact on health (which includes both negative impacts on the health of persons who carry a negative perception of age as also the overall health of society), the economy (both by denying older persons the opportunity to engage in meaningful economic activity where needed or to contribute to the economy where desired), as well as conditions of inequity or denial of rights where a segment of society faces discrimination and barriers to a life of freedom and equity (pp. 14–15). Ageism further intersects with sexism, racism, classism, and so on, with discriminatory consequences like pay disparity, workplace harassment, housing instability, inadequate access to health care, and food insecurity, faced disproportionately for instance by older women, and more so, women of colour (pp. 16, 98). Yet, ‘age’ is not expressly mentioned as a ground of discrimination in most international human rights instruments,10 and the argument that it can be addressed under the category of ‘other’ is seen as lacking strength and furthering invisibility.11 The Constitution of India, too, in its non-discrimination provisions, does not mention ‘age’.12
The notion of retirement (pp. 51–53, 64–65), which became the norm in the post–World War II era in view of developed social security systems, and which is based on the criterion of age alone, rather than interest, need, or ability, also raises a set of issues. The concept, which is a social institution but has become equated to a life-stage, reinforces stereotypes about elders as ‘useless’ and ‘unfit for work’ or ‘non-productive’ and ‘non-contributing members of society’ (pp. 50, 52–53). Moreover, it involves, to an extent, a forced withdrawal from active society, and is blind to or overlooks vulnerability factors such as race, gender, education, and social class which impact the ability to retire as well as the fact that for many, engagement in a productive work life provides a sense of purpose which is summarily dismissed irrespective of ability.13 In the Indian context, where the retirement age is among the lowest in the world,14 workplace discrimination based on age is reported by a substantial segment of the population,15 significant levels of poverty prevail among elders,16 and no strong constitutional and statutory framework17 exists for combatting discrimination so as to address ageism in the workplace; thus these issues need to be considered from both legal and social perspectives.
Relatedly, the notion of retirement as a life-stage has seen the development of retirement communities, marketed as a sort of aspiration,18 but which result in segregation and the creation of age-restricted communities denied healthy everyday interactions with members of other age groups including their own families (a situation intensified in the pandemic). Barriers are thus created in living a meaningful life for not only elders themselves but also society as a whole, which can develop in a healthy manner through regular interactions between people of all ages.
Flowing from ageism, and linked to perceptions of elders being seen as somehow withdrawn from or no longer part of active society, is the lack of consideration of their needs and interests in broader society. This lack of consideration manifests in advertising that targets them, which sees them as only interested in certain products like assistive devices, hearing aids, and so on rather than a broader range of products for general utility, well-being, and self-actualisation (p. 109). It also manifests in the context of technology, where opinions of elders are rarely reflected in studies on digital practices (and more generally in the context of all technology) (pp. 32, 114), leading to ageist development of technology, thus worsening and perpetuating stereotypes regarding elders’ inability to handle technology. In the health sector, it reflects in patronising and infantilising attitudes towards elder patients, denying them personhood, agency, and voice (for instance, by addressing family members over the patients themselves), and seeing pain and suffering as ‘expected’ in old age (pp. 45–47). An issue in the health care sector which has legal implications is the tendency of systems like Medicare19 to limit elders’ access (p. 47).20
The pandemic exacerbated the already prevalent ageism with many expressing the opinion that the elder segment was ‘disposable’, as also by the perception that the entire segment of the population was equally vulnerable and susceptible to the disease (see pp. 125, 128–130).21 Not only that, caregivers and facilities caring for the elderly also saw a devaluing as compared to other caregiving roles.
Some learnings
The book under review raises a number of issues regarding ageism, discrimination against and the ‘othering’ of elders, as well as provides many insights on age and ageing which are relevant from the perspective of law and policy-making (in addition to their relevance for broader society).
In the present-day context, with increasing longevity over the past many decades, the demographic situation has seen a significant change. As noted by World Population Ageing: 2019,22 in 2019, there were 703 million persons aged 65 and above the world over,23 comprising 9 per cent of the population. This figure is projected to reach 1.5 billion or 16 per cent of the population in 2050. Soft law measures like the Vienna Action Plan on Ageing adopted in the World Assembly on Ageing in 1982,24 the UN Principles on Older Persons in 1991,25 Madrid International Plan of Action on Ageing in 2002,26 besides an important general comment,27 have been introduced in the recent past. Significantly, an Open-Ended Working Group on Ageing was set up in 201028 towards strengthening the human rights of older persons. Debates and discussions are under way regarding a specific international convention for the protection of the rights of older persons29 in view of the specific forms of rights violation and ageism experienced by them and dispersed standards of protection in existing frameworks.
In this regard, the issues raised and points highlighted by the book become relevant. In discussing the various forms that ageism, including that against elders, can take, the book brings up the notion of positive ageism which, while based on a view of age which is tinged with respect or kindness, can perpetuate prejudices by being ‘patronizing or infantilizing’, thereby limiting older people’s opportunities through ‘over-accommodating behaviors’ (p. 11). Legislation that is seemingly for the protection of elders, such as the Older Americans Act 1965 highlighted by the book, seeks to address the needs of older persons but in doing so defines ‘all older persons as vulnerable and needy’ (p. 65; emphasis in original). This denies the heterogeneity of the segment of older persons and also further perpetuates stereotypes against them. Recognition and protection of the human rights of older persons should imply enabling them to live a meaningful life with dignity as any other member of society and thus must ensure that the provision of protection, where required, does not become an act of denying them agency and creating barriers to their living a life with all its opportunities and facets.30
Instead, as highlighted by the example in the context of ageist structural barriers (p. 123), what may be needed is not merely protection, but, drawing from the case of rights of persons with disabilities, the need for society to make space for elders as equal and contributing members, whereby they can exercise all human rights, bringing about the required structural inclusivity.
The law and policy process can also draw lessons from the book’s insight that ageism or ageist attitudes are impacting negatively not simply on elders alone but on society as a whole. This is because such perspectives and self-directed ageism can result in the realisation of stereotypes, which is shown to be linked with the higher presence of markers of Alzheimer’s, lower physical and cognitive function, and depressive symptoms, among others (p. 14). Moreover, a society where people from all walks of life can meet, interact, and learn together is seen as key to combatting fears of ageism (pp. 74–75).
Conclusion
Negative attitudes towards old age and discrimination against elders have become deeply ingrained in society and are seen in laws, policies, and structural constraints, and, as the book highlights, in cultural messaging, practices, and daily microaggressions (such as fairy tales portraying stereotypical depictions of age, jokes based on age, or advertising for ‘anti-ageing’ products that plays on the fear and shame that have become associated with age). This is an issue that has social dimensions and also requires changes in laws, policies, and practices (p. 142).
The important insights provided by the book under review can help in identifying and recognising these negative perceptions and addressing them both at the individual level and also more broadly, in law and policy. Such insights include the inaccurate association of ageing with old age alone, the equating of old age with illness and inability and the application of such an understanding to deny the heterogeneity of elders, positive ageism impeding elders in exercising their rights and living life with all its possibilities, the extent to which such attitudes have become deeply embedded in society, and the negative impacts of such practices on society as a whole.
While the book, in tracing historical attitudes and perceptions regarding elders and in making recommendations for practices that can address the prevalent ageism in society, draws from different cultures and parts of the world, the core chapters are essentially focused on the American scenario and experience. Including negative and positive instances and trends from other parts of the world in line with the concerns it highlights and the recommendations it makes, would make it a richer resource.31
Overall, the book provides understandings and highlights concerns, taking note of which can be the first step towards ensuring that elders can live as active, informed citizens with value and agency and exercise their human rights.
Declarations
Conflict of interest
The author has no conflicts of interest to declare that are relevant to the contents of this article.
1 Tracey Gendron, Ageism Unmasked: Exploring Age Bias and How to End It (Steerforth Press 2022). This is a substantially revised version of a short review of this book which has appeared previously on the author’s blog: https://potpourri2015.wordpress.com/2022/03/05/book-review-ageism-unmasked-exploring-age-bias-and-how-to-end-it-by-tracey-gendron/ (Accessed 12 September 2022) and parallelly on Goodreads.
2 The recent World Health Organization (WHO) Global Report on Ageism looks into the issue of ageism against older as well as younger segments of the population. See WHO, Global Report on Ageism (2021). https://www.who.int/teams/social-determinants-of-health/demographic-change-and-healthy-ageing/combatting-ageism/global-report-on-ageism. Accessed 22 July 2022.
3 The more specific reasons Gendron offers for this focus are ageism against older persons being a social justice issue; the absence of an alternative narrative to the view of older adulthood as a period of decline; and the severe and damaging consequences of ageism for people’s health and happiness across their lives. Gendron, Ageism Unmasked (n 1) 10–11.
4 https://gerontology.chp.vcu.edu/our-team/tracey-gendron-ms-phd.html. Accessed 22 July 2022.
5 The term is used by Gendron to capture the complexity, dimensions, and directionalities of the ageing experience. Gendron, Ageism Unmasked (n 1) 17.
6 As discussed in the previous section, it is interesting to note that even historically, both positive (reverent) and negative views of ageing prevailed at different times and in different cultures. See Gendron, Ageism Unmasked (n 1) 21–24, 25.
7 In fact, even care for elders within the family has become something that is seen as a burden or undesirable, as against care for children which is seen as a ‘normal’ part of life. See Gendron, Ageism Unmasked (n 1) 34.
8 For another view arguing that the classification or equating of old age as a disease ‘is potentially detrimental and deleterious from clinical, research and humanitarian points of view’, see Debanjan Banerjee et al., ‘Not a Disease: A Global Call for Action Urging Revision of the ICD-11 Classification of Old Age’ (2021) 2(10) Lancet: Health Longevity E610. https://www.thelancet.com/journals/lanhl/article/PIIS2666-7568(21)00201-4/fulltext. Accessed 11 May 2022. Further, as the authors also note, ‘Considering chronological age as a sole cause for diseases may be largely inaccurate and misleading.’ Ibid. E610. Also, on these lines, Gendron argues that it is both problematic and unethical for medical decision-making to be dictated by age alone, as it is not of itself an indicator of health status. See Gendron, Ageism Unmasked (n 1) 46. A contrary view, according to which this move of equating age with illness would lead to ‘economic and health care benefits for all stakeholders’ and better resource allocation, is Alex Zhavoronkov and Bhupinder Bhullar, ‘Classifying Aging as a Disease in the Context of ICD-11’ (November 2015) Frontiers in Genetics. https://www.frontiersin.org/articles/10.3389/fgene.2015.00326/full. Accessed 11 May 2022. It may be noted, however, that a subsequent piece of literature refers to the terms ‘old age’ and ‘pathological’ as having been ultimately withdrawn from ICD-11. See Kiran Rabheru, Julie E. Byles, and Alexandre Kalache, ‘How Old Age Was Withdrawn as a Diagnosis from ICD 11’ (2022) 3(7) Lancet: Health Longevity E457.
9 As explained by Carver and Buchanan, according to the ‘biomedical model’, ‘successful aging requires that the elder is disease free, disease-related disability free and engaged in activities with family and/or community.’ Lisa F Carver and Diane Buchanan, ‘Successful Aging: Considering Non-biomedical Constructs’ (2016) 11 Clinical Interventions in Aging 1623–1630.
10 Two exceptions are the International Convention on the Protection of the Rights of All Migrant Workers and Members of Their Families which mandates against age discrimination (see arts 1, 7); and the Convention on the Rights of Persons with Disabilities in terms of access by older persons with disabilities to social protection and poverty reduction programmes (art 28). See International Convention on the Protection of the Rights of All Migrant Workers and Members of Their Families (adopted 18 December 1990) General Assembly Resolution 45/158; Convention on the Rights of Persons with Disabilities (adopted 13 December 2006, 61st session of the General Assembly) Resolution A/RES/61/106. Also see UNGA, ‘Report of Secretary General: Follow Up to Second World Assembly on Ageing’, A/66/173 (22 July 2011) para 5. https://www.ohchr.org/en/documents/reports/follow-report-second-world-assembly-ageing. Accessed 25 July 2022.
11 See UNGA, ‘Report of Secretary General’ (n 10) paras 5–6; UN Department of Economic and Social Affairs, ‘Report of Expert Group Meeting: Rights of Older Persons’ (Bonn, 05–07 May 2009) 15. http://www.un.org/esa/socdev/ageing/documents/egm/bonn09/report.pdf. Accessed 21 May 2022.
12 Constitution of India, arts 15, 16. The Constitution does however mention ‘old age’ in the context of public assistance (art 41) and also ‘age’ in the context of rights of children (for example, arts 21A, 24, 45) and qualifications of various office holders, members of Parliament, and so on. Indirectly however, the issue of age discrimination in employment (although in the context of younger persons) was considered in Anuj Garg v Hotel Association of India AIR 2008 SC 663.
13 For a discussion on the myths surrounding and implications of mandatory retirement, see Elaine Fox, ‘Mandatory Retirement: A Vehicle for Age Discrimination’ (1974) 61(1) Chicago Kent Law Review 116; also see Lynn MacDonald, ‘The Evolution of Retirement as Systematic Ageism’ in Patricia Brownell and James J Kelly (eds), Ageism and Mistreatment of Older Workers (Springer 2013) 69.
14 The retirement age for private employees in India is 58. See ‘At 58, Retirement Age in India Is One of the Lowest Worldwide’ (Times of India, 25 April 2018). https://timesofindia.indiatimes.com/world/at-58-retirement-age-in-india-is-one-of-the-lowest/articleshow/63905499.cms. Accessed 28 June 2022. As per a circular issued by the Department of Personnel and Training (No. 25012/8/98-Estt. A dated 30 May 1998), the recommendations of the Fifth Pay Commission were accepted thereby raising the retirement age of central government employees from 58 to 60. It may be noted, however, that the relevant fundamental rule (56) of the Ministry of Personnel, Public Grievances and Pensions allows grant of extension to certain persons (for instance, experts, secretaries to the Government of India in certain departments such as defence, foreign affairs, and so on), while the age of superannuation for employees in teaching positions is fixed at 65. See https://dopt.gov.in/sites/default/files/Extracts%20of%20provisions%20in%20FR%2056.pdf. Accessed 13 September 2022. An unstarred question, No. 576, in the Lok Sabha brought up the issue of retirement of employees. In the response dated 16 September 2020, the Minister of State in the Ministry of Personnel, Public Grievances and Pensions, Dr Jitendra Singh, clarified that there was no proposal to change the retirement age of central government employees. See Lok Sabha, Unstarred Question No. 576 (Shri LS Tejasvi Surya). http://164.100.24.220/loksabhaquestions/annex/174/AU576.pdf. Accessed 13 September 2022.
15 See, for instance, S Bhatt, ‘Does India Need an Age-Based Employment Discrimination Law’ (Economic Times, 10 February 2022). https://hr.economictimes.indiatimes.com/news/workplace-4-0/does-india-need-an-age-based-employment-discrimination-law/89467382. Accessed 28 June 2022. Bhatt takes note of a survey wherein nearly 33 per cent employees reported having faced age-based discrimination at work. See also Diksha Madhok, ‘India’s Workplaces Have an Ageism Problem’ (Scroll.in, 30 September 2019). https://scroll.in/article/938761/ancient-menopausal-old-man-indian-workplaces-have-a-hypocritical-ageism-problem. Accessed 13 September 2022.
16 For instance, a study by Akanksha Srivastava and Sanjay K Mohanty estimates about 22.4 per cent elderly households live below the poverty line. See Akanksha Srivastava and Sanjay K Mohanty, ‘Poverty among Elderly in India’ (2012) 109(3) Social Indicators Research 493.
17 In the Indian context there is no specific statute that addresses the issue of ageism or age discrimination. The Maintenance and Welfare of Parents and Senior Citizens Act 2007, however, deals with some issues in the context of the elderly, such as enabling them to claim maintenance from children or relatives (this is in addition to remedies under personal law, the Code of Criminal Procedure 1973, and state legislation); providing punishment for abandonment by those responsible for their care; and protecting against loss of property by false promises, besides establishment of old age homes in each district. These provisions are however relevant in the context of elders who are either in need of care or protection, or have suffered due to certain acts of others.
18 Gendron highlights this aspect in the context of the American experience, but in India too, in the present context, we can see the development and ‘marketing’ of several senior-living facilities, which can go on to create a similar scenario. Some advertisements for such facilities use what we can now see as ageist language even if positively couched such as ‘age is just a number’, or referring to ‘looking and feeling young in your golden years’. For instance, Ashiana Senior Living, https://www.ashianahousing.com/senior-living-india. Accessed 13 September 2022. Alternatively, some perceive those older than 50 as requiring special facilities for ‘a stress-free, self-reliant and enlivening’ life, thus incorporating more negative connotations of ageism. See Ananya’s Nana Nani Retirement Homes. https://www.nanananihomes.in/?gclid=CjwKCAjw1ICZBhAzEiwAFfvFhPYNiPxbNxT1pAZsj2dCDgYsB2IxhqrR5aeudmikmNUpiJaOScBKpxoCogwQAvD_BwE. Accessed 13 September 2022.
19 Medicare is a federal health insurance programme for those aged 65 and over (as well as certain other categories like younger people with disabilities and those with end-stage renal failure) in the United States. See https://www.medicare.gov/what-medicare-covers/your-medicare-coverage-choices/whats-medicare. Accessed 15 September 2022. By permitting doctors to opt not to accept Medicare, thereby not treating many older patients, not covering certain preventive and support services, and its different reimbursement rates for older persons vis-à-vis those for younger patients under private insurance, elders’ access and standards of care become limited. See Gendron, Ageism Unmasked (n 1) 47.
20 Also Robert L Kane and Rosalie A Kane, ‘Ageism in Healthcare and Long-Term Care’ (2005) 29(3) Generations 49.
21 Also highlighting how ageism was sharply brought into focus during the pandemic is the ‘Report of the Independent Expert on the Enjoyment of All Human Rights by Older Persons, Claudia Mahler’, Human Rights Council (48th Session, 04 August 2021) A/HRC/48/53. https://ngocoa-ny.org/recent-documents-of-interes/a_hrc_48_53_ie-report-on.pdf. Accessed 25 July 2022. See also Sarah Fraser et al., ‘Ageism and Covid 19: What Does Our Society’s Response Say about Us’ (2020) 49(5) Age and Ageing 692. Vervaecke and Meisner discuss the nuances and implications of ‘compassionate ageing’ which also manifested during the pandemic. Deanna Vervaecke and Brad A Meisner, ‘Caremongering and Assumptions of Need: The Spread of Compassionate Ageing during Covid 19’ (2021) 61(2) Gerontologist 159.
22 Department of Social and Economic Affairs, Population Division, World Population Ageing: 2019, ST/ESA/SER.A/430 (United Nations 2019). https://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2019-Highlights.pdf. Accessed 05 May 2019.
23 In the case of India, in 2019, over 10 per cent of the population (139 million) was aged over 60, which figure is expected to nearly double to 19.5 per cent or 319 million people by 2050. See ‘Ageing Population in India’. https://ageingasia.org/ageing-population-india/. Accessed 10 May 2022.
24 United Nations, Vienna International Plan of Action on Aging (1983). https://generationen.oehunigraz.at/files/2012/07/Wiener-Aktionsplan-zur-Frage-des-Alterns-1982.pdf. Accessed 13 September 2022.
25 Adopted by General Assembly Resolution 46/91, 16 December 1991 (A/RES/46/91), Annexure.
26 Political Declaration and Madrid International Plan of Action on Ageing, Second World Assembly on Ageing, Madrid, Spain (8–12 April 2002) (United Nations 2002). https://www.un.org/esa/socdev/documents/ageing/MIPAA/political-declaration-en.pdf. Accessed 15 September 2022.
27 Committee on Economic, Social and Cultural Rights, General Comment No. 6 on ‘The Economic, Social and Cultural Rights of Older Persons’ (adopted at the 13th Session of the Committee on Economic, Social and Cultural Rights, 08 December 1995) E/1996/22.
28 ‘Follow-Up to the Second World Assembly on Ageing’ (Resolution adopted by the General Assembly on 21 December 2010) A/RES/65/182 (7 February 2011) para 28.
29 See, for instance, Israel Doron and Itai Apter, ‘The Debate around the Need for an International Convention on the Rights of Older Persons’ (2010) 20(5) Gerontologist 568; John Williams, ‘An International Convention on the Rights of Older People?’ in Marco Odello and Sofia Cavandolli (eds), Emerging Areas of Human Rights in the 21st Century: The Role of the Universal Declaration of Human Rights (Routledge 2014) 128; Mallika Ramachandran, ‘Older Persons and the International Human Rights Framework: Arguments for a Specific International Convention’ (2014) 56(4) Journal of the Indian Law Institute 523; Paul Harpur, ‘Old Age Is Not Just Impairment: The CRPD and the Need for a Convention on Older Persons’ (2016) 37(3) University of Pennsylvania Journal of International Law 1027.
30 Such an approach is in line with the perceptions of older adults with regard to dignity, as noted in a study pertaining to dignity in mental health care which points out that ‘being respected as an individual, independence, safety, privacy and participation’ are seen as essential components of dignity. Debanjan Banerjee, et al., ‘Role of Dignity in Mental Healthcare: Impact on Ageism and Human Rights of Older Persons’ (2021) 29(10) American Journal of Geriatric Psychiatry 1000.
31 For instance, the book does not address the controversy raised some years ago around elders in Germany being sent to care homes in other, lower-income countries. See Bouke de Vries, ‘Granny-Export: The Morality of Sending People to Care Homes Abroad’ (2021) 18 Journal of Bioethical Inquiry 455; Naomi Kresge, ‘Exporting Grandma: Germany’s Answer for Elder Care’ (Toronto Star, 06 October 2013). https://www.thestar.com/news/world/2013/10/06/exporting_grandma_germanys_answer_for_eldercare.html. Accessed 12 May 2022. Another issue it bypasses is the notion of combining toddler and elder day care. See Ashley McGuire, ‘Toddlers and Seniors Together: The Benefits of Intergenerational Care’ (IFS Studies, 29 March 2019). https://ifstudies.org/blog/toddlers-and-seniors-together-the-benefits-of-intergenerational-care. Accessed 12 May 2022.
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J Prev Alzheimers Dis
J Prev Alzheimers Dis
The Journal of Prevention of Alzheimer's Disease
2274-5807
2426-0266
Springer International Publishing Cham
36471007
96
10.14283/jpad.2022.96
Article
15th Conference Clinical Trials Alzheimer’s Disease, November 29- December 2, 2022, San Francisco, CA, USA: Symposia - Oral Communications - Late Breaking Abstracts (Clinical Trial Alzheimer’s Disease)
3 12 2022
2022
9 Suppl 1 850
© Serdi 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
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pmcSymposia
Clinical Trial Alzheimer’s Disease
S1- CTAD 2022 FLUID BIOMARKER SYMPOSIUM: RECENT ADVANCES IN PLASMA AND CSF ALZHEIMER BIOMARKERS TO IMPROVE CLINICAL PRACTICE AND TRIALS. R. Bateman 1, M. Mielke 2, O. Hansson 3, K. Blennow 4 (1. Washington University School Of Medicine — St. Louis (United States), 2. Wake Forest University School Of Medicine — Winston-Salem (United States), 3. Lund University — Lund (Sweden), 4. University Of Gothenburg — Gothenburg (Sweden))
Presentation 1: Relationship between blood plasma and CSF measures of Aβ 42/40, tau, and NfL species for tracking drug effects in clinical trials of Alzheimer’s disease, Randall J. Bateman (Washington University School of Medicine, St. Louis, MO, (United States))
Background: Recent advances in the development of novel Alzheimer’s disease (AD) measures of amyloid, tau, and neurodegeneration in blood have enabled the ability to track drug effects in clinical trials of AD. The discoveries of novel tau species in brain, CSF, and blood, such as specific phospho-tau (p-tau) and truncated species including the microtubule binding region (MTBR) region that comprises tangles, have greatly expanded our understanding of tau biology, target development, and drug effect tracking. Longitudinal Aβ, tau, and neurofilament light chain (NfL) changes previously measured in CSF are now being measured accurately in blood, enabling the ability not only to screen and enroll much larger and diverse populations, but also to design secondary and primary prevention trials and measure drug effects. These advances promise to accelerate treatment and prevention development for AD. Methods: We analyzed blood plasma measures of Aβ42/40, multiple p-tau species, and NfL in sporadic AD and dominantly inherited AD cohorts and determined concordance with CSF, amyloid and tau aggregation measures by Positron Emission Tomography (PET) scans, and clinical and cognitive measures in local and international clinical cohorts. Some of these measures were also used to measure plaque-removing drug effects. Results: The findings indicate that CSF and blood plasma Aβ42/Aβ40 ratio and phosphorylation of specific tau species (e.g., p-tau217, p-tau181) mirror decreases in amyloid plaques with anti-amyloid antibody treatments as measured by amyloid PET. Further, findings from CSF suggest that quantitative measures of tau aggregation can be made with specific tau MTBR fragment species, enabling tracking tau aggregation effects separately from amyloid effects. Conclusions: Our results demonstrate that biomarkers to track soluble or aggregated amyloid, and now tau aggregation, are highly precise measures of brain amyloidosis, tauopathy, and neurodegeneration. Use of these novel biomarkers can enable larger and more diverse AD studies and improve the understanding of drug impacts on pathophysiology in clinical trials.
Presentation 2: Consideration and use of AT(N) blood-based biomarkers for community screening, Michelle M. Mielke, Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC (United States))
A major benefit of the use of blood-based biomarkers in screening for Alzheimer’s disease pathology, or diagnosis, is that collection of blood is less invasive and costly than cerebrospinal fluid or neuroimaging markers, and more feasible at the primary care levels where most individuals will present with cognitive symptoms. Blood-based biomarkers of amyloid (A), phosphorylated tau (T), and neurofilament light (N) are already clinically available or nearing clinical use. This talk will highlight some next steps needed before the biomarkers can be implemented for screening or diagnosis at the population level: 1) the identification of factors that may affect the interpretation of the biomarkers (e.g., sex, race/ethnicity, co-morbidities), 2) further discussion regarding how to include the biomarkers in clinical care (e.g., measurement of all 3 biomarkers, development of algorithms or 1 biomarker suitable), and 3) disclosure and ethical considerations.
Presentation 3: Implementation of plasma biomarkers into clinical practice and trials, Oskar Hansson (Lund University, (Sweden))
Plasma biomarkers for Alzheimer’s disease (AD) have already been started to be used in clinical practice and trials. In this presentation I will summarize the Alzheimer’s Association appropriate use recommendations for plasma AD biomarkers, and the key steps needed to be taken before widespread use in e.g. primary care. I will show head-to-head comparisons of different p-tau assays, revealing the high performance of certain mass spectrometry-based assays. I will also show how plasma p-tau217 can be used to drastically lower the need for CSF and PET assessments in the clinical diagnostic work-up of patients with cognitive impairment and still retain a very high diagnostic accuracy. Further, I will describe high-performing plasma-based algorithms for detection of preclinical AD, as well as prediction of cognitive decline in such an early AD population. Longitudinal analyses show that especially plasma p-tau217 is a promising marker for detecting change in AD pathology during the preclinical disease stages. Finally, the effects of potential confounding factors (such as kidney disease) on plasma AD biomarkers will be described, and their effects on the clinical performance will be shown. In summary, plasma AD biomarkers, especially certain p-tau assays, seem to be able to revolutionize the clinical practice and trials in the coming years.
S2- DECENTRALIZED APPROACHES FOR CLINICAL TRIALS ON ALZHEIMER’S DISEASE. H. Massett 1, J. Langbaum 2, P. Maruff 3, R. Lee 4, E. Lee 4, A.M. Wessels 5, K.C. Holdridge 5, M.B. Ferguson 5, R. Yaari 5(1. National Institute on Aging — Baltimore, MD (United States), 2. Banner Alzheimer’s Institute — Phoenix, AZ (United States), 3. Cogstate Ltd — Melbourne, VIC (Australia), 4. Irvine Clinical Research — Irvine, CA (United States), 5. Eli Lilly and Company — Indianapolis, IN (United States))
Introduction: Decentralized trials (DCTs) offer flexibility typically limited in traditional trial design, which may increase geographical and ethnic/racial diversity in trial populations, improve participant engagement and retention, and reduce trial cost. The first DCTs were conducted in the early 2000s and trials with remote designs have exponentially increased within the past 5 years. The need for DCTs was further intensified by the COVID-19 pandemic when trial participants were unable to visit or access facilities for clinical trial assessments, which prompted the FDA to suggest draft guidance on DCT methods to clarify best practices for remote data collection methods. Research utilizing DCT approaches continues to identify both potential benefits and challenges of decentralizing trial research. Clinical trials focused on Alzheimer’s disease (AD) pose specialized challenges to remotely assessing cognitive outcomes. This symposium will feature presentations focused on examples of DCTs addressing AD and cognitive health measures, including benefits and limitations of DCTs in the AD population.
Presentation 1: Remote assessments in a follow-on study from TRAILBLAZER-ALZ, Jessica Langbaum (Banner Alzheimer’s Institute, Phoenix, AZ, (United States))
The comparison of remote, at-home to in-clinic administration of cognitive and functional assessments most often used in clinical trials remains a gap in the literature. To address this, the TRAILBLAZER-EXT (NCT04640077) study Part A was conducted as a multicenter, randomized, non-drug, multiple crossover design that evaluated the reliability of at-home, video teleconferencing (VTC) assessments of cognitive and functional abilities. Participants with AD underwent alternating at-home and in-clinic visits. The reliability of VTC compared with on-site administration of the ADAS-Cog13, ADCS-ADL, MMSE, and CDR-SB was assessed by estimating the intraclass correlation coefficient between the two test modalities. The results from these comparisons will be presented.
Presentation 2: Effects of supervision on cognitive and functional assessment outcomes, Paul Maruff (Cogstate Ltd, Melbourne, VIC, (Australia))
Clinical and cognitive outcomes validated for their sensitivity to early AD must be altered slightly for their administration in telehealth settings. Performance on various cognitive and functional tests were compared between in-clinic and telehealth assessment contexts in a sample of community dwelling, cognitively unimpaired (CU, N=31; mean age (sD)= 67 (9); 16 females) or MCI (CDR 0.5, N=23, mean age (SD)= 69 (13); 16 females) adults recruited from the Australian Dementia Network (ADNET) online registry and the Australian Imaging Biomarkers and Lifestyle (AIBL) study. Recruited participants completed the CDR, Cogstate PACC tests [International Shopping List Test (ISLT), Continuous Paired Associate Learning Test (CPAL), International Digit Symbol Substitution test-Medicines (IDSSTm)], the C3 battery, and the MOCA in-clinic and telehealth assessment contexts with context order randomized and CDR raters blinded to clinical status. For the CDR, there was high agreement in clinical classification between in-clinic and telehealth contexts (Kappa=0.93). Associations between scores on the individual neuropsychological tests in the two assessment contexts were also high (R-value range: 0.87–0.92). Magnitudes of impairment in the MCI group compared to the CU group on the neuropsychological tests ranged between −1 to −1.8 and were not significantly different when derived from in-clinic or telehealth contexts. Based on the results, clinical and neuropsychological tests commonly used to assess adults with early AD (preclinical and MCI) are valid for administration using telehealth contexts.
Presentation 3: Decentralized approaches in TRAILBLAZER-ALZ 3, Roy Yaari (Eli Lilly and Company, Indianapolis, IN (United States))
The TRAILBLAZER-ALZ 3 (TB3) (NCT05026866) study is an ongoing Phase 3 research trial with a decentralized design, testing donanemab in preclinical AD. This talk will provide an overview of key decentralized design characteristics utilized in the study. The remote screening process, which includes mobile research units and health fairs, uses the modified Telephone Interview for Cognitive status (TICS-m) to help select for cognitively unimpaired individuals. Plasma AD assays assess an AD biomarker as part of the inclusion screening criteria and minimize participant burden. Optional remote genetic counseling for APOE disclosure is offered as well as two optional sub-studies testing amyloid PET and tau PET. Clinical outcomes are assessed throughout the study using central raters who administer the Clinical Dementia Rating scale (CDR) to study partners and participants, and psychometric examinations to study participants. Self-administered tests are proctored remotely by a centralized study coordinator who also helps coordinate and facilitate all remote appointments for the participant and study partner. Infusion and imaging centers outside of traditional study «sites» are available in order to improve participant convenience and access throughout the study. In addition to key DCT design elements, screening data influenced by the DCT design will be presented.
Presentation 4: Investigator experience in a decentralized clinical trial on Alzheimer’s disease, Ralph Lee (Irvine Clinical Research, Irvine, CA (United States))
The investigator experience is particularly valuable when it comes to identifying and addressing practical considerations with DCTs. An experienced brick and mortar site, Irvine Clinical Research, will share challenges and opportunities faced in participating in its first DCT conducted within the TRAILBLAZER-ALZ 3 study design. The site conducted in-person trial screening events using mobile research units (MRUs) off-site in the community. A fully site investigator-staffed outreach model and an outsourced model were both tested. The outcomes, effect on diversity of participants, and operational challenges of this decentralized approach will be discussed and may help to identify key approaches to further refine future DCT designs.
Readouts
Clinical Trial Alzheimer’s Disease
TOPLINE RESULTS OF PHASE III GRADUATE I & II PIVOTAL TRIALS WITH SUBCUTANEOUS GANTENERUMAB. R. Bateman 1, J. Smith 2, M.C. Donohue 3, P. Delmar 4, R. Abbas 4, S. Salloway 5, J. Wojtowicz 4, K. Blennow 6,7, T. Bittner 4,8, S.E. Black 9,10, G. Klein 11, M. Boada 12, T. Grimmer 13, A. Tamaoka 14, R.J. Perry 15, R.S. Turner 16, D. Watson 17, M. Woodward 18, A. Thanasopoulou 4, C. Lane 2, M. Baudler-Klein 4, N.C. Fox 19,20, J.L. Cummings 21, P. Fontoura 4, R.S. Doody 4(1. Department of Neurology, Washington University School of Medicine — St. Louis, MO (United States), 2. Roche Products Ltd — Welwyn Garden City (United Kingdom), 3. Alzheimer’s Therapeutic Research Institute, Keck School of Medicine, University of Southern California — San Diego, CA (United States), 4. F. Hoffmann-La Roche Ltd — Basel (Switzerland), 5. Butler Hospital and Warren Alpert Medical School of Brown University — Providence, RI (United States), 6. Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg — Mölndal (Sweden), 7. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital — Mölndal (Sweden), 8. Genentech, Inc. — South San Francisco, Ca (United States), 9. Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre — Toronto, Ontario (Canada), 10. LC Campbell Cognitive Neurology Research Unit, Dr Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto — Toronto, Ontario (Canada), 11. F. Hoffmann-La Roche Ltd, Basel, Switzerland — Basel (Switzerland), 12. Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya - Barcelona (Spain), 13. Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich — Munich (Germany), 14. Department of Neurology, Faculty of Medicine, University of Tsukuba — Tsukuba (Japan), 15. Department of Brain Sciences, Faculty of Medicine, Imperial College London — London (United Kingdom), 16. Department of Neurology, Georgetown University School of Medicine — Washington, DC (United States), 17. Alzheimer’s Research and Treatment Center — Wellington, FL (United States), 18. Medical and Cognitive Research Unit, Heidelberg Repatriation Hospital, Austin Health - Melbourne, Victoria (Australia), 19. Dementia Research Centre, Department of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London — London (United Kingdom), 20. UK Dementia Research Institute, Queen Square Institute of Neurology, University College London — London (United Kingdom), 21. Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV) — Las Vegas, NV (United States))
Objectives: GRADUATE I and II are two identically designed ongoing global Phase III parallel-group, placebo-controlled, randomized trials investigating the efficacy and safety of subcutaneous gantenerumab in people with early AD (i.e., mild cognitive impairment [MCI] due to AD or mild AD dementia), as well as its effect on biomarkers of AD pathology and neurodegeneration. Methods: Eligible participants (50–90 years) were diagnosed with MCI due to AD or mild AD dementia; demonstrated abnormal memory using the Free and Cued Selective Recall Test; met criteria for the Mini-Mental State Examination (MMSE≥22) and the Clinical Dementia Rating—Global Score (0.5 or 1); with evidence of amyloid positivity confirmed by Aβ positron emission tomography (PET) scan or cerebrospinal fluid (CSF) analysis. Participants were randomized 1:1 to subcutaneous gantenerumab or placebo, administered at the study site or at home using home nursing. Gantenerumab was up-titrated over a 36-week period to a target dosage of 510 mg every 2 weeks (Q2W), irrespective of apolipoprotein E ε4 (APOE ε4) genotype. The primary endpoint was the change from baseline to Week 116 in Clinical Dementia Rating scale — Sum of Boxes (CDR-SB). Secondary confirmatory efficacy endpoints evaluated the change from baseline to Week 116 in cognition and function, including the Alzheimer’s Disease Assessment Scale — Cognitive Subscale (ADAS-Cog 13), Alzheimer’s Disease Cooperative Study — Activities of Daily Living (ADCS-ADL) and Functional Activities Questionnaire (FAQ). In addition to the safety assessments, the studies also included further secondary and exploratory efficacy measures, as well as Tau and amyloid PET, and cerebrospinal fluid (CSF) and plasma biomarker assessments. Results: In total, 1,965 participants (n = 985 for GRADUATE I; n = 980 for GRADUATE II) from 288 sites across 30 countries were enrolled. Data will be presented on baseline demographics and disease characteristics for both GRADUATE I and II studies. The presentation will focus on the top-line efficacy, safety and biomarker results of the two studies. Conclusion: Results of GRADUATE I and II will build on evidence from previous studies and provide a robust dataset informing the overall benefit:risk profile of subcutaneous gantenerumab in early AD. References: Klein G, et al. J Prev Alzheimers Dis 2021;8:3–6. Roche.com. Roche’s anti-amyloid beta antibody gantenerumab granted FDA Breakthrough Therapy Designation in Alzheimer’s disease. Accessed online at: https://www.roche.com/investors/updates/inv-update-2021-10-08 on 6 October 2022. Acknowledgement: «GRADUATE I and GRADUATE II participants, their families, clinical investigators and the gantenerumab study group»
TACKLING AGITATION IN ALZHEIMER’S DEMENTIA: BREXPIPRAZOLE PHASE III TRIAL RESULTS. G. Grossberg 1, D. Lee 2, M. Slomkowski 2, N. Hefting 3, D. Chen 2, K. Larsen 3, E. Kohegyi 2, M. Hobart 2, J. Cummings 4(1. Department of Psychiatry and Behavioral Neuroscience at Saint Louis University School of Medicine — St. Louis, Missouri (United States), 2. Otsuka Pharmaceutical Development & Commercialization Inc. — Princeton, New Jersey (United States), 3. H. Lundbeck A/S — Valby, Copenhagen (Denmark), 4. Chambers-Grundy Center for Transformative Neuroscience at School of Integrated Health Sciences University of Nevada Las Vegas (UNLV) — Las Vegas, Nevada (United States))
Background: Agitation is highly prevalent among patients with Alzheimer’s dementia, in community and long-term care settings (1, 2). The presence of agitation in Alzheimer’s dementia (AAD) increases the risk of institutionalization (3), negatively impacts patient quality of life, and increases caregiver distress (4). Currently, there are no FDA-approved pharmacological treatments for the management of AAD. Brexpiprazole, which acts on noradrenergic, serotonergic, and dopaminergic neurotransmitter systems (5), has been investigated as a potential AAD therapy. Objectives: To assess the efficacy, safety, and tolerability of brexpiprazole in patients with AAD based on results of a recently completed Phase III trial, together with two previously completed Phase III trials. Methods: The first two trials (NCT01862640, NCT01922258), completed in 2017, were 12-week, randomized, double-blind, placebo-controlled, parallel-arm trials of brexpiprazole versus placebo in patients with AAD (6). Patients were required to have a baseline Neuropsychiatric Inventory (NPI) Agitation/Aggression domain score of ≥4. One trial investigated fixed doses of brexpiprazole (0.5, 1 or 2 mg/day [0.5 mg/day arm discontinued]), whereas the other investigated a flexible dose (0.5-2 mg/day). The third trial (NCT03548584), completed in 2022, also had a 12-week, randomized, double-blind, placebo-controlled, parallel-arm design, but differed in terms of the dose administered (2 or 3 mg/day), and agitation requirements, which comprised the NPI criterion, the International Psychogeriatric Association (IPA) provisional definition, and a criterion based on Cohen—Mansfield Agitation Inventory (CMAI) Factor 1. In all three trials, change in CMAI Total score was the primary endpoint, and change in Clinical Global Impression — Severity of illness (CGI-S) score, as related to agitation, was the key secondary endpoint. Safety was also assessed. Results: In the first fixed-dose trial, the highest brexpiprazole dose (2 mg/day) demonstrated statistically significant improvement versus placebo in CMAI Total score change from baseline to Week 12 (least squares mean difference [LSMD], −3.77; p=0.040); the 1 mg dose did not separate from placebo. In the flexible-dose trial, although brexpiprazole 0.5–2 mg/day was not superior to placebo on CMAI Total score, a post hoc analysis of patients titrated to the 2 mg dose showed reduced agitation versus placebo (LSMD, −5.06; p=0.012). A post hoc analysis of both trials indicated that patients who did not meet CMAI Factor 1 criteria at baseline had insufficient baseline agitation severity to show measurable change over time. Hence, the third trial investigated higher doses (2 or 3 mg/day [3 mg tested for efficacy and safety, per FDA request]) in an enriched sample who met CMAI Factor 1 criteria at baseline. In the third trial, brexpiprazole 2 or 3 mg/day demonstrated statistically significant improvement versus placebo from baseline to Week 12 in CMAI Total score (LSMD, −5.32; p=0.0026) and CGI-S score as related to agitation (LSMD, −0.27; p=0.0078). Pooled response rate across the three trials (CMAI criteria) was higher with brexpiprazole versus placebo. Across all three trials, the incidence of treatment-emergent adverse events (TEAEs) was 51.1% with brexpiprazole (all doses pooled) and 45.9% with placebo. Across all three trials (pooled), no single TEAE had an incidence >5% with brexpiprazole (all doses pooled) and more than in placebo-treated patients. TEAEs that occurred in ≥2% of patients receiving brexpiprazole and more than in placebo-treated patients were insomnia (3.7% versus 2.8%), somnolence (3.4% versus 1.8%), nasopharyngitis (2.7% versus 2.6%), and urinary tract infection (2.6% versus 1.5%). The incidence of falls was 1.7% (brexpiprazole) versus 2.6% (placebo). Overall, 6.3% of patients receiving brexpiprazole discontinued treatment due to TEAEs, versus 3.4% receiving placebo. Six brexpiprazole-treated patients (0.9%) and one placebo-treated patient (0.3%) died during double-blind treatment; no deaths were considered related to brexpiprazole treatment. Conclusion: Across three Phase III trials in patients with AAD, brexpiprazole doses of 2 or 3 mg/day showed a statistically significant improvement versus placebo on agitation in Alzheimer’s dementia. Brexpiprazole was generally well tolerated, which is of critical importance in this vulnerable patient population. References: 1. Halpern et al. Using electronic health records to estimate the prevalence of agitation in Alzheimer disease/dementia. Int J Geriatr Psychiatry 2019;34(3):420-431. 2. Fillit et al. Impact of agitation in long-term care residents with dementia in the United States. Int J Geriatr Psychiatry 2021;36(12):1959-1969. 3. Cloutier et al. Institutionalization risk and costs associated with agitation in Alzheimer’s disease. Alzheimers Dement (N Y) 2019;5:851-861. 4. Khoo et al. The impact of neuropsychiatric symptoms on caregiver distress and quality of life in persons with dementia in an Asian tertiary hospital memory clinic. Int Psychogeriatr 2013;25(12):1991-1999. 5. Maeda et al. Brexpiprazole I: in vitro and in vivo characterization of a novel serotonin—dopamine activity modulator. J Pharmacol Exp Ther 2014;350(3):589-604. 6. Grossberg et al. Efficacy and safety of brexpiprazole for the treatment of agitation in Alzheimer’s dementia: two 12-week, randomized, double-blind, placebo-controlled trials. Am J Geriatr Psychiatry 2020;28(4):383–400.
Roundtables
Clinical Trial Alzheimer’s Disease
ROUNDTABLE 1- INVESTMENTS IN INNOVATION: ADVANCING THE PATH FORWARD TO NEW ALZHEIMER’S TREATMENTS. N. Bose 1, H. Fillit 2, L. Barker 3, P. Scheltens 4(1. Gates Ventures, Seattle, WA (United States), 2. Alzheimer’s Drug Discovery Foundation (ADDF), New York City, NY (United States), 3. Dementia Discovery Fund (DDF), London (United Kingdom), 4. LSP Dementia Fund at EQT Life, Alzheimer Centre Amsterdam (University Medical Centre Amsterdam, Amsterdam (The Netherlands))
Topline summary: Over six million people in the United States and 55 million globally are living with Alzheimer’s and related dementias, and that number is expected to grow substantially with an aging population. There is a large global unmet medical need with few effective treatments available for patients, creating an urgency to accelerate efforts to develop novel and effective therapies that target a whole host of underlying pathologies that contribute to Alzheimer’s. It is important to foster collaboration across various sectors — government, academia, industry, and philanthropy — combine resources and capital, and utilize innovative and creative approaches to successfully conquer this disease. This roundtable will pull expertise from four influential global investment organizations — all with a venture-minded approach that span across drug discovery and development to commercialization — focused on identifying and investing in innovative, impactful therapies. The will panel will discuss where we are now in the field and where we want to be 10 years from now and more importantly, the path forward, which is only possible when leading scientists and entrepreneurs are connected and have access to capital. The panel will cover their interests, approach, resources, and funding opportunities to support and accelerate research of new drugs, technologies, and breakthrough innovations. Lastly, they will discuss recommendations and provide evidence where these have been used in practice. Recommendations include: ● Leveraging the modern era of Alzheimer’s research to explore drugs beyond amyloid and tau proteins and focusing the next phase of research, based on the biology of aging, which is centered on promising drugs that target a host of underlying pathologies that contribute to Alzheimer’s. ● Highlighting the need for more rigorously designed clinical trials enabling the field to more rapidly and efficiently evaluate whether a drug should move to the next stage of clinical development. ● Emphasizing the importance of biomarkers as it relates to drug development and precision medicine, with an emphasis on the need for new biomarkers that can measure the impact of each biology of aging target, and the drugs designed to treat them
ROUNDTABLE 2- THE ALZHEIMER’S DISEASE PATIENT PATHWAY FROM A SEX AND GENDER LENS. F.C. Quevenco 1,2, M.C. Tartaglia 3, M. Carrillo 4, P. Ferrell 5, P. Poulsen 6, A. Santuccione Chadha 2,7, M.T. Ferretti 2, M.F. Iulita 2,8(1. Roche (Switzerland), 2. Women’s Brain Project (Switzerland), 3. University Of Toronto (Canada), 4. Alzheimer’s Association (United States), 5. Eli Lilly (United States), 6. Novo Nordisk (Denmark), 7. Altoida (United States), 8. Memory Disorders Unit, Hospital Sant Pau — Barcelona (Spain))
The topic of sex differences is now positioned as a top priority in neurology research, particularly in the context of precision medicine and personalized care. There is a growing literature about sex differences in Alzheimer’s disease manifestations, highlighting sex and gender-specific factors that are not captured in a standard patient pathway. A patient pathway takes a patient-centric approach to describe an individual’s journey from symptom onset to treatment completion. It is a crucial resource for persons living with Alzheimer’s disease, physicians, and clinical trial sponsors. When considering sex and gender differences, there are likely deviations between a male and female patient journey. To address this, an ongoing study led by the Women’s Brain Project and collaborators is mapping a comprehensive patient pathway that is able to capture these differences. The goal of this symposium is to discuss the importance of why this patient pathway is needed in Alzheimer’s disease and why it is relevant for clinical trials by inviting different stakeholders.
Oral Communications
Clinical Trial Alzheimer’s Disease
OC1- ACI-35.030 AND JACI-35.064, TWO NOVEL ANTI-PHOSPHO-TAU VACCINES FOR THE TREATMENT OF ALZHEIMER’S DISEASE: INTERIM PHASE 1B/2A DATA ON SAFETY, TOLERABILITY AND IMMUNOGENICITYE. J. Streffer 1,2, J. Mermoud 1, O. Sol 1, M. Vukicevic 1, E. Fiorini 1, E. Gollwitzer 1, V. Hliva 1, D. Hickman 1, J. Gray 1, P. Donati 1, M.P. Lopez Deber 1, J. Rongère 1, A. Pfeifer 1, M. Kosco-Vilbois 1, P. Scheltens 3(1. AC Immune SA — Lausanne (Switzerland), 2. University of Antwerp — Antwerp (Belgium), 3VUMC — Amsterdam (Netherlands))
Background: Tau deposition is a key pathological feature of Alzheimer’s disease (AD) and other neurodegenerative disorders. The spreading of Tau neurofibrillary tangles across defined brain regions is associated with cognitive decline in AD. It is hypothesized that Tau spreading throughout the brain involves extracellular phosphorylated Tau (pTau). Immunotherapy offers the potential to interfere with the spreading of Tau neuropathology and prevent or reduce cognitive impairment. In particular, active vaccination targeting pTau species that seed pathological aggregation, represents an attractive strategy for long-term treatment and potentially prevention of AD as well as other Tauopathies. Objectives: This Phase 1b/2a clinical trial, NCT04445831, aims to evaluate two first-in-class anti-pTau vaccine candidates, ACI-35.030 (i.e., liposome-based) and JACI-35.054 (i.e., conjugate-based) for the treatment of AD. We report here interim results of immunogenicity as well as safety and tolerability. Methods: This currently ongoing multicenter, double-blind, randomized, placebo-controlled study evaluates the safety, tolerability and immunogenicity of different doses of two anti-pTau vaccines, ACI-35.030 and JACI-35.054, in subjects with early AD. The antibody response is evaluated using ELISAs and measuring binding of the antibodies generated over time to the immunizing peptide, i.e., pTau, as well as against brain derived paired helical filaments (ePHF) and non-phosphorylated Tau. Epitope profiling is also employed using a specifically developed assay to cover phosphorylated and nonphosphorylated epitopes. Each dose-level subcohort comprises 8 subjects randomized in a 3:1 active/placebo ratio with the option to expand sub-cohort(s) up to 24 subjects to enlarge the assessment of safety, tolerability and immunogenicity. The study population is characterized as 50–75 year-old, male and female subjects with a diagnosis of mild AD or MCI due to AD according to NIA-AA criteria, CSF Aß42 levels consistent with AD pathology, a CDR global score of 0.5 or 1 and a MMSE score ≥ 22. Subjects receive injections of ACI-35.030 (Cohort 1), JACI-35.054 (Cohort 2) or placebo (Cohorts 1 and 2) at weeks 0, 8, 24 and 48. Results: 41 subjects have been randomized in the 3 dose-levels of cohort 1, and 16 subjects in the 2 dose-levels of cohort 2. Both vaccines are considered safe and well tolerated as no clinically relevant safety concerns associated related to the study vaccines have been observed at the time of abstract submission. Subjects immunized with the liposomal vaccine, ACI-35.030, show a high, specific and sustained anti-pTau and anti-ePHF IgG response, with an apparent dose-response between the low-and mid-dose with evidence of immunoglobulin class switch from IgM to IgG. Individual responder rates were high and consistent, especially for anti-pTau and ePHF antibodies. Over time, the data demonstrates that the IgG response matures towards a stronger preference for binding ePHF, the more pathologic species while concomitantly lowering antibody titers towards the non-pathological, non-phosphorylated Tau. Subjects immunized with the conjugate vaccine, JACI-35.054, display a high anti-ePHF and anti-pTau IgG response with no apparent dose-effect observed between the low- and mid-dose. The IgG response shows maintained binding capacity to both pTau and the non-pathological, non-phosphorylated Tau. To further profile the antibody response for breadth and selectivity towards pathological pTau, epitope mapping was performed on the subjects’ sera after 3 vaccinations. For ACI-35.030, the IgG response of the subjects was relatively homogenous displaying a broad epitope coverage as binding occurred across the pTau sequences tested and importantly, without end terminal specificity or substantial binding to nonphosphorylated sequences. The subjects vaccinated with JACI-35.054 demonstrated a more heterogeneous response with a strong disproportional binding to end terminal antibodies. These results further elucidate the differences produced by the two vaccines as well as the conclusion of IgG maturation with ACI-35.030 and not JACI-35.054 over time. Finally, as expected, no antibody responses are observed in placebo-treated subjects. Conclusions: The clinical study is successfully ongoing despite the challenges of being performed during the restrictions of the Covid-19 pandemic demonstrating that vaccination with either ACI-35.030 or JACI-35.054 is safe and well tolerated, inducing IgG responses to the immunizing peptide as well as ePHF. However, overall ACI-35.030 emerges as the superior vaccine candidate in terms of responder rate, number of immunizations to achieve the initial antibody titer, homogeneity of the antibody response across subjects, epitope coverage, with evidence of antibody maturation towards pathologic forms of Tau. As both vaccines contain the same antigenic peptide sequence, the differences observed so far in antibody response can be ascribed to the different technologies used to present the antigenic peptide to the immune system.
OC02- RESULTS OF A PHASE 2/3 PLACEBO-CONTROLLED, DOUBLE-BLIND, PARALLEL-GROUP, RANDOMIZED STUDY TO EVALUATE THE EFFICACY AND SAFETY OF 12 WEEK TREATMENT WITH THE PHOSPHODIESTERASE 9 (PDE9) INHIBITOR IRSENONTRINE (E2027) IN SUBJECTS WITH DEMENTIA WITH LEWY BODIES.} M. Irizarry 1, R. Lai 2, S. Hersch 1, K. Pinner 2, S. Dhadda 1, L. Kramer 1(1. Eisai Inc. — Nutley (United States), 2. Eisai Ltd. — Hattfield (United Kingdom))
Objectives: To assess the safety and efficacy of irsenontrine for treatment of cognition in patients with Dementia with Lewy Bodies (DLB). Methods: Study 201 was a phase 2/3, 12-week study in subjects with DLB (N=196) randomized 1:1 to irsenontrine 50 mg or placebo. The co-primary endpoints were change from baseline in the electronic Montreal Cognitive Assessment (eMoCA) and the electronic Clinician’s Interview Based Impression of Change Plus Caregiver Input (eCIBIC-plus) at 12 weeks. Secondary outcomes included the Neuropsychiatric Inventory (NPI), Mini-Mental State Examination (MMSE), Cognitive Function Inventory (CFI), and the Clinician’s Global Impression of Change — Dementia with Lewy Bodies (CBIC-DLB). Results: The study did not meet its primary objective of determining the superiority of irsenontrine compared with placebo on both the cognitive endpoint of MoCA and the global clinical endpoint of CIBIC-Plus after 12 weeks of treatment in the overall population. The irsenontrine group tended to show less decline from Baseline to Week 12 in the MoCA total score in the overall population compared with the placebo group, but the difference between treatment groups was not statistically significant (MMRM analysis: least square (LS) mean difference [95% CI] of 0.181 [−0.716, 1.078], p=0.69). Subjects in the irsenontrine group showed minimal improvement in the CIBIC-Plus compared with the placebo group at Week 12; the difference between treatment groups was not statistically significant (GLMM analysis: odds ratio [95% CI] of 1.018 [0.695, 1.492]; p=0.83). Secondary efficacy endpoints did not show a significant treatment difference between irsenontrine and placebo in the overall population. Exploratory post-hoc analyses suggested that irsenontrine performed better than placebo on the primary efficacy endpoints in a key subgroup of subjects without amyloid copathology (identified by plasma amyloid Aβ42/40 ratio ≥0.092 [C2N assay], N=26 and 30 for the placebo and irsenontrine groups, respectively): For subjects without amyloid copathology (“pure DLB”), irsenontrine treatment resulted in an improvement from Baseline to Week 12 MoCA compared with a decline in the placebo group. The difference approached statistical significance (LS mean difference [95% CI] of 1.567 [−0.024, 3.157]; p=0.05). For subjects without amyloid copathology, the irsenontrine group tended to show greater improvement in CIBIC-Plus at Week 12 compared with the placebo group, with more subjects showing improvement in the irsenontrine group compared with the placebo group: odds ratio (95% CI) of 1.596 [0.753, 3.386]; p=0.47. In the small number of subjects for whom data were available (n=4), 9 weeks of irsenontrine treatment resulted in an average of 168% increase in cGMP in the CSF. Irsenontrine was generally well-tolerated with similar incidence rates of Serious Adverse Events (SAEs) and Treatment Emergent Adverse Events (TEAEs) between the irsenontrine and placebo groups. Worsening DLB, dizziness, somnolence, orthostatic hypotension, and aggression occurred with a higher incidence in the irsenontrine group (3.0% to 5.1%) compared with the placebo group (0% to 1.0%). Conclusions: In the overall DLB population, 12 weeks treatment with irsenontrine 50 mg daily did not improve cognition relative to placebo. The suggestion of efficacy in the “pure” DLB subgroup, lacking AD co-pathology, generated the hypothesis that irsenontrine preferentially increases CSF cGMP in pure DLB relative to mixed DLB due to relative preservation of synapses (the site of action of PDE9 inhibition) in patients lacking amyloid co-pathology. This hypothesis is explored in the translational medicine Study 203.
OC03- HMTM TOPLINE RESULTS OF PHASE 3 LUCIDITY — THE FIRST TAU AGGREGATION INHIBITOR. B. Schelter 1,2(1. TauRx Therapeutics Ltd — Aberdeen (United Kingdom), 2. University of Aberdeen — Aberdeen (United Kingdom))
LUCIDITY interim data are currently being analysed and this symposium will provide an opportunity to present new data analysis not yet in the public domain. Part 1: History of HMTM and its development (Prof Claude Wischik). Hydromethylthionine mesylate (HMTM) is a tau aggregation inhibitor shown to have exposure-dependent pharmacological activity on cognitive decline and brain atrophy in two completed Phase 3 trials in mild/moderate Alzheimer’s disease (AD). The role of tau pathology in AD and the mode of action of HMTM will be presented. Context will be provided using data from 2 completed Phase 3 clinical trials in AD. Part 2: Update on the interim data, including safety, key endpoints and biomarkers (Prof Bjoern Schelter). The ongoing Phase 3 LUCIDITY trial (NCT03446001) investigates 16 mg/day as monotherapy as the optimal treatment regime compared to placebo. The trial comprises a 12-month double-blind, placebo-controlled phase followed by a 12-month modified delayed-start open-label treatment phase. The trial is being conducted across 76 clinical research sites in North America and Europe. It recruited 598 subjects in total with probable AD or MCI-AD with 545 in the final version of the protocol. Participants were assigned randomly to receive HMTM at doses of 16 mg/day, 8 mg/day or placebo at a 4:1:4 ratio during the double-blind phase. All participants in the open-label phase receive the 16 mg/day dose. The study has co-primary clinical outcomes comprising the 11-item Alzheimer’s Disease Assessment Scale (ADAS-cog11) and the 23-item Alzheimer’s Disease Cooperative Study — Activities of Daily Living (ADCS-ADL23). Secondary biomarker measures include whole-brain atrophy measured by MRI and temporal lobe 18F-fluorodeoxyglucose positron emission tomography. 470 participants completed the 12-month placebo-controlled phase by April 2022. During this symposium updates will be provided from ongoing interim data analysis of the double blind and open label phases of LUCIDITY. Part 3: What does this mean for the AD landscape? (Dr Richard Stefanacci). LUCIDITY is the only late-stage clinical trial targeting tau pathology. The trial is novel in design as it includes individuals with mild — moderate AD and MCI under the same protocol. A unique feature among tau- and amyloid-targeting approaches in development is that HMTM is an oral drug which has a proven benign safety profile including lack of ARIA risk. The possibility for a medication to come to market to treat a broad range of AD severity and be accessible to patients presents a significant opportunity to transform the AD treatment landscape. This presentation will consider the extent of the potential transformation of the patient pathway that HMTM could provide.
OC04- JANSSEN SIMOA PLASMA P217+TAU ASSAY AS A PRECISION PRESCREENING TOOL IN AUTONOMY PH2 ANTI-TAU MONOCLONAL AB TRIAL IN EARLY ALZHEIMER’S DISEASE. G. Triana-Baltzer 1, Z. Saad 1, S. Moughadam 1, R. Slemmon 1, M. Quiceno 1, D. Henley 1, H. Kolb 1(1. Janssen Research & Development — San Diego (United States))
Background: It is hypothesized that anti-amyloid or anti-tau therapies should be most effective in Alzheimer’s Disease (AD) when initiated early in disease. CSF and PET-based measures have proven utility in identifying subjects with AD pathology even prior to clinical symptom manifestation, however they are burdensome to the patient and costly. Phosphorylated tau (p-tau) as measured in CSF, and most recently plasma, has emerged as one of the most sensitive and specific biomarkers for AD pathology and appears to predict amyloid and tau PET positivity as well as gross cognitive state. Janssen has developed a highly sensitive and precise assay for measuring p217+tau in plasma, with good ability to predict amyloid and tau PET status with a cutoff of ≥0.1 pg/ml. This assay is unique from others specific for phosphorylated T217 in that it has enhanced signal when tau is phosphorylated at neighboring amino acids as well, as is often found in pathological tau species. We have studied the utility of this non-invasive assay for clinical trial enrollment with confirmation of performance via comparison to tau PET. Objectives: The Autonomy trial (63733657ALZ2002) seeks to enroll early AD (MCI/mild AD dementia) patients who are tau PET positive (standardized uptake value ratio (SUVR) Z-score > 1 in bilateral inferior temporal cortex) but without having widespread tau tangles [SUVR Z-score > 5 in each of Braak 4, 5, and 6 regions of interest (ROI)]. Patients within this range are further stratified into high and low groups based on SUVR in the Braak 4 ROI. We report on the performance of the Janssen Simoa plasma p217+ tau assay as a prescreening tool for identifying patients who are likely tau PET positive. Methods: The plasma p217+tau assay developed on Simoa platform was validated at Quanterix (Billerica, MA) and performed on screening samples from participants presenting with early AD in weekly batches. Technical performance across the initial 55 batches was evaluated. Participants presenting with plasma p217+tau levels ≥ a pre-specified cutoff of 0.1 pg/ml progressed to tau PET (18F-MK6240) screening. Concentrations of plasma p217+tau in this population and prevalence of tau PET positivity in the plasma p217+tau positive participants was studied. To assess the assay’s ability to predict participant stratification, we performed a post-hoc ROC analysis using one year’s worth of screening data. Results: From February 2021 to April 2022, 55 batches of plasma p217+tau screening were performed at Quanterix. A panel of 3 peptide Quality Control (QC) samples (0.1, 0.4, and 1.6 pg/ml) were run in duplicate in each batch revealing excellent intra-run precision (average CV = 6.0, 4.5, and 5.5%, respectively) and inter-run precision (7.6, 6.6, and 13.3% CV, respectively). Precision was also acceptable with clinical trial samples, as amongst N=725 plasma samples the mean CV was 7.9% (0–120% range), with an estimated LLOQ of 0.030 pg/ml (based on mean concentration where CV>20%). Of the 787 early AD patients in which plasma p217+tau was measured, 72% had levels ≥0.1 pg/ml, and hence were slated to have tau PET imaging performed. Of the 346 patients imaged to date, 86% were tau PET positive and 64% satisfied the trial tau PET eligibility criteria of intermediate tau burden. While assay screening performed as expected, 59% of eligible patients were in the high stratum. ROC analysis shows the plasma p217+tau assay can predict patient stratum with an AUC of 0.8, making it possible to use the assay for further patient enrichment for a desired stratification profile. Conclusion: Accurate, sensitive, and precise measurement of p-tau isoforms in plasma has emerged as the most promising non-invasive method for detecting aberrant amyloid and tau processes. Longer fragments of multi-phosphorylated tau, containing at least phosphorylation at amino acid 217, have been reported as one of the isoforms most associated with AD pathology and may begin accumulating in CSF and plasma 10–20 years before cognitive decline. Janssen has developed a robust and highly sensitive assay to measure plasma p217+tau which can quantify signal in all early AD participants, suggesting utility for pre-screening participants for anti-tau trials such as Autonomy. Pressure testing of the plasma p217+tau assay in the Autonomy phase-2 clinical trial has shown good precision within and between batches, and demonstrated the ability to enrich populations for tau PET positivity. This “low friction” tool should enable faster and more efficient AD clinical trial enrollment now, and due to its ability to quantify signal in even preclinical AD could potentially be used in the future as a tool to identify the earliest stages of disease in the general population. Additional work should focus on refining cutoffs to stage AD subjects based on time to onset of clinical symptoms and/or amyloid and tau PET progression. Conflicts of Interest: GTB, ZSS, SM, RS, MQ, DH, and HCK are employees of Janssen R&D.
OC05- LONG TERM AND ECONOMIC OUTCOMES FOR MIRTAZAPINE AND CARBAMAZEPINE VERSUS PLACEBO: NEW DATA FROM THE SYMBAD RCT. S. Banerjee On Behalf Of The Symbad Group 1(1. University Of Plymouth — Plymouth (United Kingdom))
Background: Agitation is common in people with dementia and impacts negatively on the quality of life of both people with dementia and carers. Non-drug patient-centred care is the first-line treatment, but there is a need for other treatment when this fails. Current evidence is sparse on safer and effective alternatives to antipsychotics. We assessed efficacy and safety of mirtazapine (an antidepressant) and carbamazepine (an anticonvulsant) prescribed for agitation in dementia. Here we present new data from the SYMBAD trial including those on carbamazepine and long-term and economic outcomes. Objectives: To assess the safety, clinical and cost effectiveness of mirtazapine and carbamazepine in the treatment of agitation in dementia (Cohen Mansfield Agitation Inventory (CMAI) score), with 12 weeks follow up the primary outcome, and long-term follow up at 6 and 12 months. Registered ISRCTN17411897 and ClinicalTrials.gov NCT03031184, funded by UK National Institute for Health Research. Methods: Pragmatic, phase III, multi-centre, double blind, superiority, randomised, placebo-controlled trial of the clinical and cost-effectiveness of mirtazapine and carbamazepine over 12 weeks. Approved by Hampshire A South Central Research Ethics Committee (15/SC/0606) and MHRA (58810/0001/001-0001). Eligible participants randomised to receive either mirtazapine (target dose 45mg), carbamazepine (target 300mg), or placebo. Participants eligible if the following criteria were met: (i) clinical diagnosis of probable or possible Alzheimer’s disease; (ii) co-existing agitated behaviours; (iii) evidence the agitated behaviours have not responded to management; (iv) CMAI score of 45+; (v) written informed consent from participant or consultee if capacity lacking; and (vi) availability of suitable informant. Exclusion criteria: (i) currently on antidepressants, anticonvulsants, or antipsychotics; (ii) contraindications to mirtazapine or carbamazepine; (iii) second degree atrioventricular block; (iv) bone marrow depression or hepatic porphyria; (v) case too critical for randomisation (eg suicide risk or risk of harm to others); and (vi) females of childbearing potential. Participants were drawn from 26 UK sites, allocated in a 1:1:1 ratio to receive placebo or carbamazepine or mirtazapine, each with treatment as usual. Random allocation block stratified by centre and type of residence with random lengths. The trial was double-blind, with drug and placebo identically encapsulated. Analyses were based on intention-to-treat, the primary outcome (CMAI at 12 weeks) was analysed using a general linear regression model including baseline CMAI score as a covariate. General linear regression models were created for secondary outcomes. The primary outcome for the economic evaluation was the incremental cost per 6-point difference in CMAI score at 12 weeks, from a health and social care system perspective. Due to slower than expected recruitment the carbamazepine arm was discontinued in August 2018 with 1:1 randomisation to mirtazapine or placebo thereafter. Results: Between January 2017 and February 2020, 244 participants were recruited and randomised to either the mirtazapine (n=102), the placebo (n=102), or the carbamazepine arm (n=40). Mean CMAI scores at 12 weeks were not significantly different between participants allocated to receive mirtazapine and placebo (adjusted mean difference −1.74, 95% CI −7.17 to 3.69, p=0.53). The number of controls with adverse events (65/102 [64%]) was similar to that in the mirtazapine group (67/102 [66%]). There were more deaths in the mirtazapine group (n=7) by week 16 than in the control group (n=1), with post-hoc analysis suggesting this was of marginal statistical significance (p=0.065), but this difference did not persist at 6- and 12-month follow-up. At 12-week follow-up, the costs of unpaid care by the dyadic carer over the prior 6 weeks were significantly higher in the mirtazapine than placebo group (difference: £1,120 (95% CI £56, £2,184)). In the cost-effectiveness analyses mean raw and adjusted outcome scores and costs of the complete cases samples showed no differences between groups. The cost effectiveness analyses showed no evidence of benefit of mirtazapine over placebo. The carbamazepine arm had only 40 randomisations, we therefore lack the statistical power for the planned comparisons with placebo, however exploratory analyses using the same modelling as for mirtazapine versus placebo showed there was also no evidence of any benefits compared to placebo at 12 weeks (adjusted mean difference 2.46, 95% CI -5.01 to 9.93, p=0.52) or at long-term follow-up, with similar levels of adverse events reported. Conclusions: This is a trial with negative findings and clinical implications. The data suggest that mirtazapine is not clinically effective or cost-effective (compared to placebo) for clinically significant agitation in dementia. Our findings suggest that there is no reason to use mirtazapine for people with dementia who experience agitation. The data also provide no signal that carbamazepine might have any positive effect on agitation in dementia above that seen in the placebo group and no evidence of long-term benefit of either drug. These data bring into question the use of antidepressants for agitation in dementia.
OC06- COMBINATION OF REGIONAL FLORTAUCIPIR QUANTIFICATION AND EVENT-BASED MODELING IN CLINICAL TRIAL ANALYSES. I. Higgins 1, A. Morris 1, J. Sims 1, M. Mintun 1, S. Shcherbinin 1(1. Eli Lilly and Company — Indianapolis (United States))
Background: Positron emission tomography (PET) imaging of brain tau burden, topography, and propagation is used to evaluate Alzheimer’s disease (AD) progression and treatment response. Regions of interest (ROIs) brain analysis for tau levels may be more sensitive than global whole brain tau estimates (Leuzy et al, Molecular Psychiatry, 2019). Topographic PET staging methods can incorporate a priori established ROI sequences (e.g. Braak staging and Lobar Classification, Schwarz et al, Alzheimer’s & Dementia, 2018) and data-driven methodologies, such as an Event-Based Model (EBM, Fonteijn et al, Neuroimage, 2012, Young et al, Brain, 2014, Berron et al, Brain, 2021) that can deliver an ordering scheme in a discrete-event dynamic system to determine a sequence from which a set of ROIs transition to abnormally high tau burden. Objectives: Assess an ordered sequence of cortical atlas-based brain regions reflecting tau propagation across the Alzheimer’s disease spectrum. Examine data from an interventional trial with donanemab (Mintun et al, NEJM, 2021) for the potential utility of EBM in the efficacy measurements on tau PET. Methods: Baseline flortaucipir PET scans from 1238 participants from observational and interventional trials were combined to develop and validate the model. Analyzed images were collected in 1) observational phase 2/3 18F-AV-1451-A05 study (NCT02016560); 2) EXPEDITION 3 phase 3 solanezumab trial (NCT01900665); 3) NAVIGATE-AD phase 2 trial with BACE inhibitor (NCT02791191); 4) AMARANTH phase 2/3 trial with lanabecestat (NCT02245737); and 5) DAYBREAK-ALZ phase 3 trial with lanabecestat (NCT02783573). Flortaucipir images pertaining to 57 elderly cognitive normal participants, 229 participants with mild cognitive impairment (MCI), and 936 participants with AD were included in the consolidated cross-sectional dataset. As regional outputs, standardized uptake value ratios (SUVRs) were calculated with respect to a reference signal intensity in white matter (PERSI, Southekal et al, JNM, 2018) and to an average signal in cerebellar gray matter region (Pontecorvo et al, Brain, 2017). Bilateral cortical ROIs from the Automated Anatomical Labeling (AAL, Tzourio-Mazoyer et al, Neuroimage, 2002) brain atlas were utilized as targets. The EBM assessed each brain region in our consolidated cross-sectional dataset as either “tau unburdened” or “tau burdened”, where Gaussian probability density functions governed the distribution of tau SUVRs under these two settings. A brain region experienced “an event” when it switched from normal/unburdened to abnormal tau levels. The ordered sequence of regions was determined from the regional tau SUVR dataset. The EBM ran for 250,000 iterations, where at each step the algorithm swapped the positions of two brain regions and accepted the new sequence if the data fit improved. A simple subject-level resampling scheme permitted estimation of numerous ordered sequences from which regional variation about the characteristic sequence was evaluated. A permutation test was used to determine whether the characteristic sequence was better supported by data than a randomly ordered sequence. To assess the robustness of EBM performance, sensitivity analyses were conducted by varying the reference signal utilized in SUVR measurements. The sequence was also applied to post hoc exploratory analyses of tau PET data from 172 participants with baseline PET scans collected in the multicenter, randomized, double-blind, placebo-controlled phase 2 TRAILBLAZER-ALZ trial (NCT03367403), to assess the efficacy of donanemab in early, symptomatic patients with AD (Mintun et al, NEJM, 2021). Results: EBM-generated sequences for temporal, parietal, and frontal lobe AAL ROIs were generally consistent with previously reported staging schemes (Schwarz et al, A&D, 2018), in that tau largely propagated along the temporal-parietal-frontal axis as AD progressed. Specifically, EBM placed the inferior temporal region at the beginning of the tau spread sequence followed by lateral temporal and parietal regions. All nine frontal ROIs were positioned at the end. The characteristic sequence was largely unchanged when the cerebellar crus was used as the reference region rather than PERSI. In TRAILBLAZER-ALZ post hoc exploratory analyses, regional SUVR values using cerebellar gray as a reference suggested that SUVR values showed more pronounced separation between placebo and donanemab-treated participants in regions identified later in the EBM sequence. Specifically, a significant separation was observed in frontal, temporal, and parietal ROIs (p<0.05), but there was no significant difference in tau change in “earlier” inferior temporal ROIs (p>0.05). Overall, more slowing in tau was observed (p<0.001) across the EBM sequence in participants treated with donanemab relative to placebo. Conclusions: Our analyses suggest that EBM can provide useful information in multi-regional analyses of flortaucipir images by ordering brain regions according to the pathologic sequence of tau progression. The EBM approach may better illustrate the therapeutic effect of AD treatment on tau PET by providing evidence of tau spread (Schwarz, Neurotherapeutics, 2021) to complement global tau measures. Larger trial data can further confirm these observations. Conflict of Interest: Ixavier A. Higgins is an employee and stockholder of Eli Lilly and Company.
OC07- LONGITUDINAL TAU PET INCREASE IS HIGHEST IN BRAIN REGIONS WITH STRONGEST FUNCTIONAL CONNECTIVITY TO REGIONS WITH MOST NFT AT BASELINE: AN INDEPENDENT VALIDATION. Z.S. Saad 1, R. Datta 1, C. Rowe 2, H.C. Kolb 1(1. Janssen R&D, Johnson & Johsnon — San Diego (United States), 2. Austin Health and University of Melbourne — Melbourne (Australia))
Background: Tau PET is the gold standard for in-vivo quantification of tau Neuro Fibrillary Tangles (NFT), which along with amyloid plaques and neurodegeneration, constitute the pathological hallmarks of Alzheimer’s Disease (AD). Since NFT presence and accumulation is heterogenous across patients and brain regions, assessments need to be individualized. Identifying regions most likely to show NFT progression can improve detection of treatment effects and result in smaller trials. Objectives: Franzmeier et al. (1) showed based on Flortaucipir PET that future NFT increases were highest in brain regions with the strongest functional connectivity to regions with the highest NFT levels at baseline. We have performed an independent validation of this work using MRI and Tau PET data obtained with a different tracer, MK6240, and an in-house implementation of the analysis pipeline. Methods: NFT levels were quantified using MK6240 SUVR in 232 brain regions (reference region: cerebellar gray). Longitudinal tau PET data was analyzed from 18 amyloid positive MCI patients who fit the profile for inclusion in Janssen’s Autonomy trial, and 36 Cognitively Normal (CN), amyloid negative subjects as controls. Quantification pipeline was implemented using FreeSurfer (2) and AFNI (3) software. For each subject, NFT epicenter consisted of regions with the top 10% of NFT levels that are at least one standard deviation above uptake in CN controls. All remaining regions were assigned a rank based on the strength of their average functional connectivity to the epicenter. Functional connectivity matrix for 232 cortical and sub-cortical regions (4, 5) was derived using resting state FMRI data from 500 healthy subjects available from the Human Connectome Project ((6, 7). Rank of connectivity to the epicenter was used to group regions into four quartiles Q1 to Q4, with Q1 having the strongest connectivity. Results: In the CN amyloid negative cohort, average annualized SUVR changes were close to 0 across quartiles with means and standard deviations of: Q1=−0.02 (0.04), Q2=−0.01 (0.04), Q3=−0.01 (0.04), Q4=−0.01 (0.03). In contrast, SUVR change for the MCI cohort was highest at Q1 and progressively lower across the four quartiles: Q1=0.05 (0.07), Q2=0.04 (0.07), Q3=0.03 (0.07), Q4=0.01 (0.09). Epicenter SUVR change of 0.01 (0.06) was comparable to that in Q4. In the MCI cohort, SUVR changes in Q1 and Q2 were nominally significantly greater than 0 (p<0.05) with effect sizes of 0.74 and 0.53, respectively. Conclusions: We have validated a patient-centered approach (1) for predicting future NFT increases using the patient’s specific pattern of Tau NFT at BL and whole brain functional connectomics. This validation, conducted using independent pipelines, patient cohorts, and a different PET tracer, confirms that NFT increases are largest in brain regions with the strongest functional connectivity to the epicenter at BL. This precision approach may increase the efficiency of AD clinical trials. References & Acknowledgements: 1. Franzmeier et al., Nat Commun. 2020; 2. Reuter et al., Neuroimage. 2012; 3. Cox RW, Comput Biomed Res. 1996; 4. Schaefer et al., Cereb Cortex. 2018; 5. Tian et al., Nat Neurosci. 2020; 6. Glasser et al., Neuroimage. 2013; 7. Smith et al., Neuroimage. 2013. The authors would like to acknowledge the following institutions for contributing the imaging data to the Cerveau consortium: University of Wisconsin, Massachusetts General Hospital, Biogen Inc. Author conflicts of interest statements: ZSS, RD, and HCK are employed by Janssen Pharmaceuticals and may hold stock or stock options. Author CR has received research grants from NHMRC, Enigma Australia, Biogen, Eisai and Abbvie. He is on the scientific advisory board for Cerveau Technologies and consulted for Prothena, Eisai, Roche and Biogen Australia.
OC08- INDIVIDUALISED TAU-PET MEASURES MIGHT BE SUPERIOR TO GROUP LEVEL MEASURES WHEN DETERMINING CHANGE IN TAU DEPOSITION OVER TIME IN ALZHEIMER’S DISEASE. A. Leuzy 1, A. Pichet-Binette 1, J. Vogel 2, G. Klein 3, E. Borroni 3, M. Tonietto 3, O. Strandberg 1, N. Mattsson-Carlgren 1, S. Palmqvist 1, E. Stomrud 1, R. Ossenkoppele 1, R. Smith 1, O. Hansson 1(1. Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden — Lund (Sweden), 2. Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, USA — Philadelphia (United States), 3. F. Hoffmann-La Roche Ltd, Basel, Switzerland — Basel (Switzerland))
Background: Though clinical trials in Alzheimer’s disease (AD) typically use change in cognition as a primary outcome, the use of longitudinal tau positron emission tomography (PET) as a (secondary) outcome is becoming increasingly more common. Regions of interest (ROIs) are typically used to summarize change in tau-PET signal over time. To date, most ROIs have been based on neuropathological studies or data-driven approaches where the same ROI is used for each subject (i.e., group-level ROI). However, given the inter-individual heterogeneity in spatial patterns of tau-PET, a key question is whether the use of subject-specific (i.e., individualized) ROIs might offer any advantages over group-level ROIs in AD clinical trials. Objectives: To i) compare longitudinal change in tau-PET estimated using group-level vs. individualized ROIs; ii) assess the number of patients required to detect a 25% reduction in the rate of change of either regional tau-PET or cognition across the different clinical stages of AD using group-level or individualized ROIs. Methods: Our sample consisted of 215 participants from BioFINDER-2 with longitudinal (baseline, 2-year) tau-PET using [18F]RO948 and longitudinal cognition. This included 97 Aβ-positive cognitively unimpaired individuals (preclinical AD), 77 Aβ-positive MCI patients (prodromal AD) and 41 patients with mild AD dementia (MiniMental State Examination [MMSE] ≥ 22). Longitudinal cognitive measures included MMSE and the modified Preclinical Alzheimer’s Cognitive Composite (mPACC). Annual change in [18F]RO948 standardized uptake value ratio (SUVR) was calculated ROI-wise as the difference between follow-up and baseline, divided by baseline uptake and multiplied by the time interval between scans in years: ([follow-up SUVR — baseline SUVR] / baseline SUVR) × 100 / Δtime. Group-level ROIs included i) five ROIs reflecting event-based modelling stages (data-driven stages) and ii) six ROIs reflecting Braak stages; iii) a temporal meta-ROI and iv) a whole-brain composite-ROI. Individualized ROIs included i) epicenter (top 10% of regions with highest tau at baseline), ii) Q1 (top quartile of regions closest to subject-specific epicenter based on functional connectivity), iii) probability-based approach (Gaussian mixture modelling was performed on cross-sectional data to extract probabilities of being tau-positive across individual FreeSurfer ROIs; percent change in SUVR was then calculated for different probability intervals, with selection based on the interval that provided the highest annual percent change in SUVR). A final individualized approach was used based on calculating change in tau-PET in iv) highest data-driven stage that showed abnormal tau-PET signal at baseline using Gaussian mixture modelling-based cut-offs. Change in MMSE and mPACC were calculated as slopes derived from linear mixed models. Power calculations were performed using group-wise analyses (preclinical AD, prodromal AD, mild AD dementia) of tau-PET and cognition data to determine sample size estimates for an intervention with a hypothetical intervention effect of 25%. Results: Using the group-level ROIs, the greatest changes in tau-PET SUVR were seen using the data-driven stage I (preclinical AD, 5.14%), II (prodromal AD, 6.23%) and IV (mild AD dementia, 8.90%), which encompassed medial temporal, temporal and frontal lobe regions, respetively. In comparison to group-level ROIs, higher annual change in tau-PET SUVR was seen using individualized ROIs, with the approach iv (“highest data-driven stage approach”) performing best in all groups (preclinical AD, 6.4%; prodromal AD, 8.67%; mild AD dementia, 10.72%). In comparison to longitudinal cognition as an outcome, tau-PET using best-performing group-level ROIs as an outcome resulted in greater sample size reductions (preclinical AD: data-driven stage I, 58% fewer subjects compared to mPACC and 63% compared to MMSE; prodromal AD: data-driven stage II, 54% fewer subjects compared mPACC and 65% compared to MMSE; mild AD dementia: data-driven stage IV, 64% fewer subjects compared mPACC and 51% compared to MMSE). Using the best performing individualized ROI (highest data-driven stage) resulted in even greater differences compared to cognitive measures (74% for preclinical AD compared to mPACC, 71% for prodromal AD and 67% for mild AD dementia compared to MMSE). Conclusion: Using longitudinal tau-PET as an outcome in early phase trials require fewer participants when compared to cognitive decline in AD clinical trials. If using longitudinal tau-PET as outcome, individualized ROIs appear to carry an advantage over group-level ROIs.
OC09- PREVALENCE AND LONGITUDINAL CLINICAL OUTCOMES OF VISUALLY 18F-FLORTAUCIPIR PET-POSITIVE INDIVIDUALS ACROSS THE ALZHEIMER’S DISEASE SPECTRUM. A. Moscoso 1, F. Heeman 1, V. Camacho 2, M. Van Essen 3, M.J. Grothe 4, L. Lin 5, I. Mainta 6, F. Ribaldi 7, M.D. Devous 8, M.J. Pontecorvo 8, G.B. Frisoni 7, V. Garibotto 7, M. Schöll 1(1. Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg — Gothenburg (Sweden), 2. Department of Nuclear Medicine, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain. — Barcelona (Spain), 3. Department of Clinical Physiology, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden. — Gothenburg (Sweden), 4. Movement Disorders Group, Institute of Biomedicine of Seville-IBiS, Seville, Spain. — Sevillla (Spain), 5. Department of radiology, the third affiliated hospital of sun yat-sen university. — Guangzhou (China), 6. Division of Nuclear Medicine, Geneva University Hospitals, Geneva, Switzerland. — Genève (Switzerland), 7. Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland — Genève (Switzerland), 8. Avid Radiopharmaceuticals, Philadelphia, PA, USA — Philadelphia (United States))
Background: The advent of positron emission tomography (PET) imaging with [18F]flortaucipir has allowed in-vivo visualization of aggregated tau in Alzheimer’s disease (AD). Recently, a visual interpretation method for [18F]flortaucipir was developed and validated using neuropathological data, showing that tau-PET positivity can be regarded as a marker of advanced Braak stages (V–VI). This led to the approval of [18F] flortaucipir by the US Food and Drug Administration (FDA) as the first PET radiopharmaceutical indicated to ‘estimate the density and distribution of aggregated neurofibrillary tangles’. In the clinical trials realm, this visual tau-PET positivity is one of the key eligibility criteria for inclusion in Donanemab trials. Yet, despite the relevance of this novel visual interpretation method, relevant variables for trialists such as the prevalence of visual tau-PET positivity across the AD spectrum or the longitudinal outcomes associated to visual tau-PET-positivity have not been investigated before. Objectives: 1) To estimate the prevalence of visual tau-PET positivity across the AD spectrum. 2) To establish the longitudinal clinical course of visually tau-PET-positive individuals across the AD spectrum. Methods: We included cognitively unimpaired individuals and patients with mild cognitive impairment (MCI) and AD dementia from five observational cohort studies — Alzheimer’s Disease Neuroimaging Initiative (ADNI), Harvard Aging Brain study (HABS), A4 study, AVID’s A05 study and Geneva Memory Clinic cohort — all of which had available FTP PET scans (n=1828, 1219 unimpaired, 425 MCI, and 184 AD dementia). Furthermore, Aβ status, established with Aβ-PET, was available in 1724 participants (94%), and longitudinal clinical and cognitive data was obtained for 523 unimpaired (median follow-up: 2 years) and 372 impaired (median follow-up: 1.5 years) participants. A trained reader (AM), blinded to clinical and imaging information, scored each [18F]flortaucipir PET scan as either negative or positive, and additionally classified positive [18F]flortaucipir PET scans as either moderate or advanced AD patterns. Multinomial generalized additive models (GAM) were fitted to obtain prevalence estimates of each [18F]flortaucipir AD pattern. Linear mixed models were used to estimate cognitive decline trajectories across Aβ- and visual tau-PET groups (A±vTAU±). Results: Among all cognitively unimpaired individuals, the prevalence of visual tau-PET positivity was 13.4%, with similar prevalence of moderate and advanced AD patterns (6.1% and 7.3%, respectively). The prevalence of tau-PET-positivity increased non-linearly with age from ∼5% at 65 years to 17% at 90 years. Tau-PET positivity was strongly dependent on Aβ status, showing high specificity (97.8%) for Aβ pathology. In the Aβ-positive unimpaired cohort of the A4 study, 24% of the participants were tau-PET-positive. In longitudinal analyses, A+vTAU+ unimpaired individuals showed the fastest rates of cognitive decline (Aβ+/vTAU+: ΔPACC3 = −0.34/y, p=0.02; Aβ+/vTAU-: ΔPACC3 = −0.16/y, p=0.06; Aβ-/vTAU- as reference). In cognitively impaired individuals, the overall prevalence of tau-PET positivity was 38.1% for MCI and 71.2% for AD dementia, with a much higher relative prevalence of the advanced pattern compared to the moderate (only 8.0% of MCI and 5.4% of AD dementia participants showed a moderate pattern). For both MCI and AD dementia participants, the prevalence of the advanced pattern decreased with age while that of the moderate pattern increased with age. As for unimpaired individuals, tau-PET positivity was highly specific for Aβ pathology (95%). In the longitudinal analysis, only the advanced AD pattern was significantly associated with faster clinical deterioration in MCI or AD dementia patients as measured by longitudinal MMSE, CDR-SB or ADAS-Cog 11. There were no significant differences between the clinical trajectories of A-vTAU- and A+vTAU- individuals. Conclusion: Our large-scale study provides the first robust estimates of the prevalence and longitudinal clinical outcomes of tau-PET positive individuals as defined using a clinically applicable, FDA-approved method. These estimates indicate that a non-negligible fraction of the cognitively unimpaired elderly population is tau-PET positive, indicative of advanced Braak stages. This prevalence increases even more among Aβ-positive unimpaired persons, with approximately 1 out of 4 unimpaired Aβ-positive being tau-PET-positive in the A4 study. Together with the fact that tau-PET-positive subjects show the fastest rates of clinical decline, this relatively high prevalence of unimpaired individuals in advanced Braak stages may be relevant for prevention trials using anti-amyloid therapies. The fact that A-vTAU- and A+vTAU- impaired individuals had similar longitudinal clinical outcomes suggests that the advanced AD pattern, and not amyloid pathology, is the main driver for AD-related clinical symptoms.
OC10- CONCORDANCE OF VISUAL AND QUANTITATIVE ANALYSIS FOR AMYLOID PET IMAGING WITH THREE 18F TRACERS IN THE CHARIOT-PRO SUBSTUDY. G. Novak 1, Z. Saad 2, D. Scott 3, C. Udeh-Momoh 4, L. Bracoud5, C. Ritchie 6, L. Middleton 7(1. Janssen R&D — Titusville, Nj (United States), 2. Janssen R&D — La Jolla, Ca (United States), 3. Clario (formerly Bioclinica) — San Mateo, CA (United States), 4. Imperial College — London (United Kingdom), 5. Clario (formerly Bioclinica) — Lyon (France), 6. University of edinburgh — Edinburgh (United Kingdom), 7. Imperial College — Edinburgh (United Kingdom))
Background: Assessment of amyloid burden has been essential in the selection of patients most likely to be informative of safety and efficacy in clinical trials of amyloid-directed therapy. Three 18F PET tracers are approved for assessment of amyloid status through visual interpretation; quantitative thresholds of Standard Uptake Value Ratios (SUVR) have been defined for each, and a common centiloid (CL) scale has been derived from the linear relationship among the SUVRs of each tracer. While it may be desirable to choose a specific tracer for use within a single clinical trial, the long duration of these studies and their geographic range may require one to use 2 or more approved tracers, and the potential impact of this on consistency of performance needs to be explored. Objectives: To compare the performance of three 18F amyloid tracers in an observational trial, the CHARIOT-PRO Substudy (CPSS). Methods: The CPSS aims to assess the rate of longitudinal cognitive change in equal numbers of cognitively unimpaired elders with and without biomarker evidence of increased cerebral amyloid burden (A+ and A−, respectively). Participants were recruited at 2 centers in the UK, Imperial College London and the University of Edinburgh; the majority had amyloid assessment via PET. PET exams were acquired using a uniform scanning protocol that minimizes between-site differences in PET systems, as characterized with a Hoffman phantom exam. All exams were acquired in 3D mode, with correction for attenuation (CT-based), scatter and random coincidence. Visual assessments of the scans were performed by one of 3 neuroradiologists in a central laboratory, according to the prescribing information for each tracer, blinded to SUVR. Quantitative analysis involved coregistration of the image to each participant’s baseline 3DT1 MRI. A composite SUVR was calculated as the volume-weighted average across FreeSurfer target and reference subregions derived from native-space MRI. The thresholds for amyloid positivity for each tracer were: Florbetapir, > 1.14 referred to whole cerebellum; Florbetaben, > 1.20 referred to cerebellar gray matter; and Flutemetamol > 1.21 referred to whole cerebellum. Composite SUVRs were then re-scaled to centiloid units, using linear regression derived by the central laboratory. PET positivity was determined using a hybrid approach. Concordant visual and quantitative assessments were accepted as A+ or A−, respectively. A negative visual read with an above threshold SUVR was considered A+. In case the visual read was positive and SUVR was below threshold, a second reader considered both results and made a final determination via consensus with the first reader. In case the SUVR was deemed unreliable, results were determined by consensus of 2 visual readers. Agreement between the visual read and SUVR were quantified by the kappa statistic. The sensitivity and specificity of the SUVR threshold defined for each tracer was calculated with respect to the results of the visual read, and an optimized SUVR cutpoint for predicting visual reads was determined by ROC analysis. Similarly, sensitivity and specificity were derived for a CL value of > 22 relative to the visual read, and this was compared to the optimized CL values resulting from ROC analysis. Results: A total of 1170 participants had amyloid PET, of whom 1112 had complete visual reads and SUVR (207 A+, 905 A−). Overall concordance was 95.5% and kappa = 0.837, indicating good agreement, but 49 of the 50 discordant cases were visual positive (V+) and SUVR negative (SUVR-). Thus, the visual read identified a higher proportion of participants as A+ (18.6%) than did SUVR (14.3%). Concordance was nominally higher for Florbetapir (n=178, A+=20.8%, concordance=98.3%, kappa=0.948) than for Florbetaben (n=615, A+=17.7%, concordance=95.1%, kappa=0.812) and Flutemetamol (n=319, A+=18.6%, concordance=94.7%, kappa=0.807). With respect to the visual read, the defined cutpoints yielded a sensitivity/specificity of 94.6%/99.3% for Florbetapir, 67.9%/100% for Florbetaben, and 70.1%/100% for Flutemetamol. These observations suggested that the defined SUVR cutoffs for the latter 2 tracers, derived from limited data available in 2015 and 2014 respectively, were too conservative. ROC analysis identified less stringent SUVR thresholds for each tracer (> 1.115 for Florbetapir and Florbetaben, and > 1.08 for Flutemetamol).; in the pooled population, sensitivity/specificity were 94.7%/96.6%. Using a defined threshold CL value > 22, 20.1% of the pooled population was identified as A+, with a sensitivity/specificity of 94.2%/96.7%. ROC analysis yielded an identical CL threshold of > 22. Discussion: Use of a universal CL threshold of 22 units allowed for a consistent mapping of quantitative to qualitative assessments in a study that used 3 different amyloid tracers for participant selection. While there was no direct within-subject tracer comparison in this study, the concordance of the visual assessment with a CL cutoff of 22 across tracers suggests their performance is comparable and supports the use of multiple tracers for patient selection in clinical trials. ZSS, GN, and SB are employed by Janssen and may hold stock or stock options; LB and DS are employees of Clario but declare no conflicts.
OC11- AMYLOIDIQ QUANTIFICATION STRONGLY AGREES WITH BOTH HISTOPATHOLOGY AND VISUAL READS ACROSS MULTIPLE AMYLOID TRACERS. A. Whittington 1, S. Bullich 2, L. Porat 1, R.N. Gunn 1(1. Invicro — London (United Kingdom), 2. Life Molecular Imaging — Berlin (Germany))
Background: Neuritic plaques formed predominantly of misfolded Amyloid-β (Aβ) are one of 2 pathological hallmarks of Alzheimer’s Disease (AD). Amyloid PET imaging with one of the FDA approved amyloid PET radiotracers provides a method to detect Aβ pathology in vivo. Scans are routinely classified as either positive (Aβ+) or negative (Aβ−) by visual assessment (often with a majority read from multiple independent reads). With the advance of quantitative algorithms such as AmyloidIQ, which has shown strong performance in cross-sectional and longitudinal studies, it now becomes possible to provide an automated quantitative assessment of Aβ pathology in the brain. Objectives: In this work, we assess the performance of AmyloidIQ against gold-standard post-mortem histopathology data and against visual assessment performed by trained readers for the PET tracers [18F]Florbetaben and [18F]Florbetapir. Within these analyses we also compared the performance of AmyloidIQ with a PET only pipeline and also with an associated structural MRI image available. Methods: There were 3 distinct analyses performed on different datasets in this work. In the first, histopathology (either Bielschowsky silver staining (BSS) or Immunohistochemistry (IHC)) was compared to AmyloidIQ quantification of ante-mortem [18F] Florbetaben scans for both PET-MR and PETOnly AmyloidIQ pipelines (PET-MR: n = 80, 25 Aβ-, 35 Aβ+ and PETOnly: n = 88, 35 Aβ−, 54 Aβ+). The second and third were comparisons of AmyloidIQ quantification with visual reads with [18F] Florbetaben (PET-MR: n = 345, 173 Aβ−, 172 Aβ+ and PETOnly: n = 439, 246 Aβ−, 193 Aβ+) and [18F]Florbetapir (PET-MR and PETOnly: n=610, 313 Aβ−, 297 Aβ+) respectively. The visual reads for both analyses were performed by 5 experienced independent readers. The AmyloidIQ algorithm models spatially normalised SUVR images as the linear combination of two canonical images (carrying capacity image K and non-specific image NS) to produce a single continuous outcome measure, Amyloid Load (AβL), which quantifies the global amyloid burden. AmyloidIQ was successfully run on all scans from all 3 datasets with the only difference between PET-MR and PETOnly pipeline being the spatial normalisation algorithm (PET-MR: nonlinear using DARTEL, PETOnly: affine). Histopathology and visual reads provided a classification of the presence or absence of amyloid pathology (Aβ pathology was considered present, if any of the 6 regions sampled had moderate or frequent neuritic plaques either by BSS or IHC or both) and Aβ+/Aβ− respectively. ROC curve analyses produced optimum thresholds for AβL for classification for both PET-MR and PETOnly pipelines. The accuracy of each methodology was evaluated at these optimum thresholds using both histopathology and visual reads as a gold standard. Results: The comparison of AmyloidIQ against post-mortem data yielded a strong agreement (PET-MR: Accuracy 95.0% with sensitivity 94.5% and specificity 96.0% and PETOnly: Accuracy 95.5% with sensitivity 94.4% and specificity 97.1%) at the optimum thresholds (PET-MR: 35.6% and PETOnly: 42.3%). Visual reads also exhibited a strong agreement with AmyloidIQ regardless of the tracer used. More specifically, in the [18F]Florbetapir data, the accuracy of the PET-MR pipeline was 93.4% and the accuracy of the PETOnly pipeline was 93.1%. The optimum AβL thresholds for the two pipelines were similar (PET-MR: 32.5% and PETOnly: 35.6%). The [18F]Florbetaben results were remarkably similar. The accuracy of the PET-MR pipeline was 94.5% at the optimum AβL threshold of 35.6% and the accuracy of the PETOnly pipeline was 93.2% and cut-off at the optimum AβL threshold of 42.3%. Conclusion: AmyloidIQ analysis of [18F]Florbetaben scans exhibits a very strong agreement with both histopathology (IHC/BSS) data and visual assessment. Further, AmyloidIQ analysis of [18F]Florbetapir also showed a very strong agreement with visual assessment. AmyloidIQ classification was unaffected without an associated MRI scan which paves the way for the straightforward deployment in the clinical setting. The optimum thresholds found in all circumstances were extremely similar and the carrying capacity image can be calibrated to produce a standardised threshold of 33% across all tracers and pipelines hence providing a global and easily interpretable scale for AβL. This extensive assessment of AmyloidIQ against the gold-standard measures of post-mortem data and visual reads shows that its quantification can be used to both detect amyloid burden in the brain and automate the visual assessment of amyloid PET scans.
OC12- TOPLINE RESULTS OF EXERT: CAN EXERCISE PROTECT AGAINST COGNITIVE DECLINE IN MCI? C. Cotman 1, H. Feldman 2, A. Lacroix 2, A. Shadyab 2, D. Jacobs 2, D. Salmon 2, R. Thomas 2, S. Jin 2, J. Pa 2, J. Katula 3, R. Rissman 4, J. Brewer 2, Y. Jung 5, J. Zhang 2, L. Baker 6(1. UCI (United States), 2. UCSD (United States), 3. Wake Forest University (United States), 4. USC (United States), 5. UC Davis (United States), 6. Wake Forest University School of Medicine (United States))
Background: There are currently no effective therapeutic options to delay the progression of Alzheimer’s disease (AD). The potential benefits of exercise on brain health in older adults at risk for AD are supported by preliminary studies and warrant further investigation. The EXERT trial (NCT02814526) was a Phase 3, multicenter, randomized single-blind study that examined the effects of regular exercise on cognition and other measures of brain function in a planned sample of 300 older adults with amnestic mild cognitive impairment (MCI). Objective: To test whether 12 months of supervised moderate intensity aerobic exercise versus an active control of stretching and balance protected against cognitive decline and other measures of AD progression in adults with MCI. Methods: EXERT was conducted at 14 sites and coordinated by the Alzheimer’s Disease Cooperative Study (ADCS), in partnership with Wake Forest School of Medicine and the YMCA of the USA (Y-USA) for oversight of intervention delivery. Participants were randomized to complete aerobic exercise (AX) training or stretching, balance, and range of motion (SBR) activities for 18 months. For the first 12 months, exercise was completed with supervision of YMCA trainers twice per week, and independently twice per week. In the final 6 months (Months 13–18), participants completed exercise without supervision. The AX group completed moderate intensity exercise indicated by elevated heart rate (65–70% of heart rate reserve) and ratings of exertion. The SBR group exercised at a lower heart rate (<35% heart rate reserve) and ratings of exertion. Objective measures of adherence were tracked and monitored regularly by exercise specialists (Wake Forest, Y-USA). Outcomes assessments were completed in the clinic at baseline, and at Months 6, 12, and 18. The primary endpoint included outcomes obtained at Months 6 and 12. A modified version of the ADAS-Cog13 that included select subtests with additional measures of executive function (referred to as the ADAS-Cog-Exec) was validated and used as the primary outcome. Additional tests of executive function and memory were administered, blood was collected for AD biomarker analysis, and brain MRI was completed. In addition, 12-month changes in the ADAS-Cog-Exec and Clinical Dementia Rating Sum of Boxes (CDR-SB) were compared for both EXERT intervention groups relative to propensity-matched samples from other cohorts (e.g., ADNI-1) to estimate treatment effects relative to no intervention (i.e., “Usual Care”). Results: A total of 296 participants were enrolled from September 2016 to March 2020. Over 31,000 exercise sessions were completed in the 12-month supervised phase of the study, 18,045 of which (58.2%) were supervised by a trainer. For the AX group, 81% of expected supervised sessions were completed; for the SBR group, 87% of expected supervised sessions were completed. During the COVID-19 pandemic when the study was paused, >60% of participants reported continued exercise. The AX and SBR groups were balanced in baseline characteristics; 13.2% represented communities of color, and 40% did not have a college degree. Baseline MMSE and CDR-SB scores indicated that EXERT participants had mild cognitive impairments (mean MMSE=27.9; mean CDR-SB=1.5), and 25% of the sample were APOE4 carriers. Using a modified ITT approach (i.e., ppts must have initiated exercise and completed at least 1 follow-up assessment) to data analysis, neither the AX group nor the SBR group showed cognitive decline on either the ADAS-Cog-Exec or the CDR-SB over 12 months of follow-up. There were no significant treatment differences between AX and SBR on these outcomes. In the Usual Care analysis comparing ADNI-1 and EXERT participants matched on several key variables (demographics, baseline cognitive function, APOE4), ADNI-1 participants showed the expected 12-month decline on the ADAS-Cog-Exec but the EXERT AX and SBR groups did not (ADNI-1: vs. AX: p=0.012; vs. SBR: p=0.00049). Conclusions: In past smaller trials, exercise-related benefits were observed showing relative ‘protection against decline’ vs. the control group that showed expected rates of decline for adults with MCI. In EXERT, the expected 12-month declines for the control group did not occur. Our findings suggest that both exercise interventions stalled cognitive decline for adults with MCI. EXERT is the longest exercise trial in MCI conducted to date, and it is possible that greater ‘volume’ of exercise provided more protection, regardless of exercise intensity. In addition, both groups were provided with equal amounts of socialization, which may have also protected against decline. These results are particularly noteworthy given that the trial was conducted during the COVID-19 pandemic. Funding: NIH/NIA U19 AG010483
OC13- SENOLYTIC THERAPY TO MODULATE THE PROGRESSION OF ALZHEIMER’S DISEASE (STOMP-AD) — PILOT STUDY RESULTS ON CENTRAL NERVOUS SYSTEM PENETRANCE AND ALZHEIMER’S DISEASE BIOMARKERS. M. Gonzales 1, V. Garbarino 1, T. Kautz 1, R. Petersen 2, T. Tchkonia 2, J. Kirkland 2, S. Craft 3, S. Seshadri 1, N. Musi 1, M. Orr 3(1. Ut Health San Antonio — San Antonio (United States), 2. Mayo Clinic — Rochester (United States), 3. Wake Forest School Of Medicine — Winston-Salem (United States))
Objectives: Cellular senescence, a hallmark of biological aging, is a novel therapeutic target for neurodegenerative disease, which leverages the geroscience approach to disease prevention and treatment. Accumulation of senescent cells across tissues, including the brain, increases with aging. Senescent cells can produce a noxious secretome of cytokines and chemokines, which propagates inflammation and induces tissue dysfunction if not efficiently cleared by immune system. In the brain, senescent cells frequently colocalize with neuropathology. Preclinical studies have demonstrated that pharmacological ablation of senescent cells dampens inflammation, reduces ventricular enlargement, preserves neuronal and synaptic density, attenuates neuropathological burden, and improves cognitive behavior. However, the safety and efficacy of this novel therapeutic approach, referred to as “senolytics”, in humans with cognitive impairment remains unestablished. Herein, we conducted a vanguard open-label clinical trial of senolytic therapy for Alzheimer’s disease. The primary objectives were to evaluate the safety profile of intermittent orally-administered dasatinib and quercetin and determine central nervous system penetrance of the compounds. We also aimed to gain preliminary data into treatment effects on cognitive function, fluid biomarkers of AD pathogenesis, and senescence-associated inflammation. Methods: Participants with a clinical diagnosis of early-stage AD (CDR Global = 1) were enrolled in an open-label twelve-week pilot trial of intermittent orally-delivered dasatinib (100 mg) and quercetin (1000 mg). Safety was continuously monitored with adverse event reporting, vitals, and laboratory work. Plasma and cerebrospinal fluid (CSF) levels of dasatinib and quercetin were assessed before treatment and within four hours after final study drug administration using HPLC with tandem mass spectroscopy. CSF levels of Ab40, Ab42, phosphorylated tau 181 (p-tau 181), p-tau 231, neurofilament light (NFL), and glial fibrillary acidic protein (GFAP) were assayed using the Simoa HD-X Analyzer. For purposes of rigor, we also used Lumipulse to measure Ab40, Ab42, total tau, and p-tau 181; and capillary electrophoresis for measuring total and phosphorylated tau. Target engagement was assessed by investigating treatment-related changes in plasma and CSF markers of senescence and the senescence-associated secretory phenotype by Meso Scale Discovery Immunoassays. Paired t-tests were used to examine differences in biomarker levels pre- and post-treatment. Results: Five participants (40% female) with a mean age of 76±4 years completed the open-label trial. The treatment was well-tolerated with no significant changes in vitals, complete blood counts, and comprehensive metabolic panels (all p>0.05). The primary cognitive endpoints, the CDR Sum of Boxes (CDR SOB, t(4)=2.449, p=0.070) and the Montreal Cognitive Assessment (MoCA, t(4)=−0.196, p=0.854) were stable from pre- to post-treatment. Dasatinib was detected in plasma (t(4)=3.612, p=0.023) and CSF (t(4)=3.123, p=0.035) following treatment. Plasma quercetin levels were higher post-treatment (t(4)=2.847, p=0.047), whereas quercetin levels in CSF were undetectable across timepoints. Simoa results demonstrated that in four out of five participants, CSF levels of p-tau 181 and the p-tau 181/Ab1-42 ratio decreased from pre- to post-treatment. There was a significant increase in CSF GFAP levels across timepoints (t(4)=3.354, p=0.028). Mean treatment-related changes in all other AD biomarkers did not reach statistical significance (Ab40: t(4)=0.274, p=0.797, Ab1-42: t(4)=−0.092, p=0.931, p-tau 181: t(4)=−1.521, p=0.203, p-tau 181/Ab1-42: t(4)=−0.869, p=0.434, p-tau 231: t(4)=−0.152, p=0.887, NFL: t(4)=− 0.096, p=0.928). Lumipulse data indicated that in four out of five participants, Ab1-42 increased and the tau/Ab1-42 ratio decreased, although the results for the whole sample did not reach statistical significance (Ab1-42: t(4)=2.338, p=0.0795, p-tau 181/Ab1-42: t(4)=1.606, p=0.1835). Capillary electrophoresis demonstrated that high molecular weight p-tau 181 significantly decreased in all subjects with treatment (t(4)=2.941, p=0.0424) though the lower molecular weight tau did not change (t(4)=0.8199, p=0.4583). Assays of senescence in plasma and CSF are underway. Conclusion: Results from the first clinical trial of senolytic therapy in older adults with AD indicates that the treatment was well-tolerated. Preliminary data from our open-label pilot supports central nervous system penetration of dasatinib. While early results are promising, fully powered, double-blinded, placebo-controlled studies are needed to evaluate the potential of disease modification with the novel approach of targeting cellular senescence in AD.
OC14- A RANDOMIZED, DOUBLE-BLIND, PLACEBO-CONTROLLED STUDY TO EVALUATE THE SAFETY, PHARMACODYNAMICS AND PHARMACOKINETICS OF TW001 IN ALZHEIMER PATIENTS. R. Van Der Geest 1, A. Lili 1, O. Van Loosbroek 1, A. Almeida 1, M. Oosthoek 2, C. Teunissen 2, S. Sikkes 3, E. Vijverberg 2(1. Treeway TW001AD BV — Tilburg (Netherlands), 2. Neurochemistry Laboratory, Department of Clinical Chemistry, Vrije Universiteit Amsterdam, Amsterdam UMC — Amsterdam (Netherlands), 3. Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC — Amsterdam (Netherlands))
Background: The pathological hallmarks of Alzheimer’s disease (AD) are the amyloid-beta (Aβ) plaques and the tau neurofibrillary tangles. Recent failures in phase 3 studies of anti-amyloid agents and tau aggregation inhibitors in patients with early stage, mild or mild to moderate AD suggest that novel approaches to drug development are urgently needed. Oxidative stress has been reported to be a prominent early event in the pathogenesis of AD. Reactive oxygen species (ROS) can alter the physical structures of proteins and accompanied by reactive nitrogen species (RNS) can induce cell membrane lipids to undergo peroxidation under oxidative stress conditions. All these oxidative stress products accumulate and trigger AD development. Accumulated in vitro and in vivo evidence has demonstrated that edaravone, a free radical scavenger with the ability to cross the blood brain barrier, can be effective in AD. In particular, edaravone can reduce oxidative stress in animal models of AD, measured by a reduction of pro-oxidants or products of lipid peroxidation or an increase in antioxidants in different brain regions. Moreover, the studies indicate that there is a neuroprotective effect of edaravone on several other levels that could reduce the rate of AD progression. This is indicated by the effect of edaravone on pro-inflammatory cytokines and on the cholinergic system, the latter being the main system targeted by current medication in the treatment of AD dementia. The intravenous formulation of edaravone is already on the market in a variety of countries (including the US and Japan) initially to reduce neuronal damage caused by Acute Ischemic Stroke (AiS) and later in the treatment of Amyotrophic Lateral Sclerosis (ALS). Treeway B.V. has developed an oral formulation for edaravone (TW001) to overcome the challenges related to intravenously administered edaravone for the treatment of ALS. This formulation is currently being tested in a Pivotal Phase 3 Clinical Trial in Europe. Objectives: It is hypothesized that an antioxidant therapy, such as edaravone, might also be a promising treatment strategy for AD, as oxidative stress plays a pivotal role in the development and progression of the disease. Current treatment options for AD, however, do not target oxidative stress. This is the first study that aims to investigate the effect of an antioxidant treatment for early AD. In light of this, Treeway B.V., supported by ADDF, has planned to initiate a Phase IIA clinical trial in AD patients in collaboration with the VU Medical Center and the Brain Research Center in Amsterdam. Methods: This is a double-blind, randomized, placebo-controlled, phase IIA proof-of-concept study to evaluate the safety, pharmacodynamics and pharmacokinetics of TW001 in mild AD patients. Although the primary objective of this highly innovative study design is to investigate the effect of oral edaravone on a series of disease and target engagement (e.g., oxidative stress) biomarkers, the study will also explore the early effect of edaravone on a variety of individual biomarkers and surrogate endpoints such as EEG, to define a potential composite biomarker that can be used in subsequent long-term clinical studies. In addition, a newly developed and highly sensitive clinical assessment tool (Cognitive-Functional Composite — CFC), developed by the Alzheimer Center of Amsterdam, will be tested in the study as a clinical outcome measure to potentially detect early changes in cognitive function. Conflict of interest: Treeway TW001AD B.V. has received a research grant in November 2019 sponsored by ADDF (reference GC-2013807) for the development of this project.
OC15- PROTEIN BIOMARKERS IN AUTOSOMAL DOMINANT ALZHEIMER’S DISEASE CEREBROSPINAL FLUID IDENTIFY EARLY CHANGES IN BRAIN GLUCOSE METABOLISM AND THE MATRISOME. S. Bian 1, E.K. Carter 1, R. Haque 1, C. Watson 1, B. Gordon 2, L. Ping 1, D. Duong 1, M. Epstein 1, J. Lah 1, B. Roberts 1, A. Fagan 2, N. Seyfried 1, A. Levey 1, E. Johnson 1(1. Emory University — Atlanta (United States), 2. Washington University — St. Louis (United States))
Background: Alzheimer’s disease (AD) is characterized by multiple pathological brain alterations beyond amyloid-β (Aβ) and tau dyshomeostasis. How these pathological changes evolve over the course of the disease and are reflected by current AD biomarkers is currently unknown. Objectives: To better understand the natural history of AD pathology, we analyzed cerebrospinal fluid (CSF) from autosomal dominant AD (ADAD) mutation carriers and family member controls by targeted mass spectrometry to measure the levels of multiple proteins related to disease. The proteins were mapped to different AD brain pathologies as recently described in a consensus proteomic brain co-expression network of late-onset AD. Methods: 59 proteins were measured in 284 ADAD mutation carriers and 183 non-carriers in the Dominantly Inherited Alzheimer Network (DIAN). Measurements were obtained from baseline visits, and protein levels for each subject were placed in a longitudinal framework by the estimated year of disease onset (EYO). To better approximate protein levels sampled at a discrete set of EYO time points, we modeled EYO using a restricted cubic spline transformation with three knots at the 0.10, 0.50, and 0.90 quantiles. To achieve uncertainty estimates and to account for random effects imposed by shared genetic background, the Bayesian regression model was built using a Markov Chain Monte Carlo algorithm and was applied to model the relationship between protein levels and fixed effects including mutation status, EYO, and the interaction effect. Differences in protein levels between mutation carriers and non-carriers at the 99% confidence interval were inferred using the posterior coefficient estimates from the Bayesian regression model at discrete EYO 0.5 year intervals between −36 and 26. The time at which protein biomarker levels in carriers were noted to diverge from non-carriers was compared to other biomarker changes measured in DIAN. Results: 29 proteins out of the 59 targeted for measurement were found to be different between mutation carriers and non-carriers at any EYO time point, with most proteins increased in mutation carrier CSF. Proteins derived from the brain matrisome co-expression module associated with Aβ deposition were among the earliest to change in mutation carriers—earlier than the absolute decreased levels of Aβ42 and nearly 30 years prior to symptom onset—followed by synaptic proteins and proteins associated with glucose metabolism. Markers of glucose metabolism were elevated at approximately the same time point as tau phosphorylated at residues 217 and 181 (pTau217 and pTau181). Multiple proteins associated with inflammation were noted to increase concomitantly with decreases in brain tissue and metabolism as assessed by MRI and metabolic imaging. Decreased levels of proteins from the granin family were found to be associated with cognitive impairment and functional decline. Conclusion: Proteomic approaches are able to identify novel brain-based biomarkers for AD. Measurement of these AD biomarkers in DIAN provides insight into the natural history of AD pathophysiology, which begins approximately three decades prior to the onset of cognitive symptoms in ADAD. The authors declare no competing interests. On behalf of the Dominantly Inherited Alzheimer Network.
OC16- LEVERAGING NOVEL TECHNOLOGIES TO DESIGN AND IMPLEMENT MORE PATIENT FOCUSED CLINICAL TRIALS. D. Miller 1(1. Unlearn.AI — Berkeley (United States)) Late-stage Alzheimer’s disease (AD) randomized controlled trials (RCTs) are typically characterized by enrolling a large number of participants, high screen failures, and a trial duration commonly ranging from 2 to 4 years. It is then critical to bring efficiency to these late-stage AD clinical trials to accelerate the drug development process while maintaining the reliability of the evidence being generated. Unlearn’s novel clinical trial participant-focused approach, called TwinRCTs, enables reducing the number of participants in the control arm for a desired power while maintaining a strict control of type I error rate as with the traditional RCT. Unlearn’s approach has received a draft qualification opinion from the European Medicines Agency (EMA) novel methodologies program for a 3-step procedure called PROCOVA, the foundation of our TwinRCTs. The PROCOVA procedure consists of 3 steps: Step 1 is to build and evaluate a prognostic machine learning model for use in a particular planned trial (the Target Trial); Step 2 is to estimate the sample size and plan the Target Trial using PROCOVA for the primary analysis. Step 3, taking place after Target Trial database lock, is to estimate the treatment effect using a linear model while adjusting for the prognostic score. For the Step 1 of the PROCOVA procedure, Unlearn has developed machine learning methods to build models trained with historical data that are highly suitable to be used with the PROCOVA procedure. Among our current models, we have developed an AD model leveraging historical AD data that has been used in collaboration with a number of pharmaceutical companies for determining potential use cases in their existing AD clinical programs. We have shown that for a completed Phase 2 AD clinical trial, Unlearn’s approach could enable the reduction of the control arm by more than 20%. TwinRCTs, including the PROCOVA procedure, are faster, participant-focused, and more efficient RCTs that generate regulatory-suitable clinical evidence.
OC17- AMYLOID AND TAU PET POSITIVE COGNITIVELY UNIMPAIRED INDIVIDUALS: DESTINED TO DECLINE? R. Ossenkoppele 1, A. Pichet Binette 1, C. Groot 1, R. Sperling 2, C. Masters 3, W. Van Der Flier 4, W. Jagust 5, P. Ronald 6, C. Jack 6, O. Hansson 1(1. Lund University — Lund (Sweden), 2. Mgh — Boston (United States), 3. The Florey Institute Of Neuroscience And Mental Health Melbourne Victoria Australia — Parkville (Australia), 4. Amsterdam University Medical Center — Amsterdam (Netherlands), 5. Uc Berkeley — Berkeley (United States), 6. Mayo Clinic — Rochester (United States))
Background: A major unanswered question in the dementia field is whether cognitively unimpaired individuals who harbor both Alzheimer’s disease (AD) neuropathological hallmarks (i.e., amyloid-beta plaques and tau neurofibrillary tangles) can preserve their cognition over time or are destined to decline. Consequently, there is fundamental disagreement between the National Institute on Aging and Alzheimer’s Association (NIA-AA) criteria and the International Working Group (IWG) criteria about the nomenclature for cognitively unimpaired individuals who harbor one or both AD hallmark neuropathological features. For example, a cognitively unimpaired individual with positive Abeta (A+) and tau (T+) biomarkers is classified as “preclinical AD” by the NIA-AA criteria, while the IWG criteria would label such an individual “at risk for progression to AD”. Objective: In this large multi-center amyloid and tau-PET study (n=1325), we examined the risk for future progression to mild cognitive impairment and the rate of cognitive decline over time among cognitively unimpaired individuals who were amyloid-PET-positive (A+) and tau-PET positive (T+) in the medial temporal lobe (A+TMTL+) and/or in the neocortex (A+TNEO+) and compared them with A+T- and A-T- groups. Methods: Participants were recruited from the Mayo Clinic Olmsted Study of Aging (n=680), the Swedish BioFINDER-1 (n=56) and BioFINDER-2 (n=228) studies, the Berkeley Aging Cohort study (n=109), the Harvard Aging Brain Study (n=162), the Australian Imaging Biomarkers and Lifestyle Study of Ageing (n=48) and the Amsterdam Dementia Cohort (n=42). All participants were i) cognitively unimpaired at baseline defined by neuropsychological test scores within the normative range given an individuals’ age, sex and educational background, ii) had amyloid-PET available to determine Abeta-status, iii) underwent a tau-PET scan before January 1, 2019, to allow for sufficiently long follow-up duration, and iv) had at least one clinical follow-up visit available. Follow-up data was collected until April 1st, 2022. Abeta-status was determined using center-specific cut-offs or visual read metrics using [18F] flutemetamol, [11C]Pittsburgh compound-B, [18F]florbetapir or [18F]NAV4694 PET. Tau-PET was performed using [18F] flortaucipir across all cohorts, except BioFINDER-2 where [18F]RO948 was used. We computed tau-PET status for a medial temporal lobe (MTL; unweighted average of bilateral entorhinal cortex and amygdala) and a neocortical (NEO; weighted average of bilateral middle temporal and inferior temporal gyri) region-of-interest. The threshold was determined for each cohort separately, based on the mean+2*standard deviation across all Abeta-negative participants within each cohort. Based on amyloid and tau-PET status we generated four different biomarker groups: A-T-, A+T-, A+TMTL+ (defined as tau-PET positive in the MTL but not in the neocortex) and A+TNEO+ (defined as tau-PET positive in the neocortex and/or in the MTL). First, we examined progression from cognitively unimpaired to mild cognitive impairment (MCI) using Cox proportional hazard models, adjusting for age, sex, education and cohort using A-T- as the reference group. Second, we examined differences in cognitive trajectories between groups on the modified preclinical Alzheimer cognitive composite 5 (mPACC5) and the Mini-Mental State Examination (MMSE) using linear mixed effect models with random intercepts and slopes, adjusting for age, sex, education and cohort. Statistical significance for all models was set at p<0.05 two-sided. Results: We included 1325 cognitively unimpaired participants, of whom 843 (63.6%) were A−T−, 328 (24.8%) A+T−, 55 (4.2%) A+TMTL+ and 65 (4.9%) A+TNEO+. During clinical follow-up, 26/781 (3.3%) of A−T−, 26/292 (8.9%) of A+T−, 25/51 (49.0%) of A+TMTL+ and 32/60 (53.3%) of A+TNEO+ participants progressed to MCI. Cox proportional hazard models, adjusted for age, sex, education and cohort, showed an increased risk for future progression to MCI in the A+TNEO+ (Hazard ratio [HR]=19.2[95% confidence interval: 10.9-33.7], p<0.001), A+TMTL+ (HR=14.6[8.1–26.4], p<0.001) and A+T− (HR=2.4[1.4–4.3], p=0.002) groups compared to the A−T− (reference) group. Pairwise log-rank tests showed that the A+TMTL+ and A+TNEO+ groups (both p<0.001) had steeper survival curves compared to the A+T− group, while the A+TMTL+ and A+TNEO+ groups did not differ from each other (p=0.19). Fifty percent of the A+TNEO+ and A+TMTL+ groups had progressed to MCI after 42.8 and 43.6 months, respectively. Linear mixed effect models adjusting for age, sex, education and cohort indicated that the A+TNEO+ (standardized b [stb] of interaction with time in months ± standard error=-0.020±0.002, T=−10.14, p<0.001), A+TMTL+ (stb=−0.017±0.002, T=−8.84, p<0.001) and A+T− (stb=−0.005±0.001, T=−5.26, p<0.001) groups showed faster decline over time on the mPACC5 compared to the A−T− (reference) group. On the MMSE, the A+TNEO+ (b=− 0.056+0.005, T=−11.55, p<0.001), A+TMTL+ (b=−0.024±0.005, T=−4.72, p<0.001) and A+T− (b=−0.008+0.002, T=−3.46, p<0.001) groups showed faster decline over time compared to the A−T− (reference) group. The A+TNEO+ (T=−9.51, p<0.001) and A+TMTL+ (T=−3.04, p=0.002) groups progressed faster than the A+T− group, and the A+TNEO+ group declined faster than the A+TMTL+ group (T=−4.82, p<0.001). Conclusion: Evidence of advanced AD pathological changes provided by amyloid and tau-PET is strongly associated with short-term (i.e., 3–5 years) cognitive decline in cognitively unimpaired individuals and is therefore of high clinical relevance. This supports the NIA-AA criteria-based classification of A+T+ cognitively unimpaired individuals as “preclinical AD”, especially when “T” is defined by PET.
OC18- PLASMA NT1-TAU CORRELATES WITH AGE AND COGNITIVE DECLINE IN TWO LARGE DOWN SYNDROME COHORTS. A.M. Stern 1, K.L. Van Pelt 2, L. Liu 1, A.K. Anderson 1, B. Ostaszewski 1, D.J. Selkoe 1, F. Schmitt 2, E. Head3(1. Ann Romney Center For Neurologic Diseases, Brigham And Women’s Hospital, Harvard Medical School — Boston, Ma (United States), 2. Sanders-Brown Center For Aging, Department Of Neurology, University Of Kentucky — Lexington, Ky (United States), 3. Department Of Pathology And Laboratory Medicine, University Of California, Irvine — Irvine, Ca (United States))
Background: New plasma biomarker assays can predict cognitive decline and pathology in patients with or at risk for Alzheimer disease (AD). There is a need to expand upon novel plasma biomarker profiles in people with Down syndrome (DS), who nearly universally develop AD pathology. We previously found the NT1-tau assay can predict cognitive decline and imaging biomarker changes in sporadic non-DS AD. We have also recently developed plasma assays for Aβ37, Aβ40, and Aβ42. Objectives: To determine whether plasma Aβ isoforms and the ratios between them, and NT1-tau, predict cognitive decline in people with DS. Methods: The discovery cohort from the University of Kentucky (UKY) consisted of 104 participants with 416 longitudinal plasma samples. After excluding outliers and missing data, 85 participants with 220 observations were included in the analysis. The validation cohort from the Alzheimer’s Biomarker Consortium Down Syndrome (ABC-DS) consisted of 297 cross-sectional plasma sample. The NT1-tau assay was run on the Quanterix Simoa HD-X instrument, and the Aβ isoform assays on the SP-X instrument. Linear mixed models first assessed change in biomarkers in the discovery cohort over time, with covariates including baseline age, sex, level of intellectual disability (ID), and consensus diagnosis. No longitudinal effect of time was observed in the linear mixed models, so individual regressions for each biomarker were used in a cross-sectional manner for baseline discovery cohort samples to correlate with age, sex, performance on the Dementia Scale for People with Learning Disabilities (DLD), or consensus diagnosis. The regression models developed in the discovery cohort were evaluated in the validation cohort by comparing the model-predicted vs actual values. Results: In the discovery cohort, the Aβ42 and NT1-tau linear regression models demonstrated significant main effects of baseline age (Aβ42: F(6, 78) = 3.37, p = 0.005, R2adj = 0.14, RMSE = 18.18, β = −0.70; NT1-tau: (F(6, 78) = 4.98, p < 0.001, R2adj = 0.22, RMSE = 1.32, β = 0.05). NT1-tau was not independently associated with DLD-Total or DLD subscores when controlling for age, sex, ID, and clinical diagnosis. However, NT1-tau was significantly associated with DLD-Cognitive (β = 1.76, R2adj = 0.095, p = 0.003), DLD-Social (β = 1.45, R2adj = 0.11, p = 0.002), and DLD-Total (β = 3.20, R2adj = 0.12, p = 0. 001) scores when Aβ40, Aβ42, Aβ37 were the only covariates. The linear regression model for NT1-tau developed in the discovery UKY cohort predicted the NT1-tau level in the validation ABC-DS cohort (correlation between actual and predicted NT1-tau r = 0.38, p < 0.001). Conclusions: Plasma NT1-tau and Aβ42 correlate with age in people with DS. Plasma NT1-tau correlates with cognitive decline, and its predictive power holds across two large independent cohorts. Conflicts of Interest: DJS is a director of Prothena Biosciences and a consultant to Eisai. KLVP is now an employee of Synaptek, LLC.
OC19- SPECIFIC ASSOCIATIONS BETWEEN PLASMA BIOMARKERS AND POST-MORTEM AMYLOID PLAQUE AND NEUROFIBRILLARY TAU TANGLE BURDEN. G. Salvadó 1, R. Ossenkoppele 1,2, N.J. Ashton 3,4,5, T.G. Beach 6, G.E. Serrano 6, G. Kollmorgen 7, H. Zetterberg 3,8,9,10, S. Janelidze 1, K. Blennow 3, O. Hansson 1,11(1. Clinical Memory Research Unit, Department Of Clinical Sciences, Malmö, Lund University — Lund (Sweden), 2. Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center — Amsterdam (Netherlands), 3. Department Of Psychiatry And Neurochemistry, Institute Of Neuroscience And Physiology, The Sahlgrenska Academy, University Of Gothenburg — Gothenburg (Sweden), 4. Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Institute Clinical Neuroscience Institute, King’s College London — London (United Kingdom), 5. NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley, NHS Foundation — London (United Kingdom), 6. Banner Sun Health Research Institute — Sun City (United States), 7. Roche Diagnostics GmbH — Penzberg (Germany), 8. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital — Mölndal (Sweden), 9. Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square — London (United Kingdom), 10. UK Dementia Research Institute at UCL — London (United Kingdom), 11. Memory Clinic, Skåne University Hospital — Malmö (Sweden))
Background: Multiple plasma biomarkers have been recently developed and have shown promise as diagnostic and prognostic tools for Alzheimer’s disease (AD). However, their specific relationship with post-mortem pathological burden is still not fully understood. Objectives: We aimed to investigate the specific associations between multiple plasma biomarkers (phosphorylated tau217 [p-tau217], p-tau181, p-tau231, amyloid-β42/40 [Aβ42/40] ratio, glial fibrillary acidic protein [GFAP], and neurofilament light [NfL]) and core pathological measures of AD pathology (amyloid plaques and neurofibrillary tau tangles) assessed at autopsy. Methods: We included 132 participants from the Banner Sun Health Research Institute with a post-mortem neuropathological exam and available plasma biomarkers. Plasma p-tau217 and p-tau181 were measured using immunoassay developed by Lilly Research Laboratories (IN, USA); plasma p-tau231 was analysed using in-house single molecular arrays (Simoa) developed at the University of Gothenburg and; Aβ42, Aβ40, GFAP and NfL were analyzed using in-house Elecsys prototype plasma immunoassays (not commercially available, Roche Diagnostics International Ltd). We created a global measure for both plaques and tangles, which were measured in a semi-continuous scale (0–3) in five different regions (hippocampus, entorhinal cortex, and frontal, temporal, and parietal lobes). To assess specific associations between plasma makers and each of the two AD pathological measures, we performed linear regression models with plasma biomarkers as dependent variables and measures of both plaques and tangles as independent variables. The relevance of AD pathology was also assessed using the AD neuropathological change level (ADNC) based on the NIA-AA criteria, which considers presence of both plaques and tangles. We then investigated the diagnostic accuracy of plasma biomarkers in predicting presence (intermediate/high) of ADNC using receiver operating characteristic (ROC) curve analysis. The most parsimonious models for predicting pathological measures were selected based on the corrected Akaike criterion (AICc). We inverted the Aβ42/40 ratio from the usual practice, so that higher standardized betas would represent higher pathology for an easier comparison with the other markers. Results: We included 54 participants with none/low ADNC and 78 participants with intermediate/high ADNC. Participants had a mean(SD) age of 84.5(8.6) years at death and 52 (39.4%) were women. In univariate analyses, all markers except NfL were associated with plaques (0.35≤β≤0.67, p<0.001) and tangles (0.25≤β≤0.60, p<0.011). When both plaques and tangles were included in the same model, the Aβ42/40 ratio and p-tau231 were associated with plaques (βinverted Aβ42/40[95%CI]=0.57[0.36,0.77]; βp-tau231[95%CI]=0.33[0.10,0.55], both p<0.001), while GFAP was associated with tangles (βGFAP[95%CI]=0.34[0.15,0.53], p=0.001). In contrast, p-tau217 and p-tau181 were associated with both plaques (βp-tau217[95%CI]=0.51[0.37,0.65]; βp-tau181[95%CI]=0.48[0.32,0.64], both p<0.001) and tangles (βp-tau217[95%CI]=0.32[0.17,0.47], p<0.001; βp-tau181[95%CI]=0.23[0.06,0.40], p=0.008), with p-tau217 showing a significantly higher correlation coefficient with tangles than p-tau181 (βdiff[95%CI]=0.09[0.00,0.18], p=0.038). A model combining p-tau217 and the Aβ42/40 ratio showed the highest accuracy for predicting presence of ADNC (AUC[95%CI]=0.89[0.82,0.96], R2=0.62) as semi-quantitative measures of plaques (R2=0.55), while p-tau217 alone showed the highest accuracy to predict semi-quantitative measures of tau tangles (R2=0.45). Conclusion: We observed that some plasma biomarkers are strictly associated with only amyloid pathology (the Aβ42/40 ratio and p-tau231) or only tau pathology (GFAP), whereas p-tau181 and, particularly, p-tau217 are independently associated with both pathologies. These results may have important applications for clinical trials targeting one or both hallmarks of AD as they reveal specific associations with actual pathology. We suggest that the combined use of the Aβ42/40 ratio and p-tau217 may be useful in selecting participants for trials targeting amyloid-b pathology, whereas the use of plasma p-tau217 alone may be sufficient for participant selection in trials targeting tau pathology. Conflicts of interest: GK is a full-time employee of Roche Diagnostics GmbH. Work at the authors’ research center was supported by the Swedish Research Council (2016-00906), the Knut and Alice Wallenberg foundation (2017-0383), the Marianne and Marcus Wallenberg foundation (2015.0125), the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University, the Swedish Alzheimer Foundation (AF-939932), the Swedish Brain Foundation (FO2021-0293), The Parkinson foundation of Sweden (1280/20), the Konung Gustaf V:s och Drottning Victorias Frimurarestiftelse, the Skåne University Hospital Foundation (2020-O000028), Regionalt Forskningsstöd (2020-0314) and the Swedish federal government under the ALF agreement (2018-Projekt0279). The funding sources had no role in the design and conduct of the study; in the collection, analysis, interpretation of the data; or in the preparation, review, or approval of the abstract.
OC20- SYSTEMIC INFLAMMATION AND REDUCED CEREBRAL AB CLEARANCE TRIGGERED BY PANCREATIC AMYLIN. F. Despa 1, N. Verma 1, E. Winford 1, P. Nelson 1, G. Jicha 1, L. Goldstein 1, C. Troakes 2, H. Zetterberg 3, J. Hardy 3, T. Lashley 3(1. University Of Kentucky — Lexington (United States), 2. King’s College London — London (United Kingdom), 3. Dementia Research Institute At Ucl — London (United Kingdom))
Background: Overexpression or/and impaired clearance of amyloidogenic proteins such as islet amyloid polypeptide (amylin) and β-amyloid (Aβ) are critical pathological pathways in both type-2 diabetes and Alzheimer’s disease (AD). Data from different research teams (including our own) show cerebral amylin deposits in humans with both sporadic and familial AD; however, a potential relationship between blood amylin concentrations and AD pathology remains unclear. Objectives: Because amylin deposits can be detected within the cerebral blood vessels in humans with AD, we hypothesized that amylin secreted from the pancreas disturbs cerebral Aβ clearance. To test this hypothesis, we measured blood amylin concentrations in humans with and without AD and assessed the relationships with brain parenchymal and vascular Aβ; transgenic rats were used to determine how pancreatic amyloid-forming human amylin affects cerebral Aβ clearance. Methods: Blood and brain tissue were collected as part of the University of Kentucky (UK) prospective cohort study (n=172). Additional formalin fixed temporal cortex tissues from familial AD (fAD) mutation carriers were provided by the Queen Square Brain Bank for Neurological Disorders at UCL Queen Square Institute of Neurology and King’s College London. Observer-masked analyses were conducted on blood samples from cohort participants spanning the continuum of being cognitively unimpaired (CU; n=42) to mild cognitive impairment (MCI; n=19) and dementia (DEM; n=19) using amylin ELISA and flow cytometry. The results were communicated to UK-AD Research Center to assess the relationship between blood amylin concentrations and cognitive function. To assess the brain amylin-Aβ relationship, we measured amylin and Aβ42 concentrations in temporal cortex homogenates from persons with sporadic (sAD) (n=42) and CU individuals (n=18) by ELISA. Both plasma and frozen brain tissue were available from 20 participants. Histological evidence of cerebrovascular amylin-Aβ co-localization was tested in fAD (n=27) and sAD (32) brain slices by immunohistochemistry (IHC), confocal microscopy and proximity ligation assay (PLA) with anti-amylin and anti-Aβ antibodies. Results: CU, MCI and DEM groups had similar blood glucose concentrations (112.9 ± 5.71 mg/dL vs. 119.1 ± 9.43 mg/dL vs. 113.2 ± 5.10 mg/dL; oneway ANOVA, P = 0.79) and age (79.35 ± 2.18 years vs. 81.35 ± 1.78 years vs. 77.60 ± 0.66 years; one-way ANOVA, P = 0.14). Blood amylin concentrations were higher in DEM vs. CU groups with estimated medians of 4.33 (2.84–6.56, interquartile range) and 1.53 (1.12–3.43, interquartile range) (Kruskal-Wallis one-way analysis of variance, P < 0.001). Blood samples with amylin concentrations in the upper quartile contained increased fractions of CD14+ monocytes positive for amylin, with an estimated mean rank difference of difference of 38 (Kruskal-Wallis one-way analysis of variance, P < 0.0001). Confocal microscopic imaging confirmed amylin inclusions in circulating CD14+ monocytes. Brain amylin concentrations were higher in sAD vs. control groups with an estimated difference between medians of 4.653 (unpaired t test, P<0.01). Increased brain amylin concentrations were associated with greater Aβ42 concentrations (r = 0.34; P < 0.05), consistent with the amylin-Aβ42 relationship recently reported in fAD brains. The point estimate of the pairwise correlation coefficient suggests a possible relationship between blood amylin levels and brain amylin accumulation (r = 0.40; P = 0.09) (potential outliers were excluded from the analysis). The IHC analysis detected amylin in approximately 2/3 of the total blood vessels staining positive for Aβ in AD brains. Aβ deposits were present in perivascular spaces and blood vessel walls, whereas amylin accumulated within the lumen and on the luminal side of blood vessel walls. Confocal microscopic analysis of brain section triple stained with anti-amylin, anti-Aβ, and anti-α smooth muscle cell actin antibodies showed co-localization patterns in which Aβ was present in perivascular areas and amylin within the blood vessel wall. The PLA signal showed an overall consistency with amylin-Aβ colocalization within the arteriolar wall. In rats, pancreatic expression of human amylin indeed induced systemic inflammation, cerebrovascular amylin deposits and local perivascular inflammation. LRP1-mediated Aβ transport across the blood-brain barrier (BBB) and Aβ clearance through interstitial fluid drainage along vascular walls were impaired, as indicated by Aβ deposition in perivascular spaces. At the molecular level, cerebrovascular amylin deposition altered immune and hypoxia-related brain gene expression. Conclusions: Three interdependent factors underlie amylin-induced impairment of cerebral Aβ clearance: blood amylin concentrations are increased in dementia vs. cognitively unimpaired individuals; chronically increased concentrations of amyloid-forming amylin in blood promote amylin accumulation in circulating monocytes reflecting systemic inflammation and leading to cerebrovascular amylin deposition; and cerebrovascular amylin deposition disturbs LRP1-mediated Aβ transport across BBB and Aβ clearance through interstitial fluid drainage along vascular walls, as indicated by amylin-Aβ co-localization in blood vessel walls and perivascular spaces. Future studies are needed to clarify these relationships and test whether screening for pancreatic amylin dysregulation could identify people at increased risk for brain microvascular and AD pathologies. Altering pancreas-derived amylin in blood could potentially reduce cerebrovascular amylin deposits, Aβ pathology, and the risk of diabetic brain injury and cognitive impairment.
OC21- PRAZOSIN FOR AGITATION IN ALZHEIMER’S DISEASE: PEACE-AD. E. Peskind 1, M. Raskind 2, R. Thomas 3, G. Jicha4, N. Patel 5, A. Pierce 6, S. Brangman 7, M. Sano 8, J. Kaye 6, M. Lim 6, M. Au-Yeung 6, M. Herman 9, G. Leger 9, K. Messer 9, H. Feldman 9(1. VA Northwest Mental Illness Research, Education and Clinical Center (MIRECC), Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine — Seattle (United States), 2. VA Northwest Mental Illness Research, Education and Clinical Center (MIRECC) and Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine — Seattle (United States), 3. Departments of Family Medicine and Neurosciences, University of California San Diego — La Jolla (United States), 4. Department of Neurology, University of Kentucky — Lexington (United States), 5. Department of Family and Community Medicine, UT Health San Antonio — San Antonio (United States), 6. Department of Neurology, OHSU School of Medicine — Portland (United States), 7. Department of Geriatrics, SUNY Upstate Medical University — Syracuse (United States), 8. Department of Psychiatry, Mount Sinai School of Medicine — New York (United States), 9. Department of Neurosciences, University of California San Diego — La Jolla (United States))
Background: To evaluate the efficacy and safety of prazosin for the treatment of disruptive agitation in Alzheimer’s disease participants residing at home or in a long-term care facility in a national multicenter randomized controlled trial conducted by the NIA-funded Alzheimer’s Disease Cooperative Study (ADCS). Methods: In this multi-site randomized controlled trial (RCT) in which recruitment was substantially handicapped by the COVID-19 pandemic, participants were randomized to prazosin or placebo using a 2:1 permuted block randomization. Prazosin was titrated over 4 weeks to a maximum possible dose of 4 mg mid-morning and 6 mg at bedtime based on tolerability and persistent agitation. Adverse events and orthostatic blood pressure and heart rate were monitored. Primary outcome measure was the ADCS-Clinical Global Impression of Change-Agitation (CGIC-A) targeting disruptive agitated behaviors. Secondary outcomes were the 17-item Neuropsychiatric Inventory (NPI), Cohen Mansfield Agitation Inventory (CMAI), ADCS-Activities of Daily Living (ADCS-ADL) for severe dementia, and total number study days completed. An exploratory outcome was the NPI 5-item subscale reflecting agitation. Due to COVID-19 restrictions, methods were adapted to allow for remote consent, participant screening, and outcome and safety assessments. In addition, caregivers were trained to measure blood pressure and heart rate using the Omron automated blood pressure machine. Results: Thirty-five participants were randomized 2:1 to prazosin or placebo for 12 weeks. Mixed Models Repeated Measures analysis was performed. There were no significant differences in the CGIC-A or total NPI scores. In the prazosin group, 7 of 18 participants were moderately or markedly improved on the CGIC-A compared to 1 of 4 participants in the placebo group (NS). Change from baseline in CMAI score significantly favored prazosin (−5.5 ± 4.1 [mean ± SEM] in the prazosin group vs. +10.0 + 6.0 in the placebo group, p=0.04) and the 5-item NPI Agitation subscale numerically favored prazosin (NS). Kaplan-Meier Survival Analysis numerically favored prazosin with 63% of prazosin participants completing all 12 weeks compared to 38% of placebo participants (NS). The adverse event (AE) profile was as anticipated for prazosin; AEs that occurred in >5% of prazosin participants and >2X the occurrence in the placebo group included syncope, dizziness, nausea, and somnolence. Remote consenting, screening, and assessments allowed continuation of the study without the necessity of in-person clinic visits. Conclusion: PEACE AD provides some additional evidence of the potential efficacy in this small RCT testing of prazosin in the treatment of disruptive agitation in AD. While the assessment of both efficacy and safety were limited by the small number of participants, particularly in the placebo group, there was some benefit with prazosin seen across measures of behavioral assessment over 12 weeks of treatment, with the expected safety profile. PEACE AD was successfully conducted during COVID 19 using fully remote visits with home dwelling participants and technology support. It demonstrates the feasibility and significant advantages of performing a randomized controlled trial for disruptive agitation in AD using remote technology in home dwelling participants. This approach permitted the inclusion of severely agitated AD outpatients for whom attendance at frequent clinic visits would itself have been extremely challenging. A larger multi-center study of prazosin for moderate-severe disruptive agitation in AD is necessary and warranted to extend these results with lessons from this trial applied in its design and methods. The PEACE-AD study was funded by the National Institute on Aging via the Alzheimer’s Disease Cooperative Study (U19 AG010483) with additional support from the Alzheimer’s Association (SG-20-690388).
OC22- DEMOGRAPHIC ANALYSIS OF INDUSTRY SPONSORED ALZHEIMER’S DISEASE TRIAL POPULATIONS IN THE UNITED STATES. S. Peroutka 1(1. Ppd, Part Of Thermo Fisher Scientific — Carmel (United States))
Background: The Food and Drug Administration (FDA) stated in 2020 that “sponsors should enroll participants who reflect the characteristics of clinically relevant populations with regard to age, sex, race, and ethnicity”. Moreover, the FDA Guidance recommended that sponsors include a plan for inclusion of clinically relevant populations no later than the end of the Phase 2 meeting for all drugs and biological investigational therapeutics. In view of the significant amount of clinical trial research in Alzheimer’s Disease, a comprehensive demographic evaluation of the study populations used in Alzheimer’s disease trials seems prudent. Objectives: Although it has been noted for at least 30 years that Alzheimer’s Disease trials have enrolled predominantly White subjects, a thorough analysis of industry-sponsored, US-based Alzheimer’s trials has yet to be performed. The present study therefore evaluated all available demographic data on industry-sponsored Alzheimer’s trials of 100 or more subjects, performed solely in the United States. Global Alzheimer trials were excluded since sponsors rarely report demographics on a country by country basis. The objective was to determine if the gender and racial distributions of the trial subjects were representative of the known epidemiological characteristics of Alzheimer’s Disease. Methods: A search of the ClinicalTrials.Gov website (https://clinicaltrials.gov/) for “Alzheimer’s Disease, Industry-sponsored” clinical trials was made on March 31, 2022. A total of 1,145 trials were identified. The trials data were then screened for US only trials with at least 100 enrolled Alzheimer’s Disease subjects. Finally, the analysis dataset included all trials that had gender and/or racial demographic results available on either the ClinicalTrials. Gov website or in an associated publication identified via a PubMed search. Results: There were 35 identified trials with gender and/or racial demographic results, comprised of nearly 13,000 enrolled Alzheimer’s Disease subjects. These trials were completed between 1997 and 2019. The subject enrollment per trial ranged from 100 to 1,649 individuals. The gender distribution was available from 35 trials involving 12,912 subjects. There were 6,970 (54%) females and 5,942 (46%) males in these trials. The racial distribution was available from 24 trials, involving 10,872 Alzheimer’s Disease subjects. There were 10,017 (92%) Whites, 341 (3.1%) Black or African Americans, 49 (0.5%) Asians and 464 (4.3%) Others (e.g., Mixed, Unknowns, etc.). The percentage of White subjects per study in the 24 trials ranged from 84%-99%. Conclusion: The analysis of 35 industry-sponsored Alzheimer’s trials, performed solely in the US, showed an increased frequency of female vs. male participation in the trials. This observation is consistent with the known epidemiology of Alzheimer’s Disease. By contrast, the analysis of 24 US only, industry sponsored trials identified a major discrepancy between the racial distribution in the trial subjects compared to the known epidemiology of Alzheimer’s Disease in the United States. Large scale epidemiological studies in Alzheimer’s Disease across the entire US population have not been performed. However, 92% of Alzheimer’s Disease trial subjects have been White over the past 25 years in the United States. This fact is clearly a significant deviation from the known racial demographics of Alzheimer’s Disease. These data suggest that significant modifications of subject recruitment methods are needed to increase the enrollment of underrepresented populations into Alzheimer’s Disease trials. The consistently high percentage of White subjects per trial, in all 25 studies analyzed, suggests that these racial disparities are a likely result of a recruitment process that has not focused on subject diversity. The result is that an immediate need exists to increase the enrollment of multiple underrepresented populations of Alzheimer’s patients in US clinical trials. Based on a review of clinical trial participation barriers in underrepresented populations (e.g., Black or African-America, Hispanic and American Indian), two consistent themes have emerged that limit research participation: mistrust and lack of information. These obstacles can be overcome, but they require a significant and long-term investment in community outreach programs. In the short-term, information about ongoing trials needs to be communicated effectively to underrepresented communities via local health centers, senior community centers, neighborhood association meetings, pharmacies, churches, etc. Although limited research has suggested that this approach can be successful, more research is needed to determine the optimal ways to inform underrepresented populations about clinical trial research opportunities. A more challenging need is to determine the optimal ways to build trust between the medical research community and historically underrepresented populations in Alzheimer’s Disease clinical trials.
OC23- PLASMA BIOMARKER FINDINGS FROM THE ALZHEIMER’S PREVENTION INITIATIVE AUTOSOMAL DOMINANT ALZHEIMER’S DISEASE COLOMBIA TRIAL. E.M. Reiman 1, F. Lopera 2, S. Rios-Romenets 2, C. Schiffman 3, D. Hibar 3, G. Kollmorgen 4, M. Giraldo 2, N. Acosta 2, A. Espinosa 2, G. Villegas 2, C. Muñoz 2, L. Serna 2, K. Herrera 2, Y. Su 1, R. Alexander 1, Y.T. Quiroz 5, R.S. Doody 3, J.B. Langbaum 1, P.N. Tariot 1, K.M. Sink 3, T. Bittner 1(1. Banner Alzheimer’s Institute — Phoenix, Arizona (United States), 2. Neurosciences Group of Antioquia, University of Antioquia — Medellín (Colombia), 3. Genentech, Inc., — South San Francisco, Ca (United States), 4. Roche Diagnostics GmbH — Mannheim (Germany), 5. Massachusetts General Hospital and Harvard University — Boston, MA (United States))
Background: Crenezumab is an anti-amyloid monoclonal antibody that binds to beta-amyloid (Aβ) oligomers and is hypothesized to prevent the buildup of pathogenic Aβ plaques and to modify Alzheimer’s disease (AD) progression with a low risk of amyloid-related imaging abnormalities (ARIA). The Alzheimer’s Prevention Initiative (API) Autosomal Dominant AD (ADAD) Colombia trial evaluated crenezumab using clinical and biomarker endpoints in cognitively unimpaired presenilin 1 (PSEN1) E280A mutation carriers recruited from the world’s largest ADAD kindred (NCT01998841). Blood-based biomarkers (BBBMs) of amyloid and tau pathophysiology, neurodegeneration, and neuroinflammation have the potential to inform the development of AD-modifying and prevention therapies. Objectives: To describe baseline and change from baseline treatment-related BBBM findings from participants of the API ADAD Colombia trial. Methods: This randomized, double-blind, placebo-controlled, parallel-group trial evaluated the efficacy, safety, and tolerability of crenezumab in cognitively unimpaired 30-60-year-old Colombian PSEN1 E280A kindred members whose median age of mild cognitive impairment onset is 44 years. The 252 trial participants included mutation carriers who were randomized to crenezumab, mutation carriers who were randomized to placebo, and non-carriers who received placebo distributed in an approximately 1:1:1 ratio. Participants and researchers were blinded to mutation status. While participant inclusion in the trial was independent of baseline amyloid positron emission tomography (PET) findings, 40% of the carriers were found to have a negative amyloid PET scan prior to treatment. While dosing started with 300 mg subcutaneously every 2 weeks, it evolved over time and participants were eventually treated with either crenezumab (up to 720 mg subcutaneously every 2 weeks or 60 mg/kg intravenously every 4 weeks) or placebo for 5–8 years using a common close design. The primary endpoint family included the change in the API ADAD Cognitive Composite Test score and the Free and Cued Selective Reminding Test score. Most of the participants had serial amyloid PET, tau PET, 18F-FDG PET, magnetic resonance imaging, cerebrospinal fluid (CSF) and BBBM measurements and other assessments. Blood samples collected annually were measured using Elecsysâ robust prototype immunoassays. Biomarkers tested included plasma phosphorylated tau (p-tau)181 and p-tau217, which provide information about Aβ plaque burden and Aβ-related tau pathophysiology; plasma neurofilament light (NfL), which provides information about neuronal injury and neurodegeneration; and plasma glial fibrillary acid protein (GFAP), chitinase 3-like 1 (YKL-40) and soluble triggering receptor expressed on myeloid cells 2 (TREM2), which provide information about neuroinflammation. Results: After summarizing trial aims and design, participant characteristics, and treatment-related clinical, cognitive, imaging and CSF biomarker findings, we will describe the participants’ baseline BBBM and treatment-related BBBM findings, including in mutation carriers with positive and negative baseline Aβ PET scans, and relationships between BBBM and clinical effects. Conclusion: The API ADAD Colombia Trial was intended to characterize the efficacy, safety, and tolerability of crenezumab in the prevention of AD; explore the treatment’s differential biomarker effects in amyloid-positive and amyloid-negative participants at virtually certain AD risk; clarify relationships between the treatment effects on biomarker and clinical outcomes; provide a shared resource of data and samples for the field; help to establish a new era in AD prevention research; and advance the role of emerging BBBMs in these endeavors. Conflict of interest statement: Our work on this particular study was supported by NIH grants, F. Hoffmann-La Roche, philanthropic donations to Banner Alzheimer’s Foundation, and grants from the state of Arizona. Eric. M. Reiman is a Co-Founder & Advisor of ALZPath. He is a scientific advisor to Alzheon, Aural Analytics, Denali, Retromer Therapeutics, Vaxxinity and has Institutional Research Agreements with F. Hoffmann-La Roche/Genentech, Avid/Lilly.
OC24- NEUROIMAGING DATA FROM A PHASE 2, OPEN-LABEL STUDY OF NE3107 IN PATIENTS WITH COGNITIVE DECLINE DUE TO DEGENERATIVE DEMENTIAS. K. Jordan 1, K. Mahdavi 1,2, J. Haroon 1, E. Rindner 1, M. Zielinski 1, V. Venkatraman 1,2, S. Becerra 2, D. Goodenowe 3, C. Ahlem 4, C. Reading 4, J. Palumbo 4, B. Pourat 5, S. Jordan 1,2(1. The Regenesis Project — Santa Monica (United States), 2. Synaptec Network — Santa Monica (United States), 3. Prodrome Sciences USA LLC — Temecula (United States), 4. Biovie Inc. — Carson City (United States), 5. Pourat MD — Beverly Hills (United States))
Background: Alzheimer’s disease (AD) affects more than 6 million Americans and is associated with substantial healthcare costs and suffering. Unfortunately, therapies targeting neurodegenerative and abnormal protein deposits in the brain, including amyloid beta (Aβ) and phosphorylated tau (P-tau), have shown unclear clinical benefit, and more effective therapies are urgently needed. AD is associated with imbalances or deficiencies in neuronal glutathione levels and significant synapse and dendritic spine loss in parts of the brain, among other neurophysiological deficiencies. During the past decade, chronic inflammation and impaired glucose metabolism have been recognized as important contributors to the pathophysiology of AD. Neuroinflammation, insulin resistance (IR), and Aβ and P-tau pathologies form a feed-forward loop in AD progression. Therefore, targeting neuroinflammation and IR are attractive strategies in the treatment of AD. NE3107 is a well-tolerated, blood-brain permeable oral agent that selectively inhibits several inflammatory mediators and improves insulin signaling. Across several clinical studies, NE3107 increased insulin sensitivity and restored metabolic homeostasis in patients with type 2 diabetes and inflammation. It was also shown to alter inflammatory biomarkers that have been associated with cognitive decline. Multimodal imaging in patients with dementia has demonstrated certain qualities that would reflect associated changes in brain structure and function. These include change in regional neural dysfunction as shown in arterial spin labeling (ASL), change in interstitial free water and neurite density as found in diffusion tensor imaging — neurite orientation dispersion and density imaging (DTI-NODDI), change in redox stress as reflected in glutathione magnetic resonance spectroscopy (MRS), and change in seed-based functional connectivity of the nucleus basalis of Meynert as found in blood oxygen level dependent (BOLD) imaging. Objectives: This is a Phase 2, open-label study to evaluate the potential efficacy of NE3107 in patients with mild cognitive impairment (MCI) or mild dementia using advanced neuroimaging endpoints, AD and inflammatory biomarkers, changes in glucose metabolism, and cognitive performance testing. The primary objective of this study is to evaluate changes in neurophysiological health using multi-modal brain MRIs obtained at baseline and treatment termination (3 months). Secondary objectives of this study include a longitudinal comparison of glucose homeostasis, cognitive impairment as defined by neuropsychological testing, and AD and inflammatory markers. Methods: Twenty-three participants were enrolled and received 20-mg oral NE3107 twice daily for 3 months. Participants were between 50–89 years old with MCI or mild dementia (Quick Dementia Rating Scale [QDRS] cutoff range: 1.5–12.5; Clinical Dementia Rating [CDR] score range: 0.5–1). AD markers (Aβ and P-tau) were evaluated at baseline and treatment termination. Primary endpoints evaluated neurophysiological health using multi-modal brain MRIs at baseline and treatment termination, including stabilization or increase in glutathione levels (measured by MRS), enhancement of arterial perfusion (quantified by ASL), increased functional connectivity of the nucleus basalis of Meynert (visualized by seed analysis of BOLD imaging), and improvements in dendritic density and interstitial free water (measured by DTI-NODDI). Secondary endpoints evaluated changes in serological inflammatory markers, glucose and insulin homeostasis, and cognitive functioning—including changes in Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog 12) from baseline at treatment termination. Results: Participants had a mean age of 71.6 (SD = 9.63) years and 15 (65%) were females. At baseline, the mean QDRS score was 5.07, 18 (78%) participants had a CDR score of 0.5, and 5 (22%) participants had a CDR score of 1. Results of neuroimaging analyses will be presented at the conference. Conclusion: Using an array of advanced neuroimaging techniques to ascertain changes in participants’ neurophysiological health before and after treatment with NE3107, this study aimed to demonstrate the potential therapeutic efficacy associated with NE3107 treatment in patients with MCI. Funded by: BioVie Inc. Disclosures: ER, KM, KJ, JH, MZ, VV, SB, and SJ have received grant support from BioVie Inc. DG has nothing to disclose. BP has nothing to disclose. CA, CR, and JP are employees of BioVie Inc.
OC25- HOPE4MCI TRIAL TARGETING HIPPOCAMPAL OVERACTIVITY FOR THE TREATMENT OF MILD COGNITIVE IMPAIRMENT DUE TO ALZHEIMER’S DISEASE WITH AGB101: BASELINE TAU AND MRI IMAGING CHARACTERISTICS. R. Mohs 1, S. Rosenzweig-Lipson 1, A. Bakker 2, E. Chang 2, N. Rani 2, R. Barton 1, M. Gallagher 1,2(1. AgeneBio, Inc — Baltimore (United States), 2. Johns Hopkins University — Baltimore (United States))
Background: No effective therapies exist to halt or reverse Alzheimer’s Disease (AD). With a predicted prevalence of AD cases rising to over 100 million worldwide by 2050, the need for such therapy is urgent. Novel therapies are primarily focused on patients with amnestic mild cognitive impairment (aMCI) due to AD, recognized as a prodromal phase between normal aging and a clinical diagnosis of dementia, as interventions will likely confer the greatest clinical benefit during the early phases of the disease. In addition to tau and amyloid accumulation, hippocampal hyperactivity has been recognized as a characteristic feature of aMCI with strong evidence from both studies of animal models and humans observing that hyperactivity in neuronal circuits contributes to the accumulation and spread of AD pathology and forecasts subsequent cognitive decline. Clinical studies in patients with aMCI have demonstrated that treatment with low dose levetiracetam normalizes hippocampal hyperactivity and improves memory function in these patients (Bakker et al., 2012). The HOPE4MCI trial is a randomized placebo-controlled study of AGB101, a once daily extended-release formulation containing 220 mg of levetiracetam (NCT03486938). Objectives: The objective of the HOPE4MCI study is to examine the efficacy of AGB101 compared to placebo in patients with aMCI due to AD using the Clinical Dementia Rating Scale Sum of Boxes score as well as secondary functional and cognitive measures. In addition, the HOPE4MCI trial includes several cutting-edge biomarker measures including longitudinal structural magnetic resonance imaging (MRI), and longitudinal FMK-6240 PET measures of tau in a subset of participants. Methods: The HOPE4MCI trial is a multicenter randomized, double-blind placebo-controlled 78-week, fixed dose study of AGB101. Participants are between 55–85 years old, meeting NIA-AA criteria for MCI due to AD based on a corroborated subjective memory complaint, objective memory impairment, and amyloid positivity by PET scan. Results: The HOPE4MCI trial is fully enrolled with 164 participants meeting criteria for aMCI due to AD and is expected to complete data collection by the end of 2022. A subgroup of 49 participants completed both structural MRI and FMK-6240 tau PET at baseline. Image analysis of the structural MRI data was completed using Freesurfer generating volumetric measures of the hippocampus, entorhinal cortex, and amygdala and measures of cortical thickness of the entorhinal cortex, areas that show neurodegeneration as a function of disease progression in aMCI. In addition, volume and cortical thickness were obtained for control areas where primary disease related neurodegeneration has not been observed. FMK-6240 Tau PET analysis was similarly completed using Freesurfer, generating measures of tau accumulation in the hippocampus, entorhinal cortex, and amygdala. Previous work using the FMK-6240 tau marker has shown that tau accumulation in these regions can be used to assess disease progression consistent with Braak staging (Pascoal et al., 2020). Results will be presented to show convergence of biomarkers with associations between regional tau accumulation and localized neurodegeneration particularly in the entorhinal cortex. These results will be presented in the context of cognitive and functional measures obtained from these participants establishing a richly characterized sample of patients with aMCI. Conclusion: The current study includes multiple measures relevant to dementia due to AD in a prodromal condition recognized as transitional between normal aging and progression to a clinical AD diagnosis. The use of additional biomarkers in the subset of patients is informative for further characterization of MCI due to AD. The association between structural MRI and FMK-6240 Tau PET in this sample will also be informative bridging to the full dataset of 164 enrollees in which structural MRI was obtained in all participants. In addition, the imaging biomarkers were also obtained within-subject at the end of the 78 week protocol providing additional opportunities to examine change in these measures as a function of treatment (AGEB 101 compared to placebo). References: Bakker A, Krauss GL, Albert MS, Speck CL, Jones LR, Stark CE, Yassa MA, Bassett SS, Shelton AL, Gallagher M. Reduction of hippocampal hyperactivity improves cognition in amnestic mild cognitive impairment. Neuron. 2012 May 10;74(3):467–74. Pascoal TA, Therriault J, Benedet AL, Savard M, Lussier FZ, Chamoun M, Tissot C, Qureshi MNI, Kang MS, Mathotaarachchi S, Stevenson J, Hopewell R, Massarweh G, Soucy JP, Gauthier S, Rosa-Neto P. 18F-MK-6240 PET for early and late detection of neurofibrillary tangles. Brain. 2020 Sep 1;143(9):2818-2830.
OC26- DESIGN OF THE ABCA1 AGONIST CS6253 PHASE 1 SAD AND MAD STUDY IN MALE AND FEMALE, APOE4 AND NON-APOE4 CARRIERS TO ASSESS SAFETY, PK AND BIOMARKER EFFICACY. J. Johansson 1, H. Yassine 2, D. Michaelson 3, H. Zetterberg 4, J. Cummings 5, B. Winblad 6(1. Artery Therapeutics, Inc. — San Ramon (United States), 2. USC — Los Angeles (United States), 3. Tel Aviv University — Tel Aviv (Israel), 4. U of Gothenburg — Gothenburg (Sweden), 5. U Nevada Las Vegas — Las Vegas (United States), 6. Karolinska Insitute — Stockholm (Sweden))
Background: Apolipoprotein E (APOE) ε4 genotype, the main genetic late-onset Alzheimer’s disease (AD) risk factor, is characterized by the apoE4 protein (as opposed to apoE3) having impaired interaction with astrocyte’s ATP-binding cassette transporter A1 (ABCA1). The ABCA1 agonist CS6253 appears to correct this deficit. Current anti-amyloid AD-therapies in development have ARIA side effects in the APOE4 carriers and development of alternative therapies is warranted. The CS6253 IND is now open and a Phase 1 Single Ascending dose (SAD) and Multiple Ascending Dose (MAD) study has been designed to assess safety, PK and efficacy in male and female subjects with and w/o APOE4. Efficacy assessment will be guided by results from mice pharmacology studies and cynomolgus monkey studies showing CS6253 effects on lipoprotein and AD variables related to neuron protection and cognition. Methods: SAD: In 4–6 cohorts of 8 subjects (6 active: 2 placebo) CS6253 starting with 1 mg/kg will be administered and plasma and CSF collected simultaneously over 24 hours to assess PK and effect markers. MAD: In 3–4 cohorts of 8 subjects (6 active: 2 placebo) CS6253 will be administered starting with 75% of the SAD maximum tolerated dose. Prior to dosing and in conjunction with the 4th/last dose, plasma and CSF will be collected simultaneously over 24 hours. PK will be analyzed by LC-MS. Plasma and CSF will be analyzed by ELISA for ApoE and Ab42/40 (Simoa). Results: In APOE4 targeted replacement mice CS6253 20 mg/kg QOD ip for 6 weeks increased plasma apoE 37% (p<0.05). In cynomolgus monkeys CS6253 10 mg/kg IV single and repeat 4 doses increased plasma apoE with concomitant increase in plasma Ab42/40-ratio (all p<0.05). Human SAD results are expected the summer of 2023 and human MAD data the fall of 2023. Conclusion: The Phase 1 SAD and MAD study of the ABCA1 agonist CS6253 treatment will evaluate safety, PK and biomarker efficacy in male and female subjects with and w/o APOE4. Simultaneous collection and analysis in plasma and CSF of PK and of PD markers including apoE and Ab42/40-ratio will assess clinical potential for this cholesterol-targeting treatment of hereditary APO4-associated AD.
OC27- SIGNIFICANT EFFECTS OF ORAL ALZ-801 ON PLASMA BIOMARKERS OF ALZHEIMER’S DISEASE: 12-MONTH INTERIM ANALYSIS OF PHASE 2 BIOMARKER STUDY IN APOE4 CARRIERS WITH EARLY AD. S. Abushakra 1, J. Hey 1, K. Blennow 2, P. Scheltens 3, J. Hort 4, K. Sheardova 5, N. Prins 6, S. Rutgers 6, P. Dautzenberg 7, L. Pazdera 8, P. Kesslak 1, A. Power 1, M. Tolar 1(1. Alzheon Inc. — Framingham, Ma (United States), 2. Gothenburg University, Institute of Neuroscience & Physiology — Molndal (Sweden), 3. Amsterdam University Medical Center — Amsterdam (Netherlands), 4. Charles University Dept. of Neurology — Prague (Czech Republic), 5. St. Anne University Hospital & International Clinical Research Center — Brno (Czech Republic), 6. Brain Research Center — Amsterdam (Netherlands), 7. Brain Research Center — Den Bosch (Netherlands), 8. Vestra Research Clinic — Rychnov Nad Knĕžnou (Czech Republic))
Background: ALZ-801 (valiltramiprosate) is in development as an oral disease-modifying treatment for Alzheimer’s disease (AD). ALZ-801 is a brain-penetrant, small molecule inhibitor of amyloid oligomer formation. A fully enrolled Phase 2 study is ongoing in APOE4 carriers with Early AD to evaluate ALZ-801 effects on core AD neuropathologies, including plasma biomarkers of beta amyloid (Aβ) and hyperphosphorylated tau (p-tau). A pivotal APOLLOE4 Phase 3 placebo-controlled study is currently enrolling APOE4/4 homozygotes with Early AD. Advances in blood-based biomarker assays of AD support their use to assess efficacy in drug trials. Plasma p-tau, a marker of Aβ-induced neuronal stress and injury, is elevated in AD, and can be detected using new sensitive assays. Agents that inhibit Aβ toxicity in brain are expected to reduce p-tau release into blood. Indeed, the amyloid antibodies lecanemab and aducanumab, at doses that demonstrate clinical efficacy, both showed significant plasma p-tau181 reduction at 18 months. Objectives: To evaluate effect of oral ALZ-801 on core AD pathologies including fluid biomarkers (p-tau181, Aβ42, Aβ40), hippocampal volume (HV), and on clinical outcomes over 2 years of treatment. Methods: The Phase 2 biomarker study of ALZ-801 is an ongoing, open-label study at 7 sites in the Czech Republic and the Netherlands. Enrolled subjects (MMSE 22–30, CDR-G 0.5 or 1) have either APOE4/4 or APOE3/4 genotype and prior positive amyloid-PET or CSF biomarkers fulfilling A+/T+ criteria. CSF criteria are ratio of CSF Aβ42/40 ≤ 0.061, and p-tau181 ≥ 61 pg/ml. Subjects receive oral ALZ-801 as 265 mg BID tablets over 2 years and undergo serial assessments of plasma, CSF, volumetric MRI (vMRI), cognitive and functional tests. All fluid biomarker analyses are conducted at the Neurochemistry Laboratory of Dr. Blennow (Molndal, Sweden), and blinded to subject’s demographics or genotype. CSF biomarker assays are analyzed using Lumipulse (Fujirebio) and plasma assays utilized Simoa platform. vMRI analyses of HV are conducted at Bioclinica/ Clario. Cognitive tests include the Rey Auditory Verbal Learning Test (RAVLT: immediate, delayed and recognition memory) and Digit Symbol Substitution Tests (DSST), and a composite cognitive Z-score is calculated (3-item RAVLT + DSST). Change from baseline analyses performed on the modified intent-to-treat population (mITT) population includes all observed data, using paired t-tests and 2-sided p-values. The primary biomarker outcome is p-tau181 and total HV is the primary imaging outcome. HV atrophy rate on MRI using tensor-based morphometry is measured on each side, and total HV (left + right) atrophy compared to external control subjects from the ADNI database, who are matched for genotype and disease stage. Interim analyses to detect early biomarker effects of ALZ-801 were pre-specified. Results: A total of 84 APOE4 carriers enrolled and received ALZ-801, and 80 and 75 subjects completed 26 and 52 weeks, respectively. The mITT population baseline demographics were mean age 69 years, 51% female, MMSE 26.0, CDR-G 0.6, 70% MCI and 30% Mild AD. Plasma p-tau181 reduction was significant at 13 and 26 weeks and reached -41% at 52 weeks (p=0.016). Plasma Aβ42 and Aβ40 showed significant elevation at 13–26 weeks followed by significant reduction at 52 weeks (both −5%, p =0.002 & p=0.005). Reductions of p-tau181/Aβ42 were significant at each time point (−37%% at 52 weeks, p=0.032). Bilateral HV atrophy at 1 year was reduced by 25% compared to the matched ADNI subjects. The composite cognitive test (RAVLT memory scores + DSST) Z-score improved significantly at 13 and 26 weeks (p=0.002, 26 weeks), and remained numerically above baseline at 52 weeks. The effects on the 3-item RAVLT memory test showed significant correlations to effects on left HV (correlation coefficient 0.3, p=0.01). Most common adverse events were mild nausea and COVID infection, with no drug-related serious events and no events of ARIA-E in 75 subjects at 52 weeks. Conclusions: This 1-year interim analysis of ALZ-801 in APOE4 carriers with Early AD shows significant, progressive, and sustained reduction of plasma p-tau181, reaching a robust 41% reduction at 52 weeks. The time course of Aβ42 and Aβ40 changes in plasma suggests clearance of soluble Aβ monomers from brain to plasma, with significant reduction at 52 weeks. These effects are consistent with the molecular mechanism of ALZ-801, namely preventing the formation of soluble toxic amyloid oligomers. The cognitive composite outcome showed initial symptomatic improvement over 26 weeks followed by stability at 1 year compared to baseline. Reduction of hippocampal atrophy compared to matched controls suggests a neuroprotective effect on brain volume and showed significant correlation to memory benefits. The convergence of positive effects on plasma biomarkers, hippocampal volume and clinical benefits supports the disease modifying profile of ALZ-801. These data strengthen the case for a Phase 3 study in APOE4 carriers. The favorable safety, low risk of ARIA-E, and convenience of a simple oral regimen, make ALZ-801 an attractive potential disease-modifying treatment with wide access for AD patients, and very suitable for future AD prevention trials.
OC28- MEASURES OF CORTICAL MICROSTRUCTURE ARE LINKED TO AMYLOID PATHOLOGY IN ALZHEIMER’S DISEASE. N. Spotorno 1, O. Strandberg 1, G. Vis 2,3, E. Stomrud 1, M. Nilsson 2,4, O. Hansson 1(1. Clinical Memory Research Unit, Department Of Clinical Sciences, Lund University — Lund (Sweden), 2. Diagnostic Radiology, Institution For Clinical Sciences, Lund University — Lund (Sweden), 3. Memory Clinic, Skåne University Hospital — Malmö (Sweden), 4. Memory Clinic, Skåne University Hospital — Malmö (Sweden))
Background: Markers of downstream events are a key component of clinical trials of disease-modifying therapies for Alzheimer’s disease, especially during later stages to monitor the response of the participants to the treatment. Clinical and cognitive scores are the most obvious primary outcome measures at this point. However, when targeting upstream pathological events, such as Aβ misfolding and accumulation, therapies will likely be more effective during pre-symptomatic or prodromal disease stages before overt and irreversible neurodegeneration become more evident. In this context, clinical readout might become more challenging and putative makers will be of critical importance. Morphological metrics like cortical thickness are established measures of atrophy but are not sensitive enough to detect Aβ-related changes that occur before overt atrophy become visible. Objectives. We aimed to investigate to what extent diffusion MRI can provide sensitive markers of cortical microstructural changes and to test their associations with multiple aspects of the Alzheimer’s disease pathological cascade, including both Aβ and tau accumulation, astrocytic activation and cognitive deficits. Methods: We applied the mean apparent diffusion propagator model (MAP-MRI) to diffusion MRI data from 492 cognitively unimpaired elderly and patients with mild cognitive impairment from the Swedish BioFINDER-2 cohort. Participants were stratified in Aβ-negative/tau-negative, Aβ-positive/tau-negative, and Aβ-positive/tau-positive based on Aβ- and tau-PET uptake. Cortical regional values of MAP-MRI metrics and cortical thickness were compared across groups. Associations between regional values of MAP-MRI metrics and both Aβ- and tau-PET uptake were also investigated along with the association with plasma level of glial fibrillary acidic protein (GFAP), a marker of astrocytes activation (available in 292 participants). Results: Mean square displacement (MSD) from MAP-MRI revealed widespread microstructural differences already between Aβ-negative/tau-negative and Aβ-positive/tau-negative participants with a spatial distribution that closely resembled the pattern of Aβ accumulation, including retrosplenial regions extending to the precuneus, neocortical temporal regions, as well as rostral anterior cingulate and rostral middle frontal cortex (p-values FDR corrected, p <0.05, standardized-β coefficients range: 0.18 – 0.30). In contrast, differences in cortical thickness were clearly more limited (only entorhinal cortex, parahippocampal gyrus and temporal pole p-values FDR corrected, p <0.05). MSD was also highly correlated with both Aβ- and tau-PET uptake even independently from one another and independently from cortical thickness. Further, analysis focusing on a composite ROI encompassing regions that accumulate Aβ early in the disease process confirmed MSD exhibited significantly stronger correlations with Aβ-PET uptake than cortical thickness (significant difference between the β coefficients of MSD and cortical thickness: p < 0.01). Similar results were found when focusing on a temporal meta-ROI where MSD was more strongly associated to tau-PET uptake than cortical thickness (p<0.01). Regional MSD values were also positively correlated with the glial marker GFAP with a pattern that resemble Aβ accumulation (standardized-β coefficients range: 0.14 — 0.20), and GFAP partially mediated the association between Aβ accumulation and MSD. Further, impairments in executive functions were significantly more associated with MSD extracted from the early-Aβ meta-ROI than with cortical thickness (p<0.05). Similarly, impairments in memory functions were significantly more associated with MSD extracted from the temporal meta-ROI, than with cortical thickness (p<0.05). Further longitudinal analyses to investigate the possible use of diffusion MRI for tracking disease changes over time are undergoing and the results will be presented at the conference. Conclusions: Metrics of cortical microstructural alteration derived from diffusion MRI are highly sensitive to multiple aspects of the Alzheimer’s disease pathological cascade. Of particular interest is the link between MSD, Aβ-PET and GFAP which suggests that MSD might reflect microstructural changes related to the astrocytic response to Aβ aggregation. Therefore, MSD might be an important outcome measure in anti-Aβ treatments clinical trials for detecting drug-induced changes in early Aβ-related microstructural changes. Competing interest: The corresponding author has no competing interests to report.
OC29- A BRIEF, AUTOMATED SPEECH-BASED SCREENER FOR MILD COGNITIVE IMPAIRMENT TO SUPPORT ONLINE RECRUITMENT AT SCALE. C. Skirrow 1, J. Weston 1, M. Meszaros 1, U. Meepegama 1, E. Fristed 1(1.Novoic — London (United Kingdom))
Background: Cognitive changes occurring during the early stages of Alzheimer’s disease (AD) are reflected in how someone speaks, where sensitive patterns can be extracted using audio- and text-based machine learning models. Automated speech-based testing makes an excellent candidate for at-scale screening and recruitment into larger research projects and clinical trials. Participants can self-administer tests at home in a few minutes using a range of personal mobile devices. Recorded speech samples can be automatically analysed to produce sensitive diagnostic screening data, which can facilitate onward referral for further clinical evaluation in key participant groups. Objectives: Develop a short, automated speech-based AI system to screen for MCI based on automatically transcribed speech alone. Methods: Data was taken from the AMYPRED-UK (NCT04828122) and AMYPRED-US (NCT04928976) studies, comprising 200 participants age 54–85 with established amyloid beta (Aβ) and clinical diagnostic status (MCI, mild AD or cognitively unimpaired). Participants engaged in optional remote once-daily speech-based assessments for up to 8 days using their own smart devices. Assessments included the Automatic Story Recall Task (ASRT). Responses were recorded and then transcribed manually and using an out-of-the-box Automatic Speech Recognition (ASR) system. Data was extracted from two immediate and one delayed recall of two short ASRT stories administered in the same test session, to emulate a brief screening set-up. Differences in the original story source text and transcribed participant retelling were evaluated via a generalized matching score (“G-match”). G-match is computed in Python as the weighted sum of the cosine similarity between the embeddings of ASRT source text and the transcribed retellings. G-match was evaluated separately for manual and ASR transcribed speech data, in the full sample and after restriction of the cognitively impaired group to those with MCI only. Logistic regression models were trained to predict clinical labels (MCI/mild AD vs. cognitively unimpaired) using 5-fold cross-validation, producing Receiver Operating Curve (ROC) outputs. 95% confidence intervals for Area Under the Curve (AUC) were computed using the standard error of the 5-fold AUC samples. The ASRT models were evaluated relative to a demographic comparison (combining age, gender and years in education), and the Preclinical Alzheimer’s Clinical Composite with semantic processing (PACC5), a more extensive supervised clinical assessment battery. The reduction in in-person clinical assessment required with pre-screening using G-match was evaluated in a simulated US population sample age 65+ (MCI prevalence 15.4%), using the sensitivity and specificity of the G-match model for differentiating MCI and cognitively unimpaired participants. Results: The participant sample completing the abbreviated test battery, and included in the current analysis comprised 96 adults (N=55 cognitively unimpaired, N=34 MCI, N=7 mild AD; N=48 Aβ positive, N=48 Aβ negative; 51 female, 45 male). The abbreviated assessment battery collected an average of 2.4 minutes of speech per participant. G-match of the brief test battery showed good prediction of MCI/mild AD status using ASR transcripts with AUC=0.87 +/− 0.03. Results remained consistent when restricting analyses to comparisons between MCI and cognitively unimpaired participants alone with AUC=0.82 +/− 0.04. Differences between ASR and manually transcribed data were not statistically significant (p≥0.33). G-match models were significantly superior to random performance (p≤0.001), and outperformed the demographic comparison (p≤0.01). PACC5, a longer, multi-task battery evaluated in-person during a clinical assessment, outperformed G-match for the analysis restricted to the MCI and cognitively healthy group alone (AUC=0.91 +/-0.04, p=0.02), but not for the combined MCI/mild AD group (p=0.26). Screening based on G-match (ASR transcription; sensitivity 0.94 and specificity=0.54 at Youden’s index) was simulated in a population sample age 65+. For a targeted sample of MCI patients for research, the ASRT system screening is estimated to reduce the number of in-depth clinical assessments required by 43.2%, but require 5.9% more participants at the recruitment and screening stage. Conclusion: Combined with an advanced AI language model, brief speech-based testing offers simple and accessible screening for MCI. Such testing could be used at scale to screen for appropriate patients for treatment, research and clinical trials. The ASRT system does not require trained personnel or specialist equipment and could help to reduce the costs of clinical trials by enriching recruited samples. The ASRT system has potential to reduce the quantity of more in-depth clinical assessments required, reducing clinical resource bottlenecks and costs of research and clinical trials. Funding and competing interests: All authors are employees of Novoic and option holders or shareholders of Novoic.
OC30- AB-STRUCTURE AS PRECISE RISK PLASMA BIOMARKER FOR FUTURE CONVERSION TO ALZHEIMERS DISEASE 17 YEARS IN ADVANCE. K. Gerwert 1,2(1. Ruhr-University Bochum — Bochum (Germany), 2. Center for Protein Diagnostics (ProDi) — Bochum (Germany))
Background: The identification and validation of early-stage biomarkers is coming into focus. Especially, early stage diagnosis in a symptom-free stage before significant amyloid plaques have been formed might provide the best therapy response. In recent years, the development of highly sensitive analytical methods enabled the identification of non-invasive and low costs blood-based biomarkers. Blood-based biomarkers allow beside expensive PET scans and invasive CSF measurements pre-screening of the elder population. In contrast to the widely studied concentration-based analyses of Aβ and P-tau biomarkers in body fluids we have examined Aβ and tau misfolding as structure biomarkers. The misfolding of Aβ from a monomeric/unstructured to a β-sheet enriched isoform is one of the earliest events in AD pathogenesis. With the patented infrared-immuno-sensor (iRIS) we are able to measure the secondary structure distribution of all Aβ isoforms as structure biomarker (1). Initial misfolding of Aβ takes place about 15–20 years before AD is clinically diagnosed and is followed by β-sheet oligomerization and aggregation to much larger fibrils on the nanometer scale. After several years, this Aβ misfolding becomes visible at the macroscopic scale as deposits in large amyloid plaques. We have shown in a discovery study that the structure biomarker indicates probable Alzheimer’s disease in a prospective cohort (1). We extended this to prodromal AD in the BioFINDER cohort (2). Furthermore, we have shown that the structure biomarker is prognostic and predicts the conversion to AD in older adults in the population based ESTHER cohort 14 years in advance (2). There was an added value when including APOEe4 as risk factor for identifying preclinical AD states 14 years before disease onset increasing the AUC over 0.87 (3). Additionally, the combination of other biomarkers such as tau misfolding in CSF or plasma Aβ42/40 showed added values as well. Analyzing tau misfolding in CSF and Aβ misfolding in plasma increases the sensitivity to 89% and specificity up to 97% as compared to clinical diagnosis (4). Beside the general threshold <1644 cm-1 indicating abnormal misfolding in diseased individuals, a second threshold >1646 cm-1 was introduced indicating a normal Aβ secondary structure distribution as observed in individuals without AD (4). Frequencies between both thresholds indicate low misfolding. This analysis enables the risk stratification by means of the misfolding status as already proven on SCD subjects from the Amsterdam dementia cohort (5). Objectives: We investigated the performance of Aβ misfolding as a prescreening plasma biomarker for AD development in a population based cohort up to 17 years before clinical manifestation. Additionally, the performance was compared to the concentration biomarkers GFAP, NfL and P-tau181 measured with SIMOA (6). Methods: Baseline plasma samples of 308 subjects taken between 2000–2002 were analyzed using the infrared-immuno sensor (iRIS). The obtained structure biomarker results were compared with GFAP, P-tau181 and NfL levels obtained by the SIMOA platform. Results: Baseline plasma analysis revealed significant differences for all plasma biomarkers in AD subjects compared to the controls. Additionally, the misfolding biomarker showed the best prognostic performance at 17-year follow-up relative to all concentration biomarkers. Furthermore, a biomarker panel of Aβ misfolding and GFAP levels showed an added value. Interestingly, the prognostic performance of P-tau181 was limited to 8 years before symptom onset. It could not predict AD conversion more than 8 years in advance. Conclusions: Aβ misfolding allows the identification of individuals who will develop AD up to 17 years before clinical manifestation. This highlights the potential of the misfolding biomarker as a simple blood biomarker and as a screening method for the aging population, analyzing symptom-free stages and determining the risk of future AD development. Thus, prevention and early intervention of Alzheimer’s can be achieved. References: 1. Nabers A, et al. J.Biophotonics. 2016;9(3):224-34. 2. Nabers A, et al. EMBO.Mol.Med. 2018May;10(5). 3. Stocker H, et al. Alzheimer’s and Dementia. 2020;16:283-91. 4. Nabers A, et al. Alzheimer’s and Dementia (Amst). 2019 Mar 12;11:257-263. 5. Stockmann J and Verberk I et al. Alz Res and Ther. 2020, 6. Beyer L and Stocker H, et al. Alzheimer’s and Dementia. 2022, in press
OC31- NVG-291 PHASE 1 RESULTS AND PHASE 1B/2A STUDY DESIGN IN INDIVIDUALS WITH MILD COGNITIVE IMPAIRMENT OR MILD DEMENTIA DUE TO ALZHEIMER’S DISEASE. D. Mikol 1, J. Toews 1, M. Farlow 2, B. Lamb 2, G. Perry 3, R. Sperling 4, M. Weiner 5, H. Zetterberg 6, J. Cummings 7(1. Nervgen — Vancouver (Canada), 2. Indiana University School Of Medicine — Indianapolis (United States), 3. University Of Texas, San Antonio — San Antonio (United States), 4. Harvard Medical School — Cambridge (United States), 5. University Of California, San Francisco — San Francisco (United States), 6. University Of Gothenburg — Gothenburg (Sweden), 7. University Of Nevada, Las Vegas — Las Vegas (United States))
Background: Chondroitin sulfate proteoglycans (CSPGs) are increased at sites of central nervous system (CNS) damage, including regions with beta-amyloid plaques and neurofibrillary tangles of Alzheimer’s disease (AD). CSPGs inhibit neural repair mechanisms, in part through their interaction with the receptor protein tyrosine phosphatase sigma (PTPσ). NVG-291 is a subcutaneously (SC) administered peptide that modulates PTPσ. In various animal models of CNS damage, NVG-291 treatment resulted in functional improvements due to enhanced axonal regeneration, plasticity, and remyelination. It is hypothesized that NVG-291 treatment of individuals with impaired cognition due to AD will lead to improved function of CNS neurons as a result of enhanced plasticity and strengthened synaptic connections, which may be measured using functional brain imaging techniques. Objectives: Present Phase 1 results (healthy subjects) and Phase 1b/2a study design (subjects with mild cognitive impairment or mild dementia due to Alzheimer’s disease). Methods: The single ascending dose (SAD) portion of the Phase 1 trial in healthy subjects enrolled 37 subjects in 6 dose cohorts of NVG-291 or placebo. The multiple ascending dose (MAD) portion of the study is dosing up to 18 subjects randomly assigned into 3 dose cohorts to receive NVG-291 or placebo SC once-daily for 14 days. Additional subjects treated with open-label NVG-291 are undergoing cerebrospinal fluid (CSF) analysis to measure NVG-291 concentration. NVG-291 doses being investigated in the MAD portion of the study exceed human equivalent levels that showed efficacy in animal models. The primary objective of the multicenter Phase 1b/2a trial is to assess the safety, tolerability and pharmacokinetic profile of NVG-291 in subjects with AD. Secondary objectives are to investigate the biological effects of NVG-291 by assessing change in the standardized uptake value ratio of 18F-fluorodeoxyglucose (18FDG) in a pre-specified region of interest and by voxel-based subtraction analysis using 18FDG-positron emission tomography; and to assess change in cognition using the AD Assessment Scale-Cognitive Subscale (ADAS-Cog) 13 and Clinician Interview-Based Impression of Change, plus caregiver interview (CIBIC-plus). Exploratory objectives include assessment of cerebral resting state functional connectivity using functional magnetic resonance imaging Blood Oxygenation Level Dependent (BOLD) sequences and to assess episodic/working memory, reaction time, learning, executive function and activities of daily living using additional cognitive instruments. The Phase 1b/2a trial will enroll ∼80 subjects aged 55–85 with mild cognitive impairment or mild dementia due to AD, mini-mental state exam score 22–28, abnormal paragraph recall, and evidence of AD biology. Subjects will be randomized 1:1 to NVG-291 or placebo administered by daily SC injection × 12 weeks, followed by no intervention for 12 weeks. Results: NVG-291 has been safe and well-tolerated through 6 completed SAD cohorts and two completed MAD cohorts. In the SAD cohorts, all adverse events (AEs) were mild and transient; the most common AE was injection site related. Blinded analysis of safety in the MAD (dose cohorts 1 and 2) has shown that AEs were mild except for a single event of moderate migraine; the most common AE was injection site related. There were no serious AEs, and no effect on vital signs or ECGs in any subjects, and NVG-291 has shown promising pharmacokinetic characteristics. Conclusion: NVG-291 appears well tolerated after administration of multiple ascending doses in healthy subjects. Upon completion this year, the Phase 1 study will establish the safety/tolerability/pharmacokinetics of NVG-291 to support advancement to the Phase 1b/2a clinical trial in subjects with mild cognitive impairment or mild dementia due to AD. The Phase 1b/2a study in AD will assess change in functional and advanced structural imaging measures and cognition following treatment, with the trial expected to initiate in late 2022. Disclosures: DM and JT are employees of NervGen. MF, BL, GP, RS, MW, HZ, and JC are paid consultants of NervGen
OC32- INTRODUCTION TO THE VERI-T TRIAL: A PHASE 1 RANDOMIZED, DOUBLE-BLIND, PLACEBO-CONTROLLED, MULTICENTER TRIAL OF VERDIPERSTAT IN PATIENTS WITH SVPPA DUE TO FTLD-TDP. P. Ljubenkov 1, A. Staffaroni 1, L. Vandevrede 1, J. Rojas-Martinez 1, M. Koestler 1, A. Porsteinsson 2, M.B. Pascual 3, J. Masdeu 3, I. Grant 4, D. Irwin 5, D. Knopman 6, R. Bowser 7, M. Grossman 5, I. Qureshi 8, A. Boxer 1(1. UCSF Memory and Aging Center — San Francisco (United States), 2. University of Rochester — Rochester (United States), 3. Houston Methodist — Houston (United States), 4. Northwestern University — Chicago (United States), 5. University of Pennsylvania — Philadelphia (United States), 6. Mayo Clinic Rochester — Rochester (United States), 7. Barrow Neurological Institute — Phoenix (United States), 8. Biohaven Pharmaceuticals — New Haven (United States))
Background: Nuclear depletion and cytoplasmic accumulation of TAR DNA-binding protein 43 (TDP-43) is a major cause of dementia, present in about 20% of patients with Alzheimer’s disease and about half of patients with frontotemporal dementia. There is currently an unmet need for dementia clinical trials targeting sporadic TDP-43 pathology, but TDP-43 mislocalization is typically difficult to diagnose prior to autopsy. The semantic variant of primary progressive aphasia (svPPA) is over 80% predictive of frontotemporal lobar degeneration with TDP-43 mislocalization (FTLD-TDP) and is thus an ideal cohort in which to conduct the first wave of therapeutic trials targeting sporadic TDP-43 pathology. Potential therapeutic targets in FTLD-TDP include oxidative stress, which promotes mislocalization of TDP-43 in neurons. Verdiperstat is a potent, oral, CNS-penetrant, myeloperoxidase inhibitor that reduces production of oxidative species from microglia. The Veri-T trial (NCT05184569) will explore the therapeutic potential of verdiperstat in the first clinical trial to focus on patients suffering from svPPA. The Veri-T trial is also the first clinical trial to leverage the recruitment resources of the ARTFL LEFFTDS Longitudinal FTLD (ALLFTD) research network of clinical centers. Objectives: The primary objective of this study is to determine the safety and tolerability of verdiperstat in patients svPPA due to FTLD-TDP. The secondary objective of this study is to determine the pharmacokinetic (PK) profile of verdiperstat in patients with svPPA. Exploratory objectives will include investigation of verdiperstat’s effects on candidate pharmacodynamic markers and candidate markers of efficacy for future trials in patients with FTLD-TDP. Exploratory endpoints include plasma myeloperoxidase activity, cerebrospinal fluid (CSF) biomarkers of glial activity (chitinase-family proteins), neurodegeneration (neurofilament light chain), and unbiased CSF proteomics (via SOMAmer reagent assays), as well as volumetric MRI changes unique to svPPA, and cognitive and language impairment measures assessed via ALLFTD’s Smartphone app. Methods: This is a multisite, phase 1, randomized, double-blind, placebo-controlled trial. N=64 participants with svPPA will be randomized 1:3 to placebo or oral verdiperstat (titrated to a dose of 600mg BID) for 6 months of double-blind therapy. Neuropsychological assessments, plasma and CSF, and volumetric brain imaging will be collected prior to and upon conclusion of treatment. Recruitment will occur at 5 ALLFTD research network clinical centers. Results: The first participant was randomized April 19th, 2022 and recruitment remains ongoing. To date, no dose-limiting toxicities have occurred. Conclusion: The Veri-T trial examines the safety, tolerability and pharmacokinetic properties of verdiperstat in svPPA and explores novel pharmacodynamic biomarkers and outcome measures that could be employed in future efficacy studies targeting sporadic FTLD-TDP.
OC33- A PHASE 1, OPEN-LABEL, 52-WEEK, MULTICENTER STUDY TO EVALUATE THE SAFETY AND BIOCHEMICAL EFFICACY OF AAV GENE THERAPY (LX1001) IN PATIENTS WITH APOE4 HOMOZYGOTE ALZHEIMER’S DISEASE — INTERIM DATA. M. Kaplitt 1, P. Leopold 2, E. Noch 3, J. Ivanidze 4, L. Chazen 4, R. Crystal 2, S. Kaminsky 2, H. Bowe 2, M. Wang 2, D. Ballon 4, J. Dyke 4, D. Sondhi 2, S. Gandy 5, G. Giannantoni-Ibelli 6, J. Barth 6(1. Department of Neurological Surgery, Weill Cornell Medical College — New York (United States), 2. Department of Genetic Medicine, Weill Cornell Medical College — New York (United States), 3. Department of Neurology, Weill Cornell Medical College — New York (United States), 4. Department of Radiology, Weill Cornell Medical College — New York (United States), 5. Departments of Neurology and Psychiatry, Icahn School of Medicine at Mt Sinai — New York (United States), 6. LEXEO Therapeutics, Inc. — New York (United States))
Background: Alzheimer’s disease (AD), a progressive neurodegenerative disorder, is associated with a strong genetic risk resulting from polymorphisms of the apolipoprotein E (APOE) gene. The APOE4 allele is a well-recognized genetic risk factor for late-onset AD. While this allele increases risk and reduces the age of AD onset, the E2 allele decreases risk and delays the age of AD onset. APOE4 homozygotes have a 15-fold greater risk of developing AD compared with the APOE3 homozygotes, the most common genotype. The marked reduction in AD risk among APOE2/E4 heterozygotes suggests a potential protective effect of APOE2, yet only 5% of the population carry an APOE2 allele. LX1001 is an adeno-associated viral vector (AAV) investigational gene therapy (AAVrh.10hAPOE2) designed to deliver the protective apolipoprotein E2 (APOE2) gene into the central nervous system of APOE4 homozygous AD subjects in order to halt or slow the disease progression, mediated by the APOE4 allele. Objectives: The primary objective of this first-in-human trial is to evaluate the safety of LX1001 administered into the cerebrospinal fluid (CSF) at the craniocervical junction (via CT-guided C1-C2 or intracisternal route), given the equipoise regarding the potential effects of both overexpressing APOE2 in the AD brain and of widespread CSF delivery of AAV vectors in the degenerating human brain. This trial is also designed to evaluate the feasibility of converting CSF from the APOE4 homozygous profile to an APOE4/E2 profile as a biomarker of successful gene delivery. Additional secondary endpoints include analysis of other CSF AD biomarkers, including Aß42, total tau (T-tau), and phosphorylated tau (P-tau) along with amyloid-targeted PET, structural MRI imaging, and cognitive tests. Methods: This is a Phase 1, open label, dose-finding study evaluating the safety and tolerability of LX1001 in AD. LX1001 is being evaluated in three ascending single-dose cohorts (5.0E10, 1.6E11 and 5.0E11 gc/ml CSF), with the dose for each subject determined based on CSF volume measured by MRI. Each of 3 dose cohorts consists of ∼5 APOE4 homozygotes. Enrollment criteria include APOE4 homozygous genetic profile, age 50 years or older, positive amyloid-targeted PET, CSF biomarkers consistent with AD, and mild cognitive impairment to mild or moderate dementia due to AD. After completing this study, subjects are invited to enroll into an extension study for evaluation of long-term safety and efficacy for an additional 4 years post gene transfer. Results: A total of five subjects were dosed in the low-dose (5E10 gc/ml CSF) cohort. Based on data available to date, among all subjects in cohort 1 (n=5, age 59–73 years, with MCI or moderate dementia due to AD), treatment with LX1001 was well-tolerated with no serious adverse events reported to date. Follow-up data for evaluation of efficacy are available for 4 subjects, aged 59–73 years, with MCI or moderate dementia due to AD. Preliminary data for cohort 1 demonstrated that post-vector administration APOE2 was expressed in CSF in all 4 subjects with follow-up data ≥ 3 months. Both subjects with 12-month data demonstrated a decline in the CSF T-Tau and P-Tau. One subject showed a CSF T-Tau reduction from baseline over 12-months of ∼20% and CSF P-Tau reduction of ∼9%. The other subject showed a CSF T-Tau reduction from baseline over 12-months of ∼4% and CSF P-Tau reduction of ∼14%. Conclusion: LX1001 is the first investigational gene therapy to directly address APOE, a well-recognized genetic risk factor of AD. Initial data in the low-dose cohort supports technical feasibility of conferring APOE2 expression in the CNS of human APOE4 homozygotes and indicates that there were no serious adverse events from either CSF delivery of LX1001 or from documented expression of APOE2 in these subjects. These data support further exploration of APOE2 gene therapy as a potential therapeutic for APOE4 homozygous AD patients. Conflicts of Interest: None at this time.
OC34- PRELIMINARY EVIDENCE FOR RELIABILITY AND VALIDITY OF THE INTERPERSONAL FUNCTIONING AND DAILY ACTIVITIES QUESTIONNAIRE (IFDAQ) IN THE A4/LEARN PRE-RANDOMIZATION SAMPLE. C.J. Edgar 1, R. Amariglio 2, J.M. Barbone 3, J.M. Chandler 4, S.J. Coons 5, M. Donohue 6, W.R. Lenderking 7, R. Sperling 8(1. Cogstate — London (United Kingdom), 2. Departments of Neurology, Brigham and Women’s Hospital and Massachusetts General Hospital, Harvard Medical School — Boston (United States), 3. Cogstate — New Haven (United States), 4. Eli Lilly and Company — Indianapolis (United States), 5. Clinical Outcome Assessment Program, Critical Path Institute — Tucson (United States), 6. Alzheimer’s Therapeutic Research Institute, University of Southern California — San Diego (United States), 7. Patient-centered Research, Evidera — Bethesda (United States), 8. Department of Neurology, Brigham and Women’s Hospital — Boston (United States))
Introduction: There is an unmet need for patient-reported measures reflecting relevant domains of treatment benefit in early Alzheimer’s disease (AD) that have been developed using best practices, including appropriate input from persons with Mild Cognitive Impairment (MCI) and caregivers. The Cognition Working Group (WG) of the Critical Path Institute’s Patient-Reported Outcome (PRO) Consortium developed a new PRO instrument, intended as a “fit-for-purpose” efficacy endpoint measure in clinical trials in prodromal AD or MCI due to AD. Using a targeted literature review, focus groups, and interviews, concepts of importance for complex activities of daily living (cADLs) and interpersonal functioning (IF) were identified. Using FDA feedback, advice from clinical experts, and concept elicitation interviews in 79 MCI, probable AD and non-impaired controls, and 65 informants, a conceptual framework was developed for a draft measure (the Interpersonal Functioning and Daily Activities Questionnaire (IFDAQ)) that included both cADL (16 items e.g., managing finances and planning skills) and IF domains (10 items e.g., conversational skills) (Gordon et al., 2016). Each item had response options of “Never” (0), “Rarely” (1), “Sometimes” (2), “Often” (3), and “Always” (4) to measure the frequency with which people with early AD experience difficulties in each domain. Objectives: To provide initial evidence for the reliability and validity of the IFDAQ using a large dataset derived from the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s study (A4) and the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) studies’ pre-randomization data. The A4 Study is a secondary prevention trial in preclinical AD and has a companion observational study called LEARN. The A4 study aims to prevent or slow the onset of AD symptoms in healthy adults with amyloid-beta plaque to see if treatment can show a benefit in preventing or slowing cognitive decline i.e., progression from clinical stages 1 and 2 (preclinical) to clinical stage 3 (prodromal or MCI). Methods: The IFDAQ was included in A4/LEARN and is part of the assessment schedule for two pre-randomization visits conducted within 90 days of each other (Screening Visits 1 and 3 (Note: Amyloid PET imaging occurred at Screening Visit 2, followed by disclosure of amyloid status)). Analyses were performed on the total score (26 items, range 0–104) and the IF (10 items, range 0–40) and cADL (16 items, range 0–64) subscales. Reliability was evaluated using Cronbach’s alpha for internal consistency and ICC (A,1) for test retest. Validity was evaluated using known groups validity (t-test and Cohen’s d) at Visit 1 comparing between the group with CDR Global =0 and that with CDR Global =0.5. Results: Data were available for a total of N=6203 participants (mean age 71.5 (SD 4.8); 58% female; mean years of education 16.5 (SD 2.93), with IFDAQ data available for N=5402 participants at Visit 1 and N=4264 participants at Visit 3. Item level missing data increased marginally over the length of instrument with a maximum of 4.3% of data missing for the final item (#26). Internal consistency reliability was high for the cADL and IF subscales (α=0.90 (95% CI 0.89, 0.90) and α=0.87 (95% CI 0.87, 0.88), respectively), and the total score (α=0.93 (95% CI 0.93, 0.93)). Test-retest reliability was adequate for the cADL and IF subscales (ICC=0.76 (95% CI 0.75, 0.77) and ICC=0.75 (95% CI 0.74, 0.76), respectively), and the total score (ICC=0.78 (95% CI 0.77, 0.79)). Known groups validity analyses showed statistically significant differences between CDR Global =0 (N=5230) and CDR Global =0.5 (N=101) groups at Visit 1 (unequal variances t-test p<0.001). Higher scores indicating worse participant reported function were seen in the CDR 0.5 group for cADL (Cohen’s d=1.06; mean 12.6 (SD 6.79) and mean 16.7 (SD 7.72) respectively), IF (Cohen’s d=0.68; mean 10.3 (SD 5.34 and mean 12.4 (SD 6.10) respectively), and the total score (Cohen’s d=0.97; mean 22.9 (SD 11.20) and mean 29.2 (SD 12.66) respectively). Conclusion: Initial analyses of the IFDAQ in a largely cognitively normal population undergoing screening for the A4 secondary prevention trial in preclinical AD and the companion observational study LEARN, support its validity and reliability as a PRO measure assessing interpersonal function and complex ADLs. In the know groups validity analyses a larger difference was evident for cADL items versus IF items, which may suggest cADL difficulties were more prominent and/or reflect concept coverage in the CDR. The IFDAQ has potential utility for the measurement of early changes in the frequency with which people with predementia/prodromal AD (clinical stages 1–3) experience difficulties in complex ADLs and interpersonal functioning. References: Gordon, M. F. et al. (2016) ‘Development of a patient-reported outcome instrument to assess complex activities of daily living and interpersonal functioning in persons with mild cognitive impairment: The qualitative research phase’, Alzheimer’s and Dementia. doi: 10.1016/j.jalz.2015.04.008. Disclosures: Chris J Edgar is a fulltime employee of Cogstate.
OC35- APOE-TARGETED EPIGENOME THERAPY FOR ALZHEIMER’S DISEASE. B. Kantor 1,2, O. Chiba-Falek 1,3(1. Duke University — Durham (United States), 2. CLAIRIgene LLC — Durham (United States), 3. CLAIRIgene — Durham (United States))
Background: There is an urgent need to refocus Alzheimer’s disease (AD) drug discovery on new targets and shifting the paradigm of AD drug development towards precision medicine. Apolipoprotein E gene (APOE) is the strongest and most reproducible genetic risk factor for late-onset Alzheimer’s disease (LOAD). Moreover, 50% reduction in APOE levels showed beneficial effects in AD cellular and mouse models. Thus, APOE gene holds promise as a potential therapeutics target for LOAD. Objectives: In this study we developed an epigenome therapy platform to reduce APOE expression generally and APOEe4 allele specifically by targeted modification of the epigenome landscape within APOE locus. Methods: Our gene therapy strategy is based on CRISPR/deactivated (d)Cas9 editing technology fused with an effector molecule and delivered by viral-based vehicles. Our gRNAs were designed to target regulatory elements within the APOE promoter/intron 1 region and in exon 4 sequence overlapping the SNP that defines the APOEe4 allele. We evaluated our epigenome therapy platform in vitro using human hiPSC-derived neurons and in vivo by stereotactic injection of reporter gene into the hippocampus of mice. Results: The viral dCas9-repressor vector showed decreased APOE-mRNA and protein overall levels in hiPSC-derived neuronal model. To specifically target the APOEe4 allele we utilized the VRER-dCas9 protein. Evaluation of the system specificity showed a reduction in APOE-mRNA levels in the hiPSC-derived neurons with the e4 allele while there was no effect in the isogeneic hiPSC-derived neurons homozygous for the e3 allele. Moving onto in vivo studies in mice, administration of the viral dCas9-repressor vector and the green fluorescent protein (GFP) reporter gene into the hippocampus showed a significant decrease in GFP expression with strong repression effect, demonstrating promising preliminary data. Collectively, our results provided in vitro and in vivo proof-of-concept for the utility and efficacy of the APOE-targeted epigenome therapy. Conclusions: Our epigenome therapy strategy for fine-tuning of APOE expression based on dCas9 technology is translational toward the development of a therapeutics approach to prevent and/or delay LOAD onset. Furthermore, the technology offers the opportunity to refine the platform for the development of gene-specific and even allele- and cell-type- specific therapies, and by that enables the advancement of strategies for precision medicine in LOAD.
OC36- CONFOUNDING FACTORS OF ALZHEIMER’S DISEASE PLASMA BIOMARKERS AND THEIR IMPACT ON CLINICAL PERFORMANCE. A. Pichet Binette 1, S. Janelidze 1, N. Cullen 1, J.L. Dage 2, R.J. Bateman 3, H. Zetterberg 4,5, K. Blennow 1, E. Stomrud 1, N. Mattsson-Carlgren 1, O. Hansson 1(1. Clinical Memory Research Unit, Faculty Of Medicine, Lund University — Lund (Sweden), 2. Department Of Neurology, Indiana University School Of Medicine — Indianapolis (United States), 3. Department Of Neurology, Washington University School Of Medicine — St. Louis (United States), 4. Department Of Psychiatry And Neurochemistry, The Sahlgrenska Academy, University Of Gothenburg — Gothenburg (Sweden), 5. UK Dementia Research Institute, University College London — London (United Kingdom))
Background: Plasma biomarkers will likely revolutionize the diagnostic work-up of Alzheimer’s disease (AD). However, before widespread clinical use, it is important to determine which, if any, confounding factors might affect the levels of these biomarkers, and their clinical utility. Here we studied whether common comorbidities, as well as proxies of kidney function (plasma creatinine) and blood volume (body mass index [BMI]) confounded the levels of several state-of-the-art plasma biomarkers for AD and neurodegeneration. Objectives: First, we investigated associations between plasma biomarkers levels and comorbidities/medication use, creatinine, and BMI, which allowed us to identify key potential confounding factors. Second, we studied whether the performance of plasma biomarkers was improved when adjusting for such potential confounding factors in two contexts: i) associations between individual plasma biomarkers and their CSF counterparts, and (ii) the ability of plasma biomarkers to predict conversion to AD dementia or all-cause dementia in non-demented individuals. Methods: Participants with plasma and CSF biomarkers, creatinine, BMI, and medical history data from the Swedish BioFINDER-1 (n=748) and BioFINDER-2 (n=421) cohorts were included. Beta-amyloid (Ab42, Ab40), phosphorylated tau (p-tau217, p-tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP) were measured in plasma and CSF, using high-performing assays (mass-spectrometry [MS], Meso Scale Discovery or Sioma for plasma and all Elecsys assays for CSF, apart from p-tau217). For plasma Ab, results were validated across three assays, including the WashU-IP-MS. Linear regression models first assessed associations for plasma biomarkers with BMI, creatinine, and comorbidities. Next, we used bootstrapping to assess if models with or without confounding factors as covariates (linear regressions for plasma-CSF correspondence and logistic regressions for progression to dementia) were significantly different. Results: In both cohorts, creatinine and BMI, but not comorbidities or medication use, were the main factors associated with plasma biomarkers. Creatinine was positively correlated with NfL and GFAP in both cohorts (average standardized coefficients of 0.2, all p<0.05). In BioFINDER-1, creatinine was also positively correlated with p-tau217 and p-tau181 (average standardized coefficients of 0.14, all p<0.05). BMI was negatively correlated with NfL, GFAP, and to a lesser extent with p-tau217 and p-tau181 in both cohorts (average standardized coefficients of −0.15, all p<0.05). No associations were found with the Ab42/Ab40 ratio. Adjustment for BMI and creatinine had minor effects in models predicting either the corresponding levels in CSF or subsequent development of dementia. In both cohorts, NfL was the main biomarker for which accounting for confounding factors consistently improved the plasma coefficient (by 6 to 10%) in relation to CSF. Smaller improvements (2-3%) were seen when accounting for confounding factors with the Ab42/Ab40 ratio, only on MS-based assays. Regarding progression to subsequent dementia, plasma p-tau217 and NfL odds ratio were improved by 4.5% to discriminate between stable participants and those progressing to AD dementia or all-cause dementia respectively. However, the discriminative accuracies between models (AUC of 0.82 for p-tau217 and 0.71 for NfL) were virtually the same, with a maximum change in AUC of 0.01. Conclusion: In two large cohorts, creatinine and BMI were related to certain plasma biomarkers levels. Still, the improvements in models were modest when including these two confounding factors, suggesting their limited clinical relevance for the majority of individuals.
OC37- ADUCANUMAB AND LECANEMAB LABEL INSOLUBLE, FIBRILLAR, DIFFUSIBLE AB AGGREGATES IN AQUEOUS EXTRACTS OF HUMAN ALZHEIMER DISEASE BRAIN. A.M. Stern 1, A.L. Meunier 1, W. Liu 1, M. Ericsson 2, D.J. Selkoe 2(1. Ann Romney Center For Neurologic Diseases, Brigham And Women’s Hospital, Harvard Medical School — Boston (United States), 2. Harvard Medical School Electron Microscopy Core — Boston (United States))
Background: Monoclonal antibodies that bind aggregated forms of Aβ are FDA-approved or are completing confirmatory phase 3 trials, and some may enter the clinic in common use in the next few years. Detailed studies have described their binding to synthetic Aβ and in vitro-derived aggregates thereof, but less is known about the nature of aggregates these antibodies bind in human brain. Binding and removing small aqueously soluble “oligomers” has been proposed to be one mechanism of action for these antibodies and an attractive target for future generations. Lecanemab has been shown to bind small oligomeric synthetic Aβ species aggregated in vitro, but in human subjects it reduces amyloid PET signal, which measures large fibrillar forms of Aβ. Objectives: We sought to describe the quantity, size and shape of aggregates bound by clinical antibodies in the aqueous fraction of extracts from human AD brain tissue. Methods: Aqueous extracts of postmortem AD and control cortical tissue were prepared by mincing and then soaking in TBS buffer followed by ultracentrifugation, retaining the supernatant. Immunoprecipitation followed by denaturation and detection with an Aβ42-specific ELISA were used to quantify Aβ species in the extracts reactive to therapeutic antibodies. Extracts were adsorbed onto carbon-coated grids, and immuno-electron microscopy with protein A-gold was used to study binding of therapeutic antibodies to Aβ fibrils found in the aqueous extracts. Results: Aducanumab, lecanemab, and bapineuzumab all immunoprecipitated the majority of Aβ detectable in aqueous AD brain “soaking” extracts. Negative staining and immunogold transmission EM revealed the presence of pelletable, fibrillar Aβ species in aqueous “soaking” extracts of all thirteen human AD brains examined. Aducanumab, lecanemab, and bapineuzumab all decorated these Aβ fibrils. Lecanemab immunoprecipitation of Aβ did not occur from the supernatants of very high ultracentrifugation speeds (250,000 – 475,000 g) but did occur from supernatants prepared at lower speeds (20,000 – 100,000 g). Conclusions: At least some oligomers present in aqueous diffusible extracts of AD brain are fibrillar and can bind therapeutic monoclonal antibodies. The fibrils are lost with very high-speed ultracentrifugation. The results suggest that the mechanism of action of therapeutic anti-amyloid antibodies may in part be due to binding insoluble Aβ aggregates. This agrees with the observation of robust clearance of amyloid PET by these antibodies, including lecanemab. Disclosures: DJS is a director of Prothena Biosciences and a consultant to Eisai.
OC38- A MULTIMODAL CLINICAL AND LIFESTYLE INTERVENTION INDUCES MULTIOMIC SYSTEMIC EFFECTS AND IMPROVES COGNITIVE OUTCOMES IN ALZHEIMER’S DISEASE. J.C. Roach 1, L.E. Edens 1, S. Rajbhandari 1, J. Hara 2, J. Bramen 3,4, M.K. Rapozo 5, C. Funk 1, W.R. Shankle 2,6,7,8, L. Hood 1(1. Institute For Systems Biology — Seattle, Washington (United States), 2. Pickup Family Neurosciences Institute, Hoag Memorial Hospital Presbyterian — Newport Beach, California (United States), 3. Pacific Brain Health Center, Pacific Neuroscience Institute — Santa Monica, California (United States), 4. Department of Translational Neurosciences and Neurotherapeutics, Saint John’s Cancer Institute — Santa Monica, California (United States), 5. Providence St. Joseph Health — Renton, Washington (United States), 6. Shankle Clinic — Newport Beach, California (United States), 7. Department of Cognitive Sciences, University of California — Irvine, California (United States), 8. EMBIC Corporation — Newport Beach, California (United States))
Background: The Coaching for Cognition in Alzheimer’s (COCOA) trial was a prospective randomized clinical trial (RCT) to test the effect of a multimodal intervention on individuals in the early stages of cognitive decline. Participants met criteria for at least one definition of either Alzheimer’s disease (AD) or a condition on the AD spectrum. AD and other dementias result from the interplay of multiple interacting dysfunctional biological systems. The motivation for COCOA was to test the hypothesis that personalized multimodal lifestyle and clinical interventions could ameliorate cognitive decline in this population. Coached interventions in COCOA were tailored to personal, clinical, and molecular data for each individual — representing a form of precision medicine. Standard of care, including pharmaceutical combination therapy, was available to all individuals enrolled in the trial; the intervention arm received personalized coaching for combination lifestyle interventions and cognitive training in addition to this standard of care. Objectives: Our overarching objective is to establish (or disprove) causal paths connecting specific interventions (or combinations of them) through intermediate molecular subsystems to neurological subsystems that promote cognition. Parts of this epistemological argument should include: (i) evidence that multimodal interventions improve cognition, such as a significant change in an RCT primary outcome measure, (ii) evidence that multimodal interventions impact particular endophenotypes, including description of the particular molecular analytes comprising these endophenotypes, and (iii) evidence connecting these endophenotypes to beneficial neurological and cognitive outcomes. Our goal for the resulting knowledge is to enhance the design of future clinical trials, tweak or overhaul recommendations for multimodal interventions, and stimulate broader adoption of lifestyle interventions already suspected or known to ameliorate cognitive decline. Methods: COCOA’s trial design is as described (1). COCOA’s primary outcome measure is the Memory Performance Index (MPI), a measure of cognition. The MPI is a summary statistic of the MCI Screen (MCIS). Secondary outcome measures include the Functional Assessment Staging Test (FAST), a measure of function. In addition to testing a hypothesis of improvement in a primary cognitive outcome endpoint, COCOA was also designed to produce dense omics data to enable epistemological analyses and exploration (2). We analyzed an interim data freeze from COCOA spanning a full year of trial participation for all participants. These data included cognitive outcome measures, clinical labs, targeted serum proteomics, and comprehensive serum metabolomics. We integrated these data into a combined omics dataset and analyzed them as a connected system, using both pre-existing knowledge graphs and connections learned from the data. Dimensionality reduction techniques included multidimensional scaling, principal components analysis, and force-directed network layout. Gene set and metabolite set enrichment analyses were performed on connected subsystems. Significance was computed for subsystems as well as for individual analytes. New results were contextualized and integrated with prior knowledge using knowledge graphs summarizing existing biomedical knowledge. Results: In aggregate, both the primary cognitive outcome measure (MPI) and the functional outcome measure (FAST) significantly improved in cases compared to controls. Omics data from the 42 participants with at least two omic timepoints were considered. The multimodal intervention impacted (significantly different between cases and controls) analytes spanning overlapping systems including metabolic, immune, cardiovascular, and neurologic function. The most significant of these subsystems had functions related to protein and amino acid metabolism. A subset of the most significant proteins had neurotrophic function. A distinct set of analytes, particularly cardiovascular proteins, were correlated with better cognitive outcomes across all individuals (both cases and controls). Conclusion: Multimodal lifestyle interventions have broad impacts on many physiological systems; these impacts are reflected in hundreds of serum analytes. In aggregate, individuals receiving these interventions have better cognitive outcomes than those who do not. One possible interpretation is that some single aspect of the multimodal intervention, potentially different in each person, may convey the bulk of causal benefit. However, it is more likely that improving general health across a wide variety of connected organ systems improves multiple functions that work synergistically to improve cognitive health. These functions appear to include cardiovascular health and neurotrophic support. More generally, improved overall energetics and protein synthesis may fundamentally enable systems in the body that have been degraded by other processes, have become underpowered to maintain homeostasis, and have allowed the body to veer towards a path of cognitive decline and dementia. Revitalization of these basic metabolic processes may enhance allostasis and improve cognitive outcomes. Progression of AD may be delayed, halted, or reversed (ameliorated) in some individuals. These insights may be generalizable to other conditions of aging. Additional work remains for the analysis of this and future COCOA data freezes. Our existing results facilitate design of future clinical trials and may guide refinements to personalized multimodal interventions. References: 1. Roach et al. The Coaching for Cognition in Alzheimer’s (COCOA) Trial: Study Design. Alzheimers Dement. In Press. 2. Roach et al. 2022. Dense data enables twenty-first century clinical trials. Alzheimers Dement. e12297.
OC39- ADVANTAGES OF NEXT GENERATION SUPRAANTIGEN® PLATFORM LIPOSOMAL VACCINES TO IMMUNIZE AGAINST PATHOLOGICAL TARGETS OF ALZHEIMER’S DISEASE. M. Vukicevic 1, E. Fiorini 1, D. Hickman 1, R. Carpintero 2, M. Rincon 2, P. Lopez-Deber 2, M. Ayer 2, S. Siegert 2, C. Babolin 2, E. Gollwitzer 2, S. Delpretti-Anex 2, P. Donati 2, J. Streffer 2,3, A. Pfeifer 2, M. Kosco-Vilbois 2(1. Ac Immune SA — Lausanne (Switzerland), 2. AC Immune SA — Lausanne (Switzerland), 3. University of Antwerp — Antwerpen (Belgium))
Background: Alzheimer’s disease (AD) and certain related neurodegenerative diseases are silent pandemics that are expanding in step with our ageing global population. Amyloid plaques, composed of misfolded Abeta species such as neurotoxic pyroGlu-Abeta and oligomeric Abeta, are one of the early hallmarks of AD, appearing when people are pre-symptomatic and proliferating as disease progresses. In addition, early in the disease, Tau forms neurofibrillar tangles, rich in aggregated phosphorylated (p)Tau, the deposition of which tracks with loss of cognition and neurodegeneration. For over a decade, we have been evolving our liposome-based SupraAntigen® vaccine platform, comparing it to commonly used approaches, such as protein-conjugate-based vaccines, to create best-in-class vaccines that can slow disease progression as well as delay or prevent disease onset. Objectives: Development of vaccines that safely generate sustained, conformation-specific antibody titers with preferences for the pathological species of Abeta and Tau. Evaluation of these vaccines in mice, non-human primates (NHP) and AD patients. Methods: For Abeta, several vaccines were generated as follows: a liposome-based SupraAntigen® vaccine (i.e., optimized ACI-24) containing the antigenic peptide Abeta 1–15, an adjuvant and a universal T-helper cell peptide; and CRM-conjugated vaccines containing various antigenic Abeta peptides (e.g., ACC-001) or full-length Abeta (i.e., AN1792) mixed with adjuvant. Mice and NHPs were vaccinated as follows: mice, 3 times every 2 weeks and plasma collected one week post immunization; cynomolgus monkeys, 5 times monthly and serum collected one week post immunization. ELISA-based assays assessed the binding to various forms of Abeta. Epitope mapping was carried out assessing the binding to 8 amino acid long peptides of Abeta. For Tau, various vaccines were generated as follows: a liposomal-based SupraAntigen® vaccine (i.e., ACI-35.030) containing an antigenic phosphorylated peptide pTau, adjuvants and a universal T-helper cell peptide: and a CRM-conjugated vaccine containing an antigenic phosphorylated Tau peptide. NHPs were immunized at 0, 1, 3 and 6 months and serum collected one and three weeks after each immunization. ELISA-based assays assessed the binding to various forms of Tau. Epitope mapping was carried out assessing the binding to the peptides of non-phospho- and phospho-Tau. Results: When immunizing mice and NHPs with vaccines containing the various Abeta peptides, all animals developed anti-Abeta 1–42 titers. However, only the liposomal-based optimized ACI-24 induced a homogenous and robust response to the Abeta-toxic species, pyroglutamate (pyroGlu-Abeta). Furthermore, this protective IgG response was maintained over time and could be consistently boosted. Additional profiling of the antibody response by epitope mapping revealed the superior broad coverage of the repertoire, as only optimized ACI-24 induced antibody responses to different short peptides including the mid-domain of Abeta 1–15, while the other vaccines generated antibodies that bound mainly to the very N-terminal sequence of Abeta. For the Tau targeting vaccines, ACI-35.030 and the CRM-conjugated vaccine induced similar IgG titers to the immunizing peptide, as well as ePHF. However, ACI-35.030 induced antibodies with a strong preference towards the phospho peptide and low binding to the non-phospho peptide, while the CRM-conjugated vaccine induced a strong response to the non-phospho peptide. This was in line with the epitope mapping data, which demonstrated strong binding to the phosphorylated residues for the ACI-35.030-induced antibodies. In contrast, the CRM-conjugated vaccine induced limited coverage mainly recognizing a truncation-specific open end amino acid of the Tau peptide sequence. Importantly, both vaccines showed a favorable safety profile and did not induce Tau-specific T-cell activation. Further evaluation of the 2 Tau targeted vaccines in AD patients confirmed similar specificity of the induced antibodies observed in the NHPs. Conclusions: For both Abeta and Tau targeting vaccines, the liposome-based SupraAntigen® vaccines demonstrated a superior quality of the IgG repertoire generated post-immunization. The responses in NHPs were well tolerated, homogenous, robust and boostable over time, while broadly engaging relevant pathological epitopes. For Abeta, the liposome-based SupraAntigen® vaccine generated the highest titers of antibodies specifically targeting pyroGlu-Abeta. For Tau, only the liposome-based SupraAntigen® vaccine matured a repertoire of antibodies that broadly recognized species containing the pathological pTau. Taken together, the SupraAntigen® vaccine technology platform, using carefully chosen target peptides combined with adjuvants and universal T-helper cell peptides, creates a broad and safe antibody response to the key pathological species which translates to best-in-class clinical vaccine candidates.
OC40- U-P53AZ IN PROGNOSTICATION OF EARLY ONSET ALZHEIMER’S DISEASE UP TO 6 YEARS IN ADVANCE OF THE CLINICAL DIAGNOSIS. S. Piccirella 1, L. Van Neste 2, C.H.R.I.S. Fowler 3, C.M.A.S. Masters 3, J.U.R.G.E. Fripp 4, J.D. Doecke 4, C. Xiong 5, D. Uberti 6, P. Kinnon 1(1. Diadem SpA — Brescia (Italy), 2. Halixo BV — Hoegaarden (Belgium), 3. The Florey Institute of Neuroscience and Mental Health — Parkville (Australia), 4. The Australian e-Health Research Centre, CSIRO — Herston (Australia), 5. Washington University School of Medicine, Division of Biostatistics — St. Louis (United States), 6. Department of Molecular and Translational Medicine, University of Brescia — Brescia (Italy))
Background: The unfolded conformational variant of the p53 protein is a potential prognostic biomarker of Alzheimer’s dementia (AD) (U-p53AZ), previously observed in individuals in the prodromal and clinical AD stages. Diadem have developed AlzoSure® Predict (Piccirella et al, 2022), a simple, non-invasive, rapid blood-based test that allows the assessment of cognitive decline to AD-dementia up to 6 years in advance of any clinical symptoms by detecting the concentration of a specific sequence peptide, AZ284®, from U-p53AZ. Objectives: This study aims to confirm the prognostic performance of U-p53AZ in the onset of AD and to compare this with other AD biomarkers. Methods: In this retrospective study, we evaluate the prognostic performance U-p53AZ (detected by AlzoSure® Predict) in plasma samples from individuals participating in the Australian Imaging, Biomarkers and Lifestyle (AIBL) cohort. AlzoSure® Predict is a LC-MS/MS based method that detects a specific peptide belonging to the U-p53AZ protein, called AZ284®. AlzoSure® Predict is a CE-IVD marked test, recently designated as breakthrough device by the FDA. At baseline, this cohort consists of 237 cognitively normal subjects, including both those without and with subjective memory complaints (NMC/SMC), 98 individuals with mild cognitive impairment (MCI), 141 patients with AD, and 3 other dementia patients that were followed up every 18 months. The performance of U-p53AZ was compared with other AD biomarkers, i.e. amyloid status assessed by calibrated centiloid, tau protein, ApoE4 allele status, age, and gender. To evaluate the prognostic potential, Cox proportional hazards regression models were developed for the beforementioned markers. Results: The prognostic value of U-p53AZ was evaluated in the longitudinal AD patient subset of the AIBL cohort, removing all individual that were already diagnosed with AD at baseline. The value of AZ284® relative to other biomarkers was evaluated by fitting Cox proportional hazards models. The other risk factors that were explored include centiloid, ApoE4, tau, age, and gender, in addition to combinations of these. While AZ284® data is available for a total of 338 men and women, comparisons can only be made on subjects with a complete marker profile. Because information on tau is lacking for many, this biomarker is addressed separately. First, AZ284® was compared with 294 subjects for which amyloid, age, gender, and ApoE4 are available, of which 36 develop AD over time. In terms of individual risk factors, AZ284® clearly outperforms the other risk factors with a concordance (C) index of 95.3% ± 0.9% (standard error) (all p < .0016). The best multi-risk factor model includes AZ284® (p<.0001), amyloid (p<.0001), and age (p=.0096), resulting in a C-index of 94.3% ± 1.6%. Gender and ApoE4 were not significant and did not result in any improvement of the model and were hence not included. Similarly, the best model that does not include AZ284® consists of amyloid (p<.0001) and age (p=.0007), with a C-index of 86.4% ± 3.1%. This model has a significantly lower performance compared to the model with AZ284® (p<.0001), demonstrating the significance and synergistic performance of AZ284®. The comparison with tau protein was more difficult, since this biomarker was only available for a limited subset of the subjects. When the maximum difference in sample collection for AZ284® and tau protein was limited to 1 year, only 29 subjects were eligible, of which only 2 developed AD over time. To increase the patient number, in particular those that develop AD, AZ284® and tau were compared without time restriction. It is important to note that the lead time for AZ284® is significantly larger compared to that of the tau sample, i.e. samples were taken significantly longer before the final diagnosis (p < .0001), which is also the case for those subjects that develop AD (p=.0418), creating a slight bias in favor of tau since the time-to-event of the AZ284® was used for all. This allowed to compare both biomarkers on a set of 64 subjects, of which 6 developed AD over time. In this subset, AZ284® performs similarly compared to the larger population, with a C-index of 95.7% ± 2.1%. Total tau and p-tau 181 were assessed, both as binary marker (positive vs negative) or using the actual concentration. For both, the actual concentration performed better than the binary marker, with C-indices 88.3% ± 4.6% vs .65.8% ± 10.1% and 77.7% ± 8.8% vs 63.6% ± 10.2%, respectively. Except for total tau concentration (p=.0841), AZ284® significantly outperformed tau protein (total tau binary: p=.0024; p-tau binary: p=.0013; p-tau concentration: p= .0303). Conclusion: The present study confirms the prognostic performance of U-p53AZ for AD. Despite the small sample size, the trend clearly indicates that AZ284® is the strongest performing biomarker and would be recommended as an integral part of any biomarker-based model. More details are available on: https://link.springer.com/article/10.14283/jpad.2022.52.
OC41- IWHELD: AN RCT OF A NOVEL DIGITAL NON-PHARMACOLOGICAL INTERVENTION TO IMPROVE QUALITY OF LIFE AND REDUCE ANTIPSYCHOTICS IN 741 PEOPLE LIVING IN NURSING HOMES DURING THE COVID-19 PANDEMIC. C. Ballard 1, J. Mcdermid 1, A. Sweetnam 1(1. University of Exeter — Exeter (United Kingdom))
Background: Inconsistent quality of care in nursing homes has long been recognised as a challenging area that requires urgent action and its impact on quality of life in people living with dementia. These enduring issues have been compounded by the emergence of and ongoing pressures of the COVID-19 pandemic on nursing home settings. People living in nursing homes and long-term care facilities are often frail and have complex needs, many with dementia, neuropsychiatric symptoms, and/or other physical conditions and have been disproportionately impacted by the pandemic, affecting not only residents but also their families and the care workforce. Whilst high prescribing rates of antipsychotics in the early 2000s for people living with dementia in nursing homes had significantly reduced in recent years, the pandemic has seen a rebound increase in use. Implementation of evidence-based training and support for nursing staff into real world practice in nursing home settings is a major challenge. Digital approaches provide real potential to addressing the barriers, particularly over the difficult period of the COVID-19 pandemic. Objectives: In response to the COVID-19 crisis, to rapidly develop and implement a nursing home intervention programme to include virtual coaching, peer networking and solution sharing, alongside evidence-based elements focussing on person-centred care, personalised activities, and reduction of unnecessary antipsychotic medications. Method: iWHELD is a first-of-its-kind digital programme evolving the principles of the WHELD intervention combining person centred care, social interaction, movement, and antipsychotic review with virtual coaching and a digital resource for nursing homes. The entirely remote intervention utilising a Dementia Champion model supported by live virtual coaching set within a digital resource hub and peer networking platform was compared to usual care in a 16-week randomised control cluster study of 741 people with dementia across 149 nursing homes in the UK. The primary outcome evaluated quality of life (using the DEMQOL-Proxy) and secondary outcomes included the use of antipsychotic drugs and neuropsychiatric symptoms (using the Neuropsychiatric Inventory NH). Result: The average age of residents was 84.5 years (71% female). 64% of participating nursing homes had experienced a COVID-19 outbreak. At baseline, 28% of residents were prescribed an antipsychotic (a significant 55% increase compared to pre pandemic in previous WHELD RCT trial in 2014). 36/72 (53%) of nursing homes allocated to the active treatment arm engaged successfully with the digital intervention, with 563 residents completing the treatment period. There was significant benefit in quality of life for residents receiving the iWHELD intervention compared to those in the control group (DEMQOL-Proxy 4.76 ± 15.03 point advantage, p=.006, Cohen’s D effect size 0.32). There was also a significant reduction in antipsychotic use in the iWHELD treatment group from 49% to 31% compared to no change in the group receiving usual care (p=0.046). Analysis of neuropsychiatric symptoms indicates a significant benefit for the treatment group with respect to delusions (p= .01) with no significant differences in hallucinations or agitation in the intervention group compared to those receiving usual care indicating no significant worsening of these symptoms in the context of a significant reduction in antipsychotic prescriptions.
Conclusion: For this current large scale RCT, we successfully designed, recruited, and delivered a novel digital programme in 149 nursing homes with 741 residents and over 200 staff as part of a rapid response COVID-19 initiative. The iWHELD intervention with live virtual coaching delivered through a Dementia Champion achieved better than 50% engagement, which compares favourably with previous studies of digital interventions in other therapeutic areas. The iWHELD intervention conferred significant benefit in quality of life as well as significant reductions in antipsychotic use without any worsening of neuropsychiatric symptoms and significant benefit with respect to delusions. This study provides an important potential approach to both improving wellbeing and quality of life and to safely reducing the rise in antipsychotic use in nursing home residents with dementia that has become a major challenge during the COVID-19 pandemic. The iWHELD digital format provides a potential solution for wide-scale rollout into real world settings.
OC42- MAKING DIGITAL MEASURES FIT-FOR-PURPOSE IN ALZHEIMER’S TRIALS. F. Cormack 1, J. Sorinas 2, C. Meunier 3(1. Cambridge Cognition — Cambridge (United Kingdom), 2. Novartis — Basel (Switzerland), 3. DiMe — San Francisco (United States))
Background: Developing novel cognitive tasks or adapting existing cognitive tasks to novel technology requires an iterative and cumulative approach to validation in order to develop measures which are fit for purpose, particularly in the context of virtual clinical trials, where scalable, robust and repeatable testing are needed. Three key aspects of this are considered here: 1) the technical feasibility across device, which encompasses the accuracy of automated scoring 2) participant acceptability 3) analytical validation, focused on psychometric properties. Here we illustrate these three aspects of task development. We conducted a series of large (a total of 2,868 participants) home-based feasibility studies deploying a device-agnostic web-based technology for administering and scoring verbal neuropsychological tests (Verbal Paired Associates and Digit Span Forwards and Back and Serial Subtraction). We describe the methods developed to support automated stimulus generation to enable repeated longitudinal assessment and robust automated scoring in home settings. Technical feasibility and robustness of these methods were assessed through manual review of responses. Participant acceptability was assessed through post-test questionnaires, and the suitability of the tasks for repeat remote administration, was assessed through repeated administration of parallel forms. Qualitatively, participants reported that the automated instructions were clear and easy to understand, and that the tasks were challenging but enjoyable. We observed expected effects of task difficulty and demographic variables on task performance. We also present data on the psychometric properties of assessments, supporting the psychometric properties of the tasks and the suitability of the tests for repeat, remote administration. Together, these results illustrate developing cognitive assessment technology for use in decentralised clinical trials. Specifically, the constraints of scalable, repeatable, and robust testing were met through a combination of specifically designed algorithms to generation stimuli, and score responses, together with iterative refinements to the user interactions to ensure ease of use for participants in the absence of trained raters.
Late Breaking
Clinical Trial Alzheimer’s Disease
LB1- TAU PET ASSOCIATED WITH PLASMA P-TAU217 AND COGNITIVE TESTING IN PRECLINICAL AD: SCREENING DATA FROM THE AHEAD STUDY A3 AND A45 TRIALS. K. Johnson 1, A. Schultz 1, R. Rissman 2, O. Langford 2, E. Thibault 1, M. Meyer 3, K. Kirmess 3, M. Irizarry 4, J. Zhou 4, M. Donohue 2, R. Raman 2, P. Aisen 2, R. Sperling 1,5, A.3.4.S. Team 6(1. Massachusetts General Hospital — Boston (United States), 2. University of Southern California — San Diego (United States), 3. C2N Diagnostics — St. Louis (United States), 4. Eisai — Nutley (United States), 5. Brigham and Women’s Hospital — Boston (United States), 6. ACTC — Many Sites (United States))
Background: The emergence of more accurate plasma AD biomarkers will substantially improve screening efficiency for prevention trials. Our work in the AHEAD 3–45 Study screening cognitively unimpaired participants has recently demonstrated that plasma Aβ42/40 and p-tau217 are highly predictive of positive amyloid PET status (18F-NAV4694 PET > 20CL eligibility). In addition, plasma p-tau measures could also enable prediction of tau deposition on Tau PET, subsequent cognitive decline, response to specific therapeutic mechanisms, or as a trial inclusion criterion. Objective: In this study, we investigated the ability of plasma p-tau measures to predict level of tau deposition estimated with 18F-MK6240 Tau PET in cognitively unimpaired individuals who were eligible (>20CL on amyloid PET) for the AHEAD Study A3 and A45 trials. Methods: The AHEAD Study has a shared screening platform for both the A3 (20-40 CL) and the A45 (>40CL) trials. Only individuals who showed >20CL on NAV PET and were otherwise eligible for the AHEAD trials moved forward in screening and underwent tau PET imaging with MK6240. Samples with both plasma and PET data available in the U.S. as of March 2022 (prior to the introduction of plasma measures to determine eligibility to continue in AHEAD screening) were sent to C2N Diagnostics for batch analysis of Aß42/40, and both the phosphorylated (p-tau) and non-phosphorylated (np-tau) forms of tau181 and tau217 using C2N’s mass spectrometry platform. A concentration ratio of p-tau to np-tau was calculated to normalize for differing np-tau concentrations at each epitope (p-tau181r and p-tau217r). We tested two Tau PET aggregate regions representing tau deposition at the early and mid-stages of AD tauopathy: the medial temporal allocortex (MTL: amygdala, entorhinal, parahippocampal) and inferolateral temporal/parietal neocortex (NEO: inferior temporal, fusiform, middle temporal, inferior parietal). Tau PET was quantified with the standardized uptake value ratio (SUVr; 4mm eroded cerebral white matter reference). InVicro generated NAV4694 centiloid values for global amyloid PET quantification. Pearson correlations and multivariate linear models were calculated to predict baseline Tau PET composites and the subsample z-scored Preclinical Alzheimer’s Cognitive Composite score (PACC-5). None of the p values are adjusted for multiple comparisons in these exploratory analyses. Results: There were 303 AHEAD 3–45 amyloid eligible (>20CL) participants with plasma and screening Tau PET data available: age 69.5±5.5 years, 66% female, 75% APOE ε4 carriers, and education 16.4+2.7 years. Across the full Tau PET sample, p-tau217r showed consistently stronger associations with both Tau PET composite regions than p-tau181r. The p-tau217r correlated with Tau PET SUVr in both MTL (r=0.35; p<0.0001) and NEO (r=0.43;p<0.0001) composites. The NAV amyloid PET also correlated with MTL (r=0.33; p>0.0001) and NEO (r=0.27;p<0.0001). Interestingly, in a multi-variate model with age and APOE ε4 included, p-tau217r and amyloid NAV CL were significant independent predictors of both Tau PET composites, with stronger associations observed for ptau217r over NAV CL: MTL (ptau217r estimated β=0.088, t=4.49, p<0.0001; NAV CL β=0.003, t=2.89, p=0.004); and NEO (p-tau217r β=0.104; t=6.49, p<0.0001; NAV β=0.002, t=2.13, p=0.034). Among the full Tau PET sample, 93 individuals were eligible for the A3 trial (20–40 NAV CL) and 210 were eligible for A45 (>40 NAV CL). Among the A3 subset, p-tau217r was correlated with both MTL (r=0.27, p=0.0088) and NEO (r=0.30, p=0.0035). NAV CL was marginally correlated with MTL (r=0.20, p=0.054) but not with NEO (r=0.13, p=NS). Among the A45 subset, p-tau217r was correlated with MTL (r=0.28, p<0.001) and NEO (r=0.42, p<0.0001) Tau PET composites. NAV CL was also correlated with Tau PET MTL (r=0.20, p=0.003) and NEO (r=0.23, p=0.001) composites in the A45 subset. Finally, we evaluated the association of amyloid NAV, Tau PET composites, and p-tau plasma measures with screening PACC-5 score, with age, sex, and education covaried. Across the full Tau PET sample and within the A45 subsample alone, only the NEO Tau PET composite was associated with screening PACC-5 (β= −0.18; p=0.0006). No association with cognition at screening was observed with NAV CL, MTL Tau PET, p-tau181r, or p-tau217r.
Conclusions: In this sample of cognitively unimpaired participants who were all amyloid eligible for the AHEAD 3–45 Study, p-tau217r predicts tau deposition as measured by Tau PET imaging, above and beyond amyloid levels. Neocortical Tau PET burden was associated with screening cognitive testing, even within the restricted range of normal cognition required for eligibility in the AHEAD Study. These results suggest that plasma p-tau217r measures may be useful in identifying those preclinical AD individuals with evidence of Tau pathology, whereas Tau PET may be valuable for tracking cognitive outcomes. In particular, these markers should enable efficient screening and sensitive outcomes for upcoming trials targeting tau mechanisms at very early stages of AD.
LB2- PLASMA LEVELS OF ABETA42/40 AND P-TAU217 RATIOS INCREASE ACCURACY OF AMYLOID PET PREDICTION IN PRECLINICAL AD. R.A. Rissman 1,2, O. Langford 2, M. Donohue 2, R. Raman 2, S. Abdel-Latif 2, M. Meyer 3, K. Kirmess 3, J. Braunstein 3, M. Irizarry 4, K. Johnson 5, P. Aisen 2, R. Sperling 6, T. Ahead 3–45 Study 7(1. UC San Diego — La Jolla, Ca (United States), 2. University of Southern California — San Diego, Ca (United States), 3. C2N Diagnostics — St. Louis, Mo (United States), 4. Eisai — Indianapolis, In (United States), 5. Massachusets General Hospital, Harvard University — Boston, Ma (United States), 6. Brigham and Woman’s Hospital, Harvard — Boston, Ma (United States), 7. ACTC — San Diego, Ca (United States)
Background: Our prior data from the A4 and AHEAD Study, and that from other groups demonstrates that plasma Aβ42/40 quantification by mass spectrometry can serve as a reliable biomarker for predicting elevated brain amyloid detected by PET. We studied the value of adding plasma p-tau measures to our plasma Aβ42/40 algorithm to further streamline identification of eligible participants and reduce burden and trial cost. Objective: To determine if the addition of plasma p-tau181 and/or p-tau217 concentrations can improve plasma Aβ42/40 algorithms to correctly identify participants with amyloid burden of >20 centiloids with the NAV4694 tracer among individuals screening for participation in the AHEAD preclinical AD trial. Methods: Plasma amyloid and tau measures were quantified by C2N Diagnostics using mass spectrometry-based analytical platforms. Participant plasma samples (N=1085) collected prior to the introduction of plasma Aβ42/40 testing during screening were used. Plasma samples for these analyses consisted of those with sufficient amyloid PET levels (n = 364; 33%) to be eligible for AHEAD and those who screen failed (n = 747; 67%). C2N quantified Aβ (Aβ42/40) and various tau species, including both the phosphorylated (p-tau) and non-phosphorylated (np-tau) forms of tau181 and tau217. A ratio of p-tau to np-tau was also calculated for each epitope (p-tau181r and p-tau217r) to normalize for interindividual differences in np-tau concentrations. We conducted Receiver Operating Characteristic (ROC) curve analyses for each of these biomarkers against amyloid status defined by amyloid PET status (>20 centiloids). We also fit a Mixture of Experts model to assess the value of including p-tau181r and p-tau217r in the existing predictive algorithm (Aβ42/40, Age and APOE) for amyloid PET status using NAV4694. Results: This sample of N =1085 contained 67% Female, 13.5% Hispanic, 3.7% Black or African American with a mean age of 67.6 (SD = 6.1) years. 45% of the participants had at least one APOE4 allele. The Area Under the Curve (AUC) for plasma Aβ42/40 was 0.87 (95% CI; 0.84, 0.89), consistent with prior reports. For plasma tau markers, we observed AUCs of 0.74 (95% CI; 0.71, 0.77) with p-tau181, to 0.91 (95% CI; 0.90, 0.93) with p-tau217r. The model including covariates p-tau217r, Aβ42/40, Age and APOE improved AUC to 0.95 (95% CI; 0.93, 0.96). Conclusions: These findings demonstrate that the addition of plasma p-tau/np-tau concentration ratios for tau181 and tau217 species greatly improved the utility of plasma testing for amyloid PET positivity, with p-tau217r conferring the greatest improvement. Our data suggests that consideration of plasma p-tau217r in addition to Aβ42/40 ratio can dramatically improve anti-amyloid clinical trial screening burden and timelines for participant recruitment. In addition to determining how our results can be applied to other amyloid tracers and varying levels of neuropathology as informed by Aβ and tau PET, our current priorities involve expanding these findings to underrepresented populations to determine whether the specific levels and cutoffs of plasma Aβ and p-tau species and their relation to PET amyloid positivity are similar across different racial, ethnic and other underrepresented groups.
LB3- TRAILBLAZER-ALZ 4: TOPLINE STUDY RESULTS DIRECTLY COMPARING DONANEMAB TO ADUCANUMAB ON AMYLOID LOWERING IN EARLY, SYMPTOMATIC ALZHEIMER’S DISEASE. S. Salloway 1, E. Lee 2, M. Papka 3, A. Pain 4, E. Oru 4, M.B. Ferguson 4, H. Wang 4, M. Case 4, M. Lu 4, E.C. Collins 4, D. Brooks 4, J. Sims 4(1. Department of Neurology and Department of Psychiatry, Alpert Medical School of Brown University, Providence, RI, USA; Butler Hospital — Providence (United States), 2. Irvine Clinical Research — Irvine (United States), 3. The Cognitive and Research Center of New Jersey LLC — Springfield (United States), 4. Eli Lilly and Company — Indianapolis (United States))
Background: The amyloid cascade in Alzheimer’s disease (AD) involves the production and deposition of amyloid beta (Aβ) as an early and necessary event in the pathogenesis of AD (1). Both donanemab and aducanumab have demonstrated the ability to reduce brain amyloid plaque burden and potentially slow clinical decline (2, 3). Recently, the FDA provided accelerated approval for aducanumab for the treatment of early symptomatic AD based on its ability to reduce Aβ plaques (4) as a surrogate biomarker reasonably likely to predict a clinical benefit to AD patients. Objectives: The primary outcome of TRAILBLAZER-ALZ 4 (NCT05108922) evaluated the potential superiority of donanemab treatment compared to aducanumab on the percentage of participants with amyloid plaque clearance (≤24.1 Centiloids (CL)) at 6 months in the overall study population and subpopulation of participants with intermediate tau deposition. Methods: TRAILBLAZER-ALZ-4 is a multicenter, phase 3, open-label, active comparator study of participants with early symptomatic AD (n=148), randomized 1:1 to receive donanemab (700 mg IV Q4W for first 3 doses, then 1400 mg IV Q4W for subsequent doses) or aducanumab (per USPI4: 1 mg/kg IV Q4W for first 2 doses, 3 mg/kg IV Q4W for next 2 doses, 6 mg/kg IV Q4W for next 2 doses and 10 mg/kg IV Q4W for subsequent doses). The overall study duration is 18 months with the primary endpoints assessed at 6 months. Eligible participants are considered to have early symptomatic AD with a mini-mental state examination score of 20–30 (inclusive) and Clinical Dementia Rating-global score of 0.5 or 1.0. Other eligibility criteria included elevated Aβ as detected by florbetapir F18 PET scan, age 50–85 years; and consent to apolipoprotein E (APOE ε4) genotyping. Magnetic resonance imaging (MRI)-based exclusions included >4 microhemorrhages, superficial siderosis, and severe white matter changes; use of anti-coagulation agents was not permitted. Participant randomization was stratified by amyloid burden at baseline and APOE ε4 status. A flortaucipir F18 PET scan was also performed to identify a subpopulation with an intermediate tau level. Intermediate tau deposition was defined as an initial flortaucipir scan with moderate AD patterns based on visual assessment and neocortical standardized uptake value ratio (SUVR) between 1.10 and 1.46, inclusive, or advanced AD patterns and neocortical SUVR ≤ 1.46. Key secondary objectives include assessment of the superiority of donanemab treatment compared to aducanumab brain amyloid plaque levels percent and mean change at 6 months. Beyond standard safety assessments, MRI assessments monitored amyloid-related imaging abnormalities (ARIA) occurrence. Results: The analysis set was defined as those with at least one dose of donanemab (N=71) or aducanumab (N=69). There were N=27 participants defined as having intermediate tau in the donanemab group compared to N=28 in the aducanumab group. Baseline demographics and characteristics were well-balanced across treatment groups. Upon assessment of florbetapir F18 PET scans at 6 months, 37.9% of donanemab-treated participants achieved amyloid clearance compared to 1.6% of aducanumab-treated participants (p<0.001). In the intermediate tau subpopulation, 38.5% of donanemab-treated participants achieved amyloid clearance compared to 3.8% of aducanumab-treated participants (p=0.008). Percent change and mean change in brain amyloid levels for participants on donanemab were −65.2% +/− 3.9% (baseline: 98.29 +/− 27.83 CL, change: −62.10 +/− 3.69 CL), and −17.0% +/− 4.0% (baseline: 102.40 +/− 35.49 CL, change: −16.41 +/− 3.77 CL) for participants on aducanumab (p<0.001). In the intermediate tau subpopulation, percent and mean change in brain amyloid levels for participants on donanemab were −63.9% +/− 7.4% (baseline: 104.97 +/− 25.68 CL, change: −64.08 + /− 7.34 CL) and −25.4% +/− 7.8% (baseline: 102.23 +/− 28.13 CL, change: −23.82 +/− 7.70 CL) for participants on aducanumab (p≤0.001). 62.0% of participants treated with donanemab reported an adverse event (AE) and there were no serious AEs due to ARIA. In the aducanumab group, 66.7% of participants reported an AE, and there were 1.4% serious AEs (one event) due to ARIA. The incidence of ARIA-E in the donanemab group was 21.1%, with 2.8% symptomatic ARIA-E (13.3% of those with ARIA-E). In the aducanum b group, ARIA-E incidence was 23.2%, with 4.3% symptomatic ARIA-E of all participants in the aducanumab group (18.8% of those with ARIA-E). The incidence of ARIA-H in the donanemab group was 19.7% and in the aducanumab group was 17.4%. Infusion-related reactions (IRRs) were reported by 7.0% of the donanemab group; in the aducanumab group, 2.9% of participants reported IRRs. Conclusions: The TRAILBLAZER-ALZ 4 study provides the first active comparator data on amyloid plaque clearance in patients with early symptomatic AD. There were significantly more participants reaching amyloid clearance and significantly greater amyloid plaque reductions with donanemab compared to aducanumab at 6 months. Both agents showed similar safety profiles to their previous studies. References: 1. Selkoe DJ. JAMA. 2000;283(12):1615-1617. 2. Mintun MA, et al. NEJM. 2021;384(18):1691-1704. 3. Budd Haeberlein S, et al. JPAD. 2022;9(2):197-210. 4. Aducanumab-avwa prescribing information. ADUHELM (fda.gov).
LB4- CSF MTBR-TAU243 IS A NON-AMYLOID SPECIFIC BIOMARKER OF NEUROFIBRILLARY TANGLES OF ALZHEIMER’S DISEASE. K. Horie 1,2, G. Salvadó 3, N. Barthélemy 1, Y. Li 1, B. Saef 1, C. Chen 1, H. Jiang 1, B. Gordon 1, T. Benzinger 1, D. Holtzman 1, S. Schindler 1, O. Hansson 3,4, R. Bateman 1(1. Washington University School of Medicine — St. Louis (United States), 2. Eisai Inc. — Nutley (United States), 3. Lund University — Lund (Sweden), 4. Skåne University Hospital — Malmö (Sweden))
Background: Neurofibrillary tangles (NFTs) are a key pathological hallmark of Alzheimer’s disease (AD) and are comprised of hyper-phosphorylated tau (p-tau) and microtubule binding region of tau (MTBR-tau) species. While the levels of soluble p-tau species such as p-tau181, 217, and 231 in cerebrospinal fluid (CSF) and blood are widely used as indicators of AD tau tangles, recent studies indicate that these soluble p-tau species are more strongly associated with amyloid plaques than tau tangles. We previously discovered that CSF MTBR-tau species containing the residue of 243 (MTBR-tau243) located in the upstream region of the MTBR was the fluid biomarker most highly correlated with tau tangles as measured by positron emission tomography (PET) in a small cohort (Horie et al., Brain, 2021). Objectives: We aimed to establish a non-amyloid dependent CSF biomarker to specifically quantify NFTs in AD. We measured the novel biomarker, MTBR-tau243, in CSF samples from two cohorts: 1. The Swedish BioFINDER-2 study and 2. The Knight Alzheimer Disease Research Center (Knight ADRC). Furthermore, we compared CSF MTBR-tau243 and p-tau as predictors of amyloid pathology, tau pathology and cognitive function. Methods: BioFINDER-2 (n=448) and Knight ADRC (n=219) participants underwent a lumbar puncture within two years of an amyloid PET (Flutemetamol or Florbetapir/Pittsburgh Compound-B, respectively) and/or tau PET (RO6958948 or Flortaucipir, respectively) scan. CSF was subjected to sequential immunoprecipitation with anti-N-terminal to mid-domain antibodies for p-tau analyses (p-tau181, 205, 217, and 231) and a specific antibody targeting the upstream region of MTBR for MTBR-tau243 analyses, then evaluated via mass spectrometry. Spearman correlations were used to evaluate the relationships of CSF biomarkers with amyloid PET, tau PET measures, and the Mini-Mental State Examination (MMSE). To identify the combination of CSF biomarkers that best predicted amyloid and tau PET measures, linear regression with the Least Absolute Shrinkage and Selection Operator (LASSO) variable selection method was used. The longitudinal rates of changes for the CSF tau species were compared among groups that were amyloid and tau positive vs. negative groups at baseline. Results: The majority of participants were amyloid positive (A+, 59% for BioFINDER-2 and 62% for the Knight ADRC); 70% of BioFINDER-2 and 35% of Knight ADRC were cognitively impaired. Longitudinal CSF collected 2 years after the baseline CSF was available for 223 participants from BioFINDER-2. In both the BioFINDER-2 and Knight ADRC cohorts, CSF MTBR-tau243 concentration was the biomarker most strongly correlated with NFTs as measured by tau PET even in the amyloid-positive group (Spearman Rho=0.83 and 0.70, respectively) and was the least correlated with amyloid plaques as measured by amyloid PET in the same group (Rho=0.49 and 0.48, respectively). CSF p-tau205 occupancy was also highly correlated with NFTs even in the amyloid-positive group (Rho=0.78 and 0.70, respectively) but had a stronger association with brain amyloidosis in the same group (Rho=0.67 and 0.60, respectively) than MTBR-tau243. Linear regression with LASSO selection suggested that the combination of MTBR-tau243 level and p-tau205 occupancy was the best predictor of tau PET (adjusted R2=0.75 and 0.65, respectively), while the combination of p-tau217, 205 occupancies and Aβ42/40 was the best predictor of amyloid PET (adjusted R2=0.77 and 0.69, respectively). Notably, CSF MTBR-tau243 was the most highly correlated with the MMSE (Rho=−0.62 and −0.53, respectively) across all CSF tau measures. Linear regression with LASSO selection suggested that the combination of MTBR-tau243 level and p-tau205 occupancy was the best predictor of MMSE (adjusted R2=0.41 and 0.35, respectively), which was only slightly inferior to tau PET (adjusted R2=0.43 and 0.44, respectively). In longitudinal analyses of the BioFINDER-2 cohort, MTBR-tau243 exhibited the most significant increase in rate of change according to disease progression between amyloid-positive tau-positive (A+T+) and the other two groups (A−T−: Cohen’s d=1.48, p<0.001; A+T−: Cohen’s d=1.13, p<0.001), while p-tau205 and the other p-tau species (i.e., 181, 217, and 231) exhibited no significant change and even decreases in the rates of changes between A+T- and A+T+ groups. Conclusion: These findings suggest that CSF MTBR-tau243 reflects changes in tau pathology that occur at a later stage in AD progression than brain amyloidosis and could be used to stage AD tauopathy and track the effects of tau-targeting therapies independent of amyloid effects. The combination of CSF MTBR-tau243 and p-tau205 occupancy explained most of the total variance in tau PET and predicted MMSE almost as accurately as tau PET, which suggests high clinical utility of a biomarker panel containing MTBR-tau243. The mechanisms underlying these findings add to the growing understanding of AD pathophysiology and strategies for novel tau-targeting AD therapies.
LB5- TOP-LINE RESULTS FROM THE 2-YEAR SYSTEMATIC MULTI-DOMAIN ALZHEIMER’S RISK REDUCTION TRIAL (SMARRT). K. Yaffe 1, E. Vittinghoff 1, S. Dublin 2, C. Peltz 1, L. Fleckenstein 2, D. Rosenberg 2, D. Barnes 1, B. Balderson 2, E. Larson 3(1. University of California, San Francisco — San Francisco, Ca (United States), 2. Kaiser Permanente Washington Health Research Institute — Seattle, Wa (United States), 3. University of Washington — Seattle, Wa (United States))
Background: Modifiable risk factors account for 30–40% of dementia; yet, few trials, especially multi-domain, have demonstrated that risk reduction interventions can improve these risk factors and in turn, cognitive outcomes. We conducted the NIH-funded Systematic Multi-domain Alzheimer’s Risk Reduction Trial (SMARRT), a 2-year randomized pilot trial to test a personalized, pragmatic, multidomain dementia risk reduction intervention in an integrated healthcare delivery system. (NCT03683394). Objective: To determine whether a 2-year personalized multi-domain risk reduction intervention benefits cognition and behavioral risk factors compared to a health education group. Methods: We recruited 172 older adults at higher risk for dementia (age 70–89, subjective cognitive complaints, low-normal performance on a brief telephone cognitive screen, and ≥ two targeted modifiable risk factors) from primary care clinics of Kaiser Permanente Washington (KPWA). Modifiable risk factors that counted towards eligibility and were targeted by the intervention included poorly controlled diabetes or hypertension, use of risky prescription medications, physical inactivity, social isolation, poor sleep, smoking, and depression. Participants were randomly assigned to the SMARRT intervention or to a Health Education (HE) control. The intervention consisted of personalized risk reduction goals with health/nurse coaching for those target goals. Personalized intervention was based on the prevalence of risk factors as well as personal preference for risk reduction priority and strategy. The primary outcome was change in a composite cognition score (initially assessed in person and then due to the COVID-19 pandemic, by phone); pre-planned secondary outcomes were change in risk factors and quality of life measures. Participants were evaluated at baseline, 6, 12, 18, and 24 month assessments. Analyses of all outcomes were by intention-to-treat and used linear mixed models to compare changes from baseline, averaged across the four follow-up visits. Results: The mean age of participants was 75.7 years (sd 4.8), 63% were women and 19% were non-White; the mean number of risk factors at enrollment was 2.5 (0.6). Intervention participants had a mean of 20 (sd 3.8) contacts with the health coach or study nurse during the 2-year intervention. The trial recently was completed, and we will present top-line results on the primary and secondary outcomes. We will also present data on safety and adherence. Conclusion: The recently completed SMARRT study is the first NIH-funded personalized multi-domain trial testing a risk reduction intervention with cognitive and behavioral outcomes among high-risk older adults. Results from this trial will be critical for guiding risk reduction strategies for cognitive aging.
LB6- TWO-YEAR PROGNOSTIC UTILITY OF PLASMA P217+TAU IN THE ALZHEIMER CONTINUUM. A. Feizpour 1,2, V. Doré 2,3, J.D. Doecke 4, Z.S. Saad 5, G. Triana-Baltzer 5, N. Krishnadas 1,2, C. Fowler 1, L. Ward 1, R.N. Martins 6,7, C.L. Masters 1, V.L. Villemagne 2,8, J. Fripp 4, H.C. Kolb 5, C.C. Rowe 1,2,9(1. The Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia — Melbourne (Australia), 2. Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Victoria, Australia — Melbourne (Australia), 3. The Australian e-Health Research Centre, CSIRO, Melbourne, Victoria, Australia — Melbourne (Australia), 4. The Australian e-Health Research Centre, CSIRO, Brisbane, Queensland, Australia — Brisbane (Australia), 5. Neuroscience Biomarkers, Janssen Research and Development, La Jolla, CA, USA — San Diego (United States), 6. Edith Cowan University — Perth (Australia), 7. McCusker Alzheimer’s Research Foundation, Nedlands, — Perth (Australia), 8. Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA — Pittsburgh (United States), 9. Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia — Melbourne (Australia))
Background: Plasma p217+tau is a novel biomarker that detects tau phosphorylation at threonine 217 and is augmented by phosphorylation at threonine 212. P217+tau has shown high predictive accuracy for CSF and PET amyloid-β (Aβ) and tau status. However, the association of p217+tau with longitudinal cognition and its comparative performance to neuroimaging biomarkers of Aβ and tau in predicting prospective cognitive decline has not yet been investigated. Objectives: We examined whether p217+tau can 1) predict prospective cognitive decline on multiple well-established measures of cognition; 2) be a better predictor of cognitive decline than neuroimaging biomarkers of Aβ (18F-NAV4694) and tau (18F-MK6240); 3) provide a (pre)screening strategy to decrease sample size, and therefore cost, of therapeutic trials aiming to slow cognitive decline. All objectives were investigated in a cognitively unimpaired and a cognitively impaired cohort to assess performance of p217+tau in early and later stages of the Alzheimer’s Disease (AD) continuum. Methods: 134 cognitively unimpaired (CU) participants and 41 age-matched patients with cognitive impairment (CI: i.e., mild cognitive impairment or mild dementia) were included. Participants underwent blood sampling, 18F-MK6240 tau PET, and 18F-NAV4694 Aβ-PET at baseline. PET were quantified in Centiloid (CL) for Aβ scans and SUVR in the mesial temporal (Me), temporo-parietal (Te), and meta-temporal (MetaT) regions for tau scan using CapAIBL. Clinical and neuropsychological assessments (MMSE, CDR-SB, AIBL-PACC) were performed at baseline and follow-up (2 ± 0.6 years). Multivariable linear models were used to evaluate the association of baseline biomarkers with change in cognition (individual cognitive slopes calculated via robust linear models), after adjusting for baseline age, sex, APOE ε4, and years of education. Standardised beta coefficients (β) and their corresponding p values are reported. Binary p217+tau (pT-/pT+), Aβ (A−/A+), and tau (T−/T+) groups were created using 80% sensitivity thresholds to identify cognitive decliners. Power analysis was performed in CI to estimate sample size required to detect a 30% slope reduction on CDR-SB, with 90% power. Sample size and associated screening cost for the pT+ group was compared to those for A+ and T+ PET groups. Results: In the CI group, plasma p217+tau was a significant predictor of change in MMSE (β = −0.51, p = 0.002) and CDR-SB (β = 0.57, p < 0.001), with the effect size larger than Aβ-PET CL (MMSE β = −0.43, p = 0.021; CDR-SB β = 0.37, p = 0.045) but lower than MetaT tau SUVR (MMSE: β = −0.59, p < 0.001; CDR-SB: β = 0.64, p < 0.001). In the CU group, plasma p217+tau did not correlate with decline in AIBL-PACC score over two years (β = −0.08, p = 0.36;), similar to Aβ-PET CL (β = −0.05, p = 0.58) while MetaT tau SUVR was associated with cognitive decline (β = −0.19, p = 0.031). In CI, the biomarker thresholds based on 80% sensitivity to detect positive CDR-SB slope were 131.1 fg/ml for pT+, 1.12 SUVR for T+Me, 1.2 SUVR for T+Te, 1.18 SUVR for T+MetaT and 62 CL for A+ group. Screening pT+ CI participants into a therapeutic trial — aiming at slowing cognitive decline— led to 29% reduction in sample size compared to screening with PET for A+ and 4–16% reduction compared to screening with PET for T+ (for different ROIs). Using plasma p217+tau for trial selection rather than a PET scan would translate to a >75% test cost saving assuming a blood test cost one fifth of a PET scan, owing to both the lower cost of the test and the smaller cohort size required for the trial. In a therapeutic trial recruiting PET T+MetaT, p217+tau pre-screening followed by PET would save 4% of the PET cost in the CI group, compared to 38% in the CU group. Conclusion: This data suggests that substantial cost reduction can be achieved using plasma p217+tau alone to select participants with MCI or mild dementia for a clinical trial designed to slow cognitive decline by 30% over two years, compared to participant selection by PET. Cognitive decline in CI participants that were pT+ was slightly steeper than that in PET T+ or A+; therefore, savings would result from the lower cost of the test and the smaller cohort size required for the trial. Cost-effectiveness of using p217+tau for pre-screening in MCI and mild dementia can only be achieved if the plasma p217+tau test costs far less than one fifth of the PET scan but increases the number needed to screen and this may negate any saving from lower test cost. In contrast, in the cognitively unimpaired population, p217+tau was not able to predict cognitive decline over two years, but it provided significant cost-saving if used as a pre-screening measure for PET A+ or T+. In CU, only tau PET predicted two-year cognitive decline. These findings require replication in larger cohorts.
LB7- ALZ-NET: USING REAL WORLD EVIDENCE TO DEFINE THE FUTURE OF ALZHEIMER’S TREATMENT AND CARE. M. Carrillo 1, G. Rabinovici 2, M. Rafii 3(1. Alzheimer’s Association — Chicago (United States), 2. Memory and Aging Center, Departments of Neurology, Radiology & Biomedical Imaging, University of California, San Francisco — San Francisco (United States), 3. Alzheimer’s Therapeutic Research Institute, Keck School of Medicine of the University of Southern California — San Diego (United States))
Background: There are over 100 therapies being tested in clinical trials for Alzheimer’s disease (AD) today. With potential therapies undergoing regulatory review and a growing drug development pipeline, the field is in a new phase of treatment. Once approved and used in the community, it will be important to track longitudinal clinical and safety outcomes of novel therapies in large numbers of diverse patients being cared for in real-world clinical practice. Methods: The Alzheimer’s Association, American College of Radiology, American Society of Neuroradiology, the Department of Biostatistics, Brown University School of Public Health and the Critical Path Institute, along with international clinical, research and imaging experts, have launched the Alzheimer’s Network for Treatment and Diagnostics (ALZ-NET). ALZ-NET builds on successes of networks developed in other neurologic and systemic diseases, and leverages the groundwork from the IDEAS and New IDEAS studies. These have demonstrated large-scale, real-world data collection in dementia practice is feasible for addressing critical research questions regarding dementia care. Networks of dementia clinics and imaging facilities will provide ALZ-NET’s foundation and will expand over time to include a variety of clinical practices. Patients about to start or already receiving treatment with a novel FDA-approved AD therapy will be eligible to enroll in ALZNET. ALZ-NET is structured to collect a data set that aligns with patient care, agnostic of therapy and care setting, including, baseline demographic, medical, neurologic, genetic and biomarker data. Every 6–12 months, patients will be followed longitudinally with MMSE or MoCA (required), AD8 (optional), FAQ (required) and NPI-Q (optional). Trajectories for cognition, function and behavior over time will be evaluated, assessing the patient-specific predictors of response, including clinical response to individual drugs or combination of drugs. ALZ-NET will track all adverse events (CTCAE grade≥3), unexpected and serious adverse events. A central repository will collect baseline and longitudinal neuroimaging (MRI, PET). Existing databases will track health outcomes and resource utilization. Patients will be followed until withdrawal of consent, death or lost to follow-up. All data collection and sharing will be fully compliant with research participant protections, privacy and patient/provider autonomy. Results: ALZ-NET will collect longitudinal clinical and safety data for enrolled patients treated with novel FDA-approved AD therapies and will track long-term health outcomes (effectiveness and safety), associated with use in real-world settings. ALZ-NET aims to assess the clinical course of people from a variety of backgrounds and communities, to achieve representativeness beyond the populations historically enrolled in clinical trials. ALZ-NET has partnered with clinical sites providing care in diverse practice settings to serve as the network’s initial vanguard sites. These clinical sites are the first to enroll patients into the network and provide clinical and imaging data on the use of currently approved FDA therapy for AD. ALZ-NET continues to invite sites who already, or will, offer these therapies to their patients. Participating sites have multi-disciplinary clinical expertise and an infrastructure to support the use of novel FDA-approved AD therapies consistent with the safety monitoring outlined in applicable FDA approved labels. Aspects of a qualified participating site include: access to accredited and appropriate radiological services for diagnostic and safety brain imaging; access to infusion services; access to emergency services; and access to standard cognitive, behavioral, and functional assessments used in dementia care. Efforts have been made to minimize patient and site burden while still ensuring collection of a rigorous core dataset that can be used to answer critical research questions. ALZ-NET is designed to work collaboratively and in conjunction with affiliated studies conducted by academia, industry, federal or ALZ-NET project teams. Affiliated studies could be designed to answer broad or specific questions regarding treatment. Data are being collected in a regulatory grade manner to maximize the potential for how data can be used and applied for all stakeholders. Conclusions: ALZ-NET is actively engaging and expanding the network of sites, allowing for the collection of real-world data from enrolled patients receiving novel FDA-approved AD therapies. It is designed to answer questions for therapies available now and those on the horizon including: tracking longitudinal change of treatment (or treatments); identifying responders and non-responders or predictors of response and non-response to specific therapeutics; and comparing aggregated data on outcomes across mechanisms of action and within classes of therapeutics. Over time, ALZ-NET will be used to study clinical outcomes and resource utilization using claims and EHR. ALZ-NET will be a resource for evidence gathering, information sharing, and education across clinical and research communities, encouraging innovative, inclusive research and supporting opportunities to improve care. Note: This abstract is submitted on behalf of the ALZ-NET Project Team: Ali Atri, Banner Sun Health Research Institute; Jerome Barakos, Sutter Health California; Sharon Brangman, SUNY Upstate Medical University; Kirk Daffner, Harvard Medical School; Rebecca M. Edelmayer, Alzheimer’s Association; Constantine Gatsonis, Brown University School of Public Health; Gregory Jicha, University of Kentucky; John Jordan, American College of Radiology / American Society of Neuroradiology / Providence Little Company of Mary Medical Center-Torrance; Jennifer Lingler, University of Pittsburgh School of Nursing; Oscar Lopez, University of Pittsburgh School of Medicine; Andrew W. March, American College of Radiology; Anton P. Porsteinsson, University of Rochester School of Medicine; Katherine Possin, Memory and Aging Center, University of California, San Francisco; Klaus Romero, Critical Path Institute; Stephen Salloway, Butler Hospital / Warren Alpert Medical School of Brown University; Mary Sano, Mount Sinai School of Medicine; Sudhir Sivakumaran, Critical Path Institute; Heather Snyder, Alzheimer’s Association; Rade B. Vukmir, Alzheimer’s Association; Christopher Whitlow, Wake Forest School of Medicine / American College of Radiology; Consuelo Wilkins, Vanderbilt University Medical Center; Charles Windon, Memory and Aging Center, University of California, San Francisco. Disclosures: Maria C. Carrillo is a full-time employee of the Alzheimer’s Association. She has a daughter that is a full-time graduate student in the USC Neuroscience program. Gil Rabinovici receives research support from Avid Radiopharmaceuticals, GE Healthcare, Genentech, and Life Molecular Imaging; served on SAB for Eli Lilly, Genentech, and Roche; serves on DSMB for Johnson & Johnson; is an Associate Editor for JAMA Neurology. Michael Rafii receives research support from Eli Lilly and Eisai Inc.; chairs DSMBs for Alzheon and Biohaven; serves on the SAB for Embic; provides consultation to AC Immune SA and Keystone Bio.
LB8- TOP LINE DATA OF ANAVEX®2-73 (BLARCAMESINE) RANDOMIZED, DOUBLE-BLIND, MULTICENTER, PLACEBO-CONTROLLED PHASE 2B/3 IN PATIENTS WITH EARLY ALZHEIMER’S DISEASE (AD). S. Macfarlane 1, T. Grimmer 2, T. O’brien 3, E. Hammond 4, W. Kaufmann 4, E. Fadiran 4, C. Missling 4(1. Hammoncare — Melbourne (Australia), 2. THU Munich — Munich (Germany), 3. Monash University, Alfred Health — Melbourne (Australia), 4. Anavex Life Sciences — New York (United States))
Background: ANAVEX®2-73 (blarcamesine) is a novel, oral, investigational sigma-1 receptor (SIGMAR1) agonist with multimodal activity with previously demonstrated dose-dependent target engagement by positron emission tomography (PET) imaging as well as reduction of pathological inflammation, amyloid beta, and tau. A prior Phase 2a ANAVEX®2-73 study in patients with Alzheimer’s disease (AD) (1) demonstrated reduction in rates of cognitive (MMSE) and functional (ADCS-ADL) decline in participants with higher ANAVEX®2-73 plasma concentration (doses up to 50 mg once daily). This effect was also observed in the cohort carrying the common SIGMAR1 wild type (WT) gene variant (80–84% of worldwide population), which would be an additional confirmation of the biological relevance of the SIGMAR1 activation (2). Furthermore, in a transcriptomics analysis (RNAseq) of a randomized, placebo-controlled dementia study in patients with Parkinson’s Disease Dementia (PDD), levels of pathways and genes, which are down-regulated in AD pathology were significantly increased by the therapeutic effect of ANAVEX®2-73 (p<0.005) (3). Objectives: The ANAVEX®2-73-AD-004 study was an international, randomized, double-blind, multicenter, placebo-controlled Phase 2b/3 clinical study in participants with early AD, which included biomarkers of both drug response and AD pathology (4). Here we report efficacy over 48 weeks of ANAVEX®2-73 administration on reduction in cognitive (ADAS-Cog) and functional (ADCS-ADL) decline as well as the effect of the common SIGMAR1 wild type (WT) gene variant on efficacy outcome measures. Methods: 509 patients with early AD were randomized 1:1:1 to oral target doses of 30 mg, 50 mg ANAVEX®2-73 or placebo, once daily. The primary endpoint was reduction in cognitive and functional decline, assessed from baseline, over the 48-week period as evaluated by co-primary efficacy endpoints ADAS-Cog and ADCS-ADL in participants receiving ANAVEX®2-73 compared to placebo. Key secondary efficacy endpoint was reduction in cognitive decline as measured by the CDR-SB from baseline to end of treatment (48 weeks). Safety of ANAVEX®2-73 in this study was also evaluated. Results: The top line results of the study are expected to be available around the time of the CTAD 2022 conference. Conclusions: The conclusions of the study are expected to be available around the time of the CTAD 2022 conference. References: 1. Hampel et al. A precision medicine framework using artificial intelligence for the identification and confirmation of genomic biomarkers of response to an Alzheimer’s disease therapy: Analysis of the blarcamesine (ANAVEX2-73) Phase 2a clinical study. Alzheimer’s Dement. 2020;00:1–14; 2. Excluding the cohort carrying the SIGMAR1 rs1800866 gene variant (16%–20%): https://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=rs1800866; 3. https://www.anavex.com/_files/ugd/79bcf7_c5813c517d9f4ca5aacbeb719508827a.pdf; 4. ClinicalTrials.gov Identifiers: NCT03790709, NCT02756858.
LB9- HIGHER SENSITIVITY AMYLOID-PET DETECTION OF THE EARLIEST FOCAL BETA-AMYLOID ACCUMULATION USING SPATIAL EXTENT. M.E. Farrell 1, E.G. Thibault 1, J.A. Becker 1, J.C. Price 1, K. Gong 1, A.P. Schultz 1, M.J. Properzi 1, R.F. Buckley 1,2, H.I.L. Jacobs 1, B.J. Hanseeuw 1,3, R.A. Sperling 1,2, K.A. Johnson 1,2(1. Massachusetts General Hospital — Boston, Ma (United States), 2. Brigham & Women’s Hospital — Boston, Ma (United States), 3. Cliniques Universitaires Saint-Luc, Université Catholique de Louvain — Brussels (Belgium))
Background: The key to the prevention of Alzheimer’s disease may lie with intervening at the earliest possible point in the pathological cascade, before neurodegeneration at the earliest signs of beta-amyloid (Aβ). The current gold standard for measuring Aβ deposits in the brain relies on average measures of global neocortical burden using PET, which fails to detect earlier focal Aβ deposits and likely leaves a limited time window before neurodegeneration. Importantly, it may be possible to reliably measure Aβ below global AbPET thresholds by changing two key aspects of Aβ-PET measurement: where we look and how we measure. Prior studies indicate focusing on early-accumulating regions can aid detection of early focal Aβ, but heterogeneity across studies in where Aβ begins accumulating has impeded the development of a reliable and generalizable early Aβ PET aggregate. In the present study, we sought to allow greater flexibly by incorporating more early Aβ regions, aggregating across all regions that are reliably associated with future Aβ-PET accumulation using longitudinal PIB-PET from initially globally Aβ- adults from the Harvard Aging Brain Study (HABS). However, while an expanded early Aβ aggregate may allow greater flexibility, doing so may also dilute the early focal signals we aim to detect. To avoid this dilution, we shifted our summary Aβ from the standard measure of average burden to a measure of the spatial extent of Aβ deposits. We hypothesized that measuring the number of regions with elevated Aβ within a larger set of reliable early Aβ regions would allow for greater sensitivity to focal early Aβ deposits than requiring the average burden across the entire aggregate to surpass a detection threshold. Objective: To demonstrate the improved sensitivity and specificity of measuring spatial extent within a set of reliable early Aβ regions to predict which individuals will progress to global Aβ positivity in the future and assess its potential for improved targeting of individuals with early Aβ in clinical trials. Methods: Longitudinal Pittsburgh Compound B (PIB)-PET data from 160 clinically normal (CN) older adults from HABS with globally Aβ- PET scans at baseline were used to identify all regions for which baseline elevated PIB was not significantly associated with future local decline (a sign of vulnerability to signal noise) and was significantly associated with increasing future global PIB slope. The mean burden and spatial extent in different potential aggregates based on these results were tested for their ability to predict progression to global Aβ+ in 3 years using receiver operate characteristic (ROC) curve analysis and beyond (up to 8 years) using survival analysis. The replicability and generalizability of these results were validated in an external sample of 208 initially globally Aβ- CN older adults from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Power analyses determined the number of the individuals that would need to be screened to enroll 200 participants to detect a 50% change in Ab burden over 1 year with 80% power based on 4 possible approaches to defining the lower bound for early Aβ deposits: 1) the current global Aβ threshold, 2) a lowered global Aβ threshold, 3) mean burden in the optimized early Aβ aggregate, and 4) spatial extent in the global Aβ burden. Results: A large aggregate of reliable and predictive regions (bilateral medial frontal/parietal, cingulate, lateral parietal/occipital and left lateral frontal/parietal) developed within HABS provided high sensitivity in predict progression to global positivity while maintaining high specificity in HABS (SE=.88, SP=.97) and ADNI (SE=1.00, SP=.91), though its slight advantage in specificity over other large aggregates is small compared with the 2.5x increased rate of early detection conferred by switching from a measure of mean burden to extent (SEextent=.88, SEmean=.35). Using Spatial extent in the early Aβ aggregate resulted in a 73% reduction in the number of individuals that would need to the screened relative to a standard global Aβ threshold (nstandard=4340, nextent=1193), a 44% reduction relative to a lowered global threshold (n = 2146) and 51% relative to using the mean burden in the early Aβ aggregate (n=2543). Conclusion: Our findings demonstrate that measures of spatial extent across a broad set of neocortical regions are far more sensitive to detect early Aβ than traditional measures of average burden in two independent samples. These extent measures display great potential for improved targeting of early Aβ in both clinical trials and research into the earliest stages of amyloidosis and AD pathogenesis.
LB10- SAMPLE SIZE ESTIMATES FOR PRECLINICAL AD INTERVENTION TRIALS BASED ON WISCONSIN REGISTRY FOR ALZHEIMER’S PREVENTION LONGITUDINAL PET AMYLOID, PLASMA P-TAU217, AND COGNITIVE ASSESSMENT DATA. R. Langhough Koscik 1, D. Norton 1, T. Betthauser 1, L. Du 1, E. Jonaitis 1, K. Cody 1, B. Hermann 1, K. Mueller 1, R. Chappell 1, B. Christian 1, S. Janelidze 1, N. Mattsson-Carlgren 1, O. Hansson 1, S. Johnson 1(1. University of Wisconsin SMPH — Madison (United States))
Background: Data increasingly show that progression from amyloid onset to Alzheimer’s disease (AD)-dementia spans many years. Our study and others have demonstrated that preclinical cognitive decline is related to brain amyloid burden and how long amyloid has been present. Thus, an ideal intervention window for amyloid therapies may be at the earliest signs of amyloid accumulation, before cognitive decline has reached clinical impairment. This study uses longitudinal data from the Wisconsin Registry for Alzheimer’s Prevention (WRAP) to inform sample size estimates for preclinical AD trial designs aiming to slow amyloid accumulation and slow or delay tau accumulation and cognitive decline. Methods: WRAP, a longitudinal cohort study of persons who were without dementia at cognitive baseline (mean(sd) age=54(6)), is enriched for AD risk via oversampling for parental AD history (∼72%). WRAP participants complete biennial cognitive assessments and blood draws; a subset complete positron emission tomography (PET) amyloid scans. PET amyloid burden was quantified from eight bi-lateral ROI’s using the tracer 11C-Pittsburgh Compound B to obtain a global PiB DVR value from each scan; DVR was then used to get estimated amyloid onset age and imputed DVR’s corresponding to ages at cognitive and plasma sampling (Betthauser et al, 2022). Longitudinal plasma P-tau217 was assayed in a PET subset (Meso Scale Discovery platform). To assess the effect of baseline amyloid on expected progression on various trial-relevant outcomes, we stratified observations into groups defined by PiB DVR at each assessment age. Groups (and corresponding DVR ranges) included amyloid negative (Aneg; DVR<1.13), sub-threshold-to-low-amyloid-positive (subApos; DVR≈[1.13, 1.2); and amyloid-positive (Apos; Global PiB DVR≥1.2). Visit-level data were included in estimates for each group if: the DVR was in that group’s range; the participant was between 50–80 years and cognitively unimpaired (CU) at first visit in that group; and the participant had at least two visits for the outcome of interest. A single participant could in this way contribute in more than one group. We used linear mixed effects models to estimate group-specific slope and error for Global PiB DVR, plasma P-tau217 and a set of cognitive composites and individual tests considered sensitive to AD-related change (fixed effect: years since baseline measurement; random within-person intercepts and slopes). The fixed effects slope estimates were then used to estimate sample sizes needed to detect a range of possible treatment effects for PiB, plasma and cognitive outcomes for trials targeting CU subApos samples and CU Apos samples (power=80%; assuming 3-year PiB and plasma follow-up and 6-year cognitive follow-up). Treatment effects were calculated as percent change relative to slope estimates within the subApos and Apos groups (i.e., representing attenuation towards 0 in treatment group); for the subApos group, we also calculated sample sizes for treatment effects relative to the Aneg slopes (representing attenuation to normal preclinical age-related change in the treatment group). We report samples sizes needed per treatment arm (25% treatment effect) for the five cognitive outcomes with lowest estimates from a set that included three cognitive composites and eight individual tests. Results: In the Aneg sample, longitudinal data from 109, 93, and 330 participants, respectively, contributed to slope estimates for PiB, plasma, and cognitive outcomes (median baseline ages by outcome:62, 62, and 55). In the subApos sample, longitudinal data from 13, 28, and 68 participants contributed to PiB, plasma, and cognitive estimates (median baseline ages: 65, 65, and 61). Sample sizes needed per treatment arm to detect a 25% reduction in worsening relative to 0/aNeg are, by cognitive outcome: Digit Symbol Substitution test, 441/2697; Harvard Aging Brain Study processing speed composite (HABS-PS, Trails A and Digit Symbol), 1373/2573; WRAP 3-test preclinical Alzheimer’s Cognitive composite (PACC3; RAVLT learning, Logical Memory II, Digit Symbol Substitution), 1903/8716; WRAP 5-test PACC (PACC3 plus MMSE and CFL fluency), 2015/3242; and log(Trails B) time, 6738/5206. In the Apos sample, longitudinal data from 20, 46, and 61 participants, contributed to estimates for PiB, plasma, and cognitive outcomes (median baseline ages 66, 64, 59). Sample size needed per treatment arm to detect a 25% reduction in worsening relative to 0 are, by outcome: PiB, 117; plasma p-tau217, 377; Digit Symbol, 190; PACC5, 843; PACC3, 905; Logical Memory II, 1545; and HABS-PS, 1960. For both target samples, powering to detect such cognitive change corresponds to >80% power to detect PiB and plasma p-tau217 effects of ∼10% or higher. Conclusion: Sample size requirements for cognitive outcomes vary widely depending on the cognitive measure, the baseline amyloid range of trial participants and whether treatment effects are based on attenuation to zero or to trajectory estimates of those who are amyloid negative (i.e., presumably healthy controls). Our estimates suggest that a processing speed composite or the Digit Symbol task contributing to it may out-perform more commonly used preclinical AD composites. Studies that are adequately powered to detect slowing in preclinical cognitive decline will be adequately powered to detect clinically meaningful PiB and plasma P-tau217 treatment effects.
LB11- CEREBROSPINAL FLUID BIOMARKER EFFECTS FROM A FIXED-DOSE COMBINATION OF SODIUM PHENYLBUTYRATE AND TAURURSODIOL IN ALZHEIMER’S DISEASE: RESULTS FROM THE PEGASUS TRIAL. S.E. Arnold 1,2, N. Knowlton 3, V.J. Williams 4, J.M. Burns 5, M. Crane 6, A.J. Mcmanus 1, S.N. Vaishnavi 7, Z. Arvanitakis 8, J. Neugroschl 9, K. Bell 10, B.A. Trombetta 1, B.C. Carlyle 11, P. Kivisäkk 2,12, R.E. Tanzi 13,14, K. Leslie 15,16(1. Department of Neurology, Massachusetts General Hospital, Boston, MA, USA — Boston (United States), 2. Harvard Medical School, Boston, MA, USA — Boston (United States), 3. Pentara Corporation, Millcreek, UT, USA — Millcreek (United States), 4. Department of Medicine, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, WI, USA — Madison (United States), 5. University of Kansas Alzheimer’s Disease Center, Kansas City, KS, USA — Kansas City (United States), 6. Genesis Neuroscience Clinic, Knoxville, TN, USA — Knoxville (United States), 7. Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA — Philadelphia (United States), 8. Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA — Chicago (United States), 9. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA — New York (United States), 10. Department of Neurology, Columbia University, New York, NY, USA — New York (United States), 11. Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, England, United Kingdom — England (United Kingdom), 12. Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA — Boston (United States), 13. Harvard Medical School, Boston, MA, USA — Boston (United Kingdom), 14. Department of Neurology, Genetics and Aging Research Unit, McCance Center for Brain Health, Massachusetts General Hospital, Harvard University, Boston, MA, USA — Boston (United States), 15. Amylyx Pharmaceuticals, Inc., Cambridge, MA, USA — Cambridge (United States), 16. Present address: Division of Biology and Biological Engineering Graduate Program, California Institute of Technology, Pasadena, CA, USA — Pasadena (United States))
Background: An oral, fixed-dose combination of sodium phenylbutyrate and taurursodiol (PB and TURSO) is hypothesized to simultaneously mitigate endoplasmic reticulum stress and mitochondrial dysfunction, pathways relevant in neurodegenerative diseases. Oral PB and TURSO was shown to significantly slow functional decline and prolong survival in a randomized, placebo-controlled trial in amyotrophic lateral sclerosis (ALS). Preclinical studies have shown activity of PB and TURSO individually and in combination in animal models of Alzheimer’s disease (AD). PEGASUS (NCT03533257) was the first-in-indication clinical trial designed to evaluate the safety and biologic activity of PB and TURSO in AD, with an aim of informing the design of future studies of PB and TURSO in AD and other neurodegenerative diseases. Objective: Report final safety and full biomarker results from PEGASUS. Methods: PEGASUS was a phase 2, multicenter, randomized, double-blind, placebo-controlled trial enrolling adults aged 55 to 89 years with mild cognitive impairment or mild to moderate dementia (baseline Montreal Cognitive Assessment [MoCA] score ≥8) with supporting biomarkers of AD pathology. Participants were randomized to receive oral PB and TURSO or matching placebo for 24 weeks and were permitted to continue on stable dosing regimens of standard-of-care AD medications. The primary outcome of the study was safety and tolerability of PB and TURSO. The secondary outcome was efficacy assessed using a global statistical test for change from baseline to week 24 combining 3 univariate end points: Mild/Moderate Alzheimer’s Disease Composite Scale (MADCOMS), Functional Activities Questionnaire, and hippocampal volume on volumetric magnetic resonance imaging. Exploratory outcome measures consisted of cerebrospinal fluid (CSF) biomarkers, namely, changes from baseline in core AD biomarkers (amyloid beta species [Aβ42, Aβ40, and Aβ42/Aβ40 ratio], total tau [t-tau], and phospho-tau 181 [p-tau]) as well as biomarkers of neurodegeneration (neurofilament light chain, fatty acid binding protein-3 [FABP3]), synaptic integrity (neurogranin), inflammation and immune modulation (interleukin [IL]-6, IL-8, IL-15, monocyte chemoattractant protein-1, glial fibrillary acidic protein, chitinase 3-like protein 1 [YKL-40]), neurovascular/neuropil remodeling (matrix metalloproteinase-10), oxidative stress (8-hydroxy-2’-deoxyguanosine [8-OHdG]), and metabolic dysregulation in the brain (24S-hydroxycholesterol, leptin, soluble insulin receptor). Based on feasibility, a sample size of approximately 100 participants was chosen. Mean between-group differences in change from baseline at week 24 were compared between active and placebo arms for all efficacy outcomes and declared significant if P<.05 without multiplicity adjustment. No hypothesis testing was performed for safety variables. Results: A total of 95 participants with an average age of 70.7 years were randomized (PB and TURSO, n=51; placebo, n=44). Approximately 67% of participants were receiving donepezil and 37% participants were receiving memantine at baseline for cognitive impairment. Baseline demographics and biomarker values were generally well matched between the groups; however, participants randomized to PB and TURSO had evidence of greater baseline cognitive impairment based on mean Alzheimer’s Disease Assessment Scale—Cognitive Subscale, MoCA, and MADCOMS scores (all P≤.007 vs placebo). No new safety signals were observed compared to the previous study in ALS, despite the older participant population in PEGASUS. Adverse events were predominantly gastrointestinal. This study was not powered to see differences in clinical efficacy end points, and no significant between-group differences were observed for the primary or secondary clinical end points. However, mean (SD) changes from baseline to week 24 directionally favored PB and TURSO versus placebo for the core AD biomarkers Aβ42/Aβ40 ratio (+0.004 [0.004] vs −0.005 [0.004]; P=.005), t-tau (−64.9 [15.5] vs +8.82 [15.2] pg/mL; P<.0001), and p-tau (−14.6 [3.0] vs −0.27 [2.9] pg/mL; P=.0002), as well as FABP3 (−344.6 [85.9] vs +102.9 [80.6] pg/mL; P=.0004), neurogranin (−81.2 [16.5] vs −8.3 [15.9] pg/mL; P= .0003), IL-15 (−0.02 [0.08] vs +0.25 [0.07] pg/mL; P=.01), YKL-40 (−14,635.4 [3954.0] vs +1507.9 [3776.8] pg/mL; P=.004), and 8-OHdG (+0.31 [0.16] vs −0.13 [0.15]; P=.006). Other biomarkers did not show significant mean between-group differences. Conclusions: Compared with placebo, PB and TURSO significantly improved CSF amyloid, tau, and neurodegeneration markers and other biomarkers relevant to AD pathophysiology. Results from PEGASUS provide the first-in-human evidence for a treatment effect of PB and TURSO on AD pathology and pathways of inflammation, synaptic function, oxidative stress, and neurodegeneration, complementing preclinical studies that showed a biologic effect for PB and TURSO both individually and in combination in AD models. Taken together, these findings may be used to inform the design of subsequent trials and provide support for further clinical development of PB and TURSO for AD and other neurodegenerative diseases. Disclosures: Conflicts of interest will be listed in the presentation at CTAD.
LB12- USE OF A BLOOD-BASED BIOMARKER TEST IMPACTS CLINICAL DECISION MAKING AMONG NEUROLOGISTS EVALUATING PATIENTS WITH SYMPTOMS OF COGNITIVE IMPAIRMENT. J. Braunstein 1, M. Monane 1, K. Johnson 2, B.J. Snider 3, R. Scott Turner 4, J. Drake 5, D. Jacobs 6, J. Ortega 1, J. Henderson 1, T. West 1(1. C2N Diagnostics — St Louis (United States), 2. Duke University — Durham (United States), 3. Washington University — St Louis (United States), 4. Georgetown University — Washington (United States), 5. Lifespan — Providence (United States), 6. Neurological Services of Orlando — Orlando (United States))
Background: A critical need exists for early, accurate diagnosis of Alzheimer’s disease (AD) to guide patients to current and emerging anti-AD therapies as well as to rule out AD to allow for other diagnostic considerations. There is also a need for safe, less resource-intensive, easily accessible, and broadly available tests that identify the presence or absence of brain amyloid plaques, a pathologic hallmark of AD. Blood-based biomarkers (BBMs) offer advantages over imaging and cerebrospinal fluid (CSF) measurements, potentially fulfilling these unmet needs. The PrecivityAD™ blood test quantifies plasma concentrations of amyloid beta 42 and 40 (Aβ42 and Aβ40) and determines the presence of apolipoprotein E (ApoE)-specific peptides to establish the APOE genotype. The Aβ42/40 ratio + APOE genotype + patient’s age are used to calculate the Amyloid Probability Score (APS), which is the test result, by way of a validated regression model. The PrecivityAD blood test has demonstrated 92% sensitivity and 77% specificity in a large trial incorporating patients from the Plasma Test for Amyloidosis Risk Screening (PARIS) study (NCT02420756), a prospective add-on to the Imaging Dementia-Evidence for Amyloid Scanning study, as well as the MissionAD study (BCT02956486). While clinical validity for the PrecivityAD blood test has been demonstrated, this study focuses on clinical decision making associated with results of BBM testing. Objectives: The study objective is to assess patient selection and score interpretation of the PrecivityAD blood test and the APS as well as post-test changes in diagnostic certainty and management of symptomatic patients being evaluated for AD or other causes of cognitive decline. Methods: The Quality Improvement PrecivityAD (QUIP I) Clinician Survey (NCT05477056) is a prospective, single cohort study conducted at outpatient sites among patients 60 years and older presenting to a neurologist with signs or symptoms of mild cognitive impairment (MCI) or dementia. All patients received PrecivityAD blood testing and an APS result. The APS reflects the likelihood that a patient, on a scale of 0–100, will be amyloid positive on an amyloid PET scan, with low APS (0–35), intermediate APS (36–57), and high APS (58–100) as established score categories representing low, intermediate, and high likelihood of amyloid PET positivity, respectively. Physician surveys focused on quality improvement were conducted after receipt of the test results. Collected data included subject demographics, APS result, diagnostic certainty pre- and post-blood testing, and planned drug therapy. Results: Participating clinicians from 13 sites submitted 272 surveys between March 2021 and July 2022. The surveys reflected patients with a median age of 73 years old, 56% female, and 90% white. The mean APS was 45 (range 0–100): 46% (n=125) patients had low scores, 14% (n=39) had intermediate scores, and 40% (n=108) had high scores. The mean probability of AD diagnosis was rated by physicians as 63% pre-test and 52% post-test (p<0.0001). The mean probability of physicians’ estimates of AD changed pre-test to post-test from 56% to 20% (low APS group), 63% to 47% (intermediate APS group), and 70% to 89% (high APS group) (p<0.0001). Anti-AD drug therapy was noted in 50% of patients pre-test and 57% of patients post-test; however, 25% (69/272) of patients had planned changes in anti-AD drug therapy. Of note, 85% (33/39) of patients with increased drug therapy were in the high APS group, and 93% (28/30) of patients with decreased drug therapy were in the low APS group (p<0.0001). Conclusions: In summary, the PrecivityAD blood test showed clinical utility in its association with physician decision-making around diagnostic certainty and drug therapy management in patients evaluated for mild cognitive impairment or dementia, with 86% of patients deriving clinically useful low or high APS results. Low APS patients were evaluated by neurologists to have lower AD likelihood post-test and were less likely to be managed with anti-AD drugs, consistent with ruling out AD. High APS patients were judged by neurologists to have higher AD likelihood post-test and were more likely to be managed with anti-AD drugs, consistent with ruling in AD. While previous studies have demonstrated that the use of amyloid PET and CSF biomarkers have been associated with changes in diagnostic confidence of AD as well as changes in anti-AD drug therapy, this study is one of the first to show clinical management changes using a BBM test assessing the presence or absence of brain amyloidosis among symptomatic patients being evaluated for AD or other causes of cognitive decline.
LB13- PHASE 1 PHARMACOKINETIC AND CNS TARGET ENGAGEMENT PROPERTIES OF THE ORALLY ADMINISTERED O-GLCNACASE INHIBITOR ASN51 IN HUMANS. R. Schubert 1, R. Pokorny 1, B. Permanne 1, P. Fang 1, V. Teachout 1, M. Nény 1, S. Ousson 1, J. Hantson 1, A. Sand 1, R. Ahmed 1, M. Schneider 1, J.F. Stallaert 1, A. Quattropani 1, E. Yuen 1, D. Beher 1(1. Asceneuron — Lausanne (Switzerland))
Background: Inhibition of the O-linked-β-N-acetylglucosaminidase (OGA) enzyme blocks the removal of O-linked GlcNAc carbohydrate moieties from the hydroxyl groups of serine and threonine residues on target proteins. One protein that is markedly O-GlcNAcylated in response to OGA inhibition is the microtubule associated protein tau. Tau is best known for its central role in the onset and progression of neurofibrillary tangle (NFT) pathology in Alzheimer’s disease (AD) and related forms of dementia. The O-GlcNAcylation of tau proteins prevents their incorporation into insoluble NFTs and maintains tau in a soluble state (O-tau). The ability of orally administered OGA inhibitors to slow the development of neurofibrillary tangle pathology in vivo across multiple preclinical tauopathy models has raised the visibility and potential of this new therapeutic class. Recent work has further shown that O-GlcNAcylation slows the aggregation of α-synuclein proteins by increasing the amount of O-synuclein, with therapeutic implications for Parkinson’s disease and related disorders. ASN51 is a novel, oral, brain-penetrant, active-site-directed, reversible OGA inhibitor that is being evaluated as a clinical candidate for the treatment of Alzheimer’s and Parkinson’s disease. Objectives and Methods: Two Phase 1 studies examined the human safety, tolerability, pharmacokinetic, pharmacodynamic and CNS target engagement properties of ASN51 in healthy volunteers. The first study, ASN51-101, was a randomized, double-blind, placebo-controlled safety, tolerability, pharmacokinetic and pharmacodynamic study of oral ASN51 in healthy young volunteers administered single doses of 20 mg and 50 mg and in healthy elderly volunteers administered ten daily doses of 20 mg. The second study, ASN51-102, was an open-label OGA positron emission tomography (PET) study in healthy adult volunteers administered two single doses of 5 to 15 mg, to determine the relationship between plasma concentration and brain target engagement of ASN51. Results: ASN51 was safe and well tolerated throughout the two clinical studies, reaching meaningful plasma and CSF concentrations. Exposures increased in proportion to dose with plasma half-lives in the multiple dose healthy elderly cohort ranging from 38 to 48 hours in steady state. The O-GlcNAcylation of PBMC proteins after ASN51 administration was measured as a surrogate biomarker of O-tau and was >2-fold the baseline value 8 hours after administration of a single 20 mg dose. The OGA PET study indicated that single daily doses of 10 mg can yield OGA enzyme occupancies >95% occupancy at trough. Conclusions: Altogether, the Phase 1 data suggest that daily doses of 10 mg or lower will maintain a therapeutic level of OGA inhibition with elevated O-tau and O-synuclein throughout the entire day. ASN51 thus demonstrates safety, pharmacokinetic and pharmacodynamic target engagement properties that are ideal for a once-daily, low dose CNS therapy. Based on ASN51’s optimal safety and human pharmacology profile in Phase 1, ASN51 is being advanced to a Phase 2A tau PET proof of mechanism biomarker study in early symptomatic AD patients.
LB14- ANALYSIS OF 15 SOFTWARE PIPELINES FOR VALIDATION OF [18F]FLORBETABEN PET QUANTITATION. A. Jovalekic 1, N. Roe-Vellve 1, N. Koglin 1, M. Lagos Quintana 1, A. Nelson 2, M. Diemling 3, J. Lilja 3, J.P. Gomez Gonzalez 4, V. Dore 5, P. Bourgeat 5, A. Whittington 6, R. Gunn 6, A. Stephens 1, S. Bullich 1(1. Life Molecular Imaging — Berlin (Germany), 2. MIM Software — Cleveland (United States), 3. Hermes Medical Solutions — Stockholm (Sweden), 4. QuBiotech — A Coruna (Spain), 5. CSIRO — Brisbane (Australia), 6. Invicro — London (United Kingdom))
Background: Amyloid positron emission tomography (PET) with [18F]florbetaben is an established tool for detecting Aβ deposition in the brain in vivo and has been approved for routine clinical use since 2014 as Neuraceq® based on visual assessment (VA) of PET scans. Quantitative measures are however commonly used in the research context, with many of the available PET software packages capable of calculating amyloid burden both on a regional and a composite level, allowing continuous measurement of amyloid burden in addition to the approved dichotomous VA. Objectives: This study aimed to provide scientific evidence of the robustness and additional value of florbetaben PET quantification, with a focus on Centiloid-based analysis. The diagnostic performance (i.e., sensitivity and specificity) of quantification against the histopathological confirmation of Aβ load was estimated and compared to the effectiveness of the approved VA method. Additionally, the concordance between visual and quantitative evaluation of florbetaben PET scans was assessed. The reliability and comparability of the different analytical pipelines was further tested. Methods: This is a retrospective analysis of florbetaben PET images that had been acquired in previous clinical trials. The study population consisted of 589 subjects with at least one available florbetaben PET scan. Florbetaben PET scans were quantified with 15 analytical pipelines using nine software packages (MiMneuro, Hermes BRASS, Neurocloud, Neurology Toolkit, statistical parametric mapping (SPM8), PMOD Neuro, CapAIBL, non-negative matrix factorization (NMF), AmyloidIQ (Whittington et al., 2019)) that used several metrics to estimate Aβ load (SUVR, Centiloid, amyloid load and amyloid index). Six analytical methods reported Centiloid (MiMneuro (Piper et al., 2014), standard centiloid pipeline (Klunk et al., 2015, Rowe et al., 2017), Neurology Toolkit, SPM8 (PET-only), CapAIBL (Bourgeat et al., 2018), NMF (Bourgeat et al., 2021). For some software packages, different analytical methods were tested using different reference regions, for example, without using the T1-weighted MRI scan. All the scans were quantified in batch mode to minimize operator intervention. The operators were different for each software package and blinded to the diagnosis of subjects, demographics, visual PET assessment, histopathology results and all other clinical data. All results were quality controlled. Results: The mean sensitivity, specificity and accuracy was 96.1±1.6%, 96.9±1.0% and 96.4±1.1%, respectively, for all quantitative methods tested. Centiloid-based approaches yielded a comparable mean sensitivity, specificity and accuracy of 96.1±1.6%, 97.4±1.2% and 96.7±1.2%, respectively. The mean percentage of agreement between binary quantitative assessment across all 15 pipelines and visual majority assessment was 92.4±1.5%. For the Centiloid-based sub-analysis the mean percentage of agreement with visual majority assessment was 93.2±0.4%. Substantial agreement was observed across software packages using different measures. Intra-software reliability based on re-analysis of selected scans (n=84) ranged between R2=0.98 and 1.00. Conclusion: Results from this retrospective analysis demonstrate that software quantification methods, for example Centiloid analysis, can complement visual assessment of florbetaben PET images. Such robust, validated methods could enable readers to augment their visual analysis with optional quantitative tools. Adjunct use of quantification software tools could be beneficial for newly trained or inexperienced operators in instances when images are visually assessed with relatively low confidence, or when amyloid levels of patients are close to «pathology» thresholds, or in longitudinal studies for studying amyloid accumulation or removal. Based on this study, quantification of [18F]florbetaben PET as an adjunct to visual assessment was recently approved by the European Medicines Agency (EMA) in the EU for Neuraceq®. References: Bourgeat, P., et al., Implementing the centiloid transformation for (11)C-PiB and beta-amyloid (18)F-PET tracers using CapAIBL. Neuroimage, 2018. 183: p. 387–393. Bourgeat, P., et al., Non-negative matrix factorisation improves Centiloid robustness in longitudinal studies. Neuroimage, 2021. 226: p. 117593. Klunk, W.E., et al., The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement, 2015. 11(1): p. 1–15 e1-4. 27. Piper, J., A. Nelson, and A. Javorek. Evaluation of a Quantitative Method for Florbetaben (FBB) PET Using SUVR. in EANM. 2014. Rowe, C.C., et al., (18)F-Florbetaben PET beta-amyloid binding expressed in Centiloids. Eur J Nucl Med Mol Imaging, 2017. 44(12): p. 2053–2059. Whittington A, and Gunn RN. Amyloid Load: A More Sensitive Biomarker for Amyloid Imaging. J Nucl Med. 2019. 60(4):536-540.
LB15- RESULTS FROM A CLINICAL STUDY OF AN ANTI-GALECTIN-3 MONOCLONAL ANTIBODY IN PATIENTS WITH MODERATE TO SEVERE ALZHEIMER’S DISEASE. D. Sun 1, G. Haig 1, S. Rasool 1(1. Truebinding Inc — Foster City (United States))
Background: Galectin-3 has been reported to be highly expressed in Alzheimer’s disease (AD) brain tissues. Our studies elucidated its intrinsic ability of acting as glue to promote oligomerization of Abeta, pTAU and other amyloid proteins in vitro. It’s antagonist monoclonal antibodies showed dramatic cognition improvement and plaque reduction in AD mice after only two-week treatment Inhibition of Galectin-3 is a novel approach to the treatment of AD. The hypothesis is disease reversal, not halting disease progression. Gal-3 is a ubiquitous endogenous protein involved the pathology of certain neurodegenerative, metabolic, and immunologic disorders. TB006 is a humanized IgG4 type monoclonal antibody with high affinity and selectivity for Gal-3. Preclinical studies in Tg mice demonstrated dramatic cognitive improvement and plaque reduction with only two weeks of treatment. In a SAD study in healthy volunteers, doses of up 5000 mg (∼70 mg/kg) were safe and well tolerated. Dosing of the clinical lead antibody TB006 in a single ascending dose (SAD) study in healthy volunteers up to 5000mg (70mg/kg) was safe and well tolerated. This phase 1b/2a study was conducted in moderate to severe AD patients to assess the safety, tolerability, PK and efficacy of five weekly TB006 doses. Methods: This was a seamless Ph 1b/2a double-blinded, placebo controlled, multicenter study. AD patients with a screening MMSE <24 and without confounding neurologic or psychiatric disease were eligible. In Ph 1b, 3 groups (140 mg, 420 mg, 1000 mg) of 8 patients in sequential ascending fashion received either weekly TB006 (6) or placebo (2) infusions for 5 doses. In Ph 2a, 1 participants16 were to be randomized (1:1) to receive either TB006 (1000mgthe highest safe and tolerated dose from Part 1) or placebo weekly for 5 doses. Ph 2a used the clinical dementia rating -sum of boxes (CDR-SB) score as the primary endpoint. Other endpoints were the mini-mental state examination (MMSE), neuropsychiatric inventory (NPI), CDR battery and plasma and imaging (MRI/PET) biomarkers. Cognition testing was done at baseline and on Days 15, 364, 64, and 104. Safety assessments were conducted at each visit. The sample size provided 80% power to detect a mean difference between TB006 and placebo of 0.25 point at Day 104 on the CDR-SB. Results: 157 patients, including 24 in Part 1, were randomized at 15 US sites. ; Nine9 subjects prematurely discontinued. TB006 was safe and well tolerated at all dose levels. There were 9 severe adverse events (SAEs), including 1 death. None were related to TB006 treatment. Most other AEs were mild, sporadic and self-limiting. Patients in Group 3 (1000 mg), as well as all placebo patients in Ph 1a were included in the efficacy analysis. The primary endpoint was met. Patients receiving TB006 showed a dramatic0.9 point reduction on the CDR-SB score compared with placebo (p<0.015). Secondary efficacy endpoints were equally robust. Mean efficacy endpoint scores in the placebo group remained consistent throughout the observation period. Conclusion: TB006 demonstrated evidence of AD reversal in this short-term treatment study. TB006 was safe and well tolerated.
LB16- PHASE 1 PREVENTIVE ADJUVANTED TAU VACCINE, AV-1980R. S. Schneider 1, A. Ghichikyan 2, R. Alexander 3, H. Zetterberg 3, E. Reiman 3, D. Tosun 4, M. Agadjanyan 2(1. USC — Los Angeles (United States), 2. Institute for Molecular Medicine — Huntington Beach (United States), 3. Banner Alzheimer’s Institute — Phoenix (United States), 4. University of California San Francisco — San Francisco (United States))
Objectives: Results from active and passive immunotherapy in early Alzheimer’s patients demonstrate that monoclonal antibodies decrease Aβ and tau pathology. A recent report stated that treatment with anti-amyloid mAb lecanemab reduced cognitive decline by 0.45 CDR-SB points. This facilitates the shift from treatment to prevention, and aligns with a longstanding tenet that safe and immunogenic preventive Aβ and/or tau vaccines should be initiated in cognitively unimpaired participants with preclinical AD. Methods: GMP grade AV-1980R was manufactured. Edematous changes, meningeal changes, micro-hemorrhages and meningoencephalitis, and brain atrophy assessed by MRI will be analyzed. Anti-tau cellular and humoral responses will be assessed by ELISPOT and ELISA, respectively. Plasma Aβ42/40, P-tau181, P-tau217, and P-tau231, neurofilament light chain, GFAP will be measured by SIMOA technology. Results: This is a randomized, multicenter, double-blind, placebo-controlled, multiple ascending dose trial consisting of 64 cognitively unimpaired individuals at risk of MCI due to AD (preclinical) determined by PET scan and blood biomarkers to determine the safety and tolerability of AV-1980R/A at 20, 100, and 300 µg, i.m. doses. Participants are injected four times at 0, 4, 12, 36 weeks and followed up for a 44-week period. to determine the safety and tolerability of AV-1980R/A at 20, 100, and 300 µg, i.m. doses. Participants will be injected four times and followed up for 44-weeks. Primary outcome is Treatment-Emergent Adverse Events (TEAEs) or Serious Adverse Events (SAEs). Secondary outcomes are humoral and cellular immune responses and blood biomarkers. Conclusion: Passive mAb immunotherapy for cognitively unimpaired people is impractical due to the complexity and need for frequent administration of very high doses. Safe and immunogenic active vaccines are suitable candidates for preventing the accumulation of tau pathology and potentially delaying onset of illness. We are evaluating for the first-time preventive vaccine, AV-1980R/A targeting the phosphatase-activating domain (PAD) of pathological tau.
| 36471007 | PMC9734311 | NO-CC CODE | 2022-12-14 23:28:27 | no | J Prev Alzheimers Dis. 2022 Dec 3; 9(Suppl 1):8-50 | utf-8 | J Prev Alzheimers Dis | 2,022 | 10.14283/jpad.2022.96 | oa_other |
==== Front
Public Organiz Rev
Public Organization Review
1566-7170
1573-7098
Springer US New York
687
10.1007/s11115-022-00687-w
Article
Administrative Reforms in the Ghanaian Public Services for Government Business Continuity During the COVID-19 Crisis
http://orcid.org/0000-0002-5793-4184
Bawole Justice Nyigmah [email protected]
1
Langnel Zachariah [email protected]
2
1 grid.8652.9 0000 0004 1937 1485 Department of Public Administration and Health services Management, University of Ghana Business School, P. O. Box LG 78, Legon, Accra Ghana
2 grid.442315.5 0000 0004 0441 5457 2 University of education, , Winneba, Ghana
5 12 2022
116
3 11 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The COVID-19 pandemic caused significant disruptions to public service delivery but heightened citizens demand for services. We examined public sector reforms implemented in the Ghanian public sector to ensure public service continuity during the COVID-19 pandemic. Using content analysis and key informant interviews we found that reforms such as flexible working schedule, redesign of offices, directorates, and installations of equipment, online monitoring and assessment of targets, and conducting services online were instrumental in ensuring the continuity of government business. We recommend that public sector managers should allocate adequate resources to digital-based public sector reforms to better prepare for wicked transboundary human threats such as Covid-19.
Keywords
Administrative reforms
Business continuity
Covid-19
Ghana
Public services
http://dx.doi.org/10.13039/501100005602 University of Ghana Business School University of Ghana Business School
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pmcIntroduction
The COVID-19 crisis is a compelling global ‘wicked problem’ that has disrupted political, social, economic, and organizational processes in epic proportions. Administrative and political leaders have been tested on how they have prepared, mitigated, and responded to the public health outbreak (Moon, 2020; Park & Fowler, 2021). The COVID-19 pandemic has demonstrated the extent to which society and the public sector may be undermined by unpredictable, inconsistent, and uncertain events (Ansell et al., 2020). The fragile nature of state institutions has been exposed by the pandemic, including the weakening of the public sector (Dunlop et al., 2020:366). Yet, the public sector has witnessed dramatic increase in service demands while striving to maintain the safety of employees. Analogous to this view, scholars have raised a legitimate question about how the public sector can continue to provide public services to citizens amidst significant disruptions during emergencies (Grossman et al., 2020; Schuster et al., 2020). Some have suggested that intense demand for services coupled with compounding disruptions, overstretched by the health crisis requires adaptation (Shi et al., 2020). The idea is that routine planning strategies may create inertial forces that hinder rapid adaptation when circumstances change and discontinuities occur (Weber & Tarba, 2014; Stivers, 2022). It may be difficult or impossible to plan for or sustain effective collaborations or respond to excessive service demands when existing routines, staffing and technologies are disrupted.
Rather, crisis conditions require robust administrative solutions to deal with the crises and reveal a need for public administration reforms that support governance during emergencies (Ansell et al., 2020). In fact, the public sector must meet turbulence with robust strategies where creative and agile public organizations adapt to the emerging new disruptive problems (Howlett et al., 2018). Consistent with this view, Roberts (2020) argues that new administrative capabilities are critical for governments to anticipate and manage crisis. For Boin et al. (2020), the contemporary governance amidst mega challenges requires sustained attention and effective interventions to overcome. Yet, how public administrations can transform and adapt themselves to turbulence and scale-up solutions is less explored in the literature (Ansell & Trondal, 2018). So far, scholars and practitioners alike are questioning what works and what does not work during the COVID-19 pandemic (Turrini et al., 2020). Pisano et al., (2020) also argued that the need for timely responses and revision of administrative norms is crucial for recovery. Moreover, research on the COVID-19 pandemic has largely focused on epidemiological issues (see Li et al., 2020), modelling economic consequences (see Vernengo et al., 2020), and assessing its social implications (Simonov et al., 2020). Yet, there is less emphasis on how the public administration systems are flexible enough to adapt and quickly to public emergencies. Therefore, the aim of this paper is to explore how public administration reforms are able to respond to the COVID-19 pandemic. The main questions are: what public sector reforms have been implemented to ensure the continuity of government business during the COVID-19 pandemic? How effective are such reforms to mitigate current and future emergencies? Are civil servants receptive to the new reforms? Given the bureaucratic nature of public organizations, the paper contributes to the emerging literature on how public administration can be agile and flexible enough to quickly respond to public emergencies. Moreover, the paper provides some pointers to public managers on the need to enhance administrative capacities and governance strategies that are resilient enough to deal with future public emergencies. The rest of the paper is structured as follows: the literature is presented, the methodology is discussed, findings are presented and discussed, and conclusion and policy implications offered.
Literature Review
Linking Public Sector Reforms and Public Emergencies: Theoretical Perspective
Over the years, public bureaucracies have received flaks for their inability to handle public emergencies including wicked problems (Wegrich, 2019; Ansell et al., 2020). Given the advent of complex issues, including climate change, it is becoming evident that the public sector is ill-equipped to address such megatrends. However, Ansell et al., (2020: 956) state that “the COVID-19 crisis has demonstrated a need to perceive of challenges to the public sector in a new way and is revealing the necessity, willingness, and capacity for changing the modus operandi of the public sector in pursuit of robust solutions to turbulent problems”. Yet, complex problems cannot be dealt with by only having well-trained staff and a battery of equipment ready for when unpredictable and uncertain problems hit the public sector. In fact, the theory of ‘sound governance and public administration” argues that robust governance strategies and state capacity are crucial for tackling turbulent problems (Farazmand, 2004; Ansell & Trondal, 2018). In that regard, robust administrative reforms refer to “the ability of one or more decisionmakers to uphold or realize a public agenda, function, or evaluate in the face of the challenge and stress from turbulent events and processes through the flexible adaptation, agile modifications, and pragmatic redirection of governance solutions” (Ansell et al., 2020:952). Simonovic and Arunkumar (2016) contend that since adaptation is crucial in erecting a robust governance, the political and administrative institutions, regulatory processes, and policy instruments may fail to deal with new and emerging disruptions. Therefore, whereas a traditional public administration system may resist change due to its inertia, a robust administrative system aims to transform itself to achieve an agenda (Capano & Woo, 2017).
The literature suggests a model for linking public administration to crises: first, scalability - ability to flexibly mobilize and de-mobilize resources across organizations (Ansell & Torfing, 2018). Thus, some countries created platforms for trainees and retired health workers to sign up and voluntarily assist frontline health workers based on need. Second, prototype – aims to create new adaptive solutions through testing and revision based on prompt feedbacks. Third, modularization – shows the solutions that are divided into a series of modules that can be used flexibly to deal with the emergent situation. For example, the module of testing, tracking, quarantine, lockdowns, social distancing, stimulus packages, intensive care treatment and gradual opening of economies have been adopted during COVID-19. Fourth, bounded autonomy – allows for stakeholder or broad-based ownership and commitment to an overall strategy by engaging multilevel-governance networks – regional, local, and private sector. Fifth, bricolage – aims to flexibly use and combine available ideas, tools, and resources to fashion out workable solutions during crises (Chandra & Paras, 2020). And sixth strategic polyvalence – aims to deliberately design solutions that can be taken in new directions and serve new purposes depending on situational analysis of demands, barriers, and emerging opportunities.
Ensuring Continuity of Public Services Under Crisis Situation
Public emergencies of any kind amplify citizens’ demand for public services, including, health, education, social welfare, transport, and security (Ansell & Trondal, 2018). Essentially, the response to public emergencies such as tornadoes, floods, fires, terrorist attacks, and even the COVID-19 crisis is driven by employees in the public service (Dunlop et al., 2020). It includes line ministries and street-level bureaucrats (SLBs) – working in many different roles from high-profile emergency and clinical services to low-profile refuse collection and social care services (Schuster et al., 2020). To ensure the continuity of public services, public servants are required to mobilize and utilize public resources (United Nations, 2020). In a case study conducted in the United States, Shi et al. (2020) led evidence to show that nonprofit organizations faced challenges in their quest to continue services to homeless people. In the midst of acute financial difficulties, increased service demands, and the implementation of lockdown measures, nonprofits provided hotels for quarantine services to enhance local government efforts aimed at delivering public services. Evidence from natural disasters suggests that administrative leadership capacity was crucial for crisis preparedness, responses, and recovery (Kapucu & Ustun, 2018). The work by Nolte et al. (2020) revealed that public administration reforms that decrease bureaucratic inertia enhanced responses to refugee crisis. Moreover, other scholars observed that collaboration with citizens and other important stakeholders has proven to be a useful public sector reform during crisis (Switzer et al., 2020; Steen & Brandsen, 2020). Given the strange and complex nature of the COVID-19 crisis, recent studies have focused on digital technology as a critical public sector reform. Though COVID-19 appears to be moving faster than public administration, technology has made it possible to continue public service provision despite significant disruptions. Digitization is the process of adopting and using digital technology within individual, organization, and social contexts (Legner et al., 2017). Reis et al., (2018) argue that digital transformation suggests that fundamentally, new capabilities are created in public administration and in people and society’s life. Both public and private organizations are leveraging on the opportunities provided by digital technology to forge business continuity (Trischler & Westman Trischler, 2021). The COVID-19 pandemic lockdown has had the effect of forcing an abrupt shift from face-to-face towards the digital realm. To ensure continuity of public services, the immediate public administration reform is to ensure that activities are moved online - schools conduct teaching and learning, increased food delivery by restaurants and grocery shops, ordering goods from e-commerce platforms, and other technologically related platforms (Faraj et al., 2021). Due to the pandemic, public servants were forced to work in accordance with new procedures using new technologies. This means that government business processes were redesigned in an unplanned and revolutionary manner at an unprecedented speed (Gabryelczyk, 2020). Indeed, the pandemic has made the public sector more “accidentally agile” (OECD, 2020), and that crisis demonstrated that governments can be agile and adaptive (Janssen & Van Der Voort, 2020). For example, in Germany, Wegrich (2021) observed that innovation labs were set up to coordinate and respond to the pandemic. However, the author further observed the slow pace of the digitalization of public services and calls for a re-assessment of the prevailing image of public sectors reforms. In Singapore, Abdou (2021) observed that technology is effectively deployed to follow-up, diagnose cases of COVID-19, and enhance the delivery of essential services.
Data and Methods
A content analysis approach supported by key informant interviews was employed in this study to understand public service continuity during the COVID-19 crisis. The content analysis approach helped to analyze data collected from the websites of state agencies, official COVID-19 related reports, and news articles. With respect to the official reports, 2020 and 2021 Annual Performance reports of the civil service of Ghana were analyzed. The Annual Civil Service performance report is published by the Office of the Head of Civil Service. It should be noted that the COVID-19 crisis was first identified in Ghana on 12 March 2021 and continued through 2021 to April 2022 when all related restrictions and measures have completely been lifted. In that regard, 2020 and 2021 reports covered all emergency public sector reforms and public services implemented to prepare, mitigate and recover from the threatening health pandemic. Moreover, content analysis of websites of public agencies was also important source of data for the study since state agencies displayed COVID-19 related protocols and actions taken on their respective websites for easy access. Content analysis is an important source of information in qualitative research (Hsieh & Shannon, 2005). These sources of information facilitated the triangulation of information to ensure a verification of the realities on the ground (Moran-Ellis et al., 2006). Additionally, ten key informant interviews were conducted with government officials at the ministries of Health and Public Sector Reforms, Chief Executive Officers (CEOs) of State-owned Enterprises, officials at the Office of the Head of Civil Service, and Public Services Commission to identify and assess the effectiveness of public sector reforms implemented during the COVID-19 crisis to ensure government business continuity (Table 1). Employing a purposive sampling technique, the interviewees were selected based on their knowledge or direct participation in the reforms undertaken to manage COVID-19 in Ghana.
Table 1 Category and number of interviewees
Category of interviewees Total number interviewed
Officers at the ministries of Health and Public Sector Reform Secretariat 3 interviews
CEOs of SOEs 2 Interviews
Officers at the Office of Head of Civil Service 2 Interviews
Officers at the Public Services Commission 3 Interviews
Total 10 interviews
Source: Field Data
Thematic analysis based on the suggestion by Braun and Clarke (2006) was adopted. Therefore, systematic coding ensured that the fundamental concepts were carefully extracted, labelled and defined, and key patterns and relationships regarding public sector reforms and public service continuity were identified and assessed. The themes generated formed the basis for analyses and discussions.
The COVID-19 Situation in Ghana
Ghana recorded its first case of COVID-19 in March 2020. Soon thereafter, President Akufo-Addo announced nationwide travel and social restrictions effective from 16 March. Key amongst them were closure of all borders and three weeks’ partial lockdown of cosmopolitan cities of Accra and Kumasi; a ban on public gatherings including conferences, workshops, political rallies, and religious activities; and closure of basic and tertiary educational institutions. However, businesses such as retail outlets, restaurants, hotels, transport operators, and local markets could continue to operate but had to adhere to social distancing and enhanced hygiene measures after the three-week partial lockdown. Five-pronged objectives underscored Ghana’s response strategy: contain the spread; provide adequate care for the sick; limit the impact of the virus on social and economic life; and inspire the expansion of domestic capability and deepen Ghana’s self-reliance. Conversely, the Ministry of Finance (MoF) had earlier conducted rapid assessment of the likely budgetary impacts of the COVID-19 restrictions on the economy. On the revenue side, government expected to lose GH¢ 5.68 billion in oil revenue due to the two-thirds decline in crude oil prices. Non-oil revenues were expected to fall by GH¢ 2.25 billion due to the slowdown in economic growth (MoF, 2020). Government also faced significant unforeseen costs associated with the COVID-19 response programmes, including the National Preparedness and Response Programme (GH¢ 572 million), and the Coronavirus Alleviation Programme (GH¢ 1.20 billion). The latter makes provision for various stimulus packages and support measures, including GH¢ 600 million in the form of soft loans to small and medium enterprises, to which private sector banks will contribute a further GH¢ 400 million; GH¢ 320 million to supplement healthcare workers’ incomes; and GH¢ 280 million for household water supply subsidies, food packages, and public grain procurement from smallholder farmers (MoF 2020). In order to finance these costs and to cover losses in revenue, government obtained a loan facility of US$ 1 billion (GH¢ 5.72 billion) from the International Monetary Fund (IMF). The IMF loan, which came at a time when Ghana’s debt stock is already GH¢ 200 billion or 60% of GDP (MoF, 2019), is expected to cover about half of the COVID-19 costs and revenue losses. Government further proposes to defer interest spending on existing loans from the Bank and to temporarily reduce or suspend payments to sovereign investments funds, such as the Stabilization Fund and the Heritage Fund. It also plans to reduce planned capital and current expenditure by GH¢ 1.25 billion in 2020 (MoF, 2020). Following the mixed reactions that greeted the lifting of three weeks partial lockdown, the government of Ghana later adopted a gradualist approach to easing restrictions. On June 5 2020, schools were asked to reopen for final year students, and conferences, weddings, private burials, non-contact spot, religions activities, and political activities, all with less than 100 participants were permitted to resume. Additionally, the Electoral Commission and National Identification Authority also resumed their activities. However, festivals, sporting events, nightclubs, cinemas, and political rallies remain banned, and the closure of Ghana’s border had been extended indefinitely. As at May 2022, Ghana’s total COVID-19 cases stood at 161, 000 with 1,445 deaths.
Results and Discussion
Enablers of Public Service Continuity During the COVID-19 Crisis
Outbreaks of crisis intensify demands for public services in households, homes, and in public office buildings. Essentially, services range from healthcare, electricity supply, public security, education, health, water supply, social welfare, transport, food security, and financial services. While public demands for these wide-range of public services are amplified during global public health crisis, public agencies’ service provision is affected due to the significant disruption of regular functioning of state institutions. Nevertheless, the public sector needs to secure uninterrupted delivery of these services through state entities and State-owned enterprises for effective response and recovery process. It emerged that the Public Sector Reform Secretariat together with the Public Services Commission (PSC) and the Office of the Head of Civil Service (OHCS) developed Business Continuity Plans (BCP) for all government ministries, departments and agencies to drive the whole process of government business continuity during the pandemic. Thus, critical areas of government’s operations that were likely to be undermined by the COVID-19 pandemic were the focal point. Our analysis identified some reforms that constituted the entire architecture of BCP: political and administrative leadership commitment to reforms, reforms related to the digitization of public services, and business-related reforms. These were key public sector reforms that contributed to an effective COVID-19 response, mitigation and recovery in Ghana.
Political and Administrative Leadership Commitment to Reforms
Leadership is key for the success of human institutions. Essentially, leadership is crucial for the reform of public administration and the extent to which public administration would respond to crisis situation. In that regard, our findings revealed that the President of Ghana exhibited high level of political commitment in ensuring that essential services were readily available for the citizens. During the initial stages of the COVID-19 crises in 2020, the President in his bi-weekly COVID-19 addresses to the nation (popularly referred to as “fellow Ghanaians”) maintained a consistent call on the utilities’ providers such as the Electricity Company of Ghana (ECG) and Ghana Water Company limited (GWCL) to ensure that electricity and water were running without interruption. The President also announced free water and electricity for the lifeline consumers and a subsidy for those who consume more than 50 kWh per month from April to December, 2020 (Nkrumah et al., 2021). To implement the President’s policy directives, the Ministries of Health and Finance provided leadership and facilitated the development of a National Strategic COVID-19 Response Plan and Ghana COVID-19 Alleviation and Revitalization of Enterprises Support (CARES) programme (GHS 100 billion) to reduce the incidence of and mortality from the COVID-19 pandemic and to mitigate its socio-economic impacts on citizens. The Ministry of Information in collaboration with Ministry of Health prepared a community engagement and a communication strategy to inform and educate the public to stay safe and stop community spread of the virus. Moreover, the Public Sector Reform Secretariat under the presidency provided the needed logistics to the PSC and the OHCS to initiate COVID-19 related reforms in state agencies for continued provision of services while dealing with restrictive measures for their staff. The call was necessary because with the COVID-19 restrictions, citizens were required to stay at home, meaning that water and electricity were on high demand both for domestic purposes and for dealing with the pandemic. The high level of political and administrative commitment from the executive President, PSC and OHSC put Ghana’s public sector in a firm position to respond and recover from the COVID-19 pandemic. It should be emphasized that the executive President has a wide range of powers under the 1992 Constitution (Gyimah-Boadi & Prempeh, 2012), and with that high level of commitment from the top during the COVID-19 crisis motivated and compelled all actors at central and sub-national levels to act.
Reforms on Digitization of Public Services Delivery
One major public sector reform during the COVID-19 crisis is the digitization of public services. It is crucial for the government to lead an effective response to the health pandemic through policy, institutional reforms, coordination, funding, and implementation in a manner that requires fast, and agile action. However, the government is constrained by its own mitigating measures including social distancing, which impair the work flow of public servants and requires new processes and technologies for continued essential business operations. The analysis shows that for an effective response to the COVID-19 public health crisis, the PSC and OHCS initiated the following digital-based reforms to mitigate its impacts and engendered continuity of government business.
Flexible Working Schedule and Annual Leave
It emerged that all state agencies, ministries, and departments were tasked to formulate Business Continuity Plans (BCP). Within the framework of the BCP, some staff worked in the office while others worked from home. Moreover, some non-essential employees who had outstanding leave were asked to take their leave. This COVID-19 related workplace measures enabled the decongestion of public offices to avoid overcrowding and subsequently facilitated strict adherence to the COVID-19 protocols. A duty roster was also created and facilitated the rotation of staff.
Redesign of Offices, Directorates and Installation of Equipment
As part of reforms during the COVID-19 crisis, some offices and directorates were carefully redesigned and restructured to alter the number of officers sharing offices, their work schedule and the number of hours allocation for a day, week, and month.
Moreover, to ensure that public servants are able to work effectively while off-site, the Public Sector Reform Secretariat through PSC and OHCS procured and ensured that right logistics including laptops, turbo net (for internet access) and other internet facilities for public service continuity were made accessible. The ICT centers in all departments and agencies were promptly renovated, equipped and linked to the national digital center for reliable and stable internet connectivity. Also, Web Hosting Services were provided, which allowed staff to deliver and meet work schedule deadlines regardless of their location. In that regard, virtual conferences platform such as zoom, skype, Microsoft Teams, and other video conferencing platforms facilitated meetings, sharing of ideas, team work and ensured ultimate delivery of public services.
Reforms Related to Monitoring and Assessment of Targets
The findings demonstrate that daily deliverables and deadlines were agreed and set between Divisional Directors and Staff. Staff were required to be readily available to answer calls and respond to emails as well as offer their inputs for some required tasks. The assessment of performance was done with respect to the deadlines agreed upon for response to queries and the delivery of assigned tasks.
Conducting Services Online
It emerged from the analyses that the ability to conduct government business online was an important public sector reforms that ensured the continuity of public service delivery. For instance, electronic justice (e-justice) system was implemented, which allowed judges to sit on cases via Zoom, skype, or Microsoft Team. Schools were also moved online to ensure that the academic calendar was not unduly affected. Interviews with the official of PSC and OHCS revealed that an online electronic application system (e-application) was erected during COVID-19 through which staff were recruited. This was necessary because some staff were diagnosed to have gotten the COVID-19 virus and had to isolate for some weeks to recover. This brought pressure on the other staff members who were under extreme pressure to provide essential services. For instance, a good number of medical doctors, nurses, and civil servants in state agencies and departments who were diagnosed of COVID-19 had to isolate for further management. There was an urgent need to recruit new essential staff providers to optimize staff strength in public offices so that public service provision would not be interrupted. The analyses also show that virtual processes were used to promote officers who were due for promotion to their next levels. An officer at the OHCS stated that between 2020 and 2021 when face-to-face meeting was not possible due to the COVID-19 crisis, about 1, 707 officers, comprising 331 Assistant Director II, 643 Assistant Director I, and 733 sub professional grades participated in the novel virtual promotion interviews. It must be emphasized that Ghana operates unitary system where the decision-making resides in a centralized government. In that regard, the virtual promotion processes enabled candidates and panel members outside the capital city of Accra to take part in the process. The recruitment reform reduced the risk and traveling time to interview locations, minimized human interface, and further reduced the risk of spread of COVID-19 among Civil Service Staff. Most of the recruited staff were posted to the regional and district offices, which ensured optimum staff strength to meet an increased citizens’ demand for public services during COVID-19 crisis.
Business-Related Reforms
Our analyses further revealed that the government was agile enough to implement some urgent public sector reforms to ensure that the supply chain of some essential commodities and groceries were not interrupted during the COVID-19 crisis. The business-related reforms were operationalized in two areas: reforms at the country’s port and reforms to support local manufacturers.
Reforms at the Country’s Ports
It came up that some reforms were put in place to ensure that the domestic supply of essential goods and services were not severely impacted as a result of the disruption in the global supply chain caused by the COVID-19 crisis. To achieve this, Customs officials at the various ports of entry were excluded from the COVID-19 restrictions, and the officials were only required to show their identity cards for safe movement during the lockdown periods. Moreover, the Public Sector Reform Secretariat and the Ministry Finance scaled-up the implementation of Integrated Customs Management System (ICUMS) that allows importers and exporters to process their request online from any location. The ICUMS did not only prevent overcrowding and adherence to COVID-19 measures but also enabled the business community to clear their goods with much convenience.
Moreover, special permits were granted to port operators and shipping agencies that are required to ensure continuity of services during COVID-19 crisis. The permits granted the essential service operators a safe passage through the police and military barriers that were to ensure compliance with the COVID-19 protocols. The cumulative effect of these measures is that goods and services were exported and imported without significant disruptions.
NPM-Style Contract-Based Reforms to Provide Essential Services
At the initial stages of the COVID-19 pandemic, the public administration in Ghana activated the New Public Management (NPM) style of reforms that was informed by the contract-based approach to the delivery of public services. Thus, some local garment and textile manufacturing companies contracted and supported to enhance their production capacity, which enabled them to deliver essentially large-scale government procured orders of personal protection equipment (PPEs). Moreover, the Zipline company was contracted where drones were used to deliver medical consumables to hard-to-reach rural communities. Also, contracts were entered with pharmaceutical companies to manufacture and supply some medical supplies. The contract-based approach adopted during the COVID-19 pandemic ensured that essential commodities that were critical for combating the virus were readily available in the domestic market despite the disruption of global supply chain. An analysis of the official reports revealed that as at December 2020, a total of 18.6 million face masks, 90,000 headcovers, and 60,000 medical scrubs were produced and supplied to the government. It must be emphasized that at the end of March 2020 when Ghana went into lockdown, the demand for hand sanitizers, facemasks, and PPEs had intensified and the industry players took the undue advantage to astronomically increase prices thereby making it impossible for citizens to buy for the purposes of complying with the COVID-19 protocols. Therefore, the government’s decision to contract the private sector to provide and ensure continuity of such essential services contributed to effective response and recovery process.
Business Regulatory Reform Programme
The government through the Public Sector Reform Secretariat came up with Business Regulatory Reform (BRR) programme. The BRR is an interactive web-based programme which was used and still being used to facilitate consultations between public institutions and the private sector on government’s policies and regulations during the COVID-19 crisis. The BRR project was used as a platform to disburse soft loans to businesses through the Ghana Enterprises Agency (formerly National Board for Small Scale Industries). The medium and small-scale businesses (SMEs) were required to apply online and upload their particulars for subsequent disbursement of funds.
Challenges to the Implementation of Reforms to Ensure Public Service Continuity During the COVID-19 Crisis
The COVID-19 crisis has underscored the need for flexible and responsive public sector that can adapt to changing circumstances and coordinate complex and transboundary issues. Despite the effective business continuity plans and measures that were put in place to ensure the continuity of government business during the COVID-19 crisis, some ostensible challenges were recorded.
Unavailability of Reliable Internet Connectivity for Remote Working
Ghana is a lower middle-income country with less robust information technology coverage. This limitation posed serious challenge to a major digital reform implemented to ensure continuity of public service delivery in the midst of the pandemic. The internet connectivity was not reliable for officers and even in some places the connectivity was completely not available for service to be delivered online. The use of platforms such as Zoom and Microsoft Teams to facilitate on-site and off-site interactions and communication among the staff in the form of meetings, interviews, and training were severely undermined. This impacted adversely on the smooth flow of work, productivity of workers, and the general quality of service delivery were conversely undermined. Coupled with the erratic nature of the internet connectivity, analyses revealed that high cost of internet services and data packages hindered the productivity of workers.
Inadequate Logistics to Facilitate Remote Working
The flexible work reform was not only undermined by internet connectivity but also other critical logistics were not adequate. This became critical because during the pre-COVID era, employees shared working tools such as computers and other accessories. But since COVID-19 restrictions require that a majority of staff should work from home, the emergency logistics procured were woefully inadequate. Consequently, some staff did not have personal computers at home and other necessary logistics thereby making it extremely difficult to share information to meet deadlines and other assigned responsibilities.
Inadequate Capacity and Rigidity of some Staff
Some public and civil servants lack the required skills in the use of technology. Some were unable to log into Zoom, Microsoft Teams, and skype platforms for video conferencing, meetings, workshops, and for team-based tasks, which posed significant challenges. Coupled with that, some other staff could not adjust to the new working mode since they are used to their old system which is deemed to be ineffective in responding to turbulent times. Some staff did not have conducive workspaces at home and with children not in school, home became not so suitable for work.
Discussion
There is consensus in the literature that a crisis situation requires flexible and innovative public sector reforms to ensure the continuity of the government business (Legner et al., 2017; Kapucu & Ustun, 2018; Reis et al., 2018). This is underpinned by the view that a typical public administration with its bureaucratic structures tends to hinder rapid response and adaptation to complex issues (Janssen & Van der Voort, 2020; Christensen & Lægreid, 2020). In fact, public bureaucracy is often seen as an antithetical to adaptation during crisis. Consistent with this view, Shava and Hofisi (2017) have argued that crisis situation opens a window of opportunity for public sector innovation and reforms. Therefore, this paper led evidence to show that Ghana’s public sector was agile to implement reforms that ensured the continuity of public service provision during the COVID-19 crisis. Scholars of public administration have long identified political and administrative leadership commitment as critical for the success of public sector reforms (McCourt, 2003; Batley et al., 2012). Our review established that the commitment from political and administrative leadership in the Ghanaian public sector played a significant role during the COVID-19 pandemic. Given the uncertainty that surrounded the pandemic at its initial stages, the executive President issued a number of executive instruments during his COVID-19 addresses to the nation to the Public Sector Reform Secretariat, PSC and OHCS to initiate administrative and institutional reforms to help respond, mitigate, and ultimately ensure the continuity of government business. Scholars have led evidence to demonstrate how political and administrative commitment from the top ensured continuity of public service delivery during national response to natural disasters and calamities (Kapucu & Ustun, 2018). In response to the President’s directives, a Business Continuity Plan was agilely erected at the initial stage of the pandemic within which several agile reforms where activated. These include ensuring flexible working schedule and granting annual leave, redesign of offices, directorates and installation of equipment, online monitoring and assessment of results, and moving service provision online. The activation of these digital reforms is due to the strange and complex nature of the COVID-19 pandemic which requires draconian measures such as social distancing and restrictions on movements of people. Moreover, since the pandemic was moving faster than the public administration (Trischler & Westman Trischler, 2021), the only reform that could allow the continuity of public service delivery is the deployment of technology. The implementation of these digital initiatives created new capabilities for the public administration to forge business continuity. The shift to the digital realm did not only allow the delivery of government’s services to be moved online, but also, it revolutionized service delivery in the private business sector. Thus, digital payments, restaurants, electronic commerce platforms were adopted to continue the delivery of services (Bai et al., 2021; Faraj et al., 2021). The other public innovations that were implemented during the pandemic include reforms at the country’s port of entry, contracting out service delivery, and regulatory reforms. The Ministry of Health, for instance, used the services of Zipline Drone Company to transport medical consumables to hard-to-reach rural communities (Lamptey & Serwaa, 2020). These public sector reforms decreased the bureaucratic nature of the public administration and enhanced the continuity of government business. However, some challenges were recorded with respect to the flexible reforms that were implemented to respond to the COVID-19 as well as ensure the continuity of government business. Chiefly among them are; unavailability of reliable internet connectivity for remote working, inadequate logistics to facilitate remote working, inadequate capacity and rigidity of some staff.
Conclusion and Policy Implications
The COVID-19 pandemic caused significant disruptions to public service delivery. Yet, it has also heightened citizens demands for services. In that regard, public sector organizations are required to find innovative ways to ensure the continuity of government business. This paper examined public sector reforms implemented in the Ghanian public sector to ensure public service continuity during the COVID-19 pandemic. The paper observed that digital reforms such as flexible working schedule, redesign of offices, directorates, and installations of equipment, online monitoring and assessment of targets, and conducting services on online were instrumental in ensuring the continuity of government business during the pandemic. Other initiatives include reforms at the country’s ports of entry, contracting out of service delivery to private organizations, and regulatory reforms. Specialized service such as using drone technology to transport medical consumables to hard-to-reach rural communities was also part of the business continue plan of Ghana’s public sector. However, many challenges were encountered with respect to the desperate reforms that were initiated, including, unavailability of reliable internet connectivity for remote working, inadequate logistics to facilitate remote working, inadequate capacity and rigidity of some staff, financial constraints, and cyber security issues. Though the COVID-19 crisis posed significant threats and disruptions, it has conversely presented a unique opportunity for public administration to generate new solutions to respond to emerging global wicked problems. Therefore, it is critical for the public sectors to embrace these agile reform initiatives powered by the technology to eliminate obsolete and rudimentary processes. Governments and policy makers should pay adequate attention and ensure that much resources are allocated to digital-based public sector reforms since the COVID-19 crisis and other wicked transboundary human threats are likely to stay with us for a very long time.
Funding Information
University of Ghana Business School Research Grant and the AXA Research Grant.
Declarations
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J Clin Psychol Med Settings
J Clin Psychol Med Settings
Journal of Clinical Psychology in Medical Settings
1068-9583
1573-3572
Springer US New York
36482056
9922
10.1007/s10880-022-09922-4
Article
Diversity, Equity, and Inclusion within Pediatric Adherence Science
http://orcid.org/0000-0003-1763-5507
Williford Desireé N. [email protected]
1
Sweenie Rachel 1
Ramsey Rachelle R. 12
McGrady Meghan E. 12
Crosby Lori E. 12
Modi Avani C. 12
1 grid.24827.3b 0000 0001 2179 9593 Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Center for Treatment Adherence and Self-Management, College of Medicine, University of Cincinnati, 3333 Burnet Ave. MLC 7039, Cincinnati, OH 45229 USA
2 grid.24827.3b 0000 0001 2179 9593 Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH USA
8 12 2022
112
31 10 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Given the long-standing history of systemic racism in psychological science, diversity, equity, and inclusion (DEI) efforts are increasingly vital to the advancement and improvement of the field. This commentary extends the seminal work of the article Upending Racism in Psychological Science: Strategies to Change How Our Science is Conducted, Reported, Reviewed, and Disseminated (Buchanan et al., Am Psychol, 10.31234/osf.io/6nk4x, 2020) by providing tangible applications and recommendations to improve DEI integration into pediatric adherence science. Real-world adherence examples are discussed regarding the challenges faced in systematically integrating DEI principles, potential solutions to overcoming barriers, and the implications of these efforts on scientific advancement in an effort to address and dismantle research practices that perpetuate inequity and White supremacy. Specifically, we provide discourse and practical guidance related to the conduct, reporting, reviewing, and dissemination of pediatric adherence science to promote dialog and produce actionable change toward the promotion of health equity and social justice.
Keywords
Self-management
Compliance
Systemic racism
Treatment
Communities of color
http://dx.doi.org/10.13039/100009633 Eunice Kennedy Shriver National Institute of Child Health and Human Development T32HD068223 T32HD068223 Williford Desireé N. Sweenie Rachel http://dx.doi.org/10.13039/100000050 National Heart, Lung, and Blood Institute K23HL13992 Ramsey Rachelle R. National Cancer InstituteK07CA200668 McGrady Meghan E.
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pmcIntroduction
Recent estimates in the United States suggest that approximately 19 percent of U.S. children and adolescents currently have at least one chronic health condition (Child and Adolescent Health Measurement Initiative, 2020) including highly prevalent medical or genetic conditions such as asthma, epilepsy, type 1 diabetes, cancer, and sickle cell disease. Treatments for these conditions comprise a variety of regimens which may include a combination of daily and rescue oral and inhaled medications, injections, use of medical technology (e.g., blood glucose monitors, insulin pumps), infusion therapies, physical and occupational therapies, and/or lifestyle adaptations (e.g., diet, exercise, sleep hygiene, hydration; see Modi & Driscoll, 2020 for a comprehensive review of disease self-management among pediatric populations). Adherence as a construct has been defined as “…the extent to which a person’s behavior (in terms of taking medications, following diets, or executing lifestyle changes) coincides with medical or health advice” (Haynes et al., 1979). Despite the advent of new therapies and medical advancements, suboptimal adherence remains a prevalent concern among youth with chronic health conditions, significantly impacting health outcomes and symptoms (e.g., Schwartz et al., 2010; Walsh et al., 2014), treatment efficacy or escalation (e.g., Carmody et al., 2019), quality of life, and health care utilization and costs (e.g., Hommel et al., 2017; McGrady & Hommel, 2013). Further, developmental considerations add complexity to understanding adherence. For example, the level of caregiver involvement in a youth’s life, school environments, an increasing desire for independence during adolescence, and the transition to adult care all play a significant role in disease self-management, including adherence (e.g., Feldman et al., 2018; Gray et al., 2018; Gutiérrez-Colina et al., 2018; Schmidt et al., 2020). While these data demonstrate pediatric adherence to be a vitally important construct to address in clinical research, it is important to note that these studies have been conducted with predominately White samples that lack diversity. Additionally, health disparities exist across various conditions and racial and ethnic groups, as suboptimal levels of adherence are disproportionately observed in patients and families from underserved and historically marginalized communities (e.g., Centers for Disease Control & Prevention, 2013; McQuaid & Landier, 2018). While these data are unfortunately not unique to pediatrics, pediatric adherence science has a unique opportunity to integrate DEI into adherence promotion efforts early in life while health behaviors are being developed. Pediatrics is also well poised to help us better understand aspects of intersectionality and interactions with adherence behaviors. While our patients and families may experience inequities directly in the care and management of the child’s chronic illness, families of these youth bring with them their personal life experiences, including experiences of racism and discrimination. These experiences of inequity and oppression in the larger context of families’ lives may also impact the child’s individual disease management. Further, within families, individual members may also have varied experiences, adding additional complexity to pediatric disease management. Examples may include differences in cultural beliefs, varied language proficiency, or exposure to systemic racism in healthcare settings between pediatric patients and their caregivers. Moreover, pediatric adherence science as a field of study is in need of increasing dedication to diversity, equity, and inclusion (DEI).
Across the field, the term “adherence” has grown in acceptance as compared to its predecessor, “compliance.” Following an important reconceptualization of the term and field more broadly, adherence was chosen to reduce blame (i.e., for suboptimal health outcomes) on patients and/or families and to better reflect the active and important role patients and families have in selecting, consenting to, and implementing treatment recommendations discussed with medical providers (Lutfey & Wishner, 1999; Modi & Driscoll, 2020). Still, adherence has its own negative connotations. For example, there is growing recognition in the type 1 diabetes literature highlighting how adherence may still imply engaging in behaviors that someone else (i.e., a medical provider/team) wants to occur (Dickinson et al., 2017; Modi & Driscoll, 2020).
Despite ongoing discussion around terminology, research efforts have demonstrated a growing appreciation for patients and families, including increasing emphasis and acceptance of shared decision making, wherein patient, caregiver(s), and clinicians arrive at a decision regarding medical care collaboratively (Barry & Edgman-Levitan, 2012). Given the focus on attending to patient and family rights and values, shared decision making is currently considered best practice (National Quality Forum, 2018). However, the use of shared decision making across providers and clinical care environments remains variable. Further, inequitable use of shared decision making has been observed with patients from communities of color (e.g., Couët et al., 2015; Jolles et al., 2019).
The re-examination and adaptation of research practices and procedures with greater respect to diversity, equity, and inclusion (DEI) has become of utmost importance within the larger scientific literature. In psychological science in particular, recent articles such as Buchanan et al. (2020) have highlighted “epistemic oppression” (p. 3) within the field, including the systematic exclusion, underrepresentation, segregation, and overgeneralization of communities of color1 in psychological research. Buchanan et al. (2020) share how these practices, which are widespread and over-relied upon in psychological science, beget research that is inherently inequitable and biased. As such, Buchanan et al. (2020) provide suggestions to eliminate such oppression and produce research that is more equitable, just, actively antiracist, and closer to being free of systemic biases.
While this work has generated conversation and movement in the field, more work is needed to apply these principles systematically and across domains of psychology, including adherence science. The purpose of this commentary is to extend the recommendations outlined by Buchanan et al. (2020) by applying their DEI principles, particularly around race and ethnicity, to pediatric adherence science. Application of these principles is particularly important in psychological science, which includes adherence science, given the history of medical injustices perpetrated against individuals and communities of color. In accordance with the topics delineated in their article, we will discuss potential methods for conducting, reporting, reviewing, and disseminating equitable and inclusive pediatric adherence and self-management research.
Conducting Adherence Science
Conceptualization
Accurately conceptualizing adherence and self-management require grounding research in a theoretical model that considers DEI. There are limited DEI theories specific to pediatric adherence science; however, the Pediatric Self-Management Model is one example that considers the relationship between race and ethnicity, social determinants of health, and adherence, and provides a framework for incorporating these variables into adherence research (Modi et al., 2012). When designing research studies guided by this model, it is recommended that researchers consider conceptualizing these constructs as mechanisms of change rather than demographic variables to control for (Modi et al., 2012). Research guided by this approach, for example, may assess racism as a potential moderator or mediator of adherence rather than simply including the patient or caregiver’s race as a control covariate.
Despite its’ strengths, the Pediatric Self-Management Model was developed using available research, which was conducted among samples composed of predominantly White individuals. Diversity Science approaches may be particularly informative as we continue to advance our theoretical models of adherence and increase population representativeness in our adherence research. For example, Minority Stress Theory considers how experiences of stigma, prejudice, heteronormativity, rejection, and internalized homophobia impact health outcomes and behaviors (Meyer, 2003). In adherence science, this theory has recently been applied to examine the use of pre-exposure prophylaxis (PrEP) among young men who have sex with men (Meanley et al., 2021). The Pediatric Self-Management Model could also be informed by this theory as experiences of minority stress can have implications on self-management behaviors across individual, family, community, and healthcare system domains. For example, while the present model discusses the importance of illness-related stigma, it could be expanded to include how internalized racial or ethnic stigma in the presence or absence of societal prejudice and experiences of systemic racism may contribute to underutilization of healthcare, suboptimal adherence, and/or feelings of mistrust towards healthcare providers and the healthcare system.
Black Feminist Theory, as another example, highlights the intersections of racism, sexism, and classism and their influence on maternal and child health (Barlow & Johnson, 2021; Simmons, 2021). While the Pediatric Self-Management Model presently alludes to the influences of racism, particularly in acknowledging known health disparities, the model could be adapted to explicitly address racism, sexism, and classism and implications at each domain level. Relatedly, the Intersectionality Framework further highlights the intersection between socioeconomic position and race and ethnicity (Crenshaw, 1989) and can be applied to understanding health outcomes. The Pediatric Self-Management Model could benefit from additional information on how factors such as race and racism, sex and sexism, class and classism, as well as socioeconomic position, intersect and influence self-management and adherence outcomes. For example, this framework has recently been applied to examine glycosylated hemoglobin (HbA1c) trajectories, a marker of self-management, among youth and young adults with type 1 diabetes (Liese et al., 2022). Moreover, incorporating these theories and intersecting constructs into existing models, such as the Pediatric Self-Management Model, may provide an improved and inclusive framework for understanding adherence challenges, thus, facilitating the development and selection of more equitable study designs.
Once a guiding theoretical model has been selected, representative stakeholders should be involved in defining and refining the research question (i.e., Phase I design) (Czajkowski et al., 2015). Community-based participatory research (CBPR) approaches may be particularly useful during this step. For example, community-advisory board approaches have demonstrated success in addressing disparities in health outcomes among adults (e.g., Cooper et al., 2016). At our own institution, we utilized a Stakeholder Advisory Council, wherein patients with sickle cell disease and their caregivers were involved in developing and implementing a shared decision-making self-management intervention (Hood et al., 2021). Ensuring that key stakeholders represent the racial and ethnic make-up of the patient population can maximize the likelihood that an engaging, inclusive study design, and/or adherence intervention is developed.
Recruiting & Training Researchers and Staff
Efforts should be made to enhance recruitment of study staff of color to support inclusive research and increase diversity within research teams (i.e., research assistants, students/volunteers, other research team members). Institutions and principal investigators should utilize equitable employee and volunteer recruitment methods within the community, rather than those restricted to academia and/or other internal processes. Efforts should also be made to engage high-school and undergraduate students from diverse backgrounds in adherence science, for example by offering summer training opportunities (e.g., https://www.cincinnatichildrens.org/education/research/high-school/biomedical-research-internship-minority) and courses that address pediatric adherence science (e.g., Introduction to Clinical Child/Pediatric Psychology offered at the University of Florida). Research teams should also obtain feedback from those hired and re-evaluate procedures routinely to promote more inclusive and equitable recruitment and retention of staff from diverse backgrounds over time.
Beyond research staff, institutions and research teams should also make efforts to enhance recruitment and retention of diverse investigators and collaborators. This may include promoting diversity cluster hires (Sgoutas-Emch et al., 2016) followed by strategic retention practices and diversity funding initiatives to demonstrate value and commitment to diverse scholars (e.g., Syed et al., 2018). Research has also suggested that efforts to support retention of diverse faculty and trainees may also be an effective path to increasing overall faculty diversity over time (e.g., Allen-Ramdial & Campbell, 2014). Additionally, training faculty search committees to improve diversity in hiring procedures (e.g., Cavanaugh & Green, 2020) and/or requesting that Human Resource departments give particular attention to diversity characteristics and provide a diverse applicant pool to be reviewed by search committees may be helpful. This is particularly important for adherence science as the biomedical literature suggests that diverse research teams publish more frequently, are cited more often, produce studies of higher quality and clinical significance, and are better equipped to address health disparities given complementary skill sets, greater variety in thought processes and experiences, and increased willingness to pursue innovative and creative ideas and solutions (e.g., Adams, 2013; Eckstrand et al., 2016; Freeman & Huang, 2014; Swartz et al., 2019).
Once hired, researchers and staff should be trained in DEI issues relevant to the patient population prior to study start-up. Historical mistrust of research and health care contexts (e.g., Jaiswal & Halkitis, 2019; LaVeist et al., 2009) may prohibit study enrollment (e.g., George et al., 2014; Stevens et al., 2016). Further, in adherence science specifically, personal and cultural beliefs about a chronic health condition and/or treatment regimens may also influence adherence behaviors (e.g., Shahin et al., 2019) and subsequently a patient and/or family’s desire to participate in adherence-focused research. As such, an understanding of sociocultural influences on adherence is critical to treating participant families with empathy and understanding, rather than judgment, regardless of their adherence behaviors or willingness to participate in research.
Many institutions have embarked on training staff in DEI approaches broadly (e.g., Enders et al., 2021) and researchers could extend this training into their own labs. Training should emphasize how the historical trauma that research has imposed on individuals of color (e.g., the Tuskegee Syphilis Study) and the history of racism in medicine (e.g., forced sterilization of Indigenous women) may impact adherence. For example, investigators and staff funded by the National Institutes of Health are now required to be trained in Good Clinical Practice. This e-course, available through the Society of Behavioral Medicine, may be particularly helpful for adherence scientists and research teams working with diverse participant populations. Researchers may also want to encourage their staff to take the Implicit Association Test, available through Project Implicit, to understand their own biases prior to working with research participants. Staff should also be trained on using equitable and inclusive language, for example, by becoming familiar with the American Psychological Association and American Medical Association’s guidelines (see Table 1 for a list of resources described in this section). Ongoing conversations about language should consider perspectives on various terms utilized throughout the field and implications of each. This promotes more DEI-conscious conversation and scientific inquires among research teams (e.g., Atkin et al., 2022). Finally, staff should be aware of issues of equity, particularly in terms of collecting adherence data. For those conducting adherence research in type 1 and type 2 diabetes, for example, it is important that staff are aware of racial, ethnic, and socioeconomic inequities around access to diabetes-related technology (e.g., Addala et al., 2021; Akturk et al., 2021; Majidi et al., 2021). Such inequities necessitate that staff take extra care to ensure that interventions are delivered, and data are collected in equitable and inclusive manners. Sitting with a child and taking the time to go through the stored blood glucose levels in their meter may be necessary given that not all youth with diabetes have access to a continuous glucose monitor or software, internet, and a computer to upload their data.Table 1 Resources for Training Research Staff
Resource title Organization Website
Good Clinical Practice eCourse Society of Behavioral Medicine https://www.sbm.org/training/good-clinical-practice-for-social-and-behavioral-research-elearning-course
Implicit Association Test Project Implicit https://implicit.harvard.edu/implicit/takeatest.html
Equity, Diversity, and Inclusion: Inclusive Language Guidelines American Psychological Association https://www.apa.org/about/apa/equity-diversity-inclusion/language-guidelines.pdf
Advancing Health Equity: A Guide to Language, Narrative and Concepts American Medical Association https://www.ama-assn.org/about/ama-center-health-equity/advancing-health-equity-guide-language-narrative-and-concepts-0
In addition, training research staff to gently inquire about participation hesitancies and declines during recruitment, as well as ensuring an understanding of all procedures through ample time for questions throughout the research process, may facilitate trust and confidence for prospective participants. Similarly, inclusion of testimonials of similar participants or word of mouth referrals from trusted community partners could increase engagement and trust in the research process.
Sample Considerations
Efforts to enroll representative samples to accurately capture the voices of diverse participants are pertinent to a comprehensive understanding of adherence. While some chronic diseases occur in predominately White individuals (e.g., type 1 diabetes, inflammatory bowel disease, cystic fibrosis) relative to other racial and/or ethnic minority groups (e.g., LatinX, Black, Asian), adherence research in these diseases should not be relegated to only include White patients and families. Similarly, adherence research in sickle cell disease, a disease identified more predominately in Black individuals, should also include individuals and families from non-Black backgrounds. For example, Harry et al. (2019) conducted focus groups with adolescents and young adults with lupus, specifically ensuring the population included Black women, a group that is typically underrepresented in this area of research. Consistent with broad recommendations by Buchanan et al. (2020), it may also be beneficial to recruit samples exclusively comprised of individuals of color, in order to increase understanding and appreciation of adherence barriers and facilitators within diverse samples, rather than relying on comparisons between broadly defined racial and ethnic groups (Buchanan et al., 2020).
Enrolling representative samples often requires different recruitment and retention approaches to engage particular populations. For example, Ellis et al. (2021) reported the need for persistence in reaching out to some families of Black children with type 1 diabetes to make contact and recruit for clinical trials. Use of various contact approaches (e.g., mail, phone calls, texts, in-person) was critical in ensuring families were able to understand and participate in the research process; though in many cases, this persistence may not be common in the conduct of traditional research studies. This study, moreover, is a key example of the multimodal recruitment strategies that may be necessary to engage underrepresented communities of color in historically White-centered research.
Measuring Adherence
Research teams should also be trained in how the history of racism in medicine may impact the perception of tracking adherence via electronic monitoring devices (e.g., MEMS® pill bottles), a common adherence measurement strategy. For example, some patients and/or caregivers may express reservations about health data being collected via adherence monitoring devices and have concerns about who will have access to their data and what those individuals will be able to see (Ramsey et al., 2018). Research teams should be trained in how to assess for, be sensitive to, and respond to these concerns during open, honest, and empathetic conversations (e.g., “I hear you have concerns about providing this information to our team. Can you tell me more about that?”; “How can we make your family feel more comfortable?”)
Despite the reconceptualization from compliance to adherence science, adherence research, by nature, assumes that patients and families are following medical recommendations that are decided upon by a medical expert. This can perpetuate a power differential between families and providers. While shared decision-making efforts have grown in acceptance and are designed to help mitigate these effects (Barry & Edgman-Levitan, 2012), the opinions of patients and families about their medications or proposed adherence regimens are not always included in the final regimen decisions. A lack of acknowledgment of a patient or family’s health beliefs, particularly if differing, may lead to patients and families to engage in adherence behaviors which are discrepant from prescribed regimens (e.g., Conn et al., 2007; Elliott et al., 2001). Thus, acknowledging the power differential between physicians and patients (e.g., Durand et al., 2014; Frosch et al., 2012), adherence researchers may wish to assess and address patient and caregiver understanding of, and agreement with, their prescribed regimen prior to measuring adherence and interpreting adherence data.
Discrepancies between patients and providers regarding treatment recommendations may also be related to issues of health literacy (e.g., difficulty calculating insulin ratios as part of diabetes management), access (e.g., patient splits time between households but was only prescribed one inhaler), or experiences of racism and discrimination (e.g., differences in prescribing practices, inequitable use of shared decision making). Further, to maximize the likelihood of obtaining an accurate estimate of adherence, research teams should recognize that the level of responsibility a youth has in managing their chronic condition changes across development and may vary across racial, ethnic, and sociocultural backgrounds (Yinusa-Nyahkoon et al., 2010). Research teams are encouraged to acknowledge and normalize that adherence is difficult every day and that all families and/or patients have things that get in the way of doing treatments. Such conversations open the dialog between researchers and patients and may allow for patients/families to accurately describe their adherence and barriers (Modi et al., 2009; Ramsey et al., 2018). We recommend multi-method assessment of adherence and adherence barriers, inclusive of qualitative descriptors (e.g., interviews, focus groups, written feedback), medical chart review, and quantitative data collection (e.g., electronic monitoring, Medical Adherence Measure, Barriers to Adherence Tool; Varnell et al., 2017; Zelikovsky & Schast, 2008) for a richer understanding of adherence-related concerns.
Finally, we acknowledge that many of the available measures of adherence were validated among samples described as predominantly White and that self-reported measures may only be available in English and/or be written at advanced reading levels. Further, analyses typically rely on normative data approaches and are conducted by comparing group differences, which may lose nuances in adherence when samples contain small numbers of participants of color. Given these limitations, adherence researchers should also strive to validate existing measures among samples of color and develop new measures and approaches to measurement and analysis as needed.
Reporting Adherence Science
Use System-Centered Language
When preparing study findings for publication, language selection is critical as the words we use can blame a person’s identity rather than the inequities inflicted upon that group. We recommend selecting language that does not perpetuate negative stereotypes of communities of color and adequately describes the researcher’s conceptualization and definition of race and ethnicity. Specifically, to describe the relationship between race and adherence, researchers could choose to say, “children with asthma who identify as Black or African American are exposed to additional harms that drive suboptimal adherence” instead of “Black or African American children with asthma are at-risk for non-adherence.” To provide readers with a broader understanding of this conceptualization, we recommend that researchers then elaborate on the “additional harms” that they believe may be associated with suboptimal adherence. For example, additional harms might include experiences of discrimination and racism in the medical system or broader red-lining practices that contribute to sub-standard living environments for individuals of color that can make adherence more difficult (e.g., exposure to environmental hazards, limited access to healthy food or social services). These recommendations align with guidelines for inclusive language recently published by the American Psychological Association (American Psychological Association, 2019, 2021) suggesting the importance of selecting language that reflects person-first and identity-first perspectives; denounces White-centeredness, hierarchies among populations, and historical patterns of epistemic racism; and communicates topics related to racial and ethnic identity with honor, inclusivity and respect.
Within adherence science, the term “adherence” has largely replaced the term “non-compliance,” as non-compliance carries with it connotations of blame. However, terms like “non-adherent” or “non-compliant” are often still ascribed to diverse populations and using either of these terms to describe specific populations can link negative and blaming language to those individuals rather than to systemic adherence barriers. Dickinson et al. (2017) have provided recommendations specifically around the use of language in diabetes care and education, suggesting the use of neutral, non-judgmental, person-first, and strengths-based language. In this consensus report, Dickinson et al. (2017) also discuss how adherence may be a less preferred term when communicating directly with patients and families and recommends consideration of alternative language such as engagement, participation, involvement, or medication taking (i.e., “They take their medication whenever their family is able to afford it.”)
Define Race and Culture Contextually
Researchers must be aware that race is socially and politically constructed, and that race and ethnicity are separate constructs (Braveman et al., 2017, 2022). When race, ethnicity, or other sociodemographic or cultural factors are included in statistical models, researchers should clearly describe why these variables were included and how these variables were collected and reported. To assist in these efforts, Palermo et al., (2021) provide instructions for reporting race and ethnicity in the Journal of Pediatric Psychology, a prominent journal for pediatric adherence science research. Specific attention is to be paid to terminology, sources used to identify race and ethnicity, reporting of race and ethnicity in sample description, and interpretation of race and ethnicity findings with explicit recognition of limitations (see Palermo et al., 2021).
In adherence science, the implications of not defining race and ethnicity contextually can lead to inaccurate generalizations about adherence among populations of color. For example, asthma prevalence and morbidity are higher among youth of color, and this discrepancy likely reflects that these youth face greater barriers to adherence than White youth due to systemic and structural factors (Asthma & Allergy Foundation of America, 2020). It is insufficient to say that youth of color experience “worse” adherence without acknowledging the existence of these adherence barriers. If adherence rates are consistently lower among communities of color, authors should discuss why this might be the case and propose hypotheses regarding the role of systemic racism on adherence behaviors (e.g., inequitable use of shared decision making) and in the broader field of science (e.g., measurement biases). A recent example examining adherence barriers in two racial groups (White and Black) found that Black children and caregivers experienced more healthcare system and community barriers that influenced seizure outcomes compared to White children who experienced more individual and family level barriers (Gutierrez-Colina et al., 2022). Authors discuss how systemic racism influences racial differences in adherence barriers and recommendations to reduce health disparities that affect Black children with epilepsy are highlighted.
Report Sample Heterogeneity
Race and ethnicity are often examined as predictors of adherence by comparing adherence among communities of color to White samples. Adherence researchers should consider whether such categorization and comparisons are appropriate or necessary for their work. As discussed by Buchanan et al. (2020), an alternative, more equitable approach is to report sample heterogeneity, particularly within samples or subsamples of communities of color, and to discuss differences in adherence across groups, rather than singling out a specific race or ethnicity and presuming that all participants come from similar backgrounds.
Reviewing Adherence Science
Recommendations for reviewing adherence science within a DEI framework are broadly applicable to all types of health and pediatric research. The review of research occurs at many stages of the research process, from inception to dissemination, but is often incumbent on grant reviewing agencies, institutional review boards, journal reviewers, associate editors, and editors to ensure the application of best practices around DEI. Standards around reviewing should be established by journals to include review of systems centered and bias free language, with checks and balances built into the review process by authors, reviewers, and editors. Further, standards around reporting race and ethnicity (more than 4–5 categories) and gender (male, female, transgender, etc.) would benefit psychological science broadly (Boyd et al., 2020). For example, Buchanan et al. (2020) highlight a problematic double standard that studies conducted among predominantly White samples do not typically identify their sample demographics in the paper title, whereas studies conducted among predominantly individuals of color are often required to indicate and provide rationale for doing so. Until journals and granting agencies require specific, equitable attention to DEI throughout the research process, scientists are less likely to be intrinsically motivated to ensure representation in their research (Burlew et al., 2019; Roberts et al., 2020).
Reviewing boards, whether for the National Institutes of Health or a specific journal, should have diverse representation, both from who the reviewer is demographically, to their areas of expertise and the types of research methodologies they conduct (Roberts et al., 2020). For example, community-engaged research (e.g., CBPR), qualitative and mixed-methods research (e.g., focus groups, in-depth interviews, usability testing), and dissemination-implementation science expertise should be represented among reviewer groups. These methods allow for diverse scientific thinking and a deeper understanding of the sociocultural influences on adherence science, leading to more thorough and inclusive review of adherence research. Consequently, attention to these considerations on review boards and panels incentivizes researchers to draw specific attention to these methodologies and other key DEI principles in submitted work, thereby advancing science bi-directionally.
Adherence science is conducted across a wide array of pediatric chronic conditions, which impact children from all walks of life. As reviewers, we should strongly critique research in which efforts were not made to reflect the true diversity of the population and which do not address the limitations of the study sample. To use clinical trials as an example, adherence science is substantially hindered when we develop and test interventions for the majority instead of incorporating the diverse and underrepresented perspectives of the full disease community.
Journal reviewers and editors should also consider the complexity of intersectionality as it influences adherence and self-management behaviors and encourage examination of data with this lens (Boyd et al., 2020; Raque et al., 2021). Further, journals should encourage authors to discuss the influence of social determinants of health and systemic racism on their findings (Boyd et al., 2020). However, for this level of reviewing to be possible, adherence researchers will need to measure and report on multiple constructs to ensure that race and ethnicity are not confounded with socioeconomic status (and other social determinants) or vice versa, and for both researchers and reviewers to evaluate factors predicting adherence through the lens of inclusion and intersectionality.
Finally, editors should be primed to review for inclusivity in publication practices to ensure an increasing trend in highlighting the work of individuals from diverse backgrounds. Given the gross underrepresentation and systematic exclusion of communities color in the scientific literature, including in pediatric adherence science, Buchanan et al. (2020) encourages recommending and prioritizing the citation and publication of articles and grants with representative samples, as well as works spearheaded by authors from communities of color. Editors and reviewers should note when citations do not appear representative of the diversity within the field and/or the particular area of research focus. Editors should also make efforts to collect demographic information inclusively and track outcomes over time to identify and systematically address any inequities in their publication or award practices.
Disseminating Adherence Science
Engaging communities of color in all phases of research is strongly recommended; however, it is particularly vital to the dissemination process. Traditionally, researchers develop dissemination plans without the input of the population served, limiting their reach and effectiveness. Consequently, messages about research findings may lack cultural relevance and dissemination strategies may fail to “meet communities where they are” (Bodison et al., 2015). Individuals and organizations within communities of color can help research teams leverage natural sources of dissemination (e.g., barber shops, churches, health collaboratives), craft culturally meaningful messages, and recommend the most relevant technology platforms (e.g., social media, blogs) for community dissemination. For example, while churches and related religious organizations may be a culturally relevant dissemination source, recruitment efforts in these locations without stakeholder input and support, particularly for psychological research, may be detrimental to community relationship building, stigmatizing, or even harmful, despite well-intended actions (e.g., McDade et al., 2021).
It is essential that adherence research findings and related interventions are disseminated to key stakeholders in the lives of children from communities of color, such as extended family members, teachers, school nurses, coaches, and other community organizations (e.g., faith-based youth groups, recreational centers). Stakeholders can be instrumental in (1) preventing suboptimal adherence (e.g., school nurses who can administer medications during school; Salazar et al., 2018), (2) mitigating adherence barriers (e.g., community health workers building trust with families to understand cultural beliefs around medications and support discussions with medical providers; Segal et al., 2020), and/or (3) sustaining ongoing adherence behaviors and health promotion behaviors (e.g., community pediatricians, coaches, or salient community stakeholders can encourage youth to continue to take their medications; Nieuwlaat et al., 2014; Spray & Hunleth, 2022). While recent examples suggest a growing desire to integrate stakeholders into dissemination plans, these groups have not been historically or universally included in dissemination plans. Barriers exist to implementation (e.g., time, experience, skill) and there is a need for more systematic guidance, training, and tools for successful use of strategies (e.g., Byrnes et al., 2019). In fact, recent research has suggested that even scientists with expertise in dissemination and implementation science, which not all adherence scientists may have, report varied and inconsistent use of strategies for including stakeholders as well as understanding which strategies are most effective and sustainable over time (e.g., Knoepke et al., 2019).
For pediatric adherence science, applying an ecological model, like the Pediatric Self-Management Model (Modi et al., 2012), to dissemination planning helps ensure that research findings and interventions will be disseminated beyond the family to the larger community stakeholders who could greatly benefit from findings. For example, researchers could plan to disseminate findings to the community via postcards summarizing best practices that could be hung in school clinics or recreation centers. For pediatric adherence science, specifically, lay summaries around general adherence outcomes (e.g., known barriers and facilitators of adherence behaviors) and/or effective behavioral intervention strategies (e.g., pill swallowing, environmental restructuring, use of reminders) can be provided to school nurses to assist with self-management in that environment. Further, school nurses may also be utilized as a key stakeholder to help provide important feedback on how to build upon or disseminate behavioral interventions more broadly into school environments beyond a specific clinical trial or to establish an effective sustainability plan at the end of a given study.
To promote broader use of research and intervention findings in the community, the research team could participate in a community dissemination conference where community stakeholders not involved in the research (e.g., agencies with after-school programs) are invited to consider how the findings might be applied in their setting (Khodyakov et al., 2014). This can provide critical information on not only the utility of our work, but also address perceived need in the community. Further, insights provided by community members who join can be vital to understanding potential barriers and facilitators to community dissemination and problem solving around issues raised. Finally, stakeholder voices, especially those with influence in the community (e.g., pastors, community leaders) can collaborate with researchers through this platform to inform, review, and modify existing dissemination plans as well help design future research efforts around effectiveness and translatability.
Adherence research findings often fail to reach policy makers and healthcare organizations who develop healthcare and adherence-related reimbursement guidelines. Incorporating policy and public health-focused strategies (e.g., policy briefs, storytelling/patient narratives) can facilitate dissemination to these groups. This is especially important when the research is conducted with communities of color, as existing policies and practices may have been developed using data from predominantly White populations and may not adequately address social determinants experienced by communities of color (e.g., access to care). Thus, we encourage researchers to disseminate research findings (e.g., adherence preferences, what works best for whom) through policy briefs and op-eds as a means for advocating for equitable practices to optimize adherence. More information on how to write these documents has been published by the American Psychological Association (Lee, 2018; Wong et al., 2017).
Conclusion
DEI efforts are gaining increasing momentum within academic medical centers and have the potential to dramatically change pediatric psychology clinical practice and research. Recent world events, including the COVID-19 pandemic and public racial injustices against communities of color, have resulted in critical action steps that will hopefully result in a necessary paradigm shift. Buchanan et al. (2020) is one such example in which detailed guidance is provided for dismantling racism and White supremacy in psychological science, including pediatric psychology.
Adherence science, as an overarching topic that affects almost all pediatric populations, presents unique considerations relevant to DEI, including the known relations between suboptimal adherence and disparate health and psychological outcomes for communities of color (e.g., Schwartz et al., 2010; Walsh et al., 2014). Systemic barriers further contribute to variability in adherence behaviors across race, ethnicity, sex, socioeconomic advantage/disadvantage, and more (e.g., Gregerson et al., 2019; Lee et al., 2019; Majidi et al., 2021; Redmond et al., 2021), which may subsequently contribute to health inequities. Despite these known truths, we have not systematically integrated DEI strategies into pediatric adherence research. One driver of this gap may be a lack of practical guidance on how to optimally integrate DEI into adherence science; the purpose of this manuscript was to begin to address this need.
There remains a vital need for psychological science, including adherence science, to take meaningful action toward the application and practice of DEI in all aspects of our work. However, the road to accomplishing these goals is neither linear nor without complications. Adherence scientists will undoubtedly continue to face the challenges of adapting to and implementing DEI principles into practice over time, and well-intended action may not always produce desired equitable effects. In anticipation of these challenges and the likelihood that we will be most successful in DEI efforts if we can learn from the experiences of our colleagues, the current commentary described several challenges or barriers faced by our adherence science colleagues and offers potential, practical solutions to overcome them based on this experience. Doing so will better equip our scientists and practitioners to deliver equitable science and care.
Author Contributions
All authors have contributed to the conceptualization, drafting, and editing of the current manuscript and approve submission of this manuscript in its present form.
Funding
Several authors are funded by training and career development awards from National Institutes of Health. These include the Eunice Kennedy Shriver National Institute of Child Health and Human Development (T32HD068223; DW, RS), the National Heart, Lung, and Blood Institute (K23HL13992; RR), and the National Cancer Institute (K07CA200668; MM).
Data Availability
Not applicable.
Code Availability
Not applicable.
Declarations
Conflict of interest
The authors declare no conflict of interest.
Ethics Approval
Not applicable.
Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
1 “Communities of color” is used throughout this manuscript per recent inclusive language guidelines published by the American Psychological Association (2021). For more details, visit: https://www.apa.org/about/apa/equity-diversity-inclusion/language-guidelines.pdf.
Authors self-identify as a multiracial, Afro-Latina and Indigenous woman (DW); a White woman (RS, RR, MM); an African American woman (LC); and an Asian Indian woman (AM).
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Lori E. Crosby and Avani C. Modi are Co-Senior Authors.
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Current business challenges mean that understanding elements that can affect organizational performance represents a differential factor in maintaining competitiveness. In this context, the objective of this article is to conduct a Systematic Literature Review (SLR) of the relationship between dynamic capabilities, strategic behavior, and organizational performance. For this, A three-stage SLR protocol was used: (i) planning, (ii) conduct, and (iii) knowledge development. A total of 118 articles covering the publication period of 2006–2021 were included, which evidenced: (i) the grouping of words into three classes: “Knowledge Management,” “Measurement Instrument,” and “Organizational Environment”; (ii) the methodological framework; (iii) directions for future research. The findings reinforce the importance of the theoretical, methodological, and empirical relationship between the three constructs. Furthermore, the results indicate the relationship between the set of terms selected in each class, highlighting the strong connection between dynamic capabilities and competitive intensity. The main findings of the research show that organizations can expand or modify their processes by building and using dynamic capabilities as institutional factors, shaping strategic behavior to advance better performance.
Keywords
Organizational performance
Dynamic capabilities
Strategic behavior
Systematic literature review
JEL Classification
D23
L2
L21
M1
http://dx.doi.org/10.13039/501100002322 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Finance Code 001 Drago Henrique Faverzani issue-copyright-statement© Springer Nature Switzerland AG 2023
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pmcIntroduction
To achieve sustained competitive advantage and efficiency, organizations need to adapt to both internal and external environments to establish a position in their sector based on available resources, skills and capabilities (Behl et al. 2022). They must thus focus on strategic behaviors (Al-Ansaari et al. 2015; Adewunmi et al. 2017; Bilgili et al. 2022); a company’s strategic behavior informs its approach to activity monitoring and performance achievement (Masa’deh et al. 2018; Tsai and Tsai 2022).
In highly dynamic sectors, both the approach based on the Structure-Conduct-Performance paradigm and a resource-based approach are limited in their ability to explain the sources of competitive advantage and performance achieved from strategic choices. By emphasizing a company's adjustment to highly dynamic environments, while facing the challenges imposed by volatile sectors through integration, reconfiguration, competencies and renewal of resources, dynamic analysis is a promising alternative to understanding sustainable sources of advantage and how such advantages are developed and implemented (Medeiros et al. 2020). The current business environment is challenging for organization survival. Performance measurement mechanisms can guide strategy implementation through performance monitoring, enabling organizations to achieve strategic goals and collect useful data to improve their performance (Owais and Kiss 2020; Pekovic and Vogt 2021).
It is particularly important to understand how the relationship between the three constructs of dynamic capabilities, strategic behavior and organizational performance has been addressed in the scientific literature. The present article presents a Systematic Literature Review (SLR) of the relationship between these three constructs using the Scopus and Web of Science (WoS) databases. The SLR protocol comprises three phases: (i) planning the SLR; (ii) conducting the SLR; (iii) dissemination of knowledge (Tranfield et al. 2003; Kitchenham 2004; Biolchini et al. 2007).
Previous research has not incorporated a joint analysis of these three constructs. An example is Zhou et al. (2019), which showed how dynamic capability leads a company to obtain a competitive advantage and improve its organizational performance, understanding that dynamic capabilities will be effective when leveraged by good strategies. Ringov (2017) addressed the juxtaposition of conflicting statements about the relationship between coded dynamic capabilities and company performance at different levels of environmental dynamism, concluding that the performance contribution of coded dynamic capabilities decreases as the dynamic environment increases. Shams and Belyaeva (2018) only analyzed dynamic capability, but related it to similar themes such as strategic management and competitive advantage. Analysis of the relationship between strategic behavior and performance was identified and verified by Silveira-Martins et al. (2014) in the context of the wine industry in Portugal. Benitez and Damke (2016) addressed the relationship between strategic behavior and organizational performance, analyzing the behavior of small companies in the retail sector from the perspective of the Miles et al. (1978) typology.
This SLR is therefore the first to offer a joint analysis of all three constructs of dynamic capabilities, strategic behavior and organizational performance. It aims to further understand the evolution of these themes and provide insights for future empirical research. It demonstrates how a combination of these constructs can influence companies' organizational performance and help improve their strategies to achieve better results and organizational effectiveness.
After this brief introduction, the article is structured as follows: in the second section, the theoretical bases of the research are discussed; the third section contains a discussion of the applied methodological procedures; the results are presented and analyzed in the fourth section; the fifth section contains the conclusions and limitations of the review.
Background
Figure 1 shows the relationship between dynamic capabilities, strategic behavior and organizational performance.Fig. 1 Relationship between dynamic capabilities, strategic behavior and organizational performance.
Source: Research data
Dynamic capabilities
In recent years, a new approach has emerged from strategy theory that allows organizations to revise their tactics towards a comprehensive chance of success (Zea-Fernández et al. 2020; Michaelis et al. 2021). This novel approach, known as Dynamic Capabilities (DC) theory, focuses on an organization’s ability to create, renew, modify, integrate and reconfigure its mix of resources in a rapidly changing environment to achieve high returns, sustainability and long-term competitiveness (Teece et al. 1997; Londoño-Patiño and Acevedo-Álvarez 2018; Weiss and Kanbach 2021). There are three key aspects that motivate the use of DC theory: first, that companies with a high level of dynamic capabilities are intensely entrepreneurial; second, that these are formed by innovation and collaboration with other organizations; third, that the knowledge asset is the most difficult to replicate (Teece 2011; Villafuerte-Godínez and Leiva 2015).
According to Vivas-López (2013), dynamic capabilities, in addition to being a source of new resources for the company, are a powerful tool for organizational strategists. These capabilities will enable the activation and reorientation of the complex network of economic and organizational factors, helping to control the company’s evolution and enhance future options or business opportunities. Dynamic capabilities are therefore key factors in innovating and optimizing the overall strategic course. In a dynamic context (Schumpeterian, evolutionary, rapid change or high speed, according to different authors), if a company intends to maintain its competitive advantage, it must be able to change (adapt, evolve, renew, adopt and reconfigure) (Teece et al. 1997; Eisenhardt and Martin 2000). Consequently, the concept of dynamic capabilities emphasizes the ability of a company and its managers to continuously modify resource allocation in a flexible and adaptable way in response to environmental changes (Vivas-López, 2013).
The new school of dynamic capabilities indicates that one source of knowledge is universities. Dynamic capabilities contribute to the development of knowledge within higher education institutions, integrating curricula, encompassing the importance of creativity, knowledge transfer, protection of intangible resources, technological know-how, relationships and new forms of organization. Emphasis is placed on soft assets (knowledge) that allow the synchronization of internal and external resources to address environmental challenges (Teece 2007; Rodríguez-Lora et al. 2016).
According Vodovoz and May (2017), DC theory allows us to understand the value creation process mediated by operational capabilities. Case studies have reaffirmed the tendency of DC theory to represent, with quality, a theoretical framework for the analysis of aspects related to value creation (new products and service channels) and new business model development (to achieve new consumer niches).
Strategic behavior
Strategic behavior is of particular importance in organizations because it is linked to outcomes (Bruner et al. 1986) and considers the potential future reactions of others (Burks et al. 2009). According Mintzberg (1987), strategic behavior involves setting goals, determining actions and mobilizing resources to achieve these goals. Therefore, it also involves planning and executing behaviors that make achieving those goals possible. Behling and Lenzi (2019) argued that strategic behavior encompasses the process of organizational adaptation to environmental turbulence, involving the internal dynamics of the organization. In other words, strategic behavior is characterized by the ways companies align themselves with the external environment and the choices they make over time. Likewise, Krishnamoorthi and Mathew (2018) showed that strategic behavior is the result of an orientation to international evidence regarding competition, innovation, opportunities and added value through organized actions in adapting to market changes. According to Agrell and Teusch (2020), companies can collude with their rivals to increase company profits, for example, by setting prices above competitive levels. Furthermore, it may be rational for companies to propose mergers, even in the absence of any merger-related efficiencies, because the transaction allows them to exercise market power and raise prices (unilateral effects) or because mergers facilitate collusion (coordinated or collusive effects).
By separately analyzing the concept of strategy, Svobodová and Rajchlová (2020) claimed that the creation and implementation of strategy are essential for operational planning, as it increases efficiency and leads to long-term benefits. Each company must determinei its strategy based on the environment in which it operates, its portfolio and the specification of concepts, principles and detailed plans for development and behavioral approach. Hughes et al. (2021) presented the idea that contrasting behaviors drive strategic entrepreneurs and that opportunity-seeking behavior is a function of a company's entrepreneurial strategy. Through entrepreneurial behaviors, a company is expected to develop competence by identifying a flow of rich opportunities to foster innovation. However, opportunities alone cannot create innovation, as the latter also depends on the resources attracted to the company. The strategic management of resources is a construct that conceptualizes behavior in search of advantage (Yin et al. 2021).
According to Hussein and Hafedh (2020), strategic behaviors are one of the most important topics in the field of strategic management and organizational behavior. They also focus on the nature of the behaviors adopted by senior management when dealing with human resources within the organization and with other external parties. These behaviors represent the mechanism for many strategic future decisions. Strategic behavior influences human resources at different organizational levels, but reflects the orientations of senior leadership and affects the nature of strategic direction.
Effective decision-making is an area of strategic behavior that has been widely discussed in practice and research (Staszkiewicz and Szelągowska 2019; Khanin et al. 2021). Research is mainly based on two theories: the principal agent theory and the theory of market competition. The former emphasizes that managers are motivated by shareholders or owners to reduce production costs and optimize production processes; the latter highlights the impact of market competition outside a company on the strategic behavior of production, research and development (Zhao et al. 2021; Ball 2021).
Organizational performance
Organizational performance is the result of the ability of entrepreneurs to formulate strategies that align the organization with the increasingly complex and dynamic environmental changes, and is concerned with the measurable fulfillment of organizational objectives (Meinhardt et al. 2018; Abubakar et al. 2019; Schwens and Wagner 2019; Marzall et al. 2022). Laaksonen and Peltoniemi (2018) and Rehman et al. (2019) believe that organizational performance is a significant indicator in achieving established organizational goals and objectives. Lee and Choi (2003) and Martín-Castro (2015) showed that organizations that learn more efficiently show better long-term results than their competitors. Performance can also be enhanced by improving individual knowledge within a culture of continuous organizational learning.
Nitzl et al. (2019) suggested that the use of information related to organizational performance is intended to facilitate decision-making to fulfill predefined goals. Such uses (decision facilitators) include monitoring (setting and monitoring goals, comparing expected and actual results), focus of attention (providing guidelines for the organization) and uses of strategic decision-making (supporting non-routine decisions). Zehir et al. (2016) argued that when implementing a planned strategic objective, the goals are intended to achieve efficiency, effectiveness and innovation. Wood and Ogbonnaya (2018) showed that a model of mutual gains is capable of producing superior organizational performance, as it supports the high involvement of managers and leads to a mutually beneficial situation for both employees and employers. It is therefore a distinct management approach as it delivers high levels of employee satisfaction and well-being and encourages employees to take a positive attitude towards the organization. A high-performance work system promotes a strong organizational environment in which employees feel that they belong and thus are willing to make extra efforts to achieve organizational objectives and improve performance (Kellner et al. 2016). In other words, a high-performance work system results in an increase in the value, individuality and inimitability of employees’ knowledge and skills, which, in turn, generate a competitive advantage and better performance (Zhang and Morris 2014).
Considering the emergence of new technologies and changes in the market, customers and suppliers, in addition to crises, the dynamic capabilities of innovation, entrepreneurship, organizational learning and market orientation are recognized as capabilities to achieve advantage and improve the relationship between resources and organizational performance (Henri 2006).
Materials and methods
The present study comprises an SLR that aims to address the following research question: “How has the relationship between dynamic capabilities, strategic behavior and organizational performance been addressed in the scientific literature?”. The purpose is to highlight theoretical gaps to inform new research. We adopted the protocol developed by Tranfield et al. (2003), with three steps: (i) planning the SLR; (ii) conducting the SLR; (iii) disseminating knowledge. This protocol is widely used to review scientific literature in the field of management (Klewitz and Hansen 2014; Araújo et al. 2018; Rojon et al. 2021; Guido et al. 2022; Fabrizio et al. 2022).
Planning the SLR
To guarantee the originality of our review, we searched the Scopus and WoS (Core Collection) databases to identify possible reviews involving the concepts of organizational performance, dynamic capabilities and strategic behavior together. Scopus and WoS (Core Collection) are widely used in different fields of knowledge. In addition to serving as a tool for retrieving information, they also facilitate the selection and analysis of scientific literature from a range of interests published in many languages (Barnett and Lascar 2012; Okhovati et al. 2017). Although WoS records date back to 1945, the publications from Scopus start from 1960, therefore we used 1960 as the starting point in both databases. Table 1 presents the resulting strings and number of systematic review articles. Table 1 Strings and number of systematic review articles from the database searches
Databases String Results
Scopus (TITLE-ABS-KEY ((“organizational performance*”) AND (“dynamic capacity*” OR “dynamic capability*” OR “strategic behavior*”) AND (“systematic review” OR “systematic literature review”)) AND DOCTYPE (ar OR re) AND PUBYEAR > 1959 AND PUBYEAR < 2022) AND (LIMIT-TO (LANGUAGE, “English”)) 1
Web of Science TS = ((“organizational performance*”) AND (“dynamic capacity*” OR “dynamic capability*” OR “strategic behavior*”) AND (“systematic review” OR “systematic literature review”)) AND LANGUAGE:(English) Indexes = SCI-EXPANDED, SSCI, AandHCI, CPCI-S, CPCI-SSH, ESCI Timespan = 1960–2021 0
Source: Research data
Our preliminary search found only one article that jointly addressed the three constructs and sought to demonstrate the important role of dynamic capabilities in the relationship between knowledge asset management and company performance (Moustaghfir 2008). The article argued that effective knowledge asset management increases the value of organizational competencies, which in turn support organizational processes, products and services. In this respect, dynamic resources assume the role of continuously modeling operational routines and competencies and, consequently, offer superior long-term performance. Moustaghfir (2008) drew attention to dynamic capabilities as a missing component in the relationship between knowledge assets and company performance. These insights represent the theoretical basis for the development of a conceptual framework for how the effective management of knowledge assets affects the overall performance of the business and improves value-generating activity. However, strategic behavior involves the accumulated knowledge that arises from decisions based on the organizational adaptation process in the face of environmental turbulence, which involves the dynamism of the organization (Behling and Lenzi 2019). It is therefore timely to conduct an SLR focusing on the direct relationship between dynamic capabilities, strategic behavior and organizational performance in view of this theoretical gap.
Conducting the SLR
The second step involved a broad and unbiased search of articles in the corpus with the aim of minimizing selection bias. The search strategy involved using keywords within the topics of organizational performance, dynamic capabilities and strategic behavior, combined with Boolean operators, as shown in Table 2.Table 2 Number of articles founded in Scopus and Web of Science
Databases String Results
Scopus TITLE-ABS-KEY ((“organizational performance*”) AND (“dynamic capacity*” OR “dynamic capability*” OR “strategic behavior*”)) AND (PUBYEAR > 1959 AND PUBYEAR < 2022) AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO ( SUBJAREA, “BUSI”) OR LIMIT-TO ( SUBJAREA, “ECON”)) AND ( LIMIT-TO (LANGUAGE, “English”)) 117
Web of Science TS = ((“organizational performance*”) AND (“dynamic capacity*”OR “dynamic capability*” OR “strategic behavior*”)) and Management or Business or Economics (Web of Science Categories) and English (Languages) and Article (Document Types) Indexes = SCI-EXPANDED, SSCI, AandHCI, CPCI-S, CPCI-SSH, ESCI Timespan = 1960–2021 69
Source: Research data
Of the 186 articles identified (Fig. 2), 24 duplicates were removed. Reputation and representativeness of journals were evaluated through the citation quartiles of Scimago Journal and Country Rank (SCIMAGO 2019); of the 162 remaining articles, 138 belonged to the first two citation quartiles (Q1 and Q2), meaning that a further 24 articles were excluded.Fig. 2 Selection process of textual corpus.
Source: Research data
Figure 2 shows that the 138 articles were from journals classified in SJR Q1 and Q2 (SCIMAGO 2019). Journals in these first two quartiles have lower acceptance rates than those in the third and fourth quartiles or those Without Classification (WC). They are therefore more productive, more selective and publish higher quality work than those in Q3 and Q4 (Gu and Blackmore 2017; Kaczam et al. 2022).
The thematic adherence of the articles was then evaluated. After reading the abstracts, 118 articles were determined to constitute the textual corpus, 79 from Scopus and 39 from WoS.
Knowledge dissemination
Knowledge dissemination comprises the presentation and discussion of the results and conclusions. We used RStudio, Gephi® and Iramuteq software (Bastian et al. 2009; Souza et al. 2018; Guleria and Kaur 2021). Descriptive information was obtained from the textual corpus and emerging themes via RStudio. The bibliographic coupling network was extracted with the aid of Gephi®. Word clusters and relationships between words were extracted with Iramuteq.
Results
This section presents the results of the textual corpus analysis (118 articles). First, a descriptive analysis of general characteristics is presented. Second, the bibliographic coupling network shows how the articles in the corpus are connected. Third, based on the descending hierarchical classification of words, a typology of three classes is presented. Finally, a set of suggestions for future research is provided.
Descriptive analysis
Figure 3 shows the annual distribution of the 118 articles in the textual corpus in the period between 2006 and 2021. A total of 12 articles were published in the first five years (2006 to 2011), which is equivalent in relative terms to 10.17% of the published works and a geometric average of 1.84%. This reveals a slight growth trend of this theme over time.Fig. 3 Annual distribution of the textual corpus.
Source: Research data
Between 2012 and 2016, 28 articles were published, corresponding to an accumulated 23.73% and an average production of around 4.40%. This revealed a rise of 2.56% from the previous five-year period, with particular mention for 2016 in which nine articles were published. The last 5 year period, 2017–2021, saw a further 78 articles, which corresponds in relative terms to 66.10% of the works published. This period also saw more diversity in the journals in which publications appeared, in addition to an average production of around 10.18% and a growth of 5.78% compared with the previous 5 year period. The year 2020 stands out as particularly productive, when there were 31 articles published, corresponding to 26.3% of the articles in the whole corpus. Figure 4 shows descriptive statistics on the topic of study.Fig. 4 Corpus bibliometric indicators.
Source: Research data
Figure 4 shows that the corpus contains 89 journals and 350 authors and co-authors. There was an average of 33.69 citations per document, 5.27 citations per year per published document, 0.34 documents per author and 2.97 authors per published document. Regarding the level of cooperation between authors, there is a lack of researchers with single authorship, with some authors involved in more than one work. In other words, 31 authors share authorship with other authors, generating a collaboration index of 2.79, in addition to 3.04 co-authors per document. There were 499 keywords plus from the databases and 212 keywords defined by the authors, which is relevant to the formulation of the analyses of Zipf's third bibliometric law (Piantadosi 2014).
Bibliographic coupling analysis
The bibliographic coupling analysis aims to show which authors are closer to others in their reference lists, thus establishing theoretical alignment between these authors. This section aims to assess which authors are more bibliographically coupled in terms of intensity, highlighting the most prominent themes. The degree of theoretical or methodological proximity is evaluated from a list of references of pairs of researchers, based on the assumption that if two works refer to the same source, they have proximity. This consequently favors emergence of new research fronts, as advocated by Kessler (1963) and Zhao and Strotmann (2008).
To design the bibliographic coupling network using the Gephi® software, we used the distribution algorithm developed by Fruchterman and Reingold (1991). For the grouping of network elements, we used the modularity class statistic as indicated by Newman (2006) and where the size of the vertices is proportional to the eigenvector centrality statistic (Prell 2012).
Figure 5 shows the network formulation containing 34 authors coupled from the textual corpus based on their bibliographic references, distributed in three clusters:(i) Green cluster: composed of seven articles, in which the main highlight is Singh and El-Kassar’s (2019) “Role of big data analytics in developing sustainable capabilities”, published in the Journal of Cleaner Production. The main objective of this work was to examine the extent of sustainable capabilities driven by corporate commitment resulting from the integration of big data technologies, green supply chain management and green human resource management practices, and the extent to which these capabilities can improve the performance of the company as a whole. The results of this study show the influence of big data-driven strategies on business growth in terms of sustainable performance, considering the internal processes that constitute sustainable capabilities.
(ii) Red cluster: formed by 17 articles, in which the work of Wilden et al. (2013), entitled “Dynamic Capabilities and Performance: Strategy, Structure and Environment”, is highlighted. This paper was published in Long Range Planning and its main objective was to assess, theoretically and empirically, whether the effects contingent on the organic organizational structure facilitate the impact of dynamic capabilities on organizational performance. The research evidenced the performance effects of the internal alignment between organizational structure and dynamic capabilities, and the external adjustment of dynamic capabilities with competitive intensity.
(iii) Blue cluster: formed by 10 articles, the main highlight being the work by Moon (2010), entitled “Organizational Cultural Intelligence: Dynamic Capability Perspective”, published in Group and Organization Management. The main objective was to propose a nomological network for organizational cultural intelligence (CQ) models that sheds light on the role of organizational CQ and the underlying mechanism of the relationship between organizational CQ and organizational performance, as well as intermediate performance outcomes (international performance). The results showed that the organizational CQ approach attempts to provide a coherent framework that can integrate the conceptual theory of cultural intelligence at the micro level and build on the theoretical foundations of dynamic capability.
Fig. 5 Network of bibliographically linked documents.
Source: Research data—estimated by Gephi® software
In all the clusters, the above authors had the highest estimated values for the betweenness centrality statistic (32.23, 10.47 and 8.11, respectively). They also shared the most references with two other authors in the network. The coupling analysis makes it possible to show, in a generalized way, the close theoretical relationship of the highlighted authors, with convergence in terms of citation of classic authors.
Word cluster analysis
This section is intended to provide a detailed analysis of the keywords in the corpus, grouping them according to frequency of occurrence, to enable the identification of lexical content and centrality (Mendes et al. 2016). We used the method of Reinert (1990), reported as Descending Hierarchical Classification (DHC), to present the formulated classes grouped into classes considering the 118 article abstracts. Iramuteq software allows for different forms of analysis of the textual corpus, such as the classic lexical analysis through co-occurrences, as shown in Fig. 6.Fig. 6 Grouping of highlighted words in the textual corpus.
Source: Research data
The formulation of the word groupings displayed in Fig. 6 considered the most frequent terms extracted from the abstracts. The terms described in their literal form contained in the search string (Organizational Performance, Dynamic Capability and Strategic Behavior) were excluded, since they would naturally be present in the formulation process. We considered the word incidence matrix, where the size of the terms and their centering in the word map is proportional to their occurrence.
To conduct this analysis, 660 text segments were evaluated, equivalent to 74.55% correctly classified extracts. The retention of text segments must be at least 70% for the DHC analysis to be adequate, therefore this analysis can be considered statistically representative (Camargo and Justo 2013). We show that 23,570 occurrences emerged, categorized as words or forms, with 1,871 words characterized as active forms. In addition, there were three classes containing the following compositions: Class 1, with 236 text segments (47.97%); Class 2, with 133 text segments (27.03%); Class 3, with 123 text segments (25.00%). The corpus content was subsequently analyzed, leading to the categorization of the three classes that were renamed from the content analysis technique.
To provide detail on the content of the classes contained in Fig. 6, we present the eight terms containing the highest probability values (p value) associated with the chi-square statistic. We set a minimum frequency of five occurrences and considered a critical value of the chi-square statistic as greater than 3.80 (χ2 > 3.80), so that the terms are statistically significant or, alternatively, a probability value lower than 5% (p value < 0.05). A p value < 0.05 refers to the level of significance adopted so that there is an association between words and classes, as recommended by Reinert (1990).
When analyzing the words contained in Class 1, “Knowledge Management”, the following are highlighted in descending order of frequency of occurrences and chi-square statistics: Knowledge, Resource, Ability, Relationships, Competency, Innovation, Opportunity, Creation. These terms are recurrent in other findings, as described by Moustaghfir (2008), Criado-García et al. (2020) and Arun and Ozmutl (2021). It can be clearly seen that the respective authors sought to apply the concept of dynamic capabilities linked to organizational competencies that, consequently, influence the processes, products and services necessary for rapid changes for the development of dynamic capabilities and improved performance. Tseng and Lee (2014) showed that dynamic capability is an important intermediary organizational mechanism through which knowledge capability benefits are converted into enterprise-level performance effects. In other words, knowledge capacity increases the dynamic capability of organizations, which, in turn, increases organizational performance and provides competitive advantages.
For the analysis of Class 2, “Measurement Instruments”, the following words are highlighted in descending order of frequency of occurrences and chi-square statistics: Equation, Hypothesis, Partial Least Square, Questionnaire, Respondent, Methodology, Regression, Correlation. Since 92.47% of the articles are characterized as quantitative, using data collection instruments such as structured questionnaires, it is to be expected that the overwhelming majority of articles used relational data analysis tools. We highlight the research conducted by Wilden et al. (2013), Zhou et al.(2019), Cake et al. (2020) and Lee et al. (2020). In all the quantitative studies evaluated here, several used methodologies to test theoretical hypotheses for principles that support the reduction of waste, increase of efficiency and maximization of organizational performance. For example, Hair Jr. et al. (2005) showed that the form of relational modeling, through the technique of structural equations, seeks to explain the interrelationships between variables from a series of multiple regression equations. These equations aim to describe the constructs which, in turn, are latent factors composed of multiple variables. In the analyzed articles, we noted, from the use of confirmatory models with theoretical support, the testing of hypotheses that sought to relate organizational dynamic capabilities with the alignment of processes and their organizational performance. For this purpose, moderator variables and their organizational performance were used in a complementary way, as mediators as well as the “multigroup” analysis technique, by incorporating sociodemographic characteristics or linked to managerial aspects.
Analogous to the previous class, in Class 3, “Organizational Environment”, the following terms can be highlighted: Practitioner, Exploitation, Exploration, University, Public, Organization, Strategy, Ecosystem. Greater occurrences of these terms occurred in Yang et al. (2016), Vogus and Rerup (2018), Napathorn (2021) and Widianto et al. (2021). In a generalized way, we perceived that the articles assess the development of skills of organizations in the institutional context by evaluating the relationships between the characteristics of the mental model of a given work team in making strategic decisions for organization performance. In this context, the set of terms selected have a strong connection with the effects of dynamic performance capabilities which, in turn, depend on the competitive intensity faced by companies. The organizational environment evidenced from the co-occurrences of the words in the corpus is justified based on variables related to public institutions, strategies adopted by companies and in the market, so that such organizations can expand or modify their processes.
Suggestions for future research
The results of this SLR lead to some suggestions for future research on the theme of dynamic capabilities, strategic behavior and organizational performance in different types of organizations. In particular, future research should: (i) empirically test the insertion of moderating or mediating variables, using structural equation models to assess their relationships with dynamic and substantive capabilities (Ali et al. 2012); (ii) investigate the effect of organizational capabilities, such as marketing, research and development, IT and supply chain capabilities on organizational performance (Yu et al. 2018); (iii) investigate how CEOs' personal beliefs influence the dynamic capabilities of companies in longitudinal terms, so that dynamic components and their individual characteristics can be captured (Von den Driesch et al. 2015); (iv) survey whether the effect of organizational learning moderates the relationship between strategic changes and organizational performance, in addition to empirically testing the effect of company size and industry type on organizational performance (Yi et al. 2015); (v) investigate the effects of COVID-19 on resilient organizations’ superior performance, from the perspective of strategic behavior (Eklund 2021).
Conclusion
This SLR contributes to a greater understanding of the interface between dynamic capability, strategic behavior and organizational performance in theoretical, methodological and empirical terms. In methodological terms, 25 of the articles, (21.19%) are qualitative studies and 93 (78.81%) are quantitative. Interest in the subject has grown over the time, with the highest number of published articles appearing in the last five years; 66.10% of the articles were published between 2017 and 2021 and the most productive year was 2020. Theoretically, there was an average of 33.69 citations per document, 5.27 citations per year per published document, 0.34 documents per author and 2.97 authors per published document. In empirical terms, among the quantitative metrics, structural equation modeling occurred in 65 works (69.89%), followed by regression analysis technique with 11 occurrences (11.83%).
The network formulation contains 34 authors coupled according to their bibliographic references, distributed over the three clusters of articles in the corpus, where each node represents one of these articles. Among the highest values of the betweenness centrality statistic, in the seven articles in the green cluster, prominent authors are Singh and El-Kassar (2019), of the 17 articles in the red cluster, the work of Wilden et al. (2013) is highlighted and in the 10 articles in the blue cluster, Moon (2010) is noteworthy.
Word analysis in terms of frequency of occurrence and chi-square statistics showed the following: Class 1 (“Knowledge Management”) contained Knowledge, Resource, Ability, Relationships, Competency, Innovation, Opportunity, Creation; Class 2 (“Measurement Instruments”) featured Equation, Hypothesis, Partial Least Square, Questionnaire, Respondent, Methodology, Regression, Correlation; Class 3 ("Organizational Environment") highlighted Practitioner, Exploitation, Exploration, University, Public, Organization, Strategy, Ecosystem.
As a general result, the relationship between the set of terms selected in the class has a strong connection with the effects of dynamic performance capabilities which, in turn, depend on the competitive intensity faced by companies. Furthermore, the organizational environment evidenced from the co-occurrences of the words in the corpus is justified based on variables related to public institutions, strategies adopted by companies and the market. In this respect, the data relatively confirm the fact that such organizations can expand or modify their processes by building and using dynamic capabilities as institutional factors as they seek to shape their behavior.
Both the theoretical framework and the results of bibliometrics point towards future research on the theme of dynamic capabilities, strategic behavior and organizational performance. Although this work presents a systematic and exhaustive review of the literature, there are some limitations, especially with regard to the selection of the textual corpus, which can be considered in future research: (i) the scientific production for analysis was limited to the Scopus and WoS databases; (ii) only articles published in English were considered; (iii) we only considered articles published in journals; (iv) “Accounting”, “Business”, “Management”, “Economics” and related fields were used as filters. Therefore, it is possible that relevant research has been published in other formats (e.g., books, book chapters, conference proceedings,), in different languages, in other databases or in other areas of knowledge.
Acknowledgements
We are grateful to the financial support and granting scholarship by the Research Support Foundation of the State of Rio Grande do Sul (FAPERGS), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES). We also thank the Editor Neda Ghatrouei and anonymous reviewers for their contributions and recommendations. All comments were constructive, and we believe our revised manuscript was significantly improved by addressing the comments and suggestions.
Funding
Research Support Foundation of the State of Rio Grande do Sul (FAPERGS), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.
Availability of data and materials
Not applicable.
Declarations
Conflict of interest
There are no conflicts of interest for the authors listed.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
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| 0 | PMC9734316 | NO-CC CODE | 2022-12-14 23:28:27 | no | SN Bus Econ. 2023 Dec 8; 3(1):5 | utf-8 | SN Bus Econ | 2,022 | 10.1007/s43546-022-00392-2 | oa_other |
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J Plant Pathol
Journal of Plant Pathology
2239-7264
Springer International Publishing Cham
1287
10.1007/s42161-022-01287-9
Review
Understanding tobamovirus-plant interactions: implications for breeding resistance to tomato brown rugose fruit virus
Sánchez-Sánchez Mario 1
Carrillo-Tripp Jimena 2
Aispuro-Hernández Emmanuel 1
Quintana-Obregón Eber Addí 3
http://orcid.org/0000-0003-2667-5455
Martínez-Téllez Miguel Ángel [email protected]
1
1 grid.428474.9 0000 0004 1776 9385 Laboratorio de Fisiología Vegetal, Centro de Investigación en Alimentación y Desarrollo A.C., Carretera Gustavo Enrique Astiazarán Rosas, No. 46, Col. La Victoria, 83304 Hermosillo, Sonora México
2 grid.462226.6 0000 0000 9071 1447 Departamento de Microbiología, Centro de Investigación Científica y de Educación Superior de Ensenada, Carretera Ensenada-Tijuana No. 3918, Zona Playitas, 22860 Ensenada, Baja California México
3 grid.428474.9 0000 0004 1776 9385 CONACYT-Centro de Investigación en Alimentación y Desarrollo A.C., Carretera Gustavo Enrique Astiazarán Rosas, No. 46, Col. La Victoria, 83304 Hermosillo, Sonora México
7 12 2022
112
5 7 2022
21 11 2022
© The Author(s) under exclusive licence to Società Italiana di Patologia Vegetale (S.I.Pa.V.) 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The genus Tobamovirus comprises a group of single-stranded RNA viruses that affect a wide variety of vegetables of economic importance. Tobamoviruses express a series of proteins that interact with the plant’s cellular machinery, allowing viral infection; during incompatible interactions, active defense is mediated by host proteins encoded by resistance genes. The genes conferring viral resistance and tolerance in non-susceptible hosts have been studied for their ability to transfer desired resistance traits to different crops. The N gene from Nicotiana spp., the repertoire of Tm genes in Solanum spp., the L locus from Capsicum spp., and TOM genes are the most studied genetic sequences for understanding resistance to tobamoviruses. Through classical plant breeding and genetic engineering techniques, it has been possible to introgress these resistance genes (R) into new species. However, new reports highlight the ability of tobamoviruses to overcome R-mediated defense. One of the most notorious recent cases is the tomato brown rugose fruit virus (ToBRFV). The main characteristic of ToBRFV is its capacity to overcome the resistance mediated by the Tm-22 gene, resulting in a limited repertoire of options to combat the virus. To defeat emerging viruses, it is necessary to apply the knowledge from other tobamoviruses-host relationships and use new technologies such as genome-wide association studies (GWAS) to understand and associate the architecture of resistance genes present in the Solanaceae family for the benefit of plant breeding. Although new genomic tools such as CRISPR systems open the possibility of coping with viral diseases, there are no commercial ToBRFV-resistant tomato varieties. Hence, the world’s leading seed suppliers compete to develop and bring these varieties to market.
Supplementary Information
The online version contains supplementary material available at 10.1007/s42161-022-01287-9.
Keywords
Tobamoviruses
ToBRFV
Resistance
Virus-host interactions
http://dx.doi.org/10.13039/501100003141 Consejo Nacional de Ciencia y Tecnología Postdoctoral fellowship http://dx.doi.org/10.13039/501100013395 Sistema Nacional de Investigadores Sistema Nacional de Investigadores
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pmcIntroduction
Tomato (Solanum lycopersicum) production has become one of the most profitable agroeconomic activities worldwide. With yields on the rise, according to the Food and Agriculture Organization of the United Nations (FAO), global tomato production exceeded 180 million tons in 2020, with China and India being the principal producers (Guan et al. 2021; FAO 2020). Their nutritional value and antioxidant properties make tomatoes the world’s second most important vegetable crop (Viuda-Martos et al. 2014). The increase in product demand has also brought about a paradigm shift at the economic and technological levels. Thus, controlling viral diseases that affect tomato crops is currently one of the main challenges of sustainable farming.
Tobamoviruses have a positive single-stranded RNA genome that codes for four proteins: an RNA-dependent RNA polymerase (RdRp) and a small replicase subunit protein (SrSp), both of which are involved in the viral replication process; the coat protein (CP), which is responsible for encapsulating the genetic material; and lastly, a cell-to-cell movement protein (MP) (Oladokun et al. 2019; Panno et al. 2019). In some tobamoviruses, an extra ORF6 has been reported to code for a factor related to pathogenicity (Canto et al. 2004). These proteins allow the development of viral infection through the cells and the plant; therefore, they are critical targets of plant resistance gene products, which in many cases determine the success or failure of the infection (King et al. 2012).
The members of the Brassicaceae, Cucurbitaceae, Malvaceae, and Solanaceae plant families are natural hosts of tobamoviruses (Adkins et al. 2003; Antignus et al. 2001). Tobamoviruses have no natural vectors and are easily transmitted mechanically during cultivation (Candemir et al., 2012). Characteristic lesions caused by tobamovirus infection include yellow necrotic spots on the fruit and a mosaic appearance that leads to the partial discoloration of sick leaves. Some relevant tobamoviruses used as study models are tobacco mosaic virus (TMV), tomato mosaic virus (ToMV), tomato mottle mosaic virus (ToMMV), cucumber green mottle mosaic virus (CGMMV), and tomato brown rugose fruit virus (ToBRFV). All of them are of great importance due to the losses in yield and quality associated with the symptoms they induce in plants or fruits.
ToBRFV causes an emerging disease that was detected for the first time in Jordan in 2015 (Salem et al. 2016). Reports of ToBRFV outbreaks around the world are on the rise (Hamborg and Blystad 2021; Jones 2021), and they translate into significant economic losses. Regarding the dispersal route, ToBRFV, like other tobamoviruses, is transmitted by mechanical contact. Levitzky et al. (2019) showed, through controlled experiments, that ToBRFV can be spread by pollinating insects such as bumblebees (Bombus terrestris). Novel experiments suggest that the virus is present in the tomato seed coat and that the infection can be mechanically triggered when in contact with other seeds. This type of transmission is particularly dangerous due to the possible long-distance movement of infected material from one country to another within a short time (Caruso et al. 2022, Salem et al. 2021). A study by Salem et al. (2022) also reported plant species from up to 8 different families (including Amaranthaceae, Asteraceae, Malvaceae, and others) harboring ToBRFV. The studies to date suggest that the ecological niche of ToBRFV is not restricted to tomatoes or peppers alone, but it has a broad range, assuming greater probabilities of infecting essential crops.
In this review, we focus on the tobamovirus-host relationships, the spectrum of mechanisms involved in plant defense against tobamoviruses, and efforts to combat them. We emphasize the successful trials in which viral infection was overcome by employing transgenesis, introgression, or RNA interference tools. We also summarize experimental approaches, new technologies, and strategies reported for various study models that can serve as practical examples for developing ToBRFV-resistant tomato germplasm. Lastly, we present the current developmental state of commercial ToBRFV-resistant seeds based on available public information.
Dominant resistance (R) and recessive gene repertoire against tobamoviruses
Successful viral infection is the result of a complex molecular interplay between host plants and invading viruses (Wang 2015). The intricacies of these interactions give a range of options, from the total inability of the virus to replicate to replication of the virus without inducing symptoms. Paudel and Sanfaçon (2018) explain tolerance as an interaction in which viruses accumulate to some degree without causing significant loss of vigor or fitness in their hosts. Although the molecular mechanisms for tolerance are not yet well defined, understanding these mechanisms is a fundamental premise of breeding programs for vegetables that are still susceptible to some tobamoviruses.
According to Ishibashi and Ishikawa (2016), the replication of tobamoviruses is related to the suppression of plant defenses. One of the most studied and characterized plant defense mechanisms is mediated by resistance genes (R). Most R genes code for a protein with a nucleotide binding site and a leucine-rich repeat domain (NBS-LRR) (Shi et al. 2021). When NBS-LRR-containing proteins recognize avirulence factors encoded by pathogen Avr genes, programmed cell death is induced as part of a hypersensitive response (HR) (Jubic et al. 2019; Mur et al. 2008). Genes such as N, L, Tm-1, Tm-2, and Tm-22 encode this type of protein and are fundamental to comprehending the processes of viral infection (Table 1) (Fraile and García-Arenal 2018).
The N gene of Nicotiana glutinosa was the first characterized tobamovirus resistance gene. Its introgression into commercial tobacco plants (N. tabacum) is considered a crucial milestone in combatting viral diseases caused by tobamoviruses, specifically TMV (Scholthof 2017). Although the N gene confers partial resistance against TMV, the virus rapidly and systemically moves into the leaves of the plants, developing limited local infections. A new study by Ikeda et al. (2021) showed an increase in the viral resistance of transgenic N. benthamiana against TMV by inducing the expression of different introns of the N gene. This combination of N transgenes revealed that the presence of two specific introns allowed for a more significant accumulation of premature and mature N transcripts and lesser viral spread. Although the mechanisms of intron regulation are still far from being fully understood, the methodology of using introns as transcriptional “enhancers” could be helpful in the development of total resistance against tobamoviruses.
In the Capsicum genus, the L locus, which is composed of four similar genes (L1-L4), mediates resistance against tobamoviruses (Kenyon et al. 2014). The type of virus determines the degree of resistance; TMV, ToMV, or TMGMV (tobacco mild green mosaic virus) cannot develop infection; other tobamoviruses with narrow geographic distribution are also incapable of triggering infection in peppers. Similarly, in a study conducted by Vélez-Olmedo et al. (2021), strains of yellow pepper mild mottle virus (YPMMoV) and chili pepper mild mottle virus (CPMMoV) were mechanically inoculated and classified as unable to overcome the resistance mediated by sequences of the L locus. However, other tobamoviruses can overcome the resistance mediated by the L alleles. A study by Luria et al. (2018) reported a strain of paprika mild mottle virus (PaMMV) capable of overcoming L3-mediated resistance in pepper plants. Although PaMMV can infect other plant species, the host range is narrow, and its viral titers are minimal in these plants. In an attempt to determine the behavior of this PaMMV strain in other Solanaceae plants, an increase in infection was found when co-inoculated with other tobamoviruses in tomatoes. Based on the interactions of emerging tobamoviruses, the authors concluded that the breaking of resistance occurred, and there was risk imposed by the possibility of coordinated co-infection by several viruses.
In Solanum spp., the Tm-1 and Tm-2 alleles mediate resistance against ToMV by binding the corresponding R proteins to the viral replication complex (Tm-1) or viral MP (Tm-2) (Ishibashi and Ishikawa 2013). Although the alleles have shown durability over time, the appearance of new mutant viral variants could easily lead to a hampered plant resistance mechanism (Ishibashi et al. 2012). A study by Hussain et al. (2024) evaluated the variability of 24 tomato lines native to Pakistan and other global reference lines against infection by ToMV and tomato yellow leaf curl virus (TYLCV, genus Begomovirus). Twenty-three native lineages were sensitive to ToMV infection but displayed different lesion patterns. Considering parameters such as disease severity and the percentage of infection or symptoms due to ToMV, only one accession (Acc-17,878) was asymptomatic and was considered resistant. The authors highlighted that the available germplasm variability helps achieve genetic improvement to deliver high-yielding resistant tomato varieties.
ToMMV is a tobamovirus that was first reported in tomato greenhouses in Mexico (Li et al. 2013) and has close to 85% sequence identity at the nucleotide level to ToMV and TMV. However, there is special attention to its underrepresented prevalence due to the lack of serological tests that discriminate it from ToMV (Turina et al. 2016). ToMMV can infect pepper plants (Nagai et al. 2019) and overcome resistance to tobamoviruses (Lovelock et al. 2020). Interestingly, some reports show that the tomato Tm-22 gene could mediate resistance against ToMMV (Nagai et al. 2019). Li et al. (2017) highlighted that two viral proteins, 126 kDa and 54 kDa, were essential to the ToMMV replication process and are therefore important for understanding the mechanism of viral infection. Tu et al. (2021) developed infectious cDNA clones of ToMMV that were able to infect N. benthamiana plants. These recombinant ToMMV clones proved to be highly infectious and pathogenic, which introduces the possibility of exploring the pathogen-host relationship through gene silencing or other means.
An alternative to R-mediated plant breeding is recessive resistance. Unlike the strategy mediated by a dominant gene, this strategy relies on suppressing recessive genes function. In Arabidopsis thaliana, the tobamovirus multiplication 1 (TOM1) gene family is required for the multiplication of TMV (Ishikawa et al. 1991). Simultaneous loss-of-function mutations of TOM1 and its putative paralog TOM3 result in near-complete inhibition of tobamovirus multiplication. These and other genes code for proteins in the tobamovirus replication complexes (Yamanaka et al. 2002; Nishikiori et al. 2011). The knockout of genes essential for the virus cycle has shown promising results for ToBRFV control (Ishikawa et al. 2022; Zhang et al. 2022), as described below.
In Cucurbitaceae plants, the wild species Cucumis africanus shows high levels of resistance against CGMMV. However, most commercial cucurbits are still susceptible to infection by the virus (Mandal et al. 2008). One of the first approximations to elucidate tobamovirus resistance mechanisms in cucurbits was reported in Cucumis melo L. ‘Chang Bougi,‘ a cultivar with partial resistance to CGMMV. The products of the recessive genes cucumber green mottle mosaic virus resistance-1 (cgmmv-1) and cucumber green mottle mosaic virus resistance-2 (cgmmv-2) could be responsible for resistance to the virus and serve as a starting point for developing plant breeding programs in cucurbits (Sugiyama et al. 2007). A study by Ruiz et al. (2021) evaluated 47 different accessions of cantaloupes from Asia and Europe; after the mechanical inoculation of the leaves and the development of the infection, 16 Cucumis melo accessions presented partial resistance against CGMMV. Interestingly, the Japanese cultivar Freeman’s cucumber and two Spanish accessions (Rochet (BGV004884) and Alficos (BGV004853)) were reported to be resistant to CGMMV.
To date, CGMMV has been positioned as one of the main threats to cucurbits because there are no known commercial cultivars with total resistance to this virus. However, studies such as these introduce the possibility of future breeding programs. A simplified schematic representation of plant-tobamovirus interactions based on R-mediated defense and recessive resistance is depicted in Fig. 1, showing promising methodologies for breeding tobamovirus-resistant cultivars of commercial plants.
The search for resistance against ToBRFV
Regarding efforts to obtain ToBRFV resistance, a patent by Hamelink et al. (2019) referred to the invention of a genetically modified tomato with the characteristics of some genotypes of S. pimpinellifolium and S. habrochaites (which are naturally resistant to ToBRFV). At the time, it was considered a transcendent step in developing resistance against ToBRFV (Ashkenazi et al. 2020; Ykema et al. 2020). In similar attempts, an evaluation of S. ochrantum (a close relative of wild tomatoes) exhibited high levels of resistance to ToBRFV and other tobamoviruses. However, transferring these traits to conventional tomatoes is difficult due to sexual incompatibility between S. ochrantum and S. lycopersicum. A potential alternative to overcome this genetic barrier would be somatic hybridization (Jewehan et al. 2022a; Pertuzé et al. 2002).
In another recent study, Jewehan et al. (2022b) reported the evaluation of wild tomato accessions (S. habrochaites and S. peruvianum) infected by ToBRFV. From 173 samples, nine accessions of S. habrochaites and one of S. peruvianum were highly resistant. These plants showed no symptoms at 24 °C, and no virus could be detected on the inoculated leaves. However, when resistant plants inoculated with ToBRFV were incubated at 33 °C, they expressed mosaic and deformation symptoms, indicating that resistance is broken at high temperatures. These findings demonstrate that some wild tomato species may have unknown ToBRFV resistance genes, opening a new path to discovering sequences involved in resistance against ToBRFV. However, the same research group reported a ToBRFV mutant (Tom2M-Jo) capable of breaking the natural resistance in S. habrochaites and S. peruvianum (Jewehan et al. 2022c). Tom2M-Jo has two substitutions in the MP gene that result in amino acid changes in the 30 kDa MP (Phe22 → Asn and Tyr82 → Lys). These substitutions have not been reported before in ToBRFV isolates, highlighting the difficulty of finding long-term resistance.
A critical study to develop ToBRFV-resistant tomato varieties by Zinger et al. (2021) reported the identification of loci related to resistance and tolerance against ToBRFV. For this purpose, tomato varieties susceptible (VC532) and tolerant (VC554) to ToBRFV were crossed and self-pollinated to obtain the F2 population (n = 160). By characterizing the ToBRFV response in parental, F1, and F2 plants and high-throughput sequencing, it was found that parental plants share common genome regions (on chromosome 11) that control tolerance to ToBRFV. The results indicate that the resistance trait is partially dominant and that Tm-1 and Tm-2 are ineffective at controlling ToBRFV infection in tomato plants. The new sequences found in the locus of chromosome 11 will allow for a better understanding of how some tomato varieties’ natural resistance to ToBRFV is governed. Although the evaluated alleles are strongly associated with tolerance control, the authors concluded that other loci participate in the resistance process. Studies on the interactions between loci as a way to search for resistance against ToBRFV open up new possibilities for developing phenotypes resistant to the virus. The introgression of these resistance alleles can be assisted by plant breeding with gene-editing technologies.
The suppression of functional genes and viral proteins is also emerging as a promising alternative to confer resistance against tobamoviruses. Examples worthy of note are the works of Ishikawa et al. (2022) and Kravchik et al. (2022), in which sequences homologous to genes indispensable for tobamovirus replication from Arabidopsis were edited in tomato using CRISPR/Cas9 (see below). Notably, several genetic sequences with unknown functions have been proposed as potential candidates for inhibiting ToBRFV components (e.g., NBS, RLP, and RLK genes reported by Andolfo et al., 2013). However, the characteristics of the new ToBRFV mutants (Tom2 M-Jo) raise the possibility of a change in the current paradigm, in which the main resistance trait relies on the presence of R genes. New approaches to addressing ToBRFV infection are needed, and the previously mentioned recessive resistance examples seem to support this idea. In either case, the functional analysis of ToBRFV proteins is essential to advance the understanding of their role in the viral infection and resistance processes, as described below.
Characterizing ToBRFV proteins, a fundamental step for breeding resistance
Currently, the breeding of plants of commercial interest is performed by gene introgression and the specific suppression of gene expression (Nishiguchi et al. 2019). In tomato plants, some R genes have been introduced from wild tomatoes, such as S. pimpinellifolium or S. habrochiates, to commercial lines of S. lycopersicum to observe and preserve the desired phenotype. During the breeding of plants resistant to ToBRFV, it is imperative to know which genes and proteins interact in the infection process and to determine which sequences are more likely to develop the desired resistance.
Weber et al. (2004) described one of the first approaches to understanding the function and characteristics of the MP avirulence factor of ToMV (MPToMV). By using Craigella tomato cultivars GCR 26 (without any resistance gene against ToMV), GCR 236 (Tm-2/Tm-2) and GCR 267 (Tm-22/Tm-22), and different MPToMV transgenes, the authors elucidated some similarities and differences between resistance genes Tm-2 and Tm-22 against ToMV. Both genes can induce HR after recognizing MPToMV, but Tm-2 recognizes a domain at the N-terminus of the MP, while Tm-22 most likely interacts with more than one region of the MP.
Upon studying the complete genome sequencing of ToBRFV (Salem et al. 2016, Luria et al. 2017, Chanda et al. 2020), it was clear that ToBRFV has a high identity and a short evolutionary history with other tobamoviruses. However, one main characteristic that differentiates ToBRFV from other tobamoviruses is its ability to avoid the polypeptide encoded by the Tm-22 gene (Luria et al. 2017). Any tobamovirus infectious process is partly mediated by its MP, which has been described as a crucial viral element that triggers intracellular invasion by increasing virus permeability and movement within the plasmodesmata (Sheshukova et al. 2020).
To understand the function of MPToBRFV, which breaks the resistance mediated by the Tm-22 gene product, Hak and Spiegelman (2021) reported the essential characteristics of this viral protein. First, they demonstrated that expressing MPToBRFV alone is not enough to activate the function of Tm-22, as opposed to MPTMV. Then, using hybrid infective clones with other tobamoviruses, the authors demonstrated that the amino acid sequence of MPToBRFV from residues 1 to 216 plays a determining role in the development of the infection, because the C-terminal fraction of MP alone is not sufficient to activate Tm-22 gene-mediated plant resistance. Lastly, ToBRFV infection spreads slower than TMV, as evidenced by the detection of a higher signal in N. benthamiana leaves inoculated with a TMV-GFP (green fluorescent protein) hybrid compared to plants inoculated with a TMV-GFPMP−ToBRFV hybrid.
Yan et al. (2021) evaluated the role that MPToBRFV plays in triggering (or not triggering) HR. Using MP constructs (GFP-MPToBRFV and GFP-MPTMV) and chimeras, they established that the central amino acid region of MPToBRFV is involved in overcoming Tm-22-mediated resistance. By interchanging regions of MPToBRFV and MPTMV, the authors determined that residues 60–186 of the virulence factor MPToBRFV are responsible for overcoming resistance by ToBRFV; specifically, residues H67, N125, K129, A134, I147, and I168. Since MPToBRFV is essential to overcoming Tm-22-mediated resistance, evaluating the spread of viral infection by monitoring HR-related markers could accurately determine the behavior of new hybrids and help better understand the interrelation between the MP protein and the Tm-22 product. Recently, Rivera-Márquez et al. (2022) used an in silico approach to find new and potential mutations for Tm-22 that would increase the binding affinity to MPToBRFV, H384W, and K385L. Studies such as this one show the relevance of bioinformatics to help design strategies that can be confirmed by in vitro methods later on.
Silencing, editing, and new approaches for the development of ToBRFV-resistant tomato cultivars
RNA interference (RNAi) is a cellular mechanism that regulates gene expression via small RNAs (Hung and Slotkin 2021). The study of the RNAi pathway in tobamovirus-plant models allows us to use this information to design control strategies, for example, designing small interfering RNAs (siRNAs) specific to critical sequences of the genome or viral transcripts (hot spots). Virus-derived small interfering RNAs (vsiRNAs) and artificial microRNAs (amiRNAs) are some of the most commonly used methodologies for gene silencing against viruses.
Jiao et al. (2022) characterized small RNAs against pepper mild mottle virus (PMMoV). Leaves of peppers (Capsicum annuum L. cv. Zunla-1) were inoculated with PMMoV, and viral small interfering RNAs (vsiRNAs) were identified. These vsiRNAs were characterized in silico using the miRanda algorithm, and their expression was evaluated. PMMoV infection in peppers generates a wide variety of vsiRNAs, and those from (+) RNA are the most abundant. Additionally, PMMoV infection significantly increased the proteins that play crucial roles in generating vsiRNAs, such as CaDCL2 and CaRDR1. In light of the abundance of 21–22 nt vsiRNAs found in their study, the authors suggested that they were produced during the development of a successful infection.
One of the most effective attempts to combat tobamovirus in cucurbits was reported by Liang et al. (2019), who developed and evaluated three amiRNAs to target viral CP, MP, and replicase gene sequences through a gene silencing methodology. After N. benthamiana infiltration, a positive correlation was observed between amiRNA expression and tolerance to CGMMV, as evidenced by a reduced viral load. The most promising results were observed after CP silencing. Subsequently, in a follow-up to Liang’s work, Miao et al. (2021) reported on transient expression assays with polycistronic amiRNA and synthetic trans-acting small RNAi in N. benthamiana and cucumber protoplasts. The study demonstrated that the polycistronic amiRNA construct conferred long-lasting resistance to CGMMV in cucumbers.
In a study by Li et al. (2016), vsiRNAs were characterized in cucumber seedlings infected with CGMMV 14 days post-infection. RNAs measuring 21–22 nt in length were predominant in leaves infected with CGMMV, suggesting that 21-nt vsiRNAs represent the main antiviral silencing component in the plant, which are produced by the DCL4 protein. Although the effects of 21-nt vsiRNAs are known in plants (Mitter et al. 2013; Zhang et al. 2015), the authors suggested that vsiRNAs could also have a role during the CGMMV infection cycle since several genes involved in cellular processes, regulation, and structure were predicted to be targeted by these vsiRNAs.
Using gene silencing in plants has benefited the development of varieties with better responses to stress. Under this premise, an in silico study by Gaafar and Ziebell (2020) reported possible therapeutic targets of microRNAs against ToBRFV. On the assumption that S. lycopersicum encodes mature miRNAs that may have a protective effect against ToBRFV infection, a total of 147 tomato miRNA sequences were analyzed by five different RNA target prediction tools (miRanda, RNAhybrid, RNA22, Tapirhybrid, and psRNATarget). Up to 11 miRNAs were found to share some regions of the ToBRFV genome as a common target. The authors concluded that the sequences they found may effectively increase S. lycopersicum immunity against ToBRFV; however, it is also necessary to validate their results using amiRNAs (Song et al. 2014). Considering the scope of the study and if those results could be extrapolated to in vitro technology, the strategy would undoubtedly serve as a model for developing ToBRFV-resistant tomato plants.
Gene silencing in S. lycopersicum provides an obvious opportunity to understand the genetic architecture of the RNAi machinery and the sRNAs (length or sequence) and how they are used against ToBRFV. The following steps toward understanding the ToBRFV-S. lycopersicum interactions should be aimed, among other goals, at unveiling the genetic similarity among specific sRNAs synthesized under infections in S. lycopersicum by close tobamoviruses such as ToBRFV vs. ToMMV. To date, no studies have addressed this question, which could clarify how the viral defense system of S. lycopersicum against tobamovirus has evolved.
In the quest for ToBRFV control, genetic editing is another strategy that has regained particular interest over the past decade since the development of the clustered regularly interspaced short palindromic repeats (CRISPR) editing system and its applications in plants.
One of the cases in which genetic editing was successfully used in plants to confer resistance against a virus was the work described by Aman et al. (2018). They achieved interference to turnip mosaic virus (TuMV, genus Potyvirus) in recombinant N. benthamiana lines using the CRISPR-Cas 13a system. First, transactivating CRISPR RNAs (crRNAs) were designed to target four different regions of the TuMV-GFP hybrid genome. After infiltrating leaves with crRNAs targeting HC-Pro (coding for a helper component-proteinase silencing suppressor) and GFP2 sequences, an ~ 50% reduction in the GFP signal level was observed. The authors concluded that Cas 13a is an RNA-guided ribonuclease that can be programmed to target and degrade viral RNA genomes.
Although drawbacks in using CRISPR-Cas 13 have been described due to RNA degradation (Ali et al. 2018), other systems have been used to obtain plant phenotypes with desired characteristics. For instance, Ghorbani et al. (2020) reported a significant reduction in viral DNA compared to control plants when S. lycopersicum (cv. Moneymaker) seeds were inoculated with TYLCV. Single guide RNAs (sgRNAs) were designed from the intergenic region (IntR) and CP sequences of TYLCV. Then, sgRNAs were separately infiltrated into tomato plants; these plants presented a lower viral load than the control plants when inoculated with the virus. The authors demonstrated the effectiveness of using the CRISP-Cas 9 system in targeting TYLCV. Although off-target effects are one of the main limitations of the CRISPR-Cas 9 system, this problem was overcome in this study due to modifications in the activating promoter of endonuclease Cas 9.
Another interesting report regarding resistance to ToBRFV infection in tomatoes is the work of Ishikawa et al. (2022). Based on the nucleotide sequence of TOM1 (gene essential for tobamovirus multiplication) from A. thaliana, similar sequences were identified in S. lycopersicum; subsequently, using CRISPR-Cas 9, a quadruple knockout of the homologous TOM1 genes (SlTOM1a–e) was performed in tomato. A single mutation in the TOM1 homolog did not affect ToBRFV accumulation in the inoculated plants. However, in Sltom1 triple mutants, CPToBRFV accumulation was reduced compared to that in wild-type plants. It is also suggested that the contribution of SlTOM1 genes to ToBRFV multiplication was in the order SlTOM1a > SlTOM1c > SlTOM1d > SlTOM1b. The knockout of homologous TOM1 genes generated long-lasting resistance in tomatoes, suggesting that genome editing using CRISPR-Cas 9 could aid in the development of ToBRFV-resistant tomato plants.
Using the same editing tool, Kravchik et al. (2022) reported resistance to ToBRFV in S. lycopersicum cv. M82. After both SlTOM1a and SlTOM3 were knocked out, the resulting plants were asymptomatic in response to ToBRFV infection, and their SlARL8a3 susceptibility gene expression was reduced. Although SlARL8a3 alone did not contribute to ToBRFV resistance, it was observed that the double mutants were susceptible to TMV and ToMV, establishing that sometimes the effects observed in some study models cannot be extrapolated to other viral species.
As noted in these reports, the study of recessive resistance in S. lycopersicum (TOM genes) has made it possible to establish different action methods in the struggle to develop tomato plants resistant to ToBRFV infection. The CRISPR-Cas system has overcome limitations (such as low target recognition and stability) present in alternative methodologies (e.g., site-directed mutagenesis, the Cre-Lox system (Cre recombinase and Lox sequences), or the TALEN system (transcription activator-like effector nucleases)). In this way, CRISPR-Cas allows gene editing with greater precision and control over the study model.
To achieve resistance to ToBRFV and other viruses, researchers need to develop new strategies to search for host sequences capable of constraining ToBRFV infection alongside new strategies to look for them. After completing the reannotation of the NB-LRR genes of S. lycopersicum (cv. Heinz 1706) using the resistance gene enrichment and sequencing (RenSeq) approach (Andolfo et al. 2014), Andolfo et al. (2021) determined the impact of the vast repertoire of NB-LRR genes on plant breeding strategies. In general, the NB-LRR genes showed genetic expansion and divergence with respect to other Solanaceae family members, such as potatoes. Interestingly, approximately 80% of the annotated genes have a single catalytic domain whose function is still unknown. In the tomato genome, chromosome nine harbors signaling-related sequences such as Sw5 (resistance against tospovirus) and Tm-22 genes that lead to plant cell immunity. The sequences of the NB-LRR gene repertoire open the door to gene editing techniques to improve the immune defense machinery of plants of agroeconomic interest, such as tomatoes.
Complementary to these efforts, genome-wide association studies (GWAS) could serve as a starting point for searching sequences of interest. In tomato, GWAS have been aimed at seeking the usefulness of genetic sequences associated with the organoleptic traits of the product, such as flavor (Tieman et al. 2017), weight, and ripening time (Wang et al. 2019). However, with the fast-rising -omics sciences, the utility of this tool diversified. Following a GWAS, Bauchet et al. (2017) reported more than 11,000 single nucleotide polymorphisms (SNPs) in accessions of different tomato varieties and classified the sequences into six genetic groups associated with agroeconomic interest traits, such as fruit weight and resistance to diseases. For the disease resistance trait, up to seven genes were identified on three different chromosomes. The diversity of genetic sequences associated with resistance is due to a strong impact of genetic introgression developed partly by breeding different tomato species.
Patents and companies involved in ToBRFV-resistant seed development
To date, reports on the development of commercial tomato varieties resistant to ToBRFV are lacking (Zhang et al. 2022). A considerable number of patents are registered (Supplementary material 1) in the World Intellectual Property Organization (WIPO). Most of these inventions describe phenotypes in which ToBRFV replication is delayed, reduced, or inhibited in the plants of interest. With a focus on a commercial perspective, transnational companies engage in research efforts to develop varieties of tomato seeds with total resistance to ToBRFV. The varieties intended for introduction to the agricultural market are defined by the degree of resistance to ToBRFV. The seeds with a new genotype supporting intermediate resistance (IR) are among the first to be developed. Although infected (PCR tests may be positive for the presence of ToBRFV), IR plants are asymptomatic or develop only mild symptoms in their leaves and fruits. Although this finding represents a genuine advance, studies on the agricultural and economic impact of IR tomato varieties are missing. Additionally, introducing highly resistant phenotypes stands out as one of the short- and medium-term leading prospects.
The development of tomato seeds with high resistance to ToBRFV is still in the experimental phase, with the idea of formulating seeds that limit the infection and spread of the disease (Table 2). Undoubtedly, this viral disease represents a challenge for companies dedicated to developing seeds, not because of their access to monetary funds or technology but because of each country’s agricultural regulations for using and commercializing seeds. Disagreements between developer agents and farmers can be expected due to the use of intellectual property and the benefit that the seed represents as innovation.
Conclusion and future trends
The Tobamovirus genus represents a threat to commercially important species of Brassicaceae, Cucurbitaceae, Malvaceae, and Solanaceae. Although members of these groups of plants naturally present a resistance response against viruses, sometimes the defense is overcome. Like other tobamoviruses, CGMMV and ToBRFV can be combated by avoiding mechanical transmission; however, no commercial cultivars resistant to these viruses are available to help prevent outbreaks.
Since MPToBRFV can overcome host resistance mediated by the product of Tm-22, an R gene present in commercial tomato varieties, new unknown sequences from wild Solanaceae plants are becoming relevant to look for means to inhibit this emergent virus. Nevertheless, the search should not be limited to dominant resistance genes; the study of recessive resistance in S. lycopersicum has attracted renewed interest and opens a new promising picture for developing tomato lines resistant to ToBRFV.
We consider that the new trends in the short term should be aimed at understanding the genomic architecture and the functioning of a wide range of resistance mechanisms that still remain unknown in a more robust way. We may face a new scenario in plant breeding research, in which obtaining ToBRFV-resistant tomato varieties could rely on next-generation sequencing and gene editing tools such as CRISPR-Cas and gene silencing. These strategies will shorten research times and have very high confidence thresholds. Additionally, prior to testing in experimental crops, it is necessary to construct effective in vitro models to inhibit virus replication.
Table 1 Tobamovirus proteins and corresponding resistance genes in plants of commercial interest
Virus Protein Resistance Crop Reference
TMV CP and p50 helicase domain N gene Tobacco Whitham et al. (1994)
ToMV Helicase domain
MP
Tm1 gene
Tm2 gene
Tomato Ohmori et al. (1998)
Hall (1980)
ToMMV MP Tm22 gene Tomato Nagai et al. (2019)
CGMMV Unknown cgmmv 1 and 2 Cucumber Sugiyama et al. (2007)
PMMoV,
PaMMV
Unknown L1-L4 genes Pepper Antignus et al. (2008)
Matsumoto et al. (2008)
Table 2 ToBRFV-resistant seeds are expected to be launched on the market in the coming years
Company Product launch date Product (seed) Resistance against ToBRFV Reference
Bayer (Germany) 2024 NA Intermediate Bayer News (2021)
BASF (Germany) 2020* Teenon F1 Intermediate de Domènech (2020)
Syngenta (Switzerland) 2021* Barosor
Lansor
High
Intermediate
Syngenta Group News Service (2021)
Enza Zaden (Netherlands) 2022 NA High Enza Zaden Group (2020)
*Original estimated closing. Due to the COVID-19 pandemic, new launching closings and market availability in regions or countries are not available. NA: Not available.
Fig. 1 Plant defense mechanisms against tobamovirus infections and promising methodologies for plant breeding. Tobamovirus infection begins with the internalization of the virion into the plant cytoplasm. The coat that protects the genetic material disintegrates, releasing viral genomic RNA (+). After expressing RdRp, the complementary genomic RNA strand (-) is synthetized and will serve as a template for the synthesis of new genomic RNA strands (+). SrSp plays a fundamental role in viral replication. With the help of MP, the viral replication complex travels from cell to cell through the plasmodesmata to continue the cycle. This process can be hampered by host defenses mediated by resistance genes. Tm-1, Tm-2, Tm-22, L, and N are dominant resistance genes from the NBS-LRR class. TOM, cgmmv-1, and cgmmv-2 are recessive genes. The CRISPR-Cas system, GWAS, and RNAi are new generation procedures that will shorten the time in the search and/or development of resistance against tobamoviruses.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1
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Int J Pept Res Ther
Int J Pept Res Ther
International Journal of Peptide Research and Therapeutics
1573-3149
1573-3904
Springer Netherlands Dordrecht
10475
10.1007/s10989-022-10475-1
Article
Multi Epitopic Peptide Based Vaccine Development Targeting Immobilization Antigen of Ichthyophthirius multifiliis: A Computational Approach
Ghosh Pratik 1
Patra Prasanta 1
Mondal Niladri 12
Chini Deep Sankar 1
Patra Bidhan Chandra [email protected]
1
1 grid.412834.8 0000 0000 9152 1805 Department of Zoology, Vidyasagar University, Midnapore, 721102 West Bengal India
2 grid.257409.d 0000 0001 2293 5761 Department of Biology, Indiana State University, Terre Haute, Indiana, 47809 USA
9 12 2022
2023
29 1 1113 11 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The white spot disease causes significant damage to global aquaculture production. A prominent vaccine, eliciting the immunogenicity of freshwater fishes against Ichthyophthirius multifiliis yet to be developed. Thus, an Immunoinformatic drive was implemented to find out the potential epitopes from the surface immobilization antigens. B-cell derived T-cell epitopes are promiscuous elements for new generation peptide-based vaccine designing. A total of eight common B and T-cell epitopes had filtered out with no overlapping manner. Subsequently, the common epitopes are linked up with EAAAKEAAAKEAAAK linker peptides, we also added L7/L12 ribosomal protein adjuvant at the N- terminal side of peptide sequence for eliciting the immune response in a better way. The secondary and tertiary structural properties of the modeled 3D protein revealed that the protein had all the properties required for a protective immunogen. Afterward, three globally used validation server: PROCKECK, ProSA and ERRAT were used to justify the proper coordinate. NMR, Crystallographic range and error plot calculation for vaccine model also been done respectively. This was followed by molecular docking, MD simulation, NMA analysis, in silico cloning and vaccine dose-based immune response simulation to evaluate the immunogenic potency of the vaccine construct. The in silico immune simulation in response to multi-epitopes show antibody generation and elevated levels of cell-mediated immunity during repeated exposure of the vaccine. The favourable results of the in silico analysis significantly specify that the vaccine construct is really a powerful vaccine candidate and ready to proceed to the next steps of experimental validation and efficacy studies.
Graphical Abstract
Supplementary Information
The online version contains supplementary material available at 10.1007/s10989-022-10475-1.
Keywords
Spot disease
Immunoinformatics
MD simulation
NMA
B-cell
T-cell
Docking
issue-copyright-statement© Springer Nature B.V. 2023
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pmcIntroduction
Aquarium fishes are widely kept as a pet and important source of fish trade communities for its gleaming aesthetic significance (Andrews 1990). These are mostly reared in the fish aquariums (Basu et al. 2012). However, pathogenic microorganisms cause harm to aquarium fishes and many fresh water fishes. Among the lots of pathogenic disease, ‘White spot disease’ play a harsh role in fresh water aquaculture and fish development (Koppang et al. 2015). Aquarium fishes like goldfish (Carassius auratus), Siamese shark (Pangasius sutchi), black angel (Pterophyllum scalare) and gold gourami (Trichopodus trichopterus) are highly susceptible (ABDULLAH-AL MAMUN, M., et al. 2021; Elsayed, et al. 2006). Culture fishes like Grass carp (Ctenopharyngodon idella), Cyprinus carpio, and catfish (Pangasianodon hypophthalmus) (Zhao, et al. 2013; Mamun, et al. 1878) are also affected.
Ichthyophthirius multifiliis, a holotrichous ciliate protozoan parasite, which is main causative agent of ‘White spot disease’ or Ichthyophthiriasis of fresh water fishes and aquarium fishes (Dickerson and Findly 2014; Stoskopf 2015; Matthews 2004; Trujillo-González et al. 2018). This parasite invades into the fish body through epithelial tissue of the skin and gills tissue where it create infections in host (Clark et al. 1988). I. multifiliis belongs to Ichthyophthiriidae family known for parasitic disease causing protozoans (Dickerson et al. 2011; Dickerson and Dawe 2006). The immobilization antigens are antigenic element that localized in surface membrane of I. multifiliis and predominantly targets the host immune responses (XU, C. et al. 1995; Wang et al. 2002).
Fish are very interesting vertebrate phylum that possesses both adaptive and innate immune system, play a great role on boosting immunity against pathogen (Magnadottir 2010). Therefore, it is easy to develop a predominant vaccine candidate that might be used against the pathogenic infections. It focuses on the design and study of vaccine candidate construction along with using different algorithms for mapping potential B cell and T-cell epitopes.
This research work focuses on computer-aided vaccine designing techniques, also known as an ‘Immunoinformatic’ approach. The method utilizes several prediction software and server to identify the specific antigenic epitopes within the targeted protein region. This approach takes account of various methodology that adopted to recognize and characterize T and B-cell epitopes for designing promiscuous multi epitope based peptide (MEBP) vaccine against protozoan parasite Ichthyophthirius multifiliis. This technique is very much easy, reliable and less expensive but effective one. MEBP vaccines are proved to be effective against cancer, filarial disease, multi-drug resistance pathogens, Tuberculosis, COVID-19, malaria and many more. These vaccines furnishes better options to provide immunogenicity, reproducibility and experimental control over antigenic protein of pathogens (Salaikumaran, et al. 2022). In the modern science, proteomic data of different pathogen which is easily and widely available in globally used software and server came into play.
Despite these advantages this Immunoinformatic technique has some limitation too; it requires highly skilled professionals to practice properly. It is widely dependent on available databases but sometimes those data found to be limited to apply this technique. The technique requires several in vitro and in vivo validation trial before implementation (Ishack and Lipner 2021).
Materials and Methods
A summarized steps of the designing multi-epitope vaccine is presented as flowchart shown in Fig. 1.Fig. 1 Schematic representation of the workflow for the development of multi-epitope vaccine against parasite Ichthyophthirius multifiliis
Retrieval of Amino Acid Sequences of the Target Protein
The primary amino acid sequences of immobilization antigen (I-antigen) retrieved from National Center for Biotechnology Information database (https://www.ncbi.nlm.nih.gov/protein/) (Coordinators 2016). The retrieved amino acid sequences of targeted protein were used to process in computer-based server and software for designing vaccine candidate against protozoan pathogen I. multifiliis.
B-Cell Epitope Identification
The Immune Epitope Database (IEDB) was used for the prediction of B cell epitopes in the protein sequences. In this current work, we used BepiPred 2.0 prediction method integrated in IEDB database (Jespersen, et al. 2017; Kim et al. 2012). B-cell prediction is one of the crucial steps for epitopic vaccine development. B-cell epitopes stimulate host humoral immune system.
T-Cell Epitope Identification
T-cell epitopes had predicted within the B-cell epitope for enhancing the immunity against pathogenic infections. In this work, identified T-cell epitopes have the affinity towards the multiple HLA alleles. An antigen-presenting cell (APC) represents T-cell epitopes bound to major histocompatibility molecules (MHC) for boosting immune response (Patronov and Doytchinova 2013). MHC-II and MHC-I binding region in antigenic protein sequence were predicted though Propred and Propred-I web server respectively (Singh and Raghava 2001, 2003).
Multi-Epitope Subunit Vaccine Construction and Modeling
The subunit vaccine constructed by joining of the selected proposed antigenic epitope using suitable linker peptide. The subunit vaccine has played an effective role in activating both innate and adaptive immune response (Dhal et al. 2019). Here, we were using EAAAKEAAAKEAAAK linker peptides to link the epitopes, which has self-regulating immunogen as well as it will be elicit immune response (Pentel and LeSage 2014). This linker can increase the stability and folding pattern of vaccine candidate. Subsequently, tertiary structure of a protein having a promising role to regulate its functionality in sub-cellular manner (Roy et al. 2012). Therefore, here we generated 3D structure of vaccine component with help of SPARKS-X server (Yang et al. 2011).
Analysis of the Physicochemical and Secondary Structural Properties, Allergenicity of the Vaccine Model
The physicochemical properties of vaccine model analyzed through the Expasy Protparam tool (https://web.expasy.org/protparam/). Additionally, SOPMA; tool used for predicting Secondary structural properties of the targeted protein (Geourjon and Deleage 1995). The Expasy, Protparam server predicts the Grand Average Hydropathy (GRAVY), atomic composition, molecular weight, extinction coefficients, estimated half-life, aliphatic index and Instability index. Next, we selected Protein-Sol web server for testing of solubility of vaccine candidate in aqueous solution (Hebditch, et al. 2017).
In order to evaluate the safety measure and effectiveness of the vaccine candidate here we used AllerTOP v.2.0 and AllergenFP v.1.0 (Dimitrov, et al. 2014a, 2014b). Both servers are used auto-cross covariance (ACC) transformation algorithm for allergic prediction.
Justification of Vaccine Model
In this section, we discussed about the subsequent validation of vaccine component that was modeled via SPARKS-X. Evaluation of structural properties of vaccine candidate had carried out by Ramachandran Plot through PROCHECK server (Laskowski et al. 2006). Ramachandran plot assesses the model quality, compute with phi–psi torsion angles for each amino acid residue. Finally, those residues are categorized as favored, generously allowed and disallowed regions. Conversely, the protein folding energy was assessed by using ProSA web-server (Wiederstein and Sippl 2007). ProSA delivered us ‘Z’ score value that specifies the overall model quality. Lastly, the protein model was passed through ERRAT web server for further validation. It calculates the statistical value of non-bonded interactions between different atoms and plots different value of the error function versus position of a 9-residue sliding window, considered by a comparison with statistics from highly refined structures (Colovos and Yeates 1993).
Antigenicity Prediction of the Vaccine Element
Antigenicity of constructed vaccine component is the most significant criteria for efficient vaccine designing. The immunological studies reveal, high antigenic score value would encompasses better immunization. Here, we used VaxiJen v.2.0 protective antigen prediction server for calculating antigenic score of vaccine component (Doytchinova and Flower 2007). This server needs amino acid sequences as an input and selected the parasite as a field with the prescribed threshold value of 0.5 and the prediction accuracy of 87% (Gededzha et al. 2014). VaxiJen v2.0 permits classification of antigen depicted on the physicochemical properties of proteins.
Molecular Docking
Protein-peptide interactions regulate the several cellular processes including signalling pathways as well as regulate various morphogenic pathways (Zhong et al. 2007). Therefore, Molecular docking is a prime process for supporting the drug designing and eventually discover their cellular interactions (Pagadala et al. 2017). Here, we used PatchDock server for performing molecular docking analysis (Schneidman-Duhovny, et al. 2005). This docking server used image segmentation and object recognition technique for carrying out better computer based visioning (Pradhan and Sharma 2014). PatchDock server needs two PDB file (one for ligand and another for receptor) as an input and Clustering RMSD had selected as 4.0 Å as default.
Molecular Dynamics Simulation
Molecular Dynamics simulation is a key aspect to compute the structural stability of the protein-peptide complex (Aalten et al. 1997; Hasan, et al. 2020). Complexes of vaccine construct with TLR2 were simulated at 1200 Picoseconds (ps) time scale using NAMD 2.14 by following the energy minimization with NVT (Phillips et al. 2005). The trajectories were saved for each complex after every 2 fs and root mean square deviation (RMSD) and root mean square fluctuations (RMSF) analysis were performed using VMD 1.9.3 tools (Humphrey et al. 1996).
Immune Simulation
An in silico immune simulation was accomplished through C-IMMSIM server (https://150.146.2.1/C-IMMSIM/index.php), to validate immunological response of designing vaccine construct (Rapin et al. 2010). This server simulates mainly primary and secondary immune responses. The vaccine candidate has tested for the ability for immune response against various types of immune cells like HTL, CTL, B-cells, NK cells, dendritic cells, Immunoglobulin and cytokines. Clinical recommendation of minimum interval between two doses of vaccine is four weeks (Castiglione, et al. 2012). In this experiment, we administered three injections at a standard time period gap; using C-ImmSim immuno stimulatory server, with the recommended interval of four weeks (1, 84 and 168 time-steps parameters were set as 1 time-step is equal to eight hours of real life) for a total of 1025 steps of simulation. Other parameters were kept as default for better result.
NMA Analysis
Molecular dynamics (MD) motion is key method for analyzing the physical movements of atoms and molecules. It also demonstrates the stability of protein–protein complex. Herein, we run the iMODS server to elucidate the collective motion of protein complex via analysis of normal modes (NMA) in its internal coordinates (López-Blanco, et al. 2014). This server predicted the direction and extended of the innate motions of the protein complex in the forms of B-factors, eigen values, deformability, Variance and covariance map (Ghosh 2021). In order to described the motion stiffness of normal mode the eigen value assessed its stiffness (Saha et al. 2021). In the NMA dihedral coordinates, it naturally mimics the combined functional motions of modeled protein molecules as a set of atoms connected by harmonic springs.
Codon Optimization and In-silico Cloning
Very often, the codon uses varies in target and host species so codon adaptation is implemented to increase the translation efficiency of cloned genes within the host. Codon optimization of vaccine construct had accomplished via online web server called Java Codon Adaptation Tool (JCAT) (http://www.jcat.de/) for high-level expression of the vaccine sequence in E. coli K12 strain (Grote, et al. 2005). We had taken pET28a (+) expression vector from “addgene” vector database (Kamens 2015) to implement in silico cloning of vaccine sequence. SnapGene 5.1.7 restriction cloning software has implemented for finalizing the in silico cloning (Biotech, G.J.U.s.c. 2015).
Result
Retrieval of Amino Acid Sequences of the Target Protein
Primary amino acid sequence of I-antigen of I. multifiliis had retrieved from NCBI with the GenBank accession ID: ACH87654.2. The I-antigen has contained 452 amino acid sequences and downloaded those amino acid sequences as FASTA format.
B-cell Epitope Identification
We founded a total six linear sequential B-cell epitopes along with various lengths. B-cell epitopes within I-antigen of I. multifiliis could played a robust role in provoking immunity. Here, following B-cell epitopes were positioned into Table 1 with their positional value, amino acid sequence, and length. The epitopes are graphically symbolized with the remarkable yellow color, and green color peaks represents non-epitopes one showing in Fig. 2.Table 1 Selected B-cell epitopes from IEDB and BepiPred 2.0 prediction method
Sl no Start point End point Amino acid sequence Length
1 1 44 MKFNILIILIISLFINELRAVNCPNGAAIANGQSDTGAADINTC 44
2 46 46 H 1
3 50 223 HFYFNGGNPAGQAPGAGQFNPGVSQCIACQVHKADSQHRQGGDANLAAQCSNLCPAGTAVEDGSPTFTQSLTQCVNCKPNFYFNGGNPTGQAPGAGQFDPTQLIANPDLANNPEVPNVSSPNGQCVACQVNKSDSQLRPGAQANLATQCNNECPTGTAIQDGAIFIYTQSISQC 174
4 229 324 DFYFNGGNPSAQNPGNGQFTPGQLIANPDAATSAQIPMVPGPNSKCVACESKKTNSQSRSGLEANLAAQCGTECPAGTLVTDGVTPTYTVSLSQCV 96
5 327 412 KAGFYQNSNFEAGKSQCNKCAVSKTGSASVPGNSATSATQCQNDCPAGTVVDDGTSTNFVALASECTKCQANFYASKTSGFAAGTD 86
6 415 452 TECSKKLTSGATAKVYAEATQKAQCASSTFAKFLSMSL 34
Fig. 2 The threshold level (antigenicity) of top B-cell linear epitopes
T-Cell Epitope Identification
Surface-exposed B-cell epitopes of the prioritized proteins with high antigenicity were taken into consideration to predict B-cell derived T-cell epitope. T-cell epitopes prediction was based on the binding of epitopes to both MHC-I and MHC-II molecules. Here we filtered out common B and T cell epitopes that mostly binding with MHC alleles tabulated in Table 2 and graphically reflects in Fig. 3 for better visualization.Table 2 Selected T-cell epitopes common for both MHC molecules predicted from B-cell epitopic regions
Sl No Sequence MHC-I allele MHC-II allele Position
1 FNILIILII HLA-A2.1
HLA-B*5101
HLA-B*5102
HLA-B*5201
HLA-B*5301
HLA-B*5401
HLA-B*51
HLA-Cw*0602
MHC-Db
MHC-Kd
MHC-Kk
DRB1_0101
DRB1_0102
DRB1_0401
3–11
2 FYFNGGNPA HLA-B*5301
HLA-B*5401
HLA-B*51
HLA-Cw*0401
MHC-Kd
DRB1_0101
DRB1_0102
DRB1_0401
DRB1_0426
51–59
3 FTQSLTQCV HLA-A*0201
HLA-A2.1
HLA-B*2702
HLA-B*3501
HLA-B*5301
HLA-B*51
HLA-B*5801
HLA-B61
MHC-Db revised
MHC-Ld
DRB1_0401
DRB1_0421
DRB1_0426
DRB1_0701
DRB1_0703
116–124
4 FIYTQSISQ HLA-A3
HLA-B*2702
HLA-B*2705
HLA-B*5301
HLA-B*5401
HLA-B*51
HLA-B*5801
HLA-B62
MHC-Kb
DRB1_0305
DRB1_0401
DRB1_0402
DRB1_0405
DRB1_0408
DRB1_0701
DRB1_0703
DRB1_0804
DRB1_1101
DRB1_1307
DRB1_1321
DRB5_0101
DRB5_0105
DRB1_0817
214–222
5 MVPGPNSKC HLA-A*0205
HLA-A3
HLA-B*3501
HLA-B*5101
HLA-B*5102
HLA-B*5103
HLA-B*51
HLA-B61
HLA-B7
HLA-B*0702
MHC-Ld
DRB1_0306
DRB1_0307
DRB1_0308
DRB1_0311
DRB1_0402
DRB1_0404
DRB1_0423
DRB1_1107
DRB1_1501
DRB1_1506
266–274
6 VVDDGTSTN HLA-B*3701
HLA-B*4403
HLA-B61
MHC-Kk
DRB1_0301
DRB1_0306
DRB1_0307
DRB1_0308
DRB1_0311
DRB1_0410
DRB1_1107
DRB1_0401
DRB1_0421
376–384
7 FAKFLSMSL HLA-B*5101
HLA-B*5102
HLA-B*5103
HLA-B*5301
HLA-B*5401
HLA-B*51
HLA-B*5801
HLA-B60
HLA-B7
HLA-B*0702
HLA-B8
DRB1_0308
DRB1_0311
DRB1_0410
DRB1_1107
DRB1_0401
444–452
8 FYFNGGNPS HLA-B*5401
HLA-B*51
HLA-Cw*0401
MHC-Kd
DRB1_0101
DRB1_0305
DRB1_0401
DRB1_0421
DRB1_0426
DRB1_0813
DRB1_1114
DRB1_1120
DRB1_1302
DRB1_1323
230–238
Fig. 3 Graphical representation of T − cell epitopes along with binding alleles
Multi-Epitope Subunit Vaccine Construction and Modeling
Multi-epitopic peptide-based vaccine comprises antigenic component of a pathogenic organism to induce an immunogenic reaction in the host body. In this study, the predicted T cell within B cell epitopes were combined in a sequential manner to construct the final vaccine candidate and server generated 3D structure Vaccine model showing in the Fig. 4. Sequentially, final vaccine construct has been associated with adjuvant L7/L12 ribosomal protein at the N-terminal side of vaccine model Fig. 5. Interactions of adjuvant with goldfish (Carassius auratus) toll like receptor-2 (TLR2) and common vaccine epitopes stimulate robust immune-reaction.Fig. 4 The tertiary structure of the designed vaccine construct
Fig. 5 Structural arrangement of the final vaccine construct
Analysis of the Physicochemical and Secondary Structural Properties, Allergenicity of the Vaccine Protein
In this subsection, we assumed the physicochemical properties of vaccine construct using Expasy Protparam server, calculating various values of incorporated parameters. The Molecular weight of vaccine model is 23055.04 Dalton, extinction coefficients values are 6085 M−1 cm−1(assuming all pairs of Cys residues form cystines) and 5960 M−1 cm−1(assuming all Cys residues are reduced), instability index (II) value 26.87, estimated half-life was 30 h, the value of aliphatic index is 66.80 and GRAVY value of I-antigen of I. multifiliis was − 0.140 (Table 3). As well as Protein-Sol server calculated the soluble capacity of vaccine candidate, and the results show that the vaccine candidate soluble with water (Fig. 6).Table 3 Antigenicity, allergenicity, solubility, and physicochemical property assessments of the primary sequence of multi-epitope-based vaccine construct
Sl. No Features Assessment
1 Antigenicity 0.5914 (probable ANTIGEN)
2 Allergenicity Probable non-allergen (AllerTOP v.2.0)
Probable non-allergen (AllergenFP v.1.0)
3 Solubility 0.717 (Soluble)
4 Number of amino acids 231
5 Molecular weight 23055.04 Dalton
6 Theoretical isoelectric point (pI) 7.83
7 Total number of atoms 3251
8 Formula C1012H1631N277O326S5
9 Estimated half-life 1 h (mammalian reticulocytes, in vitro)
> 30 min (yeast, in vivo)
> 10 h (Escherichia coli, in vivo)
10 Instability index 26.87 (Stable)
11 Aliphatic index 66.80
12 Grand average of hydropathicity (GRAVY) − 0.140
Fig. 6 Solubility index of Vaccine construct showing in the plot
From the SOPMA; we were getting the secondary structural values like the alpha helix, extended strand, beta turn and random coil were calculating 81.39%, 7.36%, 3.46%, and 7.79%, respectively depicted in the Fig. 7. This server also calculates window width value that was 17, Similarity threshold value-8 and numbers of states was four.Fig. 7 Probability score graph of occurrence of helix (Purple), strand (Green), turn (Red), and coil (Light blue) at each amino acid position in the secondary structure of the final Vaccine construct. Each residue position is characterized by the greater probability score associated secondary structure
Both two servers predicted that the vaccine showed non-allergenic in nature and it’s safe in respect of allergen (Table 3).
Justification of Vaccine Model
The modeled vaccine candidate had justified and validated through ERRAT, PROCHECK and ProSA web server. Here, the distribution of amino acid showed within Ramachandran map and exposed after homology modeling processes in order to validate the 3D vaccine protein model Fig. 8A. The Ramachandran plot of vaccine protein model revealed that 96.8% of residues lies in the most favorable zones, 2.8% of residues lies in allowed zones and only 0.5%in disallowed areas and statistical data plotted in the Table 4. ProSA-web and ERRAT confirmed the quality, energy value and potential errors in a crude 3D model respectively. ERRAT calculates 75.336% of quality factor that evaluates modeled protein’s overall quality reflects in Fig. 8D. Though the ProSA-web exposed a Z-score of − 0.93, Fig. 8C, for the query 3D model of vaccine, which lies outside the range of scores of NMR and lies between X-ray value commonly found in comparable size native Proteins. In the Fig. 8B represented the local energy model that have another significant sign for model quality assessment.Fig. 8 Validation of the tertiary structure of the vaccine. A The Ramachandran plot statistics represent the most favorable, accepted, and disallowed regions with a percentage of 96.8%, 2.8%, and 0.5%, respectively, B Local quality assessment plot C The ProSA-web representing the Z-score of − 0.93 for the vaccine model. D ERRAT error plot showing percentage of error of the vaccine model protein
Table 4 Distribution of amino acid residues showing in Ramachandran plot
Type of amino acid residue(s) No. of amino acid residues Percentage
Most favoured region [A, B, L] 211 96.8%
Additional allowed region [a, b, l, p] 6 2.8%
Generously allowed region [~a, ~b, ~l, ~p] 0 0.0%
Disallowed region 1 0.5%
Total Number of non-glycine and non-proline residues 218 100%
End-residues except glycine and proline 2 –
Glycine residues 7 –
Proline residues 4 –
Total number of residues 231
Antigenicity Prediction of the Vaccine Element
The VaxiJen server predicted the vaccine candidate has the antigenic property. The antigenic score for the vaccine candidate is 0.5914. This score crosses the threshold value and confirms antigenic property of vaccine component. We concluded the significant antigenic propensity from the antigenic value.
Molecular Docking
The molecular interaction between TLR2 and the designed vaccine candidate was studied by molecular docking method. The server predicted the complex structure based on complementary score, ACE (Atomic Contact Energy) and estimated interface area of the complex protein (Supplementary table-1). Among the first 20 results, only the top rank complex model depending on ACE value had taken. The ACE of the selected docking complex was -160.05 kcal/mol, the negative value indicates spontaneous molecular affinity between the putative vaccine molecules and immune receptors TLR2. The result showed that vaccine construct interacted with TLR2 receptor with significantly lower binding energy Fig. 9.Fig. 9 Molecular docking between the vaccine and the TLR-2 receptor
Molecular Dynamics Simulation
The molecular dynamics simulation was studied on the basis of Root mean square fluctuation (RMSF), Root mean square deviation (RMSD), Radius of gyration (Rg) and Solvent-accessible surface area (SASA) values present as a fraction of Picoseconds (Aspects of time) in Fig. 10A–D respectively. The conformational changes of the complex structure was calculated by RMSD values with the range of 0–1200 ps. The RMSD values steadily increased from 0 to 200 ps, and reached stable state throughout the simulation.Fig. 10 Molecular dynamics simulation A Root mean square deviation (RMSD) B root mean square fluctuation (RMSF) analysis of protein backbone and side chain residues of MD simulated vaccine construct C Radius of Gyration (Rg) plot in during MD simulation and D SASA plot
NMA Assay
Vaccine construct stability investigated through NMA mobility analysis (Fig. 11A), deformability analysis, B-factor (Fig. 11C), eigen value analysis, covariance map, variance map and elastic network plot. Results revealed that the placements of hinges in the chain was insignificant (Fig. 11B) and the B-factor column gave an averaged RMS (Fig. 11C). The estimated higher eigen value 1.780809e-06 (Fig. 11D) indicated low chance of deformation of vaccine candidate. The correlation matrix and elasticity of the vaccine candidate was shown in Fig. 11E and G, respectively. The variance associated to each normal mode is inversely related to the eigen value. Colored bars show the individual (red) and cumulative (green) variances showing in Fig. 11F.Fig. 11 Molecular dynamics analysis of vaccine protein-TLR2 complex; stability of the protein–protein complex was investigated through A NMA mobility showing with arrows B B-factor values C deformability plot D eigen value E covariance of residue index F Variance map and G elastic network analysis
Immune Simulation
The in silico simulated immune response of the vaccine construct was eliciting high primary and secondary immune responses by triggering the immune system, including CTL, HTL, sustainable memory cells and other cells. A high level of (IgM + IgG) antibodies, as well as other immune cells were drastically rising after administration of vaccine and primary immune cells like IgG and IgM immunoglobulins showed the prolonged effects against pathogens reflects in Fig. 12a. Moreover, the elevation of CTL cells, which reaches a maximum of 1207 cells/mm3 after 10 days of vaccine administration and decreases slowly after 23 days showing in Fig. 12d. Elevated CTL cells subsequently evoked to generate a high number of memory cells presented in Fig. 12e. The innate immunity mainly increased along with B-cell population was found to be increased with B isotype IgM and B-memory cell also be increased up to 600–700 cells/mm3(Fig. 12b–c). Memory cells play a central role to regulate prevention of viral infection/ re-infection through self-memorization upon encountering pathogens. Subsequently, administration of vaccine found to be elevated other central regulators of immune system (cytokines, interleukins, and NK cells) (Fig. 12f–i). These results signify the designed vaccine as a potent candidate to elicit a robust immune response to fight against fish pathogen.Fig. 12 In silico immune response simulation of the multi-epitope-based vaccine construct. a Production of immuno globulins upon antigen exposure, b Population of B lymphocytes after three injections, c Population of B-cell per cell state, d cytotoxic T lymphocytes population, e Amount of Cytotoxic T lymphocytes population per state, f Population of Natural Killer cells, g Population of Macrophages, h Population of Dendritic cells, i Concentration of cytokines and interleukins with Simpson index [D]
Codon Optimization and In-silico Cloning
Due to dissimilarity in the regulatory systems of E. coli and fishes, codon adaptation was accomplished within the host to justify its expression. Vaccine candidate was reverse transcribed for the codon adaptation, and the server predicted codon adaptation index (CAI) was 1; ensuring the higher proportion of most abundant adapted codons. The GC content of the optimized codons was 50.73%. Finally, the optimized codon sequence inserted into pET28a ( +) plasmid vector along with XhoI and XbaI restriction sites. A clone of 5887 base pair was produced comprising 778 bp desired sequence and shown in red color in between the sequence of pET28a ( +) vector and the rest belonging to the vector only (Fig. 13).Fig. 13 In silico restriction cloning of the multi-epitope vaccine sequence into the pET28a (+) expression vector. The red region represents the vaccine coding gene and the black circle represents the vector backbone
Discussion
Ichthyophthirius multifiliis is a protozoan parasite (an obligate parasite of fish), causing the ‘White spot disease’, major burden for fisherman and aqua culturist globally (Gersdorff Jørgensen 2017). The immobilization antigen are the highly abundant surface protein, mainly observed on a number of holotrich ciliates. At least five different serotypes of this parasite exists, characterized by differences in the surface I-antigen (Dickerson et al. 1993). The protozoan parasite infects (infective theront stage of the parasite invades) the skins and the gills of freshwater fishes (Ewing et al. 1985; Ventura and Paperna 1985). Recently the growing economic status of fish parasitosis for aquaculture sector and as well as fisheries sector has boosted the interest to develop the defense mechanisms against White spot disease. Interestingly, fish has both innate and adaptive immune responses to control protozoan parasite infections (Alvarez-Pellitero 2008). Innate immune response imitates immune recognition controlled by the activation of pathogen recognizing receptors (PRRs) which recognized pathogen-associated molecular patterns (PAMPs) (Medzhitov and Janeway 2002). Fishes have a number of PRRs, mainly Toll-like receptors (TLRs) that mainly activated and relies cascade mechanism through PAMP binding within the receptor (Roach et al. 2005; Purcell et al. 2006).
Hitherto, there is no prophylactic treatment or putative vaccines available while repetitive treatments with supplementary medicines are needed to control the infection. Historically, a large number of chemicals and drugs have been applied to combat White spot disease but due to changing norms and regulations with recognition of carcinogenic effects on environment the most efficient compounds are now prohibited. Researchers continuously search for novel substances like vaccine or non-carcinogenic drugs, which might be highly effective against the parasite as well protozoan organisms and harmless for the fish at the same time. These compounds should be ecofriendly and cost-effective.
Present research design was carried out to develop a multi-epitope based potential vaccine targets against protozoan I. multifiliis the causative agent of White spot disease (Ichthyophthiriasis) which have burden in the global fish culture industry. The multi-epitope-based vaccination is the new approach because of the epitope-based vaccine can generate precise immune responses against pathogens and enhance binding interaction with the target molecules. Now, in this work we selected I-antigen as target protein to design a MEBP vaccine through next generation peptide vaccine formulation methods. For the better response or the dual-purpose response (both humoral and cytotoxic immune response), T-cell epitopes derived from B-cell epitopes were identified in this study. Afterwards, both MHC-I and MHC-II epitopes were predicted successfully by the help of ProPed and ProPed-I server respectively. Next, we have identified common B and T-cell epitope and linked them up with suitable peptide linkers for construction of this vaccine candidate. 50S ribosomal protein L7/L12 adjuvant has been linked at N terminal with proper linker to boost up immunogenicity at cellular level which was used by other researcher for vaccine development.
In this study the 3D structure of vaccine candidate developed through SPARKS-X web server, and it’s validated through ProSA, PROCHECK and ERRAT error plot web-server. Subsequently ‘Z’ score, local energy plot was evaluated to comprehend its proper folding. Ramachandran plot of each amino acid residues of vaccine candidate was studied. Whereas, ProSA predicted ‘Z’ score negative value of − 0.93, indicating a good sign of model quality assessment (MacKerell et al. 2000). Along with the local quality model analysis which indicates its reliable structural quality.
PatchDock automatic web server demonstrated the binding interaction of the design vaccine candidate and TLR2, and calculated ACE score is noted as − 160.05 kcal/mol. The negative ACE value indicates the spontaneous reaction and proper interaction between the protein–peptide complex (Patra, et al. 2019) (Fig. 9). The stability of the vaccine structure was accessed through molecular dynamics simulation with the help of NAMD software package and it is CHARMM topology-based server (MacKerell et al. 2000). With the help of VMD 1.9.3 graphical software we analysed the RMSF, RMSD, radius of gyration and SASA plot of vaccine candidate (Fig. 10). From the result of the RMSD plot we concluded that the binding interaction is stable and flexible with TLR2. Whereas, NMA study performed to analyse the molecular mobility, comparative deformability, and B factor with PDB value. The eigen value was calculated as 1.780809e-06 which indicates the better flexibility of the vaccine candidate with receptor molecule. Finally, the Snap Gene cloning software appointed to amplify the desired vaccine sequence within the pET28b ( +) expression vector.
Conclusion
There is no completely effective vaccine reported against spot disease yet. In this work, a successful attempt was made to design a multi epitope-based protein (MEBP) vaccine against spot disease. Immunoinformatics and reverse vaccinology approaches were used to develop a potential and safe vaccine candidate that could trigger two types of immune responses: humoral and cellular. Our vaccine candidate is highly immunogenic, safe, stable and strongly interacting with fish TLR2 receptors. Here, eight common epitopes (FNILIILII, FYFNGGNPA, FTQSLTQCV, FIYTQSISQ, MVPGPNSKC, VVDDGTSTN, FYFNGGNPS, and FAKFLSMSL) filtered out and linked with stable peptide linker to generate an effective vaccine candidate that predominantly binds with fish TLR2 that facilitate the innate as well as adaptive immune systems and accelerates the immune response against pathogens. It was fascinating that our designed vaccine may able to stimulate neutralizing antibody as well as other central cellular responses well up to 350 days after the last third booster shot as computationally derived. Though our study has some limitations and recommended that the study may performed with in vitro and in vivo techniques to ensure the exact effectiveness.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 13 kb) Supplementary table 1: PatchDock predicted docking score (top 20) which predicted the rank based on complementary score, ACE (Atomic Contact Energy) and estimated interface area. (Here bold model has been taken for its high negative ACE value).
Acknowledgements
This research work is supported by the Council of Scientific & Industrial Research (CSIR) sponsored Junior Research Fellowship Program (File No. 09/599(0087)/2019-EMR-I).
Author contributions
PG and PP designed the model of the computational framework, in silico analysis and wrote the manuscript. PG, NM, DSC and BCP carried out the implementation and validations. BCP, NM and DSC helped with the analysis and editing the manuscript.
Data Availability
All data generated or analyzed during this study are included in this published article.
Declarations
Conflict of interest
The authors declare no conflict of interest.
Ethics approval
Not Applicable.
Consent for publication
Not applicable.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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| 0 | PMC9734321 | NO-CC CODE | 2022-12-14 23:28:27 | no | Int J Pept Res Ther. 2023 Dec 9; 29(1):11 | utf-8 | Int J Pept Res Ther | 2,022 | 10.1007/s10989-022-10475-1 | oa_other |
==== Front
Inflamm Res
Inflamm Res
Inflammation Research
1023-3830
1420-908X
Springer International Publishing Cham
36463339
1664
10.1007/s00011-022-01664-1
Original Research Paper
Triazoles with inhibitory action on P2X7R impaired the acute inflammatory response in vivo and modulated the hemostatic balance in vitro and ex vivo
Pinheiro Nathalia Gugick 12
Gonzaga Daniel Tadeu Gomes 34
da Silva Aldo Rodrigues 15
Fuly Andre Lopes 15
von Ranke Natalia Lidmar 36
Rodrigues Carlos Rangel 36
Magalhães Betina Quintanilha 37
Pereira Julianne Soares 12
Pacheco Paulo Anastácio F. 48
Silva Ana Cláudia 12
Ferreira Vitor Francisco 389
de Carvalho da Silva Fernando [email protected]
38
Faria Robson Xavier 12
1 grid.411173.1 0000 0001 2184 6919 Institute of Biology, Universidade Federal Fluminense – Postgraduate Program in Science and Biotechnology, Campus Valonguinho, Niterói, RJ Brazil
2 grid.418068.3 0000 0001 0723 0931 Laboratory of Environmental Health Assessment and Promotion, Instituto Oswaldo Cruz, Avenida Brasil 4365, Rio de Janeiro, RJ CEP 21040-900 Brazil
3 grid.411173.1 0000 0001 2184 6919 Faculty of Pharmacy, Department of Pharmaceutical Technology, Universidade Federal Fluminense, Niterói, RJ CEP 24241-000 Brazil
4 grid.412211.5 0000 0004 4687 5267 Department of Pharmacy, Organic Synthesis Laboratory – State University of Rio de Janeiro, West Zone Campus, Aveniada Manuel Caldeira de Alvarenga, 1203, Rio de Janeiro, RJ CEP 23070-200 Brazil
5 grid.411173.1 0000 0001 2184 6919 Laboratory of Venoms and ToxinsAnimal and Inhibitor Assessment, Instituto de Biologia, Fluminense Federal University, Bloco M - Rua Prof. Marcos Waldemar de Freitas Reis - São Domingos, Niterói, RJ 24210-201 Brazil
6 grid.8536.8 0000 0001 2294 473X Laboratory of Molecular Modeling and QSAR (ModMolQSAR), Faculty of Pharmacy, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
7 grid.411173.1 0000 0001 2184 6919 Laboratory of Endocrine Physiology and Metabology, Department of Physiology, UFF, Niterói, RJ CEP 24241-000 Brazil
8 grid.411173.1 0000 0001 2184 6919 Faculty of Pharmacy, Department of Pharmaceutical Technology, Universidade Federal Fluminense, Postgraduate Program in Applied Health Sciences, Niterói, RJ CEP 24241-000 Brazil
9 grid.411173.1 0000 0001 2184 6919 Department of Organic Chemistry, Institute of Chemistry, Universidade Federal Fluminense, Campus Do Valonguinho, Niterói, RJ CEP 24020-150141 Brazil
Responsible Editor: John Di Battista.
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Objective
The present study aimed to investigate five triazole compounds as P2X7R inhibitors and evaluate their ability to reduce acute inflammation in vivo.
Material
The synthetic compounds were labeled 5e, 8h, 9i, 11, and 12.
Treatment
We administered 500 ng/kg triazole analogs in vivo, (1–10 µM) in vitro, and 1000 mg/kg for toxicological assays.
Methods
For this, we used in vitro experiments, such as platelet aggregation, in vivo experiments of paw edema and peritonitis in mice, and in silico experiments.
Results
The tested substances 5e, 8h, 9i, 11, and 12 produced a significant reduction in paw edema. Molecules 5e, 8h, 9i, 11, and 12 inhibited carrageenan-induced peritonitis. Substances 5e, 8h, 9i, 11, and 12 showed an anticoagulant effect, and 5e at a concentration of 10 µM acted as a procoagulant. All derivatives, except for 11, had pharmacokinetic, physicochemical, and toxicological properties suitable for substances that are candidates for new drugs. In addition, the ADMET risk assessment shows that derivatives 8h, 11, 5e, and 9i have high pharmacological potential. Finally, docking tests indicated that the derivatives have binding energies comparable to the reference antagonist with a competitive inhibition profile.
Conclusions
Together, the results indicate that the molecules tested as antagonist drugs of P2X7R had anti-inflammatory action against the acute inflammatory response.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00011-022-01664-1.
Keywords
P2X7 receptor
Antagonists
Paw edema
Synthetic substances
Triazoles
http://dx.doi.org/10.13039/501100002322 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Financial Code 001) (Financial Code 001) (Financial Code 001) (Financial Code 001) (Financial Code 001) Pinheiro Nathalia Gugick da Silva Aldo Rodrigues Magalhães Betina Quintanilha Pereira Julianne Soares Pacheco Paulo Anastácio F. http://dx.doi.org/10.13039/501100004586 Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro E-26/203.246/2017, E-26/211.025/2019, E-26/200.982/2021, E-26/203.191/2017, E-26/202.800/2017, E-26/010.101106/2018, E-26/200.870/2021, E-26/201.369/2021 and SEI-260003/001178/2020 E-26/203.246/2017, E-26/211.025/2019, E-26/200.982/2021, E-26/203.191/2017, E-26/202.800/2017, E-26/010.101106/2018, E-26/200.870/2021, E-26/201.369/2021 and SEI-260003/001178/2020 E-26/203.246/2017, E-26/211.025/2019, E-26/200.982/2021, E-26/203.191/2017, E-26/202.800/2017, E-26/010.101106/2018, E-26/200.870/2021, E-26/201.369/2021 and SEI-260003/001178/2020 E-26/203.246/2017, E-26/211.025/2019, E-26/200.982/2021, E-26/203.191/2017, E-26/202.800/2017, E-26/010.101106/2018, E-26/200.870/2021, E-26/201.369/2021 and SEI-260003/001178/2020 E-26/203.246/2017, E-26/211.025/2019, E-26/200.982/2021, E-26/203.191/2017, E-26/202.800/2017, E-26/010.101106/2018, E-26/200.870/2021, E-26/201.369/2021 and SEI-260003/001178/2020 E-26/203.246/2017, E-26/211.025/2019, E-26/200.982/2021, E-26/203.191/2017, E-26/202.800/2017, E-26/010.101106/2018, E-26/200.870/2021, E-26/201.369/2021 and SEI-260003/001178/2020 E-26/203.246/2017, E-26/211.025/2019, E-26/200.982/2021, E-26/203.191/2017, E-26/202.800/2017, E-26/010.101106/2018, E-26/200.870/2021, E-26/201.369/2021 and SEI-260003/001178/2020 E-26/203.246/2017, E-26/211.025/2019, E-26/200.982/2021, E-26/203.191/2017, E-26/202.800/2017, E-26/010.101106/2018, E-26/200.870/2021, E-26/201.369/2021 and SEI-260003/001178/2020 Gonzaga Daniel Tadeu Gomes Fuly Andre Lopes von Ranke Natalia Lidmar Rodrigues Carlos Rangel Silva Ana Cláudia Ferreira Vitor Francisco de Carvalho da Silva Fernando Faria Robson Xavier http://dx.doi.org/10.13039/501100003593 Conselho Nacional de Desenvolvimento Científico e Tecnológico (301873/2019-4, 316568/2021-0, and 306011/2020-4) (301873/2019-4, 316568/2021-0, and 306011/2020-4) (301873/2019-4, 316568/2021-0, and 306011/2020-4) Ferreira Vitor Francisco de Carvalho da Silva Fernando Faria Robson Xavier
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pmcIntroduction
Inflammation is a physiological response that plays an important role in the maintenance of tissue homeostasis [1]. The inflammatory response is triggered by the detection of infection or tissue damage through the recognition of pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs), respectively [2]. In sterile inflammation, endogenous molecules, such as ATP, uric acid, and some cytoplasmic and nuclear proteins released by damaged cells, are DAMPs [3]. These signaling molecules recognize a set of specific receptors triggering the inflammatory process by the activation of blood vessels, release of soluble mediators, and recruitment of leukocytes to the site of inflammation [4]. After the ceasing or elimination of the cause of inflammation, the reestablishment of tissue homeostasis initiates the resolution phase. However, whether the resolution of inflammation does not occur properly and whether the cause of inflammation is not eradicated by the acute inflammatory response, a chronic inflammatory process that can lead to tissue damage and dysfunction, has been established [5].
Extracellular ATP is now a well-established DAMP signal that exerts its inflammatory effects through activation of plasma membrane receptors expressed by immune cells, known as purinergic receptors [6]. In general, the action of ectonucleotidases keeps extracellular ATP at low concentrations [7]. However, several lines of evidence demonstrate the accumulation of extracellular ATP at sites of inflammation and infection [8]. Under these conditions, ATP can reach high concentrations sufficient to stimulate purinergic receptors, and trigger a series of proinflammatory responses [9]. Thus, these subtypes of receptors have been considered relevant pharmacological targets for the development of novel anti-inflammatory drugs to avoid or mitigate the side effects of traditional COX inhibition-based therapy. Among them, the P2X7 receptor (P2X7R) is the subtype most directly involved in inflammatory responses, regulating the release of proinflammatory cytokines, such as IL-1β [10].
P2X7R is an ATP-gated ion channel that is preferentially permeable to mono- and divalent cations [11]. This receptor has pharmacological characteristics that differentiate it from other purinergic receptors in the P2X family. Its activation requires ATP concentrations of approximately 100 µM (EC50 ≥ 100 µM) [12–14], which are higher than those observed to activate other family subtypes. In addition, P2X7R treatment with high agonist concentrations or sustained stimulation evokes the formation of a high-conductance nonselective pore, which allows the passage of molecules of up to 900 Da across the cell plasma membrane. These cell modifications result in disruptions in cell ionic homeostasis [15–18]. This receptor also has another important and exclusive characteristic, which is the ability to initiate the release of intracellular ATP on a large scale that is associated with the intrinsic ability to form pores or due to the association with hemichannels of pannexins, promoting a positive-feedback loop in purinergic signaling and an increase in inflammation [13, 19].
P2X7R activation in several cells induces the production and secretion of different inflammatory mediators, such as TNF-α, MPC-1 (monocyte chemoattractant protein-1), and IL-6, as well as the cleavage of metalloproteinases, CD23, selectin-1, CD27, and matrix [20–24] (induced by lipopolysaccharide (LPS) and elevates mRNA levels for iNOS (inducible nitric oxide synthase). In mast cells, it induces an increase in the expression of TNF-α, IL-4, IL-6, and IL-13 [25–28]. Furthermore, P2X7R participates in the formation of the NLRP3 inflammasome, which is responsible for the maturation and release of IL-1β and IL-18 [21]. Since P2X7R is involved in several inflammatory dysfunctions and there is an urgent need for the development of new anti-inflammatory drugs with novel mechanisms of action, this receptor has become an attractive molecular target to circumvent the toxicity issues associated with traditional anti-inflammatory therapy.
A method for classifying the P2X7R antagonist is according to the binding site of the molecules into orthosteric ligands and allosteric ligands. The first category is composed of molecules that bind to the ATP-binding site, while the second category of ligands are defined by molecules that bind to the receptor in a different region, decreasing its endogenous ligand effectiveness [29]. To date, no selective agonists for this receptor have been described with effective therapeutic action [29]. Therefore, it is urgently necessary to search for new and selective human P2X7R antagonists. Previously, our research group assessed the antagonistic activity of a series of 1,2,3-triazole compounds and identified that some derivatives were able to potentially block the formation of the P2X7R pore in dye uptake assays performed in mammalian cells (J774. G8 cells and peritoneal macrophages). These molecules were also able to inhibit the release of IL-1β mediated by P2X7R. In addition, molecule 6c was able to decrease ATP-induced dye uptake, and molecule 9e partially inhibited intracellular dye uptake, giving them the potential to antagonize P2X7R. In addition, our previous molecular docking studies indicated the ATP-binding pocket as a potential binding site for the 1,2,3-triazole compounds [30]. We named the synthetic compounds with inhibitory activity on mP2X7R and hP2X7R antagonists 5e, 8h, 9i, 11, and 12. The choice criterion was the previous results demonstrating antagonistic in vitro action against P2X7R at nanomolar concentrations [30].
Methodology
In vivo studies
Male mice (Swiss Webster), weighing approximately 30.0 g, treated with a PURINA-LABINA balanced ration, water "ad libitum" and a light–dark cycle of 12 h were used in the paw edema assays. These tests are carried out in accordance with CEUA-FIOCRUZ with license number 043/18.
Single-dose toxicity
Triazole toxicity was evaluated by the in vivo test, according to [31], with modifications. All triazoles (1000 mg/kg) or saline solution was injected intraperitoneally (i.p.) into the abdominal region of the mice. Then, behavior and mortality were observed for 24 h.
Paw edema assay
For paw edema induction, the mice received a subplantar injection in one of the hind paws with ATP (10 mM/paw). A solution of 0.9% NaCl was applied to the contralateral paw. One hour before the application of the phlogistic agent, the following treatments were performed: G1 and G2—negative control groups (NaCl 0.9% intraperitoneal); G3—positive control group (Diclofenac 10 mg/kg/intraperitoneal); and G4, G5, G6—test groups (tested compounds at 100, 500, and 1000 ng/kg/intraperitoneal), respectively. After 60 min of preincubation with antagonists and 60 min after the application of ATP, the paw volume was evaluated with the aid of a plethysmometer device (UGO-BASILE). All substances administered to the hind paw were administered at a volume of 20 μL/paw, and all substances administered into the peritoneum were administered at a volume of 100 μL/intraperitoneal.
Peritonitis
For the peritonitis assay, the animals were treated intraperitoneally with 100 µL of the molecules tested at concentrations of 10 mg/kg (carrageenan), 10 mg/kg (dexamethasone), and 0.1 mg/kg (5e, 8h, 9i, 11, and 12). A one X solution of PBS (phosphate-buffered saline) at pH 7.4 (applied in a volume of 100 µL) and carrageenan were used as negative and positive controls. The groups that were pretreated before receiving the inflammation inducer received dexamethasone, 5e, 8h, 9i, 11, and 12 with pretreatment and 1 h later received carrageenan. Three hours after the first administration, intraperitoneal fluid was collected, and the differential and total leukocyte counts were determined. The total leukocyte count was performed using a Neubauer chamber. For the differential leukocyte count, we fixed the slide containing the microtube samples in methanol for 5 min and then stained it with Giemsa solution for 15–20 min. Differential counting was performed by counting 100 cells per slide, including mast cells, eosinophils, macrophages, lymphocytes, and neutrophils.
Platelet aggregation tests
Platelet aggregation was monitored turbidimetrically in a platelet aggregometer (Model 490 2D—Chrono-log Corporation, Pennsylvania, USA) according to [32], using platelet-rich plasma (PRP) obtained from healthy volunteer donors. Blood samples from healthy volunteer donors were collected through venipuncture using 3.8% w/v sodium citrate (citrate/blood 1:9) as an anticoagulant. The donors declared that they did not have any disorder related to hemostasis and that they did not use any medication that could affect the results. Blood was centrifuged at 1800 rpm for 12 min at room temperature to obtain platelet-rich plasma (PRP) in the supernatant. Platelet-poor plasma (PPP) was obtained by centrifuging the remaining blood at 2,500 rpm for 12 min. Assays were performed using 300 µL of PRP kept at 37 °C for 1 min in siliconized glass cuvettes under constant agitation. Then, platelet aggregation was initiated by the addition of the physiological agonist ADP (15 µM). The 100% platelet aggregation was obtained with the supramaximal platelet response to the addition of agonist after 6 min of reaction, and 0% platelet aggregation was determined by transmittance caused by PRP alone prior to the addition of agonist. Plasma with saline was used to adjust the apparatus (basal), and then, the treatments were realized. To evaluate the effect of the derivatives on platelet aggregation, they were preincubated with 300 µL of PRP for 5 min at 37 °C under constant agitation. Then, the agonist was added, and platelet aggregation was monitored. As a control, PRP was incubated with 0.15 M NaCl, and the compound was incubated in the absence of ADP in both cases. The same procedure was performed as described above. As a result, it was observed that compounds in the absence of ADP were not capable of inducing platelet aggregation (data not shown). Following a pattern of graphic demonstration present in the literature, a platelet aggregation of 100% was obtained as a response of PRP to the addition of the agonist, with nongraphic representation of the percentage of inhibition of platelet aggregation obtained by compounds [32].
Coagulation assays
All coagulation tests were performed in a Multichannel Coagulometer (Model KC4A micro—Amelung—Lemgo, Germany). The plasma was obtained from healthy volunteer donors who declared to not have any hemostasis or bleeding disorders and to not use any medication that could affect the results obtained in coagulation assays. Blood was collected by venipuncture using 3.8% w/v sodium citrate, as an anticoagulant (1:9, anticoagulant: blood), and centrifuged at 3000 rpm for 10 min at room temperature for subsequent removal of the plasma. Then, we pooled and stored the plasma of at least three different donors in plastic tubes at −20 °C until use.
Prothrombin time test
The prothrombin time (TP) test assesses the extrinsic pathway and common pathways of the coagulation cascade. In this assay, the Soluplastin kit (Wiener Lab, Rosario, Argentina) was used, following the manufacturer's instructions. Plasma (100 µL) was maintained for 2 min at 37 °C, and the reaction was started by the addition of thromboplastin with calcium (100 µL). Then, 50 µL of the derivatives was incubated with plasma for 5 min at 37 °C, and 100 µL of thromboplastin with calcium was added to trigger coagulation of plasma. We monitored the clotting time (in seconds) in the coagulometer and compared it with data obtained in a control tube containing saline.
Activated partial thromboplastin time (aPTT) test
The activated partial thromboplastin time (APTT) test assesses the initiation or propagation pathway of the coagulation cascade. In this assay, the APTT kit (Wiener Lab) was used according to the manufacturer’s instructions. The pool of plasma (100 µL) was preincubated with 100 µL of activated cephalin for 10 min at 37 °C in the absence or presence of the derivatives. The reaction was started by the addition of 100 µL of CaCl2 (8.3 mM, final concentration, previously heated to 37 °C), and coagulation was monitored in seconds in the coagulometer. Similarly, saline was added to the reaction medium, as a control.
Plasma recalcification time test
The recalcification time test evaluates calcium-dependent coagulation cascade factors through the addition of CaCl2 to plasma. The pool of plasma pool (100 µL) was incubated with saline or with the derivatives at 37 °C for 10 min, 50 µL of CaCl2 (12.5 mM, final concentration) was added to the reaction medium, and the clotting time was monitored in the coagulometer.
Toxicity hemocompatibility
The toxicity of the triazoles was evaluated by the hemocompatibility test, according to [33], with modifications. All the compounds (100 μg/mL) or saline (negative control) were incubated with a 13% (v/v) red blood cell suspension for 3 h at 37 °C. Then, the samples were centrifuged for 3 min at 1800 rpm, and lysis of the cells was detected by measuring hemoglobin at an absorbance of 578 nm using a microplate reader (SpectraMax, Model M4, Molecular Devices, California, USA). One hundred percent hemolysis (positive control) was achieved by adding Triton X-100 (1%, v/v) or water to the red blood cell suspension.
Statistical analysis
Analysis of calcium, paw edema, and peritonite assay data
Statistical comparisons are represented as the mean ± SD (standard deviation), as shown in the text. The statistical significance of the differences between means was tested by one-way ANOVA followed by Tukey's test. A bicaudal p < 0.05 was considered significant. The results were plotted using GraphPad Prism version 5.0.
Analysis of platelet aggregation and coagulation data
The results are expressed as the mean ± SEM (standard error of the mean) of the indicated number of experiments performed. The results obtained were analyzed by Student's t test using the GraphPad Prism 6 program. Values of p < 0.05 were considered significant.
In silico studies
Pharmacokinetic and toxicological profile of triazoles
ADMET Predictor® (Simulation Plus) predicted the pharmacokinetic and toxicological profiles [34]. The structures of the triazole-derived molecules that were evaluated are shown in Fig. 1.Fig. 1 1,2,3-Triazole compounds to be evaluated in this work for antagonistic activity against P2X7R
Analysis of the binding potential of triazoles in the P2Y12 receptor
To complement the platelet aggregation and coagulation assays, we performed molecular docking to explore the P2Y12 purinergic receptor as a potential additional target for the triazole derivatives. Since the P2Y12 receptor is a purinergic receptor that is well known to be involved in the coagulation process [35, 36] and represents an important pharmacological target for the development of antithrombotic drugs [37], blind molecular docking was performed against the entire structure of the P2Y12 receptor to identify whether this receptor could be a target for triazole derivatives. For that, we selected the crystal structure deposited under the PDB code of 4PXZ, since it is the only structure available to date of P2Y12 in complex with an antagonist, AZD1283. To validate and compare the results, redocking was performed with the ligand AZD1283. The methods used to perform the molecular docking are described in the work by [38].
Results
Paw edema assays
As mentioned before, P2X7R is directly associated with the inflammatory response, since its activation on several cells induces the production and secretion of different inflammatory mediators, such as TNF-α, nitric oxide, and several proinflammatory cytokines, such as IL-1β, IL-6, and IL-18. The experimental model selected to study the potential in vivo anti-inflammatory effect of the derivatives was ATP-induced paw edema. The molecules selected for this study were 5e, 8h, 9i, 11, and 12, which exhibited potent antagonist activity toward P2X7R in previous in vitro studies [38]. We tested all compounds at different doses: 100, 500, and 1000 ng/kg. As shown in Fig. 1, treatment with 10 mM ATP induced a 30% increase in paw edema compared to the saline group, corroborating its action as a physiological agent in this experimental model (Fig. 2A). In addition, all tested compounds at different doses exhibited significant inhibitory effects on paw edema formation compared to the reference drug diclofenac (Fig. 2B–F). Additionally, treatment with selective P2X2 and P2X4 receptor antagonists did not inhibit the ATP effect (Supplemental Figure 1) [39, 40].Fig. 2 Inhibition of ATP-induced paw edema formation. A Paw edema was induced by the injection of 10 mM ATP (positive control) and saline solution was used as a negative control. We administered the tested compounds (100, 500, and 1000 ng/kg) or diclofenac (10 mg/kg) 1 h before edema formation. B Compound 8h. C Compound 12. D Compound 5e. E Compound 9i. F Compound 11. The volume of the paws was read using a plethysmometer device (UGO-BASILE) 60 min after the administration of the phlogistic inducer. Graph showing the decrease in the percentage of paw edema in the groups treated with only saline, 10 mM ATP, 0.8% diclofenac and the tested compounds (100, 500, and 1000 ng/kg) (**p < 0.01 and ***p < 0.001 compared to ATP treatment). Three paw edema experiments were performed on different days
Carrageenin-induced peritonitis model
Previously, we evaluated the anti-inflammatory effect of triazoles on a mouse paw edema model [31]. Thus, we used a carrageenan-induced peritonitis model to investigate the inhibitory action of triazole derivatives in the recruitment of inflammatory cells to the peritoneal cavity. Treatment with carrageenan-induced peritonitis and saline was used as a negative control. Before carrageenan stimulation, the animals received the molecules (0.1 mg/kg) and dexamethasone (10 mg/kg) via intraperitoneal injection. Three hours later, we collected the intraperitoneal fluid, and determined the total and differential leukocyte counts. The experimental group that received carrageenan as a phlogistic agent exhibited an intense inflammatory response characterized by an increase in white blood cells compared to the saline group (Fig. 3). This effect was reversed by pretreatment with dexamethasone. Nonetheless, the groups that received the triazole derivatives in association with carrageenan presented a significant decrease in the number of total leukocytes, particularly 5e and 9i, suggesting in vivo anti-inflammatory action (Fig. 3). Treatment with selective P2X2 and P2X4 receptor antagonists did not inhibit this effect (Supplemental Figure 2).Fig. 3 Inhibition of peritonitis by treatment with 5e, 8h, 9i, 11, and 12 molecules. Total leukocytes. The graph shows the decrease in total leukocytes in the groups treated with dexamethasone, 5e and 5e + carrageenan, 8h and 8h + carrageenan, 9i and 9i + carrageenan, 11 and 11 + carrageenan, and 12 and 12 + carrageenan. **p < 0.05 and ***p < 0.001 compared to carrageenan treatment. Three experiments were performed for each molecule on different days to build this graph
We evaluated the effect of treatments on each cell population and observed that pretreatment with 5e, 8h, 9i, 11, and 12, all at a concentration of 10 mg/kg, inhibited the effect of carrageenan, maintaining the number of mononuclear cells and decreasing the number of segmented cells (Table 1), including in the group that received dexamethasone (Table 1; Fig. 3K, L).Table 1 Effect of triazoles on the differential counting of cells after carrageenan-induced peritonitis in mice
Treatments % Mononuclear cells % Neutrophils
Saline 81 ± 4ª 16 ± 10ª
10 mg/kg Carrageenan 28 ± 8 67 ± 10
10 mg/kg dexamethasone 90 ± 6ª 9 ± 7ª
Carrageenan + dexamathasone 72 ± 9ª 27 ± 9ª,g
10 mg/kg 5e 89 ± 5ª,b 11 ± 7ª,b
10 mg/kg 8h 96 ± 5ª,c 5 ± 4ª,c
10 mg/kg 9i 93 ± 3ª,d 6 ± 3ª,d
10 mg/kg 11 84 ± 2ª,e 16 ± 4ª,e
10 mg/kg 12 86 ± 5ª,f 7 ± 3ª,f
Carrageenan + 5e 56 ± 8ª 40 ± 12ª
Carrageenan + 8h 76 ± 7ª 17 ± 11ª
Carrageenan + 9i 65 ± 10ª 40 ± 10ª
Carrageenan + 11 55 ± 10ª 37 ± 4ª
Carrageenan + 12 71 ± 10ª 40 ± 5ª
Triazoles’ effect on differential counting cells after carrageenan-induced peritonitis in mice
ap > 0.005 compared with Carrageenan treatment
bp > 0.05 compared with Carrageenan + 5e
cp > 0.05 compared with Carrageenan + 8h
dp > 0.05 compared with Carrageenan + 9i
ep > 0.05 compared with Carrageenan + 11
fp > 0.05 compared with Carrageenan + 12
gp > 0.05 compared with Carrageenan + dexamethasone
Coagulation and platelet aggregation
Nonsteroidal anti-inflammatory drugs (NSAIDs) can hinder platelet aggregation via selective or nonselective inhibition of cyclooxygenase COX-1 and COX-2 isoenzymes and, therefore, the synthesis of prostaglandins, prostacyclin, and thromboxane [42]. In this sense, these drugs can increase the risk of bleeding, especially gastrointestinal bleeding. In addition, there are reports of an increased risk of bleeding in patients who use anticoagulants in association, such as patients with atrial fibrillation [41, 42].
The effect of derivatives on coagulation was investigated using three in vitro tests, prothrombin time (PT), activated partial thromboplastin time (APTT), and recalcification test (RC), which are regularly used in the clinic to assess procoagulant or anticoagulant disorders; the effect of the derivatives at two concentrations on the PT test. Derivatives 5e (15.8 ± 1 s, p = 0.075), 8h (16.4 ± 2 s, p = 0.056), 9i (15.8 ± 1 s, p = 0.057), 11 (15.4 ± 1 s, p = 0.055), and 12 (15.5 ± 1 s, p = 0.056) added at concentrations of 1 µM or 5e (15.7 ± 2 s, p = 0.064), 8h (15.4 ± 0.7 s, p = 0.064), 9i (15.2 ± 0.06 s, p = 0.058), 11 (15.2 ± 0.5 s, p = 0.065), and 12 (15.4 ± 0.8 s, p = 0.056) at a concentration of 10 µM did not cause alterations in the prothrombin time compared to the control group (15 ± 2 s). Treatment with the selective P2X2 receptor antagonist, NF770, at concentrations of 1 µM (15.5 ± 1 s, p = 0.068) and 10 µM (15.7 ± 1 s, p = 0.06) did not inhibit this effect. In a similar manner, treatment with the P2X4 receptor antagonist, 5-BDBD, at concentrations of 1 µM (15.4 ± 0.7 s, p = 0.054) and 10 µM (15.7 ± 0.5 s, p = 0.057) did not inhibit.
All derivatives at 1 and 10 µM for 8h, 11, and 12 significantly prolonged the clotting time in the aPTT test. Treatment with 1 µM 5e (44.5 ± 0.6 s, p = 0.043), 8h (47.8 ± 0.7 s, p = 0.044), 9i (44.5 ± 0.7 s, p = 0.046), 11 (51.5 ± 0.8 s, p = 0.045), and 12 (51.4 ± 0.7 s, p = 0.046) augmented the anticoagulant activity compared with control (41.3 ± 0.7 s). Treatment with 10 µM for 8h (48.3 ± 0.7 s), 11 (48.5 ± 1 s), and 12 (52 ± 0.9 s) increased the clotting time, in contrast to 5e (48.6 ± 9 s, p = 0.07) and 9i (50.65 ± 10 s, p = 0.09). Treatment with the selective P2X2 receptor antagonist, NF770 at concentrations of 1 µM (40.9 ± 1 s, p = 0.062) and 10 µM (42.2 ± 1 s, p = 0.057) did not inhibit this effect. In a similar manner, treatment with the P2X4 receptor antagonist, 5-BDBD, at concentrations of 1 µM (41.9 ± 0.6 s, p = 0.06) and 10 µM (42.5 ± 0.8 s, p = 0.054) did not inhibit.
The effect of the derivatives on the plasma recalcification time was also evaluated. The incubation of plasma with the derivatives 1 µM 5e (284.4 ± 4 s, p = 0.038), 1 µM 8h (284.7 ± 20 s, 0.044), 1 µM 9i (244.3 ± 11 s, p = 0.042), 1 µM 11 (277.7 ± 8 s, p = 0.037) or 10 µM (336.6 ± 36 s, p = 0.022), 1 µM 12 (260.2 ± 10 s, p = 0.047) or 10 µM (281.2 ± 12 s, p = 0.031) prolonged the clotting time when compared to the control (196.9 ± 9 s). On the other hand, derivative 5e, at concentration of 10 µM, decreased the clotting time (189.1 ± 3 s, p = 0.048) when compared to the control, indicating that it might act as a procoagulant agent at this concentration. Treatment with the selective P2X2 receptor antagonist, NF770, at concentrations of 1 µM (200.4 ± 12 s, p = 0.055) and 10 µM (198 ± 8 s, p = 0.054) did not inhibit this effect. In a similar manner, treatment with the P2X4 receptor antagonist, 5-BDBD, at concentrations of 1 µM (204.6 ± 9 s, p = 0.062) and 10 µM (195 ± 7 s, p = 0.059) did not inhibit.
The triazoles increased the closing time in a mechanism independent of the P2X2 and P2X4 receptors. Therefore, we decided to evaluate the effect of the triazole derivatives on platelet aggregation induced by ADP that binds to the P2Y12 purinergic receptor on platelets. This receptor is also involved in the coagulation process [43]. As show in Fig. 4, the derivatives (1 µM) inhibited ADP-induced platelet aggregation, as did clopidogrel (1 µM), which is a selective antagonist of the P2Y12 receptor.Fig. 4 Effect of the derivatives or clopidrogel at a concentration of 1 µM on platelet aggregation of platelet-rich plasma (PRP) induced by ADP. The results are expressed as the mean ± SEM (n = 9)
Analysis of the binding potential of triazole derivatives molecules in the P2Y12 receptor target
Based on the results that suggested an anticoagulant action of the compounds and the absence of an effect of other P2X receptor subtypes such as P2X2 and P2X4 receptors, we performed molecular docking to analyze the binding potential of triazole molecules to the P2Y12 receptor. Redocking assays were performed, and the results were able to reproduce most of the interactions of AZD1283 with its binding site, particularly the interactions between the piperidinyl and benzylsulfonyl moieties of the ligand with helices VI and VII (Fig. 5). The redocking reproduced the hydrogen bonding of the carboxyl group with the Arg256 residue and the hydrophobic interaction with the Phe251, Tyr105, Tyr259, and Lys276 residues (Fig. 5).Fig. 5 Representative redocking result for the AZD1283 ligand against the P2Y12 receptor. The P2Y12 receptor is shown in cartoon and colored cyan. The AZD1283 conformation derived from the crystal structure (PDB: 4NTJ) is depicted in green, and the AZD1283 conformation derived from redocking is depicted in pink (Color figure online)
The docking results of the triazole derivatives indicated that the most populated cluster is in the same AZD1283 binding pocket. In addition, the triazole derivatives and AZD1283 presented similar values of binding energy (Supplemental Table 1), indicating a potential affinity of the derivatives for the P2Y12 receptor and a competitive profile for the triazole derivatives.
It is important to mention that molecular docking estimates the binding energy via a scoring function. However, despite being a method efficient in the identification of ligands and nonligands, the scoring functions of docking are not able to discriminate between the interactions of ligands with less than 1 kcal/mol [44]. As the binding energy evaluated for all compounds in this work was less than 1 kcal/mol, we can only infer that they might present similar binding affinity.
Interestingly, the favorable conformation of all triazoles presented a similar binding mode, corroborating the affinity toward this binding site.
In all triazole derivatives, the benzyl group was oriented in the same direction as the benzylsulfonyl group of the ligand AZD1283 toward helices VI and VII, thus performing hydrophobic interactions with residues Phe251, Tyr105, Tyr259, and Lys276. It was also possible to note a hydrogen-bond interaction with residue Arg256, and such a hydrogen bond might have an important contribution to the antagonist bioactive conformation (Fig. 6).Fig. 6 Representation of the most favorable conformation for each triazole derivative. Green represents the binding mode of the ligand AZD1283 derived from the crystal structure (PDB: 4NTJ). Each triazole derivative is represented in pink (Color figure online)
These data are relevant, because the in vivo administration of triazoles could affect both receptors. However, the inhibitory effect of triazoles on the P2X7 receptor occurs in at nanomolar concentrations and dosages [45], and on the P2Y12 receptor at micromolar concentrations.
In vitro and in vivo toxicity of triazole analogs
Toxicity in red blood cells
The toxicity of triazoles was assayed through an in vitro hemocompatibility test using RBCs. The treatment of cells with Triton X-100 or water lysed 100% RBCs (positive groups), while treatment with saline (negative control) did not result in lysis. All triazoles (100 μg/mL) lysed approximately 3% of red blood cells (data not shown), and according to [33], hemolysis below 10% means that the compound or molecule is devoid of toxicity; thus, triazoles can be considered nonhemolytic or nontoxic molecules. However, it is worth noting that the concentration of triazoles in this toxicity test (100 μg/mL) was approximately 10–100 times higher than any tested concentration in the assays of coagulation or platelet aggregation.
Single-dose toxicity
We performed an assessment of mortality resulting from the probability of survival for the 1000 mg/kg dose [31]. This demonstrated that there was no lethal or behavioral toxic effect after inoculation of the triazole compounds during the 24 h of observation.
In silico study
Evaluation of the physical–chemical and pharmacokinetic properties
As we intend to use triazoles as anti-inflammatory drugs, we compare them in silico with commercial anti-inflammatory drugs to have a clearer forecast of the feasibility of progressing or not advancing these studies. The evaluation of the physicochemical properties indicates that most triazole derivatives have similar profiles to other commercially available anti-inflammatory drugs (Supplemental Table 2). The exception was 11, which violated one Lipinski’s Five Rule (7.158 LogPb) [34].
The pharmacokinetic parameters of the triazole analogs are shown in Supplemental Table 3. As the reference drugs (diclofenac, ibuprofen, and naproxen), all derivatives had a high probability of crossing the blood–brain barrier, and none acted as a substrate of P-glycoprotein. Regarding effective jejunal permeability, only derivatives 5e and 11 presented values lower than the reference drugs. All derivatives presented higher values of volume of distribution in humans (Vd) than commercial drugs, with emphasis on 11, which presented a Vd approximately 4 times higher, possibly due to two chlorines in the aromatic ring. Finally, the ADMET risk prediction indicated that derivatives 8h, 9i, 5e, and 9i have a high pharmacological profile, particularly since they presented lower values than commercial anti-inflammatory drugs.
Toxicological profile
Supplemental table 4 presents the values of the toxicological parameters analyzed for the triazoles. Through the evaluation of the TOX_Risk parameter, all the tested triazole analogs exhibited low toxicological risk (Table 4). Although none of the derivatives had the potential to block the cardiac potassium channel (hERG), some of the derivatives presented an elevated probability of causing alterations in liver enzymes, suggesting potential hepatotoxicity, although further studies are necessary to confirm this possibility. Derivatives 5e and 9i showed high mutagenic potential, which could explain the increase in the TOX_Risk parameter when compared to the other derivatives. However, its values are still lower than those of commercial anti-inflammatory drugs.
Discussion
P2X7R has been widely studied as a target for anti-inflammatory disorders. The experimental models used in this work helped us to understand the mechanism of action of triazole-derived molecules toward P2X7R, as previously reported [45, 46], by showing their inhibitory potential against P2X7R-mediated inflammatory processes in vivo.
In general, the inflammatory process is characterized by tissue responses, such as pain, heat, redness, edema, and loss of function, the so-called cardinal signs. Activation of P2X7R induces the production and secretion of different inflammatory mediators that promote tissue responses, such as edema. To study the effect of the triazole compounds on the P2X7R-mediated inflammatory response, we evaluated the experimental model of paw edema using ATP as the phlogistic agent.
The present study indicated satisfactory results from the in vivo model of paw edema. Triazole-derived molecules tested as P2X7R antagonists 8h, 12, 5e, 9i, and 11 showed significant anti-inflammatory action in the acute inflammation protocol. In this assay, we used a paw edema model strictly triggered by purinergic activation, using 10 mM ATP as an inflammation inducer [45]. All tested concentrations caused a significant reduction in paw edema, comparable to 0.8% diclofenac, a currently marketed standard anti-inflammatory drug.
The anti-inflammatory action of triazole derivatives in the in vivo model of paw edema has been previously demonstrated in studies in vitro and in vivo [38], from which the molecules to be tested were selected. The protocol performed by the authors induced paw edema by carrageenan and ATP, measuring the edema 30 min after their applications in the mouse paw. The 9d triazole inhibited the paw edema, with an ID50 value of 79.84 ng/kg, in the edema induction by ATP at 1 mM. Using carrageenan as an edema inducer, 9d also inhibited the formation of paw edema with an ID50 value of 94.35 ng/kg. Additionally, the authors also carried out an oral treatment in which they induced edema formation by ATP and carrageenan and observed the inhibition of paw edema with greater potency than intraperitoneal treatment, obtaining ID50 values of 59 and 80.49 ng/kg, respectively.
In another work by Faria et al., the same model of ATP-induced paw edema was used to study the antagonistic potential of boronic acid derivatives. The substances NO-01 and NO-12 had a better effect than BBG and a similar effect to A740003, a selective P2X7R inhibitor, in reducing paw edema. The results presented in this work show that triazole derivatives could reduce paw edema at a lower dose than substances NO-01 and NO-12. Our results agree with other studies in the literature that indicate an anti-inflammatory potential for this class of compounds. In studies by Almasirad et al., another family of 1,2,3-triazole derivatives was also tested. Six triazole derivatives were tested at a concentration of 50 μmol/kg using the carrageenan-induced paw edema model, and the anti-inflammatory action of three of these compounds was observed.
A hallmark of inflammation is the migration of immune cells to the injured site. As purinergic signaling is involved in this cellular response, we used a carrageenan-induced peritonitis model to analyze the action of triazole molecules in inhibiting or reducing cell migration into the peritoneal cavity. The use of carrageenan as an inflammatory inducer was shown in studies by de Souza et al. [47], which demonstrated the capacity of carrageenan to generate an inflammatory response in rat and mouse paws. Moreover, carrageenan is frequently used in experimental models for the induction of peritonitis [48, 49]. The molecules 8h, 12, 5e, 9i, and 11 (0.1 mg/kg) reduced the recruitment of cells into the peritoneum when compared to control treatment, indicating that they can decrease the inflammatory process in vivo [47]. In the present study, carrageenan in the peritonitis model induced histological alterations comparable to those observed in previous studies [50, 51], in which an inflammatory response with an elevated leukocyte infiltrate in the peritoneum was reported. As expected, the administration of saline did not cause peritonitis, whereas in the dexamethasone-administered group, there was inhibition of the effect of carrageenan. Interestingly, the molecules 8h, 12, 5e, 9i, and 11 presented an inhibition profile comparable to that of the dexamethasone group. In addition, when administered alone, the triazole compounds did not induce alterations in peritoneal leukocytes.
The triazole lysed approximately 3% of red blood cells; a result considered a very low profile of toxicity. According to [33], hemolysis below 10% means that the compound or molecule is devoid of toxicity, and thus, triazoles can be considered nonhemolytic or nontoxic molecules. Moreover, toxicity assessment was performed in vivo, observing the probability of mortality at a dose of 1000 mg/kg [31]. The derivatives were neither lethal nor induced behavioral toxic effects in mice during the 24 h of observation.
A fundamental step in drug research is the evaluation of the ADMET properties of new chemical entities. These in silico analyses allow us to predict the viability of promising hit compounds to advance throughout the different stages of drug development, and together with the biological results, they are essential to determine the efficacy and safety of a new drug candidate [52]. The analysis of ADME properties showed a high pharmacological potential for most of the triazole compounds evaluated. Only 11 presented a violation of Lipinski's rule of five by exhibiting an inadequate log P (LogP) value, indicating that this compound could have a low oral absorption [53]. Interestingly, all derivatives showed a high probability of crossing the blood–brain membrane and a low probability of interacting with P-glycoprotein (P-gp), two desirable characteristics for new drug candidates [54]. Furthermore, all derivatives, except for 5e and 11, had a total volume of distribution comparable to that of the reference drugs, such as diclofenac and ibuprofen. Finally, the prediction of toxicity by the ADMET and TOX_Risk parameters together indicates that the derivatives have a low potential to cause toxic effects, such as elevation of liver enzymes or blockade of the cardiac potassium channel (hERG).
The anti-inflammatory drugs currently marketed have the COX-2 enzyme as a therapeutic target. They were developed to avoid the side effects of previous anti-inflammatory drugs, which had COX-1 as a pharmacological target. However, several further studies showed that drugs that block COX-2 also lead to serious side effects, such as cardiovascular and thrombotic effects [55], since this enzyme is responsible for the generation of prostacyclins, substances that promote an antithrombotic effect, vasodilation, and reduce platelet aggregation and adhesion [56, 57].
In this work, we tested molecules aiming to identify new substances with anti-inflammatory action that target P2X7R, therefore acting in a pathway independent of COX-2 blockade. Therefore, we expected to minimize the mentioned side effects, such as thromboembolic side effects.
Therefore, it is of great importance to test these molecules in platelet aggregation and coagulation assays, since there is evidence of P2X7R involvement in thrombus formation [57]. In this sense, the molecules tested as P2X7R antagonists, 8h, 12, 5e, 9i, and 11, showed an anticoagulant effect through some current clinical tests, such as aPTT and RC. Only substance 5e used at a concentration of 10 µM acted as a procoagulant and may be a promising substance when compared to other anti-inflammatory drugs that increase the risk of bleeding.
P2Y12 receptor is a purinergic receptor subtype directly involved in the coagulation process. Therefore, we tested whether the triazole analogs could also inhibit platelet aggregation. Our results indicated that these triazoles exhibited a similar inhibition profile to Clopidrogel, which is an inhibitor of the P2Y12 receptor, suggesting the possibility that these triazoles could also act as P2Y12 receptor antagonists. It is noteworthy that the docking assays performed for the selected triazoles indicated binding energy values comparable to AZD1283, a high-affinity antagonist of the P2Y12 receptor, corroborating our in vitro results and indicating a competitive profile in relation to the reference drug.
Conclusion
In the present study, the molecules 5e, 8h, 9i, 11, and 12 exhibited an anti-inflammatory action in ATP-induced acute inflammation and ameliorated paw edema. Molecules 5e, 8h, 9i, 11, and 12 had similar action to the anti-inflammatory drug diclofenac. Molecules 5e and 8h also showed an anti-inflammatory action on peritonitis induced by carrageenan, comparable to the reference drug dexamethasone. Regarding platelet aggregation results, triazole derivatives tested as P2X7R antagonists had good prospects in coagulation assays. The substances 5e, 8h, 9i, 11, and 12 had an anticoagulant effect, and the molecule 5e (10 µM) acted as a procoagulant, being a good alternative to replace anti-inflammatory drugs that present risks of bleeding. All derivatives, except for 11, had adequate pharmacokinetic, physicochemical, and toxicological properties. In addition, the ADMET risk assessment indicates that derivatives 8h, 12, 5e and 9i have high pharmacological potential. Docking assays for the P2Y12 receptor indicated that the derivatives exhibit binding energies comparable to the reference antagonist with a competitive inhibition profile. However, the affinity for this binding, when compared with the concentration and dose to inhibit the P2X7R-mediated function, is more than 100 times smaller. Finally, 8h, 12, 5e, and 9i reversed the inflammatory response through P2X7R inhibition and caused anticoagulant action on P2Y12R. This series may be relevant to combat COVID-19 infection involvement for acting on inflammation and blood aggregation.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file1 (TIF 74 KB) Supplemental Figure 1. Inhibition of ATP-induced paw edema formation. A. Paw edema was induced by the injection of 10 mM ATP (positive control) and saline solution was used as a negative control. We administered the tested 5-BDBD and NF770 (1 mg/kg) or diclofenac (10 mg/kg) one hour before edema formation. The volume of the paws was read using a plethysmometer device (UGO-BASILE) 60 minutes after the administration of the phlogistic inducer. Graph showing the decrease in the percentage of paw edema in the groups treated with only saline, 10 mM ATP, 0.8% diclofenac and the tested compounds (1 mg/kg) (*** p<0.001 compared to ATP treatment). Three paw edema experiments were performed on different days.
Supplementary file2 (TIF 153 KB) Supplemental Figure 2. Inhibition of peritonitis by treatment with 1 mg/kg 5-BDBD and 1 mg/kg NF770 molecules. Differential counting. One hundred cells per slide were counted, and macrophages, lymphocytes, monocytes (mononuclear cells) and neutrophils were found. Three experiments were performed for each molecule on different days to build this graph. *p<0.05, **p<0.05 and ***p<0.001 compared to saline treatment. #p<0.05, ##p<0.05 and ###p<0.001 compared to saline treatment. Three experiments were performed for each molecule on different days to build this graph.
Supplementary file3 (TIF 64 KB)
Supplementary file4 (TIF 85 KB)
Supplementary file5 (TIF 97 KB)
Supplementary file6 (TIF 107 KB)
Acknowledgements
We thank the IOC, LABTOXo, and LAPSA for their support.
Author contributions
N.G.P., A.D.S., B.Q.M., and J.S.P. performed the biological assays. N.L.R. and C.R.R. performed the in silico assays. D.T.G.G., V.F.F., and F.C.S. synthetized the molecules. D.T.G.G., F.C.S., and V.F.F. prepared the figures, coordinated the synthesis assays, wrote, and revised the paper. J.S.P., A.D.S., A.C.S., A.L.F., and R.X.F. revised the biological assays and wrote and revised the paper.
Funding
The fellowships granted by CNPq (301873/2019–4, 316568/2021–0, and 306011/2020–4), CAPES (Financial Code 001), and FAPERJ (E-26/203.246/2017, E-26/211.025/2019, E-26/200.982/2021, E-26/203.191/2017, E-26/202.800/2017, E-26/010.101106/2018, E-26/200.870/2021, E-26/201.369/2021, and SEI-260003/001178/2020) are gratefully acknowledged.
Data availability statement
The authors declare that all data supporting the findings of this study are available within the article and its supplementary information files.
Declarations
Conflict of interest
The authors declare that they have no conflicts of interest.
Ethical approval
All procedures performed in studies involving animals followed the institution's ethical standards or practice at which the studies were conducted.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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29. De Marchi E Orioli E Dal Ben D Adinolfi E P2X7 receptor as a therapeutic target Adv Protein Chem Struct Biol 2016 104 39 79 10.1016/bs.apcsb.2015.11.004 27038372
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| 36463339 | PMC9734322 | NO-CC CODE | 2022-12-14 23:28:27 | no | Inflamm Res. 2022 Dec 3;:1-14 | utf-8 | Inflamm Res | 2,022 | 10.1007/s00011-022-01664-1 | oa_other |
==== Front
Ann Surg Oncol
Ann Surg Oncol
Annals of Surgical Oncology
1068-9265
1534-4681
Springer International Publishing Cham
36479662
12916
10.1245/s10434-022-12916-z
Aso Research Letter
Vaccination Against SARS-CoV-2 Decreases Risk of Adverse Events in Patients who Develop COVID-19 Following Cancer Surgery
Verhagen Nathaniel B. BS 1
Koerber Nicolas K. BS 1
Szabo Aniko PhD 2
Taylor Bradley MBA 3
Wainaina J. Njeri MD 4
Evans Douglas B. MD 1
http://orcid.org/0000-0001-6544-8832
Kothari Anai N. MD [email protected]
13
On behalf of the N3C ConsortiumWilcox Adam B.
Lee Adam M.
Graves Alexis
(Jerrod) Anzalone Alfred
Manna Amin
Saha Amit
Olex Amy
Zhou Andrea
Williams Andrew E.
Southerland Andrew
Girvin Andrew T.
Walden Anita
Sharathkumar Anjali A.
Amor Benjamin
Bates Benjamin
Hendricks Brian
Patel Brijesh
Alexander Caleb
Bramante Carolyn
Ward-Caviness Cavin
Madlock-Brown Charisse
Suver Christine
Chute Christopher
Dillon Christopher
Wu Chunlei
Schmitt Clare
Takemoto Cliff
Housman Dan
Gabriel Davera
Eichmann David A.
Mazzotti Diego
Brown Don
Boudreau Eilis
Hill Elaine
Zampino Elizabeth
Marti Emily Carlson
Pfaff Emily R.
French Evan
Koraishy Farrukh M
Mariona Federico
Prior Fred
Sokos George
Martin Greg
Lehmann Harold
Spratt Heidi
Mehta Hemalkumar
Liu Hongfang
Sidky Hythem
Hayanga J. W. Awori
Pincavitch Jami
Clark Jaylyn
Harper Jeremy Richard
Islam Jessica
Ge Jin
Gagnier Joel
Saltz Joel H.
Saltz Joel
Loomba Johanna
Buse John
Mathew Jomol
Rutter Joni L.
McMurry Julie A.
Guinney Justin
Starren Justin
Crowley Karen
Bradwell Katie Rebecca
Walters Kellie M.
Wilkins Ken
Gersing Kenneth R.
Cato Kenrick Dwain
Murray Kimberly
Kostka Kristin
Northington Lavance
Pyles Lee Allan
Misquitta Leonie
Cottrell Lesley
Portilla Lili
Deacy Mariam
Bissell Mark M.
Clark Marshall
Emmett Mary
Saltz Mary Morrison
Palchuk Matvey B.
Haendel Melissa A.
Adams Meredith
Temple-O’Connor Meredith
Kurilla Michael G.
Morris Michele
Qureshi Nabeel
Safdar Nasia
Garbarini Nicole
Sharafeldin Noha
Sadan Ofer
Francis Patricia A.
Burgoon Penny Wung
Robinson Peter
Payne Philip R. O.
Fuentes Rafael
Jawa Randeep
Erwin-Cohen Rebecca
Patel Rena
Moffitt Richard A.
Zhu Richard L.
Kamaleswaran Rishi
Hurley Robert
Miller Robert T.
Pyarajan Saiju
Michael Sam G.
Bozzette Samuel
Mallipattu Sandeep
Vedula Satyanarayana
Chapman Scott
O’Neil Shawn T.
Setoguchi Soko
Hong Stephanie S.
Johnson Steve
Bennett Tellen D.
Callahan Tiffany
Topaloglu Umit
Sheikh Usman
Gordon Valery
Subbian Vignesh
Kibbe Warren A.
Hernandez Wenndy
Beasley Will
Cooper Will
Hillegass William
Zhang Xiaohan Tanner
1 grid.30760.32 0000 0001 2111 8460 Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI 8701 USA
2 grid.30760.32 0000 0001 2111 8460 Department of Biostatistics, Medical College of Wisconsin, Milwaukee, WI USA
3 grid.415100.1 0000 0004 0426 576X Clinical and Translational Science Institute of Southeastern Wisconsin, Froedtert and Medical College of Wisconsin Health Network, Milwaukee, WI USA
4 grid.30760.32 0000 0001 2111 8460 Division of Infectious Disease, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI USA
7 12 2022
14
9 11 2022
22 11 2022
© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
==== Body
pmcEarly in the COVID-19 pandemic, mortality rates were observed to exceed 25% in patients who developed postoperative SARS-CoV-2 infections.1 This prompted numerous perioperative structural and process changes to mitigate this risk.2,3 As the pandemic has progressed, the emergence of novel therapeutic and preventative measures have proven effective in decreasing the overall burden of SARS-CoV-2 infection. These advances likely reduce the risk in surgical patients; however, this has not been reexamined at a population level. This study reports 30-day adverse postoperative event rates in patients who develop postoperative COVID-19 and measures the impact of vaccination on these outcomes.
Methods
This was a retrospective cohort study using the National COVID Cohort Collaborative (N3C) Data Enclave. The study cohort included patients who underwent surgical resection for cancer. Patients positive for COVID-19 were identified based on a positive lab measurement (PCR or antigen) or a positive COVID-19 diagnosis (ICD10-CM code U07.1). Postoperative COVID-19 was defined as a new SARS-CoV-2 infection that occurred within 30 days of surgery. Fully vaccinated patients were characterized as having at least two COVID-19 vaccines 14 days or more before surgery. This analysis included patients from January 2020 to August 2022.
Construction of the study cohort was executed using Observational Health Data Science and Informatics’ (OHDSI) ATLAS tool. Patients with cancer were identified using the Malignant Neoplastic Disease standard concept and benign concepts were excluded.4 Major oncologic surgery concepts were created using standard codes obtained from ATLAS. Exclusion criteria included (1) endoscopic or natural orifice procedures; (2) percutaneous approaches; (3) diagnostic procedures (unless performed open); (4) cosmetic procedures; (5) nononcologic procedures; and (6) transplants.
The primary outcome of the study was composite adverse event that occurred within 30 days of surgery. Adverse events included mortality, hospital readmission, pneumonia, respiratory failure, pulmonary embolism, sepsis, cardiac arrhythmia, renal failure, urinary tract infection, and deep vein thrombosis. SNOMED concepts were used for defining surgical morbidity, because there is not a validated crosswalk to other surgical classification frameworks presently available in the N3C Data Enclave (i.e., Clavien-Dindo). Multivariable logistic regression models assessing postoperative outcomes were adjusted for relative surgical risk, comorbidities, age, sex, and race. All analyses were performed within the N3C Data Enclave.
Results
Of 126,216 patients who underwent oncologic surgery, 1,091 (0.9%) developed a SARS-CoV-2 infection within 30 days after surgery. Patients who developed postoperative COVID-19 were at increased risk for 30-day readmission (47% vs. 15%, P < 0.001), pulmonary complications (20% vs. 4.4%, P < 0.001), nonfatal adverse events (47% vs. 20%, P < 0.001), and mortality (5.3% vs. 1.0%, P < 0.001). Following adjustment, postoperative COVID-19 was an independent risk factor and noted to increase the odds of all observed adverse events (Table 1).Table 1 Risk of adverse 30-day outcomes following surgery in patients with and without postoperative SARS-CoV-2
Outcomes No postoperative COVID-19 (N = 125,125) Postoperative COVID-19 (N = 1,091) P value Adjusted OR (95% CI)
Mortality 1,239 (1.0%) 58 (5.3%) < 0.001 5.73 (4.20-7.66)
Any complication 24,987 (20%) 510 (47%) < 0.001 3.64 (3.18-4.17)
Pulmonary complication 5,474 (4.4%) 220 (20%) < 0.001 5.99 (5.05-7.08)
Hospital readmission 14,767 (15%) 396 (47%) < 0.001 5.17 (4.44-6.02)
Pneumonia 2,010 (1.6%) 168 (15%) < 0.001 12.1 (9.99-14.6)
Respiratory failure 3,154 (2.5%) 145 (13%) < 0.001 6.52 (5.33-7.92)
Pulmonary embolism 1,571 (1.3%) 30 (2.7%) < 0.001 2.00 (1.30-2.93)
Sepsis 2,625 (2.1%) 118 (11%) < 0.001 5.56 (4.46-6.88)
Cardiac arrhythmia 9,395 (7.5%) 168 (15%) < 0.001 2.37 (1.96-2.85)
Renal failure 5,520 (4.4%) 149 (14%) < 0.001 3.60 (2.95-4.37)
Urinary tract infection 2,819 (2.3%) 43 (3.9%) < 0.001 1.81 (1.29-2.47)
Deep vein thrombosis 1,044 (0.8%) 21 (1.9%) < 0.001 2.30 (1.42-3.50)
OR odds ratio; CI confidence interval
At the time of surgery, 12,220 (9.7%) patients were fully vaccinated. Patients who were fully vaccinated did not have a decreased risk of developing postoperative COVID-19 (adjusted odds ratio [aOR] 1.12 [0.91–1.37]). Of patients with postoperative SARS-CoV-2 infection, 112 (10.2%) were fully vaccinated. These patients were observed to be at a decreased risk for 30-day readmission (aOR 0.54 [0.31–0.91]), pulmonary complications (aOR 0.34 [0.16–0.67]), and nonfatal adverse events (aOR 0.62 [0.39–0.97]) compared with those who were not fully vaccinated. Notably, patients that were fully vaccinated and developed COVID-19 after surgery did not have any 30-day mortality.
Discussion
In this study of cancer patients undergoing surgical resection, postoperative SARS-CoV-2 infection remains a significant risk factor for mortality and morbidity. Vaccination decreases the risk of adverse postoperative events, however, does not prevent developing COVID-19 after surgery. While the incidence of postoperative COVID-19 infection is low, it remains a devastating complication in patients undergoing oncologic resection. Vaccination against SARS- CoV-2 provides a powerful, widely available measure to mitigate this risk and should be a mandatory part of preoperative optimization.Fig. 1 Association between preoperative vaccination and adverse surgical outcomes in patients with postoperative COVID-19
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 28 kb)
Acknowledgment
The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave covid.cd2h.org/enclave and supported by CD2H - The National COVID Cohort Collaborative (N3C) IDeA CTR Collaboration 3U24TR002306-04S2 NCATS U24 TR002306. This research was possible because of the patients whose information is included within the data from participating organizations (covid.cd2h.org/dtas) and the organizations and scientists (covid.cd2h.org/duas) who have contributed to the ongoing development of this community resource (cite this https://doi.org/10.1093/jamia/ocaa196). We gratefully acknowledge the following core contributors to N3C: Adam B. Wilcox, Adam M. Lee, Alexis Graves, Alfred (Jerrod) Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E. Williams, Andrew Southerland, Andrew T. Girvin, Anita Walden, Anjali A. Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, Caleb Alexander, Carolyn Bramante, Cavin Ward-Caviness, Charisse Madlock-Brown, Christine Suver, Christopher Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A. Eichmann, Diego Mazzotti, Don Brown, Eilis Boudreau, Elaine Hill, Elizabeth Zampino, Emily Carlson Marti, Emily R. Pfaff, Evan French, Farrukh M Koraishy, Federico Mariona, Fred Prior, George Sokos, Greg Martin, Harold Lehmann, Heidi Spratt, Hemalkumar Mehta, Hongfang Liu, Hythem Sidky, J.W. Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Islam, Jin Ge, Joel Gagnier, Joel H. Saltz, Joel Saltz, Johanna Loomba, John Buse, Jomol Mathew, Joni L. Rutter, Julie A. McMurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Kellie M. Walters, Ken Wilkins, Kenneth R. Gersing, Kenrick Dwain Cato, Kimberly Murray, Kristin Kostka, Lavance Northington, Lee Allan Pyles, Leonie Misquitta, Lesley Cottrell, Lili Portilla, Mariam Deacy, Mark M. Bissell, Marshall Clark, Mary Emmett, Mary Morrison Saltz, Matvey B. Palchuk, Melissa A. Haendel, Meredith Adams, Meredith Temple-O'Connor, Michael G. Kurilla, Michele Morris, Nabeel Qureshi, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A. Francis, Penny Wung Burgoon, Peter Robinson, Philip R.O. Payne, Rafael Fuentes, Randeep Jawa, Rebecca Erwin-Cohen, Rena Patel, Richard A. Moffitt, Richard L. Zhu, Rishi Kamaleswaran, Robert Hurley, Robert T. Miller, Saiju Pyarajan, Sam G. Michael, Samuel Bozzette, Sandeep Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T. O'Neil, Soko Setoguchi, Stephanie S. Hong, Steve Johnson, Tellen D. Bennett, Tiffany Callahan, Umit Topaloglu, Usman Sheikh, Valery Gordon, Vignesh Subbian, Warren A. Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, Xiaohan Tanner Zhang. Details of contributions available at covid.cd2h.org/core-contributors. The following institutions whose data is released or pending: Available: Advocate Health Care Network — UL1TR002389: The Institute for Translational Medicine (ITM) • Boston University Medical Campus — UL1TR001430: Boston University Clinical and Translational Science Institute • Brown University — U54GM115677: Advance Clinical Translational Research (Advance-CTR) • Carilion Clinic — UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia • Charleston Area Medical Center — U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI) • Children’s Hospital Colorado — UL1TR002535: Colorado Clinical and Translational Sciences Institute • Columbia University Irving Medical Center — UL1TR001873: Irving Institute for Clinical and Translational Research • Duke University — UL1TR002553: Duke Clinical and Translational Science Institute • George Washington Children’s Research Institute — UL1TR001876: Clinical and Translational Science Institute at Children’s National (CTSA-CN) • George Washington University — UL1TR001876: Clinical and Translational Science Institute at Children’s National (CTSA-CN) • Indiana University School of Medicine — UL1TR002529: Indiana Clinical and Translational Science Institute • Johns Hopkins University — UL1TR003098: Johns Hopkins Institute for Clinical and Translational Research • Loyola Medicine — Loyola University Medical Center • Loyola University Medical Center — UL1TR002389: The Institute for Translational Medicine (ITM) • Maine Medical Center — U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • Massachusetts General Brigham — UL1TR002541: Harvard Catalyst • Mayo Clinic Rochester — UL1TR002377: Mayo Clinic Center for Clinical and Translational Science (CCaTS) • Medical University of South Carolina — UL1TR001450: South Carolina Clinical & Translational Research Institute (SCTR) • Montefiore Medical Center — UL1TR002556: Institute for Clinical and Translational Research at Einstein and Montefiore • Nemours — U54GM104941: Delaware CTR ACCEL Program • NorthShore University HealthSystem — UL1TR002389: The Institute for Translational Medicine (ITM) • Northwestern University at Chicago — UL1TR001422: Northwestern University Clinical and Translational Science Institute (NUCATS) • OCHIN — INV-018455: Bill and Melinda Gates Foundation grant to Sage Bionetworks • Oregon Health & Science University — UL1TR002369: Oregon Clinical and Translational Research Institute • Penn State Health Milton S. Hershey Medical Center — UL1TR002014: Penn State Clinical and Translational Science Institute • Rush University Medical Center — UL1TR002389: The Institute for Translational Medicine (ITM) • Rutgers, The State University of New Jersey — UL1TR003017: New Jersey Alliance for Clinical and Translational Science • Stony Brook University — U24TR002306 • The Ohio State University — UL1TR002733: Center for Clinical and Translational Science • The State University of New York at Buffalo — UL1TR001412: Clinical and Translational Science Institute • The University of Chicago — UL1TR002389: The Institute for Translational Medicine (ITM) • The University of Iowa — UL1TR002537: Institute for Clinical and Translational Science • The University of Miami Leonard M. Miller School of Medicine — UL1TR002736: University of Miami Clinical and Translational Science Institute • The University of Michigan at Ann Arbor — UL1TR002240: Michigan Institute for Clinical and Health Research • The University of Texas Health Science Center at Houston — UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • The University of Texas Medical Branch at Galveston — UL1TR001439: The Institute for Translational Sciences • The University of Utah — UL1TR002538: Uhealth Center for Clinical and Translational Science • Tufts Medical Center — UL1TR002544: Tufts Clinical and Translational Science Institute • Tulane University — UL1TR003096: Center for Clinical and Translational Science • University Medical Center New Orleans — U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • University of Alabama at Birmingham — UL1TR003096: Center for Clinical and Translational Science • University of Arkansas for Medical Sciences — UL1TR003107: UAMS Translational Research Institute • University of Cincinnati — UL1TR001425: Center for Clinical and Translational Science and Training • University of Colorado Denver, Anschutz Medical Campus — UL1TR002535: Colorado Clinical and Translational Sciences Institute • University of Illinois at Chicago — UL1TR002003: UIC Center for Clinical and Translational Science • University of Kansas Medical Center — UL1TR002366: Frontiers: University of Kansas Clinical and Translational Science Institute • University of Kentucky — UL1TR001998: UK Center for Clinical and Translational Science • University of Massachusetts Medical School Worcester — UL1TR001453: The UMass Center for Clinical and Translational Science (UMCCTS) • University of Minnesota — UL1TR002494: Clinical and Translational Science Institute • University of Mississippi Medical Center — U54GM115428: Mississippi Center for Clinical and Translational Research (CCTR) • University of Nebraska Medical Center — U54GM115458: Great Plains IDeA-Clinical & Translational Research • University of North Carolina at Chapel Hill — UL1TR002489: North Carolina Translational and Clinical Science Institute • University of Oklahoma Health Sciences Center — U54GM104938: Oklahoma Clinical and Translational Science Institute (OCTSI) • University of Rochester — UL1TR002001: UR Clinical & Translational Science Institute • University of Southern California — UL1TR001855: The Southern California Clinical and Translational Science Institute (SC CTSI) • University of Vermont — U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • University of Virginia — UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia • University of Washington — UL1TR002319: Institute of Translational Health Sciences • University of Wisconsin-Madison — UL1TR002373: UW Institute for Clinical and Translational Research • Vanderbilt University Medical Center — UL1TR002243: Vanderbilt Institute for Clinical and Translational Research • Virginia Commonwealth University — UL1TR002649: C. Kenneth and Dianne Wright Center for Clinical and Translational Research • Wake Forest University Health Sciences — UL1TR001420: Wake Forest Clinical and Translational Science Institute • Washington University in St. Louis — UL1TR002345: Institute of Clinical and Translational Sciences • Weill Medical College of Cornell University — UL1TR002384: Weill Cornell Medicine Clinical and Translational Science Center • West Virginia University — U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI). Submitted: Icahn School of Medicine at Mount Sinai — UL1TR001433: ConduITS Institute for Translational Sciences • The University of Texas Health Science Center at Tyler — UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • University of California, Davis — UL1TR001860: UCDavis Health Clinical and Translational Science Center • University of California, Irvine — UL1TR001414: The UC Irvine Institute for Clinical and Translational Science (ICTS) • University of California, Los Angeles — UL1TR001881: UCLA Clinical Translational Science Institute • University of California, San Diego — UL1TR001442: Altman Clinical and Translational Research Institute • University of California, San Francisco — UL1TR001872: UCSF Clinical and Translational Science Institute. Pending: Arkansas Children’s Hospital — UL1TR003107: UAMS Translational Research Institute • Baylor College of Medicine — None (Voluntary) • Children’s Hospital of Philadelphia — UL1TR001878: Institute for Translational Medicine and Therapeutics • Cincinnati Children’s Hospital Medical Center — UL1TR001425: Center for Clinical and Translational Science and Training • Emory University — UL1TR002378: Georgia Clinical and Translational Science Alliance • HonorHealth — None (Voluntary) • Loyola University Chicago — UL1TR002389: The Institute for Translational Medicine (ITM) • Medical College of Wisconsin — UL1TR001436: Clinical and Translational Science Institute of Southeast Wisconsin • MedStar Health Research Institute — UL1TR001409: The Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) • MetroHealth — None (Voluntary) • Montana State University — U54GM115371: American Indian/Alaska Native CTR • NYU Langone Medical Center — UL1TR001445: Langone Health’s Clinical and Translational Science Institute • Ochsner Medical Center — U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • Regenstrief Institute — UL1TR002529: Indiana Clinical and Translational Science Institute • Sanford Research — None (Voluntary) • Stanford University — UL1TR003142: Spectrum: The Stanford Center for Clinical and Translational Research and Education • The Rockefeller University — UL1TR001866: Center for Clinical and Translational Science • The Scripps Research Institute — UL1TR002550: Scripps Research Translational Institute • University of Florida — UL1TR001427: UF Clinical and Translational Science Institute • University of New Mexico Health Sciences Center — UL1TR001449: University of New Mexico Clinical and Translational Science Center • University of Texas Health Science Center at San Antonio — UL1TR002645: Institute for Integration of Medicine and Science • Yale New Haven Hospital — UL1TR001863: Yale Center for Clinical Investigation.
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| 36479662 | PMC9734328 | NO-CC CODE | 2022-12-14 23:28:27 | no | Ann Surg Oncol. 2022 Dec 7;:1-4 | utf-8 | Ann Surg Oncol | 2,022 | 10.1245/s10434-022-12916-z | oa_other |
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Acta Neurol Belg
Acta Neurol Belg
Acta Neurologica Belgica
0300-9009
2240-2993
Springer International Publishing Cham
36478546
2150
10.1007/s13760-022-02150-5
Original Article
Triggers and clinical changes of childhood primary headache characteristics during COVID-19 pandemic lockdown
http://orcid.org/0000-0002-7492-5255
Dedeoglu Özge [email protected]
1
Konuşkan Bahadır [email protected]
2
1 grid.512925.8 0000 0004 7592 6297 Department of Pediatric Neurology, Ankara City Hospital, Ankara, Turkey
2 Department of Pediatric Neurology, Dr. Sami Ulus Children Hospital, Ankara, Turkey
7 12 2022
16
28 11 2021
28 11 2022
© The Author(s) under exclusive licence to Belgian Neurological Society 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Objective
Children with primary headache are particularly vulnerable to the negative impacts of the pandemic due to factors like increased social isolation, disruption of sleep and impairment of healthy diet. We aimed to investigate the clinical changes and triggering factors for childhood primary headaches to demonstrate the impact of the pandemic lockdown.
Method
Children aged between 60 months and 18 years with headache complaint attending the general outpatient clinic between December 2019 and December 2020 were included in the study. Patients were classified according to ICHD-3 regarding clinical and laboratory data. Primary headaches diagnosed before (December 2019–March 2020) and during the pandemic lockdown (April 2020–December 2020) were divided into two groups as migraine and tension-type headache (TTH). Clinical picture and triggering factors were compared between groups to illustrate the effect of the lockdown.
Results
The study included 612 subjects, with 463 patients (76%) classified in the primary headache group and 149 (24%) in the secondary headache group. Among the first group, 267 patients (58%) had migraine and 196 patients (42%) had TTH. Comparisons between before and during the pandemic lockdown showed significant increased frequency of TTH, but no difference in the frequency and duration of migraine. Both screen exposure and sleep pattern changes were found to be significantly increased in the TTH group during the pandemic lockdown.
Discussion
We found a significant increase in the attack frequency for TTH patients during the pandemic lockdown. Reduction in screen time is an important strategy in preventing primary headache attacks in children.
Keywords
Childhood headache
Tension-type headache
Migraine
ICHD-3
COVID-19 pandemic
==== Body
pmcIntroduction
Headache is one of the most frequently reported health problems among children and frequency seems to increase with age. In a recent review of 64 cross-sectional studies published in 32 different countries and including a total of 227,249 subjects, the estimated overall mean prevalence of headache was 54.4% (95% CI 43.1–65.8) and the overall mean prevalence of migraine was 9.1% (95% CI 7.1–11.1) [1].
According to the third edition of the International Classification of Headache Disorders (ICHD-3), headache can be divided into two main categories as primary headache disorders like migraine and TTH and secondary headaches due to refractive errors, sinusitis, hypertension, brain tumour, dental caries, etc. [2]. It was clearly demonstrated that chronic headache, especially migraine, has an important impact on various areas of daily functioning. Headache can have a negative effect on quality of life, especially academic performance of children [3, 4]. Determination of triggering factors and headache category is very important for therapy and prognosis, but the inability of children to express their exact symptoms and lack of laboratory/neuroradiological diagnostic criteria make it challenging for clinicians [5].
Although the ongoing outbreak of coronavirus disease most prominently affects the respiratory system, there is emerging literature about its impact on the central and peripheral nervous systems [6]. Additionally, lifestyle changes, decreased social activities, increased screen exposure, and staying at home also have psychological and emotional effects which have received less emphasis so far [7]. It was previously documented that quarantine is a remarkable risk factor for psychological and psychiatric disturbances and patients with primary headache are particularly vulnerable to the drastic negative impacts of these factors [8]. This study aimed to analyse primary headache characteristics in childhood and investigate the impact of the pandemic lockdown.
Materials and methods
The research was conducted with children aged 60 months to 18 years with headache complaints, who attended the general outpatient clinic in Mardin State Hospital between December 2019 and December 2020. Demographic findings including age, gender, neonatal and family history were noted. At the initial visit, all patients filled out a detailed headache questionnaire. The questions about headaches were mainly designed to determine whether the patient suffered from headache or not. As far as headache is concerned, patients were asked about the occurrence of recurrent headache in their medical history. The survey questions/statements were prepared by ÖD and BK through discussions about the questions/statements in the third edition of the ICHD-3 [2]. Out of the 11 questions/statements, nine were designed to be answered on a scale about characteristics of the disease and two questions (questions no: 10, 11) were open ended involving the following points: (A) triggering factors and (B) accompanying findings. The age of onset, duration, frequency of attacks, location, character, severity, effect of physical activity, accompanying nausea, vomiting, and phono/photophobia were also noted. Participants were asked about triggering factors like sleep patterns, stress, hunger, fast food consumption, and increases in screen exposure. All patients were subjected to detailed physical and neurological examination and were evaluated by the ophthalmology and otolaryngology departments if necessary. Electroencephalogram (EEG) was performed in 43 patients, and lumbar puncture in two patients who were suspected of having high intracranial pressure.
Patients were classified according to ICHD-3 regarding clinical and laboratory data. After the pandemic lockdown was announced in March 2020 in Turkey, patients enrolled before that date (December 2019–March 2020) were included in the group “Before” the pandemic lockdown, while patients seen after that date (April 2020–December 2020) were included in the group “During” the pandemic lockdown. Clinical picture and triggering factors for primary headaches were compared during the pandemic lockdown and before the pandemic lockdown. Recommendations for lifestyle behaviour changes like maintaining regular sleep (9–12 h in primary school children, 8–10 h in adolescents), avoidance of skipping meals and having a healthy diet, adequate hydration, regular exercise, control of screen exposure time, avoiding triggering foods (caffeine, cheese, chocolate, red meat, and canned foods), decreasing daily stress, and avoiding the abuse of painkillers (not using analgesics such as acetaminophen and ibuprofen more than 3 times a week) were given to all study participants by the study investigators.
Qualitative findings were compared with the Chi-square test or Fisher’s exact test, and other findings were compared with mean values using the independent t test. A p value < 0.05 was considered significant. The study was approved by Diyarbakır Gazi Yaşargil Training and Research Hospital ethics committee (02.07.2021-811).
Results
The study included 612 participants. Of the participants, 59% were female (n = 367), while 41% were male (n = 273). The mean age of participants was 11.34 ± 2.7 years. According to ICHD-3, 76% (n = 463) of the patients were classified in the primary headache group and 24% (n = 149) were in the secondary headache group.
Primary headache
Migraine was diagnosed in 267 (58%) patients and TTH in 196 (42%) patients. All patients above 10 years of age (n = 214) were females; of these, 178 (84%) had migraine. Aura symptoms like blurred vision, zigzags and bright lights were described only by 0.5% of migraine patients. Among the 185 patients with primary headache before the pandemic lockdown, 105 (57%) had migraine and 80 (43%) had TTH. There was no statistically significant difference between mean values for age, gender and family history between patients before and during lockdown. There was no difference in the frequency and duration of attacks in migraine patients diagnosed before the pandemic lockdown with those diagnosed during the pandemic lockdown (p = 0.9; p = 0.73). However, a significant increase was found for the attack frequency of TTH (p = 0.001). Even though photophobia was the least common symptom among the migraine group with a percentage of 15% before the pandemic, it increased to 32% and became the most common symptom during the pandemic lockdown. The rate of photophobia was also increased in the TTH group with rates of 8% before and 13% during the pandemic lockdown. During the pandemic lockdown, the frequency of bilateral TTH was significantly higher than before lockdown (p = 0.001). Before the pandemic lockdown, 21% of migraine patients stated their headache developed with physical activity. Although this rate increased to 41% during the pandemic, it was not statistically significant (p = 0.14). Demographic data and characteristics of patients with primary headache before and during lockdown are shown in Table 1.Table 1 Demographic data and characteristics of patients with primary headache before and during lockdown
Demographic data Migraine headache Tension-type headache
Before lockdown During lockdown p value Before lockdown During lockdown p value
Number of patients (%) 105 (39%) 162 (61%) 80 (41%) 116 (59%)
Mean age in months 11.2 11.2 0.84 11 11.3 0.26
Gender (female/male) 65/40 103/59 0.78 53/27 72/44 0.55
Family history 40 (38.1%) 59 (36.4%) 0.72 27 (33.7%) 44 (37.9%) 0.51
Mean of attack frequency (per month) 1.39 1.4 0.9 1.31 1.89 0.001*
Characteristics of headache
Pulsating 77 (73%) 107(66%) 0.75 4 (5%) 5 (4%) 0.93
Compressive 16 (15%) 34 (20%) 47 (58%) 71 (61%)
Stinging 12 (11%) 21 (12%) 29 (36%) 40 (34%)
Mean duration of the attack in hours 9.8 8.5 0.73 10.6 11.2 0.59
Severe headache history 68 (64%) 110 (67%) 0.59 7 (8%) 15 (12%) 0.35
Localization (unilateral) 65 (61%) 103 (63%) 0.89 58 (72%) 24 (20%) 0.001*
Increase with physical activity 56 (53%) 110 (67%) 0.14 18 (22%) 33 (28%) 0.34
Nausea and vomiting 50 (47%) 59 (36%) 0.78 5 (6%) 12 (10%) 0.29
Photophobia 42 (40%) 86 (53%) 0.37 16 (20%) 26 (22%) 0.68
Phonophobia 43 (41%) 59 (36%) 0.46 10 (6%) 13(11%) 0.78
*p value < 0.05 significant
While 21% of migraine patients before the pandemic lockdown had screen exposure as a precipitating factor, this rate increased to 40%, which was statistically significant (p = 0.029). Concerning TTH, both screen exposure and sleep pattern changes were found to be significantly increased during the pandemic lockdown (p = 0.002; p = 0.001). The triggering factors for headache are shown in Table 2.Table 2 Comparison of triggers of primary headache before and during lock down
Triggers of primary headache Migraine headache Tension-type headache
Before lockdown During lockdown p value Before lockdown During lockdown p value
Number of patients (%) Number of patients (%) Number of patients (%) Number of patients (%)
Stress 27 (10%) 47(17%) 0.55 38 (19%) 70 (35%) 0.78
Hunger 55 (20%) 66 (24%) 0.62 33 (16%) 31 (16%) 0.37
Consumption of specific food 43 (16%) 46 (17%) 0.37 9 (4%) 17 (8%) 0.48
Sleep pattern changes 27 (10%) 43 (16%) 0.88 22 (11%) 60 (30%) 0.001*
Screen exposure 56 (21%) 108 (40%) 0.029* 44 (22%) 89 (45%) 0.002*
*p value < 0.05 = significant
Treatment of primary headache
The treatment was individualized for each patient considering age, personality and pain characteristics if necessary. Patients were recommended to attend a check-up visit at the end of the first month of treatment. A total of 211 migraine patients (86 patients attending before pandemic lockdown; 125 patients attending during pandemic lockdown) were administered pharmacological treatment for the following indications; 164 patients for more than one attack per week; 44 patients who stated their daily life was negatively affected by attacks; and three patients who were suspected of having hemiplegic migraine. The most preferred drug was flunarizine prescribed to 41%, the second drug was amitriptyline given to 18% and cyproheptadine hydrochloride was chosen for 16% of patients. Regarding the treatment of TTH, 17 of the patients before and 27 patients during the pandemic lockdown had prophylaxis with nonsteroidal anti-inflammatory treatment.
Secondary headache
Secondary headache was diagnosed in 149 patients (24%). On neurological examination, focal neurological deficit was found in three patients (central nervous system tumour), and sensory deficit in one patient (multiple sclerosis). Two patients with chronic headache and blurred vision had high CSF pressure and responded well to acetazolamide treatment. Myopia was revealed in 26 patients and astigmatism in 19 patients.
Laboratory
Eighty-eight percent of the patients had normal brain MRI. Pathological findings were detected as follows; 44 patients had sinusitis, 12 patients had arachnoid cyst, and one patient had cortical dysplasia. Cervical MRI was performed in two patients; one patient with neck pain and trauma history and one patient suspected of having multiple sclerosis. EEG was performed in 43 patients and four patients (one secondary to cortical dysplasia, three absence epilepsy) were diagnosed with epilepsy.
Discussion
This study investigated the impact of the pandemic lockdown on childhood primary headache characteristics. There is clear evidence in the literature about the negative effects of the pandemic on the psychological and physical well-being of children and adults [8]; however, little information is available about the effect on prevalence of primary headache in children. Concordant with our results, a nationwide study in Austria and studies performed in school samples from Vienna and Istanbul found the prevalence of migraine was slightly higher than TTH [9, 10]. When the literature was evaluated, we could find just two studies performed on children about pandemic effects on migraine. An Italian study asked participants to express a general opinion about the “trend of the headache” and revealed an improvement in frequency and intensity of symptoms during lockdown. The symptoms of patients were found to improve in 323 patients (46%), remained stable in 277 (39%) and worsened in 107 (15%) [11]. Similarly, Dallavalle et al. demonstrated significant reductions in the intensity and frequency of migraine symptoms in children and adolescents during the COVID-19 lockdown phase [12]. Contrary to these studies, a cross-sectional, internet-based study performed with adult patients found that 59.6% of the respondents reported increased worsening of migraine frequency and 10.3% reported a transformation to chronic migraine. Severity was also increased for 64.1% of the respondents [13]. Likewise, Martinez et al. investigating habits and medical care during lockdown, found out that 47.3% of patients reported subjective worsening of migraine symptoms during the lockdown, while 14% reported improvement [14]. Studies in the adult period generally demonstrate an increasing trend in the frequency and severity of migraine, nonetheless two studies showed improvements similar to childhood studies [15, 16]. Migraine attacks are thought to be caused by natural fluctuations in neuronal excitability with precipitation by trigger factors like sleep deprivation, hunger, etc. [17]. Although we expected that migraine attacks would be aggravated during lockdown, there was no difference in the frequency and duration of migraine attacks.
TTH has not been as thoroughly investigated as migraine so far and unfortunately this is also valid during the pandemic lockdown. One study of medical personnel during the pandemic revealed a significant relationship between TTH, excessive daytime sleepiness, sleep quality and fatigue syndrome [18]. The triggers for TTH include the same triggers as for migraine headaches; however, emotional stress such as depression and anxiety play a larger role [19]. Papetti et al. in their study of children with headache pointed out that 88% of the responders were receiving online education and about 50% of patients reported a reduction in school effort. They found that headache improvement was strongly correlated with the decrease in school anxiety and school effort. Consequently, although screen exposure was increased, the decrease in daily effort caused a reduction in headaches [11]. In our study despite the increase in screen time, there was no increase in migraine frequency during the pandemic lock down. Similar to our study, Hassel et al. did not find a correlation between worsening of migraine frequency and severity with age and screen time exposure. However, they found correlations with disruption of sleep, dietary habits, and presence of symptoms of anxiety and depression [13]. In the current study, both screen exposure and sleep pattern changes were found to be significantly increased during the pandemic lockdown in TTH patients.
There are some limitations of this study. First of all, headache characteristics were investigated via questionnaire; the results were based on the responses of the patients, with the potential for reporting bias. Secondly this cohort did not include testing for COVID-19 or questioning about current or previous infection so there may also be some patients who were not tested for COVID-19 due to the lack of other accompanying symptoms. The effect of COVID-19 disease could have affected migraine and TTH findings in the study participants. Also, we ignored emotional factors while investigating trigger factors and did not obtain sufficient information about socio-economic factors.
Conclusion and recommendations
Characteristics and triggers of primary headaches changed during the pandemic lockdown, in addition to specific physical manifestations. New strategies are needed to deliver quality care for children with primary headaches during pandemic lockdowns. In addition to controlling psychological distress and screen time exposure, maintaining regular sleep hygiene, healthy diet and good hydration should be ensured. Large-scale longitudinal studies are needed to develop appropriate management strategies for childhood primary headache.
Acknowledgements
The authors are grateful to the parents of the study children for taking the time to answer the questionnaire.
Author contributions
ÖD contributed to conception and design, acquisition and analysis, drafted the manuscript and agrees to be accountable for all aspects of work ensuring integrity and accuracy and there is no potential conflict of interest. BK contributed to conception and design, acquisition and analysis, drafted the manuscript and agrees to be accountable for all aspects of work ensuring integrity and accuracy and there is no potential conflict of interest. All authors read and approved the final version of the manuscript.
Funding
There was no funding.
Availability of data and material
Not applicable.
Declarations
All procedures performed in studies involving human participants were in accordance with the ethical standarts of the institutional committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standarts.
Conflict of interest
There is no conflict of interest.
Code availability
Not applicable.
Consent to participate
Written informed consent was obtained from the parent of the patient.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
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3. Powers SW Patton SR Hommel KA Hershey AD Quality of life in paediatric migraine: characterization of age-related effects using PedsQL 4.0 Cephalalgia 2004 24 2 120 7 10.1111/j.1468-2982.2004.00652.x 14728707
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6. Abboud H Abboud FZ Kharbouch H Arkha Y El Abbadi N El Ouahabi A COVID-19 and SARS-Cov-2 infection: pathophysiology and clinical effects on the nervous system World Neurosurg 2020 140 49 53 10.1016/j.wneu.2020.05.193 32474093
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8. Qiu J Shen B Zhao M Wang Z Xie B Xu Y A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: implications and policy recommendations Gen Psychiatr 2020 33 2 e100213 10.1136/gpsych-2020-100213 32215365
9. Philipp J Zeiler M Wöber C Wagner G Karwautz AFK Steiner TJ Wöber-Bingöl Ç Prevalence and burden of headache in children and adolescents in Austria—a nationwide study in a representative sample of pupils aged 10–18 years J Headache Pain 2019 20 1 101 10.1186/s10194-019-1050-8 31694547
10. Wöber-Bingöl Ç Wöber C Uluduz D Uygunoğlu U Aslan TS Kernmayer M Zesch HE Gerges NT Wagner G Siva A Steiner TJ The global burden of headache in children and adolescents—developing a questionnaire and methodology for a global study J Headache Pain 2014 15 1 86 10.1186/1129-2377-15-86 25496532
11. Papetti L Alaimo Di Loro P Tarantino S Grazzi L Guidetti V Parisi P Raieli V Sciruicchio V Termine C Toldo I Tozzi E Verdecchia P Carotenuto M Battisti M Celi A D'Agnano D Faedda N Ferilli MA Grillo G Natalucci G Onofri A Pelizza MF Ursitti F Vasta M Velardi M Balestri M Moavero R Vigevano F Valeriani M I stay at home with headache. A survey to investigate how the lockdown for COVID-19 impacted on headache in Italian children Cephalalgia 2020 40 13 1459 1473 10.1177/0333102420965139 33146039
12. Dallavalle G Pezzotti E Provenzi L Toni F Carpani A Borgatti R Migraine symptoms improvement during the COVID-19 lockdown in a cohort of children and adolescents Front Neurol 2020 8 11 579047 10.3389/fneur.2020.579047
13. Al-Hashel JY Ismail II Impact of coronavirus disease 2019 (COVID-19) pandemic on patients with migraine: a web-based survey study J Headache Pain 2020 21 1 115 10.1186/s10194-020-01183-6 32972360
14. Gonzalez-Martinez A Planchuelo-Gómez Á Guerrero ÁL García-Azorín D Santos-Lasaosa S Navarro-Pérez MP Odriozola-González P Irurtia MJ Quintas S de Luis-García R Gago-Veiga AB Evaluation of the impact of the COVID-19 lockdown in the clinical course of migraine Pain Med 2021 22 9 2079 2091 10.1093/pm/pnaa449 33659991
15. Parodi IC Poeta MG Assini A Schirinzi E Del Sette P Impact of quarantine due to COVID infection on migraine: a survey in Genova, Italy Neurol Sci 2020 41 8 2025 2027 10.1007/s10072-020-04543-x 32613542
16. Verhagen IE van Casteren DS de Vries Lentsch S Terwindt GM Effect of lockdown during COVID-19 on migraine: a longitudinal cohort study Cephalalgia 2021 41 7 865 870 10.1177/0333102420981739 33430642
17. Goadsby PJ Holland PR Martins-Oliveira M Hoffmann J Schankin C Akerman S Pathophysiology of migraine: a disorder of sensory processing Physiol Rev 2017 97 2 553 622 10.1152/physrev.00034.2015 28179394
18. Budianto P Putra SE Hafizhan M Tyas FNI Febrianty AF Prabaningtyas HR Relationship between tension-type headache and quality of sleep, excessive daytime sleepiness, and fatigue syndrome among healthcare workers during COVID-19 Glob Med Health Commun 2021 9 3 185 192 10.29313/gmhc.v9i3.8530
19. Puca F Genco S Prudenzano MP Savarese M Bussone G D'Amico D Cerbo R Gala C Coppola MT Gallai V Firenze C Sarchielli P Guazzelli M Guidetti V Manzoni G Granella F Muratorio A Bonuccelli U Nuti A Nappi G Sandrini G Verri AP Sicuteri F Marabini S Psychiatric comorbidity and psychosocial stress in patients with tension-type headache from headache centers in Italy. The Italian collaborative group for the study of psychopathological factors in primary headaches Cephalalgia 1999 19 3 159 64 10.1046/j.1468-2982.1999.1903159.x 10234463
| 36478546 | PMC9734329 | NO-CC CODE | 2022-12-14 23:28:27 | no | Acta Neurol Belg. 2022 Dec 7;:1-6 | utf-8 | Acta Neurol Belg | 2,022 | 10.1007/s13760-022-02150-5 | oa_other |
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Eye (Lond)
Eye (Lond)
Eye
0950-222X
1476-5454
Nature Publishing Group UK London
36481958
2341
10.1038/s41433-022-02341-7
Article
A lack of an association between COVID-19 vaccination and corneal graft rejection: results of a large multi-country population based study
http://orcid.org/0000-0002-3590-0856
Roberts Harry W. [email protected]
12
Wilkins Mark R. 13
http://orcid.org/0000-0002-3504-3145
Malik Mohsan 1
Talachi-Langroudi Melody 3
Myerscough James 45
http://orcid.org/0000-0002-6419-6941
Pellegrini Marco 56
http://orcid.org/0000-0001-5654-3942
Yu Angeli Christy 56
http://orcid.org/0000-0003-3635-3521
Busin Massimo 56
1 grid.436474.6 0000 0000 9168 0080 Corneal and External Diseases Unit, Moorfields Eye Hospital NHS Foundation Trust, London, UK
2 West of England Eye Unit, Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
3 grid.83440.3b 0000000121901201 UCL Institute of Ophthalmology, London, UK
4 grid.412711.0 0000 0004 0417 1042 Southend University Hospital, Southend, UK
5 Department of Ophthalmology, Ospedali Privati Forlì “Villa Igea”, Forlì, Italy
6 grid.8484.0 0000 0004 1757 2064 Department of Translational Medicine, University of Ferrara, Ferrara, Italy
8 12 2022
14
2 6 2022
29 11 2022
29 11 2022
© The Author(s), under exclusive licence to The Royal College of Ophthalmologists 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Purpose
The aim of the study was to present the rates of corneal transplant rejection from 2018 to 2022 at both Moorfields Eye Hospital UK, and Ospedali Privati Forli (OPF) “Villa Igea”, Italy and evaluate the purported association between COVID-19 vaccination and rejection.
Methods
We performed a retrospective review of rejection cases presenting to the two units. Monthly rates were correlated against regional vaccination programme rates. At OPF, conditional Poisson regression model was employed to estimate the incidence risk ratio (IRR) of graft rejection following COVID-19 vaccination risk period compared with the control period.
Results
Between January 2018 and March 2022, there were 471 (Moorfields), 95 (OPF) episodes of rejection. From the start of vaccination programme in the UK in late January 2021, the median number of graft rejections per month at Moorfields was 6 (range: 5–9), which was not significantly different to post-lockdown, pre-vaccination programme (March 2020–January 2021), p = 0.367. At OPF, the median rates of rejection before and after initiation of the vaccination programme were not significantly different (p = 0.124). No significant increase in incidence rate of rejection in the risk period following COVID-19 vaccination was found (IRR = 0.53, p = 0.71).
Conclusion
No notable increase in rates of transplant rejection was noted in year 2021 when COVID-19 vaccination was broadly implemented. The apparent temporal relationship between COVID-19 vaccination and corneal graft rejection highlighted in several case reports may not represent a causative association.
Subject terms
Corneal diseases
Risk factors
==== Body
pmcIntroduction
COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has, as of May 2022, resulted in excess of 15 million deaths worldwide, notwithstanding the morbidity burden caused by long term effects of infection and curtailment of personal freedoms imposed on many populations by their national governments in public health measures to restrict the replication of the virus [1].
However, the development of a number of vaccines has led to a reduction in the morbidity and mortality of this novel infection and in many countries have had significant benefits on alleviating pressures on healthcare systems and reducing imposed limitations on personal freedoms. Phase Ill clinical trials from various manufacturers have confirmed the high efficacy of vaccines against serious COVID-19 infection with low incidence rates of major adverse events [2, 3]. However, the relatively limited sample sizes and follow-up durations of phase III clinical trials inherently restrict the ability to detect rare and serious adverse vaccine-associated outcomes.
Since the inception of various nation-based vaccination programmes, ophthalmologists have identified and sought to publish cases of corneal transplant rejection with a temporal association to the SARS-CoV-2 vaccine and many such case reports have been published to date [4–16]. However, case reports are low level evidence which can be subject to observer, reporting and publication bias. As physicians, we have the duty to offer our patients sound medical advice based on the best available evidence and to not unnecessarily contribute to vaccine hesitancy, if unwarranted.
In order to evaluate whether national SARS-CoV-2 vaccine programmes have had an impact on the incidence of immunological rejection of corneal transplants we conducted an observational retrospective cohort study to investigate the rates of graft rejection presenting to the Emergency Department at Moorfields Eye Hospital, City Road, London, UK and Ospedali Privati Forlì “Villa Igea”, Italy both before and after the national SARS-CoV-2 vaccination programme. Moorfields Eye is responsible for over 20% of all corneal graft surgery nationally (A Rahman, Eye Bank Manager, Moorfields Lion Eye Bank, email communication, July, 2020) and provides specialised tertiary level ophthalmic care to the greater London area including running 24-h 7 days a week walk-in ophthalmic emergency department (ED), which prior to the pandemic would welcome on average over 1900 attendances per week. On the other hand, Ospedali Privati Forlì “Villa Igea” is tertiary care eye centre seeing 200–300 cornea cases weekly and performing over 10% of corneal transplants performed in Italy (D Ponzin, Fondazione Banca degli Occhi del Veneto Onlus, personal communication, March 2022).
Methods
This multicentre retrospective cohort study was approved as a Clinical Audit report by the Clinical Audit Committee at Moorfields Eye Hospital (London, UK) and Institutional review board/ ethics Committee approval was obtained from the Comitato Etico Ospedali Privati Forlì (Forlì, Italy). The study was performed in accordance with the tenets of the Declaration of Helsinki.
We performed a retrospective review of cases presenting to the cornea and external disease service at Moorfields Eye Hospital, City Road and Ospedali Privati Forlì “Villa Igea” (OPF) from January 2018 to March 2022. Inclusion criteria included all patients 18 years or older with a clinical diagnosis of graft rejection. At Moorfields, the case notes of all included cases were then reviewed by two independent observers experienced in cornea in order to perform a final confirmation of the diagnosis with the benefit of medical notes from subsequent follow up in the corneal service. All cases presenting at Ospedali Privati Forlì “Villa Igea” were seen by a cornea specialist. Graft rejection was defined as patients who developed an epithelial rejection line, subepithelial or stromal infiltrates, keratic precipitates or anterior chamber cell reaction with or without clinically apparent increase in stromal thickness or clarity.
Data analysis
All data had been collected prospectively and entered into the patient’s electronic medical records from the Moorfields Electronic Patient Record (EPR) System (OpenEyes, Apperta Foundation CIC, Sunderland, UK), which mandates recording.
Using the Moorfields dataset, we statistically compared new case presentations using non-parametric independent group Mann–Whitney U pre- and post- lockdown (March 2020), and pre- and post-vaccination roll out (February 2021). Similarly, data from the electronic database of OPF were also analysed. Vaccine status was ascertained among patients who developed corneal graft rejection. Conditional Poisson regression analysis was used to evaluate whether the association exists between COVID-19 vaccination and corneal graft rejection. Incidence risk ratio (IRR) of corneal graft rejection was calculated to compare the COVID-19 vaccination risk period, defined as the interval between vaccination and 60 days from the last dose with the control period defined as the observation period excluding risk period.
Furthermore, we obtained cumulative regional vaccination statistics from the Public Health England open-source data set to undertake a regression analysis to ascertain the effect of the UK COVID-19 vaccine programme on new case presentations [17]. A p value less than 0.05 was considered clinically significant.
Results
Moorfields Eye Hospital
Between January 2018 and March 2022, there were 471 corneal graft rejection episodes with a median rate of 9 patients per month (Range 3–18) at Moorfields Eye Hospital. 62% were male (n = 292), and the average age was 56 (range 18–97).
In the 26 months prior to national lockdown from January 2018 to February 2020 the median number of corneal graft rejections per month was 12 (range 8–18), which was significantly different to post lock down (March 2020–March 2022, median 6 cases per month, p = 0.001). From the start of vaccination programme in the United Kingdom late January 2021, the median number of corneal graft rejections per month was 6 (Range 5–9), which was not significantly different to post-lockdown, pre-vaccination programme (March 2020–January 2021), p = 0.367.
In total, 44 million received the first dose, 41 million people have had the second dose, and 32 million have received the third (booster) dose of the COVID-19 vaccine in England (as of March 2022). The cumulative percentage uptake of the COVID-19 vaccine in London was reported to be 70%, 65%, 46% for first, second and third dose respectively (Fig. 1). Regression analysis did not demonstrate a significant relationship between regional cumulative percentage vaccination uptake (first, second or third dose) and the number of corneal graft rejection episodes per month following vaccination roll out (r2 = 0.09, p = 0.667).Fig. 1 The number of presentations of immunological graft rejection at the Emergency Department, Moorfields Eye Hospital, City Road, London compared with the SARS-CoV-2 vaccine uptake in London.
Blue line - number of immune mediated graft rejection in Emergency Department, Moorfields Eye Hospital, City Road, London. Orange line - cumulative percentage uptake of COVID vaccination in London (first dose). Grey line - cumulative percentage uptake of COVID vaccination in London (second dose). Orange line - cumulative percentage uptake of COVID vaccination in London (third dose).
Ospedali Privati Forlì
During the same time period, 95 episodes of corneal graft rejection were diagnosed at OPF with a median rate of 2 per month. Of the 95 cases, 82 (86%) patients had received COVID-19 vaccination, compared to 85% of the overall population. The median rates of rejection before and after initiation of the vaccination programme were not significantly different (p = 0.124). Figure 2 shows the uptake of the COVID-19 vaccines in Emilia-Romagna, Italy and frequency of rejection episodes diagnosed over the same time period. No notable increase was noted when COVID-19 vaccination was broadly implemented.Fig. 2 The number of presentations of immunological graft rejection at Ospedali Privati Forlì “Villa Igea” compared with the SARS-CoV-2 vaccine uptake in Emilia-Romagna, Italy.
Blue line - number of immune mediated graft rejections in Forli, Italy. Orange line - cumulative percentage uptake of COVID vaccination in Emilia-Romagna, Italy (first dose). Orange line - cumulative percentage uptake of COVID vaccination in Emilia-Romagna, Italy (second dose).
Using conditional Poisson regression analysis of rejection episodes between January 2018 and March 2022, we found no significant increase in incidence rate of rejection between COVID-19 vaccination and 60 days from the last vaccine dose (IRR = 0.53, p = 0.71).
Discussion
Before the national lockdown in March 2020, Moorfields ED saw an average of 12 corneal graft rejections a month. From the start of the UK national vaccination programme the average number was 6 rejections per month. Regression analysis found no significant effect on vaccines on the number of presentation of rejections (r2 = 0.09, p = 0.667). Similarly, no increase in rejection cases were observed at OPF, Italy. If SARS-CoV-2 vaccines were associated with even a slight association of risk of corneal graft rejection, then one would expect that by vaccinating the majority of the population in the space of a few months there would have been an increase in the number of rejection episodes presenting at both centres. Given that the risk of rejection persists throughout the lifetime of the corneal graft even without an identifiable trigger, background incidence can account for rejection episodes that expectedly occur at any time over any given period following keratoplasty. We, therefore, suggest that based on our routinely collected longitudinal data using standard case definitions and methods of ascertainment, our data do not lend support to an association between COVID-19 vaccination and corneal graft rejection. This is further supported as this finding is replicated across two large European centres, each of which provide a large proportion of their national corneal transplantation workload.
Our results are in keeping with the only other two publications we are aware of which have evaluated the risk of rejection associated with vaccines. The Corneal Preservation Time Study group found that vaccines within the previous three months were not a significant factor associated with corneal graft rejection in Descemet stripping automated endothelial keratoplasty [18]. Equally, a Wills Eye Hospital prospective case–control study evaluating trigger factors for penetrating keratoplasty rejection did not find an association with recent vaccinations in their 22 rejection patients compared with controls (immunisation exposure equally prevalent in both groups) [19]. Both of the above studies occurred prior to COVID vaccinations.
There are a number of methodological issues with a study of this nature. We examined the monthly rates of corneal transplant rejections but did not characterise these clinically. At Moorfields, the vaccine status of corneal graft rejection patients presenting during this time frame was not rigorously recorded, but at OPF the conditional Poisson regression analysis did not find a significant increase in incidence rate of rejection between COVID-19 vaccination and 60 days from the last vaccine dose.
At Moorfields we observed a reduction in the rate of rejection which occurred in keeping with the first national lockdown. It is well recorded that healthcare seeking behaviours were affected by the pandemic. Face-to-face attendances at Moorfields ED did reduce by over 50% in the early phase of the first national lockdown in March–April 2020 [20]. However, the case mix and severity of conditions also shifted. Prior to the pandemic, blepharitis was the most common cause for presentation, after the onset of the pandemic it became acute anterior uveitis [20]. Hence to some extent, the reduction in attendances would have been skewed against more minor conditions, and may have had less of an effect on severe conditions, although it is well documented that rates of presentations of rhegmatogenous retinal detachments reduced during the first lockdown [20, 21].
A reduction in recent corneal transplant rates may have had an impact on rates of corneal graft rejection as 50% of corneal rejection episodes may occur during the first twelve months after surgery [18]. On average, approximately 4,000 corneal transplants are performed in the UK each year, however, this would have been significantly less in the 12 months prior to the vaccination programme. There was a 92% reduction in corneal elective work at Moorfields Eye Hospital during the first national lockdown [22]. Services were likewise significantly reduced in OPF Italy [23]. In OPF patients were not advised to increase their topical steroid dose at the time of vaccine. At Moorfields, where there are more than ten corneal consultants, management may have been more heterogenous. Nevertheless, there is no available evidence to support increasing topical steroids around the time of vaccination, this practice is speculative.
Our real world data reflects overall trends during a unique time period with inherent limitations on the use of more complex analytic methods requiring more granular information to account for potential confounding variables such as care at other sites, self quarantining, self treatment, untreated rejections, etc. Both Moorfields and OPF continued ophthalmic services for urgent care throughout the pandemic (indeed Moorfields continued to offer a 24/7 emergency service while other smaller London units closed). While both centres observed a sharp decline in healthcare utilisation in March–April 2020, there was a recovery of numbers after this period.
Despite the clear limitations of this study, we advocate that if there was a true association between the SARS-CoV-2 vaccine and corneal graft rejection then the effects of vaccinating nearly the entire population over a period of a few months would have impacted in a visible way upon the number of presentations.
In the future, it may be more helpful to perform a matched case-control study through large transplant registries to identify in a more robust way whether there may be any association not detected by this study in both centres.
In summary, national vaccination programmes in the UK and Italy for SARS-CoV-2 has not seen an associated increase in the incidence of corneal graft rejection which may be expected if there were an association.
Summary
What was known before
In the last 2 years many case reports or case series have questioned an association between SARS-CoV-2 vaccine and corneal transplant rejection.
What this study adds
This paper demonstrates that 2 major corneal centres, Moorfields in London UK and Ospedali Privati Forli “Villa Igea”, Italy did not see an increase in the rate of rejection presentations during the national vaccination campaigns, strongly indicating a lack of evidence for any association.
Author contributions
All authors have made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND Drafting the work or revising it critically for important intellectual content; AND Final approval of the version to be published.
Data availability
Data available upon written request to the corresponding author.
Competing interests
HR has undertaken paid consultancy work for Alcon Inc (Fort Worth, TX, USA) in the past 36 months and has received honoraria from Thea Pharmaceuticals Ltd (Keele, UK). The other authors have no financial interest to disclose.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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8. Phylactou M Li J-PO Larkin DFP Characteristics of endothelial corneal transplant rejection following immunisation with SARS-CoV-2 messenger RNA vaccine Br J Ophthalmol 2021 105 893 6 10.1136/bjophthalmol-2021-319338 33910885
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15. Rajagopal R Priyanka TM Stromal rejection in penetrating keratoplasty following COVID-19 vector vaccine (Covishield)—A case report and review of literature Indian J Ophthalmol 2022 70 319 21 10.4103/ijo.IJO_2539_21 34937268
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| 36481958 | PMC9734330 | NO-CC CODE | 2022-12-14 23:28:27 | no | Eye (Lond). 2022 Dec 8;:1-4 | utf-8 | Eye (Lond) | 2,022 | 10.1038/s41433-022-02341-7 | oa_other |
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Stoch Environ Res Risk Assess
Stoch Environ Res Risk Assess
Stochastic Environmental Research and Risk Assessment
1436-3240
1436-3259
Springer Berlin Heidelberg Berlin/Heidelberg
2351
10.1007/s00477-022-02351-7
Original Paper
To what extent the traffic restriction policies can improve its air quality? An inspiration from COVID-19
Xu Si-qing 12
He Hong-di [email protected]
1
Yang Ming-ke 1
Wu Cui-lin 1
Zhu Xing-hang 1
Peng Zhong-ren 3
Sasaki Yuya 4
Doi Kenji 5
Shimojo Shinji 6
1 grid.16821.3c 0000 0004 0368 8293 Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications, State-Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China
2 grid.16821.3c 0000 0004 0368 8293 Data-Driven Management Decision Making Lab, Shanghai Jiao Tong University, Shanghai, 200240 China
3 grid.15276.37 0000 0004 1936 8091 International Center for Adaptation Planning and Design, College of Design, Construction and Planning, University of Florida, PO Box 115706, Gainesville, FL 32611-5706 USA
4 grid.136593.b 0000 0004 0373 3971 Graduate School of Information Science and Technology, Osaka University, Suita, Japan
5 grid.136593.b 0000 0004 0373 3971 Cyber Media Center, Osaka University, Suita, Japan
6 grid.136593.b 0000 0004 0373 3971 Graduate School of Engineering, Osaka University, Suita, Japan
7 12 2022
117
18 11 2022
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
In hazy days, several local authorities always implemented the strict traffic-restriction measures to improve the air quality. However, owing to lack of data, the quantitative relationships between them are still not clear. Coincidentally, traffic restriction measures during the COVID-19 pandemic provided an experimental setup for revealing such relationships. Hence, the changes in air quality in response to traffic restrictions during COVID-19 in Spain and United States was explored in this study. In contrast to pre-lockdown, the private traffic volume as well as public traffic during the lockdown period decreased within a range of 60−90%. The NO2 concentration decreased by approximately 50%, while O3 concentration increased by approximately 40%. Additionally, changes in air quality in response to traffic reduction were explored to reveal the contribution of transportation to air pollution. As the traffic volume decreased linearly, NO2 concentration decreased exponentially, whereas O3 concentration increased exponentially. Air pollutants did not change evidently until the traffic volume was reduced by less than 40%. The recovery process of the traffic volume and air pollutants during the post-lockdown period was also explored. The traffic volume was confirmed to return to background levels within four months, but air pollutants were found to recover randomly. This study highlights the exponential impact of traffic volume on air quality changes, which is of great significance to air pollution control in terms of traffic restriction policy.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00477-022-02351-7.
Keywords
COVID-19 lockdown
Traffic volume
Air quality
System resilience
Urban management
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pmcIntroduction
On March 11, 2020, the World Health Organization (WHO) declared that the COVID-19 disease had been characterized as a pandemic and could cause serious health problems (Shakil et al. 2020). The main transmission routes of COVID-19 are direct, aerosol, and contact transmission. Therefore, limiting human-to-human contact is an effective way of slowing the spread of the virus (Lal et al. 2020). Most countries and regions had implemented full or partial lockdown policies during the COVID-19 pandemic, such as travel, commerce, and traffic restrictions. These lockdown measures and other preventive measures (such as the use of masks, gloves, and disinfectants) play an important role in controlling the spread of the virus (GÜNER et al. 2020).
The transportation sector was significantly affected due to lockdown policies during the pandemic. The implementation of lockdown policies during the COVID-19 pandemic led to a reduction in vehicle use (Lal et al. 2020; Muhammad et al. 2020). Many studies have used satellite imagery or government-released traffic flow monitoring data to compare traffic changes before and after the lockdown to illustrate the impact of COVID-19 pandemic. NASA’s Planet Labs captured satellite images of traffic and parking lots near Wuhan Railway Station before and after the lockdown and found almost no vehicles on the Wuhan Yangtze River Bridge after the policy’s implementation (Wang and Su 2020). Traffic flow monitoring data in Bengbu and Changzhou released by the National Bureau of Statistics of China showed that traffic in Anhui and Zhejiang decreased by 75% (Li et al. 2020). A study of the Veneto region near the Northeast Adriatic sea showed that during the lockdown from March to April 2020, shipping volume decreased by 69% and passenger volume decreased by 78% compared with that in the same period in 2017 (Depellegrin et al. 2020). A similar trend was observed in Rio de Janeiro, Brazil; in the first two weeks of partial lockdown, passenger vehicle traffic reduced by 70−80% (Siciliano et al. 2020).
As a side effect, the changing characteristics of pollutants during the COVID-19 pandemic have also been widely studied. Remarkable changes in air quality were observed in various countries after the implementation of the lockdown policies. In Malaysia, during the lockdown period, the PM2.5 concentration reduced by up to 58.4% (Abdullah et al. 2020). In the Yangtze River Delta region of China, during the first-level response period, SO2, NOX, PM2.5, VOCs, and other major pollutants decreased by 26, 47, 46, and 57%, respectively (Li et al. 2020). In summary, plenty of evidence has confirmed that the COVID-19 lockdown resulted in improved air quality and lower concentrations of pollutants, such as nitrogen oxides and particulate matter, in many cities. Evidently, in contrast to the previous studies (Cai and Xie 2011; Chen et al. 2022), Covid-19 lockdown policy provides a good chance to use the natural experiment to explore the air quality change in restrictions policy.
Traffic is a major source of air pollutants, therefore, some scholars attempted to add the consideration of traffic to the study of air pollutants variations. During COVID-19 period, Wu et al. (Wu et al. 2021) investigated the air quality change on roadside air quality monition stations to reveal the impact of traffic on air pollutions variations. They found that in contrast to non-roadside stations, the air pollutants such as NOx and PM on roadside stations present obvious reduction during the lockdown period. Lin et al. (Lin et al. 2022) analyzed the impact of different vehicle types (passenger cars, light goods vehicles, heavy goods vehicles, and long HGVs) on PM2.5 and PM10 on the M25 motorway in the UK during the COVID-19 pandemic, and it was found that long HGVs had a greater impact on pollutants. Chen et al. (Chen et al. 2021) used the DID method to evaluate the relationship between different vehicle restrictions and air pollution during the COVID-19, and found that restricting fuel vehicles and restricting according to the last digit of the license plate number were more effective in reducing air pollution. Wang et al. (Wang et al. 2022) adopted traffic indices and air quality parameters to develop a complex network to analyze the impact of traffic on air pollution in different regions and at different stages of lockdown. Kovács and Haidu (2022) collected static traffic facility density data in French cities to study its impact on nitrogen dioxide concentrations during the pandemic and found a strong negative relationship (R²=0.808) between transport infrastructure and NO2 concentrations. Munir et al. (Munir et al. 2021) analyzed mobility and pollutant data during the COVID-19 and found a strong positive correlation between traffic and nitrogen oxides. Kumar et al. (2020) found a linear relationship between traffic flow and PM2.5 (R²=0.69).
Obviously, the emergence of lockdown policies during the COVID-19 pandemic has led to changes in traffic and pollutants, providing an opportunity to use actual data to study the characteristics of traffic and pollutants and their relationship. In previous studies, most scholars used the real data during the COVID-19 period to analyze the impact of traffic changes on pollutants and provide different viewpoints to understand it. However, most of the current studies only qualitatively pointed out whether there was a positive or negative correlation between traffic and pollutants, or started from the characteristics of vehicles and traffic facilities themselves to study the changes in pollutants under different types of vehicle restrictions and different facilities. Due to lack of the measurement data, the quantitative analysis of the specific relationships between urban traffic and air pollutants have been seldom conducted. The recovery process of urban traffic and pollutants after lockdown policy are also rarely investigated.
Hence, to address these research gaps, this study collected data on air pollutants and traffic volume in four large cities in Spain and United States. Traffic is the main source of nitrogen oxides in the atmosphere, and the concentration of O3 is affected by nitrogen oxides via titration reactions (Sicard et al. 2020; Menut et al. 2020). Therefore, NO2 and O3 were selected as the main research objects for this study. Firstly, the changes in NO2 and O3 concentrations and traffic volume during COVID-19 in these cities were analyzed. Then, the impact of urban traffic on air pollutants and the relationship between the two were discussed. Finally, the recovery process in the later stage of the COVID-19 pandemic was explored. The conclusions obtained will have far-reaching significance for formulating green and environmentally friendly traffic management policies and protecting human health.
Materials and methods
Study areas
The study area comprises of four metropolitan cities located in Spain and the United States, namely Madrid, Barcelona, Los Angeles, and Providence. Madrid is the capital and most populous city in Spain. Barcelona is the second most populous municipality in Spain and is a transport hub. Los Angeles is the second largest city in the United States, which has more than 10 million inhabitants. Providence is the capital and most populous city of the U.S. state of Rhode Island, with a population of over 190,000.
After the outbreak of COVID-19 in 2020, the governments of the study cities implemented different levels of lockdown policies to slow the spread of the virus. Madrid and Barcelona confirmed their first cases of COVID-19 on January 31, 2020 and proposed social distancing for residents on March 3. On March 14, 2020, Madrid and Barcelona started the lockdown and strengthened it on March 29, announcing that all non-essential workers to remain at home for the next 14 days. California, where Los Angeles is located, had the most confirmed cases of COVID-19 in the United States. It confirmed the first case of COVID-19 on January 26, 2020, and multiple cases were confirmed the following month. The government declared a state of emergency on March 4, and imposed a lockdown on March 19, issuing a statewide stay-at-home order. Rhode Island, where Providence is located, had its first two confirmed COVID-19 cases on March 1, 2020, and the governor declared a state of emergency on March 9. Since March 12, it was mandated to ban large-scale events and maintain social distancing in Providence. On March 28, the government issued a stay-at-home order that required residents who traveled non-essentially to stay at home. The lockdown timeline for each city is shown in the Fig. 1. The lockdown policies of the study cities mainly included the following: closing bars, restaurants, parks, cinemas, and other entertainment venues; schools and companies working in an online mode; prohibiting residents from gathering, enforcing social distancing norms; and requiring residents to stay at home except for essential travel.
Fig. 1 Lockdown timeline and specific lockdown policies for the considered study cities
Data selection
The pollutant (NO2, O3) concentration data of the study cities during the COVID-19 pandemic in 2020 were obtained from the environmental monitoring stations in these countries (https://aqicn.org/data-platform/covid19). Monitoring stations in each study city are described in the Table 1. Average monitoring data from several monitoring stations in each country were used to represent the country’s air quality and meteorological conditions. The data provided by the monitoring stations were considered reliable (Kuerban et al. 2020; Lin et al. 2014), and there have been many studies based on monitoring station data (Abdullah et al. 2020; Nakada and Urban 2020; Siciliano et al. 2020). Pollutant data for 2019 were collected for subsequent comparison. Mobility data (private and public travel volume) from January 2020 to October 2021 were collected from Apple’s mobility data monitor (http://covid19.apple.com/mobility). Sannigrahi et al. (2020) and Munir et al. (Munir et al. 2021) used these data to study traffic changes during the COVID-19 and confirmed that these data was reliable and creditable. It should be noted that the data in this web is no longer available after April 14, 2022. We are willing to share it if other scholars need it in the future. More information about the data information can be found in the appendix file.
Table 1 Information of monitoring stations in each study city
City Number of stations Station’s name Description of the data
Madrid 10 Torrejon de ardoz, Cuatro Caminos, Media Red, Fernandez Ladreda, alcorcon, Mendez Alvaro, Castellana, Plaza De Castilla, Escuelas Aguirre, Casa De Campo The data is updated every 24 h, and the data is the average of monitoring stations in the city.
Barcelona 14 Poblenou, Eixample, Sant Adrià de Besòs, Sabadell (Av. Gran Via), Santa Perpètua de Mogoda, Sant Vicenç dels Horts, Gràcia-St. Gervasi, Barcelona, Palau Reial, l ' Hospitalet de Llobregat, Rubi (Ca n ' Oriol), Montcada i Reixac (Can Sant Joan), Parc Vall d ' Hebron, Montcada i Reixac
Los Angeles 5 North Long Beach (Long Beach), Los Angeles-North Main Street, South Long Beach,Burbank, Long Beach-Route 710 Near Road
Providence 4 Narragansett, E Providence, Near Road, Providence
Besides, in order to remove the meteorology factors’ impact, the change of meteorology factors and air quality between 2020 and 2019 were compared (Table S1). According to the compassion of meteorology factors, the humidity as well as pressure, temperature and wind speed present small difference, that means the air quality change in 2020 were dominantly influenced by lockdown policy.
Methodology
The regression discontinuity design (RDD) method was first proposed by sociologists Thistlethwaite and Campbell in 1960 to evaluate social projects. Discontinuous regression designs are considered close to randomized controlled trials and have the greatest internal validity for alternative quasi-experimental estimators (Lee and Lemieux 2010; Qin et al. 2017; Zhu et al. 2022), which is a common and valid method for assessing policy effects. The RDD models can identify discontinuities in a variable by identifying jumps on either side of a breakpoint. If such discontinuities are tested and found to be statistically significant, a causal relationship between policy interventions and changes in that variable can be concluded. The main assumption of RDD is that only policy variables change significantly around the threshold, whereas other factors change continuously, and it is an effective and robust method for assessing policy effects (Zeng et al. 2020). RDD methods can be used to assess the impacts of policies on pollutants. Previous studies have used RDD to evaluate the changes in air quality after the promulgation of ecological protection-related regulations or after the implementation of the restriction policy during the G20 Hangzhou summit in China (Zeng et al. 2020; Wang et al. 2021).
In this study, after the COVID-19 outbreak, lockdown policies were implemented in each of the study cities, resulting in abrupt changes in traffic volumes and pollutant concentrations, largely consistent with the scenarios described in the RDD methodology. Therefore, this study applied a discontinuous regression design to assess the impact of lockdown on traffic volume and pollutants.
The dummy variable dt was defined to represent whether the lockdown policy was implemented, as shown in the Eq. (1). Further, the model for estimating the effect of the lockdown policy using RDD was defined as shown in the Eq. (2). Here, Yt is the outcome variable representing the pollutant concentration or traffic volume at time t. α is a constant term, β0β~3 are coefficients, k is the power of a polynomial, and the value of k is usually between one and four. ϵt is a random error term and (t-cutpoint) is the distance between time t and the breakpoint (the time point when the lockdown policy is implemented).1 dt=0,lockdownpolicyisnotimplemented1,lockdownisimplemented
2 Yt=α+β0·dt+β1·t-cutpointk+β2·dt·t-cutpointk+ϵt
The concept of resilience was first introduced in the field of ecology by Holling (1973) and later applied to various fields such as economics and psychology, which aroused the interest of many researchers (Hosseini et al. 2016). System resilience is used to evaluate the resilience of a system under disturbance events, and can be understood as the ability of the system to reduce the chances of a shock, absorb a shock if it occurs, and recover quickly after a shock (re-establish normal performance) (Bruneau et al. 2003). Under the disturbance of events, system performance can be divided in three stages: before disturbance, during disturbance, and after disturbance. Before a disturbance event occurs, the system operates under normal conditions, and the performance of the systems, such as capacity and demand, is not affected. When a disturbance event occurs, the system operates under the disturbance. During this period, the system is damaged, and its performance in providing various services degrades. The system begins to recover shortly after the disturbance event, a phase that can take a longer time compared to the duration of the disturbance event, during which the system performance begins to improve (Shafieezadeh and Burden 2014). The ratio of the area of the system performance curve under disturbance events to the area of the system performance curve under undisturbed conditions is often used to evaluate the system resilience (Shafieezadeh and Burden 2014; Ouyang et al. 2012; Barker et al. 2013). Urban transportation systems are prone to disruptions owing to natural disasters, human influences and so on. Although there is no unified definition of the resilience of transportation systems, various studies have analyzed it as normal urban transportation systems are important for smooth functioning of the cities (Ni et al. 2021). The lockdown policy during COVID-19 could also be regarded as a disturbance to the normal operation of a city. Some studies have examined the interruption-recovery patterns of urban air pollution under the impact of COVID-19 pandemic (Cai et al. 2021).
The lockdown policies implemented during the COVID-19 pandemic can be seen as disruptions to the system. In this study, based on the definition of system resilience (Barker et al. 2013), a similar model was developed to evaluate system performance during the COVID-19 pandemic, as shown in the Fig. 2. When not disturbed by lockdown policy, the system performance remains stable, and the performance curve is shown by the function ϕt, while under the influence of lockdown policy during COVID-19, the system performance curve is shown by the function ϕ′t. Time t1 is the moment when the lockdown policy began to be implemented. After time t1, the system performance began to decline owing to the influence of the lockdown policy. After some time, it starts to recover from time t3, and the system performance improves and finally recovers to more than 95% of the system performance equivalent to the pre-lockdown at time t3. The definition of system resilience is shown in the Eq. 3, and is used to evaluate the resilience of transportation under the impact of COVID-19.
Fig. 2 System performance curves under disturbance events [Φ(t): System function without event disturbance; Φ’(t): System function under disturbance event]
3 system=∫t0t3φ′t/φtdt
For convenience of discussion, the stages of traffic volume change in each study city were divided. Figure 3a shows the changing pattern of travel volume under the influence of the lockdown. Figure 3b shows the actual change curve of travel volume (taking Madrid as an example, the change curves of the other study cities are similar to Madrid, which will be described in detail later). For each study city, the period of rapid decline in travel volume is defined as the descending stage, and the period when travel volume rebounds is defined as the rising stage (recovery stage).
Fig. 3 Variation modes of travel volume
Results and discussions
Change in traffic volume as a response to Covid-19 lockdown
During the COVID-19 pandemic in 2020, urban traffic volumes changed remarkably. To observe the impact of the lockdown policies on traffic, the RDD method was first used, and then the traffic volumes at different lockdown stages in each study city were compared. The different stages of the lockdown policy were as follows: pre-lockdown (no lockdown policy), partial lockdown (including social distancing, restrictions on large events), lockdown (stay-at-home order), and the release of lockdown.
The lockdown time was a certain value, and all factors (pollutants and traffic) interfered after the lockdown. This fitted the situation described by the sharp RDD model; therefore, the sharp RDD model was used here. The date when each city started lockdown was the breakpoint (day 0), and the optimal parameters of the model were determined using the AIC criterion (Anderson and Burnham 2004; Akaike 1974). The regression discontinuity plots of travel volume in Madrid are shown in the Fig. 4. The black points represent the original variations of traffic volume in pre-lockdown and lockdown periods. The red line in the Fig. 4 shows the change trend of the travel volume fitted using the RDD method. Before the lockdown, public and private traffic volumes were relatively stable and rose slowly with time. Significant breakpoints in traffic volumes were observed after the lockdown, with both public and private traffic volumes falling significantly and remaining low for some time. As the scatter plots in Barcelona, Los Angeles, and Providence were similar to those in Madrid, they are not shown in this study.
Fig. 4 Regression discontinuity plot of travel volumes in Madrid a Private travel, b Public travel
Besides, the envaulted results obtained by RDD for all the study cities are shown in the Table 2. As we known, RDD is generally used to assess the impacts of policies on variable change. The symbol “*” stands for significance. From the table, it can be found that the RDD values of traffic and NOx in four cities were negative, implied that the lockdown policy makes the traffic volume and NOx present obvious decrease. Conversely, the lockdown policy makes the O3 present increased trend. It can be mainly explained by an unprecedented reduction in NOx emissions leading to a lower O3 titration during COVID-19 period, which can also be confirmed with previous studies (Pierre Sicard et al. 2020).
Table 2 RDD values for traffic volumes and air pollutants in four cities
City Private traffic Public traffic NO2 O3
Madrid −0.728*** −0.813*** −0.138*** 0.118***
(0.159) (0.165) (0.043) (0.044)
Barcelona −0.903*** −0.987*** −0.118*** 0.163***
(0.189) (0.506) (0.034) (0.041)
Los Angeles −0.775*** −0.856*** −0.113** 0.084**
(0.189) (0.177) (0.047) (0.033)
Providence −0.303* −0.492*** −0.094*** 0.104**
(0.155) (0.160) (0.032) (0.040)
The symbols *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively
Specially, the change rates for traffic volumes and air pollutants during the different lockdown stages were investigated (Fig. 5). Both private and public travel volumes in Madrid, Los Angeles, and Providence decreased because of the implementation of the partial lockdown policy. Barcelona’s travel volume was close to that of the pre-lockdown period, which might be related to some essential activities in the city. After the implementation of full lockdown, compared with partial lockdown, the travel volume further decreased and reached the lowest value. At this time, the travel volume only reached approximately 10−40% of that before the lockdown. After the gradual release of the lockdown, traffic volume in each study city increased when compared with the time of lockdown but was still lower than before the lockdown. On comparing European cities and North American cities, it could be observed that travel volume decreased slightly after a partial lockdown in European cities, which decreased significantly after the full lockdown, while the traffic volume of North American cities after partial lockdown and full lockdown decreased by a similar amount. This was due to the different lockdown times and policies in these cities. In the Fig. 5, number next to the boxplot for each lockdown stage is the percentage change in travel volume in that phase relative to the travel volume in the previous phase. After the implementation of the lockdown, the decline in public travel volume in each study city was greater than that in private travel volume, indicating that public transport was more responsive to lockdown policies (Pozo et al. 2022; Shibayama et al. 2021; Corazza et al. 2021).
Fig. 5 Travel volume in different lockdown stages (presented as a percentage calculated based on January 13, 2020)
Change in air quality as a response to Covid-19 lockdown
As a side effect of the lockdown, the pollutant concentrations changed significantly during this period. Similar to traffic, the RDD method was used to analyze pollutants, and the concentrations were compared at different stages of the lockdown.
The regression discontinuity plots of pollutants (NO2, O3) and travel volume in Madrid are shown in the Fig. 6 (patterns found in Barcelona, Los Angeles, and Providence were similar to those in Madrid). Before the lockdown, the concentrations of NO2 and O3 fluctuated above and below the fitting line, the fluctuation range was large, and the pollutants did not show an obvious change with the date. After the lockdown, there were clear breakpoints in the concentration of the pollutants. The concentration of NO2 decreased and that of O3 increased and continued to decrease (NO2) or increase (O3) with time. The concentration also fluctuated above and below the fitting line; however, the fluctuation range was small. The regression discontinuity design results for the pollutants are presented in the Table 2. The lockdown policy had a significant promoting effect on the reduction of NO2 concentrations and an increase in O3 concentrations.
Fig. 6 Regression discontinuity plot of pollutants in Madrid a NO2; b O3
Figure 7 presents the concentrations of pollutants (NO2 and O3) in the study cities at different stages of the lockdown. Affected by the lockdown policy, the concentration of NO2 in the study cities decreased significantly after the partial lockdown and continued to decrease after the full lockdown. In contrast, the concentration of O3 increased during both the partial lockdown and lockdown phases. Traffic is the main source of NO2 in the atmosphere (Wang et al. 2020), and the reduction in travel volume is the main reason for the reduction in NO2 concentration. The increase in O3 concentration is related to its chemical reaction with NOx, and the reduction in nitrogen oxides (NO) reduces the consumption of O3 (titration, NO + O3 = NO2 + O2), resulting in an increase in O3 concentration (Tobías et al. 2020). In the Fig. 7, number next to the boxplot for each lockdown stage is the percentage change in pollutant concentration in that phase relative to the pollutant concentration in the previous phase. The NO2 concentration decreased by approximately 50%, and the O3 concentration increased by approximately 40% during the lockdown period. During the release of the lockdown period, NO2 concentrations slightly increased in Madrid, Barcelona, and Providence, whereas those in Los Angeles decreased slightly. O3 concentrations increased in Madrid and Los Angeles; and decreased in Barcelona and Providence. At this stage, the factors affecting the concentration of pollutants were not only the changes in travel volume but also the release of other activities and seasonal factors; therefore, the regularity of pollutants was not as obvious as in the lockdown stage.
Fig. 7 Concentration of NO2 and O3 in different lockdown stages in study cities
Correlation between travel volume and air quality during Covid 19 lockdown
To further understand the impact of the lockdown policy on travel volume and pollutant concentrations during COVID-19, the functional relationship between the rate of change in travel volume and the rate of change in pollutant concentration was explored. The descending stage was studied according to the description shown in the Fig. 3.
Figure 8 shows the fitting relationship between the rate of change of private traffic volume and the rate of change of NO2 concentration during the descending stage of travel volume. It can be seen from the Fig. 8 that in study cities, there was an exponential function relationship between these two (R²: 0.69 in Madrid, 0.75 in Barcelona, 0.43 in Los Angeles, 0.44 in Providence), and the more private travel volume decreased, the more NO2 concentration reduced. When the reduction rate of private traffic volume was less than 40%, the NO2 concentration decreased slowly. When the reduction rate of private traffic volume exceeded 40%, the NO2 concentration decreased rapidly. Such findings are different to the previous studies in which the linear relationships were obtained in it (Munir et al. 2021; Wang et al. 2020).
Fig. 8 Relationship between private travel and NO2 in study cities
The relationship between the rate of change of O3 concentration and rate of change of private traffic volume was also fitted (Fig. 9), and it can be observed that the relationship between these two also followed an exponential function (R²:0.86 in Madrid, 0.60 in Barcelona, 0.54 in Los Angeles, 0.63 in Providence). Contrary to the relationship between NO2 and traffic volume, the greater the decrease in private traffic volume, the greater was the increase in O3 concentration. When the reduction rate of private traffic volume was higher than 40%, the O3 concentration increased significantly. From the fitting results, the fitting effect between private traffic volume and O3 concentration was slightly better than that of NO2.
Fig. 9 Relationship between private travel and O3 in study cities
This function was also fitted to public travel volume and pollutant concentrations. All the fitting results are listed in the Table 3. The relationship between public travel volume and pollutants (NO2, O3) concentration also conformed to an exponential function [R² (NO2):0.67 in Madrid, 0.71 in Barcelona, 0.60 in Los Angeles, 0.42 in Providence; R² (O3):0.87 in Madrid, 0.56 in Barcelona, 0.51 in Los Angeles, 0.57 in Providence], similar to private travel volume, when the reduction rate of public travel volume was higher than 40%, significant changes in pollutant concentrations could be observed. The goodness of fit of the exponential function for European cities was slightly higher than that for North American cities, which might be due to the different lockdown policies in Spain and United States.
Table 3 Relationship between public travel and pollutants in study cities (The numbers in parentheses on the right side of the fitting equation were the values of R².)
City Type Fitting formula (NO2) Fitting formula (O3)
Madrid Private Y= − 0.0064*exp(− x/0.19) + 0.020 (0.69) Y = 0.053*exp(− x/0.25) − 0.41 (0.86)
Public Y= − 2.79*exp(− x/0.12) + 0.0062 (0.67) Y = 0.0022*exp(− x/0.14) − 0.27 (0.87)
Barcelona Private Y= − 0.081*exp(− x/0.37) + 0.32 (0.75) Y = 0.0062*exp(− x/0.18) + 0.0034 (0.60)
Public Y= − 0.11*exp(− x/0.45) + 0.25 (0.71) Y = 0.056*exp(− x/0.35) − 0.041 (0.56)
Los Angeles Private Y= − 0.0042*exp(− x/0.15) − 0.38 (0.43) Y = 0.79*exp(− x/0.88) − 1.2 (0.54)
Public Y= − 4.04*exp(− x/0.086) − 0.36 (0.60) Y = 0.63*exp(− x/0.75) − 0.9 (0.51)
Providence Private Y= − 0.16*exp(− x/0.47) − 0.013 (0.44) Y = 0.33*exp(− x/1.1) − 0.18 (0.63)
Public Y= − 0.045*exp(− x/0.34) − 0.13 (0.42) Y = 0.14*exp(− x/0.75) + 0.019 (0.57)
The results of fitting all data points of the study cities are shown in the Fig. 10. Combining all the data of four cities, although the goodness of fit was slightly lower than when fitting each city separately, the relationship between traffic travel volume and pollutant concentration still conformed to an exponential function [R² (Private):0.42 (NO2), 0.64 (O3); R² (Public):0.47 (NO2), 0.69 (O3)]. As the travel volume decreased, the NO2 concentration decreased and the O3 concentration increased. This shows that this exponential relationship is widely applicable between traffic and pollutant concentrations, and this conclusion can provide a reference for emission reduction control of urban traffic pollutants.
Fig. 10 Relationship between traffic and pollutants in all study cities
Correlation between travel volume and air quality during recovery period
Figure 11 shows the recovery curves of public and private traffic volumes for the study cities during the 2020 COVID-19 pandemic (as described in Sect. 2.3, when the system performance recovers to more than 95% of the system performance before the disturbance event, it can be considered that the system has been recovered, and the research period of each city was selected accordingly). Before the outbreak of COVID-19, the periodicity of traffic volumes in various cities were observed. After the implementation of the lockdown policies, travel volumes dropped sharply and then remained low for a while before slowly recovering. The decline in the public traffic volume was even greater. The recovery process for private transport in Madrid, Barcelona, and Los Angeles continued until mid-to-late June 2020 and in Providence until late May 2020. The lockdown in Providence lasted for a shorter period than that in the other three cities, which led to differences in travel volumes during recovery times. The recovery process for private transport lasted approximately four months, whereas the recovery for public transport took longer. Figure 11 also shows the system resilience of private transport and public transport in each city [Madrid:0.53 (private), 0.40 (public); Barcelona:0.51 (private), 0.44 (public); Los Angeles:0.71 (private), 0.50 (public); Providence:0.76 (private), 0.56 (public)]. The system resilience of private transport in the study cities was higher than that of public transport. Public transport was more vulnerable to policies and took longer to recover, and during the COVID-19 pandemic, residents might have been more inclined to choose private transport to avoid gatherings.
Fig. 11 Travel volume recovery curves for study cities a Madrid, b Barcelona, c Los Angles and d Providence
Due to the large time span between the recovery stage and the pre-lockdown period, to avoid seasonal differences in pollutants, the pollutant concentrations in 2020 were compared with the concentrations for same month in 2019 when studying the recovery process of pollutants. Figure 12 shows a comparison of pollutant (NO2 and O3) concentrations in the study cities in 2019 and 2020. It could be observed that the annual trends of pollutants in these two years were consistent. The dates were divided into intervals based on the time when the lockdown policy was implemented, and the daily average difference in pollutant concentrations between 2020 and 2019 in the study cities in each interval was calculated. After the gradual opening of the lockdown, compared with the lockdown period, the difference between the concentrations of NO2 and O3 in 2020 and 2019 gradually became smaller (phase III) and finally approached that before COVID-19 (phase IV). At this time, it can be considered that the pollutant concentrations had recovered. The pollutant recovery process lasted for approximately one month.
Fig. 12 Pollutant recovery curves for study cities (The number represents the average of the difference in NO2 and O3 concentration between 2020 and 2019 during this phase)
To further explore the relationship between traffic and pollutants, the methodology described in the Sect. 3.3 was also used to explore functional relationships for the recovery stage in Fig. 13. It should be noted that the recovery process only in Madrid are shown in the Fig. 13 to reveal the rising stage clearly and the corresponding results in other cities are shown in appendix file (Figs. S1−S3). During the recovery stage, only the rate of change of public and private traffic volumes in Madrid fitted an exponential relationship with the rate of change in NO2 concentration, and no obvious fitting relationship was observed in other cities. As mentioned earlier, the traffic recovery process in the study cities lasted approximately for four months, while the pollutant recovery only lasted approximately for a month, indicating that the impact on pollutants from traffic reductions caused by lockdown policies was a short-term effect. When the lockdown policy was first implemented, traffic became the main factor affecting the concentration of pollutants, owing to the sudden and substantial reduction in traffic volume. At this stage, there is an exponential relationship between traffic volume and pollutant concentrations. With time, other activities of the residents gradually resumed, and the meteorological conditions began to change. At this time, traffic may no longer be the most important factor affecting the pollutant concentrations. Therefore, during the recovery stage, it was reasonable to conclude that there was no obvious functional relationship between the traffic volume and pollutant concentrations.
Fig. 13 Relationship between travel and pollutants in Madrid (rising stage)
Discussion
Using real-world data collected during the COVID-19 period, this study investigated the relationships between traffic and pollutants and compared their recovery processes. Based on the original data, we discovered that the traffic volume and NO2 concentration decreased, while the O3 concentration increased during the lockdown. The RDD method was used to verified that these changes were both caused by the lockdown policy. In previous studies, Wu et al. (Wu et al. 2021) used the air pollution data at roadside monitoring stations to confirm that traffic had a significant effect on pollutants while Munir et al. (Munir et al. 2021) verified a negative correlation between traffic and NO2, but did not specify the relationship. Different to them, we determined an exponential function to represent the relationships between traffic and pollutants (O3, NO2). NO2 concentration decreased exponentially and the O3 concentration increased exponentially as traffic volume decreased. When the change in traffic volume was greater than 40%, the change in pollutant concentration was evident, providing quantitative data for determining the relationship between traffic and pollutants. Additionally, we used the data during the lockdown to explore the recovery process for pollutants and traffic. The recovery process of the transportation system showed clear regularity. Specially, the system resilience of public transportation was greater than that of private transportation. In contrast to it, the recovery of pollutants was less predictable. These findings contributed to a greater comprehension of the relationship between transportation and pollutants.
Currently, many cities have formulated diverse traffic restriction policies in an effort to reduce traffic congestion and improve air quality. This study offered some insights from the standpoint of pollution reduction, and the conclusions could be applied to estimate the extent to which traffic restriction policies would simultaneously reduce pollution. In addition, the promotion of public transportation would reduce the use of private vehicles. When formulating policies to promote public transportation, the conclusions of this study would also offer suggestions for reducing pollution. Finally, the conclusions obtained in this study showed good consistency in the four studied cities. Although the parameters of the conclusions drawn in this study might be different in different cities due to different city scales and development levels, the analyzed procedure we proposed could be well generalized to other cities around the world.
Of course, this study still has some limitations. Data from the ground monitoring stations used in this study were monitored at fixed locations. The use of data from more monitoring locations may lead to more accurate conclusions. In the future, mobile monitoring data can be considered for more analysis. For example, the data from Sentinel-5 TROPMI remote sensing data is also an excellent choice for studying this problem (Bauwens et al. 2020; Shami et al. 2022). It can be considered to combine remote sensing data with ground data to more accurately assess the impact of lockdown policies on pollutants.
Conclusion
This study explored the changes in traffic volumes and air pollution in response to the COVID-19 lockdown in four metropolitan areas in Spain and the United States. A regression discontinuity design model was used to reveal the impact of the traffic restriction policy during the COVID-19 pandemic on traffic and air pollutants. The correlation between them was analyzed to determine the extent to which traffic restriction policies can improve air quality. Finally, the recovery processes for traffic and air pollutants were compared. The main conclusions are summarized as follows.
First, the COVID-19 lockdown policies were confirmed to lead to an obvious decrease in traffic volume and an evident change in NO2 and O3 concentrations. Both the public and private traffic volumes in the study cities decreased from 60 to 90%. Public transport was more susceptible to being affected by the lockdown policy than private transport was. In addition, the NO2 concentration maximally decreased by 50%, while the O3 concentration maximally increased by 40%.
Second, NO2 and O3 concentrations were verified to exhibit exponential variations as the traffic volume linearly decreased. In particular, NO2 concentration decreased exponentially, whereas O3 concentration increased exponentially. Additionally, both pollutants were found to vary smoothly at a low decrease in traffic volume but showed sharp variations when the traffic volume was reduced by more than 40%.
Third, the resilience of traffic volume and air pollutants was analyzed and compared through the investigation of their recovery processes during the post-lockdown period. The traffic volume was confirmed to recover to normal situations within four months and showed good resilience. In contrast to it, the air pollutant were found to recover randomly, implying that the variations of air pollutants are complicated and the resilience of the recovery process was weak.
In summary, these conclusions provide a reference for urban pollutant control using vehicle restrictions. The exponential relationship between traffic and pollutant concentrations could provide city managers with a better understanding of the quantitative relationship between traffic changes and pollutant concentrations and provide suggestions for how to control traffic to improve air quality.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 290 KB)
Author contributions
Si-qing Xu and Hong-Di He: research design; Ming-ke Yang: data collection; Xin-hang Zhu and Cui-lin Wu: data analysis; Zhong-ren Peng and Yuya Sasaki: results interpretation; Si-qing Xu: draft manuscript; Kenji Doi and Shinji Shimojo: revised manuscript. All authors reviewed the results and approved the final version of the manuscript.
Funding
This work was supported by the National Natural Science Foundation of China (No.12072195) and the National Planning Office of Philosophy and Social Science (No. 16ZDA048).
Declarations
Conflict of interest
The authors declare that there is no conflict of interests regarding the publication of this article.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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SN Bus Econ
SN Bus Econ
Sn Business & Economics
2662-9399
Springer International Publishing Cham
384
10.1007/s43546-022-00384-2
Review
Predicting inflation component drivers in Nigeria: a stacked ensemble approach
http://orcid.org/0000-0002-8201-4072
Akande Emmanuel O. [email protected]
1
Akanni Elijah O. [email protected]
2
Taiwo Oyedamola F. [email protected]
2
Joshua Jeremiah D. [email protected]
2
Anthony Abel [email protected]
2
1 CAPE Economic Research and Consulting, Lagos, Nigeria
2 Central Bank of Nigeria, Abuja, Nigeria
9 12 2022
2023
3 1 97 1 2022
22 11 2022
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Our study examined the disaggregation of inflation components in Nigeria using the stacked ensemble approach, a machine learning algorithm capable of compensating the weakness of an ensemble and a base learner with the strength of another. This approach gives flexibility of a synergistic performance of stacking each base learner and produces a formidable model that yields a high level of accuracy and predictive ability. We analyzed the test data, out-of-sample, and our analyses reveals a robust inflation prediction results. In particular, we show that food CPI is the most important driver for headline urban, and rural inflation while bread and cereals is the most important driver for food inflation in Nigeria. Also, biscuits, agric rice, garri white were found to be among the top main drivers of bread and cereal inflation. Our study further shows that some components of the CPI baskets that majorly drive inflation were assigned lower weights. Hence, attention to CPI weights only, without recourse to understanding the tipping source, may undermined a successful control of inflation in Nigeria. Tracing and tracking the source of inflation to the least sub-component will help resolve inflation problem.
Supplementary Information
The online version contains supplementary material available at 10.1007/s43546-022-00384-2.
Keywords
Headline inflation
Stacked ensemble
Machine learning
Base learner
JEL Classification
C55
C53
E31
E50
issue-copyright-statement© Springer Nature Switzerland AG 2023
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pmcIntroduction
Intellectual discourse on dynamics and drivers of inflation has received overwhelming attention in modern macroeconomic renditions. This profound attention stems from the deleterious effects associated with persistent and unrestrained inflation in an economy. Beyond this point, inflation is known to generally impose constraints on a spectrum of important macroeconomic variables such as savings, investments, exchange rate, trade balance, wellbeing, competitiveness and other parameters that influence overall economic performance of a country (Ashraf et al. 2013; Mbutor 2013; Shaibu and Osamwoni 2020; Tumala et al. 2017). In Nigeria, inflation has been consistently high and stuck in double digits for many years. Over the period of 2018–2020, headline inflation rate averaged 11.95 in 40 years in spite of swift policy responses from the monetary authority. The persistent increase in inflation makes poverty bite harder in a country where over 82.5 million citizens are already poor. The adverse effect of inflation also includes reduction in purchasing power of fixed income earners and decline in general welfare through misallocation of consumption (Krugman et al. 1982). Therefore, management and control of inflation by the monetary authorities is crucial for the attainment of macroeconomic stability. Hence, Central banks make effort to forecast inflation with utmost precision to ensure the effectiveness of monetary policy (Doguwa and Alade 2013).
Existing studies have adopted several short term forecasting models to predict inflation trend. These models vary from multivariate (Kelikume and Salami 2014; Gaomab 1998; Capolongo and Pacella 2020) to univariate structural models (Akdogan et al. 2012; Junttila and Korhonen 2011; Pufnik 2006). Specific examples include the augmented Philips curve models (Greon et al. 2013; Stock and Watson 2008) and the random walk models. These models are, however, not without their shortcomings. They suffer from overfitting problem which results from low dimensionality1. Overfitting occurs when a complex model fits trained data but fails to fit the test data thereby weakening the predictive power of the model. An overfitted model yields high prediction error when forecasting outside the sample. For this reason, many time series models are less robust and yields less accurate inflation forecasts. Machine Learning provides an alternative approach that resolves the overfitting problem. Solving overfitting problems have been a major breakthrough in Machine Learning (ML) algorithms and it has been used to forecast inflation with high accuracy. To rectify overfitting, predictors are pre-selected using some theoretical construct by fine tuning the parameter of the training sample, this is called ‘hyperparameters’ method.
Recently, some ML models, such as Principal Components Analysis, random forest, LASSO regression (Least Absolute Shrinkage and Selection Operator), Decision Trees etc, have been used to forecast inflation (see details in (Baybuza 2018) and (Medeiros et al. 2019)). In spite of numerous advantages of using ML for analyzing and forecasting inflation, a single model or an ensemble approach may be insufficient in addressing the variance-bias problem associated with ML algorithms.Stack ensemble approach becomes sought after to address the problem. It involves combining the prediction results of selected ML models, through a meta-learner to make final predictions. A Meta learner is selected by comparing its loss function estimated by the Root Mean Squared Errors (RMSE) with that of other base learners. Through this,the weaknesses of each learners are eliminated and their strengthens are harnessed to produce a more accurate predictions. Capistran et al. (2010) paper shows that the best forecasts are obtained by combining individual models to use the information contained in them.
Inflation forecast studies have been conducted in isolation from inflation drivers and many of the studies have focused more on macroeconomic variables. In this study, the two issues - first is the identification of CPI-inflation drivers and second is the forecast using the drivers - will be addressed in Nigeria economic context using stacked ensemble approach. To the best of our knowledge, this study is the first to adopt stacked ensemble approach to track the sources of inflation by identifying its drivers and then use the same drivers to predict the inflation. To do this, we disaggregate inflation data into different components and conduct a comparative analysis of the base learners by stacking each base learner into ensemble model to understand the inflation framework in Nigeria. In addition, we trace the sources of inflation by identifying the main drivers of each inflation component. Finally, we predict the test data of each inflation components and as well forecast it over twenty-five months. Two ensembles were trained, namely Random Forest (RF) and Gradient Boosting Machines (GBMs) while Generalized Linear Models (GLM) served as base learners. Each of this base learners were trained to obtain the meta learner for our stacked ensemble and used to predict inflation drivers for Nigeria, an approach that is peculiar to this study. Our results are robust when compared with the baseline model, the two individual ensembles, and the GLM.
The study will be useful to economists and other policy makers as it proposes the use of machine learning for inflation forecasting in Nigeria. The paper also offers procedural insight needed to explore this better approach. Finally, the study will help the government direct policy to specific components of CPI that exacerbate inflation. In particular, food CPI has been assigned a higher weight among inflation components but the specific item in the basket of food CPI that is responsible for the higher weights can easily be tracked. Hence, policy could be directed to that specific component instead of the entire food CPI basket. The rest of the paper is structured as follows; “Literature review” contains a brief review of theoretical and empirical literature. “Methods” contains discussion of the methods while the subsequent section presents the “Results” of the study. Finally, “Conclusion and policy recommendation” contains the summary, conclusion and policy recommendations of the study.
Literature review
The theoretical underpinnings of inflation drivers are embodied by the expositions of orthodox economists from the Classical to Neo-Structuralist. Leading monetarists like (Brunner and Meltzer 1976; Friedman 1956, 1970; Parking 1975) postulated that the rate of growth and the change in money supply explain the rate of inflation and its acceleration. Hence, money supply is the main driver of inflation. Keynesians viewed inflation as a phenomenon driven by excessive spending relative to available goods and services at full employment. Thus, money supply in excess of potential output will, in effect, drive inflation (Javed et al. 2010). Structuralists were persuaded that inflation is mainly driven by imbalances in an economy, especially in developing countries. Such imbalances include infrastructural bottlenecks, emergence of monopolistic and oligopolistic market structure (market imperfection), agricultural bottlenecks, government budget constraints, income elasticities, distortion in government policies and exchange rates amongst others (Agnor and Montiel 1996; Kirkpatrick and Nixon 1987). Empirically, a plethora of studies have lent credence to the above theoretical submissions and further identified other key drivers of inflation using various econometric models. Inflation in Nigeria has been found to be driven by money supply, exchange rate, net exports, interest rates, fiscal factors, agro-climatic factor and real output (Agnor et al. 2018; Asogu 1991; Bayo 2011; Fakiyesi 1996; Imimole and Enoma 2011; Moser 1995; Odusanya and Atanda 2010). Other determinants include petroleum prices, expected inflation, lagged CPI and real exchange rate (Olubusoye and Oyaromade 2008).
In an empirical attempt to forecast inflation in Bangladesh, Younus and Roy (2016) employed an Unrestricted VAR Model. Inflation rate was modelled alongside other macroeconomic variables which included broad money (M2), exchange rate, private sector credit, interest rates, global food price real GDP growth rate. The result suggested that money supply (M2) and interest rates were the most relevant variables for predicting inflation in Bangladesh. Bjornland et al. (2009) used a combination of six models, namely; Vector Autoregressive (VAR), Bayesian Vector Autoregressive (BVAR), Autoregressive Integrated Moving Average (ARIMA), Error Correction Model (ECM), Factor Model and Dynamic Stochastic General Equilibrium (DSGE) models to forecast inflation in Norway. Among the variables adopted were interest rates, inflation, exchange rate, oil price, investment growth and employment rates for the period spanning 1987–1998. The result showed that model combination approach yield better forecast than individual models.
Review of inflation disaggregation
Following the disaggregation of inflation components, some scholars have argued that core inflation can efficiently predict headline inflation (Crone et al. 2013; Le Bihan and Sedillot 2000; Tekatli 2010; Freeman 1998). Thus, core inflation is a good measure for capturing trends in headline inflation and this justifies why it is preferred as a guide for monetary policy (Miskin 2007). Yet, there are counter arguments against the use of core inflation to predict total inflation. First of such arguments is that core inflation loses its predictive power as a result of removal of items on which people spend most of their income. Also, changes in energy consumption caused by changes in price will eventually exert pressure on all other prices in the economy (Bullard 2011). Pincheira et al. (2016) empirically investigated the ability of core inflation to forecast headline inflation in 33 countries using in-sample and out-of-sample analysis. The in-sample analysis confirmed predictability from core to headline while out-of-sample analysis showed that core predicted headline in about two-third of countries in the study. Nevertheless, in literature, there are other approaches of predicting inflation. In addition to previous studies, Stock and Watson (2008) argued that forecasting and measuring inflation is a difficult task but it is critical for effective monetary implementation . This task generally requires the disaggregation of inflation into its transitory and persistent components (Atuk and Ozmen 2009). Headline inflation, which is the transitory component, poses a challenge in the process of determining its underlying trend. This is due to its susceptibility to shocks which are beyond the control of policy makers(Odo et al. 2016; Roger 1998). This shortcoming necessitated the call for exclusion of items driving volatility in general price level (Bryan and Cecchetti 1994; Cecchetti and Wiggins 1997) thereby birthing the concept of Core inflation which is regarded as a persistent component of inflation. Core inflation is further disaggregated into Core 1 and Core 2, where Core 1 excludes food beverages and tobacco Core 2 excludes food, beverage, tobacco, energy prices, and mortgage interest from the food basket. This exclusion is based on the fact that, historically, food and energy have proven to be highly volatile (Pincheira et al. 2016). In this study, we simplified each CPI basket to its main components and focus on the important variables which can provide more information on the main source of inflation. This approach provides a clear direction to addressing persistent inflation in Nigeria.
Review of methods of inflation forecasting
The literature is rich with empirical studies on inflation forecasting. These studies adopt diverse models and methods across countries in their quest to attain higher precision in forecasting inflation. Specifically, (Stock and Watson 2007) and (Gurkaynak et al. 2005) employed unobserved components stochastic volatility model. Cogley and Sbordone (2008) used the New Keynesian Model while De-Graeve et al. (2009) adopted on New Keynesian Phillips curve (NKPC) approach. Medium-scale macro-finance Dynamic Stochastic General Equilibrium (DSGE) Model was used by Gonzales et al. (2011); penalised likelihood by Dotsey et al. (2017), while Clark and Doh (2011) used Bayesian methods to forecast inflation. An extensive review on literature has been done by (Faust and Wright 2013). Since early 1960s, Phillips curve models have been widely used for inflation forecasting. But in the past two decades, empirical studies began to cast shadow on the predictive power of Phillips curve models as they could not outperform the naive method in terms of precision (Dotsey et al. 2017). The authors found that forecasts from Phillips curve models tend to be unconditionally inferior to those of univariate forecasting models. In fact, Atkeson and Ohanian (2001), found that Phillips-curve models are less accurate than naive models in forecasting inflation. There is evidence in literature that Phillips curve alone is not sufficient to accurately forecast inflation. Stock and Watson (1999) proposed the introduction of supply side variables for better performance. Univariate and multivariate models have also been employed for inflation forecasting over the years. Univariate models (based on ARIMA and ARCH models) are usually adopted for short term forecasting while multivariate models (VAR and cointegration) are famous for long term forecasting (Fawad et al. 2015). Even the renowned Phillips curve are mostly based on VAR models. These models have been used to analyze and forecast inflation in several countries. For instance, Pufnik and K (2006) used univariate model to forecast Croatia’s inflation while Gaomab (1998) used multivariate model to forecast inflation in Namibia. Akdogan et al. (2012) adopted univariate ARIMA model in Turkey and compared results obtained with those of other models and found that models with more economic information produces better forecast. Kelikume and Salami (2014) employed both univariate and multivariate models to forecast inflation in Nigeria and found that VAR model had smaller errors in terms of the minimum square error and it is the closest approximate to current inflation in Nigeria. It has however been argued that univariate models are mere scientific guesses with some confidence interval and are poor in predicting turning points. Such models therefore, provide weak forecast when volatile and high frequency data are involved (Meyler et al. 1998). Multivariate models on the other hand have been criticized for being too complex and prone to misspecification errors and overfitting problems.
In forecasting monthly inflation rate in Nigeria, the findings of Amadi et al. (2013) suggested that SARIMA was the best model to adopt. Similarly, Doguwa and Alade (2013) proposed four short term forecasting models using SARIMA and SARIMAX processes. The models incorporated some endogenous variables which include PMS price, government expenditure, net credit to central government, average monthly rainfall in cereals producing north central zone, nominal Bureau-de-change exchange rate, broad money supply (M2), official nominal exchange rate, reserve money, credit to private sector and average monthly rainfall in vegetables producing southern zone. Based on the result, the paper recommended that all-item CPI, estimated using SARIMAX model, should be adopted for short-term forecasting of headline inflation in Nigeria. It also suggested that SARIMA model was the best for forecasting core inflation in Nigeria. Omekara et al. (2013) employed Periodogram and Fourier series analysis to model Nigerian monthly inflation rates. The forecasts were found to be accurate and reliable for Nigeria. Okafor and Shaibu (2013) adopted ARIMA model in line with Box Jenkins (1976) to forecast inflation using CPI data from 1981 to 2010. The paper found ARIMA (2,2,3) as the most appropriate for the country. On the contrary, Yemitan and Shittu (2015) applied Kalman filter technique and found it more efficient than Box Jenkins. Our methods in identifying main drivers of inflation includes the ARIMA, which is the baseline, the cross validated and hyper-parameter tuning of the ensembles, RF and GBM, and the cross validated and hyper-parameter tuning of the GLM. All these methods address the fundamental questions of what the best model should be. The weakness of each ensemble model necessitated the adoption of cross validated stacked ensemble approach to enhance predictability of the model. This is one of the strengths of this study. We found that the optimal hyper-parameters we obtained provides the best predictive accuracy. In addition, we confirmed that the base learners have high variability and are uncorrelated.
Review of inflation drivers and its forecasts using machine learning
Inflation narratives have been influenced by studies exploring its drivers and future trajectory through forecasts. In many cases, these two strands of literature has been explored separately. In exploring its drivers, just like this study, few empirical studies have identified inflation drivers using machine learning algorithm. Benalal et al. (2004) investigates whether the forecast of the Harmonized Index of Consumer Prices (HICP) components improve upon the forecast of overall HICP. Giannone et al. (2014) constructed Bayesian Vector Autoregressive model (BVAR) that captures the inter-relationships between the main components of the HICP and their determinants in the Euro area while Oren et al. (2021) used the Recurrent Neural Networks (RNNs) for predicting disaggregated inflation components of the Consumer Price Index (CPI). In addition to the identification of inflation drivers and its components, empirical studies on inflation forecast using other macroeconomic variables have also received a considerable attention. Inoue and Lutz (2008) adopted bagging, factor models, and other linear shrinkage estimators to forecast inflation in the US. Similarly, Medeiros and Mendesy (2016) employ adaptive LASSO to forecast US inflation while Medeiros et al. (2019) show that LASSO and Random forest are more accurate forecasts than the standard benchmarks. Other ML methods that have been used for inflation forecasting include heuristic and variable selection method (Kapetanios et al. (2016), shrinkage and complete subset regression (CSR) method (Gracia et al. 2017). Baybuza (2018) in particular applied several ML methods such as RF, Least Absolute Shrinkage and Selection Operator (LASSO), Ridge, Elastic Net and Boosting to forecast inflation in Russia. Findings of the study confirm the possibility of forecasting inflation with a higher level of precision compared to other traditional models like Random Walk and Autoregression.
There are four important features of ML; nonlinearities, regularization, cross-validation and alternative loss function, out of which nonlinearity feature was found to be the true game changer for macroeconomic forecasting (Coulombe et al. 2019). Gracia et al. (2017) used ML methods to forecast inflation in Brazil and found that LASSO model is best for shorter forecast. Similarly, Gu et al. (2018) found significant improvement in the prediction of out-of-sample (test data) stock return using ML methods on 30,000 samples, over 900 baseline signals and hundreds of predictors. Medeiros et al. (2019) in forecasting US inflation found that ML models, with a large number of covariates, are systematically more accurate than the benchmarks for several forecasting horizons both in the 1990s and the 2000s. The ML method that deserves more attention is the RF, which dominated all other models in several cases. Malhotra and Maloob (2017) employed gradient Boosted Regression Trees (BRT) technique of ML to analyzed inflation in India and submitted that all predictor variables used in the model were significant in predicting food inflation in India. In addition to what has been written, Onimode et al. (2015) used the artificial neural networks (ANN) and found that neural network is more efficient than univariate autoregressive models in forecasting inflation up to four quarters ahead.
This study differs from other studies as it proposes the application of a robust approach in identifying inflation drivers and providing a forecast for its components. In essence, we obtained the drivers to identify the source of each CPI-inflation components, predict the out-of-sample CPI-inflation data, and forecast inflation in twenty-five horizons.
Stylised facts on inflation trends in Nigeria (1973–2020)
Since early 1970s, the country has been experiencing sequence of inflation episodes. Thus, inflation has remained one of the major macroeconomic problems in Nigeria. The first oil boom of 1973 brought about a sudden upsurge in government revenue and as a result, government began to embark on massive developmental projects across the country as part of its reconstruction efforts after the civil war (Asekunowo 2016). Resultantly, there was a sudden spike in money stock in the economy without a corresponding increase in production of goods and services. Inflationary pressure was further aggravated by the enormous increase in minimum wage following the recommendation of the Udoji committee in 1974. Inflation rate in Nigeria soared to an average of 33.7% in 1975. As inflationary pressure continued to mount, policy makers came under intense pressure to respond appropriately. One of the policy responses was the change in monetary policy framework from exchange rate targeting to monetary targeting in 1974. Other policy measures taken include credit expansion to productive sector of the economy and the liberalisation of import which encouraged huge importation of cheaper goods. Consequently, by 1979, inflation rate had fallen to 11.8% (Nse et al. 2018). Again, inflationary pressure began to mount up in the early 80s as the country had become import dependent with attendant balance of payment problems. By 1984, inflation had risen to 41.2%, which necessitated the devaluation of the naira and the adoption of price control measures to bring down inflation to 5.5% and 5.4% in 1985 and 1986 respectively. Inflation in Nigeria reached its all-time peak in 1995 when it rose to 79.9%. By 1999 it had fallen to 6.6% consequent upon the adoption of effective monetary, fiscal and exchange rate policy.
Over the period 2003 to 2005, inflation rate in Nigeria averaged 15.7% owing to increasing budget deficits. However, following the implementation of sound monetary and fiscal policies, coupled with robust agricultural harvests, inflation rate declined to an average low of 5.4% by 2007 (Doguwa 2012). The advent of the Global Financial Crisis resulted in another spike in inflation rate to 12.6% by 2009. The rate of inflation remained high at over 12% in 2012 owing to non-monetary factors such as severe flooding in some regions. By 2013, CPI inflation had declined to 8% but could not be sustained as a result of oil price shock in the period 2014–2018. Again, recovery in oil price led to a decrease in inflation rate to an average of 11.4% in 2019.
A disaggregated analysis of inflation trend in Nigeria showed that there is a co-movement in headline, core and food components for most years except for periods between 1998–1999 and 2001–2004 (See Fig. 1)2.Fig. 1 Quarterly headline, core, and food inflation
Most recently, there has been a persistent upward movement in the three inflation components in Nigeria attributed largely to the adverse effect of COVID-19 pandemic and other policies like the proposed removal of fuel subsidy which aimed at improving the fiscal position of government. Besides, the recent hike in electricity tariff is expected to exacerbate inflation problem. According to National Bureau of Statistics(NBS), Nigeria’s headline and core inflation for August 2020 stood at 13.22% and 10.52% respectively which was the highest in 29 months since March 2018 (13.24%)3.Fig. 2 Headline and core inflation in Nigeria (Jan–Aug 2020)
Inflation persistence is the tendency for price shocks to push the inflation rate away from its steady state-including an inflation target-for a prolonged period (Roache 2013). In other words, it is a measure of tendency of inflation rate to retain its current status. The mandate of most monetary authorities is to reduce the extent to which inflation persists overtime. Recent empirical study of inflation persistence in Nigeria by Tule et al. (2020), using fractional cointegration VAR model, revealed evidence of high inflation persistence with a lower trend after the global financial crisis. Inflation data of Nigeria from 1973 to 2013 shows that out of forty-seven (47) year observations, headline inflation has hovered or persisted around double digits for 37 years. This is as shown in Fig. 3 below.Fig. 3 Annual headline inflation in Nigeria (1970–2020)
Comparatively, recent inflation trends in Nigeria, specifically from 2013, have shown that there is no significant disparity in headline, rural and urban inflation. Though inflation appears to follow the same upward trend in both urban and rural areas, it is lower in rural region compared to the urban. This is due to the differences in lifestyle and consumption pattern between the two regions. This suggests that inflation in Nigeria is a macroeconomic challenge whose brunt is borne by all citizens regardless of location. Hence the need for more empirical studies to bring this phenomenon to a level that is economically sustainable and politically acceptable. More information in Fig. 4.Fig. 4 Inflation—headline, urban and rural
The importance of applying ML to Nigeria’s inflation data at both economic and statistical standpoint cannot be overemphasized. From the economic perspective, ML provides a pathway to where policy about inflation control should be directed. Although, all inflation studies conducted in Nigeria used macroeconomic variables and most of the studies excluded CPI components. This approach will undermine the main component of CPI basket that drives inflation. From statistical standpoint, many econometric models that include inflation studies in Nigeria suffer from ‘curse dimensionality’ problem, a statistical problem that emanates from organizing and analyzing data in high-dimensional-spaces. This problem limits the forecast ability of many of the extant models, this is because information extracted from the few predictors may not be sufficient to forecast inflation at a higher level of accuracy. Therefore, ML algorithms offer the flexibility of fine-tuning predictor parameters in the event of model overfitting. This flexibility enriches ML models and enhances the sophistication of providing a better statistical relevance to the existing tools of analysis in Nigeria. In addition, ML approach is not entirely a standalone algorithm, as other time series model might be, it offers many options where models can be stacked or unstacked.
Methods
Consider the following forecasting model,1 yt+h=T(Xt)+Ut+h
where yt+h is the variable at time t+h, Xt=(X1t,.....,XnTt)′ is nT-vector of explanatory variables, Ut is a difference process of martingale. The objective is to estimate the target function T(Xt). The model and forecast performance are to assess the predictive accuracy through a loss function. The loss function is estimated using the Root Mean Square Error(RMSE) and it is calculated thus,2 RMSE=1T-To-1∗∑t=T0T(yt-yt^)2
The objective here is to minimize the square error. Though other reported loss functions for base learners include Mean Squared Error (MSE), Mean Absolute Error (MSE) and Root Mean Square Logarithmic Error (RMSLE), the RMSE remains the commonly used loss function for both classification and regression models. We compare the traditional inflation model, Autoregression Integrated Moving Average (ARIMA) as a baseline model4, with other base learners.
Predictions are obtained from the test data and the prediction from the training data are used to check for model overfitting and performance.
ARIMA model
ARIMA is a univariate model that is fitted with Box-Jenkins method and it is specified by three order parameters(p, d, q). p is the Auto-Regressive (AR) parameter that indicates the number of lags to include in the model. The equation below follows AR(2) process;3 Xt=α+θ1Xt-1+θ2Xt-2+ϵt
Where Xt is time series, θ1 and θ2 are coefficients of the AR terms in period one and two. The d component is the degree of differencing that is integrated in the order (I(d)). It is used for stabilizing data when the assumption of stationarity fails. In other words, it also represents the number of times a time series must be differenced to induce stationarity. The q component is the Moving Average (MA) that makes up the non-seasonal aspect of ARIMA model. The MA(q) represents the combination of the previous error terms of the model. q is the order of previous error term to include in the model. The example of MA(2) is given below,4 Xt=β+ϕ1ϵt-1+ϕ2ϵt-2+ϵt
Where ϕ1 and ϕ2 are coefficients of the MA terms in period one and two. Therefore, the main ARIMA model is given by combining equation 3.3 and 3.4. The time series Xt is stationary if d=0 else ARIMA(p,d,q) reduces to ARMA(p,q)5 Xt=ϕ0+∑i=1pθiXt-1-∑i=1qϕiϵt-i+ϵt
This study uses Akaike Information Criterion(AIC) and Bayesian Information Criterion (BIC) for model selection. The two criteria is conjecture on the extent to which the fitted values approximates the true value of the model. Furthermore, AIC and BIC are statistic functions that penalise the goodness of fit used in estimating the statistical model. This penalty prevents model overfitting however, as the number of estimated parameters increase, the penalty increases.
Ensemble models-base learners
In this study, we adopt three relevant base learners in our ensemble catalogue, namely, Random Forest (RF), Gradient Boosting Machines (GBMs) and Generalized Linear Models (GLM). New predictions are made by combining the predictions from the individual base models that make up the ensemble. RF and GBM are all ensemble models because each uses decision trees as its base-learner. While RF is applicable where data has high variance and low bias, GBM is useful where there is low variance and high bias. Hence, the strength of one ensemble is the weakness of the other and vice-versa. Random Forest (RF): RF is based on an algorithm in which decision trees are bootstrapped on the original training data then, new data are formed and new predictions are made by averaging all predicted values from the decision trees5. Constructing decision trees in stages help to divide the entire sample space by breaking a quality function Q(X, 1, p), into two sub-samples. The first sample is given as X1(i,p)={X|Xi≤p} while the second sample is given as X2(i,p)={X|Xi>p}. Each sub-sample is therefore, broken down iteratively and stops when individual criterion is specified and n leaf corresponding to each to each sub-sample is created (Baybuza 2018). While the tree holds the regression solutions that breaks down the sub-sample into two, the leaf nodes hold the quality function, which is the predicated values of the explanatory variables. The quality function is given below; 6 Q(X,i,P)=H(X)-|X1||X|H(X1)-|X2||X|H(X2)
Where H(X) is the information criterion and it explains homogeneity of the explanatory variables in the sub-sample. According to Baybuza (2018), The purpose is to maximize the homogeneous characteristics of the explanatory variables while minimizing the spread prevalent in the explanatory variables so that; 7 H(X)=1|X|∑(yi-1|X|∑yj)2
Along this process, RF6 is not only capable of reducing variance and minimize overfitting but its ability to introduce a more random component into the tree building process gives the result of the ensemble model a robust predictive performance.
For example we can fit L independent weak learners, one for each sub-sample;8 w1(.),w2(.),w3(.).......wL(.)
and then aggregate them into some kind of averaging process to get an ensemble model with a lower variance. For example, we can define our strong model such that9 sL(.)=1L∑l=1Lwl(.)
Important variable are identified and measured based on the sum of the reduction in the loss function (e.g., SSE) attributed to each variable at each split in a given tree. Therefore, for bagged decision trees, we compute the sum of the reduction of the loss function across all splits and then aggregate this measure across all trees for each feature(variable). The features with the largest average decrease in loss functions are considered most important (Fisher et al. 2018). The important driver for each of our response variable in RF is identified through a permutation-based (PB). Permutation approach captures the most important explanatory variable. This is obtained by calculating the increase in the model’s prediction error after permuting the variable (Bradley and Brandon 2020). In the PB7 approach (seeBradley and Brandon 2020 for more detail on permutation), the out-of-the bag(OOB) sample for each tree is passed down the tree and the prediction accuracy is obtained, the values of each explanatory variable are randomly permuted and the accuracy is again computed. Due to random shuffling of explanatory values, there is a decrease in the level of accuracy; this decrease is then averaged over all trees for each explanatory variable. The explanatory variable with the largest average decrease in accuracy is considered most important (Bradley and Brandon 2020; Breiman 2001).
To increase the predictive strength and avoid tree correlation and many noisy predictors, we increase the hyperparameter8, mtry, and number of tree, ntree, to 1000 each while we decrease the tree depth to maximum of 30 and also use 10-fold cross validation for the RF. 2. Gradient Boosting Machines (GBMs): GBMs, which was first proposed by Friedman (2000), build an ensemble of shallow trees in sequence with the aim of learning each tree and improving on the previous errors. The shallow trees are weak learners and thus, produce weak predictions but they can be “boosted” to produce a strong and powerful ensemble models(Bradley and Brandon 2020). Recall that new predictions are made by combining the predictions from the individual base models that make up the ensemble (e.g., by averaging in regression). Because RF is more effectively applicable to models with high variance and low bias, averaging prediction across decision trees (as in RF), reduces variance of models while boosting work effectively on models with high bias and low variance (Greenwell et al. 2018). In addition, while RF trained the base models independently, GBMs do not. Baybuza (2018) describes the algorithm as follows; The first model is trained on the 100% of the sample such that; 10 b1(x)=argminb∑i=1l(b(xi)-yi)2
The ensemble GBM algorithm leads to the first trained base ensemble 11 B1(x)=b1(x)
The residuals, which is the difference between the actual value and the predicted value based on the first GBM model are then calculated, such that 12 ei1=yi-B1(xi)
The model below is then trained on the residuals 13 b2(x)=argminb∑i=1l(b(xi)-ei1)2
We also add a new model to the previous algorithm using a ‘step reduction’ relating to a certain co-efficient γ∈(0,1). This method improves the model and avoids model overfitting and a new model is obtained; 14 B2(x)=B1(x)+γb2(x)
We run the process iteratively until the final model, which is given below, is produced. 15 BN(x)=∑i=1Nγi-1bi(x)
The training cycles are completed when the algorithm terminates and the variable of importance measure are obtained in similar procedure as RF.
Unlike RF, our GBM was trained with ntrees=5000, maximum depth9 of 3, minimum rows of 5, learning rate10 of 0.01, and 10-fold cross validation. 3. Generalized Linear Models (GLM): The basic idea behind GLM estimation is to fit a regression model such that the predicted probability, P^(Xi), of our response variable is close as much as possible to the probability of response variable being observed. To yield an estimate equivalent to ordinary linear regression normal, we use “family=gaussian′′, which assumes an errors to be distributed normally. This idea can be fully formalized in a “likelihood function” as follows; 16 l(β0,β1)=P(xi)Πi:y=1(1-P(xi′))Πi′:y′=1
The estimates, β0^ and β1^ are chosen to maximize this likelihood function and the resulting estimates are the predicted probability of the response variable.
Once our regression model is identified we then interpret how the features are influencing the results. Our variable of importance is determined by the magnitude of absolute value of the z-statistics for each coefficient, which is similar in structure to random forest.
Stacked ensemble
Methods based on RF still have significant bias error problems while method based on GBM have variance issues. Hence, stacking these ensembles tend to solve the variance-bias trade off prevalent in machine learning. Stacking involves training a new learning algorithm to combine the predictions of several base learners. First, the base learners are trained using the available training data, then a combiner or meta algorithm, called the super learner, is trained to make a final prediction based on the predictions of the base learners. Super learners will learn an optimal combination of the base learner predictions and will typically perform as well as or better than any of the individual models that make up the stacked ensemble (Bradley and Brandon 2020).
The algorithm is simple but it evolves in three phases; Set up the ensemble Specify a list of L base learners
Specify a meta learning algorithm
Train the ensemble Train each of the L base learners on the training set.
Perform k-fold cross validation (CV) on each of the base learners and collect the cross-validated predictions from each.
The N CV predicted values from each of the L algorithms can be combined to form a new NxL feature matrix 17 n{[p1]....[pL][y]→n{[Z]⏞L[y]
Where p1.....pL are the predicted values, NxL is the Z, and y is the response vector.
Train the meta learning algorithm on y=f(Z). The “ensemble model” consists of the base learning models and the meta learning model, which can then be used to generate predictions on new data.
Predict on new data To generate ensemble predictions, first generate predictions from the base learners.
Feed those predictions into the meta learner to generate the ensemble prediction.
Variable of importance for stacked ensemble: The novel contribution of this study is that it uses permute approach to measure the feature of importance of the meta learner from stacked ensemble. To the best of our knowledge, this is the first study that measure feature of importance directly from the stacked ensemble. After stacking the base learners and selecting the meta learner for the stacked ensemble, we compute the variable of importance score by calculating the percentage increase in the model’s prediction error after feature permutation. We follow the traditional permutation approach of base learners as described in Bradley and Brandon (2020). Following the usual performance degradation for permuting the training data set, we use the difference between the RMSE and the measure obtained after permuting the values of a specific feature in the training data set. The permutation-based variable/feature of importance algorithms for the feature set as hand is derived below; I Compute the RMSE loss function in Eq. (2)
II for variable in the training data set i in {1,2,3....,p} do the following randomize the training data set
apply the stacked ensemble model
estimate RMSE or any loss function
simulate the model using a fraction of the training data set
III set the prediction metrics
IV compute the feature importance by obtaining the difference between the permuted loss and the original loss.
V Sort variable in descending order.
The results will demonstrate the generalizability and interpretability of the stacked ensemble model.
Data description
We use monthly CPI data from June 2010 to April 2020 and then convert it into machine learning data interface which then results in 119 observations. Data was obtained from Nigeria Bureau of Statistics and it was filtered and sub-grouped into headline (Headline), headline less farm produce (core 1), headline less farm produce and energy (core 2). We also grouped our data into urban and rural inflation. The headline, urban, and rural inflation components have 61 variables each, core inflation has 57 variables, and core 2 has 52 variables. We incorporate the h2o object in cloud computing from R data frame into this study to make our ensemble models algorithms more flexible and seamless for prediction. It is common to use ensemble models to forecast inflation with the test data by selecting a forecast range date but we believe that there is a tendency for some data within certain date range to be influenced by external factors and thus, yields the same pattern. For instance, some data in the test data frame within certain date range might fall under recession, consequently, predicting such data may be similar and they are more likely than not yield the same pattern. In our study, we use a split command that randomly selects 70% of the sample as the training data and the remaining 30% as the test data. So, the idea is to predict a randomly selected test data, doing so will eliminate any issues that may likely arise from arbitrarily selecting range of dates for test data. By tradition, in macroeconomic models, variables are transformed into stationary series, in the event that they are non stationary, to avoid spurious results. However, latest study by Baybuza (2018) shows that transformed variables performed poorly for machine learning methods especially, the RF ensemble model. Hence, We follow the same procedure as (Baybuza 2018).
Results
In this section we present our main results for inflation using the CPI data. The first subsection fields the baseline results of the ARIMA model. The time series nature off all the inflation components are capture in this sections. The results include the drift parameters of all the inflation subgroups. The second subsection presents the performance of the base learners and the baseline model. These include analysis of different loss functions for training data set and cross-validation for different base learners and selection of a meta learner for training the stacked ensemble model. The third subsection presents the model performance and predict inflation subgroups. The last subsection deals with variable selection and variable importance for stacked ensemble model. We did this by identifying specific drivers of inflation components and make prediction afterwards.
The baseline results
The results of the ARIMA model is presented in Table 1 below. The results suggest that headline inflation dampens the trend in linear exponential smoothing. This is because the drift parameters has one autoregressive term, two seasonal differences that renders the Headline inflation nonstationary, and one lagged forecast errors in predicting the equation. The headline inflation is also the only feature in the suit of inflation components with only autoregressive term. However, all other components require linear exponential smoothing because they require two differencing to render inflation variable stationary. Similar to headline inflation, other inflation components have one lagged forecast errors for prediction. In a nutshell, headline inflation is different from other components inflation due to its autoregressive term.Table 1 The baseline model
Headline CORE 1 CORE 2 URBAN RURAL
Drift (1,2,1) (0,2,1) (0,2,1) (0,2,1) (0,2,1)
AR(1) 0.2907 – – – –
MA(1) − 0.8995 − 0.8793 − 0.8904 − 0.8719 − 0.8937
σ2 1.916 2.262 2.609 3.269 2.819
AIC 315.56 329.67 321.83 334.26 322.26
BIC 323.03 334.65 326.81 339.07 327.08
log-likelihood − 154.78 − 162.84 − 158.92 − 165.13 − 159.13
The variance of the inflation components are also reported. The results show that headline inflation is less volatile than other components while urban inflation is more persistent than other components.
Performance of the base learner models versus ARIMA
In table 2, we present two loss functions namely; MSE and RMSE for each base learners of our training data set in Table 2 and cross validation11 (see Table 4 in Appendix A for details). Building 1000 trees, with maximum depth of 30 and maximum tries of 10, on 119 observations (see Table 8 in Appendix C for details) with more than 50 predictors, RF appears to have a lower accuracy on the training data when compared with other base learners and and the baseline model. Unlike RF, GLM appears to have a better accuracy than all the base learners and the baseline model. However, we cannot conclude so at this point, since generalization on test data has not been made yet. Since the predictors represent about 40% of the observations, the likelihood of high variation or noisy predictors could have been responsible for accuracy distinctions across base learners. For detail analysis, we built a shallow model with lower trees and lower depth and also adopt a hyperparameter tuning strategy through a grid search, the results did not seems different from our initial experiment without grid search. In our case, higher RMSE in RF or any of the base learners for the training data should not pose a problem, since all the base learners are communed into stack ensemble model and the performance of the stacked model is what matters in the end.Table 2 Different loss functions for base learners and ARIMA model-training data
Headline Core 1 Core 2 Urban Rural
Random Forest MSE 8.3955 5.8182 5.5240 8.2512 6.9754
RMSE 2.8975 2.4121 2.3503 2.8725 2.6411
GBM MSE 2.6738 2.1448 1.8429 3.4532 2.9332
RMSE 1.6352 1.4645 1.3575 1.8583 1.7127
GLM MSE 0.0010 0.0333 0.0900 0.0019 0.0019
RMSE 0.0324 0.1826 0.3000 0.0441 0.0433
ARIMA (Baseline) MSE 1.8322 2.1871 2.000 3.1524 2.7179
RMSE 1.3536 1.4789 1.4144 1.7755 1.6486
Our study further shows that GLM outperforms GBM, RF and the baseline model for all the inflation components; Headline, core 1, core 2, urban, and rural. Our baseline model clearly outperforms GBM in Headline inflation, urban, and rural inflation while GBM has more accuracy than baseline in core 1 and core 2 inflation. A lower RMSE on the training data set for GLM and GBM does not imply that the two base learners generalize on our data pretty well, since we have not yet evaluated each on the test data12. However, this is the first stage of selecting the best meta learner to train the stacked ensemble. Thus, a higher loss functions are not a threat or a problem at the moment and a lower loss functions are not considered perfect as well. We retain RMSE as the sole loss function for this study because it is the most widely use for model evaluation and model predictions.
Although the RMSE of RF is higher than other base learners for training data set in Table 2, we realized that it generalizes better on the test data reported in Appendix A. since the RMSE of its test data is almost the same as the RMSE of its training data set. Whereas, the baseline model clearly overfits in all the inflation components. The performance of the GBM is also not too bad as it generalizes well also on the test data set, especially the performance of GBM on core 2. Though, GLM yielded lower RMSE on the training data set, it does not generalizes well on the test data on all the inflation subgroups in fact, it conspicuously suffers from marginal over-fitting although, it does not pose a serious concern for our stacked ensemble model since the staked ensemble will need to generate its own RMSE that is independent of base learner RMSE. So, obtaining lower RMSE on training data set only for any base learner will not suffice to conclude that it has a higher predictive power or perform well than others except we evaluates such models on the test data. This is the second stage of selecting our meta learner to train the stacked ensemble. The baseline model clearly suffers from overfitting problem despite auto selecting the parameters. The results show that the inflation series is not all linear thus, needs some exponential smoothing.
The results in Table 2 reveals one of the best ways to choose the most performing model for forecasting, these results are obtained from the training data set (in-sample). However, Table 3 depicts the validation results, the test (out-of-sample) CPI data of the stacked ensemble and the ARIMA models. The stacked ensemble model did not only generalizes well on the test data but also outperform the baseline model13. We stacked the ensemble model, using each base learner as a meta learner. In addition, the performance of the models reveal thatTable 3 Model validation of ARIMA and the stacked ensemble
Meta learner Headline Core 1 Core 2 Urban Rural
Stacked Ensemble (R2) 0.94 0.91 0.93 0.92 0.89
Random Forest 2.69 2.30 2.08 3.61 3.39
GBM 4.06 2.52 2.48 3.52 3.31
GLM 2.64 2.41 2.23 2.79 2.58
ARIMA 6.30 4.81 4.42 8.58 7.95
The RMSE are standardized for all our ensembles, the meta learner with the lowest RMSE across the inflation components are chosen for meta learner for our stacked ensemble model. RF as a meta learner performs better than GBM and GLM for headline, core 1, and rural inflation. Whereas, GLM performs better than other base learners for core 2 and urban inflation if it is used as the meta learner for the stacked ensemble model. The model with the lowest bias14 as a meta learner are trained on the stacked ensemble and consequently used in predicting and selecting inflation drivers. The coefficient of determination (R2) of our models validates the superiority of stacked ensemble over the ARIMA model. The R2 of the inflation components, out-of-sample, are at least 90%, hence, the stacked ensemble significantly out perform the ARIMA model both in evaluation, validation, and in performance.
The model performance and prediction of inflation components
The stacked ensemble results presented in table 4 a uses RMSE as the basis for model performance. when we trained the stacked ensemble model with RF, the model performs better on CPI data for headline, core 1, and rural inflation while GLM outperforms other base learners in core 2 and urban inflation. The model prediction and the selection of most important driver, using the test data, for each inflation subgroup absolutely yields the same result when we switched RF for GLM as a meta learner for the stacked ensemble model core 2 inflation. The predictive accuracy of the stacked ensemble on training and test data for all inflation subgroups is presented in Appendix B (Fig. 5).Fig. 5 Model performance actual vs predicted
When we trained each base learner as a meta learner for our stacked ensemble, the RMSEs were standardized and that process changed the potential model selection for our predictions. RF becomes potentially accurate for all inflation subgroups when we generalize it on the test data, except for core 2 and urban inflation. But, criteria for selecting a trained meta learner for stacked ensemble is not limited to how small the RMSE of the training data of each model is, but how well the training data predicts the subgroup inflation. The predicted values of the training inflation data tracks the actual inflation data pretty well. This is not surprising since the stacked ensemble model is trained on the same data thus, predicting its value may not be an issue. But the model did not generalize well on the predicted value of the training data when we trained stacked model using the GBM as a meta learner. This is because the bias of GBM is higher than other base learners when used as a meta learner for the stacked ensemble.
A trained model does not suffer from model overfits15 if it generalizes well on the test data. Using the stacked ensemble models for predicting and forecasting inflation (see Figure 10 in Appendix B for details), the predicted test data fits the actual CPI data of all the inflation subgroups very well. This is because our strategy of selecting the meta learner to train our stacked ensemble model works perfectly. This strategy yields a smaller RMSE of the test data for almost all our ensemble models across the subgroup inflation. Thus, suggesting that stacked model is more accurate for predicting inflation in Nigeria than a standalone model, as it is common in econometric analysis. The jumps might be due to a smaller randomly selected sample; as mentioned earlier, we performed a random split of 70% for training data and 30% were randomly allocated to test data consequently, the predicted and actual test data may not converge well. But this is not a concern at this point.
Variable of importance using stacked ensemble
The main idea of this section is to use our stacked ensemble model to select the most important predictors, which are the main drivers, for the inflation components. To do this, we first trained a new learning algorithm that combine all the inflation predictors of the base learners then, a meta algorithm, also known as super learner, is trained with 70% of the CPI data to make a selection of the most important variable. This way, the stacked ensemble models outperforms individual base learners and has proved to asymptotically create an optimal system for learning (see Laan et al. 2003). The stacked ensemble whose trained meta learner yields the best performance and generalizes well on the test data is use for selecting important inflation subgroup drivers and for prediction. Consequently, we use RF to select important drivers of headline, core 1, and urban inflation and use GLM to select important driver of core 2 and rural inflation, this is because the meta learner for the stacked model has the lowest bias.
We identified ten most important predictors in each subgroups however, some of the components of the inflation subgroups also have subcomponents. For instance, food as a component also have about nine subcomponents which are also predictors for food inflation. As part of the inflation drivers, food is the most important driver for headline, urban, and rural inflation. This is not surprising because food has the highest weight, 50.7%, in the inflation baskets in Nigeria. Moreover, research has shown that households spend more than 50% of their income on food therefore, it is not surprising for our stacked algorithms to identified it as the most important driver. Our modeling does not include the weight of the predictors but the stacked model was able to identify food as the most important driver because it recognizes the pattern of food CPI is more sensitive to income changes when compared with other predictors, implying that policy may have huge implication for household spending. While electricity is the fifth most important driver for headline inflation, it is the eighth most important driver for rural inflation. Electricity is an issue in Nigeria and its inability to come up as one of the first-tenth most important driver in urban inflation is not surprising. Majority of the urban households and firms already created alternative source of energy but such alternatives are not common in rural areas therefore, spending on electricity will be more sensitive to inflation in the rural areas in Nigeria. Air transportation is the second most important driver for Headline and urban inflation but the most important driver for core 1 inflation. Fuels and lubricants for personal transport equipment is the fourth most important driver for headline, fifth most important driver for rural inflation, and sixth most important driver for core 1 inflation. After excluding all farm produce and energy from headline, carpets and flooring become the most important driver for core 2 inflation and medical services appears as the most second important driver. So, changes in income spending on carpets and flooring and medical services can cause changes in CPI in Nigeria.
Having identified food as the most important driver for headline inflation, we examine the nine components16 (see Table 9 in Appendix G for details ) and identify the component that is important in driving food inflation. So, we use our stacked ensemble algorithm to select the most important driver of food inflation and predict same using the test data. Figure 6 shows the most important driver of food inflation.Fig. 6 Variable of importance for food
From the above figure, bread and cereals is the most important variable17, vegetables as the second most important driver, and meat as the third. However, fish is the last most important driver of food inflation. This is also not surprising, among the components of food inflation bread and cereals has the highest weight and this weight is 21.7% out of 50.7% of the food inflation from headline inflation. This result implies that the total household expenditure on bread and cereals is almost half of total expenditure on food. The low weight of vegetables among the classes or components food inflation, is about 5% out of 50.7% of the food inflation, does not pose a barrier for its position as one of the leading drivers in food inflation. The implication of the results is that vegetables may not occupy a large proportion of the household expenditures but changes in household consumption may help policy makers understand and control food inflation. Potatoes,Yam & Other Tubers are the component of food inflation with the highest weight after bread and cereals but it is the fourth most important food driver. So, despite their relatively larger share of household expenditure on food, they are not as important to drive inflation as meat and vegetables in Nigeria. Household income on fish should not pose a serious concern for controlling food inflation, since they are the least important driver for food inflation.
We present the performance of the stacked ensemble for food inflation in Fig. 7. The Figure shows the predicted CPI for training and test data.Fig. 7 Food CPI
Figure 7a depicts the actual training CPI and the predicted CPI data. The performance is not surprising since it was the data that was used to train our model however, it will help to explain whether the predicted CPI data in Fig. 7b have similar pattern with the CPI for the training data. We could therefore, conclude that our actual CPI data generalizes well on the predicted CPI of the test data. The model performed poorly when meta learner was switched to GBM and RF.
Bread and cereals in food inflation component have twenty-three subcomponents for food inflation. Therefore, the main goal is to identify the most important driver of bread and cereals inflation by following the same procedure as food inflation. We start with the stacked ensemble model results for each meta learner, these results are in depicted in Table 6. The results show that RF clearly overfits while GBM and GLM generalizes well on test data but GLM is better because of its lower bias therefore, we selected GLM as our meta learner. As usual, we trained GBM as the main meta learner but its performance is not as good as performance of GLM. The ten most important driver for bread and cereal inflation is depicted in Fig. 8, and the prediction of training and test CPI data is available in Fig. 9.Fig. 8 Variable of importance for bread and cereals
The foremost driver of bread and cereal inflation is biscuits and the second most important driver is sausage. Rice agric is the third most important driver but it is the component of bread cereal inflation with the highest weight; out of 21.6% weight of bread cereals in food inflation, rice and agric shares 3.1%. However, cabin biscuits has 0.36% weights out of the total weight of bread and cereal inflation. By weight, rice agric, garri yellow, rice local, maize grain, millet, and sorghum are the six most important predictors, their individual weight is at least more than 2%, but only rice agric and millet are in the tenth most important drivers of bread and cereals inflation. In addition, components that are too low by weights mostly drive cereal and bread inflation. This suggests that changes in households expenditure allocated to components of bread and cereals which has a lower weights can pose a threat to inflation management in Nigeria. Unfortunately, lack of monetary target, in the medium-term, of these food subcomponents might have been responsible as to why food inflation has been difficult for monetary authority to control; if these items have a lower weights but are important drivers of the food subcomponents inflation then, it is possible that government might have ignored its impact on headline inflation due to its negligible weights.
Cabin biscuit, rice agric, garri white, and semovita turnout as the topmost driver of bread and cereals, Bread and cereal is the main food inflation driver, while food inflation is the main headline, urban, and rural inflation driver. Moreover, air transportation, household textile, liquid fuels also top the ten most important driver in core 1 inflation while carpets and flooring, which has about 0.03% in the entire inflation weight, are the main driver in core 2 inflation. Some of the components may have lower weights but they are very significant in driving inflation in Nigeria. In fact, the performance of our model is very robust in capturing the predicted bread and cereals CPI data pretty well. Figure 9a shows the actual and predicted CPI for bread and cereal using the training sample while Fig. 9b reveals the actual and predicted CPI for bread and cereals using the test data.Fig. 9 Bread and cereals CPI
The two figures are similar and it shows that our stacked ensemble has a predictive ability over standalone model. This is because when we randomly split the data, the stacked ensemble model predicts and forecasts the randomly selected test data accurately. Doing so, attests to the better predictive performance of the stacked ensemble model.
Forecast horizons of selected inflation variables of our model is depicted in appendix C. The RMSE forecast from the first to ninth horizon is below 9.5 but a 1.7 point jump from 8.8 in ninth horizon to 10.5 in tenth horizon is noticeable. However, the forecast was fairly stable starting from from twentieth horizon.
Conclusion and policy recommendation
We examined the performance of stacked ensemble model by identifying the inflation drivers in Nigeria. To our knowledge, this is the first study that utilized stacked ensemble to identify and analyse inflation drivers for any country. The stacked ensemble approached was used to identify the variable of importance that is stable across the predicting horizons. The evidence suggests that the main driver of inflation in Nigeria is Food inflation. Of the components of food inflation, we identified Bread and cereal, vegetables and meat as the main drivers of food inflation. The components of bread and cereal were analysed further as the top driver of food inflation; biscuits, agric rice and garri white were the main drivers of inflation in bread and cereal.18.
Shifting focus from headline to food inflation subcomponents, such as bread and cereal, to understand inflation drivers reveal a piece-wise information that may not otherwise be available if the price indices are not disaggregated. The argument about the significance of “food prices” in driving headline inflation is an evidence of a larger proportion of households disposable income allocated to food items. With so much spending on food, the CPI of headline, urban, and rural inflation will continue to rise. The high cost of imported capital goods for manufacturing 800g of cabin biscuits pack and sausage beef is a narrative indicative of cost-push inflation. The higher cost automatically disrupts the supply chain of cabin biscuits and sausage beef and thus, renders the two food items as the most food CPI drivers. However, the price of “rice agric sold loose” and “garri white sold loose” are main drivers of food prices. A high disposable income of households competing for these two staple foods items can lead to a demand-pull inflation in Nigeria.
This study has also proved the flexibility and viability of ML algorithms in predicting Nigerian inflation. As evidenced from our results, each base learner were trained as a meta learner for our stacked ensemble. The results of which identified “RF” as the best performing meta learner for training the stack ensemble model. Since some of the inflation subgroups are best predicted with “RF”. It thus, suggests that some inflation data are best predicted with nonlinear models. Recent advances in ML methods can improve forecasts where the target variable can be explained by many predictors and more than two-dimensional space. In this regards, stacked ensemble deserves a special attention as it has the potency of compensating the weakness of a base learner with the strength of another base learner. Consequently, the synergistic performance of stacking each base learner produces a formidable model that yields the highest level of accuracy and best predictive ability. In our study, we analyzed the test data, out-of-sample, and our results show a strong accuracy in predicting inflation, as the time horizon increases, contrary to recent studies (seeMedeiros et al. (2019)), the accuracy of our model in predicting out-of-sample data did not diminish. In addition, volatility problems do not constrain nor affect the prediction. The more volatile and the less volatile inflation subgroups, such as “core 1” and “core 2”, were all predicted with high accuracy.
Adequate trace of the source of inflation to the least component of each subgroups will help design an appropriate policy in addressing inflation problems. Moreover, some of the CPI items that mostly drives inflation have lower weights19 while others have higher weights. Therefore, focusing entirely on CPI weights as a policy guide will stymied a successful control of inflation in Nigeria. However, establishing the relationship between the persistence of food inflation and central bank credibility using stacked ensemble would be an interesting area to explore further.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file 1 (pdf 107 KB)
Author contributions
EmA write-up the methodology, results and analyses, and the conclusion. ElA and OT jointly did the introduction and literature review sections while JJ and AA collect the data and prepare it for use.
Funding
There is no funding sources.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
The views expressed in this paper are those of the authors, and do not necessarily reflect the views of anyone else affiliated with the Central Bank of Nigeria and CAPE Economic Research and Consulting. Any correspondence should therefore, be shared through the emails provided.
Declarations
Conflict of interest
The authors declare that there is no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
1 A potentially large number of predictors with a small number of observations is what Stock and Watson (2011) referred to as ‘curse of dimensionality’.
2 Figure 1 is the plot of Core 1 inflation. The fluctuation of core 1 inflation in Nigeria is due to the components included in the basket of its computation. National Bureau of Statistics (NBS) does not exclude energy prices such as premium motor spirit (PMS), gas, and utility prices. In addition, the high exchange rate market pressure has also contributed significantly to the fluctuation of prices of many of the items in the core basket. This is because the Nigerian economy is highly import dependent and many of the item in core 1 basket are also imported.
3 See Fig. 2 for inflation trend in Nigeria from January to August 2020.
4 Some of these univariate models such as Autoregression Moving Average (ARMA) and UNobserved Components and a Stochastic Volatility (UC-SV) model has been found to be less accurate and less robust when compared with machine learning ensemble models (see Baybuza 2018; Coulombe et al. 2019; Medeiros et al. 2019 for details).
5 The main idea is to minimize the sum of the weighted average variance within the resulting sub-samples (Bradley and Brandon 2020). Using the tree constructed, we can predict values for the target variables with the newly created values of the explanatory variables. The decision tree models allow us to create an effective nonlinear dependence by minimizing the variance target variable.
6 In other words, random forests are modification to bagged decision trees that build a large collection of de-correlated trees to further improve predictive performance (Breiman 2001).
7 Permuting the value of an important explanatory variable in the training data will degrade the training performance of model. This is because a permuted variable will reduce the relationship between and explanatory variables and the response variable (target variable). The approach uses the difference between some baseline performance, such as Root Mean Squared Error (RMSE) and the performance measure obtained after permuting the values of a a particular explanatory variable in the training data set. In other words, an explanatory variable is “important” in driving a response variable if permuting its values increase the chances of higher model error relative to the other explanatory variables, since the model relied on the important variable for its prediction. Whereas an explanatory variable is ’least important” if permuting its values render the model error relatively unchanged, since the model did not recognize the explanatory variable for the prediction.
8 The parameter that controls the split-randomization of the explanatory variables; Segal (2004) showed that data with many noisy predictors can have an improved performance if the mtry is higher.
9 Values range between 3-8. A higher depth allow the model to capture specific interactions but increase the chances of over-fitting while a smaller depth is computationally efficient and may avoid over-fitting.
10 Evaluates the impact of each tree on the final outcome. It also controls the speed at which the algorithm proceeds down the gradient descent or learns.
11 The process involve splitting the training data set into two parts
12 The exclusion of food and farm produce from headline inflation, core 1, and exclusion of food, all farm produce, and energy from headline inflation, core 2, render core 1 and core 2 to be less volatile than headline inflation.
13 The results are consistent with Baybuza (2018); Coulombe et al. (2019), and Medeiros et al. (2019) studies.
14 see Table 4 in Appendix A for details, it is calculated as the difference between the RMSE of CPI using the training data and RMSE of the predicted CPI using the test data.
15 Although, the RMSE of the test data of RF in Table 4a is higher than the RMSE of the test data of GBM and GLM but RF seems to generalize better relatively because the RMSE of its test data of all inflation subgroups are much lower than RMSE of its training data.
16 Including Bread and cereals, meat, fish, milk, cheese and eggs as a subcomponent, oil and fats, as a subcomponent, fruits, vegetables, potatoes, yam, and other tubers as a subcomponent, and sugar jam etc as a subcomponent.
17 The importance variable parameters are the important predictors designed to determine the drivers. The derivations of different variable of importance of each algorithms are explained in Sect. Ensemble models-base learners.
18 We identified food as the main driver for headline, urban, and rural inflation thus, we analyze food inflation components to gain more insight into relevant drivers, while air transportation is the core 1 main driver, carpet and flooring is the main driver for core 2 inflation. Among the food components, “bread and cereals” are the main driver while “vegetables” is the second most important driver of food inflation. Cabin biscuits, a components of Bread and cereals CPI and subcomponent of food CPI, is the main inflation driver our stacked ensemble model was able to identified.
19 Ignoring such items in policy intervention will make inflation difficult to control; this problem is very crucial in food inflation.
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| 0 | PMC9734342 | NO-CC CODE | 2022-12-14 23:28:28 | no | SN Bus Econ. 2023 Dec 9; 3(1):9 | utf-8 | SN Bus Econ | 2,022 | 10.1007/s43546-022-00384-2 | oa_other |
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Virtual Real
Virtual Real
Virtual Reality
1359-4338
1434-9957
Springer London London
719
10.1007/s10055-022-00719-2
Original Article
Using interpretative phenomenological analysis to gain a qualitative understanding of presence in virtual reality
http://orcid.org/0000-0001-8652-6844
Kelly Nathan James [email protected]
grid.57686.3a 0000 0001 2232 4004 Alumni of Master of Research in Psychology, University of Derby, Derby, England
7 12 2022
113
30 6 2021
3 11 2022
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Quantitative methods have thus far been the predominant methodological stance of virtual presence research, leaving much to be desired in terms of qualitative understanding. Yet, virtual experiences are a highly personal engagement, unique to each individual, and their presence in virtual reality can be viewed in terms of its experiential individuality. This aspect of the virtual experience is overlooked by conventional quantitative methods, which clusters ratings or scores to form group deductions. Therefore, to address the qualitative gap in the literature and provide an appropriate examination of virtual experiences from the perspective of the individual, an Interpretative Phenomenological Approach was undertaken. This alternate methodology sought to reveal which aspects of virtual experiences users identify as enabling feelings of presence. Examination of common themes among accounts of individuals were performed, to investigate the generation of feelings of presence in virtual reality. Online recruitment provided six interviewees who participated in online semi-structured interviews, prior to Interpretive Phenomenological Analysis. Three superordinate themes were identified: visual satisfaction, freedom of interaction and suspension of real life. Expectance, realism and prevention of disbelief are among the sub-themes identified that contributed to the interviewee’s highly present experiences. The identified themes demonstrated the greatest influences of enabling a deeper sense of presence, in turn enhancing their experiences within virtual reality. In acknowledging these mitigating influences, it is hoped this may enable future virtual systems to build upon the research provided and produce consistently high-presence experiences. Consequently, this can aid educational, therapeutic and entertainment applications of virtual reality.
Keywords
Virtual reality (VR)
Virtual environment (VE)
Presence
Interpretive phenomenological analysis (IPA)
Immersion
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pmcIntroduction
Virtual reality, presence and immersion
Virtual reality (VR) is a digitally generated 3D environment, with systems typically consisting of a powerful computer alongside a head-mounted display (HMD). Other more complex systems are available, such as the CAVE (Cave Automatic Virtual Environment). Virtual reality’s aims to captivate users, often including peripheral devices for auditory and haptic stimuli, such as handheld sensors or finger-tracking. Since its conception, a phenomenon has arisen in which people perceive virtual environments (VEs) as highly realistic or overtly disassociated from the real world. This is currently considered the result of at least two factors: the degree of immersion and level of presence a user experiences (Gorini et al. 2011; Shubert et al. 2001). VR’s roles within healthcare (Hoffman et al. 2001a, b; Pillai and Mathew 2019), education (Merchant et al. 2014), tourism (Yung et al. 2021) and entertainment industries (Kodama et al.2017) depend upon successful user engagement, resulting from both presence and immersion. The stronger these factors, the greater associated user experience. It is hoped that by gaining insight into the mechanics of ‘being present’, we can continue to build upon our current understanding of virtual presence. This research aspires to assist VR applications across all sectors by influencing greater user engagement, improving its effectiveness as a therapeutic, educational and entertainment system.
Immersion is described by Slater (2003) as the objective level of sensory fidelity that is provided by the electronic system. For example, the headset and accompanying devices synchronise to cultivate a vivid environment. Slater and Wilbur (1997) describe immersion in terms of features such as ‘inclusivity’ and ‘surroundings’. That is, ‘inclusivity’ would, in a perfect world, fully exclude the non-virtual world, delivering only the virtual stimuli, meanwhile ‘surroundings’ would fully encompass the user with an unrestricted view of the virtual world, rather than provide a panoramic view of a single viewpoint. Primarily, immersion appears relatively quantifiable and can be manipulated in terms of frame rates, graphic fidelity and degrees of freedom.
Presence relates to the psychological state of participation and engagement of the user's environment. Whilst not a universal definition, Slater (2003) described presence as the user’s subjective psychological response to a VR system, accrediting action and purpose to be as true to the virtual world, as in the real world. A highly present user should identify themselves within the virtual environment (VE), being able to act upon and influence their surroundings. Sheridan (1996) alternatively defines presence as where “the human participant feels herself to be present at a location which is synthetic”, whilst Seth et al. (2012) states it is rather a more basic quality of normal consciousness during the experience. Sanchez-Vives and Slater (2005) also state presence as being the phenomenon of experiencing consciousness in virtual reality, and with that, the ability to respond to virtual stimuli as though they were real. The experience of becoming psychologically captivated by the VE describes the origins of presence, dissimilar to immersive aspects. The greater presence achieved for the user, the more realistic the outcome. However, issues occur in measuring presence when research adopts different definitions of presence, in absence of a unanimous concept.
Research regards immersion and presence as having complex simultaneous mechanisms yet does not appear to reliably indicate a direct relationship. Uno and Slater (1997) investigated immersive qualities, yielding mixed results in factors positively correlating with greater presence, while others failed to correlate. Cummings and Bailenson (2016) similarly highlighted numerous immersive factors as significant to presence, whilst others, such as image quality, were not. Contrastingly, Baños et al. (2004) observed how multiple displays and content produced results ranging from presence being mostly dependent on immersion for non-emotive content, to least important for emotive content. They concluded that both content and stimuli provide significant roles in presence, stating that the pursuit of presence should not focus on technology alone. Further, Krijn et al. (2004) found no difference between high and low fidelity VR when examining their effects during VR exposure therapy.
Difficulties arise when examining interactions between presence and immersion, often due to the lack of unanimous terminology, wherein after almost three decades of VR research, calls remain to unify these two definitions (Kardong-Edgren et al. 2019). This linguistic conflict was primarily addressed by Slater (2003) who highlights discrepancies between the Presence Questionnaire and the Immersive Tendencies Questionnaire as both being founded on misaligned concepts. It is vital that the VR community rapidly addresses this issue in order to guide us to a more unanimous disposition. In doing so, it is hoped that the variation amongst literature may clarify somewhat, and that future developments assist in stabilising future research. It can be concluded by most that presence is an internal psychological phenomenon, contrary to externally-influenced immersion.
‘The presence problem’
VR literature primarily concerns itself with the manipulation of technological factors such as; frame rates (Raaen and Kjellmo 2015), fields-of-view (Ragan et al. 2015) and peripheral stimuli (Fröhlich and Wachsmuth 2013; Laukkanen, et al. 2022). Consequently, literature frequently describes how VEs appear to a user, side-lining how it feels to the individual. The role of immersion is suited to manipulating these quantifiable aspects; however, such experiments report results surrounding both presence and immersion through the same quantitative lens. The experimental method provides us with evidence such as that of Slater et al. (1996), who demonstrated the role of technological components, highlighting how VR may enhance situational performance. Similarly, Hoffman et al. (2006) provided evidence of analgesic influences of immersion. However, it is concerning that the quantitative methodology has gone unquestioned when applied to presence research, resulting in its submission to computer-manipulated experiments.
This is further aggravated by research claiming to examine presence, when immersion is in fact the subject focus, as noted with Krijn et al. (2004). Successful applications of VR also fundamentally require a highly present experience to reach optimum potential (Jerome and Witmer 2002). They discuss how these phenomena indicate a causal relationship, where a highly captivating VE requires strong influences from both realms. Bowman and McMahan (2007) also investigated the contributions of presence specifically within a highly immersive environment, noting participants regarded the experience as a more involving platform when greater presence was observed.
Amongst VR literature, presence has been approached as though it and immersion were of the same origins, disregarding the fundamentals of presence as an internal quality, unique to individuals, as demonstrated by a survey paper by Schuemie et al. (2001). It is proposed that when trying to measure experiences of presence, other methods have been considered. Undeniably, quantitative methods are successful in many ways, yet we remain unable to explain the phenomenon of presence, whilst claiming to be able to measure it. Whilst investigating immersion experimentally, it is unclear if it is possible to control for presence, nor to observe any extraneous relationship between the two. The quantitative method has remained the favourable domain for investigating presence, yet alternative methods have not sufficiently been considered in favour of the classic experiment.
VR literature currently employs multiple questionnaires to investigate presence such as; the Temple Presence Inventory (Lombard et al. 2000), the Igroup Presence Questionnaire (Schubert et al. 2001), the ITC-Sense of Presence Inventory (Lessiter et al. 2001) and Witmer et al.’s Presence Questionnaire (2005). These do indeed differ, for example the Temple Presence Inventory concerns itself with social presence, compared with the ITC establishing a cross-media measurement. They therefore have varied approaches on how to measure presence using different variables as their respective focus.
It is a concern that when observing an individual’s experience of presence, the use of questionnaires demonstrates reductionist efforts to capture the user’s degree of cognitive engagement at the expense of explanatory information. Their application risks loss of informative data surrounding the experience, in favour of rapid numerical analysis. This view is shared by Slater (2003), who also recognises that questionnaires are not a preferable option to measure presence. Instead, qualitative data could provide insight into the user’s subjective experience, providing a wealth of critical information that may otherwise have gone unexplored.
There are well-established techniques that are frequently used to explore individuals’ personal experiences, and these lie within qualitative psychology (Brocki and Wearden 2006). The use of qualitative methods is grossly less frequent than those of quantitative, contrary to the conclusions of Usoh et al. (2000) and Freeman et al. (1999). Those discussed by Usoh et al. are that within the experimental method of self-reported presence data, participants “relativise” their responses to the context of the domain, signifying their design to be inappropriate. Similarly, limitations discussed by Freeman et al. of the experimental method to interpret presence scores via handheld sliders, stated their own data acquisition methods may be potentially unstable, noting future research should recognise this.
The validity of quantitative analyses should then be challenged within this research realm. Aside from the well-recognised risks of using questionnaires as a methodology, such as respondent subjectivity, questionnaires themselves carry potential investigator biases of constructional intentions and leading concepts. Consequently, it is surprising that within the wealth of academia surrounding virtual reality, so little has been provided from the qualitative approach.
Technological and individual factors have both been the subject of interest surrounding presence, yet the literature has yet to reach conclusive evidence, using quantitative methods, as to why such experiential variations occur. The ability to measure and observe differences in presence has been noted, usually through experimental manipulation of immersive factors; however, it cannot be inferred by any research observed so far that we understand the source of presence, nor how to observe this phenomenon independent from immersive technological manipulation. Therefore, it is proposed to take a novel approach to understanding the experiences of virtual reality, solely using qualitative methods.
A novel approach
The interpretative phenomenological analysis (IPA) method specialises in developing our understanding through the perspectives of others, examining personal experiences and their meanings for the individual, taking interest in their own construction of events (Smith et al. 2009). The nature of this project, experiences of virtual presence, therefore lends itself towards the IPA method, rather than other qualitative approaches, and so was elected the most appropriate technique. With IPA adopting an idiographic approach, and concerning itself with phenomenology, the uniqueness and importance of an individual’s own interpretations of the experience will be the focus of the analysis, acknowledging that no two people experience phenomena in the same way (Smith and Osborn 2003).
However, in following Heidegger’s (1992) line of thought, the researcher is intrinsically a part of any phenomenological research, initially with ‘being there’ to extract the nature of the experience. He discusses how reality is put forth by the researcher, in describing the subject-matter, yet should strive to validate the phenomenon ‘as itself’. The challenge with IPA, however, lies within the knowledge that natural experiences are observed as a “person-in-context”. That is, we are surrounded by preconceptions of the subject and interpretations from the participant’s own understanding. It will therefore be key to observe the analytical focus of participants, from as objective a standpoint as possible. Adopting a realist approach will enable analysis to focus directly on the phenomenon individuals have experienced, in the hope of investigating the experience as true to itself as possible. And so, with a research focus of ‘experiences of presence within a virtual environment’, this should methodologically complement our aim to gain an understanding of how one interprets themselves within the situation. That is, the focus lies with the user's constructs of presence, whilst within the VE.
IPA will permit access to verbal self-reports surrounding how users truly felt about their experiences, free from the constraints of questionnaires and scales, allowing for greater data enrichment and a more intrapersonal perspective. This method permits analysis of individuals’ unique constructs, and what contributes to their individual experiences of virtual presence. Using IPA, interview data will be analysed for concurrent themes in order to gain insight into common features that influence feelings of presence in VR. By allowing each participant to discuss their perspective from an idiographic approach, to guide the conversation as they see fit, the constraints of quantitative responses are removed in place of rich and insightful data surrounding how present they consider themselves. Interpretive phenomenological analysis is critical to gaining a qualitative understanding of an individual's true, idiographic, lived experience (Smith 2017).
Methodology
With over 330 million active users, the online forum Reddit.com facilitated potential candidates for this research. Reddit.com is the world’s 19th largest website, and the largest online forum-dedicated website (http://www.alexa.com/topsites, 2020). It has been previously noted as a resource for research purposes by Shatz (2017), who recognised its open community participation and suitability for researchers.
Its user-moderated design permits its use for the public domain as non-copyrighted, as well as providing adequate scope, reach and fit for approaching populations with similar interests as the project, such as the ‘sub-Reddit’ r/virtual reality. Within this sub-forum, a thread was opened outlining the purpose of the research, paraphrasing the information sheet, inviting contact through a University email address. A targeted, purposeful sample was achieved by those initiating contact. Typically, literature surrounding VR research employs opportunist University students (Baños et al. 2000), though other samples are also recognised (Hoffman et al. 2000). However, the nature of interpretive phenomenological analysis (IPA) research requires a homogenous group, and the topics of discussion to be as personal as possible (Pietkiewicz and Smith 2014), therefore student populations were deemed less desirable, as a wider range of potential incentives may occur (course credit or shared research interests) than those solely with the intent to participate.
In order to achieve homogeneity, only those having used an Oculus VR system were eligible, with the environment having been provided via a head-mounted display (HMD). The Oculus was chosen for being the most popular virtual hardware, aside from the Sony (which typically comes with a games console bundle). This ensures the research engages the interests of the wider population by utilising the most popular hardware, whilst ensuring as similar virtual experience as possible. Aside from participants being required to have experienced an Oculus system, exclusion criteria dictated that interviewees must be aged eighteen or above, as this technology is popular with users of all ages. Additionally, candidates must have no mental or physical vulnerabilities that may affect participation, as well as no history of adverse reactions to virtual reality. These were essential to minimise potential risk for the researcher and potential harm for those wishing to participate.
To further ensure homogeneity, the participants' experiences were required to be one in which users engage with only the environment, as opposed to including AI’s, ‘bots’, or other real-world VR users, in order to avoid the additional complexity of VR social interactions (Riva et al. 2003). For example, the relevance of high-vs-low resolution engagement, emotional recognition and quality of interaction are not fully understood. Although literature has examined variables such as shared environments and co-presence (Schroeder 2012), this research takes a novel qualitative approach; therefore, it does not seem appropriate to involve additional complexities at this time. Experience of presence as an individual VR user may deviate from group interactions, as in the real world, with the company of others significantly altering one’s behaviours (Li and Zhao 2019). Thus, it is required that those participating have experienced an exploratory environment.
Demographic exclusion criteria were considered, however were found to be irrelevant, as virtual reality has been shown to be resilient to such variations. A large 3-study paper by Sharar et al. (2007) demonstrated no variations in age, ethnicity nor sex when using VR in a clinical setting. Whilst the only paper of its kind, comprehensively observing multiple demographics, it demonstrates results consistent with other literature. Consideration was given to the duration users spend within virtual experiences; this was found to be an inconsequential factor (Hoffman et al. 2001a, b; Hoffman et al. 2001). Therefore, although considered, there does not appear to be justification for their use as exclusion criterion. With homogenous criteria, alongside a target sample population, the intended represented population are the typical VR users who seek to use the technology for leisure (i.e. non-research) purposes. Although inclusion/exclusion criteria were outlined on the sub-forum to establish participation requirements, these were later re-confirmed via screening during the consent process.
Upon establishing contact, participants were provided with the information, consent and interview schedule forms. This outlined that participation was voluntary, informed of their involvement and data security and highlighted their right to withdraw. Informed consent was obtained via email by returning a consent document, acknowledging each exclusion criteria and requiring an electronic signature prior to their interview to ensure full transparency of the research intentions.
Participants selected a pseudonym which they signed on the consent form, with transcripts and recordings being stored using this false identity to protect confidentiality. Interviews were conducted via Skype or Zoom, depending on the participants’ preference. Interviewees were not required, nor requested, to engage in a visual interview, however each initiated visual contact without prompting. Any identifying information disclosed in the interview was redacted during transcription to further ensure confidentiality, with all data being securely stored and erased within an appropriate time frame. Participants were verbally and scripturally debriefed post-interview. Protective measures were discussed should any participant have become distressed, with support services outlined; however, no such support was requested and no concerns observed. Participants were reminded of their right to withdraw from the study at any point up to two weeks after the interview, without reason, which was clearly outlined in the information sheet, and debrief, provided immediately after each interview. Six participants were achieved.
As is typical with IPA, a semi-structured interview approach was undertaken in order to guide the conversation whilst allowing for free-flowing discussions, encouraging interviewees to lead conversations with their own line of thought (Miles and Gilbert 2005). Interviews were conducted on a one-to-one basis in order to avoid co-construction of experiences, deemed most appropriate in accordance with guidance from Smith et al. (2009). The interview schedule consisted of three discussion topics with fourteen prompt items, though not to be strictly adhered to. The interview items were developed using items from the Presence Questionnaire (PQ) (Witmer et al. 2005). Witmer and Singer first produced the PQ in 1994 (Witmer and Singer 1994) and have since refined it on multiple occasions. Furthermore, their work surrounds presence as VR involvement, which is more relevant to this paper than other questionnaires concerned with spatial presence, for example. Their 2005 paper illustrated their most recent revision of the PQ, re-exploring the concept of presence with greater validity.
To effectively select the most appropriate items from the PQ to translate into the interview schedule, those most powerfully relating to ‘involvement’ were of greatest, but not selective, interest. Justification for this lies within Witmer et al.’s, paper (2005), in which a tripartite factor analysis found that the most influential contributor repeatedly encountered is that of ‘involvement’. They state “Involvement is clearly the most dominant dimension measured by the Presence Questionnaire” whilst examining the multi-facticity of presence. The PQ totals 29 questions, 12 of which surround ‘involvement’. In order to solidify a strong theoretical basis for our selection, factor loading coefficients of question items across both the most recent 2005 publication and the earlier 1998 version were analysed (Witmer and Singer 1998). Using these coefficients, the greater each item's strength, the greater the theoretical basis for its inclusion in the schedule. Six items were confirmed as the strongest predictors of presence (3, 6, 1, 2, 8, 18), five of which were also the strongest items within the involvement factor, a confirmatory basis for their inclusion. Item 18 was supported by remarks of being “a conceptually pure involvement item” (page 308). Items reporting lower coefficients were then used to guide the creative development of further items in the schedule to elicit discussion without restricting the dialogue to involvement alone, particularly those strongly supporting presence.
During development, questions relating to individual preferences were included with the intention of inducing in-depth engagement on a personal level, aiding personal engagement. These then established three discussion topics, with additional prompts, to guide the interviews if required. The first discussion surrounded recent VR experiences and users’ feelings surrounding this, in order to elicit discussion. The second topic led to discuss feelings of VR whilst playing, to shift the focus on to individuals’ feelings of presence or absence from the virtual environment (VE). The final topic approached the similarities and differences between the real world and the virtual, in order to highlight the distinct nature and provide a greater, more complex insight of the users’ experiences.
It remained vital that the developed schedule encouraged the participant to reflect upon their personal experiences, in order to discuss them in a retrospective light. All interview items were therefore adapted into an open-ended style, to elicit the interviewee to assume control of the dialogue and address their own experiences. This inductive emphasis provides a greater voice to the participant, rather than the researcher. This included only minor linguistic adjustments, to refrain from bias or leading phrases. Post-interview, audio files were transferred from the dictation device to secure digital storage using the participant’s desired pseudonym. Recordings were transcribed verbatim on an ad-hoc basis, in order to allow reflection on style and technique, with audio files being destroyed post-transcription.
Guidance from Smith et al. (2009) was critical to ensuring adherence to the theoretical framework and phenomenological underpinning of IPA. Each individual’s experience was considered “as is” with vital consideration of hermeneutics, and how research should attempt to focus on the individual’s own personal experience, free from outsider influences and interpretations. Double hermeneutics, for example, are a concern of IPA research, requiring a reflexive mind-set in order to minimise any potential impacts.
Furthermore, Alase (2017) also provides a comprehensive guide centred on accessing the “lived experience” of another without distortion. With virtual presence and individual experiences at the core of this paper, IPA is undoubtedly the most appropriate route to adopt with its fundamentals lying in phenomenology, hermeneutics and idiographic focus. Further comprehensive advice by both Pietkiewicz and Smith (2014) and Breakwell (2008) were also considered to follow the phenomenological approach adopted in this paper, as was those of Creswell and Poth (2016). In particular, his arguments for the usefulness of internet-based data collection aided in establishing an interview format over a digital platform, with an appropriate anticipation of its deviance from the standard face-to-face interview.
Transcripts were revisited multiple times in order to re-familiarise and become immersed in the perspective of the interviewee by paying specific attention to the reports of the individual, and how they themselves experienced VR. Note taking followed, with exploratory remarks and relevant content outlined at the side. Reflective comments were then noted and followed by a more abstract, in-depth examination for conceptually similar emerging themes. This process was repeated for each transcript, relationships between emergent themes were sought within the entire dataset, in order to cluster themes together, creating superordinate themes and noteworthy subordinate sub-themes within each. IPA analysis was undertaken on a case-wise basis in order to remain true to its idiographic origins.
Analysis
Within this stage, several themes emerged, generating a greater range of superordinate themes than expected, covering an array of subordinate themes across the dataset. Of these, three demonstrated the greatest importance to the research aims and were analysed in greater depth, which are listed in Table 1 along with corresponding subordinate themes. Regrettably, it was not possible to explore each theme in detail due to the quantity of data provided and research limitations, and so those best fitting to address the research question were specifically selected to report upon.Table 1 Super- and sub-ordinate themes
Superordinate Themes Subordinate Themes
Visual Satisfaction Imperfection ignorance
Visually feeling real
Visual realism
Environmental attraction
Believability
Real-world replication
Freedom of Interaction Interactivity
Ability to engage
Having extraordinary influence
Freedom of ability to lead
Suspending Real Life (RL) Ignoring reality
Suspension of RL awareness
Maintaining virtual belief
Brain as a mitigator
Visual satisfaction
Analysis highlighted that visual satisfaction played a large role in our interviewees’ sense of presence in the VE. This was not in terms so much as graphic fidelity as much as being mentally satisfied with the environment observed. Visual expectations, and the degree of satisfaction these were met with, were found to be associated with feelings of realism and presence."So it won’t just be like, a wall with wallpaper. it’ll have reality-wear on the wall that looks as if it’s been there a long time. so, there’s all these visual clues that make you, it adds, sort of context. and urm, it adds history. so your brain will start to fill in the gaps".
L discusses the cognitive role of the visual environment observed. He assigns a unique context to his environment, aided by the detail the VE provides. The addition of context permits the environment to be more understood, with a purpose in his virtual space. This demonstrates the environment having a role in VR, irrespective of his attendance. This detail legitimises his virtual world, in that his environment has experienced a history prior to his own presence there. He describes this as “reality-wear”, as though to appear more real to him, or that the wear is in fact real. He has built a mental framework around his VE, enabling a greater sense of reality. This satisfies the literature’s definition of presence."It’s just, you know, looking under a table and it’s just what you’d expect to be underneath a table. There’s a magazine, and a TV remote. And it’s kind of just that rewarding level of someone’s actually taking the time to make this so realistic".
This extract from M highlights how his satisfactions with the virtual environment induces greater feelings of realism. During his exploration of a VE, he found “just what you’d expect” and found this “rewarding” and “realistic”. In finding reward from his expectations, the VR provided him with satisfaction, and high realism. This suggests he typically finds things different from his expectations, less satisfying and subsequently less real. The degree of feeling “realistic” relates directly to visual expectations being satisfied within VEs. This realism is complementary to higher presence and can be drawn from satisfying users’ visual expectations."I remember an experience which was a seated experience, and not so much exploration in the sense of moving around, where the scenery changed when you looked away, like you looked that way, and when you look back you saw something different. Something impossible happened. Where you didn’t see it. And so that’s kind of confusing, through a short story, and those are things are that kind of stay with you. Like things that are not possible in real life, but possible in VR and still make you feel, like you know, there".
N reported feeling “like, you know, there”, emphasising his feelings of presence in VR. This is prompted by observing “something impossible”, or rather it happens off screen “where you didn’t see it”. The continuity of the unobserved is synonymous to true life. However, the impossibility of the event conflicts with this. The event is “not possible in real life, but possible in VR”, asserting that VR can provide users with experiences not possible in the real world. This “stays with” him, as a lasting, memorable experience. Therefore, he considers VR a method to experience extraordinary things, in which he feels “there”, although not true-to-life. Event realism can therefore be assumed of little importance to him. Yet observing something possible only in VR induced a response that could “still make you feel” as though he were truly “there”. For N, visual events provided realistic presence, in the absence of true realism."It’s like a platforming game, but it’s just this huge lush jungle. As far as you can see. And you’re right in the middle of it playing with a little mouse. But like, the appeal of the game isn’t necessarily the platform. It’s more this believable forest-scape with, you know, the beautiful lighting and the butterflies floating through the trees. And you’re like ‘holy shit that, that is real’. You know, it’s such a strong sense of atmosphere that it teleports you to another place".
O finds himself “right in the middle” of this VE, stating his “appeal” to originate from “this believable forest-scape”. Believability, being able to ascertain the VE as genuine, demonstrates his satisfaction that his environment is realistically convincing. These feelings are strongly exclaimed as he shares “holy shit that, that is real”. The influence of ‘believability’ provides him with “such a strong sense of atmosphere” that “teleports” him “to another place”, this being the VE. In being deeply satisfied with the credibility of the visual environment, and being able to indulge in the atmosphere, he is powerfully compelled to believe in the VE, influencing his presence within it.
Freedom of interaction
Individuals’ sense of freedom to interact, and the degree to do so at will, became apparent during analysis. The ability to act, and interact, within an environment enabled the users to engage within a novel realm and aided in creating a sense of presence, of ‘being’ within the VE. This co-operative relationship between user & VE, the cause and effect, demonstrated a higher level of experiential depth. Users must feel ‘able’; able to manipulate, to experience, and to explore at their own desire. Discussions found that in having the freedom to interact within respective VEs, users felt a greater degree of presence within VR."The first time you do it your brain goes ‘it’s definitely not the same set of actions’. But like, after a few repetitions, your brain goes ‘well, that’s just the action that we need to pick things up right now, so we’ll just accept that’, and then it just disappears from your mind. And again, kinaesthetic projection kicks in and you forget that there’s any real difference. You just sort of think ‘I want to do this’ and then you do the thing. It’s with, with full presence, I think like, all of that none one-to-one stuff, it stops mattering, it all fades away".
Here, O describes how his interactions in VR enabled his sense of presence. He reports noticing no “real difference” between real-world and virtual actions. This demonstrates being able to replicate his real-world impact. When he feels as though “I want to do this” and then being able to “do the thing”, he highlights his lack of restriction and freedom to act as desired. Regardless of the “none one-to-one”, to which he refers to the translation between his physical actions and his virtual, he recalls this “stops mattering”. He discusses an interesting consequence of how being fully present permitted his action disparity to have little importance. Therefore, his virtual actions clearly relate directly to his feelings of presence. Finally, he highlights how his brain “accepts” dissimilarities between real-world and virtual action, and how this “disappears from your mind”. He points out that this took “a few repetitions” suggesting that acceptance of his actions was gained by experience and is not a natural phenomenon. Once achieved, his interaction was accepted and it ‘disappears from his mind’. The interaction became more natural, in turn enabling presence."You’re constantly interacting with either people, or things. You can pick things up and play with them. And in Eleven Eleven it’s a viewing narrative experience, so whilst you can go off and explore the world I couldn’t like, pick up a flower or go in the tavern and pick up the drink".
R describes a high level of constant interaction in an experience and recalls her interactions as “play”. This symbolises her interactions evoke a sense of fun and enjoyment. She also, however, describes the limitations she reaches in a VE. She contrasts “so whilst you can” and “I couldn’t” as though she wishes to be able to perform the described actions. Being unable to “play” with her surroundings therefore is cast in a negative light, with potentially less enjoyment. She chose to mention her inability to act upon objects, after describing how a user is in constant interaction. This acts to iterate that the inability to do so is noteworthy to her. And so, the freedom or restriction of potential action is clearly a distinguishable feature that can empower or restrict the user. It is this ability to engage that users sought to experience, aiding in feeling present and joyful during VR."I would probably say it’s more about the level of sort of interaction and control that I myself am allowed to dictate, whether it’s based on the controllers or uh whatever I’m using to, you know, move within the simulation, opposed to just the headset tracking me and being allowed to be more free"
Here, M indicates his preference towards VEs that provide him with a level of “interaction and control”. His ability to control and interact as he wishes enables him to feel present. In “being allowed to be more free”, this assists in the transference from his real-world setting to that of the virtual. This appears to be irrespective of “whatever I’m using to, you know, move”, suggesting that this freedom can be provided through either technology or in-VR action. And so, whilst real-world translation of action is not key, to feel present in VR he requires the ability to “dictate” his own actions with the freedom of real life. Restrictive aspects, or inability to lead the VE, therefore inevitably obstruct presence."It’s also about the, oh, what’s the, I think the similitude. Like with racing simulations and flight simulation speed. Being able to do something that approximates the real thing has really boosted up by being in VR, like really having the feeling of sitting in a cockpit be it a race car or an aeroplane".
For N, “being able” induces “the feeling of sitting in a cockpit”. Stating he felt “boosted up” by VR, his ability to perform within the VE with “similitude” to real life enabled “really having the feeling”. His ability within VR coincides with his feelings of realism. He recalls how his freedom to act as he wishes, unrestrictedly, provided him with a greater sense of truth to VR. His ability to perform “something that approximates the real thing” is what provides his sense of pseudo-reality. This similarity, but not perfect replication, of real life within VR, provided enough of a sense of “really having the feeling”. Consequently, his presence improves from “being able” to act, and to do so with “similitude” to the real world. Providing the freedom to perform actions, and avoiding disparity, enables his virtual presence.
Suspending real life
The ability to place on-hold one’s attentiveness to the real-world frequently arose during interviews. Attending the real world is incompatible with a highly present virtual experience. Instead, users were able to suspend their real-world focus in favour of the virtual. This was discussed as a passive role, rather than an active commitment."Urm and I was playing it one summer when it was really hot, so I put my fan on my desk, and I had this cold air fan blowing on to me so that I was you know, cooling down and the VR headset wasn’t getting too hot. And in the same, I stood out on this bridge over a river…it just, the wind blowing in my face suddenly just made me completely forget I was in VR…I was fully there in that space in time".
In this extract, L is subjected to a real-world haptic stimulus whilst immersed in VR, and passively experiences the stimulus in a hybrid state. He verbally identifies within VR, stating “he stood out on this bridge”. He refers specifically to his position in virtual space, speaking as though it were his physical body, self-locating within the VE. Simultaneously, he experiences “the wind blowing” in his face. This stimulus had the potential to influence his mental positioning and was covertly assigned to his virtual embodiment. He experiences the wind as his virtual self, supporting his digital embodiment, which “just made me completely forget I was in VR”, eliminating his awareness of his true body. In being “made” to forget, he addresses this not as a choice, but rather a consequence of the presented stimuli. He had, to some degree, assumed the role of his virtual self, which resulted in an additional stimulus being subconsciously assigned, dispelling the fact it was a true body experience. This encouraged his commitment to VR, as he found himself “fully there”."At times indistinguishable, I think, from reality. Urm, again, with the help of, like, the brain being such a malleable little bugger. It is. I mean I am using the knuckles and there’s a there’s a few games now designed for knuckles and at times it can be incredible just how much you forget that they're there and you can just reach out and grab an object. But even in crappy games like the sense of interaction with the physical world can feel so natural. Again, that your brain forgets that it's not doing a direct one-to-one. Sort of, you know, it’s not one-to-one. But that non-translation happens really naturally".
O leads by remarking how he can find VR “at times indistinguishable” from reality. He attributes a high degree of presence to VR in being unable to distinguish the two. By stating “at times”, he confirms that this is not a consistent state of mind but is instead subjective. His uncertainty is fuelled by “the help of, like, the brain”, which he coins a “malleable little bugger”. His interesting terminology playfully suggests his mind provides him with dissatisfaction, as if to concede his elected reality is beyond his control. Exclaiming that “it can be incredible just how much you forget that they’re there”, he demonstrates surprise at how inattentive he became to the handheld peripherals. This distraction from the devices resembles a cognitive shift from his real-world actuality to his virtual self. This is reinforced by his confidence in being able to “just reach out and grab an object” in VR when, in physicality, he cannot feel the item. As an individual, he powerfully assumes his role in VR, overlooking his true self. This can feel “so natural” to him, as his digital assumption is evident “even in crappy games”, as well as being unaffected by “not doing a direct one-to-one” in terms of actions performed. His visual and haptic stimuli are both uninfluential to his mental location. This represents a powerful ‘locking out’ of the real world, only being able to confess “sort of, you know, it’s not one-to-one” whilst admitting “that non-translation happens really naturally” within conscious decision."I was really there, you know, I mean for a while I, you know, the world was gone, and I had the headset on I was really there because I was so curious about the whole world. I mean, that one has to be the one that I was the most intensely immersed in of all the games I’ve played. Urm, because it was just so interesting. I mean it was almost real the things that went on and the sizes of things and you didn’t have to suspend belief".
Here, A considers herself “really there” in her experience, although denotes this as “for a while”. She points out this was not a permanent state, and so must be a degree of presence she transitioned into. Acknowledging “the world was gone” whilst she “had the headset on”, she draws a contrast between recognising the role of physical hardware, and how this removed her awareness of the real world. Interestingly then, once wearing the headset, she was able to ignore its location as a real-world object on her. Evidentially, she states this occurred because “I was so curious about the whole world”. This high-order cognition, the desire of exploration, provided a compelling ‘draw’ that she was able to become present in VR. Furthermore, “because it was so interesting” she remarks it as “the most intensely immersed” she had felt. Interest and curiosity therefore appear keyf to A as a mitigator of feeling highly present in VR and removing her sense of the real-world. These influences appear powerful enough to act passively, as she “didn’t have to suspend belief” actively. Her highly present experience was conveyed greatly by cognition, allowing her to believe she was “really there” and the VE was “almost real” to her."So it is very frightening of how real it can be, but at the same time those sort of little bits of VR which make it just as equally as not very real and jumps you back into the world"
M reported having difficulties maintaining presence which grants us an inverse view. He believes it to be “very frightening of how real it can be”, meaning he has previously experienced presence sufficiently to initiate an emotional response. If it can be real, or cannot be, he does not experience these feelings consistently. His use of “can be” defines his experiences of ‘real’ as something that is triggered or influenced by something other than himself. He indicates that the source of this variation is a result of “those sort of little bits of VR” in that they “make it just as equally as not very real”. And so, his perspective of “how real” depends on the VR system itself, rather than his own state of mind, reporting he feels that it “jumps you back into the world”. In being the result of VR, he outlines a lack of control over his feelings of real or unreal. Accordingly, his depiction of ‘real’ is dictated by immersive technological aspects. This demonstrates he experiences a relationship between his mental acceptance of a ‘real’ VE and the headset. If the “bits of VR” do not permit a sense of “real”, he finds himself unable to assimilate the VE and instead is left with a “not very real” experience.
Discussion
Interviewees provided accounts of their experiences of presence in VR and between them identified; visual satisfaction, freedom of interaction and suspension of real life as commonly noteworthy. Across each account, presence was considered a positive feature of virtual reality, inducing greater senses of a ‘real environment’ within the digital space. This confirms the definitions of presence in accordance with previous literature (Sanchez-Vives and Slater 2005; Sheridan 1996).
Self-accounts of visual satisfaction, the first theme, were reported across the range of individuals’ experiences. Cross-account analysis found frequent user expectations of what should be observed within the VE. This appears to originate from previous real-world experiences, such as expecting items to exhibit wear and tear, or for typical household items to be in a location suited to where one would expect. Interviewees reported experiences as either similar or dissimilar to their expectations, upon which, synonymous to life experiences satisfied the users’ expectations. This visual satisfaction generated visual realism, enabling feelings of a realistic environment.
Event realism was briefly discussed, appearing not to have the same cognitive value. A visually realistic environment conveyed believability to the user as a genuine space to act within. Further to this, within an environment that was not a true-to-life experience, an impossibility, an interviewee still reported high feelings of realism and of ‘being there’. Although visual satisfaction appears to be a multi-faceted influence, individuals repeatedly refer to its consequences as feeling ‘real’. This empowered the virtual space as a ‘real’ space for the individuals, and in turn, a space to act within.
Graphic fidelity was rarely mentioned and was not stated to induce the same feelings of presence, with variations between accounts of its relevance. Surrounding the accounts of the VE appearing real, believable, or visually realistic, individuals attributed themselves as being ‘within’ the virtual environment as if with synchronicity. Being visually satisfied with the presented VR permitted the cognitive sensation of being within it. These accounts support literature such as Bowman and McMahan (2007) in line with immersive fidelity not being the sole inducer of presence. Visual satisfaction, the fulfilment of users preconceived expectations, provided realistic and present experiences, rather than high graphic fidelity alone. This provides the literature with newly gained insight into the role of the visual aspects of VR, not being solely based upon the immersive fidelity of vision, but also its relevance directly to the individual. This may assist in providing insight into mixed results when investigating immersive qualities, such as research by Baños et al. (2004) and Uno and Slater (1997). A VE should also seek to satisfy individuals’ preconceptions, and abide by expectations of the real world, in order to provide greater depth of presence.
Individuals’ accounts also brought to light the importance of the user’s freedom of interaction, the second superordinate theme. Whilst on the surface this may appear to be a matter of game development, individuals instead approach the phenomenon as a reciprocated engagement, hereby demonstrating relevance to more than just the VE. The desire to interact with the virtual world could be considered as quasi-natural, as one interviewee analogised it as being like a “kid with a cake in front of them and told not to eat it”. Game mechanics were mentioned on occasion but did not form the basis for individuals’ discussions. Instead, individuals reported being actively accepting of a non ‘one-to-one’ translation between handheld control and their actions within VR. This hereby side-lines the role of game mechanics currently.
Commonalities between accounts focused on the ability to interact and act with freedom. Absence of restriction, whilst immersed, was key. Users recounted their experiences as deeply explorative, rapidly rejecting the phenomenon of presence if unable to virtually participate in the digital world. It was necessary to act and engage within the environment. With being able to do so, VR became a greater attentive experience, often resulting in users forgetting the real world. Desired interactions varied between accounts, from the desire to play, to the ability to assume control. Yet the freedom to do so at will, to act as they wanted within their own world, was common throughout all discussions.
Perhaps this highlights a role of individuality, for it may be assumed each person would not take the same virtual path as another. However, withholding over-ambition, individuals stated a more direct similarity. The ability to act with freedom and engage with the environment constantly yielded feelings of presence. It was reported how being able to act within VR felt natural and so can be assumed a restrictive VE would feel less so. The natural aspect of being able to act at will, may seek to replicate real life, for as in the real world, our interactive capabilities are unbound. The feelings of natural interaction were not opposed by peripherals, but instead were rapidly accepted and indifferences faded.
Alongside interactive experiences came greater enjoyment, freedom and play. When able to interact, the similitude between VR and real life was greater, universally attributing their interactivity as closer to reality and with a greater naturalistic feel. The feelings of experiencing a synthetic environment as natural and realistic abides by Slater’s (2003) definition of presence, whilst also clear from individuals’ accounts, as the inability to differentiate between the two worlds. Interaction therefore encompasses a range of desired actions, unique to the preferences of the individual, however the desire of freedom to act in a variety of ways remains unifying, and inductive of presence. Examining literature on virtual reality interactions found it almost solely dedicated to communicative interactions with users, for example within educational applications, and so it was not possible to compare these findings to previous research. No relevant literature was found to address the cognitive roles of freedom, interaction, and presence. It is hoped such interactions will be considered for future research.
The final theme surrounded the interviewees’ experiences of being able to suspend real life, in terms of being passively able to ignore or suspend attention to real-world presence. It was found that several passive occurrences amongst the interviewees supported a presence-inducing environment. The covert suppression of real-world information repeatedly led to an enhanced experience within VR. One individual reversibly described difficulties in developing presence, occasionally being unable to ignore the overt real-world, yet still able to on occasions.
For each case, ignorance of real-world awareness directly influenced their feelings of self-location. Upon the suspension of real life, attentiveness to VR consumed the user, providing an enhanced degree of presence. This was consistently portrayed as passive, with one interviewee confirming that ‘suspending belief’ was not necessary. It is key to state that no individual acquired presence as a result of this passive influence, rather it acted to support their experience, providing deeper cognitive involvement. The origin of this phenomenon acts as if to be a dichotomous switch, which when triggered, permits what some reported as “full presence” or “fully there”. This choice of language infers that they couldn’t become any more present, and that they had reached the maximum potential of reality within VR. Conversely, this may also be interpreted as users having reached their minimum potential of awareness of their real selves. This state of real-world suspension was retold as a variety of positive assessments, often with individuals in surprise or disbelief. This reinforces recent findings by Kim et al (2021) in which virtual self-location, potential to act and enjoyment were all strongly correlated in a longitudinal study surrounding VR presence.
The individual who struggled to maintain presence consistently recounted the potential reality of VR as ‘frightening’. Although this was evident in the extract provided in the analysis, this did not represent his general feelings towards experiencing presence. The source of real-world suspension (or the inability to suspend) was not universal, rather it was unique to each personal account. No two accounts reported the accession to deeper presence as occurring from the same origins. This should therefore be considered as a matter of individuality. Schuemie et al. (2001) had previously suggested that suppression of information that is incompatible with VR is of vital importance for presence. Whilst one individual’s account appears to support this theory, it is noted that the remaining accounts do not clearly specify incompatible information, rather building upon their experience or self-investment.
Of the themes identified during analysis, none appeared any more relevant or effective to experiences of presence than the other. Greater presence was universally considered to improve the virtual experience, enabling the user to feel located within the environment and heighten their sense of ‘real’. There was a high level of individuality when examining the emerging themes; however, commonalities provided guidance to the origins of presence, rather than recommendations on how to elicit the phenomenon. Care should be taken not to assume a one-fits-all approach when looking to enable a highly present VE, as this risks suitability for some individuals whilst exclusively restricting others. The degree of individuality in VR has been previously recognised, where Usoh et al. (2000) drew attention to the potential ineffectiveness of quantitative methodologies, in favour of a more idiographic approach. After all, it appears individuals demonstrate flexibility and willingness to accept VR to fit themselves, rather than requiring perfection. According to our findings, it appears the mind is willing to ‘fill in the gaps’.
Analytical findings examined accounts of several individuals and how they personally experienced presence in a virtual environment in a way that is currently lacking within this specific research area. In applying the novelty of qualitative methods, a variety of themes have been identified as being common throughout all participants as contributing to feeling present. It is requested that both qualitative and quantitative research take note of both; the relevance of these identified themes for the virtual participant, as well as the utility of this method whilst investigating presence beyond the scope of technological manipulation and questionnaires. That is, the role of the individual user is key to their experiences, and so quantitative experiments may not always be the most appropriate for VR research, depending on the research aims.
It is hoped that the contributions of this paper will enable technologies, researchers, and VR applications in clinical and therapeutic uses to recognise and encourage the role of the key factors identified. To suggest some specific potential uses for these findings: using VR as a pain-mediator, such as with burn patients, a VE that has a greater ‘ability to engage’ and ‘freedom of ability to lead’ will assist greater ‘Freedom of Interaction’ and, as findings would suggest, induce greater virtual presence. Greater presence has been shown to reduce perceived pain (Hoffman, et al., 2006). Therefore, understanding precisely which subordinate factors can facilitate greater presence can lead to less painful and more comfortable pain treatments, simply by specifically electing for VEs that the identified factors of presence. Similarly, within the entertainment industry, understanding that ‘imperfection ignorance’ and ‘believability’ are both contributors to ‘Visual Satisfaction’, can assist game development. That is, creating a visually perfect and complete environment is not crucial, and yet, alongside believable game mechanics (such as gravity and actions upon object) users are able to mitigate the imperfections and permit themselves to become highly present. Finally, cognitive treatments using VR may find a great deal of use in applying the findings of the superordinate theme ‘Suspending Real Life’ to be of importance. Electing for a VE that encourages continuous stimulation (‘maintaining virtual belief’) as well as in-game focus (‘ignoring reality’) would enable a deeper virtual presence for the user and encourage more attentiveness to the treatment.
IPA demonstrated itself as a powerful method of analysis, highly appropriate for investigating experiences of virtual reality. Its fundamental principles align fittingly to investigate modern VR phenomena, adopting an idiographic approach. It is important to note, however, that as this paper is the first to adopt a novel qualitative approach, comparisons to previous qualitative literature are either methodologically complex or inappropriate. Beyond that, some findings lack prior research upon which to compare. This being said, findings can still be seen to support, enlighten upon or conflict with some of the literature previously discussed. In addition, the data obtained from the small number of interviews conducted provided a much larger insight and breadth of analysis than anticipated, and it is with reluctance that not all identified themes could be reported upon in depth. With this in mind, it is strongly encouraged that future research should look to explore IPA as an alternative to quantitative methods should it be appropriate to the research aims, with a view to obtaining further comparable research and gain even further insight into individuals’ experiences of virtual reality.
Reflexivity
During the literature review process, it became clear that whilst ‘immersion’ is a well-defined concept, ‘presence’ is less so. This recognition arose from several researched publications, and frequently during interviews. After all, if academics fail to agree on a unanimous concept, it would be unjust to expect casual users of VR to be well-informed of the terminological difference. Furthermore, this is almost to be expected, when it has been previously noted that presence and immersion co-occur. As such, it arose repeatedly where insights into presence were described as ‘feeling so immersed’ or ‘highly immersed’. Complications arose with the conflict of whether to suggest they were referring to high presence, or perhaps they themselves were absolute in what they meant. This level of interpretation did not suit the methodological approach, and so remained as told to the interviewer. As such, there may have been potential loss of valuable data.
In terms of participants, at least half of the interviewees were involved with VR as their predominant occupation. During interviews, and again during analysis, emotive language was not noticeably favourable, describing both positive and negative aspects of the VR experiences each had gained. It cannot be said if this may, or may not, have affected the research. Upon realisation of this similarity between interviewees, attention was paid towards any potential influence this may have. This was also reflected upon during the analytical and conclusive stages of research development, however no subsequent influence was noted, yet it cannot be ruled out completely.
The data acquisition method performed in this research was not the primary choice. Initially, data collection was to be performed with in-person interviews yet remaining in line with IPA guidance. The causes for the change were beyond control, and a matter of social benefit during the COVID-19 pandemic. This resulted in adaptation to online recruitment which dramatically influenced the research. Online data collection was highly time consuming with a surprisingly low level of respondents. This may have also been a result of subject novelty, which would have been negated with in-person interviews, at the intended recruitment site (a VR arcade). Nevertheless, the secondary method was adopted, and research continued. This is expected to have affected the number of interviewees, as well as their disposition. It would be ideal for future research to utilise in-person data collection methods when appropriate. Although it is believed the analytical findings are not warped as a result of the alternative data collection method, it should be recognised when these findings are being applied to practical uses and future research.
Funding
No funding was received in light of this research project.
Declarations
Conflict of interest
No conflicts of interest are declared.
Ethical approval
This research was conducted under approval from the University of Derby Ethical Committee for research with human participants.
Informed consent
Informed consent was obtained from each participant prior to research engagement.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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| 0 | PMC9734343 | NO-CC CODE | 2022-12-14 23:28:28 | no | Virtual Real. 2022 Dec 7;:1-13 | utf-8 | Virtual Real | 2,022 | 10.1007/s10055-022-00719-2 | oa_other |
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Curr Microbiol
Curr Microbiol
Current Microbiology
0343-8651
1432-0991
Springer US New York
36474044
3102
10.1007/s00284-022-03102-1
Article
Innate and Adaptive Immune Responses Induced by Aspergillus fumigatus Conidia and Hyphae
Luo Yingzhi 1
Liu Fang 2
Deng Lin 3
Xu Jie 2
Kong Qingtao 2
Shi Yi [email protected]
4
http://orcid.org/0000-0001-7188-6740
Sang Hong [email protected]
1
1 Department of Dermatology, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002 China
2 grid.440259.e 0000 0001 0115 7868 Department of Dermatology, Jinling Hospital, Nanjing, China
3 grid.13402.34 0000 0004 1759 700X Department of Dermatology Affiliated, Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310006 China
4 Department of Respiratory and Critical Medicine, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210000 China
6 12 2022
2023
80 1 282 7 2022
22 10 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Previous research indicated that hyphae of Aspergillus fumigatus (A. fumigatus) rather than conidia could successfully build a pulmonary aspergillosis model in immunocompetent mice. In this study, we compared the immune responses induced by hyphae and conidia to explore the possible mechanism of this striking phenomenon. Herein, a novel method was designed and adopted to quantify hyphal fragments. Murine macrophages RAW264.7 and human peripheral blood mononuclear cells were stimulated by A. fumigatus hyphae and conidia in vitro, respectively, and then immunological reactions were measured. Male C57BL/6 mice were challenged with conidia and hyphae through intratracheal inoculation. Dynamic conditions of mice were recorded, and RNA-seq measured corresponding immune responses. The results of the study confirmed that hyphae could induce more intensive inflammation than conidia in vitro and in vivo. However, macrophages revealed a higher production of ROS and M1 polarisation in response to conidia stimuli. Additionally, conidia could promote Th1 cell differentiation, while hyphae could increase the CD4/CD8 ratio. RNA-seq validated the fact that those multiple immunologically relevant pathways were more strongly activated by hyphae than conidia, which also promoted Th2 cell differentiation and suppressed Th1 signalling. Both hyphae and conidia could activate Th17 signalling. In general, conidia and hyphae induced distinctly different host immune responses, and the immune responses induced by conidia played a better protective effect. Therefore, the unique function of hyphae in the spread and infection of Aspergillus should be emphasised, and more research is required to clarify the underlying mechanisms for better understanding and management of aspergillosis.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00284-022-03102-1.
http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 81871630 81330035 Sang Hong issue-copyright-statement© Springer Science+Business Media, LLC, part of Springer Nature 2023
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pmcIntroduction
Pulmonary aspergillosis refers to a spectrum of clinical syndromes caused by airborne Aspergillus spp. These are a series of saprophytic moulds abundant in indoor and outdoor environments that mainly affect the respiratory tract of immunocompromised patients. Depending on the host characteristics, aspergillosis was categorised into invasive pulmonary aspergillosis (IPA), chronic pulmonary aspergillosis and allergic bronchopulmonary aspergillosis [1–4]. With the widespread use of broad-spectrum antibiotics and immunosuppressive agents, the high incidence of aspergillosis remains a public concern [5–8]. Moreover, the pandemic of the coronavirus disease 2019 (COVID-19) has shown a high frequency of co-infection with Aspergillus spp., leading to severe outcomes and heavy medical burdens [9–14].
Aspergillus fumigatus (A. fumigatus) is the most common etiologic species of aspergillosis. In its life cycle, numerous asexual conidia are produced and spread, which can be inhaled by humans and easily reach the alveoli owing to their tiny size [15]. For immunocompetent individuals, invading conidia will get cleared by airway immune systems, including epithelium, macrophages, neutrophils, and other immune cells. Once the host immune system gets compromised, the uneliminated conidia will swell and germinate to form invasive hyphae, resulting in different types of aspergilloses [15, 16]. The mechanism of host immunity against Aspergillus is stage-specific. During swelling and germination, the outermost hydrophobic rodlet layer of conidia gets lost, and the melanin layer is disorganised, thus resulting in the exposure of the inner layer β-1,3-glucan [17]. These molecular changes account for various pathogen-associated molecular patterns (PAMPs) in the surface of A. fumigatus conidia and hyphae, including chitin, β-glucan, galactomannan, and the like. As for the host immune system, various pathways are activated to defend against the pathogen via specific pattern recognition receptors (PRRs). These include Toll-like receptors (TLRs) and C-type lectin receptors (Clecs), which can recognise the PAMPs in the surface of conidia or hyphae and trigger a series of pro-inflammatory and anti-inflammatory reactions [16].
In the past decades, researchers focused much more on conidia as the inhalable causative agent of pulmonary aspergillosis. However, hyphal fragments can also be airborne and directly invade the respiratory tract [18, 19]. It is common to see nonsporulating moulds, especially A. fumigatus, be isolated and identified as the pathogenic microorganism in the respiratory tract samples [20]. We had previously obtained two clinical nonsporulating A. fumigatus strains from the pulmonary specimens of two immunocompetent patients. To prove their pathogenicity, Zhang Z et al. found an interesting phenomenon that hyphae of A. fumigatus rather than conidia could cause pulmonary aspergillosis in immunocompetent hosts [21]. Hyphal fragments have been reported that they could induce stronger inflammatory cytokines secretions than conidia, indicating that hyphae may have stronger immunogenicity [22]. However, the previous reports only exhibited the superficial secretion phenomenon in macrophages, which has not been generalised to the whole immune system and correspondingly short of conviction. Therefore, more research must be performed to determine hyphae’s unique effect on aspergillosis. This study's main objective was to compare host immune responses against airborne conidia and hyphae in vitro and in vivo, laying a foundation for future in-depth mechanistic research.
Materials and Methods
Mould Preparation
A. fumigatus wild-type strain Af293 (purchased from FGSC, the Fungal Genetics Stock Center, University of Missouri, USA) was cultured in rich media YAG containing 0.5% (w/v) yeast extract, 2% (w/v) glucose and 0.1% (v/v) trace elements at 37 °C. Conidia and hyphal fragments were cultured and harvested as described in a previous study [23].
Fungal Quantification
To better compare conidia and hyphal fragments, the ratio of surface area to volume was adopted as the quantitative parameter. At first, the cross-sectional areas of conidia and hyphae were measured, respectively, by Image J software after taking fluorescent pictures with Calcofluor white (Sigma, USA) staining. Further, a hemacytometer was used to keep an equal volume of each picture.
The cross-sectional areas of conidia and hyphal fragments measured by Image J software were, respectively, named S1 and S2. The total surface areas of conidia and hyphae were named Sc and Sh, respectively. Then, the radius of the conidia was set to be r, while the radius and length of the hyphal fragments were set to be R and L, respectively. The numbers of conidia and hyphal fragments were n and N, respectively, while that of the original hyphae was N2 (N2≪N). The process of whole conversion was as follows:S1=πr12+πr22+πr32+⋯+πrn2
S2=2RL1+2RL2+2RL3+⋯+2RLN
Sc=4πr12+4πr22+4πr32+⋯4πrn2=4S1
Sh=2πRL1+2πRL2+2πRL3+⋯+2πRLN+2πN2R2≈πS2
The final concentration of fungal suspension was calculated and standardised by the multiplicity of infection (MOI) ratios of conidia to cells. Conidia and hyphae were inactivated at 95 °C in the water bath for 30 min.
RAW264.7 Macrophages Culture and Stimulation
RAW264.7 macrophages were maintained in humidified air at 37 °C, 5% CO2 in DMEM supplemented with 10% fetal bovine serum (FBS; Gibco, USA). Macrophages were stimulated with gradient concentrations of conidia or hyphal fragments. They were then incubated with live conidia (LC) and live hyphal fragments (LH) for 6 h and with heat-inactivated conidia (HIC) and hyphal fragments (HIH) for 24 h (MOI ranging from 0.1 to 100). Supernatants were collected and stored at −80 °C until examination.
CCK-8 Assay
Cell Counting Kit-8 (CCK-8) assay was performed to measure cell viability. RAW264.7 macrophages were plated in 96-well flat-bottom culture plates at 5*103 cells/well density and incubated overnight. Thereafter, cells were stimulated with gradient live organisms for 6 h and inactivated ones for 24 h. After washing with PBS (Hyclone, USA) twice, it was added with fresh medium and 10 μL CCK-8 reagent in each well for 1-h incubation. Finally, the absorbance value was measured at the wavelength of 450 nm using an ELISA plate reader. The viability index of the cells was determined using the following equation:Cellviability=(Aexperimentgroup-Ablankgroup)/(Acontrolgroup-Ablankgroup)×100%,
control group: group without fungal stimuli, blank group: group without cells and fungal stimuli.
ROS Assay
RAW264.7 macrophages were washed and collected with PBS after fungal stimulation (MOI = 10), as mentioned above. Macrophages were then incubated with dihydroethidium (Beyotime, China) at 37 °C for 30 min. Next, fluorescence intensity was detected at 488 nm (excitation wavelength) and 535 nm (emission wavelength) through flow cytometry (Invitrogen, USA).
PBMC Isolation and Stimulation
With written informed consent upon approval of the ethics committee of Jinling Hospital, 20 mL EDTA blood samples were collected from healthy volunteers. Peripheral blood mononuclear cells (PBMCs) isolation was performed by Ficoll-Paque density-gradient centrifugation. After washing and centrifuging, PBMCs were resuspended in an RPMI 1640 (Gibco, USA) culture medium containing 10% FBS and then stimulated with live or heat-inactivated fungi for different durations. PMA (25 ng/mL) + ionomycin (1 μg/mL) stimulation for 6 h was used as a positive control. Supernatants were collected and stored at -80 °C until cytokine assays were performed. Cells from different donors were used for the biological replicates.
T Cell Subset Analysis
Live conidia and hyphal fragments stimulated PBMCs for 6 h and inactivated ones for 24 h. At the last 6 h of incubation, PMA (25 ng/mL), ionomycin (1 μg/mL) and brefeldin A (10 μg/mL) were added. Next, cells were collected and washed with PBS, followed by extracellular staining with anti-human CD3 PE-Cy7, CD4 APC-Cy7 and CD8 PE-Cy5.5 for 15 min. After that, PBMCs were centrifuged and resuspended in Fixation/permeabilisation reagents (Biogems, USA) for 30 min to perform the subsequent intracellular cytokine staining with anti-human IL-4 R-PE, IL-17a APC and IFN-γ Alexa Flour™ 700 (all antibodies: BioLegend, USA). Cell populations were analysed through flow cytometry. T lymphocyte was defined as CD3+ cells, and T cell subsets were defined as Th1: CD4+ IFN-γ+, Th2: CD4+ IL-4+ and Th17: CD4+ IL-17a +. CD4 (%) = CD3+CD4+/CD3+, CD8 (%) = CD3+CD8+/CD3+, Th1 (%) = CD3+CD4+IFN-γ+/CD3+CD4+, Th2 (%) = CD3+CD4+IL-4+/ CD3+CD4+, Th17 (%) = CD3+CD4+IL-17a+/ CD3+CD4+
Mice and Infection
Forty-eight male C57BL/6 mice (aged 6–8 weeks) were purchased from Qinglongshan animal centre (Jiangsu, China) and acclimatised. Mice were randomly divided into three groups, with 16 mice in each group. Ten mice from each group were used for survival observation, three for histopathology examination and three for RNA-seq. Chloral hydrate (350 mg/kg) was intraperitoneally injected into mice for anaesthesia. The three groups were challenged intratracheally with 50 μL conidia suspension (109/mL, conidia group), hyphal fragments suspension (hyphae group, equal concentration of conidia), or normal saline (control group), respectively. The mice were observed every 12 h for 10 days, and their lungs were immediately removed after anaesthesia or death. Lungs from three mice in each group were harvested, frozen and ground for RNA extraction 24 h after fungal inoculation, and the RNA products were stored for RNA-seq after quality testing. On the fourth day of the intervention, lungs from another three mice in each group were removed and fixed with 10% (v/v) formalin for periodic acid Schiff (PAS) staining. All animal experiments were carried out per the Institutional Animal Care and Use Committee of the Jinling Hospital.
Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR)
According to the instructions, total RNA was isolated from RAW264.7 cells or murine lung tissues using TRIzol reagent (Invitrogen, USA). OD260/280 ratio of RNA between 1.8–2.0 tested by spectrophotometer was qualified for the subsequent experiment. A total of 1 μg RNA was reverse-transcribed into a cDNA template, and cDNA products were diluted 1:5 with diethylpyrocarbonate-treated water without DNAse treatment. Quantitative real-time polymerase chain reaction (20 μL reaction volume) was performed in triplicate using forward and reverse primers (0.8 μL and 10 μmol/L, respectively), a 2 μL cDNA aliquot and 10 μL TB Green® Premix Ex TaqTM II (Takara, China). Housekeeping gene β-actin was used for the standardisation of mRNA, and relative expression analysis was performed using the 2−ΔΔCt method. All primers were validated by NCBI Primer-Blast (https://blast.ncbi.nlm.nih.gov/Blast.cgi) to ensure the specificity and the details of the primers are shown in Table S1.
RNA-Seq
Total RNA was isolated from murine lung tissues harvested 24 h after fungal inoculation using the TRIzol reagent. mRNA was purified using Oligo(dT)-containing beads and fragmented into small pieces. Then, the products were reverse transcribed to first- and second-strand cDNA. Afterwards, A-Tailing Mix and RNA Index Adapters were added by incubating to end repair. cDNA fragments amplification was performed via PCR, and the products were purified by Ampure XP Beads. Subsequently, quality control was performed on the Agilent Technologies 2100 bioanalyser. The double-stranded PCR products were denatured and circularised, and the synthesised single-strand circle DNA (ssCir DNA) was formatted as the final library. The final library was amplified with phi29 to make a DNA nanoball (DNB) that had more than 300 copies of one molecule. DNBs were loaded into the patterned nanoarray, and 50 single-end base reads were generated on the BGIseq500 platform (BGI-Shenzhen, China). The sequencing data were filtered with SOAPnuke to obtain clean reads and stored in FASTQ format. The Dr. Tom Multi-omics Data mining system (https://biosys.bgi.com) was adopted for subsequent analysis and data mining. Then the obtained clean reads were aligned to the gene set built by BGI (Beijing Genomic Institute in ShenZhen) using software Bowtie2. The gene expression level was calculated by RSEM (v1.3.1), and the heatmap was drawn by pheatmap (v1.0.8). Essentially, DESeq2 (v1.4.5) was applied for differential expression analysis with Qvalue ≤ 0.05. Furthermore, KEGG (https://www.kegg.jp/) enrichment analysis of differentially expressed genes was performed using Phyper (https://en.wikipedia.org/wiki/Hypergeometric_distribution) based on the Hypergeometric test. Qvalue ≤ 0.05 was the significant threshold for the correction of terms and pathways.
ELISA
Supernatants of macrophages and PBMCs were collected after stimulation and centrifuged for enzyme-linked immunosorbent assay (ELISA). Commercially available ELISA kits for mouse TNF-α, IL-6 and human TNF-α, IL-6, IL-1β and IL-4 (Novusbio, USA) were used according to the manufacturers’ instructions.
Data Analysis
The analysis of flow cytometric samples was performed in FlowJo software (TreeStar, USA). Statistical analyses were performed in GraphPad Prism 7 (GraphPad Software Inc., USA). One- or two-way analysis of variance (ANOVA) was used to analyse data sets of CCK-8, qRT-PCR and ELISA, depending on the number of explanatory variables in the experimental design. Results were presented as mean ± standard deviation (SD) of triplicate experiments. Flow cytometry results about T cell populations were analysed by the Friedman test. P < 0.05 was considered statistically significant.
Results
Macrophages RAW264.7 Were Classically Activated by A. fumigatus Conidia and Produced More ROS than Hyphal Stimulation
Cells were stimulated with the A. fumigatus in different stages to elucidate the different responses of immune cells against conidia or hyphae. In vitro, immune cells were exposed to live fungi for 6 h to mimic the early dynamic stage of fungal invasion. Meanwhile, accumulated inflammatory responses were observed when exposed to heat-inactivated ones for 24 h.
Concerning macrophages, inducible nitric oxide synthase (iNOS) mRNA expression after the stimulation of Aspergillus was concentration and fungal state dependent. As shown in Figs. 1a and b, the iNOS mRNA expression is a marker of classical macrophage activation (M1 polarisation), which was increased in conidia-stimulated macrophages rather than hyphae-stimulated ones.Fig. 1 Immune responses of macrophages induced by A. fumigatus conidia and hyphae. a The iNOS mRNA expression of macrophages induced by A. fumigatus conidia and hyphae in different conditions (stimulation time and fungal activity). b The iNOS mRNA expression of macrophages induced by A. fumigatus conidia and hyphae at different concentrations. c Cell viability of macrophages after stimulation by A. fumigatus different at different concentrations. MOI ranging from 1:10 to 100:1. Data represent mean ± standard deviation (n = 3).*P < 0.05 vs. the controls. Live Conidia LC, Live Hyphal fragments LH, Heat-inactivated Conidia HIC, Heat-inactivated Hyphal fragments HIH
In host defence against fungi like alveolar macrophages, reactive oxygen species (ROS) play an essential role. In this study, intracellular superoxide (O2–) was detected by DHE staining. Macrophages stimulated by conidia revealed a higher ROS production than the control and hyphae group (Supporting Information Fig. S1 a & b), which was consistent with the cellular iNOS levels.
Fungal Stimulation Promotes the Proliferation of Macrophages RAW264.7
CCK-8 assay was used to measure the cell viability after fungal stimulation. Both conidia and hyphal fragments stimulation exhibited a concentration-dependent rise in the cell proliferation (P < 0.05), and the live fungus groups indicated a more obvious alteration than the inactivated ones (P < 0.05), though no significant difference was found between the two groups (P > 0.05) (Fig. 1c).
Cytokine Profile of A. fumigatus-Stimulated Macrophages RAW264.7 and PBMCs
The cytokine profile can intuitively reflect inflammatory reactions of macrophages after exposure to different fungal particles. As shown in Fig. S2, TNF-α and IL-6 indicated a concentration-dependent increase in secretion. Additionally, the releases of TNF-α and IL-6 were more remarkable after exposure to hyphal fragments for 6 h than exposure to conidia.
The cytokine profile of PBMCs induced by A. fumigatus conidia and hyphal fragments were detected to explore the host immune response. The human TNF-α, IL-6, IL-4 and IL-1β were directly proportional to stimuli concentration (Fig. 2), consistent with the cytokine profile of macrophages. In most cases, the human TNF-α, IL-6 and IL-4 concentrations of hyphae-stimulated PBMCs were higher than that of conidia-stimulated cells at the same concentration.Fig. 2 Cytokine profile of murine macrophages and PBMCs stimulated by A. fumigatus conidia and hyphae measured by ELISA. a human TNF-α, b human IL-6, c human IL-1β, and d human IL-4. Data represent mean ± standard deviation (n = 3). *P < 0.05 vs. the control group. Live Conidia LC, Live Hyphal fragments LH, Heat-inactivated Conidia HIC, Heat-inactivated Hyphal fragments HIH
Dynamic T Cell Subset Changes Induced by Conidia and Hyphae
Dynamic T cell subset changes were observed after exposure to conidia or hyphae (Fig. 3& Fig. S3). In general, hyphae-stimulated cells showed an obvious elevation of the CD4/CD8 ratio compared with conidia-stimulated ones (P < 0.05). The percentage of CD4+ cells was significantly increased (P < 0.05) and CD8+ slightly decreased compared to that of conidia-stimulated group (P > 0.05). The percentage of Th1 cells (Th1%) increased greatly in both LC and HIC groups (P < 0.05). In contrast, the hyphae group exhibited a higher proportion of Th2 cells than the corresponding conidia group without statistical significance (P > 0.05). Neither group showed any changes in Th17 cell differentiation (P > 0.05).Fig. 3 T cell expression of human PBMCs after stimulation by A. fumigatus conidia and hyphae. a CD4/CD8 ratio, b Th1, c Th2, d Th17. Each geometric symbol represents a sponsor (n=4). Live Conidia LC, Live Hyphal fragments LH, Heat-inactivated Conidia HIC, Heat-inactivated Hyphal fragments HIH
Survival of Immunocompetent C57BL/6 Mice Following Inhalation of A. fumigatus Conidia or Hyphal Fragments
In the murine pulmonary aspergillosis model, the mortality of hyphae-stimulated mice reached 40% (P < 0.05). Meanwhile, the mice stimulated with conidia and normal saline all survived to the end point of the assay (Fig. 4a). Additionally, focal infection of Aspergillus was only observed on the pulmonary histopathology of hyphae-inoculated mice four days post-inoculation (Fig. 4b). The fungal culture of murine lungs further confirmed the aspergillosis.Fig. 4 RNA-seq profile of the immunocompetent murine pulmonary aspergillosis model 24 h post-inoculation. a Unique gene expression. b Volcano map of DEGs. c KEGG pathway enrichment bubble chart. Note: The size of the bubble represents the number of genes annotated to the KEGG pathway. The colour represents the enriched significance. Rich Ratio = Term Candidate Gene Number/Term Gene Number. d KEGG pathway classification
The Immune Responses Against Hyphae and Conidia have Similarities and Differences in the Murine Pulmonary Aspergillosis Model
RNA-seq detected a total of 18,984 transcripts in three groups. Only 234 genes were significantly differentially regulated between the control and the conidia groups (|log2FC|≥ 2, Q-value ≤ 0.05). On the contrary, the number of differentially expressed genes (DEGs) between the hyphae and the control groups was up to 1,123, with 633 up-regulated and 490 down-regulated (Fig. 4a). However, 712 significant DEGs (Fig. 4a and b) between the conidia and the hyphae groups were analysed. KEGG pathway enrichment analysis showed a series of immunologically relevant pathways that were involved, including the IL-17 signalling pathway, TNF signalling pathway, Toll-like receptor signalling pathway, NOD-like receptor (NLR) signalling pathway, and others (Fig. 4c). Additionally, KEGG pathway classification revealed that signal transduction and immune system were common DEG-intensive categories (Fig. 4d).
Hyphae could induce more intensive inflammatory responses in murine lungs than conidia. The expression of genes associated with immunologically relevant pathways mentioned above was activated more strongly in the hyphae group than in the conidia group. We subsequently identified a series of differentially expressed cytokines as well as essential receptors, including TLRs, Clecs and NLRs, to gain insight into immune responses against Aspergillus (Fig. S5).
Thereafter, to better understand immune signalling patterns amongst the murine model, T cell differentiation pathway-associated genes were analysed by qRT-PCR. As shown in Fig. 5, both groups tended to activate the Th17 cell differentiation pathway after intratracheal mould injection. The expressions of IL-17a, IL-17f, and stat3 were up-regulated. Besides, expressions of IL-17 signalling-associated chemokines CXCL1, CXCL2, CXCL5, CXCL10 and CCL2 were likewise significantly increased. Regarding Th1 and Th2 cell differentiation, RNA-seq revealed that Th2 cell differentiation tended to get activated, and Th1 differentiation was relatively silenced in the hyphae group. The genes related to Th2 cell differentiation, including IL-13 and IL-4Ra, were significantly up-regulated, while the key genes in the Th1 cell differentiation pathway, such as stat1 and T-bet, were down-regulated. However, in the conidia group, the gene expressions of Th1 and Th2 cell differentiation pathways were not significantly altered except for the mild elevation of IL-4Ra.Fig. 5 T cell differentiation and inflammation in the murine pulmonary aspergillosis model. a The fold change of multiple immunological genes detected by RNA-seq and b The fold change of multiple immunological genes detected by qRT-PCR (b and c). Data represent mean ± standard deviation (n = 3)., *P < 0.05 vs. the control group
Discussion
A. fumigatus, the causative agent of a series of human infections, is a kind of ubiquitous fungus in the environment. IPA is the most dangerous and fatal type of aspergillosis. The current worldwide prevalence of COVID-19 leads to the elevated risk of IPA due to dysfunction of host immunity and disruption of normal lung structure. Therefore, more efforts should be made to clarify the interaction between A. fumigatus and host immunity.
This research is based on Zhang’s experimental results, which were published in 2020 [21]. As mentioned above, Zhang found A. fumigatus hyphae rather than conidia could successfully build a pulmonary aspergillosis model in immunocompetent mice. Previous studies generally chose swollen or germinated conidia to replace mature hyphae for experiments, which are convenient to quantify and easy to compare with the conidia group. However, it cannot well represent the natural state of mature hyphal infection. To simulate the inhalable hyphae infecting the respiratory tract naturally, we obtained properly sized hyphal fragments by grinding the mature hyphae rather than shortening the incubation time to get budding ones. This inevitably brought great difficulties in quantification. In the past reports, several methods for hyphal quantification showed advantages and disadvantages. Direct counting with a hemacytometer can be simple and fast to quantify newly germinated hyphae with nearly similar sizes, just like the common quantification of conidia [24]. However, this method cannot quantify mature hyphae or hyphal fragments because hyphal particles differ in size and growth activity, and thus, the systematic error of the process is difficult to measure. Colony-forming units have the same limitation as direct counting with a hemocytometer. Gravimetry has ever been adopted for fungal quantification, but the complicated operations of sample drying by freezing or heating, which often inevitably destroy the fungal activities limit its usage [25]. Meanwhile, the dried samples are easier to spread in the environment, leading to biological contamination for health risks. Additionally, the sample required for gravimetry is extremely large, causing great inconvenience for the prevalence of this method. Thus, this study made some improvements to Zhang’s protocol with a novel method for the quantification of mycelia. We first adopted the ratio of surface area to volume for mycelial and conidia quantification, which can be used to quantify other types of filamentous fungus. Admittedly, the fungal surface with different PAMPs is the first-line structure of Aspergillus to directly contact the host. Hence, the surface area is a good parameter to study the immune interaction between fungus and host.
Another critical light spot of this study is the successful establishment of in vitro and in vivo immunocompetent infection model of mature A. fumigatus hyphae. Innate and adaptive responses against Aspergillus conidia and hyphae were studied by co-incubating different immune cells and Aspergillus in vitro. Unlike most immunocompromised models, we constructed a murine immunocompetent aspergillosis model with hyphal fragments. Meanwhile, RNA-seq analysis was first performed to analyse the immune responses against respirable mature hyphal fragments in vivo.
In the study, we confirmed that hyphae of A. fumigatus could induce stronger host immune responses than conidia, as hyphae-stimulated cells secreted more inflammatory factors. The immunological differences of cytokine secretions and T cell differentiation in inactivated conidia and hyphae were larger than in active ones, probably attributed to extended incubation time. In the murine model, we again validated that immunologically relevant genes and pathways were activated more significantly in hyphae than conidia group. Consistently, faster germination of conidia has been reported to drive to greater lung damage and inflammation, which means stronger invasiveness and lethality of hyphae [26]. Meanwhile, germinating conidia could induce stronger TNF-α, IL-1, IL-1β, IL-6 and MIP-2 secretion by alveolar macrophages, which is correlated to the levels of surface-exposed beta-glucans via dendritic cell-associated C-type lectin-1 (dectin-1) receptor [27].
As for immune cells, macrophages are known to be a decisive part of innate immunity to resist the infection from A. fumigatus. Both iNOS and ROS levels are important markers of the functions of macrophages, which were significantly higher in conidia-stimulated cells than that in hyphae-stimulated cells. iNOS was mainly induced in M1 macrophages, and nitric oxide produced by iNOS would be scavenged to generate ROS, leading to extra ROS production [28]. Macrophage polarisation is an essential symbol to distinguish its functions. M1 macrophages are generally found to be polarised by lipopolysaccharide- or Th1-associated cytokines [29]. As feedback, M1 macrophages can direct T cells towards Th1 through IL-12 and antigen presentation. Subsequent flow cytometry analysis indicated that the conidia-stimulated PBMCs expressed higher levels of IFN-γ (Th1 signalling), which was consistent with the macrophage polarisation state.
T lymphocytes are an indispensable part of adaptive immunity. Regarding T cell subset change, the percentage of CD8+ T cells tended to get down after hyphal stimulation, which usually indicated a poor prognosis in IPA patients [30]. Therefore, we speculate that the decrease of CD8+ T cell proportion in severe IPA patients is likely to be owing to the overgrowth of invasive hyphae at the late stage of infection, which can hardly be eliminated by cytotoxic CD8+ T cells. Concerning CD4+ T cells, cell differentiation clearly reflected its functions. Th1 signalling drives to fight viruses, bacteria and other intracellular infections, such as A. fumigatus conidia while Th2 signalling drives to defend extracellular organisms. Th1/Th2 is in a dynamic balance, and over-activation of either pathway can down-regulate the other [31]. Besides, Th17 also plays an important role in host defence against micro-organisms, which is associated with neutrophil migration and increased inflammation [32]. However, there are contradictions and unities in the in vitro and in vivo experimental results. In the murine model, the expression of Th1 and Th2 cell differentiation-related genes were not significantly altered after inoculation of conidia, which was inconsistent with the result of an experiment in vitro. We speculate that the phenomenon might be ascribed to the effective anatomical elimination of conidia in an immunocompetent host. Only a small portion of escaped conidia can challenge the subsequent immune system [16, 33]. Besides, Th1 and Th17 cells showed antagonistic action against each other and activation of IL-17 signalling would suppress Th1 cell differentiation [34]. Overall, it concludes that conidia stimulation promotes Th1 cell differentiation, while hyphal stimulation leads to the opposite result and activates Th2 cell differentiation. Furthermore, both conidia and hyphae could activate the IL-17 signalling pathway, which may make a dual-directional regulation related to antifungal immune resistance [16].
It is a sophisticated orchestration for innate and adaptive immunity to protect from fungal invasion. The host immune responses induced by A. fumigatus conidia and hyphae are distinctly different, mainly attributed to the different PAMPs on the surface of hyphae and conidia. Various PAMPs can be recognised by specific PRRs of immunocytes, activating downstream intracellular signal transduction pathways and different inflammatory responses [15]. For instance, hyphae and conidia of A. fumigatus can activate TLR2-related cytokine synthesis, but TLR4-related pathways can only be induced by conidia [35]. Additionally, both dectin-1 and dectin-2 receptors can recognise swollen conidia and hyphae to induce host immune responses, but neither can recognise resting conidia [36, 37]. Besides, hyphae can trigger the NLR pyrin domain-containing 3 inflammasome assembly, which cannot be effectively activated by resting conidia [38]. As for the explanation of the initial phenomenon in the study, it is a paradox that hyphal fragments successfully constructed the aspergillosis model rather than conidia despite that those hyphae induced more potent inflammation. In other words, the host immune responses induced by conidia played a better protective role. As reported, various effective ways were reported to eliminate conidia by host immunity, from anatomical barriers to the synergistic action of multiple immune cells. However, the hyphae are too large to get internalised and can hardly be cleared. Even neutrophil extracellular traps (NETs), an effective form of neutrophil-mediated antimicrobial defence, can mostly inhibit hyphal growth instead of killing them [16, 39]. Moreover, hyphal invasion tends to activate Th2 cell differentiation, which is known for its suppressive function in immunity and usually leads to unfavourable outcomes [40].
However, there are still some limitations of the study. Firstly, the live conidia would be swelling and germinating during the 6 h incubation with tremendous changes in PAMPs. However, if the incubation time was cut down, the immune responses against fungus would become indistinct or non-significant. For compensation, Aspergillus conidia and hyphae were inactivated to stop the growth, and the incubation time was extended to 24 h to explore the cumulative immune effects. However, the inactivated fungus could only keep partial immune characteristics from the live ones. The second limitation is the time point of lung tissue harvest for RNA-seq analysis in the murine model. In fact, 24 h post-inoculation was chosen based on Wang’s work that the early immune cells infiltration and differentiation in the bronchi and lung tissues can be clearly scanned 24 h after fungal inoculation intratracheally [41]. However, a single time point is insufficient to elucidate the progressive process of the early immune response against Aspergillus and setting more time points can make the study protocol more logical and convictive. Thirdly, the study did not include the comparison of neutrophils defending against Aspergillus conidia and hyphae. Germinating rather than resting conidia has been reported could recruit neutrophils to the airways [42]. Meanwhile, two distinct neutrophil-mediated killing mechanisms of A. fumigatus conidia and hyphae were characterised depending on CR3 binding or IgG binding, respectively [43]. However, the function and mechanism of NETs against Aspergillus need more study, and NETs-related Aspergillus capture experiments will be carried out in subsequent studies.
Conclusion
To summarise, the hyphae state is a neglected pathogenetic form of aspergillosis. We conducted a preliminary study about immune response against hyphae in vivo and in vitro and confirmed the different host defence against hyphae and conidia. However, the underlying mechanism of the immunological differences remains unclear and future research is needed.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file1 The conidia of fumigatus induced more ROS production in macrophages than hyphae. RAW264.7 cells were stimulated by a living fungi and b heat-inactivated fungi (TIF 3007 KB)
Supplementary file2 Cytokine profile of murine macrophages stimulated by A. fumigatus conidia and hyphae measured by ELISA. a murine TNF-α, b murine IL-6, Data represent mean ± standard deviation (n = 3). *P < 0.05 vs. the control group. Live Conidia LC, Live Hyphal fragments LH, Heat-inactivated Conidia HIC, Heat-inactivated Hyphal fragments HIH (TIF 1419 KB)
Supplementary file3 T cell expression of human PBMCs after stimulation by A. fumigatus conidia and hyphae. a CD4, b CD8. Each geometric symbol represents a sponsor (n=4). Live Conidia LC, Live Hyphal fragments LH, Heat-inactivated Conidia HIC, Heat-inactivated Hyphal fragments HIH (TIF 792 KB)
Supplementary file4 a The Kaplan–Meier survival curves of mice inoculated with normal saline, conidia, and hyphal fragments. b Histopathology of lung tissues 4 days post-inoculation with PAS staining. The difference of survival rate between hyphae group and conidia or control group reaches statistical significance (n = 10, P < 0.05). The histopathology results of lung tissue from the control and the conidia-treated groups were basically normal, and no evidence of fungal infection was found in the sections. In contrast, in the lung tissue from the hyphae-treated group, Aspergillus hyphae were positive for PAS staining. (TIF 31922 KB)
Supplementary file5 Profile of TLR, Clec, and NLR gene expression. Colour indicates the level of log2(FPKM + 1). FPKM: Fragments per Kilobase of exon per Million of mapped reads (TIF 7079 KB)
Supplementary file6 (DOCX 74 KB)
Supplementary file7 (DOCX 67 KB)
Acknowledgements
We thank Dr Weida Liu and his team in the Department of Medical Mycology, Institute of Dermatology, Chinese Academy of Medical Science and Peking Union Medical College, for their help with the experiment.
Author Contributions
YL, HS and YS contributed to the study conception and design. YL, LD and JX performed the experiments; YL, FL and QK analysed and interpreted results; YL wrote the first draft of the manuscript; FL reviewed; and all authors contributed and approved the final version of the manuscript.
Funding
This work was financially supported by the National Natural Science Foundation of China (NSFC) [Grant Number 81871630 and 81330035].
Data Availability
The data of RNA-seq was submitted in GEO data sets (GSE183979). The data-sets used and analysed during the current study are available from the corresponding author upon reasonable request.
Declarations
Conflict of interest
The authors have no competing interest to declare.
Ethical approval
The study was approved by the ethics committee of Jinling Hospital (2015NJKY-035–02) and the Animal Care and Use Committee of Nanjing Normal University (20200703).
Informed Consent
All volunteers the blood samples obtained from knew their rights and gave informed consents.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yingzhi Luo, Fang Liu and Lin Deng have contributed equally to this work.
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| 36474044 | PMC9734344 | NO-CC CODE | 2022-12-14 23:28:28 | no | Curr Microbiol. 2023 Dec 6; 80(1):28 | utf-8 | Curr Microbiol | 2,022 | 10.1007/s00284-022-03102-1 | oa_other |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
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24604
10.1007/s11356-022-24604-2
Research Article
Water consumption prediction and influencing factor analysis based on PCA-BP neural network in karst regions: a case study of Guizhou Province
Yang Zhicheng 1
http://orcid.org/0000-0002-3283-5999
Li Bo [email protected]
12
Wu Huang 1
Li MengHua 1
Fan Juan 3
Chen Mengyu 1
Long Jie 4
1 grid.443382.a 0000 0004 1804 268X Key Laboratory of Karst Georesources and Environment, Ministry of Education, Guizhou University, Guiyang, Guizhou, 550025 China
2 grid.443382.a 0000 0004 1804 268X College of Resource and Environmental Engineering, Guizhou University, Guiyang, Guizhou, 550025 China
3 grid.440720.5 0000 0004 1759 0801 College of Geology and Environment, Xi’an University of Science and Technology, Xian, 710077 Shanxi China
4 grid.418569.7 0000 0001 2166 1076 State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012 China
Responsible Editor: Philippe Garrigues
8 12 2022
112
18 5 2022
1 12 2022
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Water consumption prediction is an integral part of water resource planning and management. Constructing a highly precise water consumption prediction model is of great significance for promoting regional water resource planning and high-quality development of the socio-economy. This paper focuses on the case of the typical karst region in Guizhou Province in China. Based on data on water consumption and its influencing factors spanning 2000–2020, the principal component analysis method was applied to reduce the dimensionality of 16 influencing factors of water consumption in Guizhou; the principal components extracted were used as input samples of the BP neural network and a PCA-BP neural network water consumption prediction model was conducted to predict water consumption of Guizhou Province in the next 10 years. The results show that the mean absolute error and mean relative error of prediction based on the constructed PCA-BP neural network were 2.8% and 2.9%, respectively, with superior performance in terms of prediction error and trends compared with other models. This paper discusses the main influencing factors of water consumption and analyzes their influence on the water consumption forecasting model so that the parameters of the water consumption forecasting model can be selected more efficiently and provide a reference for regional water consumption analysis and water resource planning and management.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11356-022-24604-2.
Keywords
Karst region
Water consumption prediction
Principal component analysis
BP neural network prediction
Influencing factor
http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 42162022 41702270 Li Bo http://dx.doi.org/10.13039/501100005329 Natural Science Foundation of Guizhou Province Qian Ke He Ji Chu [2019]1413 Qian Ke He Zhi Cheng [2020]4Y048 Li Bo
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pmcIntroduction
Water is an indispensable resource for humans. With economic development and population growth around the world, the contradiction of supply and demand for water resources has become aggravated, and under the background of the current COVID-19 pandemic, the pattern of global carbon emissions has changed. The rational use of water resources is an important part of carbon emissions so making water resource planning is increasingly crucial (Wang et al. 2018a, b; Liu et al. 2021; Wang and Su 2020; Li et al. 2022). Precisely predicting water consumption is the first and foremost task for water resource planning and management and the prediction results directly influence the reliability and practicality of water resource planning and decision-making (Hao et al. 2009; Sivapalan et al. 2012). In the meantime, water consumption is subject to the influences of many uncertain factors such as the total amount of water resources, climate, population, and economy, significantly increasing the difficulty of accurately predicting water consumption (Fan et al. 2017).
Many contributions have been made to water consumption prediction by previous generations. They have proposed a series of water consumption prediction models, including the autoregressive-moving average model (ARIMA), support vector regression model (SVR), gray theory model GM (1, 1), random forest regression model, and neural network model (He, Fang et al.). The influencing factors of high degree are selected to establish an improved coupling model of the grey system and multiple regressions to predict water consumption in Wuhan. The applied research showed that the forecast effect of the improved coupled model is good with a relative error of less than 1%, and successfully predicted the water consumption data of Wuhan City in 2015 (He and Tao 2014). Q, Wang et al. combined the projection pursuit algorithm and the real-coded accelerated genetic algorithm to establish a comprehensive model, which used 17 high-dimensional, non-normal, and nonlinear complex index data to evaluate renewable energy. Sustainability achieved good results (Wang and Su 2020). Dos Santos, DC et al. used an artificial neural network (ANN) system approach to predict water consumption in the metropolitan area of São Paulo with low prediction error, considering the influence of weather and environmental factors on water consumption (Dos Santos and Pereira Filho 2014). Farias, RL et al. used the Qualitative Multi-Model Predictor Plus (QMMP +) model to predict water use in Barcelona and compared it with the Radial Basis Function Artificial Neural Networks (RBF-ANN), the statistical Autoregressive Integrated Moving Average (ARIMA), and Double Seasonal Holt-Winters (DSHW) models, which have higher prediction accuracy (Lopez Farias et al. 2018). Pu et al. proposed a variable structure support vector regression (VS-SVR) water use prediction model, and the results showed that the VS-SVR model prediction reduced the error of the prediction results by 1.2% compared to the SVR model (Pu et al. 2015). Q, Wang et al. developed two combined ARIMA-BPNN and BPNN-ARIMA simulation methods to simulate carbon emissions in China, India, the USA, and the European Union under the COVID-19 no-pandemic scenario. The average relative error of the simulation is about 1% (wang et al.2021). Almanjjahie et al. proposed a multiplicative seasonal autoregressive integrated moving average model (SARIMA), which adequately takes into account the seasonal characteristics of water consumption and obtains better prediction results (Almanjahie et al. 2019). Sebri, M et al. quarterly time series of household water consumption in Tunisia is forecasted using a comparative analysis between the traditional Box-Jenkins method and artificial neural networks approach. Results indicate that the traditional Box-Jenkins method has higher prediction accuracy than the neural network model and is closer to the actual data (Sebri 2013). Q, Wang et al. developed grey theory-based single-linear, hybrid-linear, and non-linear forecasting techniques based on grey theory are developed to more accurately forecast energy demand in China and India (wang et al.2018a, b). Piasecki, A et al. prediction of water use on the Czerniewice estate using a multilayer perception (MLP) as artificial neural network approach in combination with nine factors, including meteorology, with good prediction results (Piasecki et al. 2016). Chen et al. a multiple random forests model, integrated wavelet transform and random forests regression (W-RFR), proposed for the prediction of daily urban water consumption in the southwest of China and the results showed that the W-RFR model can not only meet the prediction accuracy of water consumption but also has higher prediction accuracy than the RFR model and FFNN model (Chen et al. 2017).
Despite certain achievements made in research based on the above water consumption prediction methods, their applications are not without limitations. For example, the autoregressive integrated moving average (ARIMA) model puts more consideration into changing patterns in the yearly water consumption data without giving the effect of water consumption influencing factors on the series into account (Wu et al. 2021). Although BP neural network considers the effect of other factors on water consumption, the method tends to produce local optimal solutions (Xu et al. 2020). Existing research on water consumption tends to focus on prediction models themselves without considering the combined effect of influencing factors on water consumption. In addition, given the large number of water consumption influencing factors, introducing too many influencing factors would complicate the model, making it difficult to ensure prediction accuracy (Azimi et al. 2018; Lili et al. 2021). On the other hand, water consumption influencing factors entail complicated relationships and overlapping information; to eliminate the correlation of complex data, it is necessary to simply influence factor data (Wu et al. 2021). This paper proposes a principal component analysis method to reduce the dimensionality of water consumption influencing factors and extract highly correlated principal components to replace extremely complicated influencing factors. Using data of the extracted principal components as inputs of the BP neural network effectively alleviates the model’s tendency to produce local optimal solutions, thereby increasing the precision of the prediction model. As such, based on the PCA-BP neural network model, this paper employs the data of water consumption and its influencing factors spanning 2000–2020 in the karst region of Guizhou, China to predict water consumption and analyze the effect of influencing factors on the prediction model. The proposed method is conducive to alleviating regional contradictions of water supply and demand and improving water resource planning and management.
Methodology and data
Principal component analysis (PCA)
Initially proposed by K. Pearson in 1901 and improved by Hotelling in 1933, principal component analysis is a data dimensionality reduction method that transforms multiple indicators into a few composite indicators (Castura et al. 2022; Heo et al. 2009). Given a certain level of information overlap in raw data, PCA conducts linear spatial projection on the raw dataset based on analyzing matrix characteristics, then converse raw data into a new-characteristic space to extract the principal linear component; a few principal component variables that are most likely to represent the information contained by the original variables are used to replace the original variables, thereby reducing the dimensionality of a multi-variable system and transform it into a system containing individual and non-correlated variables (Wu et al. 2021; Liu et al. 2003). The algorithmic steps are as follows:Step 1: Conduct standardization processing over the raw data matrix to obtain a new data matrix;
Step 2: After the standardization, construct the coefficient matrix of variables R;
Step 3: Compute the characteristic values of the correlation matrix R and order them in a sequential manner; find the characteristic vector corresponding to each characteristic value;
Step 4: Find the contribution rate Pm and cumulative contribution rate αm. The following principle is usually followed for the selection of principal components: the characteristic value is larger than 1 and the cumulative contribution rate is about 85%;
Step 5: Calculate the loading of the principal component, denoted by Zm, that is, the correlation coefficient between principal components and variables;
Step 6: Obtain principal components data after dimensionality reduction based on characteristic values and their corresponding characteristic vectors.
BP neural network
Neural networks construct data processing models by imitating the structure of brain neurons and their reflection process (Xu et al. 2018). As one type of the neural network, BP neural network is a multi-layer feedforward neural network composed of the input layer, hidden layer, an output layer, input vector, hidden weight value, threshold, the activation function of the hidden layer, and the activation function of the output layer and output function. Neurons of a layer only receive neural signals from the previous layer and those at the same layer do not have any connections with each other. The number of hidden layers, the number of neurons in each layer, and the network learning rate can be adjusted or set based on specific needs (Jia and Wu 2020; Zhu et al. 2021), as shown in Fig. 1. In the meantime, the processes of forward propagation network error and back propagation errors are repeatedly cycled based on the actual and expected errors of the BP neural network to constantly adjust the weight and threshold values until the error between the network output value and the expected output value of samples is reduced to an acceptable level and the preset number of cycles is reached so that the model is as accurately fitted as possible (Wu et al. 2021).Fig. 1 The working principle of the BP neural network
Combined prediction model of PCA-BP neural network
Using the principle of error correction, the PCA-BP neural network combination method is formed by combining the principal component analysis method and the bp neural network model. Combination model combines the advantages of the principal component analysis method and the BP neural network and makes up for the shortcomings. When the BP neural network model predicts, the simplicity and complexity of the input variables are one of the keys to affect the prediction effect. However, there are many factors affecting water consumption, and all the inputs will inevitably affect the prediction accuracy, so the number of input variables needs to be reduced. In order to ensure that the original information is preserved as much as possible while reducing the number of input variables. The principal component analysis method is used to reduce the dimension of the influencing factors of water consumption, and the formed new principal component data is input into the BP neural network model to obtain the final prediction data. Specific steps are as follows:Step 1: Conduct a principal component analysis on the influencing factors of water consumption.
Step 2: Select the newly formed principal components to make their cumulative variance contribution rate greater than 85%.
Step 3: Input the principal components selected in step 2 into the bp neural network model to predict water consumption.
Model accuracy test
To test model errors, this paper adopts mean absolute error (MAE) and mean relative error (MRE) to analyze the prediction results of different models. The MAE refers to the mean of absolute errors between prediction values and real values. While the MRE refers to the mean of the quotients between the prediction values and real values. Both indicators can effectively reflect model accuracy.1 MAE=1m∑i=1mx-y
2 MRE=-1m∑i=1mx-yy×100%
Overview of the researched region
Located in the inland region of southwestern China, Guizhou is one of the three contiguous karst regions in the world and the central one in East Asia covering an area of about 176,200 km2. Located in the watershed region of the Yangtze and Zhujiang river basins, Guizhou province harbors densely distributed river networks and has an average annual precipitation of about 1200 mm. The main supply source of water resources is atmospheric precipitation and surface and groundwater mutually compensate for each other in frequent hydrologic cycles. Despite abundant water resources due to its coverage of the Yangtze and Zhujiang river systems, the region suffers from ground karst development and special binary structures between surface and groundwater cause severe permeation of groundwater, making it difficult to form contiguous catchment areas. With extremely poor water holding capacity, part of the rivers may dry up during seasonal droughts. Furthermore, deeply buried groundwater has diverse occurrence modes, making it difficult to exploit. For such a typical karst region like Guizhou, accurately predicting water consumption helps the government formulate well-informed water resource management policies, addressing the contradiction between water demand and supply and promoting sustainable development of regional water resources see Fig. 2.Fig. 2 Geographical location of Guizhou Province
Data source
This research adopts statistical data derived from the Guizhou Water Resources Bulletin (Guizhou Provincial Department of Water Resources 2020), Statistical Yearbook of Guizhou Province (Guizhou Provincial Bureau of Statistics 2020), and Statistical Bulletin of National Economic and Social Development of Guizhou Province (Guizhou Provincial Bureau of Statistics 2020). Seventeen indicators including water consumption, precipitation, the total amount of water resources, population, GDP, and total industrial output were selected. See Table S1 for more details see Fig. 3.Fig. 3 Water consumption influence factors
Analysis of influencing factors
Water consumption influencing factors are an organic collection of multiple indicators. Based on precedent studies in combination with the actual conditions of Guizhou, 16 influencing factors for water consumption in Guizhou were selected from aspects of agricultural, industrial, domestic, and eco-environmental water consumption (Guizhou Provincial Water Resources Bulletin 2020; Chen et al. 2012; Sandiford et al. 1990; Duan and Chen 2020; Keshavarzi et al. 2006). Specifically, agricultural water consumption is related to effective irrigation area, total agricultural output value, value-added of farming, forestry, animal husbandry, and fishery, and total grain production; industrial water consumption is closely related to total industrial output value and value-added of industry; domestic water consumption is subject to influences of population, GDP, and water supply penetration rate; eco-environmental water consumption is reflected by precipitation, the total amount of water resources, groundwater resources, temperature, annual sunshine hours, forestry area, and total wastewater discharge (Romano et al. 2016; Guizhou 2020; Fan et al. 2017).
Results and analysis
Data standardization
Influencing factors for water consumption in Guizhou are numerous and entail different dimensions. To reduce the influences of different dimensions on the final prediction results, the standardization procedure was performed on data first. The equation is as follows: where Q denotes the standardized variable; qxy denotes the y influencing factors corresponding to the x-th sample; qy denotes the sample mean of the y-th influencing factor; and sy denotes the standard deviation of the y-th influencing factor.3 Q=qxy-qySy,x=1,2,⋯m;y=1,2,⋯n
In the meantime, this paper adopts the following variables to replace influencing factors: X1-X16 are precipitation, the total amount of water resources, total grain production, total industrial output value, population, GDP, water supply penetration rate, annual sunshine hours, groundwater resources, added-value of industry, added-value of farming, forestry, animal husbandry and fishery, temperature, total wastewater discharge, forestry area, effective irrigation area, and total agricultural output value. The results are shown in Table S2.
Principal component analysis
Given the different influencing degrees of each variable on the predicted target, it is difficult to obtain ideal results if all 16 influencing factors are entered into the prediction model as characteristics. Therefore, this research adopts the PCA method to perform dimensionality reduction and characteristic selection on data. Based on standardized data, the contribution rates and cumulative contribution rates of the covariance matrix, characteristic value of matrix, eigenvector, and principal components were calculated.
As shown in Table 1, the KMO statistic is 0.695 and the Bartlett’s test value is 0, indicating that each water consumption influencing factor is separately independent of others and the PCA method can be used to reduce dimensionality.Table 1 The KMO and the Bartlett’s test
The KMO statistic The Bartlett’s test
The approximate chi-square Degree of freedom Significance
0.695 661.922 120 0.000
As can be known from Table 2, when 4 principal components are extracted, the eigenvalue is 1.015 (≥ 1) and the cumulative variance contribution rate is 90.62%, indicating that these components contain the majority of information on water consumption influencing factors. Therefore, 4 principal components were selected to replace water consumption influencing factors.Table 2 Principal component eigenvalue and variance contribution rate
Serial number Initial eigenvalue
Eigenvalue Variance contribution rate (%) Cumulative variance contribution rate (%)
1 8.882 55.512 55.512
2 3.217 20.105 75.618
3 1.384 8.651 84.269
4 1.015 6.346 90.615
5 0.790 4.935 95.549
6 0.321 2.004 97.553
7 0.189 1.180 98.734
8 0.096 0.599 99.333
9 0.046 0.290 99.622
10 0.028 0.172 99.795
11 0.016 0.100 99.895
12 0.009 0.054 99.949
13 0.006 0.035 99.984
14 0.002 0.015 99.999
15 0.000 0.001 100.000
16 0.000 0.000 100.000
The scoring coefficient of the principal components reflects the degree of correlation between principal components and water consumption influencing factors. As shown in Table 3, for the 1st principal component (F1), the loading contributions of total agricultural output value, effective irrigation area, forestry area, total wastewater discharge, GDP, value-added of industry, and water supply penetration rate are relatively large, and thus principal component 1 can be generalized as the socio-economic development and intra-provincial eco-environmental factor; the 2nd principal component (F2) mainly reflects conditions of water resources in Guizhou province, which is significantly positively correlated with principal components, total amount of water resources and groundwater resources but significantly negatively correlated with annual sunshine hours and effective irrigation area, which is because sunshine and irrigation cause water consumption, leading to a reduction in total amount of water resources; principal component 3 (F3) has a relatively large loading on total grain production and thus mainly represents the agricultural influencing factor; principal component 4 (F4) has a large loading on annual sunshine hours and can be generalized as the weather factor. Principal components 1 through 4 reflect the comprehensive conditions of water consumption in Guizhou from different perspectives. These 4 factors can be used as major influencing factors for water consumption in Guizhou for further prediction, and based on the eigenvectors corresponding to eigenvalues, the principal component data of F1, F2, F3, and F4 can be obtained, as shown in Table S3.Table 3 Component score coefficient
Influencing factor Component
1 2 3 4
Precipitation 0.508 0.826 0.106 0.044
The total amount of water resources 0.440 0.851 0.161 − 0.012
Total grain production 0.502 − 0.250 0.606 − 0.273
Total industrial output value 0.961 − 0.157 0.032 − 0.032
Population − 0.352 0.623 − 0.373 0.393
GDP 0.981 − 0.073 − 0.113 0.117
Water supply penetration rate 0.898 − 0.345 0.113 − 0.167
Annual sunshine hours − 0.033 − 0.536 0.200 0.736
Groundwater resources 0.339 0.857 0.102 − 0.134
Added-value of industry 0.987 − 0.121 − 0.067 0.064
Added-value of farming 0.725 0.155 − 0.307 0.008
Temperature 0.070 0.318 0.785 0.335
Total wastewater discharge 0.97 − 0.047 − 0.134 0.027
Forestry area 0.96 0.023 − 0.152 0.198
Effective irrigation area 0.961 − 0.221 − 0.006 − 0.062
Total agricultural output value 0.979 − 0.007 − 0.094 0.148
BP neural network
The F1, F2, F3, and F4 data obtained from the PCA were used as input layer data, with the number of input nodes set as 4, the target error as 10−5, and the maximum training times as 1000. Using the annual water consumption of Guizhou as a prediction object, the water consumption data of Guizhou spanning 2000–2020 were divided as the training set and test set, respectively. Based on the 7:3 division principle, the data spanning 2000–2013 was divided as the training set while data spanning 2014–2020 was divided as the test set. The BP neural network prediction model was constructed to test the prediction performance of the PCA-BP neural network model.
As can be found from the fitting results shown in Figs. 4 and 5, after using the PCA to reduce the dimensionality of water consumption influencing factors, using the BP neural network to predict water consumption in Guizhou resulted in a fairly good fitting performance, with the fitting values of training and prediction are 0.99084 and 0.96045, respectively. In terms of the selection of the number of neurons at the hidden layer, an ideal fitting performance can be obtained if the number is set around 2 k + 1 if the number of input nodes is k. According to the test conducted in this research, the optimal effect can be obtained when the number of hidden layers is 7. The prediction results are shown in Table 4.Fig. 4 Training results fitting
Fig. 5 Prediction results fitting
Table 4 Prediction results of different models
Year Real water consumption (108m3) PCA-BP forecast (108m3) BP forecast (108m3) ARIMA forecast (108m3) GM(1,1) with fractional order accumulation (108m3)
2014 95.31 93.26 99.24 93.4 97.6071
2015 97.49 96.53 100.82 97.2 97.6102
2016 100.31 98.46 104.51 97.7 97.612
2017 103.51 97.59 104.7 99.6 97.613
2018 106.79 106.13 104.75 101.6 97.6136
2019 108.06 106.97 95.94 103.4 97.6139
2020 90.08 97.5 98.53 102.9 97.6142
MAE 2.8 5.03 4.48 5.45
MRE 2.90% 5.10% 4.60% 5.36%
In the meantime, to demonstrate that the PCA-BP neural network has a higher prediction accuracy, a comparison with the BP neural network, the GM(1,1) with fractional order accumulation, and the ARIMA model was performed. Table 4 shows the results of 7-year water consumption prediction and errors in Guizhou based on different models.
According to Table 4 and Fig. 6, the overall trend of the prediction results based on the PCA-BP neural network is more skewed to real water consumption, with the MAE being 2.8 and MRE being 2.9%, lower than those of other prediction models. Notably, despite a better error performance of the BP neural network when influencing factors are directly input, it derives a poorer prediction trend. For example, the real water consumption was a gradually increasing trend in 2014–2018 followed by a sudden dropping trend in 2019–2020; while the trends derived from the BP neural network were gradual growth in 2014–2018, followed by a sudden decrease in 2019 and then a rise in 2020. This indicates a poorer trend of prediction compared with that derived by entering dimensionality-reduced factors generated by the PCA method. This is because the massive information of influencing factors and numerous input factors of the BP neural network complicated the model and affected to some extent its prediction trend. The comparison sufficiently demonstrates that entering too many influencing factors affects prediction results, thus pointing to the importance of reducing the dimensionality of water consumption influencing factors.Fig. 6 Prediction results of different models
The above PCA-BP neural network model can reasonably predict future water consumption in Guizhou. Based on the average growth rates and mean values of water consumption influencing factors in 2000–2020 in Guizhou, as shown in Table S3, the influencing factor data in 2021–2030 were predicted. Furthermore, water consumption data of Guizhou in 2021–2030 were predicted using the PCA-BP neural network model, with the results shown in Table 5.Table 5 Water consumption forecast of Guizhou in 2021–2030
Year Predicted value (108m3) Year Predicted value (108m3)
2021 97.48 2026 101.19
2022 98.58 2027 100.96
2023 99.59 2028 100.31
2024 100.43 2029 99.33
2025 100.99 2030 98.02
As can be seen from prediction data in Table 5 and Fig. 7, water consumption in Guizhou in the next 10 years first shows a period of increase followed by a decrease, exhibiting a “heap”-shape trend. Such a first-increasing then-decreasing trend is a result of combined effects like socioeconomic advancement, development, and more attention to eco-environmental construction in recent years. As can be seen from Table S4, the 16 influencing factors of water consumption in Guizhou all showed positive average growth in the past 20 years; thus, it can be deduced that over the short term water consumption in the province will continue to grow. In addition, during the 13th five-year plan period, Guizhou province has put in vigorous effects to develop the Guizhou Jiayan Water Diversion Project and Guizhou Huangjiawan Key Water Resources Project to promote the scientific development of water resources, comprehensively increase the level of water security. This will inevitably cause continuous growth in industrial and eco-environmental water consumption, thereby resulting in an increasing trend in water consumption in Guizhou. Despite the positive average growth rates of all influencing factors, the average growth of population, total grain production, water supply penetration rate, and temperature are relatively small at 0.15%, 2.22%, 2.22%, and 0.08%, respectively. The guiding policies of Guizhou Province, for example, the 14th five-year plan, point out that the province will gain a foothold in resource environmental bearing capacity to deploy ecologically functional spaces in an orderly fashion. With the industrial restructuring and increasingly sophisticated water conservation facilities in the province, the declining trend of domestic and eco-environmental water consumption will be inevitable, further driving the declining trend of water consumption in Guizhou over the long term in the future.Fig. 7 Water consumption forecast of Guizhou in 2021–2030
Discussion
The overall water consumption in Guizhou province has projected a growing trend in recent years. However, factors like precipitation and the total amount of water resources exhibited a smooth trend. This has led to a strain on the supply–demand relationship and thus, accurate prediction of water consumption is urgently needed in order to provide a reasonable basis for future water resource planning. As water consumption is subject to the effect of numerous factors like precipitation, sorting out the interactive relationship between water consumption and its influencing factors is the key to accurate prediction. The effects of water consumption influencing and input factors on the prediction model are discussed as follows:➀ To determine the effect of influencing factors on water consumption, the sums of the absolute values of the F1-4 scores corresponding to each influencing factor shown in the principal component scoring coefficient table (Table 3) were calculated based on the PCA principle, and then the influencing factors were sequenced based on the magnitude of the value. See Table S5 for more details. The value reflects the magnitude of the correlation between an influencing factor and water consumption; the larger the value, the higher the degree of correlation. As can be seen from Table S5, factors having a relatively higher correlation with water consumption are population, total grain production, water supply penetration rate, and temperature.
Compared with previous detailed studies on water consumption by population and water price, the influence indicators selected in this paper and the main influencing factors of water consumption are undoubtedly more comprehensive (Nosvelli et al. 2009).
It can also be found from Fig. 8 that water consumption has an almost the same trend with population, total grain production, water supply penetration rate, and temperature, that is, increasing first and then decreasing over time. All of them reach minimum values around 2011 and then gradually grow. This further validates that the above influencing factors are significantly correlated with water consumption in Guizhou.➁ As shown in Fig. 6, the 2019 prediction and real water consumption derived from the single BP neural network prediction model are hugely different from those of the PCA-BP neural network. A comparison of the prediction processes of the BP neural network model and the PCA-BP neural network reveals that the entry of influencing factors is the main cause of such differences.
Fig. 8 Trends in influencing factors
Different from other articles, this article will select the indicators of the input bp model and reduce the input indicators while ensuring the completeness of the information as much as possible (Chen et al.2020).
In the 2018–2019 data of all influencing factors, the values of total agricultural output value, value-added of industry, annual sunshine hours, temperature, total grain production, and effective irrigation area all exhibited different levels of decrease. The decrease in values of these 6 influencing factors affected the 2019 prediction results of the BP neural network model. However, based on the analysis presented earlier, these 6 influencing factors largely have a low correlation with water consumption in Guizhou, but they nevertheless affected the overall prediction trend and reduced the accuracy of the model. Here, the indicators with low correlation with water consumption are successfully eliminated, which also makes the impact indicators more suitable for the characteristics of karst areas, which points to the importance of performing dimensionality reduction on influencing factors for model accuracy.
The PCA-BP neural network model also has some drawbacks in predicting water consumption. First, due to various restrictions, only the 2000–2020 water consumption data and those of the 16 influencing factors were collected. On the one hand, the data samples are relatively few; on the other hand, the data may not cover all the influencing factors of water consumption. Second, there is still room for improving model accuracy. However, the model proposed in this paper is still capable of providing technical support for water resources management in Guizhou Province. In the future, the comprehensibility of the data should be improved and water consumption influencing factors be analyzed based on socioeconomic principles.
Conclusion
This paper proposes a PCA-BP neural network prediction method to improve the prediction accuracy while incorporating the indicators that affect water consumption into the prediction model. Water consumption prediction is affected by numerous influencing factors that have complicated relationships. BP neural network has a fairly good prediction performance when multiple factors are involved but are prone to produce local convergence and high randomness, leading to a necessity to reduce the number of input nodes. Thus, this paper proposes a PCA-BP neural network model to predict water consumption in Guizhou Province. ➀ With a sufficient consideration of the actual conditions of Guizhou, 16 major water consumption influencing factors were selected and the PCA method was introduced to reduce their dimensionality, deriving 4 principal components with a cumulative variance contribution rate of 90.62%.
② Principal components and water consumption data were entered into the BP neural network to validate the water consumption in 2014–2020; a comparison with the prediction results of grey GM(1,1), time series ARIMA and single BP neural network was performed and the results indicate that the MAE and MRE of the prediction based on the PCA-BP model are 2.8 and 2.9%, respectively, lower than those of other prediction models. In particular, compared with the single BP neural network, PCA effectively reduced data redundancy and improved prediction accuracy.
➂ On that basis, the PCA-BP neural network model was used to predict water consumption in 2021–2030 in Guizhou. Water consumption in Guizhou Province in 2021–2030 exhibits a “heap”-shape trend that first increases then decreases.
This paper uses the PCA-BP model to provide a new idea for water consumption prediction, which has certain reference significance for promoting the planning and management of water resources in karst areas. However, the selection of water consumption impact indicators in this paper is affected by the characteristics of water resource consumption in karst areas. Therefore, if this model needs to be used in the planning and analysis of water consumption and water resources in a wider area, the general applicability of the model can be improved by further data mining of the impact indicators, to predict the regional water consumption more accurately and reasonably.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 40 KB)
Author contribution
Zhicheng Yang: writing-original draft and formal analysis. Bo Li: writing-review and editing and project administration. Huang Wu: software, investigation, and writing-original draft. MengHua Li: writing-review and editing and software. Juan Fan: investigation and formal analysis. Mengyu Chen: writing-review and editing. Jie Long: data analysis and investigation.
Funding
This research was financially supported by Natural Science Foundation (42162022; 41702270), Guizhou Province Excellent Youth Science and Technology Talent Project (Qian Ke He Ping Tai Ren Cai [2021]5626), Guizhou Science and Technology Department Project (Qian Ke He Ji Chu [2019]1413; Qian Ke He Zhi Cheng [2020]4Y048; Qian Ke He Zhi Cheng [2020]4Y007; Qian Ke He Zhi Cheng[2020]4Y005), Guizhou Provincial Department of Education Foundation ([2018]113), Shanxi Province Coal Mine Water Hazard Prevention and Control Technology Open Fund (2020SKMS01).
Data availability
All data generated or analyzed during this study are included in this published article and its supplementary information files.
Declarations
Ethics approval
Not applicable. This manuscript does not involve researching about humans or animals.
Consent to participate
All of the authors consented to participate in the drafting of this manuscript.
Consent for publication
All of the authors consented to publish this.
Conflict of interest
The authors declare no competing interests.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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| 36480138 | PMC9734345 | NO-CC CODE | 2022-12-14 23:28:28 | no | Environ Sci Pollut Res Int. 2022 Dec 8;:1-12 | utf-8 | Environ Sci Pollut Res Int | 2,022 | 10.1007/s11356-022-24604-2 | oa_other |
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