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==== Front
Vaccine
Vaccine
Vaccine
0264-410X
1873-2518
Elsevier Ltd.
S0264-410X(22)00444-3
10.1016/j.vaccine.2022.04.028
Letter to the Editor
Screening for SARS-CoV-2 antibodies to save vaccine doses
Fenollar Florence
Aix Marseille Univ, IRD, AP-HM, SSA, VITROME, Marseille, France
IHU-Méditerranée Infection, Marseille, France
Thomas Laurence
IHU-Méditerranée Infection, Marseille, France
Raoult Didier
Aix Marseille Univ, IRD, AP-HM, SSA, VITROME, Marseille, France
Aix Marseille Univ, IRD, MEPHI, Marseille, France
Gautret Philippe ⁎
Aix Marseille Univ, IRD, AP-HM, SSA, VITROME, Marseille, France
IHU-Méditerranée Infection, Marseille, France
⁎ Corresponding author at: VITROME, Institut Hospitalo-Universitaire Méditerranée Infection, 19-21 Boulevard Jean Moulin, 13005 Marseille, France.
12 4 2022
12 4 2022
30 6 2021
17 3 2022
6 4 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.
Keywords
COVID-19
SARSCoV-2
Serology
Screening
Vaccine
==== Body
pmcTo the Editor
We read with interest the paper by Ferrari et al. [1] showing that previously SARS-CoV-2 infected individuals had a strong humoral immune response after a first dose of COVID-19 vaccine suggesting that a single dose of vaccine should be proposed to seropositive individuals. As of June, 21, 2021, more than 31 million persons received at least one dose of vaccine against COVID-19 in France which global population is about 67 million [https://solidarites-sante.gouv.fr/grands-dossiers/vaccin-covid-19/article/le-tableau-de-bord-de-la-vaccination]. The Haute Autorité de Santé (HAS) recommends that one dose only of anti-SARS-CoV-2 vaccine be proposed to persons previously infected with the virus. Because infected patients may have been asymptomatic and not tested, screening through rapid serologic test is recommended in individuals who are unsure about their status. This may avoid injecting unnecessary second doses of vaccine in already infected persons and saving doses for those in need [2]. In a serological surveillance study conducted in the Provence-Alpes-Côte d’Azur region, from May, 11 to May 17, 2020, 3.3% of 397 individuals tested positive [3].
In our vaccination center we implemented a pre-vaccination serological screening strategy starting on May 10, 2021 and provide here an interim analysis of results until June 15, 2021. All persons consulting for a first dose of vaccine against COVID-19 were proposed a rapid whole-blood finger-stick immune-chromatographic serologic test (Biosynex COVID-19 BSS, SW40005, Biosynex, Switzerland). The BioSynex test was selected because of its good sensitivity and specificity reported previously [4], [5]. Data presented herein were collected retrospectively from the routine care setting using the electronic health recording system of the hospital.
541 persons were included of whom 309 (57.1%) were female, with a mean age of 40 years (ranging 18–97 years). The vast majority, (522 (96.5%)) had a negative result and were eligible for two doses of vaccine. 19 were positive (3.5%). Among 19 persons with a positive result, ten (52.6%) were female and their mean age was 45 years (ranging 20–69 years), which did not significantly differed from negative individuals. Most positive patients had positive IgG only, three had both positive IgG and IgM and one had positive IgM only (Table 1). Among these 19 individuals, ten had a PCR documented past SARS-CoV-2 infection and received one dose of vaccine only. Systematic testing allowed identifying nine additional individuals with a positive anti-SARS-CoV-2 serology who were not aware of a COVID-19 infection and who received one dose of vaccine only. Of note, two of these nine patients experienced symptoms compatible with COVID-19 and one was in close contact with her husband that had a confirmed SARS-CoV-2 infection. Systematic serological testing allowed therefore saving 9/541 (1.7%) doses of vaccine.
To conclude, the benefit of systematically screening individuals before a first dose of COVID-19 vaccine appears to be limited, in our experience, allowing saving less than 2% vaccine doses. Comparison with similar studies conducted in area with a higher incidence of COVID-19 will be of interest.
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.
Acknowledgement
We thank Cédrick Hamidou for his excellent technical work.
Ethical approval
This study was approved by the Ethics Committee of our institute (Ref. 2021-022).
Consent to participate
Participant were proposed serological testing as part of routine care. No written informed consent was required.
Consent to publish
Not applicable.
Authors’ contribution
FF, DR and PG contributed to the experimental design, data analysis, statistics, interpretation and writing. LT coordinated the laboratory work.
Funding
This study was supported by the Institut Hospitalo-Universitaire (IHU) Méditerranée Infection, the National Research Agency under the “Investissements d’avenir” programme, reference ANR-10-IAHU-03, the Région Provence Alpes Côte d’Azur and European funding FEDER PRIMI.
Availability of data and materials
Data available on request.
==== Refs
References
1 Ferrari D. Di Resta C. Tomaiuolo R. Sabetta E. Pontillo M. Motta A. Long-term antibody persistence and exceptional vaccination response on previously SARS-CoV-2 infected subjects Vaccine 39 31 2021 4256 4260 34147292
2 Haute Autorité de Santé. Décision n° 2021.0139/DC/SEESP du 31 mai 2021 du collège de la Haute Autorité de santé complétant les recommandations du 11 février 2021 relatives à « la vaccination des personnes ayant un antécédent de Covid-19 » [available from https://www.has-sante.fr/upload/docs/application/pdf/2021-06/decision_n2021.0139_dc_seesp_du_31_mai_2021_du_college_de_la_has_completant_les_reco_du_11_fevrier_2021_relatives_a__la_vacc.pdf, accessed June 24, 2021].
3 Le Vu S. Jones G. Anna F. Rose T. Richard J.-B. Bernard-Stoecklin S. Prevalence of SARS-CoV-2 antibodies in France: results from nationwide serological surveillance Nat Commun 12 1 2021
4 Velay A, Gallais F, Benotmane I, Wendling MJ, Danion F, Collange O, et al Evaluation of the performance of SARS-CoV-2 serological tools and their positioning in COVID-19 diagnostic strategies. Diagn Microbiol Infect Dis 2020;98(4):115181.
5 Péré H, Mboumba Bouassa RS, Tonen-Wolyec S, Podglajen I, Veyer D, Bélec L. Analytical performances of five SARS-CoV-2 whole-blood finger-stick IgG-IgM combined antibody rapid tests. J Virol Method 2021;290:114067.
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PMC009xxxxxx/PMC9001175.txt |
==== Front
J Pediatr Surg
J Pediatr Surg
Journal of Pediatric Surgery
0022-3468
1531-5037
Elsevier Inc.
S0022-3468(22)00267-6
10.1016/j.jpedsurg.2022.03.034
Article
The COVID-19 pandemic and associated rise in pediatric firearm injuries: A multi-institutional study
Collings Amelia T. a⁎
Farazi Manzur b
Van Arendonk Kyle J. b
Fallat Mary E. cd
Minneci Peter C. e
Sato Thomas T. b
Speck K. Elizabeth f
Deans Katherine J. e
Falcone Jr Richard A. g
Foley David S. cd
Fraser Jason D. h
Gadepalli Samir K. f
Keller Martin S. i
Kotagal Meera g
Landman Matthew P. a
Leys Charles M. j
Markel Troy A. a
Rubalcava Nathan f
St. Peter Shawn D. h
Flynn-O'Brien Katherine T. b
Midwest Pediatric Surgery Consortium
a Department of Surgery, Indiana University, 545 Barnhill Dr., Emerson 125, Indianapolis, IN, United States
b Children's Wisconsin, Milwaukee, WI, United States
c Norton Children's Hospital, Louisville, KY, United States
d Hiram C. Polk, Jr Department of Surgery, University of Louisville, KY, United States
e Center for Surgical Outcomes Research, Abigail Wexner Research Institute and Department of Surgery Nationwide Children's Hospital, The Ohio State University College of Medicine, Columbus, OH, United States
f Division of Pediatric Surgery, Mott Children's Hospital, Ann Arbor, MI, United States
g Division of Pediatric General and Thoracic Surgery, Cincinnati Children's Hospital Medical Center, Department of Surgery, University of Cincinnati College of Medicine, Cincinnati, OH, United States
h Children's Mercy Kansas City, Kansas City, MO, United States
i Division of Pediatric Surgery, Washington University School of Medicine, St Louis, MO, United States
j Division of Pediatric Surgery, Department of Surgery, University of Wisconsin, Madison, WI, United States
⁎ Corresponding author.
12 4 2022
7 2022
12 4 2022
57 7 13701376
17 1 2022
24 3 2022
31 3 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.
Background
Firearm sales in the United States (U.S.) markedly increased during the COVID-19 pandemic. Our objective was to determine if firearm injuries in children were associated with stay-at-home orders (SHO) during the COVID-19 pandemic. We hypothesized there would be an increase in pediatric firearm injuries during SHO.
Methods
This was a multi institutional, retrospective study of institutional trauma registries. Patients <18 years with traumatic injuries meeting National Trauma Data Bank (NTDB) criteria were included. A “COVID” cohort, defined as time from initiation of state SHO through September 30, 2020 was compared to “Historical” controls from an averaged period of corresponding dates in 2016–2019. An interrupted time series analysis (ITSA) was utilized to evaluate the association of the U.S. declaration of a national state of emergency with pediatric firearm injuries.
Results
Nine Level I pediatric trauma centers were included, contributing 48,111 pediatric trauma patients, of which 1,090 patients (2.3%) suffered firearm injuries. There was a significant increase in the proportion of firearm injuries in the COVID cohort (COVID 3.04% vs. Historical 1.83%; p < 0.001). There was an increased cumulative burden of firearm injuries in 2020 compared to a historical average. ITSA showed an 87% increase in the observed rate of firearm injuries above expected after the declaration of a nationwide emergency (p < 0.001).
Conclusion
The proportion of firearm injuries affecting children increased during the COVID-19 pandemic. The pandemic was associated with an increase in pediatric firearm injuries above expected rates based on historical patterns.
Keywords
Pediatric trauma
Firearm violence
COVID-19
Pandemic
Gun violence
2020
Abbreviations
WHO, World Health Organization
CDC, disease control and prevention
UNESCO, United Nations Educational, Scientific, and Cultural Organization
MWPSC, Midwest Pediatric Surgery Consortium
SHO, stay-at-home orders
NTDB, national trauma data bank
SVI, social vulnerability index
ITSA, interrupted time series analysis
ISS, injury severity score
SRS, simple random sampling
==== Body
pmc1 Introduction
With the spread of the SARS-CoV2 virus (COVID-19) causing a worldwide pandemic, there have been unprecedented alterations in daily living. Following World Health Organization (WHO) recommendations for social distancing, more people began working from home, unemployment increased, and both public and private schools transitioned to temporary closure and remote education. The United Nations Educational, Scientific, and Cultural Organization (UNESCO) reported that 1.37 billion school-aged children worldwide were at one point out of school [1]. Simultaneously, as children were forced to stay at home, families experienced job loss and increased financial strain. According to the United States (U.S.) Bureau of Labor Statistics, the United States reached an unemployment rate of almost 15% at its peak [2] .
During this time, Americans began to purchase firearms at an extraordinary rate. In the first four months of the pandemic, 4.3 million more firearms were purchased in the U.S than expected based on historical trends, an 85% increase [3]. In addition, the United States experienced heightened racial tensions in 2020 with several high-profile police shootings. Many of these events occurred within the Midwest region, where there was heightened awareness that children were increasingly becoming victims of firearm injuries [4], [5], [6].
The objectives of this study were to determine the association of the start of the COVID-19 pandemic with firearm injuries and to explore the change in predictors of firearm injuries and outcomes before and after SHO. We hypothesized that the COVID-19 pandemic would be associated with an increase in firearm injuries among children.
2 Methods
2.1 Study design and population
In this multi institutional study, nine Level I pediatric trauma centers from the Midwest Pediatric Surgery Consortium (www.mwpsc.org) contributed local trauma registry data from January 1, 2016 to September 30, 2020. Study inclusion criteria comprised injured children <18 years old who met National Trauma Data Bank (NTDB) standards, such as those admitted, transferred, and/or died. A complete list of inclusion criteria can be found on the American College of Surgeons’ NTDB website [7]. Institutional review board approval was obtained at each site with a waiver of consent.
The primary aim of the study was to evaluate the association of the COVID-19 pandemic with changes in pediatric firearm injuries. The primary outcome of interest was change in the volume of pediatric firearm-related injuries. Secondary outcomes included mortality, hospital length of stay (LOS), and hospital disposition. Intensive care unit (ICU) stay and days on mechanical ventilation were analyzed only for those patients who had an ICU LOS or ventilator days ≥ 1 day, respectively.
The study population was divided into two cohorts. The first was the “COVID” cohort, which was defined as patients who were injured from the date of the SHO to September 30, 2020. Each site's individual SHO orders were defined based on local and state ordinances. A “Historical” cohort was used as the comparator. Patients from corresponding SHO dates to September 30 from 2016 to 2019 were averaged in order to minimize outlying effects from any one year. In addition, a sensitivity analysis comparing the COVID cohort to patients evaluated over the same period in 2019 alone was completed to account for temporal changes in care (e.g., population changes, catchment area re distribution, triage pattern changes including centralization or decentralization). Demographics, injury characteristics, and outcomes were compared between cohorts. Using the U.S. Department of Agriculture's definition of rural counties, a crosswalk was used to code resident zip codes into urban or rural categories [8]. Rural was defined as open countryside, towns with <2500 people, and cities with 2500–49,999 people not part of a larger metropolitan area. Specific injuries were categorized by body region using the International Classification of Disease 10th revision (ICD-10) diagnosis codes to obtain a more granular description of the firearm injuries. Location External Cause Codes were used to categorize those injuries that occurred inside or outside a home. Data for this variable were only available from 7 of the 9 sites, and only those sites were included in this specific analysis.
Predictors of pediatric firearm violence were explored. The year 2020, following SHO dates, was the exposure period of interest. With known associations between social determinants of health and firearm violence [9], we set out to determine the association between firearm violence and social vulnerability. The CDC's Social Vulnerability Index (SVI) ranks each census tract on 15 social factors compromising 4 domains: socioeconomic status, household composition and disability, minority status and language, and housing and transportation. An SVI of 0 indicates the population of lowest level of vulnerability and 1 the highest vulnerability. SVI as a predictor of pediatric firearm violence was examined.
2.2 Statistical analysis
The Pearson Chi Square was used to compare categorical variables, while Student's t-test and Wilcoxon rank-sum were used to compare normally distributed continuous variables and non parametric continuous variables, respectively. If missing data comprised >5% then it would be presented in the results section. Significance was set at p < 0.05. Using a LOESS smoothing technique, month-to-month variations in gun violence were graphed for the two cohorts, COVID vs. Historical. We considered both the average number of firearm injuries per month and the cumulative burden of firearm injuries throughout the year.
Among all injured patients, logistic regression was used to assess a child's odds of firearm injury post COVID-19 pandemic in 2020 compared to the Historical cohort, controlling for age, sex, race, SVI. To minimize any regional confounders, clustering by site was also included in the model. Simple random sampling (SRS) of the non firearm injuries was compared to firearm injuries owing to the imbalance in population sizes. The SRS model was bootstrapped 1000 times to limit any potential rare event bias.
An interrupted time series analysis (ITSA), using a Poisson distribution, was used to determine the difference in expected and observed rates of firearm injuries in a longitudinal fashion, while still controlling for the known temporal variations of traumatic injuries. An “interruption” of March 13, 2020 was used as this was the date of declaration of a state of emergency in the U.S in response to the COVID-19 pandemic. Local and regional SHO went into effect shortly following this announcement. Variations between expected versus observed rates of firearm injuries per month were compared. All analyses were performed using R statistical software (RStudio, version 1.4.1717 © 2009–2021 RStudio, PBC) [10].
3 Results
From January 1, 2016 to September 30, 2020, a total of 47,385 pediatric trauma patients met inclusion criteria. Of this population, 1,062 children (2.2%) sustained firearm injuries. Overall, the COVID cohort experienced significantly more firearm injuries than the averaged Historical cohort (COVID 215 patients (3.04%) vs. Historical 108 patients (1.83%); p < 0.001). This was consistent in the sensitivity analysis using just 2019 data (COVID 215 patients (3.04%) vs. Historical 109 patients (1.79%); p < 0.001). The number of firearm injuries observed each month significantly increased in March through September 2020 relative to the Historical cohort (p = 0.006, Fig. 1 a) with an increased cumulative burden of firearm injuries in 2020 when compared with prior years (Fig. 1b).Fig. 1 (a) Frequency of firearm injuries by month across all sites with a LOESS smoothing line; (b) Cumulative firearm injuries by month across all sites.
Fig 1
There were no significant differences in baseline characteristics of sex, age, SVI, or Injury Severity Score (ISS). There was a significant difference in ethnicity across cohorts (Table 1 ); however, this was driven by a decrease in “unknowns” in the COVID cohort. There were no significant differences in the intent of firearm injuries and the proportion of injuries that occurred in the home (Table 1). The sensitivity analysis using 2019 as a recent historical control did not vary greatly from the averaged Historical cohort in terms of demographics or outcomes (Supplement Tables 1 and 2). There was no significant change in the COVID cohort with respect to the specific types of injuries resulting from firearms when compared to the Historical cohort (Supplement 3). In addition, there were no significant differences in the mortality, hospital or ICU length of stay, ventilator days, or hospital disposition between cohorts (Table 2 ).Table 1 Demographic and injury characteristics; Number (%).
Table 1 Historical average 2016–2019
Total N = 431
Averaged N = 108 COVID 2020
N = 215 p-value
Male 82 (76.6) 164 (76.3) 0.696
Age, years 0.948
<1 2 (1.9) 3 (1.4)
1–4 12 (11.1) 26 (12.1)
5–9 13 (12.0) 20 (9.3)
10–14 31 (28.7) 64 (29.8)
15–17 50 (46.3) 102 (47.4)
Mean age, years (SD) 12.3 (4.8) 12.5 (4.8) 0.55
Race 0.464
White 23 (21.3) 39 (18.1)
African American 76 (70.4) 149 (69.3)
Other 9 (8.3) 27 (12.6)
Ethnicity <0.001
Hispanic 4 (3.7) 17 (7.9)
Non-Hispanic 79 (73.8) 187 (87.0)
Unknown 24 (22.4) 11 (5.1)
Residence 1.0
Rural 6 (5.6) 11 (5.1)
Urban 101 (94.4) 203 (94.9)
Social Vulnerability Index, quartiles 0.277
1st Quartile, least vulnerable 8 (7.5) 7 (3.3)
2nd Quartile 19 (17.8) 48 (22.3)
3rd Quartile 38 (35.5) 69 (32.1)
4th Quartile, most vulnerable 42 (39.3) 91 (42.3)
Weighted Mean SVI (SD) 0.64 (0.21) 0.66 (0.20) 0.3
Median Household Income 0.201
1st Quintile 53 (49.1) 105 (48.8)
2nd Quintile 23 (21.3) 49 (22.8)
3rd Quintile 14 (13.0) 28 (13.0)
4th Quintile 9 (8.3) 27 (12.6)
5th Quintile 9 (8.3) 6 (2.8)
Payor 0.289
No Insurance 9 (8.4) 22 (10.2)
Private 19 (17.8) 26 (12.1)
Public 77 (72.0) 156 (72.6)
Unknown/Missing 2 (1.9) 11 (5.1)
Injury Severity Score (ISS) 0.779
0–15 44 (41.5) 97 (45.1)
16–24 14 (13.2) 24 (11.2)
25+ 48 (45.3) 94 (43.7)
Injury Intent 0.299
Unintentional 29 (26.9) 52 (24.2)
Assault 64 (59.3) 136 (63.3)
Suicide 5 (4.6) 3 (1.4)
Other 10 (9.3) 24 (11.2)
Location of Injury* 0.93
Home 31 (40.8) 60 (40.5)
Other 44 (57.9) 85 (57.4)
⁎ Includes 7 of the 9 sites.
Table 2 Outcomes after firearm injury; Number (%).
Table 2 Historical average 2016–2019
Total N = 431
Averaged N = 108 COVID 2020
N = 215 p-value
Overall mortality 9 15 0.999
Death in ED 4 (44.4%) 8 (53.3)
Inpatient death 5 (55.6) 7 (46.7)
ED Disposition 0.731
Floor/Observation 45 (41.7) 90 (41.9)
ICU 17 (15.7) 24 (11.2)
Operating Room 26 (24.1) 46 (21.4)
Transfer Out 2 (1.9) 7 (3.3)
Morgue 4 (3.7) 8 (3.7)
Home 10 (9.3) 32 (14.9)
Unknown 4 (3.7) 8 (3.7)
Hospital Disposition 0.457
Morgue 5 (5.4) 7 (4.3)
Home & Home Services 72 (77.4) 133 (81.1)
Other Facility 9 (9.7) 14 (8.5)
Jail or Against Medical Advice 3 (3.2) 2 (1.2)
Unknown 4 (4.3) 8 (6.9)
Median vent days [IQR]^ 2.5 [1], [2], [3], [4], [5], [6], [7] 4 [1], [2], [3], [4], [5] 0.64
Median LOS, ICU [IQR]^ 3 [2–6.5] 3 [2], [3], [4], [5] 0.95
Median LOS, hospital [IQR] 2 [1], [2], [3], [4], [5], [6], [7] 2 [1], [2], [3], [4], [5], [6] 0.98
^ Of patients that had vent or ICU LOS ≥1day.
In univariate analyses, age, sex, race and SVI were associated with increased odds of pediatric firearm injury (Table 3 ) and were thus included in the multivariate model. In adjusted analysis, injured children were at almost 80% increased odds of suffering a firearm injury post SHO in 2020 compared to prior years (adjusted odds ratio (aOR) 1.78, 95% CI 1.45–2.12, p < 0.001; Table 4 ). In the sensitivity analysis using bootstrapped SRS, a similar effect size was demonstrated, with children at 63% higher odds of sustaining a firearm injury in 2020 (aOR 1.63, 95% CI 1.17–2.27, p = 0.018; Supplement Table 4).Table 3 Univariate regression for the association with firearm injuries compared to any other type of injury.
Table 3Variable Odds Ratio 95% CI p-value
Age, years
< 1 0.49 0.25–0.89 0.030
1–4 1.55 1.11–2.14 0.009
5–9 Ref – –
10–14 3.30 2.52- 4.36 <0.001
15–17 10.38 8.06–13.56 <0.001
Sex (Male) 2.06 1.72–2.48 <0.001
Race
Caucasian Ref – –
African American 15.91 13.11–19.44 <0.001
Minority, other 4.25 2.90–6.24 <0.001
Other 3.93 2.51–5.82 <0.001
Social Vulnerability Index (SVI), Quartile
1st, most resourced Ref – –
2nd 1.9 1.34–2.75 <0.001
3rd 3.631 2.62–5.16 <0.001
4th least resourced 12.81 9.27–18.18 <0.001
Table 4 Multivariate regression with aggregate population measuring the association of the COVID pandemic (2020) and firearm injury, accounting for demographic factors.
Table 4Variable Odds Ratio 95% CI p-value
Historical Control Ref – –
COVID Cohort 1.78 1.45–2.12 <0.001
Age, years
< 1 0.37 0.18- 0.69 0.002
1–4 1.31 0.94–1.82 0.108
5–9 Ref – –
10–14 2.89 2.20–3.85 <0.001
15–17 8.41 6.47–11.06 <0.001
Gender (Male) 1.65 1.37–2.01 <0.001
Race
Caucasian Ref – –
African American 10.65 8.61–13.25 <0.001
Minority, other 4.24 2.83–6.18 <0.001
Other 3.39 2.23–5.17 <0.001
Social Vulnerability Index (SVI), Quartile
1st, most resourced Ref – –
2nd 1.57 1.10–2.29 0.015
3rd 2.30 1.64–3.3 <0.001
4th least resourced 3.63 2.56–5.27 <0.001
After the declaration of a state of emergency, there was an increase in observed firearm injuries over the expected rate (Fig. 2 ). This difference was statistically significant within the Poisson model, which calculated 87% higher odds of injured children suffering firearm injuries during the COVID-19 pandemic following SHO (OR 1.87, 95% CI 1.54–2.28, p < 0.001).Fig. 2 Interrupted time series with the interruption point representing the declaration of state of emergency in the U.S. (March 13, 2020).
Fig 2
4 Discussion
This study found that during the COVID-19 pandemic, there was a significant change in the volume of pediatric firearm injuries. There was an increased cumulative burden of firearm injuries in 2020 compared to a historical average. After controlling for multiple risk factors, injured children were at higher odds of sustaining a firearm injury in 2020 compared to prior years. Furthermore, there was an increase in the observed rate of firearm injuries above that of the expected rate after the onset of the COVID-19 pandemic. Although our study group found that occupancy motor vehicle collisions decreased during the pandemic, there was an increase in falls and injuries from bicycles and ATVs, leading to a net increase in overall trauma [11]. Thus, the rise in the proportion of firearm injuries is not fully explained by an overall decrease in other mechanisms of injury.
To date, there have been two other studies published evaluating rates of pediatric firearm injuries during the COVID-19 pandemic. Cohen et al. queried the Gun Violence Archive for patients <12 years old and found that from March to August 2020, firearm injuries both suffered and perpetrated by children increased when compared to historical controls [12]. Although our study also included adolescents, similar trends were observed. Gastineau et al. probed the Pediatric Health Information System and reported an almost 40% increase in firearm-related encounters in 2020 when compared to 2017–2019, although there was no change in patient demographics between time points [13]. We observed increased firearm injury rates during the COVID-19 pandemic, supporting the findings of these studies. Our study uniquely found that the least resourced children, measured by those in the 4th SVI quartile, were at the greatest risk during this time.
In adults, analogous findings have been observed. Several single and multi institutional studies have shown that firearm injuries in adults increased after SHO when compared to previous years [14], [15], [16], [17]. There may be a regional effect on the change in frequency in firearm injuries, with Midwest and urban areas experiencing a greater increase in firearm injuries compared to Western states and rural areas [16,18]. Our study confirms that the increase in firearm injuries after SHO in the Midwest also affected children with an increased cumulative burden of firearm injuries and a significant increase in the odds of an injured child sustaining a firearm injury compared to prior years.
The downstream consequences of the COVID-19 pandemic and SHO have yet to be completely understood; however, as fear, mistrust, and uncertainty increased, many individuals obtained firearms for protection [19,20]. There was an unprecedented surge in US gun and ammunition sales in 2020, often by first-time buyers who may also be less likely to know how to safely use, secure and store weapons [3,19,21]. Additionally, some parents reported that fear of protests, home invasion, or the unknown caused them to make firearms more accessible to their adolescent children [20].
There were several limitations to our study. First, this was a retrospective study with its inherent limitations, including an inability to determine causation rather than association between events. We chose to use institutional trauma registry data and therefore were limited to evaluating patients whose injuries met NTDB inclusion criteria, thus excluding patients who were discharged directly home from the ED or who died before hospital arrival. Implementation of SHO orders varied between different regions and states, which may have caused some site variations in its effects. Despite the large catchment areas of the included trauma centers, this study was not a population-based assessment and thus generalizability may be limited.
5 Conclusions
The proportion of firearm injuries affecting children increased during the COVID-19 pandemic. After controlling for significant demographics, injured children were at higher odds of sustaining a firearm injury in 2020 compared to previous years. The pandemic was associated with an increase in pediatric firearm injuries above expected rates based on historical patterns. Future work must determine if this increase was a transient shift during the acute phase of the pandemic or rather a sustained level of increased violence. This work will help direct resources toward firearm injury prevention should the trend continue.
Level of evidence: III.
Declaration of Competing Interest
The authors have no financial disclosures. The study had no source of funding.
Appendix Supplementary materials
Image, application 1
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Image, application 4
Acknowledgments
We would like to thank the research coordinators and trauma registrars at each of the participating sites for their help in obtaining institutional data, including Sarah Fox, Jill Jaeger, Kristin Braun, Jane Riebe-Rodgers, Suzanne Moody, Taunya Kessler, Jessica Johnson, Carley Lutz, Michelle Bainter, Jodi Raymond, Pete Muenks, Elizabeth McClure, Jennifer Seay, Linda Cherney, Benjamin Eithun, Loran Zwiefelhofer, Connor Fairfax, and Amanda Truelove. A special thanks to Sarah Fox, the MWPSC Project Manager, for her efforts in helping the MWPSC to run smoothly and efficiently.
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jpedsurg.2022.03.034.
==== Refs
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PMC009xxxxxx/PMC9001176.txt |
==== Front
Vaccine
Vaccine
Vaccine
0264-410X
1873-2518
Elsevier Science
S0264-410X(22)00447-9
10.1016/j.vaccine.2022.04.031
Article
Post-authorization surveillance of adverse events following COVID-19 vaccines in pregnant persons in the vaccine adverse event reporting system (VAERS), December 2020 – October 2021
Moro Pedro L. a⁎
Olson Christine K. a
Clark Elizabeth b
Marquez Paige a
Strid Penelope c
Ellington Sascha c
Zhang Bicheng a
Mba-Jonas Adamma d
Alimchandani Meghna d
Cragan Janet e
Moore Cynthia e
a Immunization Safety Office, Division of Healthcare Quality Promotion, National Center for Zoonotic and Emerging Infectious Diseases, Centers for Disease Control and Prevention (CDC)
b Fertility Epidemiology Studies Team, Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, CDC
c Emergency Preparedness and Response Team, Field Support Branch, Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, CDC
d Center for Biologics Evaluation and Research, Food and Drug Administration
e Division of Birth Defects and Infant Disorders, National Center on Birth Defects and Developmental Disabilities, CDC
⁎ Corresponding author.at: Immunization Safety Office, Division Of Healthcare Quality Promotion, NCEZID, Centers for Disease Control and Prevention, 1600 Clifton Rd, MS V18-4, Atlanta, GA 30329-4027.
12 4 2022
26 5 2022
12 4 2022
40 24 33893394
25 1 2022
13 3 2022
6 4 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
Pregnant persons are at increased risk of severe illness from COVID-19 infection, including intensive care unit admission, mechanical ventilation, and death compared with non-pregnant persons of reproductive age. Limited data are available on the safety of COVID-19 vaccines administered during and around the time of pregnancy.
Objective
To evaluate and summarize reports to the Vaccine Adverse Event Reporting System (VAERS), a national spontaneous reporting system, in pregnant persons who received a COVID-19 vaccine to assess for potential vaccine safety problems.
Methods
We searched VAERS for US reports of adverse events (AEs) in pregnant persons who received a COVID-19 vaccine from 12/14/2020–10/31/2021. Clinicians reviewed reports and available medical records. Crude reporting rates for selected AEs were calculated, and disproportional reporting was assessed using data mining methods.
Results
VAERS received 3,462 reports of AEs in pregnant persons who received a COVID-19 vaccine; 1,831 (52.9%) after BNT162b2, 1,350 (38.9%) after mRNA-1273, and 275 (7.9%) after Ad26.COV2.S. Eight maternal deaths and 12 neonatal deaths were reported. Six-hundred twenty-one (17.9%) reports were serious. Pregnancy-specific outcomes included: 878 spontaneous abortions (<20 weeks), 101 episodes of vaginal bleeding, 76 preterm deliveries (<37 weeks), 62 stillbirths (≥20 weeks), and 33 outcomes with birth defects. Crude reporting rates for preterm deliveries and stillbirths, as well as maternal and neonatal mortality rates were below background rates from published sources. No disproportional reporting for any AE was observed.
Conclusions
Review of reports to VAERS following COVID-19 vaccines in pregnant persons did not identify any concerning patterns of maternal or infant-fetal outcomes.
Keywords
Adverse events
Epidemiology
Coronavirus
COVID-19
mRNA vaccines
SARS-CoV-2
Adenovirus type 26
Pregnancy
Surveillance
Vaccine safety
==== Body
pmc1 Introduction
Pregnant and recently pregnant persons with coronavirus disease 2019 (COVID-19) have an increased risk of severe illness that results in intensive care unit admission, mechanical ventilation, and death compared with non-pregnant women of reproductive age [1], [2], [3], [4]. Pregnant persons with COVID-19 are at increased risk of preterm birth and may be at risk of other adverse pregnancy outcomes compared with pregnant persons without COVID-19. Three COVID-19 vaccines were authorized for emergency use in the United States (U.S.) by the Food and Drug Administration (FDA) during the study period, with two of these vaccines receiving full FDA approval since the end of the study period. Two COVID-19 vaccines are messenger ribonucleic acid (mRNA) based and were authorized in December 2020: BNT162b2 from Pfizer Inc/BioNTech and mRNA-1273 from Moderna [5], [6]. Ad26.COV2.S from Janssen Pharmaceuticals Companies of Johnson & Johnson uses a recombinant replication-incompetent adenovirus type 26 (Ad26) vector to stimulate an immune response and was authorized for emergency use at the end of February 2021 [7]. Data on safety of these vaccines in pregnancy were limited because pregnant persons were excluded from pre-authorization clinical trial enrollment and were not eligible to receive additional doses of the vaccine if found to be pregnant during the studies [8], [9]. The Centers for Disease Control and Prevention’s (CDC) Advisory Committee on Immunization Practices (ACIP) COVID-19 vaccine initial allocation guidance stated that pregnant people may choose to get vaccinated [10], [11], [12]. In August 2021, CDC updated its interim clinical considerations for use of COVID-19 vaccines to recommend COVID-19 vaccination during pregnancy [13]. The CDC and the FDA leveraged existing and implemented new vaccine safety monitoring systems to capture information about vaccination during pregnancy to better understand the safety profiles of COVID-19 vaccines in pregnancy.
A study of the safety of mRNA COVID-19 vaccines in pregnant persons during the first two months of the vaccination program found no safety concerns for these vaccines [14]. This preliminary analysis included data from three systems: v-safe [15] the v-safe COVID-19 Vaccine Pregnancy Registry, and the Vaccine Adverse Event Reporting System (VAERS) [16]. During the time since that initial publication, additional reports of adverse events among pregnant persons have been submitted to VAERS. This manuscript describes VAERS reports for pregnant persons who received any of the three authorized COVID-19 vaccines since the start of the U.S. vaccination program in December 2020.
2 Material and methods
2.1 Vaccine adverse events reporting system (VAERS)
VAERS is a national passive vaccine safety surveillance system, implemented in 1990 and co-administered by the CDC and the FDA that receives spontaneous reports of adverse events (AEs) from healthcare providers, vaccine recipients, manufacturers, and other reporters following vaccination [16]. Vaccine manufacturers are required, by law, to report AEs that come to their attention, and healthcare providers are required to report AEs that are considered a contraindication to further doses of vaccine and those specified in the VAERS table of reportable events following vaccination [16]. VAERS data are monitored in real time to detect new, unusual, or rare vaccine AEs as well as increases in known AEs.
Signs and symptoms of AEs reported to VAERS are coded by trained personnel and entered into a database using the Medical Dictionary for Regulatory Activities (MedDRA), a clinically validated, internationally standardized medical terminology [17]. A VAERS report may be assigned one or more MedDRA preferred terms (PT). A PT is a distinct descriptor for a symptom, sign, disease, diagnosis, therapeutic indication, investigation, surgical, or medical procedure, or medical, social, or family history characteristic [17]. Reports are further classified as serious as per the U.S. Code of Federal Regulations if one of the following is reported: death, life-threatening illness, hospitalization or prolongation of hospitalization, permanent disability, or a congenital anomaly [18]. Methods used to analyze and categorize pregnancy reports in VAERS have been described previously [19].
2.2 Review of reports
We identified U.S. reports of pregnant persons who received COVID-19 vaccine and who reported an AE to VAERS by using a pregnancy-status question in the VAERS form, specific MedDRA codes, and a text-string search of the symptom field in the VAERS form. Medical records were requested for all serious reports. Physicians (PM, CO) manually reviewed all VAERS reports to distinguish pregnancy from non-pregnancy reports. Obstetricians reviewed medical records of case-reports containing more specialized pregnancy information (CO, EC). Reports indicating that COVID-19 vaccination was administered prior to the last menstrual period or during the post-partum period were excluded. When more than one AE was reported for the same person, we selected what we believed was the primary clinical event of concern after medical review. If the report described an AE in the pregnant person and their fetus or infant, we considered this a single report but treated AEs as separate. AEs were classified as pregnancy-specific (e.g., spontaneous abortion, stillbirth), non-pregnancy specific (e.g., local or systemic reaction), infant or neonatal (e.g., birth defect), or as certain pre-specified conditions of special interest (e.g., anaphylaxis, SARS-CoV-2 infection after COVID-19 vaccination).
2.3 Analysis
We calculated frequencies of the most common MedDRA coding terms, demographic and selected pregnancy and fetal outcomes, and reporting rates for selected outcomes using SAS version 9.3 (SAS Institute, Cary, NC).
We used empirical Bayesian (EB) data mining to identify AEs that were reported more frequently than expected following COVID-19 vaccines compared to other vaccines in VAERS. We also identified COVID-19 vaccine-adverse event combinations that were reported more frequently than expected among pregnant women aged 16–54 years [20]. We conducted the analyses using the Multi-Item Gamma Poisson Shrinker (MGPS) algorithm [20], [21] in Oracle’s Empirica™ Signal System. The main statistical scores computed were EBGM, EB05, EB95, representing the empirical Bayes geometric mean and the 90% confidence interval. We used published criteria to identify AEs that were reported at least twice as frequently as would be expected following a COVID-19 vaccine (i.e., lower bound of the 90% confidence interval surrounding the EB geometric mean [EB05] > 2) [21].
2.4 Reporting rates
We used national vital statistics data on monthly live births and fetal deaths to calculate rates. To account for the study period of interest, we applied a proportion of 0.58 to December birth and fetal death values as vaccines were only available for a portion of December. To estimate the number of live births, we used national provisional counts by month for 2020 and 2021 [22]. We estimate 3,173,387 live births occurred in the United States during our study period. Fetal death microdata from 2019 was used to determine monthly fetal deaths [23]. Consistent with National Center for Health Statistic methods [24] we included fetal deaths of 20 weeks’ gestation or more as determined by obstetric estimate, among women 15–44 years, excluding foreign residents, for December 2020 through October 2021. Approximately 18,945 stillbirths occurred during the study period. We then applied COVID-19 vaccination coverage rates for women receiving vaccination during pregnancy by month. A vaccination coverage of 2.6% was applied to monthly live births and stillbirths for December 2020, January 2021, and February 2021 as only cumulative data were available [25]. Based on weekly vaccination coverage data available for April through October 2021, a monthly average was applied to live births and stillbirths occurring these months providing the number of live births and stillbirths that occurred where a COVID-19 vaccine may have been received during pregnancy [25]. The rate of stillbirths was calculated monthly by dividing the number of stillbirths observed by the total number of stillbirths and live births that occurred where a vaccine may have been received during pregnancy. The rate of preterm births was calculated by dividing the number of preterm births observed by live births where a vaccine may have been received. Similarly, the maternal and infant mortality rate after COVID-19 vaccination was calculated by dividing the number of maternal or infant deaths observed by live birth. See supplementary material
2.5 Ethics
Because VAERS is a routine public health surveillance program that does not meet the definition of research, it is not subject to Institutional Review Board review and informed consent requirements.
3 Results
From December 14, 2020 through October 31, 2021, VAERS received a total of 603,786 reports after receipt of any COVID-19 vaccine; 3,462 of these reports involved pregnant persons: 1,831 (52.9%) after BNT162b2, 1,350 (39.0%) after mRNA-1273, 275 (7.9%) after Ad26. COV2. S and 6 had unknown manufacturer. Medical records were obtained for 713 (20.6%) of 3,462 reports. Characteristics of pregnancy reports into VAERS can be seen in Table 1 . Six-hundred twenty-one (17.9%) reports were coded as serious, including eight maternal deaths. Most COVID-19 vaccines were reported as administered during the first (1,040; 45.5%) or second trimester of pregnancy (727; 31.8%). The most frequent pregnancy-specific AEs reported following any COVID-19 vaccine administration (Table 2 ) were spontaneous abortion (SAB) in 878 women (25.4%), vaginal bleeding in 101 (2.9%), premature delivery in 76 (2.2%) and stillbirth in 62 (1.8%). Most (81.3%; 490/603) SABs with gestational age data were reported to occur at < 12 weeks’ gestation and in 338/859 (39.3%) the pregnant person was 35 years of age or older. Among 849 SAB reports with onset interval information (onset interval is the period of time from vaccination to presentation of symptoms/signs of the adverse event), the onset interval was 0–3 days in 26.4%, 4–7 days in 12.0%, 8–14 days in 15.5%, and ≥ 15 days in 46.1%Table 1 Characteristics of VAERS reports received following COVID-19 vaccines in pregnant persons, United States, December 14, 2020-October 31, 2021.
Characteristic
All vaccines a BNT162b2 Vaccine mRNA-1273 Vaccine Ad26.COV2.S Vaccine
Total reports 3,462 1,831 1,350 275
Maternal age in years, median (IQR) b 33 (30–36) 33 (30–36) 33 (30–36) 33 (30–36)
Reports with maternal, age ≥ 35 years b, n (%) 1,233 (36.3) 660 (36.0) 477 (35.3) 95 (34.5)
Interval from vaccination to adverse event in days, median (IQR)c 2 (0–15) 2 (0–17) 2 (0–14) 1 (0–11)
Gestational age in weeks at time of vaccination, median (IQR) d 15.0 (7–26) 16 (6–27) 14 (7–25) 18 (9–28)
Reports of serious adverse events, n (%)e 621 (17.9) 359 (19.6) 217 (16.1) 42 (15.3)
Type of reporter, N (%)
Patient/parent 2,213 (63.9) 1,116 (61.0) 920 (68.1) 176 (64.0)
Provider 747 (21.6) 401 (21.9) 296 (21.9) 46 (16.7)
Other 157 (4.5) 83 (4.5) 60 (4.4) 14 (5.1)
Manufacturer 345 (10.0) 231 (12.6) 74 (5.5) 39 (14.2)
Trimester of pregnancy at time of vaccination, N (%) 2,288c 1,268 851 167
First (0 – 13 weeks) 1,040 (45.5) 567 (44.7) 413 (48.5) 60 (35.9)
Second (14 – 27 weeks) 727 (31.8) 396 (31.2) 271 (31.8) 59 (35.3)
Third (>= 28 weeks) 520 (22.7) 304 (24.0) 167 (19.6) 48 (28.7)
IQR (interquartile range).
a Brand unknown in 6 reports;
b Maternal age unknown in 69 reports.
c Onset interval unknown for 106 reports with adverse events
d Gestational age at time of vaccination is unknown for 1,174 reports.
e A report is defined as serious when one of the following is reported: death, life-threatening illness, hospitalization or prolongation of hospitalization, permanent disability, a congenital anomaly[12]
Table 2 Reported pregnancy-specific and infant adverse events (AEs)† in pregnant persons following receipt of COVID-19 vaccines, VAERS, December 2020-October 2021.
Adverse events†
All vaccines BNT162b2 Vaccine mRNA-1273 Vaccine Ad26.COV2.S Vaccine
Total reports 3,462 1,831 1,350 275
N (%)
Pregnancy specific‡ 1,377 (39.8) 783 (42.8) 506 (37.5) 84 (30.5)
Spontaneous abortion (<20 weeks gestation) 878 (25.4) 479 (26.2) 341 (25.3) 55 (20.0)
Vaginal bleeding 101 (2.9) 59 (3.2) 36 (2.7) 6 (2.2)
Preterm delivery (<37 weeks) 76 (2.2) 48 (2.6) 25 (1.9) 3 (1.1)
Stillbirth (≥20 weeks gestation) 62 (1.8) 38 (2.1) 20 (1.5) 4 (1.5)
Premature rupture of membranes 25 (0.7) 17 (0.9) 8 (0.6) 0
Placental abnormalities 34 (1.0) 17 (0.9) 13(1.0) 4 (1.5)
Preeclampsia/gestational hypertension 43 (1.2) 20 (1.1) 13 (1.0) 9 (3.3)
Ectopic/molar pregnancy 17 (0.5) 9 (0.5) 6 (0.4) 2 (0.7)
Gestational diabetes 15 (0.4) 10 (0.5) 4 (0.3) 1 (0.4)
Maternal deaths 8 (0.2) 6 (0.3) 2 (0.1) 0
Other a 91 (2.6) 59 (3.2) 27 (2.0) 5 (1.8)
Infant† 108 (3.1) 64 (3.5) 40 (3.0) 4 (1.5)
Neonatal death 12 (0.3) 9 (0.5) 1 (0.07) 2 (0.7)
Birth defects 26 (0.8) 11 (0.6) 15 (1.1) 0
Infant in intensive care unit (diverse abnormalities) 21 (0.6) 10 (0.5) 10 (0.7) 1 (0.4)
bOther infant conditions 49 (1.4) 34 (1.9) 14 (1.0) 1 (0.4)
†Adverse events are not mutually exclusive; ‡Percentages for pregnancy and infant conditions calculated using total pregnancy reports. aFor other pregnancy-specific: Delivery (29), contractions (18), fetal growth restriction (11), amniotic fluid anomalies/polyhydramnios (9), decreased fetal movement (7), elective ab (5), subchorionic hematoma/hemorrhage (7), chromosomal abnormalities (7), unviable pregnancy (3), bleeding (2),and one each report of: shortened cervix, gestational thrombocytopenia/fever/chills/fetal heart rate deceleration, fetal loss, cervical insufficiency, umbilical cord prolapse, facial numbness/thrombus in placenta, fetal tachycardia, HCG testing, rupture of membranes, twin pregnancy, increased fetal movement, chorioamnionitis, postpartum, small for gestational age, fetal hypokinesia, rash/urticaria/vaginal birth of normal infant, postpartum hemorrhage, single umbilical artery, induced high blood pressure, conjoined twins, irregular heartbeat, fetal death/high blood pressure, twin pregnancy, fetal development disorder, prelabor rupture of membranes/breech presentation/post-partum hemorrhage, hydrops/termination of pregnancy, rupture of membranes, meconium stained fluid, uncomplicated pregnancy/term infant, false labor/fetal evaluation, term baby, uterine inversion/ mastitis/endometriosis, marginal cord insertion
bOther infant conditions include: preterm infant (5), low birth weight (4), fetal hydrops (3), premature closure of ductus arteriosus (2), healthy infant (2), large for gestational age, preterm infant/injured brain tissue, fetal tachycardia, infant seizures/ischemic brain injury, eroded skin lower legs, sub-amniotic hematoma/neonatal pneumonia, stopped lactating, laryngomalacia, baby brain not developing well, hypoxic-ischemic encephalopathy, fetal arrhythmia, hypoglycemia in infant, small for gestational age, respiratory distress/suspected spontaneous gastrointestinal perforation, cardiac abnormalities, normal delivery, LBW/infant with low blood glucose/high bilirubin, seizure, jaundice, neonatal disorder, bell’s palsy infant, brain hemorrhage in fetus, platelet disorder in infant-bruise easily, hypertension/unilateral auditory neuropathy, pleural effusion in fetus, nummular eczema in infant
Eight maternal deaths were reported. Cause of death could be ascertained through medical record review in four reports: pulmonary embolism (case 1), amniotic fluid embolism (case 2), eclampsia with peripartum cardiomyopathy (case 3), and acute cerebellar intraparenchymal hemorrhage secondary to severe persistent thrombocytopenia due to acute myelogenus leukemia (case 4). In two other cases (cases #5-#6) the patients experienced sudden clinical deterioration leading to death and in one of them, amniotic fluid embolism, a rare but acute life-threatening complication of childbirth [26], was suspected as the event leading to death. No medical records were available for two reports (cases #7-#8), and the information in the VAERS reports was very limited. A brief description of each report is available in supplementary Table S1.
The most common non-pregnancy specific AEs were injection site and systemic reactions (Table 3 ), irrespective of type of vaccine or brand. Among pre-specified conditions of interest, 17 reports of Bell’s palsy or facial paralysis were reported but only two could be verified through medical record review. Eleven were after BNT162b2 and 6 after mRNA-1273. Median onset interval was 7 days (range, 0-70 days). Three reports were serious. One report of Guillain Barré Syndrome – Miller Fisher variant was reported and verified in a woman at 27 weeks’ gestation expecting her first child, 3 weeks after receiving the Ad26.COV2.S vaccine. This patient was hospitalized for 5 days and by the time of discharge she was significantly improved and fetal surveillance did not show findings of concern. A second report of Guillain Barre Syndrome (GBS) after mRNA-1273 was reported but could not be verified as no medical records were available. Ten reports of anaphylaxis or possible anaphylaxis were reported. Two were verified by review of medical records. Eight patients had recovered but no information was available for other two. Four reports of myocarditis and one report of pericarditis that met CDC’s case definition for myopericarditis [27] were reported to VAERS. For myocarditis, two reports were after BNT162b2, one report after mRNA-1273 and one after Ad26.COV2.S. The one report of pericarditis was after mRNA-1273. All 5 patients recovered.Table 3 Most common MedDRA codes after COVID-19 vaccines, combined and by brand, among non-pregnancy specific reports (1,611), VAERS, December 2020 – October 2021†
All vaccine brands
(N = 2,002) n BNT162b2 vaccine (N = 1,006) n mRNA-1273
vaccine (N = 809) n Ad26.COV2.S vaccine (N = 185) n
Headache 434 (21.7) Headache 211 21.0% Headache 160 (19.8) Pyrexia 77 (41.6)
Fatigue 422 (21.1) Fatigue 209 20.8% Fatigue 155 (19.2) Chills 70 (37.8)
Pyrexia 381 (19.0) Chills 159 15.8% Pyrexia 152 (18.8) Headache 63 (34.1)
Chills 369 (18.4) Pain 153 15.2% Pain 145 (17.9) Fatigue 58 (31.4)
Pain 342 (17.1) Pyrexia 152 15.1% Chills 139 (17.2) Pain 43 (23.2)
Nausea 299 (14.9) Nausea 143 14.2% Pain in extremity 123 (15.2) Nausea 36 (19.4)
Pain in extremity 266 (13.3) Dizziness 122 12.1% Nausea 120 (14.8) Myalgia 24 (12.9)
Dizziness 225 (11.2) Pain in extremity 122 12.1% Injection site erythema 88 (10.9) Pain in extremity 21 (11.4)
Injection site pain 186 (9.3) Vomiting 85 8.5% Injection site pain 87 (10.8) Vomiting 18 (9.7)
Vomiting 165 (8.2) Injection site pain 84 8.4% Dizziness 86 (10.6) Dizziness 17 (9.2)
† MedDRA codes are not mutually exclusive meaning one report may have several MedDRA codes
872 reports from vaccine manufacturers (not included in tables above) were received in large batches over a short period of time and were not manually reviewed. Automated analysis revealed the most common adverse events from this group were: pain in extremity (83;9.5%), headache (58;6.7%), fatigue (50; 5.7%), chills (38; 4.4%), injection site pain (38;4.4%), myalgia (38; 4.4%), fever (37; 4.2%), pain (34; 3.9%) nausea (27; 3.1%), dizziness (19; 2.2%), and spontaneous abortion (18; 2.1%)
Fifty-eight reports of SARS-CoV-2 infection after COVID-19 vaccination in pregnant persons were reported to VAERS. Fourteen were coded as serious but for reasons unrelated to COVID-19 infection (e.g., elevated blood pressure, scheduled cesarean section). Twenty were asymptomatic, 25 were symptomatic and 13 did not indicate if the patient presented symptoms. Thirty-eight SARS CoV-2 infections were after BNT162b2, twelve after mRNA-1273, 7 after Ad26.COV2.S vaccine, and one of unknown brand.
One hundred seven infant conditions were reported which included 12 neonatal deaths (Table S2). Medical records noting the cause of death were available for three reports which included prematurity. One of these reports has been described before [14]. Thirty-three reports described a major birth defect or chromosomal abnormality. Eighteen and fifteen infants with birth defects were reported for BNT162b2 and mRNA-1273 vaccines, respectively. In 16 reports the vaccine was administered during the first trimester. Supplementary table S3 shows the specific birth defects by brand of COVID-19 vaccine.
3.1 Data mining
Disproportionality analysis of COVID-19 vaccines did not reveal an elevated EB05 (>2) for any MedDRA PTs among pregnancy reports reported to VAERS.
Crude reporting rates
Using published [25] vaccination coverage data for COVID-19 vaccines, we estimated that the crude reporting rate of stillbirths is 17.3 reports per 100,000 stillbirths and live births, for preterm deliveries 21.4 reports per 100,000 live births, for maternal deaths 2.3 per 100,000 live births, and for neonatal deaths 3.4 per 100,000 live births (Table S4). All crude reporting rates were below published background rates for these conditions [28], [29], [30].
4 Discussion
During December 14,2020 through October 31, 2021, VAERS has received and processed 603,786 reports of AEs after any COVID-19 vaccine. Of these, 3,462 (0.5%) were reports of pregnant persons. An initial assessment of pregnancy reports after the mRNA vaccines during the first two months of vaccine roll-out [14] did not identify unexpected or unusual increased reporting of any adverse event. In the following months since this initial report, we have monitored pregnancy reports received by VAERS for any unexpected increase in adverse events, particularly pregnancy specific conditions. We did not find disproportional reporting of any adverse event nor increased reporting rates for certain pregnancy-specific conditions compared to their background rates. A small number of maternal and neonatal deaths were reported but the mortality rates estimated from them were below published statistics on maternal and neonatal mortality [28], [29].
The most common pregnancy-specific AE reported was SAB which accounted for a quarter of all reports submitted. The one common risk factor for 39% of them was an advanced age of ≥ 35 years. SAB are relatively common during pregnancy and their rates increase with increasing age with rates as high as 80% at 45 years of age [30]. We did not find disproportional reporting for any MedDRA PT for SAB in data mining analysis. Stillbirths (≥20 weeks gestation) were reported much less frequently than expected with 62 reports overall and an estimated reporting rate of 17.3 reports per 100,000 stillbirths and live births, which is well below the background rate of 595 per 100,000 live births and fetal death [30]. The reporting rate to VAERS for stillbirths may be due to underreporting, which is a limitation in VAERS. Our findings in VAERS for SAB reports and other pregnancy-specific conditions after the COVID-19 vaccines did not show any safety concern but given the limitations of VAERS any finding needs to be interpreted carefully. A recent study from the v-safe pregnancy registry found a cumulative risk of spontaneous abortion after a mRNA COVID-19 vaccine of 13%, consistent with background rates of this condition [31]. Another study in the Vaccine Safety Datalink found that among women with spontaneous abortions, the odds of COVID-19 vaccine exposure were not increased in the prior 28 days compared with women with ongoing pregnancies [32]. Preterm delivery is a pregnancy outcome of interest which in 2020 occurred at a rate of 10.1% [33]. Many studies on the effect of vaccination and preterm delivery have been done with the seasonal or 2009 H1N1 influenza vaccines [33]. Most studies have either shown a protective association between influenza vaccine and preterm delivery or no effect. A few showed a slight increased risk [34]. COVID-19 vaccines are different from influenza vaccines, which are conventional inactivated viral subunit vaccines. However, we did not observe increased reporting or a concerning reporting rate for preterm births following COVID-19 vaccination.
Among 33 infants/fetuses with birth defects or chromosomal abnormalities reported to VAERS, we did not observe unusual clustering of birth defects (Table S3). For reports describing non-pregnancy specific AEs the most common conditions reported were local and systemic reactions which was consistent with findings from pre-authorization studies in non-pregnant persons [5], [6], [7] and from pregnant persons enrolled in the v-safe pregnancy registry [15].
Other pre-specified conditions identified during pregnancy included 17 reports of Bell’s palsy or facial paralysis. One verified report of Guillain Barré Syndrome (GBS) was reported in a pregnant person after the Ad26.COV2.S vaccine. GBS is an acute, immune-mediated paralytic disorder of the peripheral nervous system [35] which has been found to be associated with administration of the Ad26.COV2.S vaccine [36]. Ten reports of anaphylaxis or possible anaphylaxis were reported but only two could be confirmed as anaphylaxis. COVID-19 infections following vaccination were reported in 58 pregnant persons. Consistent with increased reporting of myopericarditis reports in the VAERS database, we noted a small number of these reports in pregnant persons that met case definition for this condition [37].
VAERS is a national surveillance system used to detect signals of potential adverse events following vaccination. During the post-authorization monitoring of AEs after the COVID-19 vaccines, VAERS has fulfilled its mission by being able to identify rare AEs although no disproportional reporting was observed for pregnancy-specific events [16]. For example, increased reports in VAERS of myocarditis following receipt of a second dose of mRNA COVID-19 vaccines, prompted changes in the emergency use authorization provider information sheets [37]. Passive surveillance systems such as VAERS have a number of important limitations and their data cannot be compared directly with findings from randomized or observational studies; findings therefore need to be interpreted with caution. VAERS may be prone to biased reporting (over- or under-reporting) and inconsistency in the quality and completeness of reports. VAERS also generally cannot determine whether a vaccine caused an AE [16]. Stimulated reporting can occur following publicity around a potential AE and AEs occurring closer to vaccination or those more serious in nature may be reported more frequently [16]. VAERS does not collect data on the number of vaccinees and generally it is not possible to calculate rates of adverse events. However, during the COVID-19 vaccination program, CDC collected data on the number of COVID-19 vaccines administered. Vaccination coverage data for pregnant women from the Vaccine Safety Datalink allowed for calculation of crude reporting rates in this study [25].
5 Conclusions
The earliest data on the safety of COVID-19 vaccines came from the v-safe pregnancy registry and VAERS [14] and filled a gap in knowledge on the safety of the COVID-19 vaccines in pregnant persons. As more pregnant people get vaccinated and pregnancies come to completion, more complete data will be available and signal detection for rare events may be possible. Furthermore, large, linked database systems such as the Vaccine Safety Datalink can evaluate rates and risks for specific adverse events, including those initially detected in surveillance systems. Together these systems work to rapidly and comprehensively study the safety of COVID-19 vaccines among pregnant persons.
Our review of pregnancy-related safety for the COVID-19 vaccines from VAERS identified no disproportionate reporting of any pregnancy specific condition Moreover, reporting rates for important conditions were well below background rates. CDC and FDA will continue to closely monitor the safety of COVID-19 vaccines in pregnant persons in VAERS complementing the safety data from other systems. Timely results from enhanced maternal safety monitoring following COVID-19 vaccination has informed federal agencies, healthcare providers, domestic immunization partners, and the public on the safety of these new vaccines in pregnant persons.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A Supplementary material
The following are the Supplementary data to this article:Supplementary Data 1
Supplementary Data 2
Acknowledgements
We thank the staff of the Immunization Safety Office, and General Dynamics Information Technology, for their work and dedication to public health during the COVID-19 pandemic. No funding from any organization, agency or entity was received for this study.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.vaccine.2022.04.031.
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16 Shimabukuro T.T. Nguyen M. Martin D. DeStefano F. Safety monitoring in the Vaccine Adverse Event Reporting System (VAERS) Vaccine 33 36 2015 4398 4405 26209838
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25 Razzaghi H. Meghani M. Pingali C. Crane B. Naleway A. Weintraub E. COVID-19 Vaccination Coverage Among Pregnant Women During Pregnancy — Eight Integrated Health Care Organizations, United States, December 14, 2020–May 8, 2021 MMWR Morb. Mortal. Wkly. Rep. 70 24 2021 895 899 34138834
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27 Oster M.E. Shay D.K. Su J.R. Gee J. Creech C.B. Broder K.R. Myocarditis Cases Reported After mRNA-Based COVID-19 Vaccination in the US From December 2020 to August 2021 JAMA 327 4 2022 331 35076665
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32 Kharbanda EO, Haapala J, DeSilva M, et al. Spontaneous Abortion Following COVID-19 Vaccination During Pregnancy. JAMA. 2021 Sep 8. 10.1001/jama.2021.15494. Epub ahead of print. PMID: 34495304.https://jamanetwork.com/journals/jama/fullarticle/2784193?resultClick=1 Accessed January 23, 2022
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35 Kasper D. Fauci A. Hauser S. Longo D. Jameson J. Loscalzo J. Harrison's Principles of Internal Medicine, 19e New York 2014 McGraw-Hill NY Guillain-Barré Syndrome and Other Immune-Mediated Neuropathies http://accessmedicine.mhmedical.com/content.aspx?bookid=1130§ionid=79720773
36 Rosenblum HG, Hadler SC, Moulia D, et al. Use of COVID-19 Vaccines After Reports of Adverse Events Among Adult Recipients of Janssen (Johnson & Johnson) and mRNA COVID-19 Vaccines (Pfizer-BioNTech and Moderna): Update from the Advisory Committee on Immunization Practices — United States, July 2021. MMWR Morb Mortal Wkly Rep 2021;70:1094-1099. 10.15585/mmwr.mm7032e4
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PMC009xxxxxx/PMC9001177.txt |
==== Front
Disabil Health J
Disabil Health J
Disability and Health Journal
1936-6574
1876-7583
The Author(s). Published by Elsevier Inc.
S1936-6574(22)00066-8
10.1016/j.dhjo.2022.101326
101326
Brief Report
Physical activity levels and shoulder pain in wheelchair users during COVID-19 restrictions
Warner Martin B. PhD ab∗
Mason Barry S. PhD c
Goosey-Tolfrey Victoria L. PhD c
Webborn Nick FSEM (UK) cde
a School of Health Sciences, University of Southampton, Southampton, UK
b Centre for Sport, Exercise and Osteoarthritis Versus Arthritis, UK
c Peter Harrison Centre for Disability Sport, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
d IPC Medical Committee, Bonn, Germany
e Centre for Sport and Exercise Science and Medicine (SESAME), School of Sport and Service Management, University of Brighton, Brighton, UK
∗ Corresponding author. School of Health Sciences, University of Southampton, Southampton, Hampshire SO17 1BJ, UK.
12 4 2022
12 4 2022
10132627 7 2021
21 1 2022
1 4 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
Manual wheelchair users are at high risk of developing shoulder pain. However, it is not known if restrictions to limit the spread of the COVID-19 virus affected physical activity, wheelchair use and shoulder pain.
Objective
The aim of the study is to determine whether COVID-19 related restrictions caused changes in physical activity levels and the presence of shoulder pain in persons who use a wheelchair.
Methods
Manual wheelchair users completed a survey about the presence and severity of shoulder pain in a cross-sectional study design. Participants completed the Leisure Time Physical Activity Questionnaire and were asked about daily wheelchair activity before and during lockdown. A logistic regression examined the relationship between increase in shoulder pain severity and change in activity levels.
Results
Sixty respondents were included for analysis. There was no significant change in physical activity during lockdown. There was a significant reduction in number of hours of daily wheelchair use and number of chair transfers during lockdown. Of the respondents, 67% reported having shoulder pain and 22% reported their shoulder pain becoming more severe during lockdown. No significant relationship was observed between the change in activity levels and increasing severity of shoulder pain.
Conclusion
Restrictions to reduce the spread of the COVID-19 virus resulted in no changes in physical activity levels in a sample of adult manual wheelchair users; however, there was a reduced time using a wheelchair each day and fewer chair transfers. The changes in wheelchair activities were not related to the worsening of shoulder pain.
Keywords
Shoulder pain
Physical activity
Wheelchair
COVID19
==== Body
pmcShoulder pain is common in persons reliant on manual wheelchairs for daily mobility with prevalence reported up to 76%.1 The cause of shoulder pain has been attributed to the increased demand placed on the shoulder through wheelchair use,2 where increased wheelchair use and associated activities, such as number of chair transfers, is associated with increased risk of shoulder pain.3 , 4 The activities of wheelchair propulsion and weight bearing place the scapula into positions that potentially increase the risk of impingement and reduce sub-acromial space.5, 6, 7 The technique and frequency of chair transfers, in particular, are associated with shoulder pathology,7 , 8 and should be considered to avoid the risk of developing shoulder pain.
Physical activity, defined as activity over and above typical daily wheelchair activities,9 is essential for wheelchair users to prevent the onset of secondary health conditions such as obesity and cardiovascular disease.10 , 11 The global pandemic due to the COVID-19 respiratory virus caused unprecedented restrictions to reduce the spread of the virus. In the United Kingdom (UK), restrictions were first implemented in March 2020 and required people to remain at home and were only permitted to leave their home for essential reasons. Physical activity outside of the home was limited to once per day and led to significant reductions in physical activity levels in children and young adults with disabilities in the UK.12 However, some individuals reported increasing their physical activity during lockdown.12 In a sample of Spanish adult wheelchair users, there was a significant decrease in time spent on vigorous, moderate, and light intensity physical activity during lockdown.13 The effects of physical activity on shoulder pain in people who are reliant on a wheelchair is not fully understood. Whilst there is some evidence to suggest that physical activity increases the risk of shoulder pain,14 other studies have found no effect of physical activity on shoulder pain.15 Conversely, the study of Fullerton et al.16 suggests that physical activity reduces the risk of shoulder pain, where non-athletes are twice as likely to develop shoulder pain.
Given the COVID-19 related restrictions imposed, it is not known if the physical activity levels of adults in the UK who use a wheelchair changed and whether there is an association with the onset or worsening of shoulder pain. The restrictions only allowed for essential travel outside of the home; therefore, other typical activities of daily living, such as wheelchair propulsion and chair transfers, might have also been affected. Typical activities of daily living are associated with shoulder pain and changes in the exposure to these activities might influence the presence of shoulder pain. The aim of the study is to investigate physical activity levels, defined as activity over and above typical daily activity, of wheelchair users before and during COVID-19 related restrictions and the presence of shoulder pain.
Method
Survey development and deployment
An online survey was developed and distributed in a cross-sectional study design via Microsoft Forms. There are currently 1.2 million wheelchair users in the United Kingdom, where two thirds are regular users (NHS England). The inclusion criteria consisted of wheelchair users over the age of 18 years of age based in the UK who self-propelled using a manual or hybrid wheelchair (manual wheelchair with power assisted wheels). People with a cognitive impairment that limited their ability to complete the survey were excluded from the study. The survey was advertised via social media and on the websites of charities associated with wheelchair use from May 2020. All participants provided electronic informed consent. The study received ethics approval from the University of Southampton Research Ethics Committee in line with the principles of the Declaration of Helsinki.
The survey consisted of the following constructs: chronic shoulder pain, athletic status, physical activity over and above typical wheelchair activities, typical daily wheelchair use and demographics. The chronic pain construct asked whether participants were currently experiencing shoulder pain, the severity of their current pain, whether they had pain before lockdown measures, whether their pain had become more severe during lockdown measures and, if so, the severity of their pain before lockdown. If participants did not have current shoulder pain they were asked if they had shoulder pain in the past. The athletic status construct asked whether they were an international athlete, retired international athlete, national athlete, retired national athlete, recreational sports person or whether they did not take part in any physical activity or sport (i.e. sedentary). Participants completed the Leisure Time Physical Activity Questionnaire for People with spinal cord injury9 to determine the amount of physical activity participants undertook during their free time over and above typical daily wheelchair activities. Participants completed the Leisure Time Physical Activity Questionnaire twice; firstly, recalling their physical activity level in the week before lockdown measures and then repeating for the seven days prior to the point of completing the survey. Participants were then asked to recall the number of hours of wheelchair use per day, number of chair transfers per day and number of weight relief per day before and during lockdown. Participants provided details of their disability that requires wheelchair use and demographic details, including employment status.
Data analysis
Due to non-normal data distributions and missing data a non-parametric statistical analysis approach was utilised. A Wilcoxon test was used to compare leisure time physical activity, for each level of physical activity intensity, and wheelchair activities before to during lockdown. A logistic regression was used to determine if there was a relationship between the onset or worsening of shoulder pain and the change in physical activity levels and typical daily wheelchair activities. Subgroups were formed consisting of employment status (employed and unemployed) and athletic status (athletic and sedentary). Leisure time physical activity at each intensity and wheelchair activities were compared before and during lockdown measures for each sub-group using a Wilcoxon test.
Results
A total of 65 people responded to the survey between May 2020 and July 2020; however, five were excluded for non-compliance with the inclusion criteria, which consisted of below 18 years of age or not being a resident of the UK. There were 31 males and 28 females (1 non-response) with a mean age of 49.1 ± 13.4 years. Of the respondents, 67% (N = 40) had a neuromuscular related disability and 28% (N = 17) had a musculoskeletal related disability. Of the respondents, 17% (N = 10) were elite athletes, 8% (N = 5) were retired elite athletes, 15% (N = 9) took part in recreational activity and 60% (N = 36) were sedentary.
During lockdown there was a significant (P < 0.001) reduction in the number of hours of wheelchair use per day (Table 1 ). The number of chair transfers per day significantly (P = 0.01) reduced during lockdown compared with before lockdown (Table 1). There was no significant (P = 0.372) difference in the number of weight relief exercises performed during lockdown compared with before lockdown (Table 1). There was no significant difference in leisure time physical activity at mild (P = 0.239), moderate (P = 0.439) or heavy (P = 0.414) intensities during lockdown compared with before lockdown (Fig. 1 ).Table 1 Wheelchair activities prior to and during COVID-19 related lockdown measures for the entire sample of respondents and sub-groups within the sample.
Table 1 Wheelchair use (hours per day) Chair transfers (number per day) Weight relief (number per day)
Prior to lockdown During lockdown Prior to lockdown During lockdown Prior to lockdown During lockdown
Entire sample (n = 60) Median
IQR
Range 12∗
13
0–24 9∗
13
0–24 7∗
9.5
0–28 7∗
8
0–20 3
10
0–40 2
8
0–40
Sub-groups
Employed (n = 31) Median
IQR
Range 11∗
11.25
0–18 4∗
13.5
0–18 6
10
0–20 2
8.5
0–16 2
7.5
0–30 2
6
0–30
Unemployed (n = 22) Median
IQR
Range 14∗
11
1–24 12∗
15
0–24 6∗
7
0–25 5.3∗
7
0–20 2.5
8.3
0–40 2.5
9.8
0–40
Non-athletic (n = 50) Median
IQR
Range 11∗
11.3
0–24 5∗
14
0–24 6∗
7.3
0–20 4∗
9
0–20 1
6
0–40 1.5
5.1
0–40
Elite athletes (n = 10) Median
IQR
Range 15
9.5
0–18 11.5
11
0–18 10
18.5
0–28 8
17
0–20 2
8
0–15 4
9
0–15
∗ denotes significant (p < 0.05) difference between prior to lockdown and during lockdown.
Fig. 1 Physical activity of wheelchair users from the Leisure Time Physical Activity Questionnaire prior to and during COVID-19 lockdown restrictions at mild, moderate, and heavy intensities.
Fig. 1
Of the respondents, 67% (N = 40) reported having current chronic shoulder pain, rated on average as ‘severe’, and 17% (N = 10) reported having chronic shoulder pain in the past. Thirteen (22%) respondents reported their shoulder pain becoming more severe and two respondents (3%) developed shoulder pain during lockdown. Logistic regression analysis revealed there was no relationship between change in physical activity level or wheelchair activities and the onset or worsening of shoulder pain.
Sub-group analysis demonstrated that the unemployed group (N = 22) significantly reduced the number of hours using a wheelchair and number of chair transfers performed per day during lockdown (Table 1). The employed (N = 31) group also had a significant reduction in number of hours of wheelchair use during lockdown (Table 1). There was no significant difference in physical activity levels when comparing before to during lockdown for either group (Fig. 2 ). There was a significant between-group difference in physical activity at mild and moderate physical activity levels (Fig. 2).Fig. 2 Mild, moderate, and heavy physical activity levels of employed (N = 31) unemployed (N = 22), athletic (N = 10) and non-athletic (N = 50) wheelchair users from the Leisure Time Physical Activity Questionnaire prior to and during COVID-19 lockdown restrictions. There were no significant differences in physical activity levels when comparing prior to and during lockdown. Significant differences were observed between employed and unemployed for mild and moderate physical activity. Significant differences were observed between the sedentary group and athletic group at each physical activity level with the exception of mild activity during lockdown.
Fig. 2
Sub-group analysis of athletic status demonstrated there was no significant change in physical activity levels before compared with during lockdown for either the athletic (N = 10) or sedentary (N = 50) groups (Fig. 2). There was a significant difference between the sedentary group and athletic group at each physical activity level, except for mild activity during lockdown (Fig. 2). There was a significant decrease in the number of hours of wheelchair use and the number of chair transfers per day for the sedentary group before to during lockdown (Table 1), but no significant difference in wheelchair activities for the athletic group (Table 1).
Discussion
The nationwide restrictions in the UK to reduce the spread of the COVID-19 virus led to substantial changes in how people led their lives. The results of this study observed no significant change in physical activity levels in people who are reliant on a wheelchair during lockdown. There were, however, changes to the amount of typical daily wheelchair activities with people spending less time using their wheelchair and performing fewer chair transfers. The reduction in chair transfers, which was most evident in the employed group, was likely due to a decreased car use. People were not commuting to a place of work and eliminated the need for multiple chair/car/chair transfers.
During lockdown 25% of the respondents reported their shoulder pain becoming more severe or had developed shoulder pain, but there was no relationship between the change in wheelchair related activities and the onset or worsening of shoulder pain. Wheelchair related activities have been suggested as the cause of shoulder pain in wheelchair users with factors such as increased number of chair transfers and duration of wheelchair use increasing the risk of developing shoulder pain.4 The observed decrease in typical daily wheelchair activities potentially reduces the risks of developing shoulder pain and suggests other factors are responsible for the increase in shoulder pain observed in a quarter of respondents. Weaker shoulder abduction strength and trunk flexion have been associated with increased risk of shoulder pain.17 , 18 With a known positive correlation between strength and hand-rim force,19 it is possible that wheelchair users experienced a decrease in shoulder strength through less wheelchair use during lockdown resulting in an increased risk of shoulder pain. The relationship between shoulder strength and daily wheelchair activities requires further investigation to further elucidate on the possible mechanisms in the development of shoulder pain.
The lack of change in physical activity levels observed in the sample of adult wheelchair users contrasts other studies who observed reductions in physical activity for children and young adults in the UK with a disability and adults in Spain.12 , 13 The average physical activity was generally similar to previous studies,9 , 20; however, 29 respondents performed very little physical activity and did not meet physical activity guidelines for people with a spinal cord injury.21 Given the low levels of physical activity in half of the sample a measurable decrease in physical activity is not expected. Inactivity in wheelchair users can lead to secondary health conditions, such as obesity and cardiovascular disease, which can have greater impact on wheelchair users compared with the general population.22 The promotion of physical activity is critical to prevent a secondary consequence of the pandemic of increased risk of cardiovascular disease and obesity.23 When considering possible explanations of why respondents who undertook physical activity did not reduce their activity levels, the UK's restrictions allowed people to leave the house once per day to undertake physical activity. The lack of ability to undertake typical activities of daily living, such as commuting to work and visiting friends and family, people may have had more time during the day to undertake some form of physical activity. It must also be considered that some respondents were elite athletes, it is likely they continued to train for their sport to maintain fitness levels. It is not known what type of physical activities that were undertaken and whether the type of activity differed during lockdown. Due to sport centres, gyms and other physical activity facilities being closed, it is likely people completed different forms of physical activity to maintain an active lifestyle. The apparent discordance between a reduction in wheelchair use and the lack of change in physical activity can be attributed to a reduced need to use a wheelchair to undertake activities of daily living, as described above. Although participants may still have been using a wheelchair for physical activity, the overall amount of wheelchair use on a given day reduced. In addition, some physical activity may have been undertaken without the use of a wheelchair (e.g. resistance training).
Several study limitations should be noted, which are predominantly related to the reliance on retrospective reporting of physical activity levels and shoulder pain before lockdown. It is possible that recall bias influenced the data provided for the pre-lockdown related data and caution should be aired when interpreting these results. The incidence of shoulder pain in the sample was 67%, which was 83% when including people who had a history of, but not current, chronic shoulder pain. Although studies have found high incidence of shoulder pain in wheelchair users,1 a recent meta-analysis of musculoskeletal pain in wheelchair users demonstrated a pooled prevalence of 44% for shoulder pain.24 Given the high prevalence of shoulder pain observed in the present study, there may have been a bias towards people who have experience of shoulder pain and the sample may not be representative of the wider population. It should also be recognised that the sample of participants included elite athletes, the relative number of which is likely to be higher than found in the general population of wheelchair users. The generalisability of these results to the wider population of manual wheelchair users should be viewed with caution. The respondents completed the survey between May 2020 and July 2020, therefore, the exposure time to lockdown restrictions will have differed across participants. The effect on physical activity and shoulder pain might differ across the participants and should be considered when interpreting these data. Lastly, participants were not asked whether their shoulder pain improved and, therefore, it is not known whether the observed reduction in typical daily wheelchair activities resulted in possible improvements in shoulder pain.
Conclusion
The restrictions to reduce the spread of the COVID-19 virus resulted in no significant changes in physical activity levels in a small sample of active and inactive adult manual wheelchair users in the United Kingdom. Among this sample, lockdown measures resulted in wheelchair users spending less time per day in their chair and doing fewer wheelchair transfers; however, the study results revealed that these changes were not related to onset or worsening chronic shoulder pain. The relationship between shoulder pain and physical activity and wheelchair use remains unclear.
Funding
This work was supported by the Centre for Sport, Exercise and Osteoarthritis Versus Arthritis.
Conflicts of interest
The authors declare no conflict of interest.
==== Refs
References
1 Heyward O.W. Vegter R.J.K. de Groot S. van der Woude L.H.V. Shoulder complaints in wheelchair athletes: a systematic review PLoS One 12 11 2017 e0188410 29161335
2 Chow J.W. Levy C.E. Wheelchair propulsion biomechanics and wheelers' quality of life: an exploratory review Disabil Rehabil Assist Technol 6 5 2011 365 377 20932232
3 Irwin R.W. Restrepo J.A. Sherman A. Musculoskeletal pain in persons with spinal cord injury Top Spinal Cord Inj Rehabil 13 2 2007 43 57
4 Ferrero G. Mijno E. Actis M. Risk factors for shoulder pain in patients with spinal cord injury: a multicenter study Musculoskeletal surgery 99 2015
5 Morrow M.M.B. Kaufman K.R. An K.N. Scapula kinematics and associated impingement risk in manual wheelchair users during propulsion and a weight relief lift Clin BioMech 26 2011 352 357
6 Lin Y.S. Boninger M. Worobey L. Farrokhi S. Koontz A. Effects of repetitive shoulder activity on the subacromial space in manual wheelchair users BioMed Res Int 2014 583951 2014 25215283
7 Mozingo J.D. Akbari-Shandiz M. Murthy N.S. Shoulder mechanical impingement risk associated with manual wheelchair tasks in individuals with spinal cord injury Clin BioMech 71 2020 221 229
8 Haubert L.L. Mulroy S.J. Hatchett P.E. Car transfer and wheelchair loading techniques in independent drivers with paraplegia Front Bioeng Biotechnol 3 139 2015
9 Martin Ginis K.A. Phang S.H. Latimer A.E. Arbour-Nicitopoulos K.P. Reliability and validity tests of the leisure time physical activity questionnaire for people with spinal cord injury Archives of Physcial Medicine and Rehabilitation 93 2012 677 682
10 Abel T. Platen P. Rojas Vega S. Schneider S. Struder H.K. Energy expenditure in ball games for wheelchair users Spinal Cord 46 12 2008 785 790 18521095
11 Alrashidi A.A. Nightingale T.E. Currie K.D. Exercise improves cardiorespiratory fitness, but not arterial health, after spinal cord injury: the CHOICES trial J Neurotrauma 38 21 2021 3020 3029 34314235
12 Theis N. Campbell N. De Leeuw J. Owen M. Schenke K.C. The effects of COVID-19 restrictions on physical activity and mental health of children and young adults with physical and/or intellectual disabilities Disability and Health Journal 2021 101064 33549499
13 Marco-Ahulló A. Montesinos-Magraner L. González L.M. Morales J. Bernabéu-García J.A. García-Massó X. Impact of COVID-19 on the self-reported physical activity of people with complete thoracic spinal cord injury full-time manual wheelchair users J Spinal Cord Med 2021 1 5
14 Curtis K.A. Dillon D.A. Survey of wheelchair athletic injuries: common patterns and prevention International Medical Society of Paraplegia 23 1985 170 175
15 Finley M.A. Rodgers M.M. Prevalence and identification of shoulder pathology in athletic and nonathletic wheelchairs users with shoulder pain: a pilot study J Rehabil Res Dev 41 3B 2004 365 402
16 Fullerton H.D. Borckardt J.J. Alfano A.P. Shoulder pain: a comparison of wheelchair athletes and nonathletic wheelchair users Med Sci Sports Exerc 35 12 2003 1958 1961 14652488
17 Walford S.L. Requejo P.S. Mulroy S.J. Neptune R.R. Predictors of Shoulder Pain in Manual Wheelchair Users vol. 65 2019 Clin Biomech Bristol, Avon) 1 12
18 Mulroy S.J. Hatchett P. Eberly V.J. Haubert L.L. Conners S. Requejo P.S. Shoulder strength and physical activity predictors of shoulder pain in people with paraplegia from spinal injury: prospective cohort study Phys Ther 95 7 2015 1027 1038 25721123
19 Ambrosio F. Boninger M.L. Souza A.L. Fitzgerald S.G. Koontz A.M. Cooper R.A. Biomechanics and strength of manual wheelchair users J Spinal Cord Med 28 5 2005 407 414 16869087
20 Urbański P.K. Conners R.T. Tasiemski T. Leisure time physical activity in persons with spinal cord injury across the seasons Neurol Res 43 1 2021 22 28 32912101
21 Martin Ginis K.A. van der Scheer J.W. Latimer-Cheung A.E. Evidence-based scientific exercise guidelines for adults with spinal cord injury: an update and a new guideline Spinal Cord 56 4 2018 308 321 29070812
22 Ferri-Caruana A. Millán-González L. García-Massó X. Pérez-Nombela S. Pellicer-Chenoll M. Serra-Añó P. Accelerometer assessment of physical activity in individuals with paraplegia who do and do not participate in physical exercise J Spinal Cord Med 43 2 2020 234 240 30547733
23 Hall G. Laddu D.R. Phillips S.A. Lavie C.J. Arena R. A tale of two pandemics: how will COVID-19 and global trends in physical inactivity and sedentary behavior affect one another? Prog Cardiovasc Dis 64 2021 108 110 32277997
24 Liampas A. Neophytou P. Sokratous M. Musculoskeletal pain due to wheelchair use: a systematic review and meta-analysis Pain Ther 10 2 2021 973 984 34387846
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PMC009xxxxxx/PMC9001178.txt |
==== Front
Dis Mon
Dis Mon
Disease-a-Month
0011-5029
1557-8194
Published by Elsevier Inc.
S0011-5029(22)00070-0
10.1016/j.disamonth.2022.101386
101386
Visual Case Discussion
Reprint of: Pulmonary embolism with Hampton Hump in COVID-19 patient
Williamson Jonathan a⁎
Brown Joe b
Arthur Jason c
a MS4, University of Arkansas for Medical Sciences, United States
b PGY-2, Dept of Emergency Medicine, University of Arkansas for Medical Sciences, United States
c Dept of Emergency Medicine, University of Arkansas for Medical Sciences, United States
⁎ Corresponding author.
12 4 2022
9 2022
12 4 2022
68 9 101386101386
25 6 2020
14 7 2020
10 12 2020
© 2022 Published by Elsevier Inc.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Keywords
Pulmonary embolism
Hampton Hump
COVID-19
==== Body
pmc1 Discussion
Pulmonary embolism (PE) is a life-threatening condition with numerous causes and various presentations. Symptoms of PE include shortness of breath, pleuritic chest pain, cough, or hemoptysis. There is a broad spectrum of presentation of PE, from minimal symptoms to profound shock or cardiac arrest.1 A massive PE is defined as one that presents with hemodynamic instability, while a low risk PE is seen in the setting of normal vital signs. In the intermediate group, or a submassive PE, patients may present with borderline blood pressure and evidence of ventricular remodeling.1 Diagnostic evaluation for a suspected PE in a hemodynamically stable patient includes a determination of pre-test probability using Wells' Criteria, Geneva Criteria, or clinical gestalt. Patients with a low to moderate pre-test probability often undergo testing with a D-dimer. Those with either an elevated D-dimer or high pre-test should undergo diagnostic imaging with either computed tomography pulmonary angiography (CTPA), a V/Q scan, or pulmonary angiography.1 Chest radiography is routinely obtained in patients presenting with dyspnea or chest pain; however, it has poor test characteristics for the diagnosis of PE. Therefore, its utility is typically in the diagnosis of other more common diseases rather than to diagnose or exclude PE. Rarely, a significant pulmonary artery obstruction may lead to a wedge shaped infarct that is visible on chest radiograph, a sign known as a Hampton Hump.1 The lungs are normally protected from infarction by a dual blood supply from the pulmonary and bronchial arteries. As result, a Hampton Hump is a low sensitivity marker for PE that occurs in up to 36% of these patients.2 The presence of a Hampton Hump (Fig. 1 ) should further increase suspicion of a PE. The patient in this case presented with shortness of breath and chest pain several weeks after diagnosis of SARS-CoV-2 and pneumonia. COVID-19, the disease process that is caused by SARS-CoV-2, is thought to be a disproportionately prothrombotic condition relative to the hypercoagulability of critical illness.2 Fig. 1 Chest radiograph showing a wedge-shaped area of opacity consistent with a Hampton Hump on the right side (arrow).
Fig 1
2 Visual case discussion
A 74 year old female with a history of diabetes, hypertension, end-stage renal disease, and seizures presented to the ED via ambulance due to altered mental status, shortness of breath, and chest pain. She recently was admitted for pneumonia at another facility, during which time she tested positive for SARS-Cov-2/COVID-19. Since discharge she had missed multiple dialysis appointments and had become progressively more confused. Family reported that she had suffered from similar episodes of confusion in the past which were attributed to uremic encephalopathy and improved with dialysis. Due to the patient's confusion, she was unable to provide a flowing description of her chest pain and dyspnea. On exam she was alert and oriented but slow to answer questions. She was hypertensive, mildly tachycardic, afebrile, with normal oxygen saturation on room air. Her cardiovascular exam revealed a sinus tachycardia with no murmurs, rubs or gallops. Pulmonary exam showed rales with normal effort. Bilateral lower extremity edema was present. Other examinations were unremarkable. Chest radiograph showed signs of a peripherally based, wedged shaped consolidation near the right middle lobe which was concerning for either a right middle lobe pneumonia or a Hampton Hump (Fig. 1). In light of her recent hospitalization, diagnosis of SARS-CoV-2/COVID-19, and concerning radiograph, a CTPA was ordered to delineate if this was a pneumonia or a pulmonary infarct. CTPA demonstrated bilateral pulmonary embolism with clot burden (Fig. 2 ) and infarction with possible overlying infection (Fig. 3 ). She was started on broad-spectrum antibiotics for hospital associated pneumonia, anticoagulated with heparin, and admitted to the hospital for further care.Fig. 2 Computed tomography pulmonary angiogram confirms PE with clot burden at the bifurcation of the descending pulmonary arteries bilaterally (arrows). A left sided pleural effusion and bilateral airspace opacities are also noted.
Fig 2
Fig. 3 Computed tomography pulmonary angiogram showing wedge-shaped airspace opacity likely representing pulmonary infarction with possible overlying infection (arrow). Diffuse airspace opacities noted bilaterally, concerning for further infection.
Fig 3
Questions
1. What is the initial treatment goal in a patient that presents with a pulmonary embolism and signs of cardiogenic shock?A Fibrinolysis to decrease cardiac afterload
B Anticoagulation to decrease clot formation
C Aspirin and statin therapy to prevent future thrombi
D Oxygen therapy to improve quality of life
E Cardioversion
2 What is the next step in management for a patient that you suspect might have an acute PE after a thorough history and physical exam?A Fibrinolysis
B Troponin level
C Determine the pretest probability of PE
D Computed tomography angiogram
E Ventilation Perfusion Scan
Answers
1. Explanation: A massive PE can present with syncope, systemic arterial hypotension, cardiogenic shock, or cardiac arrest. A massive PE can result in right ventricular overload, right heart failure, and ultimately death if left untreated. Therefore, the goal in treating hemodynamically unstable patients with signs of shock due to underlying PE is to relieve right ventricular pressure by clot removal. This can be done via fibrinolytic therapy, or embolectomy if indicated.
Reference: Piazza, G., & Goldhaber, S. Z. (2010). Fibrinolysis for Acute Pulmonary Embolism. Vascular Medicine. 15(5), 419–428. https://pubmed.ncbi.nlm.nih.gov/20,926,501/.
2. Explanation: The next step in workup for a PE is to determine the pretest probability. These scores classify the patient as low, intermediate, or high risk for having a PE. A d-dimer can be used with a low or intermediate pretest probability to determine the next step. Patients with a negative d-dimer and a low or intermediate pretest probability can be considered unlikely to have a PE.
Reference: Thompson, BT., Kabrhel, C., Pena, C. Clinical presentation, evaluation, and diagnosis of the nonpregnant adult with suspected acute pulmonary embolism. UpToDate, edited by Ted. W. Post, published by UpToDate in Waltham, MA, 2020.
Appendix Supplementary materials
Image, application 1
This article is a reprint of a previously published article. For citation purposes, please use the original publication details; Visual Journal of Emergency Medicine, 2021 (22C), 100960.
DOI of original item: http://dx.doi.org/10.1016/j.visj.2021.100960
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.visj.2020.100960.
==== Refs
References
1 Thompson, B.T., Kabrhel, C., Pena, C. Clinical presentation, evaluation, and Diagnosis of the nonpregnant adult with suspected acute pulmonary embolism. UpToDate, edited by Ted W. Post, published by UpToDate in Waltham, MA, 2020.
2 Patel U.B. Ward T.J. Kadoch M.A. Radiographic feature of PE: hampton’s Hump Postgrad Med J 2014 420 421 24894313
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PMC009xxxxxx/PMC9001179.txt |
==== Front
Journal of Academic Librarianship
0099-1333
0099-1333
Elsevier Inc.
S0099-1333(22)00046-5
10.1016/j.acalib.2022.102530
102530
Article
Crisis Librarianship: An Examination of Online Librarianship Roles in the Wake of the COVID-19 Pandemic
Reed Karen Nourse a⁎
Kester Brittany b
Kaufmann Karen F. c
Homol Lindley d
Crampsie Camielle e
a Middle Tennessee State University, Walker Library, MTSU Box 13, 1301 East Main St., Murfreesboro, TN 37128, United States of America
b University of Florida, 1500 Norman Hall, PO Box 117016, 618 SW 12th St., Gainesville, FL 32611-7016, United States of America
c Seminole State College of Florida, 100 Weldon Blvd., Sanford, FL 32773, United States of America
d Northeastern University, Snell Library, 300 Huntington Ave., Boston, MA 02115, United States of America
e University of South Florida, 140 7th Ave S., POY 118, St. Petersburg, FL 33701, United States of America
⁎ Corresponding author.
12 4 2022
7 2022
12 4 2022
48 4 102530102530
31 1 2022
8 4 2022
8 4 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.
This study surveyed the members of a professional library organization for their perceptions of their online librarianship role. In particular, the survey sought to examine any change in online librarianship roles after March 2020 as a result of the COVID-19 pandemic lockdowns. Participants were administered a survey comprised of both quantitative and qualitative response options. Findings present a nuanced professional environment post-lockdown in which individual job duties largely remained the same; however participants reported increased demands stemming from workplace issues, including attrition and lack of resources.
Keywords
Academic libraries
Academic librarianship
Online learning
Subject specialist librarians
Embedded librarian
Library instruction
==== Body
pmcIntroduction
Academic librarians have long supported university instruction on and off campus using virtual and online modalities (Cahoy & Moyo, 2006; York & Vance, 2009). This type of virtual or online support has evolved from a concept of “distance education librarianship”, which focused on supporting students removed from close geographic proximity to the physical campus services, to today's more encompassing term of “online librarianship”. Services performed in support of online librarianship include such tasks as creating Springshare LibGuides, providing librarian support via the course management system, and collection development of born-digital materials. Although these online librarianship duties are supported by a range of librarians, they are frequently the responsibility of subject specialist librarians (also referred to as “liaison librarians”) who work directly with specified academic programs or colleges.
The authors, all experienced subject specialist librarians, were employed at different American university libraries prior to the March 2020 COVID-19 lockdowns. Online librarianship tasks were already a regular part of their work; however, the lockdowns forced an abrupt shift to online learning at their universities. The authors noticed a pronounced change in their work in terms of requests for online services and collections, and they wanted to understand the experiences of others employed in similar roles across the United States. They surveyed librarians through a professional organization for their work experiences both before and after the onset of the pandemic. The goal was to contribute to the dialogue regarding librarian contributions to higher education modes of learning and instruction which is increasingly online.
Literature review
This review of the literature will discuss the trend away from the term “distance education” to a more holistic view of “online learning” and its accompanying responsibilities for librarianship. This was a movement which had already taken place before the COVID-19 lockdowns of March 2020. The literature review continues with a discussion of both the American and international academic librarian response to the sudden shift in services resulting from the COVID-19 lockdowns. It specifically looks at literature from March 2020 to February 2021 in accordance with the deployment of our study.
Online librarianship prior to March 2020
Online librarianship has its origins in the concept of distance education. Distance education emphasizes the “physical separation of teachers and students during instruction and the use of various technologies to facilitate student-teacher and student-student communication” (Berg & Simonson, 2016, para. 1). Distance education began with correspondence schools during the 19th century, and by the 1970s incorporated technology such as educational television programming and computer-based instruction. Throughout the different iterations of technology used to deliver the instructional content, one common aspect of distance education was the idea that students were physically located away from the main college campus.
Although academic librarians have long supported university instruction delivered away from the physical campus, professional interest in this area began in earnest in the 1980s. In 1981, the Association of College and Research Libraries (ACRL) established the Extended Campus Library Services Discussion Group as a means of connecting librarians who were providing services to off-campus and/or branch campus students. As professional interest in this area grew, the discussion group was formalized and replaced in 1990 with a new ACRL section, the Extended Campus Library Services Section (ECLSS) (Frederickson, 2004). In 1998, the section was renamed to the Distance Learning Section (DLS), reflecting the technological advances which made such instruction increasingly popular. Over the next twenty years, the pace of developments in educational technology quickened such that the line between the concepts of “distance education” and “online education” began to blur. In 2016, ACRL's Standards for Distance Learning Library Services were revised to make a distinction between these terms:Although often informally used interchangeably, distance learning and online learning are not synonymous, since online learning can be used as a tool in settings that do not involve distance learning at all… such as main campuses, or even commercial learning facilities. Similarly…online learning can occur as a tool of distance learning.
These revised standards therefore reiterated the idea that distance learning implies education at a geographical distance from the campus (even if the student is located within the same town). Today's distance learners access their classes through online learning technology, but not all online learners are distance education students. Behr and Hayward (2016) researched the apparent streamlining of reference and instruction services present by this time, concluding that “This evidence suggests that a large group of institutions in our survey do not consider distance learners to have specific needs or to require specific staff members to work with them” (p. 97). By then it had become clear that the profession needed to focus on the needs of both distance learners and online learners as a whole. In keeping with this shift, the ACRL section was renamed yet again in 2019, this time to the Distance and Online Learning Section (DOLS) (ACRL Distance and Online Learning Section, 2021).
The means by which the librarian profession has served these learners has evolved over time. One early approach to creating greater library integration within online learning classes was the use of embedded librarianship. York and Vance (2009) explained that the term embedded librarianship had several meanings, including librarians who kept a physical office in an academic department as well as those who took an active teaching role in face-to-face semester-long classes. The authors chose to focus their research on the most widely used connotation of the term which are those librarians who assist students directly through the online course management system (CMS). Their research surveyed 159 academic librarians for their experiences performing embedded librarianship within the CMS. The most common job duties cited by the survey participants were: providing links to online library content (36%), communicating with students through email (39%) and discussion boards (33%), and writing and administering quizzes (22%). Although Tumbleson (2016) also described the embedded librarian's primary job duties as promoting the library collection and communicating with students, she emphasized the librarian's role in providing research support through individual consultations, clarification of the research process, and guiding students to select authoritative and scholarly resources. Pati and Majhi (2019) similarly affirmed the research instruction role of embedded librarians, but also identified the liaison librarian work which some embedded librarians performed. It is possible that the embedded librarianship role matured in the ten-year period between York and Vance's research and these later researchers, such that the role was expanded to include the greater responsibilities of providing advanced research support as a subject specialist and/or liaison librarian.
More recent literature has sought to define the role of online learning librarians and identify best practices for online librarianship. Withorn and Willenborg (2020) identified seven distinct aspects of online librarianship work, including such tasks as creating online content, providing instructional design, being a point of contact (either as a liaison or subject specialist), coordinating online learning tasks, training, and advocacy. Their last distinct job area was called “slasher” and was intended to point out that the online learning tasks are most commonly performed in addition to another defined job position; that is, very few librarians doing this work are 100% devoted to online librarianship as indicated by their job title. The authors note that this situation bodes problems for workload as well as resources, and necessitates clearly defined roles in the organization. Moran and Mulvihill (2017) had similar concerns with the sustainability and scalability of online library instruction. They introduced best practices for many common online librarianship tasks, for example moderating class discussion boards as well as creating online content. For both of these tasks, they pointed out the importance of producing reusable content, which by necessity lends itself to more generalized and less individualized content.
To review, the literature demonstrates that prior to March 2020, library services to students outside the traditional classroom had experienced a steady evolution in terms of the scope of the work. The concept of “distance education” was no longer a distinct concern but rather academic libraries sought to provide services to students learning in a virtual environment (regardless of their geographical location). As technology innovated and allowed for greater instruction outside of the brick-and-mortar classroom, librarianship adapted to support students through a far greater range of online support services. By 2020, the literature demonstrates that libraries sought to determine the best way to provide these services in a manner scalable to their finite resources.
Online librarianship during and after the onset of COVID-19 (March 2020 to February 2021)
The COVID-19 pandemic became a worldwide problem in March 2020. Prior to this time, a range of online librarianship support existed at academic libraries, with some libraries having full-scale programs and others providing very little. For those university libraries already providing these services, they suddenly became essential as universities were thrust into a singular virtual environment in the wake of pandemic-forced lockdowns. Unfortunately, many universities were not prepared to offer a range of online librarianship support in March 2020; these libraries often faced a steep learning and implementation curve. The following section will review the literature, both domestic (American) and international, regarding online library services from the range of March 2020 to February 2021. This time period was selected to coincide with the deployment of this paper's survey instrument in February 2021.
Domestic perspectives on online librarianship (March 2020 to February 2021)
Published research regarding the American response to offering library services in the immediate aftermath of COVID-19 lockdowns reveals two primary concerns: maintaining services/instruction through online sources, and communicating these services to the university campus. It is important to recall that during the period of March through December 2020, some university libraries remained closed due to the lockdowns while others had reopened but had reduced services. In such a tumultuous time of rapid change, prompt communication between the library and the university campus was essential. The library website appears to have been the primary means by which these changes were communicated (Condic, 2021), however social media and library chat technology such as Springshare's Ask-A-Librarian LibChat were also used (Decker, 2021).
Several researchers have documented their university library's heavy reliance on online librarianship as a means of maintaining services and instruction after March 2020. For some libraries, this was a profound shift as they had been primarily dependent on face-to-face interactions prior to COVID. Two such universities were the University of Alabama (UA) and Southern Illinois University School of Medicine (SIU-SOM). Decker (2021) described the rapid deployment of the UA library's LibChat system, technology which was already in place but not used until the COVID-19 pandemic forced librarians to move solely to online communications with patrons. Similarly, SIU-SOM had focused primarily on in-person library transactions pre-pandemic due to the preferences of their students and faculty (Howes et al., 2021). The School's sudden move to virtual instruction in March 2020 caused a surge in the number of requests for technological assistance in creating online lecture materials. Another area of dramatic increase post-lockdown was the number of literature search requests which in May 2020 increased by 233% as compared to the previous year. Additional online tasks performed by the librarians post-lockdown were the creation of LibGuides as well as video orientations and tutorials. The authors also noted that the pandemic revealed a shortcoming of their collection, a reliance on print materials and very few e-books, which required attention. In all, online librarianship was key to maintaining services during the difficulty period of the COVID-19 lockdowns, and the literature indicates that American university libraries found ways to quickly adapt.
International perspectives on online librarianship during the pandemic (March 2020 to February 2021)
The literature regarding the international academic library response to the COVID-19 lockdowns is divided primarily along economic lines. Wealthier nations in Europe as well as China mirrored many of the same concerns as the United States, such as maintaining library instruction and services, and communicating these services to students and faculty. For developing nations in Africa, India, and the Caribbean, libraries scrambled to do their best with limited infrastructure.
Two European studies described the use of social media by academic librarians to maintain services post-lockdown. Martinez-Cardama and Pacios (2020) examined the use of Twitter communications by 56 Spanish university libraries during the period of March 15 to April 26, 2020, and Gmiterek (2021) collected data posted by Polish university libraries from March 11, 2020 through June 2020 to four social networking sites. Both researchers documented the use of social media as a means of conveying timely information about library services as well as conducting online library programming events. Additionally, Gmiterek described the use of YouTube by five Polish university libraries as a means of maintaining instructional services. These libraries published videos on such topics as utilizing specific databases and making use of different online services.
Like their European colleagues, Guo and Huang (2021) were similarly concerned with preservation of services post-lockdown. Examining China's academic library approach, the researchers gathered data from 42 academic library websites and their associated social media accounts on the WeChat and Bilibili platforms. They found overall that these universities were able to respond quickly to the sudden shift to remote learning required post-lockdown. The researchers cited the use of both library-created online tutorials as well as the purchase of vendor produced online learning modules in order to sustain instruction. Although the aforementioned European literature discussed the use of social media for online programming events to bolster campus community, Guo and Huang described the Chinese university library social media experience as being predominantly used for instructional purposes.
The concerns of European and Chinese universities were in stark contrast to the interests of developing nations during the same time period. Literature from university libraries in Africa, India, Mexico, and the Caribbean all echo concerns regarding the lack of resources needed to move instruction from in-person to remote. In Africa, a lack of reliable electricity, internet connectivity, and home computers were widely cited as blocks to remote instruction (Abubakar, 2021; Ajibade & Mutula, 2021; Ifijeh & Yusuf, 2020; Tsekea & Chigwada, 2021). Ajibade and Mutula (2021) described the reliance on smartphones by university students as a way to access lectures and instruction modules. Ifijeh & Yusef (2020) discussed the problems experienced by Nigerian university libraries in moving to remote instruction post-lockdown. Aside from the aforementioned infrastructure problems, the authors described a university culture heavily reliant on in-person instruction and with few functional university websites. Like their colleagues in Africa, researchers in Mexico characterized their university student population as being heavily dependent on smartphones as a reliable means of accessing the internet (Ortega-Martínez et al., 2021). They also described significant growth post-lockdown in the use of remote learning lectures and video conferencing to sustain student instruction. In India, Lobo and Dhuri (2021) described an academic library environment plagued by unreliable internet connectivity yet advanced in its applications of digital literacies and widespread use of social media for communications. The researchers found that the rapid transition to remote instruction in the wake of COVID-19 lockdowns prompted needed improvements to librarian training through e-learning platforms. And finally, researchers at the University of the West Indies in Jamaica documented the experiences of its university library in meeting student learning needs after March 2020 (Newman & Newman, 2021). They explained that their country's internet infrastructure was not ready for the rapid shift to increased bandwidth demands, nor was the library ready for the sudden switch to remote learning. Newman and Newman described their university as being focused primarily on physical collections rather than e-content, and with few devices to give to staff to use in working from home. Despite these challenges, the university's library worked hard to provide timely communications through the library website, expand their online resources and provide staff training on digital literacy skills.
Methodology
Research context
This study was conducted by academic librarians working as education subject specialists at different institutions throughout the United States. Noting a difference in their workload after the COVID-19 lockdowns began in March 2020, the authors wanted to understand possible widespread impacts across the profession as a result of the sudden push to virtual learning. As members of the Education and Behavioral Sciences Section (EBSS), a subgroup of the Association of College and Research Libraries (ACRL), the authors sought to survey other members for insights into their experiences.
Research purpose and research questions
The purpose of this study was to understand the nature of common online librarianship tasks performed prior to and after March 2020. EBSS members were asked to indicate their responsibilities from a list of work tasks, as well as give their perceptions regarding a set of statements focused on identifying possible changes to the work environment since March 2020.
The study sought to examine the following questions:
RQ1: Which specific online librarianship job duties did participants perform prior to March 2020?
RQ2: Did participants experience a change in specific online librarianship job duties after March 2020, and if so how?
RQ3: Did participants perceive any change in their working relationship with faculty after March 2020?
RQ4: Did participants feel professionally prepared to perform online librarianship job duties after March 2020?
Data collection
The authors selected a cross-sectional survey approach for data collection, whereby one survey would be deployed to gather data at one point in time (Creswell & Creswell, 2018; Lavrakas, 2008). The survey was developed using the Qualtrics XM software, and contained questions gathering both quantitative and qualitative data.
A permalink to the survey was generated from Qualtrics and subsequently embedded into an emailed solicitation for participation distributed through two EBSS social media platforms. The professional organization approved the study's recruitment of its members in advance of the solicitation. Additionally, the authors obtained institutional review board (IRB) approval from their respective institutions prior to recruitment of participants.
The Qualtrics-based survey included: (a) an informed consent information and signature page, (b) four multiple-choice questions to collect demographic information, (c) eleven questions related to job duties, (c) seven Likert-scaled questions related to librarian perceptions, and (d) three open-ended questions so that participants could describe in their own words their thoughts regarding their professional responsibilities and any changes that occurred after March 2020 (See Appendix A). Participants were allowed to skip questions, therefore result totals for individual questions vary and are reported accordingly.
Librarians were solicited for participation at the end of February 2021, and the survey was available to participants for three weeks. At the end of March 2021 the data was harvested and cleaned, including the removal of personally-identifiable information collected by Qualtrics such as IP addresses.
Participants
This study utilized a convenience sample of 46 participants who responded to an emailed solicitation to participate in an online survey. Approximately 905 members of the Education and Behavioral Social Sciences (EBSS) were originally solicited through a group discussion board as well as a Facebook group page, yielding a response rate of 5%. EBSS members are composed primarily of education liaison librarians who support university education programs, but also include liaison librarians specializing in journalism, communications studies, psychology, and social work. Other members are library administrators, and librarians working with collection development and acquisitions. Although participants were not required to be members of EBSS, they must have been employed as an academic librarian during 2020 in order to participate in the survey.
The survey began with several demographic questions to understand the nature of participants' work role. Of the 46 survey respondents, 61% (n = 28) identified their predominant work role as subject specialists or liaisons, 13% (n = 6) identified as being a library manager/supervisor or administrator, 11% (n = 5) identified as a generalist/reference/instruction librarian, 2% (n = 1) worked with collection development and acquisitions, and 13% (n = 6) categorized their librarian role as “other”. Only one participant in the study identified their predominant work role as “online learning librarian”; for everyone else, online librarianship tasks were performed as part of broader responsibilities.
The survey also gathered data to identify the types of institutions where participants were employed. 54% (n = 25) of survey participants worked at four-year research-intensive colleges or universities, 26% (n = 12) worked at teaching-focused four-year colleges or universities with limited graduate programs, and 20% (n = 9) worked at teaching-focused four-year colleges or universities that offer a significant number of graduate programs. None of the survey's participants worked at a community college or two-year degree-granting institution.
To further understand the respondent pool please see Table 1 . The term “faculty” in the table refers to both tenured and tenure-track librarians, while the term “faculty equiv.” refers to those participants in positions equivalent to faculty but whose institutions do not offer tenure to librarians.Table 1 Participants as categorized by workplace and role.
Table 1Demo-graphics 4-year college or university (teaching focused institution; limited graduate programs) 4-year college or university (teaching focused institution; significant graduate programs) 4-year college or university (research intensive institution)
Faculty Faculty equiv. Staff Faculty Faculty equiv. Staff Faculty Faculty equiv. Staff
Subject specialist/ liaison 2 1 1 3 0 0 13 3 5
Generalist/reference/instruction 1 1 1 1 0 0 0 0 1
Library manager/supervisor/administrator 1 0 1 0 0 1 3 0 0
Collection development/acquisitions 1 0 0 0 0 0 0 0 0
Other: 0 2 0 3 0 1 0 0 0
Data analysis
To analyze the quantitative and qualitative data collected by the survey instrument, a convergent mixed methods design was selected. Under this single-phased methodology, the two groups of data were analyzed separately and then compared to see if the findings supported or disproved each other (Creswell & Creswell, 2018).
The quantitative data collected from the multiple-choice questions was analyzed using descriptive statistics and the crosstabs feature in the Qualtrics software. Descriptive statistics (including means and standard deviations) were also collected from Qualtrics for the Likert-scaled items. Descriptive analysis of the data was conducted to collate and summarize the findings. The raw quantitative data was downloaded to a Microsoft Excel spreadsheet for further filtering and triangulating of data for analysis and interpretation.
The qualitative data collected through the open-response questions was analyzed separately under an inductive process of open coding. Separate Microsoft Word files were created for each of the four open-response questions (question 5 section 12, and questions 8, 9, and10). These files were uploaded to the NVivo 12 Plus Enterprise software where they were coded for themes present in participant responses. Eleven individual codes emerged and were categorized into a hierarchy of common themes.
Table 2 summarizes the data analysis process by indicating which survey questions corresponded to the appropriate research question.Table 2 Research questions and their corresponding survey questions
Table 2 Item on survey
Research question Quantitative findings Qualitative findings
Research Question 1: Which specific online librarianship job duties did participants perform prior to March 2020? See question 5. See questions 5.12, 8, 9, and 10.
Research Question 2: Did participants experience a change in specific online librarianship job duties after March 2020, and if so how? See question 5. See questions 8, 9, and 10.
Research Question 3: Did participants perceive any change in their working relationship with faculty after March 2020? See question 6. See question 9 and 10.
Research Question 4: Did participants feel professionally prepared to perform online librarianship job duties after March 2020? See question 7. See questions 8 and 9.
Findings
RQ1: Which specific online librarianship job duties did participants perform prior to March 2020?
The goal of this research question was to establish a baseline of work responsibilities for our participants prior to the COVID-19 lockdowns of March 2020. Analysis for this research question examined the data categorized in Table 3 . Few tasks were completed only prior to March 2020; these were primarily: working a new schedule to provide in-person library coverage (n = 7), and online librarian support via the course management system of an in-person course (n = 4). Instead, nearly all of the eleven specific work responsibilities listed in Table 3 were regularly completed by our participants prior to (and after) March 2020. In particular, the most popular online librarianship tasks completed were: creation of online LibGuides (95%), individualized research support to students (93%), production of online learning materials (91%), and individualized research support to faculty (89%). Two of the work responsibilities listed in Table 3 did not refer to specific online librarianship tasks, but were included so that the authors could better understand the range of participant responsibilities: these were “Management of library personnel, including student employees” (with 51% of respondents reporting that they never did this), and “Working a new schedule to provide in-person library coverage” (with 52% of respondents reporting that they never did this).Table 3 Results of Survey Question 5.
Table 3Question Please check if you did this ONLY prior to March 2020: Please check if you did this ONLY after March 2020: Please check if you have always done this: Please check if you have never done this: Total
1. Embedded librarian support within online courses (defined as: providing remote librarian services via a course management shell) 2 5 16 20 43
2. Online librarian support via the course management system of an in-person course 4 3 19 17 43
3. Production of online learning materials
(examples; library resource guides, instruction videos, tutorials) 0 4 40 0 44
4. Creation of online LibGuides 1 0 42 1 44
5. Website management 2 0 19 22 43
6. Individualized research support to students
(remotely via means such as email, chat, or video conferencing) 0 3 41 0 44
7. Individualized research support to faculty
(remotely via means such as email, chat, or video conferencing) 0 4 39 1 44
8. Management of library personnel, including student employees 3 4 14 22 43
9. Additional collection development areas, including eBook and eResource procurement 0 2 34 7 43
10. Working a new schedule to provide in-person library coverage 7 9 4 22 42
11. Helping faculty members move their classes online, through technology and/or instruction support 0 13 4 26 43
12. Other: 0 1 1 3 5
The qualitative data largely mirrored these findings. Of the 37 participants who provided qualitative feedback on this area of inquiry, 20 cited specific online librarianship tasks that they performed prior to March 2020. These job duties included: giving instruction classes and research consultations; creation of online learning objects; creation of LibGuides; and assistance to student inquiries via chat, text, and email.
These quantitative and qualitative findings support the prior research of Withorn and Willenborg (2020) who identified very similar common online librarianship responsibilities in the workplace prior to the COVID-10 lockdowns. Our participants seemed to embody the “slasher” role as described by Withorn and Willenborg, because all but one participant performed these duties outside of a designated online librarian job title.
RQ2: Did participants experience a change in specific online librarianship job duties after March 2020, and if so how?
As demonstrated in the analysis for RQ1, most of our participants were already completing the vast range of online librarianship tasks listed in Table 3 prior to March 2020. The most profound change for our participants after March 2020 was with regard to their work location: our demographic data indicated that 91% of participants (n = 42) moved their workplace from in-person to remote for one semester or more.
Although the majority of their online librarianship work remained the same, the survey findings identified 46 instances of librarians being asked to complete tasks that they had not performed prior to March 2020. The task with the highest number of “Please check if you did this ONLY after March 2020” (see Table 3, Column 2) responses was helping faculty members move their classes online, through technology and/or instruction (n = 13). None of the participants responded that they only did this task prior to March 2020, although four participants did this task both before and after the lockdowns. The crosstab analysis in Table 4 shows the distribution of types of librarians asked to complete this additional responsibility, with subject specialists being the most common.Table 4 Cross tab analysis: helping faculty members move their classes online, by librarian type
Table 4Q5: Please select the following tasks you regularly complete, indicating when you have provided these services (prior to March 2020, after March 2020, or both): Helping faculty members move their classes online, through technology and/or instruction support
Please check if you did this ONLY prior to March 2020: Please check if you did this ONLY after March 2020: Please check if you have always done this: Please check if you have never done this: Total
Q1: Please select the classification which best describes our work: Selected Choice Subject specialist/liaison 0 9 3 14 26
Generalist/reference/instruction 0 0 0 4 4
Library manager/supervisor/administrator 0 2 1 2 5
Collection development/acquisitions 0 0 0 1 1
Other: 0 2 0 4 6
Total 0 13 4 25 42
Although it appears from the quantitative data that few librarians took on completely new online librarianship job duties after March 2020, the qualitative data provided rich descriptive information as to the specific work realities for our participants during this time. The qualitative data recorded feedback from 32 individuals on this topic, and 53% (n = 17) of these respondents noted that their job duties did not change significantly after March 2020. For the other 47% of individuals, most described an increase in job duties. The reasons for the increased responsibilities varied. Seven participants described an increase in job duties because of colleagues who left the organization due to retirements, individuals who quit, and in one sad instance the death of a colleague. These organizational changes forced some of our study's participants to step into roles they had not necessarily anticipated; here are some examples:I assumed coverage of an entire school after a colleague retired last summer. I was most qualified due to my professional background. However I still have ALL the duties of my full-time position and no let-up or leeway to work with the new school's programs.
Two librarians in our department quit, meaning we've had to pick up extra work.
My department chair retired in December and I have assumed her duties. She retired early because she did not like working remotely and had a lot of pandemic related family stress.
Other participants described having to take on additional duties due to financial stress at their institutions:
As a result of losing student enrollment, the university fired several people in the library. In addition to my duties as acquisitions and collection development librarian, I have now taken over everything once done by the acquisition clerk and the copy cataloger.
We had a part-time evening/weekend position that opened up, frozen, so we have had to take turns working some evenings and Saturdays.
Finally, one participant spoke in positive terms regarding a new job duty post-March 2020:
… the team of librarians that I lead has taken on… advising loads (about 8–15 students per librarian). The advising seemed daunting at first, but in the end it hasn't been a heavy lift. Instead, it's helped us get to know the bureaucratic details of our institution from students' perspectives, and it will help advocate for change over time. Additionally, librarians don't always get to know individual students particularly well. Through advising appointments we've gotten to build good coaching and nurturing relationships with some of our graduate students.
These qualitative results indicate the resiliency of our participants to handle unforeseen administrative issues at their institutions, including staffing and financial challenges, which occurred after March 2020. They also capture a nuance in the data collection which was not gathered through the quantitative survey questions: although our participants were largely not performing new tasks after March 2020, many of them were taking on additional workload.
RQ3: Did participants perceive any change in their working relationship with faculty after March 2020?
Question 6 of the survey contained seven individual questions designed to measure librarian perceptions of specific transactions with faculty members post-lockdowns. As previously mentioned, participants had the option to skip individual survey questions; therefore responses vary and are indicated for each individual question in Table 5 by the “Count” field.Table 5 Results of Survey Question 6.
Table 5Survey question Minimum Maximum Mean (M) Std deviation Variance Count
1. The COVID crisis strengthened my connection with faculty in that they have a new awareness of what the library can do. 1.00 5.00 2.86 1.20 1.44 35
2. I have noticed more faculty members requesting technology help from the library since March 2020. 0.00 5.00 2.47 1.38 1.90 34
3. I have noticed more faculty members requesting instruction support from the library since March 2020. 0.00 5.00 2.52 1.33 1.77 29
4. I have noticed a positive difference in the level of communication between faculty members and the library since March 2020. 0.00 4.00 2.60 1.28 1.64 30
5. I have noticed a positive difference in faculty members' opinions of the library since March 2020. 0.00 5.00 3.03 1.29 1.67 33
6. I have noticed more faculty members requesting an embedded librarian since March 2020. 0.00 5.00 1.76 1.36 1.86 25
7. I have noticed more faculty members requesting online learning objects (ex. LMS modules, LibGuides, video tutorials) since March 2020. 0.00 5.00 2.97 1.54 2.36 36
Interpretation of participant responses to these Likert-based questions focused on the mean results per question. The survey was scaled from 0 to 5, with 0 meaning 0% agreement with the statement and a 5 meaning 100% agreement. The first question in this group resulted in a minimum participant response of 1, meaning that all 35 participants who answered this question had at least some minor agreement with the statement. The midpoint of this survey section's Likert-scaled range was a 2.5, therefore any question with a mean greater than 2.5 meant that the average participant held moderate agreement with the statement.
The questions in this section of the survey were largely delineated into two groups: individual job duties/tasks, and individual engagements/collaborations. Please see Table 5 for the resulting descriptive statistics by question. Regarding the job duties/tasks, the survey found that an increase in embedded librarian requests (Q6.6, M = 1.76) was not indicated as being impacted significantly by respondents during the pandemic, as compared with other areas such as faculty asking librarians to assist with providing or creating new learning objects (Q6.7, mean 2.97), requesting instructional support (Q6.3, M = 2.52), and requesting technology assistance from librarians (Q6.2, M = 2.47). Regarding librarian perceptions of engagement with faculty during the pandemic, respondents indicated they perceived a positive difference in faculty members' opinions of the library during the COVID crisis (Q6.5, M = 3.03). Participants also perceived there to be a new awareness by faculty of what the library can do resulting in a strengthened connection with faculty (Q6.1, M = 2.86). Finally, there was also the perception that since March 2020 librarians experienced a positive difference in communication between faculty members and the library (Q6.4, M = 2.60).
The qualitative data provided evidence of several specific faculty-librarian interactions after March 2020. It was apparent that at many institutions, the need for faculty to quickly transition from in-person to online classes post-March 2020 motivated them to reach out to librarians for help. The qualitative data suggests that working relationships between librarians and faculty were already in place prior to the lockdowns, and faculty relied on these existing partnerships in their time of need. One librarian illustrated this phenomenon by writing that overall her workload had not changed post-March 2020, “with the slight exception that because they now know it's available to them, faculty are more likely to ask for an online learning object.” Another participant suggested that this increase in creating online content did not diminish the integral relationship-building aspects of the job, writing “I was able to create more online resources and content and still maintained a solid relationship with the faculty.”
Another participant described an unintended negative consequence of trying to assist faculty through collection development during the very difficult period post-March 2020:
We… do not have the budget to license tons of films and the requests for those increased significantly with so many classes being offered online. I felt that we promised more than we were actually offering to support and the subject librarians were left having to say no to people. I was frequently put in the position of having to explain our Hathitrust agreement to angry faculty and pressured by upper administrators to just promote OER and ebooks and that would solve everything. (Though I am a huge fan of OER, it does not work in all situations and my faculty were barely prepared to transition to online much less adopt open textbooks or library ebooks). It was not a realistic expectation.
RQ4: Did participants feel professionally prepared to perform online librarianship job duties after March 2020?
Although seven participants abstained from answering this question, there were clear trends among the 39 who responded. Among the respondents, there were exactly twice as many participants who indicated that they felt professionally prepared than those who did not (26 said Yes and 13 said No). Interestingly, of the 13 unprepared people, 85% of that group came from 4-year colleges or universities with either significant graduate programs or a research-intensive institutional focus.
For those who did feel professionally prepared to perform online librarianship tasks post-March 2020, a common theme emerged in participant writings regarding the value of prior online librarianship work experience. Of the 26 participants who responded that they felt professionally prepared, 23 provided comments in the open response section; all of these 23 comments described prior work experience with online librarianship tasks. One librarian cited their prior familiarity with technology as contributing to their feeling of preparedness:
I have always been encouraged to stay abreast of current technologies and to incorporate them into my work. Because of this, I felt prepared to use them to develop the online learning objects that were necessary in a fully remote environment.
One-third of the respondents to this question did not feel professionally prepared to move to remote work and perform their job duties after March 2020. For this group of self-identified unprepared librarians, eight were subject specialists/liaisons, two identified as “library manager/supervisor/administrator”, one was a generalist/reference/instruction librarian, and two selected “other” for their role. These participants were asked to add the tasks of embedded librarianship, faculty research support, management of library personnel, and helping faculty members move their classes online to their job duties. In addition to these tasks, the eight subject specialist librarians were additionally asked to work new schedules, provide individual student research support, and provide online librarian support.
Of the 13 participants who indicated that they did not feel professionally prepared, two primary themes emerged in the open response writings: lack of training/experience and also technology issues such as lack of equipment. For example, one librarian cited both problems:
… with the pandemic there was a new high volume of requests for me [to] develop customized asynchronous materials (videos and research guides) in addition to the usual load. We did not have and still don't have the technology support to produce high quality videos from home. There was also no formal training provided for creating this content and no centralized management of the videos produced within the library system (no infrastructure for hosting this content).
Another librarian cited issues of having to quickly pivot to support a higher volume of requests for the assistance, without the training or workflows necessary for managing the requests:
My library's culture of saying yes to the patron no matter what was very detrimental during this time, especially to subject librarians. We needed additional technological support and training, as well as a standardized workflow to manage all the new content creation requests. It would have been good to hire someone who specialized in producing videos to help us. We can't do it all at that volume.
Both of these librarians point to an additional underlying issue in that library workflows did not previously exist to manage the switch to a focus on online content delivery.
Discussion and implications
Theme 1 online librarianship tasks
Our results indicate that prior to March 2020, many librarians had already taken on tasks related to online librarianship. Creating online content including materials for LibGuides and Learning Management Systems, conducting individualized research support for students and faculty, and purchasing digital materials such as eBooks and electronic journal subscriptions were all common occurrences for the majority of our participants in the years leading up to the COVID-19 lockdowns.
After the lockdowns in March 2020 however, our results indicate that the nature of work duties did not change for our participants as much as the intensity or allocation of their work. We found that professional responsibilities in some cases drastically changed as librarians moved to a remote setting or greatly reduced their in-person interaction with patrons. Librarians were challenged to help professors move their courses online, work new schedules, create online learning objects, and embed in online courses. For the majority of our participants who were doing these tasks prior to March 2020, this increased pace was stressful but achievable; for those participants who did very little online librarianship prior to March 2020, however, the shift in workload appears to have been much more difficult. In some instances, librarians did not have the tools they needed to make a swift transition to online librarianship, be it through previous training, technology infrastructure, or administrative policies in place. There was also the added pressure for librarians that had to take on additional work load and/or new roles left by colleagues that retired, resigned, or passed away.
These findings coincide with the literature review of American and international academic libraries during this time period. For those libraries who were already well-versed in online librarianship prior to March 2020, the move to a largely online learning environment was far more manageable in terms of preserving library instruction, services, and communications (Decker, 2021; Guo & Huang, 2021; Howes et al., 2021). Having existing information systems and policies in place allowed librarians to concentrate on the added workload from faculty and student requests. For those libraries who focused on face-to-face interactions prior to the lockdowns, however, the transition was much more difficult as library management struggled to acquire necessary technology infrastructure and provide needed technology training to personnel (Lobo & Dhuri, 2021; Newman & Newman, 2021).
Theme 2 librarian perceptions of faculty relationships
The quantitative findings suggest that there was a greater awareness during the pandemic among discipline faculty as to the ways in which librarians might contribute to their work, particularly in their time of need. Our participants perceived a greater demand for their services post lockdown in helping faculty by creating learning objects and providing other technical assistance. Perhaps consequently, participants also perceived greater levels of professional respect from faculty members in terms of heightened opinions of the library and its services, as well as greater communication. The qualitative data echoed the idea that while this work was nothing new for our participants, discipline faculty may have been more inclined to reach out for help after March 2020. This strengthened connection between discipline faculty and librarians for collaborative work could be the direction going forward in supporting student learning, and could possibly create more co-teaching opportunities.
Theme 3 managing expectations in the face of crisis
Although the quantitative findings indicated that our survey participants' job responsibilities largely did not change after March 2020, the qualitative findings provided a more detailed view of workplace expectations for our participants. Specifically, many of our participants described their struggles with additional duties allotted to them upon coworkers leaving, lack of technology and technological support, and trying to maintain the library culture of saying “yes” to all outside requests. Several examples from our participants illustrated the difficult situations they were put in due to the decisions of their library administration, such as insufficient staffing or promotion of open educational resources (OER) materials without clear marketing of their limitations. It would seem to be the responsibility of library administrators to provide a buffer between librarians and outside patrons, such that administrators could adequately convey expectations of assistance during a crisis period. Another consideration is the turnaround time it takes to implement new services or offerings: there is a big difference between creating an online learning object for a faculty member versus making large-scale collection development changes as some print-focused libraries were forced to do. Some decisions, such as moving one's collection to e-resources and/or OER, take time to implement and have inherent limitations in their capabilities; library administrators need to convey these issues to patrons in order to mitigate potential misunderstandings and disappointments.
The stress of additional duties, saying “yes” to every request, and lack of equipment and support, combined with perceived lack of support from library administration can lead to burnout for many librarians. Academic librarian burnout was already a well-documented phenomena prior to the COVID-19 pandemic (Nardine, 2019; Wood, Guimaraes, Holm, Hayes, Brooks, 2020), however these findings suggest that COVID presented many additional workplace challenges. Our findings demonstrate the resiliency of our participants during a worldwide crisis, but also point to the necessity of managing expectations in the workplace; this issue may be even more important part of librarian work life post-March 2020.
Study limitations
This study obtained its participants through convenience sampling, a method which produces both positive and negative consequences. Although this sample was helpful in identifying the experiences of a group of academic librarians, the study was limited by the sample's heavy concentration of subject specialist librarians. Due to the membership composition of the Education and Behavioral Social Sciences (EBSS) group, a preponderance of subject specialist librarians was to be expected in the sample; however, the disproportionate sample (61% subject specialist) possibly overemphasized the perspectives of this subset of librarians. Furthermore, the small sample size (only a 5% response rate) is another factor which limits generalizability of the results to the larger academic librarian population.
Conclusion
The purpose of this study was to survey academic librarians about their online librarianship duties prior to and after the March 2020 COVID-19 lockdowns. The study found that overall our participants were experienced in providing online librarianship services pre-COVID, and were therefore prepared to move quickly to predominantly online services at the onset of pandemic-driven lockdowns. We were also pleased to find that many of our participants perceived strengthened connections with their university faculty as a result of their added support, largely in the form of technological assistance and instructional services, during the COVID-driven shift to online education.
These positive aspects of our participants' professional response to the COVID lockdowns need to be weighed in the context of their articulated struggles. Several participants described a library culture of saying “yes” to patron requests which added to their stress levels, particularly in light of having to take on additional workload at the loss of colleagues through attrition and even death. Professional burnout has been a documented problem in academic librarianship for some time, however post-COVID this issue is perhaps even more salient.
This study adds to the growing body of knowledge on this unprecedented period of academic librarianship by providing a view of online librarianship concerns at the onset of the COVID-19 pandemic. Future research may explore how post-pandemic librarianship continues to evolve.
Appendix A Supplementary data
Supplementary material
Image 1
Data availability
OSF Storage, (Original data)
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.acalib.2022.102530.
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PMC009xxxxxx/PMC9001180.txt |
==== Front
Sustain Cities Soc
Sustain Cities Soc
Sustainable Cities and Society
2210-6707
2210-6715
The Author(s). Published by Elsevier Ltd.
S2210-6707(22)00219-0
10.1016/j.scs.2022.103896
103896
Article
Impact of the COVID-19 pandemic on the energy performance of residential neighborhoods and their occupancy behavior
Todeschi Valeria ab⁎
Javanroodi Kavan a
Castello Roberto ac
Mohajeri Nahid d
Mutani Guglielmina e
Scartezzini Jean-Louis a
a Solar Energy and Building Physics Laboratory (LESO-PB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
b Future Urban Legacy Lab (FULL), Department of Energy, Politecnico di Torino, Torino, Italy
c Swiss Data Science Center (SDSC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
d UCL Institute for Environmental Design and Engineering, University College London, London, United Kingdom
e Responsible Risk Resilience Centre (R3C), Department of Energy, Politecnico di Torino, Torino, Italy
⁎ Corresponding author at: Solar Energy and Building Physics Laboratory (LESO-PB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
12 4 2022
7 2022
12 4 2022
82 103896103896
22 12 2021
11 3 2022
10 4 2022
© 2022 The Author(s). 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.
Several contrasting effects are reported in the existing literature concerning the impact assessment of the COVID-19 outbreak on the use of energy in buildings. Following an in-depth literature review, we here propose a GIS-based approach, based on pre-pandemic, partial, and full lockdown scenarios, using a bottom-up engineering model to quantify these impacts. The model has been verified against measured energy data from a total number of 451 buildings in three urban neighborhoods in the Canton of Geneva, Switzerland. The accuracy of the engineering model in predicting the energy demand has been improved by 10%, in terms of the mean absolute percentage error, as a result of adopting a data-driven correction with a random forest algorithm. The obtained results show that the energy demand for space heating and cooling tended to increase by 8% and 17%, respectively, during the partial lockdown, while these numbers rose to 13% and 28% in the case of the full lockdown. The study also reveals that the introduced detailed occupancy scenarios are the key to improving the accuracy of urban building energy models (UBEMs). Finally, it is shown that the proposed GIS-based approach can be used to mitigate the expected impacts of any possible future pandemic in urban neighborhoods.
Graphical abstract
Image, graphical abstract
Keywords
COVID-19 pandemic
GIS
Space heating and cooling
Random forest
Occupancy profile
Urban morphology
==== Body
pmcNomenclature
Symbols and units
A Area m2
ACH Air change rate h−1
Bn Beam irradianceW/m2
BCR Building coverage ratio m2/m2
BD Building density m3/m2
BH Building height m
C Effective heat capacity of a conditioned space J/K
F Reduction factor -
G Global radiation W/m2
D Diffuse irradiance W/m2
H/Havg Relative height m/m
H/W Height-to-width ratio or canyon effect m/m
Isol Solar irradiance W/m2
ML Machine learning
MAPE Mean absolute percentage error %
NBH Neighborhood
RH Relative humidity %
S Scenario
SD Standard deviation
S/V Surface-to-volume ratio m2/m3
SVF Sky view factor -
T Temperature °C, K
U Thermal transmittance W/m2/K
V Volume m3
WWR Window-to-wall ratio %
α Solar radiation absorption coefficient
ξ Percentage of shadow on the vertical wall %
ρ Solar reflectance of the external environment -
τ Total solar energy transmittance-
Φ Heat flow rate, thermal power W
Subscripts
a Air
C Cooling
H Heating
h Horizontal
I Internal heat gains
sol Solar
t Transmission
v Ventilation
Acronyms
CDD Cooling degree days
DHW Domestic hot water
DSM Digital surface model
GIS Geographic information system
HDD Heating degree days
RF Random forest
TS Thermodynamic system
UBEM Urban building energy model
1 Introduction
The COVID-19 pandemic has introduced an unexpected and significant impact on the energy sector. Moreover, it has caused an unprecedented crisis that has affected the human and social capital, institutions, communities (Saif-Alyousfi & Saha, 2021), industrial processes (Zhou et al., 2021), energy use, as well as the financial investments of individuals (Giovannini et al., 2020). As a result of the uncertainties that have arisen from the duration and intensity of the COVID-19 pandemic, its impacts on the energy sector and policy responses have opened a wide range of possible future energy scenarios. Energy policies should consider the complexities and interconnections of this crisis when defining measures to achieve the energy and climate targets set for 2030 and 2050.
The daily life and behavioral patterns of individuals have changed drastically during the pandemic. These changes have had a significant impact on the energy demand of buildings because people, and in particular those over 65 or anyone advised to shield for health reasons, now spend much more time at home (Balest & Stawinoga, 2022; Saadat et al., 2020). Moreover, many workers and students have been encouraged to work or study at home, which may involve using a computer or tablet (Cuerdo-Vilches et al., 2021; Mouratidis & Papagiannakis, 2021). As a result, an increase in domestic energy consumption, especially electricity, and a reduction of electricity in industrial and commercial sectors has been observed (Bahmanyar et al., 2020; Zhang et al., 2021). Reports and research publications have also highlighted the impact of the COVID-19 pandemic on the energy demand of buildings and the behavioral patterns of users around the world. The International Energy Agency (IEA) has estimated that the global energy demand and CO2 emissions decreased by 5% and 7% in 2020 (International Energy Agency (IEA), 2020). In March and April 2020, during the COVID-19 pandemic, electricity consumption decreased by 10% in the United States and in European countries, compared to the same period in 2019 (IAEE, 2020; Ruan et al., 2020). Italy and Spain are the countries that have been affected the most with reductions of about 30% and 20% in the electricity demand, respectively (IAEE, 2020). A different trend has been observed in Northern Europe. The total electricity consumption has remained almost constant in Denmark, Finland, Norway, and Sweden. The only exception is Switzerland, which has shown an 8% increase in the total electricity demand (IAEE, 2020).
Countries have controlled the pandemic in different ways by imposing various restrictive measures. In addition, the perception of risk varies from country to country, which can affect the effectiveness of the imposed measures (Siegrist & Bearth, 2021). Nevertheless, such measures have led to significant changes in the lifestyles of people. It is well-known that the users’ behavior has a major impact on the energy demand, in part related to the amount of time they use electricity and heating and in part related to the status of window and door openings (Delzendeh et al., 2017). There is a close correlation between the occupants’ behavior and energy demand in buildings (Ahn & Park, 2016). The occupancy profiles of buildings play a crucial role in energy consumption (Buttitta & Finn, 2020; Motuzienė et al., 2022). Human behavior, different occupancy densities, and variations in thermal and lighting preferences contribute significantly to the gap between simulated and real energy performance in buildings (Dong et al., 2021; Martinaitis et al., 2015; Wu et al., 2020). However, the literature on defining occupancy scenarios during the COVID-19 pandemic is still scarce, and contrasting effects have been observed in different sectors and countries. It is important for research to define a methodology that is able to model future energy scenarios during pandemic phenomena, given the possibility of the emergence of other pandemics in the future (Thoradeniya & Jayasinghe, 2021).
This study addresses this gap by introducing a flexible GIS-based approach, which is readily applicable to other contexts and can consider the effect of the users’ behavior during the pandemic on the energy performance of residential urban neighborhoods.
1.1 Research background and gap
The current state of the art of the impact assessment of the COVID-19 outbreak on the energy demand has focused chiefly on electricity consumption. During the COVID-19 pandemic, there has been a reduction in electricity consumption for the industrial and commercial sectors and an increase in the residential sector. The reviewed literature has been categorized according to the type of building (e.g., residential, commercial, industrial, municipal, or educational) and the type of energy use (e.g., electricity, heating, cooling, or DHW). The studies that have focused on the impacts of the pandemic on the electricity demand are mainly based on analyzing measured electricity data or on questionnaires. In Italy, a reduction of up to 37% in the national electricity sector has been observed, and the daily load profile has changed significantly (Ghiani et al., 2020). Furthermore, a significant reduction in the overall use of electricity was noted in the UK (Liu & Lin, 2021) during the lockdown. Madurai Elavarasan et al. (Madurai Elavarasan et al., 2020) found that the residential electricity demand in India increased during the lockdown, while they also observed a substantial decrease in commercial and industrial consumption.
Analogous results were observed in the province of Ontario in Canada, where a reduction in the overall electricity demand of 14% was observed in April 2020, as well as a flattening of the load demand, especially during peak hours (Abu-Rayash & Dincer, 2020). A similar trend has been reported for South Asia (Lowder & Leisch, 2020) and Romania (Soava et al., 2021). Analyzing the types of users independently, the residential electricity demand increased, showing a shift and change in the shape of the load profiles, while the electricity in the commercial and industrial sectors decreased. Geraldi et al. (2021) found that the electrical consumption of administrative buildings in Florianópolis (Brazil) decreased by 38%, and that of elementary and nursery schools decreased by around 50% during the lockdown.
In Lagos (Nigeria), the electricity data of 259 residential, commercial, and industrial users were analyzed before and during the lockdown (partial and full lockdown). No significant changes between the baseline scenario and the partial lockdown figures were observed in the residential sector, while consumption decreased in the commercial sector (Edomah & Ndulue, 2020). Burleyson et al. (2020) analyzed electricity consumption data pertaining to 3.8 million residential and non-residential buildings in Illinois (the USA). In April 2020, the electricity demand for the non-residential sector decreased by 16%, while residential consumption increased by 12%. Ding et al.(2021) developed energy signature curve models to assess the annual electricity use in Norway and found that the annual electricity demand decreased in educational buildings (kindergartens and schools) during the lockdown, while the electricity use increased in residential buildings, with significant differences related to the building typology (apartments underwent an increase of 27% and townhouses of 1.3%). In New York, the electricity demand of residential users during the lockdown increased over the weekdays, with higher morning peaks between 8 and 10 am (Chen et al., 2020). A similar trend was observed in Warsaw, where dwellings showed a higher daily electricity use on weekdays, but without any increment in the average daily peak demand, and a flattened profile in the morning hours (Bielecki et al., 2021).
Although the electricity consumption during the COVD-19 pandemic has been studied widely, only a few works have investigated the use of heat in non-residential and residential buildings. The literature on assessing the heating demand in buildings during the COVID-19 pandemic can be divided into two major groups, that is, measured data and simulated data. In the UK, the consumption of electricity and heat in the non-residential sector (industrial and commercial) decreased by 15.6% and 12%, respectively, during the first lockdown, while the second lockdown led to reductions of 6.3% for electricity and 4.1% for heat. The use of thermal energy in residential buildings did not change during the first lockdown but increased by 6.1% in the second one (Mehlig et al., 2021). In Canada, the energy demand of 40 social housing buildings was investigated during the lockdown (Rouleau & Gosselin, 2021). The average daily electricity increased by 2%, the domestic hot water (DHW) consumption increased by 17%, while the space heating consumption showed minimal variations, mainly due to the increase in window openings. Cheshmehzangi (2020) reported an increase in the energy use of 352 households in China during the lockdown. The author observed an increase of 40% in the energy demand for cooking between January and March 2020 and an increase of 60% in the energy demand (cooling and heating) and of 40% in lighting from January to February 2020. An analysis of the use of heat was carried out in educational buildings in Norway before and during the lockdown using a model based on the energy signature curve (Ivanko et al., 2021). The authors hypothesized different scenarios assuming lockdown conditions. From their results, it emerged that the consumption of schools, kindergartens, and universities could be reduced by up to 54% during the lockdown. They also hypothesized that about 77 Wh/m2 per day (equal to 21 kWh/m2 per year) could be saved.
Energy simulation tools have also been widely used to assess the impact of restriction measures on energy demand. Non-residential energy use decreased during the lockdown, while residential consumption showed different trends, depending on the input setting. For example, Zhang et al. (Zhang et al., 2020), adopting the urban modeling interface (UMI) tool (Reinhart et al., 2013), investigated the energy demand in Sweden by simulating different scenarios. The use of electricity in residential buildings increased during the full lockdown, and the heating decreased due to the internal gains (no variations in ventilation and window openings were considered during the lockdown). Schools and offices needed less energy for electricity and heating during the lockdown period (Zhang et al., 2020). Cvetković et al. (2021) simulated the energy consumption of a residential building in Kragujevac (the fourth largest city in Serbia) using EnergyPlus software. Their results showed an increase of over 21.3% in the heating demand during a partial lockdown, and a higher use of electricity of over 54% and 58.4% during a partial lockdown and full lockdown, respectively. Table 1 offers an overview of the effects of the COVID-19 pandemic on energy use in buildings.Table 1 Overview of the effects of the COVID-19 pandemic on the energy use in the building sector.
Table 1Ref. Energy simulation Energy use Users Case study Main findings
(Ghiani et al., 2020) Real measured data Electricity All the users Italy A reduction in the electricity consumption of up to 37% was observed during the full lockdown period,
(Liu & Lin, 2021) Machine learning model Electricity All the users The UK A reduction in the overall electricity consumption was observed during the lockdown period
(Madurai Elavarasan et al., 2020) Real measured data Electricity Residential, commercial, industrial users India A significant increase in the residential electricity demand and a reduction in industrial and commercial consumption were observed during the lockdown period
(Abu-Rayash & Dincer, 2020) Real measured data Electricity All the users Ontario, Canada The overall electricity demand for the Ontario province decreased by 14% in April 2020 during the lockdown.
(Lowder & Leisch, 2020) Real measured data Electricity Residential, commercial, industrial users South Asia An increase in the residential electricity consumption and a reduction in the commercial and industrial usage were observed during the lockdown and this led to a shift and change in the shape of the load profiles
(Soava et al., 2021) Real measured data Electricity Residential, commercial, industrial users Romania An increase in the residential electricity consumption and a reduction in non-residential electricity consumption were observed during the lockdown.
(Geraldi et al., 2021) Real measured data Electricity Municipal users Florianópolis, Brazil A reduction in the electricity consumption of municipal buildings was observed during the lockdown: 11.1% in health centers, 38.6% in administrative buildings, 50.3% in elementary schools, and 50.4% in nursery schools.
(Edomah & Ndulue, 2020) Real measured data Electricity Residential, commercial, industrial users Lagos Nigeria, Africa An increase in the residential electricity consumption, from 3.72 MW/week to 3.87 MW/week, and a reduction in the industrial and commercial electricity consumption, from 2.54 MW/week to 1.41 MW/week and from 3.07 MW/week to 2.63 MW/week, respectively, were observed during the lockdown.
(Burleyson et al., 2020) Real measured data Electricity Residential, commercial, industrial users Illinois, The USA The electricity consumption for the non-residential sector decreased by 16%, while residential consumption increased by 12%. The weekday load profiles in the residential sector became very similar to those of the weekends during the lockdown (April 2020).
(Ding et al., 2021) Energy signature curve models Electricity Residential, educational users Norway An increase in the residential electricity consumption of 27% was observed for apartments and 1.3% for townhouses during the lockdown. The electricity consumption for the education sector decreased.
(Chen et al., 2020) Household surveys Electricity Residential users New York, The USA It emerged, from the interviews, that the electricity consumption of households was higher during the lockdown; only a few households reported a lower energy usage.
(Bielecki et al., 2021) Real measured data Electricity Residential users Warsaw, Poland An increased daily electricity consumption was observed on weekdays, but the average daily peak demand did not increase, while the profiles were flattened in the morning during the lockdown.
(Mehlig et al., 2021) Real measured data Electricity, heating, DHW All the users The UK The electricity consumption of non-residential users in the first lockdown reduced by 15.6%, while heat consumption reduced by 12.0%, and then by less than half in the second lockdown. The energy consumption of residential users did not change during the first lockdown but increased by 6.1% in the second one.
(Rouleau & Gosselin, 2021) Real measured data Electricity, heating, DHW Residential users Canada The average daily electricity consumption increased by 2%, and the DHW consumption increased by 17%, but no significant change in space heating use was observed during the lockdown.
(Cheshmehzangi, 2020) Real measured data Electricity, heating, cooling Residential users China A 40% increase in energy consumption for cooking, a 60% increase for cooling and heating, and a 40% increase for lighting were observed during the lockdown.
(Ivanko et al., 2021) Energy signature curve models Heating, DHW School, kindergarten, university users Norway Consumption was reduced by up to 54% (21 kWh/m2 per year) during the lockdown
(Zhang et al., 2020) UMI tool, Rhino 6 Electricity, heating, cooling, DHW Residential, school, office users Sweden An increase in residential electricity consumption and a reduction in heating consumption were observed, due to more internal heat gains during the lockdown. Schools and offices needed less energy for electricity and heating
(Cvetković et al., 2021) EnergyPlus software Electricity, heating, DHW Residential users Kragujevac, Serbia The heating consumption increased by 21.3% during a partial lockdown, electricity consumption increased by 54% during the partial lockdown and 58.4% during a full lockdown (a similar trend was observed for DHW consumption)
The main findings of the literature review can be categorized as follows:- The electricity consumption of all the users (industrial, commercial and public) tended to decrease during the lockdown period, except for the residential sector.
- By analyzing the electricity consumption of different users during the lockdown period, it is possible to confirm that: (i) there was an increase in the residential electricity consumption, and the weekday load profiles became very similar to those of the weekend; (ii) there was a reduction in industrial and commercial electricity consumption.
- The heat demand of residential buildings tended to increase during the lockdown, but not markedly, due to higher internal gains.
- The heat demand for the non-residential sector tended to decrease in schools, offices (since they were closed) and retail buildings (since they were open fewer hours per day).
1.2 Research objective and contribution
The unprecedented impact of the pandemic has caused the energy consumption trend to change unexpectedly. As a result, it is essential to develop energy models to evaluate future energy trends. Only a few studies have evaluated the impacts of the lockdown on the energy heat demand. As measured energy data are not always available, energy simulation tools are often used to assess the impact of the pandemic on the energy demand. Several contrasting results have been reported in the existing literature regarding urban energy use simulations and occupancy profiles. The definition of occupancy scenarios is fundamental to take into account the behavior of residents during the pandemic. This study introduces detailed scenarios that take into account different occupancy behavioral patterns. As the GIS-based approach presented here is flexible, the input data can readily be updated according to the scenarios that has to be analyzed. In addition, the presented model allows energy assessments to be conducted at the urban scale, and the simulation runtime is much less than that of the existing simulation engines.
The aim of this study has been to address the research gaps presented in the literature review by using measured and simulated data to analyze the energy trend during the COVID-19 pandemic. More specifically, the aims of the study are:(i) To develop and verify a bottom-up approach in order to explore the impacts of the COVID-19 pandemic on the space heating and cooling demands of residential buildings using a "GIS-based engineering model".
(ii) To develop and analyze detailed occupancy scenarios that describe the behavior of the occupants during the partial and full lockdowns.
(iii) To develop a data-driven model in order to improve the accuracy of the GIS-based energy model using a machine learning approach.
2 Methodology
This work investigates the impacts of the COVID-19 pandemic on the space heating and cooling energy performances of three residential neighborhoods located in the Canton of Geneva, Switzerland. The proposed methodology consists of three main phases (Fig. 1 ). In the first phase, the input data are processed. This methodology combines different types of data. Climate data, building data, occupancy profiles, and morphological parameters are processed and elaborated with the support of GIS tools. The energy demand of urban neighborhoods is investigated using a GIS-based engineering model (Mutani et al., 2020, 2021; Todeschi et al., 2021). The GIS-based model is verified in the energy simulation phase, and the simulated annual energy consumption is compared with the measured data. We use a machine-learning algorithm to improve the accuracy of the model. In the third phase, the impact of the COVID-19 pandemic on the space heating and cooling demand is assessed by investigating three scenarios that consider the occupancy scenarios, that is, pre-pandemic, partial lockdown, and full lockdown.Fig. 1 Flowchart of the GIS-based workflow.
Fig 1
2.1 Studied area
The proposed GIS-based approach is here implemented for the Canton of Geneva. The climate in this canton is temperate with cold winters, warm summers, adequate precipitations, and a north-easterly wind (Köppen climate classification: Cfb (Peel et al., 2007)). Fig. 2 shows the hourly weather data of Geneva collected from Meteonorm 8.0.4 for the "contemporary" period from 2000 to 2019. The relative humidity (%) and external air temperature ( °C) refer to a weather station in Geneva (46°25′N, 6°12′E). During the winter season, the air temperature drops to −6.9 °C in January, while the temperature rises to 34.8 °C in July. The coldest months are January and December, with average monthly air temperatures of 2.2 and 2.9 °C, respectively. The hottest months are July, with an average monthly air temperature of 20.8 °C, and August, with an average temperature of 20 °C.Fig. 2 An example of the hourly weather data of Geneva: relative humidity (in red) and external air temperature (in blue). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig 2
2.2 Case studies
The urban morphology affects the energy performance of buildings to a great extent due to the relationship between the building and its surroundings (e.g., shading, heat exchanges between buildings) and the type of outdoor surfaces (Ahn & Sohn, 2019; Javanroodi et al., 2018; Mangan et al., 2021). It is possible to reduce the space heating and cooling energy demand of buildings by optimizing the urban morphology (Perera et al., 2021, 2021). The urban morphology also modifies the microclimate conditions in urban areas, particularly in extreme weather conditions (Javanroodi & Nik, 2020). It is thus essential to consider the impact of the urban morphology when developing a sound energy simulation model at the urban scale. In this study, the urban morphology of the three neighborhoods in the Canton of Geneva was retrieved from the GIS database, focusing on the urban form and density. The 2D building characteristics and the digital surface model (DSM) were acquired from the Swisstopo (Federal Office of Topography) database at a resolution of 0.5 × 0.5 m.
Several parameters, referring to the urban form and urban density, have been considered in the literature to describe the urban morphology of a location. In this study, six major parameters have been considered to define the morphology of the neighborhoods: (i) the building height (BH), that is, the average height of the buildings in the sample area, (ii) the relative height (H/Havg), that is, an index to describe the solar exposition concerning the building heights (Chatzipoulka et al., 2016), (iii) the building coverage ratio (BCR), that is, the total built area in the sample area divided by the sample area (Mohajeri et al., 2016; Wei et al., 2016) (iv) the building density (BD, m3/m2), that is, the total building volume in the sample area divided by the sample area (Mohajeri et al., 2016; Quan et al., 2020), (v) the height-to-width ratio (H/W) (Javanroodi et al., 2019; Martin et al., 2017), which is the ratio of the building height to the distance between buildings, and (vi) the sky view factor (SVF), which is used to measure the portion of sky visible from a given point (Javanroodi et al., 2022; Middel et al., 2018). SVF is used in the GIS-based engineering model to account for the solar exposition of the urban morphology and to quantify the thermal radiation lost to the sky considering a 200 × 200 meter grid size.
A total number of 18 urban neighborhoods were assessed in the Canton of Geneva using the procedure mentioned above, in which three neighborhoods were selected on the basis of the urban density (i.e., BH and BD) and the urban form (i.e., H/Havg, BCR, H/W, and SVF). In this regard, a BH range of 10 to 25 m was considered for the urban density, for which neighborhood 1 (hereafter referred to as NBH1) was 10.8 m, neighborhood 2 (hereafter referred to as NBH2) was 23.7 m, and neighborhood 3 (hereafter referred to as NBH3) was 15.6 m, while BD varied from 1.6 to 8.2 m3/m2. A range of 0.15 to 0.4 m2/m2 for BCR and a range of 0.25 to 0.8 m2/m2 for H/W were considered for the urban form; the BCR values were 0.16, 0.38 and 0.15 m2/m2 for neighborhoods 1, 2 and 3, respectively, while the H/W values were 0.25, 0.81 and 0.30 m2/m2. Although the H/Havg and SVF values were found to be similar across the studied neighborhoods, they were considered in the assessments due to their importance in modifying the urban climate.
This investigation focused on the residential sector, and the neighborhoods with a high percentage of residential buildings were therefore identified. Thus, three neighborhoods were selected considering the characteristics of the residential building stocks. In these neighborhoods, 90% of the buildings are residential, and most of the energy data for annual heating consumption is known for these buildings. Fig. 3 shows a map of the Canton of Geneva with the location of the three selected neighborhoods (the locations of the neighborhoods are marked in red). NBH1 (46° 24′ N, 6° 20′ E) and NBH2 (46° 21′ N, 6° 15′ E) are small urban areas in the Vésenaz district and Pâquis district, respectively, while NBH3 (46° 19′ N, 6° 11′ E), which has a larger total area, is located in the Lancy and Onex districts.Fig. 3 Map of the Canton of Geneva using World Imagery from ESRI to show the locations of the three neighborhoods considered as case studies.
Fig 3
Table 2 presents the values of the morphological parameters considered for each neighborhood. The morphological parameters that have the most significant variability are BH and BD, which were used to describe the urban density, and the canyon effect, which was evaluated as a function of the H/W ratio. Neighborhoods 1 and 3 are less dense than NBH2, which has higher BH, BCR, BD, and H/W values.Table 2 Morphological parameters: the average values of each neighborhood and the standard deviation (SD).
Table 2Neighborhood BH H/Havg BCR BD H/W SVF
(m) (m/m) (m2/m2) (m3/m2) (m2/m2) (-)
1 10.79 1.32 0.16 1.61 0.25 0.82
SD 2.14 0.13 0.07 1.03 0.15 0.02
2 23.69 1.28 0.38 8.15 0.81 0.74
SD 5.29 0.29 0.17 3.47 0.29 0.18
3 15.60 1.51 0.15 2.02 0.30 0.81
SD 6.95 0.46 0.08 1.50 0.27 0.01
Around 1800 buildings with heating/cooling systems were selected from over 3200 buildings in the three neighborhoods. These buildings were then classified into seven categories: assembly (church, public, sports center, temple), business (service, government, offices, post office, police), commercial (commercial, retail), educational (kindergarten, school, university), industrial (manufacture, atelier), institutional (hospital), and residential (condominium, detached house, retail). Fig. 4 depicts the buildings classified on the basis of their function in the selected neighborhoods.Fig. 4 Building classification by type of users in the three considered neighborhoods in the Canton of Geneva: (a) NBH1, (b) NBH2, and (c) NBH3.
Fig 4
The residential buildings were classified, according to their year of construction, into eight classes: before 1945 (class 1), between 1946 and 1960 (class 2), between 1961 and 1970 (class 3), between 1971 and 1980 (class 4), between 1981 and 1990 (class 5), between 1991 and 2000 (class 6), between 2001 and 2010 (class 7), and after 2010 (class 8). The characteristics of the residential buildings in these three neighborhoods are described in Table 3 (for further information, see (Perez, 2014)).Table 3 Characteristics of the residential buildings in the three considered neighborhoods.
Table 3NBH. % of residential buildings No. of residential buildings Average height(m) Average S/V(m2/m3) Prevalent year of construction
1 95 396 9.4 0.74 Class 7 (2001–2010)
2 84 542 22.1 0.36 Class 1 (before 1945)
3 92 702 11.5 0.71 Class 6 (1991–2000)
NBH1 has over 420 heated buildings, 95% of which were identified as residential users. The residential buildings have an average S/V ratio of 0.74 m2/m3 (i.e., detached house). Almost 40% of the buildings were built after 1991 and only 16% before 1945. A total of 34 residential buildings, for which the measured energy consumption was known, were selected from this database to verify the accuracy of the GIS-based model.
There are nearly 650 heated buildings in NBH2, 84% of which were identified as residential users. Most residential buildings (42%) were built before 1945, 25% between 1946 and 1970, and only four buildings (1%) were constructed after 2010. The measured energy consumption of 283 residential buildings in this district was available for model verification purposes. The third neighborhood has over 750 heated buildings, 92% of which are residential buildings with an average S/V of 0.71 m2/m3. The prevalent construction year is class 6 (21% of the residential buildings were built between 1991 and 2000), 17% were built before 1945, and 7% after 2010 (47 buildings). Overall, 134 residential buildings with available energy data were selected from this neighborhood.
It was crucial to verify simulated data by comparing them with the measured energy data to improve their reliability. Thus, a total of 451 buildings out of a possible 1640 were selected on the basis of the available data. The measured annual heating consumption and the construction year used to define the thermophysical properties of the building were known for these buildings. Fig. 5 shows the residential buildings selected for the analysis in green (for which the measured energy consumption was known), while the rest are highlighted in red.Fig. 5 Identification of the selected residential buildings (in green) and the other residential buildings (in red): (a) NBH1, (b) NBH2, and (c) NBH3. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig 5
2.3 Data pre-processing
2.3.1 Climate data
The GIS-based model presented in this work can be applied to different urban settings and larger scales than the one considered here (city, regional, or national scales). It has been formulated with a view toward future pandemics and weather conditions, considering different sources of uncertainties. Thus, it is crucial to tune the model considering the available coarse weather and GIS dataset in cities (historical weather data for a specific location and date with an hourly resolution are not always available freely). In the present case, the hourly climate data were collected from Meteonorm 8.0.4 for the "contemporary" period, that is, from 2000 to 2019. Meteonorm utilizes an urban heat model to account for the urban heat island effect on temperature and relative humidity, based on the f ERA-Interim/urbclim model (www.urban-climate.be). Subsequently, the climate data are calibrated according to the urban morphology (see section 2.4.). Data recorded and statistically interpolated by the local weather station (46°25′N, 6°12′E in Geneva) were elaborated for three different locations, one for each of the analyzed neighborhoods. Table 4 shows the main characteristics of the three locations (cool temperate zones with a sub-maritime climate). It is possible to observe, from the annual climate data, that there are no significant differences between the three studied areas.Table 4 Measurements and the annual climate data of the three considered sites.
Table 4Neighborhood Location Elevation Measurement Ta RH Gh Bn Dh
(m) ( °C) (%) (kWh/m2)
1 46°24′N - 6°20′E 406 statistical interpolation 11.2 70 1291 1351 571
2 46°25′N - 6°12′E 420 weather station 11.2 70 1291 1309 591
3 46°19′N - 6°12′E 398 statistical interpolation 11.8 68 1292 1298 603
The following weather profiles were used as input data for the GIS-based engineering model: the external air temperature (Ta in °C), the relative humidity (RH in%), the global horizontal radiation (Gh in W/m2), the beam irradiance (Bn in W/m2), and the diffuse horizontal irradiance (Dh in W/m2). This work has not considered the wind effect on the convective heat exchange.
No significant differences emerged when the Heating Degree Days (HDD) and Cooling Degree Days (CDD) in the Canton of Geneva were compared for 2019 (pre-pandemic period) and for 2020 (pandemic period). The HDDs for 2019 and 2020 were 2755 and 2654, respectively; the CDDs were 298 in 2019 and 290 in 2020 (source: www.meteoswiss.admin.ch). Therefore, the energy simulations in the analysis were carried out using a typical weather year on the basis of historical weather data (2000–2019) retrieved from Meteonorm.
2.3.2 Building data
The geometrical characteristics of each building (e.g., the building footprint, number of floors, height, volume), the surface-to-volume (S/V, m2/m3) ratio, which is a variable that is able to describe the compactness of the building, the year of construction, and the user type (i.e., residential, school, office, industrial) were identified and processed using different databases, that is, Swisstopo (Federal Office of Topography), SITG (Système d'information du territoire à Genève), and Switzerland's OSM (Open Street Map). The first step involved identifying the heated/cooled buildings. Garages and low-rise buildings of less than 3 m in height and with a total area of less than 50 m2 are classified as unheated buildings (without an energy system). After identifying the buildings that had an energy system, we selected only the residential users. The second step involved developing a comprehensive database of the residential buildings located in the neighborhoods using GIS tools. We defined the thermophysical properties of the buildings, according to the construction year, using a study performed on the city of Neuchâtel, Switzerland, (Perez, 2014) as a reference. We identified the thermal transmittances and thermal capacity of the windows and opaque elements, the infiltration rate, the total solar energy transmittance of the glazing, and the window-to-wall ratio (WWR, -) values of each building.
2.3.3 Occupancy scenarios
One aspect that affects energy consumption is the behavior and habits of the inhabitants/users (Buttitta & Finn, 2020; Csoknyai et al., 2019). We defined three occupancy profiles to evaluate the effect of the COVID-19 pandemic on the energy demand of the residential users. Three aspects were considered: (i) the hours of operation of the energy system, (ii) the internal heat gains, due to the presence and activity of people in the buildings, (iii) the heat losses, due to window openings. We defined the following scenarios: (i) a baseline scenario (S1), in which the energy demand was simulated considering the occupants’ behavior in a typical year; according to the SIA 2024 Swiss norm (Zurich, 2006), it is assumed that people stay at home 12 h per day; (ii) a partial lockdown scenario (S2), in which people stay at home 18 h per day; (iii) a full lockdown scenario (S3), in which people stay at home all day (24 h a day) (Cvetković et al., 2021; Zhang et al., 2020). In these scenarios, we considered that the heating system was switched on/off as a function of the building temperature. The heating/cooling system was always turned on to achieve a comfortable internal air temperature of 22–20 °C in winter and 26–28 °C in summer. The heating system turned off when the internal air temperature reached a comfortable temperature.
Figs. 6, 7, and 8 show the heating and cooling schedules for the three scenarios in blue, where the weekdays are distinguished from the weekends. In the graphs, the value 0 indicates that the internal air temperature of the building was set at 20 °C in winter and 28 °C in summer, while the value 1 indicates that the internal air temperature was set at 22 °C and 26 °C in winter and summer, respectively. This means that the energy system was always in operation to keep the building temperature at 22 °C or 20 °C during the heating season and at 26 °C or 28 °C during the summer season, according to the literature (Tardioli et al., 2020) and as required by the SIA 2024 Swiss norm (Zurich, 2006).Fig. 6 Occupancy schedules of the baseline scenario (S1): (a) weekday, (b) weekend.
Fig 6
Fig. 7 Occupancy schedules in the partial lockdown scenario (S2): (a) weekday, (b) weekend.
Fig 7
Fig. 8 Occupancy schedules in the full lockdown scenario (S3): (a) weekday, (b) weekend.
Fig 8
The internal gains were considered to depend on the number of occupants per building and the occupants' activities. The number of occupants was calculated by referring to the SIA 380–1 Swiss norm (Zurich, 2009), which indicates that the surface area per person is 40 m2/P for residential buildings, with an S/V ratio equal to or less than 0.71 m2/m3 (typical of condominiums) or 60 m2/P with a higher S/V than 0.71 m2/m3 (typical of detached houses).
According to the type of activity, the metabolic flux was assumed as 72 W for a person who is sleeping, 108 W for one who is sitting, 126 W for one who is standing, 175 W for one who is cooking, 207 for one who is walking, and 210 for one who is cleaning (Cvetković et al., 2021). The occupancy schedule for the baseline scenario (S1) is indicated in Fig. 6 and, according to the SIA 2024 Swiss norm (Zurich, 2006), people stayed at home 12 h a day. In Figs. 7 and 8, it is assumed that people stayed at home 18 h during a partial lockdown (S2) and 24 h during the full lockdown (S3) (Cvetković et al., 2021; Zhang et al., 2020).
Heat losses were quantified according to the values of the air change rate (ACH, h−1) for the infiltration indicated in Table 5 . Constant ACH values were assumed during the day (24 h), considering natural ventilation through infiltrations. People open windows more often when they stay at home longer (Lepore et al., 2021); thus, the ACH values were increased in the S2 and S3 scenarios, compared to the baseline scenario.Table 5 Air change rate (ACH, h−1) per construction year for the three scenarios (Perez, 2014).
Table 5Scenario Before 1945 1946–1960 1961–1970 1971–1980 1981–1990 1991–2000 After 2001
Baseline 0.70 0.60 0.55 0.50 0.40 0.35 0.30
Partial lockdown 0.80 0.70 0.65 0.60 0.50 0.45 0.40
Full lockdown 0.90 0.80 0.75 0.70 0.60 0.55 0.50
2.3.4 Measured energy data
The measured annual energy data for space heating were used to verify the accuracy of the GIS-based engineering model. The measured energy data were obtained from the SITG (Système d'information du territoire à Genève) database. However, no energy data were available in the SITG database for detached houses. Therefore, this building typology was excluded from the first investigation. After verification of the model, the analysis will be extended to the full residential heritage.
The following information was acquired for each building: the annual heat consumption for space heating and domestic hot water, expressed in MJ/year and MJ/m2/year, the share of energy used for DHW, the heated surface, the measurement year (from 1999 to 2010) and the energy vector.
The model used in this work simulates the energy demand for space heating under certain climatic conditions. From the measured data, only the share of energy for space heating was considered to verify the model. The residential buildings in the three neighborhoods use natural gas as the energy vector to heat the buildings. However, as no different values of system efficiency were available, we assumed a system efficiency of 0.85, which, according to the SIA 380–1 Swiss norm, is typical of gas systems (Zurich, 2009), to calculate the energy demand.
The HDD presented in Table 6 were used to normalize the measured energy data, while the typical weather data from Meteonorm 8.0.4 were used for the simulations. The HDD for the three neighborhoods can be compared in the last three columns of Table 6, referring to a typical Meteonorm year. It can be seen that the relative difference between the HDD in the neighborhoods is minimal.Table 6 Heating Degree Days (HDD, °C) in the Canton of Geneva (source: www.meteoswiss.admin.ch).
Table 6Year 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Neighb. 1 Neighb. 2 Neighb. 3
HDD 3018 2718 2812 2724 3005 2895 3163 2799 2728 2926 2781 3180 2790 2829 2718
2.4 Energy simulation model
The energy demand for the space heating and cooling of residential buildings in the three neighborhoods was investigated for one year using a bottom-up approach. The "GIS-based engineering model" introduced by Mutani et al. (Mutani et al., 2020) is a dynamic urban building thermal balance based on three thermodynamic systems (TSs) . The model is based on an hourly calculation and considers the influence of hourly variations in the weather and operation. Energy balance equations are used to assess the temperatures of the three TSs per hour using an iterative method or to solve other variables if these temperatures are known. The model was verified in detail against the actual energy data of residential buildings in Turin and Fribourg (see (Mutani et al., 2020, 2021; Todeschi et al., 2021) for more information on the validation study). In this study, the validated model has been used to simulate the hourly energy demand of 451 residential buildings located in neighborhoods with different urban morphologies.
2.4.1 Urban building thermal balance
The GIS-based engineering model presented in this study is based on the ISO 52016–1:2017 (ISO-52016–1:2017(en), 2017) and ISO 52017:2017 (ISO-52017–1:2017(en), 2017) standards. The dynamic model considers a sensible thermal balance that was adapted from the building scale to the neighborhood scale using two morphological parameters: SVF and H/W. The two parameters are calculated, using GIS tools, at the mesh-scale for a grid with a dimension of 200 × 200 m. According to this methodology, it is possible to include the mutual shading and view factors of the surrounding built-up context in the energy simulation by evaluating the heat fluxes between the block of heated-cooled buildings and the external environment.
As previously mentioned, this model is based on three TSs: (i) the opaque envelope, composed of all the opaque surfaces that separate the heated volume of the buildings from the external environment; (ii) the glazing component, which separates the heated zone from the external environment; (iii) the inside part of the building, which includes the internal partitions and structures, air, occupants, and furniture.
Eqs. (1) and (2) describe the dynamic balance for the heating and cooling seasons, respectively. For each TS, C (J/K) is the heat capacity; T is the temperature of the TSs (K); t is the time (s); ∅sol is the heat flow rate from solar gains; ∅I is the heat flow rate from internal gains; ∅H−C is the heat flow rate from the heating or cooling system; ∅t is the heat flow rate from transmission; ∅v is the heat flow rate from ventilation.(1) CTSdTTSdt=∅sol+∅I+∅H−(∅t+∅v)
(2) CTSdTTSdt=∅sol+∅I−(∅t+∅v+∅C)
The computational model used to calculate ∅sol is improved, compared with the previous version of the model (Mutani et al., 2020; Todeschi et al., 2021). In this version, the direct solar irradiation, diffuse solar irradiation and reflected solar irradiation components are used (Eqs. (3a) and 3b).
Eq. (3) shows the heat flow rate from solar gains (∅sol), which is obtained from absorption (Eq. (3a)) or from transmission (Eq. (3b)), considering the solar irradiation observed and transmitted through the opaque and transparent building elements (k).(3) ∅sol=∅sol,α+∅sol,τ
(3a) ∅sol,α=∑αk·Ak·[(Isol,Bh·ξ·Fk+Isol,Dh·Fr)·(1+ρsol)]
(3b) ∅sol,τ=∑τG·Ak·[(Isol,Bh·ξ·Fk+Isol,Dh·Fr)·(1+ρsol)]
The first term in Eq. (3a), αk (-), is the solar absorption coefficient and the first term in Eq. (3b), τG (-), is the total solar energy transmittance. The second term, Ak (m2), is the opaque and transparent envelope area exposed to the sun.
The incident solar irradiance on walls is assessed considering: (i) the direct solar irradiance Isol,Bh (W/m2) calculated according to the orientation and the inclination of the surfaces of the building envelope, (ii) the hourly variation in the sunlight percentage ξ (-) calculated as a function of the solar height and the aspect ratio H/W, (iii) and the reduction factor Fk (-), which considers the percentage of the area exposed to the sun.
The quota of the diffuse solar irradiance Isol,Dh (W/m2) is multiplied by the reduction factor Fr (-), which is the form factor between a building element and the sky, calculated as a function of the SVF and the surface inclination (e.g., ½ of the SVF is considered for vertical walls).
The quota of the reflected solar irradiance is calculated taking into account the quota of direct and diffuse solar irradiance reflected by the urban canyon surfaces (ρsol is the solar reflectance of the external environment, which is assumed equal to 0.20, in accordance with the Italian UNI 10349–1:2016 standard (UNI 10349–1:2016, 2016)). Reference (Mutani et al., 2020) shows how the other heat fluxes, ∅I, ∅t and ∅v, are calculated.
2.4.2 Input data
The calculation procedure depends on the availability of input data. In the case of existing buildings, the information on the composition of building element assemblies is limited. The following primary input data are used to apply the GIS-based engineering method:- The hourly local climate conditions elaborated by Meteonorm 8.0.4. The weather variables are the hourly external air and sky temperatures, the relative humidity, and the horizontal direct and diffuse irradiance.
- The geometrical characteristics of the buildings, such as the S/V ratio, the heat loss surfaces, the glazing area that is quantified using the WWR ratio, and the heated net volume (80% of the gross volume).
- The thermophysical properties of building elements, which are estimated according to the year of construction using values indicated in standards and literature (Le Guen et al., 2018; Perez, 2014; Todeschi et al., 2021).
Table 7 indicates the input values used according to the year of construction: the thermal capacity of opaque components (Copaque, kJ/m2/K), the thermal transmittances (U, W/m2/K) of the wall, roof, ground slab (distinguishing between layers with and without insulation) and glass, the total solar energy transmittance of glazing (g-value, -) and the WWR (%). These values are assumed to be representative of this area in Switzerland, where window substitution is the most common retrofitting intervention.Table 7 Thermophysical properties of the buildings (Perez, 2014).
Table 7Period Copaque Uwall Uroof Uground Uglass g-value WWR
kJ/m2/K W/m2/K – %
Before 1945 660 0.94 0.70 1.60 2.30 0.47 25
1946–1960 487 1.35 0.70 1.50 2.30 0.47 25
1961–1970 355 1.03 0.65 1.30 2.30 0.47 25
1971–1980 356 0.88 0.60 1.10 2.30 0.47 25
1981–1990 493 0.90 0.43 0.68 2.30 0.47 25
1991–2000 494 0.69 0.31 0.49 2.30 0.47 25
2001–2010 495 0.51 0.25 0.35 1.70 0.49 35
After 2010 507 1.35 0.22 0.25 1.70 0.49 35
- The operating and boundary conditions, which are defined according to the occupancy behavior.
- The morphological parameters used as input are SVF and H/W. These parameters are used to quantify the heat flow rate from solar gains as a function of the urban morphology.
2.4.3 Output data
The heat flow components of the urban building energy balance (∅sol, ∅I, ∅t, ∅v, ∅H and ∅C) and the temperatures of the three TSs (building, envelope, and glazing) are the main outputs of the engineering model.
As the model is based on GIS, the results are georeferenced and can be used to create energy maps. Energy maps can describe the distribution of energy consumption from the building scale to the neighborhood or city scale. Starting from information about the energy consumption at different urban levels, it is possible to identify critical areas and then to pilot energy efficiency policies to promote sustainable and resilient cities.
Outliers and missing values in the input data, especially in the thermal properties of the buildings, decrease the reliability of the results. A data-driven correction is applied to improve the outcome of the model.
2.5 Data-driven correction to the model
A machine learning-based method is used to define a data-driven correction with a random forest (RF) algorithm to tune the GIS-based model results. We use a data-driven model to extract scale factors to improve the accuracy of the "GIS-based engineering model". Such scale factors are defined as the ratios between the actual energy demand and the simulated one. Therefore, we train an RF algorithm (Breiman, 2001) (on the sample where we have actual measurements) to make a data-driven prediction of the ratio between the actual energy demand and the simulated one for buildings where we do not have any measured values. This correction is motivated by the fact that the building characteristics used to simulate the consumed energy are sometimes approximated in the GIS-based model, together with the fact that the model has its intrinsic accuracy. The model is trained using an initial set of 25 features extracted from climate databases, simulated energy data, building, and urban attributes.
Fig. 9a depicts the relative importance of each input feature in relation to the task, extracted by means of an embedded function in the RF model implementation (Breiman et al., 1984). The most relevant variables are those that describe the geometric characteristics of the building (e.g., S/V, building footprint, heat loss surface) and the energy consumption simulated with the GIS-based model. Variables that describe the thermal properties of the building, morphological parameters, and occupancy behavior (number of people and ACH) have a medium/low impact. The HDDs are very similar in the three neighborhoods and do not have a significant impact. Finally, the features related to the properties of the transparent building envelope have no meaningful impact. Therefore, the geometrical variables have a significant impact on the prediction of the scale factors. This is due to the fact that the building database suffers from some geometric errors (e.g., in some cases, the geometries of the buildings overlap erroneously) and this in turn leads to errors in the calculation of the geometric variables of the buildings.Fig. 9 The importance of the variables: (a) all the variables, (b) six variables.
Fig 9
In a second step, the model is trained using only six of the most relevant variables: the S/V ratio, the heat loss surface, the simulated annual heating demand, the building footprint, the height, and the volume. These variables have the most significant impact on predicting the targets (i.e., the scale factor for each building). Fig. 9b shows the importance of these six inputs on the performance of the model.
The dataset composed of 451 buildings is randomly divided into training and test subsets by a ratio of 75/25. The hyperparameters of the model are tuned using K-fold cross-validation to improve the precision of the predictions. The final RF model is validated using the training called Out-Of-Bag (OOB) (Liaw & Wiener, 2002), and it has a mean absolute error of 15.3%, a mean squared error of 5.2%, and a root mean squared error of 22.8%. Table 8 shows the hyperparameters of the RF model.Table 8 Hyperparameters of the RF model.
Table 8Hyperparameter Description Value Tested range
Number of estimators Number of trees in the forest algorithm 400 200–2000
Min samples split Min. number of data points placed in a node before the node is split 3 2–6
Min samples leaf Min. number of data points allowed in a leaf node 4 1–4
Max features Max. number of features considered to split a node sqrt auto, sqrt
Max depth Max. number of levels in each decision tree 85 10–160
Bootstrap Method used to sample the data points True True/False
An example of the decision tree is indicated in Fig. 10 . The depth of the trees in the forest is limited to three levels to show an understandable scheme. The variable (i.e., simulated energy) and the value used to split the node are indicated in the root node, where "mse" is the mean square error, "samples" is the number of data points in this node, and "value" is the prediction (in our case, the scale factor) for all the data points in this node.Fig. 10 Decision tree: maximum depth of the three considered levels.
Fig 10
3 Results
This section shows the main findings of the analysis. The purpose of the first part of this study was to verify the accuracy of the model used to simulate the energy demand of residential users and to improve its precision through the integration of a machine learning model. In the second part of the results, the impacts of the COVID-19 pandemic on the energy demand are described for three different scenarios.
3.1 Model verification and improvement
The "GIS-based engineering model" was designed to simulate the energy demand of a group of buildings at an urban scale. In this work, it has been applied at a building scale, and the energy demand has thus been simulated for each building, not for a group of buildings or a cluster, but with some urban variables at an urban scale (as they were not available at a building scale). The energy consumption for heating and cooling of 451 residential buildings has been simulated. Since the measured consumption has an annual temporal resolution, an annual data verification has been carried out. The results of the annual heating demand have been compared with the measured energy data according to the baseline scenario (pre-pandemic conditions).
Despite uncertainties from the input data, the developed model shows a reliable energy demand estimation. A comparison of the simulated and measured energy data shows that the GIS-based model has an average mean absolute percentage error (MAPE) of 26% (the median MAPE is 20%). The MAPE varies in the three neighborhoods. Neighborhoods 1 (34 buildings) and 2 (283 buildings) have an average MAPE of 29%, while NBH3 (134 buildings) has a lower average MAPE equal to 18%. The NBH3 results are more accurate than the other ones, even though the model slightly overestimates the energy uses.
A system efficiency of 0.85 has been assumed for all the buildings to calculate the energy demand from the measured energy consumption. It is possible to define different system efficiencies, depending on the year of construction of the buildings, and the average MAPE could on average be reduced by 6% (in NBH1 the MAPE remains equal to 29%, while it can on average be reduced by 9% and 1%, respectively, in neighborhoods 2 and 3).
Fig. 11 shows a comparison of the energy data expressed in kWh/year and the frequency distribution of the MAPE in the three neighborhoods. In neighborhoods 1 and 2, 61–62% of the simulated data have a lower MAPE than 30%. More accurate results are obtained for NBH3, where 85% of the simulated data have a lower MAPE than 30%, and 61% of the data have a lower MAPE than 20%. With the GIS tools, the MAPE is mapped at the building level in the three neighborhoods (Fig. 12 ).Fig. 11 Results of energy simulations in the three neighborhoods: (a) comparison between the measured and simulated heating demand and (b) frequency distribution of MAPE.
Fig 11
Fig. 12 MAPE at a building level: (a) NBH1, (b) NBH2, and (c) NBH3.
Fig 12
The scale factor, calculated as the ratio between the measured energy demand and the simulated one, is used to improve the energy simulations. This factor is calculated in two ways:- Using a constant scale factor, that is, the average value calculated over the data from the 451 buildings.
- Using an ad-hoc scale factor for each building, calculated from the RF model.
Fig. 13 shows the results pertaining to 113 residential buildings located in the three neighborhoods identified as the test set. This group of buildings has not been used to train the RF model. It is possible to observe that the average MAPE decreases from 26% to 23% when the constant correction factor is used. More accurate results are obtained when the RF model is applied, with an average MAPE of 16%. The GIS-based model tends to overestimate the energy data for buildings that have a higher heating demand than 150,000 kWh/year, mainly because energy retrofitting interventions are not considered. This trend is less marked when the constant correction factor is used. The bias is corrected by the RF model.Fig. 13 GIS-based model, constant correction factor, and RF model: (a) comparison between the measured and simulated heating demand and (b) frequency distribution of MAPE.
Fig 13
Therefore, it is possible, through the use of a data-driven correction based on the RF algorithm, to (i) augment the precision of the GIS-based model results (increasing the R2 and decreasing the MAPE in Fig. 13b) and (ii) improve its accuracy by removing any potential systematic bias (see the slope of the linear regression close to 1 in Fig. 13a).
Fig. 14 shows an example of an hourly profile for the heating and cooling demand for one year. These results refer to a building with a MAPE close to 0%. This building is a terrace house in NBH3. It was built between 1961 and 1970, and therefore has moderate thermal insulation. The annual heating demand is 142 kWh/m2/y (the heating season is from 7 October to 18 May), and the annual cooling demand is 8 kWh/m2/y (the cooling season is from 19 May to 6 October). The maximum daily demand for heating is in January, with an energy demand of 1383 kWh/day and an average outdoor air temperature of −1.4 °C. During the summer season, a maximum daily cooling demand of 354 kWh/day is reached with an outdoor air temperature of 28.3 °C (on 30 June).Fig. 14 Hourly profiles of the heating (in red) and cooling (in blue) energy demands of a terrace house built between 1961 and 1970. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig 14
3.2 Impacts of the COVID-19 pandemic on the energy demand
As has emerged from the results, in the S1 scenario, the annual energy demand in the three neighborhoods is 76,024 MWh/y and 5681 MWh/y for space heating and cooling, respectively. In partial lockdown conditions (S2), the energy demand increases, reaching 81,948 MWh/y (+8%) for heating and 6625 MWh/y (+17%) for cooling. The energy demand during the full lockdown (S3) increases by 13% for heating and by 28% for cooling, compared to S1. During S3, the annual heating demand is 85,753 MWh/y (+9729 MWh/y with respect to S1 and +3805 MWh/y with respect to S2), and the annual cooling demand is 7286 MWh/y (+1606 MWh/y with respect to S1 and +661 MWh/y with respect to S2). Table 9 shows the results for each scenario. What stands out in the table is that the energy demand increases more for cooling than for heating. The internal gains due to the presence of people during the heating season partially compensate for other factors that increase the use of heating. During the summer, the internal gains have an opposite effect on the cooling demand. In NBH1, the increase in cooling demand during the restriction measures is less marked than in the other two neighborhoods. This could depend on the year of construction of the buildings; in this zone, most of the buildings were built after the year 2000 and the envelopes and buildings have a lower thermal capacity.Table 9 Annual energy demand in the three neighborhoods for the three different scenarios.
Table 9Neighborhood S1 - Annual demand (MWh/y) S2 - Annual demand (MWh/y) S3 - Annual demand (MWh/y)
Heating Cooling Heating Cooling Heating Cooling
1 2184 511 2406 (+10%) 550 (+7%) 2527 (+16%) 603 (+18%)
2 54,684 3164 58,784 (+7%) 3773 (+19%) 61,453 (+12%) 4202 (+33%)
3 19,156 2005 20,758 (+8%) 2302 (+15%) 21,773 (+14%) 2481 (+24%)
Total 76,024 5681 81,948 (+8%) 6625 (+17%) 85,753 (+13%) 7286 (+28%)
*The percentage increase in energy demand for the S1 scenario is indicated in brackets.
The annual heating and cooling demand, expressed in kWh/m2/y, for 543 residential buildings is indicated in Fig. 15 for the three scenarios. What emerged from Table 9 is confirmed. The energy use in buildings increases during partial and full lockdown conditions. In addition, it can be observed that older buildings consume more in winter and less in summer than buildings built in recent years.Fig. 15 The annual (a) heating and (b) cooling demand (kWh/m2/y) of 543 residential buildings for the three considered scenarios.
Fig 15
As can be seen in Fig. 15, the specific heat demand for buildings built before 1945 increases by 9.7 kWh/m2/y (+7%) during S2 and by 16.9 kWh/m2/y (+12%) during S3 (compared to S1). The increase is greater for new buildings (built after 2000) than for old ones (older buildings consume more and the energy demand increase is less noticeable). The heat demand increases by 8.1 kWh/m2/y (+11%) and 12.2 kWh/m2/y (+17%) during S2 and S3, respectively.
In the cooling season, the energy demand is higher for new buildings due to the thermal properties of the materials, which allow good thermal insulation with low inertia, but restrictive measures have a more significant impact on old buildings. The specific cooling demand for buildings built before 1945 goes from 6.6 kWh/m2/y (S1) to 8.1 kWh/m2/y during a partial lockdown, and to 9.4 kWh/m2/y (S3), which is 42% more than the initial consumption. The consumption in buildings built after 2000 ranges from 21.7 kWh/m2/y (S1) to 23.1 kWh/m2/y (S2), and to 25.1 kWh/m2/y during a full lockdown. In this case, the cooling demand increases by 6% in S2 and by 16% in S3. These results indicate that the thermophysical properties of the building have a significant impact, not only on the energy performance but also on to what extent the COVID-19 pandemic affects the final consumption. In addition, the impact of the pandemic on the heating/cooling demand is not as marked as could be expected from the electricity consumption.
Figs. 16 shows an example of two buildings in NBH3 built in the same period (between 1961 and 1970) but with different shapes. One is a terrace house with an S/V of 0.34 m2/m3 (4 floors), and the other is a condominium with an S/V of 0.25 m2/m3 (10 floors). The annual energy demand is indicated for each scenario. The energy demand for cooling is significantly lower than that for heating. The compact building with a lower S/V (condominium) has lower consumption, and the impact of the COVID-19 pandemic is more significant in the terrace house. During the partial lockdown, the demand increases by 7–8% for heating and 18–23% for cooling. An increase from partial to full lockdown is always noted but is less marked, 4–5% for heating and 5–9% for cooling.Fig. 16 Annual heating and cooling demand (kWh/m2/y) of two residential buildings built in the 1961–1970 period for the three scenarios: (a) terrace house and (b) condominium.
Fig 16
The hourly heating and cooling profiles for the three scenarios during the coldest/hottest week are indicated in Fig. 17 . These results refer to the same building described in Figs. 14 and 16a.Fig. 17 Hourly heating and cooling demand (kWh) of a terrace house built between 1961 and 1970 for the three scenarios: (a) the coldest week and (b) the hottest week.
Fig 17
As shown in Fig. 17, before the lockdown and during a partial lockdown, there are two peak demands during the 24 h, due to the changes in the indoor air temperature settings. The internal air temperature of the building is constant during the day in the full lockdown conditions (S3); it is set at 22 °C in winter and 26 °C in summer.
The energy intensity for heating is similar for the three scenarios. On weekdays, the daily demand for the three scenarios is 1121 kWh/day (S1), 1207 kWh/day (S2), and 1273 kWh/day (S3), while on the weekend, it is 1172 kWh/day (S1), 1217 kWh/day (S2) and 1290 kWh/day (S3). The heat demand for the entire week increases by 6% under partial lockdown conditions and by 12% for full lockdown, compared to S1.
The differences are more pronounced during the hottest week. The weekday consumption without any lockdown measures is 156 kWh/day; during the weekend, it is 237 kWh/day. The cooling demand becomes 207 kWh/day (+33%) and 243 kWh/day (+2%) in the partial lockdown. With more restrictive measures (full lockdown), energy use reaches 245 kWh/day and 271 kWh/day (S3). Considering the energy use of the week, the cooling demands for the three scenarios are 1326 kWh/week (S1), 1588 kWh/week (S2), and 1838 kWh/week (S3).
The differences in the energy demand mainly depend on the occupancy behavior and the external outdoor conditions.
Fig. 18 shows the annual space heating demand, expressed in kWh/m2/y at the building level, for the three scenarios. The results refer to a block of buildings located in NBH3, in which the GIS-based model is accurate with an average MAPE of 18%. In this block of buildings, the average heating demand of these 42 residential buildings is 99 kWh/m2/y during S1, 108 kWh/m2/y during S2, and 114 kWh/m2/y during S3. Therefore, there is an increase of 15 kWh/m2/y from S1 to S3.Fig. 18 Annual space heating demand of a block of buildings in NBH3: (a) baseline; (b) partial lockdown; (c) full lockdown.
Fig 18
All together, these results provide important insights into the impacts of the COVID-19 pandemic on the energy performance of residential buildings. The pandemic has caused an increase in the energy demand for heating and cooling. In the three analyzed neighborhoods, the energy use increased by 13% and 28% during the full lockdown for heating and cooling, respectively. The findings on the peak demand variation can be used to manage the energy system. In order to optimize the energy use of the entire system, it is fundamental to carry out these analyses at a neighborhood scale and not at a building level.
4 Discussion
During the COVID-19 pandemic, the lifestyle of individuals has changed drastically. Such changes have led to larger peak loads and higher average energy demand intensities in the residential sector.
Literature shows contradictory impacts of the COVID-19 pandemic on the use of energy in residential buildings, mainly when the results are based on simulations. Several studies have shown that the total energy demand increases, whereas several other studies have shown a decrease. These contradictory results are primarily due to a lack of input data, including information on the occupants’ behavior in residential buildings during the pandemic. Thus, we have developed a GIS-based simulation and bias-corrected random forest method to quantify the impacts of the pandemic on the hourly energy demand for space heating and cooling in three residential neighborhoods. Detailed occupancy scenarios have been defined to improve the accuracy of the energy simulation. Since the proposed methodology is based on a bottom-up GIS-based engineering model that includes a quick energy simulation process, there is no need for computationally intensive models at the building scale.
The results show to what extent the energy demand for heating and cooling has changed during the period of restrictive measures (i.e., partial and full lockdown). The impact of the pandemic on the energy use in residential buildings is influenced to a great extent by the occupancy behavior and by the thermophysical properties of the buildings. An increase in energy demand of 15 kWh/m2/y for space heating and 3 kWh/m2/y for space cooling was observed during the full lockdown scenario. This trend may have led to an increase in CO2 emissions and an increment in energy bills.
The results provide important information that can be used for the evaluation of the energy trends in Switzerland. According to the Swiss Federal Office of Energy, the country's electricity consumption in 2020 decreased by 2.6% (Swiss Federal Office of Energy SFOE, 2020). In addition to the effects of the lockdown, the economic trend, the weather conditions, and the increase in energy efficiency led to a reduction in energy consumption (Swiss Federal Office of Energy SFOE, 2020). The GIS-based approach presented in this study can be used to evaluate thermal consumption, when all the different aspects that affect the energy performance of buildings are considered. It can also provide more reliable information on energy trends at the urban scale than other tools at the existing state of the art. Thus, this model could be used to mitigate the energy effects of COVID-19 and other future pandemics in urban areas if used as a support decision-making tool.
The GIS-based simulation method has been developed for the residential sector. However, the model could also be applied to other sectors, such as offices, commercial, and industrial buildings, in the future. A limitation is related to the availability and accuracy of the input data. As in all energy simulation models, the accuracy of the results depends on the precision of the input data. The input data in this work includes the local climate conditions, the geometry of the buildings and the thermal properties, occupancy profiles, and morphological parameters. The thermal properties of the buildings are defined according to the year of construction. However, it is not known whether the considered buildings have undergone any energy renovation measures. It is also not feasible to update the input data as thermal capacities and thermal transmittances of the materials. This limitation could reduce the accuracy of the model (Mutani & Todeschi, 2021). To overcome this limitation, a machine learning model was developed to improve the accuracy of the GIS-based engineering model. Starting from a limited dataset of about 550 buildings, a data-driven error correction was used to define the RF model. The precision of the GIS-based model does not depend on the urban thermal balance equations, but on the low accuracy of the input data. Therefore, the machine learning-based method has not been embedded in the GIS-based engineering model equations. The method has only been used to apply an a-posteriori, data-driven correction using the RF algorithm.
The purpose of the present work has been to show that an ML-based method can be used to correct, at a large scale, the intrinsic biases and imprecision that the GIS-based model contains, provided there is a sufficiently heterogeneous dataset for its training, thus making the simulation of the heating demand even more realistic. When the RF model was applied, the average MAPE in the energy simulation, considering the pre-pandemic conditions, was reduced by 10%. The error-corrected random forests method can be applied at the national scale, in combination with the GIS-based model, to obtain more accurate results. Although the measured energy data used for model verification may be inaccurate in a few cases, the obtained results indicate that the error-corrected random forests model does not depend on the occupancy behavior variables. Fig. 19 depicts the relationship between the scale factor, that is, the ratio between the measured and simulated energy data and the occupancy behavior variables. There is no correlation between the mentioned variables. Therefore, the RF model can also be used to improve the results of the COVID-19 pandemic scenarios.Fig. 19 Relationship between the scale factor and: (a) internal gains; (b) the heat transfer coefficient resulting from ventilation.
Fig 19
5 Conclusion
The purpose of the current study has been to quantify the impacts of the COVID-19 pandemic on the energy performance of urban neighborhoods. A GIS-based simulation and a bias-corrected random forests method have been developed to quantify the impacts of the pandemic on the hourly energy demand in three residential neighborhoods. Detailed occupancy scenarios have been defined to take into account the behavior of the residents during the pandemic. Detailed occupancy behavior significantly improves the accuracy of energy simulations. The use of heat in residential buildings in the Canton of Geneva, Switzerland, was simulated by investigating three scenarios: pre-pandemic, partial lockdown, and full lockdown.
The hourly energy demand for space heating and cooling was assessed for a large number of residential buildings. The analyzed buildings, which had different urban forms, were located in three neighborhoods and the energy data for the annual heating consumption of residential users from 1999 to 2010 were known.
A dynamic "GIS-based engineering model" was used to simulate the energy data for the three scenarios. The accuracy of the model was improved by adopting a data-driven correction that was able to predict the ratio between the measured energy demand and the simulated heating demand. It has emerged that the GIS-based model tends to overestimate the energy data for buildings that have a higher heating demand than 150,000 kWh/year. Through the application of the data-driven error correction, which uses a random forest algorithm, it was possible to improve the precision of the energy simulations during the pre-pandemic conditions.
The results regarding the energy simulations of the three scenarios indicate that the energy demand for the space heating and cooling of residential buildings tends to increase during lockdown conditions. In the energy simulations, the hours of operation of the energy system, the internal heat gains due to the presence and the activity of people in the buildings, and the heat losses due to window openings were adapted for the partial and full lockdown scenarios. During the partial lockdown, the space heating demand increased by 8% and the cooling demand by 17%. During full lockdown conditions, the energy demand increased by 13% and 28% for space heating and cooling, respectively (compared to the pre-pandemic conditions). However, these percentages differ for new and old buildings.
This work provides some useful insights into the impacts of the COVID-19 pandemic on the use of heat in buildings. The method used for the energy assessment can be applied to other residential buildings, or to other neighborhoods, districts, and cities. The findings of this study have important implications on obtaining a better understanding of the energy performance of urban neighborhoods in the case of unexpected events, such as energy price fluctuations, disruptions of the energy supply, and any possible future pandemics. The scope of this work has been to help make cities more resilient during any future pandemics. Further research should be undertaken to explore the energy trend in other sectors (such as in the commercial, industrial and educational sectors) and to apply the proposed approach at a national scale.
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.
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PMC009xxxxxx/PMC9001193.txt |
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Curr Res Environ Sustain
Curr Res Environ Sustain
Current Research in Environmental Sustainability
2666-0490
The Authors. Published by Elsevier B.V.
S2666-0490(22)00030-5
10.1016/j.crsust.2022.100152
100152
Article
The mitigating role of climate smart villages to the impacts of COVID-19 pandemic in the Myanmar rural communities
Barbon Wilson John a⁎
Myae Chan a
Vidallo Rene b
Thant Phyu Sin a
Zhang Yuntian d
Monville-Oro Emily b
Gonsalves Julian c
a International Institute of Rural Reconstruction, Myanmar Program Room 402, (7+1) D Apartment, Parami Condominium, U Thin Pe St., Hlaing Township, Yangon, Myanmar
b International Institute of Rural Reconstruction, Philippines Program, Silang, Philippines Km 39 Aguinaldo Highway, Biga-2, Silang, Cavite, Philippines
c International Institute of Rural Reconstruction Asia Regional Center, Silang, Philippines Km 39 Aguinaldo Highway, Biga-2, Silang, Cavite, Philippines
d International Institute of Rural Reconstruction, Head Office, 99 Wall Street Suite #1258, New York, NY 10005, United States
⁎ Corresponding author.
12 4 2022
12 4 2022
10015228 9 2021
7 4 2022
9 4 2022
© 2022 The Authors. Published by Elsevier B.V.
2021
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.
Climate smart village approach is identified as an important strategy laid out in the Myanmar Climate Smart Agriculture Strategy (MCSAS, 2016) Four climate smart villages were established in 2017 to facilitate participatory action research to develop the CSV approach as well as to generate evidence of outcomes. The CSV approach is based on the principle of community-directed research process where community-members collaborate with an external researcher to investigate community challenges and their solutions. Like other countries in 2020, the height of the COVID-19 pandemic, Myanmar implemented wide-scale national and local restrictions on mobility that impacted trade and business resulting to an economic slowdown. Rural communities dominated by smallholder agriculture in Myanmar are not spared from the negative impacts of these restrictions. This paper seeks to assess the impacts of the COVID-19 pandemic to the 4 climate smart villages in Myanmar by analyzing household survey data (N = 527) collected in 2020 during the height of economic disruptions and comparing these data to the household survey conducted during the pre-pandemic period of 2018. Our analysis indicated that overall, the effect of the pandemic to agriculture production in 2020 production season in the 4 CSVs has been minimal as evidenced by the continued agriculture production at the same levels as the pre-pandemic conditions in 2018. The effects to household food security and diet diversity has been varied. Sakta village in Chin state in the highlands have demonstrated that diversified production systems enable them to achieve food security in the pandemic year of 2020.
Keywords
Climate smart village
Climate smart agriculture
Myanmar
Community-based adaptation
COVID-19 impacts
Food security
Diet diversification
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pmc1 Background
Rural communities make up 44% of the global population according to the World Bank in 2019. A big part of these communities bears a disproportionate burden of poverty, poor health, and poor quality of life (Steiner and Fan, 2019). Southeast Asia has around the same proportion of rural population despite the rapid progress of urbanization within the region (Arfanuzzaman and Dahiya, 2019). In 2019, Myanmar has a rural population at 69% making it primarily a rural country (The World Bank Data, 2019a).
The rural economy is crucial to global economic growth. In 2018, agriculture accounted for 4% of global GDP (The World Bank, 2019). In some developing countries, rural production contributes to up to 25% of the national economy (Food and Agriculture Organization (FAO), 2011). In Myanmar agriculture remains an irreplaceable economic pillar as it continues to constitute 21% of Myanmar's GDP in 2018 (The World Bank Data, 2019b), and employs more than half of the country's labor force (Myint et al., 2016).
Climate change impacts refer to the effects of extreme weather and climate events, and the effects of climate change on natural and human systems (Inter-governmental Panel on Climate Change (IPCC), 2014). It is commonly believed that the impact of climate change falls disproportionately on the rural and poor populations regardless of countries and regions. In a study on the Greater Mekong Subregion (GMS) showed that rural people in this region are particularly vulnerable to climate change due to their dependence on rainfed agriculture and other climate-sensitive natural resources such as nontimber forest products. Increased appropriate investments in climate change adaptation are necessary to protect rural livelihoods and stimulate economic development within GMS (Asian Development Bank (ADB), 2014).
The CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) developed the Climate-Smart Village (CSV) approach to address knowledge gaps and scaling of climate smart agriculture. Building on the early work of CCAFS, the International Institute of Rural Reconstruction (IIRR) developed its own version of the climate smart village as a location where development interveners can demonstrate context-specific community adaptation processes, not just the development and scaling of climate smart agriculture. The broader consideration for the scope of the IIRR's CSVs presents itself the potential of not just addressing climate risks and vulnerabilities but also building robust adaptive capacities anchored on development outcomes. A broad-based study of the overall development impact of the CSV approach seems absent. This is probably due, on one hand, to the fact that the CSV is a relatively young model, and on the other hand, to the highly localized characteristic of climate-smart villages with diverse backgrounds and focuses.
Climate smart villages are identified as an important strategy laid out in the Myanmar Climate Smart Agriculture Strategy (Hom et al., 2015). With support from CGIAR-CCAFS and the International Development Research Center-Canada, IIRR established 4 climate smart villages in 2017 to conduct participatory action research directed at developing methods and tools to establishing CSVs in Myanmar as well as generate evidence of development outcomes such as food security and nutrition (Barbon et al., 2021; Hanley et al., 2021).
The climate smart village approach as espoused by IIRR is based on the principles and processes of participatory action research (PAR). The PAR approach is defined as a community-directed research processes where community-members collaborate with an external researcher to investigate community challenges and their solutions. PAR as it progresses enables community participants to build capacities, create ownership and autonomy (Maguire, 1987; St. Denis, V., 1992; Hoare et al., 1993).
A menu of socio- technical methodologies and tools was also developed and implemented to facilitate engagement with members of the four Myanmar CSVs. Consistent with the location-specificity of adaptation to climate change, a “portfolio approach” to adaptation was implemented. This “portfolio’ of adaption options include technologies (e.g. new varieties of crops) and practices (e.g. having a home vegetable garden, using green manure, etc.) aimed at reducing the impacts of climate change to agriculture production and livelihoods. By having a portfolio of adaptation options, opportunities for climate change adaptation are created based on each community member's unique agro-ecological and socio-economic situation. This approach therefore ensures inclusivity, that there is an adaptation option even for the poorest and the most vulnerable members of the CSV.
The World Health Organization (WHO) on March 2020, has declared the spread of COVID-1 as a global pandemic (WHO, 2020) After this declaration, countries have taken a number of steps to control the spread of the disease by mainly restricting mobility in communities including national lockdowns (Lunn et al., 2020; Mishra et al., 2020) These national lockdowns have impacted economic growth nationally and globally (Mishra et al., 2020).
One of the significant impacts of the national lockdowns is the increase of food insecurity in vulnerable communities. This is caused by loss of income from closures of businesses or reduction of work caused by lockdowns. Another driver of food security was the disruption of markets that affected the movement of food products to consumers but also the movement of farm inputs required in food production. As a result, there was widescale food wastage in one location but hunger in the another location (Stephens et al., 2020).
Myanmar's economy was growing rapidly prior to COVID-19 (Boughton et al., 2021). However, agricultural livelihoods in Myanmar are a risky business even in normal times due to country's exposure to climate change and trade volatility (International Food Policy Research Institute and Michigan State University, 2020). Myanmar only registered a few hundred cases until August 2020 (MOHS, 2020). The democratic government of Myanmar in 2020 rapidly responded to the threat of COVID-19, both in terms of its impacts to public health as well as its economic impacts (Minoletti and Hein, 2020).
Like other countries, Myanmar also implemented national and local travel restrictions and closures of businesses. This has affected the agriculture trade resulting to difficulties of farmers selling their produce as well as acquiring farm inputs. Household incomes have dropped in the months of January to June in 2020. There was a stage of recovery in July to August but starting September to October, incomes dropped again, due to a second wave of increased COVID-cases during this period (Boughton et al., 2021).
The government established an Economic Recovery task force to develop a Comprehensive Economic Recovery Plan (CERP). However, the vulnerability of agriculture and rural livelihoods to COVID-19 was not initially well recognized by the Economic Recovery task force (Boughton et al., 2021). Although agriculture plays a major role in rural areas where food prices are a key factor affecting nutrition security for rural households, the share of household expenditure for agriculture production has been far too small which has prevented food and nutrition insecurity of households (Boughton et al., 2021).
This paper seeks to assess the impacts of the COVID-19 pandemic to the 4 climate smart villages in Myanmar by analyzing household survey data collected in 2020 during the height of economic disruptions caused by the pandemic in Myanmar and comparing it household survey data collected in pre-pandemic period of 2018. The impacts are assessed in terms of livelihood activities, subjective assessment of ability to cope, household well-being using household wealth scores, income generation from agriculture and food security.
2 Materials and methods
2.1 Household surveys
As part of the participatory action research conducted by IIRR, a household survey was conducted in 2018 to generate a baseline for the communities. The household survey was conducted in full enumeration, all the households in each of the four CSVs were included in the data collection. The survey questions collected data on demographics of the household, livelihoods including land ownership, list of household items to be used as metric for household wealth. It also included the questions to determine Household Food Insecurity and Access Scores (HFIAS) and Household Diet Diversity Scores (HDDS).
In 2020 at the height of the lockdowns in Myanmar, IIRR conducted the same survey in the 4 CSVs to gather household data during the time of extreme stress from the social and economic impacts of the pandemic. Both surveys in 2018 and 2020 were implemented at around the same time of the year between October to November to ensure that other external conditions are the same except that it's a pandemic year in 2020. These surveys provided the data to analyze the effects of the pandemic to the CSVs by comparing these with 2018 pre-pandemic data.
The questionnaire was prepared in English and translated to the Myanmar language. It was also pre-tested with farmers from nearby communities. A total 527 households were included in the surveys in 2018 and 2020, Htee Pu = 243 HH, Taung Khamauk (TKM) = 85 HH, Ma Sein = 87 HH and Sakta = 112.
The survey data were then encoded in Microsoft Excel and data analysis was made using Statistical Package for Social Sciences (SPSS).
2.2 Statistical analysis
After encoding the survey into MS Excel, we exported these to SPSS and created a panel data to allow for a one to one, before and after statistical analysis of the collected household data. The following the statistical analysis measures were deployed in the analysis.a) Descriptive statistics, includes frequency distribution, percentages, mean and median.
b) Tests for Significant Difference to test for significant difference between 2018 and 2020 data. As the data in most variables are presented as proportions (percentages), the McNemar test was used. This test is used to analyze pretest-posttest study designs, as well as being commonly employed in analyzing matched pairs in before and after studies. The non-parametric Mann-Whitney U test for independence was also used to determine the difference between 2018 and 2020 data for food consumption.
c) Principal Component Analysis to determine wealth index of each household in the CSVs. This method of generating household wealth index is based on the Demographic and Health Surveys (DHS) wealth index construction tools and guidance specific to Myanmar. The DHS wealth index is a measure of a household's living conditions. It is determined by calculating easy to collect data of household ownership to selected assets that serve as a proxy for wealth and well-being such as owning television sets, motorbikes or the materials used to build the household house. (Rutstein, S no date indicated) (Rutstein, 2022).
d) Analysis of Variance (ANOVA) to determine if there is significant difference across the CSVs with regard to the household wealth scores in 2018 and 2020.
3 Results and discussion
3.1 Understanding the socio-economic context of the Myanmar climate smart villages
The four CSVs in Myanmar represent 4 major agro-ecologies of the country namely—semi arid dry zone, delta floodplains, uplands and hilly mountainous. These CSVs were selected to demonstrate the differences of agro-ecologies, agriculture systems, climate change impacts as well as socio-economic contexts.
Table 1, presents the unique features of the 4 CSVs and Table 2 presents the demographic profile of the four Myanmar CSVs. (See Fig. 1 .)Table 1 Profile of the Myanmar climate smart villages.
Table 1Name of village Htee Pu Taung Khamauk (TKM) Ma Sein Sakta
Agro-ecology Dry Zone Upland Delta Highlands
Major crops Groundnut, pigeon pea, green gram Rice, millets. Corn Rice Rice, corn, vegetables
Township (Tsp) Nyaung-Oo Nyaung-Shwe Bogale Hakha
State/Region Mandalay Shan Ayeyarwaddy Chin
Total households 275 94 103 200
Total Population 11,180 405 453 865
Female 603 215 249 445
Male 577 190 214 420
Distance from Tsp. nearest 35 km 20 km 11 km 32 km
Ethnic Group Burmese Pa-o Burmese Chin
Source: IIRR Myanmar.
Table 2 Demographic data of the climate smart villages, Myanmar, 2020.
Table 2Demographics Htee Pu TKM Ma Sein Sakta
1. Sex (%)
Male 46.63 53.39 49.37 48.43
Female 53.37 46.61 50.63 51.57
2. Age (%)
0–18 17.54 35.06 29.56 38.59
19–30 20.70 22.13 17.92 20.96
31–45 25.82 19.54 24.53 15.96
46–60 19.61 16.38 19.81 14.84
60-above 16.34 6.90 8.18 9.65
5. Land Ownership
Yes 80.25 91.76 24.14 95.54
No 19.75 8.24 75.86 4.46
7. Livelihood Activities (%)a
Domestic Work 70.87 83.04 44.34 99.70
Farming 62.39 83.48 11.32 99.70
Livestock 65.11 73.48 11.01 99.70
Fishing/Hunting 3.70 10.43 0.00 1.18
Business/IGA 26.96 22.17 5.97 6.51
Casual Labor 35.43 72.17 34.91 10.65
Unskilled Formal 4.13 1.30 0.00 0.89
Skilled Formal 2.50 0.43 0.63 2.66
a Only adult members were included in the analysis.
Fig. 1 Map of the 4 climate smart villages in Myanmar.
Source: Myanmar Information Management Unit.
Fig. 1
The proportion of men and women was almost the same in all studied sites . In the CSV program, it was considered important to provide opportunities to women to enhance inclusiveness . With regards to age distribution, the age groups of 19–45 years old dominate the populations in all the four CSVs. This offers an opportunity for finding ways for these target groups in all the CSVs, to contribute to the intensification and expansion of climate resilient agriculture using a range of crop, tree and small livestock-based options.
Land ownership is a key element affecting investment in sustainable and resilient agriculture production and associated livelihoods. With the majority of the households in three CSVs owning farm land (except in Ma Sein CSV where 76% of the total households are landless) many opportunities for resilience-conferring agriculture have surfaced. Domestic work and casual labor is the dominant livelihood activity in Ma Sein. Casual labor in Myanmar is short-term employment in nearby towns and are paid with a daily wage.
Interestingly (in an earlier paper) it was noted that between 2018 and 2020, the size of landholdings has also change in all 4 CSVs from owning 1 acre or less of farm land, this has increased to 2 acres or more (Barbon et al., 2021).
3.2 Effects of the pandemic to household income from agriculture
To determine the effects of the pandemic to household income in the 4 CSVs, the household-level agriculture production in 2020 was analyzed, during the period of disruptions and compared it to pre-pandemic production trends of the same households in 2018. Fig. 2 suggests that production activities have not been severely affected by the 2020 lockdown in Myanmar. Comparing this to the 2018 pre-pandemic data of the same households in the CSVs, we noted the increase in the number of households raising more chickens and goats, growing vegetables and growing staple crops such as rice and corn. Mentioned in other studies, there was mention of short period of recovery in the months of July to August 2020 in Myanmar (Boughton et al., 2021). This short window of recovery was considered timely as it coincided with the onset of the monsoon months and the initiation of agriculture production activities in all 4 CSVs. This short window of recovery allowed farmers to access important inputs to production allowing them take advantage of the monsoon rains, to grow annual crops, plant fruit trees, and raise animals.Fig. 2 Total number of households growing crops and animals in 2018 and 2020.
Fig. 2
From the survey data in the studied locations of the conditions during the pre-pandemic period and during the 2020 lockdown, we noted that there was no major disruption in the production activities in the 4 CSVs in Myanmar. We examined the gross cash earned from selling the agriculture produce of the households in 2020. Fig. 3 shows comparison between pre-pandemic year of 2018 to 2020 of the total cash received from the agriculture for all households in the 4 CSVs. In the survey, the respondents gross cash receipts in the Myanmar Kyat (MMK) currency. We standardized this for comparison with the 2018 gross cash receipts by using the foreign exchange rate in 2018 which is USD1 = MMK 1300. The average rate in 2020 was USD1 = MMK 1600.Fig. 3 Total gross cash received from agriculture in the 4 CSVs in 2018 and 2020 in US dollars.
Fig. 3
Our analysis indicated that the households in all 4 CSVs recorded more cash inflows (in US dollar) from agriculture activities in 2020, compared to the pre-pandemic year of 2018 with Ma Sein CSV making the biggest jump in gross cash received. This increase in cash inflow in Ma Sein is driven largely by the increased floor price of rice set by the Government in 2020 (Food and Agriculture Organization, 2020). Rice is the main agricultural output of Ma Sein village.
In the 4 CSVs, IIRR the implementor of the program has promoted the use of improved varieties of the crops cultivated in the CSVs such as varieties that are short-duration and drought-tolerant upland rice, pigeon pea, peanut, vegetables and corn. IIRR also promoted the diversification into small livestock production, fruit tee production and even, small-scale aquaculture.
All of these interventions featured the strategic use of external inputs, making the options cost effective for farmers and relied on approaches and principles that local farmers were already familiar with. Moreover, a special effort was made to engage women is using homesteads (the space around the residence) as production areas where vegetables are cultivated and small livestock are raised. These homesteads are spaces where women had special control and decision-making authority.
3.3 Effects of the pandemic to household wealth scores in the CSVs
Table 3, Table 4 present the change in the wealth quintiles of the four CSVs and the results of the ANOVA of wealth scores across the CSVs. Taung Khamauk (TKM) CSV exhibited significant decrease in the ultra-poor quintile.Table 3 : Descriptive statistics of the wealth index during the baseline (2018) and the analysis of variance (ANOVA), Myanmar.
Table 3 2018 Baseline 2020 Endline
Village Minimum Maximum Mean Std. Dev. F-test Minimum Maximum Mean Std. Dev. F-test
Htee Pu −1.55 2.02 0.59 0.78 199.41⁎⁎ −1.08 1.94 0.60 0.66 391.75⁎⁎
Taung Khamauk −1.65 1.53 −0.33 0.65 −1.34 1.35 −0.08 0.55
Ma Sein −2.52 0.67 −1.46 0.65 −2.78 −0.05 −1.78 0.37
Sakta −1.13 1.29 0.10 0.45 −1.11 1.30 0.15 0.42
⁎⁎ Significant at 1%.
Table 4 : Wealth ranking (quintile) by CSV, Myanmar.
Table 4Wealth quintiles Htee Pu (%) Taung Khamauk (%) Ma Sein (%) Sakta (%)
2018 2020 (p-value)a 2018 2020 (p-value)a 2018 2020 (p-value) a 2018 2020 (p-value)a
Wealthiest 39.92 39.09 0.864 3.53 5.88 0.688 0.00 0.00 NA 4.59 4.46 1.000
Above middle class 23.87 27.57 0.313 17.65 16.47 1.000 3.45 0.00 0.250 26.61 22.32 0.584
Middle Class 18.93 14.40 0.161 16.47 22.35 0.424 1.15 0.00 1.000 40.37 45.54 0.551
Poor 13.58 16.46 0.324 38.82 42.35 0.710 10.34 2.30 0.065 27.52 25.00 0.742
Ultra-poor 3.70 2.47 0.508 23.53 12.94 0.012⁎ 85.06 97.70 0.007⁎⁎ 0.92 2.68 0.625
NA - not applicable.
a McNemar's test was conducted to determine if there is a significant difference on the proportion (increase or decrease) over time.
⁎ p-value <0.05, then the proportion is statistically significant at 5%.
⁎⁎ p-value <0.01, then the proportion is statistically significant at 1%.
The ANOVA results (Table 4) indicate that the mean wealth index scores among the four CSVs are significantly different from each other. Htee Pu CSV has the highest mean wealth index score for 2018 and 2020 suggesting Htee Pu CSV is better off among the four CSVs. Ma Sein CSV has the lowest wealth index scores across the four CSVs, suggesting Ma Sein as the poorest among the four CSVs.
The wealth quintile tables is useful to determine first whether there is significant change in the wealth ranking in the CSVs between pre-pandemic and pandemic years and secondly, whether the is change in the percentage of ultra-poor households in the village. Our analysis suggest that between pre-pandemic year of 2018 and pandemic year of 2020—in general there are very few statistically significant changes in the wealth ranking in the CSVs except for the category of ultra-poor households. Focusing on this category, it is noted that Htee Pu CSV and TKM CSV (significant with p = 0.012) showed a decline in the percentage of households categorized as ultra-poor. This is considered an improvement as more households have moved up either to poor or middle-class categories. This is opposite for Ma Sein CSV (highly significant with p = 0.007) and Sakta CSV where it indicated an increase in the ultra-poor households in 2020.
This trend in the percent of ultra-poor households in 2020 suggest that in Htee Pu and TKM CSVs—the community was able to cope with the negative effects of the pandemic while in Ma Sein and Sakta CSV it is the opposite. Particular to Ma Sein, CSV, we noted earlier the increased percent of household not owning agricultural land yet this village exhibited the highest gross cash received in 2020. This would imply that agriculture lands have been concentrated to very few rich households in the village. Anecdotal accounts from the field researchers in Ma Sein CSV, also indicated that most households due to lack of cash capital driven by COVID-19 pandemic economic slowdown, sold their farm land and many migrated to towns for casual labor. In the case of Sakta CSV—considering their location in the highlands of Chin state—when mobility was restricted, the CSV experienced difficulties accessing markets to trade their products and access other socio-economic support.
3.4 Effects to perceived ability of the household to meet basic needs
Table 5 presents a subjective measure of how the household feel at the time of the survey of their ability to meet basic needs. Htee Pu and Sakta CSV indicated a significant increase in the percent of households saying they are “doing well”. Taung Khamauk and Ma Sein CSV (highly significant p = 0.004) indicated a decrease in the percent of households saying their “doing well” in 2020. All four CSVs indicated an increase in 2020 in the percentage of households responding to “struggling” and “unable to meet basic needs”. While the earlier analysis indicated huge improvements in the gross cash income of the households in the CSVs, this has not led to better self-confidence to cope with the pandemic in 2020. This is mostly likely driven by increasing food prices in 2020 as well as the uncertainties brought by the pandemic.Table 5 Ability to meet basic needs by CSV, Myanmar (N = 527).
Table 5Responses Htee Pu Taung Khamauk (TKM) Ma Sein Sakta
2018 (%) 2020 (%) p-valuea 2018 (%) 2020 (%) p-value a 2018 (%) 2020 (%) p-valuea 2018 (%) 2020 (%) p-valuea
Doing well 5.35 13.99 0.002⁎⁎ 16.47 8.33 0.167 24.14 5.81 0.004⁎⁎ 2.78 27.93 0.000⁎⁎
Doing just OK/breaking even 85.19 62.14 0.000⁎⁎ 67.06 46.43 0.014⁎ 58.62 70.93 0.175 82.41 38.74 0.000⁎⁎
Struggling 7.82 21.81 0.000⁎⁎ 12.94 40.48 0.000⁎⁎ 16.09 22.09 0.458 13.89 14.41 1.000
Unable to meet household needs 1.65 2.06 1.000 3.53 4.76 1.000 1.15 1.16 1.000 0.93 18.92 0.000⁎⁎
a McNemar's test was conducted to determine if there is a significant difference on the proportion (increase or decrease) over time.
⁎ p-value <0.05, then the proportion is statistically significant at 5%.
⁎⁎ p-value <0.01, then the proportion is statistically significant at 1%.
3.5 Effects to food security and diet diversification
To assess the effect of the COVID-19 pandemic to food security and nutrition in the 4 CSVs, we collected and analyzed the Household Food Insecurity and Access Scores (HFIAS) and the Household Diet Diversity Scores (HDDS) for 2018 and 2020. HDDS was used as an indirect measure for better nutrition as a diverse diet has been linked to better nutrition outcomes as it makes the diet more balanced or the appropriate distribution of nutrient sources (Kant et al., 1993).
Table 6 is the difference of HFIAS in the 4 CSVs between pre-pandemic and pandemic periods. In HFIAS—the higher the score, the higher is the food insecurity of the household (Coates et al., 2007). Except for Sakta CSVs—3 CSVs have higher food insecurity scores during the pandemic period compared to the pre-pandemic period in 2018. This higher food insecurity scores may have been caused by rising food prices caused by the disruptions in trade and the overall decline of the Myanmar economy in the later part of 2020 when the household surveys were conducted. In the case of Sakta CSV—this CSV is far isolated in the highlands of Chin, the households in this CSV may have been able to address food access by growing and consuming their own produce during this period of isolation and restrictions in trade.Table 6 Mean household food insecurity and access scores (HFIAS), 2018, 2020.
Table 6Village 2018 (pre-pandemic) 2020 (pandemic) Mann-Whitney U value p value Interpretation of HH food security in 2020
Htee Pu 0.98 3.92 21,390 < 0.001 Decrease in food security (highly significant)
TKM 3.17 6.13 2744 < 0.001 Decrease in food security (highly significant)
Ma Sein 3.55 3.69 4664 0.733 Slight decrease to no change in food security (not significant)
Sakta 6.90 1.78 4011 < 0.001 Increase in food security (highly significant)
Table 7 is the analysis of the Household Diet Diversity Scores as an indirect measure of better nutrition. The analysis indicated that there is significant increase in the diet diversity of the households during the 2020 pandemic conditions compared to 2018 pre-pandemic conditions. While TKM and Ma Sein villages indicated a decrease in diet diversity in 2020.Table 7 Mean household diet diversity scores (HDDS), 2018, 2020.
Table 7Village 2018 (pre-pandemic) 2020 (pandemic) Mann-Whitney U value p value Interpretation of HH diet diversity in 2020
Htee Pu 6.46 6.84 26,599 0.003 Increase in diet diversity (significant)
TKM 6.01 5.59 3682 0.151 Decrease in diet diversity (not significant)
Ma Sein 6.89 5.93 3464 < 0.001 Decrease in diet diversity (highly significant)
Sakta 4.93 5.53 8541 < 0.001 Increase in diet diversity (highly significant)
Possible drivers of this change in HDDS in the pandemic period include the level of diversification of production of crops and animals in 2020, the more diverse is the production the more diverse is the available food materials in the community. Another driver is having more cash available to purchase diverse food materials. Finally, the extent of the household consuming their own produce from diversified production systems can also enhance diet diversity of the household.
Fig. 4 is the analysis for households consuming their own produce. Performing the non-parametric test, Mann-Whitney U to determine the statistical significance of the difference between the number of households consuming their own produce in 2018 and 2020. Overall, the number of households responding that they have consumed their produce is higher in 2020 compared to 2018, U = 245,309 p-value ≤ 0.001, suggesting highly significant difference between the pre-pandemic and pandemic years.Fig. 4 Number of households responded consuming their own produce (both crops/animals).
Fig. 4
Sakta CSV in the highlands has also recorded the highest number of households consuming their own produce compared to other CSVs which likely contributed to the better food security and diet diversity scores indicated earlier. Their location being in the highlands in Chin state, considering the disruptions in trade in 2020, this might have driven households to shift towards subsistence farming (farming for their own consumption) versus the farming to sell produce. This further confirmed the earlier analysis when Sakta CSV also registered the lowest gross cash income from their own produce.
This analysis of HFIAS and HDDS in the CSVs provides insights as to the potential of diversified agriculture production systems at the local level in addressing food insecurity and poor nutrition as a result of the restrictions in trade, mobility and general economic slowdown. This is evident in Sakta CSV which by virtue of their isolated location in the highlands—the households were still able to achieve food security and better diet diversity despite not so significant gross cash income earned from farming.
4 Conclusion
The broader consideration for the scope of the IIRR's CSVs is its potential of not just addressing climate risks and vulnerabilities, but also building robust adaptive capacities anchored on development outcomes. The role of the CSV's socio technical interventions in supplementing household and local food needs is being demonstrated during height of the COVID-19 pandemic in Myanmar in 2020. One limitation of this study is sample size of 4 villages which is too small to be representative of the total rural communities of Myanmar. Another limitation is the absence of a study and data as to the specific impacts of the COVID-19 pandemic to the 4 villages studies. The COVID-19 impacts considered are inferred from available studies conducted in Myanmar.
Despite these limitations in the methodology, our analysis indicated that overall, the effect of the pandemic to agriculture production in 2020 production season in the 4 CSVs has been minimal. This is evidenced by the continued agriculture production at the same levels as the pre-pandemic conditions in 2018. There is even an indication of expansion of production as indicated by the increased areas for cultivated land. Eventually, this lead to an increase of cash inflows in the 4 CSVs. A key factor of this reduced impact to production was the short window of respite from restrictions during the start of the monsoon in June–July 2020. This has allowed farmers in the CSVs to access important farm inputs and services to start-off the production season. This situation coupled with new varieties of crops and diversified production as promoted by IIRR in the CSVs—led to a much better performance in the production activities, some are even better than the pre-pandemic period of 2018. The implication of this is in the management of lockdowns and travel restrictions. Managers of the pandemic response should consider identifying strategic periods of production season to which some form of mobility is allowed or offering an alternative mechanism to deliver needed farming support.
While agriculture production has been sustained and potentially expanded in 2020, this has not translated to significant improvements in well-being. The study revealed that there was an increase in the percentage of households in the CSVs categorized as ultra-poor in 2020 compared to pre-pandemic data in 2018. In terms of household food security and diet diversity—some CSVs have fared better than the other CSVs. Sakta village in the highland areas of Chin state fared very well in food security and diet diversity despite of the situation that their cash inflows have not change a lot in the pandemic year of 2020. As trade in Chin state has been disrupted, people in the CSVs have consumed more of their produce contributing to better food access and diet diversity. This is evidence that there is a potential for smallholder diversified agriculture production in ensuring a more secure local food system for communities in the event of trade disruption and general economic slowdown. Having own food to consume during crisis has also the potential to ease the burden on women to find food for the household, a task still traditionally assigned to women.
The future scope of this study includes studies on developing quantitative metrics of measuring household level resilience to the impacts of not only climate change but also of other risks such as COVID-19 pandemic and the current economic crisis. This study has provided potential indicators of household resilience such as cashflows, wealth scores, food security and diets that can be included in developing a household resilience metric. As a support to the recovery and further protection of the most vulnerable, a systematic assessment of social and economic support mechanisms provided to rural communities in Myanmar since the start of the pandemic in 2020 will valuable to inform future rural development programs in the country.
Funding
This work was supported by the International Development Research Center-Canada under grant number 108748–001.
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
The authors would like to acknowledge the valuable support provided by the following:
Community Development Association (CDA), Karuna Mission and Social Services (KMSS), Kalyana Mitta Development Foundation (KMF) and Radanar Ayar Rural Development Association (RDA)—all are Myanmar NGOs who are partners of the International Institute of Rural Reconstruction (IIRR) responsible in implementing the field activities in the Myanmar climate smart villages since 2018.
Annie Wesley for leading the technical and financial support provided by the International Development Research Center-Canada to the 3-year action research project in the Myanmar climate smart villages.
Leo Sebastian former regional lead of the CGIAR-CCAFS in Southeast Asia for providing support in the early work of promoting climate smart agriculture and climate smart villages in Myanmar. Through his leadership and support at CCAFS, the Myanmar government adopted the Myanmar Climate Smart Agriculture Strategy to which CSVs are important. CGIAR-CCAFS also funded the early establishment of the Myanmar climate smart villages.
Yin Min Latt, former IIRR-Myanmar country program officer instrumental in the establishment of the Myanmar CSVs in 2018.
Kirstein Itliong, Nutrition Officer of IIRR-Philippines for providing valuable technical assistance in developing nutrition education materials for the Myanmar climate smart villages.
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PMC009xxxxxx/PMC9001194.txt |
==== Front
Pers Individ Dif
Pers Individ Dif
Personality and Individual Differences
0191-8869
0191-8869
The Authors. Published by Elsevier Ltd.
S0191-8869(22)00170-2
10.1016/j.paid.2022.111666
111666
Article
Don't believe it! A global perspective on cognitive reflection and conspiracy theories about COVID-19 pandemic
Kantorowicz-Reznichenko Elena a⁎
Folmer Chris Reinders b
Kantorowicz Jaroslaw c
a Rotterdam Institute of Law and Economics (RILE), Erasmus School of Law, Erasmus University Rotterdam
b Department of Jurisprudence, Center for Law and Behavior, Amsterdam Law School, University of Amsterdam, the Netherlands
c Institute of Security and Global Affairs and Department of Economics, Leiden University, the Netherlands
⁎ Corresponding author.
12 4 2022
8 2022
12 4 2022
194 111666111666
27 12 2021
27 3 2022
9 4 2022
© 2022 The Authors
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
The COVID-19 pandemic increased the saliency of an old phenomenon – conspiracy theories. In times of a global crisis and an unprecedented access to information, fake news seems to spread as fast as the virus. A global pandemic requires more than ever self-compliance. Only behavior change and vaccination on a large scale can bring us to normality. Yet believing in conspiracy theories about COVID-19 is expected to undermine such compliance. What determines susceptibility to believing in misinformation? In this study, using data on mostly representative samples of 45 countries around the world (38,113 participants), we found evidence that people with more deliberate thinking are less likely to believe in conspiracy theories. Furthermore, on the individual level people who are more prone to believe in conspiracy theories are less likely to comply with behavior change. We are in the midst of the biggest coordination game and such insights in social psychology can inform policymakers.
Keywords
COVID-19 pandemic
Conspiracy theories
Compliance
Analytical thinking
==== Body
pmc1 Introduction
From theories that present G5 networks as the source of the virus (Van Prooijen, 2020) to claims that vaccines are just a pretext to inject microchips (Carmichael & Goodman, 2020; Lee, 2021), conspiracy theories about COVID-19 seem to have spread almost as fast as the virus itself. While not a new phenomenon in itself (Van Prooijen & Douglas, 2017), the spread of misinformation and its negative consequences seem to be especially salient during this world pandemic. During such a pandemic, compliance with mitigation measures (such as social distancing) is essential for managing the virus until a sufficient number of people are vaccinated. Such compliance can save lives. Yet because this virus can affect anyone, relying on external enforcement of rules is prohibitively costly. Accordingly, self-compliance is essential: pandemic mitigation requires that people change their behavior to comply with mitigation measures. However, belief in conspiracy theories may impede self-compliance.
Therefore, it is critical to gain insight into what shapes belief in COVID-19 conspiracy theories, and how this may impact compliance and support for pandemic mitigation policies. Moreover, given this is a global phenomenon which is not restricted to one country, it is important to understand how these processes may manifest themselves across the globe, in different communities and cultures, where belief in such conspiracies may differ. To do so, the current research focuses on the role of analytical thinking, which has been shown to predict belief in conspiracy theories in Western societies (Swami et al., 2014; in context of COVID-19, see Pummerer et al., 2022; Erceg et al., 2020; Pennycook et al., 2020; Stanley et al., 2021; Swami & Barron, 2020, Imhoff & Lamberty, 2020). We examine whether across a broad range of communities and cultures, people who use more deliberative thinking may be less susceptible to believing in COVID-19 related conspiracy theories. Furthermore, we examine if due to their lower conspiracy belief, people who use more deliberative thinking may show greater (self-reported) compliance with behavior changes, and greater support of pandemic mitigation policies (such as closure of public institutions). We examine these questions in 45 countries from around the world, in mostly representative samples with a total of nearly 40,000 participants.1 By doing so, we firstly assess how the relationship between deliberative thinking, conspiracy belief, and compliance may apply to individuals across different communities and cultures from around the world. Moreover, we examine how these processes may vary between these settings, in communities and cultures where there may be relatively stronger or weaker tendencies toward deliberative thinking, COVID-19 related conspiracy belief, and self-compliance or support for mitigation measures.
In an era of proliferation of fake news, and especially when it has such a critical impact on human lives, it is crucial to understand the determinants and consequences of susceptibility to follow misinformation. Furthermore, it is key to understand how these may vary across different communities and cultures around the globe. By examining the role of deliberative reasoning, the present research may help to identify possible avenues that could help people to screen out false information – such as improving reasoning skills or activating more deliberative modes of thinking. In this way, the present research may also point at possible strategies for public policy to promote and sustain compliance (recommendation versus mandatory rules; communication strategies).
The data used in this study is part of a larger set of data collected in a large-scale comparative project on COVID-19 social and moral psychology titled “International Collaboration on the Social & Moral Psychology of COVID-19 (ICSMP)”, led by Jay Van Bavel, Mark Alfano, Paulo Sérgio Boggio, Valerio Capraro, Aleksandra Cichocka, Aleksandra Cisłak and Hallgeir Sjåstad, measuring general attitudes as well as related to the COVID-19 pandemic, and different personality traits. The initial analysis was exploratory where we have examined our predictions on 10% of the collected data and pre-registered our predictions to examine on the full sample.2
2 Analytical thinking, conspiracy theories and compliance
During times of crisis, which are often characterized by high levels of uncertainty, conspiracy theories emerge to help people to make sense of the situation (Van Prooijen & Douglas, 2017, p. 324). However, belief in conspiracy theories can have harmful consequences, for example by affecting related health behaviors (Oliver & Wood, 2014). In the context of COVID-19 pandemic, this implies that widespread conspiracy belief may have substantial harmful consequences for society as a whole. From the initial waves of the pandemic, behavioral change such as social distancing, hand washing, and disinfection of items, has been crucial for containing the spread of the virus – and this will remain the case until a critical mass of people can be vaccinated. Moreover, belief in conspiracy theories may even undermine the latter outcome, as studies on COVID-19 and other vaccines have shown that such beliefs can also reduce willingness to vaccinate oneself (e.g., Jolley and Douglas, 2014a; Bertin et al., 2020). Initial evidence for such negative outcomes has been already demonstrated in different studies. For example, Marinthe et al. (2020) conducted several studies in France to examine the effects of “conspiracy mentality”, i.e., the higher tendency to believe in conspiracy theories. In one of their studies, they have found that people with conspiracy mentality were less willing to obey the confinement rule installed by the government during the first wave. Similar results were found in the UK (Allington & Dhavan, 2020), and in Croatia (Banai et al., 2021). The latter study found that the link between belief in conspiracy theories and compliance was partially mediated by trust in government officials. In the context of vaccines against COVID-19, Earnshaw et al. (2020) found negative correlation between belief in conspiracy theories and intentions to vaccinate against COVID-19 among a sample of U.S. participants. Similar results were found in Israel and in the UK (Kantorowicz-Reznichenko et al., 2021).3
In sum, conspiracy beliefs about COVID-19 may undermine self-compliance, and more widespread conspiracy belief thus may constrain the ability of public policy to contain the pandemic. But what predicts people's tendency to believe in COVID-19 conspiracy theories? Does such conspiracy belief indeed undermine self-compliance, as well as support for COVID-19 mitigation policies? And how do these tendencies differ between different communities and cultures? These questions are the center of this study.
In the present research, we focus on the role of analytical thinking, in line with previous research that has associated this with conspiracy belief (Swami et al., 2014). By doing so, we follow dual-process theories of cognition (Evans & Stanovich, 2013), which separate two modes of processing information. Type 1 process is intuitive, automatic, less effortful but more prone to biased responses. Type 2 process, on the other hand, is slower, more reflective, requires more effort, but can reduce biases in judgment (Evans & Stanovich, 2013, p. 225). Following this theory, it can be expected that people who engage in more reflective (Type 2) processing when judging incoming information might be better equipped to avoid decisional biases than people who engage in less reflective (Type 1) processing. Therefore, upon reflection such people can for example identify inconsistencies in the theories, or its implausibility. Furthermore, more reflective people might seek for additional proof for the theories before adjusting their behavior accordingly. In turn, they might be more likely to detect misinformation and challenge it. Since COVID-19 related conspiracy theories are considered to be misinformation, we predict that people who are more deliberate and reflective in their processing of information will be less likely to believe in COVID-19 conspiracy theories than people who are less reflective (H1). This prediction is in line with previous research on Western samples, which has found generally that analytical thinking reduces belief in conspiracy theories (Swami et al., 2014), also in the context of COVID-19 (e.g., Erceg et al., 2020; Pennycook et al., 2020; Stanley et al., 2021; Swami & Barron, 2020). However, the present study examines this relationship across a broad range of communities and cultures from around the globe and explores how the role of analytical thinking in conspiracy belief may vary between these.
Secondly, we predict that in the context of COVID-19 pandemic people who believe in conspiracy theories to a larger extent (especially the denial theories) 4 will comply less with behavior change/support less anti-corona policies than people who believe such theories to a lesser extent (H2). This prediction also aligns with findings obtained in specific (Western) samples, which have found that acceptance of COVID-19 conspiracy theories was associated with lower levels of compliance (Swami & Barron, 2020, e.g., in the UK), behavior change (Pennycook et al., 2020), and social distancing and handwashing (Erceg et al., 2020; Pummerer et al., 2022; Stanley et al., 2021). In the present study, we examine this association across a broad range of mostly representative samples, for a fixed set of behaviors (compliance with behavior change and support for anti-corona policies).
Finally, given H1 and H2, we predict that the effect of deliberative thinking on compliance and support of anti-corona policies will be mediated by the belief in conspiracy theories (H3). As such, we examine whether there is an indirect effect of deliberative thinking on self-compliance by reducing conspiracy belief about COVID-19. This firstly will demonstrate whether at the individual level, more deliberative thinking may promote compliance by reducing conspiracy belief. Moreover, this will also illuminate whether at the superordinate level (i.e., communities and cultures), settings where conspiracy beliefs are generally more common may also show lower rates of analytical thinking and compliance.
By doing so, the present research further deepens and extends our understanding of the psychological processes which lead to increased belief in conspiracy theories in the context of COVID-19 pandemic, which so far has still been limited in focus (e.g., by focusing on Western, and often nonrepresentative samples; see Sternisko et al., 2021; Jolley & Douglas, 2017; Pennycook et al., 2020; Stanley et al., 2021; Erceg et al., 2020; Swami & Barron, 2020). In this study, we look whether such links exists across communities. Moreover, given that COVID-19-related conspiracy theories are part of a general problem of misinformation spread through social media (Vosoughi et al., 2018), we also contribute to this more general literature on the susceptibility to fake news (Bago et al., 2020; Pennycook & Rand, 2019; Pennycook & Rand, 2020).
3 Method
The present study was conducted as part of a large-scale international collaboration project conducted in 69 countries in April and May 2020 (Van Bavel et al., 2022). In each of these countries, a team administered an identical survey to (in majority of cases) a representative sample of at least 500 participants. The total sample consisted of 51,916 participants, nested within 69 countries. The study has been approved by the University of Kent (UK) Research Ethics Committee. Written consent has been obtained from the participants.
In some countries (i.e., 24), less than 400 cases with complete data on our focal variables were collected. These countries were excluded from our analysis. Our final sample therefore consisted of 38,113 participants (49.1% male, 50.6% female, 0.3% other, M age = 43.91, SD = 16.01) nested in 45 countries. For the countries which were included in the analysis see Fig. 1 . Furthermore, detailed information about the participating countries and characteristics of the samples can be found in Table S1 in the Supplementary Materials.Fig. 1 Countries included in the analysis.
Fig. 1
For the purpose of this study, we first focused on the two variables of interests – analytical thinking and belief in conspiracy theory. These variables were measured through two sets of questions. (1) Performance on three questions of Cognitive Reflection Test (CRT) as a measurement of analytical versus intuitive thinking (Frederick, 2005; Pennycook et al., 2015; Pennycook et al., 2020; Toplak et al., 2011). An index of cognitive reflection was constructed by computing the proportion of correct answers (out of 3). (2) The level of agreement with five statements reflecting different conspiracy theories (e.g. “The coronavirus (COVID-19)… is a hoax invented by interest groups for financial gains”). Responses were provided on a 11-point Likert scale (0 = “Strongly disagree”, 5 = “Neither agree nor disagree”, 10 = “Strongly agree”), and were aggregated into a scale measure (α = 0.92), with higher scores indicating greater belief in COVID-19 conspiracy theories. For the specific questions see variables 1 and 2 in the Supplementary Materials.
Second, in order to investigate the relationship between cognitive deliberation, belief in conspiracy theories and compliance and support for COVID-19 related policies, we used in addition a set of questions measuring compliance and policy support (see variables 3 and 4 in the Supplementary Materials).
Self-compliance was measured by means of five items (e.g., “During the days of the coronavirus (COVID-19) pandemic, I have been… Staying at home as much as practically possible”). Responses were provided on a 11-point Likert scale (0 = “Strongly disagree”, 5 = “Neither agree nor disagree”, 10 = “Strongly agree”). All items revealed good internal consistency, except for item 2 - “Visiting friends, family, or colleagues outside my home” (reverse coded) (item-total r = 0.30). As such, items 1 and 3–5 were aggregated into a scale measure (α = 0.78), with higher scores indicating greater physical distancing.
Policy support was measured by means of five items (e.g., “During the days of the coronavirus (COVID-19) pandemic, I have been in favor of… closing all schools and universities”). Responses were provided on a 11-point Likert scale (0 = “Strongly disagree”, 5 = “Neither agree nor disagree”, 10 = “Strongly agree”) and were aggregated into a scale measure (α = 0.87), with higher scores indicating greater support for COVID-19 mitigation policies.
Given the literature on other relevant features for belief in conspiracy theory, we also controlled for the level of collective narcissism (Sternisko et al., 2021), and political ideology (Pennycook et al., 2020; Van Prooijen et al., 2015). Collective narcissism was assessed by means of three questions (e.g., “My national group deserves special treatment”). Responses were provided on a 11-point Likert scale (0 = “Strongly disagree”, 5 = “Neither agree nor disagree”, 10 = “Strongly agree”), and were aggregated into a scale measure (α = 0.87), with higher scores indicating greater collective narcissism. Participants' political ideology was assessed by asking them to indicate “what would be the best description of your political views?” Responses were provided on a 11-point Likert scale (0 = “Very left-leaning”, 5 = “Centre”, 10 = “Very right-leaning”).
We also controlled for risk perception. The level of risk itself can determine the level of compliance and support for restrictive policies. Besides being an intuitive presumption, this is also supported by empirical evidence (e.g., Pennycook et al., 2020). Risk perception was assessed by means of two questions to assess participants' perceived risk of being infected with COVID-19 themselves, and the likelihood an average person in their country would be infected. Responses were provided on a 11-point Likert scale (0% = Impossible, 100% = “Certain”). Answers were highly correlated (r = 0.69, p < .001) and hence were aggregated into a scale measure, with higher scores indicating greater perceived COVID-19 infection risk. For the full set of questions measuring the control variables see the Supplementary Materials.
3.1 Analysis strategy
To confirm the structure of our focal measures, factor analysis was conducted (for a full description, see Supplementary Materials, Tables S2–S8 and Fig. 6). For this purpose, the sample was split randomly into two groups, and exploratory factor analysis (EFA) was conducted on the former, and confirmatory factor analysis (CFA) on the latter. The EFA revealed that the items indeed separated into four dimensions, which corresponded with our measures of physical distancing, policy support, collective narcissism, and conspiracy belief. The CFA confirmed that this four-factor solution showed adequate to good model fit. Thus, we proceeded with our planned analyses, in which the relationship between these constructs was assessed.
To test Hypotheses H1–H2, we relied on linear mixed-effects models conducted in Stata. We compare three models: a model with fixed effects only (Model 0), a model with fixed effects and random (country-level) intercepts (Model 1), and a model with fixed effects and random (country-level) intercepts and slopes (with unstructured covariance structure; Model 2). All models control for collective narcissism, risk perception, and political ideology at the individual level, and utilize robust (Huber-White) standard errors.
To test Hypothesis H3, two multilevel mediation models were estimated by means of the MLMED macro (Hayes & Rockwood, 2020) in SPSS. These models utilized maximum likelihood estimation (10,000 Monte Carlo resamples) and unstructured covariance and residual covariance matrices. In these models, CRT score was the independent variable (X), physical distancing or policy support the dependent variable (Y), and conspiracy belief the mediator (M), and observations were clustered by country. The models included all random effects, including random intercepts and random slopes for each path (i.e., effect of X on M [path a], effect of M on Y [path b], and effect of X on Y [path c]), except when models failed to converge in this fashion. In such instances, we follow the recommendations of Bates et al. (2015) and Barr et al. (2013) and decrease the complexity of the maximally specified random effects structure by eliminating random slopes that prevented the model from converging (here typically that for path c).
4 Results
4.1 The effect of CRT score on conspiracy belief
Results are displayed in Table 1a, Table 1b . Relative to Model 0, which included fixed effects only, Model 1 added the country-level intercepts as a random effect. The intra-class correlation was 0.13, such that 13% of the total variance in conspiracy belief was explained by country differences (when controlling for all individual-level variables). A likelihood ratio test indicated that compared to Model 0, the -2 log likelihood of Model 1 was significantly lower (by 4605.05, exceeding the Chi Square(1) threshold value of 10.83 at alpha = 0.001). However, by adding the country-level slopes in Model 2 as a random effect, the fit was improved even further (by 102.41, exceeding the Chi Square(2) threshold value of 13.82 at alpha = 0.001).Table 1a Model summaries, conspiracy belief.
Table 1a Model 0 (no random effects) Model 1 (random intercept only) Model 2 (random intercept and slope)
Residual variance 6.88*** 6.06*** 6.04***
Intercept variance (country) 0.89*** 1.12***
Slope variance 0.20***
Slopes and intercepts covariance −0.31***
Intra-class correlation 0.13 0.17
Log pseudolikelihood −90,835.42 −88,532.89 −88,481.69
*p < .05; **p < .01; ***p < .001.
Table 1b Estimates of fixed effects, conspiracy belief.
Table 1bPredictors Model 0 Model 1 Model 2
Β RobustSE Β Robust SE B Robust SE
Intercept 1.30*** 0.05 1.47*** 0.29 1.47*** 0.29
CRT (% correct) −1.41*** 0.04 −1.22*** 0.08 −1.26*** 0.07
Collective narcissism 0.33*** 0.00 0.26*** 0.02 0.26*** 0.02
Risk perception 0.00 0.00 0.00 0.00 0.00 0.00
Political ideology 0.14*** 0.05 0.13*** 0.03 0.13*** 0.03
*p < .05; **p < .01; ***p < .001.
Fixed effects (Table 1b) for Model 2 indicated that conspiracy belief was significantly lower at higher levels of CRT score, such that for every problem that participants solved correctly, their reported belief in COVID-19 conspiracy theories was 1.26 scale points lower. This confirms H1. Also, belief in conspiracy theories was significantly greater among participants with higher collective narcissism or more right- leaning political ideology.
The effect of CRT score on conspiracy belief was found to vary significantly by country, however (Table 1a). For an overview of this variation see scatterplot in the Supplementary Materials (Fig. S1). Firstly, Model 2 revealed significant variance in intercepts, such that there were significant differences between countries in average conspiracy belief. Moreover, the model revealed significant variance in slopes, such that countries differed in the degree to which higher levels of CRT score reduced conspiracy beliefs. Last, the model revealed significant negative covariance between intercepts and slopes, such that slopes of the relationship between CRT score and conspiracy beliefs were dependent on the average national level of conspiracy belief. More specifically, for countries where average conspiracy belief was high, higher levels of CRT score more strongly reduced belief in conspiracy theories (i.e., slopes are more negative) than for countries where average conspiracy belief was lower (i.e., slopes are less negative). As such, higher CRT score especially reduces conspiracy belief in countries where average belief in COVID-19 conspiracies is relatively high.
4.2 The effect of conspiracy belief on physical distancing
Table 2a, Table 2b display the results for physical distancing. Relative to Model 0, which included fixed effects only, Model 1 (adding country-level intercepts) revealed an intra-class correlation of 0.07; i.e., 7% of the total variance in physical distancing was explained by country differences (controlling for individual-level variables). A likelihood ratio test indicated that relative to Model 0, the log likelihood of Model 1 was significantly lower (by 2152.23, exceeding the Chi Square(1) threshold value of 10.83 at alpha = 0.001). The fit was improved even further by adding the country-level slopes as a random effect in Model 2 (by 185.44, exceeding the Chi Square(2) threshold value of 13.82 at alpha = 0.001).Table 2a Model summaries, physical distancing.
Table 2a Model 0 (no random effects) Model 1 (random intercept only) Model 2 (random intercept and slope)
Residual variance 2.94*** 2.76*** 2.74***
Intercept variance (country) 0.20*** 0.17***
Slope variance 0.00***
Slopes and intercepts covariance 0.00
Intra-class correlation 0.07 0.06
Log pseudolikelihood −74,604.15 −73,528.04 −74,435.32
*p < .05; **p < .01; ***p < .001.
Table 2b Estimates of fixed effects, physical distancing.
Table 2bPredictors Model 0 Model 1 Model 2
Β Robust SE Β Robust SE B Robust SE
Intercept 8.42*** 0.03 8.44*** 0.12 8.39*** 0.12
Conspiracy belief −0.12*** 0.00 −0.11*** 0.01 −0.11*** 0.01
Collective narcissism 0.07*** 0.00 0.05*** 0.01 0.06*** 0.01
Risk perception 0.00*** 0.00 0.00*** 0.00 0.00*** 0.00
Political ideology −0.02*** 0.00 −0.00 0.01 −0.00 0.01
*p < .05; **p < .01; ***p < .001.
When examining the fixed effects (Table 2b) for Model 2, the results revealed that physical distancing was significantly lower at higher levels of conspiracy belief. More specifically, for an increase of one scale point in conspiracy belief, participants reported 0.11 scale point less physical distancing. Reported distancing was greater among participants with higher collective narcissism and risk perception. This confirms H2.
The relationship between conspiracy belief and physical distancing differed between countries, however (Table 2a). For an overview of this variation see scatterplot in the Supplementary Materials (Fig. S2). Specifically, Model 2 revealed significant variance in intercepts, such that there were significant differences between countries in average physical distancing. Furthermore, the model indicated significant variance in slopes, such that countries differed in the strength with which higher conspiracy beliefs reduced physical distancing. No significant covariance between intercepts and slopes was observed, however. Thus, it was not the case that higher conspiracy belief especially reduced physical distancing in countries where average physical distancing was relatively high (or low).
4.3 The effect of conspiracy belief on policy support
Table 3a, Table 3b display the results for policy support. Relative to Model 0 (fixed effects only), Model 1 (adding country-level intercepts) showed an intra-class correlation of 0.15, such that 15% of the total variance in policy support was explained by country differences (controlling for individual-level variables). Furthermore, a likelihood ratio test indicated that the log likelihood of Model 1 was significantly lower than that of Model 0 (by 6097.48, exceeding the Chi Square(1) threshold value of 10.83 at alpha = 0.001). The fit was improved even further in Model 2 (by 493.33, exceeding the Chi Square(2) threshold value of 13.82 at alpha = 0.001), where the country-level slopes were included.Table 3a Model summaries, policy support.
Table 3a Model 0 (no random effects) Model 1 (random intercept only) Model 2 (random intercept and slope)
Residual variance 4.84*** 4.10*** 4.04***
Intercept variance (country) 0.73*** 0.78***
Slope variance 0.01***
Slopes and intercepts covariance −0.02
Intra-class correlation 0.15 0.16
Log pseudolikelihood −84,144.59 −81,095.85 −80,849.19
*p < .05; **p < .01; ***p < .001.
Table 3b Estimates of fixed effects, policy support.
Table 3bPredictors Model 0 Model 1 Model 2
Β Robust SE Β Robust SE B Robust SE
Intercept 7.24*** 0.04 7.59*** 0.20 7.49*** 0.20
Conspiracy belief −0.15*** 0.00 −0.18*** 0.02 −0.17*** 0.01
Collective narcissism 0.19*** 0.00 0.12*** 0.01 0.13*** 0.01
Risk perception 0.01*** 0.00 0.01*** 0.00 0.01*** 0.00
Political ideology −0.05*** 0.01 −0.02 0.01 −0.02 0.01
*p < .05; **p < .01; ***p < .001.
Results for the fixed effects (Table 3b) revealed that policy support was significantly lower at higher levels of conspiracy belief. More specifically, in Model 2, for an increase of one scale point in conspiracy belief, policy support decreased by 0.17 scale points. This confirms H2. Policy support was greater among participants with higher collective narcissism and risk perception.
The relationship between conspiracy belief and policy support also differed between countries (Table 3a). For an overview of this variation see scatterplot in the Supplementary Materials (Fig. S3). Model 2 indicated significant variance in intercepts; thus, there were significant differences between countries in average support for COVID-19 mitigation policies. Furthermore, the analysis revealed significant variance in slopes between countries; i.e., the negative effect of conspiracy belief on policy support differed in strength between countries. However, no significant covariance between intercepts and slopes was observed. It was not the case, therefore, that higher conspiracy belief especially reduced policy support in countries where average support for COVID-19 mitigation policies was relatively high (or low).
4.4 Mediation analysis
4.4.1 Physical distancing
For physical distancing, the multilevel mediation model that included all random intercepts and slopes did not converge. To resolve this, we decreased the complexity of the maximally specified random effects structure (see Barr et al., 2013; Bates et al., 2015). To do so, we omitted the random slope for the path between CRT score and physical distancing (path c), which showed a high correlation (r = 0.94) with the slope for the path between CRT score and conspiracy belief (path a). Doing so enabled the model to converge successfully; hence, these results are reported here.
Fig. 2 displays the multilevel mediation model. At Level 1 (individuals), the indirect effect of CRT on physical distancing via conspiracy belief was significant, IND CB = 0.29, SE = 0.02; z = 14.00, p < .001, 95% CI = [0.25; 0.33]. Accordingly, at the level of individuals, CRT predicted greater physical distancing by reducing conspiracy belief. The effect of CRT on physical distancing was significant and negative when the effect of conspiracy belief was controlled for, c′ = −0.19, SE = 0.03, t (1429.66) = −7.44, p < .001, 95% CI = [−0.24; −0.14]. As such, CRT continued to predict physical distancing when the effect of conspiracy belief was controlled for.Fig. 2 Multilevel mediation model, physical distancing.
Fig. 2
At Level 2 (countries), the indirect effect of CRT on physical distancing via conspiracy belief was not significant, IND CB = 0.26, SE = 0.29; z = −0.91, p = .36, 95% CI = [−0.20; 0.94]. Accordingly, there were no indications that countries where (average) CRT was higher displayed greater (average) physical distancing due to lower (average) conspiracy belief. For a graphical illustration, please see Fig. S4 in the Supplementary Materials.
4.4.2 Policy support
For physical distancing, the multilevel mediation model that included all random intercepts and slopes also did not converge. Here too, we omitted the random slope for path c (between CRT score and policy support), which did allow the model to converge successfully.
The multilevel mediation model is displayed in Fig. 3 . At Level 1 (individuals), the indirect effect of CRT on policy support via conspiracy belief was significant, IND CB = 0.47, SE = 0.03; z = 14.57, p < .001, 95% CI = [0.41; 0.54]. Thus, at the level of individuals, CRT predicted greater policy support by reducing conspiracy belief. Furthermore, the effect of CRT on policy support was significant and negative when the effect of conspiracy belief was controlled for, c′ = −0.60, SE = 0.03, t (578.92) = −10.05, p < .001, 95% CI = [−0.66; −0.54]. Therefore, CRT also continued to predict policy support when the effect of conspiracy belief was controlled for.Fig. 3 Multilevel mediation model, policy support.
Fig. 3
At Level 2 (countries), the indirect effect of CRT on policy support via conspiracy belief was not significant, IND CB = 0.26, SE = 0.34; z = 0.75, p = .45, 95% CI = [−0.26; 1.11]. Accordingly, countries where (average) CRT was higher did not display greater (average) policy support due to lower (average) conspiracy belief. For a graphical illustration, please see Fig. S5 in the Supplementary Materials.
5 Conclusions and discussion
In this study, we sought to examine some of the determinants of people's susceptibility to believe in conspiracy theories regarding COVID-19, and the consequences of doing so, in terms of their (self-reported) compliance with, and support for, pandemic mitigation policies. To do so, we utilized a cross-national perspective, which examined these questions in representative samples from 45 countries from around the world. By doing so, our findings provide unique insight into the relationship between analytical thinking, belief in conspiracy theories, and self-compliance, and its robustness across communities and cultures.
Our findings revealed that across communities and cultures, individual deliberative thinking was associated with lower belief in COVID-19 conspiracy theories. Furthermore, our findings revealed that belief in such conspiracy theories was associated with lower compliance, and lower support for mitigation measures. Thus, our findings also confirmed the expected mediating relationship, such that more deliberative thinking predicted greater compliance and support, by reducing belief in conspiracy theories. Our findings also contribute to prior research on belief in conspiracy theories (in relation to COVID-19 as well as more generally) by providing a cross-national perspective on these processes. Whereas prior research has studied these processes mostly in a select set of Western countries, the present research provided a cross-national perspective based on mostly representative samples from more than 40 countries.
Belief in conspiracy theories differed considerably between countries; however, the indirect effect of analytic thinking on compliance and support via conspiracy belief was robust, and occurred regardless of local differences in e.g. culture, spread of the pandemic, or approach toward mitigating it. There were indications, however, that the relationship between analytic thinking and conspiracy belief varies between countries and was stronger in countries where belief in conspiracies was more widespread.
Our study aimed to evaluate whether the indirect effect via conspiracy belief applied beyond the narrow subset of countries in which it has previously been studied. We indeed confirmed those previous findings. Our results also provided some indications that the strength of this indirect effect may differ between countries (see Figs. S4–S5 in the Supplementary Materials). Our study was not designed to deeply explore the reasons why the influence of conspiracy belief might differ between particular communities. Indeed, the observed clusters do not seem to align with existing typologies of national culture (e.g., Hofstede, 2001; Schwartz, 2006; also see Beugelsdijk & Welzel, 2018), since similar countries according to these typologies nevertheless showed notable differences in the strength of the indirect effect. Further research therefore is needed to understand why the indirect effects of CRT via conspiracy belief may be relatively more or less pronounced within particular countries. Moreover, future research could move beyond our present cross-sectional approach by dynamically examining the relationship between deliberative thinking, conspiracy beliefs and compliance behavior in a longitudinal design.
Our findings are important for public authorities all around the world who are currently struggling in managing the COVID-19 pandemic. By highlighting the importance of deliberative thinking, our findings imply that activating more deliberative forms of thinking – for example in communication and campaigns – could be an important instrument for countering conspiracy belief and promoting compliance. This may apply not just to self-compliance with social distancing, as in the present research, but perhaps also to vaccination. Here too, misinformation may reduce people's willingness to follow governmental instructions to vaccinate, and thereby jeopardize authorities' ability to control the pandemic, and to render the costly mitigation measures on which the present research focused obsolete. Further research is needed to understand these questions, but the present findings underline that people's susceptibility to be influenced by misinformation should not be ignored or underestimated. The problem of misinformation is growing in recent times, and leads to negative effects in many areas, of which health related behavior is one. Even though we have focused on the context of COVID-19 pandemic, we believe our findings can be relevant to other fields as well. Previous research has demonstrated the negative association between belief in conspiracy theories and welfare-enhancing behavior, for example, in the context of climate change (Jolley and Douglas, 2014b), or measures to reduce HIV infections (Bogart & Thorburn, 2005; Grebe & Nattrass, 2012). Therefore, building on such results as presented in this study, future research can also empirically investigate mechanisms to appeal to people's deliberative thinking. This in turn, may improve people's decisions in different areas.
CRediT authorship contribution statement
Elena Kantorowicz-Reznichenko took part in running the survey, has conducted the research in the field, and pre-registered the study. She has also prepared and written the theoretical framework and the predictions for the study. She has significantly contributed to the writing of the discussion. Finally, this author contributed to the final editing of the paper.
Chris Reinders Folmer conducted the statistical analysis, has written the results section and prepared the supplementary materials. He has also contributed to the other parts of the paper. Finally, this author contributed to the final editing of the paper.
Jaroslaw Kantorowicz took part in running the survey, conducted the pre-analysis which served as the basis for the predictions of the core analysis in this paper. Finally, this author contributed to the final editing of the paper.
Elena Kantorowicz-Reznichenko is a professor of Quantitative Empirical Legal Studies at Rotterdam Institute of Law and Economics (RILE), Erasmus School of Law, Erasmus University Rotterdam. Her researcher focuses, among others, on examining how behavioral insights can be implemented in public policies. She is publishing her work in leading journals and publishing houses such as Cambridge University Press, Journal of Economic Psychology, Journal of Intentional Criminal justice.
Chris Reinders Folmer is assistant professor at the Center of Law and Behavior at Amsterdam Law School, University of Amsterdam. His research integrates psychological, legal and economic perspectives to empirically test the assumptions that underlie legal practice and policy making, to identify possible discrepancies, and to develop alternatives informed by these perspectives. This research is published in leading journals such as, Psychological Science, Law and human behavior, and Journal of Experimental Social Psychology.
Jaroslaw Kantorowicz is an Assistant Professor at the Institute of Security and Global Affairs and Department of Economics, Leiden University. His research interests center around political economy issues, public perception of institutions and empirical legal studies. His publications appeared in, among others, Journal of Economic Behavior and Organization, European Journal of Political Economy and Research & Politics.
Appendix A Supplementary data
Supplementary material
Image 1
1 Despite the initial plan to collect data on representative samples, in some countries, convenience samples were used. Nevertheless, the majority of samples are still representative (33 out of 45). For more details see Table S1 in the Supplementary Materials.
2 The information about the ICSMP project and the leading team is availbe at https://icsmp-covid19.netlify.app/index.html. We did not have access to the full dataset when doing the exploratory analysis. This was reassured by the organizing team. The subsample (10% of the data) on which we have performed our initial analysis is avaible at https://osf.io/k7s9p/, and our pre-registered predictions are availble at https://osf.io/pmn47/?view_only=3fd1dea29a884e4db27591132d7f15c9. The full data used for this article can be found here https://osf.io/8nhzr/.
3 Despite the evidence suggesting negative correlation between belief in conspiracy theory and support and/or compliance with governmental rule in the context of COVID-19 prevention, a few studies found different results. For example, Peitz et al., 2021, conducting a study on a UK sample found that the relationship is not straightforward. The effect of believing in a conspiracy theory, depends on the emotion it evokes (e.g., while anger led to higher perceived importance of governmental restrictions, anxiety achieved just the opposite). Alper et al. (2021) did not find evidence that belief in conspiracy theories is associated in any way with levels of preventive measures.
4 With denial theories we mean those theories that challenge the mere existence or the danger of the pandemic. This can be contrasted with other conspiracy theories which accept the fact there is a pandemic but misinform about the source of it (for example, that the government is responsible for this).
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.paid.2022.111666.
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PMC009xxxxxx/PMC9001195.txt |
==== Front
Vaccine
Vaccine
Vaccine
0264-410X
1873-2518
The Author(s). Published by Elsevier Ltd.
S0264-410X(22)00445-5
10.1016/j.vaccine.2022.04.029
Short Communication
Increased delta variant SARS-CoV-2 infections in a highly vaccinated medical center in Japan
Yan Yan a
Naito Toshio ac⁎
Tabe Yoko b
Ito Kanami c
Nojiri Shuko d
Deshpande Gautam A. a
Seyama Kuniaki ce
Takahashi Kazuhisa e
a Department of General Medicine, Juntendo University Graduate School of Medicine, Japan
b Department of Clinical Laboratory Medicine, Juntendo University Graduate School of Medicine, Japan
c Department of Safety and Health Promotion, Juntendo University, Japan
d Medical Technology Innovation Center, Juntendo University Graduate School of Medicine, Japan
e Department of Respiratory Medicine, Juntendo University Graduate School of Medicine, Japan
⁎ Corresponding author at: Department of General Medicine, Juntendo University Graduate School of Medicine, Hongo 2-1-1, Bunkyo-ku, Tokyo 113-8421, Japan.
12 4 2022
20 5 2022
12 4 2022
40 23 31033108
10 10 2021
24 3 2022
6 4 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 Delta variant has dominated SARS-CoV-2 infections in Tokyo, Japan from June 2021 to date. We conducted a retrospective cohort study to assess BNT162b2 vaccine effectiveness during the surge in Delta among 3,911 healthcare workers (HCWs) at a medical center of Tokyo with a high vaccination rate of 84.1%. With strict infection control protocols including universal masking, only a small number of cases among vaccinated and unvaccinated HCWs were identified before June. As Delta spread in Tokyo, 16 cases among 3,289 fully vaccinated HCWs and 11 cases among 574 unvaccinated HCWs were reported in July and August (case rate in August: 4.0 vs. 19.2 per 1,000). All breakthrough cases were confirmed as Delta. While our study confirms a robust vaccine effectiveness of BNT162b2 vaccine against Delta, rising breakthrough cases suggest that continued infection control measures are warranted in higher risk environments, even when high rates of vaccination coverage are achieved.
Keywords
Delta variant
BNT162b2 mRNA vaccine
Breakthrough
Vaccine effectiveness
Japan
Asia
==== Body
pmc1 Introduction
Japan has experienced multiple waves of coronavirus disease 2019 (COVID-19) infections since the pandemic started and, as of mid-September 2021, is currently in its fifth wave. Compared to previous waves, record numbers of daily new cases have been confirmed, with a peak of 25,851 cases on Aug 20 [1]. Vaccination roll-out in Japan began with healthcare workers (HCWs) in mid-February 2021, starting with the BNT162b2 (Pfizer-BioNTech) mRNA vaccine, followed by the mRNA1273 (Moderna) vaccine in May 2021. Full vaccination (two doses) was achieved in 41.9% of the overall population by the end of August, with coverage reaching 88.3% of those over 65 [2]. In contrast, vaccination for the younger and middle-aged individuals (aged 15–64 years old) has been slower with just 28.9% fully vaccinated by August 31 [2].
Spread of the Delta variant has been observed across Japan, with a major outbreak first seen in Osaka in March, then reported in Tokyo by June 2021, causing increased number of daily confirmed cases. ( Supplementary Materials ) Since the first week of June, Delta gradually replaced other observed variants, accounting for over 90% of all PCR positive tests in the Tokyo metropolitan area by the end of August 2021 [3]. Delta was the predominant variant in countries with high vaccination coverage, including the United Kingdom and Singapore [4], [5]. This highly transmissible variant has also caused recent outbreaks in China, where vaccination coverage is high and infection control measures remain strict [6].
Previous studies have shown the Delta variant has increased transmissibility even in some fully vaccinated individuals [7], [8]. Resurgence of SARS-CoV-2 infections has also been reported in medical centers with high rates of vaccination [9]. However, reports addressing Delta’s impacts on breakthrough infections among fully vaccinated individuals mainly come from North America and Europe, where the social dynamics of compliance with public health measures may differ from other parts of the world. Moreover, effectiveness of mRNA vaccines in ethnic groups of the Western Pacific region may also differ compared to populations in North America and Europe, as suggested by our recent report of increased reactogenicity among Japanese HCWs receiving BNT162b2 vaccines [10]. To explore these potential differences, we conducted a retrospective cohort study to assess BNT162b2 vaccine effectiveness during the Delta surge among HCWs at a tertiary level center with a high vaccination rate in Tokyo, Japan.
2 Study methods
This retrospective cohort study assessed SARS-CoV-2 infections from March 1 to August 31, 2021 at Juntendo University Hospital (JUH), a multispecialty, 1051-bed tertiary level academic hospital in Tokyo, Japan that provides screening, diagnosis, and acute ward/ICU care for COVID-19 patients. A total of 3,911 registered JUH HCW employees who agreed to participate via web consent were included in the study. Relevant vaccination and infectious disease data were extracted from the electronic charts of each participant. On-site employee vaccinations with BNT162b2 began on March 17; the first batch from March 17 to April 23 for the first dose, and April 7 to May 19 for the second dose. mRNA-1273 vaccines were not available at JUH until September 1; employees who received mRNA-1273 during the study period were vaccinated in other facilities or mass vaccination centers managed by the central government of Japan.
Regarding infection control protocols, masks are mandated for employees at all times except while eating. Outside of work hours, dining with more than two non-family members is strongly discouraged. Temperature is checked and self-reported daily by all employees; those with temperature above 37.5 °C are required to submit a PCR test for further examination. Additional PCR testing is conducted at the discretion of an employee health physician after clinical evaluation for classified close contacts, and/or those who have flu-like symptoms such as fatigue, cough, runny nose, etc.. ( Supplementary Materials ) For further infection control, all patients visiting our hospital are strongly encouraged to wear face masks, with virtually universal compliance; free masks are provided to visitors without one. At the time of this study, all inpatients were required to take a COVID-19 PCR test prior to admission. Except in extenuating circumstances, visitors were not allowed onto wards.
For diagnosis of SARS-CoV-2 infection, nasopharyngeal and saliva tests, both showing high sensitivity and specificity in previous studies, were performed [11]. Nasopharyngeal swabs were performed following a standardized procedure (WHO 2006) [12]. For saliva sampling, the participants collected 1–2 mL of unstimulated saliva into a sterile 50-mL polyethylene tube. Nasopharyngeal swabs and saliva samples were submitted for RT-PCR testing within 3 h after collection [13]. RT-PCR was carried out using the 2019 Novel Coronavirus Detection Kit (nCoV-DK; Shimadzu Corporation, Kyoto, Japan). The nCoV-DK assay uses the “2019-nCoV_N1″ primer and probe sequences as described by the U.S. CDC’s ”2019-Novel Coronavirus Real-time rRT-PCR Panel Primers and Probes“ [14]. This assay also includes internal control oligonucleotides. Real-time PCR analysis was run on a Light Cycler System (Roche, California, USA). Specific spike protein variations (L452R, N501Y, E484K, E484Q) were detected with the VirSNiP SARS-CoV-2 Mutation Assays (Roche Diagnostics, Rotkreuz, Switzerland) according to the manufacturer instructions. In addition, serological tests were conducted in June after two doses of vaccine. ( Supplementary Materials ).
The primary outcome of this study was case rate (attack rate) among fully vaccinated versus unvaccinated HCWs. In addition, detailed data on variant type, along with duration between full vaccination to infection and vaccine-induced anti-S IgG levels, were analyzed to more thoroughly assess the impacts of the Delta variant and efficacy of vaccination. Statistical analyses were performed using IBM SPSS Statistics for Windows, version 27 (IBM Japan). In addition, the bootstrap method (1000 replications) was used to produce the confidence interval (CI) for the case rate of infection. The CI is based on normal-theory, assuming that log(case rate) is normally distributed. This study was approved by Juntendo University Institutional Review Board (No. 2021055).
3 Results
Among 3,911 healthcare workers (mean age, SD; 36.6, [±11.8]) at JUH, 1,296 (33.1%) men and 2,615 (66.9%) women were enrolled in the study. Characteristics of the studied cohorts are shown in Supplementary Table 1. ( Supplementary Materials ) By August 31, 2021, a cumulative number of 3,289 (84.1%) HCWs had received two doses of mRNA vaccine; 48 had received one dose and 574 remained unvaccinated. During the study period, 16 fully vaccinated HCWs were confirmed to have SARS-CoV-2 infection, 1 infection was identified among those partially vaccinated, and 13 infections were confirmed among unvaccinated HCWs. Monthly case rate among fully vaccinated and unvaccinated groups are presented in Table 1 , with 95% CI of 0.17 to 1.05 and 1.05 to 2.97 for these two groups, respectively, in August 2021. Monthly case rates are also illustrated in Fig. 1-1 . No hospitalization or deaths were reported.Table 1 Vaccination status, SARS-CoV-2 infections, and case rates among fully vaccinated and unvaccinated HCWs, March through August 2021.
March April May June July August
No. of health workers: 3,911*
Administered vaccines
BNT162b2 (Pfizer-BioNTech) 2,804 390 33 53 20 14
mRNA-1273 (Moderna) 0 0 0 1 10 12
Vaccination status (cumulative no. of staff)
Fully vaccinated 47 2,866 3,172 3,221 3,261 3,289
Partially vaccinated 2,757 328 55 60 50 48
Unvaccinated 1,107 717 684 630 600 574
Fully vaccinated workers (%) 1.2 73.3 81.1 82.4 83.4 84.1
Workers receiving at least 1 dose of vaccine (%) 71.7 81.7 82.5 83.9 84.7 85.3
SARS-CoV-2 infections
Fully vaccinated workers 0 0 0 0 3 13
Partially vaccinated workers 0 1 0 0 0 0
Unvaccinated workers 0 2 0 0 0 11
Case rate (attack rate) per 1000
Fully vaccinated workers 0.0 0.0 0.0 0.0 0.9 4.0
(95% CI: confidence interval) † – – – – (0.00 to 0.52) (0.17 to 1.05)
Unvaccinated workers 0.0 2.8 0.0 0.0 0.0 19.2
(95% CI) – (0.00 to 0.70) – – – (1.05 to 2.97)
Notes:
*No. of health workers comprises staff registered as Juntendo University Hospital employees by August 31, 2021.
†Bootstrap analysis was performed to determine the CIs (shown above).
Fig. 1 Case rate and COVID-19 variants among infected HCWs in JUH (March through August 2021).
L452R mutation, the most representative mutation of the Delta variant, was confirmed among all breakthrough cases occurring in July and August with available nasopharyngeal or saliva samples (13 out 16). (Table 2 ). Regarding infections in unvaccinated HCWs, except for one case with N501Y mutation, all cases with available nasopharyngeal or saliva samples were identified as having L452R mutation. ( Fig. 1, Fig. 2, Fig. 1, Fig. 2 ) The prevalence of Delta variant in JUH is shown in Fig. 2 and was consistent with that of the Tokyo metropolitan area. Among all reported positive tests, including PCR tests for non-employees such as inpatients before admission, the Delta variant comprised 88.9% of all cases, increasing to 98.0% after exclusion of cases in which nasopharyngeal or saliva samples were unavailable.Table 2 Confirmed breakthrough SARS-CoV-2 infections from March through August 2021.
Vaccination* Confirmed SARS-CoV-2 infections IgG level test
Cases Sex Age 1st dose 2nd dose Date of infection confirmed PCR Testing methods† Days from the 2nd dose Break-through cases‡ Ct value Mutation IgG level testing date Anti-S (U/ml)
Case 1 F 43 2021/03/23 2021/04/13 2021/07/05 Saliva 83 Yes 22.40 L452R 2021/6/11 417
Case 2 M 45 2021/03/17 2021/04/07 2021/07/24 Nasopharyngeal 108 Yes 30.74 L452R 2021/6/9 477
Case 3 F 29 2021/03/19 2021/04/09 2021/07/24 Nasopharyngeal 106 Yes 20.08 L452R 2021/6/10 1,476
Case 4 F 43 2021/03/30 2021/04/20 2021/08/03 – 105 Yes – – 2021/6/18 976
Case 5 F 26 2021/03/23 2021/04/13 2021/08/05 Saliva 114 Yes 22.00 L452R 2021/6/11 3,713
Case 6 M 31 2021/04/03 2021/04/23 2021/08/05 Saliva 104 Yes 18.96 L452R 2021/6/16 518
Case 7 F 24 2021/03/24 2021/04/14 2021/08/07 – 115 Yes – – 2021/6/18 1,459
Case 8 F 28 2021/04/22 2021/05/13 2021/08/10 Saliva 89 Yes 29.65 L452R – –
Case 9 M 46 2021/03/23 2021/04/13 2021/08/15 – 124 Yes – – 2021/6/17 1,113
Case 10 M 32 2021/03/22 2021/04/12 2021/08/16 Nasopharyngeal 126 Yes 20.66 L452R 2021/6/11 1,282
Case 11 F 23 2021/03/25 2021/04/15 2021/08/17 Nasopharyngeal 124 Yes 29.09 L452R 2021/6/8 3,793
Case 12 F 22 2021/04/22 2021/05/13 2021/08/18 Saliva 97 Yes 32.42 L452R – –
Case 13 F 25 2021/03/23 2021/04/13 2021/08/19 Nasopharyngeal 128 Yes 18.30 L452R 2021/6/9 761
Case 14 F 24 2021/03/25 2021/04/15 2021/08/21 Nasopharyngeal 128 Yes 20.20 L452R 2021/6/14 1,683
Case 15 F 49 2021/03/24 2021/04/14 2021/08/21 Nasopharyngeal 129 Yes 16.60 L452R 2021/6/16 594
Case 16 F 42 2021/03/24 2021/04/14 2021/08/25 Saliva 133 Yes 33.00 L452R – –
Notes:
*All received BNT162b2 mRNA COVID-19 vaccine.
†PCR testing method and variant details are not available for cases that were tested at facilities outside of JUH, but reported via JUN’s monitoring system.
‡Breakthrough cases are defined as infections at least 14 days after receiving second vaccination.
Fig. 2 PCR positive cases with L452R mutation (Delta variant) as % of total positive cases in JUH, March through August 2021.
For the 16 breakthrough cases, none had known or confirmed infection prior to vaccine rollout in March 2021. A wide range of anti-S IgG levels, ranging from 417 to 3,793 U/ml (mean, SD; 1,405, [±1,122]), were reported for these cases. Ct (threshold cycle) values of RT-PCR positive cases with available nasopharyngeal or saliva samples ranged from 16.60 to 33.00 (mean, SD; 24.16, [±5.88]). ( Table 2 ) For the 14 non-breakthrough cases, results of serological tests and Ct values are provided in Supplementary Materials .
4 Discussion
During the Delta spread, our study of HCWs working in a highly vaccinated medical environment with strict infection control protocols identified a number of SARS-CoV-2 infections in recent months regardless of vaccination status. Nonetheless, the case rate among those fully vaccinated was substantially lower compared to those unvaccinated at 4.0 vs. 19.2 per 1,000 persons, respectively, in August 2021. No hospitalizations or deaths were reported for either group. Our findings are consistent with previous studies that the BNT162b2 vaccine remains effective against Delta, protecting vaccinated individuals from severe infections and hospitalization [5].
With strict infection control protocols such as universal masking and actively encouraging employees to minimize social events, few infections among HCWs were reported before June when the wild-type, Alpha, and other variants dominated the Tokyo Metropolitan area. In contrast, with the rapid spread of the Delta variant beginning in mid-June, several infections occurred throughout July and August, even among fully vaccinated individuals. A previous retrospective cohort study in Singapore reported lower Ct values and longer viral shedding associated with the Delta variant, suggesting increased transmissibility [8]. Although the correlation between Ct values and the amount of virus per specimen is imperfect, our study showed relatively low Ct values associated with the Delta variant among the vaccinated HCWs, supporting the findings of the Singapore study [8], [15].
The breakthrough cases found in our study received two doses of the BNT162b2 vaccine in March and April, with an average of less than four months between full vaccination and infection, shorter than the six month period currently being considered for booster vaccines [16]. Marginally diminished vaccine effectiveness against Delta has been reported in several previous studies, and is corroborated in our population [17], [18]. The breakthrough cases found in our highly vaccinated medical center are likely to be the result of a combination of marginally lower vaccine effectiveness against Delta and increased transmissibility of the variant. However, even during Delta spread, our overall breakthrough case numbers remained low. Strict infection control protocols including universal masking were continued even after implementation of our vaccination program. Effectiveness of face masks in preventing airborne transmission and reducing SARS-CoV-2 cases has been shown in multiple previous studies, and many governmental and professional organizations continue to recommend indoor mask-wearing to prevent COVID-19 infections during Delta spread [19], [20]. Strict universal masking has likely contributed to overall lower surge numbers in the Japanese setting.• Limitations
There are several limitations to our study worth addressing. First, although PCR testing is performed for those reporting temperature above 37.5 °C during daily checks, close contacts of confirmed cases, and/or those having flu-like symptoms, asymptomatic cases may be missed; therefore, actual case rate are likely underestimated in our results. Second, due to overall low case rates in this hospital, prior infections among all healthcare workers not infected during the studied period were not analyzed in this study. However, all studied breakthrough cases were checked and found not to have prior infections. Lack of analysis on acquired immunity via prior infections among all healthcare workers might have underestimated the vaccine effectiveness. Third, although the strict infection control measures implemented in this tertiary level hospital may represent the general compliance of Japanese citizens at large, the level of control in the healthcare workplace setting is likely higher than average; our findings are particularly applicable to high-risk healthcare environments and extrapolation to the general population should be interpreted with caution.
5 Conclusion
Our retrospective cohort study provides real-world evidence of maintained robust BNT162b2 vaccine effectiveness against Delta in an Asian population, yet also revealed increased breakthrough cases in this highly vaccinated medical center. With demand for vaccinations in Japan continuing at a strong pace, all individuals willing to receive COVID-19 vaccines are expected to be fully vaccinated by the end of November 2021; rapid vaccination programs are expected in other countries of the Western Pacific region in the coming months as well. Nonetheless, our findings emphasize the need for ongoing vigilance toward breakthrough infections despite an environment with high vaccination coverage and strict public health protocols. From a public health policy perspective, given the current prevalence of the Delta variant and the possible future emergence of highly transmissible variants, our data suggest that infection control measures such as masking, personal hygiene, and social distancing will continue to be required in high-risk settings.
Authorship statement
Prof Naito had full access to all data in the study and takes responsibility for the integrity of the data and the accuracy of data analysis.
Consept and design: All authors.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Yan, Deshpande, Naito.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Ito, Tabe, Nojiri, Seyama.
Administrative, technical, or material support: Tabe, Seyama, Takahashi.
Supervision: Naito, Takahashi.
All authors meet the ICMJE authorship criteria.
Conflict of interest statement
The authors declare no conflict of interest.
Ethical statement
Study protocol was approved by the Institutional Review Board (IRB) of Juntendo University Faculty of Medicine, Juntendo University. (No. 2021055) All participants agreed to participate in this study.
Funding
This research was supported by Japan Agency for Medical Research and Development (AMED) under Grant Number JP20fk0108472.
CRediT authorship contribution statement
Toshio Naito: Supervision.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A Supplementary data
The following are the Supplementary data to this article:Supplementary data 1
Acknowledgement
We thank healthcare workers in Juntendo University Hospital, Tokyo, Japan for their participation. We thank Dr. Koji Tsuchiya and Dr. Yoshie Hosaka of Department of Clinical Laboratory Medicine, Juntendo University Hospital, for their contribution on collecting and analyzing sampling data. We thank Kristin Thurlby, assistant professor in the Department of Science, Johnson County Community College, Kansas, USA, for her editorial support.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.vaccine.2022.04.029.
==== Refs
References
1 Ministry of Health, Labour and Welfare, Japan. https://covid19.mhlw.go.jp/extensions/public/index.html. Accessed Sep 20, 2021. (in Japanese).
2 Data of prefectures of Japan. https://uub.jp/cvd/cvd.cgi?Y=X&T=38&TY=2021&TM=8&TD=31. Accessed Sep 23, 2021. (in Japanese).
3 Bureau of social welfare and public health, Tokyo metropolitan. https://www.fukushihoken.metro.tokyo.lg.jp/iryo/kansen/corona_portal/henikabu/screening.files/screening_09160101.pdf. Accessed Sep 23, 2021. (in Japanese).
4 Sheikh A. McMenamin J. Taylor B. Robertson C. SARS-CoV-2 Delta VOC in Scotland: demographics, risk of hospital admission, and vaccine effectiveness The Lancet 397 10293 2021 2461 2462 10.1016/s0140-6736(21)01358-1
5 Chia P.Y. Ong S.W.X. Chiew C.J. Virological and serological kinetics of SARS-CoV-2 Delta variant vaccine-breakthrough infections: a multi-center cohort study Clin Microbiol Infect 2021 10.1016/j.cmi.2021.11.010 S1198-743X(21)00638-8
6 Li B. Deng A. Li K. Viral infection and transmission in a large well-traced outbreak caused by the delta SARS-CoV-2 variant Nat Commun 13 1 2022 460 10.1038/s41467-022-28089-y 35075154
7 Campbell F. Archer B. Laurenson-Schafer H. Increased transmissibility and global spread of SARS-CoV-2 variants of concern as at June 2021 Euro Surveill 26 24 2021 2100509 10.2807/1560-7917 34142653
8 Ong S.W.X. Chiew C.J. Ang L.W. Clinical and virological features of SARS-CoV-2 variants of concern: a retrospective cohort study comparing B.1.1.7 (Alpha), B.1.315 (Beta), and B.1.617.2 (Delta) Clin Infect Dis 2021 10.1093/cid/ciab721 ciab721
9 Keehner J. Horton L.E. Binkin N.J. Laurent L.C. Pride D. Longhurst C.A. Resurgence of SARS-CoV-2 infection in a highly vaccinated health system workforce N Engl J Med 385 14 2021 1330 1332 10.1056/NEJMc2112981 34469645
10 Saita M. Yan Y. Ito K. Sasano H. Seyama K. Naito T. Reactogenicity following two doses of the BNT162b2 mRNA COVID-19 vaccine: Real-world evidence from healthcare workers in Japan J Infection Chemotherapy 28 1 2022 116 119 10.1016/j.jiac.2021.09.009
11 Yokota I. Shane P.Y. Okada K. Unoki Y. Yang Y. Inao T. Mass Screening of Asymptomatic Persons for Severe Acute Respiratory Syndrome Coronavirus 2 Using Saliva Clin Infect Dis 73 3 2021 e559 e565 10.1093/cid/ciaa1388 32976596
12 World Health Organization. Collecting Preserving and Shipping Specimens for the Diagnosis of Avian Influenza A (H5N1) Virus Infection. Guide for Field Operations. https://www.who.int/ihr/publications/MainTextEPR_ARO_2006_1.pdf. Accessed Sep 29, 2021.
13 Pandit P. Cooper-White J. Punyadeera C. High-yield RNA-extraction method for saliva Clin Chem 59 7 2013 1118 1122 10.1373/clinchem.2012.197863 23564756
14 Centers for Disease Control and Prevention. Research Use Only 2019-Novel Coronavirus (2019-nCoV) Real-time RT-PCR Primers and Probes. https://www.cdc.gov/coronavirus/2019-ncov/lab/rt-pcr-panel-primer-probes.html. Accessed Sep 29, 2021.
15 Centers of Disease Control and Prevention. Frequently Asked Questions about Coronavirus (COVID-19) for Laboratories. https://www.cdc.gov/coronavirus/2019-ncov/lab/faqs.html#Interpreting-Results-of-Diagnostic-Tests. Accessed Sep 29, 2021.
16 Centers of Disease Control and Prevention. Who Is Eligible for a COVID-19 Vaccine Booster Shot? https://www.cdc.gov/coronavirus/2019-ncov/vaccines/booster-shot.html. Accessed Oct 5, 2021.
17 Puranik A. Lenehan P.J. Silvert E. Comparison of two highly-effective mRNA vaccines for COVID-19 during periods of Alpha and Delta variants prevalence MedRxiv 2021 10.1101/2021.08.06.21261707
18 Seppala E, Veneti L, Starrfelt, et al. Vaccine effectiveness against infection with the Delta (B.1.617.2) variant, Norway, April to August 2021. Euro Surveill. 2021;26(35):2100793. doi: 10.2807/1560-7917.ES.2021.26.35.2100793.
19 Ueki H. Furusawa Y. Iwatsuki-Horimoto K. Imai M. Kabata H. Nishimura H. Effectiveness of face masks in preventing airborne transmission of SARS-CoV-2 mSphere 5 5 2020 10.1128/mSphere.00637-20
20 Mitze T. Kosfeld R. Rode J. Walde K. Face masks considerably reduce COVID-19 cases in Germany PNAS 117 51 2020 32293 32301 10.1073/pnas.2015954117 33273115
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PMC009xxxxxx/PMC9001200.txt |
==== Front
Pulmonology
Pulmonology
Pulmonology
2531-0429
2531-0437
Sociedade Portuguesa de Pneumologia. Published by Elsevier España, S.L.U.
S2531-0437(22)00084-8
10.1016/j.pulmoe.2022.04.002
Letter to the Editor
Idiopathic pulmonary fibrosis mortality in the Italian epicenter of COVID-19 pandemic
Faverio P. a
Conti S. b
Madotto F. c
Franco G. a
Renzoni E. de
Mantovani L.G. bc
Luppi F. a⁎
a School of Medicine and Surgery, University of Milano Bicocca; Respiratory Unit, San Gerardo Hospital, ASST Monza, Monza, Italy
b Research Centre on Public Health, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
c Value-based Healthcare Unit, IRCCS Multimedica, Sesto San Giovanni, Italy
d Interstitial Lung Disease Unit, Royal Brompton and Harefield NHS Foundation Trust, London, UK
e National Heart and Lung Institute, Imperial College London, London, UK
⁎ Corresponding author at: School of Medicine and Surgery, University of Milano Bicocca; Respiratory Unit, San Gerardo Hospital, ASST Monza, via Pergolesi 33, 20900, Monza, Italy
12 4 2022
12 4 2022
12 3 2022
1 4 2022
© 2022 Sociedade Portuguesa de Pneumologia. Published by Elsevier España, S.L.U.
2022
Sociedade Portuguesa de Pneumologia
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
==== Body
pmcTo the Editor,
Idiopathic pulmonary fibrosis (IPF) is a progressive, life-threatening interstitial pneumonia of unknown cause1, affecting elderly, frail individuals with a median age at diagnosis of 661 and a median estimated survival of 2.5–3.5 years after diagnosis.2, 3
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) rapidly spread worldwide and the absence of effective therapies or vaccines at the beginning of the pandemic led Governments to enforce strict measures in their efforts to limit the virus transmission.4 On March, 9th 2020 Italy went into a full lockdown. In parallel, hospital infrastructures were redirected towards maximizing intensive care resources which resulted in routine clinical practice, including IPF outpatient clinics, being considerably reduced.5
The aim of our study was to assess the mortality of IPF patients included in the cohort of the tertiary outpatient IPF clinic at the “San Gerardo” Hospital, located in Monza (Lombardy, the most populated Italian region) in relation to the social and healthcare changes due to COVID-19 pandemic.
We analyzed a cohort of 212 patients recruited between May 2008 and April 2021 and alive on January 1st 2018. We recorded mortality data comparing the characteristics between patients who died in January 1st, 2018 and February 28th, 2020 (pre-pandemic and pre-lockdown period) to those who died between March 1st, 2020 and April 30th, 2021 (pandemic and post-lockdown period) using Chi-square or Fisher's exact tests for categorical variables and Mann–Whitney U test for continuous ones. Thereafter, we computed monthly average crude mortality rates for each of the two periods, with related exact 95% confidence intervals (95%CIs) based on a Poisson distribution, and we compared them through incidence rate ratios (IRR). Similarly, we computed trimestral-specific monthly average mortality rates for the biennium 2018-2019, and we compared them with those of 2020. Person-time at risk (in months) was computed for each subject from January, 1st 2018 or the day of IPF diagnosis, until death or the end of the period of interest. 95%CIs for IRR were based on the exact distribution of the rate of two Poisson counts, as well as 2-sided p-values. All analyses were performed using SAS version 9.4 (The SAS institute, Cary, NC) and R version 4.0.3 (R Core Team, Vienna, Austria) with the packages epitools and rateratio.test. The study received Ethics Committee approval (ASST Monza, 1538, November 14th 2019).
In the pre-lockdown period, we documented 39 deaths in our IPF cohort; in contrast, in the post-lockdown period, 33 IPF patients died (Fig. 1 a). We observed a significantly younger median age at death and a trend toward younger median age at diagnosis in the pre-lockdown compared to the post-lockdown period with similar median disease duration, pulmonary function tests and severity of the disease evaluated through Gender-Age-Physiology (GAP) index (Table 1 ). We did not detect statistically significant differences regarding gender or antifibrotic treatment. The burden of comorbidities was similar between the two groups with the exception of pulmonary hypertension that was more common in the post-lockdown period.Fig. 1. (a) Study flow chart; (b) Trimestral-specific comparison of the monthly average mortality rate in 2018/2019 and in 2020 (2-sided tests) * p-value 2-sided test <0.05. IPF= idiopathic pulmonry fibrosis.
Fig 1 (
Table 1 Demographics and clinical characteristics of study population.
Table 1 Death Total p-value
Before March 3rd 2020 On or after March 3rd 2020
N subjects 39 33 72
Year of death - N(%)
2018 17(43.59) 0(0.00) 17(23.61)
2019 14(35.90) 0(0.00) 14(19.44)
2020 8(20.51) 22(66.67) 30(41.67)
2021 0(0.00) 11(33.33) 11(15.28)
Males - N(%) 35(89.74) 30(90.91) 65(90.28) 1
Age at death - Median (Q1-Q3) 73(68 - 78) 79(72 - 82) 74(69 - 81) 0.0311
Time between last visit and death (months) - Median (Q1-Q3) 4(3 - 9) 6(3 - 9) 5(3 - 9) 0.2651
Disease duration# at death (months) - Median (Q1-Q3) 36(19 - 60) 35(25 - 58) 36(23 - 59) 0.4834
Age at diagnosis - Median (Q1-Q3) 69(65 - 75) 75(68 - 78) 71(67 - 77) 0.0588
FVC% of predicted* – Median (Q1-Q3) 67(56 – 77) 62(52 – 76) 64(52 - 77) 0.4834
DLCO% of predicted* – Median (Q1-Q3) 29(21 – 34) 26(16 – 39) 29(19 - 35) 0.2180
GAP index* – N (%) 0.9692
Stage 1 1(2,56) 1(3,03) 2(2,78)
Stage 2 20(51,28) 16(48,48) 36(50,0)
Stage 3 18(46,15) 16(48,48) 34(47,22)
Antifibrotic therapy at death - N(%) 0.2118
No 12(30.77) 5(15.15) 17(23.61)
Yes 21(53.85) 19(57.58) 40(55.56)
Previous, but interrupted 6(15.39) 9(27.27) 15(20.83)
Months between interruption and death - Median (Q1-Q3) 19.0(9.4) 23.3(17.3) 21.6(14.4) 0.8596
Nintedanib 12(30,77) 10(30,30) 20(27,78)
Pirfenidone 9(23,08) 9(27,27) 18(25,00)
Comorbidities - N(%) 29(74.36) 27(81.82) 56(77.78)
Combined pulmonary fibrosis and emphysema 10(25.64) 6(18.18) 16(22.22) 0.4481
Obstructive sleep apnea syndrome 4(10.26) 1(3.03) 5(6.94) 0.3662
Coronary artery disease 13(33.33) 12(36.36) 25(34.72) 0.7878
Chronic Heart Failure 6(15.39) 5(15.15) 11(15.28) 0.9781
Pulmonary hypertension 11(28.21) 17(51.52) 28(38.89) 0.0432
Gastroesophageal reflux disease 11(28.21) 8(24.24) 19(26.39) 0.7038
Lung cancer (active) 1(2.56) 1(3.03) 2(2.78) 1
Type 2 diabetes 11(28.21) 10(30.30) 21(29.17) 0.8453
Hypothyroidism 2(5.13) 0(0.00) 2(2.78) 0.4965
DLCO= diffusing capacity for carbon monoxide; FVC= Forced Vital Capacity; GAP= Gender-Age-Physiology
We estimated that monthly average mortality rates rose from 1.03 per 100 person-months (95%CI: 0.72-1.42) during the pre-lockdown period to 1.67 (95%CI: 1.17-2.33) post-lockdown: such increase was borderline significant, corresponding to an IRR of 1.63 (95%CI: 1.00-2.66, p=0.05). In detail, comparing the various trimestral periods, we observed a statistically significant increase in mortality in the last trimester (October/December) of 2020, as compared to the last trimester 2018: monthly average mortality rates increased from 0.68 (95%CI: 0.25-1.48) to 2.48 (1.24-4.44) per 100 person-months (IRR: 3.64, 95%CI: 1.24-12.00, p=0.008, Fig. 1b).
In the lockdown periods, patients included in our IPF cohort were regularly followed-up with telephone calls and continuously received antifibrotic treatment. Asking family members, we were able to determine that 3 out of 33 patients (9.1%) were hospitalized and died because of a confirmed diagnosis of Coronavirus disease (COVID-19) and that the great majority, 30/33 (90.9%), died at home or in long-term facilities without signs or symptoms suggestive of COVID-19.
This study showed a significant increase in mortality in our IPF cohort during the post-lockdown period that, in most of the cases, did not appear directly related to COVID-19.
In line with our results, Marcon and colleagues showed an excess of IPF-related deaths during the first wave of the COVID-19 pandemic.6 However, the authors did not differentiate between deaths directly related to COVID-19 and other etiologies.
In our study, we observed a marginally significant increase in mortality during post-lockdown period compared to pre-lockdown. We believe that the increase in mortality is mainly related to the increased frailty and to limitation of access to the IPF Referral Center for a worsening of the disease during the peak of the pandemic. This is corroborated by the results of the trimestral analysis which shows an increase in the period of the COVID-19 second wave (October/December 2020), when our province (Monza-Brianza) reached the highest level of incidence of SARS-CoV-2 infection.
In our study, a number of limitations should be acknowledged. The cause of death was not confirmed for the majority of the patients. Moreover, this study was performed in a single center, limiting the generalizability of the results. Finally, given the small sample size, we were not able to run a Cox-analysis that would have been the best way to address the risk factors for mortality adjusting for possible confounders.
In conclusion, we report a statistically significant increase in mortality within our IPF cohort during the COVID-19 second wave. To the best of our knowledge, only in a minority of patients was the cause of death directly related to SARS-CoV-2 infection. In most patients, the cause of death was possibly related to the limitations to reaching the hospital and ILD-physicians of the IPF referral Center in relation to the COVID-19 pandemic in case of worsening of the disease.
Authors’ contributions
FL is the guarantor of this research. PF, SC, GF, FM, LGM and FL were responsible for study concept and design. PF, SC, GF, FM and FL contributed to data acquisition. PF, SC, GF, FM, LGM and FL performed data analysis. PF, SC, GF, FM, ER, LGM and FL contributed to the drafting of this manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The study received Ethics Committee approval (ASST Monza, 1538, November 14th 2019).
Consent for publication
Written informed consent was waived given the retrospective design of the study.
Conflicts of interest
The authors have no conflicts of interest to declare.
Avalilability of data and materials
Individual participant data referring to this article (i.e. text, tables and figures) will be made available upon reasonable request. The study protocol will be made available for researchers who provide a methodologically sound proposal. Proposals should be directed to paola.faverio@unimib.it
Acknowledgments
We acknowledge that this research was partially supported by the Italian Ministry of University and Research (MIUR)—Department of Excellence project PREMIA (PREcision MedIcine Approach: bringing biomarker research to clinic).
Funding sources
The authors have no funding to declare.
==== Refs
References
1 Richeldi L Collard HR Jones MG. Idiopathic pulmonary fibrosis Lancet. 389 10082 2017 1941 1952 10.1016/S0140-6736(17)30866-8 28365056
2 Ley B Collard HR Jr King TE Clinical course and prediction of survival in idiopathic pulmonary fibrosis Am J Respir Crit Care Med 183 4 2011 431 440 10.1164/rccm.201006-0894CI 20935110
3 Vancheri C Failla M Crimi N Raghu G. Idiopathic pulmonary fibrosis: a disease with similarities and links to cancer biology Eur Respir J 35 3 2010 496 504 10.1183/09031936.00077309 20190329
4 Koh WC, Alikhan MF, Koh D, Wong J. Containing COVID-19: Implementation of early and moderately stringent social distancing measures can prevent the need for large-scale lockdowns. Ann Glob Health. 2020; 86 (1): 88. 10.5334/aogh.2969.
5 Weisel KC Morgner-Miehlke A Petersen C Implications of SARS-CoV-2 infection and COVID-19 crisis on clinical cancer care: report of the university cancer center hamburg Oncol Res Treat 43 6 2020 307 313 10.1159/000508272 32380501
6 Marcon A Schievano E Fedeli U. Mortality associated with idiopathic pulmonary fibrosis in Northeastern Italy, 2008-2020: a multiple cause of death analysis Int J Environ Res Public Health 18 14 2021 7249 10.3390/ijerph18147249 34299699
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PMC009xxxxxx/PMC9001203.txt |
==== Front
Vaccine
Vaccine
Vaccine
0264-410X
1873-2518
The Authors. Published by Elsevier Ltd.
S0264-410X(22)00441-8
10.1016/j.vaccine.2022.04.025
Article
Vaccine effectiveness against onward transmission of SARS-CoV2-infection by variant of concern and time since vaccination, Belgian contact tracing, 2021
Braeye Toon a⁎
Catteau Lucy a
Brondeel Ruben a
van Loenhout Joris A.F. a
Proesmans Kristiaan a
Cornelissen Laura a
Van Oyen Herman ae
Stouten Veerle a
Hubin Pierre a
Billuart Matthieu a
Djiena Achille b
Mahieu Romain c
Hammami Naima d
Van Cauteren Dieter a
Wyndham-Thomas Chloé a
a Department of Epidemiology and Public Health, Sciensano, Juliette Wytsmansstraat 14, 1000 Brussel, Belgium
b Agence pour une Vie de Qualité, Rue de la Rivelaine 11, 6061 Charleroi, Belgium
c Common Community Commission Brussels, Rue Belliard 71/1, 1040 Brussels, Belgium
d Agency for Care and Health, Infection Prevention and Control, Flemish Community, Koningin Maria Hendrikaplein 70 bus 55, 9000 Gent, Belgium
e Department of Public Health and Primary Care, Ghent University, Corneel Heymanslaan 10, 9000 Gent, Belgium
⁎ Corresponding author at: Juliette Wytsmansstraat 14, 1000 Brussel, Belgium.
12 4 2022
11 5 2022
12 4 2022
40 22 30273037
20 1 2022
4 4 2022
5 4 2022
© 2022 The Authors
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Background
During the first half of 2021, we observed high vaccine effectiveness (VE) against SARS-CoV2-infection. The replacement of the alpha-‘variant of concern’ (VOC) by the delta-VOC and uncertainty about the time course of immunity called for a re-assessment.
Methods
We estimated VE against transmission of infection (VET) from Belgian contact tracing data for high-risk exposure contacts between 26/01/2021 and 14/12/2021 by susceptibility (VEs) and infectiousness of breakthrough cases (VEi) for a complete schedule of Ad26.COV2.S, ChAdOx1, BNT162b2, mRNA-1273 as well as infection-acquired and hybrid immunity. We used a multilevel Bayesian model and adjusted for personal characteristics (age, sex, household), background exposure, calendar week, VOC and time since immunity conferring-event.
Findings
VET-estimates were higher for mRNA-vaccines, over 90%, compared to viral vector vaccines: 66% and 80% for Ad26COV2.S and ChAdOx1 respectively (Alpha, 0–50 days after vaccination). Delta was associated with a 40% increase in odds of transmission and a decrease of VEs (72–64%) and especially of VEi (71–46% for BNT162b2). Infection-acquired and hybrid immunity were less affected by Delta. Waning further reduced VET-estimates: from 81% to 63% for BNT162b2 (Delta, 150–200 days after vaccination). We observed lower initial VEi in the age group 65–84 years (32% vs 46% in the age group 45–64 years for BNT162b2) and faster waning. Hybrid immunity waned slower than vaccine-induced immunity.
Interpretation
VEi and VEs-estimates, while remaining significant, were reduced by Delta and waned over time. We observed faster waning in the oldest age group. We should seek to improve vaccine-induced protection in older persons and those vaccinated with viral-vector vaccines.
Keywords
Vaccine effectiveness
Transmission
SARS-CoV2
Infection
Contact Tracing
Susceptibility
Infectiousness
Bayesian analysis
Covid19
==== Body
pmc1 Introduction
Understanding the magnitude of vaccine-induced protection over time and against SARS-CoV2-variants of concern (VOC) is a public health priority [1]. Vaccine effectiveness against the onward transmission (VET) of infection during contacts can be separated into two components; infectiousness (VEi) and susceptibility (VEs). Early analyses showed that vaccines reduced susceptibility of vaccinated persons and, if a breakthrough infection occurred, they reduced infectiousness of breakthrough cases [2]. From Belgian contact tracing data collected during the first half of 2021, we estimated the VET to be over 90% for the mRNA-vaccines BNT162b2 and mRNA-1273 [3]. We could however only include data collected from recently vaccinated persons and on infections that were likely caused by the alpha-VOC (Alpha). These early studies were also limited by the small number of breakthrough cases. This was especially true for the viral-vector vaccines, ChAdOx1 and Ad26.COV2.S, which were included later in the vaccination campaign and used less. In addition to the uncertainty around the early VE-estimates, two important evolutions required further investigation of VE-estimates: the delta-VOC (Delta) replaced Alpha from mid-2021 onwards in Belgium and early reports on the waning of neutralizing vaccine-induced-antibodies were published [4], [5], [6].
VET-estimates are obtained by explicitly including the potential ‘infectors’ and their vaccination status into the model. Data for these models typically comes from either household-surveys or contact tracing. In Belgium, contact tracing started in May 2020. During 2021, all persons with a positive test (PCR or antigenic) for SARS-CoV-2 were called and asked to report their high-risk exposure contacts (contacts for >15′ at <1.5m without face masks, or direct physical contact, close contacts) [7]. We refer to the initial cases as index cases and to their high-risk exposure contacts as HREC. Index cases with a recent infection (positive PCR or antigenic test in the past 90 days), were excluded from contact tracing and recently infected HREC were not required to get tested.
Belgium’s vaccination campaign started in January 2021 and the vaccination strategy prioritized nursing home residents and healthcare workers after which an age- and risk-based approach was taken to vaccinate the general population. By 30 November 2021, 86·7% of the adult and 75% of the total Belgian population was fully vaccinated. There was a three- to five-week interval between doses for the mRNA-vaccines and a eight to 12-week interval for ChAdOx1. For further details on the strategy we refer to the Scientific Institute of Public Health FAQ [8]. Belgium started administering additional doses to persons with reduced immunity and booster doses to selected populations, including nursing home residents, healthcare workers and those aged 65 years or older from mid-September 2021 onwards. Eventually booster-vaccination was offered to all Belgian adults.
1.1 Objectives
We estimated VE against transmission of SARS-CoV-2-infection by VOC and time since vaccination for the four vaccine brands used in Belgium (Ad26.COV2.S, ChAdOx1, BNT162b2, mRNA-1273) and compared VE-estimates to protection offered by previous infection (=infection-acquired immunity) and by the combination of vaccine-induced and infection-acquired immunity (=hybrid immunity).
2 Methods
2.1 Data included
We included data from 26 January 2021 to 14 December 2021. From 26 January 2021 onwards a second PCR-test was required when the first test was negative. The first test was carried out as soon as possible. The second test was carried out seven days after the last contact. A single negative test sufficed for vaccinated persons during the summer holidays (July-August) and if there had been no contact with the index case in the last three days. We included test results of first and second tests from fully vaccinated and unvaccinated persons. Persons who received a single dose of a two-dose vaccine schedule (incomplete vaccination) or an additional or booster dose at the time of testing were excluded.
Alpha was the dominant strain during the first months of 2021, being detected in 60–85% of sequenced samples. Delta was first detected in Belgium in April 2021. We defined 18 June 2021, the date at which 20% of the sequenced samples were identified as Delta, as the end of the Alpha-dominant period. On 15 July 2021, 86% of the sequenced samples were identified as Delta. This percentage increased to 99.6% by 30 November 2021. We defined the period from 15 July to 14 December 2021 as the period during which Delta was dominant. On 15 December 2021, 4% of sequenced sampled were identified as Omicron. Samples collected between the Alpha and Delta-dominant periods, from 19 June 2021 to 14 July 2021, were excluded.
Contacts with a negative duration of exposure were excluded: (1) when the HREC was tested earlier than the index case and (2) when the date of last contact between index and HREC was more than three days before the date of symptom onset of the index case. For index cases with more than three HREC, we randomly selected three HREC for inclusion and excluded the other HREC.
2.2 Variables included
Person-level data on test-results (result of the test, sampling and testing date) were linked to data from the vaccination registry (vaccine brand and date of vaccination) and contact tracing data (age, sex, date of symptom onset, date of last contact, household-membership) by National Registry Number (NRN).
A person was considered fully vaccinated 14 days after the second dose of ChAdOx1/ mRNA-1273, seven days after the second dose of BNT162b2 and 21 days after a single dose of Ad26.COV2.S [9]. A previous SARS-CoV2-infection was defined as having had a positive PCR or antigenic test more than 90 days prior to the date of sampling.
Biological sex and age at sampling were obtained from the national registry. Age groups were 0–5, 6–11, 12–24, 25–44, 45–64 and 65–84 years old. As we could only include a small number of persons aged 85 years and older, these were excluded from the analysis. VEs was not estimated for persons younger than 12 years as this age group was not eligible for vaccination during the study period.
We included a dummy variable to indicate if the index case and HREC were part of the same household. We included whether the test was a first test or a second test (in combination with the test result of the first test). Calendar time, the week during which the sample was taken, was included as a random effect into the model. Finally, the background exposure was included as the positivity rate (centered 7-day moving average) of all PCR and antigenic tests of the province of the HREC at the sampling date.
2.3 The model
We fitted a multilevel Bayesian regression model to the test results of the HREC. The probability of a positive test was a function of characteristics of the index and HREC (age, sex, household, vaccination and previous infection (pC.Vacc)), the dominant VOC, background exposure and the calendar week.PpostestHREC~ageindex+sexindex+ageHREC+sexHREC+household+VOC+backgroundexposure+calendarweek+(Index)ImmAgeGroup,Sex,pC.Vacc,t,VOC+(HREC)ImmAgeGroup,Sex,pC.Vacc,t,VOC
2.3.1 The effect of vaccination and previous Covid-19 infection
The effect of the immunity-conferring event (Imm) was included for the index case (effect on infectiousness) and the HREC (effect on susceptibility) as an initial effect (pC.Vacc+age+sex) in interaction with the VOC (VOCpC.Vacc) and waning (WaningAge,Sex,pC.Vacc,t). ImmAge,Sex,pC.Vacc,t,VOC=pC.Vacc+age+sex∗WaningAge,Sex,pC.Vacc,t∗VOCpC.Vacc
Previous infection and vaccination (pC.Vacc) were included as a factor with 10 factor-levels (2 * 5: yes/no previous infection and unvaccinated/Ad26.COV2.S/ChAdOx1/BNT162b2/mRNA-1273). WaningAge,Sex,pC.Vacc,t is included as a linear spline over 50-day periods since the last immunity-conferring event with a single knot at 150 days. The spline’s coefficients are determined by age, sex and the combination of vaccination and previous infection.WaningAgeGroup,Sex,pC.VaccAgeGroup+Sex+pC.Vacc
One way to interpret the model is to look at its three levels. The first level represents a baseline for transmissibility/infectiousness/susceptibility defined by age, sex (of index case and HREC), household, VOC, background exposure and calendar week. The second level represents the initial effect of the vaccination/previous infection (first 50 days after last immunity-conferring event). The third level represents the waning of this initial effect. Note that variables such as age, sex, vaccination (brand)/previous infection are included on all three levels. The model allows age to be associated with changes in susceptibility, changes in vaccine effectiveness and faster or slower waning. Other variables, such as the VOC, were included on two levels (1) baseline transmissibility/infectiousness/susceptibility and (2) ‘vaccination/previous infection’-effect. The model allows for a different VOC-effect on infection-acquired immunity, vaccine-induced immunity and hybrid immunity. The model does not allow VOC-specific waning.
We reported 95% credible intervals as CI. The Bayesian model was fitted using the R-package nimble. Code for the model and the priors used can be found in supplementary material.
Females aged 45–64 years old without a previous infection were used as reference category in this paper. Whenever VE is reported without additionally mentioning sex, age group and previous infection, it refers to females aged 45–64 years of age. BNT162b2 is the most frequently administered vaccine in Belgium and is therefore often used as reference in this study.
2.4 Role of the funding source
This study was supported by the Belgian Federal and Regional Authorities through funding for the LINK-VACC project and organizing and financing of contact tracing. The funding source had no role in the study design, collection, analysis, interpretation, writing of the report or deciding to submit the paper.
3 Results
3.1 Numbers included and characteristics of those included
Over the study period 1,281,260 Covid19-cases were recorded. A total of 931,518 (72%) index cases were successfully contacted, 85.6% reported contacts (low and/or high-risk) and 50% reported HREC. A median of 3 HREC were reported per index case reporting HREC. A total of 1,341,084 HREC were to be contacted. To be included in the analysis, the HREC needed to be successfully contacted and provide a NRN. This was available for 1,037,677 HREC (78.5% of all HREC).
Adherence to the testing strategy was high: test results were available for 90.3% of HREC with an NRN (N = 934,285). Among those testing negative on a first test, a test result of a second test was available for 65%.
Over the study period 1,446,605 test results were available. Results were excluded because of missing variables for the HREC (N = 5920), missing variables for the index case (N = 144,380), sampling during a period in which the dominant VOC was unclear (N = 24,884), incomplete vaccination or booster vaccination (N = 144,411), second tests in fully vaccinated persons during summer (N = 21,618), an index case or HREC aged 85 years or older (N = 10,150), more than 3 HREC per index case (N = 188,926) and misclassification (e.g. testing of HREC before testing of index case, N = 78,683). We included 941,320, of which 194,128 positive, test results (20.6%) from 321,279 index cases and 567,986 HREC in the analysis.
We included a number of descriptive tables on previous infection and vaccine brand by age group for HREC and index case (Table 1, Table 2 ). More descriptive statistics on age, sex, index case-HREC interactions and the temporal evolution of the unadjusted attack rate in HREC are provided in supplementary material. Notably, persons included in the analysis were most frequently aged around either 15 or 42 years. Also, about 66% of HREC were household-members of the index case and most tests were from March-April (3rd Belgian Covid19-wave) and October-November 2021 (4th Belgian Covid19-wave).Table 1 Number of the included index cases (upper) and High-Risk Exposure Contacts (bottom) by previous infection and age group (at the time of high-risk exposure contact). The positivity rate of the first test of the HREC (or HREC reported by the index case) is presented in brackets (%). Index cases are included once per tested HREC. Belgian contact tracing, 26/01/2021–14/12/2021.
Index case 0–5 6–11 12–24 25–44 45–64 65–84
No prev. Infection (% HREC positive) 24,145 (19%) 120,391 (17%) 115,502 (17%) 202,067 (24%) 120,951 (25%) 23,031 (27%)
Prev. Infection (% HREC positive) 102 (17%) 1570 (9%) 3317 (9%) 5394 (12%) 2476 (12%) 329 (10%)
HREC 0–5 6–11 12–24 25–44 45–64 65–84
No prev. Infection (% positive) 28,912 (27%) 77,622 (31%) 124,959 (21%) 193,047 (20%) 131,711 (20%) 32,682 (22%)
Prev. Infection (% positive) 218 (11%) 2024 (10%) 6994 (7%) 12,717 (7%) 7429 (5%) 960 (5%)
Table 2 Number of the included index cases (upper) and High-Risk Exposure Contacts (HREC bottom) by vaccination status (vaccine brand or unvaccinated) and age group (at the time of high-risk contact). The positivity rate of the first test of the HREC (or HREC reported by the index case) is presented in brackets (%). Index cases are included once per tested HREC. Belgian contact tracing, 26/01/2021–14/12/2021.
Index case 0–5 6–11 12–24 25–44 45–64 65–84
Unvaccinated (% HREC pos) 24,247 (19%) 121,958 (17%) 91,307 (18%) 114,679 (27%) 64,393 (30%) 10,675 (30%)
Ad26.COV2.S (% HREC pos) NA NA 3826 (11%) 4614 (22%) 5286 (21%) 135 (30%)
ChAdOx1 (% HREC pos) NA NA 1698 (10%) 11,713 (20%) 15,520 (20%) 3413 (25%)
BNT162b2 (% HREC pos) NA NA 21,100 (11%) 69,077 (20%) 34,889 (19%) 8649 (25%)
mRNA-1273 (% HREC pos) NA NA 888 (10%) 7378 (17%) 3339 (14%) 448 (18%)
HREC 0–5 6–11 12–24 25–44 45–64 65–84
Unvaccinated (% positive) 29,130 (27%) 79,639 (30%) 82,413 (26%) 89,862 (24%) 60,805 (24%) 12,372 (28%)
Ad26.COV2.S (% positive) NA NA 2422 (16%) 4031 (20%) 5007 (20%) 196 (20%)
ChAdOx1 (% positive) NA NA 792 (13%) 9336 (17%) 16,296 (18%) 5373 (17%)
BNT162b2 (% positive) NA NA 44,606 (9%) 90,037 (15%) 50,603 (15%) 14,449 (19%)
mRNA-1273 (% positive) NA NA 1720 (7%) 12,498 (11%) 6429 (11%) 1252 (12%)
3.2 Baseline susceptibility and infectiousness
The baseline susceptibility and infectiousness as obtained from the multivariate model (adjusted for VOC, vaccination/previous infection, background exposure, household-membership and characteristics of index/HREC) were lowest for the youngest age group and highest for the oldest age group (Fig. 1 ). Susceptibility was lower in males (OR 0·96, CI 0.95–0.97) compared to females, infectiousness was not-significantly different (OR 1.01, CI 0.99–1.02).Fig. 1 Baseline Odds Ratio (95% CI) for susceptibility (upper) and infectiousness (bottom) by age group, Belgian contact tracing, 26/01/2021–14/12/2021.
The odds of transmission during the period when Delta was dominant increased with 40.4% (CI 38.9–41.8) compared to the period when Alpha was dominant.
3.3 First 50-day effects of vaccine and previous infection on susceptibility, alpha-VOC
In persons without previous infection, during the Alpha-dominant period, we observed significant differences in VEs-estimates by vaccine brand. mRNA-1273 offered the highest VEs (82%, CI 79–84) and VEs was lowest for Ad26.COV2.S (38%, CI 34–44). The VEs-estimates for BNT162b2 and ChAdOx1 were 72% (CI 70–74) and 56% (CI 51–59) respectively. Infection-acquired immunity did not offer significantly different protection compared to mRNA-1273-vaccination. The estimated reduction in susceptibility for re-infection was 83% (CI 80–88). Hybrid immunity provided the highest protection; in previously infected persons, VEs was estimated around 87%, without significant differences between vaccine brands.
In addition to reducing susceptibility, the infectiousness of breakthrough cases without previous infection, was reduced by 76% (CI 72–79) for mRNA-1273 and 44% (CI 41–48) for Ad26.COV2.S. The VEi-estimates for BNT162b2 and ChAdOx1 were 71% (CI 68–74) and 53% (CI 49–57) respectively. Infection-acquired immunity reduced infectiousness with 73% (CI 68–82). The reduction associated with hybrid-immunity was estimated around 80%, without significant differences between vaccine brands.
3.4 VOC-effects
The protective effects of vaccines were smaller for Delta compared to Alpha. The observed decrease was greatest for persons without previous infection and was greater for VEi (19–25 percentage points) compared to VEs (5–8 percentage points).
The dominance of the delta-VOC also resulted in a decrease in protection conferred by previous infection but to a lower extent and 95% credible intervals overlapped (Fig. 2 ).Fig. 2 (Top) VE-susceptibility (95% CI) and (Bottom) VE-infectiousness (95% CI) by vaccine brand and previous infection and by VOC, 0–50 days after vaccination (alpha = black, delta = orange, hybrid immunity not presented), Belgian contact tracing, 26/01/2021–14/12/2021. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
3.5 Waning of initial effects
We observed waning of vaccine-induced and immunity-acquired protection for both VEi and VEs. Over a period of 150–200 days after vaccination, VEs decreased by 11–20 percentage points and VEi decreased by 1–12 percentage points (depending on the brand). The VET-estimate for BNT162b2 decreased from 81% to 63% (for females 45–64 years old, Delta). The reduction in susceptibility ((1-RR) * 100) by previous infection to Delta-infection without vaccination went from 79% (CI 74–83) to 64% (CI 61–66). For hybrid immunity, we observed waning from 87% (CI 84–88) to 82% (81–83) (BNT162b2, Delta, 150–200 days). There is considerable uncertainty surrounding these estimates with wide 95% credible intervals (Figs. 3 and 4 ).Fig. 3 VE-susceptibility (95% CI) by vaccine brand and previous infection and by time since vaccination (0–50 days (black) and 150–200 days after vaccination/infection (green), Delta), Belgian contact tracing, 26/01/2021–14/12/2021. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 4 VE-infectiousness (95% CI) by vaccine brand and previous infection and by time since vaccination (0–50 days (black) and 150–200 days after vaccination/infection (green), Delta), Belgian contact tracing, 26/01/2021–14/12/2021. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
3.6 Effects of age groups and sex on vaccine effectiveness/effect of previous infection
Male sex was associated with higher VEs (lower odds of infection after vaccination of HREC compared to females OR 0.83 CI 0.84–0.93), but lower VEi (higher odds of infection after vaccination of index cases compared to females OR 1.08 CI 1.01–1.17). In addition, faster waning of VEs was observed in males compared to females, but the size of the effect was small.
Faster waning of VEi and VEs was observed in the oldest age group. This observation was accompanied by a lower initial VEi in the oldest age group. We observed higher VEs in the youngest age group (12–25 years) (Fig. 5 ).Fig. 5 (Top) VE-susceptibility (95% CI) and (Bottom) VE-infectiousness (95 %CI) for the different age groups (index case and HREC from the same age group, 0–50 (black) and 150–200 (green) days after full vaccination, fully vaccinated with BNT162b2 no previous infection, Delta), Belgian contact tracing, 26/01/2021–14/12/2021. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
3.7 Vaccine and previous infection effectiveness against transmission
The combined initial effects of VEi and VEs resulted in VET-estimates of 90% (CI 89–92) for high-risk contacts between fully vaccinated (BNT162b2) females aged 65–84 years without previous infection for Alpha. For Delta and 150–200 days after full vaccination, these estimates were reduced to 54% (CI 50–57). The largest part of the reduction in VET for this age group is associated with waning. For younger age groups, the VOC-effect and waning over a 150–200 days period were more equally associated with the decrease (Figs. 6 and 7 ).Fig. 6 VE-Transmission (95% CI) for females aged 65–84 years old without previous infection, by vaccine brand, VOC and time since vaccination (0–50 and 150–200 days), Belgian contact tracing, 26/01/2021–14/12/2021. The first column presents VE-estimates against susceptibility (protection of the HREC by vaccination against exposure from an unvaccinated index case), the first row presents VE-estimates against infectiousness (protection of unvaccinated HREC through reduced infectiousness of an index case by vaccination).
Fig. 7 VE-Transmission (95% CI) over time since vaccination by age groups and sex (HREC and Index case fully vaccinated with BNT162b2, from the same age group and same sex), Belgian contact tracing, 26/01/2021–14/12/2021.
4 Discussion
In this study, we estimated the effects of vaccination on transmission of SARS-CoV2 during high-risk exposure contacts from Belgium’s contact tracing data. We found that, while the delta-VOC and time since vaccination lowered vaccine-induced protection, significant protection remained. We first discuss the initial effect of vaccination and then discuss how this effect waned over time. Finally, we discuss infectiousness of vaccinated cases.
Initial vaccination effects differed by VOC and by vaccine type. Delta was associated with a decrease of 19–25 percentage points for VEi and 5–8 for VEs. mRNA-vaccines offered more protection than viral-vector vaccines. The lowest VE-estimates were associated with the single dose viral-vector vaccine Ad26.COV2.S. This has been observed in other studies [10], [11]. Also in accordance with other studies [12], [13], [14], [15], we observed a high level of protection against re-infection after vaccination. We estimated VEs at 85% for hybrid immunity (BNT162b2, Delta), with non-significant differences between vaccine brands. Hybrid immunity offered more protection than previous infection or vaccination alone. Without vaccination, protection by previous infection was comparable to protection by mRNA-1273. Our estimate for infection-acquired relative risk reduction for susceptibility (1-RR: 83%, Delta) was at the lower limit of the range (80–100%) reported by an overview study [16]. While we observed more cross-neutralization between Alpha and Delta by infection-acquired compared to vaccine-induced immunity, this finding cannot be extrapolated to other VOCs [17]: neutralizing antibody responses are strongest against variants sharing certain spike mutation with the immunizing exposure [18].
We observed waning for both VEi and VEs. The waning observed for VEi was age-specific: increasing with age and not significant for the youngest age groups. Because, compared to HREC, a lower number of vaccinated index cases were included, our estimates for VEi are more uncertain. This is especially true for the viral-vector vaccines which were administered less. We observed an initial steep decrease of VET-estimates over the first 4 months, a loss of around 20%. Estimates continued to wane, but at a slower speed. Our waning estimate is within the 20–30% range over a six month period reported by a systematic review [19]. Other studies reported faster or comparable waning of VE-estimates. Over five months, from February to October 2021, VE declined from 80% to 43% in the UK [20] and 81% to 46% in the USA [5]. Another UK study reported waning 20 weeks after full vaccination to 44.3 (CI 45–50) and 66.3 (CI 69–71) against the Delta variant for ChadOx1 and BNT162b2 respectively [21]. A population study from Sweden reported waning after vaccination with BNT162b2 from 92% to 47% 121–180 days later [22]. Serum antibody levels have been shown to decline by 57% in six months [23]. For persons aged over 65 years, we report faster waning. We associated hybrid immunity with slow waning. It was identified as the most durable form of immunity by an Israeli study [24]. Even among older people [preprint] [25] and after a single dose [26], hybrid immunity was associated with a durable IgG response [20].
We found significant VEi-estimates against both Alpha and Delta. Other studies have also reported significant VEi against Alpha; an Israeli study found VEi-estimates of 23% [27], UK studies on households and healthcare workers found estimates of 35–60% [28], [29], [30]. Studies on Delta have reported no significant or, compared to Alpha, lower VEi-estimates. A Singapore, Isreali and a UK household-study found no significant VEi-estimates [27], [31], [32], while another UK household-study reported VEi-estimates around 40% for two doses of BNT16b2 and ChAdOx1 [preprint] [28]. Estimates from contact tracing data in the Netherlands found significant VEi-estimates: 63% in unvaccinated household-contacts and 40% in vaccinated household-contacts [33]. This was a significant decrease however from the estimates they reported for Alpha [2]. We estimated VEi at 25–51% against Delta for 25–44 year olds. Estimates for the 65–84 years old were considerably lower (5–37%) and waned faster. Comparable results on waning of VEi were obtained from an English study on contact testing. They found an initial significant reduction in transmission for BNT162b2 (aRR = 0·50) and ChAdOx1 (aRR = 0·76). These estimates were lower than those obtained for the index cases infected with Alpha and VEi was no longer significant after 12 weeks for ChAdOx1 and attenuated substantially for BNT162b2 [34].
4.1 Strengths and limitations
We offer detailed estimates of VEi/VEs/VET for four different vaccine brands obtained from systematic and repeated testing of HREC regardless of vaccination status and symptomatic state. We adjusted baseline infectiousness/susceptibility-, VE- and waning-estimates for personal characteristics. This detailed analysis was possible because we could include a large number of person-level observations into a multilevel model. The use of contact tracing data limited possible confounding by test seeking and contact behavior differences between vaccinated an unvaccinated persons. In addition, VEi/VEs-estimates avoid a confounding-bias between the vaccination statuses of the potential infector and at-risk person. An unadjusted VE-estimate will be a combination of the VEs-estimate of the at-risk person and the VEi-estimate of the (unidentified) infector. For example, some studies have reported faster waning of VE-estimates in older age groups [35], [36], [37], while others found no significant difference [11]. We did observe significantly faster waning of VEs in older age groups, but the faster decrease of VET for older age groups was mostly linked to the faster waning of VEi. The distinction between VEi and VEs also allows for a more detailed analysis of Delta’s transmissibility. Adjusted for vaccination, the odds of transmission compared to Alpha increased with 40%. While other studies have reported even larger increases [38], in our study this finding is accompanied by a large decrease in VEi against Delta.
We also investigated susceptibility and infectiousness for baseline, adjusted for vaccination, rates and found them to increase with age. Comparable observations, have been made from Belgian case data [preprint] [39] and internationally [31], [34], [38], [40]. While we accounted for some of the characteristics of the potential infector, we cannot exclude a remaining effect of within ‘index case’ clustering of HREC. We included a maximum of three HREC per index case to limit such an effect. Possible misclassification; e.g. a HREC infecting the index case or an unknown common infector is another limitation of this study.
Undetected infections remain a possible cause of, typically downward, biased VE-estimates. In our study the age group from 25 to 44 years reported both the lowest effect of vaccination and the slowest waning. This observation could be explained both by a larger relative (to other age groups) amount of undetected infections and/or age-specific immunological effects. In addition, even with 90% of HREC taking a first test, symptomatic HREC might still be more likely to get tested, biasing our VE-estimates against infection towards VE-estimates against symptomatic infection.
Our model did not allow for VOC-vaccine brand interactions or VOC-specific waning. The VOC had different effects on VEi and VEs, but effects were assumed equal for the included brands. We explored more complex models, but these exploratory analyses and the observation by Cromer et al. [41] that “whether immunity was acquired through infection or vaccination (and which vaccine was used) was not significantly associated with the loss of neutralization” (by VOC) indicated that this assumption was acceptable [42].
For hybrid immunity, we did not differentiate infection followed by vaccination from vaccination followed by infection. In addition, the time between vaccination and previous infection is discarded and only the time since the last immunity-conferring event (either vaccination or previous infection) is used. Likewise, we did not focus on the time between vaccination doses. We did not include corrections for underlying medical conditions or clinically vulnerable groups. We did not differentiate between severe and mild infections. VE-estimates against severe outcomes (hospitalization and deaths) are not included in this study. We excluded persons over 84 years. Only small number were available for this age group since we excluded persons who received booster vaccination and long-term care facilities have separate contact tracing systems. Our study period precedes significant circulation of Omicron.
5 Conclusion
We report significant VEi and VEs-estimates for both Alpha and Delta. Both increasing time since vaccination and Delta were associated with a decrease in VET-estimates. In addition, Delta increased baseline transmission. Infection-acquired immunity was less affected by Delta and, in combination with vaccination, showed slower waning compared to vaccine-induced immunity. VET-estimates were highest for hybrid immunity. We observed the fastest waning of VET in persons aged 65 to 84 years, mainly because the effect of vaccination on the infectiousness of breakthrough cases waned fastest in this age group.
Statements
Data sharing statement
The person-level contact tracing data will not be shared. General descriptive statistics from the contact tracing are available from https://covid-19.sciensano.be/nl/covid-19-epidemiologische-situatie. Code for the nimble-model is provided in supplementary materials.
Authors’ contributions
TB, KP, DvC, LuC and CWT have conceptualized the study. TB, KP, RB, PH, MB, AD, RM, EB, NH were responsible for data curation and investigation and did the formal data analysis. KP, TB, RB, MB, PH accessed and verified the data. TB, LaC, VS, JvL, TB, CWT prepared the initial draft of the manuscript. LuC, DvC, HvO, CWT oversaw the project. TB, LuC, RB, JvL, KP, LaC, HvO, VS, PH, MB, AD, RM, EB, NH, DvC, CWT reviewed and edited the manuscript.
The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Role of the funding source
This study was supported by the Belgian Federal and Regional Authorities through funding for the LINK-VACC project and organizing and financing of contact tracing. The funding source had no role in the study design, collection, analysis, interpretation, writing of the report or deciding to submit the paper.
Ethics committee approval
Data linkage and collection within the data-warehouse has been approved by the information security committee.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A Supplementary material
The following are the Supplementary data to this article:Supplementary data 1
Acknowledgements
We wish to dedicate this study to all persons who played a key role by providing and processing the data technically: especially physicians, clinical microbiology laboratories, and the colleagues of healthdata.be. Without their dedication and efforts this study could not have been conducted.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.vaccine.2022.04.025.
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PMC009xxxxxx/PMC9001251.txt |
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Cancer Cell
Cancer Cell
Cancer Cell
1535-6108
1878-3686
Elsevier Inc.
S1535-6108(22)00165-9
10.1016/j.ccell.2022.04.003
Letter
Enhanced SARS-CoV-2 breakthrough infections in patients with hematologic and solid cancers due to Omicron
Mair Maximilian J. 1
Mitterer Manfred 2
Gattinger Pia 3
Berger Julia M. 1
Trutschnig Wolfgang 4
Bathke Arne C. 4
Gansterer Margaretha 5
Berghoff Anna S. 1
Laengle Severin 1
Gottmann Lynn 1
Buratti Thomas 2
Haslacher Helmuth 6
Lamm Wolfgang W. 1
Raderer Markus 1
Tobudic Selma 7
Fuereder Thorsten 1
Valenta Rudolf 3
Fong Dominic 28
Preusser Matthias 18∗
1 Division of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria
2 Hemato-Oncological Day Hospital Unit, Franz Tappeiner Hospital, Meran/Merano, Italy
3 Division of Immunopathology, Department of Pathophysiology and Allergy Research, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
4 Department of Artificial Intelligence and Human Interfaces and Intelligent Data Analytics Lab Salzburg, University of Salzburg, Salzburg, Austria
5 Faculty of Management and Economics, University of Klagenfurt, Klagenfurt, Austria
6 Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
7 Division of Infectious Diseases and Tropical Medicine, Department of Medicine I, Medical University of Vienna, Vienna, Austria
∗ Corresponding author
8 These authors contributed equally
12 4 2022
9 5 2022
12 4 2022
40 5 444446
© 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.
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pmcMain text
Patients with cancer are at high risk for severe clinical courses of COVID-19 and delays of antineoplastic treatment due to SARS-CoV-2 infections (Pinato et al., 2022). SARS-CoV-2 vaccinations were shown to be quite effective and well tolerated according to post-authorization data and real-life studies of hemato-oncological cohorts (Corti et al., 2022). However, we (Mair et al., 2022a; 2022b) and others have shown that antibody levels in patients with cancer are lower than in healthy controls, and specific subgroups such as patients receiving B cell-targeting treatments exhibit particularly low seroconversion rates.
With the emergence of immune-evading variants of concern (VOC) such as Delta (B.1.617.2) and Omicron (B.1.1.529), vaccination efficacy against symptomatic infections is considerably impaired in the general population, although protection against severe courses and hospital admission seems to be maintained (Andrews et al., 2022; Collie et al., 2022). Also in patients with cancer, disease severity in Omicron-infected individuals appears to be lower than with previous virus variants (Lee et al., 2022). Therefore, precautionary measures are gradually being lifted due to increasing vaccination coverage and the seemingly lower pathogenicity of the Omicron VOC in the general population. Nevertheless, mild SARS-CoV-2 infections and subsequent quarantine measures may disrupt anticancer treatment and thereby potentially impact survival prognosis in these patients. Data on the impact of VOC on vaccination efficacy and neutralizing ability of vaccination-induced antibodies, particularly the Omicron variant, are scarce in patients with different types of cancer with and without systemic treatment (Fendler et al., 2022).
Here, we analyzed the time course of the occurrence of SARS-CoV-2 infections in a large cohort of patients with cancer in Austria and Italy throughout the pandemic (Supplemental information). In total, 3,959 patients were included, of whom 3,036/3,959 (76.7%) had been diagnosed with a solid tumor and 923/3,959 (23.2%) with a hematologic malignancy. Of note, 2,737/3,959 (69.1%) did not undergo systemic antineoplastic treatment at the time of vaccination. Between February 24, 2020, and database lock (February 28, 2022), 950/3,959 (24.0%) patients had been infected with SARS-CoV-2. Moreover, 3,368/3,959 (85.1%) patients had received at least one vaccination dose, whereas 588/3,959 (14.9%) were unvaccinated. Baseline characteristics are shown in Table S1.
The weekly numbers of SARS-CoV-2 infections and COVID-associated hospitalizations according to vaccination status over time are illustrated in Figure S1A. With the emergence of the Delta VOC, 54/125 (43.2%) infected patients had been previously vaccinated. However, breakthrough infections were more common during the subsequent Omicron wave (204/289, 70.6%; odds ratio [OR]: 3.15, 95% confidence interval (CI): 1.99–4.99; p < 0.001, Fisher exact test, Figure S1B). Among all infected patients, breakthrough infections during the Delta and Omicron waves were more frequent in patients with cancer who were undergoing systemic antineoplastic treatment (79/95, 83.2%) as compared to patients without ongoing anticancer therapy (179/319, 56.1%, OR: 3.85, 95% CI 2.12–7.39; p < 0.001, Fisher exact test, Figure S1C), indicating a particularly impaired vaccination-induced immunity against VOCs in patients receiving systemic antineoplastic agents.
In addition, we observed that hospital admissions were less common during the Omicron wave than during the Delta wave, irrespective of vaccination status (Figure S1A). Vaccinated patients had a tendency for shorter hospital stays (median/range: 15 [1–41] days) than unvaccinated patients (median/range: 9 [1–79] days; p = 0.126, Mann-Whitney-U test, Figure S1D), suggesting a retained protection against severe COVID-19 in vaccinated individuals. In line, only 1/11 (9.1%) patients requiring intensive care unit (ICU) admission was attributable to a breakthrough infection.
To gain deeper insights underlying the higher rate of breakthrough infections due to Omicron compared to Delta, we investigated humoral immunity after SARS-CoV-2 vaccination against VOCs. In particular, we measured levels of antibodies specific for the receptor-binding domain (RBD) on the SARS-CoV-2 spike protein of VOCs and their ability to inhibit the interaction of RBD with the human angiotensin-converting enzyme 2 (ACE2) receptor in a subgroup of patients with cancer undergoing antineoplastic treatment (Gattinger et al., 2022) (Supplemental information).
In total, 78 patients (28 with solid tumors, 26 with hematologic malignancies receiving B cell-targeted treatments, and 24 with hematologic malignancies receiving other therapy) and 25 healthcare workers (HCWs) as controls were included (Table S1). With regard to total anti-spike (S) protein IgG levels, there were significant differences between cohorts (p = 0.009, Kruskal-Wallis test, Figure S2A). Anti-S IgG levels were higher in HCWs (median optical density [OD]: 1.917, range: 1.513–2.793) than in patients with solid tumors (median OD: 1.787, range: 0.957–2.474, uncorrected p = 0.036) or hematologic malignancies receiving B cell-targeted agents (median OD: 1.750, range: 0.061–2.475, p = 0.014).
Differences between groups were more accentuated for RBD-specific antibodies. Antibody levels to RBD of wild-type (hu-1) were lowest in patients receiving B cell-targeted agents (median OD: 0.435, range: 0.058–2.435), followed by hematologic malignancies not receiving B cell targeted agents (median OD: 1.185, range: 0.123–2.441), solid tumors (median OD: 1.244, range: 0.088–2.406), and HCWs (median OD: 2.070, range: 0.442–2.883; p < 0.001, Kruskal-Wallis test, Figure S2B). Similar results were seen for RBD-Delta (p < 0.001, Figure S2C) and RBD-Omicron levels (p < 0.001, Figure S2D). Multivariate non-parametric analysis for RBD levels between groups confirmed these findings (p < 0.001), with significant differences (p < 0.05) for each VOC-specific RBD individually. Corrected pairwise comparisons between cohorts showed significant differences (p < 0.05) for each pair except between patients with hematologic malignancies without B cell-targeted treatment and patients with solid tumors. Of note, RBD-specific antibody levels numerically decreased from hu-1 to Delta and Omicron in all cohorts.
In addition, we performed molecular interaction assays to measure the inhibition of RBD-ACE2 binding by patients’ sera. Most patients with solid tumors and hematologic malignancies without B cell-targeted treatment exhibited inhibition of RBD-ACE2 binding of more than 50% for hu-1 (median inhibition solid tumors: 98.5%, range: 15.7–100.6%; median hematologic malignancies: 91.8%, range: 22.7–100.6%; Figure S2E) and Delta VOC (median inhibition solid tumors: 94.4%, range: −13.7–100.6%; median hematologic malignancies: 59.2%, range: −49.7–100.8%, Figure S2F). In contrast, patients receiving anti-B cell treatment showed considerably lower values (median inhibition hu-1: 21.7%, range: −11.0–101.4%; median inhibition Delta: 13.1%, range: −48.4–100.9%). Of note, inhibition of RBD-ACE2 binding was markedly impaired for the Omicron variant in patients with solid tumors (median inhibition: 16.6%, range: −9.5–94.6%) as well as hematologic malignancies receiving B cell-targeting agents (median inhibition −1.07%, range: −62.6–81.0%) or other treatments (median inhibition: 5.6%, range: −43.6–99.2%), while HCWs as controls had considerably higher values (median inhibition: 79.4%, range: −1.7–99.8%; p < 0.001, Kruskal-Wallis test, Figure S2G) than the other groups. However, inhibition of the Omicron RBD-ACE2 interaction was considerably lower than for RBD of hu-1 and Delta. Multivariate non-parametric analysis for RBD-ACE2 inhibition levels between groups confirmed these findings (p < 0.001), with significant differences (p < 0.05) for each VOC-specific RBD individually. Corrected pairwise comparisons between cohorts showed significant differences (p < 0.05) for each pair except between patients with hematologic malignancies without B cell-targeted treatment and patients with solid tumors, as well as between both hematologic patient cohorts. Again, RBD-ACE2 binding inhibition decreased from hu-1 to Delta and Omicron within all cohorts.
Our data provide a comprehensive overview on SARS-CoV-2 infections and hospitalizations during the last Delta and Omicron waves of the pandemic in patients with different forms of cancer with and without treatment. We observed an increase of SARS-CoV-2 breakthrough infections with the occurrence of the Omicron variant as compared to the Delta wave, whereas hospital admissions decreased. However, as in the general population, our data do not allow conclusions on whether reduced hospitalization rates in patients with cancer were attributable to the seemingly lower pathogenicity of the Omicron variant or increasing infection and/or reduced vaccination coverage.
We found immunological evidence of highly impaired vaccine-induced neutralization of the Omicron variant, but not against the wild-type hu-1 strain and the previous Delta VOC in patients with hematologic and solid cancers in comparison to HCW. Indeed, comprehensive data on the neutralizing ability of vaccination-induced antibodies against VOCs are rare in hemato-oncologic patients, in particular concerning the Omicron variant (Fendler et al., 2022). While previous studies investigating prior virus variants showed impaired humoral responses mainly in the subpopulation of patients with hematologic malignancies receiving B cell-depleting therapies (Obeid et al., 2022), our immunological data indicate virtually lacking humoral vaccine response against the most recent Omicron VOC across a much broader population of patients with cancer.
Our study has some limitations, including the retrospective design, which is inherently linked to heterogeneity, as well as missing data. Whereas the number of received vaccinations was known in all patients, missing information on vaccination dates impeded further testing on time-dependent risks for infection and hospitalization. In addition, we only measured neutralizing RBD antibody levels and their inhibitory capacity on RBD-ACE2 binding, whereas immunity against VOCs may be based also on T cell-associated immunity and other factors.
In conclusion, the increasing rates of breakthrough infections and hospital admissions of vaccinated cancer patients associated with SARS-CoV-2 VOC highlight the need for further protective measures, not only for effective control of the ongoing pandemic but also to prepare for the potential emergence of further immune-evading VOCs. The high rates of breakthrough infections in patients with hematologic and solid cancers documented in our study highlight the unresolved pandemic-related challenges in oncology, including a reduction and delay of routine care. In addition, adapted VOC-specific vaccines (Gattinger et al., 2022) might be needed to protect hemato-oncological patients and maintain cancer care during the ongoing pandemic.
Supplemental information
Document S1. Figures S1 and S2, supplemental methods, and supplemental references
Table S1. Baseline characteristics of the included cohorts
Document S2. Article plus supplemental information
Acknowledgments
The authors thank Zoltan Vass for support with blood sampling. Author contributions: Study design and its implementation: M.J.M., M.M., P.G., J.M.B., W.T., A.C.B., M.G., A.S.B., S.L., L.G., T.B., H.H., W.W.L., M.R., S.T., T.F., R.V., D.F., and M.P.; Data analysis and interpretation: M.J.M., M.M., P.G., W.T., A.C.B., R.V., and M.P.; Manuscript writing and editing: M.J.M., P.G., W.T., A.C.B., R.V., and M.P.. All authors read and approved the final version of the manuscript. D.F. and M.P. contributed equally and are co-last authors. M.J.M. and M.P. had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Funding/Role of funder statement: This study was funded by the research budget of the 10.13039/501100005788 Medical University of Vienna , the budget of the Südtiroler Sanitätsbetrieb and partly by a grant from the Federal State of Lower Austria, Grant: Danube Allergy Research Cluster (Danube ARC, R.V.). The funding organizations had no role/influence in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Declaration of interests
A.S.B. has received research support from Daiichi Sankyo and Roche; honoraria for lectures; consultation or advisory board participation from Roche, Bristol-Meyers Squibb, Merck, and Daiichi Sankyo; and travel support from Roche, Amgen, and AbbVie. T.F. has received honoraria for lectures and consultation or advisory board participation from the for-profit companies Merck Sharp & Dohme (MSD), Merck Darmstadt, Roche, Bristol-Myers Squibb, Accord, Sanofi, and Boehringer Ingelheim as well as travel support from Roche, MSD, and Bristol-Myers Squibb. The following for-profit companies have supported clinical trials and contracted research conducted by T.F. with payments made to his institution: MSD, 10.13039/100004334 Merck Darmstadt, Bristol-Myers Squibb. R.V. has received research grants from HVD Life-Sciences, Vienna, Austria; WORG Pharmaceuticals, Hangzhou, China; and Viravaxx AG, Vienna, Austria. He serves as consultant for Viravaxx AG and WORG. M.P. has received honoraria for lectures and consultation or advisory board participation from the following for-profit companies: Bayer, Bristol-Myers Squibb, Novartis, Gerson Lehrman Group (GLG), CMC Contrast, GlaxoSmithKline, Mundipharma, Roche, BMJ Journals, MedMedia, Astra Zeneca, AbbVie, Lilly, Medahead, Daiichi Sankyo, Sanofi, Merck Sharp & Dome, and Tocagen. The following for-profit companies have supported clinical trials and contracted research conducted by M.P. with payments made to his institution: Boehringer-Ingelheim, 10.13039/100002491 Bristol-Myers Squibb , 10.13039/100004337 Roche , Daiichi Sankyo, 10.13039/100004334 Merck Sharp & Dome, Novocure, 10.13039/100004330 GlaxoSmithKline , and 10.13039/100006483 AbbVie . All other authors declare that they have no conflict of interest related to the present study.
Supplemental information can be found online at https://doi.org/10.1016/j.ccell.2022.04.003.
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PMC009xxxxxx/PMC9001745.txt |
==== Front
9607835
20545
Mol Psychiatry
Mol Psychiatry
Molecular psychiatry
1359-4184
1476-5578
34642460
10.1038/s41380-021-01331-7
nihpa1744562
Article
A multimodal study of a first episode psychosis cohort: potential markers of antipsychotic treatment resistance
Yang Kun PhD a#
Longo Luisa MD a&#
Narita Zui MD, PhD a
Cascella Nicola MD a
Nucifora Frederick C. Jr. DO, PhD a
Coughlin Jennifer M. MD a
Nestadt Gerald MD a
Sedlak Thomas W. MD, PhD a
Mihaljevic Marina MD, PhD a
Wang Min PhD e@
Kenkare Anshel BS a
Nagpal Anisha BS g
Sethi Mehk BS h
Kelly Alexandra BS a
Di Carlo Pasquale MD i&
Kamath Vidyulata PhD a
Faria Andreia MD, PhD e
Barker Peter DPhil ej
Sawa Akira MD, PhD abcdf*
a Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
b Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
c Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
d Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
e Departments of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
f Department of Mental Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA
g Department of Public Health Studies, Johns Hopkins University Zanvyl Krieger School of Arts and Sciences, Baltimore, MD 21218, USA
h Department of Applied Math and Statistics, Johns Hopkins University Whiting School of Engineering, Baltimore, MD 21218, USA
i Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
j F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA
& Current address: Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
@ Current address: College of Biomedical Engineering and Instrument Science, Zhejiang University, China
Contributors
The current research was designed by AS. The analytic pipeline was designed by KY. The data was analyzed by KY, LL, ZN, AN, MS, and PDC. Data analysis and interpretation regarding clinical scales were assisted by NC, and MM. Data analysis and interpretation regarding smell test were assisted by VK. Data analysis and interpretation regarding 7T MRS data were assisted by MW and PB. Data analysis and interpretation regarding brain volume data were assisted by AF. Study participants were recruited and/or interviewed by NC, FCN, JMC, GN, TWS, AK (Kenkare), and AK (Kelly). The manuscript was drafted by KY, LL, and AS. All authors contributed to the discussion of the results and have approved the final manuscript to be published.
# These authors contributed equally to this work
* Corresponding Author: Akira Sawa, M.D., Ph.D., 600 N. Wolfe Street, Meyer 3-166, Baltimore, MD 21287, Tel: 410-955-4726; Fax: 410-614-1792; asawa1@jhmi.edu
12 10 2021
2 2022
12 10 2021
02 5 2022
27 2 11841191
Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms
Treatment resistant (TR) psychosis is considered to be a significant cause of disability and functional impairment. Numerous efforts have been made to identify the clinical predictors of TR. However, the exploration of molecular and biological markers is still at an early stage. To understand the TR condition and identify potential molecular and biological markers, we analyzed demographic information, clinical data, structural brain imaging data, and molecular brain imaging data in 7 Tesla magnetic resonance spectroscopy, from a first episode psychosis cohort that includes 136 patients. Age, gender, race, smoking status, duration of illness, and antipsychotic dosages were controlled in the analyses. We found that TR patients had a younger age at onset, more hospitalizations, more severe negative symptoms, a reduction in the volumes of the hippocampus (HP) and superior frontal gyrus (SFG), and a reduction in glutathione (GSH) levels in the anterior cingulate cortex (ACC), when compared to non-TR patients. The combination of multiple markers provided a better classification between TR and non-TR patients compared to any individual marker. Our study shows that ACC GSH, HP and SFG volumes, and age at onset could potentially be biomarkers for TR diagnosis, while hospitalization and negative symptoms could be used to evaluate the progression of the disease. Multimodal cohorts are essential in obtaining a comprehensive understanding of brain disorders.
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pmcIntroduction
Psychotic disorder is one of the most debilitating mental conditions, and its disease trajectory is heterogenous [1]. A significant proportion of patients with psychosis continue to have symptoms and poor outcomes despite treatment [2–4]. Clozapine is the only evidence-based medicine for treatment resistant (TR) cases [2], a possible subset of schizophrenia. Clozapine can provide a significant benefit to a substantial portion of TR patients [5]. Nevertheless, due to severe adverse effects [2, 6], clozapine use is still limited in many countries [2]. Paradoxically, it is also known that delayed initiation of clozapine is related to poor response to this medication in TR patients [7, 8]. Thus, identification of patients that are TR, or have susceptibility to TR, at the first clinical visit is very important in order to potentially start clozapine as early as possible. To achieve this goal, it is imperative to establish predictive markers for TR.
In the past decades, multiple groups have put forth major efforts in identifying markers associated with TR. In these studies, the criteria for TR are relatively consistent but also vary depending on the goal of the study. The core criteria for TR patients includes unsuccessful use of two or more non-clozapine antipsychotics and/or the use of clozapine in their past history [2–4, 9, 10]. Thus far, these studies that compare between TR patients and non-TR patients have mainly focused on clinical and demographic markers, and reported that TR patients tend to have a longer duration of untreated psychosis (DUP), more severe negative symptoms, younger age of onset, and poor pre-morbid functioning [4, 11–13]. In addition, some studies have also pointed out severer cognitive deficits in TR patients compared with non-TR patients [14, 15].
Meanwhile, some studies have assessed treatment response during a short interval (for example, 12 weeks) to test whether candidate biological markers at both molecular and anatomy/circuitry levels are associated with treatment response [16–26]. Through such a candidate target approach, increased level of the sum of glutamate and glutamine (Glx) in the anterior cingulate cortex (ACC) estimated through 3-Tesla magnetic resonance spectroscopy (3T-MRS), was reportedly associated with persistent psychotic symptoms despite antipsychotic treatment [18–20]. Whereas, elevated striatal dopamine synthesis capacity was observed in treatment responders measured by positron emission tomography (PET) [21–23]. One group hypothesized that patients with higher glutathione (GSH) levels in the ACC would demonstrate a shorter time to treatment response, and this hypothesis was proven through a 7T-MRS study [24]. Furthermore, other studies have indicated cortical folding defects, white matter regional vulnerability [25], lower cortisol awakening response, and higher interleukin (IL)-6 and IFN-γ for poor treatment response [16, 17].
In the present study, we used a first episode psychosis (FEP) cohort with multimodal datasets, including demographic and clinical information as well as structural and molecular brain imaging data [27–31]. Olfactory functions were also evaluated in this study, since olfactory deficits frequently accompany FEP and may have a predictive value for disease severity and poor prognosis [28, 32, 33]. The goal of this study is to identify markers that can differentiate TR from non-TR patients, in which TR is defined based on medication history [2–4, 9, 11–15], rather than treatment response [16–24]. By taking advantage of multimodal datasets, rather than single layer data, we aimed to obtain a comprehensive landscape associated with TR in psychosis.
Subjects and Methods
Study participants
This study was approved by the Johns Hopkins Medicine Institutional Review Board. All study participants provided written informed consent. Initially, 138 patients were recruited from within and outside of the Johns Hopkins Hospital. The study psychiatrists did not make treatment decisions regarding medications. The inclusion criteria for all study participants were: 1) between 13 and 35 years old; 2) no history of traumatic brain injury, cancer, abnormal bleeding, viral infection, neurologic disorder, or mental retardation; 3) no drug or alcohol abuse (not including cannabis or synthetic cannabinoid receptor agonists) in the past three years; 4) no illicit drug use in the past two months. The inclusion criterion for patients was: at the first visit (baseline), patients must be within 24 months of the onset of psychotic manifestations as assessed by study team psychiatrists using the Structured Clinical Interview for DSM-IV (SCID) and collateral information from available medical records. In the present study, we refer to these patients as FEP patients to differentiate this cohort from recent-onset psychosis cohorts in which patients with a longer duration of illness (e.g., with onset within 5 years) are usually included [34, 35]. We acknowledge that a 2-year-window is a relaxed definition of FEP. Nevertheless, this definition has been used in many published studies and meta-data analyses [36–39]. The majority of patients, except 6 patients, were already medicated at their first (baseline) visit. The 6 medication-naive patients at the baseline started medication during the follow-up period. Five patients were initially diagnosed as substance-induced psychotic disorder because of the use of cannabis or synthetic cannabinoid receptor agonists. However, 2 out of 5 have been later diagnosed as bipolar disorder with psychotic features, and 1 out of 5 as schizophrenia. The rest of two subjects were excluded from downstream analysis. Taken together, the number of patients used for the entire analyses was 136: schizophrenia (n=73), schizoaffective disorder (n = 14), schizophreniform disorder (n = 3), bipolar disorder with psychotic features (n = 29), major depressive disorder with psychotic features (n = 9), brief psychotic disorder (n=2), not otherwise specified psychotic disorder (n=6). Healthy controls (n=115) were also recruited but not used in the present study. After the enrollment (first visit), the study participants were followed up to 4 years.
Symptomatic and neurocognitive assessment
We used the Scale for the Assessment of Negative Symptoms (SANS) and the Scale for the Assessment of Positive Symptoms (SAPS) to evaluate the presence and severity of negative and positive symptoms respectively [40]. The global and total scores collected at the first (baseline) visit were used in the data analysis. In addition, we used a comprehensive neuropsychological battery we previously developed and have used in multiple publications [27, 29, 41–43]. In brief, we obtained cognitive scores scaled in normally distributed standardized units that covered 6 domains: 1) processing speed (calculated from the combined scores of the Grooved Pegboard test and the Salthouse test); 2) attention/working memory (Digit Span and Brief Attention Memory test); 3) verbal learning and memory (Hopkins Verbal Learning test); 4) visual learning and memory (Brief Visuospatial Memory test); 5) ideational fluency (Ideational Fluency assessment for Word Fluency and Acceptable Designs); and 6) executive functioning (Modified Wisconsin Card Sorting test). These 6 cognitive scores and their average (referred to as the composite score) obtained at the first (baseline) visit were used in the analysis.
Smell Test
The olfactory functioning of study participants was tested. Specifically, subjects were first administered the Sniffin’ Sticks Odor Identification and Discrimination test to evaluate the ability to identify and discriminate 16 different types of odors. Next, participants were administered 2 odor detection threshold tasks utilizing lyral and citralva as active odorants in a counterbalanced order. The task followed a single reversing staircase, forced-choice format in which individuals were presented 2 vials, one with mineral oil and one containing the active odorant diluted in mineral oil. A total detection threshold score was created that reflected the weakest odor concentration reliably identified as stronger than mineral oil. The detailed procedures were described in our previous publications [28, 31]. In the present study, the data obtained at the study participant’s first visit (baseline) was used for analysis.
7T-MRS
Participants were scanned using a 7T scanner (Philips ‘Achieva’, Best, The Netherlands) equipped with a 32-channel head coil (Nova Medical, Wilmington, MA). The ‘LCModel’ software package (Version 6.3–0D) was used to analyze the spectra. Metabolite levels were calculated relative to the water or tCr (total creatine) signal from the same voxel and expressed in institutional units (IU, approximately millimolar) [44]. The detailed protocol for acquiring and processing MRS scans has been described previously[27]. In the present study, we used the data obtained at the first visit (baseline) for glutamate (Glu), gamma-aminobutyric acid (GABA), N-acteylaspartate (NAA), and glutathione (GSH) levels in 5 brain regions: the anterior cingulate cortex (ACC), semiovale (CSO), dorsolateral prefrontal cortex (DLPFC), centrum orbitofrontal cortex (OFR), and thalamus (Thal).
3T structural brain imaging: brain volume
T1 high-resolution-weighted images (T1-WI) were obtained from a 3T scanner with the following parameters: sagittal orientation, original matrix 170×170, 256 slices, voxel size 1×1×1.2 mm, TR/TE 6700/3.1 ms. The images were automatically segmented and post-processed through the MRICloud [45] (www.MRICloud.org), as described previously [29]. Briefly, in MRICloud the images were processed through the following steps: 1) orientation and homogeneity correction; 2) two-level brain segmentation (skull-stripping, then whole brain); 3) image mapping based on a sequence of linear, non-linear algorithms, and Large Deformation Diffeomorphic Mapping (LDDMM); 4) multi-atlas labeling fusion (MALF), adjusted by PICSL [46]. In this study, we used the multi-atlas set “Adult22_50yrs_26atlases_M2_V9B” that matched with the demography of our population, through which we collected and analyzed the volumes of 136 brain regions, as defined in the parcellation “level 4” which covered the whole brain (Table S1) [47].
TR patients
Patients who had previously taken more than two non-clozapine antipsychotics and/or were currently taking clozapine were classified as TR. Specifically, we collected medication records and clinical interview notes from the patients’ attending physicians prior to the baseline visit and during the follow-up period (up-to-4-years). Experienced psychiatrists (N.C. and L.L.) carefully reviewed these records to determine how many antipsychotics each patient had tried because of poor response to a medication. If a patient switched antipsychotics due to adverse effects, non-adherence, or other reasons that were independent of his/her response to the medication, the individual was not considered TR.
Among 136 patients for the entire analyses, 32 satisfied the TR criteria. Eighteen had been TR before their first (baseline) visit and fourteen patients became TR after their first (baseline) visit. The length of follow-up varied among patients. The maximal length of follow-up from disease onset in TR patients was 39 months. In the non-TR group, 69 patients were followed longer than 39 months after disease onset. Some of the remaining 35 patients, though we expect the number to be minor, may turn out to be TR once they are followed longer than 39 months after disease onset. Given this potential caveat, we conducted the analysis using datasets from 32 TR patients and 69 non-TR patients who were followed for at least 39 months after disease onset.
In addition, we performed an analysis for 32 TR patients and 104 non-TR patients (all non-TR patients) and disclosed the results in the supplementary document.
Statistical analysis
R 3.5.1 was used to perform statistical analysis. Group comparisons of demographic and clinical data were calculated using Welch’s t-test for continuous variables, and a Chi-square test for categorical data. General linear regression with age, gender, race, smoking status, chlorpromazine (CPZ) equivalent dose estimated by the Defined Daily Doses (DDDs) method [48], and duration of illness (DOI) at baseline as covariates was performed when global and total SANS/SAPS, neuropsychological data, olfactory functions, brain volumes, and metabolite levels from 7T MRS were compared between TR patients and non-TR patients. When we assessed neuropsychological data, we also included education as a covariate due to its influence on a subject’s capabilities in interpretation and execution of neuropsychological tests [42]. In the assessment of brain volumes, we also considered handedness in addition to the basic set of confounding factors described above to account for the effects of laterality on brain volumes. A permutation test was performed to evaluate statistical significance. The Benjamini-Hochberg (BH) procedure was used for multiple comparison correction. P values corrected with the BH procedure are presented as q values. The analyte was considered significant if its q value was smaller than 0.05. Finally, we constructed general linear regression models to evaluate the classification performance of individual markers as well as the combination of multiple markers. Leave-one-out cross-validation receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate performance. The 95% confidence interval of AUC was calculated. Additionally, Akaike information criterion (AIC) was calculated to compare models.
Results
Characteristics of the cohort
In the present study, we examined 136 FEP patients, including 32 TR and 104 non-TR patients, but did not use 115 healthy controls for the analysis (see Method section). Using baseline data collected at the first visit, we found that TR patients are younger [mean (TR) = 20.53; mean (non-TR) = 22.78; p value = 4.96E-03], have fewer years of education [mean (TR) = 12.13; mean (non-TR) = 13.26; p value = 0.03], and are less employed (TR = 29.03%; non-TR = 53.13%; p value = 0.03) than non-TR patients (excluding those with short follow-ups) (Table S2A). There were no significant differences in gender, race, psychiatric family history, or smoking status. The analysis between TR and all non-TR patients, including those with short follow-ups (see Method section for more detail), led to the consistent conclusion with significant differences in age, education, and employment (Table S2B).
Analysis of clinical data between TR and non-TR patients
Next, we compared the clinical characteristics between TR and non-TR patients (excluding those with short follow-ups). As summarized in Table 1A, TR patients were found to be younger at disease onset and have more hospitalizations. DOI and antipsychotic dose was not significantly different between TR and non-TR patients. Next, we compared the SANS/SAPS global and total test scores (Table 1B). TR patients had a significantly higher SANS total score and global score in avolition (q-value < 0.05), while no significant differences in either the SAPS total or global scores were observed between TR and non-TR patients. Furthermore, we compared the neuropsychological composite score and sub-domain scores and didn’t observe any significant differences between TR and non-TR patients (Table 1C). Similar findings were obtained via comparisons between TR and all non-TR patients (including those with short follow-ups) (Table S3A, B, C).
Analysis of olfactory test scores between TR and non-TR patients
Previously, our group reported that FEP patients have significantly different odor identification, discrimination, and detection sensitivities of lyral and citralva compared to healthy controls [28]. Thus, we tested whether smell function is different between TR and non-TR patients. We didn’t find significant differences in these olfactory test scores between TR and non-TR patients including or excluding those with short follow-ups (Table 1D, S3D).
Analysis of brain volume between TR and non-TR patients
Changes in brain volume have been observed in patients with psychosis compared to controls [49–51]. Thus, we wondered whether TR patients have more prominent anatomical changes compared with non-TR patients, and conducted the comparison based on the segmentation in the MRICloud [45]. In this analysis, no regions met a stringent cutoff of q-values smaller than 0.05. Thus, to select promising brain regions, nevertheless, reasonably controlling type I error, we performed a permutation test and introduced a relaxed cutoff (group comparison p-value < 0.05 and permutation test p-value < 0.05). Under this relaxed cutoff, 10 out of 136 regions of study were different in the volume between TR patients and non-TR patients (excluding those with short follow-ups) (Table 2), whereas 3 regions showed a difference in the volume between TR patients and all non-TR patients (including those with short follow-ups) (Table S4). The right hippocampus (HP), left superior frontal gyrus (SFG), and left gyrus rectus (GR) were different between TR patients and non-TR patients in both comparisons (TR vs. non-TR, excluding or including non-TR patients with short follow-ups).
Analysis of 7T-MRS data between TR and non-TR patients
We previously studied 5 distinct brain regions (ACC, DLPFC, OFR, CSO, and Thal), and reported that FEP patients have significantly different levels of Glu, GABA, NAA, and GSH, compared with healthy controls [27]. With the hypothesis that some of these brain metabolites may be associated with TR, in the present study, we compared these metabolites in all 5 brain regions between TR and non-TR patients. We found that GSH in the ACC was the only significant metabolite (water reference: q-value = 0.03): GSH levels in the other brain regions and the other metabolite levels in all 5 brain regions were not significantly different between TR and non-TR patients (excluding non-TR patients with short follow-ups) (Figure 1, Table S5A, B). Similar findings were obtained via comparison between TR and all non-TR patients (including non-TR patients with short follow-ups) (Table S5C, D).
Influence of clozapine within TR patient group.
Nine of 32 TR patients in our cohort took clozapine at the first visit when all biological and other assessments were made. To test whether the use of clozapine affected the outcome measures in the present study, we compared TR patients who took clozapine with TR patients who didn’t take clozapine at the first visit. We did not observe any differences in any variables between these two groups, which include age of onset, number of hospitalizations, negative symptoms, 7T MRS data, and brain volumes (Tables S6, S7, S8).
Multimodal data for TR and non-TR classification
One unique aspect of the present study is that multimodal data was collected from this FEP cohort, which gives us an opportunity to integrate different types of data to obtain a comprehensive view for TR. Thus far, at least from each individual modality, we observed that TR and non-TR groups are different in age of onset, number of hospitalizations, negative symptoms, the level of GSH in the ACC (ACC-GSH), and the volume of specific brain regions (Table 3).
We next asked whether multimodal datasets could provide a better understanding of TR that could differentiate TR patients from non-TR patients. Markers that differentiate these two groups may include state indicators that change over time. On the other hand, markers whose values are stable over the disease course or at least in early disease stages are useful to predict the risk or biological vulnerability to TR. Thus, in the present study, we paid attention to stable markers for TR (markers that were shared among patients who met the TR criteria before and after the baseline visit) rather than signatures associated with the TR state. Under such premise, we used classification models with stable markers to evaluate the performance of individual markers as well as the combination of them. Our previous longitudinal assessment for the same cohort used in the present study showed that the ACC-GSH levels were stable in patients between 15–35 years of age [52]. Using the same analytic pipeline that we used to assess longitudinal changes in the ACC-GSH level [52], we found that the right HP volume and the left SFG volume were also stable in FEP patients (Table S9: see also details in the supplementary methods). Additionally, age of onset won’t change over time. The SANS total score and the number of hospitalizations were not considered as stable markers under the cutoff (q-value > 0.3) that we defined in a conservative manner. Altogether, we identified 4 stable markers (ACC-GSH, right HP volume, left SFG volume, and age of onset), which were further evaluated using classification models.
Interestingly, using TR patients and non-TR patients excluding those with short follow-ups, we found that the integration of markers could largely improve the classification performance with an AUC of 0.83, while the AUCs for age of onset, ACC-GSH, right HP, and left SFG were 0.64, 0.69, 0.60, and 0.55, respectively (Figure 2, Table S10A). The AICs for models using age of onset, ACC-GSH, right HP, left SFG, and all the four markers are 69.06, 67.49, 71.26, 72.64, and 51.92, respectively. Similarly, analysis using TR and all non-TR patients (including those with short follow-ups) found that the combination of multimodal markers outperformed individual markers (Figure S1, Table S10B). Furthermore, when we exclude right HP and left SFG, which were included with a less stringent cutoff, and only used age of onset and ACC-GSH for the multimodal assessment, we obtained an AUC of 0.75 (excluding non-TR patients with short follow-ups), which was still better than the performance of individual markers (age of onset 0.64, ACC-GSH 0.69, as described above). Together, these results imply that multimodal datasets could provide complementary or non-redundant information to reach a better understanding of TR. Without a single factor highly associated with TR, a combination of biomarkers may complement the small effect size of individual factors and help us to identify TR patients at an early stage.
Discussion
We have established a FEP cohort with multimodal data collection. The percentage of TR patients was consistent with previous findings that about 20–30% of patients develop TR [2–4, 53]. We confirmed that younger age of onset, more hospitalizations, and severer negative symptoms were associated with TR patients when compared with non-TR patients, which is also consistent with previous reports from multiple groups [2–4]. Notably, we showed that GSH was selectively reduced among several key metabolites such as Glu, GABA, and NAA in the ACC in TR patients, when compared with non-TR patients. Furthermore, the difference in GSH between the groups was observed only in the ACC, but not in any other brain regions (DLPFC, Thal, CSO, OFR) examined in the present study. Interestingly, our previous longitudinal study found that the levels of ACC-GSH were stable over time [52]. In addition, by using an unbiased approach using MRICloud [45], we observed differences in volumes in some brain regions between TR and non-TR patients. Furthermore, the volumes of these brain regions (e.g., right HP and the left SFG) were stable over time. At last, we showed that clozapine use did not affect outcome measures in the present study within our cohort.
One unique aspect of this study is that multimodal data was obtained from the same subjects. By taking advantage of this, we constructed classification models with multimodal data that were stable in the trajectory of early disease phases, and demonstrated that a combination of multiple markers led to much better classification performance than individual markers. This suggests that using multiple markers may overcome the issue of small effect sizes of individual markers and identify TR patients at early stages. The performance of the classification models in the present study may not directly impact clinical practice, nevertheless, the goal of this study is to explore the direction of early diagnosis and intervention for TR. Diagnostic strategies using multimodal data have achieved success in the clinical practice of many diseases. For example, a variety of invasive and non-invasive tests including physiological, imaging, and molecular markers are routinely used for the diagnosis of cardiovascular disease [54]. With additional molecular markers from future studies at the epigenetic, gene, or protein levels, we believe that a clinical-grade diagnostic tool employing multimodal data could be developed for TR.
In this study, we used medication records to define TR. Instead of closely tracking the treatment response to antipsychotics during a short interval (such as half to one year), we chose this approach because medication records can be easily collected. Furthermore, this strategy is easily expanded globally beyond potential differences involving hospitalization that tends to be affected by social systems. For example, discoveries from this study can be further validated by population studies or consortia research like ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) [55]. Nevertheless, it is important that study psychiatrists carefully determine that the switches of antipsychotics are not because of adverse effects, non-adherence, or for other unrelated reasons. Our biological observations that were made in a comprehensive manner (semi-unbiased or unbiased approaches) are compatible with the biological changes that were reported in a candidate target approach for antipsychotic treatment response [24]. These observations suggest that, although study designs based on antipsychotic treatment response for a short period and those that stand on medication records are somewhat disconnected in the overall TR research field, both of them have room to be interpreted in an integrative and comprehensive manner.
In the present study, we observed that TR patients had a significantly higher SANS total score and global score in avolition, whereas no significant differences in either the SAPS total or global scores were observed between TR and non-TR patients. Given that TR is defined as poor response to antipsychotics in regard to psychosis, these results may sound paradoxical. Nevertheless, thus far, more than one meta-data analysis has consistently reported that negative symptoms rather than positive symptoms were significantly different between TR and non-TR groups [56, 57].
A reduction in the HP volume or a decrease in the SFG volume have been reported in patients with psychotic disorder [58–61]. Here, we observed a volume reduction in both the HP and SFG in TR patients compared with non-TR patients, making the observation more specific. Furthermore, we also observed a significant reduction in the level of GSH in the ACC. GSH is a physiological reservoir for Glu through the GSH cycle [62], and a reduction in the ACC GSH level, as shown in the present study, implies not only redox imbalance but also glutamatergic neurotransmission imbalance [63] involving the ACC in TR patients. The brain regions underscored in the present study mediate multiple higher brain functions, including associative learning and memory, in which glutamatergic and non-dopaminergic neurotransmission play important roles [64, 65]. This may be one of the reasons why D2-targeted antipsychotics, regardless of first or second generation, may not work for TR patients. We previously reported gray-matter abnormalities in deficit schizophrenia, including major changes in the SFG and ACC [66]. A recent report pointed out the connectivity between the HP and ACC as a potential marker to predict treatment response to second generation antipsychotics in FEP patients [26]. These reports may be interpreted in the same mechanistic context as what we have observed in the present study.
The present study may have potential limitations. First, this study does not include a replication cohort. However, the goal of this study is to discover new directions by using a relatively unique cohort in which multimodal data are available for FEP patients. For this reason, we believe that the inclusion of a replication cohort is beyond the scope of the present work. Nevertheless, the main message is to extract multimodal markers for TR that can be easily collected in many institutions and countries. The MRS data for ACC-GSH may be difficult for some hospitals in some countries. To overcome this dilemma, efforts to identify a surrogate blood marker that correlates with ACC-GSH may be important. In addition, we acknowledge that some of the 104 patients classified as non-TR, might become TR if they were followed longer. To address this possibility, we performed analysis using non-TR patients with and without excluding those with short follow-ups. The results and conclusions of the two-group comparisons were in essence the same. Thus, this dilemma should not affect the overall conclusion. Furthermore, in the present study, the majority of the patients were medicated. Statistical analysis can’t fully address the effects of antipsychotics on brain structures and metabolites. Further studies with medication-naïve patients are expected to confirm our findings. Lastly, we used general linear regression models to evaluate the performance of multimodal markers, which may not be the most efficient way for data fusion. Nevertheless, we employed this approach in the present study, as this is straightforward and can be easily verified by other cohorts.
The research presented here demonstrates the utility and advantages of multimodal study cohorts for psychiatric research. By utilizing deep phenotyping data covering genetics, metabolomics, proteomics, brain imaging, and clinical tests, we can obtain a holistic view of complex and heterogeneous diseases, like psychosis, through hypothesis and/or data-driven approaches (Figure S2). We expect that well-maintained multimodal study cohorts will become essential resources for tackling the diagnosis and treatment of brain disorders.
Supplementary Material
Supplementary document
Acknowledgements
This study is supported by National Institutes of Mental Health Grants MH-092443 (to AS), MH-094268 (to AS), MH-105660 (to AS), and MH-107730 (to AS); foundation grants from Stanley (to AS), RUSK/S-R (to AS), and a NARSAD young investigator award from Brain and Behavior Research Foundation (to AS, KY). The original recruitment of study participants was partly funded by Mitsubishi Tanabe Pharma Corporation. The authors thank Drs. Brian Caffo for kindly contributing to scientific discussions and feedback related to this work. The authors appreciate Ms. Yukiko Lema for research management and manuscript organization, and thank Dr. Melissa A Landek-Salgado for critical reading of the manuscript.
FIGURE 1. Box plots of glutathione (GSH) levels (water reference) in treatment resistant (TR) and non-TR patients.
The box represents standard deviation and the solid line in the middle of the box shows the mean value. Black dots represent individual subjects. Symbol * denotes significant results (q-value < 0.05). Abbreviations: ACC, anterior cingulate cortex; Thal, thalamus; OFR, orbital frontal cortex; DLPFC, dorsolateral prefrontal cortex; CSO, centrum semiovale, and IU, institutional unit, approximately millimolar. In this analysis, non-TR patients excluding those with short follow-ups were compared with TR patients.
FIGURE 2. Classification models of treatment resistant (TR) and non-TR patients.
We compared the performance of five classification models: 1) model of age of onset (denoted as Onset), blue, AUC=0.64; 2) model of glutathione in anterior cingulate cortex (denoted as GSH), purple line, AUC=0.69; 3) model of right hippocampal volume (denoted as HP), green line, AUC=0.60; 4) model of left superior frontal gyral volume (denoted as SFG), orange line, AUC=0.55; and 5) model of multimodal markers (denoted as Onset + GSH + HP + SFG), red line, AUC=0.83. In this modeling, the data from non-TR patients excluding those with short follow-ups and TR patients were used.
Table 1 Comparison of clinical data between treatment resistant (TR) and non-TR patients.
Clinical data were compared between TR (n=32) and non-TR (n=69) patients (non-TR patients with short follow-ups were excluded). Significant results (q-value < 0.05) are highlighted in bold with a gray shadow. Abbreviations: SANS, the scale for the assessment of negative symptoms; SAPS, the scale for the assessment of positive symptoms; and PFTD, positive formal thought disorder.
A) Clinical variables
Characteristics mean (TR) mean (non-TR) p-value q-value
CPZ dose (mg) 385.88 280.95 0.06 0.08
Age of onset (years) 19.28 21.80 1.37E-02 0.03
No. of Hospitalizations 2.69 1.66 7.19E-03 0.03
Duration of illness (months) 15.82 14.15 0.38 0.38
B) SANS/SAPS
Characteristics mean (TR) mean (non-TR) p-value q-value
SAPS
Total score 4.31 3.46 0.48 0.86
Hallucination 1.83 1.22 0.43 0.86
Delusion 1.66 1.47 0.58 0.86
Bizarre behavior 0.45 0.32 0.86 0.86
PFTD 0.38 0.45 0.85 0.86
SANS
Total score 10.34 6.81 0.02 4.80E-02
Affective flattening 1.97 1.34 0.15 0.15
Alogia 1.83 1.03 0.10 0.15
Avolition 2.38 1.35 2.98E-03 0.02
Anhedonia 2.45 1.76 0.12 0.15
Attention 1.79 1.32 0.13 0.15
C) Neuropsychological test
Characteristics mean (TR) mean (non-TR) p-value q-value
Composite score 95.77 96.39 0.54 0.94
Processing speed 85.97 86.48 0.51 0.94
Attention memory 87.11 86.03 0.15 0.94
Verbal learning and memory 86.67 87.10 0.94 0.94
Visual learning and memory 84.93 90.38 0.46 0.94
Ideational fluency 95.36 96.32 0.75 0.94
Executive functioning 88.70 89.30 0.94 0.94
D) Smell test
Characteristics mean (TR) mean (non-TR) p-value q-value
Odor discrimination 9.59 9.79 0.74 0.96
Odor identification 11.18 11.54 0.75 0.96
Detection sensitivity: Citralva −4.44 −4.66 0.12 0.48
Detection sensitivity: Lyral −4.35 −4.27 0.96 0.96
TABLE 2. Analysis results of brain volume between treatment resistant (TR) and non-TR patients.
Brain volumes were compared between TR (n=32) and non-TR (n=69) patients (non-TR patients with short follow-ups were excluded). The top 20 brain regions are listed.
Brain region Hemisphere Mean (TR) Mean (non-TR) p-value p-value (permutation test) q-value
Hippocampus right 3608.11 3856.06 5.24E-03 3.40E-03 0.58
Gyrus rectus left 5390.74 5666.04 0.02 0.02 0.58
Superior frontal gyrus left 24406.95 25294.23 0.03 0.02 0.58
Cuneus left 6235.74 6526.36 0.04 4.89E-02 0.58
Cuneus right 5639.63 5864.89 0.04 0.02 0.58
Occipital sulcus left 1038.95 1158.70 0.04 0.04 0.58
Occipital sulcus right 1184.68 1307.47 4.52E-02 0.03 0.58
Parietal sulcus right 7136.21 7797.64 4.54E-02 0.04 0.58
Middle frontal gyrus right 22359.63 22887.74 4.65E-02 0.03 0.58
Hippocampus cingulum right 1498.37 1565.00 4.75E-02 0.03 0.58
Middle frontal gyrus left 23062.74 23857.11 4.79E-02 5.34E-02 0.58
Middle occipital gyrus left 15138.05 15890.55 5.14E-02 0.06 0.58
Limbic right 1894.00 1971.47 0.07 0.04 0.70
Gyrus rectus right 5478.47 5692.02 0.08 0.31 0.70
Hippocampus left 3546.58 3701.38 0.08 0.20 0.70
Cingulate cortex right 3182.32 3439.49 0.09 0.26 0.70
Cingulate gyrus right 24413.21 25127.13 0.10 0.13 0.70
Parietal parts of syllabus sulcus left 206.63 253.74 0.10 0.08 0.70
Peripheral occipital white matter left 25707.00 26277.60 0.10 0.12 0.70
Frontal parts of sylvian sulcus right 1518.26 1417.85 0.11 0.27 0.70
TABLE 3. Summary of significant findings.
This table summarizes the p-values for CPZ dose, number of hospitalizations, age of onset, ACC GSH, SANS scores, and brain volumes. It also summarizes the range of p-values for multiple scores from smell test, neuropsychological test, and SAPS. Stable markers for TR are highlighted in gray shadow. Abbreviations: TR, treatment resistant; CPZ, chlorpromazine; ACC, anterior cingulate cortex; GSH, glutathione; HP, hippocampus, SFG, superior frontal gyrus; SANS, the scale for the assessment of negative symptoms; and SAPS, the scale for the assessment of positive symptoms.
TR vs non-TR
(short follow-ups excluded) TR vs non-TR
(short follow-ups included) Change over time
CPZ dose 0.06 0.03 1.43E-03
No. of Hospitalizations 7.19E-03 1.50E-03 0.08
Age of onset 0.01 3.78E-03 1.00
ACC GSH 1.68E-03 6.25E-03 0.77
Volume of right HP 5.24E-03 0.01 0.77
Volume of left SFG 0.03 0.02 0.35
Volume of left gyrus rectus 0.02 0.04 1.68E-03
Smell test > 0.05 > 0.05 < 0.05
Neuropsychological test > 0.05 > 0.05 < 0.05
SAPS: total/global score > 0.05 > 0.05 < 0.05
SANS: avolition 2.98E-03 5.21E-03 0.02
SANS: total score 0.02 0.01 0.11
Conflict of interest
We declare that we have no conflict of interest. As noted in the acknowledgement section, the original recruitment of study participants was partly funded by Mitsubishi Tanabe Pharma Corporation. However, this company is not involved in this specific study.
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PMC009xxxxxx/PMC9001818.txt |
==== Front
Ir J Med Sci
Ir J Med Sci
Irish Journal of Medical Science
0021-1265
1863-4362
Springer International Publishing Cham
35412213
2997
10.1007/s11845-022-02997-w
Original Article
Incidence of SARS-CoV-2 re-infection in anti-nucleocapsid IgG-positive healthcare workers: a prospective cohort study
Mehboob Saima drsaima758@gmail.com
1
Rehman Asif 2
Haq Mohsina 3
Rajab Hala 3
Haq Momina 3
Haq Hala 4
Ahmad Jawad 5
Ahmad Sajjad 3
Abbas Mohammed 3
Anwar Saeed 2
Haq NajibUl 1
1 grid.414839.3 0000 0001 1703 6673 Department of Medicine, Peshawar Medical College, Riphah International University, Islamabad, Pakistan
2 grid.414839.3 0000 0001 1703 6673 Department of Community Health Sciences, Peshawar Medical College, Riphah International University, Islamabad, Pakistan
3 grid.414839.3 0000 0001 1703 6673 Department of Pathology, Peshawar Medical College, Riphah International University, Islamabad, Pakistan
4 grid.444783.8 0000 0004 0607 2515 Department of Pathology, Fazaia Medical College, Air University, Islamabad, Pakistan
5 grid.444779.d 0000 0004 0447 5097 Institute of Basic Medical Sciences, Khyber Medical University, Peshawar, Pakistan
12 4 2022
2023
192 2 915918
29 7 2021
9 3 2022
© The Author(s), under exclusive licence to Royal Academy of Medicine in Ireland 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.
Background
Since the pandemic of SARS-CoV-2 began, our understanding of the pathogenesis and immune responses to this virus has continued to evolve. It has been shown that this infection produces natural detectable immune responses in many cases. However, the duration and durability of immunity and its effect on the severity of the illness are still under investigation. Moreover, the protective effects of antibodies against new SARS-CoV-2 variants still remain unclear.
Objectives
To assess the incidence and associated demographic features of SARS-CoV-2 infection in anti-nucleocapsid IgG-positive and anti-nucleocapsid IgG-negative healthcare workers.
Material and methods
This prospective longitudinal cohort study was conducted in Peshawar Medical College group of hospitals of Prime Foundation. Anti-nucleocapsid IgG sero-positive and anti-nucleocapsid IgG sero-negative healthcare workers were followed for a period of 6 months (from 1 Aug 2020 to 31 Jan 2021), and the incidence of SARS-CoV-2 was confirmed by RT-PCR.
Results
A total number of 555 cohorts were followed for a period of 6 months; of them 365 (65.7%) were anti-nucleocapsid-negative (group A) and 190 (34.3%) were anti-nucleocapsid-positive (group B) healthcare workers. The mean age of the study cohort was 33.85 ± 9.80 (anti-N (–), 34.2 ± 10.58; anti-N ( +), 33.5 ± 9.50). The median antibody level in anti-nucleocapsid-positive HCWs was 15.95 (IQR: 5.24–53.4). Male gender was the majority in both groups (group A, 246 (67%), group B, 143 (48%)) with statistically significant difference (P < 0.05). Majority of the HCWs were blood group B in both groups (34% each). None of the 190 anti-nucleocapsid-positive HCWs developed subsequent SARS-CoV-2 re-infection, while 17% (n = 65) HCWs developed infection in anti-nucleocapsid-negative group during the 6-month follow-up period.
Conclusion
In conclusion, none of the anti-nucleocapsid-positive HCWs developed SARS-CoV-2 re-infection in this study, and the presence of IgG anti-nucleocapsid antibodies substantially reduce the risk of re-infection for a period of 6 months.
Supplementary information
The online version contains supplementary material available at 10.1007/s11845-022-02997-w.
Keywords
Anti-nucleocapsid antibody
Healthcare workers
Incidence
Re-infection
SARS-CoV-2
issue-copyright-statement© The Author(s), under exclusive licence to Royal Academy of Medicine in Ireland 2023
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pmcIntroduction
The SARS-CoV-2 pandemic began in December 2019 in Wuhan, Hubei Province of China as a cluster of pneumonia-like illness [1].The infection spread rapidly throughout the world and was declared a global pandemic by WHO (World Health Organization) on 11 March 2020 [2]. Since then the number of infections continue to rise. Our understanding of the pathogenesis and immune responses to this virus continue to evolve, and it has been shown that this infection produces natural detectable immune responses in many cases. However, the duration and durability of immunity and its effect on the severity of the illness is still under investigation [3]. Moreover, the protective effects of antibodies against new SARS-CoV-2 variants still remain unclear [3–5].
There is limited evidence regarding the post-infection immunity; despite the emergence of new variants with faster rate of transmission and a very high global rate of infections, there is little evidence of re-infection [6]. One of the main reasons could be a lack of diagnostic facilities especially in developing countries. Although there are some reports suggesting that naturally developing antibodies maybe associated with protection up to a few months, longitudinal studies comparing infection rates in sero-positive and sero-negative individuals are still lacking [7].
Following infection, immunity may be contributed by both the innate and adaptive immune systems of the human body having different cells with specific functions. The adaptive immune system comprises B cells, CD4 + T cells, and CD8 + T cells. B cells produce antibodies. CD4 + T cells possess a range of helper and effecter functions, and the CD8 + T cells kill virus infected cells. COVID-19 virus is an enveloped, single-stranded RNA virus and is composed of 16 nonstructural (NS) proteins and 4 structural proteins named as Spike (S), Envelope (E), Membrane (M), and nucleocapsid (N) [8]. Serologic tests based on the presence of antibodies against the antigens to these structural proteins (S) and (N) have increased our understanding of the epidemiology, sero-prevalence rates, and identifying potential convalescent plasma donors. However, the duration of persistence of antibodies and its neutralizing role confirming immunity against a subsequent infection is still an area of intense clinical research [9].
We performed a prospective cohort study to assess and compare the incidence and associated demographic features of subsequent SARS-CoV-2 infection confirmed by real-time polymerase chain reaction (RT-PCR) in anti-nucleocapsid-positive and anti-nucleocapsid-negative healthcare workers for a period of 6-month follow-up.
Methodology
This prospective cohort study was conducted from August 01, 2020, to January 31, 2021. All the study participants (HCWs) were employees of Peshawar Medical College (PMC) group of hospitals of Prime Foundation. The study was approved by the Institutional Review Board (IRB) of Prime Foundation, Pakistan. Participant of the study were selected using non-probability consecutive sampling technique. After written and informed consent, physical examination was performed and data regarding the co-morbidities and socio-demographic characteristics were recorded on a structured proforma. At the time of study, none of the participants was vaccinated against the virus. Standard protocols were followed in the hospitals and at home by all the participants to limit the spread of infection; this included hand hygiene, social distancing, and using of personal protective equipment (PPE).
Baseline SARS-CoV-2 antibody testing were performed using Roche Elecsys®S-CoV-2kit [9]. The assay uses a recombinant protein representing the nucleocapsid (N) antigen in a double-antigen sandwich assay format. On the basis of antibody testing, the healthcare workers (HCWs) were then divided into antibody positive and negative. Both groups were followed for a period of 6 months. Symptomatic HCWs were offered real-time polymerase chain reaction (RT-PCR) testing of nasopharyngeal swab specimen. The PCR analyses were made using Geneproof ® SARS-CoV-2 PCR kit. Immunocompromised healthcare workers and those above 60 years and below 18 years of age were excluded from the study.
Results
A total number of 555 cohorts were followed for a period of 6 months; of them, 365 (65.7%) were anti-nucleocapsid-negative (group A) and 190 (34.3%) were anti-nucleocapsid-positive (group B) healthcare workers. The mean age of the study cohort was 33.85 ± 9.80 (anti-N (–), 34.2 ± 10.58; anti-N ( +), 33.5 ± 9.50). The median antibody level in anti-nucleocapsid-positive HCWs was 15.95 (IQR: 5.24–53.4). Majority of the HCWs in group A were in the age group 31–40 years (n = 128, 35%), while age group 20–30 years has the highest number of HCWs in group B (n = 92, 48%). Male gender was the majority in both groups (group A, 246 (67%); group B, 143 (48%)) with statistically significant difference when chi-square test was applied (P < 0.05). (Table 1)
Majority of the HCWs were identified with blood group B in both groups (34% each) followed by blood group O (27%), blood group A (26%), and blood group AB (13%) in anti-nucleocapsid-negative group and blood group A (32%), blood group O (23%), and blood group AB (11%) in anti-nucleocapsid-positive healthcare workers. In both groups, RH factor (negative) were the highest (group A 95%, group B 77%) compared to RH factor (positive) (group A 5%, group B 23%). When blood groups of the COVID-19 re-infected HCWs were checked, majority had blood group A (33.8%) followed by O (29.2%) and B (20%), while only 16% had blood group AB. (Table 3)
None of the 190 anti-nucleocapsid-positive healthcare workers developed subsequent COVID-19 re-infection, while 17% (n = 65) HCWs developed infection in anti-nucleocapsid-negative group during the 6-month follow-up period. (Table 2)
.
Discussion
Since December 2019, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been rapidly spreading across the globe. Over the past 1 year, our understanding of the pathogenesis and diagnostic possibilities of the disease has been rapidly evolving.
Serology can play a major role in the true estimation of prevalence of SARS-CoV-2 infection as it detects the asymptomatic or subclinical cases too. These assays have a high sensitivity, and antibodies can be detected in 1 up to 2 weeks following infection [10]. The serologic tests developed so far are limited to the detection of antibodies against one or two antigens, and they may cross-react with antibodies to other human corona viruses. The serology for detection of emerging coronaviruses is largely focused on antibodies against the spike (S) protein, particularly the S1 domain, and the nucleocapsid protein (N) but how to optimally combine antigens to most accurately detect strain-specific coronavirus antibodies still remains a debatable issue [11]. There is some data available regarding the sero-prevalence in various population subsets and the degree to which they are at risk of developing infection with SARS- CoV-2 [12]. There is currently little knowledge available regarding the duration of persistence of the antibodies and their durability as well as their protective role in human subjects [13].
The results of our study show several interesting features. It was found that age has a positive association with the antibody titers so antibody response may differ in various age groups as shown in Table 1. Similarly, it was observed that Rh status also affects the antibody levels; however, the ABO status has no significant association with the levels of antibodies achieved (Tables 2 and 3). Gender of the participants also had a significant association with the N antibody response (Table 1).Table 1 Socio-demographic characteristics
Anti-nucleocapsid negative
(Group A) Anti-nucleocapsid positive
(Group B) P value
Mean age in years (± SD) 33.5 ± 9.50 34.2 ± 10.58
Median (IQR) antibody level (AU/ml) – 15.95 (5.24–53.4)
Age Group
20–30 124 (34%) 92 (48%) < 0.01
31–40 128 (35%) 58 (31%)
41–50 62 (17%) 24 (13%)
51–60 51 (14%) 16 (8%)
Gender
Male 246 (67%) 143 (75%) < 0.05
Female 119 (33%) 47 (25%)
Blood group
A 95 (26%) 61 (32%) > 0.05
B 124 (34%) 65 (34%)
AB 47 (13%) 21 (11%)
0 99 (27%) 44 (23%)
RH factor
Positive 18 (5%) 44 (23%) < 0.01
Negative 347 (95%) 146 (77%)
Total 365 (100%) 190 (100%)
Table 2 Subsequent re-infection distribution among anti-nucleocapsid-positive and anti-nucleocapsid-negative participants
Subsequent/infection
Developed Subsequent re-infection
Not developed Total
Anti-nucleocapsid positive 0 (0%) 190 (100%) 190 (100%)
Anti-nucleocapsid negative 65 (17.8%) 300 (82.2%) 365 (100%)
Table 3 Blood group distribution among re-infected participants
COVID-19 infection
Blood Group (n) (%)
A 22 33.8%
B 13 20.0%
AB 11 16.9%
O 19 29.2%
Total 65 100%
None of the antibody-positive participants developed a subsequent re-infection, and it is safe to assume that anti-nucleocapsid antibodies can confer immunity for at least 6 months irrespective of the antibody titers. Further follow-up may clarify the duration for which the antibodies persist and continue to provide protection against this deadly virus.
A study shows that the N-antibody may persist up to 8 months [14]. Lumley, Sheila F et al. also found similar results; in their study, the presence of anti-nucleocapsid IgG antibodies was associated with a reduced risk of SARS-CoV-2 reinfection [15]. In their study, the antibody persisted for 6 months similar to our findings. In another the trajectory of the neutralizing antibodies (S and N antibodies) showed that the median neutralization potency decreased by 45% per month, and S-based serological assay best predicted the neutralization potency [16]. In contrast, our study found that the antibody titers of the N type provided immunity and all participants with positive antibodies remained infection free for six months.
In our study, the major limitation was the fact that following up the RT-PCR was done only on those who became symptomatic and the actual incidence of subsequent infections could be higher if the PCR was done on the asymptomatic participants as well. Despite this limitation, the results have provided new insights into the pathogenesis of SARS-CoV-2 infections, and further studies with longer follow-up are needed to confirm and quantify the protective role of these antibodies.
Conclusion
In this longitudinal study, none of the anti-nucleocapsid-positive HCWs developed SARS-CoV-2 re-infection, and the presence of IgG anti-nucleocapsid antibodies substantially reduces the risk of re-infection for a period 6 months. Further studies are needed to assess the risk of re-infection in sero-positive individuals beyond a 6-month period and in diverse population.
Supplementary information
Below is the link to the electronic supplementary material.Supplementary file1 (PDF 173 KB)
Author contribution
Dr. Saima Mehboob: manuscript writing, concept, study design. Asif Rehman: data processing and critical review. Changed aspects of manuscript as per the reviewers’ suggestions. Mohsina Haq, Hala Rajab, Momina Haq, Hala Haq: sample collection and processing of laboratory data. Jawad Ahmad, Sajjad Ahmad, Mohammed Abbas: data collection. Saeed Anwar: data processing and critical review. NajibUl Haq: original idea and critical review.
Declarations
Ethics approval and consent to participate
Ethical approval was granted from the Institutional Review Board of Prime Foundation, Peshawar Medical College, Pakistan.
IRB approval number: Prime/IRB/2020–269(a).
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References
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11. Nielsen S Yang F Jackson K Hoh R Röltgen K Jean G Human B Cell Clonal Expansion and Convergent Antibody Responses to SARS-CoV-2 Cell Host Microbe 2020 28 4 516 525.e5 10.1016/j.chom.2020.09.002 32941787
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Silicon
Silicon
1876-990X
1876-9918
Springer Netherlands Dordrecht
1865
10.1007/s12633-022-01865-7
Original Paper
Sensitivity Enhancement of Dual Gate FET Based Biosensor Using Modulated Dielectric for Covid Detection
http://orcid.org/0000-0003-4912-7435
Kumar Saurabh saurabh.k2u@gmail.com
Chauhan R.K. rkchauhan27@gmail.com
Kumar Manish er.manish.k@gmail.com
Department of Electronics & Communication Engineering, M.M.M. University of Technology, Gorakhpur, India
12 4 2022
110
2 12 2021
31 3 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 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.
This paper presents a dual gate dielectric modulated FET (DGDMFET) biosensor with enhanced sensitivity for covid detection. In earlier literature, the biosensors are operated using the surface interaction with the virus biomolecules that are reflected through a channel or gate. The downside of these types of sensors has limited sensitivity. In this paper, we have considered that the change in the dielectric constant due to virus proteins results in a significant shift in the threshold voltage of FET. Enhancement of sensitivity is done by using the novel dual metal gate arrangement with different work functions (higher at the source end and lower at the drain end) and the chromic oxide (Cr2O3) layer, which is carved out vertically to form nanogap. At the same time, interface charge density is maintained nearly equal to 1.0 × 1011 cm−2 at the Si-SiO2 layer. To demonstrate the proposed biosensor, electrical parameters (electron concentration, surface potential, energy band distribution, and electric field) and the absolute percentage sensitivity of threshold voltage, subthreshold slope, ON current, and transconductance are evaluated and compared with related literature. The ATLAS device simulator is used for the simulation of the proposed device.
Keywords
Biosensor
Dielectric modulation
Nanogap
Dual gate
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pmcAuthors Contribution
All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were done by Saurabh Kumar, R.K.Chauhan, and Manish Kumar. The first draft of the manuscript was written by Saurabh Kumar, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Data Availability
This manuscript has no associated data.
Declarations
Ethics Approval
The author has followed all ethics standards
Consent to Participate
Not Applicable
Consent for Publication
Not Applicable
Conflict of Interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
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6. Vishwanathan, all you need to know about Corona Virus in India (2020). https://www.unicef.org/india/coronavirus/covid-19, UNICEF India
7. Hoehl S Rabenau H Berger A Kortenbusch M Cinatl J Bojkova D Behrens P Böddinghaus B Götsch U Naujoks F Neumann P Schork J Tiarks-Jungk P Walczok A Eickmann M Vehreschild MJGT Kann G Wolf T Gottschalk R Ciesek S Evidence of SARS-CoV-2 infection in returning travelers from Wuhan, China N Engl J Med 2020 382 1278 1280 10.1056/NEJMc2001899 32069388
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9. Baig A M and Erin C S (2020) Potential Neuroinvasive Pathways of SARS-CoV-2: Deciphering the spectrum of neurological deficit seen in coronavirus disease-2019 (COVID-19). Journal of medical virology 92:1845–1857. 10.1002/jmv.26105, 2020
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11. E. Preparedness, Emergency Preparedness and Response CDC Emergency Operations Center Activations. https://emergency.cdc.gov/recentincidents/index.asp, (2020) 2018–2019
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13. Chu DKW Pan Y Cheng SMS Hui KPY Krishnan P Liu Y Ng DYM Wan CKC Yang P Wang Q Peiris M Poon LLM Molecular diagnosis of a novel coronavirus (2019-nCoV) causing an outbreak of pneumonia Clin Chem 2020 66 549 555 10.1093/clinchem/hvaa029 32031583
14. Asif M Ajmal M Ashraf G Muhammad N Aziz A Iftikhar T Wang J Liu H The role of biosensors in coronavirus disease-2019 outbreak Current Opinion in Electrochemistry 2020 23 174 184 10.1016/j.coelec.2020.08.011 32984642
15. C.-E. Igg, A. Test, Accelerated Emergency Use Authorization (Eua) Summary Covid-19 Elisa Igg Antibody Test (Mount Sinai Laboratory), (2013) 51–58. https://www.fda.gov/media/137029/download
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18. Lee CS Kyu Kim S Kim M Ion-sensitive field-effect transistor for biological sensing Sensors 2009 9 7111 7131 10.3390/s90907111 22423205
19. Moon D-I Han J-W Meyyappan M Comparative study of field effect transistor based biosensors IEEE Trans Nanotechnol 2016 15 956 961 10.1109/TNANO.2016.2615855
20. Kalra S Kumar MJ Dhawan A Reconfigurable FET biosensor for a wide detection range and electrostatically tunable sensing response IEEE Sensors J 2020 20 2261 2269 10.1109/JSEN.2019.2952333
21. Vu CA, Chen WY (2019) Field-effect transistor biosensors for biomedical applications: recent advances and future prospects. Sensors (Switzerland) 19. 10.3390/s19194214
22. Sadighbayan D Hasanzadeh M Ghafar-Zadeh E Biosensing based on field-effect transistors (FET): recent progress and challenges TrAC Trends Anal Chem 2020 133 116067 10.1016/j.trac.2020.116067
23. Mukhopadhyay S, Sen D, Goswami B, Sarkar S (2020) Performance evaluation of dielectrically modulated extended gate single cavity InGaAs/Si HTFET based label-free biosensor considering non-ideal issues. IEEE Sensors Journal PP. 10.1109/JSEN.2020.3033576
24. Kanungo S, Rahaman H, Chattopadhyay S (2015) Investigating the performance of short gate insulator less dielectrically modulated tunnel field effect transistor based bio-sensors. In: 2015 6th international conference on computers and devices for communication (CODEC). Pp 1–4. 10.1109/CODEC.2015.7893189
25. Paliwal A Tomar M Gupta V Complex dielectric constant of various biomolecules as a function of wavelength using surface plasmon resonance J Appl Phys 2014 116 23109 10.1063/1.4890027
26. Chan M Assaderaghi F Hu C Recessed-Channel structure for fabricating ultrathin SOI MOSFET with low series resistance IEEE Electron Device Letters 1994 15 22 24 10.1109/55.289474
27. Koh YH Choi JH Nam MH Yang JW Body-contacted SOI MOSFET structure with fully bulk CMOS compatible layout and process IEEE Electron Device Letters 1997 18 102 104 10.1109/55.556094
28. Colinge JP, Chen J, Flandre D, et al (1996) A low-voltage, low-power microwave SOI MOSFET. In: 1996 IEEE international SOI conference proceedings. Pp 128–129
29. Abdullah, M.M., Rajab, F.M., & Al-Abbas, S.M. (2014) Structural and optical characterization of Cr2O3 nanostructures: evaluation of its dielectric properties. AIP Adv, 4:0–11.10.1063/1.4867012
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31. Singh D Patil GC Performance analysis of feedback field-effect transistor-based biosensor IEEE Sensors J 2020 20 13269 13276 10.1109/JSEN.2020.3006986
32. Parihar MS Kranti A Enhanced sensitivity of double gate junctionless transistor architecture for biosensing applications Nanotechnology 2015 26 145201 10.1088/0957-4484/26/14/145201 25771821
33. Talley K Ng C Shoppell M Kundrotas P Alexov E On the electrostatic component of protein-protein binding free energy PMC Biophys 2008 1 2 10.1186/1757-5036-1-2 19351424
34. Huang Y, Yang C, Xu X feng, et al (2020) Structural and functional properties of SARS-CoV-2 spike protein: potential antivirus drug development for COVID-19. Acta Pharmacol Sin 41:1141–1149. 10.1038/s41401-020-0485-4
35. Igor P, Pushkar R, Tsu-Jae K, Hu C, (2004) Dual work function CMOS gate technology based on metal interdiffusion, U.S. patent, no. US 2004O238859A1,
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Mol Biol Rep
Mol Biol Rep
Molecular Biology Reports
0301-4851
1573-4978
Springer Netherlands Dordrecht
35412175
7433
10.1007/s11033-022-07433-x
Short Communication
A novel prothrombotic role of proprotein convertase subtilisin kexin 9: the generation of procoagulant extracellular vesicles by human mononuclear cells
Scalise Valentina 1
Lombardi Stefania 2
Sanguinetti Chiara 1
Nieri Dario 1
Pedrinelli Roberto 1
http://orcid.org/0000-0002-1364-9568
Celi Alessandro alessandro.celi@unipi.it
1
Neri Tommaso 1
1 grid.5395.a 0000 0004 1757 3729 Centro Dipartimentale di Biologia Cellulare Cardio-Respiratoria, Dipartimento di Patologia Chirurgica, Medica, Molecolare e Dell’Area Critica, University of Pisa, 56126 Pisa, Italy
2 SSD Analisi ChimicoCliniche ed ImmunoAllergologia, USL1 Massa e Carrara, Italy
12 4 2022
2022
49 5 41294134
24 2 2022
25 3 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 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.
Background
Proprotein convertase subtilisin kexin 9 (PCSK9) is a serin protease synthesized mainly in the liver that binds the receptor of low-density lipoprotein and promotes its degradation in lysosomes. PCSK9 is considered a promising target for the development of new therapies for the treatment of hypercholesterolemia and related cardiovascular diseases. Extracellular vesicles represent a heterogeneous population of vesicles, ranging in size between 0.05 and 1 μm involved in numerous pathophysiological processes, including blood coagulation. We investigated whether PCSK9 stimulation induces the release of procoagulant extracellular vesicles from human mononuclear cells (PBMCs) and THP-1 cells.
Methods and results
PBMCs and THP-1 cells were stimulated whit PCSK9, the generation of EV was assessed by the prothrombinase assay and by cytofluorimetric analysis. EV-associated tissue factor activity was assessed by a one-stage clotting assay. PCSK9 induced an increase in extracellular generation by PBMCs and THP-1 cells as well as an increase in extracellular vesicle-associated tissue factor. Pre-treatment with inhibitors of the toll like receptor, TLR4 (C34), and of NF-κB signaling (BAY 11-7082), downregulated PCSK9-induced extracellular vesicle generation and of extracellular- bound tissue factor. Similar effect was obtained by an anti-PCSK9 human-monoclonal antibody.
Conclusions
PCSK9-mediated generation of procoagulant EV could contribute to increase the prothrombotic status in patients with cardiovascular diseases.
Keywords
PCSK9
Extracellular vesicles
Tissue factor
Procoagulant activity
issue-copyright-statement© Springer Nature B.V. 2022
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pmcIntroduction
Proprotein convertase subtilisin kexin 9 (PCSK9) is a serine protease synthesized in the form of a soluble zymogen mainly in the liver which, following an auto-catalytic process in the endoplasmic reticulum, is transformed into pro-protein convertase. PCSK9 is secreted into the bloodstream, where it binds the low-density lipoprotein (LDL) receptor in its extra-cellular portion and its degradation products in lysosomes, preventing its recycling on the hepatocyte membrane [1]. PCSK9 gain-of-function mutations are associated with autosomal dominant hypercholesterolemia and premature atherosclerosis [2]. PCSK9 loss-of-function mutations conversely lead to low levels of LDL-cholesterol and cardiovascular protection [3]. Pharmacological inhibition of PCSK9 with monoclonal antibodies leads to a 60% LDL-cholesterol reduction in patients already treated with maximum tolerated statin therapy [4]. High circulating levels of PCSK9 predict cardiovascular events in patients with atrial fibrillation and in those with stable coronary artery disease (CAD), including patients with well-controlled LDL-cholesterol levels [5].
Tissue factor (TF) is an integral membrane protein constitutively expressed in subendothelial tissues and exposed to the blood following injury. TF is a high affinity receptor and essential cofactor for coagulation factor (F) VII(a); upon binding to TF, FVII(a) activates both FX and FIX thus initiating a series of reactions that eventually lead to fibrin generation [6]. An inducible form of TF is also synthesized by blood and vascular cells upon stimulation by different agonists. Circulating monocytes are endowed with a potential for synthesizing and expressing substantial amounts of TF [6]. Quite notably, monocytes represent the effector arm of the innate immune system deeply involved in endogenous inflammatory processes [7] and an extensive crosstalk links these two systems, whereby inflammation activates coagulation and coagulation also considerably affects inflammatory activity [8]. Lipopolysaccharide (LPS, endotoxin) is one of the best characterized agonists capable of stimulating TF synthesis by monocytes. LPS is the principal glycolipid component of the outer membrane of Gram-negative bacteria and a well characterized inflammatory stimulus, signaling through the Toll-like (TL) receptor pathway [9].
Extracellular vesicles (EV) are cell membrane-derived vesicles ranging between 0.1 and 1 µm in diameter shed upon activation and/or during apoptosis by virtually all cells including monocytes [10]. The discovery that EV could harbor functional TF [11] has contributed to modify the paradigm of circulating monocytes and endothelial cells as the only sources of intravascular procoagulant material released in response to inflammatory stimuli [12]. Exposure to LPS [13] as well as other agonists, including angiotensin II [14], rosiglitazone [15], environmental particulate matter [16] and leptin [17] causes an increase in procoagulant, monocyte-derived TF-bearing EV.
We recently provided evidence that PCSK9 stimulation elicits TF expression in cells of monocytic lineage through the activation of the TLR4/NF-κB signaling pathway [18]. Based on the above data and on the observation that both PCSK9 and EV are increased in thrombotic diseases [1, 19] we hypothesized that PCSK9 induces prothrombotic, TF-bearing EV generation by monocytes, possibly via TLR4/NF-κB activation.
Materials and methods
Reagent and materials
RPMI-1640, penicillin, streptomycin, trypan blue, Histopaque®-1077, sodium citrate, LPS from Escherichia coli O55:B5, hrPCSK9, BAY 11–7082 (BAY), β-mercaptoethanol and Fetal bovine serum (FBS) were obtained from Sigma-Aldrich (Milan, Italy). Human relipidated full length recombinant human TF was from BioMedica Diagnostics (Windsor, Canada). Ultrapure LPS-RS, CLI-095 (CLI) were purchased from InvivoGen (San Diego, California). The human monoclonal antibody anti-PCSK9 was a generous gift from Amgen Inc. C34 was purchased from Tocris Bioscience (Bristol, UK). LAL chromogenic endpoint assay was obtained from Hycult Biotech (Uden, The Netherlands). Annexin V was purchased by Biosciences (Dublin, Ireland). Carboxyfluorescein succinimidyl ester (CFSE) was purchased by Abcam (Cambridge, UK). THP-1 cells were from the European Collection of Authenticated Cell Cultures (ECACC).
Cell cultures
Human peripheral blood mononuclear cells (PBMCs) were obtained as described [18]. Briefly, suspensions derived from single-donor buffy coats were obtained from the local blood bank and represented leftovers otherwise destined to disposal. The procedure was approved by the local ethics committee. The buffy coats were kept at room temperature and utilized within 4 h from withdrawal. PBMCs were isolated by centrifugation of fresh buffy coats on Histopaque®-1077 at 400×g for 30 min at 20 °C. Cells collected at the interphase were washed twice in sodium citrate 0.38% and resuspended in RPMI-1640 supplemented with 1% penicillin–streptomycin. The final PBMCs preparations typically contain 25–35% monocytes, negligible proportions of neutrophils (< 5%), 65–75% lymphocytes and some contaminating platelets. Cell viability was assessed by dimethyl thiazolyl diphenyl tetrazolium (MTT) (Sigma-Aldrich, Milan, Italy): high viability (> 85%) was confirmed for all preparation. To account for interindividual variability among donors as well as interferences from copurified lymphocyte- and contaminant platelet-derived TF, cells of the monocytic lineage, THP-1, were also used in selected experiments. Cells were cultured in RPMI 1640 medium supplemented with 10% heat-inactivated FBS, 5% β-mercaptoethanol, 2 mM l-glutamine, 50 U/mL penicillin, and 50 g/mL streptomycin and incubated in a 5% CO2 humidified atmosphere at 37 °C. Throughout the study procedures, THP-1 cells were maintained in a logarithmic growth phase at a concentration between 3 and 5 × 105 cells/mL.
Cell cultures suspended in polypropylene tubes, PBMCs (3 × 106 cells/mL) and THP-1 (1 × 106 cells/mL), were incubated with PCSK9 (1 and 5 µg/mL; 18 and 4 h for the two cell types, respectively) prior to EV-associated TF-PCA and total EV analysis. LPS at concentrations of 0.1 and 10 μg/mL for PBMCs and THP-1 respectively was used as a positive control in all set of experiments. Antagonists were added to cell culture 30 min prior to stimulation.
To prevent LPS contamination, glassware was exposed to high temperature (200 °C for 4 h). In addition, plasticware, reagents and solutions used for in vitro cell cultures were preliminarily tested on a routine basis with a sensitive chromogenic LAL assay. Reagents with endotoxin concentrations > 60 pg/mL were discarded.
Assessment of EV generation
EV generation was investigated by two independent methods. The Zymuphen MP-activity kit (Hyphen BioMed, Neuville-sur-Oise, France) measures the concentration of phophatidylserine (PS) based on the rate of prothrombin conversion to thrombin in a solid-phase chromogenic assay in which the availability of PS is the rate limiting step [20]. Thrombin generation is then converted to PS concentration by mean of a standard curve generated with known concentrations of PS, according to the manufacturer’s instructions. PS concentration was assessed in the cell-free conditioned medium of PBMC and THP1 cells, after culture in different conditions. Under these experimental conditions, EV are considered the only source of PS. A cytofluorimetric analysis was conducted using a FACScanto™II flow cytometer (BD Biosciences, San Jose, CA, USA). EV were first discriminated by size, using commercially available calibration beads (Megamix Plus—SSC; Stago, Milan, Italy), as events conforming to a light scatter distribution within the 0.16−0.5 μm range in a SSc vs. FSc window and further identified as annexin V+, to identify medium-large EV expressing phosphatidylserine [10], and CFSE+ events after incubation with allophycocyanin (APC)-annexin V and CFSE, in a APC vs. FITC window. Annexin-V binds to PS and is therefore used as a marker for PS-exposing EV. CFSE was used as a marker of EV integrity [21]. Events acquisition was obtained at low flow rate and stopped after 180 s.
Assessment of EV-associated TF activity
TF activity was measured in EV derived from PBMCs (3 × 106 cells/mL) and THP-1 cells (1×106 cells/mL) by a one-stage clotting time test using a STart Max semi-automated Coagulation analyzer (Diagnostica Stago S.A.S., Milan, Italy). The conditioned media were cleared by centrifugation at 16,000×g for 5 min at 4° to remove dead cells and big cell fragments that might have detached, and then submitted to further centrifugation at 16,000×g for 45 min at 4 °C. The pellets were resuspended in 125 µL normal saline, lysed by three freeze–thaw cycles and added to an equal volume of pooled, EV-free normal human plasma. Time to clot formation upon recalcification with 25 mM CaCl2 was recorded. All experiments were carried out at 37 °C. For each experimental session, calibration curves were generated using recombinant human relipidated TF (pg/mL) (BioMedica Diagnostics-Windsor-NS Canada). In our experimental conditions, clotting times of 627 ± 84 s and 19 ± 2 s (mean ± SD) were obtained with 0.001 pg/mL and 100 pg/mL TF, respectively. Experiments were run in triplicate and averaged.
Statistical analysis
Data are shown as mean ± SEM. Comparisons between two groups were performed using unpaired t-test; comparisons among more than two groups were performed using ANOVA for independent measures followed by Sidak’s post-hoc analysis. All tests are two-tailed. Prism software (GraphPad, San Diego, CA, USA) was used for analysis and graph generation.
Results and discussion
First, we investigated whether PCSK9 stimulation induces EV generation by monocytic cells. As sown in Fig. 1A, incubation of PBMCs with PCSK9 (1 μg/mL) causes a significant increase in procoagulant EV generation, as assessed by an assay that measures phosphatidylserine (PS) concentration. Similar results were obtained with cells of the monocytic line, THP-1 (Fig. 1B). To confirm the specificity of the phenomenon, we used evolocumab, an inhibitory human monoclonal antibody directed against PCSK9. The antibody abolished the upregulation of EV generation by PBMC after incubation with PCSK9 (Fig. 1C).Fig. 1 EV generation, expressed as PS concentration, by PBMCs (A) and THP-1 (B) incubated in the absence and in the presence of PCSK9. ** and ***p < 0.01 and 0.001, respectively for PCSK9 stimulated cells versus unstimulated cells; n = 13 (A) and 11 (B); Student’s t-test. C: Inhibition of PCSK9-induced EV generation by evolocumab; *p < 0.05 for EV generation in the presence PCSK9 and evolocumab vs. PCSK9 alone; n = 5; ANOVA with Sidak’s post-hoc analysis.
We next investigated some of the mechanisms of PCSK9 induced upregulation of EV generation. BAY-117082 (BAY), a compound which inhibits NF-κB by preventing the phosphorylation and subsequent degradation of IκB abolished EV generation upon PCSK9 stimulation both in PBMCs and THP-1 (Fig. 2A, B, respectively). A similar inhibitory effect was induced by the TLR4 inhibitor, C34 (Fig. 2C).Fig. 2 Inhibition of EV generation, expressed as PS concentration, by PBMCs (A) and THP-1 (B, C) by BAY-117082 (A, B) and C34 (C). * and ***p < 0.05 and 0.001, respectively, for inhibited versus unhibited; n = 5 (A, B) and 4 (D). ANOVA with Sidak’s post-hoc analysis
The results described above were obtained analyzing the concentration of PS in the conditioned medium. As this medium is cell-free, EV are likely the only source of PS. However, to confirm the results we also analyzed EV by flow cytometry. EV were defined as events conforming to light scatter distribution within the 0.16–0.5 μm bead range in a SSc vs FSc window and further identified as annexin V positive events, and therefore expressing PS, and carboxyfluorescein diacetate succinimidyl ester (CFSE) positive events. CFSE is a membrane permeant molecule that, upon cleavage by intracellular esterases, produces a fluorescent dye. Accordingly, CFSE positivity confirms that the events detected are closed vesicles with an intact membrane, enclosing esterases rather than cell debris. With this approach, we also confirmed that PCSK9 induces the release of EV by THP-1 (235 ± 18 events SSC+/annexinV+/CFSE+ in basal condition vs 565 ± 122 events SSC+/annexinV+/CFSE+ after stimulation with PCSK9; mean ± SD, p < 0.05).
The EV generated upon PCSK9 stimulation are procoagulant, due to the exposure of PS, a negatively charged phospholipid required for the assembly of the multimolecular complexes that participate in the coagulation cascade. However, EV also might contribute to the coagulation processes through the exposure of TF [12]. Indeed, EV generated by both PBMC and THP1 cells upon stimulation with PCSK9 showed a TF-dependent procoagulant activity as assessed by a one-stage clotting assay (Fig. 3A, B, respectively). Again, direct PCSK9 inhibition with evolocumab and blocking of the of NF-κB-dependent signaling pathway by BAY abrogated PCSK9-induced generation of EV-bound TF-dependent procoagulant activity both in PBMCs (Fig. 3C) and THP-1 cells (not shown).Fig. 3 Effect of PCSK9 on the induction of TF-dependent procoagulant activity by PBMC (A) and THP-1 (B). ** and ***p < 0.01 and 0.001, respectively, n = ; Student’s t-test. C Inhibition of PCSK9 induced TF-dependent procoagulant activity by evolocumab and BAY117082. ***p < 0.001; n = 5 (A, C), and 8 (B); ANOVA followed by Sidaks post-hoc analysis
Our data indicate that PCSK9 induces the generation of prothrombotic, TF-bearing EV by cells of monocytic lineage. A specific monoclonal antibody to PCSK9, evolocumab, blocked the phenomenon, confirming its specificity and effectively ruling out, for example, a potential role of LPS contamination of the reagents. Inhibition with BAY-117982 and C34 suggest that PCSK9-induced stimulation of EV shedding is mediated through NF-κB and TLR4.
The role of EV in numerous pathophysiologic phenomena has gained much attention over the last several years. Specifically in the field of coronary heart diseases, increased numbers of EV have been demonstrated in the peripheral blood of patients with acute coronary syndromes; this observation has been linked, at least in part, to the procoagulant role of PS and TF on their surface [12].
PCSK9 has been extensively studied for its tole as a key regulator of cholesterol metabolism [1]. However, it has become clear over the years that the effects of this molecule in the pathogenesis of atherothrombosis are more complex. Indeed, PCSK9 promotes vascular inflammation through TLR4/NF-κB-mediated modulation of proinflammatory cytokine release [22], an activity that this molecule shares with LPS [23].
We have recently shown that PCSK9 also induces TF expression by monocytes, again via a TLR4/NF-κB pathway [18]; this observation adds to the general understanding of PCSK9 as an agonist with pleiotropic proinflammatory and prothrombotic effects. Our current data further expand on the same line.
The analysis of EV is notoriously complex and none of the available methods (that include, among others, flow cytometry, nanotrack analysis, electron microscopy, PS analysis) is considered sufficient to clearly identify and enumerate specific vesicles [10]. In the current work we have used two independent approaches. One approach measures PS concentration based on the kinetics of thrombin generation in a chromogenic assay in which PS is the rate limiting component. The approach has the advantage of being insensitive to the dimensions of the EV but does not distinguish between EV and cell fragments. In contrast, flow cytometry does recognize closed vesicles enclosing intact cytoplasm via the use of CFSE, a molecule that fluoresces only upon cleavage by intracellular esterases; flow cytometry, however, does not recognize events smaller than approximately 200 nm in size [24, 25]. The use of both approaches increases the reliability of the results.
We had originally planned to perform most experiments with PBMC in parallel with those with THP-1; however, the SARS-CoV2 pandemic has prevented us from accessing blood-derived products for research purposes.
In conclusion, we demonstrate that PCK9 might contribute to the atherothrombotic process through the induction of EV expressing both PS and TF and therefore capable of activating the coagulation cascade. Further studies will investigate more rigorously the mechanisms of PCSK9-induced EV generation.
Author contributions
The study conception was performed by AC, CS, DN, VS and TN. material preparation, data collection and analysis were performed by SL, VS and TN; The first draft of the manuscript was written by AC, RP, VS and TN and. All authors read and approved the final manuscript.
Funding
This research was funded by the Dipartimento di Patologia Chirurgica, Medica, Molecolare e dell’Area Critica, Università di Pisa.
Declarations
Conflict of interest
The authors have no relevant financial or non-financial interests to disclose.
Ethical approval
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Pisa University Hospital (Protocol code 558).
Consent to participate
Buffy coats obtained from healthy donors used for this study were, “waste of the sample examined” not used for the dosage of biochemical analytes. Data security was maintained by the impossibility of tracing the patient’s identity and, therefore, any further information.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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PMC009xxxxxx/PMC9001821.txt |
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Arch Microbiol
Arch Microbiol
Archives of Microbiology
0302-8933
1432-072X
Springer Berlin Heidelberg Berlin/Heidelberg
35412092
2862
10.1007/s00203-022-02862-5
Mini-Review
Polyphenolic phytochemicals as natural feed additives to control bacterial pathogens in the chicken gut
http://orcid.org/0000-0003-1792-3170
Al-Mnaser Afnan q8science@hotmail.com
12
Dakheel Mohammed 3
Alkandari Fatemah 4
Woodward Martin 15
1 grid.9435.b 0000 0004 0457 9566 Department of Food and Nutritional Sciences, School of Chemistry, Food and Pharmacy, University of Reading, Reading, RG6 6DZ UK
2 grid.452356.3 0000 0004 0518 1285 Dasman Diabetes Institute, Dasman, Sharq, Kuwait
3 grid.411498.1 0000 0001 2108 8169 Department of Veterinary Public Health, College of Veterinary Medicine, University of Baghdad, Baghdad, Iraq
4 Department of Plant Protection, Public Authority of Agriculture Affairs and Fish Resources, Al-Rabia, Kuwait
5 Folium Science, Unit DX, Bristol, BS2 0XJ UK
Communicated by Erko Stackebrandt.
12 4 2022
2022
204 5 2538 8 2021
17 3 2022
20 3 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.
Poultry provides an important protein source consumed globally by human population, and simultaneously, acts as a substantial reservoir of antibiotic resistant bacterial species such as Escherichia coli, Salmonella, Campylobacter, Clostridium perfringens. These bacterial species can include commensal strains with beneficial roles on poultry health and productivity, and pathogenic strains not only to poultry but zoonotically to man. This review paper evaluates the role of phytochemicals as possible alternatives to antibiotics and natural anti-bacterial agents to control antibiotic resistance in poultry. The focus of this paper is on the polyphenolic phytochemicals as they constitute the major group; carvacrol oil (the active ingredient of oregano), thymol oil (the main ingredient of oregano), oregano oil, and tannins oil as feed additives and their mechanism of actions that might enhance avian gut health by controlling antibiotic-resistant bacterial strains spread in poultry.
Keywords
Escherichia coli
Salmonella
Campylobacter
Clostridium perfringens
Phytochemicals
AMR
issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2022
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pmcIntroduction
Improvements in white meat productivity have increased significantly in the recent years, to meet the continuous increase in local and global demand for white meat and feed the growing population (Delgado 2005). As a result, huge efforts have been put to achieve higher level of effectiveness in poultry production such as improving diet and husbandry practices (Thornton 2010) which collectively led to improving feed conversion ratio (FCR) to 1.4 (Science 1999). High-quality diet costs around 60% of the production costs, but it increases the standards of poultry production due to its ability to maintain healthy intestines of these birds and avoid developing pathogenic diseases (Porter 1998). These transformations had positive impact on modern poultry industry such as decreasing the time required to get to the commercial weight (Zuidhof et al. 2014), improving food economy and ensuring its sustainability (Flock and Preisinger 2002), and reducing environmental pollution (Gerber et al. 2007).
Antibiotics used as a prophylactic agent have shown to have positive effects on the growth performance of chicken as a presumed result of reduced pathogen load, reduction in competition for nutrients in the small intestine, reduction of inflammation, and improvement of digestion (Thomke and Elwinger 1998). Also, they were used to fight bacterial infections as a therapeutic drug and at sub-therapeutic levels as feed ingredients, because they were shown to enhance growth, but unfortunately this led to the rise of the first incident of resistant Salmonella enterica ser. Typhimurium in 1963 (Dewey et al. 1997). The growth promoting effect of antibiotics was discovered in the 1940s (Hughes and Datta 1983), and later in the 1950s and 1960s, they were authorised with set guidelines by the EU to be used in animal feeds (Castanon 2007), which also made a contribution to an improved poultry productivity (Bunyan et al. 1977), but this resulted in negative consequences of selecting highly resistant bacteria leading to the emergence of global antibiotic resistant bacteria (ARB) carrying antibiotic resistant genes (ARG) which was marked in the 1980s (Aarestrup 2003). This raised worries as these ARG would be transferred through the food chain from animals to man (Greko 2001), and this was proved by a European surveillance study conducted in 2005 that demonstrated the presence of ARB of animal origin among patients admitted to intensive care units (ICU) (Hanberger et al. 2009) which indicates that humans are direct recipient of these ARB and ARG. ARB were the reason behind high numbers of medical illnesses and even death among human (Cosgrove 2006).
As a result of the antimicrobial resistance (AMR) issue, the World Health Organization (WHO) set guidelines and recommendations to stop the use of antibiotics as growth promoters in 1997 (Caron et al. 2009). A year later in 1998, the EU imposed an initial ban on the use of antibiotics as feed additives in poultry feed and water (Dibner and Richards 2005), and this was followed by a complete ban on the use of prophylactic antibiotics in animal feed in 2006 (Millet and Maertens 2011). Later in 2013, the United States (US) Food and Drug Administration (FDA) ordered major medical manufacturers to stop labelling antibiotics as growth promoters. This came as a result of AMR and consumers demand for healthier antibiotic-free poultry products (Food and Administration 2013). Also recently in 2017, the FDA imposed new rules restricting the use of clinical antibiotics for the purpose of growth promotion in animal husbandry (Brüssow 2017). These precautionary measurements are being taken into consideration as they are important for the poultry welfare and its sustainability, and moreover for human (Casewell et al. 2003) as they are at the top of the food chain hierarchy. The emergence of poultry diseases has a negative economic impact and an increase in financial costs due to loss in poultry productivity and emergence of public health problems (Bryan and Doyle 1995). The previously mentioned rising issues in the poultry production has prompted a search for alternatives to control diseases (Si et al. 2006), and therefore looking for antibiotics alternative to serve the purpose of growth promotion and enhancement in the gut microbiota since diet has a direct impact on productivity and animal health (Borda-Molina et al. 2018), but this needs to be achieved without the current issue of AMR. There are many antibiotic alternatives used currently at commercial level such as probiotics, prebiotics, synbiotics, and bacteriophages (Gadde et al. 2017), but the focus of this literature review paper will be on phytochemicals.
Bacterial infections in poultry
Bacterial infections in poultry are inevitable and are the cause of high mortality rate among birds (Porter 1998). These infections can be caused by a variety of Gram-negative and Gram-positive bacteria.
Escherichia coli (E. coli)
E. coli is a facultative anaerobe (Finegold et al. 1983), Gram-negative bacterium (Scheutz and Strockbine 2015) belonging to the phylum Proteobacteria (Marchesi et al. 2016) and the family Enterobacteriaceae (Ewing 1986). E. coli is normally part of the human and animals’ intestinal natural microbiota (Ørskov and Ørskov 1992), being the most dominant aerobic bacterium (Savageau 1983) with 106–109 colony forming unit (CFU) per cm of the poultry (chicken and turkey) intestine (Leitner and Heller 1992), and it is one of the first species to colonise the gut of human (Mitsuoka 1973) and animal (Hudault et al. 2001). Also, it is one of the best studied bacterial species and often used as a model microorganism because of their different commensal and pathogenic types, and with E. coli K12 being the most common reference strain (Hobman et al. 2007). The pathogenic type such as avian pathogenic E. coli (APEC) strains are the causative agent of colibacillosis in poultry (Gross 1994), which studies have shown to be of multi-resistant nature to 10 or more antibiotics (Lima Barbieri et al. 2017). Moreover, E. coli can be easily grown in the laboratory, as it needs simple growth requirements, grows at a fast rate, and extensive information is already provided in literature (Donachie and Begg 1970).
In terms of antibiotic resistance, E. coli is the most common carrier of extended spectrum β-lactamase (ESBL) genes which are located on plasmids that facilitate their transfer (Donachie and Begg 1970), and these genes are widespread among chickens (Machado et al. 2008). Ingestion of food of animals origin containing ARB becomes a source of ARB and their ARG in the human gut, and this might affect antibiotics use or might cause opportunistic diseases in the future (Smith et al. 2002). In human hosts, it is the main causative agent and is responsible for most cases of urinary tract infection (UTI) (Stamm and Hooton 1993). Therefore, the need for alternative control measurement and this can be through using natural phytochemicals. There are many studies that suggest the efficacy of using phytochemicals to control E. coli growth as summarised in Table 1.Table 1 A summary of the mechanisms of actions of carvacrol, thymol and oregano (at sub-MIC level) against some of the bacteria responsible for poultry infections
Phytochemical Bacteria Target site Mode of action References
Thymol/carvacrol E. coli Heat and oxidative stress responses and iron transportation Increased expression of membrane genes (pspD and pspG), heat responses genes (ibpB), oxidative stress responses genes (grxA and soxS) and iron transport gene (feoA) Yuan et al. (2018)
Carvacrol/oregano E. coli Survival mechanism and multi-drug efflux system Missense mutation in cadC and marR Al-Mnaser and Woodward (2020)
Carvacrol E. coli Redox sensor system and multi-drug efflux system Missense mutation in soxR and frameshift in marR Chueca et al. (2018)
Carvacrol/oregano Salmonella Stress response Influence on the rpoS gene Cariri et al. (2019)
Carvacrol Salmonella Oxidative stress response Single nucleotide modification in the transcriptional regulators (yfhP and soxR) Berdejo et al. (2020)
Thymol E. coli Multi-drug efflux system Non-sense mutation in acrR gene encoding for the AcrAB repressor Al-Kandari et al. (2019)
Thymol Salmonella Thermal stress response Upregulation in the expression of the chaperones (GroEl and DnaK) Di Pasqua et al. (2010)
Thymol/carvacrol Salmonella Virulence genes Downregulation in the expression of the main virulence genes (hilA, prgH, invA, sipA, sipC, sipD, sopB, sopE2) Giovagnoni et al. (2020)
Carvacrol Campylobacter Motility systems Downregulation in the expression of genes encoding for motility systems (flaA, flaB and flgA) Wagle et al. (2019)
Carvacrol Campylobacter Thermal stress response Upregulation in the expression of the stress response genes (dnaK, grpE and groEL) Windiasti et al. (2019)
Salmonella
Salmonella is a facultative anaerobe, Gram-negative bacilli bacterium belonging to the phylum Proteobacteria and the family Enterobacteriaceae (MacConkey 1905). It usually resides in the intestinal tract of animals and humans and it is the causative agent of Salmonellosis disease, where it infects food such as poultry meat and egg (Hikasa et al. 1982). Salmonellosis as a food-borne disease is known as a public health issue causing concerns in industrial countries (D'Aoust et al. 1992) and responsible for high morbidity and mortality cases among human as indicated by the US Center for Disease Control and Prevention (CDC) (Mead et al. 1999). Among food-borne pathogens in the USA, Salmonella was found to be responsible for the highest percentage of these disease (Gast and Porter 2020). Ingestion of foods of animal origin contaminated with AMR-resistant Salmonella is likely to be responsible for most of Salmonellosis diseases (Angulo et al. 1998). These AMR-resistant Salmonella were first arising from the misuse of antibiotics in poultry industry, and due to its zoonotic nature, it found its way to human (Angulo et al. 2000). In Denmark, it was found out that by decreasing the use of sub-therapeutic in poultry industry, it led to a significant decrease in the prevalence of antibiotic-resistant Salmonella in broilers (Evans and Wegener 2003).
The genus Salmonella includes six sub-species of Salmonella enterica which is found to be responsible for diseases among warm-blooded animals (Gast and Porter 2020). There are more than 2600 serotypes of Salmonella enterica (Achtman et al. 2012), but Salmonella enterica serovars Heidelberg (SH) and Typhimurium (ST) are widespread among human and animal hosts (Zhao et al. 2008; Glenn et al. 2013). These bacterial pathogens are usually found to be responsible for food-borne outbreaks in human due to consumption of food products of animal origin (Authority et al. 2017). The misuse of antibiotics in poultry industry has led to the dissemination of antibiotic resistant Salmonella to ampicillin, chloramphenicol, quinolones and sulphonamide (Su et al. 2004). There are many factors that control the epidemiology of Salmonella infections such as (1) human demography, (2) human lifestyle, (3) human behavior, (4) industrial and technological revolutions, (5) changes in aviation industry, (6) bacterial adaptation, (7) status of public health infrastructure (Oaks et al. 1992), (8) human knowledge in food safety and health practices (Bruhn and Schutz 1999).
Campylobacter
Campylobacter is a microaerophilic, Gram-negative spiral-curved bacilli bacterium (Skirrow 2006) belonging to the phylum Proteobacteria and the family Campylobacteriaceae (Huang et al. 2020). It can be found in the intestinal tract of animals and human oral cavity with the ability to cause diseases in both hosts (Lee et al. 2016). Campylobacter species which inhabit poultry intestine and are associated with poultry diseases are Campylobacter jejuni and Campylobacter coli (Pezzotti et al. 2003) with the former specie being responsible for most of the infection cases. These bacteria species are the causative agents of enteritis and linked to chronic gastritis, gastric ulceration, and gastric cancer in human (Lee and Newell 2006). Campylobacter infection in human results from handling or ingestion of undercooked poultry meat contaminated with this pathogen, which 80% of the raw meat in the UK was found to be contaminated with it (Corry and Atabay 2001). Moreover, practicing low hygienic levels and food safety skills in the kitchen can lead to the spread of Campylobacter contamination with other undercooked food (Lee and Newell 2006).
Campylobacter and Salmonella share similar infection outcome and zoonotic nature, but Campylobacter are fastidious and differ in their metabolic and stress responses as demonstrated by genomic analysis (Lee and Newell 2006). Among zoonotic diseases, Campylobacteriosis has been reported to be the most frequently spread among humans in the EU (Hugas et al. 2009; Westrell et al. 2009), and the major source of infection was fresh broiler chicken meat infected with Campylobacter jejuni (Authority 2005; Wingstrand et al. 2006). Therefore, reducing the occurrence of Campylobacter in poultry meat would decrease its infection cases among human and that would be through various preventative measures: (1) vaccination (Wyszyńska et al. 2004), (2) bacteriophages (Wagenaar et al. 2005), (3) bacteriocins (Line et al. 2008), (4) organic acids (Chaveerach et al. 2004), (5) probiotics (Ghareeb et al. 2012), (6) antibiotics. However, administration of fluroquinolone antibiotic in poultry industry has led to the emergence of resistant Campylobacter and was found to be responsible for 10% of Campylobacter diseases in human (Randall et al. 2003). Hence, the need to look for alternative ways.
Clostridium perfringens
Clostridium is an anaerobic, Gram-positive spore-forming bacterium (Songer and Meer 1996) belonging to the phylum Firmicutes and the family Clostridiaceae (Wiegel 2015). Clostridium perfringens is a bacterium of ubiquitous nature and it is part of animal and human gut microbiota (Miller et al. 2010). At the same time, it is one of the frequent zoonotic bacterial pathogens that causes foodborne diseases outbreak in humans after Campylobacter and Salmonella (Buzby and Roberts 1997). It costs poultry industry economically as it is the main causative agent of necrotic enteritis (Van der Sluis 2000). Necrotic enteritis infections can be in the form of acute or subclinical, with the former being responsible for higher mortality rates among broiler chickens (Kaldhusdal and Lovland 2000). The acute form of the infection leads to the formation of severe necrosis in the mucosal layer of the small intestine, thereby increasing the rate of death (Gazdzinski and Julian 1992). While, the subclinical form of the infection causes reduced absorption and digestion, decrease in the weight gain and an increase in the FCR due to the damage in the intestinal mucosa (Kaldhusdal and Hofshagen 1992). At the level of bacterial gut microbiota, this infection leads to its disturbance which can be reversed by feeding a monoculture of Lactobacillus acidophilus or Streptococcus faecalis (Fukata et al. 1991). The classification of Clostridium perfringens puts them into five toxinotypes (A, B, C, D and E) according to the ability to produce four major toxins (α, β, ε and ι). The subclinical form of the infection is caused mainly by Clostridium perfringens type A producing alpha toxin and to a less degree by Clostridium perfringens type C producing alpha and beta toxin (Songer and Meer 1996). The European ban on the use of antibiotics as a growth promoter has been associated with the wide spread of necrotic enteritis (Immerseel et al. 2004). Hence, the need to find alternatives to control this pathogen.
Phytochemicals
Phytochemicals are natural plant products produced as secondary metabolites of which some possess antimicrobial effects (Metabolhtes 2004), and natural sources of feed additives and have been proven to be generally recognised as safe (GRAS) (Hashemi et al. 2008). These secondary metabolites can be non-digestible carbohydrates and compounds such as lignin, resistant protein, polyphenols and carotenoids, some of which are considered anti-oxidants (Saura-Calixto et al. 2000) and display anti-microbial activities (Wink 2004). They differ in chemical structure, biological activity, plant source, and method of production. In other words, they are natural sources of growth promoters coming from plants, herbs, or spices (Hashemi and Davoodi 2010). They come in different materialistic form: solid in the form of dried leaves or ground powder, or liquid in the form of an essential oil (Gadde et al. 2017). They differ in their composition, extraction method, storage conditions, growth stage, the source of plant and its geographical location (Dhami and Mishra 2015). Generally, phytochemicals are categorised into five main groups; terpenoids, polyphenols, organosulfur compounds, phytosterols, and alkaloids (Somani et al. 2015). However, polyphenols represent the major and main compounds of these phytochemicals (Gadde et al. 2017), hence the focus of this current paper.
Presently, plant-based natural therapies are of increased popularity, because consumers are becoming aware of concerns regarding synthetic additives (Hammer et al. 1999) as well as the dangers of antibiotic use as discussed earlier. Scientific research changed the perception of food including phytochemicals from being an energy source to that of health promoting supplements because of their bioactive roles (Berner and O’Donnell 1998). Therefore, it is crucial to understand the scientific background behind the beneficial roles of phytochemicals as anti-microbial agents (Mitscher et al. 1987), and the process of using them as an alternative to antibiotics. The scientific interests in phytochemicals are due to the rising problem of ARB in poultry industry, consumers demand, and the EU ban on the usage of antibiotics for growth promotion. The biological mechanism of action of these phytochemicals is not very well-understood, but it depends on their chemical structure (Hashemi and Davoodi 2010).
Phytochemicals used as poultry feed additives can improve animal’s health and performance because of their anti-microbial, anti-stress (Wang et al. 1998) and anti-oxidant properties (Valenzuela 1995), and their ability to modulate gut microbiota (Hashemi et al. 2009) and enhance immune responses (Chowdhury et al. 2018). The improvements in the animal health performance can be observed in the form of an increased body weight, feed intake and FCR. Physically, the positive effects included an improved carcass quality, meat quality and nutritional values (Valenzuela-Grijalva et al. 2017). The efficiency of these phytochemicals is determined by intrinsic and extrinsic factors such as animal’s nutrition and health, type of diet and environment (Giannenas et al. 2003). They can act as prebiotics by enhancing the growth of beneficial bacteria and suppressing the growth of pathogenic bacteria (Cencic and Chingwaru 2010) which leads to the enhancement in the gut microbiota (Hashemi and Davoodi 2010). Thus, they reward the host by shaping gut microbiota in a beneficial way (Laparra and Sanz 2010). On the bacterial level, previous research has demonstrated that using phytochemicals as feed additives results in a decrease in the population of E. coli and also an increase in the activity of specific digestive enzymes (Jang et al. 2007) such as amylase in the intestinal system of female broiler chickens (Lee et al. 2003) and maltase in the intestinal system of male broiler chickens (Xu et al. 2003).
Carvacrol, thymol and oregano
Thymol (2-isopropyl-5-methylphenol) (Fig. 1) and carvacrol (5-Isopropyl-2-methylphenol) (Fig. 2) are phenolic compounds (Kim et al. 2016) and they are the main constituents of the essential oils of oregano (Fig. 3). They are structural l isomers, sharing the same chemical structure in the form of a phenolic ring but differing in the location of hydroxyl groups (Ultee et al. 2002). Moreover, carvacrol is the key ingredient of oregano essential oil that is extracted from plants of the genus Origanum (Kintzios 2002), but its abundance in plants differs from one species to another (Gounaris et al. 2002). Thymol, carvacrol, and oregano share the same chief components which are monoterpenic phenols consisting of two main ingredients of γ-terpinene and p-cymeme (Kokkini 1996). Carvacrol and oregano exhibit anti-microbial activities against pathogenic microorganisms whether from plant, animal or human sources, and these microorganisms include bacteria and fungi (Baricevic and Bartol 2002).Fig. 1 Chemical structure of thymol (Kim et al. 2016)
Fig. 2 Chemical structure of carvacrol (Kim et al. 2016)
Fig. 3 Chemical structure of oregano (Kim et al. 2016)
Carvacrol and thymol as feed additives showed enhanced growth promoting effects on anti-oxidant enzyme activities, immune responses, digestive enzyme activities among broiler chickens (Hashemipour et al. 2013). Oregano oil containing carvacrol and thymol is effective against E. coli in a dosage-dependent manner (Friedman et al. 2002; Al-Mnaser 2019; Alvarez et al. 2019). Generally, the phytochemicals (e.g. carvacrol) with a high percentage of other phenolic compounds display potent anti-bacterial properties (Guynot et al. 2003). As anti-bacterial agents, the main mechanism of action appears to be disruption of the integrity and functionality of the cell wall and cell membrane structures (Sikkema et al. 1995). At minimum inhibitory concentration (MIC) level, they disrupt the outer membrane structure of Gram-negative bacteria, increasing the permeability of cell membrane, leading to leakage of cellular energy sources in the form of adenosine tri-phosphate (ATP) (Gill and Holley 2006) and may also result in the bursting of the bacterial cell (Sikkema et al. 1995). These essential oils are highly hydrophobic and thus can readily integrate into and transition across the bacterial cell membrane (Sikkema et al. 1995). Interestingly, exposing bacteria to sub-lethal concentrations of these phytochemicals leads to changes in the ratio of unsaturated and saturated fatty acid component of the cell membrane (Di Pasqua et al. 2006) suggesting that bacteria develop an adaptive response upon exposure. Furthermore, oregano oil exhibits high biological activities resulting in growth promotion when used as feed additives in poultry (Giannenas et al. 2005). Another study showed that oregano extract (Origanum vulgare) contains a high phenolic content that exhibits anti-oxidant properties (Gómez-Estaca et al. 2009). More recent studies showed that broiler chickens fed diet supplemented with oregano resulted in the following: (1) significant increase in the digestive enzyme chymotrypsin and enhanced protein digestion (Basmacioğlu Malayoğlu et al. 2010), (2) significant increase in body weight, higher anti-oxidant activity of serum, significant decrease in cecal E. coli population resulting in an increased growth performance (Roofchaee et al. 2011), (3) significant increase in body weight and significant decrease in FCR among broilers chickens infected with Eimeria species (Pajić et al. 2019). Moreover, oregano and other herb extracts can suppress the growth of harmful coliform bacteria, but do not affect the growth of beneficial bacteria (Namkung et al. 2004).
In vitro study demonstrated the anti-bacterial activity of carvacrol and oregano by decreasing the number of Salmonella and Campylobacter jejuni in chicken cecal content (Johny et al. 2010), which was further supported by an in vivo study suggesting the efficacy of using these phytochemicals as feed additives in 10 days old broiler chickens due to the significant reduction in the number of Campylobacter in chicken ceca (Arsi et al. 2014). The mode of action of the carvacrol treatment against Campylobacter jejuni can be due to its ability to act as membrane destabilization agent and therefore increasing the susceptibility and cell membrane damage of this bacteria (Windiasti et al. 2019). A longer period study covered 35 days showed that the absence of Campylobacter sp. at day 21 was due to the increase in the number of Lactobacillus sp. with probiotic beneficial effects and thereby improving chicken health by preventing Campylobacter infection (Kelly et al. 2017). As for the spore-forming bacteria Clostridium perfringens, carvacrol, oregano and thymol have showed their ability in preventing their sporulation and controlling their numbers in meat (Juneja and Friedman 2007). Another study further supported the anti-bacterial efficacy of thymol and carvacrol when used as feed additives in broiler chickens challenged with Clostridium perfringens leading to an improve in the chicken gut health supported by the presence of Lactobacillus strains with probiotic beneficial properties (Du et al. 2015). Table 1 provides a summary of the proposed mode of actions of these phytochemicals in further details and at the genetic level of the bacterial cell.
Tannins
Tannins are polyphenolic compounds [2,3-dihydroxy-5-[[(2R,3R,4S,5R,6S)-3,4,5,6-tetrakis[[3,4-dihydroxy-5-(3,4,5-trihydroxybenzoyl)oxybenzoyl]oxy]oxan-2-yl]methoxycarbonyl]phenyl] 3,4,5-trihydroxybenzoate] (Fig. 4) (Kim et al. 2016) categorised into four groups: (1) condensed tannins or proanthocyanidins, (2) hydrolysable tannins, (3) phlorotannins from brown algae, and (4) complex tannins (Suvanto et al. 2017; Brus et al. 2018), and come in different chemical structures (Lillehoj et al. 2018). The presence of different hydroxyl groups at different positions in its structure believes to be behind its ability to bind with the carboxyl groups of the proteins (Wang et al. 2016). Moreover, different chemical composition and structure of tannins makes it bacterial species-specific (Huang et al. 2018). In nature, tannins originate in several plant species, specifically in the inedible parts (i.e. bark or wood) (Brus et al. 2018). Some of these tannins are responsible for defending the plants and others give the plants their odor or color (Redondo et al. 2014). Tannins have been known for their ability to promote growth and their anti-microbial properties, making them a broiler feed additive of choice in South America (Lee et al. 2021). Though, the exact mechanism of action of tannins is still poorly understood (Smith and Mackie 2004), the proposed inhibitory activities involve its interaction with proteins, bacterial cell membrane (Hemingway and Laks 2012), and chelation of iron metals (Scalbert 1991). Other intracellular activities include inhibiting enzymatic and metabolic activities that result in bacterial cell morphological changes (Liu et al. 2013). A recent study showed the immunomodulation effect of tannins when used as a feed additive, and this was due to a significant increased expression of cytokines (IL-6 and IL-10) in the cecal cells. This resulted in an altered metabolism, enhanced gut health and bird growth, and feed efficiency (Lee et al. 2021).Fig. 4 Chemical structure of tannins (Kim et al. 2016)
On a larger scale, inclusion of tannins in the poultry diet had health promoting effects on growth and intestinal effects (Schiavone et al. 2008), and increased feed efficiency (Redondo et al. 2014). The health promoting effects on growth can include a decrease in lipid oxidation and cholesterol level, an increase in the content of beneficial fatty acids, and an increase in body weight (Starčević et al. 2015). On the bacterial level, it can lead to changes in the gut morphology and type of bacteria and thereby increasing their biodiversity in the gut of broiler chickens (Viveros et al. 2011). Also, it can act as an anti-oxidant and provides a source of vitamin E in animal nutrition (Brenes et al. 2008). On the physiological level of the broiler, inclusion of tannins leads to the following: (1) red blood cell growth and maturation of the small intestine (Iji et al. 2001), (2) elongation of the villi of the small intestine, and (3) increased number of cell mitosis of the duodenum (Khambualai et al. 2009). Collectively, these result in increased weight gain on a daily basis and in total, and enhanced feed utilization (Lee et al. 2021). On the broiler production level, tannins increase the quality and nutritional values of meat (Mannelli et al. 2019), and eggs (Minieri et al. 2016).
Tannins as an anti-bacterial agent can reduce the occurrence of avian diseases and transmission of zoonotic pathogens (Hassan et al. 2020). Examples on this can include the following: (1) inhibit the growth of E. coli by acting as anti-biofilm and anti-motility agents (Dakheel et al. 2020), (2) inhibit the growth of Salmonella by acting as anti-quorum sensing and anti-virulence agent (Sivasankar et al. 2020), (3) inhibit the growth of Campylobacter sp. which may be resorted to their ability to bind to the proteins and enzymes within the bacterial cell (Nagayama et al. 2002), (4) inhibit the growth of Clostridium perfringens by acting as a membrane destabilization agent (Kaimudin and Manduapessy 2020) and as an anti-toxin agent (Elizondo et al. 2010), (5) chelates iron that is crucial for the growth of most pathogenic bacteria (Chung et al. 1998). Table 2 provides a summary of the proposed mode of actions of tannins in further details and at the genetic level of the bacterial cell.Table 2 A summary of the mechanisms of actions of tannins (at sub-MIC level) against some of the bacteria responsible for poultry infections
Phytochemical Bacteria Target site Mode of action References
Tannins E. coli Biofilm formation and motility genes Repression in the production of curli genes (csgB and csgD) and downregulation in the expression of motility genes (fimA, fimH, flhD, motB, qseB, qseC) Yang et al. (2016)
Tannins E. coli Motility genes and quorum sensing Downregulation in the expression of motility genes (fliA, fliY, fljB, flhC, fimD) and repression in the expression of quorum sensing genes (sdiA and srgE) Li et al. (2014)
Tannins/phenolic compounds Salmonella Type III secretion system/pathogenicity genes Downregulation in the expression of the type III secretion system-related genes (hilA, hilC, invA, invF, sirA and sirB) Salaheen et al. (2016)
Condensed tannins (proanthocyanidins) Salmonella Pathogenicity island 1/virulence secretion system Suppression in the secretion of the pathogenicity island SPI1 Morita et al. (2016)
Conclusion
Antibiotic resistance issue made the scientific community shift their perspective and search for antibiotic alternatives. One of the antibiotic alternatives is the use of natural plant products, phytochemicals. This review has focused on four polyphenolic phytochemicals with promising results which make them good candidates to be used as feed additives in the poultry industry instead of antibiotics. However, more studies need to be done to increase our understanding in the long-term used of these phytochemicals and how will they affect us as humans considering that we are at the end of the food chain. This AMR issue will continue to increase in the coming years due to the current overuse of anti-bacterial compounds during this coronavirus pandemic. Therefore, the need to increase our understanding in this area and find effective alternatives is very crucial and of high importance.
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Research on Optimization of Supermarket Chain Distribution Routes Under O2O Model
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International Business College, Zhejiang Industry & Trade Vocational College, Wenzhou, Zhengjiang 325003 China
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Dealing with the store distribution problems in transformation and upgrading of supermarket chains under Online-to-Offline (O2O) e-commerce model and considering the factors that affect the distribution cost and customer satisfaction, such as distribution distance, number of distribution vehicles, and delivery time, an O2O store distribution optimization model has been constructed with the purpose of minimizing total distribution cost. Moreover, a two-stage heuristic algorithm for ordering nearest distribution and mileage saving planning has been designed. The applicability and effectiveness of the model and method have been verified by enterprise case data collected. The results have shown that the total cost of distribution is obviously reduced compared with the accounting results of related models, which is suitable for supermarket chains or single-store retail enterprises with low probability of order splitting and distribution due to the shortage of certain products.
Keywords
O2O
Chain stores
Optimization model for distribution
Mileage saving method
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pmcIntroduction
With the rapid development of China’s e-commerce, online shopping has gradually become one of the main shopping methods for consumers. Especially with the “COVID-19 epidemic,” the measures of “home isolation” have further promoted consumers’ recognition of online shopping methods for fresh department stores and accelerated the popularization and application of O2O e-commerce model. The increasingly mature O2O e-commerce model has become the preferred path for traditional retail enterprises to transform and upgrade. The so-called O2O e-commerce model is essentially an operation mode that connects offline real business with online users. The offline real economy can tap and attract consumer traffic online, and consumers can place orders online or experience in physical stores. The development of O2O e-commerce model has brought a new strategic decision direction for the operation upgrade of supermarket chains. With the help of digital technologies such as network information technology, cloud computing, and big data, traditional supermarket chains implement O2O e-commerce mode, build new sales channels, and expand consumer markets. However, if traditional large supermarket chains want to give full play to the advantages of O2O e-commerce mode, they must create a good shopping experience for consumers. According to the investigation of five large supermarket chains implementing O2O e-commerce mode, one of the key factors affecting consumers’ shopping experience is the delivery time of goods. Whether the goods can be delivered within the time expected by consumers depends on two logistics links: sorting and distribution, while the “last mile” instant distribution problem is more difficult to control and optimize. The “last mile” instant delivery problem is a “stuck neck” problem that restricts the rapid development of O2O e-commerce mode. According to the high sensitivity of consumers to the delivery time of goods, the distribution problem of supermarket chains in online to offline can be attributed to VRPTW (Vehicle Routing Problem with Time Windows). For supermarket chains, it can effectively solve the “last mile” instant delivery problem, which can enhance the decision-making effectiveness of traditional retail enterprises in implementing O2O e-commerce mode.
Material and Methods
On the Solution of VRPTW Problem
Clark and Wright proposed Clark-Wright algorithm (C-W cost saving method) in 1964, which was used to solve VRPTW problem. Mester et al. (2007) put forward the multi-parametric evolution strategies algorithm, which is mainly used to solve the vehicle routing problem of VRPTW and CVRP. Yang et al. (2006) improved the simulated annealing algorithm to solve VRPTW problem, and introduced the representation method of solutions arranged directly by customers, which improved the solution efficiency. Meng et al. (2014) proposed the HPBIL algorithm (Hybrid Population-based in Cremental Learning Algorithm) for the VRPTW problem, which can minimize the total driving distance and the number of vehicles at the same time.
Research on Vehicle Routing Problem Under Online to Offline
On the one hand, it studies the distribution of supermarket stores. On the basis of considering the constraints of time window, Yao (2014) analyzed the various costs in the distribution process of fresh products in supermarket chains, establishes a model of minimizing the total distribution cost, and optimizes the distribution vehicle route by using the mileage saving method. In order to minimize the travel time of vehicles in the distribution process, Wu et al. (2015) established an uncertain opportunity constraint model. On the other hand, it is the study of customer (consumer) distribution. According to the characteristics of online to offline (O2O) fresh take-out orders with high dynamics and strong timeliness of distribution services, Li and Chen (2020) established an O2O fresh take-out instant distribution path optimization model with a hard time window. In order to reduce the distribution cost of fresh food distribution enterprises, Wu et al. (2015) the improved NSGA II (Non-Dominated Sorting Genetic Algorithm II) to solve the model. Yao (2014) etc., considering the factors such as commodity types, inventory capacity, and customer returns, put forward the order splitting strategy of splitting only by commodity types rather than quantity, and set up an O2O store distribution optimization model based on order splitting with the goal of minimizing the total distribution cost.
To sum up, there is a lack of research on the optimization of distribution path for large supermarket chains to implement online to offline. Therefore, this paper will mainly focus on the real-time distribution of supermarket chains in online to offline, investigate the logistics operation data of a supermarket chain, and explore the model of real-time distribution path optimization, hoping to provide reference for retail enterprises in implementing O2O e-commerce model.
Construction of Distribution Optimization Model Based on O2O Stores
Problem Description and Hypothesis
Company A, a supermarket chain enterprise, is using the e-commerce platform to implement online to offline and give full play to the advantages of online traffic. After the customer of company A places an order through the e-commerce platform, the system will automatically assign the order to the nearest store according to the customer’s location, and the store will organize personnel to pick and distribute the goods, with the distribution distance of the store being 3–5 km. The main products consumed by customers belong to fresh department stores, and the order quantity is generally small, but there are many varieties, which are usually packaged into several parcels for delivery to customers. Because customers have higher requirements on delivery time, the timely delivery of goods is the key factor to improve online traffic. According to the data collected randomly from a store in A company for 1 month, the customer order cancellation rate is 17.20%, of which 45.23% are rejected or cancelled due to delivery overtime, and the customer order delivery overtime rate is 22.12%. The time for customers to place orders is mainly from 9:00 a.m. to 12:00 p.m. and from 13:00 p.m. to 16:00 p.m. During this time interval, the average number of orders placed by customers per hour is 200, and customers’ demands are random, but there are requirements for delivery time. In view of the uncertainty of customer demand and the limitation of service time window, each delivery vehicle can deliver packages from multiple customers, and the vehicles start from the store and finally return to the store to deliver the next batch of orders. Therefore, it is necessary to arrange the delivery schedule according to the customer’s location, order placing time, and delivery time. Assuming that customer demand obeys random distribution, according to these characteristics, it constitutes a logistics instant distribution routing problem with time windows under random demand.
According to the problem description, the following assumptions are made to facilitate the construction of distribution optimization model.I. Chain supermarket stores have sufficient inventory, and there is no shortage, resulting in the loss of orders;
II. Each customer’s order product can be packaged into several packages according to the commodity category and standard packaging size;
III. Each delivery vehicle can deliver packages from multiple customers, but it cannot exceed the maximum loading capacity of the vehicle;
IV. Each car must start from the store and return to the store after delivery.
Model Structure
Developing an optimization model needs to define the objective functions and constraints (Xu et al. 2021; Zhu et al. 2021; Wang et al. 2021, 2022a, b; Zhang et al. 2022). Therefore, the objective function and constraints of the O2O store distribution optimization model are as follows:1 MinC=∑m∈Δ∑k∈Γm∑i∈m∪I∑j∈m∪I,j≠iGSijxijmk+∑m∈Δ∑k∈Γmi∑jϵI,j≠iFXijmk+∑m∈Δ∑k∈Γm∑i∈m∪I∑j∈m∪I,j≠iδjxijmk
s.t.2 ∑i∈I∑k∈ΓmPidmkyidmk≤Wmd,∀mϵΔ;∀dϵDi
3 δi=α,Timk>Ti;δj=0,Timk≤Ti,∀i∈I,∀m∈Δ,∀k∈Γm
4 ∑iϵI∑dϵDiPidmkyidmk≤U,∀m∈Δ;∀k∈Γm
5 ∑m∈Δ∑k∈Γmyidmk=1,∀i∈I;∀d∈Di
6 ∑m∈Δ∑k∈ΓmDiyidmk≤Widmk,∀i∈I,∀d∈Di
7 ∑i,j∈I,i≠j∑k∈Γmxijmk≤Km,∀m∈Δ
8 ∑i∈Ixmimk=∑i∈Iximmk≤,∀m∈Δ;∀k∈Γm
9 ∑iϵIxmimk=∑iϵIximmk=0,∀m∈Δ;∀k∈Γm
10 ∑jϵm∪Ixijmk=∑jϵm∪Ixjimk≤1,∀i∈I;∀m∈Δ;∀k∈Γm
11 ∑iϵm∪ITimk+timk+Sij/Vxijmk=Tjmk,∀j∈I;∀m∈Δ;∀k∈Γ
where I = {1, 2,⋯, i} set of customers; Δ = {0, 1, 2, ⋯, m} collection of stores; Γm=1,2,⋯,k,⋯,Km the store m distributes a collection of vehicles, in which k represents the serial number of vehicles and Km represents the total number of vehicles dispatched and distributed by store m; N = {1,2,⋯,n} collection of distribution lines; U = maximum loading capacity of vehicle; V = travel speed of vehicle; Wmd = inventory in store m that meets the number of packages for all customers; F = fixed cost of one distribution vehicle; G = driving cost per unit distance of distribution vehicles; Di = package number of goods by customer i, Di = {1,2,⋯,d}; Sn = the nth distribution route is the distance from store m to customer I and then to store m, i∈I;Sij; j = distance from store (or customer) i to store (or customer) j, i,jϵm∪I,i≠j; δi = customer i’s time deviation penalty; Ei= order placing time of customer i; Ti′ = the latest delivery time of customer i; timk = the residence time of the delivery vehicle k from the store m at the customer i; xijmk = 0–1 variable, if the vehicle k departing from the store m is from customers i to j, then xijmk takes 1, otherwise, it takes 0, i ≠ j, jϵI; yidmk = 0–1 variable, if the vehicle k departing from the store m delivers the parcel d for the customer i, then yidmk takes 1, otherwise, it takes 0; Tjmk = time when the vehicle k departs from the store m and arrives at the customer j; Pidmk = The store m uses the vehicle k to deliver the package d to the customer i; and C = total cost of distribution.
Equation (1) was the objective function which means that the total cost of logistics distribution is the smallest, and the first item is the total cost of distribution vehicles, which is proportional to the distribution distance; item 2 is the fixed total cost of delivery vehicles, which is directly proportional to the number of delivery vehicles, and item 3 is the time penalty cost, which is directly proportional to the number of overtime delivery customers (Yao 2014).
In the constraint equations, Eq. (2) indicates that the store inventory meets the demand of its distribution customers; Eq. (3) indicates that the customer delivery time deviates from the value of penalty fee; Eq. (4) indicates that the number of goods loaded and distributed by each vehicle when starting from the store cannot exceed its loading limit, and its empirical value of vehicle loading is 8 packages; Eq. (5) means that several packages for each customer can only be completed by one store and one delivery vehicle; Eq. (6) indicates that each customer’s order demand must be met; Eq. (7) indicates the restriction on the number of vehicles distributed in stores; Eq. (8) indicates that the vehicle starts from the store and returns to the store after distribution; Eq. (9) indicates that the delivery vehicle cannot go from one store to another; Eq. (10) means that each delivery vehicle departing from the store can deliver to customers at most once; and Eq. (11) represents the time when the delivery vehicle arrives at the customer.
Algorithm Design
The O2O store distribution optimization model based on customer order system allocation is essentially a new extension of the vehicle routing problem with time windows in a specific mode, and it is also a NP-hard problem. From the model (1), it can be made clear that the variables that affect the total cost of logistics distribution are the distribution distance, the number of distribution vehicles, and the delivery time. Firstly, according to the distance between customers and stores, the system distributes orders to corresponding stores according to the principle of proximity, and sets service time windows according to the speed of vehicles to ensure that customer orders can be delivered on time, and at the same time forms a directed graph from stores to customers in the service time windows, which is convenient for path calculation and planning; Then, combined with mileage saving method, the distribution route is optimized, so as to achieve the maximum distribution customers, the shortest distribution trip, and the maximum probability of on-time delivery, thus reducing the fixed cost, transportation cost, and time penalty cost of vehicles.
Design of Undirected Graph from Store to Customer
Customer demand and location are random. Any store has the goods required by a certain customer. After the system receives the order, it only needs to send the order to the corresponding store according to the principle of proximity, and at the same time, select the customer object with optimized distribution path on the condition of service time window.Step 1: Calculate the distance between the ordering customer and each store, and distribute the order to a certain store;
Step 2: Set a service time window based on the customer’s order time and the set earliest delivery time and latest delivery time;
Step 3: Calculate the distance between customers in the service time window;
Step 4: Output the distribution directed graph from the store to each customer within the service time window.
Path Optimization Using Mileage Saving Method
Combined with directed graph, the optimal distribution route is found by mileage saving method. The core idea of mileage saving method is to optimize and merge two loops in transportation problem into one loop in turn according to the mileage saving size. After each route optimization and merger, the total transportation distance decreases to the maximum extent, until the loading limit of one vehicle is reached, the next vehicle is optimized, and the best distribution route is gradually found to realize efficient distribution, so as to minimize the distribution time, the shortest distance, and the lowest cost. The specific steps are as follows:Step 1: Expression of distribution route optimization solution. For node numbers such as stores and customers, P0 represents stores and P1 represents customers, then the distribution route for customers is P0-P1-P0, and the distribution route forms a loop from stores to customers and then back to stores;
Step 2: According to the directed graph from the store to the customer, taking the store as a fixed node, two customer nodes are selected in turn to form a triangular distribution route. According to the principle that the sum of the two sides is greater than the third side, the distribution mileage saved by the two nodes is calculated to form a mileage saving table;
Step 3: Sort according to the saved mileage to form a sorted list of saved mileage;
Step 4: Select two customers with large mileage savings, connect with the store to form a primary distribution line, and then add customers with large mileage savings according to the maximum vehicle load. Until the vehicle load on the distribution line is less than or equal to the maximum vehicle load, but it cannot be overloaded, the first distribution line is solved, and the solution is expressed as P0-P1-P2-P3-0;
Step 5: Solve the 2nd, 3rd, …, and n distribution routes according to step 4, until all customers are on the distribution routes within the service time window, and then the optimization of the distribution routes is finished, that is, the distribution routes are solved into n distribution routes.
Example Analysis
In view of the model problem studied, the time range of placing orders is set from 10:00 to 10:30, and 150 customer orders are randomly selected from the back-office system of company A for distribution and mileage saving calculation, so that the total cost of logistics distribution can be minimized on the basis of meeting customers’ time demands. According to the statistical analysis of the company’s operation data, the fixed cost of distribution vehicles is 3 CNY/train number, the variable cost is 1.4 CNY /km, the maximum load of vehicles is 8 parcels, the average driving speed is 40 km/h, and the penalty for late arrival is 3.5 CNY per order.
Analysis of Undirected Graph from Store to Customer
According to the principle of proximity, among the 150 customer orders extracted by the system, 12 customer orders are assigned to a certain store, with store coordinates of 120.72123, 28.004919 and user-defined store serial number of P0, i.e., m = 0. After the order is processed by the information system, the package number, location (latitude and longitude), order placing time, and the set latest delivery time corresponding to each customer’s order number are shown in Table 1. Combined with the data analysis of GPS software, the system forms a directed graph covering 12 customers including P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, P11, and P12 from store P0 (He 2021) as shown in Fig. 1. At the same time, calculate the shortest travel path distance between nodes, as shown in Table 1.Table 1 Order customer information
Customer node Business order number Package number Longitude Latitude When the customer placed the order Latest delivery time of order
P1 6,000,207,333,778,580,969 1 120.694314 28.001572 10:06:24 11:30:00
P2 6,000,208,585,408,960,969 2 120.72425 28.001054 10:06:27 11:30:00
P3 6,000,207,951,816,650,969 2 120.698593 28.005429 10:09:29 11:30:00
P4 6,000,207,952,084,330,969 2 120.702874 28.014095 10:09:29 11:30:00
P5 6,000,208,406,320,910,969 3 120.711418 28.002773 10:17:23 11:30:00
P6 6,000,208,408,640,930,969 1 120.717967 27.986901 10:10:28 11:30:00
P7 6,000,208,410,131,000,969 2 120.725948 27.998592 10:06:27 11:30:00
P8 6,000,208,406,964,410,969 2 120.694548 28.005544 10:06:27 11:30:00
P9 6,000,208,407,048,880,969 1 120.715534 28.004111 10:16:31 11:30:00
P10 6,000,208,411,228,880,969 3 120.719552 28.00485 10:29:21 11:30:00
P11 6,000,208,414,860,360,969 2 120.725653 27.995724 10:29:21 11:30:00
P12 6,000,208,413,018,490,969 3 120.717154 27.996256 10:29:21 11:30:00
Fig. 1 Location distribution map of stores and customers
Calculation Results and Analysis
Calculated by the Method in This Paper
Based on the directed graph analysis from store to customer, the route is optimized by mileage saving method (Fig. 2). The distribution from store P0 to 12 customers, such as P1, P2, P4…, P12, can complete the distribution task within the time window, and can be completed through three distribution routes, that is, the first optimal distribution route is P0-P3-P4-P8-P1-P0. The second optimal distribution route is P0-P12-P6-P11-P7-P0. The optimal distribution route is P0-P10-P2-P5-P9-P0, then δi=0, there are 3 distribution lines N and 3 vehicles K, and the total distribution distance is S=∑n∈NSn=23.9 (km) (Table 2). According to the O2O store distribution optimization model, the fixed total cost of distribution is 9 CNY, the distribution cost is 25.30 CNY, and the penalty cost for late arrival is 0, so MinC=34.30CNY.Fig. 2 Optimal solution distribution road map
Table 2 The shortest driving distance between the store and the customer and the customer
P0
P1 3.2 P1
P2 0.83 3.6 P2
P3 2.7 0.82 3.1 P3
P4 3.9 1.5 4.1 1.3 P4
P5 1.4 2.2 1.5 1.7 2.7 P5
P6 3.4 3.7 2.6 3.8 4.8 2.5 P6
P7 1.4 4 0.62 3.5 4.6 1.9 2.2 P7
P8 3.5 0.97 3.9 1.1 1.3 2.5 4.6 4.3 P8
P9 0.97 2.5 1.3 2 2.9 0.72 2.5 1.5 2.8 P9
P10 0.39 3.1 0.82 2.6 3.6 1.1 2.7 1.4 3.4 0.89 P10
P11 2.1 3.6 1.4 3.5 4.5 1.7 1.7 0.85 4.3 1.7 1.7 P11
P12 1.4 3.1 1.2 3.1 4.1 1.3 1.8 1.8 3.8 1.2 1.5 0.78 P12
Calculation by the Method in Reference Xin et al (2020)
According to this example, the store has sufficient inventory, and there is no order splitting. However, according to the assumption of literature Xin et al (2020), one package is packed for each customer’s order, and only one customer is delivered to each vehicle. Figure 3 shows the distribution route from the store to the customer and then back to the store. According to the statistical analysis of the company’s system, this kind of direct distribution can be 100% guaranteed to be delivered within the set latest delivery time, then δi=0, that is, the total fixed cost of distribution is 36 CNY, the distribution cost is 70.53 CNY, and the penalty cost for late arrival is 0, so MinC=106.53CNYFig. 3 The distribution route map planned by Xin et al. (2020)
Conclusion
In this paper, we have discussed the joint optimization of order allocation and distribution path of large supermarket chains in online to offline, and built a model around three variables that affects distribution cost: distribution distance, number of distribution vehicles, and delivery time. Sample orders are randomly selected through time window constraints, and the orders are allocated according to the principle of proximity and the set delivery time range. Taking the order delivery of a certain store as the path optimization object, the route optimization is carried out by using the mileage saving method, saving the delivery mileage by 26.5 km, and the mileage saving rate is 52.58%; According to the constructed O2O store distribution optimization model, the total cost of logistics distribution has decreased by about 67.80% compared with Xin et al (2020), which shows that the model is feasible and suitable for large supermarket chains or single-store retail enterprises with low probability of order split distribution due to the shortage of certain products. In the algorithm design, the empirical value is adopted to set the delivery service time window. On the one hand, the rationality of setting the time window is not analyzed, and on the other hand, the difference between soft and hard time windows is ignored. In the future, in-depth research will be carried out in these two aspects to improve the applicable scope of the model.
Acknowledgements
The authors acknowledge the Research Initiation Fund Project of Introducing Talents of Zhejiang Industry & Trade Vocational College: “Research on the Collaborative Development of Logistics Industry Cluster in Zhejiang Province Based on Service Ecosystem.”
Data availability
All data are available from the corresponding author.
Declarations
Conflict of Interest
The author declares 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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PMC009xxxxxx/PMC9001827.txt |
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EuroMediterr J Environ Integr
EuroMediterr J Environ Integr
Euro-Mediterranean Journal for Environmental Integration
2365-6433
2365-7448
Springer International Publishing Cham
35434265
300
10.1007/s41207-022-00300-y
Thematic Issue
Evaluating the importance of urban green spaces: a spatial analysis of citizens’ perceptions in Thessaloniki
http://orcid.org/0000-0003-2184-9268
Latinopoulos D. dlatinop@plandevel.auth.gr
grid.4793.9 0000000109457005 School of Spatial Planning and Development, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Responsible Editor: Antonis Zorpas.
12 4 2022
2022
7 2 299308
7 2 2022
16 3 2022
© Springer Nature Switzerland AG 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.
In Greek cities, urban green spaces are scarce and well below acceptable standards. However, policy makers and planners are not prioritizing long-term planning strategies for urban green and do not attempt to engage citizens in relevant decision-making and urban planning processes. In this context, a web-based public survey was conducted in the city of Thessaloniki (Greece) during the COVID-19 pandemic, aiming to identify citizens’ attitudes, satisfaction levels, actual behaviour and future expectations about urban green spaces (UGS). It also aimed to measure the effect of COVID-19 (mobility) restrictions on UGS visitation. All these issues were explored through a spatial lens, by developing measurable and mappable results suitable for future urban planning decisions. According to these results, citizens tend to report a very low satisfaction level about the current state of UGS (in terms of their adequacy and quality), and they tend to travel a great distance to reach an urban park (about 2 km on average). Moreover, the results indicate that spatial differences are very significant in terms of UGS availability and accessibility. Another important outcome of this study is that, unlike in other cities, the frequency of visiting green spaces in Thessaloniki did not increase during the pandemic. On the contrary, a slight downward trend was observed, maybe due to the combined effect of restriction measures and the lack of proximity/availability of UGS to local population groups. The maps produced in this study may thus facilitate well-informed planning decisions related to the development of new green projects.
Keywords
Public green spaces
Urban greening
Spatial analysis
Resilient cities
Citizen’s attitude and expectations
issue-copyright-statement© Springer Nature Switzerland AG 2022
==== Body
pmcIntroduction
Urban green spaces (UGS) are usually seen as spaces that are directly used for active or passive recreation or are indirectly used by virtue of their positive influence on the urban environment, are accessible to citizens, and serve their diverse needs (Grunewald et al. 2017). Hence, green spaces can provide significant social, economic, environmental, and health benefits to city residents, and contribute to the quality of life in the urban setting (Tzoulas et al. 2007; Ambrey et al. 2014). Some of these benefits are: (1) reduction of respiratory and cardiovascular illnesses, (2) increase of shade/thermal comfort and reduction of heat-related illnesses, (3) provision of recreational spaces and promotion of outdoor recreational activities, (4) reduction of anxiety and mental fatigue, (5) aesthetic appreciation and increased inspiration, (6) increase property values, (7) enhancement of social interactions, communication skills and social/neighborhood ties (Lee and Maheswaran 2011; Pappas et al. 2021; Semeraro et al. 2021). For this reason, several recent movements in urbanism, such as ecological urbanism, ecological landscape urbanism and landscape urbanism, emphasize that it is vital for the quality of life of cities to prioritize nature and ecological considerations. UGS can play a very important role in this direction (Mostafavi and Doherty 2016; Steiner 2011; Waldheim 2016). Taking into account the multiple ecosystem services that UGS may supply to the urban environment (e.g. stormwater runoff, reducing the heat island effect, supporting urban biodiversity, improving air quality, carbon sequestration, etc.), the European Commission highlighted the importance of transforming the traditional concept of isolated UGS (parks and gardens) into a comprehensive vision of green infrastructure (EC 2013), which seeks to balance “people, planet and profit” (Borgström and Kistenkas 2014). This vision has been additionally reinforced in the New Green Deal (EC 2019).
Since March 2020, due to the COVID-19 pandemic, a huge change in everyday life has taken place in cities, with unprecedented restrictions on their inhabitants in terms of both individual mobility and recreation and leisure options. Restrictions on various leisure facilities (such as restaurants, shopping malls and recreational places), cancellations of social activities and the need for self-quarantine and social distancing have made parks and green spaces very popular and vital for public (physical and mental) health, and they provide social benefits (Geng et al. 2021; Ritchie et al. 2020). Access to natural settings such as UGS has emerged as a likely component of the resilience to the pandemic (Venter et al. 2020). Hence, it seems that the pandemic has acted as a catalyst for the re-evaluation of the urban environment by city dwellers, and consequently for a re-assessment of the value of public green spaces.
At the same time, all over the world, scientists, municipal officers and decision makers are working together intensively in order to create new green spaces or to enhance the existing UGS in the context of urban resilience to climate change. Besides, as emphasized in the resilience strategies of many cities, the strengthening of urban resilience is inextricably linked to the (quantitative and qualitative) upgrading of UGS, while green infrastructure is considered one of the best planning tools for adaptation to climate change (Yiannakou and Salata 2017).
In Greece, UGS are scarce and well below the acceptable standards (9 m2 of green space per city dweller), as suggested by the World Health Organization (WHO, 2010). This is also the case for the city of Thessaloniki, a typical compact city facing a lack of open and green space, as the share of green area per person is only 2.6 m2. Moreover, the size of an UGS in Thessaloniki is usually quite small, as only 30% of them are larger than 500 m2 (Latinopoulos et al. 2016). This shortage and fragmentation of green spaces has significant environmental and health impacts (e.g. its impact on air quality) and lowers resilience to climate change (e.g. urban heat island phenomena). Despite all the obvious advantages of UGS, the development of a long-term planning strategy for urban green infrastructure has never been a key priority for local (city and regional) policy makers. A reason for this is that local policy makers usually consider only the aesthetic services (values) of UGS, disregarding their critical ecological, health and social functions (Latinopoulos et al. 2016). Therefore, it was not surprising that, during the pandemic, the UGS of Thessaloniki received little attention and remained subject to considerable development pressures, as city planners and local authorities remained reluctant to articulate the environmental, social, health and economic value of these areas.
In light of the above, it is now particularly necessary to integrate citizens’ concerns, preferences and perceptions into the decision-making and planning processes regarding urban green infrastructure. Human perceptions regarding the urban environment are subjective and differ from person to person (Langemeyer et al. 2015). Therefore, the benefits derived from UGS and their objective properties should be interpreted individually (Kothencz and Blaschke 2017). A number of studies have focused on investigating public attitudes and perceived benefits related to UGS (e.g. Baur et al. 2013; Jim and Chen 2006). Some recent studies particilarly examined the relationship between green spaces and well-being during the pandemic (Ugolini et al. 2020; Xie et al. 2020). Citizens’ attitudes towards UGS have been measured largely though structured questionnaire surveys (Balram and Dragićević 2005), but there are also other techniques/methods available to understand citizens/visitors’ attitudes towards UGS (mainly related to recreational capacity and aesthetic appreciation), such as the use of crowd-sourcing data that utilize geotagged UGS (Kothencz et al. 2017).
On the other hand, GIS-based methods tend to examine some objective indicators/attributes of UGS (e.g. NDVI index, the distribution of urban vegetation in a city and the residents’ access to public green spaces) or attributes that may be used as decision factors for new urban green development (e.g. temperature, the urban heat island effect, population density, accessibility to green space, air quality, etc.) (Landry and Chakraborty 2009; McConnachie and Shackleton 2010; Nesbitt et al. 2019). Furthermore, GIS-based tools have also been applied in order to evaluate accessibility to—and the quality of—UGS with the aim of supporting decision making and planning at the urban scale (Stessens et al. 2017). Studies that explore the correlation between perceived and objective attributes are scarce, but they usually reveal that subjective evaluations are very important, as they are likely to differ from objective data. Interestingly, such a study was recently conducted in the city of Thessaloniki to examine how the built environment characteristics, and particularly the proximity to UGS (e.g. large parks) relate to (self-reported) health and well-being before and during COVID-19 (Mouratidis and Yiannakou 2022). In another relevant study, Bertram and Rehdanz (2015) combined spatially explicit survey data with spatially disaggregated GIS data on urban green space in order to explore the effect of UGS to the self-reported well-being (life satisfaction) of the residents of Berlin. Another method which shows much promise in incorporating socio-spatial information in strategic green space planning is Public Participation Geographic Information Systems (PPGIS) (Rall et al. 2019). An interesting review of studies applying PPGIS to urban systems, and particularly to UGS is presented by Ives et al. (2017).
The present study aims to couple the subjective evaluations/preferences of citizens with a geographical information system (GIS) in order to develop a handy spatial analysis tool for UGS. In this context, a geo-questionnaire survey (i.e. a questionnaire linked with interactive maps) was conducted to analyse and assess urban residents’ access to public UGS, to measure citizens’ attitudes towards UGS, as well as to investigate the subjective evaluations of visitors to UGS. This method can be considered as a top-down PPGIS and the feedback of this analysis can be interpreted in order to support future urban green infrastructure planning in the city of Thessaloniki. The findings of the present study may also apply to cities in similar climatic conditions (e.g. in many Mediterranean cities), as well as, to cities that face similar challenges (i.e. the challenge of increasing green infrastructure in order to improve the living standards for their residents and to underpin nature-based solutions for urban resilience to climate change).
Materials and methods
A web-based survey with 25 questions was conducted among residents of the Thessaloniki urban area. The questionnaire was designed using LimeSurvey® software as a survey tool, an open-source online survey application (web-server-based software) written in PHP (Hypertext Preprocessor) and distributed under the GNU General Public License (LimeSurvey GmbH, Hamburg, Germany, http://www.limesurvey.org). The survey was optimized for both computers and mobile devices, published online (hosted on Aristotle University’s server), and distributed through emails and social media platforms from 3 February 2021 to 3 March 2021. A total of 1824 clicks were received, generating a final sample of 1049 survey responses.
The questionnaire consisted of four parts. The first part surveyed respondents’ general attitudes and beliefs about the current situation regarding green spaces. Respondents were also asked to mark on a map (through an interactive user interface) their home location as well as the location of the green space/park that they spend the most time in (i.e. the most frequently visited green space). For this purpose, a broader definition of (public) green areas was adopted, following the objective assessment of respondents (i.e. based on the greenery and open spaces available to users for recreational activities). The two marked points for each respondent were used to estimate the distance the citizen travels from their home (supply point) to their preferred green space (demand point).1 It should be noted that the distance of UGS from home is usually considered the most important precondition for the use/selection of green spaces (e.g. Grahn 1994). Another important factor that affects a citizen’s choice of green space is the functional level of the green space, i.e. the range of functions and activities that each UGS is able to support. Therefore, a decisive criterion in park selection by citizens is the size of the UGS, which is likely to determine the range of functions or activities that the UGS is able to support. Hence, residents may prefer to travel longer distances than the distance to their nearest UGS in order to reach a larger park that offers more amenities, more potential uses and therefore more benefits (Stessens et al. 2017). In this context, all the UGS selected by the respondents were identified through Urban Atlas data sourced from Copernicus (https://land.copernicus.eu/local/urban-atlas, accessed on 9 November 2021), and their areas were calculated using the “measure area” function of the QGIS 3.18 software (QGIS, Zurich, Switzerland, https://www.qgis.org/en/site/).
In the second part of the questionnaire, the citizens of Thessaloniki were asked about (a) their motivations for selecting particular parks/urban green spaces, (b) the special features of and problems with those areas, (c) the means of transport used to reach them, (d) the time they stay on-site, as well as (e) the frequency that they visited urban green spaces prior to the pandemic and during the pandemic (excluding from the analysis the period during which there were very strict lockdown restrictions).
The third part of the questionnaire contained questions about the citizens’ “vision” with regard to the future planning and management of UGS in Thessaloniki, including the need for new urban green infrastructure. In this part, participants were asked to vote for a future large-scale redevelopment program for the city of Thessaloniki concerning the area where the Thessaloniki International Fair (TIF) is currently situated. Two alternative scenarios were provided: (a) to redesign the site based on the current development plan, in which the fair remains on-site and new commercial and tourist-oriented facilities (providing new green spaces that will cover approximately 30% of the site area) are established, or (b) to transform the whole site into a large metropolitan park, thus creating a single (autonomous) open-space area with a safe and relaxed urban environment for cultural and recreational activities. In the second scenario, the TIF will be relocated to new premises outside the city centre.
The final part of the questionnaire consisted of several questions regarding the socio-economic and household characteristics of the respondents, including sex, age, education level, occupation, household members, number of children under age 18, income, etc. The results are reported in the following section according to the grouping of questions presented above.
Results and discussion
The sample in this study consisted of 1049 urban residents of Thessaloniki. The spatial distribution of participant location is given in Fig. 1, which shows a quite homogeneous distribution that covers the entire urban area. About 44% of the participants were male and 56% were female. Their average age was about 42 years old (median = 43). The average number of household members was 2.8 and the average household income was approximately 1600€/month.Fig. 1 Spatial distributions of participant location (residence) and satisfaction level concerning the UGS in their neighbourhood (based on spatial interpolation)
Participants were first asked to rank on a 1–10 Likert scale their satisfaction level concerning the actual/current situation regarding UGS in both quality and quantity terms. This question was repeated at different spatial scales ranging from the participant’s neighbourhood to the whole urban area of Thessaloniki. An important result that emerged from these answers is that residents gave a very low score for their satisfaction with the existing UGS, no matter the spatial scale. Namely, in the case of the entire urban area, 90% of the participants evaluated the current UGS as below average (as indicated by a score of 5/10 or lower), while the mean value was found to be 3.2/10. When evaluating the green areas in their neighbourhood, below-average scores were assigned by 78% of the participants, while the mean value was found to be slightly higher (4.3/10). The spatial variation in these answers (based on the satisfaction level at the neighbourhood scale) is depicted in Fig. 1. This shows that, according to the citizens’ objective perceptions, there are considerable differences and inequalities in the quality and availability of UGS. This spatial variation can be attributed to the variation among participants in their proximity to significant UGS (with respect to size and/or number) UGS. It is also worth noting that the lowest satisfaction levels were found in the historical centre of Thessaloniki (represented by a black circle in Fig. 1), where UGS are in fact very scarce.
Identifying the spatial patterns of human activity in UGS is likely to provide evidence of the utility of these elements, thus supporting future urban planning and management decisions (See et al. 2016). In this context, a spatial analysis was performed, using the QGIS 3.18 software, to geographically specify the locations of the green areas that the residents of Thessaloniki prefer to visit. An interesting outcome of this analysis is that more than half of the survey participants (61%) were found to visit green areas located either on the urban waterfront or are situated far from the city centre, in some cases even on the outskirts of the city (e.g. in the suburban forest of Seich Sou as well as in many parks located in suburban municipalities). Consequently, only four out of ten residents choose to visit all the other UGS located within the urban fabric. Figure 2 shows a heat map based on the actual visits to the UGS by the citizens of Thessaloniki, which can help to identify the above-mentioned spatial hotspots of green space use (i.e. the most popular/visited UGS). Most of these hotspots areas are green parks, except the waterfront area, which is a newly regenerated open access space, stretching over 3.2km, with a wide promenade along the sea, featuring a bicycle line and lined with a chain of eight thematic parks (so the urban green space comprises the vegetation in these parks and the trees along the promenade) (Athanassiou 2021). Another important indicator related to the usage of green space is the visitor density in the area. The first step in the creation of a visitor density map was to quantify the total number of observed visitors (based on the survey data) and their activity levels (according to their average time spent on-site) in terms of the total UGS area (visitors hour−1 hectare−1). Then, a density index was created by classifying these results into five equal-sized groups (quintiles) ranging from very low to very high relative density (according to the actual data). Figure 3 presents this density map, which was created using the GeoDa spatial modelling software (Center for Spatial Data Science, The University of Chicago, Chicago, IL, USA, https://geodacenter.github.io/). In this figure, a dark-mode basemap (Carto Dark) was selected in order to highlight the UGS elements. It is worth noting that the last category (very-high density) is almost exclusively related to small green spaces.Fig. 2 Heat map of UGS attractiveness based on visit count analysis
Fig. 3 UGS visitor density map (qualitative indicator based on the sample data)
Concerning the main motivations for selecting an urban green space, according to the survey results, the two most important criteria were distance (i.e. accessibility), which was reported by 80% of the respondents, and size/area (larger areas are particularly preferred as compared to the smaller ones), reported by 72% of the respondents. Apart from size, two other important motivations, which also reflect the functional level of green spaces, were the provision of sport/recreational activities (54.8% of respondents) and the provision of activities for children (28% of respondents). Concerning the main problems with existing UGS, the participants’ rankings, as shown in Fig. 4, indicate that the three most important problems are (a) poor infrastructure maintenance (e.g. of benches, fences, playgrounds, etc.), (b) a lack of cleanliness and (c) poor green maintenance (i.e. poor care and maintenance of lawns, bushes and flower gardens).Fig. 4 Participants’ rankings of existing problems with the UGS in Thessaloniki
As described in the methodology section above, the respondents were asked to mark on a map (through an interactive user interface) their home location and the location of the green space/park that they spent the most time. Based on these data, it was easy to estimate the average distance that a citizen travels in order to visit a green space (see Fig. 5a). The farther a visited place is from a respondent’s home, the more likely it is that the green space has a positive wellbeing influence (Samuelsson et al. 2021) and/or that the respondent has limited accessibility to UGS of an acceptable quality.Fig. 5 Histograms of the distance travelled by a citizen to visit UGS (a) and the duration of their visit (b)
An important outcome of this analysis is that, on average, the citizens of Thessaloniki travel about 1.8 km (measured as the Euclidean distance). It is also worth mentioning that half of the respondents travel at least 1 km, while only 20% of the sample are visiting a green space at a distance of no more than 400 m, which is commonly used as the distance that indicates accessibility (i.e. walking distance) for all age groups (Van Jerzele and Wiedemann 2003). This outcome is very close to the estimation of Barboza et al. (2021), who found that 87.7% of Thessaloniki’s urban population did not meet the WHO standards for a healthy urban environment (i.e. green spaces of at least 0.5 hectares should be accessible within a linear distance of 300 m from their residence).2 As a consequence of these findings, one in four residents never walks to an UGS, thus being forced to use some means of transport (car, bus, bicycle, etc.). Figure 6 shows a map of the average distances that citizens of Thessaloniki are travelling to visit a green space (once again a dark mode basemap was selected to accentuate the visualizations of the UGS data). It is worth mentioning that long distances are travelled not only in suburban/peri-urban green areas but also in some areas located within the urban fabric, most of which correspond to large UGS (e.g. green areas on the urban waterfront). It is also worth noting that the average distance travelled to visit 55 of the 171 sites (i.e. 32% of the UGS) is greater than 1 km. Figure 6 presents citizens’ preferences for visiting green spaces, based on the locations of existing UGS (i.e. based on the actual supply of green space). On the other hand, in order to explore demand-driven spatial relationships as well as to assess the current spatial inequalities in UGS accessibility, it is necessary to estimate the travel distances with regard to the residence areas of the respondents. Thus, a map of spatial accessibility was developed (Fig. 7) by interpolating actual travel times from the residence point data over the study area. The resulting map shows the expected travel time from any origin (residence) to a green space that is likely to maximize wellbeing.Fig. 6 Map of average distances travelled to visit a green space
Fig. 7 Spatial accessibility of UGS
The importance of visits to UGS can be valued non-monetarily by the total amount of time spent on-site (i.e. in UGS) during the citizens’ visits. In this framework, it was necessary to estimate (a) the length of on-site stay, (b) the frequency of visits to UGS and (c) the time taken to travel (both ways) to visit green spaces. The duration of visits to UGS was examined for each respondent (according to their answer), and then the average duration of an on-site stay at each green space was estimated. As shown in Fig. 5b, the mean time spent on-site is equal to 67 min, while about 20% of the respondents spend 2 h or more in parks and other natural areas within the city. The time taken to travel to the UGS for each respondent (based on his/her pinpointed locations) was calculated using the Google Maps travel time estimator, while data concerning the frequency of visits to the UGS were also extracted from the questionnaire. Taking into account these data, it was then possible to estimate the average time that a citizen of Thessaloniki spends every year travelling to/from (45 h) and at (108 h) an UGS.
The importance of visits to UGS can be also valued monetarily using a travel cost method (TCM). The application of a detailed TCM was beyond the aim of this study (mainly due to the lack of other on-site expenses). Thus, a simplified TCM was used to value the benefits provided by the UGS of Thessaloniki, based on the opportunity cost of time spent (on the road and on-site), which is usually measured as a percentage of the wage rate. By using the aforementioned results and a conservative estimate (Bowker et al. 1996) for the opportunity cost of travel and/or leisure time (equal to 25% of the wage rate), it was possible to approximate the total value of recreational hours spent in UGS, which was found to be equal to 260 million euros per year. If we consider the on-site time to be positive (i.e. a benefit and not a cost) and subtract it from the analysis, the total value of this recreation is equal to 83.4 million euros per year.
As already mentioned, the demand for urban parks and outdoor green spaces all over the world has increased since the pandemic outbreak (except for the period of strict lockdown restrictions during the first month). This fact highlights the important role and the benefits provided by green spaces, and particularly by UGS, during the COVID-19 pandemic (Geng et al. 2021). However, in Thessaloniki, the small number and sizes of green spaces, the long distances between homes and UGS, and the restrictive measures implemented due to the pandemic make it very difficult (or even impossible) for large population groups to visit green areas over a very long period. So, as the study results confirm, the frequency of visits to green spaces has not increased during the pandemic; on the contrary, it has slightly fallen (as depicted in Fig. 8). Namely, 83.1% of the respondents stated that before the pandemic they visited a green area at least once a week, while this percentage decreased to 76.9% during the pandemic. This outcome reveals that the city of Thessaloniki has a low resilience to the COVID-19 pandemic, as it partially failed to adapt to the pandemic disturbance, and thus failed to sustain the wellbeing of urban residents.Fig. 8 Frequency of visiting UGS a before and b during the pandemic
All of the above confirm the need to reconsider the current urban planning strategies and to integrate new green spaces into the urban environment of Thessaloniki. One of these potential sites is the area where the Thessaloniki International Fair (TIF) is located. The redevelopment/transformation of this site into a large metropolitan plan was one of the key elements of the city’s masterplan to address the environmental and spatial problems the city is facing. A previous study (Latinopoulos et al. 2016) has shown that the residents of Thessaloniki attribute very high value to transforming this area into a large metropolitan park. The provision of new walking/leisure/activity areas and the contribution to residents’ wellbeing (in terms of aesthetic value and air quality improvements) emerged as the most important benefits of this project. However, this option has been recently overshadowed by a renovation project with significant exhibition areas and commercial uses (including new hotel infrastructure) but limited public green space. So far, the issue of citizen participation in the planning process has been quite problematic. Hence, in order to explore the residents’ views about the future use of this space, they were asked (in the context of this study) to choose between the two different scenarios described above: (a) the recently proposed renovation project or (b) the creation of a large metropolitan park. According to the survey findings (shown in Fig. 9), the vast majority of the respondents (78.4%) opted for the metropolitan park, emphasizing in their individual comments that a large urban green park is more than necessary for the city of Thessaloniki. This outcome validates the previous results of Latinopoulos et al. (2016) about the social preferences implied by this site/project, confirming once more that urban parks and green spaces are extremely important for the wellbeing of the citizens of Thessaloniki.Fig. 9 Results of the citizens’ vote on the future use of the area
Conclusions
It is a common belief that the popularity of UGS in Thessaloniki is growing; their significance is increasingly being recognized due to their ability to improve the city’s resilience to environmental (e.g. climate change) and health (e.g. the COVID-19 pandemic) risks. Therefore, it was essential to get more information about the demand for and the value of UGS. In this framework, a public survey was conducted to shed some light on the citizens’ attitudes, satisfaction level, actual behaviour towards and future expectations about UGS in Thessaloniki. The results of this survey demonstrate that there are many reasons to invest in urban green infrastructure options. The potential benefits of such investments are expected to be substantial, as citizens are dissatisfied with the current green areas and are very supportive of the development of new (and especially large-scale) green projects. Therefore, the planning and development of new UGS in Thessaloniki is a demand-driven and cost-effective option that city planners and local authorities should consider in their future decisions.
Future research on this topic will focus on identifying the specific areas that are most in need of new or improved UGS, by coupling the subjective perceptions of citizens with objective spatial data/indicators. In other words, a planning support tool for optimizing location of new green infrastructure could be developed. Finally, the survey could be replicated in other Greek, or even Mediterranean cities, in order to identify similarities and differences in the preferences, values and expectations of citizens regarding UGS.
Acknowledgements
An earlier version of this paper was presented at the Eighth International Conference on Environmental Management, Engineering, Planning and Economics (CEMEPE 2021), July 20–24, 2021, Thessaloniki, Greece.
Funding
No funding was received for conducting this study.
Declarations
Conflict of interest
The author has no competing interests to declare that are relevant to the content of this article.
1 The margin of error for this procedure was considered to be within acceptable limits of accuracy.
2 In our sample, 11.3% of the citizens are visiting a green space (of at least 0.5 hectares) at a distance of less than 300 m.
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PMC009xxxxxx/PMC9002022.txt |
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Medizinrecht
Medizinrecht
Medizinrecht
0723-8886
1433-8629
Springer Berlin Heidelberg Berlin/Heidelberg
6178
10.1007/s00350-022-6178-x
Rechtsprechung
Entscheidungsbefugnis fur die Durchführung einer Schutzimpfung nach 1628 BGB – kein medizinisches Sachverständigengutachten zur Frage der Impffähigkeit erforderlich
BGB 1628, 1697a
OLG Frankfurt, Beschl. v. 8.3.2021 – 6 UF 3/21 (AG Dieburg)
12 4 2022
2022
40 4 322324
© Springer-Verlag 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.
Wird in einem sorgerechtlichen Verfahren betreffend die Entscheidungsbefugnis für die Durchführung einer Schutzimpfung nach 1628 BGB die Frage der Impffähigkeit des betroffenen Kindes aufgeworfen, ist zu dieser Frage im Regelfall kein medizinisches Sachverständigengutachten einzuholen, weil nach den Empfehlungen der Ständigen Impfkommission beim Robert-Koch-Institut und der Schutzimpfungs-Richtlinie des Gemeinsamen Bundesausschusses vom zuständigen Arzt Kontraindikationen zu beachten sind und damit eine Prüfung der Impffähigkeit vor der jeweiligen Impfung zu erfolgen hat.
issue-copyright-statement© Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature 2022
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Medizinrecht
Medizinrecht
Medizinrecht
0723-8886
1433-8629
Springer Berlin Heidelberg Berlin/Heidelberg
6195
10.1007/s00350-022-6195-9
Rechtsprechung Kurz Berichtet
Rechtsprechung kurz berichtet
Wever Carolin
grid.469935.4 Kanzlei Bergmann und Partner, Josef-Schlichter-Allee 38, 59063 Hamm, Deutschland
12 4 2022
2022
40 4 371372
© Springer-Verlag 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
issue-copyright-statement© Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature 2022
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J Gen Intern Med
J Gen Intern Med
Journal of General Internal Medicine
0884-8734
1525-1497
Springer International Publishing Cham
35412178
7559
10.1007/s11606-022-07559-5
Original Research: Qualitative Research
The Impact of COVID-19 on Primary Care Teamwork: a Qualitative Study in Two States
http://orcid.org/0000-0002-0014-0850
DePuccio Matthew J. PhD, MS matthew_j_depuccio@rush.edu
1
Sullivan Erin E. PhD 23
Breton Mylaine PhD 4
McKinstry Danielle MHA 2
Gaughan Alice A. MS 5
McAlearney Ann Scheck ScD, MS 56
1 grid.262743.6 0000000107058297 Department of Health Systems Management, College of Health Sciences, Rush University, Chicago, IL USA
2 grid.264352.4 0000 0001 0684 8852 Sawyer School of Business, Suffolk University, Boston, MA USA
3 grid.38142.3c 000000041936754X Center for Primary Care, Harvard Medical School, Boston, MA USA
4 grid.86715.3d 0000 0000 9064 6198 Department of Community Health Sciences, Université de Sherbrooke, Longueuil, Canada
5 grid.261331.4 0000 0001 2285 7943 The Center for the Advancement of Team Science, Analytics, and Systems Thinking in Health Services and Implementation Science Research (CATALYST), College of Medicine, The Ohio State University, Columbus, OH USA
6 grid.261331.4 0000 0001 2285 7943 Department of Family and Community Medicine, College of Medicine, The Ohio State University, Columbus, OH USA
11 4 2022
6 2022
37 8 20032008
23 11 2021
30 3 2022
© The Author(s), under exclusive licence to Society of General Internal Medicine 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Background
The emergence of coronavirus disease 2019 (COVID-19) disrupted how primary care physicians (PCPs) and their staff delivered team-based care.
Objective
To explore PCPs’ perspectives about the impact of stay-at-home orders and the increased use of telemedicine on interactions and working relationships with their practice staff during the first 9 months of the pandemic.
Design
Qualitative research.
Participants
Participants included PCPs from family and community medicine, general internal medicine, and pediatrics.
Approach
One-on-one, semi-structured video interviews with 42 PCPs were conducted between July and December 2020. Physicians were recruited from 30 primary care practices in Massachusetts and Ohio using a combination of purposeful, convenience, and snowball sampling. Interview questions focused on work changes and work relationships with other staff members during the pandemic as well as their experiences delivering telemedicine. All interviews were audio-recorded, transcribed verbatim, and coded using deductive and inductive approaches.
Key Results
Across respondents and states, the context of the pandemic was reported to have four major impacts on primary care teamwork: (1) staff members’ roles were repurposed to support telemedicine; (2) PCPs felt disconnected from staff; (3) PCPs had difficulty communicating with staff; and (4) many PCPs were demoralized during the pandemic.
Conclusions
The lack of in-person contact, and less synchronous communication, negatively impacted PCP-staff teamwork and morale during the pandemic. These challenges further highlight the importance for practice leaders to recognize and attend to clinicians’ relational and work-related needs as the pandemic continues.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11606-022-07559-5.
KEY WORDS
Primary care
Teamwork
Qualitative research
Healthcare workforce
COVID-19
http://dx.doi.org/10.13039/100000905 Commonwealth Fund issue-copyright-statement© The Author(s), under exclusive licence to Society of General Internal Medicine 2022
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pmcINTRODUCTION
In the early months of the coronavirus disease 2019 (COVID-19) pandemic, social distancing, stay-at-home orders, and restrictions on in-person patient visits were common strategies deployed by primary care practices to protect patients and healthcare providers from contracting and spreading COVID-19.1–3 In addition, the rapid implementation of telemedicine enabled primary care physicians (PCPs) and their staff (e.g., nurses and medical assistants) to provide essential services without having to occupy the same workspace, representing a drastic change for primary care practice 4. In the wake of these changes, reports have indicated that the COVID-19 pandemic has exacerbated PCP exhaustion and burnout.5–7 If left unaddressed, declining physician mental health could negatively affect patient care quality as well as physicians’ job satisfaction. 8–10
The hallmarks of team-based primary care—stable teams, clear roles, and effective communication—have the potential to mitigate the symptoms of burnout and improve both physician satisfaction and patient outcomes.11–14 To this end, primary care practices have adopted a variety of strategies including, but not limited to, forming teamlets (i.e., physician-staff dyads) or multidisciplinary care teams, using non-physician staff for care coordination, introducing care manager roles, implementing team huddles, and integrating behavioral health to facilitate coordinated and continuous primary care.15–17 Previous research indicates that cultivating teamwork and fostering communication between healthcare providers can reduce PCP burnout and improve physician work satisfaction.13,14,18
However, as far as we know, there have been no systematic examinations of COVID-19’s impact on primary care teamwork from the perspective of PCPs. It is unclear how measures taken by practices to protect individuals from infection by the coronavirus (e.g., social distancing, masking protocols) and increased use of telemedicine have affected primary care teamwork, specifically as it relates to interpersonal interactions between PCPs and staff. We begin to address this gap by describing the findings from our analysis of data from a multi-state qualitative study designed to understand the changes to PCP work practices during the COVID-19 pandemic. The objective of this study was to examine PCPs’ perspectives about primary care teamwork between July and December 2020 of the COVID-19 pandemic to improve our understanding of how practice changes required by the pandemic impacted primary care teamwork.
METHODS
Study Design, Setting, and Participants
We designed a qualitative study using content analysis to understand PCPs’ perspectives about the impact of COVID-19 on primary care and how their work changed. We originally planned to employ a purposeful, regional multi-site sampling strategy that would identify and recruit PCPs from the USA. However, due to the varied nature and timing of pandemic surges in different geographic areas, only the research sites in Massachusetts and Ohio were able to recruit PCPs for the study. In these two states, the research sites employed a combination of purposeful, convenience, and snowball sampling to recruit physicians who practiced general and internal medicine (GIM), family and community medicine (FM), or pediatrics, and were affiliated with either a primary care research center (Massachusetts) or an academic medical center (AMC) (Ohio).
With the approval of the institutions and department chairs, the research team recruited PCPs via two mechanisms: (1) an invitation via a primary care newsletter, which had approximately 7,000 subscribers at the time of the study; and (2) via emailed recruitment letters to 106 PCPs describing the purpose of the study and offering them an opportunity to participate. Up to three follow-up emails were sent to non-respondents. Medical residents were excluded from the study as they had not been practicing long enough to meaningfully compare primary care practice before and during the COVID-19 pandemic. This project received approval from the Institutional Review Board at [Harvard Medical School, The Ohio State University, and Suffolk University] prior to data collection. The study followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines.
Interview Guide Development
We developed a semi-structured interview guide during May and June of 2020 using the emerging literature on the COVID-19 pandemic to inform our questions. The interview guide included the following domains: physician background and work information; changes in work since the emergence of COVID-19; perspectives about confidence and competence as a PCP during the pandemic; and impacts of telemedicine and virtual care on PCPs and patient care (see Appendix: Interview Guide). A panel of health services researchers including a PCP reviewed the interview guide and questions were refined based on their feedback. The study team (MJD, EES, MB, ASM) has extensive expertise in conducting qualitative research in primary care settings, including developing interview protocols, conducting physician interviews, and analyzing interview data.
Interview Procedures
Interviews were conducted between July and December 2020, before any vaccines against COVID-19 had become available. On average, interviews lasted 30 min. Participants provided verbal consent prior to being interviewed. The authors (MJD, EES, MB, DM, ASM) conducted all interviews for their respective sites using the Zoom videoconference platform. All interviews were audio-recorded, transcribed verbatim, and de-identified. Participants received no compensation for their participation. We conducted a total of 42 semi-structured interviews, at which time data saturation, or information redundancy, occurred.19–21
Data Analysis
De-identified transcripts were entered into qualitative data analysis software (NVivo and Atlas.Ti) and were thematically indexed and coded. Following the tenets of deductive and inductive thematic content analysis,22–24 each team (Ohio/Massachusetts) reviewed the transcripts from their respective state using a core set of common codes derived from the interview guide, but also allowing for the identification of new codes during analysis. Consensus was reached about the final codes and definitions through Zoom meetings held between the authors (MJD, EES, MB, AAG, ASM). A member from each team (AAG/DM) initially coded all their data using the core set of common codes, with frequent meetings held among and across teams to ensure agreement about emergent themes. For this analysis, two team members (MJD/EES) used data from two common codes of the full dataset (i.e., teams, relationships with colleagues) to identify and characterize sub-codes related to teamwork in primary care.
RESULTS
We interviewed 42 PCPs from 30 primary care practices affiliated with 6 AMCs and 8 non-academic healthcare organizations. Table 1 describes the characteristics of the respondents including their gender, specialty, affiliation, years in practice, and geographic location. Table 1 Study participant characteristics
State
Characteristic Massachusetts Ohio Total
(N=22) (N=20) (N=42)
Gender, N (%)
Female 10 (45.5) 11 (55.0) 21 (50.0)
Physician specialty, N (%)
Family and community medicine 8 (36.4) 13 (65.0) 21 (50.0)
General and internal medicine 9 (40.9) 6 (30.0) 15 (35.7)
Pediatrics 5 (22.7) 1 (5.0) 6 (14.3)
Practice affiliation, N (%)
Academic medical center 15 (68.2) 20 (100.0) 35 (83.3)
Community health center 2 (9.1) -- 2 (4.8)
Community hospital 2 (9.1) -- 2 (4.8)
Other* 3 (13.6) -- 3 (7.1)
Years in practice, average (range) 18 (2–36) 16 (3–41) 17 (2–41)
*Includes one direct primary care practice (n=1) and two not-for-profit health systems (n=2)
Across interviewees and states, we characterized four themes related to PCPs’ perspectives about the impact of COVID-19 on primary care teamwork (Table 2): (1) staff members’ roles were repurposed to support telemedicine; (2) PCPs felt disconnected from staff; (3) PCPs had difficulty communicating with staff; and (4) many PCPs were demoralized during the pandemic. Since PCP perspectives were similar regardless of geography and practice setting, we report our findings from MA and OH in the aggregate. Table 2 Qualitative themes and illustrative quotations from interviews
Themes Illustrative quotations
Repurposed: Physicians describing the changing roles and responsibilities of staff to support telemedicine “There’s been issues with some of our staff being deployed to other locations, so we’re having staff doing work that either they wouldn’t ordinarily do or is an overload of what their typical responsibilities are because they’re having to cover for other people…So everybody’s kind of shifted in other ways and pulled and kind of stretched a little bit….” (GIM, 10 years in practice)
“And so we had to repurpose [staff] to doing kind of virtual rooming processes prior to us seeing them and trying to get whether it be paperwork and rating scales filled out or whether it be [a] vital from home measurements and whatever else done.” (GIM, 7 years in practice)
Disconnected: Physicians describing how being detached from other staff members effects team continuity “I did not go into this field because I like to work in isolation. This is a collaborative project. I need to have a team because my MA (medical assistant) hears things that I wouldn’t hear and I hear things that my MA wouldn’t hear[….] [T]his is a collaborative affair and I’m completely alone and isolated in my dining room and house and community.” (GIM, 21 years in practice)
“So just things like that where kind of the erosion of that cohesion of a team now became a lot more disjointed, I guess.” (FM, 15 years in practice)
Difficulty Communicating: Physicians describing the challenges of communicating with staff “So the support staff has trouble communicating with me, and I have trouble communicating with them.” (FM, 10 years in practice)
“[…] I missed that communication. I mean, I still sign out patients, I call them my colleagues on the phone, etc. But it just was much more fluid. So, I mean, basically I missed the office environment.” (Pediatrics, 35 years in practice)
Demoralized: Physicians describing the impact of COVID-19 on physician and staff morale “[The COVID-19 pandemic] has affected just morale in general because we just don’t have enough people to help us and those people that are here are some of our best, [...] you know, they can only do so much, like no one can be on 100% all the time. And so, you just see morale issues.” (FM, 1 year in practice)
“And so, our workforce is dramatically being impacted, I would say, by [workforce hospitalizations] and it is adversely affecting our ability to deliver high quality care...” (FM, 5 years in practice)
Repurposing of Roles to Support Telemedicine
Physicians relied heavily on telemedicine during the early phases of the pandemic resulting in expanded roles for medical assistants (MAs) and other primary care staff who needed to help patients engage with telemedicine. Interviewees elaborated on the new responsibilities of MAs and nurses in preparing patients for virtual visits, which prior to the COVID-19 pandemic, rarely occurred:So the medical assistant, what they'll do is call the patient, prime them for the visit, review their medication list with them and then there's a way in the chart that the medical assistant can denote that this patient is ready to begin the visit and then that lets me know, I can shoot them the invitation to the telemedicine visit. (FM, 5 years in practice)
Roles of MAs were repurposed to facilitate the virtual rooming process and to provide technology support to patients. These roles were in addition to existing responsibilities essential to primary care delivery. There was general agreement that this repurposing of staff roles was fundamental to implementing virtual visits while PCPs were working offsite during the early part of the COVID-19 pandemic.
Disconnection from Staff
While stay-at-home orders were in place, physicians were unable to work side-by-side and maintain usual lines of communication with their staff. As one PCP noted: “With virtual health, I’m kind of by myself. We do a lot of sending out messages and stuff but still not that visual contact, we all kind of felt separated from each other.” (FM, 35 years in practice) The physical separation between PCPs and staff also made it difficult to maintain work relationships essential for delivering team-based care. For example, two PCPs explained how being separated from their team made it “incredibly challenging” to know what was going on with patients and made them feel “on [their] own” in the physical absence of staff.
A major drawback of PCP and staff separation was that physicians could not interact or consult with other team members as they did before the pandemic—resulting in a more individualized care experience:So primarily me and my patient interact really in a different way than having the rest of the care team integrated. I think we went from a really good, robust care team working together to take care of patients and being able to bounce things off people quickly to more of an individualized [manner where] me and my patient [were] working together without the medical assistant, or the nurse, or other folks. (FM, 15 years in practice)
Another physician described how telemedicine altered how healthcare providers engaged with each other: “People realize that this was going to be the system for holding [it] all together because you can’t just run upstairs. You can’t just pick up the phone, we are now dispersed.” (Pediatrics, 6 years in practice)
Communication Challenges
Electronic modes of communication via electronic health records, voice messages, and/or text messaging replaced synchronous in-person conversations between PCPs and staff. These modes of communication were less useful when team members wanted to interact to respond to emergent patient care needs:We've had to move to a lot more electronic conversations...with staff. And so it's changed the dynamic of the quick simple conversation as you pass someone [in] the hallway [... it] requires [a] phone call or a text message, which is often more time consuming and challenging, and so [it is] all together harder to make on the fly changes. (GIM, 7 years in practice)
Some physicians noted the disadvantages of not having face-to-face communication and having to switch to virtual platforms to communicate with patients. One PCP described the impact of not having these face-to-face encounters: “...and now that whole chain is kind of broken and so they're missing a lot of those interactions. I miss that too because sometimes I find out [a] bunch of information that I might need because they’ll [the patient] tell the MA something[…] And so sometimes that's really helpful.” (FM, 23 years in practice) Another PCP commented on the difficulties of communicating with staff in their practice: “So there’s something going on with the call with a patient that I need resolved right away, it’s not always immediately obvious to me how to connect to that [staff] person[...].” (FM, 17 years in practice) Despite these challenges, asynchronous communication was reportedly important to help coordinate day-to-day work activities.
Demoralization of PCPs
Physicians remarked about the impact the pandemic had on the morale of their staff. For example, some staff were redeployed to hospital units to help manage the surge of COVID-19 patients and others had to pick up shifts to cover for the redeployed co-workers, as well as co-workers who were sick. One participant explained the potential ramifications of the situation:And, as people are dropping out sick, as people are struggling one way or another, morale takes [a] really big hit. And so what we are really struggling with now in a pretty profound way is, I think, [a] devastatingly low level of morale, to the point of which my medical director was overheard the other day saying that he feels like this is the end of our practice. (FM, 5 years in practice)
One participant described how staff turnover also increased anxiety within practices, especially for long-serving staff members: “And then you also are losing members of your clinical team and can't really, you know, replace them. And so I think it’s creating just a lot of angst.” (GIM, 10 years in practice) Isolation also contributed to a poorer work environment, such that physicians, staff, and patients could not have simultaneous conversations critical to the delivery of coordinated care: “But definitely the mood was much more negative because…they’re physically isolated from us and from their families and from their patients and from each other because they’re all having to sit far apart from each other so they can't even have that usual conversation.” (GIM, 21 years in practice) Overall, with fewer PCPs working in person, morale decreased as some staff had to work harder to continue providing in-person primary care during the pandemic.
DISCUSSION
Effective teamwork is an essential component of high-quality and efficient primary care delivery. However, there has not been a systematic investigation into how the COVID-19 pandemic, and the subsequent uptake of telemedicine, affected primary care teamwork. Our findings suggest that the pandemic disrupted teamwork, and the rapid shift to telemedicine altered previously defined roles of staff, with both resulting in fractured connectedness and communications between PCPs and their staff. While the scope of our study did not address the impact of these changes on patient outcomes, our findings suggest that the COVID-19 pandemic had a negative impact on physician and staff morale. These findings were consistent across interviewees from two states up until December of 2020. Despite PCPs having transitioned back to delivering in-person care, our findings highlight the challenges facing PCPs during a time when practices were in flux. As primary care leaders consider sustaining and potentially expanding the use of telemedicine, they should also think about implementing strategies that will foster PCP-staff connectedness and teamwork as the practice undergoes such changes.
During the early months of the pandemic, participants described a lack of clear roles and responsibilities as primary care teams learned to function in a virtual office. This required greater coordination as described by participants, but it also highlighted the challenges of clarifying the roles of physicians and staff in delivering telemedicine. In the context of COVID-19 and the rapid shift to telemedicine, optimal task delegation or team-based models have yet to be explicitly defined and operationalized, which may continue to hinder team functioning. Technology challenges notwithstanding, it is likely more tailored training, resources, and strategies to support the delivery of team-based telemedicine will be necessary to ensure primary care practices do not lose the pre-pandemic momentum many have made during their transformation into high-performing primary care teams.25
Previous research shows that co-location and ongoing communication are critical components of high-performing primary care practices.25 Daily check-ins and team huddles provide opportunities for physicians and staff to communicate and ensure PCPs and staff members are aware of upcoming patient visits.26 But despite the availability and increased use of technology to maintain communication, we found that the lack of co-location and team continuity negatively affected PCP and staff morale. Like other research,4 physicians in our study described feeling demoralized as stay-at-home orders and virtual visits posed challenges to interdisciplinary teamwork. Thus, our findings suggest that PCP-staff separation inhibited healthcare providers from working together and it may have had negative downstream effects—that is, it contributed to lower physician and staff morale. Organizational approaches to advance physician and staff well-being, for example, soliciting feedback from physicians regarding the practice environment, should consider addressing the communication needs and workflow challenges of staff as the pandemic has continued in order to improve teamwork and prevent the exacerbation of physician and staff burnout.18,27
Our findings also suggest that the implementation of video visits and the development of separate clinical workspaces may also affect care team processes. New workflows that accommodate telemedicine should provide opportunities for physicians and staff to collaborate with each other and to encourage the sharing of best practices to support telemedicine use.28 This may include expanding the use of asynchronous communication tools (e.g., secure messaging and electronic triaging systems) and ensuring staff are appropriately trained to use such systems to maintain communication between physicians and staff. More research is warranted to investigate how physicians and staff use telemedicine to optimize teamwork and foster communication in order to provide insights about the design and implementation of telemedicine workflows. Because PCPs in our study were employed at different primary care practices, it is possible that other practice characteristics could have influenced our findings, but more research at the organization level is needed to better understand these relationships.
Although we capture the perspectives of PCPs across two states, our study has limitations. First, we interviewed participants at a single point in time at which some PCPs were transitioning back to delivering in-person care, so participant accounts may suffer from recall bias. As hybrid models of in-person care and telemedicine emerge,29 understanding the impact of these new care models on PCPs and teamwork is an area for future research. Second, we did not include primary care staff in our study given that many nurses and MAs were extremely busy at that time delivering in-person care during the pandemic. Further examination of staff perspectives on the impact of telemedicine and primary care teamwork could uncover additional themes that were not identified in this research. Third, we did not ask participants about their experiences using telemedicine prior to the pandemic which could have influenced participants’ perspectives about the advantages and disadvantages of virtual care on team functioning. Fourth, as most respondents worked in academic, or academic-affiliated, practices, it is possible that the experiences of physicians from other practice types may differ with respect to the impacts of COVID-19 due to variability in factors such as patient panel characteristics, telemedicine infrastructure, and/or access to resources such as staff. Finally, due to the time-sensitive nature of this research, we did not interview patients to understand if and how their interactions with PCPs and staff were affected by using telemedicine.
CONCLUSION
The COVID-19 pandemic and its associated increased use of telemedicine have revealed both challenges and opportunities to improve team functioning. As practices consider augmenting in-person care with virtual visits, it will be important to examine how these new workflows engage PCPs and staff to ensure the delivery of high-quality team-based care. Also, an important element reported by physicians is the impact of the absence of face-to-face interactions with their colleagues on morale. Future studies should focus on the more relational and emotional aspects of practice changes.
Supplementary Information
ESM 1 (DOCX 23 kb)
Acknowledgements
The authors would like to acknowledge the physicians who participated in this research.
Funding
Mylaine Breton was supported by a fellowship funded by the Commonwealth Fund to conduct this research.
Data availability
The datasets generated during the current study are available from the corresponding author on reasonable request.
Declarations
Conflict of interest
The authors declare that they do not have a conflict of interest.
Disclaimer
The Commonwealth Fund had no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.
Matthew J. DePuccio and Erin E. Sullivan are co-first authors.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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==== Front
Internist (Berl)
Internist (Berl)
Der Internist
0020-9554
1432-1289
Springer Medizin Heidelberg
1326
10.1007/s00108-022-01326-8
Schwerpunkt: Immunität und Infektionsschutz
Prophylaktisches und therapeutisches Management erhöhter Infektionsanfälligkeit bei Immundefekten
Prophylactic and therapeutic management of increased susceptibility to infection in patients with immunodeficiencyHanitsch Leif G. leif-gunnar.hanitsch@charite.de
grid.6363.0 0000 0001 2218 4662 Immundefekt-Ambulanz, Institut für Medizinische Immunologie, Charité – Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Deutschland
Redaktion Ivo Grebe, Aachen
12 4 2022
17
30 3 2022
© The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Infektionen sind ein wichtiges Warnzeichen für ein geschwächtes Immunsystem. Erworbene (sekundäre), insbesondere medikamentös induzierte Immundefekte sind in der internistischen Praxis deutlich häufiger als angeborene (primäre) Immundefekte. Das Management beginnt bereits in der Planungsphase vor Einleitung der Immunsuppression. Es sollten infektiologische Risiken individuell stratifiziert und Schutzimpfungen komplettiert werden. Je nach Immunsuppression kann die Notwendigkeit einer präventiven Therapie bestehen, etwa bei latenter Tuberkulose oder Hepatitis B. Ebenfalls müssen serologische Befunde zu Varizella-Zoster- und JC-Viren berücksichtigt werden. Als immunologische Basisdiagnostik stehen das Differenzialblutbild sowie die Bestimmung von Immunglobulinen (IgG, IgA, IgM) vor und auch während der immunsuppressiven Therapie zur Verfügung. Bei relevanten Laborauffälligkeiten vor Therapieeinleitung sollte ein angeborener Immundefekt gezielt abgeklärt werden, da dieser oft auch mit Zeichen der Immundysregulation einhergeht. Abhängig von der Art des Erregers sowie von Lokalisation, Häufigkeit, Dauer und Schweregrad der Infektion kann eine prophylaktische Antibiotikagabe erfolgen. Bei dauerhafter schwerer Lymphozytopenie, insbesondere bei CD4-positiven (Helfer‑)Zellen < 200/µl, besteht ein erhöhtes Risiko opportunistischer Infektionen, sodass eine Antibiotikaprophylaxe empfohlen wird. Bei deutlich erhöhter Infektionsneigung und Nachweis einer relevanten quantitativen (IgG < 4 g/l) und/oder qualitativen Antikörperstörung (eingeschränkte Impfantwort) kann eine ergänzende Immunglobulinersatztherapie erforderlich sein, die intravenös (IVIG) wie auch subkutan (SCIG) als Heimtherapie erfolgen kann. Entsprechend der Infektionslokalisation sollte eine multidisziplinäre Abklärung und Betreuung erfolgen.
Infections are an important warning sign for a weakened immune system. In the internal medical practice acquired (secondary), particularly drug-induced immunodeficiencies, are much more frequent than congenital (primary) immunodeficiencies. The management starts as early as the planning phase before initiation of immunosuppression. The risk of infection should be individually stratified and protective vaccinations should be completed. Depending on the immunosuppressive treatment, there can be a necessity for preventive treatment, e.g. for latent tuberculosis infection or hepatitis B. The serological results on varicella zoster virus and JC polyomavirus must also be considered. The basic immunological diagnostics include differential blood count and the determination of immunoglobulins (IgG, IgA, IgM) prior to and during immunosuppressive treatment. Relevant conspicuous laboratory results before initiation of treatment should prompt advanced immunological work-up for the identification of primary immunodeficiencies, which are often accompanied by clinical signs of immune dysregulation. Depending on the type of pathogen, localization, frequency and duration as well as the severity of the infection, prophylactic antibiotic treatment may be required. Patients with chronic severe lymphocytopenia, in particular with CD4 positive T (helper) cells < 200/µl, are at increased risk for opportunistic infections so that an antibiotic prophylaxis is recommended. In patients with significantly increased proneness to infections and detection of a relevant quantitative (IgG < 4 g/l) and/or qualitative antibody deficiency (impaired vaccine response), additional immunoglobulin replacement therapy may be necessary and can be administered intravenously (IVIG) or subcutaneously (SCIG) as home treatment. In accordance with the localization of the infection, multidisciplinary clarification and management is warranted.
Schlüsselwörter
Primäre Immundefekte
Sekundäre Immundefekte
Impfungen
Antikörpermangel
Immunglobulintherapie
Keywords
Primary immunodeficiency diseases
Secondary immunodeficiency diseases
Vaccination
Antibody deficiency
Immunoglobulin therapy
==== Body
pmcInfektionen als Hinweis auf primäre und sekundäre Immundefekte
Häufige oder schwere Infektionen sind ein wichtiges Warnsignal für Immundefekte. Je nach Ursache werden angeborene (primäre) und erworbene (sekundäre) Immundefekte unterschieden. Sowohl primäre als auch sekundäre Immundefekte können mit Schwächungen der humoralen und zellulären sowie der angeborenen und erworbenen Immunität einhergehen. Je nach Art des Defekts kann eine allgemein erhöhte Infektionsneigung oder auch ein erhöhtes Risiko für spezielle Infektionen bestehen.
Bei auffälliger Infektionsneigung werden eine immunologische Basisdiagnostik mittels Differenzialblutbild und die Bestimmung der Immunglobulinhauptklassen (IgG, IgA und IgM) empfohlen. Je nach Art des Immundefekts und dessen Ursache unterscheidet sich das therapeutische Vorgehen. Neben allgemeinen Behandlungsstrategien werden in dieser Übersichtsarbeit auch Schutzimpfungen, die antimikrobielle Prophylaxe und die Substitutionstherapie mit polyvalenten Immunglobulinen als wesentliche Behandlungsmöglichkeiten vorgestellt.
Primäre Immundefekte
Klinisch relevante primäre Immundefekte (PID) sind in der internistischen Praxis seltener und werden daher oft erst mit Verzögerung diagnostiziert [1]. Im Gegensatz zu den sekundären Immundefekten (SID) finden sich bei PID oft auch Zeichen einer fehlgeleiteten Immunantwort. Wichtige Warnzeichen dieser Immundysregulation sind in dem Merkakronym GARFIELD zusammengefasst:Granulome
Autoimmunität
Rezidivierendes Fieber unklarer Ursache
Ekzeme
Lymphoproliferation
Darmentzündungen
Insgesamt sind mittlerweile > 450 monogenetische PID bekannt [2]. Bei Verdacht auf einen PID sollte die weiterführende immunologische, genetische und funktionelle Diagnostik sowie die Evaluation hinsichtlich der (spezifischen) Therapieindikation stets in Zusammenarbeit mit einem in Diagnostik und Behandlung erfahrenen Zentrum für Immundefekte erfolgen. An dieser Stelle sei auch auf die Leitlinien der Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften (AWMF) zur Diagnostik und Therapie von PID verwiesen [2, 3].
Sekundäre Immundefekte
Einem SID begegnen Internisten deutlich häufiger. Aktuellen Schätzungen zufolge tritt allein der sekundäre Antikörpermangel mindestens 30-mal häufiger auf als der primäre Antikörpermangel [4, 5]. Ebenfalls lassen sich häufig chronische Lymphozytopenien oder Neutropenien nachweisen, wobei insbesondere verminderte CD4-positive (Helfer‑)Zellen < 200/µl und eine Reduktion der neutrophilen Granulozyten < 500/µl mit einem erhöhten Risiko von Infektionen einhergehen.
Sekundäre Immundefekte sind deutlich häufiger als primäre Formen
In vielen Fällen besteht eine medikamentös induzierte sekundäre Immundefizienz, die in den letzten Jahren weiter zugenommen hat [5]. Andere mögliche Ursachen eines sekundären Antikörpermangels sind hämatologisch-onkologische Grunderkrankungen (vor allem chronische lymphatische Leukämie [CLL], multiples Myelom [MM] und Lymphomerkrankungen), Erkrankungen mit relevantem Eiweißverlust (vor allem nephrotisches Syndrom, chronisch-entzündliche Darmerkrankungen), Störungen der lymphatischen Zirkulation, aber auch Infektionen oder Mangelernährung ([3]; Tab. 1).Lymphoproliferative Erkrankungen Chronische lymphatische Leukämie, multiples Myelom, Non-Hodgkin-Lymphome, Hodgkin-Lymphome, follikuläres Lymphom, Mantelzelllymphom, Marginalzonenlymphom
Eiweißverlust Nephrotisches Syndrom, enteraler Eiweißverlust
Erkrankungen der Lymphwege Intestinale Lymphangiektasien (Morbus Waldmann), Yellow-nail-Syndrom, Chylothorax, Proteus-Syndrom
Medikamentenassoziiert Rituximab, CAR-T-Zell-Therapie, Steroide, Cyclophosphamid, Clozapin, Imatinib, Ibrutinib, Abatacept, Phenytoin, Carbamazepin, Lamotrigin, Valproat u. a.
CAR chimärer Antigenrezeptor
Aufgrund der Vielzahl unterschiedlicher Immunsuppressiva kann im Folgenden nur auf einige wenige Wirkstoffe und exemplarische Szenarien eingegangen werden. Für die detaillierten Vorgehensweisen bei Patienten mit Tumorerkrankung oder Transplant sei auf die Leitlinien der Fachgesellschaften verwiesen. Eine detaillierte Übersicht möglicher infektiöser Komplikationen unter immunsuppressiver Therapie wurde unter Leitung der European Society of Clinical Microbiology and Infectious Diseases (ESCMID) zusammengestellt [6–11].
Relevante Aspekte vor Beginn einer immunsuppressiven Therapie
Allgemeines
Auf Basis einer Anamnese können vor Therapiebeginn bereits wichtige Schritte zur Risikoreduktion eingeleitet werden. Vor dem Hintergrund des Wirkmechanismus der geplanten Therapie sollte eine gezielte Infektionsanamnese erfolgen. Zusätzlich ermöglichen klinische Algorithmen wie der RABBIT-Risikoscore (RABBIT „Rheumatoide Arthritis: Beobachtung der Biologika-Therapie“), das individuelle Risiko infektiöser Komplikationen besser abzuwägen [12].
Das Risiko potenzieller Arzneimittelwechselwirkungen sollte geprüft werden, so etwa ein erhöhtes Risiko für Agranulozytose unter Azathioprin und Allopurinol. Für Patienten aus bestimmten endemischen Regionen kann ein erhöhtes Risiko parasitärer Infektionen bestehen, beispielsweise mit Geohelminthen wie Strongyloides stercoralis oder Ascaris lumbricoides; daher sollte auch die Reise- bzw. Migrationsanamnese erfasst werden.
Auch bei Patienten ohne auffällige Infektionsneigung empfiehlt sich, bereits vor Einleitung einer immunsuppressiven Therapie die oben genannte Basisdiagnostik zur Risikostratifizierung durchzuführen, auch da unter Immunsuppression nicht mehr sicher zwischen angeborener und medikamentös induzierter Immunschwäche unterschieden werden kann.
Screening auf eine mögliche Reaktivierung latenter Infektionen
Das erhöhte Risiko von Tuberkulosereaktivierungen unter Tumor-Nekrose-Faktor-α(TNF-α)-Blockern ist das wohl bekannteste Beispiel für die Notwendigkeit einer infektiologischen Risikostratifizierung vor Immunsuppression [13]. Weitere wichtige Infektionen sind Hepatitis B [14], Herpes zoster und eine John-Cunningham(JC)-Polyomavirus-Infektion als Auslöser der progressiven multifokalen Leukenzephalopathie (PML; [6–11, 14, 15]). Je nach geplantem Medikament und Risikoprofil empfiehlt sich eine vorherige infektiologische Abklärung (Beispiele in Tab. 2).Empfohlene Untersuchung vor Therapiebeginn Medikamentengruppe
Tuberkulose-IGRA TNF-α-Blockade (z. B. Infliximab)
IL-1-Hemmung (z. B. Anakinra, Canakinumab)
Abatacept (CTLA-4-Fusionsprotein)
IL-6-Blockade (Tocilizumab, Siltuximab)
IL-12/23-p40-Blockade (Ustekinumab)
mTOR-Inhibitoren (Sirolimus, Everolimus)
Serologische Untersuchung (JC-Virus-IgG) Integrinantagonisten (v. a. Natalizumab)
Seltener unter CD20-Depletion (z. B. Rituximab, Ocrelizumab, Ofatumumab)
Hepatitis B: HBsAg und serologische Untersuchung, ggf. PCR-Diagnostik CD20-Depletion (z. B. Rituximab, Ocrelizumab, Ofatumumab)
TNF-α-Blockade (z. B. Infliximab, Etanercept)
IL-6-Blockade (Tocilizumab, Siltuximab)
Interleukin-12/23-p40-Blockade (Ustekinumab)
mTOR-Inhibitoren (Sirolimus, Everolimus)
JAK-Inhibitoren (z. B. Ruxolitinib, Tofacitinib, Baricitinib)
Serologische Untersuchung (Varizella-Zoster-Virus-IgG) Sphingosin-1-Phosphat-Rezeptor-Modulator (Fingolimod)
JAK-Inhibitoren (z. B. Ruxolitinib, Tofacitinib)
Anti-CD38-Therapie (Daratumumab)
Proteasominhibitor (Bortezomib)
Kursiv hervorgehoben sind die Medikamentengruppen mit besonders hohem Risiko
CTLA‑4 „cytotoxic T‑lymphocyte-associated protein 4“, HBsAg „hepatitis B surface antigen“, IgG Immunglobulin G, IGRA „interferon‑γ release assay“, IL Interleukin, JAK Januskinase, mTOR „mechanistic target of rapamycin“, PCR Polymerase-Kettenreaktion, TNF‑α Tumor-Nekrose-Faktor‑α
Präventive Therapien vor Immunsuppression
Die häufigsten präventiven Therapien betreffen die Tuberkulose und Hepatitis B. Bei latenter Tuberkuloseinfektion wird vor geplanter Behandlung mit TNF-α-Blockern und anderen Medikamenten (Tab. 1) eine präventive Chemoprophylaxe mit Isoniazid für 6–9 Monate oder mit Rifampicin für 4 Monate empfohlen [14, 16]. Bei der Wahl der Therapie sollten die Hepatotoxizität von Isoniazid und das Risiko von Arzneimittelinteraktionen unter Rifampicin berücksichtigt werden.
Bei positivem HBsAg im Screening besteht ein erhöhtes Risiko der Hepatitis-B-Reaktivierung
Bei Patienten mit positivem „hepatitis B surface antigen“ (HBsAg) im Screening besteht ein erhöhtes Risiko einer Hepatitis-B-Reaktivierung. In Absprache mit einem Hepatologen ist vor Einleitung der Immunsuppression eine antivirale Therapie mit den Nukleosidinhibitoren Entecavir oder Tenofovir indiziert. Bei durchgemachter Hepatitis B mit positiven Anti-HBc-Antikörpern bei HBsAg-Negativität hängt die Notwendigkeit einer antiviralen Therapie von Ausmaß und Art der Immunsuppression sowie vom Alter und weiteren Risikofaktoren ab. Ein Monitoring der Hepatitis-B-Viruslast wird empfohlen. Die Betroffenen sollten einem Hepatologen vorgestellt werden.
Bei Seropositivität für das JC-Virus steht derzeit leider keine spezifische präventive Option zur Verfügung. Die ESCMID empfiehlt die Bestimmung von JC-Virus-IgG-Antikörpern vor und alle 6 Monate während einer Therapie mit sogenannten Integrinantagonisten (beispielsweise Natalizumab, Vedolizumab und Efalizumab). Bei einem Antikörperindex > 1,5 darf die Therapie nicht begonnen bzw. muss sie sofort beendet werden [7]. Unter Vedolizumab, einem darmselektiven α4β7-Integrin-Antagonisten für die Behandlung chronisch-entzündlicher Darmerkrankungen, wurden bisher keine PML-Fälle gemeldet.
Schutzimpfungen
Der Impfstatus des Patienten und die Notwendigkeit von Auffrischungen bzw. zusätzlichen Schutzimpfungen sollte frühzeitig überprüft werden. Da die Impfantwort unter immunsuppressiver Behandlung stark beeinträchtigt sein kann, sollten alle Impfungen idealerweise spätestens 2–4 Wochen vor Behandlungsbeginn abgeschlossen sein. Grundsätzlich werden gemäß Ständiger Impfkommission (STIKO) bei erworbener Immundefizienz folgende Schutzimpfungen empfohlen:
Pneumokokken.
Alle Patienten mit angeborener oder erworbener Immunschwäche sollten eine sequenzielle Pneumokokkenimpfung erhalten (Konjugatimpfung mit Prevenar13® [13-valent] gefolgt von der Polysaccharidimpfung mit Pneumovax23® [23-valent]). Derzeit wird eine Auffrischung mit dem Polysaccharidimpfstoff alle 6 Jahre empfohlen.
Influenza.
Alle Patienten mit angeborenen oder erworbenen Immundefekten sollten jährlich gegen Influenza geimpft werden. Des Weiteren empfiehlt die STIKO jährliche Impfungen für alle volljährigen Haushaltsmitglieder. Seit dieser Saison empfiehlt die STIKO die Verwendung des quadrivalenten Hochdosisinfluenzaimpfstoffs für alle Patienten > 60 Jahre.
Herpes zoster.
Für Patienten mit angeborenen oder erworbenen Immundefekten wird ab 50 Jahren eine 2‑malige Schutzimpfung gegen Herpes zoster mit dem adjuvantierten Totimpfstoff (Shingrix®) empfohlen. Des Weiteren haben bestimmte Immunsuppressiva ein deutlich erhöhtes Risiko für Herpes-zoster-Infektionen. Hierzu zählen vor allemJanuskinase(JAK)-Inhibitoren (beispielsweise Ruxolitinib und Baricitinib),
Proteasominhibitoren (unter anderem Bortezomib) und
Anti-CD38-Therapien (beispielsweise Daratumumab).
Wird im vorherigen serologischen Screening kein Schutz gegen das Varizella-Zoster-Virus (VZV) nachgewiesen (VZV-IgG negativ), ist zunächst eine Immunisierung gegen VZV (cave: Lebendimpfstoff, siehe unten) erforderlich.
Meningokokken.
Eine Impfung gegen Meningokokken sollte alle impfpräventablen Serotypen (A, C, W und Y sowie B) beinhalten und ist vor allem bei angeborenen Komplementdefekten, funktioneller oder anatomischer Asplenie sowie vor einer Therapie mit Komplementinhibitoren wie Eculizumab empfohlen. Die Impfung gegen die Serotypen A, C, W und Y sollte mittels Konjugatimpfstoff erfolgen und alle 5 Jahre aufgefrischt werden. Für den Serotyp B liegen noch keine Daten zur Auffrischung vor.
Humane Papillomaviren (HPV).
HPV-Infektionen sind bei Immungeschwächten häufiger als in der gesunden Bevölkerung. Die 9‑valente HPV-Impfung sollte daher angeboten werden, allerdings muss bei Patienten > 18 Jahre im Vorfeld die Kostenübernahme durch die Krankenkasse geklärt werden.
Hepatitis B.
Für Immungeschwächte gilt, genauso wie für Immungesunde, eine Empfehlung zur Impfung primär bei beruflicher und nichtberuflicher Exposition sowie bei Reisen in Risikoländer. Immunsupprimierte Personen mit erhöhtem Risiko, etwa bei Koinfektion mit „human immunodeficiency virus“ (HIV) oder Hepatitis-C-Virus, entwickeln öfter schwere Verläufe und sollten stets geimpft werden.
Es gibt keine Evidenz für ein erhöhtes Risiko von Krankheitsschüben bei Autoimmunerkrankungen nach Impfung. Hingegen konnte in diversen Studien ein Krankheitsprogress nach impfpräventablen Erkrankungen belegt werden [17, 18]. Lebendimpfstoffe werden bei Patienten mit erworbener Immunschwäche generell nicht empfohlen. Es gilt jedoch, jeden Fall individuell zu prüfen, da das Risiko einer impfinduzierten Infektion vor allem von der Höhe der CD4-positiven (Helfer‑)Zellen abhängt [17, 18]. Bei starker Immunschwäche und Kenntnis eines protektiven Schwellenwerts für die Höhe der spezifischen Antikörper wird eine serologische Kontrolle der Impfantwort empfohlen. Detaillierte Handlungsanweisungen können den aktuellen STIKO-Empfehlungen entnommen werden [17, 18].
Weitere Behandlungsansätze bei primären Immundefekten
Patienten mit klinisch relevanten PID sollten stets unter Beteiligung eines in Diagnostik und Therapie erfahrenen Immundefektzentrums betreut werden. Die möglichen Behandlungsoptionen reichen von einer regelmäßigen Immunglobulinsubstitutionstherapie bei pathologischer Infektionsneigung und Antikörpermangel über eine immunsuppressive (Kombinations‑)Therapie bei zusätzlicher Immundysregulation bis hin zur hämatopoetischen Stammzelltransplantation. Bei einigen monogenetischen Erkrankungen sind zielgerichtete Behandlungen möglich, beispielsweise der Einsatz des CTLA-4-Fusionsproteins Abatacept bei Patienten mit Cytotoxic-T-lymphocyte-associated-protein-4(CTLA-4)-Haploinsuffizienz oder Lipopolysaccharide-responsive-beige-like-anchor(LRBA)-Defizienz. An dieser Stelle sei auf die aktuelle Leitlinie verwiesen [3].
Weitere Behandlungsansätze bei sekundären Immundefekten
In einigen Fällen kann durch die Beendigung oder Umstellung der immunschwächenden Medikation eine ausreichende Rekonstitution des Immunsystems erreicht werden. In anderen Fällen ist eine Therapieumstellung in Bezug auf die Grunderkrankung jedoch klinisch nicht möglich.
Sekundärer Antikörpermangel
Bei vielen hämatologisch-onkologischen Erkrankungen kommt es zu begleitenden Hypogammaglobulinämien. Insbesondere bei CLL, MM und Lymphomerkrankungen lassen sich oft verminderte Immunglobuline messen. Die Häufigkeit der infektiösen Komplikationen korreliert hierbei mit dem Ausmaß der Hypogammaglobulinämie [19, 20]. Eine Hypogammaglobulinämie kann Teil der Grunderkrankung sein oder aber als Behandlungsfolge entstehen (Tab. 1). Insbesondere B‑Zell-depletierende Medikamente wie Rituximab und Ocrelizumab, die als monoklonale Antikörper den B‑Zell-Oberflächenmarker CD20 erkennen, können eine Hypogammaglobulinämie auslösen. Ist eine Immunrekonstitution durch Therapieumstellung nicht möglich, kann bei auffälliger Infektionsneigung eine regelmäßige Immunglobulinersatztherapie erforderlich sein.
Seit 2019 sind die Zulassungskriterien der Europäischen Arzneimittel-Agentur (EMA) für die Immunglobulinersatztherapie auf alle Patienten mit sekundären Antikörpermangelzuständen erweitert, die zusätzlich zur pathologischen Infektionsneigung trotz versuchter Antibiotikaprophylaxe eine relevante Hypogammaglobulinämie (IgG < 4 g/l) und/oder eine nachgewiesenermaßen eingeschränkte Impfantwort (< 2-facher Anstieg der spezifischen IgG-Antikörper nach Impfung mit Pneumokokkenpolysaccharidvakzine oder Peptidimpfung und jeweils auffällig niedrige Ausgangswerte) haben [21].
Immunglobuline werden von gesunden Plasmaspendern gewonnen, eine schriftliche Aufklärung vor Therapiebeginn ist obligat. Ziel der regelmäßigen Gabe ist es, durch Erhöhung des IgG-Werts eine Verbesserung der Infektionsneigung zu erreichen. Ein Anstieg der Immunglobuline IgA und IgM ist nicht möglich. Während für Patienten mit PID eine Dosis von 0,4 bis 0,8 g/kgKG pro Monat empfohlen wird, wird die Dosis bei Patienten mit SID primär etwas niedriger angegeben (0,2–0,4 g/kgKG pro Monat). In beiden Fällen ist jedoch stets von einer individuellen Dosisfindung auszugehen. Die Gabe von Immunglobulinen kann sowohl intravenös (IVIG) als auch subkutan (SCIG) erfolgen. Unter SCIG-Therapie werden weniger systemische Nebenwirkungen beobachtet. Die SCIG-Therapie ist in Deutschland als Heimtherapie zugelassen [3].
Die Indikation zur Immunglobulinersatztherapie sollte individuell und sorgsam geprüft werden
Vor dem Hintergrund eines globalen Engpasses in der Versorgung mit Immunglobulinen sollte die Indikation individuell und sorgsam geprüft werden. Ebenfalls sollte Patienten und Behandlern bewusst sein, dass eine regelmäßige Immunglobulinersatztherapie nicht alle potenziellen Infektionen effektiv verhindern kann. So besteht gute Evidenz für die Verhinderung von Pneumonien, aber nur ein eingeschränkter klinischer Effekt auf rezidivierende Sinusitiden [22, 23].
Auch bei laufender Immunglobulinersatztherapie werden die oben genannten Schutzimpfungen empfohlen. Denn einerseits kommt es auch zu einem „Training“ der T‑Zell-Immunität, andererseits sind in den Immunglobulinen nicht ausreichend spezifische IgG-Antikörper gegen alle impfpräventablen Erreger enthalten, beispielsweise gegen Meningokokken. Dies gilt auch für Erreger mit wechselnder Antigenzusammensetzung (jährliche Influenza und „coronavirus disease 2019“ [COVID-19]).
Antibiotikaprophylaxe
Eine prophylaktische Gabe von Antibiotika als Schutz vor Infektionen sollte bei allen Patienten mit häufigen bakteriellen Infektionen und Notwendigkeit der Antibiotikabehandlung diskutiert werden. Evidenz aus randomisierten, kontrollierten Studien besteht vor allem für das Makrolidantibiotikum Azithromycin, beispielsweise bei Patienten mit Bronchiektasen [24] und bei Patienten mit PID [25]. Vor dem Einsatz von Makrolidantibiotika (in der Regel 250–500 mg Azithromycin 3‑mal/Woche) muss eine Besiedlung bzw. Infektion mit atypischen Mykobakterien ausgeschlossen werden. Ebenfalls ist ein Elektrokardiogramm wegen des Risikos einer QT-Zeit-Verlängerung erforderlich. In der klinischen Praxis wird darüber hinaus Trimethoprim/Sulfamethoxazol (TMP/SMX, beispielsweise Cotrimoxazol), Doxycyclin oder Amoxicillin eingesetzt [26, 27]. Für Patienten mit angeborenen (terminalen) Komplementdefekten sowie bei einigen Risikopatienten unter Behandlung mit Komplementinhibitoren ist eine zusätzliche (meningokokkenwirksame) Antibiotikaprophylaxe mit Penicillin oder Ciprofloxacin zu empfehlen.
Bei allen Patienten mit einer chronischen Verminderung der CD4-positiven (Helfer‑)Zellen auf < 200/µl besteht formal ein erhöhtes Risiko opportunistischer Infektionen, wobei die klinische Evidenz vor allem auf den Erfahrungen bei Patienten mit HIV-Infektion basiert. Erste Wahl ist hier die Kombination TMP/SMX, die mit 480 mg täglich bzw. mit 960 mg 3‑mal wöchentlich dosiert wird. Bei Unverträglichkeiten können Atovaquon, Dapson oder die Inhalation von Pentamidin zum Einsatz kommen. Wichtig ist eine individuelle und umfangreiche Aufklärung des Betroffenen, damit im Falle einer Infektion bereits früh an das Vorliegen opportunistischer Erreger gedacht werden kann.
Antivirale Behandlung und Prophylaxe
Bei schwer immunsupprimierten Patienten kann auch eine ergänzende antivirale oder antimykotische Prophylaxe erforderlich sein. Dies betrifft in der Regel Patienten nach Organ- und Knochenmarktransplantation oder Patienten unter Chemotherapie. Die ESCMID empfiehlt darüber hinaus eine Valaciclovir- bzw. Aciclovirprophylaxe bei VZV-Seropositivität während der Induktionsphase mit Proteasominhibitoren (Bortezomib) sowie bei Behandlung mit dem Sphingosin-1-Phosphat-Rezeptor-Modulator Fingolimod in Kombination mit Steroiden [7].
Immunsuppressive Therapie während Infektionen
Die Entscheidung über eine Unterbrechung oder Fortführung der immunsuppressiven Therapie während Infektionen hängt von Schweregrad und Art der Infektion ab und muss individuell getroffen werden. Während die Unterbrechung einer TNF-α-blockierenden Therapie in der Regel unproblematisch ist, besteht insbesondere bei autoinflammatorischen Erkrankungen ein erhöhtes Risiko von Krankheitsschüben, wenn eine JAK-Inhibitoren- oder Anti-IL-1-Behandlung abrupt unterbrochen wird [10, 11].
Multidisziplinäres Management, supportive und begleitende Maßnahmen
Entsprechend der Infektionslokalisation sollte eine multidisziplinäre Abklärung und Betreuung erfolgen. Patienten mit häufigen und/oder chronischen Infektionen der Nasennebenhöhlen sollten Hals-Nasen-Ohren-ärztlich vorgestellt werden, auch um eine Allergie auszuschließen. Viele Betroffene profitieren von einer regelmäßigen Spülung der Nasennebenhöhlen mit Kochsalzlösung (sogenannte Nasendusche).
Bei bekannten Bronchiektasen ist eine Betreuung beim Pneumologen und Atemtherapeuten erforderlich. Maßnahmen zur Verbesserung der mukoziliären Clearance beinhalten regelmäßige Inhalationen mit Kochsalzlösung, den Einsatz vibrationserzeugender Hilfsmittel sowie Lungensport. Regelmäßig sollten mikrobiologische Sputumuntersuchungen erfolgen.
Umfangreichere Informationen sind den Leitlinien der European Respiratory Society (ERS) zu entnehmen [26]. Eine nationale deutsche Leitlinie befindet sich derzeit in Vorbereitung. Bei Infektionen der oberen und unteren Atemwege ist eine strikte Nikotinkarenz zwingend zu empfehlen.
Komplementärmedizinische Ansätze
Das Angebot frei verkäuflicher „Immunstimulanzien“ ist groß. So sehr es einerseits auch gilt, eine proaktive Haltung der Patienten zur Verbesserung ihrer Gesundheit zu unterstützen, so sollte andererseits auch klar kommuniziert werden, dass viele komplementärmedizinische Ansätze ohne bzw. mit nur minimaler Evidenz erfolgen. Neben der finanziellen Belastung müssen auch etwaige Nebenwirkungen thematisiert werden [28].
Elemente der Versorgung von Patienten mit Immunschwäche und erhöhter Infektionsneigung sind in Abb. 1 illustriert.
Fazit für die Praxis
Angeborene und erworbene Immundefekte präsentieren sich oft mit einer erhöhten Infektionsneigung.
Angeborene Immundefekte gehen oft mit Zeichen der Immundysregulation einher.
Vor Immunsuppression sollte eine individuelle Risikostratifizierung und Impfberatung erfolgen.
Alle Schutzimpfungen sollten idealerweise 2–4 Wochen vor Therapiebeginn abgeschlossen sein.
Das Differenzialblutbild und die Immunglobuline G, A und M sollten vor und während einer immunsuppressiven Therapie bestimmt werden.
Je nach Immunsuppression sind ein infektiologisches Screening (latente Tuberkuloseinfektion, Hepatitis B, Varizella-Zoster-Virus, JC-Virus) und gegebenenfalls eine präventive Therapie erforderlich.
Bei persistierenden Infektionsbeschwerden und Hypogammaglobulinämie (Immunglobulin G < 4 g/l) kann eine regelmäßige intravenöse oder subkutane Immunglobulinersatztherapie erfolgen.
Je nach Infektionslokalisation sollte eine multidisziplinäre Abklärung und Betreuung erfolgen.
Kochsalzinhalationen bzw. -spülungen sind bei Bronchiektasen oder chronischer Sinusitis wichtige supportive Maßnahmen.
Einhaltung ethischer Richtlinien
Interessenkonflikt
L.G. Hanitsch gibt an, dass kein Interessenkonflikt besteht.
Für diesen Beitrag wurden vom Autor keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.
QR-Code scannen & Beitrag online lesen
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J Public Health Policy
J Public Health Policy
Journal of Public Health Policy
0197-5897
1745-655X
Palgrave Macmillan UK London
35414693
348
10.1057/s41271-022-00348-8
Letter to the Editors
Prioritizing COVID-19 test utilization during supply shortages in the late phase pandemic
http://orcid.org/0000-0003-1130-7609
Amirian E. Susan ea25@rice.edu
grid.21940.3e 0000 0004 1936 8278 School of Social Sciences, Rice University, 6100 Main St., Houston, TX 77005 USA
12 4 2022
2022
43 2 320324
17 3 2022
© The Author(s), under exclusive licence to Springer Nature Limited 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
issue-copyright-statement© Springer Nature Limited 2022
==== Body
pmc Dear Editor,
From the beginning of the pandemic, shortages in the COVID-19 testing supply in the United States (U.S.) have undermined the nation’s ability to maintain sufficient, consistent, and equitable testing across the population, hampering the public health response [1–3]. As we enter the third year of the pandemic, the U.S. still struggles to meet spikes in testing demand during surges [4, 5], such as during the Omicron peak. Reports of long lines, regional shortages, and stark disparities in testing resurfaced [6, 7], just months after alleged destruction of millions of test kits by a manufacturer [8]. Even concerns surrounding competition for testing supplies between states and the federal government re-emerged, reminiscent of the 2020 bidding wars for test kits and personal protective equipment [9].
To alleviate the situation, in Winter 2021, the Biden administration initiated distribution of free at-home COVID-19 antigen tests, announced the launch of new federal testing sites, and evoked the Defense Production Act to accelerate test production [10]. While laudable, these efforts will neither eliminate inequitable access to COVID-19 testing nor substantially ameliorate supply strains during future periods of high demand. Multiple factors affect test supply chains, including risks that may not be easy to manage soon. Historic underinvestment in public health laboratories and other testing infrastructure compounds the difficulty [11]. Demand for testing will fluctuate with waves of the pandemic, and testing supplies will remain crucial for timely clinical care of patients and effective public health practice. Therefore, unless the testing supply chain can be secured in the immediate future, the U.S. will need to mount a robust national effort to triage and coordinate use of diagnostic and screening test resources strategically in times of high demand (COVID-19 surges, holidays, start of school year), even as we transition into the endemic phase.
Crisis triage of inadequate medical resources makes it possible to optimize health outcomes and maximize life-saving potential while upholding fundamental ethical and societal values [12]. During the pandemic, society should consider COVID-19 tests a core medical resource that testing service providers may need to triage according to public health guidance when acute shortages arise. Some suppliers and local agencies have made piecemeal, ad hoc efforts to prioritize access to COVID-19 tests (including seller-enforced household purchase limits on at-home tests) [6]. However, lack of carefully crafted national guidance for efficient leveraging of the national testing supply to support public health has left consumers and test suppliers with little clarity about how to achieve our most basic shared aims: ensuring testing for those with medical need, reducing test waste, and aligning best practices.
Testing access currently depends on financial resources to purchase at-home tests, strong advocacy (by powerful labor unions, or employers that sponsor testing), or time and technological literacy to find available tests or appointments, or all of these. Even during scarcity, part of the supply goes to low priority uses, including recreational, non-essential travel screening, or resource-intensive occupational surveillance programs (such as frequent testing in the entertainment industry) [13]. All these uses have epidemiological justification but may not constitute the most epidemiologically sound or ethically justifiable expenditures of limited resources. Such uses may limit the supply for symptomatic patients, COVID-exposed individuals, and frontline workers (or their household members) for quick case isolation and early treatment. Ideally, testing sites would preserve same-day appointments and walk-in testing for individuals with medical need. Instead, during the 2021–2022 Omicron surge, anecdotal reports emerged of molecular tests reserved for recreational or non-essential business travel by private testing facilities (such as urgent care centers) while wait and turnaround times for PCR tests soared for symptomatic individuals [7, 14]. For test-seekers who can pay for faster testing options, there may be other adverse effects of supply shortages including price gouging or risks associated with pop-up test sites [5, 13, 15].
Given the dearth of national coordination, profound disparities in COVID-19 testing have emerged across racial and ethnic groups, geographic regions, and occupations [16–19]. These disparities further exacerbate the already disproportionate impact of COVID-19 morbidity and mortality on uninsured, low-income, and minority or immigrant communities, stymying efforts to prioritize protection of those most vulnerable.
Moving forward, the U.S. Centers for Disease Control and Prevention (CDC) should convene a COVID-19 testing task force with representatives from the public and private sectors to develop clear guidance, detailed recommendations, and tools to guide prioritization and distribution of tests during shortages. Stakeholders include state health officials, industry representatives from major national test suppliers, and national experts in epidemiology, medicine, bioethics, and health disparities. Policy makers, including those in U.S. Congress and in the Executive branch and its agencies, such as the Centers for Medicare & Medicaid Services, can create incentive programs (government subsidies) to encourage private test providers to replace “first come, first serve” testing with triaging practices under pre-determined conditions. Planners can identify thresholds for offering incentives—based on COVID-19 incidence and test positivity rates and time to supply exhaustion estimates, among other criteria—to apply once any geographic region meets specific disease and supply related metrics.
The author suggests that such a triage system should prioritize patients with medical need among those seeking testing (outside of medical care settings) as follows:All symptomatic patients experiencing COVID-consistent illness;
Asymptomatic individuals:with known COVID-19 exposures;
who are frontline workers;
who reside in high-exposure-risk households;
Required screening for essential travel, followed by non-essential business or leisure travel;
For any other reason.
If a supply shortage is extreme, planners could consider additional prioritization within Tiers 1-2c: the person’s age, underlying medical conditions, and frequency of contact with susceptible individuals.
To streamline implementation, the CDC could develop a standard patient questionnaire to be integrated into appointment systems that, during shortages, would categorize test-seekers into priority tiers based on self-reported data and automatically organize appointment options by tier. For walk-in sites, test-seekers could complete the questionnaire on paper or through a mobile phone application to facilitate manual assignment to tier-specific lines. The federal government could also devise innovative strategies to enhance surge capacity for testing while balancing test quality and supply. It would be useful to pilot test the impact of providing provisional Clinical Laboratory Improvement Amendments (CLIA) certifications to academic research laboratories experienced in conducting molecular testing. The Centers for Medicare & Medicaid Services and the Food and Drug Administration could modify CLIA certification standards to permit select labs to run “non-waived” COVID-19 tests on a temporary, as-needed basis. “Non-waived” tests are those of moderate to high complexity. The extra manpower and lab capacity from these provisionally certified laboratories could complement the increased production of test components. If successful, this program could also augment diagnostic lab capacity for future epidemics. Finally, the CDC should provide aggregated data from testing service providers on how the national test supply is being used on its website to promote public accountability and transparency. The CDC’s COVID-19 testing task force could also utilize the data, using metrics such as the ratio of tests consumed per positive case detected per week (or 7-day average test positivity rates considered in the context of tests consumed per person per week) to identify large-scale programs that involve imprudent test expenditure when test supplies or staffing are limited. The task force could then target communication strategies or other interventions to mitigate wasteful test usage by these programs if the amount or proportion of tests expended on them is high enough to exacerbate regional testing shortages.
In medicine and public health, crisis triage is not a foreign concept; the prospect of rationing resources for the common good is generally accepted by the public during times of emergency. We now have a more robust armamentarium of testing options than earlier in the pandemic, but society will not appreciate its full benefit until our overwhelmed public health system can devise and communicate a fair and equitable strategy for efficient use of these resources when they are most needed. During a pandemic that has killed over 955,000 people as of the beginning of March 2022 in the U.S. alone, the CDC, state and local health departments, and policymakers should play more proactive roles in helping optimize testing to reduce loss of life and preserve societal function.
Acknowledgements
None
Funding
No funding was received for this work.
Declarations
Conflict of interest
The corresponding author states there is no conflict of interest. The corresponding author reports employment with McKesson, Corp. that is unrelated to this work.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
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Educ Inf Technol (Dordr)
Educ Inf Technol (Dordr)
Education and Information Technologies
1360-2357
1573-7608
Springer US New York
35431598
11052
10.1007/s10639-022-11052-1
Article
Exploring factors affecting the adoption of MOOC in Generation Z using extended UTAUT2 model
http://orcid.org/0000-0002-5843-9679
Meet Rakesh Kumar rakeshmeet111@gmail.com
12
http://orcid.org/0000-0003-4539-4608
Kala Devkant devkala@gmail.com
3
http://orcid.org/0000-0001-5688-1503
Al-Adwan Ahmad Samed a.adwan@ammanu.edu.jo
4
1 Marketing Department, Doon Business School, Dehradun, India
2 grid.444415.4 0000 0004 1759 0860 University of Petroleum and Energy Studies, Dehradun, India
3 grid.444415.4 0000 0004 1759 0860 School of Business, University of Petroleum and Energy Studies, Dehradun, India
4 grid.116345.4 0000000406441915 Electronic Business and Commerce Department, Al-Ahliyya Amman University, Amman, Jordan
12 4 2022
123
7 2 2022
6 4 2022
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The advent of Internet heralded the rise of scalable educational technology dubbed as massive open online course (MOOC). Easy to use, access, economical as well as flexible, provide students lot of freedom and the advantage of self-paced learning. Despite all these merits, MOOC adoption is low in the higher educational institutions (HEIs) of India. The aim of this study is to explore the factors affecting the behavioural intention to adopt MOOCs among Generation Z (Gen Z) enrolled in the Indian HEIs. The study uses the extended UTAUT2 model with additional constructs of language competency and teacher influence to explore MOOC adoption among the Gen Z. Using online survey, data of 483 students was collected from HEIs of India using stratified random sampling and analysed using partial least square-structure equation modelling (PLS-SEM) technique. The results establish the general applicability of UTAUT2 model in context of MOOC in Indian settings with explanatory power of 69.9% and highlights the positive influence of price value, hedonic motivation, facilitating conditions, performance expectancy and effort expectancy on MOOC adoption. However, the constructs of social influence, habit, language competency, and teacher influence unexpectedly do not have an impact on Behavioural Intention of Gen Z towards MOOC adoption. Based on the research findings, study implications and future directions of the research have been suggested.
Keywords
MOOC
Higher education
Price value
Hedonic motivation
Effort expectancy
UTAUT
Gen Z
India
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pmcIntroduction
The internet has influenced almost every aspect of our daily life including education. It has transformed the way we used to attain education from the close confines of a traditional classroom to now scalable and innovative medium in modern education named MOOC which is accessible from any place in the world using an internet connection and a mobile device (Albelbisi et al., 2021a; Ma & Lee, 2019). Online classrooms are not only complementing the traditional classrooms but are also making the students learn from lectures designed, curated and delivered by the world’s best professors teaching in the best universities of the world accessible to large population almost free of cost (Al-Adwan, 2020; Albelbisi et al., 2021b; Deng et al., 2019; Ma & Lee, 2019). MOOCs are considered to be a good medium for encouraging lifelong learning which is one of the important goals of Sustainable Development Goals (SDG4) listed by United Nations for achievement by member countries by 2030 (Lambert, 2020; Meet & Kala, 2021). The COVID-19 pandemic made online learning a necessity for many especially school and college students to the working professionals (Anand Shankar Raja and Kallarakal, 2020; Altalhi, 2021). Growing substantially in number in the last few years, MOOCs have attracted thousands of users across nations (Larionova et al., 2018; Classcentral.com, 2020; Altalhi, 2021).
The majority of present generation MOOC learners belongs to Gen Z – defined as individuals born between 1995 and 2010 (Francis & Hoefel, 2018). Born in the digital era, Gen Z is the first generation whose life hinges on technology and modern technological solutions are all the part of their living ecosystem (Larionova et al., 2018). It is important to note that Gen Z also known as digital natives are highly technology-driven; thus, deploying digital means to engage and teach them assumes significant importance. In India, government owned MOOC platform viz. SWAYAM has a registered user base of 16 million (Classcentral.com, 2020) and the country has a world’s largest population of 500 million people in the age bracket of 5–24 years, providing huge opportunity to the education sector to further grow and evolve (IBEF, 2021). To accommodate rise in number of students in the HEIs of India, country needs to have at least another 800 new universities and 40,000 new colleges by 2030 (Thestatesman.com, 2019). India’s higher education Gross Enrolment Ratio (GER) in 2019–20 stood at 27.1%, which is calculated for 18–23 years of age group and is way below the GER of many developed and developing nations (AISHE, 2019–20) which calls for massive thrust on education sector (Christensen & Alcorn, 2013). This imminent requirement has given researchers an area to study and explore as how online education through MOOCs can be furthered and what are the factors influencing MOOC adoption. The reasons for choosing Gen Z as a subject of study on MOOC adoption are majorly three. Firstly, MOOC offerings resonates well with the likings of Gen Z of convenience, comfort, quality and quick access (Larionova et al., 2018). Secondly, it is also to be studied that how Gen Z who have been brought up in the digital world adopt to the new virtual learning environment other than the traditional classrooms in achieving their educational and vocational goals (Szabó et al., 2021) and Thirdly, in India the higher education GER exit 2019–2020 is at 27.1 calculated on the age group of 18–23 years (AISHE, 2019–20), which falls in the age bracket of Gen Z.
In the extant literature, many research has investigated MOOCs adoption by students in general (Al-Adwan, 2021; Al-Adwan & Khdour, 2020; Wan et al., 2020; Fianu et al., 2018). However, to the best of the authors’ knowledge, this is the first study that targets the adoption of MOOCs by Gen Z students, particularly in India. Given that a considerable percentage (27.1%) of students in the Indian higher education belongs to Gen Z students, the finding of this study would be very useful to guide the efforts toward a successful adoption of MOOCs in India. Furthermore, Christensen and Alcorn (2013) in their study revealed that HEIs must acknowledge imbalance in demand and supply in affordable quality of higher education and they should actively participate in creating and curating MOOC in Indian-language especially on subjects in demand catering to Indian students from diverse cultural and geographical backgrounds. Similar findings are echoed by Aldahdouh and Osório (2016) and Connolly (2016) highlighting the significance of language proficiency in MOOC participation and suggested that students enrol in only those MOOCs which are available in their language. Garcia Mendoza et al. (2017) highlighted the need to examine the impact of language competency on MOOC adoption as communication plays a vital role in every form of learning be it online or offline; learners have a better learning outcomes in their native language (UNESCO, 2016). Similarly need was felt to evaluate teachers’ or instructors’ characteristics and influence on learning processes and outcomes of MOOC participants (Littlejohn et al., 2020). Teacher’s influence refers to the role of a teacher in motivating and directing a student to use MOOC for his better understanding and knowledge of the subject. Teacher has a positive influence on the offline and online learning activities of a student and teacher’s prior exposure to MOOC as a student or creator, comfort and ease of handling education technology, and teaching experience could be a possible influencer besides promoting positive attitude towards MOOC learning (Garrison, 2000; Tseng et al., 2019; Jung & Lee, 2020).
Realizing the potential of MOOC to disrupt the education sector, and the dearth of research in the area of MOOC adoption in Indian context (Virani et al, 2020), particularly among the students pursuing higher education in Indian Universities and Institutes has motivated the scholar to undertake this research with Gen Z as a subject of study and language competency and teacher influence as an additional constructs positively impacting MOOC adoption.
Literature review and hypotheses development
Over a past decade, educational technology is gaining much attention, interest and reviews and is becoming a part of learning mechanism for millions of learners across the world (Albelbisi et al., 2021a; Ma & Lee, 2019). MOOCs are evolving and emerging as a cost efficient, and an attractive way to bridge the current and huge gap in the country’s education system (UNESCO, 2016; Al-Adwan, 2020). Many research in the field of technology adoption resulted into theory and model development which further have explained organizations’ and individuals’ intention to use technological innovations, which have their origins in information systems, psychology, sociology and anthropology (Venkatesh & Davis, 2000; Davis, 1989; Venkatesh et al., 2003). The theoretical models in this field of research identifies certain independent variables that positively or negatively influence the dependent variable of intention to use and which in turn, may impact actual use of the technology. Literature review revealed that UTAUT is one of the well-researched and widely applied theory for explaining technology adoption and usage majorly on the premises of it being a result of synthesis of as many as eight theories (Williams et al., 2015). Four constructs of the UTAUT namely performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC) were tested and applicability of it is shown to significantly influence behavioural intention (BI) of the learners in e-learning settings (Dečman, 2015; Rosaline and Wesley, 2017; Fianu et al., 2018; Persada et al., 2019). The extended version of UTAUT namely UTAUT2 model has been applied and validated by researchers on varied technologies however not much research has happened on validating UTAUT 2 in the educational context (Mittal et al, 2021). Only limited studies have validated UTAUT2 in the educational settings (Mittal et al., 2021; Tseng et al., 2019), therefore, contemplating inconsistency in generalization of research available, more research is necessitated to validate UTAUT2 as a theoretical framework (Khalid et al., 2021). A study by Venkatesh (2012)demanded the extension of UTAUT2 to enhance the explanatory power in other consumer technology use. Based on the suggestion and previous research (Littlejohn et al., 2020; Radovan & Kristl, 2017; Tseng et al., 2019), we carried out our study in two parts. Firstly, we test to validate the influence of existing UTAUT2 constructs on Gen Z Behavioural Intention to adopt MOOC in the Indian settings. Second, we extended UTAUT2 by incorporating the additional two constructs of language competency (Deng et al., 2019; Jung & lee, 2020) and teachers’ influence (Chang et al., 2015; Pynoo et al., 2011; Tseng et al., 2019) which is believed to have a significant influence on Gen Z Behavioural Intention to adopt MOOCs.
It is important to note that the original UTAUT includes moderators (gender, age, and experience). These moderators are not examined in this study for several reasons. Eliminating the moderating variables results in generating more simplified models that can be employed to test the direct relationships between the constructs (Dwivedi et al., 2017, 2020). Additionally, excluding the moderators contributes significantly to building models that could be utilized in any context (Rana et al., 2017). Finally, moderators may not influence on the use and adoption context (Dwivedi et al., 2019).
An individual adopts a technology only when he feels that the use of technology will enhance their performance. Existing studies on technology adoption have highlighted the significant influence of PE on the BI to adopt e-learning (Dečman, 2015; Fianu et al., 2018; Jambulingam, 2013; Persada et al., 2019). During the Pandemic, PE was found to be major reason to adopt online teaching and learning owning to its usefulness (Kala & Chaubey, 2022; Mittal et al, 2021). In this study, it is assumed that the techno-savvy Gen Z studying in HEIs and confined to their homes to prevent the further outbreak of the COVID-19 pandemic may consider MOOCs to enhance their knowledge and skill and subsequently their employability in the professional world. Therefore, it was hypothesized that:H1. Performance expectancy influences Gen Z Behavioural Intention to adopt MOOC.
Another variable in UTAUT is effort expectancy which is similar to ease of use (TAM) defined as innovation perceived to be used or handled with ease and without much efforts (Davis, 1989). Previous researches have emphasized on the positive impact of effort expectancy on BI to adopt new technology (Venkatesh et al., 2003). A study by Al-Adwan (2020) reveal the positive influence of perceived ease of use which is a variable of effort expectancy on user’s BI towards MOOC. It is perceived that Gen Z who’s innate familiarity with digital devices and online world (Weinswig, 2016) may find MOOC usage easy. Hence, it was hypothesized that:H2. Effort Expectancy influences Gen Z Behavioural Intention to adopt MOOC.
Social influence is defined as ways and means in which people adjust or change their behaviour to conform to the societal norms and it has an influence on an individual when it comes to technology usage (Venkatesh et al., 2003) and is also corroborated by previous studies on technology adoption (Tseng et al., 2019). Study reveal that the young generation rely on family, friends and peers opinion when it comes to digital learning (Rosaline and Wesley, 2017; Persada et al., 2019). Hence, it was hypothesized that:H3. Social influence influences Gen Z Behavioural Intention to adopt MOOC.
Facilitating conditions (FC) refers to consumers’ schema of availability of necessary resources and support ecosystem to do a task (Brown & Venkatesh, 2005; Venkatesh et al., 2003). Researchers reveal that FC influence BI and use behaviour of the learners (Chang et al., 2019; Kala & Chaubey, 2022; Persada et al., 2019). Taking cognizance of this, it is projected that FC influence the BI towards MOOC adoption. Hence, we hypothesized that.H4. Facilitating conditions influences the Behavioural Intention of Gen Z to adopt MOOC.
Hedonic motivation (HM) is defined as degree of fun, pleasure and enjoyment derived using a technology (Brown & Venkatesh, 2005). The online technology adoption depends on the pleasure an individual derive from it (Yang et al., 2012). HM is an antecedent of BI to adopt online and internet based technologies such as learning management software, mobile learning, e-learning, digital social media, mobile banking etc. (Baptista & Oliveira, 2015; Moorthy et al., 2019; Raman & Don, 2013). Previous studies have found HM as a significant predictor of BI to adopt technology (El-Masri & Tarhini, 2017; Moghavvemi et al., 2017). Digitization and social media fuelled peer pressure has encouraged Gen Z to value experiences more than any other generations do and to lead a turbo charged, interesting, fun, experience-rich lives. Gen Z’s innate familiarity with the technological products and services affirm that they will be at forefront of adopting all the new online consumer technologies (Weinswig, 2016). Hence, it was hypothesized that:H5. Hedonic motivation influences Gen Z Behavioural Intention to adopt MOOC.
The price value (PV) is described as an individual users’ cognitive barter between the perceived benefits derived by using a technology and the money spent on using it (Venkatesh, 2012). The direct connect between PV and BI have been proved by previous studies on online learning (Raman & Don, 2013; Tseng et al., 2019). It is assumed that Gen Z perceive the benefits offered by MOOCs are more than the money spent as they get an access to online education from instructors teaching in one of the world’s best universities free or at a subsidized cost enhancing knowledge and skill resulting in improved employability quotient. Hence, it was hypothesized that:H6. Price Value influences Gen Z Behavioural Intention to adopt MOOC.
Habit (HT) is explained as exhibiting behaviour in an auto mode as a result of learning (Limayem et al., 2015). HT is found to have a positive influence on BI and use behaviour (Venkatesh, 2012). Previous investigations affirmed the positive influence of HT on internet based technologies (El-Masri & Tarhini, 2017; Gupta & Dogra, 2017). In this study, it is expected that Gen Z by virtue of innate familiarity with internet devices and technologies (Weinswig, 2016) have a conditioned behaviour towards using the technology which may influence their intention to adopt MOOCs. The influence of habit on student’s use of MOOC is not studied especially in educational settings of India. Hence, it was hypothesized that:H7. Habit influences Gen Z Behavioural Intention to adopt MOOC.
Language competency refers to students’ knowledge and proficiency in language in which online learning is being conducted. In information systems research, language has been found to influence technology acceptance (Deng et al., 2019). In the developing countries, language has a strong influence on students opting for MOOCs (Aldahdouh & Osório, 2016; Anand Shankar Raja and Kallarakal, 2020). Previous studies have highlighted the need of establishing the influence of language on online education (Deng et al., 2019; Jung & lee, 2020). Contemplating different languages spoken widely in India, it is important to establish the significance and the influence of language competency towards MOOC adoption (Christensen & Alcorn, 2013) and also given the ubiquity of non-native English MOOC learners it is expected that the language competencies influences the BI of Gen Z towards MOOC adoption. Hence, it was hypothesized that:H8. Language competency influences Gen Z Behavioural Intention to use MOOC.
Teacher’s influence refers to the role of a teacher in motivating and encouraging a student to use online learning tools for his better understanding and knowledge of the subject. It is found that teachers who are regarded as important social agents and nation builders have a positive influence on students’ mental makeup and behaviour and their independent use of technology for learning (Huang et al., 2019; Hoi & Mu, 2021; Al-Adwan et al., 2021a) and also as a key reason of participants enrolling in MOOC and promoting positive attitude towards MOOC learning (Chang et al., 2015; Jung & Lee, 2020; Tseng et al., 2019). Students consider teachers as their mentors and given the emergent need to adopt blended learning made mandatory by the COVID 19 there is a need to re-consider the changing role of teachers from sage on the stage to the facilitator on the side who can influence the learning strategies and processes adopted by learners and the subsequent outcomes (Littlejohn et al., 2020) (Fig. 1). Hence, it was hypothesized that:H9. Teacher influence influences Gen Z Behavioural Intention to use MOOC.
Fig. 1 Conceptual Framework
Research methodology
The study adopted quantitative research approach to create and test the conceptual framework (Rodrigues et al., 2021). Recent review papers by Alemayehu and Chen (2021) and Meet and Kala (2021) found that the quantitative research approach was extensively used in MOOC research. Furthermore, constructs of the UTAUT model and the PLS-SEM technique for examining relationships among constructs were widely used in MOOC adoption. Accordingly, the quantitative approach using the UTAUT model and PLS-SEM was employed in this study. At first, literature review was undertaken to figure out gap in research followed by development of a research instrument viz. survey questionnaire. Similar to most of UTAUT-based research in the field of educational technology adoption (Al-Adwan et al., 2018a, 2018b, 2021b), and particularly UTAUT-MOOCs research (Fianu et al., 2018; Wan et al., 2020), this study used survey questionnaire as the main data collection method to validate the research model. The items for the constructs of PE, EE, SI, FC, and BI referred to as UTAUT constructs were adapted from the research of Venkatesh et al. (2003) and modified in context of MOOCs. The items measuring HT, HM and PV were adapted from Venkatesh (2012) and modified in context of MOOCs. Similarly, the language competency and teacher influence items were adapted from the research work of Barak et al. (2015) and Sebastianelli et al. (2015) respectively and modified into the MOOC context. The items on the scale were meant for the students to specify their degree of agreement on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). A committee of experts comprising three academicians, two research scholars, and two industry experts working with MOOC providers vetted the questionnaire and finalized it. Later, a pilot survey was conducted on a sample of 100 students (excluded in the full-scale survey) of Indian HEIs who completed MOOCs. The Cronbach alpha of 0.953 added to the reliability of pilot study and paved way for full-scale survey which was completed over a period of 14 weeks during June–September 2021.The primary data of 483 students were collected from Gen Z respondents from various HEIs (Table 1) in Northern cities of India using stratified random sampling (Alraimi et al., 2015; Altalhi, 2021; Fianu et al., 2020; Šumak & Šorgo, 2016).Table 1 Type of University in Northern India
Types of University Private State Central Deemed IOE* IONR* Total
University (In No.s) 160 124 19 37 7 21 368
Respondent (In No.s) 179 158 57 51 12 26 483
*IOE—Institution of eminence
*IONR—Institution of national repute
Demographic details of the sampled students are shown in Table 2.Table 2 Demographic Profile (n = 483)
Demographic Characteristics Frequency Percentage
Age 20 and less 155 32.1
21–25 328 67.9
Education Undergraduate 269 55.7
Postgraduate 214 44.3
Gender Male 240 49.7
Female 243 50.3
Data analysis
Table 2 shows the demographic details of the sampled Gen Z participants. Of the total, 32.1% of the participants were in the age group of less than 20 and 67.9% were in 21–25 years. 55.7% of participants were pursuing undergraduate and 44.3% were pursuing postgraduate. 49.7% were male and 50.3% were female.
Measurement model
The partial least squares (PLS) method was used for primary data analysis and validate the conceptual framework. PLS has the capability to evaluate the measurement model and the structural model simultaneously (Hair et al., 2014). In comparison to the covariance-based structural equation modeling (CB-SEM), PLS-SEM is chosen for data analysis as it works well on both, small and large sample sizes, and has no restriction on normal distribution (Chin, 1998). PLS-SEM is considered to be adequate and accurate for validating explanatory power and appraising complex models (Hair et al., 2014). For the given reasons, PLS is deemed fit for analysis. SmartPLS 3.0 software (Ringle et al., 2015) is used for analysis.
In the measurement model, properties of reliability and validity of the constructs were calculated. Each construct’s internal consistency and item reliability was assessed by Chronbach alpha (α), Composite Reliability (CR) and Average Variance Extracted (AVE). Reliability indicators suggest that the values for Cronbach’s α, composite Reliability (CR) should be higher than 0.7, and the critical value for AVE should be higher that 0.5 (Fornell & Larcker, 1981). As depicted in Table 3, high level of reliability and internal consistency of all the constructs were established as the value of Cronbach’s α was above 0.7 (Nunnally, 1978). CR values more than the threshold of 0.7 confirms the level of reliability and internal consistency of all the constructs. Convergent validity was measured by evaluating factors loading of each construct. The convergent validity for all constructs was verified on account of AVE values were found to be greater than the threshold value of 0.5 (Hair et al., 2014) barring construct TI having a borderline AVE value of 0.472. Discriminant validity (DV) measures the degree of difference between one construct with another and two of the prominent measure of assessing it were the Fornell and Larcker (1981) criterion and the HTMT (heterotrait-monotrait ratio) criterion (Henseler et al., 2015). Discriminant validity was achieved when the squared root of each construct’s AVE was greater than any correlations with other constructs (Fornell & Larcker, 1981). As seen in Table 4, DV criteria was met.Table 3 Construct Operationalization
Variable & Construct Mean SD Factor Loading VIF
Performance Expectancy (PE) (α = 0.870, CR = 0.911, AVE = 0.720)
PE1 I find Online Courses (MOOCs) useful in my studies 3.894 0.914 0.850 2.230
PE2 Online Courses (MOOCs) increases my chances of achieving knowledge that is important to me 3.986 0.929 0.878 2.664
PE3 Online Courses (MOOCs) enables me to accomplish my task more quickly 3.648 0.978 0.833 1.917
PE4 Online Courses (MOOCs) increases my productivity (It adds to my knowledge) 3.890 0.947 0.833 2.106
Effort Expectancy (PE) (α = 0.868, CR = 0.919, AVE = 0.790)
EE1 How to use Online Courses (MOOCs) is easy for me 3.650 1.124 0.867 2.118
EE2 My interaction with Online Courses (MOOCs) is clear and understandable 3.600 1.011 0.903 2.427
EE3 I find Online Courses (MOOCs) easy to use 3.851 1.004 0.897 2.309
Social Influence (SI) (α = 0.884, CR = 0.928, AVE = 0.812)
SI1 People who are important to me think that I should use Massive Open Online Courses (MOOCs) 3.658 1.000 0.886 2.388
SI2 People who influence my behavior think that I should use Massive Open Online Courses (MOOCs) 3.609 1.023 0.912 2.638
SI3 People whose opinions that I value prefer that I use Massive Open Online Courses (MOOCs) 3.602 1.005 0.905 2.518
Facilitating Condition (FC) (α = 0.723, CR = 0.833, AVE = 0.565)
FC1 I have the resources necessary to use Online Courses (MOOCs) 3.834 1.161 0.749 1.379
FC2 I have the knowledge necessary to use Massive Open Online Courses (MOOCs) 3.536 1.059 0.868 2.690
FC3 Online Courses (MOOCs) is compatible with other technologies (Mobile/Laptops/Tablets) I use 3.710 1.029 0.845 2.485
FC4 I can get help from others when I have difficulties using Massive Open Online Courses (MOOCs) 3.839 1.037 0.481 1.079
Hedonic Motivation (HM) (α = 0.910, CR = 0.943, AVE = 0.847)
HM1 Using Online Courses (MOOCs) are enjoyable 3.712 0.968 0.915 2.832
HM2 Using Online Courses (MOOCs) are very entertaining 3.511 1.044 0.929 3.521
HM3 Using Online Courses (MOOCs) are fun 3.503 1.056 0.917 2.969
Price Value (PV) (α = 0.763, CR = 0.862, AVE = 0.676)
PV1 Online Courses (MOOCs) are reasonably priced 3.712 1.119 0.816 1.509
PV2 Online Courses (MOOCs) are a good value for the money 3.511 1.127 0.791 1.561
PV3 At the current price, Online Courses (MOOCs) provides a good value 3.503 1.124 0.859 1.576
Habit (HT) (α = 0.725, CR = 0.841, AVE = 0.639)
HT1 The use of Online Courses (MOOCs) has become a habit for me 3.124 1.032 0.752 1.408
HT2 I am addicted to using Online Courses (MOOCs) 2.934 1.071 0.790 1.517
HT3 I must use Massive Open Online Courses (MOOCs) 3.230 1.133 0.853 1.385
Behavioural Intention (BI) (α = 0.888, CR = 0.930, AVE = 0.817)
BI1 I will always try to use Online Courses (MOOCs) in my daily life 3.174 1.125 0.889 2.272
BI2 I plan to continue to use Online Courses (MOOCs) frequently 3.350 1.025 0.925 3.105
BI3 I intend to continue using Online Courses (MOOCs) in the future 3.617 1.050 0.897 2.672
Language Competency (LC) (α = 0.771, CR = 0.845, AVE = 0.524)
LC1 Students can actively participate in learning if the language of instruction is what they understand well 4.058 0.866 0.695 1.418
LC2 Language used in Online Courses (MOOCs) is important for me to adopt it 3.824 0.957 0.825 1.996
LC3 Language which the students may not be confident with may affect their approach to learning 3.853 0.962 0.775 1.872
LC4 I find it easy to develop rapport with the teacher delivering Online Courses (MOOCs) in my mother tongue 3.588 0.992 0.674 1.278
LC5 I believe that the Online Courses (MOOCs) if delivered in regional languages will have far wider acceptability 3.911 1.015 0.633 1.316
Teacher Influence (TI) (α = 0.707, CR = 0.810, AVE = 0.472)
TI1 I believe my teacher is an expert of his subject 4.106 0.870 0.500 1.244
TI2 My teacher is my role model 3.874 1.051 0.794 1.822
TI3 I follow my teacher’s instructions on study related matter 3.998 1.004 0.815 1.971
TI4 My college encourages enrolment in online course (MOOCs) to gain additional knowledge and learn new skills 4.017 0.972 0.759 1.620
TI5 My teachers give additional weightage during evaluation on the successful completion of an online course (MOOCs) 3.739 1.125 0.491 1.235
Table 4 Fornell-Larcker Criterion
Constructs EE FC HT HM LC PE PV SI TI
EE 0.889
FC 0.544 0.752
HT 0.324 0.460 0.800
HM 0.318 0.420 0.507 0.920
LC 0.329 0.600 0.399 0.417 0.724
PE 0.396 0.470 0.440 0.608 0.553 0.849
PV 0.260 0.400 0.700 0.536 0.350 0.414 0.822
SI 0.350 0.442 0.448 0.488 0.449 0.601 0.414 0.901
TI 0.154 0.404 0.301 0.325 0.527 0.345 0.279 0.294 0.687
Bold digits represent the square roots of AVEs
The HTMT measures similarity between predictor variables. If the HTMT is less than one, DV is considered as established (Henseler et al., 2015). Table 5 confirms that all HTMT values are well within the cut-off value. Thus, the results of these tests indicates that discriminant validity was verified.Table 5 HTMT Criterion
Construct BI EE FC HT HM LC PE PV SI TI
EE 0.501
FC 0.784 0.686
HT 0.843 0.402 0.636
HM 0.683 0.353 0.542 0.613
LC 0.623 0.397 0.810 0.516 0.492
PE 0.669 0.450 0.621 0.537 0.682 0.677
PV 0.836 0.312 0.541 0.815 0.643 0.432 0.494
SI 0.579 0.395 0.573 0.553 0.544 0.546 0.683 0.495
TI 0.486 0.215 0.569 0.424 0.417 0.720 0.474 0.373 0.400
Structural model
With reliability and validity criteria of the model met, PLS results of structural model was analysed to investigate the association between the constructs. The results of bootstrap are shown in Table 6. In PLS path models, structural model and hypothesis testing is done by measuring path coefficients (β value) and the path models does not need the data to be normally distributed, it is computed with squared multiple correlations (R2) for each latent construct which reflects the fitment of model to the hypothesized relationships. For evaluating hypothesis relevance and importance, bootstrapping procedure was used (Chin, 1998). Table 6 reflects the hypothesized path coefficient values besides the T-statistics values. The results revealed that PV is a strong predictor of intention to adopt MOOCs. The association between PV and BI is significant with β = 0.316 and has positive influence on BI towards MOOC adoption which is in support of the extant study (Venkatesh, 2012; Raman & Don, 2013), however, contradicting the findings of El-Masri and Tarhini (2017). The BI changes in accordance to PV with a coefficient of 0.316.Table 6 Path coefficient and T-Statistics value
Hypothesis Path β Values P Values Decision
H1 PE—> BI 0.127 0.005 Supported
H2 EE—> BI 0.066 0.039 Supported
H3 SI—> BI 0.026 0.547 Not Supported
H4 FC- > BI 0.238 0.000 Supported
H5 HM- > BI 0.145 0.001 Supported
H6 PV- > BI 0.316 0.000 Supported
H7 HT- > BI 0.121 0.084 Not Supported
H8 LC- > BI 0.035 0.355 Not Supported
H9 TI—> BI 0.044 0.181 Not Supported
Hence, H6 is proved. Other predictor variables having significant positive impact on intention to adopt MOOCs are PE (β = 0.127) and EE (β = 0.066) which is in support of previous studies (Al-Adwan, 2020; Fianu et al., 2018; Venkatesh et al., 2003). Hence, H1-H2 are proved. Consistent with prior research, FC (β = 0.238) and HM (β = 0.145) also have positive influence on BI of Gen Z towards MOOC adoption confirming the previous literature (Brown & Venkatesh, 2005; Raman & Don, 2013; Tseng et al., 2019). Hence, H4-H5 are proved. The relationship between SI & BI is not significant with β = 0.02 and T-value = 0.60 contradicting the previous studies (Khalid et al., 2021; Persada et al., 2019; Raman & Don, 2013) and supporting the studies of Jeng and Tzeng (2012) and Fianu et al. (2018). Hence, H3 is rejected. The independent variable of HT ((β = 0.121) has an insignificant influence on BI contradicting the findings of Gupta and Dogra, (2017) and in line with the findings of Raman and Don, (2013), hence, H7 is rejected. LC does not have a strong association with BI with β = 0.035 contradicting previous studies (Aldahdouh & Osório, 2016; Anand Shankar Raja and Kallarakal, 2020) and supporting the findings of Barak et al. (2015) rejecting H8. TI does not have a significant impact on BI with β = 0.044 thus contradicting the findings of extant studies (Huang et al., 2019; Hoi & Mu, 2021; Al-Adwan et al., 2021a). Hence, H9 is rejected.
Measuring the value of R2
In PLS path models, the squared correlation values of 0.75, 0.50 and 0.25 are viewed as substantial, moderate and weak respectively (Hair et al., 2014). R2 statistics explains the change in the dependent variable explained by the independent variable(s). The R2 value of latent dependent construct is of 0.69 as shown in Fig. 2 is greater than 0.50 and close to 0.75 therefore the R2 value is considered to be moderate to high value.Fig. 2 Structural model
Effect size f 2
The effect size is the measure of influence of each independent variable on the dependant variable. In PLS path model, when an independent variable is excluded from the model, it measures the variation in squared correlation values and ascertain whether the excluded independent variable has a strong effect on the value of dependent variable. The formula of effect size f2 (Chin, 1998) is as under –f2=R2included-R2excluded/1-R2included
The impact of predictor variable is high at the structural level if f2 is 0.35 and it is medium if f2 is 0.15 and small if f2 is 0.02 (Cohen 1988). Inference of data analysed is as per Table 7.Table 7 Effect size f 2
Independent Construct Dependent Construct Effect Size Inference
Effort Expectancy Behavioural Intention 0.010 Small Effect
Facilitating Condition 0.088 Medium Effect
Habit 0.008 Small Effect
Hedonic Motivation 0.036 Medium Effect
Language Competency 0.002 Small Effect
Performance Expectancy 0.024 Small Effect
Price Value 0.059 Medium Effect
Social Influence 0.001 Small Effect
Teacher Influence 0.005 Small Effect
Predictor independent constructs of FC (0.088), HM (0.036) and PV (0.059) have a medium effect on the dependent construct of BI to adopt MOOC whereas other constructs have a small effect.
Discussion and Implications
This study aims to validate and extend the UTAUT2 model in the context of MOOC and identify different factors influencing the intentions towards MOOC adoption. It is found that the predictor variables of PV, HM, FC, EE and PE have a significant influence on BI, which implies that they are important for BI of Gen Z MOOC learners to adopt MOOC. It is observed that PV has the strongest positive influence on BI towards MOOC adoption (β = 0.316) and is an important predictor of MOOC adoption among Gen Z learners attaching greater value to the trade-off between price of MOOC and the perceived benefit received in terms of their up-skilling and enhancing their employability. Now a day’s majority of MOOC developers offer the course enrolment and content free of cost however charge for the certification, which troubles financially weak students hence MOOC developers need to keep in mind the variable of PV at the time of pricing their course. PV plays a vital role in influencing Gen Z intention to use new technology (Tseng et al., 2019). This indicates that developers and marketers of MOOCs must promote and position value and the perceived benefits of doing MOOC courses greater than the price paid for the course or the certification to attract Gen Z. HM also found to influence the BI of Gen Z learners towards MOOC adoption which is in line with the extant literature (Yang et al., 2012; El-Masri & Tarhini, 2017; Baptista & Oliveira, 2015). Gen Z, born in the digital world loves to live life online (Weinswig, 2016) finds learning through MOOCs exciting, and fun filled and an element of peer pressure makes Gen Z exhibit online behaviour. FC found to significantly influence learner’s BI to adopt MOOC (Chang et al., 2019). The very thought of adding knowledge or a new skill to their existing skillset influences the BI of young under graduates and post graduates to adopt MOOC (Jambulingam, 2013). Ongoing pandemic and subsequent home confinement also influenced the intention to adopt online learning owning to their usefulness (Al-Adwan, 2020; Mittal et al, 2021) which in turn supported PE. Since MOOC courses doesn’t require much of effort in enrolment and are easy to access and manage, EE too influence the intention of learners (Al-Adwan, 2020; Kala & Chaubey, 2022).
The study found the insignificant influence of the predictor variables of SI on BI which contradicts the existing studies (Rosaline & Reeves, 2017; Al-Adwan & Khdour, 2020; Persada et al., 2019) and supporting the previous studies (Fianu et al., 2018; Jeng & Tzeng, 2012). This indicates that the Gen Z ability and knowledge regarding MOOC is as a result of self-awareness which is gained by self-study therefore they need no external influence or social support to adopt MOOC. The variables of HT has insignificant impact on BI supporting the previous study of Raman and Don, (2013) and contradicting the findings of Gupta and Dogra, (2017). This could be because of students using MOOCs for educational purposes only and it is yet to become a part of their daily routine. Further research is required to identify the root cause.
Relationship between TI and BI is insignificant contradicting the existing research (Al-Adwan et al., 2021a; Chang et al., 2015; Hoi & Mu, 2021). Result indicates that Gen Z does not get any encouragement or support from teacher fraternity to pursue MOOCs and it is not currently an integral part of their university curriculum or the evaluation criteria thus the influence of teacher who is considered a change agent found to be insignificant. This finding also substantiates the insignificance of habit construct on BI as MOOCs are yet to become an integral part of education system indicating its use is need based and not habitual. The association between LC and BI is not significant contradicting the existing research (Aldahdouh & Osório, 2016; Anand Shankar Raja and Kallarakal, 2020) and supporting the research findings of Barak et al. (2015). This indicates that the students enrolled in higher education programs of the universities sampled and studied are well versed in communication skills and language competencies and are comfortable with MOOCs content delivery and its understanding. They do not find language as a major determinant of MOOC adoption. This finding also underlines the fact that India is second largest English speaking nation in the world (mapsofworld.com, 2021). With the R2 value of 0.699, this study confirms the moderate to high explanatory power of UTAUT2 model towards the intention to adopt MOOC in the Indian settings.
Theoretical implications
This research work adds to the existing pool of knowledge related to the literature on factors affecting technology adoption especially internet based technologies. It examines the factors influencing the intention towards MOOC adoption in India and contributes to the extant literature on MOOCs using UTAUT model (Mittal et al., 2021; Persada et al., 2019). The outcome of this study highlights the important role PV, HM, FC, PE and EE plays in influencing the intention of Gen Z towards MOOC adoption that validates UTAUT2 model. The study investigated the impact of extended constructs of LC and TI on MOOC adoption, however, found it to be insignificant which contradicts the existing literature (Aldahdouh & Osório, 2016; Anand Shankar Raja and Kallarakal, 2020).
Practical implications
The outcomes of this study provide newer insights on educational technology adoption by Gen Z. The study highlights that PV has strong influence on the BI (El-Masri & Tarhini, 2017; Tseng et al., 2019) of a learner to adopt MOOC which developers and marketers of MOOCs must keep in mind to increase its spread and usage which will not only complement studies in the physical classrooms but also help multitude of economically weaker section of the society to attain education (Meet & Kala, 2021). Developers and marketers should also look into integrating or enhancing the component of gaming and fun while developing MOOCs to attract Gen Z who pays much attention to HM (Baptista & Oliveira, 2015; Moorthy et al., 2019; Raman & Don, 2013) and this can be done by the gamification of courses, animations, simulations, enhanced peer to peer interaction, blended learning giving learners the feel of online and offline learning. FC is important for Gen Z before adopting MOOCs, therefore, all the stakeholders engaged in the proliferation of MOOCs must ensure that a complete ecosystem of online learning ((Chang et al., 2019; Persada et al., 2019) is created at the HEIs level with students getting appropriate credits for MOOC certifications so that their efforts are justified and the outcomes valued. The study highlights positive influence of PE and EE on BI to adopt MOOC. Therefore, MOOC developers and marketers must design and market courses which are contemporary, industry relevant and can be accessed through mobile devices while on move, providing learners to re-skill and up-skill themselves, to enhance their competency and employability at workplace. The results reveal the insignificant impact of SI and TI on BI of Gen Z reflecting no impression of normative social influence on them and no encouragement from the teacher to pursue MOOCs alongside their regular studies. Teachers must encourage enrolments in MOOCs (Huang et al., 2019; Hoi & Mu, 2021; Al-Adwan et al., 2021a) and include them in their evaluation criteria to see the proliferation of MOOCs which offers contemporary courses which are not the part of many universities course curriculum helping students to up-skill themselves and enhance employability. MOOC can help governments in the massification of higher education, which is the need of hour as GER of many developing nations is much below the average GER of developed nations (AISHE, 2019–20). Governments should look at MOOCs as a tool that can bridge the digital divide and its integration with National Educational Policy shall contribute majorly in achieving SDG4 educational goals of country by 2030 as earmarked in United Nation’s SDGs (Meet & Kala, 2021).
Limitations and future research directions
Future studies must be carried out to address the limitations and better generalizability of these results. First, the study carried out cross sectional research on Gen Z. A longitudinal research can suggest the change in the intention and behaviour of Gen Z over a period of time for better generalizability for the model. Second, the model explains 69% of the factors that affect the intention of MOOCs adoption, leaving 31% unanswered. The UTAUT2 model should be extended with additional constructs to enhance the explanatory power. Third, future research should consider K12 students as subject of their study to know their outlook towards MOOCs and how it can be integrated to their classroom education. Fourth, future research to study the impact of Gen Z demographic moderators and educational characteristics on BI and use of MOOCs. Fifth, a cross cultural research on MOOCs among countries would help in knowing the impact it creates on the society and nations at large in democratising education using MOOCs as a tool to improve literacy rates and employability of Gen Z in the developing nations.
Conclusion
MOOCs by virtue of ease of access and free education have been in limelight from last one decade and the outbreak of COVID 19 has re-emphasized its importance in complementing offline learning however, despite the merits, MOOCs adoption among students in higher education is low. Knowing the significance of MOOCs in education and the gap in existing literature on student’s BI to adopt MOOCs, this study sought to investigate factors affecting BI of Gen Z towards MOOC adoption by extending UTAUT2 theory with two additional constructs of language competency and teacher influence to examine enhancement in the explanatory power of theory. Results reveal that the extended UTAUT2 model explains 69.9% of BI to adopt MOOCs with the constructs of PE, EE, PV, HM and FC having direct positive influence on BI underlining its robustness to predict BI on MOOC adoption. Furthermore, the results deviated from the existing studies by indicating at the insignificant influence of constructs of SI, HT, LC and TI on BI towards MOOC adoption. The study confirms that the most critical factor affecting the future intention to adopt MOOCs is PV followed by FC, HM, PE and EE. This study adds to the extant literature on UTAUT2 model by testing it’s applicability on BI to adopt MOOCs in Indian settings that has not been done before. It also provides crucial knowledge to advance online learning literature and executable insights significantly required by MOOCs creators and academicians to ramp up MOOC enrolments that is much desired in developing countries like India towards democratizing education.
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1Lara Khoury, Ad E, DPhil, BCL, LLB,
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is an Associate Professor at McGill University’s Faculty of Law, Montréal, Québec, Canada
Couture-Ménard Marie-Eve 2Marie-Eve Couture-Ménard, DCL, LLM, LLB,
is an Associate Professor at the University of Sherbrooke, Faculty of Law (Faculté de droit, Université de Sherbrooke), Sherbrooke, Québec, Canada
Hammond Kathleen 3Kathleen Hammond, JD/BCL, PhD, MPhil, BA,
is an Assistant Professor at The Lincoln Alexander School of Law at Ryerson University, Toronto, Ontario, Canada
1 grid.14709.3b 0000 0004 1936 8649 Faculty of Law, McGill University, 3644 Peel St., Montreal, QC Canada
2 grid.86715.3d 0000 0000 9064 6198 Faculté de Droit, Université de Sherbrooke, 2500 Bd de l’Université, Sherbrooke, QC Canada
3 grid.68312.3e 0000 0004 1936 9422 The Lincoln Alexander School of Law, Ryerson University, 350 Victoria street, Toronto, ON Canada
12 4 2022
2022
43 2 222233
21 3 2022
© The Author(s), under exclusive licence to Springer Nature Limited 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.
The COVID-19 pandemic—with its wide-reaching social, political, and economic implications—showcases the importance of public health governance. Governmental accountability is at the forefront of societal preoccupations, as state actors attempt to manage the pandemic by using sweeping emergency powers which grant them significant discretion. Though emergency measures have tremendous impacts on citizens’ lives, elected officials and civil society have little input in how governments wield these powers. We reviewed available mechanisms in Canadian private, constitutional, and criminal law and found them to be unlikely sources of much-needed accountability. Therefore, we propose that provincial and territorial legislatures modify public health legislation to expand mechanisms to foster public confidence in decision-makers, and bolster accountability to parliaments and citizens.
Keywords
COVID-19
Pandemic response
Governmental accountability
Emergency powers
Public health legislation
McGill Interdisciplinary Initiative in Infection and Immunity (MI4)issue-copyright-statement© Springer Nature Limited 2022
==== Body
pmcKey messages
Canadian provinces and territories have extensive public health emergency powers with tremendous impacts on citizens’ lives.
Court intervention grounded on private, criminal, and constitutional law is insufficient to ensure state accountability in times of emergency.
Provincial and territorial legislatures need to add public accountability mechanisms to their public health legislation.
These mechanisms should include the following: periodical accounts to legislatures when renewing a declaration of a public health emergency, public reporting on emergency measures taken, after the emergency has ended, and reports to Parliament in order to learn from mistakes and successes and to improve public health management for the future.
Introduction
To protect population health during the COVID-19 pandemic, governments across the globe have exercised extensive emergency powers, leading to unprecedented measures and responses. Governments often implement these measures and responses swiftly, with little input from the electorate and from civil society organizations. Accountability serves many purposes, such as preventing abuses of power and lack of responsiveness, ensuring compliance with procedures and standards, and improving performance and learning [1]. These purposes are especially important during pandemics, which escalate inequalities and disproportionately affect vulnerable populations. Using Canada as an example, we discuss the lack of legal accountability of governments when they exercise broad public health emergency powers to manage a pandemic. We then propose that provincial and territorial legislatures amend their respective public health laws to enhance state accountability to parliaments and citizens. They should accomplish this by requiring public authorities to provide more information on emergency management and explain their decisions, during and after the crisis.
Broad public health emergency powers
Because extraordinary threats require extraordinary means, all three levels of Canada’s government (federal, provincial/territorial, and municipal) wield a large range of emergency powers, many of which are listed in public health legislation. Canada is a federal state made up of ten provinces and three territories. Of these, the two most populated provinces are Ontario and Quebec. While Canada’s 1867 Constitution Act established the different powers of the federal and provincial governments, territorial governments were created by, and obtained their powers through, federal statute. Despite shared jurisdictional authority over public health between federal and provincial/territorial governments, provinces and territories bore the bulk of responsibility for the COVID-19 crisis response. This responsibility was exercised through the exceptional powers that their respective public health laws grant them. The Canadian literature has rarely discussed emergency powers in public health legislation, which vary from one province or territory to another in terms of content, trigger process, and the authorities that exercise them. Nevertheless, they share common features.
In most provinces or territories, the first criterion to trigger use of emergency powers is the existence of a public health emergency, often defined as an imminent or immediate threat that poses a significant or serious risk to public health. The second criterion is that mitigating or remedying the threat and protecting population health requires coordination or special measures. When public authorities use their emergency powers, they are not required to consult with elected representatives or other democratic forums beforehand. As a result, public authorities have considerable discretion to act quickly.
As mentioned, the pandemic response in Canada came very predominantly from the provinces and territories, rather than from the federal government or municipal authorities. Therefore, we limited our review to the public health acts of each province and territory in aims of identifying the emergency powers they have at their disposal to respond to an emergency situation like the COVID-19 pandemic. This review allowed us to identify fifty emergency powers in public health legislation across Canada. We classify these emergency powers into three categories:Powers to mobilize human and material resources to respond to the overwhelming demands for health care and other services: These include the possibility for government to grant medical practitioners in other provinces or territories temporary permits if there is urgent need for professionals (for example, in the Northwest Territories and the Yukon); the Chief Public Health Officer’s power to direct health care providers (such as pharmacists) to administer immunizations (for example, on Prince Edward Island); and the power of the Minister of Health and Long-Term Care to control material resources, including medication and medical supplies, facilities, and property (for example, in Ontario).
Powers to prevent spread of communicable disease by restricting the movement or gathering of people: Several provinces used this power to order closing of public areas and places of assembly, including educational institutions, restaurants, and gyms. Governmental authorities may also restrict travel by prohibiting entry into certain areas within a province or territory, or by restricting travel between them, as done by the Atlantic provinces (forming the “Atlantic bubble”). Medical preventive measures, such as compulsory vaccination (in Quebec and Alberta) and obligatory wearing of masks in public places also belong in this category.
Powers for authorities to act outside of usual legislative requirements: These powers eliminate processes and formalism that would hinder a quick and efficient response to a public health threat. These powers allow authorities to dispense with delays or in-writing requirements, inspect premises without a warrant, and take extraordinary measures to obtain, use, or disclose relevant information. The province of Quebec used this power to compel bar and restaurant owners to keep a register of all customers (and customers’ name, phone number, email address if available) on the premises during a particular period of time [2].
More generally, some provinces grant public authorities the power to modify laws, thereby transferring legislative power to the executive branch (as done in Alberta and Quebec). Additionally, the public health legislation of six provinces/territories grants government authorities power to take any other measure necessary to protect the health of the population.
Emergency powers are extensive, and the measures associated with them can have tremendous impacts on citizens’ lives. Consequently, accountability for state conduct is paramount, especially when a public health crisis is long-lasting. Since the start of the pandemic, citizens have sought such accountability through appeals to judicial intervention. Indeed, mechanisms within tort law, criminal law, and constitutional law offer the courts tools to intervene to impose state accountability. In the following section, we argue that these avenues of accountability are, however, insufficient.
Limited accountability through private, criminal, and constitutional law
When faced with disaster, we often look to assign blame and allocate responsibility. Indeed, victims of the pandemic have undertaken an increasing number of liability lawsuits. Class action liability lawsuits are ongoing in Quebec, Ontario, and Alberta against long-term care homes claiming damages for residents’ deaths which occurred as a result of alleged negligence during the first wave of the pandemic. Moreover, in response to outbreaks in a restaurant and federal prisons, patrons and inmates have undertaken liability class action lawsuits against, respectively, an Alberta restaurant owner and the Attorney General of Canada.
Substantial hurdles hinder liability claims against governments. Public health and civil emergency laws across Canada provide varying forms of immunity against liability for decisions taken by public actors to curtail the COVID-19 pandemic. For instance, the Quebec Public Health Act grants immunity to the government, the Minister of Health and Social Services or “another person” for acts performed in good faith in the exercise of powers (or in relation to the exercise of powers) held under a declaration of public health emergency. This effectively protects almost all governmental decisions taken in Quebec during the pandemic.
State actors’ decisions not explicitly targeted by a legislative immunity are, in theory, subject to liability. However, Canadian courts have upheld a public law immunity, which protects governmental policy decisions from civil liability, unless found to be irrational or taken in bad faith. For instance, this immunity has protected public authorities’ decisions related to the imposition of budgets and the allocation of resources, the establishment of priorities in the fight against certain diseases, and the establishment and implementation of screening programs [3, 4]. The immunity also prevents courts from interfering with how governments choose to regulate a particular matter [5]. For example, under Quebec’s Public Health Act, the Minister of Health and Social Services has the power to establish standards concerning the disinfection or decontamination of persons, premises or things in contact with certain agents, so as to avoid more widespread contagion or contamination. Should the Minister decide to exercise this power, the standards they establish are considered a discretionary policy decision that courts will usually not interfere with. In the context of the COVID-19 pandemic, the category of state action immune to liability could include, for instance, the priorities that provincial governments set for vaccination of the population, and their decision to require a proof of vaccination to enter certain premises.
Public law immunity does not extend to the operational sphere of state action—actions concerned with the execution or implementation of policy decisions. The Canadian judiciary itself struggles with the policy/operational distinction, however. To explain the distinction, the Supreme Court of Canada offers the example of where a government agency decides to regulate the inspection of certain premises to ensure their safety, in addition to regulating the frequency of inspections that must occur given available resources. While that decision is likely a policy decision, the actual manner in which the government agents carry out the inspections imposed by regulation falls into the operational sphere.
In addition to the public law immunity, if the state exercises powers by virtue of legislation that imposes duties on government to act in the public interest (as public health laws often do), common law courts (i.e., courts of all the Canadian provinces/territories except the province of Quebec where liability is governed by civil law) are generally reluctant to superimpose a private law duty of care to address specific interests of individuals or groups [6], except in exceptional circumstances. In this way, Ontario courts refused to certify class action lawsuits against the province by victims of the West Nile Virus and SARS epidemics. For instance, in one of the class action lawsuits against Ontario in the aftermath of the SARS epidemic, the Ontario Court of Appeal refused to recognize that the province had a specific duty to the victims who claimed injury resulting from certain actions (or inaction) of the province. Included actions, among others, were the province’s failure to control an outbreak which led to the second wave of the epidemic, the province’s failure to maintain a public health system adequately equipped to deal with outbreaks, failure to issue proper directives to hospitals to control or limit the spread of SARS, and finally the premature lifting of the state of emergency before the first outbreak was eradicated [7].
Canadian courts offer public health-based justifications for some of the above protections against liability. These justifications include that public authorities need to prioritize the interests of the general population in public health matters, taking into account the varied and divergent interests it is composed of. Courts are also concerned that imposing duties to the benefit of only specific individuals or groups may conflict with the duties the state owes to the public at large [8]. For instance, recognizing a duty of care to protect the health of healthcare workers during an epidemic may conflict with the public interest in ensuring healthcare facilities remain open for patients who need access to them. Furthermore, under the threat of possible future lawsuits related to the handling of the COVID-19 pandemic, public authorities may be tempted to prioritize the voices of those with wealth and power—for instance, industrial actors with a financial interest in reopening the economy—to the detriment of vulnerable populations most affected by the pandemic—for instance, the elderly.
In sum, judges’ predictable reluctance to use civil liability principles to review government decisions taken to manage the COVID-19 pandemic, compounded by the lengthy delays and costs associated with liability litigation, demonstrates that this avenue is not the best tool for securing governments’ accountability.
We draw similar conclusions with regard to criminal law mechanisms, particularly those for reviewing discretionary police enforcement. Many public health and emergency measures the provinces and territories relied upon in the pandemic expand the role of law enforcement. These include prohibitions of risk-associated behaviors backed by fines or even imprisonment, as well as new warrantless search powers. The executive branch of government, taking action without the usual extent of public debate and scrutiny before elected representatives within the legislative assembly, may not consider how sanctions for violating emergency measures disproportionately affect marginalized groups. Those with limited resources and without adequate housing, for instance, may have difficulty meeting the demands of social distancing and stay-at-home orders. Racialized groups may be less likely to benefit from police discretionary forbearance. Civil society actors monitoring patterns of police enforcement of COVID-related restrictions, such as restricted use of parks and physical distancing, have reported troubling enforcement that tracks race and social status [9].
Police enforcement in Canada is already under growing criticism for lack of transparency and accountability, both in terms of fairness in distribution of sanctions and the extent to which enforcement achieves public objectives [10]. Nonetheless, Canadian courts have maintained the high value typically placed on police discretion in Anglo-American legal traditions. For example, the Supreme Court of Canada acknowledges the duty incumbent on police officers to use their discretion to ‘adapt the process of law enforcement to individual circumstances and to the real-life demands of justice,’ so long as they can justify their decisions rationally [11]. Though the Court states that the exercise of discretion based on cultural, social, and racial stereotypes cannot be justified, courts have played little role in identifying and sanctioning enforcement that falls disproportionally on racialized people or people living on the streets. This continues to be the case, even as research demonstrates that race and class play a role in influencing discretionary decision-making by law enforcement [12], and despite recent government reports decrying systemic discrimination in exercises of police discretion. For example, an independent report prepared for the Montreal (Quebec) police service in 2019 and using the service’s own data found that the black community is disproportionately challenged by the police force, and that Indigenous people, black people, and young Arabs are several times more likely to be arrested than white people [13].
Although the power of the state to use coercion to prevent the spread of disease is a mainstay of public health law, and there is little debate that states may use force in service of public health goals, the wisdom of any particular measure in any given circumstance is a matter of political and scientific debate. Consistent with the public health insight that haphazard enforcement undermines trust needed for compliance, criminologists note that certainty of enforcement plays a more important role in deterrence than severity of sanction [14]. When people subject to the prohibitions view them as confusing, arbitrary, or mutually inconsistent, trust is further undermined [15]. Public health benefits of coercive approaches can be difficult to measure. The value of heavy penalties for impaired driving, for instance, is contested [16]. Likewise, concerns from global actors over stigma and discrimination cast the early reliance in some jurisdictions on coercive measures in response to HIV exposure and transmission as antithetical to public health [17].
We have not found any scientific studies on whether fines are effective for controlling the spread of a virus like COVID-19. Provinces that have issued the most tickets have not always benefitted from consequent decreases in infection rates, nor fared better than those that have favored an “education first” approach [9].
In the absence of meaningful judicial oversight of police enforcement patterns, review of government measures for compliance with provincial and federal human rights instruments offers an alternative avenue of accountability. There are credible arguments that aspects of Canada’s pandemic response may infringe constitutional rights and freedoms, including freedom of expression, assembly, religion, mobility rights, privacy rights, rights to liberty, and security of the person, as well as equality rights. But none of these rights in Canadian constitutional law is absolute; each may be limited by government to the extent that state measures are proportionate to a valid government objective. Specifically, governments must be able to demonstrate that they are pursuing a “pressing and substantial objective,” that they are doing so in a way that is “rationally connected” to that objective and that any impairment of rights is minimally impairing and proportionate [18]. Though these requirements do not force governments to formally account for the wisdom and effectiveness of their punitive measures, they allow the courts to require justification for any rights-infringing responses of governments, in the absence of which judges may strike them down.
However, the nature of the COVID-19 pandemic means that governments are likely to be accorded greater deference than usual—at least in the short term—for rights-infringing conduct. In other words, courts are less likely to intervene in the context of COVID-19, even where government action infringes on citizens’ rights. The reasons are similar to those related to limitations on liability claims. Deference to rights-infringing government action is higher when governments are balancing numerous interests [19, 20], protecting the vulnerable [21], and where science is unclear [22]. The Supreme Court of Canada has specifically cited epidemics as a circumstance that gives states greater leeway at each step of proportionality analysis [22].
That said, the governmental designation of the COVID-19 pandemic as an emergency does not entail automatically that courts will be willing to defer to the governmental conclusion that right infringements are justified in the current circumstances. The existence of an emergency adds little to the list of factors contemplated in justifying judicial deference in other contexts: lack of information, need to protect the vulnerable, and multiple competing interests. If emergencies attract deference beyond these factors, it is because they are temporary. The longer an emergency continues—and certainly COVID-19 has endured longer than previous public health emergencies—the less deference is justified. Further, deference due to lack of information should similarly abate as we learn more about COVID-19 responses, their effectiveness, and alternative approaches. Finally, while courts show deference to governments when they protect the vulnerable and marginalized, less deference may be afforded where government measures disproportionately burden those groups or neglect to properly account for their situations [23]. For example, the fact that social distancing disproportionately impacts low-income youth and families, already in situations of disadvantage, may encourage courts to inquire as to the proportionality of the measure vis-a-vis the government’s objectives.
Canadian courts are unlikely to strike down most emergency measures. Nonetheless, by requiring governments to justify the rationality of their measures and the proportionality of their impacts in light of growing knowledge in the field, constitutional review offers an important avenue of accountability. Yet governments, especially in emergency times, may not have the capacity or inclination to subject proposals to thorough analysis in anticipation of future constitutional challenge. They may also anticipate judicial deference, even if this may abate somewhat as the pandemic period extends, as new information emerges, and as vulnerable groups bear the brunt of ill-considered emergency orders. As previously discussed, the review by courts of the constitutionality of some measures implemented to fight the pandemic disproportionately favors those with the resources to bring constitutional claims. As a result, rights review remains a marginal mechanism of accountability.
Proposing better state accountability to parliament and citizens
Because the limitations for securing sufficient state accountability through private law and constitutional rights litigation and certain criminal law safeguards are substantial, we argue in favor of ways to reinforce public accountability through democratic channels other than the courts. The COVID-19 pandemic affords a crucial opportunity to reflect on ways to reinforce accountability mechanisms by including into public health legislation mechanisms of continuous oversight on state action.
A first option is for legislatures to require that public authorities periodically justify the renewal of a public health emergency declaration before the legislature in order to renew emergency measures. This option would require that provincial/territorial legislatures amend their current public health legislation to encode this requirement in statute. Current Canadian public health laws allow authorities to declare public health emergencies without legislative approval. Declarations are typically limited in time, ranging from ten to thirty days (with the possibility of repeated renewal), except in British Columbia where there is no time limit. The moment at which a government renews a public health emergency declaration could be an important time for public officials to explain and justify their conduct, and to outline their reasons for maintaining the state of emergency. If governments would do this periodically (but not necessarily at each renewal), this would allow elected representatives to garner feedback about the nature and impact of the measures enacted since the previous renewal of the public health emergency declaration and about the evolving data justifying another renewal. While Quebec and Alberta’s laws provide for a formal oversight mechanism, their governments can avoid it either by renewing the declaration for shorter periods of time (Quebec) or by declaring another state of emergency once the preceding one has elapsed (Alberta).
Critics such as the media, political oppositions, and affected citizens have complained throughout the pandemic that governments fail to adequately disclose information such as their pandemic response plans and the reasons underlying their responses. This information void undermined public trust and undercut compliance with public health measures [24]. For instance, in October 2020, owners of gyms and fitness studios in Quebec threatened to reopen their establishments if the government did not provide data justifying their closure. Public health authorities and their superiors in government have few informational duties during an emergency. Specifically, they are only required to provide information when accompanying the publication of a public health emergency declaration, its renewal, or its termination. Even in these instances, they are only required to publish extremely limited information. Saskatchewan is an exception: legislation in the province requires the Minister of Health or the medical officer who issues an emergency order to “set out the reasons for the order” [25]. Therefore, a second option for reform is for lawmakers to amend public health laws by adding a requirement for governments to produce periodic public reports with information on the rationale behind measures, so as to facilitate their subsequent evaluation and the reporting of results to the public. The reports containing the rationale could include, for instance, relevant data available at the time, advice received from experts or civil society, and reasons justifying the choice to order a given measure instead of alternatives. Public health legislation could further require that public authorities only publish reports once a delay has elapsed after adoption of a given measure. This would allow officials to include information on compliance and enforcement once measures have been implemented.
Finally, in some Canadian provinces, public authorities must report to their legislature after a public health emergency has ended; however, it is not always clear whether the report must be evaluative. For instance, Quebec public health legislation only requires the report to specify the nature and the cause of the threat (if determined), the duration of the declared emergency, as well as the power exercised, and measures implemented. In Newfoundland and Labrador and in Nova Scotia, the relevant Minister must review and report on the cause and the duration of the emergency, and on measures implemented. No Canadian jurisdiction requires that the report evaluate, for instance, the health, social, and economic consequences of any emergency, nor obstacles faced by authorities (in terms of resources, enforcement, or compliance). Given the magnitude of the COVID-19 pandemic, there will almost certainly be evaluative reports in its aftermath. However, we argue that legislators should amend public health laws to impose mechanisms for retrospective evaluation and reporting of results to the public. An advance promise for a thorough evaluation of the state’s response to a pandemic may reinforce public trust and help victims, all while providing an incentive for public authorities to act in the public interest.
Additional public accountability mechanisms
Our research reveals that accountability through litigation is ineffective and could, in certain cases, negatively impact public health management, while risking exacerbating race and class-based inequalities. Civil liability litigation, which requires enormous financial expenditures (especially when scientific issues are raised), will be less available to those most adversely affected by the pandemic, and outcomes for victims will be poor due to the many limits on state liability imposed by courts and legislation. Moreover, enforcing public health orders through policing aggravates accountability issues; criminal law offers little opportunity to review such exercises of enforcement discretion. These circumstances elevate risks of governments’ use of emergency powers affecting disproportionately marginalized groups, and often for uncertain public health benefits. Cases inviting courts to review whether pandemic curtailing measures respect constitutionally protected human rights serve as an important backstop against government excesses. However, they remain a last resort measure of accountability, as Canadian courts are likely to find constitutional violations only in cases of the most extreme or irrational rights infringements by the state.
Given those important limits and the need to maintain public trust and compliance with public health measures, especially in the context of long-lasting emergencies, we argue that the aftermath of the COVID-19 outbreak should be used to, notably, improve the public accountability mechanisms surrounding public authorities’ public health powers. In order to do so, provincial and territorial legislators must modify their respective public health legislation to include an obligation for governments to:(i) periodically account to legislatures when renewing a declaration of a public health emergency,
(ii) produce periodic public reports on emergency measures taken, and
(iii) disseminate, post-emergency, a public report that should include an evaluation sufficient for public authorities, and society, to learn from mistakes and successes and to improve public health management for the future.
Such oversight mechanisms are sure to provide for more predictable and transparent state accountability during public health emergencies. That said, the exact design of such legislative oversight mechanisms deserves more research, as well as the engagement of elected representatives and insights from organizations, such as advisory committees, commissions of inquiry, and audit offices, whose task is to advise governments on ways to improve the performance of systems.
Conclusion
Emergency powers in public health laws across Canada equip governments with tools to respond quickly and effectively to extraordinary threats. However, the need for these emergency powers, which became obvious in the aftermath of the SARS and H1N1 outbreaks, may have eclipsed the equally important need for a robust system of state accountability. The COVID-19 pandemic provides us with a unique opportunity to review and discuss such a system. The issue of public accountability of the state is not a solely Canadian preoccupation; governments around the world used exceptional and far-reaching powers to respond to the COVID pandemic. Inevitably, questions of state accountability in times of crisis arise in the global public health community as they do in Canada. Our work demonstrates that even in a democratic country with a strong parliamentary system like Canada, governments hold extensive discretionary powers to face emergencies without adequate systems of accountability. Accordingly, judicial tort or rights-based interventions are likely to prove insufficient at remedying this situation, which instead calls for reforms of public health legislation to promote public accountability.
Funding
This study was funded by the McGill Interdisciplinary Initiative in Infection and Immunity (MI4) with seed funding from the MUHC Foundation.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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J Gen Intern Med
J Gen Intern Med
Journal of General Internal Medicine
0884-8734
1525-1497
Springer International Publishing Cham
35412179
7530
10.1007/s11606-022-07530-4
Original Research
Advance Care Planning and Treatment Intensity Before Death Among Black, Hispanic, and White Patients Hospitalized with COVID-19
Barnato Amber E. MD, MPH, MS 12
Johnson Gregory R. MD 3
Birkmeyer John D. MD 13
Skinner Jonathan S. PhD 14
O’Malley Allistair James PhD 15
Birkmeyer Nancy J. O. PhD Nancy.J.Birkmeyer@Dartmouth.EDU
1
1 grid.254880.3 0000 0001 2179 2404 The Dartmouth Institute for Health Policy & Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH USA
2 grid.254880.3 0000 0001 2179 2404 Department of Medicine, Geisel School of Medicine at Dartmouth, One Medical Center Drive, Lebanon, NH USA
3 Sound Physicians, Tacoma, WA USA
4 grid.254880.3 0000 0001 2179 2404 Department of Economics, Dartmouth College, Hanover, NH USA
5 grid.254880.3 0000 0001 2179 2404 Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH USA
11 4 2022
6 2022
37 8 19962002
18 10 2021
29 3 2022
© The Author(s), under exclusive licence to Society of General Internal Medicine 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Background
Black and Hispanic people are more likely to contract COVID-19, require hospitalization, and die than White people due to differences in exposures, comorbidity risk, and healthcare access.
Objective
To examine the association of race and ethnicity with treatment decisions and intensity for patients hospitalized for COVID-19.
Design
Retrospective cohort analysis of manually abstracted electronic medical records.
Patients
7,997 patients (62% non-Hispanic White, 16% non-Black Hispanic, and 23% Black) hospitalized for COVID-19 at 135 community hospitals between March and June 2020
Main Measures
Advance care planning (ACP), do not resuscitate (DNR) orders, intensive care unit (ICU) admission, mechanical ventilation (MV), and in-hospital mortality. Among decedents, we classified the mode of death based on treatment intensity and code status as treatment limitation (no MV/DNR), treatment withdrawal (MV/DNR), maximal life support (MV/no DNR), or other (no MV/no DNR).
Key Results
Adjusted in-hospital mortality was similar between White (8%) and Black patients (9%, OR=1.1, 95% CI=0.9–1.4, p=0.254), and lower among Hispanic patients (6%, OR=0.7, 95% CI=0.6–1.0, p=0.032). Black and Hispanic patients were significantly more likely to be treated in the ICU (White 23%, Hispanic 27%, Black 28%) and to receive mechanical ventilation (White 12%, Hispanic 17%, Black 16%). The groups had similar rates of ACP (White 12%, Hispanic 12%, Black 11%), but Black and Hispanic patients were less likely to have a DNR order (White 13%, Hispanic 8%, Black 7%). Among decedents, there were significant differences in mode of death by race/ethnicity (treatment limitation: White 39%, Hispanic 17% (p=0.001), Black 18% (p<0.0001); treatment withdrawal: White 26%, Hispanic 43% (p=0.002), Black 28% (p=0.542); and maximal life support: White 21%, Hispanic 26% (p=0.308), Black 36% (p<0.0001)).
Conclusions
Hospitalized Black and Hispanic COVID-19 patients received greater treatment intensity than White patients. This may have simultaneously mitigated disparities in in-hospital mortality while increasing burdensome treatment near death.
KEY WORDS
COVID-19
racial disparities
terminal care
mortality
intensive care unit
mechanical ventilation
do not resuscitate order
advance care planning
hospital medicine
medical decision-making
http://dx.doi.org/10.13039/100000049 National Institute on Aging P01AG019783 Skinner Jonathan S. issue-copyright-statement© The Author(s), under exclusive licence to Society of General Internal Medicine 2022
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pmcINTRODUCTION
People who are Black and Hispanic people are more likely to contract COVID-19, require hospitalization, and die than White people.1 These differences are attributed to racial/ethnic group-related exposures and comorbidity risk.2 An early study from a single 40-hospital health system in Louisiana found that, conditional upon hospitalization for COVID-19, risk-adjusted case fatality was the same for Black and White patients,1 a finding confirmed by at least four other regional analyses.3–6 A much larger cohort study of nearly 45,000 hospitalized Medicare Advantage beneficiaries, however, found that the Black-White mortality disparity among hospitalized COVID-19 patients persisted after administrative risk adjustment and could be explained by the hospitals treating Black patients.7 Another study of nearly 35,000 all-payer hospitalized patients found a survival advantage among Black patients after clinical risk adjustment for COVID-19 complications, such as organ failures (e.g., acute respiratory failure, shock, sepsis, acute kidney failure, liver damage).8 Finally, a multicenter study with more granular clinical risk adjustment data found that neighborhood disadvantage, as measured by the area deprivation index (ADI), independently predicted in-hospital COVID-19 mortality.9 Taken together, this body of literature suggests that case fatality rates among Black patients hospitalized with COVID-19 are mitigated by younger age and female sex but aggravated by pre-existing chronic conditions, severity of organ failure, and residential segregation that results in neighborhood disadvantage and differential access to high-quality hospitals.
Missing from this literature is any information about preferences for life-supporting treatment and associated treatment intensity. Individual preferences for life-supporting treatment strongly influence outcomes of critical illness.10 White people are more likely to have advance care planning (ACP) conversations and advance directives (ADs) than minoritized groups.11 Disparities in ACP and differences in life-supporting treatment may influence outcomes across racial/ethnic groups hospitalized for COVID-19. In this study, we describe differences between non-Hispanic White, non-Black Hispanic, and Black or African American patients hospitalized with COVID-19 in their rates of inpatient ACP conversations, do not resuscitate (DNR) orders, intensive care unit (ICU) admission, mechanical ventilation, and in-hospital mortality.
METHODS
Setting
The data for this study comes from Sound Physicians, a national medical group that specializes in hospital medicine, critical care, and emergency medicine. At most of the 200 community hospitals where it is based, this group is the only hospital medicine provider and manages the majority of admissions and discharges. This medical group serves many hospitals in states that were impacted by the early COVID-19 surge, including Washington, Michigan, and Ohio as well as several in the broader metropolitan area of New York City. Since 2017, the medical group has implemented a multilevel quality improvement initiative to increase ACP among inpatients.
Patients
This analysis is based on a database that includes review of the electronic health records (EHR) for adult patients who were hospitalized for treatment of COVID-19 infection between March and June 2020. Patients being treated for COVID-19 were identified using the medical group’s electronic billing platform which provides clinical diagnoses supplied by treating physicians (hospitalists) who are prompted on patient admission to identify whether patients are being treated for COVID-19. Stratified (by month) random sampling of COVID-19 patients was used to restrict the number of records for review to 100 patients per hospital. We restricted analyses for the current study to those (n=7,997) treated at hospitals (n=135) with electronic health record systems that allowed chart reviewers employed by the medical group to access the ICU portions of the health record.
The EHR review was performed by trained abstractors at each hospital using a templated instrument specific to the EHR used in their hospital. The data abstracted included the following: patient demographics (age, sex, race/ethnicity) and comorbidity (cancer, coronary artery disease/myocardial infarction, cardiovascular disease/stroke, dementia, diabetes, HIV/AIDS, hypertension, heart failure, kidney disease, liver disease, respiratory disease, obesity, and smoking12), information regarding the elicitation (presence or absence of a billed (CPT codes 99497 or 99498) advance care planning conversation) or documentation of treatment preferences (code status: do not resuscitate (DNR), full code, or other), use of intensive treatments including intensive care unit (ICU) admission and mechanical ventilation (MV), and patient outcome (in-hospital mortality).
We focus on race/ethnicity in this study to explore different experiences with the healthcare system by racialized minority groups. In the USA, non-White race and Hispanic ethnicity are associated with adverse health exposures, poorer access to healthcare, and discrimination in their interactions with the health system due to systemic racism.13–19 Studying healthcare delivery by racial/ethnic group should not be interpreted as reflecting any genetic or biologic risk for COVID-19 illness severity. We collected race and ethnicity data following NIH guidelines; race: White, Black or African American, Asian, American Indian or Alaska Native, and Native Hawaiian or Pacific Islander and ethnicity: Hispanic or non-Hispanic. For the purposes of this analysis, numbers of American Indian or Alaska Native (n=97), Asian (n=157), and Native Hawaiian or other Pacific Islander (n=22) were deemed too small for reliable estimates and so were dropped from the analysis. We further classified patients into mutually exclusive categories: non-Hispanic White (n=4,918), non-Black Hispanic (n=1,254), and Black or African American (n=1,825). We recognize that such categorizations are oversimplifications and do not measure the ways that intersectional identities (e.g., Black racial identity and Hispanic/Latinx ethnic identity) may further exacerbate inequities.
Among decedents, we classified the mode of death into four mutually exclusive groups based upon treatment intensity and code status: treatment limitation (no MV/DNR), treatment withdrawal (MV/DNR), maximal life support (MV/no DNR), and other (no MV/no DNR) and examined adjusted differences by race/ethnicity.
Statistical Analyses
Standard statistical methods including t-tests for continuous variables and chi-square tests for categorical variables were used to evaluate the statistical significance of differences in demographic characteristics and comorbidity for patients in each race/ethnic group. We used mixed effects logistic regression to examine the relationships between race/ethnicity and treatment intensity and mortality adjusted for adjusting for age category, sex, comorbidity, month of hospitalization, and clustering within hospital. We used the White patient group as the reference standard in regressions because this group had the largest sample size.
Ethical Review and Approval
The analysis was approved by the Dartmouth College Committee for the Protection of Human Subjects.
RESULTS
Table 1 compares patient characteristics by race/ethnicity category. The most striking difference among the race/ethnic categories was in age; 22% of White, 9% of Hispanic, and 12% of Black patients were >80 years of age (p<0.0001). Hispanic patients were significantly more likely to be male (54%) than White (49%) or Black (48%) patients. In general, White patients had higher rates of cancer, heart disease, and dementia than Hispanic or Black patients. However, Black patients had higher rates of obesity, diabetes, hypertension, renal failure, and asthma than White or Hispanic patients. Similar trends in patient characteristics by race/ethnicity were apparent in the subgroup of decedents. Table 1 Demographic and Clinical Characteristics of White Non-Hispanic, White Hispanic, and Black Patients Hospitalized with COVID-19 in 135 US Community Hospitals, March–June 2020
Variable Overall Decedents
White Hispanic Black p-value White Hispanic Black p-value
n 4,918 1,254 1,825 586 77 203
% 61 16 23 68 9 23
Age category: <30 years 3 7 4 <0.0001 1 0 1 <0.0001
30–39 years 5 15 8 1 6 0
40–49 years 8 19 11 3 6 3
50–59 years 16 22 22 8 18 12
60–69 years 23 17 26 17 25 31
70–79 years 23 11 17 30 25 27
80+ years 22 9 12 41 19 25
Male 49 54 48 0.002 52 73 57 0.002
Cancer 10 4 7 <0.0001 15 4 11 0.021
Cirrhosis 2 2 2 0.290 2 1 1 0.372
CAD/MI 17 7 11 <0.0001 21 17 17 0.412
CVA/stroke 7 4 8 <0.0001 9 8 10 0.791
Dementia 8 3 4 <0.0001 16 13 7 0.006
Diabetes 28 32 34 <0.0001 29 46 42 <0.0001
HIV/AIDS 1 1 2 <0.0001 0 1 1 0.396
Hypertension 49 39 56 <0.0001 54 53 60 0.368
Heart failure 16 8 14 <0.0001 23 17 19 0.329
Chronic kidney disease 9 6 9 0.001 14 10 15 0.622
Renal failure 4 5 7 <0.0001 4 10 10 <0.0001
Asthma 8 5 9 0.002 6 0 4 0.087
Emphysema 22 6 10 <0.0001 24 12 12 <0.0001
Obesity 14 16 18 <0.0001 13 10 18 0.129
Smoker 22 11 18 <0.0001 17 13 15 0.478
Total comorbidities ≥3 35 22 34 <0.0001 42 38 45 0.474
Crude in-hospital mortality rates (Fig. 1) were significantly lower among Hispanic (6%) than among White (12%) or Black (11%) patients (p<0.0001). In adjusted analyses (Fig. 1), in-hospital mortality was similar between White (8%) and Black patients (9%, OR=1.1, 95% CI=0.9–1.4, p=0.254), and lower among Hispanic patients (6%, OR=0.7, 95% CI=0.6–1.0, p=0.032). Fig. 1 Crude and adjusted in-hospital mortality rates among COVID-19 patients by race/ethnic group. Logistic regression models adjusted for age category, sex, comorbidity, month of hospitalization, and clustering within hospital.
Table 2 compares ACP, code status, and treatment intensity by race/ethnicity category. Overall and in the decedent subgroup, the crude rates of ICU and MV use were higher among Black and Hispanic patients and crude rates of ACP and DNR were significantly higher among White than among Hispanic or Black patients. In adjusted analyses (Table 3), Black and Hispanic patients were significantly more likely to be treated in the ICU (White 23%, Hispanic 27%, Black 28%) and with mechanical ventilation (White 12%, Hispanic 17%, Black 16). Rates of ACP were similar (White 12%, Hispanic 12%, Black 11%), yet Black and Hispanic patients were less likely to have a DNR order (White 13%, Hispanic 8%, Black 7%). Table 2 Crude Rates of Advance Care Planning (ACP) Conversations, Do Not Resuscitate Orders, Admission to the Intensive Care Unit, and Receipt of Invasive Mechanical Ventilation Among White Non-Hispanic, White Hispanic, and Black Patients Hospitalized with COVID-19 in 135 US Community Hospitals, March–June 2020
Variable Overall Decedents
White Hispanic Black p-value White Hispanic Black p-value
n 4,918 1,254 1,825 586 77 203
% 61 16 23 68 9 23
Advance care planning 15 11 12 <0.0001 26 25 17 0.034
Do not resuscitate order 22 9 10 <0.0001 71 60 47 <0.0001
Intensive care unit 25 26 28 0.008 63 84 81 <0.0001
Mechanical ventilation 13 16 18 <0.0001 46 78 70 <0.0001
Table 3 Adjusted Rates of Advance Care Planning (ACP) Conversations, Do Not Resuscitate Orders, Admission to the Intensive Care Unit, and Receipt of Invasive Mechanical Ventilation Among White Non-Hispanic, White Hispanic, and Black Patients Hospitalized with COVID-19 in 135 US Community Hospitals, March–June 2020
Variable Overall adjusted Decedents adjusted
Rate OR LB 95% CI UB 95% CI p-value Rate OR LB 95% CI UB 95% CI p-value
ACP (14% overall)
White 12% 26%
Hispanic 12% 0.9 0.7 1.2 0.478 30% 1.3 0.7 2.4 0.482
Black 11% 0.9 0.7 1.1 0.161 18% 0.6 0.4 0.9 0.022
ICU (26% overall)
White 23% 65%
Hispanic 27% 1.2 1.0 1.4 0.014 84% 3.2 1.5 6.5 0.002
Black 28% 1.3 1.1 1.5 0.001 80% 2.3 1.5 3.5 <0.0001
MV (15% overall)
White 12% 48%
Hispanic 17% 1.5 1.2 1.8 <0.0001 75% 3.9 2.0 7.6 <0.0001
Black 16% 1.4 1.2 1.6 <0.0001 66% 2.4 1.6 3.6 <0.0001
DNR (17% overall)
White 13% 69%
Hispanic 8% 0.6 0.5 0.8 <0.0001 68% 0.9 0.5 1.8 0.861
Black 7% 0.5 0.4 0.6 <0.0001 50% 0.4 0.3 0.6 <0.0001
We follow biostatistics recommendations to treat the subgroup with the largest sample size as the “reference standard.” This reference standard should not be interpreted to mean that the characteristics and outcomes of Whites are superior to those in racialized minority groups
Among those who died (Fig. 2), there were significant differences in mode of death by race/ethnicity (treatment limitation: White 39%, Hispanic 17% (OR=0.3, 95% CI=0.1–0.6, p=0.001), Black 18% (OR=0.3, 95% CI=0.2–0.5, p<0.0001); treatment withdrawal: White 26%, Hispanic 43% (OR=2.6, 95% CI=1.4–4.8, p=0.002), Black 28% (OR=1.2, 95% CI=0.8–1.8, p=0.542); and maximal life support: White 21%, Hispanic 26% (OR=1.4, 95% CI=0.7–2.7, p=0.307), Black 36% (OR=2.4, 95% CI=1.5–3.47, p<0.0001)). Fig. 2 Adjusted mode of death by race/ethnic group. Mixed effect regression models adjusted for age category, sex, comorbidity, month of hospitalization, and clustering within hospital. The “other” category includes patients who died shortly after admission, with or without attempted cardiopulmonary resuscitation.
DISCUSSION
This analysis of medical records from patients hospitalized across the USA reproduces findings from many other studies of COVID-19—non-Hispanic White, non-Hispanic Black, and Hispanic persons had very different epidemiologic experiences of serious illness. After accounting for the substantial differences in age distributions across the three groups, we found similar rates of documented ACP conversations overall, but fewer DNR orders and greater treatment intensity among Black and Hispanic patients. Black and Hispanic patients’ higher use of ICU and MV may reflect the absence of treatment limitations or greater unmeasured illness severity, in which case Black and Hispanic patients’ greater treatment intensity would need to have been protective against inpatient death for these groups to have similar risk-adjusted death rates to White patients. Regardless, care patterns among decedents suggest different modes of death for the three groups: White patients were most likely to die with treatment limitations, Hispanic patients were most likely to die after a trial of life-supporting treatment, and Black patients were most likely to die on maximal life-supporting treatment.
Many studies have demonstrated that Black and Hispanic patients have lower rates of outpatient ACP and advance directive completion.20,21 These differences persist across populations at high risk of dying, including patients with advanced cancer22 and nursing home residents,22 and have been variously attributed to religious and cultural values, lack of knowledge, problems with the trustworthiness of our health system, and failure by providers to broach the topic with minorities.20,23–32 In the first weeks of the US COVID-19 surge, there were urgent calls for ACP and decisions about DNR orders.33 In our sample, 14% of patients admitted with COVID-19 had a documented and billed ACP conversation. Time-based CPT billing codes are an accurate measure of conversations about treatment preferences because they require adherence to time and documentation requirements. Indeed, due to stringent documentation requirements, including completion of a separate ACP progress note, this is likely to be an undercount of conversations to establish the patient’s healthcare proxy or to probe for pre-existing AD documentation. Interestingly, among all COVID-19 admissions, there were no statistically significant differences in the risk-adjusted rate of billed ACP by race/ethnicity; however, among decedents, Black patients were significantly less likely to have billed ACP than non-Hispanic White and Hispanic decedents.
Rates of DNR orders are lower among Black hospitalized patients across multiple conditions.34–37 COVID-19 is no exception; in our sample overall, risk-adjusted DNR rates were lower among Black and Hispanic patients; however, among decedents, Black but not Hispanic patients had lower risk-adjusted DNR rates. While such differences may represent true preferences for more aggressive medical care, it is also possible that this is the outcome of conversations by hospitalists who carry explicit and implicit biases and beliefs about Black patients’ treatment preferences.38,39 Indeed, conditional on palliative care consultation with skilled goals of care discussions, race-based differences in code status tend to disappear.40,41
We used DNR orders as a crude proxy for broader life-sustaining treatment preferences. There are limitations to this approach, since DNR orders only govern advance cardiac life support and cardiopulmonary resuscitation in the event of pulselessness. A DNR order should not inform intubation and MV preferences in the event of hypoxemic respiratory failure, which is the most common antecedent of death in COVID-19 patients. DNR orders may proxy illness severity rather than preferences since they are commonly written when a patient is actively dying to avoid burdensome CPR at the time of death.10 Nevertheless, we used the combination of DNR status and receipt of MV to infer the mode of a patient’s death from COVID-19. Even after adjusting for age, comorbidity, and hospital, we found that White patients were more likely to die with a DNR order and no MV, suggesting that life-supporting treatment was never started. In contrast, Hispanic patients were more likely to die with a DNR order and MV, suggesting that life-supporting treatment was started but, at the very least, CPR was withheld; MV may have been withdrawn. Finally, Black patients were more likely to die without a DNR order and with MV, suggesting that life-supporting treatment was not limited and they died on full support. While these are speculative conclusions regarding care patterns, if accurate, they raise concerns regarding race-based differences in burdensomeness of end-of-life care for COVID-19 patients. Such patterns would be consistent with our knowledge of non-COVID end-of-life care for Black patients.20,42–44
Our study has many strengths, including a large sample size drawn from hospitals across the USA. Our analyses adjust for hospital random effects, which is key given our knowledge of the influence of hospital practice patterns on racial differences in end-of-life care.45 However, our study is also subject to several limitations. Our findings may not be generalizable, given that these hospitalizations were managed by a national medical group that has focused on improving the frequency of ACP among inpatients since 2017. However, this universal focus on ACP may have mitigated disparities in broaching these conversations. We relied on retrospective chart review, and race and ethnicity may not be reliably documented in the EHR and all datapoints are subject to the accuracy and completion of electronic documentation by the care team. We did not collect information regarding COVID-19 illness severity, such as admission vital signs, laboratory values, or organ failures. We also did not have data regarding language barriers to communication, area-level measures of socioeconomic status, or insurance status. We did not have information about the quality or timing of ACP conversations, nor did we abstract information about palliative care consultation. We do not know whether DNR orders were pre-existing or new. We did not abstract other orders governing life-sustaining treatment, such as “do not intubate” or “do not transfer to the ICU.” Finally, our categories of “mode” of death are imperfect approximations of complex care patterns and may be subject to misspecification. Finally, we do not know if treatment was goal concordant. Future research could explore these issues via in-depth review of clinical chart documentation. Additional approaches to studying racial bias in communication and medical decision-making include ethnography and case-based simulation.46 Addressing disparities in goal-concordant medical decision-making requires specialized knowledge of intercultural communication theory and preference construction.47
CONCLUSION
In this national sample from early in the COVID-19 pandemic, hospitalized Black and Hispanic COVID-19 patients received greater treatment intensity than White patients. This may have simultaneously mitigated disparities in in-hospital mortality while increasing burdensome treatment near death. White patients were most likely to die with treatment limitations, Hispanic patients were most likely to die after a trial of life-supporting treatment, and Black patients were most likely to die on maximal life-supporting treatment. These observations highlight profound differences in the experiences of hospitalized COVID-19 patients from different racial and ethnic groups.
Authors Contribution:
NJB, AEB, JSS, and JDB developed the research question and analysis plan. JSS obtained research funding and JDB obtained the data. NJB conducted data analysis with oversight by AJO. AEB and NJB co-wrote the manuscript and JSS, JDB, GRJ, and AJO provided critical feedback on the manuscript.
Declarations
Conflict of Interest
This work was funded by a research grant awarded to Dartmouth from the National Institute on Aging (P01AG019783)
The work was performed at The Dartmouth Institute for Health Policy & Clinical Practice.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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J Ultrasound
J Ultrasound
Journal of Ultrasound
1971-3495
1876-7931
Springer International Publishing Cham
35412125
666
10.1007/s40477-022-00666-3
Letter to the Editor
COVID-19 and lung ultrasonography in out of hospital settings correspondence
Mungmunpuntipantip Rujittika rujittika@gmail.com
1
Wiwanitkit Viroj 2
1 Private Academic Consultant, Bangkok, Thailand
2 grid.444604.6 0000 0004 1800 5248 Dr. D. Y. Patil University, Pune, India
12 4 2022
3 2023
26 1 289289
17 1 2022
17 1 2022
© Società Italiana di Ultrasonologia in Medicina e Biologia (SIUMB) 2022
issue-copyright-statement© Società Italiana di Ultrasonologia in Medicina e Biologia (SIUMB) 2023
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pmcDear Editor,
We would like to share ideas on the publication "Lung Ultrasonography for COVID-19 Patients in Out of Hospital Settings [1]" Abd Wahab et al. concluded that "The diagnostic imaging and staging of COVID-19 patients using lung ultrasound in out-of-hospital settings showed LUS detected lung pleural disease more often than CXR for stage 3 COVID-19 patients [1]." We agree that the lung ultrasonography is useful for diagnosing COVID-19. The use of lung ultrasonography in out-of- hospital setting might be an alternative management during pandemic. The use of point-of-care lung ultrasonography is still a challenge. There are different concerns in different settings. The availability of the tool and proficiency of practitioner on interpretation should be discussed [2]. The good diagnostic efficacy in the study by Wahab et al. might be due to the good knowledge of local practitioners. Also, the specific data on the pattern of lung ultrasonographic findings are still limited [3]. Training is necessary and it is an actual challenge during current pandemic. Finally, to use point of care lung ultrasonography, we should not forget to follow standard infectious control principle. The decontamination of the tool is necessary and has to be recognized.
Declarations
Conflict of interest
None.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References
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PMC009xxxxxx/PMC9002040.txt |
==== Front
Educ Inf Technol (Dordr)
Educ Inf Technol (Dordr)
Education and Information Technologies
1360-2357
1573-7608
Springer US New York
35431603
11044
10.1007/s10639-022-11044-1
Article
Teachers’ beliefs and practices of technology integration at a school for students with dyslexia: A mixed methods study
Bice Holli hbice11@yahoo.com
http://orcid.org/0000-0002-8846-7654
Tang Hengtao htang@mailbox.sc.edu
grid.254567.7 0000 0000 9075 106X Department of Educational Studies, University of South Carolina, Columbia, SC 29208 USA
12 4 2022
127
18 1 2022
6 4 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, 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.
The amount of technology available in schools has increased steadily over the past two decades, but higher-level uses have not followed, and many teachers continue to struggle integrating technology in their classrooms. The purpose of this study was to describe teachers’ beliefs about technology in the classroom and identify whether their beliefs are reflected in practices of integrating technology at a small, private school for students with dyslexia. A convergent mixed methods action research study was conducted to understand how teachers’ beliefs may be affecting technology integration at the school. Quantitative data was collected through a survey administered to all 55 teachers at the school to describe how technology was being used throughout the school. From this sample, six participants were selected for three rounds of follow-up interviews and observations. Quantitative data revealed more teacher-centered beliefs and practices of teachers at the school. Qualitative findings showed teachers with more student-centered beliefs integrated technology more in their classrooms. Findings also revealed the school culture influenced teachers’ beliefs about the role of technology. Implications are provided on offering professional development adapted to teachers’ levels of technology integration.
Keywords
Teachers
Technology integration
Belief
Alignment
Action research
==== Body
pmcIntroduction
The past three decades have seen a tremendous increase in the technology available in schools. In 1995 only 8% of public schools contained a computer with Internet access for instructional purposes, yet that number had increased to 98% by 2008 (U.S. Department of Education, 2016). The question is no longer whether technology should be used in schools, but how it can be used to enhance learning. Despite the increases in technology available to teachers and students, it is not being fully integrated into classrooms for learner-centered, high-level uses (An & Reigeluth, 2011; Hsu, 2016; Palak & Walls, 2009).
It has been evidenced that teachers’ beliefs about technology predict their technology use in the classroom (Ottenbreit-Leftwich et al., 2010). When teachers perceive technology to have value in the teaching and learning process, they are more likely to use it (Hsu, 2016; Mama & Hennessy, 2013; Miranda & Russell, 2012; Sadaf & Johnson, 2017; Taimalu & Luik, 2019). Furthermore, researchers have found a connection between constructivist teaching beliefs and technology use (Hermans et al., 2008; Hsu, 2016; Kim et al., 2013; Tondeur et al., 2017). Teachers who possessed more learner-centered beliefs about teaching were found to have more seamless integration of technology into lessons (Kim et al., 2013). There is some research to support teachers’ espoused beliefs aligning with their classroom practices (Deng et al., 2014; Hsu, 2016; Kim et al., 2013). In addition, teachers’ beliefs about technology use in the classroom may also be one of the strongest barriers to integration. For example, teachers’ beliefs may affect their ability to overcome other barriers due to the relative weight they place on each barrier (Blackwell et al., 2013; Ertmer, 1999; Ertmer et al., 2012; Miranda & Russell, 2012; Walker & Shepard, 2011).
Therefore, exploring the beliefs that underlie classroom practices can bring about the change necessary to enhance student learning through transformative, technology-rich lessons. However, teacher beliefs are complex. Particularly, teachers’ beliefs about technology use may not be constant but vary by time and conditions (Tang et al., 2020, 2021; Xie et al., 2021). Existing research has mainly focused on performing one-shot data collection to describe teachers’ beliefs but overlooked the variation across time (Tang & Bao, 2021). It is necessary to develop a longitudinal understanding of teachers’ beliefs about technology so as to devise sustainable strategies to support teachers’ use of technology in their classrooms. In addition, teachers’ enacted beliefs tend to divert from their espoused beliefs in the practices of technology integration (Ertmer et al., 2012). Understanding the alignment between teachers’ beliefs and practices is also needed to effectively support teachers’ technology integration.
The purpose of this action research study was to describe teachers’ beliefs about technology integration over a period of nine months and investigate the alignment between teachers’ beliefs and their classroom practices of technology integration. Research has indicated the need to understand teachers’ beliefs as a necessary step in integrating technology effectively (e.g., Ertmer & Ottenbreit-Leftwich, 2010, 2013; Tondeur et al., 2017). This study sought to establish a longitudinal account of teachers’ beliefs about technology integration by which we hope to set up the first step in designing effective professional development and providing the skills necessary to integrate technology for student-centered learning (An & Reigeluth, 2011; Ertmer et al., 2012; Ottenbreit-Leftwich et al., 2010).
Literature review
Technology integration
An and Reigeluth (2011) defined technology integration as “the use of technology for instructional purposes” (p. 55). Effective technology integration helps teachers meet learning goals that they would not be able to accomplish otherwise (Cifuentes et al., 2011). Despite the substantial increase in access to technology within schools, an increase in use and more substantive uses have not followed. Teachers continue to use technology in ways that support their professional needs, but they are not using technology as an instructional tool for learning (Ertmer & Ottenbreit-Leftwich, 2010). For example, Karsenti (2016) found teachers used interactive whiteboards for presenting material to the class, displaying websites, and in place of blackboards.
In addition to traditional uses of technology, most teachers employ a teacher-centered pedagogical approach (Dawson, 2012; Palak & Walls, 2009). In such an approach, teachers impart knowledge to students who passively receive information. For illustrative purposes, Dawson (2012) found that teachers used technology 43% of the time for direct instruction, and 38% of the time for collaborative learning. Similarly, Polly and Rock (2016) noted that teachers used technology 65% of the time while students only used it 35% of the time. Moreover, when teachers are using technology, the tasks tend to tap low-level thinking skills, such as remembering, more than high-level skills, such as evaluating (Dawson, 2012; Ruggiero & Mong, 2015). For example, a study of teacher candidates’ lesson plans integrating technology revealed most technology uses focused on basic skills (Polly & Rock, 2016). Using technology for low-level tasks, such as drill and practice activities, provides students with repetition. However, high-level tasks utilizing technology could provide opportunities for students to engage and actively learn through communication and collaboration with others.
Teachers’ beliefs about technology
What teachers believe about teaching and learning impacts their behavior in the classroom. Pajares (1992) defined teachers’ beliefs as “[teachers’] attitudes about education—about schooling, teaching, learning, and students” (p. 316). Identifying teachers’ beliefs and what drives the enactment of beliefs is necessary to understand the choices teachers make within their classrooms. Research into technology integration has identified teachers’ personal beliefs as a factor affecting integration (Ertmer et al., 2006; Vannatta & Fordham, 2004). Several studies have identified teachers’ beliefs about the benefits of technology for student learning as one of the strongest predictors of use (McCulloch et al., 2018; Miranda & Russell, 2012; Petko, 2012). Vannatta and Fordham (2004) argue that teachers’ philosophy and willingness to change are significant factors affecting integration. Technology teachers identified as exemplary technology users rated internal beliefs and commitment to student learning as the most influential factors guiding their technology use (Ertmer et al., 2006). Teachers’ self-efficacy with technology has been found to be another significant predictor of technology use (Gu et al., 2013; Holden & Rada, 2011; Vareberg & Platt, 2018). Additionally, the perceived usefulness and importance of technology for teaching was recognized as one of the most significant factors affecting teachers’ decisions to adopt technology (Miranda & Russell, 2012; Vareberg & Platt, 2018).
However, teachers’ beliefs are broad and cover a variety of different beliefs (Taimalu & Luik, 2019), such as pedagogical beliefs and value beliefs. Research suggests these beliefs are interrelated and serve as the main predictors of technology integration (Hsu, 2016).
Pedagogical beliefs
Teachers’ pedagogical beliefs relate to what teachers believe about the nature of teaching and learning (Tondeur et al., 2017). They are often classified in the literature as being either teacher-centered or student-centered (Deng et al., 2014). Teacher-centered beliefs are typically associated with behaviorism and may be called traditional pedagogical beliefs, whereas student-centered beliefs are associated with constructivism (Deng et al., 2014). Student-centered activities allow learners to construct knowledge actively rather than passively receiving knowledge from the teacher.
What teachers believe about the nature of knowledge influences their pedagogical beliefs (Deng et al., 2014; Kim et al., 2013). Teachers who feel knowledge comes from authority tend to use more of a teacher-centered approach to instruction (Deng et al., 2014). These traditional beliefs negatively impact technology use (Hermans et al., 2008; Taimalu & Luik, 2019; Tondeur et al., 2017). Teacher-centered instruction involving technology has been associated with low-level cognitive tasks, such as practice activities (Polly & Rock, 2016). Researchers have noted teachers with constructivist beliefs use technology more for teaching and learning (Hermans et al., 2008; Tondeur et al., 2017) and use it for constructivist purposes (Deng et al., 2014). Additionally, teachers who use technology for student-centered instruction have shown more seamless integration (Kim et al., 2013). A constructivist teaching style also impacts the intensity with which teachers use technology in the classroom (Petko, 2012). This suggests that teachers who hold pedagogical beliefs favoring student-centered instruction are able to create meaningful, authentic tasks for students through the use of technology.
However, teachers’ pedagogical beliefs are not mutually exclusive. Teachers may hold both student-centered and teacher-centered beliefs (Tondeur et al., 2017; Walker & Shepard, 2011). Tondeur et al. (2017) found that pedagogical beliefs and technology use exist in a bi-directional relationship, suggesting teachers’ beliefs can influence teachers’ use of technology and vice versa. These findings imply positive uses of technology can shape teachers’ beliefs about how technology can be used for learning.
Value beliefs
Teachers’ value beliefs relate to “the belief about the value of technology for their teaching practice” (Vongkulluksn et al., 2018, p. 71). The value teachers believe that technology has in helping achieve their instructional goals can impact their integration decisions (Hew & Brush, 2007; Ottenbreit-Leftwich et al., 2010). Researchers have found value beliefs positively affected teachers’ technology knowledge and their integration (Sadaf & Johnson, 2017; Taimalu & Luik, 2019). Furthermore, positive beliefs about the value of technology for teaching and learning predicted how much teachers and students used technology (Miranda & Russell, 2012; Mueller et al., 2008). Value beliefs also predicted the quality and quantity of technology integration (Vongkulluksn et al., 2018).
Beliefs teachers hold about the value of technology can impact technology use in other ways as well. Value beliefs can affect teachers’ use of technology for professional needs as well as student needs. For example, Ottenbreit-Leftwich et al. (2010) found that teachers valued technology as a means to increase their efficiency and effectiveness, but they also valued technology to engage students, enhance reading comprehension, and teach technology skills. Additionally, teachers’ value beliefs may affect how they perceive external barriers to integration. For example, teachers who value technology may perceive limited access to technology resources differently than teachers who value technology less because they work around the constraints (Vongkulluksn et al., 2018).
Alignment of beliefs and practices
Teachers’ beliefs play a significant role in the behaviors they enact in the classroom. However, research into the alignment of teachers’ beliefs and practices has drawn inconsistent results, with some researchers finding alignment between beliefs and practices while others do not (Ertmer, 2005; Fives & Buehl, 2012).
Studies examining teachers’ beliefs have found that teachers with constructivist beliefs exhibited alignment in their classroom practices (Deng et al., 2014; Ertmer et al., 2012; Hsu, 2016). Deng et al. (2014) determined teachers’ epistemic beliefs and pedagogical beliefs existed in a nested relationship, and their instructional uses of technology were in alignment with their beliefs. Ertmer et al. (2012) examined 12 award-winning teachers recognized for their technology integration and found through interviews and observations that their enacted practices aligned with their stated beliefs. For example, one teacher stated technology should be used for student-centered, authentic applications, and her observed classroom practices included using iMovie software and digital storytelling (Ertmer et al., 2012). Additionally, Hsu (2016) determined a significant majority (75%) of the teachers in her study held constructivist beliefs, and their beliefs were in alignment with their classroom practices. Alignment was evidenced through multiple high-level learning activities in teachers’ lessons (Hsu, 2016).
Researchers studying alignment of teachers’ beliefs and practices have found teachers do not always enact their espoused beliefs in the classroom (Chen, 2008; Liu, 2011; Mama & Hennessy, 2013; Polly & Hannafin, 2011; Shifflet & Weilbacher, 2015). A case study of two teachers found their stated beliefs about technology use in the classroom were positive and aligned with constructivist uses of technology, yet some of their classroom practices did not match their stated beliefs (Shifflet & Weilbacher, 2015). A larger qualitative study found even when teachers held positive beliefs about technology use in education and recognized its value for teaching and learning, teachers’ practices did not reflect these beliefs (Mama & Hennessy, 2013). In addition, cultural emphasis on academic achievement may drive teachers to utilize teacher-centered methods (Chen, 2008; Liu, 2011). Another factor affecting alignment of beliefs and practices may be teachers’ lack of fully understanding how to implement constructivist strategies (Chen, 2008). Polly and Hannafin (2011) observed teachers with constructivist beliefs who felt they were using student-centered strategies in their classrooms, although they were not. However, when these teachers participated in professional development focused on student-centered instruction, the alignment between their beliefs and practices increased (Polly & Hannafin, 2011). While the research is inconclusive concerning whether teachers’ beliefs align with their practices, there is a need to understand what teachers believe about teaching and learning in order to determine why these discrepancies in alignment are occurring.
Therefore, this research addressed the following research questions (RQ):What are teachers’ beliefs about the role of technology in teaching and learning?
How do teachers’ observed classroom practices align with their stated beliefs about technology?
Method
A convergent mixed methods design (Creswell & Plano Clark, 2018) was applied in this action research project (Mertler, 2017) to understand the beliefs that teachers held toward technology for learning and how their beliefs were reflected in their practices of integrating technology. Central to mixed methods research is the belief that combining quantitative and qualitative data will provide a more complete understanding of the research problem (Creswell, 2014). Ultimately, findings from two sources of data collection were converged to develop a comprehensive understanding of teachers’ beliefs about technology and the alignment between teachers’ beliefs and their practices of integrating technology (Creswell & Plano Clark, 2018).
Setting
This study took place at a small, private school in the southeastern United States. The first author served as Curriculum and Instruction Technology Coordinator at this institution. All students attending the school were diagnosed with the learning disability dyslexia. The school offers reading remediation for students with dyslexia through an intensive phonics-based method, the Orton-Gillingham Approach. This approach emphasizes multisensory methods of instruction, and teachers are trained to use these multisensory instructional methods for remediation of dyslexia. The school maintains a population of 250 students enrolled in kindergarten through sixth grade. Classes are capped at 10 students in order to provide specialized reading instruction. Every classroom is assigned two teachers who work together in a co-teaching relationship and share teaching responsibilities throughout the day. In addition to 50 classroom teachers, the school employs five teachers who cover curriculum on digital and print media, art, music, and physical education. Therefore, the total number of faculty members at the school is 55. Teachers had ample access to technology. In addition to every classroom being equipped with a Smart Interactive Display, all teachers were issued an Apple MacBook Pro laptop computer. Students had access to Apple iPads or Macbook in a one student to one device ratio. Despite the abundance of technology available, teachers’ comfort levels and ability to use these resources for student-centered learning varied.
Participants
Survey participants
The sample for the quantitative phase included all 55 faculty members, including 51 female teachers and four male teachers. Teachers ranged in age from 23 to 74 years old. Twenty-three teachers had earned a bachelor’s degree, 31 had earned a master’s degree, and one had obtained a specialist degree. Levels of experience varied among teachers with 51% having less than five years of experience at the school and 29% having more than 10 years of experience at the school.
The survey was distributed as a Google Form, only allowing one submission per teacher. The form collected email addresses to identify participants in the qualitative phase of the study. A total of 29 teachers responded to the survey but one did not consent to participate in the study. Therefore, the response rate for the survey was 51%.
Interview and observation participants
Participants were selected for follow-up interviews and observations using a purposeful sampling method (Merriam, 1998) based on their level of technology integration reflected in survey responses in sections about technology integration. Quartiles for their scores were calculated and two teachers were chosen for each level of technology integrators (e.g., experienced, intermediate, and novice, see Table 1).Table 1 Six participants selected for the interview and observation
Pseudonym Group Quartile Subjects
Amelia Experienced 1 Phonics, math, social studies
Rachel Experienced 1 Math, writing, social studies, science
Charlotte Intermediate 2 Phonics, math, writing, social studies
Emma Intermediate 3 Phonics, math, writing, social studies, science
Olivia Novice 4 Phonics, math, social studies
Sophia Novice 4 Phonics, math, writing, social studies, science
Data collection
Before data collection began, an Institutional Review Board (IRB) approval was granted. Quantitative data was collected through a survey. Survey data was used to inform the selection of participants for the qualitative phase (Creswell, 2014). Qualitative data was collected through interviews and classroom observations on the selected participants (Creswell, 2014). In the end, findings from qualitative data were combined with the quantitative survey data to answer the research questions (Creswell & Plano Clark, 2018) (Table 2). Table 2 Research questions and data sources
Research Questions Data Sources
RQ1: What are teachers’ beliefs about the role of technology in teaching and learning? Survey
Interviews
RQ2: How do teachers’ observed classroom practices align with their stated beliefs about technology? Interviews
Observations
Survey
A survey instrument was adapted from the Survey of Technology Integration and Related Factors (STIR) (Pittman & Gaines, 2015) and the Technology Skills, Beliefs, and Barriers scale (Brush et al., 2008). Our survey included 49 items divided into five sections. The first section collected demographic information and qualifications, such as teachers’ age, years of teaching experience, and highest degree. Participants were asked to rate each statement on using a Likert-type scale ranging from (1) Strongly Disagree to (5) Strongly Agree. Statements addressed 1) access to technology and the availability of technical support at the school, 2) beliefs about technology in teaching and learning, 3) students’ use of technology in their classrooms, and 4) teachers’ use of technology. Internal consistency was tested using Cronbach’s alpha coefficients. All sections show acceptable or higher internal consistency (α > 0.70, Gliem & Gliem, 2003) (Table 3). Table 3 Cronbach’s alpha for the survey
Survey Section Cronbach’s alpha
Technology Access and Support (Items 7–13) 0.86
Importance of Technology in Teaching and Learning (Items 14–32) 0.91
Technology Use by Students (Items 33–40) 0.75
Technology Use by Teachers (Items 41–49) 0.84
Interviews
Three rounds of semi-structured interviews were conducted with the six selected participants in order to develop detailed understanding of teachers’ beliefs regarding the role of technology in teaching and how those beliefs aligned with practices (Creswell, 2014). By interviewing participants three times, we established a longitudinal trajectory to identify changes in their beliefs and practices of technology integration. Through maximum variation sampling (Bloomberg & Volpe, 2016), diverse perspectives of technology integration were gleaned from interviews. Interviews were conducted via video conferencing software and recorded based on participants’ consent. Each interview was transcribed for analysis.
The first interview with participants lasted approximately 40 min and took place before any classroom observations. This interview followed a semi-structured format to pose the same questions to participants, but also ask clarifying questions as needed (Creswell, 2014). Two to eight interview questions were generated for each research question along with probing questions to elicit further elaboration or clarification from participants. Before initial interviews began, the interview protocol was pilot tested with two teachers in order to refine and finalize questions prior to data collection. Based on their feedback, revisions were made to clarify the distinction between technology for teaching and technology for learning.
The second interview with participants took place after the initial classroom observations and lasted approximately 30 min. The follow-up questions asked were unique to each participant and related to the specific technology tools they chose to use in the lessons observed.
The third interview with participants took place after each of them was observed a second time. The interviews lasted approximately 30 min and followed a semi-structured format. The interview protocol contained the same questions included in the second interview. Follow-up questions were individualized for each participant based on the technology tools they used in the second lessons observed.
Observations
For this study, classroom observations served to identify how teachers were using technology and whether their stated technology beliefs matched their classroom practices (Mack et al., 2005). Observations also served as reference points to inform questions regarding actions and behaviors observed during the second and third interviews (Merriam & Tisdell, 2016). Two rounds of observations were conducted by the primary investigator who assumed the role of observer as participant. The first round took place after the initial interview with participants via video-conferencing software due to the school utilizing distance learning. The second round took place in-person. Each observation lasted the length of the class (30–60 min).
All observations followed a semi-structured format to assess the same aspects of technology use in each teacher’s classroom while also flexibly note other events and interactions observed (Mertler, 2017). A semi-structured observation protocol was created in consultation of the Looking for Technology Integration Instrument (LoFTI), Teaching Dimensions Observation Protocol (TDOP), and ISTE Classroom Observation Tool (ICOT). Finally, six sections were included: settings, groups, teacher activity, student engagement, technology activities, and technology tools used.
Data analysis
Quantitative
Descriptive statistics were obtained for each section to understand teachers’ and students’ uses of technology throughout the school and teachers’ beliefs about the role of technology in instruction.
Qualitative
Thematic analysis (Braun & Clarke, 2006) was applied by reviewing interview transcripts and observation notes to assign codes. Specifically, two cycles of coding were completed during quantitative data analysis. We organized the data in Delve by participant and coded all data points for one participant before moving on to the next participant.
The first cycle involved coding the most essential data (Creswell, 2014). This cycle consisted of three rounds of coding utilizing four different methods: in vivo, descriptive, process, and values coding (Saldaña, 2015).
We employed in vivo coding for the interviews and descriptive coding for the observations to develop each individual story as a whole. This also established an account of participants’ experiences with and perceptions of technology integration at a specific point so as to identify changes over time. In vivo coding was appropriate for our goal of having participants describe their technology integration experiences and beliefs using their own words (Saldaña, 2015). Descriptive coding was used to analyze observations since it provides a focused filter for analyzing data and builds a foundation for future rounds of coding (Miles et al., 2020). We assigned codes to meaningful units of text and placed in vivo codes in quotation marks to separate them from descriptive codes.
Process coding was performed to explicitly tell the story of how participants were using technology as indicated by their actions (e.g., gerunds) and how their use changed over time (Bogdan & Biklen, 1998). This method uses gerunds as codes to “imply action intertwined with the dynamics of time” (Saldaña, 2015, p. 111). The use of gerunds also captured the actions that occurred during observations. In total, 312 process codes were assigned to the data.
Values coding was a particularly appropriate method for this study because it captures participants’ “values, attitudes, and beliefs, representing his or her perspectives and worldview” (Saldaña, 2015, p. 131). This method helped understand the aspects of teaching that participants valued, their attitudes toward technology, their beliefs about the role of technology in teaching and learning, and how these changed during the study. As such, codes were labeled with a B, V, or A to represent participants’ beliefs, values, and attitudes (see Fig. 1). This round generated 597 values codes.Fig. 1 Values coding in Delve
After coding one participant in each round, we conducted peer debriefing with a scholar expertized in technology integration. The scholar reviewed the codes, questioned what codes meant, offered feedback, and provided guidance.
To transition to the second cycle of coding, we exported all coded transcripts in Delve as Microsoft Excel files. We reviewed all the codes generated in the first cycle and began visualizing the data by considering how codes answered research questions (Bogdan & Biklen, 1998). We highlighted codes that seemed related in order to give structure to the data.
The second cycle served to reorganize and reanalyze the data in order to generate categories and eventually themes (Saldaña, 2015). We performed two rounds of pattern coding, which involves arranging similarly coded data into categories that attribute meaning (Saldaña, 2015). We created a pattern code for the related codes with a narrative sentence of how we arrived at the code and what it meant. After coding all participants’ data points, we copied all the pattern codes into a new spreadsheet, devoting a separate sheet to each participant. We printed each page of this spreadsheet and cut them out in order to arrange the codes for participants (see Fig. 2). We also made notes detailing a description of participants’ beliefs and practices of technology integration. This step helped us deepen our understanding of individual patterns for each participant. During this process, we would step away and come back to see if we agreed with the codes and categories. Working in this manner was helpful as we changed categories a few times and divided some categories into smaller categories. Meanwhile, we also took notes in our researchers’ journals as themes began to emerge.Fig. 2 Codes arranged in categories
The final step was to generate themes using pattern coding (Saldaña, 2015). We stepped away from the analysis for a few days to keep a clear mind when eliciting the themes. Then, we revisited the categories as well as the journal notes we had recorded. Once a theme became apparent, we wrote it down on an index card and placed it above the categories and codes (see Fig. 3). We met to discuss the emerging themes and re-examine the themes to make any necessary edits.Fig. 3 Codes organized by theme
Our qualitative data analysis resulted in four themes and seven categories. To verify the accuracy of these themes, we conducted member checking by providing participants an opportunity to review and verify whether the findings accurately depict their experiences and perspectives (Merriam & Tisdell, 2016). All six participants responded expressing agreement with the themes without suggestions for changes. Qualitative findings are presented below using rich, thick descriptions from participants (Creswell, 2014).
Results
Quantitative results
Descriptive statistics were obtained for each section of the survey.
Technology access and support
The mean scores for all items within this section fell between (3) Adequate and (4) Good. Overall, teachers felt there was acceptable access to technology and support for technology at the school, and the mean score for statements in this section (M = 4.24, SD = 0.82) aligned with Good.
Importance of technology in teaching and learning
The mean score (M = 4.00, SD = 0.80) revealed most teachers agreed with the statements regarding the importance of technology for teaching and learning. This section addressed both teachers’ beliefs about the importance of technology for teaching (items 14–22) and for student learning (items 23–32). Mean scores revealed teachers placed slightly higher importance in the use of technology for teaching (M = 4.07, SD = 0.79) than learning (M = 3.94, SD = 0.80). Means for responses to each item ranged between (3) Neutral and (4) Agree.
Technology use by students
The overall mean score for this section was 2.70 with a standard deviation of 1.26, indicating considerable variations in students’ technology use for class-related activities. The most frequently used technology by students was drill and practice/learning games (M = 2.96, SD = 1.17). The majority of teachers (n = 19) stated students used these at least once per week. Word processing (M = 2.93, SD = 1.46) and presentation tools (M = 2.93, SD = 1.30) were also used almost weekly by students. However, there was significant disparity among responses.
Technology use by teachers
The mean score for this section fell between (3) Once per week and (4) Several times per week (M = 3.26, SD = 1.49), revealing teachers use some of the technology tools frequently. Results revealed Communication with Parents/Students (M = 4.32, SD = 1.24) was the most frequently used and Website Creation or Maintenance (M = 2.14, SD = 1.33) was the least used. The use of Organization/tracking software showed the most disparity (M = 3.18, SD = 1.70).
Teachers were also asked to describe their level of technology integration as (1) Nonexistent, (2) Limited, (3) Average, (4) Above Average, or (5) Excellent. The mean for this item (M = 3.39, SD = 0.74) revealed most teachers felt their level of technology integration was (3) Average.
Qualitative findings
Four themes were generated from the qualitative interviews and classroom observations. Table 4 presents each theme along with the categories, example pattern codes, and first-cycle codes associated with it.Table 4 Themes that emerged from qualitative data
Themes Categories Pattern Codes Codes
Teachers' beliefs about the role of technology are influenced by their level of technology integration Technology as a supplement Technology supports teaching
Technology supports learning
Supporting Instruction with Technology, Assisting the Teacher, “technology is useful to put on top”
Technology to engage students Engagement increases learning
Technology engages students
Playing Smartboard games,
Technology Enhances Learning,
“a good tool to engage kids”
Teachers believe technology use should be balanced with multisensory methods Teachers’ perceptions of multisensory methods Hands-on methods are important
Multisensory component
Incorporating Multisensory Activities,
“integrate the sensory motor aspect”
Balancing technology and multisensory methods Using technology for multisensory component
Balancing technology and hands-on methods
Finding Balance in Technology Use, “technology versus other hands on methods”
Teachers are motivated to use tools that are easy for them and their students Ease of use Ease of use
Motivated by ease of use
Selecting Technology That’s Easy to Use, “easiest for me to grade,”
Teachers’ beliefs are dynamic Recognizing the benefits of technology Shifting attitude toward technology
Personal productivity
Connection with Distance Learner, “faster that typing a sheet”
Concern of overreliance on technology Worried about technology overuse
Reducing technology use
“these kids have been on the screen too much,”
Balancing Technology Use
Theme 1: Teachers' beliefs about the role of technology are influenced by their level of technology integration
This theme describes how teachers’ beliefs about the role of technology varied by their level of technology integration. The two experienced integrators viewed technology as an essential tool for their instruction because it engaged students, thus increasing their learning. However, two novice integrators and two intermediate integrators viewed technology as a tool to support, or supplement, their instruction. This discrepancy in how teachers view the role of technology has been recognized in the literature. Ertmer and Ottenbreit-Leftwich (2010) noted, “it is time to shift our mindsets away from the notion that technology provides a supplemental teaching tool and assume, as with other professions, that technology is essential to successful performance outcomes (i.e., student learning)” (p. 256). This theme consisted of the categories (a) technology as a supplement and (b) technology to engage students.
The category Technology as a Supplement describes how novice and intermediate integrators viewed technology as supplemental to their instruction but not an essential component. Some of the ways they used technology to supplement instruction were to provide a visual, evaluate students, and reinforce concepts. These uses align with teacher-centered methods of instruction. There were also discrepancies between novice and intermediate integrators in how they viewed technology as a supplement.
Olivia and Sophia, who were novice integrators, held teacher-centered pedagogical beliefs as Olivia described “I am definitely coming from teacher-centered.” Olivia and Sophia displayed limited technology use during their initial observations and stated in interviews that they did not use technology frequently. Despite their limited use, both teachers expressed beliefs that technology use was expected, if not required. Their perception that technology use is required, however, has not motivated them to use it more.Sophia: I feel like [technology]’s becoming less of an option....before you were like ‘oh, that’s a nice tool, but I’m still gonna do that this way and not use the technology.’
Olivia: I think that [the lesson] represents me using [technology] as an added experience, not so much a main avenue.
Intermediate integrators Emma and Charlotte mentioned technology as a supplement to their instruction and used technology to support their instruction during observations. Unlike the novice integrators, they recognized technology could support student learning and provided more learning opportunities for their students that involved technology. Emma said, “[technology] just assists the teacher in letting them work with the content in other ways than just a teacher standing in the front of the room presenting material.” One way she enacted this belief in her classroom was by having students play a learning game on the website Kahoot.
Additionally, their pedagogical beliefs were not squarely teacher-centered. Emma described her beliefs as “in the middle” while Charlotte said her beliefs were teacher-centered but “moving towards student-centered.”Emma: It’s an aid to what I’m trying to teach or make them aware of…I think it’s there to support the lesson that we’re doing and to give another way for students to interact and show what they know and be creative and really just be a supplement to the content and the lesson.
Charlotte: Because of technology, different types of books are accessible to [students] that are read to them that they might not have picked up and been able to understand or to read or comprehend because they weren’t able to read them.
The category Technology to Engage Students is defined as teachers using technology to engage students. Two intermediate integrators and two experienced integrators expressed beliefs about technology engaging students. Teachers used technology to engage students by making learning relevant to their lives and giving them multiple opportunities to interact with the content. These uses align with student-centered instruction (An & Reigeluth, 2011; Hermans et al., 2008).
Charlotte and Emma, who were intermediate integrators, used technology to engage students, but they expressed different reasons for doing so. Emma stated she uses technology “to pique their interest and keep [students] excited.” She saw technology as an opportunity to engage students by making them interested in the content and sustaining their attention. This was witnessed during her first observation when she had students shift between synchronous direct instruction and self-paced independent practice in Seesaw and Kahoot. Charlotte also used technology to engage students by making the content relevant to their lives. She recognized that harnessing students’ interests through technology could keep them engaged.Charlotte: Students become more engaged when you teach them based on what they like or what they enjoy learning and based on their interest, and I think technology can help with that interest.
Like the intermediate integrators, experienced integrators used technology to create excitement and interest for students. Amelia noted using technology “gives [students] one more opportunity to do something that is not what they’re used to and it wakes up their brain.” Experienced integrators used several different methods to engage students through technology. Rachel believed that making content relevant to students increased their engagement. She noted, “if they’re more engaged and they’re taking charge of their learning, then it’s going to be a lot more relevant for them and they’re going to be more excited about it.” During the first observation, Rachel tapped into students’ interests for her math lesson on adding decimals. She showed students the Target website and told them they could each pretend to buy a game. She scrolled through the different games available on the website and students picked one they liked. Rachel then recorded the price of each game the students selected into an addition problem for the class to solve. Rachel made the math problem relevant to students by giving them choice in selecting a game and providing a real-world context where they would be adding decimals.
One difference between the experienced and intermediate integrators was the belief by experienced integrators that technology was essential to their practice. Amelia and Rachel held the most student-centered beliefs of all participants.Rachel: It’s both because some of our apps like Braining Camp, it engages them, but it’s also tied directly to our learning goals. So I think they go hand in hand.
Amelia: If I didn’t have a computer and all of [this hardware and software], I would have to completely change how I do planning and communicating and collaborating.
Theme 2: Teachers believe technology use should be balanced with multisensory methods
A core principle of the Orton-Gillingham Approach is the use of multisensory instructional methods. This theme encompasses teachers’ belief that any technology use integrated in the classroom must not replace multisensory instruction but be used in conjunction with it. This theme consists of the categories (a) teachers’ perceptions of multisensory methods and (b) balancing technology and multisensory methods.
Multisensory methods are defined as instructional methods that require students to use multiple modalities, such as seeing, hearing, and feeling. The importance of multisensory methods was a unanimous sentiment expressed by teachers with different levels of technology integration. Teachers also saw benefits to using technology for instruction, particularly experienced integrators who used their technology knowledge and content knowledge to make instructional decisions.Olivia: I always try to put a lot of multisensory aspects into my lessons, meaning hearing, seeing, spelling, so that drives my lesson plan. They need to have a strong visual, after they have the strong visual, they need to hear it, but they also need to be doing something as a motor component where they write.
Emma: [technology] is just another way to tap into all of the different learning modalities that we use….When we’re talking about phonics or math, we can teach the content and then they can actually try to apply it by different manipulatives or tools that they’re using in whatever app or software that we’re offering to them.
Teachers expressed a desire to balance technology use with these methods. For example, Amelia thought about balancing technology with multisensory methods during each lesson as well as throughout the week. She stated about learning activities in her lessons, “I try to do a sandwich of pen paper, bulk of technologies, mostly Pear Deck, and then a game or an activity that’s hands-on.” She built her lessons by using multiple learning activities, and students shifted from pencil and paper activities to technology-based activities. This was witnessed during her second observation of a math lesson. Early in the lesson, students solved math problems using pencil and paper. Then, they used virtual manipulatives in the Braining Camp app to create t-tables and solve problems. After that, students completed an exit ticket in Classkick before starting a math game the teacher had created that was printed on cardstock. In addition to each lesson, Amelia thought about slowly increasing how much technology students used throughout the week. She stated, “we begin the week with pen to paper work and hands-on activities, but then as we continue on throughout the week, I’ll incorporate more technology.” She was strategic in deciding when to use technology and how much to use.
Theme 3: Teachers are motivated to use tools that are easy for them and their students
Previous research has found teachers’ decisions to use technology in their classrooms are influenced by the perceived ease of use of that technology (Vareberg & Platt, 2018). Similarly, all of the teachers in this study cited ease of use, either on their part or the students’ part, as a factor in deciding to use technology.Rachel: I think that’s a pretty big factor because there’s just so many options out there like Seesaw and Pear Deck and Google Slides and Google Drive and all of that, and sometimes I pick one over the other just because it’s a little bit easier.
Amelia: I would mostly say I pick Pear Deck and then Classkick [because] those are the two easiest for me to make and grade and to send back corrections.
In addition to their own ease of use, participants considered how easy tools were for their students to use. Selecting tools by viewing how students would experience them was a priority for the participants.Amelia: I try to use technology that requires very little steps and then kind of look at it from a student perspective and see if it’s something that’s doable for a fourth grader.
Emma: If it's too hard and we're spending a lot of time teaching them how to use an app as opposed to we're using this app to support our content that we're trying to teach, then I'm not going to use it.
Theme 4: Teachers’ beliefs are dynamic
Teachers’ beliefs toward technology changed over the course of this study. Three teachers recognized new uses for technology within their classrooms while two teachers grew concerned over how much technology was being used. A previous longitudinal study found that teacher’s beliefs are not linear and changed over time, which in turn affected their practices (Levin & Wadmany, 2006). The technology-rich distance learning environment was a catalyst driving this change. This theme consists of the categories (a) recognizing the benefits of technology and (b) concern of overreliance on technology.
The category Recognizing the Benefits of Technology describes a change in teachers’ beliefs toward identifying beneficial uses for technology over the course of this study. Three teachers increasingly recognized technology played an essential role in in instruction. For example, Sophia stated, “I would say it’d be supplementary” when asked in her initial interview about the role of technology in the classroom, but in her second interview, she responded to the same question saying, “It’s crucial. I think it can make or break a lesson or make and break a school year.” Sophia realized that without technology, she would not be able to continue teaching her students for the remainder of the school year. The role of technology in her classroom changed from being a supplementary tool to one that was essential for instruction.
In addition, teachers realized technology could be used in ways they had not used it before. For example, Emma, an intermediate integrator, found ways to use technology to replace her classroom activities in the distance learning setting. One activity teachers commonly used to help students learn to spell difficult words was called technique. Emma found she was still able to have students do technique during class by using Seesaw. She stated, “I’ll send them off to Seesaw, and they’ll do technique on Seesaw as opposed to in their book.” Emma also expressed enthusiasm regarding efficiency when using an online lesson plan book. During our third interview, she shared that she was using technology to streamline the distribution of work to students and using less paper.Emma: I’m even using an online plan book this year that I didn’t use before because I just find it easier....instead of having to write my lessons down or make copies for [my co-teacher when I’m out] I [can] just share it with her virtually.
Two teachers also discovered technology increased their personal productivity. For instance, when asked what factors she considered when choosing a technology tool, Olivia responded, “I am really looking towards efficiency.” She recognized and appreciated the efficiency of Seesaw in creating activities. She stated enthusiastically, “What is great about Seesaw is that edit and copy feature. Within minutes you have a new activity. It’s faster than typing a sheet.” Olivia found these features in Seesaw a quick and effortless way to create new activities for her students based on ones she had created previously, thus, increasing her personal productivity.
Two experienced integrators, who were enthusiastic about technology during their initial interviews, expressed growing concerns about technology use. Rachel had initially expressed the belief that technology was appropriate for any setting and content, yet after the return to in-person learning she grew concerned about how much technology was being used in her classroom. Striking a balance between using technology and using other methods was important to her. She expressed concern over how much screen time students were getting at several points in her third interview and acknowledged her changing beliefs toward technology. When asked how the lesson we observed during her second observation aligned with her pedagogical beliefs, she stated, “I think that has kind of changed because I’m all about technology, but there have been sometimes where I’m like, okay, these kids have been on the screen too much and it’s too much for them.” This change in beliefs was evident in her observation. During the lesson, most of the instruction was teacher-centered and students were observed using technology only during the second half of class. She elaborated on the struggle she felt between wanting to use technology but being concerned about how much she was using. Amelia also made statements about how prioritizing technology affected her classroom and her beliefs.Rachel: [I’m] just kind of finding the balance between too much technology and technology for the benefit of education. I’m kind of still trying to find that balance and finding some days we’re just going to do paper and pencil, cause we’re not going to get the iPad out today because we’ve been looking at a screen all day.
Amelia: Before distance learning, incorporating technology was something exciting for me. I really enjoy adding it in because it didn’t need to be added in. So being able to incorporate these new things was so awesome and cool for the kids and they weren’t used to it and things like that. But now, because it’s needed every day, it kind of has lost its novelty for the kids, and for me a little bit.
Discussion
The purpose of this study was to, in a longitudinal perspective, describe teachers’ beliefs and practices relating to technology integration at a school for students with dyslexia. To answer the research questions, quantitative and qualitative research findings were integrated. The findings add to the existing literature on technology integration. The discussion of findings is organized by the two research questions of the study.
What are teachers’ beliefs about the role of technology in teaching and learning?
Teachers’ beliefs about technology and their practices of technology integration
Teachers’ beliefs about the role of technology affect their integration. Research suggests that teachers with student-centered beliefs are more likely to integrate technology and teacher-centered beliefs can negatively impact integration (Hermans et al., 2008). Teacher responses to the survey revealed more teacher-centered beliefs and practices among faculty than student-centered ones. These findings suggest that teachers at the school used technology more for teacher-centered instruction and teachers perceived that student use involved low-level thinking skills, which aligns with previous research findings (Dawson, 2012; Palak & Walls, 2009; Polly & Rock, 2016).
Qualitative inquiry of interview and observation data revealed teachers’ beliefs differed by their levels of integration. Experienced integrators demonstrated more student-centered aspects in their lessons, such as providing students with choice and connecting content to real-world scenarios. They made statements regarding technology being essential to their practice during interviews. Furthermore, experienced integrators demonstrated robust technology use during their observations. These findings support previous research that teachers with more student-centered beliefs demonstrate more seamless integration of technology (Kim et al., 2013) and positive value beliefs toward technology (Hsu, 2016; Taimalu & Luik, 2019). Novice integrators, by contrast, displayed teacher-centered characteristics during lessons, such as drill and practice activities and direct instruction. Observations and interviews revealed they frequently used technology to provide a visual for students, which often involved using presentation software. Novice integrators expressed beliefs that technology was a supplemental piece of their instruction and could be used to support their lessons. They viewed technology as an extra component that could be added into their instruction but was not necessary. These findings concur with previous research indicating traditional teacher-centered beliefs negatively impact technology use (Hermans et al., 2008).
School culture influenced teachers’ beliefs
This study found the school culture impacted teachers’ beliefs about the role of technology for instruction of students with dyslexia, echoing prior research findings that teachers’ beliefs are influenced by external factors such as school characteristics (Hew & Brush, 2007; Tondeur et al., 2017). One aspect of the school culture that shaped teachers’ beliefs was the emphasis on multisensory methods of instruction. All teachers relayed the importance of multisensory methods in their interviews, and these instructional methods were observed in their classroom practices. This aligns with Hew and Brush’s (2007) finding that teachers resist adopting technology they perceive as contradicting the norms of the subject culture. Regardless of their varying views on how technology can be combined with multisensory methods, the use of multisensory methods is a deeply ingrained school norm.
Teacher beliefs evolved with time
Our longitudinal investigations found five teachers expressed changes in their beliefs about technology after the experience of distance learning, which supports previous research findings that teachers’ beliefs changed after participating in a technology-rich environment (Levin & Wadmany, 2006), such as distance learning (Barbour & Reeves, 2009). Levin and Wadmany (2006) found that the process of educational change involving technology was unique to each teacher. This study corroborates those findings as each integrator experienced unique changes in their beliefs based on their own individual circumstances.
How do teachers’ observed classroom practices align with their stated beliefs about technology?
This research question sought to determine if teachers’ beliefs aligned with their classroom practices. Five teachers in this study did not describe their beliefs as fitting clearly into one category or the other, corroborating prior findings of a meta-analysis by Tondeur et al. (2017). Olivia took a strong stand labeling her beliefs teacher-centered, but all other teachers described their beliefs as being between student-centered and teacher-centered to some degree. These varying beliefs can be reflected in teachers’ classroom practices as they exhibited both traditional and constructivist classroom practices (Orlando, 2013; Shifflet & Weilbacher, 2015).
Four teachers with different levels of integration experience demonstrated alignment of their stated beliefs and classroom practices, supporting prior findings (Ertmer et al., 2012; Hsu, 2016). For example, Amelia, an experienced integrator, said her beliefs contained aspects of both student-centered and teacher-centered practices, and classroom observations revealed this to be true. Olivia, a novice integrator, recognized her beliefs as teacher-centered. She delivered direct instruction, presented information to students, and had them engage in independent learning. However, one intermediate integrator and one novice integrator displayed misalignment between their beliefs and practices. Emma and Sophia felt their beliefs were in between student-centered and teacher-centered, but their instruction was teacher-centered. For example, Emma’s observations were primarily didactic with students completing independent learning activities that promoted storing and remembering information. The finding is aligned with research that has shown teachers’ beliefs are not always in alignment with their practices (Chen, 2008; Liu, 2011; Mama & Hennessy, 2013; Polly & Hannafin, 2011; Shifflet & Weilbacher, 2015).
Previous research provides a few reasons why teachers’ beliefs are misaligned with their practices (Chen, 2008; Liu, 2011; Orlando, 2013). Two teachers in this study made statements expressing the belief that phonics required teacher-centered methods, echoing Orlando (2013) that some teachers felt the curriculum dictated teacher-centered instruction. Chen (2008) suggested teachers may not fully understand pedagogical beliefs in order to accurately describe their own. This could explain why Emma’s and Sophia’s stated beliefs did not align with their observed practices. During our first interview, Emma asked for clarification about what was meant by teacher-centered and student-centered when asked to describe her pedagogical beliefs. Sophia’s response to the question about her pedagogical beliefs was contradictory. She stated her beliefs were in the middle, but her description of teacher-centered beliefs was not relevant. For these teachers, not fully understanding pedagogical beliefs may have been the reason they stated their beliefs were in-between student-centered and teacher-centered.
Implications for practice
Finding from this research study provide several implications for the school. Professional development is recommended to address gaps and deficiencies in teachers’ technological knowledge. Professional development should not only build teachers’ technical skills, but also show them how technology can be used to enhance content and what pedagogical approaches best support technology use. In addition, due to the school norm of providing multisensory instruction, professional development that provides teachers ways to marry multisensory methods with technology is advised. Using phonics content relating to the Orton-Gillingham Approach will create meaningful, authentic learning experiences for teachers (Ertmer & Ottenbreit-Leftwich, 2010; Ertmer et al., 2015). Additionally, this training will build teachers’ technological content knowledge by showing teachers ways to use technology with their phonics content (Ertmer, 2005).
Limitations and future research
As with the majority of research studies, the current study is subject to limitations. First, this study collected responses from participants through a self-reported survey, which may reflect self-presentation bias and teachers may inaccurately report their own practices (Kopcha & Sullivan, 2007). Second, for qualitative inquiry, the researcher’s own subjectivities can present biases in how findings are interpreted (Roulston & Shelton, 2015). We kept a researcher’s journal to document our thoughts and to self-examine practices and assumptions (Roulston & Shelton, 2015). However, findings from this research should be viewed in light of this limitation. Finally, restrictions due to COVID-19 placed limitations on this study. Namely, initial observations had to be conducted through virtually that limited our observation of their actions.
Future research could examine technology integration with an emphasis on identifying pedagogical strategies for online learning that work well with the Orton-Gillingham Approach at schools for students with dyslexia. In addition, future research may identify teachers’ specific barriers in a longitudinal perspective so as to reduce or eliminate those barriers with minimal or considerable effort.
Conclusion
Technology has continued to advance over the last few decades, yet classrooms are not taking advantage of the increase in access by using technology in authentic, meaningful ways. Effective technology integration requires a pedagogical shift whereby teachers utilize technology as a cognitive tool. It is necessary to understand teachers’ beliefs to understand their intentions and actions within the classroom. Identifying teachers’ deep-seated beliefs about the role of technology in teaching and learning is a crucial step in affecting this change. The longitudinal nature of this study revealed teachers’ beliefs about technology changed over time driven by their level of technology integration experience and also school culture. All teachers at the school were thrust into an online teaching environment without having any prior experience and with minimal training. Teachers learned new uses for technology for their students and themselves. They also recognized technology use must be purposeful and carefully planned so that reliance on technology is not too great.
Declarations
Conflict of interest
There is no any potential conflict of interest in the work.
Preprint
This manuscript was based on the first author’s dissertation. The bibliographic information for this dissertation is as follows.
Bice, H. (2021). Teachers’ beliefs, barriers, and classroom practices: A mixed methods study of technology integration at a school for students with dyslexia. (Publication No. 28320939) [Doctoral dissertation, University of South Carolina]. ProQuest Dissertations & Theses Global.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Educ Inf Technol (Dordr)
Educ Inf Technol (Dordr)
Education and Information Technologies
1360-2357
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Article
Gamification in education: A scientometric, content and co-occurrence analysis of systematic review and meta-analysis articles
http://orcid.org/0000-0002-6257-2325
Nadi-Ravandi Somayyeh snadi2006@gmail.com
12
http://orcid.org/0000-0002-1212-9835
Batooli Zahra batooli91@gmail.com
34
1 grid.444768.d 0000 0004 0612 1049 Health Information Management Research Center, Kashan University of Medical Sciences, Kashan, Iran
2 grid.444768.d 0000 0004 0612 1049 Educational Development Center, Kashan University of Medical Sciences, Kashan, Iran
3 grid.444768.d 0000 0004 0612 1049 Social Determinants of Health (SDH) Research Center, Kashan University of Medical Sciences, Kashan, Iran
4 grid.444768.d 0000 0004 0612 1049 Faculty of Health, Kashan University of Medical Sciences, Kashan, Iran
12 4 2022
132
23 1 2022
6 4 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, 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.
This study seeks scientometric, content and co-occurrence analysis of systematic review and Meta-analysis articles in the field of gamification in education. In terms of purpose, this is an applied study and regarding type, it is a scientometric and co-occurrence analysis. The researchers conducted a search in WoS, Scopus and PubMed databases. The abstract and full text of 25 out of 71 articles were selected to be included in the study. Then, the citation and altmetrics indicators were investigated. In addition, VOSviewer software was utilized to analyze and visualize keywords and map of articles. Finally, the full texts of all articles were analyzed to be provided more information about the types of analyses in these articles. The findings showed that 25 articles were published between 2016 and 2021. Co-occurrence map of articles showed that the three variables of motivation, learning, and engagement have been considered in gamified education studies and most studies have examined gamification in the e-learning environment. Finally, the content analysis of the articles showed that 344 articles were included and analyzed in these 25 systematic reviews and meta-analyses. The types of analyzes performed on these 344 articles categorized them in 7 categories including Country/Territory, Duration of intervention, Lessons/content and the level of gamified educational course, the number of learners, platforms, the game elements and the theories. The results of the study illustrate that different dimensions of the gamification in articles in the field of Education have been considered by the researchers.
Keywords
Gamification
Education
Content analysis
Co-occurrence analysis
==== Body
pmcIntroduction
In the current age, computer games are one of the things that digital culture has brought to modern life. The latest concept in this field is termed gamification, which acts as a broad umbrella for the use of video game components in order to improve the experience and increase users' participation in environments and contexts that are not related to the game (Hamari et al., 2014).
Gamification, which is related to the field of digital media industry (Schönen, 2014), means "the use of game design elements in non-gaming spaces" (Deterding et al., 2011). In fact, gamification is the use of tools and mechanism, aesthetic aspects and game thinking to make people more engaged and motivated to behave specifically, and encourage them to learn and solve problems (Kapp, 2012). Now, it covers a very wide and diverse range such as education and learning, health, e-commerce, environment, and hotel management (Deterding et al., 2011). Thus, gamification is the use of game-like thinking and characteristics in areas that are not inherent in the game (Huotari & Hamari, 2012); however, it uses game structures such as foundations, stimuli and components of game to solve life problems (Chou, 2016).
The word gamification was coined in 2002, but the concept first became popular in the scientific literature in the second half of 2010, and gained popularity among researchers in 2011. Gartner predicted that by the end of 2015, more than 40% of the world's top 1,000 organizations will benefit from gamification components in terms of customer orientation and product quality improvement (Schönen, 2014) and in the near future, there will be significant progress in the field of internalization of internal processes as well as external interactions, i.e. attracting more users and customers (Burke, 2012). Currently, there are a lot of websites and experts in this field and many articles have been written on this topic and its sub-categories. This generated knowledge can inherently provide valuable information about the role ofof gamification in various aspects of life. A simple Google search for new teaching and learning methods shows that gamification is a fascinating method alongside other methods such as flipped learning, project-based learning, cooperative learning, problem-based learning, design thinking, thinking-based learning, and competency-based learning (Realinfluencers, 2019). Interestingly, this method itself has the ability to be integrated with other methods so that, an inverted learning method, for instance, can be linked to a collaborative learning using gamification. Discovering and using this information in the field of learning and teaching requires a look through the literature in this field., Systematic review and meta-analysis articles are most probably the best, shortest and fastest ways to obtain valid information in this regard. These types of articles aim to evaluate, select, and synthesize quality studies in a specific field to provide more accurate results, which can not only provide high-quality evidence but also make decisions about reviewing original studies easier and faster. On the other hand, the results of the research by Hamri, Quisto and Sarsa testify to the claim that the most widely used concept of gamification has been in the field of "teaching and learning" (Hamari et al., 2014). Therefore, an analysis of systematic review and meta-analysis articles in this subject area can provide valuable information for researchers and those interested in the status of studies related to the gamification in the field of education.
In addition, altmetric analysis of high-quality articles in a subject area contributes to our greater knowledge of research, topics, and trends. This type of analysis shows the process of dissemination, the evolution of knowledge and the evidence-based practice of a subject. Therefore, due to the increasing applications of scientometrics in the evaluation and measurement of scientific products, the purpose of this study is scientometric analysis of the systematic review and meta-analysis articles related to gamification (Mostafavi & Bazrafshan, 2011). Examination of citation and altmetric indices of articles shows their scientific and social impact (Lora et al., 2020).
Co-occurrence analysis is also one of the types of scientometric analysis namely content analysis, which is obtained through the co-occurrence of words with the concepts in texts and sources, which can be used to identify the main concepts of a field or scientific field. As a result, patterns and conceptual events, scientific structure, conceptual network, hierarchical relationships of concepts and conceptual categories of the field under study are discovered, plotted and managed (Zhang et al., 2016). This conceptual network is drawn by counting the number of thematic words in the text and its association with other topics. In other words, if two terms are used together in a document and are repeated as much as possible, it means that these two words are more semantically related. The co-occurrence of two terms or two words is also used to discover the connection between two topics in a field of research, and in this way the development and progress of that field of science can be traced (Ahmadi & Osareh, 2017).Among the studies that have dealt with the co-occurrence of words in the scientific productions of different subject areas are Covid-19 (Al-Zaman, 2021), Coronavirus (Atlasi et al., 2021), Artificial Intelligence (Chen et al., 2020), Dentistry (Ghaffari et al., 2019), Diabetes (Makkizadeh et al., 2016), Blockchain technology (Niknejad et al., 2021), Child abuse (Tran et al., 2018). Therefore, considering that the use of gamification in education has caught many researchers' attention and numerous systematic review and meta-analysis studies has been done in this regard, this study seeks scientometric, content and co-occurrence analysis of systematic review and meta-analysis articles in the field of gamification in education.
Method
The purpose of this study is a scientometric, content and co-occurrence analysis of systematic review and meta-analysis articles in the field of gamification in education. In terms of purpose, this is an applied study and regarding type, it is a scientometric and co-occurrence analysis.
Literature search strategy
In order to retrieve systematic review and meta-analysis articles related to gamification, using related keywords (Systematic review, meta-analysis, Game, Games, Gamification, Gameful), a search was conducted in WoS, Scopus and PubMed databases. Education-related keywords were not added to the search strategy, and after reviewing the titles and abstracts of the articles, articles in the field of education were selected.
Inclusion and exclusion criteria
Inclusion criteria
Articles of systematic review and meta-analysis in the field of Gamification in Education.
Exclusion criteria
Articles which were not based on the definition of gamification (Deterding et al., 2011; Kapp, 2012; Nicholson, 2015) (using game elements in non-game environments to change behavior and solve problems) and were not related to the use of Gamification in Education.
Identification, selection, and coding
In the identification phase, 421 articles were retrieved with our search strategy. After removing duplicates, 208 articles remained for further review. Afterwards, the articles were screened, out of which71 articles were selected. Finally, the abstract and full text of selected articles were reviewed and only 25 articles (based on inclusion and exclusion criteria) selected to be included in the study. Figure 1 shows the flowchart of the present study process.Fig. 1 Flowchart of present study process
Data analysis
Citation and Altmetric analysis
In order to investigate the scientific and social impact of the articles, their citation and altmetrics indices were extracted from WoS and Scopus. Citation indicators such as WOS and Scopus Citation, the scientific impact of articles and altmetrics indicators such as Mendeley Read, Facebook Share, Tweets, Scopus View, WOS Usage Count Since 2013, and Altmetric Attention Score, show their social impact.
Co-occurrence analyses
Finally, using VOS viewer software, the word map of articles was drawn. The mapping of a scientific field is a technique that provides a structured overview of the science. One of the techniques used to map science is the visualization of similarities (VOS). By drawing a graphic of each discipline, science maps have paved the way for a better and more accurate identification of that branch of human knowledge and the transformation of the abstract concept of the discipline into a more objective concept. These maps are drawn with various techniques and methods. One of the purposes and applications of co- occurrence analysis is to draw the structure of science or to draw scientific maps. In a keyword co-occurrence map, the size of the circles shows the number of repetitions of the keywords. In other words, the larger the circle, the more frequent the vocabulary of that domain. In a keyword co-occurrence map analysis, the relative distance of one concept to another is emphasized. The proximity of keywords at the point means that more concepts are related to each other. In addition, the thicker and shorter the lines are, the stronger the connection between the words exists.
Content analysis
In order to analyze the content of the articles, the following items were extracted from 25 reviewed articles: Subject, Number of articles reviewed in each article, Names of databases searched in 25 articles for resource extraction (ERIC, Science Direct, ACM Digital Library, IEEE Xplore, Scopus, Springer Link, Wiley Online Library, Google Scholar), Extraction period of resources reviewed in 25 articles, Language of resources reviewed in 25 articles (English, Spanish, etc.), and type of publication reviewed in 25 articles (journal article, conference article, dissertation, book).
Results
Citation and Altmetric analysis
In this study, 25 systematic reviews (18 articles) and meta-analyses (seven articles) in the field of gamified educational environments were studied. All articles were published between 2016 and 2021 that 6 articles were conference proceedings and the others were journal articles. Table 1 shows the findings of top articles regarding the altmetric and citation indicators.Table 1 Top articles in terms of citation and altmetric metrics
Metrics No. Of Metrics Author Title Year Journal
WoS Citation 104 Subhash & Cudney, 2018 Gamified learning in higher education: A systematic review of the literature 2018 Computers in Human Behavior
Scopus Citation 142
Scopus View 522
Usage Count Since 2013 202
Mendeley Read 835
FWCI 33.57 Kalogiannakis et al., 2021 Gamification in science education. A systematic review of the literature 2021 Education Sciences
Facebook share & Tweets 413 Osatuyi, Osatuyi et al., 2018 Systematic review of gamification research in IS education: A multi-method approach 2018 Communications of the Association for Information Systems
AAS Sailer & Homner, 2020 The Gamification of Learning: a Meta-analysis 2020 Educational Psychology Review
It can be seen from Table 1, in terms of the number of citations and views in WoS and Scopus and the number of read in Mendeley, the article by Subhash & Cudney (2018) is the best one among all articles. The highest number of FWCI belongs to the article by Kalogiannakis et al. (2021).
Co-occurrence analyses
Next, the co-occurrence map was comprisedof 64 keywords from authors and database keywords by using VOSviewer software, which is shown in Fig. 2. The size of the circles indicates the amount of knowledge available in each concept. Nodes represent concepts and lines show how they are related.Fig. 2 co- occurrence maps of articles
Figure 2 shows that gamification keyword is at the center of the map. The keywords Motivation, student learning, computer aided instruction, engagement, learning achievement, learning performance are also keywords that have a larger node. Figures 3, 4, 5 and 6 highlights the relationship between the four most frequent keywords with other keywords.Fig. 3 Relationship of Motivation with Gamification
Fig. 4 Relationship of Engagement with Gamification
Fig. 5 Relationship of Computer aided instruction with Gamification
Fig. 6 Relationship of Student learning with Gamification
As can be seen from the maps (3-6), the three variables, including motivation, learning, and engagement have been considered in gamified education studies. It is also clear that most studies have examined gamification in the e-learning environment.
Content analysis
In the next step, the full texts of all 25 articles were examined and information such as databases, period, language, type of included resources (conference article, journal article, thesis and book) and the number of included articles were extracted. Table 2 shows that the included articles in these 25 systematic reviews and meta-analyses were indexed in 48 databases, the most repeated one belongs to WoS with 12 cases and ERIC, Science Direct, ACM Digital Library, IEEE Xplore, Scopus, Springer Link, Wiley Online Library, Google Scholar are in the next ranks. In addition, the most and least number of articles in these 25 studies are 148 and 6 cases, in turn. Most of the which are in English. However, there were some articles in Dutch, Portuguese and Spanish in some studies.Table 2 Bibliographic information on articles reviewed in systematic reviews and meta-analyses (Database, Date, No. of Article, Publication Type, and Language)
Citation Database date No. of Article Publication Type Language
Malicki et al., 2020 PubMed, CINAHL, ERIC, and Cochrane to 2018 23 articles Journal English
Bai et al., 2019 ACM Digital Library, EBSCO, IEEE Xplore Digital Library, INSPEC, ProQuest, Scopus, Web of Science to 2018 13 articles Journal, conference proceedings English
Huang et al., 2020 ACM Digital Library, ACS Publications, DOAJ, EBSCOhost, Gale Databases, HEINONLINE, IEEE Xplore Digital Library, LearnTechLib, NCBI Databases, Ovid, ProQuest, Sage Journals, ScienceDirect, SpringerLink, Taylor & Francis Online, Web of Science, Wiley Online Library, World Cat 2009-2018 Qualitative: 118 Quantitave: 30 Journal, Dissertation/thesis, Conference proceeding English
Zainuddin et al., 2020 ScienceDirect, EBSCOhost Web, Emerald Insight, Taylor & Francis Online, Wiley Online Library and SpringerLink 2016-2019 46 articles Journal English
Indriasari et al., 2020 ACM Digital Library, IEEE Xplore, ScienceDirect, Springer Link, Scopus, Web of Science Core Collection, and ERIC 2011-2018 39 articles Journal, conference proceedings, book chapter English
Subhash & Cudney, 2018 Academic Search Complete, ACM Digital Library, Education Full Text, ASEM Digital Collection, IEEE Xplore, PsychINFO, and Scopus to September 2017 41 articles Journal English
Ortiz-Rojas et al., 2017 Web of Science 2000–2016 23 articles Journal Spanish, English
Kim & Castelli, 2021 The Academic Search Complete, Communication & Mass Media Complete, Education Source, ERIC, Library Information Science & Technology Abstracts, and PsycINFO 2010-2019 18 articles Journal, conference proceedings English
Garcia et al., 2020 Springer Link, ACM Digital Library, Science Direct and IEEE Xplore the last five years 45 articles Journals English, Portuguese
Kalogiannakis et al., 2021 Science Direct, Eric,Wiley Online Library, SpringerLink, Sage Journals, Taylor & Francis Online, and JSTOR. Google Scholar 2012-2020 24 articles Journal, conference proceedings English
Bai et al., 2020 ACM Digital Library, EBSCO host research databases (including Academic Search Premier, British Education Index, ERIC, TOC Premier), Emerald Insight, Science Direct, Scopus, and Web of Science 2010-2018 quantitative: 24 Qualitative: 32 Journal, conference proceedings English
Yıldırım & Şen, 2019 Web of Science and Google Scholar 2010-2016 40 articles Journal, Book chapter, Thesis/ Dissertation Conference proceeding, all languages
Dehghanzadeh et al., 2019 Scopus, ERIC, and Web of Science 2008-2019 22 articles Journal, conference proceedings English
Van Gaalen et al., 2021 Academic Search Premier; CINAHL; EMBASE; ERIC; Psychology and Behavior Sciences Collection; PsychINFO, PubMed and the Cochrane Library to April 2018 44 articles Journal Dutch or English
Alomari et al., 2019 Google scholar, Springer, ERIC (education resources information system), IEEE Xplore and Science Direct 2016-2018 40 articles Journal, conference proceedings English
Dos Santos et al., 2020 ACM, IEEE Xplore, Springer Link, Science Direct, in conjunction with the CAPES Periodicals Portal from the Brazilian Ministry of Education 2013-2018 43 articles Journal English
Ortiz et al., 2016 Web of Science 2000-2016 30 articles Journal, conference proceedings Spanish, English
Sailer & Homner, 2020 ACM Digital Library, ERIC, IEEE Xplore, JSTOR, PubMed, ScienceDirect, and SpringerLink, Google Scholar to March 2017 38 articles Journal, conference proceedings English
Osatuyi et al., 2018 Journals: ISR, JAIS, JCIS, JISE And major IS conference proceedings available in the Association for Information Systems 2008-2017 41 articles Journal, conference proceedings
Ekici, 2021 The Web of Science, Scopus, Wiley Online Library, ERIC and Science Direct 2010-2019 22 articles Journal English
Brick et al., 2020 ERIC, Emerald and Elsevier 2014-2018 6 articles Journal -
Da Silva et al., 2019 ISI Web of Science and Scopus to December 2017 104 articles Journal -
Manzano-León et al., 2021 Wos, scopus, dialent 2016–2020 14 articles Journal English, Spanish
Tenório et al., 2018 high quality journal; learning as primary field; computers in education as secondary field. Selected journals to this review.: IJDET, JEMH, JITE, JSET, C&E,CE, RLTE all year 14 articles Journal English
Pinto et al., 2021 ACM, IEEE Xplore, Mary Ann Liebert, Scopus,Wiley and Web of Science 1990–2020 32 articles Journal, conference proceedings English, Portuguese
344 articles
In 25 systematic reviews and meta-analysis articles, 344 articles were included and analyzed. Having read the full text of all 25 articles, the researchers extracted the types of analyzes performed on these 344 articles and categorized them in 7 categories including:Country/Territory related to studies in the field of gamified education studies
Duration of intervention
Lessons/content and the level of gamified educational course
The number of learners
The used platforms
The game elements
The used theories in the studies
Country/territory
Cultural differences can create different expectations and attitudes in learners about gamified learning in different countries. Table 3 shows that only 11 out of 25 studied articles reported country/territory where intervention was conducted.Table 3 Country/Territory, where gamified educational interventions were conducted
Citation Country/Territory Citation Country/Territory
Subhash & Cudney, 2018 Spain, US, Germany, United Kingdom; Ortiz et al., 2016 Europe, America, Asia , Africa and not specified
Ortiz-Rojas et al., 2017 Europe:12, America: 5; Asia: 4, not specified: 2 Bai et al., 2019 Asia, Europe: 4, America:3 Countries: Spain, Hong Kong and Turkey
Bai et al., 2020 East Asia:15, Western Asia: 5; Northern America: 2, South America: 2; Southern Europe: 2 Ekici, 2021 Spain, Great Britain, Taiwan, Indonesia, Hong Kong, Turkey and Cyprus, Singapore, Mexico, Germany
Yıldırım & Şen, 2019 USA: 9, Turkey: 9, Spain: 4, England: 3; Taiwan: 3, Hong Kong: 2 Osatuyi et al., 2018 USA 53, Germany 44, Australia 22, Switzerland 12 Hong Kong 8, UK 8, Finland 7
van Gaalen et al., 2021 USA or Canada Malicki et al., 2020 United States (6), Canada (4), Australia (2), Finland (2), Norway, Portugal, Spain (2)
Dos Santos et al., 2020 Spain
As it is clear from Table 3, most of the studies in the field of gamified education were conducted in the US, Canada and Spain, respectively.
Duration of gamified educational intervention
One of the most important items in the field of gamified education considered by researchers is course effectiveness based on the length of the intervention. Table 4 shows that only in 8 studies, the duration of gamified intervention were reported.Table 4 Duration parameter in gamified educational interventions
Citation Length of Gamified Interventions
Ortiz-Rojas et al., 2017 <1 month: 4, 2—4 months: 5, 1 semester: 5, Not Stated: 6, < 1 semester: 3
Kim & Castelli, 2021 less than 1 h: 5, 2–16 weeks: 11, 1–2 years: 2
Bai et al., 2020 1 month-3 months: 10, <1 weeks: 6, ≥1 semesters: 5, 3 months-1 semester: 4, 1 week-1 month: 3, No data reported:2
Dehghanzadeh et al., 2019 < 1 Hour: 6, 1 Hour: 2, < 3 weeks: 4, 3-6 weeks: 4, 6 weeks: 2, 2 months: 1, 3 months: 1, 6 months: 1
Ortiz et al., 2016 1 semester: 16, 1-14 weeks: 7, 1-4 months: 2, 1-24 h: 1, ≥ 1 year: 1, Not specified: 1, Other: 2(14)
Bai et al., 2019 three-quarters of a term: 4, one quarter of a term: 3, two-quarters of a term: 3, more than one term: 1, No data: 2
Sailer & Homner, 2020 1 day or less, 1 week or less (but longer than 1 day), 1 month or less (but longer than 1 week), half a year or less (but longer than 1 month), more than half a year
Ekici, 2021 between 0 and 4 months long: 16, six months long: 2, shorter than a month: 3
Table 4 indicates that the maximum and minimum lengths of each course are "less than one hour" and "one to two years", in turn.
Participants (lesson/content/discipline and grade)
Another important item in systematic reviews and meta-analyses was the Lesson/content and the grade of gamified educational course for the participants. Table 5 shows the results of 19 articles examined this item in their studies.Table 5 Discipline and Participants’ levels of education in gamified educational interventions*
Citation Lesson/content/ Discipline of the intervention Participants’ levels of education
Huang et al., 2020 Social science (Psychology, Education): 10; Engineering Computing: 6; Arts and Humanities: 5, Science (Biology, Physics): 4, Math: 2; Health (Nursing and Medicine): 1, Business, nformation Systems: 1; Other: 1 Undergraduate: 13; K-12: 10; Higher education: 3; Graduate: 2; Mixed higher education: 1; Mixed higher education and K-12:1
Zainuddin et al., 2020 - Adult/Higher education: 36; Primary schools: 5; Secondary school: 5
Indriasari et al., 2020 Physical Science, Mathematics & Computer Science: 10; Engineering: 8, Education: 7, Social Sciences: 3; Business: 3; Health Professions: 2, Other majors:1, Unknown: 6 Elementary; High school; University
Subhash & Cudney, 2018 Computing: 14, Business, science: 5, Science: 4; Academic Distribution: 3, Entrepreneurship: 2, Communication: 2; Civil: 1, Language: 1, Manufacturing Engineering: 1; Mechanical Engineering: 1, Nursing:1, Pedagogy: 1, Psychology:1 Higher education
Ortiz-Rojas et al., 2017 Science, Technology, Engineering and Mathematics (STEM): 19; Communication: 1, Financing: 1, Use of Photoshop: 1; Designing Questionnaires: 1 Higher Education: 19; High School: 2; Middle School: 2
Kim & Castelli, 2021 - College students: 10, Adults: 6, k-12: 2
Garcia et al., 2020 Mathematics, Teacher:2, Student:33, Both: 1, Unknown: 9 Higher Education: 19, Middle School:16, Both: 1, unknown :8
Kalogiannakis et al., 2021 Biology or Health: 8, Physics: 5, Chemistry: 2, Natural Sciences: 9 Higher education: 10; Secondary education: 9; Primary education: 5
Bai et al., 2020 Arts: 3, Computer & information: 9, Language: 7, Research methodology: 3, Science: 3, Health education: 2, Mathematics: 2 Undergraduate: 10; Elementary school: 9; High school: 5; Postgraduate: 3; No data reported: 2
Yıldırım & Şen, 2019 Non-technology: 30; Technology: 15; Overal: 45 Primary school: 5; Secondary school: 6; High school: 4; University: 30; Overall: 45
Dehghanzadeh et al., 2019 English, Learning vocabularies: 15, Grammar:5: Pronunciation:4 Speaking:5, Writing: 3, Listening: 4 High schools: 10; Higher education:7; Elementary schools: 4
Van Gaalen et al., 2021 Heallth, Surgery, Anatomy & physiology, Anatomy learning Attitudes towards aging, Auscultatory skills, Biology Breast imaging, Clinical reasoning, Critical care, Hypertension treatment, Internal medicine , Learning about, Learning critical thinking, Life-support training, Microbiology , Pediatric knowledge Pharmaceuticals, Physiology, Quality improvement, Radiology Resuscitation principles, Scientific writing, Urine catheterization Residents: 11, Medical students: 14 Nursing: 4, Allied health students: 1 Specialists:3, Osteopathic students:1 Pharmacy students: 1 Nursery: 3, Primary care physicians:1 Speech-Language and Hearing Science: 1, Medicine (mixed): 3, Dentistry:1
Dos Santos et al., 2020 - High/technical school:18; General education: 11; Higher education: 7; Elementary school:5; Early childhood education: 2
Ortiz et al., 2016 STEM, computer science: 25, Science/Technology: 2, Math: 1 Chemistry:1, Not specified: 1 Higher Education
Bai et al., 2019 - Undergraduate:9, Elementary school:1; High school: 1; High school + undergraduate + postgraduate: 1,Postgraduate: 1
Sailer & Homner, 2020 - School setting, Higher education setting
Ekici, 2021 STEM, information science and information and communication technologies: 12, educational sciences: 2 English learning: 2 personal and professional development: 2 College: 18; High school: 3; Primary school: 1
Manzano-León et al., 2021 Physical education, Foundations of the curriculum and physical Education, Sciences, Industrial technology, MSc in Software Engineering for the Web, Advanced quantum mechanics course Online seminar of psychology, Second language(English), Ethical education, Matter and Energy subject in Primary, Education Degree Psychology courses, Math University: 6; School: 3; High School: 3; School and high school: 2
Pinto et al., 2021 Language: English: 21; German: 3; Chinese: 2; Japanese: 1; Basque: 1; Mandarin: 1; Amazigh: 1; Danish: 1 Primary education or first stage of basic education: 9; Lower secondary or second stage of basic education: 5; Pre-primary: 2; Secondary: 2; First stage of tertiary: 2; Post-secondary non-tertiary: 1; Second stage of tertiary: 1; Participants recruited from the university community: 1; 21 years old: 1; 21 to 55 years old: 1; Students from an higher education foreign language course: 1; Do not inform: 9
* The numbers in the table refer to the number of articles with this situation in each systematic or meta-analysis(Citation)
Table 5 shows that some studies focused only on one lesson or content such as English or Mathematics while the majority of systematic reviews and meta-analyses investigated studies with intervention on different contents. Based on the findings, Science, Technology and Mathematics (STEM) accounted for a significant number of studies. Furthermore, in terms of educational grade, according to Table 4, the gamified educational interventions were conducted in all grades from pre-primary to postgraduate that most of them were conducted in Higher education.
The number of learners
Table 6 indicates that only 5 systematic reviews and meta-analyses examined the number of learners participating in interventions in included articles.Table 6 Samples size in gamified educational interventions
Citation Sample size
Ortiz-Rojas et al., 2017 21-100 students: 12; 101-200 students: 3; 201-300 students: 2; ≥301 students: 5; Not mentioned: 1
Bai et al., 2020 <50 students: 13; 50–100 students: 8; ≥150 students:8
Dehghanzadeh et al., 2019 <50 students: 11; 50–100 students: 7; >100 students: 3
Ortiz et al., 2016 <10 students: 1; 11-60 students: 13; 61-110 students: 5; 111-470 students: 9; 2263 students: 1; Not mentioned: 1
Ekici, 2021 <60 students: 8 l <120 students: 14; Not mentioned: 2
According to Table 6, the lowest and the highest sample size were less than 10 and 2263 participants, respectively. The sample size in most of the articles was less than 100 learners.
The used platforms in the gamified educational interventions
Table 7 indicates that four studies reported the name of the used platforms in included articles. Findings show that the majority of examined articles in systematic reviews and meta-analyses used some of the most exciting gamification platforms such as Cahoot and Quizziz.Table 7 Platforms used in gamified educational interventions*
Citation Platforms
Zainuddin et al., 2020 Adapted gamification platforms: ClassDojo and ClassBadges, Ribbonhero of Microsoft Rain classroom, Quizbot, Duolingo Kahoot and Quizizz, Math Widgets, Google + CommunitiesiSpring Learn LMS learning management system: MOOCs (Coursera, Udacity, and edX), wiki platforms, moodle platforms or institutional LMS
Kalogiannakis et al., 2021 Pre-existed gamified platform: Kahoot, ClassDojo, Socrative, Quizziz, Zondle, and 3D GameLab
Dehghanzadeh et al., 2019 WordBricks, Duolingo, Kahoot, Babbel, Jeopardy, ClassDojo, Lifeline, Feelbot, Brainscap
Ekici, 2021 Moodle, Kahoot, Blackboard, Socrative, iSpring Learn LMS, The Minimum Learning Judgement System, VoiceTube, Quizziz, Khan Academy LMS, Electronic Book
Table 7 shows that the most popular platforms are Kahoot, ClassDojo, Duolingo, Moodle, Quizziz and Khan.
The game elements
In every study in the field of the gamified educational environment, one or more game elements have been used. The game elements in educational interventions are one of the most important items that systematic review and meta-analyses articles reported them. Table 8 shows that 17 out of 25 studies examined the game elements in the included articles.Table 8 Game elements used in gamified educational interventions*
Citations Game elements (the number of studies that used the element) and the number of articles using these elements in every citation
Huang et al., 2020 Points/experience (24), Leaderboards (23), Badges/awards (22), Competition (21 ), Responsive feedback (19), Advancement/levels (14), Quests/missions/modules (12), Collaboration (9), Avatars/customization (8), Timed activity (6), Performance graphs (6), Non-linear navigation (5), Adaptivity/personalization (5), Narrative/storytelling (5)
Zainuddin et al., 2020 Point (38), Leaderboard (33), Badges (33), Levels (21), Trophies (7), Avatars (6), Gift (5), Progress bar (5), ranking (5)
Indriasari et al., 2020 Points (27), Leaderboards (22), Badges (26), Progress Bar (5), Virtual Gift (5), Level (4), Mission/ Quest (2), Prize (1)
(Subhash & Cudney, 2018) Point, leaderboard, badge, level, feedback, collaboration, graphics, design (goals, rules, time limit, competition), narrative, freedom to fail, real reward, role play
Ortiz-Rojas et al., 2017 Badges (13), Leaderboard (10), Points (6), Levels (4), Ranking (4), Challenges (3), Trophies (3), Virtual Currency (1), Feedback (1), Hearts (1), Quests (1), Scoring (1), Achievements (1), Avatars (1), Awards (1)
Kim & Castelli, 2021 Badges 15, leaderboard 14, points 13, progress bar 5, Challenge2, levels 2, avatar2, goals 1, peer assessment 1 , storytelling1, prize1
Garcia et al., 2020 Feedback (29), Pontuation (29), Levels (25), Rewards (21), Goals (19), Cooperation (15), Narrative (15), Real time (8), Objective History (1)
Kalogiannakis et al., 2021 Competition (15), points (13), levels (12), Leaderboard (12), Progression (11), Badges (6), Time- pressure (5), Rewards (4), Cooperation (4), Storytelling (3), Quizzes (3), Avatar (3), Score (2), Story-based (2), Narrative (2), Challenges (2), Collaboration (2), Stats (1), Repeat-testing (1), Puzzle (1), prizes (1), Goals/objectives (1), Feedback (1)
Dehghanzadeh et al., 2019 Feedback (22), Challenge (12), Reward (11), Point (11), Leaderboard (8), Level (7), Time pressure (6), Progress bar (6), Badge (5), Score system (3), Like or dislike (3), Narration (2), Answer question (2), Quest (2), Story (2),Achievement (2), Avatar (2), Character system (2), Curiosity (2), Emoticon (1),Fantasy (1), Mission (1), Virtual credit(1), Medal (1), Performance graph (1), Use of social media (1), Warning signal (1), Wall (1), Control (1), Appreciation (1), Freedom to fail (1), Chatting with users (1), Message (1), User guidance (1), Status (1), Rule (1), Specific phrases (1), Competition (1), Uploading (1), Chunking(1), Correctness bar (1), Peer assessment (1), Error typing (1), Profile (1), New feed (1)
Van Gaalen et al., 2021 Scoring/Points (15),Competition (13), Rewards (7), Time (6), Teams (4), Levelling (3), Crossword puzzle (2), Spaced-learning (2), Social network (2), Surprise (2), Role playing (1), Avatar (1), curiosity (1), Progress (1), signposting (1) , Mystery character (1), Awards (1), Badges (1), Chance (1)
Alomari et al., 2019 Points (30), Badges (27), leaderboards (25), Levels (14), Progress bar (5), Challenge (4), Feedback (4), Achievement rewards (3), Avatars (3), Quests (2), Ranking (2), Rewards (2), Social engagement (1), Storyline (1), Thumbs –ups (1), Trophies (1), Win-state (1), Real gifts (1), Reputation (1), Narrative (1), Progressive levels (1), group competition (1), Comparisons (1), Constraints (1), Cards (1), Awards (1)
Ortiz et al., 2016 Combination (18), Badges (7), Leaderboard (2), Points (1), Challenge (1), Quests (1)
Ekici, 2021 Points (17), Badges (14), Leaderboard (8), Levels (2), Progress bar (1), Virtual coins (1), Virtual Objects (1), Rewards (1)
Manzano-León et al., 2021 Points (10), Narrative (8), Badges (7), Ranking (6), Rewards (6), Challenge (4), Prize (3), Levels (3), Playful activities (2), Tasks (2), Events (1), Roles (1), Feedback (1), Choices (1), Competition (1), Achievements (1)
Tenório et al., 2018 Badges (9), Points (8), Leaderboard (6), Level (5), Avatar image (4), Teams (4), Avatar in 3D (3), Social Graph (3),Virtual Goods (2)
Bai et al., 2020 Badges + leaderboard/rank + points (8), Badges + leaderboard/rank + levels/unlock + points (6), Badges + points (4), Points (3), Badges + levels/unlock + points (3), Avatar+leaderboard/rank+levels/unlock +points+progress bar+team (collaboration, competition) (3)
Bai et al., 2019 Badges + leaderboard/rank + level/unlock + points: (4), Badges + leaderboard/rank + points (2), Badges + points + progress bar (1), Badges + leaderboard/rank + level/unlock + progress bar (1), Badges + leaderboard/rank + level/unlock (1), Badges (1), Badges + leaderboard/rank (1), Level/rank + points (1), Avatar + badges + leaderboard/rank + level/unlock + points + word notification (1)
* The numbers in the table refer to the number of articles with this situation in each systematic or meta-analysis(Citation)
Table 8 shows that the game elements used in educational programs are very different at different levels, but some elements are used more than others. The majority of the used game elements are Point, Leaderboard, Badge, Level, Feedback, Progress bar, Challenge and Avatar.
The theories applied in the gamified educational interventions
Theories that are the basis of designing gamified learning environments are among the cases that have been studied in these types of articles.Table 9 shows the titles of these theories.Table 9 Theories used in gamified educational interventions
Citation Theories
Zainuddin et al., 2020 Self-determination theory; flow theory; The goal-setting theory; Cognitive evaluation theory; Cognitive load theory; Behaviour reinforcement theory; Social comparison theory; Theory-driven gamification design model: goal, access, feedback, challenge and collaboration; Theory of reasoned action; Rational choice theory; Taxation theory; Information systems success model/information systems theory; Presence pedagogy model; Eisenkraft's 7E instructional Model; Felder-Silverman learning style model; Unified Modelling Language; Fogg's behavior model; Merrill's first principles of instruction design theory; Landers' theory of gamified learning; Social development theory: zone of proximal development and scaffolding; Self-efficacy theory; Constructivist learning theory; Technology-enhanced training effectiveness model
Kalogiannakis et al., 2021 self-determination theory; flow theory; goal-setting theory; cognitive theory of multimedia learning; motivation theory to learn
Osatuyi et al., 2018 Self-determination theory; Flow theory; Situated learning theory; Experiential learning theory; Uses andgratifications theory; Zone of proximal development; Achievement goal theory; Activity theory; Andragogy theory of adult learning; Cognitive evaluation theory; Cognitive load theory; Constructivist theories of learning; Grounded theory; social capital theory; Social cognitive theory; Social exchange theory; The frame model; The organismic integration theory (OIT); The SNAP: model of motivation; Trans-theoretical model of behavior change (TTM)
Van Gaalen et al., 2021 Experiential Learning Theory; Reinforcement Learning Theory; Social Comparison Theory; Self-Directed Learning; Deliberate Practice Theory
As the findings in Table 9 shows, the two theories of Self-determination theory and Flow theory in three studies and four theories, each in two studies have been reported as the most frequent theories.
Discussion
This study is a scientometrics, systematic, and co-occurrence analyses of systematic review and meta-analysis articles in the field of gamified education. According to or findings, 7 out of 24 articles were systematic reviews and the rest were meta-analyses with publication dates from 2000 to 2020.
Co-occurrence analysis of words indicated that motivation, learning and engagement are the most important concepts studied in articles in the field of gamified education. The results of a study showed that performance, participation, attitude, motivation, pleasure, perceived learning, satisfaction, practical skills, and increased learner competition are some benefits observed in studies related to gamification in education (Subhash & Cudney, 2018). In fact, learning engagement and motivation, learning achievement, interaction and social connection are some effects of these kinds of intervention. Gamified tests at the beginning and the end of each class increase learners' mastery of lesson content and engagement during class activities, as well as improve their cognitive, emotional, and behavioral engagement (Zainuddin et al., 2020). Gamification can be directly related to increasing learners' learning performance. However, some studies reflect weaker statistical differences between on-game and off-game environments (Ortiz-Rojas et al., 2017). The results of the studies indicate that in some gamified educational interventions, no improvement was observed in final exam scores, but perceived learning was widely concluded as a positive effect of gamification learning. Improving learners' performance in presenting higher quality projects, improving learning outcomes, reducing failure rates and higher average scores are also observed in game-based learning groups (Subhash & Cudney, 2018). The results of another study also showed that the level of participation had a higher effect size than the test score. Therefore, gamification has a greater effect on the level of learners' participation than the test score. Increasing the level of participation can develop learning skills and academic achievement. Thus, educators are expected to improve learners' participation levels using gamification strategies (Kim & Castelli, 2021).
In terms of content analysis, researchers extracted 7 fundamental categories. In the following, we have discussed every category.
Country/territory
Because of cultural differences in every country, learners' attitude and expectations might be different about learning via gamification (Subhash & Cudney, 2018). In addition, based on educational subjects, learners in different countries have different tendencies to gamified learning. For instance, in the field of higher education, Spain is the first country in regard with the highest number of studies in gamified learning and United States, Germany, and the United Kingdom are in the next ranks, respectively (Ortiz-Rojas et al., 2017). To measure the student learning outcomes, East Asia with 15 and Western Asia with 5 articles are in the first and second ranks (Bai et al., 2020). About the effect of gamification on academic success in students, both USA and Turkey (9 articles) and Spain (4 articles) have the most studies (Yıldırım & Şen, 2019). A systematic review by Gaalenet al. showed that in the field of medical education, the majority of studies were conducted in the USA and Canada (Van Gaalen et al., 2021).However, in terms of the use of gamification in collaborative learning, Spain had had conducted the highest number of studies (Dos Santos et al., 2020). European countries are pioneers in research on the application of gamification in Science, Technology, Engineering and Mathematics (STEM), followed by America, Asia and Africa, respectively (Ortiz et al., 2016). Moreover, researchers in countries such as Spain, Hong Kong and Turkey have shown great interest in gamified learning methods in measuring learners' learning performance in this field (Bai et al., 2019). Flipped learning is another field that has attracted gamification. While the studies in this field have been conducted in 12 different countries, Spanish researchers have the first rank and more than half of studies have been conducted in European countries (Ekici, 2021). The investigation of studies in the field of gamified education in information systems (IS) showed that Americans,Australians and German' researchers published 53, 44 and 22 articles, in turn (Osatuyi et al., 2018). Eventually, in the gamified nursing education, the United States and Canada have the highest number of publications (6 and 4, respectively) (Malicki et al., 2020).
Duration
An important parameter in gamified learning is how long the intervention s has taken place. In fact, this is important whether the effects of gamification last long time or not (Dichev & Dicheva, 2017; Hamari et al., 2014; Seaborn & Fels, 2015). Because duration of gamified course is considered as a potential modifier of effects on the results of cognitive, motivational and behavioral learning. However, there are conflicting findings in this regard. According to Wouters et al. (2013) when the participants participate in several sessions and play for longer period of time, the effects of games are greater, while findings of Kim and Castelli (2021) about the effect of gamification on behavioral change showed that gamified interventions lasting some days is more effective than those lasting one or two years. Thus, the studies recommend the short courses rather than longer ones in gamified learning. Another analysis carried out by Sailer and Homner indicated that both long and short-term interventions are useful in cognitive and behavioral learning. However, the interventions that lasted for half a year or less (but more than 1 month) have a moderate effect on motivational learning outcomes, while the effectiveness of interventions of one-day courses or less were negligible. In fact, for motivational outcomes, it may even take longer time to affect motivation. However, this does not lead to any conclusions about the durability of the effects obtained (Sailer & Homner, 2020). Based on Ekici (2021), the duration of gamification used in learning is up to 4 months. Hanus and Fox's findings highlights the negative effect of long-term gamification courses on intrinsic motivation, academic achievement and satisfaction while many studies have been conducted in a relatively short period of time (less than four months) (Hanus & Fox, 2015). For example, Hung (2017) used Kahoot for gamifying their education course in the control group, and the results showed that it was effective on increasing motivation and academic achievement in the short term. In another study, Chen and Hwang (2019) used Kahoot for only six weeks.
Game elements
There are three important issues with game elements including the type of element, the number of elements and the type of combination of game elements.
Type of used element
Our findings show that the game elements used in learning interventions do not have the same effects on learners' learning. For example, Huang et al. (2020) found that using a timed activity element produces a smaller effect size than other elements, while environments that do not use this element have a larger effect size. The same is true about the leaderboard element, though the difference is not significant. Even some studies showed that the most controversial element is the leaderboard that may harm learners' motivation when they are doing an explicit competition. Howeverr, in almost all articles, leaderboards were introduced as the most attractive elements of the game (Zainuddin et al., 2020). One study showed that the main game elements used in learning are points, medals, rankings, and narratives (Manzano-León et al., 2021). Nevertheless, a large number of studies showed that the most frequent elements are points, badges, and leaderboard (Ekici, 2021; Indriasari et al., 2020; Subhash & Cudney, 2018; Tenório et al., 2018; Zainuddin et al., 2020). On the other hand, few studies have reported quest, virtual goods or gifts as the game elements (Indriasari et al., 2020).Collaboration was also one of the most common game elements used in the form of teammates and discussion boards.The results of Subhash and Cudney' study illustrated that points, badges, leaderboard, levels, feedback, and graphics, as the most important game elements, are suitable for higher education environments (Subhash & Cudney, 2018).
Number of elements
There are also challenges regarding the number of elements used in gamification. Ekici's findings showed that most studies used more than one game element (Ekici, 2021) because when only one or two game elements such as points or badges are used in educational interventions, the effects on students' motivation are becoming less or even negative. Manzano-León et al.'s research reinforces the idea that a diverse gaming environment is more motivational and can meet the needs of its players according to their characteristics, a result consistent with that of the Kocadere and Çaglar (Manzano-León et al., 2021).According to Indriasari et al., 62% of studies used a combination of game elements, while only 15 studies reported using only one element (Indriasari et al., 2020). However, Manzano-León et al. (2021) showed that there was no significant difference between the effects of interventions that used more elements of the game with other interventions. Based on their findings, in different interventions, four elements, three elements, two elements, one element and six game elements have been used, respectively.
Combination of game elements
One of the important issues is the combination of game elements. While the greatest effect size for the gamification design feature was observed in the use of quests/missions/modules in the interventions (Huang et al., 2020), the results of several studies revealed that in most of the gamified educational interventions, the combination of badges+leaderboards+points is often used (Ekici, 2021; Kalogiannakis et al., 2021; Ortiz-Rojas et al., 2017). Bai et al. (2019) indicated that in some studies, the most used combination include badges, leaderboard/rank, level/unlock and points, followed by badges, leaderboard/rank, and points. The ranking of the impact of elements in terms of effect size alone or in combination with other elements in Huanget al.'s study is as follows: Quests/missions/modules, Collaboration, Avatars/customization, Adaptivity/personalization, Non-linear navigation, Responsive feedback, Advancement/levels, Narrative/storytelling, Points/experience, Badges/awards, Competition, Leaderboards, Performance graphs, Timed activity (Huang et al., 2020).
Participants (field of study and grade)
Participants’ field of study
The study by Bai et al. (2020) showed that there is no significant difference between gamification in different fields of study. They found that the effect size was not affected by student'gradelevel of education (e.g., elementary, high school and college students) and subject disciplines (e.g., computer and information science, math, science). However, various studies indicated that in some areas of science, gamification is more highlighted. The study by Huang et al. (2020) illustrated that most of the fields in which gamification was introduced were "social sciences" and "engineering and computer", both of which had a statistically significant effect size. In contrast, in subject areas such as "arts and humanities" with the effect size, was not statistically significant. Subject areas such as Math, Health care, and Business used less gamification in their educational settings. However, the study by Indriasari et al. indicated that most types of the gamification was applied in the fields of Physical sciences, Mathematics and Computer science, Engineering and Education. In addition, Science, Technology, Engineering, and Math (STEM) are disciplines in which most of the peer-to-peer review activities (Indriasari et al., 2020) and flipped gamification learning (Ekici, 2021) were reported. Subhash and Cudney also showed that 14 out of the 37 studies in the field are computing (Kim & Castelli, 2021).
Participants’ degree
In terms of participants, the results are somewhat contradictory. According to a study by Bai et al. (2020) the effect size in the high school environment was significantly larger than those in undergraduate and graduate levels. Their findings showed that the effect size was not affected by the participants' degree. Even Yıldırım and Şen (2019) showed that the effect of gamification on students' progress in different degrees of education was not different. Bai et al. (2020) showed that most of the studies were conducted with undergraduate students with the highest effect size and K-12 students were in the next rank but there was no significant difference. Yet, it is not surprising to see more studies on undergraduate education because they are more accessible for researchers in their institutions. However, the results of Bai et al. showed that the effect size of undergraduate students is almost twice as much as that of K-12 students. Zainuddin et al. (2020) also illustrated that most of the articles studied were related to adult learners or higher education students. The results of the study by Indriasari et al. (2020) also confirmed that a small number of studies were related to high school and primary school, and most of the articles were conducted in the university level, even in the studies of flipped education (Ekici, 2021). Perhaps, this is why, despite the results of some studies (Bai et al., 2020; Sailer & Homner, 2020; Yıldırım & Şen, 2019), gamification is not statistically significant in high school level (Yıldırım & Şen, 2019) and the effects of gamified interventions are much more effective for adults than K-12 and college students. In fact, it is possible that younger people and adults are more interested in the gamified factors in education than the age groups of college students. Because adults showed the highest participation rate compared to college students and K-12 students (Kim & Castelli, 2021). Only Sailer and Homner's study showed that gamified cognitive learning in school was better than other educational environments (Sailer & Homner, 2020). What is certain is while the majority of the research were related to students, a small number of studies in this field have provided solutions that are directly aimed at teachers (Garcia et al., 2020).More information about study field of the intervention and the participants’ level of education is shown in Table 6.
Gamification platforms
The platforms and applications used in gamified learning research are other considerable issues. The results of one of these studies by Zainuddin et al (2020) showed that most of the articles used existing platforms from different sources such as ClassDojo and ClassBadges, Ribbonhero of Microsoft Rain classroom, Quizbot, Duolingo, Kahoot and Quizizz, Math Widgets, Google + CommunitiesiSpring Learn LMS. The most common of these is Kahoot (Kalogiannakis et al., 2021). The integration of game elements in the Learning Management System (LMS) is also used. For example, by integrating gamification using Web 2 tools, new functions are created for MOOCs (Coursera, Udacity, and edX), wiki and moodle platforms, and enterprise learning management systems (Aparicio et al., 2019; Huang et al., 2019; B. Huang & Hew, 2018; Jurgelaitis et al., 2019; Özdener, 2018). In addition, the National Budget Forecasting project is another platform used (Buckley & Doyle, 2017). Some researchers have developed their game development platforms to prioritize user-centric needs and help to provide an effective online experience for a diverse range of users. Their goal is to improve the performance and participation of inclusive learning (8, 9) and to participate in online discussions using the tools of the game (Bouchrika et al., 2019; Ding, 2019; Ding et al., 2017, 2018). In terms of applying gamification in teaching English, different types of digital learning environments such as WordBricks, Duolingo, Kahoot, Babbel, Jeopardy, ClassDojo, Lifeline, Feelbot, Brainscap have been used to play LESL (Dehghanzadeh et al., 2019). The predominant environment/tool of gamification in flipped game education research are learning management systems such as Moodle, iSpring LMS, Blackboard, The Minimum Learning Judgment System, and Khan Academy LMS. Moodle was used in 9 studies and Kahoot in 7 studies, which were ranked first and second (Ekici, 2021).
Theories in gamified learning
The theories used in the design of gamified educational environments are the other important element. Self-determination and Flow theories (Kalogiannakis et al., 2021; Osatuyi et al., 2018) are the most frequent theories used in gamified studies. These two theories have been widely used in gamified studies of educational environments (Zainuddin et al., 2020). According to Kalogiannakis et al. (2021) that conducted a systematic review of articles related to gamification in science education, most of the articles included in the systematic review had no theoretical basis. Of the 24 studies reviewed, only six articles implicitly stated their theoretical framework, which self-determination theory is one of the most comprehensive and significant one. The results of a study by Osatuyi et al. (2018) showed that only 17 out of 41 existing articles were theoretically based. The results of this study showed that the following theories were dominant among the theoretical frameworks used in game development research:Social theories (such as theories that support psychological processes such as social exchange theory, social capital theory, social cognitive theory);
Cognitive theories (such as cognitive evaluation theory, cognitive load theory, Kolb’s experiential learning theory, Lave’s situated learning theory, andconstructivist theories of learning);
Behavioral theories (such as self-determination theory (SDT) and flow theory).
Conclusion
The results showed that 344 articles in the field of gamified learning and education were reviewed in 25 systematic review and meta-analysis articles, most of which were in English. Therefore, there is a lack of systematic review research for articles in other languages. Content analysis showed that these 25 articles can be categorized in 7 categories based on the most important elements in the field of gamification and learning, including country/territory, duration of intervention, lessons/content, the number of learners, platforms, the game elements, theories. Based on results, all these items were not analyzed in all 25 articles. Therefore, it is suggested that these seven items be considered in subsequent systematic reviews studies and meta-analyses. In addition, the results showed that most of these studies have implemented gamification in online learning environments. There is a need for more research to gamify face-to-face classes.
On the other hand, most of the review articles were in the field of "social sciences" or "engineering and computer". It is suggested that studies be conducted to examine interventions in other disciplines and courses. The results showed that in most studies, due to time and cost issues, they preferred to use existing platforms and LMS.
The results of some studies showed that educational interventions were effective in promoting learning, motivation and participation of learners, but in most of these studies, the definite effect of gamification was not mentioned and among their research suggestions, the need for further studies was suggested. The results of some studies also reflected weaker statistical differences between gamified and non-gamified environments. Therefore, it is suggested that the higher quality studies (two groups with pre-test and post-test) be performed to determine the effect of gamification on variables. Finally, due to the inconsistency of the results of these studies, it is suggested that systematic review and meta-analysis studies focusing on the seven variables proposed in the present study.
Author contributions
Author 1: Contributed to conception, design, data acquisition and interpretation, performed all analyses, drafted and critically revised the manuscript.
Author 2: Contributed to conception, design, data acquisition and interpretation, performed all analyses, drafted and critically revised the manuscript.
Funding
Funding for this project was provided by the by Kashan university of medical sciences, Kashan, Iran, with No. 400062 (IR.KAUMS.NUHEPM.REC.1400.032).
Declarations
Conflict of interest
The authors declare that there is no conflict of interest.
Ethical standards
This research did not involve Human Subjects.
All authors gave their final approval and agree to be accountable for all aspects of the work.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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PMC009xxxxxx/PMC9002047.txt |
==== Front
Econ Change Restruct
Economic Change and Restructuring
1573-9414
1574-0277
Springer US New York
9396
10.1007/s10644-022-09396-2
Article
Does China’s green economic recovery generate a spatial convergence trend: an explanation using agglomeration effects and fiscal instruments
Kong Qunxi 1
Li Rongrong 1
Ni Y. 1
Peng Dan 787450198@qq.com
2
1 grid.440844.8 0000 0000 8848 7239 School of Industrial Development, Nanjing University of Finance and Economics, Nanjing, 210003 China
2 grid.41156.37 0000 0001 2314 964X School of Business, Nanjing University, Nanjing, 210093 China
12 4 2022
128
6 6 2021
9 3 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, 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.
China’s urbanization process has entered a period of rapid development, and cities have become key to driving regional economic development. This paper uses data from 286 cities in China in the period 2005–2018 to construct an urban economic growth quality index system and examine the influence of spatial factors on the convergence trend of China’s urban economic growth quality. It is found that there is a β absolute convergence trend of economic growth quality in Chinese cities across the whole country. After controlling for the initial conditions of individual economies, spatial factors strengthen the spatial convergence trend of urban economic growth quality and significantly increase the corresponding convergence rate. Among the areas studied, the western region has the fastest convergence rate, followed by the central and eastern regions, and the convergence rates of both the central and western regions are higher than the national average. Agglomeration economies and fiscal policy tools are important for the promotion of the urban economic growth quality. The agglomeration of productive service industries significantly improves the spatial convergence rate of urban economic growth quality. This effect is mainly due to the spatial spillover of industrial agglomeration. The expansion of government fiscal expenditure also contributes to the spatial convergence trend of urban economic growth quality. Local economic growth quality is also affected by government fiscal expenditure in neighboring cities.
Keywords
Green economic growth
Spatial convergence
Spatial spillover
Agglomeration economy
Fiscal expenditure
http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 71303105 Kong Qunxi http://dx.doi.org/10.13039/501100012325 National Office for Philosophy and Social Sciences 19FJYB039 Kong Qunxi http://dx.doi.org/10.13039/501100007166 Jiangsu Provincial Department of Education KYCX21_1430 Li Rongrong
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pmcIntroduction
Over the past 40 years since China's reform and opening up, its economic development has achieved remarkable results that have caught the world's attention. A period of high economic growth has emerged under the impetus of the globalized economy, especially since China became a member of the World Trade Organization (WTO) in 2001. However, behind this unprecedented growth trend is the factor-input type of ‘sloppy growth’. This process has brought about many hidden dangers, including the depletion of local resources, economic inefficiency, increased environmental pollution, economic structural imbalance, and unbalanced economic development, among others (Chen and Chen 2018; Zhang 2021). As China’s economy moves into a ‘new normal’, the past high-input, high-consumption resource-input-based approach to economic growth is no longer sustainable. The internal and external conditions that supported past economic growth have changed, and China’s economic development requires a change to its approach to growth (Zhao and Ge 2019; Zhang and Kong 2022).
High-quality development has become the main feature of contemporary China’s society and economy. It can be said that the relationship and focus between the quantity of economic growth and the quality of economic growth have begun to change, and the quality of economic growth has become a critical factor in determining economic development (Zhang 2022). At the same time, in a stage during which the quantity of economic growth in China is rapidly increasing, differences in the level of economic development between different regions are subsequently expanding due to differences in natural endowments, policy tilts, etc. There is obvious heterogeneity in both the quality of economic development and the growth rate, with both factors being higher in the eastern regions and lower in the western regions (Su and Guo 2021; Kong et al. 2021b; Wong et al. 2021a). Therefore, reducing regional disparity cannot be ignored when encouraging high-quality economic growth. The spatial correlation of regional economic growth indicates that the economic growth process of a region is closely related to the regions surrounding it. Spatial proximity, spatial accessibility, and knowledge spillover between regions are important mechanisms for convergence in economic growth clubs (Bin et al. 2016; Zhang et al. 2020).
The relationship between the urbanization process and the quality of economic growth is extremely close, and urban economic growth is a highly important component of China’s economic growth (Kong et al. 2021a; Zhang et al. 2021). China has also entered into a phase of rapid urbanization. According to statistics, China’s urbanization rate has increased by 50 percentage points from 1949 to the present and has exceeded 60% in 2019.1 As the core space and important carrier of regional economic development, the economic growth of China's cities is closely linked to the nation’s overall socioeconomic growth. It can be said that cities bear the burden of economic development, and urban economic growth is an important component of China’s economic growth (Jiang and Yang 2020; Kong et al. 2021c; Wong et al. 2021b). Improvements in the quality of China’s economic growth must therefore be specifically reflected in the quality of economic growth in cities. This begs the question, how can the quality of urban economic growth be understood? How can it be measured? Furthermore, how has the quality of China’s urban economic growth evolved over time? What kind of spatial dependence and heterogeneity do territorial units have at the microscopic scale? What development trends are likely to emerge in the future? And considering that the agglomeration economy is the source and driver of urban expansion and economic growth (Sun et al. 2015), what will the trend of urban economic growth influenced by the agglomeration economy be?
This paper intends to answer these questions by taking the following three approaches. First, this paper takes 284 Chinese cities of prefecture level and above as research objects and measures the quality of urban economic growth in these cities in four dimensions: economic growth dynamics, structure, mode, and outcome. On this basis, we attempt to generalize their spatial and temporal evolution characteristics to determine the spatial convergence trend of economic growth quality in Chinese cities. The results of this study can help to clarify the future trend of urban economic development quality and to objectively understand the source of the gap in China’s urban economic development quality; this study can also help to deepen understanding of the current situation of regional economies and provide a reference for future urban economic development quality.
Second, this study will identify the key factors affecting the spatial convergence of urban economic growth quality and their degrees of influence. Since this paper takes cities as the object of investigation, and considering that urban economic growth has distinctive spatial aggregation characteristics compared with traditional economic growth, it is necessary to focus on distinguishing urban economic growth from traditional economic growth in the study of urban economic growth quality. Since 1978, spatial shift and agglomeration of industries have occurred in China, and widening regional economic disparity in China is closely related to this phenomenon. Especially after 1995, industrial agglomeration has become the dominant factor affecting regional disparity and industrial structure (Geo 2020). Therefore, the second problem that this paper aims to solve is the identification of whether the effect of agglomeration economy on the spatial convergence of urban economic growth quality is a result of specialized agglomeration or diversified agglomeration.
Third, the balanced development of regional economic growth is a key concern of government macro-control, and fiscal policy plays an active role in coordinating the balanced development of regional economies. The reasonable selection and application of fiscal policy tools can effectively optimize the supply structure, transform growth dynamics, and promote the synergistic development of heterogeneous regional spaces (Bian et al. 2019; Zhang and Yang 2021). Therefore, this paper further introduces fiscal policy tools, which can be used to analyze the factors influencing the spatial convergence of urban economic growth quality, and examines the changes in impact brought about by changes in the scale of fiscal expenditure. The study’s results are used to analyze the relationship and path of influence between the regional economic growth gap, the spatial convergence trend, and fiscal policy tools in China in order to provide reliable empirical evidence for policy makers.
Literature review
The study of spatial convergence of economic growth quality can be based on the relationship between economic growth and convergence (Lee and Yu 2012; Song et al. 2020). Early mainstream studies of economic convergence theory are mainly situated within the fields of neoclassical growth theory and endogenous growth theory. Early neoclassical economics assumed technology invariance as a premise, but neoclassical growth theory achieved a theoretical breakthrough by putting forward a theory of technological progress, arguing that total factor productivity has a role in economic growth and revealing the process by which economic growth converges to a long-term steady state (Solow 1956). However, due to the assumption of exogenous technology in neoclassical theory, it is difficult for it to explain differences in economic growth quality in the long run. Endogenous growth theory thus proposes the core concept of endogenous technology. It assumes that the factors involved have diminishing marginal returns and that the endogenous driving force of continuous technological progress lies in the exogenous accumulation of knowledge; this formulation better explains the external fact of differences in growth rates in the long run. In addition to these two mainstream theories, most scholars have combined new economic geography with the spatial convergence of economic growth quality. New economic geography assumes that individual economies operate with the same initial conditions and economic structure, and explores endogenous evolutionary divergence in the spatial dimension. Research in new economic geography finds that the systemic endogenous forces of two homogeneous individual economies, independent of other external influences, can contribute to regional evolutionary divergence, industrial agglomeration, and even the formation of core–edge structures. This finding has important implications for studying the spatial attributes of regional economic growth, which affect club convergence (Borsi and Metiu 2015).
The existence of convergence in neoclassical growth theory has been supported empirically by domestic and international scholars using country and regional samples. Initially, Baumol (1986), basing their work on Maddison’s (1982) data analysis, obtained a more pronounced productivity convergence in sample countries during the period 1870–1979, and Summers and Heston (1984) used output per capita data to find similar results. De Long (1988) argues against this. His study obtained σ convergence, indicating that the type of convergence is controversial, but convergence is unquestionable. Following these initial studies, studies on σ convergence have increased in number (Chen and Fleisher 1996; Jian et al. 1996). Sala-i-Martin (2002) has reconfirmed the relevance of neoclassical growth theory by using different country samples over different examination periods. Based on the availability of country-specific data and the consideration of excessive sample variation, scholars began to focus on the convergence of regional economic differences within countries. For example, Barro et al. (1992) used data from 48 US states. Based on the absolute β convergence model, they concluded that there was significant absolute convergence in per capita income or per capita output in each state. In 1995, Barro applied the same method to study convergence in the US, Japanese, and European regions and again confirmed the existence of absolute convergence. However, Young et al. (2008) conducted a distribution function analysis using 1970 and 1998 US county-level cross-sectional data and found that both counties and states displayed a single-peaked distribution; that is, growth is parallel between counties and between states and there is no σ convergence. Caggiano and Leonida (2009) find that economic growth is conditionally convergent in OECD countries. Moreover, Caggiano and Leonida (2013) argue that the presence of clustering and/or polarization may be a common problem in regional models, but do not exclude the existence of absolute convergence.
In the 1970s, Weeks and Yao (2003) found large differences between China’s regional economies, but these differences narrowed after the agricultural reform. When industrialization became widespread in different regions of China in the 1990s, economic disparity between regions reemerged as a result of the different levels of industrialization in different regions. Due to the advantage of endowment combined with policy inclination, the economic development of the eastern region was far in advance of other regions. The inter-regional economic growth rate was clearly higher in the east and lower in the west, and this gap continues to widen (Démurger et al. 2002). However, club convergence and conditional β convergence are evident within the eastern region (Shen and Ma 2002).
Furthermore, to investigate whether there a trend of convergence was evident at the region-wide level in China, Lin and Liu (2003) used economic growth data from 1978 to 1999 for different Chinese provinces, and Xu and Li (2004) produced the first analysis of economic convergence in China using urban data. Both these studies found that there was indeed a trend of convergence. Jiang (2012), on the basis of the Solow growth model, found that labor productivity converges rapidly and conditionally across Chinese provinces. Wang et al. (2013) used non-stationary factor analysis to find that the conditional convergence of China's regional economies is extremely weak. Using a static spatial panel data model, Chen et al. (2018) found absolute and conditional convergence in urban economic growth in China, while Sun and Cao (2018) found that club convergence can be observed in China's urban economy through a nonlinear time-varying factor model study.
With the development of spatial economics, the spatial convergence of economic growth has received wide attention. The foreign scholars Seya et al. (2012), and Ahmad and Hall (2017) used spatial error and spatial Durbin model empirical analysis to examine spatial convergence. The Chinese scholar Ying (2000) was the first to study the spatial correlation of economic growth. Following Ying, many scholars such as Pan (2010), and Huang and Yuan (2014) explored the spatial convergence of China’s economy at the provincial level, the city level and the three major regional levels of east, central and west using spatial econometric models based on the consideration of geographical factors. Their findings all suggest the existence of conditional β convergence or spatial club convergence in China, but do not support the existence of absolute convergence. Following the significant impact on global economic growth of the outbreak of COVID-19 in 2020 (Nandan and Mallick 2021), scholars have initiated a new discussion on the spatial convergence of economic growth. Wang et al. (2020) established a spatial Durbin model to empirically test the spatial convergence of economic growth between provinces in China. They found that economic growth between provinces in China is congruent with the law of conditional convergence. Cartone et al. (2021) studied differences in the determinants of economic growth between 187 regions of 12 European countries and used spatial quantile regression methods to find differences in the rates of conditional convergence of investment, population growth, and human capital in these European regions.
In conclusion, research on the spatial convergence of economic growth quality can be divided into general economic growth convergence theory and spatial economic growth convergence theory. General economic growth convergence theory mainly revolves around neoclassical growth theory and endogenous growth theory. Neoclassical theory assumes that technology is exogenous, which can account for the convergence of economic growth to a steady state in the long run, to a certain extent. However, its assumption of technological exogeneity can hardly explain differences in economic growth quality in the long run. The endogenous growth model, which treats the dynamics of economic growth as resulting from technological progress, solves this difficulty precisely. With the rise of spatial economics, geographic factors have been given full attention in the study of economic growth convergence. The theory of spatial convergence of economic growth, with new economic geography theory as its core, was thus born. This theory holds that individual economies interact spatially, resulting in spillover effects in economic development.
Analysis of regional differences in the green economic growth of Chinese cities
Indicator system establishment
Drawing on the research of the scholar Guo et al. (2020) and combining the characteristics of urban economy and data availability, this section selects four dimensions (economic growth dynamics, structure, mode, and outcome) to construct an indicator system for urban economic growth quality. The index system includes 9 secondary indicators and 18 tertiary indicators. The specific content of the indicators is shown in Table 1.Table 1 Index system of economic growth quality of Chinese cities
Target Primary indicators Secondary indicators Tertiary indicators Attributes
Quality of economic growth Economic growth dynamics Technological progress Number of inventions acquired +
Human capital Local financial expenditure on education +
Number of higher education schools +
Economic growth structure Industrial structure The proportion of tertiary industry to GDP +
Fixed asset investment as a proportion of GDP +
Trade structure Total imports and exports as a proportion of GDP +
Share of foreign direct investment in GDP +
Economic growth mode Resource conservation Electricity consumption of 10,000 Yuan GDP −
Water consumption of 10,000 Yuan GDP −
Environmental protection Green space coverage rate of built-up areas +
Harmless treatment rate of domestic waste +
Economic growth results Economic development GDP per capita +
Fiscal revenue as a proportion of GDP +
Urban registered unemployment rate −
Public services Books in public libraries per 100 people +
Social security Basic pension insurance coverage rate +
Basic medical insurance coverage rate +
Number of hospitals and health centers per 10,000 people +
Compiled by the author
Measurement process of urban economic growth quality index
In this paper, the entropy weighting and Delphi methods are applied to establish a comprehensive urban economic growth quality index. The entropy method of assigning weights is based on the amount of information reflected by the level of data evolution, which can objectively reflect the importance of each indicator in the evaluation system as a whole. This can reduce the influence of human subjective factors on the indicator weights. The Delphi method is an expert opinion survey method. Combining the advantages of subjective assignment and objective assignment, this paper assigns equal weights to indicators of the four dimensions of economic growth dynamics, structure, mode and result: each dimension accounts for 25%. The specific measurement process is as follows. Standardization. The indicators' units and orders of magnitude are very different, and direct calculation will cause large errors, so the original data need to be invariant.1 xij′=xij-minxijmaxxij-minxijPositiveindicators
2 xij′=maxxij-xijmaxxij-minxijNegativeindicators
In Eqs. (1) and (3), i represents the city, j represents the measured index, xij represents the value of the j-th index of the i-th city, and xij′ is the result after data normalization.
Calculate the share of the i-th city under the j-th indicator.3 Sij=xij∑i=1nxij
Calculation of the entropy value of the j-th indicator.4 ej=-k∑i=1nsijlnsij
where k=1lnn, ej≥0 and satisfies ej≥0.
Calculation of information entropy redundancy.5 d=1-ej
Calculation of the weights of each indicator.6 wj=dj∑j=1mdj
Measurement of the composite index of economic growth quality. This formula is calculated through a multiple linear weighting function, as follows.7 QEGi=∑j=1mwj×sij
In Eq. 7, QEGi represents the economic growth quality of city i, QEG⊆0,1. The larger the QEGi index, the higher the economic growth quality of the city. Conversely, the smaller the QEGi index, the lower the economic growth quality of the city.
Measurement results and analysis
Based on the economic growth quality measured in the previous section, this section examines overall differences in the economic growth quality of 284 cities in China in terms of mean, standard deviation, minimum, median, and maximum values. The statistical information of the basic data is shown in Table 2. On this basis, the trend of growth rate of each indicator relative to 2005 was plotted, as shown in Fig. 1.Table 2 Descriptive statistics of economic growth quality in Chinese cities
Year Mean Std Min Median Max
2005 0.2609 0.0730 0.1492 0.2559 0.5113
2006 0.3008 0.0690 0.1794 0.2942 0.5488
2007 0.2934 0.0639 0.1812 0.2893 0.5427
2008 0.2550 0.0511 0.1747 0.2403 0.4989
2009 0.2970 0.0615 0.1831 0.2889 0.5629
2010 0.2913 0.0525 0.1915 0.2824 0.5391
2011 0.2700 0.0490 0.2002 0.2571 0.5235
2012 0.3199 0.0533 0.2288 0.3085 0.5647
2013 0.3199 0.0543 0.2158 0.3116 0.6017
2014 0.3052 0.0526 0.2124 0.2893 0.5820
2015 0.3272 0.0518 0.1695 0.3191 0.5617
2016 0.3546 0.0534 0.2176 0.3452 0.6076
2017 0.3186 0.0377 0.1779 0.3126 0.5137
2018 0.2993 0.0426 0.1816 0.2951 0.5240
Compiled by the author
Fig. 1 Growth rate of descriptive statistics
In general, China’s urban economy shows a fluctuating upward trend. Specifically, average urban economic growth quality rose from 0.2609 in 2005 to 0.2993 in 2018, an increase of 14.71%, implying an overall improvement in the quality of China's urban economic growth. A more pronounced trough of fluctuation occurred in 2008, when urban economic growth quality dropped to 0.2550, a 2.26% decrease compared to 2005. The main reason for this trough is that the global systemic financial crisis affected the economic system in 2008, leading to a rapid economic decline and a consequent drop in the quality of economic growth. However, with the help of the ‘four trillion’ investment plan, China's real economy recovered rapidly. At this time, investment differed greatly between different cities, leading to another divergence in the development of each city's economy, and the differences between cities began to expand again. The growth curve of standard deviation shown in Fig. 1 always lies below the zero level and shows a fluctuating decline. It shrank from 0.0730 in 2005 to 0.0426 in 2018, an overall reduction of 41.65%, indicating a trend of narrowing differences in the quality of urban economic growth. The minimum value gradually increased from 0.1492 in 2005 to 0.1816 in 2018, while the maximum value expanded from 0.5113 in 2005 to 0.5240 in 2018. The average value across all years is higher than the median value of the maximum and minimum values, indicating that the difference in urban economic growth quality had a narrowing trend and balanced development.
Next, we used nonparametric kernel function estimation to empirically analyze urban economic growth quality distribution characteristics over the years. In this section, representative years at three-year intervals (2005, 2008, 2011, 2014, and 2017) were selected to plot the dynamic evolution of nonparametric estimations of urban economic growth quality in Fig. 2.Fig. 2 Evolution of the distribution of economic growth quality levels in Chinese cities
Overall, the basic shape of the size distribution curve of urban economic growth quality is consistent over the years. China shows a single-peaked state in the evolution of urban economic growth quality distribution during 2005–2018. There is a significant narrowing of the wave width from 2005 onward. The wave width in 2017 is the narrowest among the representative years, which to some extent indicates a narrowing trend in absolute differences in the quality of urban economic growth. From the evolution of the crests, urban economic growth quality moves to the right and the height of the main crest increases over time, with the crest reaching its highest point in 2017. This indicates that the quality of urban economic growth has improved, which is consistent with the findings of previous studies.
According to the distribution characteristics of the density curve, cities with low levels of development of economic growth quality occupied a large proportion of this distribution before 2011 and gradually transitioned to medium–high levels of economic growth quality after 2011. In the early period, China's economy pursued an increase in total economic volume by extensive and rough economic growth, ignoring the problems of environmental pollution, resource depletion, low economic efficiency and structural imbalance (Chen and Chen 2018). As China's economic growth enters a ‘new normal’, the problem of non-synchronization of the quality of economic growth and the quantity of economic growth is gradually being exposed. A series of key measures for high-quality development emphasize quality as the core of development, and the Chinese economy is gradually entering a new stage in which quality is given primacy. Results at the city level also show that China's urbanization level has been developing rapidly since 2011, indicating a highly agglomerated stage of economic development.
Research design
Model setup and data description
As a first step, a general convergence model was constructed. β convergence is an important means for examining economic convergence among regions, and σ convergence among regions will only hold if β convergence exists among regions. Therefore, the general convergence model used here takes β convergence as the initial measurement model. An absolute β convergence model of economic growth quality was constructed.8 dlnQEGit=lnQEGit-lnQEGit-1=α+βlnQEGit-1+εit
The economic growth quality condition β convergence model is based on absolute β convergence controlling for the initial characteristics of economic individuals, i.e., the absolute β convergence is based on the introduction of control variables.9 dlnQEGit=lnQEGit-lnQEGit-1=α+βlnQEGit-1+γXit+εit
where εit∼iidN0,σ2, QEGit denotes the economic growth quality of city i in year t, and QEGit-1 denotes the economic growth quality of city i in year t-1. Xit in Eq. 9 is the set of control variables. If the coefficient β is less than 0 and statistically significant, this means that there is absolute β convergence and conditional β convergence in the quality of urban economic growth, which eventually converges to the steady-state γ0. The convergence steady-state value γ0=α1-β, the convergence rate θ=-ln1+βt, and the convergence half-life cycle τ=ln2θ can be calculated from the estimated value of the convergence coefficient β.
In the second step, a spatial convergence model was constructed. Cities are divided into artificial administrative divisions, cities are open in spatial scope, and there are economic interactions between different cities. In addition, the spatial correlation pattern of economic growth quality and the boundaries between cities may not be uniform, generating neighborhood measurement errors. The statistics in the research process are related to the sample space, and subsequently, the quality of economic growth in different cities may be affected by spatial correlation. If the spatial factor is ignored, large errors may result, so a spatial lag model (SAR) and a spatial error model (SEM) were constructed. The specific expressions of these models are given in Eqs. (10) and (11).10 dlnQEGit=lnQEGit-lnQEGit-1αS+ρWnlnQEGit-lnQEGit-1+βlnQEGit-1+γXit+μit
11 dlnQEGit=lnQEGit-lnQEGit-1αS++βQEGit-1+γXit+φit,φit=λWφit+μit,φit=λWφit+μit
where εit∼iidN0,σ2, S is the spatial unit column vector, and W is the spatial autocorrelation weight matrix. Equation (10) is the spatial lag model, where ρ is the spatial autocorrelation parameter. When ρ is greater than 0, this indicates a positive spatial correlation of spatial economic growth quality among cities; when ρ is less than 0, this indicates a negative spatial correlation of spatial economic growth quality among cities. β is the convergence coefficient consistent with Eqs. (8) and (9). Equation (11) is the spatial error model, where λ is the parameter measuring the spatial correlation between the regression residuals.
In the third step, the impact of the aggregation of economic and fiscal policy instruments on the spatial convergence of the quality of urban economic growth was examined. A specific model was constructed as follows.12 dlnQEGit=lnQEGit-lnQEGit-1=αS+ρWnlnQEGit-lnQEGit-1+η1SPit+η2DVit+η3FOSit+βlnQEGit-1+γXit+μit
13 dlnQEGit=lnQEGit-lnQEGit-1=αS++βQEGit-1+η1SPit+η2DVit+η3FOSit+γXit+φit,φit=λWφit+μit
In Eqs. (12) and (13), SP denotes the degree of specialization and agglomeration of productive service industries, DV denotes the level of diversification and agglomeration of productive service industries, FOS denotes the scale of government fiscal expenditure, and Xit is the set of control variables.
Setting of spatial weight matrix
In spatial econometric analysis, the spatial weight matrix is a powerful tool for the conceptualization of spatial relationships, which reflects the structure and intensity of spatial effects and determines the degree of contribution of spatial units to neighboring units. In addition to geographical factors, studies have shown that spatial correlation among cities is also related to the level of economic development (Gu and Pang 2008). This paper therefore set the spatial weight matrix according to two sets of factors: geographical factors and economic factors.
Distance matrix of neighboring weights
If two cities are adjacent, then the weight is 1, and if they are not adjacent, the weight is 0.14 Wijn=1,i=j0,i≠j,Wij′n=Wijn∑jWijn,i≠j
In Eq. 17, i and j denote the i-th and j-th cities. To simplify the model and to easily interpret the empirical results, the spatial weight matrix is row normalized so that the sum of the elements in each row is 1, yielding Wij′n.
Geographical distance weight matrix
According to the general rule of spatial correlation between regions, the shorter the distance interval between regions, the stronger the correlation between regions. As the distance interval expands, the correlation between regions will gradually weaken. Therefore, this paper assigns weights according to the inverse of the geographical distance between different cities, as shown in Eq. (15).15 Wijd=1/dij,i≠j0,i=j,Wij′d=Wijd∑jWijd,i≠j
where dij is the geographic distance between two places, the weight is assigned to 0 when the inverse of the geographic distance is infinite, and Wij′d is the normalized matrix.
Economic distance weight matrix
Given that competition and spillover effects are more likely to be triggered between cities with similar levels of economic development, an economic distance weight matrix was constructed. This matrix combines both spatial and economic factors which lead to differences, as shown in Eq. (16).16 Wije=WijddiagY¯1/Y¯,Y¯2/Y¯,…Y¯n/Y¯Wij′e=Wije∑jWijei≠j
where Y¯i is the economic indicator of the i-th city, Y¯i=1n, Y¯=1n, is the mean value of economic indicators for all cities in the total observation period, and the normalized weight is Wij′e.
Variable descriptions and data sources
In this paper, the quality of economic growth in Chinese cities was selected as the research target. The sample data were obtained from the China City Statistical Yearbook. The data of the number of invention indicators were obtained from the CNRDS database. Data processing was performed in three main steps, as follows. Different processing methods were selected to process cases of missing values for individual indicators. Indicators with low levels of missing data were filled with the average value of the two periods before and after the missing value, or with the value of the previous and later periods. We chose the interpolation method for indicators with missing data in individual years for all cities. For indicators with high levels of missing data, we considered replacing the variables.
Some indicators required simple calculation. The relevant proportion indicators were measured, and the import and export data and foreign direct investment data expressed in US dollars were converted according to the exchange rate of US dollars to RMB in the relevant year.
Because there were serious missing data problems for Tibet, Bijie, Tongren and several other cities, these cities were excluded from the sample. After this processing, panel data for 284 cities from 2005 to 2018 were ultimately obtained.
The core variables included the concentration of productive service industries and the scale of fiscal spending. Production service industry agglomeration was divided into the degree of specialized agglomeration and the level of diversified agglomeration. Referring to Combes (2000), this paper expresses the production service industry specialization agglomeration (SP) indicator as: SPi=E¯is/Ei/E¯s/E, where SPi is the degree of specialization and agglomeration of productive service industry in city i; E¯is is the number of productive service employment in city i; Ei is the total employment in city i; E¯s is the number of people employed in productive services nationwide; and E is the overall number of people employed nationwide. The level of of the diversification agglomeration (DV) indicator is tabulated as DVi=∑sEis/Ei×1∑s′=1,s′≠snEis′/Ei-Eis2/1/∑s′=1,s′≠snEs′/E-Es2, where Eis′ denotes the number of people employed in a particular productive service industry s′ (in city i and industry s); Es′ is the number of people employed in a productive service industry s′ nationwide in addition to industry s; and Es represents the number of people employed in productive service industry s nationwide. The fiscal expenditure size (FOS) is expressed using the ratio of local fiscal general budgetary expenditure to GDP, drawing on the methodology of Wu (2021).
In this paper, we selected the level of informatization (INF), city size (POP), physical capital (K), transportation accessibility (TRAV), degree of government intervention (GOV), economic policy uncertainty index (EPU), and industrial SO2 emissions (SO2) as control variables. The level of informatization was measured using postal service revenue per capita. City size was measured using the total year-end population of the municipality. Physical capital was measured by capital stock calculated by the perpetual inventory method, and the capital stock depreciation rate was borrowed from Zhang et al. (2004) (set at 9.6%). Transportation accessibility was measured by total freight transportation per capita. The degree of government intervention was expressed using the city's fiscal revenue as a percentage of GDP. The Economic Policy Uncertainty Index was based on Baker's et al. (2016) methodology, which first calculates the proportion of daily news articles in the South China Morning Post (published in Hong Kong, China) that contained the keywords “China”, “economy”, “policy”, and “uncertainty” to the total number of articles published by the South China Morning Post in that month. Baker’s methodology then uses the index of global economic policy uncertainty published on an economic policy uncertainty website2 to measure the index. This paper construct enterprises' pollution emission intensity index using industrial SO2 emissions, which are typically used to represent air pollution.
Empirical results and discussion
General convergence results
Table 3 shows the results of the estimation of the absolute β convergence model of economic growth quality. To consider the possibility of club convergence in China, this section empirically analyzes absolute β convergence at the national level and the three regional levels of east, central and west. The results show that the β coefficient is significantly smaller than 0 in the national sample, indicating the existence of absolute β convergence in China. Similarly, the eastern, central, and western samples show a trend of club convergence. The western region converges the fastest, followed by the central and eastern regions, while the central and western regions converge faster than the national average. However, the absolute convergence rate is still relatively slow for each sample.Table 3 Test of absolute β convergence of urban economic growth quality
(1) (2) (3) (4)
National East Central West
α − 0.3763*** − 0.3144*** − 0.4691*** − 0.5165***
(− 29.76) (− 15.48) (− 19.92) (− 19.09)
β − 0.3184*** − 0.2833*** − 0.3863*** − 0.4118***
(− 31.20) (− 15.82) (− 20.82) (− 20.10)
Convergence speed 2.949 2.562 3.756 4.082
Convergence period 23.51 27.05 18.46 16.98
R2 0.3800 0.3983 0.3568 0.4008
N 3692 1313 1300 1079
* , ** , *** represent passing the coefficient significance test at 10%, 5%, and 1% significance levels, respectively; Z values in parentheses; convergence rate in % and convergence period in years
Spatial convergence analysis
Table 4 shows the spatial conditional β convergence results obtained using the maximum likelihood estimation method (ML). Columns (1)–(3) are the results of spatial lagged dynamic panel model estimation, and columns (4)–(6) are the results of spatial error dynamic panel model estimation. This paper uses a combination of Hausman and Lagrange multiplier (LM) tests to make judgments with regard to the choice of spatial model. The results of the Hausman statistical test tended to favor the empirical results of the fixed-effects model rather than the random-effects model. The LM test results indicated that the surface spatial lag model was more suitable for the empirical analysis in this paper. Therefore, this section mainly focuses on the results in columns (1)–(3). Although the spatial error model is slightly less reasonable than the spatial lag model, it still reflects the robustness of the results of this study.Table 4 Quality conditions for China's economic growth β spatial convergence test
SAR SEM
(1) (2) (3) (4) (5) (6)
Adjacency weighting Geographical distance weight Economic distance weight Adjacency weighting Geographical distance weight Economic distance weight
ρ 0.5004*** 0.6040*** 0.6422***
(42.84) (54.84) (42.53)
λ 0.3927*** 0.2325*** 0.0615***
(57.35) (47.77) (48.51)
lnQEGit-1 − 0.3691*** − 0.3638*** − 0.4117*** − 0.3393*** − 0.3172*** − 0.5024***
(− 43.83) (− 43.37) (− 39.42) (− 45.49) (− 48.68) (− 43.29)
INF 0.4703*** 0.4038*** 0.2072*** 0.3471 0.5221** 0.0530
(6.41) (7.27) (7.02) (0.63) (2.22) (1.32)
K 0.3528*** 0.4914*** 0.6469*** 0.2872*** 0.3180*** 0.0492***
(10.42) (7.46) (9.23) (5.26) (4.94) (6.13)
GOV 0.1426*** 0.1139*** 0.5269*** 0.2552*** 0.1977*** 0.1157***
(6.61) (6.39) (7.07) (7.20) (5.25) (6.15)
TRAV − 0.2274 − 0.5266* − 0.3746* − 0.2186 − 0.2468 − 0.1456
(− 1.47) (− 1.83) (− 1.86) (− 0.60) (− 0.56) (− 0.95)
POP 0.0441*** 0.0429*** 0.0384*** 0.2520 0.3090 0.4103
(6.52) (6.31) (6.56) (0.54) (1.07) (1.16)
EPU − 0.3602 *** − 0.4164 *** − 0.5613*** − 0.6564 *** − 0.3797 ** − 0.0227**
(5.66) (6.10) (5.36) (4.97) (2.27) (2.19)
SO2 0.3103** 0.3343** 0.5224* 0.2097*** 0.2291*** 0.3614***
(2.01) (2.15) (1.71) (4.10) (6.61) (4.73)
Convergence speed 3.543 3.479 4.081 3.188 2.935 5.369
Convergence period 19.56 19.92 16.99 21.74 23.62 12.91
LMlag 179.74 155.43 137.16
0.0000 0.0000 0.0000
LMerr 34.24 25.10 39.64
0.0000 0.0000 0.0000
Hausman 125.17 95.31 143.66 153.61 115.43 163.45
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
R2 0.4193 0.3915 0.2507 0.2745 0.3035 0.3162
N 3692 3692 3692 3692 3692 3692
*, **, *** represent passing the coefficient significance test at 10%, 5%, and 1% significance levels, respectively; Z values in parentheses; convergence rate in % and convergence period in years
The lnQEGit-1 coefficient is significantly negative, indicating that urban economic growth quality still eventually converges to a steady state after the inclusion of spatial factors; that is, there is spatial conditional β convergence. For different spatial weight matrices, the coefficients of lnQEGit-1 differ. The absolute value of the convergence coefficient corresponding to the economic distance weight matrix is the largest, followed by the neighboring weight matrix, and the geographical distance weight matrix. This indicates that convergence is more significant between neighboring cities with similar economic development levels. In addition, the spatial effect coefficient ρ is significantly positive for the neighboring weights, geographic distance weights, and economic distance weights. This indicates that, due to the positive spatial spillover effect, the convergence of economic growth quality in the sampled cities is accelerated and the convergence period is shortened.
Explanations for the spatial convergence of the urban economic growth quality
A scatter plot was drawn with the specialization agglomeration index and diversification agglomeration index as the x-axis and the quality of economic growth as the y-axis, respectively. As shown in Fig. 3, there is a linear relationship between specialization and diversification agglomeration and the quality of urban economic growth. Specialization agglomeration is positively related to the quality of urban economic growth, while diversification agglomeration is inversely related to the quality of urban economic growth.Fig. 3 Scatter plot of productive service industry agglomeration and urban economic growth quality
Table 5 shows the estimation results of the spatially conditional β convergence model, which reflect the effects of productive service industry agglomeration and fiscal expenditure.Table 5 Results of considering productive services agglomeration and fiscal expenditures
SAR SEM
(1) (2) (3) (4) (5) (6)
Adjacency weighting Geographical distance weight Economic distance weight Adjacency weighting Geographical distance weight Economic distance weight
ρ 0.3076*** 0.3175*** 0.5930***
(32.56) (38.12) (44.27)
λ 0.6739*** 0.5719*** 0.4853***
(39.20) (34.71) (36.52)
lnQEGit-1 − 0.5312*** − 0.4354*** − 0.7167*** − 0.4892*** − 0.3242*** − 0.6385***
(− 38.05) (− 33.80) (− 48.55) (− 31.46) (− 25.79) (− 39.07)
SP 0.0013 0.0015* 0.0036** − 0.0021 − 0.0037 0.0006
(1.14) (1.85) (2.21) (− 0.67) (− 0.54) (0.37)
DI − 0.1937*** − 0.1539*** − 0.1706*** − 0.0587 0.6062* − 0.0275
(− 14.83) (− 13.45) (− 16.76) (− 0.53) (1.90) (− 0.34)
FOS 0.5378*** 0.3533*** 0.6676*** 0.7772*** 0.4467*** 0.3706**
(6.64) (5.25) (6.58) (5.41) (6.78) (2.16)
CONTROLS YES YES YES YES YES YES
Convergence speed 5.828 4.397 9.702 5.168 3.014 7.827
Convergence period 11.89 15.76 7.14 13.41 23.00 8.86
LMlag 156.31 214.23 111.48
0.0000 0.0000 0.0000
LMerr 65.24 33.16 43.87
0.0000 0.0000 0.0000
Hausman 63.26 75.69 98.18 157.38 98.27 231.40
0.0000 0.0000 0.0000 0.0000 0.0369 0.0000
R2 0.3091 0.2631 0.3700 0.2784 0.3236 0.2606
N 3692 3692 3692 3692 3692 3692
*, **, *** represent passing the coefficient significance test at 10%, 5%, and 1% significance levels, respectively; Z values in parentheses; convergence rate in % and convergence period in years
The specialized agglomeration of productive service industries plays a positive role in the spatial convergence of economic growth quality of cities. However, the higher the diversification agglomeration is, the more diffuse the economic growth quality is. After the agglomeration economy is factored in, the convergence speed of urban economic growth quality increases and the convergence period is shortened. The spatial lag model’s convergence speeds based on the neighborhood weight, geographic distance weight, and economic distance weight are 5.828%, 4.397%, and 9.702%, respectively. In the spatial condition β convergence model without considering industrial agglomeration, the convergence speeds corresponding to the three weight matrices are 3.543%, 3.479%, and 4.081%, respectively. The convergence speeds are increased by 64.49%, 26.39%, and 137.74%, respectively.
In terms of policy effects, the FOS coefficient is significantly positive, indicating that increasing the scale of fiscal expenditure can significantly promote the spatial convergence of urban economic growth quality. Therefore, fiscal policy instruments are important factors influencing the spatial convergence of urban economic growth quality. Finally, there is a significant positive spatial spillover effect among cities. This indicates that cities should consider the local industrial development level and the industrial development patterns of the surrounding areas when planning and designing industrial development.
Robustness test
The spatial spillover effects obtained from the spatial lag model and spatial error model include local effects and indirect effects from other cities. In order to ensure the robustness of the above conclusions, and considering that in practice cases of spatial lag and spatial error may exist simultaneously, this section uses the spatial Durbin model for re-testing. In addition, considering the possibility of spatial correlation jumps due to the length of the examined period, the examined period is further divided into the sub-periods 2005–2012 and 2013–2018. The results are presented in Table 6. In Table 6, W1, W2, and W3 correspond to the adjacency matrix, geographic distance matrix, and economic distance matrix, respectively.Table 6 Results of spatial Durbin model
2005–2018 2005–2012 2013–2018
(1) (2) (3) (4) (5) (6) (7) (8) (9)
W1 W2 W3 W1 W2 W3 W1 W2 W3
ρ 0.5880*** 0.5398*** 0.5576*** 0.7033*** 0.6674*** 0.6423*** 0.5249*** − 0.5807*** 0.4495***
(34.39) (30.59) (26.96) (25.00) (33.08) (30.82) (34.15) (− 7.08) (15.41)
lnQEGit-1 − 0.6577*** − 0.4249*** − 0.7327*** − 0.5761*** − 0.6866*** − 0.5221*** − 0.5911*** − 0.6435*** − 0.8668***
(− 32.64) (− 34.57) (− 39.20) (− 27.01) (− 31.46) (− 33.50) (− 28.23) (− 26.99) (− 33.67)
SP 0.0119* 0.0121*** 0.0172*** − 0.0032 − 0.0046 − 0.0368 0.0184 0.0151 0.0031
(1.75) (4.61) (4.16) (− 0.51) (− 0.46) (− 0.62) (1.47) (1.52) (1.10)
DI − 0.0004 − 0.0008 0.0003 0.0005 0.0028** 0.0041 − 0.0013** − 0.0024* − 0.0009
(− 0.23) (-0.56) (0.13) (0.24) (2.20) (1.16) (− 2.16) (-1.75) (− 0.59)
FOS 0.2558*** 0.3296*** 0.2853*** 0.0174** 0.5500*** 0.3590*** 0.2777*** 0.4716*** 0.2766**
(7.06) (8.90) (8.29) (2.03) (9.49) (8.52) (6.78) (8.36) (6.19)
W*lnQEGit-1 0.2664*** 0.4339*** 0.3475*** 0.3744*** 0.3764*** 0.1889*** 0.3394*** − 0.9079*** 0.5347***
(9.13) (8.36) (8.62) (8.64) (9.10) (8.35) (8.64) (− 9.13) (8.84)
W*SP 0.1520* 0.1118*** 0.0042* 0.0063 0.1634*** 0.0064* 0.0083*** − 0.0642 0.0036
(1.93) (4.64) (1.65) (1.01) (3.52) (1.81) (3.68) (− 1.01) (0.05)
W*DI − 0.0321*** − 0.2131*** − 0.1981*** − 0.3207*** − 0.2686*** − 0.3811*** − 0.0598*** 0.5587*** − 0.1662**
(− 6.52) (− 6.71) (− 5.31) (− 6.21) (− 4.65) (− 7.21) (− 5.53) (5.92) (− 2.01)
W*FOS 0.4420*** 0.3096*** 0.2497** 0.5224*** 0.3691*** 0.3374*** 0.4968*** 0.2291** 0.6370***
(6.12) (7.71) (2.14) (6.34) (5.12) (6.34) (4.27) (2.10) (6.19)
CONTROLS YES YES YES YES YES YES YES YES YES
W*CONTROLS YES YES YES YES YES YES YES YES YES
Convergence speed 8.247 4.255 10.149 6.602 8.925 5.680 6.879 7.934 15.507
Convergence period 8.41 16.29 6.83 10.50 7.77 12.20 10.08 8.74 4.47
Hausman 46.21 76.26 54.55 43.72 37.31 67.33 113.53 117.43 66.43
0.0000 0.0000 0.0000 0.0109 0.0000 0.0000 0.0000 0.0000 0.0000
*, **, *** represent passing the coefficient significance test at 10%, 5%, and 1% significance levels, respectively; Z values in parentheses; convergence rate in % and convergence period in years
The results in Table 6 show that the specialized agglomeration of productive services is positive for the spatial convergence of the quality of urban economic growth, while diversified agglomeration increases the gap in urban economic growth quality. This is consistent with the results given in Table 5. Furthermore, from 2005 to 2012, specialization agglomeration is shown to be key to promoting the convergence of the urban economic growth quality gap; from 2013 to 2018, diversification agglomeration plays a major role in promoting economic growth quality. This result again proves that agglomeration economy is a key factor in the convergence of economic growth quality in cities. In addition, W*SP is significantly positive, while W*DV is significantly negative, indicating that specialized agglomeration in some cities can significantly contribute to the convergence of economic growth quality in other cities. Still, the specialized agglomeration of industries in neighboring cities contributes to the widening of regional differences. This situation is always present in both the short and long runs. Finally, we look at the impact of fiscal policy instruments. The coefficient of FOS is significantly positive, indicating that an increase in the size of fiscal spending can significantly contribute to the convergence of the quality of economic growth in cities. The estimated coefficient of W*FOS also has a positive value, indicating that fiscal spending in some cities also affects the convergence of the quality of economic growth in other cities.
Conclusions and policy recommendations
This paper takes 284 prefecture level and above cities in China from 2005 to 2018 as the research objects, constructs comprehensive indicators of urban economic growth quality using the entropy value method, and tries to summarize their spatiotemporal evolution characteristics to initially determine the convergence of urban economic growth quality in China. Given the possible spatial correlation of different cities, this paper examines the spatial convergence of urban economic growth quality by using a spatial econometric model starting from a general convergence trend. Furthermore, this paper also tries to explain the spatial convergence of urban economic growth quality fin terms of both agglomeration economy and fiscal policy instruments.
The study draws the following conclusions. First, there is a general convergence trend in China across the whole territory. The western region converges the fastest, followed by the central and eastern regions, while the central and western regions converge faster than the national average. However, the absolute convergence rate is still relatively slow, regardless of the sample. Second, in terms of spatial convergence, the convergence between neighboring cities with similar economic development levels is more significant. Due to the existence of a positive spatial spillover effect, the speed of economic growth quality convergence among cities in China is accelerated and the convergence period is shortened. Third, an examination of the factors influencing the spatial convergence of urban economic growth quality reveals that the specialized agglomeration of productive service industries has a positive influence on the spatial convergence of urban economic growth quality, while the influence of diversified agglomeration is negative. After factoring in agglomeration economy, the convergence speed of urban economic growth quality increases and the convergence period is shortened accordingly. The empirical results of this paper also indicate that fiscal policy instruments are important factors affecting the spatial convergence of urban economic growth quality. The increase of fiscal expenditure scale can significantly promote the convergence of urban economic growth quality. The fiscal expenditure of other cities also significantly affects the spatial convergence trend of local urban economic growth quality.
The empirical analysis of this paper shows that the quality of China’s urban economic growth is currently converging spatially. However, there is still a problem in that there is a low level of convergence and a decreasing rate of convergence. Therefore, measures need to be taken to break this bottleneck. This paper recommends the following policies.
First, the spatial distribution of cities should be coordinated and the horizontal and vertical interactions between regions should be strengthened. This paper demonstrates a significant spillover effect on the convergence of economic growth quality in China’s cities and an obvious center-periphery pattern. The level of economic growth quality is unevenly distributed; this is related to the spatial distribution of cities to a certain extent. Therefore, the government should improve regional transportation structure, promote the construction of an inter-city rapid railroad network, and build a more rational urban spatial structure system to improve spatial accessibility and the degree of inter-regional knowledge spillover.
Second, inter-regional industrial sectors should be promoted; this will encourage regional industries to drive each other and encourage factor circulation. The government should focus on lowering technical barriers, promoting the flow of outstanding talents, capital and technology, and encourage in-depth cooperation between regions with similar technical structures. The advantage of doing so is that these policies can improve the regional linkage effect and avoid inter-regional policy fragmentation in order to adjust the disadvantages of regional economies and stimulate their growth.
Third, a city network system should be built and the city network structure should be optimized. A scientific and rational urban network system will be beneficial for the optimal allocation of resources by market players and will help to realize urban economies of scale and the benefits of agglomeration. Given the systemic characteristics of different regional areas, the government should implement active fiscal policies and accurately grasp the different functional characteristics and service levels of cities in multiple networks to realize the complementary development and overall optimization of regional urban functions. The government should enhance network organization efficiency by improving the network resource domination ability of regional central cities and strengthening the support provided by node cities to the network system.
Acknowledgements
We gratefully acknowledge financial support from the National Natural Science Foundation of China (NO. 71303105), the National Social Science Foundation of China (No. 19FJYB039; 20BJY194), and the Postgraduate Research and Practice Innovation Program of Jiangsu Province (No. KYCX21_1430).
Funding
National Natural Science Foundation of China, 71303105, Qunxi Kong, National Office for Philosophy and Social Sciences, 19FJYB039, Qunxi Kong, Jiangsu Provincial Department of Education, KYCX21_1430, Rongrong Li.
1 Relevant statistics for 1949 are from ‘50 Years of New China Cities’, Xinhua Publishing House, December 1999. Data from 1978 onward are obtained from the China Economic Statistics Database. Data in the table are presented in current year prices.
2 http://policyuncertainty.com/global_monthly.html.
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PMC009xxxxxx/PMC9002048.txt |
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Appl Microbiol Biotechnol
Appl Microbiol Biotechnol
Applied Microbiology and Biotechnology
0175-7598
1432-0614
Springer Berlin Heidelberg Berlin/Heidelberg
35412129
11908
10.1007/s00253-022-11908-z
Biotechnological Products and Process Engineering
The pre-induction temperature affects recombinant HuGM-CSF aggregation in thermoinducible Escherichia coli
Restrepo-Pineda Sara 1
Sánchez-Puig Nuria 2
Pérez Néstor O. 3
García‑Hernández Enrique 2
Valdez-Cruz Norma A. 1
http://orcid.org/0000-0002-7497-4452
Trujillo-Roldán Mauricio A. maurotru@gmail.com
maurotru@biomedicas.unam.mx
14
1 grid.9486.3 0000 0001 2159 0001 Departamento de Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad de México, CP 04510 México
2 grid.9486.3 0000 0001 2159 0001 Universidad Nacional Autónoma de México, Instituto de Química, Ciudad Universitaria, Ciudad de México, 04510 México
3 Probiomed S.A. de C.V. Planta Tenancingo, Cruce de Carreteras Acatzingo-Zumpahuacan SN, Tenancingo, CP 52400 Estado de México México
4 grid.9486.3 0000 0001 2159 0001 Departamento de Biología Molecular y Biotecnología, Unidad de Bioprocesos, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad de México, CP 04510 México
12 4 2022
2022
106 8 28832902
2 11 2021
28 3 2022
30 3 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.
Abstract
The overproduction of recombinant proteins in Escherichia coli leads to insoluble aggregates of proteins called inclusion bodies (IBs). IBs are considered dynamic entities that harbor high percentages of the recombinant protein, which can be found in different conformational states. The production conditions influence the properties of IBs and recombinant protein recovery and solubilization. The E. coli growth in thermoinduced systems is generally carried out at 30 °C and then recombinant protein production at 42 °C. Since the heat shock response in E. coli is triggered above 34 °C, the synthesis of heat shock proteins can modify the yields of the recombinant protein and the structural quality of IBs. The objective of this work was to evaluate the effect of different pre-induction temperatures (30 and 34 °C) on the growth of E. coli W3110 producing the human granulocyte–macrophage colony-stimulating factor (rHuGM-CSF) and on the IBs structure in a λpL/pR-cI857 thermoinducible system. The recombinant E. coli cultures growing at 34 °C showed a ~ 69% increase in the specific growth rate compared to cultures grown at 30 °C. The amount of rHuGM-CSF in IBs was significantly higher in cultures grown at 34 °C. Main folding chaperones (DnaK and GroEL) were associated with IBs and their co-chaperones (DnaJ and GroES) with the soluble protein fraction. Finally, IBs from cultures that grew at 34 °C had a lower content of amyloid-like structure and were more sensitive to proteolytic degradation than IBs obtained from cultures at 30 °C. Our study presents evidence that increasing the pre-induction temperature in a thermoinduced system allows obtaining higher recombinant protein and reducing amyloid contents of the IBs.
Key Points
• Pre-induction temperature determines inclusion bodies architecture
• In pre-induction (above 34 °C), the heat shock response increases recombinant protein production
• Inclusion bodies at higher pre-induction temperature show a lower amyloid content
Supplementary Information
The online version contains supplementary material available at 10.1007/s00253-022-11908-z.
Keywords
Growth temperature
Recombinant protein
Thermoinduction
Inclusion body
Chaperones, rHuGM-CSF
http://dx.doi.org/10.13039/501100006087 Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México IN-210822 IN-211422 IV-201220 Valdez-Cruz Norma A. Trujillo-Roldán Mauricio A. issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2022
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pmcIntroduction
The production of recombinant proteins for therapeutic use has become of great interest in current biotechnology and biomedicine. To achieve high product yields, make bioprocesses efficient, and minimize production costs, different biological platforms, expression systems, culture medium designs, and purification strategies have been explored (Assenberg et al. 2013; Terol et al. 2021; Kumar et al. 2020; Kaur et al. 2018). Escherichia coli as a host for recombinant protein production has numerous advantages compared to other bacteria, yeasts, or mammalian cells (Baeshen et al. 2015; Sánchez-García et al. 2016). E. coli can grow on inexpensive substrates in a short time, it is a well-known organism, high cell density cultures can be easily reached, and the accumulation of the recombinant protein can represent 30% or more of the total cellular protein (Huang et al. 2012; Rosano and Ceccarelli 2014; Rosano et al. 2019). Additionally, within the biopharmaceuticals approved through 2018, ~ 22% were produced in E. coli (Walsh 2018).
When recombinant proteins are overexpressed in E. coli, the bacteria tend to form protein aggregates called inclusion bodies (IBs) (Baneyx and Mujacic 2004; Williams et al. 1982). IBs are recombinant protein-enriched deposits with spherical or pseudo-spherical shapes and diameters between 50–800 nm (De Marco et al. 2019; Margreiter et al. 2008; Castellanos-Mendoza et al. 2014). Its formation is a dynamic phenomenon that occurs mainly due to the interactions between partially folded or misfolded polypeptide chains during the overexpression of recombinant proteins, stressful conditions, or the shortage of heat shock proteins (HSPs), which are responsible for maintaining the cell proteome homeostasis (Baneyx and Mujacic 2004; Fahnert et al. 2004; Carrió and Villaverde 2003; Hartl and Hayer-Hartl 2009). Although IBs were considered an obstacle in obtaining soluble and active recombinant proteins, recent studies have shown that proteins with native-like structure and polypeptides with amyloid characteristics coexist within IBs, giving them both biological functionality and mechanical stability (González-Montalbán et al. 2007; Cano-Garrido et al. 2013; Rinas et al. 2017; Singhvi et al. 2020). This progress in the perception of IBs has increased the scientific interest in the study of their molecular organization and application in various areas as materials for cell proliferation (García-Fruitós et al. 2010), drug release agents (Villaverde et al. 2012; Pesarrodona et al. 2019) and biocatalysts (Jäger et al. 2020). Nowadays, it is known that bioprocess conditions such as pH (Castellanos-Mendoza et al. 2014; Calcines-Cruz et al. 2018), temperature (de Groot and Ventura 2006; Peternel et al. 2008; Restrepo-Pineda et al. 2019), agitation (Valdez-Cruz et al. 2017), among others, can determine the formation, composition, and structure of IBs (De Marco et al. 2019). However, the effect of different induction strategies on its properties, for example, the use of a temperature-induced system, is a subject that remains poorly explored.
The λpL/pR-cI857 thermoinducible expression system is a widely applied strategy in the industry to produce recombinant proteins in E. coli (Valdez-Cruz et al. 2010, 2011; Restrepo-Pineda et al. 2021). The use of this system avoids the addition of chemical inducers such as isopropyl-β-D-1-thiogalactopyranoside (IPTG), the possibilities of contamination are minimized through the external control of temperature, and target recombinant protein yields of at least 30% can be obtained regarding the total protein (Remaut et al. 1981; Caspeta et al. 2013; Singha et al. 2018). Below 37 °C, recombinant protein expression is regulated by the binding of the cI857 thermolabile repressor to the operator regions of the pL and pR promoters derived from bacteriophage λ, facilitating the formation of a DNA loop that inhibits RNA polymerase activity (Dodd et al. 2004; Lewis et al. 2016). By raising to 37 °C, cI857 is released and allows the transcription of the gene of interest (Caulcott and Rhodes 1986; Villaverde et al. 1993; Valdez-Cruz et al. 2010). However, it has been reported that above 34 °C, E. coli initiates the heat shock response (HSR) to cope with heat stress and maintain cellular homeostasis (Yamamori et al. 1978; Morita et al. 1999; Yano et al. 1990; Yura 2019). HSR is controlled by the transcription factor σ32 (RpoH), which regulates the expression of an extensive network of chaperones and proteases involved in folding of nascent proteins and the removal of damaged/unfolded proteins (Guisbert et al. 2004, 2008; Baneyx and Mujacic 2004; Balchin et al. 2016). The temperature upshift during recombinant protein synthesis also involves a metabolic adaptation, reflected in the reduction of the specific growth rate, accumulation of organic acids, mainly acetate, and a readjustment in metabolic fluxes (Hoffmann and Rinas 2004; Hoffman et al. 2002; Wittmann et al. 2007; Restrepo-Pineda et al. 2021). Recombinant protein production typically follows a two-phase strategy; initially, cells are grown at 30 °C (in batch or feed-batch cultures), and then recombinant protein expression is induced at a constant temperature between 38 °C and 42 °C (Caspeta et al. 2009, 2013; Valdez-Cruz et al. 2010; Restrepo-Pineda et al. 2021).
In a thermoinduced system in addition to HSR, recombinant protein overexpression and the IBs formation co-occur (Valdez-Cruz et al. 2010, 2011; Restrepo-Pineda et al. 2021). Also, HSPs are overexpressed in response to elevated temperature and recombinant protein accumulation (Gill et al. 2000; Carrió and Villaverde 2003). During thermoinduction, the mRNA levels of heat shock genes (dnaK, dnaJ, and groEL) increase between 2 and 9 times compared with 30 °C (Valdez-Cruz et al. 2011). At the proteomic level, Hoffmann and Rinas (2000) found that just 30 min after thermoinduction at 42 °C, HSPs reach their maximum synthesis rate, and IBs analyzed one-hour post-induction contains chaperones such as DnaK, GroEL, IbpA, and IbpB. Previous findings in our laboratory revealed that using a thermoinducible system, GroEL is present in both the soluble protein fraction and in the IBs. At the same time, DnaK predominated in the soluble fraction (Restrepo-Pineda et al. 2019). The presence of these chaperones in IBs has been related to their function in the dissolution of these aggregates or preventing their aggregation during thermoinduction (Rinas et al. 2007). Moreover, the assembly and morphology of IBs can also be modified depending on the absence or presence of specific HSPs (García-Fruitós et al. 2010). Furthermore, IBs harvested from E. coli cultures induced by IPTG at high temperatures (39 or 42 °C) were more accessible to be solubilized in urea than those obtained at lower temperatures (20 and 30 °C) (Singh et al. 2020).
Granulocyte–macrophage colony-stimulating factor (GM-CSF) is a protein whose primary function is to stimulate germinal hematopoietic cells for the formation of the differentiated myeloid lineage and takes part in regulating a wide variety of inflammatory responses (Francisco-Cruz et al. 2014; Wicks and Roberts 2016; Hamilton 2019; Dougan et al. 2019). GM-CSF has been recombinantly expressed in mammalian cells, yeasts, and bacteria, receiving FDA (Food and Drug Administration) approval in 1991 for the treatment of neutropenia (Mehta et al. 2015; Dougan et al. 2019). Even though the human GM-CSF contains O- and N- glycosylation sites (Walter et al. 1992), the non-glycosylated form produced in E. coli is biologically active and has therapeutic relevance (Okamoto et al. 1991; Cumming 1991). Recent studies have elucidated the protective role of GM-CSF in autoimmune diseases such as pulmonary alveolar proteinosis (Trapnell et al. 2020; Zhang et al. 2020), its use in oncolytic immunotherapies, and adjuvant in cancer vaccines (Kaufman et al. 2014) as well as its possible administration in treatments against COVID-19 (Lang et al. 2020; Bonaventura et al. 2020).
Although some studies revealed the influence of induction temperature and induction time on IBs structure using a thermoinducible system (Caspeta et al. 2009, 2013; Restrepo-Pineda et al. 2019), this report aimed to elucidate the effect of growth (or pre-induction) temperature on the subsequent formation process, protein composition and structural characteristics of IBs from an E. coli bacterial culture producing the recombinant human GM-CSF (rHuGM-CSF). Here, bioreactor cultures of E. coli W3110 grown at either 30 or 34 °C were subsequently thermoinduced at 42 °C. Growth curves, carbon source consumption, and acetate production were analyzed. SDS-PAGE evaluated the expression kinetics of the rHuGM-CSF protein in the total protein fraction and IBs. Changes in the expression of some HSPs (DnaK/J, GroEL/ES) were identified by western blot. The structural analysis of the aggregates was carried out using attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), binding to an amyloidogenic dye, proteolytic digestion, and denaturation with a chaotropic agent. Finally, the secondary structure of the refolded and purified rHuGM-CSF from IBs was studied by circular dichroism (CD) to elucidate its biological activity indirectly.
Materials and methods
Strain, media composition, and bioreactor conditions
E. coli W3110 (ATCC® 27325™) was used as a host to produce the recombinant human granulocyte–macrophage colony-stimulating factor (rHuGM-CSF). The coding sequence for the rHuGM-CSF (GenBank accession number OL419360) was cloned in the pV3 plasmid containing the gene of the cI857 thermolabile repressor and the pL promoter from bacteriophage λ (Lowman and Bina 1990). pV3 is a low copy number plasmid based on the RepA-CopB from R100 plasmid (Olsson et al. 2004) and the par sequence to increase plasmid stability from plasmid pSD101 (Miller et al. 1983). A working cell bank containing aliquots of 1 ml at an optical density at 600 nm (OD600) of 1.09 absorbance units (AU) was generated with 40% (v/v) glycerol and stored at -75 °C (Restrepo-Pineda et al. 2019).
For inoculums and bioreactor cultures, a defined culture medium was prepared as described in Restrepo-Pineda et al. (2019), as follows (in g/l): 4.0 (NH4)2HPO4; 13.3 KH2PO4; 1.7 citric acid; 1.2 MgSO4·7H2O; 0.045 thiamine; 0.1 kanamycin; 17.5 glucose; 3.0 casamino acids and trace elements (2.0 ml/l of 500X stock). Glucose, MgSO4, and trace elements stocks were separately sterilized at 121 °C and 22 psig for 30 min (ES-215 sterilizer, Tomy Digital Biology, Tokyo, Japan). Thiamine, casamino acids, and kanamycin solutions were sterilized using 0.22-μm-pore-size filters (Merck Millipore, Billerica, MA, USA) and added before inoculation. The pH of the culture medium was adjusted to 7.0 ± 0.1 with 3 N NaOH and 8 N HCl.
For preparing the inoculum, 500 µl of the working cell bank was cultured in conventional 250-ml Erlenmeyer flasks with 50 ml filling volume. Cells were incubated overnight (~ 14 h) at 30 °C and 200 rpm (New Brunswick Scientific Classic C25, Enfield, CT, USA). The inoculum volume was decided based on the OD600 measurement to start the bioreactor experiments with the same cell concentration (~ 0.1 AU). The batch cultures were carried out in 1.2-l bioreactors (Applikon, Delft, Netherlands) with a working volume of 800 ml. Dissolved oxygen tension (DOT) was controlled at 35% with respect to air saturation by a cascade of agitation between 100–1000 rpm and constant airflow rate (1 vvm, volume of air per volume of culture medium). Medium pH in bioreactors was maintained at 7.0 ± 0.1 by adding 3 N NaOH or 3 N HCl. The temperature was controlled to either 30 or 34 °C (42 °C for induction) using a heating/cooling circulating water bath (PolyScience, Niles, IL, USA). To avoid foaming, a sterile antifoam agent was added manually when necessary. pH, DOT, and temperature were monitored and controlled online with the BioXpert software (Applikon, Delft, Netherlands).
Cell concentration estimation
Growth of the strain E. coli W3110 producing rHuGM-CSF was determined by following the OD600 (Spectronic Genesys 5, Thermo Electron Corporation, Westmont, IL, USA). OD600 measurements were converted to dry cell weight (DCW) through a linear correlation standard curve. Briefly, 10 ml samples from three independent cultures were centrifuged at 8,000 × g for 10 min. The cell pellet was washed with 1X PBS (pH 7.5) twice and filtered using 0.22-μm-pore-size filters (Merck Millipore, Billerica, MA, USA). The wet cell paste was dried at 90 °C for 48 h. After complete drying, the filters were weighed again. The difference in mass was used to calculate the DCW. 1.0 AU was equivalent to 0.33 ± 0.04 g/l of DCW.
Recombinant protein thermoinduction
Bioreactor cultures were grown at either 30 or 34 °C until reaching the pre-stationary phase (OD600 of 2.0–3.0 AU). At this point, the thermoinduction of rHuGM-CSF production was carried out by increasing to 42 °C, maintaining DOT and pH control (Restrepo-Pineda et al. 2019). The heating rate was the same in both conditions (0.5 °C/min). Samples of 1.0 ml were taken at different post-induction times (1, 3, 5, 10, and 18/20 h) and centrifuged at 10,000 × g for 10 min. Supernatants were used for glucose and acetate estimation, while pellets were stored at -20 °C for further analysis. All experiments were performed in triplicate.
Glucose and acetate quantification
Supernatants were filtered using sterile syringe filters with a 0.22-µm-pore-size before injection. Glucose concentration was determined in a biochemistry analyzer YSI 2900D (YSI Inc, Yellow Springs, OH, USA) equipped with a glucose oxidase membrane (YSI 2365), a buffer solution (YSI 237), and a standard calibrator solution (2.5 g/l of glucose). The concentrations of acetate were determined by high-performance liquid chromatography (HPLC) in a Shimadzu LC-20AT (Shimadzu, Kyoto, Japan) using an Aminex HPX-87H column (300 × 7.8 mm; 9-μm internal diameter, Bio-Rad, Hercules, CA, USA). The mobile phase consisted of 0.008 N NH2SO4 with 0.6 ml/min at 50 °C and 215 nm UV absorbance. A commercial standard solution was used for acetate (No. 125–0586, Bio-Rad), and data obtained were processed in the LC Solution software (Shimadzu, Kyoto, Japan).
Recovery and purification of IBs
For IBs isolation, the method described in Calcines-Cruz et al. (2018) was followed with minor modifications. Briefly, the cell biomass pellets from 1 ml culture were diluted in lysis buffer (50 mM Tris–HCl, 100 mM NaCl, 1 mM EDTA, pH 7.5) containing protease inhibitor (0.1 mM PMSF, phenylmethylsulfonyl fluoride). Each sample was disrupted by sonication using a Soniprep150 (Sanyo Gallenkamp PLC, Loughborough, UK) at 8 µm amplitude in 3–10 cycles of 30 s, keeping on ice. The lysed cell mixture was centrifuged at 14,000 × g for 15 min at 4 °C, and both the supernatant with the total soluble protein and the pellet with the insoluble protein (IBs) were recovered. Insoluble protein fraction was incubated in lysis buffer with 1% (v/v) IGEPAL (Sigma Aldrich, St. Louis, MO, USA) for 30 min under agitation at 4 °C and centrifuged at 14,000 × g for 5 min. The pellet was resuspended in lysis buffer containing 0.5% (v/v) Triton X-100 and centrifuged at 14,000 × g for 15 min. Finally, the pellet was washed 3 to 5 times with deionized water, centrifuged between each wash (14,000 × g for 15 min), and the purified IBs were stored at -20 °C.
Protein quantification, rHuGM-CSF identification, and chaperones immunodetection
The concentration of total soluble protein and protein in the IBs was measured by Bradford assay (Bio-Rad, Hercules, California, USA) according to supplier recommendations. Insoluble proteins were previously solubilized in isoelectric focusing (IEF) buffer (final 1:5 dilution) at room temperature for at least 3 h. Calibration curves with bovine serum albumin (BSA, Equitech-Bio, Kerrville, TX, USA) were made. Both samples and standards were prepared in triplicate, and OD600 was measured on a Stat Fax 2100 Microplate Reader (Awareness Technology Inc., Palm City, FL, USA).
Samples collected were used to analyze the production of the total soluble protein and rHuGM-CSF accumulation in IBs on 15% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE). Previously, equal amounts of protein were solubilized using 2.5% SDS for 12 h at room temperature, and 20 µg of protein was loaded in each lane. Gels were stained with Coomassie Brilliant Blue R-250 (Bio-Rad, Hercules, CA, USA), and the percentage of rHuGM-CSF in IBs was determined by densitometry using the Image-Lab software and Gel Doc EZ Imager (Bio-Rad, Hercules, CA, USA).
The specific detection of chaperones was carried out by western blot, as reported by Restrepo-Pineda et al. (2019). Proteins in the polyacrylamide gels were transferred to a polyvinylidene difluoride (PVDF) membrane (Immobilon, Millipore, Bedford, MA, USA) by a semi-wet approach via Trans-Blot system (Bio-Rad, Hercules, California, USA). Membranes were blocked for 40 min with 1X TBS plus 5% skim-milk at room temperature and gentle shaking. Two washes for 10 min each were made with wash buffer (1X TBS plus 0.06% Tween-20), and primary antibodies (mouse DnaK ADI-SPA-880 dilution 1:7500, rabbit GroEL ADI-SPA-875 dilution 1:7500, rabbit DnaJ ADI-SPA-410 dilution 1:2000, and rabbit GroES ADI-SPA-210 dilution 1:7500; Enzo Life Sciences, Farmingdale, NY, USA) were added for 1 h at room temperature. Later, membranes were washed three times, followed by incubation with the corresponding secondary antibodies (goat anti-mouse IgG A9044 dilution 1:2500 and goat anti-rabbit IgG A0545 dilution 1:2500, HRP-conjugated, Merck-Sigma-Aldrich, St. Louis, MI, USA) for 1 h at room temperature. Three more washes were done, and the proteins were detected by chemiluminescence using SuperSignal West Pico and Femto substrates (Thermo Fisher Scientific, Waltham, MA, USA) in a C-DIGIT blot scanner (LI-COR, Lincoln, NE, USA). A homemade chemiluminescence marker was used to reveal the images, and the membranes were incubated with mild stripping buffer (glycine, SDS, Tween-20, pH 2.2) to remove the antibodies.
ATR-FTIR spectroscopy
The components of the secondary structure of IBs were identified by attenuated total reflection (ATR)–Fourier transform infrared (FTIR) spectroscopy (Valdez-Cruz et al. 2017; Calcines-Cruz et al. 2018; Singh et al. 2020; Gil-Garcia et al. 2020). Freshly purified IBs were dried at room temperature for 1 h using a speed vacuum concentrator and placed on Specac Quest ATR diamond accessory (Specac Limited, Slough, UK) coupled to an infrared spectrometer IRAffinity-1S (Shimadzu, Kyoto, Japan). Each sample consisted of 40 acquisitions with a resolution of 2 cm−1 in the range of 1500–1700 cm−1 and subsequently averaged. After 13 points of smoothing, the second derivatives of the spectrum of the amide I region were determined with the IR LabSolutions program (Shimadzu, Kyoto, Japan) and normalized with respect to the absolute value of the tyrosine peak (~ 1508 cm−1) (Ami et al. 2005). The analysis of the location frequencies indicated the abundance of the different elements of the secondary structure in the IBs.
Thioflavin T binding assay
The amyloidogenic properties of IBs were evaluated by measuring the fluorescence after binding to the thioflavin T (Th-T) dye (Castellanos-Mendoza et al. 2014; Calcines-Cruz et al. 2018; Singh et al. 2020; Gil-Garcia et al. 2020). 50 mg/ml of protein in IBs were resuspended in phosphate buffer (pH 7.5) containing 75 µM of Th-T (Sigma Aldrich, St. Louis, MO, USA) and incubated for 1 h at 25 °C. The fluorescence signal was measured in a Cary Eclipse Fluorescence Spectrophotometer (Agilent Technologies, Palo Alto, CA, USA) with an excitation wavelength of 440 nm and 5 nm bandwidth. The emission spectrum was recorded from 450 to 560 nm with a spectral resolution of 5 nm. Each spectrum was acquired five times, averaged, and smoothed. The spectrum of Th-T without protein was obtained as a control.
Proteinase K digestion
The resistance of the IBs to enzymatic degradation was determined by incubating 50 mg/ml of sample with 25 µg/ml of proteinase K (PK, Sigma Aldrich, St. Louis, MO, USA). The proteolytic digestion was carried out in 1 ml of buffer (50 mM Tris–HCl and 150 mM NaCl, pH 8.0) at room temperature. Changes in absorbance were monitored at 350 nm for 100 min in a UV/Vis DU®730 spectrophotometer (Beckman Coulter Inc., Brea, CA, USA) mixing by pipetting every minute. Data were normalized with respect to the initial absorbance value (Upadhyay et al. 2012; Castellanos-Mendoza et al. 2014; Valdez-Cruz et al. 2017; Calcines-Cruz et al. 2018; Singh et al. 2020).
Stability in guanidinium chloride
The stability of IBs against chemical solubilization was examined by adding 1.0 mg/ml of protein in IBs to 100 μl of 10 mM Tris–HCl buffer (pH 7.5) containing different concentrations (0, 1, 3 and 5 M) of guanidinium hydrochloride (GndHCl, Sigma-Aldrich, St. Louis, MO, USA). After 24 h of gentle shaking at room temperature, the samples were centrifuged at 8000 × g for 10 min. The supernatants were recovered, and the solubilized protein was quantified by the Bradford method described above. The solubility profiles of the IBs harvested at different times after induction were obtained by plotting the soluble protein concentration against GndHCl concentration (De Groot and Ventura 2006; Espargaró et al. 2008; Castellanos-Mendoza et al. 2014).
IBs solubilization, refolding, and purification of rhGM-CSF
Purified IBs of rHuGM-CSF were resuspended in a solubilization buffer (6 M GndHCl, 50 mM 2-mercaptoethanol in 100 mM Tris base, pH 8.2, adjusted with 3 M HCl) at 700 rpm, 1 h and 25 °C to a final protein concentration of 1.0 mg/ml. Then, the protein folding process was carried out at 25 °C, 100 rpm, and 4 h by drop-by-drop dilution (1:10) of the solubilized product in a Tris base buffer (20 mM, pH 8.2). The reaction was stopped by adding 4 M acetic acid to a final concentration of 0.2 M (Burgess et al. 1987; Belew et al. 1994; Thomson et al. 2012).
The folded protein solution was purified by reverse-phase HPLC in a Shimadzu LC-20AT (Shimadzu, Kyoto, Japan) using a Zorbax Eclipse XDB-C8 column (Agilent Technologies, Santa Clara, CA, USA). A standard curve of the European Pharmacopoeia reference standard for human GM-CSF (Molgramostim, Y0000251, Sigma Aldrich, St. Louis, MO, USA) was carried out at 1.345 mg/ml, 0.672 mg/ml, and 0.336 mg/ml. The folded protein sample (~ 10 µg) was loaded in the HPLC at 50 °C with a detector wavelength of 214 nm and a maximum column pressure of 3000 psig. Solutions of 0.1% v/v trifluoroacetic acid (TFA) in water (mobile phase A) and 0.1% v/v TFA in 90% acetonitrile (mobile phase B) were used for the linear gradient elution at a flow rate of 1.2 ml/min (Nicola et al. 1983; Das et al. 2011).
Circular dichroism (CD) spectroscopy
CD spectra of purified rHuGM-CSF in water were recorded at 37 °C in the far-UV region with a JASCO J-720 spectropolarimeter (Jasco Inc., Easton, MD) described elsewhere (Luviano et al. 2019). Protein solutions of ~ 0.05 mg/ml were loaded into a quartz cell of 0.1-cm length path. Each spectrum corresponded to the average of three repetitive scans and was corrected by the buffer signal. Ellipticities are reported as mean residue ellipticity, [θ]mrw. Secondary structure content was calculated from CD spectra using the deconvolution software K2D3 (Louis-Jeune et al. 2012).
Statistical analysis
Statistically significant differences between the data were calculated using analysis of variance (ANOVA), followed by Tukey’s test. The quantitative results are expressed as mean ± standard error of the mean. A p-value < 0.05 was considered statistically significant.
Results
Pre-induction temperature affects the specific growth rate (µ) in thermoinduced E. coli cultures.
Cultures of E. coli W3110 were grown at either 30 or 34 °C until reaching the pre-stationary phase of growth (OD600 nm of 2.0–3.0 AU). Thermoinduction (at 42 °C with a heating rate of 0.50 °C/min) was done in the pre-stationary phase to have a large number of viable cells and take advantage of the fact that the stress responses associated with entering the stationary phase, due to nutrients limitation or the presence of secondary metabolites, has not been triggered (Hengge-Aronis, 1993; Overton, 2014). The dry cell weight (DCW) determination indicated that 1.0 AU was equivalent to 0.33 ± 0.04 g/l of DCW, similar to that reported in other works: 0.32 g/l for recombinant E. coli W3110 (Sandoval-Basurto et al. 2004); 0.31 ± 0.05 g/l for recombinant E. coli 53,606 (Restrepo-Pineda et al. 2019) and 0.28 g/l for a recombinant strain derived from E. coli K-12 (Mansey et al. 2014). Figure 1 compares the kinetics of biomass, the consumption of the carbon source, and the production of acetate. The maximum biomass (Xmax) for the cultures grown at 30 °C → 42 °C was 3.48 ± 0.19 g/l at 21 h (Fig. 1A, Table 1) and for those grown at 34 °C → 42 °C was 3.48 ± 0.31 g/l at 15 h (Fig. 1B, Table 1).Fig. 1 Kinetics of bacterial growth (A, B), glucose consumption (C, D), and acetate production (E, F) of E. coli W3110 producing the rHuGM-CSF protein in 1.2-l bioreactors. Two pre-induction temperatures were evaluated: 30 °C (filled circles) and 34 °C (open circles) with subsequent thermoinduction at 42 °C. Vertical dotted lines indicate the start of the temperature increase (7 h for 30 °C and 4 h for 34 °C), which correspond to an OD600 of ~ 2.0 AU. The graph presents the mean with their respective standard deviation of three independent experiments
Table 1 Comparison of the kinetic parameters of E. coli W3110 bioreactor cultures growing at two different temperatures: 30 °C and 34 °C with subsequent thermoinduction of rHuGM-CSF at 42 °C. The mean and standard deviation of three biological replicates per condition are presented
Growth at 30 °C
Induction at 42 °C Growth at 34 °C
Induction at 42 °C
Before induction After induction Before induction After induction
A µ (h−1) 0.53 ± 0.01 a 0.14 ± 0.03 b 0.90 ± 0.07 c 0.11 ± 0.01 b
td (h) 1.30 ± 0.03 a 0.78 ± 0.08 b
B Xmax (gDCW/L) 3.48 ± 0.19 a 3.48 ± 0.31 a
C YX/S (gDCW/gGLC) 0.20 ± 0.02 a 0.25 ± 0.03 a
D qs (gGLC/gDCW·h) 2.64 ± 0.20 a 3.74 ± 0.34 b
E YAC/X (gAC/gDCW) 1.61 ± 0.76 a 1.56 ± 0.20 a
F qP (gAC/gDCW·h) 0.86 ± 0.41 a 1.41 ± 0.20 b
Total protein, TP (g/l) 1.01 ± 0.10 a 0.85 ± 0.03 b
G rHuGM-CSF in TP (%) 34 ± 3 a 30 ± 4 a
rHuGM-CSF in TP (g/l) 0.34 ± 0.10 a 0.26 ± 0.04 a
H YTP/X (gTP/gDCW) 0.29 ± 0.08 a 0.24 ± 0.05 a
I rHuGM-CSF in IBs (%) 45 ± 2 a 53 ± 3 b
J YRP/X (gRP/gDCW) 0.06 ± 0.03 a 0.12 ± 0.02 b
Abbreviations: µ, specific growth rate; td: doubling time; Xmax, maximum biomass concentration; DCW, dry cell weight; GLC, glucose; AC, acetate; YX/S, biomass per substrate yield; YAC/X, acetate per biomass yield; qS, specific glucose consumption rate; qP, specific acetate formation rate; TP, total protein; IBs, inclusion bodies; RP, recombinant protein; YTP/X, total protein per biomass yield; YRP/X, rHuGM-CSF per biomass yield; rHuGM-CSF, recombinant human granulocyte–macrophage colony-stimulating factor
Data are presented as mean ± standard deviation
A non-statistically significant test result (P > .05) is represented with the same letter, and a statistically significant test result (P < .05) is represented with a different note
A: μ after induction was calculated from cell growth just after thermoinduction until the beginning of steady state
B: Xmax was reached at 21 h in cultures grown at 30 °C and 15 h in cultures grown at 34 °C
C, E: YX/S and YAC/X were calculated using the glucose and acetate concentrations at Xmax
D, F: qs and qp were calculated using the μ before induction and the yields obtained at Xmax
G, I: YTP/X and YRP/X were calculated using maximum values of protein concentrations
H, J: Percentage of rHuGM-CSF in TP and IBs was based on the densitometric analysis from bands identified in SDS-PAGE gels
A significant difference in the specific growth rate (µ) before thermoinduction is observed between cultures of 30 °C → 42 °C (0.53 ± 0.01 h−1) and cultures of 34 °C → 42 °C (0.90 ± 0.07 h−1) as shown in Table 1. This represents an increase of ~ 69% in the µ of recombinant E. coli growing at a higher temperature (34 °C). Glucose was consumed entirely in both cases, reaching values close to zero after 20 h of culture (Fig. 1C, D). Interestingly, no significant differences were observed in the biomass per glucose yield (YX/S) and the acetate per biomass yield (YAC/X) between cultures with different pre-induction temperatures (30 and 34 °C; Table 1). However, cultures growing at 34 °C → 42 °C consumed the carbon source faster, yielding a specific glucose consumption rate (qs) of 3.74 ± 0.34 g/g·h, which is 1.5 higher than for cultures grown at 30 °C → 42 °C with qs of 2.64 ± 0.20 g/g·h (Table 1). Similarly, the specific acetate production rate (qp) was ~ 65% higher in the cultures at 34 °C → 42 °C than at 30 °C → 42 °C (Table 1). Acetate reached similar maximum concentrations of 6.58 ± 0.39 g/l at 30 °C → 42 °C and 6.38 ± 0.63 g/l at 34 °C → 42 °C after 17 h of culture (Fig. 1E, F).
The DOT was controlled in the bioreactors through a proportional–integral–derivative (PID) control algorithm (Trujillo-Roldán et al. 2001). DOT oscillated around the setpoint of 35%, confirming no oxygen limitation in the cultures (Supplemental Fig. S1A, B). Likewise, the pH of the medium was kept close to 7.0 ± 0.1 using an automatic addition system of 3 N NaOH or 3 N HCl (Supplemental Fig. S1C, D).
rHuGM-CSF is preferentially accumulated in IBs using a thermoinducible system, and the amount of recombinant protein within IBs increased to 34 °C → 42 °C
To determine the amount of rHuGM-CSF produced after thermoinduction, fractions of total protein obtained from cultures growing at either 30 °C → 42 °C or 34 °C → 42 °C were analyzed on 15% SDS-PAGE (Supplemental Fig. S2). Samples of different post-induction times (1, 3, 5, and 18/20 h) were loaded in gels, and a sample before thermoinduction (0 h) was used as a negative control. A band corresponding to the molecular weight of the rHuGM-CSF protein (~ 14–15 kDa) was seen after thermoinduction for both conditions (Supplemental Fig. S2). The densitometric analysis of the gel 1 h after thermoinduction revealed that rHuGM-CSF corresponds to ~ 22% of the total protein for the 34 °C → 42 °C cultures (Supplemental Fig. S2B), while for the 30 °C → 42 °C cultures, rHuGM-CSF band represented ~ 11.5% of the total protein (Supplemental Fig. S2A). The amounts of recombinant protein increased with the induction time, reaching at the end of the culture percentages of ~ 34% of rHuGM-CSF at 30 °C → 42 °C and ~ 30% of rHuGM-CSF at 34 °C → 42 °C (Supplemental Fig. S2). Similar final concentrations of 0.34 ± 0.10 g/l at 30 °C → 42 °C and 0.26 ± 0.04 g/l at 34 °C → 42 °C of rHuGM-CSF were obtained (Table 1). Our results agree with previous reports, where recombinant protein yields close to 30% of the total protein were achieved in a thermoinducible expression system (Remaut et al.1981; Valdez-Cruz et al. 2010).
Afterward, IBs from E. coli cultures growing at 30 or 34 °C with induction at 42 °C were purified by multiple washing steps. The band corresponding to rHuGM-CSF was visible only in the IBs after the upshift to 42 °C, regardless of the growth temperature, but not in the soluble protein fraction (Supplemental Fig. S3). Subsequently, to determine the rHuGM-CSF yields in the insoluble fraction, 20 µg of IBs obtained at different post-induction times (1, 3, 5, 10, and 18/20 h) from E. coli cultures that grew at 30 or 34 °C were analyzed on 15% SDS-PAGE (Fig. 2). The European Pharmacopoeia reference standard for human GM-CSF (Molgramostim) was used as a positive control (Molgra St. in Fig. 2). The accumulation of rHuGM-CSF in IBs showed differences depending on the growth (pre-induction) temperature (Fig. 2). Densitometric analysis of the rHuGM-CSF protein in IBs indicated the content of ~ 45 ± 2% and ~ 53 ± 3% of recombinant protein in thermoinduced cultures, from 5 h to the end of the culture, that grew at 30 and 34 °C, respectively (Table 1). Although the total protein concentration at the end of cultures was higher in those that follow 30 °C → 42 °C, (Fig. 3A), the amount of protein (Fig. 3B) and rHuGM-CSF within the IBs was higher in those 34 °C → 42 °C (Table 1). The rHuGM-CSF per biomass yield (YRP/X) was two times higher for cultures of 34 °C → 42 °C (0.12 ± 0.02 g/g) than for those of 30 °C → 42 °C (0.06 ± 0.03 g/g), demonstrating that the bacterial growth at 34 °C favored the accumulation of rHuGM-CSF in IBs during thermoinduction (Table 1).Fig. 2 Analysis of protein in IBs by 15% SDS-PAGE gel stained with Coomassie blue. Purified IBs from E. coli W3110 bioreactor cultures growing at 30 °C (A) or 34 °C (B) with subsequent rHuGM-CSF thermoinduction at 42 °C are presented. Lane rE. coli 30 °C and lane rE. coli 34 °C: total protein of the recombinant E. coli W3110 without thermoinduction growing at 30 or 34 °C, respectively; Lane WT E. coli: total protein from wild-type E. coli W3110 strain. Lane MW: molecular weight marker. IBs from different post-induction times (1, 3, 5, 10, and 18/20 h) to the two conditions evaluated are shown. Molgra St: Molgramostim reference standard (2 µg). Arrows indicate the band corresponding to the rHuGM-CSF protein (~ 14 kDa)
Fig. 3 The concentration of total protein (A) and protein in IBs (B) of E. coli W3110 cultures under different pre-induction temperatures: 30 °C (black bars) or 34 °C (white bars) and subsequent rHu-GM-CSF thermoinduction at 42 °C. The mean and standard deviation for three biological replicates per condition are shown
Main folding chaperones (DnaK and GroEL) are associated with IBs and their co-chaperones (DnaJ and GroES) to the soluble protein fraction during thermoinduction
Here, immunodetection of the main folding chaperones (DnaK and GroEL) and their co-chaperones (DnaJ and GroES) was carried out (Fig. 4). As positive controls, total protein lysates of the recombinant E. coli W3110 growing at 30 or 34 °C without thermoinduction and whole protein lysates from wild-type E. coli W3110 were used. In both control fractions, the HSPs mentioned were identified, as expected (Lanes 1 and 2, Fig. 4). DnaK chaperone with an approximate molecular weight of 70 kDa (Bardwell and Craig, 1984) was found weakly expressed 1 and 3 h after thermoinduction, but the intensity of the band increased at 5 h after thermoinduction and returned to a baseline level at the end of cultivation, both in IBs from E. coli that grew at 30 °C → 42 °C (Fig. 4A, Panel 1) and 34 °C → 42 °C (Fig. 4B, Panel 1). However, at 34 °C → 42 °C the band for DnaK (Fig. 4B, Panel 1) was noticeably stronger than at 30 °C → 42 °C (Fig. 4A, Panel 1). In the case of the GroEL chaperone, a band close to 60 kDa was observed in the IBs obtained either at a growth temperature of 30 °C (Fig. 4A, Panel 3) or 34 °C (Fig. 4B, Panel 3), being of similar intensity during all time after thermoinduction at 42 °C.Fig. 4 Immunodetection of DnaK, DnaJ, GroEL, and GroES chaperones in rHuGM-CSF IBs from cultures under different pre-induction temperatures: 30 °C (A) or 34 °C (B) and subsequent thermoinduction at 42 °C. Lanes 1A and 1B: total protein lysates of the recombinant E. coli W3110 without thermoinduction growing at 30 or 34 °C, respectively. Lanes 2A and 2B: whole protein lysate from wild-type E. coli W3110 strain. Lane MW: molecular weight marker. IBs from different post-induction times (1, 3, 5, 10, and 18/20 h) to the two conditions evaluated are shown. Arrows indicate the bands corresponding to DnaK (~ 70 kDa, panel 1), DnaJ (~ 41 kDa, panel 2), GroEL (~ 60 kDa, panel 3), and GroES (~ 15 kDa, panel 4). The amount of protein in IBs (20 µg) loaded on the SDS-PAGE gels was used as a loading control for western blotting
The immunodetection of the co-chaperones DnaJ with ~ 41 kDa (Fig. 4, Panel 2) and GroES with ~ 15 kDa (Fig. 4, Panel 4) was performed, and the bands corresponding to these two proteins were only observed in lanes 1 and 2, which belong to the positive controls. That is, DnaJ and GroES are not associated with IBs under either of the two pre-induction temperatures tested but remain in the soluble fractions of the non-induced recombinant and the wild-type strains. Finally, GroES and DnaJ co-chaperones were immunodetected in the total soluble protein from E. coli cultures growing at 30 °C and 34 °C with thermoinduction at 42 °C (Supplemental Fig. S4).
The pre-induction temperature influenced the content of amyloid-like structure in IBs in a thermoinducible system
The effect of pre-induction temperature (30 or 34 °C) on the amyloid content of rHuGM-CSF IBs obtained under thermoinduction at 42 °C (1 h, 3 h, 5 h, 10 h, and 18/20 h) was analyzed by ATR-FTIR (Fig. 5). ATR-FTIR is a sensitive technique to determine the secondary structure of proteins and studying aggregates formation (Miller et al. 2013). In particular, the absorbance spectra were obtained in the amide I region (1,700–1,500 cm−1), and second derivatives were used to identify the major bands and assign them to the protein secondary structure components (Fig. 5A, B). The major band at 1654 cm−1 was assigned as α-helices/random coil, while the bands at 1636 cm−1 and 1625 cm−1 were designated as β-sheets in native structure (β-sheets) and intermolecular β-structures related to amyloid conformation (aggregates), respectively (Ami et al. 2006; Li et al. 2019; Singh et al. 2020). The rHuGM-CSF IBs from cultures growing at 30 °C → 42 °C (Fig. 5A) exhibited a higher spectral intensity of the band at 1625 cm−1 compared to IBs from cultures growing at 34 °C → 42 °C (Fig. 5B), indicating a decrease in the content of amyloid aggregates when the pre-induction temperature is increased. The content of α-helices and β-sheets in the IBs did not differ significantly for both pre-induction temperatures. Furthermore, no clear differences were observed between the ATR-FTIR spectra of the IBs concerning the post-induction time (Fig. 5A, B). The minima of the second derivative of each structural component allow to observe the differences between the secondary structure content under the evaluated conditions (Fig. 5C, D, E). From the first hour after thermoinduction, the content of amyloid aggregates was higher for IBs from cultures grown at 30 °C than at 34 °C (Fig. 5E). Moreover, the content of α-helices (Fig. 5C) and β-sheets (Fig. 5D) was similar in rHuGM-CSF IBs, regardless of the pre-induction temperature and post-induction time.Fig. 5 Amyloid content in rHuGM-CSF IBs by ATR-FTIR. Second derivatives of the absorbance spectra for IBs from cultures growing at 30 °C (A) or 34 °C (B) and harvested at different times after thermoinduction at 42 °C: 1 h (solid line), 3 h (dashed line), 5 h (dotted line), 10 h (dashed-dotted line) and 18/20 h (dashed double-dotted line). Data were normalized with respect to the tyrosine peak (~ 1508 cm−1), and the major bands were used to identify and assign structural components as α helix/random coil (~ 1654 cm−1), β sheets (~ 1636 cm−1), and amyloid aggregates (~ 1625 cm−1). Spectra represent the average of three biological replicas. Comparison of the second derivatives minima corresponding to α helix/random coil (C), β sheets (D), and amyloid aggregates (E) of IBs from cultures at 30 °C (black bars) or 34 °C (white bars) collected at different times after thermoinduction at 42 °C. The mean and standard deviation are shown for three independent experiments
Alternatively, amyloid-diagnostic dyes are used to determine amyloid-like structure in IBs (Carrió et al. 2005; De Groot et al. 2009; Singh et al. 2020). Thioflavin T (Th-T) is a specific marker to study the amyloid conformation in aggregates since it binds to the surface of channels formed by cross-linked β-sheets (Krebs et al. 2005; Wu et al. 2009). A higher fluorescence indicates a higher amyloid content (LeVine 1995; Castellanos-Mendoza et al. 2014). The change in the fluorescence spectra of Th-T was evaluated after incubation with the rHuGM-CSF IBs (Fig. 6). The maximum emission fluorescence was around 485 nm, both for IBs from cultures 30 °C → 42 °C and 34 °C → 42 °C, which is a typical feature of amyloid aggregates (Singh et al. 2020). IBs collected from cultures grown at 30 °C → 42 °C showed a gradual increase in fluorescence intensity over time, that is, the amount of amyloid structure was greater at 18 h post-induction (Fig. 6A). In contrast, the fluorescence signal of Th-T was minimal for the IBs produced in cultures carried out at 34 °C → 42 °C (Fig. 6B), suggesting either a lack of amyloid structures in these aggregates or the inability of binding to them due to greater compaction or a small size (Carrió et al. 2005).Fig. 6 Fluorescence emission spectra of Th-T binding to rHuGM-CSF IBs obtained under pre-induction temperatures of 30 °C (A) or 34 °C (B) and thermoinduction at 42 °C. IBs were harvested at different times post-induction: 1 h (thin solid line), 3 h (dashed-dotted line), 5 h (dotted line), 10 h (dashed line), and 18/20 h (thick solid line). The spectrum of Th-T without protein was used as a control (dotted gray line), and the mean of two biological replicates per condition is shown
Pre-induction temperature impacts the resistance to proteolytic degradation and solubilization of IBs.
Evaluation of the IBs resistance to proteolytic degradation with proteinase K (PK) has been helpful to characterize the molecular organization and stability of aggregates, as well as an indication of their amyloid content (Castellanos-Mendoza et al. 2014; Calcines-Cruz et al. 2018; Restrepo-Pineda et al. 2019; Singh et al. 2020). PK is a serine protease that exhibits low activity on regions structurally dominated by β-sheets, typical of amyloid fibrils, but it is highly active in hydrophilic domains enriched by loops and α-helices (De Groot et al. 2009; Vázquez-Fernández et al. 2012; Macedo et al. 2015). The enzymatic activity of PK on the IBs was monitored for 100 min at 350 nm (Fig. 7). IBs from E. coli cultures of 34 °C → 42 °C were more susceptible to proteolytic attack by PK (Fig. 7B) than IBs from cultures of 30 °C → 42 °C (Fig. 7A). In the first hours after thermoinduction, IBs from 30 °C → 42 °C appeared to be more susceptible to PK digestion than IBs collected during the final hours (Fig. 7A); the same behavior was observed at 34 °C → 42 °C, where IBs obtained at 1, 3 and 5 h post-induction showed less resistance to PK activity than IBs harvested at 10 and 20 h (Fig. 7B). This means that the pre-induction temperature and the post-induction time affect the structural arrangement of the protein aggregates and, at the same time, their resistance to enzymatic digestion.
Fig. 7 Kinetics of proteolytic digestion of rHuGM-CSF IBs with proteinase-K from cultures obtained at different pre-induction temperatures: 30 °C (A) or 34 °C (B) and subsequent thermoinduction at 42 °C. IBs were collected at different times post-induction: 1 h (black circles), 3 h (white circles), 5 h (triangles), 10 h (white squares) and 18/20 h (black squares). The progressive degradation was followed by absorbance at 350 nm for 100 min, and data were normalized. Traces represent the average of at least two independent experiments
Solubilization of rHuGM-CSF IBs against increasing concentrations of guanidinium chloride was determined. GndHCl is a strong chaotropic agent whose ionic nature causes denaturation of globular proteins and provides an estimate of the conformational stability of IBs (Monera et al. 1994; Del Vecchio et al. 2002; Castellanos-Mendoza et al. 2014). In Fig. 8, the solubilization profiles of rHuGM-CSF IBs exhibit a similar trend under the two evaluated pre-induction temperatures (30 and 34 °C). Protein aggregates obtained after thermoinduction were sensitive to chemical solubilization. At 1 M GndHCl, the amount of solubilized protein was small, but it increased at higher concentrations of GndHCl (3 and 5 M) (Fig. 8). Statistical analysis indicated that the concentrations of solubilized protein did not present significant differences with respect to the post-induction time (Fig. 8).Fig. 8 Solubilization profiles of rHuGM-CSF IBs obtained from pre-induced cultures at 30 °C or 34 °C after different times of thermoinduction at 42 °C (1, 3, 5, 10 and 18/20 h). The amount of solubilized protein (mg/ml) after 24 h of incubation with 1 M (A), 3 M (B), and 5 M (C) concentrations of guanidinium chloride is presented. Bars indicate the mean and standard deviation of the data obtained at each time from three independent experiments
After IBs solubilization in GndHCl (6 M) and folding process, rHuGM-CSF was purified by HPLC showing a retention time of ~ 24.5 min, in both, 30 °C → 42 °C and 34 °C → 42 °C (Fig. 9). A similar retention time was observed when the European Pharmacopoeia reference standard for human GM-CSF was injected in the HPLC (inset of Fig. 9). All the small peaks observed before and after the rHuGM-CSF peak are surely the host cell proteins from the IBs. Finally, the refolding ability of the solubilized rHuGM-CSF was assayed by CD spectroscopy. In the far-UV region, the asymmetric environments of peptide bonds yield CD signals that are characteristic of each different structural element, allowing the secondary structure of a protein to be estimated. As shown in Fig. 10, protein samples obtained from both thermoinduction regimens showed DC spectra largely overlapping each other, exhibiting two minima centered ~ 208 and ~ 220 nm that are typical of helical-type secondary structures. Deconvolution analysis of these spectra yielded α-helix and β-strand contents of 36% and 15%, respectively, which agree with those of the crystallographic structure of the protein, α-helix = 39% and β-strand = 9% (PDB code 1csg; Walter et al. 1992).Fig. 9 Purification of the folded rHuGM-CSF by reverse-phase HPLC. The folded protein solution (~ 10 µg) from pre-induced cultures at 30 °C (continuous line) or 34 °C (dotted line) after thermoinduction at 42 °C was loaded in the HPLC at 50 °C with a detector wavelength of 214 nm. A standard curve (inset) of the European Pharmacopoeia reference standard for human GM-CSF (Molgramostim, Y0000251, Sigma Aldrich, St. Louis, MO, USA) was carried out at 1.345, 0.672, and 0.336 mg/ml
Fig. 10 Far-UV circular dichroism spectra of purified rHuGM-CSF in water from IBs obtained from pre-induced cultures at 30 °C (filled circles) or 34 °C (open circles) after thermoinduction at 42 °C. Each spectrum corresponded to the average of three repetitive scans and was corrected by the buffer signal
Discussion
Innumerable bioprocesses of recombinant protein production for therapeutic use are carried out in E. coli, and the formation of bacterial aggregates has become a common phenomenon that, contrary to being relegated, has gained interest in recent years (García-Fruitós et al. 2010; De Marco et al. 2019; Pesarrodona et al. 2019; Jäger et al. 2020). Inclusion bodies (IBs) are enriched reservoirs of recombinant protein, which can be considered a previous protein purification step (De Marco et al. 2019; Restrepo-Pineda et al. 2021). In thermoinducible systems, the recombinant protein overexpression, the heat shock response, and IBs formation co-occur in the cells (Valdez-Cruz et al. 2010; Restrepo-Pineda et al. 2021a). Therefore, understanding the communication between molecular responses and physiological events in this system can be useful to design optimized production bioprocesses that allow higher yields of biologically active recombinant protein with inexpensive and straightforward recovery steps from IBs (Rosano et al. 2019; Restrepo-Pineda et al. 2021).
In thermoinducible systems, the temperature upshifts and the over synthesis of recombinant proteins and HSPs cause an increase in energy demand, metabolic alterations, and a decrease in cell growth (Hoffmann and Rinas 2004; Restrepo-Pineda et al. 2021). Our study shows the effect of pre-induction temperature on cell growth, chaperone composition, and structural characteristics of IBs collected at different times of thermoinduction from cultures producing the rHuGM-CSF. E. coli W3110 growing either at 30 °C or 34 °C reached similar maximum biomass. However, a significant increase in the specific growth rate (µ), the specific glucose consumption rate (qs), and the specific acetate formation rate (qp) was evidenced by increasing the pre-induction temperature (Fig. 1, Table 1). In previous publications, the production of recombinant proteins under the λpL/pR-cI857 thermoinducible system was also accompanied by the acetate accumulation, reporting values near 4.0 g/l in E. coli BL21 (Caspeta et al. 2009, 2013); 0.2 g/l in E. coli K-12 (Mansey et al. 2014); 3.5–7.4 g/l in E. coli 53,606 (Restrepo-Pineda et al. 2019) and 5.9–11.3 g/l in E. coli W3110 (this work). Aerobic E. coli cultures under conditions of excess glucose (20–40 g/l) are accompanied by metabolic overflow, which can lead to high excretion of acetate and other by-products (Wittmann et al. 2007; Phue and Shiloach 2004). This acetate accumulation is due to a flow redirection from pyruvate dehydrogenase to pyruvate oxidase to remedy the pyruvate node load during induction (Wittmann et al. 2007; Shiloach and Rinas 2009). Concentrations above 2.4 g/l of acetate in the culture medium can generate a decrease in bacterial growth and inhibit recombinant protein production (Dittrich et al. 2005; Eiteman and Altman 2006), which could be associated with the fall in biomass at the end of thermoinduced cultures (Fig. 1) as it can also be due to a complete depletion of the carbon (Restrepo-Pineda et al. 2019).
Bacterial growth at 34 °C favored the accumulation of rHuGM-CSF in IBs during thermoinduction at 42 °C in comparison with a pre-induction of 30 °C, possibly due to those bacterial cells grown at 34 °C have an accelerated rate of cellular processes, reflected in a higher specific growth rate compared to 30 °C. This may be associated with an increased rate of translation, a higher concentration of nascent recombinant polypeptide in the cytosol, and, therefore, the probability of stereo-specific interactions that lead to the increase of rHuGM-CSF aggregation (Singh et al. 2020; Adachi et al. 2015). In addition, it is possible that cells cultivated at 30 °C require more resources to deal with heat stress; while the cells that grew at 34 °C already have a physiological and metabolic pre-adaptation to thermal stress, favoring productivity (Cullum et al. 2001). According to these, the pre-induction temperature is modifying not only the specific growth rate but also the post-induction recombinant protein synthesis rate and its accumulation in IBs. The cellular growth rate has been related to the IBs amount, and biological activity of the recombinant protein produced in them. Iafolla et al. (2008) found that at the fastest growth rate, more active EGFP (enhanced green fluorescent protein) was present in IBs, while at slower growth rate IBs are less abundant and with less active EGFP (Iafolla et al. 2008).
The tendency of GM-CSF to aggregation makes it an ideal candidate protein to study the behavior at the structural and compositional level of IBs obtained from thermoinduced cultures. The first studies about the GM-CSF production in bacteria were based on a temperature-inducible plasmid (from 28 to 42–48 °C). They reported that both murine GM-CSF (DeLamarter et al. 1985) and human GM-CSF (Burgess et al. 1987) accumulated in IBs when produced in E. coli, which agrees with our results. This knowledge has been corroborated in subsequent reports using chemical induction with IPTG (Schwanke et al. 2009; Thomson et al. 2012) or autoinduction (Malekian et al. 2019a). However, those did not analyze the protein secondary structure or the amyloid content of the aggregates, they only demonstrated the bioactivity of the recombinant GM-CSF obtained from IBs. The human GM-CSF produced in E. coli under the regulation of a heat-inducible promoter showed a specific activity of 2.9 × 107 units/mg with bone marrow cells (Burgess et al. 1987). Recombinant Murine-derived GM-CSF stimulated the growth of granulocyte and macrophage colonies of mouse bone marrow cells (DeLamarter et al. 1985). The refolded and purified rHuGM-CSF promoted the cell growth in a human hematopoietic cell line in a similar way to the commercially available protein (Schwanke et al. 2009; Thomson et al. 2012). Notably, these results indicate that the recombinant protein reaches an active conformation after recuperation and refolding from IBs and, confirms that the absence of glycosylation or the addition of an extra N-terminal methionine residue does not affect the bioactivity of the rHuGM-CSF obtained on a bacterial platform (Burgess et al. 1987; DeLamarter et al. 1985).
E. coli has robust control systems to assist the folding of newly synthesized proteins in the cytosol, including the DnaK chaperone with its DnaJ and GrpE co-chaperones, and GroEL chaperone with its GroES co-chaperone (Houry 2001; Bhandari and Houry 2015). In this work, molecular chaperones were differentially associated with the protein fractions, detecting DnaK and GroEL in the IBs. At the same time, DnaJ and GroES were found in the total soluble protein, possibly reflecting the order in which they interact with the protein folding intermediates during aggregation. Differences in DnaK expression due to pre-induction temperature suggest that DnaK is preferentially required at elevated temperatures to maintain viable cell growth and other multiple bacterial functions (Mayer 2021). Previous results indicate that E. coli cells with dnak null mutations can grow slowly at 30 °C and 37 °C, but at 42 °C they lose the ability to form colonies after 2 h of exposure (Paek and Walker 1987). In contrast, the constant production of GroEL throughout the thermoinduction stage and under the two growth temperature conditions evaluated corroborates its importance as a central regulator for protein folding in E. coli (Hayer-Hartl et al. 2015). Some studies have reported that GroEL complies a job of protecting the bacterial growth in a wide range of temperatures, from low (17 to 30 °C; Fayet et al. 1989) to normal/high temperatures (20 to 40 °C; Kusukawa and Yura 1988). In brief, GroEL seems to act as a key piece in supporting growth at normal physiological temperatures, whereas DnaK might be essential mostly at higher temperature. Some studies have revealed the presence of chaperones such as DnaK, GrpE, GroEL, GroES, IbpA and IbpB in IBs isolated from E. coli after induction by IPTG or temperature increase, but they did not find DnaJ (Hoffmann and Rinas 2000; Rinas et al. 2007; Jürgen et al. 2010). The absence of DnaJ was not discussed in those reports, probably because is assumed that it remains in the soluble fraction like most HSPs (> 90%; Hoffmann and Rinas 2000).
In recent years, it has been proposed that the structural properties of IBs define the protocols of isolation, solubilization, and refolding, mainly during recovery of a biologically active recombinant protein (Singh and Panda 2005; Singhvi et al. 2020). Structural changes of IBs depend not only on the dynamic of aggregation and nature of the recombinant protein but also on the bioprocess conditions (Castellanos-Mendoza et al. 2014; Calcines-Cruz et al. 2018; Restrepo-Pineda et al. 2019; De Marco et al. 2019). Structural analyses by ATR-FTIR and binding to Th-T indicated that at a higher pre-induction temperature (34 °C), the IBs have a lower proportion of amyloid-like conformations than at 30 °C (Figs. 5 and 6). Moreover, previous studies revealed that regions with higher amyloid-like conformation in IBs are more resistant to proteolysis with by PK (Upadhyay et al. 2012). Therefore, the degradation profiles in Fig. 7 also confirm the results observed in the secondary structure analysis by ATR-FTIR and binding to Th-T, suggesting that the rHuGM-CSF IBs obtained at 30 °C → 42 °C have a higher proportion of amyloid-like structure compared to IBs obtained at 34 °C → 42 °C.
Usually, high concentrations of urea or GndHCl (6–8 M) are used in the solubilization of IBs, which can cause a significant disturbance in the structure of the recombinant protein folded state and result in a low recovery of bioactive protein (Upadhyay et al. 2012, 2016; Singhvi et al. 2020). In this study, the rHuGM-CSF IBs were effectively solubilized in low concentrations of GndHCl (3 M and 5 M, Fig. 8), which is important in the downstream process at the industrial level. When IBs can be solubilized at the lowest possible denaturant concentration, it can result in proteins that retain part of their folded structure, making refolding processes more efficient and consequently recovering biological activity (Singh et al. 2015). Indeed, the removal of the denaturing agent yielded folded proteins with native-like secondary structure contents, as revealed by the HPLC purification recovering the folded rHuGM-CSF (Fig. 9), as also by solution CD spectroscopy (Fig. 10). CD is a widely used tool to identify changes in the secondary structure of proteins, which can impact their mechanism of action or in the regulation of their biological activity (Kelly and Price 2000; Kelly et al. 2005). The application of CD to analyze the protein structure after denaturalization, and to associate these data with the rate of recovery of biological activity is common (Kelly and Price 1997). In this study, CD spectra of rHuGM-CSF from E. coli cultures growing either at 30 or 34 °C were similar (Fig. 10) A typical helical conformation with two negative shoulders at ~ 208 and ~ 220 nm was observed. Calculations of the secondary structure suggest 36% α-helix and 15% β-strand for both conditions, which agrees with previously reported values of ~ 30% α-helix content for recombinant GM-CSF by far-UV CD (Malekian et al. 2019b; Wingfield et al. 1988). The refolded rHuGM-CSF characterization by CD allowed obtaining valuable insights to understand the protein structure–function relationship.
The claims obtained in this study could serve as a methodological proposal to produce biopharmaceutical proteins in IBs using thermoinduced systems. By modifying the growth or pre-induction temperature, it is possible to have conformationally different IBs with a high content of recombinant protein, and easier to solubilize, lowering bioprocesses efforts and costs. Moreover, the similarity in the secondary structure content between the recombinant protein isolated from bacterial aggregates and the reported structure of rHuGM-CSF revealed the possibility of obtaining biologically active protein after solubilization and refolding processes.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file1 (PDF 1615 KB)
Acknowledgements
Sara Restrepo-Pineda is a doctoral student from “Programa de Doctorado en Ciencias Biológicas” of the “Universidad Nacional Autónoma de México” (UNAM) and received fellowship from CONACYT (CVU 589949), authors thank both institutions for the support provided. EG-H, NS-P, NOP, MAT-R and NAV-C are members of the Sistema Nacional de Investigadores, Consejo Nacional de Ciencia y Tecnología. This project was developed under the Institutional Program of the Instituto de Investigaciones Biomédicas UNAM: “La producción de biomoléculas de interés biomédico en bacterias y hongos”. Authors also appreciate the technical support by Dr. Axel Luviano in CD experiments and Diego Rosiles-Becerril and M. Sc. Luis Pablo Ávila-Barrientos in protein refolding and HPLC development.
Author contributions
SR-P, NAV-C, EG-H, NS-P contributed to experimentation; SR-P, EG-H, NAV-C, and MAT-R helped in conceptualization; MAT-R and NAV-C contributed to funding acquisition; MAT-R and NAV-C contributed to project administration; MAT-R, NOP, NS-P, EG-H, and NAV-C helped in resources; SR-P, MAT-R, and NAV-C contributed to writing—original draft; SR-P, NOP, NS-P, MAT-R, and NAV-C helped in writing—review and editing.
Funding
This work was supported by “Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica, Universidad Nacional Autónoma de México” (PAPIIT-UNAM IN210822: NAVC, IN211422, IV201220: MATR). The funders had no role in data collection and analysis, decision to publish, or preparation of the manuscript.
Data availability
The authors confirm that all relevant data are included in this article and its supplementary information files.
Declarations
Ethics approval
This article does not report any studies with human participants or animals performed by the authors.
Conflict of interest
NOP works in Probiomed S.A. de C.V., which manufactures recombinant human therapeutic proteins.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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PMC009xxxxxx/PMC9002100.txt |
==== Front
J Psychiatr Res
J Psychiatr Res
Journal of Psychiatric Research
0022-3956
1879-1379
Elsevier Ltd.
S0022-3956(22)00199-6
10.1016/j.jpsychires.2022.04.003
Article
Psychological distress among outpatient physicians in private practice linked to COVID-19 and related mental health during the second lockdown
Frajerman Ariel abcd∗
Colle Romain ab
Hozer Franz ef
Deflesselle Eric ag
Rotenberg Samuel ab
Chappell Kenneth a
Corruble Emmanuelle ab
Costemale-Lacoste Jean-François a
a MOODS Team, INSERM U1178, CESP, Université Paris-Saclay, Faculté de Médecine Paris-Saclay, Le Kremlin Bicêtre, F-94275, France
b Service Hospitalo-Universitaire de Psychiatrie de Bicêtre, Mood Center Paris Saclay, Assistance Publique-Hôpitaux de Paris, Hôpitaux Universitaires Paris-Saclay, Hôpital de Bicêtre, F-94275, France
c Inserm U1266–GDR 3557, Institut de psychiatrie et neurosciences de Paris, Institut de Psychiatrie, Paris, France
d Université de Paris, Paris, France
e UNIACT Lab, Psychiatry Team, NeuroSpin Neuroimaging Platform, CEA Saclay, Gif-sur-Yvette, France
f AP-HP Centre-Université de Paris, Hôpital Corentin-Celton, Département Médico-Universitaire de Psychiatrie et Addictologie, 92130, Issy-les-Moulineaux, France
g Département de Médecine Générale, Université Paris-Saclay, Faculté de Médecine Paris-Saclay, Le Kremlin Bicêtre, F-94275, France
∗ Corresponding author. Université de Paris, Inserm U1266–GDR 3557, institut de psychiatrie et neurosciences de Paris, 102-108, Rue de la Santé, 75014, Paris, France.
12 4 2022
7 2022
12 4 2022
151 5056
30 8 2021
26 3 2022
4 4 2022
© 2022 Elsevier Ltd. All rights reserved.
2022
Elsevier Ltd
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Background
Outpatient physicians in private practice, as inpatient physicians, are on the frontline of the COVID-19 pandemic. Mental-health consequences of the pandemic on hospital staff have been published, but the psychological distress among outpatient physicians in private practice due to COVID-19 has never been specifically assessed.
Methods
A French national online cross-sectional survey assessed declared psychological distress among outpatient physicians in private practice linked to COVID-19, sociodemographic and work conditions, mental health (Copenhagen Burn-out Inventory, Hospital Anxiety and Depression Scale, and the Insomnia severity Index), consequences on alcohol, tobacco, and illegal substance misuse, and sick leave during the 2nd COVID-19 wave.
Findings
Among the 1,992 physicians who answered the survey, 1,529 (76.8%) declared psychological distress linked to COVID-19. Outpatient physicians who declared psychological distress linked to COVID-19 had higher rates of insomnia (OR = 1.4; CI95 [1.1–1.7], p = 0.003), burnout (OR = 2.7; CI95 [2.1; 3.2], p < 0.001), anxiety and depressive symptoms (OR = 2.4; CI95 [1.9–3.0], p < 0.001 and OR = 1.7; CI95 [1.3–2.3], p < 0.001) as compared to physicians who did not. They also had higher psychotropic drug use in the last twelve months, or increased alcohol or tobacco consumption due to work-related stress and were more frequently general practitioners.
Interpretation
The feeling of being in psychological distress due to COVID-19 is highly frequent among outpatient physicians in private practice and is associated with mental health impairment. There is a need to assess specific interventions dedicated to outpatient physicians working in private practice.
Keywords
Outpatient physicians
Private practice
Covid-19
Lockdown
Mental health
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pmc1 Introduction
The coronavirus 2019 (COVID-19) is the longest pandemic ever experienced by the modern health system. Outpatient physicians, like in-patient physicians, are on the frontline against the pandemic.
By increasing the need of an already overstretched and understaffed health system across the globe, physicians’ well-being has been negatively impacted (Goddard and Patel, 2021). Indeed, more than usual, physicians have experienced fears over being infected or infecting their families. Despite these risks, patient care was prioritised before their own health. For some physicians, the fear of transmitting COVID-19 led them to isolate from their families for months (Mehta et al., 2021). The COVID-19 pandemic could be considered as a new form of traumatic event and a source of post-traumatic stress disorder (Bridgland et al., 2021; Unützer et al., 2020). Stressors linked to the COVID-19 pandemic are summarized in Supplementary Data 1. Often, difficult decisions were made based on insufficient resources, such as postponing a hospitalization or a surgery, selecting which patient to transfer to the intensive care unit, or considering off-label care. Thus, physicians had to frequently struggle with different ethical dilemmas (Marazziti and Stahl, 2020). In parallel, the fear of infection has been maintained by the worldwide shortage of personal protective equipment (Burki, 2020), especially during the first wave. Physicians have also had to quickly adopt new technologies, such as telemedicine, to help patients while limiting the risk of contagion (Mehta et al., 2021). Additionally, by increasing uncertainty and limiting physicians in their ability to save lives, COVID-19 has created among them a sense of helplessness and failure (Sederer, 2021). Psychological distress has also been reinforced by the stress of successive lockdowns and the worldwide economic crisis (Adibe, 2021). However, other factors—work-related or otherwise—such as age, sex, and poor work environment, could be linked to increased stress in physicians (Chatterjee et al., 2021; Dyrbye et al., 2013; West et al., 2018; Wijeratne et al., 2020; Zhou et al., 2020).
High levels of depression (Azoulay et al., 2020; Kannampallil et al., 2020; Lai et al., 2020; Park et al., 2020; Tiete et al., 2020; Zhu et al., 2020), anxiety (Azoulay et al., 2020; Kannampallil et al., 2020; Lai et al., 2020; Park et al., 2020; Tiete et al., 2020; Zhu et al., 2020), and insomnia-related symptoms (Barua et al., 2020; Lai et al., 2020; Pappa et al., 2020; Şahin et al., 2020; Wu et al., 2021; Yang et al., 2021), burnout (Cravero et al., 2020; Huang et al., 2021; Kok et al., 2021; Park et al., 2020; Tiete et al., 2020; Torrente et al., 2021), and psychological distress (Giusti et al., 2020; Park et al., 2020) have been reported among inpatient physicians during the COVID-19 pandemic.
A study of 40 Catalan general practitioners (GP) showed the mental health impact of the pandemic on doctors, with a significant increase in severe burnout from 10% before the pandemic in 2019 to 50% during the pandemic in October 2020 (Seda-Gombau et al., 2021). In Singapore, a study of 257 GPs found anxiety (21%), depression (26.6%), and burnout (82%) (Lum et al., 2021). Another study of 215 Italian GPs during the first wave found the presence of anxiety, depression, and burnout symptoms in 36%, 18%, and 25%, respectively (Lasalvia et al., 2021).
However, psychological distress linked to COVID-19 and the related mental health of outpatient physicians in private practice have never been specifically assessed.
Such symptoms could impact other factors such as alcohol or illegal drug abuse, tobacco consumption, or one's ability to work. Due to the culture of silence in medicine (e.g., suffering without complaining because you chose to be a doctor (Robertson and Long, 2018; Shapiro and McDonald, 2020), and the stigma linked to the perception that vulnerability is a sign of weakness, physicians often think that emotional exhaustion is a part of their job and not a good reason to seek psychological help (Shapiro and McDonald, 2020). For these reasons, there is an urgent need to study outpatient physicians' psychological distress.
Aims of the Study: We aim to assess among outpatient physicians in private practice the prevalence of psychological distress linked to COVID-19 during the 2nd lockdown in France, its associated factors, and related mental health.
2 Material and methods
2.1 Design
We conducted an online survey for physicians in private practice registered on Doctolib®, the most used interface software by physicians in private practice to schedule medical appointments in France. The survey was administered using the online software LimeSurvey®. The reporting of the study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement (Elm et al., 2007; Group, 2007).
Approval for this study was obtained from the local institutional review board at the University of Paris-Saclay, France. The questionnaires were collected anonymously. Respondents gave their informed consent to participate.
2.2 Participants
Emails to respond to an online survey were sent on October 29th and November 24th, 2020 to outpatient physicians of all specialties in private practice who were users of Doctolib®. Among the 15,722 which received the mail, 2,377 opened the survey, and 1,992 completed it (Fig. 1 ).Fig. 1 ESTEEM study Flow chart.
Fig. 1
The second lockdown in France took place between October 29th and December 15th, 2020.
2.3 Measures
The following variables were collected: age range, sex, medical specialty, and the French county of practice. Participants were then invited to specify the nature of their practice: alone or in a group, with or without a medical assistant, and as an employee or not. Finally, they were invited to complete a questionnaire on their mental health, comprising declared psychological distress linked to COVID-19, declared stress linked to work, and questions related to insomnia, anxiety, depression, burnout, psychotropic medication use, substance abuse or misuse, and sick leave associated to the declared work-related stress.
2.4 Declared psychological distress linked to the COVID-19 pandemic
Psychological distressed linked to the COVID-19 pandemic was assessed with the following statement: “the COVID-19 epidemic we are going through is currently a source of excess stress, psychological suffering or professional burnout” and a Y/N answer. Based on this question, the whole population was split in 2 groups: physicians who responded “Yes” were considered as having declared a psychological distress linked to COVID-19 (population with psychological distress linked to COVID-19) and those who responded “No” as the population without psychological distress linked to COVID-19.
2.5 Burnout syndrome
The Copenhagen Burnout Inventory (CBI) (Doppia et al., 2011; Hardy et al., 2020; Kristensen et al., 2005; Nuss et al., 2020; Perumalswami et al., 2019) was used. The scale examines exhaustion and its attribution on three subscales: personal burnout, work-related burnout, and patient-related burnout. Each question is a Lickert scale of five-points: “Always” for 100, “Often” for 75, “Sometimes” for 50, “Seldom” for 25, and “Never/almost never” for 0. The cut-off for each subscale is a mean score >50. Burnout syndrome was defined by a score higher than the cut-off on at least one of the subscales (Madsen et al., 2015).
2.6 Psychiatric symptoms
The Insomnia Severity Index (ISI) (Morin et al., 2011) was used to measure sleep complaints. The ISI is a 7-item self-report questionnaire that assesses the nature, severity, and impact of insomnia. The total score ranges from 0 to 28. The cutoff ≥8 was chosen since it is useful at detecting physicians with sleep problems (Morin et al., 2011).
Anxiety and depression symptoms were assessed by the Hospital Anxiety and Depression Scale (HADS) (Zigmond and Snaith, 1983). This questionnaire consists of 7 questions related to anxiety (HADS-A) and 7 related to depression (HADS-D), ranging from 0 to 21 on each subscale. We used the cut-off ≥8 (HADS-D8 or HADS-A8) because of its sensitivity in case-finding ability (Brennan et al., 2010).
2.7 Statistical analyses
Descriptive statistics were provided as effectives (percentages) for age range, sex, and work characteristics. Bivariate analyses were based on chi-square and Fisher's exact tests to compare prevalence between groups (physicians with versus without declared psychological distress linked to COVID 19). Subsequently, logistic regression models adjusted for age and sex were performed to control for potential age or sex effects for statistically significant associations in bivariate analyses (Model 1). We then controlled logistic regression models for variables related to the nature of practice (i.e. alone or in a group, with or without a medical assistant, and working as an employee or not) (Model 2). Statistical significance was evaluated using two-sided tests with an alpha risk set a priori at 0.05. Statistical analyses were performed using SPSS 20 software (IBM SPSS Statistic 20). Statistical power was tested with G*Power (“Heinrich-Heine-University Software, Apps, and Games Free Download,” n.d.).
3 Results
Among the 1,992 physicians who answered the survey, 57.9% were women and 1,529 (76.8%) declared a psychological distress linked to COVID-19. Outpatient physicians with a psychological distress linked to COVID-19 were more frequently general practitioners (Table 1 ). There was no other difference between outpatient physicians with and without a declared psychological distress linked to COVID-19 in terms of gender, age, and type of work (Table 1).Table 1 Sociodemographic and work condition characteristics associated with psychological distress linked to COVID-19.
Table 1 Total prevalence Psychological distress linked to COVID-19 (n = 1,529) Psychological distress linked to COVID-19 (n = 463) Psychological distress linked to COVID-19 (n = 1,529) Total prevalence p valuea Adjusted Odds-ratioc AOR[95%CI] p valueb
Women n(%) 1,152 (57.9) 904 (59.1) 248 (54) 904 (59.1) 1,152 (57.9) 0.053 1.22 [0.99; 1.51] 0.060
Age Range n(%) 20–30 years 29 (1.9) 42 (2.1) 13 (2.8) 29 (1.9) 42 (2.1) 0.042 0.98[0.99; 1.51] 0.6
31–40 years 480 (31.4) 597 (30) 117 (25.3) 480 (31.4) 597 (30)
41–50 years 370 (24.2) 499 (25.1) 129 (27.9) 370 (24.2) 499 (25.1)
51–60 years 261 (17.1) 354 (17.8) 93 (20.0) 261 (17.1) 354 (17.8)
>60 years 389 (25.4) 500 (25.1) 111 (24.0) 389 (25.4) 500 (25.1)
Nature of practice
Not employee n(%) 1,695 (85.3) 1,306 (85.4) 389 (84.7) 1,306 (85.4) 1,695 (85.3) – – –
Employee n(%) 12 (0.6) 7 (0.5) 5 (1.1) 7 (0.5) 12 (0.6) 0.138 0.45 [0.14; 1.43] 0.175
Mixed activity n(%) 281 (14.1) 216 (14.1) 65 (14.2) 216 (14.1) 281 (14.1) 0.851 0.99 [0.85; 1.14] 0.848
Work alone n(%) 731 (36.8) 551 (36.0) 180 (39.2) 551 (36.0) 731 (36.8) 0.216 1.14 [0.91; 1.42] 0.247
No assistant n(%) 770 (38.7) 601 (39.3) 169 (36.8) 601 (39.3) 770 (38.7) 0.337 1.09 [0.89; 1.36] 0.432
Specialties
General practitioners n(%) 952 (47.9) 764 (50.0) 188 (41.0) 764 (50.0) 952 (47.9) 0.001 1.43 [1.16;1.77] 0.001
Mixed activity = time shared between not employee and employee work.
Bold: significant p value < 0.05.
a Bivariate analyses.
b Multivariate logistic regression.
c adjusted on age and gender.
In bivariate analyses, psychological distress linked to COVID-19 was associated with higher rates of insomnia, burnout, anxiety symptoms, depressive symptoms, psychotropic drug use in the last twelve months, or increased alcohol or tobacco consumption due to work-related stress (Table 2 , Fig. 2 ). In subscales of burnout, declared psychological distress linked to COVID-19 was associated with work-related burnout, personal burnout, and patient-related burnout (Table 2, Fig. 2).Table 2 Mental health of physicians with and without declared psychological distress linked to COVID-19.
Table 2 No psychological distress linked to COVID-19 (n = 463) Psychological distress linked to COVID-19 (n = 1529) Bivariate Odds-ratio [95%CI]a p value Model 1: Adjusted Odds-ratio AOR [95%CI] p value Model 2: Adjusted Odds-ratio AOR[95%CI] p value
Declared stress linked to work n(%) 204 (44.4) 1,164 (76.1) 3.99 [3.20;4.96] <0.001 3.97 [3.19;4.94] <0.001 3.96 [3.18;4.93] p < 0.001
Sick leave (12 months) due to stress linked to work n(%) 6 (1.3) 38 (2.5) 1.92 [0.81; 4.58] 0.139 1.84 [0.77; 4.39] 0.169 1.863 [0.78; 4.45] p = 0.845
Taking psychotropic drugs (12 months) due to stress linked to work n(%) 50 (10.9) 267 (17.5) 1.73 [1.25;2.39] 0.001 1.72 [1.25;2.38] 0.001 1.73 [1.25;2.39] p = 0.001
Increasing consumption of alcohol or tobacco due to stress linked to work n(%) 37 (8.1) 247 (16.2) 2.20 [1.53;3.16] <0.001 2.21 [1.54;3.18] <0.001 2.22 [1.54;3.20] p < 0.001
Taking illegal drugs (12 months) due to stress linked to work n(%) 4 (0.9) 13 (0.9) 0.98 [0.32; 3.01] 0.965 1.03 [0.33; 3.19] 0.959 1.06 [0.34; 3.31] p = 0.911
Burnout n(%) 252 (54.9) 1,168 (76.4) 2.66 [2.14; 3.31] <0.001 2.62 [2.10; 3.27] <0.001 1.75 [1.35;2.27] p < 0.001
Work-related burnout n(%) 227 (49.5) 1099 (71.9) 2.61 [2.11;3.24] <0.001 2.58 [2.08;3.21] <0.001 2.38 [1.91;2.95] p < 0.001
Personal burnout n(%) 194 (42.3) 991 (64.8) 2.52 [2.03;3.11] <0.001 2.49 [2.01;3.09] <0.001 2.63 [2.11; 3.29] p < 0.001
Patient-related burnout n(%) 126 (27.5) 659 (43.1) 2.00 [1.59;2.52] <0.001 2.00 [1.59;2.52] <0.001 2.58[2.08;3.20] p < 0.001
Insomnia n(%) 182 (39.7) 729 (47.7) 1.39 [1.12;1.72] 0.003 1.40 [1.13;1.74] 0.002 2.50 [2.02;3.11] p < 0.001
Depressive symptoms n(%) 88 (19.2) 443 (29.0) 1.72 [1.33;2.22] <0.001 1.73 [1.33;2.33] <0.001 2.03 [1.61;2.55] p < 0.001
Anxiety symptoms n(%) 195 (42.5) 976 (63.8) 2.39 [1.93;2.96] <0.001 2.36 [1.90;2.93] <0.001 1.42 [1.15;1.76] p = 0.003
Model 1: multivariate logistic regression adjusted for age and sex.
Model 2: multivariate logistic regression adjusted for age, sex and the work condition (alone or in group, with or without medical assistant and as employee or not).
a Bivariate analyses.
Fig. 2 Prevalence of psychiatric symptoms in physicians with or without declared psychological distress linked to COVID-19.
Fig. 2
These associations were significant in multivariate analyses controlled for age, gender, and work characteristics (alone or in a group, with or without a medical assistant, and working as an employee or not) (Table 2).
4 Discussion
This survey shows that three-quarters of the responding outpatient physicians in private practice declared psychological distress linked to the COVID-19 pandemic during the second lockdown in France. These physicians are more often general practitioners and have higher mental health impairment than those without psychological distress linked to COVID-19. Our study is the first to specifically assess outpatient physicians of all specialties in private practice during the second lockdown.
Regarding the effects of the pandemic on the mental health of healthcare workers, our study is the first to assess the psychological distress linked to COVID-19 by asking physicians a simple closed-ended question. This simple question is relevant since a “yes” answer is associated with increased insomnia, burnout, anxiety, and depressive symptoms among outpatient physicians in private practice. Prevalence of psychiatric symptoms by specialties are described in Supplementary Data 2.
Compared to other studies conducted mainly on inpatient physicians during the COVID-19 pandemic, our sample of physicians in private practice with psychological distress linked to COVID -19 show almost the same rates of burnout syndrome as the most impacted physicians (76% in Romanic hospital resident physicians) (Fiest et al., 2021). In physicians that declared psychological distress linked to COVID-19, the rate of insomnia (47.7%) was slightly higher than in previous studies: 41.6% in a recent meta-analysis of inpatient physicians caring for COVID-19 patients (Salari et al., 2020), 45% among anaesthesiologists and intensive care practitioners working in public hospitals (Richter et al., 2015), and 40.9% among radiologists (Florin et al., 2020).
The prevalence of anxiety symptoms among physicians in private practice with declared psychological distress linked to COVID-19 during the 2nd lockdown is much higher (76.4%) than radiologists (35%) during the first wave (Florin et al., 2020).
The prevalence of depressive symptoms among physicians with declared psychological distress linked to COVID-19 (29%) is similar to the prevalence among French radiologists during the first wave (30.6%)(Florin et al., 2020). According to a survey of 989 French GPs, 49% felt very anxious during the first wave (Dutour et al., 2021).
We report that outpatient physicians with psychological distress linked to COVID-19 are more often general practitioners than other specialties in private practice. Three previous studies explored the mental health of general practitioners during the first wave of COVID-19, including burnout (Di Monte et al., 2020) and depression or anxiety symptoms (Amerio et al., 2020; Monterrosa-Castro et al., 2020). The prevalence of such symptoms was lower than in our global sample and largely lower than the prevalence among physicians that declared psychological distress linked to COVID-19 during the second lockdown in our study. These results suggest that the prevalence of psychiatric symptoms related to the second wave of the pandemic among general practitioners could be more important.
Prior to the COVID-19 pandemic, a meta-analysis of 22 studies (Kansoun et al., 2019) showed a prevalence of burnout among French physicians of 44% among anaesthesiologists and intensive care practitioners, 55% among emergency practitioners, and 48% among general practitioners. Each prevalence here is almost 20 to 30 percentage points lower than the prevalence of burnout linked to COVID-19 in our study (76.4%) but close to the prevalence of burnout among physicians without declared psychological distress linked to COVID-19 in our study (54.9%).
4.1 Strengths
This study has several strengths. First, it is the only one to directly assess psychological distress linked to COVID-19. Second, assessing insomnia, burnout, anxiety, and depression at the same time allowed us to identify associated mental health impairment. Third, our study is the largest to date to investigate the mental health of outpatient physicians in private practice during the COVID-19 pandemic.
4.2 Limitations
Our study has several limitations. First, this is a cross-sectional study without a comparative group. Second, its response rate was low (12.8%) but close to other mental health studies among physicians (17.8%) (West et al., 2020). However, this could be a selection bias. Indeed, according to Pierce et al. (2020), 2 hypotheses could be made. One, the more the physicians were stressed, the more they took part in the survey or two, the less the physicians were stressed, the more they took part in the survey. Third, only 32,655 French private practice physicians out of 123,727 could theoretically participate, and 15,722 received the survey. Since Doctolib® is a web-based device used to schedule medical appointments, physicians using it may be more likely to be younger and working with a computerized support. Therefore, this population might not be representative of all French outpatient physicians. Fourth, this is a cross-sectional study without prospective assessment. Since the pandemic is still active in France, the prevalence of current psychological distress linked to COVID-19 should now be higher than during the second wave. Fifth, we only used one question to assess stress, psychological suffering, or professional burnout related to COVID 19, which does not allow us to distinguish the different possible causes (fear of infection, overwork, isolation, etc.)
4.3 Perspectives
Physicians cannot easily report that they have psychological distress since they tend to think that it is normal to be exhausted during the pandemic. The simple question we used to assess psychological distress could be more acceptable than a longer psychological evaluation and allow for screening 3/4 of the population with a particularly high risk of burnout, anxiety, depression, or insomnia due to COVID-19. To date, there is no systematic screening of psychological distress and no specific interventions set up for outpatient physicians in private practice. Thus there is a need to set up and evaluate effective psychological interventions such as stress reduction therapies, educational interventions targeting physicians’ self-confidence, communication skills, organizational interventions or small group discussions (Panagioti et al., 2017; West et al., 2016), and communities of peer support (Hu et al., 2012; Sederer, 2021; Shapiro and McDonald, 2020). Following international recommendations, psychiatrists should be mobilized to develop self-help, group, or individual supports and/or treatments for distressed colleagues and their families (Kuzman et al., 2020; Stewart and Appelbaum, 2020).
5 Conclusion
Reports of psychological distress due to COVID-19 are dramatically elevated in outpatient physicians and are associated with mental health impairment. There is a need to assess specific interventions dedicated to outpatient physicians working in private practice. It would also now be necessary to reassess the mental health of these physicians in order to understand its evolution.
Disclaimer
The views and opinions expressed in this report are those of the authors and should not be construed to represent the views of any of the sponsoring organizations or agencies.
Contributors
Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources: Jean-François Costemale-Lacoste, Formal analysis and Writing - original draft: Ariel Frajerman, Romain Colle and Jean-François Costemale-Lacoste, Software: Ariel Frajerman and Jean-François Costemale-Lacoste, Supervision: Romain Colle and Jean-François Costemale-Lacoste, Validation: Emmanuelle Corruble, Visualization: Emmanuelle Corruble, Writing - review and editing: Franz Hozer, Eric Deflesselle, Samuel Rotenberg, Kenneth Chappell, Emmanuelle Corruble, Ariel Frajerman conducted the statistical analyses under the supervision by JFCL and RC. He wrote the draft of the manuscript and helped proofread it. Romain Colle supervised the statistical analyses and served as an advisor during the writing procedure. He co-wrote the entire manuscript and proofread it, Franz Hozer is a physician in private practice. He helped to improve the entire manuscript and acted as an advisor, Eric Deflesselle is a general practitioner in private practice. He helped to improve the entire manuscript and acted as an advisor, Samuel Rotenberg is a physician in private practice. He helped to improve the entire manuscript and acted as an advisor, Kenneth Chappell proofread the entire manuscript as a native English speaker, Emmanuelle Corruble is the head of the MOODS laboratory at Paris-Saclay University. She acted as advisor and supervisor for the survey, Jean-François Costemale-Lacoste supervised the entire process of the study. He built the survey in collaboration with a panel of outpatient physicians in private practice and MOODS’ team researchers. He conducted the collaboration with Doctolib and supervised the collection of the data. He supervised the data analyses. He wrote the manuscript and proofread it.
Declaration of competing interest
No conflict of interest arises from this research.
Appendix A Supplementary data
The following is the Supplementary data to this article:Multimedia component 1
Multimedia component 1
Acknowledgement
No financial support, aside from the authors' salaries provided by the Nationals Health Systems, was involved in the preparation of this manuscript. Doctolib allowed the transmission of the survey but was not involved in any data management process. No financial support was provided from Doctolib.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jpsychires.2022.04.003.
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PMC009xxxxxx/PMC9002143.txt |
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Disaster Med Public Health Prep
Disaster Med Public Health Prep
DMP
Disaster Medicine and Public Health Preparedness
1935-7893
1938-744X
Cambridge University Press New York, USA
35241209
S1935789322000507
10.1017/dmp.2022.50
Report from the Field
Transport Safety Concerning a Patient Infected With SARS-CoV-2 and Emergency Service Officers in an Ambulance Accident—A Case Study
Mikos Marcin PhD 1
Dymura Krzysztof PhD 2
Gałązkowski Robert 3
Rzońca Patryk PhD, DSc 3
Żurowska-Wolak Magdalena PhD 4
1 Andrzej Frycz Modrzewski Krakow University, Kraków, Poland
2 City Police Headquarters, Nowy Sącz, Poland
3 Medical University of Warsaw, Warsaw, Poland
4 Jagiellonian University Medical College, Kraków, Poland
Corresponding author: Patryk Rzońca, Email: przonca@wum.edu.pl.
04 3 2022
04 3 2022
14
02 3 2021
30 10 2021
21 2 2022
© The Author(s) 2022
2022
The Author(s)
simple This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means subject to acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
The manifestation of a new pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), constitutes a new problem for modern health care systems. Developing updated standards for all emergency services working at an accident site during the pandemic has been a continuous challenge. The principal method of preventing the transmission of the SARS-CoV-2 virus is the use of personal protective equipment, such as protective suits, masks and goggles, or face shields. The study aims to present the recommended on-site procedures during the coronavirus pandemic based on the description of an accident of an ambulance transporting a patient with confirmed SARS-CoV-2 infection, emphasizing the actions taken by the emergency services sent to the accident site.
Keywords:
accident
personal protective equipment
PPE
rescue services
SARS-CoV-2
Abbreviations:
COVID-19 coronavirus disease
EMS emergency medical services
FFP2/FFP3 filtering face piece (protection class 2/3)
SARS-CoV-2 severe acute respiratory syndrome coronavirus 2
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pmcIntroduction
According to the Ministry of Health data, around 2 992 401 infections and 75 869 fatalities have resulted from the acute respiratory tract infection (coronavirus disease [COVID-19]) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Poland so far (October 10, 2021). 1 The pandemic poses a challenge for modern health care systems and medical rescue systems—the first link in the event of a health emergency. Unfortunately, modern medical rescue systems exhibited a low level of preparedness in the early stage of the coronavirus epidemic worldwide. The insufficient knowledge concerning the new pathogen and inadequate provision of personal protective equipment (PPE) constituted the main issues. Therefore, at the onset of the pandemic, the priority was a rapid development of appropriate procedures for medical emergency rescue teams and medical dispatchers, and those actions were hindered by limited knowledge concerning the virus itself. With time, key research regarding the virus effect was published, increasing its understanding, and allowing for measures to prevent the spread of the virus, prepare effective disinfectants, and prepare equipment protecting medical rescue teams, for example, protective suits, masks, or isolation chambers to be developed. 2–4
Transport of a Patient Infected With SARS-CoV-2 in Poland
Transport of patients affected by COVID-19 in Poland is coordinated by the chief medical dispatcher in the emergency medical dispatch center, with updated information concerning available hospital beds within its area of operation. That information mediates transport by the emergency medical services (EMS) to the nearest hospital. Furthermore, interhospital patient transfer to an institution with a higher level of care is undertaken by ambulance staff in the hospital structure, ambulances of private entities, or the most severe clinical cases of Helicopter Emergency Medical Services. 5
The SARS-CoV-2 pandemic required additional safety procedures for emergency services, including ambulance, police, and fire brigade working under emergency conditions. On February 28, 2020, the Chief Sanitary Inspector published guidelines for the police, border guard, state fire service, Internal Security Agency, Foreign Intelligence Agency, among others, on the safety requirements for officers and employees who have contact with persons suspected of being infected with the new type of coronavirus: SARS-CoV-2. The guidelines described necessary safety precautions in the event of direct contact with an infected person while conducting their duties. 6
According to the data obtained from the police, 1 accident of an ambulance with a patient infected with SARS-CoV-2 has been reported in Poland, so far. 7
The study aims to present the procedure applicable to the accident site in the era of SARS-CoV-2 in Poland, based on the example of an accident of an ambulance transporting an infected patient.
Material and Method
The presented manuscript employs qualitative research—a case study with a research technique of an uncategorized interview. The questions were open-ended and focused on the transport mode, the circumstances of the accident, the rescue actions, epidemiological procedures, epidemiological safety of the event participants, and possible infections that could have occurred during the ambulance accident. The respondents were also asked to present the conclusions drawn from the accident. The questions were directed 30 days after the incident, as access to publicly available information, to units participating in the rescue operation, that is, fire brigades, police, dispatcher of EMS, sanitary inspection, and to the hospital, which ordered the transport of a patient infected with SARS-CoV-2 to a hospital (a hospital that only treats patients infected with SARS-CoV-2 created during the pandemic in Poland).
Accident Description
On July 12, 2020, a transfer of a patient with confirmed SARS-CoV-2 infection on the route from Krynica Zdrój to Kraków (Małopolskie Voivodeship) was initiated. The distance between the medical institutions was approximately 140 kilometers. The patient was transferred to the University Hospital in Krakow, a specialized hospital for patients with SARS-CoV-2. An ambulance with 2 paramedics onboard conducted the transfer.
During the transport, an ambulance collided with a passenger vehicle of a 4-person family. The medical dispatcher, notified of the incident via the emergency number (112), registered the report and dispatched appropriate resources to deal with the incident. The medical dispatcher was also informed that one of the accident participants was the transferred patient with a confirmed SARS-CoV-2 infection. The warning concerning an infected patient was provided to all services at the accident site. As a result, EMS, volunteer fire brigade, state fire service, and the police were sent to the accident site. The incident was also reported to the Sanitary Inspectorate (Figure 1).
Figure 1. An accident of an ambulance transporting an infected patient (own source).
Actions of the Fire Brigade
Fifteen firefighters were at the incident, including 9 firefighters from the state fire service and 6 from the volunteer fire brigade. The officers were equipped with special firefighter clothing, FFP3 masks, goggles, protective footwear, safety helmets, and latex gloves. After securing the accident site, the fire services designated a safe zone with no access for unauthorized personnel and secured access to the infected patient in the damaged ambulance. After the initial triage, the total number of injured was 5.
Among the injured, a paramedic and 4 passengers from the second vehicle received qualified first aid. In addition, the fire brigade performed preliminary decontamination of the injured paramedic, and the firefighters provided first aid at the scene. Under this level, decontamination constituted a simple wet method using a damp towel soaked in a cleaning solution followed by a safe undressing procedure (removal of the contaminated clothing, providing a substitute one).
Actions of the EMS
Four EMS were dispatched for the incident, including 1 physician-staffed EMS team and 3 non-physician-staffed EMS teams. The non-physician-staffed EMS teams consisted of 2 paramedics each. The physician-staffed EMS team included a physician and 2 paramedics. All members of dispatched EMS were equipped with PPE, that is, protective suits, FFP2/FFP3 masks, goggles or face shields, and shoe covers. The first EMS team was at the scene 4 minutes after receiving the emergency call, that is, 11:37.
After arriving at the site, EMS took over the casualties from the firefighters, reassessed their clinical state, and implemented the necessary medical care. After being stabilized, the injured were transported to local hospitals. The infected patient was admitted to the destination hospital in Krakow 50 minutes after the EMS teams were dispatched to the accident site.
Six casualties were transported to hospitals, as described in the following:The patient with confirmed SARS-CoV-2 infection suffered a contusion of the anterior wall of the chest and abdomen, reported pain in the cervical spine area, and was transported to the University Hospital in Krakow.
An injured paramedic was admitted to the nearest hospital in Brzesko with a suspected upper extremity fracture.
The passenger vehicle driver suffered head trauma and lost consciousness and was admitted to the local hospital in Brzesko.
The other 3 casualties, including 2 children with minor injuries, were transported to the local hospital in Brzesko.
Police Interventions
The officer on duty of the District Police Headquarters in Brzesko sent 1 patrol—2 traffic police officers to the site to secure the accident scene. The police officer on duty was warned of the infected patient and reminded of the safety precautions. Before the inspection, the ambulance involved in the incident was disinfected, and, additionally, the police officers conducted the required procedural steps remaining outside of the vehicle in question. Police officers were equipped with FFP3 face masks with a filter and gloves during their proceedings. Collecting witness statements and documentation procedures were conducted, avoiding direct contact, and maintaining recommended physical distance. Other procedural steps, for example, Preliminary Breath Test, were performed only after disinfection of accident participants.
According to the information provided by the police, it was a crash of the ambulance of the EMS team with a passenger car due to the ambulance driver’s mistake of running a red light at the intersection.
Actions of the Sanitary Inspection
Sanitary inspectors did not attend the accident scene. Instead, their activities consisted of determining the exposure risk of incident participants by phone, including emergency service officers based on an epidemiological interview with commanders of particular services and a paramedic from the ambulance transporting the patient with confirmed SARS-CoV-2 infection.
Discussion
The presented case study concerning a road accident of an ambulance transporting a patient with confirmed SARS-CoV-2 infection was the first such case reported in Poland. Appropriate actions by the medical dispatcher receiving the accident report, PPE used at the incident site by attending emergency services responding to the scene, the flow of information between the emergency services and cooperation with the sanitary inspection was a result of the preparation of appropriate procedures in the current epidemic situation.
It should be emphasized that PPE in the era of the SARS-CoV-2 pandemic is of interest to researchers, which is reflected in numerous scientific publications. 8–12 In the current pandemic, researchers and organizations present recommendations on adequate and necessary PPE for health care professionals when in contact with infected patients worldwide, with particular emphasis on donning face masks, eye protection, gloves, or protective suits. 8,9 Donning face masks reduces the risk of upper respiratory tract infections, which is vital for health care workers and communities worldwide. 10,11 Tabah et al. (2020) conducted an international study on the safety of intensive care units’ employees and their protective equipment in the era of SARS-CoV-2. 12 The research highlighted that more than half of the surveyed health care workers currently use N95/FFP2 masks, protective suits, face shields, and double gloves in their daily care for patients. 12
In sum, the assessment of the sanitary inspection emphasizes that awareness of the risk of SARS-CoV-2 infection, compliance with anti-epidemic procedures, and professional responsibility of emergency service officers are the greatest way to prevent transmission of coronavirus infections. In turn, according to the EMS dispatcher, the driver of an ambulance transporting a patient infected with SARS-CoV-2 should be entirely excluded from medical care for the patient, isolated from the medical compartment, and should not use protective goggles and protective suit, as they cause discomfort while driving, limit vision and perception, affecting the safety of the transportation. In the opinion of police, a post-accident investigation by road traffic police officers displayed that the universal procedures when using PPE, tactics of taking legal action, and transfer of information between the officers and on-duty dispatcher are sufficient and adequately protect the health of the officers who intervene. Although there are no detailed guidelines regarding the safety proceedings at the road accident site involving a person infected with SARS-CoV-2, the application of general safety regulations seems to be sufficient.
Neither the sanitary inspection nor the dispatcher of the emergency medical system nor the commanding officers of the fire service and the police employed any preventive measures concerning any of the incident participants in the form of isolation, quarantine, forced hospitalization, or diagnostic tests for SARS-CoV-2.
Moreover, 30 days after the incident, no SARS-CoV-2 infection was found among the EMS officers who participated in the activities at the scene of the accident.
Conclusions
Medical transport of a patient with confirmed infection of SARS-CoV-2 due to PPE that restricts the freedom of movement and the field of view while driving an ambulance should be considered a high-risk procedure for patients and paramedics. At the same time, the limited space in the ambulance and care including various medical interventions during transport result in a higher risk of virus transmission compared to other health care facilities.
To reduce the risk of SARS-CoV-2 transmission during medical rescue operations at the scene of the incident, and during patient’s transport, the information gathered by the emergency number dispatch operator is of key importance. Also, PPE of emergency officers at an incident site as a minimum standard and cooperation between services based on jointly developed procedures are required.
It seems necessary to conduct further research on the use of adequate and required PPE for health care professionals, particularly employees of emergency medical teams, during patient care, including hospital transport.
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References
1. Website of the Republic of Poland. Temporary hospitals across the country. Accessed October 10, 2021. https://www.gov.pl/web/koronawirus/lista-szpitali
2. van Doremalen N , Bushmaker T , Morris DH , et al. Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. N Engl J Med. 2020;382 (16 ):1564-1567.32182409
3. Sharps MC , Hayes DJH , Lee S , et al. A structured review of placental morphology and histopathological lesions associated with SARS-CoV-2 infection. Placenta. 2020;101 :13-29.32911234
4. Li Y-C , Bai W-Z , Hashikawa T . The neuroinvasive potential of SARS-CoV-2 may play a role in the respiratory failure of COVID-19 patients. J Med Virol. 2020;92 (6 ):552-555.32104915
5. Ministerstwo Zdrowia. Strategia walki z pandemią COVID-19 jesień 2020. Wersja 3.0. November 3, 2020. Accessed April 12, 2021. https://www.gov.pl/web/zdrowie/strategia-walki-z-pandemia-covid19
6. Główny Inspektor Sanitarny MSWiA. Wytyczne GIS MSWiA. February 28, 2020. Accessed April 12, 2021. https://www.sgsp.edu.pl
7. Komenda Główna Policji. Wypadki drogowe w Polsce w 2020 roku. February 14, 2021. Accessed April 12, 2021. https://statystyka.policja.pl/st/ruch-drogowy/76562,Wypadki-drogowe-raporty-roczne.html
8. Cook MT. Personal protective equipment during the coronavirus disease (COVID) 2019 pandemic—a narrative review. Anaesthesia. 2020;75 :920-927.32246849
9. Park SH. Personal protective equipment for healthcare workers during the COVID-19 pandemic. Infect Chemother. 2020;52 (2 ):165-182.32618146
10. Chou R , Dana T , Jungbauer R , et al. Masks for prevention of respiratory virus infections, including SARS-CoV-2, in health care and community settings: a living rapid review. Ann Intern Med. 2020;173 (7 ):542-555.32579379
11. Cheng VC , Wong SC , Chuang VW , et al. The role of community-wide wearing of face mask for control of coronavirus disease 2019 (COVID-19) epidemic due to SARS-CoV-2. J Infect. 2020;81 (1 ):107-114.32335167
12. Tabah A , Ramanan M , Laupland KB , et al. Personal protective equipment and intensive care unit healthcare worker safety in the COVID-19 era (PPE-SAFE): an international survey. J Crit Care. 2020;59 :70-75.32570052
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PMC009xxxxxx/PMC9002145.txt |
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Disaster Med Public Health Prep
Disaster Med Public Health Prep
DMP
Disaster Medicine and Public Health Preparedness
1935-7893
1938-744X
Cambridge University Press New York, USA
35241208
S1935789322000519
10.1017/dmp.2022.51
Original Research
Management Strategies During the COVID-19 Pandemic Crisis: The Experiences of Health Managers from Iran, Ardabil Province
Shamshiri Mahmood PhD 1
Ajri-Khameslou Mehdi PhD 1
https://orcid.org/0000-0002-7628-5749
Dashti-Kalantar Rajab PhD 1
Molaei Behnam PhD 2
1 Department of Critical Care Nursing, School of Nursing and Midwifery, Ardabil University of Medical Sciences, Ardabil, Iran
2 Department of Psychiatric Nursing, School of Nursing and Midwifery, Ardabil University of Medical Sciences, Ardabil, Iran
Corresponding author: Rajab Dashti-Kalantar dashtikalantar.r@gmail.com.
04 3 2022
04 3 2022
17
25 4 2021
10 1 2022
25 2 2022
© The Author(s) 2022
2022
The Author(s)
simple This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means subject to 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:
The coronavirus disease 2019 (COVID-19) outbreak is the most threatening public health challenge in the 21th century, and more than 200 countries are affected. Considering that Iran was one of the first countries influenced by the COVID-19 pandemic, this study aimed to explain the crisis management strategies during the COVID-19 pandemic in Ardabil province.
Methods:
This study used a qualitative method using content analysis in which 12 health-care managers or decision-makers involved in the management of the COVID-19 crisis were recruited through purposeful sampling. In-depth, semi-structured interviews were used to collect data, which continued until data saturation.
Results:
Data analysis led to nine categories, including prior preparation for the COVID-19 crisis; challenges and management of workforce shortages; benefiting from the participation of volunteer staff; challenges and strategies for physical space, supplies, and personal protective equipment (PPE); designation of referral centers for COVID-19; protocolized patient transport; benefiting from donations and charity support; management of information about COVID-19; and learning from the prior stages of crisis.
Conclusion:
This study revealed that, in critical situations, managers use multiple and, to some extent, unique strategies for decision-making and crisis control. Therefore, the health system can use the findings of the current study for proper response to similar crises and training of future managers.
Keywords:
crisis management
management strategies
SARS-CoV-2
COVID-19
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pmcCoronavirus disease 2019 (COVID-19) is an ongoing global pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), and more than 200 countries or territories around the world have been facing with COVID-19 pandemic. The disease was reported on December 31, 2019, with a report of 27 cases of pneumonia of unknown etiology in Wuhan, China. 1 Due to the COVID-19 outbreak, health-care systems in the most countries faced critical situations and unique challenges in controlling the outbreaks and providing care to patients with COVID-19. However, COVID-19 as an emerging disease is not yet fully understood by physicians, nurses, and scientists. There are also many unknown dimensions of SARS-CoV-2 and its pathogenicity, incidence, and resistance to existing drugs. 2
Global crises, especially ongoing viral pandemics, require rigorous response at different levels of global, national, regional, and local. Given that the distribution of resources, per capita income, and countries’ approach to health care are not the same, countries may take different measures to combat a crisis. In low-income countries, minimal resources when the COVID-19 pandemic occurred led to a more catastrophic situation. However, the global experience showed that high-income countries may also encounter a catastrophic situation in the pandemic such as COVID-19. 3 It must be admitted that crises are mainly unpredictable, and each one is unique in nature. Despite this fact, when a crisis occurs, the prior experience from other similar events can be useful for crisis management. 4
Management of health-care systems is usually based on normal structures and conditions. The occurrence of a health crisis challenges the routine activities and decision-making of the health-care organization. 5 However, in response to crises and threats to public health, the demands of society are rapidly increasing. 6 The COVID-19 pandemic has imposed difficult conditions for governments in making proper decisions and controlling the crisis. Accordingly, even large-scale decisions at the national level were changed periodically. 7 The most common health-care deficiencies in responding to the COVID-19 pandemic are the lack of centralized, intelligent, and reliable screening, weakness in the referral system, inadequate monitoring, lack of capacity to provide integrated care and lack of planning based on situation analysis. 6 Moreover, confusion, delay in practical intervention, lack of effective response, poor coordination, and lack of integrated management have been reported as the common managerial challenges in fighting the COVID-19 pandemic. 8,9
According to the official report of the World Health Organization (WHO), Iran was one of the first countries after China affected by the COVID-19 pandemic. 10 The COVID-19 pandemic requires managing the crisis and controlling the spread of the SARS-CoV-2 in the community. The first outbreak of COVID-19 in Iran was reported February 19, 2019, from Qom city and gradually spread to other cities such as Tehran, Arak, Rasht, Gorgan, Ardabil, and other provinces. 11 According to the latest official statistics dated February 21, 2021, of the population of 84 million people in Iran, 1,574,012 people have been diagnosed with COVID-19 and 59,483 people have lost their lives. In Iran, health system, treatment, and medical education are managed by the medical universities in each province, which are under the supervision of the Ministry of Health and Medical Education. 12 Ardabil province is located in the northwestern of Iran, and according to the latest National Statistical Center, Ardabil province has a population of 1,270,420 people. 13
There is currently no single effective way to prevent the spread of SARS-CoV-2 in the world, but confirmed data suggest that effective crisis management, disease prevention, and home-based quarantine and immediate vaccination can decrease the number of incidences. 14 The present study was conducted with the aim of explaining crisis management strategies during the COVID-19 pandemic in Ardabil province.
Methods
This research used a qualitative method to explain the management strategies to control COVID-19 pandemic crisis in Ardabil Province, Iran. Through purposive sampling, 12 managers and officials working in the hospitals and health-care centers were recruited to the study. The province high level health-care managers were invited by telephone to participate in the interview. After the initial agreement, the interviews were conducted at the workplace of the participants. Inclusion criteria were having direct responsibility or experience in organizations working to control the COVID-19 crisis, expressing informed consent to participate in the interview, and the ability to narrate the experiences.
In-depth semi-structured interviews were used to collect data. The first and opening question was an open-ended question: “Please tell me about the management of COVID-19.” Then, more structured questions were used to collect purposive data such as: “Tell me about your experiences in planning to fight the Coronavirus pandemic.” The interviews continued until data saturation and the formation of a comprehensive image of the experiences of dealing with the COVID-19 crisis management. Moreover, participants’ answers to the primary questions were used as a guide for the next questions. The subsequent questions were used for more details and further explanation of the initial themes.
Inductive qualitative content analysis introduced by Elo and Kyngäs was used for data analysis. This method has 3 stages, including preparation, organization, and reporting. 15 Data analysis was performed using MAXQDA-10 software. The audio-taped data were immediately transcribed into plain texts verbatim. Researchers reviewed the texts several times and reflected on each word, phrase, and sentence. At first phase, open coding was performed to extract the primary meanings or messages of the interviews. Progressively, the initial codes were identified and through the subsequent reflective process condensed to the primary categories. After the emergence of the primary categories, the similar or related categories were grouped under higher order or generic categories. Ultimately, the main categories emerged through the process of abstraction. 15 Table 1 represents more description of the phases of this study and the researchers’ activities during each phase.
Table 1. Phases of the study and the researchers’ activities during each phase
Phase Researchers’ activities
Preparation • Formulating the main research question
• Inviting the participants to the interview
• Taking informed consent
Data collection • Conducting in-depth interviews and recording voices
• Note-taking
• Verbatim transcription of audio-tapes
Data analysis • Listening to the audios, reading the transcripts several times, immersing in the data
• Open coding
• Grouping of the primary codes
• Categorization
• Abstraction of the categories
Reporting • Description of the analyzing process
• Reporting the results and making discussion in relation to the literature
Trustworthiness
The Guba and Lincoln’s criteria were used to improve and ensure the trustworthiness or validity of this study. For this purpose, some activities were used. First, key informant participants were selected for data collection. Furthermore, long-term engagement with participants, data collection triangulation through recording interviews, taking field notes, simultaneous data collection and analysis, detailed descriptions, audit trial, rich documentation, and member check of the extracted themes with the participants were strategies that promoted trustworthiness in the current study. 16,17
Results
The average age of the participants was 42.2 y. Two-thirds of the participants were males; 33.30% were females (Table 2). The results of qualitative content analysis led to the emergence of 9 categories, including prior preparation for the COVID-19 crisis; challenges and management of workforce shortages; benefiting from the participation of volunteer staff; challenges and strategies for physical space, supplies, and personal protective equipment (PPE); designation of referral centers for COVID-19 patients; protocolized patient transport; benefiting from donations and charity support; management of information about COVID-19 disease and learning from early stages of crisis.
Table 2. Demographic characteristics of the participants
Participant code Academic
degree Age (y) Administrative
position Work
experience (y)
1 PhD 42 Head of health and medicine education development center 16
2 BSc 53 Hospital manager 27
3 MSc 46 Director of the health center 21
4 MD 42 University’s vice chancellor 12
5 MSc 47 University’s head manager of nursing 19
6 BSc 36 Hospital manager 13
7 PhD 41 Clinical Psychologist 15
8 MD 43 Director of university crisis management center 14
9 MSc 40 Hospital’s head manager of nursing staff 13
10 MSc 37 Executive manager of a hospital 8
11 MD 45 Hospital chief 17
12 MSc 34 Head of emergency medical services 6
Prior Preparation for the COVID-19 Crisis
The establishment and conducting of the COVID-19 management center has been introduced by the participants as the cornerstone of the health-care system to respond the COVID-19 crisis. Participant 4 said: “Following the news of the coronavirus outbreak in China and before its spread in Iran, we health managers came together to make some crucial decisions to be prepared for the pandemic.” With the official confirmation of the COVID-19 outbreak, decision-making policies have shifted toward coordination and organized activities in the health centers. Participant 3: “In the early days of the COVID-19 pandemic, the immediate priorities were determined, including social distancing, early detection, isolation and treatment, and community education through the media.” Moreover, the majority of the participants emphasized on solidarity as a main principle of coordination and preparation against the COVID-19. Participant 8 said: “One of our greatest achievements was our solidarity in all parts of the health system, which led to excellent coordination.” Participant 12 said: “Based on the last evidences, guidelines were developed and communicated for use in homes, offices, and public spaces.”
Challenges and Management of Workforce Shortages
Data analysis showed that workforce shortage was a vital demand during the COVID-19 pandemic following the increase in referrals to medical centers. Participant 10: “The biggest challenge was personnel shortages, when patients rushed to the medical centers at once.” One of the challenging issues regarding workforce management was the staff’s fear of getting the disease from patients. Participant 5: “The biggest problem for our staff was fear of COVID-19, which exacerbated the staff shortage crisis.” Participant 9 said in an interview: “Due to the nature of the crisis, the staff families often tried to avoid the staff from going to work.” Participant 7: “In the conflict between managers and staff families, convincing them has been a difficult process.”
In addition, the incidence of COVID-19 among staff was another barrier for managing staff that profoundly influenced patient care process. Participant 5: “Staff with positive test needed long-term quarantine, for at least two weeks, which kept them out of work.” Another major factor in terms of workforce shortage was the prolongation of the crisis. Participant 6: “Due to the staff shortages, a large number of nurses were kept at the hospital for two months because of consecutive work shifts that led to fatigue and exhaustion among them.” Managers emphasized that their first strategy for managing the shortage of workforce was to overcome the pervasive fear through novel strategies. “Along with the general fear of Corona, we have selected and identified volunteers and encouraged them, which has greatly diminished the feeling of resistance,” said the university’s director of nursing workforce. In addition, “we first tried to convince the experienced nurses to work in COVID-19 settings. When they got to work, the others came.” Another strategy was to consider financial incentives. Participant 12: “Financial incentives were also considered to make it attractive.” Participant 3: “Incentives include: financial incentives, written incentives, and special payments.”
Benefiting From the Participation of Volunteer Staff
Recruiting of volunteers and recalling inactive health workers were effective methods used to compensate staff shortages. For example, Participant 10: “After an excessive workforce shortage, complementary personnel were allowed to be recruited.” Participant 2 said: “A number of students and faculty members came to the medical centers as volunteers and they were very helpful in feeding and accompanying the patients,” said the director of a hospital. Participant 9: “We had a group of 25 clergymen at our center who helped feed patients and their general needs.” Participant 5: “The presence of clergymen who were very helpful both mentally and in helping patients and reducing staff workload.” Participant 5: “We transported clinical personnel from small towns in the province that had fewer workloads to bigger cities.”
Challenges and Strategies for Physical Space, Supplies, and PPE
One of the main challenging areas in the management of the COVID-19 crisis was related to the provision of space, supplies, and PPE. Participants acknowledged that the lack of physical space, equipment, and PPE was a major logistical challenge in the fight against COVID-19.
In terms of providing the physical space and capacity for patients with COVID-19, managers pointed to these challenges. Participant 6: “A new hospital was set up and had to be equipped and supplied within 24 hours.” Participant 2: “In our center, 12 intensive care beds with ventilators were prepared and set up in a short time, which is really difficult.” Another challenge in this area was the evacuation of the hospital, which required the discharge of elective and nonemergency patients to create sufficient capacity. Participant 5: “We had to evacuate the hospital and it had to be done within 2 hours.”
“We have had tremendous challenges in providing PPE,” said the director of the University Crisis Management Center. In addition, ensuring the proper and standard operation of PPE was another major challenge raised by the participants. Participant 3: “What criteria did we have to check the proper functioning of PPE?” Participant 5: “Non-standard masks could easily cause the disease to the personnel.” Opinions differ on the cause of the initial shortage of PPE and devices; “If we had been more aware and informed faster, we could have insured ourselves by providing equipment,” said Participant 10. On the other hand, some have another opinion. Participant 12: “The forecast was made, but because we did not have much liquidity, we had difficulty preparing it.” Other than the reasons given, the other participants stated that the reason for the initial surprise was: “We do not have anything to save for emergencies,” said Participant 9, and Participant 12 added: “The biggest problem is that we do not have the ability to store or back up financial resources and equipment.”
Participants stated that special planning and flexibility were provided to overcome the challenges associated with physical space, equipment, and PPE. “First of all, we monitored the number of daily visits and hospitalizations and predicted future events. For example, what should we have done if 300 patients needed hospitalization? What should we have done if there were 400 patients?” said Participant 11. Participant 2: “Planning was done in the province in such a way that we always had an unused ward ready to receive or transfer patients. And this was usually the closest ward to the last active ward in the same hospital.” “If the capacity of one hospital was completed, it would be ready for admission according to the next hospital plan,” said the deputy director of treatment. Some measures were taken on a large scale at the university in province, “In addition to the physical space of the hospitals, we have equipped sheds that can be used if the hospitals are full. However, due to the lack of need, it was only used as a convalescent home,” said the provincial director of crisis and emergency services.
An important strategy was to rebuild used tools and equipment, which had positive effects in this critical situation. “We already had permission for all the university’s demolition equipment to be given to the emergency services. In this way, the equipment was repaired and ready for use on a low budget. We prepared a hundred beds in this way,” said Participant 12.
One of the most effective ways to deal with the COVID-19 pandemic was cross-sectoral coordination and cooperation with other institutions. One of the auxiliary institutions was the police force, which was emphasized by participant 4: “For the quarantine of patients, the police force provided us with a hall and we took care of its equipment and personnel.” Elsewhere, they said, “We prepared the Azad University dining hall with 100 beds due to its close proximity to the COVOD-19 referral hospital, and connected it to the main building of the hospital through a corridor.”
As the results show, various strategies have been used in the field of supply and management of PPE and devices. “As soon as the coronavirus outbreak was announced, accurate statistics were provided through the consumption curve with analysis by the Nursing Office and the Quality Improvement Office,” participant 1 told me about consumption management in educational and medical centers. In the distribution dimension, the centralized management method was used, participant 5: “The responsibility of delivering and distributing PPE was transferred to the province emergency center. This center distributed the equipment based on the level of exposure in the medical centers.”
Participants emphasized that preparedness to deal with known health crises similar to the crisis of COVID-19 was an important factor in equipment management in the current crisis. “Our previous preparation for the flu helped us a lot to get the equipment we needed for the initial response,” said participant 6.
Due to the increase in consumption and the severe shortage of equipment and consumables in the early phase of the crisis, a basic strategy has been to empower managers to supply equipment in unusual ways. “One of the positive decisions was that all centers and units were empowered to use all possible means to provide PPE, even outside of administrative principles and procedures,” said participant 11. The next step was to guide donors to provide the equipment needed for the corona crisis. “People’s contributions and donations, and even previously available resources, have been diverted to control of SARS-CoV-2,” said one of the city’s crisis managers. Another measure was to divert the products of some factories, especially textile factories, to equipment needed for the corona crisis, such as masks. “The necessary coordination was done through the governor’s office, and the textile factory started to produce masks and special protective clothing,” said participant 3.
To provide physical space, one of the strategies has been to create a convalescent home. Accordingly, participant 9 said “Two observatories were prepared for patients”; participant 10said “Patients were transferred to the observation post after discharge and in the absence of managed care at home to be monitored and monitored.”
Designation of Referral Centers for COVID-19 Patients
The experience of managers involved in coronavirus crisis management showed that the allocation of special hospitals was a vital strategy in response to COVID-19 disease. Participant 8: “In large cities, referral hospitals were identified and isolated very early to prevent contamination of other health care departments.” In small towns, the separation plan is implemented differently. Participant 4: “For cities that had only one hospital, the separation of the COVID-19 department took place within the hospital,” said the director of a city hospital. Measures were also taken to separate patients before admission to the emergency department. Participant 11: “Initial additional triage was deployed before the patient entered the emergency department and triage so that patients with COVID-19 could not come into contact with other patients.” One strategy was to conduct new inpatient wards based on newly emerging needs due to Covid-19. “For pregnant cases with COVID-19 who needed Normal Vaginal Delivery or Cesarean section, a special ward was set up at the Coronavirus Referral Center with the NICU for newborns,” said participant 5.
From the participants’ point of view, restrictions were used on the entrance of patients’ families to the coronavirus ward. “Accompanies were not allowed to enter, and the equipment brought by the patients’ families was delivered, packaged, and disinfected by a specific person and then transported to the ward,” said participant 2. In addition to these standards, space and equipment disinfection have also been considered. “The hospital environment was completely disinfected three times a day,” one participant said.
Given the direct exposure of clinical staff, the need to isolate them was also critical. For this purpose, accommodation conditions were provided for the personnel who were working in the inpatient wards of COVID-19. It was necessary to provide isolated accommodation, both to prevent further outbreaks and to reassure staff. Participant 3: “One of the requests of the clinical staff of the boarding house was that, with the coordination of the governorate, a boarding house with suitable facilities for the use of the clinical staff be determined.”
Protocolized Patient Transport
Based on the participants’ experiences, one of the helpful strategies in management of COVID-19 crisis has been to learn from other leading provinces involved in the COVID-19 epidemics. One of the members of the Coronavirus Crisis Committee emphasized on the important role of learning from the experiences of other cities and provinces, participant 8: “The situation in neighboring provinces was monitored in person and reported to the university and the governor’s office. The data was used in crisis management planning.” One area in which the experiences of others were used was the transfer of patients with COVID-19. “At the beginning of the outbreak, patient transport was not principled,” said participant 12: “Therefore, the personnel of the emergency centers, after the training course, professionally took the responsibility of transporting the infected patients in each city.” According to this, “The establishment of a special center for COVID-19 in medical emergencies was done before the national instructions in Ardabil.”
In times of crisis, people need guidance. “The 190-telephone system, which was under the control of the Ministry of Health, was handed over to the provincial capitals during the crisis,” said the director of the Emergency Medical Center. To guide the people, participant 7: “Faculty members and experienced personnel were used as volunteers.” “Faculties, nurses, midwives, environmental health, etc. were professionally answering the questions of the people of the province.” said participant 1
Benefiting From Donations and Charity Support
Another way to deal with the COVID-19 crisis was to attract the support of health donors and charity institutions. This strategy was recognized as really helpful, while the budget was limited. “The involvement of the donors has been very effective. Both in providing the equipment and in motivating the staff,” said one hospital manager. The donors’ participation has been both financially and in the field. Participant 6 said: “They would take a list of supplies from us and then bought equipment such as: ventilator, CPAP, and PPE. They acted really smart and purposeful.” Sometimes, “donors sent gifts to staff at medical centers to make them motivated,” said a hospital manager. In addition, the staff themselves conducted charitable work, participant 9: “The staff themselves also performed actions to boost confidence, for example, spontaneously and sincerely accepting the cost of a meal for their co-workers.” For the participants, these behaviors created a good atmosphere and were very effective in boosting morale.
Management of Information About COVID-19
The participants emphasized on the important role of the Internet in facilitating communication and coordination between managers. Participant 1: “The possibilities of the Internet were used to coordinate with managers and provide quick instructions.” This feature also facilitated the conditions at the implementation levels. Participant 11: “Formal instructions and guidelines received and followed through the Internet.” Participant 10: “We were receiving reports of the preparations continuously,” said the director of the provincial nursing office. Cyberspace has facilitated the necessary conditions for communication in these special circumstances. Participant 11: “Virtual communication of managers with officials and clinical staff continued.”
Learning From the Prior Stages of Crisis
Managers highlighted that the intensification and recurrence of the COVID-19 crisis outbreaks was a common concern that required prospective decision-making in the early stages. “We have to be prepared for the next phases of crisis and the lessons we learned are so important for the next phases of crisis” said participant 4. In addition, participant 7 stated: “My view is that the crisis will not finish and will continue, with only a difference in the sensitivity of the people, so we need to work with the public to keep them active against the Coronavirus.”
Participants stated that they had been thinking about planning since the early stages of the crisis. “From early phases of crisis, an epidemic management training program was held to identify the needs of the future to provide greater efficiency for operational managers,” said participant 10. Regarding the quality of care for patients in need of intensive respiratory care, the relevant director stated: “In the scientific committees of the crisis, maneuvers are carried out to review scientific experiences and findings.”
Discussion
This qualitative study explored the experiences of health managers to reveal the strategies or policies that have been used to respond to the outbreak of COVID-19. Data analysis showed that the initial reaction of WHO to COVID-19 as a global public health emergency, was a turning point in response to COVID-19 crisis management. 18,19 Health managers had been assumed the rapid outbreak of the coronavirus to be a public health emergency, describing it in terms such as emergency, surprise, shock, or general fear for people and even medical personnel. The findings showed that health managers have considered the primary epidemics as global health crises when the first warning was issued by the World Health Organization (WHO). 18 Previous studies have similarly highlighted that the right understanding of COVID-19 as a severe contagious disease is central to adopt proper response and protective behaviors by the public and health system. 20,21
Based on the current study, workforce management has been one of the main challenges in provision of clinical care for patients with COVID-19. In this regard, the fundamental strategies have been used to deal with the workforce shortage were recruiting, training, development, and retention of personnel. These findings are consistent with the results of previous studies. 22,23 The most important challenges of workforce management were workforce shortage, fear of getting COVID-19 and getting out of service, and exhaustion of staff due to prolonged crisis. Strategies such as using a volunteer workforce, considering physical and spiritual incentives, and transferring forces from less involved areas, and most importantly, creating an atmosphere of solidarity among staff have been effective solutions to combat crisis-related workforce shortages. Similarly, a study from Iran has reported similar strategies which had been effective in workforce management including recruitment of volunteer workforces, flexible work schedule, rearrangement of workforce, motivational measures, and psychological support. 21,24 Moreover, in the United States, the employment of retired physician volunteers and nurses, as well as undergraduate medical students, have been cited as strategies for workforce compensation. 25 Another study highlighted financial and psychological support during prolonged crises. 26 Numerous studies have mentioned managerial characteristics such as charisma and ability to influence, effective communication skills, and building trusting relationship with staff. 27 Moreover, management of negative emotions of personnel during the early stages of the COVID-19 outbreak has been reported as a crucial activity that would be accomplished by the health managers. 28
Use of nursing students, volunteer workforces, and recalling inactive health workers were extra strategies related to the workforce management. Due to the fact that a large number of new workforces were recruited and rushed to the aid of hospital staff, they needed special training to provide appropriate care for COVID-19 patients. Therefore, personnel training, especially for new ancillary workforces, was of special importance. Similarly, Bourgeault and et al. emphasized on recruitment of volunteer health professionals particularly retired workers, last-year nursing students, and inactive health workers as strategies for workforce shortage compensation. 29 Given that COVID-19 is a new phenomenon with unknown aspects, 30 hospital staff also needed to acquire updated and evidence-based knowledge. 22
One of the challenges of the health system during COVID-19 was the lack of medical equipment, particularly PPE, mechanical ventilator, surgical gown, gloves, and masks. According to a study by Iqbal and Chaudhuri in the United Kingdom, two-thirds of health professionals believed that insufficient PPE was a serious challenge during the early stages of COVID-19 pandemic. 26 Due to the fact that the lack of medical equipment, especially PPE, can endanger public health and hospital staff during health-care service provision, it requires special resource management. 18 Despite the initial shortage, the current study revealed that centralized management of available resources, resource allocation by consumption analysis, staff training on how to properly use PPE, supporting of producers to deliver PPE, and cooperation with the governmental agencies have been very effective in management of medical supplies and equipment during COVID-19 pandemic.
Findings of the present study emphasized on designating of referral and specialized centers both at primary level of health-care provision and at the hospital. Allocation and designation of special referral hospitals was an effective approach to manage COVID-19 patients during the early phases of the COVID-19. 31 Based on a survey in Jakarta, in addition to the deployment of primary interventions, such as handwashing, public education, patient screening, use of PPE, distancing and etc., referral specialized hospitals played a crucial role in the management of patients with COVID-19. 32 Adherence to infection control guidelines, employee movement control, immediate screening, patient isolation, and patient management have been highlighted as the main measures to control contagious diseases such as COVID-19 in hospital. 33,34 Comprehensive Hospital Preparedness Checklist for COVID-19 can also be used for hospitals’ readiness to provide an effective care for COVID-19. 35
As the results showed, the field activity of managers and activating the capacity of societies were effective strategies for overcoming the COVID-19 crisis. In the present study, the capabilities of charity institutions and health volunteers were reported as financial resources to combat with COVID-19. The National Institutes of Health has assigned more than 300 roles to health volunteers as they make a significant contribution to the health and well-being of the community by using their time, skills, and expertise to support health promotion programs. 36 In crisis events, when the need for workforce and financial assistance increases, it is necessary to use the capacity of volunteers and health charities. It is important to note that volunteers are not a substitute for skilled professionals and should be used under supervision. 37
The results revealed that information management and data sharing is a crucial aspect of crisis management during COVID-19 pandemic. In the current study, managers emphasized on the capacity of Internet-based media as well as mobile phone applications for sharing and delivery of updated information regarding COVID-19. Studies recommend that the use of the Internet and technology is identical in crisis management. Delivery of daily updated instructions, training of personnel, following up the measures taken to prevent and control the crisis, establishing direct communication between managers, and creating a chain of communication between officials and personnel are examples of fundamental usefulness of cyberspace in management of COVID-19. 38 Other studies highlighted the role of the Internet in various functions such as self-management of symptoms, contact tracing, test results reporting, online consultation, and access to information and support. 39 In addition to the ample advantages and opportunities of cyberspace, the spread of false information on social media also should be considered by health administrators. 40
Another strategy for managing the COVID-19 crisis was to visit and learn about neighboring provinces that were previously affected by the SARS-CoV-2. This benchmarking activity was reported as a necessary strategy to benefit from positive experiences and avoid repeating ineffective actions against COVID-19. 41
The present study had some limitations. Given that this study was a qualitative research project, the findings of our context may not be fully generalizable to other backgrounds, and it is necessary for readers to use the findings with special considerations of qualitative studies. Another limitation was due to the fact that the national COVID-19 vaccination had not begun during our data collection, so the management strategies related to the immunization program have not been explored.
Conclusions
This study, which explained the lived crisis management strategies of managers involved in COVID-19, showed that, in a crisis situation, managers adopt multiple and unique methods. However, crises can always happen, but it is important to learn from experiences from the crisis that can be helpful to better manage the subsequent crises. Therefore, according to the results of this study, the most important COVID-19 crisis management strategies were including the forming of crisis management teams, workforce management, management of resource and essential supplies, designation of COVID-19 referral hospitals, screening and isolating patients and staff, learning from pandemic-stricken provinces, benefiting from donors and charities, recruiting volunteer personnel, use of cyberspace capabilities, and learning from the prior steps of crisis to overcome the subsequent challenges. These actions of managers may follow a special model that needs to be explained by further studies in the future. It is necessary for health system to maintain the organizational and managerial preparedness created to manage the COVID-19 crisis to be active quickly in similar future crises.
Acknowledgments
This study is the result of a research project registered at Ardabil University of Medical Sciences with the ethics code of IR.ARUMS.REC.1399.011. We thank the participants as well as the staff working in the research deputy of Ardabil University of Medical Sciences.
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PMC009xxxxxx/PMC9002147.txt |
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Infect Control Hosp Epidemiol
Infect Control Hosp Epidemiol
ICE
Infection Control and Hospital Epidemiology
0899-823X
1559-6834
Cambridge University Press New York, USA
35351217
S0899823X22000502
10.1017/ice.2022.50
Original Article
Effectiveness of two coronavirus disease 2019 (COVID-19) vaccines (viral vector and inactivated viral vaccine) against severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection in a cohort of healthcare workers
https://orcid.org/0000-0002-7577-7688
Marra Alexandre R. MD, MS 1 2 3
https://orcid.org/0000-0002-4788-6254
Miraglia João Luiz MD, MPH 4
Malheiros Daniel Tavares PhD 5
Guozhang Yang BSc 5
Teich Vanessa Damazio MSc 5
https://orcid.org/0000-0003-2550-0469
da Silva Victor Elivane MSc 1
https://orcid.org/0000-0003-3999-0489
Pinho João Renato Rebello MD, MPH 6
Cypriano Adriana MSc 5
Vieira Laura Wanderly BA 7
Polonio Miria RN 7
Ornelas Rafael Herrera MD 4
de Oliveira Solange Miranda MD 7
Borges Junior Flavio Araujo MD 4
Shibata Audrey Rie Ogawa MD 6
Schettino Guilherme de Paula Pinto MD 8
de Oliveira Ketti Gleyzer MSc 6
Ferraz Santana Rúbia Anita MSc 6
de Mello Malta Fernanda PhD 6
Amgarten Deyvid Phd 6
Boechat Ana Laura PhD 6
Trecenti Noelly Maria Zimpel MSc 6
https://orcid.org/0000-0003-4751-6859
Kobayashi Takaaki MD 2
https://orcid.org/0000-0002-9193-820X
Salinas Jorge L. MD 9
Edmond Michael B. MD, MPH, MPA, MBA 10
Rizzo Luiz Vicente MD 1
1 Instituto Israelita de Ensino e Pesquisa Albert Einstein, Hospital Israelita Albert Einstein, São Paulo, Brazil
2 Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, United States
3 Center for Access & Delivery Research & Evaluation (CADRE), Iowa City Veterans’ Affairs Health Care System, Iowa City, Iowa, United States
4 Saúde Populacional, Diretoria de Medicina Diagnóstica Ambulatorial, Hospital Israelita Albert Einstein, São Paulo, São Paulo, Brazil
5 Health Economics Department, Hospital Israelita Albert Einstein, São Paulo, São Paulo, Brazil
6 Research and Development Sector, Clinical Laboratory, Hospital Israelita Albert Einstein, São Paulo, Brazil
7 Saúde do Trabalho, Diretoria de Medicina Diagnóstica Ambulatorial, Hospital Israelita Albert Einstein, São Paulo, São Paulo, Brazil
8 Instituto Israelita de Responsabilidade Social Albert Einstein, Hospital Israelita Albert Einstein, São Paulo, Brazil
9 Stanford University, Stanford, California, United States
10 West Virginia University School of Medicine, Morgantown, West Virginia, United States
Author for correspondence: Alexandre R. Marra, MD, E-mail: alexandre.marra@einstein.br or alexandre-rodriguesmarra@uiowa.edu
30 3 2022
30 3 2022
17
02 11 2021
07 2 2022
15 2 2022
© The Author(s) 2022
2022
The Author(s)
simple This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means subject to 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:
We investigated real-world vaccine effectiveness for Oxford-AstraZeneca (ChAdOx1) and CoronaVac against laboratory-confirmed severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection among healthcare workers (HCWs).
Methods:
We conducted a retrospective cohort study among HCWs (aged ≥18 years) working in a private healthcare system in Brazil between January 1, 2021 and August 3, 2021, to assess vaccine effectiveness. We calculated vaccine effectiveness as 1 − rate ratio (RR), with RR determined by adjusting Poisson models with the occurrence of SARS-CoV-2 infection as the outcome and the vaccination status as the main variable. We used the logarithmic link function and simple models adjusting for sex, age, and job types.
Results:
In total, 13,813 HCWs met the inclusion criteria for this analysis. Among them, 6,385 (46.2%) received the CoronaVac vaccine, 5,916 (42.8%) received the ChAdOx1 vaccine, and 1,512 (11.0%) were not vaccinated. Overall, COVID-19 occurred in 6% of unvaccinated HCWs, 3% of HCWs who received 2 doses of CoronaVac vaccine, and 0.7% of HCWs who received 2 doses of ChAdOx1 vaccine (P < .001). In the adjusted analyses, the estimated vaccine effectiveness rates were 51.3% for CoronaVac, and 88.1% for ChAdOx1 vaccine. Both vaccines reduced the number of hospitalizations, the length of hospital stay, and the need for mechanical ventilation. In addition, 19 SARS-CoV-2 samples from 19 HCWs were screened for mutations of interest. Of 19 samples, 18 were the γ (gamma) variant.
Conclusions:
Although both COVID-19 vaccines (viral vector and inactivated virus) can significantly prevent COVID-19 among HCWs, CoronaVac was much less effective. The COVID-19 vaccines were also effective against the dominant γ variant.
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pmcHealthcare workers (HCWs) are at risk of coronavirus disease 2019 (COVID-19) due to high levels of exposure. When compared to the general population, frontline HCWs have >10 times the risk of testing positive for severe acute respiratory coronavirus virus 2 (SARS-CoV-2), and those reporting inadequate access to personal protective equipment (PPE) have a 23% higher risk. 1,2 In addition, when compared to HCWs reporting adequate access to PPE and who were not caring for patients with COVID-19, HCWs caring for patients with documented COVID-19 had a nearly 5-times higher risk of testing positive if they had adequate access to PPE and a nearly 6-times higher risk if they had inadequate access to PPE. 2 These reports emphasize the need for effective vaccines, especially among frontline HCWs.
Over the last few months, multiple studies have yielded a large amount of data from different institutions that provided real-world data on short-term vaccine effectiveness. 3,4 The great majority of these studies examined COVID-19 mRNA vaccines that significantly prevented symptomatic and asymptomatic severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection among HCWs. 5 However, data regarding the vaccine effectiveness of other COVID-19 vaccines (eg, viral vector or inactivated virus) are limited.
We investigated real-world vaccine effectiveness for Oxford-AstraZeneca (ChAdOx1), and CoronaVac against laboratory-confirmed SARS-CoV-2 infection among HCWs.
Methods
Population and setting
This retrospective cohort study was conducted between January 1, 2021, and August 3, 2021, in Brazil. We included all adult HCWs (aged ≥18 years) working at the Hospital Israelita Albert Einstein (HIAE). The HIAE is a Brazilian, nonprofit, healthcare, education, and research organization, with its headquarters in the city of São Paulo. HIAE manages a diverse healthcare system, including primary healthcare to tertiary-care services, in the public and private healthcare sectors. This hospital operates 40 healthcare units, mainly in the state of São Paulo, and in 2020 it had ∼700,000 emergency department visits, 900,000 outpatient visits, and 70,000 hospital discharges overall. Since the beginning of the COVID-19 pandemic, HCWs with COVID-19 symptoms had access to free-of-charge SARS-CoV-2 RT-PCR testing conducted by the institution’s laboratory. Vaccination with 2 doses of CoronaVac vaccine, 21 days apart, and with 2 doses of the ChAdOx1 vaccine, 12 weeks apart, were evaluated for COVID-19 vaccine effectiveness. HCWs were considered unvaccinated if no COVID-19 vaccine doses were received. Individuals who tested positive for SARS-COV-2 prior to the first vaccine dose, between vaccine doses, or before 14 days after the second vaccine dose, and individuals who had been vaccinated before the study period, were excluded from the study. We also excluded HCWs who received the Pfizer COVID-19 vaccine because the sample size (∼130 HCWs) was too small to obtain an estimate of vaccine effectiveness (Supplementary Appendix 1 online).
Real-time polymerase chain reaction (RT-PCR) methodologies for SARS-CoV-2 detection
The diagnostic confirmation of COVID-19 was performed using RT-PCR on specimens obtained via nasopharyngeal swab, according to the protocol instituted at the hospital. The following RT-PCR kits were utilized: XGEN MASTER COVID-19 (Mobius, Pinhais, Paraná, Brazil); cobas SARS-CoV-2 Test (Roche Molecular Systems, Branchburg, NJ); Xpert Xpress SARS-CoV-2 (Cepheid, Sunnyvale, CA); and Abbott RealTime SARS-C0V-2 (Abbott Molecular, Des Plaines, IL).
Next-generation sequencing of viral full-length genome
We extracted total nucleic acid from the naso-oropharyngeal (NOP) swab samples with QIAamp Viral RNA Mini kit (QIAGEN, Hilden, Germany). After purification and concentration, DNAse I treatment, and depletion of human ribosomal RNA, the samples were subjected to random amplification. 6 The preparation of sequencing libraries for the Illumina platform was carried out with DNA Prep (Illumina, San Diego, CA) using the random 2-step PCR amplification product as input. The libraries were quantified with the Qubit instrument (Thermo Fisher Scientific, Waltham, MA) and were loaded on the NextSeq 550 equipment (Illumina) for sequencing with MID 300 paired-end reads (Illumina).
Outcome measures and statistical analyses
Laboratory-confirmed COVID-19 was considered the primary outcome to calculate vaccination effectiveness after 2 doses of a COVID-19 vaccine (CoronaVac or ChAdOx1). RT-PCR testing for the diagnosis of COVID-19 was performed only on symptomatic HCWs. Hospitalization related to COVID-19, length of stay, ICU admission, necessity of mechanical ventilation and death were secondary outcomes. Vaccination status and SARS-CoV-2 RT-PCR results of all study participants were obtained from institutional electronic records. We excluded those with a positive COVID-19 diagnosis before January 15, 2021, because it corresponded to data from the first vaccine (January 1, 2021) plus 14 days. For those vaccinated, the initial follow-up date was 14 days after the second vaccine dose. The last date was defined as the date COVID-19 was diagnosed, or up to August 3, 2021, for the censored cases without a positive diagnosis of COVID-19.
The qualitative variables were characterized using absolute and relative frequencies in general and by interest groups; for comparisons, we used the χ 2 or the Fisher exact test. The quantitative variables have been reported as medians, interquartile range (IQR, first and third quartiles), minimum and maximum values due to the asymmetry observed in the variables, 7 and comparisons were performed via nonparametric Kruskal-Wallis tests. Vaccine effectiveness was calculated as 1 − rate ratio (RR), 8 with RR obtained by adjusting Poisson models with SARS-CoV-2 infection confirmed by RT-PCR as the outcome and vaccination status as the main exploratory variable, in addition to 95% confidence intervals. We used the logarithmic link function to estimate unadjusted models and models adjusted for sex, age, and HCW job type (ie, direct patient contact vs no direct patient contact). The cumulative incidence curves of COVID-19 for the vaccinated and unvaccinated groups were estimated using the Kaplan-Meier method 9 and the cumulative incidence estimated at 30 and 90 days with unadjusted models. All analyses were performed with the R software environment for statistical computing and graphics version 4.1.0. 10 All reported tests were 2-sided, and P < .05 was considered significant. The study was approved by the Hospital Israelita Albert Einstein Ethics Committee (no. CAAE 47110421.7.0000.0071), which waived the need for informed consent.
Results
By the end of the study period, 18,359 individuals were screened for eligibility to evaluate the COVID-19 vaccination effectiveness after the second vaccine dose. Overall, 13,813 HCWs met inclusion criteria (Supplementary Appendix 1 online). Among this cohort, 6,385 (46.2%) received the CoronaVac vaccine, 5,916 (42.8%) received the ChAdOx1 vaccine, and 1,512 (11.0%) were unvaccinated. Most were female (71.0%), and the median age of the entire study population was 35 years. Unvaccinated workers were younger, and women more frequently received 2 doses of CoronaVac (Table 1). The proportions of HCWs with direct patient contact were ∼20% among the unvaccinated, 80% among those with CorovaVac, and ∼20% among those with ChAdOx1. Of 13,813 HCWs, past medical history was available for 10,786 HCWs (78.1%). Among them, 2,783 (25.8%) had at least 1 comorbidity: obesity (n = 1,009, 9.4%), hypertension (n = 843, 7.8%), dyslipidemia (n = 592, 5.5%), asthma (n = 478, 4.4%), and diabetes mellitus (n = 250, 2.3%).
Table 1. Baseline Characteristics of Study Participants, Hospital Israelita Albert Einstein, São Paulo, Brazil, from January 1, 2021, to August 3, 2021
Charaacterstics Unvaccinated
(n = 1,512) 2 Doses of CoronaVac Vaccine
(n = 6,385) 2 Doses of ChAdOx1 Vaccine
(n = 5,916) Total
(13,813) P
Value
Sex, no. (%) <.0001 a
Female 1,043 (69.2) 4,672 (73.2) 4,090 (69.1) 9,805 (71.0)
Male 465 (30.8) 1,710 (26.8) 1,826 (30.9) 4,001 (29.0)
Missing 4 (0.26) 3 (0.05) 0 (0) 7 (0.05)
Age, median y <.0001 b
Median (IQR) 32 (26–38) 36 (30–42) 35 (28–42) 35 (29–42)
Minimum–Maximum 18–84 18–82 18–83 18–84
Job type <.0001 a
No direct patient contact 1,174 (77.6) 1,333 (20.9) 4,603 (77.8) 7,110 (51.5)
Direct patient facing 338 (22.4) 5,052 (79.1) 1,313 (22.2) 6,703 (48.5)
Comorbidity c <.0001 a
No 752 (79.7) 3,568 (75.1) 3,683 (72.3) 8,003 (74.2)
Yes 191 (20.3) 1,181 (24.9) 1,411 (27.7) 2,783 (25.8)
Hypertension c <.0001 a
No 901 (95.5) 4,391 (92.5) 4,651 (91.3) 9,943 (92.2)
Yes 42 (4.5) 358 (7.5) 443 (8.7) 843 (7.8)
Diabetes mellitus c .0856 a
No 929 (98.5) 4,645 (97.8) 4,962 (97.4) 10,536 (97.7)
Yes 14 (1.5) 104 (2.2) 132 (2.6) 250 (2.3)
Obesity c .0102 a
No 871 (92.4) 4,331 (91.2) 4,575 (89.8) 9,777 (90.6)
Yes 72 (7.6) 418 (8.8) 519 (10.2) 1,009 (9.4)
Dyslipidemia c .0126 a
No 911 (96.6) 4,479 (94.3) 4,804 (94.3) 10,194 (94.5)
Yes 32 (3.4) 270 (5.7) 290 (5.7) 592 (5.5)
Asthma c .0984 a
No 908 (96.3) 4,554 (95.9) 4,846 (95.1) 10,308 (95.6)
Yes 35 (3.7) 195 (4.1) 248 (4.9) 478 (4.4)
Follow-up between COVID-19 vaccine doses, d <.0001 d
Median (IQR) … 25 (22–27) 84 (80–88) 35 (25–84)
Minimum–Maximum … 15–172 50–164 15–172
Follow-up period, d e <.0001 b
Median (IQR) 214 (214–214) 151 (144–154) 78 (73–83) 138 (78–153)
Minimum–Maximum 17–214 1–176 1–118 1–214
SARS-COV-2 infection (by PCR) <.0001 a
No 1,421 (94.0) 6,194 (97.0) 5,873 (99.3) 13,488 (97.6)
Yes 91 (6.0) 191 (3.0) 43 (0.7) 325 (2.4)
No. of hospitalizations .0048 f
0 1,501 (99.3) 6,371 (99.8) 5,904 (99.8) 13,776 (99.7)
1 11 (0.7) 14 (0.2) 11 (0.2) 36 (0.3)
2 0 (0.0) 0 (0.0) 1 (0.0) 1 (0.0)
Length of hospital stay, d .0154 b
Median (IQR) 10 (7–21) 4 (3–6) 6 (3–9) 6 (3–10)
Minimum–Maximum 1–40 1–7 2–20 1–40
ICU .3392 f
No 6 (54.5) 11 (78.6) 10 (83.3) 27 (73.0)
Yes 5 (45.5) 3 (21.4) 2 (16.7) 10 (27.0)
Mechanical ventilation .0050 f
No 7 (63.6) 14 (100.0) 12 (100.0) 33 (89.2)
Yes 4 (36.4) 0 (0.0) 0 (0.0) 4 (10.8)
Note. ChAdOx1 vaccine, Oxford-AstraZeneca vaccine; IQR, interquartile range; ICU, intensive care unit.
a χ 2 test.
b Kruskal-Wallis test.
c Information available for 10,786 participants, 943 unvaccinated, 4,749 with 2 doses of CoronaVac vaccine and 5,094 with 2 doses of ChAdOx1 vaccine.
d Mann-Whitney test.
e Follow-up was initiated 15 days after the second dose for those vaccinated.
f Fisher exact test.
During the study period, 325 HCWs (2.4%) were diagnosed with COVID-19. For the unvaccinated HCWs, the cumulative incidences of COVID-19 were 0.73% at 30 days of follow-up and 3.57% at 90 days of follow-up. The cumulative incidences were 0.85% at 30 days and 2.32% at 90 days for those vaccinated with the CoronaVac vaccine. The cumulative incidences were 0.58% in 30 days and 0.73% in 90 days for those vaccinated with the ChAdOx1 vaccine (Fig. 1). None of the HCWs (vaccinated or unvaccinated) died during the study period.
Fig. 1. Cumulative incidence of COVID-19 infection (by RT-PCR) among vaccinated (2 doses of CoronaVac vaccine, and 2 doses of ChAdOx1 [Oxford-AstraZeneca] vaccine) and unvaccinated healthcare workers.
The estimated vaccine effectiveness for CoronaVac after 2 doses was 50.3% (95% CI, 36.2%–61.3%), and the vaccine effectiveness for ChAdOx1 vaccine after 2 doses was 87.9% (95% CI, 82.6%–91.6%). After controlling for sex, age, and professional category, the estimated vaccine effectiveness rates were 51.3% (95% CI, 34.6%–63.7%) for CoronaVac after 2 doses and 88.1% (95% CI, 82.8%–91.7%) for ChAdOx1 vaccine after 2 doses (Table 2).
Table 2. Observed Rate Ratios and Vaccine Effectiveness Among Healthcare Workers after COVID-19 Vaccine Second Dose and COVID-19 Infection by RT-PCR, Hospital Israelita Albert Einstein, São Paulo, Brazil, from January 1, 2021, to August 3, 2021
Variable RR (95% CI) P Value Vaccine Effectiveness (95% CI)
COVID-19 infection
Unvaccinated 1.0 (Reference)
CoronaVac 0.497 (0.387–0.638) <.001 50.3% (36.2%–61.3%)
ChAdOx1 0.121 (0.084–0.174) <.001 87.9% (82.6%–91.6%)
COVID-19 infection adjusted for covariates
Unvaccinated 1.0 (Reference)
CoronaVac 0.487 (0.363–0.654) <.001 51.3% (34.6%–63.7%)
ChAdOx1 0.119 (0.083–0.172) <.001 88.1% (82.8%–91.7%)
Sex, male 0.859 (0.669–1.105) .237
Age, y 0.996 (0.984–1.008) .540
HCW job type (direct patient exposure) 1.020 (0.782–1.331) .885
Note. RT-PCR, real-time polymerase chain reaction; RR, rate ratio; CI, confidence interval; ChAdOx1, Oxford-AstraZeneca vaccine; HCW, healthcare worker.
Whole-genome sequencing analysis
From March to June 2021, 19 SARS-CoV-2 samples from 19 HCWs were screened for mutations of interest. Of those, 18 were the P1 strain (γ variant), and 1 B.1.1.7 strain (ie, the α [alpha] variant) was identified (Table 3).
Table 3. Characteristics of Participants With SARS-CoV-2 Variants of Concern (n=19), Hospital Israelita Albert Einstein, São Paulo, Brazil, from January 1, 2021, to August 3, 2021 a
SARS-CoV-2
Variant of Concern Date Age, y Sex Job Type COVID-19
Vaccine Hospitalization ICU Mechanical Ventilation Death
P1 3/20/2021 37 Female DPF CoronaVac No … … No
P1 3/21/2021 33 Female DPF CoronaVac No … … No
B.1.1.7 3/25/2021 26 Female DPF CoronaVac No … … No
P1 3/25/2021 46 Male DPF CoronaVac No … … No
P1 3/26/2021 30 Female DPF CoronaVac No … … No
P1 4/5/2021 48 Female DPF CoronaVac No … … No
P1 4/26/2021 43 Female DPF CoronaVac No … … No
P1 4/27/2021 42 Female DPF CoronaVac No … … No
P1 5/4/2021 42 Female NDPC CoronaVac No … … No
P1 5/10/2021 35 Female DPF CoronaVac No … … No
P1 5/12/2021 51 Female DPF CoronaVac No … … No
P1 5/17/2021 39 Male DPF CoronaVac Yes No No No
P1 5/31/2021 37 Female NDPC ChAdOx1 No … … No
P1 6/2/2021 42 Female DPF CoronaVac No … … No
P1 6/3/2021 37 Female DPF CoronaVac No … … No
P1 6/4/2021 44 Female DPF CoronaVac No … … No
P1 6/4/2021 31 Female DPF CoronaVac No … … No
P1 6/5/2021 44 Female DPF ChAdOx1 No … … No
P1 6/7/2021 27 Female DPF CoronaVac No … … No
Note. P1, γ (gamma) variant; B.1.1.7, α variant; ChAdOx1, Oxford-AstraZeneca vaccine; DPC, direct patient facing; NDPC, no direct patient contact.
a The whole-genome sequencing was performed from March to June 20
Discussion
This retrospective study revealed that the estimated vaccination effectiveness among HCWs against symptomatic COVID-19 were 51.3% for CoronaVac and 88.1% for AstraZeneca after adjusting for age, sex, and job type. Both a viral vector vaccine (ChAdOx1) and an inactivated viral vaccine (CoronaVac) reduced the number of COVID-19 cases, the number of hospitalizations, the length of hospital stay, and the need for mechanical ventilation. These vaccines were even effective against a new variant of concern in Brazil, the γ (gamma) variant.
Based on a recent published systematic literature review evaluating short-term vaccination effectiveness between December 2020 and April 2021, COVID-19 vaccines (primarily the mRNA vaccines) decrease symptomatic COVID-19 infection with vaccine effectiveness of 92.8%. 5 Our study showed that the estimated vaccine effectiveness rates after 2 doses of CoronaVac and ChAdOx1 among HCWs were lower than the vaccine effectiveness rates of mRNA COVID-19 vaccines among the general population reported in the randomized trials 11,12 and also in a noncontrolled setting. 3 A randomized clinical trial evaluating vaccine effectiveness of CoronaVac among HCWs in Brazil reported vaccine effectiveness after 2 doses of 50.7%. 13 Another randomized clinical trial of CoronaVac in Turkey reported an estimated vaccine effectiveness after 2 doses of 83.5%. 14 Both clinical trials were conducted in 2020, prior to the emergence of the variants of concern. More recent observational studies evaluating the vaccine effectiveness of the CoronaVac vaccine did not include genomic surveillance for SARS-CoV-2 virus but reported the circulation of at least 2 viral lineages considered to be variants of concern: B.1.1.7 (α variant) 15 and P.1 (γ variant). 15,16 Results from these studies after 2 doses, in a prospective national cohort study from Chile, demonstrated that the estimated vaccine effectiveness of CoronaVac in a general population was 65.9%. 15 Results from a test-negative case–control study of the vaccine effectiveness of CoronaVac vaccine among HCWs in Manaus, Brazil, where the γ variant was also predominant, showed that the estimated vaccine effectiveness after 2 doses was low (36.8%) against COVID-19. 16 In terms of the ChAdOx1 vaccine, a randomized clinical trial conducted in Brazil, South Africa, and the United Kingdom showed that the vaccine effectiveness of ChAdOx1 after 2 doses was 62% against COVID-19. 17 Results from a test-negative case–control study evaluating the vaccination effectiveness of ChAdOx1 vaccine after 2 doses for the B.1.1.7 (α variant) and the δ (delta) variant were 74.5% and 67.5%, respectively. 18 Although it is not clear why the vaccine effectiveness rates in our present study were higher compared to vaccine effectiveness rates in previously published studies. 17,18 Possible explanations are the strict infection control policies in our institution and adequate PPE throughout the pandemic while many other institutions suffered critical shortages of PPE. 1,2 Although a peak of COVID-19 was observed in March–June 2021, the community incidence of COVID-19 was relatively stable and the γ (gamma) variant was dominant during the study period.
ChAdOx1 (Oxford-AstraZeneca) and CoronaVac were the first COVID-19 vaccines authorized by the Brazilian Heath Surveillance Agency, 19 and HCWs were considered the priority group to receive them as of January 2021. 20 We observed that HCWs who received 2 doses of CoronaVac were more likely to provide direct patient care in comparison to HCWs that received 2 doses of the ChAdOx1 vaccine. This difference can be explained by the CoronaVac vaccine being the first available COVID-19 vaccine in our institution, and for that reason, the frontline HCWs were prioritized to receive the COVID-19 vaccine. Later in the pandemic, our institution started using ChAdOx1 vaccine, which was mainly given to nonclinical persons. The duration of our study (8 months) among HCWs is justified, particularly to understand the short-term vaccination effectiveness in the context of a global pandemic with a novel pathogen. 21 We collected data during a rapid vaccination campaign during a period with one of the highest community transmission rates of the pandemic, which allowed for a relatively short follow-up period and the estimation of the prevention of COVID-19 cases, related hospitalization, necessity of mechanical ventilation, and ICU stay. Both CoronaVac and ChAdOx1 vaccines were effective at preventing COVID-19 and serious illness (hospitalizations, necessity of mechanical ventilation and ICU care).
During the HCW COVID-19 vaccine campaign, the dominant variant in circulation was P.1 (γ variant), and both COVID-19 vaccines (CoronaVac and ChAdOx1) showed effectiveness against this variant. More studies are needed regarding the SARS-CoV-2 variants of concerns (VOC) that have multiple spike-protein mutations and appear to be more infectious or cause more disease than other circulating SARS-CoV-2 variants. 22 Some deletions in the spike-protein mutations can alter the shape of the spike and may help it evade some antibodies. 23 No COVID-19 vaccine is 100% effective against SARS-CoV-2 infection, consistent with COVID-19 breakthrough infections reported in HCWs after COVID-19 vaccination. 24,25 We detected a clear effect of the vaccines against the new variants (mainly P.1).
Our study had several limitations. First, this was an observational study, subject to multiple biases 26 ; however, this is the most common study design in the infection prevention literature. 26 Second, we estimated vaccine effectiveness based on short-term duration, and longer-term observational studies are needed to assess sustained immune response and vaccine effectiveness. Third, due to the uncertainty related to the number of days required to develop immunity postvaccination, we decided to adopt the CDC definition for the CoronaVac vaccine and for the ChAdOx1 vaccine, which defines people fully vaccinated as being ≥14 days after the second dose in a 2-dose series (Pfizer/BioNTech or Moderna), or ≥14 days after a single-dose vaccine (Johnson & Johnson/Janssen). 27 Other studies adopted different definitions of a fully vaccinated person. 5 Currently, no postvaccination time limit on fully vaccinated status has been established. In addition, the CDC defines unvaccinated people as individuals of all ages including children who have not completed a vaccination series or have not received a single-dose vaccine. 27 Fourth, we have not reported nonneutralizing viral antigen-binding antibody levels in our HCW cohort study. However, the US Food and Drug Administration (FDA) does not recommend antibody testing for SARS-CoV-2 to determine immunity or protection from COVID-19, especially among those who are vaccinated. 28 Lastly, since our study focused only the short-term vaccination effectiveness among HCWs, we could not evaluate the need for a third dose. Consolidated knowledge indicates that each HCW needs to get 2 doses of CoronaVac or 2 doses of ChAdOx1 vaccine; thus, we decided to not report the analysis of vaccine effectiveness after 1 dose only. Considering our data regarding vaccine effectiveness for both COVID-19 vaccines, our institution began administering a third dose to HCWs in October 2021, after authorization from the Ministry of Health.
In conclusion, both COVID-19 vaccines (viral vector and inactivated virus) can significantly prevent COVID-19 among HCWs. The 2 COVID-19 vaccines were also effective among HCWs even after an emergence of a new variant (ie, the γ variant). More observational studies are needed to evaluate vaccination effectiveness of other COVID-19 vaccines (eg, other types of viral vector or inactivated virus). Studies are also needed to evaluate the impact of COVID-19 vaccines on personal protective equipment among HCWs, on vaccine effectiveness, and on COVID-19 breakthrough infection. Also, the vaccine effectiveness of unmatched COVID-19 vaccines as a third dose should be evaluated. Further genomic surveillance is needed for better understanding of vaccine effectiveness against the new SARS-CoV-2 variants.
Acknowledgments
We thank all the participants for their contributions to this study.
Supplementary material
For supplementary material accompanying this paper visit https://doi.org/10.1017/ice.2022.50.
click here to view supplementary material
Financial support
The sequencing reactions carried out to characterize the circulating SARS-CoV-2 described in this study were supported by Chamada MCTIC/CNPq/FNDCT/MS/SCTIE/Decit 07/2020 in Brazil (grant no. 402669/2020-7).
Conflicts of interest
All authors report no conflict of interest relevant to this article.
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Br J Nutr
Br J Nutr
BJN
The British Journal of Nutrition
0007-1145
1475-2662
Cambridge University Press Cambridge, UK
S0007114522000745
10.1017/S0007114522000745
Corrigendum
Implementation Strategies for Improving Vitamin D Status and Increasing Vitamin D Intake in the UK: Current Controversies and Future Perspectives. Proceedings of the 2nd Rank Prize Funds Forum on Vitamin D – CORRIGENDUM
Buttriss Judy L.
Lanham-New Susan A.
Steenson Simon
Levy Louis
Swan Gillian E.
Darling Andrea L.
Cashman Kevin D.
Allen Rachel E.
Durrant Louise R.
Smith Colin P.
Magee Pamela
Hill Tom R.
Uday Suma
Kiely Mairead
Delamare Gael
Hoyland Alexa E.
Larsen Lise
Street Laura N.
Mathers John C.
Prentice Ann
11 4 2022
11 4 2022
11
© The Author(s) 2022
2022
The Author(s)
simple This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means subject to 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.
DOI: https://doi.org/10.1017/S0007114521002555 (First published online: 21 July 2021)
==== Body
pmcBritish Journal of Nutrition, First View, pp. 1 - 21
Details: Correct author spelling
Currently reads: Collin P. Smith
This should read: Colin P. Smith
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Disaster Med Public Health Prep
Disaster Med Public Health Prep
DMP
Disaster Medicine and Public Health Preparedness
1935-7893
1938-744X
Cambridge University Press New York, USA
35241205
S1935789322000520
10.1017/dmp.2022.52
Original Research
Factors Influencing Wearing Face Mask in Public During COVID-19 Outbreak: A Qualitative Study
https://orcid.org/0000-0001-7844-6457
He Wei 1
Cai Duanying 2
https://orcid.org/0000-0002-3498-2204
Geng Guiling 3
Klug David 1
1 School of Nursing, Queensland University of Technology, Brisbane, Queensland, Australia
2 School of Nursing, Jiujiang University, Jiujiang City, Jiangxi Province, China
3 School of Nursing, Nantong University, Nantong City, Jiangsu Province, China
Corresponding author: Guiling Geng, Email: gengguiling@163.com.
04 3 2022
04 3 2022
17
25 5 2021
20 1 2022
17 2 2022
© The Author(s) 2022
2022
The Author(s)
simple This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means subject to 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:
Wearing face masks is believed to mitigate coronavirus disease 2019 (COVID-19) virus transmission by filtering respiratory droplets. This study was to explore the factors influencing wearing face masks in public in China during COVID-19 outbreak.
Methods:
This study was a qualitative semi-structured interview research design and was guided by the Protection Motivation Theory. Participants from Jiangxi Province China were interviewed by means of WeChat video call. Thematic analysis was used to analyze the data.
Results:
Recruitment efforts were suspended when 21 participants (aged 23 to 72 y) were successfully enrolled and the data reached thematic saturation. Four themes were identified when participants described factors influencing them to wear face masks: knowledge of disease (subthemes were severity of disease, and individual vulnerability to disease), environmental facilitators and constraints (subthemes were government recommendations, public opinion, and affordability and availability of face masks), understanding of protection effectiveness (subthemes were protection effectiveness of wearing face masks, and selection of protective measures), and past experiences.
Conclusions:
Individuals’ decision to wear face masks was influenced by the combination of factors identified. Identification of these factors provides guidance for explaining wearing face masks in public and helps policy-makers develop feasible recommendations for wearing face masks during COVID-19 outbreak.
Keywords:
mask
COVID-19
coronavirus
qualitative research
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pmcCoronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, 1 was initially reported in China in December 2019 2 and spread rapidly around the globe over the next few months. COVID-19 has high virus transmission rate (by means of respiratory droplets and contact routes) and mortality rate. 3 According to a recent World Health Organization COVID-19 epidemiological update, 4 until October 17, 2021, over 240.2 million cases have been reported globally with over 4.8 million deaths. Due to the absence of specific and effective treatment for COVID-19, interrupting virus transmission is the primary strategy used to prevent infection and control outbreak. 5
Wearing face masks mitigates COVID-19 virus transmission by filtering respiratory droplets 6 and is increasingly being adopted by many people in public. There are 3 considerations when wearing face masks. First, different types of face masks have varying levels of protection against COVID-19 virus transmission. There are 3 main types: fabric face masks (eg, cloth covering and scarf); medical face masks (also referred to as surgical masks); and filtering face piece respirators (eg, N95, KN95, and FFP2). 7 Fabric face masks provide very limited protection for the wearers against virus transmission and are not appropriate in health-care settings. However, they can be used in public because their physical barrier can reduce the risk of droplet transmission. 7 Compared with fabric face masks, medical face masks are thought to have more effective filtering capabilities due to the strict requirements of production standards designed to provide protection against infection. However, there is limited evidence supporting the protection of medical face masks against COVID-19 virus transmission. 3 Respirators can filter over 95% of droplets when inhaling and provide effective protection for the wearers against virus transmission. 7 Second, face mask use alone is not sufficient to suppress COVID-19 virus transmission. 3 Individuals should be aware of the false sense of security when wearing face masks. Whether face masks are worn or not, compliance with hand hygiene, physical distancing, and other infection prevention and control measures are critical to prevent COVID-19 virus transmission. 3 Third, people who have received COVID-19 vaccines should not be exempt from wearing face masks because vaccines do not provide 100% protection. 8 Although vaccines can prevent people from developing symptoms, it is still possible for vaccinated people to be infected by the virus without showing symptoms. If vaccinated people are infected and do not wear face masks, they can become silent spreaders of the virus and potentially put unvaccinated people at risk.
Countries worldwide are experiencing different stages of COVID-19 trajectory and have issued different policies for wearing face masks. In China, wearing face masks is required in public places with high population density, where ventilation is insufficient, and where physical distancing (> 1.0 meter) is difficult to maintain. 9 However, there are discrepancies between government recommendations and observed public behaviors, indicating that government recommendations might not be the only factor influencing wearing face masks in public.
Understanding the factors influencing wearing face masks in public is important for policy-makers to develop feasible guidelines and help educate people why, when, and how to properly wear face masks during COVID-19 outbreak. To the best of our knowledge, no previous study has explored factors influencing wearing face masks in public. This study aims to explore the influencing factors of wearing face masks in public during COVID-19 outbreak.
Theoretical Framework
The Protection Motivation Theory (PMT) 10 was used as the theoretical framework for this study. The PMT was originally developed in 1975 to understand the impact of fear appeal on behaviors and then revised in 1983 to describe the cognitive processes of performing behaviors (Figure 1). In the PMT, behavioral performance (wearing face masks) is determined by protection motivation (intention to wear face masks) in response to a threat (COVID-19 outbreak). The development of protection motivation encompasses 2 appraisal processes: threat appraisal and coping appraisal. First, the threat appraisal process consists of appraising severity of the threat (harm from COVID-19), vulnerability to the threat (probability of being infected with COVID-19), and rewards of maladaptive responses (benefits of not wearing face masks). Threat severity and vulnerability can decrease the likelihood of maladaptive responses, while rewards of maladaptive responses can increase the likelihood of maladaptive responses. Second, the coping appraisal process consists of appraising adaptive response efficacy (efficacy of wearing face masks in preventing COVID-19), self-efficacy of performing adaptive responses (confidence in wearing face masks), and adaptive response costs (negative consequences of wearing face masks). Adaptive response efficacy and self-efficacy of performing adaptive responses can increase the likelihood of adaptive responses, while adaptive response costs can decrease the likelihood of adaptive responses. 11 The PMT has been widely used to explain and predict health behaviors, such as exercise, dietary behavior, smoking, alcohol drinking, safe sex, and medical adherence. 11
Figure 1. The Protection Motivation Theory.
There are 2 reasons for using the PMT in this study. First, the PMT encompasses the theoretical constructs (factors) that increase/decrease (influence) the likelihood of adaptive/maladaptive responses (wearing face masks or not) to a threat (COVID-19 outbreak). This aligns with the purpose of this study—to explore the factors that influence wearing face masks in response to COVID-19 outbreak. Second, the PMT outlines cognitive responses that result from fear appeal, 11 and fear is a likely psychological response to the high transmission and high mortality rates of COVID-19. In this study, the PMT was used to develop the interview questions and discuss the results. Conscious efforts were made not to use the PMT to identify a priori factors influencing wearing face masks in public.
Methods
Research Design
This study was a qualitative semi-structured interview research design.
Settings and Participants
Participants were recruited from Jiangxi Province, China, between March 31 and May 25, 2020. Jiangxi Province covers 170,000 km2 and has a population of 46.5 million. 12 The number of accumulative confirmed COVID-19 cases was 935 at the beginning of data collection, 13 and there was no new confirmed case during the period of data collection. 14 Inclusion criteria for recruiting participants were ≥ 18 y of age and able to use WeChat video call. Exclusion criteria included suspected or confirmed COVID-19 cases and cognitive impairment.
Data Collection
This study used purposive sampling to recruit participants. Pedestrians were approached and invited as potential participants, provided an explanation about the study, and screened for eligibility. Eligible participants who agreed to participate in this study were asked to provide their WeChat IDs for the purpose of video interview. To avoid the risk of COVID-19 transmission, interviews were conducted by means of WeChat video call on the days appointed. Written informed consent was obtained from all participants before data collection.
Semi-structured one-to-one interviews were conducted to collect data. Interview questions (Table 1) were first prepared after considering the PMT constructs, then tested, reviewed, and revised by means of 3 pilot interviews before they were used in the major interviews. The question “Can you please tell me what you know about COVID-19?” reflects participants’ understanding of the disease, including severity and vulnerability. The question “Can you please tell me the impact of wearing a face mask in public on you during COVID-19 outbreak?” reflects participants’ understanding of benefits and costs of wearing face masks in public (rewards of maladaptive response and adaptive response costs). The question “Can you please tell me how you understand the role of wearing a face mask in protecting against COVID-19?” reflects participants’ understanding of efficacy of wearing face masks in preventing COVID-19 (adaptive response efficacy). In the pilot interviews, the participants had difficulty in responding to the question reflecting confidence in wearing face masks (self-efficacy) – “Can you please tell me how your confidence in wearing face masks influences the usage of face masks?”. Therefore, this interview question was excluded from the major interviews. Participants’ demographic characteristics were also collected during the interviews.
Table 1. Interview questions
Interview questions
1 Can you please tell me whether you wear a face mask in public during COVID-19 outbreak? If yes,
• What type of face mask do you usually wear in public (medical face mask, respirator, fabric face mask, or others)?
• How often do you wear a face mask in public (rarely, sometimes, often, or always)?
2 Can you please tell me what reasons/factors make you (not) wear a face mask in public during COVID-19 outbreak?
3 Can you please tell me what you know about COVID-19?
4 Can you please tell me the impact of wearing a face mask in public on you during COVID-19 outbreak?
5 Can you please tell me how you understand the role of wearing a face mask in protecting against COVID-19?
Interviews were conducted by a female PhD nurse researcher (D.C.) with expertise in qualitative methods and health behaviors. Participants had no previous contact or relationship with the interviewer before being interviewed. Each interview took approximately 1-1.5 h and was audio recorded. The length of interviews varied according to participants’ wishes and topic requirements. 15 Sampling was stopped when the data collected reached thematic saturation—the point when no new themes emerged from data analysis. 16 To enable the detection of thematic saturation, data analysis was intertwined with data collection from the beginning.
Data Analysis
Interviews were transcribed verbatim and de-identified before data analysis. Thematic analysis was used to analyze the data and consists of 6 phases. 17 The first phase is familiarizing with data, involving repeated reading of the data to become immersed and intimately familiar with the content. The second phase is generating codes, involving generating succinct labels (codes) that identify important features of influencing factors of wearing face masks in public. The third phase is generating candidate themes, involving examining the codes and collated data to identify significant broader patterns of meaning underpinned by a central concept or idea (themes). The fourth phase is reviewing themes, involving checking candidate themes against the dataset to determine whether they tell a convincing story of the data and refining them if needed. The fifth phase is defining themes, involving working out the scope and focus of each theme and deciding on an informative name for each theme. The final sixth phase is producing the report, involving weaving the analytic narrative and data extracts and then contextualizing the analysis in relation to existing literature. Although thematic analysis is introduced here as a linear 6-phase method, it is an interactive and reflective process involving constant movement between phases. During the process of thematic analysis (W.H. and D.C.), analytical memos and notes containing ideas and thoughts about the data and reasons for coding and grouping the data were recorded. 18
Lincoln and Guba 19 developed 4 criteria (credibility, dependability, confirmability, and transferability) to judge the merits of qualitative research. The strategies to ensure trustworthiness of this study are summarized in Table 2.
Table 2. Strategies to ensure trustworthiness
Criterion Strategy Activity
Credibility Prolonged engagement Each interview lasted 1-1.5 h. The sufficient interview time ensured engagement in the field with participants, and enabled participants to answer interviewer’s questions and support their statements with examples.
Persistent observation During data analysis, the researchers constantly read and re-read data to become immersed and intimately familiar with the content, generated codes and themes, reviewed and revised themes, and produced the report.
Investigator triangulation Two researchers analyzed the data independently and compared their results. If results differed, they discussed the results until the most suitable results were found to represent the data. They held regular meetings during the process of data analysis.
Member check All participants were invited to review the interview transcripts and give further comments to make additional contributions or strengthen accuracy.
Transferability Thick description A thick description of the participants and the context was provided to enable the readers to evaluate the transferability of this study.
Dependability and confirmability Audit trail A 5-phase audit 20 was conducted by an independent external auditor who was not directly involved in this study and 2 research team members. The independent auditor possesses knowledge and expertise in qualitative methods and health behaviors.
Ethical Considerations
Written informed consent was obtained from all participants before data collection. Ethical approval was obtained from Jiujiang University Ethics Committee (2020-JS-031).
Results
Recruitment efforts were suspended when 21 participants were successfully recruited (2 declined and 1 could not be reached on the day appointed) as thematic saturation was reached after data analysis. The participants’ demographic characteristics and frequencies and types of face masks worn are summarized in Table 3. Four themes were identified from participants’ description about the factors influencing wearing face masks: knowledge of disease, environmental facilitators and constraints, understanding of protection effectiveness, and past experiences (Table 4).
Table 3. Participants’ demographic characteristics and wearing face masks
Characteristic N or Min - Max
Age (y) 23-72
Gender
Male 12
Female 9
Education
Bachelor’s degree or higher 5
Certificate or diploma 9
Middle school or lower 7
Frequency of wearing face masks in public
Always 5
Often 13
Sometimes 3
Rarely 0
Never 0
Type of face masks worn in public
Medical face masks only 16
Medical face masks and N95 respirators 3
Medical face masks and fabric face masks 2
Others 0
Table 4. Themes and subthemes
Themes and subthemes The Promotion Motivation Theory constructs reflected by subthemes
1 Knowledge of disease
1.1 Severity of disease Severity
1.2 Individual vulnerability to disease Vulnerability
2 Environmental facilitators and barriers
2.1 Government recommendations –
2.2 Public opinion Rewards of maladaptive response
2.3 Affordability and availability of face masks Adaptive response costs
3 Understanding of protective measures
3.1 Protection effectiveness of wearing face masks Adaptive response efficacy
3.2 Selection of protective measures –
4 Past experiences
Theme 1: Knowledge of Disease
Individuals’ knowledge of COVID-19 greatly influenced their decision to wear face masks, from 2 perspectives (subthemes): severity of disease and individual vulnerability to disease. These 2 subthemes reflect the 2 PMT constructs of “severity” and “vulnerability.” Participants described COVID-19 as an extremely contagious disease and that the consequences of being infected are very likely severe. Their understanding of COVID-19 severity led them to believe that it was important to wear face masks.It was reported that many people died of COVID-19. It is very contagious. You know, human-to-human transmission. I have to be cautious and wear a face mask. (Participant 2)
Some participants thought that it was necessary to wear face masks due to their previous history of vulnerability to other respiratory infectious diseases. Other participants who believed they were strong and seldom sick still chose to wear face masks due to the high virus transmission and high mortality rates of COVID-19.I am easily infected by the people with common cold. … I need to wear a face mask to protect myself. (Participant 7)
I think that my disease resistance is okay. But COVID-19 has a high virus transmission rate and high mortality rate. … It is not worth taking the risk of not wearing face mask. (Participant 15)
Theme 2: Environmental Facilitators and Constraints
Environmental factors, such as government recommendations, public opinion, and affordability and availability of face masks, either facilitated or constrained participants’ wearing face masks. One participant described the impact of government recommendations on the decision to wear face masks.The country is requiring and educating us to wear face masks. … I am not clear about the disease because I have not experienced it before. But I surely will follow government policy anyway. (Participant 5)
Public opinion refers to the prevalent views on wearing face masks in public. Participants perceived public opinion regarding the importance of wearing face masks, and subsequently adjusted their behavior to conform to the public opinion in the social groups to which they belong. The subtheme “public opinion” reflects the PMT construct of “rewards of maladaptive response.” Participants would receive “negative” rewards from public opinion if they conducted maladaptive response (not wearing face masks in public).There are so many community workers and complex securities supervising your wearing face masks in public. If you do not wear a face mask, you will be seen differently. … You will not be allowed to enter complexes, food markets, and supermarkets without a face mask. (Participant 10)
Most participants chose to wear medical face masks because they are more affordable compared with the respirators. Availability of face masks in market also influenced participants’ choice. The subtheme “affordability and availability of face masks” reflects the role of the PMT construct of “adaptive response costs” in wearing face masks.N95 respirators are more expensive than medical face masks and not always available in shops. … I only used N95 respirators in shopping malls or supermarkets where the population density is high. (Participant 13)
Theme 3: Understanding of Protective Measures
Participants’ decision to wear face masks was influenced by their understanding of protective measures against COVID-19. Some participants believed that medical face masks meet medical standards during production and, therefore, provide effective protection for the wearers against COVID-19. Others believed that only respirators could effectively protect them against COVID-19. The subtheme “protection effectiveness of wearing face masks” reflects the PMT construct of “adaptive response efficacy.”Medical face masks must meet the medical standard during production. … I feel safer with a medical face mask on. (Participant 7)
I normally wear N95 respirators in public, because only respirators provide effective protection against COVID-19. … Sometimes I have to wear medical face masks if N95 respirators are not available. (Participant 18)
Some participants thought that it was unnecessary to wear face masks in public when physical distancing could be achieved.I do not see that the face mask adds any further protection of what we are already doing in terms of physical distancing. … I only wear face masks in places with high population density. (Participant 20)
Theme 4: Past Experiences
Another influence on participants’ decision to wear face masks was the recollection of the severe acute respiratory syndrome (SARS) outbreak in 2003, when many people wore face masks as a protective measure against SARS virus. They similarly took a precaution by wearing face masks during COVID-19 outbreak. SARS was first reported in China and affected 26 countries with more than 8000 deaths in 2003. 21 It resulted in substantial detrimental effects on the economy and daily life in China. Those participants who experienced SARS outbreak were inclined to wear face masks.The spread of COVID-19 makes me recall the SARS outbreak in 2003. I was in Beijing at that time. … So many people wore face masks. We must learn a lesson from SARS and wear face masks to protect against the virus. (Participant 10)
SARS has caused such a huge damage to economy and daily life. I saw from the news that many people wore face masks. Now COVID-19 seems to be worse than SARS. Wearing face masks is so important and necessary now. (Participant 13)
Discussion
Wearing face masks is an effective, affordable, and easy-to-implement measure in the battle against COVID-19. 22 This study deepens the understanding of wearing face masks in public during COVID-19 outbreak. Individuals’ decision to wear face masks in public is influenced by a combination of 4 factors: knowledge of disease, environmental facilitators and constraints, understanding of protective measures, and past experiences.
Factors Influencing Wearing Face Masks in Public
Individuals’ knowledge of attributes and consequences of a disease influences their intention to perform health behaviors to combat the disease. 23 In this study, participants’ knowledge of COVID-19 was found to influence their decision to wear face masks through several routes. Continuous education about COVID-19 by the government provided knowledge regarding its high infectivity and severe consequences, which evoked fear and led people to take protective measures, 11 such as wearing face masks. Also, participants described concerns about their vulnerability to COVID-19 and the importance of wearing face masks regardless of individual levels of resistance to disease.
Environmental facilitators and constraints are important factors influencing the implementation of health behaviors. 18,24 Government recommendations and public opinion about the importance of wearing face masks in China increased the usage of face masks in public. Chinese has been identified as the representation of collectivist culture. 25 Different from individualist culture that primarily considers individual needs and attitudes, collectivist culture prioritizes public opinion when deciding individual behaviors. 25 When the government and public opinion encourage people to wear face masks, individuals who do not adopt this protective measure are more likely to adapt their opinions, revise their beliefs, and change their behaviors as a result of social interactions with other people. 26
Factors outside of individual control, such as supply shortage, can make wearing face masks difficult or impossible. During COVID-19 outbreak, initial shortages were observed for several reasons: (1) the demand of face masks was dramatically increased; (2) the beginning of COVID-19 outbreak coincided with the Chinese New Year holiday, which meant a reduced workforce and insufficient storage of raw materials in face mask manufactories; (3) city lockdowns and transport restrictions made it challenging for the face mask manufacturing workforce to return to work; and (4) panic buying worsened the shortage of face masks in the market. 27 Despite the boosted production capacity of face masks, there could potentially be another wave of shortages if COVID-19 outbreak is not controlled and more countries implement a universal face mask wearing policy, because respirators and medical face masks are designed for single use.
Individuals’ understanding of protection effectiveness of wearing face masks influenced their usage of face masks. Most participants in this study believed that medical face masks meet medical standards during production and can, therefore, provide wearers effective protection against COVID-19 virus transmission. However, evidence on the effectiveness of wearing face masks is limited and inconsistent, and the World Health Organization (WHO) continues gathering scientific data to inform it. 3 The result of this study indicates that some people might overestimate the effectiveness of wearing medical face masks in public, and that clearer education on where, when, how, and what type of mask should be worn are needed.
Past experiences were found to influence future behaviors because experiences shape individuals’ beliefs about the behaviors, which in return influence behavioral intention and subsequent behaviors. 11 In some East Asian regions, particularly after SARS in 2003, wearing a face mask in public during flu seasons or other disease outbreaks is considered a reasonable measure to constrain disease transmission in the community and has become a social norm for outbreak control. 28 In this study, some participants wore face masks because they understood the importance of wearing face masks against virus transmission after experiencing SARS outbreak.
Extension of the PMT
This study extended the PMT when explaining the influencing factors of wearing face masks in public. Although some themes identified in this study reflect the PMT constructs, participants also described some other influencing factors that do not reflect the PMT constructs, such as the subtheme “government recommendations,” the subtheme “selection of protective measures,” and the theme “past experiences.” Some studies stated that the PMT is not a sufficient model of health behavior and will benefit from the inclusion of additional constructs. 11 A Japanese research 29 investigated the factors influencing wearing face masks against COVID-19 and reported that only 34% variance of wearing face masks could be explained by severity of COVID-19, self-efficacy, response efficacy, rewards, norm, and impulse to take necessary actions. 29 Their result indicates that the remaining 66% variance may be explained by additional factors other than the 6 factors that they investigated. This current study provided evidence for additional factors that should be considered when explaining the factors influencing wearing face masks in public.
Considerations When Applying the Results to Other Ethnic Groups
The results of a study are normally interpreted with the study context and sample. This study recruited participants in China. As a result, there may be a misconception that the results are only appliable to a Chinese population. In reality, many people in countries other than China have also adopted wearing face masks as a protective response to COVID-19 outbreak. It is reasonable to assume that some factors influencing wearing face masks identified in this study would be shared by other ethnic groups.
However, several considerations should be taken into account when applying the results of this study to other ethnic groups. First, countries worldwide are experiencing different stages of COVID-19 trajectory. On October 24, 2021, the number of new confirmed COVID-19 cases was 56 in China, 2041 in Australia, and 78,075 in the United States. 30 People from areas with poor control of COVID-19 outbreak are more likely to take protective measures, such as wearing face masks. Second, there are discrepancies in the recommendations for wearing face masks among different countries. 31 Although governments recommend people to wear face masks when physical distancing is difficult to maintain, the definitions of social distancing are not consistent: > 1.0 meter in China, 9 > 1.5 meter in Australia, 32 and > 6 feet or 1.8 meter in the United States. 33 The various government recommendations for social distancing might influence wearing face masks in public. Third, the difference in people’s reaction to their governments’ recommendations should be noted. The collectivist nature of Chinese culture may demonstrate a high degree of compliance with government recommendations and constraint on individual behaviors, 34,35 whereas an ethnic group with individualist culture may demonstrate greater variance in compliance with government recommendations. Fourth, availability of face masks may differ among countries. The access to face masks in China is easier compared with other countries because China contains the largest amount of face mask manufacturers and the largest production capacity globally. Fifth, the theme “past experience” might not be referenced in other ethnic groups due to the absence of a comparable event to COVID-19. Other countries experienced much fewer cases of SARS, whereas in 2003, SARS primarily affected China (7748 cases in China out of global 8422). 36
Limitations
There are 3 limitations in this study. First, the strategy of recruiting participants might limit the generalization of the results. Future studies can use a stratified purposive sampling method to promote generalization. 37 Also, collecting data from areas with different COVID-19 infection rates should be considered. Second, the limitation of applying the results of this study to other ethnic groups cannot be excluded. Further studies are needed to confirm the influencing factors in other ethnic groups. Third, the disadvantages of video interviews cannot be ignored. For example, an interviewer might miss the opportunities to observe the participants’ physical space and respond to their body language and emotional cues. 38 Also, there is a potential risk of biased results to exclude those who were unable to use WeChat video call. Future studies could conduct face-to-face interviews when protection measures (eg, vaccination and social distancing) are secured.
Conclusions
This study identified 4 themes as factors influencing wearing face masks in public in China. These include knowledge of disease, environmental facilitators and constraints, understanding of protective measures, and past experiences. Identification of these factors provides guidance for explaining wearing face masks in public and helps policy-makers develop feasible recommendations for wearing face masks during COVID-19 outbreak.
Supplementary material
For supplementary material accompanying this paper visit https://doi.org/10.1017/dmp.2022.52.
click here to view supplementary material
Author Contributions
Drs. He and Cai contributed equally to this manuscript.
W.H. and D.C. contributed to the study conceptualization and design, data collection and analysis, and writing. G.G. contributed to the study design and writing. D.K. contributed to the data analysis and writing. All authors read and approved the final manuscript.
Conflict(s) of interest
The authors declare that there is no conflict of interest.
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PMC009xxxxxx/PMC9002152.txt |
==== Front
Disaster Med Public Health Prep
Disaster Med Public Health Prep
DMP
Disaster Medicine and Public Health Preparedness
1935-7893
1938-744X
Cambridge University Press New York, USA
35241197
S1935789322000568
10.1017/dmp.2022.56
Brief Report
Depression Among Medical Students in the United States During the COVID-19 Pandemic: The role of Communication Between Universities and Their Students
Ecker Alexandra BS 1
Berenson Abbey B MD, PhD 2
Gonzalez Sandra J PhD, LCSW 3
Zoorob Roger MD, MPH, FAAFP 3
https://orcid.org/0000-0002-0126-7845
Hirth Jacqueline M PhD, MPH 3
1 University of Texas Medical Branch, School of Medicine, Galveston, TX, USA
2 University of Texas Medical Branch, Department of Obstetrics and Gynecology, Center for Interdisciplinary Research on Women’s Health, Galveston, TX, USA
3 Department of Family and Community Medicine, Baylor College of Medicine, Houston, TX, USA
Corresponding author: Jacqueline M Hirth, Email: jacqueline.hirth@bcm.edu.
04 3 2022
04 3 2022
18
14 9 2021
27 12 2021
21 2 2022
© The Author(s) 2022
2022
The Author(s)
simple This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means subject to 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:
Medical students are vulnerable to stress and depression during medical school and the COVID-19 pandemic may have exacerbated these issues. This study examined whether the risk of depression was associated with COVID-19 pandemic-related medical school communication.
Methods:
A 144 - item pilot cross-sectional online survey of medical students in the US, was carried out between September 1, 2020 and December 31, 2020. Items on stress, depression, and communication between students and their medical schools were included. This study examined associations of student perceptions of universities’ communication efforts and pandemic response with risk of developing depression.
Results:
The sample included 212 students from 22 US states. Almost 50% (48.6%) were at risk of developing depression. Students felt medical schools transitioned well to online platforms, while the curriculum was just as rigorous as in-person courses. Students at risk of developing depression reported communication was poor more frequently compared to students at average risk. Students at risk of depression were also more than 3 times more likely to report their universities’ communication about scholarships or other funding was poor in adjusted analyses.
Conclusion:
Universities communicated well with medical students during the pandemic. However, this study also highlights the need for ongoing efforts to address student mental health by medical schools.
Keywords:
SARS-CoV-2
students
medical
universities
mental health
curriculum
==== Body
pmcThe COVID-19 pandemic disrupted education globally, contributing to poor mental health among medical students. 1–5 This disruption affected classes and clinical rotations as medical students were not allowed to have clinical contact with patients. 5,6 Medical schools in the US transitioned to online curriculums in the months of March and April, 2020 to decrease potential exposure to the SARS CoV-2 virus. This rapid transition required extensive coordination and communication, as well as adapting the curricula to new platforms while ensuring education was as rigorous as in-person instruction. The numerous adjustments early in the pandemic contributed to the stress and isolation among students. 3
Medical students are vulnerable to stress and depression during medical school in the US. 7,8 Internal and external pressure to perform at a high level despite events occurring locally, nationally, or globally contribute to rates of depression and suicide that exceed that of the general US population. 8,9 Personal sources of stress, such as tuition funding, having inadequate time to participate in stress-reducing activities, and coping with a fast-paced curriculum designed to quickly impart basic medical knowledge and skills in a limited time period, all contribute to an increased level of stress, anxiety, and depression. 10 Understanding the effects of a sudden change in curriculum, and communication about these changes during a pandemic or any other disaster on the mental health of students is critical to planning for future disaster events that affect this vulnerable population.
The purpose of this study was to examine medical student perception of their institutions’ communication efforts during the COVID-19 pandemic, and the association of pandemic-related curriculum changes and communication with depression in the Fall of 2020, among students enrolled in a US medical school during the previous (2019 - 2020) school year.
Methods
This convenience sample of medical students using an online REDCap survey recruited students through social media and email from participating medical schools. The survey was open from September 1, 2020 to December 31, 2020. Study methods were approved by the University of Texas Medical Branch Institutional Review Board (IRB), and data analyses were approved by the Baylor College of Medicine IRB.
Students who did not give their consent were exited from the survey, while those who enrolled from 2019 - 2020 were excluded. There were 144 multiple-choice or free-text questions. Participants were not compensated for participation.
Depression Measure
The Center for Epidemiological Studies-Depression (CESD-10) is a 10-item measure of depressive symptoms during the past week. Responses ranged from 0 = “Rarely or none of the time (less than 1 day)” to 3 = “Most or all of the time (5 - 7 days”). To develop a dichotomous risk of depression outcome, a standardized cutoff of 10 was used to determine elevated risk of depression (≥ 10, “at risk”) compared to no elevated risk of depression (< 10, “average” risk).
Medical School Communication and Response
A total of 10 questions about medical school communication had Likert scale responses that were collapsed into “Very poor/ poor,” “Fair,” and “Well/ Very well.” Of the remaining questions, 17 were about medical schools’ preparedness, and they had 5-point Likert scale responses ranging from “Strongly disagree” to “Strongly agree.” Also, 11 questions characterized how concerned students felt their medical schools were with respect to the well-being of students, student ability to access facilities and services, how well online classes prepared them, and passing courses. They had 5-point Likert scale responses, ranging from “Not concerned at all,” to “Very concerned.” These survey questions were pilot tested by 10 medical students and 3 medical school educators, and included written and verbal feedback.
Exposure to SARS-CoV-2 Virus and Vaccination
Students were asked about personal testing or hospitalization for the virus and whether any of their family or friends had tested positive, been hospitalized, or had passed away due to the SARS-CoV-2 virus. They were also asked about their intention to get vaccinated if a vaccine was approved by the FDA (as this survey was administered before vaccine approval). Those who answered “Maybe I will get it” were further asked what would encourage them to get vaccinated.
Statistical Analyses
Bivariate analyses were conducted using Chi-Square tests. Logistic regression was then used to determine the odds of depression risk after ensuring that all significant variables had been controlled. All analyses were conducted using SAS Statistical Software version 9.4 (Cary, NC).
Results
A total of 145 medical schools were contacted; of these, 76 (52.4%) responded, and 26 (34.2%) emailed advertisements to their students. Also, among the 367 medical students who accessed the survey, 22 did not consent, and 133 did not complete it for a sample size of 212. Respondents lived in 22 US states and represented 28 medical schools. This study was estimated to have a 6.7% margin of error in a population of approximately 75000 medical students.
At least 48.6% of respondents were at risk of developing depression, with 61% of first year students at risk. Demographics were similar between students at high risk compared with average risk students, (Table 1) except for their year in medical school. A large proportion (98%) reported communication from their universities occurred via email, 55.6% reported university announcements, 43.1% reported online announcements, and 36.6% reported communication through their faculty. Some (16.7%) mentioned other methods of communication in free-text responses. Virtual town hall meetings were considered particularly helpful methods of communication by those reporting this method.
Table 1. Characteristics of medical student sample by risk of developing depressive disorder, N = 212
Total n
(column %) Average risk n
(row %)* At risk n
(row %)* P -value
Race/ ethnicity 0.3
White 156 (73.2) 84 (54.6) 70 (45.4)
Hispanic 16 (7.5) 5 (35.7) 9 (64.3)
Other 41 (19.3) 19 (46.3) 22 (53.7)
Missing 3
Gender 0.55
Male 68 (31.5) 37 (54.4) 31 (45.6)
Female 148 (68.5) 72 (50.0) 72 (50.0)
Marital status 0.98
Married/ domestic partnership 64 (29.6) 33 (51.6) 31 (48.4)
Single/ divorced/ widowed 149 (69.0) 76 (51.4) 72 (48.6)
Year in medical school during 2019-2020 0.03
MS1 77 (35.7) 30 (39.0) 47 (61.0)
MS2 49 (22.7) 31 (63.3) 18 (36.7)
MS3 73 (33.8) 39 (53.4) 34 (46.6)
MS4 13 (6.0) 9 (69.2) 4 (30.8)
Education before medical school 0.49
Bachelor’s degree 186 (87.7) 94 (50.5) 92 (49.5)
Master or doctorate degree 26 (12.3) 15 (57.7) 11 (42.3)
Moved after switch to online instruction 0.71
No 129 (60.8) 65 (50.4) 64 (49.6)
Yes 83 (39.2) 44 (53.0) 39 (47.0)
Number of people living with respondent 0.85
alone or 1 person 121 (57.1) 61 (50.4) 60 (49.6)
2-4 people 70 (33.0) 36 (51.4) 34 (48.6)
5+ people 21 (9.9) 12 (57.4) 9 (42.9)
Report of change in depression symptoms from Spring 2020 to Fall 2020 semester *** <0.001
Improved 44 (20.8) 32 (72.7) 12 (27.3)
Stayed the same 59 (27.8) 46 (78.0) 13 (22.0)
Worse 109 (51.4) 31 (28.4) 78 (75.7)
Change in residence after medical school changed to online-only instruction
Moved in with family 62 (28.7)
Moved in with friends 5 (2.31)
Moved to be with spouse/ partner 8 (3.7)
Moved to a different state 22 (10.2)
Moved to a different country 2 (0.9)
Moved within 50 miles of campus 5 (2.3)
Moved more than 50 miles from campus 49 (22.7)
Communication with university during SARS CoV-2 pandemic **
email 211 (97.7)
Online announcement 93 (43.1)
Blackboard 34 (15.7)
Automated phone message 3 (1.4)
Text 9 (4.2)
Focused online messages 24 (11.1)
Messages relayed through faculty 79 (36.6)
University announcements 120 (55.6)
Other 36 (16.7)
Townhall meetings (virtual)
Zoom or WebEx meetings
Canvas notifications
Student government
Intranet website
Zoom office hours
Video messages
* Center for Epidemiological Studies of Depression, short-form (CESD-10), contained 10 questions with responses ranging from 0 to 3. The “average risk” score was <10 and the “at risk” score was ≥ 10. This cutoff of depressive symptomology for this measure is established in the literature.
** Medical students responded to a question about whether their ability to cope with events surrounding the SARS CoV-2 pandemic period had changed since the beginning of the pandemic.
*** Medical students responded to a question about whether their feelings related to the 10 depression symptoms changed since the Fall of 2019.
A majority of students were willing to get a COVID vaccine (163/212; 76.9%), or would consider it (48/212; 22.6%). Among the 114 students tested for the SARS CoV-2 virus, 5.3% (6/114) tested positive. Close to 46% of students (97/212) reported a family member or friend had tested positive for SARS CoV-2. Among those, 23.7% (23/97) reported hospitalization and 10.3% (10/97) reported a related death among their family or friends.
There were differences in schools’ communication about online learning by depression risk (Table 2). Students at risk of developing depression reported communication was poor at a higher frequency compared to average risk students. After controlling for variables associated with the risk of developing depression in bivariate analyses, students had lower odds of being at risk for depression if they felt their universities did a good job communicating: the transition to online learning, test dates and times by administration, plans to return to classes in the Fall, and new or changing curriculum requirements. Students who felt communication about scholarships or other funding was poor had more than 3 times the odds of being at the risk of depression in adjusted analyses.
Table 2. Description of how well medical school communicated with students by risk of developing depressive disorder (N = 212)
Total n (%) Average risk* n (%) At risk*
n (%) p-valuea aOR (95% CI)b
Academic standard ratings during pandemic 0.007
Far below/ below standards 38 (17.9) 11 (29.0) 27 (71.0) 2.47 (0.97 −6.28)
Meets standards 126 (59.4) 69 (54.8) 57 (45.2) Reference
Above/ far above standards 48 (22.6) 29 (60.4) 19 (39.6) 0.70 (0.31 −1.59)
Communication about transition to online learning during pandemic 0.007
Very poor/ poor 29 (14.2) 11 (37.9) 18 (62.1) 0.83 (0.27 −2.54)
Fair 55 (26.8) 21 (38.2) 34 (61.8) Reference
Well/ very well 121 (59.0) 73 (60.3) 48 (39.7) 0.34 (0.15 −0.78)
Communication about how long medical school expected to utilize new learning methods 0.001
Very poor/ poor 65 (31.6) 24 (36.9) 41 (63.1) 1.77 (0.76 −4.12)
Fair 64 (31.1) 31 (48.4) 33 (51.6) Reference
Well/ very well 77 (37.4) 52 (67.5) 25 (32.5) 0.53 (0.23 −1.21)
Communication about test dates and times by administration 0.06
Very poor/ poor 32 (15.8) 12 (37.5) 20 (62.5) 0.95 (0.32 −2.88)
Fair 47 (23.2) 20 (42.6) 27 (57.5) Reference
Well/ very well 124 (61.1) 71 (68.9) 53 (42.7) 0.43 (0.19 −0.99)
Communication about summer research, practicums, or other curriculum 0.007
Very poor/ poor 51 (26.2) 17 (33.3) 34 (66.7) 1.97 (0.76 −5.10)
Fair 59 (30.3) 30 (50.8) 29 (49.2) Reference
Well/ very well 85 (43.6) 52 (61.2) 33 (38.8) 0.52 (0.23 −1.22)
Communication about return to classes in the Fall <0.001
Very poor/ poor 54 (27.0) 24 (44.4) 30 (55.6) 0.44 (0.16 −1.22)
Fair 51 (25.5) 14 (27.4) 37 (72.6) Reference
Well/ very well 95 (47.5) 62 (65.3) 33 (34.7) 0.21 (0.08 −0.52)
Communication of new or changing curriculum requirements <0.001
Very poor/ poor 47 (22.8) 16 (34.0) 31 (66.0) 0.78 (0.28 −2.18)
Fair 48 (23.3) 16 (33.3) 32 (66.7) Reference
Well/ very well 111 (53.9) 73 (65.8) 38 (34.2) 0.26 (0.11 −0.62)
Communication about delays or cancellations in national tests 0.04
Very poor/ poor 59 (35.1) 25 (42.4) 34 (57.6) 0.63 (0.22 −1.80)
Fair 37 (22.0) 15 (40.5) 22 (59.5) Reference
Well/ very well 72 (42.9) 44 (61.1) 28 (38.9) 0.46 (0.16 −1.29)
Communication of information about changes to school fees 0.005
Very poor/ poor 94 (48.7) 37 (39.4) 57 (60.6) 2.12 (0.87 −5.16)
Fair 43 (22.3) 28 (65.1) 15 (34.9) Reference
Well/ very well 56 (29.0) 34 (60.7) 22 (39.3) 0.86 (0.32 −2.34)
Communication about scholarships or other funding 0.001
Very poor/ poor 59 (33.0) 19 (32.2) 40 (67.8) 3.37 (1.30 −8.71)
Fair 53 (29.6) 30 (56.6) 23 (43.4) Reference
Well/ very well 67 (37.4) 43 (64.2) 24 (35.8) 0.55 (0.22 −1.39)
Communication about eligibility for CARES Act grant funds 0.3
Very poor/ poor 76 (41.3) 35 (46.1) 41 (54.0) 0.95 (0.37 −2.42)
Fair 41 (22.3) 19 (46.3) 22 (53.7) Reference
Well/ very well 67 (36.4) 39 (58.2) 28 (41.8) 0.75 (0.29 −1.94)
aOR= adjusted odds ratios; 95% CI = 95% confidence interval
*Center for Epidemiological Studies of Depression, short-form (CESD-10), contained 10 questions with responses ranging from 0 to 3. The “average risk” score was < 10 and the “at risk” score was ≥ 10. This cutoff of depressive symptomology for this measure is established in the literature.
aChi-Square test p-value for unadjusted bivariate analyses.
baOR adjusted for year in medical school, change in coping, and change in depressive symptoms.
Bold odds ratios indicate significance at P < 0.05.
Table 3. Student agreement or disagreement with statements about their medical school’s communication and response during the SARS CoV-2 pandemic period by risk of developing depressive disorder (N = 212)
Total n (%) Average risk* n (%) At risk* n (%) p-valuea aOR (95% CI)b
Transitioned to online learning quickly 0.42
Strongly disagree/ disagree 16 (7.6) 9 (56.2) 7 (43.8) 0.22 (0.40 - 1.23)
Neither agree or disagree 21 (9.9) 8 (38.1) 13 (61.9) Reference
Agree/ strongly agree 175 (82.5) 92 (52.6) 83 (47.4) 0.41 (0.12 - 1.45)
Had a good infrastructure related to communication and course preparation in place. 0.002
Strongly disagree/ disagree 46 (21.7) 13 (28.3) 33 (71.7) 2.94 (1.00 - 8.67)
Neither agree or disagree 43 (20.3) 19 (44.2) 24 (55.8) Reference
Agree/ strongly agree 123 (58.0) 77 (62.6) 46 (37.4) 0.56 (0.24 - 1.31)
Communicating plans for preceptorships or clinical experience was well done. 0.002
Strongly disagree/ disagree 65 (30.7) 23 (35.4) 42 (64.6) 2.87 (1.18 - 7.02)
Neither agree or disagree 59 (27.8) 30 (50.8) 29 (49.2) Reference
Agree/ strongly agree 88 (41.5) 56 (63.6) 32 (36.4) 0.77 (0.35 - 1.71)
Communicating plans for research practicums or other research experiences was well done. 0.007
Strongly disagree/ disagree 61 (28.8) 21 (34.4) 40 (65.6) 2.59 (1.06 - 6.36)
Neither agree or disagree 91 (42.9) 53 (58.2) 38 (41.8) Reference
Agree/ strongly agree 60 (28.3) 35 (58.3) 25 (41.7) 0.97 (0.45 - 2.12)
It took longer for grades to be posted compared to when classes were conducted in person. 0.01
Strongly disagree/ disagree 103 (48.6) 62 (60.2) 41 (39.8) 0.44 (0.20 - 1.00)
Neither agree or disagree 58 (27.4) 29 (50.0) 29 (50.0) Reference
Agree/ strongly agree 51 (24.0) 18 (35.3) 33 (64.7) 1.18 (0.47 - 2.98)
Plans for the remainder of the semester were well understood. 0.02
Strongly disagree/ disagree 81 (38.2) 32 (39.5) 49 (60.5) 2.39 (1.00 - 5.71)
Neither agree or disagree 47 (22.2) 29 (61.7) 18 (38.3) Reference
Agree/ strongly agree 84 (39.6) 48 (57.1) 36 (42.9) 1.12 (0.47 - 2.67)
Communication between [medical school] and me could have been done differently to be more effective 0.1
Strongly disagree/ disagree 55 (25.9) 35 (63.6) 20 (36.4) 0.65 (0.25 - 1.67)
Neither agree or disagree 50 (23.6) 25 (50.0) 25 (50.0) Reference
Agree/ strongly agree 107 (50.5) 49 (45.6) 58 (27.4) 1.32 (0.58 - 3.02)
The objectives related to the content of tests was made clear to me. 0.01
Strongly disagree/ disagree 33 (15.6) 9 (27.3) 24 (72.7) 1.43 (0.47 - 4.33)
Neither agree or disagree 52 (24.5) 28 (53.9) 24 (46.2) Reference
Agree/ strongly agree 127 (59.9) 72 (56.7) 55 (43.3) 0.62 (0.28 - 1.37)
Faculty were readily available for questions. 0.004
Strongly disagree/ disagree 20 (9.4) 8 (40.0) 12 (60.0) 0.23 (0.05 - 1.08)
Neither agree or disagree 28 (13.2) 7 (25.0) 21 (75.0) Reference
Agree/ strongly agree 164 (77.4) 94 (57.3) 70 (42.7) 0.16 (0.05 - 0.49)
Administration were readily available for questions. 0.006
Strongly disagree/ disagree 34 (16.0) 12 (35.3) 22 (64.7) 0.63 (0.20 - 2.00)
Neither agree or disagree 42 (19.8) 16 (38.1) 26 (61.9) Reference
Agree/ strongly agree 136 (64.2) 81 (59.6) 55 (40.4) 0.36 (0.15 - 0.85)
Curriculum was just as rigorous as it was when classes were conducted in person. 0.25
Strongly disagree/ disagree 65 (30.7) 30 (46.2) 35 (53.8) 1.68 (0.68 - 4.16)
Neither agree or disagree 47 (22.2) 29 (61.7) 18 (38.3) Reference
Agree/ strongly agree 100 (47.2) 50 (50.0) 50 (50.0) 0.85 (0.34 - 2.11)
I am as well-prepared for the next courses as I would have been before online-only courses were implemented. 0.008
Strongly disagree/ disagree 73 (34.4) 28 (38.4) 45 (61.6) 1.14 (0.51 - 2.57)
Neither agree or disagree 62 (29.2) 32 (51.6) 30 (48.4) Reference
Agree/ strongly agree 77 (36.3) 49 (63.6) 28 (36.4) 0.56 (0.24 - 1.25)
My professors were well-prepared for online teaching. 0.007
Strongly disagree/ disagree 86 (40.6) 33 (38.4) 53 (61.6) 2.26 (1.01 - 5.06)
Neither agree or disagree 65 (30.7) 40 (61.5) 25 (38.4) Reference
Agree/ strongly agree 61 (28.8) 36 (59.0) 25 (42.0) 1.02 (0.44 - 2.38)
I was well-prepared for the changes in how the curriculum was presented. <0.001
Strongly disagree/ disagree 60 (28.3) 19 (31.7) 41 (68.3) 2.16 (0.88 - 5.32)
Neither agree or disagree 58 (27.4) 30 (51.7) 28 (48.3) Reference
Agree/ strongly agree 94 (44.3) 60 (63.8) 34 (36.2) 0.86 (0.34 - 1.93)
I got a lot of information from other students before official announcements were made. 0.13
Strongly disagree/ disagree 80 (37.7) 48 (60.0) 32 (40.0) 0.50 (0.20 - 1.27)
Neither agree or disagree 40 (18.9) 20 (50.0) 20 (50.0) Reference
Agree/ strongly agree 92 (43.4) 41 (44.6) 51 (55.4) 0.70 (0.28 - 1.74)
Communication about changes was done in a timely manner. 0.01
Strongly disagree/ disagree 73 (34.4) 29 (39.7) 44 (60.3 1.43 (0.60 - 3.39)
Neither agree or disagree 53 (25.0) 26 (49.1) 27 (50.9) Reference
Agree/ strongly agree 86 (40.6) 54 (62.8) 32 (37.2) 0.61 (0.26- 1.42)
I needed to put extra effort into finding out information critical to my success as a student. 0.003
Strongly disagree/ disagree 66 (31.1) 45 (68.2) 21 (31.8) 0.38 (0.14 - 1.06)
Neither agree or disagree 40 (18.9) 20 (50.0) 20 (50.0) Reference
Agree/ strongly agree 106 (50.0) 44 (41.5) 62 (58.5) 1.13 (0.45 - 2.85)
aOR= adjusted odds ratios; 95% CI = 95% Confidence Interval
*Center for Epidemiological Studies of Depression, short-form (CESD-10), contained 10 questions with responses ranging from 0 to 3. The “average risk” score was < 10 and the “at risk” score was ≥ 10. This cutoff of depressive symptomology for this measure is established in the literature.
aChi-Square test p-value for unadjusted bivariate analyses.
baOR adjusted for year in medical school, change in coping, and change in depressive symptoms.
Bold odds ratios indicate significance at P < 0.05.
Most students felt universities transitioned quickly to online learning, and that the curriculum was rigorous (47.2%) or were neutral (22.2% ; Table 3). They also felt communication could have been done differently to be more effective (50.5%). In adjusted analyses, students were more than 3 times the odds of being at risk for developing depression if they disagreed that: their university had a good infrastructure for communication and course preparation in place, that plans for preceptorships or clinical experience were well done, or that communication about plans for research practicums or other research experiences were well done. Students had more than 2 times the odds of being at risk for developing depression if they disagreed that: plans for the remainder of the semester were well understood or that their professors were well-prepared for online teaching. Students who disagreed that their universities took longer to post grades during online learning had lower odds of being at risk for depression. Students who agreed that faculty were readily available for questions or that administration staff were readily available for questions had lower odds of being at risk of developing depression.
Discussion
A substantial proportion of medical students who participated in this survey were at risk of developing depression. Depression among US medical students ranges from 21.7% to 59.1% according to studies with a cutoff score of ≥16 on the CESD-20. 11 Among the medical students sampled early in the pandemic, 24% were found to be depressed. 3 Although the observed rate of 48.6% falls within previously reported limits, it is still problematic as close to 50% of medical students in this sample were at risk of developing depression. These students need resources and guidance to address the distress they are feeling. A particularly high rate of first year students were at the risk of depression (61%) during the 2019 - 2020 academic year. These students may have been particularly vulnerable, as previous research found students had the lowest physical, emotional, and overall health at the end of year 1, and at the time of this survey, many participants would have been in the first part of their second year. 12 The addition of physical isolation from their fellow students may have exacerbated the stress encountered by these first year students. Inclusion of virtual meetups organized by student societies or by universities may help, but depressed students may have difficulty utilizing such opportunities. Facilitating the process of moving back in with their families may offer more support if remote education becomes necessary again in the future. Universities may need to proactively push for federal policies that can fund and make allowances available for medical students to participate in medical education across state or country borders during periods of time when remote education is the only way to maintain student health and safety.
Students who felt that communication about scholarships or other funding was poor or very poor had more than 3 times the odds of being at risk of developing depression. Students who depended on loans or external funding, such as scholarships for higher education, were more likely to experience anxiety and stress that could lead to depression. 13–15 This study indicates it is important for medical schools to communicate regularly with students who rely on scholarships or other funding. It is also possible that students who felt more depressed were more likely to need financial assistance with tuition, or were more likely to perceive their universities’ communication about scholarships and other funding as poor. Regardless, these results indicate the importance of strong communication with students about funding. Although it may be difficult to provide individualized communication with students, messages about funding, providing reassurance to students on a regular basis with general information and acknowledgment of the administrations’ awareness about the importance of this issue could help alleviate some student anxiety. Future research could focus on the development of solutions to this issue of communication and evaluate the optimum intervals between information offerings to improve reassurance without causing a communication burn-out for students.
Most communication between medical schools and their students took place via email, however, in situations that cause stress, medical schools may consider utilizing a variety of methods for communication with students. College students who used computer mediated communication in a previous study preferred synchronous and interactive methods of online communication such as instant messaging, and social networks. 16 These previous findings, combined with students freely reporting satisfaction in this study with remote town halls, suggest future paths for strengthening communication to alleviate communication - related anxiety among medical students during times of uncertainty or in emergency situations. 16 Town halls may provide a forum for students to ask questions or provide suggestions to improve communication and how the curriculum is offered or adjusted in a way that is appropriate for each university’s program, enabling them to feel more empowered in the decisions related to their future. Furthermore, using these opportunities to discuss how to identify stress and depression, how to address it with classmates or family, share experiences with mental health struggles, and understand what services are available may assist with the normalization of discussing mental health issues, and thereby encourage help-seeking. 17 Training faculty preceptors or facilitators to recognize and address mental health in small group settings, such as problem-based learning groups could also help to identify and normalize help-seeking among students.
There is evidence that even short programs aimed at reducing stress through group stress management and self-care workshops for medical students can decrease stress and increase mindfulness. 18 Similar programs that enhance communication between medical students and their universities could be developed and adapted for remote access, as well as in-person access. As medical students are at a heightened risk of experiencing poor mental health, it is important that interventions are not temporary, as it is apparent that medical students need mental health services, especially during disaster events.
In general, most students felt that their universities communicated well regarding the changes in curriculum during the COVID-19 pandemic. In particular, good communication about curriculum expectations and anticipated changes was associated with lower odds of having an elevated risk of developing depression. As concerns about the learning environment and academic performance are associated with increased anxiety and stress among students, it is reassuring that good communication about these issues is associated with lowered odds of being at risk for depression. 10
This study was developed with direct input from medical students. The concern they felt about the curriculum and communication was reflected in the questions, as well as their response to the survey. Future research should focus on the quantity and quality of communication between medical schools and students, as well as mental health among medical students with methodology informed by both medical students and medical school administration. In addition, future research should evaluate how the COVID-19 pandemic affects the cohort of students that were represented by this survey. Maintaining the well-being and health of these future healthcare providers is critical to ensuring the continuity of a quality healthcare system.
Limitations
The strength of this study lies in the national recruitment in the US, with students being eligible to participate even if their universities did not send email invitations. Several universities were represented, but the results of this study may not have reflected the experience of all medical schools. Although the sample is small, it may be representative of a significant population of medical students. Biases may have existed within the sample as participants who felt that there were issues with their medical school’s communication may have been more likely to participate than those who felt that their institutions responded well to the pandemic. The sample had a high proportion of first and second year students, so the experiences of more advanced medical students may not have been adequately captured.
Conclusion
In conclusion, universities appeared to do a good job communicating with their students during the pandemic. However, this study also highlights the need for ongoing efforts to address mental health by medical schools, and to better understand the effects of disasters, not just the COVID-19 pandemic, on their well-being. Improving communication on this issue through normalizing help-seeking behavior, offering more interactive forms of communication, particularly during disasters, and ensuring that communication about funding is clear may assist medical students in feeling less stress related to their education, and begin to address high rates of depressive symptoms in this population.
Acknowledgments
We would like to acknowledge the 26 medical schools that informed their students about the survey via email, including those faculties who helped the authors of this manuscript with submitting studies to committees or IRBs in a manner consistent with their university’s policies for gaining approval for surveys originating outside their own institution.
Conflicts of interest
The authors have no conflicts of interest to declare.
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PMC009xxxxxx/PMC9002153.txt |
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Disaster Med Public Health Prep
Disaster Med Public Health Prep
DMP
Disaster Medicine and Public Health Preparedness
1935-7893
1938-744X
Cambridge University Press New York, USA
35027098
S1935789322000155
10.1017/dmp.2022.15
Concepts in Disaster Medicine
Implementation of SARS-CoV-2 Monoclonal Antibody Infusion Sites at Three Medical Centers in the United States: Strengths and Challenges Assessment to Inform COVID-19 Pandemic and Future Public Health Emergency Use
https://orcid.org/0000-0001-6822-9969
Lambrou Anastasia S. 1 2
Redd John T. 1
Stewart Miles A. 1 2
Rainwater-Lovett Kaitlin 1 2
Thornhill Jonathan K. 1 2
Hayes Lynn 1
Smith Gina 1
Thorp George M. 1
Tomaszewski Christian 3 4
Edward Adolphe 3 4
Elías Calles Natalia 5
Amox Mark 6
Merta Steven 6
Pfundt Tiffany 1
Callahan Victoria 1
Tewell Adam 1
Scharf-Bell Helga 1
Imbriale Samuel 1
Freeman Jeffrey D. 1 2
Anderson Michael 1
Kadlec Robert P. 1
1 Office of the Assistant Secretary for Preparedness and Response, US Department of Health and Human Services, Washington, DC, USA
2 Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
3 El Centro Regional Medical Center, El Centro, CA, USA
4 UC San Diego Health, San Diego, CA, USA
5 TMC HealthCare, Tucson, AZ, USA
6 Sunrise Hospital and Medical Center, Las Vegas, NV, USA
Corresponding author: John T. Redd, Email john.redd@hhs.gov.
14 3 2022
14 3 2022
111
01 4 2021
18 11 2021
11 1 2022
© The US Department of Health and Human Services and The Author(s) 2022
2022
The US Department of Health and Human Services and The Author(s)
simple This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means subject to 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.
Monoclonal antibody therapeutics to treat coronavirus disease (COVID-19) have been authorized by the US Food and Drug Administration under Emergency Use Authorization (EUA). Many barriers exist when deploying a novel therapeutic during an ongoing pandemic, and it is critical to assess the needs of incorporating monoclonal antibody infusions into pandemic response activities. We examined the monoclonal antibody infusion site process during the COVID-19 pandemic and conducted a descriptive analysis using data from 3 sites at medical centers in the United States supported by the National Disaster Medical System. Monoclonal antibody implementation success factors included engagement with local medical providers, therapy batch preparation, placing the infusion center in proximity to emergency services, and creating procedures resilient to EUA changes. Infusion process challenges included confirming patient severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positivity, strained staff, scheduling, and pharmacy coordination. Infusion sites are effective when integrated into pre-existing pandemic response ecosystems and can be implemented with limited staff and physical resources.
Keywords:
COVID-19 pandemic
infusion
medical countermeasure
monoclonal antibody
pandemic response
==== Body
pmcIntroduction
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019 and ignited a global pandemic with detrimental impacts on health systems across the world. This novel virus caught the globe unprepared without targeted medical countermeasures (MCMs), such as therapeutics, to treat individuals with coronavirus disease (COVID-19). As the pandemic progressed and scientific progress was rapidly stimulated, the therapeutic toolkit to treat COVID-19 evolved to include monoclonal antibodies. 1 Monoclonal antibody therapeutics to treat COVID-19 are composed of laboratory-synthesized SARS-CoV-2 neutralizing antibodies, most often isolates from infected individuals, and isolated for specific immunologic properties such as binding, neutralization, and effector functions. 2 Multiple formulations and forms of administration of monoclonal antibodies have been authorized by US Food and Drug Administration’s under Emergency Use Authorization (EUA) for both post-exposure prophylaxis and treatment. 3 Recent clinical trials on monoclonal antibody therapies suggest that early use of these drugs can reduce COVID-19 symptom severity, SARS-CoV-2 viral load, and hospitalization in infused outpatient populations as compared to individuals given placebos. 4–6 Real-world effectiveness studies have also provided evidence that monoclonal antibody infusions reduce hospitalization rates in high-risk patient populations. 7–9 These monoclonal antibody therapies are currently administered as intravenous infusions to treat individuals with mild to moderate COVID-19. The EUAs also specify monoclonal antibody infusion eligibility requirements for potential patients at high risk for COVID-19 complications, such as age, Body mass index, and pre-existing conditions (SI Table 1). EUAs are regulatory tools used during public health emergencies, such as pandemics, to expand use, system implementation, and further study of new therapeutics. 10
Despite the EUAs and promising clinical trial results, monoclonal antibody therapies are currently underutilized as a treatment for COVID-19 across the United States. This is hypothesized to be due to gaps in outreach to both providers and patient communities, strict EUA criteria, and infusion site implementation barriers during the ongoing pandemic, such as staffing, resources, and infection control. 11 Incorporating monoclonal antibodies into COVID-19 response efforts may relieve stress on medical centers through reducing disease severity and hospitalizations. 12 Monoclonal antibody use is increasing in some settings across the United States, but there is limited research on the implementation of this therapy, resources needed to maintain an infusion site, and lessons learned to inform the scale-up of this pandemic response tool. Monoclonal antibody therapeutics may also play a critical role in future emerging biological threats, including the newly described, emerging variant SARS-CoV-2 isolates, as they can be rapidly manufactured and can be used as a treatment before other MCMs, such as vaccines, evaluated, and distributed. 13 Vaccines may also require multiple weeks or doses to elicit protection, while monoclonal antibodies serve as a treatment to reduce the burden of a novel pathogen. It is critical to learn from the ongoing implementation of monoclonal antibody infusions during the COVID-19 pandemic to inform the scale-up of this therapy, and other biologics, during the current and future emergencies.
The purpose of this investigation was to describe monoclonal antibody infusion site implementation and requirements during the COVID-19 pandemic using data from 3 sites in the United States, supported by the Office of the Assistant Secretary for Preparedness and Response (ASPR). A set of standard metrics was utilized to evaluate site infusion process staffing model, resources, strengths, and challenges. Diagrams of the monoclonal antibody infusion process components and infusion site physical environment illustrate various therapy implementation layouts. The descriptive metrics analysis informs the implementation of a monoclonal antibody infusion site for the COVID-19 pandemic response efforts and for future use to tackle emerging infectious disease threats. This is a critical window during the pandemic in the United States to examine the implementation of monoclonal antibody infusion sites for outpatients as the response is currently marked by recent therapy EUAs and the steadily growing mass distribution of COVID-19 vaccines.
Infusion Site Process Assessment
Data were collected from 3 medical centers in the United States, El Centro Regional Medical Center (El Centro, CA), TMC HealthCare (Tucson, AZ), and Sunrise Hospital and Medical Center (Las Vegas, NV) between January and February 2021. These sites recently implemented monoclonal antibody infusions during the pandemic to treat individuals with mild and moderate COVID-19 using EUA criteria and by collaborating with ASPR’s National Disaster Medical System (NDMS) Disaster Medical Assistance Teams (DMATs). All 3 medical sites then transitioned to maintaining their own monoclonal antibody infusion sites without ASPR support and incorporated monoclonal antibody infusion into their COVID-19 pandemic response workflows. This investigation was concerned with describing the infusion site process workflows after the DMATs departed and the medical systems transitioned their processes to ensure sustainability during the COVID-19 pandemic. These sites were selected due to their early adoption of monoclonal antibody delivery while under pandemic stress as COVID-19 “hotspots.” The 3 sites also delivered care to diverse and underserved patient populations, and exhibited different process approaches, infrastructures, and physical locations that can inform monoclonal antibody infusion process scale-up across the United States. Two of the sites were temporary tent-based infusion sites and 1 site was a converted former primary care clinic. This clinical support activity was conducted as part of the ASPR public health response to the COVID-19 pandemic and at the request of the host institutions. Under the US Department of Health and Human Services, Office of Health Research Protection guidelines, it was judged a non-research COVID-19 response activity. The Johns Hopkins University Applied Physics Laboratory (JHU/APL) Environmental Health Services Board and all 3 medical sites also deemed this work non-human subjects research exempt from institution review board approval.
Data Collection and Analysis
Data were collected through 3 mechanisms to inform the monoclonal antibody infusion process assessment, model, and recommendations: (1) key informant interviews, (2) on-site observations, and (3) infusion records. A process assessment framework informed the 7 key metrics on which data were collected to ensure standard data collection at each site (SI Figure 1): logistics, timing, staffing, physical environment, resources, monitoring and resilience, and engagement (SI Table 2). The 7 framework metrics describe critical quantitative and qualitative characteristics of the infusion process to inform the assessment and propose future recommendations.
Semi-structured key informant interviews were conducted at each site using an interview guide to collect data on infusion process assessment metrics to ensure standard data collection. Interviews were conducted with the medical center’s Chief Medical Officer, infusion site logistics lead, infection control lead, director of pharmacy, and infusion site staff. Each of the 3 different medical centers’ monoclonal antibody infusion sites was visited by the study team to observe and map the infusion process workflow. Each step in the infusion process was timed for multiple patients, and the staff, resources, and information needed for the step were recorded. The on-site observations also facilitated validating data from the key informant interviews.
A descriptive analysis of the monoclonal antibody infusion process was conducted to examine the timing, staffing needs, resources, and information flow of each component of the process. The process was examined from patient engagement through the infusion appointment and discharge from the infusion site. The physical environment of each infusion site was also mapped to analyze resource and implementation needs for this new therapy option. Data on each process metric from the process assessment framework were synthesized and compiled for each site.
Infusion Site Workflow and Metrics
A descriptive analysis of 3 medical center monoclonal antibody infusion sites was conducted using a process assessment to inform recommendations to strengthen infusion site implementation during current pandemic response efforts. This investigation evaluated the process of monoclonal antibody infusion and staffing equipment, physical space, and resource requirements during the COVID-19 pandemic. A general monoclonal antibody infusion site workflow process (Figure 1) was developed to integrate the data from the 3 data collection sites. It is important to note that there was not a single standard monoclonal antibody infusion site process workflow. Each site exhibited common process components, staffing models, and resources, yet adapted the system to address local policies, patient populations, and medical center characteristics. An effective monoclonal antibody infusion site optimized the volume of infused patients and minimized patient appointment time and stress on the underlying medical system.
Figure 1. General monoclonal antibody infusion site process workflow examining the network of physical environments, patients, information, calls, staff, and resources, informed by the workflows and assessments of each data collection site.
The sites exhibited 2 major medical center mechanisms of implementing a monoclonal antibody infusion site: (1) an outpatient infusion clinic model, and (2) an emergency department (ED) medication visit model (Table 1). Site 1 employed a model tied to ED operations, while Sites 2 and 3 operated as outpatient infusion sites co-located with a medical center. The infusion sites also presented 2 appointment types: 24/7 walk-up and scheduled appointments during business hours. The 3 sites started infusions at different times: first Site 1 started on December 30, 2020, and Sites 2 and 3 initiated infusions the same week, respectively, on January 7 and 8, 2021. Site 1 completed 397 infusions since starting the site with an average rate of 7 infusions per day. Site 2 recorded the highest number of infusions with 824 patients infused, amounting to a rate of approximately 16 infusions per day. Last, Site 3 completed 402 infusions with a rate of 8 patients infused per day. The average rate of patient infusions per day from the field sites was 10 patients per day.
Table 1. Monoclonal antibody infusion process logistics and timing metrics from the 3 National Disaster Medical System-supported infusion sites and related strengths and challenges to inform implementation
Logistics and timing metrics Site 1 Site 2 Site 3 Implementation considerations
Strengths Challenges
Infusion site type Walk-up tent infusion site Appointment-based outpatient infusion site Appointment-based tent infusion site Walk-up sites were beneficial in communities with low health care. system engagement
Appointment-based sites facilitated batch preparation of monoclonal antibody infusion doses, shortening the overall time of the appointment.
30-minute staggering between patient group arrivals improved patient flow due to 15- to 30-minute intake process. Walk-up sites exhibited longer wait times for on-demand pharmacy preparation of the monoclonal antibody.
Batch preparation of monoclonal antibodies resulted in unused doses for walk-up systems.
Walk-up site had large variability in timing due to confirming the patient’s SARS-CoV-2 positivity upon arrival.
Appointment-based sites required increased staffing and planning to schedule patients.
Process type Emergency medical visit Outpatient infusion procedure Outpatient infusion procedure
Infusion site start date Dec 30, 2020 Jan 7, 2021 Jan 8, 2021
Total patients infused during study period
(Start-Feb 26 2021) 397 824 402
Average rate (patients/day) 7 16 8
Most significant logistics barriers Confirming SARS-CoV-2 patient positivity criteria Coordination with pharmacy for monoclonal antibody preparation Coordination with pharmacy for monoclonal antibody preparation
Coordination with pharmacy for monoclonal antibody preparation Staffing needs for scheduling process Staffing needs for scheduling process
Hours of operation 24 hours/day Monday-Friday Monday-Friday
7 days a week 9:00 am-5:00 pm 9:00 am-5:00 pm
Generally, the process components were initiated by a prospective patient testing positive for SARS-CoV-2, and with scheduling-based infusion sites, patients having first to obtain a provider referral for monoclonal antibody treatment with confirmation that they meet the EUA robust criteria, and timely local SARS-CoV-2 test result turnaround was critical to effective monoclonal antibody implementation, as the current EUA requires the infusion to occur within 10 days of symptom onset in patients with a documented positive COVID-19 viral test result. Areas with SARS-CoV-2 testing turnaround close to 1 week delayed patient referral and created monoclonal antibody uptake obstacles. Infusion site appointments had 3 major components. The first component was a pre-infusion intake process to confirm patient eligibility, collect vitals, obtain patient consent, and insert an IV. The next component was the monoclonal antibody infusion process, which ranged from 16–60 minutes depending upon the specific therapy available and size of infusion bags. This time was EUA-dependent and this process must remain flexible to changes in infusion requirements, as the guidelines changed from 60 to 16 minutes during the study period. The last component was the EUA-specified 60-minute patient observation period of each patient to monitor for any adverse events.
Three process components contributed the most to patient visit time variability: (1) scheduling appointments, (2) pre-infusion patient intake, and (3) monoclonal antibody coordination with the medical center pharmacy. These 3 process components also created stresses on already constrained staffing resources. A critical barrier of the infusion process at each of the 3 sites was the pharmacy’s preparation of the monoclonal antibody and coordination with the infusion site on therapy doses and timing. Scheduling-based infusion site pharmacies were equipped with data to enable pre-preparation of monoclonal antibody doses in batches before patients arrive. The 3 infusion sites emphasized that coordination with the pharmacy is difficult due to physical proximity and the need to conserve any prepared doses. Monoclonal antibody infusion process workflows were strongly shaped by EUA requirements regarding drug preparation, storage, timing, and delivery.
Infusion Site Staffing
Similar to the infusion process components, the infusion site staffing metrics varied between sites. The different staffing models relied on the same underlying requirements to ensure monoclonal antibody referral, prescription, preparation, and administration (Table 2). Staffing models differed due to state policies and the different underlying staffing structures of the 3 medical centers. Each staffing model consisted of an advanced practice provider (APP) or physician, a nursing team, and a pharmacy team. The infusion site operations relied heavily on the nursing team, and the more effective infusion process workflows separated the nursing team into 2 distinct task areas: patient pre-infusion intake tasks and the infusion-related tasks. The consistent recommendation from the infusion sites for the minimal staffing needs estimated 2 registered nurses (RNs) are needed for every 10 infusion patients. Informed by initial implementation experience, sites recommended developing a process workflow split into 2 staffing components with 1 RN completing pre-infusion and intake processes such as patient initial vitals, data collection, and consent. All sites also recommended integrating paramedics, to start IVs and monitor patients, into the staffing model to alleviate stress on constrained medical center nursing staff. One site leveraged a local medical volunteer organization to support staffing the infusion site during the ongoing pandemic to reduce stress on the medical center’s pandemic response staffing. Each of the 3 sites also strongly recommended initiating a multidisciplinary staffing meeting between the medical center’s leadership, pharmacy, infection control, ED, nursing, information technology, and security to coordinate the implementation process and medical center staffing allocation. These representatives were not needed for the day-to-day operations of the monoclonal antibody infusion site, but their expertise and support were for developing the initial workflow and staffing models at the 3 sites.
Table 2. Monoclonal antibody infusion process staffing metrics from the 3 National Disaster Medical System-supported infusion sites and strengths and challenges related to staffing and implementation decision-making
Staffing metrics Infusion site 1 Infusion site 2 Infusion site 3 Implementation considerations
Strengths Challenges
Staffing model 1-3 Registered Nurses (RNs): staff infusion site while also staffing Emergency Department (ED) overflow 3-4 RNs: 2-3 RNs Recommended staffing model for monoclonal antibody infusion sites consists of 2 RNs for every 10 infusion patients/chairs.
Staffing models were strengthened by delegating tasks between the 2 RNs with 1 RN dedicated to the pre-infusion/intake process (vitals, registration, consent, etc.) and the other RN dedicated to IV insertion, infusion start, and observation process.
Medically accredited volunteers or paramedics in the community may serve as critical staffing resources for future sites.
Infusion site scheduler or arrival coordinator staffing facilitated shorter total appointment times.
Infusion process is not heavily physician staffing dependent. Therapy implementation during an ongoing pandemic created large staffing barriers and staff were relocated based upon dynamic medical left needs.
Difficult to dedicate pharmacy staff only to monoclonal antibody preparation.
Staff time and resources are spent on the physical transfer of the monoclonal antibody therapy from the pharmacy to the infusion site.
Scheduling, requests and outreach can encompass large amounts of staff time and resources.
Staffing plans require flexibility as EUA changes also change staff needs, training, and protocols.
1 Physician or Advanced Practice Provider (APP): based in the ED, but oversees referrals and prescriptions 1 Nurse Practitioner (NP): 1 Medically-Credentialed Volunteer:
1-2 Pharmacists: prepare the monoclonal antibody and transfer to tent 1 Pharmacist: 1 Physician: on-call hospitalist used to oversee referrals and prescriptions
1 Pharmacy Technician: 1-2 Pharmacists
1 Courier: transfers prepared monoclonal antibody from pharmacy to infusion site 1 scheduler (dedicated to infusion site)
1 Scheduler: multiple types of infusions 1 intake and tent entrance coordinator
1 Front Desk Staff Member
Full-time staff 0 5-6 5-6
Support staff 3-6 4 2-3
Total staff 3-6 9-10 7-9 (1 volunteer)
Physical Environment and Resources
The different external and internal physical environments exhibited by the 3 monoclonal antibody infusion sites were influenced by infection control, resource transport, staffing, and emergency response plan considerations (SI Figures 2–3). Monoclonal antibody recipients are all laboratory-confirmed SARS-CoV-2-positive patients and likely infectious; consequently, it was critical to separate the infusion site from other medical center operations with uninfected individuals. Two of the sites created temporary tent-based infusion sites next to their ED to maintain a separate physical space and HVAC system for infection control purposes but remain near emergency services for potential adverse events and the pharmacy for monoclonal antibody preparations. One site converted a former primary care clinic located a short distance away from the main medical center into a monoclonal antibody infusion site. This building was only being used by monoclonal antibody patients and the therapy was transferred by a driving courier from the pharmacy in the main medical center campus to the site.
The sites differed in the total number of patients who could be infused at one point in time. While the indoor site allocated 6 rooms for infusion, the 2 tent sites had 10 and 30 infusion chairs (Figure 2). Medical and technological infusion site resources were needed to perform the infusion process, record patient data, and ensure an infection-controlled environment. The resources did not vary greatly between the 3 infusion sites; however, some sites improved the overall monoclonal antibody infusion process by using a mobile, miniature refrigeration unit to store batches of the monoclonal antibody, and scanners to rapidly send prescription and paperwork (Table 3). The temporary tent sites required more infrastructure resources such as electricity sources, power strips, lights, HVAC systems, and generators to remain self-sufficient while adjacent to the medical center. At the current stage in the pandemic, the 3 infusion sites did not report any supply chain barriers related to the physical environment and infusion-related resources.
Figure 2. Monoclonal antibody infusion site physical environment schematics of Sites 1–3, indicating resources, site type, and layout.
Table 3. Monoclonal antibody infusion process physical environment and resource metrics from the 3 National Disaster Medical System-supported infusion sites and related strengths and challenges
Physical
environment & resource metrics Site 1 Site 2 Site 3
Implementation considerations
Strengths Challenges
Physical environment type Temporary tents with heating, venting, and air condition (HVAC), electricity, generator, and outdoor mobile restroom Offsite indoor infusion site Temporary tent with HVAC, electricity, generator, and outdoor mobile restroom and handwashing station Temporary tents can lend themselves to easier infection control measures.
Temporary tents may allow for closer proximity to emergency services.
Indoor infusion sites can be more climate resilient and may have pre-existing resources such as electricity and furniture. Temporary tents are difficult to implement in inclement weather and are less sustainable for the site long-term.
Temporary tent may need services such as electricity, security, wireless internet, generator, and bathroom.
Temporary tent rent can be an additional cost if not provided by other entity.
Indoor site must have separate entrance, exit, bathroom, and HVAC system from other medical services treating SARS-CoV-2-negative patients.
Adjacent, outdoor location to ED removed a significant amount of parking required by increased patient demand at medical lefts.
Monoclonal antibody type(s) infused Bamlanivimab and REGN-COV2 Bamlanivimab Bamlanivimab Easier to allocate and share common resources, such as infusion towers, when in a tent layout.
Bamlanivimab recently EUA approved reduced infusion times to as little as 16 minutes.
Refrigeration capacity at infusion site can allow for unused preparations to be stored for 24-36 hours for future use, depending on specific therapy.
Phone capabilities allow for communication with the medical left, emergency services, and other stakeholders.
Integrating the infusion site technology with the electronic health record system and electronic communications supported more effective processes. Tent sites require technological and furniture resources and may require resource storage during off hours.
REGN-COV2 can take approximately 10-15 minutes longer to prepare due to vials and packaging.
Products are both preservative-free and require immediate use after preparation unless refrigerated.
Medical lefts needed to ensure open supply chains for required medical resources.
Infusion sites must be incorporated into biohazard waste medical left plans.
Medical resources Intravenous (IV) supplies IV supplies IV supplies
Infusion towers/dials Infusion towers Infusion towers/dials
Infusion chairs Infusion chairs Infusion chairs
Hospital beds PPE PPE
Personal protective equipment (PPE) Disinfectant Disinfectant
Disinfectant Crash cart Blanket warmers
Crash cart Emergency oxygen Crash cart
Emergency oxygen Sharps container Emergency oxygen
Sharps container Biohazard waste disposal Mini refrigerator (therapy storage)
Biohazard waste disposal Sharps container
Biohazard waste disposal
Technologic resources Vitals monitors Vitals monitors Vitals monitors
Computer to interface with electronic health record Computer to interface with electronic health record Computer to interface with electronic health record
Fax machine Infusion site specific phone line Fax machine to interface with pharmacy
Lights Infusion site specific phone line
Power cords Lights
Electricity generator Power cords
HVAC system Electricity generator
HVAC system
Security cameras and system
Resilience, Monitoring, and Engagement
Sustaining infusion sites through the pandemic required process resilience, monitoring, and engagement (Table 4). Two major barriers that affected process resilience were monoclonal antibody infusion-related adverse events and disruptions to the infusion schedule. The 3 sites had comprehensive plans and resources in place to address a potential adverse event, including the presence of a crash cart at the infusion site, availability of oxygen, patient transport equipment, and medications to treat allergic reactions. The temporary tent sites were also placed adjacent to the ED of the medical centers to ensure close proximity to emergency services if needed. This was a challenge for the off-site physical environment of Site 2 as emergency services would need to be called in the event of an adverse reaction requiring further medical assistance. Disturbances to the schedule were not a potential challenge for Site 1 as it was walk-in based including referrals of ED patients. Sites 2 and 3 emphasized the importance of quickly refrigerating or relabeling an unused monoclonal antibody dose due to patients not arriving for their appointments. This proved to be difficult for sites on Fridays as they were closed on the weekends, and the preservative-free monoclonal antibody drug products must be infused within 24 hours of preparation. Infusion process monitoring and evaluation varied greatly from site to site: 1 site did not conduct any real-time analysis and other sites implemented dashboards to monitor progress such as average patients per day, tracking adverse events, and patient appointment time estimates. A large barrier to monoclonal antibody infusion site implementation during the COVID-19 pandemic was engagement with patients and providers for education, outreach, and referrals.
Discussion
In these 3 ASPR-supported monoclonal antibody infusion sites, our primary finding was that existing processes do not need to be reinvented to implement a successful infusion site during public health emergencies, as the therapy lends itself well to integration into existing outpatient infusion processes and ED/Urgent Care medical visits. The sites implemented various personnel, equipment, and resource modifications to successfully provide monoclonal antibody therapies in communities with large burdens of COVID-19. The general structures of the 3 monoclonal antibody process workflows described here are similar and have consistent major compartmental steps. Process variations were introduced to address state and local requirements on staffing, prescription orders, and to maintain medical center integration with other COVID-19 response workflows. As the COVID-19 pandemic and EUAs evolve, infusion site implementation and maintenance must remain adaptable to changes in therapeutic administration, clinical criteria, requirements, resources, and site needs.
Although a successful monoclonal antibody infusion site can be implemented with minimal staffing needs from the underlying health care system, the physical environment, resources, and work require planning and systems integration to ensure effectiveness, robust infection control, and safety. Medical volunteers or local paramedics can aid in staffing needs and also reduce the burden on the health care system during an emergency. The major strengths of these diverse sites derived from strong community and medical provider engagement on monoclonal antibodies, resilience to process disruptions, and optimized workflows of separating pre-infusion tasking and infusion-related activities between 2 nursing teams. The 3 sites demonstrated successful implementation during a pandemic through strong leadership and staff, collaboration with the NDMS, and flexibility to test and evaluate infusion process workflows. Common barriers and challenges across the sites included coordinating the preparation of the monoclonal antibody in the pharmacy, as it was not prepared at bedside. However, it is important to note that the EUA allows for the therapy to be prepared at bedside and this preparation mechanism may be more effective at particular types of sites, such as nursing homes, and at-home infusions. Infusion sites that scheduled patients were better able to address this barrier by batch preparing infusion bags and storing them in a refrigerator. Scheduling monoclonal antibody infusion appointments was time- and staff-intensive; however, scheduling enabled more efficient workflows and monoclonal antibody preparation.
Confirming patient test positivity and scheduling individuals within 10 days of their symptom onset were other barriers to optimal monoclonal antibody infusions. Rigorous and timely testing and result communication was a necessary foundation for infusion site success due to the requirement for evidence of a positive test result. Future EUA changes and additional authorizations may address some of the logistical challenges and barriers in infusion site implementation such as reducing infusion times, changing storage and preparation requirements, and expanding patient criteria. Demand for this therapy has not yet been maximized in many communities, and the sites’ process workflows can accommodate more patients than their average numbers. Community and provider engagement is critical for any new public health measure, but even more so during a pandemic, as all 3 sites reported challenges addressing misinformation and disinformation on COVID-19 treatments and control in their local communities.
The limitations of this descriptive analysis are rooted in its small sample size of 3 sites and limited geographic scope. However, this study has been uniquely conducted during the pandemic to inform ongoing public health action and infusion site implementation during this emergency. These therapies are not yet widely available internationally, and lessons learned now in the United States may be generalizable to other settings implementing monoclonal antibodies for an emerging infectious disease.
Recommendations for Current and Future Use
The monoclonal antibody infusion site process description and assessment has informed general recommendations for the current implementation and future use of these therapies to tackle public health emergencies (Table 5). For current use, this monoclonal antibody infusion site process assessment has also supported the development of a data-informed decision support tool called “The mAbs Calculator” hosted for free public use on the ASPR Public Health Emergency website. 14 This calculator provides infusion metrics, recommendations, and data visualizations for monoclonal antibody infusion sites to plan and adapt their staffing, resources, and outputs based upon capacity and workflow inputs. 15 Infusion process workflow and environment adaptability are critical as infusion times, requirements, and staffing change in emergencies. A primary recommendation is to build workflows that can be sustainably maintained in existing pandemic response ecosystems. Optimal staffing models require the minimal number of individuals with the appropriate targeted skills. Medical volunteers, paramedics, and other medical emergency support staff can be leveraged from local services to reduce the burden on the health system.
Table 4. Monoclonal antibody infusion process resilience, monitoring, and engagement metrics from the 3 National Disaster Medical System-supported infusion sites and related strengths and challenges
Resilience, monitoring, & engagement Site 1 Site 2 Site 3
Implementation considerations
Strengths Challenges
Potential adverse events protocol Crash-cart located within the tent Crash-cart located within the tent Crash-cart located within the tent Strong engagements with the local community members, providers, and other medical sites built trust and increased therapeutic demand
Utilizing an infusion dashboard and daily data metrics supported productive monitoring and evaluation
Infusion site proximity to ED optimized rapid care for adverse events
Dose repurposing or dose storage plan critical to address schedule and logistical disruptions
Infusion site processes integrated into the pre-existing medical center pandemic response ecosystem Difficult to engage and build trust with particular patient and vulnerable communities due to misinformation and disinformation on the COVID-19 pandemic
Pandemic strain and fatigue served as barriers to engaging providers
Barrier to stronger patient and community engagement was the delay in monoclonal antibody effectiveness data in outpatient populations
Site located adjacent to Emergency Department (ED) to address potential adverse events Offsite of main medical campus, must call 911 for adverse events or related-emergencies Site located adjacent to ED to address potential adverse events
Schedule disruption impacts Lacked pre-established schedule Doses from scheduled patients who do not arrive were stored in refrigerator for next infusion appointment block within 24 hours Doses from scheduled patients who do not arrive are stored in refrigerator for next infusion appointment block within 24 hours
Monitoring & evaluation of infusion site No formal monitoring and evaluation tools Utilized dashboard and electronic health records to monitor and evaluate progress and adjust process Uses whiteboard and electronic health records to monitor, evaluate, and adjust infusion process and schedule
Patient engagement Social media engagement such as Facebook Live Newspaper and online media Newspaper and online media
Local billboards and newspaper articles Provider referral system News media interviews
Provider referral system
Provider engagement Paper-based referral forms sent to provider offices Provider and urgent care sites via email, fax, and phone Provider and urgent care sites via email, fax, and phone
Table 5. Monoclonal antibody infusion therapy and process recommendations for the COVID-19 pandemic and future emerging public health threats
Monoclonal antibody recommendation Description
Incorporate monoclonal antibodies into pandemic preparedness and response and existing health systems as an early intervention Monoclonal antibodies can:
Be manufactured rapidly after neutralizing antibody identification
Provide immediate immunologic support when other medical counter measures (MCMs) are under development or require time to achieve full effectiveness such as vaccines
Serve as prophylaxis for individuals at high risk for infection
Adapt to many forms of deployment during a public health emergency
Integrate into existing health system processes such as existing outpatient infusion processes and ED/Urgent Care med visits
Strengthen process workflow and environment flexibility during public health emergency Adjust monoclonal antibody administration process to policy changes
Critical to monitor and evaluate process workflow to optimize and remain flexible to public health emergency conditions
Adapt monoclonal antibody administration environment to infection control, weather, drug, and staffing changes
Adapt staffing models to minimize burden, and maximize targeted skills Establish workflow with minimal staffing needs
Balance staffing needs with other emergency response activities
Integrate non-traditional health care workers such as medical volunteers and paramedics
Infusion site location expansion and innovative administration Community-based sites: multiple medical centers partner to implement a monoclonal antibody infusion site, share resources and staffing, and minimize individual burden
Rapid testing adjacent sites: co-locate monoclonal antibody site with rapid testing capabilities to refer and immediately treat patients
Car-based infusion or injection: alleviate the physical environment by delivering monoclonal antibodies and observing patients in cars
Home administration: administer monoclonal antibodies in patients’ homes
Nursing homes: administer monoclonal antibodies in nursing homes or long-term care facilities
Ensure strong engagement and equity Engage with local communities to dispel misinformation and disinformation regarding treatments
Empower communities and providers with the knowledge of new therapeutic options and impact data
Ensure monoclonal antibody allocation equity by directing information to populations that are vulnerable, most in need, and likely to meet eligibility criteria
Improved therapy formulations and delivery mechanisms Expand and improve routes of drug administration for therapies, especially rapid methods such as subcutaneous, intramuscular, and microneedle transdermal administration
Strengthen temperature stability and minimize drug product preparation requirements
Standard data collection and effectiveness study integration for outpatients Establish data collection standards for early adopters of monoclonal antibody infusion to permit rapid assessment and large-scale evaluation
Pair monoclonal antibody distribution with data collection network to better understand the therapeutic impact during EUA periods
Sustainable use and public health integration through other disease targets Promote monoclonal antibodies in emerging infectious disease preparedness and response toolkit
Build upon the therapeutics momentum from the pandemic
Continue innovative monoclonal antibody research and study delivery mechanisms and emergency implementation techniques
Partner with organizations researching the application of monoclonal antibodies for other disease targets and public health threats
In public health emergencies, it is important to innovatively expand potential monoclonal antibody administration sites beyond traditional settings. A future outbreak or pandemic could be ignited by a more transmissible pathogen, in which it would be prudent to further minimize staff and patient interactions. One potential solution is patient infusion or injection of a monoclonal antibody therapy with observation in patients’ vehicles, decreasing interactions in a physical environment, space, and indoor infection control systems. This intervention may not be suitable for all settings and vulnerable populations, but it can reduce the strain on physical environments and decrease potential transmission events between patients and health care workers. Further integration of monoclonal antibody delivery into communities could occur by co-locating infusion sites with rapid testing sites so that patients notified of positivity and meeting eligibility criteria could easily access treatment. Infusions and injections may also be administered in the home, 16 removing the need for a physical environment, but potentially increasing the staffing needs and time. As novel treatments arise, such as monoclonal antibodies, strong engagement with the public and equitable distribution of such therapeutics to vulnerable populations is critical. 17 Currently, monoclonal antibodies are delivered via intravenous infusion and subcutaneous administration and research may soon enable intramuscular delivery. 3,18,19 Expanding and improving drug administration routes, especially more rapid methods such as subcutaneous, intramuscular, and microneedle transdermal administration, can support more rapid and less intensive therapy deployment. There is evidence that current monoclonal antibody therapies may show reduced neutralization and potential effectiveness against novel SARS-CoV-2 virus variants to which the drugs were not optimized. 20 However, a strength of monoclonal antibodies is rooted in their adaptability and rapid production. Monoclonal antibody therapies can act as a platform biologic that can be updated as emerging infectious diseases evolve and evade targeting.
Measuring the effectiveness of new therapies, especially in outpatient populations, during a public health emergency, is difficult because resources are focused on saving lives. Establishing site data collection standards to rapidly assess effectiveness and pairing these with the early distribution of new therapies during an emergency, such as monoclonal antibodies, would improve large-scale evaluation. Implementation lessons learned can be translated for the next pandemic. Innovative research, delivery mechanisms, and implementation techniques for monoclonal antibodies must be further studied and optimized, and this can be accomplished through the lens of other pathogens and public health threats.
The emerging infectious disease preparedness and response toolkit is growing to incorporate monoclonal antibodies, and building upon the therapeutics momentum in the current pandemic is important for the next pandemic.
Acknowledgments
The authors acknowledge the significant efforts of the members of the Disaster Medical Assistance Teams (DMATs) who coordinated the monoclonal antibody infusion site setup, initiation, and integration with the collaborating medical centers. The authors would also like to thank the incredible infusion site leadership, implementation, and clinical staff at El Centro Regional Medical Center (El Centro, CA), TMC HealthCare (Tucson, AZ), and Sunrise Hospital and Medical Center (Las Vegas, NV), who provided extensive time and support for this study.
Supplementary material
For supplementary material accompanying this paper visit https://doi.org/10.1017/dmp.2022.15.
click here to view supplementary material
Funding statement
This study was supported by the US Department of Health and Human Services (HHS), Office of the Assistant Secretary for Preparedness and Response (ASPR) through HHS/ASPR contract #: 75A50121C00003.
Conflict(s) of interest
None of the authors received any payments or influence from a third-party source for the work presented, and none reports any potential conflict of interest.
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2. Ju B , Zhang Q , Ge J , et al. Human neutralizing antibodies elicited by SARS-CoV-2 infection. Nature. 2020;584 :115-119. doi: 10.1038/s41586-020-2380-z 32454513
3. COVID-19 Treatment Guidelines, Anti-SARS-CoV-2 Monoclonal Antibodies . National Institutes of Health (NIH). Published 2021. Accessed April 1, 2021. https://www.covid19treatmentguidelines.nih.gov/therapies/anti-sars-cov-2-antibody-products/anti-sars-cov-2-monoclonal-antibodies/
4. Chen P , Nirula A , Heller B , et al. SARS-CoV-2 neutralizing antibody LY-CoV555 in outpatients with COVID-19. N Engl J Med. 2020;384 :229-237. doi: 10.1056/NEJMoa20298433113295
5. Weinreich DM , Sivapalasingam S , Norton T , et al. REGN-COV2, a neutralizing antibody cocktail, in outpatients with COVID-19. N Engl J Med. 2020;384 :238-251. doi: 10.1056/NEJMoa203500233332778
6. Gottlieb RL , Nirulla A , Chen P , et al. Effect of bamlanivimab as monotherapy or in combination with etesevimab on viral load in patients with mild to moderate COVID-19: a randomized clinical trial. JAMA. 2021;325 :632-644. doi: 10.1001/jama.2021.0202 33475701
7. Rainwater-Lovett K , Redd JT , Stewart MA , et al. Real-world effect of monoclonal antibody treatment in COVID-19 patients in a diverse population in the United States. Open Forum Infect Dis. 2021;8 :ofab398. doi: 10.1093/ofid/ofab398 34409125
8. Bariola JR , McCreary EK , Wadas RJ , et al. Impact of bamlanivimab monoclonal antibody treatment on hospitalization and mortality among nonhospitalized adults with severe acute respiratory syndrome coronavirus 2 infection. Open Forum Infect Dis. 2021;8 (7 ):ofab254. doi: 10.1093/ofid/ofab254 34250192
9. Webb BJ , Buckel W , Vento T , et al. Real-world effectiveness and tolerability of monoclonal antibody therapy for ambulatory patients with early COVID-19. Open Forum Infect Dis. 2021;8 (7 ):ofab331. Published 2021 Jun 23. doi: 10.1093/ofid/ofab331
10. Rizk, J. G , Forthal DN , Kalantar-Zadeh K , et al. Expanded access programs, compassionate drug use, and emergency use authorizations during the COVID-19 pandemic. Drug Discov Today. 2020. doi: 10.1016/j.drudis.2020.11.025
11. Donnenberg VS. AAMC discusses monoclonal antibody therapeutics for SARS-CoV-2 infection. J Clin Pharmacol. 2021;61 :280-281. doi: 10.1002/jcph.1820 33474735
12. Cohen MS. Monoclonal antibodies to disrupt progression of early COVID-19 infection. N Engl J Med. 2021;384 :289-291. doi: 10.1056/NEJMe2034495 33471984
13. Sempowski GD , Saunders KO , Acharya P , et al. Pandemic preparedness: developing vaccines and therapeutic antibodies for COVID-19. Cell. 2020;181 :1458-1463. doi: 10.1016/j.cell.2020.05.041 32492407
14. The mAbs Calculator. US Department of Health & Human Sevices ( HHS). Published June 22, 2021. Accessed November 17, 2021. https://www.phe.gov/emergency/mAbs-calculator/Pages/default.aspx
15. Caglayan C , Thornhill J , Stewart MA , et al. Staffing and capacity planning for SARS-CoV-2 monoclonal antibody infusion facilities: a performance estimation calculator based on discrete-event simulations. Front Public Health. 2022(2415). doi: 10.3389/fpubh.2021.770039
16. Malani AN , LaVasseur B , Fair J et al. Administration of monoclonal antibody for COVID-19 in patient homes. JAMA Netw Open. 2021;4 :e2129388–e2129388. doi: 10.1001/jamanetworkopen.2021.29388 34648014
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20. Hu J , Peng P , Wang K , et al. Emerging SARS-CoV-2 variants reduce neutralization sensitivity to convalescent sera and monoclonal antibodies. Cell Mol Immunol. 2021;18 :1061-1063. doi: 10.1038/s41423-021-00648-1 33633321
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Urologe A
Urologe A
Der Urologe. Ausg. a
0340-2592
1433-0563
Springer Medizin Heidelberg
1826
10.1007/s00120-022-01826-3
BvDU Kurz notiert
BvDU Kurz notiert
12 4 2022
2022
61 4 450452
© The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
issue-copyright-statement© Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2022
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pmc
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Nat Rev Endocrinol
Nat Rev Endocrinol
Nature Reviews. Endocrinology
1759-5029
1759-5037
Nature Publishing Group UK London
35414023
676
10.1038/s41574-022-00676-5
In Brief
Dexamethasone in patients with diabetes mellitus
Greenhill Claire nrendo@nature.com
Nature Reviews Endocrinology, http://www.nature.com/nrendo/
12 4 2022
2022
18 6 333333
© Springer Nature Limited 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.
Subject terms
Type 2 diabetes
issue-copyright-statement© Springer Nature Limited 2022
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pmcPatients with COVID-19 who require supplemental oxygen and/or mechanical ventilation are routinely treated with dexamethasone. However, glucocorticoids can exacerbate dysglycaemia, and the benefits of dexamethasone treatment in patients with diabetes mellitus were unclear. A retrospective analysis has assessed data from the first two waves of the COVID-19 pandemic in the UK. Mortality was reduced in the second wave compared with the first wave, with dexamethasone being independently associated with reduced risk of admission to the intensive care unit and/or death. Furthermore, a multivariate analysis demonstrated that the independent effect size of dexamethasone was similar for patients with and without diabetes mellitus. The authors conclude that dexamethasone is beneficial for patients with severe COVID-19 and diabetes mellitus, but that treatment guidelines need to incorporate strategies to identify and manage steroid-induced hyperglycaemia.
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References
Original article
Eng PC The benefit of dexamethasone in patients with COVID-19 infection is preserved in patients with diabetes Diabetes Obes. Metab. 2022 10.1111/dom.14692 35293117
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Med Sci Educ
Med Sci Educ
Medical Science Educator
2156-8650
Springer US New York
35433107
1544
10.1007/s40670-022-01544-7
Letter to the Editor
Student and Trainee Research Collaboratives Can Support Early Exposure to Research, Networking, and Socialisation
http://orcid.org/0000-0003-4128-1390
Kinder Florence florence_kinder@hotmail.co.uk
12
Hayes Siena 23
Dominic Catherine 24
Byrne Matthew H. V. matthew.byrne@nds.ox.ac.uk
256
on behalf of MedEd CollaborativeKinder Florence
Hayes Siena
Dominic Catherine
1 grid.9909.9 0000 0004 1936 8403 School of Medicine, University of Leeds, Leeds, UK
2 MedEd Collaborative, Oxford, UK
3 grid.5600.3 0000 0001 0807 5670 Cardiff University School of Medicine, Cardiff University, Cardiff, UK
4 grid.4868.2 0000 0001 2171 1133 Barts and the London School of Medicine, QMUL, London, UK
5 grid.4991.5 0000 0004 1936 8948 Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
6 grid.410556.3 0000 0001 0440 1440 Oxford University Hospitals Trust, Oxford, UK
12 4 2022
4 2022
32 2 583584
23 3 2022
© The Author(s) under exclusive licence to International Association of Medical Science Educators 2022
issue-copyright-statement© The Author(s) under exclusive licence to International Association of Medical Science Educators 2022
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pmcKehoe et al. highlight some of the key ways to support the recruitment, retention, and progression of clinical academics, with particular reference to early exposure to research, networking, and socialisation [1]. We want to draw attention to the role of student and trainee research collaborative groups in helping to achieve this.
Within the undergraduate medical curriculum, there is a lack of formal research and audit training and opportunities for participation [2], which equates to a lack of confidence and participation when opportunities arise [2]. The structure of collaboratives such as STARSurg and the MedEd Collaborative allows large-scale participation from students as collaborative authors who may be involved in data collection all the way to involvement in study design and steering and writing groups [3]. These experiences are often accompanied by training in research methodologies and provide valuable experience in the fundamentals of research and audit [4].
It is becoming widely accepted that improving the quantity and quality of mentors is necessary to ensure the continued success of medical science. Within student and trainee research collaboratives, the members are at varying stages of training. This provides an ideal platform for networking and near-peer mentorship, which has been shown to aid both personal and professional development and aid transition [5].
Inadequate academic socialisation has been said to hinder group projects, particularly when conducted at a distance, and this has been compounded by social distancing in response to COVID-19 [6]. However, collaborative student and trainee research groups are particularly adapted to promote socialisation amongst distant participants. Collaborative groups often traverse multiple geographical locations and meet regularly, virtually. For example, STARsurg holds representatives at each university in the UK alongside a national committee. We also draw attention to the global networks developed by GlobalSurg for their collaborative work, CovidSurg, which brought together some 15,000 collaborators across 80 countries. [7]
We agree with Kehoe et al. that early research exposure embedded in networks that promote socialisation and mentorship is key. We believe collaborative research groups are fundamental in providing these, alongside their ability to produce high-quality research output.
Author Contribution
FK and MHVB was responsible for conceptualisation. FK, SH, CD, and MHVB were responsible for writing the first draft and revisions. MHVB was responsible for supervision. All authors were responsible for revisions.
Declarations
Ethics Approval
NA.
Consent to Participate
NA.
Conflict of Interest
FK, SH, and CD are student members of MedEd Collaborative. MHVB is chair of MedEd Collaborative. MedEd Collaborative is a student and trainee led medical education research collaborative.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References
1. Kehoe A Crampton P Buchanan J Tips to support the recruitment, retention, and progression of clinical academics Med Sci Educ 2022 10.1007/s40670-022-01512-1
2. Collaborative S. Medical research and audit skills training for undergraduates: an international analysis and student-focused needs assessment. Postgrad Med J. 2018;94(1107):37-42. 10.1136/postgradmedj-2017-135035.
3. Byrne MH, Ashcroft J, Alexander L, Wan JC, Arora A, Brown ME, Harvey A, Clelland A, Schindler N, Brassett C, Allan R. COVIDReady2 study protocol: cross-sectional survey of medical student volunteering and education during the COVID-19 pandemic in the United Kingdom. BMC Med Educ. 2021;21(1):1-7.10.1186/s12909-021-02629-4.
4. Chapman SJ, Glasbey JC, Khatri C, Kelly M, Nepogodiev D, Bhangu A, Fitzgerald JE. Promoting research and audit at medical school: evaluating the educational impact of participation in a student-led national collaborative study BMC Med Educ. 2015;15(1):1-1. 10.1186/s12909-015-0326-1.
5. Akinla O, Hagan P, Atiomo W. A systematic review of the literature describing the outcomes of near-peer mentoring programs for first year medical students. BMC Med Educ. 2018. 10.1186/s12909-018-1195-1.
6. Minocha S, Tingle R. Socialisation and collaborative learning of distance learners in 3D virtual worlds. In: Proceedings of Researching Learning in Virtual Environments International Conference (RELIVE08). 2008. The Open University, Milton Keynes, UK. Available at http://oro.open.ac.uk/19499/. 9 Mar 22.
7. Lobo D Devys J Timing of surgery following SARS-CoV-2 infection: an international prospective cohort study Anaesthesia 2021 10.1111/anae.15458
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TechTrends
TechTrends
Techtrends
8756-3894
1559-7075
Springer US New York
723
10.1007/s11528-022-00723-y
Column: Book Reviews
Review of Merging the Instructional Design Process with Learner-Centered Theory: The Holistic 4D Model by Charles M. Reigeluth and Yunjo An
Roman Tiffany A. tiffany.roman@kennesaw.edu
grid.258509.3 0000 0000 9620 8332 Kennesaw State University, Kennesaw, GA USA
12 4 2022
2022
66 3 560562
© Association for Educational Communications & Technology 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
issue-copyright-statement© Association for Educational Communications & Technology 2022
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pmcReigeluth and An’s new book, Merging the Instructional Design Process with Learner-Centered Theory: The Holistic 4D Model, is impeccably timed. As COVID-19 becomes endemic and the economy stabilizes, teacher attrition at the national level remains a concern (Goldhaber & Theobald, 2021). For educators considering a shift to the instructional design (ID) field, acquiring a copy of Reigeluth and An’s book is wise as it provides practical guidance and foundational instructional design knowledge. A mixture of a textbook and job aid, the 200-page book is packed with resources, classroom exercises, and templates that can also be found on a companion website. The targeted readers of the book include (1) practicing instructional designers, (2) students of instructional design, and (3) teachers, instructors, and trainers. The book also benefits those considering a career shift to the instructional design field, as well as instructional design faculty, given the book’s potential to be a foundational text across multiple ID courses.
From the onset of the book, Reigeluth and An masterfully detail the need for their work. The authors argue that ID process models have not kept pace with advances in learning, instructional strategies, technological tools, and instructional design processes. To address this need, Reigeluth and An put forth what they refer to as a Holistic 4D Model of ID, which contrasts with fragmented hierarchical ID approaches. Concisely, the process can be summarized as Define – Design – Develop – Deploy, with three levels (Top-level, Mid-level, Lower-level) of holistic design and iterative cycles of analysis, design, and evaluation (ADE; see Fig. 1).Fig. 1 Holistic 4D Model of ID
Born out of a year-long ID process model update for the U.S. Air Force, the authors highlight several key benefits of their holistic approach to ID, which include the following innovative features: A holistic design process.
Analysis-design-evaluation cycles and design document templates.
Integration with instructional theory.
Teaching topic and task expertise.
Holistic instructional sequences.
Learner-centered instruction.
Non-instructional interventions.
Rapid prototyping.
Designer objectives and demonstration objectives.
Product and process evaluations.
It is imperative to note that several of the innovative features within the model are not overtly visible in the diagram that the authors provide (Fig. 1). For example, the figure does not encapsulate the guidance detailed within the book with respect to instructional theory or learner-centered instruction, topic and task expertise, etc. To fully understand the Holistic 4D Model of ID, one must look beyond the diagram (Fig. 1) and dive into the text where the reader will find detailed guidance on sequencing and executing key activities – analysis, design, development, implementation, and evaluation – within an ID project.
The authors ground the application of the model in real examples that are relatable to all readers. For example, when speaking to task complexity, Reigeluth and An have the reader envision learning how to drive a car and how that may vary according to the complexity of the task (e.g., weather conditions, difficulty of route, manual shifting). The authors break down the Holistic 4D Model of ID in detail in chapters 5, 6, 7, and 8. Readers with strong ID backgrounds will find chapters 5 through 8 particularly informative, as the top, mid, and lower levels of the holistic cycles of ADE are detailed in-depth. The book advocates not only for a holistic approach to instructional design and task analysis, but also a holistic approach to instructional sequences.
The book is broken into “Units” that guide the reader through the overarching steps of the Holistic 4D Model of ID (Define, Design, Develop, Deploy). Reigeluth and An are always transparent noting when action steps are NOT necessary. The authors do not expect instructional designers to carry out unnecessary processes or analyses that are outside the nature or scope of a given project, so that money, time, and resources are not wasted. They regularly remind readers that instructional designers play multiple roles and work in collaboration with Subject Matter Experts (SMEs), project managers, task experts, and instructional experts. The authors do an excellent job detailing how to carry out critical skills required of an ID practitioner (e.g., task expertise, topic expertise) and offer tips and rules of thumb that can streamline one’s work (e.g., have an SME as part of one’s development team; develop a design document when working on large-scale ID projects; list all subjects and topics that should be taught within a domain; do not duplicate assessments at the lower-level ADE process).
Reigeluth and An do so many things incredibly well throughout their book. They clearly define relevant terms (e.g., technology, media) and concepts (e.g., instruction, good design), followed with explanations that are stated in an alternative way, which are complemented by examples situated in a familiar context. They clarify possible points of confusion (e.g., instructors vs. instructional designers, learning theory vs. instructional theory). If an instructional strategy is particularly important, the authors bold parts of sentences for additional emphasis. The authors point the reader in the direction of additional readings where appropriate (e.g., instructional theories); however, this is an area for growth in the next edition. For example, when speaking to learning theories or design theory, suggesting additional seminal books in those areas could be beneficial to the reader (e.g., The Cambridge Handbook of the Learning Sciences, The Design Way).
Reigeluth and An are also skilled at guiding the reader through the book by providing compelling overviews of all units and chapters, signposting throughout each chapter, detailing to the reader what they are about to read, and summarizing key points from this chapter using this cue: Where Are We? Additionally, the authors provide classroom-based exercises for teachers who may use the book as part of an ID course. Reigeluth and An wisely recommend that educators select an ID course project for their students to work on as they progress through the book as a means of practicing the skills covered within each chapter. The authors encourage educators to have students work in groups of three and to remain in those groups throughout the duration of the project.
The authors are also excellent at defining limitations as it relates to the scope of their work. For example, Reigeluth and An note that due to the complexity of affective learning and the emerging research in that area, they limit their instructional guidance to attitudes and values in the affective learning domain. Readers with K-12 teaching experience will appreciate examples grounded in classroom contexts (e.g., teacher-centered vs. learner-centered perspectives as it relates to duties of an elementary school math teacher; conceptual vs. theoretical understanding in biology). Those with military backgrounds will recognize the military references throughout the book, particularly the “Deploy” component of the Holistic 4D Model.
At the end of every chapter, the authors invite readers to provide feedback stating frequently that they, “would love suggestions for improvement.” Their sincerity is refreshing and sincere. At the end of the book, the authors encourage readers with ID expertise to help the Holistic 4D Model evolve. Reigeluth and An note that they would like the next edition of the book to have sections authored by other ID experts. The authors also request that ID faculty reach out to share ideas and resources for using the book within ID courses, as the companion website (www.reigeluth.net/holisitic-4d) can house student projects, syllabi, examples, etc. As a reader, I would argue that the book’s companion website is a valuable resource for ID students, practitioners, and faculty, as the authors note that the main limitation of their Holistic 4D model is that it does not yet include all kinds of instruction for all kinds of situations. As the companion website grows, it will be beneficial to see concrete examples and case studies of how the Holistic 4D Model is used in practice.
The greatest strength of this book, its timelessness, could also be considered a limitation. Since the application of the model depends on many variables and contexts, ID practitioners must employ design judgment frequently (see Boling et al., 2017; Honebein & Reigeluth, 2021). Truly, Reigeluth and An do their best to bring awareness of the sheer number of design judgments that practitioners need to make and provide templates that aid ID practitioners in that work. As a tip to faculty who decide to integrate this book into their instructional design courses, one should be aware that the high/mid/lower-level ADE process put forth in the book will likely be intimidating to novice instructional designers given the complexity of ID work. To address this issue, Reigeluth and An recommend keeping the scope of projects reasonable (e.g., a very small ADE document) as students practice and apply the skills within the Holistic 4D Model. The authors also write, “In many situations, the most effective medium is an expert, typically working one-on-one with the learner on real problems in real situations” (p. 108). Those new to the ID field will find that this book will work best in conjunction with an expert (a professor or ID mentor) who can help guide them through the Holistic 4D Model while working on a real project in collaboration with others.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References
Boling E Alangari H Hajdu IM Guo M Gyabak K Khlaif Z Kizilboga R Tomita K Alsaif M Lachheb A Bae H Ergulec F Zhu M Basdogan M Buggs C Sari A Techawitthayachinda RI Core judgments of instructional designers in practice Performance Improvement Quarterly 2017 30 3, 199, 219 10.1002/piq.21250
Goldhaber D Theobald R Teacher attrition and mobility over time Educational Researcher 2021 10.3102/0013189X211060840
Honebein, P. & Reigeluth, C. M. (2021). Making good design judgments via the instructional theory framework. In J. K. McDonald & R. E. West (Eds.), Design for Learning: Principles, Processes, and Praxis. EdTech Books. https://edtechbooks.org/id/making_good_design
Nelson, H. G., & Stolterman, E. (2014). The design way: Intentional change in an unpredictable world. MIT press.
Reigeluth CM An Y Merging the instructional design process with learner-centered theory: The Holistic 4D Model 2021 Routledge.
Sawyer, R. K. (Ed.). (in press). The Cambridge handbook of the learning sciences (3rd ed). Cambridge University Press.
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Adv Compos Hybrid Mater
Adv Compos Hybrid Mater
Advanced Composites and Hybrid Materials
2522-0128
2522-0136
Springer International Publishing Cham
35434523
460
10.1007/s42114-022-00460-z
Original Research
Combined bactericidal process of lignin and silver in a hybrid nanoparticle on E. coli
Ran Fangli 12
Li Chenyu nk_lcy710430@hotmail.com
1
Hao Zhenxin 12
Zhang Xinyuan 12
Dai Lin dailin@tust.edu.cn
2
Si Chuanling 2
Shen Zhiqiang 1
Qiu Zhigang 1
Wang Jingfeng wangjingfeng0116@163.com
12
1 Department of Environment and Health, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050 People’s Republic of China
2 grid.413109.e 0000 0000 9735 6249 Tianjin Key Laboratory of Pulp and Paper, College of Light Industry and Engineering, Tianjin University of Science and Technology, Tianjin, 300457 People’s Republic of China
12 4 2022
111
25 1 2022
25 2 2022
21 3 2022
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 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.
Among multiple engineered nanoparticles that have been used in the bactericidal application, silver nanoparticles (Ag NPs) are the most explored bactericidal functional materials with their high efficiency and broad-spectrum bactericidal properties. However, environmental toxicology and lack of modifiability restrict their further development. In this study, a simple and economic method was established to fabricate lignin and silver hybrid nanoparticles (Lig-Ag NPs) with bactericidal ability. Afterwards, material characterization, bactericidal evaluation, and mechanism exploration were implemented to explore the properties of Lig-Ag NPs. The results indicated that Lig-Ag NPs not only demonstrated remarkable dispersity, uniformity, and encapsulation efficiency but also possessed approximated bactericidal ability on Escherichia coli and better durability compared with the same concentration of Ag NPs on E. coli. On the other hand, flow cytometry and transcriptomic analysis were used to further explore the bactericidal mechanism of Lig-Ag NPs. The results showed that oxidative stress was the possible leading bactericidal mechanism of Lig-Ag NPs. The formation approaches of reactive oxygen species production were various including the slow release of silver ion and generation of quinone/semi-quinone radicals on account of the combined effect of lignin and silver.
Graphical abstract
Lig-Ag NPs exhibited remarkable dispersity, uniformity, encapsulation efficiency, and possessed approximated bactericidal ability and better durability compared with Ag NPs.
Supplementary information
The online version contains supplementary material available at 10.1007/s42114-022-00460-z.
Keywords
Lignin
Silver
Nanoparticles
E. coli
Oxidative stress
Special FundAWS18J004 2019CXTD04 20QNPY136 2019-JCJQ-JJ-163 http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 51678565 42177414 Qiu Zhigang Wang Jingfeng http://dx.doi.org/10.13039/501100006606 Natural Science Foundation of Tianjin City 19JCYBJC23800 Wang Jingfeng
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pmcIntroduction
The public panic about the widespread of COVID-19 has triggered recently a proliferated use of bactericidal products [1, 2]. In recent years, nanoparticles are heavily used to disinfect microorganisms due to their toxicological effect and small size effect [3–9]. Among multiple engineered nanoparticles that have been used in bactericidal application, silver nanoparticles (Ag NPs) are the most explored bactericidal functional materials with their high efficiency and broad-spectrum bactericidal properties [10–14]. However, the waste Ag NPs cannot recover or deactivate in refuse processing plants or sewage treatment plant [11]. As time goes on, the continuous exposure of Ag nanocore may lead to several ecological problems, such as the change of microbial community composition [15] and horizontal transfer of antibiotic resistance genes [16–18]. On the other hand, unmodified Ag NPs are hard to directly form applicable bactericidal products blaming on lack of an active group to chemical graft [19, 20]. Core–shell structure is an ideal solution to construct a more stable composition of Ag NPs materials. Khan et al. [21] established a novel bactericidal composite that was synthesized by coating ZnO on the surface of biogenic Ag nanoparticles which had been formed using the leaf extracts of Hibiscus sabdariffa. Guo et al. [22] synthesized core–shell Ag@ZIF-8 nanowires with bactericidal activity tested against Bacillus subtilis and Escherichia coli BL21, while these reported [23, 24] shell structures mostly acted as a protective layer of Ag NPs with mass transfer resistance and theoretically had an obviously negative influence on bactericidal efficiency.
Lignin, a cheap, abundant, and green material, is the second-largest natural biopolymers on the earth and the main waste product from agro-waste and papermaking industries [25–31]. Nature lignin is known to possess many advanced properties, such as good bactericidal activity and antioxidant properties [32–35]. Therefore, in the area of antibacteria, lignin has been widely used as a potential matrix material. Chen et al. [36] used kraft lignin to prepare an aminated lignin-silver complex, which was proved as an ideal alternative bactericidal agent against gram-positive (Bacillus cereus, Staphylococcus aureus) and gram-negative (Salmonella enterica) bacteria. Li et al. [37] designed a lignin-based bactericidal hydrogel for antimicrobial application. The biocompatible hydrogel has good broad-spectrum bactericidal properties due to the enhanced bactericidal effect of both the hydrogel and silver nanoparticles. Wang et al. synthesized silver nanoparticles incorporated quaternized lignin (QAL) composites with the assistance of microwave radiation. Owing to the electrostatic effect of QAL on bacteria, Ag@QAL exhibits the highest antibacterial activity with 3.72 log10(> 99.9%) and 5.29 log10(> 99.999%) CFU/mL reduction against E. coli and S. aureus, respectively. But for all this, above synthesized composites had yet to verify those antibacterial abilities versus equivalent Ag NPs simple substance. On the other hand, the bactericidal mechanism needed to be further explored at a molecular level.
In this study, complex lignin-silver nanoparticles (Lig-Ag NPs) were synthesized with a core–shell structure. Industrial alkaline lignin was used to reduce silver ions (Ag+) to Ag NPs in situ. The phenolic hydroxyls or methoxy groups on the lignin can reduce Ag+ to metallic Ag NPs and then convert them to dynamically stable semi-quinone radicals. Lignin produced free radicals through REDOX, and silver ions released from Lig-Ag NPs played a positive role in the killing of Escherichia coli (E. coli), which achieved the bactericidal effect by inducing oxidative stress reaction and damage to the bacterial cell membrane. The shell structure helped to reduce the toxic effect of silver nanoparticles, and the bactericidal effect on E. coli was more durable than Ag NPs. Our results demonstrated that the application of green and facile design strategy may allow the synthesis of bactericidal agents with higher bactericidal activity and less environmental impact.
Experimental section
Chemicals and reagents
Alkaline lignin was kindly supplied by Shan Dong Sun Paper Industry Joint Stock Co., Ltd. Silver nitrate (AgNO3) was obtained from Sinopharm Chemical Reagent CO., Ltd. (Shanghai, China). Ag NPs were purchased from Sigma-Aldrich, Co., Ltd. (< 100 nm particle size, 99.5% trace metals basis). Ammonium hydroxide (NH3·H2O, AR) and sodium hydroxide (NaOH, AR) were purchased from the Damao Chemical Reagent Factory (Tianjin, China).
Preparation of bacteria
Escherichia coli K12-MG1655 (ATCC 700,926) were inoculated in Luria–Bertani broth (LB, Sangon Biotech, China) and cultured at 37 ℃ constant temperature shaker (THZ-82A, Tianjin Sateris Experimental Analysis Instrument Company, China), at 150 rpm for 12 h. The bacterial suspensions were centrifuged at 4629 g for 5 min (5804R, Eppendorf, Germany) and washed with phosphate-buffered saline (PBS, pH 7.2 ~ 7.4) 3 times to remove any broth medium residuals. In the end, bacterial cells were resuspended in PBS and stored at 4 ℃ for reserve.
Synthesis of Lig-Ag NPs
Lig-Ag NPs were fabricated by the nanoprecipitation method in an alkaline system with a reduction process. 0.2 g of alkaline lignin was fully dissolved in 4 mL of NaOH solution (5% w/v) by ultrasonic treatment for getting solution A. Subsequently, 5 mL of AgNO3 solution (10 g/L Ag+) was slowly added into 2.5 mL NH3·H2O (7.6% w/v) under magnetic stirring for getting solution B. Finally, solution A was dropwise added to solution B under magnetic stirring. The product was dialyzed in freshly deionized water for 3 days and made up to 100 mL with deionized water for getting Lig-Ag NPs stock solution.
Characterization
The microstructures of the samples were observed by field-emission scanning electron microscope (FE-SEM). The diluted and dispersed Lig-Ag NPs solution was dropped onto a silicon slice to prepare samples. After air-drying, in order to avoid charging effects, the samples were coated with platinum prior to FE-SEM analysis (Sigma 300, Zeiss, Germany) at an accelerating voltage of 5.00 kV. Element mapping analysis was carried out by an energy disperse spectroscopy [38] (EDS, Ultim Extreme, Oxford Instrument, UK).
For the bacterial suspension, the samples were dehydrated using graded ethanol and freeze-dried (FDU-1200, EYELA, Japan). Subsequently, platinum was used to coat samples for FE-SEM analysis. The particle size was analyzed by DLS (Malvern Nano-ZS Zeta Sizer, Malvern Instruments, UK). Lig-AP NPs were diluted with deionized water and ultrasonically dispersed for 3 min before analysis.
The wettability of Lig-Ag NPs and Ag NPs was investigated by determining the static water contact angel (WCA) by a sessile drop method [39], employing an automated WCA apparatus (ZR-SDJ-B3, defnuo, China). For each measurement, 10 mL specific concentration Lig-Ag NPs or Ag NPs was dispersed over a piece of polyamide membrane. After airing, a deionized water droplet (~ 6.5 μL) was deposited onto the surface, and images were recorded to analyze WCA.
The chemical structures were confirmed by Fourier transform infrared (FT-IR), ultraviolet–visible (UV–vis), and X-ray photoelectron spectroscopy (XPS). Samples (Lig-Ag NPs and Lignin) were incorporated into KBr and pressed to formulate a 3-mm tablet by a table press (HY-12, Tianjin optical instrument factory, China). The infrared spectrum from 2000 to 400 cm was acquired from an FT-IR spectroscopy (Nicolet iS5, Thermo Scientific Co. Ltd., USA) operating at a resolution ratio of 2 cm−1. Second-derivative spectra were obtained with the use of the Nicolet software (DR2, Thermo Scientific Co. Ltd., USA). For the UV–vis spectroscopy, lignin and Lig-Ag NPs suspension were diluted over the wavelength range from 200 to 800 nm using a UV–vis spectrometer (UV-2600, SHIMADZU, Japan). NaOH solution was used as a blank control group. XPS of samples (Lignin, Lig-Ag NPs, and Ag NPs) was detected by a photoelectron spectrometer (EscaLab Xi + , Thermo Scientific Co. Ltd., USA) with Al Kα radiation (1486.6 eV) and hemispherical electron energy analyzer. The radiation source operated at 14.4 kV and 13.6 mA. The vacuum pressure was kept around 8 × 10–10 pa all along. The binding energy scale was corrected by referring to the C1s spectrum as being 284.80 eV.
Evaluation the bactericidal ability of Lig-Ag NPs
0.2 ml of Lig-Ag NPs stock solution was added to 9.8 mL of bacterial suspension (about 1 × 107 CFU/mL), diluted from the bacterial stock solution with PBS to derive a final sliver concentration of 0, 0.5, 1, 5, and 10 mg/L. Meanwhile, Ag NPs and lignin experimental groups were prepared similarly, but with equal concentration Ag NPs or lignin instead of Lig-Ag NPs. All the bacterial suspensions were exposed to experimental materials on a constant temperature shaking table (HNYC-203 T, Honour Co. Ltd., China). After the designed exposure time, the live bacteria numbers were determined by counting the numbers of CFUs on solid LB agar plates, which were incubated at 37 ℃ for 16–18 h. The bacterial survival rate was expressed as N/N0 (× 100%), in which N and N0 represent the remaining and initial numbers of live bacteria, respectively. In the performance attenuation experiments, the Lig-Ag NPs and Ag NPs were respectively placed in freshly deionized water for 3 months. Afterwards, the bactericidal materials were dialyzed for 3 days and ultrasonically dispersed for 3 min as well to test the bactericidal ability. In addition, ROS scavenger (N-acetyl-L-cysteine, 100 μM) to quantitatively analyze whether Lig-Ag NPs could inhibit E.coli growth or not via ROS generation.
Measurement of cell membrane permeability
Cell membrane permeability of E. coli was determined by using Syto-9 and propidium iodide (PI) dyes [40] (live/dead biofilm viability kit, Thermo Scientific Co. Ltd., USA). Briefly, E. coli suspensions were exposed to designed concentrations of bactericidal materials (Lig-Ag NPs and Ag NPs) for 2 h. Two milliliter of bacterial cells was stained with Syto-9/PI (100 μM) combination of dyes. Samples were kept at room temperature in the dark and incubated for 10 min and then immediately analyzed by flow cytometry. One hundred thousand events were analyzed. Forward scatter (FSC), green fluorescence (FL1 515–565 nm), and red fluorescence (FL3 > 605 nm) were measured using a flow cytometer (FCM, S3 cell sorter, Bio-rad Inc., USA). The FCM was equipped with 100 mW, 488 nm, solid laser. FSC characteristics were used as a trigger signal. Finally, data analysis was accomplished with FlowJo 10 (FLOWJO, LLC and BD Biosciences, USA). All samples were sorted by the same oval gate in scatter plots with FL1 X-coordinate and FL3 Y-coordinate.
RNA extraction, genome-wide sequencing, and transcriptomic analysis
The bactericidal systems were established as described above, with dosages of 5 mg/L Ag element within Lig-Ag NPs and Ag NPs. After 2 h of exposure, the samples were submitted to omics laboratory (Novogene Co., Ltd., China) for genome-wide RNA sequencing. The RNA samples were analyzed and subjected to quality control. After this, the cDNA libraries were constructed and sequenced on the Illumina NovaSeq6000 Platform (Illumina Inc., San Diego, CA). The fragments per kilobase of a gene per million mapped reads (FPKM) were measured to quantify the gene expression. Differences in the gene expression between the control and the Lig-Ag NPs and Ag NPs were presented as log2 fold changes of the averaged FPKM values.
Statistical analysis
All experiments were conducted independently at least in biological triplicate. Phenotypic data statistical analysis was performed using SPSS 25.0 (Chicago, USA) with analysis of variance (ANOVA) and independent-sample t-test methods and expressed as mean ± standard deviation (SD). The corrected p values of < 0.05 were considered to indicate statistical significance.
Result and discussion
Synthesis of Lig-Ag NPs
The environmentally friendly synthesis steps and theoretically bactericidal process of Lig-Ag NPs are shown in Fig. 1. Alkaline lignin was dissolved by NaOH and added into [Ag(NH3)2]+ solution by dripping slowly. During mixture and blend, the methoxy (-OCH3) and phenolic hydroxyl (-OH) were oxidized into quinone or semi-quinone free radicals. In the meantime, Ag+ was reduced to form several Ag cores inside a lignin compact shell. The core–shell structure has some possible pathways to carry out bactericidal functions. Obviously, the Ag core could continually release silver ions and cross the lignin shell to influence the bacterial cells. On the other hand, the oxidized active groups (quinone and semi-quinone free radicals) dispersed on the surface of the shell might generate a mass of oxidative stress, which was an important approach to accelerate the death of bacterial. Hereinafter, a series of characterization, evaluation, and mechanism analysis experiments were accomplished to prove and discuss the above hypothesis.Fig. 1 The preparation and possible bactericidal process of Lig-Ag NPs
Characterization of Lig-Ag NPs
The morphology characterization results of Lig-Ag NPs are demonstrated in Fig. 2. FE-SEM images (Fig. 2a, e) showed that Lig-Ag NPs possessed a regular spherical structure and uniform particle size. According to the result of DLS (Fig. 2f and Table S1), the effective diameter of Lig-Ag NPs was 53.2 nm, and the polydispersity was 0.216. Nanodimension gave the particles a small size effect, which might destroy the bacteria by perforation of the cell membrane in theory. The zeta potential of Lig-Ag NPs was − 62.7 mV due to the negatively charged functional groups (such as hydroxyl group) on the surface of Lig-Ag NPs, which might prevent them from touching bacterial cells on account of electrostatic repulsion. As shown in Fig. 2b, c, carbon and silver are densely distributed in the whole image. Although the colocalization phenomenon was hardly photographed due to the small size, the density of the elements presented an ideal encapsulation efficiency. For further quantitative analysis of element proportion, the total element content of distribution images is shown in Fig. 2d. The proportion of Ag, C, and O was 27.9%, 54.54%, and 17.57%, respectively, which indicated the volume ratio of lignin shell and Ag core. In addition, the wettability of Lig-Ag NPs was further investigated via WCA measurement. As shown in Fig. S1a, d, WCA are 48.4° and 55.8° when 1 mg/L Lig-Ag NPs and Ag NPs disperse over the surface of the polyamide membrane, respectively. These WCAs decreased to 36.7° (Fig. S1b) and 43.0° (Fig. S1e) when the concentrations of the materials were adjusted to 100 mg/L. WCA analysis results demonstrated that Lig-Ag NPs possessed well hydrophilicity, which endowed Lig-Ag NPs potential application in hydrophilic membrane and antibacterial wipes preparation.Fig. 2 Morphology characterization of Lig-Ag NPs. (a, e) FE-SEM images of Lig-NPs, (b) element mapping of (b) carbon and (c) silver, (d) EDS spectra, and (f) DLS analysis of Lig-Ag NPs
FT-IR analysis was used to identify the oxidation of lignin functional groups. As depicted in Fig. 3a, the characteristic peaks at ~ 1610, ~ 1215, and ~ 1037 cm−1 correspond to the benzene ring bands, the phenolic hydroxyl (-OH) groups, and methoxy groups (-OCH3) [41, 42]. The intensity of -OH and -OCH3 peaks obviously decreased in the spectrum of Lig-Ag NPs compared with that of pure lignin. On the other hand, the characteristic peaks at ~ 1610 cm−1 were broadened due to the emergence of a new peak at ~ 1628 cm−1, which corresponded to the carbanyl group (C = O) of quinone. FT-IR results indicated that the synthesis process of Lig-Ag NPs made the inherent groups (-OH and -OCH3) of lignin oxidated to C = O along with the reduction of Ag+ to Ag NPs. UV–vis analysis was used to characterize the surface plasmon resonance. In the UV–vis spectra (Fig. 3b), the enhancement of the quadrupole plasmon resonance was located in the region around 400 nm due to the presence of media interface between macromolecule (lignin) and metal (Ag NPs).
The information about the molecular structure of Lig-Ag NPs was further developed by XPS. As shown in Fig. 4, peak differentiation and fitting are operated in several narrowed ranges (C1s: 292–282 eV; Ag3d: 376–364 eV; O1s: 538–528 eV) of binding energy to study the valent state of carbon, silver, and oxygen [43–45]. The C1s spectra (Fig. 4b) showed an obvious decline in contents of C-O (286.1 eV) and a corresponding increase in the contents of C = O (288.1 eV). Similar variation trend of C-O (532.4 eV) and C = O (531.2 eV) contents was presented again in the spectra of O1s (Fig. 4d), which described the functional group change of lignin during the redox process particularly. On the other hand, Ag–O-Ag (530 eV) could ascribe to the hydrogen-bonding interaction between the Ag NPs and lignin. According to Fig. 4c, Ag 3d 3/2 and Ag 3d 5/2 peaks of Lig-Ag NPs arise at 373.2 eV and 367.2 eV, respectively. According to the XPS database (http://www.lasurface.com/), the above peaks were both ascribed to silver oxide (Ag2O) which moved down compared with those of elemental Ag NPs (373.8 eV and 367.7 eV). These results of Ag3d described the changes of chemical environment around Ag atoms, which might be attributed to the capping lignin which electrostatically grafted to positively charge silver nanoclusters through ionogenic groups.Fig. 3 (a) FT-IR and (b) UV–vis spectra of lignin and Lig-Ag NPs
Effect of Lig-Ag NPs on bacterial culturability
Bacterial culturability experiments were carried out in PBS buffer, in which the bacteria cannot grow, but were viable. Firstly, E. coli were exposed to different concentrations of Lig-Ag NPs, and the bacterial survival graph was plotted. As depicted in Fig. 5a, a significant dosage effect of Lig-Ag NPs is observed. The percentage of bacteria survival was decreased as the concentration of Lig-Ag NPs and exposure time increased. Of particular note is the concentration of 5 mg/L and 10 mg/L of Lig-Ag NPs can kill more than 90% bacterial cells within 4 h (Fig. 5b) and completely killed 107 CFU/mL cells within 6 h. To display the bactericidal effect intuitively, the live bacteria colony on solid LB agar plates exposed to different Lig-Ag NPs concentrations within 4 h are photographed as shown in Fig. 5c.Fig. 4 XPS of Lig-Ag NPs, lignin, and Ag NPs. (a) Wide-scan XPS spectra. (b) high-resolution XPS of C1s for Lig-Ag NPs and lignin. (c) High-resolution XPS of Ag3d for Lig-Ag NPs and Ag NPs. (d) High-resolution XPS of O1s for Lig-Ag NPs and lignin
Secondly, E. coli were exposed to comparable bactericidal materials with the same lignin or silver concentration, and the bacterial survival graph was plotted. As depicted in Fig. 5d, the bactericidal effect had a significant difference between Lig-Ag NPs and Ag NPs within the first 2 h. In this phase, 5 mg/L Lig-Ag NPs and Ag NPs can kill about 21% and 46% bacterial cells, respectively. However, these bactericidal effects were not invariable over time. In the performance attenuation experiments (Fig. 5e), after 3 months of storage, the bactericidal effect of Ag NPs suffered a significant decline due to the continuous consumption of silver ions. In contrast, that of Lig-Ag NPs had no significant change during storage.
The aforesaid sustaining bactericidal activity of Lig-Ag NPs might be derived from the core–shell structure. Outer lignin shell presented a barrier between inner silver core and bacteria, and mass transfer resistance of silver increased. Furthermore, a deeper question that needed to be discussed was the combined bactericidal mechanism. It was obvious that only the release process of silver ion cannot provide Lig-Ag NPs a comparable bactericidal effect compared with Ag NPs on account of lignin shell structure and electronegativity. Accordingly, cell membrane permeability, ROS scavenger experiments, and transcriptomic analysis were carried out to explore the combined bactericidal mechanism.
Effect of Lig-Ag NPs on membrane permeability of E. coli
Although the change of membrane permeability was unavoidable during cell death, the proportion of cells with a change in membrane permeability demonstrated the physical damage induced by Lig-Ag NPs. In these experiments, E. coli exposed to Ag NPs was set as a control group to analyze the degree of cell membrane damage. Syto9/PI dyes and flow cytometry were used to identify the membrane permeability at single-cell resolution. Briefly, the red fluorescent nucleic acid stain PI was used for identifying dead cells because it is supposed to penetrate only cells with disrupted membrane and is generally excluded from viable cells [40]. On the contrary, the green fluorescent Syto9 can enter both live and dead bacterial cells. Therefore, in the scatter diagram results (Fig. 6a–d), X-axis fluorescence intensity (Syto9) can be used to identify whether it is a bacterial cell or signal noise. On the other hand, Y-axis fluorescence intensity (PI) represented the permeability increase degree of the cell membrane. R1 and R2 oval regions were drawn artificially by graphical clustering analysis, in which the cells represented membrane damaged cells and membrane intact cells, respectively.Fig. 5 Culturability loss of Escherichia coli induced by bactericidal materials exposure in PBS. (a) Variations in bacteria survival with different concentrations of Lig-Ag NPs within 6 h. (b) Variations in bacteria survival with different concentrations of Lig-Ag NPs for 4 h (*p < 0.05). (c) Photos of the live bacteria colony on solid LB agar plates exposure to different Lig-Ag NPs concentrations for 4 h. (d) Variations in bacteria survival with comparable concentration of Lig-Ag NPs (Ag-5 mg/L), Ag NPs (5 mg/L), and lignin (20 mg/L) within 6 h. (e) Variations in bacterial survival with Lig-Ag NPs and Ag NPs for 2 h before and after storage for 3 months (*p < 0.05)
Fig. 6 Cell membrane permeability analysis of E.coli after 4 h exposure to specific concentration Lig-Ag NPs (a 1 mg/L, c 5 mg/L) and Ag NPs (b 1 mg/L, d 5 mg/L) by flow cytometry. (e) Variation in cell population ratio of R1/R2 with different concentration of bactericidal materials (*p < 0.05); FE-SEM images of E. coli after 4 h exposure to (f) PBS, (g) Ag NPs, and (h) Lig-Ag NPs
Fig. 7 (a) Fold changes in the expression of core genes related to ROS production, SOS response, and cell membrane permeability. (b) Variations in bacteria survival with the scavenger- and no scavenger-treated groups exposed to 5 mg/L Ag NPs and Lig-Ag NPs for 4 h (*p < 0.05). (c) Possible combined bactericidal mechanisms underlying E. coli exposed to Lig-Ag NPs
As shown in Fig. 6a, b, the membrane damaged cell proportions were 4.5% and 6% exposed to 1 mg/L Lig-Ag NPs and Ag NPs at 4 h, respectively. These proportions increased to 5.6% (Fig. 6c) and 21% (Fig. 6d) when the concentrations of the bactericidal materials were adjusted to 5 mg/L. Specifically, the ratios of R1/R2 were quantitatively analyzed. As shown in Fig. 6e, the membrane permeabilities are enhanced as the concentration of Lig-Ag NPs increased. However, compared with Ag NPs, the membrane permeability-increasing trend of Lig-Ag NPs was significantly slower. The possible cause for these results was the electronegativity of Lig-Ag NPs and bacterial cells which decreased the contact probability. To demonstrate the change of membrane permeability intuitively, the bacterial cell morphology was photographed exposed to PBS (Fig. 6f), Ag NPs (Fig. 6g), and Lig-Ag NPs (Fig. 6h) at 4 h. PBS group presented E. coli cells with rhabditiform outline and unbroken membrane. The cells in the Ag NPs group had seriously damaged membrane, and bits of Ag NPs were attached to the membranes. The cell membrane morphology had also changed in Lig-Ag NPs groups. Different from the Ag NPs group, the membranes were atrophic and plicated instead of broken, which might be on account of the endogenous death process. As reported in our previous studies [16, 17, 46], it had been proved that nanomaterials could promote the transfer of antibiotic resistance genes by conjugation and transduction due to the increase of membrane permeability. Therefore, the decline in contact frequency and membrane broken degree was an ecological friendly characteristic of Lig-Ag NPs.
The combined bactericidal process of lignin and silver
It has been previously reported [18, 47, 48] that Ag NPs could induce oxidative stress, activate SOS response, and increase cell membrane permeability during the bactericidal process. In this work, we attempted to analyze the relevant bactericidal mechanism of Lig-Ag NPs at the gene expression level. As depicted in Fig. 7a and Table S2, Lig-Ag NPs and Ag NPs treatments result in a 1.3- and 1.8-fold increase in the expression of alkyl hydroperoxide reductase gene ahpF. The expression of rutB increased by 3.3- and 4.2-fold, and soxS exhibited a 4.7- and 5.3-fold increase in expression upon treatment with Lig-Ag NPs and Ag NPs, respectively. Of particular note was that the upregulation level of some genes (hemH [18], rutD [49], sufA [18], and sufS [18] ) in Lig-Ag NPs group about ROS production was higher than that in the Ag NPs group. Moreover, Lig-Ag NPs and Ag NPs also increased the expression of SOS response related genes. These results indicated that Lig-Ag NPs-induced oxidative stress was a main mechanism in the bactericidal process. To further explore the bactericidal contribution of ROS production, ROS scavenger experiments were carried out. As shown in Fig. 7b, ROS scavenger blocks the process of oxidative stress and significantly weakens the bactericidal effect of Lig-Ag NPs and Ag NPs. The Lig-Ag NPs group with ROS scavengers no longer even had the bactericidal ability. These results demonstrated that oxidative stress was the leading bactericidal mechanism of Lig-Ag NPs.
In addition, the expression of cell membrane permeability genes was investigated as well. The results showed that Lig-Ag NPs could also alter the transcription level of cell membrane related genes. For instance, the expression of csgF [49], waaA [18], waaB [18], and yfaZ [49] increased by ~ 3.0-fold when exposed to 5 mg/L Lig-Ag NPs. These genes are related to lipopolysaccharide synthesis (waaA and waaB) and putative outer membrane (csgF and yfaZ). Contrary to the ROS production results, the upregulation level of most genes about cell membrane in Lig-Ag NPs group was lower than that in Ag NPs group, which might be derived from the negative feedback of membrane damage being consistent with the results in Chapter 3.4.
Although oxidative stress was the possible leading bactericidal mechanism of Lig-Ag NPs, the formation approaches of ROS production are various on account of the combined effect of lignin and silver as depicted in Fig. 7c. Firstly, silver ion was slowly released and transferred across the lignin shell. This process might be accelerated on account of the negative charge surface, and the redox reaction between silver ion and a reducing group of lignin continuously proceed during the mass transfer process. In the meantime, quinone and semi-quinone radicals were consistently generated as intermediate products of the redox reaction. Secondly, these radicals could further generate reactive oxygen molecules such as hydroxyl radical (·OH), superoxide (O2−), and hole (h+). Finally, whether these reactive oxygen molecules or silver ions were able to get inside the bacterial cells produce oxidative stress and inactivate them.
Conclusions
In this study, a simple and economic method was established to fabricate Lig-Ag NPs with bactericidal ability. Afterwards, materials characterization, bactericidal evaluation, and mechanism exploration were further implemented to explore the properties of Lig-Ag NPs. In summary, Lig-Ag NPs possessed a series of advantages. Firstly, the prepared Lig-Ag NPs demonstrated remarkable dispersity, uniformity, and encapsulation efficiency. Secondly, Lig-Ag NPs had approximated bactericidal ability compared with the same concentration of Ag NPs. Moreover, Lig-Ag NPs were endowed with better durability attributed to the core–shell structure and combined bactericidal mechanism. Last but not the least, Lig-Ag NPs were environmentally friendly which was not just from the green synthesis process, but also because theoretically lower ARGs transfer risk and environmental hypotoxicity due to lignin shell. In brief, this green hybrid nanoparticle would provide a new idea for the development of bactericidal materials.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 165 KB)
Funding
This study was funded by the Special Fund (grant numbers AWS18J004, 2019CXTD04, 20QNPY136, and 2019-JCJQ-JJ-163), the National Natural Science Foundation of China (grant numbers 51678565 and 42177414), and the Natural Science Foundation of Tianjin, China (grant number 19JCYBJC23800).
Declarations
Conflict of interest
The authors declare no competing interests.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Software Qual J
Software Quality Journal
0963-9314
1573-1367
Springer US New York
9590
10.1007/s11219-022-09590-5
Article
Editorial
Gaston Christophe christophe.gaston@cea.fr
1Dr. Christophe Gaston
is a senior researcher at CEA LIST. His research topics focus on formal methods using models. His field of expertise includes techniques such as model-based testing, symbolic execution and run-time verification as well methodological approaches based on formal frameworks, such as algebraic specifications, category theory, automata theory, process algebras, rewriting and various theories of conformance testing. His recent works focus on the application of such techniques to the verification of distributed systems. Christophe Gaston supervised 7 thesis in Formal methods. He is a member of several committees of international conferences and workshops including the International Conference on Testing Software and Systems (IFIP-ICTSS). He is a founding member of the Model-Driven Engineering Verification and Validation (MoDeVVa) workshop.
Kosmatov Nikolai nikolaikosmatov@gmail.com
2Dr. Nikolai Kosmatov
works as an expert in software verification at Thales Research and Technology (Palaiseau, France) since 2019, where he focuses on applying various verification techniques and tools to industrial projects. He is also an invited researcher at CEA List, where he had previously worked for 13 years as an expert researcher at Software Safety and Security Lab. He obtained PhD in Mathematics in 2001 from St.Petersburg State Univ., MS in Computer Science in 2003 from Univ. of Besançon, and Habilitation in Computer Science (HDR) from Univ. Paris-Sud in 2018. His research interests include software testing, formal verification, combinations between static and dynamic analysis techniques and runtime verification. He co-authored 4 patents and more than 60 scientific papers in international conferences and journals. He was PC co-chair of several international events related to verification and testing, e.g. TAP 2015, IFIP-ICTSS 2019, ACM SAC-SVT 2020 and 2021. He is co-responsible for the working group on software testing (MTV2) of the French CNRS network on software engineering (GDR GPL) and organizes its annual workshops. Dr. Kosmatov contributed to the design and development of several software verification tools. He is the main author of the PathCrawler-online.com testing web service. Personal website: https://nikolai-kosmatov.eu/
Le Gall Pascale pascale.legall@centralesupelec.fr
3Pascale Le Gall
is a Full Professor in Computer Science at the CentraleSupélec engineering school of the University of Paris-Saclay. She leads the Logimics research team in MICS laboratory and the Doctoral School Interfaces of the Graduate School of Engineering and System Sciences. She supervises or has supervised more than 30 PhD students, is co-author of more than 70 international publications. She has a strong expertise in formal methods, Model-Based Testing (test case generation, verdict computation), graph transformations, symbolic execution technics and models for timed reactive distributed systems. She was PC co-chair of several international events related to verification and testing, e.g, IFIP-ICTSS 2019, ACM SAC-SVT 2022. She is co-responsible for the working group on software testing (MTV2) of the French CNRS network on software engineering (GDR GPL) and organizes its annual workshops.
1 grid.457331.7 Université Paris-Saclay, CEA, List, F-91120 Palaiseau, France
2 grid.410363.3 0000 0004 1754 8494 Thales Research & Technology, Palaiseau, France
3 grid.460789.4 0000 0004 4910 6535 Laboratoire de Mathématiques et Informatique pour la Complexité et les Systèmes CentraleSupélec, Université Paris-Saclay - Plateau de Moulon, 9 rue Joliot-Curie, F-91191 Gif-sur-Yvette Cedex, France
12 4 2022
12
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
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pmcThis Special Issue follows the 31st IFIP International Conference on Testing Software and Systems (IFIP-ICTSS). IFIP-ICTSS has become a traditional event of the WG 6.1 working group of the International Federation for Information Processing (IFIP). The conference was held in Paris, France, from October 15 to October 17, 2019.
IFIP-ICTSS is a series of international conferences addressing conceptual, theoretical, and practical problems of testing software systems, including communication protocols, services, distributed platforms, middleware, embedded and cyber-physical systems, and security infrastructures. It is a forum for researchers, developers, testers, and users. Its goal is to review, discuss, and learn about new approaches in the field of testing of software and systems. The topics of interest include new concepts, theories, methodologies, tools, and experience reports.
The authors of the papers accepted to ICTSS 2019 were invited to submit an extended version to this Special Issue. However, the call for papers was also open to external contributions.
After a rigorous selection process, 10 papers were accepted to appear in the Special Issue. They cover a large range of subjects such as test-case generation, testing in relation with artificial intelligence, proof and verification techniques, performance, and empirical studies and domain-specific applications.
The preparation of the Special Issue was strongly impacted by the COVID-19 pandemic. We would like to thank the authors for their contributions and the reviewers for their hard work on paper evaluation in this particularly uneasy period. We are also grateful to the Journal Editorial Office for their efficient support during the paper selection process.
We hope that the readers will find this Special Issue inspiring and challenging.
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CJEM
CJEM
Cjem
1481-8035
1481-8043
Springer International Publishing Cham
35412259
295
10.1007/s43678-022-00295-z
Original Research
Closed loop communication in the trauma bay: identifying opportunities for team performance improvement through a video review analysis
http://orcid.org/0000-0002-1890-2745
Bhangu Avneesh abhangu@qmed.ca
1
Notario Lowyl Lowyl.Notario@sunnybrook.ca
234
Pinto Ruxandra L. Ruxandra.Pinto@sunnybrook.ca
5
Pannell Dylan Dylan.Pannell@sunnybrook.ca
6
Thomas-Boaz Will Will.Thomas-Boaz@Sunnybrook.ca
234
Freedman Corey corey.freedman@sunnybrook.ca
24
Tien Homer Homer.Tien@sunnybrook.ca
467
Nathens Avery B. Avery.Nathens@sunnybrook.ca
468
da Luz Luis Luis.DaLuz@sunnybrook.ca
46
1 grid.410356.5 0000 0004 1936 8331 School of Medicine, Faculty of Health Sciences, Queen’s University, Unit 505 - 91 King Street East, Kingston, ON K7L 2Z8 Canada
2 grid.413104.3 0000 0000 9743 1587 Department of Emergency Services, Sunnybrook Health Sciences Centre, Toronto, ON Canada
3 grid.17063.33 0000 0001 2157 2938 Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON Canada
4 grid.17063.33 0000 0001 2157 2938 Tory Regional Trauma Program and the Evaluative Clinical Sciences Program, Sunnybrook Research Institute, Toronto, ON Canada
5 grid.413104.3 0000 0000 9743 1587 Department of Critical Care Medicine, Sunnybrook Health Sciences Centre, Toronto, ON Canada
6 grid.413104.3 0000 0000 9743 1587 Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, ON Canada
7 Ornge, Mississauga, ON Canada
8 grid.17063.33 0000 0001 2157 2938 Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON Canada
12 4 2022
2022
24 4 419425
8 12 2021
9 3 2022
© The Author(s), under exclusive licence to Canadian Association of Emergency Physicians (CAEP)/ Association Canadienne de Médecine d'Urgence (ACMU) 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.
Objectives
Communication among trauma team members in the trauma bay is vulnerable to errors, which may impact patient outcomes. We used the previously validated trauma-non-technical skills (T-NOTECHS) tool to identify communication gaps during patient management in the trauma bay and to inform development strategies to improve team performance.
Methods
Two reviewers independently assessed non-technical skills of team members through video footage at Sunnybrook Health Sciences Centre. Team performance was measured using T-NOTECHS across five domains using a five-point Likert scale (lower score indicating worse performance): (1) leadership; (2) cooperation and resource management; (3) communication and interaction; (4) assessment and decision making; (5) situation awareness/coping with stress. Secondary outcomes assessed the number of callouts, closed loop communications and parallel conversations.
Results
The study included 55 trauma activations. Injury severity score (ISS) was used as a measure of trauma severity. A case with an ISS score ≥ 16 was considered severe. ISS was ≥ 16 in 37% of cases. Communication and interaction scored significantly lower compared to all other domains (p < 0.0001). There were significantly more callouts and completed closed loop communications in more severe cases compared to less severe cases (p = 0.017 for both). Incomplete closed loop communications and parallel conversations were identified, irrespective of case severity.
Conclusion
A lower communication score was identified using T-NOTECHS, attributed to incomplete closed loop communications and parallel conversations. Through video review of trauma team activations, opportunities for improvement in communication can be identified by the T-NOTECHS tool, as well as specifically identifying callouts and closed loop communication. This process may be useful for trauma programs as part of a quality improvement program on communication skills and team performance.
Supplementary Information
The online version contains supplementary material available at 10.1007/s43678-022-00295-z.
Résumé
Objectifs
La communication entre les membres de l'équipe de traumatologie dans la salle de traumatologie est vulnérable aux erreurs, ce qui peut avoir un impact sur les résultats des patients. Nous avons utilisé l'outil de compétences non techniques en traumatologie (T-NOTECHS) précédemment validé pour identifier les lacunes en matière de communication pendant la prise en charge des patients dans la salle de traumatologie et pour informer les stratégies de développement visant à améliorer les performances de l'équipe.
Méthodes
Deux examinateurs ont évalué de manière indépendante les compétences non techniques des membres de l'équipe au moyen de séquences vidéo réalisées au Sunnybrook Health Sciences Centre. La performance de l'équipe a été mesurée à l'aide de la T-NOTECHS dans cinq domaines à l'aide d'une échelle de Likert à cinq points (un score plus bas indiquant une performance plus faible) : (1) leadership ; (2) coopération et gestion des ressources ; (3) communication et interaction ; (4) évaluation et prise de décision ; (5) conscience de la situation/ gestion du stress. Les résultats secondaires ont évalué le nombre d'appels, de communications en boucle fermée et de conversations parallèles.
Résultats
L'étude a porté sur 55 activations de traumatismes. Le score de gravité des blessures (ISS) a été utilisé comme mesure de la gravité du traumatisme. Un cas présentant un score ISS ≥ 16 était considéré comme grave. L'ISS était ≥ 16 dans 37 % des cas. La communication et l'interaction ont obtenu des scores significativement plus faibles par rapport à tous les autres domaines (p<0,0001). Il y avait significativement plus d'appels et de communications en boucle fermée terminées dans les cas plus graves que dans les cas moins graves (p = 0.017 pour les deux). Des communications incomplètes en boucle fermée et des conversations parallèles ont été identifiées, indépendamment de la gravité du cas.
Conclusion
Un score de communication plus faible a été identifié en utilisant le T-NOTECHS, attribué à des communications incomplètes en boucle fermée et à des conversations parallèles. Grâce à l'examen vidéo des activations de l'équipe de traumatologie, les possibilités d'amélioration de la communication peuvent être identifiées par l'outil T-NOTECHS, ainsi que l'identification spécifique des appels et de la communication en boucle fermée. Ce processus peut être utile pour les programmes de traumatologie dans le cadre d'un programme d'amélioration de la qualité sur les compétences de communication et la performance de l'équipe.
Keywords
Nontechnical skills
T-NOTECHS
Team performance
Communication
Closed loop communication
Callout
issue-copyright-statement© Canadian Association of Emergency Physicians (CAEP)/ Association Canadienne de Médecine d'Urgence (ACMU) 2022
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pmcClinician’s capsule
What is known about the topic?
Communication during patient management within the trauma bay presents opportunities for errors, which may impact team performance and patient outcomes.
What did this study ask?
Can the T-NOTECHS tool be leveraged through video review to identify communication gaps in the trauma bay for improvement purposes?
What did this study find?
A lower communication score was identified using T-NOTECHS, attributed to incomplete closed loop communications and parallel conversations.
Why does this study matter to clinicians?
Evaluating trauma team performance using T-NOTECHS through video review may be useful to trauma programs for quality improvement purposes.
Introduction
It is estimated that approximately 70–80% of healthcare errors are due to poor communication [1]. Communication techniques within the trauma bay are diverse and present opportunity for errors, such as misunderstanding, interruptions, and a hesitation to speak up [2]. Additionally, environmental noise such as equipment noise and side conversations may interrupt the flow of communication [3]. Ultimately, poor teamwork (and inherently poor communication) impacts patient safety and outcomes. Previous literature has suggested that human error in the context of trauma care may contribute to excess length of stay and mortality [4]. Quality improvement studies have demonstrated that training designed to improve nontechnical skills (such as teamwork and communication) amongst trauma teams may improve patient metrics, such as time from arrival to CT scanner, to endotracheal intubation, and to the operating room [5].
Evidently, communication amongst trauma team members is critical to patient care. As such, standardized communication techniques such as callouts and closed loop communication play a key role in effective communication in trauma [2]. A callout is defined as when a trauma team member states an important patient finding for other team members to hear clearly. Closed loop communication is a communication technique in which a sender gives a message, and a receiver repeats back the message confirming understanding. The aviation industry has demonstrated that adopting standardized behavior (such as callouts and closed loop communication) and assessment tools is highly effective in improving teamwork and reducing risk [6]. One such assessment system is the non-technical skills (NOTECHS) tool, used to define cognitive and social skills needed to carry out safe operations [7]. The NOTECHS tool has since been modified and implemented within healthcare, such as in the operating room [8–10] and trauma bay [11]. In 2012, Steinmann et al. evaluated the reliability and correlation of the trauma non-technical skills (T-NOTECHS) tool with clinical performance parameters at a level II trauma centre [11]. T-NOTECHS was rapidly adopted after minimal rater training and was used for assessment and debrief. Furthermore, a significant improvement in clinical parameters, as assessed using the T-NOTECHS tool, was reported after teamwork training [11]. This suggests clinical relevance of the tool.
Video review of care provided in the trauma bay has also grown in the past decade and provides an ideal opportunity to apply the T-NOTECHS tool to assess the effectiveness of team communication, and evaluate the use of callouts and closed loop communication [12]. Studies have shown that T-NOTECHS may be used to evaluate nontechnical skills in the trauma bay for quality improvement purposes [11, 13]. Furthermore, video review technology can be applied in a healthcare setting [14]. However, there is limited research to suggest whether trauma team communication can be assessed and improved upon using video review technology in Canada. The purpose of this study was to use the T-NOTECHS tool using video review technology to identify communication gaps during the assessment and management of patients in the trauma bay and to develop strategies for improvement.
Methods
Study design and time period
This was a quality improvement study which followed SQUIRE 2.0 guidelines [15]. Two reviewers (medical student and emergency department nurse) independently assessed non-technical skills of team members during the care of trauma patients through video footage via the Trauma Team Video Review Program. Reviewers were responsible for video footage review and data collection. The study was deemed exempt from full Research Ethics Board review and approval because it was considered a quality improvement study. Furthermore, Research Ethics Board approval for quality improvement initiatives and research projects had already been attained and is part of the Trauma Team Video Review Program policy. Two out of three trauma bays beds were outfitted with audio–video recording equipment including omnidirectional microphones which collected high-quality audio and cameras positioned over each bed. One camera was positioned to provide a bird’s eye view of the bed, while the other was positioned over the head of bed to observe any airway interventions. Data were prospectively collected for all cases by two data collectors over eight consecutive weeks from July 1st, 2020 to August 31st, 2020. This timeline included a 1-week pilot period where eight footages were reviewed allowing for calibration between data reviewers and for refining of data forms.
Study setting and population
This study was conducted at Sunnybrook Health Sciences Centre (SHSC)—a regional trauma centre located within Toronto, Ontario, Canada. SHSC is a leading academic and clinical institution in the country that receives over 2000 trauma patients each year [16]. Trauma cases brought to the trauma bay by emergency medical services for whom a trauma code was activated and required attention from the full trauma team [trauma team leader, a junior trainee trauma team leader, two nurses, general surgery resident, orthopedics resident, anesthesia resident, respiratory therapist, and an X-ray technologist] were included. For critically injured patients with possible indication of an emergent surgical procedure, the trauma surgeon was also paged as part of the full trauma team. Team members from obstetrics, burns/plastic surgery and neurosurgery were activated ad hoc as necessary.
Outcome measures
Our study assessed for patient demographics, team performance (primary outcome), and secondary characteristics specific to team communication (secondary outcomes), using video recordings within the Trauma Team Video Review Program. The T-NOTECHS tool (Online Resource 1), as described by Steinmann et al. [11], was used to collect primary outcome data. Final data collection metrics included: (1) Patient demographics [age; male sex; Injury Severity Score (ISS) as a measure of trauma severity (ISS ≥ 16 was considered more severe at SHSC, a threshold commonly used to define major trauma [17]); impaired airway, breathing, and/or circulation as determined by the trauma team; mechanism of injury]; (2) Primary outcome: team performance as assessed across the five domains of T-NOTECHS (Leadership; Cooperation and resource management; Communication and interaction; Assessment and decision making; Situation awareness/coping with stress) on a five-point Likert scale; (3) Secondary outcomes (Number of callouts during patient assessment and management; number of times closed loop communication was properly completed; number of times closed loop communication was initiated and not properly completed; number of times parallel conversations occurred; number of times the charting nurse had to ask a team member to repeat themselves; number of times the trauma team leader or other team member had to reinforce the crowd and noise control during patient care).
Data analysis
All statistical tests were conducted using IBM SPSS v24.0 [18] and SAS software v9.4 [19]. Descriptive statistics such as median and interquartile ranges (IQR) were calculated after averaging scores between data collectors. A Wilcoxon signed rank test was performed to assess significance amongst the five T-NOTECHS domains. A Wilcoxon two sample test was conducted to assess significance amongst secondary outcome characteristics. A p value of less than 0.05 was considered statistically significant for all calculations.
Results
Participant demographics
Fifty-five trauma activations were included in the study. The ISS was ≥ 16 in 37% of cases. Table 1 demonstrates further demographic characteristics of cases included in our study.Table 1 Demographic features of 55 patients admitted to trauma bay as full trauma activations
Demographic feature Value
Age, Median (IQR) 35 (25–61)
Male sex, n (%) 35 (65)
ISS ≥ 16, n (%) 19 (37)
Airway, Breathing, and/or Circulation Impaired, n (%) 15 (27)
Motor vehicle collision, n (%) 15 (27.8)
Cyclist injury, n (%) 4 (7.4)
Pedestrian injury, n (%) 5 (9.3)
Fall, n (%) 13 (24.1)
Gunshot injury, n (%) 8 (14.8)
Stab injury, n (%) 6 (11.1)
Other mechanism, n (%) 3 (5.5)
Injury Severity Score (ISS) was used as a measure for trauma severity. Less severe cases were defined as ISS < 16. More severe cases were defined as ISS ≥ 16. One patient with unreported data for patient demographic and mechanism of injury information was missing. Three deceased patients excluded from ISS calculations used to describe secondary outcomes
Primary outcome (team performance assessment)
As seen in Table 2, the median/IQR score on the domain of communication and interaction was significantly lower (p < 0.0001) compared with each of the other T-NOTECHS domains. However, when comparing each of the other domains amongst themselves, no statistical difference was identified.Table 2 Primary outcomes as measured using the T-NOTECHS scale across five domains for 55 full trauma activations
T-NOTECHS domain Median (IQR) p value (relative to Communication and Interaction)
Communication and Interaction 4 (3–4.5) –
Leadership 4.5 (4.5–5) < 0.0001
Cooperation and Resource Management 4.5 (4–5) < 0.0001
Assessment and Decision Making 4.5 (4.5–5) < 0.0001
Situation Awareness and Coping with Stress 4.5 (4.25–5) < 0.0001
p values calculated relative to the communication and interaction domain
The intraclass correlation coefficient (ICC) between the two data collectors was 0.52 for overall T-NOTECHS score.
Secondary outcomes
Table 3 shows there were significantly more callouts and completed closed loop communications in more severe cases compared to less severe cases (p = 0.017 for both). No statistical difference was identified in more severe cases for number of incomplete closed loop communications compared to less severe cases [2 (0.5–4) vs. 1.5 (0.5–2), p = 0.30]. There was no significant difference between more severe and less severe cases in terms of number of parallel conversations, number of times charting nurses asked a team member to repeat themselves, or number of times the trauma team leader had to conduct noise control.Table 3 Secondary outcomes collected by both data collectors after reviewing 55 consecutive full trauma activations
Secondary outcome ISS < 16
Median (IQR) ISS ≥ 16
Median (IQR) p value
Number of callout’s 4 (2.5–6.5) 6 (5–10) 0.017
Number of times closed-loop communication was properly completed 5 (3–8) 9 (5–12) 0.017
Number of times CLC was initiated and not properly completed 1.5 (0.5–2) 2 (0.5–4) 0.30
Number of times that parallel conversations occurred 2 (1–4) 1 (0.5–3) 0.35
Number of times the charting nurse had to ask a team member to repeat themselves 1 (0.5–2) 1.5 (1–2) 0.33
Number of times the TTL or other team member had to reinforce the crowd and noise control during patient care 0 (0–0.5) 0 (0–0.5) 0.96
Total # of times TTL was asked to repeat themselves 0.5 (0–1) 1.5 (0.5–2) 0.084
Discussion
Main findings
Our study identified that communication and interaction scored significantly lower relative to all other domains using the T-NOTECHS tool. The low communication score in our study could be explained by incomplete closed loop communications and parallel conversations amongst trauma team members, which were present in both severe and less severe cases. Closed loop communication was often not completed when communication was not directed towards specific team members. This may be due to the high level of trainee turnover in the trauma bay who often have limited training in crisis resource management, which implements closed loop communication techniques. We also identified that there were significantly more callouts and completed closed loop communication in more severe cases compared to less severe cases. This phenomenon could largely be explained by the increase in verbal communication expected in increasingly complex cases seen in the trauma bay. According to the Yerkes–Dodson law, team performance improves as pressure and arousal increase as cases become more and more severe and challenging, explaining an increase in callouts and completed closed loop communication [20].
Comparison to previous literature
The presence of incomplete closed loop communication (irrespective of case severity) likely contributed to the deficit in overall communication score identified on the T-NOTECHS scale. However, deficits in closed loop communication can also lead to a decrease in overall team performance which may impact patient care. Bowers et al. [21] found that flight crews using closed loop communication were higher performing compared to crews not using closed loop communication. Furthermore, in a study conducted by Abd El-Shafy et al. [22], their team suggests closed loop communication not only prevents medical errors, but also has the potential to increase the speed and efficiency of tasks in the setting of pediatric trauma resuscitation. As such, it is possible that the lack of closed loop communication within the trauma team could have contributed to decreases in task efficiency and consequent team performance.
Parallel conversations were also noted throughout our study which may have impacted team member`s communication, including closed loop communication. As seen in the study conducted by Andersen et al. [23], multiple simultaneous orders called out “in the air” led to task overload in resuscitation teams. Härgestam et al. [2] further suggest multiple orders in the context of trauma teams may have a negative influence on team performance, as reflected by decreased T-NOTECHS communication scores in our study.
Strengths and limitations
Our study effectively demonstrated that video review technology in a Canadian setting can be used to perform a comprehensive performance assessment of trauma team members using the T-NOTECHS tool, which accounts various communication characteristics intrinsic to trauma teams. The introduction of video review technology at our centre provided opportunity to assess team performance remotely during the first wave of the COVID-19 pandemic, avoiding presence of research personnel in the trauma bay to collect data. The ability to replay cases further highlights the advantages of the Trauma Team Video Review Program, as it limited the possibility of recall bias and allowed for details related to case specifics to be reviewed and accurately collected [14]. In addition, we used two reviewers, corroborating with the study performed by Maarseveen et al. [24] which suggested that video analysis of trauma team performance by multiple raters using T-NOTECHS leads to a higher ICC compared to resuscitations observed by live raters, suggesting greater reliability. As such, from a methodological perspective, having two reviewers collect data using video review technology allowed for more robust data collection in our study.
Our study was susceptible to limitations. Firstly, given its observational nature, it is possible that trauma team members unconsciously performed better than usual [i.e. the Hawthorne effect—the notion that participants may alter their behavior when studied) [25]. However, the Trauma Team Video Review program had been instituted at SHSC seven months prior to the initiation of our study. Thus, it is possible this “observer” bias played a smaller role after having become desensitized to video monitoring. Secondly, due to COVID, we implemented different initiatives to protect trauma team members in case patients needed aerosol generating procedures, such as endotracheal intubation, cricothyrotomies, and chest tube insertions. We isolated one of the trauma bay beds with walls, limited the number of providers inside the room (including the charting nurse), which affected communication flow. However, after an initial phase where communication was more challenging, our teams ended up adapting to these initiatives. We believe that, at the time of conducting this study, communication was not importantly affected. Furthermore, despite the professional-grade omnidirectional microphones, some conversations were inaudible due to overlapping conversations and extraneous noise in the environment. As such, during video review, it was unclear if some messages were received and silently being acknowledged, or simply not received and therefore neglected. The multiple raters allowed for some conversations to be captured that were not noted by one rater but noted by the other. The microphone placed closest to the trauma team leader’s position had the highest quality audio for any team member in that area due to proximity to the microphone. Conversations outside of the trauma bay or off-camera were not captured. However, most conversations occurred within proximity of the microphones.
Clinical and research implications
The implementation of the T-NOTECHS tool in the trauma bay allowed for the identification of communication gaps, which our team aims to improve in subsequent plan-do-study-act cycles. On a local scale, we will first introduce a mandatory crisis resource management training video for incoming trainees. Furthermore, we plan to reinforce closed loop communication during the pre-briefing checklist, in situ simulations, and Trauma Team Video Review rounds for all trauma team members. Finally, we will use the Trauma Team Video Review Program and the T-NOTECHS tool to re-assess trauma team communication after these measures have been implemented.
On a broader scale, studies have shown that teamwork and communication in trauma care can be improved through using validated assessment tools such as T-NOTECHS, and subsequently implementing programs such as in situ simulation [26, 27]. In our study, we used the T-NOTECHS tool to assess team communication during trauma care, as it has demonstrated robust reliability and validity to assess nontechnical skills and trauma team performance in authentic and simulation settings [28]. Furthermore, our study has shown that video review technology can be leveraged to assess nontechnical skills (such as team communication) using T-NOTECHS. Introducing video review in trauma institutions is not an insurmountable feat, as previous studies have shown important considerations and challenges in implementing such a program [29, 30]. Of course, quality improvement initiatives may be unique and trauma program specific. Therefore, it is important to gather input from front line healthcare professionals involved in trauma team interactions, communicate the vision to key stakeholders, and demonstrate tangible improvements in team communication to create the necessary culture change to implement standardized closed loop communication within trauma care.
Conclusion
Through video review of trauma team activations, opportunities for improvement in communication can be identified by the T-NOTECHS tool, as well as specifically identifying callouts and closed loop communication. This process may be useful for trauma programs as part of a quality improvement process on communication skills.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 750 kb)
Funding
None.
Declarations
Conflict of interest
The authors declare that there is no conflict of interest.
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Urologe A
Urologe A
Der Urologe. Ausg. a
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10.1007/s00120-022-01817-4
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Aktuelles aus der DGU-Pressestelle
12 4 2022
2022
61 4 447449
© The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
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Nat Rev Immunol
Nat Rev Immunol
Nature Reviews. Immunology
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Nature Publishing Group UK London
35414124
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10.1038/s41577-022-00720-5
Comment
Estimating disease severity of Omicron and Delta SARS-CoV-2 infections
http://orcid.org/0000-0001-8571-2004
Sigal Alex alex.sigal@ahri.org
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http://orcid.org/0000-0003-1641-2299
Milo Ron 5
Jassat Waasila 67
1 grid.488675.0 0000 0004 8337 9561 Africa Health Research Institute, Durban, South Africa
2 grid.16463.36 0000 0001 0723 4123 School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
3 grid.418159.0 0000 0004 0491 2699 Max Planck Institute for Infection Biology, Berlin, Germany
4 grid.428428.0 0000 0004 5938 4248 Centre for the AIDS Programme of Research in South Africa, Durban, South Africa
5 grid.13992.30 0000 0004 0604 7563 Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel
6 grid.416657.7 0000 0004 0630 4574 Division of Public Health Surveillance and Response, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
7 grid.481194.1 0000 0004 0521 9642 Right to Care, Centurion, South Africa
12 4 2022
2022
22 5 267269
© Springer Nature Limited 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.
The Omicron variant of SARS-CoV-2 has been reported to cause milder disease in adults but lead to increased hospital admissions in children. How can we compare disease severity in Omicron and Delta infections, and how should differences be interpreted?
Subject terms
SARS-CoV-2
Infection
issue-copyright-statement© Springer Nature Limited 2022
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pmcMeasuring COVID-19 disease severity in a population has been important for understanding the public health impact of each variant of concern. It also impacts immunologists and virologists closely as it reflects population immunity and the mechanisms of viral infection. A watershed moment in the COVID-19 pandemic was the emergence of the Omicron (B.1.1.529) variant of SARS-CoV-2 with widespread reports of lower disease severity relative to previous variants such as Delta (B.1.617.2). The lower disease severity seen in populations during the Omicron wave of the SARS-CoV-2 pandemic infection1 can be attributed to changes in the virus that limit its ability to spread in the lungs and, probably most importantly, to increased immunity in the population from previous SARS-CoV-2 infection and vaccination2. However, in children, Omicron infections led to more hospital admissions than in previous waves3,4. Does this mean that Omicron is causing more severe disease in children, or is the difference based on how disease severity is defined? While the current Omicron wave is waning globally, a clearer concept of disease severity should help us evaluate the variants to come.
Disease severity is measured using outcomes such as total hospital admissions, requirement for supplemental oxygen and ventilation, and death (Fig. 1a). Hospital admission is a measure that is not very specific: it does not indicate cause, and there can be a wide range of severity. People may also be admitted with, but not because of, SARS-CoV-2 infection. However, severity measures become more specific for lower respiratory tract damage caused by COVID-19 disease as severity increases.Fig. 1 Estimating disease severity.
a | A simplified severity scale for COVID-19 disease. b | Ballpark estimate of the fraction of unreported infections based on UK surveillance data. c | Periods for the USA Delta (15 July 2021 to 15 November 2021) and Omicron (15 December 2021 to 15 March 2022) infection waves used in the analysis. The period between 15 November and 15 December was not analysed to avoid times when both variants were circulating. d,e | Cumulative number of deaths (d) and reported cases (e) since the start of each infection wave from Centers for Disease Control and Prevention (CDC) data. f | Cumulative number of estimated total infections in each wave, calculated by dividing the number of cumulative reported cases by the UK estimated fraction of reported to total infections as determined from data in (b). g | Case fatality ratio, the cumulative number of deaths divided by the cumulative number of reported infections per wave. h | Infection fatality ratio, the cumulative number of deaths divided by the cumulative number of estimated total infections per wave.
Measurement generally has one of three forms: 1) per unit time, usually daily, for example number of new hospital admissions per day; 2) as an integral over a time interval, for example total excess mortality over an infection wave; and 3) as a fraction of infections, for example fraction of deaths out of the total number of people infected within a defined period. These measurements all involve disease severity but are used differently. Knowing daily hospital admissions would be important for planning sufficient hospital capacity. Total disease or mortality may be important to calculate the human and economic cost of an infection wave. Fraction of infections that are severe is important to know to answer questions such as: “Is my child more or less likely to become severely ill if infected with this variant?” with implications for risk assessment, behaviour and mechanisms of pathogenicity.
In terms of immunological mechanisms, inferring disease severity from a daily or cumulative measure may be misleading because a higher number of deaths, hospital admissions or other metrics may result from an increase in infections, not increased severity. Higher infection prevalence can happen because the virus has evolved to transmit better, or because there was no lockdown or non-pharmacological interventions in place. Normalizing by the number of infections should eliminate this dependence. However, it introduces a new complication: what is the denominator? Not all infections are reported, either because they are asymptomatic, or because people have difficulty accessing testing or choose not to test.
To give an example, a clinical trial recruiting people during the Omicron infection wave in South Africa5 showed that 31% of apparently healthy individuals arriving to enrol were qPCR positive for SARS-CoV-2. Given the population of South Africa is approximately 59 million, this would equate to about 18 million people infected. By contrast, the total number of reported SARS-CoV-2 cases in South Africa between 25 November 2021 and 15 February 2022 (the Omicron wave) was 692,153 (see Related links). This gives a ratio of 26 to 1 of unreported to reported infections.
A more precise way to estimate unreported infections may be community surveys. The REACT-1 study in the UK randomly surveys about 100,000 people monthly for SARS-CoV-2 and may capture about a 2-week window of infection per sampling, given that SARS-CoV-2 is detectable by qPCR in most people for this period. The study found an infection prevalence of 2.9%6 between 8 February and 1 March 2022, when Omicron dominated. Between 19 October to 5 November 2021, when Delta was dominant, infections were at 1.6%7. Extrapolating this to the UK population of 67 million and comparing the resulting number to the number of UK reported cases (see Related links) in a two-week interval within the surveyed Omicron and Delta periods gives 27% reported and 73% unreported infections for Omicron. The estimate for Delta is 58% reported and 42% unreported infections (Fig. 1b). Estimates for unreported infections may be dependent on vaccination prevalence, age of the infected population, and likely many other factors, and so may be specific to the population surveyed. They are used here as ballpark figures to illustrate how they influence the assessment of relative disease severity between Omicron and Delta.
To compare relative disease severity, we used data from the Delta and Omicron infection waves in the USA (Fig. 1c) available from the Centers for Disease Control and Prevention (see Related links). The number of cumulative deaths in the Omicron wave (analysed from 15 December 2021 to 15 March 2022) was very similar to that seen in the Delta wave (analysed from 15 July 2021 to 15 November 2021; Fig. 1d). However, the number of confirmed cumulative cases during this period was twofold higher with Omicron (Fig. 1e). Based on the ballpark figures for unreported infections stated above, the number of total estimated infections was about fivefold higher for Omicron (Fig. 1f). Normalized by the number of confirmed cases — the case fatality ratio — Omicron infection had about a twofold lower mortality relative to Delta (Fig. 1g). Normalized by the total number of infections — the infection fatality ratio — the difference became approximately fivefold (Fig. 1h). For comparison (see Related links), the same time-windows in South Africa, also corresponding to Delta and Omicron dominated periods, had 699,236 reported cases and 23,894 deaths (Delta) and 492,181 cases and 9,555 deaths (Omicron). This gives a case fatality ratio of 3.4% for Delta and 1.9% for Omicron, again about a twofold difference. Therefore, Omicron does have lower severity by these measures, with the precise severity drop relative to Delta dependent on how the number of infections is estimated.
But is Omicron more severe in children? In children, the fraction of SARS-CoV-2-positive cases admitted to hospital in South Africa doubled during the Omicron wave compared to in the Delta wave8,9. This seems to point to higher disease severity. However, the in-hospital case fatality ratio of children under 5 in the Omicron wave was 0.5% versus 0.6% in the Delta wave9. A similar trend, but with lower case fatality, was seen in older children. Consistent with this, the fraction of ventilated children under 5 in the UK was 2.9% in the Omicron wave versus 5.1% in other waves10.
Although the adult severity scale may miss paediatric-specific symptoms such as seizures, which are more prevalent with Omicron8, it seems that measures of high severity and death do not support that Omicron is more severe than Delta in children. Also, a lower fraction of children than adults die with both variants. So why are more children admitted with Omicron? One possibility is that, like in adults, there were more unreported cases of infection in the Omicron wave and this drove the higher admissions, but admissions did not result in as many severe outcomes as with the other variants. For example, in South Africa, 61% of children were admitted with fever or dehydration from diarrhoea and vomiting8. There are clinical protocols for paediatric management that require children to be admitted for fever or fluid management. These are generally short admissions for supportive care and may not lead to the more dangerous respiratory symptoms.
A related explanation is that Omicron leads to a shift in symptoms (for example, more fever). This may be harder to discern in adults possibly because of the gap in immunity between adults and children (that is, adults are more likely to be vaccinated). Also, most adults may not require admission for fever.
What all this may show is that measures of disease severity should be interpreted with caution. As seen in children, the metric of hospital admissions as a fraction of cases may not be a good measure, as it does not fully reflect more severe disease. The number of deaths is an easily accessible measure and captures the most severe outcome, but may be misleading if it is not normalized by the number of infections. The infection fatality ratio may therefore be the most informative metric, and population surveys measuring active infection prevalence should be used to get an accurate estimate of this.
Competing interests
The authors declare no competing interests.
Related links
Centers for Disease Control and Prevention. COVID Data Tracker: https://covid.cdc.gov/covid-data-tracker/#datatracker-home
The National Institute for Communicable Diseases, National COVID-19 Daily Report: https://www.nicd.ac.za/diseases-a-z-index/disease-index-covid-19/surveillance-reports/national-covid-19-daily-report
UK Coronavirus dashboard: https://coronavirus.data.gov.uk/details/cases?areaType=overview&areaName=United%20Kingdom
==== Refs
References
1. Abdullah F Decreased severity of disease during the first global Omicron variant Covid-19 outbreak in a large hospital in Tshwane, South Africa Int. J. Infect. Dis. 2021 116 38 42 10.1016/j.ijid.2021.12.357 34971823
2. Sigal A Milder disease with Omicron: is it the virus or the pre-existing immunity? Nat. Rev. Immunol. 2022 22 69 71 10.1038/s41577-022-00678-4 35046570
3. New York State Department of Health. Pediatric COVID-19 update: January 7, 2022 https://www.health.ny.gov/press/releases/2022/docs/pediatric_covid-19_hospitalization_report.pdf (2022).
4. UK Health Security Agency. SARS-CoV-2 variants of concern and variants under investigation in England. Technical briefing 34 https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1046853/technical-briefing-34-14-january-2022.pdf (2022).
5. Garret N High rate of asymptomatic carriage associated with variant strain Omicron medRxiv 2021 10.1101/2021.12.20.21268130
6. Chadeau-Hyam M The Omicron SARS-CoV-2 epidemic in England during February 2022 medRxiv 2022 10.1101/2022.03.10.22272177
7. Chadeau-Hyam M REACT-1 round 15 final report: increased breakthrough SARS-CoV-2 infections among adults who had received two doses of vaccine, but booster doses and first doses in children are providing important protection medRxiv 2021 10.1101/2021.12.14.21267806
8. Cloete J Paediatric hospitalisations due to COVID-19 during the first SARS-CoV-2 omicron (B.1.1.529) variant wave in South Africa: a multicentre observational study Lancet Child Adolesc. Health 2022 10.1016/S2352-4642(22)00027-X 35189083
9. Jassat W Clinical severity of COVID-19 patients admitted to hospitals during the Omicron wave in South Africa medRxiv 2022 10.1101/2022.02.22.21268475 35043121
10. COVID-19 Clinical Information Network. CO-CIN update January 2022 https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1046475/S1483_CO-CIN_Child_admissions_and_severity_by_epoch.pdf (2022).
|
PMC009xxxxxx/PMC9002224.txt |
==== Front
Support Care Cancer
Support Care Cancer
Supportive Care in Cancer
0941-4355
1433-7339
Springer Berlin Heidelberg Berlin/Heidelberg
35412075
7049
10.1007/s00520-022-07049-8
Original Article
Body image distress among cancer patients: needs for psychosocial intervention development
Nikita nikitaarya123456@gmail.com
1
Rani Ruchika ruchikaheera@gmail.com
2
http://orcid.org/0000-0002-7504-5620
Kumar Rajesh rajeshrak61@gmail.com
2
1 grid.413618.9 0000 0004 1767 6103 All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Odisha India 751029
2 grid.413618.9 0000 0004 1767 6103 Department of Nursing, All India Institute of Medical Sciences (AIIMS), Rishikesh, Uttarakhand India 249203
12 4 2022
2022
30 7 60356043
29 12 2021
6 4 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.
Objectives
This study examines the body image distress among patients with head and neck cancer (HNC) visiting a tertiary care hospital for follow-ups.
Design
A cross-sectional survey purposively enrolled 170 head and neck cancer (HNC) patients who had undergone cancer surgery at a newly established tertiary care hospital, North India.
Methods
A structured pre-tested socio-demographic and clinical profile checklist and the Derriford Appearance Scale-24 (DAS-24) were used to collect information. An appropriate descriptive and inferential statistic was applied to compute the findings.
Results
The median age of the participants was 46.0 years, and 80% of the participants were unemployed. The mean body image distress score was 57.95 (SD = 10.3, 47–66.75, range 42–77). The body image distress shows a significant association with age (p < .001), gender (p = 0.003), and working status (p = 0.032) of the HNC patients. Multilinear regression reported gender as an independent predictor (95% CI: 0.615–8.646, p = 0.025) for body image distress in HNC patients.
Conclusions
HNC patients reported substantial body image distress due to changes in body appearance. Female patients who had undergone surgery at young age reported higher body image distress. Recommending cosmetic surgery and nurse-led psychosocial nursing intervention on routine follow-ups are other potential strategies to improve facial appearance to overcome the negative impact of body image.
Keywords
Body image
Head and neck neoplasm
Appearance
Distress
issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2022
==== Body
pmcIntroduction
Head and neck squamous cell carcinoma (HNC) is the sixth most common cancer worldwide and accounts for 30–40% of all cancer in India [1, 2]. Mouth and tongue cancers are more common in India, with a higher incidence in the North-East region [1]. However, extensive use of tobacco, pan masala, and gutkha is common and may be linked to the higher incidence of HNC in the geographical area [3].
HNC patients undergo long, physically demanding, and multimodal treatment approaches, including surgery, radiotherapy, and chemotherapy or a combination [1]. These treatment modalities result in significant alterations and debilitating changes in the body involving loss of a body part, disfigurement, scars development, and skin changes, leading to overall disturbed body image [4]. Unlike other forms of cancer, the disfigurement after surgical excision for HNC cannot be hidden and can enormously influence the individual’s identity [5, 6]. Surgical treatment can distort normal symmetry and landmarks of the skin and lead to psychological distress among patients [6]. Likewise, radiotherapy may cause fibrosis, swelling, and changes in skin pigmentation and may disrupt normal skin integrity and tone [7].
Furthermore, a person’s identity is an intricate construction that extends beyond their physical appearance. Body image is a similar yet multifaceted concept encompassing perceptions, thoughts, feelings, and behaviors associated with the whole body and its different domains [7]. It has been described as a subjective perception of one’s insight regarding own body, based on self-interpretation and reactions to the judgment of others, with associated features such as identification, imitation, social factors, and emotional expression [8]. A visible disfigurement and functional impairment in the typical appearance of the body leads to a substantial negative impact on one’s psychological health, referred to as body image distress [9]. Additionally, body image distress denotes perceived physical anomaly such as obsessively examining oneself in the mirror, grooming to hide or fix the perceived flaw, and seeking reassurance from others about their appearance without satisfaction [10]. According to the cognitive-behavioral model, body image is the notion of satisfaction and dissatisfaction with one’s body in terms of appearance investment and self-evaluation, where appearance investment refers to an individual’s view of the significance of look, and physical attributes and self-evaluation indicate the degree to which an individual is satisfied with her appearance [11].
Body image distress in HNC patients refers to an individual’s identity as a by-product of their social and psychological experiences shaped by their impressions and sense of the bodily appearance every day [12]. The evidence suggests that 75% of patients face innumerable problems related to acknowledgment and embarrassment about unwanted physical changes and body image, which further evoke painful experiences and constantly remind of body disfigurement [13]. Furthermore, body image distress is common in HNC patients, with prevalence rates ranging from 25 to 77% [14]. However, the prevalence of body image distress found varies in oral and oropharyngeal cancer [15]. It may be linked with relationship conflict, social isolation, damage to self-image, disfigurement, stress, and moderate levels of anxiety and depression experienced by an individual [15, 16].
However, the extent of body image distress experienced after different treatment modalities is poorly understood and negatively impacts patients’ daily functioning and psychosocial health [17]. Clearly, further research is needed to address the concern and issues related to the impact of body image distress on health-related quality of life and psychological status as a part of treatment in head and neck cancer (HNC).
The first aim of the present investigation is to study the body image distress among HNC patients and whether socio-demographic and clinical factors are associated with body image distress. The present study’s findings will develop more insight into other body image-associated issues, facilitating emotional needs targeting HNC patients with body image distress.
Material and methods
A cross-sectional survey design was used to understand the body image distress among HNC patients who underwent surgery at one of the tertiary care hospitals, North India. All India Institute of Medical Sciences, Rishikesh, Uttarakhand, is among the six-apex healthcare institutes established under the Ministry of Health and Family Welfare (MOHFW), India, to correct regional imbalances in health care services across the country. It is a tertiary care institute with a 1000-bed hospital to provide multispecialty health care services to the underserved population. Other specialized services to oncology patients are catered by hematology, medical oncology, radiation, surgical oncology, and integrated breast care center (IBCC).
HNC patients who had undergone surgery with 6 weeks of follow-up were purposively recruited in the study. A sample size considering 75% expected prevalence and 5% margin of error was used to calculate the survey and was 288 [18]. However, the study sample size was limited to 170, considering restriction and decreased patients’ load during the second wave of the COVID-19 pandemic. The data collection was completed between Dec 18, 2020, to Jan 18, 2021. Patients equal to or more than 18 to 65 years of age and converse in Hindi and English languages are included in the study. Patients with other malignancies and unwilling to be part of the study were excluded from the study.
Self-reported questionnaires
The data collection tools consisted of a clinical and socio-demographic sheet, and Derriford Appearance Scale (DSA-24) is used to measure body image distress among HNC patients.
Socio-demographic and clinical profile sheet consists of information on age, gender, religion, working status, marital status, family type, education, residential area, and monthly income. Furthermore, a brief clinical profile was obtained using a pre-tested clinical profile sheet consist information on the duration of cancer, tumor site, types of surgery and treatment, comorbidities, and history and time of alcohol and tobacco use.
The Derriford Appearance Scale 24 (DAS-24) [19] is a 24 items short form of the Derriford Appearance Scale 59 (DAS-59) psychometric scale designed to measure adjustment to problems of visible difference and disfigurement in the body after undergoing different types of surgery in cancer patients [19]. Participants were asked to respond to the scale using a 5-point rating scale, “almost always (4)” to “never/almost never (0).” Some of the items in the scale are rated on “extremely (4)” to “not at all (0).” The scale has a total score of 11–96 (min.–max.). The scale is continuous, and getting a high score indicates more psychological distress due to poor or dissatisfaction with body image. The scale was translated into Hindi, and the back-translation method [20] was used to measure the scale’s consistency. The tool was translated from the original language, English, to Hindi with the help of an expert in Hindi literature and retranslated into the origin language by an English language expert to know the language consistency [20]. The translated tool reliability is calculated using the split-half test and reported 0.82 (r = 0.82) for the present study. The scale’s internal consistency was tested using Cronbach’s alpha and reported 0.86 (α = 0.86). The translated Hindi version scale was pre-tested before using it for the final study.
Ethical considerations
The Institutional Ethics Committee (ICE, 43/IEC/M.Sc./2020) approved the study. Written informed consent was obtained from each patient after giving a due explanation of the purpose of the study. Data collectors ensured privacy and confidentiality at each point of research and publication of the findings.
Statistical analyses
Descriptive statistics analyze the frequency, percentages, and other relevant statistics for socio-demographic and clinical profiles. Independent sample t-test and one-way ANOVA were applied to find the association of socio-demographic variables with body image distress among patients. Odds ratio (ORs) with 95% confidence interval (CI) was used to quantify the association between socio-demographic characteristics and the body image distress of patients. The SPSS Window, Version 23.0 Armonk, NY: IBM Corp is used for data analysis. The level of significance was set at P < 0.05 (two-sided).
Results
Descriptive analysis and preliminary analyses
Table 1 describes the demographic characteristics of the participants. The median age of the participants was 46.0 years. Around an equal number of participants were in a category of 41–50 years (26.46%) and 51–60 years (27.06%). Furthermore, more than two-thirds of the participants were not working (80%) and were married (88.24%). More than half of the participants (52.36%) belonged to urban areas and completed secondary education (54.12%).Table 1 Socio-demographic variables and body image distress in participants (n = 170)
Socio-demographic variable f (%) Mean ± SD p-value
Age (years, median) 46.00
≤ 40 51 (7.05) 67.81 ± 5.63 < .001
41–50 45 (26.46) 56.69 ± 8.59
51–60 46 (27.06) 47.83 ± 5.51
> 60 28 (16.48) 59.82 ± 9.80
Gender
Female 47 (27.65) 62.49 ± 9.76 0.003*
Male 123 (72.35) 56.22 ± 10.02
Working status
Not working 136 (80.00) 61.32 ± 10.07 0.032*
Working 34 (20.00) 57.11 ± 10.08
Marital status
Married 150 (88.24) 57.91 ± 10.31 0.887
Unmarried/widow 20 (11.76) 58.10 ± 10.83
Family type
Joint family 72 (42.35) 57.62 ± 10.27 0.627
Nuclear family 98 (57.65) 58.4 ± 10.44
Education
Up to primary 48 (28.23) 58.05 ± 9.76 0.932
Secondary 92 (54.12) 57.62 ± 10.24
Graduate and above 30 (17.65) 59.03 ± 11.37
Residential area
Rural 26 (15.29) 55.42 ± 9.53 0.942
Semiurban 55 (32.35) 58.51 ± 10.04
Urban 89 (52.36) 58.31 ± 10.71
Monthly income#
≤ 10,001 09 (05.29) 54.78 ± 10.77 0.089
10,002–29,972 141 (82.95) 57.52 ± 10.35
29,973–49,961 20 (11.76) 62.41 ± 9.05
#Classification based on Kupuswami scale updated in 2020; #-Muslim and Sikh; *p-value < 0.05
Table 2 presents the clinical profile of the participants. More than half (58.24%) of the participants have oral cancer, followed by nasopharynx (14.11%) and nasal cavity (13.53%) with a mean duration of 11.55 (SD: 6.91) years of cancer since the first diagnosis of cancer. More participants (38.24%) underwent a combination of treatment including radiotherapy, surgery, and chemotherapy (38.24%) and underwent mouth angle scarified surgery (27.06%) and glossectomy (21.76%). Approximately half of the participants have one or another comorbidity, including hypertension and diabetes, and cancer. More participants reported alcohol use (42.94%) than tobacco (20%), with a mean duration of 13. 43(SD: 6.56) years and 12.82 (SD: 5.06) years, respectively.Table 2 Clinical profile of cancer participants (n = 170)
Clinical profile f (%)
Tumor site
Nasal cavity 23 (13.53)
Nasopharynx 24 (14.11)
Oral cavity 99 (58.24)
Hypopharynx 07 (04.12)
Others* 17 (10.00)
Duration of cancer^ (Yrs, mean ± SD) 11.55 ± 6.91
Type of treatment
Surgery + chemotherapy 26 (15.29)
Surgery + chemotherapy + radiotherapy 65 (38.24)
Surgery + radiotherapy 46 (27.06)
Surgery only 33 (19.41)
Type of surgery
Facial skin-sacrificed 30 (17.65)
Mouth angle-sacrificed 46 (27.06)
Glossectomy 37 (21.76)
Inferior maxillectomy 01 (00.59)
Other types 56 (32.94)
Comorbidity (yes) 76 (44.70)
Type of comorbidity
Diabetes mellitus 03 (3.89)
Diabetes mellitus and hypertension 36 (46.75)
Hypertension 37 (49.35)
History of alcohol consumption (yes) 73 (42.94)
Alcohol consumption duration
(Yrs, mean ± SD, n = 73)
13.43 ± 6.56
≤ 10 23 (31.52)
11–15 42 (68.48)
History of tobacco consumption (yes) 34 (20.00)
Tobacco consumption duration
(Yrs, mean ± SD, n = 34)
12.82 ± 5.06
≤ 10 13 (38.23)
≥ 11 21 (61.75)
*Paranasal cavity, larynx, salivary glands; ^Duration is since the first diagnosis of cancer
Furthermore, body image distress was found significantly higher in females (p = 0.003) and younger participants (p < 0.001) visiting the outpatient department for follow-ups. Additionally, this distress was reported higher in participants staying at home or not working (p = 0.032) Table 1.
Significant problems measured by DAS-24
The DAS-24 measures significant problems are summarized in Table 3. We have considered the critical issues reported as worst outcomes (e.g., extremely/moderately, almost always, or often). A total of 98.9% of participants were self-conscious about their features and believed (98.3%) that surgical treatment had an adverse effect on work. Similarly, 95.9% of participants responded that self-consciousness makes them irritable at home and (81.3%) distressed while watching a mirror or window. More than two-thirds of participants (80.7%) express folding arms and covering their face while facing other people. Likewise, 82.5% said to avoid using communal changing rooms and avoid going (89.5%) shopping at departmental stores and supermarkets. An equal number of participants (74.3%) refused to attend a social event and reported adverse effects on sexual life (74.8%) after surgical excision.Table 3 Response to the Derriford (DAS 24) Questionnaire
Derriford Appearance Scale (DAS-24) items Significant problem on DAS-24 Significant problem on DAS-24
F %
How confident do you feel Not at all/slightly 41 24.0
How distressed do you get when you see yourself in the mirror/ window Extremely/moderately 139 81.3
My self-consciousness makes me feel irritable at home Almost always/often 164 95.6
How hurt do you feel Extremely/moderately 102 59.7
At present, my self-consciousness has an adverse effect on my work Almost always/often 168 98.3
How distressed do you get when you go to the beach Extremely/moderately 117 68.4
Other people misjudge me because of my feature Almost always/often 132 77.2
How feminine/masculine do you feel Not at all/slightly 166 99.4
I am self-conscious of my feature Almost always/often 169 98.6
How irritable do you feel Extremely/moderately 72 42.1
I adopt certain gestures (e.g., folding my arms in front of other people, covering my mouth with my hand) Almost always/often 138 80.7
I avoid communal changing rooms Almost always/often 144 82.5
How distressed do you get by shopping in department stores/supermarkets Extremely/moderately 153 89.5
How rejected do you feel Extremely/moderately 22 12.9
I avoid undressing in front of my partner Almost always/often 26 15.2
How distressed do you get while playing sports/games Extremely/moderately 00 0.00
I close into my shell Almost always/often 10 5.8
How distressed are you by being unable to wear your favorite clothes Extremely/moderately 102 5.8
How distressed do you get when going to social events Extremely/a fair amount 127 59.7
How normal do you feel Extremely/moderately 127 74.3
At present, my self-consciousness has an adverse effect on my sex life Almost always/often 63 36.8
I avoid going out of the house Almost always/often 128 74.0
How distressed do you get when other people make remarks about your feature Extremely/a fair amount 101 59.1
I avoid going into pubs/restaurants Almost always/often 96 56.2
F frequency, % percentage
Furthermore, findings reported that participants aged less than 40 years were feeling more body image distress while going shopping in departmental stores/supermarkets than their counterparts (p = 0.035). In contrast, feelings of rejection were significantly higher in participants aged 51–50 than older and younger (p = 0.016). However, the feelings of body image distress were substantially higher in the participants who belonged to the 41–50 years of age category in contrast to the younger cohort (p = 0.006). Findings reported that females were significantly more concerned with body image distress after surgical excision (Table 4).Table 4 Body Image distress and selective socio-demographic variables of participants (n = 170)
Item Age/gender/working status Mean ± SD p-value
Feeling distressed while shopping in department stores/supermarkets ≤ 40 years
41–50 years
51–60 years
> 60 years
3.43 ± 0.57
3.20 ± 0.55
3.07 ± 0.74
3.29 ± 0.60
0.035*
Feeling of rejection ≤ 40 years
41–50 years
51–60 years
> 60 years
2.00 ± 0.63
1.96 ± 0.42
2.37 ± 0.90
2.00 ± 0.72
0.016*
Feeling distressed while playing sports/games ≤ 40 years
41–50 years
51–60 years
> 60 years
0.80 ± 0.83
1.29 ± 0.82
0.78 ± 0.84
0.71 ± 0.81
0.006*
Avoiding communal changing rooms Female
Male
2.91 ± 0.69
3.17 ± 0.65
0.025*
Avoid going to pubs/restaurants Female
Male
3.13 ± 0.99
2.76 ± 0.97
0.028*
I close into my shell Female
Male
1.51 ± 0.55
1.28 ± 0.59
0.025*
I feel close into my shell Not working
Working
1.31 ± 0.60
1.53 ± 0.51
0.048*
*p-value significant < 0.05
Furthermore, multilinear regression was applied to quantify the strength of association of variables that show significant association with DAS-24. Findings represent that gender has a considerable impact (p = 0.025) on body image distress among participants. The model explained 91% of (Nagelkerke R2) variance on the body image distress (Table 5).Table 5 Multilinear regression to identify predictors of body image distress (n = 170)
Variable B SE ß t-value p-value 95% CI
Constant 58.295 3.074 18.962 0.000 52.040–64.550
Age 0.437 0.754 .093 .579 0.566 − 1.098–1.971
Gender 4.630 1.974 0.393 2.346 0.025* 0.615–8.646
Working status − 3.044 2.073 − .248 − 1.469 0.151 − 7.260–1.173
Model fit calculated from valid cases F (2.205), p = 0.106, adjusted R2 = 0.091; SE, standard error; CI, confidence interval; *p-value < 0.05
Discussion
The study was conducted to identify body image distress among HNC patients attending follow-up services at a newly established tertiary care hospital in North India. The median age of participants was 46.0 years, with a higher proportion of male participants than females. These demographics were found in concurrence with the previous Indian epidemiological study, which reported higher HNC incidence in the age group of 40–60 years and sixteen times higher in the male population conducted in Western Uttar Pradesh [21]. Likewise, these demographic trends were found similar in other studies performed in southern states of India [22]. Demographic trends further supported with other relevant literature emphasizing 2–4 times higher risk of HNC in men than women [23]. A higher proportion of the male participants in this study and previous literature support a higher prevalence of HNC in the cohort. In addition, males in the Northeast region had the highest prevalence of developing cancer than females (11–25% vs. 3–18%) [24].
In clinical profile, oral cancer reported more frequent cancer in the studied population with a mean duration of 11.55 (± 6.91) years since the first diagnosis. Furthermore, a more significant number of participants were undergoing a combination of treatment, including chemotherapy, radiation, and surgery. Indian studies reported 40% of oral cancer, where cancer of the tongue and mouth contributed more than one-third of total cancer [25]. However, a declining trend for oral cancer was observed in men above 40 years of age during 1986–2000, but this trend remains unchanged in adult men below 40 [26]. A decline in tobacco use may be postulated as a possible reason for this group’s sudden drop in oral cancer. However, a significantly higher cancer incidence indicates continued use of tobacco and alcohol in the population. Excessive alcohol (42.9%) and tobacco (20%) use was higher in female participants in the current cohort. Besides tobacco use, the harmful effects of alcohol and other local tobacco products are apparent risk factors for oral cancer in India and elsewhere [27, 28]. Furthermore, it has been attributed that regular alcohol use increases the risk of oral cancer [25]. In addition, smoking and alcohol use further intensifies the incidence of oral cancer compared to alcohol use only [29, 30].
Furthermore, the mean scores of body image distress were 57.95 ± 10.3, ranging from 42 to 77, suggesting higher distress among participants. The higher distress in participants shall be correlated with dissatisfaction with personal appearance or disfigurement after surgery [31] which is similar to earlier work on HNC patients [32]. Likewise, other concerns noticed among surgically treated HNC patients were negative body image and poor quality of life [15, 32]. The incidence of negative self-evaluation about the health dimension of body image, appearance, and not being attractive or embarrassed about bodily changes is well documented in cancer patients [33]. In a qualitative investigation, disfigurement reported a constant reminder for ruptured self-image and other dysfunctions in cancer patients [34]. However, the prevalence of body image distress varies, ranging from 25 to 77%, higher in newly diagnosed younger participants [13].
HNC patients’ age, gender, and working status reported a significant association with body image distress. Findings said that younger participants (< 40 years) felt more distress while shopping in a departmental store. Conversely, the adult cohort reported a feeling of rejection and distress while playing sport. These findings on distress are consistent with the work conducted by Melissant HC et al. (2021) in Netherland reported higher distress among the younger age cohort while having social interactions [6]. Likewise, male participants were more embarrassed while using communal changing rooms, and refraining from visiting restaurants and public places is more frequently observed in the female cohort. A qualitative investigation reported that participants with this kind of cancer face more problems while eating in public places or restaurants while holding the fluid in their mouth, starring people, and prothesis-related issues that further potentiate frustration and embarrassment [17]. Symptom progression becomes a struggle to perform activities of daily living, including eating, swallowing, speaking, and pain in addition to changes in appearance [35]. In addition, surgery-specific complications may substantially impact normal eating, chewing, and swallowing food, making it a challenge for the patient to dine out at public places [36].
Body image distress is more common in young participants with cancer in a study conducted by Bahrami M et al. (2017) reported feelings of being more apprehensive and isolated, and rejected [33], participants expressed negative body image experiences related to the asymmetric appearance of the face and created an older look. However, it has been reported by the participants that it will take a longer time for them to restore normalcy in working, living, or sports [34]. Furthermore, disfigurement related to poor self-esteem and higher body image-distress may also impede the normal grieving process and may take a longer time for participants to restore everyday living [37].
In the present study, 80% of the participants were unemployed. Cancer survivors often change or quit their work due to one or another reason, including physical functions and endurance, appearance-related discomfort, fatigue, and strategies to reduce cancer-related symptoms and the need for long-term treatment [31]. Debilitating anxiety after surgical reconstruction made HNC patients more reluctant to join the work and social gathering. Similarly, unclear speech and difficulty in eating further made patients hesitate to continue daily work [12]. Equally, diagnosis of tumor and side effects of treatment-induced alopecia, surgical scar, a cushingoid appearance from corticosteroid use, cranial deformities, and the weird attitude of work supervisor makes it challenging to return to work are associated with poor work productivity [15, 17].
On the contrary, body image distress reported a significant association with the working status of the participants. Relative younger age and need of income to support cancer treatment and family might have contributed to higher distress in the studied sample. Cancer rehabilitation sometimes has long-lasting effects on employment and the ability to work. Changes in appearance and physical and emotional distress postulated contributing to the high adjusted risk of quitting the job in HNC patients [31]. Further exploration of this relationship in HNC patients is needed.
Female cancer cohort reported higher body image distress than male counterparts. Facial disfigurement has a higher negative impact on female participants than males and may have a negative effect on body image [38]. In general, women are more sensitive about their appearance and appreciate beauty as their emotional strength [17, 39]. Disruption to the developmental goals and tasks imposed by the physical impact after surgery could be a probable reason for higher body image distress in the female cohort, similar to earlier findings on HNC females [39].
Our study has found that body image distress negatively impacts psychological health and health-related quality of life. One potential model of support recognizes the specialized role of nurses to intervene in such issues among patients. There is evidence reporting the effect of a nurse-led intervention to decrease cancer-related psychosocial morbidity and quality of life among newly diagnosed cancer patients [40]. However, a role division for oncology nurses in screening, referral, and treatment needs to be studied in future separate research. Notably, the nurse-led psychosocial screening at follow-ups may necessitate a change in cancer care to improve mental health outcomes.
The study should be appraised under many good points. It is one of the modest attempts to explore neglected yet significant psychological concerns that need immediate attention in the target population. The study included a large sample of HNC patients with different kinds of head and neck tumors and treatment modalities. The present study results add to the knowledge that screening and timely intervention of body image distress in HNC patients can curb many psychological issues and improve health-related quality of life. Additionally, based on our results, efforts should be made to screen the HNC patients for body image distress and other associated psychosocial consequences at routine follow-up or rehabilitation.
The study has many methodological limitations. First, a one-time cross-sectional survey may not attribute cause and effect relationships. Secondly, the response to body image distress was self-reported and hence may carry subjective reporting bias and should be extrapolated carefully. Third, there is a lack of a control group and a single-center study; even though one of the largest institutes in the regions, the findings’ generalizability might be limited to the area only. Upcoming, the phenomenological or case–control multicentric approach might verify the work results.
Conclusions
The body image distress was predominately observed in young female and working patients with head and neck cancer. For clinical practice, it is necessary to identify the patients with higher body image distress when visiting the clinic. Evidence on effective supportive care targeting body image distress in head and neck cancer patients is scarce, indicating more research.
Author contribution
NK conceived of the study, defined variables, search literature, collected data, and drafted the manuscript; RR participated in design of the study, literature search, write, and reviewed the draft; RK participated in data analysis, final draft preparation, and approved final draft. All authors read and approved the final draft of the manuscript. All authors contributed equally to the work.
Data availability
Not applicable.
Code availability
Not applicable.
Declarations
Ethics approval
Ethical approval obtained (ICE, 43/IEC/M.Sc./2020).
Consent to participate
Appropriate consent obtained.
Consent for publication
Authors consented to publish.
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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PMC009xxxxxx/PMC9002226.txt |
==== Front
J Community Health
J Community Health
Journal of Community Health
0094-5145
1573-3610
Springer US New York
35412189
1086
10.1007/s10900-022-01086-4
Original Paper
Long Haul COVID-19 Videos on YouTube: Implications for Health Communication
http://orcid.org/0000-0003-2441-1982
Jacques Erin T. et2592@tc.columbia.edu
1
Basch Corey H. 2
Park Eunsun 3
Kollia Betty 3
Barry Emma 2
1 grid.212340.6 0000000122985718 Department of Health & Human Performance, York College, CUNY, 94-20 Guy R. Brewer Blvd., Jamaica, NY 11451 USA
2 grid.268271.8 0000 0000 9702 2812 Department of Public Health, William Paterson University, Wayne, NJ 07470 USA
3 grid.268271.8 0000 0000 9702 2812 Department of Communication Disorders & Sciences, William Paterson University, Wayne, NJ 07470 USA
12 4 2022
16
16 3 2022
© This is a U.S. government work and not under copyright protection in the U.S.; 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.
The term COVID-19 “long haul” originated on social media and was later studied by the scientific community. This study describes content related to persistent COVID-19 symptoms on YouTube. The 100 most viewed English-language videos identified with the keywords “COVID-19 long haul” were assessed for video origin, engagement, and content related to COVID-19 long-haul. The findings indicate that the majority of videos were uploaded by television or internet news (56%), followed by consumers (members of the public, 32%), health professionals (only 9%), and lastly by entertainment TV (non-news programs, 3%). Videos originating from entertainment TV were significantly more likely to be “liked” than videos from other sources. The most commonly mentioned long-haul symptoms in the videos were physical (fatigue, 73%; difficulty breathing/shortness of breath, 56%; and joint or muscle pain, 49%) and cognitive (difficulty thinking or concentrating; 69%). The case of COVID-19 long haul demonstrates that social media are significant fora whereon the public identify health concerns. It is necessary for healthcare professionals to assume an active and responsible role in social media.
Keywords
Long haul
Long COVID
Health communication
COVID-19
YouTube
==== Body
pmcIntroduction
The COVID-19 pandemic has caused a global public health crisis [1, 2] and those who are afflicted experience a wide range of symptoms of varied severity. The onset of the pandemic brought an urgent need for the scientific community to provide clear recommendations to the public [3]. However, in the early phases of the pandemic, news and social media coverage of COVID-19 were mixed with political and economic discourse [4]. The emerging information meant that guidelines kept shifting, and the fragmented public health infrastructure caused many to feel hesitancy in following the advice of the scientific community [4, 5]. Professional organizations have information on their websites for their members, on the communication and cognitive symptoms of COVID-19 long haul, but this information is not typically accessed by the public [6–8].
COVID-19 survivors found each other on social media [9] and began to discover that they shared prolonged symptoms, being the first to indicate that COVID-19 could lead to protracted illness in the post-recovery phase [1]. Given that half of COVID-19 survivors experience prolonged symptoms [10], many survivors found solace in social media groups [11]. Before the phenomenon of persistent COVID-19 symptoms was well-documented, social media discussions validated the presence of lingering symptoms, while medical professionals may have been dismissive [9, 11]. As a result of these discussions, COVID-19 survivors with persistent symptoms [12] colloquially became known as “long haulers,” a term coined by COVID-19 survivors on social media [13].
As COVID-19 continues to evolve, the scientific community continues to learn more about the virus’ long-term health effects [14, 15]. The long-term sequelae of COVID-19 vary from person to person [16–20]. Through self-reports and diagnostic assessments [21], health professionals saw a disruption of nearly every organ system [15, 20, 22–27], including a high incidence of cognitive dysfunction, stroke, and other neuropathologies. There were additional physical problems, including weight loss, critical illness weakness, severe pressure ulcers, renal failure, vascular problems, and significant psychological effects. Intervention should ideally be provided by a rehabilitation team (which includes speech language pathologists), who are urged to treat this novel situation as they would any population with neurocognitive and communication disorders [24].
For many experiencing COVID-19 long haul the social support provided by online communities has been essential [28]. At the same time, information garnered online served as a valuable tool for researchers seeking to understand and improve public health outcomes. The novel way in which long term effects of COVID-19 were identified and defined, i.e., through social media, points to an intersection between scientific research and the use of social media by the public to discuss health issues.
Considering that 4.48 billion people use social media worldwide [29], these platforms provide real-time opportunities to track, evaluate, and gain insight into emerging diseases [30]. YouTube, a well-known social media platform, has 122 million visits per day globally [31]. Viewers spend on average 18 min on YouTube per day, totaling more than a billion hours of content viewing per day [31]. While the chronic symptoms experienced by many people who recovered from COVID-19 are not well understood [32], no identified research to date has investigated the presence of this information on YouTube. Thus, the purpose of this study was to describe the content of videos related to COVID-19 long-haul on YouTube, with a view to a better synergy between the scientific community and the public seeking information on social media.
Methods
The methodology for this study is based on that of an earlier study of COVID-19 long-haul news coverage [33]. The data were collected in December of 2021. The videos were identified on YouTube (via Google Chrome) by searching with the keywords “COVID-19 long-haul.” They were then filtered by the number of views. The first 100 videos with the highest view counts were included. Thirty-three videos were excluded due to exceeding 45 min in length or failing to be relevant to long-haul COVID-19. They were then replaced with the next 33 videos on the view-count list. The source of the video was put into one of the following categories: (1) consumer, (2) professional, (3) television or internet-based news, (4) entertainment TV.
The metadata for each video were then identified. This included the URL, number of views, upload date, length in minutes and the number of likes/dislikes. The study used the following content categories: the length of time symptoms persisted, tiredness and/or fatigue, brain fog or memory loss, ear ringing, sleep disturbance, stroke, fear and/or worry, hair loss, headache/sinus pain, loss of smell or taste, dizziness, heart palpitations, chest pain, difficulty breathing and/or shortness of breath, lack of mobility, joint or muscle pain, depression or anxiety, fever, diarrhea, symptoms getting worse after physical or mental activities, multisystem inflammatory syndrome (MIS), post-intensive care syndrome (PICS), post-traumatic stress disorder (PTSD), difficulty getting help, available treatment, symptoms being worse in women, symptoms being worse in older populations, job loss/inability to work, other related life issues related to long haul symptoms, waves of symptoms, relief after vaccine, other symptoms. Responses were coded as “yes”/1 if the categories were included in the video or “no”/0 if they were not. Additional written information was added to the “other symptoms” column as this category required further specification.
Statistical analyses were performed using IBM SPSS Statistics for Windows (version 23, Armonk, NY) [31]. A value of p < 0.05 was used to determine statistical significance. Descriptive statistics were generated for categorical variables. The number of “likes” and the length of YouTube Videos by the origin of the video uploads were analyzed using one-way ANOVA.
Since this study did not involve research with human subjects, the Institutional Review Board (IRB) at William Paterson University determined the study did not require ethics review.
Results
Data from 100 YouTube videos based on the highest number of views were analyzed. The 100 YouTube videos had been watched a cumulative total of 15,319,997 times. The videos were uploaded in the 18 month period from July 2020 to December 2021. The highest number of videos was uploaded in both March 2021 (8%) and July (8%) 2021.
Origin: The origin of upload of the videos fell in the following categories: television or internet-based news, consumer (i.e., the general public), medical professionals, and entertainment television. Television or internet-based news videos accounted for 56%, consumer-created video accounted for 32%, professional video accounted for 9%, and entertainment TV created video accounted for 3%.
Engagement: The number of “likes” in each video origin was significantly different (p < 0.001). The mean number of “likes” for videos uploaded by entertainment TV was 16,108 [226–47,000], compared with 9,236 [0–58,000] for videos uploaded by professionals, compared with 1,715 [21–26,000] for videos uploaded by television or internet-based news, and compared with 963 [1–6,000] for videos uploaded by consumers. The mean length of YouTube videos was not significantly differentiated by the origin of video upload (p = 0.17); 691.3 [506.9–875.5] seconds for videos uploaded by consumers, 644.0 [323.7–964.3] seconds for videos uploaded by medical professionals, 484.2 [379.2–589.2] seconds for videos uploaded by television or internet-based news, 680.7 [155.3–1206.1] seconds for videos uploaded by entertainment TV.
Content: 56% of the YouTube videos report the length of time that COVID-19 long haul symptoms persist. The length of time was reported to range from one month to about one year. The YouTube video reports included the following COVID-19 long-haul symptoms: tiredness or fatigue (73 cases; 73%), difficulty thinking or concentrating (69 cases; 69%), difficulty breathing or shortness of breath (56 cases; 56%), joint or muscle pain (49 cases; 49%), symptoms that get worse after physical or mental activities (37 cases; 37%), headache/sinus pain (33 cases; 33%), other related life issues related to long haul symptoms (33 cases; 33%) treatment available (30 cases; 30%), chest pain (26 cases; 26%), waves of symptoms (26 cases; 26%), fast beating or pounding heart (25 cases, 25%), loss of smell or taste (24 cases; 24%), sleep disturbances (24 cases; 24%), depression or anxiety (17 cases, 17%), fever (15 cases; 15%), job loss/inability to work (15 cases; 15%), dizziness (15 cases; 15%), difficulty getting help (14 cases; 14%), relief after vaccine (14 cases; 14%), hair loss (10 cases; 10%), fear or worry (10 cases; 10%), diarrhea (5 cases; 5%), autoimmune conditions (5 cases; 5%), ear ringing (4 cases; 4%), stroke (4 cases; 4%), worse in women (4 cases; 4%), worse in older population (4 cases; 4%), post-intensive care syndrome (2 cases; 2%), post-traumatic stress disorders involving long-term reactions to a very stressful event (2 cases; 2%). A summary is shown in Table 1.Table 1 Content regarding Long-Haul COVID-19 symptoms on YouTube videos (N = 100)
Contents Number of cases Percent (%) of total
Tiredness or fatigue 73 73
Difficulty thinking or concentrating 69 69
Difficulty breathing or shortness of breath 56 56
Joint or muscle pain 49 49
Symptoms that get worse after physical or mental activities 37 37
Headache or sinus pain 33 33
Life issues related to long haul symptoms 33 33
Treatment availability 30 30
Chest pain 26 26
Waves of long-haul symptoms 26 26
Fast beating or pounding heart 25 25
Loss of smell or taste 24 24
Sleep disturbances 24 24
Depression or anxiety 17 17
Fever 15 15
Job loss or inability to work 15 15
Dizziness on standing 15 15
Difficulty getting help 14 14
Relief after vaccine 14 14
Hair loss 10 10
Fear and/or worry 10 10
Diarrhea 5 5
Autoimmune conditions 5 5
Ear ringing 4 4
Stroke 4 4
Worse symptoms in women 4 4
Worse symptoms in older populations 4 4
Post-intensive care syndrome 2 2
Post-traumatic stress disorders involving long-term reactions to a very stressful event 2 2
Discussion
In the aftermath of the first wave of the COVID-19 pandemic, lingering physical and psychological health problems among survivors were widely reported on social media [34]. Meanwhile, various cognitive, emotional, and other neuropsychiatric persistent symptoms began to also be noted in COVID-19 survivors [35]. In this study we examined the information conveyed via YouTube videos in terms of who uploaded videos, how much the public reacted to them, and the breadth of coverage regarding long COVID. Our analysis focused on the most widely viewed 100 YouTube videos uploaded from July 2020 to December 2021. During this period, medical professionals have been learning how to improve the care of patients with COVID-19 [36]. The findings of this study indicate that videos developed by TV/internet-based news sources and consumers accounted for the majority of the highly-viewed videos. In contrast, videos developed by health professionals represented a minority of the referenced sample (9%). It appears that health professionals continue to miss opportunities to fill information gaps and connect with the public on issues that align with their specialties and the public’s health concerns.
In terms of the content covered by the videos, the findings of the present study indicate that cognitive problems were the second most reported symptom, mentioned in 69% of the videos we sampled, along with a report that general symptoms get worse after physical or mental activities (37%). In addition, psychological distress persists long into the post-recovery phase of COVID-19. Mental health and well-being in the forms of sleep disturbances, depression or anxiety, fear or worry, and post-traumatic disorder were reported in a combined 53% of complaints. Hence, there is a need to prioritize knowledge of the impact of long haul COVID-19 on cognitive and mental health. Our findings suggest that COVID-19 infection may serve as a predictor of long-term psychological distress and cognitive challenges. The mental health implications brought on by the pandemic necessitate that healthcare professionals integrate psychosocial support into the general physical pandemic care. Furthermore, raising awareness of the likely impending, long-term cognitive and mental health problems associated with COVID-19 may better prepare health professionals to monitor, direct services, and support patients. Similarly, it may help patients recognize such symptoms and prepare for the relevant services that they may need. The physical symptoms reported in the videos included a variety of non-respiratory problems ranging from fatigue (73%), joint or muscle pain (49%), headache or sinus pain (33%), chest pain (26%), to tinnitus and stroke (4% for each). The content of the videos also included a number of social issues, such as life issues related to long haul symptoms (33%), availability of treatment (30%), and job loss (15%). Symptoms were reported to be worse in women (4%) and in older persons (4%). Clearly, much and varied information is contained in these videos on YouTube.
Given that the public discussion regarding COVID-19 long haul on social media was the impetus for getting the medical community to examine the condition, it is not surprising that the public, non-professionals, are driving the conversations on YouTube. Further, our findings suggest that COVID-19 long-haul is extensively discussed on YouTube: the videos in this study had garnered more than 15 million views. It is important that health professionals have a more prominent and responsible place on the platform.
Lastly, it is interesting that COVID-19 long haul, as an illness, was initially rejected by the medical community [37], while social media became the outlets where survivors reported their conditions. Social media reports on COVID-19 long haul started a movement [37, 38] that expanded the known symptomatology to include protracted illness [39] and directed the spotlight on the sequelae of the disease. According to Callard and Perego [9], COVID-19 long haul “has a strong claim to be considered the first illness to be collectively made by patients finding one another through Twitter and other social media” (p. 4). The digital era facilitated the interconnectedness of long haulers to communicate and share their experiences within the larger, global community [19, 38, 40]. Researchers have studied COVID-19 long-haul conversations on Twitter and Reddit [9, 19, 40, 41], but to our knowledge, this is the first study to address this topic on YouTube.
The limitations of this study, methodological and inherent to YouTube, include a cross-sectional design, which does not represent changes over time. Second, YouTube is a popular video sharing platform and the findings of this study may not apply to other social media platforms with different intent and features. Third, new videos with evolving content are uploaded at a high rate. This could influence which videos are most popular at different points in time. Fourth, the most popular views are filtered by view count and the extent to which YouTube’s algorithms influenced this are not accounted for. Fifth, this study was limited to English language videos despite there being considerable content in other languages on this topic on YouTube. Sixth, one set of keywords was used in this study, so a variety of related keywords could influence the yield.
The collective experiences shared by patients in online communities led to shifts in the recognition of “long COVID” and subsequent support in health, media, and policy channels [37, 38]. After the initial mapping of patients suffering from COVID-19 long haul [9], the mining of social media datasets to understand the clinical course and symptomatology of COVID-19 long haul has become prevalent [9, 19, 41]. At the same time, medical and immunologic evidence presented by scientists continues to emerge [42–46]. In addition to the medical literature, health professionals should recognize the importance of social media and social listening to learn about patients' quality of life and functioning after contracting COVID-19. These insights can potentially inform educational efforts for patients and healthcare providers on the challenges and services necessary for COVID-19 long-haul patients.
Author Contributions
BK, CHB and ETJ conceptualized the study. EB collected the data, EP conducted the data analysis. All authors contributed to the manuscript production.
Funding
Not applicable.
Data Availability
Not applicable.
Code Availability
Not available.
Declarations
Conflict of interest
Not applicable.
Consent to Participate
Not applicable.
Consent of Publication
Not applicable.
Ethical Approval
Since this study did not involve research with human subjects, the Institutional Review Board (IRB) at William Paterson University determined the study did not require ethics review.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
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PMC009xxxxxx/PMC9002254.txt |
==== Front
BMJ Support Palliat Care
BMJ Support Palliat Care
bmjspcare
bmjspcare
BMJ Supportive & Palliative Care
2045-435X
2045-4368
BMJ Publishing Group BMA House, Tavistock Square, London, WC1H 9JR
35383045
bmjspcare-2021-003333
10.1136/bmjspcare-2021-003333
Original Research
Complementary and integrative medicine intervention in front-line COVID-19 clinicians
http://orcid.org/0000-0002-4058-3672
Ben-Arye Eran 12
Gressel Orit 1
Samuels Noah 3
Stein Nili 4
Eden Arieh 5
Vagedes Jan 6
Kassem Sameer 7
1 Integrative Oncology Program, The Oncology Service, Lin, Carmel, and Zebulun Medical centers, Clalit Health Services, Haifa, Israel
2 Ruth and Bruch Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
3 Center for Integrative Complementary Medicine, Shaarei Zedek Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
4 Department of Community Medicine and Epidemiology, Carmel Medical Center, Haifa, Israel
5 Lady Davis Carmel Medical Center, Haifa, Israel
6 Department of Pediatrics, University Hospital Tubingen, Tubingen, Germany
7 Department of Internal Medicine, Lady Davis Carmel Medical Center, Haifa, Israel
Correspondence to Professor Eran Ben-Arye, Integrative Oncology Program, Oncology Service, Lin Medical Center, Haifa, Israel; eranben@netvision.net.il
EB-A and OG are joint first authors.
4 2022
5 4 2022
5 4 2022
bmjspcare-2021-00333318 8 2021
08 3 2022
© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.
2022
https://bmj.com/coronavirus/usage This article is made freely available for personal use in accordance with BMJ’s website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.
Objective
To assess the impact of a multidisciplinary complementary and integrative medicine (CIM) intervention on physical and emotional concerns among front-line COVID-19 healthcare providers (HCPs).
Methods
A multimodality CIM treatment intervention was provided by integrative practitioners to HCPs in three isolated COVID-19 departments. HCPs’ two main concerns were scored (from 0 to 6) before and following the CIM intervention using the Measure Yourself Concerns and Wellbeing questionnaire. Postintervention narratives identified reflective narratives specifying emotional and/or spiritual keywords.
Results
Of 181 HCPs undergoing at least one CIM treatment, 119 (65.7%) completed post-treatment questionnaires. While HCPs listing baseline emotional-related concerns benefited from the CIM intervention, those who did not express emotional or spiritual concerns improved even more significantly following the first session, for both leading concerns (p=0.038) and emotional-related concerns (p=0.023). Nevertheless, it was shown that following subsequent treatments HCPs who expressed emotional and spiritual concerns improved more significantly than those who did not for emotional-related concerns (p=0.017).
Conclusions
A CIM intervention for front-line HCPs working in isolated COVID-19 departments can significantly impact emotional-related concerns, more so after the first treatment and among HCPs not using emotional-spiritual keywords in post-treatment narratives. Referral of HCPs to CIM programmes for improved well-being should avoid referral bias to those not expressing emotional/spiritual concerns.
COVID-19
communication
complementary therapy
access-typefree
==== Body
pmcKey messages
What was already known?
Front-line COVID-19 healthcare providers report physical/emotional concerns.
What are the new findings?
Complementary and integrative medicine treatments reduced healthcare provider-reported emotional-related distress.
Healthcare practitioners not expressing emotional-spiritual keywords showed greater response to these treatments.
What is their significance?
Healthcare directors should consider providing complementary and integrative medicine to COVID-19 healthcare practitioners aiming to improve their emotional concerns.
Future research needs to examine the impact of integrative medicine intervention on burnout/resilience among healthcare providers.
Introduction
The current COVID-19 pandemic has created a number of challenges to healthcare providers (HCPs) across the globe. These include the need for hospitals to quickly assemble multidisciplinary teams of HCPs from a wide range of clinical departments, while addressing the strain placed on available resources and staff.1 The need for a quick response and the uncertainty of its success have been compared with a ‘battlefield’, with significant psychosocial impact on clinical and non-clinical staff.2 Psychological morbidity seen among front-line COVID-19 HCPs ranges from emotional exhaustion and distress, to depression, anxiety, burnout, post-traumatic stress and inadequate sleep.3 4 The constant need to wear personal protective equipment has created its own challenges, making communication with patients and other staff members extremely difficult. This has led to an exacerbation of feelings of isolation among COVID-19 HCPs, who have also experienced diminished social support, with increased rates of burnout and depression.5 6
In order to address these challenges, especially emotional-related concerns, distress and burnout, many hospitals have been directing resources to address issues such as job protection, communication about ensuring a safe COVID-19 environment, personal protective equipment and professional counselling services, which in many cases have been found to be underutilised.7 In order to overcome the need for social distancing in these departments, many counselling programmes have moved to an online format.8 Finally, many hospitals are providing complementary and integrative medicine (CIM) programmes to COVID-19 workers, with a significant beneficial effect found with mind-body modalities in alleviating anxiety, stress and insomnia among this HCP population.9 10
The present study sets out to examine the impact of an HCP-tailored CIM intervention designed to address emotional and physical concerns among HCPs and other personnel working in three isolated COVID-19 departments in a hospital in northern Israel. HCP narratives were searched for emotional and/or spiritual keywords in order to assess the impact of the CIM intervention on this aspect of care.
Methods
Study design
The study was designed within a prospective, participant-preference format. It was considered to be unethical to randomly assign HCPs to a non-treated control arm in light of the intensity of the clinical setting in which front-line COVID-19 HCPs are working on a daily basis.
Primary study outcome
The primary study outcome was the impact of the CIM intervention on the quality of life of the study participants, focusing predominantly on the two most significant concerns, especially emotional-related issues.
Study setting
The study took place in three isolated COVID-19 departments at Carmel Medical Center in Haifa, Israel, during a 10-week outbreak immediately following the national COVID-19 immunisation initiative in February 2021. The study participants were clinical and non-clinical personnel working in isolated COVID-19 departments, one of which served as an intensive care unit. HCPs and non-clinical personnel working in the three COVID-19 departments were referred by the hospital administration and the departments’ senior physicians and nurses to an initial consultation with an integrative physician, a medical doctor dually trained in integrative medicine and supportive care. The 10 min consultation with an integrative physician took place in a room adjacent to the COVID-19 departments, outside the isolation area, during which the study format was described in detail. Following signing of the study informed consent form, the integrative physician and the study HCP codefined the two most significant concerns to be addressed during the CIM treatment programme. Sessions lasted 30 min and included at least two of the following modalities: acupuncture, mind-body therapies (eg, relaxation and breathing) and touch-movement modalities (eg, reflexology, acupressure, anthroposophic medicine, qi gong and Feldenkrais method). Each CIM session was followed by a brief follow-up assessment of the concerns addressed, with reflections on the experience and impact on well-being.
CIM consultations and treatments were provided by nine CIM-trained personnel (two physicians, two nurses and five therapists) from the Integrative Oncology Program, who had until then been providing CIM supportive and palliative care to patients undergoing chemotherapy at Clalit Health Services Oncology Service (Lin, Zebulun and Carmel medical centres in Haifa, Israel). The CIM team underwent 6 hours of special training in preparation for working in an isolated COVID-19 setting. This included instruction on infection-related preventive measures; learning about CIM research being conducted in COVID-19 departments in China, Italy and Israel; and sharing case studies with CIM-trained colleagues who had launched a parallel CIM project in COVID-19 departments at Bnai Zion Medical Center, Haifa, Israel. In addition to treating COVID-19 HCPs and personnel, CIM was also provided to patients hospitalised in these departments by four CIM personnel (two physicians and two therapists), addressing their concerns and well-being as well. All CIM personnel were able to attend daily debriefing sessions in the departments, sharing their reflections and experiences, with the goal of promoting resilience and facilitating a learning process to be implemented in subsequent CIM sessions. Staff meetings were also attended by a social worker who, together with the CIM team, was then able to provide guidance to CIM practitioners in the treatment of patients and staff.
Assessment of HCP concerns
Assessment of HCP concerns was conducted during the initial CIM consultation and at the end of each subsequent CIM session using the Measure Yourself Concerns and Wellbeing (MYCAW) questionnaire. The MYCAW is composed of a Likert-like questionnaire which asks patients to list their two main concerns, scoring them from 0 (of no concern) to 6 (of greatest concern). Patients are also asked to score their general feeling of well-being (0, as good as it could be; 6, as bad as it could be). At follow-up visits, patients are asked to answer two open-ended questions about ‘other issues related to your health’ and ‘what has been the most important issue for you?’11
In the present study, the MYCAW questionnaire was used to assess the impact of the CIM programme on participating HCPs working in the COVID-19 department. Preintervention physician-administered questionnaires asked participants to list their two most significant concerns, while acknowledging the therapeutic setting (‘Please write down one or two concerns or problems which you would most like us to help you with’). They were then asked to score the two concerns and their ‘general feeling of wellbeing’ from 0 (not bothering me at all) to 6 (bothers me greatly), while emphasising a subjective context (‘bothering’) rather than an objective symptom intensity. At each post-CIM assessment, HCPs were asked to rescore the two leading concerns and well-being, as well as complete two additional open-ended questions about their experience during and following the CIM session. These reflections were considered short narratives and were qualitatively analysed.
Data analysis
Statistical analyses were conducted using the IBM SPSS Statistics V.24.0 program, with mean and SD or median and IQR for continuous variables, and numbers and proportions for categorical variables.
Demographic traits of the study cohort were analysed in accordance with use (or non-use) of emotional and spiritual keywords, which included terms such as ‘calming’, ‘release’, ‘relaxation’ and ‘disengagement’, at post-treatment assessment. Identification of these keywords was conducted through a qualitative analysis, using ATLAS.Ti software for systematic coding of MYCAW free-text narratives provided by the HCPs following the CIM treatment.12 In the present study, the use of these keywords in HCP narratives was considered an independent variable reflecting a willingness to share emotional experience, and not just the response to a specific outcome. Demographic and clinical characteristics of both groups (HCPs using vs not using the keywords) were analysed using χ2 test (for categorical variables) and an independent t-test/Mann-Whitney for continuous variables. Within-group differences between pre-CIM and post-CIM treatment assessments were analysed for the two leading concerns listed on the study questionnaire, for specific groups of concerns (eg, fatigue, emotional distress, pain) and for well-being scores, using Wilcoxon signed-rank test for each group separately. Prescore to postscore differences between groups were analysed using Mann-Whitney test.
A multivariate logistic regression model was designed following a univariate analysis, where variables with p<0.1 were included (age, mentioning an emotional concern at baseline assessment and undergoing acupuncture treatment) to predict the associations between use of emotional-spiritual keywords and demographic and treatment-related characteristics. Additional logistic regression analysis was performed to predict improvement in severity scores (at least 2 points on the questionnaire, on a scale ranging from 0 to 6) among HCPs attending the first CIM session. In patients reporting two concerns at baseline, improvement was considered only if both concerns had improved by at least 2 points.
Participation in the study was voluntary, with no incentives offered such as payments or the like. All participating HCPs gave written consent.
Results
Description of the study group
Of the 299 HCPs and personnel working in the three COVID-19 departments, 181 provided written consent and underwent at least one CIM treatment. Of these, 105 (58%) attended only a single session (181 sessions), with 76 attending between 2 and 8 sessions (124 sessions), for a total of 305 CIM sessions. The study cohort included the following professional characteristics: 57 physicians, 90 nurses, 17 adjuvant personnel (eg, administration, cleaning), 11 technicians (eg, respiratory, X-ray) and 6 paramedical practitioners (eg, physiotherapists, occupational therapists, social workers). The cohort was of a diverse social-cultural-religious make-up, with majority of HCPs reporting Arabic as their primary language (47.2%), followed by Hebrew (29%) and Russian (22.7%). Only 2 of 181 participants met with a social worker during the study period, although this service was available and recommended by the hospital administration in order to enable them to express their concerns in a non-formal or psychotherapeutic setting.
Of the cohort of 181 HCPs, 119 (65.7%) were found to use emotional-spiritual keywords in their post-treatment narratives (table 1). Both groups had similar baseline demographic and clinical-related characteristics, although HCPs in the group using the keywords were younger (p=0.002), less likely to be physicians or nurses (p=0.032) and more likely to list emotional concerns at their baseline questionnaire assessment (p=0.001). When compared with the group not using the keywords, a multivariate logistic regression analysis indicated that HCPs who used the keywords were more likely to include emotional concerns at their baseline assessment (OR: 2.63 (95% CI 1.36 to 5.1), p=0.004).
Table 1 Comparison of healthcare practitioners undergoing CIM treatments using emotional-spiritual keywords* in their reflective narratives and those who did not
Characteristics Total cohort Not using the keywords Using the keywords P value
N=181 n=62 n=119
Age
Mean±SD (median) 36.8±9.5 39.7±10.3 35.2±8.7 0.002
Gender/sex
Female 109 (60.2) 41 (66.1) 68 (57.1) 0.241
Primary language
Hebrew 51 (29.0) 14 (23.3) 37 (31.9) 0.235
Arab 83 (47.2) 31 (51.7) 52 (44.8) 0.389
Russian 40 (22.7) 15 (25.0) 25 (21.6) 0.647
Other 2 (1.1) 0 2 (1.7)
Familial status
Single 65 (36.1) 18 (29.0) 47 (39.8) 0.152
Residence
Haifa 66 (39.1) 18 (32.7) 48 (42.1) 0.242
Profession
Physician 57 (31.5) 14 (22.6) 43 (36.1) 0.062
Nurse 90 (49.7) 31 (50.0) 59 (49.6) 0.957
Other 34 (18.8) 17 (27.4) 17 (14.3) 0.032
Original department
Internal medicine 82 (54.7) 24 (49.0) 58 (57.4) 0330
ICU 37 (24.7) 14 (28.6) 23 (22.8) 0.440
Others 31 (20.7) 11 (22.4) 20 (19.8) 0.707
Weekly hours in COVID-19
Mean±SD (median) 40.4±15.7 39.1±13.0 41.1±17.0 0.428
Ever diagnosed with COVID-19?
Yes 8 (16.1) 7 (11.3) 21 (17.6) 0.262
Prior CAM use
Yes 95 (52.5) 35 (56.5) 60 (50.4) 0.441
Referral source
Secretary 87 (66.9) 27 (69.2) 60 (65.9) 0.714
Physician 30 (23.1) 10 (25.6) 20 (22.0) 0.650
Nurse 13 (10.0) 2 (5.1) 11 (12.1) 0.342
Leading concerns at baseline
Emotional 119 (65.7) 31 (50.0) 88 (73.9) 0.001
Pain 102 (56.4) 39 (62.9) 63 (52.9) 0.200
Fatigue 72 (39.8) 30 (48.4) 42 (35.3) 0.088
Insomnia 16 (8.8) 4 (6.5) 12 (10.1) 0.414
Dyspnoea 6 (3.3) 4 (6.5) 2 (1.7) 0.183
Gastrointestinal 4 (2.2) 1 (1.6) 3 (2.5) 0.99
Baseline well-being 2.51±1.3 2.46±1.4 2.54±1.3 0.848
Mean±SD (median) 3 (1, 3) 3 (1, 3) 3 (2, 3)
Number of IM treatments
Only 1 (vs >1) 76 (42.0) 25 (40.3) 51 (42.9) 0.743
Integrative modalities practised during the first session
Touch-movement 167 (94.4) 58 (95.1) 109 (94.0) 0.99
Acupuncture 137 (77.4) 41 (67.2) 96 (82.8) 0.019
Mind-body 105 (59.3) 33 (54.1) 72 (62.1) 0.305
Anthroposophic medicine 41 (23.3) 15 (24.6) 26 (22.7) 0.448
*Based on the Measure Yourself Concerns and Wellbeing (MYCAW) questionnaire.
CAM, Complementary and Alternative medicine; CIM, complementary and integrative medicine; ICU, Intensive Care Unit; IM, Integrative Medicine.
Integrative medicine modalities
Patients in the group using the keywords were most likely to be treated with acupuncture (p=0.019), with the other CIM modalities equally distributed between the two study groups. Less than half (42%) of HCPs received only one modality during the first CIM session, with the rest undergoing as many as four treatment modalities concurrently. Safety-related issues associated with the CIM intervention were documented during and following each intervention. Only a small number of adverse effects were reported, including local discomfort during acupuncture needle insertion and a temporary experience of difficulty relaxing at the beginning of mind-body interventions, which resolved shortly after.
HCPs’ concerns: assessment following first CIM treatment
The 181 HCPs undergoing the first CIM session listed a total of 340 concerns on their questionnaires, of which 292 were available for a pre-to-post treatment assessment. At baseline, patients in the group not using the keywords specified 90 concerns, while those using the keywords listed 202 concerns. HCPs in both groups had similar severity scores in their two leading baseline concerns (table 2), including fatigue, emotional, pain and well-being. Baseline-to-post CIM treatment scores improved significantly within the two groups for all concerns. However, patients not using the keywords improved more significantly in their overall scores for the two leading concerns on the questionnaire (p=0.038), as well as for emotional concerns (p=0.023). A multivariate logistic regression analysis indicated that improvement in scores for the specified concerns was associated more significantly with previous use of complementary medicine (OR: 2.51 (95% CI 1.003 to 6.26), p=0.049), but not with expression of emotional/spiritual keywords (p=0.565).
Table 2 Impact of the CIM programme before and after the first treatment: comparing HCPs using versus those not using emotional-spiritual keywords
Parameter Pretreatment assessment Post-treatment assessment Pretreatment assessment Post-treatment assessment P value*
Score, mean±SD (median) Score, mean±SD (median)
HCPs reporting MYCAW† concerns during the first IM session
Not using keywords
n=62 Using keywords
n=119
Two leading MYCAW concerns scores n=90‡
4.307±1.2
4 (3, 5) n=90
1.67±1.6
1.5 (0, 3) n=202
4.40±1.1
4 (4, 5) n=202
2.0±1.4
2 (1, 3) P1=0.444, P2<0.0001, P3<0.0001, P4=0.038
Fatigue score n=24
4.08±1.1
4 (3, 5) n=24
1.95±1.8
2 (0, 3) n=38
4.50±0.98
4 (4, 5) n=38
2.18±1.4
2 (1, 3) P1=0.122, P2<0.0001, P3<0.0001, P4=0.389
Emotional score n=25
4.84±1.1
5 (4, 6) n=25
1.28±1.5
1 (0, 2) n=81
4.48±1.1
5 (4, 5) n=81
2.0±1.5
2 (1, 3) P1=0.147, P2<0.0001, P3<0.0001, P4=0.023
Pain score n=33
4.06±1.3
4 (3, 5.5) n=33
1.69±1.4
2 (0, 3) n=54
4.09±1.08
4 (3, 5) n=54
1.89±1.3
2 (1, 3) P1=0.717, P2<0.0001, P3<0.0001, P4=0.484
Well-being score n=24
2.50±1.5
3 (1, 3.75) n=24
0.92±1.18
1 (0, 1) n=59
2.81±1.2
3 (2, 4) n=59
1.36±1.2
0 (1, 2) P1=0.871, P2<0.0001, P3<0.0001, P4=0.616
*P values are presented with the following comparisons between groups: P1= comparison between those using vs. not using emotional-spiritual keywords for baseline scores; P2= comparison between those using vs. not using keywords for within-group score changes, from baseline to post-CIM treatment assessment; P3= comparison between those using vs. not using keywords for within-group score changes from baseline to post-CIM treatment assessment; P4= comparison between those using vs. not using keywords for group changes from baseline to post-CIM treatment assessment
†The MYCAW questionnaire scores the two most significant concerns, ranging from 0 (not bothering me at all) to 6 (bothers me greatly).
‡n is the number of MYCAW concerns reported by HCPs.
CIM, complementary and integrative medicine; HCPs, healthcare providers; MYCAW, Measure Yourself Concerns and Wellbeing.
HCPs’ concerns: assessment following subsequent CIM treatments
The 76 HCPs undergoing additional CIM treatment sessions (range: 2–8, total 124 sessions) listed 223 leading concerns on their questionnaires, of which 197 were available for pre-to-post treatment assessment (124 from the group not using the keywords; 73 from those who did). The two groups had similar scores for their two leading concerns at baseline (table 3), including fatigue, emotional, pain and well-being scores. As with scores following the first CIM session, baseline-to-post CIM treatment scores improved significantly for all concerns during subsequent (2–8) treatment sessions. However, in contrast to the post-treatment assessment following the first session, patients using the keywords showed a more significant improvement in subsequent CIM sessions for both the two leading concerns (p=0.005) and for emotional-related concerns (p=0.017).
Table 3 Impact of the CIM programme before and after subsequent (2–8) treatment sessions: comparing HCPs using versus not using emotional-spiritual keywords
Parameter Pretreatment assessment Post-treatment assessment Pretreatment assessment Post-treatment assessment P value*
Score, mean±SD (median) Score, mean±SD (median)
HCPs reporting MYCAW† concerns during the second to eighth IM sessions
n=76
HCPs not using keywords HCPs using keywords
Two leading MYCAW concerns scores n=124‡
4.4±1.7
4 (3, 5) n=124
2.14±1.4
2 (1, 3) n=73
4.0±1.3
4 (3, 5) n=73
1.56±1.3
1 (0, 2.5) P1=0.055, P2<0.0001, P3=0.006, P4=0.005
Fatigue score n=26
4.42±1.2
4 (3, 6) n=26
2.73±1.6
2.5 (1.7, 4) n=9
4.11±1.6
4 (3, 5.5) n=9
1.78±1.4
2 (0.5, 3) P1=0.753, P2<0.0001, P3<0.0001, P4=0.138
Emotional score n=46
4.52±1.2
5 (3.75, 6) n=46
2.02±1.2
2 (1, 3) n=27
4.22±1.3
4 (3, 5) n=27
1.3±1.4
1 (0, 2) P1=0.351, P2<0.0001, P3<0.0001, P4=0.017
Pain score n=47
4.23±1.1
4 (3, 5) n=47
1.83±1.3
2 (1, 3) n=31
3.84±1.3
4 (3, 5) n=31
1.74±1.4
2 (0, 3) P1=0.222, P2<0.0001, P3<0.0001, P4=0.773
Well-being score n=41
3.15±1.4
3 (2, 4) n=41
1.71±1.3
2 (1, 2.5) n=23
2.57±1.5
2 (1, 3) n=23
1.43±1.2
1 (1, 2) P1=0.121, P2<0.0001, P3=0.003, P4=0.397
*P values are presented with the following comparisons between the groups: P1: comparison between those using versus not using emotional-spiritual keywords for baseline scores; P2: comparison between those using versus not using keywords for within-group score changes from baseline to post-CIM treatment assessment; P3: comparison between those using versus not using keywords for within-group score changes from baseline to post-CIM treatment assessment; P4: comparison between those using versus not using keywords for group changes from baseline to post-CIM treatment assessment.
†The MYCAW questionnaire scores the two most significant concerns, ranging from 0 (not bothering me at all) to 6 (bothers me greatly).
‡n is the number of MYCAW concerns reported by HCPs.
CIM, complementary and integrative medicine; HCPs, healthcare providers; MYCAW, Measure Yourself Concerns and Wellbeing.
Discussion
The present study explored the impact of a CIM treatment programme on the concerns and well-being of front-line HCPs and personnel working in three isolated COVID-19 departments. The study setting presents intense physical and emotional challenges to a medical team working in suboptimal conditions, with the need to communicate with patients despite cumbersome protective gear, work with inorganic teams created from diverse departments in the hospital, with limited knowledge and treatment options for the virus and its complications, and being exposed to a real risk of infection with the COVID-19 virus from the medical team and others working in this environment.13 14 A setting such as this would not seem to be one in which a short (30 min) CIM intervention would be of any beneficial effect, especially since treatments would need to take place in this isolated and stressful environment, requiring the IM staff to work within the same conditions as the COVID-19 staff and during the intensive hours of the work shift.
Despite these challenges, the results of the study indicate a significant improvement in baseline-to-post CIM treatment for the two leading concerns and well-being, including for specific concerns such as fatigue, emotional-related concerns or pain. The impact of the CIM programme may reflect non-specific effects (eg, the ability to take a short rest during the work shift, in a relatively peaceful setting, in a supportive environment, with the ability to address their ‘moral injury’),15 as well as specific effects resulting from the direct impact of the intervention. It is of interest to note that following the first CIM session HCPs who did not use the keywords not only showed greater improvement in their two leading concerns, but also in their emotional-related scores. This is in contrast to subsequent (2–8) sessions, during which HCPs who did not use the keywords improved less significantly than those in the group who did, for both the two leading concerns as well as emotional-related concerns.
A number of explanations could be given for this change in the impact of the CIM programme. To begin with, it is possible that HCPs who did not use the keywords showed greater improvement in their concerns following the first CIM session as a result of a specific effect, which for this group of HCPs decreased in subsequent sessions. It is, however, more likely that HCPs who did not use the keywords were initially unaware or unwilling to share their emotional/spiritual narratives with an unfamiliar integrative physician and experienced a more pronounced therapeutic effect during the first visit. The significant in-between group baseline-to-post session change in this group of respondents may reflect the impact of the CIM intervention among participants who did not verbalise their emotional experience, who in ‘normal’ conditions would not experience CIM treatments, at least not in a hospital personnel setting.
The change created by this experience appeared to be more dramatic among those who were limited in their ability to use expressive keywords. It is thus possible that HCPs who did not use the keywords may have been less ‘in touch’ with their emotional-related concerns and were thus more impacted by the first encounter with the CIM intervention. However, as motives for undergoing CIM treatments have been found to change from the initial session to subsequent treatments (eg, values and ideology),16 here too the pronounced effect may have changed. As a result, in subsequent treatment sessions, the group that used the keywords were more likely to attend these sessions and more consistent in their response to these treatments, with a greater impact of the therapeutic process.
The present study also highlights the association between prior use of complementary medicine and a proclivity towards these practices, which may have influenced the referral of the HCPs to such treatments.17–20 In the present study, previous use of complementary medicine was similar (about 50%) in both groups. However, it is likely that this prior experience took place in a much less intense and restrictive environment than that of the isolated COVID-19 department. It is possible that encouragement by the hospital administration, as well as the opportunity to have a ‘break’, may have encouraged even sceptical HCPs to experience at least one CIM session. This might explain why of the 181 HCPs undergoing the first CIM session, only 76 continued with further treatments.
The findings of the present study have important implications for planning of future research examining interventions with the goal to address HCP concerns, increase resilience and prevent burnout with CIM interventions. The findings suggest the need to be less judgemental and selective in recruiting participants in order to address a potential referral bias based on the interest in, openness to and experience with complementary medicine. It is possible that HCPs may not express an initial interest in participating in a CIM programme, as is the case participation in psychosocial consultations provided to COVID-19 staff.21 22 In 2020, Pollock et al23 published a Cochrane meta-analysis reviewing the interventions supporting the resilience and mental health of front-line HCPs, concluding that these HCPs may not be fully aware of what they needed to support their mental well-being. The present study found limited use of an available social worker consultation, with CIM shown as a potential option to enrich the spectrum of psychoemotional and spiritual support, serving as a bridge to overcome barriers to the implementation of other interventions. Research on this potential has demonstrated the feasibility and effectiveness of mind-body and breathing therapies (eg, yoga,24 mindfulness-based intervention25 26) in decreasing stress and augmenting resilience among HCPs.
The present study has a number of methodological limitations which need to be addressed in future research. First and foremost, the pragmatic approach entailed the absence of a control group. A control group could, in theory, comprise HCPs not undergoing CIM, but passively given a 30 min rest period or actively undergoing a psychosocial consultation, for example. However, as stated, it was considered unethical and impractical to randomly deny participants access to the CIM treatment. As such, the lack of a control group reflected commitment to the welfare of front-line HCPs, an aspect of the study recognised in the reflective narratives and analysed qualitatively in a separate presentation of the study.
Another study limitation is the risk of referral bias, including selection bias in which HCPs with a proclivity to complementary medicine may have been referred more frequently to the CIM programme, whereas more sceptical HCPs were not. This potential bias may have, however, been offset by the active referral of all COVID-19 HCPs and other staff to the CIM programme by the hospital administration, as well as the proximity of the intervention to the COVID-19 department.
The decision to designate keywords such as ‘calming’, ‘release’, ‘relaxation’ and ‘disengagement’ as emotional-spiritual keywords was based on an earlier qualitative study which analysed HCP narratives following a CIM treatment programme.12 Qualitative research exploring patients’ experience following CIM treatments has supported the use of similar keywords.27 28 Still, the use of ‘emotional-spiritual keywords’ has not, to the best of our knowledge, been reported in the literature with respect to the CIM treatment experience. As such, the decision in the present study to include specific keywords in this category remains to be shown as a valid methodological approach.
In the present study, comparison between HCPs using and not using the keywords is a qualitative-based parameter that was defined at postintervention assessment. This outcome served as a marker of a verbal expressive trait, rather than as an effectiveness outcome. Finally, the present study took place in a single hospital, which despite the rich diversity of the demographic and social-cultural characteristics of participating HCPs may not be applicable to other sites and settings. Future research will need to explore the generalisability of the CIM intervention.
In conclusion, front-line HCPs working in isolated COVID-19 departments report improved concerns, including emotional distress and well-being, following an HCP-tailored CIM treatment programme. The study supports the feasibility of offering CIM to HCPs who are working in extremely challenging and stressful clinical settings, regardless of their experience or use of emotional-spiritual keywords. Directors of these healthcare settings should be encouraged to refer their personnel to CIM services with the goal of improving their quality of life-related concerns. Future research will need to explore the impact of the CIM programme on additional parameters such as prevention of burnout and enhancement of resilience among HCPs.
We are grateful to Dr Avi Goldberg MD, Director of Carmel Medical Center, and Shani Brosh, Ahuva Tal and Keren Marom, for their unlimited support; the medical personnel of the three COVID-19 departments at the Carmel Medical Center; Professor Elad Schiff MD and the integrative medicine team at Bnai Zion Medical Center in Haifa, Israel for sharing their insights regarding the pilot design of integrative medicine intervention in their hospital; and the integrative medicine teams at the Lin, Zebulun and Carmel medical centres, who despite the risk agreed to treat patients and medical personnel at the COVID-19 departments: Dafna Wolf, Avigail Sagi, Sagi Shalev, Galit Galil, Dana Goldenblum, Raviv Peleg and Meital Manches.
Data availability statement
Data are available upon reasonable request.
Ethics statements
Patient consent for publication
Not required.
Ethics approval
This study involves human participants and was approved by Carmel Medical Center Ethics Committee (Helsinki Committee; CMC-20-0202) in Haifa, Israel. Participants gave informed consent to participate in the study before taking part.
EB-A and OG contributed equally.
Contributors: EB-A, OG and SK planned the trial. EB-A, OG, AE and SK conducted the study and collected the data analysed in the study. EB-A, NSa and NSt carried out the analysis and wrote the draft manuscript. All authors participated in the revision of the manuscript. EBA is responsible for the overall content as the guarantor. EBA (the guarantor) accepts full responsibility for the work and/or the conduct of the study, had access to the data, and controlled the decision to publish.
Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests: None declared.
Provenance and peer review: Not commissioned; externally peer reviewed.
==== Refs
References
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12 Ben-Arye E, Zohar S, Keshet Y, et al . Sensing the lightness: a narrative analysis of an integrative medicine program for healthcare providers in the COVID-19 department. Support Care Cancer 2022;30 :1–8.10.1007/s00520-021-06546-6 33742245
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PMC009xxxxxx/PMC9002257.txt |
==== Front
J Investig Med
J Investig Med
jim
jim
Journal of Investigative Medicine
1081-5589
1708-8267
BMJ Publishing Group BMA House, Tavistock Square, London, WC1H 9JR
35379701
jim-2021-002274
10.1136/jim-2021-002274
Original Research
Treatment with 3-day methylprednisolone pulses in severe cases of COVID-19 compared with the standard regimen protocol of dexamethasone
Dafni Maria
Karampeli Maria
http://orcid.org/0000-0002-3314-3329
Michelakis Ioannis
http://orcid.org/0000-0002-3420-1420
Manta Aspasia
Spanoudaki Anastasia
Mantzos Dionysios
Krontira Sofia
Georgiadou Victoria
Lioni Athina
http://orcid.org/0000-0003-3137-645X
Tzavara Vasiliki
1st Department of Internal Medicine, Korgialenio-Benakio Red Cross General Hospital, Athens, Greece
Correspondence to Vasiliki Tzavara, 1st Department of Internal Medicine, Korgialenio-Benakio Red Cross General Hospital, Athens, 11526, Greece; vtzavara2015@gmail.com
4 2022
3 4 2022
3 4 2022
jim-2021-00227403 3 2022
© American Federation for Medical Research 2022. No commercial re-use. See rights and permissions. Published by BMJ.
2022
https://bmj.com/coronavirus/usage This article is made freely available for personal use in accordance with BMJ’s website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.
Since the outbreak of COVID-19, research has been focused on establishing effective treatments, especially for patients with severe pneumonia and hyperinflammation. The role and dose of corticosteroids remain obscure. We evaluated 58 patients with severe COVID-19 during two periods. 24 patients who received methylprednisolone pulses (250 mg/day intravenously for 3 days) were compared with 34 patients treated according to the standard dexamethasone protocol of 6 mg/day. Among non-intubated patients, the duration of hospitalization was shorter for those who received methylprednisolone pulses (9.5 vs 13.5, p<0.001). In a subgroup analysis of patients who required intubation, those treated with the dexamethasone protocol demonstrated a relative risk=1.89 (p=0.09) for dying, in contrast to the other group which showed a tendency towards extubation and discharge from the hospital. A ‘delayed’ need for intubation was also observed (6 vs 2 days, p=0.06). Treatment with methylprednisolone pulses significantly reduced hospitalization time. Although there was no statistically significant influence on the necessity for intubation, methylprednisolone pulses revealed a tendency to delay intubation and hospital discharges. This treatment could benefit patients in the hyperinflammatory phase of the disease.
COVID-19
Glucocorticoids
Inflammation
Methylprednisolone
access-typefree
==== Body
pmcSignificance of this study
What is already known about this subject?
Corticosteroids have been useful in the treatment of severe cases of COVID-19, especially for the hyperinflammatory phase of the disease.
The optimal dose and regimen remain a matter of debate.
What are the new findings?
High doses of corticosteroids should be considered in the treatment of the hyperinflammatory phase of COVID-19.
Compared with the standard dose of dexamethasone, methylprednisolone pulses seem to shorten hospitalization time.
Methylprednisolone pulses could also delay intubation and favor extubation.
How might these results change the focus of research or clinical practice?
These results, alongside further research that needs to be done, could potentially affect the existing clinical practice concerning the role of corticosteroids in the treatment of COVID-19.
Introduction
In December 2019, COVID-19 was first reported in Wuhan, the capital of Hubei, China.1 The high infective capacity of SARS-CoV-2 infection led to its rapid spread around the world, causing a sustained global outbreak.2 Finally, on March 12, 2020, the WHO declared the COVID-19 outbreak as a Public Health Emergency of International Concern.3 The clinical spectrum of COVID-19 ranges from asymptomatic, mild pneumonia to critically ill cases of acute serious respiratory failure and multiple organ dysfunction syndromes.4 The course of the disease has been divided into three phases: a first phase characterized by a local propagation of the virus in the respiratory tract but a limited innate immune response; a secondary pulmonary phase characterized by the propagation and migration of the virus down the respiratory tract along the conducting airways, triggering a more robust innate immune response; and a third hyperinflammatory phase.5 The hyperinflammatory phase is driven by a dysregulated host innate immune response which is characterized by overproduction of early response proinflammatory cytokines that can lead to multiorgan failure and death.6 The hyperinflammatory phase is linked to the highest mortality rate.7 Thus, several studies have postulated that immunomodulators such as tocilizumab, anakinra or corticosteroids at different doses could be useful treatment for patients experiencing severe COVID-19.8–11
Corticosteroids were a point of disagreement at the outbreak of the pandemic. Due to the possibility of delayed viral clearance, as well as the risk of adverse effects, recommendations warned against the use of systemic corticosteroids based on experience during the epidemics of SARS-CoV and Middle East Respiratory Syndrome-CoV.12 The RECOVERY trial came to establish the use of 6 mg/day of dexamethasone in the standard of care (SOC) treatment in hospitalized patients requiring supplemental oxygenation.13 In several studies, higher doses of corticosteroids were used, with the question of ‘one dose of corticosteroids does not fit for all patients’ to remain a hot point of interest.14
In this retrospective study, we investigated the effect of 3-day methylprednisolone pulses (MPs) on the prognosis and the need for endotracheal intubation in hospitalized patients with severe COVID-19, in comparison with SOC.
Study population
We conducted a single-center retrospective observational study and analyzed a cohort of 58 patients with severe, proven COVID-19 infection admitted to the First Department of Internal Medicine of General Hospital ‘Korgialenio-Benakio Red Cross’, Athens, Greece, during two different periods of the pandemic. Period 1 was defined from September to December 2020 and period 2 from January to March 2021. During period 1, 34 patients were treated according to the standard regimen of dexamethasone (6 mg/day), while, in the second period, a total of 24 patients were treated with MPs (250 mg/day intravenously for 3 days), followed by standard dose regimen of 6 mg/day dexamethasone. Both groups received remdesivir and anticoagulation with low molecular weight heparin. Antimicrobial agents were prescribed as needed.
Inclusion and exclusion criteria
All patients older than 18 years old, non-vaccinated, testing positive on reverse transcriptase-PCR assay for SARS-CoV-2 in nasopharyngeal swabs were eligible to participate in the study. Only patients with significant lung involvement (SpO2/FiO2 <300 and a CT scan with bilateral and over 50% of the lung parenchyma distribution of opacities, either ground glass or consolidated) were included in the study. We excluded patients on different corticosteroid regimens than the ones described above. Pregnant women, patients who were intubated or died in the first 24 hours after admission, those who died of causes non-related to COVID-19 infection, as well as terminally ill patients or suffering from active malignancies, were also excluded (online supplemental figure 1).
10.1136/jim-2021-002274.supp1 Supplementary data
Data collection and variables measured
We carried out a retrospective analysis of epidemiologic and clinical data retrieved from paper and electronic records in our department, regarding all patients meeting the inclusion criteria. Medical charts of patients were retrospectively reviewed and clinical, laboratory and therapeutic parameters were recorded. Data collected included demographic and clinical parameters, date of onset of the COVID-19 symptoms and the oxygen saturation levels were registered. Regarding the medical history, the following details were registered: smoking history, body mass index (BMI), hypertension, chronic heart failure, coronary artery disease, diabetes mellitus (DM) and chronic obstructive pulmonary disease. In addition, the patient’s usual home medication regimen and any treatment prescribed in the outpatient setting (before hospitalization) were registered. On admission, all patients had an in-depth laboratory testing. Markers tested included hemogram, renal function, liver function tests, creatine kinase, triglycerides, lactate dehydrogenase, high-sensitivity troponin, C reactive protein, procalcitonin, ferritin, immunofixation electrophoresis, quantitative serum immunoglobulin tests, lymphocyte immunophenotyping, prothrombin time, partial thromboplastin time, D-dimer and fibrinogen. During their hospital stay, we evaluated the need for oxygen supplementation, the maximum oxygen flux required and the need for non-invasive mechanical-assisted ventilation. We also registered all the medications prescribed during hospitalization.
Outcomes
In the present study, the effect of two corticosteroid schemes on the clinical course of patients with severe SARS-CoV-2 infection was investigated. We analyzed differences between the two groups, regarding in-hospital and 30-day mortality, the need for mechanical ventilation and the days of hospitalization. Serious adverse events related to treatment protocols were also recorded.
Statistical analysis
Continuous variables were expressed as mean value and SD or median value and IQR, whereas categorical variables as frequencies and percentages. To investigate the differences between baseline demographic, clinical and immunophenotyping variables between patients with different therapeutic schemes, the t-test and Mann-Whitney U test for independent samples for continuous variables and the χ2 and Fisher’s exact test for categorical variables were applied. Univariate linear regression analyses for estimating the association between different characteristics of our patients and the duration of their hospitalization in the department were performed. Data were analyzed using Stata V.13.0 software (Stata Corporation, College Station, Texas, USA), and significance was set at α=0.05. All tests proceeded as two tailed.
Results
Description of study population
All patients admitted to the First Department of Internal Medicine of General Hospital of Athens ‘Korgialenio-Benakio Red Cross’ during the two different periods of the pandemic in Greece were potentially eligible to be enrolled in the present study. Out of 232 patients, 58 were finally enrolled. A total of 58.6% of them received a 3-day MP scheme, while 41.4% were treated with 6 mg/day dexamethasone. Based on demographic and clinical characteristics, no major difference was noted regarding age, sex, BMI, smoking habits or medical history between the two groups. Baseline demographic characteristics of each treatment group are displayed in tables 1 and 2. Table 3 presents the clinical and laboratory findings on admission day. None of the basic laboratory findings, with the exception of D-dimers (1.2 vs 0.9, p=0.06), was notably different between the study groups.
Table 1 Comparison of basic demographic characteristics among patients who received MP or 6 mg/day dexamethasone
Characteristics Treatment with SOC Treatment with MP P value
Mean (±SD), N/% N=34 patients N=24 patients
Sex 0.15
Female 7/20.6 9/32.5
BMI (kg/m2) 0.32*
18.5–24.9 4 (11.7) 6 (25)
25–29.9 16 (47) 6 (25)
30–34.9 11 (32.3) 4 (16.6)
35–39.9 0 (0) 4 (16.6)
>40 3 (9) 4 (16.6)
Smoking 0.72*
No 22 (65) 18 (75)
Past 7 (22) 6 (25)
Current 5 (13) 0 (0)
Age (y) 65/12 61/14 0.21
independent-sample t-test and Χ2 tests.
*Fisher’s exact tests.
BMI, body mass index; MP, methylprednisolone pulse; SOC, standard of care.
Table 2 Medical history of patients who received MP or 6 mg/day dexamethasone
Comorbidities
Treatment with SOC Treatment with MP P value
Hypertension
No 17 (50) 17 (73) 0.14
Yes 17 (50) 7 (27)
Diabetes mellitus
No 30 (88) 20 (84) 0.68
Yes 4 (12) 4 (16)
Coronary artery disease
No 32 (92) 18 (78.9) 0.37
Yes 2 (8) 6 (21.1)
Atrial fibrillation
No 32 (96) 23 (95) 1
Yes 2 (4) 1 (5)
COPD
No 32 (96) 24 (100) 1
Yes 2 (4) 0 (0)
Χ2 tests.
COPD, chronic obstructive pulmonary disease; MP, methylprednisolone pulse; SOC, standard of care.
Table 3 Clinical and laboratory findings on admission day of patients with severe pneumonia due to SARS-CoV-2 infection
Variables Treatment with SOC Treatment with MP P value
Mean (±SD), median/IQR N=34 patients N=24 patients
SpO2/FiO2 264 (±122) 267 (±74) 0.91
Respiratory rate (n/min) 26 (±6) 24 (±8) 0.51
White cell count (109 c/L) 6.8/4.7 5.15/5.25 0.33
Lymphocytes (c/μL) 700/500 700/300 0.39
D-dimers (ng/dL) 1.2/0.6 0.9/0.7 0.06
CRP (mg/L) 85.5/72.6 67.9/73.4 0.17
Ferritin (μg/L) 872/878 621/870 0.50
Fibrinogen (mg/dL) 669/205 612/154 0.27
LDH (U/L) 410/188 391/188 0.83
CPK (µg/L) 106/88 162/125 0.18
Troponin (ng/mL) 0.01/0.01 0.01/0.01 0.26
PCT (ng/mL) 0.11/0.11 0.08/0.1 0.37
Fisher’s exact tests.
CPK, creatine kinase; CRP, C reactive protein; LDH, lactate dehydrogenase; MP, methylprednisolone pulse; PCT, procalcitonin; SOC, standard of care.
Study outcomes
We investigated the effects on prognosis and the need for endotracheal intubation of the administration of MP to hospitalized patients, in comparison with the standard administration of dexamethasone. It was a more reasonable approach to examine the effects separately for the patients who required endotracheal intubation and admitted to the intensive care unit (ICU) and those who had a milder clinical course. Among the non-intubated patients, those who were treated with MP tend to require less days of hospitalization (coefficient=−4.91, p=0.01) (online supplemental figure 2).
In linear regression analysis (table 4), age, female gender and current smokers (vs non-smokers) seemed to have a negative effect, prolonging the hospitalization period in non-intubated patients (not statistically significant results), while a greater score in the Mini-Mental State Examination test was associated with fewer days in hospital (coefficient=−1.14, p=0.08). No other laboratory or clinical characteristic was found to affect the time until discharge or death.
Table 4 Factors associated with the duration of hospitalization in non-intubated patients
Variables β-coefficient 95% CIs P value
MP vs SOC −4.91 −8.75 to -1.07 0.01
Age 0.07 −0.09 to 0.25 0.37
Female vs male 0.50 −4.06 to 5.08 0.82
Smoking (past vs no) −2.72 −8.2 to 2.74 0.31
Smoking (current vs no) 7.43 −1.13 to 16.01 0.08
Mini-mental test score −1.14 −2.39 to 0.10 0.07
Linear regression analysis.
MP, methylprednisolone pulse; SOC, standard of care.
The analyses were performed for the intubated patients as well; patients with DM had a greater risk of a prolonged need for hospitalization (coefficient: 10, p=0.03). No effect was noted from the study of other variables. Treatment with MP was not associated with lower risk of endotracheal intubation (54.1% vs 52.9%, p=0.92). However, in a subgroup analysis of patients who required endotracheal intubation (n=27 patients), those who received MP (n=11 patients) demonstrated a relative risk=2.03 (p=0.09) for extubation, weaning and discharge from the hospital. They also appeared to have a ‘delayed’ need for intubation, in contrast to the 6 mg/day dexamethasone group (6 vs 2 days, p=0.06) (table 5).
Table 5 Endpoints in the clinical course of patients who required or not endotracheal intubation
Patients who required endotracheal intubation P value Patients who did not require endotracheal intubation P value
SOC MP SOC MP
Median/IQR Mean/SD
Hospitalization days until intubation/discharge 2/5 6/4 0.06 13.5/5 9.5/4.5 <0.001
N (%)
Outcome 0.09 0.41
Death 11 (69) 4 (36) 0 (0) 1 (8)
Discharge 5 (31) 7 (64) 18 (100) 12 (92)
Mann-Whitney U tests.
IQR, Interquartile Range; MP, methylprednisolone pulse; SOC, standard of care.
In our cohort, there was only one death observed in patients who did not require invasive ventilation. No death was recorded 30 days after discharge for both groups (p=1). In the MP treatment group, no major adverse events such as infections were recorded.
While there were no significant differences regarding the demographic and clinical characteristics of patients suffering from severe COVID-19, the lymphocyte immunophenotyping assay demonstrated a worse pattern of disease during the second period. Results concerning the expression of CD8, NK and CD19 cells were statistically significant (p=0.05, p<0.01, p=0.01 and p<0.01, respectively) (figure 1), as expected from the dominance of the British variant in Greece since January 2021 (online supplemental table 1).
Figure 1 Lymphocyte immunophenotyping assay, regarding the expression of CD8, NK and CD19 cells in patients presented during the two study periods and were treated with different corticosteroid scheme. MP, methylprednisolone pulse.
Discussion
Cytokine storm induced by proinflammatory cytokines is related to most severe cases of COVID-19.15 Corticosteroids, among other immunomodulators, have been studied as a possible treatment option in these cases. After the initial distrust,12 the RECOVERY trial established the use of 6 mg dexamethasone to be included in the SOC in patients requiring supplemental oxygen.13 Since then, several studies have confirmed the beneficial effect of systemic corticosteroids in reducing mortality.16 Although the accurate timing of initiation of corticosteroid administration seems to be universally accepted to be the second week of the disease, the appropriate dosage and regimen remain controversial. In addition, organizing pneumonia as well as acute fibrinous pneumonia have been demonstrated as the main imaging and histopathological pattern respectively in the majority of moderate or severe cases,17 implying that higher doses of corticosteroids should be considered.18 As ‘one size does not fit all’ in the treatment of COVID-19 with corticosteroids,14 several studies have introduced the usefulness of the MP in severely ill hospitalized patients.14 16 19–22
In this retrospective study, we compared the effects of 3-day 250 mg MP with the standard dose protocol of 6 mg/day dexamethasone in hospitalized patients with severe COVID-19. While there was no statistically significant difference in mortality or the need for intubation between the two groups, patients who were not intubated were hospitalized for less days (p<0.001) and, even for those requiring intubation, there was a ‘delay’ in intubation time (p=0.06). Less days of hospitalization had financial benefits for the healthcare system and psychological benefits for the individuals, as patients with COVID-19 infection and especially those who are admitted to the hospital have been reported to suffer from high levels of stress and potentially some form of post-traumatic stress disorder.23 While some studies showed prolonged recovery time for patients treated with MP,24 25 our results coincide with Ranjbar et al who also found that the use of MP resulted in less days of hospitalization.26
In addition, MP treatment in our study led to 'delayed’ intubation compared with SOC, meaning that more days were required from the day of admission to the day of intubation. We interpret this outcome as a positive one, as in a healthcare system so deeply impacted by the pandemic, ‘offering’ a few more days could possibly help relieve the ICU system and provide more time for different treatments to act collectively. Among patients who eventually required mechanical ventilation, there was a trend towards extubation and discharge from the hospital for those who received MP (p=0.09). It should also be underlined that there were no major side effects associated with MP treatment, such as serious infections or prolongation of time to recovery. Interestingly, patients with higher Mini-Mental State Examination scores had better outcomes. Experience from infections caused by different strains of coronavirus shows involvement of the central nervous system in various ways.27 The difference observed in our study could be associated with a lower burden of disease.
The results of lymphocytic phenotypes indicate a more aggressive disease profile throughout the second period of the study, during which all patients were treated with MP, pointing out that in spite of disease severity patients benefit from MP. Despite the fact that several studies have advocated MP as a treatment choice, our study is one of the very few that directly compare short-term treatment with MPs with the standard dose of 6 mg dexamethasone. Fatima et al did not report any difference between treatment with MP and dexamethasone,28 while the study of Ko et al was conducted in ICU patients.29 The results of Ranjbar et al were in accordance with our results regarding the days of hospitalization. It should be underlined though that Ranjbar et al used a different MP protocol.26 To the best of our knowledge, no other study has reported ‘delayed’ intubation and the trend towards extubation and discharge from ICU, in patients treated with MP compared with 6 mg/day dexamethasone. There are several limitations to the study, including its retrospective nature, the limited sample size, as well as the possibility that different disease phenotypes dominated during the two study periods.
Conclusion
Despite accumulating data supporting the benefits of corticosteroids in individuals with COVID-19, the optimal dose and duration of corticosteroid therapy in various clinical settings remain unknown. In this study, we assessed the effect of MP compared with the standard dose of dexamethasone. Our research revealed that hospitalized patients during the hyperinflammatory phase of the disease, considered as the most life-threatening, could benefit from the administration of short-term MP without an increase in the risk of severe adverse effects. Additional research is needed to determine the best corticosteroid regimen in order to achieve the desired therapeutic impact while minimizing side effects.
Authors would like to acknowledge all the support received on this project from the head of the Immunology Laboratory of Korgialenio-Benakio, Red Cross General Hospital, Kremasmenou E. We would also like to thank our colleagues in the First Department of Internal Medicine.
Data availability statement
Data are available upon reasonable request. All data relevant to the study are included in the article or uploaded as supplemental information. The authors confirm that the data supporting the findings of this study are available within the article and its supplemental materials. Further data of this study are available from the corresponding author, VT, upon reasonable request.
Ethics statements
Patient consent for publication
Obtained.
Ethics approval
This study involves human participants and was approved by the Institutional Review Board (IRB 11444/10.5.2021) of General Hospital 'Korgialenio-Benakio Red Cross', Athens, Greece. The study subjects provided informed consent.
Contributors: Conceptualization—MD, MK, AL and VT. Acquisition—IM, AM, AS, DM, VG and SK. Statistical analysis—IM. Interpretation of data—IM, AM, AS and DM. Drafting the work—IM, AM, AS and DM. Revision—MD and MK. All authors have read and agreed to the published version of the manuscript and are accountable for all aspects of the work. VT is responsible for the overall content as the guarantor.
Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests: None declared.
Provenance and peer review: Not commissioned; externally peer reviewed.
Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
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PMC009xxxxxx/PMC9002283.txt |
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J Natl Cancer Inst
J Natl Cancer Inst
jnci
JNCI Journal of the National Cancer Institute
0027-8874
1460-2105
Oxford University Press
34893865
10.1093/jnci/djab225
djab225
Articles
Editor's Choice
AcademicSubjects/MED00010
Impact of the COVID-19 Pandemic on Treatment Patterns for Patients With Metastatic Solid Cancer in the United States
https://orcid.org/0000-0003-2692-6306
Parikh Ravi B MD, MPP Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, PA, USA
Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
https://orcid.org/0000-0002-1173-3846
Takvorian Samuel U MD, MSHP Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
https://orcid.org/0000-0002-2103-5304
Vader Daniel PhD Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
Paul Wileyto E PhD Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
https://orcid.org/0000-0002-3685-6535
Clark Amy S MD, MSCE Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Lee Daniel J MD Division of Urology, Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA
https://orcid.org/0000-0001-6148-5177
Goyal Gaurav MD Division of Hematology and Oncology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
https://orcid.org/0000-0003-4188-9785
Rocque Gabrielle B MD, MSPH Division of Hematology and Oncology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
https://orcid.org/0000-0002-5762-512X
Dotan Efrat MD Department of Medical Oncology, Fox Chase Cancer Center, Philadelphia, PA, USA
https://orcid.org/0000-0002-1423-5295
Geynisman Daniel M MD Department of Medical Oncology, Fox Chase Cancer Center, Philadelphia, PA, USA
https://orcid.org/0000-0003-4054-526X
Phull Pooja MD Department of Medical Oncology, Fox Chase Cancer Center, Philadelphia, PA, USA
https://orcid.org/0000-0002-5723-1972
Spiess Philippe E MD, MS, FRCS(C), FACS Department of Genitourinary Oncology, Moffitt Cancer Center, Tampa, FL, USA
https://orcid.org/0000-0002-8262-484X
Kim Roger Y MD Division of Pulmonary, Allergy, and Critical Care, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
https://orcid.org/0000-0001-8141-9249
Davidoff Amy J PhD, MS Healthcare Delivery Research Program, National Cancer Institute, Bethesda, MD, USA
https://orcid.org/0000-0002-4974-935X
Gross Cary P MD Cancer Outcomes Public Policy and Effectiveness Research, Yale School of Medicine, New Haven, CT, USA
Neparidze Natalia MD Cancer Outcomes Public Policy and Effectiveness Research, Yale School of Medicine, New Haven, CT, USA
https://orcid.org/0000-0003-3194-5122
Miksad Rebecca A MD, MPH Flatiron Health, New York, NY, USA
https://orcid.org/0000-0002-7744-3518
Calip Gregory S PharmD, MPH, PhD Flatiron Health, New York, NY, USA
https://orcid.org/0000-0001-5843-4926
Hearn Caleb M MPH Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, PA, USA
https://orcid.org/0000-0001-9966-1973
Ferrell Will MPH Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, PA, USA
https://orcid.org/0000-0001-8661-473X
Shulman Lawrence N MD Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
https://orcid.org/0000-0002-8267-5356
Mamtani Ronac MD, MSCE Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
https://orcid.org/0000-0003-0879-0994
Hubbard Rebecca A PhD Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
the PRACTICE Investigators
‡ Ravi B. Parikh and Samuel U. Takvorian contributed equally to this manuscript as co-first authors.
§ Ronac Mamtani and Rebecca A. Hubbard contributed equally to this manuscript as co-senior authors.
¶ Pandemic-Related Advanced Cancer Treatment In the COVID-19 Era.
Correspondence to: Ravi B. Parikh, MD, MPP, 423 Guardian Dr, Blockley 1102, Philadelphia, PA 19104, USA (e-mail: ravi.parikh@pennmedicine.upenn.edu).
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Abstract
Background
The COVID-19 pandemic has led to delays in patients seeking care for life-threatening conditions; however, its impact on treatment patterns for patients with metastatic cancer is unknown. We assessed the COVID-19 pandemic’s impact on time to treatment initiation (TTI) and treatment selection for patients newly diagnosed with metastatic solid cancer.
Methods
We used an electronic health record–derived longitudinal database curated via technology-enabled abstraction to identify 14 136 US patients newly diagnosed with de novo or recurrent metastatic solid cancer between January 1 and July 31 in 2019 or 2020. Patients received care at approximately 280 predominantly community-based oncology practices. Controlled interrupted time series analyses assessed the impact of the COVID-19 pandemic period (April-July 2020) on TTI, defined as the number of days from metastatic diagnosis to receipt of first-line systemic therapy, and use of myelosuppressive therapy.
Results
The adjusted probability of treatment within 30 days of diagnosis was similar across periods (January-March 2019 = 41.7%, 95% confidence interval [CI] = 32.2% to 51.1%; April-July 2019 = 42.6%, 95% CI = 32.4% to 52.7%; January-March 2020 = 44.5%, 95% CI = 30.4% to 58.6%; April-July 2020 = 46.8%, 95% CI= 34.6% to 59.0%; adjusted percentage-point difference-in-differences = 1.4%, 95% CI = −2.7% to 5.5%). Among 5962 patients who received first-line systemic therapy, there was no association between the pandemic period and use of myelosuppressive therapy (adjusted percentage-point difference-in-differences = 1.6%, 95% CI = −2.6% to 5.8%). There was no meaningful effect modification by cancer type, race, or age.
Conclusions
Despite known pandemic-related delays in surveillance and diagnosis, the COVID-19 pandemic did not affect TTI or treatment selection for patients with metastatic solid cancers.
National Cancer Institute 10.13039/100000054 K08-CA-263541–01
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pmcThe COVID-19 pandemic has led to declines in patients seeking care for life-threatening conditions, such as acute myocardial infarction and stroke, as well as care delays for screening and management of chronic medical conditions (1-5). For patients with cancer, who may be particularly vulnerable to COVID-19 infection (6–8), early research suggested changes in practice patterns leading to care delays and treatment modifications (9–17). Some of these changes were supported by guidelines issued during the pandemic (18), which encouraged consideration of nonmyelosuppressive regimens despite mixed evidence linking the risk and severity of COVID-19 infection to immunosuppression from cancer therapy (8,19–21). These care disruptions may have been particularly prominent for patients with metastatic cancer for whom treatments are palliative rather than curative. A recent systematic review identified 62 studies evaluating pandemic-related delays across the cancer care continuum; however, the majority of these studies used single-institution data and did not focus on patients with metastatic cancer (22). Thus, little is known about the impact of the pandemic on changes in treatment patterns for patients with metastatic cancer.
Because treatment delays cause patient distress and are associated with increased mortality for patients with cancer (23–27), time to treatment initiation (TTI) is a patient-centered quality metric and outcome that has been used to evaluate the impact of health policies on cancer care (9,28,29). TTI may also serve as a barometer of capacity limitation and care delivery disruption during the COVID-19 pandemic (30–34). Moreover, pandemic-related delays or changes in cancer treatment may have disproportionately affected minority groups, including African American patients, who even before the pandemic were less likely to receive guideline-concordant systemic therapy for metastatic cancer than White patients (35–40). It is thus critical to identify whether the COVID-19 pandemic resulted in changes in treatment patterns for patients with metastatic cancer, with potential downstream consequences that could adversely affect patient outcomes and equitable cancer care.
The objective of this study was to evaluate the impact of the COVID-19 pandemic on TTI and treatment selection for patients newly diagnosed with metastatic solid cancer, with attention to race- and age-based disparities. We hypothesized that the pandemic would be associated with delays in initiation of systemic therapy and increased use of nonmyelosuppressive therapies.
Methods
Study Design
We applied a retrospective controlled interrupted time series approach to evaluate associations between the COVID-19 pandemic period and changes in TTI and use of myelosuppressive therapy. The study adhered to Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines and was exempted by the University of Pennsylvania and WCG Institutional Review Boards before study conduct due to use of deidentified data only.
Data Source
This study used the nationwide Flatiron Health database, an electronic health record (EHR)-derived, longitudinal database comprising deidentified patient-level structured and unstructured data curated via technology-enabled abstraction (41,42). During the study period, data originated from approximately 280 US cancer clinics (approximately 800 sites of care). The majority of patients in the database originated from community oncology settings. The data were deidentified and subject to obligations to prevent reidentification and protect patient confidentiality.
Participants
The main study sample included adult patients (aged ≥18 years) with a new diagnosis of metastatic solid cancer from January 1 to July 31, 2019, or January 1 to July 31, 2020. Metastatic status was determined using both structured data and abstracted unstructured data from clinical, imaging, and pathology notes and included de novo (defined as stage M1 at initial diagnosis) or recurrent (M0 at initial diagnosis) diagnoses. Eligible cancer types were breast, colorectal, non-small cell lung carcinoma (NSCLC), pancreas, prostate, renal cell, or urothelial cancer. Patients were included regardless of treatment status, including those who did not receive systemic therapy during the study period. We excluded patients with incomplete historical treatment data (defined as 90 days or more) between diagnosis and the earliest date of structured activity (defined as a clinical visit, laboratory check, or treatment receipt documented in the EHR [n = 1631]), fewer than 2 documented clinical visits after metastatic cancer diagnosis (n = 1275), multiple metastatic malignancies (n = 66), first-line treatment starting before recorded metastatic diagnosis date (n = 682), or who were receiving therapy that was not part of National Comprehensive Cancer Network guidelines (n = 344). We also excluded patients diagnosed during a 30-day “washout” period (March 8 to April 7) encompassing the start of most state stay-at-home orders in 2020 (Supplementary Table 1, available online) and historical control patients the comparable period in 2019 (n = 2127). Supplementary Figure 1 (available online) illustrates our cohort selection.
We evaluated changes in treatment selection in a subsample of patients diagnosed with metastatic breast, NSCLC, prostate, or urothelial cancer during the study period who received a systemic therapy within 60 days of metastatic diagnosis (n = 6721). We selected these 4 cancers because they have guideline-based myelosuppressive and nonmyelosuppressive options for frontline therapy. Furthermore, frontline treatment guidelines (43–46) for these metastatic cancers did not change substantially during the study period, allowing for comparisons with historical controls. In addition to exclusions applied to the main study sample, patients were excluded if they received first-line treatment directed at a targetable mutation (EGFR, ALK, ROS-1, or BRAF for NSCLC; HER-2 for breast) or microsatellite instability (n = 759). These patients were excluded because their treatment decisions were likely influenced by the presence of an actionable genetic or molecular aberration rather than by factors related to the pandemic.
Main Outcomes and Measures
The primary outcome was TTI, defined as the number of days from metastatic diagnosis to receipt of first-line systemic therapy. Patients were censored at their last structured activity within the Flatiron Health network or 90 days after diagnosis, whichever occurred first. The secondary outcome was receipt of myelosuppressive treatment. Myelosuppressive treatment was defined as any regimen containing cytotoxic chemotherapy or a cyclin-dependent kinase inhibitor. Checkpoint inhibitors (NSCLC, urothelial) and hormone therapies (breast, prostate) without concurrent myelosuppressive therapy were considered nonmyelosuppressive (see Supplementary Table 2, available online for treatment categorizations).
The primary exposure was time period (April 8-July 31 vs January 1-March 8) and year (2020 vs 2019) of metastatic cancer diagnosis. These intervals corresponded with time periods in 2020 when the COVID-19 pandemic would be more vs less likely to influence patient treatment based on the date of most states’ stay-at-home orders. In our controlled interrupted time series approach, the comparison of interest was defined as the change in TTI (or receipt of myelosuppressive therapy) across time periods in 2020 compared with the change across time periods in 2019.
Covariates included age, sex, race (Hispanic, non-Hispanic Black, non-Hispanic White, or other [includes Asian American, American Indian or Alaska Native, Hawaiian or Pacific Islander, and multiracial]), insurance type (commercial, government, or other), Eastern Cooperative Oncology Group performance status (<2 or ≥2), documented opioid medication order (yes or no), calendar day of metastatic cancer diagnosis, and cancer type. All covariates were ascertained at the time of metastatic cancer diagnosis.
Missing baseline covariate data were accounted for using multiple imputation via chained equations with 10 imputations. Continuous variables were imputed using an approach that allowed for heterogeneous within-group variance by practice (47). Categorical and dichotomous variables were imputed using multinomial logistic regression and logistic regression, respectively.
Statistical Methods
Frequencies and proportions of baseline characteristics were summarized by time period. Standardized mean differences were used to describe differences in baseline characteristics across the 4 time periods; a standardized mean difference greater than 0.1 was considered a meaningful difference (48). The Kaplan-Meier estimator was used to estimate unadjusted median TTI within each time period. We conducted adjusted analyses of TTI using Cox proportional hazards regression. The primary exposure was an interaction between period (April-July vs January-March) and year (2020 vs 2019) of metastatic cancer diagnosis. All models were adjusted for age, sex, race, insurance, Eastern Cooperative Oncology Group, opioid prescription, a linear time trend for calendar day of metastatic cancer diagnosis, and cancer type and used robust standard errors to allow for within-practice correlation. Our primary analysis included an additional 3-way interaction between period, year, and cancer type to investigate effect modification by cancer type. Exploratory analyses excluded the cancer type interaction and included 3-way interactions between period, year, and race or, in a separate model, period, year, and age group, to investigate effect modification by race or age group, respectively. After fitting the Cox models, we used marginal standardization to estimate the predicted probabilities of treatment within 30 days of metastatic cancer diagnosis within each time period. Estimates across the 10 imputations were combined using Rubin’s rules (47,49).
Analyses of the subsample of patients who initiated treatment within 60 days of diagnosis used a similar approach using logistic regression rather than Cox regression to model use of myelosuppressive therapy (vs not). Marginal standardization was applied to logistic regression estimates to obtain adjusted probabilities of receiving myelosuppressive therapy.
Sensitivity Analyses
We performed a sensitivity analysis to verify the robustness of our findings to an alternate definition of pandemic period exposure. Rather than defining 1 exposure period for all study participants that encompassed most state stay-at-home orders, we varied the exposure period for individual participants, defining the start of the 30-day washout period using the stay-at-home order date of a patient’s state of residence (see Supplementary Table 1, available online for dates).
Data analyses were conducted between November 2020 and April 2021 using R, version 4.0.4. The statistical significance of interaction terms was tested by comparing the full model (with interaction terms) with the corresponding nested model (without interaction terms) using the Wald-like tests for multiple parameters for use with multiply imputed data (50). All hypothesis tests were 2-tailed with alpha = 0.05. Missing data were imputed using the mice package, version 3.13.0 (51). Cox proportional hazards models were fit using the survival package, version 3.2.11 (52), and regression standardization conducted using stdReg, version 3.4.1 (53). The functional form of continuous variables (age and calendar day of diagnosis) in Cox models was assessed using Martingale Residuals, and the proportional hazards assumption was evaluated for all variables using Schoenfeld Residuals. All analytic code is available at https://github.com/PRACTICE-research-group/COVID19-treatment-patterns.
Results
Baseline Characteristics
Table 1 shows the distribution of patient characteristics in the main study sample by time period and year. Of 14 136 patients with documented newly diagnosed metastatic solid cancer during the study period, 2954 (20.9%) were diagnosed from January to March 2019, 4745 (33.6%) from April to July 2019, 2640 (18.7%) from January to March 2020, and 3797 (26.9%) from April to July 2020. There were no meaningful differences in the distributions of age, sex, race, insurance, practice setting, and performance status by time period within each year (standardized mean differences <0.1). The most common cancers were NSCLC (41.3%), colorectal (18.4%), and breast (11.6%); there were no differences in the distribution of cancers by time period. Overall, 62.9% of patients were diagnosed with de novo metastatic disease; as a proportion of overall new metastatic cancer diagnoses, de novo metastatic diagnoses were more common in the COVID-19 period (April-July 2020 67.0%) than in the pre-COVID-19 periods (January-March 2019 = 61.2%; April-July 2019 = 61.5%; January-March 2020 = 61.5%; standardized mean difference = 0.11). Supplementary Table 3 (available online) describes the subsample of patients (n = 5962) who were diagnosed with metastatic NSCLC, breast, prostate, or urothelial cancer and treated within 60 days of diagnosis. The distribution of baseline characteristics in this subsample was similar to the full cohort.
Table 1. Population characteristicsa
Variable 2019 2020 Total
January 1-March 8 April 8-July 31 SMD January 1-March 8 April 8-July 31 SMD (N = 14 136)
(n = 2954) (n = 4745) (n = 2640) (n = 3797)
Cancer, No. (%)
Breast 382 (12.9) 589 (12.4) 0.089 282 (10.7) 393 (10.4) 0.066 1646 (11.6)
Colorectal 553 (18.7) 862 (18.2) 466 (17.7) 722 (19.0) 2603 (18.4)
NSCLC 1170 (39.6) 1987 (41.9) 1122 (42.5) 1562 (41.1) 5841 (41.3)
Pancreatic 261 (8.8) 388 (8.2) 251 (9.5) 368 (9.7) 1268 (9.0)
Prostate 282 (9.5) 383 (8.1) 214 (8.1) 347 (9.1) 1226 (8.7)
RCC 133 (4.5) 274 (5.8) 147 (5.6) 178 (4.7) 732 (5.2)
UCC 173 (5.9) 262 (5.5) 158 (6.0) 227 (6.0) 820 (5.8)
Median age (IQR) 70 (61-77) 70 (61-77) 0.013 69 (61-77) 69 (62-77) 0.008 70 (61-77)
Sex, No. (%)
Female 1330 (45.0) 2295 (48.4) 0.067 1250 (47.3) 1784 (47.0) 0.007 6659 (47.1)
Male 1624 (55.0) 2450 (51.6) 1390 (52.7) 2013 (53.0) 7477 (52.9)
Race, No. (%)
Hispanic 164 (6.3) 240 (5.7) 131 (5.6) 197 (5.9) 732 (5.9)
Non-Hispanic Black 270 (10.3) 458 (10.9) 242 (10.4) 355 (10.6) 1325 (10.6)
Non-Hispanic White 1801 (68.7) 2912 (69.1) 0.031 1554 (66.8) 2211 (66.3) 0.015 8478 (67.8)
Other 386 (14.7) 606 (14.4) 400 (17.2) 571 (17.1) 1963 (15.7)
Missing 333 529 313 463 1638
Insurance, No. (%)
Commercial 1435 (48.6) 2315 (48.8) 0.048 1343 (50.9) 1915 (50.4) 0.026 7008 (49.6)
Government 585 (19.8) 1015 (21.4) 556 (21.1) 775 (20.4) 2931 (20.7)
Unknown/not documented/self-pay 934 (31.6) 1415 (29.8) 741 (28.1) 1107 (29.2) 4197 (29.7)
Practice type, No. (%)
Academic 279 (9.4) 471 (9.9) 0.016 235 (8.9) 341 (9.0) 0.003 1326 (9.4)
Community 2675 (90.6) 4274 (90.1) 2405 (91.1) 3456 (91.0) 12 810 (90.6)
ECOG performance status, No. (%)
0-1 1176 (82.1) 1816 (82.1) 0.001 1086 (83.5) 1540 (81.7) 0.047 5618 (82.3)
≥2 257 (17.9) 396 (17.9) 214 (16.5) 344 (18.3) 0.047 1211 (17.7)
Missing 1521 2533 1340 1913 7307
Opioid prescription, No. (%) 220 (7.4) 362 (7.6) 0.007 179 (6.8) 274 (7.2) 0.017 1035 (7.3)
De novo metastatic, No. (%) 1647 (61.2) 2676 (61.5) 0.006 1491 (61.5) 2329 (67.0) 0.113 8143 (62.9)
Missing 261 391 217 319 1188
a ECOG = Eastern Cooperative Oncology Group; IQR = interquartile range; NSCLC = non-small cell lung carcinoma; RCC = renal cell carcinoma; SMD = standardized mean difference; UCC = urothelial cell carcinoma.
Time to Treatment Initiation
Across all periods, the median time to systemic treatment initiation was 35 days, with 44.0% (95% confidence interval [CI] = 43.2% to 44.8%) of patients initiating treatment within 30 days of metastatic diagnosis. Unadjusted and adjusted probabilities of treatment initiation within 30 days are shown in Table 2. In our primary analysis, the difference in the proportion of patients initiating treatment within 30 days in April-July compared with January-March was similar in 2019 and 2020 (adjusted probability of treatment within 30 days: January-March 2019 = 41.7%, 95% CI = 32.2% to 51.1%; April-July 2019 = 42.6%, 95% CI = 32.4% to 52.7%; January-March 2020 = 44.5%, 95% CI = 30.4% to 58.6%; April-July 2020 = 46.8%, 95% CI = 34.6% to 59.0%; adjusted percentage-point difference-in-differences = 1.4%, 95% CI = −2.7% to 5.5%) (Table 2). There was no evidence of effect modification by cancer type (Pinteraction = .25) (Figure 1), race (Supplementary Table 4, available online; P = .10), or age (Supplementary Table 5, available online; P = .65).
Figure 1. Changes in the adjusted probability of treatment initiation within 30 days of metastatic diagnosis between COVID-19 and pre–COVID-19 periods. This figure displays the differential effect of the COVID-19 period on the probability of 30-day treatment initiation by cancer type, race, and age (years) among patients with newly diagnosed de novo or recurrent metastatic solid cancer. The error bars represent the 95% confidence intervals. NSCLC = non-small cell lung carcinoma; RCC = renal cell carcinoma; UCC = urothelial cell carcinoma.
Table 2. Adjusted probability of treatment within 30 daysa
Model and category 2019 2020 Difference in differences
January 1-March 8 April 8-July 31 Difference January 1-March 8 April 8-July 31 Difference 2020-2019
Est (95% CI) Est (95% CI) Est (95% CI) Est (95% CI) Est (95% CI) Est (95% CI) Est (95% CI)
Unadjusted 0.429 (0.407 to 0.452) 0.415 (0.383 to 0.447) −0.014 (−0.032 to 0.003) 0.453 (0.433 to 0.472) 0.453 (0.426 to 0.487) 0.004 (−0.014 to 0.075) 0.018 (0.000 to 0.036)
Adjusted
Combined 0.417 (0.322 to 0.511) 0.426 (0.324 to 0.527) 0.009 (−0.044 to 0.061) 0.445 (0.304 to 0.586) 0.468 (0.346 to 0.590) 0.023 (−0.029 to 0.075) 0.014 (−0.027 to 0.055)
Breast 0.546 (0.363 to 0.728) 0.573 (0.337 to 0.809) 0.027 (−0.223 to 0.277) 0.637 (0.360 to 0.914) 0.635 (0.298 to 0.972) −0.002 (−0.190 to 0.185) −0.029 (−0.385 to 0.326)
Colorectal 0.402 (0.259 to 0.545) 0.410 (0.296 to 0.524) 0.008 (−0.089 to 0.106) 0.430 (0.233 to 0.628) 0.434 (0.307 to 0.561) 0.004 (−0.112 to 0.120) −0.005 (−0.115 to 0.106)
NSCLC 0.397 (0.308 to 0.486) 0.390 (0.301 to 0.480) −0.007 (−0.075 to 0.062) 0.404 (0.284 to 0.524) 0.436 (0.300 to 0.572) 0.032 (−0.007 to 0.071) 0.039 (−0.031 to 0.108)
Pancreatic 0.508 (0.290 to 0.727) 0.549 (0.228 to 0.869) 0.040 (−0.188 to 0.268) 0.541 (0.327 to 0.755) 0.546 (0.293 to 0.798) 0.005 (−0.293 to 0.302) −0.036 (−0.299 to 0.227)
Prostate 0.369 (0.212 to 0.526) 0.363 (0.182 to 0.545) −0.006 (−0.232 to 0.221) 0.371 (0.197 to 0.545) 0.438 (0.262 to 0.614) 0.067 (−0.184 to 0.317) 0.072 (−0.385 to 0.530)
RCC 0.343 (0.176 to 0.510) 0.361 (0.006 to 0.716) 0.018 (−0.365 to 0.402) 0.396 (0.068 to 0.724) 0.440 (0.132 to 0.748) 0.044 (−0.283 to 0.371) 0.025 (−0.293 to 0.344)
UCC 0.345 (0.195 to 0.495) 0.390 (0.224 to 0.556) 0.045 (−0.170 to 0.260) 0.408 (0.237 to 0.579) 0.418 (0.264 to 0.572) 0.010 (−0.205 to 0.225) −0.035 (−0.236 to 0.166)
a Results are from cancer type: period interaction model. Est = estimate; CI = confidence interval; NSCLC = non-small cell lung carcinoma; RCC = renal cell carcinoma; UCC = urothelial cell carcinoma.
Treatment Selection
Among the 5962 patients who received first-line systemic therapy within 60 days of diagnosis, 67.2% received myelosuppressive therapy (range = 3.2% for prostate cancer to 81.0% for breast cancer). The difference in the adjusted probability of receiving myelosuppressive therapy in April-July compared with January-March was similar in 2019 and 2020 (January-March 2019 = 69.8%, 95% CI = 65.1% to 74.4%; April-July 2019 = 66.7%, 95% CI = 60.9% to 72.5%; January-March 2020 = 68.3%, 95% CI = 65.1% to 71.4%; April-July 2020 = 66.8%, 95% CI = 63.3% to 70.2%; adjusted percentage-point difference-in-differences = 1.6%, 95% CI = −2.6% to 5.8%) (Table 3). There was no evidence of effect modification by cancer type (P = .21) (Figure 2), race (Supplementary Table 6, available online; P = .13), or age (Supplementary Table 7, available online; P = .48).
Figure 2. Changes in the adjusted probability of receiving myelosuppressive therapy after metastatic diagnosis between COVID-19 and pre–COVID-19 periods. This figure displays the differential effect of the COVID-19 period on the probability of receiving myelosuppressive therapy by cancer type, race, and age (years) among patients with newly diagnosed de novo or recurrent metastatic solid cancer. The error bars represent the 95% confidence intervals. NSCLC = non-small cell lung carcinoma; UCC = urothelial cell carcinoma.
Table 3. Adjusted probabilities of receipt of myelosuppressive therapya
Model and category 2019 2020 Difference in differences
January 1-March 8 April 8-July 31 Difference January 1-March 8 April 8-July 31 Difference 2020-2019
Est (95% CI) Est (95% CI) Est (95% CI) Est (95% CI) Est (95% CI) Est (95% CI) Est (95% CI)
Unadjusted 0.687 0.676 −0.012 0.691 0.658 −0.033 −0.022
(0.651 to 0.723) (0.622 to 0.729) (−0.059 to 0.036) (0.656 to 0.727) (0.626 to 0.690) (−0.062 to −0.004) (−0.081 to 0.038)
Adjusted
Combined 0.698 0.667 −0.031 0.683 0.668 −0.015 0.016
(0.651 to 0.744) (0.609 to 0.725) (−0.065 to 0.002) (0.651 to 0.714) (0.633 to 0.702) (−0.038 to 0.008) (−0.026 to 0.058)
Breast 0.815 0.776 −0.039 0.827 0.807 −0.019 0.020
(0.746 to 0.883) (0.701 to 0.851) (−0.122 to 0.044) (0.783 to 0.870) (0.767 to 0.848) (−0.073 to 0.034) (−0.077 to 0.116)
NSCLC 0.835 0.808 −0.026 0.807 0.783 −0.024 0.003
(0.797 to 0.872) (0.761 to 0.856) (−0.071 to 0.018) (0.773 to 0.841) (0.749 to 0.817) (−0.059 to 0.012) (−0.056 to 0.061)
Prostate 0.046 0.025 −0.022 0.025 0.041 0.016 0.037
(0.000 to 0.101) (0.004 to 0.045) (−0.080 to 0.036) (0.008 to 0.042) (0.012 to 0.071) (−0.015 to 0.047) (−0.025 to 0.100)
UCC 0.558 0.497 −0.061 0.583 0.589 0.007 0.068
(0.452 to 0.664) (0.402 to 0.593) (−0.190 to 0.068) (0.505 to 0.660) (0.518 to 0.661) (−0.081 to 0.095) (−0.086 to 0.221)
a Results are from cancer type: period interaction model. Est = estimate; CI = 95% confidence interval; NSCLC = non-small cell lung carcinoma; UCC = urothelial cell carcinoma.
Sensitivity Analyses
Results from a sensitivity analysis using a state-specific exposure definition based on dates of state stay-at-home orders were consistent with results from the primary analysis (Supplementary Tables 8 and 9, available online).
Discussion
In this large, multi-site cohort of patients with metastatic solid cancer, we assessed the impact of the COVID-19 pandemic on TTI and treatment selection using a quasi-experimental approach. We did not find evidence that the pandemic period was associated with delayed systemic therapy or increased use of nonmyelosuppressive therapy. We did observe changes in disease presentation during the COVID-19 period—most notably, an increased proportion of patients presenting with de novo metastatic disease. Our analysis suggests that previously reported pandemic-associated diagnostic delays may have resulted in more acute presentations of metastatic disease but not delays in systemic treatment initiation or preference against use of myelosuppressive therapies.
Our findings stand in contrast to earlier studies evaluating COVID-19 pandemic–related disruptions in cancer care, which found evidence of care delays across the cancer continuum (11,13,17). Several factors may account for this discrepancy. First, previously reported declines in cancer screening and diagnoses may have contributed to greater available capacity in outpatient clinics and infusion suites for those needing prompt treatment (16). Second, we observed a 5-6 percentage-point increase in the proportion of de novo metastatic diagnoses in the COVID-19 period compared with pre-COVID periods. Relative to recurrent metastatic diagnoses, which are often detected via routine surveillance imaging or laboratory testing when patients may not be symptomatic, de novo metastatic diagnoses are associated with greater symptomatic burden and worse overall mortality (54). It is possible that known pandemic-related decreases in routine imaging and laboratory surveillance contributed to the observed relative increase in presentation of potentially more symptomatic de novo metastatic diagnoses, which has been suggested in prior single-institution studies (52). Consequently, any pandemic-related delays in treatment initiation may have been balanced by the need for quicker treatment initiation for more symptomatic cases. Our findings of COVID-related impacts on de novo metastatic presentation are hypothesis-generating, and this study was not well powered to assess this. Future studies with longer follow-up will be necessary to evaluate whether the relative increase of de novo presentations will persist and what the consequences of this potential shift will be on future cancer-related outcomes. Nevertheless, delays in detection and diagnosis of recurrent metastatic disease during the early phase of the COVID-19 pandemic may be a harbinger for increased rates of symptomatic metastatic disease and cancer-associated mortality in later stages of the pandemic.
We did not find evidence of changes in the type of treatment selected despite early professional society guidance in some cases cautioning against use of myelosuppressive therapy (18). The mechanisms behind this finding are unclear. An increased proportion of de novo metastatic diagnoses presenting with symptomatic disease may have led more physicians and patients than expected to prefer chemotherapy to achieve rapid debulking and disease control (55). Additionally, evidence emerged during the pandemic suggesting that myelosuppressive therapies might not, as initially suspected, be associated with increased COVID-19 severity or mortality among patients with cancer (8). Oncologists may have thus grown more comfortable with using myelosuppressive therapy during the pandemic period.
Our study has several advantages compared with prior studies examining pandemic-related treatment delays. First, we studied a large national cohort using EHR–derived data with minimal data lag, allowing for broad geographic coverage that accounted for state-specific stay-at-home orders, strong representation of community oncology practices, and greater data recency compared with other administrative databases. Second, we used a real-world dataset that harnesses technology-enabled chart abstraction to ascertain diagnoses and treatments rather than relying solely on administrative claims from the COVID-19 pandemic period, which may be subject to measurement error and data lag (56,57). Finally, we used a quasi-experimental design to account for temporal confounding, such as known seasonal patterns in diagnoses and treatment-seeking behavior (58).
Our study has several limitations. First, it is a retrospective study of a sample of predominantly community-based US oncology practices, and therefore our findings may not be reflective of all oncology practice. However, this database has been shown to be broadly representative of US oncology practices and patients (41). Second, outpatient EHR data may incompletely capture important variables that contribute to treatment patterns, such as patient preference or comorbidities, thus raising the possibility of unmeasured confounding. However, our quasi-experimental approach should account for these unmeasured confounders, assuming such confounders were consistent across time periods. Third, although we used the most up-to-date data available, there may be COVID-related delays in data capture affecting completeness of data from more recent time periods. In particular, the pandemic could affect capture of metastatic cancer diagnoses. Although this remains a hypothetical concern, future analyses should address this possibility. Fourth, our cohort was limited by a relatively small proportion of racial minorities and those with noncommercial insurance. This may have resulted in limited power for analyses of race- or age-based interactions, though notably there was some non-statistically significant evidence of delayed treatment among African American patients. Given the disproportionate impact of the pandemic on care for minority groups, future analyses with larger, more diverse cohorts are needed. Finally, although we did not find any delays in systemic therapy initiation, our study was not designed to evaluate possible changes in rates of systemic therapy initiation (or lack thereof) over time or to assess changes in systemic therapy dosing or schedules that may have occurred during the pandemic.
In this large, nationwide study of patients newly diagnosed with metastatic solid cancer, we did not find evidence of treatment delays or preferential use of nonmyelosuppressive therapies associated with the COVID-19 pandemic. An increased proportion of patients presenting with de novo metastatic cancers during the pandemic may portend a backlog of recurrent metastatic diagnoses stemming from pandemic-related delays in surveillance and diagnosis. Future studies with longer follow-up should assess whether COVID-related delays in presentation affect cancer-related outcomes among patients with metastatic cancers.
Funding
This study was supported by the National Cancer Institute K08-CA-263541–01 (to RBP).
Notes
Role of the funder: The funders had no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; and the decision to submit the manuscript for publication.
Author disclosures: Ravi Parikh and Ronac Mamtani have received reimbursement from Flatiron, Inc for travel and speaking. Rebecca Miksad and Gregory Calip report employment at Flatiron Health, Inc. There are no other conflicts of interest relevant to this submitted work.
Author contributions: All authors have directly participated in the planning, execution, or analysis of the study, and have approved the final version of this manuscript. RBP, SUT, DV, RM, RH: Conception and design. DV, EPW: Data Curation, Formal Analysis, Software. WF and CH: Project Administration. All authors: Methodology. RBP and SUT: Writing—Original Draft. All authors: Writing—Review and Editing.
Disclaimers: The views expressed in this article are those of the authors, and no official endorsement by the National Cancer Institute, National Institutes for Health, or the Department of Health and Human Services is intended or should be inferred.
Prior presentations: This was previously presented as a Poster Presentation at the 2021 American Society of Clinical Oncology (ASCO) Annual Meeting (Virtual).
Data Availability
All deidentified data generated or analyzed during this study is available upon request to Ravi Parikh, MD, MPP, ravi.parikh@pennmedicine.upenn.edu.
Supplementary Material
djab225_supplementary_data Click here for additional data file.
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References
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PMC009xxxxxx/PMC9002296.txt |
==== Front
Indoor Built Environ
Indoor Built Environ
IBE
spibe
Indoor + Built Environment
1420-326X
1423-0070
SAGE Publications Sage UK: London, England
10.1177/1420326X211048537
10.1177_1420326X211048537
Original Papers
How community medical facilities can promote resilient community constructions under the background of pandemics
https://orcid.org/0000-0001-6038-2002
Wang Fang 1
Fang Yuanyang 1
Deng Handuo 2
https://orcid.org/0000-0002-3709-5803
Wei Fangzhen 3
1 NSFC-DFG Sino-German Cooperation Group on Urbanization and Locality (UAL), Peking University, College of Architecture and Landscape, Peking University, Beijing, P. R. China
2 College of Urban and Environmental Sciences, Peking University, Beijing, P.R. China
3 Peking University Hospital, 12465 Peking University , Peking University, Beijing, P.R. China
Fangzhen Wei, Peking University Hospital, Peking University, Beijing, P.R. China. Email: pkuh-wfz@pku.edu.cn
4 2022
4 2022
4 2022
31 4 10181027
6 9 2021
© The Author(s) 2022
2022
International Society of the Built Environment
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.
Nowadays, urban and community resilience have become the core issues of urban theoretical research and construction practices. While there are many studies on climate change, natural hazards and environmental pollution, relatively less attention has been paid to public and human health. However, the current COVID-19 pandemic, which is a major global public health crisis, is posing severe challenges to the resilience of cities and communities in the context of high-mobility, high-density and high-intensity, as well as expands the connotation of community resilience to public health. To compensate for the lack of current research, this study examined the characteristics of community medical facilities in response to pandemics at urban, community and individual multi-spatial scales based on a thorough review of current research and relevant practice. It also emphasized the significant role played by community medical facilities in improving resilient community constructions in the face of large-scale public health emergencies. These characteristics were fully utilized to explore ways to build and govern the ‘resilience' of communities in the future, help people to survive better as well as develop in complex and changeable external environments.
COVID-19
Resilience
Healthcare facilities
Community governance
Urban planning
Peking University’s Humanities and Social Sciences Special Project 34 typesetterts10
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pmcIntroduction
The outbreak of SARS-CoV-2, a novel coronavirus and its related diseases, coronavirus disease 2019 (COVID-19), has triggered a global public health crisis, which has seriously affected people's normal life, work schedules, physical and mental health,1 and has caused devastating consequences like the loss of human lives and economic decline in countries all over the world. Such sudden public health events pose a serious challenge to urban planning and community governance under the background of high mobility, high density and high intensity.2 How to systematically improve the ‘resilience’ of human settlements in the face of sudden changes and disturbances, and how to help people survive better and develop in complex and changeable external environments, are particularly important problems, having both theoretical and practical value.
In a sudden or pandemic situation, if proper community medical facilities are not available in a locality, prevention/mitigation and recovery of and preparedness and response for such pandemic towards the betterment of the community people could be broken, which would ultimately affect community resilience. For a better understanding of the related key issues of community resilience and community medical facilities, and their relationship, this paper discusses the following issues in subsections.
Community resilience
The resilience of human settlements refers to the aggregation of a series of human abilities to deal with uncertainty3 and instability. While the former two abilities are passive, the latter two are active. These capability sets run through the three stages of emergencies, disasters or hazards: prevention/mitigation and preparedness; response; and recovery/reconstruction.4 Due to spatial scale differences, the subjects of resilience form patterns comprising the following levels: families/individuals, local communities, urban, regional, national and global.5,6 Among them, cities, the most complex social ecosystem, have been continuously suffering from various impacts and disturbances from the outside and inside since their formation. Community, as the basic unit of a city, is the cellular organization of social organisms and has been at the forefront of disaster prevention and mitigation. As a result, ‘community resilience’ has become the core issue of relevant theoretical research and construction practices.
Resilience has been gradually applied to various disciplines and fields related to human communities and social systems,7,8 after Holling,7 an ecologist in Canada, introduced it into ecosystem research in 1973. The concept of ‘disaster resilient community’ was first proposed by Mileti and Noji.9 Subsequently, scholars and organizations such as Adger,10 Bruneau,11 UNDRR (The United Nations Office for Disaster Risk Reduction)12 and CARRI (Community and Regional Resilience Institute)13 further enriched the connotation and extension of ‘community resilience’.
Presently, the ability of communities to cope with and recover from large-scale emergencies is often referred to as ‘community resilience’.14 There have been many studies on climate change, natural hazards and environmental pollution in the research and practice of community resilience.
Community resilience and public heath
Firstly, community resilience emphasizes disaster prevention and mitigation capabilities to cope with the impact of extreme weather and various natural hazards caused by climate change on human settlements. A series of international slogans or proposals show that disaster prevention and mitigation has become an international consensus as to the primary goal of community resilience enhancement. The management forum of the World Conference on Disaster Reduction in 1999 proposed that ‘communities should be regarded as the basic unit of disaster reduction’.15 In 2001, on the International Day for Disaster Reduction, the United Nations put forward the slogan of ‘developing community-based disaster reduction strategy’.12 The 2005 World Conference on Disaster Reduction listed community disaster reduction as important content and proposed to establish emergency response mechanisms for improving emergency response capacities in all social strata, especially communities.16 Specifically, Australian scholars are concerned about the rising energy costs caused by climate change and the impact of freezing, heat waves and storms on communities and families, and have considered transforming residences using block designs to adapt to bad weather.17 In the Middle East, urban planning has paid attention to the impact of extremely high temperatures, water shortages, floods and power interruptions on vulnerable communities in summer, when designing key infrastructure.18 American scholars found that the inherent conditions of communities (including the environment, social economy and industry) play a key role in post-disaster recovery.19
Secondly, community resilience is often associated with ‘sustainability’ and emphasizes the ecological environment and inclusive growth,20 which is a new way to guide the sustainable development of modern cities based on the traditional planning theory. The 2015 United Nations Conference on Sustainable Development proposed, ‘make cities inclusive, safe, resilient and sustainable’ in the next 15 years.21 The New Urban Agenda of the 2016 United Nations Conference on Housing and Urban Sustainable Development22 put forward the vision of urban development:ensure that all inhabitants, of present and future generations, without discrimination of any kind, are able to inhabit and produce just, safe, healthy, accessible, affordable, resilient and sustainable cities and human settlements, to foster prosperity and quality of life for all.
With the development of the economy and the increase of the urban population, cities are facing challenges such as environmental pollution, traffic congestion, ageing population and the shortage of education, medical and social resources. Therefore, in this context, community resilience construction pays more attention to comprehensive sustainable development in order to improve people's quality of life and promote urban development.
The current measurement of community resilience is also focused on disaster prevention, ecology and environment, as well as on macro-level evaluations.23 Bene24 corresponded the index of community resilience to governance cost, while Chang and Shinozuka25 extended monetary measurement to organizations, technology and society. Tian et al.26 proposed a framework for measuring community resilience based on five aspects: original conditions, coping capacity, adaptability, disaster loss and disaster exposure. Although scholars construct a community resilience evaluation system from multi-dimensional and multi-scale synthesis, they pay less attention to public and human health aspects. Correspondingly, research on community resilience in major public health emergencies is also relatively less.27
Since 2020, the community's response to the outbreak of the novel coronavirus pneumonia pandemic has enriched the connotation of community resilience and led to a rethinking about the community spatial resilience strategy based on public health crisis management. In the global scope, the response speed and governance effect of different communities in different countries, regions and cities are not the same,20 which reflects the resilience strength of comprehensive management of public health crisis at two levels: city and community.
Community medical facilities and community resilience
Community action has been proven to be a vital part of the public health effort in the pandemic.14 Community medical facilities, at the core of enhancing community resilience in public health, have attracted considerable attention during the pandemic. They have acted as the ‘brain’ of the community defence system construction, which drives the community residents' self-organization and self-governance, and plays a crucial role in promoting the ‘resilience’ construction of the community.
In the pandemic, community medical facilities are another important medical resource outside the hospital system, including hardware facilities such as health centres, sanatoriums and soft facilities such as human capital. Various medical services based on community medical facilities play a key role in flattening the transmission curve, improving the rescue rate and reducing the mortality rate. Community medical facilities are the key line of defence for primary prevention and rescue. Early detection, contact tracking, risk assessment, isolation and other measures can relieve the pressure of hospitals and maintain the health of the population28 and are more important for areas having scarce per capita hospital resources and vulnerable groups.
During the economic recession following the pandemic, some studies show that disinvesting in maternal and child health will sow the seeds of later health inequality and Non-infectious Chronic Disease (NCD) risk, which will undermine community resilience to future health emergencies.29 Therefore, the community medical facilities concerned with these problems will contribute to the construction of community resilience.
In addition to pandemic periods, community medical facilities also protect people's health in their daily lives. Community-based family monitoring and care can help prevent and predict diseases and reduce social medical costs. The patient-centred community medical centre is an important way of primary health care, which helps to improve residents’ access to care, enhances the utilization of medical services and reduces the overall cost of medical care.30 Community medical facilities can also build social support networks, which are conducive to the mental health of patients in recovery, which is proven to help in improving community resilience and decreasing the impact of the threats of the COVID-19 pandemic.31
Although many countries have established, or are in the process of establishing, a complete hierarchical diagnosis and treatment system and strongly emphasize the importance of community medical facilities, there is a lack of research on how community medical facilities can continue to play a role in the pandemic, communicate with normalized medical care and various emergency measures, and improve the public health safety dimension’s community resilience.
This paper studies how exactly community medical facilities can improve community resilience based on a multi-scale analytic framework. At the beginning of the next section, the three spatial scales of community medical facilities and the characteristics are introduced. Then, the specific effects of these characteristics at different spatial scales improving community resilience are concretely reviewed. The discussion part claims the need to build a healthy urban governance system with community medical facilities as the core and take the community as the ‘health unit’ of the city in order to build sustainable cities. Finally, the paper concludes that community medical facilities play a key role in enhancing community resilience in public health. Community medical facilities drive the construction of medical support networks at different spatial scales and promote the improvement of abilities, processes, goals and other aspects of community resilience ultimately.
Community medical facilities improving community resilience
Although the academic community has recognized the importance of community medical facilities in strengthening the community resilience system, few studies have focused on the role of community medical facilities in constructing medical support networks at different spatial scales. Therefore, to compensate for the lack of current research, this study aims to expound on the promotion of community medical facilities to community resilience construction from multiple spatial scales (i.e. urban, community and individual facility). The paper aims to contribute to deepen the understanding of the role of community medical facilities in community resilience under the background of a pandemic situation.
Community medical facilities are an important entry point to enhance the resilience of community public health, which is of great significance in the three spatial scales of cities, communities and individuals. In general, community medical facilities can help to improve urban resilience at the urban level with policy flexibility and pertinence; at the community network level with network and node nature, and at the individual facilities level with infrastructure and functionality of facilities; ultimately promoting abilities, processes, goals and other aspects in community resilience (Figure 1).
Figure 1. Community medical facilities’ characteristics in three space scales to improve resilience.
Urban policy: Flexibility and pertinence
As the core of the community’s ‘defence unit’, community medical facilities can promote the construction of urban public medical and health systems. As a research subject, they can champion the formulation of special planning of medical facilities and emergency plans of relevant government departments for flexibly responding to a variety of complex situations and solving corresponding problems.
Implementation of special planning for medical and health facilities
The planning and health departments of the government carry out special planning of medical and health facilities, which builds a medical and health system covering urban and rural areas. It also provides high-quality services based on community medical institutions, public health institutions and various specialized hospitals to realize the efficient operation of medical facilities and carry out a comprehensive balance to ensure the implementation of the planning.32
The special planning is particularly important to prevent, control and reduce as much as possible the spread and harm of infectious diseases that have a great impact on the daily work and life of residents. There is a need to consider how the daily life and production of cities can be carried out normally, without being affected or less affected when a pandemic occurs and how to utilize the community residents’ digital information resources,33 so that regional infectious diseases can be detected and nipped in the bud, and the spread of the pandemic can be prevented in advance.
Formulation of emergency response plan for large-scale public health events
The urban space system, with community medical facilities as the core of the ‘defence unit’, can boost the level of emergency plans for large-scale public health events, improve the process management of emergency plans with the scenario as the mainline and facilitate the emergency drills of each unit, to optimize emergency mechanisms and the quality of residents’ preparedness to deal with emergencies.
In this way, emergency plans which are compatible with extreme conditions can realize effective space control and supply of materials; maintain the operation order of multi-level spaces under extreme conditions and ensure basic travel and living needs of communities and families under normal and abnormal conditions, while avoiding high concentration of personnel.34
In addition, the participation of various social groups from the private and public sectors can be included in consideration of emergency plans. These groups and organizations can coordinate various projects such as health care, construction, safety and hotel management. Such strong cross-organizational cooperation and clear communication channels can effectively utilize social resources and better guarantee the establishment and operation of emergency plans.35
Construction land reserved for temporary emergency facilities
During the implementation of special plans for medical and health facilities and the formulation of emergency response plans for large-scale public health events, reserving construction land for temporary emergency facilities based on community medical facilities is an important measure for improving the community emergency response capabilities. It is also necessary to fully consider emergency beds under public health events along with the ventilation and filtering systems,36 material storage systems, surgical lighting and other ancillary facilities to meet the needs of hospitals and combine peacetime and wartime with disaster relief, to ensure rapid function conversion in special periods and realize the efficient utilization of space resources. For example, the ‘mobile cabin hospitals’ in Wuhan, China, provided a safe treatment place and an effective isolation area for patients with mild symptoms of COVID-19 when the pandemic broke out; thus, effectively preventing its spread.37
The principle of ‘small and even’ can be referred to in relation to the preparation of dispersed public spaces for function conversion,38 and for providing sufficient, simple medical and disaster prevention facilities. There is also a need to consider the needs of medical facilities for space and site, such as well-ventilated and compatible garbage disposal sites,39,40 along with the allocation of certain public spaces for the community’s education and learning during special periods. Community residents’ good knowledge literacy in response to disasters and pandemics is also an important way to improve the ‘resilience’ of communities.
Community network: Network and node
As a typical node, the community, the basic unit of urban space and the cellular organization of the organism,41 can participate in the formation of urban populations, spaces and organization networks because of its universality.42 The networking of community medical facilities has two connotations. On the one hand, as the core of the communities’ defence system, it becomes an important node of the urban public health network constructed by the government to effectively prevent and respond to emergencies by establishing community basic unit self-governance; while on the other hand, it connects individuals, families and communities, as well as connects and associates each basic node of pandemic prevention and control, and builds a community-level pandemic prevention and control support network by guiding residents' health behaviours. In short, community medical facilities promote a ‘public health defence network’ to reasonably organize the integration and blocking of social and urban spaces and effectively reduce the transmission capacity of viruses in high-density, high-mobility urban spaces.43
Establishing a basic community governance unit
The novel coronavirus pneumonia pandemic highlights the importance of a community-based medical system during pandemics. Some scholars have begun to reflect on the concept of patient-centred care in the past,44,45 since pandemics affect not only individuals but also families and communities related to the disease. If barriers are not built to prevent infection at the community level, and only the hospital system is relied upon to fight the pandemic, the healthcare system will collapse. Community autonomy means that when public health emergencies occur, the pressure of hospitals and large-scale public health places will be distributed to grassroots community hospitals and small clinics, ‘distributed reception and centralized treatment’. In this way, as a key node of the urban public health system, community medical facilities help to improve the utilization efficiency of social medical resources and cope with the lack of hospitals and critical medical facilities.
Taking community medical facilities as the guiding institution of community governance, full play can be given to residents’ self-organization and governance power from the bottom-up by guiding them in supporting and helping each other and promoting the improvement of their community governance abilities. The rise of community power can clearly be seen in the pandemic environment, and community governance issues will be comprehensively upgraded. The COVID-19 pandemic has made it necessary for governments to urgently overcome the obstacles of institutional weaknesses: weak administrative capacity, rigid bureaucracy and conflicts among political leaders, which are structural constraints,46 as well as to encourage responsive grassroots governance. Therefore, there is a need to establish a social governance pattern of co-construction, co-governance and sharing by decentralizing the responsibility and power of community governance at the grassroots level, as well as guiding and encouraging community autonomous governance, which will make the city, the basic cell, really live.47
Guiding residents' health behaviour
Community medical facilities and related medical staff, regarded as key nodes in the urban public health network, provide urban residents with risk assessment and health knowledge education,48 and guide their health behaviours. Research shows that medical facilities and medical staff in the community can promote the improvement of community-based health levels and disease prevention; effectively improve per capita health levels and the utilization rate of medical care49; build a social knowledge network on environment and health; enhance residents' sense of participation.50 Community public health intervention measures should not only focus on the construction of a healthy living environment but also pay attention to guiding residents' health behaviour, which is the key to promoting the quality of the living environment and the construction of resilient communities.51
In addition, the gradual increase of population density in urban residential areas will affect residents’ physical and mental health as well as the community’s social management to a certain extent. As a professional department, community medical facilities can carry out a comprehensive and systematic health risk assessment; health knowledge education and popularization; effectively integrate material and social spaces to guide individuals/families’ health behaviours through regular health examinations and health education, which will greatly improve the community’s anti-risk levels.52
Community medical facilities should also play a key role in protecting vulnerable groups’ health rights and interests. For example, from the perspective of age groups, the elderly are the most vulnerable group, since they often lose the opportunity of forming social networks with the outside world because they live alone and are at a disadvantage in receiving health care services. Community medical facilities can provide support in these two aspects, for example, by volunteering to provide regular health examinations and trying to help the single elderly contacting with others. European scholars have found that community medical facilities can help improve the relief rate of the infected population in poor communities and immigrant groups.33 Studies have shown that communities with strong social ties are more resilient.53 By strengthening the connection, trust and reciprocity between individuals, community medical facilities can also help to enhance social capital, strengthen the network support system at the community level, and enhance the effect of collective action and local governance.
Individual facilities: Basic and functional
An essential prerequisite for dealing with public health emergencies is the construction of communities with substantial basic medical facilities that will ensure the infrastructure and functionality of grassroots facilities. Opening and closure are a city’s normal and abnormal states, respectively. Thus, to strike a suitable balance is necessary, considering the city’s vitality and safety. Since infrastructure construction is one of the key nodes, there is a need to grasp the spatial layout and allocation of resource elements.
Considering prefabricated prefinished volumetric construction
Modular design is a new standardized mode developed in the late 20th century. It can be used to deal with complex and diverse problems,54 including uncertain emergencies and external environmental changes, through its convenient process of prefabrication, transportation, installation and disassembly, as well as its unique adaptability and economy. Thus, it is a better way to improve the resilience of communities.
Under the background of the novel coronavirus pneumonia pandemic, the modularization design in community health systems can be used to improve the configuration and management guarantee efficiency in the module, establish a mode of cooperation and independent functionality, as well as protect the whole area from being affected.55 It also helps to reduce the difficulty of governance.
Moreover, modular designs can make the layout of key infrastructure and related facilities, the lifeline system of disaster relief, realize equalization and modularization based on stable operation for coping with unexpected uncertainties and changing situations. In case of emergencies, modular spaces and sites can provide activity spaces for residents and effectively inhibit the survival and spread of the virus by creating an environment with good lighting and ventilation.56
Guaranteeing infrastructure construction
The present COVID-19 pandemic clearly showed home isolation to be a very effective measure. To guarantee the basic quality of life of those living at home requires stable infrastructure, material supplies and community services, such as communication facilities, networks, transportation, logistics, energy, water supply, distance education and entertainment.
In addition, digital technology can be considered helpful for improving the level of community infrastructure construction and quality of community services, necessary to promote the improvement of community functions.57 The pandemic’s outbreak has clearly shown that the construction of an intelligent pandemic prevention system in the community depends on various new technologies, such as contactless takeout and express deliveries, personal itinerary cards, and so on. Thus, intelligent management networks based on the new generation of communication technology cover everyone and contributions to the control and protection of pandemic situations.58
Infrastructure construction should not only meet the needs of daily life, but also of situations during special periods. Only in this way can effective space control and material supplies under extreme conditions be achieved with the community as the basic unit. Such a multi-level spatial operational plan will be the infrastructure construction work that every city must plan for.
Discussion
Novel coronavirus pneumonia prevention should be implemented in the grassroots community, and the community should be the last line of defence for pandemic prevention and control, and the key role of community medical facilities should be brought into full play. It is an effective strategy for the world to cope with the outbreak of the COVID-19 pandemic because resilient communities can help people face and deal with all kinds of losses and external pressures caused by emergencies.
Nowadays, because of the normalization of the pandemic’s prevention and control, the important role of community medical facilities cannot be ignored. It is necessary to make full use of community medical facilities’ characters at the urban policy level, the community network level and the individual facilities level, integrate the theory and practice of resilience into the multi-disciplinary environment of economy, society and ecology, and explore methods and ways to adapt to various emergencies and complex environments. These strategies help to build and manage community ‘resilience’ and guide sustainable urban development in the future.
People can never reach a city without disease but should have a city that is safe, healthy, prosperous and able to cope with all kinds of dangers, emergencies and long-term challenges. Therefore, to build a healthy urban governance system with community medical facilities as the core and take the community as the ‘health unit’ of the city is necessary. This goal can be achieved through three measures. First, set up a ‘health unit’ based on a ‘15-minute community life circle’, optimize residents' lifestyle with the main purpose of promoting exercise activities and social interaction and implement emergency measures related to public space with the main purpose of epidemic prevention, isolation and rescue. Second, decision-makers in city planning and related scholars should cooperate to promote the health planning program, clarify the health needs of different groups of people and fully consider the health effect of space in the daily design and use of public space. Third, they should promote the Health Impact Assessment (HIA) of large-scale urban construction projects, which is also a health policy vigorously promoted by the World Health Organization.
The core of urban governance to deal with the pandemic situation lies in: based on the community life circle, taking the public health unit as the core, aiming at the outbreak of infectious diseases and the growth of chronic diseases, integrating all kinds of health promotion facilities, resources and work, forming an efficient and high-quality health governance model and constructing a healthy urban governance system. This pandemic prevention and control are undoubtedly an arduous, lasting and comprehensive ‘urban defence war’. We believe that the prospect of a ‘beautiful community’ in the new era will come.
Conclusion
In response to the current novel coronavirus pneumonia pandemic, community medical facilities play a key role in enhancing community resilience in public health. As an additional important medical resource outside the hospital system, community medical facilities are the key line of defence for primary prevention and relief in various regions. They can promote the construction of medical support networks in different spatial scales.
At the urban policy level, community medical facilities have policy flexibility and pertinence. As a research subject, it can encourage relevant government departments to implement special plans relating to medical and health facilities, formulate emergency plans for extreme cases of large-scale public health events and reserve construction land for facilities, so as to flexibly respond to a variety of complex situations and solve corresponding problems with pertinence.
At the community network level, community medical facilities have a network and nodal nature. On the one hand, as the core of the community defence system, community medical facilities participate in the construction of urban public health networks to prevent and respond to health emergencies. On the other hand, individuals and families, the basic nodes of pandemic prevention and control, are closely connected with the community to build community-level pandemic prevention and provide control support.
At the individual facilities level, community medical facilities reflect a basic and functional nature. The guarantee of the construction of basic facilities and the control of the spatial layout and allocation of resource elements can promote effective space control and material supplies during special periods, with the community, as the basic unit.
The medical facilities in the community are the core and ‘brain’ of the whole community defence system construction. Community medical facilities drive the self-organization and self-governance of community residents as well as the construction of medical support networks at different spatial scales, and ultimately promote the improvement of abilities, processes, goals and other aspects of community resilience. Furthermore, the ‘resilience’ of human settlements can help people survive and develop better in complex and changeable external environments.
Authors' contribution: Fang Wang provided research inspiration for the research design and organised and promoted the study through the entire process. Yuanyang Fang was primarily responsible for the entire research, writing and revising process. Handuo Deng was involved in the writing and revising processes. Fangzhen Wei offered valuable research ideas and collected some information and data.
Authors Note: Author Handuo Deng is also affiliated to The Department of Land Economy, University of Cambridge.
Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: from the Peking University’s Humanities and Social Sciences Special Project (Grant No. 34).
ORCID iDs: Fang Wang https://orcid.org/0000-0001-6038-2002
Fangzhen Wei https://orcid.org/0000-0002-3709-5803
==== Refs
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PMC009xxxxxx/PMC9002297.txt |
==== Front
Value Health
Value Health
Value in Health
1098-3015
1524-4733
International Society for Pharmacoeconomics and Outcomes Research, Inc. Published by Elsevier Inc.
S1098-3015(22)00146-2
10.1016/j.jval.2022.03.004
Health Policy Analysis
Real-World Impact of Transferring the Dispensing of Hospital-Only Medicines to Community Pharmacies During the COVID-19 Pandemic
Murteira Rodrigo PharmD 1
Romano Sónia PharmD 1
Teixeira Inês BA, MSc 1
Bulhosa Carolina MSc 1
Sousa Sérgio MSc 1
Conceição Maria Inês PharmD 2
Fonseca-Silva Anabela PharmD 2
Martins Humberto PharmD 2
Teixeira Rodrigues António PharmD, PhD 134∗
1 Centre for Health Evaluation & Research/Infosaúde, National Association of Pharmacies, Lisbon, Portugal
2 Infosaúde, National Association of Pharmacies, Lisbon, Portugal
3 Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
4 ICVS/3Bs PT Government Associate Laboratory, Braga/Guimarães, Portugal
∗ Correspondence: António Teixeira Rodrigues, PharmD, PhD, Centre for Health Evaluation & Research/Infosaúde, National Association of Pharmacies, Rua Marechal Saldanha 1, Lisbon 1249-069, Portugal.
12 4 2022
12 4 2022
4 3 2022
© 2022 International Society for Pharmacoeconomics and Outcomes Research, Inc. Published by Elsevier Inc.
2022
International Society for Pharmacoeconomics and Outcomes Research, 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.
Objectives
In Portugal, the dispensing of most outpatient specialty medicines is performed exclusively through hospital pharmacies and totally financed by the National Health Service. During the COVID-19 first wave, the government allowed the transfer of the dispensing of hospital-only medicines (HOMs) to community pharmacies (CPs). This study aimed to measure the value generated by the intervention of CP in the dispensing of HOM.
Methods
A single-arm, before-and-after study with 3-month follow-up was conducted enrolling a randomly selected sample of patients or caregivers with at least 1 dispensation of HOM through CP. Data were collected by telephone interview. Main outcomes were patients’ self-reported adherence (Measure Treatment Adherence), health-related quality of life (EQ-5D 3-Level), satisfaction with the service, and costs related to HOM access.
Results
Overall 603 subjects were recruited to participate in the study (males 50.6%) with mean 55 years old (SD = 16). The already high mean adherence score to therapy improved significantly (P < .0001), and no statistically significant change (P > .5757) was found in the mean EQ-5D score between baseline (0.7 ± 0.3) and 3-month follow-up (0.8 ± 0.3). Annual savings account for €262.1/person, arising from travel expenses and absenteeism reduction. Participants reported a significant increase in satisfaction levels in all evaluated domains—pharmacist’s availability, opening hours, waiting time, privacy conditions, and overall experience.
Conclusions
Changing the dispense setting to CP may promote better access and satisfaction. Moreover, it ensures the persistence of treatments, promotes savings for citizens, and reduces the burden of healthcare services, representing a crucial public health measure.
Keywords
community pharmacies
COVID-19
health services research
hospital-only medicines
patient satisfaction
public health
==== Body
pmcIntroduction
The COVID-19 pandemic deeply challenged health systems capacity to handle current and emerging threats worldwide. In Portugal, several measures were adopted to contain the transmission of the virus and the spread of the disease, starting on March 18, 2020, when the state of emergency was first time declared. The law imposed unprecedented measures, with stricter restrictions over domestic and international movements and social distancing rules, reducing the number and proximity of contacts.1 In this context, a decrease in the delivery of healthcare, through a lower number of diagnosis,2, 3, 4 emergency visits,5, 6, 7 and medical appointments,8 , 9 was alarming, given the risk of increased severe and long-term health problems. This scenario emphasized the need of public health measures that ensure the continuation of care, including strategies to promote continuous access to medication.10
In Portugal, the dispensing of outpatient high-cost medicines for oncology, multiple sclerosis, and human immunodeficiency virus (HIV), among other pathologies, is performed exclusively through hospital pharmacies (HPs) and totally financed by the Portuguese National Health Service. Nevertheless, in response to this pandemic crisis, national authorities took several transitional emergency measures, such as allowing patients to receive their hospital-only medicines (HOMs) in community pharmacies (CPs).11 This policy measure aimed to ensure the proximity and safety of the dispensing act in a period of extreme workload in hospitals and, consequently, promote the protection of risk groups by avoiding nonessential travel. In this context, a nationwide collaborative program, known as “Operação Luz Verde” (OLV) (hereinafter referred to as OLV Initiative), was launched ensuring the access to these medicines through CP, as a free service that enabled the continuity of care and freedom of choice. This initiative was supported by the “Pharmacist Support Line” (LAF), a service coordinated by the Portuguese Royal Pharmaceutical Society that secured the exchange of information among stakeholders.12 Starting from March 23, 2020, cross-country CP guaranteed the access in proximity to HOM.
This study aimed to measure the value generated by the intervention of CP in the dispensing of HOM within OLV Initiative.
Methods
Study Design
The OLV Initiative study (OLV study) was a national single-arm, before-and-after cohort study of patients and/or caregivers who had access to HOM through CP, engaged in the OLV Initiative.
Data Sources, Population, Time Period, and Variables
Three data sources were used for study purposes: pharmacy dispensing software Sifarma®, telephone questionnaires, and LAF database.
From the pool of patients of the LAF database, a simple random sampling technique was used to select OLV participating subjects (individuals who had at least 1 dispensed HOM recorded in the CP’s dispensing software Sifarma on April 24, 2020). From May 15, 2020 to July 7, 2020, subjects were contacted by telephone, by the research team, for eligibility assessment and invited to participate in the study. Subjects had to be aged ≥ 18 years and understand and speak Portuguese. After giving verbal informed consent, participants—patients or caregivers—were interviewed for baseline data collection using a structured questionnaire regarding sociodemographic data (patient date of birth, employment status of the individual who gets the medicines at HP and/or CP), previous experience with the HP and the current experience with CP (satisfaction with business hours, waiting time, privacy during dispense, pharmacist’ availability, global experience with the service), waiting and travel time, means of transportation (car, subway, train, bus, taxi, walk) and related costs (€), working absenteeism defined as working-day time lost (none, half-day, one day), hospital-pharmacy frequency visits (once a month, 2/2 months, 3/3 months, 6/6 months, other), health-related quality of life (HRQOL), and adherence to therapy.
A second telephone interview was performed 3 months after the index date (baseline questionnaire date) for follow-up data collection, namely HRQOL, adherence, and preferences regarding the setting to access the specialty medicines in the future. The last visit of the last patient occurred on October 10, 2020. In cases where the questionnaires were answered by the caregiver, the HRQOL and adherence measurements were not applied. The remaining variables were collected only for participants who reported being themselves to get the medicines at HP and/or with the transferring, at CP. Additionally, the CP dispensing software was used to assess the patients’ sex and dispensed therapeutic indication of HOM.
The study received ethics clearance by the Ethics Committee of the Institute for Bioethics of the Portuguese Catholic University of Porto, ethics screening report number 03.2020. All principles of ethical research were followed according to the Helsinki Declaration. An oral informed consent was obtained from all participants. No incentives were provided for participation in the study.
Outcome Measures
Participants’ satisfaction with CP and HP was measured using a 5-item Likert scale, and preference regarding the place of dispensing by a dichotomous closed-ended question.
Adherence was measured using the self-reported 7-item Measure Treatment Adherence (MTA) tool validated for the Portuguese Population.13 The MTA is a psychometric tool derived from the Morisky et al14 questionnaire and evaluates the individuals’ behavior in relation to the daily use of medication.
HRQOL was assessed using the EQ-5D 3-level (EQ-5D-3L) generic instrument. The EQ-5D-3L covers 5 dimensions of health (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression) each with 3 levels of functioning (1 = no problems, 2 = some problems, and 3 = severe problems).15 , 16
The economic impact per patient/visit and patient/year was estimated, through working absenteeism (days) and transportation costs (costs of mean of transportation and travel time) data.
Sample Size
To assure a maximum absolute error in the global estimates of 4% with a 95% confidence level, a sample of 600 subjects were estimated to be sufficient. The sample was randomly selected out of the total 9782 subjects registered in the OLV Initiative with at least one dispensed medication, at the beginning of the study.
Statistical Analysis
Descriptive statistics were estimated for the whole data set. Categorical variables were summarized by absolute and relative frequencies, including counts of missing observations. Continuous variables were summarized by the number of nonmissing values, using measures of central tendency and dispersion (mean and SD).
HRQOL and adherence were assessed using the EQ-5D-3L16 and MTA13 indexes, respectively, at baseline and 3-month follow-up. For participants that completed follow-up, mean change index score (endpoint-baseline) were also computed and compared using the Wilcoxon signed rank test with continuity correction, after rejection of normality assumption. Mean differences between the satisfaction and characterization of journey to CP and HP were also tested, using the appropriate test (paired Student’s t test or Wilcoxon signed rank test, if the normality assumption was rejected). Working absenteeism per visit (CP and HP) was calculated considering the national average salary for men and women.17 Transportation costs were estimated considering the mean of transportation and official tariffs. For subjects who refer to use their car, toll road charges were not considered. Average costs and savings per patient per year, arising from absenteeism and traveling changes, were calculated for both settings (CP and HP), multiplying the costs per visit per setting, by the average number of the participants’ HP reported visits per year. Additionally, an extrapolation of total costs was conducted for the total number of patients who had received at least one HOM in CP by the end of the study period (n = 15 441).
All statistical tests consider a 5% significance level. Sampling was conducted in SAS software (SAS Institute, Cary, North Carolina). Data analysis was performed using MS Office, SAS, and R software (https://www.r-project.org/).
Results
Participants’ Flow and Characteristics
A total of 659 subjects (patients/caregivers) were contacted by the research team. Of those, 38 did not meet the inclusion and exclusion criteria and 18 declined to participate, resulting in 603 participants enrolled. Compared with the participants, refusals had similar sex (P = .3283) and therapeutical indication distributions (P = .6438). Most participants (84.6%) were the patients themselves, that is, the users of the HOM. Over the study period, 28 participants were lost to follow-up. Participants’ flow is depicted in Figure 1 .Figure 1 Participants’ flow.
The mean patients’ age was 55 years (SD = 16) and 50.6% were male. Overall, more than half of the individuals who got the medication were employed (50.4%), and the most prevalent therapeutic included HIV (25.2%) and oncology (20.6%). In most cases, the respondents (either the patients or caregivers) were the ones who travel to obtain the medicines. The baseline characteristics of participants are summarized in Table 1 .Table 1 Baseline sociodemographic and clinical characteristics.
Variables Total (N = 603)
Age, years, mean (SD) (NR = 0) 55 (16)
Sex (male), n (%) (NR = 2) 305 (50.6)
Employment status, n (%) (NR = 4)
Employed 302 (50.4)
Pensioner 220 (36.7)
Unemployed 46 (7.7)
Student 8 (1.3)
Other 23 (3.8)
More prevalent therapeutical indications,∗ n (%)
HIV 152 (25.2)
Oncology 124 (20.6)
Transplant 67 (11.1)
Transplant/Crohn disease/colitis 59 (9.8)
Multiple sclerosis 46 (7.6)
Rheumatoid arthritis/psoriasis/psoriatic arthritis 22 (3.6)
Participants, n (%) (NR = 0)
Patient 510 (84.6)
Getting the medicine in person at HP 450 (88.2)
Getting the medicine in person at CP 373 (73.1)
Caregiver 93 (15.4)
Getting the medicine in person at HP 69 (74.2)
Getting the medicine in person at CP 72 (77.4)
CP indicates community pharmacy; HIV; human immunodeficiency virus; HP, hospital pharmacy; NR, nonrespondent.
∗ Accordingly, to the summary of product characteristics.
HRQOL and Adherence
The HRQOL and adherence scores are summarized in Table 2 . Overall, most patients were adherent to HOM therapeutics. The transferring of the local of dispense from HP to CP not only maintained the high level of adherence to therapy but also significantly increased the mean score (P < .0001). Regarding HRQOL, there were no statistically significant changes (P > .5757) in the mean EQ-5D-3L score between baseline (0.7 ± 0.3) and 3-month follow-up (0.8 ± 0.3).Table 2 Patients mean scores for EQ-5D-3L index and MTA.
Variables Baseline n = 504 (NR = 6) 3 months n = 486 (NR = 5) P value∗
EQ-5D index score, mean (SD) 0.7 (0.3) 0.8 (0.3) .5757
MTA score
Mean (SD) 5.8 (0.2) 5.9 (0.2) < .0001
Score ≥ 5, n (%) 500 (99.2) 486 (100.0)
EQ-5D-3L indicates EQ-5D 3-Level; MTA, 7-item Measure Treatment Adherence; NR, nonrespondent.
∗ Wilcoxon signed rank test.
Participants’ Satisfaction and Preferences
An improvement of satisfaction levels at CP was observed compared with HP. This increase was statistically significant in all the evaluated domains—pharmacist’s availability, business hours, waiting time, privacy during dispense, and global experience. Additionally, 91% of participants reported to prefer to continue to have access to their medication at CP in a postpandemic scenario.
The satisfaction domain with lower scores in both settings was the privacy conditions. Results of satisfaction per setting and domain are depicted in Figure 2 .Figure 2 Participants’ satisfaction with the assessed dimensions of the community and hospital pharmacies.
NR indicates nonrespondent.
Characterization of a Journey to the CP and HP
In this study, a journey to the pharmacy (community or hospital) refers to the act of leaving home, arriving to the pharmacy, and returning home, which can take more than one travel mode. Additionally, the waiting time to be attended in the pharmacy was also considered. The mean number of travel modes was 1.0 ± 0.1 and 1.2 ± 0.4 for CP and HP, respectively, with a significant mean difference of 0.2 ± 0.4 modes. The main travel mode for HP was the subjects’ car (62.8%), whereas walking (55.3%) was the most referred for CP. The mean journey time to HP and CP was 138.9 ± 117.2 and 23.9 ± 18.3 minutes, respectively, with a significant time gain of 114.5 ± 120.6 minutes per visit to CP. The average estimated saving per transferred visit to CP was €35.8 ± 52.4. A total of 27.6% of employed participants reported missing work at least half-day to get the medicines at the HP compared with 0.4% at the CP. Overall results are summarized in Table 3 .Table 3 Journey, absenteeism, and estimated costs per patient.
Variables HP CP Mean difference P value∗
Travel modes (number)
Mean (SD) 1.2 (0.4) 1.0 (0.1) 0.2 (0.4) < .0001
Car 326 (62.8%) 195 (43.8%) — —
Public transportation 168 (32.4%) 8 (1.8%) — —
Walking 51 (9.8%) 246 (55.3%) — —
Taxi 24 (4.6%) 3 (4.6%) — —
Travel time (min)
Mean (SD) 116.8 (109.5) 14.7 (13.0) 101.1 (112.3) < .0001
Waiting time (min)
Mean (SD) 22.0 (27.4) 9.0 (10.7) 13.2 (29.5) < .0001
Journey time (min)
Mean (SD) 138.9 (117.2) 23.9 (8.3) 114.5 (120.6) < .0001
Work absenteeism n (%)
None/less half-day 181 (72.4%) 235 (99.6%) < .0001
Half-day 36 (14.4%) 1 (0.4%)
One day 33 (13.2%) 0 (0.0%)
n 250 (NR = 4) 236 (NR = 0)
Frequency visits/year
Mean (SD) 8 (3.5) —
Estimated costs/visit (€)
Travel, mean (SD) 33.1 (47.8) 1.3 (3.1) 31.0 (47.2) < .0001
Absenteeism,† mean (SD) 9.4 (16.6) 0.1 (1.6) 9.0 (16.3) < .0001
Total, mean (SD) 37.7 (52.8) 1.4 (3.3) 35.8 (52.4) < .0001
Estimated costs/year/patient‡ (€)
Travel, mean (SD) 246.3 (415.0) 8.7 (17.1) 226.8 (405.1) < .0001
Absenteeism,† mean (SD) 68.0 (132.2) 0.6 (9.7) 64.8 (130.9) < .0001
Total, mean (SD) 278.5 (460.6) 9.1 (18.3) 262.1 (452.1) < .0001
CP indicates community pharmacy; HP, hospital pharmacy.
∗ Paired Student’s t test or Wilcoxon signed rank test, as appropriate.
† Estimated costs of absenteeism were only calculated to employed subjects.
‡ For CP setting, it was assumed that the frequency visits per year was the same as for HP.
Annual savings estimated from the reported reduction in travel expenses (€226.8) and absenteeism (€64.8) account for a total of €262.1 per patient. Considering the 15 441 patients who received, at least once, their HOM in CP by the end of this study through this initiative, savings rose to approximately €4 million.
Discussion
During the first wave of the COVID-19 outbreak in Portugal, OLV Initiative was launched as a public health measure to promote access and continuation of care to patients receiving HOM.11 , 12 Health professionals and stakeholders, with the institutional support of professional organizations and patient associations, joined efforts to guarantee access to outpatient specialty medicines to patients in a nationwide initiative. This study investigated the impact of transferring the dispensing of several HOM to CP.
The results suggest that switching the dispense of these medicines, from HP to CP, may have a significant and positive impact on patients and caregivers’ satisfaction, savings, and adherence to therapy, with no known risks for patients nor negative impact on their quality of life. Community pharmacists worked collaboratively with the patients’ hospital healthcare team—pharmacists and prescribers—creating a supporting network to patients, which guaranteed a timely, safe, and quality access to HOM.
The larger business hours, reduced waiting time, and proximity of CP seems to be very convenient and probably contribute to significantly higher satisfaction with CP than HP. It is worth emphasizing that high levels of satisfaction with the CP have been consistently reported nationally and internationally.18, 19, 20 Moreover, higher convenience of CP location is reflected in the travel mode preferred by respondents, with the majority reporting going on foot to CP versus by car to HP. Given that geographical accessibility is a key dimension of access to medicines,21 the proximity of CP to subjects also seems to contribute to reduce absenteeism and traveling time, resulting in savings to patients and caregivers. Similar findings were found in a pilot study22 previously conducted in Portugal and framed by the National Strategy for Medicines 2016 to 2020 that foresaw the valuation of CP as healthcare providers, including the possibility of HOM dispensing at that setting.23 The pilot was conducted in 2016/2017 and included people living with HIV under antiretroviral therapy, from one hospital in the country’s capital—Lisbon.22 Results showed a significant improvement of patients’ satisfaction in all domains assessed and also a reduction in traveling time (34 ± 29 minutes) and waiting time (17 ± 29 minutes) for CP compared with HP.22 Higher gains on travel times were found in this national study (101.1 ± 112.3 minutes), to the extent that hospitals outside large municipalities serve a population more dispersed and thus further away from hospital centers.24
The satisfaction dimension assessed with lower value for both settings in the OLV study was the “privacy during dispense.” Privacy constraints are widely recognized and frequently reported as a barrier, especially for people living with stigmatizing conditions as those taking some HOM.18 , 25, 26, 27 This fact underlies the importance of the results presented in this study, showing a statistically significant improvement in the privacy domain. Nationwide, large efforts have been made by Portuguese pharmacies, who have invested in creating private counseling areas as their scope have been shifting to service provision.28 , 29 This may explain the fact that > 86% of participants were satisfied or very satisfied with the privacy dimension.
To the best of our knowledge, at least 3 other European countries allowed, under emergency measures, the CP to expand their roles and dispense high-cost medicines that were previously only accessible in the hospital setting.30 Indeed, the regular dispensing of specialty medicines that do not require in-hospital administration for treatment, as the case of some therapeutics for hepatitis C, HIV, multiple sclerosis, psoriatic arthritis, and others, is already provided by both the CP and HP in most jurisdictions.31 Some examples are Australia with medicines for HIV and viral hepatitis,32 , 33 Canada34 with oral anticancer drugs, Belgium with immunosuppressants,35 , 36 and France and Germany with antiretroviral therapy,37 , 38 with CP being reimbursed for the service.
Consistent with previous international evidence,37 when Portuguese participants were asked about the preferred setting to continue to refill their HOM, a very high proportion reported preference to continue to have access to their medication at CP, close to their home or place of work. The results highlight the importance of giving citizens freedom of choice, but also the relevant public health role performed by the network of CP in interprofessional collaboration, contributing to the system’s efficiency.
Regarding HRQOL, no significant impact was observed with the transfer of HOM to CP, with patients maintaining their overall status at 3-month follow-up. Additionally, a significant, but possibly not clinically relevant, increase of adherence to HOM at CP was found, with patients presenting high levels of adherence at baseline. These findings suggest that the access to medicines and clinical stability of all patients were maintained given that adherence is crucial for the success of the therapy.39 , 40 In line with these results, a recently published systematic review demonstrated the positive influence of medicines’ dispensing by CP on health outcomes, quality of life, and satisfaction of patients.41
Overall results highlight the feasibility of enlarging the role of CP, making HOM available with improved convenience, and guaranteeing the continuing of care. The provision of this service by CP can also reduce some existent inequalities, given that patients from the inland (the most disadvantaged areas of the country) are those who had to travel greater distances to obtain their medicines.24 Moreover, the transferring can be an important strategy to reduce the existent workload on hospital-pharmacy services.42 Recent studies have demonstrated the new societal demands, suggesting that a fully integrated, intersectoral, and interprofessional collaboration is required, to build upon the lessons and experiences of this COVID-19 global crisis.43, 44, 45
Strengths and Limitations
The main strength of this study is the use of real-world data of a nationwide representative sample of people using outpatient HOM and had the dispense transferred to CP, allowing a real picture of the impact of this transfer. Moreover, this study presented a high retention rate and included participants on different therapeutic classes of medicines, capturing a global picture and not only the view of a specific group.
This study also has some limitations. First, the study design was a before-and-after with no control group, thus being subject to bias, such as recall bias concerning the experience in the HP setting. To minimize this bias, the experience regarding HP setting (satisfaction and journey) was asked at baseline (before), reducing the time span between the last visit to the HP and the data collection point. Second, a social desirability bias could have occurred because participants were asked about subjective satisfaction measures. Third, variation in the interviewers may have contributed to intrainterview variability and influence participants’ response, although this possibility was reduced by training the telephone interviewers. Fourth, considering the main data collection tool, questionnaires bias could also have occurred. Nonetheless, only validated tools and questions already piloted and used in previous similar studies were included in the telephone questionnaire.
Finally, this was a short-period study, which is insufficient to estimate long-term effects of the transferring. Therefore, it would be desirable to be able to continue with similar national initiatives or have access to big data from different data sources, to assess the long-term impacts of the initiative.
As future research, we aim to access the value of transferring the dispensing of HOM to the CP from the National Health Service perspective, to estimate a value to reward their contribution for the delivery of this service and encourage policy makers to reimburse it.
Conclusions
The study findings suggest that changing the dispense setting of outpatient specialty medicines from HP to CP will have a positive impact on people’s HRQOL, adherence to therapy, satisfaction, access times, and savings, representing a crucial public health measure, although some major challenges need to be overcome, namely a sustainable regulatory and remuneration framework to leverage this service in the CP.
Article and Author Information
Author Contributions:Concept and design: Murteira, Martins, Teixeira Rodrigues
Acquisition of data: Murteira, Bulhosa, Sousa, Conceição, Fonseca-Silva
Analysis and interpretation of data: Murteira, Romano, Teixeira Rodrigues
Drafting of the manuscript: Murteira, Romano, Teixeira
Critical revision of the paper for important intellectual content: Murteira, Romano, Teixeira, Bulhosa, Sousa, Conceição, Fonseca-Silva, Martins, Teixeira Rodrigues
Statistical analysis: Bulhosa, Sousa
Provision of study materials or patients: Murteira, Conceição, Fonseca-Silva
Administrative, technical, or logisticsupport: Teixeira, Conceição, Fonseca-Silva
Supervision: Martins, Teixeira Rodrigues
Conflicts of Interest Disclosures: Drs Murteira, Romano, Teixeira Rodrigues, Teixeira, Bulhosa and Sousa are employed by Centre for Health Evaluation & Research/Infosaúde, a company owned by the Portuguese National Association of Pharmacies. Drs Conceição, Fonseca-Silva, and Martins are employed by Infosaúde, a company owned by the Portuguese National Association of Pharmacies. No other disclosures were reported.
Funding/Support: This study was supported by the Portuguese National Association of Pharmacies.
Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Acknowledgment
The authors are grateful to all subjects who voluntarily agreed to participate in the study. Moreover, the authors express great appreciation to the community and hospital pharmacies and pharmacists of Portuguese Royal Pharmaceutical and Medical Societies, Portuguese National Association of Pharmacies, Portuguese Pharmacies Association, Portuguese Full-Service Pharmaceutical Distributors Association, Portuguese Pharmaceutical Industry Association, Patient Associations, Portuguese Association of Hospital Administrators, and Dignitude Association, who supported the nationwide pharmaceutical response to the COVID-19 pandemic.
Availability of Data and Material: Data generated from this study are available from the corresponding author on reasonable request.
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PMC009xxxxxx/PMC9002318.txt |
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J Am Coll Radiol
J Am Coll Radiol
Journal of the American College of Radiology
1546-1440
1558-349X
American College of Radiology
S1546-1440(22)00261-7
10.1016/j.jacr.2022.02.037
Civil Discourse
Training and Education
The Radiology Residency Application Arms Race—Is Preference Signaling the Answer?
Slanetz Priscilla J. MD, MPH a∗
Ngo Michael BS b
Ali Kamran MD c
Chapman Teresa MD, MA d
a Vice Chair of Academic Affairs in the Department of Radiology and Associate Program Director of the Diagnostic Radiology Residency at Boston University Medical Center, Boston, Massachusetts; Director of Early Career Faculty Development and Co-Director of the Academic Writing Program for Boston University Medical Group; President of Massachusetts Radiological Society; Vice President of the Association of University Radiologists; and Subspecialty Chair of the ACR Appropriateness Criteria Breast Imaging Panels
b Department of Radiology, Boston University Medical Center, Boston, Massachusetts
c Diagnostic Radiology Residency Program Director, University of Kansas School of Medicine, Wichita, Kansas; President of the Wichita Radiological Group; Chair of the Small/Non-University Committee for APDR; and Councilor for the Kansas Radiological Society
d Diagnostic Radiology Residency Program Director, University of Washington, Seattle, Washington
∗ Priscilla J. Slanetz, MD, MPH: Boston University Medical Center, Department of Radiology, 820 Harrison Avenue, FGH-4, Boston, MA 02118.
12 4 2022
6 2022
12 4 2022
19 6 779781
© 2022 American College of Radiology.
2022
American College of Radiology
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 coronavirus disease 2019 (COVID-19) pandemic has made virtual interviews the norm as students seek to secure a residency position. With the United States Medical Licensing Examination (USMLE) Step 1 examination becoming pass-or-fail next year, residency programs are faced with devising a new way to select appropriate candidates from an increasingly overwhelming number of applications for the available positions. In fact, some programs are experiencing a 20% to 25% increase in applications this year alone. Although programs are being encouraged to implement a more “holistic” approach when selecting students for interviews, the increasing number of applications is making this process quite challenging. Recently, the Association of American Medical Colleges introduced a pilot of preference signaling, a process by which applicants applying to specific specialties can indicate, or “signal,” their interest to up to five training programs.
For radiology, is preference signaling the solution that will make the residency selection process better for programs and applicants or will this approach only complicate the process further?
Response from Michael Ngo, BS, Fourth-Year Medical Student, Boston University School of Medicine
With the switch to virtual interviews and a dramatic increase in applications, radiology residency programs are tasked with finding applicants who are not only qualified but also genuinely interested. Preference signaling has been proposed as a possible solution to help programs with this goal. However, requiring applicants to signal programs could make the application process more complicated without adding substantial benefits for the applicant. In addition, it might not even solve the underlying problem of overapplying.
Signaling will add another level of complexity that applicants would need to navigate. Without a clear understanding of how programs will use signals in their assessment, there will be differing opinions on the best way to leverage these signals to maximize their chances of obtaining an interview, which could disadvantage applicants without strong mentors to help guide them through this new system. Additionally, the benefits of preference signaling seem minimal. Applicants can already express their interest in a program by participating in away rotations, writing letters of interest, and customizing personal statements. Because these methods exist, the incorporation of signaling may only increase the amount of work for applicants.
Furthermore, preference signaling is unlikely to fix the core problem of overapplying. Programs will likely interview applicants who did not signal to their program; thus, overapplying to many programs will increase an applicant’s chance of receiving an interview and eventually matching. Rather than implementing a new system that could complicate the application process without addressing the underlying issue of overapplying, efforts should be concentrated on piloting ideas that could minimize the advantage that comes with sending more applications.
Response from Kamran Ali, MD, Program Director-Diagnostic Radiology, University of Kansas School of Medicine-Wichita
Preference signaling is an intriguing concept to a program director of a small or university-affiliated program in the heartland of America. Virtual interviews have been beneficial to our program because more candidates get to “see” our program without the added cost of travel. Yet we are never sure who is genuinely interested in the program or using as a filler on their way to interviews at programs with ivory towers. As Step 1 scores become pass or fail, we anticipate even more applications to our program. With limited resources and limited faculty bandwidth to conduct a rigorous review of applications, the applicant review will certainly be daunting for small programs.
Would preference signaling help a program like ours, which is in a geographically isolated part of the country? We have many applicants with stellar credentials who interview at numerous other programs. Although many of our matched applicants are from the Midwest, having a coastal applicant preference signal our program would be highly valuable in knowing their genuine interest in relocating to the Midwest. Although a seasoned interview committee can certainly help, many interviewees blend in with an impressive body of work in medical school and polished interview skills. Ranking these candidates can become an educated guessing game into “they are great, but will they really come to Wichita?”
Although preference signaling is not “the” proliferation treaty that will solve the application arms race, it will certainly offer value in the form of another metric small programs can use to facilitate holistic reviews and interview selections. We do not anticipate a deluge of preference signals, but even one or two may help tilt the odds slightly further in a program’s favor to a successful match of a small complement of residents.
Response from Teresa Chapman, MD, MA, Diagnostic Radiology Residency Program Director, University of Washington
The goals of a residency program director (PD) during the recruitment season are multifold. We are tasked with recruiting, selecting, and successfully admitting the candidates best suited for our training programs’ intended aims. This requires an initial team of qualified, energetic, and dedicated selection members who can conduct holistic reviews of the submitted applications and a second team of interviewers with dedicated time to meet with the selected candidates. Ensuring high-quality holistic reviews of every individual within this ever-enlarging pool of applications is an impossible task.
Some form of cursory screening process is required in this selection process—either a rapid reading of the application by an individual or a filtering system based on data such as USMLE or COMLEX scores. Both methods inevitably risk skipping qualified applicants who, under different circumstances, might have been admitted to our program and thrived. A recent study showed that without available USMLE Step 1 scores, applicant selection is likely to lean more heavily on Step 2 scores and medical school reputation [1]. This has the potentially harmful effect of discounting exceptional individuals with backgrounds from lower socioeconomic resources. Strategies are necessary to reduce the number of applications requiring review.
The stated primary goal by the Association of American Medical Colleges for preference signaling is to provide a process for sharing genuine interest in a program that enhances accuracy and fairness [2]. I, and others, believe this will be an important part of the solution to address overapplication—preference signaling is supported by most radiology PDs surveyed about mitigating the overapplication phenomenon [3]. As a PD, I want certainty that our incoming trainees will be happy in their new job. Knowing they are aiming to be in the region or at our university is undeniably reassuring. Key to implementing this feature is the Program Code of Conduct, requiring that (1) programs shall not disclose which applicants signaled or did not signal; (2) programs shall not ask interviewees where they signaled; and (3) programs shall not disclose the number of signals received. The only concern I have about preference signaling is that some programs may make the mistake of limiting their consideration of candidates exclusively to those who signaled, and this is not the intended design outcome.
Summary
In summary, both graduating medical students and training programs are facing challenges with the residency match related to an exponential increase in applications exacerbated by virtual interview platforms adopted since the COVID-19 pandemic. Given limited interview slots, programs desire to holistically review applicants but realistically need tools to filter and identify the candidates that will excel in the program and are genuinely interested in matching. Students are focused on maximizing their ability to match and often apply broadly to an excessive number of programs to ensure that they match successfully.
Given that virtual interviews are here to stay, several approaches to de-escalate the rise in applications and help programs make more informed interview decisions have been considered, including increasing the cost of applications, setting an application cap, requiring standardized letters of recommendations, implementing secondary application questions, allowing applicants to rank geographic preferences, and asking applicants to “signal” up to six programs of interest. A recent survey of 2021 otolaryngology applicants and PDs showed that signaling statistically increased an applicant’s chance of receiving an interview and a majority viewed signaling positively [4]. If selective (limited to a small number of programs), signaling does achieve its intended result of indicating genuine interest [5], although many applicants have accomplished this same effect for years through targeted emails, personalized personal statements, or telephone calls to residency program leadership from faculty or a medical school dean.
Is preference signaling the answer? The data are not in yet. However, in the 2022 match, radiology residency programs will be participating in a pilot that will not only entail preference signaling, but also allow applicants to rank up to three geographic preferences and answer supplemental application questions highlighting up to five meaningful past experiences. The hope is that this approach will make it easier for programs to select applicants and that applicants will focus on targeting fewer training programs. It remains unclear how this tiered approach will help except that it will create two new ERAS filters that programs almost certainly will use—one based on signaling and one on geographic location. That information, in addition to Step 2 scores, may become the next tools by which programs narrow down the applicant pool. Realistically, however, a holistic review is the only sure way that programs and applicants will find the best match for postgraduate training.
Dr Chapman is a member of the ACGME Radiology Review Committee. The content here represents Dr Chapman’s input as a program director and does not reflect the opinions of the Radiology Review Committee. The other authors state that they have no conflict of interest related to the material discussed in this article. Dr Ali is on a partnership track; and the other authors are non-partner/non-partnership track/employees.
==== Refs
References
1 Maxfield C.M. Montano-Campos J.F. Chapman T. Factors influential in the selection of radiology residents in the post-Step 1 world: a discrete choice experiment J Am Coll Radiol 18 2021 1572 1580 34332914
2 Association of American Medical Colleges Supplemental ERAS application guide Published 2021. Available at: https://students-residents.aamc.org/media/12326/download
3 Moran S.K. Nguyen J.K. Grimm L.J. Should radiology residency interviews remain virtual? Results of a multi-institutional survey inform the debate Acad Radiol 18 2021 S1076-6332(21)00491-8
4 Pletcher S.D. Chang C.W.D. Thorne M.C. Malekzadeh S. The otolaryngology residency program preference signaling experience Acad Med 2021 10.1097/ACM.0000000000004441
5 Salehi P.P. Azizzaseh B. Lee Y.H. Preference signaling for competitive residency programs in NMRP J Grad Med Educ 11 2019 733 734 31871581
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PMC009xxxxxx/PMC9002334.txt |
==== Front
Virology
Virology
Virology
0042-6822
1096-0341
Published by Elsevier Inc.
S0042-6822(22)00063-0
10.1016/j.virol.2022.04.005
Article
Flavonols and dihydroflavonols inhibit the main protease activity of SARS-CoV-2 and the replication of human coronavirus 229E
Zhu Yue a
Scholle Frank b
Kisthardt Samantha C. b
Xie De-Yu a∗
a Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, USA
b Department of Biology, North Carolina State University, Raleigh, NC, USA
∗ Corresponding author.
12 4 2022
6 2022
12 4 2022
571 2133
24 2 2022
7 4 2022
7 4 2022
© 2022 Published by Elsevier Inc.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Since December 2019, the deadly novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the current COVID-19 pandemic. To date, vaccines are available in the developed countries to prevent the infection of this virus; however, medicines are necessary to help control COVID-19. Human coronavirus 229E (HCoV-229E) causes the common cold. The main protease (Mpro) is an essential enzyme required for the multiplication of these two viruses in the host cells, and thus is an appropriate candidate to screen potential medicinal compounds. Flavonols and dihydroflavonols are two groups of plant flavonoids. In this study, we report docking simulation with two Mpro enzymes and five flavonols and three dihydroflavonols, in vitro inhibition of the SARS-CoV-2 Mpro, and in vitro inhibition of the HCoV 229E replication. The docking simulation results predicted that (+)-dihydrokaempferol, (+)- dihydroquercetin, (+)-dihydromyricetin, kaempferol, quercetin, myricentin, isoquercitrin, and rutin could bind to at least two subsites (S1, S1’, S2, and S4) in the binding pocket and inhibit the activity of SARS-CoV-2 Mpro. Their affinity scores ranged from −8.8 to −7.4 (kcal/mol). Likewise, these compounds were predicted to bind and inhibit the HCoV-229E Mpro activity with affinity scores ranging from −7.1 to −7.8 (kcal/mol). In vitro inhibition assays showed that seven available compounds effectively inhibited the SARS-CoV-2 Mpro activity and their IC50 values ranged from 0.125 to 12.9 μM. Five compounds inhibited the replication of HCoV-229E in Huh-7 cells. These findings indicate that these antioxidative flavonols and dihydroflavonols are promising candidates for curbing the two viruses.
Keywords
(+)-dihydrokaempferol
(+)-dihydroquercetin (taxifolin)
(+)-dihydromyricetin
Kaempferol
Quercetin
Myricentin
Isoquercitrin
Rutin
Flavan-3-ols
==== Body
pmc1 Introduction
SARS-CoV-2 is the abbreviation of the novel severe acute respiratory syndrome coronavirus 2. This virus was firstly reported to cause a severe pneumonia in December of 2019 in Wuhan, China (Ding et al., 2020; Wang et al., 2020a; Zhu et al., 2020). On February 11, 2020, the World Health Organization (WHO) designated this pneumonia as coronavirus disease 2019 (COVID-19). COVID-19 then rapidly spread in different countries. On March 11, 2020, WHO announced the COVID-19 pandemic (WHO, 2020a, b). This pandemic has rapidly spread across all over the world. By June 21, 2021, based on the COVID-19 Dashboard by Center for Systems Science and Engineering at Johns Hopkins Coronavirus Resource Center, 117,553,726 infected cases and 3,867,641 deaths have been reported from more than 200 countries or regions. No strategy to stop the spread of this virus was available until January 2021, when several vaccines started to be approved for vaccination in several countries (CDC, 2021; Dooling et al., 2021; Gharpure et al., 2021; Kim et al., 2021; Knoll and Wonodi, 2021; Painter et al., 2021). On the one hand, since the start of vaccination, the number of infections has started to decrease. On the other hand, due to the insufficient vaccine quantities and vaccination hesitancy even where available, in the first week of February 2021, the daily infection cases and deaths were still more than 400,000 and 11,000, respectively. By Feb. 9, 2021, the case numbers still increased by more than 300,000 daily. Meanwhile, the use of vaccines has also indicated that developing effective medicines is necessary to stop COVID-19. A recent study showed that mutations in the spike protein of SARS-CoV-2 might cause the escape of new variants from antibody (McCarthy et al., 2020). The variant B.1.351 found in South Africa was reported to be able to escape vaccines developed by AstraZeneca, Johnson & Johnson (J&J), and Novavax (Cohen, 2021). Merck & Co has stopped their race for vaccines due to the lack of effectiveness of their products, instead, they continue to focus on antiviral drug development (Kenilworth, 2021). Unfortunately, to date, effective medicines are still under screening. Although chloroquine and hydroxychloroquine were reported to be potentially effective in helping to improve COVID-19 (Wang et al., 2020b), the use of these two anti-malarial medicines has been arguable in USA because of potential risk concerns (Bull-Otterson et al., 2020). Other potential candidate medicines are the combination of α-interferon and anti-HIV drugs lopinavir/ritonavir (Cao et al., 2020), and remdesivir (Holshue et al., 2020; Wang et al., 2020b). In recent, PaxlovidTM was approved for emergency use (FDA, 2022; Roberts et al., 2022; Wang and Yang, 2021). Given that the efficacy of all these medicines being repurposed has not been conclusive, further studies are necessary to apply them for treating COVID-19.
SARS-CoV-2 is a single stranded RNA virus. Its genomic RNA contains around 30,000 nucleotides and forms a positive sense strand with a 5′ methylated cap and a 3′ polyadenylated tail that encodes at least six open reading frames (ORF) (Chen et al., 2020; Hussain et al., 2005). This feature allows it to be able to use the ribosomes of the host cells to translate proteins. The longest ORF (ORF1a/b) translates two polyproteins, which are cleaved by one main protease (Mpro, a 3C-like protease, 3CLpro) and another papain-like protease (PL2pro) into 16 nonstructural proteins (NSPs), which include RNA-dependent RNA polymerase (RdRp, nsp12), RNA helicase (nsp13), and exoribonuclease (nsp14). The NSPs subsequently produce structural and accessory proteins. The structural proteins include an envelope protein, membrane protein, spike (S) protein, and nucleocapsid protein (Fig. 1 ) (Ramajayam et al., 2011; Ren et al., 2013). The S protein is a type of glycoprotein and plays an essential role in the attachment and the infection of the host cells (Zhao et al., 2020). It binds to the human angiotensin converting enzyme 2 (ACE2) to help the virus enter the human cells (Hoffmann et al., 2020; Yan et al., 2020). Since May 2020, the alterations of amino acids of the S protein have created a large number of variants, such as alpha, beta, gamma, and delta variants. These new ones have shown more pathogenic and transmissible, thus caused potential challenges to use vaccines to completely control the pandemic (Abdool Karim and de Oliveira, 2021; Altmann et al., 2021; Fontanet et al., 2021; Walensky et al., 2021).Fig. 1 A diagram showing the function of the SARS-CoV-2 main protease in the virus replication in the host cells. Once the virus enters into the host cells. Its positive sense and single stranded RNA uses the ribosomes to translate open reading frames 1a and 1b to polyproteins (PP), in which the main protease and papain-like protease cleaves PPs to non-structural proteins (NSPs). Three NSPs, RNA dependent RNA polymerase (RdRp), RNA helicase, and exoribonuclease, are involved in the transcription of the positive RNA to negative sense and single stranded RNA, which is further transcribed to positive sense and single stranded RNA. Finally, structural proteins and a positive single stranded RNA assembly together to form a virus progeny.
Fig. 1
Human coronavirus 229E (HCoV-229E) is a pathogenic virus in the genus Alphacoronavirus (Woo et al., 2010). It is one of the causative viral agents of the common cold (Gaunt et al., 2010; Shirato et al., 2017). Its genome consists of a positive sense and single-stranded RNA with 27,317 nucleotides (nt). Its genome size commonly varies in different clinical isolates. For example, HCoV-229E strains 0349 and J0304 were two clinical isolates causing the common cold (Farsani et al., 2012). The entire genome of these two clinical isolates were reported to be about 27, 240 nt, which included 38.07% GC content is in 0349 and 38.13% GC content in J0304. In general, the genome of HCoV-229E is characterized with a gene order of 5′-replicase ORF1a/b, spike (S), envelope (E), membrane (M), and nucleocapsid (N)-3’ (Fehr and Perlman, 2015). Like SARS-CoV-2, the spike protein is the determinant of infections to host cells (Shirato et al., 2012). The ORF1a/b of HCoV-229E encodes 16 non-structural proteins (NSPs). The NSP5 encodes its Mpro that is required for the replication in the host cells (Farsani et al., 2012). Given that HCoV-229E is allowed to be studied in BSL2 laboratories, this pathogenic virus is an appropriate model to screen therapeutics for the treatment of both common cold and COVID-19.
Given that the SARS-CoV-2 Mpro plays a vital role in the cleavage of polyproteins and the humans do not have a homolog, it is an ideal target for anti-SARS-CoV-2 drug screening and development (Kim et al., 2016; Yang et al., 2005). It belongs to the family of cysteine proteases and has a Cys-His catalytic dyad, which is an appropriate site to design and screen antiviral drugs (Dai et al., 2020). Its high-resolution crystal structure was elucidated in April 2020 (Jin et al., 2020). Based on the crystal structure, the medicine screening from the existing antiviral medicines or designed chemicals revealed that cinanserin, ebselen, GC376, 11a, and 11b showed inhibitory effects on the Mpro activity (Chen et al., 2005; Dai et al., 2020; Jin et al., 2020; Ye et al., 2020; Zhang and Liu, 2020). A common feature is that these molecules deliver their carbonyl group (aldehyde group or ketone group) to the thiol of the 145-cysteine residue to form a covalent linkage, thus inhibit the Mpro activity. The potential application of these molecules is still under studies to evaluate their effectiveness and side effects. In addition, we recently found that flavan-3-ol gallates, such as (−)-epigallotechin-3-gallate, (−)-catechin-3-gallate, and (−)-epicatechin-3-gallate, and dimeric procyanidins promisingly inhibited the Mpro activity (Zhu and Xie, 2020). Docking simulation indicated that their inhibitory activity likely resulted from the formation of hydrogen bonds between these compounds and several amino acids in the binding domain of Mpro.
Flavonols and dihydroflavonols (Fig. 2 ) are two main groups of plant flavonoids (Fowler and Koffas, 2009; Hostetler et al., 2017; Yi et al., 2009). Quercetin, kaempferol, and myricetin are three flavonol molecules widely existing in plants. Likewise, dihydroquercetin, dihydrokaempferol, and dihydromyricetin are three dihydroflavonol molecules in plants (Xie and Dixon, 2005; Xie et al., 2004). In general, flavonols and dihydroflavonols are strong antioxidants with multiple benefits to human health (Chopra et al., 2000; de Vries et al., 1998; Egert et al., 2008; Hertog et al., 1993b; Kolhir et al., 1996; Moon et al., 2001; Murota and Terao, 2003; Teselkin et al., 1998, 2000; Weidmann, 2012). Furthermore, studies have reported that quercetin and its derivatives have antiviral activity (Cheng et al., 2015; dos Santos et al., 2014; Mehrbod et al., 2021). Based on these previous findings, we hypothesized that flavonols and dihydroflavonols might inhibit the Mpro activity of both SARS-CoV-2 and HCoV-229E. In this study, to test this hypothesis, we performed docking simulation for three dihydroflavonols, three flavonols, and two glycosylated quercetins. Then, we tested these compounds’ inhibition against the recombinant Mpro activity of SARS-CoV-2 in vitro. More importantly, five available compounds were evaluated to determine their inhibitive activity against the replication of HCoV-229E in Huh-7 cells. The resulting data showed eight compounds effectively inhibited the Mpro activity of SARS-CoV-2 and five tested compounds inhibited the replication of HCoV-229E in Huh-7 cells.Fig. 2 Structures of ebselen and ten flavonoids. Two flavan-3-ols: (−)-epicatechin and (+)-catechin; three dihydroflavonol aglycones: (+)-dihydroquercetin (taxifolin), (+)-dihydrokaempferol, and (+)-dihydromyricetin; three flavonol aglycones: kaempferol, quercetin, and myricetin; two glycosylated flavonols: quercetin-3-O-glycoside (isoquercitrin) and rutin.
Fig. 2
2 Materials and methods
2.1 Dihydroflavonols, flavonols, cell line, and coronavirus
Flavonols used in this study included kaempferol, quercetin, myricetin, quercetin-3-O-glycoside, and rutin. Dihydroflavonols used were (+)-dihydroquercetin (DHQ, taxifolin), (+)-dihydrokaempferol (DHK), and (+)-dihydromyricetin (DHM). Two flavan-3-ols, (−)-epicatechin, and (+)-catechin, were used as compound controls. Ebselen was used as a positive control. These compounds were purchased from Sigma-Aldrich (https://www.sigmaaldrich.com/)
Huh-7 cells, a human hepatocellular carcinoma cell line, are an appropriate to study the replication of different viruses (Dewi et al., 2020; Logue et al., 2019; Nakabayashi et al., 1984; Shih et al., 1993; Thongsri et al., 2019). This cell line was used for infection and propagation of virus and for testing antiviral activity of compounds. Human coronavirus 229E (HCoV-229E) is a positive sense and single-stranded RNA virus that infects the human respiratory system (Bucknall et al., 1972; Friedman et al., 2021; Hierholzer, 1976; Kennedy and Johnsonlussenburg, 1976; Macnaughton and Madge, 1978). HCoV-229E was propagated in Huh-7 cells and tittered with TCID50 assay.
2.2 Docking simulation of the SARS-CoV-2 Mpro
We recently reported the docking simulation of flavan-3-ols, such as epicatechin and catechin (Fig. 2) [44]. Herein, we used the same steps for docking simulation of flavonols and dihydroflavonols. In brief, three main steps were completed, protein preparation, ligand preparation, and protein-ligand docking. The first step was protein preparation. The SARS-CoV-2 Mpro was used as a receptor to test ligands. Its ID is PDB ID: 6LU7 at Protein Data bank (https://www.rcsb.org/), from which its 3D structure was downloaded to a desktop computer and then was prepared as a receptor of ligand via the Dock Prep tool of UCSF-Chimera (https://www.cgl.ucsf.edu/chimera/). Because Mpro contains the inhibitor peptide N3, we removed N3 prior to docking simulation. Hydrogens and charges were added and optimized to allow determining the histidine protonation state. The second step was ligand preparation. The 3D structures of compounds (Fig. 2) were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/) and then used as ligands. All structures were minimized by using the minimize structure tool of UCSF-Chimera. Hydrogens and charges were added to the ligands, which were then saved as mol2 format for the protein-ligand docking simulation. The third step was protein-ligand docking. The modeling of protein-ligand docking was performed via the publicly available AutoDock Vina (http://vina.scripps.edu/) software. The protein and ligand files were loaded to the Auto-Dock Vina through the UCSF-Chimera surface binding analysis tools. A working box was created to contain the whole receptor. The box center was set at x = −27, y = 13, and z = 58. The box size was set as x = 50, y = 55, and z = 50, which framed the entire receptor to allow free position changes and ligand binding to the receptor at any potential positions.
2.3 Docking simulation of the HCoV-229E Mpro
HCoV-229E needs its Mpro (3C-like protease) for the replication in the host cells (Ziebuhr et al., 1995, Ziebuhr et al., 1997, Ziebuhr et al., 1998). Accordingly, the Mpro is a target for screening anti-HCoV-229E medicines. The HCov-229E Mpro was downloaded from Protein Data Bank. Its PDB ID is 2ZU2 (Chuck et al., 2013; Lee et al., 2009; Prior et al., 2013). Given its 3D structure contained two subunits (chains A and B) and inhibitor EPDTC, we removed its chain B and EPDTC. The chain A was used for docking simulation. The software, docking preparation, and docking method were the same as those used for SARS-Cov-2 Mpro described above. The sequence of the HCoV-229E Mpro was obtained from the GenBank and then used for an alignment and docking simulation. The steps of simulation were the same as described above.
2.4 Inhibition assay of the SARS-CoV-2 Mpro activity
(+)-DHQ, (+)-DHK, (+)-DHM, quercetin, kaempferol, myricetin, isoquercitrin, rutin, (−)-epicatechin, (+)-catechin, and ebselen were dissolved in DMSO to prepare a 1.0 M stocking solution. A SARS-CoV-2 Assay Kit (BPS bioscience, https://bpsbioscience.com/) was used to test the inhibitory activity of these compounds. The steps of in vitro assay followed the manufacturer's protocol as performed in our recent report (Zhu and Xie, 2020). In brief, each reaction was carried out in a 25 μl volume in 384-well plates. Each reaction solution contains 150 ng recombinant Mpro (6 ng/μl), 1 mM DDT, 50 μM flu-orogenic substrate, and one compound (0, 0.02, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100, 150, and 200 μM) in pH 8.0 mM Tris-HCl and 5 μM EDTA buffer. GC376 (50 μM) and ebselen were used as two positive controls, while (−)-epicatechin and (+)-catechin were used as two negative controls. The reaction mixtures were incubated for 2 h at room temperature. The fluorescence intensity of each reaction was measured and recorded on a microtiter plate-reading fluorimeter (BioTek's Synergy H4 Plate Reader for detect fluorescent and luminescent signals). The excitation wavelength was 360 nm and the detection emission wavelength was 460 nm. Each concentration of every compound was tested five times. A mean value was calculated with five individual replicates. Plots were built with the per-centiles of catalysis versus log [μM] values of concentrations to show the effect of each compound on the Mpro activity. Statistical evolution is described below.
2.5 Inhibition assay of human coronavirus 229E
Huh-7 cells were grown in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (10% FBS) and 1% antibiotics. HCoV-229E was propagated in Huh-7 cells. Virus containing supernatants were harvested 72 h post infection and stored at −80 °C. The virus titer was determined by the Median Tissue Culture Infectious Dose 50 (TCID50) assay in Huh-7 cells.
Then, we performed virus inhibition assays. Huh-7 cells were seeded in 96 well plates at a density of 25,000 cells/well and incubated overnight. HCoV-229E was diluted in MEM with 1% FBS, 1% HEPES buffer, and 1% antibiotic solution (MEM 1 + 1+1). The cells were inoculated with HCoV-229E at a multiplicity of infection (MOI) of one in a total volume of 50 μl. The infected plates were incubated at 35 °C with 5% CO2 for 1 h. Phytochemicals dissolved in DMSO were added in cell culture medium to the following concentrations: 0 μM, 2.5 μM, 5 μM, 10 μM, 20 μM, and 50 μM.
After 1 h, virus and medium were removed from the infected cells, which were further washed once with 200 μl of PBS. 100 μl of each compound master mix was added to triplicate wells for each concentration. Virus was allowed to grow in the presence of each compound at 35 °C and 5% CO2 for 24 h. Supernatants were harvested and virus titers on Huh-7 cells were determined with TCID50 assay (Barrett et al., 1996). Plates were incubated at 37 °C and 5% CO2 for 96 h, inspected visually for cytopathic effect (CPE) and TCID50/ml was calculated using the Spearman-Kaerber method (Kärber, 1931; Spearman, 1908). A mean value was calculated using three replicates. Plots were built with TCID50/ml versus concentrations to show the effect of each compound on the replication of virus in Huh-7 cells. The minimum level of detection in this assay was 632 TCID50/ml.
2.6 Statistical evaluation
One-way analysis of variance (ANOVA) was performed to evaluate the statistical significance. The P-value less than 0.05 means significant differences.
3 Results
3.1 Ligand-receptor docking of flavonols and dihydroflavonols to the Mpro of SARS-CoV-2
Docking simulation was completed with the UCSF-Chimera and AutoDock Vina software to predict the binding abilities of flavonols and dihydroflavonols to the SARS-CoV-2 Mpro. The Mpro structure is featured with a substrate-binding pocket (Fig. 3 a). When the 3D structure of the protein was downloaded from the public database, the peptide inhibitor N3 was shown to bind to this pocket. During protein preparation, N3 was removed for docking. The simulation results showed that (+)-DHQ, (+)-DHK, (+)-DHM, quercetin, kaempferol, myricetin, isoquercitrin, rutin, (−)-epicatechin, (+)-catechin, and ebselen bound to the binding pocket (Fig. 3b–c). The resulting affinity scores for (+)-DHQ, (+)-DHK, (+)-DHM, quercetin, kaempferol, myricetin, isoquercitrin, and rutin ranged from −8.8 to −7.4 (kcal/mol), lower and better than the score of ebselen (−6.6) (Table 1 ). The scores among the aglycones of (−)-epicatechin, (+)-catechin, three dihydroflavonols, and three flavonol aglycones were close, either −7.4 or −7.5 (kcal/mol). These data suggested that dihydroflavonols, flavonols, and glycosylated flavonols could potentially inhibit the Mpro activity.Fig. 3 The binding of eleven compounds to the substrate pocket of the SARS-CoV-2 main protease (Mpro, ID: PDB ID: 6LU7) shown by a ligand-receptor docking simulation. a, this image shows the 3D surface view of the SARS-CoV-2 Mpro, on which the red rectangular frame indicates the substrate-binding pocket. b and c, eleven flavonoids and ebselen bind to this pocket. Two flavan-3-ols: (+)-catechin (CA) and (−)-epicatechin (EC); three dihydroflavonol aglycones: (+)-dihydroquercetin (taxifolin, DHQ), (+)-dihydrokaempferol (DHK), and (+)-dihydromyricetin (DHM); three flavonol aglycones: kaempferol, quercetin, and myricetin; two glycosylated flavonols: isoquercitrin (quercetin-3-O-glycoside), and rutin. S1, S1′, S2 and S4: four subsites.
Fig. 3
Table 1 Affinity scores of 11 compounds binding to the main proteases of SARS-CoV-2 and HuCoV-229E.
Table 1Compounds Affinity score (SARS-CoV-2) (kcal/mol) Affinity score (229E) (kcal/mol) Molecular weight (Da)
Rutin −8.8 −7.8 610.5
Isoquercitrin −8.7 −7.5 464.1
Kaempferol −7.7 −7.6 286.2
(+)-DHK −7.6 −7.6 288.2
(+)-DHM −7.5 −7.6 320.2
Myricetin −7.4 −7.1 318.2
Quercetin −7.4 −7.7 302.2
(+)-DHQ −7.4 −7.8 304.2
(+)-catechin −7.5 −7.6 290.2
(−)-epicatechin −7.5 −7.6 290.2
Ebselen −6.6 −6.0 274.2
DHK, DHM, and DHQ: dihydrokaemferol, dihydromyricetin, and dihydroquercetin.
3.2 Docking features at the binding pocket of the SARS-CoV-2 Mpro
As we reported recently (Zhu and Xie, 2020), the Mpro substrate-binding pocket includes four subsites, S1′, S1, S2, and S4 (Fig. 3a). Cys145 is a critical residue located at the space among subsites S1, S1′, and S2 (Fig. 4 a) (Dai et al., 2020; Jin et al., 2020). Several studies have reported that the thiol of the Cys145 residue is crucial for the catalytic activity of Mpro and if a compound binds to this residue, it can inhibit the Mpro activity (Chen et al., 2005; Dai et al., 2020; Ramajayam et al., 2011). When ebselen was used as our positive compound for simulation, as we reported recently (Zhu and Xie, 2020), it bound to this residue featured by three rings facing to the S1 and S1′ subsites (Fig. 4b). The docking simulation results showed that three dihydroflavonols, three flavonol aglycones, and two glycosylated flavonols bound to 2 to 4 subsites via the Cys145 residue. In three dihydroflavonols tested, (+)-DHK and (+)-DHQ showed a similarity in their occupation in the binding site. The A and B rings of (+)-DHK and (+)-DHQ dwelled in the S1′ and S2 subsites and their heterocycle C ring resided in the space between the S1 and S2 subsites (Fig. 4c–d). The A and B rings of DHM occupied the S1 and S4 subsites and the heterocycle C ring resided in the space between the S1 and S2 subsites (Fig. 4e). In three flavonol aglycones tested, the occupation of kaempferol was different from that of quercetin and myricetin. The A-ring, B-ring, and heterocycle C-ring of kaempferol resided in the S1′, S2, and the space between S1 and S2, respectively (Fig. 4f). The A-ring, B-ring, and the heterocycle C-ring of quercetin and myricetin dwelled in the S1, S4, and the space between S1 and S2 (Fig. 4g–h). These simulation results supported previous reports that kaempferol and quercetin could theoretically reside in the binding site of the Mpro protein (Rehman et al., 2021; Xia et al., 2020). In comparison, the residing positions of isoquercitrin and rutin were more complicated. The A-ring, B-ring, heterocycle C-ring, and 3-glucose of isoquercitrin occupied the S2, S1′, the space between S1 and S2, and S1 (Fig. 2i). The A-ring, B-ring, heterocycle C-ring, 6-β-glucopyranose, and 1-L-α-rhamnopyranose of rutin occupied S4, S1’, the space between S1/S2, S1, and S4 (Fig. 4j). The features of the binding of rutin to these subsites were similar to those characterized by a previous virtual study (Xu et al., 2020). These occupations in the binding sites suggested that these compounds might have an inhibitive activity against Mpro.Fig. 4 Orientation features of compounds binding to subsites. a, a surface image shows the four subsites in the binding pocket. b-j, images show the binding positions of nine compounds. ebselen; three dihydroflavonol aglycones: (+)-taxifolin (DHQ), (+)-dihydrokaempferol (DHK), and (+)-dihydromyricetin (DHM); three flavonol aglycones: kaempferol, quercetin, and myricetin; two glycosylated flavonols: isoquercitrin and rutin.
Fig. 4
3.3 Ligand-receptor docking of flavonols and dihydroflavonols to the Mpro of HCoV-229E
The Mpro of HCoV-229E was used for docking simulation. A sequence alignment revealed that the identity between the Mpro homologs of HCoV-229E and SARS-CoV-2 was 42.81% (Fig. 5 a). The binding domains were highly conserved. Furthermore, a 3D modeling revealed that the conformation and binding pocket of the HCoV-229E Mpro were similar to those of SARS-CoV-2 Mpro (Fig. 5b and c). A further superimposition of two proteins indicated that the two-substrate binding sites were overlaid together (Fig. S2). The simulation results were also similar to those of the Mpro of SARS-CoV-2 described above. (+)-DHQ, (+)-DHK, (+)-DHM, quercetin, kaempferol, myricetin, quercetin-3-O-glycoside, rutin, (−)-epicatechin, (+)-catechin, and ebselen could bind to the binding pocket of the HCoV-229E Mpro (Fig. 6 ). The affinity scores of these compounds ranged from −7.8 to - 7.1 (kcal/mol) (Table 1). The scores of rutin and isoquercitrin (two glycosides) binding to the HCoV-229E Mpro were −7.8 and −7.5 (kcal/mol), higher than −8.8 and −8.7 (kcal/mol), the scores of the two compounds binding to the SARS-CoV-2 Mpro (Table 1). This result indicates that compared with the affinity score of quercetin, these two types of glycosylation reduce the affinity scores binding to the SARS-CoV-2 Mpro, but do not affect the affinity scores binding to the HCoV-229E Mpro.Fig. 5 Amino acid sequence alignment of the SARS-CoV-2's and HCoV-229E's Mpro homologs and comparison of their three-dimensional (3D) models. a, amino sequence alignment, in which three rectangle frames highlight three conserved domains forming the substrate binding pocket; b, a comparison of the 3D models of the SARS-CoV-2's (bronze color) and HCoV-229E's (blue color) Mpro homologs; c, yellowish, orange, and reddish colors showing the binding pocket formed from three conserved binding domains highlighted with three rectangle frames in a, in which the reddish and yellowish spaces include Cys-His catalytic dyad.
Fig. 5
Fig. 6 The binding of eleven compounds to the substrate pocket of the HCoV-229E main protease (PDB ID: 2ZU2) shown by a ligand-receptor docking simulation. The first image shows the 3D surface view of the HCoV-229E Mpro, on which the red rectangular frame indicates the substrate-binding pocket. Ten flavonoids and ebselen bind to this pocket. Two flavan-3-ols: (+)-catechin (CA) and (−)-epicatechin (EC); three dihydroflavonol aglycones: (+)-taxifolin (DHQ), (+)-dihydrokaempferol (DHK), and (+)-dihydromyricetin (DHM); three flavonol aglycones: kaempferol, quercetin, and myricetin; two glycosylated flavonols: isoquercitrin (quercetin-3-O-glycoside) and rutin.
Fig. 6
3.4 In vitro inhibitory effects of five flavonols and two dihydroflavonols on the SARS-CoV-2 Mpro activity
(+)-DHK, (+)-DHM, (+)-DHQ, quercetin, kaempferol, myricetin, isoquercitrin (quercetin-3-O-glycoside), and rutin were used to test their inhibitory effects on the Mpro activity. In addition, based on our recent report (Zhu and Xie, 2020), (−)-epicatechin and (+)-catechin were used as negative controls. The resulting data showed that (+)-DHQ, (+)-DHK, (+)-DHM, quercetin, kaempferol, myricetin, isoquercitrin (quercetin-3-O-glycoside), and rutin inhibited the SARS-CoV-2 Mpro activity. The half-maximum inhibitory concentrations (IC50) were 0.125–20.3 μM (Fig. 7 ). Among the tested seven compounds, rutin had the lowest IC50 value with the most effectiveness to inhibit the Mpro activity (Fig. 7h), while (+)-DHK had the highest IC50 value with the lowest inhibitive activity (Fig. 7f). In addition, ebselen inhibited the activity of Mpro and its IC50 value was approximately 0.47 μM, this value of which supported a previous report (Jin et al., 2020). Further, one hundred μM was used to compare the inhibitive effects of these compounds on the Mpro activity in a given time. The resulting data showed the most effectiveness of rutin (Fig. 7i). In addition, as we reported previously, (+)-catechin and (−)-epicatechin did not show an inhibitory effect on the Mpro activity in the range of concentrations from 0 to 200 μM. For example, the two compounds did not inhibit the catalytic activity of the Mpro at 100 μM (Fig. 7j).Fig. 7 Inhibitory effects of twelve compounds on the Mpro activity of SARS-CoV-2. a-i, nine plots show the inhibitory curves of eight flavonoids and ebselen against the Mpro activity. All dots in each plot are an average value calculated from five replicates. IC50 value for each compound is inserted in each plot. “95% Cl” means 95% confidence internal. “(value 1, value 2)” means values in the range with 95% Cl. j, a comparison shows the inhibitory effects of 12 compounds at 100 μM on the Mpro activity. GC376 and ebselen (Ebs) are inhibitors used as positive controls. (+)-catechin, (−)-epicatechin, and water are used as negative controls. Two flavan-3-ols: (+)-catechin (Ca) and (−)-epicatechin (Ep); three dihydroflavonol aglycones: (+)-taxifolin (DHQ), (+)-dihydrokaempferol (DHK), and (+)-dihydromyricetin (DHM); three flavonol aglycones: kaempferol (Ka), quercetin (Qu), and myricetin (My); two glycosylated flavonols: isoquercitrin, (Iso), and rutin.
Fig. 7
3.5 Inhibitory effects of five compounds on the replication of HCoV-229E in Huh-7 cells
Quercetin, isoquercitrin, and DHQ were tested to examine their inhibitory effects on the replication of HCoV-229E in Huh-7 cells. In addition, epigallocatechin gallate (EGCG) and epicatechin, two examples of flavan-3-ols, were tested. The reason was that we recently reported that EGCG effectively inhibited the SARS-CoV-2 Mpro activity, while epicatechin could not (Zhu and Xie, 2020), however, whether they could inhibit coronavirus replication in the host cells was untested. It was essential to test them. The resulting data indicated that all five compounds showed an inhibition against the replication of HCoV-229E in Huh-7 cells (Fig. 8 ). Based on TCID50/ml values, DHQ started to show its inhibition at 2.5 μM and its inhibitory activity increased as its concentration was increased. Quercetin started to have inhibition at 5.0 μM. As its concentrations were increased, its inhibitive activities were more effective. At a concentration tested higher than 10 μM, quercetin could strongly inhibit the replication of the virus. Its EC50 value was estimated to be 4.88 μM (Fig. 8b). Isoquercitrin strongly inhibited the replication starting with 2.5 μM. EGCG started to show its inhibition against the replication of the virus at 2.5 μM and its inhibition became stronger as its concentrations were increased. It was interesting that epicatechin could strongly inhibit the replication starting at 20 μM.Fig. 8 Inhibition of five compounds on the replication of HCoV-229E in Huh-7 cells. a, plots were built with TCID50/ml versus concentrations of each compound. b, this plot was built with the inhibition rate (%) versus log [μM] values to estimate the EC50 of quercetin. “95% Cl” means 95% confidence internal. “(value 1, value 2)” means values in the range with 95% Cl. Bars labeled with “*” means were significant difference compared with control without adding compounds (P-value less than 0.05). DHQ: dihydroquercetin (taxifolin) and EGCG: epigallocatechin gallate.
Fig. 8
4 Discussion
The development of medicines is necessary to complement the use of vaccines to control COVID-19. The SARS-CoV-2 Mpro is one of the targets to screen, repurpose, or develop drugs to treat or prevent SARS-CoV-2 (Dai et al., 2020; Ramajayam et al., 2011; Ren et al., 2013). One strategy is to inhibit the Mpro activity via delivering a compound to the Cys145 residue at the space across the region of S1′ and S1 subsites (Dai et al., 2020). Ebselen is a small molecule candidate that has been found to inhibit the Mpro activity with an IC50 0.46 μM (Jin et al., 2020). Its structure featured with three rings was revealed to be an effective vessel to deliver its carbonyl group to the CYS145 residue (Fig. 4b). We recently reported another strategy. We have found that epicatechin gallate, epigallocatechin gallate, gallocatechin gallate, catechin gallate, and procyanidin B2 could effectively inhibited the activity of Mpro likely via the formation of hydrogen bonds with different amino acids in the binding pocket (Zhu and Xie, 2020). Our findings indicated that the formation of peptide bonds was effective to screen more flavonoids to intervene COVID-19. Quercetin and other flavonols are common nutraceuticals with antiviral activities against different viruses, such as influenza virus, hepatitis B virus, Zika virus, and Ebola viruses (Mehrbod et al., 2021; Parvez et al., 2020; Qiu et al., 2016; Wong et al., 2017). In this study, we took advantage of our recent strategy to perform the docking simulation of flavonols and dihydroflavonols. These two groups of compounds (Fig. 2, Fig. 4) have C4 keto and 3-OH structures on the heterocycle C-ring. Like flavan-3-ol gallates, the structures of these two groups might have a potential to reside in the spaces of S1 and S2 subsites. In the present study, our ligand-docking simulation showed that these two groups of compounds could bind to the substrate-binding pocket of Mpro and occupied their heterocyclic C ring in the crossing region between S1 and S2. Furthermore, the docking results predicted the A-ring and B-ring of two, three, two, and one compounds could bind to S1′ and S2, S1 and S4, S2 and S1′, and S4 and S1′, respectively (Fig. 4). The docking results further showed that a glycosylation of quercetin increased the dwelling capacity in the binding site. Quercetin was predicted to dwell at S1, S4, and the space between S1 and S2. Isoquercitrin results from the glycosylation of quercetin at 3-OH. The docking simulation predicted that the A-ring, B-ring, C-ring, and glucosyl group of isoquercitrin occupied S2, S1′, the space between S1 and S2, and S1, respectively. Rutin derives from the glycosylation of quercetin at 3-OH by a disaccharide rutinose (α-l-rhamnopyranosyl-(1 → 6 (-β-d-glucopyranose). The docking simulation predicted that the A-ring, B-ring, C-ring, glucosyl group, and rhamnosyl group dwelled at S4, S1’, the space between S1 and S2, S1, and S4, respectively (Fig. 4 j). The increase of binding subsites was reflected by the affinity scores of Mpro-ligands. Rutin and isoquercitrin had the lowest and second lowest score values (Table 1). These data suggested that not only might these compounds have an inhibitive activity but also a lower and better affinity score might indicate a strong inhibition against the Mpro activity. Further in vitro assays substantiated the prediction of docking simulation. Seven available compounds inhibited the activity of Mpro with IC50 values from 0.125 to 20.3 μM. To exclude the potential existence of water in compounds that could be detected by a near-infrared spectroscopy (Zhang et al., 2019) and the possible effects of water on compound concentrations, all compounds used were completely dehydrated prior to use. Therefore, the concentrations of all compounds used were accurate. Taken together, these data imply that these compounds might be potential therapeutics.
Given that SARS-CoV-2 can be only handled in the BSL-3 laboratories, we cannot access this deadly virus to test the effects of these compounds on its replication in host cells. Instead, we selected the less pathogenic HCoV-229E to test the inhibitory activity of these compounds. We hypothesized that inhibitory compounds screened against this virus might be appropriate for the potential therapy of COVID-19. The reason is that like SARS-CoV-2, the replication of HCoV-229E also depends on its Mpro activity in human cells and the active site is conserved between HCoV229E and SARS-CoV-2. Accordingly, the resulting data might help design medicines for the therapy of both COVID-19 and HCoV-229E respiratory diseases. An amino acid sequence alignment revealed that the identity of HCoV-229E and SARS-CoV-2 Mpro homologs was approximately 48%. The binding domain of substrates between the two homologs was conserved. Docking simulation and the resulting affinity scores further indicated that these compounds could reside in the binding pocket to potentially inhibit the activity of the Mpro of HCoV-229E (Fig. 6 and Table 1). Based on these data, we could test five compounds with HCoV-229E. In tested concentrations, DHQ and isoquercitrin starting from 2.5 μM showed a significant inhibition of HCoV-229E replication in Huh-7 cells. Quercetin could slightly reduce the replication of HCoV-229E at 2.5 μM and significantly inhibited the replication of this virus at higher concentrations (Fig. 8a). These positive results not only supported the docking simulation results that these compounds bound to the Mpro of HCoV-229E (Table 1), but also substantiated the results of in vitro assays that these compounds effectively inhibited the SARS-CoV-2 Mpro activity (Fig. 7). We previously demonstrated that EGCG could effectively inhibit the activity of the SARS-CoV-2 Mpro (Zhu and Xie, 2020). Herein, we used it as a positive control. The resulting data showed that EGCG starting with 2.5 μM could significantly inhibit the replication of HCoV-229E in Huh-7 cells. This positive control result further supported that DHQ, isoquercitrin, and quercetin inhibited the replication of HCoV-29E via the reduction of the Mpro activity. In addition, we tested epicatechin, which was not shown to have an inhibitive activity against the SARS-CoV-2 Mpro in vitro. It was interesting that epicatechin starting with 20 μM tested could inhibit the replication of HCoV-229E (Fig. 8 a), which was supported by the results of the docking simulation and its affinity score (Fig. 6 and Table 1). Accordingly, this datum indicates the difference between HCoV-229E and SARS-CoV-2. Taken together, these data indicate that HCoV-229E is appropriate substitute to screen inhibitors of SARS-CoV-2 by targeting the Mpro of these two viruses.
Quercetin, isoquercitrin, and rutin are three common supplements, given that their nutritional values benefit human health (Amanzadeh et al., 2019; da Silva et al., 2019; Kolarevic et al., 2019; Ragheb et al., 2020; Seifert, 2013; Xu et al., 2021). Our data suggest that quercetin, isoquercitrin, and rutin might be helpful to intervene COVID-19. These compounds are plant natural flavonoids that their bioavailability, metabolism, and toxicity have been studied extensively (Hostetler et al., 2017). In general, these compounds are safe nutrients sold as supplements or in food products such as onion and common dinner table fruits (Burak et al., 2017; Careri et al., 2003; Egert et al., 2012; Erlund et al., 2002; Meng et al., 2004; Snyder et al., 2016). More importantly, quercetin can be absorbed into the human body from the intestines. A large number of human health studies have reported the presence of quercetin and its derivatives in the blood plasma and their nutritional benefits after consumption (Day and Williamson, 2001; Huang et al., 2020; Mohammadi-Sartang et al., 2017; Shi and Williamson, 2016). For example, the quercetin concentration in plasma was reported to reach 5.0 ± 1.0 μM after the intake of 150 mg in 1 h (de Whalley et al., 1990; Olthof et al., 2000). In addition, these compounds are potent antioxidants (Justino et al., 2002; Terao et al., 2001). The intake of quercetin can inhibit the oxidation of LDL and prevent the cardiovascular diseases (de Whalley et al., 1990; Hertog et al., 1993a; Manach et al., 1998). Moreover, quercetin and its derivatives have strong anti-inflammation activity (Carullo et al., 2017; Chen et al., 2016; Li et al., 2016; Sato and Mukai, 2020; Tejada et al., 2017). All of these functions can benefit people's health.
Declaration of interest statement
The authors confirm that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
CRediT authorship contribution statement
Yue Zhu: performed the Docking and SARS-Cov-2 Mpro inhibitory experiments, Formal analysis, prepared all figures. Frank Scholle: performed the hCov-229E inhibitory experiments, Formal analysis, this project, Writing – original draft, Supervision. Samantha C. Kisthardt: performed the hCov-229E inhibitory experiments, Formal analysis. De-Yu Xie: Conceiving and designing the project, Formal analysis, Writing-original draft and finalization, Supervision.
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.virol.2022.04.005.
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PMC009xxxxxx/PMC9002601.txt |
==== Front
J Mol Graph Model
J Mol Graph Model
Journal of Molecular Graphics & Modelling
1093-3263
1873-4243
Elsevier Inc.
S1093-3263(22)00064-X
10.1016/j.jmgm.2022.108185
108185
Article
Molecular modelling identification of phytocompounds from selected African botanicals as promising therapeutics against druggable human host cell targets of SARS-CoV-2
Uhomoibhi John Omo-Osagie
Shode Francis Oluwole
Idowu Kehinde Ademola
Sabiu Saheed ∗
Department of Biotechnology and Food Science, Faculty of Applied Sciences, Durban University of Technology, PO Box 1334, Durban, 4000, South Africa
∗ Corresponding author.
12 4 2022
7 2022
12 4 2022
114 108185108185
5 2 2022
26 3 2022
28 3 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.
The coronavirus disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is highly pathogenic and transmissible. It is mediated by the binding of viral spike proteins to human cells via entry and replication processes involving human angiotensin converting enzyme-2 (hACE2), transmembrane serine protease (TMPRSS2) and cathepsin L (Cath L). The identification of novel therapeutics that can modulate viral entry or replication has been of research interest and would be germane in managing COVID-19 subjects. This study investigated the structure-activity relationship inhibitory potential of 99 phytocompounds from selected African botanicals with proven therapeutic benefits against respiratory diseases focusing on SARS-CoV-2's human cell proteins (hACE2, TMPRSS2, and Cathepsin L) as druggable targets using computational methods. Evaluation of the binding energies of the phytocompounds showed that two compounds, Abrusoside A (−63.393 kcal/mol) and Kaempferol-3-O-rutinoside (−58.939 kcal/mol) had stronger affinity for the exopeptidase site of hACE2 compared to the reference drug, MLN-4760 (−54.545 kcal/mol). The study further revealed that Verbascoside (−63.338 kcal/mol), Abrectorin (−37.880 kcal/mol), and Friedelin (−36.989 kcal/mol) are potential inhibitors of TMPRSS2 compared to Nafamostat (−36.186 kcal/mol), while Hemiphloin (−41.425 kcal/mol), Quercetin-3-O-rutinoside (−37.257 kcal/mol), and Myricetin-3-O-galactoside (−36.342 kcal/mol) are potential inhibitors of Cathepsin L relative to Bafilomycin A1 (−38.180 kcal/mol). The structural analysis suggests that these compounds do not compromise the structural integrity of the proteins, but rather stabilized and interacted well with the active site amino acid residues critical to inhibition of the respective proteins. Overall, the findings from this study are suggestive of the structural mechanism of inhibitory action of the identified leads against the proteins critical for SARS-CoV-2 to enter the human host cell. While the study has lent credence to the significant role the compounds could play in developing potent SARS-CoV-2 candidate drugs against COVID-19, further structural refinement, and modifications of the compounds for subsequent in vitro as well as preclinical and clinical evaluations are underway.
Graphical abstract
Image 1
Keywords
COVID-19
Host cell targets
Molecular dynamics simulation
Phytocompounds
SARS-CoV-2
==== Body
pmc1 Introduction
The coronavirus disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is highly pathogenic and transmissible. The first identified and reported case of COVID-19 was in Wuhan, China in December 2019. As of 14 January 2022, WHO has reported 315 345 967 confirmed cases of COVID-19 with 5 510 174 deaths [1]. The most common clinical features of COVID-19 are tiredness, pyrexia, cough, and dyspnea, while some may remain asymptomatic [2].
Although, some vaccines have been approved and are currently in use, the vaccination program itself is riddled with problems including, misinformation and disinformation about the effectiveness of the various vaccines that are available, reduced potency against the variants, and post-vaccination symptoms, such as thromboembolism and thrombocytopenia and other adverse effects [3]. Hence, the development of novel drug candidates that can stand the test of time in offering therapeutics that will be globally acceptable, affordable, and easily accessible, is imperative. Drug development from plants has been demonstrated against several debilitating diseases including viral infections [4,5] and recent reports have lent credence to exploration of plant secondary metabolites as leads against druggable targets in SARS-CoV-2 infection using computational approaches [[6], [7], [8]].
As part of the human host cell proteins, important druggable targets including the human angiotensin converting enzyme 2 (hACE2), transmembrane protease serine 2 (TMPRSS2), and cathepsin L (Cath L), are required to facilitate and aid the viral entry and replication in SARS-CoV-2 infection. Specifically, infection with SARS-CoV-2 occur when the interaction between the viral spike protein and the human host cell is facilitated through processes involving hACE2, TMPRSS2 and Cath L [9]. In fact, the inhibitory effect of the depletion of hACE2 and TMPRSS2 on SARS-CoV-2 replication in different cell lines has been reported [10]. Similarly, the expression of Cath L is up-regulated during chronic inflammation as observed with cytokine storm in COVID-19 infection and is also implicated in extracellular matrix degradation, a critical process for SARS-CoV-2 viral entry into the host cell [11]. Generally, the host cell proteins are genetically more stable than viral structural proteins, which are also typical druggable targets on the viral genome [12,13]. Hence, the development of novel phytotherapeutics that can modulate viral entry or replication through inhibition of hACE2, TMPRSS2, and Cath L would be of immense benefit to mankind as we grapple with the COVID-19 pandemic.
To identify Phytocompounds as potential therapeutic agents against SARS-CoV-2 and its associated target proteins, several in silico and in vitro studies have explored the potentials of African rich plants. Dwarka et al. [14], reported four compounds (Uzarin, Hypoxide, l-canavanine and arabic acid) from South African medicinal plants as potent therapeutics against COVID-19 in an in silico study [14]. Similarly, Amaranthin from Amaranthus tricolor commonly used in Nigeria, Kenya and Tanzania was reported to be a potential inhibitor of the main protease enzyme of SARS-CoV-2 [15]. Compounds such as licoleafol, methyl rosmarinate, A-terpineol, P-cymene, T-anethole, and thyhydromoquinone are some of the other therapeutics that have been identified from African medicinal plants against the main protease, hACEE-2 and other key druggable targets of SARS-CoV-2 [[15], [16], [17], [18], [19], [20], [21]]. As part of the ongoing efforts to develop anti-SARS-CoV-2 drugs, the current study adopted a computational structure-activity relationship approach exploring 99 plant secondary metabolites from eight African medicinal plants (Leonotis leonurus, Ocimum gratissimum, Macaranga barteri, Abrus precatorius, Artemisia afra, Carapa procera, Alepidea amatymbica, and Drosera madagascariensis) with proven therapeutic benefits against respiratory diseases and related infections [[22], [23], [24], [25], [26], [27], [28], [29], [30]], against the SARS-CoV-2 host cell targets.
2 Materials and methods
2.1 Molecular docking and simulation
2.1.1 Protein acquisition, preparation, and docking
The protein structures of the hACE2 (PDB ID: 1R4L), TMPRSS2 (PDB ID: 5CE1) and Cath L (PDB ID: 5MQY) were obtained from the RSCB Protein Data Bank (https://www.rcsb.org). The preparation of all the protein structures were carried out on the UCSF Chimera version 1.14 by eliminating water molecules, nonstandard naming, and protein residue connectivity [31]. Prior to molecular docking, protein structures with missing atoms in their sidechains and protein backbone are rectified by adding such missing atoms. The reference/standard drugs as well as the phytocompounds used in this study were accessed and downloaded from PubChem [32]. Using Avogadro software [33], the 3-D structures of the selected 99 phytochemicals (Supplementary Table S1) and reference drugs [bafilomycin A1 (BFA), nafamostat (NFM), and MLN 4670 (MLN)] were all prepared in readiness for docking. While BFA was used as a known Cath L inhibitor, NFM and MLN were adopted as potent inhibitors of TMPRSS2 and hACE2, respectively [[34], [35], [36]].
For the molecular docking, the Autodock package on Chimera version 1.14 was used with default parameters [8]. Briefly, to the ligands were added Gasteiger charges and all the hydrogen atoms that are non-polar were joined to carbon atoms. Docking of all prepared secondary metabolites (ligands) into the binding site of the respective proteins (hACE2, TMPRSS2, and Cath L) by defining the grid box with a spacing of 1 Å each and size (26 × 24 × 25), (60 × 76 × 68) and (50 × 40 × 46) pointing in x, y and z directions, respectively. The resulting complex with the best geometric pose in each case was thereafter subjected to molecular dynamics (MD) simulation.
2.2 Molecular dynamics simulations, post-dynamic analyses and binding free energy calculation
The MD simulations were done as earlier reported [8] with the AMBER 18 suite Leap module [37] where hACE2 protein was numbered from residues 19 to 615, while Cath L, and TMPRSS2 proteins were numbered from residues 1 to 220 and 1 to 370, respectively. ANTECHAMBER was used to generate atomic partial charges for the ligands by utilizing the Restrained Electrostatic Potential (RESP) and the General Amber Force Field (GAFF) procedures. The Leap module of AMBER 18 allowed for the addition of hydrogen atoms, Na+ and Cl− counter ions for the three proteins, respectively, to neutralize all systems. Thereafter, the systems were anchored within an orthorhombic box of TIP3P water molecules within 10 Å [38], allowing a stepwise heating 0–300 K (50 ps) and a pressure of 1 bar [39]. The total time for the MD simulations conducted was 100 ns. In each simulation, the SHAKE algorithm was employed to constrict hydrogen atoms' bonds [40].
For the post-dynamic analysis of root mean square fluctuation (RMSF), root mean square deviation (RMSD), and radius of gyration (RoG), the CPPTRAJ module was adopted [41], and the resulting plots were generated in Origin V1.4 [42]. For the binding affinity, the binding free energy, Molecular Mechanics/GB Surface Area method (MM/GBSA) was estimated over 1 × 105 snapshots drawn from the 100 ns trajectory [43].
2.3 Pharmacokinetic properties
An assessment of the chemistry and drug likeness of the lead phytocompounds was done using the SwissADME online software [44].
3 Results and discussion
3.1 Docking scores and thermodynamic binding free energy
The results of the molecular docking of the 99 phytocompounds investigated against hACE2, TMPRSS2, and Cath L in this study are presented in Supplementary Table S1. Molecular docking enables the assessment of the geometric fitness and affinity of a molecule upon its binding at the active site of the receptor and the higher the negative score, the better the pose and interaction of the compound with the protein [45,46]. For hACE2 (Table S1), 16 phytocompounds had docking scores between −10.1 and −9.1 kcal/mol and better affinity for the protein compared to the reference standard, MLN-4760 with a docking score of −7.3 kcal/mol (Table S1). For TMPRSS2 (Table S1), 11 phytocompounds had docking scores between −9.2 and −7.9 kcal/mol compared to −8.1 kcal/mol for the reference standard, NFM while for Cath L (Table S1), 27 phytocompounds had docking scores between −8.7 and −7.0 kcal/mol compared to BFA (−8.1 kcal/mol). Since molecular docking assesses only the pose and affinity of a molecule in the receptor active site or protein, the most promising of these compounds against each target were further taken through MD simulation and the results of those with better or close binding free energy post-MD simulation are presented in Supplementary Tables S2, S3, and S4 while the results of the most promising ones judging by the highest negative values are shown in Table 1, Table 2, Table 3 . Specifically, for hACE2, Abrusoside A (ABA) and Kaempferol-3-O-rutinoside (KOR) had the best binding free energy values of −63.393 kcal/mol and −58.939 kcal/mol, respectively, relative to MLN (−54.545 kcal/mol) (Tables 1 and S2), while Verbascoside (VBS), Abrectorin (ABC), and Friedelin (FDL) had the best binding free energy values of −63.338 kcal/mol, −37.880 kcal/mol, and −36.989 kcal/mol, respectively against TMPRSS2 relative to −36.186 kcal/mol for NFM (Tables 2 and S3). While the observation on hACE2 is indicative of ABA's affinity for the protein and suggestive of its ability to be a better inhibitor of hACE2 than KOR, VBS could be suggested as a strong inhibitor of TMPRSS2 relative to both ABC and the reference standard NFM. Since TMPRSS2 is responsible for cleaving the viral spike glycoprotein, VBS could be a promising therapeutic drug candidate targeting the entry stage of SARS-CoV-2 replication cycle. It was also noteworthy that both ABC and FDL bound strongly with TMPRSS2 judging by their binding energy values that were comparable to that of NFM. On the other hand, Hemiphloin (HPN): -41.425 kcal/mol, Quercetin-3-O-rutinoside (QOR): -37.257 kcal/mol, and Myricetin-3-O-galactoside (MOG): -36.998 kcal/mol were identified as prominent compounds against Cath L (Tables 3 and S4). The results suggest that HPN is the best inhibitor against Cath L as it showed higher binding affinity relative to BFA and other compounds used in this study. The observations noted regarding the binding energy values of the tested compounds against hACE2, TMPRSS2 and Cath L are similar with previous reports [47,48], where compounds with the highest negative binding energy values were proposed as having the best affinity for SARS-CoV-2 host cell proteins and suggested as potential inhibitors of the respective druggable target.Table 1 Thermodynamic binding free energy values for MLN, ABA, and KOR towards hACE2.
Table 1Complex MLN 4670 (MLN) ABA KOR
ΔEvdW −32.851 ± 4.761 −67.659 ± 3.783 −43.747 ± 6.103
ΔEelec −942.921 ± 19.627 −23.862 ± 0.163 −99.020 ± 15.204
ΔGgas −975.773 ± 20.535 −91.521 ± 7.533 −142.768 ± 12.274
ΔGsolv 921.228 ± 16.585 28.127 ± 5.659 83.828 ± 9.960
ΔGbind −54.545 ± 7.029 −63.393 ± 4.757 −58.939 ± 5.538
ΔEelec electrostatic energy, ΔEvdW van der Waals energy, ΔGbind total binding free energy, ΔGsolv solvation free energy, and ΔGgas gas-phase free energy.
Table 2 Thermodynamic binding free energy values for NFM, VBS, ABC, and FDL towards TMPRSS2.
Table 2Complex NFM VBS ABC FDL
Δ EvdW −34.007 ± 3.207 −56.875 ± 4.933 −33.932 ± 2.578 −36.624 ± 3.949
ΔEelec −250.376 ± 20.327 −80.487 ± 16.014 −8.281 ± 0.068 −7.647 ± 0.116
ΔGgas −284.383 ± 20.514 −137.363 ± 14.311 −42.213 ± 4.033 −47.272 ± 6.592
ΔGsolv 248.196 ± 19.035 74.025 ± 9.886 17.333 ± 2.702 15.235 ± 3.517
ΔGbind −36.186 ± 4.572 −63.338 ± 7.493 −37.880 ± 2.596 −36.989 ± 4.036
ΔEelec electrostatic energy, ΔEvdW van der Waals energy, ΔGbind total binding free energy, ΔGsolv solvation free energy, and ΔGgas gas-phase free energy.
Table 3 Thermodynamic binding free energy values for BFA, QOR, HPN, and MOG towards Cath L.
Table 3Complex BFA QOR HPN MOG
Δ EvdW −49.969 ± 5.756 −38.005 ± 3.345 −40.191 ± 3.216 −32.119 ± 5.521
ΔEelec −13.595 ± 5.542 −38.535 ± 4.666 −26.703 ± 3.067 −42.1420 ± 0.564
ΔGgas −63.564 ± 9.447 −76.540 ± 14.689 −71.896 ± 7.478 −74.261 ± 9.889
ΔGsolv 25.384 ± 4.956 44.283 ± 2.141 30.470 ± 5.052 42.263 ± 5.297
ΔGbind −38.180 ± 5.985 −37.257 ± 7.588 −41.425 ± 3.869 −36.998 ± 3.097
ΔEelec electrostatic energy, ΔEvdW van der Waals energy, ΔGbind total binding free energy, ΔGsolv solvation free energy, and ΔGgas gas-phase free energy.
3.2 Interaction plots of the phytocompounds with the host cell proteins
The inhibitory characteristics of the promising or lead compounds, judging by their binding affinities and those of the respective reference standards against the evaluated targets as a function of interactions with the amino acid residues at the active site of each protein are presented in Fig. 1, Fig. 2, Fig. 3 . Different degrees of bond interactions such as van der Waals (vdW) overlaps, halogen, hydrogen bonds, alkyl, π-alkyl, π-π stacked interaction, and π-π T-shaped were observed. Specifically, Fig. 1 revealed that ABA bound strongly to hACE2 with 4 conventional hydrogen bonds (with Gly187, Glu190, Asn192 and Lys544) and 13 van der Waals’ interactions (with Leu55, Glu80, Gln84, Tyr178, Tyr184, Val191, Val194, Leu373, Leu374, Asn376, Tyr492, Ser545 and Gln546) while 6 conventional hydrogen bonds (with Ala330, Glu357, Asp364, Glu380, Asn490 and Arg496) and 14 van der Waals interactions (with Hie327, Pro328, Trp331, Asp332, Ile361, Tyr367, Asn376, Gly377, Hie387, Glu384, Phe486, Hie487, Asp491 and Tyr497) were observed with KOR. MLN on the other hand had a total of 13 hydrogen and van der Waals interactions with hACE2. In addition, ABA had four alkyl interactions with Phe22, Trp51, Phe372 and Ala81 compared to two strong π-π stacked interactions with Tyr492 and Tyr184 for KOR and two alkyl interactions with Phe372 and Tyr492 as well as one π-π stacked interaction with Phe22 for MLN (Fig. 1). The interactions observed with both ABA and KOR towards hACE2 could be a probable justification for their higher binding affinities relative to MLN. Hydrogen bonds as well as van der Waals and other non-covalent interactions such as alkyl interactions are known to significantly add to the binding energy values of ligands after binding to a receptor [49,50] and based on this observation, ABA and KOR could be identified to have had good interactions with the protein in a manner that enhanced affinity suggestive of their potential inhibitory effect on hACE2.Fig. 1 2D interaction plots of MLN, Abrusoside A (ABA) and Kaempferol-3-O-rutinoside (KOR) with the active site amino acid residues of hACE2.
Fig. 1
Fig. 2 2D interaction plots of Nafamostat (NFM), Verbascoside (VBS), Friedelin (FDL), and Abrectorin (ABC) with the active site amino acid residues of TMPRSS2.
Fig. 2
Fig. 3 2D plots of Bafilomycin A1 (BFA), Myricetin-3-O-galactoside (MOG), Quercetin-3-O-rutinoside (QOR), and Hemiphloin (HPN) with active site amino acid residues of cathepsin L.
Fig. 3
Fig. 2 shows the 2D interaction plots of VBS, ABC and FDL with the active site amino acid residues of TMPRSS2 in comparison with NFM. More interactions including 24 hydrogen and van der Waals forces and 3 strong amide-stacked bonds with His158, Val330, and Pro161 were observed between VBS and the active site amino acid residues of TMPRSS2, justifying its higher negative binding energy value and better affinity for the protein relative to others (Fig. 2). Although, strong amide-stacked and alkyl interactions involving either carboxylic group or benzyl group were observed in all the compounds including NFM, however, compared to VBS, NFM, FDL and ABC had lower number of interactions with the amino acid at the binding site of the protein (Fig. 2), and this also correlates with their respective lower binding energy values.
The structure of Cath L showed the fold of the papain-like enzyme composed of two domains (left (L-) and right (R-)), and between the two domains, is a V-shaped active site cleft on which the L- and R-domain catalytic residues C25 and H163 are positioned [51], and the ligands binds at this active site. For Cath L, the ligand-interaction plot revealed that the reference standard, BFA had a total of 11 interactions consisting of 4 π-alkyl interactions with Met70, Leu69, Ala210, and Ala135, 2 conventional hydrogen bonds, as well as 5 van der Waals forces (Fig. 3). Unlike the BFA, the study compounds (MOG, QOR and HPN) had higher number of hydrogen bonds in addition to van der Waals forces. Furthermore, BFA did not show any π-alkyl interaction with the protein as observed with the three study compounds. Thus, of the three studied ligands (MOG, QOR and HPN), HPN by virtue of the increased number of electrostatic interactions exhibited, will have better binding affinities than the reference drug BFA. This finding is affirmed by the fact that the magnitude of the binding is a measure of how strong the interactions are between the ligand and the protein, and this is usually increased by interactions such as van der Waals forces, electrostatic interactions, and hydrogen bond between two molecules [49].
3.3 Dynamic stability, compactness, and flexibility of hACE2, TMPRSS2, and cathepsin L bound and unbound complexes
The data obtained with respect to structural analyses of hACE2, TMPRSS2, and Cath L complexes are presented in Fig. 4, Fig. 5, Fig. 6 , respectively alongside the calculated average values of RMSD, RoG, SASA, and RMSF (Table 4 – 6). The RMSD is a parameter that assesses how stable a complex is. The lower the average RMSD value, the more stable the complex [52,53]. The RMSD plot for the hACE2 complexes (Fig. 4a) showed that the bound and unbound (apo-enzyme) complexes converged at approximately 5 ns, and thereafter both the apo-enzyme and the respective ligand-bound complexes displayed a favorable stability throughout the simulation with overall average values of 1.771 Å, 1.756 Å, and 1.873 Å), for MLN, ABA and KOR, respectively (Table 4). Although, binding of KOR revealed an insignificant marginally higher RMSD value when compared to ABA and MLN, the binding of the three compounds do not alter the overall structural stability of hACE2. In the case of TMPRSS2, the average RMSD values for VBS (2.487 Å), FDL (2.238 Å), and ABC (2.409 Å) complexes are relatively higher than that of NFM (1.809 Å) but lower than the value for the apo-enzyme (2.589 Å) while the reference drug NFM had an average value of 1.809 Å (Fig. 5a, Table 5 ). This observation on the RMSD values for TMPRSS2 indicates that the binding of NFM, VBS and FDL induced more structural stability on TMPRSS2, which does not only agree with the results of the binding energy values and suggestive of favorable interactions between the protein and both VBS and FDL but also identifying them as promising prospect against TMPRSS2 inhibition. For the Cath L complexes, the binding of the reference standard, BFA (2.287 Å) and MOG (1.723 Å) raised the average RMSD value of the complexes, while compounds HPN (1.012 Å) and QOR (1.382 Å) lowered the RMSD values when compared with the apo-enzyme (1.572 Å) (Table 6 ). The RMSD plots revealed that both BFA and MOG complexes at approximately 55 ns induced unstable conformational changes on the protein structure as evidenced by the relatively high RMSD values (Fig. 6a). Although, the result of the binding energy suggests that the phytochemicals might be effective inhibitors of Cath L, however, the inhibition mechanism displayed by HPN and QOR may differ to that of BFA and MOG. Generally, the observations on the RMSD values and patterns for the druggable targets in this study are good indications of the test compounds as prospective drug candidates judging by their values which were <3.5 Å acceptable limit and agrees with a previous study [54], where plant secondary metabolites induced significant structural stability at the druggable sites of SARS-CoV-2.Fig. 4 Comparative plots of C-α atoms of hACE2 with MLN, ABA and KOR displayed as (a) RMSD, (b) RoG, (c) SASA, and (d) RMSF, post-100 ns MD simulation.
Fig. 4
Fig. 5 Comparative plots of C-α atoms of TMPRSS2 with NFM, VBS, ABC, and FDL displayed as (a) RMSD, (b) RoG, (c) SASA, and (d) RMSF, post-100 ns MD simulation.
Fig. 5
Fig. 6 Comparative plots of C-α atoms of Cathepsin L with BFA, QOR, MOG, and HPN, displayed as (a) RMSD, (b) RoG, (c) SASA, and (d) RMSF, post-100 ns, MD simulation.
Fig. 6
Table 4 Calculated average values of RMSD, RoG, SASA, and RMSF of hACE2 complexes.
Table 4Complex RMSD (Å) RoG (Å) SASA (Å2) RMSF (Å)
hACE2 1.822 ± 0.101 24.012 ± 1.733 25065.454 ± 800.34 1.119 ± 0.021
hACE2 + MLN 1.771 ± 0.081 24.116 ± 2.034 25475.381 ± 723.43 1.199 ± 0.143
hACE2 + ABA 1.756 ± 0.123 24.076 ± 2.120 25040.439 ± 523.33 1.180 ± 0.474
hACE2 + KOR 1.873 ± 0.072 24.161 ± 3.012 25722.981 ± 635.24 1.219 ± 0.352
Table 5 Calculated average values of RMSD, RoG, SASA, and RMSF of TMPRSS2 complexes.
Table 5Complex RMSD (Å) RoG (Å) SASA (Å2) RMSF (Å)
TMPRSS2 2.589 ± 0.212 21.703 ± 2.423 17007.673 ± 903.423 1.382 ± 0.324
TMPRSS2 + NFM 1.809 ± 0.039 21.617 ± 3.024 16556.672 ± 950.245 1.194 ± 0.154
TMPRSS2 + VBS 2.487 ± 0.322 21.610 ± 1.948 16460.933 ± 746.324 1.347 ± 0.424
TMPRSS2 + FDL 2.238 ± 0.313 21.661 ± 3.242 16807.211 ± 932.394 1.295 ± 0.322
TMPRSS2 + ABC 2.409 ± 0.301 21.738 ± 2.452 17032.312 ± 732.421 1.294 ± 0.324
Table 6 Calculated average values of RMSD, RoG, SASA, and RMSF of Cathepsin L complexes.
Table 6Complex RMSD (Å) RoG (Å) SASA (Å2) RMSF (Å)
Cath L 1.572 ± 0.422 16.791 ± 2.341 9435.845 ± 232.494 1.093 ± 0.322
Cath L + BFA 2.287 ± 0.221 17.019 ± 3.494 9857.129 ± 195.353 1.305 ± 0.234
Cath L + QOR 1.382 ± 0.123 16.816 ± 2.344 9490.176 ± 302.355 1.036 ± 0.156
Cath L + MOG 1.723 ± 0.164 16.634 ± 1.344 9143.020 ± 235.462 1.126 ± 0.253
Cath L + HPN 1.012 ± 0.423 16.678 ± 3.223 8907.722 ± 321.452 0.820 ± 0.012
The RoG is a parameter used to evaluate the structural compactness of proteins when they bind with molecules [55]. A lower and higher RoG values indicate more stable and unstable systems, respectively [56]. The hACE-2 RoG plot results correlates with its RMSD plot revealing the that binding of the molecules do not alter the hACE-2 structural stability. Fig. 4b as well as Table 5 showed a relatively close average RoG values of 24.012 Å, 24.116 Å, 24.076 Å and 24.161 Å for the apo-enzyme, MLN, ABA and KOR, respectively. For the TMPRSS-2 complexes, lower RoG values and more stable complexes were observed for the apo-enzyme as well as ligand bound complexes (Fig. 5b). The average RoG values of 21.703 Å, 21.617 Å, 21.610 Å, 21.661 Å and 21.738 Å were recorded for the apo-enzyme, NFM-, VBS-, FDL- and ABC- complexes, respectively. These results validate the structural stability observed with the RMSD plot. As observed in the RMSD plot for BFA complex with Cath L, the binding of BFA increased its average RoG values (17.019 Å) compared to the apo-enzyme (16.791 Å) and other ligands with average RoG values of 16.816 Å (QOR), 16.634 Å (MOG) and 16.678 Å (HPN) (Fig. 6b, Table 6). Results revealed that the apo-enzyme together with QOR, MOG, and HPN had low and relatedly close values, suggesting an increased structural compactness and stability, while the increased average RoG value observed with BFA-complex indicates a decrease in Cath L structural compactness.
The SASA plot measures the exposure of the protein structure to solvent environment, and the lower the SASA value, the more exposed the hydrophobic amino acid residues of the proteins are, and the systems stability also increases [57]. From Fig. 4c and Table 4, the average SASA values of the hACE2 complexes were 25040.439 Å2, 25722.981 Å2 and 25475.381 Å2 for ABA, KOR and MLN, respectively. ABA has a lower value than the standard drug (MLN) (25475.381 Å2) while KOR has a higher value than the apo-enzyme as well as the MLN-hACE2 complex. These values are relatively close to the value for the apo-enzyme (25065.454 Å2). This result showed that, the binding of ABA and KOR to hACE2 compared favorably with that of MLN and does not alter the exposure of the buried hydrophobic residues of hACE2, and ultimately did not adversely impact the systems’ stability. Unlike hACE2, the SASA values for the TMPRSS2 complexes showed that, binding of NFM (16556.672 Å2), VBS (16460.933 Å2), and FDL (16807.211 Å2) marginally lowered the SASA values compared to the apo-enzyme (17007 Å2) (Fig. 5c, Table 5), which is an indication of increased exposure of hydrophobic amino acid residues of TMPRSS2, suggestive of increased structural stability. However, the binding of ABC (17032.312 Å2) showed no effect on the exposure of the hydrophobic residues of TMPRSS2 to solvent environment and the overall observation regarding the SASA values of the TMPRSS2 complexes in this study agrees with those of RMSD and RoG following binding of VBS, FDL, and ABC and a further attestation that, the structural integrity, which is significant for inhibitory activity of TMPRSS2, was not compromised. It was observed that BFA displayed a similar trend in its SASA, RMSD and RoG plots, where the binding of BFA increased the average SASA value (9857.129 Å2) compared to the apo-enzyme (9435.845 Å2) (Fig. 6c, Table 6). This suggests that the interactions of BFA with Cath L reduced the exposure of hydrophobic residues of Cath L and its structural stability. The binding of HPN (8907.722 Å2), and MOG (9143.020 Å2) lowered the average SASA values, while the binding of QOR (9490.176 Å2) does not affect the SASA values compared to the apo-enzyme. These observations suggest that the binding of HPN and MOG increased the exposure of hydrophobic amino acid residues of Cath L leading to its increased stability. A recent report by Hassan [58] indicated MOG to be a promising inhibitor against SARS-CoV-2 spike glycoprotein (Sgp) in silico. This result adds support to the recommendation of MOG as a potential inhibitor of SARS-CoV-2 based on the findings of our study.
The RMSF is an assessment of how the amino acid residues of a receptor move or fluctuates as a result of a binding of a drug [50,59,60]. Increased RMSF value is an indication of increase in flexible movements of the alpha-carbon atoms [8]. In this study, the binding of KOR (1.219 Å) and MLN (1.199 Å) marginally increased the average RMSF value compared to the apo-enzyme (1.119 Å) for the hACE2 complexes (Fig. 4d, Table 4), indicating the two ligands caused an overall amino acid residues flexibility of hACE2. However, the binding of ABA lowered the average RMSF value (1.180 Å), and this could be indicative of restricted movement of the hACE2 active site amino acid residues. However, relatively high fluctuations were observed at residues 75–85, 305–310, 535–545. Similarly, a relatively close, low average RMSF values were observed with both the unbound and bound complexes in the TMPRSS2 systems (Fig. 5d, Table 5). Nevertheless, high residual fluctuations at residues 150–190, 250–275 recorded. The binding of NFM and the studied phytochemicals (VBS, FDL, and ABC) for TMPRSS lowered the RMSF values compared to the apo-enzyme (Table 5), indicating an overall flexible movement and stable complexes. This stable ligand-enzyme complexes correlates with the observations with RMSD, RoG, and SASA plots for TMPRSS2. For the Cath L systems, the binding of BFA (1.305 Å) and MOG (1.126 Å) increased the average RMSF values of the complexes as compared to the unbound enzyme (Cath L) (1.093 Å) (Fig. 6d, Table 6), suggesting their binding leads to a decrease or restricted movement of the amino acid residues, making the protein less flexible. In contrast, the binding of HPN and QOR resulted in the structural flexibility of the enzyme's amino acid residues as indicated by the lowered RMSF values of 0.082 Å and 1.036 Å, respectively (Fig. 6d, Table 6).
3.4 Pharmacokinetics
As shown in Table 7 , nine compounds including the standards (KOR, VBS, ABC, QOR, MOG, HPN, NFM, BFA, and MLN) are either moderately soluble or soluble in water. This will enhance the bioavailability of the nine compounds. Four compounds (ABA, FDL, ABC, and HPN) passed the drug-likeness test. Lipinski's rule of five is a tool used in assessing the drug-likeness compounds whereby a determination is made as to whether any compound that possesses certain pharmacological activities has oral drug properties that will allow it to be administered in humans [61]. On the other hand, VBS, KOR, QOR and MOG did not pass Lipinski's by violating three rules.Table 7 Predicted ADME parameters for the most prominent compounds.
Table 7Compound Bioavailability
Score Lipophilicity (iLOGP) BBB
Permeability GIT
Absorption Water
Solubility Drug Likeness (Lipinski)a
ABA 0.55 7.44 No Low Poor 1 (Mwt)
KOR 0.17 −0.73 No Low Soluble 3 (Mwt,HBD, HBA)
VBS 0.17 −0.43 No Low Soluble 3 (Mwt, HBD, HBA)
FDL 0.55 7.44 No Low Poor 1 (LogP)
ABC 0.55 2.44 No High Moderately soluble 0
QOR 0.17 −1.12 No Low Soluble 3 (Mwt, HBD, HBA)
MOG 0.17 −0.96 No Low Soluble 2 (HBD, HBA)
HPN 0.55 −0.25 No Low Soluble 1 (HBD)
NFM 0.55 2.14 No Low Moderately soluble 0
BFA 0.55 4.10 No Low Soluble 1 (Mwt)
MLN 0.55 2.06 No High Soluble 0
a Mwt = molecular weight; HBD = number of hydrogen bond donor groups; HBA = number of hydrogen bond acceptor group Parts; LogP = Partition Coefficient.
All the compounds and reference standards except for ABC and MLN were predicted to exhibit low gastrointestinal tract absorption (GIT). Nevertheless, low GIT absorption does not alter the therapeutic activity of drugs as many drugs (such as Nafamostat, Bafilomycin A1, and Lopinavir) with low GIT absorption are commercially available and therapeutically potent. The level and rate at which the active moiety or metabolite enters systemic circulation are measured by drug bioavailability [61]. Compared with the reference standards, four compounds, KOR, VBS, QOR, and MOG exhibited low bioavailability scores of 0.17, while the bioavailability scores predicted for the other compounds (ABA, FDL, ABC, HPN) and the three reference standards was 0.55.
4 Conclusion
The modulation of the entry steps and inhibition of these host cell druggable targets could help achieve the inhibition of the viral entry by preventing the interactions between the host cell and the viral proteins. In this study, two compounds (ABA and KOR) have strong affinity for the exopeptidase site of hACE2, interacted tenaciously with the essential amino acid residues (Glu384, Asn376, His383, Arg496, Thr329, Asp364) needed for the catalytic activity of hACE2. The study further revealed each of the three compounds [(VBS, FDL and ABC) and (QOR, MOG and HPN)] as potential inhibitors of TMPRSS2 and Cath L proteins, respectively, as they do not compromise the structural integrity of the proteins, but rather stabilized and established catalytic interactions with the vital amino residues needed for inhibition of the respective targets. In a nutshell, the results obtained in this study are suggestive of the structural mechanisms of inhibition of the identified leads against the proteins critical for SARS-CoV-2 to enter the human host cell and cause infection. Further refinement and development of the compounds for subsequent in vitro and other preclinical and clinical evaluations are underway.
Data availability
The data used to support the findings of this study are included within the article.
Author's contribution
SS conceptualized and supervised the study. UJO, IKA and SS generated and analyzed the data. SFO co-supervised the project, while UJO wrote the manuscript. All authors read and contributed to critical review of the manuscript for intellectual content and approved the submission for publication.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A Supplementary data
Supplementary description: Additional data for the molecular docking are presented in the supplementary file (Supplementary Tables S1, S2, S3 and S4).
The following is the Supplementary data to this article:Multimedia component 1
Multimedia component 1
Acknowledgements
The authors specially acknowledge the financial assistance of the Directorate of Research and Postgraduate Support, 10.13039/100007648 Durban University of Technology , and the Centre for High Performing Computing (CHPC), Cape Town, South Africa for providing the computing platform for this study. Research reported in this publication was also supported by the 10.13039/501100001322 South African Medical Research Council (SA MRC) under a Self-Initiated Research Grant to Dr. Sabiu. The views and opinions expressed are those of the authors and do not necessarily represent the official views of the funders.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jmgm.2022.108185.
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PMC009xxxxxx/PMC9002684.txt |
==== Front
J Mol Struct
J Mol Struct
Journal of Molecular Structure
0022-2860
1872-8014
Elsevier B.V.
S0022-2860(22)00680-9
10.1016/j.molstruc.2022.133019
133019
Article
African derived phytocompounds may interfere with SARS-CoV-2 RNA capping machinery via inhibition of 2′-O-ribose methyltransferase: An in silico perspective
Gyebi Gideon A. a⁎
Ogunyemi Oludare M. b
Adefolalu Adedotun A. c
Rodríguez-Martínez Alejandro d
López-Pastor Juan F. d
Banegas-Luna Antonio J. d
Pérez-Sánchez Horacio d⁎
Adegunloye Adegbenro P. e
Ogunro Olalekan B. f
Afolabi Saheed O. g
a Department of Biochemistry, Bingham University, Karu, Nigeria
b Human Nutraceuticals and Bioinformatics Research Unit, Department of Biochemistry, Salem University, Lokoja, Nigeria
c Department of Biochemistry, Federal University Lafia, Nigeria
d Structural Bioinformatics and High Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), Spain
e Department of Biochemistry, Faculty of Life Sciences, University of Ilorin, Ilorin, Nigeria
f Department of Biological Sciences, KolaDaisi University, Ibadan, Nigeria
g Department of Pharmacology and Therapeutics, Faculty of Basic Medical Sciences University of Ilorin, Ilorin, Nigeria
⁎ Corresponding authors.
12 4 2022
15 8 2022
12 4 2022
1262 133019133019
9 11 2021
1 4 2022
5 4 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.
Despite the ongoing vaccination against the life-threatening COVID-19, there is need for viable therapeutic interventions. The S-adenosyl-l-Methionine (SAM) dependent 2-O’-ribose methyltransferase (2′-O-MTase) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents a therapeutic target against COVID-19 infection. In a bid to profile bioactive principles from natural sources, a custom-made library of 226 phytochemicals from African medicinal plants with especially anti-malarial activity was screened for direct interactions with SARS-CoV-2 2′-O-MTase (S2RMT) using molecular docking and molecular dynamics (MD) simulations as well as binding free energies methods. Based on minimal binding energy lower than sinefungin (a reference methyl-transferase inhibitor) and binding mode analysis at the catalytic site of S2RMT, a list of 26 hit phytocompounds was defined. The interaction of these phytocompounds was compared with the 2′-O-MTase of SARS-CoV and MERS-CoV. Among these compounds, the lead phytocompounds (LPs) viz: mulberrofuran F, 24-methylene cycloartenol, ferulate, 3-benzoylhosloppone and 10-hydroxyusambarensine interacted strongly with the conserved KDKE tetrad within the substrate binding pocket of the 2′-O-MTase of the coronavirus strains which is critical for substrate binding. The thermodynamic parameters analyzed from the MD simulation trajectories of the LPs-S2RMT complexes presented an eminent structural stability and compactness. These LPs demonstrated favorable druggability and in silico ADMET properties over a diverse array of molecular computing descriptors. The LPs show promising prospects in the disruption of S2RMT capping machinery in silico. However, these LPs should be validated via in vitro and in vivo experimental models.
Graphical abstract
Image, graphical abstract
Keywords
Coronavirus
SARS-CoV-2
2-O’-ribosemethyltransferase
Phytochemicals
Molecular docking
Molecular dynamics
Mulberrofuran F
==== Body
pmc1 Introduction
The coronavirus disease-19 (COVID-19), was classified a worrisome global pandemic by the World Health Organization (WHO), following the virulent infection rate of Severe Acute Respiratory Syndrome Coronavirus 2 – (SARS-CoV-2) in humans [1]. Recent epidemiological findings present a cumulative total of about over 246 million confirmed cases and 5 million deaths have been reported since the start of the outbreak [2]. The SARS-CoV-2 belongs to one of the two zoonotic coronaviruses, the other ones being the Middle East Respiratory Syndrome Coronavirus (MERS-CoV). MERS-CoV and SARS-CoV have engendered severe respiratory disorder in mankind since the 21st century commenced [3]. SARS-CoV-2 has been described to be part of the most virulent viruses of this century, with the most fatalities till date [4]. Coronaviruses are described as rapidly evolving viruses, with a high rate of genomic mutation [5]. Recently, several variants of SARS-CoV-2 have been identified: the United Kingdom (UK), South Africa and Brazil variants are cited in the several literatures as (B.1.1.7 for UK, 501Y.V2 or 20C/501Y.V2B.1.351 for South Africa and P.1 for Brazil variants) [6]. This, along with a high infection rate has made the development of drugs quite elusive. Like the earlier coronaviruses, SARS-CoV-2 makes use of its cell environment for its replication and survival [7]. The viral RNA maintains its integrity through the “cap”, a unique organization towards the 5‘ end of the RNA molecule which comprise of a C-2′-O-methyl-ribosyladenine and N-methylated guanosine triphosphate; an arrangement similar to the host cell's RNA [8,9]. The “cap” structure plays significant function in pre-mRNA splicing, mRNA export, RNA stability and escaping the cellular innate immune system [10]. However, in humans, the cap is established in the nucleus of the cell, on the newly transcribed RNA to which the virus has no access. Consequently, they have to own their specified cap-synthesizing enzymes [9]. The last methylation step that cumulates into the RNA cap requires two enzymes, non-structural proteins (nsp) 14 and 16. The nsp 14 for GTP nucleobase, N-7 methylation while the nsp 16 for C-2′-O methylation of the following nucleotide. Both enzymes are methyltransferases (MTases) that depend on S-adenosylmethionine (SAM) [11,12]. Nsp14 when complexed to nsp10 has been reported to reduce cases of mismatched nucleotides via its exoribonuclease domain (ExoN) [13]. The 2′-O-ribose methyltransferase (2′-O-MTase) activity of nsp16 is also influenced by the enzyme's association with nsp10 [13], The activity of nsp16 functioning as a 2′-O-ribose methyltransferase (2′-O-MTase) is also influenced by its association with nsp10 [14,15]. These properties indicate nsp14 and nsp16 as promising therapeutic targets for SARS-CoV-2, especially nsp16 being a very promising molecular target for structural drug design. The 2′-O methyltransferase (MTase) is also essential for coronaviruses replication (in cell cultures) [16,17]. Identifying bioactive compounds with therapeutic activities against these targets is a necessary step to designing potent antiviral agents. Initial large-scale screening of bio-active agents capable of inhibiting target proteins, using bioinformatics tools have been variously reported [18], [19], [20], [21], [22], [23]. The use of plants and their parts, ‘herbal remedies’, in traditional medicine has been well documented. These plants are used as concoctions, decoctions, infusions etc. Indeed, the efficacy of many of these remedies has been attributed to their bioactive phyotcompounds [24]. Compounds derived from plants have been known to possess enormous structural diversity that has served as good starting points for investigating new drug [25].
There are several reports that focuses on the use of computation methods to screen different databases and libraries of natural compounds for potential inhibitors of several targets of SARS-CoV-2, this information have been compiled in some reviews [26,27]. Though there are few reports that targets 2′-O-MTase as a viable therapeutic target [28,29], there is no report on the repurposing of antimalarial compounds against SARS-CoV-2 2′-O-MTase
The inhibitory potential of phytocompounds against viral methyltransferases have been well documented [30,31]. This approach can be exploited in the quest for inhibitors of important targets against the novel SARS-CoV-2. In the wake of the ravaging (and still evolving) effect of the COVID-19 pandemic, the dearth of effective anti-viral drugs, and the relatively long process of drug discovery, computational simulation techniques has been a viable tool employed to study the evolving mutations [32,33], and for screening possible novel drug candidates [34], [35], [36], [37], [38]. In this study, we employ computational techniques to predict the interactions of a list of bioactive phytocompounds (BP) that were compiled from literature search and known to be derived from African medicinal plants against SARS-CoV-2 and other coronaviruses 2′-O-MTase.
2 Methods
2.1 Retrieval and preparation of protein structure for molecular docking
The 3D structure of nsp-16/10 of SARS-CoV-2 complexed with its native substrate (PDBID: 6WRZ), and previous viruses SARS-CoV (PDB ID: 3R24) and MERS-CoV (PDB ID: 5YNB), were retrieved from the Protein Data Bank (http://www.rcsb.org). Existing ligands and water molecules associated with the protein structures were removed and missing hydrogen atoms were added. Using MGL-AutoDockTools (ADT, v1.5.6), the Kollamn charges were added as the partial atomic charge [39]. The non-polar hydrogens were merged while the polar hydrogens were added to the proteins. This procedure was applied to all proteins and then saved into a dockable pdbqt format for docking calculations.
2.2 Ligand preparation for molecular docking
The structure data format (SDF) of the reference inhibitors (sinefungin and S-adenosyl-L-homocysteine (SAH)) and 226 phytocompounds were downloaded from the PubChem database (www.pubchem.ncbi.nlm.nih.gov). These ligands were converted to mol2 using Open babel [40]. Compounds that were unavailable on the database were drawn using ChemDraw version 19, the same was converted to mol2 chemical format.
2.3 Virtual screening and active site targeted molecular docking of phytocompounds
The screening of the 226 bioactive compounds against SARS-CoV-2 2′-O-ribose methyltransferase (S2RMT) was performed using AutoDock Vina [41].]. Based on the docking scores, interaction in the catalytic site and binding poses, 26 hit phytocompounds were selected. These hit phytocompounds were docked for interaction with the active pockets of the S2RMT of other strains (SARS-CoV and MERS-CoV). For all the docking
In OpenBabel that is incorporated into PyRx 0.8. the Universal Force Field (UFF) was used as the energy minimization parameter and conjugate gradient descent as the optimization algorithm. The energy of all the ligands were minimized using conjugate gradient descent as the optimization algorithm in OpenBabel that is incorporated into PyRx 0.8. The active sites of the three enzymes were defined by the grid boxes and presented in Table 1 . All other parameters were kept as default.Table 1 Binding site coordinates of nsp16 protease of coronaviruses.
Table 1Dimensions SARS-CoV-2 (Å) SARS-CoV (Å) MERS-CoV (Å)
Center_x 89.26 54.25 89.26
Center_y 16.92 60.82 16.92
Center_z 26.44 65.17 26.44
Size x 31.00 26.34 31.00
Size y 29.63 25.41 29.63
Size z 31.34 20.81 31.34
2.4 Molecular dynamics simulation
Desmond module of Schrodinger 2019–4 was employed for the MD simulation of the LPs-S2RMT. Water boxes were added to the proteins subsequent to addition of the missing hydrogen atoms and removal of any ligand in the TIP3P molecules solvent system [42] under orthorhombic periodic boundary conditions for 10 Å, buffer region with OPLS3 force field. An isothermal–isobaric ensemble (constant number of particles N, constant pressure P and constant temperature T) which is an ensemble of Nose-Hoover thermostat [43] and barostat was applied to maintain the constant temperature (310 K) and pressure (1 bar) of the systems, respectively. An energy minimization of 1000 steps with steepest descent followed by conjugate gradient algorithms was utilized. The Parameters such as temperature, salt concentration, and pH were set at the physiological values (310 K, 0.154 M NaCl and 7.0, respectively) during the simulation period. Multiple time step RESPA integration (Reference System Propagator Algorithms) was used in the dynamics study for bonded, near and far-bonded interactions with 2, 2 and 6 fs, respectively. The data were collected for every 100 ps, and the obtained trajectory was analyzed with Maestro graphical interphase (Schrödinger Release 2021–1: Maestro, Schrödinger, LLC, New York, NY, 2021). Various structural parameters, like Root Mean Square Deviation (RMSD), Root Mean Square Fluctuations (RMSF), Radius of Gyration (rGyr), Intramolecular Hydrogen Bonds (intraHB), Molecular Surface Area (MolSA), Solvent Accessible Surface Area (SASA) and Polar Surface Area (PSA) were calculated as a function of time to explore the structural behavior of the proteins, ligands and protein-ligand complexes. In order to estimate the free energy change that describes the binding of these LPs through the MD trajectories, MM-GBSA calculations were carried out, and free energy estimations were computed for 11 snapshots (one every 10 ns as shown in Fig. S19: supplementary data).
2.5 PCA and FEL analysis and covariance matrix generation
PCA and FEL analysis and covariance matrix generation were performed through covar and anaeig GROMACS modules with Desmond MD trajectories and represented by matplotlib Python library.
2.6 Physicochemical and pharmacokinetic study
The LPs for S2RMT were submitted for drug-likeness and ADMET filtering analysis. The SwissADME webserver (http://www.swissadme.ch/index.php) was used to analysis the drug-likeness using the Lipinski and Veber filtering tools [44]. Several molecular descriptors on the SuperPred webserver (http://lmmd.ecust.edu.cn/admetsar1/predict/) was used to analysis the predicted Absorption, Distribution, Metabolism, Excretion and toxicity (ADME/tox [45]. The canonical SMILES of the LPs were used for the analysis.
3 Results
3.1 Molecular docking of phytocompounds with the target protein
The virtual screening of 226 bioactive phytocompounds from African medicinal plants against S2RMT demonstrated varying degrees of estimated binding energies as presented in Table S2: supplementary data. From the results obtained, a hit list of 26 BP with binding affinities higher than the reference inhibitors, Sinefungin (−7.7 Kcal/mol) and SAH (−8.2 Kcal/mol), with notable interaction with the catalytic residues. The interactions of the top 26 ranked BP with S2RMT were further compared with those of SARS-CoV and MERS-CoV2’-O-MTase. From these analyzes, the four lead phytocompounds (LPs) with the highest binding affinity to the S2RMT were further selected viz: mulberrofuran F, a flavonoid; 24-methylene cycloartenol ferulate, a pentacyclic triterpenes; 10′-hydroxyusambarensine, an indole alkaloid; and 3-benzoylhosloppone, an abietane diterpenes (Table 2 ) with quantified free binding energy of (−10.7, −10.1, −9.4 and −9.2 Kcal/mol, respectively). The LPs interacted with SARS-CoV and MERS-CoV 2′-O-MTase with binding affinities of (−9.4, −8.9, −10.5 and −9.6 Kcal/mol) and (−8.7, −8.9, −10.1, and −9.9 Kcal/mol), respectively (Fig. 1 ). It was observed the Mulberrofuran F (−10.7 Kcal/mol) the topmost ranked phytocompound to the S2RMT displayed a lower binding affinity of −9.2 and 8.7 Kcal/mol to SAR CoV and MERS-CoV2’-O-MTase. On the other hand, 10-Hydroxyusambarensine demonstrated the highest binding affinity to SARS-CoV and MERS-CoV 2′-O-MTase (−10.5 and −10.1 Kcal/mol, respectively). Thus, the compounds displayed selectivity for different strain of the coronaviruses base on their affinity. The structural stability of the S2RMT complexed with the LPs was analyzed through MD simulations.Table 2 Structure of reference inhibitors (sinefungin and SAH) and the top docked BP with the active site residues of SAR CoV-2 2′-O-MTase.
Table 2S/No Bioactive Compounds Class of compound Plant species (Family)
S1 Sinefungin Image, table 2 Nucleoside
S2 S-adenosyl-l-homocysteine(SAH) Image, table 2 Nucleoside
1 Mulberrofuran F Image, table 2 Isoprenylated flavonoids Morusmesozygia (Moraceae)
2 24-Methylene cycloartenol ferulate Image, table 2 Pentacyclic triterpenes Entandrophrag maangolense (Meliaceae)
3 10 -Hydroxyusambarensine Image, table 2 Indole alkaloids Strychnosus ambarensis (Loganiaceae)
4 3- Benzoylhosloppone Image, table 2 Abietane diterpenes Hoslundiaopposita (Lamiaceae)
Fig. 1 Binding energies of the ten lead phytocompounds from the docking analysis of 226 phytocompounds and reference compounds docked to the active site of coronaviruses 2-O-methyltransferase. The red dotted line shows the top 4 docked compounds. 168 = 2, 3, 19 -trihydroxy-urs-12–20-en-28-oic acid.
Fig 1
3.2 Molecular interactions between the lead phytocompounds and coronaviruses 2′-O-MTase
The interactions of the LPs with the amino acid residues of coronaviruses 2′-O-MTase is given in Table 3 . The active site directed docking of sinefungin and SAH to SARS-CoV-2, SARS-CoV and MERS-CoV 2′-O-MTase revealed that it actively interacted with the catalytic site residues majorly through conventional hydrogen-bond, in most cases the reference compounds served as H-Donor from its hydrozyl or amino group. In the three coronaviruses 2′-O-MTase, sinefungin and SAH were docked into the substrate binding cavity as SAM (Fig. 2 ). In the same manner mulberrofuran F, 24-methylene cycloartenol ferulate, 10-hydroxyusambarensine and 3-benzoylhosloppone interacted with the catalytic residues of the substrate binding pocket (SBP) that is localized in a canyon of the three coronaviruses [46]. Mulberrofuran F interacted via a hydrogen bond to ASP6897 which is part of the amino acid involved in methionine binding in the pocket of the second subdivision of the SBP [46]. The benzofuran-2-yl ring of Mulberrofuran F was responsible for the hydrophobic interaction with the SBP via Pi-Pi, T-shaped, Pi-alkyl and alkyl interactions (Table 3 and Fig. 2). 24-Methylene cycloartenol ferulate interacted with both the nucleoside and amino acid (methionine binding) pocket sub unit of SAM binding cleft of S2RMT. The same binding pattern was observed with SARS-CoV and MERS-CoV 2′-O-MTase. The 3-O-feruloyl moiety was directed into the cleft of S2RMT, interacting via H-bonds, while the cycloartenol moiety interacted with the nucleoside binding residue via Pi-alkyl and alkyl hydrophobic interaction (Fig. 2). The hydroxyl and amino group of the 9H-pyrido[3,4-b]indol-6-ol moiety of 10-Hydroxyusambarensine interacted with catalytic residues of S2RMT such as ASP6897 and ASP6912 via hydrogen bonds while the 1H-indolo[2,3-a]quinolizine moiety formed most of the hydrophobic (P-alkyl and alkyl) interactions. A Pi-Sigma interaction with PRO6932 alongside carbon hydrogen bonds were formed between the indolopyridocoline moiety and the SAM binding residues (ASN6899 and GLY6871) of S2RMT (Fig. 2 and Table 3). For 3- Benzoylhosloppone the carboyl and hydroxyl group were responsible for the conventional and carbon hydrogen bonds to the catalytic residues S2RMT. The benzoyl ring interacted via Pi-alkyl and alkyl interactions to MET6929 and LEU6898 residues belonging to the amino acid (methionine binding) pocket a subunit of the SAM binding cleft of S2RMT. In a similar binding mode as the reference compounds, it was further observed that the LPs interacted with SARS-CoV and MERS-CoV2’-O-MTase in similar binding pattern as with S2RMT. They were docked into the ligand binding cleft of the two proteins and interacted with the catalytic and substrate binding residues (Fig. 2, Fig. 3, Fig. 4 ). The interacting amino acids are represented in Table 3.Table 3 Interactions of top docked compounds and reference inhibitors with active site residues of coronaviruses 2′-O-MTase.
Table 3Compounds Coronavirus Hydrogen bonds (Bond distance) Other interactions
Sinefungin SARS-Cov-2
TYR6930(3.36) ASP6912(3.00, 2.97) ASP6897(2.29) SER6872 MET6929(3.37) TYR6930 (2.01) ASP6928(2.71, 2.83, 2.01) GLY6869(2.78) ASP6897(2.61, 2.70) CYS6913(2.91) CYS6913MET6929
S-Adenosyl-l-Homocysteine TYR6930(3.29) ASP6912(3.00, 2.79) ASP6897(2.29) ASP6928(2.65, 2.83, 2.01) GLY6869(2.36) ASP6897(1.98) SER6872 MET6929 ASP6897 TYR6930
Mulberrofuran F SER6872(2.41) GLY6869 (2.13) ASP6897(2.80) ASP6897 ASP6899 PHE6947 PRO6932 LEU6898 MET6929 CYS6913
24-Methylene cycloartenol ferulate SER6999(3.31) GLU6971(2.81) HIS6972(3.37) TYR6930(3.56) THR6934(3.56) LYS6935(3.72) SER7000 (3.50) LYS6935 LEU6898 MET6929TYR6930 HIS6972
10 -Hydroxyusambarensine ASP6897(2.27) TYR6930(2.16) ASP6912(2.54) ASN6899(3.60) PRO6932 MET6929 PHE6947 LEU6898
3- Benzoylhosloppone LYS6968(3.05) ASP6928(2.79) GLY6869(3.61) ASP6897(3.76) TYR6930 PRO6932 LEY6898 MET6929
Sinefungin SARS-COV GLY75(2.77) GLY71(2.39) ASP73(2.54) ASP130 (2.18, 1.87) SER74(3.25) ASP99(3.32) TYR132(3.23) LEU100 (2.95, 3.52,4.80)
S-Adenosyl-l-Homocysteine TYR132(3.22) ASP114(2.65) ASP130(2.13) ASN43(2.70) ASP99(2.11) SER74 PRO80 ASP99 TYR132
Mulberrofuran F SER201(2.73) SER202(3.21) GLY71(2.40) TYR132 PRO134 MET42
24-Methylene cycloartenol ferulate ASP133(2.04) GLY71 ASP114 PRO134 TYR132 LEU100
10 -Hydroxyusambarensine ASP99(2.41) GLY71(2.35) ASP75 ASP99 ASP130 LEU100 PRO134 TYR132 MET131 CYS115
3- Benzoylhosloppone LYS170(2.81) ASP130(2.60) GLY71 PRO134 LEU100MET131
Sinefungin MERS-CoV ASN101(2.07) ASN98 (2.51) ASP75(3.02) ASP99(1.92, 3.36) ASP130(3.72) GLY71 (2.35) GLY73 (3.72, 3.133) LYS170 ASN43
S-Adenosyl-l-Homocysteine ASN43(2.48) TYR47(2.78) GLY81(2.99) CYS111(2.96) GLY71(2.81) ASP130(2.85) GLY73(2.98) ASP99(2.75) MET131(3.60) LEU100 (3.60)
Mulberrofuran F TYR132(2.74) ASN101(3.72) ASP99(3.72) PRO134MET131ASP75 PHE149LEU100
24-Methylene cycloartenol ferulate ASP114(3.44) TYR132(2.74) LEU100 (3.25) PRO134 PHE149 LEU100 LYS76 HIS41 CYS115
10 -Hydroxyusambarensine TYR132(2.54) ASP114(3.07) ASP99(2.37) LEU100 (3.75) PRO134 MET131 PHE149
3- Benzoylhosloppone TYR132(2.74) GLY71(3.25) ASP99(3.72) ASP130(3.72) LEU130(3.72) LYS170 MET131 PHE149
Fig. 2 Amino acid interactions of top lead phytocompounds from the docking analysis and reference inhibitors in substrate binding cavity SARS-CoV-2 2′-O-MTase. (S) solvent-accessible surface view. The top four ranked phytocompounds in sticks representation are represented by colors: (a) cyan: sinefungin (b) orange: SAM (c) gold: mulberrofuran F (d) red: 24-methylene cycloartenol ferulate (e) blue: 10–hydroxyusambarensine (f) Green: 3-benzoylhosloppone. Types of interactions are represented by light purple-dotted line: Green-dotted lines: H-bonds; hydrophobic interactions (Pi-Alkyl, Alkyl and pi-stacking); yellow-dotted lines: purple-dotted line: Pi-Pi T Shaped; Pi-sulfur interactions, pi-stacking interactions, with three-letter abbreviations of amino acids.
Fig 2
Fig. 3 Amino acid interactions of phytocompounds and reference inhibitors in substrate binding cavity SARS-CoV 2′-O-MTase. (S) solvent-accessible surface view. The top four ranked phytocompounds in sticks representation are represented by colors: (a) cyan: sinefungin (b) orange: SAM (III) gold: mulberrofuran F (d) red: 24-methylene cycloartenol ferulate (e) blue: 10–Hydroxyusambarensine (f) Green: 3-benzoylhosloppone.
Fig 3
Fig. 4 Amino acid interactions of phytocompounds lead phytocompounds from the docking analysis and reference inhibitors in substrate binding cavity MERS-CoV2’-O-MTase. (S) solvent-accessible surface view. The top four ranked phytocompounds in sticks representation are represented by colors: (a) cyan: sinefungin (b) orange: SAM (c) gold: mulberrofuran F (d) red: 24-methylene cycloartenol ferulate (e) blue: 10–hydroxyusambarensine (f) green: 3-benzoylhosloppone.
Fig 4
3.3 Result from molecular dynamic analysis
An in-depth 100 ns MD simulation was performed on the LPs complexed with S2RMT. In other to access the stability of the bound system and the structural integrity upon the binding of the phytochemicals, the MD simulation trajectories of the complex systems were compared to that of the unbound systems. The following thermodynamic parameters (RMSD, RMSF SASA, RoG, and number of H-bonds) protein secondary structure, ligand properties and protein-ligand contacts were computed from the trajectories, the plots were presented as a function of time frame.
3.3.1 Protein secondary structure
Protein secondary structure elements (SSE) of the S2RMT such as the alpha-helices and beta-strands were monitored throughout the simulation. Fig. 8a shows the SSE distribution by residue index. Fig. 8b summarizes the SSE composition, while Fig. 8c monitors each residue and its SSE assignment over time. The result of the analysis showed that 19.75% was Helix, 15.28% was strands, while 35.05% was Total SSE (Fig. 5 ).Fig. 5 Secondary structural analysis of SARS-Cov-2 2′-O-MTase during 100 ns MD simulation (a) SSE distribution by residue (b) summary of the SSE composition for each trajectory frame (c) residue and its SSE assignment over time.
Fig 5
3.3.2 Thermodynamic parameters
Root mean square deviation analysis
The RMSD plots for the five systems show that they were equilibrated before 10 ns. The systems exhibited the same progression of RMSD with minimal fluctuation with average RMSD values of 6.83043, 5.674218, 6.124726, 6.369042 and 5.989651 Å for the unbound enzyme, and S2RMT complexed to 10-Hydroxyusambarensine, Mulberrofuran F, 3-Benzoylhosloppone, 24-Methyono cycloartenol, respectively. The binding of the lead phytochemicals reduced the fluctuation in the phytochemical-enzyme complex system; this indicates a more compacted structure upon the binding of the phytochemicals (Fig. 6 ). The LP- S2RMT systems were further analyzed in Figs. S1–S4 (suplemantery data) The Cα shows the RMSD evolution of a protein (left Y-axis). The ligand RMSD (right Y-axis) indicates how stable the LPs are with respect to the S2RMTP and its binding pocket.Fig. 6 The Backbone-Root Mean Square Deviation (RMSD) plots of molecular dynamics (MD) simulation of SARS-Cov-2 2′-O-MTase complexed to the four lead phytochemicals from the docking analysis.
Fig 6
Root mean square fluctuation analysis
The RMSF plots reveal the flexibility of the amino acid residues of the protein. Higher fluctuations are observed at the N and C terminal ends of the proteins due to terminal motions. The mean RMSF values for the systems are 2.37097, 2.642513, 3.20722, 2.304798 and 2.51605 Å for the unbound enzyme and S2RMT complexed to 10-Hydroxyusambarensine, Mulberrofuran F, 3-Benzoylhosloppone, 24-Methyono cycloartenol, respectively. The phytochemicals bound SARS-Cov-2 2′-O-MTase complexes displayed higher RMSF values when compared to the unbound enzyme (Fig. 7 ).Fig. 7 Per residue Root Mean Square Fluctuations (RMSF) plots of molecular dynamics (MD) simulation of SARS-Cov-2 2′-O-MTase complexed to the four lead phytochemicals from the docking analysis.
Fig 7
The RMSF plots of the LP- S2RMT systems were analyzed to reveal the secondary structure elements (alpha-helical and beta-strand) regions that interacted with the LP. For the 4 LP- S2RMT systems, the highest fluctuation was observed with the amino acid residues close to residue no. 300 and after residue no. 350. These residues weren't involved in interaction with the ligand. A minimal fluctuation was observed with the interacting amino acid residues before amino acid residue no. 150. The catalytic and substrate binding residue were stable throughout the simulation period. (Fig. S5: supplementary data)
The RMSF of the LP with respect to the S2RMT complexes was further analyzed. The atomic breakdown of the LP that corresponds to the 2D structure in the top panel (Fit Ligand on Protein) line shows the ligand fluctuations, with respect to the protein. A large degree of fluctuation was observed during the simulation period in Mulberrofuran F atoms especially around atom no. 33 with respect to the protein, though the internal atoms of the ligands experienced fewer fluctuations (Fig. S6a). For 24-Methylene cycloartenol ferulate-protein complex the highest fluctuation was around atom no. 34 and 44 (Fig. S6b). Atoms around the later interacted via hydrophobic contacts with the binding site residue. In the case of 10–Hydroxyusambarensine-protein complex the atoms were stable with a lesser degree of fluctuations at atom no. 34 a hydroxyl moiety (Fig. S6c). Atoms of the benzoyl ring moiety of 3- Benzoylhosloppone were the most stable, while the alkyl, carbonyl and hydroxyl group on hosloppone moiety caused some level of fluctuations (Fig. S6d).
The radius of gyration (RoG) analysis
The extent of the compactness of the enzyme upon binding of the ligands is measured from the RoG plots and values. A stably folded protein structure presents a steady RoG plot. Fig. 8 shows the RoG plots of the five systems. The plots for the systems show a steady progression with minimal fluctuations. The mean RoG values calculated for the S2RMT systems are 21.94529, 22.34246, 22.72907, 22.32701 and 22.66686 for the unbound enzyme and the enzyme complexed to 10-Hydroxyusambarensine, Mulberrofuran F, 3-Benzoylhosloppone, 24-Methyono cycloartenol, respectively. The unbound and the enzyme complexed to the lead phytochemicals displayed very close mean RoG values indicating compacted systems (Fig. 8).Fig. 8 The Radius of gyration (RoG) plots of molecular dynamics (MD) simulation of SARS-Cov-2 2′-O-MTase complexed to the four lead phytochemicals from the docking analysis.
Fig 8
The surface accessible surface area analysis
The measure of solvent accessible by the surface of the enzymes was computed from the generated SASA values for the systems. Both RoG and SASA plots indicates the level of structural unfolding of proteins with reference to its original structure. Fig. 9 show the SASA plots for the enzymes systems. The average SASA values for the S2RMT systems are 20,326.16, 21,156.28, 21,112.91, 20,899.48 and 20,900.15 for the unbound enzyme and the enzyme complexed to 10-Hydroxyusambarensine, Mulberrofuran F, 3-Benzoylhosloppone, 24-Methyono cycloartenol, respectively (Fig. 9).Fig. 9 The Surface Accessible Surface Area (SASA) plots of molecular dynamics (MD) simulation of SARS-Cov-2 2′-O-MTase complexed to the four lead phytochemicals from the docking analysis.
Fig 9
The changes in the number of H-bonds
The average number of hydrogen bonds for the unbound enzyme, 10-Hydroxyusambarensine, Mulberrofuran F, 3-Benzoylhosloppone, 24-Methyono cycloartenol complexes are 53.72927, 46.89011, 48.31968, 50.53147 and 48.3956. In the AChE systems, a slight reduction in average number of hydrogen bond was observed in the complexes when compared to the unbound protein (Fig. 10 ).Fig. 10 The changes in the number of H-bonds during the MDS trajectory of SARS-Cov-2 2′-O-MTase complexed to the four lead phytochemicals from the docking analysis.
Fig 10
3.3.3 Protein-ligand contacts
The S2RMT interactions or contacts with the LPs were monitored throughout the simulation. The 2D-trajectory interaction diagram (Fig. 11, Fig. 12, Fig. 13, Fig. 14 ) and the stacked bar interactions plots were categorized by type (Hydrogen Bonds, Hydrophobic, Ionic and Water Bridges) and summarized in Fig. 11, Fig. 12, Fig. 13, Fig. 14. From the total of 21 amino acids contact made, CYS6913 and ASP6873 maintained contact for about 50% and 20% of the simulation time, respectively, via H-Bonds to mulberrofuran F. TYR6930 maintained the highest contact via hydrophobic interaction mulberrofuran F. Almost all the residue maintained some level of contact during the simulation time via water bridges (Fig. 11b). 24-Methylene cycloartenol ferulate made contact with 25 amino acid residues. H-bond contacts were sparsely maintained, while residue like TYR6930, VAL6937 and LEU6898 interacting via hydrophobic interaction-maintained contact for ∼40% 18% and 15% of the simulation time, respectively. Most residues interacting with water bridges maintained some level of prolonged contact (Fig. 12 b). From the total of 18 contacts made, LYS6933, TYR6930 and ASP6912 interacting via H-bond maintained contact for at least 30% of the simulation time with 10–Hydroxyusambarensine. PHE6947 maintained the highest hydrophobic contact time (>50%) with 10–Hydroxyusambarensine Unlike the first 2 compounds 10–Hydroxyusambarensine maintained short ionic contact with ASP6879 and ASP6912. The 2D-trajectory interaction diagram (Fig. 13 a,b) depicts that 3- Benzoylhosloppone maintained a H-bonding with TYR6930 for 66% of the simulation time, while it maintained a hydrophobic contact for about 33% of during the period of simulation with PHE6947 (Fig. 14 a,b).Fig. 11 (a) A schematic details of binding groups of mulberrofuran F interacting with the amino acid residues of SARS-Cov-2 2′-O-MTase (S2RMT) during the period of 100 ns MD simulation analysis. Interactions that occured more than 30.0% of the simulation time in the selected trajectory (0.00 through 100.00 ns), are shown (b) simulation interactions plot showing categorized S2RMT- mulberrofuran F interactions.
Fig 11
Fig. 12 (a)A schematic details of binding groups of 24-Methylene cycloartenol ferulate interacting with the amino acid residues of SARS-Cov-2 2′-O-MTase (S2RMT) during the period of 100 ns MD simulation analysis. Interactions that occured more than 30.0% of the simulation time in the selected trajectory (0.00 through 100.00 ns) are shown (b) simulation interactions plot showing categorized S2RMT- 24-Methylene cycloartenol ferulate interactions.
Fig 12
Fig. 13 (a) A schematic details of binding groups of 10 -Hydroxyusambarensine interacting with the amino acid residues of SARS-Cov-2 2′-O-MTase (S2RMT) during the period of 100 ns MD simulation analysis. Interactions that occurred more than 30.0% of the simulation time in the selected trajectory (0.00 through 100.00 ns) are shown (b) simulation interactions plot showing categorized S2RMT-10 -Hydroxyusambarensine interactions.
Fig 13
Fig. 14 (a) A schematic details of binding groups of 3-Benzoylhosloppone interacting with the amino acid residues of SARS-Cov-2 2′-O-MTase (S2RMT) during the period of 100 ns MD simulation analysis. Interactions that occurred more than 30.0% of the simulation time in the selected trajectory (0.00 through 100.00 ns) are shown (b) simulation interactions plot showing categorized S2RMT-3-Benzoylhosloppone interactions.
Fig 14
A timeline representation of the interactions and contacts (H-bonds, Hydrophobic, Ionic, Water bridges) summarized in Figs. S11–S14 is presented in the supplementary data.
3.3.4 Ligand properties
The LPs properties analyzed on its reference conformation. From the plots (Figs. S7a–10a), we observed stable RMSD fluctuations (<3.0 Å) for most cases indicating no huge dynamical alterations during the course of simulations. All the compounds in ligand-protein systems showed a stable rGyr profile, suggesting no conformational alterations (expansion or compression) (Fig. S7b–S10b). Except for mulberrofuran F the other three displayed no intramolecular hydrogen bond during the simulation run (Figs. S7c–S10c). The MolSA, SASA and PSA plots for all the four compounds during the simulation run showed minimal fluctuations, indicating an impressively stable complex upon the binding of compounds to the active sites of the protein. Other ligand properties such as the Ligand Torsion Profile (LTP) was analyzed, the results are presented in the Figs. S15–S18 (supplementary data). The ligand torsions plot summarizes the conformational evolution of every rotatable bond (RB) in the ligand throughout the simulation trajectory (0.00 through 100.00 ns). The 2D schematic of a ligand, rotatable bonds, conformation and torsion are represented in the Figs. S15–S18 (supplementary Data).
3.3.5 MM-GBSA method for estimating phytocompound binding free energy
The computed free energy estimations for 11 snapshots (one every 10 ns) are summarized in average values and their standard deviation in Table 4 . All the LPs yielded dG values that collaborates the docking analysis, with Mulberrofuran F possessing the highest binding free energy (dG). The evolution of the binding free energy for the four systems is graphically presented as a function of the time during the simulation (Fig. S19: supplementary data).Table 4 MMGBSA obtained dG average values and their standard deviation for the four studied compounds.
Table 4Compound RMSD value at 100 ns (Å) dG Average (kcal/mol) dG Standard deviation
10-Hydroxyusambarensine 9.618 −112.4300034 21.67643475
Mulberrofuran F 6.735 −140.1412904 18.02256363
3-Benzoylhosloppone 4.481 −132.1901051 12.52935498
24-Methyono cycloartenol 6.782 −139.3845749 26.35954092
4 Principal component and free energy landscapes analysis
The free energy landscape representations generated by the two first principal components (PC1 and PC2) of the complexes with each one of the inhibitors show similar PCA distribution in the SARS-Cov-2 2′-O-MTase-ligand complexes. Additionally, all of them have differences with the PCA distribution for free protein system. It was observed more different metastable conformations with low-energy states, represented as free energy basins in the blue regions, for those complexes with inhibitor respect to the observed in the free protein. Besides, only one region near to the minimum energy was detected, while rest of complex show more of one metastable region with the minimum value (Fig. 15 ).Fig. 15 Free energy landscape (FEL) between first and second principal components (PC1, PC2) graph representation for SARS-Cov-2 2′-O-MTase complexed with (a) mulberrofuran F (b) 24-methylene cycloartenol ferulate (c) 10 –hydroxyusambarensine (d) 3- benzoylhosloppone and (e) without any compound systems.
Fig 15
Regarding the traces of covariance matrix, the most relevant evidence is the difference of trace between free protein (10.7905 nm2) and protein binds with Mulberrofuran F (8.35602 nm2). Thus, these results suggest SARS-Cov-2 2′-O-MTase structure obtains a greater compaction when is binding of Mulberrofuran F due to trace decrease in the complex. The rest of ligands don't show a considerable increase in complex compaction respect protein (Table 5 ).Table 5 Trace of the covariance matrix for each SARS-Cov-2 2′-O-MTase-compound complex.
Table 5Compound Trace of covariance matrix (nm2)
No compound (Free protein) 10.7905
10-Hydroxyusambarensine (16) 10.9227
Mulberrofuran F (113) 8.35602
3-Benzoylhosloppone (119) 10.9818
24-Methyono cycloartenol (164) 18.2716
4.1 Drug-likeness and pharmacokinetic properties of selected compounds
The result for the predictive druglikeness and ADMET filtering analyzes for the LPs presented in Table 6 . For ADMET analyzes, the molecular descriptors used for the filtering included blood brain barrier (BBB) penetration, this who compounds could cross the blood brain barrier, the aqueous solubility (AS), predicts the solubility of each LPs in water at 25 °C. The various cytochrome P450 descriptors were used to assess the cytochrome P450 inhibitory activities of the LPs. The human intestinal absorption (HIA), predicts the intestinal absorption of the LPs after oral administration. The drug likeness and ADMET analysis of 10–hydroxyusambarensine have been reported in our previous paper [18] while the 3 reported herein (mulberrofuran F, 24-methylene cycloartenol ferulate, and 3- benzoylhosloppone) fulfilled the all the requirement for Lipinski analysis with corresponding favorable predicted ADMET parameters. The in silico druglikeness and ADMET properties suggested mulberrofuran F to have low GI absorption, while 24-methylene cycloartenol ferulate, and 3- benzoylhosloppone have high GI absorption. The three compounds had high probability of absorption, subcellular distribution, and low toxicity [47]. The ADMET analysis shows that the LPs have the ability to be absorbed in the human intestine, high aqueous solubility, low acute oral toxicity with a good bioavailability score (Table 6).Table 6 Physicochemical properties of the top-binding phytocompounds from African plants to SARS-CoV-2 2′-O-MTase.
Table 6a) Physiochemical properties Mulberrofuran F 24-Methylene cycloartenol ferulate 3- Benzoylhosloppone
Molecular weight (g/mol) 630.68 630.68 418.52
Num. heavy atoms 47 44 31
Num. arom. heavy atoms 27 6 6
Num. rotatable bonds 4 9 4
Num. H-bond acceptors 8 4 4
Hydrogen bond donor 5 1 1
cLogP 4.55 4.55 2.61
Molar Refractivity 179 179 101.11
TPSA (Ų) 132.75 55.76
Lipinski violation 1 1 0
Drug likeness
Lipinski Yes Yes Yes
Veber Yes Yes Yes
Bioavailability Score 0.55 0.17 0.55
(b) ADMET SAR Absorption (Probability)
Blood-Brain Barrier BBB+ (0.565) BBB+ (0.649) BBB+ (0.835)
Human Intestinal Absorption HIA+ (0.984) HIA+ (0.973) HIA+ (0.974)
Caco-2 Permeability Caco2+ (0.577) Caco2+ (0.745) Caco2+ (0.678
P-glycoprotein Substrate Non-substrate (0.727) Substrate (0.795) Substrate (0.752)
P-glycoprotein Inhibitor Non-inhibitor (0.656) Non-inhibitor (0.606) Non-inhibitor (0.806)
Renal Organic Cation Transporter Non-inhibitor (0.910) Non-inhibitor (0.797) Non-inhibitor (0.814)
Distribution (Probability)
Subcellular localization Mitochondria (0.786) Mitochondria (0.802) Mitochondria (0.838)
Metabolism Metabolism Metabolism
CYP450 2C9 Substrate Non-substrate (0.780) Non-substrate (0.758) Non-substrate (0.777)
CYP450 2D6 Substrate Non-substrate (0.852) Non-substrate (0.812) Non-substrate (0.912)
CYP450 3A4 Substrate Non-substrate (0.567) Non-substrate (0.822) Non-substrate (0.813
CYP450 1A2 Inhibitor Non-inhibitor (0.5154) Non-inhibitor (0.5814) Non-inhibitor (0.5814)
CYP450 2C9 Inhibitor Non-inhibitor (0.8197) Non-inhibitor (0.500) Non-inhibitor (0.539)
CYP Inhibitory Promiscuity Low CYP Inhibitory Promiscuity (0.8818) Low CYP Inhibitory Promiscuity (0.729) Low CYP Inhibitory Promiscuity (0.815)
Toxicity
AMES Toxicity Non-AMES toxic (0.506) Non-AMES toxic (0.50) Non-AMES toxic (0.882)
Carcinogens Non-carcinogens (0.934) Non-carcinogens (0.9712) Non-carcinogens (0.912)
Acute Oral Toxicity III (0.429) IV (0.607) III (0.749)
Rat Acute Toxicity LD50, mol/kg 3.3280 1.4139 1.9882
Aqueous solubility (LogS) −4.3480 −5.8146 −4.7646
Pharmacokinetics
GI absorption low High High
Log Kp (skin permeation) cm/s −4.53 −1.42 −1.42
5 Discussion
SARS-CoV-2 is a virulent and highly evolving virus, whereas the drug discovery process has not matched the increasing therapeutic need of this viral infection [48]. Naturally existing phytocompounds from plants are potential bioactive repositories, including antiviral activity, which, if adequately explored, could provide affordable, accessible and available use as therapeutic agents against coronavirus infections [49]. Like other coronavirus, the SARS-CoV-2 evades host immune detection and reduces the chance of immune response in the incubation period of 2 to 14 days. This evasion of immune detection is projected to be achieved through the modification of viral mRNA by 2′ O-methyltransferase activity of nsp16/nsp10 which enables the virus to escape detection by the host's innate immune mechanism [50]. Compounds that block viral immune evasion through the suppression of viral RNA 2′-O-methylation, will encourage early expression of interferon-stimulated genes which in turn will serve to impede SARS-CoV-2 replication [51]. The 2′-O-Methyl transferase activity of nsp-16 is S-adenosyl-methionine (SAM)-dependent and regulated by nsp10 binding. The binding of SAM induces essential conformational changes, in the enzyme, that favors RNA affinity and methylation [52]. Hence it is expected that compounds that interacts with the SAM binding site may elicit host response against the virus. Structure based drug design has employed molecular docking to predict the binding-conformation of ligands in the binding site of target receptor and the strength of association (binding affinity) [53]. In the present study, we screened 226 bioactive phytocompounds from various African plants against nsp16 of SARS-CoV 2. The docking, interactive and binding free energy analysis identified the LPs (Mulberrofuran F, 24-Methylene cycloartenol ferulate, 10-Hydroxyusambarensine and 3-Benzoylhosloppone) with high potential and selective inhibition of the coronaviruses nsp16 protein. Hydroxyusambarensine is an alkaloid from the roots of Strychnosusambarensis, previously reported as an antimalarial [54]. Mulberrofuran F isolated from Morus alba, has been used to treat hypotension [55,56]. 24-Methylene cycloartenol ferulate, also called γ-Oryzanol (OZ) has been identified in various cereals, including barley, rice bran and corn [57]. It has been reported to exhibit antioxidant, anti-lipidemic, anti-diabetic and neuro-modulatory properties [57,58]. These LPs interacted with the surface residues (Lys-46, Asp-130, Lys-170 and Glu-203) at the bottom of the central groove, thatcatalysis the transfer SAM methyl group within the substrate binding pocket [46]. In all strains of CoV, the catalytic tetrad (Lys46, Asp130, Lys170) and Glu203 are conserved [59], this may have been responsible for the high binding potential to the three CoV understudied. Though the LPs interacted with the catalytic residues in a similar binding pattern as the SAH (the product of methylation of SAM) and sinefugine (a known inhibitor), they interacted with a stronger binding affinity than these compounds. Thus, these compounds may be able to bind to the S2RMT tightly and hence compromise the RNA methylation function of the enzyme, this will in turn, disrupt the capping machinery, prevent evasion of recognition by the host innate immune system [60], [61], [62] and preclude the viruses from resisting the IFN-mediated antiviral response [10,16]. To further understand the dynamic behavior of the LPs at the binding site of S2RMT, MD simulation was employed [63]. The binding patterns and per-residue amino acid interactions of the LPs-S2RMT complexes in the dynamic state collaborated with those done from the static docking analysis. The various thermodynamics parameters that were analyzed from the 100 ns atomistic MDS trajectory files of the LPs-S2RMT complexes revealed stable complexes that can be adapted into other forms of experiments. The comparison of the RMSD plots for the complex systems shows that the binding of the LPs to S2RMT did not cause any structure deformation in the protein [64]. From the RMSF plots analysis of the four systems, the higher fluctuation that was observed with the interacting residues is consistent with previous reports, where higher structural fluctuations occurred in ligand binding sites of catalytic loop regions [65]. The RoG and SASA plots of all the systems did not show fluctuation above the optimum of >2 Å further indicating that the structural integrity of the proteins was preserved [66]. The binding free energy that is measured from the simulation trajectories provides more accurate computation of ligand binding affinities than the static docking analysis [67]. These results were calculated based on the total binding free energy of the complex. In these calculations, the binding free energy (∆Gbind) measures the affinity of a ligand to its target protein. Thus, the ∆Gbind calculations are important to gain in-depth knowledge about the binding modes of the hits in drug design [68]. The results from the binding free energy calculation (MM-GBSA) agreed with that from the docking analysis; further establishing Mulberrofuran F as the most potent phytocompound. Also, from the predictive drug-likeness, pharmacokinetic and ADMET filtering analyzes, the top docked phytocompounds were predicted to be druggable and nontoxic. The result from the filtering analyzes showed descriptors that suggests a favorable ADMET and pharmacokinetic properties. This further indicates the druggable potential of the LPs [69,70]. The LPs displayed properties that suggest their ability to cross the BBB, hence their potential to ensure overall viral clearance in the brain cells [47]. Also, the LPs expressed high possibility of human intestinal absorption and not susceptible to the permeability-glycoprotein (P-gp, a drug efflux pump). Therefore, it is suggested to be well absorbed into the blood stream, subverting the restraining effect of the P-gp to pump compounds back into the intestinal lumen [71].
6 Conclusion
Herein, we have virtually screened a list of 226 bioactive phytocompounds compiled from a literature search of compounds from African medicinal flora with reported bioactivity against infectious diseases (including viral infection). Altogether the top four docked compounds demonstrated higher binding affinity than the reference inhibitors to the coronaviruses 2′-O-MTase, nevertheless, they displayed similar binding pattern as the reference inhibitors. These phytocompounds were identified to interact with important catalytic residue in the substrate binding site of SARS-CoV-2, SARS-CoV and MERS-CoV 2′-O-MTase as the reference inhibitors, hence, they may disrupt the RNA capping machinery, the replication and survival of the viruses. These potential inhibitors of SARS-CoV-2 2′-O-MTase were stable in a simulated dynamic condition and exhibited positive drug-likeness in the ADMET studies, thus, they are well adaptable for a recommended in vitro and in vivo experimental studies as anti-COVID-19 agents.
Funding
This work has been funded by the Fundación Séneca de la Región de Murcia under Project 20,988/PI/18. This research was partially supported by the omputer resources and the technical support provided by Barcelona Supercomputing Center (BCV-2021–1–0010), Poznan Supercomputing Center, the e-infrastructure program of the Research Council of Norway via the supercomputer center of UiT−the Arctic University of Norway, and by the supercomputing infrastructure of the NLHPC (ECM-02), Powered@NLHPC
Institutional review board statement
Not applicable.
Informed consent statement
Not applicable.
Data availability
All data supporting the findings of this study are available within the article and its supplementary materials.
Ethical approval
Not required.
CRediT authorship contribution statement
Gideon A. Gyebi: Conceptualization, Visualization, Writing – original draft, Methodology. Oludare M. Ogunyemi: Methodology, Writing – review & editing. Adedotun A. Adefolalu: Writing – review & editing. Alejandro Rodríguez-Martínez: Methodology. Juan F. López-Pastor: Methodology. Antonio J. Banegas-Luna: Methodology. Horacio Pérez-Sánchez: Supervision. Adegbenro P. Adegunloye: Writing – review & editing. Olalekan B. Ogunro: Writing – review & editing. Saheed O. Afolabi: Writing – review & editing.
Declaration of Competing Interest
The authors declare that they have no competing interests.
Appendix Supplementary materials
Image, application 1
Acknowledgements
This work is funded by grants from the Spanish 10.13039/501100003329 Ministry of Economy and Competitiveness (CTQ2017-87974-R), and by the Fundación Séneca de la Región de Murcia under Project 20988/PI/18. This research was partially supported by the supercomputing infrastructure of Poznan Supercomputing Center, the e-infrastructure program of the Research Council of Norway via the supercomputer center of UiT−the Arctic University of Norway, and by the supercomputing infrastructure of the NLHPC (ECM-02), Powered@NLHPC. The authors appreciate the members of the BioNet-AP: Bioinformatics Network for African Phytomedicine.
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.molstruc.2022.133019.
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PMC009xxxxxx/PMC9003152.txt |
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Osteoporos Int
Osteoporos Int
Osteoporosis International
0937-941X
1433-2965
Springer London London
6340
10.1007/s00198-022-06340-y
Article
IOF REGIONAL 2021 8th Asia Pacific Osteoporosis Conference
12 4 2022
2022
33 Suppl 1 120
© International Osteoporosis Foundation and National Osteoporosis Foundation 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
issue-copyright-statement© International Osteoporosis Foundation and National Osteoporosis Foundation 2022
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pmc
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PMC009xxxxxx/PMC9003157.txt |
==== Front
Curr Hypertens Rep
Curr Hypertens Rep
Current Hypertension Reports
1522-6417
1534-3111
Springer US New York
35412188
1186
10.1007/s11906-022-01186-5
Telemedicine and Technology (HB Bosworth, Section Editor)
A Systematic Review of the Role of Telemedicine in Blood Pressure Control: Focus on Patient Engagement
http://orcid.org/0000-0002-0172-9421
Khanijahani Ahmad khanijahania@duq.edu
1
Akinci Nesli 2
Quitiquit Eric 1
1 grid.255272.5 0000 0001 2364 3111 Department of Health Administration and Public Health, John G. Rangos School of Health Sciences, Duquesne University, 600 Forbes Avenue, Pittsburgh, PA 15282 USA
2 grid.261241.2 0000 0001 2168 8324 Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Davie, FL USA
12 4 2022
112
9 3 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, 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 of Review
To systematically review and synthesize the existing evidence on the effects of different telemedicine interventions on improving patient engagement among patients with hypertension. Patient engagement is defined as patients’ knowledge, skills, ability, and willingness to manage their healthcare within the context of interventions designed to promote positive patient behaviors.
Recent Findings
Telemedicine is a rapidly growing method of healthcare services delivery. Telemedicine interventions are mainly used to facilitate communication between the patient and provider, measure, record, and track blood pressure, and educate and train patients about managing their blood pressure. Findings from several studies indicate the evidence of patient engagement, adherence to the care plan, improvement in knowledge about blood pressure, and patient satisfaction with telemedicine interventions for blood pressure.
Summary
Telemedicine interventions need to be customized depending on patient demographics and socioeconomic characteristics such as age and education level to ensure optimal patient engagement.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11906-022-01186-5.
Keywords
Telemedicine
Telehealth
Blood pressure
Hypertension
Patient engagement
Patient adherence
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pmcIntroduction
Telemedicine is continuously growing as a practical method of healthcare service delivery nationally and globally across various healthcare settings. The advent of the COVID-19 pandemic has brought telemedicine interventions to the forefront of healthcare offerings and highlights more than ever the importance of optimizing telemedicine interventions so they can best serve patients, providers, and healthcare systems [1••, 2••]. Though individual telemedicine offerings vary in their structure and organization, telemedicine is broadly defined as the act of delivering healthcare services over a distance using information and communication technologies to aid in the diagnosis, treatment and prevention, research and evaluation, and the continuing education of healthcare providers [3]. Despite potential challenges in implementing telemedicine interventions, such as protecting the privacy of patient data, technological costs, and the heterogeneity of these interventions, the use of telemedicine has increased substantially over time. In 2010, 35% of hospitals in the USA were fully or partially implementing a computerized telehealth system and, by 2017, this percentage rose to 76% [4]. A 2016 World Health Organization (WHO) survey found that 87% of countries (n = 109) reported implementing at least one mobile health (e.g., telemedicine through mobile phones and patient monitoring devices) program and that 57% of the responding countries (n = 70) recognized telehealth at a national policy level [5].
Telemedicine is becoming increasingly popular to manage patients with chronic conditions such as diabetes, heart disease, and hypertension. Blood pressure (BP) monitoring and control through telemedicine is particularly of interest as nearly 50% of US adults have hypertension, and only about 24% of them have their hypertension under control [6]. In 2017, over 472,000 individuals’ primary or contributing cause of death in the USA was hypertension, and hypertension cost the USA approximately $131 billion each year between 2003 and 2014 [7, 8]. Healthcare systems must develop and implement efficient hypertension management methods to lower the disease burden nationally and globally. Previous studies, including randomized controlled trials, suggest that home blood pressure telemonitoring (HBPT) improves blood pressure control in patients with hypertension. Additional studies have demonstrated a significant reduction in BP through regular HBPT than usual care [9]. Telemedicine, therefore, presents a promising method of facilitating the care and management of patients with hypertension.
Although previous studies demonstrate improvements in BP control and reductions in BP through telemedicine interventions, less is known about the effects of telemedicine on patient engagement among patients with hypertension. In this review, we define patient engagement as patients’ knowledge, skills, ability, and willingness to manage their healthcare within interventions designed to promote positive patient behaviors [10]. Increasing and improving patient engagement may be a strategy whereby healthcare systems, providers, and patients observe improved health outcomes, lower costs, and better patient care [10]. Optimizing patient engagement may also contribute to achieving a more patient-centered approach to healthcare, which is increasingly valued in the current healthcare climate [11]. Therefore, assessing the effects of telemedicine on improving patient engagement among patients with hypertension can offer essential insights to enhance the care of hypertensive patients. Insufficient patient adherence to treatment and clinical inertia (the inability of healthcare providers to initiate or intensify therapy appropriately) are the two major causes of inadequate BP control [12]. Telemedicine offers a unique approach to target such concerns and can potentially improve outcomes for patients. To our knowledge, to date, there is no comprehensive review of empirical studies on the effects of telemedicine on improving patient engagement in patients with hypertension. The purpose of this systematic review was to compile and assess the existing relevant literature, fill current knowledge gaps by providing comparative evidence about the impact of telemedicine on improving patient engagement in patients with hypertension, and inform future research and policy development on this topic.
Methods
Databases and Search Strategies
We searched PubMed, Wiley Online Library, Scopus, and Embase for relevant studies until July 2020. After restricting our search terms to include different variations of telemedicine, patient engagement, and hypertension, the initial search yielded 775 results. Table 1 includes the search terms and the general search strategy.Table 1 Keywords and search strategy
Category 1: telemedicine Category 2: patient engagement Category 3: hypertension
“e-Health” OR “eHealth” OR “telemedicine” OR “telehealth” OR “tele-health” OR “telecare” OR “health information technology” OR mhealth OR “mobile health” “patient involvement” OR “patient empowerment” OR “patient participation” OR “patient activation” OR “patient engagement” OR adherence “blood pressure” OR hypertension
Search strategy: 1 AND 2 AND 3
Inclusion and Exclusion Criteria
Only original research articles published in peer-reviewed journals were included in this review. Other formats, such as book chapters, viewpoints, and comments, were excluded. If possible, we restricted the results to journal articles in the search process. Otherwise, we eliminated the other publication formats during the review of the search results. Only articles published in English were included in this review. Table 2 details the study selection and inclusion criteria. Table 2 Study selection and inclusion criteria
1. Intervention: introduction or use of telemedicine or telehealth technology among those with hypertension
2. Outcome: changes in patient participation, engagement, or adherence
3. Language: only published in English
4. Format: only peer-reviewed original research papers. Exclude other publications such as, book chapters, viewpoints, commentaries, and letters to the editor
Study Screening and Selection Criteria
The flow diagram in Fig. 1 demonstrates the study selection process for this systematic review based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) [13]. A total of 775 studies were initially identified through database searching (PubMed, Wiley Online Library, Scopus, and Embase). After removing duplicated results, 580 studies remained. Of those studies, 560 studies were deemed irrelevant and excluded from further analysis after reviewing the title of the studies. Twenty studies remained and were assessed for eligibility. Another seven studies were excluded after a complete review of these studies with relevant reasons listed in Fig. 1. Ultimately, thirteen studies were included in this systematic review.Fig. 1 Flow diagram of article selection based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines
Results
Summary Study Characteristics
Table 3 summarizes the sample sizes, study designs, interventions, and primary findings for the thirteen studies included in this systematic review. The publication dates for the included articles ranged from 2012 to 2019. Seven studies were from North America (six from the USA and one from Canada), one from South America (Bolivia), three from Western Europe (two from Sweden and one from England), one from East Asia (China), and one study location could not be identified.Table 3 Summary characteristics and findings of the included studies
First author, year Population/sample Sample size Design/data collection Telemedicine intervention Main findings
Aberger, 2014 [14] Patients in renal transplant clinic within 700 bed urban hospital in the USA 66 post-transplant patients Survey research using convenience sampling and self-reported data by patients via home electronic BP monitoring Telemedicine system in which patients self-record BP values at home using an uploadable BP monitor. BP values are uploaded to a patient portal that is accessible to pharmacist as well as physician who can tailor treatment accordingly to BP values. Patient portal contains messaging platform that sends automated and tailored feedback messages to engage and reinforce patients 75% of patients enrolled in the study monitored their BP at least once and 69% of patients took 6 readings and obtained a BP average. Statistically significant reductions in average SBP and DBP readings upon 30 days and 180 days of study enrollment were observed
Bengtsson, 2018 [23] Hypertension patients and their healthcare providers in 4 primary care centers in Sweden 20 patients and 7 healthcare providers Qualitative and exploratory study design 8-week use of a mobile phone-based BP self-monitoring system that incorporates the following features: daily BP and pulse measurements, motivational messages that encourage maintenance of lifestyle changes, and graphs displaying self-recorded BP data Patients demonstrate greater engagement in and contribution to follow-up consultations with their healthcare providers regarding their BP values after 8-week use of the mobile phone-based telemedicine intervention. Importantly, during follow-up visits, patients contextualized elevated or reduced BP readings based on what they were doing on the days those BP readings were taken and therefore assumed a more active role in the interpretation and management of their hypertension
Cottrell, 2015 [15] Hypertensive patients enrolled in one of four national telemedicine-based hypertension protocols in England 2963 patients Patient registration data and data entered by patients Mobile phone-based telemedicine platform (Florence) to which patients can upload self-recorded BP values, and receive reminder text messages about uploading BP data as well as messages on next steps to take if their BP readings are outside an acceptable range. In this study, patients are enrolled in one of four national telemedicine initiatives/protocols in which protocol success criteria is defined by how engaged patients are in BP monitoring based on the number of text messages they send via Florence in a defined period of time Patient engagement in protocols was satisfactory in the first month; however, engagement rapidly declined over the following 2 months. As patient medical records were not examined and patient interviews were not conducted, it was not possible to gain clarity on the specific reasons why patient engagement declined over time
Frias, 2017 [16] Patients with uncontrolled type 2 diabetes and hypertension in the USA 109 patients 12-week, open-label, prospective, cluster-randomized controlled study Digital medicine offering (DMO) that consists of an ingestible pill with a sensor, adhesive skin patch that collects medication usage data, as well as a mobile app that compiles medication adherence data from skin patch. Providers can view adherence data via a web portal and make medication adjustments as needed. DMO use outcomes were evaluated at 4 weeks and 12 weeks 91% of patients reported that using the DMO data was useful to manage their health and 93% reported that the data improved their health. 91% stated that sharing data with their provider gave them a better understanding of their care plan. Patient engagement was evaluated by a 10 question patient activation measure (PAM). Participants in the DMO group had a nonsignificant greater increase in PAM scores compared to the control group
While the DMO groups demonstrate greater reductions in SBP and HbA1c at weeks 4 and 12 compared with the control group, the SBP reduction at week 4 was the only statistically significant finding
Guo, 2017 [17] Patients with atrial fibrillation in China. Hypertension was among the most common comorbidities in both the intervention and control groups 209 patients Cluster randomized design study Mobile atrial fibrillation (mAF) app that stores patient health records, automatically assesses stroke and bleeding risk and provides treatment regimen suggestions, offers educational modules on atrial fibrillation, and encourages patients to self-monitor their heart rate and BP Statistically significant improvements seen in patients’ knowledge on atrial fibrillation, adherence to drug therapy, anticoagulation satisfaction, quality of life, and self care
Hallberg, 2018 [18] Patients with high BP and their treating healthcare providers in Sweden 20 patients and 7 healthcare providers Face-to-face semi-structured interview 8-week use of a mobile phone-based BP self-monitoring system that incorporates the following features: daily BP and pulse measurements, motivational messages that encourage maintenance of lifestyle changes, and graphs displaying self-recorded BP data The mobile phone-based BP monitoring system is viewed in a generally positive way by both patients and providers. Patients were more cognizant of how lifestyle modifications impacted their BP values and came into their follow-up visits better informed about their BP data. Patients assumed a more active role in the interpretation of their BP data when interacting with their provider and reported an increased motivation to make lifestyle changes and sustain these changes
Jean-Jacques, 2012 [26] Black and white patients in a general internal medicine ambulatory care practice in Chicago 8919 eligible patients Secondary data with time-series models Health information technology (HIT) initiative within electronic health record that includes clinical reminders, decision support tools, and performance feedback directed to healthcare providers Quality in patient care improved for 14 out of 17 measures for white patients and 10 out of 17 measures for black patients whose providers received HIT intervention. For one measure (blood pressure control in patients with diabetes), quality improved for black patients only. For one measure (blood pressure control for patients with hypertension), quality improved for neither group
Kaplan, 2017 [19] Patients with two or more BP recordings 5115 patients Single-arm retrospective observational study Mobile health program (Hello Heart app) that allows patients to track their BP recordings, provides BP measurement reminders, and contains educational modules 2 weeks after initial download of the Hello Heart app, 74% of patients were still recording their BP, 45% were still recording at 4 weeks, 21% at 8 weeks, 6% at 16 weeks, and 1.9% at 22 weeks. Mean app visits were 15 times per week across the different subgroups. App visit to BP recording ratio was 3:1
Levine, 2018 [20] Primary care patients with hypertension in the USA 1786 patients Retrospective cohort study Asynchronous virtual primary care visit occurring within 21 to 180 days following an in-person visit. During the virtual visit, the patient enters 5 blood pressure readings on a mobile-friendly website, notes medication adherence, reviews medication side effects, and can ask questions of their primary care clinician Compared to usual care patients, patients who received virtual visits had reduced utilization of in-person primary care visits only. No significant difference was observed in SBP control between the experimental and control groups
Masi, 2012 [25] Primary care providers in urban Federally Qualified Health Centers (FQHCs) 12 primary care providers Prospective cohort study with a comparison group 12-session telehealth educational program for primary care providers that consisted of lectures by hypertension specialists and case presentations about patients with uncontrolled hypertension Significant increase in primary care providers’ mean hypertension knowledge test score as well as a significant increase in the mean self-assessed competency score upon completion of the 12-session curriculum
Piette, 2016 [21] Patients with diabetes and/or hypertension in Bolivia 72 patients Randomized trial Weekly automated interactive voice response (IVR) calls that occur for up to 4 months to patients as the standard mHealth intervention group. Weekly IVR calls to patients coupled with IVR calls to informal caregivers as the mHealth + informal caregiver intervention group Patients with an informal caregiver who also received IVR calls completed significantly more IVR calls than patients in the standard mHealth intervention group (62% vs 44% p < 0.047)
Price-Haywood, 2017 [24] Adults 50 years or older with hypertension and/or diabetes in the USA 247 patients Cross-sectional survey MyOchsner online patient portal that is a part of the Ochsner Health System in Louisiana. Patient portal allows patients to check lab values, make healthcare appointments, and ask their providers medical questions. Patients were surveyed on their portal usage habits as well as general internet use and eHealth literacy e-Health literacy was positively associated with MyOchsner portal usage and interest in health-tracking tools. Portal users had significantly greater interest in using mHealth interventions to track their health parameters such as blood pressure, weight, exercise, medication, and heart rate
Tobe, 2019 [22•] Canadian First Nations people with uncontrolled hypertension 122 participants Randomized controlled study Active (hypertension management-specific) and passive (general healthcare) SMS text messages sent to participants twice a week No difference was observed in BP reduction between the active and passive SMS groups. BP control was not improved by active SMS messages
Types and Purposes of Telemedicine Technologies Used in the Studies
Communication with Patient and Reminders
A variety of telemedicine platforms and technologies were utilized in the reviewed studies. In most studies (n = 10), patient communication was predominantly achieved through mobile phones [14–21, 22•, 23]. The communication strategies utilized in these nine studies included a smartphone application [15–19, 23], text messaging [22•], interactive voice response (IVR) calls [21], and a mobile-friendly website [20]. In one study, patients uploaded their BP data to an online portal via a home computer or clinic kiosk [14]. Another study employed an online portal that could be accessed by mobile phone or computer [24]. In two studies, the telemedicine intervention primarily targeted healthcare providers and included a teleconference system [25] and the electronic medical record [26].
Seven studies included a component of the telemedicine intervention that sent reminders to patients. These reminders were sent to remind patients about taking medications [16, 18], recording and submitting BP readings [14, 15, 19], and about their upcoming appointments [17, 24].
Recording, Tracking, and Monitoring Blood Pressure
The studies varied in terms of how BP was measured and recorded. In eight studies [14, 15, 18, 19, 21], BP was self-recorded by patients. In two studies [16, 22•], BP was recorded in a clinical setting. Feedback provided to patients included automated email or text messages to reinforce monitoring in patients who were actively monitoring BP [14], tailored text messages to patients who were not providing enough BP recordings [14], a five-star rating system to rate the patient’s adherence to BP monitoring [14], automated responses that described actions patients should take if BP readings were outside of an acceptable range [15], graphs and figures of self-reported data [14, 17, 18], and immediate feedback after each BP recording that included an enthusiastic animation and positive reinforcement language [19]. Patients’ BP data were reviewed by a clinician either periodically [14–16, 18, 20] or as seen fit, and patients could be contacted with further information if needed [15, 16, 20]. Clinician notifications could also be prompted when patients were not taking medications or their BP and glucose level values were worrying [21].
Patient Training and Education
Nine studies [14–19, 21, 22•, 23] included a patient education or training component. Patients were educated about the benefits of managing their BP on their overall health and well-being, and clinicians identified target levels for patients’ systolic BP (SBP) and diastolic BP (DBP) values [14]. Patients were also sent text messages containing educational information [15, 22•] and received training on measuring their BP [15, 16]. In two studies, the smartphone application had an educational component that consisted of modules that informed patients about their condition and how to manage it at home [17, 19]. Patients also received education about disease self-management based on their responses to IVR call questions [21].
Patient Outcomes
Patient Engagement and Adherence
Three studies demonstrated improvements in patient engagement upon implementing a telemedicine intervention [19, 21, 23]. Patients were actively involved in their medical care by self-monitoring their heart rates and blood pressures. They experienced significantly better drug therapy adherence and significantly improved quality of life scores [17]. In addition, through self-monitoring and self-reporting BP and other health data, patients could contextualize their BP values within their daily lives and participate more equally in their follow-up consultations with their providers [23]. Patients who regularly used online health portals had a significantly greater interest in using websites and mobile phone apps to monitor and record their BP, weight, exercise, and medication usage data [24]. Patients with the most significant clinical need showed the highest engagement levels, and higher engagement levels were associated with a more significant BP reduction [19]. Additionally, the presence of an informal caregiver who received health information regarding the patient was found to increase patient engagement significantly [21]. In one study, patient engagement was promising for the first month but then fell off sharply during the next 2 months [15].
Patient Knowledge, Attitudes, and Behaviors
Compared to the usual care group, patient knowledge was significantly improved (p < 0.05) in patients using a telemedicine intervention [17]. Patients felt a greater sense of responsibility in monitoring their health and felt greater motivation to make and maintain lifestyle changes [18]. In older patients, e-Health literacy was positively associated with online portal usage and an interest in using health-tracking tools [18]. e-Health literacy scores were also positively associated with higher education and negatively associated with age [24].
Patient Satisfaction
In studies that assessed patient satisfaction with the telemedicine intervention [15–18, 23, 24], patients generally viewed the intervention positively. In one study, over 90% of the patients reported that the app was user-friendly and helpful [17]. In another study, patients reported that the intervention was a user-friendly tool and could enable healthcare providers to better understand the patient perspective [18]. Some suggestions that patients and healthcare workers had for improving the telemedicine-based intervention were making graphs easier to understand and tailoring the system according to personal preferences [18]. When comparing patients who used an online health portal with portal non-users, a significantly greater proportion of the portal users rated viewing lab results, checking health records for accuracy, and receiving test reminders as useful [24]. Some concerns among portal non-users about portal use were the privacy and security of their health data, not seeing the need for using the portal to manage their health and the lack of personalization in using technology [24]. Concerns about computer literacy, the difficulty of remembering passwords and logging into portal accounts, lack of technical support, provider availability for online appointment scheduling, and response times to medical messages were expressed by portal users [24]. In one study, the majority of patients who responded to a survey about the telemedicine intervention reported feeling more confident understanding their BP and taking their own BP measurements upon using the intervention [15].
Reduction in High Blood Pressure
Three studies reported statistically significant reductions in blood pressure levels upon using a telemedicine intervention [14, 16, 19]. In one study, statistically significant decreases in both average SBP and DBP readings were observed upon 30 days (p < 0.01) and 180 days of study enrollment (p < 0.05) [14]. In another study, the intervention group had a greater reduction in SBP at weeks 4 and 12 compared with the usual care group. However, the decline in SBP in week 4 was the only statistically significant finding [16]. In the third study, a statistically and clinically significant reduction in SBP was observed in less than 4 weeks, where 10% of the participants experienced a drop of at least 10 mm Hg in SBP (p < 0.001) [19]. On the other hand, two studies did not report significant differences in blood pressure levels upon using a telemedicine intervention [20, 22•] (Table 4).Table 4 Differences in the blood pressure reduction between the intervention and control groups
First author, year Changes in blood pressure between the telemedicine intervention and the control/standard care/usual care
Aberger, 2014 [14] Baseline vs 30 days after intervention: significant decrease in SBP and DBP, 6.0 mm Hg and 3.0 mm Hg, respectively (p values < 0.01). Baseline vs 180 days after intervention: significant decrease in SBP and DBP, 6.6 mm Hg and 5.0 mm Hg, respectively (p values < 0.5)
Cottrell, 2015 [15] BP control was achieved by only 5–22% of 1495 patients signed up to one of the three monitoring protocols. No data on exact or average changes in blood pressure
Frias, 2017 [16] At week 4, the intervention resulted in a statistically greater SBP reduction than usual care (mean difference − 9.1, 95% CI − 14.0 to − 3.3 mm Hg) and sustained even more reduction at week 12
Kaplan, 2017 [19] Blood pressure reduction was achieved for 22–25% of application users between weeks 4 and 22 compared to the baseline
Levine, 2018 [20] There was no significant difference in systolic blood pressure (SBP) change from baseline, comparing the virtual visit and the usual care
Tobe, 2019 [22•] There was no significant difference in systolic 0.8 (95% CI − 4.2 to 5.8 mm Hg) or diastolic − 1.0 (95% CI − 3.7 to 1.8 mm Hg, p = 0.5) blood pressure between groups from baseline to final stage
Discussion
Previous studies that have examined the effects of telemedicine interventions on the management of hypertensive patients have primarily focused on changes in SBP and DBP values and general BP control. The effects of telemedicine interventions specifically on improving patient engagement among patients with hypertension are not sufficiently studied. Understanding how telemedicine interventions impact patient engagement can optimize patient care and patient outcomes among hypertensive patients. Thirteen studies out of the 775 initially identified studies were included in this systematic review. This systematic review aimed to synthesize the existing evidence on telemedicine’s effects on improving patient engagement among patients with hypertension.
In the reviewed studies, communication with patients mainly took place through a mobile platform, specifically through smartphones, with nine out of thirteen studies utilizing some form of smartphone communication. The ubiquitous use of smartphones and the increasing number of health-monitoring apps, in particular, may hold promise for expanding the scope of telemedicine interventions. Mobile health (mHealth) allows for faster transmission of patient health data to healthcare providers, offers convenience to both patients and healthcare providers, and has beneficial impacts on chronic disease management [27]. Although more evidence for the efficacy and cost-effectiveness of mHealth interventions is needed, mHealth nonetheless remains very much at the forefront of telemedicine developments and technologies [28].
BP data in eight of the all included studies were self-reported by patients, and in two other studies were recorded in a clinical setting. When caring for hypertensive patients, BP measurements taken in the clinical setting can often be inadequate or misleading [29]. For this reason, home blood pressure monitoring (HBPM) can be advantageous in improving patient care and prime patients to assume a more active role in managing their condition. One of the included studies explicitly states that patients received training on using an automated sphygmomanometer [15]. It is unclear in most studies if patients were adequately trained on how to record their BP. Integrating educational modules into blood pressure monitoring apps with videos demonstrating how to adequately take BP measurements can empower patients by increasing their knowledge and self-sufficiency in managing their condition.
Several studies included a feedback mechanism to inform patients of their BP data and encourage them to take BP recordings. One such feedback mechanism was using graphs and figures to display BP data over time. Another was enthusiastic animations with positive reinforcement language immediately upon patient submission of a BP recording. Some of the feedback was in the form of automated responses, while some feedback consisted of tailored messages. These feedback systems are an important method of ensuring patient engagement on a continual basis with the telemedicine intervention. When patients receive reinforcement and confirmation that they are on track with their BP measurements and have access to their BP data over time in a graphical manner, they can evaluate the BP data within the context of their daily lives and adopt a more health-conscious mindset. It is unclear which feedback systems are the most effective in encouraging patients to manage and record their BP. More robust research is needed to gain a complete understanding of the characteristics of the most effective feedback systems for optimal patient engagement.
Three studies demonstrated an apparent increase in patient engagement upon using the telemedicine intervention. In these studies, patients contextualized their BP values within their daily lives and contributed more equally to conversations with their health providers. Those with the greatest clinical need showed the highest engagement levels. However, one study noted that while engagement was promising for the first month, it fell off sharply during the following 2 months. It is essential to gain a complete understanding of the factors that drive patient engagement and how patient engagement manifests over the long run. A limitation of some of these studies was that the study duration was not very long. Although patient engagement increased during the study period, it is impossible to say if this increase in engagement would be sustainable over time.
Overall, in those studies which assessed patient satisfaction with the telemedicine intervention, patient satisfaction was generally high. Patients viewed the intervention as user-friendly. However, patients did have concerns about health data privacy and ease of use of the system. Concerns about the ease of use of the system were of note among the elderly population. It has been shown that eHealth literacy score is usually negatively associated with patient age [30]. However, many times, elderly patients have more morbidities and, because of issues such as poor mobility, have a greater need for what these telemedicine interventions can offer [31]. The simplification of telemedicine and mHealth interventions can potentially improve elderly engagement in the process of receiving care and providing feedback to healthcare providers.
Telemedicine interventions targeted at healthcare providers may also contribute to better outcomes in the treatment of hypertensive patients. Primary care providers who participated in a 12-session telemedicine-based hypertension education program experienced a statistically significant increase in both hypertension management knowledge and self-assessed competency [25]. Increasing provider knowledge and competency can allow for more nuanced monitoring of patients’ BP values and encourage provider-directed education of hypertensive patients.
Limitations
The studies included in this review come from different patient populations and small settings from different countries. These sociodemographic and cultural heterogeneities threaten the generalizability of the findings from these studies. Besides, in some studies, the sample consisted of well-educated individuals who were more likely to use technology. Additionally, the heterogeneity of the telemedicine and mHealth interventions in different studies makes identifying the most proper and effective intervention more challenging.
Another limitation of this systematic literature review is that only studies published in English were included in the review. It is possible that relevant studies were missed during the screening process due to excluding non-English publications and limiting the search to four databases. Therefore, information and insights about the topic of interest may have been excluded from this review. Despite these limitations, this study provides a comprehensive understanding of the different dimensions of utilizing telemedicine to control BP.
Future of Telemedicine Systems
Telemedicine and the use of telemedicine technologies within the current and future healthcare climate remain more relevant than ever, mainly due to the advent of the COVID-19 pandemic. Due to social distancing measures during the pandemic, many services previously offered in-person were offered via a telehealth format, and governments and health providers provided additional expansion of telemedicine options and funding [32•]. For instance, private health providers began providing their telemedicine services to the public free of charge, and medium- to small-sized medtech companies provided their telemedicine platforms to public health providers [32•]. Telemedicine has proven to be a cost-effective and indispensable tool during the pandemic and will undoubtedly be here to stay in the long run. Interestingly, it seems that previous hesitations in adopting telemedicine offerings are changing, and governments are looking more favorably on the utility of telemedicine interventions. For example, in South Korea, since 2018, there have been many controversies about adopting telemedicine systems. However, during the COVID-19 pandemic, the Seoul National University Hospital began actively offering telemedicine services to COVID-19 patients [32•].
A concern moving forward with the more widespread adaptation of telemedicine systems is the privacy and security of patient data. As echoed by the findings of studies in this systematic review, patients are indeed concerned about the confidentiality of their health data, which can prevent them from engaging fully with telemedicine interventions. The concern of third-party advertiser access to patient health data is particularly of note. Additionally, during the COVID-19 pandemic, patient consent was overlooked for the sake of public interest and public health in some circumstances [32•]. With the ever-growing use and relevance of telemedicine interventions, it is paramount that patient privacy and confidentiality be strictly protected to offer patients a sense of privacy and security.
Conclusions
Because the majority of the studies reviewed were published less than 10 years ago, it appears that the implementation of telemedicine interventions to improve patient engagement among those with hypertension is relatively new. Although the findings show the significant impact of telemedicine in achieving clinical improvements and patient engagement and communication, more is to be known about the effectiveness of different telemedicine initiatives, especially compared to in-person visits to healthcare providers. Studies on the cost-effectiveness of different telemedicine and mHealth interventions can help understand potential cost savings for patients, healthcare personnel, and healthcare organizations. Given that high BP is more common among the elderly, ensuring the use of user-friendly and simplistic approaches might increase patient engagement and improve communication between healthcare providers and patients. Moreover, additional support such as hotlines and translator services can potentially ensure the telemedicine intervention’s acceptability among culturally and linguistically diverse communities.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 40 KB)
Author Contribution
All authors contributed to the research process in various forms, including original draft preparation, summarizing findings, writing, reviewing, and editing. AK conceptualized and designed the study, performed the database search, and outlined the manuscript. EQ and NA reviewed the studies, identified the relevant studies, and performed the data extraction.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
Human and Animal Rights and Informed Consent
This is a review of publicly available and published research papers. No human subject or identification data is collected or analyzed in this study.
This article is part of the Topical Collection on Telemedicine and Technology
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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18. Hallberg I Ranerup A Bengtsson U Kjellgren K Experiences, expectations and challenges of an interactive mobile phone-based system to support self-management of hypertension: patients’ and professionals’ perspectives Patient Prefer Adherence 2018 12 467 476 10.2147/PPA.S157658 29643739
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J Chin Polit Sci
J Chin Polit Sci
Journal of Chinese Political Science
1080-6954
1874-6357
Springer Netherlands Dordrecht
35431530
9799
10.1007/s11366-022-09799-y
Retraction Note
Retraction Note: A Discourse Analysis of Quotidian Expressions of Nationalism during the COVID-19 Pandemic in Chinese Cyberspace
https://orcid.org/0000-0001-8009-6476
Zhao Xiaoyu zhaoxiaoyu@u.nus.edu
grid.4280.e 0000 0001 2180 6431 Department of Political Science, National University of Singapore, Singapore, Singapore
12 4 2022
11
© Journal of Chinese Political Science/Association of Chinese Political Studies 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
==== Body
pmc Retraction Note: Journal of Chinese Political Science (2020) 26:277–293.
10.1007/s11366-020-09692-6
The Editor in Chief has retracted this article because it contains material that substantially overlaps with the following thesis [1]. Xiaoyu Zhao does not agree to this retraction.
The original article can be found online at 10.1007/s11366-020-09692-6.
==== Refs
Reference
1. Guo, B. 2019. Not all nationalists are zealots: A discourse analysis of Chinese nationalism among Zhihu users. Masters Thesis. Singapore: National University of Singapore.
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Osteoporos Int
Osteoporos Int
Osteoporosis International
0937-941X
1433-2965
Springer London London
6343
10.1007/s00198-022-06343-9
Abstract
IOF REGIONAL 2021 8th Asia Pacific Osteoporosis Conference : Poster Abstracts
12 4 2022
2022
33 Suppl 1 3344
© International Osteoporosis Foundation and National Osteoporosis Foundation 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
issue-copyright-statement© International Osteoporosis Foundation and National Osteoporosis Foundation 2022
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pmc P101
ROLE OF ALENDRONATE/TERIPARATIDE IN STEROID INDUCED OSTEOPOROSIS IN A DEVELOPING COUNTRY
M. Muzzammil 1
1Services Hospital Karachi, Karachi, Pakistan
This is double-blinded randomized controlled trial that was conducted in tertiary care hospitals of a developing country from January 2015 to June 2019. In this study, comparison of alendronate with teriparatide in 214 women and men with osteoporosis (ages, 22-65 y) who had received glucocorticoids for at least 3 months. A total of 107 patients received 20 μg of teriparatide, and 107 received 10 mg of alendronate once daily. Significant difference between the groups was reached by 6 months (P<0.001). At 12 months, BMD at the hip had increased more in the teriparatide group. Fewer new vertebral fractures occurred in the teriparatide group than in the alendronate group (6.0% vs. 0.4%, P=0.004). Patients with osteoporosis who were at high risk for fracture, BMD increased more in patients receiving teriparatide than in those receiving alendronate.
P102
ROLE OF TERIPARATIDE IN DISTAL RADIUS OSTEOPOROTIC FRACTURE PATIENTS IN A DEVELOPING COUNTRY
M. Muzzammil 1
1Services Hospital Karachi, Karachi, Pakistan
Objective: Osteoporotic distal radius fractures result in serious health problems and decrease health-related quality of life (HRQoL). Faster time-to-union is important for early return to daily activities and reduction of complications. Teriparatide has been shown to accelerate fracture healing, but the literature is deficient at time of study on osteoporotic distal radius fracture. The aim of this study is to assess whether teriparatide accelerates fracture healing.
Methods: Double-blind randomized controlled trial that was conducted in tertiary care hospital Karachi, Pakistan from January 2015 to June 2019, patients with osteoporotic distal radius fractures extra-articular managed in casting are included. Group 1 included patients who were not on any osteoporosis medication prior to fracture and who postoperatively received only calcium and vitamin D; patients in Group 2 were not on any osteoporosis medication prior to fracture, and received teriparatide and calcium and vitamin D postoperatively. A total of 100 patients received 20 μg of teriparatide, and 100 received placebo once daily. Demographics, time-to-union, HRQoL (short-form health survey [SF]-12 physical component summary and SF-12 mental component summary), morbidities, mortalities, and radiographic and functional outcomes between groups were compared.
Results: Significant difference between the groups was reached by 6 months (P<0.001). Complications and mortality were also markedly reduced in the teriparatide treated groups.
Conclusion: Post fracture use of teriparatide for 6 months appears to be an effective adjunct therapy in the treatment of patients with osteoporotic distal radius fractures.
P103
EAST VS. WEST: RECENCY OF SENTINEL FRACTURES AND ITS IMPACT ON CONVENTIONAL ESTIMATES OF FRACTURE PROBABILITY USING FRAX
J. A. Kanis1,2, H. Johansson1, N. C. Harvey1, V. Gudnason1, G. Sigurdsson1, K. Siggeirsdottir1, M. Lorentzon1, M. Liu1, L. Vandenput1, E. Mccloskey1
1Centre for Metabolic Bone Diseases, University of Sheffield Medical School, Sheffield, UK, 2Mary McKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia
Prior fragility fracture, a well-established risk factor for a future fracture which is accommodated within FRAX. The population relative risk of having a hip fracture or other osteoporotic fracture is approximately 2-fold higher for most types of prior fracture. However, the increase in risk is not constant with time or age. The risk of a subsequent osteoporotic fracture is particularly acute immediately after an index fracture and wanes progressively with time. The early phase of particularly high risk has been termed imminent risk. The question arises how to accommodate the impact of a recent fracture on conventional estimates of fracture probability with FRAX.
To address this question, data were extracted from the Reykjavik Study fracture register that documented prospectively all fractures at all skeletal sites in a large sample of the population of Iceland. Fracture probabilities were determined after a sentinel fracture (humeral, clinical vertebral, forearm and hip fracture) from the hazards of death and fracture. Fracture probabilities were computed on the one hand for sentinel fractures occurring within the previous two years and on the other hand, probabilities for a prior osteoporotic fracture irrespective of recency. The probability ratios provided adjustments to conventional FRAX estimates of fracture probability for recent sentinel fractures.
Probability ratios to adjust 10-year FRAX probabilities of a major osteoporotic fracture for recent sentinel fractures were age dependent, decreasing with age in both men and women. Probability ratios varied according to the site of sentinel fracture with higher ratios for hip and vertebral fracture than for humerus or forearm fracture. Probability ratios to adjust 10-year FRAX probabilities of a hip fracture for recent sentinel fractures were also age dependent, decreasing with age in both men and women with the exception of humerus fractures.
Probability ratios provide adjustments to conventional FRAX estimates of fracture probability for recent sentinel fractures. The quantification of imminent risk enables the targeting of anabolic treatments to individuals identified to be at very high risk.
P104
BODY MASS INDEX (BMI) RELATION TO OSTEOPOROSIS: A RADIOFREQUENCY ECHOGRAPHIC MULTISPECTROMETRY (REMS) SCAN-BASED REPORT IN URBAN POPULATION OF MEDAN AT ROYAL PRIMA HOSPITAL
A. Khu1, M. Sumardi1
1Royal Prima Hospital, Medan, Indonesia
Objective: To determine the relationship between BMI and osteoporosis using REMS.
Methods: This study was the cross-sectional study that involved the patients aged 21 years old and above who underwent REMS scan from the period of October 2018 to September 2019 in Royal Prima Hospital, Medan, Indonesia, were divided into normal, osteopenia and osteoporosis based on the densitometry parameters. Meanwhile the patients’ BMI were classified into underweight (<18.5 kg/m2), normal weight (18.5-22.9 kg/m2), overweight (23-24.9 kg/m2), pre-obese (25-29.9 kg/m2), obese type 1 (BMI 30-40 kg/m2), and obese type 2 (40.1-50 kg/m2).
Results: 300 patients became the sample of this study.
Table 1. Correlation of Spine Osteoporosis and BMI by Spearman Correlation Spine BMD BMI (kg/m2)
[Median (IQR)]
P-Value R
Normal 28.09 (6.84) <0.01 -0.390
Osteopenia 25.48 (6.45)
Osteoporosis 23.24 (5.25)
There was a significant correlation between spine osteoporosis and BMI (P-value<0.05), and the decrease of BMI leads to an increase in the potential of spine osteoporosis.
Table 2. Correlation of Neck of Femur Osteoporosis and BMI by Spearman Correlation The neck of femur BMD BMI (kg/m2)
[Median (IQR)]
P-Value R
Normal 30.84 (5.43) <0.01 -0.690
Osteopenia 25.58 (5.71)
Osteoporosis 22.51 (4.19)
There was a significant correlation between neck of femur osteoporosis and BMI (P-value<0.05), and the decrease of BMI leads to an increase in the potential of neck of femur osteoporosis.
Conclusion: Lower BMI increased the risk of osteoporosis. It was caused by poor nutritional state, which could result in decreasing bone density.1 Meanwhile, high BMI had a linear correlation with high BMD because of the conversion of androgen to estrogen, which increased bone mass in both men and women.2
References: Kim Y-S, et al. Osteoporossarcopenia 2017;3:98-103.
Salamat MR, et al. Hindawi Publ Corp 2013;2013(205963):1-7. doi:10.1155/2013/205963
P105
OSTEOPOROSIS AND ANEMIA IN LONG-LIVING PATIENTS WITH CORONARY ARTERY DISEASE
S. Topolyanskaya1, T. Eliseeva2, O. Vakulenko2, L. Dvoretski1
1First Moscow State Medical University (Sechenov University), 2War Veterans Hospital N3, Moscow, Russia
Objective: Limited and controversial data are available on relationships between osteoporosis and anemia. Therefore, we evaluated BMD and its relationship with erythropoiesis in patients with coronary artery disease (CAD) over 90 years of age (long-livers).
Methods: This work was cross-sectional study performed in the War Veterans Hospital. The study enrolled 197 patients (138 women and 59 men) aged 90-106 y (mean age 92.4±2.3 y) hospitalized with CAD. BMD was analyzed by DXA.
Results: Patients with osteoporosis had lower hemoglobin and erythrocyte counts compared to patients with normal BMD: hemoglobin - 117.3 and 125.9 g/l, respectively (p=0.003), erythrocytes - 3.8x1012/l and 4,1x1012/l (p=0.04), MCV - 88.7 and 93.5 fl (p=0.02), MCH - 30.6 and 31.0 pg (p=0.07). Patients with anemia had lower total BMD (973 and 1036 mg/cm3, p=0.001), BMD of upper (772 and 845 mg/cm3, p=0.001) and lower (956 and 1059 mg/cm3, p=0.0003) extremities, BMD of trunk (805 and 851 mg/cm3, p=0.004), ribs (607 and 642 mg/cm3, p=0.005), pelvis (889 and 935 mg/cm3, p=0.03) and spine (973 and 1034 mg/cm3, p=0.02). Correlation analysis revealed significant direct relationships between hemoglobin level and all BMD parameters (r=0.3; p=0.00003). Significant correlations were also established between all BMD parameters and erythrocytes MCV (r=0.27; p=0.0001) as well as MCH (r=0.22; p=0.002). Significant direct relationships between blood iron concentration and all BMD parameters were found (r=0.28; p=0.003).
Conclusion: The study results indicate presence of relationships between BMD and erythropoiesis in centenarians. It is advisable to further study these relationships in long-livers involving a large sample of patients.
P106
CERULOPLASMIN: A SURROGATE MARKER OF OSTEOPOROSIS
S. das1, S. Verma2
1Ram Manohar Lohia Hospital, Delhi, 2School of Medical Sciences & Research, Greater Noida, India
Objective: Osteoporosis is a bone disorder and is currently a major global health issue. Ceruloplasmin (CP) is an acute phase reactant and antioxidant, characterized by ferroxidase activity and increases in inflammation. C-reactive protein (CRP), an acute phase protein belonging to pentraxin family of proteins. This study was designed to investigate the role of acute phase reactants CP and CRP in the screening of osteoporosis. Our aim was to evaluate the role of serum CP and CRP as biomarkers of osteoporosis.
Methods: This study was conducted in the bone clinic and the biochemistry department of a tertiary care hospital. 120 participants were included in the study belonging to the age group of 50-80 y. Participants in the group were divided into two groups, group I comprising of patients with osteoporosis, and group II consisted of patients without osteoporosis (n=56) (control group) (n=64). Patients were classified into the two groups on the basis of BMD measurements using DXA scanning. CRP and serum CP levels were analyzed in blood samples by immunoturbidimetry.
Results: Serum CP levels were significantly higher in osteoporosis patients as compared to the control group. A significant positive correlation (r=0.92, p<0.05) was observed between higher serum levels of CP and higher levels of CRP in the osteoporosis patients. There was a significant difference in the CP levels of the osteoporosis group (68.4±7.2 mg/dl) and the control group (37.3±4.9 mg/dl) (p<0.05). CRP levels also differed significantly among the osteoporosis patients (2.23±0.68 mg/dl) and the control participants (1.07±0.42 mg/dl) (p<0.05).
Conclusion: Our study demonstrates that measurement of serum CP levels has potential as a surrogate marker for patients with osteoporosis.
P108
TIME TO REVISIT ‘ABSOLUTE’ AND ‘RELATIVE’ CONTRAINDICATIONS OF VERTEBROPLASTY: CASE SERIES OF 24 OSTEOPOROTIC VERTEBRA PLANA WITH POSTERIOR/ANTERIOR WALL DEFECTED NEUROLOGICAL DEFICIT PATIENTS TREATED WITH VERTEBROPLASTY AND SHORT SEGMENT FIXATION
G. Kakadiya1, D. Joshi2
1Fortis Hospital, Mohali, India, 2Consultant Spine Surgeon, Head of Spine Surgery Department, Fortis Hospital, Mohali Panjab, India
Objective: To evaluate the safety and efficacy of vertebroplasty with short segmented cement augmented pedicle screws fixation for severe osteoporotic vertebral compression fractures (OVCF) with posterior/anterior wall fracture patients.
Methods: A retrospective study of 24 patients of DGOU type-4 (vertebra plana) OVCF with posterior/anterior wall fracture, were treated by vertebroplasty and short segment PMMA cement augmented pedicle screws fixation. Radiological parameters (kyphosis angle and compression ratio) and clinical parameters visual analogue scale (VAS) and Oswestry disability index (ODI) were analysed.
Results: A significant improvement was noted in VAS (preoperative, 7.90±0.60; final follow-up 2.90±0.54) and ODI (77.10±6.96 to 21.30±6.70), (
P109
SUBLAMINAR MERSILENE TAPE AUGMENTED PEDICLE SCREWS FIXATION FOR OSTEOPOROTIC VERTEBRAL COMPRESSION FRACTURE: A NOVEL AND LOW COST MODALITY
G. Kakadiya1, K. Chadhary2
1Fortis Hospital, Mohali, 2P.D Hinduja Hospital, Mumbai, India
Objective: To assess the safety and efficacy of sublaminar mersilene tape augmented pedicle screws fixation as a novel and low-cost modality for osteoporotic vertebral compression fractures (OVCF) instrumentation fixation.
Methods: A retrospective study of 40 consecutive patients of the OVCFs. All patients were operated with open decompression, pedicle screw fixation, and sublaminar mersilene tape augmentation. Preoperative and postoperative clinical (visual analog scale [VAS], modified Oswestry disability index [M-ODI], neurologic deficit, revision surgeries, and infection) and radiological (axial collapse, fracture union, implant failure/back out,) parameters were compared to describe the utility of sublaminar mersilene tape augmented pedicle screws for OVCFs treatment.
Results: Complete neurological improvement was noted in 38 patients and two patients had Frankel Garde D neurology. The mean VAS was significantly improved from preoperative 8.98±0.60 to 2.76±0.54, final follow-up and M-ODI from 80.10±6.90 to 15.30±6.90. The mean local kyphosis angle was improved from 23.20°±5.90° preoperative to 5.30°±3.9° postoperatively, and 3.30°±2.50° loss of correction at final follow-up. There was no pseudoarthrosis and implant failure noted. No iatrogenic dural or nerve injury.
Conclusion: Sublaminar mersilene tape augmentation relies on the lamina for its hold, which is the strongest part of an osteoporotic vertebra. Sublaminar mersilene tape augmented pedicle screws fixation is a novel and low cost modality for OVCFs. It provides significant improvement in clinical and radiological outcomes. This technique is an easy learning curve, user-friendly and safe, which makes this a viable alternative option for OVCFs fixation.
P110
DENOSUMAB IN MEN: 6 YEARS RETROSPECTIVE, REALWORLD CLINICAL PRACTICE SINGLE CENTRE STUDY (OSTEOPROM) – OSTEOPOROSIS TREATMENT, FRACTURES AND SAFETY DATA
M. R. S. Pokšāne1, D. R. Rasa2
1Rīga Stradiņš University, 2Riga East Clinical University Hospital, Rīga, Latvia
Objective: Osteoporosis (OP) represents a considerable threat to global health and national healthcare systems. OP in men is an underrecognized and undertreated disease.
Methods: We analyzed the effectiveness of OP treatment in men with denosumab (Dmab) from August 2014 to January 2021. We studied BMD changes and fractures in the men retrospective cohorts in a realworld clinical setting using patient (pt) data from single-centre RECUH. We collected and analyzed at the beginning and end of the study: BMD changes of the spine L1/L4 and in some cases total spine, right and left femoral neck by using DXA and 1 case by QCT; risk factors; lab data (serum Ca, iPTH, vitamin D); comorbidities and concomitant medications. Men were divided into 6 groups according to the number of Dmab injections (inj): group (gr) nr. 1 (12–13 inj), gr nr. 2 (9–11 inj), gr nr. 3 (8 inj), gr nr. 4 (6–7 inj), gr nr. 5 (4–5 inj), gr nr. 6 (1–3 inj).
Results: Over the last 6 y, women with OP was 691 (89.2%) and men only 84 (10.8%). We analyzed a total of 37 (44% of 84 men, who received Dmab) men with an average age of 63.2±10.4 SD. DXA scans analyzed BMD in 36 pts (97.3%) and QCT in 1 pt (2.7%). Men with idiopathic OP were 83.8%, GIO 10.8%, secondary OP 5.4%. At the beginning of the study, men with at least 1 fracture were 56.7% (62.0% in the spine, 9.5% hip, 14.3% forearm, 28.6% ribs, 28.6% other types of fractures). At the end of the study, men with at least 1 fracture were 2 (5.4%). DXA was made for all pts at the beginning of the study (n=37) and at the end of the study 43.2% (n=16). The most significant BMD increased in the gr nr.1– 12.8% (n=2). The least significant gain was in the gr nr. 3 – 2.8% (n=2). Total right and left BMD was analyzed (n=12). The more significant BMD gain was in the gr nr.1– in the right hip 4.7% and left hip – 5.9% (n=2). Lab data, data of comorbidities and concomitant medications will be presented later. During the study, no cardiovascular events were detected.
Conclusion: Dmab is effective in increasing BMD at the lumbar spine and the hip. The most significant BMD increased after 12–13 Dmab injections. The study indicates that Dmab is effective and safe.
P111
PHOSPHORUS SERUM LEVEL: 15 YEARS STUDY RESULTS FROM LATVIA
M. R. S. Pokšāne1, D. R. Rasa2
1Rīga Stradiņš University, 2Riga East Clinical University Hospital, Rīga, Latvia
Objective: We evaluated phosphorus serum level status among adults in Latvia from the E. Gulbja lab electronic database over the past 15 y. Hypophosphatemia may be the reason, e g., for X-linked hypophosphatemia (XHL), an inherited disorder characterized by low phosphorus levels in the blood.
Methods: We analyzed retrospectively the data from 803 patients admitted to the study from January 2004 to December 2020. All hypophosphatemia levels (reference range: 0.80-1.60 mmol/L) were divided into 3 groups depending on serum phosphorus level – mild (0.81-0.65 mmol/L), moderate (0.65-0.32 mmol/L) and severe (<0.32 mmol/L). Also, serum calcium (Ca), vitamin D and iPTH were analyzed. The reference range for serum Ca: 2.1-2.6 mmol/L, vitamin D 30-100 ng/mL, and iPTH 12-72 pg/mL.
Results: A total of 3173 serum phosphorus level samples were analyzed – 1760 females, 1413 males; average age 60.2±14.9 SD; the average age of females 60.4±15.0SD and males 60.0±14.8SD. The number of blood samples with reduced serum phosphorus levels was 1803 (56.8%), where mild was 1414 (78.4%): 767 females, 647 males; average age 60.0±14.7SD; the average age of females 60.2±14.4SD; the average age of males 59.7±15.0SD; moderate were 381(21.1%): 206 females, 175 males; the average age was 56.6±15.7SD; the average age of females 54.1±17.5SD; the average age of males 59.5±12.8SD and severe were 8 (0.5%): 5 females, 3 males; the average age was 56.1±16.4SD; the average age of females 3.0±19.4SD; the average age of males 61.3±11.0SD. A total of 1719 (95.3%) serum Ca levels were analyzed, a total of 972 (53.9%) iPTH levels and a total of 456 (25.3%) vitamin D levels were also analyzed.
Conclusion: Reduced serum phosphorus level is more often than commonly believed, and it needs to be examined for various reasons, e.g., hyperparathyroidism, vitamin D deficiency, XLH.
P114
THE RESULTS OF DRUG CORRECTION OF ORTHOPEDIC PATHOLOGY IN PATIENTS WITH VARIOUS FORMS OF FIBROUS DYSPLASIA
Y. Guk1, A. Zyma1, T. Kincha-Polischuk1, A. Cheverda1, O. Skuratov1, R. Vyderko1
1SI “The Institute of Traumatology and Orthopedics” by NAMS of Ukraine, Kyiv, Ukraine
Objective: To improve medication correction of disorders of the structural state and metabolism of bone tissue in patients with fibrous dysplasia.
Methods: There were 16 patients with FD who were receiving medication treatment. Age of patients was 6-28 y. All patients underwent basic antiosteoporotic therapy, of which 10 were treated with pamidronic acid. Serum Ca and Vit D3 levels were screened before and during treatment. The structural state of bone tissue was studied according to the Z- and T-criteria, bone metabolism - the study of bone markers: total P1NP, β-CTx, osteocalcin.
Results: Based on paraclinical studies in patients with FD, depending on the clinical manifestations, form of the disease, age, indications for medication treatment with the use of pamidronic acid have been developed. Basic treatment and basic treatment in combination with pamidronic acid were used. Pamidronic acid medications were used at a dose of 0.5-1.0 mg/kg/d for 1-3 infusions, the interval between cycles was 3-4 months. Schemes, doses, combinations of pamidronic acid with other antiosteoporotic drugs depending on changes in the condition and metabolism of bone tissue in FD were determined. Indications for basic therapy were: β-CrossLaps up to 0.500 ng/ml and Z-test up to -1.0 SD. Basic therapy included: preparations of Ca "Osteogenon" 1-2 capsules 2 times a day and vitamin D3 in a dose of up to 2000 IU. Indications for basic therapy in combination with the use of pamidronic acid at a dose of 0.5-1.0 mg/kg/d were severe pain, a significant area of long bone lesions, changes in β-CrossLaps from 1.5 ng/ml and above, Z-test from - 1.5 SD and below. The effectiveness of therapy was assessed by changes in the level of β-CrossLaps in the serum and the Z-test of the lumbar vertebrae. The effectiveness of the used treatment did not depend on the dose of pamidronic acid and the form of FD. The relation between changes in the marker of osteoresorption and its reduction depending on the initial values (Wilcoxon's test p=0.0045) was reliably found - the higher the rate, the more effective the treatment, the intensity of pain decreased, the bone structure improved.
Conclusion: The results of medication treatment in patients with FD indicate its significant effectiveness (Wilcoxon test p=0.0045): reduction and elimination of pain, improvement of the structural condition and metabolism of bone tissue.
P115
ECONOMIC BURDEN AND THE EFFECTS OF EARLY VS. DELAYED HOSPITALIZATION ON THE TREATMENT COST OF PATIENTS WITH ACUTE FRAGILITY HIP FRACTURES
K. A. Cortez1, I. A. Tabu1, J. G. Lai1
1Dept. of Orthopedics, University of the Philippines Manila, Philippine General Hospital, Manila, Philippines
Objective: Fragility hip fractures present not only as a significant cause of morbidity and mortality to the elderly population but also as an important source of financial burden due to staggering costs of treatment. This study determines the effect of timing of hospitalization to the treatment cost of patients with acute fragility hip fractures.
Methods: In this retrospective cohort study, the patient database of the Orthogeriatric Multidisciplinary Fracture Management Model and Fracture Liaison Service was reviewed to investigate the effects of timing of hospitalization to the treatment cost of patients with acute fragility hip fractures admitted in a university-based tertiary government hospital. The economic burden of this group of patients was also computed.
Results: A total of 118 patients were enrolled in the study with 54 patients in the early hospitalization (EH) group (≤ 3 days from injury) and 64 in the delayed hospitalization (DH) group (4-28 days from injury). Mean interval of injury to hospitalization is 1.3 days among the EH group and 12.5 days among the DH group. Median treatment cost is less among the EH group than those who were in the DH group (P=0.0362). The computed economic burden of patients with acute fragility hip fractures is PhP 1,094,048,363.00 (USD 22,595,007.79) per year in the Philippines.
Conclusion: Fragility hip fractures impose significant financial impact; and therefore, we recommend early hospitalization to lessen treatment cost. Future studies should also be undertaken to investigate other interventions that may help alleviate this burden.
P117
SYSTEMIC BONE LOSS AFTER FRACTURES: EXPERT OPINION FROM CHINA
C. S. Chunli1, M. Z. Melissa2
1Peking University Third Hospital, Beijing, 2GlaxoSmithKline Consumer Healthcare Pvt Ltd, Shanghai, China
Objective: To understand the systemic bone loss after local fractures and gather expert opinion on how to support the recovery.
Methods: 9 KOLs participated in an Advisory Board in Beijing to discuss systemic bone loss after fracture, including evidence of fractures leading to reduced bone mass and increased refracture risk, mechanisms and risk factors and, calcium and vitamin D’s roles in fracture healing.
Results: Current literature report 2-15% reduction in total bone mass post-fracture, peaking within the first 2 years. Even after fracture healing, bone mass may not return to pre-fracture level. This increases refracture risk. Mechanisms of systemic bone loss are complex, involving disuse, inflammation and hormones etc. It is also affected by age, gender (more in males, elderly) and trauma severity. Clinically, there are some cases of significant bone loss within 3 months following lower-limb fracture surgeries. Calcium and vitamin D aid fracture healing and bone formation, lowering refracture risk. More research is needed to validate if local fractures cause systemic absorption and mobilization of calcium to the fracture site.
Conclusions: Systemic bone loss after fractures increases the risk of reduced bone mass and refractures. Increasing calcium and vitamin D intake may help in recovery.
Disclosure: Meeting was sponsored by GSK Consumer Healthcare China.
P118
CLOSE TO THE BONE: A CASE OF CONCURRENT MULTIFOCAL OSTEONECROSIS AND PERIPHERAL SPONDYLOARTHROPATHY
M. B. Macedo1, H. A. M. Giardini1, L. K. N. Guedes1, R. M. R. Pereira1
1Rheumatology Division of the Clinics Hospital of the University of Sao Paulo - HCFMUSP, Sao Paulo, Brazil
Objective: Avascular necrosis at three or more sites is defined as multifocal osteonecrosis (MFON), which is a rare condition traditionally reported in long-term users of glucocorticoids, or patients with sickle cell disease. To our knowledge, no previous case of MFON in association with peripheral spondyloarthropathy (pSpA) has ever been described.
Methods: Medical records review. Informed consent was obtained from the patient.
Results: A 49-year-old Afro-Brazilian woman presented with acute onset of severe pain on her left shoulder, knees and ankles bilaterally. She had been diagnosed with HIV infection for 21 years. Her last CD4 was of 823/mm3, and her viral load was undetectable. Whole body computed tomography evidenced extensive areas of bone infarct on femoral, tibial, and left humeral head. Extensive investigation for acquired thrombophilia, including antiphospholipid syndrome, resulted negative. Her current highly active antiretroviral therapy had no established association with MFON. An age-appropriate screening for occult malignancy was also negative. 99mTc-MDP bone scintigraphy evidenced hyperconcentration on the joints compromised by osteonecrosis, but also on left tarsal bones, 1st and 5th left metatarsophalangeal joint, suggesting a chronic inflammatory process. Magnetic resonance imaging of feet revealed synovitis of tibiotalar and posterior talocalcaneal joints, with no signs of osteonecrosis on those joints. pSpA was then hypothesized as the cause of her ankle and feet arthritis. The patient tested positive for HLA-B27. She was started on anticoagulation with warfarin and received zoledronic acid as adjunct therapy. Sulfasalazine was also introduced, with good response. A year after the initial presentation, the patient had complete resolution of pain.
Conclusion: We presented a rather atypical case of MFON in a well controlled HIV patient with pSpA, showcasing multiple mechanisms for her joint pain.
P119
PREVENTION OF SECONDARY MEN’S HIP FRACTURES IN BELARUS: THE COST-EFFECTIVE MODEL OF GENERIC ALENDRONIC AND ZOLEDRONIC ACID
H. Ramanau1, E. Rudenka2, N. Serdyuchenka3
1Gomel State Medical University, Gomel, 2Belarusian State Medical University, Minsk, 3National Academy of Sciences of Belarus, Minsk, Belarus
Objective: To calculate the cost-effectiveness of Belarusian generic alendronic (ALN) and zoledronic acid (ZOL) in men aged 50 years and older with low-energy proximal femur (PF) fractures for prevent secondary fractures.
Methods: The calculation of the expected number of PF fractures in men was carried out on the basis of our own epidemiological data on the primary incidence of PF fracture in men aged 50 years and older with an interval of 5 years. The cost of treatment with generic ALN and ZOL was calculated based on the average cost of medications in the pharmacy. The cost of the course of treatment is calculated for 3 years of treatment with ALN and for 2 years of treatment with ZOL after primary PF fracture. GDP per capita of the Republic of Belarus in 2020 amounted to 6 678 USD.
Results: According to calculations, 2873 low-energy PF fractures are expected per year in men 50 years of age and older in Belarus. The ALN treatment for 3 years will prevent about 130 secondary PF fractures (NNT=22) and will save 601 years of life and years of healthy life for men. In case of ZOL treatment for 2 years will prevent about 151 secondary PF fractures (NNT=19) and will help to save 695 years of life and years of healthy life. The total cost of treatment with ALN will be 344 054 USD for 3 years, and ZOL - 554 519 USD for 2 years (FX rate of the National Bank of Belarus 01/11/2019). The cost of 1 year of a saved and saved healthy life with the treatment of ALN will range from 376 USD at the age of 50-54 to 894 USD at the age of 85+. The cost of 1 year of saved and saved healthy life will be from 524 USD at age 50-54 to 1245 USD at age 85+ with ZOL treatment. The total direct economic costs for PF fracture treatment in Belarus is 1174 USD.
Conclusion: Treatment of ALN and ZOL for prevent secondary PF fractures in men 50+ is cost-effective at any age period and the cost of saved 1 year life does not exceed 40% of GDP per capita in Belarus.
P120
THE EFFECTIVENESS OF THE INFLUENCE ON BONE MINERAL DENSITY OF DENOSUMAB FOR THE TREATMENT OF POSTMENOPAUSAL OSTEOPOROSIS
A. Adamenka1, E. Rudenko2, V. Alekna3, M. Tamulyaitiene3, A. Rudenko4, O. Samokhovets5
1Republic Medical Center, Minsk, Belarus, 2Belarusian State Medical University, Minsk, Belarus, 3Vilnius University, Vilnius, Lithuania, 4Belarusian Medical Academy of Postgraduate Education, Minsk, Belarus, 5Minsk City Center for Osteoporosis & Musculoskeletal System Diseases, Minsk, Belarus
Objective: The efficacy of denosumab in the treatment of postmenopausal osteoporosis has been studied.
Methods: We observed 83 patients (median age 64.1 years) with a diagnosis of postmenopausal osteoporosis who had been receiving denosumab therapy in combination with calcium and vitamin D for at least 1 year. The criteria for the effectiveness of the therapy were an assessment of the dynamics of the BMD of the lumbar spine (L1-L4) and the femoral neck (SB) before the introduction of denosumab and in dynamics after 12 months of therapy, the absence of new fractures.
Table. Dynamics of BMD of the lumbar spine and SB of participants (n=83) after 12 months from the start of the study. The data are presented as a median [25%; 75%]. DXA regions Initial BMD BMD after 12 months p
Т-score L1-L4, SO -2.60 [-3.18; -2.20] -2.30 [-2.90; -1.80] <0.05
BMD L1-L4, g/cm2 0.84 [0.79; 0.91] 0.88 [0.82; 0.96] <0.05
BMD SB, g/cm2 0.76 [0.67; 0.83] 0.79 [0.74; 0.85] <0.05
T-score SB, SO -2.00 [-2.58; -1.60] -1.80 [-2.30; -1.30] <0.05
Results: In 69 (83.1%) patients receiving denosumab, a statistically significant increase in BMD of the lumbar spine and SB was found (p>0.05); new fractures were not recorded in the study group during the observation period.
Conclusion: Denosumab is an effective drug that increases BMD and reduces the risk of fractures in postmenopausal women with osteoporosis, which is not associated with significant side effects.
P121
BIFIDOBACTERIUM LONGUM INHIBITS BONE LOSS IN OSTEOPOROTIC MICE VIA INDUCING PTREGS IN GUT
A. Bhardwaj1, L. Sapra1, R. K. Srivastava1
1All India Institute of Medical Sciences, New Delhi, India
Objective: Probiotics are defined as viable microorganisms that upon administration in adequate amount confer various health benefits by inducing alterations in composition of gut microbiota (WHO). Very few bacterial strains have been studied till date in relation to their effect on bone health. The decrease in number of Tregs leads to various inflammatory conditions of the bone such as osteoporosis, rheumatoid arthritis, etc. Probiotics induce the differentiation of peripherally derived Treg (pTreg) cells from naive CD4+ T cells in GUT resulting in prevention of various inflammatory diseases by regulating immune homeostasis. Based on these facts we were interested in investigating the effect of probiotics intake on the modulation of bone health via its effect on the induction of pTregs in GUT. Thus, in present study we selected Bifidobacterium longum (BL) strain to examine its effect on bone health in ovariectomy (Ovx) induced osteoporotic mice model. We were interested in investigating the effect of BL intake on modulation of bone health via its effect on the induction of pTregs in GUT. We thus hypothesized to study the effect of BL on bone health in Ovx induced osteoporotic mice model.
Methods: Female C57BL/6 mice were divided into three group’s viz. Sham, Ovx and Ovx + BL. BL was administered orally (109 CFU/ml) and after 45 days mice were sacrificed and tissues were analyzed for accessing the role of BL on bone health via various cutting-edge technologies such as SEM, AFM, μCT, FACS and ELISA.
Results: We observed that oral administration of BL protected mice from Ovx-induced bone loss, which was confirmed by SEM, AFM, FTIR and μCT analysis of bone samples. We further observed that BL-intake enhanced bone density in both cortical and trabecular bones of Ovx mice. Interestingly, it was observed that BL-intake enhances percentage of CD4+Foxp3+NRP-Treg cells (pTregs) in both GUT (mesenteric lymph nodes, peyer’s patches, small intestine and large intestine) and bone marrow (prime sites of osteoclastogenesis). Furthermore, serum cytokine analysis revealed that Ovx mice administered with BL had significantly decreased levels of osteoclastogenic cytokines IL-6, IL-17 and TNF-α along with significantly enhanced levels of anti-osteoclastogenic cytokines IL-10, IL-4 and IFN-γ with respect to Ovx group.
Conclusion: Taken together our results for the first time establish an osteoprotective role of BL on bone health via induction of pTregs in GUT of Ovx mice.
P122
PREVALENCE OF HYPOVITAMINOSIS D IN ADULT POPULATION OF THE REPUBLIC OF BELARUS
A. Rudenka1, O. Krasko2, O. Ganchar3, S. Vasukovich3, I. Nazarchik4, D. Lukyanionak4, E. Rudenka5
1Belarusian Medical Academy of Postgraduate Education, 2United Institute of Informatics Problems, 3SYNLAB-EML Foreign Unitary Enterprise, 4Foreign LLC Synevo, 5Belarusian State Medical University, Minsk, Belarus
Objective: Hypovitaminosis D in both adults and children is detected in various geographic zones of our planet with high frequency. At the same time, vitamin D has number of positive effects on human health and is an important micronutrient for the prevention of some diseases. The purpose of this study is to identify the incidence of vitamin D deficiency and insufficiency in the adult population of the Republic of Belarus.
Methods: We analyzed the results of laboratory tests of total vitamin D, produced in 2019 and 2020 in persons over 18 years old living in the Republic of Belarus. 147673 results were analyzed, performed in laboratories located in various regions of the country. Determination of the level of total vitamin D (25(OH)D) in serum in all laboratories was carried out by the method of electrochemiluminescence (Cobas e411 apparatus manufactured by Roche Diagnostic, Germany) using original reagents from Roche Diagnostics GmbH. Level of vitamin D was considered to be normal at values of 25(OH)D ≥30 ng/ml, values of 20-29.9 ng/ml were defined as insufficiency, <20 ng/ml as deficiency, <10 ng/ml as severe deficiency.
Results: The average level of vitamin D in the surveyed population during the study period did not reach normal values in all age groups in both men and women. Hypovitaminosis D was observed in the majority of the surveyed with a frequency from 65.3% to 77%. The highest frequency of hypovitaminosis D was observed in the autumn-winter period and reached 81.6% in January 2019 and 77.8% in January 2020. In the summer months, normal levels of vitamin D were observed in less than 50% of the surveyed: the maximum values were 40.7% in July 2019 and 45.8% in July 2020.
Conclusion: Due to the high prevalence of hypovitaminosis D, it is advisable to carry out measures for the prevention and treatment of hypovitaminosis D in residents of the Republic of Belarus at the population level.
P123
NOVEL AND PREVIOUSLY DESCRIBED PATHOGENIC MUTATIONS IN COLLAGEN‐RELATED OSTEOGENESIS IMPERFECTA
P. Marozik1, A. Pachkaila2, E. Rudenka3, K. Kobets1
1Institute of Genetics & Cytology of the National Academy of Sciences of Belarus, 2Belarusian Medical Academy of Postgraduate Education, 3Belarusian State Medical University, Minsk, Belarus
Objective: Osteogenesis imperfecta (OI) is a rare genetic bone fragility disorder. Over 85% of OI cases are associated with mutations in the procollagen type I genes (COL1A1 or COL1A2). The purpose of the study was to analyses the spectrum of collagen mutations in Belarusian patients with OI and reveal their association with particular clinical phenotypes.
Methods: 90 Belarusian patients, diagnosed with OI by clinical standards, were included in the study. Genomic DNA was extracted from peripheral blood leukocytes. The sequencing of COL1A1 and COL1A2 protein coding regions was performed using Illumina MiSeq (USA). The raw data were mapped to the human reference genome hg19 using Illumina MiSeq Reporter. The variants were confirmed by Sanger sequencing. Variants not described in OI variant database were analyzed in silico using predictive programs and described as novel mutations.
Results: In total, 35 unique pathogenic COL1A1/2 variants were identified in 59 (64%) patients with OI. The whole spectrum of mutations included 32 missense, 8 nonsense, 11 frameshift, 7 splice site and 2 intronic mutations. The majority of the pathogenic variants were located in the COL1A1 gene (69.5%), 22% of them were novel (Table). At the same time, 66.7% of the COL1A2 mutations were novel (Table). All pathogenic variants were heterozygous, suggesting dominant inheritance. Glycine (Gly, G) substitutions, affecting triple-helical domains of collagen chains, were present in 19 (59.3%) of the missense variants and in 5 novel mutations.
Table. The spectrum of identified novel mutations in COL1A1 and COL1A2 genes
Conclusion: We identified 12 novel heterozygous missense mutations, associated with OI in Belarusian patients. We also revealed significant association of different mutations with clinical phenotype. This study expands the mutation spectrum of the COL1A1/2 genes and contribute toward the increased understanding of OI.
P124
ASSOCIATION OF SERUM 25(OH)D LEVELS WITH VDR GENE VARIATION IN POSTMENOPAUSAL WOMEN
P. Marozik1, E. Rudenka2, K. Kobets1, A. Rudenka3, V. Samokhovec3
1Institute of Genetics & Cytology of the National Academy of Sciences of Belarus, 2Belarusian State Medical University, 3Belarusian Medical Academy of Postgraduate Education, Minsk, Belarus
Objective: Vitamin D effects have been widely investigated in various populations with regards to its possible effect on osteoporosis (OP) risk. The huge interest in the vitamin D is explained primarily by its activity in calcium homeostasis, bone formation and regulation of BMD, realized through its receptor, coded by VDR gene. The aim of this work was to reveal the effects of VDR gene ApaI rs7975232, BsmI rs1544410, TaqI rs731236, FokI rs2228570 and Cdx2 rs11568820 variants on 25(OH)D level in Belarusian women with OP.
Methods: Patients were recruited at 1st Minsk city clinic (Minsk, Belarus). In total, 602 women met inclusion criteria, of them 355 patients with OP and 247 subjects from control group. BMD was evaluated by DXA (GE Lunar, USA), serum vitamin D was determined by electrochemiluminescence immunoassay (Cobas e411, Roche, Switzerland). VDR gene variants markers were determined using the quantitative PCR.
Results: We revealed significant association of rs1544410, and rs731236 gene variants with 25(OH)D level, which is gene/dose dependent: the lowest vitamin level was typical for reference genotype, intermediate for heterozygotes and the highest for the bearers of minor homozygous genotypes (P<0.01). The opposite gene/dose relationship was revealed for rs11568820 variant (Figure). At least rs7975232, rs1544410, rs731236, and rs11568820 might help to identify individuals with increased PMO risk and vitamin D status. Revealed considerable variation in serum 25(OH)D in individuals with different VDR genotypes further suggest that a one-size-fits-all approach to vitamin D supplementation may not be appropriate.
Figure. The association of serum 25(OH)D levels with VDR gene variation.
Conclusion: The data show that the increased level of circulating vitamin D level is observed in bearers of unfavorable VDR genotypes, associated with decreased receptor expression, possibly due to altered metabolic feedback loops or effectiveness of vitamin metabolism. VDR gene variants should be considered for personalized vitamin D supplementation.
P125
EGFL6 MODERATES BONE METASTASIS OF LUNG ADENOCARCINOMA CELLS AND PROMOTES OSTEOCLAST DIFFERENTIATION
X. T. Song1, X. Cheng1, Z. Y. Li1, L. W. Zhang1, D. Hong1
1Wenzhou Medical University Affiliated Taizhou Hospital, Taizhou City, China
Epidermal growth factor-like domain multiple 6 (EGFL6) belongs to EGF-like ligands, is implied to play a role in tumor growth, migration and invasion of breast cancer, gastric cancer and nasopharyngeal carcinoma, etc. EGF-like ligands have been reported that they can stimulate osteoclastogenesis by affecting on osteoblastic cells through indirectly decreasing OPG expression and increasing MCP1 expression in an EGFR-dependent manner. Here, we proved that EGFL6 was a secreted protein and found that EGFL6 was highly expressed in lung adenocarcinoma tissues and positively correlated with bone metastasis of lung adenocarcinoma. Over-expressive EGFL6 obviously potentiated the proliferation, migration and invasion of lung adenocarcinoma cells partly through Wnt and PI3K/AKT/mTOR signaling pathways while silencing EGFL6 presented the opposite results. Intriguingly, EGFL6 promoted bone destruction of nude mice through enhancing osteoclast differentiation via NF-κB signaling pathway but affected little on osteoblast differentiation. Therefore, we elucidate that EGFL6 could be acknowledged as a precative factor in bone metastasis of lung adenocarcinoma.
P126
OSTEOPOROSIS, SARCOPENIA AND HIGH FRACTURE RISK IN RHEUMATOID ARTHRITIS PATIENTS
O. Dobrovolskaya1, N. Toroptsova1, A. Efremova1, N. Demin1, A. Feklistov1
1V.A. Nasonova Research Institute of Rheumatology, Moscow, Russia
Objective: To assess the frequency of osteoporosis (OP), sarcopenia (SP) and high fracture risk in rheumatoid arthritis (RA) patients (pts).
Methods: 155 women (mean age 59±9 years) with RA who met the ACR/EULAR criteria (2010) were enrolled. 126 (81%) women were postmenopausal, the postmenopause duration was 12 [5; 17] years. The duration of RA was 8 [5; 14] years. 60 (39%) pts took glucocorticoids (cumulative dose 7346 [3650; 18388] mg in prednisolone equivalent). A questionnaire was conducted on risk factors of low energy fractures (LEF). BMD and body composition were evaluated using DXA. Sarcopenia was diagnosed according to criteria European Working Group on Sarcopenia in Older People (EWGSOP2, 2018). The 10-year probability of a major osteoporotic fracture was assessed using the FRAX tool in postmenopausal women.
Results: 41 (27%) women had OP among them 35 (85%) were postmenopausal, 64 (41%) pts had osteopenia and 50 (32%) normal BMD. 37 (24%) pts had a prior LEF, among them OP was found in 21 (57%) women, osteopenia in 13 (35%) and normal BMD in 3 (8%) persons. 113 (73%) pts had overfat, but only 41 (26%) persons had BMI corresponding to obesity. SP was found in 32 (21%) pts, among them 11 (7%) had osteosarcopenia, 18 (12%) osteosarcopenic obesity, 3 (2%) isolated sarcopenia. SP was found in 26 (21%) postmenopausal and in 6 (21%) premenopausal women (p>0.05). The median FRAX value was 16% [12; 23]. 31 (89%) pts with OP and 25 (27%) pts without OP had very high or high risk of LEF; 4 (11%) and 66 (73%), respectively, low risk (p<0.0001). 18 (69%) pts with SP and 47 (47%) without SP had very high or high risk, 8 (31%) and 53 (53%) pts, respectively, were at low risk of LEF (p=0.043).
Conclusion: Our study demonstrated that the presence of SP as well as OP increased risk of LEF in RA women.
P127
EVALUATION OF ENVIRONMENTAL AND SOCIOECONOMIC FACTORS CONTRIBUTING TO FRAGILITY FRACTURES IN INDIANS
V. Singh 1
1Trauma Centre and Super Specialty Hospital, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India
Objective: Osteoporosis causes fragility fractures that also occur in patients with BMD in the normal or osteopenic range, suggesting role of risk factors that are unrelated or partially related to BMD. The study aims at highlighting the link between 3 conditions, that are environment and occupation related risk factors and that are widely prevalent in India, and development of fragility fractures.
Methods: A case control study was done by recruiting 110 cases with history of recent fragility fractures and 84 controls with no history of recent fractures. 3 study parameters, village dwelling, conventional farming, and poverty, were chosen the presence or absence of which were documented in participants. This was followed by an odds ratio analysis.
Results: The odds of village dwellers, conventional farmers, and socioeconomically poor individuals to develop fragility fractures were both significant and large.
Conclusion: Urbanization is a risk in the development of fragility fractures. However, this study points that village dwelling in India is associated with the development of fragility fractures. Similarly, odds of farmers exposed to pesticides and agrochemicals to develop fragility fractures is large and significant. Pesticides and agrochemicals act as endocrine disruptors and bone health is closely linked to endocrine system. Fragility fractures among farmers may be due to endocrine disrupting properties of pesticides and agrochemicals. Socioeconomic deprivation is a known risk in the development of osteoporosis. This study too highlights that the odds of individuals living in poverty to develop fragility fractures is significant and large.
P128
10-YEAR DIFFERENCES IN RADIOGRAPHIC HIP OSTEOARTHRITIS PREVALENCE AND EFFECT OF HANDGRIP STRENGTH IN JAPANESE MEN AND WOMEN
T. Iidaka1, S. Muraki1, H. Oka2, C. Horii3, K. Nakamura4, T. Akune5, S. Tanaka3, N. Yoshimura1
1Dept. of Preventive Medicine for Locomotive Organ Disorders, 22nd Century Medical & Research Center, Faculty of Medicine, University of Tokyo, Tokyo, 2Dept. of Medical & and Management for Musculoskeletal Pain, 22nd Century Medical & Research Center, Faculty of Medicine, University of Tokyo, Tokyo, 3Dept. of Orthopaedic Surgery, Faculty of Medicine, University of Tokyo, Tokyo, 4Towa Hospital, Tokyo, 5National Rehabilitation Center for Persons with Disabilities, Saitama, Japan
Objective: We investigated the 10-year differences in radiographic hip osteoarthritis (OA) prevalence in Japanese men and women based on data from a large-scale nationwide cohort study (Research on Osteoarthritis/Osteoporosis Against Disability Study).
Methods: We analyzed the data of 2924 participants (1026 men; 1898 women) aged 40–89 y (mean 70.7 y) from urban, mountainous, and coastal communities from a baseline survey conducted in 2005–2007. We also analyzed the data of 2347 participants (726 men; 1621 women) aged 40–89 y (mean 69.2 y) obtained from a fourth survey in 2015–2016. Anthropometric measurements such as height and weight were taken. Handgrip strength was measured, and the larger value was noted as the maximum handgrip strength. Radiographs were scored using the Kellgren−Lawrence (KL) grading system; radiographic hip OA was defined as a KL score ≥2.
Results: The prevalence of radiographic hip OA in men and women was 18.4% and 14.4% in the baseline survey and 16.0% and 10.7% in the fourth survey, respectively. In the fourth survey on men and women in their 40s to 60s, the prevalence of radiographic hip OA was significantly lower than in the baseline survey, whereas height and handgrip strength measurements were significantly higher. The mean values of weight and BMI had nearly no difference between the baseline and fourth surveys. Logistic regression analysis performed after adjusting for age, sex, height, weight and residence showed that the prevalence of radiographic hip OA in the fourth survey was significantly lower than in the baseline survey (odds ratio 0.54, 95%Cl 0.45–0.65), and handgrip strength was significantly associated with radiographic hip OA (-1 kg, 1.02,1.00-1.04).
Conclusion: Two large-scale cross-sectional cohort studies reported 10-year differences in radiographic hip OA prevalence. Handgrip strength might affect declining the prevalence of radiographic hip OA.
P129
THAI OSTEOPOROSIS FOUNDATION (TOPF) POSITION STATEMENT ON MANAGEMENT OF OSTEOPOROSIS 2021
N. Charatcharoenwitthaya1, U. Jaisamrarn2, T. Songparanasilp3, V. Kupniratsaikul4, A. Unnanuntana5, C. Sritara6, H. Nimitphong7, L. Wattanachanya8, S. Chaiamnuay9, T. Valleenukul10, P. Chotiyarnwong11, T. Amphansap12, O. Phruetthiphat3, S. Chaikittisilpa2, W. Somboonporn13, W. Kitisomprayoonkul14, P. Dajpratham4, V. Srinonprasert15, A. Petchlorlian16, S. Tejavanija17
1Division of Endocrinology & Metabolism, Dept. of Medicine, Faculty of Medicine, Thammasat University, Pathumthani, 2Dept. of Obstetrics & Gynecology, Faculty of Medicine, Chulalongkorn University, Bangkok, 3Dept. of Orthopedic Surgery, Phramongkutkloa Hospital, Bangkok, 4Dept. of Rehabilitation Medicine, Siriraj Hospital, Mahidol University, Bangkok, 5Dept. of Orthopedic Surgery, Siriraj Hospital, Mahidol University, Bangkok, 6Dept. of Nuclear Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, 7Division of Endocrinology and Metabolism, Ramathibodi Hospital, Mahidol University, Bangkok, 8Division of Endocrinology & Metabolism, Chulalongkorn University, Bangkok, 9Division of Rheumatology, Phramongkutklao Hospital, Bangkok, 10Dept. of Orthopedic Surgery, Bhumibol Adulyadej Hospital, Bangkok, 11Dept. of Orthopedic Surgery, Siriraj Hospital, Mahidol University, Bangkok, 12Dept. of Orthopedic Surgery, Polic General Hospital, Bangkok, 13Dept. of Obstetrics & Gynecology, Khon Kaen University, Khon Kaen, 14Dept. of Rehabilitation Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, 15Division of Geriatric Medicine, Dept. of Medicine, Siriraj Hospital, Mahidol University, Bangkok, 16Division of Geriatric Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, 17Division of Nutrition, Phramongkutklao Hospital, Bangkok, Thailand
Objective: To update the Thai Osteoporosis Foundation (TOPF) Position Statement on Management of Osteoporosis, published in 2016.
Methods: TOPF enlisted a panel of experts in the field of osteoporosis to review and update the 2016 TOPF position statement. Primary writers submitted their first drafts, which were subsequently reviewed, discussed, and integrated into the final document. Recommendations are based on reviews of the clinical evidence and expert’s opinions.
Results: The executive summary of this updated position statement contains 90 recommendations. New or updated topics in this position statement include the diagnosis and evaluation of osteoporosis, patient stratification according to fracture risk, management, and treatment monitoring according to fracture risk, osteoporosis management during the COVID-19 situation, multidisciplinary care of osteoporosis, atypical femoral fracture, and osteonecrosis of the jaw.
Conclusion: This updated position statement is a practical tool for physicians who take care of osteoporosis patients in Thailand.
P130
GUT METABOLITE 5’-HYDROXYINDOLEACETIC ACID (HIAA) INHIBITS OSTEOCLASTOGENESIS
L. Sapra1, A. Bhardwaj1, R. Srivastava1
1All India Institute of Medical Sciences, New Delhi, India
Objective: Several evidence suggests that microbiota dependent metabolites and cometabolites, by acting as aryl hydrocarbon receptor (AHR) ligands facilitates the bidirectional communication between the host machinery and microbiota and thus modulates host physiology. In recent years, tryptophan metabolites viz. 5’-hydroxyindoleacetic acid (HIAA) by binding and activating AHR receptor play fundamental roles in various physiological mechanisms. But no study till date has reported the direct effect of HIAA on osteoclastogenesis. Thus, in the present study, we aim to investigate the direct effect of gut metabolite HIAA on osteoclastogenesis. The present study aims to examine the potential of gut metabolite (5’HIAA) in modulating osteoclastogenesis.
Methods: To investigate the role of 5’-HIAA in regulating bone health, we carried out in vitro studies. For determining the direct effect of HIAA on osteoclastogenesis, we cultured bone marrow cells with different concentrations of HIAA in osteoclastogenic media supplemented with M-CSF and RANKL factors for 5 d. To assess the effect of HIAA on osteoclastogenesis, TRAP staining was performed. F-actin ring polymerization was performed for investigating the effect of HIAA on osteoclasts functional activity.
Results: Our in vitro data clearly indicated that 5’-HIAA significantly inhibits osteoclastogenesis in a dose dependent manner. We observed significant reduction in number of multi-nucleated TRAP positive osteoclasts in HIAA treated cultures.
Conclusion: Our data clearly indicate that HIAA inhibits osteoclastogenesis. The present study thus highlights the potential of gut metabolite HIAA as novel therapeutics in the treatment and management of several bone related diseases including osteoporosis.
Acknowledgements: This work was financially supported by projects: DST-SERB (EMR/2016/007158), Govt. of India sanctioned to RKS. LS thanks UGC for research fellowship and AB thanks DST SERB for research fellowship.
P131
THE FREQUENCY OF LOW BONE MINERAL DENSITY, LOW MUSCLE MASS AND SARCOPENIA IN PATIENTS WITH SYSTEMIC SCLERODERMA
A. Efremova1, N. Toroptsova1, O. Dobrovolskaya1
1V.A. Nasonova Research Institute of Rheumatology, Moscow, Russia
Objective: To identify the frequency of low BMD and low muscle mass in patients with systemic scleroderma (SSc).
Methods: 51 women >40 years (median age 53.9 [48.0;62.0] years old) who met the 2013 ACR/EULAR classification criteria for SSc were recruited: 33 (64.7%) patients with limited and 18 (35.3%) - with diffuse form of the disease. Pregnant or breastfeeding women and patients with overlapping rheumatic syndromes were not included. 13 (25.5%) women were premenopausal and 38 (74.5%) postmenopausal. Median duration of the disease was 6.0 [1.0;12.0] years. All patients underwent wholebody DXA (DXA). The appendicular muscle mass index (AMI) was calculated as the ratio of appendicular muscle mass (AMM) to height squared (kg/m2). Muscle strength was measured using hand dynamometry and “chair rising” test. Physical performance was assessed using a gait speed test and the short battery of physical performance (SPPB). Sarcopenia (SP) was diagnosed according to the revised European consensus on definition and diagnosis (EWGSOP2).
Results: Low BMD was found in 40 (78.4%) patients: 35 (92.1%) in postmenopausal and 5 (38,5%) in premenopausal women. Among postmenopausal persons osteoporosis was discovered in 26 (68,4%) and osteopenia in 9 (23.7%) cases, and among premenopausal women in 1 (7,7%) and 4 (30.8%) persons, respectively (p=0.03). Low muscle mass was discovered in 13 (25.5%) persons: 12 (31.6%) in postmenopausal and 1 (7.7%) in premenopausal women (p>0.05). All 13 (25.5%) SSc patients with low muscle mass had low BMD, 11 (21.6%) had also low muscle strength, so these women were classified as having SP, among them 6 (33.3%) patients with diffuse and 5 (27.8%) with limited form of the disease (p>0.05). SP had 1 (7.7%) premenopausal and 10 (26.3%) postmenopausal women (p>0.05). Patients with SP did not differ from patients without SP in age, BMD and the frequency of OP, number of fractures, skin score, positivity of Scl 70 and ACA, the nutritional status assessed by MNA-SF. At the same time, they had more often falls (p=0.044), a lower BMI (p=0.044), a longer disease duration (p=0.039) and a higher cumulative dose of glucocorticoids (GC) use (p=0.045).
Conclusion: Low BMD was detected in 78.4% and low muscle mass in 25.5% cases. 21.6% of SSc women had SP with no significant difference between the limited and diffuse forms of the disease. Patients with SP had a lower BMI, a longer SSc duration and a higher cumulative dose of GC use, they fell more often than women without SP.
P132
ZOLEDRONIC ACID IS THE TIME TESTED, COST-EFFECTIVE, ADHERABLE AND DEPENDABLE ANTIOSTEOPOROSIS MEDICATION FOR BOTH FEMALES AND MALES
S. B. Bajaj 1
1Falls Institute of India, Nagpur, India
Objective: An effort to establish that zoledronic acid is the time tested, cost-effective, adherable, dependable antiosteporosis medication for both females and males. Methods: The principal author is Consultant Geriatrician. He is advising inj zoledronic acid to the elderly people having osteopenia and osteoporosis since 05 June 2012. The dose is given after doing DXA scan and knowing FRAX score. Before giving the infusion comprehensive geriatric assessment (CGA) of each patient is done. Also vit D 3 and serum calcium levels are corrected. The principal author has innovated a consent form for evaluation, education and information of the probable adverse events to the patient and the relatives. The beneficiaries are routinely given one strip of tab paracetamol 650 to take prophylactically. Also they are also given one strip each tab omprazole 20 mg and tab ibuprofen 400 mg to consume depending upon the symptoms postinfusion. Many deserving elderly patients were given the infusion by home visits. Total 230 patients were given inj zoledronic acid between 05 June 2012 to 10 Sep 2021. The monitoring visits by the trained orthgeriatric nursing staff routinely done for next 2-3 days in the "ZA PLAN". Results: The results of giving i.v. zoledronic acid are encouraging. The age groups was between 53-92 years in both male and female population. It is best medicine to adhere, remember, remind and also comparatively affordable to even elderly from lower S-E status. Conclusion: The PI author came to the conclusion over the study of last 10 yrs with 230 beneficiaries that the zoledronic acid is the most convenient, cost-effective, easy to administer, least time consuming and best to adhere antiosteoporosis medication available at present.
P134
SOY PROTEIN SUPPLEMENTATION AND MUSCLE HEALTH IN OLDER ADULTS AND ELDERLY: SYSTEMATIC REVIEW AND META-ANALYSIS OF RANDOMIZED CONTROLLED TRIALS
L. I. Octovia1, J. R. Tandaju2, F. Witjaksono1
1Dept. of Nutrition, Faculty of Medicine, Universitas Indonesia, Dr. Cipto Mangunkusumo Hospital, Indonesia Osteoporosis Association, 2 Faculty of Medicine, Universitas Indonesia, Dr. Cipto Mangunkusumo Hospital, Jakarta, Indonesia
Objective: To determine potential benefits of soy protein towards muscle health in older adults and elderly.
Methods: Systematic review and meta-analysis was done based on PRISMA statement on PubMed, Scopus, ProQuest, EBSCOHost, and WileyOnline. Studies were screened and included studies were processed for quality assessment and data synthesis. Quantitative analysis was done by RevMan 5.4 with fixed- or random-effects model based on heterogeneity, with additional tests performed if applicable to determine bias.
Results: Six randomized controlled trials (RCT) involving 831 subjects were found. Appraisal shown good results with analysis shown several relations between soy protein and muscle health. Quantitative analysis on 598 subjects shown that there was no significancy of soy supplementation towards muscle mass (MD 0.39; 95% CI -0.02, 0.74), handgrip (MD 4.33; 95% CI -2.02, 10.68), knee extension (MD 0.88; 95% CI -4.45, 6.20), and lat pull down (MD 0.37; 95% CI -1.94, 2.69). However, there was significant relation between soy protein supplementation and bench press results (MD 7.41; 95% CI 2.43, 12.39).
Conclusion: Soy consumption was related to better muscle performance on older adults and elderly, thus improving performances. However, more studies should be done to determine its benefits on muscle profile.
Figure. Relation between soya protein supplementation and muscle profile.
P135
OSTEOPOROSIS IN RHEUMATOID ARTHRITIS: FOCUS ON THE RISK FACTORS
A. A. Ansarova1, E. V. Kalinina2, M. S. Zvonorenko2, K. S. Solodenkova3, A. R. Babaeva2
1Volgograd State Medical University, Dept. of the Internal Diseases, Volgograd, 2Volgograd State Medical University, Dept. of the Internal Diseases, Volgograd, 3First Moscow Medical University (Sechenov University), Dept. of the Outpatient Internal Diseases, Moscow, Russia
Objective: Osteoporosis (OP) and its complications contribute in the outcome of rheumatoid arthritis (RA). On the other hand clinical peculiarities of RA as well as some medications impact on the development of secondary OP. Combination of numerous risk factors (RF) contribute in the pathogenesis of OP in patients with RA. Consideration of the full spectrum in each patient with RA plays an important role in the individualized assessment of the risk of OP and the rational management in daily practice. The aim of our investigation was to analyze the traditional and disease-associated RF of OP in RA patients observed at outpatient clinics.
Methods: 120 pts with determined RA (ACR/EULAR criteria) who were managed at outpatient clinics were included in this study. We have analyzed classical and disease-associated RF of OP in observed cohort. Comparison of prevalence of RF in RA pts with and without evidenced OP was performed using χ² criterion.
Results: The observed RA cohort was presented predominantly with erosive arthritis, disease duration >5 years, RF and/or ACCP positivity, high/moderate activity. Based on the available instrumental data, systemic OP was diagnosed in 38.3% of patients, while DXA densitometry was performed only in 7.5% of patients with osteoporotic fractures. The most common traditional RF in RA cohort were female sex (70%), low physical activity (76.7%), age >50 years (65%), BMI <20 kg/m2 (27.5%). From disease-associated RFs, the most common were RA history >5 years (82.5%), DAS28 >3.2 (73.3%), high titer of RF and ACCP (79.2%), X-ray stage >2 (71.7%), long term systemic GC use (67.5%). The prevalence of numerous traditional and disease-associated RF was significantly higher in group with confirmed OP (χ²>4.0, p<0.05).
Conclusion: Obtained results suggest that in routine practice the diagnosis of OP is based on standard radiography preferentially resulting in delayed detection, prevention and treatment of OP. Majority of patients have a wide range of traditional and specific RA-related RF of OP. Prevalence of RF both conventional and RA-related was higher in patients with documented OP. These data highlight reasonability of timely monitoring for systemic OP in primary care, as well as RF assessment and effective OP prevention in RA.
P136
FREQUENCY OF LOW-ENERGY FRACTURES IN WOMEN WITH OSTEOARTHRITIS OF THE KNEE JOINT IN POSTMENOPAUSE
E. V. Usova1, M. V. Letaeva1, J. V. Averkieva1, M. V. Koroleva1, O. S. Malyshenko1, T. A. Raskina1
1Federal State Educational Institution of Higher Education “Kemerovo State Medical University” Ministry of Health of the Russian Federation, Kemerovo, Russia
Objective: To estimate the frequency of occurrence of low-energy fractures in women with osteoarthritis (OA) of the knee joint in postmenopause.
Methods: Medical cards of 78 women were analysed: 42 (median age - 63.0 [59.3;69.8] years) with OA of the knee joint established according to ACR criteria (1991), 36 women without OA (median age - 65.0 [61.8;71.0] years). The presence of low-energy fractures in the history and their localization was evaluated. The low-energy fractures were considered osteoporotic and non-traumatic. The criteria of non-inclusion were related diseases that affect bone metabolism, taking any glucocorticoids for >3 months.
Results: It has been found that in women with OA knee joint, fractures at the minimum level of injury were statistically less frequent than in the control group: in 14 (33.3%) and 36 (55.5%) women respectively (p=0.048). There is no statistically significant impact of OA on reducing the chances of low-energy fractures in women in postmenopause (OR=2.5; 95%CI=0.160-1.00, p>0.05). Among the cases analysed, fractures of the forearm were most frequent, both in the group of women with OA of the knee joint and in the control group: 9 (20.9%) and 12 (30.2%) women, respectively. Compression fractures of the vertebrae were less common in 6 (16.2%) and 4 (9.3%) patients. The fracture of the humerus was found in 1 (12.3%) of women with OA and 4 (10.8%) of women without OA, rib fractures in 1 (2.7%) and 1 (2.3%) of women, respectively. Only 1 woman (2.7%) without OA has a femur fracture. There is no statistically significant difference in the location of fractures between the studied groups (p>0.05).
Conclusion: The results indicate a statistically significantly lower incidence of low-energy fractures in women with OA knee joint compared to women without OA. The groups of women with OA knee joint and without OA were comparable in the localization of fractures.
P137
MORBIDITY OF ANTICOAGULANT OR ANTIPLATELET MEDICATION IN HIP FRACTURE PATIENTS
A. Elete1, Y. Panwar1, J. Dannaway1, J. Chen1, B. Thomas1
1Blacktown Hospital, Blacktown, Australia
Objective: Hip Fractures represent a prevalent geriatric cause of morbidity and mortality. The presence of multiple comorbidities requiring the use of an anticoagulant/antiplatelet medication adds complexity to management and influences outcomes. International guidelines suggest expedited surgery within 48 hours, however anticoagulants/antiplatelet medications commonly cause delays. There is limited research exploring health outcomes in this group. Therefore we aimed to determine the impact of anticoagulants/antiplatelet medications on key health outcomes and time to surgery.
Methods: A retrospective cohort study of hip fractures was performed at a tertiary hospital over a three-year period from January 1, 2018 to December 31, 2020. Data collected included demographics, time to surgery, length of stay, postoperative blood transfusion, acute coronary syndrome (ACS), stroke, hospital acquired infections and 120-day mortality. Patients were categorised based on the use of direct oral anticoagulants (DOAC), warfarin and antiplatelets medications. Categorical data was assessed with the chi-square test whilst continuous data were evaluated with the Kruskall-Wallis test and the independent samples median test.
Results: 474 patients were included of which 43.5% were on an anticoagulant or antiplatelet. These patients had increased overall complication rate (p<0.001) which included a higher ACS risk (p=0.02), postoperative blood transfusion (p=0.021), and infection rate (p=0.01). Anticoagulant/antiplatelet patients had a higher rate of time to surgery >48 h (41.7% vs. 17.2%, p=0.021) with this being highest in the DOAC group (92.7%). Time to surgery >48 h was associated with worse outcomes including a higher overall complication rate and a higher 120-day mortality (p=0.035).
Conclusion: There is a significantly higher incidence of complications in hip fracture patients on anticoagulant/antiplatelet medications as well as a greater time to surgery. This is significantly associated with greater morbidity and 120-day mortality. Patients on anticoagulant/antiplatelet medications are at increased risk of poor health outcomes. In a well resourced tertiary hospital outcomes may have room for improvement. Guidelines tailored to the resources of the institution are required to expedite early safe surgery in this high risk patient group.
P138
QUANTITATIVE DIAGNOSIS & SCREENING OF OSTEOPOROSIS USING ABDOMINAL COMPUTED TOMOGRAPHY SCANS OBTAINED FOR OTHER INDICATIONS
G. Kakadiya1, D. Joshi2
1Fortis Hospital, Mohali, India, 2Consultant Spine Surgeon, Head of Spine Surgery Department, Fortis Hospital, Mohali Panjab, India
Objective: Osteoporosis is a prevalent condition in current developing era but undiagnosed or underdiagnosed condition in especially developing countries like India. The study aim was to evaluate computed tomography (CT)-derived BMD assessment compared DXA measures for identifying osteoporosis by using CT scans performed for other clinical indications.
Methods: It was a cross-sectional study. A total of 1867 adults undergoing CT and DXA (n=2067 pairs) within a period over 5 years. CT-attenuation values (in Hounsfield units [HU]) of trabecular bone between the T12 and L5 vertebral levels, with an emphasis on L1 measures (study test); DXA BMD measures (reference standard). Sagittal CT images assessed for moderate-to-severe vertebral fractures.
Results: CT-attenuation values were significantly lower at all vertebral levels for patients with DXA-defined osteoporosis (P<0.001). An L1 CT-attenuation threshold of 160 HU or less was 90% sensitive and a threshold of 110 HU was more than 90% specific for distinguishing osteoporosis from osteopenia and normal BMD. Positive predictive values for osteoporosis were 68% or greater at L1 CT-attenuation thresholds less than 100 HU; negative predictive values were 99% at thresholds greater than 200 HU. Among 119 patients with at least 1 moderate-to-severe vertebral fracture, 62 (52.1%) had nonosteoporotic T-scores (DXA false-negative results), and most (97%) had L1 or mean T12 to L5 vertebral attenuation of 145 HU or less. Similar performance was seen at all vertebral levels. Intravenous contrast did not affect CT performance.
Conclusion: Abdominal CT images obtained for other reasons that include the lumbar spine can be used to identify patients with osteoporosis or normal BMD without additional radiation exposure or cost. The potential benefits and costs of using the various CT-attenuation thresholds identified were not formally assessed.
P139
SYSTEMATIC REVIEW OF POST-SURGERY INTERVENTIONS FOR HIP FRACTURE
Y. H. Kwan1, Z. Y. Lim2, W. Q. Yee2, L. L. Low2
1Duke-NUS Medical School, 2Singapore General Hospital, Singapore,
Objective: We aim to summarise the existing literature on post-surgery interventions provided in the acute, subacute and community settings in order to improve outcomes for patients with hip fractures.
Methods: We performed a systematic literature review guided by the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA). We included articles that were (1) randomized controlled trials (RCTs), (2) involved post-surgery interventions that were conducted in the acute, subacute or community settings and (3) conducted among older patients above 65 years old with any type of non-pathological hip fracture that was surgically treated, and who were able to walk without assistance prior to the fracture. We excluded (1) non–English language articles, (2) abstract-only publications, (3) articles with only surgical interventions, (4) articles with interventions that commenced pre-surgery or immediately upon completion of surgery or blood transfusion, (5) animal studies. Due to the large number of RCTs identified, we only included “good quality” RCTs with Jadad score ≥3 for data extraction and synthesis.
Results: Our literature search has identified 109 good quality RCTs on post-surgery interventions for patients with fragility hip fractures. Among the 109 RCTs, 63% of the identified RCTs (n=69) were related to rehabilitation or medication/nutrition supplementation, with the remaining RCTs focusing on osteoporosis management, optimization of clinical management, prevention of venous thromboembolism, fall prevention, multidisciplinary approaches, discharge support, management of post-operative anemia as well as group learning and motivational interviewing. 44 RCTs were carried out in the inpatient setting (40.4%), 28 RCTs in the outpatient setting (25.7%), and 37 RCTs in the inpatient to outpatient setting (33.9%).
Conclusion: The identified RCTs regarding post-surgery interventions were heterogeneous in terms of type of interventions, settings and outcome measures. Recovery post-surgery usually spans from the acute inpatient stay to subacute to post-discharge. Greater improvement of various outcomes like physical function recovery, nutritional status recovery, reduction of various postoperative complications, shortening of length of stay etc., could be better achieved through combining various interventions across various settings instead of a specific setting.
P140
THE DEMOGRAPHIC, HEALTH RISK FACTORS AND PHYSICAL FITNESS CHARACTERISTICS OF A VIRTUAL OSTEOPOROSIS PERWATUSI GROUP EXERCISE PROGRAM IN INDONESIA: A DESCRIPTIVE STUDY
A. Anggunadi1, A. Tobing1, A. A. Kurniawan1
1Perkumpulan Warga Tulang Sehat Indonesia, PERWATUSI, Jakarta, Indonesia
Objectives: To describe the demographic, osteoporosis risk factors and physical fitness characteristics of a virtual Perwatusi Osteoporosis group exercise program that will be analyzed to design further improvement for the program.
Methods: An online survey was distributed on April 2021 for 3 weeks among the virtual Perwatusi Osteoporosis group exercise’s participants to collect demographic and osteoporosis risk factors, and also asking the participants to do simple muscle endurance and flexibility test at home (Chair stand test and Apley Scratch test). The virtual group exercise was started due to COVID-19 pandemic on 2020.
Results: From about 300 participants joining this 3 times/week (1 h/session) group exercise, a total of 164 subjects (mean age 68.2 y.o.; 93.9% of them are female) join the survey. Demographically, though about 62% of the subjects were in Jakarta, but the exercise program has succeeded to reach participants from outside of Java Island (8% of the subjects) mostly through the Zoom meeting media, but also through YouTube and Live Instagram (18.3% and 1.8%, respectively). About the osteoporosis risk factors, 50.6% of the subjects are obese, whereas <5% of them are underweight. The most frequent osteoporosis risk factors found were early menopause (20.1%), followed by smoking (17.3%), diabetes (15.9%), gastrointestinal disorders (15.9%), genetic factor (14%) and thyroid gland disorders (7.3%). Most of the subjects had poor flexibility (42.7%). On the other hand, most of the subjects had moderate muscle endurance (50.6%).
Conclusion: Based on the results description, it is necessary to design strategies to: (1) invite the Perwatusi branches located in outside Java Island to actively participate and broadcast about the group exercise to society living outside Java Island; (2) increase the participants’ knowledge about the osteoporosis risk factors and its management; (3) evaluate how routine is the participation to analyze their physical fitness improvement better.
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PMC009xxxxxx/PMC9003166.txt |
==== Front
Support Care Cancer
Support Care Cancer
Supportive Care in Cancer
0941-4355
1433-7339
Springer Berlin Heidelberg Berlin/Heidelberg
35412076
7039
10.1007/s00520-022-07039-w
Original Article
Development and evaluation of the efficacy of a web-based education program among cancer patients undergoing treatment with systemic chemotherapy: a randomized controlled trial
http://orcid.org/0000-0002-3356-3120
Bektas Hicran hbaydin@akdeniz.edu.tr
1
Coskun Hasan Senol hs.coskun@yahoo.com
2
Arikan Fatma farikan64@gmail.com
1
Ozcan Keziban kezocan_20@hotmail.com
2
Tekeli Aysel acaraysel@hotmail.com
2
Kondak Yasemin ycelebioglu@hotmail.com
2
Sezgin Merve Gozde gozdesezgin1990@gmail.com
1
Yangec Elcin elcnyngc.2013@hotmail.com
1
Kalav Simge skalav@adu.edu.tr
1
1 grid.29906.34 Department of Internal Medicine Nursing, Faculty of Nursing, Akdeniz University, Dumlupinar Bulvari, 07058 Antalya, Turkey
2 grid.411268.8 0000 0004 0642 4824 Akdeniz University Hospital Medical Oncology Unit, Antalya, Turkey
12 4 2022
2022
30 7 60216033
1 11 2021
4 4 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
The study aimed to develop a web-based education program among cancer patients undergoing treatment with systemic chemotherapy and to evaluate the efficacy of the program on symptom control, quality of life, self-efficacy, and depression.
Methods
A web-based education program was prepared in line with patient needs, evidence-based guidelines, and expert opinions and tested with 10 cancer patients. The single-blind, randomized controlled study was conducted at a medical oncology unit of a university hospital. Pretests were applied to 60 cancer patients undergoing treatment with systemic chemotherapy, and the patients (intervention: 30, control: 30) were randomized. The intervention group used a web-based education program for 3 months, and they were allowed to communicate with researchers 24/7 via the website. The efficacy of a web-based education program at baseline and after 12 weeks was evaluated. The CONSORT 2010 guideline was performed.
Results
In the first phase results of the study, it was found that most of the patients with cancer wanted to receive education about symptom management and the side effects of the treatment. Expert opinions on the developed website were found to be compatible with each other (Kendall’s Wa = 0.233, p = 0.008). According to the randomized controlled study results, patients who received web-based education reported significantly fewer symptoms (p = 0.026) and better quality of life (p = 0.001), but there was no statistically significant difference in the self-efficacy and depression levels during the 3-month follow-up period (p˃0.05). The most frequently visited links in the web-based education program by the patients with cancer were the management of chemotherapy-related symptoms (62.6%).
Conclusion
A web-based education program was found to be efficacy in remote symptom management and improving the quality of life of cancer patients.
Trial registration
www.clinicaltrials.gov, NCT05076916 (October 12, 2021, retrospectively registered).
Supplementary Information
The online version contains supplementary material available at 10.1007/s00520-022-07039-w.
Keywords
Cancer
Symptom management
Quality of life
Web-based education
Tele-nursing
Supportive care needs
http://dx.doi.org/10.13039/501100004410 Türkiye Bilimsel ve Teknolojik Araştirma Kurumu 113S924 issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2022
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pmcIntroduction
Cancer is a life-threatening disease with negative physical, psychological, and socioeconomic effects [1, 2]. Many patients with cancer receive outpatient chemotherapy, often requiring them to manage the side effects of treatment at home, without the support of a healthcare professional. Patients with cancer and their families need to be supported not only in the hospital but also in the home environment [3]. Patients want to receive information about strategies for coping with symptoms or problems that they frequently encounter after treatment [4]. During treatment, many patients do not feel able to participate in self-management education in the clinical setting. As such, access to relevant and useful support is required [5].
Recently, health reforms and policies have emphasized that patients should be supported to take an active role in the management of their disease. It is stated that interactive health communication practices, which provide patients with education, equipment, and self-efficacy to better manage their diseases, play an important role in improving patient care [6]. Today, most adults have access to the Internet, and many patients use it as a primary source of information that is easily accessible [7–9]. Considering the increase in the number of patients with cancer, it is stated that a web-based education program can be used as a cost-effective tool to support them [10]. It is possible to reach a wide audience with web-based education programs, and the rate of individuals using web-based information has increased with the development of effective online education programs [9, 11]. With health education on the web, interactive, economical, efficient, and appropriate content can be provided to all users, such as patients, their families, and health professionals. In the chronic health setting, web applications have been used to improve communication between patients and health professionals, manage symptoms, provide health information, social interaction, and recovery using motivational games [9, 12–14]. It has also been reported that effective online communication can reduce anxiety and improve clinical patient outcomes [15].
Web-based education has been used with positive effects in several chronic diseases populations including diabetes, hypertension, stroke, respiratory diseases, cancer, and high-risk health problems, such as obesity, anxiety, and depression [5, 16–25]. The results of the studies in the literature have shown that web-supported education improves disease management and reduces symptom burden [6, 26, 27] and that there are improvements in the pain and depression symptoms of patients who have received web-based education [28]. Web applications can play an important role in the management of cancer treatment, symptoms, and providing cancer-related information [13].
Additionally, today’s pandemic conditions have increased the need for web-based education for patients with cancer. It has been suggested that web-based programs should be planned for the needs of cancer patients. It has been reported that the usage rates and efficacy of web-based education programs designed in this way will be higher [29]. Delivering education and follow-up via the web may mitigate the challenges of social isolation and the risk of contracting COVID while attending in-hospital face-to-face appointments. The primary aim of the study is to develop a web-based education program for cancer patients undergoing treatment with systemic chemotherapy and to test the validity of this program. The secondary aim is to evaluate the efficacy of this web-based education program on symptom control, quality of life, self-efficacy, and depression in cancer patients undergoing treatment with systemic chemotherapy.
Methods
Design and participants
In the first stage of the study, the needs of patients for web-based education were determined using a descriptive design, and in the second stage, the efficacy of a web-based education program was evaluated using a randomized controlled trial design, with the participants being single-blinded. A randomized controlled trial based on the Consolidated Standard of Reporting Trials—CONSORT 2010-guidelines was performed [30] (Supplementary File 1). In the second stage of the study, the sample size was calculated on the G*POWER software package based on an 85% power and a 95% confidence interval. A randomized controlled trial study was conducted with patients with cancer (n = 60), including 30 in the intervention and 30 in the control groups. The study consisted of patients who were over the age 18, received at least two cycles of systemic chemotherapy, had no verbal communication disorder, were literate, had Internet access, and use the Internet. Those with a diagnosis of a psychiatric disorder were excluded from the study. Data of both stages of the study were collected at a medical oncology unit of a university hospital between May 7, 2014, and February 17, 2016.
Ethics of the study
This study was conducted in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of University B (22.08.2013/23–5). The objective of the study was explained to the participants. Participants were assured of their right of refusal to take part in or to withdraw from the study at any stage with no negative consequences. The validity and reliability studies of all scales used in the study were conducted, and permission of the authors of the scales was obtained via e-mail.
Preparation of the website
In the first stage of the study, 30 patients with cancer were interviewed to determine their expectations regarding their needs for web-based education. The researchers developed a “Web-Based Education Needs Assessment Form for Patients with Cancer” based on a review of the literature [1, 3, 16, 26–28, 31] (Supplementary File 2). Quantitative questions were used to assess their Internet use for health, patient information needs about the disease and treatment, expectations and suggestions for web-based education needs, and the content they want to see on a website specific to cancer patients. The expectations and suggestions of the 30 patients with cancer, who were treated with systemic chemotherapy, regarding the need for web-based education, were evaluated with this form.
In line with the needs analysis and literature, the research team created a web-based education program. The Web site’s quality was analyzed using DISCERN, a tool created by Charnock et al. in 1999 [32] to assess the quality of training materials that provide textual information about treatment alternatives for health problems. The overall score varies from 15 to 75 for the 16 items. Each item is scored on a scale of one to five. A 16th component is examined independently, and it gives a general assessment. Low DISCERN ratings indicate poor quality, whereas high values indicate excellent quality. Gokdogan translated DISCERN into Turkish in 2003 [33]. A group of 10 experts evaluated the content of the program separately, including three oncology physicians, four nursing faculty members, and three nurses. Experts rated the content 1 “inappropriate,” 2 “partly appropriate,” 3 “appropriate,” or 4 “completely appropriate.” After obtaining expert opinions, a pilot study was conducted with 10 different patients with cancer who met the sampling criteria. Patients with cancer included in the first stage of the study were excluded from the second stage. The intelligibility and usability of the program were tested in a pilot study.
The topics included in the developed website content were as follows: what is cancer?, causes of cancer, the most common types of cancer, cancer prevention, early diagnosis, and screening methods, cancer treatment, let’s understand chemotherapy, problems/recommendations related to chemotherapy, nutritional recommendations, emergencies, preventive recommendations during chemotherapy, coping with stress, Department of Medical Oncology, complementary and supportive medicine practices, advice to family/caregivers in the cancer process, rights of cancer patients, information on health and social services, announcements, events, gallery, contact us. During the 3-month follow-up phase of the study, statistical information such as the usage rates of the website and the most read pages were regularly monitored by the membership to http://www.google.com/analytics/. Repeat visits to the website were considered new visits.
Procedures
The efficacy of a web-based education program on patient outcomes was analyzed by dividing the participants into groups through randomization. All patients with cancer who met the sampling criteria were assigned to the intervention and control groups through block randomization (1:1). The researchers used a randomization list created on a computer application (https://www.randomizer.org/). Pretest data of the study were collected from patients who consented to participate in the study by two independent researchers who were not involved in the implementation of the study. For assigning each patient to a group, the other team members were called by the same independent researcher, and the subjects were assigned to the groups according to the randomization list created. Two independent researchers conducted the assignment of the participants on the intervention and control groups and the evaluation of the outcome measurement data. Oncology patients were given verbal training by the oncology education nurses with manual education materials. It was mentioned that a study is being prepared to improve these instructional materials for cancer patients as part of the research’s goal. The patients in the control group were given routine education by the oncology education nurses. In addition to the routine training, the website address of the study was given to the intervention group who received web-based education. The patients were blinded because they were unaware of their randomization status or the study hypotheses. Since the researchers conducted the interventions, they were not blinded.
A web-based education program was introduced to the patients with cancer in the intervention group during face-to-face interviews. They were asked to examine the program for at least 2 h a week for 3 months. During the follow-up period, the patients in the intervention group were called twice a week and reminded to use the education program. The researchers got in contact with the patients online via the website. During the follow-up period, the patients contacted the research team by calling or writing messages via the website 24/7. An e-mail account was created by the research team using the email system of the university, and it was shared with the patients. The clinical researchers of the team answered the patients’ questions via this e-mail account. Within the scope of the study, a new cell phone number was purchased, and the researchers used it on a rotating basis to answer patients’ questions as a phone response system. During the follow-up, patients’ questions were answered on the phone. The control group received routine patient education and routine hospital follow-ups given by oncology education nurses during the 3-month follow-up period. In the third month, after the follow-up stage of the study was completed, the posttests were administered to the patients in the intervention and control groups who came to the hospital for follow-up or treatment. A flowchart of the study is given in Fig. 1.Fig. 1 Flow diagram of the randomized controlled study (CONSORT 2010)
Measures
Questions about the descriptive and disease characteristics of the patients included age, gender, marital status, education level, cancer type, stage, metastatic status, and treatment type. The outcome measures of the study were determined as the symptoms, quality of life, self-efficacy, and depression levels at baseline and 3 months.i. The Rotterdam Symptom Checklist was used to evaluate the patient-reported side effects of treatment. The items on the scale are scored between 1 and 4 by using a Likert-type scoring system. The scale consists of 39 items and has 4 sub-dimensions: Physical Symptom Discomfort, Psychological Discomfort, Activity Level, and Quality of Life. The higher the scores obtained from the scale, the greater the distress. Cronbach’s alpha value of the scale was determined as 0.88 [34].
ii. EORTC-QLQ-C30 Quality of Life Scale was developed to measure the quality of life of patients with cancer. It includes 30 questions and three sub-dimensions: General Well-Being, Functional Difficulties, and Symptom Control. The maximum score on the scale is 100, and the minimum is 0. High scores on the functional sub-dimension indicate good/healthy functional status, high scores on the symptom sub-dimension indicate high levels of symptoms and/or problems, and high scores on the global health status/quality of life sub-dimension indicate good quality of life [35].
iii. Strategies Used by Patients to Promote Health is used to assess the self-confidence of individuals in fulfilling the strategies they use to improve health. It consists of 29 items under 3 sub-dimensions, namely, coping with stress, decision-making, and positive behavior development. The scores that can be obtained from the scale range from 29 to 145, and increased scores indicate an increased level of self-efficacy. Cronbach’s alpha value of the scale was found as 0.92 [36].
iv. Beck Depression Scale is a self-assessment scale that measures the symptoms of depression observed in physical, emotional, cognitive, and motivational areas. The purpose of the scale is not to diagnose depression but to objectively measure the severity of depressive symptoms. Each of the 21 items on the scale includes four statements numbered 0, 1, 2, and 3. Two of the items on the scale are reserved for emotions, eleven items for cognition, two items for behavior, five items for somatic symptoms, and one item for interpersonal symptoms. The total score that can be obtained from the scale varies between 0 and 63, and the scores are interpreted as follows: 0–9, no depression; 10–15, mild depression; 16–23, moderate depression; and 24–63, severe depression [37].
Statistical analysis
Statistical Package for Social Sciences 23.0 software was used in the analysis of the data obtained from the study. Percentages, arithmetic means, t-test, and one-way ANOVA tests were used in the analysis of the data obtained from the sample in the first stage of the study, and percentages, arithmetic means, t-test, one-way ANOVA, analysis of variance in repeated measures, chi-square tests, and intention-to-treat (ITT) analysis were used to evaluate the data obtained from the sample in the second stage of the study. Kendall’s coefficient of concordance was used to determine the level of agreement between expert ratings for DISCERN and the website’s content. Experts’ mean scores were interpreted for quality and content evaluation. The level of significance was set as 0.05 in the entire study.
Results
In the first phase of the study, the expectations and suggestions of the 30 participants with cancer, who were treated with systemic chemotherapy, regarding the needs for web-based education, were evaluated with the “Web-Based Education Needs Assessment Form for Patients with Cancer” form. Most participants (76.7%) in the study used the İnternet to obtain health information. All of them wanted to receive education and counseling on possible risks after treatment through a web-based education program. Regarding the need for web-based education, the participants wanted to receive education about what to do in case of emergency when they had a problem at home (96.7%), learn knowledge about the illness (93.3%), the side effects of the treatment (86.7%), or complementary and supportive medicine practices from the Internet (50%). They also stated that they applied the information they obtained from health websites (50%), went to a health institution (76.7%), or called their physician (43.3%) when they experienced a problem at home about their illness and/or treatment. Regarding the web-based education program, 76.7% of the participants suggested phone communication and 73.3% of them favored an online meeting with the healthcare professional.
In line with the needs analysis and literature, the research team created a simple, understandable, and user-friendly web-based education program by discussing all the contents of the website one by one. The website design was commissioned by specialist software company. The website was designed to be mobile compatible so that it could be viewed on all desktop computers, tablets, and smartphones. After the website was designed, the content of the website was evaluated by 10 independent oncology and nursing professionals using the DISCERN Guidelines. The expert opinions were found to be consistent with one another, with no significant differences between them (Kendall’s Wa = 0.233, p = 0.008). After pilot testing (n:10), minor revisions were made to symptom management on the website. The final version of the website was prepared after patient opinions, evidence-based guidelines, expert opinions, pilot study, and statistical methods that tested the comprehensibility of the website. After the study was completed, a web-based education program was made available to all patients on the university hospital’s oncology department website.
The mean age of the participants in the intervention group who received web-based education was 52.47 ± 10.57 years, and it was 55.57 ± 10.14 years in the control group. Of the participants in the intervention group, 56.7% were male, and 90% were married. In the control group, 56.7% of the participants were female, and 80% were married. Also, 36.7% of the participants in the intervention and control groups were diagnosed with gastrointestinal system cancer. The comparison of the descriptive and disease characteristics of the participants with cancer in the intervention and control groups in the study indicated that they had statistically similar characteristics (p > 0.05) (Table 1). The outcome measures of the study were tested as the symptoms, quality of life, self-efficacy, and depression levels at baseline and the results were found to be similar (Table 2, Table 3, Table 4, Table 5).Table 1 Descriptive and disease characteristics of the patients in the intervention and control groups included in the study (pretest)
Descriptive and disease characteristics Intervention Control Test
n % n % t p
Age (mean ± SD) 52.47 ± 10.57 55.57 ± 10.14 2.354 0.072
Gender
Female 13 43.3 17 56.7 1.067 0.302
Male 17 56.7 13 43.3
Marital status
Single 3 10.0 6 20.0 1.176 0.278
Married 27 90.0 24 80.0
Education level
Primary school 9 30.0 11 36.7 0.840 0.657
High school 9 30.0 6 20.0
Undergraduate and above 12 40.0 13 43.3
Type of cancer
Breast 6 20.0 9 30.0 3.800 0.803
Lung 3 10.0 2 6.7
Gastro-intestinal 11 36.7 11 36.7
Gynecological 3 10.0 2 6.7
Tumor in tongue 4 13.4 1 3.3
Bladder 1 3.3 3 10.0
Prostate 1 3.3 1 3.3
Other 1 3.3 1 3.3
Stage
Stage I 3 10.0 6 20.0 5.609 0.132
Stage II 5 16.6 11 36.7
Stage III 11 36.7 6 20.0
Stage IV 11 36.7 7 23.3
Metastasis status
No 11 37.9 18 62.1 3.270 0.071
Yes 19 62.1 12 37.9
Type of treatment
CT 8 26.7 12 40.0 2.467 0.481
CT + RT 4 13.3 3 10.0
CT + surgical 5 16.7 7 23.3
CT + RT + targeted therapy 13 43.3 8 26.7
Table 2 Compensating for the difference between the mean of the Rotterdam Symptom Checklist scores
Measurements Comparison F p
Pretest
Mean ± SD Posttest
Mean ± SD
Physical symptom discomfort
Intervention 15.93 ± 10.18 10.59 ± 9.52 Time 8.838 0.004
Control 18.06 ± 11.83 13.50 ± 12.93 Group 0.855 0.359
Time × group 0.143 0.707
Psychological discomfort
Intervention 6.23 ± 4.54 4.22 ± 4.98 Time 6.981 0.011
Control 6.03 ± 4.75 4.46 ± 5.20 Group 0.025 0.875
Time × group 0.008 0.929
Activity level
Intervention 16.40 ± 7.23 20.00 ± 3.33 Time 6.218 0.016
Control 16.06 ± 6.37 18.38 ± 5.57 Group 0.375 0.543
Time × group 1.321 0.256
Quality of life
Intervention 2.97 ± 1.54 2.30 ± .0.87 Time 0.924 0.341
Control 3.10 ± 14.03 2.77 ± 1.42 Group 1.321 0.256
Time × group 0.924 0.341
Total
Intervention 41.53 ± 13.08 37.11 ± 12.91 Time 5.252 0.026
Control 43.26 ± 14.03 39.12 ± 17.48 Group 0.365 0.549
Time × group 0.018 0.893
SD, standard deviation; F, multivariate analysis
Table 3 Comparison of the difference between the mean scores of the EORTC QLQ-C30 Quality of Life Scale
Measurements Comparison F p
Pretest
Mean ± SD Posttest
Mean ± SD
Functional score
Intervention 27.16 ± 10.03 22.33 ± 5.85 Time 6.753 0.012
Control 28.66 ± 9.54 24.76 ± 8.98 Group 1.048 0.311
Time × group 0.300 0.587
Symptom score
Intervention 23.43 ± 6.17 19.29 ± 5.65 Time 13.375 0.001
Control 24.50 ± 7.27 21.50 ± 6.38 Group 0.811 0.352
Time × group 1.334 0.254
Global health score
Intervention 9.53 ± 2.91 10.48 ± 2.59 Time 0.444 0.508
Control 8.97 ± 2.70 9.64 ± 2.75 Group 1.268 0.266
Time × group 0.227 0.639
Physical functioning
Intervention 9.97 ± 4.38 8.15 ± 2.57 Time 2.801 0.100
Control 10.10 ± 3.76 9.00 ± 3.49 Group 0.302 0.585
Time × group 1.077 0.304
Role functioning
Intervention 3.37 ± 2.13 2.48 ± 0.80 Time 2.413 0.127
Control 3.20 ± 1.79 2.81 ± 1.63 Group 0.021 0.884
Time × group 1.861 0.179
Emotional functioning
Intervention 7.23 ± 2.88 5.78 ± 2.85 Time 7.701 0.008
Control 7.47 ± 2.94 6.42 ± 2.86 Group 0.623 0.789
Time × group 0.072 0.734
Cognitive functioning
Intervention 2.97 ± 1.10 2.78 ± 0.85 Time 0.744 0.392
Control 3.73 ± 1.57 3.35 ± 1.38 Group 6.917 0.011
Time × group 0.241 0.629
Social functioning
Intervention 3.63 ± 1.85 3.15 ± 1.29 Time 4.017 0.050
Control 4.17 ± 1.80 3.19 ± 1.70 Group 0.322 0.573
Time × group 0.492 0.486
Total score
Intervention 60.13 ± 12.89 52.11 ± 10.53 Time 11.875 0.001
Control 62.13 ± 14.36 55.54 ± 12.98 Group 0.748 0.391
Time × group 0.466 0.489
SD, standard deviation; F, multivariate analysis
Table 4 Comparison of the difference between the mean scores of Strategies Used by Patients to Promote Health
Group Measurements Comparison F p
Pretest
Mean ± SD Posttest
Mean ± SD
SUPPH-stress
Intervention 33.27 ± 10.42 34.89 ± 9.10 Time 0.253 0.617
Control 33.20 ± 9.17 34.96 ± 9.37 Group 0.001 0.075
Time × group 0.011 0.919
SUPPH-decision
Intervention 11.03 ± 4.14 11.89 ± 3.71 Time 1.578 0.215
Control 11.53 ± 3.09 12.48 ± 2.90 Group 0.209 0.650
Time × group 0.171 0.681
SUPPH-positive attitude
Intervention 61.30 ± 13.35 60.63 ± 14.99 Time 0.302 0.585
Control 59.63 ± 13.00 62.90 ± 12.00 Group 0.218 0.642
Time × group 0.302 0.585
SUPPH-total
Intervention 105.60 ± 25.17 107.41 ± 26.17 Time 0.010 0.621
Control 104.37 ± 23.45 107.60 ± 22.52 Group 0.042 0.839
Time × group 0.163 0.688
SD, standard deviation; F, multivariate analysis
Table 5 Comparison of the difference between Beck Depression Scale mean scores
Group Measurements Comparison F p
Pretest
Mean ± SD Posttest
Mean ± SD
Beck Depression Scale
Intervention 10.23 ± 6.40 7.93 ± 9.65 Time 2.236 0.141
Control 12.10 ± 6.97 9.72 ± 8.89 Group 1.294 0.261
Time × group 0.116 0.735
SD, standard deviation; F, multivariate analysis
According to our results, participants who received web-based education reported significantly fewer symptoms (F = 5.252, p = 0.026), fewer physical discomfort (F = 8.838, p = 0.004), fewer psychological discomfort (F = 6.981, p = 0.011), and fewer activity problems (F = 6.218, p = 0.016). It was determined that the difference between the changes in symptom level of the two groups was statistically significant during the 3-month follow-up period from the beginning to the end of the study (Table 2).
It was found that participants who received web-based education stated significantly fewer cognitive problems (F = 6.917, p = 0.011). It was determined that the difference between the changes in functional status (F = 6.753, p = 0.012), symptom status (F = 13.375, p = 0.001), emotional functions (F = 7.701, p = 0.008), and total quality of life (F = 11.875, p = 0.001) of the two groups was statistically significant during the 3-month follow-up period from the beginning to the end of the study (Table 3).
According to the results, it was found that the health promotion strategies used by the participants who received web-based education were not statistically significant (p > 0.05) (Table 4). Also, there was no statistically significant change in the depression levels of the intervention group who received web-based education during the 3-month follow-up period (p > 0.05) (Table 5).
It was determined that the participants logged into the developed website 1707 times. The most frequently visited links in the web-based education program by the participants in the intervention group were the management of chemotherapy-related symptoms (62.6%). The symptoms and management pages have been visited a total of 2336 times, and the education programs on the website have been visited a total of 3734 times. It was found that the average stay was 1 h and 19 min in each session.
Discussion
Recently, the number of web-based education programs and applications in the field of health has increased in parallel with the developments in technology. In the first stage of our study, a needs analysis was conducted for cancer patients undergoing treatment with systemic chemotherapy for a web-based education program design. The results of the study indicated that the vast majority of the participants used the Internet to obtain health information, wanted to receive education about symptom management and the side effects of the treatment. Additionally, half of the participants stated that they applied the information they obtained from health websites. In a study, it was found that the most common source of information used by participants other than healthcare professionals was the Internet [38]. Internet users prefer the Internet for obtaining information about diseases, treatments, seeking new or alternative treatment options, or searching for support groups [13, 31, 39]. It was determined that surviving patients with oral cancer wanted to get information, especially about symptom management, and were willing to use a web-based education program to increase their quality of life [17]. Tele-health applications are a promising method for the future of self-management [5]. Symptoms of the disease and its treatment are common in oncology patients. Patients’ adherence to treatment and quality of life are thought to be influenced by how well they control their symptoms. Patients require information about treatment side effects and symptom management, and they prefer to obtain it through the use of web technology. These findings reveal the importance of web-based education programs and the necessity of developing and disseminating websites that contain evidence-based information. The need for seeking information and symptom management online has increased, especially due to pandemic-related fear, anxiety, and social isolation worldwide. Web-based education, in line with technological advancements, can help health professionals promote remote symptom management.
In our study, the symptom distress of participants who received web-based education was found to be significantly fewer than those of the control group participants. In a systematic review and meta-analysis study, web-based symptom management interventions were most effective in reducing overall physical symptoms in people with advanced cancer. Physical access barriers, transportation, and due to rapid changes in our environment, such as the COVID-19 pandemic, new technologies have been proposed to support the symptom management of cancer patients [40]. It was determined that a 12-week individualized education program with online support was effective in preventing cancer-induced fatigue, reducing anxiety, and increasing health-related quality of life [41]. It has been shown that follow-ups conducted with web support reduce the symptom burden [6, 26, 27], and psychological symptoms of patients [28]. Along with the web-based education programs, the e-mail communication forum with a clinical nurse specialist in cancer was valued as the most useful, most easy to understand, and having the highest quality of information to meet individual needs [42]. In a study examining the effects of standard education and telemonitoring on patient outcomes, it was stated that standard education was effective in symptoms such as pain, anxiety, and depression only in the first week and that education and telemonitoring provided more and long-term improvement in patient outcomes [43]. In patients with advanced non-small-cell lung cancer treated with chemotherapy, 3 months of web-based health education provided a significant reduction in the first 10 important symptoms according to the Symptom Distress Scale [21]. Considering that cancer patients experience various treatment-related symptoms and their daily life activities are negatively affected. It is thought that the symptom management skills of the patients should be improved. Web-based education is an easily accessible and economical tool for supporting and empowering cancer patients. As a result, it is critical to spread web-based education to support symptom management of cancer patients.
It was determined that participants who received web-based education stated significantly better quality of life and there was a significant difference between the intervention and control groups in terms of quality of life. In a web-based study by Ruland et al. [6], the self-efficacy and health-related quality of life scores of patients in the control group decreased over time. Web-based education was found to affect increasing the quality of life in patients with breast cancer [20], and a web-based health education implemented for 3 months had a significant effect on global quality of life and emotional functions in patients with lung cancer receiving chemotherapy treatment [21]. It is clear that web-based education programs positively affect the quality of life of cancer patients. To improve their quality of life and functional status, cancer patients need programs that they can access quickly and easily whenever they experience symptoms, gain evidence-based information, and maintain symptom self-management.
It was found that there was no difference between the intervention and control groups according to the self-efficacy status during the 3-month follow-up period. Some studies have shown that the self-efficacy levels of patients with breast cancer do not change before and after chemotherapy treatment [44], and 30–60% of them have high levels of distress for 6 months after the completion of their treatment [45]. Different factors, such as the chronic characteristics of cancer, disease, treatment-related symptom burden and psychological problems, and low socioeconomic status, have affected the level of self-efficacy [44]. To increase the level of self-efficacy in cancer patients, it can be recommended to develop and expand online programs that allow online interviews with health professionals within the scope of web-based education. In the diagnosis and difficult treatment processes of cancer patients, it can be considered to enrich applications such as online peer group interviews that can encourage and strengthen them in their cancer journey.
It was found that there was no difference between the intervention and control groups according to the depression levels during the 3-month follow-up period. While there was a mild level of depression in the intervention and control group participants at the beginning, the symptoms of depression decreased during the follow-up period. It was determined that there was no significant difference between emotional functions, social functions, depression, and fatigue levels in the initial, 6th-month, and 12th-month measurements of the effect of web-based interventions in patients with cancer [46]. It was found that a web-based cognitive rehabilitation intervention in patients with cancer did not yield a significant effect on distress, quality of life, and perception of illness during the 3-month follow-up period [24]. In the systematic review of randomized controlled trials, which evaluated the effect of technology-based interventions on depression in patients with cancer, five of the nine studies found no significant effect on depression [47]. It is known that cancer patients experience depression even after the recovery period for different reasons, such as the fact that cancer is a chronic disease, the duration of symptoms, and the inadequacy of individual coping strategies. It can be recommended to consider applications such as psychological counseling or peer group interviews within the scope of web-based education programs for cancer patients and to evaluate depression in the long term.
In our study, it was determined that the most frequently visited links on the website by the participants in the intervention group were chemotherapy-related symptoms and their management. These results revealed that patients receiving chemotherapy use cancer-related websites at home to obtain health information and that patients with cancer should support symptom management at home. It is thought that patients frequently access these links to get information about the management of the symptoms that they frequently experience. In the study by Wiljer et al. [48] to determine the information needs of patients with lung cancer, it was found that approximately half of the patients wanted to obtain information about the stage of the disease and symptoms. About a quarter of emergency department visits of patients with advanced cancer receiving palliative care are potentially preventable, and proactive efforts have been recommended to improve communication with patients and support [49]. It was observed that cancer survivors frequently examined web modules on diet, fatigue, returning to work, anxiety, depression, and physical activity in the web-based program [46]. It is thought that patients with cancer need to get information about treatment and symptom management at every stage of their disease and treatment and to communicate with health professionals. For this purpose, in our study, questions asked by the participants were sent directly to the e-mail address of the research team 24/7 via the question and answer interface of the web-based education software, and the clinical staff in the study answered immediately. While participants with cancer are given web-based education, they can also be given online support to help them handle their treatment and care more effectively.
Since there are negative effects of cancer and chemotherapy on patients’ quality of life and self-efficacy, healthcare providers should focus on designing psychosocial interventions to improve self-care, self-efficacy, and quality of life and support the cancer patients throughout their illness and chemotherapy. An online education program is an important tool to help cancer patients and their families better manage their illness, reduce symptom distress and depression, and improve self-efficacy. Also, this program may be the type of patient-centered support system highly needed to educate, equip, and empower patients to better manage their illness, improve the quality of life, and reduce needs and depression for costly specialist care. This project can serve as a guide for developing symptom management knowledge in cancer patients, and self-care strength to the disease.
Limitations
The participants of this study were literate patients with cancer, who were treated with systemic chemotherapy, and had Internet access; the results cannot be generalized to all patients with cancer treated with chemotherapy. One of the limitations of this study was the small sample size. Additionally, a web-based education program applied for 12 weeks was not enough to describe the long-term efficacy of the study. Future studies include patients with cancer undergoing different treatment modalities, and the longer-term impact of web-based education be assessed.
Conclusion
Along with the developing technology, web-based education programs are easily accessible and they involve low-cost tools that can support patients with cancer in symptom management, improving the quality of life, and coping with the disease. Considering the factors, such as the pandemic in the world today, the increase in the number of patients diagnosed with cancer and receiving treatment every day, and hospital-associated infections, it is necessary to increase the number of web-based applications for efficacy web-based symptom management, strengthening self-care and improving the quality of life of patients with cancer. The patient should be supported through remote symptom management as a health system in their cancer journey. It is recommended to increase studies on the evaluation of the efficacy of web-based education programs and to create health policies in which these practices can be implemented in health institutions.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file1 (DOC 221 KB)
Supplementary file2 (DOCX 22 KB)
Acknowledgements
The authors thank all patients who participated in the study and the Scientific and Technological Research Council of Turkey (TUBITAK) (Project Number: 113S924) for supporting the study.
Author contribution
All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by HB, HSC, FA, KO, AT, YK, MGS, EY, and SK. The first draft of the manuscript was written by HB and all authors commented on previous versions of the manuscript. All authors have read and approved the final manuscript.
Funding
This study was funded by the Scientific and Technological Research Council of Turkey (TUBITAK) (Project Number: 113S924).
Data availability
Anonymized data is securely stored with the lead author.
Declarations
Ethical approval
Ethical approval has been obtained.
Consent to participate
Informed consent was obtained from all individual participants included in the study.
Consent for publication
All authors have reviewed the manuscript and agree to its publication.
Competing interests
The authors declare no competing interests.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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SN Soc Sci
SN Soc Sci
Sn Social Sciences
2662-9283
Springer International Publishing Cham
35434644
338
10.1007/s43545-022-00338-3
Original Paper
Emergency e-learning acceptance in second-cycle institutions in Ghana: a conditional mediation analysis
http://orcid.org/0000-0002-7820-6709
Amankwa Eric amankwa@presbyuniversity.edu.gh
1
Asiedu Eric Kofi 12
1 grid.460825.d 0000 0004 0398 6338 Department of ICT, Presbyterian University College Ghana, Abetifi, ER Ghana
2 Directorate of IT Systems and Operations (DITSO), University of Environment and Sustainable Development (UESD), Somanya, Ghana
12 4 2022
2022
2 4 4218 10 2021
28 3 2022
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 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.
This paper investigates the determinants that will influence students’ acceptance of the electronic learning (e-learning) system of education after the COVID-19 emergency. Specifically, the paper assesses the attitudes and intentions of students in second-cycle institutions to accept e-learning after the pandemic, using constructs derived from the health belief model and technology acceptance model. Also, we test if there is any significant difference in the attitudes and intentions of students in public and private institutions. Using data collected from 370 students in upper and lower levels of a second-cycle institutions in Ghana, we found that student attitude is significantly influenced by perceived usefulness and moderately affected by perceived severity, whereas, student’s intention is moderately affected by the perceived severity but substantially influenced by the student’s attitude towards usage. Also, the results revealed that students’ attitudes and intentions to use e-learning are moderately affected by the severity of the ongoing COVID-19 pandemic. Finally, there were no significant differences in the attitudes and intentions of the sampled students in public and private second-cycle institutions in Ghana, regarding their acceptance and usage of e-learning after the COVID-19 emergency. Given the study’s findings, the paper concludes that students’ attitudes and intention to use e-learning are the main determinants that will influence the students’ acceptance of the e-learning system of education in second-cycle institutions in Ghana after the COVID-19 emergency. The paper contributes to knowledge by providing evidence of students’ acceptance of the e-learning system of education after the COVID-19 emergency in the context of a developing country like Ghana.
Keywords
e-learning
COVID-19
Perceived severity
Attitude and intentions to use
Perceived usefulness
Second-cycle institutions in Ghana
Perceived Ease of Use
issue-copyright-statement© Springer Nature Switzerland AG 2022
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pmcIntroduction
Electronic Learning (e-learning) is becoming more popular as a type of education due to its accessibility, practicability, and affordability (Shevchenko et al. 2021). E-learning is the use of network technology to design, deliver, select, administer, and extend learning across geographical locations to improve learning (Kulikowski et al. 2021; Sułkowski 2020). E-learning systems can be used to deliver education either in asynchronous, synchronous, or hybrid mode to learners (Yawson and Yamoah 2020). E-learning systems offer several benefits including the ability to allow learners to access content from every location. Learners do not need to be physically present in class to receive instructions, thereby saving time and money (Chen et al. 2020; Li et al. 2020). Other benefits include scalability, consistency, and personalization (Al-Harbi 2011). These benefits made e-learning systems the preferred choice during the emergency created by the COVID-19 pandemic (Roman and Plopeanu 2021).
In the first quarter of 2020, the COVID-19 (novel coronavirus disease 2019) pandemic swept through the world and affected global socio-economic activities. The pandemic led to the closure of schools at all levels across the continent and in Ghana between March 2020 and February 2021 (GES 2020). The academic schedules of schools were disrupted and had to be adjusted (Demuyakor 2020). The extent of disruptions in the educational sector was also unprecedented (Azoulay 2020; Demuyakor 2020). Educational institutions at the tertiary levels had to migrate the teaching and learning processes to e-learning systems for continuity (Demuyakor 2020; Hoq 2020; WHO 2020). The disrupted academic calendar was quickly revised for academic activities to resume in those institutions with e-learning systems in place. Academic activities including lectures, assignments, quizzes, assessments, seminars, and workshops were all organized on e-learning platforms (Almaiah et al. 2020). Some tertiary institutions integrated video conferencing and recording capabilities into their e-learning systems to deliver live and interactive lectures (Dhawan 2020; Purwanto and Tannady 2020). By the end of October 2020, the major public universities and some private Universities in Ghana that adopted the use of e-learning systems had completed the academic year and organized virtual graduation ceremonies. These institutions had e-learning systems in place before the outbreak of the pandemic. However, the severity of the COVID-19 pandemic called for a complete switch of the teaching–learning process to e-learning systems overnight (Liguori and Winkler 2020).
This emergency adoption of e-learning systems means that institutions that did not have the needed e-learning infrastructure in place had to improvise and this was largely the case across institutions in developing countries including Ghana. A 2015 study conducted by Wong and Huang (2015) on e-learning systems found limited usage in developing countries in Africa. In Ghana, the majority of second-cycle institutions in Ghana did not have e-learning systems in place before the pandemic. The second-cycle institutions in Ghana mainly relied on a face-to-face approach to teaching and learning and did not consider the implementation of e-learning. Consequently, when COVID-19 struck, all second-cycle institutions in the country had to close down for several months to restrategize on ways to resume the teaching–learning process. The emergency created by the COVID-19 pandemic, therefore, forced all institutions including second-cycle institutions that wanted to avoid the disruption of the teaching–learning process irreversibly, to migrate to e-learning systems for continuity.
The decision to accept and use e-learning systems is generally motivated by the institutions’ readiness to move academic activities to e-learning systems, the students' and teachers' attitudes, and intentions to accept and use e-learning (Nikou and Economides 2017; Wongwatkit et al. 2020). However, the severity of the pandemic forced educational institutions at all levels to migrate to e-learning platforms whether ready or not ready. Students and teachers had to endure the challenges that come with emergency technology adoption. Educational institutions at all levels either have to look for ways to circumvent these challenges or risk missing several weeks and months as the COVID-19 pandemic rages on. What is not clear in this emergency acceptance and use of e-learning systems is whether the institutions, students, and teachers would continue the usage after the emergency created by the pandemic.
This paper, therefore, investigates the determinants of students’ acceptance and use of e-learning systems after the COVID-19 emergency. Specifically, the paper assesses students’ attitudes and intention to accept the e-learning system of education, using constructs derived from the health belief model (HBM) and Technology Acceptance Model (TAM). Also, the moderating effects of the perceived severity of the COVID-19 pandemic on the relationship between these constructs and students’ intentions to accept e-learning systems are examined. Lastly, it determines if there will be any significant differences in the attitudes and usage intentions of student groups in public and private schools. The paper provides valuable information for policy design and planning to stakeholders in the educational sector as they continue to explore safe and effective ways to continue education in second-cycle institutions after the COVID-19 emergency. It is also one of the first to present the perspectives of students regarding the use and adoption of e-learning systems in second-cycle institutions.
The rest of the paper is structured as follows: the next section discusses research questions, the theoretical framework and hypotheses development. This is followed by the methods applied in the study. Next, the results will be presented and discussed in relation to the objectives of the study. Given the results of the study, implications for practice and future research possibilities are put forward.
Research questions
The key research question to be addressed by this study is: what are the determinants that will influence students’ acceptance of the e-learning system of education after the COVID-19 emergency? To address the main research question of the study, the following sub research questions are considered:What are the effects of the study’s construct (perceived severity, perceived usefulness, Perceived Ease of Use) on students’ attitudes and intention to accept e-learning?
What are the moderating effects of the perceived severity of the COVID-19 pandemic on the relationship between the study’s constructs and students’ intentions to accept e-learning systems?
IS there any significant difference in the attitudes and usage intentions of student groups in public and private schools?
Ghana’s response to the COVID-19 educational disruptions
When the first case of COVID-19 was reported in Ghana in March 2020, educational institutions at all levels had to move their operations to e-learning systems due to the imposed restrictions and closure of schools. Electronic learning systems adoption and use before the pandemic were only available in tertiary institutions and some private basic schools in Ghana. There was, however, limited or nothing to show in the second-cycle institutions. A study by Adarkwah (2021a, b) reported the lack of funds, infrastructure, effective e-learning systems, and ICT gadgets as the factors that impede the adoption of online learning in most developing countries. Ghana, like many other developing countries, faced some challenges including the lack of funds; hence the limited adoption of e-learning in second-cycle institutions.
The adoption rate in tertiary institutions is relatively high as compared to the pre-tertiary level (Sarpong et al. 2021). The institutions at the tertiary level can generate enough funds internally and are therefore able to fund capital-intensive projects like acquiring and setting up information and communication technology (ICT) systems needed for e-learning implementation. In addition, some tertiary institutions can obtain funding through research grants and donations from organizations. As a result, some institutions at the tertiary level were able to implement e-learning systems to reinforce the traditional face-to-face classroom learning before COVID-19. Teaching materials, assignments, and quizzes were delivered to students through e-learning systems. Students also submitted assignments and engaged in class discussions using the ‘discussion forum’ feature provided by the system. Some tertiary institutions integrated video conferencing features such as the Zoom and the BigBlue button that enabled the delivery of live lectures to students. Therefore when COVID-19 struck and onsite instruction was suspended in March 2020, some tertiary institutions with e-learning systems in already place were able to continue and complete the academic calendar. These institutions simply moved to their already existing e-learning systems to complete the academic year. However, the majority of the tertiary institutions had no e-learning systems in place (Adarkwah 2021a).
At the pre-tertiary level, some private basic schools that made adequate investments into the implementation of e-learning systems to support the traditional classroom teaching were also able to complete the academic calendar. Before COVID-19, these basic schools implemented e-learning systems to provide extra tuition for their students in the evening, on weekends, and during vacation. Therefore, with the closure of schools due to the pandemic, these private schools continued the rest of the academic year on the existing e-learning systems.
However, the COVID-19 engineered e-learning implementation in tertiary institutions in Ghana (particularly in the public universities and few basic schools) was not without challenges. The National Union of Ghana Students (NUGS) described it as “challenge-ridden online learning” and as a result, appealed to the government to stop the implementation (Anyorigya 2020). The implementation was challenged with “inadequate bundle incentives for lecturers and students, lack of properly laid framework for the implementation of online learning, and the plight of needy students who have been left out of the online learning platforms because of their inability to settle school bills” (Adarkwah 2021b, p. 1668). Also, within the public technical universities sector, Sarpong et al. (2021) found the lack of access to devices, unreliable internet connectivity, and inability to afford the cost of internet data as challenges that hindered the implementation process. Similarly, data from the Afrobarometer Round 8 (2019) survey in Ghana suggest that many students, especially, those living in rural or poor households.
will find it difficult or impossible to participate in the e-learning initiatives due to the lack of access to the required devices, poor internet connectivity, or lack of access to electricity (Dome and Armah-attoh 2020).
The situation was, however, different at the second-cycle level where there was limited implementation of e-learning systems before the outbreak of COVID-19. Although the goal of the ICT for Accelerated Development (ICT4AD) in 2003 was to transform Ghana into information and technology-driven high-income economy through “education any- time anywhere for everyone” (Ministry of Education 2015), this goal is yet to be realized. Schools in Ghana, especially at the second-cycle levels are faced with challenges that hinder the realization of the goal of the ICT4AD policy. These challenges include funding, lack of access to ICT resources and electricity (Adarkwah 2021b; Dome and Armah-attoh 2020). This level of Ghana’s educational system is dominated by schools and institutions established and funded solely by the government. These schools receive very little funding and cannot afford the high cost of acquiring the needed ICT equipment for e-learning systems implementation. Some schools at this level do not have well-resourced computer laboratories for ICT lessons. Some rely on the benevolence of individuals and parent–teacher associations (PTAs) for teaching and learning materials. Accordingly, the impact of COVID-19 was significantly severe in secondary level education in Ghana. Almost all second-cycle institutions in Ghana had to defer the academic calendar because there was no means to continue. Academic activities were halted and the academic calendar was delayed for several months. As a result, the Ghanaian government, through the Ministry of Education (MoE) and the Ghana Education Service (GES), established virtual learning platforms. The implementation featured television (Ghana Learning TV) and online (icampus) programs, as well as a radio reading program, to allow students to continue studying core subjects including mathematics, English, science, and social sciences, as well as selected electives (Dome and Armah-attoh 2020). These platforms did not allow live teacher-student interactions and provide limited mechanisms for receiving feedback. This type of e-learning was therefore not effective to address the educational needs of learners during the pandemic. The introduction of a more robust learning management system for educational continuity, at the second-cycle level, was thus needed, especially for final year (form 3) students who were preparing for the West Africa Senior School Certificate Examination (WASSCE).
The situation of the final year students at the second-cycle institutions during the COVID-19 lockdown, closure of schools, and the subsequent implementation of online learning were however different from other students across second-cycle institutions. The final year students in second-cycle institutions were the most affected group of students. While students in other levels could afford to relax until the government eases the COVID-19 restrictions, the final year students could not do the same due to the impending external examinations (i.e. WASSCE), which require the completion of the course syllabus and adequate preparation by the students. These students had limited time and were in dire need of effective e-learning systems to resume academic work. Consequently, the students did not have a choice other than to accept the emergency COVID-19 engineered e-learning systems with all its problems. What is, however, not clear is whether the students will continue to accept the e-learning system of education post-COVID-19. Considering the challenges faced by the second-cycle institutions during the period of the emergency e-learning adoption, there is the need to examine the factors that will influence students’ acceptance of the e-learning system of education after the COVID-19 pandemic for policy recommendations.
Theory and hypotheses development
In the formulation of a theoretical framework for this study, the HBM and TAM were considered. The choice for TAM was due to its general acceptance and wide application in information systems research for studying users’ acceptance behaviours (Lee et al. 2011; Gefen and Larsen 2017). The HBM was also selected due to the severity of the ongoing health crisis (i.e. COVID-19 pandemic). The HBM posits that people will take action to prevent illness if they regard themselves as susceptible to a condition (perceived susceptibility), if they believe it would have potentially serious consequences (perceived severity), if they believe that a particular course of action is available to them would reduce the susceptibility or severity or lead to other positive outcomes (perceived benefits), and if they perceive few negative attributes related to the health action (perceived barriers).
According to Chuttur (2009), the constantly growing demand for technology advancement in the 70s and the unprecedented failure of technology adoption in many countries was the center of attraction for many researchers but the majority of the research carried out failed. This led Davis to propose the TAM (David 1985) which was grounded on Fishbein and Ajzen's (1975) Theory of Reasoned Action. TAM was developed to predict, explain and understand users' motivation toward technology or system adoption (Chuttur 2009). Since then TAM has been demonstrated to be a theoretical model for assessing users' attitudes and behavioural intentions toward technology or system adoption (Brandon-Jones and Kauppi 2018). TAM postulates that the most important indicator of actual system use is the user’s behavioural intention (Davis 1989). This is also influenced by Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). PU describes the extent to which a user believes that the usage of a system will directly improve productivity (Davis 1989), while PEOU describes the extent to which a user considers the usage of a system as effortless (Davis 1989). Further, TAM posits that PU is directly impacted by PEOU for the reason that when users find a system easier to use, they will as well find it useful. Finally, TAM is preferred over other research models because of its scientific parsimony and rigour (Venkatesh and Davis 2000; Lee et al. 2011). Researchers have simplified TAM by removing the attitude construct found in TRA from the current specification (Venkatesh et al. 2003). Attempts to extend TAM have generally taken one of three approaches: (1) by introducing factors from related models, (2) by introducing additional or alternative belief factors, and (3) by examining antecedents and moderators of Perceived Usefulness and Perceived Ease of Use (Wixom and Todd 2005). In their paper, Gefen and Larsen (2017) demonstrated that TAM's construct relationships primarily emerge from semantic relationships between its questionnaire items. The constructs of the study are discussed next.
Perceived Usefulness
Perceived Usefulness (PU) is one of the two main constructs of the original TAM. Together with Perceived Ease of Use, PU determines the behavioural intentions of users. PU describes the belief that the use of technology will directly enhance productivity (Liu et al. 2009; Abdullah et al. 2016). Davis (1989) explained in the original TAM that PU significantly affects users’ attitudes towards the use of technology. This is corroborated in several studies that applied the TAM or its extensions. Consistent with the postulations of TAM, Al-Harbi (2011), Rizun and Strzelecki (2020), and Wu and Chen (2017) found that PU is a strong predictor of Attitude Towards Use. On the back of these findings, we hypothesize that:
H1
PU will positively affect students’ attitudes towards the acceptance of the e-learning system of education after the COVID-19 pandemic.
Perceived Ease of Use
Perceived Ease of Use (PEOU) is one of the constructs of TAM, which postulates that the best determinant of users’ behaviours is their intentions, which are also affected by PEOU and PU. PEOU refers to the consideration that the usage of a system will be effortless (Davis 1989). According to the TAM, PEOU directly affects PU for the reason that when users see a system to be effortless, they are likely to see it as beneficial. Thus, when users find technology to be useful and easy to use, they are likely to develop a positive attitude towards usage.
Several research studies have found that PEOU has a positive effect on attitude towards the use of technology. In a study to discuss the factors that influenced e-learning adoption in higher education institutions in Saudi Arabia, Al-Harbi (2011) found that PEOU explains a significant percentage of the variance in students’ attitudes towards e-learning acceptance. His study found that PEOU has a stronger effect on attitude towards technology adoption. Additionally, Gefen and Larsen (2017) and Rizun and Strzelecki (2020) found that PEOU is a strong factor that affects attitude towards technology. Contrary to these findings, Wu and Chen (2017) found a rather weak effect of PEOU on attitude and explained that e-learning platforms are relatively easier to use. As a result, PEOU becomes an important factor for consideration among a list of factors that influence attitude. We, therefore, hypothesize that:
H2
PEOU will significantly influence students’ attitudes towards the acceptance of the e-learning system of education after the COVID-19 pandemic.
H3
PEOU will be positively affected by perceived usefulness.
Health belief model
The HBM is a social psychological health behaviour change model that was designed to explain and predict health-related behaviours, particularly health care utilization (Janz and Becker 1984; Rosenstock 1974). The HBM was established in the 1950s by social psychologists at the United States Public Health Service and is now one of the most well-known and commonly utilized theories in health behaviour research (Janz and Becker 1984). According to the HBM people will take action to prevent illness if they believe they are susceptible to it (perceived susceptibility), if they believe it will have potentially serious consequences (perceived severity), and if they believe a particular course of action available to them will reduce susceptibility or severity or lead to other positive outcomes (perceived benefits) (Boon Yuen et al. 2009; Carpenter 2010; Janz and Becker 1984; Jones et al. 2015; Rosenstock 1974; Sreelakshmi and Prathap 2020a, b). The HBM originally has five main constructs, namely: perceived susceptibility, perceived severity, health motivation, perceived benefits, and perceived barriers. The combination of perceived severity and perceived susceptibility is referred to as a perceived threat. Therefore, this study applies perceived severity to assess its additive impact on students’ attitudes and intentions to accept e-learning after the pandemic.
Perceived severity of the COVID-19 pandemic
Perceived severity is described as “how threatening the condition is to the person” (Champion 1984; Sreelakshmi and Prathap 2020a, b). Researchers have evaluated perceived severity in a variety of research context; Abdullah et al. (2020) confirmed that perceived severity significantly influence human choices. Also, Melznera et al. (2014) combined the constructs of the unified theory of acceptance and use of technology (UTAUT) and theory of planned behaviour (TPB) with perceived severity (HBM) in a framework to examine the users' acceptance of mobile health applications. Similarly, Wei et al. (2020) combined perceived severity with the constructs of UTAUT to examine the factors affecting acceptance of fitness mobile applications and confirmed the indirect impact of perceived severity constructs on usage intention. Further, Zhao (2017) confirmed a significant effect of perceived severity on users’ intention to adopt the technology. Zhang et al. (2019) extended the UTAUT model to study the factors affecting the usage of diabetes management applications and found a significant effect of the HBM construct of perceived severity on the adoption of technology.
The outbreak of COVID-19 as confirmed in Wuhan, China at the beginning of 2020 has spread to almost every corner of the world and has created vast havoc around the globe (WHO 2020). World Health Organization declared it a pandemic on March 11, 2020. The outcome of the pandemic influences behaviour of an individual (Kok et al. 2010). Some studies were conducted in different places to analyse the influence of age and gender over the behavioural response and showed that older people and women take more preventive measures to fight against the pandemic than others (Quah and Hin-Peng 2004). While, in contrast to the previous study, the other study conducted in the Netherlands showed no association between age and taking preventive measures (Brug et al. 2004). Basilaia and Kvavadze (2020) stated that the transition from traditional to online setup during the COVID-19 pandemic was successful though, to ensure the quality of learning further research is required. Saxena et al., (2021) found that the perceived benefits of maintaining social distance during the COVID-19 pandemic partially moderate the relationship between e-learning quality and student satisfaction. In all cases reported in the previous studies, e-learning was planned and accepted by the learners before enrolment. However, because the present pandemic has compelled students to adopt e-learning, the factors that will influence students' acceptance of e-learning after the pandemic will differ (Hodges et al. 2020). The gap between planned e-learning and forced e-learning has not made a good impression on the large general public about online education in general (Gacs et al. 2020). The perceived severity of this pandemic may influence the students’ intention and attitude while using the e-learning system. Technology acceptance will also be influenced by the perception of the severity of this infection. PEOU and PU were both affected by external variables in past research (Abdullah et al. 2016). The study will examine the moderating influence of the external variable ‘perceived severity of the COVID-19 pandemic’ on the constructs of TAM, student attitude, and intentions to use e-learning. Basing on this, we hypothesize that:
H4
Perceived severity of the COVID-19 pandemic will have a significant effect on students’ attitudes towards the acceptance of e-learning systems in the future.
H5
Perceived severity of the COVID-19 pandemic will positively affect students’ intentions to accept e-learning systems in the future.
Attitude and intentions to use e-learning
Attitude towards action is characterized as the certainty or otherwise of individual acting. It is addressed by evaluating the individual’s feelings regarding the results developing from the action and assessing the appropriateness of the results. Overall attitude can be generally evaluated as the total of the actual results compounded by the appropriateness appraisals for every single predictable result of the action.
In some studies, user attitude demonstrates a positive effect on intentions to perform an act. In these studies (Amankwa et al. 2018; Ifinedo 2014; Rizun and Strzelecki 2020; Safa et al. 2016), attitude was found to have a significant influence on behavioural intentions. Purwanto and Tannady (2020) also confirmed that attitude influences behavioural intentions to e-learning. This finding was corroborated by Rizun and Strzelecki (2020). However, in a few studies, attitude showed no significant effect on intentions. (Masrom 2007) study, for instance, found that attitude did not affect college students’ intention to use e-learning. Nonetheless, following the postulations of the TPB, the following hypothesis is put forward:
H6
Attitude towards e-learning will positively affect students’ intentions to accept e-learning systems after the COVID-19 pandemic.
Moderating effect of perceived severity of COVID-19 pandemic
The COVID-19 pandemic which began in China has extended to nearly every country around the globe and has since generated immense mayhem for governance and the daily lives of the ordinary citizen (WHO 2020). EdTech (2020) investigated the severity of the COVID-19 pandemic on education in Africa. Their survey, which involved 1650 respondents from 52 African countries, reported that 97% of schools in African countries are closed due to the severity of the pandemic and the rate at which it is spreading globally (EdTech 2020). Furthermore, 1393 (92%) of the respondents agreed that it is necessary to close down the schools due to the severity of the pandemic (EdTech 2020). In line with this, students are forced to rely on e-learning platforms for the continuation of their studies (Baber 2020). The consideration of the perceived severity of COVID-19 (PSC) in the study is to investigate if it causes significant differences in the relationship between the constructs of the study. We, therefore, hypothesize that:
H2a
Perceived severity of COVID-19 will have a moderating influence on the relationship between perceived ease of use and students’ attitude towards e-learning systems acceptance.
H6a
Perceived severity of COVID-19 will have a moderating influence on the relationship between students’ attitudes and intentions to accept e-learning systems.
Multi-group analysis: private and public schools
Public and private schools differ in terms of infrastructure, specifically ICT and other resources. Private second-cycle institutions are usually equipped with robust ICT infrastructure for e-learning implementation. As a result, e-learning systems are used to augment face-to-face classroom delivery. This establishes positive attitudes in students from private schools in relation to e-learning usage. However, this is not the case in public schools where there is a lack of the requisite ICT resources for e-learning adoption. The majority of the students in public schools will never have a shot at e-learning systems during the period of their studies. We, therefore, hypothesize that:
H7a
The effects of the factors on attitude towards e-learning acceptance after the COVID-19 emergency will differ across student groups.
H7b
The positive effect of attitude on intentions to accept e-learning after COVID-19 will differ across the two groups.
Basing the discussions and hypotheses above, the research model in Fig. 1 is developed for the study.
Materials and methods
Population and sample
The scope of this study is within the Ghanaian context focusing on second-cycle private and public institutions in Ghana. The Ghana Education Service (GES) categorizes second-cycle institutions in Ghana into senior high schools (SHS) and technical and vocational education training institutes (TVET) (GES 2020). The study’s population involved students of three-second-cycle institutions from the technical and vocational institutes, the public and private senior high schools in Ghana. These students are either in the lower or upper levels of the selected second-cycle institutions. The reason for the selection is that e-learning is sparingly used by students in these institutions in Ghana. Moreover, the selected schools have the full complement of programmes, teachers, and students for a typical second-cycle institution. Academic programmes available in the three selected institutions include science, general arts, visual art, business, home economics, Fashion Design Technology, Electrics Engineering, and Mechanical Engineering Technology. The population is estimated at 6250 students. Yamane’s (1967) sample size formula (n = N/(1 + N e2)) was applied to select a sample of 375 students who were randomly selected from the lower and upper levels of three-second-cycle institutions in Ghana.
Data collection
A quantitative approach involving the use of a questionnaire was applied to collect data from 375 students in the lower and upper levels of the three selected second-cycle institutions in Ghana. The questionnaire was pilot-tested on a cross-section of secondary school students. This was done to ensure that difficult, confusing, ambiguous, and misleading questions are corrected. After this, the final questionnaire was printed.
The researchers after seeking the needed permission from gatekeepers of the selected schools distributed the final questionnaire to the students with the assistance of their teachers. The researchers could not get physical access to the students due to the COVID-19 pandemic and the subsequent policy to prevent visitors to the schools. Teachers were given copies of the printed questionnaires to assist with data collection. The completed questionnaires were returned to the researchers after one month.
In the final analysis, 370 valid responses collected from students in upper and lower second-cycle institutions in Ghana were considered after dropping five (5) incomplete responses from the dataset.
Scale of measurement
The measurements scaled items for the constructs Attitude Towards Use (ATU), Behaviour Intentions to Use (BIU), Perceived Usefulness (PU), and Perceived Ease of Use (PEOU) were adopted from (Coman et al. 2020) whereas items for Perceived severity of COVID-19 (PSC) were adopted from prior literature (Baber 2020, 2021; Sreelakshmi and Prathap 2020a, b). The items for the study were measured on a 5-point Likert scale ranging from Strongly Disagree (1) to Strongly Agree (5). Furthermore, the study utilized multiple measurements for each construct to get rid of the various limitations associated with high measurement dimension inaccuracies or errors of a single item. A single measurement item has a limitation in that it is highly defined to capture all the elements or attributes of a particular construct in a study.
Data analysis approach
The partial least squares-structural equation modelling (PLS-SEM) technique involving the use of the SmartPls software developed by Ringle et al. (2015) was applied for data analysis in this study. This approach was selected due to the smaller sample size of the study (Chin et al. 2003; Hair et al. 2011a, b; Wong 2013). The study conducted an outer model evaluation to test reliability and validity and an inner model evaluation to test the hypotheses.
Results
This study was set out to investigate the determinants of students’ acceptance and use of e-learning systems during the COVID-19 pandemic. Students of three-second-cycle institutions in Ghana were surveyed using an online questionnaire and the results of the PLS-SEM data analysis are discussed in the sections that follow.
Respondents’ demographics
A total of 370 valid questionnaire responses were used in the final analysis after dropping five (5) incomplete responses. Table 1 summarizes the respondents’ demographic information in terms of age, gender, class, and school type.Table 1 Respondents demographic characteristics
Variable Characteristics N % Mean ± SD
Gender Male 190 51.4
Female 180 48.6
Age 11–14 years 108 29.2
15–18 years 247 66.8 1.75 ± .52
Above 18 years 15 4.1
Form Form 1 105 28.4
Form 2 150 40.5
Form 3 115 31.1
School type Public 195 52.7
Private 175 47.3
From Table 1, 175 of the respondents were selected from private second-cycle institutions and the remaining 195 were from the public second-cycle institutions. This shows a balanced representation of students from public and private institutions for the analysis in the study.
Measurement model test: validity and reliability
The measurement model also called the outer model represents the relationships between the observed data and latent variables (unobservable variables). The outer measurement model is essential for assessing the reliability and validity levels of a study’s constructs. In this study, the measurement model was tested using confirmatory factor analysis. The study considered factor loading, composite reliability (CR), average variance extracted (AVE), and Fornell–Larcker Criterion, for assessing the validity and reliability of the study’s constructs (Hair et al. 2020).
To ensure the reliability of the instrument, indicator loadings and CR values for each construct should be 0.7 or higher (Hair et al. 2011a, b, 2020; Sarstedt et al. 2016; Wong 2013). Items that had values less than the 0.7 threshold were dropped in the final analysis. The loadings, in the final analysis, ranged from 0.937 to 0.992 and 0.968 to 0.992 for indicator loadings and CR values, respectively. This indicates the achievement of internal consistency reliability in this study. Tables 2 and 3, respectively, show the indicator loadings and CR values.Table 2 Indicator loadings and extracts of survey questions
Authors Indicator Items descriptive statistics
Items Mean ± SD Skew (Kurt) Factor loadings (γ)/[95% CI] Outcome
Perceived ease of use
PEOU_1 I believe e-learning systems are easy to use 3.75 ± 1.19 − 0.61 (− 0.71) 0.992 Significant
PEOU_2 I believe interacting with the e-learning systems will require less mental efforts 1.78 ± 1.09 − 0.94(.25) 0.341 Dropped
PEOU_3 I believe interacting with e-learning systems will NOT be frustrating 3.78 ± 1.09 − 0.04(− 0.85) 0.981 Significant
PEOU_4 I believe I will need less assistance from friends and teachers whenever I want to use e-learning systems 3.75 ± 1.17 − 0.67(− 0.66) 0.990 Significant
PEOU_5 Overall, I believe e-learning systems will be easy to use 4.09 ± 6.76 2.84 (− 0.17) 0.072 Dropped
Perceived usefulness
PU_1 I believe e-learning systems will save me money from the time and cost of travelling to school 4.01 ± .76 2.88 (− 1.17) 0.972 Significant
PU_2 I believe e-learning systems will be of significant benefit to me 5.78 ± 1.09 2.04 (− 0.85) 0.411 Dropped
PU_3 I believe e-learning systems will make it easier for me to access education from the comfort of my home .28 ± 1.09 1.84 (− 0.85) 0.363 Dropped
PU_4 I am satisfied with the services available on e-learning systems 3.83 ± .76 1.89 (− 1.03) 0.937 Significant
PU_5 Overall, I find e-learning systems to be useful 4.11 ± .76 3.94 (− 1.37) 0.951 Significant
Perceived severity of Covid-19
PSC_1 If anyone gets infected with the COVID-19, the results will be severe. 3.81 ± .88 0.56 (− 0.69) 0.958 Significant
PSC_2 If anyone gets infected with the COVID-19, the results will be risky 3.92 ± .75 2.28 (− 1.06) 0.960 Significant
PSC_3 If anyone gets infected with the COVID-19, he/she won’t be able to manage daily activities 3.97 ± .82 1.02 (− 0.78) 0.974 Significant
PSC_4 I think the COVID-19 pandemic is so severe therefore, I intend to use e-learning systems 3.94 ± .87 2.11 (− 1.14) 0.982 Significant
Attitude towards E-learning usage
ATU_1 During COVID-19, I feel e-learning will be useful 3.81 ± .88 0.56 (− 0.69) 0.987 Significant
ATU_2 I am likely to use e-learning during the COVID-19 pandemic due to fear of contracting the virus in school 5.81 ± .82 2.66 (− 0.99) 0.387 Dropped
ATU_3 In my view, it would be desirable to use e-learning during COVID-19 rather than traditional face-to-face education 8.90 ± .89 2.47 (− 0.03) 0.517 Dropped
ATU_4 During COVID-19, I feel that e-learning will be the most efficient means to receive education 4.13 ± .71 4.26 (− 2.15) 0.474 Dropped
ATU_5 I am willing to use e-learning during the COVID-19 pandemic 4.07 ± .77 2.48 (− 1.21) 0.987 Significant
Behavioral intention to use
BIU_1 I intend using e-learning for receiving course instructions during COVID-19 3.99 ± .81 1.37 (− 1.03) 0.977 Significant
BIU_2 I intend to use the e-learning for submitting all my assignments during COVID-19 4.13 ± .71 3.26 (− 1.15) 0.974 Significant
Table 3 Constructs reliability and validity
Constructs CR/a AVE ATU BIU PU PEOU PSC
Attitude Towards Use (ATU) 0.987/.973 0.974 0.987
Behaviour Intentions to Use (BIU) 0.975/.949 0.951 0.95 0.975
Perceived Usefulness (PU) 0.968/.950 0.909 0.97 0.966 0.953
Perceived Ease of Use (PEOU) 0.992/.987 0.976 0.874 0.881 0.903 0.988
Perceived Severity of Covid-19 (PSC) 0.984/.978 0.938 0.96 0.947 0.947 0.941 0.968
For validity, the values of the AVE for each construct should be 0.5 (50%) or higher (Hair et al. 2020).
From Table 3, the AVE values for the current study range from 0.909 to 0.974, confirming the achievement of convergent validity. However, discriminant validity is demonstrated when the shared variance within a construct (AVE) exceeds the shared variance between the constructs. The table also shows the assessment of discriminant validity using the Fornell–Larcker criterion. From Table 3, discriminant validity is also confirmed since the AVE values are higher than the shared variance between the constructs. The overall results of the measurement model, therefore, show that the instruments were valid and reliable for this study.
Structural model test: hypotheses testing and multi-group analysis
This study utilized the values of path coefficients (β) and squared R (R2) to present information about the path significance of hypothesized relationships. The strength of the relationship is specified by the values of the path coefficients. Figure 2 presents the summarized results of the PLS-SEM data analysis using SmartPls version 3.2 software.
From the results of the SEM data analysis presented in Fig. 2, the coefficient of determination, R2, value for the Attitude Towards Use endogenous latent variable is 0.948. This means that the three latent variables (Perceived Ease of Use, Perceived Usefulness, and perceived severity of COVID-19) substantially explain 94.8% of the variance in Attitude Towards Use. Additionally, Attitude Towards Use and perceived severity of COVID-19 together explain 91.8% of the variance in behavioural intentions to use e-learning systems during the COVID-19 pandemic. Also, Perceived Usefulness explains 81.5% of the variance in Perceived Ease of Use. This means that when students find e-learning systems useful, they will as well find them easy to use (Larsen and Eargle 2015). The inner model suggests that Perceived Usefulness has the strongest effect on Attitude Towards Use (0.629), followed by perceived severity of COVID-19 (0.511) and Perceived Ease of Use (− 0.175). Also, Attitude Towards Use has the strongest effect on behavioural intentions to use (0.521) as compared to the effect of perceived severity of COVID-19 (0.447).
The effects of perceived severity, perceived usefulness, and Perceived Ease of Use on students’ attitudes and intention to accept e-learning
To measure the effects of the perceived severity, Perceived Usefulness, and Perceived Ease of Use on students’ attitudes and intentions to accept e-learning, a bootstrapping resampling procedure (with 500 samples) was carried out to estimate the significance of paths in the structural model. Table 4 shows the results of hypothesis testing.Table 4 Summary of hypothesis testing results without moderating effect
Paths Original Sample (O) Mean (M) Standard Deviation (SD) t p values Decision
H1: Perceived Usefulness (PU) → Attitude Towards Use (ATU) 0.629 0.634 0.093 6.782 0.00 Accept
H2: PERCEIVED Ease of Use (PEOU) → Attitude Towards Use (ATU) -0.175 -0.176 0.045 3.862 0.00 Accept
H3: Perceived Usefulness (PU) → Perceived Ease of Use (PEOU) 0.903 0.903 0.008 111.275 0.00 Accept
H4: Covid-19 Severity (PSC) → Attitude Towards Use (ATU) 0.511 0.508 0.099 5.151 0.00 Accept
H5: Covid-19 Severity (PSC) → Behaviour Intentions to Use (BIU) 0.447 0.451 0.082 5.441 0.00 Accept
H6: Attitude Towards Use (ATU) → Behaviour Intentions to Use (BIU) 0.521 0.516 0.084 6.2 0.00 Accept
Table 4 shows the results of the two-tailed test with a significance level of 5%. This was computed to ascertain if the path coefficients of the inner model are significant or not. The path coefficients are significant if the T-Statistics is larger than 1.96 for a 5% significance level (Hair et al. 2020; Wong 2013). From the results presented in Table 4, the hypothesized path relationships were all significant at the 5% significance level and all path relationships had t-statistic values above the 1.96 thresholds for acceptance.
Results of the moderating effects of perceived severity of COVID-19
In a time of a pandemic such as Covid19, perceived severity becomes an important consideration in the assessment of intentions to perform an act with risk implications (Baber 2020, 2021; Sreelakshmi and Prathap 2020a, b). Accordingly, the path relationships in the study were moderated with the perceived severity of COVID-19 to determine if there will be any significant differences in the relationships. The results of the study with the moderation effect are presented in Table 5.Table 5 Summary results of hypotheses testing with moderating effect
Paths Original Sample (O) Sample mean (M) SD t P Values Decision
H1a: PU_Mod → Attitude Towards Use (ATU) 0.098 0.1 0.046 2.11 0.035 Accept
H2a: PeoU_Mod → Attitude Towards Use (ATU) − 0.111 − 0.114 0.058 1.931 0.053 Reject
H6a: ATU_Mod → Behaviour Intentions to Use (BIU) − 0.01 − 0.01 0.007 1.369 0.171 Reject
Table 5 shows the results of the moderating effect of perceived severity of COVID-19 on the hypothesized paths in the two-tailed test with a significance level of 5%. This was computed to ascertain if the effect of perceived severity of COVID-19 could strengthen the relationship between the hypothesized paths and whether the effects will be significant or not. The path coefficients are significant if the T-Statistics is larger than 1.96 for a 5% significance level (Hair et al. 2020; Wong 2013). From the results presented in Table 5, the hypothesized path relationships were all insignificant at the 5% significance level except the path Perceived Usefulness to Attitude Towards Use, which had a T-statistic value above the 1.96 thresholds for acceptance.
Differences in the attitudes and usage intentions of student groups in public and private schools
To determine if there is any significant difference in the attitude and usage intentions of students groups in public and private schools, a multi-group analysis (MGA) was conducted. The MGA assessed whether the original structural model tests are different across the two groups of schools (private and public schools) involved in the study. Table 6 shows the results of the MGA between private and public schools.Table 6 Multi-group analysis—private vs public schools
Paths Path coefficients—difference p value original 1-tailed (SchoolType_ST(2.0) vs SchoolType_ST(1.0)) p value new (SchoolType_ST(2.0) vs SchoolType_ST(1.0))
Attitude Towards Use (ATU) → Behaviour Intentions to Use (BIU) − 0.934 1 0.00
Perceived Usefulness (PU) → Attitude Towards Use (ATU) − 0.835 1 0.00
Perceived Usefulness (PU) → Perceived Ease of Use (PEUS) 0.13 0 0.00
Perceived Ease of Use (PEUS) → Attitude Towards Use (ATU) − 0.325 1 0.001
Covid-19 severity (PSC) → Behaviour Intentions to Use (BIU) 1.009 0 0.00
Private = 1 and public = 2
The figures in Table 6 show no significant differences between the two groups (i.e. students of private and public schools).
Discussions, implications, comparison, and limitations
Discussions
The purpose of the study was to investigate the determinants that will influence students’ to accept e-learning after the COVID-19 pandemic. In the literature, it is argued that attitude and behavioural intentions are essential determinants of technology acceptance and use (Al-Harbi 2011; Mohajerani et al. 2015; Nikou and Economides 2017; Purwanto and Tannady 2020; Shahzad et al. 2020). Moreover, we hypothesized that attitude is significantly predicted by Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and perceived severity of COVID-19 (PSC) by students. The study further predicted that students’ intention to use e-learning after COVID-19 will be influenced by the students’ attitude and perceived severity of COVID-19 in the e-learning system. Using structural equation modelling techniques, the research model (see Fig. 1) was tested and the results indicated a good fit for the data.Fig. 1 Research model
From the study’s results (see Fig. 2 and Table 4), all three variables that were predicted to significantly influence students’ attitudes were supported, accounting for 94.8% of the total variance in students’ attitudes towards acceptance of e-learning after COVID-19. All variables except PEOU had a direct and significant influence on students’ attitudes towards e-learning usage. This result corroborates that by Larmuseau et al. (2018) and Tiwari (2020) who found that attitude is significantly affected by the extent to which a given technology will make a user productive. Also in line with the postulation so the TAM (Davis 1989), when technology makes users productive, they will as well find it to be useful. Additionally, considering the current physical restriction and closure of schools due to the COVID-19 pandemic, technology such as e-learning systems become very useful since it provides access to education, which became impossible during the outbreak of the pandemic. However, the lack of a direct and significant relationship between PEOU and attitude deviates from the findings of studies (Al-Harbi 2011; Sipior et al. 2011; Tiwari 2020) that found PEOU to be significantly correlated with attitude. During the current period of the COVID-19 pandemic, students’ main desire is how to continue to access educational content and instructions. Therefore, a technology that provides a solution becomes an asset irrespective of how easy or difficult it may be to use. Accordingly, PEOU was found to be of less concern to students during the period of the COVID-19 pandemic. In contrast to e-learning acceptance before the COVID-19 pandemic, existing research evidence showed that students’ acceptance of e-learning was significantly affected by how easy or difficult they perceived its usage (Larmuseau et al. 2018; Yuen and Ma 2008). Besides, TAM (Davis 1989) posits that when students find a technology to be easy to use, they will as well use it and vice versa. But during a pandemic, how difficult or easy a technology is of less importance, as the drive for education continuity increases students’ Perceived Usefulness of the technology, which will ultimately affect attitude and intention to use the technology. As a result, PEOU in this study was found to have no significant and direct effect on students’ attitude towards usage of e-learning during the COVID-19 pandemic.Fig. 2 Results of the PLS-SEM analysis
The result also showed all two variables (i.e. attitude and perceived severity of COVID-19) hypothesized to positively affect students’ intentions to use e-learning were supported, explaining 91.8% of the variance in students’ e-learning usage intentions during the COVID-19 pandemic. Both attitude and perceived severity of COVID-19 had a significant and direct relationship with students’ intentions to use e-learning during the COVID-19 pandemic. This result is consistent with Purwanto and Tannady (2020), Amankwa et al. (2018) and Rizun and Strzelecki (2020). Purwanto and Tannady (2020) found that a positive attitude is crucial to creating interest in the acceptance of platforms. Amankwa et al. (2018) also found that users’ attitude will ultimately affect their behavioural intentions. More so, in a recent study to investigate students’ acceptance of the COVID-19 impact on shifting higher education to distance learning in Poland, Rizun and Strzelecki (2020) revealed that attitude has the strongest effect on intentions to use technology.
From the results of the moderating effect, the effect of perceived severity of COVID-19 was found to positively affect both attitude and intentions to use e-learning. However, in both cases, the perceived severity of COVID-19 produced the second strongest effect. This finding from the study supports Baber (2021), Sreelakshmi and Prathap (2020a, b), who found perceived severity to be superior to other constructs in the intention to accept technology. This finding shows that during a pandemic such as the ongoing COVID-19, the consideration of the perceived severity of COVID-19 becomes secondary to achieving the end goal (i.e. access to educational content and instructions). This also shows that when students find e-learning systems to be useful and able to provide the enabling environment for educational continuity or resumption, they will develop a positive attitude and intention to use the system. In such instances, perceived severity will have minimal impact on attitude and intention to use. This shows that students will accept and use e-learning systems whether or not they perceive COVID-19 to be severe. Contrary to the direct effects, the indirect effects of the perceived severity of COVID-19 were insignificant in two relationships but significant in one. The study hypothesized that the relationship between the Perceived Usefulness of e-learning and attitude towards usage is moderated by the extent to which students perceive the COVID-19 pandemic to be severe. The finding lends support to this hypothesis for acceptance. This means that students find e-learning useful for acceptance when they perceive the effects of COVID-19 to be severe. They will develop positive attitudes over time for its usage. The perceived severity of COVID-19, therefore, strengthens the relationship between usefulness and attitude to use. The study also hypothesized that the relationship between Perceived Ease of Use of e-learning systems and attitude towards usage during COVID-19 is moderated by the perceived severity of COVID-19. The findings did not support this hypothesis. This means that the perceived severity of COVID-19 does not affect the relationship between Perceived Ease of Use and attitude to use. Students may perceive COVID-19 to be severe but will not necessarily find e-learning systems easy to use and will have no influence on their attitude. Further, the study hypothesized that the relationship between attitude towards usage and intentions to use e-learning during COVID-19 is moderated by the perceived severity of COVID-19. This claim was also not supported by the finding. The finding indicates that the perceived severity of COVID-19 has no significant effect on the relationship between attitude towards usage and intentions to use e-learning. The outbreak of COVID-19 led to the closure of schools and the subsequent suspension of academic activities in schools at all levels. As a result, the attention of stakeholders is more focused on how to resume academic activities. This, therefore, explains the lack of support for this hypothesis.
In the MGA, the results showed no significant difference in attitude and intention to use between students in private and public or state-owned schools. Private second-cycle institutions are usually equipped with robust ICT infrastructure for e-learning implementation. As a result, e-learning systems are used to augment face-to-face classroom delivery. This establishes positive attitudes in students from private schools in relation to e-learning usage. However, this is not the case in public schools where there is a lack of the requisite ICT resources for e-learning adoption. The majority of the students in public schools may never have had a shot at e-learning but for the outbreak of the COVID-19 pandemic. This led to the speculation that students in private second-cycle schools will have positive attitudes toward e-learning usage whereas their counterparts in public will show negative attitudes. However, the results of the MGA showed no significant differences in the attitudes and behavioural intentions of the two groups of students.
Implications for practice
Based on the findings of the study, some recommendations to improve students’ attitudes and intentions for continuous usage of e-learning systems even after the COVID-19 pandemic are as follows:E-learning readiness From the study’s findings, it is evident that students in Ghanaian second-cycle institutions, like those in other developed countries, intend to use e-learning systems to restart and continue with education following the disruptions by the COVID-19 pandemic. However, for this intention to materialize into actual usage, second-cycle institutions should be prepared and ready to move major academic activities including teaching and assessment to e-learning systems. The institutions must have in place the requisite ICT infrastructure to support full-scale e-learning systems. It is only when the requisite ICT infrastructure is available that the implementation of e-learning systems can be plausible. Additionally, teachers should also be ready for the e-learning take-off. Teacher readiness here means that teachers should be equipped with the needed resources, knowledge, and skills to successfully deliver courses on the e-learning systems. Also, parents should be able and willing to provide the needed resources to support students’ online learning activities during the COVID-19 stay-home periods. These resources may include computers and other smart devices with internet connections for accessing e-learning systems. Finally, the government should support the provision of fast and affordable broadband internet access to students to facilitate and promote the usage of e-learning systems during the COVID-19 stay-home period.
A positive recommendation Most students of second-cycle institutions are below the age of 18 years and describe as minors (in the case of Ghana). As a result, social influence will significantly affect the behavioural intentions of these students, who are likely to continue or discontinue the usage of e-learning systems due to positive or negative recommendations by a referent. Accordingly, parents and teachers, who are students’ first point of call on issues relating to academic decisions, should always highlight the positive sides of e-learning systems to stimulate continuous usage by students. Teachers should further demonstrate mastery of e-learning systems to engender confidence and positive students’ attitude towards usage.
Availability of relevant content The findings showed that students’ attitudes incline to positivity when they perceive e-learning systems as useful. It was realized that the use of an e-learning system depends on its ability to satisfy the educational needs of students by continuously supporting teaching and learning during the stay-home period of the COVID-19 pandemic. Accordingly, teachers and heads of educational institutions need to ensure that the needed contents are always available and can be accessed by students on e-learning systems.
Constant metacognitive communication Although the findings showed that students will learn and adapt to e-learning systems when they find them useful, it is imperative to note that continuous usage cannot be sustained when a given e-learning system is overly difficult to use. It is therefore important that institutions provide simple guidelines on how to perform the basic tasks on e-learning systems. Such guidelines should be updated as and when the system is updated or when new features are introduced. Also, there should be constant communication with students regarding their progress and challenges, and words of encouragement to assuage students’ fears, anxiety, and uncertainties regarding the effectiveness of the e-learning system usage. Such metacognitive dialogues ensure efficient monitoring of the learning process and engender self-regulation skills (Patricia 2020).
Change of attitude and perception towards online learning and teaching When educational institutions were closed down as a result of COVID-19 some resorted to online teaching. Online teaching has proven to be a reliable mode of teaching. This has been the case for quite a long time in some countries like South Africa where educational institutions run programmes online up to the Ph.D. level. However, in Ghana some frown on online programmes. Holders of online degree certificates are at times not given the due recognition as their counterparts who had their degrees through face-to-face programmes. The advent of COVID-19 has demonstrated that educational institutions including second-cycle institutions cannot continue to operate within the four walls of the institutions. Second-cycle institutions should adopt a hybrid system of teaching and learning where in-person campus academic work would be complemented by online teaching and learning after COVID-19. Online teaching and learning have significant value and should be encouraged and prioritized.
Provision of ICT resources To make the running of online programmes effective, second-cycle institutions should develop and implement proactive and innovative policies that will ensure that all students and staff have personal computers, laptops, and /or tablets or have access to institutional ICT resources. The availability of such resources will make online teaching and learning easy and meaningful and will also ensure that e-learning systems become engrained in the academic structure of second-cycle institutions.
Comparison
We compare the results from the current study with other similar studies from other geographical locations to ascertain corroboration or deviations in patterns.
In the existing literature, studies on e-learning during the COVID-19 pandemic and subsequent closure of schools have been conducted mainly in higher educational institutions. Studies including (Aristeidou and Cross 2021; Kulikowski et al. 2021; Stotz et al. 2021; Vittorini and Galassi 2021; Yawson and Yamoah 2020) are some of the recent e-learning studies that were conducted during the period of the pandemic.
Unfortunately, evidence from basic schools and secondary or second-cycle institutions is limited. There exists limited research focusing on e-learning acceptance during the period of the COVID-19 pandemic in lower educational levels.
One of the few studies that focused on e-learning implementation during the period of the COVID-19 pandemic is that by Mailizar et al. (2020). The authors assessed the opinions of secondary school mathematics teachers on e-learning implementation barriers during the COVID-19 pandemic at four barrier levels, namely teacher, school, curriculum, and student. The authors collected data from 159 students from lower and upper secondary schools in Indonesia. The descriptive and inferential statistics involving means and standard deviations were employed in the final analysis to analyse and present the results using tables. ANOVA was also employed to examine significant differences in barriers across the categories. Lastly, Spearman correlation coefficients were calculated to assess relationships between barriers across the levels, and Cohen's (1992) guidelines for the interpretation of a correlation coefficient were used to interpret the correlation. According to the conclusions of their study, the student-level barrier had the greatest influence on e-learning use. Furthermore, there was a substantial positive link between the student-level barrier and the school level barrier, and the curriculum level barrier. The study found that the backgrounds of teachers did not affect the level of barriers. This study encourages additional debate on how to overcome e-learning challenges while also maximizing the advantages of e-learning during and after the pandemic by emphasizing the value of students' opinions.
On account of the recommendations made by Mailizar et al. (2020), the current study examined the factors that will influence students’ acceptance of e-learning after the pandemic using the opinions of 370 students from lower and upper levels of second-cycles institutions in Ghana. The results suggest that students’ attitudes and intentions are the main determinants that will influence the acceptance of e-learning by students after the pandemic. This finding compares favourably with the findings of Mailizar et al. (2020) that student-level barriers had the greatest influence on e-learning usage. The two studies conducted in Ghana and Indonesia, though have different environmental factors, have shown similar results across the two geographical locations. The factors that will influence students in Ghana are therefore similar to or maybe the same as those that will influence students in Indonesia in the acceptance of e-learning. This also implies that the factors that affect students’ acceptance of e-learning may be consistent across Africa and Asia.
Limitations and future study
In this study, data were collected using a questionnaire, which had items that were tested for the first time and may therefore not be standardized. Additionally, the fear and uncertainty of the COVID-19 pandemic could affect the responses provided by the study’s respondents. Moreover, the use of a questionnaire is prone to social desirability errors. There is the possibility that the information provided by the respondents may differ from their actual behaviour after the COVID-19 emergency. Finally, data collection involved students of only three-second-cycle institutions, thereby limiting the generalizability of the findings. Future studies could extend the study to cover more second-cycle institutions for a larger sample and statistical power.
Additionally, the country in which the study was conducted is a developing country in Africa, as such geographical, economic, and cultural biases may have contributed to the outcome of the study. Future studies could extend the discourse across Africa by comparing it with evidence obtained from other parts of the world.
Another limitation of the study is the focus on only attitudes and intentions. Technology infrastructure plays an important role in e-learning. For instance, a student having access to only a smartphone may have limitations when compared to a student having a laptop. Further study would be needed to observe the difference in technology infrastructure. Also, future research may focus on educational institutions' readiness to move academic activities to e-learning systems, and teachers’ intention to accept and use e-learning systems to continue with academic work during the period of the COVID-19 pandemic.
Conclusion
In this paper, we investigate the factors that will influence students’ acceptance of the e-learning system of education after the COVID-19 emergency in second-cycle institutions in Ghana. By analysing data collected from 370 students selected from three-second-cycle institutions in Ghana, we discovered that student attitude is highly impacted by Perceived Usefulness and somewhat influenced by perceived severity. Also, student intention was found to be moderately influenced by COVID-19’s perceived severity but significantly influenced by attitude toward usage. Given the study’s results, it is evident that the students’ attitudes and intentions will influence the acceptance of the e-learning system of education after the COVID-19 pandemic. A positive attitude and intention towards e-learning systems of education should therefore be developed in second-cycle institution students. Stakeholders in the education value chain must guarantee that the required steps are in place to make e-learning systems appealing, user-friendly, and valuable in the eyes of students. It was also found that positive students’ attitudes and intention to use e-learning systems are largely influenced by how useful students perceive e-learning systems. Other determinants include ease of use and the perceived severity of the COVID-19 pandemic. Although the perceived severity of a pandemic, hitherto, was of significant consideration in technology acceptance discourse, in this study, it was found to be of less importance compared to Perceived Usefulness. Consequently, the moderating effect of perceived severity of COVID-19 on the relationship between Perceived Ease of Use and attitude, and attitude and intention to use were insignificant.
The paper, therefore, concludes that the determinants that will influence the students’ acceptance of the e-learning system of education after the COVID-19 emergency are students’ attitudes and intention to use e-learning. Also, attitudes and intentions are the same across students in public and private second-cycle institutions. Consequently, factors that influence the attitudes and intentions of students in public second-cycle institutions are most likely to influence those in private second-cycle institutions.
Author contributions
All authors contributed to the study's conception and design. Material preparation, data collection and analysis were performed by EA and EKA. The first draft of the manuscript was written by EA and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Funding
The authors received no funding to support the preparation of this manuscript.
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Conflict of interest
The authors have no competing interests to declare that are relevant to the content of this manuscript.
Ethical approval
This research was given by the Presbyterian University College Ghana Research and Ethics Committee.
Informed consent
Informed consent was obtained from each participant of the study.
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==== Front
ACS Pharmacol Transl Sci
ACS Pharmacol Transl Sci
pt
aptsfn
ACS Pharmacology & Translational Science
2575-9108
American Chemical Society
10.1021/acsptsci.2c00026
Article
Hit Expansion of a Noncovalent SARS-CoV-2 Main Protease Inhibitor
https://orcid.org/0000-0003-1852-3849
Glaser Jens *†
Sedova Ada †
https://orcid.org/0000-0001-5712-2568
Galanie Stephanie †¶
Kneller Daniel W. †§
Davidson Russell B. †
Maradzike Elvis †
Del Galdo Sara ‡
Labbé Audrey †
Hsu Darren J. †
Agarwal Rupesh †
Bykov Dmytro †
Tharrington Arnold †
https://orcid.org/0000-0002-3103-9333
Parks Jerry M. †
Smith Dayle M. A. †
https://orcid.org/0000-0001-8970-8408
Daidone Isabella ‡
https://orcid.org/0000-0003-2342-049X
Coates Leighton †
Kovalevsky Andrey †
https://orcid.org/0000-0002-2978-3227
Smith Jeremy C. *†
† Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, Tennessee 37830, United States
‡ Department of Physical and Chemical Sciences, University of L’Aquila, I-67010 L’Aquila, Italy
¶ Protein Engineering, Merck, 126 East Lincoln Avenue, RY800-C303, Rahway, New Jersey 07065, United States
§ New England Biolabs, 240 County Road, Ipswich, Massachusetts 01938, United States
* glaserj@ornl.gov
* smithjc@ornl.gov
04 04 2022
08 04 2022
5 4 255265
17 02 2022
© 2022 American Chemical Society
2022
American Chemical Society
Inhibition of the SARS-CoV-2 main protease (Mpro) is a major focus of drug discovery efforts against COVID-19. Here we report a hit expansion of non-covalent inhibitors of Mpro. Starting from a recently discovered scaffold (The COVID Moonshot Consortium. Open Science Discovery of Oral Non-Covalent SARS-CoV-2 Main Protease Inhibitor Therapeutics. bioRxiv 2020.10.29.339317) represented by an isoquinoline series, we searched a database of over a billion compounds using a cheminformatics molecular fingerprinting approach. We identified and tested 48 compounds in enzyme inhibition assays, of which 21 exhibited inhibitory activity above 50% at 20 μM. Among these, four compounds with IC50 values around 1 μM were found. Interestingly, despite the large search space, the isoquinolone motif was conserved in each of these four strongest binders. Room-temperature X-ray structures of co-crystallized protein–inhibitor complexes were determined up to 1.9 Å resolution for two of these compounds as well as one of the stronger inhibitors in the original isoquinoline series, revealing essential interactions with the binding site and water molecules. Molecular dynamics simulations and quantum chemical calculations further elucidate the binding interactions as well as electrostatic effects on ligand binding. The results help explain the strength of this new non-covalent scaffold for Mpro inhibition and inform lead optimization efforts for this series, while demonstrating the effectiveness of a high-throughput computational approach to expanding a pharmacophore library.
SARS-CoV-2
drug discovery
main protease inhibitor
antiviral therapeutics
hit expansion
Oak Ridge National Laboratory 10.13039/100006228 LOIS 10126 UT-Battelle 10.13039/100016818 DE-AC05-00OR22725 document-id-old-9pt2c00026
document-id-new-14pt2c00026
ccc-price
==== Body
pmcIntroduction
The development of antiviral therapeutics is a major focus of COVID-19 research. The SARS-CoV-2 main protease, Mpro (3CLpro), is responsible for cleaving the viral polypeptide pp1a and pp1ab into functional protein subunits essential for viral replication. Because of this key role, and together with the low mutation rate of the active site2−6 which suggests that mutations will not broadly impact the efficacy of SARS-CoV-2 Mpro inhibitors, as well as a lack of homologous proteases in humans, Mpro is a prime target for antiviral drug discovery.4,7 Recent studies have confirmed the effectiveness of targeting Mpro for inhibiting viral replication.8−10 Several inhibitors of varying affinity have been discovered, including some with activities as low as 20 nM, by optimizing compounds based on structure–activity relationships (SARs).11,12 Pfizer has developed two Mpro inhibitors, PF-07321332 (Nirmatrelvir)13,14 and PF-07304814.15 The former is orally bio-available and has been authorized for emergency use in the USA with the cytochrome P450 3A4 inactivator Ritonavir under the brand name Paxlovid.16 The latter inhibitor is given intravenously and is currently in clinical trials.17 The PF-07321332 inhibitor has a kinetic inhibition constant (Ki) of 3.1 nM.14
For Mpro both covalent and non-covalent inhibitors have been discovered. We focus here on non-covalent inhibitors, which can act alone or provide starting points for optimized covalent and reversible covalent inhibitors.
Over the past two decades, efforts to develop non-covalent inhibitors of the main proteases of human coronaviruses such as MERS and SARS-CoV-1 have resulted in only a handful of non-covalent pharmacophores (scaffolds) that can be used to derive molecular series for optimization. These optimization efforts have resulted in relatively few non-covalent inhibitors with IC50 less than 1 μM in in vitro kinetic assays.18−20 We use as a starting point here selected results from the PostEra COVID Moonshot,1,21 which, through massive crowdsourcing and high-throughput experimental assays, has helped to discover a number of new motifs for non-covalent inhibitors with IC50 values below 2 μM. A large proportion of these are isoquinoline-containing compounds, such as four of the compounds shown in Figure 1. Crystal structures of many such compounds were determined through a collaboration between the Diamond Light Source XChem X-ray project and PostEra and were produced by high-throughput, automated methods. For all compounds in the isoquinoline series the isoquinoline nitrogen forms a strong hydrogen bond (2.6 Å to 2.8 Å) with His163 of Mpro. Another hydrogen bond can be formed between a carbonyl oxygen and Glu166 of Mpro; this interaction is also found in many of the compounds from this series. Of the 362 unique quinoline-containing compounds from the PostEra dataset, 327 experimentally measured fluorescence IC50 values have been reported. Of these, 179 have an IC50 under 5 μM, 48 between 5 and 20 μM, 23 between 20 and 30 μM, and 77 over 30 μM.
Figure 1 Selected non-covalent inhibitors from the COVID-19 Moonshot project1 with PostEra COVID Moonshot molecule ID, and IC50 values, in μM. The starting compound for the present hit expansion effort is labeled in bold.
Having discovered inhibitors (hits), a next step in the hit-to-lead process can be hit expansion, in which chemically similar molecules are identified, assayed, and compared. Hit expansion aims at producing inhibitors of similar or increased activity to the original hit, expanding diversity beyond a particular scaffold, and extending understanding of SARs.22 High-throughput parallel expansion has been found to be effective in this regard.23 Furthermore, serendipitous findings arising from the addition of random diversity to the hit compounds often results in discoveries of novel and unpredictable mechanisms of action.24 These types of efforts can be automated and may include cheminformatics or other high-throughput modeling approaches.25 Here we explore the ability of a fingerprint-based, automated hit expansion method to produce new Mpro inhibitors starting from a parent inhibitor.
Results
As a starting compound for our expansion we selected the chiral COVID-19 Moonshot inhibitor MAT-POS-b3e365b9-1 (bold in Figure 1). This compound is an intermediate product of a manual, crowd-sourced SAR optimization strategy, in which a weak (IC50 ≈ 25 μM) aminopyridine SARS CoV-2 Mpro inhibitor was successively modified to include an isoquinoline and a halogen group. Then, an oxane moiety was added to the m-chlorophenyl group to allow for stereoselective binding of the inhibitor, and further improvement of the affinity to an IC50 = 80 nM was achieved by replacing the out-of-plane hydrogen of the aliphatic heterocycle with a methoxy group. This starting compound was the most potent non-covalent inhibitor characterized by the COVID-19 Moonshot project at the time our expansion was performed and compounds were ordered.
We performed the automated computational hit expansion by examining 1.37 billion compounds in the Enamine REAL library (including stereoisomers and tautomers), a database of commercially available drug-like fragments complying with Lipinki’s “rule of five” 26 and Veber criteria27 for orally active compounds (molecular weight ≤ 500 Da, SlogP ≤ 5, number of hydrogen bond acceptors ≤ 10, number of hydrogen bond donors ≤ 5, number of rotatable bonds ≤ 10, and total polar surface area ≤ 140 Å2).28
Enzymatic Activity Assays
We tested 48 compounds for activity against Mpro (Figure 2). Our screening yielded 26 novel inhibitors with less than 50% residual enzymatic activity in the primary screen at 20 μM, and 21 satisfying the same threshold in both the primary and the confirmation screens (cf. Figure 3 and Table 1). Of these, we selected five inhibitors (compounds 6, 12, 17, 19, and 21) with high Z-scores for further characterization. The hits include a compound already characterized by the COVID Moonshot project (compound 21, Moonshot ID ADA-UCB-6c2cb422-1) and four others that, to our knowledge, have not been characterized previously. These compounds have the isoquinoline group in common, with either halogen substitutions at the meta position of the phenyl group (compounds 6 and 19), a methoxy (compound 12), methyl (compound 19), or 5-bromo-2,3-dihydrofuran (compound 6) group instead of the oxane moiety, or the addition of a methanesulfonyl functional group (compound 17).
Table 1 Activity of the Compounds Shown in Figure 3 against Mpro: Similarity to Starting Compound PostEra MAT-POS-b3e365b9-1, and Z-Scores from Primary and Confirmation Screen
compd MAP4 Z Z (confirmation)
1 0.6602 4.30 4.63
2 0.4404 8.50 5.01
3 0.4072 3.10 7.06
4 0.3867 2.80 5.96
5 0.3672 3.60 7.17
6 0.3652 5.00 7.73
7 0.3643 3.80 6.70
8 0.3340 9.00 5.54
9 0.3262 10.80 7.56
10 0.3232 4.30 8.06
11 0.3164 9.50 5.69
12 0.3018 5.00 11.67
13 0.2979 11.30 6.60
14 0.2910 4.50 6.97
15 0.2900 9.50 7.23
16 0.2891 2.70 5.53
17 0.2871 13.00 8.06
18 0.2871 12.00 6.54
19 0.2852 13.20 8.11
20 0.2842 11.90 6.86
21 0.2549 12.80 9.83
Figure 2 Activity of non-covalent SARS-CoV-2 Mpro inhibitors identified by scaffold expansion around MAT-POS-b3e365b9-1. Histogram of Z-scores from primary (top panel) and confirmation screend (bottom panel).
Figure 3 Top compounds assayed in the hit expansion with ≤50% residual activity at 20 μM.
These five inhibitors were further characterized in a dose–response experiment (Figure 4). Consistent with the high similarity scores to the starting compound, the IC50 values of these inhibitors (Table 2) were also similar and between 1.6 and 4.8 μM, but not statistically different from the control compound 21 or from the micromolar inhibitor MCULE-5948770040.29
Table 2 Inhibitory Concentrations (IC50 Values) of the Compounds from Figure 4
compd IC50 (μM)
6 4.8 ± 3.4
12 1.8 ± 0.8
17 2.5 ± 2.1
19 2.1 ± 1.0
21 1.6 ± 0.7
MCULE-5948770040 1.3 ± 0.7
Figure 4 Concentration curves of the top four inhibitors (solid symbols) and of control compounds 21 and MCULE-594877004029 (open symbols).
Structure–Activity Relationship (SAR) from Similarity Search
Given the size of the screened database it is interesting that, among the 48 similar molecules tested, the most potent ones exhibit only minor modifications to the original scaffold retaining the isoquinoline, the halophenyl ring, and the amide bond connecting the two. These salient features may therefore be critical for the activity of this class of inhibitors. They also demonstrate the necessity for searching a comprehensive molecule database, since more global changes to the molecules did not lead to improved inhibition. In other words, minute modifications of the scaffold would have likely been missing from smaller databases. To test the hypothesis that the similarity in activity is consistent with a SAR model generated from the COVID-19 Moonshot dataset, we used machine learning to predict the importance of individual molecular features for the reference compound and the top five inhibitors (Figure 5).
Figure 5 Selected inhibitors and molecular regions of importance for inhibitory activity, according to a support vector regression model trained on pIC50 values. The attribution weights are normalized to [−1, 1] as indicated by the color scale.
Although we do not explicitly explore the combinatorial effect of the various substitutions, e.g., by separately varying the scaffold and the chloro substitutions, the results can be interpreted in terms of a manual SAR. The slight increase in IC50 from compound 21 to compound 12 (cf. Table 2) is due to the addition of the methoxy group, in qualitative agreement with the negative weight predicted for it by the model. The IC50 increases further with compound 19, where the methylene bridge between the amide and the phenyl ring bears an additional methyl group, and the chlorine is simultaneously replaced by a fluorine. The model predicts that both contributions reduce the activity. A rigidification of the above-mentioned connection between the amide and the phenyl ring was made in compound 6, and chlorine was replaced by bromine, resulting in an even weaker binder.
Generally, the most important functional groups of the isoquinoline derivatives are the 4-aminopyridine ring fused to a phenyl ring and the halogen and its closest atoms on the second phenyl ring. On the other hand, the substitutions that differentiate our top inhibitors from the reference compound and from compound 21 are predicted to be of minor relevance for the activity. This finding is in agreement with the observed minor changes in IC50 values. Finally, the halogen accounts for an order of magnitude improvement in binding strength; this is supported by a compound nearly identical to compound 21 but without the halogen substituent (PostEra ID RAL-THA-2d450e86-1, cf. Figure 1) which has a reported IC50 = 14 μM.
Crystallography
To examine the molecular basis of inhibition of hit compounds, we determined room-temperature X-ray structures of Mpro co-crystallized with compound 12, compound 19, and compound 21 up to 1.90 Å resolution (Table S1). Other hits selected from the inhibition assay results were attempted but did not co-crystallize. Each ligand was modeled with unambiguous electron density in the active site (Figure 6a–c).
Figure 6 Room-temperature X-ray crystal structures of Mpro co-crystallized with compound 21 (Z1530724813), compound 19 (Z1530724963), and compound 12 (Z1530718726). Isoquinoline compounds (a) compound 21, (b) compound 19, and (c) compound 12 modeled into electron density as polder omit maps in blue mesh contoured at 3σ. Intermolecular interactions between Mpro and compounds (d–f) are shown with H-bonds as black dashes and possible CH−π interactions as blue dots (another possibility is a halogen−π interaction). Distances in Å.
The structures show that all three ligands form a hydrogen bond (d = 2.9 Å) between the isoquinoline and the ε-nitrogen on His163 in S1 subsite of the binding pocket. Another hydrogen bond (d = 3.1 Å) forms between the ligand carbonyl O and Glu166 backbone N in the S3 site. The amide NH group of the ligand forms a hydrogen bond with a water molecule (the water molecule is not shown in Figure 6, but it is included in the analysis of water structure below). Moreover, one discerns a weak interaction (d = 3.9 Å to 4.1 Å) between the C2–C3 edge of the m-chlorophenyl group with the imidazole of the catalytic His41 in the S2 site. Halogen bonding has been recognized as a useful tool in drug design, in part for its tunability;30 a halogen bond, with an average distance of 3 Å, is a relatively weak interaction but can contribute several kcal/mol to the binding energy of a ligand.31,32 While the geometries found in our crystal structures do not correspond to a classical RX–Y halogen bond,33 the interaction may be of a halogen−π30−32 nature. It may alternatively, or also, involve CH−π interactions. The methoxy and methyl substitutions in compounds 12 and 19, respectively, do not lead to additional interactions with the protein, which partially explains their lack of (significant) effect on activity. They can thus be understood as neutral substitutions of the ligand scaffold, potentially only having a steric or entropic effect on binding.
Potential Role of Water Molecules in Stabilizing the Ligand Pose
Given the polar nature of most of the interactions discussed here, it is natural to look at the complex–solvent interactions, their qualitative change upon ligand binding and to elucidate the role of individual water molecules. Such an approach can form the basis for even more quantitative modeling of binding strength. The important role of water in mediating protein–ligand interactions is well known and can make modeling and prediction in drug discovery difficult.34,35 We performed a set of analyses to identify the locations of both trapped and displaced water molecules that impact the stability of the ligand binding pose.
The efforts of the global community to find drugs targeting SARS-CoV-2 proteins has led to an explosion in the number of crystal structures of these proteins, creating an unprecedented collection of structures of Mpro for analysis. Making use of this wealth of data, we aligned 550 SARS-CoV-2 Mpro structures deposited in the Protein Data Bank (see Table S2 for the list of structures) and interpolated the water oxygen positions onto a 3D grid around the active site (see Methods). In Figure 7, we show the crystallographic water molecule loci satisfying our density threshold in the crystallographic ensemble as red surfaces.
Figure 7 Solvent sampling densities around the protease active site. (a) The isosurfaces shown represent the volumes within which water oxygen atoms are observed at a 1% maximum occupancy value with respect to (red) the ensemble of 280 currently available Mpro structures, which yielded 550 monomers and (blue) a 5 ns NPT simulation of rigid protein and ligand (compound 21, Z1530724813). Panels (b) and (c) show volumes in the active site that are strongly sampled by water oxygen atoms in the crystallographic ensemble and are occupied by the ligand (compound 21) and similar analogues. Both volumes shown are active hydrogen-bonding sites between the protein and solvent or ligand molecules. Displacement of solvent molecules within these volumes enables strong protein–ligand interactions to occur, thus partially explaining the strong homology of isoquinoline ligand orientation in the ensemble of crystal structures.
To elucidate water positions specific to these isoquinoline ligands and to determine if stable water molecules with kinetics too fast to capture with crystallography were also important, we performed a constrained molecular dynamics (MD) simulation of the ligand compound 21 in the binding pocket in explicit water and analyzed the water molecule positions in the same way, showing these as blue surfaces.
Generally, crystallographic and MD water locations coincide, demonstrating good qualitative agreement between simulated and crystallographic water molecule positions. A large cluster found only in the simulation and not in the crystallographic database analysis is found to interact with the amide nitrogen on the ligand, an interaction that appears to be ligand-specific (a majority of the crystal structures either do not contain bound ligands or contain ligands bound in poses that differ from the isoquinoline series). Notably, some crystallographic waters found in the set of Mpro crystal structures overlap with ligand compound 21 atoms and are therefore absent in the MD simulation, indicating water displacement. Panels (b) and (c) of Figure 7 show ligand substructures that form hydrogen bonds with the backbone of Glu166 and the side chain of His163, respectively, together with clusters of crystallographic waters found in the same positions as ligand atoms. In lieu of the ligand forming hydrogen bonds with the protein in the complex, water molecules are bonding partners in the fully solvated apo-protein structure. Key non-covalent protein–ligand interactions arise from the displacement of non-catalytic hydration water observed at reproducible positions in the binding pocket, as well as from displacement of water from the ligand solvation shell. Displacement of water molecules by ligands in protein binding sites is known to contribute to binding affinity with standard free energies of ΔG0 ≈ 8 kcal/mol36,37 and is likely a contributing factor to the binding strength of this series.
Density Functional Theory-Optimized Binding Pocket Geometry and Electrostatic Surface Analysis
Finally, using quantum chemistry, we examine some aspects of the electrostatic interactions of ligands with the active site. The electrostatic potential Vel was calculated for the ligand alone, for the protein environment alone, and for the ligand bound to protein, all using a continuum solvent mimicking an aqueous environment. The density functional theory (DFT)-optimized geometry is in excellent agreement with the corresponding X-ray structure (RMSD 0.71 Å), indicating that the choices of interacting atoms used to define the cluster and the water placement were robust. Figure 8 displays a particular result from these calculations. The electrostatic potentials of the m-chlorophenyl moiety as calculated for the ligand alone (panel a) and in the protein active site (panel b) are substantially different. The full system used for the protein–ligand calculation is shown in panels (b) and (c). The anisotropic nature of the electrostatic potential on the Cl suggests that it is interacting with three or four neighboring protein sites. This result supports the order of magnitude change in affinity associated with the halogen group discussed above, and also the effect of various halogen substitutions in a hit expansion around the similar MCULE-5948770040 scaffold, as discussed by Kneller et al.38
Figure 8 Electrostatic potential Vel at a distance of 1 Å along the surface normal, projected onto the molecular surface of the isoquinoline pharmacophore, and its interaction with protein, calculated using density functional theory. Projected potential surface map of the ligand only, (a); Projected potential surface map of ligand compound 21 in protein environment, (b); the same model as in (b) but illustrated with atomic ball-and-stick representation to guide the eye, (c). The system was modeled in a continuum solvent (see Methods). The colors correspond to the electrostatic potential values in Hartree atomic units (a.u.) on the surface as indicated by the accompanying color key.
Discussions and Conclusion
Inhibition of the SARS-CoV-2 main protease (Mpro) is a major focus of drug discovery efforts against COVID-19. The list of Mpro inhibitors is rapidly growing,39 and SARs have been identified for several series. Various modes of inhibition have been developed, including small-molecule covalent inhibitors, peptidomimetic covalent inhibitors, non-covalent inhibitors, and metal-conjugated inhibitors. We report here a hit expansion of non-covalent inhibitors of Mpro, focusing on an isoquinoline scaffold discovered as part of the PostEra COVID Moonshot.1 A novelty of the present work is the use of a cheminformatics-based hit expansion, in which we performed an automated computational search of a billion-compound database using a molecular fingerprinting approach. In this hit expansion, 48 selected compounds were identified and tested, of which 21 exhibited inhibitory activity above 50% (IC50) at 20 μM. Four new non-covalent inhibitors with IC50 ≈ 1 μM were found. The isoquinoline motif is present in each of the four strongest binders, and the success of this hit expansion in demonstrating its importance was chiefly enabled by the enormous size of the database searched. A simple machine-learning model trained on COVID-19 Moonshot data suggests that the strong homology of the ligands is consistent with the SAR implied by the Moonshot dataset, which amounts to a manual hit expansion method guided by human intuition. Room-temperature X-ray structures of co-crystallized protein–inhibitor complexes reveal essential hydrogen bonds with the binding site and water molecules. MD simulation and quantum chemical DFT calculations further probe the nature of these interactions as well as charge effects on ligand binding.
These compounds will benefit from optimization to improve binding affinity, solubility, desired anti-viral effect in live cells, and other key properties such as selectivity and metabolic stability. Nevertheless, we have demonstrated the potential of high-throughput, automated cheminformatics-based computational hit expansions for rapidly expanding the size of a set of hits for lead optimization.
Methods
Selection of Compounds
We selected compounds by scaffold similarity to the starting compound PostEra MAT-POS-b3e365b9-1. To this end, we employed the MAP4 MinHash-based, atom-pair molecule fingerprint with 1024 permutations.40 MAP4 combines chemical environments of radius r = 1 and r = 2 around pairs of atoms with their topological distance, and for every such set of descriptors returns the member with the minimum SHA-1 hash under a random permutation, resulting in a similarity measure between molecules that is an unbiased estimator of the Jaccard index.41 MAP4 has been shown to outperform comparable methods in the task of separating active binders from decoys by similarity.40
We computed MAP4 fingerprints for the Enamine REAL library and employed the dask42-distributed and NVIDIA RAPIDS43 GPU-accelerated data analytics libraries to parallelize the calculation of fingerprints and to reduce the dataset to the most similar compounds. We selected 47 unique scaffolds (cf. Supporting Information) having the highest fingerprint similarity (≥0.28125) and included a control compound (Enamine ID Z1530724813/PostEra COVID Moonshot ID ADA-UCB-6c2cb422-1, similarity 0.2549), for which an IC50 has been reported by the PostEra project. These compounds were filtered for potential pan-assay interference compounds (PAINS) and purchased from Enamine. Our experimental characterization did not include the starting compound itself, as it was unavailable from Enamine; however, we included an additional control (MCULE-5948770040) that was previously found to be a potent, non-covalent Mpro inhibitor29 in a study unrelated to the PostEra project.
SARS-CoV-2 Mpro Expression, Purification, and Enzyme Inhibition Assay
Protein purification supplies were purchased from Cytiva (Piscataway, NJ, USA). A gene construct encoding Mpro (NSP5) from SARS-CoV-2 was cloned into plasmid pD451-SR44 (Atum, Newark, CA) and expressed and purified with protocols detailed in ref (45). Briefly, the authentic N-terminus was achieved by including an NSP4-NSP5 autoprocessing sequence flanked by maltose binding protein and Mpro. At the C-terminus, a sequence encoding the human rhinovirus 3C (HRV-3C) cleavage site was followed by a His6-tag. The authentic N-terminal sequence was then created by autocleavage during expression, while the C-terminus was generated by HRV-3C treatment following Ni-immobilized metal affinity chromatography.
Compounds were purchased from Enamine as 10 mM stock solutions in DMSO and stored at 20 °C. All compounds are >90% pure by LC-MS. The assays were performed in 40 μL total volume in black half-area 96-well plates at 25 °C as previously described.29,46,47 The assay buffer contained 20 mM Tris-HCl pH 7.3, 100 mM NaCl, 1 mM EDTA, and 2 mM reduced glutathione with 5% v/v final DMSO concentration. Reaction final concentrations were 250 nM Mpro enzyme, 20 μM inhibitor, and 40 μM FRET peptide substrate. The FRET substrate DABCYLKTSAVLQSGFRKM-E(EDANS) trifluoroacetate salt was purchased from Bachem (PN 4045664). Initial rates were determined for time points in the linear range by linear regression in Excel, residual activities were determined by normalizing candidate initial rates to the average of the positive controls, and Z-scores were determined by dividing the difference between the candidate initial rate and average positive control initial rate by the standard deviation of the positive control initial rates. The Z′ statistics for the plate were calculated using the published equation.48Figure S1 shows good agreement between the primary and the confirmation screen (Spearman-ρ = 0.44, P = 0.00164).
IC50 Determination
To determine the concentration at which a compound was able to achieve 50% inhibition of Mpro activity in vitro (IC50), the FRET assay was performed at seven concentrations of inhibitor (0.03–31.6 μM) in duplicate. Initial rates were normalized to no inhibitor control (100% activity) and no enzyme control (0% activity), and nonlinear regression of the [Inhibitor] vs normalized response IC50 equation was performed to fit the data using GraphPad Prism 9, yielding IC50 and its 95% confidence interval.
Structure–Activity Model
To derive a structure–activity model, we trained a support vector regression model on 1365 fluorescence IC50 values from the COVID-19 Moonshot dataset1 using scikit-learn.49 For every molecule we computed the 2048-bit Morgan fingerprint as a feature vector using RDKit50 and the label as −log10 IC50 [μM], normalized by the mean and standard deviation of the training dataset. We then split the training fingerprints and labels into training and test sets using a 9:1 ratio. This model reproduced the order of experimentally measured activity values of the test set with a Spearman-ρ rank correlation coefficient of 0.73 and a mean-square error of 0.353 (in log10 IC50 units). To determine which parts of the molecule are important for predicting the affinity value, we used the GetSimilarityMapForModel function in RDkit, which removes a single atom and then recomputes the fingerprint for every atom in the molecule.
Crystallization
Three compounds with favorable IC50 values were crystallized in complex with Mpro and their structures determined using X-ray diffraction. Crystallization reagents were purchased from Hampton Research (Aliso Viejo, CA, USA). Crystallographic tools were purchased from MiTeGen (Ithaca, NY, USA) and Vitrocom (Mountain Lakes, NJ, USA). Mpro concentrated to ∼5.0 mg/mL in 20 mM Tris pH 8.0, 150 mM NaCl, and 1 mM TCEP was used for crystallization by sitting-drop vapor diffusion. Conditions for growing crystalline aggregates of ligand-free Mpro were identified by high-throughput screen at the Hauptman-Woodward Research Institute,51 reproduced locally, and then used for microseeding to nucleate Mpro crystals in subsequent co-crystallization experiments. Lyophilized samples of compounds 12 (Z1530718726), 19 (Z1530724963), and 21 (Z1530724813) (Enamine, Monmouth Jct., NJ, USA) were dissolved in 100% DMSO as 50 mM stocks and stored at −20 °C. Compound 21 corresponds to PostEra COVID Moonshot ID ADA-UCB-6c2cb422-1. Compounds were mixed with Mpro in a 5:1 molar ratio and allowed to incubate on ice for a minimum of 1 h. Crystals suitable for room-temperature X-ray diffraction grew after 1 week in 20 μL drops at a 1:1 mixture with 18–20% PEG3350, 0.1 M Bis-Tris pH 6.5, nucleated with 0.2 μL of 1:200 dilution microseeds after incubation at 14 °C.
Room-Temperature X-ray Data Collection and Structure Refinement
Protein crystals were mounted using a MiTeGen (Ithaca, NY) room-temperature capillary system. X-rays for crystallography were generated from a Rigaku HighFlux HomeLab employing a MicroMax-007 HF X-ray generator with Osmic VariMax optics. Diffraction images were collected using an Eiger R 4M hybrid photon-counting detector. Diffraction datasets were reduced and scaled using Rigaku CrysAlis Pro software package. Molecular replacement was performed using the ligand-free room-temperature Mpro structure (PDB ID 6WQF,44) using Phaser.52 Structure refinement was performed with Phenix.refine from the Phenix suite53 and manual refinement in COOT54 assisted by Molprobity.55 Data collection and refinement statistics are listed in Table S1. The structures and corresponding structure factors of the room-temperature co-crystal complexes have been deposited into the Protein Data Bank (PDB).
Crystal Structure Hydration Analysis
To determine the average locations of water molecules around the active site in the ensemble of Mpro structures deposited in the PDB, the Dali server56 was used to identify homologues of the SARS-CoV-2 Mpro. The query structure was the Mpro:compound 21 protein–ligand complex reported here. The search across the full PDB database returned 277 crystal structures with strong structural homology. The three room-temperature X-ray structures reported here were also included in this set of structures. Alignment of all chains in the 280 Mpro-analogous structures was performed using the align function in PyMOL,57 with the Mpro:compound 21 complex used as the target structure. Chains with strong alignments (RMSD smaller than 5.0 Å, number of aligned residues greater than 50, and alignment score greater than 150) were included in the structural ensemble; all crystallographic waters and resolved small molecules within 5.0 Å of any protein atom were maintained in the aligned structures. The final count of monomeric Mpro homologues was 550 structures. Once all structures were aligned, a 3D histogram of crystal water oxygen atom positions was calculated around the active site of the enzyme. Each of the original 280 crystal structures was given an equal weight during the creation of the histogram to avoid over-weighting of the results toward structures from multimeric Mpro or NMR datasets. Each cubic bin (voxel) was 0.25 Å × 0.25 Å × 0.25 Å. The resulting 3D histogram was then imported into Visual Molecular Dynamics (VMD)58 to enable the visualization of hydration hot spots identified from the ensemble of crystal structures.
Molecular Dynamics Sampling of Water Positions in the Constrained Mpro:Compound 21 Complex
To obtain a dynamic view of the hydration structure of Mpro specific to the isoquinoline ligands, all-atom explicit solvent MD simulations of the protonated Mpro:compound 21 complex were performed to model the protein in a bulk solvent environment. Here, both the protein and the ligand were completely restrained to prevent deviation away from the crystal structure geometry so that the solvation hot spots for the specific bound structure could be identified. Additional details regarding the MD simulation protocol are provided below. The 5 ns trajectory was analyzed in a similar fashion to the crystal structure ensemble to generate a 3D histogram of solvent oxygen atom positions around the ligand.
Parameters for the solvated Mpro:compound 21 complex were assigned in AmberTools21 tleap,59 using the ff19SB force field60 and GAFF/AM1-BCC parameters61,62 for the ligand compound 21. Resolved crystal waters were maintained. Protonation states of His residues were assigned using PropKa,63,64 and all His side chains were singly protonated. His172 and His163 were protonated at the ε-nitrogen position. The other histidines, His41 and His164, were protonated at the δ-nitrogen. The system was solvated in a box of TIP3P water molecules with a minimum distance of 12 Å from the protein to the nearest face of the box. Sodium and chloride ions were added to neutralize charge and maintain a 0.1 M ionic concentration. The OpenMM molecular simulation software package65 was used to perform the MD simulations. The protein and ligand atoms were constrained by setting their masses to zero. The particle mesh Ewald (PME) method was used to account for long-range electrostatics interactions. A Langevin thermostat was used to keep the temperature of the solvent at 310 K. A Monte Carlo barostat was used to maintain the pressure at 1 atm. A time step of 2 fs was used to propagate the solvent atoms, with frames written every 2 ps. A total of 5 ns was performed for adequate sampling around the static protein–ligand complex.
Quantum Chemical Calculations
To examine specific static binding interactions in detail, quantum mechanical calculations were performed using DFT with the ORCA package.66 We optimized the geometry of a cluster model of the ligand in the binding pocket and used this geometry to calculate the electrostatic potential in the ligand-only, protein-only, and complex configurations. Starting geometries of the ligands were derived from crystallography, and the bound-complex geometries included all residues within 5 Å of the ligand as well as six stably bound water molecules identified in the MD water analysis. Two carboxylic groups, belonging to Glu166 and Asp187, were protonated (i.e., COO– → COOH) to mimic an overall neutral charge of the protein.67 The two carboxylic groups were chosen due to their peripheral position in the model and hence limited impact on the substrate–binding pocket interaction. Constraints were imposed on the protein backbone. The model with bound ligand contained 268 atoms and 2571 basis functions. The optimized geometry and the list of all constraints are available as Supporting Information.
The model was optimized at the BP86/Def2-SVP level of theory.68−70 Grimme’s D3 dispersion corrections were applied in all calculations.71,72 Single-point energy calculations at the optimized geometries were performed at the B3LYP/Def2-SVP level of theory.73,74 These calculations included D3 dispersion corrections as well as the CPCM polarizable continuum solvent model.75 The dielectric constant was set to εr = 4 during geometry optimization to mimic a protein environment. All calculations used the resolution-of-the-identity (RI) approximation and automatically generated auxiliary basis sets as implemented in ORCA.76−78
For any single point in the vicinity of an atom, the electrostatic potential was computed using the ORCA_vpot module, with the density computed at the B3LYP/Def2-SVP level of theory in a continuum solvent with εr = 80. This computation was performed for the complex, the ligand, and the environment (including water molecules). A Python script79 was used to call the ORCA_vpot module to generate electrostatic potentials around the 4096000 = 160 × 160 × 160 points specified to surround the model of the protein active site and to convert the computed electrostatic potentials to the .cube file format for visualization. The .cube file was visualized using the Chimera program.80
Supporting Information Available
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsptsci.2c00026.Figure S1, validation of the enzymatic assay; Table S1, crystallographic data collection and refinement statistics for room-temperature structures of Mpro in complex with compounds 12, 19, and 21; and Table S2, list of Mpro crystal structures (PDB IDs) used for the hydration analysis (PDF)
Characterization of compounds, including purity, molecular formula (SMILES) strings, and activities (XLSX)
Supplementary Material
pt2c00026_si_001.pdf
pt2c00026_si_002.xlsx
Accession Codes
The PDB accession codes are 7S3K for Mpro:compound 12, 7S4B for Mpro:compound 19, and 7S3S for Mpro:compound 21.
ORNL is managed by UT-Battelle, LLC for the DOE Office of Science, the single largest supporter of basic research in the physical sciences in the United States. This manuscript has been coauthored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). The U.S. government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
The authors declare no competing financial interest.
Acknowledgments
This research was supported by the U.S. Department of Energy (DOE) Office of Science through the National Virtual Biotechnology Laboratory (NVBL), a consortium of DOE national laboratories focused on response to COVID-19, with funding provided by the Coronavirus CARES Act. CARES act funding to the Oak Ridge Leadership Computing Facility (OLCF) through DOE ASCR in support of this research is also acknowledged, as is the Laboratory Directed Research and Development Program at Oak Ridge National Laboratory (ORNL). This research used resources at the Spallation Neutron Source and the High Flux Isotope Reactor, which are DOE Office of Science User Facilities operated by ORNL. This research also used resources of the Spallation Neutron Source Second Target Station Project at ORNL. The Office of Biological and Environmental Research supported research at the ORNL Center for Structural Molecular Biology (CSMB), a DOE Office of Science User Facility. We acknowledge a computing allocation on Google Cloud for COVID-19 research that enabled the similarity search on the compound dataset. We thank Marti Head for discussions and a careful reading of the manuscript. This research also used resources of the OLCF at ORNL, which is supported by the Office of Science of the U.S. DOE under Contract No. DE-AC05-00OR22725.
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==== Front
Immunity
Immunity
Immunity
1074-7613
1097-4180
Elsevier Inc.
S1074-7613(22)00139-X
10.1016/j.immuni.2022.03.016
Review
Nonresolving inflammation redux
Nathan Carl 1∗
1 Department of Microbiology and Immunology, Weill Cornell Medicine, New York, NY 10065, USA
∗ Corresponding author
12 4 2022
12 4 2022
12 4 2022
55 4 592605
© 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.
Nonresolving inflammation contributes to many diseases, including COVID-19 in its fatal and long forms. Our understanding of inflammation is rapidly evolving. Like the immune system of which it is a part, inflammation can now be seen as an interactive component of a homeostatic network with the endocrine and nervous systems. This review samples emerging insights regarding inflammatory memory, inflammatory aging, inflammatory cell death, inflammatory DNA, inflammation-regulating cells and metabolites, approaches to resolving or modulating inflammation, and inflammatory inequity.
This review describes inflammation as part of an interacting system of homeostatic control and restoration along with the endocrine and nervous systems. A focus on recent advances in inflammation as they apply within an organism is joined with attention to the disproportionate burden of inflammation on some populations in society.
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pmcIntroduction
A review in 2010 entitled “Nonresolving Inflammation” (Nathan and Ding, 2010) began, “Perhaps no single phenomenon contributes more to the medical burden in industrialized societies than nonresolving inflammation.” That view has not changed. In 2021, a leading thinker in the field wrote that “inflammation is associated with almost every major human disease” (Medzhitov, 2021). That same year, 13,905 review articles flagged “inflammation” as a key word and 1,284 of them included “inflammation” in their titles. This not only reflects that the topic is important but underscores that we are struggling to get a grip on it.
Since 2010, the toll of inflammation on human health has not subsided, despite major advances in understanding of the underlying biology, the tireless efforts of drug developers, and the clinical success of several interventions, such as biologics that block signaling by interleukin-1β (IL-1β) or tumor necrosis factor-α (TNF-α) (Netea et al., 2017). On the contrary, since 2020, inflammatory responses to COVID-19 (Casanova and Abel, 2021; Del Valle et al., 2020; Gruber et al., 2020; Karki et al., 2021) have driven the death toll from nonresolving inflammation to the highest level in the lifetime of anyone reading these words. Meanwhile, the striking but partial success of immuno-oncology has focused attention on the ability of intra-tumoral inflammation to either frustrate or assist the immunological control of cancer. Accordingly, efforts to resolve inflammation as a treatment for autoinflammatory and autoimmune diseases have been joined by efforts to modulate inflammation in the treatment of malignancies.
The goal here is not to repeat or replace other reviews, but to both enlarge our thinking and focus it. The enlargement offers an expanded definition of inflammation and directs attention to emerging topics, among them inflammatory memory, inflammatory modulation, and inflammatory inequity. The focus is on emerging knowledge that might lead to treatments for individuals and on the need for interventions to benefit communities and populations. Necessarily, for a subject and literature this vast, these topics are selective and the citations illustrative.
What is inflammation?
Definitions of inflammation are evolving as understanding of the biology grows and as more studies assert the involvement of inflammation without its classic signs—rubor (redness), calor (heat), humor (swelling), and dolor (pain). In the 20th century, inflammation was seen as a tissue reaction to an emergent stimulus (Table 1 ). The reaction was macroscopically visible on the outside of a host or within the host at endoscopy or surgery, and microscopically apparent in tissue from an affected site as the accumulation of neutrophils, monocytes, macrophages, and/or lymphocytes in a structure that might be disordered by edema, necrosis, fibrosis, lipidosis, malignancy, or infection.Table 1 Changing conceptions of inflammation over the past two decades
Conception Detection Stimuli Causes of non-resolution or recurrence Participating cells Overall function Therapeutic concerns
20th centurya,b macroscopically or microscopically emergent; usually evident; may be single persistent stimulus cells of the immune system resolve problem or initiate an immune response infection, trauma, cancer, asthma, atherosclerosis, diabetes, autoimmune disorders, etc.
21st century V.0c,d macroscopically, microscopically, or inferred from increased production of cytokines, chemokines, non-protein mediators and products they induce dual stimuli, signaling infection plus injury; or inapparent but seemingly continual stimuli, implied by spontaneous inflammation being a phenotype of numerous gene deficiencies persistent stimulus; emergent secondary stimulus, such as autoimmune response; excessive or prolonged initial response; subnormal initial response; defective switch of cells and mediators from pro- to anti-inflammatory, depending on context; loss of a constitutively operating anti-inflammatory mechanism cells of the immune system resolve problem or initiate an immune response as for 20th century, with additional focus on metabolic and neurodegenerative diseases
21st century V.1e “any process involving signals and cells known to orchestrate the more familiar acute inflammatory response” reaction to a perturbation, or participation “in normal homeostatic processes in the absence of any perturbations”
21st century V.2f as for V.0 as for V.0 plus air pollution, temperature extremes, dietary deficiencies, and stresses of poverty and discrimination as for V.0, with additional recognition of inflammatory memory and inflammaging any cells, including microbiota as for V.1, with emphasis on joint participation with the endocrine and nervous systems in providing homeostatic control and restoration as for V.0, with additional emphasis on inflammatory modulation in immuno-oncology and societal actions to reduce inflammatory inequity
Cells in the table are unfilled when the topic was not a focus of the article cited.
a Zweifach et al. (1965)
b Gallin et al. (1988)
c Nathan, 2002
d Nathan and Ding, 2010
e Medzhitov, 2021
f This article
The early 21st century introduced a fundamentally different and still-evolving view, as summarized for versions V.0 (Nathan, 2002; Nathan and Ding, 2010), V.1 (Medzhitov, 2021), and V.2 (here) in Table 1. Versions V.0 and V.2 hold that in addition to inflammation arising in response to stimuli that are evident and new, multiple points of control act constitutively to restrain the onset of inflammation that otherwise emerges as if spontaneously, presumably in response to unnoticed contact with microbes, inanimate particulates, noxious gases, or aberrant cells. This is evidenced by the consequences of loss of function in any one of a vast number of genes (Nathan, 2002; Nathan and Ding, 2010), a number that continues to grow (Tyler et al., 2021). Inflammation is considered “nonresolving” not only when it is unremitting but when it is recurrent. Moreover, according to version V.0, inflammation can persist for reasons other than failure to remove an inciting stimulus (Nathan and Ding, 2010). COVID-19 illustrates each of these additional routes to nonresolving inflammation: an excessive or prolonged initial response, as in acute SARS-CoV-2 infection, which triggers inflammasomes (Rodrigues et al., 2021; Vora et al., 2021), elicits massive cytokine release (Del Valle et al., 2020), activates complement (Ma et al., 2021), and releases neutrophil extracellular traps (Veras et al., 2020); a subnormal initial response, as when autoantibodies or loss-of-function mutations prevent production of or response to type I interferons (Bastard et al., 2021; Zhang et al., 2020) or signaling through TLR7 (Asano et al., 2021); and emergence of secondary stimuli after viral clearance, such as when tissue damage sets up an autoimmune reaction (Wang et al., 2021). COVID-19 reminded us that perpetuation of inflammation by autoantibody formation in response to tissue damage may be operative in diverse states of nonresolving inflammation. It may contribute, for example, to atherosclerosis (Lorenzo et al., 2021), the leading cause of death before COVID-19 from a disease in which nonresolving inflammation plays a prominent part.
Recent years have revealed that immunity can involve any cells in the body, not just those of lymphohematopoietic origin, and, reciprocally, cells of lymphohematopoietic origin function in the development and homeostatic maintenance of other tissues and organs (Nathan, 2021). Medzhitov has ascribed similar features to inflammation, which is an integral part of immunity, namely, the participation in inflammation of more cells than were classically implicated, and the impact of inflammation on the development and homeostatic maintenance of tissues (Medzhitov, 2021). This is strikingly evident in the role of inflammation in normal fetal development and parturition, notwithstanding that inflammation can also contribute to fetal damage from infection (Megli and Coyne, 2022).
Definition V.2 (Table 1) is compatible with versions V.0 and V.1, but with additions in scope and changes in emphasis. According to V.2, inflammation, like the immune system of which it is a part, is a major module in a network of homeostatic bodily responses to perturbation that coordinates with the two other major systems of inter-organ, intra-tissue communication—the endocrine and nervous systems—and complements the limitations of each (Figure 1 ).Figure 1 Inflammation as a component of the homeostatic network
The immune system (with inflammation as a prominent part), the endocrine system, and the nervous system interact with each other in a meta-system of homeostatic control and restoration. Each member of the tripartite system complements the others with respect to their range of action in space and time, the diversity and nature of responses they command, and their exertion of control at levels of cells, tissues, organs, and organism.
Like the endocrine system, inflammation can send soluble signals throughout the body but can do so with molecules (cytokines) of greater molar potency than hormones, and more commonly takes advantage of the opportunity to release its signals in specific sites. The influence of the endocrine system on inflammation has long been appreciated. For example, endogenous stress-induced corticosteroids are anti-inflammatory and immunosuppressive, and corticosteroids in pharmacologic doses remain among the most powerful and widely used anti-inflammatory drugs. Many of the same hormones that regulate metabolism in other cells, such as insulin, do so in lymphohematopoietic cells as well. The reciprocal influence has also long been apparent, for example, with the recognition that inflammation is a major driver of insulin resistance in obesity (Rohm et al., 2022). Obesity, like atherosclerosis, is one of the most prevalent states of nonresolving inflammation.
More recent is the appreciation of functional similarities, complementary differences, and reciprocal interactions between inflammation and the nervous system (Kabata and Artis, 2019; Pavlov et al., 2018). Like the nervous system, inflammation can involve cell-cell contact, but is not limited to hard-wired, pre-determined contacts and has a greater number of cell-surface and secretory products that act on a larger repertoire of receptors and induce a wider range of responses. Like the nervous system, inflammation involves sensory and effector pathways. As in the nervous system, these involve some developmentally positioned cells, such as perivenular mast cells, skin-resident dendritic cells, liver-resident Kupffer cells, bone-resident osteoclasts, lung-resident alveolar macrophages, brain-resident microglia, and innate lymphoid cells resident in diverse tissues near neurons (Kabata and Artis, 2019). However, unlike nerves, inflammatory cells can migrate to any place in any tissue. The peripheral nervous system has a limited set of commands: it instructs cells to contract, relax, secrete, or excrete, and to do so within seconds or minutes. Inflammation instructs cells to change their transcriptome, metabolome, and secretome, and sometimes to die, grow, or proliferate, while commanding tissues to leak, swell, break down, or reconstruct, and issues these instructions over a period of hours to days, weeks, months, or years.
Increased understanding of interactions between inflammation on the one hand and the nervous system on the other hand represents one of the most important developments in inflammation biology of the last two decades. An explosion of insight followed the discovery that the inflammatory and nervous systems interact through the far-reaching, highly arborized vagus nerve in the “inflammatory reflex” (Tracey, 2002). T cells help induce inflammation through release of the neurotransmitter acetylcholine (Cox et al., 2019). Inflammatory cell autacoids and cytokines activate sensory neurons, while neuropeptides, neurotransmitters, and neuron-derived alarmins promote or restrain inflammation (Kabata and Artis, 2019; Moriyama et al., 2018; Nagashima et al., 2019; Yang et al., 2021). Certain neuronal guidance molecules prolong inflammation (Plant et al., 2020) or resolve it (Körner et al., 2021). Strikingly, the brain houses a form of inflammatory memory, as discussed in the following section.
Inflammatory memory
Appreciation has grown for forms of memory in the innate immune system, sometimes called “trained immunity” (Saeed et al., 2014). Likewise, evidence has mounted for an analogous form of inflammatory memory, namely, epigenetic changes that outlast a bout of inflammation and facilitate the recurrence or persistence of inflammation in the host or its emergence in the host’s offspring.
For example, inflammation can have an impact on hematopoietic precursors in the bone marrow that lasts long after inflammation has subsided. Inflammation can skew hematopoietic stem cells toward myelopoiesis, and increased numbers of myeloid cells can promote further inflammation (Chavakis et al., 2019). Inflammatory suppression of hematopoiesis can help select for the emergence of clones better able to withstand it (Avagyan et al., 2021; Caiado et al., 2021; Trowbridge and Starczynowski, 2021). Some of these clones may give rise to myelodysplastic syndrome or leukemia. However, nonmalignant clones that withstand the myelosuppression of inflammation can persist and expand in a manner that is markedly dependent on age (Jaiswal et al., 2014). In turn, this clonal hematopoiesis promotes inflammation, notably in the cardiovascular system (Libby and Ebert, 2018). In this sense, inflammation imprints a memory of itself in hematopoietic cells that is recalled and amplified in later years. Clonal hematopoeisis may be one of the major explanations for the association of age with an inflammatory diathesis, sometimes called “inflammaging,” other features of which are discussed further below.
Mature myeloid cells can also show lasting, epigenetically mediated effects of an inflammatory experience. For example, intraperitoneal injection of lipopolysaccharide (LPS) in mice induced long-lasting epigenetic changes in their microglia that impacted the responses of the microglia to inflammatory stimuli 6 months later (Wendeln et al., 2018). Pneumonia altered the epigenome of pulmonary alveolar macrophages, leading to a sustained defect in their phagocytic capacity (Roquilly et al., 2020).
Epithelial and mesenchymal cells can display inflammatory memory as well (Niec et al., 2021). For example, dermal inflammation induced by a toll-like receptor 7 (TLR7) agonist, abrasion, or fungal infection epigenetically altered epithelial stem cells such that they responded much faster to a long-delayed second insult (Naik et al., 2017). Injection of an agent that inflames the pancreas led to transcriptional and epigenetic changes in pancreatic acinar cells that reduced their inflammatory response to a subsequent challenge, while promoting their malignant transformation (Del Poggetto et al., 2021). Transient induction of inflammation in neonatal mice led to accumulation of Th2 cells alongside dermal fibroblasts, which led in turn to changes in fibroblastic responses to a subsequent injury (Boothby et al., 2021).
Prenatal inflammation can increase the incidence of inflammatory, neurodevelopmental, and behavioral disorders in adult progeny. For example, infection of pregnant mice induced IL-6, which altered the fetal stem cell epigenome in such a way as to predispose adult offspring to inflammation (Lim et al., 2021). Similarly, infection of pregnant mice induced IL-17A, which altered the maternal microbiota in such a way that offspring had epigenetic changes in their CD4+ T cells (Kim et al., 2022). Elevation of maternal IL-17A in response to injection of poly(I:C) altered their adult offspring’s behavior (Shin Yim et al., 2017). Exposure of pregnant mice to LPS altered the inflammatory responses of microglia in their adult offspring (Schaafsma et al., 2017).
Recently, Koren et al. (2021) identified specific neurons in the mouse posterior insular cortex that were activated during experimental colitis and other neurons that were activated during peritonitis. After the inflammation had subsided, the investigators activated the neurons that had responded to colitis, and this elicited a recurrence of lymphocyte accumulation in the colonic mucosa. When the investigators instead activated neurons that had responded to peritonitis, this elicited some of the inflammatory cell accumulations and cytokine elevations that characterized the original bout of peritonitis. These recall responses were mediated through the autonomic nervous system (Koren et al., 2021). This landmark study suggests the possibility that there is a fifth general route to nonresolving inflammation—understood here as recurring inflammation— besides the four routes described earlier (Nathan and Ding, 2010) and summarized in Table 1, namely, conscious or subconscious mental recall of a previous bout of inflammation. The signs of inflammation induced by activation of the neuronal “engram” were only a subset of what was seen after oral administration of dextran sodium sulfate or intraperitoneal injection of zymosan (Koren et al., 2021). However, it now seems possible that remembering the experience of inflammation might synergize with other factors to delay its resolution or contribute to flares, such as in relapsing-remitting multiple sclerosis or systemic lupus erythematosus. However, it will be difficult to establish whether this form of inflammatory memory occurs in people. And it will be important not to fault patients for reflecting on their illness. How could they not?
Inflammatory aging (“inflammaging”)
Aging promotes inflammation in additional ways besides through the inflammatory memory associated with the age-dependent expansion of clonal hematopoiesis. Aging-associated obesity is a major driver of inflammation (Rohm et al., 2022). Caloric restriction, which counteracts some effects of aging, reduced the expression of the platelet activating factor acetylhydrolase PLA2G7 in human and mouse myeloid cells, leading to decreased ceramide-dependent NLRP3 activation (Spadaro et al., 2022). Deletion of PLA2G7 led to lower amounts of circulating TNF-α and IL-1β in 2-year-old mice, implicating PLA2G7 as a mediator of inflammatory aging (Spadaro et al., 2022). Age-related de-repression of retrotransposable elements promotes type I IFN secretion (De Cecco et al., 2019). Mitochondrial function declines in aged T cells; T cells with an engineered mitochondrial deficiency drove a cytokine storm (Desdín-Micó et al., 2020). Epigenetic changes in T cells caused by residence in an aging host promoted clonal expansion of a subset of T cells that secreted granzyme K, which elicited inflammatory responses in other cells (Mogilenko et al., 2021). Decreased diurnal expression of the transcription factor Kruppel-like factor 4 accounted for functional defects in the macrophages of old mice (Blacher et al., 2022). Increased production of soluble VEGF receptor with age led to reduced VEGF signaling and impaired maintenance of microcapillaries, accompanied by an increase in circulating granulocytes, perivascular inflammatory cell infiltrates, elevated levels of monocyte chemoattractant-1 (MCP-1) and C-reactive protein (CRP), and immune cell infiltration of liver and fat (Grunewald et al., 2021). A study of leukocytes from 205,011 men in the UK biobank detected a markedly age-dependent loss of the Y chromosome (LOY) that reached a prevalence of 43.6% of men over 70 years of age (Thompson et al., 2019). Given the epidemiologic association between LOY and several diseases associated with nonresolving inflammation, such as obesity, cardiovascular disease, type II diabetes, and Alzheimer’s disease (Thompson et al., 2019), it deserves study whether LOY may contribute to inflammaging.
Anti-inflammatory functions of pro-inflammatory cells
A given type of cell can have a predominantly pro-inflammatory or anti-inflammatory impact, depending on context (Nathan and Ding, 2010) (Table 1). Recent evidence adds new examples and mechanisms. Regulatory T (Treg) cells are indispensable for resolution of inflammation (Hu et al., 2021), yet intradermal Treg cells promoted epidermal inflammation in wounded skin by driving keratinocytes to produce neutrophil-recruiting chemokines (Moreau et al., 2021). Platelets helped resolve the pulmonary inflammation associated with bacterial pneumonia in mice by physically trapping Treg cells in the lung and inducing a pro-resolution transcriptional program in macrophages (Rossaint et al., 2021). IgM+ B cells adhering within the pulmonary vasculature in the lungs of mice with pneumonitis induced by zymosan or Aspergillus produced lipoxin A4, reducing accumulation of neutrophils (Podstawka et al., 2021). Neutrophils helped restrain allergic inflammation in the lung by suppressing chemokine generation by type 2 innate lymphoid cells (ILC2s) (Patel et al., 2019). Neutrophil-like myeloid-derived suppressor cells appeared to be critical for suppressing inflammation in neonatal mice and human infants (He et al., 2018). Astrocytes drove inflammation in some contexts (Wheeler et al., 2020) and restrained it in others (Sanmarco et al., 2021).
Inflammatory cell death
Cell death from trauma, infection, intoxication, autoimmune attack, or the host’s inflammatory response can promote inflammation when cells dying by pyroptosis or necroptosis release IL-1α, IL-1β, IL-18, IL-33, HMGB1, galectin-1, or DNA (Newton et al., 2021; Orning et al., 2019; Russo et al., 2021). A kinase-caspase cascade readies gasdermins to form pores; the ensuing ionic imbalance activates cell surface NINJ1 protein to drill and rupture the plasma membrane (Kayagaki et al., 2021). Cells dying by apoptosis are considered non-inflammatory, but if they are not cleared, NINJ1 lyses them as well (Kayagaki et al., 2021). Clearance of apoptotic cells not only removes alarmins from the extracellular space, but helps trigger production of pro-resolving mediators, both processes being assisted by the protein developmental endothelial locus 1 (DEL-1) (Kourtzelis et al., 2019). Ferroptosis may also contribute to inflammation, including by fostering the generation and release of oxidized lipids and oxidized nucleosides (Chen et al., 2021b).
Inflammatory DNA
One of the most important advances in inflammation biology of the last decade has been the discovery of cGAS as a sensor of cytosolic DNA that generates cGAMP as an activator of STING, a driver of type I interferon production (Sun et al., 2013). The inflammation characteristic of type I interferonopathies can result both from gain of function in DNA-sensing molecules like STING and MDA5 and loss of function in any of a large number of nucleic acid metabolizing enzymes or pathways controlling mitochondrial integrity (Crow and Stetson, 2021; Li and Chen, 2018).
Recent findings have revealed diverse cGAS-STING-dependent processes by which DNA can come to act as an inflammatory stimulus. In senescent cells or cells undergoing DNA damage from irradiation, chemotherapy, or deficiencies in DNA repair pathways, fragments of chromatin in cytosolic micronuclei can activate cGAS (Crow and Stetson, 2021; Dou et al., 2017; Li and Chen, 2018). In people with systemic lupus erythematosus, maturing erythrocytes tend to exhibit defective mitophagy. Autoantibodies can opsonize the erythrocytes for ingestion by macrophages. The ingested mitochondrial DNA can find its way to activating cGAS (Caielli et al., 2021). Mitochondrial DNA was a STING-dependent driver of inflammatory cytokine production and cell death in endothelial cells in skin lesions from patients with COVID-19, and ingestion of dying endothelial cells activated cGAS-STING-dependent type I IFN secretion in their macrophages (Domizio et al., 2022). Similarly, mitochondrial DNA triggered STING-dependent inflammation in exercising mice with Parkinson’s disease-associated mutations in the mitophagy regulators parkin and PINK1 (Sliter et al., 2018). In mice fed a high fat diet, the skin microbiota induced keratinocytes to increase their expression and reverse transcription of endogenous retroviruses, whose cDNA activated the cGAS-STING pathway, leading to accumulation of IL17A-producing T cells (Lima-Junior et al., 2021). In mouse models of macular degeneration, RNA from Alu retroelements, rather than leading to dsDNA, acted by an unclear mechanism to promote release of mitochondrial DNA, which activated cGAS (Kerur et al., 2018).
However, oxidized mitochondrial DNA can also contribute to inflammation in other ways, such as by binding and helping to activate NLRP3 (Zhong et al., 2018). Further, endogenous retroviruses can contribute to inflammation without acting through cGAS. For example, many patients with inflammatory bowel disease have a colonic deficiency of the histone methyltransferase SETBD1; in SETBD1-deficient mice, mobilization of retroviruses triggered necroptosis in intestinal stem cells in a ZBP-1-, RIP3-dependent manner (Wang et al., 2020). DNA released from dying cells can be taken up by macrophages via its attached histones, which are recognized by the C-type lectin Clec2d, and delivered to endosomes, where the DNA can trigger TLR9-driven cytokine and chemokine release (Lai et al., 2020). Extracellular histones themselves promote inflammation when they cause membrane disruption, leading to necrotic cell death (Silvestre-Roig et al., 2019).
Inflammation-regulating metabolites
Both the host and its microbiota are sources of metabolites with pro- or anti-inflammatory actions. Several intermediates in the host’s central carbon metabolism such as pyruvate and α-ketoglutarate are α-ketoacids that can act as antioxidants by undergoing oxidative decarboxylation upon encountering hydrogen peroxide (O’Donnell-Tormey et al., 1987). Those molecules and dicarboxylic acids can also acylate proteins, modifying their activity, and some of them bind G-protein coupled receptors. It is difficult to unravel which of these effects account for the anti-inflammatory activity of fumarate (given as a dimethyl ester) in multiple sclerosis (Liu et al., 2021) or pyruvate (given as an ethyl ester) in diverse preclinical models of inflammation (Koprivica et al., 2022). Among the acylation targets of succinate is gasdermin D, which when so modified, no longer supports pyroptosis (Humphries et al., 2020). The inflammation-resolving effects of IL-33 have been traced to its promotion of increased production of itaconate (Faas et al., 2021). Among the diverse effects of itaconate are inhibition of the NLRP3 inflammasome and activation of Nrf2 and ATF3, transcription factors with anti-inflammatory regulons (Peace and O’Neill, 2022).
Many microbiotal metabolites can affect inflammation, including through epigenetic regulation (Krautkramer et al., 2021). This branch of inflammation research took off with the discovery by Hazen and his colleagues that microbiotal metabolism of dietary phosphocholine and carnitine leads to trimethylamine-N-oxide, which is both a biomarker for and a driver of vascular inflammation, leading to both atherosclerosis (Koeth et al., 2013; Wang et al., 2011) and stroke (Zhu et al., 2021). Microbiotal metabolism of L-tyrosine to p-cresol reduced chemokine production by airway epithelial cells (Wypych et al., 2021). Microbiotal metabolism of tryptophan to indole and indoxyl sulfate suppressed miR-181 in white adipose tissue, preventing the tissue from becoming inflamed (Virtue et al., 2019), and generated ligands for the arylhydrocarbon receptor, suppressing inflammation (Hezaveh et al., 2022; Lamas et al., 2020).
While the responsible microbiotal products remain to identified in many cases, a voluminous literature documents the profound influence of the microbiota on host inflammatory responses in the gut, lung, brain, and skin (Agirman et al., 2021; Blander et al., 2017; Chen et al., 2018; Rosshart et al., 2019).
Suppressing inflammation
The list of potential targets for reducing nonresolving inflammation in one or more diseases has probably never been longer. The sampling that follows is biased toward agents not included in several wide-ranging reviews (Dinarello, 2010; Goldfine and Shoelson, 2017; Henderson et al., 2020; Rohm et al., 2022). Even so, it is far from complete. The selections are meant only to suggest the diversity of potential new approaches.
Enzyme targets include cGAS (Ablasser and Chen, 2019); the kinases JAK1/2 (Hoang et al., 2021), ephrin-B3 (Clark et al., 2021), and Fgr (Crainiciuc et al., 2022); the tyrosine phosphatase SHP2 (Paccoud et al., 2021); the proteases proprotein convertase subtilisin/Kexin 9 (PCSK 9) (Patriki et al., 2022) and the immunoproteasome (Ah Kioon et al., 2021; Kirk et al., 2021); the 8-oxoguanine DNA glycosylase OGG-1 (Visnes et al., 2018); and the histone 3 Lys27 trimethyltransferase Ezh2 (Zhang et al., 2018). Adaptor protein targets include STING (Ablasser and Chen, 2019) and NRLP3 (Wang et al., 2022). Among channel targets is transient receptor potential cation channel member A1 (TRPA1) (Balestrini et al., 2021). Targets that might reduce neutrophil accumulation in inflammatory sites include dipeptidyl peptidase 1 (a setting in which it acts non-enzymatically [Choudhury et al., 2019]) and the formation of potent heterodimers of particular chemokines (von Hundelshausen et al., 2017). Synthetic versions of natural product oleanane triterpenoids have multiple targets and resolve inflammation in a wide range of preclinical models, largely through reactions with cysteine residues (Liby and Sporn, 2012), as also seen with anti-inflammatory actions of itaconate and fumarate.
Antibody to the soluble axon guidance protein neogenin promoted formation of pro-resolving mediators (Schlegel et al., 2019). A retinoic acid receptor agonist suppressed the sterile inflammation of stroke, apparently by increasing the ability of inflammatory myeloid cells to clear alarmins (Shichita et al., 2017). An unusual addition to the list of receptors as targets is the endoplasmic reticulum sigma-1 receptor, whose agonistic engagement restrained inflammatory cytokine production (Rosen et al., 2019).
Exercise induced a circulating complement inhibitor, clusterin, with anti-inflammatory properties (De Miguel et al., 2021). Inhibition of complement activation accounts for much of the inflammation-resolving action of apolipoprotein E (Yin et al., 2019). High-density lipoprotein (HDL) can also reduce inflammation, in this case by suppressing TLR levels (De Nardo et al., 2014). HDL has been used to form nanoparticles that delivered inhibitors of TOR and of CD40 signaling to macrophages, leading to acceptance of allografts (Braza et al., 2018). Other kinds of nanoparticles have been used to deliver bilirubin to suppress inflammation in rodent models of colitis and pancreatitis (Vítek and Tiribelli, 2020).
Multiple cytokines have been targeted in efforts to reverse nonresolving inflammation. Less common are studies that demonstrate the benefit of infusing a cytokine. Delivery of VEGF counteracted inflammation in aging mice (Grunewald et al., 2021). GDF15, a TGFβ superfamily member, promoted the ability of mice to tolerate inflammation (Luan et al., 2019). miR-342 was found to mediate the anti-inflammatory response of Treg cells to glucocorticoids (Kim et al., 2020). miR-223 suppressed inflammation by inhibiting expression of the NLRP3 inflammasome (Neudecker et al., 2017). BCG vaccination reduced inflammatory biomarkers in the blood of volunteers tested 3 months later (Koeken et al., 2020). Anti-inflammatory effects of electrical stimulation of the vagus nerve have been documented in the clinic (Pavlov et al., 2020).
These examples teach us that despite the complexity of inflammation, the diversity of its causes, the wide range of tissues in which it can originate, and its propensity to exert systemic effects, there is a wide variety of molecular targets whose modulation can suppress it. Unfortunately, this does not mean that these targets are mutually non-redundant and universally involved. Each anti-inflammatory intervention has a partial effect. The broader the effect, the greater the risk of toxicity. As yet, we know of no single target for a magic bullet.
Modulating inflammation
There is intense interest in “re-programming” tumor-infiltrating myeloid cells so that instead of suppressing T cell-mediated tumor rejection, they contribute to control of the malignancy. Selected examples illustrate the breadth of approaches.
Administration of an inhibitor of inducible nitric oxide synthase or iNOS (NOS2) plus taxane induced tumor shrinkage in a high proportion of women with locally advanced breast cancer or metastatic triple-negative breast cancer, and some tumors regressed completely (Chung et al., 2021). Given that taxol induces macrophages to release TNFα (Ding et al., 1990), both of the experimental drugs in that study likely modulated inflammation. Macrophages from breast cancer patients’ pleural effusions were activated to kill tumor cells when exposed to IFN-γ and monophosphoryl lipid A (an LPS derivative) and administration of those agents to mice induced their tumor-associated macrophages to express iNOS, TNFα, and IL-12 in connection with improved responses to chemotherapy (Sun et al., 2021). The combination of IFN-γ and LPS is a powerful inducer of iNOS in mouse macrophages (Xie et al., 1992). Exosomes delivering anti-sense oligonucleotide to STAT6 drove TAMs in mouse models of colorectal and hepatocellular carcinoma toward expression of IL-1β, IL-12, TNFα, and iNOS, resulting in marked suppression of tumor growth (Kamerkar et al., 2022). The findings of Chung et al. (2021), Sun et al. (2021), and Kamerkar et al. (2022) recall a decades-old literature showing that iNOS can both suppress T cells and kill tumor cells. Which effect dominates in a tumor likely depends on the flux of reactive nitrogen species, but this will not be evident from measuring the level of iNOS mRNA. Output from iNOS will depend on the supply of its substrates (L-arginine, oxygen, and NADPH) and cofactor (tetrahydrobiopterin). Less obviously, iNOS, unlike other proteins made by the same macrophages, is selectively dependent for its synthesis and stability on levels of L-arginine, which can be depleted in an inflammatory site by another macrophage product, L-arginase (El-Gayar et al., 2003), whose expression was likely reduced by suppression of STAT6 (Kamerkar et al., 2022).
Multiple receptors on tumor-associated macrophages (TAMs) have been targeted to improve tumor control. Agonistic anti-CD40 mAb plus gemcitabine led to regression of some human pancreatic carcinomas, and in mouse models, promoted infiltration of the tumors by tumoricidal macrophages (Beatty et al., 2011). Likewise, an agonistic anti-CD40 mAb combined with a CSF-1R inhibitor drove TAMs in a mouse melanoma model to secrete TNFα, IL-6, and IL-12 and suppressed tumor growth (Perry et al., 2018). Interference with the anti-phagocytic effect of tumor cell CD47 acting on macrophage SIRPα shrank tumors in mice and people and improved responses to chemo- and immunotherapy (Advani et al., 2018; Kosaka et al., 2021; Weiskopf et al., 2013). Prostate cancer cell-derived IL-1β increased MARCO expression on macrophages; MARCO engagement with lipids induced macrophages to make CCL6; CCL6 promoted prostate cancer cell metastasis; and anti-MARCO antibody reduced tumor growth (Masetti et al., 2022). A synthetic agonist of the CD206 mannose receptor reversed TAMs’ immunosuppressive phenotype and improved the response of mice to chemotherapy and immunotherapy (Jaynes et al., 2020).
TAMs that better support chemo- and immunotherapy were elicited in a mouse melanoma model by intravenous injection of nanoparticles of phospholipids, cholesterol, and apolipoprotein A that delivered a cargo of a synthetic, lipidated analog of the bacterial peptidoglycan subunit, muramyl dipeptide (Priem et al., 2020). Inhibition of CXCR2 reduced the accumulation of neutrophils and myeloid-derived suppressor cells (MDSCs) in pancreatic adenocarcinomas in mice and improved the response to checkpoint blockade (Steele et al., 2016). A diet that promoted microbial generation of STING ligands induced TAMs to produce type I IFN, and this was associated with improved responses to immunotherapy in mice, with correlative evidence in melanoma patients (Lam et al., 2021). Tumor cell-derived histamine influenced TAMs to suppress CD8+ T cell function; a blocker of histamine receptor HRH1 augmented responses to immunotherapy in mice (Li et al., 2022). Yet another approach to favorably modulating the inflammatory environment in a tumor is to irradiate the tumor (Brandmaier and Formenti, 2020).
Inflammatory inequity
People can develop nonresolving inflammation in response to their physical, economic, and psychosocial environments, including through the air they breathe, the temperatures they experience, the diet they access, and the stresses they endure from poverty, discrimination, or dysfunctional relationships (Furman et al., 2019). When these adverse influences fall disproportionately on a geographic community or a group of people whose shared geographic ancestry is associated with the biologically false social construct of “race,” we have “inflammatory inequity.” Though the supporting literature for inflammatory inequity is vast, causal influences are inter-related, mechanisms are complex, outcomes are long term, quantification is difficult, and controlled experiments are rare. Such methodologic limitations should not be taken to diminish the significance of inflammatory inequity.
For example, inhaling fine particulates causes inflammation that contributes to cardiovascular disease and insulin resistance (Bhatnagar, 2022). Metals and organic compounds borne on the particles may generate reactive oxygen species (Bhatnagar, 2022). The particles activate NLRP3 and trigger production of TNFα and IL-β (Cao et al., 2022; Zheng et al., 2018). Products of lipid and DNA (per)oxidation appear in the circulation (Bhatnagar, 2022). Sources of inflammatory particulates include power plants, automobile exhausts, forest fires (whose number and size are increasing with climate change), and unventilated indoor cooking. In the United States, communities with large Black and Hispanic contingents are disproportionately exposed to the first two sources. According to one study, “among zip codes with high levels of PM2.5 [fine particulate matter], 90% were predominantly African American” (Dey and Dominici, 2021). Mortality rates from COVID-19 have been strongly linked to levels of air pollution (Dey and Dominici, 2021; Frontera et al., 2020; Mendy et al., 2021; Pozzer et al., 2020), magnifying the inflammatory inequity.
Climate change brings many regions more days with difficult-to-tolerate temperatures. C-reactive protein, an inflammatory biomarker, rises with heat exposure (Kang et al., 2020).
Childhood under-nutrition and environmental enteric dysfunction are associated with biomarkers of inflammation (Victora et al., 2021). In Tanzania, adoption of a “western” diet in association with urbanization was associated with an inflammatory transcriptome in unstimulated whole blood (Temba et al., 2021). In industrialized countries, “food deserts” in impoverished communities lead to nutritional imbalances characteristic of the worst features of the “western diet.”
Mice stressed by social isolation, cage switching, or physical constraint increased their circulating IL-6 levels and became more likely to die when injected later with LPS (Qing et al., 2020). If a child’s family was poor when he/she was under 3 years of age, his/her IL-6 levels were likely to be higher when measured at age 9 (Kokosi et al., 2021). People who experienced early life adversity, including from poverty, had elevated production of inflammatory mediators and reduced responsiveness to the anti-inflammatory actions of glucocorticoids throughout their lifespans (Chen et al., 2021a).
Challenges
Despite an extensive preclinical and clinical anti-inflammatory pharmacopoeia (Dinarello, 2010; Goldfine and Shoelson, 2017; Henderson et al., 2020; Rohm et al., 2022), as yet there is no drug that abolishes nonresolving inflammation in the majority of people treated, in the sense that patients remain free of inflammation when they stop taking the drug. There is no single drug that benefits a substantial proportion of those treated for nonresolving inflammation no matter which inflammatory disease they have. Few drugs that afford substantial benefit by strongly mitigating nonresolving inflammation are free of the risk of major toxicities (e.g., Ytterberg et al., 2022). There is no way short of clinical trials to establish which of the diseases that nonresolving inflammation underpins will be most responsive to a given anti-inflammatory agent. We do not have a non-empirical basis for rationally designing combination anti-inflammatory therapies.
Though investigators have worked heroically and swiftly under difficult conditions to characterize and mitigate the lethal impact of acute inflammation in SARS-CoV-2 infection, it is still not clear how best to minimize tissue damage in COVID-19 through appropriately timed anti-inflammatory interventions that spare anti-viral responses. This challenge may fade if effective antivirals become widely available and vaccination and non-lethal infection increase population immunity.
However, another COVID-19 challenge is likely not to fade but to grow—post-acute sequelae of SARS-CoV-2 infection (PASC, or “long COVID”). PASC has reportedly afflicted 10%–30% of people who recovered from COVID-19 without hospitalization and 76% of those who were hospitalized (Phetsouphanh et al., 2022). Many of them show persistently elevated levels of IFN-β, IFN-γ, IFN-λ2/3, and IL-6 (Phetsouphanh et al., 2022). An increased risk of inflammation of the heart, pericardium, and arteries persists for at least a year after the diagnosis of COVID-19 (Xie et al., 2022). The hippocampi of some people who died from COVID-19 appeared free of virus but expressed IL-1β and IL-6 (Klein et al., 2021). Perhaps some of the neurological signs and symptoms in PASC reflect nonresolving inflammation in the central nervous system. Epstein-Barr virus infection precedes the onset of multiple sclerosis but can only be said to cause the disease in a minority of those infected (Bjornevik et al., 2022). One candidate for an additional and less widely distributed causal factor is the presence in the microbiota of strains of Clostridium perfringens that produce ε-toxin (Linden et al., 2015). We should be alert to the possibility that prior SARS-CoV-2 infection, in conjunction with the microbiota or other factors, might predispose to development of inflammatory neurodegenerative disease.
Immune checkpoint blockage has given hope to many cancer patients but left a greater number disappointed. “Cold tumors”—those lacking an IFN-γ-producing and -responding immune cell infiltrate—respond poorly to immune checkpoint blockade. Infiltration of a tumor by inflammatory cells can lead to regression (Agrawal et al., 2004; Mao et al., 2021), yet is also often associated with non-responsiveness to checkpoint blockade. What combination of intratumoral and systemic inflammatory and immune responses optimizes a patient’s chance of survival? How can we elicit the “right” form of inflammation when it is not present to begin with?
Despite these challenges, there is reason for optimism. Clinical advances pre-dating the period of focus in this review have been stunning, among them the impact of antagonists of IL-1β and TNF-α on autoinflammatory diseases, rheumatoid arthritis, and inflammatory bowel disease. The marked increase in basic research into inflammation gives hope for a knowledge roadmap that will identify practically actionable, highly effective, and safely addressable pathogenic pathways for patients suffering from atherosclerosis, obesity-related metabolic syndrome, asthma, rheumatoid arthritis, inflammatory bowel disease, systemic lupus erythematosus, scleroderma, non-alcoholic steatohepatitis, Alzheimer’s disease, multiple sclerosis, and other diseases in which nonresolving inflammation plays a major role. Future work will further unmask the microbiota’s storehouse of inflammation-regulating metabolites, leading to interventions based on diet, probiotics, and drugs. Electromedicine will take its place in the resolution of nonresolving inflammation refractory to molecular therapeutics.
Yet there is more we must do to reduce nonresolving inflammation that lies beyond biomedical research and the comfort zone of those who conduct it. Physicians and scientists must work to persuade voters and policymakers to act for communities and populations to prevent and reverse the degradation of neighborhoods, air, water, and climate, the sequelae of systematic discrimination, and the trans-generational perpetuation of poverty. Physicians and scientists are privileged with knowledge of the biological consequences of societal inaction. With that privilege comes a responsibility to help bring change.
Acknowledgments
Preparation of this review was supported by the Abby and Howard P. Milstein Program in Chemical Biology and Translational Medicine. The Department of Microbiology & Immunology is supported by the Randolph Hearst Trust.
Declaration of interests
The author is a co-inventor on patents related to immunoproteasome inhibitors; a scientific co-founder and equity holder in IpiNovyx, Inc; a member of the National Therapeutic Areas scientific advisory board for Pfizer External Sciences & Innovation; and a member or the scientific advisory board of Leap Therapeutics.
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==== Front
Biomacromolecules
Biomacromolecules
bm
bomaf6
Biomacromolecules
1525-7797
1526-4602
American Chemical Society
35344365
10.1021/acs.biomac.2c00112
Article
Injectable Slow-Release Hydrogel Formulation of a Plant Virus-Based COVID-19 Vaccine Candidate
Nkanga Christian Isalomboto †
https://orcid.org/0000-0002-0773-0677
Ortega-Rivera Oscar A. †∥
Shin Matthew D. †∥
https://orcid.org/0000-0002-1601-9369
Moreno-Gonzalez Miguel A. †∥
https://orcid.org/0000-0002-0130-0481
Steinmetz Nicole F. *†‡§∥⊥#
† Department of NanoEngineering, University of California San Diego, 9500 Gilman Dr., La Jolla, California 92039, United States
‡ Department of Bioengineering, University of California San Diego, 9500 Gilman Dr., La Jolla, California 92039, United States
§ Department of Radiology, University of California San Diego, 9500 Gilman Dr., La Jolla, California 92039, United States
∥ Center for Nano-ImmunoEngineering, University of California San Diego, 9500 Gilman Dr., La Jolla, California 92039, United States
⊥ Moores Cancer Center, University of California San Diego, 9500 Gilman Dr., La Jolla, California 92039, United States
# Institute for Materials Discovery and Design, University of California San Diego, 9500 Gilman Dr., La Jolla, California 92039, United States
* Email: nsteinmetz@ucsd.edu.
28 03 2022
11 04 2022
23 4 18121825
24 01 2022
09 03 2022
© 2022 American Chemical Society
2022
American Chemical Society
This article is made available via the PMC Open Access Subset 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 the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Cowpea mosaic virus (CPMV) is a potent immunogenic adjuvant and epitope display platform for the development of vaccines against cancers and infectious diseases, including coronavirus disease 2019. However, the proteinaceous CPMV nanoparticles are rapidly degraded in vivo. Multiple doses are therefore required to ensure long-lasting immunity, which is not ideal for global mass vaccination campaigns. Therefore, we formulated CPMV nanoparticles in injectable hydrogels to achieve slow particle release and prolonged immunostimulation. Liquid formulations were prepared from chitosan and glycerophosphate (GP) before homogenization with CPMV particles at room temperature. The formulations containing high-molecular-weight chitosan and 0–4.5 mg mL–1 CPMV gelled rapidly at 37 °C (5–8 min) and slowly released cyanine 5-CPMV particles in vitro and in vivo. Importantly, when a hydrogel containing CPMV displaying severe acute respiratory syndrome coronavirus 2 spike protein epitope 826 (amino acid 809–826) was administered to mice as a single subcutaneous injection, it elicited an antibody response that was sustained over 20 weeks, with an associated shift from Th1 to Th2 bias. Antibody titers were improved at later time points (weeks 16 and 20) comparing the hydrogel versus soluble vaccine candidates; furthermore, the soluble vaccine candidates retained Th1 bias. We conclude that CPMV nanoparticles can be formulated effectively in chitosan/GP hydrogels and are released as intact particles for several months with conserved immunotherapeutic efficacy. The injectable hydrogel containing epitope-labeled CPMV offers a promising single-dose vaccine platform for the prevention of future pandemics as well as a strategy to develop long-lasting plant virus-based nanomedicines.
National Science Foundation 10.13039/100000001 CHE-2116298 document-id-old-9bm2c00112
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This article is made available via the ACS COVID-19 subset 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 the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
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pmcIntroduction
The pandemic of coronavirus disease 2019 (COVID-19) is an unprecedented global public health challenge due to the transmissibility, morbidity, and mortality associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). There were more than 83 million positive cases and 3 million deaths in the first year following the initial outbreak in December 2019.1−3 Several multidose vaccines were rapidly developed and approved, including the Pfizer-BioNTech BNT162b2,4 Oxford-AstraZeneca,5 and Moderna vaccines.6 However, despite global mass vaccination campaigns beginning in December 2020, the number of positive cases had risen to more than 281 million by the end of 2021, with ∼5 million deaths.7 These data indicate that global morbidity increased 2.4-fold during the vaccination period,3 whereas the mortality rate decreased.7 In part, these figures represent the contrast between the exponential spread of the virus and the logistical and supply-chain issues facing the distribution of vaccines,8 including the requirement for cold chain continuity for some of the products9 and the choice between prioritizing first dose coverage and the completion of two-dose schedules according to clinical guidelines.10−13 In this context, a long-acting single-dose vaccine would be an ideal alternative, providing wider coverage while ensuring complete protection by eliciting sustained immunological responses.
During the pandemic, the emergence of more contagious SARS-CoV-2 variants14−17 that can overcome prior immunity18 has highlighted the potential for reinfection and loss of vaccine efficacy.19 This can be addressed by updating vaccines to maintain protection,20,21 but an alternative solution is the development of vaccines that elicit broadly neutralizing antibodies. At the end of 2021, there were 23 COVID-19 vaccines already approved for emergency use in humans and 329 vaccine candidates undergoing clinical (111) or preclinical (218) tests.22 These represented a range of conventional and novel vaccine platforms including inactivated whole viruses (e.g., CoronaVac and Covaxin), mRNA-loaded liposomes (e.g., BNT162b2 and mRNA-1273), adenovirus vectors (e.g., ChAdOx1 nCoV-19, CTII-nCoV, and Sputnik V), and virus-like particles (e.g., NVX-CoV2373).23 These vaccines elicit a neutralizing antibody response against the SARS-CoV-2 spike (S) protein and achieved 65–96% protective efficacy against morbidity and mortality in phase 3 trials.4,5,24−28 The vaccines are effective because the S protein protrudes from the virus surface and is recognized by angiotensin-converting enzyme 2 on the host cell surface, which facilitates the uptake of viral particles.29 However, the efficacy of vaccines targeting the S protein declines due to the rapid evolution of variants that accumulate mutations.30−33 Mutations occur in the N-terminal domain, including L18F, D80A, D215G, and Δ242-244; the receptor-binding domain (RBD), including K417N, E484K, and N501Y; and other regions that maintain spike stability and functionality, including D614G and P681R.34−37 It may be more appropriate to select broadly conserved epitopes for the development of vaccines rather than using the entire S protein.
The RBD is the binding site for most neutralizing antibodies against SARS-CoV-2.38 We recently demonstrated that three B-cell epitopes (peptide sequences 553–570, 625–636, and 809–826), which are common to many SARS-CoV-2 variants, are suitable for the development of effective pan-specific vaccines against SARS-CoV-2.39 To enhance the immune response, these peptide epitopes were attached to cowpea mosaic virus (CPMV) or virus-like particles derived from bacteriophage Qβ, which function as a combined adjuvant and epitope nanocarrier, promoting trafficking across draining lymph nodes and interactions with antigen-presenting cells.40,41 CPMV has a bipartite RNA genome encapsulated in a 30 nm icosahedral capsid consisting of 60 asymmetrical copies of small (24 kDa) and large (41 kDa) coat protein (CP) subunits.42 Both the capsid and RNA are immunostimulatory, therefore rendering CPMV a potent adjuvant. For example, the strong immunogenicity of native CPMV44,45 makes it an effective in situ vaccine against various tumors in mouse models41,46,47 and canine patients.48 It also serves as a delivery platform and multiple copies of the SARS-CoV-2 peptide epitopes can be displayed via chemical bioconjugation.43 When tested as soluble prime-boost formulations, microneedle patches, or slow-release poly(lactic-co-glycolic acid) (PLGA) implants, the CPMV- and Qβ-based COVID-19 vaccine candidate formulations elicited neutralizing antibodies against SARS-CoV-2, and the soluble prime-boost vaccine (CPMV conjugated to the epitope sequence 809–826) elicited a neutralization titer comparable to Moderna’s mRNA-1273 vaccine.39 The Qβ formulation maintained efficacy when formulated as a PLGA implant, but in a previous study with a similar approach against SARS-CoV, the efficacy of CPMV-based vaccines declined significantly in this format when administered as a single dose.43 This reflected the lower immunostimulatory response caused by the loss of CPMV RNA during freeze-drying, as required for implant formulation.49 The efficacy of a CPMV-based vaccine displaying the 809–826 epitope sequence (826-CPMV) could perhaps be improved by investigating alternative single-dose formulations, such as those based on the natural biopolymer chitosan.
Chitosan is a polysaccharide produced by the deacetylation of chitin.57 It is generally regarded as safe as an excipient and is therefore considered to be biocompatible, nonimmunogenic, and biodegradable.50,51 It is already approved for products such as BST-CarGel for the regeneration of cartilage.52 Many studies have reported excellent immune-enhancing capability of chitosan as a vaccine adjuvant for nasal,53 parenteral,54 and subcutaneous administrations.55 Chitosan-based hydrogels are produced by mixing chitosan with β-glycerophosphate (GP) to yield liquid formulations that are fluid at room temperature but form a gel at body temperature. This thermo-responsive behavior is driven by the interactions between GP and the polar backbone of chitosan, which prevents polymer precipitation, balances the pH, and triggers gelation when heated.56−58 Such thermo-responsive hydrogels are advantageous because they are simple to prepare and inject.59,60 Chitosan/GP hydrogels have been extensively used for drug delivery,61,62 tissue regeneration/repair,63,64 and the slow release of nanoparticles.65,66
Here, we report the development of an in situ forming chitosan/GP hydrogel loaded with 826-CPMV as a single-dose vaccine against COVID-19. We initially prepared chitosan/GP hydrogels containing native CPMV particles for formulation design and optimization before testing CPMV labeled with the fluorophore sulfo-cyanine 5 (Cy5) as a cargo model for the characterization of in vitro/in vivo release profiles by fluorescence analysis. We then prepared 826-CPMV particles formulated as chitosan/GP hydrogels and immunized BALB/c mice subcutaneously. We monitored the antibody response for 20 weeks, comparing the hydrogel to soluble formulations in terms of antibody titers and subtypes.
Experimental Section
Preparation of CPMV Nanoparticles
Preparation of Native CPMV
CMPV was propagated in and extracted from the leaves of black-eyed pea plants (Vigna unguiculata) as previously described.67,68 The frozen leaf tissue (100 g) was homogenized in 300 mL of 0.1 M potassium phosphate (KP) buffer (pH 7.0) and then filtered and centrifuged (18 500g, 20 min, 4 °C) to remove plant debris. The supernatant was extracted with 1:1 chloroform:1-butanol, and the aqueous phase was mixed with 0.2 M NaCl and 8% PEG 8000 for CPMV precipitation. The mixture was centrifuged (30 000g, 15 min, 4 °C), and the pellet was resuspended in 0.01 M KP buffer. After a further round of centrifugation (13 500g, 15 min, 4 °C) to remove aggregates, the supernatant was purified on a 10–40% sucrose gradient. The bright bands were isolated and purified by ultracentrifugation (42 000 rpm, 2.5 h, 4 °C) using an Optima L-90K centrifuge with rotor type 50.2 Ti (Beckman Coulter, Brea, CA, USA). CPMV particles were dispersed in 0.1 M KP buffer, and the CP concentration was determined using a NanoDrop 2000 UV/visible spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) at 260 nm using a molar extinction coefficient (ε260 nm) of 8.1 mg–1 mL cm–1.
Conjugation of CPMV to Sulfo-Cy5
We prepared Cy5-CPMV particles by conjugating CPMV lysine residues to the N-hydroxysuccinimide (NHS)-activated ester of Cy5 (Lumiprobe, Hunt Valley, MD, USA). Covalent attachment was achieved by reacting 25 μL of 50 mg mL–1 NHS-Cy5 (5 equiv per CP) with 10 mg of CPMV in 0.01 M KP buffer on an orbital shaker for 2 h at room temperature. The Cy5-CPMV conjugate was continuously purified using a 100 kDa molecular weight cutoff (MWCO) centrifugal filter (500g, 5 min, room temperature) until a clear filtrate was obtained. The concentration of Cy5-CPMV particles was determined by UV–vis spectrophotometry as described above, and the Cy5 absorption at 647 nm (ε647 nm = 271 000 L mol–1 cm–1) was used to estimate the dye loading per particle.
Conjugation of CPMV to Epitope 826
CPMV particles were labeled with the bifunctional PEGylated cross-linker SM(PEG)4 (Thermo Fisher Scientific) using a reactive NHS-activated ester that targets lysine residues. The reaction was performed by mixing 2000-fold molar excess of SM(PEG)4 with 2 mg of CPMV particles in 0.01 M KP buffer for 2.5 h at room temperature. The PEGylated intermediate was purified using a 100 kDa MWCO centrifugal filter (16 000g, 5 min, 4 °C). The maleimide handles of the PEGylated intermediate were then reacted with the cysteine residue of epitope 826 (GenScript Biotech, Piscataway, NJ, USA) by mixing 2 mg of PEGylated CPMV with 0.2 mL of 20% Pluronic F-127 (MilliporeSigma, Burlington, MA, USA) in DMSO69 and then adding 0.12 mL of 20 mg mL–1 epitope 826 in DMSO and stirring overnight. The 826-CPMV conjugate was purified by centrifugation on a 0.1 mL 40% sucrose cushion (50 000 rpm, 1 h, 4 °C) and dialysis against 0.01 M KP buffer for 24 h at room temperature. The 826-CPMV particles were concentrated using a 100 kDa MWCO centrifugal filter (8000g, 5 min, 4 °C) and quantified by UV–vis spectrophotometry as described above. They were also visualized by transmission electron microscopy (TEM) on a Tecnai F30 instrument (FEI Company, Hillsboro, OR, USA) after staining with 2% uranyl acetate.
Characterization of CPMV Nanoparticles
Size Exclusion Chromatography
We loaded 200 μg of CPMV particles onto a Superose6 column in the ÄKTA Explorer chromatography system (GE Healthcare, Chicago, IL, USA) and eluted them with 0.1 M KP buffer (pH 7.0) at a flow rate of 0.5 mL min–1. The capsid protein, viral RNA, and conjugated Cy5 dye were detected at 260, 280, and 647 nm, respectively.
Dynamic Light Scattering
We determined the hydrodynamic diameter, polydispersity index (PDI), and zeta potential of the particles using a Zetasizer Nano ZSP Zen5600 instrument (Malvern Panalytical, Malvern, UK). Triplicate measurements were acquired over 3–5 min at room temperature with a scattering angle of 90°.
Native Gel Electrophoresis
Particles (10–20 μg) suspended in Tris/Borate/ethylenediaminetetraacetic acid (EDTA) (TBE) buffer (45 mM Tris, 45 mM boric acid, 1.25 mM EDTA in Milli-Q water) were loaded onto 1.2% agarose gels and fractionated for 30 min at 120 V and 400 mA. The gels were documented on an AlphaImager (Protein Simple, San Jose, CA, USA) under UV, red, and white light before and after staining with Coomassie brilliant blue (CBB).
Sodium Dodecylsulfate Polyacrylamide Gel Electrophoresis
Protein samples (10 μg) were analyzed side by side with SeeBlue Plus2 prestained protein standards (Thermo Fisher Scientific) on 4–12 or 12% NuPAGE polyacrylamide gels using 1× MOPS elution buffer (Invitrogen, Thermo Fisher Scientific) at 200 V and 120 mA for 40 min. Gel images were documented on the AlphaImager system under red and white light before and after CBB staining.
Hydrogel Formulation and Characterization
Preparation of Chitosan/GP Formulations
Liquid formulations were prepared by mixing the chitosan and GP solutions and vortexing the mixture with the CPMV, Cy5-CPMV, or 826-CPMV particles. The chitosan solution was prepared by dispersing 4 g of chitosan powder (Chem-Impex International, Wood Dale, IL, USA) in 180 mL of 0.1 M HCl for 2 h, followed by autoclaving for 20 min at 121 °C and homogenization by stirring overnight at room temperature.70 We prepared chitosan solutions of low molecular weight (LMW, 250 kDa), medium molecular weight (MMW, 1250 kDa), and high molecular weight (HMW, 1500 kDa). The GP solution was prepared by dissolving 5.60 g of β-glycerophosphoric acid disodium salt (MilliporeSigma) in 10 mL of deionized water and passing the solution through a 0.22 μm filter. The chitosan and GP solutions were mixed at a 5:1 (v/v) ratio,63 and different amounts of CPMV in phosphate-buffered saline (PBS) were dispersed by vortexing to yield 0 (blank), 2.25 (0.225%), and 4.5 mg mL–1 (0.450%) CPMV nanoparticles in the final formulations (Table 1). Minitab v13 (Minitab, Coventry, UK) was used for the factorial design of nine different formulations for evaluation against gelation time. CPMV 0.45% was duly selected, and the Cy5-CPMV formulations were prepared as follows: chitosan/GP solutions were vortexed with 15 mg mL–1 Cy5-CPMV at a 7:3 (v/v) ratio yielding 0.45% formulations denoted F1, F2, and F3 representing the LMW, MMW, and HMW chitosan, respectively. Formulation F3 based on HMW chitosan achieved the shortest gelation time and prolonged release profiles and was therefore used to encapsulate 826-CPMV as described for Cy5-CPMV. Blank hydrogels were prepared under the same conditions using PBS-lacking CPMV particles.
Table 1 Formulation Parameters for the Design of CPMV/Chitosan/GP Hydrogels
level chitosan molecular weight (MW) final CPMV concentration, mg mL–1 (%)
1 low MW (250 kDa) 0 (0%)
2 medium MW (1250 kDa) 2.25 (0.225%)
3 high MW (1500 kDa) 4.5 (0.45%)
Viscosity Measurements
Viscosity was measured using a parallel plate ARG2 rheometer (TA Instruments, New Castle, DE, USA). We pipetted 200 μL of each sample into the center of the parallel plate geometry, which was set at 25 °C with a gap height of 500 μm (ensuring the liquid covered the entire gap between the plates).
Determination of the Gelation Time Using the Tube Inversion Method
We incubated 1 mL of each sample (in a 1.5 mL Eppendorf tube) at 37 °C and inverted the tube every 60 s. The gelation time point was recorded when the formulation no longer flowed in the inverted tube after 30 s65
Hydrogel Swelling and Degradation In Vitro
We incubated 0.5 mL of each hydrogel sample containing Cy5-CPMV (in a 1.5 mL Eppendorf tube) at 37 °C for 45 min to ensure complete gelation. The initial height of the gel was measured before carefully adding 1 mL of PBS and agitating the tubes at 200 rpm. At predefined time intervals, the liquid phase was removed and set aside for Cy5-CPMV characterization. We added the same amount of fresh PBS and recorded the height of gel to calculate the swelling ratio (the height at any time divided by the initial height × 100).65 Following this longitudinal incubation in PBS, exhausted gels (and fresh gels) were freeze-dried and imaged by scanning electron microscopy (SEM) using a Quanta 600 ESEM (FEI Company) operating at 10 kV.
Characterization of Cy5-CPMV Released from Hydrogels In Vitro
The liquid phase set aside from the previous step was compared to a defined amount of Cy5-CPMV in PBS as a control. Fluorescence measurements were recorded on a microplate reader (Tecan, Männedorf, Switzerland) to quantify Cy5 (λEx = 600 nm, λEm = 665 nm) and estimate Cy5-CPMV release profiles.66 The particles were separated by sodium dodecylsulfate polyacrylamide gel electrophoresis (SDS-PAGE) to confirm the molecular stability of the Cy5-CPMV CP conjugates. The intactness of the particles was confirmed by native gel electrophoresis and TEM as described above.
Animal Experiments
Ethical Statements
Animal procedures were carried out according to the guidelines of the Institutional Animal Care and Use Committee of the University of California San Diego (UCSD) following the protocols approved by the Animal Ethics committee of UCSD. For all animal experiments, we used healthy BALB/c female mice (7–8 weeks old) purchased from the Jackson Laboratory (Bar Harbor, ME, USA) and hosted at the UCSD Moores Cancer Center with unlimited food and water.
Characterization of Cy5-CPMV Released from Hydrogels In Vivo
Hydrogel formulations F1–F3 (100 μL, containing 450 μg of Cy5-CPMV) or soluble Cy5-CPMV (450 μg in 100 μL of PBS) were administered as single subcutaneous injections behind the neck of shaved mice on day 0 (five mice per group). Animals were maintained on an alfalfa-free diet 1 week before the experiment and throughout the study to prevent tissue autofluorescence. The injection site was imaged at different time points under a Xenogen IVIS 200 Optical Imaging System (Caliper Life Sciences, Hopkinton, MA, USA). IVIS software was used to determine the fluorescence intensity within a region of interest (ROI) and thus evaluate the persistence of fluorescence as a marker of slow release. The F3 formulation (200 μg single subcutaneous injection) was then selected for comparison to 2 × 100 μg doses of soluble Cy5-CPMV.
Immunization Procedure
BALB/c female mice (four mice per group) were assigned to one of the following treatment groups, with all treatments involving subcutaneous injections behind the neck: (i) group 100 = prime-boost (week 0 and week 2) injections of 100 μg of soluble 826-CPMV in 150 μL of PBS; (ii) group 200 = single injection of 200 μg of soluble 826-CPMV in 150 μL of PBS; (iii) group F3 = single injection of the F3 formulation containing 200 μg of 826-CPMV; and (iv) group blank F3 = single injection of the F3 formulation without 826-CPMV. Blood samples were collected by retro-orbital bleeding before injection (week 0) and on weeks 2, 4, 8, 12, 16, and 20. Blood samples were centrifuged (2000g, 10 min, 4 °C), and the plasma was kept at −80 °C for antibody screening.
Enzyme-Linked Immunosorbent Assay
Anti-826 antibodies were detected by enzyme-linked immunosorbent assay (ELISA) as previously reported.39 Pierce maleimide-activated 96-well plates (Thermo Fisher Scientific) were rinsed three times with 200 μL per well of PBS containing 0.05% (v/v) Tween-20 (PBST), and the same washing procedure was used between all subsequent steps. The washed plates were coated with peptide epitope 826 (20 μg mL–1, 100 μL per well) in binding buffer (0.1 M sodium phosphate, 0.15 M sodium chloride, 0.01 M EDTA, pH 7.2) overnight at 4 °C. After discarding the coating solution and washing the plates, each well was blocked with 100 μL of 10 μg mL–1 cysteine in binding buffer, and the plates were incubated at room temperature for 1 h. Following the blocking step, the plasma from immunized animals was added in PBS (100 μL per well) using dilution factors of 200, 400, 800, 1600, 3200, 6400, 12,800, 25,600, 51,200, 102,400, and 204,800. After incubating for 1 h at room temperature and washing, we added the horseradish peroxidase (HRP)-conjugated goat antimouse IgG Fc-specific secondary antibody (Invitrogen, diluted 1:5000) in PBST and incubated the plates again for 1 h at room temperature. Following another wash, we added 100 μL per well of the 1-Step Ultra TMB-ELISA substrate (Thermo Fisher Scientific) and allowed the plates to develop for 5 min at room temperature before stopping the reaction with 100 μL per well of 2 N H2SO4 and reading the optical density at 450 nm on a Tecan microplate reader.
Antibody Isotyping
The ELISA protocol for anti-826 antibody screening was slightly modified for the isotyping experiment. Instead of serial dilutions, samples from weeks 4 and 12 were diluted 1:1000 in binding buffer. As secondary antibodies, we used HRP-conjugated goat anti-mouse IgG1 (Invitrogen PA174421, 1:5000), IgG2a (Invitrogen A-10685, 1:1000), IgG2b (Abcam, Cambridge, UK, ab97250, 1:5000), IgG2c (Abcam ab9168, 1:5000), IgG3 (Abcam ab98708, 1:5000), IgE (Invitrogen PA184764, 1:1000), and IgM (Abcam ab97230, 1:5000). The IgG1/IgG2a ratio was calculated, with values < 1 considered indicative of a Th1 response and values > 1 considered indicative of a Th2 response.
Statistical Analysis
Graphical data were processed and analyzed using GraphPad Prism v9.0.2 (GraphPad Software, San Diego, CA, USA), unless otherwise indicated. Depending on the datasets, data were statistically compared by one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test or two-way ANOVA using pairwise multiple comparison followed by a posttest Holm–Šidák correction. Asterisks in figures indicate significant differences between groups (*p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001).
Results and Discussion
Preparation and Labeling of CPMV Particles
CPMV was purified from infected black-eyed pea plants yielding 0.55 mg per gram of leaf tissue. The 260/280 nm absorbance ratio was 1.75, well within the 1.7–1.8 range anticipated for pure particles.68 Surface-exposed lysine side chains were conjugated to Cy5 using NHS chemistry (Figure 1A). Five equivalents of NHS-sulfo-Cy5 per CP achieved a loading efficiency of 19 Cy5 molecules per particle, which is acceptable for fluorescence imaging.71 SDS-PAGE and native agarose gel electrophoresis confirmed the attachment of Cy5 (Figure 1B,C). Illumination of the polyacrylamide gels with red light revealed fluorescent bands matching the small and large CP bands on gels stained with CBB, indicating that Cy5 was covalently linked to both polypeptides. Illumination of the native agarose gels under red light showed a fluorescent band matching the UV band (RNA signal) and the protein band on gels stained with CBB (intact particles), thus confirming that the particles were intact following bioconjugation. This was consistent with size analysis by dynamic light scattering (DLS), which showed the presence of nanometer-scale particles in the CPMV and Cy5-CPMV samples (Figure 1D). Particle integrity was verified by the single elution peak during size-exclusion chromatography: proteins were detected at 260 nm, RNA at 280 nm, and Cy5-CPMV at 647 nm (Figure 1E). The latter also confirmed the absence of aggregates, broken particles, free proteins, or free dye molecules.
Figure 1 Characterization of CPMV and Cy5-CPMV. (A) Bioconjugation reaction, labeling of CPMV with sulfo-Cy5 using NHS chemistry. Black dots on the CPMV surface represent lysine residues. (B) SDS-PAGE comparing CPMV wild-type and Cy5-conjugated CPs, demonstrating similar electrophoretic profiles and thus successful covalent attachment. (C) Native agarose gel electrophoresis demonstrating the similar electrophoretic mobility of CPMV/Cy5-CPMV (viral proteins, RNA, and Cy5 fluorophore), suggesting that the particles are intact. (D) DLS measurements indicating the nanoparticulate nature of CPMV/Cy5-CPMV samples. (E) Size-exclusion chromatography confirming the CPMV/Cy5-CPMV particle integrity by the coelution of all viral components in the same peak. The black dashed curve represents viral CP absorbance at λ = 260 nm, the blue solid curve is the RNA signal at λ = 280 nm, and the red solid line is Cy5 detected at λ = 647 nm.
Preparation and Characterization of Hydrogels Loaded with CPMV/Cy5-CPMV
Gel Formation
Chitosan is soluble in acids due to the electrostatic repulsion between its positively charged amine-protonated chains. The addition of GP neutralizes the solution (pH = 6.5–7.3) without inducing immediate precipitation or aggregation because GP deprotonates some of chitosan’s positively charged amine groups (−NH3+), allowing electrostatic attraction between the GP backbone and chitosan’s remaining −NH3+ groups, in turn exposing the glycerol moiety of GP to neighboring chitosan chains and enhancing their solubility when the temperature is below ∼23 °C.56,72 Higher temperatures trigger the transfer of protons from chitosan’s −NH3+ groups to the GP backbone, reducing the charge density of chitosan and favoring hydrophobic interchain interactions and hydrogen bonding between chitosan chains, resulting in the formation of a gel.57,58,72,73
We investigated the gelling behavior of chitosan/GP mixtures featuring three different molecular weights of chitosan (LMW = 250 kDa, MMW = 1250 kDa, and HMW = 1500 kDa) and various concentrations of CPMV (0–4.5 mg mL–1) at 37 °C. The gelation time was assessed by the flow and turbidity of each mixture following tube inversion (Figure 2A). The gelation time decreased with increasing chitosan molecular weight, but the concentration of CPMV was also relevant (Figure 2B and Table S2). This is consistent with previous studies demonstrating that solution-to-gel transition is influenced by many formulation parameters, including chitosan molecular weight and cargo loading.74 Blank formulations gelled much faster than those containing CPMV, supporting previous observations that nanoparticles occupy the space between chitosan chains and slow gelation.66 The shortest gelling time was observed for the formulations containing HMW chitosan (5–8 min). We selected the formulations with the highest load of CPMV (4.5 mg mL–1) for further characterization because this allows the maximum dosage with the smallest volume of the excipient. The formulations containing 4.5 mg mL–1 CPMV dispersed in LMW, MMW, and HMW chitosans were named F1, F2, and F3, respectively. The liquid formulation F3 was the most viscous (0.482 Pa·s), 2.4-fold more viscous than F2 (0.202 Pa·s) and 4.8-fold more than F1 (0.099 Pa·s). The viscosity modulus of F1 (and to some degree F2) decreased abruptly as the shear rate increased, whereas the viscosity modulus of F3 declined gradually (Figure 2C). This indicates much better shear-thinning and self-healing behavior,62 reflecting the presence of stronger interchain interactions as would be anticipated from the short gelation time.
Figure 2 Preparation and characterization of hydrogels. (A) CPMV particles were dispersed in chitosan/GP hydrogels. (B) Design-of-experiment plots (from Minitab software) showing the impact of two formulation variables (chitosan molecular weight and CPMV concentration) on gelation time. (C) Rheological properties of liquid formulations, showing variations in relative viscosity at 25 °C. (D) Gel height variations measured at different time points following hydrogel incubation in PBS at 37 °C (n = 3). (E) Experimental setting used for in vitro gel swelling/degradation and release analysis, showing the homogeneous dispersion of Cy5-CPMV in hydrogel F3 vs PBS. (F) Full data set showing in vitro Cy5-CPMV release from hydrogels vs soluble Cy5-CPMV/PBS at 37 °C (n = 3). (G) Release data excerpt showing the difference between the three hydrogel formulations. Asterisks indicate significant differences between groups (*p < 0.05; **p < 0.01).
Gel Swelling, Degradation, and In Vitro Release Profiles
Next, we assessed gel swelling and degradation, as well as the Cy5-CPMV release profile over 21 days in PBS at 37 °C. Although hydrogel F1 initially showed some fluctuations (Figure 2D), all formulations ultimately showed no significant change in the gel height (Figure 2E). The apparent volume of the gel therefore remained constant regardless of the composition (loaded with Cy5-CPMV particles or blank). This agrees with one earlier report,65 but in another case, the authors observed significant height fluctuations.75 The constant apparent volume of our gel suggests that the rates of gel swelling and degradation are comparable, which implies a robustness that may interfere with cargo release. However, SEM revealed that the microstructure of fresh (nonincubated) hydrogels comprised a bulky but porous matrix, which would encourage cargo release even without degradation (Figure S1). SEM images of the exhausted gels (after incubation in PBS) included abundant salt crystals, which made it difficult to determine the matrix structure (data not shown). Despite these results, the slow-release capability of the hydrogels was confirmed directly by measuring the quantity of Cy5-CPMV particles in the liquid phase (Figure 2E). The gels remained stable throughout the 21 days of testing, but we observed the gradual release of Cy5-CPMV nanoparticles from all formulations, suggesting that the particles can diffuse through the pores identified above (Figure 2F,G). The slowest release profile was observed for F3, consistent with its rapid gelation and high viscosity, followed by F1 and then F2. This suggests that the release profile is not directly related to the molecular weight of the chitosan. We found that a free suspension of Cy5-CPMV released 100% of the particles after incubation in PBS for 10 days, which was anticipated because the particles can move freely due to Brownian motion. In contrast, only 10–12% of the particles were released from the hydrogels after 21 days, reflecting a combination of physical obstruction and chemical interactions within the gel matrix.76,77
Characterization of Cy5-CPMV Released from the Hydrogels In Vitro
Having established the potential for intermolecular interactions within the hydrogel, we investigated whether the chemical reactivity of the matrix had a negative impact on nanoparticle stability. Cy5-CPMV particles released from the hydrogels on days 7 and 14 were characterized by native agarose gel electrophoresis, SDS-PAGE, and TEM. The illumination of agarose gels with red light revealed Cy5 bands that matched the RNA signal under UV light and the protein bands under white light following staining with CBB (Figure S2A). This confirmed the presence of intact particles containing all three components. Some particles remained in the loading wells, which may reflect particle aggregation or interactions with positively charged chitosan molecules affecting electrophoretic migration toward the anode. The chemical stability of the Cy5-CP conjugates was confirmed by SDS-PAGE, which showed that the protein bands corresponding to the small and large CPs after staining with CBB appeared at the same positions as the fluorescent bands representing Cy5 (Figure S2B). This confirmed that the covalent linkage between Cy5 and the particles remained stable after 14 days in the hydrogel matrix. Finally, the structural integrity of the Cy5-CPMV particles eluted from hydrogels was confirmed by TEM (Figure 3). Taken together, these observations suggest that chemically modified CPMV nanoparticles are likely to maintain their particulate and molecular integrity following encapsulation within and release from the chitosan/GP hydrogels.
Figure 3 TEM images of Cy5-CPMV released in vitro from hydrogels following incubation in PBS for 14 days, confirming the integrity and stability of Cy5-CPMV particles within the hydrogel matrix.
In Vivo Retention and Release Profiles
Cy5-CPMV-loaded formulations F1, F2, and F3 were injected subcutaneously behind the neck of shaved BALB/c mice to determine the retention and release profiles in vivo. Cy5-CPMV in PBS was injected as a control. The local retention of Cy5-CPMV was assessed over 21 days by fluorescence imaging of the injection site and ROI analysis. The signals from the single dose of soluble Cy5-CPMV decayed rapidly compared to those from the hydrogel formulations, disappearing almost completely by day 12 postinjection due to fast diffusion and clearance62 (Figure 4A). The signals from F1 and F2 lasted until day 18, and the signal from F3 was still present at the end of the experiment, indicating depot formation in situ followed by the slower diffusion of Cy5-CPMV from the injection site. Although the hydrogel significantly increased the residence time of CPMV, the excellent tissue residence time of the soluble formulation is also notable, probably reflecting the high stability of the CPMV nanoparticles. Quantitative fluorescence intensity analysis revealed that F3 was the only formulation that differed significantly from free Cy5-CPMV in terms of fluorescence decay (Figure 4B). This agrees with the observed ability of F3 to outperform the other formulations in vitro (e.g., the shortest gelation time and slower release). We also compared Cy5-CPMV local retention following subcutaneous injections of F3 (200 μg single dose) versus soluble Cy5-CPMV (100 μg every 14 days), and the outcome was intriguing. Bright fluorescence at the injection site was observed in both groups on day 15 but only in the F3 group on day 28, confirming the prolonged tissue residence due to depot formation (Figure 4C). Although the reliability of fluorescence signals is limited by the potential for quenching or particle aggregation (especially in the confined subcutaneous injection site), the results nevertheless allowed us to compare the rate of Cy5-CPMV particle clearance when using soluble and slow-release formulations, supporting the enhanced local retention achieved by the administration of Cy5-CPMV in chitosan/GP hydrogels.78
Figure 4 In vivo retention/release of Cy5-CPMV from hydrogels (F1, F2, and F3) vs soluble Cy5-CPMV. (A) Fluorescence images and (B) fluorescence intensity at the injection site in female BALB/c mice (n = 5 per group) following a single subcutaneous injection of F1, F2, or F3 (450 μg of Cy5-CPMV) or soluble Cy5-CPMV (450 μg) on day 0. Asterisks indicate significant differences between F3 and Cy5-CPMV (*p < 0.05). (C) Comparing local retention of a single subcutaneous dose of hydrogel F3 (containing 200 μg of Cy5-CPMV) vs two doses of soluble Cy5-CPMV (100 μg injected at days 0 and 14) in female BALB/c mice. Fluorescence images demonstrating the extended tissue residence of the F3 hydrogel compared to that of the soluble Cy5-CPMV.
Efficacy of 826-CPMV-Loaded Hydrogel as a Single-Dose Vaccine
Bioconjugation of Peptide Epitope 826 to CPMV
We conjugated the B-cell epitope 826 (peptide sequence 809–826 of the SARS-CoV-2 S protein) to CPMV using our two-step protocol as previously described.39 This peptide is highly conserved and is not affected by the mutations that generated the Delta and Omicron variants of SARS-CoV-2 (Figure S3). We used NHS chemistry to attach the cross-linker SM(PEG)4 to lysine side chains on CPMV (Figure 5A). The resulting maleimide handles were quickly conjugated to the cysteine residues of peptide 826 in the presence of the polymer Pluronic F127, a surfactant used for peptide solubilization.69 The 826-CPMV particles were purified by ultracentrifugation and characterized by SDS-PAGE, native agarose gel electrophoresis, and TEM. SDS-PAGE revealed the presence of new CP bands with higher molecular weights than those of the native small and large CPs, reflecting the conjugation of the additional peptide (Figure 5B). Quantitative analysis by densitometry indicated that each nanoparticle displayed ∼60 peptide epitopes, which is in agreement with our previous study.39 Native agarose gel electrophoresis indicated that the 826-CPMV particles had a lower electrophoretic mobility than native CPMV, which can be attributed to the higher molecular weight and increase in hydrodynamic diameter (Figure 5C). The presence of a higher-mobility band that appeared to be free RNA (stained with GelRed but not with CBB) may indicate the release of RNA under the reaction conditions, in agreement with our previous work on the 826-CPMV formulation.69 While some RNA is lost during the conjugation procedure, a significant amount of the RNA is retained within the formulation. Importantly, RNA is not lost during hydrogel formulation (see Figure S2). The structural integrity of the 826-CPMV nanoparticles was confirmed by TEM, which revealed homogeneous icosahedral particles of ∼30 nm (Figure 5D). Collectively, these data confirmed the synthesis of stable 826-CPMV nanoparticles for immunization studies.
Figure 5 Conjugation of the B-cell peptide epitope 826 to CPMV. (A) Two-step synthesis of 826-CPMV conjugates. (B) SDS-PAGE analysis comparing the CPs from wild-type and modified CPMV particles. (C) Agarose gel showing the colocalization of viral RNA (under UV light) with CP (revealed by staining with CBB). (D) TEM images confirming particle integrity following the bioconjugation reaction. Scale bar = 100 nm.
Immunogenicity of Hydrogel F3 Containing 826-CPMV Particles
The immunogenicity of 826-CPMV formulated in chitosan/GP hydrogel F3 was evaluated in female BALB/c mice. Based on the previously reported dosing schedule for 826-CPMV,39 a single dose of liquid formulation F3 containing 200 μg of 826-CPMV particles was compared with the soluble particles in PBS administered as a single subcutaneous dose of 200 μg or prime-boost doses of 100 μg at the beginning of weeks 0 and 2 (Figure 6A). Blood samples were collected by retro-orbital bleeding over 20 weeks and sera were screened for antibodies against epitope 826 by ELISA (Figure 6B). The control group (F3 hydrogel without 826-CPMV particles) did not elicit antibodies, whereas all study groups produced anti-826 IgG (Figure 6C). The injectable hydrogel formulation of 826-CPMV improved the antibody titers at later time points (between weeks 12 and 20) compared to the soluble formulation (Figure 6D). Significantly high antibody concentrations were still apparent at week 20 following the administration of 826-CPMV particles in hydrogel F3. Differences in antibody titers were apparent at later time points with higher titers observed in animals immunized with the 826-CPMV particles released from the F3 hydrogel versus single administration of 200 μg of 826-CPMV particles or prime-boost with 100 μg of 826-CPMV particles (Figure 6C,D). This is consistent with the prolonged tissue residence time and slow release of CPMV from the injectable hydrogel compared to the faster clearance of the soluble CPMV formulation (Figure 4). The data provide further evidence that intact and biologically active CPMV nanoparticles released from the hydrogel retained their biological properties, supporting the in vitro stability data (Figures 3 and S2). The chitosan/GP slow-release technology is therefore highly compatible with plant virus nanotechnology. Our results are important because many nations have now initiated repeat vaccinations with shorter intervals in an attempt to control COVID-19, whereas a slow-release formulation could provide long-lasting immunity by creating a depot that releases vaccine antigens over a period of several months. The use of such formulations would therefore alleviate some of the burden on global health systems by reducing the number of vaccination appointments needed to achieve population-wide protection.
Figure 6 Antibody response following the immunization of BALB/c mice (n = 4 per group). (A) Mice were subcutaneously injected once with hydrogel F3 (containing 200 μg of 826-CPMV) or 200 μg of soluble 826-CPMV in PBS or with 2 × 100 μg of soluble 826-CPMV in PBS as a prime-boost regimen. Blood samples were withdrawn by retro-orbital bleeding according to the schedule as shown. (B) ELISA detecting IgG (from immunized mouse serum) binding to epitope 826. (C) ELISA data curves showing IgG titers of immunized mice against epitope 826 from weeks 2 to 20. (D) Longitudinal IgG titers over 20 weeks indicating that the F3 group continuously differed from the control blank group to a much greater extent than the soluble particle (with p values included for weeks 16 and 20 to show the differences). Asterisks indicate significant differences between the study group and control blank group (*p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001), with green color referring to the soluble 826 CPMV 100 (x2) group, blue to the 826 CPMV 200 group, and red to the F3 group.
Antibody Isotyping
Finally, we analyzed the Ig isotypes and IgG subclasses in plasma from weeks 4 and 12 and thus reveal whether hydrogel vaccine F3 induced a Th1-biased response (IgG1/IgG2a ratio < 1) or a Th2-biased response (IgG1/IgG2a ratio > 1). Th1 cells produce cytokines such as interferon γ that instruct B cells to produce opsonizing antibodies (IgG2a/b) and stimulate macrophages for phagocytic activity against intracellular pathogens (e.g., viruses). In contrast, Th2 cells produce interleukin 4 (IL-4) that instructs B cells to secrete neutralizing antibodies (IgG1) for humoral protection against pathogens or toxins in the extracellular environment.40 We observed comparable Ig isotype profiles in all groups at week 4 but evident differences at week 12 due to IgG1 becoming exclusively prominent in the F3 group (red arrows in Figure 7A). Based on the IgG1/IgG2a ratio, we found that F3 induced a Th1-biased response at week 4 but shifted to a Th2-biased response at week 12, while the immune response for the soluble 826-CPMV groups remained Th1-biased throughout the experiment (Figure 7B). CPMV-based vaccines were previously shown to induce Th1-biased responses against cancers,41,79,80 but Th2-biased responses at later time points have been reported for other shared epitopes from SARS-CoV and the SARS-CoV-2 S protein, reflecting a shift from Th1 typically after the second boost injection.43 The Th1/2 response was deemed to be dependent on the SARS-CoV2 S protein epitope.39,43 With regard to epitope 826, we and others39 observed only Th1-biased responses for soluble 826-CPMV administered using the prime-boost schedule, which implies that the observed shifting bias in the F3 group from Th1 to Th2 is possibly due to the immune-enhancing adjuvant capability of chitosan53−55,81 and/or the slow-release characteristics of the hydrogel F3. The first CPMV nanoparticles released from the gel can diffuse through lymph vessel pores and find their way to the lymph node, where they interact directly with B cells to induce immediate IgG2a production (Th1 bias) without prior interactions with T cells.40,82 However, longitudinal and delayed release may induce more Th2 bias because the particles are likely to interact with antigen-presenting cells due to their prominent recognition by pre-existing opsonizing antibodies.46 The comparative release profiles of soluble particles versus hydrogels may help to determine whether CPMV-based vaccines are inherently Th1-mediated adjuvants or whether the nature of the epitope is the main determinant of Th1/2 bias.
Figure 7 Antibody isotyping using mouse sera from weeks 4 and 12 (n = 4 per group). (A) Immunoglobulin isotypes and IgG subclasses, showing comparable antibody profiles at week 4 but enhanced IgG1 production by the F3 group at week 12 (three red arrows). (B) IgG profiling expressed as the IgG1/IgG2a ratio, demonstrating a Th1-biased response (IgG1/IgG2a ratio < 1) for all groups at week 4 but a remarkable shift to a Th2-biased response (IgG1/IgG2a ratio > 1) exclusively in the F3 group.
Vaccine efficacy and safety are important design parameters, and while Th2 bias is desired to elicit neutralizing IgG1 antibodies for humoral protection against viruses prior to cell entry and establishment of infection, reports highlight the risk of antibody-dependent enhancement (ADE) with the SARS and Middle East Respiratory Syndrome coronavirus vaccine candidates.83,84 Some reports suspected similar risk of ADE for SARS-CoV-2 vaccines;85,86 nevertheless, the rationale design and choice of target epitope may provide greater safety compared to subunit vaccines containing RBD or the full-length S protein.
Conclusions
We have formulated an injectable hydrogel containing CPMV conjugated to B-cell epitope 826 as a single-dose vaccine candidate for COVID-19. CPMV hydrogel formulations were prepared using chitosan and GP solutions to yield a liquid mixture that was homogenized with CPMV particles at room temperature. HMW chitosan formulations (F3) containing 0–4.5 mg mL–1 CPMV achieved a relatively fast transition from liquid solutions to gels at 37 °C (gelation time 5–8 min) and slowly released Cy5-CPMV particles in vitro and in vivo. Most importantly, F3 containing CPMV labeled with epitope 826 from the SARS-CoV-2 S protein induced high antibody titers over 20 weeks, with an associated shift from Th1-biased to Th2-biased profiles. Our findings suggest that CPMV nanoparticles can be effectively formulated in chitosan/GP hydrogels and are released over several months as intact and biologically active particles with conserved immunotherapeutic efficacy. The proposed formulation not only represents a promising single-dose vaccine candidate to address future pandemics but may also facilitate the development of long-lasting plant virus-based nanomedicines for diseases that require long-term treatment.
Supporting Information Available
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.biomac.2c00112.Formulation gelation times; SEM images of hydrogels; agarose and SDS-PAGE gels of Cy5-CPMV released; and mutations in SARS-CoV-2 variants (PDF)
Supplementary Material
bm2c00112_si_001.pdf
The authors declare the following competing financial interest(s): Dr. Steinmetz is a co-founder of, has equity in, and has a financial interest with Mosaic ImmunoEngineering Inc. Dr. Steinmetz serves as Director, Board Member, and Acting Chief Scientific Officer, and paid consultant to Mosaic. The other authors declare no potential conflicts of interest.
Acknowledgments
This work was supported by the NSF Center for the Chemistry of Molecularly Optimized Networks (MONET), CHE-2116298. Animal studies were supported by start-up funds to N.F.S. O.A.O.-R. acknowledges a UC MEXUS-CONACYT Postdoctoral Fellowship, FE-20-136. The authors thank Dr. Soo Khim Chan (UC San Diego) for technical assistance with TEM imaging and Dr. Sourabh Shukla and Young Hun Chung (UC San Diego) for assistance with animal studies.
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PMC009xxxxxx/PMC9003891.txt |
==== Front
ACS Synth Biol
ACS Synth Biol
sb
asbcd6
ACS Synthetic Biology
2161-5063
American Chemical Society
35377616
10.1021/acssynbio.1c00381
Letter
A Cell-Free Assay for Rapid Screening of Inhibitors of hACE2-Receptor–SARS-CoV-2-Spike Binding
Kikuchi Nanami ‡
Willinger Or ‡
Granik Naor ‡
Gal Reut ‡
Navon Noa †‡
Ackerman Shanny ‡
Samuel Ella ‡
Antman Tomer ‡
Katz Noa ‡
https://orcid.org/0000-0003-1062-2084
Goldberg Sarah ‡
https://orcid.org/0000-0003-0580-7076
Amit Roee *‡
‡Department of Biotechnology and Food Engineering, and †Department of Biomedical Engineering, Technion—Israel Institute of Technology, Haifa, 32000, Israel
* Email: roeeamit@technion.ac.il.
04 04 2022
15 04 2022
11 4 13891396
11 08 2021
© 2022 American Chemical Society
2022
American Chemical Society
This article is made available via the PMC Open Access Subset 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 the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
We present a cell-free assay for rapid screening of candidate inhibitors of protein binding, focusing on inhibition of the interaction between the SARS-CoV-2 Spike receptor binding domain (RBD) and human angiotensin-converting enzyme 2 (hACE2). The assay has two components: fluorescent polystyrene particles covalently coated with RBD, termed virion-particles (v-particles), and fluorescently labeled hACE2 (hACE2F) that binds the v-particles. When incubated with an inhibitor, v-particle–hACE2F binding is diminished, resulting in a reduction in the fluorescent signal of bound hACE2F relative to the noninhibitor control, which can be measured via flow cytometry or fluorescence microscopy. We determine the amount of RBD needed for v-particle preparation, v-particle incubation time with hACE2F, hACE2F detection limit, and specificity of v-particle binding to hACE2F. We measure the dose response of the v-particles to known inhibitors. Finally, utilizing an RNA-binding protein tdPP7 incorporated into hACE2F, we demonstrate that RNA-hACE2F granules trap v-particles effectively, providing a basis for potential RNA-hACE2F therapeutics.
protein−protein interaction
inhibitor
functionalized nanoparticle
drug repurposing
RNA-protein granule
Horizon 2020 Framework Programme 10.13039/100010661 851065 Technion Israel Institute of Technology NA NA document-id-old-9sb1c00381
document-id-new-14sb1c00381
ccc-price
This article is made available via the ACS COVID-19 subset 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 the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
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pmcThe current COVID-19 pandemic, caused by the SARS-CoV-2 virus,1,2 has resulted in an unprecedented need for tools that combat the spread of the virus, and for therapeutics for those infected. SARS-CoV-2 virions enters the host cells via interaction between the receptor binding domain of the viral Spike protein (RBD), and hACE2 on the host cell surface.3,4 An assay for characterization of RBD-hACE2 binding and the inhibition of this binding could be used to quantify the effect of neutralizing antibodies on the interaction of hACE2 with RBDs of emerging viral strains.5 Furthermore, it could be used to screen candidate small molecule inhibitors of RBD-hACE2 binding, thereby accelerating the inhibitor identification step of drug discovery.6
Repurposing of drugs approved by either the FDA or the EMA is perhaps the fastest path for identification of approved therapeutics for emerging diseases.7,8In silico strategies are currently being employed to identify approved drugs that could be repurposed for COVID-19.9 The standard experimental screen for candidate compounds is an in vitro viability assay,10 in which ex vivo cells are first mixed with the compounds, and then infected with the virus. The percentage of viable cells is compared to their percentage in infected+nontreated and noninfected controls. However, high-throughput screening with cell culture requires multiple days, is relatively expensive, and requires Biosafety Level 3 biocontainment conditions. Also, assay results may differ between laboratories due to differences in cell strain, growth conditions, and inherent variability in biological response. Pseudovirus assays for SARS-CoV-2 inhibitors11,12 require only Biosafety Level 2, but may still suffer from relatively high expense and inherent variability due to the cellular component. These constraints provide motivation for cell-free screening alternatives.13
Ideally, a cell-free assay for screening of inhibitors of protein–protein interaction should satisfy the following requirements: detection using standard lab equipment, repeatability, ease of use, flexibility, and low cost. Since protein sizes are well below the optical diffraction limit, some form of bulk measurement is required. To our knowledge, the only commercial cell-free option currently available for screening RBD-hACE2 inhibitors (Cayman Chemical, Cat. 502050) consists of an antibody-coated surface that binds antigen-RBD. Horseradish peroxidase (HRP)-hACE2 is introduced in the presence or absence of an inhibitor candidate. Excess HRP-hACE2 is rinsed, and HRP activity is measured optically at 450 nm via a plate reader. However, this assay requires expensive reagents, and multiple washing steps that could affect assay repeatability. In this work, we developed a particle-based fluorescence assay for rapid screening of candidate inhibitors of RBD-hACE2 interaction without the need for live cells or viruses (see Figure 1A). Our assay utilizes a fluorescent version of hACE2 (containing mCherry, for sequence see Supplementary Table 1), which eliminates the use of antibodies and any related labeling and rinsing steps. We use fluorescent particles covalently coated with RBD, which we term v-particles, as the surface on which binding occurs. Possible roles of small particles in the context of COVID-19 have been discussed elsewhere.14−16 In our assay, the v-particles provide a number of benefits: first, during v-particle preparation, unattached RBD can be removed from the v-particle stock via centrifugation, so that all RBD-binding events occur at the v-particle surface. This could enable production of a ready-to-use product that can be more easily shipped and stored than a coated microplate. Such a ready-to-use product could enable better quality control of the assay and yield more reproducible results.17 Second, the v-particles provide a versatile platform: v-particles can be prepared with any choice of viral proteins (e.g., various RBD mutants) or be adapted to display any desired component without necessitating a particular chemical modification. Finally, v-particles are large enough to be easily detectable using either flow cytometry (Figure 1B) or standard fluorescence microscopy and enable clear distinction of bound hACE2 from unbound hACE2 when assayed via flow cytometer or microscope without the need for cleanup via centrifugation or buffer exchange.
Figure 1 Schematic of the v-particle binding assay. (A) RBD is covalently attached to fluorescent polystyrene particles (green), yielding virion-like particles (v-particles). V-particles are incubated with hACE2F (red) in the presence and absence of a candidate inhibitor. (B) Experimental flow cytometry data showing v-particles in the presence (red) and absence (blue) of bound hACE2. Inhibitor activity can be quantified by the shift of the distribution to lower mCherry values.
Results and Discussion
The RBD for the v-particles in the presented data was purchased from RayBiotech [Recombinant SARS-CoV-2, S1 Subunit Protein (RBD), cat. 230–30162]. Similar results were obtained for RBD expressed in our lab from a plasmid encoding his-tagged RBD that was a gift from the Krammer lab (see sequence in Supplementary Table S1). Lab-produced RBD18 was extracted from HEK293F cells (Freestyle 293, Thermo Fisher) following the manufacturer’s protocol (for full details, see Supplementary Methods). For v-particle generation, carboxyl fluorescent yellow particles with 0.7–0.9 μm diameter were purchased (Spherotech Inc., cat. CFP-0852-2, lot no. AM01, specified batch diameter 0.92 μm). The RBD protein was attached to the particles by two-step carbodiimide cross-linker chemistry (see Figure 1A) using N-(3-(dimethylamino)propyl)-N′-ethylcarbodiimide hydrochloride (EDC, Sigma-Aldrich) and N-hydroxysulfosuccinimide sodium salt (Sulfo-NHS, Sigma-Aldrich) (see Supplementary Methods for full details).
For the v-particle binding partner, we expressed and secreted a his-tagged fusion protein containing three domains: the extracellular domain of hACE2, the fluorescent label mCherry, and the RNA-binding protein tdPP7. While not required for the binding assay, in our hands the tdPP7 domain increased hACE2F titer relative to a fusion protein lacking the tdPP7 domain. hACE2F was expressed and secreted from HEK293F (Freestyle 293, Thermo Fisher) cells. We refer to this protein as hACE2F in the following (see Supplementary Table 1 for hACE2F sequence, and Supplementary Methods for hACE2F expression and purification).
We first optimized the concentration of the v-particle protein component using conjugation of tdPP7-mCherry instead of RBD to the carboxyl fluorescent particle, since RBD does not contain any fluorescence output (see Supplementary Table 1 for tdPP7-mCherry sequence, and Supplementary Methods for tdPP7-mCherry expression and purification). The standard curve obtained with respect to tdPP7-mCherry concentration is shown in Figure 2A. Saturation of tdPP7-mCherry is observed at 0.5x protein (x = 300 000) per bead particle (see Materials and Methods for details of component ratios). On the basis of the result for tdPP7-mCherry, we estimated that an RBD/bead molar ratio of approximately 1x would be optimal for v-particle preparation.
Figure 2 Optimizing v-particle synthesis and binding assay using flow cytometry. (A) Increasing amounts of tdPP7-mCherry were covalently attached to carboxyl fluorescent yellow particles (bare bead), and mCherry fluorescence of FITC-positive events was measured by flow cytometry. Schematic is indicated on top of the figure. Plateau of fluorescence indicates saturation of tdPP7-mCherry attachment onto bare bead, which is observed at 0.5 tdPP7-mCherry ratio (x300 000) per 1 bead particle (or 150 000 tdPP7-mCherry per 1 bead particle). Bead without any attachment is indicated as bead only (green). (B) 0.34 μg hACE2F was mixed with v-particles attached with increasing ratio of RBD. V-particle was synthesized using bead/RBD ratios of 1:0.001x, 1:0.01x, 1:0.1x, 1:0.5x, 1:1x, 1:2x, 1:5x, and 1:10x (x = 300 000 RBD particle). A 0.5 μL sample of those v-particles was mixed with 0.34 μg of hACE2F. Fluorescence was measured after 45 min. Schematic is indicated on top of the figure. V-particle synthesized with low amount of RBD [0.001–0.1 RBD ratio (x300 000) per 1 bead particle] or excess amount of RBD [5, 10 RBD ratio (x300 000) per 1 bead particle] shows limited or inhibited hACE2F binding, whereas v-particle with bead/RBD ratio of 1:0.5–2 (x300 000) shows optimal binding of hACE2F. Bead without any attachment is indicated as bead only (green). (C) Specificity of v-particle binding to hACE2F. Increasing amount of hACE2F was mixed with either v-particle (red) or bare bead (green). Schematic is indicated on the left of the panel. Compared to the bare bead control, a 1–2 order-of-magnitude shift in fluorescence was achieved for hACE2F mixed with v-particle at 1 μg hACE2F (2 hACE2F per one RBD), indicating hACE2F binding to the RBD displayed on the v-particles.
We next determined the working conditions for hACE2F–v-particle binding. To determine the lower limit of detection of hACE2F–v-particle binding, we measured the dependence of the mCherry fluorescence of v-particles on hACE2F concentration. The results for the sensitivity assay are shown in Figure S1A. We found that we can detect as little as ∼0.125 μg of hACE2F using 0.5 μL of v-particles in our experimental conditions, which is equivalent to a RBD/hACE2F molar ratio of 1:0.25, though a larger amount of hACE2F could provide more sensitivity when screening candidate inhibitors. We next determined the optimal time for binding reactions. On the basis of the results (Figure S1B), we determined that 15 min is sufficient for binding reactions. In this work, we decided to incubate for 45 min.
After determining time and useful range of hACE2F concentration, we further optimized hACE2F binding by varying the RBD/bead ratio while keeping hACE2F constant. We conjugated carboxyl particles to RBD at ratios of 1:0.001x, 1:0.01x, 1:0.1x, 1:0.5x, 1:1x, 1:2x, 1:5x, and 1:10x (x = 300 000) RBD, and added a constant amount of 0.34 μg of hACE2F. The results are shown in Figure 2B. We observe a hook effect,19 which is typical for multicomponent binding assays: at low RBD concentration, all RBD can bind to the carboxyl particles, and hACE2F binding to v-particles is limited by the amount of conjugated RBD. The amount of conjugated RBD increases until optimal RBD/particle is reached, at which point hACE2F is also maximal. At higher RBD concentrations, not all RBD undergoes conjugation, and any remaining unconjugated RBD competes with v-particles for hACE2F binding, resulting in reduced mCherry fluorescence of v-particles. We determined that the 0.5x–2x RBD/carboxyl particle can achieve optimal mCherry fluorescence. Therefore, the final molecular ratio is around 1x RBD per 1 carboxyl bead.
We next verified the specificity of v-particle binding to hACE2F by comparing to carboxyl fluorescent yellow particle (bare bead) binding to hACE2F (Figure 2C). The plot shows that v-particles incubated with comparable amounts of hACE2F exhibit a modest and continuous concentration-dependent shift (red) in mCherry fluorescence, compared to the shift seen for bare bead (green), which is consistent with nonspecific binding. This indicates specific binding of hACE2F to the RBD displayed on the v-particles.
As a proof-of-concept for inhibitor screening, we measured the inhibition of v-particle–hACE2F binding in the presence and absence of synthetic peptide inhibitors, or sybodies, Sb#15, Sb#68, and GS420 (Figure 3A). For details of sybody expression see Supplementary Methods. For comparison, we measure v-particle–hACE2F binding in the absence of inhibitor, and the fluorescence of the bare-bead control. We observe a shift in fluorescence with increasing sybody concentration, indicating a reduction in v-particle–hACE2F binding. For GS4 particularly, we see a dose-dependent reduction in the fluorescence distribution. We plot the histogram of the v-particle mCherry fluorescence as a function of the GS4 dose (Figure 3B), which provides a quantitative assay of RBD-hACE2F inhibition. The flow cytometry results for the three inhibitor sybodies are plotted in Figure 3C. For comparison, we tested bovine serum albumin (BSA, New England Biolabs) and glutathione S-transferase (GST, Sigma-Aldrich cat. SRP5348), which are not known to inhibit RBD-hACE2F binding (Figure 3D). We note that GST stock concentration was low, and thus any inhibition that might be inferred may be due to the increased buffer components in the binding assay for this protein, and not to the protein itself.
Figure 3 Inhibition of v-particle–hACE2F binding by sybodies Sb#15, Sb#68, and GS4. (A) Sybodies Sb#15 (Sb15), Sy#68 (Sb68), and a fusion of Sb15 and Sb68 (GS4), were prepared and used in the assay. (B) Flow cytometry data for GS4. Top histogram shows fluorescence associated with no inhibitor, corresponding to maximum fluorescence. Bottom histogram shows fluorescence associated with bead only, or fluorescence noise level. Fluorescence peak shifts from high fluorescence to low fluorescence as the amount of GS4 (shown as a molecular ratio of GS4 to RBD) is increased. (C) Flow cytometry data obtained from v-particle–hACE2F inhibition by Sb#15, Sb#68, and GS4. Fluorescence associated with no inhibitor is indicated in red. Fluorescence associated with bead only is indicated in green. The molecular ratio of three components hACE2F, RBD, and inhibitor is indicated. (D) Flow cytometry data obtained from v-particle–hACE2F inhibition by control proteins BSA and GST. Fluorescence associated with no protein indicated in red. Fluorescence associated with bead only indicated in green. The molecular ratio of three component, hACE2F, RBD, and protein is indicated. Note that commercial BSA and GST buffer components (e.g., glycerol) may have interfered with the assay.
Finally, we utilized the v-particles to assess the efficacy of synthetic RNA-protein (SRNP) granules in binding to, and thereby depleting the active amount of, SARS-CoV-2 virions. Here, the v-particles provide a safe and microscopically visible alternative to actual virions. RNA-protein granules can be produced in vitro,21,22 and have been shown to bind cellular components.22,23 We recently showed24 that SRNP granules form specifically in vitro via self-assembly by mixing purified bacterial phage coat proteins with synthetic long noncoding RNA (slncRNA) molecules that encode multiple binding sites for the coat proteins. In this case, the protein component in the granule formulation was either tdPP7-mCherry, or hACE2F (see Supplementary Methods and Supplementary Table 1 for both proteins). The slncRNA component (slncRNA-PP7bsx14, see Supplementary Table 1 for sequence and Supplementary Methods for synthesis details) harbors 14 PP7 binding sites, to which the tdPP7 domain present in both hACE2F and tdPP7-mCherry can bind. The RNA thus increases the local concentration of hACE2F, which may facilitate virion entrapment and thus potentially function as an anti-SARS-CoV-2 decoy particle (Figure 4A). To test for selective binding of the SRNP granules to the v-particles, we prepared the following samples: v-particles with slncRNA-PP7bsx14 and tdPP7-mCherry, v-particles with hACE2F, and v-particles with slncRNA-PP7bsx14 and hACE2F. We show the results of the binding experiments in Figure 4. In the microscopy images, v-particles appear as green fluorescent beads (Figure 4B). SRNP-granules appear as large red clumps or as bead-like particles (Figure 4C), which are located on the coverslip at different positions from the v-particles. When v-particles are mixed with hACE2F, colocalization of the hACE2F protein to the v-particles is observed, as expected from our previous experiments (Figure 4D). Finally, hACE2F-SRNP-granules appear to be bound to the v-particles (Figure 4E and Figure S2), as compared with the non-hACE2F-SRNP-granules which appear to be spatially separated from the v-particles (Figure 4C). Consequently, the SRNP-hACE2F granules provide a potential decoy or anti-SARS-CoV-2 therapeutic, which should be examined in follow-up research.
Figure 4 Entrapment of v-particles by slncRNA-PP7bsx14 - hACE2F granules. (A) Schematic of the hACE2F SRNP granules sequestration assay. (Left) RNA containing PP7 binding sites is incubated with hACE2F proteins to form SRNP granules with high protein concentration. (Right) SRNP granules attach to the v-particles via hACE2-RBD binding, serving as decoys. (B–E) Overlay of fluorescence microscopy images at 585 nm (mCherry) and 490 nm (FITC) excitation wavelengths. (B) V-particles incubated with tdPP7-mCherry, (C) v-particles incubated with slncRNA-PP7bsx14 - tdPP7-mCherry granules, (D) v-particles incubated with hACE2F, and (E) v-particles incubated with slncRNA-PP7bsx14 - hACE2F granules. For (A–E), v-particle concentration was 0.1% w/v. Protein concentrations in imaged samples were (B,C) 842 nM, (D) 560 nM, and (E) 507 nM. slncRNA-PP7bsx14 concentration in imaged samples was 112.8 nM (C,E).
We have presented a particle-based assay that enables rapid, cell-free screening of candidate inhibitors of protein–protein interaction, focusing on the interaction between SARS-CoV-2 Spike RBD bound to fluorescent particles (v-particles), and fluorescently tagged hACE2 (hACE2F). The assay materials are commercially available or relatively easy to prepare and do not include antibody components. The main difficulty in assay preparation is the production of the protein components. Depending on available lab resources, researchers may choose to outsource this step. We demonstrated the utility of the assay for quantifying inhibition of RBD–hACE2 interaction by the reported inhibitor GS4, Sb#15, and Sb#68 as well as with a potential anti-SARS-CoV-2 RNP–granule decoy particle. Although we described applications specific to RBD and hACE2F interaction, the presented applications could easily be modified to quantify interaction of other peptide–receptor interaction partners, such as RBD mutants with either hACE2 or other suspected host receptors,25 or other viral proteins with their respective host partners.26 We further demonstrated that v-particles can provide a cell-free alternative to more expensive and higher-biosafety-level cell-based assays for assessing proposed SARS-CoV-2 entrapment products. We hope that the relatively straightforward preparation, ease of use, and quantitative results of our v-particles and binding assay will have a significant impact in assays involving SARS-CoV-2 variants, as well as other viruses.
Materials and Methods
Details of protein expression and purification for his-tagged RBD, hACE2F, tdPP7-mCherry, and sybodies Sb#15, Sb#68, and GS4 appear in the Supporting Information. Details of slncRNA-PP7bsx14 preparation appear in the Supporting Information. Details of v-particle preparation appear in the Supporting Information. Calculation of all component ratios appears in the Supporting Information.
Flow-Cytometry Binding Assays
V-particles and protein components were added according to the details below. BSA (20 mg/mL, New England Biolabs) was added at a ratio of 10 μg of BSA per 0.5 μL of v-particle stock (1 μL v-particle = 2.2 × 107 bead particles. According to the manufacturer one bead particle contains up to 300 000 COOH functional groups, 0.5 μL of v-particles contains up to 0.6 × 10–11 mol RBD or 3.6× 1012 RBD single protein molecules) to all binding reactions to suppress nonspecific binding of protein to the v-particles. Unless stated otherwise, samples were incubated on ice for 45 min. All sample volumes were adjusted to 100 μL with 1x PBS and measured via flow cytometry (MACSquant VYB, Miltenyi Biotec). The flow cytometer was calibrated using MacsQuant calibration beads (Miltenyi Biotec) before measurement, and 0.5 μL of 1% w/v amine polystyrene fluorescent yellow particles (Spherotech, Inc., cat. AFP-0852-2, lot No. V01-R) or carboxyl polystyrene fluorescent yellow particles in 100 μL of 1x PBS were run as a negative control. Negative controls behaved similarly. Voltages for the SSC, FSC, FITC (B1), and mCherry (Y2) channels were 400, 200, 325, and 300 V, respectively. Events were defined using an FSC-height trigger of 60, chosen using a bead-only control. Approximately 10 000 events per sample were collected. Of these, typically over 98% were FITC-positive, using a B1-area threshold of 1 × 103. Negative mCherry values (negative Y2-area below zero, indicative of noise distribution around 0, typically less than 10% of FITC-positive events) were assigned a value of zero. Boxplot measurements shown are the mCherry fluorescence values of the FITC-positive events, with black marker indicating the median, colored bar spanning from the 25th to the 75th percentile, and whiskers extending to extreme data points not considered outliers (using the Matlab boxplot function).
Optimal Loading of Protein onto the Carboxyl Fluorescent Yellow Particles
The carboxyl polystyrene fluorescent yellow particles were centrifuged after the first step of the reaction (see Supplemental Method for detailed synthesis of v-particle) for 15 min at 3000g and the supernatant was replaced with tdPP7-mCherry (Figure 2A) or RBD (for v-particle, Figure 2B) in 100 μL of 1x PBS, and incubated on ice with 145 rpm horizontal shaking for 2.5 h while protected from light. The sample was centrifuged for 15 min at 3000g, and the supernatant was replaced with 100 μL of 1x PBS, 3 times. The synthesized particle stock was stored at 4 °C, and could be used for approximately 3 weeks. Final fluorescent particle concentration in the particle stock is approximately 1% w/v. One bead particle contains up to 300 000 COOH functional groups and can therefore theoretically bind a maximum of 300 000 protein molecules. We optimized the binding ratio, using 1 bead particle to the following amounts of protein molecules: 0.001x, 0.01x, 0.1x, 0.5x, 1x, 2x, 5x, 10x (x = 300 000 protein molecules). In a Lo-Bind microcentrifuge tube, we combined 0.5 μL of presonicated protein–particle stock with 99.5 μL of 1x PBS and measured by flow cytometry as described above. For tdPP7-mCherry, we did not add any hACE2F (Figure 2A). For RBD, we added a constant amount of 0.34 μg of hACE2F (Figure 2B).
Specificity of V-particle Binding to hACE2F
In a Lo-Bind microcentrifuge tube, we combined 0.5 μL of presonicated v-particle stock or 1% w/v carboxyl polystyrene fluorescent yellow particles, 10 μg of BSA and either 0.05, 0.25, 0.5, or 1 μg of hACE2F (1 μg of hACE2F is equivalent to a RBD/hACE2F ratio of 1:2). Sample volumes were adjusted to 3 μL with 1x PBS. Samples were incubated, diluted, and measured by flow cytometry as described above.
Sensitivity of V-particles to hACE2F
In a Lo-Bind microcentrifuge tube, 0.5 μL of presonicated v-particle stock and 10 μg of BSA were mixed with one of the following amounts of hACE2F: 5 ng, 12.5 ng, 0.05 μg, 0.125 μg, or 0.5 μg hACE2F (RBD/hACE2F ratios of 1:0.01, 1:0.025, 1:0.1, 1:0.25, and 1:1). The volume was adjusted to 3 μL with 1x PBS. Samples were prepared in triplicate. Samples were incubated, diluted, and measured by flow cytometry as described above.
Optimal Time for Binding Reactions
In a Lo-Bind microcentrifuge tube, 0.5 μL of presonicated v-particle stock, 10 μg of BSA, and 1 μg of hACE2F (equivalent to RBD/hACE2F of 1:2) were added, and the volume was adjusted to 3 μL with 1x PBS. The samples were incubated for different amounts of time: 15, 45, 90, 180 min, and 24 h. Samples were prepared in triplicate. Samples were diluted and measured by flow cytometry as described above.
Maximizing Fluorescent Signal Associated with hACE2F binding to V-particle
V-particles were synthesized with carboxyl-particle/RBD ratios of 1:0.001x, 1:0.01x 1:0.1x, 1:0.5x, 1:1x, 1:2x, 1:5x, and 1:10x (x = 300 000 RBD particle = 1.4 × 10–2 pg RBD). In a Lo-Bind microcentrifuge tube, we combined 0.5 μL of presonicated v-particle stock with 10 μg of BSA and 0.34 μg of hACE2F. Total volume was adjusted to 5 μL. Samples were incubated, diluted, and measured by flow cytometry as described above.
Inhibition of V-particle–hACE2F Binding
In a Lo-Bind microcentrifuge tube, 0.5 μL of presonicated 0.5x RBD–v-particle stock (0.5 μL v-particle contains up to 2.8 × 10–12 mol RBD), 10 μg of BSA, and Sybody in one of the following amounts: 0, 34 ng, 0.17, 0.34, 1.7, 3.4 μg (ratio per 0.5 RBD: 0, 1, 10, 100 Sybody inhibitor/2.5 hACE2F) or 0.5 μL of presonicated v-particle stock (0.5 μL v-particle contains 0.6 × 10–11 mol RBD), negative inhibitor protein in one of the following amounts: BSA 0, 5, 10, 20 μg (ratio per 1 RBD: 15, 30, 60 BSA) or GST 0, 0.5, 1, 2 μg (ratio per 1 RBD: 3, 6, 12 GST) was added, and the volume was adjusted to 6 μL with 1x PBS. Next, 1.25 μg of hACE2F (0.5 RBD:2.5 hACE2F) or (1 RBD:2.5 hACE2F) was added to all the samples. Total volume was 13 μL. Samples were incubated, diluted, and measured by flow cytometry as described above.
Selective Binding of the SRNP Granules to the V-particles
SRNP experiments were performed in granule buffer (GB: 750 mM NaCl, 1 mM MgCl2, 10% PEG 4000, in water). Reactions containing 8 μL of GB, 1 μg of tdPP7-mCherry or 1.5 μg of hACE2F (in 1 μL), 0 or 1 μg of slncRNA-PP7bsx14 (in 1 μL), and 0.5 μL of Ribolock RNase Inhibitor (Thermo Fisher) were incubated at room temperature for 1 h. After 1 h, 1 μL from each reaction was deposited on a glass slide, together with 1 μL of presonicated 1% w/v v-particle stock diluted 1:5 in water. A 1 μL control sample of undiluted v-particle stock was also deposited. After 10 min, the samples were sealed with coverslips and imaged using a 100x oil immersion objective on a Nikon Eclipse Ti epifluorescent microscope with iXon Ultra EMCCD camera (Andor) and NIS-Elements software (Nikon), with 585 nm (mCherry) and 490 nm (FITC) excitation using a CooLED PE illumination system (CooLED Ltd.).
Supporting Information Available
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acssynbio.1c00381.List of the nucleotide sequences of all proteins and RNA components used in this work; description of methods used for expression and extraction of the proteins used in this work; and a description of v-particle generation (PDF)
Supplementary Material
sb1c00381_si_001.pdf
Author Contributions
N. Kikuchi, O.W., and N.G. are equally contributing first authors. N. Kikuchi, O.W., N.G., S.G., and R.A. conceived the approach and designed the experiments. N. Kikuchi prepared the v-particles and carried out binding experiments. N.G. prepared slncRNA-PP7bsx14 and carried out microscopy experiments. O.W. and S.G. expressed and purified RBD, hACE2F, and tdPP7-mCherry, and assisted with binding experiments. N.N. assisted with RBD secretion and purification. N. Katz assisted with slncRNA-PP7bsx14 design. R.G., N.G., S.G., and S.A. expressed and purified sybodies. S.A., E.S., and T.A. designed Sb#15 and Sb#68 expression vectors. N. Kikuchi, O.W., N.G., S.G., and R.A. prepared the manuscript. All authors have given approval to the final version of the manuscript.
This research was funded in part by the Zuckerman Fellowship supported at the Technion, and European Union’s Horizon 2020 Research and Innovation Programme under grant agreement 851065 (CARBP).
The authors declare the following competing financial interest(s): N. Kikuchi, O.W., N.G., S.G., and R.A. are inventors on US Provisional Patent Application No. 63/187969 concerning some of the described technologies. N. Katz and R.A. are inventors on US Patent Application 2021/0095296 A1.
Acknowledgments
We thank Omer Yehezkeli, Smadar Shulami, Onit Alalouf, and Moran Bercovici from the Technion for expert advice. N. Kikuchi acknowledges the support of the Zuckerman STEM Leadership program. We thank Integrated DNA Technologies for contributing the gBlocks encoding Sb#15 and Sb#68 to the Technion 2020 iGEM team. We thank the Florian Krammer lab for the plasmid encoding RBD. We thank the Marcus Seeger lab for the plasmid encoding GS4. Special thanks to J. D. Walter for advice regarding sybodies.
Abbreviations
RBD SARS-CoV-2 Spike receptor-binding domain
hACE2 human angiotensin-converting enzyme 2
tdPP7 tandem-dimer form of bacteriophage PP7 coat protein
hACE2F fluorescently labeled hACE2 also containing tdPP7
slncRNA-PP7bsx14 synthetic long noncoding RNA harboring 14 binding sites of bacteriophage PP7 coat-protein
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==== Front
J Chem Educ
J Chem Educ
ed
jceda8
Journal of Chemical Education
0021-9584
1938-1328
American Chemical Society and Division of Chemical Education, Inc.
10.1021/acs.jchemed.1c01154
Laboratory Experiment
Teaching Instrumental Analysis during the Pandemic: Application of Handheld CO2 Monitors to Explore COVID-19 Transmission Risks
https://orcid.org/0000-0003-0238-2367
Jensen Andrew †‡
Brown Niamh †§
Kosacki Nathalie †
Spacek Sara †
Bradley Alexander †‡
https://orcid.org/0000-0001-5673-0131
Katz Daniel †‡
https://orcid.org/0000-0001-6203-1847
Jimenez Jose L. †‡
https://orcid.org/0000-0002-0385-1826
de Gouw Joost *†‡
†Department of Chemistry and ‡Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado 80309, United States
§ Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
* Email: Joost.deGouw@Colorado.edu.
07 03 2022
12 04 2022
99 4 17941801
17 11 2021
09 02 2022
© 2022 American Chemical Society and Division of Chemical Education, Inc.
2022
American Chemical Society and Division of Chemical Education, Inc.
This article is made available via the PMC Open Access Subset 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 the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The COVID-19 pandemic has posed a challenge for maintaining an engaging learning environment while using remote laboratory formats. In this work, we describe a Student Choice Project (SCP) in an undergraduate instrumental analysis course that was adapted for remote learning without sacrificing research-based learning goals. We discuss the implementation and assessment of this SCP, selected student results, and student feedback. Students were provided handheld carbon dioxide monitors and charged with designing and implementing an investigation centered on COVID-19 airborne transmission. The real-time monitors provided experience with a new analytical tool that demanded considerations and analysis not common to other methods discussed in the course. Students were motivated by the ability to design their own projects and by the real-world implications of their findings. They performed well for all assessments, reported a positive experience, and recommended these monitors be added to the typical repertoire of instrumentation for the course.
Upper-Division Undergraduate
Analytical Chemistry
Inquiry-Based/Discovery Learning
Problem Solving/Decision Making
Atmospheric Chemistry
Laboratory Equipment/Apparatus
Quantitative Analysis
document-id-old-9ed1c01154
document-id-new-14ed1c01154
ccc-price
This article is made available via the ACS COVID-19 subset 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 the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
==== Body
pmcIntroduction
Following the global spread of the coronavirus disease (COVID-19) in early 2020, education transitioned to remote learning to limit infections, forcing adaptations from both instructors and students and sparking discussions about the benefits and limitations of various approaches.1−5 The common challenge of retaining student interest and engagement in the course material was exacerbated by the practical hurdles presented by the pandemic, particularly for inherently hands-on laboratory work which was transitioned to a remote format. In some cases, laboratories were replaced by recordings1,2 or virtual simulations,6−8 while others designed at-home experiments.9,10 Thoughtful implementation of these nontraditional laboratory experiences can effectively engage students and achieve learning outcomes.11
In a typical instrumental analysis course, learning goals center on experimental development and problem solving.12 Upper division chemistry students at the University of Colorado, Boulder take a two-semester instrumental analysis course, culminating in a nine-week Student Choice Project (SCP) to foster research skills. Similar, student-led, course-based undergraduate research experiences have reported increased student engagement and improved learning outcomes.13−16 Typically, small groups of two or three students establish a research question and hypothesis related to their interests, design and execute an investigation using available analytical techniques (e.g., flame atomic absorption spectroscopy, gas chromatography tandem mass spectrometry, etc.), and present the results. In Spring 2021, restricted in-person access to the analytical laboratory and instrumentation limited the students’ SCP options. We adapted the SCP to use low-cost monitors, which others have used in undergraduate learning outside the laboratory.14,17,18
There is mounting evidence for airborne transmission of COVID-19 which involves inhalation of virus-containing aerosol particles exhaled by infected individuals.19−22 Direct measurements of virus-laden particles is beyond the capabilities of typical instrumental analysis courses, and human-emitted aerosols are difficult to isolate from others, e.g., dust, pollen, vehicular emissions, and indoor emissions. Instead, it has been shown that carbon dioxide (CO2) can be a useful proxy for exhaled human breath and thus relative exposure risk in indoor environments.23−25 There are limitations with this approach for students to consider: other sources of CO2 exist, and masks and filters reduce exposure to aerosol26−28 but not CO2.
Here, we present the use of commercially available, easy-to-use, low-cost, handheld CO2 monitors for estimating COVID-19 exposure. We provide an overview of the project and summarize the course materials used to prepare the students. Results of selected student-designed investigations are provided to demonstrate the creative ways students approached the project. Student assessment and feedback demonstrate the effectiveness of this SCP format, and we make recommendations for improved implementation. This approach is a practical solution to remote-learning and is cost-effective for programs that lack instrumentation funding.
Materials and Methods
Course Materials and Resources
The SCP and timeline were briefly introduced to students at the beginning of the Spring 2021 semester. Starting week four, applicable lectures (3, 50 min; slides in the Supporting Information) covered the following:COVID-19 transmission;
Aerosol size, lifetimes, and emissions;
CO2 from human breath;
CO2 as a proxy for virus-containing particles;
Ventilation and filtration.
Throughout these lectures, students were asked to consider a few hypothetical situations, e.g., riding the bus or attending lecture in person, and determine which has the highest risk for COVID-19 transmission. This poll, which has no clear answer, probed students’ developing understanding of different variables that impact COVID-19 transmission such as indoor space volume, occupancy, and the rate at which indoor air is exchanged with fresh air (air exchange rate, AER). Additionally, students were assigned a homework problem: a box model of CO2 emissions in a home with ventilation (detailed in the Supporting Information).
The COVID-19 Aerosol Transmission Estimator (ATE) developed by Prof. Jimenez29 was provided as a resource. This spreadsheet employs current literature and models to estimate airborne transmission of COVID-19 and explore trends.30 For the SCP, students updated values within the calculator, e.g., occupancy or AER, to match their observations and assess transmission risk. This calculator also estimates average CO2 volume mixing ratios (VMRs), allowing for comparisons to measurements and further assessment of risk.
Ideally, the AER was determined experimentally by measuring the temporal decay of CO2 after the removal of all sources, such as measuring overnight. Fitting the decay to an exponential function yields the AER as the exponential variable. Where infeasible (e.g., in a grocery store), students were encouraged to search the literature for representative values. The ATE uses some such values in example cases.
Groups were provided a Temtop M2000 Second Generation Air Quality Monitor (Elitech; ∼$200 USD each) which measures CO2, formaldehyde, and particulate matter with diameters less than 2.5 μm (PM2.5) and 10 μm (PM10) and records data down to 1 min intervals. Some projects benefited from a second monitor. CO2 is measured with a nondispersive infrared sensor (0–5000 ppmv range with ±50 ppmv, +5% accuracy). Formaldehyde is measured with an electrochemical sensor (0–5 mg m–3 range with ±0.03 mg m–3 or ±10% accuracy, whichever is largest). PM2.5 (0–999 μg m–3 range with ±10 μg m–3 or ±10% accuracy, whichever is largest) and PM10 (0–999 μg m–3 range with ±15 μg m–3 or ±15% accuracy, whichever is largest) are measured with a laser particle sensor. Data are exported as .csv files.
Project Description
Potential SCP ideas were discussed during lecture, and then the 34 students were split into pairs. They were tasked with designing and performing a systematic study of CO2 and COVID-19 exposure in a context of interest while considering the applicable limitations when formulating conclusions. Where applicable, students were encouraged to utilize the monitor’s other measurements. The asynchronicity of this project provided flexibility but also posed challenges due to limited interactions with instructors and reduced student engagement, as noted elsewhere.8,11,31,32 A synchronous element was employed via required, but flexible, weekly meetings with teaching assistants (TAs) online, which were used to assess progress, answer questions, and provide feedback. The 17 groups were divided evenly among three TAs. Meeting length, typically 30–60 min, and structure depended on groups’ progress and needs. All meetings began with progress updates and ended with planning for the following week. Earlier meetings focused on measurement techniques and problem-solving while later meetings focused on interpretation of results. These meetings also served to monitor student growth over the course of the SCP. Below is a brief timeline of major events and deliverables:Week 1: brainstorm SCP ideas and find a partner;
Week 2: present and write an SCP proposal;
Weeks 3–8: do the measurements and analysis;
Weeks 8–9: present findings and submit a full report.
Hazards
Students were told to comply with state guidelines and mask mandates. Students were advised to not enter locations where they did not feel safe or did not normally go. Additionally, they were told to observe additional precautions as applicable to their measurement’s locations, e.g., a research lab.
Examples of Student Results and Implications for COVID-19 Exposure
Survey of Locations
Figure 1 provides an overview of locations studied in selected projects. For locations measured by multiple groups, the bars represent the averages. Monitors were placed away from sources and measured for minutes to days, depending on the project. One group cleverly used formaldehyde measurements to determine the AER in a well-ventilated research lab, where enhancements in CO2 were difficult to quantify, following a graduate researcher’s experiment. CO2 VMRs provided relative COVID-19 transmission risks, but most groups used the ATE with their observations and AERs for better estimates. All students discussed limitations relevant to their projects, commonly relating to weather conditions, lack of control over public locations, and air mixing rates.
Figure 1 Summary of students’ results including (a) average CO2 VMRs and (b) measured AERs at various locations, (c) average CO2 VMRs in a restaurant’s outdoor and indoor dining spaces given different occupancies, and (d) measured AERs with different ventilation. In (a) and (c), the horizontal line denotes the approximate atmospheric background CO2 VMR. Error bars denote standard deviations.
Average CO2 VMRs and AERs are summarized in Figure 1a and b. CO2 VMRs are expected to increase with decreasing AER and increasing occupancy, among other factors. One group showed that CO2 increases with restaurant occupancy and decreases with increased AER, as in the case of outdoor dining (Figure 1c). They recommended outdoor dining when available and indoor dining only with low occupancy. Another group tested different ventilation methods in an apartment and found a dependence on the time of day, likely due to weather conditions, and that ventilation methods, e.g., opening windows and using air conditioning, are not necessarily additive (Figure 1d). However, it is unclear if the apartment’s air conditioning system introduced outside air or recirculated indoor air. Below, we present results from three creative student projects.
Highlighted Student Project: Determining Safe Distance
One group investigated safe speaking distances by measuring CO2 concentrations at different distances from one and four people speaking. Their hypotheses stated that CO2 concentrations would decrease at greater distances from the source and increase with the number of people. They recorded CO2 for 2 min intervals at different distances away from one person reading the same passage from a book at a constant speed and volume, later repeated with four people. The students identified an exponential decay in CO2 over distance (Figure 2), supporting their first hypothesis. When comparing one and four people, the larger group increased the e-folding distance by a factor of 3.5. The students recommended distancing oneself further from groups based on the number of individuals. The students acknowledged limitations in reproducibility for CO2 emissions. With a single monitor, the different distances were recorded for independent speaking events without knowing if the emissions were consistent across events.
Figure 2 CO2 VMR enhancements above atmospheric background (ΔCO2) at different distances from one (red) and four (blue) people speaking with the corresponding e-folding distances, δ. Error bars denote standard deviations.
Highlighted Student Project: Transmission between Rooms
Another group investigated the exchange of PM2.5 and CO2 between the living room and bedroom of an apartment, separated by a closed door. The students used their exhaled breath as a source of CO2 and burned incense to produce PM2.5. The experiment was conducted with and without the apartment’s filtered ventilation system fan. When the fan was off, CO2 and PM2.5 were high in the living room but only increased at a modest rate in the bedroom (Figure 3). In contrast, the fan efficiently transmitted CO2 into the bedroom. Similarly, the fan introduced PM2.5 to the bedroom at a greater rate, despite the ventilation system’s filter. The students noted that PM2.5 transport to the bedroom was somewhat hindered by their filter but could be further reduced by filters with better PM2.5 filtration efficiency. The investigators suggested that COVID-19 transmission may increase with the use of ventilation and insufficient filtration in a household where someone is infected, even if isolated from other occupants. They also discussed limitations in their experimental setup: PM2.5 emissions from incense were not necessarily reproducible and create more PM2.5 than humans. Also, an infected person is more likely to occupy the bedroom than the living room, so transmission from the bedroom to the living room should also be investigated.
Figure 3 Transport of CO2 (blue) and PM2.5 (red) from sources in the living room of an apartment (top) to an adjacent bedroom (bottom) with closed doors. The experiment first used no additional air circulation (dashed traces) and then was repeated with the apartment’s filtered ventilation system fan (solid traces).
Highlighted Student Project: Vehicle Air Exchange Rates
A third group investigated the effects of ventilation on the AER in two cars. They used their breath to build up CO2 in the vehicles, exited, and derived the AERs from the decay. As hypothesized, the AER was greater when the windows were open ∼5 cm compared to being closed. With the windows open, AER ranged from 1.7 to 5.5 h–1, and the students found it correlated with wind speed (Figure 4). When comparing two cars, they hypothesized that the AER would be similar for both cars due to similar surface area to volume ratios. They found the AER to be much slower for the larger car, which was newer, suggesting it was better sealed and less leaky. Using the ATE’s “subway” case for parameters such as breathing rates along with their measured AERs, vehicle volumes, and reported disease prevalence in the local community, they estimated how long two people could drive together with an absolute COVID-19 transmission risk under 0.1%, per the ATE’s output. They found that two people could travel for 26 min with closed windows and 155 min with cracked windows (∼5 cm) and the average recorded wind speed. The students acknowledged limitations for predicting safe travel times: differences in the leakiness of cars, the exact wind speed and relative direction, the effect of car movement, and the accuracy of the transmission estimator.
Figure 4 Air exchange rates in a vehicle measured at different outdoor wind speeds. Windows were cracked at ∼5 cm. Error bars denote standard deviations.
Learning Objectives and Assessment
Through this SCP, students were able to design a higher-level, open-ended investigation centered on the pandemic and pertinent to their daily lives. In an end-of-semester survey (detailed in the Supporting Information), students reported improved understanding of COVID-19 transmission (Survey Figure S1), positive perceptions of their abilities to do the SCP, and high interest (Figure 5). Additionally, students learned research-based skills including the following:A. Identification of a research question;
B. Development of a research method;
C. Adaptation and problem-solving when faced with unanticipated outcomes;
D. Communication of findings.
Figure 5 Student survey results including (a) the extent to which students agreed with statements about their abilities to do the project and (b) student interest at different stages of the project. See Figures S2 and S3 for the full survey questions.
For many students, this SCP was their first opportunity to design their own investigation, allowing for growth in understanding of the research process. These broad learning goals were indirectly assessed during different stages of the SCP using the deliverables mentioned previously (templates and guides in the Supporting Information). Traditional instrumental analysis learning goals, e.g., understanding instrumentation, data analysis, keeping notebooks, etc., are not discussed here as they are well documented in the literature.
The planning phase of the SCP was assessed via an oral presentation and written proposal. Students were expected to develop research-planning skills such as identification of a problem (skill A) and formulation of a reasonable means of investigation (skill B). In the ∼5 min presentation, students identified their research topic, posed a hypothesis, and proposed the measurements and data analysis. The purpose of this informal presentation was to garner feedback from peers and instructors to support a more complete and, if necessary, modified proposal that also required background, motivation, and a timetable. During the transition from oral to written proposals, some groups narrowed their topics or modified their methods to better fit the project time frame or to better characterize a smaller scope of transmission. In doing so, they grew in their abilities to identify experimental concerns and construct a higher quality investigation. Proposals were assessed based on the quality of the research question (skill A), the feasibility of the plan (skill B), and the inclusion of sufficient information. Specifically, students were expected to address relevant considerations for their proposed projects, e.g., how to measure AERs or which statistical tests will apply, and address any concerns raised during the presentations. Students performed well with most deductions stemming from insufficient information regarding measurements and analysis. Common pitfalls included misunderstanding the use of CO2 as a proxy for virus-containing aerosol particles, which was often reflected in their question or method. Additionally, some proposals utilized the monitor as a single-sample method where real-time measurements would have been beneficial. This confusion underlines the need to include real-time measurement techniques in undergraduate education to broaden their experiences and analytical tool belt. Most students agreed that they were able to find an interesting research question and design an appropriate experiment (Figure 5a).
These SCPs were student-designed and provided opportunities to learn skills related to problem-solving and adaptability (skill C). The students experienced unforeseen challenges: reduced access to locations, control of activities in locations, and unexpected CO2 sources, among others. Instructors for in-person laboratories typically monitor students and immediately address experimental problems, but this remote format required more critical thinking on the part of the students. Students were able to resolve most issues themselves but had access to TAs during weekly check-ins for more significant concerns.
Students’ problem-solving and adaptability skills were assessed indirectly through separate creativity and effort grades. All students performed well, as reflected in student perceptions of their problem-solving capabilities (Survey Figure 5a). Creativity was assessed on the quality of the research topic (skill A) with the highest grades awarded to unique experiments that exceeded classroom examples. Similarly, creativity was assessed for confronting experimental complications with meaningful solutions (skills B and C). This aspect of creativity reflects students’ growth regarding method development as they learned how to address problems with their initial plans. Several groups gradually adapted their methods during the first few weeks of the project as they recognized limitations. In fewer cases, students adapted to their results, as in the cases of deriving an AER from formaldehyde measurements and noticing the correlation of AER and wind speed. Students’ efforts were primarily assessed based on progress, tracked via the weekly meetings, and implementation of their experiments including the use of the real-time monitor (skill B). All students agree that they learned the value of using real-time measurements compared to the single-sample methods taught in the course (Survey Figure 5a).
Students reported their results via a ∼10 min presentation and a written report, both of which were assessed for coherent scientific interpretation and communication (skill D). Students were graded on the quality of the slides, verbal presentation, scientific argument, and response to audience questions. Students presented well with mostly minor issues related to figure quality, pacing of the presentation, or missing information. There were few significant issues primarily related to the quality of the scientific argument. Following the presentation, students received instructor feedback to address before submitting the final report. The report was assessed similarly to typical lab reports and the presentation. These reports showed improvements over the presentations as students addressed relevant comments and questions brought forth by peers and instructors. Growth in scientific communication was observed during the TA meetings and between the oral and written reports. Interpretation of complicated real-time data initially required help. For example, one group struggled to link an oscillatory signal to a real-world phenomenon which was later attributed to a ventilation system. Over time, the students more confidently interpreted their results as they better understood additional variables. Commonly, students required help brainstorming which type(s) of figures would best display their results, as they were accustomed to well-defined calibration curves, but gradually demonstrated their own creative ideas.
During the SCP, students demonstrated key research skills including the proposal of scientific work, method design, problem-solving, and scientific communication. Students exhibited similar growth regarding these skills relative to previous students performing the in-lab SCP. Additionally, the inclusion of the real-time monitors provided the students a new analytical technique, prompting a different approach to method planning and data interpretation relative to the course’s other instrumentation and previous years’ SCPs. Overall, students achieved the desired learning outcomes in this well-received project as discussed in the next section.
Student Feedback and Project Improvements
Students reacted positively to the SCP (Survey Figure S4) and found that this SCP format was understandable, had reasonable expectations, and contributed to their understanding of the research process. Most students agreed that this format was a good replacement for the in-lab format when considering the limitations. Over time, student interest improved allowing for a productive and engaging learning experience (Survey Figure 5b). Further, all students supported the addition of the monitors to the suite of instruments available in future semesters.
Flexibility has been noted elsewhere as key for student success1,2,4,7,31,32 and is a central aspect of the SCP, even during a business-as-usual year. Students appreciated being empowered to design and perform an experiment relevant to their lives while working according to their own schedules (Survey Free Response A). As noted by one student, “My favorite part of this project was using my knowledge of chemistry to evaluate a real-world problem that is currently affecting millions of people.”
Students were asked to identify their least favorite part of the SCP (Survey Free Response B) and offer suggestions for improvement (Survey Free Response C). While the flexibility of this remote experiment was appreciated by some students, others found the lack of structure challenging. The weekly meetings with TAs provided a synchronous element to alleviate some remote-learning difficulties. Per one student: “Keep up the TA weekly meetings because that did help keep everyone on task...” (Survey Free Response C). Most students agreed these meetings were helpful (Figure S4), and we encourage instructors to utilize such meetings to provide students with necessary guidance. In future semesters, where groups can meet, we encourage in-person elements for remote experiments. Furthermore, we suggest sufficient discussion of the pros and cons of remote vs in-person experiments such that the students may make an informed decision.
The SCP has previously been a group project, and there have always been concerns over unequal partner contributions. Several students experienced imbalanced workloads, possibly exacerbated by being remote, and suggested groups be optional. Potential solutions require accountability via peer assessments and open discussion between instructors and students.
Some students expressed concerns that the SCP was complex and overwhelming due to significant differences from prior course material and analytical techniques. Additionally, students experienced issues with the volume of data. As described by one student, “My least favorite part of the project was the analysis of the data because there were so many things that could possibly [be] analyzed...” (Survey Free Response B). Following student suggestions, SCP lectures should begin earlier to provide more lead time to consider research topics before proposal presentations. We suggest the existing box-model homework problem be modified to include multiple, guiding steps (Supporting Information). Additional problems should be included to encourage correct analysis, e.g., fit an exponential decay to sample data to find the AER or use the ATE spreadsheet.
To improve proposed methods, students should be given the monitors earlier, allowing them to better understand how they work. Instructors may also design simple in-lab experiments or assign activities to measure, e.g., cooking, exercising, vacuuming, etc., and have students share their measurements for group analysis and interpretation. A possible in-lab experiment to investigate the AER is provided in the Supporting Information. Doing so demonstrates the benefits and complexity of real-time measurements to the students, promoting more carefully designed projects and improved understanding of such analytical techniques. Similarly, students should give a short ∼5 min update to the class during the second week. In doing so, presenters receive feedback and troubleshooting help to refine their methods while the audience may receive inspiration for their own projects.
Some students’ knowledge of Excel was insufficient for data manipulation and analysis. For example, simple exponential fits in Excel do not allow for a nonzero asymptote, which means that the background CO2 concentrations, which are not necessarily well-known, must be subtracted. The University of Colorado, Boulder and other universities offer software, e.g., MatLab, which can do these fits, or Excel’s “Solver” add-in could be used.
While this SCP was motivated by COVID-19, low-cost, handheld monitors can be applied to study various aspects of indoor air quality, as shown by D’eon et al.,14 including disease transmission, volatile chemicals, emission sources, and ventilation. Such monitors are available for various species to address different contexts, and, over time, a department can build up a suite of various monitors. With sufficient modifications of motivations and learning goals, low-cost, handheld monitors could be used in student- or instructor-designed projects for a wide range of audiences.
Conclusions
With in-person laboratory learning hampered by the pandemic, handheld CO2 monitors were used in an instrumental analysis course for student-led investigations into COVID-19 airborne transmission. From course assessments and survey responses, students achieved the targeted research-based learning outcomes. The CO2 monitors provided experience with real-time measurements and analysis. Students left the course with a better understanding of COVID-19 airborne transmission. More generally, students expressed an improved understanding of air quality with intentions to use their knowledge of ventilation and filtration in their own lives. Additionally, this project prompted postsemester discussions of the interactions between science and policy in the context of COVID-19 airborne transmission. Students expressed satisfaction with these open-ended projects due to flexibility, applicability, and the ability to design their own project. These factors all contributed to strong student engagement, bolstering the learning experience. This project can be adapted to a variety of contexts related to air quality for a variety of audiences with different skill sets. Moreover, the versatile, low-cost monitors make this form of project accessible to most programs.
Supporting Information Available
The Supporting Information is available at https://pubs.acs.org/doi/10.1021/acs.jchemed.1c01154.Student survey (PDF, DOCX)
Teaching materials (PDF, DOCX)
Lecture slides (PPTX)
Supplementary Material
ed1c01154_si_001.pdf
ed1c01154_si_002.docx
ed1c01154_si_003.pdf
ed1c01154_si_004.docx
ed1c01154_si_005.pptx
The authors declare no competing financial interest.
Acknowledgments
We would like to acknowledge Molly Larsen for her invaluable input and guidance of students. We are grateful to all the students who took part in this new Student Choice Project format, for providing feedback and for allowing us to use their data.
==== Refs
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PMC009xxxxxx/PMC9003935.txt |
==== Front
Anal Chem
Anal Chem
ac
ancham
Analytical Chemistry
0003-2700
1520-6882
American Chemical Society
35380435
10.1021/acs.analchem.2c00139
Article
Online Hydrophilic Interaction Chromatography (HILIC) Enhanced Top-Down Mass Spectrometry Characterization of the SARS-CoV-2 Spike Receptor-Binding Domain
Wilson Jesse W. †
https://orcid.org/0000-0003-2985-8249
Bilbao Aivett †
Wang Juan ‡
Liao Yen-Chen †
https://orcid.org/0000-0001-7945-9620
Velickovic Dusan †
https://orcid.org/0000-0003-3670-5654
Wojcik Roza §
https://orcid.org/0000-0002-6691-4054
Passamonti Marta ∥⊥
Zhao Rui †
https://orcid.org/0000-0003-3361-7341
Gargano Andrea F. G. ∥⊥
Gerbasi Vincent R. ‡
Pas̆a-Tolić Ljiljana †
Baker Scott E. †
https://orcid.org/0000-0003-3575-3224
Zhou Mowei *†
† Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, 3335 Innovation Boulevard, Richland, Washington 99354, United States
‡ Biological Sciences Division, Pacific Northwest National Laboratories, 902 Battelle Boulevard, Richland, Washington 99354, United States
§ National Security Directorate, Pacific Northwest National Laboratories, 902 Battelle Boulevard, Richland, Washington 99354, United States
∥ Centre for Analytical Sciences Amsterdam, Amsterdam 1098 XH, The Netherlands
⊥ Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam 1098 XH, The Netherlands
* Email: mowei.zhou@pnnl.gov.
05 04 2022
19 04 2022
94 15 59095917
10 01 2022
25 03 2022
© 2022 American Chemical Society
2022
American Chemical Society
This article is made available via the PMC Open Access Subset 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 the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
SARS-CoV-2 cellular infection is mediated by the heavily glycosylated spike protein. Recombinant versions of the spike protein and the receptor-binding domain (RBD) are necessary for seropositivity assays and can potentially serve as vaccines against viral infection. RBD plays key roles in the spike protein’s structure and function, and thus, comprehensive characterization of recombinant RBD is critically important for biopharmaceutical applications. Liquid chromatography coupled to mass spectrometry has been widely used to characterize post-translational modifications in proteins, including glycosylation. Most studies of RBDs were performed at the proteolytic peptide (bottom-up proteomics) or released glycan level because of the technical challenges in resolving highly heterogeneous glycans at the intact protein level. Herein, we evaluated several online separation techniques: (1) C2 reverse-phase liquid chromatography (RPLC), (2) capillary zone electrophoresis (CZE), and (3) acrylamide-based monolithic hydrophilic interaction chromatography (HILIC) to separate intact recombinant RBDs with varying combinations of glycosylations (glycoforms) for top-down mass spectrometry (MS). Within the conditions we explored, the HILIC method was superior to RPLC and CZE at separating RBD glycoforms, which differ significantly in neutral glycan groups. In addition, our top-down analysis readily captured unexpected modifications (e.g., cysteinylation and N-terminal sequence variation) and low abundance, heavily glycosylated proteoforms that may be missed by using glycopeptide data alone. The HILIC top-down MS platform holds great potential in resolving heterogeneous glycoproteins for facile comparison of biosimilars in quality control applications.
Congressionally Directed Medical Research Programs 10.13039/100000090 PR201356 H2020 European Research Council 10.13039/100010663 694151 document-id-old-9ac2c00139
document-id-new-14ac2c00139
ccc-price
This article is made available via the ACS COVID-19 subset 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 the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
==== Body
pmcIntroduction
The heavily glycosylated spike protein on the surface of SARS-CoV-2 virion particles mediates internalization into human cells via interactions with the cellular surface protein angiotensin converting enzyme-2 (ACE-2).1−5 Due to the direct interaction of the spike receptor-binding domain (RBD) and ACE-2, the RBD serves as a key target for neutralizing antibodies to prevent infection.1,6,7 Recombinant spike protein and RBD can potentially serve as vaccines,1,8,9 and RBDs are necessary for diagnostic purposes in immunoassays.7,9 RBD glycosylation has been demonstrated to play a role in ACE-2 binding and aids shielding of the spike protein from antibodies.2,4,10,11 In general, protein glycosylation is known to modulate the immune response,12,13 and recombinant protein glycosylation is often dependent on the expression platform employed.14−16 Thus, understanding the full glycosylation profile of the spike RBD is important for the development and quality control of novel therapeutics or vaccines,11 where knowledge of the precise combination of all post-translational modifications (PTMs) is necessary.
Individual glycosites can be occupied with many glycan structural variants, which result in different forms of the protein termed glycoforms. With glycoproteins, macroheterogeneity describes the glycan occupancy at a given glycosite, while microheterogeneity is the variation of glycan composition per glycosite. More recent glycoprotein observations have led to the idea of glycan metaheterogeneity, a higher level of glycan regulation based on the variation in glycosylation across multiple sites.17 Conventional analysis of protein glycosylation relies on bottom-up mass spectrometry (MS) approaches to produce glycopeptides using enzymatic digestion,3,18−20 and release of glycans from the protein for comprehensive glycan profiling.21 Although these approaches lead to robust identification of glycosites and glycans (macroheterogeneity and microheterogeneity, respectively), the overall connectivity between various intact glycoforms and other PTMs is lost (metaheterogeneity). Top-down MS experiments skip enzymatic digestion to analyze whole proteins where the relative abundance of exact proteoforms, proteins with varying glycosylations or PTMs, can be determined.22
To better resolve heterogeneous samples (e.g., glycoproteins), liquid chromatography is often coupled to MS to separate proteins based on their chemical properties before MS detection.22−24 Denaturing reversed-phase liquid chromatography (RPLC) is among the most widely used separation techniques with protein retention based on hydrophobic interactions.25 Capillary zone electrophoresis (CZE) coupled to MS, in comparison, separates molecules based on charge and size characteristics within a capillary using an applied electric field in the presence of a background electrolyte.26 Ion exchange is another charge-based online separation method that has recently been developed to study intact therapeutic proteins such as antibodies.27 Hydrophilic interaction chromatography (HILIC), which has the opposite selectivity to RPLC, separates molecules based on hydrophilicity with greater retention for hydrophilic molecules such as glycans.28−32
Two recent publications have used top-down MS to study RBD glycosylation.33,34 Both the studies identified O-glycosylation at site T323 of the spike S1 protein and two N-glycosylation sites at N331 and N343, the same sites that were previously identified with bottom-up glycoproteomics.3,19,20 The study from Roberts et al.34 used denatured and native top-down analysis to determine the relative abundance of O-glycoforms of the RBD, while the study from Gstottner et al.33 combined bottom-up and intact protein analysis with multiple glycosidase enzymatic steps to study the N- and O-linked glycosylation profile of the RBD. However, the modality of online separation for improving the intact protein analysis has not yet been thoroughly investigated in these reports. Herein, we compared HILIC with two other commonly used intact protein separation methods (C2 RPLC35 and CZE36) on several RBDs recombinantly expressed in HEK 293 cells from two different vendors (Sino Biological and RayBiotech). HILIC allowed the greatest separation of RBD glycoforms, which were different in their neutral glycans. Drastic differences in the glycan composition were also detected between vendor sources when the same expression platform (from the same type of cell line) was used. Additionally, our results suggest that the RBD exhibits more than 200 individual glycoforms that are assignable. Top-down MS also helped the discovery of unexpected PTMs on the proteins that may affect the structure and function. When compared with glycopeptide data, HILIC top-down analysis better detected low abundance and/or heavily glycosylated proteoforms that may be missed due to more severe detection biases at the peptide level. We anticipate that online HILIC separation has great potential for defining the metaheterogeneity of heterogeneous glycoproteins for biotherapeutic or biotechnology use.
Materials and Methods
Chemicals and Proteins
The glycoprotein standard alpha-1-acid glycoprotein (AGP, G9885) was purchased from Millipore Sigma (St. Louis MO). Spike RBD proteins expressed in HEK 293 cells were purchased from two sources: Sino Biological (Beijing, China) and RayBiotech (Atlanta, GA, USA). A wild type version of the RBD (SARS-CoV-2 spike protein amino acids 319–541) was purchased from both sources expressed with a C-terminal polyhistidine tag. Additionally, the N501Y (stronger ACE-2 binding)37 mutant from Sino Biological and the N331Q mutant (removes the N331 glycosite) from RayBiotech were purchased for comparison. Ammonium acetate, tris(2-carboxyethyl)phosphine hydrochloride (TCEP), 1,4-dithiothreitol, and iodoacetamide were purchased from Sigma (St. Louis, MO, USA). Peptide-N-glycosidase F (PNGase F, P0704S) was acquired from New England Biolabs (Ipswich, MA, USA).
Sample Preparation for Intact Protein Analysis
AGP and the RBDs from RayBiotech came as lyophilized powders and were diluted to a starting concentration of 1 mg/mL with deionized water. The RBDs from Sino Biological were also lyophilized but were diluted to a starting concentration of 0.25 mg/mL following the manufacturer’s recommendations. Samples were then buffer exchanged into 100 mM ammonium acetate using Zeba 7 kDa desalting columns (Thermo Fisher) that were equilibrated with ammonium acetate. All samples were vialed at 0.25 mg/mL in 100 mM ammonium acetate for MS analysis. Further sample preparation for PNGase F treatment of RBDs as well as glycopeptide and released glycan analysis can be found in the Supplementary Methods.
Online Liquid Chromatography for Intact Protein Mass Spectrometry
Online RPLC and HILIC separations were performed with a Waters NanoAcquity UPLC with dual pump trapping mode. The nanoflow C2 column (100 μm ID capillary) was packed in-house.35 The HILIC column used was recently developed and made in-house,38 consisting of an acrylamide-based polymer monolith stationary phase polymerized in a 200 μm ID capillary. Packed HILIC columns can be made using commercially available HILIC materials29 but have a lower LC resolution and higher baseline. Both C2 and HILIC utilized an online desalting C2 column prior to the analytical separation. Online CZE separation was performed using a CMP Scientific (Brooklyn, NY) EVE-001 capillary electrophoresis autosampler using a proprietary 100 cm PS2 coated capillary (cat: E-SC-PS2-360/150-50-100-B1) from CMP Scientific using denaturing background electrolyte conditions (10% acetic acid in water). Further details for online separation can be found in the Supporting Information Methods.
Most intact (RPLC, CZE, and HILIC) and top-down mass measurements were performed with a Thermo Fisher Orbitrap Eclipse tribrid. A Thermo Q-Exactive HF mass spectrometer was used for the Sino Biological C2 RPLC separation experiments. Additionally, a Thermo Exploris 480 was used for higher-energy collisional dissociation (HCD) top-down analysis of RBDs after PNGase F N-glycan removal. The nanoelectrospray source was set to 1.8–2.2 kV, with the transfer tube at 305 °C, source fragmentation voltage set to 35 V for HILIC experiments, and 15 V for C2 RPLC and CZE experiments. The HILIC separation experiments required higher source fragmentation voltages to diminish trifluoroacetic acid (TFA) adducts. TFA at 0.05% was used as a necessary ion-pairing agent in HILIC LC analysis.29 The RF lens was set at 70% for experiments. MS1 spectra were acquired with a mass range of 600–6000 m/z at 7500 resolution (at m/z of 400), AGC target of 8E5, a maximum injection time of 200 ms, and 5 microscans. The Eclipse and Exploris were set to intact protein mode with low pressure. The Sino Biological WT RBD was run in triplicate as per a separation method tested, while the other RBDs were run in triplicate using HILIC separation.
Data Analysis of Intact Mass Spectra
All raw mass spectra used for comparing separation techniques were deconvolved to zero-charge spectra and output as a matrix of mass, abundance, and elution time slice using Protein Metrics (Cupertino, CA) Intact Mass software (version 4.2)39 with default settings. An R script was implemented (Version 4.0.2) to compare the list of deconvolved masses from each separation method and to remove mass peaks not observed in triplicate using a mass tolerance of ±2 Da. Further details of this peak filtering approach can be found in the Supporting Information Methods. The R source code, deconvolved intact mass data, and intact mass assignment for each RBD can be found at https://github.com/EMSL-Computing/RBD-intact-peak-analysis.
Results and Discussion
HILIC Provided the Highest Degree of Separation for Resolving the Heterogeneity of RBD Glycoforms by Separating Neutral Glycans
To benchmark the separation methods for glycoform separation, we used AGP (orosomucoid 1) as a mammalian glycoprotein standard. Almost half of the mass of the ∼40 kDa AGP is from extensive N-glycosylation (5× N-glycan sites) with a high degree of sialic acid incorporation.40 Overall, the different methods showed different selectivities for AGP proteoform separation. Both CZE and HILIC demonstrated greater capacities to separate glycoforms than C2 RPLC (Figure S1). In contrast, C2 RPLC separation was mostly between the sequence variants of ORM1*F1 and ORM1*S (Figure S2). CZE separated AGP charge variants well based on the degree of sialylation per glycoform (Figure S3), while HILIC separation demonstrated a trend of increasing glycosylation with retention time (mostly based on neutral glycans). These observations were consistent with previous studies,28,40,41 and the work of Baerenfaenger and Meyer was used for AGP proteoform assignment.40
Variable amounts of sialic acid groups on the O-and the N-glycan sites have been reported on the RBD.20,33,34 Separation techniques to resolve these complex RBD glycoforms have not been systematically evaluated to the best of our knowledge. One might expect CZE to separate the RBDs well, since there is some variance in charged sialic acid, while HILIC may be able to separate the more neutral N-glycans based on total glycan composition. Figure 1A–C displays the C2 RPLC, CZE, and monolithic HILIC separation for the Sino Biological WT RBD with total ion chromatograms/electropherogram (TICs), and extracted ion chromatograms/electropherograms (XICs) of selected RBD glycoforms that only differ in their glycan compositions. The glycoform assignments were based on additional top-down, glycoproteomic, and released glycan data, which will be discussed in later sections. Minor separation was seen in both C2 RPLC and CZE for the selected high-abundance RBD glycoforms (Figure 1A,B). In contrast, HILIC produced clear chromatographic separation of RBD glycoforms, with resolvable peaks even in the TIC (Figure 1C). The limited CZE separation may be due to the small degree of heterogeneity in sialic acid seen in recent reports of recombinant RBDs. The T323 O-glycan site is decorated with 1–2 sialic acids (the most abundant O-glycan has 2 sialic acids) with Core 1 or Core 2 O-glycan structures.33,34 The identified N-glycans are complex and have between 0 and 3 sialic acids.20,33 This combination of O- and N-glycan compositions leads to the majority of RBDs containing 2–4 sialic acids,33 a narrower distribution than seen with proteins that separate well with CZE such as AGP (13–19 sialic acids).
Figure 1 Sino Biological WT RBD intact glycoform separation comparison between (A) C2 RPLC, (B) CZE, and (C) monolithic HILIC. Each XIC corresponds to a defined glycoform of the RBD (charge states 16+ except for the lightest two glycoforms with one N-glycan are 15+). HILIC separated the RBD glycoforms the best by the N-glycan occupancy and glycan composition. (D–F) Intact mass distributions for the RBD at given HILIC elution times matching three selected elution peaks marked in (C). (D) Part of the RBD population with 1× N-glycan occupied on sites N331 or N343. The mass shift from the most abundant peaks from (D) to (E) (labeled elution peaks 1 and 2) corresponds to 2144 Da adding the N-glycan H3N6F3. The 186 Da spacing observed does not match a glycan mass but is an additional unknown modification. (F) Late eluting RBD species (labeled elution peak 3) display a mass peak spacing of 146 Da, suggesting increasing fucosylation and sialic acid. The two fucose units weighs 1 Da more than one sialic acid. Labeled glycan compositions are based on matching top-down and released N-glycan data. Corresponding glycan key: hexose (H), N-acetylhexosamine (N), fucose (F), and sialic acid (S).
As seen here and previously, HILIC has a high capacity to separate neutrally charged glycans,28,30 leading to a broader elution time profile for RBD glycoforms than observed with the standard intact protein separation methods using C2 RPLC or sheath flow CZE (Figure 1C). To make the data more comparable, we selected conditions that yield similar separation windows of ∼5 min across the main protein peaks for all methods. From the monolithic HILIC elution profile alone, differences in the N-glycan occupancy become readily apparent, which would otherwise be easily missed with CZE or C2 RPLC. Figure 1D–F displays the intact mass distributions for the Sino Biological WT RBD at three different points along the HILIC elution (marked in Figure 1C). The first elution peak at 19 min (Figure 1C) contains RBD species weighing 29830.3 Da (Figure 1D), while the main elution peak at ∼21 min weighs 31974.3 Da (Figure 1E), a mass difference of 2144 Da. This mass shift of 2144 Da corresponds to the addition of the N-glycan group with the composition of H3N6F3 (hexose (H), N-acetylhexosamine (N), fucose (F), and sialic acid (S)), which was identified previously from glycoproteomics20 and is observed in our released glycan and glycoproteomics data. Additionally, a repeated peak spacing of 41 Da is observed in Figure 1D,E that can be attributed to the exchange of an H1 for N1. Late eluting species (Figure 1F) display an abundant peak spacing of 146 Da that matches to increasing amounts of fucose or sialic acid. Two fucose units weighs only 1 Da more than one sialic acid group, causing mass degeneracy that is difficult to disentangle due to peak overlap and limited mass resolution. Overall, compared to the generic RPLC and CZE methods for intact proteins, the HILIC used here provided a broader separation of RBD glycoforms based on the extent of glycosylation (increasing retention with extent of glycosylation) with distinct peaks being separated from the main protein elution area. The high resolving power of HILIC for glycoforms seen here was consistent with the different selectivities among the separation methods shown for the glycoprotein lipase.30 Although further optimization for RPLC and CZE is possible (e.g., gradient length, stationary phase/coating, and mobile phase/buffer), we herein focused on evaluating HILIC separation of RBDs because of the ease of glycoform separation.
HILIC Separation Reduced Spectral Congestion and Improved Detection of Low Abundance Glycoforms
A significant challenge in MS of heterogeneous macromolecules (such as glycoproteins) remains in the effective charge state determination prior to accurate mass determination by deconvolution. The complexity may arise from the lack of mass resolution and high spectral baselines due to peak coalescence. In addition to the advances of new algorithms42 and instruments (e.g., charge detection MS),43−45 online separations also play essential roles in reducing sample complexity. Figure 2 displays the deconvolved intact mass distributions with each tested RBD using HILIC separation. Each colored trace corresponds to the intact mass distribution at a given apex elution time from the HILIC separation. With each RBD, the intact mass increases with retention time. Visual comparison of the intact mass profiles between RBDs immediately suggests that the glycan compositions for the Sino Biological WT and N501Y RBDs (Figure 2A,D) are different from those of the RayBiotech WT RBD (Figure 2B). Importantly, the two Sino Biological RBDs (WT and N501Y) have very similar intact mass profiles that are only shifted by the N to Y amino acid substitution (49.07 Da). The RayBiotech WT RBD has a broader intact mass profile than the Sino Biological RBDs that are centered around a few high-abundance mass peaks. The N331Q mutant (removes the N331 glycosite, Figure 2D) displayed a simpler intact mass profile with reduced glycosylation.
Figure 2 Deconvolved intact mass analysis of tested RBDs separated with monolithic HILIC across the elution profile with apex times given for each elution slice. (A) Sino Biological WT RBD. The same raw data as shown in Figure 1. (B) RayBiotech WT RBD. (C) Sino Biological N501Y RBD. (D) RayBiotech N331Q RBD. The intact mass distributions (without peak filtering) for HILIC separated slices are represented by the overlaid color traces . The mass distributions of the WT RBDs are drastically different between the two vendors, while the N501Y mutant is simply shifted by the mass of the mutation in comparison to the WT RBD from the same vendor.
With each RBD, more than 200 peaks were detected due to the number of different glycoforms, creating a broad and congested mass distribution that is challenging to interpret. Figure 2 Using the Ray Biotech WT RBD as an example that had the most complex glycosylation pattern among the four RBDs, Figure 3A displays the TIC for the HILIC separation with selected mass spectra of RBD species from the elution (Figure 3B). Summing the elution window together produced a heavily congested mass spectrum (Figure 3C). Some of the lighter RBD species for this sample (red and blue mass spectral traces in Figure 3B) are easily lost without the use of the HILIC glycoform separation due to the high baseline and overlapping charge state distributions observed. For instance, the 12+ RBD species weighing 29273.3 Da (retention time 16.5 min) overlaps with heavier 13+ RBD species weighing ∼31,570 Da that elute later (retention time 19.5 min). However, taking 30 second windowed slices along the elution drastically reduces the spectral complexity before performing mass deconvolution, which eases detection of the lighter RBD species.
Figure 3 (A) Example chromatogram and (B) overlaid mass spectra from the RayBiotech WT HILIC elution. Using 30 second windowed slices (colored segments and traces, red: 16.5–17 min, blue: 18–18.5 min, and gold: 19.5–20 min) from the elution aids resolution of more RBD glycoforms due to the reduction in spectral congestion in comparison to summing the full elution window together (C) (16.5–26.5 min) where overlapping charge state distributions can be observed. For example, the 12+ RBD species weighing 29273.3 Da (most abundant) overlaps with heavier 13+ RBD species weighing ∼31,500 Da.
This spectral complexity inevitably resulted in variability and artifact peaks during deconvolution. We thus implemented a peak filtering approach to ensure the consistency of results (more details in the Supporting Methods section). In essence, elution time slices (18 total) for each separation method were deconvolved separately. The resultant mass lists were then merged and filtered by removing mass peaks not observed in three technical replicates with a tolerance of ±2 Da. Representative examples of the chromatographic reproducibility and deconvolution results after peak filtering for the Sino Biological WT are plotted in Figure S4A−C with each separation method, and in Figure S4D for comparison of the observed intact masses. This approach combined the intact mass distribution from triplicates and reduced the influence of noise in the deconvolution when comparing different samples or conditions. On comparing the number of observed RBD proteoforms, HILIC detected the highest number (261 peaks), including low abundance species, in comparison to C2 RPLC (129 peaks) and CZE (177) (Figure S4F). It is noted that the filtering step kept ∼10% of the total peaks, while ∼70 and ∼20% of peaks showed up only in one or two replicates, respectively. The average median abundances of peaks observed in all triplicates were consistently higher than those of peaks observed in one or two replicates for all separation methods, suggesting higher variability in detection of low-abundance species (Figure S5). Using this peak filtering approach, we compared the total numbers of peaks after deconvolution using the HILIC separation across time slices (the same as Figure 3B) vs summing across the full elution window (the same as Figure 3C) for all four RBDs (Figure S6A–D). Not surprisingly, separation increased the number of peaks by at least ∼3 fold (Figure S6E) and showed the lowest median abundances of the detected peaks (Figure S6F). Overall, the windowed elution slices and peak filtering approach used here best utilize the HILIC glycoform separation to detect the greatest number of proteoforms while reducing the influence of noise from the deconvolution process from spectral congestion issues. We additionally compared the chromatographic performance of a packed HILIC column (with commercially available packing material) to our monolithic HILIC column format (Figure S7).38 The monolithic HILIC column consistently had lower chromatographic baselines and better peak resolution, which thus led us to focus on the monolithic format.
HILIC Top-Down Analysis Reported more Low-Abundance, Heavily Glycosylated Proteoforms than What Were Predicted from Glycopeptide Data
From the intact mass measurements alone, initial fitting of the masses to previously reported O- and N-glycans proved very difficult. The measured masses were 206 Da higher for the Sino Biological and 733 Da higher for the RayBiotech RBDs than expected (after considering the known C-terminal polyhistidine affinity tag), suggesting additional modifications. To better confirm the protein sequence, the N-glycans were removed with PNGase F, and the protein was reduced and then denatured to produce O-glycoforms for direct infusion top-down analysis, similar to the approach used by Roberts et al.34 Removal of N-glycosylation significantly reduced spectral complexity and improved fragmentation for sequencing the protein backbones. After accounting for the expected sequence mass (including purification tags) and the O-glycoforms, the RayBiotech WT and N331Q RBDs were still 613.0 and 610.3 Da heavier than expected, respectively (Figure S8). Manual de novo sequencing of the N-terminus for the RayBiotech WT RBD by electron transfer dissociation (ETD) fragmentation (Figure S9)46 determined that five amino acids with the sequence, KSMHM (weighing 614.77 Da), from the partially cleaved signaling peptide remained. Top-down with HCD for each RBD produced only C-terminal and glycan fragments that matched to the expected O-glycoform isolated but did not reveal the N-terminus (Figure S10), possibly due to the complication of O-glycosylation. Using the same approach, N-terminal ETD fragments for the Sino Biological WT (Figure S11) explained the extra 85.1 Da by an additional serine residue remained from the signaling peptide sequence, as was previously reported for this RBD.34 We also identified potential cysteinylation at C538 based on nonreducing intact mass analysis after N-glycan removal (Figure S12) and known information in a recent report.33 Additionally, we detected an unknown 186 Da mass shift unique to the two Sino Biological RBDs (Figure S12C,D). This mass shift was present in the intact RBDs (without any treatment, Figures 1 and 2) but lost with TCEP reduction. It cannot be readily assigned to known N- or O-glycans but could be a noncovalent adduct protected by disulfides or an unknown covalent modification linked to a cysteine residue. The identity of this modification remains unclear but must be accounted for in the intact mass distribution.
Together, all the RBDs studied here had additional N-terminal residues added to the expected RBD sequence from the signaling peptide and cysteinylation, consistent with other reports by Gstottner et al.33 and Roberts et al.34 Notably, standard peptide mapping by bottom-up analysis produced 100 and 92.1% sequence coverage for the RayBiotech WT and Sino Biological WT, respectively, (Figures S13 and S14) without considering the modified N-terminal sequence. Since the expected N-termini begin with arginine, additional preceding residues are easily missed due to the trypsin cleavage. Despite the relatively short sequence of the signaling peptides at N-termini, variable N-terminal cleavage points have been observed that leave additional residues, which could complicate RBD-based seropositivity assays if recombinant RBDs with variable N-termini are used.47 Therefore, complete characterization of the sequence, especially with the power of top-down measurements, can be important for quality control of RBDs.
After quantifying intact O-glycoforms and unexpected PTMs of all four RBDs from the intact mass and top-down data, we examined bottom-up glycopeptide (Figures S15–S18) and released glycan data (Figures S19 and S20; Tables S1–S4) regarding their coverage of glycosylation. Overall, released N-glycans showed similar glycan profiles to glycopeptide data but captured few N-glycans with sialic acids, possibly due to the labile nature or detection bias of sialic acid groups.48,49 Thus, we focused on combining the RBD intact masses after N-glycan removal (O-glycoforms) with the N-glycopeptide data for reconstruction of the mass distributions for each RBD by adapting the method reported by Yang et al.50Figure 4 plots the intact mass profile after peak filtering (top, black trace) with the corresponding reconstruction (bottom, red trace). Matching the reconstruction allowed us to attach assignments to at least half of the peaks in the filtered intact mass distributions including the selected glycoforms in Figure 1. Some intact mass peaks could have multiple glycan assignments due to structural isomers or exchange of glycans between sites, where ambiguity exists due to multiple assignments overlapped in mass, and the glycopeptide abundances were used to inform which species are the most likely.
Figure 4 (A–D) Comparison of HILIC separated deconvolved RBD mass distributions after peak filtering (top, black trace) with the reconstructed mass distribution from top-down and glycopeptide N-glycan analysis (bottom, red trace) for each RBD. The Pearson correlation between the intact mass spectrum and the reconstruction is given for every RBD. Note: For the Sino Biological RBDs, only glycosite N331 was observed to be sometimes unoccupied. The relative abundances of unoccupied glycosites were estimated to be 8% based on the best fit to the intact mass distribution as described in the Supporting Method.
With all the RBDs investigated, the RayBiotech N331Q RBD (Figure 4D), which was the least heterogeneous sample, had the best fit between the reconstruction and the experimental data with a Pearson correlation of 0.83 and 52 assignable peaks in the reconstruction out of 107 experimental peaks matching within ±2 Da. The RayBiotech WT RBD had a correlation of 0.76 and matched 98 of 150 peaks. Similarly, the Sino Biological RBDs had correlations of 0.74 and 0.67 with 210/261 and 160/202 mass peaks matching the reconstruction for the WT and N501Y RBDs, respectively. Despite the overall agreements, significant amounts of higher mass peaks (>33 kDa and up to ∼30% relative abundance) in the experiment were unaccounted for in the reconstruction for the RayBiotech WT RBD (Figure 4B). Underestimations of high mass species were also seen in the two Sino Biological RBDs (the tails in the mass distribution >33 kDa in Figure 4A,C), but those species were at a much lower abundance (up to ∼5%). This discrepancy could be attributed to the detection biases in the glycopeptide analysis.
It is known that glycosylation can reduce the ionization efficiency of glycopeptides relative to unglycosylated peptides, which complicates quantitative glycan analysis without derivitization.51,52 Additionally, sialic acid is known to substantially shift the retention time of glycopeptides where more heavily sialylated peptides elute later in reverse-phase chromatography. Sialic acid also can undergo more substantial in-source fragmentation than other glycan groups.53,54 These missing glycoforms in the reconstruction likely have even larger N-glycans with more sialic acid and/or fucose present that are not readily detected from the glycopeptide or released glycan analysis. For instance, the matched reconstruction and experimental peak with the highest mass for the RayBiotech WT RBD weighs 33,296 Da (O-glycan: H2N2S2; combined N-glycans: H10N10F2S3). Many additional higher mass peak pairs (e.g.,33,587 and 33,878 Da) are exclusively observed in the experimental data that are each spaced by sialic acid (291 Da), thus supporting this possibility of suppressed signal of sialyated glycopeptides in our peptide data. In native or denaturing intact mode MS, ionization efficiency is largely driven by the protein backbone, which alleviates the ionization bias based on the extent of glycosylation.55 This hypothesis could be tested in future work using targeted methods (e.g., derivatization, negative mode) that minimize losses and enhance the signal of sialic acids.54
We also noted that both Sino Biological RBDs showed more heterogeneous glycoform distributions (∼3 times more peaks) in the reconstructions than in the experimental intact mass distribution. Herein, we collected dual enzyme digestion data to separately define the glycosylation on N331 and N343 for the two Sino Biological RBDs. We suspect that the N-glycans on these two glycosites were likely correlated and not randomly combined as assumed in the reconstruction (i.e., connectivity between the microheterogeneity). In our RayBiotech WT RBD data, the N331 and N343 glycans were defined on a single tryptic peptide, maintaining the native connectivity between the two sites and therefore yielding a more similar reconstruction to the experimental spectrum. While the connectivity and variation of glycosylation across multiple sites (i.e., metaheterogeneity) require further confirmation, our preliminary analysis showed that a top-down framework with HILIC glycoform separation can be highly beneficial for defining the combinations of glycosylation with potentially reduced detection biases.
Conclusions
Here, we demonstrated that online monolithic HILIC provided improved detection of RBD intact glycoforms compared to C2 RPLC and CZE, attributable to HILIC’s superior separation of neutral glycans.28,30 While all separation methods helped reduce spectral congestion, the heterogeneity of the glycoproteins still caused remarkable variations in detection of glycoforms even among technical replicates. The peak filtering approach used here maintained reproducible RBD proteoforms while limiting the influence of noise with these heterogeneous and difficult-to-study proteins. The intact mass profile of the detected glycoforms provides a rapid assessment of the integrity of the RBDs and readily revealed unexpected mass shifts from PTMs and sequence variations. When combined with top-down fragmentation, glycopeptide, and released glycan analyses, up to 75% of the intact masses could be assigned based on the computationally reconstructed mass profiles integrating all the data. Interestingly, our reconstruction based on glycopeptide data generally showed fewer low abundance and/or heavily glycosylated species than the top-down data, especially for the more heterogeneous RBDs. Such discrepancies were consistently reported in several recent studies of RBD/spike glycosylation,33,45 and with other glycoprotein analysis,49 to differing extents. Given the known experimental biases in glycopeptide analysis, incorporating top-down data will be highly valuable for more accurate characterization of glycosylation.
While it remains technically challenging to directly assign individual glycoforms by online top-down fragmentation, the continuing advances in MS instrumentation methods such as proton transfer charge reduction56,57 and charge detection mass spectrometry45 will likely allow more comprehensive analysis of heterogeneous glycoproteins in the near future. New online separation modalities such as the HILIC method described here will also be indispensable for reducing sample complexity prior to MS analysis. Currently, HILIC separation of intact glycoproteins is uncommon but has shown great promise in the separation of glycoproteins that vary in the glycan occupancy and the amount of neutral glycans.28,30,58 Future development could utilize the HILIC separation capacity for online top-down fragmentation of glycoforms to better define the PTMs at the intact protein level. In addition, the potential separation of glycoform isomers by HILIC should also be investigated for reducing ambiguity in intact mass assignment.
Given the importance of protein glycosylation in modulating immune responses12,13 and many other biological processes, we envision that HILIC coupled to MS will have great potential in the characterization of heterogeneous glycoprotein products. To realize this, solutions to reduce the amount of TFA used in the separation and column formats that allow for more sensitive analysis should be developed and commercialized.38
Supporting Information Available
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.analchem.2c00139.Extended methods section with experimental conditions and the data analysis procedure for peak filtering; additional mass spectra and analysis of AGP as a glycoprotein standard; comparison of the intact mass profile with each separation method for the Sino Biological WT RBD and justification of the peak filtering approach; infusion and top-down analysis of RBDs after PNGase F treatment; and RBD glycopeptide and released N-glycan analysis (PDF)
RBD-identified glycopeptide (XLSX)
Intact glycoprotein data (XLSX)
Supplementary Material
ac2c00139_si_001.pdf
ac2c00139_si_002.xlsx
ac2c00139_si_003.xlsx
Author Contributions
M.Z., L.P., V.R.G., and S.E.B. contributed to conceptualization; A.B. and J.W. helped with software; J.W.W., A.B., J.W., Y.L., D.V., and M.Z. contributed to formal analysis; J.W.W., Y.L., D.V., and R.W. contributed to investigation; M.P., A.F.G.G., V.R.G., and L.P. helped with resources; J.W.W. and M.Z. contributed to writing – original draft; A.B., A.F.G.G., V.R.G., and S.E.B. contributed to writing – review & editing; M.Z. helped with project administration; S.E.B., L.P., M.Z., and V.R.G helped with funding acquisition.
The authors declare no competing financial interest.
Notes
Raw MS data for online RBD glycoform separations, glycopeptide analysis, released glycan, and top-down experiments after N-glycan removal are available at MassIVE with accession MSV000088601. Processed data and scripts used in the analysis are available at GitHub (https://github.com/EMSL-Computing/RBD-intact-peak-analysis).
Acknowledgments
A portion of this research was performed on a project award 10.46936/intm.proj.2020.51671/60000250 from the Environmental Molecular Sciences Laboratory (EMSL), a DOE Office of Science User Facility sponsored by the Biological and Environmental Research program under Contract No. DE-AC05-76RL01830. This research was also supported by the DOE Office of Science through the National Virtual Biotechnology Laboratory, a consortium of DOE national laboratories focused on response to COVID-19, with funding provided by the Coronavirus CARES Act. A portion of this work was supported by Congressionally Directed Medical Research program award #PR201356 to VRG. M.P. acknowledges the European Research Council (ERC), Project 694151, STAMP, for funding. We would like to thank David S. Roberts and Ying Ge for helpful discussions and for data comparison; Thomas Fillmore and Ronald Moore for liquid chromatography and instrumental tuning conditions; and Matthew Monroe for data deposition.
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PMC009xxxxxx/PMC9004146.txt |
==== Front
Clin Epidemiol Glob Health
Clin Epidemiol Glob Health
Clinical Epidemiology and Global Health
2452-0918
2213-3984
The Authors. Published by Elsevier B.V. on behalf of INDIACLEN.
S2213-3984(22)00086-0
10.1016/j.cegh.2022.101044
101044
Article
COVID-19 cases and their outcome among patients with uncommon co-existing illnesses: A lesson from Northern India
Reddy D. Himanshu a
Atam Virendra a
Rai Priyanka b
Khan Farman a
Pandey Saurabh c∗
Malhotra Hardeep Singh d
Gupta Kamlesh Kumar a
Sonkar Satyendra Kumar a
Verma Rajeev e
KGMU COVID-19 working groupUsman Kauser
Chaudhary Shyam Chand
Sonkar Satyendra Kumar
Kumar Vivek
Sawlani Kamal Kumar
Gupta Kamlesh Kumar
Patel M.L.
Himanshu D.
Kumar Ajay
Verma Sudhir Kr
Gautam Medhavi
Gupta Harish
Kumar Satish
Baghchandanani Deepak
Yadav Ambuj
Lamba M.
Kumar Amit
Suhail
Prabha Rati
Bajaj Darshan
Singh Abhishek Bahadur
Mahendra Mayank
Kumar Gaurav
Kumar Narendra
Ojha Bal Krishna
Verma Rajeev
Verma Dhananjay Kumar
Kumar Vinod
Singh Suresh
Gupta Shivam
Hashim Mohammad
Verma Kuldeep
Bhardwaj Akriti
Chaudhary Anurag
Chaudhan Himanshu
Kaustubh
Dubey Kinjalk
Kumar Naveen
Rituraj
Kumar Janmajay
Srivastav Somesh
Singh Shiv Paratap
Kumari Sunita
Srivastave Sudham
Verma Jyoti
Hussain Mohmmad Ahmad
Siddiqui Ammar Sabir
Rizvi Azher
Pancholi Chitranshu
Sharma Deepak
Verma Deepak Kumar
Zothansanga David
Singh Kuldeep
Singh Prashant Kumar
Kumar Rahul
Bharti Vipin Raj
Ansari Shahnawaz Ali
Kumar Vivek
Kallani Monika
Bharti Harish
Singh Ankita
Majumdar Avirup
Verma Neeraj
Mishra Mayank
Gupta Pankaj Kumar
Shivhare Shubhanshu
Kotwal Mudit
Mahar Prashant
Mall Praduman
Parmar Krishnapal Singh
Kumar Guddoo
a Department of Internal Medicine, KGMU, Lucknow, India
b Department of Pathology, MVASMC, Basti, India
c Medicine & Infectious Diseases Unit, PMC Hospital, Basti, India
d Department of Neurology, KGMU, Lucknow, India
e Department of Internal Medicine, AIIMS, Gorakhpur, India
∗ Corresponding author. PMC Hospital, Gandhi nagar, Basti, Uttar Pradesh, 272001, India.
12 4 2022
May-June 2022
12 4 2022
15 101044101044
24 1 2022
23 3 2022
3 4 2022
© 2022 The Authors
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Introduction
Newer coexisting conditions should be identified in order to modify newer risk factors. Aim was to identify patients with non-classical or less common coexisting conditions in patients infected of COVID 19.
Method
Single centred study from June 2020 to May 2021 at a tertiary centre in North India. A preformed questionnaire was used to record clinical and laboratory parameters and to identify cases which are in addition to CDC list and Indian data.
Results
0.67% (46) cases out of 6832 patients were identified to have non-classical coexisting illness. It was divided into 2 groups-infections A (60.1%) and non-infections B (39.9%). Group A included-tuberculosis- pulmonary (14.3%) & extra pulmonary (32.9%), bacterial (25.0%) viral infections [dengue, hepatitis B & C] (14.3%), HIV disease (10.7%) and malaria (3.6%). Group B included- organ transplant (27.8%), autoimmune [myasthenia gravis, polymyositis, psoriasis] (22.6%), haematologic [Haemophilia, ITP, Aplastic anaemia, APML, CML] (27.8%), uncommon malignancies [disseminated sacral chordoma and GTN] (11.1%) and snakebite (11.1%). Serum Procalcitonin was not helpful for diagnosis of bacterial infection in COVID-19 disease. Group A had significantly longer duration of illness, hepatitis and elevated CRP. The mortality in group A & B were 32.1% and 43.8% respectively. Death in non-severe COVID cases was in tetanus and snakebite. 30.7% death among tuberculosis patients. More than 70% of deaths were attributable to COVID 19 in both the groups.
Conclusion
In Indian settings, comorbidities like tuberculosis and bacterial infections can precipitate severe COVID 19 unlike other parts of the world where tuberculosis is relatively uncommon.
Keywords
Uncommon coexisting illness
COVID 19
Tuberculosis
Tropical infections
Bacterial
Rheumatologic
India
transplatation
HIV
==== Body
pmc1 Introduction
COVID-19 affected worldwide after its origin from Wuhan, China. It adversely affected people belonging to high risk group as defined by CDC (1) and this was uniformly observed across globe. CDC has laid down risk factors according to available evidences and strongest risk factors includes-cancer, CKD, COPD, cardiac illness, cerebrovascular accidents, smoker, obesity, pregnancy, solid organ or haematopoietic cells transplant and diabetes mellitus. The remaining factors had moderate to low level of evidences for severe diseases (1).
As the disease continues to unfold itself new risk groups are expected. One such risk factor established later is tuberculosis as shown by Sarkar et al.2 We started managing this pandemic since February 2020 and our initial report by D H Reddy et al. shows presence of classical risk group and expected outcome.3 Over the time during this pandemic we encountered COVID 19 disease with underlying illnesses sparsely described in literature and variable outcomes of this disease. Thereby, we are reporting uncommon co-existing illnesses with COVID19 disease and their outcome in Indian context of this pandemic.
2 Material and methods
The study was conducted at COVID 19 treatment facility of a tertiary care centre in Northern India from June 2020 to May 2021. The patients admitted to the facility either by self-reporting for testing, contact tracing, positive test from community screening or elsewhere screening. We enrolled consecutive patients admitted with diagnosis of SARS-CoV-2 and evaluated them by pre-formed proforma to record epidemiology, history, examination and investigation. All patients were diagnosed on the basis of Indian Council of Medical Research-National Institute of Virology (ICMR-NIV) criteria4 and strict screening guidelines levied by the same. The severity of COVID-19 disease was done according to Government of India guidelines on COVID-19 management.5 The severity of the hypoxemia defines the severity of ARDS: a. mild: PaO2/FiO2 200–300 mmHg or SaO2/FiO2 237–317 mmHg; b. moderate: PaO2/FiO2 100–200 mmHg or SaO2/FiO2 155–237 mmHg; C. severe: PaO2/FiO2 ≤ 100 mmHg or SaO2/FiO2 < 155 mmHg. Hyper-inflammation or cytokine response was defined as presence of two or more of the following -serum ferritin >1000 ng/mL; C-reactive protein >150 mg/L; D-dimer > 800 μg/L; Interleukin 6 > 20 pg/mL.5
All patients with any suspected or confirmed concurrent illness or recently diagnosed were included in the study. We used CDC criteria to identify comorbidities or coexisting illness.1 After review of these criteria in Indian context we concluded that the frequency of few diseases or conditions is low and therefore we considered keeping this group among uncommon co-existing condition. This included- Solid organ or blood stem cell transplantation cases, patients on immunosuppressive medications and uncommon cancers.
The mandatory investigations included-complete blood count (CBC), liver function, kidney function test (KFT), serum electrolytes, serum C reactive protein, serum lactate dehydrogenase (LDH), ferritin, rapid testing for HbsAg, Anti-HCV IgG and HIV, EKG and chest x ray. Additional investigations were performed as per case The features positively correlating with infection by COVID-19 include fever with myalgia, fatigue along with dyspnoea, dry cough, absolute anosmia, aguesia. In this background the investigations included-neutrophilic leucocytosis, thrombocytosis, elevated levels of serum LDH, CRP, Pro-BNP, fibrinogen, procalcitonin and, D dimer with normal prothrombin time.6 , 7 Serum Procalcitonin was done by ELISA method and all patients sample was sent on day of admission along with other routine investigations.
All suspected or confirmed cases of chronic illness like tuberculosis; rheumatic diseases and malignancy in their active state were included and rest were excluded. Cases of Tuberculosis (pulmonary & extra-pulmonary) with duration of less than 2 months were included as active disease and longer duration were excluded from active co-existing illness. The coexisting illness were categorised as infectious and non-infectious. Death attributable to COVID-19 was adapted from WHO guidelines.8 All patients were followed up till discharge from hospital or expiry.
3 Statistical analysis
Statistical analysis was performed using SPSS software (version 25.0, IBM, Armonk, NY). Normally distributed data were presented as mean ± SD and data with a skewed distribution as median (IQR). The between groups differences were determined by using Student's t-test, analysis of variance (ANOVA), and nonparametric tests (Chi-square test) as well as Student's t-test being used for comparison with paired samples. In all statistical results, P < .05 was defined as statistical significance. * indicates statistical significance (*P < .05, **P < .01, ***P < .001, ****P < .0001).
4 Ethics in research
Ethical clearance was provided by institutional ethical committee.
5 Results
In our study we evaluated 6832 patients admitted in COVID-19 dedicated care and 46 (0.67%) had uncommon or non-classical co-existing illnesses. These are clubbed in 2 groups as infections and non-infections with 63.6% of infections as co-existing illness. Table 1 (Distribution of non-classical co-existing and concurrent illness in patients of COVID-19) shows distribution of coexisting illnesses. The infections group has significantly higher cases of tuberculosis 13 (56.4%) both pulmonary 4 (14.3%) and extra-pulmonary 9 (32.1%) followed by bacterial infections 7 (25%). There were 2 cases of sputum positive with rifampicin sensitive pulmonary tuberculosis (50%) and rest were diagnosed clinico-radiologically. 2 (22.2%) patients of extra-pulmonary tuberculosis were CBNAAT positive and rest were based on clinical, radiological and cytology study. The EPTB cases included- 3 cases of pleural effusion, 2 cases of tuberculous meningitis, 1 each cases of pyothorax, lymph node TB, tuberculoma, and disseminated TB. All suspected tuberculosis patients clinical features of fever with sputum for more than 1 month and in EPTB cases the duration of illness raned between 1 and 3 months. We included cases of more than 1 month of duration of illness so as to prevent over diagnosis of tuberculosis in background of COVID 19. 5 patients were already of antituberculous drugs and 2 were initiated. All bacterial infections diagnosed on basis of positive culture except 2 (pyelonephritis and intra-abdominal sepsis) due to prior antibiotics use. Malaria and 2 dengue cases were diagnosed according to WHO criteria with malaria as uncomplicated illness and each case of dengue as sever and non-severe.Table 1 Distribution of non-classical co-existing and concurrent illness in patients of COVID-19.
Table 1Co-existing illness: infectious (n = 28) Co-existing illness: non-infectious (n = 18)
Extra-pulmonary tuberculosis (9) [32.1%]- Central nervous system tuberculosis (7)-Tubercular meningitis ± tuberculoma (6)
- Potts' spine (1)
- Pleural effusion (1)
- *Abdominal tuberculosis (1)
Transplantation cases (5) [27.8%]- Liver (2)
- Renal (2)
- Post bone marrow (1)
Pulmonary tuberculosis (recently diagnosed) (4) [14.3%] –
HIV/AIDS with opportunistic infections (3) [10.7%]- *Disseminated tuberculosis (1)
- Tuberculoma/toxoplasmosis (1)
- without co-infections (Traumatic brain contusion)
Autoimmune/Rheumatologic (4) [22.3%]- Myasthenia gravis (2)
- Polymyositis (1)
- Psoriasis with arthritis (1)
Viral infection (4)- [14.3%]- *Dengue (2)
- Acute hepatitis B (1)
- Asymptomatic Hepatitis C infection without chronic liver disease (1)
Uncommon malignancy (2) [11.1%]- Disseminated Gestational trophoblastic neoplasm (1)
- Disseminated Sacral chordoma (1)
Bacterial infection (7) [25%]-- Pyelonephritis (1)
- *Tetanus (1)
- *Ruptured Appendicular abscess{Kleseilla species} (1)
- Psoas abscess {Staphylococcus aureus} (1)
- *Post-operative biliary sepsis (1)
- *Oesophageal rupture with pyothorax (1)
- *Purulent bacterial leg cellulitis {Staphylococcus aureus} (1)
Haematological conditions (5) [27.8%]- Haemophilia (1)
- Immune thrombocytopenia (ITP) (1)
- Aplastic anaemia (1)
- Acute promyelocytic anaemia (AML) (1)
- Chronic myeloid leukemia (CML) (1)
Parasitic infection- Malaria (1) [3.6%] Miscellaneous-Snake bite (2) [11.1%]
*Diagnosis associated with mortality.
The comparative data of both the groups are depicted in Table 2 (Clinical and laboratory characteristics of the population). There was no significant difference in terms mean age, sex and presence of classical co-morbidities among both the groups. Among clinical features only mean duration of illness was significantly higher among infection group although the longest duration of illness was of 180 days in myasthenia gravis. Among investigations abnormal liver function test (82.2%) and elevation in CRP (85.7%) were significantly higher in infection group. There was no significant difference among mean value of serum procalcitonin among bacterial and non-bacterial infections (5.33 v/s 3.23; p > .05). Radiological evidence of pulmonary tuberculosis was present in 100% patients with focal consolidation or cavitation, 100% patients of suspected TBM had imaging features of meningo-encephalitis with 1 case of concomitant tuberculoma.1 case of CNS tuberculoma without meningitis. A case of Polymyositis had active disease at presentation with creatine phosphokinase (CPK) levels were 5346 IU/L.Table 2 Clinical and laboratory characteristics of the population.
Table 2Indices Co-existing illness: infectious (n = 28) Co-existing illness: non-infectious (n = 18) P value
Mean age 43.62 ± 9.52 (14–76) 37.62 ± 14.88 (10–75) 0.11
Sex- Male 17 (60.8%) 9 (56.3%) 0.8
Female 11 (39.2%) 7 (45.7%) 0.6
Presence of 1 or more classical comorbidities/risk factors 12 (42.60%) 5 (31.25%) 0.46
Presence of 1 or more classical comorbidities/risk factors among expired cases 4 (14.7%) 3 (18.75%) 0.56
Mean duration of illness at admission 15.62 ± 16.54 (7–90) 5.82 ± 4.64 (6–180) <0.001
Clinical features-
Fever 20 (71.5%) 9 (56.3%) 0.31
Respiratory complaints 17 (60.7%) 9 (56.3%) 0.43
Hypoxia 18 (64.3%) 13 (81.3%) 0.23
Hypotension 5 (17.9%) 2 (12.5%) 0.64
Altered sensorium 7 (25%) 3 (18.75%) 0.63
G I complaints 7 (25%) 2 (12.5%)) 0.32
Oliguria 4 (14.7%) 1 (6.50%) 0.42
Others 7 (25%) 5 (31.25%) 0.65
Investigations-
Neutrophilic leucocytosis 12 (42.60%) 9 (56.3%) 0.38
Lymphocytosis 2 (7.20%) 1 (6.50%) 0.90
Thrombocytopenia 13 (46.60%) 9 (56.3%) 0.64
Anaemia 17 (60.70%) 8 (50%) 0.49
Abnormal liver function test 23 (82.14%) 9 (56.3%) 0.04
Abnormal renal function test 10 (35.7%) 3 (18.75%) 0.24
Presence laboratory features of cytokine storm 12 (42.60%) 5 (31.25%) 0.46
Elevated C reactive protein (CRP) 24 (85.7%) 10 (62.5%) 0.04
Elevated D-dimer 15 (53.6%) 7 (43.50%) 0.52
Elevated serum fibrinogen 7 (25%) 5 (31.25%) 0.24
Elevated fibrinogen and D-dimer 7 (25%) 4 (22.5%) 0.52
Elevated serum ferritin 13 (46.60%) 8 (50%) 0.83
Elevated serum LDH 20 (71.5%) 10 (62.50%) 0.54
Elevated serum pro-calcitonin 12 (42.60%) 8 (50%) 0.38
#Radiologic changes suggestive of COVID-19 17 (60.7%) 9 (56.3%) 0.43
Total deaths 10 (32.1%) 8 (43.8%) 0.44
No of death attributable to COVID-19 7 (70.0%) 6 (75.0%) 0.64
No of Concurrent diagnosed illness 17 (60.70%) 5 (31.25%) 0.04
Table 3 (Distribution of stages of COVID-19 infection and mortality among patient groups) shows distribution of case severity and related mortality. Total 18 (39.1%) patients developed severe COVID 19 disease. Death among tuberculosis subjects was seen in 4 (30.7%) cases with 1 each pulmonary tuberculosis, disseminated disease, abdominal and meningo-encephalitis with 75% cause attributable to COVID-19 and rest to tuberculosis. The application of WHO cause of direct death allowed us to differentiate between death directly attributable to COVID 19. Death where immediate causes were unrelated to COVID 19 included-tubercular meningitis, neurotoxic snakebite and oesophageal rupture with secondary pyothorax. Cases where cause death was difficult to delineate from COVID 19 due to overlap features and we placed them in moderate-severe category included-disseminated GTN with pulmonary involvement, acute promyelocytic leukemia with DIC and massive haemorrhage. Dengue fever with warning signs at end of critical phase developed severe COVID pneumonia and expired on 5th day. 3 patients with features suggestive of HLH were subjected to bone marrow study but were inconclusive and diagnosed as cytokine storm.Table 3 Distribution of stages of COVID-19 infection and mortality among patient groups.
Table 3Stage of COVID 19 & outcome Co-existing illness: infectious (n = 28) Diseases among infection group Death as outcome (n = 10) Co-existing illness: non-infectious (n = 18) Diseases among non-infection group Death as outcome (n = 8)
Mild 14 (50%) 1. TBM (1)
2. Potts' spine (1)
3. PTB (3)
4. Pleural effusion (1)
5. Severe Tetanus (1)
6. Hepatitis B & C (1 each)
7. Dengue (1)
8. HIV with traumatic brain injury (1)
9. Malaria (1)
10. Staphylococcal abscess (1)
1(10.0%) 5 (27.80%) 1. Myasthenia gravis (1)
2. Neurotoxic snakebite (1)
3. Haemophilia (1)
4. Post bone marrow transplant (1)
5. Renal transplant (1)
0
Moderate 4 (10.7%) 1. TBM (1)
2. PTB (1)
3. Abdominal tuberculosis(1)
4. Pyelonephritis (1)
0 5 (27.80%) 1. Liver transplant (2)
2. Immune thrombocytopenia (ITP) (1)
3. Aplastic anaemia (1)
4. snakebite (1)
1 (12.5%)
Severe 10 (39.3%) 1. TBM (1)
2. TBM (1)
3. Tuberculoma with HIV (1)
4. TB with HIV (1)
5. Cellulitis (1)
6. Dengue (1)
7. Biliary sepsis (1)
8. Bacterial peritonitis (1)
9. Ruptured Appendicular abscess (1)
10. Oesophageal rupture with pyothorax (1)
9(90%) 8 (44.40%) 1. Myasthenia gravis (1)
2. AML (1)
3. Disseminated GTN (1)
4. Psoriasis with arthritis (1)
5. Disseminated sacral chrodoma (1)
6. Polymyositis (1)
7. Renal transplant (1)
8. CML (1)
7 (87.50%)
#Diseases marked in red shows cases associated with mortality.
6 Discussion
Severe form of COVID-19 is usually seen in presence of certain risk factors and they are classical risk factors. We considered the CDC criteria and found few coexisting illnesses apart from classical risk factors. In our study classical risk factors were seen in 38.6% of all patients without any significant differences among both groups. In fact, these factors were present in 15.9% of all cases with sever disease. Additional cases of severe disease were seen in GTN with distant metastasis, tuberculous meningo-encephalitis, and intra-abdominal and biliary sepsis, abdominal tuberculosis, dengue fever and HIV disease. Therefore, it is important to identify other conditions that predispose to development of severe COVID 19 disease or complicate existing COVID-19 infection and increases morbidity and mortality.
In our study, infections constituted 63.3% of coexisting or recently diagnosed illness with tuberculosis (56.4%) as predominant infection. As India homes the highest global burden of tuberculosis, it is expected to cross ways with COVID-19. In our series 56.4% of co-existing infections were tuberculosis. The data in this regard is variable across globe. Two important studies were from Tadolini et al. and Motta et al. had 49 and 69 patients along with 12.3% and 11.6% mortality among individuals with TB-COVID-19 co-infections.9 , 10 A recent database from Southern India by M S Kumar et al. found 177 cases of active pulmonary TB-COVID19 co-infection with 15% mortality. Almost all cases of tuberculosis were diagnosed before the diagnosis of COVID 19 infection and more than 50% of mortality was seen among patients without underlying classical comorbidities.11 A study from N Gupta et al. found 22 cases of tuberculosis with mortality of 27.5%.12 Similarly, in our series the mortality was 30.7% and 75% attributable to COVID-19 showing role of active tuberculosis as a factor in causing severe COVID-19 disease among patients without underlying classical comorbidities.
Other most common group was bacterial infection and formed 25% of infectious co-existing conditions. There is very sparse data on pre-existing infections on COVID-19 outcome. Study by C G Vidal et al. showed community acquired co-infection was in 3.1% cases and all of them were pneumonia and associated blood stream infection. The organism included- Staphylococcus, Streptococcus, Haemophilus Influenza and Moraxella catarrhalis.13 In our study we found 2 cases of Staphylococcus aureus in form of blood stream infection and psoas abscess along with each case of Klebsiella spp. and E. coli . Moliere S et al. found 17.4% cases of COVID-19 among 46 patients with acute symptoms in post-operative period with 25% mortality.14 We had 2 (7%) post-operative sepsis patients who developed severe COVID 19 disease and died. Our study had 3 (10.8%) cases of tropical infections including 2 cases of dengue and 1of malaria with mortality of 1 dengue by ARDS.
S Sarkar et al. showed only few reported cases of COVID-19 dengue co-infection and dengue as risk factor for severe COVID 19 disease is still under evaluation.2 10.7% of all infectious co-existing condition in our study included HIV disease with 1 patient had disseminated tuberculosis developed severe COVID 19 disease and rest recovered from moderate disease. Now CDC considers HIV to be a significant risk factor but our data is sparse to comment.1
In our study there were 4 (25.0%) cases of rheumatologic disorders, all of them were on immunosuppression with 75% mortality. A recent review by K L Hyrich et al. showed risk factors for poor prognosis of COVID 19 infected individuals with rheumatological illness were similar to other patients apart from therapy of prednisolone daily dosage more than 10 mg.15 Last risk factor was present in all patients with 1 of each patients having hypertension and the other had multiple comorbidities.
Solid organ transplant is one of the strongest risk factor for developing severe COVID-19 disease.1 India has limited reports in this regards. The largest series by M Kumarsenum et al. had 720 kidney transplant recipients and 2.2% developed COVID-19 with mortality of 18.8% and all these patients had at least 1 classical risk factor.16 Dhampalwar S et al. and Choudhury A et al. had 12 and 6 COVID19 infected liver transplant recipients from India.17 , 18 The earlier one had mortality of 18.4% with comorbidities in all of them and later group had no mortality. Most of the mortality occurred in patients with comorbidities. Similarly, in our study both liver transplant recipients recovered from moderate and sever illness whereas the renal transplant recipient succumbed to sever disease. Therefore, our findings are in concordance with current data.
Our series is unique in a way that we are reporting cases not reported in the literature in context of a developing country like India. We had 1 case of tetanus that developed sever COVID-19 disease and died. Similarly, 2 cases of snake bite each neurotoxic as well as haematotoxic. The earlier recovered from moderate illness and later eventually developed ARDS and DIC which was not possible to differentiate from COVID as the reason. A moderate COVID-19 complicated newly diagnosed acute hepatitis B that eventually recovered. W A Aldhaleei et al. reported a case of acute hepatitis B with active hepatitis and mild COVID-19 disease.19 Similarly, 2 cases of myasthenia gravis on immunosuppression and one of them succumbed to sever COVID19 disease while the other recovered a moderate illness. We had a recently diagnosed ITP patient on prednisolone more than 10 mg per day developed progressive thrombocytopenia and moderate COVID disease that responded to pulse methyl prednisolone. American society of haematology (ASH) mentions ITP as not a risk factor for severe COVID 19 disease.20 The other unreported case in Indian setting is COVID19 among Haemophilia patients. Since haemophilia is coagulation disorder treatment of COVID 19 coagulation defects poses a challenging task and especially in severe cases as shown by De la Corte-Rodriguez H et al. but fortunately it not a risk factor for severe COVID 19 disease.21 Our patient recovered a mild illness with complete recovery and no coagulation defect. We report a newly diagnosed untreated case of CML without other comorbidities developed severe COVID disease and recovered. W Li et al. found that CML patients are at higher risk of developing COVID disease with advance illness and comorbidities carries poor prognosis.22 Similarly, we diagnosed a case of acute promyelocytic leukemia who presented to us with high grade ever with pancytopenia and on evaluation came COVID positive and during stay developed DIC and ARDS. A similar case is reported by Farmer I et al. from London but none from India.23 We diagnosed 2 cases of GTN with one recovered moderate COVID disease the other with disseminated disease died from ARDS. Bachani S et al. had 1 patient died from GTN post operatively among COVID infected pregnant female.24
We found almost no significant difference among laboratory parameters of the patients among both groups showing uniform COVID disease activity among the groups. Procalcitonin as marker of gram negative bacterial infection and it stay elevated in COVID 19 and its levels are directly proportional to the severity of COVID 19 disease.25 In our study 91% patients with severe disease had elevated levels (0.6–53.8 ng/l) without any significant difference with patients with bacterial infection. About 44% patient had thrombocytopenia and more than 60% of severe thrombocytopenia cases were seen in patients with haematological disorders, and each case of dengue fever, malaria and severe sepsis indicating severe thrombocytopenia as differentiating feature for COVID 19 infection. The unique feature of elevated d-dimer with fibrinogen6 was seen around 23% cases without any difference amongst the group predominantly in severe disease group showing COVID 19 activity. 37.5% mortality was seen with attributable death of 72.2% evenly distributed in both groups showing COVID disease as predominant cause of death.
Algorithm for patient recruitment in the study.
Image 1
7 Conclusion
Severe COVID 19 disease is associated with commonly occurring classical risk factors across globe. In Indian context there is possibility of newer coexisting illness that might precipitate severe COVID 19 disease in form of tuberculosis and bacterial infections apart from prolonged immunosuppression and uncommon malignancy as gestational trophoblastic neoplasm, myasthenia gravis and solid organ transplants. These are sparse findings in Indian settings and require close monitoring for severe disease even in absence of classical risk factors. Death attributed to COVID 19 is usually high hence these might be additional risk factors.
Funding
None.
Author statement
S Pandey: Designing the research study, Data collection, Data analysis and report preparation.
D.H Reddy: Designing the research study, Data analysis and report preparation.
V Atam: Designing the research study, Data analysis and report preparation.
F Khan: Designing the research study, Data collection, Data analysis and report preparation.
S Sonkar: Designing the research study, Data analysis and report preparation.
H S Malhotra: Designing the research study, Data analysis and report preparation.
K K Gupta: Designing the research study, Data collection, Data analysis and report preparation.
P Rai: Designing the research study, Data analysis and report preparation.
R Verma: Designing the research study, Data analysis and report preparation.
Declaration of competing interest
None.
Acknowledgment
None.
==== Refs
References
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5 https://www.ncdc.gov.in/showfile.php?lid=458.
6 Iba T. Levy J.H. Connors J.M. Warkentin T.E. Thachil J. Levi M. The unique characteristics of COVID-19 coagulopathy Crit Care 24 1 2020 Jun 18 360 10.1186/s13054-020-03077-0 PMID: 32552865; PMCID: PMC7301352 32552865
7 Behzad S. Aghaghazvini L. Radmard A.R. Gholamrezanezhad A. Extrapulmonary manifestations of COVID-19: radiologic and clinical overview Clin Imag 66 2020 Oct 35 41 10.1016/j.clinimag.2020.05.013 Epub 2020 May 18. PMID: 32425338; PMCID: PMC7233216
8 https://www.who.int/classifications/icd/Guidelines_Cause_of_Death_COVID-19.pdf?ua=1
9 Tadolini M. Codecasa L.R. García-García J.M. Active tuberculosis, sequelae and COVID-19 co-infection: first cohort of 49 cases Eur Respir J 56 1 2020 Jul 9 2001398 10.1183/13993003.01398-2020 PMID: 32457198; PMCID: PMC7251245
10 Motta I. Centis R. D'Ambrosio L. Tuberculosis, COVID-19 and migrants: preliminary analysis of deaths occurring in 69 patients from two cohorts Pulmonology 26 4 2020 Jul-Aug 233 240 10.1016/j.pulmoe.2020.05.002 Epub 2020 May 14. PMID: 32411943; PMCID: PMC7221402 32411943
11 Kumar M.S. Surendran D. Manu M.S. Rakesh P.S. Balakrishnan S. Mortality due to TB-COVID-19 coinfection in India Int J Tubercul Lung Dis 25 3 2021 Mar 1 250 251 10.5588/ijtld.20.0947 PMID: 33688819
12 Gupta N. Ish P. Gupta A. A profile of a retrospective cohort of 22 patients with COVID-19 and active/treated tuberculosis Eur Respir J 56 5 2020 Nov 19 2003408 10.1183/13993003.03408-2020 PMID: 33093125; PMCID: PMC7674774
13 Garcia-Vidal C. Sanjuan G. Moreno-García E. COVID-19 Researchers Group Incidence of co-infections and superinfections in hospitalized patients with COVID-19: a retrospective cohort study Clin Microbiol Infect 27 1 2021 Jan 83 88 10.1016/j.cmi.2020.07.041 Epub 2020 Jul 31. PMID: 32745596; PMCID: PMC7836762 32745596
14 Moliere S. Veillon F. COVID-19 in post-operative patients: imaging findings Surg Infect (Larchmt) 21 5 2020 Jun 416 421 10.1089/sur.2020.169 Epub 2020 May 13. PMID: 32401630 32401630
15 Hyrich K.L. Machado P.M. Rheumatic disease and COVID-19: epidemiology and outcomes Nat Rev Rheumatol 17 2 2021 Feb 71 72 10.1038/s41584-020-00562-2 PMID: 33339986; PMCID: PMC7747184 33339986
16 Kumaresan M. Babu M. Parthasarthy R. Clinical profile of SARSA CoV-2 infection in kidney transplant patients- A single centre observational study Indian J Transplant 14 2020 288 292 10.4103/ijot.ijot_140_20 Available from URL
17 Choudhury A. Reddy G.S. Venishetty S. COVID-19 in liver transplant recipients - a series with successful recovery J Clin Transl Hepatol 8 4 2020 Dec 28 467 473 10.14218/JCTH.2020.00061 Epub 2020 Oct 10. PMID: 33447532; PMCID: PMC7782113 33447532
18 Dhampalwar S. Saigal S. Choudhary N. Outcomes of coronavirus disease 2019 in living donor liver transplant recipients Liver Transplant 26 12 2020 Dec 1665 1666 10.1002/lt.25909 Epub 2020 Nov 5. PMID: 33021025; PMCID: PMC7675322
19 Aldhaleei W.A. Alnuaimi A. Bhagavathula A.S. COVID-19 induced hepatitis B virus reactivation: a novel case from the United Arab Emirates Cureus 12 6 2020 Jun 15 e8645 10.7759/cureus.8645 PMID: 32550096; PMCID: PMC7296884
20 https://www.hematology.org/covid-19/covid-19-and-itp.
21 De la Corte-Rodriguez H. Alvarez-Roman M.T. Rodriguez-Merchan E.C. Jimenez-Yuste V. What COVID-19 can mean for people with hemophilia beyond the infection risk Expet Rev Hematol 13 10 2020 Oct 1073 1079 10.1080/17474086.2020.1818066 Epub 2020 Sep 17. PMID: 32862729
22 Li W. Wang D. Guo J. Hubei anti-cancer association, Meng L, Jiang Q. COVID-19 in persons with chronic myeloid leukaemia Leukemia 34 7 2020 Jul 1799 1804 10.1038/s41375-020-0853-6 Epub 2020 May 18. PMID: 32424293; PMCID: PMC7233329 32424293
23 Farmer I. Okikiolu J. Steel M. Acute promyelocytic leukaemia lying under the mask of COVID-19-a diagnostic and therapeutic conundrum Br J Haematol 190 4 2020 Aug e248 e250 10.1111/bjh.16864 Epub 2020 Jun 8. PMID: 32428243; PMCID: PMC7276820 32428243
24 Bachani S. Arora R. Dabral A. Clinical profile, viral load, maternal-fetal outcomes of pregnancy with COVID-19: 4-week retrospective, tertiary care single-centre descriptive study J Obstet Gynaecol Can 43 4 2021 Apr 474 482 10.1016/j.jogc.2020.09.021 Epub 2020 Oct 28. PMID: 33349556; PMCID: PMC7591315 33349556
25 Ahmed S. Jafri L. Hoodbhoy Z. Siddiqui I. Prognostic value of serum procalcitonin in COVID-19 patients: a systematic review Indian J Crit Care Med 25 1 2021 Jan 77 84 10.5005/jp-journals-10071-23706 PMID: 33603306; PMCID: PMC7874291 33603306
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PMC009xxxxxx/PMC9004148.txt |
==== Front
Bioorg Med Chem Lett
Bioorg Med Chem Lett
Bioorganic & Medicinal Chemistry Letters
0960-894X
1464-3405
Published by Elsevier Ltd.
S0960-894X(22)00208-6
10.1016/j.bmcl.2022.128732
128732
Article
Identification of Aloe-derived natural products as prospective lead scaffolds for SARS-CoV-2 main protease (Mpro) inhibitors
Hicks Emily G.
Kandel Sylvie E.
Lampe Jed N. ⁎
Department of Pharmaceutical Sciences, Skaggs School of Pharmacy, University of Colorado, Aurora, Colorado, 80045, United States
⁎ Corresponding author.
12 4 2022
12 4 2022
1287329 12 2021
31 3 2022
8 4 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.
Graphical abstract
In the past two years, the COVID-19 pandemic has caused over 5 million deaths and 250 million infections worldwide. Despite successful vaccination efforts and emergency approval of small molecule therapies, a diverse range of antivirals is still needed to combat the inevitable resistance that will arise from new SARS-CoV-2 variants. The main protease of SARS-CoV-2 (Mpro) is an attractive drug target due to the clinical success of protease inhibitors against other viruses, such as HIV and HCV. However, in order to combat resistance, various chemical scaffolds need to be identified that have the potential to be developed into potent inhibitors. To this end, we screened a high-content protease inhibitor library against Mproin vitro, in order to identify structurally diverse compounds that could be further developed into antiviral leads. Our high-content screening efforts retrieved 27 hits each with >50% inhibition in our Mpro FRET assay. Of these, four of the top inhibitor compounds were chosen for follow-up due to their potency and drugability (Lipinski’s rules of five criteria): anacardic acid, aloesin, aloeresin D, and TCID. Further analysis via dose response curves revealed IC50 values of 6.8 μM, 38.9 μM, 125.3 μM, and 138.0 μM for each compound, respectively. Molecular docking studies demonstrated that the four inhibitors bound at the catalytic active site of Mpro with varying binding energies (-7.5 to -5.6 kcal/mol). Furthermore, Mpro FRET assay kinetic studies demonstrated that Mpro catalysis is better represented by a sigmoidal Hill model than the standard Michaelis-Menten hyperbola, indicating substantial cooperativity of the active enzyme dimer. This result suggests that the dimerization interface could be an attractive target for allosteric inhibitors. In conclusion, we identified two closely-related natural product compounds from the Aloe plant (aloesin and aloeresin D) that may serve as novel scaffolds for Mpro inhibitor design and additionally confirmed the strongly cooperative kinetics of Mpro proteolysis. These results further advance our knowledge of structure-function relationships in Mpro and offer new molecular scaffolds for inhibitor design.
Keywords
SARS-CoV-2 main protease
Mpro
3CLpro
aloe
natural products
aloesin
aloeresin D
protease inhibitors
==== Body
pmcThe global scourge of the COVID-19 pandemic has enveloped the world for two years, leaving over 5 million dead and 250 million infected in its wake 1, 2. Despite the emerging success of vaccines, a significant number of people remain unvaccinated, either due to lack of availability 3 or vaccine hesitancy 4, 5. Furthermore, “breakthrough” infections are becoming more common in the vaccinated with the identification of new variants 6, 7, leaving few therapeutic options available for life threatening infections 8, 9. This illustrates the need to develop novel small molecule chemical modalities that can be used both to treat severe SARS-CoV-2 infections and as potential prophylactic agents in high-risk population groups. While the earliest small molecule therapies targeting SARS-CoV-2 directed their activities towards the RNA polymerase target (e.g., remdesivir and molnupiravir), recent attention has turned to the 3C-like viral main protease (3CLpro or Mpro) as a possible drug target10-17. Viral protease inhibitors have previously demonstrated high clinical efficacy against other viruses, such as HIV (human immunodeficiency virus) 18, 19 and HCV (hepatitis C virus) 20-22, making Mpro an attractive target for antiviral drug development. Indeed, Pfizer, Inc. has a first-in-class Mpro inhibitor currently in Phase III clinical trials 11, pointing to Mpro inhibition as an effective therapeutic strategy. Despite this, a major liability of viral protease inhibitors is the eventual development of resistance, which necessitates their use as part of combination therapy and also the development of second and third generation compounds. This highlights the need for the identification of new lead structures that may function as Mpro inhibitor scaffolds 12, 14, 15. Since the publication of the SARS-CoV-2 Mpro crystal structure 23, a number of campaigns have been conducted to identify selective Mpro inhibitors 11, 12,14, 15, 16 .23, 24, 25, 26, 27, 28, 29 While most of these published accounts have exclusively involved computational approaches 16, 25, 26, 30 several have screened existing small molecule libraries for Mpro inhibition in vitro 13, 15, 31. However, these approaches have led to a number of redundant hits with similar mechanisms of action 32-34. In order to forestall eventual viral resistance, chemical diversity is an important consideration in identification of early protease inhibitor leads 35-39. Even small chemical libraries that are highly diverse can lead to the identification of inhibitors with unique mechanisms of action 40-42. In an attempt to expand the chemical diversity of potential scaffolds for SARS-CoV-2 Mpro inhibitor lead design, our approach focused on a high-content protease inhibitor library to filter out redundant molecules and obtain unique pharmacophores that could be further exploited by chemical optimization. Here, we report the identification of novel Aloe-derived natural product scaffolds that have the potential to be further optimized into effective clinical candidates, given their high drug-like qualities (Lipinski's rule of five 43, 44) and safety profiles. Additionally, our data confirm that SARS-CoV-2 Mpro functions as a cooperative dimer, hinting at the possibility of developing allosteric inhibitors that may target and disrupt the enzyme dimer interface.
In order to screen library compounds for inhibitory activity against Mpro in an in vitro system, we cloned, expressed, and purified the Mpro viral protein in an E. coli based heterologous expression system 45. To facilitate production of the native protease, we retained the original viral N-terminal autocatalytic cleavage site (SAVLQ↓SGFRK) and modified the C-terminal sequence with the core amino acids of the HRV (human rhinovirus)-3C protease cleavage site (VTFQ↓GP) to permit removal of the His tag needed for the protein purification (see supporting information, Figure S1). This construct resulted in high yields of the native protein (see supporting information, Figure S2). To ascertain proteolytic activity, we relied on a FRET (fluorescence resonance energy transfer)-based assay with the peptide substrate Dabcyl-KTSAVLQ↓SGFRKME-Edans-NH2, which emits a fluorescent signal at 460 nm when cleaved and excited at 360 nm 46. Once we evaluated our Mpro activity assay for linearity as a function of time and protein concentration (see supporting information, Figure S3), a kinetic experiment was performed at 100 nM Mpro, 2-128 μM FRET substrate (5% final volume dimethyl sulfoxide, DMSO) in 100 μL final reaction volume with reaction buffer comprised of 20 mM Tris HCl, 100 mM NaCl, 1 mM EDTA, 1 mM DTT, pH 7.3 at 37°C. The reaction was initiated by adding 50 μL of the FRET substrate in reaction buffer to 50 μL of Mpro in reaction buffer. Cleavage of the substrate was measured via fluorescence on a Tecan Infinite M Plex plate reader every minute for 30 min. A free Edans calibration curve from 0.1-25 μM was used to convert the initial velocities in RFU (relative fluorescence units) to pmol/s. Concentration of FRET substrate vs. initial velocity was plotted (Figure 1 ) and analyzed for kinetic parameters via GraphPad Prism (v. 9.2.0.332). Both Michaelis-Menten and allosteric sigmoidal models (Hill equation) were assessed (Figure 1A) and compared via the second order Akaike Information Criterion (AICc) 47. The resulting kinetic parameters for the Michaelis-Menten fit were Km = 44.6 ± 8.0 μM and Vmax = 359.6 ± 27.8 pmol/s/nmol Mpro, and S50 = 24.2 ± 2.0 μM and Vmax = 269.6 ± 11.4 pmol/s/nmol Mpro for the allosteric sigmoidal fit (see supporting information, Table S1).Fig 2. Figure 1 Kinetic analysis of FRET substrate proteolysis by Mpro. A: Kinetic data fit to Michaelis-Menten and allosteric sigmoidal (Hill) models. B: Eadie-Hofstee transformation of Hill equation at h = 1 (no cooperativity) and h = 1.7 (positive cooperativity). The kinetic assay was performed at 2-128 μM FRET substrate and 100 nM Mpro. Initial velocities in RFU converted to pmol/s with the free Edans calibration curve. Points represent mean of triplicate measurements ± standard deviation.
Figure 2 High-content library screening for Mpro inhibition. A total of 236 protease inhibitors were screened at 100 μM against Mpro. Hits are defined as compounds with over 50% inhibition, represented by black circles. Compounds with less than 50% inhibition are represented by gray squares. Percent inhibition was calculated against DMSO control. Data points represent the mean of three replicates ± standard deviation (several standard deviations are too small to be discernable).
Interestingly, the data best fit to the sigmoidal Hill plot with an R2 of 0.985 and an AICc correct fit probability of 99.98%. This is dramatically apparent when the data is analyzed by the Eadie-Hofstee transformation (Figure 1B). The presence of cooperativity in enzyme activity is readily explainable and somewhat expected as the enzyme is reported to be only active as a dimer 48-50. However, while allosteric cooperativity has been demonstrated previously for this enzyme 51-53, this is the first time that kinetic data have been quantitatively compared to determine the best model for enzyme activity, i.e. either the Michaelis-Menten or the sigmoidal Hill equations (Figure 1A, Table S1). The clear appearance of allosteric kinetics, as represented in the Eadie-Hofstee plot (Figure 1B), indicates that the allosteric dimer interface may also be a promising inhibitor target, as was found to be the case for the SARS-CoV-1 Mpro enzyme 50.
For our high-content library screening, a total of 236 compounds were obtained from different vendors: Protease Inhibitor Library (catalog no. L2500) and PF-00835231 from Selleck Chemicals (Houston, TX); ebselen from Sigma-Aldrich (St. Louis, MO); aloin B, aloe-emodin, calpain inhibitor II, calpain inhibitor III, calpain inhibitor VI, calpain inhibitor XII, 2-cyano-pyrimidine, E-64d, oseltamivir (phosphate), PD 150606, PSI-7977, tosyllysine chloromethyl ketone HCl, and pimodivir from Cayman Chemical (Ann Arbor, MI); aloeresin D from eNovation Chemicals (Green Brook, NJ); and 7-O-methylaloeresin A from Muse Chem (Fairfield, NJ). The inhibition screening assays were performed at 50 nM Mpro, 10 μM FRET substrate, and 100 μM inhibitor (1.5% final volume DMSO). 100 μM inhibitor concentration was selected as our goal was not to identify highly potent compounds, but rather chemically diverse leads. The Pfizer Mpro inhibitor, PF–00835231, was used as a positive control for inhibition at a concentration of 1 μM. The reactions were initialized at room temperature by the addition of 50 μL FRET substrate and inhibitor mixture, dissolved in reaction buffer, to 50 μL of Mpro also prepared in reaction buffer. The reaction buffer consisted of 20 mM Tris HCl, 100 mM NaCl, 1 mM EDTA, and 1 mM DTT, pH 7.3 at room temperature. Fluorescence was monitored for 1 hour, with readings taken every 2.5 minutes. The first 20 minutes were used to calculate the initial velocity, and percent inhibition was calculated from the slope compared to the DMSO control. Background from the FRET substrate was subtracted from all samples. All experiments were performed in triplicate and the presented values represent the mean ± standard deviation. Positive “hits” were defined as any compound that inhibited Mpro activity by 50% or more. As can be seen in Figure 2, 27 compounds fell into this category, indicating an assay hit rate of approximately 11.4%. The structures and percent inhibition are shown in Table 1 , demonstrating the chemical diversity present in the hits obtained from the initial library screening. To assess assay robustness, the Z’-factor was calculated for each screening experiment (see supporting information, Figure S4). The resulting Z’-factors were between 0.727 and 0.969, well within the guidelines of 0.5 to 1 for a high-throughput assay. 54 Table 1 Mpro inhibitors identified from high content protease inhibitor library screening.
Percent inhibition values represent mean of three replicates ± standard deviation. a Compound with background fluorescence.
Inherent limitations of the FRET-based assay led to 19 of the initial library compounds (8.1%) being eliminated from the assay screen due to either intrinsic fluorescence from the compounds themselves or issues stemming from inner filter effects. Some of the compounds we identified have been previously reported as SARS-CoV-2 Mpro inhibitors (see supporting information, Table S2), thereby increasing our confidence in the robustness of our Mpro screening assay. Interestingly, a number of the most potent compounds are natural products (e.g., anacardic acid, aloesin, aloeresin D).
Aloesin and aloeresin D come from the Aloe plant, including Aloe perryi and A. barbadensis (aka, A. vera) species 55, 56. These compounds have a long history of medicinal use as stimulant-laxatives to treat constipation 57, anti-inflammatory compounds to promote wound healing 58, and bittering agents in food supplements, thereby demonstrating their positive safety profile and bioavailability in humans 56. Furthermore, aloesin is a known tyrosinase inhibitor with an IC50 of 0.9 mM against mushroom tyrosinase 59, 60. Anacardic acid is a natural product from the cashew nut that exhibits anti-inflammatory and antinociceptive properties. 61Due to the chemical novelty of the natural products identified and the availability of readily accessible functional groups on these compounds for further modification, we decided to focus on the top three natural products with high percent inhibition in the 100 μM screen: anacardic acid (96.1% ± 0.1), aloesin (80.1% ± 2.9), and aloeresin D (72.6% ± 1.1).
We also chose to include the non-natural product TCID (a selective ubiquitin C-terminal hydrolase-L3 inhibitor), due to its performance in the high-content screen (94.2% ± 2.5).62 In order to quantitatively assess the extent of Mpro inhibition, we performed IC50 experiments using the screening assay that we had already developed. The dose response curves in Figure 3 demonstrate IC50’s in the micromolar range. It should be noted that 0% inhibition (100% activity) was not reached in all cases due to assay background fluorescence.Figure 3 Dose response curves of anacardic acid (A), aloesin (B), aloeresin D (C), and TCID (D) against Mpro. Percent inhibition calculated against the DMSO control and analyzed in GraphPad Prism via non-linear regression with a dose-response inhibition model (three parameters). Points represent mean of triplicates ± standard deviation (several standard deviations are too small to be discernable).
TCID (IC50 = 138.0 μM ± 12.9), was ruled out as a possible scaffold due to its higher IC50 value and potential toxicity 63. While the most potent inhibitory compound was anacardic acid (IC50 = 6.8 μM ± 1.0), its toxic liabilities may also prevent it from further development as an antiviral agent, whereas the two Aloe compounds (aloesin IC50 = 38.9 μM ± 8.6, aloeresin D IC50 = 125.3 μM ± 24.5), have a more favorable safety and efficacy profile in humans 56. The four hits were also pre-incubated with and without the enzyme in order to assess time-dependent inhibition (TDI) (see supporting information, Figure S5). While a single inhibitor concentration is not conclusive, the data suggest that the compounds exhibit a moderate amount of TDI. Experiments involving multiple concentrations points will need to be carried out to further delineate the TDI potential of these compounds.
Table S3 (see supporting information) illustrates Lipinski’s rule of five for the four inhibitors: aloesin and TCID meet all four criteria, while anacardic acid and aloeresin D have one and two violations respectively. Compounds meeting at least three of the four of Lipinski’s criteria generally make excellent orally active drugs 43, 44.
In an effort to expand our knowledge as to the specific mode of action of these compounds, a molecular docking study was undertaken using the AutoDock Vina algorithm, v. 1.1.2 64. Briefly, the SARS-CoV-2 Mpro crystal structure (PDB entry 6Y2E) was prepared for docking by removing ions and water molecules and adding polar hydrogens using MGL AutoDock Tools v. 1.5.7 (UCSD Molecular Graphics Lab and The Scripps Research Institute). Inhibitor (PF-00835231, anacardic acid, aloesin, aloeresin D, and TCID) structural coordinates were obtained from the Protein Data Bank (PDB: https://www.rcsb.org/ ) and parameterized by adding polar hydrogens and identifying rotatable bonds. The receptor docking grid was defined by the following parameters - grid box center: x-center = -16.022, y-center = -32.73, z-center = 4.648, and the total number of grid points in each dimension being: x-dimension = 80, y-dimension = 48, and z-dimension = 62. To facilitate an efficient docking routine, a configuration file docking script was prepared using MS Notepad in simple text format with the energy range set to 4 and the exhaustiveness search parameter set to 24. AutoDock Vina was executed using the configuration file with both PDBQT.out and log.out file options selected. The PDBQT.out file contains all of the ligand binding poses for any particular docking simulation. Output files were analyzed using the ViewDock function of UCSF Chimera v.1.15, and ranked according to binding energy (ΔG). To test the validity of AutoDock Vina to predict the correct binding pose for our selected inhibitors, we first attempted to dock PF-00835231 using the parameters described above. The reported IC50 of Pfizer’s reference inhibitor is in the low nanomolar range (6.9 nM). Here, we found that the lower IC50 value correlated with the lower docking score, both indicating higher affinity for the enzyme. The most energetically stable pose found the compound bound in the active site within close proximity of the catalytic dyad (Cys 145 and His 41) (see supporting information, Figure S6). This docking pose is strikingly similar, although not quite superimposable, with the location of the compound found in PDB entry 6HXM, where it is covalently adducted to Cys 145. The difference in position is likely due to the covalent adduct formed between PF00835231 and Cys 145 during catalysis.
As seen in Figure 4 , all four inhibitors docked within the active site of Mpro, near the catalytic dyad of Cys 145 and His 41, although with differing binding energies. Aloesin bound with the lowest predicted free energy, -7.5 kcal/mol, followed by aloeresin D at -6.8 kcal/mol, TCID at -5.8 kcal/mol, and anacardic acid at -5.6 kcal/mol. TCID, the smallest molecule, docked near the Cys 145 at subsite S1 (Figure 4D). Both aloesin and anacardic acid are bound in extended conformations, occupying the entirety of the active site, including subsites S1, S1’, S2, and S4 (Figure 4 A, B). In contrast, aloeresin D (Figure 4C) is bound in the active site in a more sterically constrained conformation, with the vinylphenol ring hovering just above the glycosyl moiety, only occupying subsites S1 and S4. This correlates well with the IC50 potencies, where aloeresin D was approximately 3-fold less potent than aloesin (Figure 3), and may indicate that the vinylphenol group of aloeresin D sterically hinders binding in the active site and in fact decreases the affinity of the ligand for Mpro. Removal and/or substitution of this group with smaller functional groups could result in more potent inhibitors that fit more snugly into the active site. In addition, removal of the vinylphenol ring of aloeresin D would likely change its Lipinski’s rules of five parameters, decreasing the number of violations and improving its drugability. We also attempted to dock vitamin K, a natural product with structural similarity to anacardic acid, but did not obtain a binding pose that met the search criteria, likely due to the unsaturated, rigid hydrocarbon tail present in the vitamin K structure. While the Aloe compounds are not extremely potent inhibitors on their own, these results illustrate their potential as molecular scaffolds that can be further developed into more efficacious Mpro inhibitors. Furthermore, the excellent safety profile and bioavailability of both aloesin and aloeresin D make it likely that minor structural modifications to improve affinity to Mpro will not result in producing significant toxic liabilities. Given the limited number of validated chemical scaffolds currently available for Mpro inhibitors, identification of these compounds further adds to the medicinal chemist’s toolkit for SARS-CoV-2 antiviral drug design.Figure 4 Docking of anacardic acid, aloesin, aloeresin D, and TCID in Mpro active site. A. Anacardic acid (magenta) docked with a predicted affinity of -5.6 kcal/mol. B. Aloesin (cyan) docked with a predicted affinity of -7.5 kcal/mol. C. Aloeresin D (purple) docked with a predicted affinity of -6.8 kcal/mol. D. TCID (red) docked with a predicted affinity of -5.8 kcal/mol. The catalytic dyad of Cys 145 and His 41 are displayed in green and orange respectively.
In conclusion, here we have identified two natural product compounds, aloesin and aloeresin D, as novel SARS-CoV-2 Mpro inhibitors through screening of a high-content protease inhibitor library. Both of these compounds are safe in humans and have functional groups that are readily accessible for modification allowing them to serve as leads for Mpro inhibitors. Anacardic acid and TCID performed well in our study but were both ruled out due to potential toxicity. Additionally, we have confirmed that SARS-CoV-2 Mpro exhibits cooperative kinetics during catalysis, suggesting that the protein dimer interface may be an attractive target for allosteric inhibitors. Combined, these results advance our knowledge of structure-function relationships in Mpro and offer new molecular scaffolds for inhibitor design.
Uncited references
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 17, 18, 19, 20, 21, 22, 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, 63, 64.
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
The authors would like to gratefully acknowledge the invaluable assistance of Dr. Phil Reigan with helpful discussions and input. This work was supported by a faculty start-up grant generously provided by the University of Colorado, Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, USA. It should be noted that the University of Colorado, Skaggs School of Pharmacy and Pharmaceutical Sciences had no editorial input on the preparation of this manuscript or the planning, excitation, or interpretation of the experimental data.
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Int Orthop
Int Orthop
International Orthopaedics
0341-2695
1432-5195
Springer Berlin Heidelberg Berlin/Heidelberg
5403
10.1007/s00264-022-05403-3
Letter to the Editor
Letter to the Editor: “Orthopaedic training during COVID-19 pandemic: should action be taken?”
http://orcid.org/0000-0001-9313-2384
Dixit Shaili shaili.dixit@hmhn.org
http://orcid.org/0000-0001-7907-8166
Makkapati Tejaswi tejaswi.makkapati@hmhn.org
grid.429392.7 0000 0004 6010 5947 Hackensack Meridian School of Medicine, 340 Kingsland St, Nutley, NJ USA
12 4 2022
12
28 3 2022
4 4 2022
© The Author(s) under exclusive licence to SICOT aisbl 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
==== Body
pmcDear Editor,
We read the article with great interest by Abdelazeem et al. “Orthopaedic training during COVID-19 pandemic: should action be taken?” [1]. It gave valuable insight into the perspective of residents and physicians during the pandemic globally. This letter addresses additional factors contributing to the larger picture of what trainees and physicians faced during the pandemic.
Initially, there was heightened anxiety and depression regarding the consequences of getting COVID. The healthcare facilities were overwhelmed during the first and second variant outbreaks of COVID. However, it appears that the survey results were obtained from the time of the third variant outbreak. The results could have been different if the survey was conducted earlier in the pandemic, during the first or second waves. Earlier, civilians were more compliant with quarantine, and the government leaned in on finding a resolution to the pandemic—there was a hope and drive to fix the problem [2]. However, further into the pandemic, the unpredictability of the different COVID variants increased burnout rates among healthcare professionals and the public due to a sustained issue with no resolution [3]. A survey study showed that increased levels of uncertainty were associated with higher levels of stress for healthcare workers during this pandemic [4]. It would be interesting to see how these results changed based on which wave of the pandemic the participants were answering the survey questions.
Additionally, orthopaedic elective cases were suspended because of the pandemic hindering these trainees’ ability to engage in the material they wanted to learn [5]. Being forced to cross-train in treating conditions in medical and ICU services rather than an orthopaedics service may have disillusioned and burned-out orthopaedic trainees before they got the chance to learn the material they were eager to learn in the first place. While they did gain valuable medical and ICU-related skills that can, ideally, transfer to their chosen specialty, they were still behind on their orthopaedic training, which they had to work longer to catch up on.
Lastly, the survey seems to have received more responses from physicians working in urban settings than those from rural areas. Rural locations may have carried different challenges in terms of availability of resources and quantity of COVID cases in contrast to urban areas. These differences can drastically change the burnout rates among trainees and physicians in these two areas. According to a study conducted in China, physicians and trainees in urban settings had higher rates of burnout secondary to patient volume overload, increased risk of exposure, and greater media and information influx about negative aspects of COVID [6]. Ultimately, those working in rural areas were not experiencing the same level of anxiety regarding COVID-19. However, it would be interesting to see more studies performed in different countries regarding this to see regional differences.
Author contribution
All authors contributed to the study’s conception and design. Material preparation and background research were performed by Tejaswi Makkapati. The first draft of the manuscript was written by Shaili Dixit and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Data availability
Not applicable.
Declarations
Ethics approval and consent to participate.
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References
1. Abdelazeem AH Khedr AM Scarlat MM Orthopaedic training during COVID-19 pandemic: should action be taken? Int Orthop 2022 46 2 159 164 10.1007/s00264-022-05307-2 35031819
2. Su Z McDonnell D Ahmad J Cheshmehzangi A Xiang YT Mind the “worry fatigue” amid Omicron scares Brain Behav Immun 2022 101 60 61 10.1016/j.bbi.2021.12.023 34973394
3. Fox J, Meisenberg B (2022) The three-fold harms of compassion fatigue during COVID-19 surges. Am J Med S0002–9343(22)00090–0. Advance online publication. 10.1016/j.amjmed.2022.01.023
4. Temsah MH, Alenezi S, Alarabi M, Aljamaan F, Alhasan K, Assiri R, Bassrawi R, Alshahrani F, Alhaboob A, Alaraj A, Alharbi NS, Alrabiaah A, Halwani R, Jamal A, Abdulmajeed N, Alfarra L, Almashdali W, Al-Eyadhy A, AlZamil F, Alsubaie S, … Als-Tawfiq JA (2022) Healthcare workers’ SARS-CoV-2 Omicron variant uncertainty-related stress, resilience, and coping strategies during the first week of the World Health Organization’s alert. Int J Environ Res Public Health 19(4), 1944. 10.3390/ijerph19041944
5. Kogan M Klein SE Hannon CP Nolte MT Orthopaedic education during the COVID-19 pandemic J Am Acad Orthop Surg 2020 28 11 e456 e464 10.5435/JAAOS-D-20-00292 32282439
6. Zhang J Zhu L Li S Huang J Ye Z Wei Q Du C Rural-urban disparities in knowledge, behaviors, and mental health during COVID-19 pandemic: a community-based cross-sectional survey Medicine 2021 100 13 e25207 10.1097/MD.0000000000025207 33787602
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Sci China Technol Sci
Sci China Technol Sci
Science China. Technological Sciences
1674-7321
1869-1900
Science China Press Beijing
2011
10.1007/s11431-021-2011-7
Review
Fabrication of subunit nanovaccines by physical interaction
Chen HaoLin 1
Liu Hong 2
Liu LiXin 1
Chen YongMing chenym35@mail.sysu.edu.cn
13
1 grid.12981.33 0000 0001 2360 039X School of Materials Science and Engineering, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, Sun Yat-sen University, Guangzhou, 510275 China
2 grid.258164.c 0000 0004 1790 3548 Zhuhai Jinan Selenium Source Nanotechnology Co., Ltd., Jinan University, Zhuhai, 519000 China
3 grid.12981.33 0000 0001 2360 039X Laboratory of Biomaterials and Translational Medicine, Center for Nanomedicine, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510630 China
8 4 2022
111
12 11 2021
9 2 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.
Vaccines can improve the quality of human life by preventing the burden of infectious diseases. Also, vaccination is becoming a powerful medication for preventing and treating tumors. Various vaccines have been developed based on the origin of the antigens. Herein, we focus on the subunit vaccines whose antigens are proteins or peptides. The advantage of subunit vaccines is safety for recipients; however, the immunogenicity of subunit antigens is relatively low. Nanoparticular delivery systems have been applied to improve the immunocompetence of subunit vaccines by targeting lymph nodes, and effectively present antigens to immune cells. Moreover, adding appropriate molecular adjuvants may strengthen the antigens to elicit immune response. In this perspective article, we first elucidate the characteristics of immunity induced by subunit nanovaccines and then summarize the strategies to fabricate subunit nanovaccines with delivering materials. Herein we highlight non-covalent interaction to fabricate nanoparticular subunit vaccines.
Keywords
nanovaccine
protein/peptide
subunit
nanoparticle
immunotherapy
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pmcThis work was supported by the National Natural Science Foundation of China (Grant Nos. 22075324, 51820105004) and the Key Areas Research and Development Program of Guangzhou (Grant No. 202007020006).
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PMC009xxxxxx/PMC9004207.txt |
==== Front
Nervenarzt
Nervenarzt
Der Nervenarzt
0028-2804
1433-0407
Springer Medizin Heidelberg
35412038
1283
10.1007/s00115-022-01283-5
CME
Sinus- und Hirnvenenthrombose
Ein Überblick über Ursachen, Diagnostik und Therapie
Cerebral venous sinus thrombosisAn overview of causes, diagnostics and treatment
Heckelmann Jan
Dafotakis Manuel
Schulz Jörg B. jschulz@ukaachen.de
grid.412301.5 0000 0000 8653 1507 Neurologische Klinik, Universitätsklinik der RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Deutschland
12 4 2022
2022
93 4 413421
28 2 2022
© The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Die Sinus‑/Hirnvenenthrombose ist eine teils fulminant verlaufende, jedoch mit einer Inzidenz von 1,32 Fällen pro 100.000 Personenjahre eher seltene neurologische Diagnose. Nichtsdestotrotz ist die Erkrankung für etwa 0,5–1 % aller Schlaganfälle verantwortlich. Die neurologische Untersuchung zeigt oft ein unspezifisches Bild, gerade bei jüngeren Patientinnen mit akut bis subakut aufgetretenen, lageabhängigen Kopfschmerzen sollte diese Differenzialdiagnose jedoch unbedingt bedacht werden. Im Rahmen dieses Artikels erfolgt die Präsentation der häufigsten Ursachen, einschließlich eines Exkurses zur vakzininduzierten immunthrombotischen Thrombozytopenie (VITT), und es werden Empfehlungen zur klinischen, laborchemischen und bildgebenden Diagnostik gegeben. Zudem werden relevante Komplikationen, mit besonderem Augenmerk auf epileptische Anfälle im Rahmen der Krankheitsentität und die leitliniengemäße Akuttherapie und Sekundärprophylaxe dargestellt.
In some cases, cerebral venous sinus thrombosis shows a fulminant progress but with an incidence of 1.32 cases per 100,000 person-years it is relatively rare. Nevertheless, the disease is responsible for around 0.5–1% of all stroke cases. The neurological examination often reveals nonspecific findings but especially in younger patients with acute to subacute position-dependent headaches, this differential diagnosis should definitely be considered. This article presents the most common causes, including a digression on vaccine-induced immune thrombotic thrombocytopenia (VITT) as well as recommendations for clinical, laboratory testing and imaging diagnostics. In addition, relevant complications with particular reference to epileptic seizures within the framework of the disease entity and guideline-based acute treatment and secondary prophylaxis are presented.
Schlüsselwörter
Schlaganfall
Vakzininduzierte immunthrombotische Thrombozytopenie
Antikoagulation
Epileptische Anfälle
Sekundärprophylaxe
Keywords
Stroke
Vaccine-induced immune thrombotic thrombocytopenia
Anticoagulation
Epileptic seizures
Secondary prophylaxis
issue-copyright-statement© Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2022
==== Body
pmcLernziele
Nach Lektüre dieses Weiterbildungsbeitrags …kennen Sie die häufigsten Ursachen der Sinus- bzw. Hirnvenenthrombose,
sind Sie in der Lage, die Akutdiagnostik beim Verdacht auf eine Sinus- bzw. Hirnvenenthrombose zu veranlassen,
können Sie Akuttherapie und Sekundärprophylaxe der Erkrankung benennen,
erkennen Sie mögliche Akutkomplikationen der Sinus‑/Hirnvenenthrombose sicher und leiten hieraus mögliche Therapieoptionen ab.
Hintergrund
Die 1825 von Ribes [1] erstbeschriebene Sinus- bzw. Hirnvenenthrombose umfasst sowohl Thrombosen der Sinus durae matris als auch der kortikalen und inneren Hirnvenen. Die Entität stellt im klinischen Alltag eine diagnostische Herausforderungdiagnostische Herausforderung
dar, da es sich um eine eher seltene Erkrankungseltene Erkrankung
handelt und im Rahmen der neurologischen Untersuchung oftmals keine relevanten Auffälligkeiten nachweisbar sind. Aufgrund der guten Prognoseguten Prognose
bei adäquater Behandlung muss das Erkrankungsbild jedoch unbedingt rechtzeitig erkannt werden. Die Diskussion der Erkrankung im Rahmen der COVID(„corona virus disease“)-Pandemie, sowohl als Folge einer Sars-CoV2(„severe acute respiratory syndrome coronavirus type 2“)-Erkrankung als auch als potenzielle Impfnebenwirkung, hat zur diesbezüglichen Wachsamkeit maßgeblich beigetragen.
Fallbeispiel
Bei einer notfallmäßigen Vorstellung einer 25-jährigen Patientin aufgrund progredienter Kopfschmerzen über mehrere Tage und psychomotorischer Auffälligkeiten zeigten sich im Rahmen der klinischen Untersuchung keine sonstigen relevanten Auffälligkeiten. In der kraniellen Magnetresonanztomographie (cMRT) mit MR-Venographie erfolgt der Nachweis einer ausgedehnten tiefen Hirnvenenthrombose mit supratentoriellem Liquoraufstau (Abb. 1a,b). Trotz sofortigem Beginn einer i.v. Heparinisierung kam es zu einer progredienten Bewusstseinsstörung mit der Notwendigkeit einer Schutzintubation. Bei zunehmendem Liquoraufstau (Abb. 2a) erfolgte die Anlage einer externen Ventrikeldrainage (EVD) und, aufgrund des fulminanten Verlaufs unter suffizienter Antikoagulation, die Entscheidung zur intraartieriellen Lyse im Rahmen einer digitalen Subtraktionsangiographie (DSA). Trotz konsekutiver partieller Wiedereröffnung der venösen Blutleiter entwickelte sich in rascher Folge eine konservativ nicht beherrschbare Hirndruckerhöhung mit bildmorphologisch beginnender Einklemmung (Abb. 2b). Daher erfolgte die Entscheidung zur Hemikraniektomie, hierbei jedoch mors in tabula durch vegetative Entgleisung.
Epidemiologie
Coutinho et al. beschrieben in einer Studie aus dem Jahr 2012 eine Inzidenz des Erkrankungsbildes von 1,32 Fällen pro 100.000 Personenjahren mit einer maximalen Inzidenz von 2,38 bei 20- bis 50-jährigen Frauen. Die GeschlechterverteilungGeschlechterverteilung
wird in der Studie mit etwa 1:3 (Männer:Frauen) angegeben [2]. Insgesamt werden etwa 0,5–1 % aller Schlaganfälle durch eine Sinus- oder Hirnvenenthrombose ausgelöst [3, 4]. Aufgrund des teils subklinischen Verlaufessubklinischen Verlaufes
ist jedoch tendenziell von einer Unterdiagnose der Erkrankung auszugehen.
Merke
Die Erkrankung tritt gehäuft bei Frauen im gebärfähigen Alter auf.
Ätiologie
Im Rahmen der Ursachenabklärung ist primär zwischen septischen und aseptischen Sinus- und Hirnvenenthrombosen zu unterscheiden. Eine septische Geneseseptische Genese
(etwa 10–15 % der Fälle) wird durch eine hämatogene Ausbreitunghämatogene Ausbreitung
einer Entzündung im Bereich der dem Sinus vorgeschalteten venösen Blutleiter ausgelöst. Entsprechend der typischen anatomischen Verhältnisse kommt es bei einem Entzündungsfokus im Bereich von Nasennebenhöhlen, der Orbita oder des Gesichtes meistens zu einer Thrombose des Sinus cavernosus, während inflammatorische Prozesse im Bereich des Ohres (im Sinne von Otitiden bzw. Mastoiditis) eher zu einer Thrombose des ipsilateralen Sinus transversus oder des Sinus sigmoideus führen [5].
Bei aseptischen Sinus‑/Hirnvenenthrombosenaseptischen Sinus‑/Hirnvenenthrombosen
gilt es eine Vielzahl nachweisbarer angeborener oder sekundär erworbener auslösender Faktoren zu beachten, idiopathische Fälle rücken durch die stetige Verbesserung der diagnostischen Möglichkeiten zunehmend in den Hintergrund (etwa 10–20 % aller aseptischen Sinus‑/Hirnvenenthrombosen), hierzu tragen insbesondere die in den letzten Jahren relevant bessere Auflösung bildgebender Untersuchungen, z. B. bei tumorbedingter Genese, und die Verbesserungen der genetischen Panel-Diagnostik bei. Bei Betrachtung aller Fälle mit gesicherter Ursache, konnte in der Literatur zumeist ein alleiniger oder additiver Zusammenhang mit einem prothrombotischen Zustandprothrombotischen Zustand
nachgewiesen werden (Tab. 1). Bei nichtgenetischen Ursachen ist hierbei die SteroidtherapieSteroidtherapie
als wichtigster Risikofaktor zu nennen (18-fache Risikoerhöhung), bei genetischen Ursachen die Protein-C-DefizienzProtein-C-Defizienz
(11-fache Risikoerhöhung; [6]). Analog zur bekannten Virchow-Trias sind zudem Veränderungen der korpuskulären Blutanteile im Sinne einer hämatologischen Grunderkrankungen sowie von Änderungen der Flusseigenschaften des Blutes durch ausgeprägte Dehydratation, insbesondere bei Kindern und geriatrischen Patienten, als mögliche Auslöser zu nennen. Auch Nikotinabusus und die Einnahme einer oralen Kontrazeption sorgen für eine relevante Risikoerhöhung, sowohl solitär als auch in Kombination.
Weitere, eher seltene, Ursachen sind rheumatologische Erkrankungenrheumatologische Erkrankungen
oder chronisch-entzündliche Darmerkrankungenchronisch-entzündliche Darmerkrankungen
, wohl auch wegen der oft notwendigen Steroidtherapie, sowie traumatische und iatrogene Ursachen im Sinne von Operationskomplikationen sowie durch die Anlage zentraler Venenkatheter oder externer Ventrikel- oder Lumbaldrainagen [4, 5, 6, 7].
Merke
Man unterscheidet die septische von der aseptischen Form.
Wichtigste Risikofaktoren sind die Protein-C-Defizienz (genetische Ursache) sowie eine Steroidtherapie (nichtgenetische Ursache).
Genetisch bedingte Gerinnungsstörungen Antithrombinmangel
Protein-C/S-Mangel
Faktor-V-Leiden-Mutation
Prothrombinmutation
Homozysteinämie durch Methylentetrahydrofolatreduktasemutation
Erworbene Gerinnungsstörungen Nephrotisches Syndrom
Antiphospholipidsyndrom
Homozysteinämie
Schwangerschaft/Wochenbett
Entzündliche Systemerkrankungen Systemischer Lupus erythematodes
Morbus Wegener
Sarkoidose
Morbus Behçet
Chronisch-entzündliche Darmerkrankungen
Hämatologische Erkrankungen Anämie
Polyglobulie/Polyzythämie/Thrombozythämie
Sichelzellerkrankung
HIT-II
VITT
Hämatologische Malignome
Medikamente Orale Kontrazeptiva
Hormonersatztherapie
Steroide
Infektionen Sinusitis/Otitis/Mastoiditis
Meningitis
Sepsis
Traumata/iatrogen Traumatische Kopfverletzung
Neurochirurgische Eingriffe
Lumbalpunktion/Ventrikelpunktion
ZVK-Anlage jugulär
Sonstiges Dehydratation
HIT heparininduzierte Thrombozytopenie, VITT vakzininduzierte immunthrombotische Thrombozytopenie, ZVK zentraler Venenkatheter
Exkurs VITT
Nach Etablierung von COVID-19-ImpfungenCOVID-19-Impfungen
wurde über das gehäufte Auftreten von Sinus- und Hirnvenenthrombosen innerhalb der ersten 30 Tage nach der (meist ersten) Impfung berichtet, die mit einer ausgeprägten ThrombozytopenieThrombozytopenie
einhergeht. Dieses Erkrankungsbild wird inzwischen als vakzininduzierte immunologische thrombotische Thrombozytopenie (VITT) bezeichnet. Es kommt durch ursächlich ungeklärte Plättchenfaktor(PF)-4-Antikörperbildung zu einer Aktivierung von Thrombozyten mit nachfolgender Thrombozytopenie. Zur Diagnose führen die für eine Sinus- und Hirnvenenthrombose typischen Symptome, meist eine Erhöhung von D‑Dimeren, der CT- oder MR-venographische Nachweis der Thrombose und der Nachweis einer Thrombozytopenie, positiver PF4-Antikörper und ein pathologischer Thrombozytenfunktionstest [8, 9]. Dieses Erkrankungsbild tritt ausschließlich nach Verwendung der VektorimpfstoffeVektorimpfstoffe
von AstraZeneca und Johnson&Johnson auf, aber nicht nach Impfung mit den mRNA-Präparaten von BioNTech oder Moderna [10].
Die Prognose der Sinus‑/Hirnvenenthrombose im Rahmen einer VITT ist, auf Basis der aktuellen Datenlage, im Vergleich zur Gesamtprognose der Erkrankung verschlechtert [11].
Merke
VITT ist nicht mit mRNA-Impfstoffen assoziiert.
Klinische Auffälligkeiten
Häufigstes Symptom einer Sinus- bzw. Hirnvenenthrombose ist der subakute bzw. akute, meist lageabhängige Kopfschmerzlageabhängige Kopfschmerz
(etwa 70 % aller Fälle; [12]). Chronische Zephalgien oder ein „thunderclap headache“ sind selten, schließen die Diagnose jedoch nicht aus. Da die Schmerzsymptomatik zumeist durch einen erhöhten Hirndruckerhöhten Hirndruck
ausgelöst wird, entweder aufgrund eines z. T. generalisierten Hirnödems oder durch eine Einblutung, gelingt fundoskopisch oft der Nachweis eines PapillenödemsPapillenödems
, teils auch die Objektivierung von Gesichtsfelddefekten durch Druckschädigung der N. opticus. Bei Thrombosen des Sinus cavernosus, in den meisten Fällen durch eine septische Sinusthrombose, werden oft Augenmuskelparesen oder eine faziale Hypästhesie beobachtet, was durch die räumliche Nähe der entsprechenden Hirnnerven zum Sinus zu erklären ist. Auch eine Bulbusprotrusion ist bei ausgeprägter Thrombosierung theoretisch möglich, jedoch eher mit einer Karotis-Sinus-cavernosus-Fistel vergesellschaftet. Ein weiteres typisches Zeichen einer Sinusthrombose ist das sog. Griesinger-ZeichenGriesinger-Zeichen
[13], eine schmerzhafte, oftmals retroaurikuläre ödematöse Schwellung durch einen Aufstau im Bereich der Venae emissariae bei septischer Sinusthrombose im Bereich des Ohres bzw. Mastoiditis.
Fokalneurologische DefiziteFokalneurologische Defizite
beim Verschluss einer oberflächlichen Hirnvene entstehen meist durch Ausbildung stauungsbedingter bzw. zytotoxischer Ödeme mit konsekutiver lokalisierter Parenchymschwellung und Verminderung der zerebralen Perfusion im betroffenen Areal [5] und der hierdurch verursachten Entwicklung ischämischer oder hämorrhagischer Stauungsinfarkte. Da es sich um venös bedingte Infarkte handelt, können hierbei klinische Befundkonstellationen auftreten, die sich nicht an „klassische“ Gefäßterritorien halten.
Bei ausgeprägten Sinus- oder Hirnvenenthrombosen mit generalisiertem Hirnödemgeneralisiertem Hirnödem
oder bei Affektion der unpaaren V. cerebri magna Galeni mit konsekutiver Stauung im Bereich beider Thalami („innere Hirnvenenthrombose“), wie im o. g. Fallbeispiel, kann es zu ausgeprägten BewusstseinsstörungenBewusstseinsstörungen
mit Notwendigkeit einer Schutzintubation kommen[14].
Merke
Typisch ist ein (sub)akuter, lageabhängiger Kopfschmerz.
Stauungsinfarkte/-blutungen verursachen teils eine Symptomatik, die klassische Gefäßterritorien überschreitet.
Exkurs Epilepsie bei Sinus‑/Hirnvenenthrombose
Ein epileptischer Anfall tritt in etwa 20–40 % aller Erkrankungsfälle auf [4, 7, 15], das Auftreten eines Status epilepticusStatus epilepticus
ist eher selten, aber mit einer verschlechterten Prognose assoziiert [16]. Statistisch treten iktale Ereignisse primär in der FrühphaseFrühphase
, d. h. als Initialsymptom bzw. innerhalb der ersten 2 Wochen, auf. Bemerkenswert ist zudem eine vergleichsweise hohe Rate an postiktalen Todd-Paresenpostiktalen Todd-Paresen
[16]. RisikofaktorenRisikofaktoren
für einen epileptischen Anfall sind das Bestehen einer Hirnvenenthrombose, die Affektion des Sinus sagittalis superior sowie ischämische supratentorielle Läsionen bzw. Hämorrhagien. Darüber hinaus scheinen Schwangerschaft bzw. Wochenbett, weibliches Geschlecht sowie die Faktor-V-Leiden-Mutation ein relevant erhöhtes Risiko für die Entwicklung von Krampfanfällen darzustellen [15, 17]. Bei Patienten, die im Rahmen einer Sinus- bzw. Hirnvenenthrombose einen Krampfanfall erlitten haben, besteht ein verschlechtertes Outcomeverschlechtertes Outcome
innerhalb des ersten Monats nach Erkrankungsbeginn, meist durch die Entwicklung eines Status epilepticus. Im Anschluss konnten in den bisherigen Studien keine Unterschiede im Vergleich zu Patienten ohne iktales Ereignis nachgewiesen werden [17].
Trotz der recht hohen Rate an epileptischen Anfällen im Rahmen einer Sinus‑/Hirnvenenthrombose wird eine prophylaktische Gabe eines AntikonvulsivumsAntikonvulsivums
in den derzeitigen Leitlinien nicht empfohlen. Auch eine rezente Studie aus dem Jahr 2020 zeigte unterstützend kein signifikant erhöhtes Risiko für epileptische Anfälle im Rahmen einer Sinus‑/Hirnvenenthrombose, falls es nicht bereits vor der Diagnose zu einem solchen Ereignis im Sinne eines Frühanfalls gekommen war [18]. Sollte es jedoch im Rahmen der Erkrankung zu einem epileptischen Anfall kommen, wird eine umgehende antikonvulsive Behandlung zur ProphylaxeProphylaxe
weiterer Anfälle bzw. eines Status epilepticus empfohlen, auch wenn formal noch keine Epilepsie vorliegen sollte [19, 20]. Die Dauer der antikonvulsiven Therapie muss individuell evaluiert werden. Sollten sich in der Verlaufsbildgebung keine relevanten, v. a. kortikalen, strukturellen Defekte zeigen und somit ein akut-symptomatischer Anfall vorliegen, kann die medikamentöse Therapie wieder beendet werden. Im Falle des Nachweises einer solchen persistierenden strukturellen Läsion und somit formalem Bestehen einer läsionellen Epilepsie, empfehlen sich eine längerfristige antikonvulsive Behandlung und die Evaluation eines Absetzversuches.
Merke
Epileptische Anfälle sind ein häufiges Symptom in der Frühphase.
Eine prophylaktische antikonvulsive Therapie wird nicht empfohlen.
Diagnostik
Basis der Diagnose einer Sinus- bzw. Hirnvenenthrombose ist eine zielgerichtete Anamnesezielgerichtete Anamnese
. Typischerweise berichten Patienten über akut bis subakut aufgetretene, im Verlauf eher zunehmende KopfschmerzenKopfschmerzen
mit Lageabhängigkeit. Im Rahmen des Anamnesegesprächs sollte außerdem eine zeitliche Assoziation zu einer hormonellen Umstellunghormonellen Umstellung
(Beginn/Umstellung einer hormonellen Kontrazeption, Schwangerschaft, Stillzeit) bzw. zu Infektionen oder Operationen im Kopfbereich erfolgen. Mögliche klinische Auffälligkeiten sind, wie im Vorabschnitt dargestellt, vielfältig und primär von Lokalisation und Ausmaß der Thrombose abhängig.
Im Rahmen der laborchemischen Untersuchungenlaborchemischen Untersuchungen
sollten, gerade bei Anhalt für die septische Form der Erkrankung, die üblichen InflammationsparameterInflammationsparameter
(Leukozyten, C‑reaktives Protein, Prokalzitonin) bestimmt werden. Die europäische Leitlinie rät zudem bei Symptomatik seit weniger als 7 Tagen zur Bestimmung der D‑DimereD‑Dimere
. Diese stellen jedoch bei Sinus‑/Hirnvenenthrombosen kein sicheres Ausschlusskriterium dar, sodass bei dringendem klinischem Verdacht auch bei negativen D‑Dimeren eine Bildgebung erfolgen sollte [18, 20].
Zur bildgebenden Abklärung muss eine Kontrastmittel(KM)-gestützte UntersuchungKontrastmittel(KM)-gestützte Untersuchung
durchgeführt werden, da mittels nativer Darstellung ein ausreichend sicherer Ausschluss der Krankheitsentität nicht möglich ist. Die MagnetresonanztomographieMagnetresonanztomographie
(MRT) mit MR-Venographie ist hierbei die primäre Modalität der Wahl, insbesondere bei jungen Patienten und im Rahmen der Schwangerschaft. Neben der fehlenden Strahlenbelastung gelingen mittels MRT der Nachweis eines konsekutiven Ödems und der frühzeitige Nachweis von Stauungsinfarkten in der Diffusionsbildgebung deutlich vor einer Demarkation in der Computertomographie (CT). Sollte es bereits zu Einblutungen gekommen sein, ist zudem durch Vergleich mehrerer Wichtungen eine grobe Schätzung der Erkrankungsdauer unter Berücksichtigung der jeweils typischen Intensitäten des Blutes möglich [21].
Aufgrund der rascheren Verfügbarkeit und kürzeren Untersuchungsdauer wird derzeit in der Praxis jedoch noch oft zunächst eine kraniale Computertomographiekraniale Computertomographie
(CT) mit venöser CT-Angiographie durchgeführt. Hierbei kann bei ausgeprägten Befunden bereits im nativen Bild ein hyperdenser Sinus (Abb. 3a) als Hinweis auf eine Sinusthrombose nachgewiesen werden, darüber hinaus sind im Nativbild etwaige Stauungsblutungen bzw. -infarkte nachweisbar. In der CT-VenographieCT-Venographie
lässt sich beim Bestehen einer Sinus- oder Hirnvenenthrombose eine konsekutive KM-Aussparung nachweisen, pathognomonisch ist hierbei das „empty triangle sign“„empty triangle sign“
(Abb. 3b), ein noch gering KM-umflossener randständiger Thrombus, der ansonsten das Lumen des Sinus komplett ausfüllt [22, 23]. Da es im Verlauf der Erkrankung zu einer relevanten Schwellung des Parenchyms kommen kann, sollten, sofern der Patient nicht ausreichend klinisch beurteilbar ist, regelmäßige bildgebende VerlaufskontrollenVerlaufskontrollen
erfolgen.
Eine digitale Subtraktionsangiographiedigitale Subtraktionsangiographie
ist den o. g. Methoden zwar bei der Darstellung von Gefäßabbrüchen oder Thrombosierungen kortikaler Venen überlegen, unter Berücksichtigung des Interventionsrisikos besteht für die Durchführung dieser Untersuchung nur dann eine Evidenz, wenn, z. B. bei Thrombosierung mehrerer Sinus oder bereits starker Hirnparenchymschwellung, der gleichzeitige Versuch einer interventionellen Therapieinterventionellen Therapie
als Ultima Ratio nötig erscheint.
Eine erweiterte Ursachenabklärung (z. B. Thrombophilie‑/Vaskultisscreening oder Positronenemissionstomographie-CT zur Malignomsuche) wird in den aktuellen Leitlinien nur bei klinischem oder (familien‑)anamnestischem Anhalt für eine entsprechende Erkrankung empfohlen. Erscheint eine ThrombophilieabklärungThrombophilieabklärung
indiziert, muss beachtet werden, dass eine Antikoagulation, sowohl mit Heparin als auch mit Cumarinen oder direkten oralen Antikoagulanzien (DOAKsDOAKs
), die Ergebnisse der Diagnostik verfälschen kann. Konsekutiv sollte die Blutentnahme idealerweise noch vor der Etablierung einer solchen Therapie erfolgen. Des Weiteren ist zu beachten, dass zur Diagnose eines AntiphospholipidsyndromsAntiphospholipidsyndroms
nach aktueller Leitlinie ein Bestätigungstest mindestens 12 Wochen nach initialer Bestimmung erfolgen muss. Auch hier kann die Diagnostik, insbesondere die Bestimmung des Lupus-Antikoagulans, durch eine Antikoagulation gestört sein [24, 25]. Basierend auf dem Gendiagnostik-Gesetz muss zudem vor einer genetischen Testung, z. B. auf Faktor-V-Leiden-Mutation oder Prothrombinmutation, eine umfassende Aufklärung des Patienten durch einen entsprechend qualifizierten Arzt und eine schriftliche Einverständniserklärung erfolgen.
Merke
D‑Dimere haben nur eine geringe diagnostische Konsequenz.
Bildgebung der Wahl bei jungen bzw. schwangeren Patienten ist die MRT, bei fehlender Verfügbarkeit ist die CT-Venographie zur Diagnostik der Thrombose heranzuziehen.
Therapie
In der Akutphase besteht die Therapie in einer umgehenden therapeutischen Antikoagulationtherapeutischen Antikoagulation
. Mittel der Wahl ist hierbei eine HeparinisierungHeparinisierung
, bei unkomplizierten Verläufen ist nach den aktuellen Leitlinien ein niedermolekulares Heparin in gewichtsadaptierter therapeutischer Dosierung zu bevorzugen. Bei Verwendung eines unfraktionierten Heparins, z. B. zur besseren Steuerbarkeit im Falle einer zu erwartenden Operation (Fokussanierung, Dekompression), sollte eine Verlängerung der Ausgangs-aPTT(„activated partial thromboplastin time“) um das 2‑ bis 3‑Fache angestrebt werden (üblicherweise 60–80s). Ziel der Therapie ist nicht die Auflösung des Thrombusmaterials, sondern die Verhinderung eines Fortschreitens der Thrombosierung und somit Aufrechterhaltung möglichst großer Anteile des venösen Blutabflusses. Im Gegensatz zu Hirnblutungen anderer Ätiologie muss auch beim Nachweis von Stauungsblutungen unbedingt eine Antikoagulation erfolgen. Eine systemische i.v. Lyse ist zur Behandlung der Sinus‑/Venenthrombose nicht indiziert, bezüglich i.a. LysetherapienLysetherapien
oder mechanischen Thrombektomien besteht nur eine schwache Evidenz, insbesondere in puncto Lysetherapie auch der Nachweis eines negativen Outcomes [26, 27], sodass es sich hier um Einzelfallentscheidungen als Ultima Ratio handelt.
Grundsätzlich sollte, zumindest zu Beginn der stationären Behandlung, eine engmaschige Überwachung der Patienten auf einer Überwachungsstation (Stroke-UnitStroke-Unit
) erfolgen, um eine klinische Verschlechterung unmittelbar zu bemerken und mögliche Sekundärkomplikationen umfassend behandeln zu können. Bei Anhalt für eine relevante Hirndruckerhöhung sollte eine maximal-konservative HirndrucktherapieHirndrucktherapie
(im Sinne einer tiefen Sedierung bei intubierten Patienten, 30°-Oberkörperhochlagerung, Normothermie, hochnormaler Natriumwert, niedrig-normaler pCO2-Wert) erfolgen. Ergänzend ist eine kontinuierliche Überwachung des intrakraniellen Drucks (ICP) sinnvoll, sofern nicht kontraindiziert über eine Ventrikel- oder Lumbaldrainage zur gleichzeitigen Möglichkeit der Entlastung, ansonsten mittels Parenchymsonde.
Als lebensrettende Intervention bei nichtkonservativ beherrschbarem Hirndruck oder drohender Einklemmung sollte, unter Berücksichtigung des jeweiligen klinischen Vorzustands, dem Ausmaß etwaiger Infarzierungen und individueller gewünschter Therapielimitationen, eine HemikraniektomieHemikraniektomie
durchgeführt werden. Aufgrund der hierfür nötigen Pausierung der Antikoagulation mit Gefahr einer weiteren Thrombosierung und hohem peri- und postoperativem Blutungsrisiko, wie auch in der obigen Fallvorstellung angesprochen, muss dies jedoch jeweils als Einzelfallentscheidung evaluiert werden [28].
Die SekundärprophylaxeSekundärprophylaxe
besteht in einer oralen Antikoagulation mit einem Vitamin-K-AntagonistenVitamin-K-Antagonisten
(z. B. Phenprocoumon), Studien mit DOAKs legen eine Nichtunterlegenheit nahe, eine entsprechende Zulassung besteht jedoch derzeit nicht [29]. Im Falle eines Antiphospholipidsyndroms als Ursache der Erkrankung sollte Phenprocoumon in Kombination mit Acetylsalicylsäure (ASS) 100 mg eingesetzt werden.
Die Dauer der AntikoagulationDauer der Antikoagulation
ist grundsätzlich von der Ätiologie der Erkrankung abhängig und nicht einheitlich festgelegt. Insgesamt wird nach aktueller Leitlinie bei erstmaligem Ereignis eine Antikoagulation über zunächst 3 bis 12 Monate3 bis 12 Monate
empfohlen. Zur individuellen Evaluation der Dauer der Antikoagulation sollte nach 3 Monaten eine MRT mit MR-Venographie erfolgen. Zeigt sich hier ein rekanalisierter Sinus, kann bei ansonsten fehlenden negativen Kontextfaktoren eine Beendigung der Therapie bereits zu diesem Zeitpunkt erfolgen. Ansonsten sollte die Antikoagulation spätestens nach 12 Monate beendet werden, auch wenn der Sinus zu diesem Zeitpunkt weiter verschlossen ist, es sei denn es besteht ein erhöhtes Wiederholungsrisiko, z. B. durch Thrombophilieneigung oder Tumorerkrankung [20]. Im Falle einer Sinus‑/Hirnvenenthrombose im Rahmen einer Schwangerschaft wird eine Thromboseprophylaxe mittels niedermolekularen Heparins bei erneuter Schwangerschaft empfohlen.
Bei dem Verdacht auf eine VITTVITT
sollen auf eine Antikoagulation mit Heparinen verzichtet und heparininduzierte Thrombozytopenie (HIT-)kompatible Präparate (z. B. Argatroban, Bivalirudin oder DOAKs) verwendet werden. Bei gesicherter VITT wird die Gabe intravenöser Immunglobulineintravenöser Immunglobuline
(2 g/kg, aufgeteilt auf 2 oder 5 aufeinanderfolgende Tage) empfohlen. Auf die Gabe von Thrombozytenkonzentraten soll verzichtet werden.
Merke
Eine Antikoagulation mittels Heparinisierung sollte umgehend begonnen werden.
Eine Antikoagulation muss auch beim Nachweis von Stauungsblutungen weitergeführt werden.
Eine Sekundärprophylaxe mit einem Vitamin-K-Antagonisten oder einem DOAK (off-label) sollte mindestens 3 Monate durchgeführt werden.
Prognose
Mit einer Restitutio ad integrumRestitutio ad integrum
bei ungefähr 80 % aller Krankheitsfälle und einer Mortalität von – nach aktueller Literatur – nur 2 % ist die Prognose der Erkrankung vergleichsweise gut [7]. Analog zum am Anfang des Übersichtsartikels dargestellten Fallbeispiel stellt eine Thrombose der inneren Hirnvenen einen negativen Kontextfaktor dar. Weitere negative Prognosefaktorennegative Prognosefaktoren
sind eine ausgeprägte Bewusstseinsstörung schon bei Aufnahme sowie eine septische Genese der Thrombose bzw. ein Auftreten im Rahmen einer VITT [11].
Bezüglich der Auswirkung einer rekanalisierenden Therapierekanalisierenden Therapie
, also i.a. Lyse oder Thrombektomie, auf das Outcome zeigt sich in der aktuellen Literatur ein inkonklusives Bild, insbesondere bezüglich der i.a. Lyse, da es, wie in unserem Fallbeispiel, auch durchaus zu SekundärkomplikationenSekundärkomplikationen
, wie z. B. intrazerebralen Blutungen, kommen kann. Insgesamt kann somit derzeit keine sichere bzw. signifikante Prognoseverbesserung konstatiert werden. Sollte aufgrund einer relevanten Hirndruckerhöhung eine DekompressionsoperationDekompressionsoperation
indiziert sein, behalten etwa 20 % der Patienten schwerste Einschränkungen zurück oder versterben [30].
Wie im vorherigen Abschnitt dargestellt, sollte nach etwa 3 Monaten eine bildgebende Verlaufskontrollebildgebende Verlaufskontrolle
mittels MRT und MRV erfolgen, um eine eventuell nicht mehr nötige Antikoagulation mit möglichen konsekutiven Sekundärkomplikationen frühzeitig beenden zu können.
Fazit für die Praxis
Die Sinus‑/Hirnvenenthrombose ist eine seltene, jedoch teils fulminant verlaufende Erkrankung, deren typisches Red-flag-Symptom der (sub‑)akut aufgetretene, lageabhängige Kopfschmerz darstellt.
Die Bildgebung sollte möglichst mittels Magnetresonanztomographie (MRT) mit MR-Venographie erfolgen. Bei fehlender bzw. nur verzögerter Verfügbarkeit oder Kontraindikationen ist leitliniengemäß die Computertomographie(CT)-Venographie nicht unterlegen. D‑Dimere sollten allenfalls additiv bestimmt werden.
Die leitliniengemäße Akuttherapie ist die umgehende Heparinisierung, auch bei bereits vorhandenen Stauungsblutungen. Nach Entlassung aus der stationären Behandlung sollte die Etablierung einer Sekundärprophylaxe mit einem Vitamin-K-Antagonisten (oder off-label mit einem direkten oralen Antikoagulans) erfolgen, die Dauer der Sekundärprophylaxe muss individuell festgelegt werden.
Die Prognose von Sinus- bzw. Hirnvenenthrombosen ist zumeist gut. Im Falle ausgeprägter Thrombosen, rascher initialer Bewusstseinsminderung, insbesondere im Rahmen der inneren Hirnvenenthrombose, oder großer Stauungsinfarkte besteht jedoch ein relevantes Mortalitäts- und Morbiditätsrisiko. Zudem ist auch bei Sinus‑/Hirnvenenthrombose im Rahmen einer vakzininduzierten immunthrombotischen Thrombozytopenie von einer schlechteren Prognose auszugehen.
CME-Fragebogen
Welche ist die häufigste nichtgenetische Ursache der aseptischen Sinus‑/Hirnvenenthrombose?
Steroidtherapie
Rauchen
Dehydratation
Anlage eines zentralen Venenkatheters
Schädel-Hirn-Trauma
Welches Symptom kommt im Rahmen der Sinus‑/Hirnvenenthrombose am häufigsten vor?
Kopfschmerz
Epileptischer Anfall
Hemiparese
Vigilanzminderung
Augenmuskelparese
Welche der folgenden Aussagen zu epileptischen Anfällen im Rahmen einer Sinus‑/Hirnvenenthrombose ist zutreffend?
Epileptische Anfälle kommen nur in weniger als 10 % aller Erkrankungsfälle vor.
Eine antikonvulsive Medikation sollte prophylaktisch zum Zeitpunkt der Diagnose begonnen werden.
Die meisten epileptischen Ereignisse treten später als 4 Wochen nach Diagnose auf.
Infratentorielle Läsionen sind ein Risikofaktor für das Auftreten eines epileptischen Anfalls.
Ein Status epilepticus ist mit einer verschlechterten Prognose assoziiert.
Die vakzininduzierte immunologische thrombotische Thrombozytopenie (VITT) mit Ausbildung einer Sinus‑/Hirnvenenthrombose …
tritt hauptsächlich bei Verwendung von mRNA-Impfstoffen auf.
tritt vorwiegend innerhalb der ersten 30 Tage nach der ersten Impfung auf.
betrifft vorwiegend Menschen > 80 Jahre.
zeigt typischerweise in den meisten Fällen keine D‑Dimer-Erhöhung.
ist nicht mit der Bildung von Plättchenfaktor(PF)-4-Antikörpern assoziiert.
Welche Maßnahme sollten sie im Rahmen einer vakzininduzierten immunthrombotischen Thrombozytopenie (VITT) keinesfalls durchführen?
Gabe von Bivalirudin
Antikoagulation mittels i.v. Heparinisierung
Gabe von intravenösen Immunglobulinen (IVIG)
Antikoagulation mittels eines direkten oralen Antikoagulans (DOAK)
Antikoagulation mittels Argatroban
Eine 25-jährige schwangere Patientin stellt sich mit lageabhängigen Kopfschmerzen in Ihrer Notaufnahme vor. Welche diagnostische Maßnahme würden Sie zum Ausschluss einer Sinus‑/Hirnvenenthrombose priorisieren?
Kraniale Computertomographie
Magnetresonanztomographie (MRT) Neurokranium
Bestimmung der D‑Dimere
Digitale Subtraktionsangiographie
Duplexsonographie
Welches der folgenden Zeichen ist ein typisches Zeichen einer Sinusthrombose im Rahmen der Bildgebung?
Griesinger-Zeichen
Hot-Cross-Bun-Zeichen
„Empty triangle sign“
„Dense artery sign“
Empty-Sella-Syndrom
Welcher Sinus bzw. welches der aufgeführten Gefäße ist am ehesten thrombosiert, wenn ein Patient mit Sinus‑/Hirnvenenthrombose bereits bei Aufnahme eine komatöse Bewusstseinsstörung aufweist?
Sinus sagittalis superior
Sinus transversus
V. cerebri magna (Galeni)
Kortikale Hirnvene
Sinus cavernosus
Welche Akuttherapie sollte leitliniengemäß nach Diagnosesicherung einer Sinus‑/Hirnvenenthrombose ohne Anhalt für eine relevante Hirndruckerhöhung zeitnah eingeleitet werden?
Heparinisierung
Duale Thrombozytenaggregationshemmung
Intravenöse Lysetherapie
Intraarterielle Lysetherapie
Mechanische Thrombektomie
Eine 30-jährige Patientin erlitt im Rahmen der ersten Schwangerschaft eine Sinusthrombose und wünscht nun im Rahmen Ihrer Sprechstunde eine Beratung bei geplanter erneuter Schwangerschaft. Welche Empfehlung geben Sie der Patientin?
Sie raten von einer erneuten Schwangerschaft ab.
Sie raten zur Einnahme von ASS (Acetylsalicylsäure) 100 mg/Tag ab Konzeption.
Sie sehen keinen Handlungsbedarf.
Sie empfehlen den Beginn einer Thromboseprophylaxe mittels niedermolekularen Heparins.
Sie schlagen monatliche magnetresonanztomographische Untersuchungen zur rechtzeitigen Diagnose einer erneuten Sinus‑/Hirnvenenthrombose vor.
Einhaltung ethischer Richtlinien
Interessenkonflikt
Gemäß den Richtlinien des Springer Medizin Verlags werden Autoren und Wissenschaftliche Leitung im Rahmen der Manuskripterstellung und Manuskriptfreigabe aufgefordert, eine vollständige Erklärung zu ihren finanziellen und nichtfinanziellen Interessen abzugeben.
Autoren
J.B. Schulz: A. Finanzielle Interessen: J. Schulz gibt an, dass kein finanzieller Interessenkonflikt besteht. – B. Nichtfinanzielle Interessen: Direktor einer Neurologischen Universitätsklinik | Mitgliedschaften: Deutsche Gesellschaft für Neurologie (DGN), Deutsche Parkinson-Gesellschaft, Deutsche Schlaganfall-Gesellschaft, American Neurologcial Association, American Academy of Neurology, International Society for Neurochemistry, Society for Neuroscience. M. Dafotakis: A. Finanzielle Interessen: M. Dafotakis gibt an, dass kein finanzieller Interessenkonflikt besteht. – B. Nichtfinanzielle Interessen: Oberarzt der Klinik für Neurologie, Uniklinik, RWTH Aachen | Mitgliedschaften: DGN, Deutsche Gesellschaft für Klinische Neurophysiologie und Funktionelle Bildgebung (DGKN), AK Botulinumtoxin. J. Heckelmann: A. Finanzielle Interessen: J. Heckelmann gibt an, dass kein finanzieller Interessenkonflikt besteht. – B. Nichtfinanzielle Interessen: Arzt in Weiterbildung, Klinik für Neurologie, Uniklinik RWTH Aachen | Mitgliedschaft: DGN.
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Die vollständige Erklärung zum Interessenkonflikt der Wissenschaftlichen Leitung finden Sie am Kurs der zertifizierten Fortbildung auf www.springermedizin.de/cme.
Der Verlag
erklärt, dass für die Publikation dieser CME-Fortbildung keine Sponsorengelder an den Verlag fließen.
Für diesen Beitrag wurden von den Autoren keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.
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20. Weimar C Cerebral venous and sinus thrombosis : S2k guidelines Nervenarzt 2019 90 4 379 387 10.1007/s00115-018-0654-6 30758512
21. Bonneville F Imaging of cerebral venous thrombosis Diagn Interv Imaging 2014 95 12 1145 1150 10.1016/j.diii.2014.10.006 25465119
22. Lee EJY The empty delta sign Radiology 2002 224 3 788 789 10.1148/radiol.2243990978 12202715
23. Shinohara Y Yoshitoshi M Yoshii F Appearance and disappearance of empty delta sign in superior sagittal sinus thrombosis Stroke 1986 17 6 1282 1284 10.1161/01.STR.17.6.1282 3810732
24. Baglin T Clinical guidelines for testing for heritable thrombophilia Br J Haematol 2010 149 2 209 220 10.1111/j.1365-2141.2009.08022.x 20128794
25. Miyakis S International consensus statement on an update of the classification criteria for definite antiphospholipid syndrome (APS) J Thromb Haemost 2006 4 2 295 306 10.1111/j.1538-7836.2006.01753.x 16420554
26. Coutinho JM Effect of endovascular treatment with medical management vs standard care on severe cerebral venous thrombosis: the TO-ACT randomized clinical trial JAMA Neurol 2020 77 8 966 973 10.1001/jamaneurol.2020.1022 32421159
27. Ilyas A Endovascular mechanical thrombectomy for cerebral venous sinus thrombosis: a systematic review J Neurointerv Surg 2017 9 11 1086 1092 10.1136/neurintsurg-2016-012938 28213478
28. Théaudin M Should decompressive surgery be performed in malignant cerebral venous thrombosis?: a series of 12 patients Stroke 2010 41 4 727 731 10.1161/STROKEAHA.109.572909 20185780
29. Ferro JM Safety and efficacy of dabigatran etexilate vs dose-adjusted warfarin in patients with cerebral venous thrombosis: a randomized clinical trial JAMA Neurol 2019 76 12 1457 1465 10.1001/jamaneurol.2019.2764 31479105
30. Ferro JM Decompressive surgery in cerebrovenous thrombosis Stroke 2011 42 10 2825 2831 10.1161/STROKEAHA.111.615393 21799156
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==== Front
Int J Hematol
Int J Hematol
International Journal of Hematology
0925-5710
1865-3774
Springer Nature Singapore Singapore
35412265
3349
10.1007/s12185-022-03349-1
Letter to the Editor
Patients with B-cell lymphoma receiving anti-CD20 monoclonal antibody-containing chemotherapies and seroreactive patterns in response to COVID-19 vaccination
http://orcid.org/0000-0002-1595-8524
Inaba Tohru inaba178@koto.kpu-m.ac.jp
1
Okumura Keita 2
Maekura Chika 3
Muramatsu Ayako 3
Kobayashi Tsutomu 3
Kuroda Junya 3
Nukui Yoko 1
1 grid.272458.e 0000 0001 0667 4960 Department of Infection Control and Laboratory Medicine, Kyoto Prefectural University of Medicine, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566 Japan
2 grid.272458.e 0000 0001 0667 4960 Faculty of Clinical Laboratory, Kyoto Prefectural University of Medicine, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566 Japan
3 grid.272458.e 0000 0001 0667 4960 Division of Hematology and Oncology, Kyoto Prefectural University of Medicine, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566 Japan
12 4 2022
2022
115 6 913914
24 1 2022
1 4 2022
3 4 2022
© Japanese Society of Hematology 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.
Keywords
COVID-19
Anti-SARS-CoV-2 antibody
Anti-CD20 monoclonal antibody therapy
SARS-CoV-2 vaccine
http://dx.doi.org/10.13039/100010795 Chugai Pharmaceutical http://dx.doi.org/10.13039/501100004095 Kyowa Hakko Kirin issue-copyright-statement© Japanese Society of Hematology 2022
==== Body
pmcTo the editor
We read the previous report by Funakoshi et al. about the limited increase in IgG anti-spike 1 (S1) protein following BNT 162b2 mRNA COVID-19 vaccination after anti-CD20 monoclonal antibody (MoAb) [1]. We have also examined IgM and IgG antibodies against SARS-CoV-2 nucleocapsid (N), S1, and neutralizing antibody (Nab) using an iFLASH Immunoassay Analyzer and respective reagents (Shenzhen YHLO Biotech, Shenzhen, China) in various individuals. Here, we present interesting data obtained from patients with follicular lymphoma (FL) receiving anti-CD20 MoAb before infection with SARS-CoV-2.
Case 1 was a male in his 70s with refractory FL treated for 8 years. He was infected with SARS-CoV-2 5 days after the 37th course of rituximab (R)-containing chemotherapy and developed severe pneumonia. He had never received a SARS-CoV-2 vaccine and died of respiratory failure 10 weeks (+ 10w) after the diagnosis of COVID-19. PCR for the SARS-CoV-2 E-gene was repeatedly positive. All anti-SARS-CoV-2 antibody tests had been persistently negative (< 10 AU/mL).
Case 2 was a male in his 70s with newly diagnosed FL. He had not received COVID-19 vaccination, and he was infected with SARS-CoV-2 7 days after the initial course of R-CHOP. After interruption of R-CHOP, he received comprehensive therapy for COVID-19. He developed sustained pneumonia, but all antibodies except IgM-S1 became positive + 4w after onset.
Case 3 was a female in her 60s who had been diagnosed with FL 3 years earlier and received six courses of R-containing chemotherapies. She experienced recurrence and received six courses of obinutuzumab–CHOP for 2 years without relapse. She also received SARS-CoV-2 vaccination twice 1 year after the last course of chemotherapy, but experienced breakthrough infection 8 weeks after the second vaccination. She experienced moderate COVID-19 pneumonia, but her condition improved without any subsequent complications. At + 2w after onset of infection, her serum sample was positive for IgM-S1 and Nab. In contrast, IgG-N, IgM-N, and IgG-S1 were consistently negative.
Case 4 was a non-infected healthy individual who had received SARS-CoV-2 vaccination twice without any severe adverse effects.
SARS-CoV-2 mRNA vaccination promotes production of anti-S Ab, including S1 as well as the receptor binding domain (RBD), and neutralizes the binding of RBD to the human angiotensin-converting enzyme 2 receptor. Therefore, anti-N antibodies are only produced in affected patients as a marker for past SARS-CoV-2 infection. Nevertheless, only Case 2 was seropositive for anti-N antibodies among the three affected cases (Cases 1–3). On the other hand, Case 3 had already acquired both IgM-S1 and Nab + 2w after infection, as well as Case 4 (non-infected vaccine recipient) at + 4w after vaccination. These findings suggest that both antibodies in Case 3 were produced by SARS-CoV-2 vaccine before infection, although we were unable to take a sample for analysis at the onset of COVID-19. In contrast, she could produce neither anti-N antibodies nor IgG-S1 at + 4w, suggesting incomplete humoral immunity against SARS-CoV-2. The mechanism of class-switch disturbance of anti-S1 antibodies in Case 3 is unclear, but such disturbance may not be serious, at least in this case.
COVID-19 patients with malignant lymphoma have poor clinical outcomes, especially when undergoing R-based chemotherapies [2]. Moreover, there have been several reports of impaired humoral response to SARS-CoV-2 mRNA vaccination in patients receiving anti-CD20 MoAb [1, 3]. Indeed, Case 3 could not acquire IgG-S1 at + 4w after onset, which was + 12w after the 2nd vaccination, and we did not evaluate her T-cell-mediated immunity to SARS-CoV-2. Nevertheless, she had a high serum titer of Nab against SARS-CoV-2 at + 2w after onset, probably due to vaccine-induced IgM-S1, which enabled her to recover from COVID-19 pneumonia despite her persistently negative IgG-S1 level.
In conclusion, we believe that the humoral effect of SARS-CoV-2 mRNA vaccination should not be evaluated solely based on IgG-S1 or IgG-RBD. Evaluation of a larger number of patients will be necessary to clarify this issue.
Acknowledgements
We would like to thank honorary Prof. Tatsuhiko Kodama (Laboratory for Systems Biology and Medicine, The University of Tokyo), Takeshi Kawamura (Isotope Science Center, The University of Tokyo) and Gen Kano (Kyoto Yamashiro General Medical Center).
Funding
Inaba T. was technically supported by YHLO Biotech and Medical & Biological Laboratories. Kuroda J. received research funding and honoraria from Kyowa Kirin and Chugai Pharmaceutical and also honoraria from Pfizer.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Change history
5/5/2022
A Correction to this paper has been published: 10.1007/s12185-022-03376-y
==== Refs
References
1. Funakoshi Y Yakushijin K Ohji G Hojo W Sakai H Watanabe M Limited increase in antibody titers following mRNA SARS-CoV-2 vaccination for more than 3 years after final dose of anti-CD20 antibody Int J Hematol 2022 115 7 10 10.1007/s12185-021-03247-y 34981433
2. Hagihara M Ohara S Uchida T Inoue M Practical management of patients with hematological diseases during the COVID-19 pandemic in Japan Int J Hematol 2021 114 709 718 10.1007/s12185-021-03175-x 34669154
3. Perry C Luttwak E Balaban R Shefer G Morales MM Aharon A Efficacy of the BNT162b2 mRNA COVID-19 vaccine in patients with B-cell non-Hodgkin lymphoma Blood Adv 2021 5 3053 3061 10.1182/bloodadvances.2021005094 34387648
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==== Front
J Community Health
J Community Health
Journal of Community Health
0094-5145
1573-3610
Springer US New York
35412190
1087
10.1007/s10900-022-01087-3
Original Paper
Addressing Barriers to COVID-19 Vaccination Among Older U.S. Veterans
http://orcid.org/0000-0003-3926-3847
Desir Marianne Marianne.Desir@va.gov
12
Cuadot Alain 1
Tang Fei 1
1 grid.413948.3 0000 0004 0419 3727 Miami Veterans Affairs Healthcare System, 1201 NW 16th Street, Miami, FL 33125 USA
2 grid.26790.3a 0000 0004 1936 8606 University of Miami Miller School of Medicine, 1801 NW 9th Avenue, Miami, FL 33136 USA
12 4 2022
14
18 3 2022
© This is a U.S. government work and not under copyright protection in the U.S.; 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.
Efforts are being made to ensure that COVID-19 vaccination among older adults is as complete as possible. Dialogue-based interventions tailored to patients’ specific concerns have shown potential for effectiveness in promoting vaccination. We implemented a quality improvement project intended to help patients in an outpatient geriatrics clinic overcome barriers to COVID-19 vaccination. We offered tailored conversations by telephone in which we discussed the barriers to vaccination that the patients were facing and offered to provide relevant information and/or logistical assistance. Of the 184 patients reached by phone, 125 (68%) endorsed having already been vaccinated and 59 (32%) did not. About one third of the unvaccinated patients were willing to participate in tailored conversations (20 patients = 34% of the unvaccinated). In follow-up calls 30 days after the intervention we found that four of these 20 patients had received COVID-19 vaccination, one patient was scheduled for vaccination, 10 continued to be deciding about vaccination, four had decided against it and one could not be reached. Dialogue-based interventions that are conducted by telephone and are tailored to the specific barriers to vaccination being faced by older adults may have some effectiveness in encouraging vaccination against COVID-19. The effectiveness of such interventions may be decreased in populations that already have high vaccination rates and in which many patients have already formed strong opinions regarding vaccination against COVID-19. Completion of Plan-Do-Study-Act cycles is a feasible way to design, implement and work to optimize quality improvement efforts related to COVID-19 vaccination.
Keywords
COVID-19
Vaccination
Older adults
Intervention
==== Body
pmcIntroduction
Older adults have been dramatically affected by the COVID-19 pandemic, which has encouraged efforts to ensure that vaccination among older adults is as complete as possible. Barriers to vaccination include access issues and vaccine hesitancy [1]. Transportation limitations, cognitive concerns (such as mild cognitive impairment or dementia) and physical disabilities are among the factors that can contribute to older adult patients having difficulty accessing vaccination. Vaccine hesitancy is “patient-level reluctance to receive vaccines” [2]. Previous work has suggested that dialogue-based interventions can be effective in addressing vaccination barriers, and that intervention strategies may benefit from being tailored to the target population and their reasons for hesitancy [3]. In the present project a tailored, dialogue-based intervention was offered to older adults enrolled in a Veterans Affairs outpatient geriatrics clinic.
Methods
The setting for the project was a Veterans Affairs Medical Center in a large urban area, in the summer and fall of 2021. In the “Plan” component of our first “Plan”-“Do”-“Study”-“Act” (PDSA) cycle, we discussed with the director of the outpatient geriatrics clinic and other staff at our institution the barriers to COVID-19 vaccination that they were finding patients to be facing, and how we might be able to help patients address these barriers [4]. We designed a plan for an intervention, and the IRB for our institution reviewed the plan and agreed that it described a quality improvement project. With the help of our informatics department we then identified patients enrolled in the medical center’s outpatient geriatrics clinic who had possibly not yet had vaccination for COVID-19. Of the 1706 patients enrolled in the geriatrics primary care clinic 573 (34%) were identified as possibly not having yet been vaccinated, due to vaccination status in their electronic medical records being listed as “unvaccinated” or “unknown”. Starting from the beginning and end of this alphabetized list of 573 patients and proceeding toward the center of the list, our team reached 184 of the 573 patients (or their caregivers; 32%) for semi-structured conversations.
In the phone conversations with patients and caregivers we discussed the patients’ actual vaccination statuses at that time. For patients who confirmed that they were unvaccinated, we attempted to identify the specific barriers to vaccination being experienced by each patient, and to tailor for each patient an intervention intended to address his/her specific barriers to vaccination. Predetermined options for questions regarding vaccination barriers were sometimes used or adapted, as assessed by the staff members to be appropriate in each conversation. Available components of the intervention included dialogue targeted to areas of concern expressed by the patient, provision of patient education targeted to the patient’s interests and provision of assistance with access issues (such as scheduling vaccination appointments or arranging transportation to the VA). The staff members completing the phone calls had received relevant training, printed materials and internet-based materials in advance.
The project staff members who had conversations with patients by phone included two employees of our Geriatric Research Education and Clinical Center who have high levels of health literacy and have worked well with other quality improvement efforts at our medical center. One completed medical school in Cuba and is fluent in English and Spanish, and the other has worked and volunteered extensively in healthcare settings and is himself an older adult Veteran. The third project staff member, who provided support for the other project team members and reached out to patients for follow-up phone calls to help address logistical issues (such as scheduling transportation), is a geriatrician who has worked at our medical center for 3 years.
Information on the patients’ self-reports of their vaccination statuses and the content of the conversations was recorded in the electronic medical record and in a secured Excel file. Approximately 1 month after the last of the 184 patient conversations, we recontacted the patients who had expressed interest in vaccination in our first conversations with them. We asked their updated vaccination status and explored whether there were new or continued barriers to vaccination with which we might assist. We then studied the overall results of the telephone-based intervention and used this information to inform the next PDSA cycle.
Results
Of the 184 patients reached for initial conversations, 125 (68%) reported themselves as already having been vaccinated (or were reported as such by their caregivers) and 59 (32%) did not endorse having already been vaccinated. Most of the unvaccinated patients reached by phone were unwilling to further consider vaccination (39 patients = 66% of the unvaccinated), but about one third of the unvaccinated patients were willing to participate in conversations tailored to their specific concerns (20 patients = 34% of the unvaccinated). Of the 20 patients who were willing to engage in tailored conversations with us regarding COVID-19 vaccination, the barriers most were facing were concerns regarding the safety and/or efficacy of the vaccines (17 patients = 85%). Only three patients endorsed physical and/or cognitive concerns that were, in association with limited informal social support, limiting their access to vaccination. Though the number of patients is small, in our experience the members of this subgroup were most amenable to assistance with addressing barriers to vaccination. Logistical supports were offered and 30 days later one of the three patients had received vaccination (33%), one patient had missed an appointment for vaccination and rescheduled for an appointment in the following weeks (33%), and the third patient could not be reached for further discussion. Of the 17 patients who engaged in tailored conversations and expressed concerns regarding the safety and/or efficacy of the vaccinations, three had received vaccination 30 days later (18%), four had decided against vaccination and ten continued to be deciding.
Table 1 provides a summary of the demographics and responses to our conversations among patients who were reached by phone. In Chi-squared tests, race and ethnicity were not found to be significantly associated with vaccination statuses or responses to our phone conversations. It should be noted, however, that the project was designed as a quality improvement initiative and not as a research study, therefore was not powered to detect the potential associations of race/ethnicity with perspectives on COVID-19 vaccination, and that other authors have found such associations [5]. Table 1 Demographics and vaccination statuses/responses to phone conversations for patients reached by phone
Patients totals Average age Male Female Black or African American White Native Hawaiian and other pacific islanders Race unknown/ patient declined to answer/No information available Hispanic or latino Not hispanic or latino Ethnicity unknown/patient declined to answer/no information available
Reached by phone/vaccinated 125 (67.9%) 78.0 (± 7.4) 125 (67.9%) 0 25 (59.5%) 82 (71.3%) 2 (66.7%) 16 (66.7%) 38 (70.4%) 82 (65.6%) 5 (100%)
Reached by phone/Not yet vaccinated but expressed interest in possible vaccination 20 (10.9%) 77.7 (± 7.1) 20 (10.9%) 0 4 (9.5%) 14 (12.2%) 1 (33.3%) 1 (4.2%) 6 (11.1%) 14 (11.2%) 0
Reached by phone/refused conversation or vaccination 39 (21.2%) 77.2 (± 6.6) 39 (21.2%) 0 13 (31.0%) 19 (16.5%) 0 7 (29.2%) 10 (18.5%) 29 (23.2%) 0
Reached (totals) 184 77.8 (± 6.6) 184 0 42 115 3 24 54 125 5
Discussion
During the project implementation we found that the pre-implementation vaccination rates in our geriatrics clinic did have room for improvement, but not as much room as had initially seemed possible. While initial data retrieved by our informatics department from a data warehouse suggested that as many as a third of the patients in our geriatrics primary care clinic might not have been vaccinated against COVID-19 (due to vaccination status in the patients’ electronic medical records being “unvaccinated” or “unknown”), after speaking with patients and manually checking individual charts we estimated that the pre-intervention vaccination rate in the geriatrics clinic was in the 80 or 90 s (as of September 2021). This would be in keeping with estimates from the Center of Disease Control for current vaccination rates among older adults in our county and in the United States [6].
In our project only about one-third of unvaccinated patients were willing to engage in tailored conversations by telephone about the barriers they were facing to vaccination, and of these the majority were facing barriers related to vaccination hesitancy. These findings may relate to the telephone intervention being offered at a point in the pandemic when most unvaccinated patients had already formed strong opinions related to vaccination, and when patients facing logistical barriers to vaccination in our healthcare system had already had opportunities to receive relevant assistance with scheduling, visit reminders, transportation, etc. This points to the possibility that the relatively small group of unvaccinated patients in our geriatrics clinic might benefit at this point from further discussions of vaccination within the context of established relationships with people they trust [5,7,8]. With studying the results of telephone-based intervention with older adult patients at our institution (the “S” of the PDSA cycle), we therefore hypothesized that supporting tailored conversations on vaccination between primary care providers in the geriatrics clinic and unvaccinated patients would be a reasonable next step. Multiple providers in our geriatrics outpatient clinic participated in training and discussion on addressing barriers to vaccination among their patients, including through use of motivational interviewing techniques and tailoring the content of conversations to the specific barriers being faced [7,8]. It was also discussed that broader public health efforts and encouraging vaccination among younger patients may be particularly important components of vaccination efforts at this point in the COVID-19 pandemic [7].
Conclusions
Our quality improvement project supported that dialogue-based interventions that are conducted by telephone and are tailored to the specific barriers to vaccination being faced by older adults may have some effectiveness in encouraging vaccination against COVID-19. Our experience also suggested that the effectiveness of such interventions might be decreased in populations that already have high vaccination rates and in which high percentages of the relatively few patients who remain unvaccinated have already formed strong opinions regarding vaccination. We found the completion of Plan-Do-Study-Act cycles to be a feasible method to for designing, implementing and working to optimize quality improvement efforts related to COVID-19 vaccination.
Author Contributions
MD and AC contributed to design and implementation of the described project. FT provided and reviewed statistical work for the project.
Funding
The authors are employees of the Veterans Affairs Healthcare System and dedicated work hours to the project. No other funding was received.
Data Availability
De-identified data that support the findings of this study are available from the corresponding author (Marianne Desir) upon request.
Code Availability
Not applicable.
Declarations
Conflict of interest
The authors have no relevant financial or non-financial interests to disclose.
Ethical Approval
The Miami VA IRB reviewed the project and designated it as quality improvement and not research.
Consent to Participate
Not applicable as this was quality improvement, not research.
Consent for Publication
Not applicable.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
1. Zhang Y Fisk RJ Barriers to vaccination for coronavirus disease 2019 (COVID-19) control: Experience from the United States Global Health Journal 2021 5 1 51 55 10.1016/j.glohj.2021.02.005 33585053
2. Puri N Coomes EA Haghbayan H Gunaratne K Social media and vaccine hesitancy: New updates for the era of COVID-19 and globalized infectious diseases Human Vaccines & Immunotherapeutics 2020 16 11 2586 2593 10.1080/21645515.2020.1780846 32693678
3. Jarrett C Wilson R O’Leary M Eckersberger E Larson HJ SAGE Working Group on Vaccine Hesitancy Strategies for addressing vaccine hesitancy—A systematic review Vaccine 2015 33 34 4180 4190 10.1016/j.vaccine.2015.04.040 25896377
4. Langley GL Moen R Nolan KM Nolan TW Norman CL Provost LP The improvement guide: A practical approach to enhancing organizational performance 2009 2 San Francisco Jossey-Bass Publishers
5. Khubchandani J Macias Y COVID-19 vaccination hesitancy in Hispanics and African-Americans: A review and recommendations for practice Brain Behavior Immunity Health 2021 15 100277 10.1016/j.bbih.2021.100277 34036287
6. COVID Data Tracker: COVID-19 Vaccinations in the United States. Centers for Disease Control and Prevention website. https://covid.cdc.gov/covid-data-tracker/#datatracker-home. Updated February 16, 2022. Retrieved February 17, 2022.
7. Omer SB Benjamin RM Brewer NT Promoting COVID-19 vaccine acceptance: Recommendations from the Lancet Commission on vaccine refusal, acceptance, and demand in the USA The Lancet 2021 398 10317 2186 2192 10.1016/S0140-6736(21)02507-1
8. Breckenridge LA Burns D Nye C The use of motivational interviewing to overcome COVID-19 vaccine hesitancy in primary care settings Public Health Nursing 2021 10.1111/phn.13003 34716618
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PMC009xxxxxx/PMC9004212.txt |
==== Front
Health Inf Sci Syst
Health Inf Sci Syst
Health Information Science and Systems
2047-2501
Springer International Publishing Cham
35432950
174
10.1007/s13755-022-00174-y
Research
MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images
http://orcid.org/0000-0003-0565-4217
Pan Liangrui 1
Ji Boya 1
Wang Hetian 1
Wang Lian 1
Liu Mingting 1
Chongcheawchamnan Mitchai 2
Peng Shaolaing slpeng@hnu.edu.cn
1
1 grid.67293.39 College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
2 grid.7130.5 0000 0004 0470 1162 Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Thailand
12 4 2022
12 2022
10 1 413 3 2022
4 4 2022
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 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.
The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID19) is life-saving important for both patients and doctors. This research proposes a multi-channel feature deep neural network (MFDNN) algorithm to screen people infected with COVID19. The algorithm integrates data over-sampling technology and MFDNN model to carry out the training. The oversampling technique reduces the deviation of the prior probability of the MFDNN algorithm on unbalanced data. Multi-channel feature fusion technology improves the efficiency of feature extraction and the accuracy of model diagnosis. In the experiment, Compared with traditional deep learning models (VGG19, GoogLeNet, Resnet50, Desnet201), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Furthermore, through ablation experiments, we proved that a multi-channel convolutional neural network (CNN) is superior to single-channel CNN, additional layer and PSN module, and indirectly proved the sufficiency and necessity of each step of the MFDNN classification method. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.
Keywords
COVID19
Chest X-ray
Multi-channel feature
MFDNN
National Key R&D Program of China2017YFB0202602 issue-copyright-statement© Springer Nature Switzerland AG 2022
==== Body
pmcIntroduction
SARS-CoV-2 causes COVID19. Since the first report, it has become a global pandemic, with 180 million confirmed cases and 3.91 million deaths. It is extremely contagious characteristics and delayed vaccination has made developing countries vulnerable to virus attacks. Now the nucleic acid detection mechanism has played an essential role in screening the flow of people. Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the most standard diagnostic technology available [1]. However, its sensitivity is relatively low, and the result is highly dependent on the sample area obtained and heavily reliant on the operator's technique [2]. The important thing is that this method takes time. However, time is a key factor in isolating, preventing and treating people infected with COVID19, which will limit the efficiency of COVID19 screening. With the global spread of COVID19, medical research has found that CXI can identify people infected with COVID19. Therefore, as a supplement to RT-PCR technology, it plays an essential role in detecting and evaluating people infected with COVID19.
Computed tomography (CT), lung ultrasound (LUS), and Chest-X ray radiography are among the most commonly used imaging modalities to identify COVID19 infections [3–5]. It is widely used in large hospitals because of the safety, painlessness, non-invasiveness, clear image, high-density resolution, and apparent morbidity of CXI. In addition, experienced doctors can make real-time diagnoses through CXI. Therefore, CXI is one of the most commonly used and readily available methods to detect COVID19 infections [6]. However, there are many similarities in the CXI characteristics of patients with COVID19 and common pneumonia, which poses a massive challenge to radiologists in diagnosing patients with COVID19.
In recent years, artificial intelligence has prompted tremendous progress in the field of biomedicine, such as medical diagnosis, intelligent image recognition, intelligent health management, intelligent drug development, and medical robots [7–9]. Machine learning-based methods have developed many applications in the accurate analysis of CXI, such as diagnosing and evaluating people infected with COVID19 [10, 11]. Standard machine learning algorithms include linear regression, random forest (RF), K-nearest neighbor (KNN), decision tree (DT), etc. [12, 13]. Abolfazl et al. used dimensionality reduction methods to extract the best features of CXI to build an efficient machine learning classifier, and the classifier distinguishes COVID19 and non-COVID19 cases with high accuracy and sensitivity [6]. Dan et al. used three different machine learning models to predict the deterioration of the patient's condition, compare them with the currently recommended predictors and APACHEII risk prediction scores, and obtain high sensitivity, specificity, and accuracy [14]. Mohamed et al. used the new fractional multi-channel exponential moments (FrMEMs) to extract features from CXI [15], p. 19].
Then, the improved Manta-Ray Foraging Optimization (MRFO) method was used for feature selection, and the KNN method was used to classify the two types of CXI [15]. However, deep learning is the hottest research direction in the field of machine learning. The CXI deep learning method for COVID19 classification has been actively explored. Linda et al. proposed a deep convolutional neural network called COVID-Net to help clinicians improve screening [16]. Ali et al. proposed five models based on pre-trained convolutional neural networks (ResNet50, ResNet101, ResNet152, InceptionV3, and Inception-ResNetV2) to implement four different binary classifications: COVID19, normal (healthy), viral pneumonia and bacterial pneumonia. CXI has achieved a high accuracy rate [17]. Loannis et al. automatically detected CXI based on the transfer learning method of convolutional neural network and achieved 96.78%, 98.66%, and 96.46% accuracy, sensitivity, and specificity [18]. Ezz et al. proposed the COVIDX-Net network based on seven deep convolutional network models with different architectures and obtained F1 scores of 0.89 and 0.91, respectively [19].
Inspired by machine learning and deep learning and the accumulation of previous work experience, we will further explore the impact of experimental data and deep convolutional neural networks on the detection algorithm or detection system in this article. However, in most databases, unbalanced label classes often occur, which will cause the convolutional neural network to be biased to correctly identify image data with many class labels. Multi-channel CNN is usually superior to single-channel CNN in terms of computational efficiency and accuracy [20–22]. A similar method has been proposed due to previous work. However, the previous work mainly focused on optimizing the processing features on multiple channels, and did not discuss fusion, nor did it optimize the features after fusion. Therefore, the experiment selected a multi-channel input single-channel output parallel deep neural network as the front part of the algorithm, additional layer as the main method of feature fusion, and added the features extracted from the multi-channel. As the tail of MFDNN, PSN can perform secondary convolution operations on the basis of feature fusion to improve the efficiency and accuracy of feature extraction. Two similar neural networks map the input to a new space and represent the output in the new space. By calculating the value of loss, the similarity between image features is calculated [23].
Therefore, the main focus of this article is to solve the accuracy of the COVID19 detection algorithm. Around this problem, we will solve the following problems separately: (1) Deal with the imbalance of sample labels. (2) Optimize the feature extraction of the deep neural network algorithm. (3) Evaluate the classification effect of the network algorithm. To achieve this goal, we first analyze the degree of imbalance in the sample data. We found that chest X-ray data of people who were not infected with COVID19 was significantly more than other categories through the data set analysis. To be able to classify the chest radiograph data set more accurately, our main contributions are as follows.To balance the impact of the unbalanced label data set on model training, when processing CXI, we embed the oversampling method into the model to balance all categories of data.
We propose an MFDNN algorithm based on multi-channel input, single-channel output, and centralized weight sharing. The algorithm model concentrates the feature maps of multi-channel chest radiographs and optimizes the feature extraction process. As the tail of MFDNN, PSN can extract features from the additional layer for the second time.
Finally, the MFDNN is compared with the classic deep neural networks (VGG19, GoogLeNet, Resnet50, Desnet201). The MFDNN model is better than other models in precision, recall, and F1 Score and confusion matrix.
Materials and methods
In this section, we first introduce the flow chart of the MFDNN algorithm. It mainly includes two parts: oversampling and the MFDNN model. The first part is primarily data preprocessing, and the second part primarily uses the MFDNN model for feature extraction and patient diagnosis. Figure 1 shows our proposed algorithm classification process.Fig. 1 MFDNN algorithm flow chart. It includes two parts, namely data oversampling, feature extraction and classification
Materials
X-rays pass through the chest, and different body parts absorb the rays, and the film will not be exposed or partially exposed. After the film is processed, this part is white, forming an imaging manifestation. As shown in Fig. 2, COVID images have the symptom of "white lung"; a large lung area is white. The lung opacity class is very similar to the normal class. The main difference is the increase in lung texture. Small area texture image blur appears in viral pneumonia images, which may be due to lung inflammation caused by other viruses. Compared with COVID, the contour of both lungs is visible, the transparency is acceptable, and the texture is slightly obvious. The proposed MFDNN model is trained and tested on a public dataset (COVID19 chest X-ray dataset). The data set consists of 3616 COVID19 positive cases, 10,192 normal, 6012 lung opaque (non-COVID lung infection) and 1345 viral pneumonia images [24, 25]. The dataset can be downloaded from the website [25]. Before the experiment, we need to have a preliminary understanding of the CXI data set.Fig. 2 Chest X-ray medical images of four different label samples (COVID, Lung_Opacity, Normal, Viral Pneumonia)
Oversampling
The classification of imbalanced data sets is still a problematic point for deep neural network classification. Different methods have been used in the literature to deal with unbalanced data [26–28]. The commonly used method is the resampling technique. In addition, the resampling method includes two methods, namely under-sampling and over-sampling. In oversampling, the minority class samples are copied to balance the size of each class in the training data. In undersampling, some majority class samples are removed during the training process to balance the size of each class. Therefore, when the model is trained on balanced data, it should exhibit unbiased behavior. Different types of undersampling methods have been proposed [26–28]. However, it is reported that random oversampling is the simplest method and exhibits similar performance to other complex methods. Therefore, in this article, we use random oversampling to balance the training process and reduce the bias in building the model.
As shown in Fig. 3a, by comparing the characteristics of the database, the chest radiographs of people infected with COVID19 are significantly less than those of normal people. However, the steps of neural networks and human brain extraction are similar. Therefore, when the probability of memorizing normal chest radiographs of the MFDNN model is greater than remembering the chest radiographs of COVID19 infected persons, the model is more likely to recognize the chest radiographs of normal people, which may lead to COVID19 infection. Therefore, the probability of screening by the user is reduced, and the model cannot be applied to the actual detection process. Therefore, the experiment chooses to oversample to generate new samples for a few categories to ensure that the model has the same probability of remembering different CXI. Figure 3b shows the result of oversampling. The sample data size of the minority class is the same as the sample data size of the majority class.Fig. 3 a The sample size of the original dataset. b The number of samples after oversampling
Multi-channel feature deep neural network
The multi-channel feature deep neural network (MFDNN) algorithm is experimentally designed. The oversampling dataset passes through three identical feature extraction modules. First, the features will be merged in the middle of the frame. Then further feature extraction is performed on the collected image features through the Siamese network, which improves image feature extraction work efficiency. Finally, we will introduce the function of each layer in detail from the feature extraction module.
The image can be seen as a high-dimensional matrix composed of feature vectors. In feature extraction, using a small convolution kernel can reduce the convolution operation's error rate, so the 3 × 3 convolution kernel is selected in the convolution layer, and the step size is 1 for matrix operation. The convolutional layer is defined as:1 Yi,j,kl=WklTXi,jl+bkl
The feature value at the position (i, j) of the kth feature map of the lth layer is Yi,j,kl. Where Wkl is the weight of the lth layer, bkl is the bias of the lth layer, and Xi,jl is the (i, j) unknown input block of the lth layer. Thus, 64 convolution kernels simultaneously perform local perception and share parameters on the input image.
Batch Normalization (BN) is widely used training technique in deep networks. A BN layer whitens activations within a mini-batch of N examples for each channel dimension and transforms the whitened activations using affine parameters γ and β, denoting by χ∈RH×W×N activations in each channel, BN is expressed as [29]:2 BN(X[i,j,n];γ,β)=γ·X^[i,j,n]+β
where3 X^[i,j,n]=X[i,j,n]-μσ2+ε
The mean and variance of activations within a mini-batch,4 μ=∑n∑i,jX[i,j,n]N·H·W
5 σ2=∑n∑i,j(X[i,j,n]-μ2)N·H·W
Select the Rectified Linear Unit (ReLU) function as the convolutional layer's activation function to reduce the probability of model overfitting [30]. The ReLU function will make part of the neuron output 0 to enhance the sparsity of the network. Besides, it reduces the interdependence between parameters and alleviates the problem of overfitting. For the MFDNN model, the ReLU function enables each neuron to exert the greatest screening effect, saving a lot of calculations in the whole process, which is defined as:6 f(x)=xifx≥0αxifx<0.
The maximum pooling method is selected to obtain the maximum value of the feature tiles from the convolutional layer as the output in the pooling layer. The down-sampling convolution kernel size is set to 2 × 2, the stride size is set to 2, and the feature matrix after the pooling operation is filled in a "same" manner to alleviate the excessive sensitivity of the convolution layer to the position. The maximum pooling layer reduces the parameters by reducing the dimension, removing redundant features, simplifying the network's complexity, and other methods to achieve nonlinear feature extraction. The input Xil of the lth layer is mapped to the output Yil through the neuron, which is defined as:7 Yil=maxXil.
The additional layer is the main part of multi-channel feature fusion. The additional layer serves as an intermediate hub for merging the output from the pooling layer, combining feature weights. Assuming the output is, its effect can be expressed as:8 Y=∑i = 1nxil
As the input Y of the global average pooling (GAP) layer, the learned "distribution feature representation" is selectively mapped to the labelled sample space. The activation function of each neuron in the GAP layer generally uses the ReLU function [31]. It can replace the fully connected layer in the traditional structure, thereby reducing the amount of storage required for the large weight matrix of the fully connected layer. Moreover, it also has the features and capabilities of easy fine-tuning of a pre-trained model with a conventional structure. Since the working principle of the fully connected layer involves calculating the inner product of the input vector and the weight of each row, the row size of the weight matrix needs to be the same as the number of input elements [32]. Therefore, as the input changes, we also need to adjust the weight matrix f, W, to a corresponding size by9 Wi,j′=∑l=(j-1)sizefm2+1j×sizefm2Wi,l
where sizefm is the size of the input feature map, i, j is the index of the output neurons and input feature maps, and W′ is the modified weight matrix [32]. Given the computational complexity in the GAP layer, the dropout layer chooses a 40% random probability to discard some feature weights to reduce the model complexity and prevent overfitting. Finally, it is classified by Softmax, and the output of multiple neurons is mapped to the interval of (0, 1), which is defined as:10 Si=ei∑jej
We use x(1),y(1),x(2),y(2),...x(m),y(m) to represent m training samples and y(i) to represent the label of i samples, and train the neural network by using gradient descent. In this article, the cross-entropy function is used to calculate the Loss of the MFDNN model. For a single example, the cross-entropy loss function can be expressed as:11 L(w,b,x(i),y(i))=-∑l=1k1{y(i)=l}logehl(x,w,b)∑lkehl(x,w,b)
where hl(x,w,b) represents the sth neuron in the output layer corresponding to the sth type, 1{.} is the indicator function. The weight parameter is continuously updated through the backpropagation loss function. In order to better integrate upsampling and MFDNN, we propose an MFDNN classification algorithm for detecting COVID19 patients.
Results and discussion
Experimental settings
Before oversampling, the data set is divided into training and test data at a ratio of 0.8:0.2. Then, the over-sampled training data is divided into training set and validation set according to the ratio of 0.8:0.2. Before training the MFDNN model, we choose to flip the data and augment the data with translation to expand the training data to avoid over-fitting the model. The experiment is set to 30 epochs, the batch size is set to 32, and the Adam algorithm is used as the optimizer of the model. The initial learning rate is 0.003, and after each epoch, the learning rate will drop by half. Before each epoch training, the training data and verification data will be randomly shuffled. Each model is trained on a single RTX3060.
Results
In this section, we will explain the evaluation indicators used to quantify model classification. To this end, we use an indicator based on a confusion matrix. These indicators include test accuracy, precision, recall, and F1 Score. To evaluate the model, we need to perform a detailed analysis of each category. Therefore, we need to count true positive (TP), false positive (FP), true negative (TN), and false positive (FN) [33].Test accuracy: the proportion of samples correctly predicted to the total samples12 Accuracy=TP+TNTP+TN+FP+FN
Precision: the ratio of true positive predictions to total positive predictions13 Precision =TPTP+FP
Recall: Ratio of true positive to the total observation made by the proposed model14 Recall=TPTP+FN
F1 Score: It is the harmonic mean of precision and recall15 F1score=2∗precision∗recallprecision+recall
Confusion matrix: It is the measurement of the performance of the model. It compares the actual and predicted values in the form of True Positive, False Negative, True Negative and False Positive16 TPFPFNTN
True Positive (TP): True positive are the forecasts which were at first positive and, additionally, anticipated by the AI model as positive.
False Positive (FP): False positives are the forecasts which were initially negative and anticipated by the AI model as positive.
True Negative (TN): True negatives are the forecasts which were initially negative and anticipated by the AI model as unfavourable.
False Negative (FN): False-negative are the forecasts which were initially positives and anticipated by the model as negative
The experiment first trained five models under the algorithm of MFDNN, namely Densenet201, ResNet50, VGG19, GoogLeNet, and MFDNN. Among them, the accuracy of the MFDNN model is 93.19%. Among them, the COVID category received a Recall of 0.9447 and an F1 score of 0.9358; the Lung_Opacity category received a precision of 0.9144 and an F1 score of 0.9106; the Normal class received a recall of 0.9431 and an F1 score of 0.9389; the Viral Pneumonia category received an F1 score of 0.9504. Table 1 details the test reports of each type of chest radiograph under different models. From the classic deep learning model analysis, for the COVID19 data set, the deeper the network layer, the worse the effect of the model. For example, the test results of the Densenet201 model only get good prediction results in a few categories. GoogLeNet obtains the best results in the classic deep learning network, but compared to the MFDNN model, the traditional deep learning model does not achieve the best test results.Table 1 Densenet201, ResNet50, VGG19, GoogLeNet, MFDNN classification technical report
Densenet201 ResNet50 VGG19 GoogLeNet MFDNN
COVID Precision 0.9272 0.9473 0.9532 0.9572 0.9369
Recall 0.8105 0.87 0.7331 0.8976 0.9447
F1 score 0.8649 0.907 0.8288 0.9264 0.9358
Lung_Opacity Precision 0.7198 0.801 0.7968 0.854 0.9144
Recall 0.9359 0.9143 0.8611 0.9002 0.9068
F1 score 0.8137 0.8539 0.8277 0.8765 0.9106
Normal Precision 0.941 0.9302 0.8627 0.9266 0.9348
Recall 0.8057 0.89 0.9001 0.923 0.9431
F1 score 0.8681 0.9097 0.881 0.9248 0.9389
Viral pneumonia Precision 0.9007 0.9675 0.956 0.9611 0.9257
Recall 0.9777 0.8847 0.8885 0.9182 0.9765
F1 score 0.9376 0.9242 0.921 0.9392 0.9504
Test accuracy 0.8544 0.8932 0.8599 0.9113 0.9319
Bold values indicate that the metric is optimal for that row
Secondly, Fig. 4 describes the confusion matrix of each model prediction test set. This can give us a rough idea of how all images are classified and where most misclassifications occur. It can be seen from the figure that the probability of the prediction error of the Normal class is greater than the probability of the prediction error of the other classes. This shows that the up-sampling method embedded in the algorithm has a positive effect. It makes the model not biased to ignore infected patients during the detection process. When the MFDNN model discriminates four types of samples, the misjudgment rate is relatively low.Fig. 4 Confusion matrix of Densenet201, ResNet50, VGG19, GoogLeNet, MFDNN model
Ablation experiment
To prove the necessity of steps 6 and 7 in the MFDNN classification model, we designed an ablation experiment to explore the accuracy of multi-channel feature fusion combined with PSN. The first step is to remove the PSN and additional layers, generate a simple CNN, train the model, and record the evaluation index value of the model. The second step is to remove the additional layer, keep the remaining part of the MFDNN model and name it DNN. Again, train the model and record the evaluation index value of the model. The third step is to use only the feature fusion method to generate a multi-channel feature convolutional neural network (MFCNN), train the model and record the evaluation index value of the model. Finally, the fourth step compares all the model detection results with the MFDNN model classification results.
The impact of additional layers
In order to verify the influence of the additional layer, we compared the performance of the three models of CNN, DNN, and MFDNN. As shown in Table 2, we analyzed the performance of CNN and DNN models from accuracy, recall, F1 score and test accuracy. In most indicators of all categories, the evaluation indicators of the MFDNN model are higher than other models. In terms of test accuracy, the MFDNN model is 3.33% higher than the DNN model.Table 2 CNN, DNN, MFDNN classification technical report
CNN DNN MFDNN
COVID Precision 0.7538 0.769 0.9369
Recall 0.9732 0.984 0.9447
F1 score 0.8496 0.8633 0.9358
Lung_Opacity Precision 0.8877 0.886 0.9144
Recall 0.8189 0.868 0.9068
F1 score 0.8519 0.8769 0.9106
Normal Precision 0.9367 0.9603 0.9348
Recall 0.8805 0.8851 0.9431
F1 score 0.9077 0.9211 0.9389
Viral pneumonia Precision 0.7472 0.8364 0.9257
Recall 1 0.9825 0.9765
F1 score 0.8553 0.9036 0.9504
Test accuracy 0.875 0.8986 0.9319
Bold values indicate that the metric is optimal for that row
Secondly, we have a detailed understanding of the classification effect of the model in different categories through the confusion matrix. By comparing the confusion matrix analysis of the CNN and DNN models in Fig. 5 with the MFDNN model in Fig. 4, we find that the MDNN model has smaller errors than the CNN and DNN models in detecting all categories. Therefore, the additional layer can capture a wider range of information from the image, thereby significantly improving the performance of the model.Fig. 5 Confusion matrix of CNN, DNN model
Role of PSN
According to the data analysis in Table 3, we found that MFCNN and MFDNN models have a large gap in accuracy, recall and F1 score. The test accuracy of the MFDNN model is 0.0333 higher than that of the MFCNN model. Compared with the confusion matrix of the MFCNN model and the MFDNN model in Fig. 6, we find that the MFCNN model has greater errors than MFDNN in detecting all categories. Because the MFCNN model does not include the PSN module, the MFDNN model includes the PSN module. Therefore, we conclude that the function of PSN is to perform secondary feature extraction based on the extracted features, so that the features can be used more effectively and the accuracy of the model can be improved.Table 3 MFCNN, MFDNN classification technical report
MFCNN MFDNN
COVID Precision 0.7911 0.9369
Recall 0.9811 0.9447
F1 score 0.8759 0.9358
Lung_Opacity Precision 0.8977 0.9144
Recall 0.8536 0.9068
F1 score 0.8751 0.9106
Normal Precision 0.9465 0.9348
Recall 0.8972 0.9431
F1 score 0.9212 0.9389
Viral pneumonia Precision 0.8513 0.9257
Recall 0.9745 0.9765
F1 score 0.9087 0.9504
Test accuracy 0.9 0.9319
Fig. 6 Confusion matrix of MFCNN, MFDNN model
Ablation experiment
This paper proposes an MFDNN algorithm to screen people infected with COVID19. The algorithm integrates data oversampling technology and a MFDNN model to carry out the training. In the experiment, we used the publicly available CXI database to train the model. First, compared with traditional deep learning models (VGG19, GoogLeNet, ResNet50, Densenet201), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Through ablation experiments, we proved that multi-channel CNN is superior to single-channel CNN, additional layer and PSN module, and indirectly proved the sufficiency and necessity of each step of the MFDNN classification method. Secondly, comparing the latest CoroDet model, the MFDNN algorithm is 1.91% higher than the CoroDet model in the four-classification experiment of COVID19 infected persons. However, the limitation of this experiment is mainly in the disadvantages of X-rays. For opaque images of the lungs, RT-PCR is needed to assist in the screening of COVID19 infections.
Acknowledgements
This work was supported by National Key R&D Program of China 2017YFB0202602, 2018YFC0910405, 2017YFC1311003, 2016YFC1302500, 2016YFB0200400, 2017YFB0202104; NSFC Grants U19A2067, 61772543, U1435222, 61625202, 61272056; Science Foundation for Distinguished Young Scholars of Hunan Province (2020JJ2009); Science Foundation of Changsha kq2004010; JZ20195242029, JH20199142034, Z202069420652; The Funds of Peng Cheng Lab, State Key Laboratory of Chemo/Biosensing and Chemometrics; the Fundamental Research Funds for the Central Universities, and Guangdong Provincial Department of Science and Technology under Grant No. 2016B090918122.
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PMC009xxxxxx/PMC9004215.txt |
==== Front
Psychiatr Q
Psychiatr Q
The Psychiatric Quarterly
0033-2720
1573-6709
Springer US New York
35412100
9983
10.1007/s11126-022-09983-6
Original Paper
Study of Impact of Telehealth Use on Clinic “No Show” Rates at an Academic Practice
http://orcid.org/0000-0003-0422-3263
Muppavarapu Kalyan muppavarapuk17@ecu.edu
1Kalyan Muppavarapu, MD, MPH
I am a Clinical Assistant Professor in the Department of Psychiatry at East Carolina University (ECU). I am board certified in Psychiatry and Sleep Medicine by American Board of Psychiatry and Neurology (ABPN).
I am the Medical Director for North Carolina Statewide Telepsychiatry Program (NC-STeP) which provides Psychiatry services to Emergency Departments and community sites across the state of North Carolina.
Saeed Sy A 1
Jones Katherine 2
Hurd Olivia 1
Haley Vickie 1
1 grid.255364.3 0000 0001 2191 0423 Department of Psychiatry and Behavioral Medicine, Brody School of Medicine, East Carolina University, East Carolina, USA
2 grid.255364.3 0000 0001 2191 0423 Department of Public Health, East Carolina University, East Carolina, USA
12 4 2022
2022
93 2 689699
2 4 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, 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.
Objective
To examine the clinic no-show rate across different modalities of care delivery (Face to Face, Telephone visits and Audio–Video visits).
Methods
Clinic no show data for adult patients was extracted from the electronic health records used by the psychiatry clinic for 10 months before pandemic and 10 months during pandemic. No show rate was analyzed by visits type (new vs return) and across different modalities (face-to-face vs Telephone vs Audio–Video) before and during COVID pandemic.
Results
There were 13,916 scheduled visits during the 10-month period before the pandemic of which 2,522 were no show. There were 13,251 scheduled visits during the 10-month period during the COVID pandemic of which 2,029 were no show. The overall clinic no show rate decreased from pre pandemic to pandemic period (18.1% vs 15.3%) after transitioning to telehealth. Across different modalities during the pandemic, the no-show rate for Telephone visits was significantly lower than for face- to-face visits. No difference was identified for no-show rates between face-to-face visits and audio–video visits during the pandemic. The no-show rate for face-to-face visits before the pandemic compared to during the pandemic also showed no difference.
Conclusion
Using technology in health care delivery can decrease the clinic no show rate. Digital literacy for patients and providers is critical for successful utilization of telehealth.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11126-022-09983-6.
issue-copyright-statement© Springer Science+Business Media, LLC, part of Springer Nature 2022
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pmcIntroduction
Psychiatry clinics, especially those associated with academic medical institutions, have long struggled with higher no-show rates [1, 2]. No-show attendance rates for outpatient psychiatry clinic are reported by various studies to differ anywhere from 2–30% [3]. Furthermore, the no-show rate for initial psychiatric evaluations is twice that of most other specialties [2].Gajwani reported a no-show rate of 31% at University of Texas Health Sciences Center at Houston, an academic institution [1]. Patients who no-show to initial psychiatric appointments have been found to have more frequent hospitalization and emergency department visits. [2, 4] In addition to the patients not being treated due to missed appointments, there is concern for the large financial burden placed on the clinics with monetary loss associated with no-show rates. At the Michael E DeBakey VA Medical Center in Houston, Texas, the average cost of a no-show patient across multiple departments (Audiology, Cardiology, Dermatology, Eye Care, Gastroenterology, Mental Health, Orthopedics, Podiatry, Primary Care, and Urology) was $196 in 2008 [5]. At the University of Missouri outpatient psychiatry clinic, the no-shows represented between $11–19 million in lost revenue [6]. Furthermore, high no-show rates create increased burden on clinicians and administrative staff in the form of contacting and rescheduling patients [7]. No show appointments not only waste clinic resources, but they also prolong the waiting time for patients wanting to see a provider. Clinical capacity and workflow are not optimally utilized during no show appointments as time and clinic resources are wasted whereby providers could be seeing other patients [8]. This prolongs the waiting time for patients waiting to see a provider to establish care and delays treatment for patients who are already established/seeking care [9].
Due to the financial burden of missed appointments and the wasted time of providers and resources, several studies have investigated reasons for missed appointments. Common attributes of patients who no-show to their appointments include lower socioeconomic status, younger age, ethnic minorities, prior history of no-show, living farther from outpatient clinics, poorly insured, and the less educated [7, 10]. The University of Missouri’s data revealed that the scheduler who made the appointments affected one-third of the probability of whether the patient would show. This was based off whether the scheduler followed protocol assigning appointments vs asking the patients’ time preference [6]. Common reasons reported for missed appointments by patients include patient illness, oversleeping, inability to get off work/scheduling conflict, transportation issues, lost appointment card, believing the appointment was on a different day or time, lack of understanding of the scheduling system, and motivational issues/avoidance of treatment [7, 10]. Forgetfulness as a factor of missed appointments is a response heard twice as often in psychiatry outpatient clinics than that other specialties [1]. Multiple studies have reported that factors affecting missed initial appointments include male sex, younger age, lower socioeconomic status, comorbid substance abuse disorders, poor family support, poor adherence to psychotropic drugs, lack of or limited health care insurance, poor social functioning, unemployment, longer periods from contact to appointment, higher numbers of previous hospital admissions, and shorter hospital stays. [2, 4], Common practices that have been shown to reduce no-show rates include collaborating with referral sources for new patient evaluations, reducing clinic wait times, making reminder calls, using behavioral engagement strategies, and creating a welcoming clinic environment [8]. Parikh et al. studied patient no-show rates among patients receiving a clinic staff reminder, automated appointment reminder or no reminder, with results indicating no show rates lower in that respective order. Staff reminder calls are most commonly employed and in general health settings reduced no-show rates from 23.1% to 13.6% [8]. Ultimately, reducing no-show rates leads to improvement for patients, physicians, and clinic workflow.
The current COVID-19 pandemic has presented challenges to delivering quality health care to both new and established patients. There was special concern for delivering continuity of care to those with mental illness, as they may be more susceptible to decompensation from fear of acquiring the virus and the isolation of quarantine and social distancing. [11, 12].
The field of psychiatry has pioneered the way for telemedicine, allowing easy implementation of telepsychiatry into current practice [13–15]. This transition was made more feasible with the lifting of some prior telehealth restrictions. The Coronavirus Preparedness and Response Supplemental Appropriations Act was signed into law on March 6, 2020 [16]. This act waived telehealth reimbursement restrictions for geographic and originating site restrictions. The Health and Human Services Office of Civil Rights has additionally announced that it would waive HIPPA penalties for using non-HIPAA compliant video conferencing software during video conferencing appointments. These include popular platforms such as Skype, FaceTime, etc. [17].
It is largely unknown how implementation of telepsychiatry visits during the COVID-19 era impacted clinic workflow. This study examined the no show rate in a large academic outpatient psychiatry clinic in eastern North Carolina before and during COVID-19. The clinic serves a multi-county catchment area. Given the widespread availability of virtual platforms to the public, it was anticipated that the no-show rate would drop, and clinic workflow would improve in the outpatient psychiatry clinic.
Methods
This study was conducted at the ambulatory psychiatry clinic at the East Carolina University which is a large academic outpatient psychiatry practice in eastern North Carolina. Like other institutions across the country, this clinic transitioned from face-to-face vits to telehealth including both telephone visits and audio–video visits during COVID pandemic. The clinic used Zoom for Healthcare (HIPPA compliant) as the virtual encounter platform. The patients were also given the option of continuing to come in person for visits. Data about clinic appointments for adult patients (age 18 and above) was collected from Electronic Medical Record system for 20 months, from May 2019 to December 2020. We compared the clinic no show rate for the 10-month pre-Covid period (from May 2019 to Feb 2020) to 10 months during Covid pandemic (March 2020 to Dec 2020). During pre-Covid period all the appointments were face to face. During the Covid period telephone visits, audio–video visits and face to face visits were included.
We defined clinic no show rate as the total number of patients who did not attend the appointment divided by the total number of patients scheduled. We did not include cancelled appointments or rescheduled appointments in calculating the no show rate. Data was analyzed by using SAS software (SAS 9.4, Cary, NC). We compared no show rates by appointment type (New vs Return visit) as well as by modality (Telephone vs Audio–Video vs Face to Face visit). This study was considered exempt from institutional review board approval.
Results
During the 10-month pre-COVID-19 period there were 13,916 scheduled appointments, of which 2,522 were no-show. During the 10-month COVID-19 period, there were 13,251 scheduled appointments, of which 2,029 were no-show (Table 1 and Fig. 1). The overall no-show rate was 18.1% for the pre-COVID-19 period and it dropped to 15.3% for the COVID-19 period, which was a significant difference (p < 0.0001). For new patients, the no-show rate dropped from 29.8% to 28% (p = 0.2696), and for returning patients it dropped from 16.4% to 13.9% (p < 0.0001).Table 1 Percent of Appointments Not Kept Before and During COVID-19
Before COVID-19 During COVID-19
Total Appointments Number of Appointments Not Kept Percent Not Kept Total Appointments Number of Appointments Not Kept Percent Not Kept X2 p
All Adult Appointments 13,916 2,522 18.1 13,251 2,029 15.3 38.4587 < .0001**
New Adult Patients 1,774 529 29.8 1,318 369 28 1.219 0.2696
Return Adult Patients 12,142 1,993 16.4 11,933 1,660 13.9 29.2963 < .0001**
* significant at the .05 level
**significant at the .01 level
Fig. 1 Percent of Appointments Not Kept Before and During COVID
Prior to the pandemic, all appointments were face-to-face. During the COVID-19 period, some appointments were face-to-face, but phone and virtual appointments were also introduced. We compared the no-show rates for face-to-face appointments before and during the pandemic to determine if there were pandemic-related differences across similar appointment types. We also compared the no-show rate during the pandemic for new and return appointments delivered by different modes (phone, virtual) to look for mode-specific differences.
Table 2 and Fig. 2 show the no-show rates for face-to-face appointments for new and return adult visits both before and during COVID-19. No show rate for face-to-face new visits before Covid was 29.8% and during Covid 28.2% (p = 0.3539). No show rate for face-to-face return visits before Covid was 16.4% and during Covid 16% (p = 0.4427). There were no significant differences in no-show rates for similar visits delivered by face-to-face across the two periods (pre-COVID-19 and during COVID-19).Table 2 Percent of Face to Face Appointments Not Kept Before and During COVID-19 by New and Return Appointment Type
Before COVID-19 During COVID-19
Total Appointments Number of Appointments Not Kept Percent Not Kept Total Appointments Number of Appointments Not Kept Percent Not Kept X2 p
New Adult Face to Face Appointments 1,774 529 29.8 1,148 324 28.2 0.8595 0.3539
Return Adult Face to Face Appointments 12,142 1,993 16.4 6,186 988 16 0.5893 0.4427
* significant at the .05 level
**significant at the .01 level
Fig. 2 Percent of Face to Face Appointments Not Kept Before and During COVI-19 by New and Return
Comparing across modes during COVID-19 period, the no-show rate for phone mode were significantly lower than face-to -face mode for both new visits (p = 0.0208) and return visits (p < 0.0001) (Table 3a, Fig. 3). No-show rates for virtual mode were no better than for face-to-face mode for new visits (p = 0.6812) and return visits (p = 0.186) (Table 3b, Fig. 3).Table 3 a and b Percent of Face to Face and Phone Appointments Not Kept During COVID-19 by New and Return
Face to Face Phone
Total Appointments Number of Appointments Not Kept Percent Not Kept Total Appointments Number of Appointments Not Kept Percent Not Kept X2 p
New Adult Patients 1,148 324 28.2 26 2 7.7 5.34 0.0208*
Return Adult Patients 6,186 988 15.9 3,362 263 7.8 127.04 < .0001**
b
Face to Face Virtual
Total Appointments Number of Appointments Not Kept Percent Not Kept Total Appointments Number of Appointments Not Kept Percent Not Kept X2 p
New Adult Patients 1,148 324 28.2 144 43 29.8 0.1688 0.6812
Return Adult Patients 6,186 988 15.9 2,385 409 17.1 1.748 0.186
* significant at the .05 level
**significant at the .01 level
Fig. 3 Percent of Appointments Not Kept During COVID by Mode and New or Return. p values are based on comparison to face to face appointments. * significant at the .05 level. **significant at the .01 level
Discussion
This study demonstrates that in the ambulatory psychiatry clinic at ECU, a large academic outpatient psychiatry practice in eastern North Carolina, the overall no show rate decreased after transitioning to telehealth during COVID pandemic. Even though COVID pandemic created numerous challenges for the society at large, it also created a necessity to adapt quickly to the innovative ways of delivering health care. Telephone and virtual delivery of care made it possible to bridge the physical distancing while maintaining the social distancing and provided opportunity to compare no show rates across different modes.
Psychiatry is well-suited for telehealth and empowers the providers to reach the patients in rural and underserved areas. Before the pandemic, many practices in the country were not using telepsychiatry to reach patients at their homes due to multitude of factors and one of the primary concerns was insurance reimbursement and requirement for being at an originating site [13, 21, 22]. On March 17, 2020, CMS released guidance allowing patients to be seen at home via live video conference without having to be at an originating site for virtual encounters [14, 15, 23]. Then on March 31, 2020, CMS temporarily waived requirements for out of state practitioners to be licensed in the state where they were providing services [14]. The DEA has also suspended the requirement of the Ryan Haight Act that requires providers conduct in person initial evaluations prior to prescribing a controlled substance [24]. DEA licensure in the state where the patient is located to electronically prescribe a controlled substance has also been waived [14, 16]. These provisions have allowed the field of psychiatry to evolve with the COVID-19 pandemic more rapidly and transition from in person to virtual encounters. Initial reports revealed that implementation of telepsychiatry improved outpatient attendance for healthcare delivery [12, 18]. Massachusetts General Hospital-based outpatient psychiatric practice reported a 22% increase in productivity and 20% decrease in no-show rates from pre-COVID period to COVID period [19]. Patient no-show rates for Boston Medical Center psychiatry department dropped to 15% from around 45% [20].
In this study the no show rate for New visits did not differ before and during COVID. No show rate for Return visits was significantly lower during the COVID than before COVID primarily because of using telephone visits. No show rate by telephone mode was significantly lower than other modes (Face to face and Virtual) for new visits (7.7%) and return visits (7.8%). Patients and providers comfort level with using telephone probably contributed to this finding. There were few New visits by Telephone mode than Return visits during COVID. No show rate for virtual mode is no better than Face to Face mode during the COVID (17.1% vs 16%). Low Socioeconomic status and lack of clear policy on what virtual platform to use during the swift transition to telehealth could be possible contributors to the differences in no show rates for telephone and virtual modes. Ease of use with telephone appears to be a driver for the very low no show rate for telephone visits when compared to audio video visits. Another potential contributor is the complexities associated with accessing virtual platforms. Even though virtual platforms have been around for a while, ability to use these platforms may be a limiting factor as it requires technical knowledge and needs some time to learn. This learning curve for the patients and the providers may be one of the factors contributing to increased no show rate for virtual visits when compared to telephone visits. Very low no show rate for telephone visits speaks to the fact that if the patients and providers are comfortable with using the technology, no show rate for virtual visits will decrease. But it can also be impacted by other external factors such as availability of technology to the patients in remote areas including computers and reliable internet, availability of IT support to the clinic as well as patient navigators to educate and guide patients about using the virtual platform that the clinic is using. In addition to these, appropriate policy modifications to support using telehealth can play a significant role in decreasing clinic no show rate.
Limitations
Specific reasons for clinic no shows were not obtained in this study. Data was collected as aggregate no show rate for each modality of care delivery and individual level data was not available to identify the change in clinical symptoms and longitudinal outcomes. The periods compared were not identical times of the year which could have caused a skew in results due to seasonality potentially playing a role in no-show rates. There was no way to determine if decrease in no show rate after transitioning to telehealth during COVID pandemic translated into improved outcomes for the patients. This study took place in an academic psychiatry clinic which may limit generalizability to other specialties and community settings.
Conclusion
Telehealth can decrease the clinic no show rate and has the potential to make a positive impact on the provider and staff time, clinic revenue, patient outcomes and the overall health care system. It is important to understand the learning curve involved for both patients and providers. Successful implementation requires educating patients and providers in using technology, staff and technical support and polices conducive to easy access and widespread utilization of telehealth.
Next Steps
Future studies looking into the reasons for clinic no shows by different modalities of care delivery can help to understand the drivers for no show rate for virtual care. Prospective studies focusing on weather decreased no show rate can translate into improved patient outcomes for telehealth are imperative. It would be helpful to have future studies to evaluate the impact of demographic and socioeconomic factors on telehealth utilization as well as no shows in variety of clinical settings and age groups.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 12 KB)
Funding
No funding was received for conducting this study. The authors have no relevant financial or non-financial interests to disclose.
Declarations
Ethical Approval
This research study was conducted retrospectively from data obtained for administrative purposes. We consulted extensively with the IRB of East Carolina University who determined that our study did not need ethical approval. An IRB official waiver of approval was granted from the IRB of East Carolina University
Informed Consent
Informed consent is not required
Conflict of Interest
The authors declare that they have no conflict of interest.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References
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4. Cheng K Huang C Tsang H Lin C Factors related to missed first appointments after discharge among patients with schizophrenia in Taiwan J Formos Med Assoc 2014 113 7 436 441 10.1016/j.jfma.2012.09.016 24961185
5. Kheirkhah P, Feng Q, Travis LM, Tavakoli-Tabasi S, Sharafkhaneh A. Prevalence, predictors and economic consequences of no-shows. BMC Health Serv Res. 2015;16(1). 10.1186/s12913-015-1243-z.
6. Butterfield S. Research reveals reasons underlying patient no-shows. 2009. Retrieved November 22, 2020, from https://acpinternist.org/archives/2009/02/no-shows.htm.
7. Defife JA, Conklin CZ, Smith JM, Poole J. Psychotherapy appointment no-shows: Rates and reasons. Psychotherapy: Theory Res Prac Train. 2010;47(3):413–417. 10.1037/a0021168.
8. Molfenter T Reducing Appointment No-Shows: Going from Theory to Practice Subst Use Misuse 2013 48 9 743 749 10.3109/10826084.2013.787098 23607670
9. Cullen BA Altering the attendance rate successfully for new patients at an outpatient mental health clinic Psychiatr Serv 2018 69 12 1212 1214 10.1176/appi.ps.201800161 30152272
10. Mehra A Hoogendoorn CJ Haggerty G Engelthaler J Gooden S Joseph M Guiney PA Reducing Patient No-Shows: An Initiative at an Integrated Care Teaching Health Center The Journal of the American Osteopathic Association 2018 118 2 77 10.7556/jaoa.2018.022 29379973
11. Stoll J Sadler JZ Trachsel M The Ethical Use of Telepsychiatry in the Covid-19 Pandemic Front Psych 2020 10.3389/fpsyt.2020.00665
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15. Deslich S, Stec B, Tomblin S, Coustasse A. Telepsychiatry in the 21st Century: Transforming Healthcare with Technology. Perspec Health Inform Manag. 2013;10.
16. American Psychiatric Association. (Updated 5/1) Telepsychiatry and COVID-19. Retrieved August 20, 2020, from https://www.psychiatry.org/psychiatrists/practice/telepsychiatry/blog/apa-resources-on-telepsychiatry-and-covid-19.
17. HHS. OCR Issues Guidance on Telehealth Remote Communications Following Its Notification of Enforcement Discretion. U.S. Dep Health Human Serv. 2020. Retrieved November 10, 2021, from https://public3.pagefreezer.com/content/HHS.gov/31-12-2020T08:51/https://www.hhs.gov/about/news/2020/03/20/ocr-issues-guidance-on-telehealth-remote-communications-following-its-notification-of-enforcement-discretion.html.
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DGNeurologie
DGNeurologie
2524-3446
2524-3454
Springer Medizin Heidelberg
429
10.1007/s42451-022-00429-8
Leitlinie
Update zu: Neurologische Manifestationen bei COVID-19
Gekürzte aktualisierte Leitlinie der Deutschen Gesellschaft für Neurologie
Update on: neurological manifestations in COVID-19Abridged version of the updated German Neurological Society guideline
Berlit Peter berlit@dgn.org
Deutsche Gesellschaft für Neurologie (DGN), Berlin, Deutschland
12 4 2022
2022
5 3 197209
8 3 2022
© DGN 2022
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
issue-copyright-statement© Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2022
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pmcWas gibt es Neues?
Nach durchgemachter COVID-19 kann es zu persistierenden neurologischen, insbesondere neurokognitiven, Symptomen kommen. Bei einem Zeitraum von mehr als 3 Monaten nach der Akutinfektion wird von einem Post-COVID-Syndrom gesprochen.
Eine vorbestehende neurologische Erkrankung ist nach aktuellem Wissensstand keine Kontraindikation gegen eine SARS-CoV-2-Impfung.
Eine SARS-CoV-2-Impfung ist generell auch unter laufender Immuntherapie sinnvoll und sicher, wobei die Impfantwort insbesondere unter breit wirksamen Immunsuppressiva sowie B‑Zell-depletierenden Therapien und S1P-Modulatoren verringert sei kann. Entsprechende Impfstrategien wurden in der aktuellen Leitlinienfassung ergänzt.
In zeitlichem Zusammenhang mit der Impfung gegen COVID‑19 wurden verschiedene neuromuskuläre Manifestationen wie Hirnnervenaffektionen, Plexopathien, Polyneuritiden und Myopathien beschrieben. Nach der SARS-CoV-2-Impfung mit Vektorimpfstoffen kann es zu einer vakzininduzierten immunologischen thrombotischen Thrombozytopenie (VITT) mit zerebralen Hirnvenen- und Sinusthrombosen kommen.
Neuroimmunologie
Auf Grundlage der bisherigen Fallberichte zum COVID-19-Verlauf unter einer Immuntherapie ist nicht von einem generell erhöhten Infektionsrisiko bzw. einer erhöhten Mortalität auszugehen. Nur für die monoklonalen Antikörper gegen das CD20-Antigen (Rituximab und Ocrelizumab) liegen Berichte vor, die ein erhöhtes Infektions- und Mortalitätsrisiko ergaben.
Ein höheres Alter, der Grad der Behinderung sowie Übergewicht scheinen prognostisch ungünstig für den COVID-19-Verlauf bei MS-Patienten zu sein.
Enzephalopathie
Enzephalopathien zeigen eine klare Assoziation mit höherer Morbidität und Mortalität.
Eine belastbare Grundlage für spezifische Therapiemaßnahmen existiert bislang nicht. Immunmodulatorische Ansätze (Steroide, IVIG, Plasmapherese, Antikörper) nehmen zu.
Enzephalitis
Histologisch ist der Nachweis von SARS-CoV‑2 in verschiedenen Hirnregionen beschrieben. Die Zeichen der Neuroinflammation korrelieren jedoch nicht mit dem Virusbefall, sodass vermutlich der lokal schädigende Einfluss des Virus keine entscheidende Rolle spielt.
Eine akute Enzephalitis kann differenzialdiagnostisch Ausdruck einer Autoimmunenzephalitis sein, auch para- oder postinfektiös infolge einer Infektion mit SARS-CoV‑2 auftreten.
Eine Virusenzephalitis durch SARS-CoV‑2 mit Virusnachweis im Liquor scheint sehr selten zu sein.
Zerebrovaskuläre Erkrankungen
Eine Infektion mit SARS-CoV‑2 geht mit einem erhöhten Risiko für ischämische Schlaganfälle einher. Bei Patienten mit COVID liegt die Inzidenz bei 1,1–1,6 %. Das Risiko ist insbesondere in den ersten Wochen nach der Infektion erhöht.
Patienten mit typischem kardiovaskulärem Risikoprofil haben ein erhöhtes Risiko, bei SARS-CoV-2-Infektion einen Schlaganfall zu erleiden. Gleichzeitig gibt es vereinzelt auch kryptogene Schlaganfälle bei jüngeren COVID-19-Patienten ohne relevantes kardiovaskuläres Risikoprofil mit einer Häufung großer Gefäßverschlüsse.
Eine Assoziation zwischen COVID und Schlaganfall kann über eine immunologisch vermittelte Aktivierung des Gerinnungssystems, aber auch über vaskuläre Komplikationen als Ausdruck schwerer sonstiger Organschäden vermittelt sein.
Patienten mit zerebrovaskulären Erkrankungen in der Anamnese haben ein höheres Risiko für einen schwereren Verlauf der Erkrankung COVID-19.
Bei der vakzininduzierten immunologischen thrombotischen Thrombozytopenie (VITT) treten zerebrale Sinus- und Hirnvenenthrombosen (SHVT) als seltene Komplikation der SARS-CoV-2-Impfung mit Vektorimpfstoffen auf.
Epilepsie
Das Neuauftreten einer Epilepsie oder von akut symptomatischen Anfällen unter einer SARS-CoV-2-Infektion ist möglich, aber selten; sie sind keine typischen Komplikationen einer SARS-CoV-2-Infektion.
Eine bestehende Epilepsie ist keine Kontraindikation gegen eine Impfung, welche unter Berücksichtigung möglicher individueller Merkmale (z. B. Allergien) im Allgemeinen zu empfehlen ist.
Bei der vakzininduzierten immunologischen thrombotischen Thrombozytopenie mit zerebraler Sinus- und Hirnvenenthrombose als seltener Komplikation nach SARS-CoV-2-Impfung können akute symptomatische Anfälle, auch als Erstsymptom, auftreten.
Nerven- und Muskelaffektionen
Eine präexistente neuromuskuläre Erkrankung ist kein grundsätzlicher Risikofaktor für eine erhöhte Morbidität und Mortalität unter einer SARS-CoV-2-Infektion.
Das Post-COVID-19-GBS spricht in der Regel auf die Standardtherapie mit i.v. Immunglobulinen sowie Plasmaaustauschverfahren an.
Die Verschlechterung einer Myasthenia gravis mit erhöhter Mortalität ist möglich.
Eine SARS-CoV-2-Persistenz nach COVID-19 konnte in autoptischer Skelettmuskulatur nicht nachgewiesen werden.
In zeitlichem Zusammenhang mit der Impfung gegen COVID‑19 sind verschiedene neuromuskuläre Manifestationen wie Hirnnervenaffektionen, Plexopathien, Polyneuritiden, Myositiden und Rhabdomyolysen beschrieben.
Störungen der Chemosensorik
Riechstörungen bilden sich nach COVID-19 bei mehr als 85 % der Patienten binnen 6 Monaten weitgehend zurück.
Eine Anosmie ist eher durch Infektion von Stützzellen im Riechepithel und lokale Entzündungsreaktion bedingt als durch Infektion (und Destruktion) der olfaktorischen Neuronen.
Neurologische Intensivmedizin
Die häufigsten neurologischen Manifestationen des Intensivpatienten sind Enzephalopathien, Koma, Neuropathien und Schlaganfälle.
Intrazerebrale Blutungen scheinen mit therapeutischer Antikoagulation und ECMO assoziiert zu sein.
Neurologische Manifestationen bei Long- und Post-COVID-19-Syndrom
Die Definition des Post-COVID-19-Syndroms erfolgt aktuell anhand zeitlicher Kriterien und kann Patienten unabhängig vom Schweregrad der Akutinfektion betreffen.
Die genauen pathophysiologischen Mechanismen des Post-COVID-19-Syndroms sind bislang noch unbekannt. Diskutiert werden neurotransmittervermittelte Veränderungen, eine postinfektiös fortbestehende Entzündung sowie (virusgetriggerte) immunvermittelte Mechanismen.
Unterschieden werden müssen Symptome, deren Auftreten gehäuft nach SARS-CoV-2-Infektion beschrieben ist, von bekannten neurologischen Krankheitsbildern, die nach COVID-19 auftreten können.
Konzentrations- und Gedächtnisstörungen, Fatigue, Kopfschmerzen, Myalgien und Neuropathien werden von Patienten auch noch 3 Monate nach der akuten SARS-CoV-2-Infektion beschrieben. Eine umfassende Diagnostik ist anzustreben.
Es existiert aktuell keine kausale Therapie. Bestehen Hinweise auf einen autoimmunologischen Erkrankungsmechanismus, kann immunmodulatorisch behandelt werden.
Aufgrund der Vielzahl der Symptome, die im Rahmen eines Post-COVID-19-Syndroms auftreten können, sind eine interdisziplinäre Behandlung und weitere Versorgung der Patienten anzustreben.
Neurorehabilitation bei Long- und Post-COVID-19-Syndrom
Bei verbleibenden relevanten Schädigungen des peripheren und/oder zentralen Nervensystems nach einer COVID-19-Akutbehandlung sollen eine neurologisch-neurochirurgische Frührehabilitation oder Anschlussrehabilitation durchgeführt werden, diese schließen fallbezogen auch eine prolongierte Beatmungsentwöhnung (Weaning) ein.
Zur Behandlung von neurologischen Post‑/Long-COVID-bedingten Einschränkungen leichterer Ausprägung sollen nach der fachärztlich diagnostischen Abklärung primär Heilmittel verordnet werden, um im Rahmen der ambulanten Versorgung die eingeschränkten Körperfunktionen wiederherzustellen und Aktivitätslimitierungen und resultierenden Teilhabeeinschränkungen in Familie, Beruf und Gesellschaft entgegenzuwirken.
Eine teilstationäre (ganztägig ambulante) oder stationäre Neurorehabilitation sollte für Post-COVID-19-Betroffene verordnet werden, wenn nach COVID-19 krankheitsbedingt nicht nur vorübergehende neurologisch bedingte Beeinträchtigungen der Teilhabe am Leben in der Gemeinschaft bestehen oder drohen, die der multimodalen fachärztlichen und therapeutischen Behandlung bedürfen, wenn also ambulante Heilmittel für die Behandlung nicht ausreichen.
COVID-19-Impfungen
Abgesehen von möglichen unspezifischen Impfreaktionen in den ersten beiden Tagen gelten COVID-19-Impfungen als nebenwirkungsarm.
Die mRNA- und Vektorimpfstoffe gewähren einen hohen Schutz gegen eine Infektion mit SARS-CoV‑2. Eine Impfung ist ab einem Alter von 5 Jahren zugelassen und soll bei allen Menschen ab einem Alter von 12 Jahren, einschließlich Schwangerer und Stillender, erfolgen. Dabei sind die aktuellen Empfehlungen der STIKO zu beachten.
Patienten mit einer ausgeprägten Immundefizienz oder unter einer immunsuppressiven Therapie insbesondere mit Anti-CD-20-Antikörpern oder unter einer Therapie mit einem S1P-Modulator sollten bereits 4 Wochen und über 60-Jährige und Risikopatienten 6 Monate nach einer Grundimmunisierung eine erneute Impfdosis (Boosterung) erhalten.
Sehr selten können die in Deutschland zugelassenen COVID-19-Vakzine nach Erstgabe zu einer akuten Rhabdomyolyse, Fazialisparesen oder einem Guillain-Barré-Syndrom (GBS) führen.
Nach Vektorimpfstoffen wurden gehäuft Sinus- und Hirnvenenthrombosen (SHVT) beobachtet. Die Gefahr ist etwa um das 10Fache im Vergleich zu mRNA-Impfstoffen erhöht, aber deutlich niedriger als das Auftreten einer Thrombose im Rahmen der Erkrankung COVID-19.
Leitbild der vakzininduzierten immunologischen thrombotischen Thrombozytopenie (VITT) sind starke Kopfschmerzen, eine reduzierte Thrombozytenzahl, erhöhte D‑Dimere, Plättchenfaktor-4-Antikörper und ein positiver VITT-Funktionstest. Therapie der Wahl sind die Gabe von Immunglobulinen und eine heparinfreie Antikoagulation.
Kernaussagen
Enzephalopathie
Bei Enzephalopathien im Rahmen einer SARS-CoV-2-Infektion sollten folgende Pathomechanismen erwogen werden: Hypoxie, Sepsis, schwere systemische Inflammation, Organversagen.
Neben supportiven und symptomatischen Therapiemaßnahmen sollte die Indikation für immunmodulatorische Ansätze (Steroide, IVIG, Plasmapherese, Antikörper) geprüft werden.
Meningoenzephalitis
Bei neu aufgetretenen zentralneurologischen Symptomen, insbesondere Bewusstseinsstörungen, akuten kognitiven Defiziten und epileptischen Anfällen, soll eine weiterführende Diagnostik mit zerebraler Bildgebung (Magnetresonanztomographie [MRT]), Elektroenzephalographie (EEG) und Liquordiagnostik erfolgen.
Neben der Routineliquor- und Erregerdiagnostik sollte eine ergänzende Bestimmung von SARS-CoV‑2 mit PCR und antineuronalen Antikörpern aus dem Liquor erfolgen.
Kalkuliert sollte bis zum Ausschluss einer Herpesenzephalitis eine Therapie mit einem Antiherpetikum erfolgen.
Der Einsatz von Kortikosteroiden in hohen Dosen kann bei anhaltender Persistenz der Symptome versucht werden.
Schlaganfall
Die Behandelnden von COVID-19-Patienten sollen bei möglichen zerebrovaskulären Komplikationen unverzüglich die notwendige Diagnostik veranlassen.
Bei akutem Schlaganfall und nachgewiesener Infektion mit SARS-CoV‑2 soll unter Einhaltung der entsprechenden Hygienemaßnahmen die gleiche Akutdiagnostik und Akutbehandlung erfolgen wie bei allen Schlaganfallpatienten.
Wenn Thrombektomien in Intubationsnarkose erfolgen, sollten diese als videolaryngoskopische Intubation in Räumlichkeiten mit Absaugung erfolgen.
Akute disseminierte Enzephalomyelitis
Bei neu aufgetretenen multifokalen neurologischen Symptome soll eine Diagnostik inklusive MRT und Liquoranalyse bei Verdacht auf akute disseminierte Enzephalomyelitis (ADEM) erfolgen.
Therapeutisch sollte initial ein 3‑ bis 5‑tägiger Zyklus mit Methylprednisolon (1 g/Tag) i.v. erfolgen.
Bei persistierenden Symptomen sollten i.v. Immunglobuline gegeben werden.
Epilepsie
Bei bestehender Epilepsie sollte eine Impfung gegen SARS-CoV‑2 erfolgen.
Bei nach einer Impfung mit Vektorimpfstoff neu auftretenden epileptischen Anfällen sollte eine Sinus- oder Hirnvenenthrombose im Rahmen einer VITT ausgeschlossen werden.
Störungen der Chemosensorik
Eine während der Pandemie neu auftretende Riechstörung/Anosmie soll Anlass geben zu Selbstisolation/Quarantäne, Testung auf SARS-CoV‑2.
Wenn sich die Riechfunktion nicht binnen 4 Wochen wieder normalisiert, sollten eine neurologische und HNO-ärztliche Vorstellung mit weiterer Diagnostik erfolgen.
Nerven- und Muskelaffektionen
Bei intensivpflichtiger COVID-19 sollten zur Unterscheidung eines Guillain-Barré-Syndroms (GBS) von einer ICUAW („ICU-acquired weakness“ [ICU: Intensivstation]) eine neurophysiologische und Liquordiagnostik erfolgen.
Bei einem GBS sollen eine Liquordiagnostik und die serologische Testung von Gangliosidantikörpern erfolgen.
Das GBS bei COVID-19 soll mit i.v. Immunglobulinen oder einem Plasmaaustauschverfahren behandelt werden.
Das GBS in zeitlichem Zusammenhang mit einer SARS-CoV-2-Impfung soll mit i.v. Immunglobulinen oder einem Plasmaaustauschverfahren behandelt werden.
Neuroimmunologie
Bei der SARS-CoV-2-Infektion eines neuroimmunologisch Erkrankten sollten Immuntherapien fortgesetzt werden.
Bei hohem individuellem Patientenrisiko kann im Einzelfall eine Deeskalationsstrategie wie eine Therapieumstellung oder eine Intervallverlängerung erfolgen.
SARS-CoV-2-Impfungen sollen auch unter laufender Immuntherapie erfolgen.
Sofern vertretbar sollte die Impfung mindestens 2–4 Wochen vor Beginn einer Immuntherapie abgeschlossen sein.
Neurologische Intensivmedizin
Nach neurologischen Manifestationen von COVID-19 sollte in der pulmonal dominierten Intensivsituation gezielt gesucht werden.
Patienten mit invasiver Beatmung mit PEEP („positive end-expiratory pressure“), einer permissiven Hyperkapnie oder in Bauchlagerung sollten bezüglich einer Erhöhung des intrakraniellen Drucks beobachtet werden.
Ein multimodales Neuromonitoring sollte bei Patienten mit potenziellen zerebralen Komplikationen wie erhöhtem intrakraniellem Druck erfolgen.
Bei Verdacht auf eine zerebrale oder auch spinale Beteiligung durch COVID-19 sollten eine CT oder eine MRT durchgeführt werden.
Neurologische Manifestationen bei Post-COVID-19-Syndrom
Bei Hirnnervenausfällen, Myalgien und Neuropathien mehr als 3 Monate nach der akuten SARS-CoV-2-Infektion soll eine umfassende Diagnostik mit neurophysiologischer Testung und Labordiagnostik (einschließlich Liquor) erfolgen.
Bei Konzentrations- und/oder Gedächtnisstörungen, Kopfschmerzen und weiteren ZNS-Symptomen mehr als 3 Monate nach der akuten SARS-CoV-2-Infektion soll eine umfassende Diagnostik mit neuropsychologischer Testung, zerebraler Bildgebung und Labordiagnostik erfolgen.
Bei Hinweisen auf einen autoimmunologischen Erkrankungsmechanismus sollte immunmodulatorisch behandelt werden.
Das Post-COVID-19-Syndrom soll interdisziplinär versorgt werden.
Neurorehabilitation bei Long- und Post-COVID-19-Syndrom
Bei verbleibenden relevanten Schädigungen des peripheren und/oder zentralen Nervensystems nach einer COVID-19-Akutbehandlung soll eine neurologisch-neurochirurgische Frührehabilitation oder Anschlussrehabilitation durchgeführt werden, diese schließt fallbezogen auch eine prolongierte Beatmungsentwöhnung (Weaning) ein.
Zur Behandlung von neurologischen Post‑/Long-COVID-bedingten Einschränkungen leichterer Ausprägung sollen nach der fachärztlich diagnostischen Abklärung primär Heilmittel verordnet werden, um im Rahmen der ambulanten Versorgung die eingeschränkten Körperfunktionen wiederherzustellen und Aktivitätslimitierungen und resultierende Teilhabeeinschränkungen in Familie, Beruf und Gesellschaft entgegenzuwirken.
Eine teilstationäre (ganztägig ambulante) oder stationäre Neurorehabilitation sollte für Post-COVID-19-Betroffene verordnet werden, wenn nach COVID-19 krankheitsbedingt nicht nur vorübergehende neurologisch bedingte Beeinträchtigungen der Teilhabe am Leben in der Gemeinschaft bestehen oder drohen, die der multimodalen fachärztlichen und therapeutischen Behandlung bedürfen, wenn also ambulante Heilmittel für die Behandlung nicht ausreichen.
COVID-19-Impfungen
Eine SARS-CoV-2-Impfung ist ab einem Alter von 5 Jahren zugelassen und soll bei allen Menschen ab einem Alter von 12 Jahren erfolgen, einschließlich Schwangerer und Stillender. Dabei sind die aktuellen Empfehlungen der STIKO zu beachten.
Patienten mit einer ausgeprägten Immundefizienz oder unter einer immunsuppressiven Therapie insbesondere mit Anti-CD-20-Antikörpern oder mit S1P-Modulatoren sollten bereits 4 Wochen nach einer Grundimmunisierung eine erneute Impfdosis erhalten.
Über 60-Jährige und Risikopatienten sollten 6 Monate nach einer Grundimmunisierung eine erneute Impfdosis erhalten.
Bei Verdacht auf eine vakzininduzierte immunologische thrombotische Thrombozytopenie (VITT) sollen Thrombozytenzahl und D‑Dimere bestimmt werden.
Bei Vorliegen einer Thrombozytopenie sollen die Suche nach Plättchenfaktor-4-Antikörpern und ein VITT-Funktionstest sowie eine zerebrale Bildgebung zum Nachweis einer Hirnvenen- oder Sinusthrombose erfolgen.
Bei Vorliegen einer VITT sollen die Gabe von Immunglobulinen und eine Antikoagulation erfolgen.
Wenn starke Kopfschmerzen 4 Tage bis 3 Wochen nach der SARS-CoV-2-Impfung mit einem Vektorimpfstoff auftreten und die Laborkriterien einer VITT erfüllt sind, können auch ohne Nachweis einer Thrombose die Gabe von Immunglobulinen und eine Antikoagulation erfolgen.
Neurologische Manifestationen bei Post-COVID-19-Syndrom
Bearbeitet von Christiana Franke und Harald Prüß, Berlin
Definition und Inzidenz
Ein Post-COVID-19-Syndrom liegt dann vor, wenn klinische Symptome während oder nach einer mit COVID-19 vereinbaren Erkrankung (fluktuierend) auftreten, für mindestens 2 Monate anhaltend sind und die Akutinfektion mindestens 12 Wochen zurückliegt und durch keine andere Diagnose erklärt werden können. Der Begriff Long-COVID-19 umfasst klinische Symptome, die während oder nach COVID-19 aufgetreten sind; die Akutinfektion liegt hierbei mindestens 4 Wochen zurück [1, 2].
Betroffen sind sowohl Patienten, die COVID-19 mit einem milden bis moderaten Verlauf durchgemacht haben und in häuslicher Quarantäne verblieben sind, als auch Patienten, die im Krankenhaus aufgenommen werden mussten oder sogar intensivpflichtig behandelt wurden [3–5].
Bei der sehr detaillierten Krankheitsüberwachung durch das britische „Office for National Statistics“ zeigte sich, dass die bei Post-COVID-19 beschriebenen vielgestaltigen Symptome zu einem relevanten Anteil auch bei Kontrollprobanden auftreten [35]. So gaben 12–16 Wochen nach der SARS-CoV-2-Infektion 5,0 % der Patienten an, mindestens 1 von 12 definierten Symptomen zu haben, in der Kontrollgruppe waren es 3,4 %. Bei Kindern bis zum 11. Lebensjahr traten die Beschwerden sogar häufiger in der nichtinfizierten Kontrollgruppe auf (4,1 % vs. 3,2 %). Die fehlende Kontrollgruppe in den meisten publizierten Studien birgt das Risiko der Überschätzung des Post-COVID-Syndrom-Risikos.
Aufgrund der derzeitigen Datenlage lassen sich folgende 6 Aussagen treffen:Die Definition des Post-COVID-19-Syndroms beruht auf dem zeitlichen Zusammenhang zur Akutinfektion. Patienten können unabhängig vom Schweregrad der Akutinfektion betroffen sein. Die sichere diagnostische Zuordnung ist dadurch erschwert, dass einzelne Symptome nicht spezifisch für das Krankheitsbild sind.
Die genauen pathophysiologischen Mechanismen des Post-COVID-19-Syndroms sind bislang noch unbekannt. Diskutiert werden neurotransmittervermittelte Veränderungen, eine endothelial-mikrozirkulatorische Dysregulation, eine (unspezifische) postinfektiös fortbestehende Entzündung sowie (virusgetriggerte) immunvermittelte Mechanismen.
Unterschieden werden müssen Symptome, deren Auftreten gehäuft nach SARS-CoV-2-Infektion beschrieben ist, von bekannten neurologischen Krankheitsbildern, die nach COVID-19 auftreten können.
Gedächtnisstörungen, Fatigue, Kopfschmerzen, Myalgien und Neuropathien werden von Patienten auch noch 3 Monate nach der akuten SARS-CoV-2-Infektion beschrieben. Eine umfassende Diagnostik ist anzustreben.
Es existiert aktuell keine kausale Therapie. Bestehen Hinweise auf eine autoimmunologischen Erkrankungsmechanismus, kann immunmodulatorisch behandelt werden.
Aufgrund der Vielzahl der Symptome, die im Rahmen eines Post-COVID-19-Syndroms auftreten können, sind eine interdisziplinäre Behandlung und weitere Versorgung der Patienten anzustreben.
Pathophysiologie
Die Pathophysiologie ist aktuell noch unbekannt. Am Beispiel der Fatigue werden neurotransmittervermittelte Veränderungen, eine endothelial-mikrozirkulatorische Dysregulation, eine (unspezifische) postinfektiös fortbestehende Entzündung sowie (virusgetriggerte) immunvermittelte Mechanismen diskutiert [6]. Für einen Nervenzelluntergang, gemessen an neuronalen Degenerationsmarkern, oder eine intrathekale SARS-CoV-2-Antikörperproduktion, die ursächlich für die neurologischen Manifestationen ist, gibt es aktuell keinen Hinweis [7, 8].
Mittels der 18FDG-PET ([18F]-Fluordesoxyglukosepositronenemissionstomographie) wurde bei 10/15 Long-COVID-Patienten mit neurokognitivem Defizit (weniger als 26/30 Punkte im MoCA-Test; „Montreal cognitive assessment“) in frontoparietalen Hirnregionen ein Hypometabolismus nachgewiesen [9]. Beim Follow-up [10] von 8 Patienten über 6 Monate zeigten sich eine Symptomverbesserung mit weitgehender Normalisierung des Hirnstoffwechsels in der PET.
Erfahrungen mit der SARS-CoV-1-Pandemie zeigten bereits, dass einzelne Patienten sehr lang anhaltende klinische Beschwerden zurückbehalten können, insbesondere Schmerzen, Fatigue, Depression und Schlafstörungen. Das Fehlen krankheitsspezifischer Biomarker erschwert die eindeutige ätiologische Zuordnung ebenso wie die Überlappung mit anderen (prämorbiden) Erkrankungen.
Symptome und Therapie allgemein
Die häufigsten neurologischen Beschwerden nach durchgemachter COVID-19 sind Fatigue, Konzentrations- und Gedächtnisstörungen, Kopf- und Muskelschmerzen sowie anhaltende Geruchs- und Geschmacksstörungen [11–13]. Auch autonome Dysregulationen sind beschrieben [14]. Die Beschwerden können sehr unterschiedlich ausgeprägt sein, stark fluktuieren und im Wechselspiel mit anderen Stressfaktoren stehen. Es besteht eine verlängerte Rekonvaleszenz nach COVID-19. Eine Besserung der Residualsymptome tritt bei einer Vielzahl der Patienten ohne spezielle Behandlung in den ersten 12 Wochen nach der Akutinfektion ein.
Nach COVID-19 können Schlaganfälle, epileptische Anfälle, Myelitiden, aber auch peripher-neurologische Erkrankungen wie ein Guillain-Barré-Syndrom (GBS), Hirnnervenausfälle, Myositiden und Plexopathien auftreten [15–18]. Auch eine autoimmune Enzephalomyelitis wurde 3 Monate nach COVID-19 beobachtet [19].
Da die Erkrankung COVID-19 die Voraussetzung für die Entwicklung eines Post-COVID-19-Syndroms ist, stellt die Vermeidung der Infektion den wichtigsten präventiven Faktor dar. Hierzu gehören neben allgemeinen Hygienemaßnahmen insbesondere die Impfung mit einem der verfügbaren Impfstoffe. Aufklärung über das Krankheitsbild, die Langzeitrisiken und Behandlungsoptionen, Unterstützung bei der Suche nach zusätzlichen psychosozialen Hilfsangeboten beispielsweise für die Wiedereingliederung in den Beruf.
Eine interdisziplinäre Behandlung unter Einbeziehung von internistischer, psychosomatischer und psychiatrischer Expertise ist sinnvoll. Besteht führend belastungsabhängige Dyspnoe, sollte eine pulmologische Vorstellung, bei Herzrhythmusstörungen und Tachykardie eine kardiologische Vorstellung erfolgen. Bei Angststörungen, Panikattacken, Depressionen und funktionellen neurologischen Störungen ist eine psychiatrische bzw. psychosomatische (Mit‑)Behandlung anzustreben. Bisher gibt es keine validen prädiktiven klinischen oder laborchemischen Parameter, die die Prognose eines Post-COVID-19-Syndroms eingrenzen lassen. Ob eine Vakzinierung zu einer Besserung der Post-COVID-19-Symptomatik führt, ist aktuell noch unzureichend untersucht.
Post-COVID-19-assoziierte Symptome, Diagnostik und Therapie im Detail
Kognitive Störungen und Fatigue
Kognitive Defizite, die sowohl im subakuten Stadium als auch im weiteren Verlauf nach COVID-19 häufiger gefunden werden, betreffen planerisches Denken, Konzentration, Gedächtnis- und/oder Sprachleistungen. Dies betrifft Patienten sowohl bei initial leichten als auch schweren COVID-19-Verläufen [20, 21]. Dies trifft auch für die Fatigue zu [22]. Fatigue ist eine subjektiv oft stark einschränkende, in Bezug auf die vorausgegangenen Anstrengungen unverhältnismäßige, sich durch Schlaf oder Erholung nicht ausreichend bessernde subjektive Erschöpfung auf somatischer, kognitiver und/oder psychischer Ebene.
Diagnostik.
Bei kognitiven Defiziten sollte eine neuropsychologische Untersuchung inklusive des „Montreal cognitive assessment“(MoCA)-Testes erfolgen. Bei Auffälligkeiten im Screening sollten die Untersuchung von Serum und ggf. auch Liquor auf ZNS-Autoantikörper gegen intrazelluläre und Oberflächenantigene, eine zerebrale Bildgebung mittels kranieller Magnetresonanztomographie und eine detaillierte neuropsychologische Diagnostik mit Fokus auf die kognitiven Domänen Aufmerksamkeit, Exekutivfunktionen, Lernen und Gedächtnis, Sprache sowie visuell-räumliche Fähigkeiten erfolgen. Eine signifikante Assoziation von neurokognitiven Symptomen und antinukleären Antikörpern (ANA) ist Hinweis für eine autoimmune Genese [23].
Zur Einschätzung von Symptomatik und Schweregrad einer Fatigue sollten einfach zu erhebende psychometrische Selbstauskunftsinstrumente wie z. B. die Fatigue-Skala (FS), die „fatigue severity scale“ (FSS) oder die „fatigue assessment scale“ (FAS) eingesetzt werden. Als mögliche Biomarker sind mannosebindendes Lektin und erhöhte Werte für Interleukin 8 beschrieben, kommen aber in der Routine noch nicht zur Anwendung [24].
Therapie.
Bei Hinweisen auf eine autoimmune neurologische Manifestation mit Autoantikörpernachweis bei kognitiven Störungen kann eine Gabe von i.v. Immunglobulinen, Kortikoiden oder therapeutischer Apherese in Abhängigkeit von Risiko und Nutzen erfolgen. Eine kausale Therapie für die Fatigue ist nicht bekannt. Nichtmedikamentöse Therapieansätze wie Entspannungsverfahren, moderate körperliche und kognitive Belastung, angepasst an die individuelle Symptomatik, und Unterstützung eines adäquaten Copingverhaltens kommen hier zur Anwendung, ggf. unterstützt durch psychotherapeutische oder psychopharmakologische Behandlung.
Kopfschmerzen
Eine Metaanalyse von Kohortenstudien gibt an, dass Kopfschmerzen in 44 % nach COVID-19 bestehen. Wenn Kopfschmerzen schon während der Akutinfektion berichtet werden, existiert eine erhöhte Prävalenz von anhaltenden Kopfschmerzen im Rahmen eines Post-COVID-19-Syndroms [25].
Diagnostik.
Zur Einschätzung von Symptomatik einschließlich des Schweregrads von chronischen Schmerzen sollten einfach zu erhebende psychometrische Selbstauskunftsinstrumente (z. B. „brief pain inventory“) verwendet werden. In Abhängigkeit von der Anamnese und körperlichen Untersuchung kann eine erweiterte Labordiagnostik zum Ausschluss anderer (z. B. rheumatologischer) Ursachen erfolgen.
Therapie.
Es ist keine kausale Therapie bekannt. Die Behandlung erfolgt gemäß den existierenden Leitlinien der Deutschen Gesellschaft für Neurologie (DGN, [26]).
Hyposmie/Anosmie und Hypogeusie/Ageusie
Eine Einschränkung bzw. ein Verlust des Geruchs und des Geschmacks können auch noch länger als 6 Monate nach der Akutinfektion anhalten [4].
Diagnostik.
Eine Hyposmie/Hypogeusie oder Anosmie/Ageusie sollten über eine Testung (z. B. mit dem „SS-16-item sniffin-sticks test“ bzw. Schmecktestung) objektiviert werden. Neben einer neurologischen und/oder HNO-ärztlichen Vorstellung mit Anamnese (u. a. auch hinsichtlich konkurrierender/alternativer Ursachen) und Untersuchung, können Labordiagnostik und Endoskopie erwogen werden. Ergänzend kann der Bulbus olfactorius MR-tomographisch untersucht werden [27].
Therapie.
Bei länger anhaltenden Riechstörungen kann eine Therapie mit konsequentem, strukturiertem Riechtraining versucht werden [28]. Ziel ist, im Bereich der Riechschleimhaut die Regeneration olfaktorischer Rezeptorneuronen anzuregen. Klassischerweise werden hierzu Rose, Zitrone, Eukalyptus und Gewürznelke eingesetzt [36] Hinsichtlich der Therapie mit intranasalen Kortikosteroiden liegen widersprüchliche Berichte vor [29].
Myalgie, Muskelschwäche und Neuropathie
Muskelschmerzen, insbesondere der proximalen Muskulatur, und Muskelschwäche werden häufig berichtet und können bis zu 6 Monate nach der Akutinfektion bestehen [3, 12].
Diagnostik.
Nach ausführlicher Anamnese und körperlicher Untersuchung sind eine laborchemische Untersuchung des Serums mit Bestimmung von Blutsenkungsgeschwindigkeit, Myoglobin, Kreatinkinase und ggf. Myositisantikörpern sowie ggf. die liquorologische Untersuchung sinnvoll. Eine elektrophysiologische Untersuchung (NLG und EMG) ist indiziert. Die Diagnostik erfolgt gemäß der existierenden DGN-Leitlinie [30–32].
Therapie.
Die Therapie erfolgt in Abhängigkeit der Ergebnisse der durchgeführten Diagnostik und dann gemäß der DGN-Leitlinie [33, 34]. Sollten die erhobenen Befunde allesamt normwertig sein, existiert keine kausale Behandlung. Physiotherapie und moderate körperliche Belastung sind zu empfehlen.
Zusammenfassung
Neurologische Manifestationen treten häufig bei Patienten mit Post-COVID-19-Syndrom auf, v. a. Gedächtnisstörungen, Fatigue, Kopfschmerzen, Myalgien und Neuropathien. Eine umfassende – ggf. interdisziplinäre – Diagnostik sollte bei Patienten eingeleitet werden, die länger als 3 Monate nach der Akutinfektion noch über residuelle oder neu aufgetretene Symptome klagen. Falls eine Immunbeteiligung nachgewiesen wird, kann eine immunmodulatorische Therapie als individueller Heilversuch begonnen werden. Eine kausale Therapie existiert bislang nicht, die symptomatische Therapie erfolgt gemäß den Leitlinien der DGN. Eine frühzeitige und parallelisiert eingeleitete psychosomatische Mitbehandlung sollte den Patienten angeboten werden.
Rehabilitation bei neurologischen Manifestationen infolge COVID-19
Bearbeitet von Thomas Platz, Greifswald
Patientengruppen mit neurologischem Rehabilitationsbedarf nach COVID-19
Die unterschiedlichen o. g. neurologischen Manifestationsformen bei COVID-19 können einzeln oder auch kombiniert auftreten. Für das klinische Management und die Feststellung eines neurologischen (Früh‑)Rehabilitationsbedarfs sind aus medizinischen, aber auch pragmatischen Gründen 2 Subgruppen von Long‑/Post-COVID-19-Patienten zu unterscheiden, die wegen alltags- und/oder berufsrelevanter Körperfunktionsstörungen der neurologischen rehabilitativen Behandlung bedürfen [37]:Gruppe A: Patienten mit neurologischen Körperfunktionsstörungen, die – häufiger nach schweren bis kritischen Verläufen – seit der Akutphase fortbestehen und
Gruppe B: Patienten, die nach primär milden und moderaten Verläufen ggf. auch erst zu einem späteren Zeitpunkt unter neurologischen Körperfunktionsstörungen leiden, die die Teilhabe am gesellschaftlichen und Arbeitsleben relevant einschränken.
Long-COVID-19 im Zusammenhang der hier gemachten Empfehlungen meint Manifestationen, die jenseits der ersten 4 Wochen nach Erkrankungsbeginn einer COVID-19 bestehen, Post-COVID-19 in einem Zeitraum jenseits der ersten 12 Wochen [1, 2, 37].
Bei Gruppe A bestehen im Rahmen schwerer und kritischer Verläufe einer COVID-19 interindividuell unterschiedliche Kombinationen aus Lähmungen, kognitiven und emotionalen Störungen teilweise über lange Zeit fort und bedürfen der neurologischen (Früh‑)Rehabilitation, sowohl primär postakut [38, 39], teilweise mit Beatmungsentwöhnungsbedarf [40], als auch ggf. (erneut) im weiteren Verlauf bei Persistenz von Funktionsstörungen, die sich durch die poststationär anschließende ambulante Behandlung nicht ausreichend verbessern lassen. Die Zustände ähneln einerseits anderen intensivpflichtigen Erkrankungen mit konsekutivem „post intensive care syndrome“ (PICS); zudem können in Zusammenhang mit COVID-19 wie o. g. verschiedene weitere spezifische Erkrankungen wie Schlaganfälle, Enzephalopathien, Enzephalomyelitiden, ein Guillain-Barré-Syndrom (GBS), Hirnnervenneuritiden, Myositiden und Plexopathien auftreten, die alle mit spezifischem Rehabilitationsbedarf einhergehen (können).
Patienten der Gruppe B charakterisiert, dass der initiale COVID-19-Verlauf nicht schwer oder kritisch war und dennoch im Weiteren trotz gutem Überwinden der primären Infektion ggf. über viele Monate persistierend alltags- und berufsrelevante neurologische Defizite fortbestehen. In prospektiven Beobachtungsstudien fanden sich 3 bzw. 6 Monate nach der Infektion gehäuft als neurologische Funktionsstörungen neben einer Hyposmie oder Anosmie eine geminderte psychophysische Belastbarkeit, periphere Lähmungen (CIP/CIM), kognitive Defizite und/oder Kopfschmerzen bzw. Muskelschmerzen, nicht selten auch begleitet von psychischen Belastungen (Depressivität, Ängste, posttraumatische Belastungsstörung, [3, 41]).
Auf eine ausführliche erneute Darstellung der Literatur kann an dieser Stelle verzichtet werden; diese ist oben bzw. in der S2k-LL SARS-CoV‑2, COVID-19 und (Früh‑)Rehabilitation (Version 2; Stand 01.11.2021) wiedergegeben [37]. Wichtig zu beachten ist, dass alle (Long‑/Post‑)COVID-19-Betroffenen mit sensorischen, sensomotorischen, kognitiven und/oder emotionalen Veränderungen einer adäquaten neurologischen Evaluation und bei Bedarf einer neurorehabilitativen Versorgung zugeführt werden sollen.
Der Behandlungsbedarf soll früh im Zuge der Beendigung der primären Akutbehandlung für die postakute neurologische (Früh‑)Rehabilitation überprüft werden sowie auch im Verlauf (z. B. nach 3–6 Monaten), um einerseits eine neurorehabilitativ behandlungsbedürftige Persistenz von neurologischen Manifestationen einer COVID-19-Infektion festzustellen oder auch erstmals einen neurorehabilitativen Behandlungsbedarf bei Post-COVID-19 mit neurologischen Funktionsstörungen nach primär nicht schwerem Verlauf.
Empfehlungen für Patienten mit neurologischem Rehabilitationsbedarf nach COVID-19
Bei verbleibenden relevanten Schädigungen des peripheren und/oder zentralen Nervensystems nach einer COVID-19-Akutbehandlung sollen eine neurologisch-neurochirurgische Frührehabilitation oder Anschlussrehabilitation durchgeführt werden, diese schließen fallbezogen auch eine prolongierte Beatmungsentwöhnung (Weaning) ein.
Zur Behandlung von neurologischen Post‑/Long-COVID-bedingten Einschränkungen leichterer Ausprägung sollen nach der fachärztlich diagnostischen Abklärung primär Heilmittel verordnet werden, um im Rahmen der ambulanten Versorgung die eingeschränkten Körperfunktionen wiederherzustellen und Aktivitätslimitierungen und resultierenden Teilhabeeinschränkungen in Familie, Beruf und Gesellschaft entgegenzuwirken.
Eine teilstationäre (ganztägig ambulante) oder stationäre Neurorehabilitation sollte für Post-COVID-19-Betroffene verordnet werden, wenn nach COVID-19 krankheitsbedingt nicht nur vorübergehende neurologisch bedingte Beeinträchtigungen der Teilhabe am Leben in der Gemeinschaft bestehen oder drohen, die der multimodalen fachärztlichen und therapeutischen Behandlung bedürfen, wenn also ambulante Heilmittel für die Behandlung nicht ausreichen.
COVID-19-Impfungen
Bearbeitet von Jörg B. Schulz, Aachen
Zu den COVID-19-Impfungen lassen sich folgende 5 Aussagen treffen:Die mRNA- und Vektorimpfstoffe gewähren einen hohen Schutz gegen eine Infektion mit SARS-CoV‑2. Eine Impfung ist ab einem Alter von 5 Jahren zugelassen und soll bei allen Menschen ab einem Alter von 12 Jahren, einschließlich Schwangerer und Stillender, erfolgen. Dabei sind die aktuellen Empfehlungen der STIKO zu beachten.
Patienten mit einer ausgeprägten Immundefizienz oder unter einer immunsuppressiven Therapie insbesondere mit Anti-CD20-Antikörpern oder unter einer Therapie mit S1P-Modulatoren sollten bereits 4 Wochen und über 60-Jährige und Risikopatienten 6 Monate nach einer Grundimmunisierung eine erneute Impfdosis erhalten.
Abgesehen von möglichen unspezifischen Impfreaktionen in den ersten beiden Tagen gelten COVID-19-Impfungen als nebenwirkungsarm.
Nach Vektorimpfstoffen wurden gehäuft Sinus- und Hirnvenenthrombosen (SHVT) beobachtet. Die Gefahr ist etwa um das 10Fache im Vergleich zu mRNA-Impfstoffen erhöht, aber deutlich niedriger als das Auftreten einer Thrombose im Rahmen einer COVID-19.
Es handelt sich um eine vakzininduzierte immunologische thrombotische Thrombozytopenie (VITT). Leitbild sind starke Kopfschmerzen, eine reduzierte Thrombozytenzahl, erhöhte D‑Dimere, Plättchenfaktor-4-Antikörper und ein positiver VITT-Funktionstest. Therapie der Wahl sind die Gabe von Immunglobulinen und eine Antikoagulation.
Indikationen
Das wirksamste Mittel, die COVID-19-Pandemie einzudämmen und zu bekämpfen, ist die Impfung gegen SARS-CoV‑2. Zum jetzigen Zeitpunkt (8/2021) sind in Deutschland und der EU 4 Impfstoffe zugelassen: 2 basierend auf mRNA-Technologie, Comirnaty® von BioNTech [42] und Spikevax® von Moderna Biotech [43], und 2 basierend auf Adenovirusvektortechnologie, Vaxzevria® von AstraZeneca [44, 45] und COVID-19 Vaccine Janssen® von Janssen-Cilag/Johnson & Johnson [46]. In den Zulassungsstudien führten alle Impfstoffe zu einem hohen Impferfolg in den getesteten Altersgruppen mit Antikörpernachweis und im Vergleich zu placebobehandelten Probanden einem Schutz von z. T. über 90 % vor einer SARS-CoV-2-Infektion.
Auch gegen SARS-CoV-2-Varianten ist ein Schutz gegeben, bei manchen Varianten, z. B. der ansteckenderen sog. Deltavariante (B.1.617.2), die in Deutschland Ende August 2021 einen Anteil von 99,3 % ausmachte, bedarf es dafür aber möglicherweise höherer Impftiter [47]. Impfdurchbrüche werden unter den neuen Varianten beobachtet, die Erkrankungsverläufe scheinen aber milder zu sein. In einer vergleichenden Real-World-Studie wurde die Effektivität der Impfungen untersucht. Die Effektivität betrug bei den mRNA-Impfstoffen nach vollständiger Immunisierung 89–91 % hinsichtlich der Aufnahme auf eine Intensivstation, Vorstellung in einer Notaufnahme oder dringlicher stationärer Behandlungsindikation [48]. Bei über 85-Jährigen wurde eine Effektivität von 83 % erreicht. Im Vergleich hierzu betrug die Effektivität von Vaxzevria® 68–73 % über alle Altersgruppen.
Stand Dezember 2021 ist laut STIKO (Ständige Impfkommission am Robert Koch-Institut) die SARS-CoV-2-Impfung ab einem Alter von 5 Jahren möglich und soll bei allen Menschen ab einem Alter von 12 Jahren erfolgen. Ab einem Alter von 5 Jahren stehen die beiden mRNA-Impfstoffe zur Verfügung, für die über 18-Jährigen zusätzlich die beiden Vektorimpfstoffe. Bei den beiden mRNA-Impfstoffen und Vaxzevria® sind jeweils 2 Impfdosen notwendig, das COVID-19 Vaccine Janssen® ist als einmalige Impfung zugelassen. Hier gibt es inzwischen aber auch die STIKO-Empfehlung einer 2. Impfdosis. Frauen im gebärfähigen Alter wird dringend zu einer Impfung geraten; inzwischen wird auch Schwangeren (ab dem 2. Trimenon) und Stillenden zur Impfung mit einem mRNA-Impfstoff geraten [49].
Patienten mit einer angeborenen oder erworbenen Immundefizienz sollen laut STIKO eine Impfserie bestehend aus 2 Impfdosen eines mRNA-Impfstoffs erhalten. Personen, die zur ersten Impfung einen vektorbasierten Impfstoff (Vaxzevria® oder COVID-19 Vaccine Janssen®) erhalten haben, sollen als weitere Impfdosis einen mRNA-Impfstoff im Abstand von mindestens 4 Wochen erhalten (STIKO, [50]). Ferner soll allen Personen mit einer Immundefizienz 6 Monate nach einer Grundimmunisierung die zusätzliche Impfdosis eines mRNA-Impfstoffs angeboten werden [49]. Bei schwer immundefizienten Patienten, das schließt Patienten unter einer Kortikoidstoßtherapie, einer Therapie mit Methotrexat (> 20 mg/Woche), Azathioprin (> 3 mg/kgKG und Tag), Cyclophosphamid oder einer B‑Zell-depletierenden Therapie mit Anti-CD20-Antikörpern (Rituximab, Ocrelizumab, Ofatumumab) sowie unter einer Therapie mit einem S1P-Modulator (Fingolimod, Siponimod, Ponesimod, Ozanimod) ein, sollte eine 3. Impfdosis bereits 4 Wochen nach der 2. Impfdosis zur Optimierung der primären Impfserie gegeben werden [49]. Bei organtransplantierten Patienten, unter Hämodialyse und bei immunsuppressiv behandelten Patienten wurde durch eine 3. Impfung die Rate der antikörperpositiven Patienten von 40 % auf 60 % gesteigert [51, 52].
Eine serologische Antikörpertestung wird nicht grundsätzlich empfohlen, kann aber bei Patienten mit schwerer Immundefizienz erwogen werden. Tatsächlich wurde bei Patienten mit multipler Sklerose, die mit den B‑Zell-depletierenden Anti-CD20-Antikörpern Rituximab und Ocrelizumab oder Sphingosin-1-Phosphat-Rezeptor-Analogon (Fingolimod) behandelt wurden, eine niedrige bzw. fehlende humorale Immunantwort beobachtet [50, 53, 54]. Daher ist eine Impfung, wenn möglich, 6 Wochen vor Einleitung einer immunmodulierenden Therapie zu empfehlen. Die Impfantwort ist besser mit zunehmendem Abstand zur Therapie mit Anti-CD20-Antikörpern.
Nebenwirkungen
Der Wirksamkeit der Impfung gegen SARS-CoV‑2 sind potenzielle, auch neurologische Nebenwirkungen gegenüberzustellen. Wie auch bei anderen Impfungen wird über ein gehäuftes Auftreten von Guillain-Barré Syndromen (GBS), Plexopathien, Hirnnervenläsionen (u. a. ein- bzw. beidseitige Fazialisparesen), Enzephalomyelitiden, Myositiden und Anaphylaxien berichtet [55]. In seltenen Fällen kann es auch zu einer Myokarditis oder Perikarditis kommen. In der Regel ist es aus methodischen Gründen schwierig, eine Impfassoziation zu belegen, da die genannten Erkrankungen auch spontan auftreten.
Aufgrund eines nachgewiesenen neuen immunologischen Mechanismus konnte jedoch ein ursächlicher Zusammenhang zwischen der Impfung mit einem Vektorimpfstoff (Vaxzevria®, COVID-19 Impfstoff Janssen®) und der Auslösung einer vakzininduzierten immunologischen thrombotischen Thrombozytopenie (VITT) mit Sinus- und/oder Hirnvenenthrombose (SVHT) nachgewiesen werden [56, 57]. Verantwortlich ist die Aktivierung von Thrombozyten durch induzierte Plättchenfaktor-4(PF4)-Antikörper-Bildung mit nachfolgender Thrombozytopenie. Zur Diagnose führen für eine SHVT typische klinische Symptome 2–30 Tage nach der Impfung, erhöhte D‑Dimere, der CT- oder MR-angiographische Nachweis einer SHVT, der Nachweis einer Thrombozytopenie, positive PF4-Antikörper und ein pathologischer VITT-Thrombozytenfunktionstest [58].
Bis zum 31.08.2021 wurden laut Angaben des Robert Koch-Instituts in Deutschland 101.877.124 Impfungen durchgeführt, davon 76.982.568 Impfungen mit Comirnaty® (BioNTech), 9.396.381 Impfungen mit Spikevax® (Moderna), 12.645.915 Impfungen mit Vaxzevria® (AstraZeneca) und 2.852.260 Impfungen mit dem COVID-19-Impfstoff von Janssen® (https://www.pei.de/DE/newsroom/dossier/coronavirus/coronavirus-inhalt.html?cms_pos=6Bis). Zu diesem Zeitpunkt wurden dem Paul-Ehrlich-Institut für Vaxzevria® insgesamt 174 Fälle einer Thrombose mit Thrombozytopenie berichtet. Dabei handelte es sich um 103 Frauen und 70 Männer. 169 Fälle bezogen sich auf die erste Impfdosis. Bezogen auf die 1. Impfung betrug die Melderate 1,83 auf 100.000 Impfdosen. Nach Impfung mit dem COVID-19-Impfstoff Janssen® wurden 13 Fälle gemeldet, davon 3 Frauen und 9 Männer.
Eine Befragung aller Kliniken für Neurologie in Deutschland identifizierte bis Mitte April 2021 45 Fälle von SHVT nach COVID-19-Impfung [58]. Von den SHVT-Patienten waren 35 (77,8 %) weiblich, und 36 (80,0 %) waren unter 60 Jahre alt. 53 Ereignisse wurden nach der Impfung mit Vaxzevria® (85,5 %), 9 nach der Comirnaty®-Impfung (14,5 %) und keines nach der Spikevax®-Impfung beobachtet. Nach 7.126.434 ersten Impfstoffdosen betrug die Inzidenzrate von SHVT innerhalb 1 Monats nach Verabreichung der ersten Dosis 0,55 (95 %-KI: 0,38–0,78) pro 100.000 Personenmonate (was einem Risiko für CVT innerhalb der ersten 31 Tage von 0,55 pro 100.000 Personen entspricht) für alle Impfstoffe und 1,52 (1,00–2,21) für Vaxzevria® (nach 2.320.535 Erstdosen). Das bereinigte Inzidenzratenverhältnis betrug 9,68 (3,46–34,98) für Vaxzevria® im Vergleich zu mRNA-basierten Impfstoffen und 3,14 (1,22–10,65) für Frauen im Vergleich zu Nicht-Frauen. Bei 26/45 Patienten mit SHVT (57,8 %) wurde eine vakzininduzierte immunologische thrombotische Thrombozytopenie (VITT) als hochwahrscheinlich eingestuft. Eine signifikant unterschiedliche Altersabhängigkeit bestand nicht, eine VITT trat auch bei Geimpften über 60 Jahren auf.
Diese erhöhte Thromboserate muss jedoch in Relation gesetzt werden zu spontanen SHVT und zu SHVT im Verlauf einer COVID-19-Erkrankung. Spontane SHVT traten in den USA im Jahr 2018 in einer Häufigkeit von 2,4/1 Mio. auf [59]. Das geschätzte Risiko einer impfinduzierten SHVT liegt zwischen 1:150.000 und 1:470.000 [60]. Das Risiko einer SHVT bei einer COVID-19-Erkrankung wird bei 537.913 betroffenen Patienten mit 1:25.000 angegeben [61], und das Risiko nach COVID-19-Impfung mit Vektorimpfstoffen mit 1:250.000. Das Risiko, im Verlauf einer COVID-19 Erkrankung eine SHVT zu erleiden, ist also 10-mal höher als nach Impfung mit einem Vektorimpfstoff. Nach einer Influenzaimpfung besteht kein erhöhtes Risiko [61].
Eine VITT-induzierte SHVT wird nach Vaxzevria®-Impfung (und vermutlich auch Impfung mit COVID-19 Vaccine Janssen®) durch eine entzündliche Reaktion und Immunstimulation mit Antikörperbildung gegen Plättchenantigene (PF4) hervorgerufen. Diese Antikörper induzieren dann unabhängig von Heparin über den FC-Rezeptor eine Thrombozytenaktivierung in Analogie zur, aber nicht identisch mit der, heparininduzierten Thrombozytopenie. Alle Patienten, die positiv auf Antikörper gegen PF4 getestet wurden, waren positiv im Thrombozytenaktivierungsassay in der Anwesenheit von PF4 und unabhängig von Heparin. Die Thrombozytenaktivierung wurde gehemmt durch Heparin, Fc-Rezeptor-blockierende monoklonale Antikörper und Immunglobuline [56].
Das klinische Bild einer SVHT nach COVID-19-Impfung unterscheidet sich nicht von dem einer spontanen SHVT. Leitsymptom sind zunehmende Kopfschmerzen – typischerweise innerhalb der ersten 14 Tage, maximal 31 Tage nach der Impfung, gefolgt von fokalen neurologischen Ausfällen und epileptischen Anfällen. Bei der Hälfte der Patienten kommt es im Rahmen der SVHT zu intrazerebralen Blutungen.
Bei zunehmenden Kopfschmerzen nach Gabe eines Vektorimpfstoffs sollte nach einer neurologischen Untersuchung zunächst die absolute Thrombozytenzahl untersucht werden. Wenn eine Thrombozytopenie vorliegt, müssen unmittelbar ein CT mit CT-Angiographie oder ein MRT mit MR-Venographie zum Ausschluss einer Sinusvenenthrombose durchgeführt werden. Parallel dazu müssen die entsprechenden Laboruntersuchungen veranlasst werden: Gerinnungstests mit Quick, PTT, Fibrinogen und D‑Dimeren, und gezielt die Bestimmung von Antikörpern gegen Plättchenfaktor 4 (PF4) mittels ELISA und möglichst auch mit einem funktionellen Plättchenaktivierungstest, der die höchste Spezifität für eine VITT hat.
Bis zum Ausschluss einer VITT sollten auf eine Antikoagulation mit Heparinen verzichtet und auf alternative, HIT-kompatible Präparate ausgewichen werden [62]. Hier kommen Argatroban, Bivalirudin, Fondaparinux oder direkte orale Antikoagulanzien (DOAK) in Betracht. Bei Patienten mit gesicherter VITT-SVHT kann der prothrombotische Pathomechanismus sehr wahrscheinlich durch die Gabe von hochdosierten i.v. Immunglobulinen (IVIG), z. B. in einer Dosierung von 1 g/kg Körpergewicht und Tag an 2 aufeinanderfolgenden Tagen oder 0,4 g/kg und Tag für 5 Tage, unterbrochen werden [63, 64]. Wenn Antikoagulation und IVIG-Gabe rechtzeitig erfolgen, können die thrombotischen Komplikationen sogar verhindert werden. In einer kleinen Fallserie wurde über 11 Patienten berichtet, die sich 5–18 Tage nach der Vaxzevria-Impfung mit starken Kopfschmerzen vorstellten, ohne dass bis dahin eine radiologisch nachweisbare SHVT vorlag. Alle Patienten wiesen aber eine Thrombozytopenie, hohe D‑Dimer-Werte und hohe Werte von Anti-PF4-Heparin-IgG-Antikörpern auf. Wenn die Behandlung mit Immunglobulinen und/oder Antikoagulation innerhalb der ersten Kopfschmerztage eingeleitet wurde, konnte das Auftreten von SHVT-assoziierten Komplikationen und eine bleibende neurologische Beeinträchtigung verhindert werden [65].
Vermutlich treten aber nicht nur VITT-assoziierte SHVT, sondern auch primäre intrazerebrale Blutungen und embolische Ischämien auf [58].
Ein Kausalzusammenhang zwischen einem Guillain-Barré-Syndrom und einer COVID-19-Impfung wurde mehrfach vermutet, aber auch hier müssen die Hintergrundinzidenzen von ca. 1,70:100.000–1,84:100.000 pro Jahr berücksichtigt werden [66], d. h. in Deutschland treten jährlich 1500 Fälle auf. Meistens wurden für COVID-19-assoziierte Guillain-Barré-Syndrome nur Fallserien beschrieben. Eine Auswertung des Paul-Ehrlich-Instituts ergibt kein Risikosignal für die mRNA-Impfstoffe, die Meldung nach Impfungen mit einem Vektorimpfstoff betrug 1 Meldung auf 100.000 bzw. 133.000 Impfdosen [67]. Basierend auf einer Beobachtungsstudie nach Marktzulassung von COVID-19 Vaccine Janssen® wurden von Februar bis Juli 2021 Nebenwirkungen erfasst und mit Hintergrundinzidenzen verglichen. Es wurde über 1 Fall pro 100.000 Impfdosen berichtet. Daraus resultiert eine leicht erhöhte Inzidenz von ca. 8,36 Guillain-Barré-Fällen/100.000 Personenjahre nach Impfung vs. 2 Fällen/100.000 Personenjahre als Hintergrundinzidenz [68].
Möglicherweise ist bei jungen Männern die Inzidenz für eine Myokarditis/Perikarditis nach Impfung mit einem mRNA-Impfstoff erhöht. Die Manifestation erfolgte in 20 % nach der ersten und in 80 % nach der 2. Impfung [67].
Einhaltung ethischer Richtlinien
Interessenkonflikt
Siehe Interessenkonflikterklärung auf www.dgn.org/leitlinien.
Für diesen Beitrag wurden von den Autoren keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.
Fachgesellschaft
Deutsche Gesellschaft für Neurologie (DGN) in Zusammenarbeit mit der Deutschen Gesellschaft für Neurointensiv- und Notfallmedizin (DGNI), der Deutschen Gesellschaft für HNO-Heilkunde, Kopf- und Hals-Chirurgie (DGHNO-KHC) und der Deutschen Gesellschaft für Neurorehabilitation (DGNR)
Federführend
Prof. Dr. med. Peter Berlit, Generalsekretär, Schriftleiter DGNeurologie, Deutsche Gesellschaft für Neurologie (DGN), Berlin, berlit@dgn.org
Redaktionskomitee
Leitlinienkoordinator
Prof. Dr. med. Peter Berlit, Generalsekretär, Schriftleiter DGNeurologie, Deutsche Gesellschaft für Neurologie (DGN), Berlin
Leitliniengruppe
Prof. Dr. med. Julian Bösel, Klinik für Neurologie, Klinikum Kassel, DGNI
Dr. med. Christiana Franke, Klinik für Neurologie mit Experimenteller Neurologie Charité – Universitätsmedizin Berlin
Prof. Dr. med. Georg Gahn, Neurologische Klinik, Städtisches Klinikum Karlsruhe, DGNI
Prof. Dr. med. Stefan Isenmann, Klinik für Neurologie und klinische Neurophysiologie, St. Josef Krankenhaus Moers
Prof. Dr. med. Dr. rer. nat. Dr. h.c. Sven G. Meuth, Klinik für Neurologie, Universitätsklinikum Düsseldorf
Prof. Dr. med. Christian Nolte, Klinik für Neurologie mit Experimenteller Neurologie und Center for Stroke Research Berlin (CSB), Charité-Universitätsmedizin Berlin
Dr. med. Marc Pawlitzki, Klinik für Neurologie mit Institut für Translationale Neurologie, Universitätsklinikum Münster
Prof. Dr. med. Thomas Platz, Institut für Neurorehabilitation und Evidenzbasierung, BDH-Klinik Greifswald, DGNR
Prof. Dr. med. Harald Prüß, Klinik für Neurologie mit Experimenteller Neurologie Charité – Universitätsmedizin Berlin
Prof. Dr. med. Felix Rosenow, Epilepsiezentrum Frankfurt Rhein-Main, Zentrum der Neurologie und Neurochirurgie, Universitätsklinikum Frankfurt
Prof. Dr. med. Benedikt Schoser, Friedrich-Baur-Institut an der Neurologischen Klinik, LMU Klinikum München
Prof. Dr. med. Jörg B. Schulz, Klinik für Neurologie, Universitätsklinikum Aachen
Prof. Dr. med. Götz Thomalla, Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, Universitätsklinikum Hamburg-Eppendorf
Prof. Dr. med. Thomas Hummel, Interdisziplinäres Zentrum für Riechen und Schmecken, Universitäts-HNO-Klinik Dresden, DGHNO-KHC
Die vollständige aktualisierte Leitlinie wurde unter https://dgn.org/leitlinien/neurologische-manifestationen-bei-covid-19/ am 20.12.2021 publiziert.
Redaktion
P. Berlit, Berlin
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PMC009xxxxxx/PMC9004218.txt |
==== Front
Early Child Educ J
Early Child Educ J
Early Childhood Education Journal
1082-3301
1573-1707
Springer Netherlands Dordrecht
35431531
1345
10.1007/s10643-022-01345-y
Article
Exploring the Challenge of Teachers’ Emotional Labor in Early Childhood Settings
http://orcid.org/0000-0002-7589-6484
Purper Cammy J. cpurper@calbaptist.edu
Thai Yvonne
Frederick Thomas V.
Farris Shari
grid.411853.a 0000 0004 0459 0896 California Baptist University, 8432 Magnolia Avenue, Riverside, CA 92504 USA
12 4 2022
19
24 3 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 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.
One challenging aspect of working in early childhood education settings is engagement in emotional labor. Research suggests that emotional labor is associated with emotional exhaustion and burnout in early childhood teachers, but there is limited research available on this issue. Research focusing on early childhood contexts in the United States is especially limited. This paper explores the concept of emotional labor by early childhood teachers and reviews some of the research on teachers’ experience of emotional labor in the classroom. Implications of the current research are discussed and suggestions for future research are provided. Possible solutions to the challenges of emotional labor are presented, focusing on mindfulness strategies.
Keywords
Early childhood
Emotional labor
Mindfulness
==== Body
pmcThe development of a sustainable workforce of early childhood educators in the United States is a growing challenge. The turnover rate for early childhood educators is high, and the causes for this are numerous: low pay and job prestige, inadequate training, and stressful working conditions are just a few. A significant number of teachers report experiencing stress and depression related to their work, with 8 to 11% of the respondents reporting clinically significant symptoms of depression (Roberts et al., 2017). The COVID-19 pandemic has exacerbated these burdens on the early childhood workforce, with many centers closing or struggling to stay afloat, and early educators-who are often from historically marginalized groups -dealing with their own personal as well as professional COVID related challenges (Payton, 2021). In our work coaching and supporting early educators, we have directly observed these rising levels of stress and exhaustion. In fact, in the past year so many of the educators we work with have reported feeling emotionally overwhelmed that we have begun to regularly help teachers set goals for stress management as part of their professional development plans.
One aspect of the work performed by early educators that has received relatively little attention by researchers is emotional labor. The term emotional labor was coined by Hochschild (1983) and is defined as work that “requires one to induce or suppress feeling in order to sustain the outward countenance that produces the proper state of mind of others” (2012, p. 7). Engagement in emotional labor is often required from service employees like nurses and teachers, who need to restrict or limit their emotional displays in order to provide an expected high level of customer service. Early childhood educators engage daily in work that is physical, mental, and highly emotional. The need for early educators to engage in emotional labor is significant as they provide supervision and care, respond to behavioral challenges, resolve conflict, and work in tandem with other professionals and parents. Several studies have established a link between preschool teachers’ engagement in emotional labor and emotional exhaustion and the quality of their interactions with children (Ansari et al., 2020; Brown et al., 2018). Since teacher–child interactions have been demonstrated to be especially important for children’s development and learning gains, this is of critical importance (Burchinal et al., 2010; Curby et al., 2009). Research focusing on supporting the work of early childhood educators is not only important for strengthening the profession as a whole but is an essential step towards the goal of enriching the lives of young children.
Recent research has also documented a connection between burnout and engagement in emotional labor in helping and service professions like teaching (Tiwari et al., 2020; Yilmaz et al., 2015; Zaretsky & Katz, 2019). However, relatively few studies focus specifically on the issue of emotional labor and burnout within the context of early childhood settings, and few of these have been conducted in the United States. Although research in other educational settings may be helpful for understanding the nature, challenge, and consequences of emotional labor for early childhood educators, more attention to the unique experiences of early childhood educators is necessary. The purpose of this paper is to discuss important issues related to emotional labor in early childhood settings in the United States. The focus will be on understanding what emotional labor is and how it can impact educators based on the limited available research. The implications of the current research and areas for future research will be discussed. Possible solutions to the challenges of engagement in emotional labor, including the use of mindfulness techniques for early childhood teachers, will be explored.
Understanding Emotional Labor and Burnout
To understand how early educators experience emotional labor, it is important to first understand how emotional labor is defined. Based on her observations of flight attendants, Hochschild asserted that there are “feeling rules” or societal norms about the appropriate type and amount of feeling that should be experienced and expressed in a particular situation (Wharton, 2009). When emotions become regulated due to other people’s expectations or organizations in public as required by work, it is then known as emotional labor. Emotional labor is essentially the act of expressing socially desired emotions during work-related transactions despite one’s authentic experience in those moments. Early childhood workers are called upon to continuously engage in emotional labor during their working hours. Ideally, in interactions with parents, other teachers, and children, early educators remain positive, calm, and encouraging, in addition to performing a multitude of other caregiving and instructional tasks.
As early educators and other service workers attempt to manage their emotions, they can engage in surface acting or deep acting. These two strategies are used by employees when they cannot express their true emotions, which allows them to perform the emotional labor that is required of them on the job (Ashforth & Humphrey, 1993; Grandey, 2003). Whereas surface acting only changes the expression of emotion, deep acting transforms our emotional state (Larson & Yao, 2005). Surface acting involves simulating emotions that are not actually felt by changing outward appearances (i.e., facial expression, posture, gestures, or voice tone) to exhibit the required emotions. In this way, the service worker feigns and conveys emotions that are not experienced. For teachers, surface acting can occur when they alter their emotional expression to correspond with social expectations by expressing an emotion they do not really feel (Grandey et al., 2013). For example, when dealing with a challenging student a teacher may smile and appear calm; however, this may not mean they are feeling these emotions, but are instead acting to conform with expected emotional display rules.
Deep acting, on the other hand, occurs when one attempts to actually experience or feel the emotions that one wishes to display. Here the early educator would attempt to induce herself to actually experience the authentic emotions (Ashford & Humphrey, 1993). Unlike surface acting, deep acting involves changing one’s inner feelings by altering more than the outward appearance. Rather than expressing unfelt feelings, individuals actively alter their inner feelings to express the emotion they wish to display or that is required by a job (Mann & Cowburn, 2005). Employees may put forth significant effort to stimulate memories, images, or thoughts to feel or suppress specific emotions at the workplace in order to express organizationally desired emotions (Schirmer & Adolphs, 2017). For example, teachers may try to induce positive memories or reframe their thoughts in a more positive way in order to alter their feelings of irritation when faced with a stressful situation or interaction in the classroom. In sum, surface acting occurs when feelings are changed from the “outside in'', whereas in deep acting, feelings are changed from the “inside out” (Hochschild, 1983).
Not surprisingly, there can be consequences of both surface acting and deep acting on early educators and other service workers. Portraying emotions that are not felt through surface acting creates a sense of strain that Hochschild (1983) terms “emotive dissonance”. This dissonance can lead to personal and work-related maladjustment, resulting in poor self-esteem, depression, and cynicism. Hochschild also argued that because emotional reactions help us to make sense of situations, deep acting may distort these reactions and impair the sense of authentic self, possibly impairing well-being. Deep acting may lead to self-alienation as the individual loses touch with the authentic self, which can impair the ability to recognize or experience real emotion (Ashforth & Humphrey, 1993). The root problem is the discrepancy between the feeling(s) one is expected to outwardly express and what is actually experienced in private.
Research suggests that the demands of emotional labor can result in negative outcomes. Generally, the most consistent findings from research focus on the relationship between surface acting and the emotional exhaustion dimension of burnout. Maslach et al. describe emotional exhaustion as “feelings of being emotionally overextended and exhausted by one’s work” (1996, p. 10). According to Wharton (2009), numerous studies show that workers who report having to display emotions that conflict with their own feelings on a regular basis are more likely than others to experience emotional exhaustion. Interaction with people, besides leading to fatigue, requires the regulation of emotions and is thought to trigger burnout (Rafaeli & Sutton, 1989). For example, a study conducted with early educators in the Philippines completed since the pandemic found that burnout was dependent on teachers’ emotional stability (Dela Cruz, 2020).
Workers employed in the categories of “high emotional labor” jobs (Hochschild, 1983) and “high burnout jobs” (Cordes & Dougherty, 1993) report significantly higher levels of employee stress than do other workers. Specifically, occupants of health care, social service, teaching, and other “caring” professions are more likely to experience burnout (Cherniss, 1993; Jackson et al., 1986; Leiter & Maslach, 1988).
Understanding and acknowledging the connection between emotional labor and burnout is important for understanding the impact and implications of emotional labor on the profession of early childhood education. This is especially critical in the face a global pandemic due to COVID-19, in which early childhood educators were challenged with how to remain operating and how to teach remotely as well. Burnout is traditionally conceptualized as a chronic stress response due to long-term emotional and interpersonal job stressors (Maslach et al., 2001). The Maslach Burnout Inventory (MBI) is the most popular tool for assessing burnout and has been used widely in many occupations and nations. The MBI consists of three dimensions. The first dimension of burnout, exhaustion, is characterized by feeling emotionally overextended, exhausted, and being unable to psychologically or physically recover from the various demands placed on the individual to complete work related tasks. Exhaustion is often used to describe the emotional experience of burnout and is the most robust of all the burnout dimensions. In a recent study of 273 early childhood teachers, researchers found that teachers’ emotional exhaustion was related to a lack of work control, a lack of collegial relationships, and difficulty in responding to children’s challenging behaviors (Schaack et al., 2020).
Research conducted since the start of the pandemic suggests that the work of early educators has become more challenging and is taking a greater emotional toll on early educators. For instance, a study of early childhood educators in the United States found that educators were more likely to report that the pandemic had adverse impacts on their mental health and had caused moderate levels of stress (Hanno et al., 2020). In another study that surveyed early educators nationwide on their experiences since COVID, 91% of teachers surveyed indicated they were somewhat to very concerned about the increase in their overall stress levels (Jones, 2020). Based on prior research related to emotional labor and burnout, more investigation into how the emotional work required in early care and education settings contributes to burnout and impacts the profession as a whole is warranted, especially in light of the COVID-19 pandemic. A greater understanding of these important elements of early childhood educators’ work could ultimately translate into the development of strategies intended to buffer early educators against burnout in the future (Maslach & Leiter, 1997).
Emotional Labor in Educational Settings
As mentioned, very limited research on emotional labor has been conducted in early childhood settings, and most of the extant literature is a result of research that has been conducted in other countries, which may not necessarily speak to the experiences of early childhood educators in the United States. However, there is a significant amount of research on the impact that stress and burnout have on the emotional well-being of teachers working in K-12 environments. In a study of United States K-12 teachers, 100% of teachers surveyed reported engagement in emotional labor (Brown et al., 2014). Like the work of early childhood educators, the work K-12 teachers perform with students and families is emotional in nature, requiring them to navigate through a range of emotions daily, both their own and that of others. How well teachers successfully regulate their own emotions as they guide and facilitate others' emotions can significantly impact the climate and culture of a classroom. According to Horner et al. (2020), unlike other emotional labor jobs, such as those within the service industry where interactions with others can be brief and random, teachers have prolonged and consistent engagements with students and families. Teachers are required to be both subject matter experts and emotional development facilitators in the classroom. Teachers are expected to regulate their emotions and respond to students and families in ways that fall within acceptable and professional rules and conduct guidelines. Horner et al. (2020) describe how teachers use the strategy of emotional acting to negotiate positive and negative encounters in the classroom. For example, teachers might engage in surface acting to regulate emotions in response to negative classroom situations such as student outbursts and disruptions, school crisis drills, and conflicts with parents. Teachers in this study described acting enthusiastically or responding positively to something in the classroom while concealing negative emotions. New teachers are often unprepared for the emotional demands of teaching as they enter the field. Historically, emotional practice has not been addressed in most teacher preparation programs, but some are working to change that.
Although research on emotional labor and burnout in K-12 settings does exist, more research specifically on emotional labor and burnout in early childhood settings is necessary because of some important differences between K-12 and early childhood educators. Early childhood educators often have less training, preparation, and support than K-12 teachers, which may exacerbate the challenges of emotional labor. In a study of over 1600 early educators in Nebraska, it was found that many early educators did not earn livable wages, and between 20 and 30% of early childhood teachers utilized public assistance to make ends meet (Roberts et al., 2017). Phillips et al. (2016) describe early childhood teachers as “some of the most erratically trained and poorly paid professionals in the United States” (p. 140). The authors draw attention to the profound “contradiction inherent in this characterization of the early care and education workforce, and its implications for the well-being of the millions of young children in early childhood care” (p. 140). While training and compensation for early educators may be lacking in many cases, the skills required are significant. Meloy and Schachner (2019) identified a lengthy list of essential competencies as part of defining the work of early educators. This long list includes providing developmentally appropriate practice and environments, ongoing observations and assessment, individualized learning and inclusion practices, cultivating family support and partnerships, and participating in continuous improvement and professionalism. The role of the early educator also involves being a responsive caregiver, a good listener, observer, actor, and content expert. Fulfilling these roles and managing the associated emotions, especially without adequate training and support, create profound stressors on early educators.
Differences in the ways in which the work of K-12 teachers and early childhood educators is valued are also important to acknowledge. Early educators are often viewed as babysitters or caregivers, and in spite of the body of research pointing to the important of high quality early care and education, “the perception that caring for young children is unimportant work persists” (Payton, 2021, p. 10). Early educators are sometimes paid less than half of teachers serving in K-12, and salaries for early childhood educators still lag well behind their counterparts in K-12 education, even with similar degree requirements. Training and access to professional development are other important issues. Phillips et al. (2016) point out that training, compensation and working conditions for K-12 teachers are relatively uniform, whereas these things are highly inconsistent for early childhood educators. In addition, professional development is often limited or unavailable to early educators. These gaps in wages, training, support, and access to professional development contribute to burnout and teacher turnover. They may also make early childhood educators more vulnerable to the challenges of coping with demands of emotional labor.
As mentioned, research focusing on emotional labor in early childhood settings is sparse and most of what currently exists has not been conducted in the United States. Research about engagement in emotional labor by early educators in other countries provides some interesting insights. For example, research by Vincent and Braun (2013) conducted in the United Kingdom explored the process in which early educators learn the emotional “rules” of working with children. They noted that early educators not only must manage their own emotions at work, but that they often consider themselves responsible for being role models for young children in the area of emotional recognition and management, helping them identify and appropriately express their emotions. The early educators interviewed generally believed that “they could and should be warm and positive towards all of the children all of the time, without getting too attached to individuals”, which is a monumental task (p. 765). Another study from China described the emotional labor of preschool teachers as being “characterized by its long duration, high intensity, and diversity in emotional interactions” (Zhang et al., 2020). A study conducted in New Zealand (Anuja, 2017) identified difficult interactions with parents and teachers, unsupportive management, and heavy workloads as contributors to early childhood teachers’ experience of emotional labor. Although additional research on the subject of emotional labor by early educators outside of the United States exists, a cross-cultural study on emotional labor in early education settings by Hong and Zhang (2019) underscores the need to be cautious about generalizing these findings because of issues related to the study of diverse populations. In a qualitative study comparing the emotional labor of early childhood teachers in Norway and China, researchers found that although both groups believed that engagement in emotional labor was important for their work, the group of teachers in Norway perceived and approached emotional labor differently than those in China. For this reason, care should be taken when generalizing findings from research completed in other countries, and additional research on emotional labor should be conducted in various early childhood settings within the United States.
Supporting Early Educators Response to Emotional Labor
Studies have found a relationship between an educator's social and emotional competency, healthier and more authentic relationships with students, and greater success in the classroom. Research findings by Yilnaz et al. (2015) show that emotional labor plays a role in job satisfaction, rate of burnout, and teacher emotional health both inside and outside the classroom. A study by Wender and DeMille (2019) addresses the need for more focus on emotional practices in teacher education programs to help prepare pre-service teachers for their roles in managing the often-unanticipated issues regarding emotional labor. This study also suggests that a proactive approach would be to intentionally address emotional well-being strategies and talk about what to expect in the classroom. The study goes on to stress the importance of journaling as well as reflection as a way for pre-service educators to acknowledge and process their emotions. Strategies for addressing emotional well-being not only benefit the teacher but also contribute to a healthy classroom climate. "By carefully attending to various aspects of teachers' emotional practice, we will be better able to support them as they work to foster the academic success and emotional growth of their students" (Horner et al., 2020, p. 25).
Because of the growing understanding of the importance of teacher’s emotional practice, professional development opportunities to help teachers learn about the role of emotional labor in their work while cultivating healthy emotional wellness practices are now being offered in school districts, workshops, and conferences. Jennings (2018) advocates for programs such as the Garrison Institute's Cultivating Awareness and Resilience for Educators (CARE). These programs are becoming more prevalent training for K-12 teachers as districts begin to invest in professional development opportunities targeting self-care. Baker (2020) suggests that self-care should be an integrated part of the day for first-year teachers. Unfortunately, these types of efforts are not yet being targeted on any scale to early childhood educators.
Few training programs, conferences, and preparation programs include targeted strategies for addressing emotional well-being or emotional practice in early childhood contexts, but some research supports their effectiveness. One study conducted with preschool teachers in China (Gu et al., 2019), for example, showed that recovery experiences protected teachers from the detrimental effects of work stress. While this study was not conducted in the United States and focused on the construct of “emotional dissonance “rather than emotional labor, it highlights the important question of the need to clearly understand what strategies might be effective to promote teachers” emotional health. Beyond individual recovery strategies, there may also be programmatic ways that promote better responses to the demands of emotional labor. For example, work by Jeon and Ardeleanu (2020) using a large group of preschool teachers from across the United States found that when teachers had a more positive work climate and felt more supported by parents they were better able to utilize emotional regulation strategies. In other research exploring the emotional labor experiences of Early Head Start teachers conducting home visits, Lane (2011) proposes reflective supervision as a means of supporting early educators. Moreover, based on a national survey on early educators, Madill et al. (2018) concluded that both formal workforce supports, like supervision and mentoring, as well as informal supports such a teamwork and a respectful work environment, were important contributors to teachers’ psychological well-being. Additional investigation into these areas could be very helpful for early childhood leaders, policy makers and developers of early childhood programs.
Recovery from emotional labor is an important process through which employees restore the resources that have been depleted during work (Sonnentag & Fritz, 2007). While deep and surface acting can cause more long term issues than they solve, recent research asserts that the effects experienced by the individual may vary depending on which emotional display rule is used. Xanthopoulou et al. (2017) argue that surface acting may be more harmful to employees as it is likely to exhaust employees’ resources in the short run and hinder the recovery process, whereas deep acting may allow employees’ to reserve or even gain resources at work that may facilitate recovery. Effective recovery involves emotional regulation strategies, which are critical for individuals to cope with negative experiences while engaging one’s work environment. These strategies are used to aid individuals in managing overwhelming experiences.
There are a myriad of strategies that can be used to regulate emotions and moods in the workplace. Gross (1998) classified these strategies into two main categories: preventative (antecedent-focused) and responsive (response-focused). When using preventative strategies, individuals try to modify how much or what type of emotion they experience before the onset of the emotion by: selecting situations, modifying situations, attention deployment, and cognitive change. For example, an early educator might employ a preventative strategy by practicing some calm verbal responses prior to meeting with an angry parent. Strategies like this are associated with deep acting. In contrast, responsive emotional regulation occurs after the emotion has been felt and it acts to “intensify, diminish, prolong, or curtail the ongoing emotional experiences, expression or physiological responding” (Gross, 1998, p. 225). Responsive approaches to emotional regulation, such as engaging in a few minutes of deep breathing after helping to calm a toddler who has been bitten by a peer, involve surface acting. Self-care techniques such as taking breaks, emotional distancing, relaxation, exercising, talking to peers, and sleep quality are other useful tools intended to improve emotional regulation (Diestel et al., 2015; Karing & Beelmann, 2019; Sonnentag & Fritz, 2007; Sutton, 2004). For example, in a study of 50 female childcare workers in the southern United States, the use of strategies such as exercising and meditation for short periods daily, often less than 15 min, was associated with less work stress (Carson et al., 2017).
One prominent cognitive self-care technique to mitigate the effects of emotional labor is mindfulness. Mindfulness is both a preventative and a responsive way to deal with emotional labor and burnout. Mindfulness is a way to increase emotion regulation by applying specific meditation practices (Hill & Updegraff, 2012). Meta-analyses on various types of mindfulness-based interventions (MBIs) have shown an improvement in overall well-being (Blanck et al., 2018; Eberth & Sedlmeier, 2012; Franca & Milbourn, 2015). MBIs have demonstrated the ability to improve negative personality traits, stress, and psychological distress (Querstret et al, 2020). One meta-analysis of Mindfulness Based Stress Reduction (MBSR), a specific type of mindfulness-based therapy, found that such treatments have the ability to reduce anxiety as well as increase empathy and self-compassion (Chiesa & Serretti, 2009). Mindfulness has demonstrated great benefits in clinical and medical samples. For instance, mindfulness-based therapies (MBTs), such as Mindfulness Based Cognitive Therapy (Segal et al., 2018), have been repeatedly shown to alleviate depressive symptoms and prevent drug relapses (Barnhofer et al., 2015; Bowen et al., 2014; Kingston et al., 2007). These findings have been extended to schoolteachers coping with burnout (Flook et al., 2013; Tarrasch et al., 2020). In a recent study of 515 Kindergarten teachers in China, the use of mindfulness was associated with deep acting and more naturally felt emotions and negatively correlated with burnout (Ma et al., 2020).
Mindfulness may be defined as awareness to the present moment. It is a shift from doing things to simply being with one’s self and observing what arises, such as thoughts, feelings, physical sensations, and more (Kabat-Zinn, 2013). The emphasis in mindfulness encourages paying attention without judgment and minimize identification with what arises (Bishop et al., 2004). Mindfulness entails bringing one’s attention to the present moment and how to bring acceptance and non-judgment into that experience (Orsillo & Roemer, 2005). The practice of mindfulness has been shown to provide relief and coping as a response to stressors. Mindfulness has robust support as a positive coping strategy in response to burnout and job stress (Frederick et al., 2018; Grover et al., 2017). Because the current research on mindfulness techniques is so promising, more work needs to be done to identify effective mindfulness techniques for teachers that translate well to working in the classroom, particularly in early childhood settings. Once these are identified, efforts should be made to incorporate this important information into teacher training programs and new teacher support systems.
Implications
Good teachers matter in the lives of children, and understanding and addressing the challenges associated with the work of early childhood educators has many potential benefits. The work of early childhood educators is important and complex, and their success and skill can translate into better outcomes for young children. Early educators’ engagement in emotional labor may be unavoidable, but research suggests that it may be possible to mitigate the burnout associated with this emotional labor through the use of appropriate strategies; however, more work must be done to uncover what strategies are effective. Ideally, future scholars in the area of early childhood education will focus on multiple domains in researching the consequences of emotional labor, job-related stress, and burnout. A concerted effort to understand the differences between early childhood educators and others in the experience of burnout would likely also be useful. Not only is the work of early educators unique, this work also occurs in many different contexts: infant/toddler centers, early intervention settings, preschools, faith-based programs, private programs, as well as government funded and regulated programs like state preschool, transitional kindergarten, and Head Start programs. Understanding more about the nuances of engagement in emotional labor in these various contexts would be helpful for finding tailored solutions.
In addition to conducting research in different settings, it is also important to investigate the experience of emotional labor in various demographic groups. In the United States, there has been less of an effort to track burnout and job stress among various careers and vocations compared with European nations. Within the United States, there is also reason to believe that some groups may be more vulnerable to the effects of emotional labor and other workplace stressors. For instance, in a study examining predictors of psychological distress for early educators, it was found that early educators belonging to certain demographic groups, including those with lower household income and those with lower levels of education, experienced greater distress (Madill, 2018). There are many areas for further investigation in order to truly understand enough about emotional labor in early education to begin to address this important challenge.
Some of the important questions to raise are related to the concept of burnout as a consequence of engagement in emotional labor, Do early childhood educators experience burnout along the lines of emotional exhaustion, indifference, and personal effectiveness as described by Maslach and colleagues, or are other nuances present (Maslach & Leiter, 1997; Maslach, et al., 1996)? What factors contribute most to the burnout and job stressors of early childhood educators? How does emotional exhaustion relate to emotional labor as described above? Researchers can also incorporate the literature on emotional labor strategies in investigating burnout and job stress for early childhood educators. Early childhood educators provide significant emotional labor for the children placed in their care, modeling and relating in emotionally healthy ways. To this end, an explicit focus on understanding how early educators use surface and deep acting in response to emotional labor (Brotheridge & Lee, 2003; Diefendorff et al., 2005) would allow scholars to measure the amount and effect of emotional labor for early childhood educators. Including emotional labor in studying burnout and job stress among early childhood educators would allow researchers to understand the relationship between burnout and surface and deep acting for individuals in this career.
The final dimension that scholars should incorporate in studying burnout and job stress among early childhood educators focuses on mindfulness. Based on the research presented above, mindfulness has been shown to be an excellent emotion regulation strategy. Further, mindfulness has been demonstrated to aid in coping with stress. Research on mindfulness for early childhood educators can begin by focusing on understanding the relationships between burnout, emotional labor strategies, mindfulness. One pressing question is: Does mindfulness mediate the relationship between emotional labor strategies and burnout for early childhood educators? Determining the nature of this relationship will form the basis of developing some interventions for early childhood educators. These strategies seem to hold great promise, but much more research is needed to understand the experience of emotional labor in United States early childhood settings so effective interventions can be developed and used to support teachers.
Author Contributions
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Int J Infect Dis
Int J Infect Dis
International Journal of Infectious Diseases
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Article
The Russia-Ukraine war could bring catastrophic public-health challenges beyond COVID-19
Ramírez Céleo 1
Durón Reyna M. 2⁎
1 Consorcio de Investigadores COVID Honduras
2 Observatorio de COVID-19, Universidad Tecnológica Centroamericana, Tegucigalpa, Honduras
⁎ Corresponding author. Reyna M. Durón, Research, Universidad Tecnológica Centroamericana (UNITEC), Zona Jacaleapa, Blv. Kennedy, Tegucigalpa, Honduras
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Key words
Armed Conflicts
Biological Warfare Agents
COVID-19
Nuclear Weapons
Radiation Effects
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pmcDear Editor,
Dahl et al. (2022) recently warned in this journal that armed conflict and human displacement by the Russia-Ukraine conflict may increase the burden of tuberculosis in Europe. But there could be more public health problems ahead. Since 2 years ago, the coronavirus disease 2019 (COVID-19) pandemic has been an important catalyst for international collaboration on public health (Jit et al., 2021). This collaboration has not been error-free, especially in relation to an equitable distribution of diagnostic tests, treatments, and vaccines between high-income and low-middle-income countries (Javed & Chattu, 2020).
The pandemic has been, at least temporarily, a kind of freezer of major war conflicts worldwide due to the joint efforts to mitigate its impact. A better understanding of the virus, growing herd immunity produced by COVID-19 vaccines and/or previous infection (Randolph & Barreiro, 2020; Radbruch & Chang, 2021), and the transition to less lethal variants (Petersen et al., 2022) have coincided with the beginning of a warfare in East Europe. The immediate consequences for Ukraine and surrounding regions are COVID-19 surges, unattended chronic diseases, emerging infections and drop in vaccination rates, including anticovid vaccination.
The risk of an escalation is latent and consequences are unimaginable in case nuclear weapons are used. All simulators of a nuclear war conclude in the inevitable figure of millions of deaths and an incalculable number of people affected by radiation (Princeton University, 2022). We already know about the immediate and long-term aftermath of the Chernobyl disaster or the Hiroshima and Nagasaki nuclear bombings in World War II (Douple et al., 2011).
Another threat is the use of biological and chemical weapons that disseminate biological agents or toxins to cause harm, disease, and death of humans or animals, and harm the environment (Janseen et al., 2014; Ekzayez et al., 2020). In no way this type of warfare can be local or easy to contain. The global scientific community must warn the world leaders about the abyss into which humanity can fall, if consensus is not reached in a timely manner to avoid self-destruction.
After the use of nuclear weapons, there will be no winners among survivors. The health systems, especially in the countries most affected by radiation, will have to deal with its short-, medium-, and long-term effects on the population. It is time that states directly or indirectly affected by the Ukraine and Russia conflict cooperate bilaterally or multilaterally to stop the ongoing war.
Conflict of interest
All authors declare no competing interest related to this paper.
Founding source
Universidad Tecnológica Centroamericana, Honduras.
Ethical approval
Not applicable
Authors’ contribution
CR and RMD developed the concept, reviewed the literature, and wrote the manuscript. References
Dahl V, Tiberi S, Goletti D, Wejse C. Armed conflict and human displacement may lead to an increase in the burden of tuberculosis in Europe. Int J Infect Dis 2022;S1201–-9712(22)00180–1. doi: 10.1016/j.ijid.2022.03.040
Douple EB, Mabuchi K, Cullings HM, Preston DL, Kodama K, Shimizu Y, Fujiwara S, Shore RE. Long-term radiation-related health effects in a unique human population: lessons learned from the atomic bomb survivors of Hiroshima and Nagasaki. Disaster Med Public Health Prep 2011;5;Suppl 1:S122–33. doi: 10.1001%2Fdmp.2011.21
Ekzayez A, Flecknoe MD, Lillywhite L, Patel P, Papamichail A, Elbahtimy H. Chemical weapons and public health: assessing impact and responses. J Public Health (Oxf) 2020;42:e334–42. doi: 10.1093/pubmed/fdz145
Jansen HJ, Breeveld FJ, Stijnis C, Grobusch MP. Biological warfare, bioterrorism, and biocrime. Clin Microbiol Infect 2014;20:488–96. doi: 10.1111/1469-0691.12699
Javed S, Chattu VK. Strengthening the COVID-19 pandemic response, global leadership, and international cooperation through global health diplomacy. Health Promot Perspect 2020;10:300–5. doi: 10.34172%2Fhpp.2020.48
Jit M, Ananthakrishnan A, McKee M, Wouters OJ, Beutels P, Teerawattananon Y. Multi-country collaboration in responding to global infectious disease threats: lessons for Europe from the COVID-19 pandemic. Lancet Reg Health Eur 2021;9:100221. doi: 10.1016/j.lanepe.2021.100221
Nuclear Princeton, Princeton Science and Global Security Nuclear War Simulation https://nuclearprinceton.princeton.edu/news/princeton-science-and-global-security-nuclear-war-simulation, 2019 (accessed 22 March 2022).
Petersen E, Ntoumi F, Hui DS, et al. Emergence of new SARS-CoV-2 Variant of Concern Omicron (B.1.1.529) - highlights Africa's research capabilities, but exposes major knowledge gaps, inequities of vaccine distribution, inadequacies in global COVID-19 response and control efforts. Int J Infect Dis 2022;114:268–72. doi: 10.1016/j.ijid.2021.11.040
Radbruch A, Chang HD. A long-term perspective on immunity to COVID. Nature 2021;595:359–60. doi: 10.1038/d41586-021-01557-z
Randolph HE, Barreiro LB. Herd immunity: understanding COVID-19. Immunity 2020;52(5):737–41. doi: 10.1016/j.immuni.2020.04.012
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Biosaf Health
Biosaf Health
Biosafety and Health
2590-0536
Chinese Medical Association Publishing House. Published by Elsevier BV.
S2590-0536(22)00049-0
10.1016/j.bsheal.2022.04.001
Case Report
Long-term asymptomatic SARS-CoV-2 infection associated with deficiency on multiple immune cells
He Gang a1
Chuai Xia b1
Liang Dan c
Chen Chunyu a
Hu Changzheng a
Ke Changwen c
Ke Bixia c⁎
Zhen Peilin a⁎
Zhang Huajun d⁎
a Jiangmen Central Hospital affiliated with Jiangmen Hospital of Sun Yat-Sen University, Jiangmen 529000, China
b Department of Pathogenic Biology, Hebei Medical University, Shijiazhuang, Hebei 050017, China
c Guangdong Center for Disease Control and Prevention, Guangzhou 511430, China
d State Key Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan 430071, China
⁎ Corresponding authors: Guangdong Center for Disease Control and Prevention, Guangzhou 511430, China (B. Ke). Jiangmen Central Hospital affiliated with Jiangmen Hospital of Sun Yat-Sen University, Jiangmen 529000, China (P. Zhen). State Key Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan 430071, China (H. Zhang).
1 These authors contributed equally to this work.
12 4 2022
12 4 2022
31 12 2021
21 3 2022
11 4 2022
© 2022 Chinese Medical Association Publishing House. Published by Elsevier BV.
2022
Chinese Medical Association Publishing House
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 immune responses and the function of immune cells among asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection cases, especially in immuno-compromised individuals, remain largely unknown. Here we present a case of asymptomatic SARS-CoV-2 infection that lasted for at least 67 days. The patient has administrated Thymalfasin as 1.6 mg per dose every other day from Day 45 to 70, plus 200 mg per dose Arbidol antiviral therapy three doses per day from Day 48 to 57. Throughout the infection, no anti-SARS-CoV-2 specific IgM or IgG antibodies were detected. Instead, the patient showed either a low percentage or an absolute number of non-classical monocytes, dendritic cells (DCs), CD4+ T cells, and regulatory T cells (Tregs), which may account for the clinical feature and absence of antibody response. This case may shed new light on the outbreak management related to control/prevention, treatment, and vaccination of SARS-CoV-2 and other virus infections in immunocompromised individuals.
Keywords
SARS-CoV-2
COVID-19
Asymptomatic infection
Antibody response
Immune cells
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pmc1 Introduction
The coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2, and the clinical manifestations were widely varied, ranging from asymptomatic to mild, moderate, and severe pneumonia, which frequently leads to death [1]. The variability of disease severity was closely related to the individual immune responses to SARS-CoV-2 after the first infection [2]. For example, Wong et al. reported that total lymphocytes, CD4+ T cells, CD8+ T cells, B cells, and natural killer (NK) cells decreased in COVID-19 patients, and severe cases had a lower level than mild cases [3]. And Zhou et al. found that acute SARS-CoV-2 infection resulted in broad immune cell reduction, including T cells, NK, monocyte, and DCs [4]. But most SARS-CoV-2 infected people, including asymptomatic individuals, developed virus-specific antibodies for up to months [5], [6].
Immuno-compromised patients are prone to progress into severe or critical types underpinned by impaired immune function. However, in this case study, we present an asymptomatic COVID-19 patient who was initially diagnosed positive in Nigeria but negative in the following three tests before traveling to Guangzhou, China, where she was tested positive again. The patient was administrated Thymalfasin plus Arbidol antiviral therapy. The patient had been positive with real-time RT-PCR for 67 days, but no SARS-CoV-2 specific IgM or IgG antibodies were detected in the sera. Flow cytometry analysis of the blood samples found that the patient had dysfunctions in immune response with a low percentage or an absolute number of non-classical monocytes, DCs, CD4+ T cells, and Tregs.
2 Case presentation
A 33-year-old female overseas worker was diagnosed as SARS-CoV-2 nucleic acid positive during quarantine when entering Guangzhou, China. The patient was initially diagnosed but without any symptoms in early January of 2021 (Day 17) in Nigeria, where she had worked since 2019. But the tests performed on Day 13, 12, and 8 showed negative, and she took an airplane on Day 6 back to Guangzhou.
During quarantine time, real-time RT-PCR (Daan Gene, China) of nasal swabs were collected on Day 3 and 2, showed negative but positive for both ORF1ab and N genes on Day 0, with CT values of 33.62 and 37.31, respectively (Fig. 1 ). Then the patient was sampled every two days. On Day 2, the CT values dropped to 23.57 and 18.53 for ORF1ab and N, respectively; then the CT values increased towards the detection limit, indicating virus clearance. However, the patient was negative for both genes until 69 and 70 days later, when she was discharged. No SARS-CoV-2 specific IgM or IgG antibodies were detected in the sera collected on Day 0, 28, 35, 41, 48, 55, 62, 69, 77 and 98 with SARS-CoV-2 IgM/IgG detection kit (Livzon, China, Fig. 1).Fig. 1 The CT value of real-time RT-PCR detecting ORF1ab and N genes. The dotted line indicates the detection limit, and the negative detection is given a CT value of 45. × indicates the negative result of SARS-CoV-2 specific IgM and IgG antibodies, ■ indicating the treatment of Thymalfasin as 1.6 mg per dose every other day, and ◆ Arbidol therapy as 200 mg per dose, three doses a day.
Although chest CT examination on Day 0 showed multiple small solid nodules in the bilateral oblique fissure of the left lung and lymph node, the left hilar showed calcification. The patient showed no other symptoms and had nothing noted about multiple tests, including BCA, liver function, biochemical tests, coagulation function, erythrocyte sedimentation rate, procalcitonin, C-reactive protein, myocardial enzymes, and myocardial injury markers. The patient was administrated Thymalfasin 1.6 mg per dose every other day from Day 45 to 70, plus Arbidol antiviral therapy 200 mg per dose three doses per day from Day 48 to 57, when the virus wasn’t cleared yet.
Blood was sampled on Day 77 and Day 98 and subjected to flow cytometry tests on various cell populations. As shown in Table 1 , total monocytes were normal, with a slight increase of classical monocytes on Day 98 and a decrease of intermediate monocytes on Day 77, both in percentage but not in absolute number. Still, compared to the reference value, the rate of non-classical monocytes in monocytes was lower on Day 77 and dropped almost 50% in both percentage and absolute number on Day 98. But DCs were reversed for both days, increasing from 0.15% to 0.27%, though the total number was significantly low. Among DCs, myeloid DCs (mDCs) showed a similar trend as DCs; however, type 1 and type 2 mDCs were relatively high in percentage. A low rate was also found for CD4+ T cells but didn’t vary much on both days. Like non-classical monocytes, Tregs were deficient and significantly decreased from Day 77 to Day 98, while Naïve Tregs had some increase but were still low on both days. CD8+ T cells and NK cells were normal in percentages and absolute numbers. The patient had slightly low B cells on Day 77 in total number, but not in rate.Table 1 Summary of flow cytometry analysis of immune cells in the whole blood.
Cell population Markers D77 D98 Reference value
Percentage Indi.* Percentage Indi. Percentage/denominator
Absolute number/μL Absolute number/μL Absolute number/μL
Monocytes CD14+ 9.51 9.81 3.00–10.90/leukocytes
360.00 370.00 144.00–702.00
Classical monocytes CD14high CD16- 92.02 93.88 ↑ 68.44–93.40/monocytes
332.00 347.00 114.00–589.00
Intermediate monocytes CD14high CD16+ 2.02 ↓ 3.83 2.60–15.80/monocytes
7.00 14.00 7.00–70.00
Non-classical monocytes CD14+ CD16high 1.97 ↓ 1.00 ↓ 2.20–16.70/monocytes
7.00 4.00 ↓ 7.00–86.00
Dendritic cells Lin- HLA-DR+ 0.15 ↓ 0.27 0.20–1.90/leukocytes
6.00 ↓ 10.00 ↓ 20.00–121.00
myeloid DC (mDC) Lin- HLA-DR+ CD11c+ 0.09 ↓ 0.16 0.10–1.70/leukocytes
3.00 ↓ 6.00 ↓ 10.00–107.00
CD16+ mDC HLA-DR+ CD11c+ CD16+ 15.54 ↓ 15.17 ↓ 33.90–98.20/mDC
1.00 ↓ 1.00 ↓ 5.00–95.00
mDC1 Lin- HLA-DR+ CD11c+ CD16- CD1c+ Clec9A- 60.33 73.12 ↑ 1.70–61.60/mDC
2.00 4.00 1.00–22.00
mDC2 Lin- HLA-DR+ CD11c+ CD16- CD1c- Clec9A+ 16.97 ↑ 5.75 ↑ 0.10–4.50/mDC
1.00 ↑ 0.00 0.00–0.90
CD4+ T cells CD3+ CD4+ 39.37 ↓ 44.07 ↓ 46.20–78.00/T cells
300.00 398.00 199.00–1414.00
CD8+ T cells CD3+ CD8+ 43.45 43.30 14.80–48.40/T cells
331.00 391.00 61.00–1,118.00
NK cells CD3- CD56+ 13.55 7.64 3.30–32.90/lymphocytes
135.00 90.00 53.00–569.00
Regulatory T cell (Treg) CD3+ CD4+ CD25high FoxP3+ CD4+ 6.94 3.13 ↓ 5.10–12.70/T cells
21.00 ↓ 12.00 ↓ 28.00–142.00
Naïve Treg CD3+ CD4+ CD25high FoxP3+ CD45RA+ 6.49 8.09 3.50–77.30/Treg
1.00 ↓ 1.00 ↓ 4.00–68.00
B cells CD19+ CD3- 4.94 7.14 3.80–21.50
49.00 ↓ 84.00 51.00–728.00
* ↑ and ↓ indicate increase and decrease compared to the reference value, respectively.
3 Discussion
In severe and critical cases, acute SARS-CoV-2 infection could reduce broad immune cells, including CD4+ T cells, CD8+ T cells, NK cells, and DCs in severe and critical cases [3], [4]. Benjamin et al. found high proportions of SARS-CoV-2-reactive cytotoxic CD4+ T cells and a reduced proportion of SARS-CoV-2-reactive Tregs in hospitalized patients [7]. But SARS-CoV-2 infection led to diverse effects on monocytes, with reduction of non-classical monocytes and accumulation of classical monocytes in severe patients [8]; Gatti et al. also reported an increase of non-classical and intermediate monocytes in patients with moderate symptoms [9]. In addition, non-classical monocytes increased in patients with infectious diseases, and in vitro cultured non-classical monocytes exhibit phenotypic and functional dendritic cell-like characteristics [10], [11], indicating they play essential roles in the immune response against pathogens.
Long et al. reported that 81% and 62% of asymptomatic patients tested positive for IgG and IgM after exposed for 3 to 4 weeks, respectively [6]. Although the low percentage or an increase in absolute number of multiple immune cells of the case might result from SARS-CoV-2 infection, the absence of SARS-CoV-2 specific antibody indicated that the patient might have dysfunctions of the immune system response. Supporting, the patient complained of frequent cough and cold, and no IgG antibody was detected against yellow fever virus (YFV) when the patient was vaccinated before she went to Nigeria in 2019 (data not shown).
Interestingly, SARS-CoV-2 RNA was detected up to 105 days after initial diagnosis in an immunocompromised female individual with chronic lymphocytic leukemia who finally cleared the virus after two doses of convalescent plasma transfusion [12]. Our patient was diagnosed positive for 67 days. Thymalfasin was administered as 1.6 mg per dose every other day from Day 45 to 70, plus Arbidol antiviral therapy as 200 mg per dose three doses per day from Day 48 to 57, when the virus wasn’t cleared yet. Our patient was initially diagnosed positive in Nigeria but became negative in the following three tests before traveling to Guangzhou, China, where she became positive again. At least 13 days when the virus was cleared between the two diagnostic results. It’s interesting to investigate how the virus was removed twice in such an individual with a dysfunctional immune response.
It’s not clear whether the second result was the reoccurrence of the first one or from another infection. However, since the patient developed no antibodies against SARS-CoV-2 and YFV, it’s possible that the second was from reinfection; in line with it, one of her colleagues was diagnosed on the day before leaving Nigeria SARS-CoV-2 with fever.
In summary, we report a case of asymptomatic SARS-CoV-2 infection up to 67 days with a low percentage or an absolute number of non-classical monocytes, DCs, CD4+ T cells, and Tregs. In addition, the patient generated no antibodies against SARS-CoV-2 or YFV despite being vaccinated. These results indicated that the patient had dysfunctions in the immune response. This case may shed new light on the outbreak management related to control/prevention, treatment, and vaccination of SARS-CoV-2 and other virus infections in immunocompromised individuals.
Ethics statement
This study has been approved by Ethic Committee of the Jiangmen Hospital (approval serial number 2020139). The patient has signed informed consent to participate in this study.
Acknowledgements
The study was funded by Natural Science Foundation of Hebei Province granted to XC (no. H2020206352) and Novel Coronavirus Project to GH by Jiangmen Science and Technology Bureau (2020020500410003915) and Guangzhou Emergency Response Plan to D.L (EKPG21-27). The funders had no role in the study’s design or the decision to publish this work.
Conflict of interest statement
The authors declare that there are no conflicts of interest.
Author contributions
Gang He: Investigation, Funding acquisition. Xia Chuai: Data curation, Funding acquisition, Writing – review & editing. Dan Liang: Investigation. Chunyu Chen: Resources. Changzheng Hu: Resources. Changwen Ke: Supervision. Bixia Ke: Conceptualization. Peilin Zhen: Conceptualization, Project administration. Huajun Zhang: Conceptualization, Writing – original draft.
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References
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2 Maggi E. Canonica G. Moretta L. COVID-19: Unanswered questions on immune response and pathogenesis J. Allergy Clin. Immunol. 146 1 2020 18 22 10.1016/j.jaci.2020.05.001 32389590
3 Wang F. Nie J. Wang H. Zhao Q. Xiong Y. Deng L. Song S. Ma Z. Mo P. Zhang Y. Characteristics of peripheral lymphocyte subset alteration in COVID-19 pneumonia J. Infect. Dis. 221 11 2020 1762 1769 10.1093/infdis/jiaa150 32227123
4 Zhou R. To K.K. Wong Y.C. Liu L. Zhou B. Li X. Huang H. Mo Y. Luk T.Y. Lau T.T. Acute SARS-CoV-2 infection impairs dendritic cell and T cell responses Immunity 53 4 2020 864 877.e5 10.1016/j.immuni.2020.07.026 32791036
5 Lei Q. Li Y. Hou H.Y. Wang F. Ouyang Z.Q. Zhang Y. Lai D.Y. Banga Ndzouboukou J.L. Xu Z.W. Zhang B. Antibody dynamics to SARS‐CoV‐2 in asymptomatic COVID‐19 infections Allergy 76 2 2021 551 561 10.1111/all.14622 33040337
6 Long Q.X. Tang X.J. Shi Q.L. Li Q. Deng H.J. Yuan J. Hu J.L. Xu W. Zhang Y. Lv F.J. Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections Nat. Med. 26 8 2020 1200 1204 10.1038/s41591-020-0965-6 32555424
7 Meckiff B.J. Ramírez-Suástegui C. Fajardo V. Chee S.J. Kusnadi A. Simon H. Eschweiler S. Grifoni A. Pelosi E. Weiskopf D. Imbalance of regulatory and cytotoxic SARS-CoV-2-reactive CD4(+) T cells in COVID-19 Cell 183 5 2020 1340 1353.e16 10.1016/j.cell.2020.10.001 33096020
8 Silvin A. Chapuis N. Dunsmore G. Goubet A.G. Dubuisson A. Derosa L. Almire C. Hénon C. Kosmider O. Droin N. Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19 Cell 182 6 2020 1401 1418.e18 10.1016/j.cell.2020.08.002 32810439
9 Gatti A. Radrizzani D. Viganò P. Mazzone A. Brando B. Decrease of non-classical and intermediate monocyte subsets in severe acute SARS-CoV-2 infection Cytometry A 97 9 2020 887 890 10.1002/cyto.a.24188 32654350
10 Ancuta P. Weiss L. Haeffner-Cavaillon N. CD14+CD16++ cells derived in vitro from peripheral blood monocytes exhibit phenotypic and functional dendritic cell-like characteristics Eur. J. Immunol. 30 7 2000 1872 1883 10.1002/1521-4141(200007)30:7<1872::Aid-immu1872>3.0.Co;2-2 10940876
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PMC009xxxxxx/PMC9004224.txt |
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Multimed Tools Appl
Multimed Tools Appl
Multimedia Tools and Applications
1380-7501
1573-7721
Springer US New York
35431610
12865
10.1007/s11042-022-12865-5
Article
PlasticGAN: Holistic generative adversarial network on face plastic and aesthetic surgery
http://orcid.org/0000-0003-3229-2637
Chandaliya Praveen Kumar 2016rcp9511@mnit.ac.in
Nain Neeta nnain.cse@mnit.ac.in
grid.444471.6 0000 0004 1764 2536 Malaviya National Institute of Technology, Jaipur, 302017 India
12 4 2022
2022
81 22 3213932160
12 11 2020
1 4 2021
10 3 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, 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.
By embracing Generative Adversarial Networks (GAN), several face-related applications have significantly benefited and achieved unparalleled success. Inspired by the latest advancement in GAN, we propose the PlasticGAN which is a holistic framework for generating images of post-surgery faces as well as reconstruction of faces after surgery completion. This preliminary model works as a helping hand in assisting surgeons, biometric researchers, and practitioners in clinical decision-making by identifying patient cohorts that require building up of confidence with the help of vivid visualizations prior to treatment. It helps them better provide the tentative alternatives by simulating aging patterns. We used the face recognition system for evaluating the same individual with and without masks on surgery face, keeping the current trends in mind such as forensic and security application and recent worldwide COVID scenario. The experimental results suggested that plastic surgery-based synthetic cross-age face recognition (PSBSCAFR) is an arduous research challenge, and state-of-art face recognition systems can negatively affect face recognition performance. This can present a new dimension for the research community.
Keywords
Plastics surgery
Generative adversarial networks
Cross-age face recognition
Face aging
Face masks
Ministry of Electronics and Information Technology of IndiaNo.4 (13)/2019-ITEA Nain Neeta issue-copyright-statement© Springer Science+Business Media, LLC, part of Springer Nature 2022
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pmcIntroduction
Usually, facial plastic surgery reshapes the structure or improves the appearance of the face or neck. Procedures typically include the nose, ears, chin, cheekbones, and neckline. People seeking this surgery might be motivated by a desire to remove irregularities introduced in the face by an injury, a disease, congenital disabilities, or post-surgical deformities. The following is according to the recent statistics released by The American Society for Aesthetic Plastic Surgery (ASPS)1 for the year 2019 and International study on aesthetic procedures (ISAP)2 for the year 2019. According to the ASPS, almost 17.7 million people underwent surgically and minimally invasive cosmetic procedures, and more than 5.9 million reconstructive procedures happened in the United States in 2019.
In 2019, According to the ISAP, more than 2 million facial rejuvenation surgical procedures were performed, and the most common were Chemical Peel, Full-Field Ablative Resurfacing, Micro-Ablative Resurfacing, Dermabrasion, Microdermabrasion, Nonsurgical Skin Tightening, and Face Rejuvenation.
In 2019, 11.1% and 8.5% of face and head plastic surgery procedures were performed in Brazil and the USA, respectively. 10.5% and 18.3% of facial rejuvenation were constituted by USA and Japan, respectively.
Plastic surgery distribution by age: 0 − 18 years constitutes 4%, 19 − 34 years constitutes 43%, 35 − 50 years constitutes 36%, 51 − 64 years constitutes 14%, and 65 years and above constitute 3% of the total number of plastic surgery procedures.
86.5% of women and 13.5% of men are now more affirmative toward plastic surgery procedure as per the data provided by the ISAP in 2019.
The statistics provided by ASPS and ISAP indicate the popularity of facial plastic surgery across all age categories, ethnicity, countries, and gender. In South Korea, every one in three women between 19 to 29 year had a cosmetic or plastic surgery.3
These surgical processes demonstrate ideal for individuals experiencing facial distortions or those who want to counter the aging process. Similarly, these procedures can be misused by individuals who are attempting to hide their personality with the goal to cause extortion or dodge law implementation [34]. These surgeries may permit anti-social elements to openly move around with no dread of being recognized by any automated face recognition framework. A considerable amount of research on plastic surgery has been performed and reported [33], cross-age face recognition(CAFR) [40], synthetic aging [5, 8], and synthetic face mask recognition [27]. Due to advances in technology in the medical field, variation in faces due to plastic/cosmetic surgery has also led to the emergence of co-variates [33] of face recognition. Furthermore, if we add synthetic aging to surgery faces then the cross-age face recognition task becomes arduous.
Facial plastic surgery is a discipline that requires years of training for a surgeon to gain the necessary experience, skill, and dexterity. As the demand of minimally invasive procedures (MIP) is increasing rapidly, the patient wants to know how the changes are reflected on their face after the surgery. But in these procedures, the surgeon’s vision often relies on their own and the patient’s imagination completely. Due to the lack of appropriate visualization techniques and technology, surgeons are bound to rely on their skills and imagination power while performing the surgery; this can make this task more challenging.
To attempt to alleviate these challenges, we propose the PlasticGAN framework which can generate diverse photo-realistic faces with respect to facial surgery; this can work as a middleware between surgeons and patients and aid clinical decisions with the help of vivid visualizations. In this manuscript, we also focus on quantifying the effect of plastic surgery with aging and wearing face masks on the performance of face recognition systems.
Our key efforts are summarized as follows: An effective Conditional Generative Adversarial Network (cGAN) based network, PlasticGAN, is proposed to solve the face aging and rejuvenation problem on faces that have undergone plastic surgery for the very first time. Specifically, age and gender are passed as conditional information into both the discriminator and generator to acquire more fine-grained information between input and synthesized results. Besides, BlockCNN-based residual blocks are adopted to remove the artifacts and improve convergence behavior.
PlasticGAN will work as a middleware between surgeons and patients in terms of motivation provider and confidence booster for the surgery by providing a better glimpse of post-surgery looks.
Our framework does not require pre- and post-plastic surgery faces in the training dataset. At the time of testing, our model synthesizes face aging, rejuvenation, and face completion on surgery faces.
We defined a new challenge in the face recognition field named plastic surgery-base synthetic cross-age face recognition (PSBSCAFR).
We evaluated the robustness of the PlasticGAN model. For this, we performed an excessive qualitative and quantitative comparison with faces with and without face masks that will contribute to the forensic and law enforcement field.
The primary aim of this paper is to add a new dimension to clinical decision-making between surgeon and patient as well as lend an impact on cross-age face recognition on faces that have undergone plastic surgery. The remainder of the paper is organized as follows: In Section 2 we present a detailed description of different types of plastic surgery. In Section 3, we provide the generative model-based related work on face images. In Section 4, we present the proposed PlasticGAN model in detail. Section 5 presents the overall objective functions used in the optimization of our model. Section 6 describes data collection, pre-processing on surgery, and mask-wearing face. Section 7 presents the qualitative study on different types of surgeries. Section 8 discusses qualitative study on mask-wearing faces. Section 9 presents extensive quantitative experiments to demonstrate the superiority and wide-range practical application of our proposed model. Section 10 presents the performance ablation study. Finally, in Section 11, we conclude the paper and discuss the challenges in the face recognition research domain.
Plastic surgery and face recognition
Primarily, plastic surgery constrained to the face can be characterized into two major categories: (1) local plastic surgery and (2) global plastic surgery. Local plastic surgery accounts for correcting defects and craniofacial anomalies and improving skin texture. Procedures for local plastic surgery include Rhinoplasty, Mentoplasty, Blepharoplasty, Browlift, Malar Augmentation, and Otoplasty. It is also used for cosmetic and aesthetic purposes [28]. Global plastic surgery remodels the overall facial structure. This plastic surgery procedure is recommended for cases of recovery from fatal burn, changing facial appearance and skin texture, and modifying facial geometries. Procedures for global plastic surgery include Rhytidectomy, Skin Peeling, Craniofacial, Dermabrasion, and Mole Removal [26, 32].
When global plastic surgery is performed on an individual, face components such as nose, lips, and eyelid geometries might be disturbed or modified. In parallel, we observed that face recognition accuracy was significantly improved by the commercial of the shelf (COTS) and open source deep face recognition system on face aging [5], plastic surgery [34], and disguise face [10].
In this paper, PlasticGAN based on Generative Adversarial model is synthesized with reference to faces that have undergone plastic surgery with and without mask considering face aging and rejuvenation effect. Subsequently, we performed face verification using Face++ app on faces generated by PlasticGAN. Hence, this creates a significant challenge for the face recognition system as it produces extensive changes in facial features. This challenge, namely plastic surgery-based synthetic cross-age face recognition (PSBSCAFR) can become a new dimension for upcoming researches.
Related work
Generative adversarial networks
GAN was proposed by Ian Goodfellow et al. which incorporates two networks, a generator which produces new instance data by accepting noise, a discriminator of the genuineness of the produced images by generator. GAN has gained interest due to its high performance in a wide range of practical applications such as facial attribute alteration [3, 19], finding missing children [4, 5], transferring and removing makeup style [7], super-resolution techniques [22], anti-forensic [11, 37] and law enforcement [1, 21], Age-Invariant Face Recognition [40], etc. Additionally, GAN can explicitly control the features or latent vector in a manner that in its given class includes categorical description of text [29], landmark-type conditions, and background and foreground details to generate images; these conditions make it a conditional GAN (cGAN) [25] model.
However, GAN still has the disadvantage of unstable training and mode collapse problems. GAN-based Single Image Super-Resolution (SISR) models such as SRGAN [22] generate photo-realistic results with respect to these problems. However, the loss function on the feature space makes it to sacrifice the issue of its. Simultaneously, Wasserstein GAN (WGAN) [2] and Wasserstein GAN-Gradient Penalty (WGAN-GP) [15] improved training techniques by adding a Wasserstein (Earth Mover) distance metric loss to address the issues of generator and discriminator training and gradient vanishing. They stabilize their training over an extensive range of architectures with almost no hyper-parameter tuning. It does not rely on weight clipping techniques but penalizes the model if the gradient norm moves away from its target norm value 1. The adversarial loss proposed by Gulrajani et al. [15] moved the distribution of the generated images to the distribution of the real images. Especially, we seek to generate photo-realistic as well as less blurry images of post-surgery faces. To accomplish this, we employ deep feature consistent principle [17] to generate comprehensible face images with natural eyes, teeth, hair texture, and nose. In parallel, we also use improved GAN training mechanism to generate images that lie in the manifold of natural images.
All these principles and improved GAN techniques motivate us to work in facial plastic and aesthetic surgery fields, benefiting the society with its needs and considering current trends such as forensic and security application and the recent worldwide COVID-19 face mask scenario.
Face aging and rejuvenation
Face age progression is the prediction of future looks, and rejuvenation is the estimation of younger faces also referred to as facial age regression. It significantly impacts a wide range of applications in various domains. Generative models [14] are progressively used to perform face aging and de-aging due to their unquestionable and plausible generation of natural face images with an adversarial training approach. For e.g Zhang et al. proposed CAAE [38] for face aging and de-aging framework that learns a face manifold. Yang et al. [36] designed pyramidal adversarial discriminator for high-level face representations in a detailed manner. Wang et al. [35] presented an identity permanence conditional GAN and used pre-trained age classification loss to estimate the age correctly. Attention-cGAN [41] used the attention-based mechanism, which is a modification of only the facial regions relevant to aging. Recently, Praveen et al. [5] proposed ChildFace specific to child-based face aging and de-aging by introducing the gender and age-aware condition vector to preserve the identity in a small age span.
The aforementioned generative models used for beautification and face rejuvenation inspired us to propose PlasticGAN that integrates both face aging and rejuvenation. Moreover, it does not require any training on the dataset with before and after plastic surgery faces. We leverage the conditional GAN-based architecture integrated with adversarial, perceptual-based identity, and reconstruction loss function. The proposed model is designed as an innovative method for vivid visualization of realistic post-surgery faces to help surgeons in building up confidence and acquiring the patient’s acceptance for surgery.
Network architecture
We propose a PlasticGAN framework. The architecture of this framework evolved from the deep feature consistency principle [18], adversarial auto encoder [23], and BlockCNN base residual block [24]. As depicted in Fig. 1, the PlasticGAN system consists of four deep networks: a) deep residual variational autoencoder including a probabilistic encoder E(x), b) probabilistic generator G(z,l), c) pre-trained VGG19 (Φ)[31] for identity preservation, and d) deep residual critic discriminator Dimg(x,x¯). This model is based on the principle of WGAN-GP [15] to improve the accuracy results for face aging and rejuvenation in terms of generating natural and realistic images. Fig. 1 Overview of training and testing phase of the proposed PlasticGAN model. Encoder (E),Generator (G), and Discriminator (Dimg) are used for reconstruction and aesthetic surgery. LKL(μ,σ) represent the KL loss, Lrec represent the reconstruction loss, Φ represent the perceptual loss, LadvG and LadvDimg represent generator and discriminator adversarial loss. For simplicity, we have omitted the total variation loss LTV
The encoder (E) compresses the input image x having the size 128 × 128 × 3 through two fully connected layers (for mean μ and variance σ), and then they are added to sample latent vector z. Furthermore, this vector z appends with identity feature maps that combine two vectors: 12 times of age vector (a) and 25 times gender vector (g). Then, the output from previous zl passes as an input to the generator (G) for generating image x¯.
Then in the next step, VGGNet takes both x and x¯ as input and extracts deep feature from these images and then constructs the perceptual loss. Meanwhile, the generated images and the real image are conveyed to the Discriminator Dimg. The idea is to take these two as inputs to perform the adversarial min-max game policy and to calculate the discrepancy between the generated and input images. In addition to this information, Dimg also considers the identity feature map l (e.g. age and gender vector) in its first hidden layer as depicted in Fig. 1; these vector values are used as a conditional setting to obtain more fine-grained age and gender information between x and x¯.
E, G, and Dimg have BlockCNN-based residual blocks after each convolution and deconvolution layer except the first one of Dimg. These blocks are used to improve convergence behavior and remove the compression artifact [24]. The spatial differences of pre-trained VGG19 network are calculated in the middle of the layer architecture and then are combined to find total perceptual loss (Φ). This loss network is based on the principle of deep feature consistency [17] and is used to capture the most prominent image features. Our model generates age-progressed and regressed plastic surgery images with comparatively better aesthetic results in terms of the reconstruction of damaged facial parts such as nose, teeth, lips, mouth, and ear textures.
Objective function
The adversarial training of PlasticGAN can be considered as a two-player min-max game in which the team of probabilistic residual encoders and generators is trained against the team of residual adversarial discriminators. Both teams have to minimize five losses: 1) KL divergence loss (LKL(μ,σ)) to regularize the feature vector z with the prior distribution P(z)∼N(0,1); 2) the reconstruction loss (Lrec) between input and generated images is adopted so that sparse aging outcomes are produced and the image background is preserved; 3) LΦ perceptual loss is computed by pre-trained high-performing CNN as VGGNet [31] the loss network captures the spatial correlation between x and x¯ face images; 4) the aim of Ladv adversarial by incorporating the WGAN-GP into PlasticGAN is to improve the perceptual quality of the output images; 5) The total variation loss (LTV) function goal is to regularize the total variation in the generated images.
KL divergence loss
LKL(μ,σ) helps the residual encoder network learn better feature space representations. For input face x image, the E network E(x) = (μ(x),σ(x)), output the mean μ and variance σ of the approximate posterior. To calculate the feature vector z forgiven x, we sample a random 𝜖 Gaussian distribution where 𝜖∼N(0,1). We sample the feature vector using z=μ+σ⊙𝜖, where ⊙ represents element-wise multiplication. LKL(μ,σ) as shown in (1). 1 LKL(μ,σ)=−12∑k(1+log(σk)−μk2−σk)
where k denotes the indexes over the dimensions of the latent vector.
Reconstruction Loss
Lrec ensures the generated image preserves the low-level image content between its input x. For this, we incorporated a mean square base reconstruction loss between x and x¯ in the image space which could be written as (2). 2 Lrec=||x−(G(E(x),zl))||22
Where G is taken in the latent vector z generated by E(x) and the identity feature map (l) concatenated with z and passed as zl with input image x.
Perceptual loss
Perceptual loss calculates the spatial difference between the layers of VGG19 [31] and effectively minimizes the perceptual distance between the synthesized x¯ and input image x. Here, we denote perceptual loss by Φ(x)l with l as the layer. Here, we exploit the intermediate activation layer feature map denoted as relu1_1,relu2_1, relu3_1,relu4_1,relu5_1 of VGG as VGG19. 3 Φ=ΣΥΦΥ
In order to calculate ΦΥ at layer l, we use Euclidean distance between the activation map of module l for input image x and generated image x¯. Here C, W, and H denote the number of filters, width, and height of each feature map, respectively. ΦΥ denotes the perceptual loss of a single layer Υ. 4 ΦΥ=12×CΥWΥHΥ∑c=1CΥ∑w=1WΥ∑h=1HΥ(Φ(x)c,w,hΥ−Φ(x¯)c,w,hΥ)2
Adversarial loss
The adversarial training between the generator G and discriminator Dimg stimulates the generated results to be realistic and identical to real ones. Besides, image generation quality and attribute immutability is also guaranteed by including the attribute of input face images as a conditional vector in adversarial training. To accomplish these two goals, our discriminator Dimg is designed to take the input and generated images with their corresponding attributes after the first convolution block. Dimg calculates the improved adversarial loss by discriminating between input image x and the image generated by G. Formally, the objective function for training the discriminator adversarial loss (LadvDimg) is shown in (5). 5 LadvDimg=Ex,l∼Pdatax,lDimgx,l−Ex,l∼Pdatax,lDimgGEx,l−λgpEx^∼Px^[(||▽x^Dimg(x¯)||2−1)2]
Where x^∼Px^ is uniformly sampled along straight lines between pairs of input x and generated x¯ images, and λgp is the penalty coefficient to penalize the gradients ncritic= 5. The generator network G is trained to confuse Dimg with visually conceivable synthetic images and the objective function is shown in (6). 6 LadvG=−Ex,l∼Pdatax,lDimgGEx,l
Total Variational Loss
(LTV) Total Variational (TV) loss is used for assuring measurable continuity and smoothness in the generated image to avoid noise and sudden changes in high-frequency pixel intensities. The TV loss is the sum of the absolute differences for adjacent pixel values in the generated image. (7) shows TV loss. 7 LTV=∑c=1C∑w=1W∑h=1H|(x¯)w+1,h−(x¯)w,h)|2+|(x¯)w,h+1−(x¯)w,h)|2
Overall objective
To generate realistic faces while also preserving the identity corresponding to input. The final objective function for the discriminator Dimg is shown in (8). 8 max||Dimg||L≤1LDimg=λadvDimgLadvDimg
where ||Dimg||L ≤ 1 represents the set of 1-Lipschitz constraint on Dimg. The final objective function for the generator G is shown in (9). 9 minE,GLG=λklLKL(μ,σ)+λrecLrec+λperΦ+λadvGLG+λtvLTV
where λkl,λrec,λper,λadvDimg,λadvG,λtv are the hyper-parameters that tune the weight of the above-mentioned loss function. In our model, we used λkl,λrec,λper, λtv, λadvDimg as 1 and λadvG as 0.0001.
Experimental results
The primary objectives of facial plastic surgery are to reconstruct faces, remove defects, and improve the appearance of the patient or preserve the facial personality. In this section, we demonstrate the power of publicly available facial dataset on a large scale. This section is further divided into 3 subsections: (1) Description of dataset which merges the different publicly available datasets. (2) Dataset preprocessing. (3) Processing of mask-to-face mapping on surgery faces.
Dataset
To train a relevant population of diverse facial plastic surgery synthesis models, one of the key elements is to generate plausible images and reasonably aged face images from different ethnicities. Thus, we have selected [1 − 40] year age range images from publicly available Cross-Age Celebrity Dataset 35,450(CACD) [6], 5822 UTKFace [38], 35,484 CLF [4, 9], and 1113 Adience [12]. In total, we have used 77,889 images of size 128 × 128 pixels and divided the dataset into 4 age groups as [1 − 10],[11 − 20],[21 − 30],[31 − 40] as shown in Fig. 2. To test our model on plastic and aesthetic surgery face images, we have web-crawled real-world pre- and post-surgery face images. In total, there are 24 paired before and after surgery face images which are referred to as plastic-surgery testing images. The test dataset contains various types of face surgeries such as Otoplasty (ear surgery), Skin Resurfacing (skin peeling), Lip Augmentation, Oral Surgery (teeth surgery), Craniofacial, and Dermabrasion. The testing dataset of plastic surgery includes pairs of before and after plastic and aesthetic surgery faces as shown in Fig. 3. Fig. 2 Training dataset group formation: shows the distribution of face images in given age ranges to train PlasticGAN model
Fig. 3 First and third row are examples of pre- and post-surgery images respectively where images were acquired using web-crawling. Second and fourth show the cropping and alignment effect of MTCNN[39] face detector. The types of plastic surgery procedures are given below the fourth row
Prepossessing on dataset
For training the proposed model to reconstruct damaged areas in the correct orientation, preprocessing the dataset is necessary. As the dataset images have improper alignment and different resolutions, we have used MTCNN [39] to detect the five landmarks points (two eyes, nose, and two mouth corners) used for proper alignment and for cropping the images to a resolution of 128 × 128 pixels as shown in Fig. 3.
Processing on Mask-to-face mapping
To test our proposed model on the mask-wearing face, we have to cover pre-surgery images with a mask; we used MTCNN [39] to crop and align pre-surgery faces. Besides, an image of 12 key points has been manually annotated on the reference mask image as show in Fig. 4. In the final stage, it has used the face-to-mask mapping on cropped and aligned images. Fig. 4 First column presents the examples of pre-surgery face images where images were acquired using web-crawling. The second column shows the cropping and alignment effect of MTCNN [39] face detector. Third column references the mask images. Fourth column presents the mask-wearing face
Implementation details
We have trained PlasticGAN model on 77,889 images and divided these into 4 equal age categories, i.e., 1 − 10, 11 − 20, 21 − 30, 31 − 40. The architecture of our model is presented in Fig. 1. In the training phase, all components are trained with batch size 24 using ADAM [20] with hyper-parameter α = 0.0001 and β = (0.5,0.999). The output of Generator (G) is restricted to [− 1,1] using the tanh activation function. After 20,000 iterations, we received competent results. In the testing phase, we included plastic surgery images with and without masks, E and G are responsible for generating age-progressed and regressed facial images. The model was trained from scratch with a learning rate of E, G, and Dimag as 0.0001. We have optimized Dimag every 5 iterations and G is updated at every iteration.
Qualitative evaluation of plastic surgery face
In order to extensively evaluate the performance of our proposed PlasticGAN framework, we use state-of-the-art work CAAE 4, AcGAN 5, AIM (Age-Invariant Model) 6 and IPCGAN 7.
The following presents important common observations related to CAAE, AcGAN, AIM, and IPCGAN on various surgery faces. As observed, CAAE does not perform well in the aging effect and even produces artifacts and blurry results due to pixel-wise loss between the input and generated images. AcGAN utilizes an attention mechanism that only modifies the regions relevant to the aging effect. Hence, AcGAN performs poorly on plastic-surgery testing images in terms of reconstruction (teeth, face, lips), aging effect, and generate wired faces. Age-Invariant Model (AIM) though addresses the challenges of face recognition with large age variations but is not capable of generating photorealistic surgery faces with the desired aging effects. IPCGAN uses Image-to-Image translation-based generator network component so it cannot properly structure the different types of plastic surgery faces into a realistic face. Compared to state-of-the-art aging frameworks, the age progress and regress images of the PlasticGAN model are photo-realistic, and it rejuvenates identity-preserved faces on plastic-surgery faces.
Evaluation I: Teeth surgery
As is evident in each dotted box 6th column of Fig. 5, the pre-surgery image is improperly aligned (crooked, missing teeth). Our model has generated a perfect-looking set of teeth and the real image for the same which is shown in post-surgery row. In 6th column, missing teeth are generated in the mouth. As seen in all age range observations, the aesthetics of the face improve with age. In addition, the respective skin texture is preserved. At the beginning of this Section 7, we have mentioned logical reasons and demonstrated why the face-aging state-of-the-art models do not perform well on surgery faces in terms of post-surgery looks. Fig. 5 Teeth Surgery: Each dotted box denotes one person’s pre- and post-teeth surgery images. In each box, from second column left to right, are images generated by CAAE, AcGAN, AIM, IPCGAN, PlasticGAN, and ablation study, respectively
Evaluation II: Face surgery
As shown in Fig. 6, the pre-surgery image has no nose and mouth. However, PlasticGAN generated both the missing components perfectly by adhering to structure and texture of the face and even improved the appearance of eyes, producing a youthful appearance. AIM addresses the challenges of cross-age face recognition with large age, however, it is not capable of generating visually appealing faces with the desired aging effect. IPCGAN and AcGAN generator network components translate the input to image space and reconstruct from this space; hence, they cannot properly structure a before-surgery face into an after-surgery face as shown in Fig. 6. Due to this reason, these frameworks are not very helpful to clinical decision-making between doctor and patients. Fig. 6 Face Surgery: Each dotted box denotes one person’s pre- and post-surgery facial images. In each box, from second column left to right, are images generated by CAAE, AcGAN, AIM, IPCGAN, PlasticGAN, and ablation study, respectively
Evaluation III: Ear surgery
In Fig. 7, the pre-surgery image shows an ear lunging outwards. In PlasticGAN, the generated images are aligned perfectly to the normal settings. In addition, PlasticGAN constructs the internal structure of the ear compared to the state-of-the-art models. Therefore, the post-surgery images resembles the age progress and regress images. In the case of IPCGAN and AcGAN, only the face region is altered. Therefore, the age progress and regress ear surgery images do not properly align. Fig. 7 Ear Surgery: Each dotted box denotes one person’s pre- and post-ear surgery images. In each box, from the second column left to right, are images generated by CAAE, AcGAN, AIM, IPCGAN, PlasticGAN, and ablation study, respectively
Evaluation IV: Lips surgery
In Fig. 8, the pre-surgery image contains a partial lip and a deformed face structure. PlasticGAN input this image and completed the lip as well as produced open eyes, depicting how the child will look in the future. In addition, PlasticGAN performed age translation and beautifies the entire face, which enhances the rejuvenation effects. Compared to our framework, CAAE produces over-smoothed surgery images with subtle changes of appearance. As for IPCGAN and AcGAN, due to their incapability of face completion, faces generated by these models are deformed as evident in Fig. 8. Fig. 8 Lip Surgery: Each dotted box denotes one person’s pre- and post-lip surgery images. In each box, from second column left to right, are images generated by CAAE, AcGAN, AIM, IPCGAN, PlasticGAN, and ablation study, respectively
Model robustness on mask wearing face
To check the robustness of the proposed model, we covered the nose and mouth areas with synthetic masks and checked the aging effect on overall plastic surgery face. As shown in Fig. 9, IPCGAN and AcGAN models could not remove the face mask, could not complete surgery tasks, and could not show the aging effect. In case of CAAE, the face mask region can be seen with a little transparency which causes artifact in generated images. These effect due to pixel-wise loss. As evident from the results for PlasticGAN, it performed well overall in the context of various parameters such as skin tone, hair color, open eyes, reconstruction, and lighter to darker beard appearance. In addition to these, it generated better facial structures with the aging effect. Fig. 9 Mask Wearing face: Each dotted box denotes the same individual’s mask-wearing and pre-surgery images. In each box, from second column left to right, are images generated by CAAE, AcGAN, AIM, IPCGAN, PlasticGAN, and ablation study, respectively
Quantitative evaluation
Most of the existing age estimation and face verification approaches have primarily focused on unconstrained face recognition and no endeavor has been made to examine their effect on synthesized face aging and rejuvenation on local and global plastic surgery faces as well as mask-wearing surgery faces. As surgery-based aging and rejuvenation procedures becomes more and more prevalent, face verification framework fails to recognize individual’s faces after surgery. In this section, we explore age estimation and verification aspect on synthesized surgery faces.
We have evaluated the aging and identity permanence accuracy on age progress and regress on plastic surgery face with/without the mask. For this, we have generated all age range [1 − 10], [11 − 20], [21 − 30], [31 − 40] faces from pre-surgery faces with/without the mask. Then, we used the online face analysis tool known as Face++ API [13] to estimate the age distribution and face verification scores. We considered twenty-four test faces and the following protocol used for our comparison:
Face+ + test: [test face, progress-face1], [test face, progress -face2], [test face, progress-face3], [test face, progress-face3], [test face, progress-face4]. (where test face is pre-plastic surgery faces with/without mask).
Age estimation
Identically, age estimation was conducted to measure aging and deaging accuracy.
Plastic surgery face without mask
Following are the observations on plastic surgery face without mask shown in Table 1. IPCGAN and AcGAN introduce an identity-preserved loss and an age classification loss with the Image-to-Image translation-based generator network. However, if the classification error value is high then the gradient for small age range is not accurate. Therefore, these models’ age estimation accuracy is lower compared to PlasticGAN in most age ranges. AIM generated similar-looking and aged faces as depicted in Figs. 5, 6, 7, and 8. Therefor, this model’s age estimation standard deviation values are high in all age ranges. In case of CAAE, the generated images are blurry owing to the fact that the generated faces in the age ranges (1 − 10, 11 − 20) are aged. PlasticGAN model provides better age estimation results in three age ranges out of four compared to other state-of-the-art models. Table 1 Estimated Age Distribution (years) on Plastic-surgery testing images by PlasticGAN, ablation, and state-of-the-art models. For simplicity, we only address the mean and standard deviation of age estimation error computed over all age ranges
Age group 1-10 11-20 21-30 31-40
CAAE [38] 14.41 ± 7.58 20.50 ± 5.18 27.22 ± 5.18 28.58 ± 6.34
AcGAN [41] 16.13 ± 14.13 34.96 ± 17.00 32.83 ± 19.33 41.26 ± 18.55
AIM [40] 24.00 ± 10.40 40.44 ± 7.42 36.67 ± 9.15 42.11 ± 7.42
IPCGAN [35] 13.81 ± 14.45 17.24 ± 9.01 22.14 ± 13.01
PlasticGAN 28.80 ± 7.83
Ablation 15.79 ± 9.11 17.63 ± 9.70 20.96 ± 11.25 23.00 ± 12.44
Plastic surgery face with mask
CAAE uses the mean square-based reconstructed loss, and AcGAN and IPCGAN generator architecture are based on image-to-image translation network. AIM disentangles the age and identity attributes. Consequently, the progress and regress images with face mask are completely deformed as they detect local and global face attributes e.g, nose, lips, mouth, eyes completely deformed. Therefore, state-of-the-art model generated the aged face in age ranges (1 − 10, 11 − 20, 21 − 30) compared to PlasticGAN as illustrated in Table 2.
Note: Face images generated by AcGAN and IPCGAN are not detected by Face++ app because due to the fact that they did not remove the face mask region and due to unstructured eye construction. Table 2 Estimated Age Distribution (years) on Plastic surgery with mask, testing images using PlasticGAN, ablation, and state-of-the-art models. For simplicity, we only address the mean and standard deviation of age estimation error computed over all age ranges
Age group 1-10 11-20 21-30 31-40
CAAE [38] 30.52 ± 10.56 35.19 ± 9.62 38.42 ± 8.52 41.14 ± 9.49
AcGAN§ [41] 25.00 ± 6.21 24.42 ± 3.99 26.84 ± 4.65 29.89 ± 5.67
AIM [40] 22.31 ± 7.90 40.77 ± 4.50 40.59 ± 5.60 45.50 ± 5.06
IPCGAN# [35] 26.75 ± 5.03 29.25 ± 7.23 30.58 ± 9.87
PlasticGAN 28.63 ± 6.55
Ablation 17.50 ± 9.49 20.77 ± 9.81 24.77 ± 9.65 27.50 ± 10.34
#: 5 face is not detected,§: 12 face is not detected
From the result in Tables 1 and 2, we have the following observation. The age estimation results of no mask compared to with mask are better.
IPCGAN and AcGAN do not unmask the surgery face as shown in Fig. 9. Therefore, the aging pattern only reflects the periocular region and forehead. Thus, the age estimation value is not properly distributed over the age ranges as shown in Table 2.
Face verification on surgery faces with and without mask
For identity permanence, face verification rates are reported along with threshold set to 76.5@FAR = 10− 5 experimented according to the Face+ + API [13]. A confidence score is then obtained for each comparison, demonstrating the similarity between two faces. The confidence range lies under [0 − 100]. A higher confidence score indicates a higher probability that two faces (real and generated) are from the same subject.
Plastic surgery face without mask
With plastic surgery, it is evident that PlasticGAN outperforms over AcGAN and AIM. Although, CAAE in the context of identity information generates surgery faces with ghosting artifacts. PlasticGAN, AIM, and CAAE aging models generate age progress and regress face by disentangling the age and personality features from the latent vector, due to which identity is also altered with age. Thus, face verification accuracy is low compared to IPCGAN and AcGAN.
Plastic surgery face with wearing mask
To verify the robustness and stability of the proposed model, even if the mask covers a region of the plastic surgery face, the features of the upper half of the face, such as eyes, eyebrow, and forehead can still be used to improve the masked cross-face recognition (MCFR). The experiment results are shown in Fig. 9; PlasticGAN can still progress and reconstruct a complete face. In AcGAN and IPCGAN, the network component is unable to unmask the masked face. These models alter the regions particularly relevant to face aging. This is why the face verification accuracy is high compared to other models as shown in Table 4. Objectively speaking, a few progress faces have distortion. Thus, the Face++ APP is unable to detect. Following are general observations from Tables 3 and 4. Without wearing mask cross-age face verification score is improved with wearing face mask in all age ranges and face aging model.
With increasing the age gap, the face verification score is decreased in all age ranges.
The face verification score of CAAE and AIM models are lower than PlasticGAN.
IPCGAN and AcGAN do not unmask the surgery face as shown in Fig. 9. Owing this, the face verification accuracy is better of these models with the no-mask surgery face.
Despite the potential of our proposed model, we can conclude that the task of cross-age face recognition on these synthetic faces after applying surgery becomes challenging as it degrades the verification score. This challenge, namely PSBSCAFR, can become a new dimension for upcoming research regarding how to improve the recognition accuracy of synthetic surgery faces generated by GAN. Table 3 Face verification results (in %) on Plastic surgery testing images by PlasticGAN and other state-of-the-art models
1-10 11-20 21-30 31-40
Verification rate (Threshold= 76.5 FAR= 1e-5)
CAAE [38] 69.22 ± 14.42 60.96 ± 12.18 54.56 ± 11.88 49.24 ± 12.22
AcGAN [41] 92.14 ± 6.56 88.98 ± 10.49
AIM [40] 65.45 ± 11.20 57.96 ± 12.18 48.75 ± 14.07 46.56 ± 13.83
IPCGAN [35] 91.63 ± 0.32 91.66 ± 0.49
PlasticGAN 73.63 ± 8.64 69.27 ± 8.87 66.53 ± 9.33 63.05 ± 9.68
Ablation 71.28 ± 10.97 67.13 ± 12.01 65.95 ± 12.30 64.55 ± 9.01
Table 4 Face verification results (in %) on plastic surgery with mask; testing images by PlasticGAN and other state-of-the-art models
1-10 11-20 21-30 31-40
Verification rate (Threshold= 76.5 FAR= 1e-5)
CAAE [38] 61.52 ± 9.19 58.83 ± 9.36 55.81 ± 9.40 52.12 ± 9.37
AcGAN§ [41] 87.29 ± 2.28 84.61 ± 3.51
AIM [40] 72.03 ± 7.20 67.79 ± 8.54 58.93 ± 11.36 57.49 ± 11.55
IPCGAN# [35] 93.88 ± 0.76 93.48 ± 0.78
PlasticGAN 62.53 ± 8.11 61.03 ± 8.87 60.53 ± 9.13 58.05 ± 9.68
Ablation 60.26 ± 9.48 58.23 ± 9.81 55.11 ± 9.49 53.41 ± 10.04
#: 12 face is not detected, §: 5 face is not detected
Comparison between surgery face with and without mask
Traditional CNN-based face recognition systems trained on existing datasets are almost ineffective on faces that have undergone surgery or that are wearing a mask. Simultaneously, new challenges create new opportunities and research direction in this field. The one which we want to include in this study is how plastic surgery face and facial mask face can be correlated based on the idea of and considering face verification of same individuals. During our experiment, we observed a few significant aspects which can also be seen in the Fig. 10. We have conducted this study by Face++ App. In many cases, masked faces are not even detected when the eyes are closed and face is not properly aligned.(In block 2 and 4 of Fig. 10).
Different types of face surgery with mask leads to many differences in the impact on face recognition reliability. When it is the case of the mask covering the lip and half nose region, the confidence score is upgraded because eyes are an important consideration at the time of verification. This score is also affected by same mask covering. When it is the case of the above-mask region, the score is degraded due to the fact that the recognition system does not find any similarity points while verification except the mask. (In block 1 and 3 of Fig. 10).
Fig. 10 Each block is an example of pre- and post-surgery face with and without mask. We have evaluated the confidence score with the help of Face++ app. ND represents the cases of no detection because the face is covered with mask and showing non similarity
Inception and Fréchet inception distance
The image quality with the diversity of the generated data is assessed in terms of the inception score (IS) [30] and the Fréchet inception distance (FID) [16]. In Table 5, the PlasticGAN achieves best IS and FID scores on plastic surgery test datasets compared to the state-of-the-art models. Meanwhile, high IS and low FID score indicate that our framework generates more realistic faces. Table 5 Comparing IS and FID on PlasticGAN and its variant with other state-of-the-art models
Model IS FID
CAAE [38] 2.10 ± 0.25 144.04
AcGAN [41] 2.38 ± 0.42 72.28
AIM [40] 1.41 ± 0.09 154.02
IPCGAN [35] 2.03 ± 0.34 74.43
PlasticGAN
Ablation 2.18 ± 0.14 107.05
Beauty Score and Gender Prediction
We evaluate beauty score and gender prediction experiment of PlasticGAN and state-of-the-art models on plastic surgery test images. For fair comparison, Face++ [13] as is used as a face analysis tool to evaluate the beauty score and gender prediction of pre-plastic surgery face and corresponding age progress in all age ranges as shown in Table 6. Table 6 Beauty score and gender prediction: The second column contains pre-surgery images followed by beautification score with gender prediction values. In column 4, the state-of-the-art model, PlasticGAN, and Ablation study are shown
S.No Pre-Surgery Image PBSGP Model GBSGP GBSGP GBSGP GBSGP
1-10 11-20 21-30 31-40
1 74.21(M) CAAE [38] AcGAN [41]AIM [40] IPCGAN [35] PlasticGAN Ablation 74.86( F) 76.93(M) 79.818( F) 74.87(M) 71.39(M) 70.30(M) 76.11( F) ND 81.86( F) 79.59(M) 71.46(M) 68.87(M) 73.32(M) 74.51(M) 82.12( F) 74.78(M) 69.19(M) 69.26(M) 72.50(M) 75.25(M) 84.07(M) 69.00(M) 70.23(M) 68.67(M)
2 73.27(M) CAAE [38] AcGAN [41] AIM [40] IPCGAN [35] PlasticGAN Ablation 79.10( F) 75.17(M) 75.17(M) 67.51(M) 76.48(M) 74.73(M) 78.22( F) 77.12( F) 77.13( F) 73.34(M) 76.17(M) 74.92(M) 77.09( F) 71.82(M) 71.83(M) 70.90(M) 74.99(M) 73.79(M) 77.94( F) 68.26( F) 68.26( F) 66.45(M) 73.43(M) 73.79(M)
3 72.56(F) CAAE [38] AcGAN [41] AIM [40] IPCGAN [35] PlasticGAN Ablation 76.49(F) 76.94( M) 77.64(F) 72.45(F) 75.49(F) 78.68( M) 78.04(F) 71.02(F) 79.52(F) 74.95(F) 77.38(F) 79.15( M) 79.44(F) 72.09(F) 77.16(F) 70.89(F) 79.81(F) 79.14( M) 77.69(F) 68.52(F) 75.49(F) 70.10(F) 77.62(F) 76.10( M)
4 74.83(M) CAAE [38] AcGAN [41] AIM [40] IPCGAN [35] PlasticGAN Ablation 79.66( F) 70.37(M) 80.38( F) 68.58(M) 76.70(M) 78.12(M) 81.08( F) 78.73(M) 80.57( F) 70.83(M) 80.77(M) 79.56(M) 82.56( F) 72.08(M) 83.71(M) 71.23(M) 78.29(M) 76.07(M) 82.22( F) 74.36(M) 84.03(M) 69.72(M) 75.59(M) 76.16(M)
5 89.61(F) CAAE [38] AcGAN [41] AIM [40] IPCGAN [35] PlasticGAN Ablation 84.23(F) 89.86(F) 79.85(F) 84.23(F) 90.49(F) 81.98(F) 80:99(F) 88.83(F) 83.30(F) 80.99(F) 90.39(F) 82.16(F) 81:31(F) 88.92(F) 82.47(F) 81.31(F) 87.58(F) 80.95(F) 78:91(F) 85.09(F) 81.51(F) 78.91(F) 87.01(F) 78.93(F)
This is followed by generated beautification score with gender of all age ranges from column 5 to 8
Note: PBSGP: pre-surgery beauty score and gender prediction, GBSGP: generated beauty score and gender prediction, ND: Not Detected face, M: male, F: female, highlighted incorrectly predicted gender in bold
General observations are as follows: CAAE generates images introducing grain-like artifact, which deteriorates image quality. Due to this reason, wrong gender prediction is shown in red color.
IPCGAN and AcGAN use an image-to-image translation-based generator network component. Hence, it cannot properly restructure a partially covered face into a realistic face. Owing to this, the destroyed face is not detected by Face++ App as mentioned (Not Detected ) ND in Table 6.
PlasticGAN is better at overcoming ghosting artifacts and color distortions. Further, it maintains uniformity in the background and face boundaries as well as shows comparative the beauty score with other state-of-the-art models.
The pre-surgery test images’ gender prediction value is either male or female. However, the generated images’ gender prediction value is changed from the ground truth. Compare to PlasticGAN, state-of-the-art models are predicting incorrectly.
In ablation study (Without KL loss), the skin color is shown lighter compared to PlasticGAN as shown in Figs. 5, 6, 7 and 8. Due to this effect, the beauty score value is low compared to PlasticGAN.
Ablation study
To comprehend the effect of LKL(μ,σ) over our proposed model, we conducted an experiment on variants of the PlasticGAN model by removing LKL(μ,σ) (sampling block). This effect can be easily seen in Figs. 5, 7, 6, and 8 for visual comparison. We have observed throughout the process that PlasticGAN produces artifacts-free age-progressed and regressed faces and applies some effects of beautification as well. As shown in the Tables 1, 2, 3, 4, 5, and 6, the Kl loss helps in face verification, age estimation, fidelity generation, and gender preservation with beautification score. This further elucidates our objective functions and network components that are designed well for face aging and rejuvenation based on social and forensic applications.
Conclusions and future research work
The advancement of generative models in beautification and rejuvenation has inspired and motivated us to propose the robust and general PlasticsGAN framework. This model integrates face aging and rejuvenation, face recognition, and face completion which relies on plastic and aesthetic facial surgery cases. This can contribute to a wide range of applications such surgeon and patient consultancy, forensics and security, digital entertainment, and even the fashion and wellness industry.
Furthermore, PlasticGAN unmasks the mask wearing face and properly structures it with aging/deaging effect. Moreover, the PlasticGAN framework does not require pre- and post-plastic surgery faces as a paired dataset during training. In the testing phase, our model paralelly synthesized face aging, rejuvenation, and face completion on faces that had undergone surgery. From the concluded qualitative and quantitative experiments and from the comparison with state-of-the-art face aging architectures on various plastic surgery faces (teeth, face, ear, lips), it was found that our model is robust and has diverse applications especially in the case of aging and rejuvenation with face completion.
As future work, we would like to enhance the framework’s performance by analyzing face aging and rejuvenation entailed in plastic surgery. This can further degrade a commercial and publicly available face recognition systems performance. When these co-occur with other factors, e.g different types of mask-wearing and synthetic surgery face. This can be a new dimension for future work.
Acknowledgements
This research is based upon work supported by the Ministry of Electronics and Information Technology (Meity), Government of India, under Grant No. 4 (13)/2019-ITEA. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the TITAN V GPU used for this research. We would like to thank Anjali Vijayvargiya for her insight and helpful comments.
Declarations
Disclosure of potential conflicts of interest
We declare that we have no financial and personal relationships with other people or organization that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled. “PlasticGAN: Holistic Generative Adversarial Network on Face Plastic and Aesthetic Surgery”.
1 https://www.surgery.org
2 https://www.isaps.org
3 https://www.isaps.org/event/aesthetic-plastic-surgery-2018-korean-society-aesthetic-plastic-surgery
4 https://github.com/ZZUTK/Face-Aging-CAAE
5 https://github.com/JensonZhu14/AcGAN
6 https://github.com/ZhaoJ9014/High-Performance-Face-Recognition/tree/master/src/
7 https://github.com/dawei6875797/Face-Aging-with-Identity-Preserved-Conditional-Generative-Adversarial-Networks
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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PMC009xxxxxx/PMC9004225.txt |
==== Front
Int J Infect Dis
Int J Infect Dis
International Journal of Infectious Diseases
1201-9712
1878-3511
The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.
S1201-9712(22)00216-8
10.1016/j.ijid.2022.04.017
Article
Evaluation of SARS-CoV-2 diagnostics and risk factors associated with SARS-CoV-2 infection in Zambia☆
Tembo John 1*
Egbe Nkongho Franklyn *2
Maluzi Kwitaka *1
Mulonga Kangwa 1
Chilufya Moses 1
Kapata Nathan 3
Mukonka Victor 3
Simulundu Edgar 4
Zumla Alimuddin 5
Fwoloshi Sombo 6
Mulenga Lloyd 6
Pallerla Srinivas Reddy 7
Velavan Thirumalaisamy P. *78
Bates Matthew *12
1 HerpeZ, University Teaching Hospital, Lusaka, Zambia
2 School of Life & Environmental Sciences, University of Lincoln, Lincoln, United Kingdom
3 Zambia National Public Health Institute, Lusaka, Zambia
4 Macha Research Trust, Macha, Southern Province, Zambia
5 Centre for Clinical Microbiology, University College London, London, United Kingdom
6 Department of Internal Medicine, University Teaching Hospital, Lusaka, Zambia
7 Institute for Tropical Medicine, University of Tubingen, Tubingen, Germany
8 Vietnamese German Center for Medical Research, Hanoi, Vietnam
⁎ Authors contributed equally
12 4 2022
7 2022
12 4 2022
120 150157
18 2 2022
7 4 2022
7 4 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.
Objectives
To conduct a diagnostic validation study of SARS-CoV-2 diagnostic kits.
Methods
We compared SARS-CoV-2 diagnostic test results from 3 RT-PCR assays used by the Zambian government between November 2020 and February 2021 (Panther Fusion assay, Da An Gene's 2019-nCoV RNA kit and Maccura's PCR Kit) with the Altona RealStar RT-PCR kit which served as the gold standard. We also evaluated results from rapid antigen testing and whether comorbidities were linked with increased odds of infection.
Results
We recruited 244 participants, 61% (149/244) were positive by at least 1 PCR assay. Da An Gene, Maccura, and Panther Fusion assays had sensitivities of 0.0% (95% confidence interval [CI] 0%–41%), 27.1% (95% CI 15%–42%), and 76% (95% CI 65%–85%), respectively, but specificity was low (<85% for all 3 assays). HIV and TB were not associated with SARS-CoV-2, whereas female sex (OR 0.5 [0.3–0.9], p = 0.026) and chronic pulmonary disease (0.1 [0.0–0.8], p = 0.031) were associated with lower odds of SARS-CoV-2 infection. Of 44 samples, 84% sequenced were Beta variant.
Conclusions
The RT-PCR assays evaluated did not meet WHO recommended minimum sensitivity of 80%. Local diagnostic validation studies should be embedded within preparedness plans for future outbreaks to improve the public health response.
Keywords
SARS-CoV-2
COVID-19
diagnostics
serology
real time- PCR
lateral flow test
RDTs
comorbidities
==== Body
pmcIntroduction
Despite the rapid development and adoption of SARS-CoV-2 diagnostics, SARS-
CoV-2 diagnostics have been deployed to resource-limited settings without rigorous evaluation. Given the need to ensure tracking of the epidemic and the lack of resources, countries in resource-limited settings end up using any available diagnostics. Attempts to establish an effective testing and tracking system have led to indiscriminate use of any available nucleic acid tests (Twohig et al.), antigen and/or antibody-based tests (Arevalo-Rodriguez et al., 2020, Kobia and Gitaka, 2020) regardless of their diagnostic performance.
In Zambia, SARS-CoV-2 testing has relied on PCR assays and antigen tests that have been donated through the African CDC (Maccura & Da An Gene RT-PCR assays and Abbott and Roche rapid antigen tests). The Zambian government then procured the Panther PCR assay with the national testing programme, switching between these 3 assays in accordance with availability. An early outpatient study showed that referral centre testing would be an essential pillar of the diagnostic response to the pandemic in Zambia (Hines et al., 2021). During November 2020–February 2021, we implemented an observational clinical diagnostic evaluation study at 2 COVID-19 referral hospitals in Lusaka, Zambia to evaluate the performance of these 3 donated PCR assays compared with the Altona Diagnostics CE-IVD certified RealStar SARS-CoV-2 RT-PCR assay, which received FDA EUA on the April 22, 2020 (Freire-Paspuel et al., 2021) and was the back-bone of the German government's testing system early in the pandemic. It has been reviewed extensively and has proven to be robust, sensitive and versatile (van Kasteren et al., 2020, Visseaux et al., 2020).
Multiple variants of SARS-CoV-2 have been documented worldwide during this pandemic. Several new SARS-CoV-2 variants recently reported, Alpha, VOC B.1.1.7, Beta, VOC B.1.351, Gamma VOC B.1.1.28 and Delta VOC B.1.617.2. The Alpha, Beta, Gamma, and Delta variants were associated with high transmission, severe illness, and increased mortality (Bal et al., 2021, Challen et al., 2021, Davies et al., 2021, Gaymard et al., 2021, Twohig et al., 2021). More recently, the Omicron variant has shown increased potential for immune evasion in various studies (Wang et al., 2022, Zhang et al., 2021). This study took place during ‘Wave 2’ of the pandemic in Zambia, which coincided with the emergence and global spread of the Beta variant of SARS-CoV-2.
The primary objective of this study was to assess intertest agreement and other important operational characteristics of the diagnostic kits being used in Zambia. Secondary objectives included analysing whether comorbidities (such as tuberculosis or HIV) were associated with infection or poorer outcomes. Whole genome sequencing was used to determine variants that were circulating within our patient population at the time of the study.
Materials and Methods
Ethics statement
Informed written consent was obtained from all the participants. The study was approved by ERES Converge, Zambia (Ref No. 2020-JUL-07).
Study population and patient recruitment
The study was conducted at 2 COVID-19 referral hospitals in Lusaka, Zambia; the University Teaching Hospital (UTH), and Levy Mwanawasa University Teaching Hospital (LMUTH) between November 2020 and February 2021. All individuals aged >18 years, attending accident and emergency with suspected SARS-CoV-2 infection were eligible for the study. After obtaining informed consent, they were enrolled in the study and underwent a questionnaire to gather patient demographics, symptoms, underlying conditions and comorbidity, and recent travel history.
Specimen collection and laboratory analysis
A nasopharyngeal swab and blood sample were collected from each enrolled participant. One nasopharyngeal swab specimen was collected and placed in 3 ml of viral transport media for rapid antigen testing and parallel RT-PCR testing by the government laboratories using any 1 of 3 RT-PCR assays used by the national testing programme at the time: the Panther Fusion assay, Da An Gene's 2019-nCoV RNA kit, and Maccura's PCR Kit and by our research laboratory, using the RealStar SARS-CoV-2 RT-PCR kit (Altona Diagnostics GmbH, Germany).
Rapid antigen testing
Testing was done at the Zambian COVID-19 testing sites using either the Abbott Panbio COVID-19 Ag Rapid Test Device (Abbott Diagnostic GmbH, Jena, Germany) or the Roche SD Biosensor SARS-CoV-2 Rapid Antigen Test Nasal Test (Roche Diagnostics, Basel Switzerland), following manufacturer's guidelines.
RT-PCR testing
Altona SARS-CoV-2 Assay
RNA extraction for our RT-PCR testing was done using the QIAamp Viral RNA Mini extraction kit (QIAGEN GmbH, Hilden, Germany), following the manufacturer's instructions. RT-PCR set up was done with 10 μl of the RNA template in a 30 μl final reaction. The RealStar SARS-CoV-2 RT-PCR kit (altona Diagnostics GmbH, Germany), which targets the E and S genes of SARS-CoV-2, was used for RT-PCR testing on the Rotor-Gene 6000 cycler (QIAGEN GmbH, Hilden, Germany).
For the government laboratories, PCR testing was done using the available kit at the time and according to the manufacturer's instructions for the specific assay. RNA extraction was done using QIAamp Viral RNA Mini extraction kit (QIAGEN GmbH, Hilden, Germany) and the PCR was set up on a Light Cycler 480 or ABI7500 system. The 3 RT-PCR assays used were Aptima Panther Fusion SARS-CoV-2 assay (Hologic, Inc, San Diego, USA), which targets the ORF1ab gene; the Maccura SARS-CoV-2 assay (Maccura Biotechnology Co., Chengdu, P.R. China), which targets ORF1ab, E and N genes, and the Da An Gene SARS-C0V-2 assay (Daan Gene Co., Guangzhou, Guangdong, P.R. China), which targets the ORF1ab and N genes.
SARS-CoV-2 sequencing & analysis
SARS-CoV-2 genomes were sequenced in our laboratory by tiling PCR and Oxford Nanopore NGS sequencing methods. Bioinformatic analysis was done using the ARTIC pipeline, as described elsewhere (Manouana et al., 2021), and the lineages were obtained using Pangolin. In brief, tiling PCR was used to amplify 1200 bp fragments in 2 pools, covering the SARS-CoV-2 genome. Amplicons were purified using AMPure bead purification and barcoded using Rapid Barcoding kit (SQK-RBK004) from Oxford Nanopore and purified again before being combined into a full library and loaded onto the MinION for sequencing. Guppy version 3.6.0 was used for base-calling and demultiplexing all runs. The ARTIC Network bioinformatics protocol was used for all genome assembly and variant calling steps, and the lineages were obtained using Pangolin tool (O'Toole et al., 2021). All genomes were aligned with Wuhan-Hu-1 strain (NC_045512.2) using multiple alignment fast Fourier transform algorithm (Katoh et al., 2002), and the subsequent phylogenetic tree was constructed with the maximum likelihood method with 1000 bootstrap iterations using the general time-reversible (GTR) model with rate heterogeneity (GTR+G) in the IQ-TREE server (Trifinopoulos et al., 2016). The final dataset was displayed using the interactive tree of life (iTOL v6).
Anti-SARS-CoV-2 antibody screening
The presence of SARS-CoV-2 antibodies was determined using the Wantai SARS-CoV-2 total Ab ELISA kit (Wantai Biological, Beijing, China) according to the manufacturer's instructions. Absorbance reading were obtained using the BioTek EL800 microplate reader (BioTek, Winooski, USA) at 450 nm wavelength. Samples were considered to have anti-SARS-CoV-2 antibodies if the absorbance value was greater than 0.03.
Data analysis
Laboratory and clinical data were entered on the EpiInfo version 7.2.4.0 (CDC, USA) and exported as a.csv file. It was imported to RStudio version 1.2.5019 (R Core Team, 2019) and cleaned. All statistical analyses were done using packages and functions in RStudio version 1.2.5019 (R Core Team, 2019). Graphics were produced using the ggplot2 package and all confidence intervals (CIs) were reported at 95% level (Wickham, 2009). A multivariable logistic regression analysis to investigate comorbidities associated with SARS-CoV-2 infection (either Altona RealStar PCR positive or negative) was done using the R package stats (R Core Team, 2019). Sex and age were added as a fixed effect to the logistic regression and all the recorded comorbidities (hypertension, HIV, diabetes, tuberculosis, chronic obstructive pulmonary disease [COPD], asthma, obesity, renal disease, cardiac) and mortality outcome were used as explanatory variables.
Results
Recruitment and Cohort Descriptives
We recruited 244 patients with suspected COVID-19, attending either the University Teaching Hospital (UTH) or Levi Mwanawasa University Teaching Hospital (LMUTH), Lusaka, Zambia. Patients were recruited between November 24, 2020 and February 5, 2021, which coincided with the second wave of the COVID-19 pandemic in Zambia (Fig. 1 ). The COVID-19 isolation ward at LMUTH was established as the primary centre for COVID-19 treatment and care. Full-capacity was soon reached and so, as cases dramatically increased, UTH recruited 134 patients during the first 2 weeks of January, accounting for 55% of all recruits.Fig. 1 Timeline showing number of patients recruited each week from each of the participating hospitals, along with the overall trajectory of the pandemic (cases/day) within Zambia nationally during the same period. UTH, University Teaching Hospital, LMUTH, Levi Mwanawasa University Teaching Hospital.
Fig. 1:
As both hospitals quickly reached capacity, home care was established, with patients triaged, and those with high oxygen saturation sent home to self-care with pulse oximeters. As such, only half of participants (48%; 115/244) were admitted to the hospital (Table 1 ).Table 1 Characteristics of 244 participants attending hospital with suspected COVID-19.
Table 1: Comorbidity n= 89 No comorbidity n= 155 All participants (%) n= 244
Characteristic
Median Age in Years (IQR) 46 (36–61) 35 (28 – 43) 38 (30–50)
Sex (male) 46 83 129 (53%)
Admitted 62 53 115 (47)
Symptoms
Cough 72 121 193 (79)
Shortness of Breath 65 72 137 (56)
Sore Throat 37 71 108 (44)
Headache 20 57 77 (32)
Chest Pain 19 30 49 (20)
Diarrhoea 13 17 30 (12)
Nausea 15 15 29 (12)
Runny Nose 11 18 29 (12)
Loss of Taste 5 13 18 (7)
Median Temperature °C (IQR) 36.5 (36–37) 36.5 (36 – 37) 36.5 (36 – 37)
Days since onset of symptoms*
0–3 Days 27 62 89 (38)
4–7 Days 30 57 87 (37)
>= 8 Days 31 30 61 (26)
SARS-CoV-2 antibodies present# 33 58 91 (37)
Altona RT-PCR Positive 42 92 134 (55)
Median RT-PCR ct value (IQR) 27.5 (21.6 – 32.7) 28.1 (22.0 – 32.3) 27.8 (21.7 – 32.4)
⁎ data missing for 7 participants
# antibodies detected with the Wantai SARS-CoV-2 total Ab ELISA kit. IQR, Interquartile range
IQR, interquartile range; RT-PCR, real-time PCR.
The median age of the study cohort was 38 years (IQR 30–50 years), and 53% (130/244) were male (Table 1). The most common symptom was persistent cough, affecting 79% (193/244) of participants, followed by shortness of breath (56%), sore throat (44%) and headache (32%). Loss of taste was relatively uncommon, being reported by just 7.4% (18/244) of participants (Table 1). The median temperature was 36.5 °C, with 21% (51/244) recorded as having fever. A total of 21% (48/244) of participants reported exposure to a known COVID-19 case and 74% (176/237) were recruited within 7 days of symptom onset (Table 1).
Diagnostic assay performance
Among the PCR assays used, the Altona RealStar RT-PCR assay had the highest yield, being positive in 55% (134/244). The yields of the 3 RT-PCR assays used by the government were highly variable, ranging from 3%–55%. In total, 61% (149/244) of participants had a positive PCR result from at least 1 RT-PCR assay. The rapid antigen test had a lower yield than the 2 leading PCR assays, being positive in only 33% (54/163) of participants (Table 2 ).Table 2 Comparison between RT-PCR assays and rapid antigen test.
Table 2 Altona RealStar Diagnostic Performance characteristics
Negative Positive Sensitivity Specificity PPVa NPVa
Da An Gene Negative 31 7 0.0% (0–41%) 96.9% (84–100%) 0.0% 81.6% (43–46%)
Positive 1 0
Maccura Negative 26 35 27.1% (15–42%) 83.9% (66–95%) 67.3% (45–84%) 48.4% (43–54%)
Positive 5 13
Panther Fusion® Negative 37 19 76% (65–85%) 80.4% (66–91%) 82.7% (72–90%) 73.2% (64–81%)
Positive 9 60
Ag RDT Negative 59 48 44.8% (34–56%) 79.7% (69–88%) 73.1% (62–82%) 54% (49–60%)
Positive 15 39
All gov't assays# Negative 87 44 67% (56–75%) 79.1% (70–86%) 80% (71–87%) 66.4% (58–74%)
Positive 23 90
a Based on a prevalence of 55.1%
# Positive case defined as positive on any of the RT-PCR assays used by the government or rapid antigen test. A positive result either on the rapid Ag or RT-PCR assays was notified as a confirmed COVID-19 case by the government.
Ag RDT, antigen detection rapid diagnostic test; gov't, government; NPV, negative predictive values; PPV, positive predictive values; RT-PCR, real-time PCR.
As the Altona RealStar assay had the highest yield, we used this as a proxy gold standard, against which to compare the other PCR assays and the antigen detection rapid diagnostic test (Ag RDT). Of the 3 assays used by the Zambian government, the Da An Gene assay performed extremely poorly, failing to detect a single positive case, resulting in a sensitivity of 0% (95% CI 0%–41%). The Maccura assay was also extremely insensitive, with a sensitivity of just 27% (95% CI 15%–42%). The Panther Fusion assay performed better with a sensitivity of 76% (95% CI 65%–85%). But the specificity of both the Panther Fusion and Maccura assays was low at 80% (95% CI 66%–91%) and 84% (95% CI 66%–95%), respectively (Table 2).
The Ag RDT test performed poorly, with a sensitivity of just 45% (95% CI 34%–56%) and specificity of just 80% (95% CI 69%–88%) (Table 2). Performance of the Ag RDT test varied depending on when patients presented themselves for testing after the date of onset of symptoms. The sensitivity and specificity were highest in participants presenting at the hospital within 3 days from onset of symptoms (53%; 95% CI 34%–72% and 84%; 95% CI 66%–95%, respectively) (Table 3 ). Test performance also improved when the Ag RDT test was evaluated among 56 participants with a high viral load (defined as an Altona RealStar RT-PCR cycle threshold [Ct] value less than 30); sensitivity was 67%; 95% CI 51%–79% and specificity was 100%; 95% CI 54%–100% (supplementary table).Table 3 Sensitivity and Specificity of the Rapid Antigen test stratified by the number of days since symptoms onset.
Table 3 Altona RealStar Diagnostic performance characteristics
Days since onset of symptoms⁎⁎ Ag RDT* Negative Positive Sensitivity Specificity
0–3 Days Negative 26 14 53.3% (34%–72%) 83.9% (66%–95%)
Positive 5 16
4–7 Days Negative 17 20 42.8% (26%–61%) 68% (47%–85%)
Positive 8 15
>= 8 Days Negative 13 14 36.4% (17%–59%) 92.9% (66%–99.8%)
Positive 1 8
All Negative 60 48 44.8% (34%–56%) 79.7% (69%–88%)
Positive 15 39
⁎ Rapid antigen test not done for 82 participants.
⁎⁎ Number of days since the onset of symptoms unknown for 7 participants.
Ag RDT, antigen detection rapid diagnostic test.
The median Ct value of confirmed SARS-CoV-2 cases on the Altona RealStar assay was 27.8 (IQR 21.7–32.4). When we compared the mean Ct value of the missed government SARS-CoV-2 cases (false negatives) to the true positives, it was significantly higher (30 vs 24; p <0.01) and 66% of the missed cases had a Ct value greater than 30 (Fig. 2 ).Fig. 2 A Plot of the Ct value (Altona assay) of confirmed SARS-CoV-2 cases showing the difference between false negatives (cases missed by the government RT-PCR assays) and true positives (participants positive on both the government RT-PCR assays and Altona RealStar assay). RT-PCR, real-time PCR.
Fig. 2:
Seroprevalence
Overall, SARS-CoV-2 antibodies were detected in 37% (91/244) of participants (Table 4 ). Seroprevalence did not differ significantly between RT-PCR positives (44%, 59/134) and RT-PCR negatives (29%, 32/110), and when stratifying by days since onset of symptoms, seroprevalence did not differ significantly by PCR status (Table 4).Table 4 Seroprevalence by Wantai ELISA stratified by Altona RealStair RT-PCR result and days since onset of symptoms.
Table 4Days since onset of symptoms⁎⁎ Altona RealStar Wantai ELISA seropositive
0–3 Days Negative 27% (12/45)
Positive 36% (16/44)
4–7 Days Negative 29% (12/41)
Positive 39% (18/46)
>= 8 Days Negative 32% (6/19)
Positive 60% (25/42)
All participants Negative 29% (32/110)
Positive 44% (59/134)
⁎⁎ Number of days since the onset of symptoms unknown for 7 participants.
RT-PCR, real-time PCR.
SARS-CoV-2 detection & comorbidity
Hypertension, HIV, diabetes, tuberculosis (Tegally et al.) and COPD were the most common comorbidities and together accounted for 78.2% (68/87) of participants with comorbidities. In univariate binary logistic regression analysis, HIV, TB, and COPD were all associated with a reduced odds of being SARS-CoV-2 PCR positive, with ORs ranging from 0.1–0.3 (Table 5 ). In a multivariate regression model that included sex and age along with the highlighted comorbidity variables, being female (OR 0.5, 95% CI 0.3–0.9) and having COPD (OR 0.1, 95% CI 0.0–0.8) were associated with reduced odds of being SARS-CoV-2 positive (Table 5).Table 5 Binary logistic regression analysis of various comorbidities as risk factors for being SARS-CoV-2 PCR positive.
Table 5 Altona RealStar Univariate Multivariate
Negative Positive OR (95% CI) p OR (95% CI) p
Median Age (IQR) 37 (29–48) 40 (30–52) 1.0 (1.0–1.0) 0.207 1.0 (1.0–1.0) 0.211
Sex (female) 58/109 53.2% 56/134 41.8% 0.6 (0.4–1.1) 0.077 0.5 (0.3–0.9) 0.026
Any Comorbidity 46/109 42.2% 41/134 30.6% 0.6 (0.36–1.0) 0.061
Hypertensive 17/109 15.6% 33/134 24.6% 1.8 (0.9–3.4) 0.086
HIV 19/109 17.4% 8/134 6.0% 0.3 (0.1–0.7) 0.007 0.4 (0.2–1.1) 0.086
Diabetes 7/109 6.4% 8/134 6.0% 0.9 (0.3–2.6) 0.884
Tuberculosis 10/109 9.2% 3/134 2.2% 0.2 (0.1–0.8) 0.027 0.4 (0.1–1.6) 0.189
CPD 8/109 7.3% 1/134 0.7% 0.1 (0.0–0.8) 0.028 0.1 (0.0–0.8) 0.031
Asthma 3/109 2.8% 2/134 1.5% 0.5 (0.1–3.3) 0.498
Obesity 1/109 0.9% 3/134 2.2% 2.5 (0.3–24) 0.436
Renal Disease 2/109 1.8% 1/134 0.7% 0.4 (0.0–4.5) 0.460
Cardiac 1/109 0.9% 2/134 1.5% 1.6 (0.1–18.3) 0.689
Outcome v Dieda 2/80 2.5% 1/85 1.2% 0.5 (0.0–5.2) 0.534
a outcome data was only available for patients who were admitted (n=165)
CPD, chronic pulmonary disease.
SARS-CoV-2 Variants
Only samples that had a Ct value of below 30 and produced a complete consensus sequence on analysis that coverage of over 90% of the genome were submitted to GISAID and are presented here. More than 3/4 of the samples sequenced (84.1%; 37/44) were the Beta variant (B.1.351 Pangoline lineage), and 4 were B.1.306, with 1 sample each being B.1.1.7 and B.1.404. A total of 16 of the 44 samples sequenced were missed by all the 3 government PCR assays, confirming these as true positives. Phylogenetic analysis was undertaken on 44 samples from this study, and 185 Zambian sequences that have been published by other research groups, representing all SARS-CoV-2 sequence data available from samples sequenced in Zambia during the study period. (Fig. 3 )Fig. 3 Phylogenetic Tree Maximum likelihood phylogenetic tree of currently available SARS-CoV-2 genomes from the Republic of Zambia collected during the study period Nov 2020 to Feb 2021. Coloured in accordance with SARS-CoV-2 variant type. Coloured id labels on nodes indicate samples sequenced by laboratory during the course of the study. All the full-length genomes retrieved from the GISAID (global database for influenza gene sequences) labelled as country of origin, GISAID ID. Branch lengths are drawn according to the number of nucleotide substitutions per site.
Fig. 3:
Discussion
The main aim of this study was to validate the diagnostics tests used by the Zambian government laboratories for the detection of SARS-CoV-2 infections against the Altona RealStar RT-PCR assay as gold standard. The overall proportion of RT-PCR positive SARS-CoV-2 cases, positive on any of the RT-PCR assays, was 61.1% (149/244); and the reference assay, the Altona RealStar SARS-CoV-2 RT-PCR kit, was the most sensitive. Of the 3 government RT-PCR assays used at the time, the Panther Fusion RT-PCR assay was most accurate (sensitivity 76% and specificity 80%), whereas the Da An Gene was least accurate (sensitivity was 0% and specificity 96.9%). Using the Altona assay as a gold standard, none of the 3 RT-PCR assays evaluated met the WHO recommended minimum sensitivity of 80% and specificity of 97% (WHO, 2020).
The analytical performance characteristics of these assays provided by the manufacturers does not correlate well with how the assays performed in a real-world clinical setting (Doust et al., 2021), consistent with their rapid development and deployment in the absence of clinical evaluations necessitated by the pandemic. Possible factors which might affect the operational performance in a real-world setting include variations in sample quality, transport and storage, and human factors such as how samples are collected and processed by both clinical and biomedical personnel (Doust et al., 2021, Fung et al., 2020). This study has illustrated the importance of local clinical validation and assay verification to characterise the performance of a diagnostic test in a specific clinical setting. For novel emerging pathogens, there is no endemic population within which novel diagnostics can be readily evaluated, and so pandemic preparedness planning should include skeleton protocols for the rapid validation of diagnostic assays.
Molecular diagnostic development is traditionally a slow and methodical process. When running 2 different tests on the same set of samples, there will always be some degree of discordance and so, careful work-up is required to elucidate the reasons for differences and to determine true positives. The SARS-CoV-2 pandemic has challenged this modus operandi and has showed that the rapid development and deployment of reliable molecular diagnostic assays is a central pillar of the pandemic response. At the time of study design and implementation, there was very little data available on the performance of the diagnostic assays being used, and the results of this study were reported in real time to the Zambian National Public Health Institute, which led to discontinuing the use of the Da An Gene assay that was being used in many African countries. At the time of writing, only 1 study had evaluated its use in Benin (Sander et al., 2021). The authors observed good analytical performance characteristics (using synthetic armoured transcripts) but poor clinical performance, which is consistent with the poor clinical performance observed in our study.
In the study herein, the median Ct value of the SARS-CoV-2 cases missed by the government assays was significantly higher than the median Ct value of the true positives (Fig. 2), indicating the poor performance might be due to low sensitivity. Conversely, a study from Ecuador reported a higher sensitivity of the Da An Gene assay (75%–100%) than that of the CDC 2019-nCoV CDC EUA assay which was considered as gold standard (Freire-Paspuel et al., 2021). This alternative gold standard has a higher LoD of 1000 copies/ml compared with 650 copies/ml for the Altona assay (Freire-Paspuel et al., 2021, Visseaux et al., 2020), and/or there could also be logistical/operational factors which contributed to the discrepancy between the 2 studies. With the rapid commercializing and scale up of manufacture, there could well have been quality control issues, which affected the performances of certain batches.
SARS-CoV-2 is evolving in both human and animal populations (Lauring and Hodcroft, 2021, Tegally et al., 2021) and when mutations occur in primer or probe sequences, this can impact assay performance (Artesi et al., 2020). Altona Diagnostics have not yet reported any mutations that they think might affect their assay, including for the recent Omicron variant (Diagnostics, 2021). The Altona RealStar assay targets both the E and S genes of the SARS-CoV-2 genome. The Aptima Panther Fusion assay targets ORF1ab; the Maccura assay targets ORF1ab, E, and N genes and the Dan An Gene assay targets the ORF1ab and N genes. We did not observe any probe failures with the Altona RealStar assay but mutations have the capacity to alter diagnostic assay performance, as has been widely documented for certain variants of concern (Valley-Omar et al., 2022, Wollschlager et al., 2021). This reinforces the need for assays which detect multiple targets and the broader need for genomic surveillance during pandemics with novel viral pathogens.
In evaluating diagnostics assays, the gold standard or reference test used must be accurate, reliable, efficient, highly sensitive and very robust to ensure the cases are correctly determined as either positives or negatives, and it should be appropriate for the population being tested (Doust et al., 2021). All study participants had 1 or more COVID-19 symptoms and were within 2 weeks of symptom onset, a period when the virus should typically be detectable by RT-PCR and antigen screening assays (He et al., 2020, Wolfel et al., 2020). Hence, the cohort of participants used was appropriate for evaluating SARS-CoV-2 diagnostic assays. The Altona Diagnostics RealStar SARS-CoV-2 RT-PCR assay has been extensively reviewed and found to be robust, versatile, and highly sensitive in detecting SARS-CoV-2 infections (van Kasteren et al., 2020, Visseaux et al., 2020). It can detect as low as 625 viral copies/mL compared with 1250 copies/mL LOD for most approved PCR assays (Visseaux et al., 2020). Moreover, the WHO recommends the use of a nucleic acid amplification test as the gold standard test to evaluate SARS-CoV-2 screening assays (WHO, 2020). Hence, the Altona RealStar assay was a credible reference test to use. We sequenced 16 of the samples that were positive only on the Altona assay but negative by the government assays, demonstrating that these were true positives.
The antigen tests evaluated in this study were not reliable in detecting SARS-CoV-2 infections in the general population as both the sensitivity (45%) and specificity (78%) were below the WHO recommended sensitivity of 80% and specificity of 97% (WHO, 2020). In other studies, the sensitivity of rapid antigen tests varies between 45% and 84.9% (Albert et al., 2021, Igloi et al., 2021, Lambert-Niclot et al., 2020, Linares et al., 2020, Osterman et al., 2021) and specificity is typically >99% (Albert et al., 2021, Igloi et al., 2021). There has been much debate about the use of less sensitive lateral flow antigen rapid tests compared to RT-PCR, with some arguing that many RT-PCR-positive cases might not be infectious, and that a less sensitive rapid Ag test is a better tool for identifying those who are at the highest risk of infecting others (Mina et al., 2021, Tom and Mina, 2020). The counter argument is that with a test from just 1 time point, you are unable to know whether the viral load might increase and so, RT-PCR is the only effective way to identify a sufficient number of infectious cases, to inform on isolation, and stop transmission.
The seroprevalence data from our study indicated that a significant minority of both PCR+ve and PCR-ves, had existing antibody to SARS-CoV-2, suggesting previous infection within wave 1, and/or possible cross-reactivity of the ELISA assay used with immunity to other circulating viruses. The overall seroprevalence of 37% among suspected COVID-19 cases is consistent with a community survey undertaken 6 months before the study, which reported seroprevalence of 9% in Lusaka district (Mulenga et al., 2021).
In our study, 78% (68/87) of the participants presented with hypertension, HIV, diabetes, Tuberculosis (Tegally et al.), and chronic pulmonary disease. Suspected cases who had HIV were not at increased risk of SARS-CoV-2 positivity, consistent with previous studies (Charre et al., 2020, Friedman et al., 2021, Inciarte et al., 2020). Our study was underpowered to evaluate whether HIV-infected cases had worse outcomes, but studies elsewhere have suggested HIV is not associated with worse outcomes (Cooper et al., 2020, Nagarakanti et al., 2021). Our observation that COPD was associated with a reduced risk of being SARS-CoV-2 positive (OR 0.1 95% CI 0.0–0.8) had a very wide 95% CI and was likely a sample size artefact. A comprehensive review found that COPD was associated with worse outcomes in COVID-19 patients (Leung et al., 2020).
Limitations of the study
The findings of our study are limited to symptomatic suspected SARS-CoV-2 cases and cannot be extrapolated to asymptomatic cases where diagnostic assay performance might vary. We could not reliably match the specific rapid antigen test used by the government laboratories to the results, and they ran out of Ag test kits during the course of the study, limiting the statistical power of the antigen test evaluation. The study was implemented during the exponential rise of cases during the second wave of the pandemic and changes in government advice/policy could have affected health seeking behaviour and clinical practice during the study.
Conclusions
The RT-PCR assays evaluated did not meet WHO recommended minimum sensitivity of 80%. This highlights the need for all governments to ensure that local plans for diagnostic validation are incorporated into pandemic preparedness planning. Molecular diagnostics have been pivotal in managing the SARS-CoV-2 pandemic and in Zambia and other countries; capacity should be maintained/developed to respond to future zoonoses and could also support much needed surveillance for ongoing endemic infectious disease threats such as antimicrobial resistance. The apparent negative association between female sex and COPD with SARS-CoV-2 had wide confidence limits and should be interpreted with caution.
Disclosures
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Funding Source
This work was funded by the European and Developing Countries Clinical Trials Partnership (EDCTP2) programme under the PANDORA-ID-NET Consortium (EDCTP Reg/Grant RIA2016E-1609). They had no role in the writing, study design; collection, analysis and interpretation of data, and decision to submit the article for publication.
Ethics statement
Informed written consent was obtained from all the participants. The study was approved by ERES Converge, Zambia (Ref No. 2020-JUL-07), in compliance with the laws of Zambia and the standards of Elsevier journals standards of ethics.
Appendix Supplementary materials
Image, application 1
Acknowledgments
JT, KM, KM, MC, NK, ES, AZ, TPV, and MB were part of PANDORA-ID-NET Consortium (EDCTP Reg/Grant RIA2016E-1609) which was funded by the European and Developing Countries Clinical Trials Partnership (EDCTP2) programme, which was supported under Horizon 2020, the European Union's framework programme for research and innovation.
☆ Evaluation the SARS-CoV-2 diagnostics and the impact of comorbidity on odds of SARS-CoV-2 infection in Zambia
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ijid.2022.04.017.
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PMC009xxxxxx/PMC9004226.txt |
==== Front
Immunobiology
Immunobiology
Immunobiology
0171-2985
1878-3279
Elsevier GmbH.
S0171-2985(22)00042-0
10.1016/j.imbio.2022.152216
152216
Article
Assessment of changes in immune status linked to COVID-19 convalescent and its clinical severity in patients and uninfected exposed relatives
Torres Rives Bárbara a⁎1
Zúñiga Rosales Yaíma a2
Mataran Valdés Minerva a3
Roblejo Balbuena Hilda a4
Martínez Téllez Goitybell a5
Rodríguez Pérez Jacqueline a6
Caridad Marín Padrón Lilia a7
Rodríguez Pelier Cira a8
Sotomayor Lugo Francisco a9
Valdés Zayas Anet b10
Carmenate Portilla Tania b11
Sánchez Ramírez Belinda b12
Carlos Silva Aycaguer Luis c13
Portal Miranda José Angel d14
Marcheco Teruel Beatriz a15
a National Center of Medical Genetics, 146 Ave No 3102, Havana 11300, Cuba
b Molecular Immunology Center. Havana, Cuba
c National School of Public Health. Havana, Cuba
d Ministry of Public Health, Havana, Cuba
⁎ Corresponding author.
1 https://orcid.org/0000-0001-9729-5172.
2 https://orcid.org/0000-0001-9483-9971.
3 https://orcid.org/0000-0002-6265-4814.
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Introduction
The immune response during and after SARS-CoV-2 infection can be complex and heterogeneous, and it can be affected by the severity of the disease. It can also contribute to an unfavorable evolution and bring about short and long term effects. The aim of this study was to characterize the lymphocyte composition according to the severity of COVID-19, as well as its degree of relationship to the specific humoral response to SARS-CoV-2 in convalescents up to 106 days after the infection and in their exposed relatives.
Methods
An applied research was carried out with a cross-section analytical design, from March 11 to June 11, 2020 in Cuba. The sample consisted of 251 convalescents from COVID-19 over 18 years of age and 88 exposed controls who did not become ill. The B and T cell subpopulations, including memory T cells, as well as the relationship with the humoral immune response against SARS-CoV-2, were identified by flow cytometry and enzyme immunoassay.
Results
Convalescent patients, who evolved with severe forms, showed a decrease in frequency and a greater proportion of individuals with values lower than the minimum normal range of B cells, CD3 + CD4 + cells and the CD4 + / CD8 + ratio, as well as a higher frequency and a greater proportion of individuals with values above the normal maximum range of CD3 + CD8 + and NK cells. Convalescent patients with severe forms of COVID-19 that exhibited IgG / RBD titers ≥ 1/200 had a lower frequency of TEMRA CD8 + cells (p = 0.0128) and TEMRA CD4 + (p = 0.0068). IgG / RBD titers were positively correlated with the relative frequency of CD4 + CM T memory cells (r = 0.4352, p = 0.0018).
Conclusions
The identified alterations of B and T lymphocytes suggest that convalescent patients with the severe disease could be vulnerable to infectious, autoimmune or autotinflammatory processes; therefore, these individuals need medical follow-up after recovering from the acute disease. Furthermore, the role of T cells CD4 + CM in the production of antibodies against SARS-CoV-2 is confirmed, and it is noted that the defect of memory T cells CD8 + TEMRA could contribute to the development of severe forms of COVID-19.
Keywords
COVID-19
Severity
Immune response
==== Body
pmc1 Introduction
The infection by the novel coronavirus SARS-CoV-2, (severe acute respiratory syndrome coronavirus 2) first reported by Huang et al. (2020), is a severe health problem worldwide that has produced the death of>5 million people until October 29, 2021 (Lu et al., 2020, MINSAP, 2021).
Within the immuno-pathology of the disease, it has not only been stated that the damage may be mediated by the virus itself, but that other factors are involved. These include a hyper-inflammatory immune response, (Chen and John Wherry, 2020, Kuri-Cervantes et al., 2020, Mathew et al., 2020), the exhaustion or dysfunction of T cells (Chen and John Wherry, 2020, Zheng et al., 2020), and the development of a cytokine storm (Kuri-Cervantes et al., 2020, Mathew et al., 2020). These elements are responsible for the large clinical spectrum of COVID-19 (Kuri-Cervantes et al., 2020, Mathew et al., 2020).
A post COVID-19 syndrome having a significant clinical repercussion has been described (Greenhalgh et al.,2020). It is expressed in that the dysfunction of the immune system may subsist for up to two years after viral infections (Wiedemann et al., 2020), the magnitude of the antibody and T cell response may be diverse, discordant, and it may be influenced by the severity of COVID-19 (Wiedemann et al., 2020). Several authors have shown that the response of the memory cells to SARS-CoV-2 will play an important role in the infection, pathogenesis and protection against the disease (Mathew et al., 2020, Sekine et al., 2020).
The study focus on the identification of the degree of alterations of the immune system that may favor the presence of sequels at the short- or long-term, in convalescents from SARS-CoV-2 (Greenhalgh et al.,2020; Shuwa et al., 2021).
The aim of this study was to characterize the lymphocyte composition according to the severity of COVID-19, as well as its degree of relationship to the specific humoral response to SARS-CoV-2 in convalescents up to 106 days after the infection and in their exposed relatives.
2 Materials and methods
2.1 Subjects
We carried out an applied research at the National Medical Genetics Center, Havana, Cuba, through a cross-section analytical design, with the patients diagnosed in Cuba with COVID-19 by means of real time polymerase chain reaction (RT-PCR) from March 11 to June 11, 2020.
The sample was selected by stratified sampling with proportional allocation method (Olayiwola Olaniyi et al., 2013), and it was finally formed by 251 convalescent individuals (asymptomatic convalescents (A) = 67, moderate convalescents (M) = 122, severe convalescent (S) = 62), that had been sick with COVID-19 (confirmed by RT-PCR for SARS-CoV-2 from nasopharyngeal swabs) and had got epidemiological discharge, which was defined as the presence of a negative RT-PCR to SARS-CoV-2, 14 days after the first negative RT-PCR (SARS-CoV −2) that granted the clinical discharge (Ministry of Public Health, 2021a; World Health Organization, 2020a) (see Supplementary material). The inclusion and exclusion criteria of the subjects to study were exposed in Table 1 . The convalescent study time was from epidemiological discharge to blood collection for the evaluation with an average of 68 days (interquartile range of 55–77 days, minimum 20 days, and maximum 106 days) (Table 2 ).Table 1 Inclusion and exclusion criteria of the subjects of study.
Convalescents Patients Uninfected Exposed individuals
Inclusion criteria - Age > 18 years
- Of both sexes
-Who accepted their participation in the study
- That had been sick with COVID-19 confirmed by RT-PCR for SARS-CoV-2.
- That were living in close contact** to a first-degree relative (mother, father, children) having COVID-19.
-That had Epidemiological discharge.*
-They did not test positive to two RT-PCR for SARS-CoV-2 done in the quarantine period (14 days) since their convalescent relative was diagnosed with COVID-19.
- These exposed individuals were also negative to specific antibodies against SARS-CoV-2.
Exclusion criteria -The individuals who were not Cuban residents
-The individuals who were residing outside the health area at the time of inclusion in the study.
-Deceased
*Epidemiological discharge: Defined as the presence of a negative RT-PCR to (SARS-CoV −2), 14 days after the first negative RT-PCR to (SARS-CoV-2) that granted the clinical discharge (Ministry of Public Health, 2021a;World Health Organization, 2020b). **Close contact: Defined as a person who lived and had been in contact or within a close distance (<1.0 m) to an individual(s) with COVID-19 in a confined space for > 24 h, from 2 days before and up to 14 days after the onset of symptoms. (Ministry of Public Health, 2021a;World Health Organization, 2020b)
Table 2 Demographic characteristics of Cuban individuals who suffered from COVID-19 up to three months after the SARS-CoV-2 infection, according to clinical severity and exposed relatives.
CONVALESCENTS / COVID-19 EXPOSED RELATIVES
Total
n (%) Asymptomatic n (%) Moderate
n (%) Severe
n (%) Total
n (%)
Distribution according to age
Total 251 (1 0 0) 67 (26.7) 122 (48.6) 62 (25.10) 88 (1 0 0)
19–29 years old 26 (10.4) 9 (13.4) 12 (19.7) 5 (8.1) 25 (28.4)
30–39 years old 40 (16) 14 (20.9) 24 (23.8) 2 (3.2) 14 (16.0)
40–49 years old 51 (20.3) 15 (22.4) 29 (23.8) 7 (11.3) 21 (23.9)
50–59 years old 61 (24.3) 17 (25.4) 29 (23.8) 25 (40.3) 19 (21.6)
≥60 years old 73 (29) 12 (17.9) 28 (22.9) 33 (53.2) 9 (10.2)
60–69 years old 34 (13.5) 9 (13.4) 13 (10.6) 12 (19.3) 3 (3.4)
70–79 years old 19 (7.6) 2 (3) 6 (4.9) 11 (17.7) 3 (3.4)
≥80 years old 20 (7.9) 1 (1.5) 9 (7.4) 10 (16.1) 3 (3.4)
Age in years, median (IQR) 51 (39–63) 48 (34–57)b1 49 (30–59)b2 64 (51–73) 28.3 (28–54)
Females, n (%) 142 (56.6)a 38 (56.7) 67 (54.9) 37 (59.6) 66 (75.0)
Males, n (%) 109 (43.4) 29 (43.2) 55 (45) 25 (40.3) 22 (25.0)
Comorbidities
High blood pressure 106 (42.2) 22 (20.7) 47 (18.7) 37 (14.7) 24 (27.3)
Obesity 21 (8.4) 3(14.3) 12 (57.1) 6 (28.6) 10 (11.4)
Diabetes mellitus 34 (13.5) 6 (17.6) 13 (38.2) 15 (44.1) 10 (11.4)
Cardiovascular diseases 23 (9.17) 1 (0.03) 9 (3.6) 13 (5.2) 3 (3.4)
Chronic pulmonary disease 43 (17.1) 12 (27.9) 19 (44.2) 12 (27.9) 0
Immunodeficiencies 10 (4) 3 (30) 3 (30) 4 (40) 1 (1.1)
Autoimmune diseases 14 (5.6) 5 (35.7) 3 (21.4) 6 (42.8) 4 (4.4)
Cancer n (%) 4 (1.6) 1(25) 1(25) 2(50) 1 (1.1)
Symptoms of the disease
Fever 101 (40.2) 0 62 (61.4) 39 (38.6) –
Coughing 69 (27.5) 0 45 (65.2) 24 (34.8) –
Myalgia 2 0 2 0 –
Fatigue 55 (22) 0 35 (63.6) 20 (36.4) –
Anosmia 2 0 2 (1 0 0) 0 –
Loss of taste 48 (19.1) 0 40 (83.3) 8 (16.7) –
Diarrhea 4 (2) 0 2 (50) 2 (50) –
Breathlessness (dyspnea) 61 (24.3) 0 30 (49.2) 31 (50.8) –
Duration of the disease* (in days), median (IQR) 16 (14–19.5) 16 (14–18) 16 (14–18.5) 17 (14–24)c –
Convalescent study time ** (in days), median (IQR) 68 (55–77) 64.5 (47.2–74.0) 68.5 (54–78.3) 68 (59.5–78.5)d –
Legend: a: statistical significance using proportion comparison between both sexes in the total number of COVID-19 convalescent individuals. b1: statistical significance between asymptomatic patients and those severely ill, b2: statistical significance between moderately ill and severely ill convalescent patients, in both cases p < 0.0001 identified through the Mann-Whitney test. c: statistical significance between the duration of the disease in persons with moderate and severe forms of COVID-19 through the Mann-Whitney test. d: statistical significance between the duration of convalescence in individuals with asymptomatic and severe forms of COVID-19 identified through the Mann-Whitney test. * Duration of the disease: was defined as the time lapse between the diagnosis made by PCR-RT of SARS-CoV-2 infection and the first negative PCR as part of the criteria for clinical discharge. ** Convalescent study time: was defined from epidemiological discharge until day of blood sampling. IQR: interquartile range. For all tests, statistical significance was considered as p < 0.05.
The COVID-19 convalescents individuals were grouped according to the clinical severity of the infection by SARS-CoV-2: Asymptomatic convalescent (A): they experienced asymptomatic COVID-19; Moderate convalescent (M): they experienced mild or moderate COVID-19; Severe convalescent (S): they experienced a severe or critical form of COVID-19.
The COVID-19 disease severity was classified, according to the guidelines for clinical management of COVID-19 of the World Health Organization (World Health Organization, 2020a) and the guidelines of the national action protocol for COVID-19 (Ministry of Public Health, 2021a) as: Asymptomatic disease: when patients infected with SARS-CoV-2 (positive to RT-PCR), present no signs or symptoms of the disease (see symptoms associated to COVID-19 and Table S1 in Supplementary material); Mild disease: when patients (positive to RT-PCR) present symptoms of COVID-19, without evidence of viral pneumonia, hypoxia or others complications; Moderate disease: when patients infected with SARS-CoV-2 (positive to RT-PCR) present clinical signs and imagines of pneumonia, but without signs of severity and with oxygen saturation as measured by pulse oximetry (SpO2) ≥ 90% on room air; Severe disease: when patients (positive to RT-PCR) present clinical signs of pneumonia (fever, cough, dyspnea, fast breathing) plus one of the following: respiratory rate > 30 breaths/min; severe respiratory distress; or SpO2 < 90% on room air; Critical disease: when patients (positive to RT-PCR) present the Acute Respiratory Distress Symptom (ARDS), sepsis, septic shock, multiple organ dysfunction or other severe complications (Table S1 in Supplementary material).
A group of 88 uninfected exposed individuals (Exposed), who were living in close contact to a first-degree relative (mother, father, children) having COVID-19, was included (Table 1 and Supplementary material). Blood samples for the study of the uninfected exposed individuals were collected the same day it was collected from their first-degree relative convalescent (minimum 42 days, and maximum 107 days from the diagnoses the convalescent relatives).
A face-to-face interview was carried out for the collection of clinical, epidemiological and social data of the patients, given directly by the patients or by their legal tutors, in the case of intellectual disability.
The variable “Duration of the disease” was defined as the time lapse between the diagnosis by RT- PCR for SARS-CoV-2 infection and the first negative RT-PCR as part of the criteria for clinical discharge (Tabla 2).
Demographics and clinical information for convalescent patients can be found in Tables 2.
2.2 Immunotyping using flow cytometry
Cellular immunotyping through flow cytometry (8 color Gallios flow cytometry, Beckman Coulter, France) was made using the peripheral blood obtained by venipuncture, with the use of the K2-EDTA anticoagulant and the lysing solution VersaLyse (BeckmanCoulter, France). According to the recommendations of the manufacturer (Beckman Coulter, France), we used the red blood cell lysis without washing. To each volume of the conjugate, we added 100 μL of blood, mixed for 3 s, and incubated it in a dark chamber for 15 min. at room temperature. Then we added 1 mL of the lysis buffer VersaLyse TM (Beckman Coulter, France) and incubated it for 10 min under the same conditions as the previous step. Finally, we immediately proceeded to the acquisition of the sample by the cytometer. The acquisition of the data was carried out through the Kaluza Acquisition v1.0 software by which we obtained a minimum of 50,000 total events. For the analysis and the report of the results we used Kaluza Analysis v1.5a. The absolute cell counts of the lymphocyte populations were performed through a dual-platform. We designed a manual and sequential window selection strategy with bi-parametric graphs (Fig. S1). For the reference values of the analyses of cellular sub-populations we used previous studies in the Cuban population (Kokuina et al, 2019).
2.2.1 Identification of B, T, NK cells
We quantified the CD 19 + lymphocytes (B cells), T CD3 + lymphocytes, T CD3 + CD4 + lymphocytes, T CD3 + CD8 + lymphocytes and CD56 + CD3- cells (NK cells). A polychromatic flow cytometry tube was used for peripheral lymphocyte immunotyping developed at the immunology laboratory of the National Medical Genetics Center (Zúñiga Rosales et al., 2020). The monoclonal antibodies conjugated with fluorochromes from MACS MiltenyiBiotec (Germany) included anti-CD45 APC-Vio770 (Clone 5B1), anti-CD19 PE-Vio700 (Clone LT19), anti-CD3 FITC (Clone BW264/56), anti-CD4 PerCP-Vio700 (Clone M−T466), anti-CD8 APC (Clone BW135/80), anti-CD56 PE (Clone REA196), (Fig. S1).
2.2.2 Identification of the memory and naive cells
We identified the memory cells as: central memory: CM, CD45RA-CD27+, effector memory: EM, CD45RA-CD27−, terminally differentiated T effector cells (TEMRA, CD45RA + CD27 − ) and naïve cells (CD45RA + CD27 + ). The monoclonal antibodies used were CD8-PE-Cy7 (invitrogen, eBioscience, clone SK1), CD45 RA APC- eF780 (invitrogen, eBioscience, clone HI100), CD3/FITC (Clone BW264/56), Anti-CD27 APC, eBioscience, clone 0323, San Diego, CA, anti-CD127-PE, BD Pharmingen, Clone HIL-7R-M21, BD Biosciences (Fig. S1).
2.3 Qualitative determination of total antibodies anti-SARS-Cov-2 in the serum
We carried out the determination in the serum of total antibodies against SARS-CoV-2 through a double antigen sandwich-type ultra-immune-enzymatic assay (UMELISA ANTI SARS-CoV-2) that was standardized and validated at the Immuno-assay Center of Cuba (CIE, according to its Spanish acronym). The SARS-CoV-2 antigens were fragments from the spike protein (S) and the nucleocapsid (N) of SARS-CoV-2. (Supplementary material).
2.4 Detection of antibodies anti-RBD in the serum of patients
At the Cuban Center of Molecular Immunology (CIM, according to its Spanish acronym), we quantified the total IgG specific RBD antibodies in the serum of patients using an enzyme-linked immunosorbent assay (ELISA). The plates were coated with RBD-mFc and incubated with serial dilutions of serum samples, starting at 1:100. The experimental titers of IgG were determined (Supplementary material).
See the supplementary material for particulars of the methods.
2.5 Statistical analysis
The normal distribution of the quantitative variables was verified using the Shapiro-Wilk test. To describe the quantitative variables, the estimates were made through the median and interquartile ranges (IQR) or the mean and standard deviation, as appropriate. The 95% confidence intervals were also calculated.
To assess the statistical significance of the association between qualitative variables and the comparison of proportions between each convalescent group and between those and the exposed group the Fisher's exact test was used.
The Mann-Whitney U test was used for comparisons between two groups for the analysis of cell subpopulations by flow cytometry and anti-SARS-CoV-2 antibody levels. The correlation between the flow cytometric variables (cell subpopulations CD3, CD4, CD8, CD19, NK and memory cells CM, EM and TEMRA) and the IgG / RBD titers and total antibodies against SARS-CoV-2, was performed using the Spearman’s rank correlation.
Using the IBM SPSS Statistics software (version 22), we carried out multivariate logistic regression analyses to evaluate the influence of age, severity and duration of the disease (we adjusted age) on the variables: CD19+, CD3+, CD3 + CD4+, CD3 + CD8+, NK. We also used the GraphPad Prism 7 (GraphPad Software, California, USA). We consider that there is statistical significance when p < 0.05.
2.6 Ethical issues
The research was carried out under the compliance of the regulations of the Helsinki Declaration of 2013 (World Medical Association, 2013). All cases participants in the research signed the informed consent before accepting their participation. This study is part of a research project approved by the Ethics and Research Committee of the National Medical Genetics Center, and by the advisory committee of the Ministry of Public Health of Cuba.
3 Results
3.1 Demographic and clinical characteristics according to the clinical severity of COVID-19 patients who were epidemiologically discharged
The sample was of 251 individuals who had been ill with COVID-19, and in the group of exposed persons we included 88 first-degree relatives who were exposed to the virus and did not become ill (Table 2).
The clinical forms of COVID-19, from mild to moderate, were more frequent in the convalescents (48.6%), followed by individuals with asymptomatic forms of the disease (67%) (p < 0.0001; 95% CI: 13.5–29.9) (Table 2). Females were predominant (n = 142, 56.6%, p = 0.0385, 95% CI: 0.73–25.1) within all patients having COVID-19 (Table 2).
The median of age was higher (p < 0.0001) in patients with severe forms of the disease compared to asymptomatic individuals (p < 0.0001) and moderate (p < 0.0001) (Table 2).
In convalescents that had severe forms of COVID-19, the time lapse between the diagnosis by RT- PCR of SARS-CoV-2 infection and the first negative PCR of the disease was slightly longer compared to patients with moderate COVID-19 symptoms (p = 0.0313) (Table 2). The Convalescent study time (adopted for the study was from epidemiological discharge until day of blood sampling collection) was of 68 days (IQR: 55.0–77.0 days, minimum 20 days, and maximum 106 days) (Table 2).
3.2 Immunotype of B, T and NK cells in convalescents according to the clinical forms of COVID-19
The absolute lymphocyte count was lower in convalescent people that had asymptomatic forms (A) and moderate forms (M) of the disease compared to the Exposed group that was not infected (Fig. 1 ). The severe convalescent patients (S) had a higher proportion of individuals with an absolute lymphocyte count (14.5%) higher than the normal reference value compared to the A (3.0%, p = 0.0184) and M (5.7%, p = 0.0464) groups (Fig. 2 ).Fig. 1 Cellular immunotypes through flow cytometry in Cuban patients who suffered from SARS-CoV-2 infection according to clinical severity and in the controls. The multi-parametric analysis (relative and absolute frequencies) is represented through the flow cytometry of the cellular sub-populations, CD19+, CD3+, CD3 + CD4+, CD3 + CD8+, the ratio CD4+/CD8 + and the NK cells of COVID-19 convalescent individuals of non-infected exposed persons. Each point represents an individual, the asymptomatic patients (A, green, n = 67), those with moderate symptoms (M, blue, n = 122), and the severely ill patients (S, red, n = 62), as well as those exposed who did not become sick (Exp, gray, n = 88). The analyses between the groups were made using the Mann-Whitney test with a level of significance of p < 0.05. The p values and their significance are: ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05. IQR: inter-quartile range.
Fig. 2 Proportion of convalescents (n = 251) and exposed persons (n = 88) with cellular sub-populations that are lower and higher than the normal reference range of the Cuban population. The proportion of individuals with cellular sub-populations having lower values than the minimum normal reference was found as follows: A: CD19+ (%), B: CD19+ (VA), C: CD3+ (%), D: CD3+ (VA),E: CD3 + CD4+ (%),F: CD3 + CD4+ (VA), G: CD3 + CD8+ (%), H: CD3 + CD8+ (VA), I: NK+ (%),J: NK+ (VA), K: Total lymphocytes (VA), L:) CD4+/CD8 + Ratio. The proportion of individuals with cellular sub-populations having higher values than the normal maximum reference was found as follows: A’: CD19+(%), B’: CD19+ (VA), C’: CD3+ (%), D’: CD3+ (VA), E’: CD3 + CD4+ (%), F’: CD3 + CD4+(VA), G’: CD3 + CD8+(%), H’: CD3 + CD8+(VA), I’: NK+(%), J’: NK+(VA), K’: Total lymphocytes (VA), L’: CD4+/CD8 + Ratio. The green bars represent the proportion of convalescents with asymptomatic clinical forms: (A), moderate clinical forms (M) (blue bars); the proportion of convalescents with severe clinical forms (S) (the red bars); those exposed (Exp) (gray bars). Where n is the total number of convalescents and those exposed with alterations in comparison to the total of each group (total number of convalescents with asymptomatic disease forms, 67; total number of convalescents with moderate disease forms, 122; total number of convalescents with severe disease forms, 62; total number of exposed individuals, 88). The cellular sub-populations are identified through flow cytometry. The 95% confidence intervals are shown in parenthesis, with which we identified the statistical significance. The between-groups significance was calculated through the proportions comparison; the statistical significance was established for p < 0.05 and it was represented as: ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05.
In the S group compared to the non-severe convalescent groups (A and M) and to those Exposed, we observed a lower relative and absolute frequency of CD19+, a lower median of relative frequency of CD3 + CD4 + and of the ratio CD4+/CD8+, as well as an increase of the median of the relative frequency of CD3 + CD8 + and NK (Fig. 1). The S group also showed (compared to A and M) an increase of the median of the absolute frequency of CD3 + CD8 + and NK (Figura1). The A and M groups had lower median of the absolute frequency of total lymphocytes, CD3+, CD3 + CD4 + and CD3 + CD8 + compared to those exposed (Fig. 1).
In another analysis we observed a higher proportion of S (21.0%) that showed values below the minimum normal of the median of the relative frequency of CD19 + compared to those of A (4.5%, p = 0.0047), M (5.7%, p = 0.0018) and the exposed group (Exposed: 5.7%, p = 0.0047). We also observed that the S group had a higher proportion of individuals (24.0%) with a median of the absolute frequency of CD19+, lower than the median reference value compared to the M (11.5%, p = 0.0257) and to the exposed (5.7%, p = 0.0047) groups. We also identified a higher proportion of S with a median of the relative frequency (8.1%, p = 0.0182) and absolute frequency (6.5%, p = 0.0345) of CD3 + CD4+, lower than the median minimum normal reference value compared to the A (0%) group. At the same time, the median of the absolute frequencies of CD3 + and CD3 + CD4 + were lower than the median minimum reference value in a larger proportion of the S group (8.1%, p = 0.0047) compared to the exposed persons (0%), with the same percentages in both sub-populations (Fig. 2).
We also observed in group S, compared to those Exposed who were not infected, a higher percentage of individuals with values higher than that of the normal established value of the median absolute frequency (severe: 21.0% vs exposed: 6.82%, p = 0.0106) and the relative frequency of NK+ (severe: 8.1% vs Exposed: 1.14%, p = 0.0325) (Fig. 2).
Interestingly, the S group had a higher proportion of individuals with values above the maximum range established as the normal value of the absolute frequency of CD3+ (12.90%), CD3 + CD8+ (12.9%) and of NK cells (21.%), compared to the A (CD3+: 1.49%, p = 0.0109; CD3 + CD8+: 0%, p = 0.0025; NK+: 3,0p = 0.0014. Similarly, the M group had a higher proportion of individuals (compared to the S group) with absolute frequencies higher than the normal maximum value of CD3+: 3.3% p = 0.0127; CD3 + CD8+: 3.3%, p = 0.0127 and NK+: 5.7%, p = 0.0018 (Fig. 2).
3.3 T Memory cells in convalescents from SARS-CoV-2 infection according to the clinical forms of COVID-19
We analyzed T CD4 + and CD8 + memory cells (TEMRA: CD45RA + CD27+, CM: CD45RA − CD27+, EM: CD45RA − CD27-) and naive (virgin) cells from 85 (convalescent) at an average of 82 days (42 days as the minimum and 107 days as the maximum) after viral clearance for SARS-CoV-2 identified by RT- PCR, who had clinical and epidemiological discharge. We also studied 29 exposed individuals.
The median of the relative frequency of total CD8 + memory cells (CM, EM, TEMRA) was significantly greater compared to the median of the T CD4 + memory cells (CM, EM, TEMRA) in all the convalescents studied (p < 0.0001) (Fig. 3 A), and this occurred in a similar manner in each one of the groups according to the clinical forms (asymptomatic: p < 0.0001, moderate: p = 0.0213 and severe: p < 0.0001) of COVID-19 (Fig. 3A).Fig. 3 Phenotype of T CD4 + and T CD8 + memory and naive cells in COVID-19 convalescents and controls. A: We analyzed the T CD4 and CD8 + memory cells (central memory cells, MC, CD45RA-CD27+; effector memory cells, ME, CD45RA-CD27−; terminally differentiated T effector cells, TEMRA, CD45RA + CD27 − ), total memory (MC + EM + TEMRA) and naïve cells (virgin cells) (CD45RA + CD27 + ) according to clinical severity. Each point represents an individual, the asymptomatic individuals (A, green, n = 17); those with moderate symptoms (M, blue, n = 20); the severe cases (S, red, n = 48); the total number of convalescents studied (T, black, n = 85); and the controls (Exp, gray, n = 29). B: We analyzed the T CD4 and CD8 + memory cells in all convalescent and exposed. The medians and inter-quartile ranges of each group are represented. The analyses between groups were carried out using the Mann-Whitney test with the significance level of p < 0.05. The p values and their significance were: ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05.
In the analysis of total memory cells in all convalescents (p = 0.0397) and severe cases (p = 0.0030) studied, we identified a higher median of the relative frequency of total T CD8 + memory cells in comparison to the Exposed cases (Fig. 3A).
Nonetheless, we observed that in the entire group of convalescents studied, the medians of the frequencies of CM, EM, TEMRA and naive cells were similar between convalescents and exposed for T CD4 + and CD8 + memory cells (Fig. 3B).
Within the subtypes of T CD8 + memory cells, the highest median of the relative frequency corresponded to TEMRA CD8+ (34.0%), followed by memory cells CM T CD8+ (24.8%), although no statistical differences were found between the median of the frequencies (p = 0.1889, 95% CI: −4.47 to 22.4) (Fig. 3). No statistical differences were observed between the median of the relative frequencies in the subpopulations of T CD8 + memory cells (TEMRA: CD45RA + CD27+, CM: CD45RA − CD27+, EM: CD45RA-CD27-) from the convalescent and Exposed groups (Fig. 3). Nevertheless, the median of the frequency of virgin cells (CD45RA + CD27 + ) in the S group was lower compared to that of the Exposed group (9.5% vs 11.6%; p = 0.0379) (Fig. 3A).
The median of the relative frequency of the total T CD4 + memory cells in relation to the Exposed group, was similar in the total convalescent group (p = 0.3263) and in those presenting asymptomatic (p = 0.7282), moderate (p = 0.2666) and severe (p = 0.4101) forms of the disease (Fig. 3B). The median of the relative frequency of the total T CD4 + memory cells were also similar between the individuals with different degrees of COVID-19 severity (Fig. 3A).
The phenotype of T CD4 + memory cells that prevailed were the cells specialized in central memory (51.1 %), followed by the EM T CD4+ (21.3%) (p < 0.0001, 95% CI: 16.6 to 43.6504) (Fig. 3A).
The medians of the relative frequencies of T CD4 + CM, T CD4 + EM memory cells and naive cells from convalescent individuals of all the clinical forms analyzed in this study, were similar to those exposed. However, the median of the relative frequency of the TEMRA T CD4 + cells was higher in the M group (6.9%) compared to the Exposed group (Exposed: 3.9%, p = 0.0158) and to the S group (4.3%, p = 0.0182) (Fig. 3A).
3.4 Correlation of B, T, NK cells, memory and naive cells with clinical epidemiological variables in convalescents from SARS-CoV-2 infection according to the clinical forms of COVID-19
Using multivariate analysis, we detected that the risk of presenting an increase in the absolute frequency of NK + in convalescents was 4.0 times greater in those presenting the severe forms of the disease (severe) (adjusted OR: 34.0; 95% IC: 1,47–10,8; p = 0.007). Furthermore, it was 1.06 times higher for each day the acute disease was extended (duration of the disease) (adjusted OR: 1.06; 95% IC: 1.0–1.11; p = 0.036). We also found that the convalescents that progressed with severe forms of the disease showed a trend towards an increase in the relative frequency of the NK + cells (adjusted OR: 2.32; 95% IC: 0.58–9.28; p = 0.2320) and a rise in the relative frequency (adjusted OR: 1.49; 95% IC: 0.37–6.08; p = 0.5790), and absolute frequency of CD3 + CD8+ (adjusted OR: 3.23; 95% IC: 0.95–11.0; p = 0.0610) (Fig. 4 , Table 1S).Fig. 4 Relationship between age ≥ 60 years old, severity and duration of the disease, on the main alterations of the cellular sub-populations in COVID-19 convalescent patients. We carried out a logistic regression analysis in 233 SARS-CoV-2 convalescent patients using for the analysis the information that was complete. We evaluated the influence of age ≥ 60 years old, severity (the characteristic of the severe disease against the non-severe case) and the duration of the disease (time lapse of the disease) on the increase of NK+ (%) [A], the increase of NK+ (VA) [A’], the increase of CD8+ (%) [B], the increase of CD8+ (VA) [B’], the decrease of CD19+ (%) [C] and of CD19+ (VA) [C’]. VA: Absolute value. In the Forest plot we show the 95% confidence intervals.
Convalescents, of 60 or more years of age, had a higher risk of decreasing the relative frequency (adjusted OR: 2.70; 95% IC: 1.07–6.78; p = 0.0390) and absolute frequency (adjusted OR: 2.84; 95% IC: 1.34–6.03; p = 0.007) of CD19+ (Fig. 4, Table 1S).
Age was positively correlated to the total number of CD8 + memory cells in convalescent patients (r = 0.5748. p < 0.0001, Spearman correlation) and a similar behavior was found in the exposed persons (r = -0.5688, p = 0.0013, Spearman correlation). Furthermore, age was negatively correlated with T CD8 + naive cells in the S and M groups (r = -0.5213, p < 0.0001, Spearman correlation, data not shown) and in those exposed +(r = -0.5491, p = 0.0020, Spearman correlation, data not shown).
The duration of the disease was found to be positively correlated with the T CD8 + EM cells (r = 0.3480, p = 0.0192 Spearman correlation, data not shown).
3.5 Correlation of B, T, NK, memory and naive cells with the response of specific antibodies to SARS-CoV-2 in convalescent cubans according to the clinical forms of COVID-19
IgG/RBD titers are positively correlated with the relative frequency of the CD8 + CM memory cells (r = 0.3132, p = 0.0320) in the S group (Fig. 5 -A). TEMRA CD8 + showed a tendency to correlate negatively, but there were no statistical differences (r = -0.2405, p = 0.1035) (Fig. 5-B).Fig. 5 Relationship of the memory cells with the specific response to SARS-CoV-2 in convalescents that progressed to severe form of COVID-19. We show the analysis of the correlation between the antibody titer IgG/RBD and the central memory cells (CM) (Fig. 1-A) and with the terminally differentiated memory cells (TEMRA) (Fig. 1-B) in convalescents that progressed to severe forms of the disease (n = 45). A non-parametric Spearman’s correlation test was used for both correlations. We show the comparison of the medians of the memory cells (central memory: CM, effector memory: EM, terminally differentiated: TEMRA) T CD4+ (Fig. 1-C) and T CD8+ (Fig. 1-D) between the severe convalescent patients with titers of < 1/200 (n = 18) and ≥ 1/200 (n = 28). This comparison was made using the Mann-Whitney test. Figures E and F show the comparison of the medians of the memory cells T CD4+.
Those of the S group with antibody titers IgG/RBD > 1/200 compared to those with titers ≤ 1/200, showed lower relative frequency of TEMRA CD8 + cells (IgG/RBD > 1/200: 28.7% vs IgG/RBD ≤ 1/200: 34.58%, p = 0,0357) (Fig. 5-C and D).
In line with the previous results of IgG/RBD, we identified a lower frequency of TEMRA T CD8 + cells (53.3% vs 27,3 %, p = 0.0137) in the peripheral blood of the positive S group for total antibodies against fragments of the N and S protein of SARS-CoV-2 (compared to those that were negative to this antitotal antibody) (Fig. 5- E).
Among the individuals with a presence or absence of antitotal antibodies to SARS-CoV-2, we identified similar relative frequencies of CD19+, CD3+, CD3 + CD4+, CD3 + CD8+, NK + and the ratio CD4/CD8 + in all ranges of severity studied (results not shown).
4 Discussion
The alterations of the B, T and NK cells, as well as the more and more frequent presence of signs and symptoms, have been reported in COVID-19 convalescents, (Greenhalgh et al.,2020; Shuwa et al., 2021). There are, however, discrepancies in the magnitude and the protective or pathogenic role of the following immune response to SARS-CoV-2 infection (Kuri-Cervantes et al., 2020, Chen and John Wherry, 2020, Mathew et al., 2020).
As observed by other authors, in this study we did not observe lymphopenia in COVID-19 convalescents (Grifoni et al., 2020, Rodriguez et al., 2020). A higher proportion of severe convalescents with values above the reference range of total lymphocytes could be influenced by the larger number of T CD8 + lymphocytes identified as a response to clear the persistence and the greater antigenic magnitude of SARS-CoV-2 and because of the use of immunomodulators according to the national protocols (Ministry of Public Health, 2021a; Hernández Cedeño et al., 2021), since it has been reported that the administration of biological therapies in COVID-19 patients, produces an increase of circulating lymphocytes (Giamarellos-Bourboulis et al., 2020, Hernández Cedeño et al., 2021).
The normalization of CD19 + cells in COVID-19 convalescents is reported in the literature (Mathew et al., 2020; Sherina Sherina). A decrease of CD19 + cells has also been observed in patients with severe forms of the disease, (Grifoni et al., 2020, Deng et al., 2020) as those found in this study. The decrease of CD19 + lymphocytes could suggest that sub-populations of this compartment, such as B regulator cells with anti-inflammatory functions, are low, and as a consequence the convalescents show a delay in their complete recovery, and are vulnerable to auto-immune, auto-inflammatory and infectious processes (Mauri and Menon, 2017).
Several mechanisms may explain the lymphopenia of CD19 + and CD3 + CD4 + observed in this study, such as the presence of a greater viral load and exposure time to SARS-CoV-2 in more severely ill patients. This leads to an increase in the direct action of SARS-CoV-2 on the cells, and the damage mediated by the immune system, the sequester of cells from the lung or peripheral lymphoid organs induced by the cytokine storm, apoptosis and the suppression of the bone marrow and the thymus (Wen et al., 2020). The literature also reports, however, that convalescent individuals have similar values of CD3 + CD4 + to those of individuals who did not become ill (Shuwa et al., 2021, Townsend et al., 2021, Wen et al., 2020).
The absence of asymptomatic convalescent patients with a decrease of the T CD4 + lymphocyte values and a higher frequency of these cells in relation to the convalescents with symptoms (moderate and severe), may correspond with the evidence that asymptomatic individuals have a higher secretion of INF -γ e IL-12, as well as a proportional secretion of IL-10 and of pro-inflammatory cytokine (IL-6, TNF-α e IL-1β). This fact suggested that the asymptomatic patients have the ability of developing a less intense inflammatory process, but their antiviral response is protective, efficient, balanced and specific, so that it protects the host and does not produce any apparent pathology (Le Bert et al., 2021).
The discrete increase in T CD3 + CD8 + lymphocytes in convalescents from the severe illness supports the role of these cells when facing a greater antigenic exposure and an exaggerated immune response to achieve effective viral clearance (Wen et al., 2020, Thieme et al., 2020). These results agree with the expansion of T CD8 + lymphocytes in convalescents reported by other authors (Shuwa et al., 2021, Wen et al., 2020). However, the recovery from lymphopenia of T CD8 + characteristic of the acute phase of the disease is also reported. This has led to the idea that the virus produces this alteration and that the effective anti-viral therapy leads to the recovery of T CD8 + cells (Zheng et al., 2020). The increase of T CD8 + lymphocytes in convalescents may have implications in the development of later infections or the perpetuation of inflammatory processes, depending on the capacity of the cytokine secretion of these cells (Shuwa et al., 2021).
Similarly, other studies have reported an increase in NK cells during the convalescent stages (Rodriguez et al., 2020; Wen et al., 2020). It has been reported that an effective therapy for SARS-CoV2 is accompanied by an increase of NK cells (Zheng et al., 2020). The increase in NK + cells is considered to be a valuable biomarker for monitoring the progression of the acute phase of the disease toward recovery stages in severe patients (Rodríguez et al., 2020). In contrast, values of NK + cells are also reported to be similar between convalescents and persons who are not infected by SARS-CoV-2 (Townsend et al., 2021, Liu et al., 2021). This research also showed that an increase in the frequency of these cells is associated to the severity and duration of the disease. This supports the antiviral role and the participation in the immunopathology of these cells on the severity of the disease and on the stages of inflammation that may persist (Townsend et al., 2021, Fox et al., 2012, Market et al., 2020).
The immunologic memory is considered to be of great importance in preventing the recurrence of severe forms of COVID-19 in individuals who are seronegative to SARS-CoV-2, whether they are exposed or not (Sekine et al., 2020). It is possible that a small part of the population infected with SARS-CoV-2, having a poor immunological memory, will be susceptible to reinfection shortly after recovering from the acute process (Dan et al., 2020).
The similarity of the memory and naive cells among all convalescents and uninfected persons identified in this study has been reported in the literature (Mathew et al., 2020).
The prevalence of central memory cells in the compartment of T CD4 + memory in the present study, as well as the identification in severe convalescents of a positive correlation between the T CD4 + CM cells and the RBD titers, corresponds to the capacity of these cells of extravasation and migration to secondary tissues. These show a high proliferative capacity and a low dependence on co-stimulators, thus favoring the formation of specific antibodies against SARS-CoV-2, as observed (Wen et al., 2020, Peng et al., 2020, Weiskopf et al., 2020; Neidleman et al., 2020, Mahnke et al., 2013).
Consistent with other reports from the literature, (Grifoni et al., 2020, Wen et al., 2020, Dan et al., 2020, Peng et al., 2020, Weiskopf et al., 2020, Neidleman et al., 2020, Yang et al., 2007), in this study there was a predominance of TEMRA cells in the sub-set of T CD8 + memory cells, which agrees with their function as potent producers of interferon -γ and perforins that mediate in the specific cytotoxicity of the antigen. This makes them highly important in viral infections (Sallusto et al.,2004).
The high frequency of TEMRA memory cells in convalescent individuals presenting moderate forms of the disease (compared to the severe forms), supports the protective role of these cells in the development of severe forms of COVID-19 and endorses the substantial role of T-cell immunity in SARS-CoV-2, (Sekine et al., 2020, Dan et al., 2020, Peng et al., 2020, Le Bert et al., 2020), and other viral infections (Sridhar et al., 2013). Hence, the formation of T memory cells has been associated to recovery from COVID-19, and it was reported that this response could predict severity and it could become a marker associated to the loss of the effectiveness of the anti-viral response (Odak et al., 2020).
Previous studies have demonstrated that the severity of the disease is inversely correlated with the immunity of T-cells (Ni et al., 2020), and that the deficient T-cell response prevents the positive action of the immune system against SARS-CoV-2 (Odak et al., 2020, Wang et al., 2020). Consistent with these reports, we observed that the convalescents who became ill with severe COVID-19 showed a lower frequency of TEMRA cells associated to higher titers of IgG/RBD and to the presence of total antibodies against fragments of the N and S proteins of SARS-CoV-2 (TEMRA CD8 + ). We also found a negative correlation between TEMRA CD4 + and the IgG/RBD titers. On the other hand, the similarity in the immune response (T CD3 + CD4+, CD3 + CD8+, NK and CD19 + ) in individuals who were seropositive or not to SARS-CoV-2, suggests that the state of protection evaluated through the detection of antibodies against SARS-CoV-2, may be underrated (Sekine et al., 2020).
Although we did not determine the specific SARS-CoV-2 T cells, we did observe that the frequency of the memory and naive cells in all convalescents was similar to that of the exposed individuals. It has been found a response of specific SARS-CoV-2 T cells in persons having close contact with COVID-19 patients, in which no positive RT-PCR was detected, nor the presence of antibodies anti-SARS-CoV-2; therefore, it suggested that there were no infections due to the limited exposure of the persons to viral particles, or the short exposure time (Sekine et al., 2020, Wang et al., 2021).
The decrease of T CD8 + naive cells in patients having severe forms of COVID-19 may be the result of the mobilization of effector cells, as the cytotoxic lymphocytes, in order to eradicate viral infection (Odak et al., 2020, Thieme et al., 2020). Other authors observed a decrease in T CD8 + naive cells in individuals having a mild or moderate SARS-CoV-2 infection (Odak et al., 2020). It should be mentioned that the group of severely ill patients were older, and that the decrease of naive T cells has been associated to immunosenescence, as a consequence of aging (García Verdecia et al., 2013, Saavedra et al., 2017).
It must be considered that older age could produce a hyper-inflammatory state, starting with the fact that during aging and immunosenescence there are changes occurring in the immune system (Sauce and Appay, 2011). These include the effect on several cellular compartments, (García Verdecia et al., 2013) deregulation of cytokine secretion and association to a state of “inflammaging” (low-degree chronic and sterile inflammation during aging), producing an increase in the frequency of infectious, neurodegenerative, and cardiovascular diseases and cancer (Fulop et al., 2018).
One limitation of this study was the fact that we did not analyze any specific cells for SARS-CoV-2. Also, nor we did not carry out a longitudinal study, with patients through the time (samples were collected between 14- and 106-days post-infection). The evaluation of specific responses to SARS-CoV-2 in a longitudinal study could have provided a more comprehensive view of the dynamics of the immune response during COVID-19 and in the convalescent study time, as reported by other authors (Liu et al., 2021, Rodriguez et al., 2020 Wen et al., 2020). It would have been interesting to have included a group of patients experiencing acute COVID-19, as well as to correlate the results of all groups with the clinical condition present in the convalescence period. (Shuwa et al., 2021, Sekine et al., 2020, Peng et al., 2020). Despite these limitations, it was observed that the identified alterations in the immune response in convalescents is influenced by the severity of the disease, similar to what other authors affirm (Peng et al., 2020, Shuwa et al., 2021, Sekine et al., 2020; Wen et al., 2021). Besides, these patients are susceptible to subsequent complications mediated by the immune system (Shuwa et al., 2021). The study of T cells specific to SARS-CoV-2 would have allowed the identification of the presence of cross reaction T cells against SARS-CoV-2 and other coronaviruses, which could have possibly been an element to explain the fact that uninfected exposed individual did not get sick (Grifoni et al., 2020, Le Bert et al., 2021, Wang et al., 2021).
5 Conclusion
The immune status of COVID-19 convalescents is influenced by the severity of the disease. The alterations of the lymphocytes CD19+, CD8+, NK cells and of the specific antibodies against SARS-CoV-2 in severe convalescents suggest that these patients could be vulnerable to infectious, autoimmune or autotinflammatory processes. These findings could be associated with a more unfavorable recovery and the instauration of new sequels of the disease, thereby needing close medical supervision. The alterations in the effector memory cells may be related to the evolution towards severe forms of the disease.
CRediT authorship contribution statement
Bárbara Torres Rives: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft. Yaíma Zúñiga Rosales: Conceptualization, Methodology, Investigation. Minerva Mataran Valdés: Formal analysis, Visualization. Hilda Roblejo Balbuena: Supervision, Conceptualization, Investigation, Writing – review & editing. Goitybell Martínez Téllez: . Jacqueline Rodríguez Pérez: Investigation. Lilia Caridad Marín Padrón: Investigation. Cira Rodríguez Pelier: Resources, Investigation. Francisco Sotomayor Lugo: Resources, Methodology, Investigation. Anet Valdés Zayas: Methodology. Tania Carmenate Portilla: Methodology, Investigation. Belinda Sánchez Ramírez: Methodology, Resources, Investigation. Luis Carlos Silva Aycaguer: Methodology, Investigation, Writing – review & editing. José Ángel Portal-Miranda: Resources, Investigation. Beatriz Marcheco Teruel: .
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A Supplementary data
The following are the Supplementary data to this article:Supplementary data 1
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.imbio.2022.152216.
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PMC009xxxxxx/PMC9004254.txt |
==== Front
Physica A
Physica A
Physica a
0378-4371
0378-4371
North-Holland Pub. Co
S0378-4371(22)00257-6
10.1016/j.physa.2022.127318
127318
Article
Phase transitions may explain why SARS-CoV-2 spreads so fast and why new variants are spreading faster
Phillips J.C. a
Moret Marcelo A. b
Zebende Gilney F. c
Chow Carson C. d⁎
a Department of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854, United States of America
b SENAI CIMATEC Salvador, BA, Brazil
c Department of Physics, State University of Feira de Santana, BA, Brazil
d Mathematical Biology Section, NIDDK, NIH, Bethesda, Md 20892, United States of America
⁎ Corresponding author.
12 4 2022
15 7 2022
12 4 2022
598 127318127318
19 2 2021
26 10 2021
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 SARS CoV-2 responsible for the COVID-19 pandemic and SARS CoV-1 responsible for the SARS epidemic of 2002-2003 share an ancestor yet evolved to have much different transmissibility and global impact 1. A previously developed thermodynamic model of protein conformations hypothesized that SARS CoV-2 is very close to a new thermodynamic critical point, which makes it highly infectious but also easily displaced by a spike-based vaccine because there is a tradeoff between transmissibility and robustness 2. The model identified a small cluster of four key mutations of SARS CoV-2 that predicts much stronger viral attachment and viral spreading compared to SARS CoV-1. Here we apply the model to the SARS-CoV-2 variants Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1) and Delta (B.1.617.2)3 and predict, using no free parameters, how the new mutations will not diminish the effectiveness of current spike based vaccines and may even further enhance infectiousness by augmenting the binding ability of the virus.
Keywords
Proteins
Spike
Virus
Evolution
Vaccines
==== Body
pmc1 Introduction
The novel coronavirus SARS-CoV-2, responsible for the COVID-19 pandemic, enters cells by binding to the Angiotensin-converting enzyme 2 (ACE2) receptor [1], [2]. The binding is done via a structure called the spike (S), which is a glycosylated fusion protein that operates as a trimer. The S glycoprotein rests in a metastable prefusion state that must undergo conformational changes before the virus can fuse to the cell membrane. Given the importance of dynamics for S function it must be understood as a thermodynamic object immersed in water, which is extremely difficult to compute ab initio. One approach to circumvent this intractability is to apply a hydropathic score to the residues of the protein with the idea that hydrophobic residues are more likely to be in the interior while hydrophilic residues are more likely to reside on the exterior. This approach has been attempted many times with varying success [3]. We adopt an approach used previously that predicted that vaccines based on S would be very effective and finding four key mutations that enhance viral attachment and infectiousness [4]. This is nontrivial as vaccine efficacy is not always guaranteed. For example, flu vaccine efficacy is usually around 50% [5].
It has long been hypothesized that some biological systems including proteins may extract important functional benefits from operating at the edge of instability, halfway between order and disorder, i.e., in the vicinity of the critical point of a phase transition [6], [7], [8]. Proteins act as folded self-organized networks that must obey steric and topological constraints. In particular, inside and outside shape extremals as measured by the interactions of amino acid side groups with water are paramount [7], [9]. Each S chain consists of over 1200 amino acid residues, with approximately 300 of these having mutated from SARS-CoV-1 (CoV-1, GenBank: AY278741, UniProtKB: P59594) to SARS-CoV-2 (CoV-2, GenBank: MN908947.3, UniProtKB: P0DTC2) [10]. Of the 300 mutations between CoV-1 and CoV-2, the previous application of the model predicted that four spike mutations affected its infectiousness [4]. The variants contain twelve or fewer spike mutations. Here, we show how the additional S amino acid mutations in the variants will not diminish the efficacy of existing S-based vaccines while increasing the binding ability of the Delta variant [11]. Our work continues a long tradition of applying concepts from thermodynamics and statistical mechanics to virus and virus–host interactions [12], [13], [14], [15], [16], [17], [18], [19], [20], [21].
2 Materials and methods
The method utilizes a thermodynamic amino acid scale [3] that considers the loss of solvent-accessible surface area (ASA) surrounding a central residue. Moret and Zebende [3] found in over 5000 different protein segments that the ASA for a specified amino acid residue at the center of a protein fragment scales as a power-law of the length of the fragment with a well-ordered negative exponent for all 20 amino acids (See Fig. 1 for a schematic of the analysis and Table 1 for the values). Power-law scaling is the hallmark for the critical point of a second order phase transition and the existence of universal amino acid–water interaction parameters is evidence for a thermodynamic phase transition model for novel coronavirus function [6], [7], [8]. The average accuracy of each of the 20 exponents is better than 1% [3]. In the absence of a phase transition critical point, the likelihood that 20 such accurate power-law exponents would coexist independently is astronomically small (less than 10−40). To our knowledge, proteins are the only large-scale networks that exhibit both first-order unfolding phase transitions and second-order conformational phase transitions described by fractals.
The 20 exponents give a measure of the average hydropathy of each residue at the center of an arbitrary background neighborhood. The smaller the magnitude of the exponent, the more hydrophilic the amino acid will be on average. This differs from the many attempts to assign hydropathy based on chemical properties of an amino acid in isolation [3]. Hydrophilic residues are more likely to reside near the outside of the protein and vice versa for the more hydrophobic. However, given that the residues are situated sequentially on a chain, the effect of neighboring residues must be considered. This requires deducing an effective domain length that dominates the protein’s conformation, whose details at the molecular level are unknown. This length may differ from protein to protein although there may be preferred lengths. It captures the scale at which dynamics of the protein may take place at a scale larger than secondary structures such as alpha helices. We conjecture that natural selection acting on a given class of proteins, such as the novel coronavirus spike, favors sequences such that the protein operates close to a thermodynamic critical point at an optimized length scale. Evidence for positive selection has been demonstrated in CoV-2 compared to CoV-1 [4]. Nearer the critical point, it can function more reversibly and at lower temperatures [6]. This conjecture has been applied successfully to other proteins [9], [22].Fig. 1 (a) Moret and Zebende [3] began with a set of 5526 protein segments of varying length M up to 45. A few short examples are shown here, with their amino acid side chains. (b) These segments were grouped into 20 subsets. Each subset has the same amino acid at its center, so each subset contains about 250 segments. (c) The amino acids side chains are each surrounded by a sphere with radius set by van der Waals interactions. Where the spheres overlap, they are cutoff by planes equidistant from their chain contacts (dotted lines). The surface area of the central amino acid that is accessible to water molecules is then calculated. These surface areas are then plotted for each amino acid as functions of chain length M. Increasing M decreases surface areas as the chains fold back upon themselves. These decreases were fitted well by power-laws; such power-laws are known to be characteristic of fractal structures and second-order phase transitions very close to critical points [6], [7].
Table 1 The shifted and rescaled hydropathic values ψ for the Moret Zebende (MZ) and Kyte-Doolittle (KD) scales. The overall correlation is 86%, and in practice the differences are large enough to be reflected in protein function. For different proteins, one or the other of the two scales is better. Here and previously2 only the MZ scale gives synchronous edges that optimize conformational changes for faster spreading for CoV-2 and its mutants.
MZ KD (KD - MZ)/(KD + MZ)
A 157 200.5 0.12
C 246 214.2 −0.07
D 87 96.0 0.05
E 94 96.0 0.01
F 218 220.2 0.00
G 156 157.1 0.00
H 152 102.0 −0.20
I 222 253.7 0.07
K 69 88.2 0.12
L 197 239.9 0.10
M 221 202.4 −0.04
N 113 96.1 −0.08
P 121 133.5 0.05
Q 105 96.0 −0.04
R 78 76.4 −0.01
S 100 149.2 0.20
T 135 151.2 0.06
V 238 247.7 0.02
W 174 147.3 −0.08
Y 222 139.4 −0.23
We thus posit a mechanism for the increase in infectiousness of CoV-2 based on synchronized attachment [4]. The analogy is to a network of coupled oscillators that have a transition between synchrony and asynchrony. The transition is facilitated when the intrinsic frequencies of the oscillators are more symmetric allowing the network to sit nearer to the critical point [23]. In the case of the oscillators, the bifurcation parameter is the coupling strength between the oscillators. As the oscillators become more symmetric the critical transition strength becomes smaller and the network synchronizes much more readily. We note that we use infectiousness here only to imply more effective binding of the virus to the cell to the ACE2. Transmissibility could also increase due to an improvement in the replication ability of the virus within the host; our model does not directly address this very important possibility.
The predictions are made by computing the coarse-grained (shifted and rescaled, see Table S1) magnitude of the exponent along S over a domain length W to create a matrix of hydropathy scores, Ψ(R,W), where a larger value means that the residue at site R acts more hydrophobically. W is the width of a sliding window centered on each R to average nearby interactions. Ψ(R,W) fluctuates between hydrophilic to hydrophobic values with decreasing amplitude and increasing length scale as W increases. Hence, W sets a length scale for which protein dynamics act at the tertiary and quaternary structural level. This scale is an emergent property of the protein which we deduce by finding the W that minimizes the coefficient of variation (standard deviation divided by mean) between six isolated local hydropathic minima (hydrophilic maxima). The minima were the nearest local minima corresponding to those in CoV-2 at a window length of W=35, which was used previously [4]. These minima are often connected to observed static structural features in other proteins [7]. The conclusion that the spike attachment dynamics are synchronized by hydrophilic extrema is preserved [4].
3 Results
Fig. 2 shows the hydropathy score as a function of W, Ψ(R,W), for CoV-1, CoV-2, Alpha, and Delta [24]. The score trends downwards and reaches a minimum at a W near 40. However, there is a clear difference in the curves for CoV-1 versus CoV-2 and its variants. CoV-2 declines faster to a more defined minimum. In fact, CoV-1 appears to have two minima.
Fig. 3 shows the profile of Ψ(R,W) as a function of R at the optimal W for the same strains as Fig. 2. (The profiles of the other variants are similar to CoV-2). The notable features include a large hydrophobic maximum near residue 1231 and 6 deep minima (hydrophilic maxima) between 400 and 1200. The large hydrophobic maximum is located in the virus transmembrane domain and likely acts as an anchor. The minima represent the boundaries or edges of hydrophobic regions. Minimum 1 (456, positions are for CoV-2) is located in the Receptor Binding Domain (RBD), 2 (572) in C-Terminal Domain 1 (CTD1), 3 in the Linker between S1 and S2 (692), 4 in the Fusion peptide in S2 (792), 5 (937) in heptad repeat 1 (HR1), and 6 (1163) in the Linker to the stem of S2 [25]. Although these minima need not necessarily be associated with any observable structural features, they seem to be located in salient regions for dynamics. The minima are much more symmetric in depth for CoV-2 and the variants compared with CoV-1 (see Table 1). This symmetry is a signature of a critical point and lends the hydrophilic domains more conformational mobility, particularly for acting in synchrony. The symmetry is also fragile and can be easily broken by mutations as confirmed by mutation simulations (data not shown). While only one minimum is located within the receptor binding domain (RBD) of the spike (residues 331-524), there are smaller minima and maxima within the RBD (see Fig. 3). We also note that minima 1 and 2 are near regions where several predicted antibody epitopes may reside [26].Fig. 2 Hydropathy as a function of window length, W. The optimal W became smaller for CoV-2 and variants compared to CoV-1. Note that W is always odd by convention.
Table 2 gives the hydropathic values for the six minima as well as the optimal W and the coefficient of variation (CV) of the minima (all six or the four deepest). There is a significant decrease in CV between CoV-1 and CoV-2 and its variants and also a decrease in W from 41 to 39 (note that W is always an odd number by definition). The CV for Delta decreased even more but that of Alpha, Beta, and Gamma increased slightly compared to CoV-2. Not much is noticeable in the hydropathy profiles in Fig. 2 between CoV-2 and the variants except that minimum 1 has shifted lower to align better with minimum 4. Such long-range or allosteric interactions occur in motor proteins, where they were quantified by hydropathic scaling [27]. They are known to occur in principle but when small they are only detectable using the hydropathic scale with 20 exponents. Equalization of hydrophobic minima was recognized as the cause of domain attachment synchronization in the evolution of CoV-1 to CoV-2 for second, but not first, order phase transitions [28], [29]. While the minima have not become more leveled in Alpha, Beta, and Gamma, the hydrophobic maximum between minima 1 and 2 is higher in all three compared to CoV-2 and Delta. This increased hydrophobicity could possibly stabilize the virus in aerosols, which then results in higher transmissibility [29]. The near leveling of the hydrophobic peak near 227 with the receptor binding peak near 380 suggests that the two peaks could bind together to aerosol surfaces. It had been noted previously that CoV-2 mutations favored hydrophobic residues [30]. Thus, we hypothesize that Delta may have become more transmissible by improving its binding ability to ACE2 while the others have preserved their ability to bind but possibly improved function elsewhere, such as being more stable outside of the host or replicating at a faster rate within the host. We do note that Delta, which seems much more transmissible than the other variants and CoV-2, likely also has improved function elsewhere. The important point is that for all variants the changes to the S protein are slight and thus leave the spike vulnerable to existing vaccines.Fig. 3 Hydropathy score Ψ(R,W) for CoV-1, CoV-2, Alpha, and Delta, at the optimal W. The six local hydropathic minima (hydrophilic maxima) are much more symmetric in CoV-2 and variants compared to CoV-1. Minimum 1 is located within the RBD (residues 331-524), which also contains other local minima and maxima.
The increased binding ability of CoV-2 and variants compared to CoV-1 may also partially explain why CoV-1 had a much higher case fatality rate than CoV-2 but with lower transmissibility [31]. It is well known that viral attachment in the upper respiratory tract, URT, (e.g. throat) leads to a less severe illness compared to attachment to the lower respiratory tract, LRT, (e.g. lung) infection [17]. Both CoV-1 and CoV-2 are equally severe when they attach to the LRT. The virus must pass through the URT to get to the LRT [32]. The optimized spike of CoV-2 and its variants may lead to a higher probability of binding to the URT and thus to a much higher transmissibility with lower total case fatality rate.Table 2 Scores for main hydropathic minima (based on CoV-2 sites) at the optimal window, W. CV is the coefficient of variation for the six minima and CV* is the coefficient of variation for the four most hydrophilic (deepest) minima.
1 2 3 4 5 6 W CV CV*
CoV-1 137.4 135.9 147.2 141.6 137.0 138.1 41 0.03 0.007
CoV-2 141.2 139.7 140.2 138.5 139.6 139.4 39 0.006 0.004
Alpha 141.2 137.9 140.9 138.5 139.6 139.4 39 0.009 0.006
Beta 141.2 139.7 142.2 138.5 139.6 139.4 39 .001 0.004
Gamma 141.2 139.7 140.2 138.5 139.6 138.9 39 0.007 0.004
Delta 138.1 139.7 139.1 138.5 140.3 139.4 39 0.005 0.004
Could more key mutations bring the edges into better agreement, increasing transmission further? This has low probability as it would require coordinated hydrophilic mutations within the narrow range of W residues surrounding an edge. Additionally, the great success of vaccines based on S was predicted because even a small disturbance in S will tend to drive it away from the critical point [33]. For the virus to attach to a cell, S must act in a coordinated fashion and this is impaired away from the critical point. Thus, the S-based vaccines that are already available are expected to be equally successful not only for the current variants, but for any future mutation as well that increases transmissibility by moving the virus still closer to its critical point.
All S mutations are easily evaluated using our measure, which can also be used to design animal experiments to test S mutations for their nearness to criticality and thus transmissibility. Our score evaluates transmissibility due to efficacy of viral attachment to cells but does not address the effect of mutations that may alter mechanisms after the viral material has entered the cell, such as replication rate. Finally the predictions (with no new parameters) were made possible through experience gained from the many previous studies that utilized global 21st century protein databases [34].
Julia code used for all computations is available at https://github.com/ccc1685/SARS-CoV-2-Spike.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
We thank Mario Borgnia for helpful discussions. CC was supported by the Intramural Program of the NIH/NIDDK, Brazil. MAM received financial support from the 10.13039/501100003593 National Council of Technological and Scientific Development - CNP, Brazil (grant number 305291/2018-1).
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==== Front
Inform Med Unlocked
Inform Med Unlocked
Informatics in Medicine Unlocked
2352-9148
The Authors. Published by Elsevier Ltd.
S2352-9148(22)00081-8
10.1016/j.imu.2022.100933
100933
Article
Supervised learning of COVID-19 patients' characteristics to discover symptom patterns and improve patient outcome prediction
Ilbeigipour Sadegh ∗
Albadvi Amir
Department of Information Technology Engineering, Industrial and Systems Engineering Faculty, Tarbiat Modares University, Tehran, Iran
∗ Corresponding author.
12 4 2022
2022
12 4 2022
30 100933100933
4 1 2022
26 3 2022
26 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.
The world today faces a new challenge that is unprecedented in the last 100 years. The emergence of a new coronavirus has led to a human catastrophe. Scientists in various sciences have been looking for solutions to this problem so far. In addition to general vaccination, maintaining social distance and adherence to government guidelines on safety precaution measures are the most well-known strategies to prevent COVID-19 infection. In this research, we tried to examine the symptoms of COVID-19 cases through different supervised machine learning methods. We solved the class imbalance problem using the synthetic minority over-sampling (SMOTE) method and then developed some classification models to predict the outcome of COVID-19 cases (recovery or death). Besides, we implemented a rule-based technique to identify different combinations of variables with specific ranges of their values that together affect disease severity. Our results showed that the random forest model with 95.6% accuracy, 97.1% sensitivity, 94.0% specification, 94.4% precision, 95.8% F-score, and 99.3% AUC-score outperforms state-of-the-art classification models. Finally, we identified the most significant rules that state various combinations of 6 features in certain ranges of their values lead to patients’ recovery with a confidence value of 90%. In conclusion, the classification results in this study show better performance than recent studies, and the extracted rules help physicians consider other important factors to improve health services and medical decision-making for different groups of COVID-19 patients.
Keywords
COVID-19
Supervised learning
Machine learning
Confidence index
Support index
Association rules mining
==== Body
pmc1 Introduction
In late 2019, the world suffered a great menace that influenced all viewpoints of human life. A new type of coronavirus has appeared in Wuhan, China [1]. The virus causes lung infection and has a very high rate of transmission [2]. Finally, with the extent of the virus in many countries, the World Health Organization on March 11, 2020, announced the prevalence of novel coronavirus as a fatal pandemic [3].
There are various types of coronaviruses in the world. Acute Respiratory Syndrome and Middle East Respiratory Syndrome are the most famous of these viruses [4]. The recently recognized virus is called SARS-COV-2 and is the object of COVID-19 disease [4].
Several efficient COVID-19 vaccines have been produced by well-known companies around the world so far. But, masking, adherence to government guidelines on safety precaution measures, and social distancing are still the three most effective actions in preventing COVID-19 [4]. However, computer science applications are an effective way to help physicians cope with coronavirus and improve hospital care. These applications generally include machine learning and deep learning techniques that are used to detect the patients with the disease or mathematical modeling and social network analysis (SNA) approaches to forecast the pattern of disease outbreaks. For example, regression and susceptible-exposed-infected-recovered (SEIR) analysis have been applied to predict the future prevalence of the disease [5]. Gene expression programming (GEP) and genetic algorithm (GA) were utilized to optimize the diameter of Nylon-6,6 nanofibers for coronavirus face masks [6]. Furthermore, some techniques based on mathematical modeling have been developed to forecast the spread rate of the disease [[7], [8], [9]]. Today, in addition to short-term estimating of the prevalence pattern and assessing the risk of infection with the COVID-19 [10,11], real-time applications are increasing in various fields of medicine, such as the diagnosis of cardiac arrhythmias [12]. Also, the social network analysis [13,14] and the partial correlation coefficients [15] approaches have been used to recognize high-risk areas of the disease and identify effective climatology factors on the prevalence of COVID-19, respectively. As a last attempt, researchers have employed different deep learning methods to distinguish positive cases based on chest X-ray and CT-Scan images [[16], [17], [18], [19], [20], [21], [22], [23]]. However, although deep artificial neural networks have a high capability in processing image and audio data, these methods greatly increase computational costs [23].
The previously presented methods in diagnosing the outcome of COVID_19 cases suffer from two problems. First, the model evaluation measures in this research show the relatively low performance of the classification models. Second, they do not provide any knowledge about the signs and characteristics of different groups of patients.
In this study, the aim is to address the problem of previous studies by enhancing the classification performance metrics and identifying important rules (participation of variables) that determine the outcome of patients infected with COVID-19. So, the main purpose of this study is to reduce COVID-19 mortality by improving medical decision-making. For this purpose, supervised machine learning methods are implemented to predict the outcome of COVID-19 cases (recovery or death) and identify the factors that have the greatest impact on their outcome. We trained decision tree, random forest, support vector machine (SVM), logistic regression, and k nearest neighbor (KNN) classification algorithms to diagnose death or recovery data classes. Our random forest model presented superior performance in predicting the outcome of patients infected with novel coronavirus than previous approaches. In the second part of the research, we identified the most important rules governing the set of COVID-19 symptoms through a rule-based technique. The discovered rules define various combinations of several important characteristics with a range of their values, which together determine the outcome of patients with more than 90% confidence. It helps physicians provide appropriate medical care for high-risk groups of patients.
In the next sections of the research, we first report the study area and collected data. After that, we describe the data preprocessing and developed models in the processing subsections. We explain our findings in the results section. Then we discuss different techniques, their results, and research applications. Finally, in the conclusion section, we state the goal of the research, our findings, limitations, and several suggestions for future works.
1.1 Related works
With the advent of the COVID-19 pandemic, artificial intelligence-based technologies helped humans in various areas of personal and public health. Utilizing these technologies has helped physicians and nurses improve patient services and facilitated efforts to find effective ways to combat the COVID-19 infection [25]. Most researches in this area focus on forecasting the number of infected cases, recoveries, or deaths [[26], [27], [28], [29], [30], [31], [32], [33], [34], [35]].
Davoudi et al. [36] investigated the effect of three different types of statins on the severity of COVID-19 infection and then diagnosed the disease severity using machine learning methods. The results of research in the first step showed that the severity of the disease is lower for patients who took Simvastatin before infection. In addition, the researchers claimed in the second step that the decision tree classifier with 87.9% accuracy outperformed other classification models in diagnosing disease severity.
Sharifi et al. [37] studied the effects of the COVID-19 pandemic on several areas of society. The researchers used digital and artificial intelligence to measure the effects of the novel coronavirus on energy, industry, and medicine areas. The results showed the effect of the new coronavirus pandemic on energy-related industries and confirmed that renewable energies are significantly effective in reducing the destructive consequences of SARS-COV-2.
In [38], researchers presented a new version of the hybrid salp swarm (HSS) and genetic algorithms to improve nursing services during the COVID-19 pandemic. The authors claimed that the proposed algorithms are well able to address the nurses' scheduling and designation problems and perform better than the proposed state-of-the-art methods.
Researchers in Ref. [39] first developed the generalized logistic growth model (GML) to simulate the novel coronavirus sub-pandemic waves in Iran and then estimated the risk of inter-provincial travel in the prevalence of the COVID-19 disease. The results revealed that the GML model can well simulate the trend of two, three, and four waves. In addition, the results indicated that traveling between small cities and the capital of Iran significantly increases the risk of disease infection.
In another study, researchers examined the relationship between the sunspots number and the emergence of new viruses in the world. The researchers smoothed the sunspot cycle using a wavelet analysis method to examine the history of previous pandemics, predict the time of emergence of the new unknown virus, and identify high-risk geographical points for virus outbreak. The results confirmed that there is a significant relationship between the occurrence of the previous pandemics and the extrema of the sunspot number. In addition, the multi-step autoregression (MSAR) model estimated about 110 years for the emergence of a new pandemic [40].
The supervised machine learning applications are extensive in various fields of medicine. The researchers in Ref. [41] used decision tree, logistic regression, and support vector machine classifiers to diagnose pneumonia in limited resource and community settings. First, the researchers identified six essential features using a feature selection technique and then trained the learning models using 4500 cases. The test results showed that the decision tree model with an AUC of 93% recorded better performance than other models.
In [42], the researchers used a combined method involving supervised machine learning to identify an effective drug against coronavirus infection. Researchers used FDA-approved drugs, natural datasets in the literature, and zinc database to develop chemical libraries. These chemical libraries were used to select compounds interacting with target proteins of the COVID-19 infection such as spike and nucleocapsid proteins. The results suggest some approved drugs against hepatitis C virus, cancer, and ssRNA virus as candidate compounds to fight the novel coronavirus.
Lybarger et al. [43] developed an automated span-based framework for extracting events from the COVID-19 clinical text notes. Text data includes 1472 notes of symptoms, tests, and diagnoses provided by various patients. This research consists of two stages: 1. Construct and train the event extraction model, and 2. Predict the COVID-19 test results. The proposed framework predicted the symptom and assertion with high performance and outperformed the similar extractor developed on MetaMapLite for event extraction. In addition, the results showed that the extracted symptom annotations significantly improved the prediction performance on the structured data.
Today, a large amount of unstructured, structured, and semi-structured data is generated from various sources. Collecting, integrating, storing, and processing this amount of data with traditional data processing technologies is very time-consuming and expensive. So, it has led to the development of a new generation of big data processing technologies [12]. On the other hand, researchers have developed various feature extraction algorithms to reduce the dimension of big data, which significantly reduces computational and spatial costs [44]. Microarray data is a type of high-dimensional data generated by microarray technology to evaluate the manifestation level of genes [45]. Reducing the dimension of microarray data is critical for gene expression because it is hard to discover knowledge and identify hidden patterns from a large number of extracted genes [45].
In the new approaches proposed to reduce the dimension of microarray data, researchers developed a solution for selecting the optimal features of the microarray data. The proposed approach consists of two steps: 1. features extraction based on independent component analysis (ICA) method, and 2. selecting the optimal set of genes based on artificial bee colony (ABC) theory. The authors claimed that the proposed method reveals superior performance than the state-of-the-art approaches developed for selecting optimal genes from microarray data for the Naïve Bayes [46] and artificial neural network [47] classification methods.
2 Method and materials
2.1 Experiment data
We gathered data through interviews, questionnaires, and medical records of patients with COVID-19 admitted to Saveh Medical Center (SMC) in Iran during the year 2020. The research data contained 1,142 samples with 39 features per case. Some important features include age, sex, hospital unit, breathing conditions, fever, cough, different underlying diseases, the length of hospitalization, blood rate oxygen, intubation, and death or recovery class labels. In Table 1 , we provided a detailed description and possible values of some important variables in this study. We picked out the most important attributes using a filter-based feature selection (extra-tree) method in the preprocessing stage. Besides, our data included 1131, and 111 recovered and died cases, respectively. Accordingly, our data suffered from a class imbalanced problem, which can affect learning models.Table 1 Definition and possible values of significant characteristics in this study.
Table 1Variable Description
Age Patient's age
Intubation 1; patient has undergone intubation, 2; patient has not undergone intubation.
Fever, cough, headache, chest pain 0 stands for absence of symptom, and 1 stands for presence of the symptom.
Contact coronavirus 0; no history of contact with COVID-19 cases, 1; history of contact with COVID-19 cases.
Section of hospital the ward where the patient has been hospitalized. 1; regular ward, 2; intensive care unit, 3; no hospitalization.
Presence of underlying diseases 0 stands for absence of underlying diseases, and 1 stands for presence of the underlying diseases.
Rate of partial pressure of oxygen, Po2 0; PO2 levels are greater than 93, 2; PO2 levels are less than 93.
Shortness of breath 0 stands for absence of symptom, and 1 stands for presence of the symptom.
Hospital duration number of hospitalization days.
Result PCR 0; negative for COVID-19, 1; positive for COVID-19, 3; test result is pending.
Condition entering the hospital 0; sever, 1; mild
CT scan manifestation 1; CT scan results for COVID-19 are negative, 2; CT scan results for COVID-19 are positive.
Death no; patient has recovered, yes; patient has died.
2.2 Data visualization
A useful tool for revealing hidden statistical properties in a dataset is data visualization. We visualize the data with different categorizes of visualization plots. Fig. 1 shows the age distribution of patients relative to hospital duration and the sex of patients based on their outcome (death or recovery), respectively. As can be seen, most of the patients were hospitalized for 1–10 days, and the number of patients who died (blue spots) was more than 60 years old (Fig. 1a). On the other hand, according to Fig. 1 b, the number of male patients (value 2) in this study is more than female patients (value 1). Furthermore, the mortality rate is almost the same among both sexes.Fig. 1 Scatter plot of the age of patients relative to their (a) sex and (b) hospital duration based on death or recovery class labels.
Fig. 1
A useful way to explore the different characteristics of patients relative to each other is to present data with a bar plot. The bar diagram indicates the values assigned to the variables of a particular case. It also makes it possible to visually compare the variables of two or more samples based on the same variable. Fig. 2 represents the intubation, hospital section, blood oxygen level, and class label variables of some patients toward their age.Fig. 2 Bar chart of Intubation, hospital unit, rate Po2, and the class features of the patients based on their age.
Fig. 2
Visualization also provides a way to show the rate of change in the values of different features relative to each other. The line chart is the tool used for this purpose. The line chart in Fig. 3 shows the rate of change in intubation, hospital ward, blood oxygen level (ratePo2), and class label (death) characteristics for 50 COVID-19 cases.Fig. 3 Line chart of intubation, hospital ward, blood oxygen level, and class label variables for 50 COVID-19 cases.
Fig. 3
Finally, we divide the data into separate sections using a facet chart and display it as a single plot. The facet chart in this study (Fig. 4 ) divides the data set into two subsets based on the class labels and shows the age distribution of patients in each subset. Accordingly, the highest age recurrence is 40 years in the recovered patients set and 80 years in the set of cases who died of infection.Fig. 4 Facet chart of the age distribution of patients based on different class labels.
Fig. 4
2.3 Data pre-processing
In data analysis applications, data should be preprocessed before training the models. In this research, the preprocessing stage includes removing outliers, replacing null values, solving the class imbalance problem, feature selection, and random shuffle sampling. The output of the preprocessing step is a set of data that is acceptably empty of redundancy. It has a significant impact on improving the performance of learning models [24]. Additionally, preprocessing steps may require data sampling and normalization based on the learning method. The pre-processed data then enters the processing stage to train machine learning models. Fig. 5 shows an overview of the methodology used in this research.Fig. 5 The block diagram of the research methodology.
Fig. 5
In this study, null values in all variables are replaced with the mean value of that variable. It assures that the learning model does not tend to a specific value in a variable. Moreover, the K-means clustering method was applied to discover outliers in the data. The K-means clustering suggests data points as outliers that are further away from cluster centers than other points.
2.3.1 Feature selection
The purpose of feature selection is to find an optimal subset of characteristics by eliminating unrelated variables in the research data. So, it leads to advancing the model performance results and reduces computational complexity [48]. The most well-known methods for picking out features are Filter, Wrappers, and Embedded procedures [48]. The filter method does attribute ranking independent of the learning algorithm. Feature ranking describes the degree of influence of a feature in separating data classes [48].
In this study, we used filter-based extra tree classifier to select the ten most important variables in predicting the death or recovery classes of COVID-19 patients (Fig. 6 ). The extra tree is an ensemble and majority-vote-based classifier that employs a set of decision trees to distinguish class labels [49]. Fig. 6 presents the most relevant features to train the learning models. According to this figure, intubation, age, and the number of hospitalization days are the most effective properties to determine class labels, respectively.Fig. 6 Top 10 influence attributes selected using the filter-based technique.
Fig. 6
2.3.2 Balancing class labels
The class imbalance problem is a common problem in supervised feature learning. Class imbalance indicates that the number of data samples with different labels is not balanced. It can greatly affect the prediction results. If the number of a particular label in a data set is very different from other labels, the data suffer from the class imbalance problem. Therefore, it is necessary to solve this problem, otherwise, the classification results will not be sufficiently reliable.
Conventional methods for solving the class imbalance problem are based on Over-sampling or Under-sampling approaches. The over-sampling methods balance class labels by replicating minority class samples. The under-sampling methods, on the other hand, balance class labels by eliminating some majority class samples.
In this study, the cases who died of COVID-19 or recovered from the disease constitute 9.8% and 90.2% of the data samples, respectively. Therefore, data classes are imbalanced in our research. So, we used the SMOTE [50] method to solve the class imbalance problem. The SMOTE algorithm is a method based on a data Over-sampling approach that incorporates the minority class samples to create new cases instead of duplicating them. This technique first randomly selects a sample from the minority class set and finds its best K nearest neighbor by the Euclidean distance measure. Then randomly selects some cases from its neighbors and generates a new synthetic sample by the selected samples [50]. This process is repeated for all the minority class samples until the number of the minority class cases equals the majority class cases. Equation (1) shows a way of combining samples in the SMOTE algorithm [50]. While Xn is the new sample, X is the sample selected from the minority class sample, and Xk is the sample selected from the neighbors set. Plus, the rand function generates a random number between zero and one.(1) Xn=X + rand(0 ‚ 1)∗ |X − Xk|
By solving the class imbalance problem in this study, the number of cases who died of new coronavirus (minority class) increased from 111 to 1131 cases, equal to recovered cases (majority class). Table 2 shows the number of patients belonging to each class label in this research after applying the SMOTE algorithm and dividing the data into a train and test set. We used the random shuffle strategy to randomize the samples, then applied the non-probable top-down sampling method to break down the dataset into train and test sets. We repeated this process several times and evaluated the classifier performance measures using the different numbers of the test samples. Accordingly, the models showed their best performance using 30% of test cases and 70% of train samples.Table 2 Number of samples in different classes in the train and test data set.
Table 2Data set Recovery class samples Death class samples Total sample
Train (70%) 729 714 1443
Test (30%) 302 317 619
All 1031 1031 2062
2.4 Processing
In this stage, we train and test classification models using preprocessed data. In the literature, researchers have proposed many machine learning approaches for different artificial intelligence applications. Supervised, unsupervised, and semi-supervised feature learning approaches are the well-known machine learning categories [24].
The supervised machine learning methods can utilize what they have seen in the past to foretell the future. In these methods, the data set is divided into two train and test data sets. In the training phase, the model learns how to separate class labels (supervised learning), and in the testing phase, we evaluate how well the model can recognize the class of the samples [24].
2.4.1 Classification
Several classification models were developed for supervised feature learning to diagnose the outcome of patients infected with the coronavirus in this study. These learning models include decision tree, SVM, KNN, logistic regression, and random forest due to their low time complexity and high efficiency in classifying relational data. In the results section, the performance metrics of the models are detailed.
2.4.2 Association rule mining
Association rules extraction is a data mining operation that finds connections between the features of a data set [24]. In other words, association analysis is the study of features or characteristics that are related to each other and tries to extract rules from these features. This method seeks to discover rules to quantify the relationship between two or more properties [24]. Association rules are defined as if and then with two indices of Support and Confidence [24].
In this research, the aim is to determine rules for predicting the novel coronavirus behavior in different patients. In the next section, we describe the concepts needed to derive the rules that may not be familiar to the readers.
Important definition: Suppose that I = [I1, I2, …, Im] is the set of total features available in the data set. Each subset of I is called a transaction, denoted by T, and D is the set of transactions in T. Then an association rule is shown as follows:
X→Y {Support, Confidence} So that, X⊂I ‚ Y⊂ I ‚ X∩Y=∅ .
Support: Indicates the percentage or number of D transactions that include both X and Y.
Confidence: Expresses the dependence of a particular feature on another feature and is calculated as Equation (2).
Strong rules: Strong rules are rules that have greater support and confidence than the determined threshold. In association rules analysis, the goal is to find and extract these strong rules.(2) Confidence(X ‚ Y)=support(X∪Y)support(X)
Lift index: Lift index is a measure for evaluating association rules and shows the attractiveness of a rule [34], which is calculated as Equation (3).(3) Lift(X→Y)=support(X∪Y)support(X).support(Y)
According to Equation (3), if the lift index for a rule is less (or greater) than one, then there is a negative (or positive) dependency between X and Y in the X → Y condition.
Many algorithms have been proposed to extract strong rules in the literature. One of the most widely used methods in this field is the Apriori algorithm, which has lower computational complexity than other methods [24]. We have used the Apriori algorithm with 30% and 90% support and confidence thresholds, respectively, to extract strong rules from the symptoms of COVID-19 disease in determining the outcome of infected patients. The extracted rules that meet all the defined conditions include 269 rules. Fig. 7, Fig. 8 show the scatter diagram and the grouped matrix of the extracted rules, respectively. The higher color intensity in Fig. 7 indicates a higher degree of confidence (close to one). But, the higher color intensity in Fig. 8 represents the lift value, and the node size describes the support value. The matrix elements in Fig. 8 show the symptoms of COVID-19 cases with specific intervals that lead to the patient's recovery or death. Although all 269 rules are important, the rules filtered in Fig. 7 (top left corner) are more important than the rest of the rules because they have higher lift and confidence indices. In the next section, we will examine the ten most important of these rules in more detail and state what important factors together determine the outcome of infected patients with high confidence.Fig. 7 The association rules extracted with the expected conditions (support = 0.3, confidence = 0.9, lift> 1).
Fig. 7
Fig. 8 Grouped matrix diagram of extracted association rules with expected conditions (support 0.3, confidence 0.9, lift> 1).
Fig. 8
2.5 Analysis environment
Our system is supported by CPU 2.3Ghz (five-core), 6 gigabytes of RAM, and one terabyte of disk space to implement algorithms in this study. We implemented visualization, preprocessing, and classification steps with the Python programming language version 3.5. Also, we use R language version 3.5.1 to discover association rules due to its capability to display results. Finally, We used dplyr (version 1.0.7), ggplot2 (version 3.3.5), plyr (version 1.8.6), kohonen (version 3.0.10), arules (version 1.7), and arulesVIZ (version 1.5-1) packages in the R statistical language, and pandas (version 1.3.5), matplotlib (version 3.5.1), seaborn (version 0.11.2), numpy (version 1.0.7), Scikit-learn (version 0.23.2), imblearn (version 0.8.1), collections (version 3.7.12), and yellowbrick (version 1.3) packages in the Python language for statistical analysis, visualization, implementation of models, and validation of results.
3 Result
In this section, we present the research results for different methods separately. First, the classification performance metrics are computed in detail for each classification algorithm and compared with the results of previous studies. Second, different sets of symptoms and the range of their values that play a significant role in determining the outcome of COVID-19 patients are identified using the association rules mining technique.
3.1 Classification performance
We adjusted the classification models with different parameters, trained with 70% of the data samples, and tested with the remaining 30%. The KNN model with parameter k = 3 and random forest model with 150 predictors recorded their best performance. To evaluate the classification performance of the models, we applied the model evaluation metrics presented in Equations (4), (5), (6), (7), (8). In these equations, true positive (TP) represents positive samples that the model accurately predicts as positive. False positive (FP) shows negative samples that the model mistakenly classifies as positive. Also, true negative (TN) is the number of negative samples that the model correctly predicts as negative. Ultimately, false negative (FN) indicates cases that the model should predict positive but wrongly considers negative [24].
Confusion Matrix is a useful tool for examining the performance of classification models in recognizing data class labels. If the data samples are grouped into M classes (where M ≥ 2), the confusion matrix C will have at least M rows (actual value) and M columns (predicted value) for different class labels. In other words, a confusion matrix represents TP, FN, FP, and TN indexes for a classifier based on its observations. There may be additional rows or columns in the matrix to represent the sum of the samples or the percentage of recognition. Accordingly, Fig. 9 illustrates the confusion matrices of the classification models developed in this research. Besides, Table 3 provides the classification performance metrics derived from the confusion matrices in Fig. 9 for the different learning models.(4) Accuracy:TP+TNTP+TN+FP+FN
(5) Precision:TPTP+FP
(6) Sensitivity:TPTP+FN
(7) Specificity:TNTN+FP
(8) F1−score:2(Precision∗Sensitivity)(Precision+Sensitivity)
Fig. 9 Confusion matrices of the models developed in this research.
Fig. 9
Table 3 Classification performance results to diagnosis the outcome of COVID-19 cases.
Table 3Model Acc(%) Pr(%) Se(%) Sp(%) F1_score(%) AUC_score(%)
Decision tree 93.21 91.29 95.89 90.39 93.53 93.14
SVM 87.07 86.23 88.95 85.09 87.57 95.66
Knn 86.91 82.24 94.95 78.47 88.14 93.02
Logistic Regression 86.59 87.74 85.80 87.41 86.76 95.48
Random forest 95.63 94.47 97.16 94.03 95.80 99.38
Fig. 10 displays the receiver operating characteristic (ROC) curve to compare the classification models. ROC curve is used to assess the performance of two or more classification models in a two-class problem [24]. This curve compares the performance of the classifier based on the rate of changes between the proportion of positive samples that the model correctly detects positive (TPR) and the ratio of negative cases that the model mistakenly labeled as positive (FPR) in different parts of the test set [24]. Meanwhile, the area under the ROC curve (AUC) for each model demonstrates a degree of the accuracy of the model. The closer the AUC score is to one, the better the performance of the model in distinguishing between positive and negative cases. Therefore, a model is selected as the final model whose AUC score is close to one and higher than the AUC score of the other models [24]. According to Table 3, the highest AUC score belongs to the random forest model that confirms its better performance than other models. So, the random forest classifier with 150 predictors is selected as the final model for outcome prediction of the COVID-19 cases.Fig. 10 ROC curve to compare the performance of the models implemented in this research.
Fig. 10
Finally, Table 4 compares the classification performance results of the random forest model in this research with the results of previous state-of-the-art studies. Based on data provided in Table 4, it is obvious that our model performs better in determining the outcome of coronavirus patients than the previous researches.Table 4 Classification performance of the proposed method and comparison with state-of-the-art methods.
Table 4Research Method Acc(%) Pr(%) Se(%) Sp(%) F1_score(%)
An, Chansik, et al. [51] Linear SVM 91.9 25.6 92.0 91.8 40.0
Chen et al. [52] Logistic Regression – – 91.4 76.0 –
Chowdhury et al. [53] Nomogram – – 92.0 92.0 –
Iwendi et al. [54] Random Forest 94.0 100 75.0 – 86.0
Sumayh S et al. [55] Random Forest 95.2 95.0 94.9 93.6 95.5
Mohammad and Mahdi [56] Neural Network 89.9 93.6 87.7 93.2 90.5
Rahila et al. [57] Bayes Net 89.0 – 92.6 86.0 –
Proposed method Random Forest 95.6 94.4 97.1 94.0 95.8
3.2 Association rules results
In this study, we used association rules mining by the Apriori algorithm to identify a set of symptoms that determine the outcome of COVID-19 patients with high confidence (0.9) and support (0.3). In each specific application, the rules generated are usually high. Therefore, we need to extract only the most essential rules that satisfy the value of the support and confidence thresholds, and the correlation between their items and their results is positive. We used the lift index to extract the ten most momentous rules in the data set by evaluating the relationship between the item(s) and the label of the rules. We visualized these rules with a parallel coordinates plot in Fig. 11 . The vertical axis of the diagram in Fig. 11 shows the set of items, and the horizontal axis represents the position of the items in different rules. According to the result, all the rules lead to the patient's recovery (death = 1). In our data set, the class label (death) column with numbers 1 and 2 describes the cases who died of COVID-19 or recovered from it, respectively. Other items include hospital section, hospital duration, patient's respiratory condition, the patient's condition when visiting the hospital, blood oxygen level, need for a ventilator, and patient's age, respectively.Fig. 11 The ten most essential association rules were discovered in this research.
Fig. 11
Before extracting the rules, the values of each attribute are discretized into different intervals. Each rule specifies the extent to which features can together determine the outcome of COVID-19 patients. In addition, it recognizes variables in which range of their values have the most occurring in the data set. The range [1,1.67) for the hospital section indicates the high range of its values and, as mentioned previously, refers to the hospital wards where patients with usual symptoms are admitted. Also, the rules record that the number of hospitalization days with a range between 0 and 11 days has an effective role in determining the patient's recovery. In this study, the set of values for lack of the patient's shortness of breath and the patient's shortness of breath is 0 and 1, respectively. Shortness of breath (interval [0,0.5)) indicates lack of shortness of breath or mild shortness of breath in patients. The next feature is the patient's condition when visiting the hospital. This feature with a value of zero describes the patients with normal conditions, and a value of one expresses the patients with severe conditions. So, the rules indicate that patients recover in a critical condition (interval [0.5,1]). This unusual situation in the rules occurs because almost all patients in our data set are hospitalized in unfavorable conditions. Blood oxygen level in patients is another feature that plays a significant role in their recovery. The higher the rate of pulmonary infection caused by the coronavirus, the lower the blood oxygen level in patients. In coronavirus, the values of 1 and 2 determine the oxygen level above 93% and less than 93%, respectively. As Fig. 11 shows, the oxygen level with intervals [1, 1.5) plays a substantial role in various rules. Moreover, the items that have been reviewed so far, the intubation feature is another essential feature that occupies a place in the set of rules. Intubation indicates whether a patient has needed a ventilator device or not. Patients who did not require intubation are marked with the number 2, otherwise, the value of the intubation variable is 1. Naturally, the rules indicate that intervals [1.5,2] are necessary for patients to recover because they do not need artificial respiration. Finally, the age property of patients as the last item is essential in determining the patient's recovery. Patients between the ages of 25 and 50 are more likely to recover.
Finally, it is principal to note that the extracted rules are one-sided. It means that although a combination of properties can result in the patient's recovery with high confidence, a patient may have recovered but not meet any of the conditions set out in Fig. 11. In other words, the rules do not guarantee that a patient who is not covered by any of the rules will die.
4 Discussion
In this study, we employed machine learning techniques to evaluate the clinical symptoms of patients who died of COVID-19 or recovered from it. Machine learning techniques in this research mainly included supervised methods. Classifying patients based on two-class labels (recovery and death) and discovering association rule mining were two supervised methods used.
One problem with data classification is the class imbalance problem. In this study, the initial number of patients who recovered from the infection or died of it was 1031 and 111 cases, respectively. So, our data suffered from the class imbalance problem. We used the SMOTE algorithm to solve the problem by increasing the cases who died of infection. We then developed the decision tree, KNN, SVM, logistic regression, and random forest models with 70% of the train data and tested them with the remaining 30% samples. Our results showed that the random forest classifier with 95.63% accuracy, 94.47% precision, 97.16% sensitivity, 94.03% specification, and 95.8% F1-score has better performance in diagnosing the outcome of COVID-19 patients than methods proposed in the recent studies (Table 4).
Random forest is an ensemble classification method based on the majority vote and uses a combination of decision trees to classify data classes. Although it has a higher computational complexity, past researches show that this classifier performs more beneficial than other classification models due to its ensemble nature. It can justify the higher performance of this model in this research.
Also, we presented a rule-based approach to determine the sets of factors that together affect the outcome of patients. We set the support and confidence values to 30% and 90, respectively. The purpose of adopting a high threshold for support and confidence measures is to find the most valid rules. In the next step, we calculated the quality for all generated rules by the lift metric and represented the ten highest quality rules through a parallel coordination diagram (Fig. 11). All extracted rules were related to the recovered patients and did not provide knowledge about patients who died of infection. However, the high number of recovered cases confirms the produced rules because they follow more patterns in the data set. Accordingly, the rules tell us what symptoms and in what range of their values with high confidence will result in the patient's recovery. The items that made up the most important rules were age, intubation, hospital section, breathing condition, the patient condition when visiting the hospital, hospital duration, and blood oxygen rate. Different combinations of these attributes led to the production of various rules (Fig. 11).
Finally, our results confirm the research presented in the past. Most studies have identified patients' age as an essential factor in the outcome of coronavirus cases. According to the latest research, more than 70% of deaths in Iran are among people over 60 years old. Besides, our results present new information to physicians. For example, specialists should consider the value of clinical variables such as hospital duration, patient's respiratory condition, hospital ward, and patient condition at the time of hospitalization as other important factors to improve treatment services for high-risk patients at different stages of patients' treatment, in addition to the features identified in the past, such as age and underlying diseases.
4.1 Limitation
This research faced some constraints. Our data lacked some of the pathological and neurological features of COVID-19 cases. A further variety of features may enhance the classification performance metrics. Besides, we considered ten features as the best attributes to improve reliability, so more variables will be investigated by raising this threshold. Besides, the deep learning-based methods could not be implemented on our data because they require a large amount of data for high-level performance and our research data did not provide this number of required samples. We can provide a Big Data framework by integrating a variety (Big Data feature) of data types such as CT-scan images and electrocardiogram (ECG) signals of COVID-19 patients to utilize deep neural networks. On the other hand, it significantly raises the computational costs of the operations.
5 Conclusion
We set a target to investigate different aspects of the new coronavirus using two supervised machine learning methods. We removed redundancies from the data and solved the class imbalance problem, then developed different classification models to diagnose the outcome of COVID-19 cases. Our random forest model outperformed the previous state-of-the-art models in this area of study. Next, we developed a rule-based method to extract the essential association rules governing the COVID-19 cases. The rules identify various combinations of 6 features and the range of their values that specify the patient's recovery with a confidence value of 90%. Accordingly, this study improved the classification performance metrics in detecting the outcome of COVID-19 cases (recovery or death), identified the most effective sets of characteristics and range of their values that together determine the outcome of COVID-19 patients, distinguished high-risk groups of patients in the early stages of the disease, and improved medical decision-making and health services by separating different groups of disease cases.
Our results help health specialists consider other factors to enhance healthcare services for the COVID-19 patients. Specialists can handle different groups of patients by measuring the characteristics that have been identified as efficient in this research. It conclusively can help to decrease COVID-19 fatality.
Data availability statement
The data utilized for finding the outcomes of this research have been taken through questionnaires and patients' medical records in the SMC, Iran. Research data was approved by the SMC in Iran and was provided by figshare repository with unique identifier "http://doi.org/10.6084/m9.fihshare.12446120.v1" and under "Attribution 4.0 (CC BY 4.0)" license.
Funding statement
The authors received no financial support for the research and/or authorship of this article.
Institutional Review Board statement
Our research was confirmed by the Institutional Review Board of Department of Information Technology Engineering, Industrial and System Engineering Faculty, Tarbiat Modares University. Ethical review and approval were waived for this study due to the data samples lacked the participants' personal information, and our study did not violate participants' privacy.
Informed consent statement
Patient consent was waived due to the data samples lacked the participants' personal information, and our study did not violate participants' privacy.
Author contributions
Conceptualization, S.I. and A.A.; Methodology, S.I.; Software, S.I; Validation, A.A and S.I; Formal Analysis, S.I; Investigation, A.A, S.I; Data Curation, S.I; Writing – Original Draft Preparation, S.I; Writing - Review & Editing, S.I; Visualization, S.I; Supervision, A.A; Project Administration, A.A.
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 would like to express our full thanks to Saveh Medical Center for providing medical data.
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PMC009xxxxxx/PMC9004257.txt |
==== Front
Ann Tour Res
Ann Tour Res
Annals of Tourism Research
0160-7383
1873-7722
Elsevier Ltd.
S0160-7383(22)00053-6
10.1016/j.annals.2022.103402
103402
Article
The impact of public health emergencies on hotel demand - Estimation from a new foresight perspective on the COVID-19
He Ling-Yang a
Li Hui a⁎
Bi Jian-Wu a
Yang Jing-Jing b
Zhou Qing c
a College of Tourism and Service Management, Nankai University, Tianjin 300350, China
b Macao Institute for Tourism Studies, Colina de Mong-Há, Macau 999078, China
c School of Management, Hangzhou Dianzi University, Hangzhou 310018, China
⁎ Corresponding author at: Nankai University College of Tourism and Service Management, No. 38 Tongyan Road, Haihe Education Park, Jinnan District, Tianjin 300350, China.
12 4 2022
5 2022
12 4 2022
94 103402103402
18 8 2021
13 3 2022
22 3 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.
This paper proposes a new foresight approach to estimate the impact of public health emergencies on hotel demand. The forecasting-based influence evaluation consists of four modules: decomposing hotel demand before an emergency, matching each decomposed component to a forecasting model, combining the predictions as the expected demand after the emergency, and estimating the impact by comparing actual demand against that predicted. The method is applied to analyze the impact of COVID-19 on Macao's hotel industry. The empirical results show that: 1) the new approach accurately estimates COVID-19's impact on hotel demand; 2) the seasonal and industry development components contribute significantly to the estimate of expected demand; 3) COVID-19's impact is heterogeneous across hotel services.
Keywords
Prediction-based management
Tourism demand forecasting
Long short-term memory (LSTM)
Support vector machine (SVM)
Complete ensemble empirical mode decomposition (CEEMD)
Handling Editor: Haiyan Song
==== Body
pmcIntroduction
A public health emergency can bring tourism to a standstill. For example, due to the restrictive measures on population mobility after the outbreak of COVID-19, tourism - an industry largely characterized by the congregation of people and essentially only ever an offline form of consumption - has been seriously affected, with a sharp decline in the demand (Sharma, Shin, Santa-María, & Nicolau, 2021). The highly contagious nature of COVID-19 has made tourists more cautious about travel. As a related sector of the tourism industry, hotels also suffer from huge operating losses owing to this pandemic. For example, early in the current pandemic, Macao's hotel occupancy rate in February 2020 fell to below 15%, and the actual gross domestic product of Macao as a whole plunged about 50% year-on-year in the first quarter of 2020 (Liu, Wang, McCartney, & Wong, 2021). As Jin, Qu, and Bao (2019) pointed out, investigating the effect of crisis events helps to understand the impact and post-event recovery in a mixed political, social, and economic context. Hence, there is an urgent need to be able to produce accurate estimates of the impact of a public health emergency on hotel demand and how quickly it will recover after a pandemic.
Qualitative and quantitative studies have examined the impact of public health emergencies on tourism and hospitality. The qualitative analysis focuses on interviewing individuals, which is suitable for impact analysis from a micro perspective (Bajrami et al., 2021). To evaluate the macro impact of public health emergencies, quantitative analysis is introduced, such as intervention analysis (Crespí-Cladera, Martín-Oliver, & Pascual-Fuster, 2021; Kuo, Chen, Tseng, Ju, & Huang, 2008; Polemis, 2021) and the event study approach (Chen, Jang, & Kim, 2007; Sharma & Nicolau, 2020). However, an appropriate control group is difficult to find for intervention analysis. Typically, in the context of public health emergencies, such as SARS and COVID-19, few regions can act as a suitable control group for intervention analysis, e.g. differences-in-differences (Polemis, 2021) and regression discontinuity (Deng, Hu, & Ma, 2019). Similarly, the event study approach is difficult to apply because one of its basic assumptions, regarding the effectiveness of the market (Peterson, 1989), is violated in the context of public health emergencies. Both supply and demand experience a substantial decline in this circumstance, and the cumulative abnormal return in an event study may not truly reflect the impact of the emergency. Thus, a new method particularly suited to estimate the impact of an emergency needs to be investigated, meeting the following characteristics of 1) providing a macro perspective, 2) no need to find a suitable control group, 3) not violating market assumptions, and 4) obtain more accurate estimation results.
To meet the above four conditions, we propose a new forecasting-based influence evaluation method, involving adaptive and hybrid forecasting on the expected hotel demand in a virtual world without public health emergencies (the control group) and comparing expected demand against the real demand. It is necessary to restrain the virtual world without a pandemic from being too complex, and so some factors relevant to hotel demand, such as online search data, weather data, and holiday data, are not considered here. Additionally, as what we predict is the development trend of hotel demand without pandemics, the judgmental inputs are also not included, although they can reflect the levels of severity in terms of a pandemic's influence (Zhang, Song, Wen, & Liu, 2021). However, factors that can reflect the future economic status are valuable for the forecasting task (Jaipuria, Parida, & Ray, 2021). Therefore, an economic indicator is added into the model. The hotel demand before a public health emergency is decomposed into independent components with specific meanings and different frequencies via complete ensemble empirical mode decomposition (CEEMD). Then, because these components have different degrees of nonlinearity, each is matched with a suitable forecasting model. The model library contains four types of models popular in hotel demand forecasting: a statistical model, called the seasonal autoregressive integrated moving average with exogenous variables (SARIMAX); a deep learning model based on long short-term memory (LSTM); and two machine learning models, namely artificial neural network (ANN) and support vector regression (SVR). The expected hotel demand during an emergency is estimated by combining the predictions of different components. Ultimately, the impact of a public health emergency on hotel demand is evaluated by comparing the difference between actual demand and expected demand.
The new method is applied to analyze the impact of COVID-19 on Macao's hotel industry to demonstrate its effectiveness. There are five principal empirical findings. 1) The new forecasting-based influence evaluation approach accurately estimates COVID-19's impact on hotel demand, and the proposed adaptive hybrid forecaster inside it performs better than a series of benchmarks. 2) The seasonal component and development trend make significant contributions to the estimation of hotel demand, respectively contributing more than 10% and 25%. 3) In the absence of COVID-19, the virtual demand for three- and five-star hotels grows, while that for two- and four-star hotels declines. 4) COVID-19's impact is heterogeneous for hotels with different star ratings: the impact on two-star hotels is the slightest, while that on five-star hotels is the most significant, followed by four-star hotels. 5) The recovery speed of the demand for hotels with different star ratings is also heterogeneous: five-star hotels recover the most rapidly, while two- and four-star hotels recover relatively slowly. Five-star hotels provide diverse services, including accommodation, entertainment, catering, among others, with marketing promotion strategies. This type of service is preferred by travelers after the pandemic. Accordingly, hotel managers should increase the diversity of their services to achieve a high standard in order to be more robust during a public health emergency.
The rest of this paper is organised as follows: The next section presents related works on evaluating the impact of public health emergencies on tourism and hospitality and those mainstream forecasting methods. The following section sets out the methodology of the study, and briefly describes the modelling strategies for estimating the impact of a public health emergency with the proposed adaptive hybrid forecaster. Then, the impact of COVID-19 on Macao's hotel industry is used as a case study to investigate the effectiveness of the new forecasting approach. Finally, the contributions and implications are presented in the conclusion section.
Literature review
Impact analysis of crisis events on tourism and hospitality
Many works have investigated the impact of public health emergencies, especially the current COVID-19, on tourism and hospitality, qualitatively or quantitatively; they have utilized interviews (Chen, Demir, García-Gómez, & Zaremba, 2020; Kaushal & Srivastava, 2021; Shapoval et al., 2021), questionnaires (Bajrami et al., 2021; Chadee, Ren, & Tang, 2021; Foroudi, Tabaghdehi, & Marvi, 2021; Kim, Kim, & Lee, 2021; Sobaih, Elshaer, Hasanein, & Abdelaziz, 2021; Tu, Li, & Wang, 2021), and laboratory experiments (Bresciani, Ferraris, Santoro, Premazzi, & Viglia, 2021; Li, Yao, & Chen, 2021). Interviews are particularly useful for the construction and development of relevant theory and are better suited to small samples. However, the findings based on interviews apply only to a particular group, and it is not wise to generalize them. Questionnaires are a conventional and widely used quantitative research method that is better suited to larger samples than interviews. Nevertheless, this method can only measure variables within an established model, and so focus on the impact of relatively few variables, from a particular perspective, such as the employees (Bajrami et al., 2021). As for the laboratory experiment, its great advantage lies in the ability to control the influence of exogenous variables in order to verify causal relationships between variables. However, findings from laboratory experiments also lack generalizability to some extent. As Bresciani et al. (2021) point out, while the results of their study with laboratory experiment are robust to hotels in different European locations, the circumstance that US hotels are rather different from European hotels might affect tourists' perceptions.
The generalizability of micro findings produced by using the above methods is limited. Therefore, to explore the macro impact of public health emergencies on tourism and hospitality, some researchers have chosen to use intervention analysis (Crespí-Cladera et al., 2021; Kuo et al., 2008; Polemis, 2021) and the event study approach (Chen et al., 2007; Sharma & Nicolau, 2020). Intervention analysis is regression-based and needs to meet strict assumptions (Box & Tiao, 1975). In such studies, the public health emergency is generally regarded as a dummy variable, which is introduced in a regression model to test whether the emergency causes changes in the development of tourism and hospitality. The impact is measured by the coefficient on the dummy variable (Zhang, Yu, Wang, & Lai, 2009). For example, Crespí-Cladera et al. (2021) used logit regression to identify which hospitality firms in Spain faced financial distress due to the COVID-19 disaster, and found that strong financial ability characterized those that survived. Looking at earlier pandemics and drawing on single-firm as well as panel data, Kuo et al. (2008) adopted the autoregressive moving average model and dynamic panel model to estimate the overall impact of SARS and avian flu on Asian countries. However, many regions will be affected by any given public health emergency, which leaves few comparable regions to act as a suitable control group in the commonly used forms of intervention analysis, such as differences-in-differences (Polemis, 2021) and regression discontinuity (Deng et al., 2019). Thus, the impact of public health emergencies evaluated by them may have systematic errors.
The event study approach is a standard analytical approach in economics and management (Mackinlay, 1997). It can assess the impact of unforeseen events, such as public health emergencies, based on the cumulative abnormal return, which is defined as the sum of differences between the actual return and the estimated return, that is, the measure of abnormality caused by events. For example, Chen et al. (2007)) explored the impact of SARS on the stock value of hotels with an event study. The findings indicated that the earnings and share prices of seven listed hotels fell sharply during the SARS outbreak. Sharma and Nicolau (2020) assessed the impact of COVID-19 on the travel industry based on a market-based valuation method and found four sub-industries - hotels, airlines, cruise lines, and car rentals - had experienced a substantial fall in valuation. However, the foundation of event studies - the assumption of the effectiveness of the market - is violated in the context of a public health emergency (Peterson, 1989). The cumulative abnormal return calculated by an event study may not truly reflect the impact of that public health emergency, and the estimation results may be detrimentally affected.
Forecasting methods related to tourism and hospitality
There are also works that analyze the impact of unforeseen events on tourism and hospitality from the perspective of forecasting. For example, Qiu, Wu, Dropsy, Petit, and Ohe (2021) provided an assessment concerning the future development of tourism under the uncertainty surrounding COVID-19 based on a two-stage three scenario forecast framework. Wu, Hu, and Chen (2022) predicted the latest hotel occupancy rates in the context of the COVID-19 pandemic based on the mixed data sampling models (MIDAS). Zhang et al. (2021) combined econometric and judgmental indicators into their model to forecast the possible paths to tourism recovery in Hong Kong. As for the forecasting method, related works can be divided into single forecasting and combined forecasting. Single forecasting mainly includes three types. The first is regression analysis based on statistics, and the representatives are VAR (Wu, Cao, Wen, & Song, 2021) and ARMA family, i.e., ARIMA (Jin et al., 2019), SARIMA (Qiu et al., 2021), ARIMAX (Hu, Qiu, Wu, & Song, 2021), and SARIMAX (Song, Qiu, & Park, 2019; Wu, Song, & Shen, 2017). They are of better adaptability to linear data. The second refers to methods based on machine learning, such as SVR (Chen, Liang, Hong, & Gu, 2015) and ANN (Claveria, Monte, & Torra, 2015). They can deal with forecasting tasks with nonlinear demand data to some extent. The third is those methods based on deep learning. Wherein, commonly used models, e.g., LSTM (Bi, Liu, & Li, 2020) and CNN (Bi, Li, & Fan, 2021), can process demand data with strong nonlinearity. Deep learning methods show a universally well performance on a relatively large sample volume. As for combined forecasting, it integrated the advantages of multiple single models and can avoid the influence of excessive forecasting error of the single model (Song et al., 2019). Presently, mainstream combinations are serial and parallel. The serial forecast the original sequence firstly; then forecast the error; finally combines these two parts. The parallel decomposes the original sequence firstly; then predict each component separately; finally integrates the prediction results of all models. The integrated model is more stable and can generally achieve more accurate predictions than single models (Athanasopoulos, Song, & Sun, 2018). Thus, we construct our model based on the combination way.
The ideas and methods of the above-mentioned forecasting works have important guiding values for our research, which convey a basic assumption on reasonable forecasting, i.e., the law of data generation is stable in a virtual world without the interference of emergencies. On the basis of this assumption, a new foresight approach is proposed by using a new adaptive hybrid forecaster to estimate the impact of public health emergencies on hotel demand. We combine statistical regression analysis and mainstream methods in artificial intelligence; then match each of them with the most suitable sequence to increase their adaptability; finally, confirm the practicability of this method by the empirical study on Macao's hotel demand under the surrounding of COVID-19.
Methodology
In this section, we propose a new forecasting-based influence evaluation method. Fig. 1 is a schematic diagram that briefly describes our methodology. Taking the COVID-19 as an example, we first divide the data before the occurrence of COVID-19 (Time < T 2) into three groups: training set (OT 0), validation set (T 0 T 1), and test set (T 1 T 2). We use the training set to train a model; use the validation set to optimize its parameters; and then make predictions on the test set. In the first sub-graph, the purple dotted line represents the prediction result of the model on the test set, which reflects its performance. If the predicted sequence is very close to the original sequence, the corresponding model will be selected. In the second sub-graph, we use the data corresponding to T 1 T 2 to update the parameters of the selected model and then predict the future development trend of hotel demand without COVID-19. Similarly, the dotted line denotes the future development trend of hotel demand without COVID-19, while the blue refers to the real demand after COVID-19.Fig. 1 A schematic diagram for the methodology.
Fig. 1
Finally, the third sub-graph illustrates how to estimate the impact of COVID-19 on hotel demand, during a period, based on the predictive result. Wherein I 1 represents the impact of COVID-19 between T 2 and T 3; I 1 + I 2 represents the impact between T 2 and T 4; I 1 + I 2 + I 3 represents the impact between T 2 and T 5. Our methodology can also be used to assess the overall impact of COVID-19 if the point when COVID-19 is over comes. In the third sub-graph, the green dotted line indicates the future end of COVID-19. When that point comes, we can get the overall impact of COVID-19, that is, I 1 + I 2 + I 3 + I 4. The proposed method is based on a new adaptive hybrid forecaster (AHF), which features complete ensemble empirical mode decomposition and a model library that includes SARIMAX, LSTM, ANN, and SVR, and a difference-comparing modular. The methodology has four stages: a) decomposing hotel demand before the public health emergency into a series of components, b) matching each decomposed component with a suitable forecasting model, c) verifying the performance of models, and d) evaluating the impact of a public health emergency on hotel demand. The framework of the methodology is illustrated in Fig. 2 .Fig. 2 The framework for evaluating the impact of public health emergencies on hotel demand.
Fig. 2
Decomposing hotel demand before a public health emergency
The obvious nonlinear and seasonal characteristics affect the forecasting of hotel demand. Thus, it is helpful to decompose demand data in advance and then construct forecasting models by incorporating the decomposed components. Previous studies have used wavelet analysis to decompose demand data (Balli, Shahzad, & Uddin, 2018). Wavelet analysis lacks adaptability, and its performance relies on the selection of the wavelets. Compared with wavelet analysis, empirical mode decomposition (EMD) has good adaptability and is more suitable for the decomposition of nonlinear, nonstationary, and complex sequences (Xie, Qian, & Wang, 2020; Tuo & Zhang, 2020). However, a drawback named mode mixing exists for EMD: a decomposed component may comprise signals with different frequencies, or signals with the same frequency are in different components. Thus, we choose complementary ensemble empirical mode decomposition (CEEMD), an improved version of EMD, to decompose hotel demand as it can cope with mode mixing by adding white Gaussian noise to hotel demand before decomposing it (Yeh, Shieh, & Huang, 2010). We do not consider the role of factors such as online search data and weather data because these factors need to be forecast in advance. The forecasting errors would inevitably accumulate and eventually lead to a greater evaluation error. But, we introduce an exogenous factor - the month-on-month growth rate of forecasting value on the consumer price index - because it can reflect and judge future economic and hotel development in a virtual world without public health emergencies. According to Wong and Song (2006), the factor is an important macroeconomic variable that describes the hospitality stock indices. The detailed steps for this stage are as follows:Step 1: Add white noise ε i(t) (i = 1, 2, ..., m) to hotel demand before a public health emergency, denoted DB(t), to form two sets of sequences:
(1) DBi+t=DBt+εit;
(2) DBi−t=DBt−εit.
Step 2: Decompose DB i +(t) and DB i −(t) into intrinsic mode functions (IMFs) and residues (RES) by using the EMD method. The process is set out in detail by Zhang, Lai, & Wang (2008).
(3) DBi+t=∑j=1n−1cij+t+ri+t;
(4) DBi−t=∑j=1n−1cij−t+ri−t.
where n − 1 refers to the number of intrinsic mode functions; c ij + and c ij − are the j-th intrinsic mode function decomposed respectively by DB i +(t) and DB i −(t); and r i +(t) and r i −(t) are the residues.Step 3: Generate the final intrinsic mode functions based on Eqs. (3), (4):
(5) cijt=cij+t+cij−t/2;
(6) Cjt=1m∑i=1mcijt;
(7) Rest=DBt−∑j=1n−1Cjt.
Here, C 1(t),C 2(t), …,C n−1(t), and Res(t) are n components identified by using CEEMD. They are all independent and of specific meanings and different frequencies.Step 4: Analyze the features of each component, such as its average period, the correlation between each component and hotel demand, and the contribution of each component to hotel demand.
Matching each decomposed component with a suitable forecasting model
The decomposed components of hotel demand based on CEEMD have different degrees of nonlinearity and no forecasting model can deal with all of them effectively. To solve this, we built a library that contains four mainstream models: SARIMAX, LSTM, ANN, and SVR. The training principles and processes of these four models are set out in detail by, respectively, Wu et al. (2017), Bi et al. (2020), Palmer, Montano, and Sesé (2006), and Sun, Wei, Tsui, and Wang (2019). Owing to the limitations on space, they are not included here. These four models are different from each other, which helps to improve the stability of the hybrid forecasting of hotel demand. Based on the library, we match each component with the most appropriate model. Specifically, the components of hotel demand and the exogenous factor are divided into three groups: the training set, the validation set, and the test set. Then, samples from the training set are used to train the models and those from the validation set are used to optimize models' parameters. The steps for Stage 2 are as follows.Step 1: Divide hotel demand, its decomposed components, and exogenous factor (EF(t)) into training sets, validation sets, and test sets, as described in Table 1 .Table 1 Description of variables.
Table 1 Hotel demand Decomposed components of hotel demand Exogenous factor
Before a public health emergency (1 ≤ t ≤ tTe) Training sets
(1 ≤ t ≤ tTr) DBTr(t) C1Tr(t) C2Tr(t) ... Cn−1Tr(t) ResTr(t) EFTr(t)
Validation sets
(tTr < t ≤ tVa) DBVa(t) C1Va(t) C2Va(t) ... Cn−1Va(t) ResVa(t) EFVa(t)
Test sets
(tVa < t ≤ tTe) DBTe(t) C1Te(t) C2Te(t) ... Cn−1Te(t) ResTe(t) EFTe(t)
After a public health emergency
(t > tTe) DA(t) – – ... – – EF(t)
Step 2: Randomly choose a combination of parameters, denoted as PC 1, and train four models based on {C 1 Tr(t), EF Tr(t)}, …, {C n−1 Tr(t), EF Tr(t)}, and {Res Tr(t), EF Tr(t)}.
Step 3: Make predictions on hotel demand components C 1 Va(t), …, C n−1 Va(t), and Res Va(t) by using four trained models. As there are four models and n components, a matrix PC 1_F AHF Va containing 4 × n prediction results can be formed:
(8) PC1_FAHFVa=fSARIMAXC1TrtEFt⋯fSARIMAXCn−1TrtEFtfSARIMAXResTrtEFtfLSTMC1TrtEFt⋯fLSTMCn−1TrtEFtfLSTMResTrtEFtfANNC1TrtEFt⋯fANNCn−1TrtEFtfANNResTrtEFtfSVRC1TrtEFt⋯fSVRCn−1TrtEFtfSVRResTrtEFt
Step 4: Analyze the forecasting performance. The root means square error (Eq. (12)) is used as the performance index, and the performance matrix, PC 1_P AHF Va, is:
(9) PC1_PAHFVa=RMSEPC1_FAHFVa11C1Vat⋯RMSEPC1_FAHFVa41C1Vat⋮⋮⋮RMSEPC1_FAHFVa1n−1Cn−1Vat⋯RMSEPC1_FAHFVa4n−1Cn−1VatRMSEPC1_FAHFVa1nResVat⋯RMSEPC1_FAHFVa4nResVatΤ
Step 5: Choose a new combination of parameters and repeat Steps 3 and 4 until all performance matrices (PC 1_P AHF Va, PC 2_P AHF Va, …,PC all_P AHF Va) are obtained. They are corresponding to different combinations of parameters (PC 1, PC 2, …,PC all).
Step 6: Match each component of hotel demand with a suitable model. For the k-th component, the suitable model with the optimal parameters is corresponding to the minimum value in the k-th column of all performance matrices.
Verifying the performance of models for forecasting hotel demand before an emergency
The forecasting performance needs to be evaluated in advance. Thus, in this stage, we use those models selected in Stage 2 to make predictions on the test sets of all components, and then add them to obtain the prediction on the test set of hotel demand. Here, three indexes are adopted to evaluate the performance of our adaptive hybrid forecaster when forecasting hotel demand before an emergency: the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). We also take the following six models as benchmarks: exponential smoothing (ETS), SARIMA, SARIMAX, LSTM, ANN, and SVR. The information about ETS can refer to Chen, Li, Wu, and Shen (2019). In this stage, the training set and validation set of hotel demand are used to train these benchmarks and adjust their parameters, respectively. Then, the trained benchmarks with the optimal parameters are employed to predict the test set of hotel demand. The steps for Stage 3 are as follows:Step 1: Train six models based on DB Tr(t), with a randomly given parameter combination, and then make predictions on the validation set DB Va(t). Here, the matrix F BM Va contains six prediction results:
(10) FBMVa=fETSDBTrtEFtfSARIMADBTrtEFtfSARIMAXDBTrtEFtfLSTMDBTrtEFtfANNDBTrtEFtfSVRDBTrtEFt
Step 2: Analyze the forecasting performance. The root means square error (Eq. (12)) is used as the performance index and the performance matrix P BM Va is:
(11) PBMVa=RMSEFBMVa11DBVatRMSEFBMVa21DBVat⋯RMSEFBMVa61DBVatΤ
Step 3: Change a parameter combination for the six models and repeat Steps 1 and 2 until the optimal parameter combination is discovered.
Step 4: Make predictions of on DB Te(t) by using the six benchmarks with optimal parameter combination.
Step 5: Make predictions on components C 1 Te(t),C 2 Te(t), …,C n−1 Te(t), and Res Te(t) by using the corresponding models with optimal parameters selected in Stage 2 and add them to obtain the prediction on DB Te(t).
Step 6: Verify the performance of all models in forecasting hotel demand. Three performance indexes are used here, which are represented by Eqs. (12–14):
(12) RMSExhx^h=1H∑h=1Hxh−x^h2,
(13) MAExhx^h=1H∑h=1H∣xh−x^h∣,
(14) MAPExhx^h=1H∑h=1H∣xh−x^h∣∣xh∣.
where H is the number of samples; and x h and x^h denote the h-th actual and predicted value respectively.
Analyzing the impact of a public health emergency
This stage investigates the impact of a public health emergency on hotel demand. To achieve a more accurate evaluation, the sequences before a public health emergency are used to train the adaptive hybrid forecaster to update its parameters. Then, the virtual demand for hotel without the public health emergency is predicted. The impact is evaluated by comparing the real demand after the emergency (the observed group) with the expected demand (the control group), and the degree of the impact is evaluated by the average value. Here, Fig. 1 is used again to illustrate how to calculate the impact and its degree. Assume that T 2 represents the month when COVID-19 occurred. T 3, T 4, and T 5 represent 1, 2, and 3 months after T 2, respectively. DA¯t denotes the purple dotted line, and DA(t) denotes the green solid line. Then, the impacts for 3 months after the occurrence of COVID-19 are respectively: I1=DA¯T3−DAT3, I2=DA¯T4−DAT4, and I3=DA¯T5−DAT5. Its degree for 3 months after the occurrence of COVID-19 are I 1, (I 1 + I 2)/2, and (I 1 + I 2 + I 3)/3. The detailed steps for this stage are as follows.Step 1: Combine the training set and validation set of each component in Stage 2 to retrain the selected model of this component, and then use the test set in Stage 2 to update their optimal parameters.
Step 2: Make a prediction of the virtual hotel demand after an emergency (the control group). The prediction result DA¯t can be regarded as demand without emergencies.
Step 3: Analyze the impact of an emergency. The impact of an emergency, I(t), can be described as the difference between the real demand DA(t) and the forecast demand DA¯t. That is:
(15) It=DA¯t−DAt.
Step 4: Analyze the degree of the impact, which is considered as the average value of I(t). Then, relevant statistical tests are introduced as an additional description for this step.
Step 5: Analyze the recovery rate after the emergency. The recovery rate, RE(t), is calculated based on the following growth rate:
(16) REt={DA¯t−DAtDBt−stTe<t≤tTe+s;DA¯t−DAtDA¯t−st>tTe+s.
where s takes a value of 1, 4, or 12 for yearly, quarterly, and monthly data, respectively.Step 6: Analyze the average recovery rate after the emergency. The average recovery rate, ARE, is calculated as:
(17) ARE=1N∑REt.
where N is the number of recovery rates. Relevant statistical tests are also introduced as an additional description in this step.
Experimental study
The COVID-19 pandemic has led to operating losses for hotels. As a city known for its tourism and service industries, Macao has been badly affected by this public health emergency. In this section, the new method is applied in the analysis of the impact of the current pandemic on hotel demand in Macao.
Data on Macao's hotel demand
The information on demand for Macao's hotels with ratings from two to five stars can be retrieved from the official website of Macao tourism department. As for the exogenous factor, we use the month-on-month growth rate of forecasting value on the consumer price index (CPI). The index, according to Wong and Song (2006), is an important macroeconomic variable that describes the hospitality stock indices, so its forecasting value reflects the future economic development in a virtual world without public health emergencies. The data on the index can be found in the iFinD database (http://www.51ifind.cn/). The sample set of this work comprises the monthly data from June 2012 to March 2021 (106 monthly values in total) and the hotel demand in Macao are divided according to its star ratings (two to five stars), resulting in 424 monthly values. Fig. 3 depicts the exogenous factors across the study period, and Fig. 4 plots the hotel demand separately for the four sets of star ratings. After the outbreak of COVID-19, demand dropped sharply in 2020. To find the most accurate estimation function for expected demand without the pandemic, for the separate samples of hotels with each star rating, samples before 2020 (i.e., from 1 to 91) are used as the training set, validation set, and test set. The lengths of the validation and test sets are both set to 12 months. Specifically, samples before 2018 are used as the training set (i.e., from 1 to 67); samples from January 2018 to December 2018 (i.e., from 68 to 79) are the validation set; and samples from January 2019 to December 2019 (i.e., from 80 to 91) are the test set.Fig. 3 The exogenous factors (forecasting value on the consumer price index) from June 2012 to March 2021.
Fig. 3
Fig. 4 The monthly demand for hotels with different star ratings, June 2012 to March 2021.
Fig. 4
Processes of decomposition of hotel demand before the emergency
Fig. 5 demonstrates the components derived by CEEMD from the demand separately for hotels with different star ratings. The parameters for CEEMD are set in a basic way: the number of repetitions is set to 100, and the standard deviation of white noise in each repetition is set to 0.2. As depicted in Fig. 5, IMFs indicate the different frequency components in hotel demand, and the RES is the component that slowly varies around the long-term average, which is essentially the average trend (Zhang et al., 2009). To analyze the characteristics and meanings of these components, we adopted the following measures: 1) the average period of each component of hotel demand is calculated, which is defined as the value derived by dividing the total number of points by the number of peaks for each component (Zhang, Lai, & Wang, 2008); 2) the correlation between each component of hotel demand and the original demand data was measured using the Pearson correlation coefficient (Xu, Tang, He, & Man, 2016); 3) the contribution of each component to general hotel demand is further analyzed. Considering that the components of hotel demand in a category are independent of each other (Zhang et al., 2008), we use the percentage of variance to explain the contribution. Table 2 shows the results and we can draw the following findings.Fig. 5 The decomposition results of demand for hotels with different star ratings, June 2012 to March 2021.
Fig. 5
Table 2 The measurement results for the components of demand for hotels with different star ratings.
Table 2 Two-star hotels Three-star hotels
Mean period
(month) Pearson
correlation Std Std as % of
(ΣIMFs + RES) Mean period
(month) Pearson
correlation Std Std as % of
(ΣIMFs + RES)
IMF1 3.68 0.51 2020.90 28.31% 2.56 0.17 4610.98 11.59%
IMF2 8.36 0.43 1524.60 21.61% 5.75 0.16 2588.88 6.51%
IMF3 15.33 0.63 1552.38 21.75% 13.14 0.02 2359.53 5.93%
RES – 0.66 2022.45 28.33% – 0.97 30,214.97 75.97%
Sum – – – 100.00% – – – 100.00%
Four-star hotels Five-star hotels
Mean period
(month) Pearson
correlation Std Std as % of
(ΣIMFs + RES) Mean period
(month) Pearson
correlation Std Std as % of
(ΣIMFs + RES)
IMF1 3.17 0.31 9958.48 19.91% 2.88 0.22 14,518.81 15.59%
IMF2 5.75 0.16 3768.82 7.53% 7.08 0.23 8315.62 8.93%
IMF3 18.40 0.30 6921.85 13.84% 15.33 0.17 7033.15 7.55%
RES – 0.92 2937.64 58.72% – 0.95 63,232.32 67.92%
Sum – – – 100.00% – – – 100.00%
Note: the mean period of RES is not provided as it reflects the long-term development trend of hotel demand.
The mean periodicity of components
For all hotels with a given star rating, the average period from IMF1 to IMF3 gradually increases. IMF1 and IMF2 are close to 3-month and 6-month periods, respectively. Thus, they can be regarded as seasonal and semi-annual periodic components, respectively. The average period of IMF3 is more than one year and can be regarded as the yearly pattern. According to Rosselló and Sansó (2017) and Li, Ge, Liu, and Zheng (2020), the longest seasonal periodic in tourism demand seems to be the annual, so here we get three IMFs. As for RES, it is the demand component that slowly varies around the long-term average and can be considered as reflecting the development trend more generally. In the short term, the hotel demand has obvious seasonality. In the long run, the hotel industry is affected by the economic situation and the changing preferences of tourists. As RES indicates, the demand for three-star and five-star hotels has increased continuously over recent years, whereas demand for four-star hotels fell after an initial rise and the demand for two-star hotels experienced an overall decline.
The contributions of the different components of hotel demand
The RES component has a high contribution to hotel demand (reaching more than 25%), which means that the development trend plays a key role in hotel demand. For all hotels with a given star rating, the component IMF1 is found to make a higher contribution than the corresponding low-frequency components, i.e., IMF2 and IMF3, whose contribution to the original hotel demand reaches more than 10%. This finding is different from the findings for other industries, where the low-frequency component generally contains more information than the high-frequency component (Zhang et al., 2008). Here, IMF1 (the seasonal periodic component) contains more information than IMF2 (the semi-annual periodic component) and IMF3 (the yearly periodic component). In summary, the components of seasonal periodicity and the general development trend mainly determine demand within the hotel industry.
Matching each component of hotel demand with a forecasting model
Since the various components of hotel demand have varying degrees of nonlinearity, it is wise to select the most suitable forecasting model for each component from a model library. The library contains SARIMAX, ANN, SVR, and LSTM. Before selecting models, the parameters need to be set. For these four models, the key parameters (those with the greatest impact on their performance) are mainly considered, while the remaining parameters are set in a default manner.
In this experiment, there are six parameters for SARIMAX: the order for differences (d), the order for auto-regressive process (p), the order for moving average process (q), the order for seasonal differences (D), the order for seasonal auto-regressive process (P), and the order for seasonal moving average process (Q). There are two parameters for LSTM: time steps and number of units. There are two parameters for ANN: size of input layers and size of hidden layers. Finally, there are two parameters for SVR: kernel types and number of features. To obtain suitable parameters, the training set is used to train the forecasting models, and the validation set is used to select the optimal parameter combinations. In other words, models with different parameter combinations are used to predict samples in the validation set, and then the parameter combination corresponding to the minimum RMSE is selected as the optimal parameter. As for the optimization method, the parameters of SARIMAX are determined by statistical testing, while the parameters of the other three models are determined by the exhaustive grid-search technique (Cho, 2003). The ranges of the corresponding parameters of the four models are shown in Table 3 . Considering that the training processes for LSTM and ANN involve a certain degree of randomness, different prediction results may be obtained for LSTM and ANN with the same parameters. Therefore, we run the LSTM and ANN five times for each parameter and take the average of the five RMSEs as the evaluation index.Table 3 The range of parameters for the four models.
Table 3Model The range of parameters
SARIMAX (p∈{1, 2, …, 6}; d∈{1, 2, 3}; q∈{1, 2, …, 6}) (P∈{1, 2, 3, 4}; D∈{1, 2, 3}; Q∈{1, 2, 3, 4}).
LSTM (time steps∈{1, 2, …, 10}; number of units∈{5, 10, …, 50}).
ANN (size of input layers∈{1, 2, …, 10}; size of hidden layers∈{2, 4, …, 20}).
SVR (kernel types∈{1 = ‘gaussian’, 2 = ‘linear’, 3 = ‘polynomial’}; number of features∈{1, 2, …, 10}).
Here, RES for two-star hotels is taken as an example to illustrate the procedure for parameter selection. Fig. 6 presents the performance of LSTM with different parameter combinations on RES for two-star hotels. In Fig. 6, each small square corresponds to a combination of parameters, and the number in the square represents the average value of five RMSEs. For the RES of two-star hotels, LSTM can obtain higher prediction accuracy when the time step is 1 and 2, which means that the development trend for two-star hotels is more short-term dependence. When the number of units and time steps are respectively 40 and 2, LSTM achieves the best performance on the validation set (the average of five RMSEs is only 249.2). Using the same method, we obtained the optimal parameter combinations of the four models corresponding to all components of demand for hotels with different stars. Given that RES needs to be differentiated multiple times to be stable, we take the natural logarithm of RES when training the SARIMAX model and then restore the prediction results to reduce data loss. The results are presented in Table 4 .Fig. 6 The performance of LSTM on the RES of two-star hotels under different parameter combinations.
Fig. 6
Table 4 The models with optimal parameter combinations corresponding to the total 16 components of hotels demand with the four different star ratings, January 2018 to December 2018.
Table 4 SARIMAX LSTM ANN SVR Selected model
Optimal parameters RMSE Optimal parameters RMSE Optimal parameters RMSE Optimal parameters RMSE
Two
-star
hotels IMF1 (2,0,1)(0,0,0) 2641.80 (1,20) 2303.05 (2,6) 2247.14 (1,8) 2292.26 ANN
IMF2 (2,0,2)(0,0,0) 681.51 (1,50) 535.21 (2,2) 712.94 (2,2) 710.37 LSTM
IMF3 (4,0,1)(0,0,0) 443.17 (8,5) 1511.45 (7,2) 757.27 (1,4) 1487.15 SARIMAX
RES (0,2,0)(0,0,0) 57.68 (2,40) 174.73 (4,4) 77.88 (3,1) 736.56 SARIMAX
Three
-star
hotels IMF1 (0,0,5)(0,0,0) 5313.05 (5,35) 4215.18 (3,2) 4343.20 (3,8) 3586.26 SVR
IMF2 (2,0,2)(0,0,0) 1649.16 (3,20) 2523.85 (9,14) 1638.57 (1,3) 1274.82 SVR
IMF3 (4,0,1)(0,0,0) 1464.84 (4,45) 442.40 (4,4) 558.00 (1,6) 515.87 LSTM
RES (0,2,0)(0,0,0) 1290.61 (2,40) 590.49 (5,4) 1332.78 (2,1) 3520.99 LSTM
Four
-star
hotels IMF1 (5,0,0)(0,0,0) 11,690.59 (3,35) 10,202.66 (1,4) 10,076.08 (1,1) 10,172.70 ANN
IMF2 (4,0,1)(0,0,0) 4787.16 (10,5) 4112.00 (9,6) 3123.74 (3,9) 4084.35 ANN
IMF3 (4,0,1)(0,0,0) 1813.10 (9,30) 1433.95 (1,10) 2608.28 (1,2) 868.54 SVR
RES (0,2,0)(0,0,0) 1588.01 (4,35) 833.79 (1,8) 1228.61 (2,3) 2652.20 LSTM
Five
-star
hotels IMF1 (3,0,1)(0,0,0) 16,861.49 (2,40) 15,939.98 (2,4) 16,364.05 (3,5) 15,844.90 SVR
IMF2 (4,0,1)(0,0,0) 8017.17 (2,10) 5994.77 (3,2) 6243.48 (1,5) 6664.49 LSTM
IMF3 (5,0,0)(0,0,0) 7202.25 (5,5) 6343.40 (4,8) 5514.25 (2,1) 5849.88 ANN
RES (0,2,0)(0,0,0) 847.53 (1,45) 5420.72 (1,2) 7211.79 (2,10) 9551.09 SARIMAX
As shown in Table 4, no model is suitable for predicting all components of hotel demand. These four models have different performances on the 16 components (the bold means the best performance of all models for each component). LSTM performs best overall and is selected five times. ANN and SVR rank second overall and are selected four times respectively. SARIMAX have average performance, only being selected three times. LSTM, especially the one with many layers, has good fitting capabilities. However, because the structure of Macao's monthly hotel demand is not overly intricate, a shallow LSTM (here, the number of layers is 1) can also achieve better performance on the training set. Furthermore, the complex generation process of demand data can also be simplified by decomposition, which also reduces the sample volume requirement of LSTM. Machine learning models, represented by ANN and SVR, are sensitive to sample size and can also adapt to nonlinear data. Thus, their performance is not as good as that of LSTM. SARIMAX has a low requirement on sample size, but it cannot fit nonlinear data well. The adaptive hybrid forecaster is expected to provide more accurate predictions than single models as it can take full advantage of each of them.
The performance of the adaptive hybrid forecaster in estimating demand before the emergency
This section presents the performance of the adaptive hybrid forecaster in forecasting hotel demand before COVID-19. In Table 4, each component of hotel demand selects the most suitable forecasting model from the model library with the optimal parameter combination. To explore the performance of the adaptive hybrid forecaster, we make predictions on the test set of the 16 components using the models and parameter combinations given in Table 4, and then integrate the results to obtain the final forecasts of demand for hotels with different star ratings. We also make predictions by using the same samples with the six benchmark models. The advantages of the adaptive hybrid forecaster are illustrated by comparing its forecasting results with those of benchmarks. Their optimal parameters are also determined with the exhaustive grid-search technique.
Fig. 7 depicts the performance of all models on the test set of demand for hotels with different star ratings before COVID-19. Compared with the benchmarks, the adaptive hybrid forecaster more accurately follows the trend and makes better predictions. Taking two-star hotels as an example, the adaptive hybrid forecaster not only tracks the development trend in the demand data but also captures the irregular fluctuations, at least to a certain extent. In contrast, the benchmarks cannot follow well the general demand trends and overestimate demand for two-star hotels. Furthermore, the adaptive hybrid forecaster seems to find a balance between volatility and trend. Owing to the sharp fluctuations in the hotel demand data, the adaptive hybrid forecaster places more emphasis on following trends to reduce the error of evaluation as much as possible, which is an advantage. To further compare the performance of the models, three indicators based on Eqs. (12), (13), (14) are calculated and presented in Table 5 (the bold means the best performance and the underlined represents the second-best). The adaptive hybrid forecaster performs better than the benchmarks. For two-, three-, four-, and five-star hotels, the MAPE values of the adaptive hybrid forecaster are lower than those of the best benchmark model, with gaps of 7.03%, 0.74%, 0.19%, and 1.15%, respectively. Therefore, according to the results presented in Table 5, it can be considered that the adaptive hybrid forecaster proposed in this paper accurately evaluates demand before the emergency for hotels with different star ratings.Fig. 7 The performance of all models on the test set of hotel demand before the emergency.
Fig. 7
Table 5 The performance of all models on the test set of hotel demand with different star ratings before the emergency.
Table 5Models The demand for two-star hotels The demand for three-star hotels
MAE RMSE MAPE (%) MAE RMSE MAPE (%)
ETS 3839.09 5012.77 21.84 7514.29 8688.63 5.65
SARIMA 5226.25 6071.21 29.71 8530.12 9507.09 6.38
SARIMAX 4622.41 5344.73 26.06 8736.02 9688.27 6.53
SVR 4707.14 6321.21 27.38 8438.84 9293.34 6.27
ANN 4385.18 5765.56 25.11 18,304.60 23,752.26 12.91
LSTM 5218.48 6187.18 29.31 12,235.43 14,943.72 9.33
AHF 2830.77 3355.92 14.81 6725.56 7702.35 4.91
Models The demand for four-star hotels The demand for five-star hotels
MAE RMSE MAPE (%) MAE RMSE MAPE (%)
ETS 28,785.08 34,650.01 14.28 40,126.04 45,080.94 9.50
SARIMA 26,680.30 31,959.57 13.38 30,218.38 33,901.42 7.09
SARIMAX 14,435.65 18,493.77 7.29 29,337.73 32,996.93 6.89
SVR 18,908.73 23,756.73 9.56 34,485.69 38,431.43 8.09
ANN 18,235.92 21,193.89 8.93 29,988.08 35,330.33 6.98
LSTM 15,398.44 20,273.77 7.67 28,840.78 32,643.22 6.78
AHF 14,108.49 17,891.01 7.10 24,820.78 28,606.31 5.63
The impact of COVID-19 on hotel demand, differentiated by star ratings
In this section, the adaptive hybrid forecaster is used to evaluate the impact of COVID-19 on the demand for hotels with different star ratings. To explore the impact of the current pandemic, we use the hotel demand before the public health emergency to train and adjust the parameters used in the adaptive hybrid forecaster, and then forecast the expected hotel demand for the virtual case where COVID-19 did not emerge. Accordingly, we have also updated the parameter combinations of the models selected in Table 5 to obtain more accurate predictions. Specifically, we use the samples from months 1 to 79 to retrain the selected models, and use the samples from months 80 to 91 to update their parameters. The impact of the pandemic is then taken to be the difference between the actual demand and that forecast for the same period but without the emergence of COVID-19.
Fig. 8 describes the actual and expected demand for hotels with different star ratings. Fig. 9 depicts the impact of the pandemic on the four categories of hotels. Firstly, without the pandemic, the trend in future demand for two- and four-star hotels declines, while the demand for three- and five-star hotels increases. Secondly, the impact of COVID-19 is heterogeneous for hotels with different star ratings: its impact on two-star hotels is relatively low, while that on five-star hotels is the most serious, followed by four-star hotels and three-star hotels. In other words, the impact of the current public health emergency accords with the following sequence: five-star hotels > four-star hotels > three-star hotels > two-star hotels. We also confirm the robustness of the finding based on statistical tests. Considering that the impacts of COVID-19 on hotels with different star ratings may be relevant, we choose Friedman's test to verify this finding. Friedman's test can make full use of the information contained in relevant samples (Pahnehkolaei, Alfi, & Machado, 2021). Fig. 11 presents the results of Friedman's test, in which rank represents the degree of impact. It can be seen that the results obtained by Friedman's test confirm the findings.Fig. 8 Hotel demand and the expected demand without COVID-19 from using the foresight method.
Fig. 8
Fig. 9 The impact of the COVID-19 pandemic on hotel demand.
Fig. 9
We also analyze the speed of recovery in hotel demand after the emergency. In Fig. 10 , the solid line reflects the rate recovery of hotel demand and the dotted line shows its average value. Since there was a gap between the emergence of COVID-19 and its impact on the hotel industry, the recovery rate corresponding to January 2020 in Fig. 10 is regarded as noise and hence is discarded. As depicted in Fig. 10, the recovery in hotel demand after the pandemic has obvious volatility. This is caused by the seasonal characteristics of the hotel industry, as the components with seasonal periodicity are important for hotel demand, as shown in Table 2. The speed of recovery in the demand for hotels with different star ratings is distinct: five- and three-star hotels have the fastest recovery, while two- and four-star hotels recover relatively slowly. Again, this finding is confirmed by Friedman's test (Fig. 12). Fig. 10 The recovery rate of hotel demand after the COVID-19 pandemic.
Fig. 10
Fig. 11 Friedman's test for the impact of the outbreak of COVID-19 on hotel demand.
Fig. 11
Fig. 12 Friedman's test for the recovery rate of hotel demand after the outbreak of COVID-19.
Fig. 12
Conclusions
To evaluate the macro impact of a public health emergency on hotel demand with an effective control group and without violating the assumption of market effectiveness, we propose a new forecasting-based influence evaluation method, and apply it in the analysis of the impact of COVID-19 on Macao's hotel industry. The main findings are as follows. 1) We find that the new approach performs well in evaluating the impact of the public health emergency on hotel demand, and the new adaptive hybrid forecaster inside it outperforms a series of benchmark models in estimating hotel demand. Either the most affected hotel category (the hotel star in the present study) or that recovering the most rapidly can be identified with the proposed new method. 2) Before the onset of the public health emergency, Macao's hotel industry was mostly influenced by the general development trend and the seasonal periodicity characteristics of hotel demand; these respectively contributed more than 25% and 10% to overall hotel demand. 3) In the virtual world without the pandemic, the expected demand for three- and five-star hotels grows rapidly, while that for two- and four-star hotels declines. 4) In the real word, the impact of a pandemic on hotel industry exhibits heterogeneity across the four sets of hotels with different star ratings. The impact on five-star hotels is the most significant, followed by that on four-star hotels, while that on two-star hotels was the least. The speed of recovery in hotel demand also shows heterogeneity across the services. Five-star hotels recover the most rapidly, while two- and four-star hotels recover relatively slowly. Since the outbreak of COVID-19, travelers have preferred hotels with diverse high-quality services.
Our work makes three key contributions. First, it supplements the current research on the macro impact of a public health emergency on tourism and hospitality by providing a new foresight perspective. The new approach not only overcomes the lack of generalizability of micro qualitative analysis but also lessens the constraints which conventional quantitative analysis requires, such as the need for the similarity between experimental group and control group. When estimating the expected demand in the virtual world without the public health emergency, the new adaptive hybrid forecaster proposed is good at estimating the general development trend component and the seasonal periodicity component in hotel demand before the public health emergency. Thus, a virtual control group can be generated, which also requires no violation of the assumption of market effectiveness. In order to find which category of hotels is most affected by the pandemic and which one will recover the most rapidly after the pandemic, a multiple impact analysis is used to compare the difference between the real hotel demand and the expected demand. The second contribution is that the new approach provided a real-time monitor or evaluation for the impact of a public health emergency on hotel demand. The method can dynamically analyze the impact of a public health emergency in real-time, thereby helping the government implement new policies or adjust the previous ones. Hotel managers can also look for ways to survive or improve hotel performance in view of this dynamic impact. The third contribution is that the new approach is validated in analyzing the impact of the COVID-19 pandemic on hotel demand in Macao. We find the two significant factors of general development trend and seasonal periodicity impact hotel demand before the pandemic. These two factors are processed well by the adaptive hybrid forecaster to generate a good estimation of expected hotel demand in the virtual world without the public health emergency.
The management implications are as follows. 1) For tourism and hospitality, managers and regulators can refer to the new method to identify the most affected product, such as different hotel star categories, and find the most rapidly recovered one. None of the existing methods is as effective in this. 2) Identification of the fastest-growing product before the public health emergency is a reference to estimate the most rapidly recovered product after the event. In Macao's hotel industry, five-star hotels would be an important product to promote tourism and hospitality development after an emergency. 3) To survive better during and after a public health emergency, hotels should establish diverse services with an effective mechanism of health protection. Diverse service promotion with health safety as a focus will attract tourists because travelers visiting Macao mainly prefer the diverse services of five-star hotels.
There are also some limitations in this research. First of all, we focus on the impact of a public health emergency on overall hotel demand, and ignore the impact on each component of demand. Subsequent work could analyze the impact of emergencies on each component of hotel demand before the emergency. Then, specialized studies focusing on error analysis with other methods would be useful to verify the superiority of the proposed adaptive hybrid forecaster. Finally, our method currently can only evaluate the impact of an emergency on hotel demand from its appearance to the present as the actual demand is a parameter for assessment. On the one hand, the virus can mutate. Once a new virus is discovered, the effects of COVID-19 may continue. On the other hand, new infections are still being discovered and appear in somewhat random places. As a result, forecasting when COVID-19 will end, or the point at which hotel demand fully recovers, relies on identification of these key factors. In future work, we will consider how to effectively predict the time when hotel demand totally recovery and forecast the overall impact of an emergency on hotel demand before the end of the emergency.
CRediT authorship contribution statement
Ling-Yang He contributed via conceptualization of the original idea, theoretical development; experimental design, algorithm design and coding, data collection, results analysis, and finished the original draft. Hui Li conceptualized the original idea, applied for research funding, and completed the following tasks: results analysis, discussion of implications and study significance, and improvement of the original draft. Jian-Wu Bi contributed by discussing the design and the conceptualization, and improving the analysis and presentation. Jing-Jing Yang contributed on data collection, and improving the theorical discussion and presentation. Qing Zhou applied for research funding and improved the presentation.
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.
Ling-Yang He is a PhD candidate at Nankai University. He focuses on tourism prediction.
Hui Li, PhD., is a Professor at Nankai University. He focuses on tourism big data mining.
Jian-Wu Bi, PhD., is a Post Doctor in Nankai University. He focuses on tourism prediction.
Jing-Jing Yang, PhD, has international teaching experience in China's Mainland and Macao, UK and New Zealand.
Qing Zhou, PhD., is a Professor in Hangzhou Dianzi University. He focusses on firm innovation.
Appendix A Supplementary data
The following are the supplementary data related to this article.Video 1
Author’s greetings and introduction
Video 1
Acknowledgements
This work was partially supported by the 10.13039/501100001809 National Natural Science Foundation of China (71932005, 71971124), the Liberal Arts Development Fund of Nankai University (ZB21BZ0106), the One Hundred Talents Program of Nankai University (63213023), the 10.13039/501100001809 National Natural Science Foundation of China (72101124), and the 10.13039/501100012325 National Social Science Foundation of China (20ZDA067; 16ZDA082).
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.annals.2022.103402.
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PMC009xxxxxx/PMC9004287.txt |
==== Front
J Chem Educ
J Chem Educ
ed
jceda8
Journal of Chemical Education
0021-9584
1938-1328
American Chemical Society and Division of Chemical Education, Inc.
10.1021/acs.jchemed.1c01259
Article
Reassessing Undergraduate Polymer Chemistry Laboratory Experiments for Virtual Learning Environments
https://orcid.org/0000-0002-9808-9079
Karayilan Metin *†§
McDonald Samantha M. †§
Bahnick Alexander J. †§
Godwin Kacey M. †§
Chan Yin Mei †§
https://orcid.org/0000-0003-4089-6916
Becker Matthew L. *†‡
† Department of Chemistry, Duke University, Durham, North Carolina 27708, United States
‡ Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708, United States
* Email: metin.karayilan@duke.edu.
* Email: matthew.l.becker@duke.edu.
31 03 2022
acs.jchemed.1c0125921 12 2021
04 03 2022
© 2022 American Chemical Society and Division of Chemical Education, Inc.
2022
American Chemical Society and Division of Chemical Education, Inc.
This article is made available via the PMC Open Access Subset 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 the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Chemistry laboratory experiments are invaluable to students’ acquisition of necessary synthetic, analytical, and instrumental skills during their undergraduate studies. However, the COVID-19 pandemic rendered face-to-face (f2f), in-person teaching laboratory experiences impossible from late 2019–2020 and forced educators to rapidly develop new solutions to deliver chemistry laboratory education remotely. Unfortunately, achieving learning and teaching objectives to the same caliber of in-person experiments is very difficult through distance learning. To overcome these hurdles, educators have generated many virtual and remote learning options for not only foundational chemistry courses but also laboratory experiments. Although the pandemic challenged high-level chemistry education, it has also created an opportunity for both students and educators to be more cognizant of virtual learning opportunities and their potential benefits within chemistry curriculum. Irrespective of COVID-19, virtual learning techniques, especially virtual lab experiments, can complement f2f laboratories and offer a cost-efficient, safe, and environmentally sustainable alternative to their in-person counterparts. Implementation of virtual and distance learning techniques—including kitchen chemistry and at-home laboratories, prerecorded videos, live-stream video conferencing, digital lab environment, virtual and augmented reality, and others—can provide a wide-ranging venue to teach chemistry laboratories effectively and encourage diversity and inclusivity in the field. Despite their relevance to real-world applications and potential to expand upon fundamental chemical principles, polymer lab experiments are underrepresented in the virtual platform. Polymer chemistry education can help prepare students for industrial and academic positions. The impacts of polymers in our daily life can also promote students’ interests in science and scientific research. Hence, the translation of polymer lab experiments into virtual settings improves the accessibility of polymer chemistry education. Herein, we assess polymer experiments in the emergence of virtual learning environments and provide suggestions for further incorporation of effective polymer teaching and learning techniques into virtual settings.
First-Year Undergraduate/General
Second-Year Undergraduate
Upper-Division Undergraduate
Distance Learning/Self Instruction
Internet/Web-Based Learning
Computer-Based Learning
Laboratory Instruction
Organic Chemistry
Polymer Chemistry
Materials Science
Polymerization
Duke University 10.13039/100006510 NA document-id-old-9ed1c01259
document-id-new-14ed1c01259
ccc-price
This article is made available via the ACS COVID-19 subset 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 the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
==== Body
pmc1 Introduction
Laboratory experiments are a crucial component of the undergraduate chemistry curriculum. Currently, face-to-face (f2f) lab experiments are the university standard and most comprehensive way to teach students necessary synthetic methodology, instrumental lab techniques, and relevant safety precautions. However, the COVID-19 pandemic raised significant barriers for implementing these types of technical laboratories. As a result, notable attention shifted toward a new way of learning and teaching chemistry experimentation: virtual learning, a topic heavily emphasized in this Journal’s special issue, “Teaching Chemistry in the Time of COVID-19”.1
Virtual and remote learning spaces have been documented since the early 1960s2 but have been largely overlooked until recently. In the wake of the COVID-19 pandemic, there has been a substantial increase in the use of virtual learning techniques3 and an additional focus on gauging its effectiveness.4,5 For example, out of the distance learning papers published in this Journal between the years 1990 and 2021, approximately 38.4% (∼185 papers) of the accepted virtual learning manuscripts were produced during the peak of the pandemic in 2020. Numerous new publications on distance learning (∼8.5% of the papers published from 1990 through 2021) were generated within the first eight months of 2021. Furthermore, 52.3% of the materials were published between the years of 2010 and 2019, while only 0.8% (four papers) of the total papers were produced in the two decades preceding 2010 (Figure 1).6 This data shows that the pandemic not only required educators to design new content for online teaching but also increased the awareness and importance of the benefits associated with virtual learning. Additionally, early and extended training in chemistry laboratories (e.g., course-based undergraduate research experiences, CUREs) that elevates student interest and skills in research, such as demonstrating scientific literacy, identifying a research question, designing an experimental plan, analyzing data, and communicating the results, can be accomplished by incorporating virtual learning components into teaching and research.7−9
Figure 1 Breakdown of distance learning papers published in the Journal of Chemical Education between 1990 and 2021. The keywords “Internet/Web-Based Learning” and “Distance Learning/Self Instruction” were identified in papers by year. The 2021 dataset does not include work published after September 2021.6
The pandemic is not the only driving force for enhancing virtual learning opportunities. Other potential issues that may necessitate virtual translation include limited resources (e.g., personnel, chemicals, equipment, lab space), safety concerns, and negative environmental impact. In addition, virtual laboratories can broaden accessibility. For example, students across different grade levels, institutions, and countries can benefit from the same virtual experiments that can be easily performed and reused in subsequent years. Educators can also design diverse virtual experiments to be more inclusive of students with learning disabilities or socioeconomic restrictions. For example, these virtual laboratories can promote greater inclusivity for students and instructors/teaching assistants (TAs) who cannot be in the lab due to physical challenges/disabilities, travel restrictions, health concerns, military deployment, and more.10 As such, virtual learning can allow for greater flexibility in chemistry laboratories and can foster a more equitable approach to chemical education. Nonetheless, virtual learning efforts assume students have access to certain levels of technology (e.g., a computer and reliable Internet connection) and an environment conducive to online learning (e.g., a quiet place to study). More robust strategies require advanced technology (e.g., virtual/augmented reality equipment and/or a smartphone), which further limits the feasibility of large-scale implementation. Existing electronic lending services and free Wi-Fi availability through universities can bridge this gap; however, particularly in the case of the COVID-19 pandemic, students may not always have access to public spaces and resources. Therefore, this is not a sufficient solution, and greater efforts will need to be made to address remote academic inclusivity.
In addition to these limitations of virtual learning efforts, certain fields have not been reflected enough in the virtual platform. Polymer chemistry laboratories, for example, are underrepresented in the virtual environment despite the fact that polymers are prominent within the chemical industry, manufacturing conglomerate (e.g., cosmetic, petroleum, food, automotive, aerospace, electronics, pharmaceutical, clothing, construction, packaging, and others), and academic research, which results in a growing demand for specialized polymer chemists.11,12 Students equipped with synthetic and instrumentation skills relevant to polymers will be better prepared for these industrial and academic positions. However, comprehensive polymer chemistry education is often missing in the undergraduate curriculum, and polymer-focused lab experiments are generally not offered in undergraduate studies. Typically, students only get an opportunity to learn about basic principles and some practical applications of polymers when/if they join a research lab working in these areas.13 This matter was brought to attention in this Journal starting in the late 50s,14−16 and the 2015 ACS Guidelines for Bachelor’s Degree Programs recently necessitated the inclusion of polymers, macromolecules, and colloids systems in the undergraduate curriculum for certified degree programs.17,18 Uncoincidentally, this Journal highlighted polymer concepts across the curriculum even before the pandemic in the 2017 special issue.19
Although there has been a considerable effort to digitize aspects of chemistry within the virtual space, there remains an unmet need for polymer chemistry experiments. Compared to organic chemistry publications within this Journal, the number of works covering the topics of polymer chemistry falls considerably short (Figure 2).6 Virtual translations of existing polymer synthesis, characterization, and processing experiments would allow for a great opportunity to fill this gap. Herein, we highlight the strategies applied to distance learning lab experiments and explore their potential utilization for polymer chemistry undergraduate lab experiments in the future. First, we discuss the virtual learning techniques that have been developed for lab experiments prior to and during the pandemic. Then, we assess these techniques to suggest pertinent ways to digitize undergraduate polymer lab experiments.
Figure 2 Laboratory experiments published in this Journal between 2010 and 2021 pertaining to polymer chemistry (front, red), organic chemistry (middle, green), and any (back, blue) topics. For the data labeled “Polymer Chemistry Lab Experiments”, the keywords “Polymerization” and “Polymer Chemistry” were identified in Laboratory Experiments or Articles with the keyword “Laboratory Instruction” for each year in data scraped from the Journal of Chemical Education website. For the data labeled “Organic Chemistry Lab Experiments”, the keywords “Synthesis” and “Organic Chemistry” were used. Papers that included both keywords were filtered as to not be counted twice. The 2021 dataset is current to mid-September 2021. Note that the overlap between synthesis and polymer topics was not eliminated.6
2 Virtual Learning Paradigm
“Virtual learning” broadly describes a learning process that utilizes technology as a substitute or complement to traditional f2f teaching.20 Under the virtual learning umbrella lies the term “distance/remote learning”, which pertains to the acquisition of knowledge outside of the institutional laboratory setting.8,21,22 The first reports of distance learning in this Journal appeared in the 1960s and described the use of “blackboard by wire” for remote instruction.2,23 Efforts to expand on distance learning continued throughout the 1980s and 1990s, including the development of “Take-Home Challenges” for at-home learning24 and remote lectures via two-way, synchronous audio-video networks.25
The COVID-19 pandemic has driven the need for new and engaging methods to teach chemistry remotely. As evidenced by this Journal’s Special Issue on “Insights Gained While Teaching Chemistry in the Time of COVID-19”, chemistry educators responded admirably to implement virtual teaching into their curricula, especially for lab experiments.1 Presently, we have observed that most approaches for virtual chemistry laboratories fall under the following categories: Kitchen Chemistry and At-Home Laboratories, Video-Based (prerecorded and live-stream), Data Analysis and Computational Chemistry, Digital Lab Environment (DLE), and Virtual Reality (VR) (Figure 3). Regardless of the approach, virtual translations of chemistry wet laboratories can demonstrate significant safety considerations without the risk associated with in-person laboratories (e.g., mixing incompatible chemical waste).26,27 In this section, we describe each of these virtual learning categories and highlight some representative works.
Figure 3 Virtual lab strategies for chemistry laboratories. Images reprinted with permission from refs (28−33). Copyright 2020 and 2021 American Chemical Society. The image for the Digital Lab Environment reprinted with permission from Labster ApS.34
2.1 Kitchen Chemistry
Kitchen Chemistry and At-Home Laboratories utilize mostly common household materials and equipment to safely conduct hands-on experiments outside of conventional teaching laboratories.35 These strategies have been used to teach project design,36 scientific writing processes,37 and hands-on practical skills, such as experiment planning, attention to detail, and awareness of one’s workspace.38−40 Students can also benefit from this approach as a break from screen-based learning that is imperative for other distance learning strategies. Kitchen chemistry has become increasingly accessible and has been proven to reinforce fundamental concepts (e.g., spectroscopy, standard purification techniques) (see Table 1, VL-1–6). For example, several authors from the United States and Spain have reported the use of cell phones to aid kitchen chemistry experiments, namely, colorimetric and fluorescence analyses in high school and college (an R1 institute) chemistry courses.41,42 Furthermore, take-home chemistry kits for high school, two-year community college, and university students from Brazil and the United States address issues with student access to supplies and tools while offering a safe and often inexpensive alternative to traditional lab experiments.43−45 Overall, there is a consistent motif of kitchen chemistry in terms of learning objectives, such as the realistic learning experiences akin to hands-on laboratories, active engagement with small-scale experiments, and problem-solving during the experiment rather than reading from a recipe. Nevertheless, kitchen chemistry does not fully mimic a laboratory setting and associated safety considerations for laboratories whose objectives are primarily derived from the acquisition of technical skills (e.g., upper-level synthesis techniques).
Table 1 Virtual Lab Strategies with Associated Main Concepts Covered, Assessment Methods Used, and the Level and Domaina,b 29,30,32,33,45,75,8898
a Assessment methods for students’ performance.
b Lower level, first/second-year; upper level, third/fourth-year. DLE, digital lab environment; HW, homework; IR, infrared; TLC, thin layer chromatography; VL, virtual lab; VR, virtual reality.
2.2 Video-Based Laboratories
Video-Based Chemistry Laboratories encompass a variety of styles and delivery methodologies, including simple video recordings, narrative/voice-over lab recordings, and real-time delivery (see Table 1, VL-7–13, -15, and -16). Early renditions include the videotaping of experiments for students to watch later, often during lectures where demonstrations may not be suitable.46 Educators in universities from Australia, Canada, and the United States have utilized prerecorded videos as a tool for students to observe experimental protocols and chemical reactions outside of a lab environment.47−51 This method is favorable in its low cost and development requirements; however, students may not fully engage with these videos and miss valuable information. As supporting information to in-person laboratories, these videos can improve student comprehension of conceptual objectives (e.g., reactivity differences, kinetics), but stand-alone virtual laboratories require greater student input, which has inspired efforts to create active-learning video experiences.52−55 For example, in response to the COVID-19 pandemic, laboratory courses at the University of California, Irvine utilized prerecorded videos of lab experiments for synchronous class meetings.30 TAs and students met online to watch videos of the experiments together, and the TAs would pause periodically to discuss key steps and concepts. A similar synchronous approach conducted live-streamed, real-time demonstrations of scheduled laboratory experiments where students recorded their own observations and engaged in small-group discussions.31 Also, student-created videos have been used to supplement as well as test student understanding at NC State University and Florida International University.52,56 Overall, video-based remote learning can provide students the opportunity to experience experimental design, reaction setup, lab techniques, instrumentation, data collection, processing and analyzing samples/data/results, waste management, and safety precautions. Integration of these video-based laboratories into undergraduate curricula shows promise for emphasizing concepts within experimental observations but cannot replicate the hands-on aspects of in-person laboratories.57
2.3 Computer-Based Learning: Data Analysis and Computational Chemistry
Computer-Based Learning has become a catch-all term to describe a wide array of learning methodologies. This learning method applied computers for chemical problems such as solving complex chemical equilibria,58 nuclear magnetic resonance (NMR) spectroscopy simulations,59 reaction mechanism determination,60 kinetics calculations,61 and quantum mechanical problems.62 Today, chemistry courses still utilize computer-based learning; however, the definition has progressed to include simulations,63 software for data capture and analysis,64 programming-based tutorials,65 and coding and computational chemistry66,67 (see Table 1, VL-6, -10–14). Coding and modeling, for example, have also been used to showcase active-learning techniques in a computer-based undergraduate lab (University of Central Lancashire, UK). The key learning objectives aimed in these studies focused on simulations (e.g., thermodynamics of chemical reactions and chemical equilibria) and 3D model visualizations (e.g., target-drug interactions).68,69 In addition, Kobayashi et al. from the Australian National University demonstrated through a remote and hands-on computational chemistry course that virtual delivery of “dry” laboratories shows promise of effective engagement but is very technology-dependent.33 Another facet of computer-based learning is data analysis laboratories, which give students the opportunity to gain hands-on experience with characterization, data processing, and interpretation techniques. Data analysis can easily be completed at home by providing students with raw data; although in some cases, it requires an online training component to make students more familiar with the analysis software (e.g., MestReNova, ChemDraw), data interpretation, and/or structure elucidation.42,70 Ultimately, the scope of the raw data analysis is limited to instrumentation and characterization experiments in which instrument operation is not required, but in these cases, virtual translations show high fidelity to their f2f counterparts.
2.4 Digital Lab Environment
Digital Lab Environments (DLEs) consist of laboratory components that exist entirely in a virtual interface, which allows students to observe or perform experiments through software offline (e.g., ChemSense, LabVIEW) or online (e.g., Labster, Beyond Labz, ChemCollective)71,72 with the goal of achieving cognitive and affective learning outcomes (Table 1, VL-15–17). Aljuhani et al. from Taibah University in Saudi Arabia, for example, demonstrated this ability with an interactive web-based platform for students to conduct chemistry experiments.73 Moore and co-workers from UC Boulder have produced a series of simulations that are readily accessible online and have been successfully integrated into lectures and laboratories.74 Similarly, “Labventures” used at Rice University and Alfred University offer a creative alternative to virtual laboratories by adopting a Choose Your Own Adventure “click-through” format to test student decision-making in a laboratory context.75,76 Ali and Ullah from the University of Malakand in Pakistan thoroughly reviewed DLEs and analyzed both 2D and 3D virtual chemistry laboratories.77 This work found that virtual chemistry laboratories adequately familiarized students to chemistry experiments, though a lack of realism can impact student engagement, instructions may be insufficient for students to navigate the DLE, and many systems cannot be adjusted to account for students’ knowledge levels. While there are some examples of DLEs developed by the universities, this approach is dominated by corporate efforts that limit the available experiments and the ability of the instructors to create specific experiments for their course. Commercially available DLEs also have costs associated with licensing and distribution, which hinder their accessibility within lower-income schools. However, some programs like ChemCollective do offer some creative flexibility, for example, furnishing digital laboratories with a customizable stockroom that can be modified by the instructor.78 In general, DLEs can help students develop a conceptual understanding of a scientific experimental design and provide engaging, dynamic, and visual feedback supports in a safe, cost-effective, and repeatable lab simulation.
2.5 Virtual Reality
Popularized in the video-game sector, Virtual Reality (VR) has been translated to chemical education and serves as a powerful supplement and alternative setting for teaching students fundamental chemistry concepts, practical lab skills, and new instrumentation through “hands-on” activities (Table 1, VL-18).79 VR provides realistic interactions with customizable, computer-generated, 3D hologram-like learning environments. This approach has been most extensively explored as a forum for interactive molecule visualization, which has allowed students to interact with an otherwise inaccessible molecular world.80−84 Within a laboratory setting, VR has been applied to both instrumentation-based (e.g., IR spectroscopy and pH meter) at NC State University and Iowa State University and synthetic experiments (e.g., synthesis of gold nanocrystals) at the Chinese University of Hong Kong.32,85,86 The results from NC State University showed that there were no significant differences in learning outcomes between the group of students that experienced and performed the VR lab and the group that did the same experiment in a traditional lab.32 The recently developed Virtual Reality Remote Education for Experimental Chemistry (VR2E2C) system features both a digital interface and real-lab live-stream, which give students control over the experiment and the opportunity to virtually observe an in-person experiment.85 Digitization of the lab environment and high interactivity that comes with it gives users a very close experience to in-person laboratories without the associated safety concerns. Overall, this translation allows for engagement with exploratory educational activities in chemistry that can enhance independent, discovery-based learning. This technology has also made improvements toward the inclusivity of chemistry laboratories, particularly for those who cannot physically participate in a lab experiment due to physical/attendance challenges or safety concerns.10 VR experiments are also more easily developed at the university level than other approaches with a similar level of interactivity (e.g., DLEs). Yet, their overall accessibility is limited by a greater upfront cost for both students and universities due to the required technology and software. In this respect, smartphone-based approaches minimize the associated costs, especially given many universities’ electronic lending services and affordable options for smartphone-compatible VR headsets like Google cardboard (∼$9).87 However, smartphones are not ubiquitous to student populations and not all universities can supplement them, so this remains a significant barrier in the effort to develop equity-driven methods.
Overall, chemistry laboratories in particular present a challenge, as the learning objectives, such as hands-on skills, executing methodology, active learning, engagement, interpersonal interactions, and team building, that are assessed and gained in an in-person teaching lab cannot be identically translated to virtual learning platforms.
3 Digitizing Polymer Laboratories for Virtual Environment
Current virtual polymer laboratory experiments are predominated by characterization experiments and simulations.99−103 As such, to the best of our knowledge, the first virtual polymer synthesis experiment implemented by the authors of this work appeared only last year in this Journal in a prerecorded video-based approach along with online collaborative learning.94 In this work, various high sulfur-content polymers were made using different styrenic comonomers through bulk free-radical polymerization. Short videos of polymerization reactions, polymer purification, and spectroscopic analysis were prepared for students to watch during the virtual lab meeting. The absence of virtual synthesis experiments, in general, indicates that conceptual learning objectives underpinning laboratories are more easily translated to a virtual environment, whereas objectives associated with reaction setup, acquiring technical skills, and understanding fundamental concepts derived from troubleshooting a laboratory experiment pose a greater challenge for virtual translation. For this reason, synthetic experiments are more difficult to adapt to a remote learning environment as a stronger emphasis is placed on hands-on techniques as well as safe lab habits—particularly for upper-level students who are already familiar with fundamental concepts. Thus, digitization methods that allow students to make decisions about reaction conditions show the most potential for retaining the value of synthetic experiments (e.g., DLE,72 VR,32 and “labventures”76) and offer some improvements toward safety education as well as material costs.26,27 For more advanced, senior-level laboratories where the goal extends beyond concept familiarity, these options fall short and represent a critical need for future development—particularly in the case of upper-division synthetic experiments.
3.1 Polymer Synthesis
Existing undergraduate polymer synthesis experiments cover a diverse subset of methods predominated by FRP,110 controlled polymerization,128−130 ring-opening polymerization (ROP),131−133 and condensation polymerization107 (see Table 2 for a selection of polymer chemistry laboratory experiments and see the papers135−151 for other polymer-related experiments that are not listed in the table). The first virtual translation of these experiments featured a prerecorded FRP reaction along with online discussions (see Table 2, PL-10).94 However, the prerecorded video approach lacks the student input critical to their acquisition of the experimental setup, active learning, and related troubleshooting skills. Combining the existing videos with an approach that allows for more student input (e.g., “labventure” approach and live stream) would allow the students to learn from incorrect decisions and build experimental intuition as they would in f2f laboratories. Ultimately, the existing video-based approach could be a valuable supplement to in-person laboratories to familiarize students with methodologies prior to performing the experiments themselves as they support the delivery of conceptual objectives (e.g., reactivity differences, kinetics).52,53 These prerecorded video-based approaches are also among the most accessible strategies; however, they struggle as a stand-alone replacement for in-person synthesis experiments as they are unable to provide hands-on technique acquisition—something particularly important for upper-level undergraduate students preparing for industry and graduate-level research.
Table 2 Selection of Polymer Chemistry Laboratory Experimentsa 94,99,105−134
a Polymerization methods: melt-condensation, interfacial polymerization, and ROP. ATRP, atom transfer radical polymerization; CRP, controlled-radical polymerization; FRP, free-radical polymerization; FTIR, Fourier-transform infrared; Mn, number-average molecular weight; NMR, nuclear magnetic resonance; PET, photoinduced electron/energy transfer; PL, polymer lab; RAFT, reversible addition–fragmentation chain-transfer; ROMP, ring-opening metathesis polymerization; ROP, ring-opening polymerization. ⧫ for synthesis; ▼ for characterization; ● for virtual.
Many key concepts in polymer synthesis are missing from the virtual landscape but could be adapted from existing experiments (see Table 2). For lower-level (1st/2nd-year) undergraduate students who have little to no background in polymer chemistry, understanding the nuances between polymerization mechanisms has been accomplished through experiments that feature multiple polymerization methods (Table 2, PL-4 and -16).108,118 These more fundamental objectives can be adapted to a virtual environment through modified video-based approaches that allow for more student input. In addition, polymerization methods with fast reaction times such as the “nylon rope trick” lend themselves to live-streamed synthesis, which can also accommodate real-time questions and foster student engagement (Table 2, PL-7).111 While the underlying concepts in these experiments can be adapted to a virtual environment using any of the previously discussed approaches, VR and DLE show the most promise for translating more tactile synthesis protocols such as air-free or Schlenk techniques114,115 for radical polymerizations to a digital forum.
Fully virtual, upper-level polymer experiments regarding the synthesis of block copolymers, hydrogels, and polyelectrolytes120,121,131 would also require strategies that more closely approximate an in-person laboratory such as VR experiments and DLE (e.g., Labster, Beyond Labz). Both VR and DLE are more immersive and offer students a greater degree of control over the outcome of the reaction.87,152 However, many of these experiments feature application-specific objectives that equip students with property-directed polymer design fundamentals. This unique polymer synthesis technique would likely require heavy modification of existing DLE infrastructure. Thus, VR experiments might be more easily developed at a university level for upper-level polymer experiments. Overall, higher student involvement in these types of experiments promotes the development of technical and soft (e.g., problem solving) skills that will be critical in students’ success in their future graduate and industrial research.
3.2 Polymer Characterization
As components of synthesis laboratories and as stand-alone experiments, characterization experiments are critical to students’ ability to confirm polymer composition (e.g., using NMR, FTIR, UV–vis spectroscopies), elucidate properties (e.g., using thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and gel permeation chromatography (GPC)), and become more familiar with the instrumentation techniques (see Table 2). For lower-level undergraduates, kitchen chemistry can be an effective technique for providing students with a distanced but still hands-on experience.99 Kitchen chemistry can successfully introduce students to fundamental polymer concepts (e.g., thermomechanical properties), but it does not provide students an opportunity to do rigorous data analysis (Table 2, PL-15). Through data interpretation, students learn how to draw conclusions on the basis of gathered/generated datasets. The absence of data interpretation is a persistent flaw with many kitchen chemistry approaches—particularly for upper-level undergraduate students since data analysis is a crucial skill for postgraduate pursuits. Existing approaches can be used to incorporate this into kitchen chemistry laboratories. For example, smartphones could be used to build quantitative data sets for kitchen chemistry laboratories using existing methods, within polymer chemistry, in a smartphone alternative to UV–vis spectrophotometry and, more generally, for various colorimetric measurements.107,153,154
Upper-level undergraduate virtual characterization experiments remain to be developed. Often, instruments for characterization (e.g., NMR spectroscopy, GPC) use automated sample loading processes, therefore bypassing direct student operation of such instruments can be sacrificed without much detriment.155 As such, many of the valuable characterization components of the aforementioned synthesis laboratories (e.g., molecular weight determination, composition verification, thermal analysis) and several characterization experiments covering more advanced topics (e.g., light-scattering, electron microscopy, X-ray photoelectron spectroscopy) could be easily translated to a virtual setting by providing students with raw data.156−158 In cases where instrument-operation is more standardized (e.g., NMR spectroscopy) or is the focus of the experiment, upper-level synthesis strategies (e.g., video-based, live-stream, DLE, and VR experiments) can prove advantageous for teaching instrumentation operations alongside data interpretation. Additionally, there are existing simulations for kinetics, viscosity measurements, and thermal properties that can be used for students to generate raw data for analysis purposes and develop an intuition for structure–property relationships by generating data influenced by student inputs.100−103
Regardless of the strategy and type of experiment, all the proposed distance learning approaches have the potential to improve students’ awareness and comprehension of safety considerations as individuals can make and observe unsafe choices without any of the associated risks.
4 Conclusion
The COVID-19 pandemic required educators to swiftly modify f2f chemistry lab experiments for virtual learning environments or develop new alternatives that are suitable for virtual settings. This sudden, involuntary shift also enabled an impromptu beta test of the rapid deployment of virtual lab experiments. Although the overall results and observations are very promising, further studies are needed to understand the actual effects of the current virtual lab experiments on students’ learning. We are all aware that in-person lab experiments are critical to teaching students synthetic and instrumental skills, fostering active learning, and maintaining human interaction and engagement. The recent studies indicated that virtual lab experiments can effectively complement these f2f lab experiments. In addition, the incorporation of virtual lab experiments into the undergraduate curriculum can be preferred in the postpandemic era due to their cost-effectiveness, safety, sustainability, and inclusivity.
With the spike in the number of virtual experiments in 2020 and 2021, some areas remain underrepresented, such as virtual polymer lab experiments. In this work, our objective was to introduce the readers to current virtual lab strategies and common polymer lab experiments and suggest how these strategies can be used to translate polymer lab experiments to the virtual environment. Richard Zare remarked in this Journal more than 20 years ago: “Which is better: face-to-face learning or computer-aided instruction?” is the wrong question. The right question is “How do we best combine both approaches?”.159 Here, we highlighted approaches for virtual laboratories that would be suitable for teaching and learning one of the most important fields of chemistry: polymers.
Author Contributions
§ M.K., S.M.M., A.J.B., K.M.G., and Y.M.C. contributed equally.
The authors declare no competing financial interest.
Acknowledgments
The authors are grateful for financial support from Duke University and thank Liam H. McDonald for generating the Journal of Chemical Education article data set used by the authors to make Figures 1 and 2.
Abbreviations Used
ATRP atom transfer radical polymerization
CRP controlled-radical polymerization
DLE digital lab environment
DSC differential scanning calorimetry
f2f face-to-face
FRP free-radical polymerization
FTIR Fourier-transform infrared
NMR nuclear magnetic resonance
PET photoinduced electron/energy transfer
PL polymer lab
RAFT reversible addition–fragmentation chain transfer
ROMP ring-opening metathesis polymerization
ROP ring-opening polymerization
SEC size-exclusion chromatography
STEM science, technology, engineering, and mathematics
TA teaching assistant
TGA thermogravimetric analysis
TLC thin-layer chromatography
UV–vis ultraviolet–visible
VL virtual lab
VR virtual reality
VR2E2C virtual reality remote education for experimental chemistry
==== Refs
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ACS Chem Neurosci
ACS Chem Neurosci
cn
acncdm
ACS Chemical Neuroscience
1948-7193
American Chemical Society
35369686
10.1021/acschemneuro.2c00164
Viewpoint
Correlating Biochemical and Structural Changes in the Brain with Clinical Features in COVID-19
https://orcid.org/0000-0003-0626-216X
Baig Abdul Mannan *
Department of Biological and Biomedical Sciences, Aga Khan University, Karachi, Sindh 74800, Pakistan
* Phone: +92-[0]333-2644-246. Email: abdul.mannan@aku.edu.
02 04 2022
acschemneuro.2c0016411 03 2022
21 03 2022
© 2022 American Chemical Society
2022
American Chemical Society
This article is made available via the PMC Open Access Subset 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 the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
With emerging reports of the deleterious effects of SARS-CoV-2 reflecting as neurological deficits in COVID-19, the biochemical and morphological changes it casts on the brain are also being investigated. This is an important niche of research as it is expected to predict and relate the neurological clinical features in the acute phase and chronic syndromic forms of COVID-19. Here debated are the biochemical and structural changes that can be related to the neurological manifestations in COVID-19.
SARS-CoV-2
COVID-19
neurological deficit
imaging
CNS damage
long-COVID
long-haulers
document-id-old-9cn2c00164
document-id-new-14cn2c00164
ccc-price
This article is made available via the ACS COVID-19 subset 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 the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
==== Body
pmcIntroduction
About 2 years ago, in the year 2020, reports and published literature appeared that heralded the neurological deficits caused by SARS-CoV-2 based on clinical outcomes in patients admitted to hospitals in Wuhan, China.1,2 The anatomical structures and known pathways of the SARS-CoV-2 to the brain were predicted1 based on the clinical features in the patients who have been admitted to the hospitals in Wuhan, China. No gross or microscopic findings had been presented as a shred of evidence, at that time, to support the pathways or specific zones of the brain involved in the cases of fatalities resulting from COVID-19 early during the pandemic. Also early in the pandemic, there was reluctance and delays in acknowledging direct neuronal injury caused by SARS-CoV-2 in COVID-19. The biopsy finding after autopsy of the brain and spinal cord coupled with neuroimaging data that has been accumulated now clearly elucidates the neurological damages in COVID-19 (Figure 1). Relating the morphological findings obtained from the biopsies performed during autopsies in COVID-19 and long- COVID to the clinical feature exhibited by the patients during the ongoing pandemic is much needed to gauge the severity of the neurological damages that are caused by SARS-CoV-2 in COVID-19. Apart from direct damage caused by SARS-CoV-2 (Figure 1A1,B), neuronal injury caused by inflammation, hypoxia, and cytokines was also implicated in the causation of neuronal damage (Figure 1A1). Although neuroimaging and other noninvasive modalities could be of diagnostic and prognostic significance in neuro-COVID, it is, however, important to mention here that clinical features in neuro-COVID can be exhibited even in the absence of morphological abnormalities seen on imaging (Figure 1C). As biochemical events are known to precede morphological change during cellular injury, the patients with COVID-19 may exhibit neurological features ahead of alterations detected in imaging modalities like CT scans and MRI reported recently.3 Keeping the latter in mind and with the wide range of clinical features in the COVID-19 and long-COVID patients, with the appearance of normal to the near-normal brain and spinal cord scans (Figure 1C), there is a need to identify CSF biomarkers and serum markers (Figure 1, top-right panel), that can hint toward an ongoing neuronal injury. Additional diagnostic modalities like nerve conduction studies, PET scans, and thorough clinical examination should be brought into consideration to include or exclude the neurological deficits in symptomatic COVID-19 patients with features of neurological damage.
Figure 1 SARS-CoV-2 causes neurological damage via direct neuronal injury or inflammation (A1). Early neuronal damage (B) at the molecular level would not appear on brain scans (C). The release of chemicals in the CSF and blood can serve as a biomarker of neuronal injury (top-right). A thinning of the gray matter and widening of sulci (D) may appear after a considerable loss of neurons in COVID-19 and long-COVID.
Susceptibility of the Neurons to Cellular Injury and Routes to the Brain
Among the tissues and organs of the human body, the neurons that are the building blocks of the human central nervous system (CNS), and glial cells, are sensitive cells that succumb easily to diverse factors capable of causing cell damage like hypoxia, extremes of pH, toxic chemicals, and infectious organisms including viruses. Also, unlike other human tissues where a cellular loss is compensated by regeneration of identical cells by the stem cells, the human CNS mostly is devoid of stem cells; therefore a neuronal loss is a permanent event and mostly is replaced by gliosis. Given the sensitivity of the neurons and glia to injurious influences, evolutionarily the CNS has built the blood–brain barrier (BBB) to protect itself from damaging influences. The toxin and microbes that enter the CNS can adopt routes that can bypass the BBB. Examples include nerve gases and parasite pathogens like Naegleria fowleri. In COVID-19 the SARS-CoV-2 has been shown5 to reach the brain via the cribriform plate of the ethmoid bone,1,4,5 bypassing the BBB (Figure 1 A).
Relating the Effects of Neuronal Injury to Syndromic Neuro-COVID
The knowledge of the functions of different areas of the brain has enabled us to relate the zone of neuronal damage to functional loss. In a patient who presents with signs and symptoms of neurological origin, a CT scan or MRI of a particular area of the brain can provide clues toward neuronal damages caused by either direct effects of the SARS-CoV-21,5 or diverse factors like hypoxia, inflammation, or cytokine-mediated injury. This correlation is not possible when the damage is ongoing at the molecular level and the changes sufficient to document an abnormality on CT scan or MRI are yet to be develop (Figure 1 C), as has been the case with many patients with long-COVID in long-haulers who exhibit neurological features like “brain fog” and diminished cognitive functions.
Need for Investing in Biomedical Research in Neuro-COVID
To prevent damages to the neurons before they can cause fatality in COVID-19 or disability in long-COVID, it is important to invest in research that can identify biochemical markers (Figure 1, top-right panel) that can be a clue toward an ongoing neuro-COVID in either acute phase or chronic syndromic form of COVID-19 as in the case of long-haulers.5 Prevention of neuro-COVID by topical drug and vaccine delivery devices that can reduce the viral load in the nose5 is also needed. The research on COVID-19 and the recently emerging long-COVID patients is in its infancy, and a huge investment in research is expected to find a resolution in the form of neurotropic antiviral agents or anti-inflammatory drugs that can cross the BBB and reduce inflammation and cytokine-mediated neuronal damage in long-COVID and COVID-19.
Discussion and Conclusion
The neurological damages that have been reported in the current pandemic of COVID-19 are of concern not only because the CNS involvement in COVID can cause mortalities more than the similar intensity of infection in any other organ or tissue but also because of the concern of the disabilities expected in the patients who survive the acute phase of COVID-19. The latter is not a prediction but a fact, as this has been seen in long-COVID and long-haulers5 that has now been recognized by WHO and CDC as a disease entity. Most of the patients with long-COVID are disabled due to particular or diffuse damage to the CNS as has been reported recently. After the first indication of the COVID-19 targeting the CNS via the nose, published in ACS Chemical Neuroscience, and the hints that the virus may opt for a pathway across the cribriform plate at the root of the nose to reach the olfactory pathways and brain,1 the finding of mRNA of SARS-CoV-2 around the cribriform plate, olfactory bulb, and diverse regions of the brain4 should have alerted us to anticipate the neurological damages in COVID-19. Similarly, this viewpoint is drawing attention toward a wide range of neurological deficits caused by SARS-CoV-2 in long-haulers and long-COVID. Immediate investment in form of funding research by the healthcare regulating bodies is needed to minimize the incidence of neurological deficits in long-COVID syndrome. Early detection by identification of biomarkers that hint neurological damage in COVID-19 is needed. Though scan-related changes would prove to be of enormous value to study CNS damages in COVID-19,3 prevention of irreversible neuronal and glial damage long before the loss of brain mass leading to changes in CT and MRI could prove to be one of the ways to contain neuro-COVID during this pandemic.
The author declares no competing financial interest.
Acknowledgments
The author thanks the members of Long Covid-19 Foundation, U.K., for their discussions into diverse causes of long-COVID. This research has no funding resources.
==== Refs
References
Baig A. M. ; Khaleeq A. ; Ali U. ; Syeda H. Evidence of the COVID-19 Virus Targeting the CNS: Tissue Distribution, Host-Virus Interaction, and Proposed Neurotropic Mechanisms. ACS Chem. Neurosci. 2020, 11 (7 ), 995–998. 10.1021/acschemneuro.0c00122.32167747
Li Y. C. ; Bai W. Z. ; Hashikawa T. The neuroinvasive potential of SARS-CoV2 may be at least partially responsible for the respiratory failure of COVID-19 patients. J. Med. Virol. 2020, 92 , 552 10.1002/jmv.25728.32104915
Douaud G. ; Lee S. ; Alfaro-Almagro F. ; et al. SARS-CoV-2 is associated with changes in brain structure in UK Biobank. medRxiv 2022, (preprint) 10.1101/2021.06.11.21258690.
Meinhardt J. ; et al. Olfactory transmucosal SARS-CoV-2 invasion as a port of central nervous system entry in individuals with COVID-19. Nat. Neurosci. 2021, 24 , 168–175. 10.1038/s41593-020-00758-5.33257876
Baig A. M. Counting the neurological cost of COVID-19. Nat. Rev. Neurol. 2022, 18 (1 ), 5–6. 10.1038/s41582-021-00593-7.34795449
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ACS Appl Nano Mater
ACS Appl Nano Mater
an
aanmf6
ACS Applied Nano Materials
2574-0970
American Chemical Society
10.1021/acsanm.2c00954
Article
Terahertz Impedance Spectroscopy of Biological Nanoparticles by a Resonant Metamaterial Chip for Breathalyzer-Based COVID-19 Prompt Tests
Sengupta Rudrarup
Khand Heena
https://orcid.org/0000-0001-6717-2235
Sarusi Gabby *
Department of Photonics and Electro-Optics Engineering, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel
* Email: sarusiga@bgu.ac.il.
04 04 2022
acsanm.2c0095403 03 2022
20 03 2022
© 2022 American Chemical Society
2022
American Chemical Society
This article is made available via the PMC Open Access Subset 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 the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
We propose a tested, sensitive, and prompt COVID-19 breath screening method that takes less than 1 min. The method is nonbiological and is based on the detection of a shift in the resonance frequency of a nanoengineered inductor–capacitor (LC) resonant metamaterial chip, caused by viruses and mainly related exhaled particles, when performing terahertz spectroscopy. The chip consists of thousands of microantennas arranged in an array and enclosed in a plastic breathalyzer-like disposable capsule kit. After an appreciable agreement between numerical simulations (COMSOL and CST) and experimental results was reached using our metamaterial design, low-scale clinical trials were conducted with asymptomatic and symptomatic coronavirus patients and healthy individuals. It is shown that coronavirus-positive individuals are effectively screened upon observation of a shift in the transmission resonance frequency of about 1.5–9 GHz, which is diagnostically different from the resonance shift of healthy individuals who display a 0–1.5 GHz shift. The initial results of screening coronavirus patients yielded 88% agreement with the real-time quantitative polymerase chain reaction (RT-qPCR) results (performed concurrently with the breath test) with an outcome of a positive predicted value of 87% and a negative predicted value of 88%.
COVID-19 prompt test
terahertz spectroscopy
metamaterials
nanoantennas
impedance spectroscopy
screening test
Ben-Gurion University of the Negev 10.13039/501100005005 1234 Ministry of Defense 10.13039/501100008121 1234 document-id-old-9an2c00954
document-id-new-14an2c00954
ccc-price
This article is made available via the ACS COVID-19 subset 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 the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
==== Body
pmcIntroduction
During the last 2 years, the coronavirus and its variants have caused a global upsurge in the number of people being infected with COVID-19. The disease courses of COVID-19 range from asymptomatic cases to mild (e.g., dry cough and fever) and severe cases (e.g., pneumonia) and even to acute respiratory distress syndrome. In a metaanalysis of 95 studies covering 29,776,306 tested individuals, 0.25% (95% CI, 0.23–0.27%) had asymptomatic infection and 40.50% (95% CI, 33.50–47.50%) of 19,884 confirmed individuals in this study were asymptomatic.1 The high percentage of asymptomatic infections highlights the potential transmission risk of asymptomatic infections in communities. Furthermore, asymptomatic patients are considered a major source of COVID-19 spread. In the wake of this development, there is a tremendous need for prompt, large-scale, accurate, and cost-effective screening detection of COVID-19 asymptomatic as well as symptomatic patients. As life gradually returns to normal, there is a great need for monitoring asymptomatic cases in order to decrease or prevent cluster outbreaks and transmission. The availability of a rapid, noninvasive, large-scale, accurate, and cost-effective COVID-19 screening test is paramount. Current biological-based methods, such as polymerase chain reaction (PCR) tests, require more than 4 h to obtain the result. In parallel, a plethora of new COVID-19 rapid test techniques with varying efficiency have emerged, but these are not always commensurate with the real-time polymerase chain reaction (RT-PCR) standard.2−4 In this work, we propose an innovative, prompt, noninvasive, nonbiological COVID-19 screening test. This test will be used at entry points, such as airports, stadiums, and shopping malls, and will enable quick differentiation between COVID-19 positive and negative cases.
In general, spectroscopy across the electromagnetic frequencies is a powerful tool for analyzing chemical and biological materials. The most commonly used spectral ranges for spectroscopy are ultraviolet, visible, and infrared radiation. Terahertz (THz) spectroscopy is a relatively recent implementation toward analyzing chemical and biological materials, apart from nanoparticles.5,6 Using metamaterials in conjunction with THz spectroscopy is an important technique toward bridging the THz gap by providing a strong response to THz radiation of the inspected material and for low-concentration nanoparticles, where most natural materials exhibit only weak responses.7−11 With recent advances in THz spectroscopy, the interaction of THz radiation with metallic micro- and nanostructures has provided insights regarding the properties of nanoparticles, in general, and viruses, in particular.7,12 Engineered metamaterial nanostructures have been used to determine the dielectric properties of chemical and biological materials when introduced into metamaterial metallic structures, which has led to various innovative techniques for virus, bacteria, and fungus detection.13−18 These methods constitute the fundamental concept of inductor–capacitor (LC) resonance-based micro- and nanoparticle testing. The metallic nanostructure is physically designed as an inductor and capacitor element, where its fundamental resonant frequency is . Here, the factor L (the inductance) is dictated by the geometric parameters of the fabricated inductor’s nanostructure, while C (the capacitance) is highly dependent on the plate area (length and thickness), capacitive gap (W), and effective dielectric constant (εeff) of the material introduced inside the capacitor gap. A change in the resonant frequency of the metamaterial structure can be brought about by any foreign substance deposited in the capacitive gap region, thereby changing the εeff and thus the capacitance, resulting in a red shift in the resonance frequency (ΔF) with respect to the pristine LC circuit in the array. Because these microstructures have an effective inductance in the nanohenry range and a capacitance in the femtofarad range, they are extremely sensitive to any decimal-point change in εeff, thereby giving an indicative ΔF with the deposition of any nanoparticle or biological particle inside the capacitor gap.17,19 Moreover, the effectiveness and sensitivity of the metamaterial structure in detecting a specific type of particle can be enhanced by engineering the outer dimensions, capacitive area, and gap of the metamaterial, which makes the metamaterial structure more physically sensitive to a specific genre of foreign substances of a specific dimension. This phenomenon has been demonstrated multiple times, where the F0 value of the metamaterial-based sensor shows a strong dependence on the capacitive gap as well as on any foreign particle present there.19
Park et al.19 demonstrated a sensitive bacteriophage detection using a THz-sensitive metamaterial made of classical, linear polarization, split-ring resonators (SRRs) with various capacitive gap widths. This THz SRR is actually a microantenna array of specific geometry-dependent inductance and capacitance, giving a discrete resonance frequency in the THz range. Upon deposition of the bacteriophages on the metamaterial sensors’ surface, the collective resonance frequency of the microantenna array is red-shifted because of an increase in εeff inside the capacitor gap. However, in order to adequately obtain a substantial ΔF, as required for high-sensitivity detection of the bacteriophages, the proposed method19 requires a very high concentration of bacteriophages (about 109 copies/mL), a concentration that can only be obtained in a laboratory experiment. If we consider using the above method to detect infected individuals having viruses related to a respiratory system infection, such an extremely high viral density cannot be obtained by any practical method—neither by a swab nor by a breath test. A recent study revealed that the viral load present in a breath or cough sample is 3000–15000 copies/mL of exhaled air for a low-to-moderate viral load and up to 2.6 × 106 ± 1.7 × 106 copies/mL for a very high viral load.19 For comparison, even for a very high viral load,20,21 a nasopharyngeal swab sample provides a maximum of 16000–80000 copies/mL. Therefore, to attain rapid testing of coronavirus carriers, out of the two methods, the most convenient and noninvasive method would undoubtedly be a breath analysis, where the exhaled breath of a tested individual is used for detection. In addition, detection of only the viruses may provide a very small spectral shift because of their low density, which can be obtained inside the capacitor gap.
Further, a variety of THz metamaterial resonator structures, including SRRs,13−19 are polarization-dependent (linear polarization nanoantenna), which necessitates careful orientation of the metastructure when using a linear-polarized spectrometer and adds to the complexity of rapid testing because of the alignment requirements. Nevertheless, because these engineered metamaterials have been extensively used for a range of bacteria and fungus detection, their use could be a promising step toward the detection of coronavirus as well, provided that a new sensitive, resonant, polarization-independent metamaterial structure design is adopted. Our work presents the detection of a variety of nanometer-to-submicron biological particles and viruses at low concentrations, with sensitivity enhancement provided by a specific polarization-independent metamaterial structure.
More particularly, this paper aims to demonstrate prompt, noninvasive screening of non-COVID-19 carriers and other respiratory-related viral diseases through a breath test with a newly engineered and sensitive LC resonant metamaterial structure in considerably lesser time than that of currently available tests.22 Our hypothesis is that any respiratory-infected subject (viral or bacterial) exhales a vast amount of biological micro- and nanoparticle-based ingredients associated with his/her disease during a breath test; these particles can be detected and analyzed using a metamaterial-based chip and THz spectroscopy. These particles could be cell debris, virus debris, cytokines, and other related proteins, fat molecules, and clusters associated with virus proliferation inside the respiratory system. Therefore, we hypothesize that this method will be sufficiently efficient to detect a combination of viruses and exhaled biological particles related to respiratory diseases that will give a specific ΔF signature, even at concentrations as low as those found in reported exhaled breath statistics.19
Metamaterial Design Considerations, Research Hypothesis, and Test Method
For the detection of COVID-19 carriers using metamaterial-based structures, we physically redesign the THz LC resonant geometry with the intention of maximizing the capacitor gap area. The capacitor gap (width) can be reduced in order to obtain a plasmonic enhancement that is associated with the nanometric scale of such a capacitor gap.7 Placing the capacitor gaps at both geometric diagonals of a square inductor enables the exhaled viruses and particles to be detected in both the S and P polarization states (when using a single polarization spectrometer) with a rectangular all-around singular inductor structure (rectangular side arm), as shown in Figure 1a,b, for its resonance detection. The initial concept of this “cross-arrowhead” structure was inspired by a single-symmetry SRR unit cell metamaterial and based on a study that dealt with various structures of THz antennas, in general.23−25 This improvement enables breathalyzer-based coronavirus and related particle detection by increasing the capacitive gap lengths compared to those of the basic SRR;19 this effectively increases the sensitivity by increasing the probability of viruses/particles falling inside the capacitive gap and yet maintaining a submicron capacitor gap, thereby enabling more pronounced resonance frequency shifts for breath samples even with a lower virus load.
Figure 1 (a) 2D schematic model of the engineered cross-polarization four-arrowhead LC resonant metamaterial structure with coronavirus carriers positioned at the capacitive gap (W), changing εeff (the given dimensions are not drawn to scale). (b) Microscopic image of the fabricated cross-polarization four-arrowhead LC resonant metamaterial structure. Conceptual design of our COVID-19 breathalyzer test, indicating a step-by-step process of the test: (c) the cap is unscrewed from the capsule, which reveals (d) our designed LC resonant metamaterial chip, containing thousands of nanoantennas; (e) the capsule is then screwed onto the mouthpiece, (f) facilitating the individual to blow into the capsule on the metamaterial chip surface; (g) finally, the mouthpiece is thrown away, and the capsule cap is screwed back in its place. The capsule, as shown in part g, can be placed in a THz spectrometer for testing after external sterilization.
In typical conditions, once an infected patient exhales onto the chip, the coronavirus and related particles will spread all over the metamaterial chip surface (which contains a plurality of nanoantenna metamaterial structures), but the virus/particles that are deposited on the capacitive gap areas will only change the resonant frequency due to the change in εeff. Moreover, the thousands of such nanoantenna structures in a few-millimeter-sized chip enables combined field enhancement, giving rise to a considerable ΔF value for COVID-19 carrier detection. The concentration of viruses only will be very low in each breath or cough test. Therefore, according to our hypothesis, the effective dielectric constant that will be measured by the metamaterial structures is a combination of all of the exhaled particles from an infected patient, which will be considerably different between an infected and a healthy person, yielding a ΔF range for coronavirus-infected patients that can be easily distinguished from that of healthy persons. Because this is a measurement of the electrical parameters of the metamaterial that is changed after the breath test, we do not aim to distinguish between any specific genetic material (variant) related to the coronavirus using this screening method. This technology proposition is expected to detect the entire combination of exhaled viruses and particles of all virus variants to screen healthy and infected individuals. If we can effectively distinguish coronavirus-infected patients from healthy ones, we can hypothesize that the collateral damage in the respiratory system due to different coronavirus variants is the same and that all infected patients exhale similar biological ingredients and particles because of the viruses’ population multiplication inside the respiratory system regardless of the variant type. Therefore, the method will be equally efficient for any mutated variation of the coronavirus. In addition, this method is currently targeted to serve as a screening test at point of entries and not a diagnostic test. A screening test identifies “who is healthy” with a high accuracy, unlike a diagnostic test, which diagnoses “who is sick” along with the “type of sickness”. Our method will likely identify any respiratory-infected individual as positive regardless of whether they have COVID-19, influenza, or another viral respiratory disease. At this moment, we do not differentiate between different types of respiratory diseases, because we want to be more specific about SARS-CoV-2, and obtain a specific threshold for “infected” versus “healthy” patients, so that we can separate healthy subjects from infected ones by a prompt screening test. In the case where the individual is found to be positive, he/she may then undergo a diagnostic biological-based test such as antigen or PCR.
For the breath test, we designed a plastic breathalyzer-like kit, semitransparent to THz frequencies, that consists of a capsule housing for the nanoantenna chip and a mouthpiece, which directs the air blown by an individual onto the nanoantenna chip surface. In our prompt coronavirus breathalyzer test shown schematically in Figure 1c–g, after the individual blows onto the chip surface enclosed in a capsule, the capsule is placed in a THz spectrometer for scanning in transmission mode within a predefined frequency range around the expected resonance frequency. The resonance region of the LC resonant nanoantenna structure is characterized by a definitive dip in the transmission amplitude at the resonance frequency, which is compared with the reference resonance frequency of the pristine LC resonant chip to obtain the ΔF value. We aim to design our entire breathalyzer test to take less than 1 min to predict the results from the moment an individual blows air into the breathalyzer kit and to employ it as a screening tool at points of entry.
Simulations
Finite-element COMSOL multiphysics simulations were initially performed to evaluate and predict the resonance frequency in transmission mode of a pristine single-element LC resonator. To incorporate the dielectric environment of the nanocircuit, an effective refractive index, εeff, was introduced in the simulation. Here, the initial εeff during the reference measurement was a linear combination of the dielectric constant of the substrate (silicon, silica, glass, etc.) and air. To emulate the sample measurement in the COMSOL frequency domain electromagnetic solver, εeff was varied by depositing carbon quantum dots (QDs) of a size similar to that of the coronavirus (∼100 nm diameter) in the capacitive gap (W), as shown in Figure 1a, in order to predict ΔF in the THz transmission function. All simulations were done with Si as the substrate, and mostly aluminum (and gold to compare the sensitivity of the nanoantennas) as the metallic metamaterial structure, where the deposition thicknesses of Al and Au are higher than their skin depths (at 0.7 THz, the skin depth of Au is around 80 nm, and for Al, it is around 98 nm7,26). Hence, the metal deposition thickness for Au was kept at 100 nm and for Al at 200 nm to avoid any penetration of THz fields into Si through the metamaterial structures and to ensure proper resonating interaction. Because the mass production of chips at CMOS technology-based fabs can be done with Al-metal only (because Au is known as a charge-carrier-lifetime killer in semiconductors), simulations were done to predict the stable range for the Al thickness that does not considerably affect F0 and ΔF (see Supporting Information section 1).
For further modeling and fabrication, we chose a capacitor gap of W = 1.5 μm, with a resonance frequency at 710 GHz, which fits well with the trade-off between an increased plasmonic effect7 (which demands a lower capacitive gap) and wider capacitive gaps that increase the probability of coronavirus/exhaled particles covering the capacitive gap. Figure 2b shows ΔF for varying surface densities of carbon QDs (100-nm-diameter particles per square micron), where we simulated ultralow QD concentrations, addressing the possibility of a low virus load in a breathalyzer-based test and taking advantage of the increased capacitive region of our cross-arrowhead structure. Now, according to the simulated results, we see an inverse relationship between εeff and the resonant frequency, thereby leading to small red shifts (positive ΔF), which linearly increase with a small increase in the QD concentration compared to the pristine sample (reference). In addition, the fact that we have around 15000 nanoantennas on a 6 × 6 mm sized chip enables us to measure the overall effect of all antennas where the exhaled particles and viruses are spread randomly. Interestingly, according to this theory, a particle of a definitive size and shape should also generate a particular change in the resultant dielectric constant in the capacitive region, leading to a deterministic change in the resonant frequency because the nanometamaterial structures are sensitive enough to capture changes in the dielectric media due to their ultralow capacitance (femtofarad range). Hence, any specific particle can be expected to be detected by a deterministic shift of ΔF, which will be unique to only that particular substance. This notion can be well extended to different genres of viruses as well. We took advantage of the finite-element COMSOL machine to simulate such a condition. In Figure 2c, we varied the QD size while keeping the surface density constant, and we see a very gradual red shift of 10 GHz with increasing QD size. This shows the possibility of virus-specific testing with our cross-arrowhead LC resonant metamaterial.
Figure 2 Finite-element COMSOL simulation results plotted at a normalized transmission amplitude for an Al (200-nm-thick) metamaterial four-arrowhead structure. (a) Blue shift of the resonance frequencies due to increasing cap gap (W), which can be well explained mathematically with the formula C = εeff(area/W). (b) Small ΔF for a low surface density of carbon QDs (100-nm-diameter particles per square micron). (c) Gradual red shift of 10 GHz with a change of the carbon QD dimensions while retaining the concentration constant at 0.1 particles per square micron. (d) Results depicting the linear relationship of ΔF with lower carbon QD surface densities and showing (inset) the gradual saturation of ΔF with QD surface densities above 2 particles per square micron (100-nm-diameter carbon QD particles) that was reported. (e) Higher sensitivity of Au nanoantennas compared to Al nanoantennas.
However, one can expect an ambiguity issue at this point if εeff related to the specific density of particles of a particular size approaches that of a different particle size with different density. Viruses/particles of different kinds (both from the respiratory system tract) might show similar ΔF values, which might increase the number of false positives related to coronavirus patients. Nevertheless, at this point of the development, we aim to achieve a screening test at strategic entry points and not a diagnostic test. The decision regarding positive/negative for a specific individual will be based on a range of ΔF values, as discussed in the following, that is based on the particle density on the metamaterial chip surface.
Further simulations graphically show the saturation of ΔF with an increase in the carbon QD concentration above a surface density of 2 particles per square micron (high surface density), whereas for lower surface densities, its relationship with ΔF is close to linear for different capacitive gaps (W), as shown in Figure 2d. When these simulations are extended to a Au metamaterial cross-arrowhead structure with a 100 nm deposition thickness (yet thicker than the skin depth), we observe higher sensitivity of the Au nanoantennas in Figure 2e compared to Al, which was also reported for SRR metamaterial structures.27 However, it is worth noting that the overall simulated sensitivity of our geometrically optimized Al cross-arrowhead structure is higher than that for Au-based linear SRRs as depicted by S. J. Park et. al.19
To calculate and predict the collective effect of a plurality of such arrowhead nanostructures fabricated on a 6 × 6 mm chip with a 725-μm-thick Si substrate (the typical thickness of an 8-in. wafer), in addition to the plastic capsule (wherein the chip is mounted during the spectral measurement), which serves as a radome to the microantenna array from the electromagnetic point of view, we performed further simulation with the CST Studio Suite. The enclosure of the chip is a THz semitransparent capsule with precisely calculated front- and back-wall thicknesses and air gaps for optimal impedance matching of THz radiation with the nanoantenna array chip. Because of the thickness of the Si substrate, these simulations accurately predict the resonance, as well as Fabry–Perot oscillations originating by the Si substrate in transmission mode, such that there is zero loss of the transmitted signal when the capsule (radome) encloses the metamaterial chip, compared to the naked chip. This is achieved by designing suitable capsule architecture, thicknesses, and material (for impedance matching in the CST simulation, see Supporting Information section 2). Figure 3a gives the optimized dimensions of a 3D simulation, which was taken further for fabrication. Figure 3b shows the THz transmission spectrum of an Al nanometamaterial four-arrowhead chip in the resonance region enclosed in a capsule compared to an experimental spectrum in decibels. A good agreement is noticed at the resonance frequency of 794 GHz and in the amplitude between the experimental results and simulations, where both results are calculated in decibels. The resonance frequency obtained from the CST simulations is not exactly the same as that obtained from the COMSOL simulations because the latter deals with only a single element of the metamaterial, while the CST simulations account for the overall antenna array effect, taking into account the 725 μm Si substrate and the capsule that serves as a radome; this upgrades the CST simulations to a system-level simulation tool in our work.
Figure 3 (a) 3D emulation of the entire LC resonant metamaterial chip with a breathalyzer capsule setup, simulated in the CST Studio Suite. The optimized dimensions of the 3D simulation, which is taken further for fabrication, are shown. (b) THz transmission spectrum of an Al nanometamaterial four-arrowhead chip of the resonance region enclosed in the capsule compared to an experimental spectrum in decibels. (c) System-level CST Studio Suite simulations to study the effect of ΔF with varying surface densities of the nanocytoplasm-like entity (100 nm diameter) with transmission parameters plotted in decibels. (d) Comparison of the ΔF values between Au and Al arrowhead LC resonator metamaterial chips, with respect to variation of the surface densities of 100-nm-diameter-sized particles whose dielectric constants are analogous to that of a biological cell.
Apart from the Fabry–Perot oscillations, the resonance region can be clearly seen with a considerably lower decibel dip, indicating blocking of the transmitted THz waves by the resonant metamaterial. Because most biological entities are filled with cytoplasmic materials, in order to emulate coronaviruses and their related nanoparticles exhaled at a biological level, we used 100-nm-diameter-sized particles with relative permittivity identical with that of a biological cell as the dielectric.28,29 In Figure 3c, we studied the effect of ΔF with varying concentrations of such nanoentities, where we maintained a very low surface density in order to mimic a breathalyzer-like test. The precise numbers of ΔF for Al metamaterial chips are given in Figure 3d and range from 3 to 10.5 GHz, varying linearly with the nanoparticle surface density. The results are further compared with Au metamaterial chips with 100 nm deposition thickness, where ΔF varies linearly from 4.5 to 21.5 GHz as well. This quantifies the effect of the higher sensitivity of Au metamaterial structures at the system level, indicating that Au-based four-arrowhead LC resonator structures will give a larger ΔF value compared to its Al counterparts for the same virus/particles load during the experiment. Nevertheless, we opted for Al-based LC resonators for our fabrication and verification trials, keeping in mind the CMOS-fab design constraints during possible future mass production. (For fabrication procedures and a working prototype, see Supporting Information section 3.)
Experimental Results and Low-Scale Verification Clinical Trials
To verify our hypothesis, we conducted low-scale verification clinical trials of our breathalyzer-based coronavirus carrier screening kit at the Soroka Medical Center in the year 2021. In the initial clinical trials, each patient simultaneously gave samples for the swab-PCR test along with our breathalyzer test. The patient was asked to deeply exhale three times into the mouthpiece (for the air-flow design of our breathalyzer kit, see Supporting Information section 4). The sealed capsule samples from the patients were externally sterilized by dipping into alcohol for a few minutes. After the alcohol dried, the samples were scanned with a frequency domain THz spectrometer (see Supporting Information section 5) within 4 h from the sample collection. All measurements undertaken by us were completely “blind”, and we correlated our predictions with the RT-PCR tests that were delivered to us 1 day after the breath trial; our results were provided within a few minutes to the trial principal investigator from the hospital. In order to effectively differentiate between “healthy” and “SARS-CoV-2-infected” patients in terms of ΔF, we included only “completely healthy” and “sick with SARS-CoV-2” individuals in our low-scale verification clinical trials. Because we intend to design a screening test and not a diagnostic test, we have not included samples of other respiratory diseases, in order to obtain a strict threshold line between “sick” and “healthy”.
Figure 4 depicts the raw THz spectra (in photocurrent) of patients infected with coronavirus, compared with healthy ones. The black spectrum is a THz scan done on pristine samples, which acts as a reference, and the red line indicates the measurement samples (after the patient exhaled). Both the reference and measurement spectra are Gaussian-fitted at the resonance region, and the minima of individual fittings are extracted to compute ΔF. According to our simulations, we anticipated a transmittance dip around 800 GHz, signifying the resonance frequency for our four-arrowhead metamaterial structure. It is interesting to note that, for a coronavirus-positive patient with a CT value of 21 (high viral load), we observed ΔF = 9.2 GHz (Figure 4a), whereas for a patient with a CT value of 30 (moderate viral load), we observed ΔF = 4.1 GHz (Figure 4b). Hence, there is a direct correlation between the viral load (CT value of the PCR) and ΔF; the CT value indicates the viral infection level in the patients, which is strongly linked to the viral load. On the contrary, for healthy individuals, ΔF ∼ 0–1.5 GHz, where the reference and measurement spectra almost completely overlap, as observed in Figure 4c.
Figure 4 Experimental THz spectra (envelope) of healthy and infected patients. (a) Transmittance spectra of a coronavirus-infected patient with a CT value of 21. (b) Transmittance spectra of a coronavirus-infected patient with a CT value of 30. (c) Transmittance spectra of a completely healthy individual. (d) Statistical representation of all of the samples (positive and negative) showing the relationship between ΔF and the viral load, which is indicated by the CT value of the PCR. The data set is linearly fitted and shows a clear linear dependence. (e) Negative samples separately shown to depict the number of correct and incorrect predictions for calculation of the specificity.
This experiment supports our initial hypothesis that ΔF due to the coronavirus-related particles exhaled by an infected patient is considerably different from that of a healthy individual. By statistically conducting this trial for 40 individuals in hospitals, we reached the conclusion that, for screening purposes using an Al-based metamaterial, any ΔF in the range of 1.5–10 GHz will signify coronavirus-infected individuals with varying viral loads. ΔF values below 1.5 GHz can certify a coronavirus-negative individual. Small ΔF values up to the range of 1.5 GHz can be attributed to numerous other micro- to nanoparticles present in the breath. Interestingly, if we study the relationship between the viral load and ΔF for infected patients, we understand that roughly a higher viral load will lead to higher εeff, leading to a decreased resonance frequency and an enlarged ΔF. The CT values are strongly linked to the viral load, which can be subsequently divided into zones of high, moderate, and low viral loads, depending on the marker used for the fluorescence of the RT-PCR process.30,31 The results are observed in Figure 4d. Here, we have assumed that the infected individual exhales properly into the breathalyzer, such that the higher viral load is properly correlated with the CT values.
The three false-negative samples were taken from severely ill patients in critical condition who were unable to breathe properly into the breathalyzer (they all had very high CT values), and hence a sufficient ΔF value was not observed for these patients. However, because this test is intended to be used as a screening test, most of the individuals are assumed to be asymptomatic and would be able to give a proper breath test. Moreover, all of the samples with CT values higher than 29 were taken from walk-in patients, who took our breathalyzer-based test along with the RT-qPCR, which proves that our screening method works for lower viral loads. Among the remaining 24 healthy individuals, we had two false positives, as shown in Figure 4e.
On the other hand, we also had one subject with a ΔF of 2.3 GHz, and with a CT value of 37, who tested negative at the time that we performed the breathalyzer test but was retested, upon showing symptoms, and found to be positive the next day. We should note at this point that the linear dependence of ΔF on the CT may serve in the future to be a tool for identifying “just infected” or “in recovery” phases of the disease, but this will require more intensive clinical trials with hundreds to thousands of tested subjects. Hence, our hypothesis seems to be confirmed and may pave the way for this technology to be used for the screening of symptomatic and asymptomatic patients with significant accuracy. An interesting correlation is also drawn between the ΔF data obtained in the clinical trials and the simulation results (see Supporting Information section 6). Upon evaluating the time taken to perform the entire breathalyzer test from the moment an individual exhales onto the chip, we could consistently deliver results within 50–55 s, which includes scanning of the sample and ΔF extraction with an automated algorithm inlayed into the software (see Supporting Information section 7). We have also included a detailed list of all patients recruited, mentioning their RT-qPCR test results along with the CT values and our predictions based on ΔF (see Supporting Information section 8).
Figure 5 shows a scanning electron microscopy (SEM) image of an Au metamaterial chip collected from a coronavirus-infected patient during the initial proof-of-concept tests (in the year 2020), with coronaviruses and carriers located in the capacitive gap region, which gives a ΔF of 19.8 GHz (this ΔF is typical for a Au-based metamaterial for COVID-positive subjects). A fixation of the viruses with formaldehyde was done before scanning with the electron microscope. The coronavirus measured about 100 nm in diameter, which is well correlated with the literature.31 We can also see various biological particles exhaled that are spread all over the metamaterial chip surface, but these are too microscopic to be resolved even at 2 μm resolution. For Au metamaterial chips, we had found that the maximum ΔF value that we can reach is up to 20 GHz for a similar viral load, which also strongly correlates with the simulation results shown in Figure 3d. Figure 5 helps to visualize our entire breathalyzer-based coronavirus carrier detection principle and mechanism with our arrowhead LC resonant metamaterial nanostructure chips.
Figure 5 (a–d) SEM images of an Au metamaterial chip collected from a COVID-19-infected patient, with coronaviruses in the capacitive gap region. (e) Transmittance spectra showing ΔF for that particular sample. We used Au as the metal in the cross-arrowhead structure during the initial proof-of-concept experiments, which gave us a ΔF of 19.8 GHz, which is well correlated with the simulation results.
Verification of Clinical Trial Result Statistics
A total of 40 randomly selected individuals were recruited into this study and tested simultaneously using the SARS-CoV-2 rapid breathalyzer test and the gold standard RT-qPCR method. A total of 16 (40%) tested positive by RT-qPCR, while 24 tested negative. Considering the PCR results, we found 13 tests to be true positive and 2 to be false positive, whereas 22 tests were true negative and 3 tests were false negative (Table 1). The test reached a sensitivity of 81% and a specificity of 92%, a positive predictive value (PPV) of 87% and a negative predictive value (NPV) of 88.0%.
Table 1 Comparison of SARS-CoV-2 RT-qPCR and Breathalyzer Rapid Test Resultsa
breathalyzer rapid test
RT-qPCR test positive negative total sens/spec
positive 13 3 16 sens: 13/16 = 81.25%
negative 2 22 24 spec: 22/24 = 91.67%
total 15 25 40 87.50% agreement with the RT-qPCR analysis
PPV/NPV PPV: 13/15 = 86.67% NPV: 22/25 = 88.00%
a sens = sensitivity; spec = specificity; PPV = positive predictive value; NPV = negative predictive value; RT-qPCR = real-time quantitative polymerase chain reaction.
Conclusion
In our proposed coronavirus screening test with cross-arrowhead LC resonant metamaterial chips, we detected a combination of viruses and related biological particles (virus debris, cytokines, cell debris, and related proteins and fat molecules) that produce an effective change in the dielectric constant of the resonant metamaterial in the capacitive-gap regions. This phenomenon red shifts the resonance frequency (ΔF), which becomes the determining factor used to effectively screen infected patients and distinguish them from healthy individuals. The simulations correlate well with the clinical trial results, which give a distinct band of ΔF = 1.5–10 GHz for infected individuals, with a linear relationship of increasing ΔF with an increase in the viral load (and thus the amount of particles exhaled). The attractiveness of this noninvasive, rapid, and cost-effective breathalyzer test lies in its ease of handling, which does not require a complex setup procedure. The entire testing and analysis procedure is performed within 50–55 s, with 88% agreement with the RT-PCR analysis, based on the low-scale verification clinical trials conducted at the Soroka Medical Center.
This method of prompt coronavirus screening will help to effectively distinguish healthy from coronavirus-infected individuals, both symptomatic and asymptomatic and with low viral loads. Because we aim to conduct this screening test at strategic entry points like airports, shopping malls, ports, etc., any individual taking the breathalyzer test may have other related respiratory and chronic diseases. This might identify as positive any individual having influenza, etc., and not just COVID-19. If the individual is screened as positive, he/she will have to undergo a biological-based test such as antigen or PCR diagnostic tests. Nevertheless, the infected person will be screened at entry points with PPV = 87% and NPV = 88%, using our prompt breathalyzer test.
Supporting Information Available
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsanm.2c00954.Dependence of the metal thickness in the material on ΔF (S1), minimizing Fabry–Perot oscillations using the capsule (radome) (S2), fabrication methods and a working prototype (S3), air-flow design of our breathalyzer (S4), information about the THz spectrometer used (S5), correlation of the ΔF data of the clinical trials with the simulation results (S6), automation procedure for ΔF extraction (S7), and low-scale verification of the clinical trial dataset (S8) (PDF)
Supplementary Material
an2c00954_si_001.pdf
The authors declare no competing financial interest.
Acknowledgments
The authors acknowledge the contribution of Ben-Gurion University of the Negev in supporting this research. We also acknowledge support of the Israeli Ministry of Defense (Mafa’at) during the initial phase of this research. The help of Dr. Ifat Abadi-Korek to write the clinical results section in the proper medical terms is highly appreciated.
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PMC009xxxxxx/PMC9004354.txt |
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Appl Environ Microbiol
Appl Environ Microbiol
aem
Applied and Environmental Microbiology
0099-2240
1098-5336
American Society for Microbiology 1752 N St., N.W., Washington, DC
35285710
02552-21
10.1128/aem.02552-21
aem.02552-21
Public and Environmental Health Microbiology
public-healthPublic HealthFactors Impacting Persistence of Phi6 Bacteriophage, an Enveloped Virus Surrogate, on Fomite Surfaces
https://orcid.org/0000-0003-0366-5799
Baker Christopher A. a
Gutierrez Alan b
https://orcid.org/0000-0003-4071-7914
Gibson Kristen E. a keg005@uark.edu
a Department of Food Science, University of Arkansas System Division of Agriculture, Fayetteville, Arkansas, USA
b Department of Animal Sciences, Emerging Pathogens Institute, University of Florida grid.15276.37 , Gainesville, Florida, USA
Editor Elkins Christopher A. Centers for Disease Control and Prevention
The authors declare no conflict of interest.
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4 2022
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American Society for Microbiology
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ABSTRACT
The persistence of Phi6 (Φ6) bacteriophage on surfaces commonly encountered in consumer-facing environments was evaluated. Φ6 has been utilized as a surrogate for enveloped viruses, including SARS-CoV-2—the causative agent of COVID-19—due to structural similarities, biosafety level 1 (BSL-1) status, and ease of use. Φ6 persistence on fomites was evaluated by characterizing the impact of the inoculum matrix (artificial saliva, phosphate-buffered saline [PBS], tripartite), inoculum level (low and high), and surface type (nonporous—aluminum, stainless steel, plastic, touchscreen, vinyl; porous—wood). Φ6 was inoculated onto surfaces at low and high inoculum levels for each inoculum matrix and incubated (20.54 ± 0.48°C) for up to 168 h. Φ6 was eluted from the surface and quantified via the double agar overlay assay to determine virus survival over time. For nonporous surfaces inoculated with artificial saliva and PBS, significantly higher D values were observed with high inoculum application according to the 95% confidence intervals. In artificial saliva, D values ranged from 1.00 to 1.35 h at a low inoculum and 4.44 to 7.05 h at a high inoculum across inoculation matrices and surfaces. D values for Φ6, regardless of the inoculum level, were significantly higher in tripartite than in artificial saliva and PBS for nonporous surfaces. In contrast with artificial saliva or PBS, D values in tripartite at low inoculum (D values ranging from 45.8 to 72.8 h) were greater than those at high inoculum (D values ranging from 26.4 to 45.5 h) on nonporous surfaces. This study characterized the impact of the inoculum matrix, inoculum level, and surface type on Φ6 survival on various surfaces relevant to fomite transmission in public settings.
IMPORTANCE An important consideration in virus contact transmission is the transfer rate between hands and surfaces, which is driven by several factors, including virus persistence on inanimate surfaces. This research characterized Φ6 persistence on surfaces commonly encountered in public settings based on various factors. The inoculum matrix, which simulates the route of transmission, can impact virus persistence, and three separate matrices were evaluated in this study to determine the impact on Φ6 persistence over time. The number of microorganisms has also been suggested to impact persistence, which was evaluated here to simulate real-world contamination scenarios on six surface types. Results from this study will guide future research utilizing Φ6 or other surrogates for enveloped viruses of public health concern.
KEYWORDS
Phi6
SARS-CoV-2
carrier
inoculum matrix
persistence
surrogate
U.S. Department of Agriculture (USDA) https://doi.org/10.13039/100000199 2020-67017-32427 Gibson Kristen E. cover-dateApril 2022
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pmcINTRODUCTION
Transmission of enveloped viruses of public health importance, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—the causative agent of COVID-19—is an important determinant to assess risk levels for future outbreaks caused by enveloped viruses. Risk assessment outcomes are guided by both direct and indirect contact transmission routes. An important consideration in virus contact transmission is the transfer rate between hands and surfaces, which is driven by several factors, including virus persistence on inanimate surfaces. In response to the COVID-19 pandemic, the stability of SARS-CoV-2 on surfaces has been investigated on inanimate surfaces to help drive risk assessments in public health (1–5).
While it is ideal to study the pathogen of interest, e.g., SARS-CoV-2, this is not always feasible. For instance, biosafety level 3 (BSL-3) facilities are required for SARS-CoV-2 research, which is inaccessible to many researchers, cost-prohibitive, and limits the number of studies and parameters that can be investigated (6–8). These limitations highlight the utility of surrogates for assessing important experimental parameters that may not be addressed with the pathogen of interest. Surrogate selection criteria have been characterized (9), and delineating how study parameters impact surrogates can help identify their utility when studying the pathogens of interest.
Phi6 (Φ6) is a segmented, double-stranded RNA bacteriophage of approximately 75 nm in diameter (10). Similar to SARS-CoV-2, Φ6 bacteriophage is lipid-enveloped. Φ6 infects Pseudomonas syringae pv. phaseolicola, which in addition to Φ6, has BSL-1 status and can be utilized as a host (11–13). Φ6 continues to be investigated as a surrogate for SARS-CoV-2 based on structural similarities of the phospholipid envelope, the relatively short analysis time (24 h), and cost-effective assays that enable experiments to be performed without specialized facilities (6, 14).
The extent of indirect contact transmission via fomites and subsequent risk of infection for SARS-CoV-2 has been controversial, as previous studies have inoculated surfaces with virus titers that do not represent real-world contamination scenarios (15, 16). Slower inactivation kinetics of viruses have been observed at higher inoculation levels (17, 18), although further evidence is needed, as inoculum matrix (e.g., artificial saliva, vomitus, feces, etc.) may impact this observation (17). Further understanding the scenarios that facilitate an increased risk of transmission due to persistence of enveloped viruses will help guide mitigation strategies in consumer-facing environments (e.g., restaurants, waiting rooms, public transportation, etc.) where fomite surfaces are commonly touched. The matrix associated with enveloped viruses has been highlighted as an important factor for persistence in the environment (19–21). Thus, evaluating how various matrices impact survival on fomites, especially in relation to virus concentration, is important to assess transmission routes driven by surface contamination scenarios.
To further substantiate Φ6 as a surrogate for enveloped viruses of public health importance, persistence data based on the inoculum matrix, inoculum level, and surface type are needed. Previous studies have evaluated the persistence of Φ6 on fomites (8, 17, 21–23). However, these studies have not characterized persistence on multiple unique surface types with various inoculum matrices and levels. This research was performed to characterize Φ6 persistence on surfaces commonly encountered in public settings under conditions relevant to real-world exposures, including when a virus is deposited at various concentrations and within different bodily fluids (e.g., respiratory secretions, fecal material).
RESULTS AND DISCUSSION
The impact of inoculum level on virus persistence on fomites has been highlighted previously (15–17, 19). Lai et al. (19) observed greater survival of SARS-CoV-1 at higher virus titers (4, 5, and 6 log 50% tissue culture infective dose [TCID50]/mL) with survival times ranging from <5 min to 48 h following inoculation on paper, a disposable gown, and a cotton gown. Bangiyev et al. (17) observed longer half-lives (overall range of 5 to 57 min) of Φ6 in a saline inoculum matrix when vacuum-dried on plastic tubes at approximately 4 log PFU versus when evaluated at lower concentrations (3 and 2 log PFU). However, longer half-lives at increased inoculation levels were not observed with Φ6 in a Luria-Bertani inoculum matrix on plastic (half-lives ranged from 9 to 18 h), which provides evidence that extended survival may not always result from an increased inoculum titer.
In the current study, a high inoculum did not consistently result in higher D values, as this was impacted by the inoculum matrix. For instance, artificial saliva and phosphate-buffered saline (PBS) matrices resulted in a faster decline of Φ6 at high inoculum, while the tripartite matrix resulted in a slower decline at high inoculum. In artificial saliva, D values (time to obtain one log10 reduction) ranged from 0.61 to 7.05 h depending on the surface type and inoculum level (Table 1). At a high inoculation level, the greatest survival was observed on touchscreen with a D value of 7.05 h, and minimal differences in D values were observed among aluminum, plastic, stainless steel, and vinyl (4.44 to 5.33 h) (see Fig. S1 in the supplemental material). Minimal differences were observed among nonporous surfaces at a low inoculum level in artificial saliva (1.00 to 1.35 h) (Fig. S2). Similar to Φ6 in artificial saliva on surfaces, greater D values were observed at high (2.02 to 4.63 h) versus low inoculum levels (0.79 to 1.52 h) for all nonporous surfaces in PBS (Fig. S3 and S4).
TABLE 1 Inactivation kinetics based on inoculum matrix, surface, and inoculum levela
Inoculum matrix Surface Inoculum level D value (95% CI) Decay rate (log PFU h−1) (95% CI) R2
Artificial saliva Aluminum High 4.60 (3.41–7.05) 0.22 (0.14–0.29) 0.67
Low 1.07 (0.80–1.63) 0.93 (0.61–1.25) 0.81
Plastic High 4.61 (3.58–6.46) 0.22 (0.15–0.28) 0.75
Low 1.14 (0.83–1.81) 0.88 (0.55–1.21) 0.78
Stainless Steel High 4.44 (3.47–6.17) 0.23 (0.16–0.29) 0.76
Low 1.00 (0.77–1.42) 1.00 (0.70–1.30) 0.85
Touchscreen High 7.05 (4.87–12.80) 0.14 (0.08–0.21) 0.55
Low 1.16 (1.00–1.39) 0.86 (0.72–1.00) 0.95
Vinyl High 5.33 (4.29–7.05) 0.19 (0.14–0.23) 0.81
Low 1.35 (0.97–2.20) 0.74 (0.45–1.03) 0.77
Wood High 0.77 (0.51–1.55) 1.30 (0.65–1.96) 0.66
Low 0.61 (0.50–0.77) 1.65 (1.29–2.00) 0.96
PBS Aluminum High 4.63 (3.37–7.41) 0.22 (0.13–0.30) 0.67
Low 1.49 (1.07–2.45) 0.67 (0.41–0.93) 0.76
Plastic High 4.30 (3.34–6.01) 0.23 (0.17–0.30) 0.75
Low 1.49 (1.17–2.06) 0.67 (0.49–0.86) 0.87
Stainless steel High 2.02 (1.52–3.04) 0.49 (0.33–0.66) 0.76
Low 0.83 (0.65–1.15) 1.21 (0.87–1.54) 0.93
Touchscreen High 2.54 (1.69–5.12) 0.39 (0.20–0.59) 0.61
Low 0.79 (0.60–1.17) 1.27 (0.86–1.68) 0.86
Vinyl High 3.86 (3.13–5.03) 0.26 (0.20–0.32) 0.81
Low 1.52 (1.14–2.31) 0.66 (0.43–0.88) 0.81
Wood High 0.50 (0.34–1.00) 1.98 (1.00–2.97) 0.80
Low 0.72 (0.55–1.06) 1.38 (0.95–1.82) 0.95
Tripartite Aluminum High 26.4 (24.2–28.9) 3.79 × 10−2 (3.46–4.13 × 10−2) 0.96
Low 72.2 (48.8–138.8) 1.39 × 10−2 (0.72–2.05 × 10−2) 0.59
Plastic High 36.3 (33.9–39.1) 2.75 × 10−2 (2.56–2.95 × 10−2) 0.97
Low 62.5 (45.5–99.5) 1.60 × 10−2 (1.01–2.20 × 10−2) 0.70
Stainless Steel High 31.3 (28.9–34.2) 3.19 × 10−2 (2.93–3.46 × 10−2) 0.97
Low 61.9 (45.8–95.3) 1.62 × 10−2 (1.05–2.18 × 10−2) 0.73
Touchscreen High 45.5 (38.7–55.3) 2.20 × 10−2 (1.81–2.58 × 10−2) 0.86
Low 45.8 (34.9–66.5) 2.19 × 10−2 (1.50–2.87 × 10−2) 0.72
Vinyl High 33.1 (29.7–37.4) 3.02 × 10−2 (2.67–3.37 × 10−2) 0.94
Low 72.8 (61.1–90.0) 1.37 × 10−2 (1.11–1.64 × 10−2) 0.90
Wood High 12.6 (8.4–25.4) 7.95 × 10−2 (0.04–0.12) 0.66
Low 1.60 (1.09–2.96) 0.63 (0.34–0.92) 0.73
a CI, confidence interval.
In tripartite matrix, there was a significant difference in D values based on inoculum level for all surfaces except touchscreens according to the 95% confidence intervals. Unlike other matrices, D values in tripartite matrix at low inoculum (D values ranging from 45.8 to 72.8 h) were greater than those at high inoculum (D values ranging from 26.4 to 45.5 h) on nonporous surfaces (Fig. S5 and S6). Similar to artificial saliva, the greatest D value was observed on a touchscreen (45.5 h, high inoculum), although a similar D value was observed at a low inoculum level on a touchscreen (45.8 h). At a low inoculum level in tripartite, Φ6 on vinyl and aluminum exhibited D values greater than 72 h (Table 1), while Φ6 on plastic and stainless steel exhibited D values greater than 60 h. Based on work by Bangiyev et al. (17) and the current study, the impact of the suspension medium/inoculum matrix, especially in relation to viral titer, should be considered when interpreting surrogate data in fomite persistence investigations.
The presence of organic matter is thought to represent a worst-case scenario when assessing virus inactivation (1, 24, 25). Viruses are presumed to be stabilized and protected in the presence of organic matter (20). Based on the greater survival observed at low versus high tripartite inoculum levels, further evaluation of Φ6 and its interaction with this matrix is warranted. At the low inoculum level, there may have been greater interaction of each virus with the organic matrix in comparison with a high inoculum in which greater virus-virus interaction could have resulted in less protection. Wood et al. (21) observed greater survival of Φ6 in a blood matrix than in PBS on various surfaces; median D values of Φ6 in PBS for glass and stainless steel were 23 and 5 h, respectively. However, in a blood matrix, median D values ranged from 103 to 283 h on glass and from 77 to 88 h on stainless steel. Bodily fluids may limit virus envelope damage via desiccation (6), and the protective effects of proteins have been postulated as an important factor for prolonging virus survival (18, 25–27).
The current study was performed at ambient temperatures with monitored relative humidity (RH) levels to best represent consumer-facing environments. The mean temperature in the biosafety cabinet during surface incubation was 20.54 ± 0.48°C with a maximum and minimum temperature of 23.35 and 20.13°C, respectively. A mean RH of 62.46 ± 2.64% was observed with a maximum and minimum RH of 72.29 and 47.49%, respectively. The mean inoculum level (final concentration on surfaces) for high and low inoculum levels among all inoculum matrices was 7.11 ± 0.45 and 3.33 ± 0.72 log PFU, respectively. Among all surfaces, the recovery efficiency with artificial saliva, PBS, and tripartite (consisting of bovine mucin, bovine serum albumin, and tryptone) was 54.2 ± 28.1, 74.5 ± 89.8, and 62.4 ± 94.7%, respectively. The recovery efficiencies and log PFU/mL loss at 0 h for each surface and inoculum matrix were calculated. Mean log PFU/mL loss following surface inoculation was below 0.50 log PFU/mL for each surface except wood (Table 2). There were no significant differences in the log PFU/mL loss among nonporous surfaces, regardless of the inoculum matrix type (P > 0.05). However, log PFU/mL loss on wood surfaces was significantly different from each nonporous surface type, regardless of inoculum matrix type (P < 0.05).
TABLE 2 Recovery efficiency (%) and log PFU loss based on surface and inoculum matrix
Surface Inoculum matrixa Recovery efficiency (%) Log PFU lossb
Aluminum AS 64.6 ± 23.0 0.24 ± 0.24 A
PBS 119 ± 138 0.14 ± 0.49 A
TP 100 ± 152 0.29 ± 0.51 A
Plastic AS 63.2 ± 23.1 0.25 ± 0.27 A
PBS 119 ± 118 0.17 ± 0.57 A
TP 58.4 ± 45.2 0.36 ± 0.38 A
Stainless steel AS 58.0 ± 26.7 0.30 ± 0.29 A
PBS 86.0 ± 82.0 0.25 ± 0.46 A
TP 60.9 ± 48.0 0.38 ± 0.44 A
Touchscreen AS 65.5 ± 26.0 0.22 ± 0.20 A
PBS 49.4 ± 25.5 0.38 ± 0.31 A
TP 48.0 ± 32.1 0.44 ± 0.37 A
Vinyl AS 54.6 ± 26.1 0.34 ± 0.21 A
PBS 91.4 ± 73.8 0.21 ± 0.47 A
TP 113 ± 172 0.28 ± 0.56 A
Wood AS 19.2 ± 20.1 1.03 ± 0.64 B
PBS 5.87 ± 14.3 2.25 ± 1.21 B
TP 13.2 ± 24.6 1.90 ± 1.29 B
a AS, artificial saliva; PBS, phosphate-buffered saline; TP, tripartite.
b Different letters indicate significant differences (P < 0.05).
For wood, a low recovery of virus was observed from the surface due to the porous structure, which suggests that lower transmission risks are likely on similar porous surfaces. Based on inoculum level, large differences in D values were observed on wood surfaces for tripartite (low, 1.60 h; high, 12.58 h), while minimal differences were observed for artificial saliva (low, 0.61 h; high, 0.77 h) and PBS (low, 0.72 h; high, 0.50 h). Among nonporous surfaces, the type of surface did not have as great of an impact on Φ6 persistence. Whitworth et al. (23) observed similar D values between stainless steel and plastic surfaces when Φ6 was suspended in an artificial test soil containing albumin proteins, hemoglobin, carbohydrates, cellulose, lipids, and salts that simulated bodily fluids. However, large differences in D values were observed for low (3.0 g/m3 [18°C, 20% RH]) and high (14.4 g/m3 [26°C, 57% RH]) absolute humidities at 14 to 18 days and 6 h, respectively (23).
The current study highlights the importance of inoculum matrix and inoculum level in relation to enveloped virus persistence on fomites and provides evidence that the survival rate of Φ6 was similar among the nonporous surface types investigated. The data generated from this study will build upon previous studies focused on utilizing the Φ6 bacteriophage as a surrogate for enveloped viruses (8, 17, 21–23). The decay rate (log PFU h−1) was included in this study for previous and future comparisons of Φ6 persistence on surfaces. Overall, the lowest decay rates were observed in the tripartite matrix (1.37 × 10−2 to 3.79 × 10−2 log PFU h−1), which are similar to those from a previous investigation by Whitworth et al. (23) (2.5 × 10−3 to 5.92 × 10−2 log PFU h−1). Much higher decay rates were observed in PBS and artificial saliva (0.22 to 1.98 log PFU h−1) in this study.
Riddell et al. (3) observed D values ranging from 33 to 42 h at 30°C and 143 to 152 h at 20°C for SARS-CoV-2 inoculated on stainless steel, glass, and vinyl surfaces with tripartite matrix. While more data are needed directly comparing different enveloped viruses simultaneously, the observed D values for Φ6 in the current study in tripartite are similar to the values observed by Riddell et al. (3) and warrant further investigation. Additionally, determining how inoculum matrix impacts inactivation kinetics for enveloped viruses will be necessary when making comparisons across persistence data sets. Factors such as protein binding sites, sources of inoculum matrix components, and virus-virus interactions, or microbial interactions in general, in the inoculum are important areas that remain to be investigated.
MATERIALS AND METHODS
Φ6 production.
Φ6 bacteriophage (HER102) stock production and Pseudomonas syringae pv. phaseolicola (HER1102) growth was performed as previously described (28). Briefly, medium used for Φ6 propagation and P. syringae pv. phaseolicola growth was lysogeny (LC) broth (10 g NaCl, 10 g tryptone, 5 g yeast extract/L ultrapure water, pH adjusted to 7.5). Φ6 stock was produced by adding P. syringae pv. phaseolicola bacterial host (200 μL) (approximately 9 log CFU/mL) and 100 μL of undiluted Φ6 (approximately 10 log PFU/mL) to 5 mL of LC soft agar, and soft agar was poured onto LC agar plates via the double agar overlay (DAL) assay (29), after which dried plates were inverted and incubated at 25°C for 20 to 24 h. Φ6 was harvested from lacy-webbed plates with a 25-cm cell scraper (VWR, Radnor, PA), and following centrifugation (10 min at 3,000 × g, 4°C) supernatant was passed through a 0.45-μm sterile polyethersulfone syringe filter (Whatman, Buckinghamshire, UK) and stored at 4°C until use.
Inoculum preparation and surface inoculation.
Low and high inoculum levels were developed in three separate inoculum matrices at a target concentration of 3.5 and 7.0 log PFU on surfaces, respectively. Sterile 1× phosphate-buffered saline (PBS), pH 7.4, was prepared by adding 100 μL of diluted Φ6 stock (high, 10-fold dilution; low, 1,000-fold dilution) to 5 mL of PBS. Similarly, 100 μL of diluted Φ6 stock (high, 10-fold dilution; low, 1,000-fold dilution) was added to 5 mL of artificial saliva consisting of 1.54 mM KH2PO4 (Sigma-Aldrich), 2.46 mM K2HPO4 (Fisher Scientific, Loughborough, UK), 0.04 mg/L MgCl2·7H2O (Alfa Aesar, Ward Hill, MA), 0.11 g/L NH4Cl (VWR), 0.12 g/L (NH2)2CO (VWR), 0.13 g/L CaCl2 (VWR), 0.19 g/L KSCN (Acros Organics, Carlsbad, CA), 0.42 g/L NaHCO3 (Fisher Scientific), 0.88 g/L NaCl (VWR), 1.04 g/L KCl (VWR), and 3 g/L mucin (Sigma-Aldrich) at pH 7 (30, 31). Lastly, the tripartite matrix (5 mL) was prepared as described in international standard ASTM E2197-17 (24) by combining 3.4 mL of PBS containing Φ6 stock (low and high) with a 1.6-mL solution consisting of 0.8 mg/mL bovine mucin (Sigma-Aldrich, St. Louis, MO), 2.5 mg/mL bovine serum albumin (VWR), and 3.5 mg/mL tryptone (VWR) to mimic fluids shed by infected individuals (1, 3, 24, 32).
Surface source, preparation, and inoculation.
Surfaces were purchased from a variety of sources (Table 3). Surfaces were cut to 5 by 5-cm (25-cm2) coupons similar to in previous studies (4, 33). Wood boards were purchased at 6 by 60 cm and were cut to 6 by 5-cm carriers.
TABLE 3 Surface details and sources investigated in this study
Surface Detail(s) Source
Aluminum 3003 grade, unpolished (mill) finish Rose Metal Products, Inc., Springfield, MO
Plastic Polyester ePlastics, San Diego, CA
Stainless steel 304 grade, unpolished (mill) finish Rose Metal Products, Inc., Springfield, MO
Touchscreen Alkali-aluminosilicate thin sheet glassa Corning Gorilla Glass, Corning, NY
Vinyl Product code FT-029b Mayer Fabrics, Indianapolis, IN
Wood Pine, item no. 50249 Lowe’s, Mooresville, NC
a Tempered glass (Gorilla Glass 3) with Native Damage Resistance.
b 86% Vinyl-phthalate free face, 14% polyester backing.
Aluminum, plastic, and stainless-steel surfaces were sprayed with 70% ethanol until saturation and held for 20 min or until air-dried. Surfaces were then washed with hot, soapy water, thoroughly rinsed with deionized (DI) water, and dried completely. Stainless-steel, aluminum, and touchscreen surfaces were wrapped in aluminum foil and steam sterilized at 121°C, 15 lb/in2, for 30 min. Plastic, wood, and vinyl carriers were placed in a biological safety cabinet and exposed to UV light for at least 30 min as an initial decontamination procedure. Prior to inoculation, each surface type was transferred to a petri dish, placed in a biological safety cabinet, and exposed to UV light for at least 30 min.
Next, 50 μL of virus inoculum was spot-inoculated (10 ± 2 droplets) on the center of each surface. Prior to each experiment, the titers of inoculum matrices were measured to determine the inoculum levels deposited on each surface by performing 10-fold serial dilutions of the inoculum in sterile 1× PBS, and P. syringae pv. phaseolicola host (200 μL) and Φ6 dilutions (100 μL) were added to LC soft agar and plated in duplicate via the DAL assay as previously described. For negative-control surfaces, inoculum matrices without Φ6 were spot-inoculated as previously described and sampled and plated to confirm virus absence.
Environmental conditions and Φ6 elution from surfaces.
Inoculated surfaces were incubated in petri dishes (without lids) in a biosafety cabinet without airflow until sampling (Fig. S7). The temperature and relative humidity were continuously monitored with a HOBO Bluetooth low-energy temperature/relative humidity data logger (Onset Computer Corporation, Bourne, MA). Destructive sampling was performed in duplicate, and time points (0, 2, 4, 12, 24, 48, 72, and 168 h) were selected based on preliminary trials of survival influenced by inoculum matrix, inoculum level, and surface type. Φ6 was eluted from each surface with 2 mL of LC broth by repeated pipetting (5 times total), after which recovered eluent was transferred to a sterile 2-mL microcentrifuge tube. Samples were serially diluted and plated in duplicate by adding 0.1 mL of sample to 0.25 mL of host in LC soft agar and plating via the DAL method in duplicate. When samples approached the limit of detection (LOD), 0.5 mL of undiluted eluent was plated in duplicate. The LOD ranged from 0.15 to 0.26 log PFU, which was influenced by the recovered eluent volume from surfaces.
Statistical analysis.
Each surface was sampled in technical duplicates with two experimental trials for each time point with duplicate plating. PFU values were log10 transformed prior to statistical analysis. Raw PFU recovery values were divided by the inoculum deposited on the surface and multiplied by 100 to obtain percentage recovery efficiency values. Log PFU/mL recovery values were subtracted from the log PFU/mL inoculum deposited on the surface to determine the log PFU/mL loss during recovery from surfaces. One-way analysis of variance was performed to compare the log PFU/mL loss based on surface type for each inoculum matrix (α = 0.05). Mean values were compared with Tukey’s honestly significant difference test (α = 0.05). Samples below the LOD were assigned a value of 0 log PFU. The log reduction of Φ6 at specific time points was calculated by subtracting the starting log PFU concentration deposited on surfaces from the log PFU concentration recovered from surfaces. Least-squares regression methods were used to fit linear models to each surface treatment for Φ6 concentrations recovered between 0 h and the first sampling time approaching the LOD. Outlier values, caused by unusually low Φ6 recovery from surfaces, were omitted from linear models. These outliers exhibited either low recovery at 0 h (<1 log PFU) or low recovery at other sampling times where subsequent samples resulted in a difference of >3 log PFU. Additionally, in the low-inoculum level with tripartite, the 24-h samples for aluminum, plastic, touchscreen, stainless steel, and vinyl were excluded from linear regression analysis due to high variability (standard deviations of 0.69 to 1.28) compared to the other sampling times (standard deviations of 0.02 to 0.50). Linear models determined the decay rate (log PFU h−1) for Φ6 on each surface, and R2 was used to assess goodness-of-fit. D values were calculated as the negative reciprocal of the decay rate (slope) for each linear model. All statistical analyses were performed in R version 4.1.1 (http://www.R-project.org).
ACKNOWLEDGMENTS
We thank Leland Schrader for guidance on cutting surfaces at the University of Arkansas Biological and Agricultural Engineering Laboratory. We thank Sylvain Moineau at Université Laval in Québec, Canada, for providing Φ6 and the P. syringae pv. phaseolicola host. We thank Denise Tremblay from Sylvain Moineau’s Laboratory at Université Laval in Québec, Canada, and Siobain Duffy at Rutgers University for providing technical assistance on Φ6 propagation.
This research was supported by USDA-NIFA grant number 2020-67017-32427.
CRediT author statement—Christopher A. Baker: conceptualization, methodology, validation, formal analysis, investigation, data curation, writing–original draft preparation, writing–review and editing, visualization; Alan Gutierrez: formal analysis, writing–review and editing, software, visualization; Kristen E. Gibson: conceptualization, methodology, resources, writing–review and editing, supervision, project administration, funding acquisition.
We declare no conflict of interest.
Supplemental material is available online only.
10.1128/aem.02552-21.1 Supplemental file 1 Fig. S1 to S7. Download aem.02552-21-s0001.pdf, PDF file, 3.1 MB
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PMC009xxxxxx/PMC9004361.txt |
==== Front
Appl Environ Microbiol
Appl Environ Microbiol
AEM
Applied and Environmental Microbiology
0099-2240
1098-5336
American Society for Microbiology 1752 N St., N.W., Washington, DC
35285246
02289-21
10.1128/aem.02289-21
aem.02289-21
Methods
environmental-microbiologyEnvironmental MicrobiologyDesign of SARS-CoV-2 Variant-Specific PCR Assays Considering Regional and Temporal Characteristics
Oh Chamteut a
Sashittal Palash b
Zhou Aijia a
Wang Leyi c
El-Kebir Mohammed b melkebir@illinois.edu
https://orcid.org/0000-0002-5461-5233
Nguyen Thanh H. a d thn@illinois.edu
a Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign grid.35403.31 , Urbana, Illinois, USA
b Department of Computer Science, University of Illinois at Urbana-Champaign grid.35403.31 , Urbana, Illinois, USA
c Veterinary Diagnostic Laboratory and Department of Veterinary Clinical Medicine, University of Illinois at Urbana-Champaign grid.35403.31 , Urbana, Illinois, USA
d Institute of Genomic Biology, University of Illinois at Urbana-Champaign grid.35403.31 , Urbana, Illinois, USA
Editor Elkins Christopher A. Centers for Disease Control and Prevention
Chamteut Oh and Palash Sashittal contributed equally to this article. Author order was determined alphabetically.
The authors declare no conflict of interest.
14 3 2022
4 2022
14 3 2022
88 7 e02289-2118 11 2021
1 2 2022
Copyright © 2022 American Society for Microbiology.
2022
American Society for Microbiology
https://doi.org/10.1128/ASMCopyrightv2 All Rights Reserved.
https://doi.org/10.1128/ASMCopyrightv2 This article is made available via the PMC Open Access Subset for unrestricted noncommercial re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
ABSTRACT
Monitoring the prevalence of SARS-CoV-2 variants is necessary to make informed public health decisions during the COVID-19 pandemic. PCR assays have received global attention, facilitating a rapid understanding of variant dynamics because they are more accessible and scalable than genome sequencing. However, as PCR assays target only a few mutations, their accuracy could be reduced when these mutations are not exclusive to the target variants. Here we introduce PRIMES, an algorithm that evaluates the sensitivity and specificity of SARS-CoV-2 variant-specific PCR assays across different geographical regions by incorporating sequences deposited in the GISAID database. Using PRIMES, we determined that the accuracy of several PCR assays decreased when applied beyond the geographic scope of the study in which the assays were developed. Subsequently, we used this tool to design Alpha and Delta variant-specific PCR assays for samples from Illinois, USA. In silico analysis using PRIMES determined the sensitivity/specificity to be 0.99/0.99 for the Alpha variant-specific PCR assay and 0.98/1.00 for the Delta variant-specific PCR assay in Illinois, respectively. We applied these two variant-specific PCR assays to six local sewage samples and determined the dominant SARS-CoV-2 variant of either the wild type, the Alpha variant, or the Delta variant. Using next-generation sequencing (NGS) of the spike (S) gene amplicons of the Delta variant-dominant samples, we found six mutations exclusive to the Delta variant (S:T19R, S:Δ156/157, S:L452R, S:T478K, S:P681R, and S:D950N). The consistency between the variant-specific PCR assays and the NGS results supports the applicability of PRIMES.
IMPORTANCE Monitoring the introduction and prevalence of variants of concern (VOCs) and variants of interest (VOIs) in a community can help the local authorities make informed public health decisions. PCR assays can be designed to keep track of SARS-CoV-2 variants by measuring unique mutation markers that are exclusive to the target variants. However, the mutation markers may not be exclusive to the target variants because of regional and temporal differences in variant dynamics. We introduce PRIMES, an algorithm that enables the design of reliable PCR assays for variant detection. Because PCR is more accessible, scalable, and robust for sewage samples than sequencing technology, our findings will contribute to improving global SARS-CoV-2 variant surveillance.
KEYWORDS
PCR assays
SARS-CoV-2 variants
in silico analysis
PRIMES
wastewater-based epidemiology
the Grainger College of Engineering Nguyen Thanh H. Jump-Arches program with OSF healthcare Nguyen Thanh H. HHS | U.S. Food and Drug Administration (FDA) https://doi.org/10.13039/100000038 FOA PAR-17-141 Wang Leyi National Science Foundation (NSF) https://doi.org/10.13039/100000001 CCF-2027669 El-Kebir Mohammed National Science Foundation (NSF) https://doi.org/10.13039/100000001 CCF-2046488 El-Kebir Mohammed cover-dateApril 2022
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pmcINTRODUCTION
SARS-CoV-2 has had an unprecedented impact on public health globally. However, despite the availability of vaccines, emerging new variants, which may have better infectivity, transmissibility, and immune evasion, threaten global public health again (1, 2). Monitoring the introduction and prevalence of variants of concern (VOCs) and variants of interest (VOIs) in a community can help the local authorities make informed decisions regarding public health (3–5). In particular, wastewater-based epidemiology (WBE) has been applied across the globe to monitor SARS-CoV-2 circulating in a community (6–9). WBE could complement clinical diagnosis, because WBE allows health authorities to monitor transmission levels in communities, including asymptomatic patients, without requiring excessive resources (10).
Although sequencing is considered the gold standard to identify SARS-CoV-2 lineages and mutations, PCR assays have attracted global attention for variant detection due to several advantages (11). First, PCR is a more accessible tool because the instruments and reagents are more affordable. Second, PCR is more scalable because it can analyze dozens or hundreds of samples in only a couple of hours, while sequencing takes a much longer time (>12 h) (12). Third, PCR is more robust for sewage samples that have low concentrations of SARS-CoV-2 genomes and contain different types of impurities (13). These advantages are beneficial for ramping up capacity for SARS-CoV-2 surveillance and facilitating deployment in regions lacking access to sequencing facilities to initiate their variant monitoring systems.
PCR assays are composed of about 20- to 30-bp-long primers or probes designed to detect single or multiple loci that characterize a target variant. Importantly, PCR assays can only examine sequences that are less than 100 bp long, while sequencing produces reads that span longer genome regions (>1,000 bp). Meanwhile, each variant or sublineage of SARS-CoV-2 is defined by a group of different mutations located throughout the genome. Therefore, distinct variants may have the same mutations, reducing specificity when used in PCR assays (https://covariants.org).
As new variants of SARS-CoV-2 emerge and fade away throughout the world, a number of different lineages (1,340 lineages as of August 2021) have been reported (14). Due to the evolutionary relationship of these lineages, they often share characteristic mutations. As such, PCR assays targeting only a few mutations (typically 1 to 3 mutations) have difficulty detecting samples from a specific lineage of interest with high specificity and sensitivity. In addition, while most lineages are limited to where they emerged, outbreaks of some lineages occasionally spread across the borders and become global concerns (such as VOCs and VOIs). Thus, regional and temporal differences in variant dynamics have to be considered for PCR assays.
In this study, we introduce PRIMES (PRIMer Efficacy Sleuth), a computational tool that can be used to analyze sequences available in open-source databases such as GISAID (14, 15) to predict the sensitivity and specificity of a PCR assay to detect specific pathogen lineages of interest (Fig. 1a). Moreover, for a given set of mutations characterizing the target variant, PRIMES can also identify a subset of variant-specific mutations for designing PCR assays with high specificity and sensitivity. Using PRIMES, we show multiple examples of previous PCR assays (13, 15, 16) that were successfully applied to certain study areas that might not work for other regions (Fig. 1b). We also demonstrate that the PCR assays designed using PRIMES successfully identify the dominant lineages in sewage samples from Champaign County, IL, USA. We conclude that PCR assays should be designed or modified considering regional and temporal variations and that in silico analyses using open-source databases can improve the sensitivity and specificity of PCR assays. These findings will allow PCR assays to be applied more reliably for SARS-CoV-2 variant surveillance.
FIG 1 (a) Schematic describing the workflow for designing PCR assays while considering regional and temporal variations in GISAID samples. After selecting a target variant, we identified variant-specific mutations, which we ranked in terms of sensitivity and specificity by using the introduced tool PRIMES. Finally, we designed primers for mutations with high sensitivity and specificity for the geographical region of interest. (b) Illustration showing the effect of regional lineages on the accuracy of variant-specific PCR assays.
RESULTS
Analysis of previously developed PCR assays.
Here, we use PRIMES to analyze the efficacy of previously developed PCR assays targeting specific spike (S) protein mutations in the SARS-CoV-2 genome. First, we analyzed PCR assays targeting S:Δ69/70 to detect the Alpha variant (13, 15, 17). S:Δ69/70 means the Δ69/70 mutation in the spike gene. These PCR assays were verified with synthetic RNA controls and local sewage samples from Israel. We then simulated the application of these assays to sequences deposited in GISAID for Israel (n = 13,932 from January 2021 to October 2021). Figure 2a shows that the Alpha variant was dominant (most prevalent variant) from January 2021 until May 2021, after which most samples were from other lineages. PRIMES predicts that the PCR assays targeting S:Δ69/70 (15) correctly assigned GISAID samples to the Alpha variants with a sensitivity of 0.95 and a specificity of 0.93. This finding can be attributed to the observation that the target mutation of these PCR assays, S:Δ69/70, is mostly exclusive to the Alpha variant in Israel, where this PCR assay was developed. However, although S:Δ69/70 was once a key mutation for the Alpha variant (B.1.1.7 first reported in February 2020), B.1.258.17 (first reported in August 2020) and B.1.620 (first reported in February 2021) and other lineages are also known to have the same mutation. Although these SARS-CoV-2 lineages are not significant in Israel (29/13,932), they have a significant prevalence in certain regions at some points in time due to local outbreaks. For example, the B.1.258.17 lineage accounted for 21.8% of all the sequences in GISAID from Slovenia for the period between January 2021 and October 2021.
FIG 2 In silico analysis of PCR assay targeting the S:Δ69/70 mutation (15) to detect the Alpha variant for GISAID samples from Israel (n = 13,932) (a), Slovenia (n = 25,528) (b), Central African Republic (n = 49) (c), and Congo (n = 183) (d). Dotted lines in the graphs on the left indicate the number of sequences used for the in silico analyses.
While this PCR assay targeting S:Δ69/70 was applied only to wastewater samples from Israel in the previous study (15), we can use PRIMES to predict the sensitivity and specificity of this PCR assay for GISAID samples from any other region. Figure 2b shows our analysis of samples from Slovenia (n = 25,528, from January 2021 to October 2021), where the prevalence of lineage B.1.258.17 was significant until May 2021 and dominating in January 2021 and February 2021. On the other hand, the Alpha variant had less than 10% prevalence in January 2021 and February 2021. However, our analysis of the PCR assays shows that the Alpha variant would have been dominant from January 2021 until June 2021. This error came from the fact that these assays would have incorrectly assigned genomes belonging to the B.1.258 and B.1.258.17 lineages to the Alpha variant. The false positives for the Alpha variant would continue until June 2021, when the B.1.258.17 lineage faded out. Thus, while the estimated sensitivity of the assay for the Alpha variant in Slovenia is 0.89, the specificity is estimated to be only 0.68. We also found that PCR assays targeting S:Δ69/70 could lead to significant numbers of false positives when applied to samples from the Central African Republic (Fig. 2c shows that the estimated specificity is only 0.46 due to samples from B.1.620) and Republic of Congo (Fig. 2d shows that the estimated specificity is only 0.64 due to B.1.620 and B.1.631).
We conducted a similar analysis for another PCR assay targeting the mutation S:Δ144/145 to detect the Alpha variant (13). This assay was applied to samples from wastewater treatment plants and selected residential buildings across the United States to track the occurrence of the Alpha variant over time in 19 communities. Figure S1a in the supplemental material shows that this assay works well for GISAID sequences from the United States, with an estimated sensitivity and specificity of 0.90 and 0.98, respectively. However, several lineages, including C.1.2, B.1.620, B.1.1.318, B.1.525 (or the Eta variant), B.1.637, B.1.625, and AZ.2 also have the same mutation, S:Δ144/145, targeted by this assay. This PCR assay can produce false results if any of the aforementioned lineages have a significant prevalence in the studied area. For example, we analyzed GISAID sequences collected from Gabon (n = 254, from January 2021 to May 2021). Figure S1b suggests that Gabon had significant sequences from the Eta variant and the B.1.1.318 lineage from February 2021 to May 2021, both of which have the target mutation S:Δ144/145. As a result, even though the number of sequences from the Alpha variant increased from February 2021 to April 2021 and then decreased in May 2021, the PCR assay would have predicted a continuous increase in the prevalence of the Alpha variant from February 2021 to May 2021 (Fig. S1b). The estimated specificity of this assay for detecting the Alpha variant in GISAID sequences from Gabon is only 0.74. We see a similar result by analyzing GISAID sequences from Togo (n = 157, from January 2021 to April 2021) in Fig. S1c. Many countries in Africa, including Nigeria and Ghana, were also expected to have lower specificity for the PCR assay targeting S:Δ144/145 because of B.1.1.338 and B.1.525 lineages (Table S1).
This propensity for false-positive results is not limited to PCR assays developed to detect samples from the Alpha variant. We demonstrate this by considering a recent PCR assay targeting mutation S:T478K of the Delta and Delta plus lineages (16). However, this mutation is also present in the B.1.1.519 lineage. This lineage accounted for only around 1.2% of sequences from the United States (n = 1,187,412, from January 2021 to October 2021), so the PCR assay targeting S:T478K was expected to work well for Illinois, USA, showing an estimated sensitivity of 0.94 and an estimated specificity of 0.97 (Fig. 3a). However, the B.1.1.519 lineage was dominant in Mexico (n = 28,956) and explained 30% of the total GISAID sequences from January 2021 to October 2021 (Fig. 3b). Therefore, our analysis shows that the PCR assay targeting S:T478K would estimate that the Delta variant was dominant from January 2021 to October 2021, when in reality, Delta variant sequences were collected and later deposited in GISAID starting in May 2021 (Fig. 3b).
FIG 3 In silico analysis of PCR assay targeting the S:T478K mutation (16) to detect the Delta variant in the United States (n = 1,187,412) (a) and Mexico (n = 28,956) (b). Dotted lines in the graphs on the left indicate the number of sequences used for the in silico analyses.
The examples of regional and temporal characteristics affecting the accuracy of PCR assays for the detection of SARS-CoV-2 samples of specific lineages of interest are not limited to the cases mentioned above. Globally, only a few VOCs and VOIs accounted for more than 1% of the total sequences in GISAID, while most of other SARS-CoV-2 lineages explain less than 1%. However, as we examine narrower regions, we may find outbreaks of certain lineages that could be overlooked when we focus on the prevalence on a global scale. For example, as of October 2021, B.1.526 lineage accounted for 1% of reported sequences in the world. However, the prevalence of the lineage increases as we narrow down the study area to the local level: 4% in the United States, 17% in New York State, and 30% in Bronx County. In the case of the B.1.429 lineage, the prevalences are 1%, 4%, 11%, and 38% in the world, the United States, California State, and Riverside County, respectively. The number of B.1.258 lineages was less than 0.5% of total sequences worldwide, but this lineage accounted for 54% of cases in Cyprus.
We document the SARS-CoV-2 lineages with the same mutation that our target variant possesses in Table S1. When we use PCR assays targeting certain mutations, this table will help identify the lineages that would interfere with our PCR assay. In Table S2, we also tabulated countries where each of the SARS-CoV-2 lineages summarized in Table S1 accounted for more than 1% of the total sequences. This table explains whether the lineages that would interfere with your PCR assays are dominant in the study areas. By interpreting Table S1 and Table S2 together, we can find various examples where certain PCR assays would not work reliably.
In conclusion, we used PRIMES to estimate the performance of previously developed PCR assays on sequences from various countries. Strikingly, our analysis shows that several previously developed variant-specific PCR assays would not be as accurate for samples collected from locations and periods beyond those included in the original study due to the presence of other lineages sharing mutations that characterized the lineage of interest. These findings motivated us to establish PCR assays for variant detection based on the characteristics of sequences reported from our target study area (Illinois, USA).
Design of variant-specific PCR assays considering regional and temporal characteristics.
We describe the proposed workflow for designing variant-specific PCR assays considering regional and temporal variant dynamics using PRIMES (Fig. 1a). Our goal is to design variant-specific PCR assays to track variants with significant prevalence in the United States, with a particular focus on the state of Illinois.
First, we investigated the prevalence of SARS-CoV-2 lineages in our regions of interest to select lineages that we need to track. To this end, we downloaded 1,187,412 SARS-CoV-2 sequences from GISAID collected between January 2021 and October 2021 in the United States, including 20,165 sequences collected in Illinois. These sequences were assigned to the most likely lineage using Pangolin (Fig. 4a and b). Focusing on the variant dynamics in the state of Illinois (Fig. 4a), we observed that the B.1.2 lineage was dominant from January (61%) to February (44%), eventually giving way to the Alpha variant. The Alpha variant became dominant in April (44%), May (61%), and June (62%). Then, the Delta variant samples replaced the Alpha variant samples and became the dominant lineage in the state (95% in July and >99% in August, September, and October). Other VOIs and VOCs, including Epsilon, Iota, and Beta variants, accounted for only 2.2%, 1.5%, and 0.4% of total sequences, respectively. Similar trends were observed in sequences collected throughout the United States (Fig. 4b). Based on the variant dynamics of our regions of interest, we decided to design PCR assays to enable monitoring of the two major variants, the Alpha and Delta variants.
FIG 4 (a and b) Variant dynamics determined by Pangolin using GISAID samples from the state of Illinois in the United States (n = 20,165) (a) and from the United States (n = 1,187,412) (b). (c and d) Focusing on the spike protein mutations in the Delta variant, we showed the sensitivity and specificity of assigning the Delta variant based on the presence of each mutation in GISAID samples from Illinois (c) and the United States (d). (e and f) Estimated assignment of GISAID samples from Illinois (e) and the United States (f) to variants using the primer designed to target mutation S:P681R.
Second, we designed PCR assays to find unique mutations exclusive to our lineage of interest. We utilized the website https://covariants.org to list nonsynonymous mutations that define target variants (Table S1). We focused on mutations of the spike gene, which has a higher frequency of mutation than other SARS-CoV-2 genes (18). Previous studies have shown that primers targeting mutations in the spike gene enable accurate detection of SARS-CoV-2 lineages in sewage samples with a low virus concentration (17). As a result, for the Alpha variant, we identified nine mutations in the spike gene: S:Δ69/70, S:Δ144, S:N510Y, S:A570D, S:D614G, S:P681H, S:T716I, S:S982A, and S:D1118H. For the Delta variant, we identified seven mutations: S:T19R, S:Δ156/157, S:L452R, S:T484K, S:D614G, S:P681R, and S:D950N.
Third, we used PRIMES to compute the sensitivity and specificity of lineage assignments performed using each of the selected mutations. We assumed that if the specificity and sensitivity of the mutations were higher than 0.99, the mutations were exclusive to the target variant. This criterion allowed us to identify the ideal target mutation for the design of the PCR assay that would yield high specificity and sensitivity in our regions of interest. For the Alpha variant, we found three acceptable mutations, S:A570D, S:T716I, and S:S982A (Fig. S2b), and we chose the S:A570D mutation because the PCR assay targeting S:A570D has already been verified to work for sewage samples; we adopted this mutation in our analysis (13). For the Delta variant, we found three mutations, S:L452R, S:P681R, and S:Δ156/157, in GISAID samples from the state of Illinois. However, if we look at all GISAID samples from the United States, the sensitivity for the S:L452R and S:Δ156/157 mutations to characterize the Delta variant drops below 0.97. Thus, regional variation can lead to a drastic change in the performance of variant-specific PCR assays. Since our goal was to develop PCR assays that are also effective in other states in the United States, we instead chose S:P681R, which has high sensitivity and specificity in both Illinois (sensitivity is 0.99, and specificity is 0.99) and the United States (sensitivity is 0.99, and specificity is 0.99). Importantly, this mutation has higher sensitivity and specificity in both regions of interest than the mutation S:T478K (sensitivity is 0.99 and specificity is 0.97 in Illinois, while sensitivity is only 0.96 and specificity is only 0.98 in the United State [Fig. 4c and d]), which was previously targeted to monitor the Delta and Delta plus variants (16).
The fourth step was to design the allele-specific primers for the selected mutations. Since both of our selected target mutations are single nucleotide polymorphisms (SNPs), we designed allele-specific quantitative PCR (qPCR) assays in which either a forward or a reverse primer targets the SNP at the 3′ end with a mismatch near the SNP location to improve the specificity of the assays (13). All reverse transcription (RT)-qPCR assays were designed using PrimerQuest (Integrated DNA Technologies [IDT], USA) to have a melting temperature (Tm) of 59 to 63°C for primers and GC contents of 30% to 60%.
Finally, we can estimate the efficacy of the candidate RT-qPCR assays using PRIMES. Specifically, we determined the sensitivity and specificity of our assays on the GISAID samples collected from the regions of interest by searching for sequences of a forward primer and a reverse primer in each query sequence. Note that the sequences of reverse primers were converted to reverse sequences to have all sequences, including primers and viruses, on the same strand. If the viral sequence includes the forward and reverse sequences, we assumed that the PCR assay would detect the viral sequence (an illustrative example is shown in Fig. S3). Some lineages should be expected to lower the sensitivity or specificity of our assays based on Table S1 (e.g., B.1.1.189, C38, and B.1.636 for Alpha variant detection and AU.3, AU.2, P.1.8, B.1.617.3, A.23.1, B.1.617.1, B.1.551, B.1.466.2, B.1.1.528, Q.4, B.1.623, B.1.1.25, C.36, and AY.28 for Delta variant detection), but importantly, those lineages were not detected or had very low prevalences in our regions of interest. The estimated sensitivity and specificity for PCR assays designed to detect viruses from the Alpha and Delta variants were all high for our study scope and in Illinois in particular (sensitivity is 0.99 and specificity is 0.99 for detection of Alpha variant, and sensitivity is 0.98 and specificity is 1.00 for detection of the Delta variant in Illinois) (see Fig. S4 for sensitivity and specificity of detecting the two variants in GISAID samples from all of the United States). These values are higher than the sensitivity and specificity estimated for the previously developed PCR assays in their regions of interest (Fig. 2 and 3). In the following section, we demonstrate this performance of our PCR assays with synthetic RNA controls and actual sewage samples collected in our community.
Verification of PRIMES-designed PCR assays by synthetic RNA controls.
Operational failures of PCR assays due to inappropriate primer design or PCR inhibitors are not considered by PRIMES. Therefore, PCR assays designed by PRIMES must be verified by in vitro experiments. We applied the RT-qPCR assays designed with PRIMES to synthetic RNA controls for the wild type (WT), the Alpha and Delta variants to experimentally confirm the sensitivity (i.e., the limit of quantification [LOQ] and limit of detection [LOD]) and specificity (i.e., cross-reactivity). Regarding sensitivity, we found that the LOQs for total SARS-CoV-2, Alpha variant, and Delta variant were all 10 gene copies (gc)/μL or 50, 30, and 30 gc/reaction mixture, respectively (Fig. 5a). Also, the LODs of RT-qPCR assays for total SARS-CoV-2, Alpha variant, and Delta variant were 1.0, 1.3, and 1.3 gc/μL or 5.0, 3.9, and 3.8 gc/reaction mixture, respectively (Fig. 5b). We used LODs as thresholds to report RT-qPCR results, so the data below the LODs were considered negative for target genes. Because LODs for our assays were close to the theoretical LODs of RT-qPCR (3.0 gc/reaction mixture) (19), we concluded that our RT-qPCR assays are sensitive to detect RNA of target variants.
FIG 5 Determination of sensitivities (i.e., LOQs and LODs) of RT-qPCR assays for total SARS-CoV-2, Alpha variant, and Delta variant. (a) Dashed lines indicate the coefficient of variation at 0.35. (b) The trend lines for positive rate (solid lines) were calculated using equation 6. The LODs were the RNA concentration at which the positive rate was 0.95 (dashed lines).
As for cross-reactivity, we found that when the concentrations of the synthetic RNA control were high (i.e., 104 and 105 gc/μL), we detected quantitation cycle (Cq) values from WT RNA controls. This finding suggests that the presence of the WT caused false positives for Alpha variant detection (Fig. 6a). However, the Cq value differences between the Alpha variant and the WT were greater than 11, which is about a 1,000-fold difference in RNA concentrations. This difference in Cq values is equivalent to less than 0.1% error when quantifying the Alpha variant, and thus we considered this error acceptable for our study. When the synthetic RNA control concentrations were low (i.e., less than 103 gc/μL), the Cq values from the WT were lower than the LOD, and these values will be disregarded in this study. Thus, false positives were not detected. We found similar results from the specificity experiments for the Delta variant assay (Fig. 6b). When the concentrations of the synthetic RNA controls were high (i.e., 104 and 105 gc/μL), the Cq value differences for the Delta variant and WT were greater than 13. At the lower concentration (i.e., less than 103 gc/μL), the Cq values from the WT (i.e., false positives) were less than the LOD. Because the measured cross-reactivities with the WT were negligible, we concluded that our RT-qPCR assays are specific for measuring target variants.
FIG 6 Cross-reactivity of PCR assays for Alpha and Delta variants. RT-qPCR assays for the Alpha (a) and Delta (b) variants were applied to the corresponding variant and the WT to determine the specificity (i.e., cross-reactivity with the WT).
We further confirmed the applicability of the PRIMES-designed PCR assay to determine predominant variants in mixtures of synthetic RNA controls. The results from the experiments with mixtures of synthetic RNA controls are presented in Fig. 7a and b. The y axis shows the prevalences, calculated as the ratio of each variant’s concentration to the total SARS-CoV-2 concentration. The variant showing the highest prevalence became the dominant variant. If none of the two targets (i.e., Alpha and Delta variants) has a prevalence higher than 0.5, the “others,” which comprises all SARS-CoV-2 lineages other than our target variants (i.e., the Alpha and Delta variant), becomes the dominant variant. With the highest total virus concentrations (104 gc/μL), our RT-qPCR assays successfully assigned the correct dominant variant to all experimental cases (P < 0.001) (Fig. 7a). For example, in the case of the mixtures between the WT and the Alpha variant, we assigned the Alpha variant to the RNA mixtures whose actual prevalences of Alpha variant were 0.7 and 0.9. In contrast, we assigned “others” when the prevalences of the Alpha variant were 0.1 and 0.3. Similarly, we also assigned the dominant variant correctly to the mixtures of the Alpha and Delta variants. Specifically, we assigned the Alpha variant as dominant when the actual prevalences of the Alpha variant were 0.7 and 0.9. At the same time, the Delta variant was assigned as dominant to the other two mixtures whose prevalences of the Alpha variant were 0.1 and 0.3. In addition, we found that the PCR assays assigned the dominant variant correctly when the total virus concentrations were 101 gc/μL for all mixing ratios (Fig. 7b). However, the statistical analysis showed that the comparisons of prevalences determined by the RT-qPCR were significant only when the mixing ratios were 0.9:0.1 or vice versa for mixtures of the WT and the Alpha variant or the Alpha variant and the Delta variant. Note that when the total virus concentrations were 101 gc/μL, concentrations of each synthetic RNA control ranged from 1 × 10° to 9 × 10° gc/μL depending on the mixing ratios, which were less than their LOQs (101 gc/μL). Based on these findings, we concluded that our RT-qPCR assays could find the dominant variant when the total SARS-CoV-2 concentrations were higher than the LOQs (101 gc/μL) and the prevalence of target variants was higher than 0.9 (Fig. 7b). When the concentrations of the SARS-CoV-2 N gene, Alpha and Delta variants become higher than the respective LOQ values (101 gc/μL), our RT-qPCR assays can assign the dominant variant when its prevalence is higher than 0.7 (Fig. 7a).
FIG 7 Dominant variants of the mixtures of synthetic RNA controls determined by RT-qPCR assays. Total SARS-CoV-2 concentrations were determined by the N gene concentrations at 104 gc/μL (a) and 101 gc/μL (b). Prevalences on the y axis indicate the ratio of the concentration of each variant to the total virus concentration. The x axis shows the mixing ratios of different synthetic RNA controls (W, A, and D represent WT, Alpha variant, and Delta variant, respectively). The label at the top of each graph (others, Alpha, or Delta) indicates the dominant variant determined by the RT-qPCR assays. A one-sample t test or a two-sample t test was conducted to compare the prevalences between the Alpha variant and 0.5 or the prevalences between the Alpha and Delta variants (nonsignificant [ns], P > 0.05; *, 0.001 < P < 0.05; **, P < 0.001), respectively.
Application of PCR assays to sewage samples and confirmation by NGS.
We applied our PCR assays to six different local sewage samples. We first obtained RNA extracts from those sewage samples. The total SARS-CoV-2 concentrations (i.e., N gene) of these RNA extracts ranged from 1.4 × 101 to 1.8 × 102 gc/μL (Table 1). After accounting for recovery efficiencies and concentration factors, the SARS-CoV-2 concentrations (i.e., N gene) of these sewage samples ranged from 1.3 × 103 to 6.0 × 104 gc/L (see equation 3 below). These concentrations agree with the SARS-CoV-2 concentrations of sewage samples analyzed previously (20). We then determined the prevalence of variants based on the ratios of the Alpha variant concentration (determined by PRIMES-designed PCR) to the total SARS-CoV-2 (N gene). We found that sample 1 has a prevalence of the Alpha variant of 0.85. Based on the results with the synthetic RNA mixtures, we assigned the Alpha variant as the dominant variant to sample 1. Similarly, we assigned the Delta variant to samples 5 and 6 because of their prevalences of 0.92 and 0.73, respectively. On the other hand, none of the Alpha and Delta variants presented a prevalence higher than 0.5 for samples 2, 3, and 4, so we assigned “others” to these three samples.
TABLE 1 Applications of RT-qPCR assays to local sewage samples
Sample Total SARS-CoV-2 concentration of RNA extracts (gc/μL) Concentration (% prevalence) Recovery efficiency Concentration factor Total SARS-CoV-2 concentration of sewage sample (gc/L) Variant
decision
Alpha variant Delta variant
1 2.7 × 101 2.3 × 101 (85) Below LOD 0.58 × 10-2 0.6 × 10-4 2.6 × 103 Alpha
2 1.4 × 101 Below LOD Below LOD 0.58 × 10-2 0.5 × 10-4 1.3 × 103 Others
3 9.9 × 101 Below LOD Below LOD 0.74 × 10-2 1.7 × 10-4 2.3 × 104 Others
4 5.6 × 101 Below LOD 3.4 × 100 (6)a 2.37 × 10-2 4.1 × 10-4 9.6 × 103 Others
5 1.7 × 102 Below LOD 1.5 × 102 (92) 0.67 × 10-2 0.9 × 10-4 4.3 × 104 Delta
6 1.8 × 102 Below LOD 1.3 × 102 (73) 0.71 × 10-2 1.2 × 10-4 6.0 × 104 Delta
a Below the LOQ.
To further confirm whether the RT-qPCR results were correct, we conducted NGS analysis to examine eight mutation markers for the Alpha variant (S:Δ69/70, S:Δ144, S:N501Y, S:A570D, S:P681H, S:T716I, S:S982A, and S:D1118H) and six mutation markers for the Delta variant (S:T19R, S:Δ156/157, S:L452R, S:T478K, S:P681R, and S:D950N) on the spike gene of two sewage samples (samples 5 and 6) and three synthetic RNA controls (WT, Alpha variant, and Delta variant). Even though we amplified the entire spike gene with the three pairs of primers, samples 1, 2, 3, and 4 were not appropriate for sequencing due to the low SARS-CoV-2 concentrations (<102 gc/μL) (21). For samples 5 and 6 classified to the Delta variant by the RT-qPCR assays, we detected all six mutations for the Delta variant. In comparison, none of the eight mutations for the Alpha variant were detected in these samples. We believe that these NGS analyses were reliable because we detected all mutation markers with the corresponding synthetic RNA controls. For example, we detected the eight Alpha variant mutations from the Alpha variant RNA samples and found the six Delta variant mutations from Delta variant RNA controls (Table 2). Therefore, the agreement between the NGS analysis and RT-qPCR assays supports that our RT-qPCR can assign the most likely variant for the local sewage samples.
TABLE 2 Comparisons between RT-qPCR assays and NGS analysisa
a + symbols in orange cells represents mutations that were detected, while − symbols in gray cells indicate mutations that were not detected.
bGISAID accession ID is EPI_ISL_10113885.
cGISAID accession ID is EPI_ISL_10113884.
DISCUSSION
PCR assays have advantages for SARS-CoV-2 variant detection in sewage samples over sequencing technologies because of low cost, fast turnaround, and robustness with environmental samples. However, PCR assays can examine only a few mutations due to size constraints of primer and probe sequences, compromising their accuracy since distinct SARS-CoV-2 lineages may share target mutations. We used the PRIMES algorithm to show that the current variant-specific PCR assays have diminished accuracy when applied outside the region where they were developed. These findings suggest that consideration of regional and temporal dynamics of variants is important to secure the sensitivity and specificity of PCR assays that target only a limited number of mutations. Subsequently, we used PRIMES and open-source databases (e.g., GISAID, Pangolin, or outbreak.info) to design PCR primers to determine the dominant SARS-CoV-2 variants (i.e., Alpha and Delta variants) in local sewage samples. Note that viral load in feces significantly varies depending on an individual’s characteristics, such as type of variants, vaccination, and so forth (22). Therefore, the dominant variant in sewage may not necessarily indicate that the variant is also dominant in a community.
The regional and temporal variations are especially critical for SARS-CoV-2 detection because various SARS-CoV-2 lineages with different genotypes have been reported worldwide. Commercial PCR kits for variant detection are currently available. However, these kits also target a few mutation markers originating from SARS-CoV-2 lineages of interest (23–25). As we showed above, targeting a single mutation might make the assay less accurate in certain regions due to the presence of other lineages that have the same mutation. In addition, our findings are not limited to PCR assays but are also relevant for other types of molecular assays such as loop-mediated isothermal amplification (LAMP), PfAgo-based assays, and CRISPR (clustered, regularly interspaced short palindromic repeats)-based assays that are designed to detect specific RNA sequences for virus detection (26–28). For example, a LAMP assay that targets N genes for SARS-CoV-2 testing might have low accuracy when applied outside of Germany or the United States, where the assays were developed and verified with clinical samples (26, 29). This low-accuracy issue might happen because their primers include sequences for N:A119S, a mutation marker for the Zeta variant (P.2 lineage). The Zeta variant was dominant in some South American countries (Suriname, Paraguay, Uruguay, and Brazil). The Food and Drug Administration (FDA) also recommended that mutations present in the sequences which molecular diagnostic tests target for virus detection should be monitored by in silico analysis (30). Our PRIMES tool allows users and developers of molecular diagnostic assays to follow this recommendation.
Perfect loci for targeting viral mutations are not realistic because viruses evolve randomly, so one that looks perfect could be affected by emerging variants. For example, the S:Δ69/70 mutation used to be a unique mutation for the Alpha variant, but the Eta variant, which appeared later, also has this mutation. Thus, if S:Δ69/70 is considered an exclusive mutation for the Alpha variant, the Eta variant will be false positive for the Alpha variant. In addition, sublineages in the target variant may not have one of the mutation markers for the target variant. For example, less than 0.5% of Q.4 (one of the sublineages for the Alpha variant and reported in December 2020) is known to have an S:P681H mutation. The S:P681H mutation is one of the mutation markers for the Alpha variant. Thus, if the S:P681H mutation is targeted for the Alpha variant, Q.4 will cause false negatives. These examples demonstrate that PCR assays could have different sensitivities and specificities depending on various lineages of SARS-CoV-2 that coexist with the target lineage.
Global genomic databases for emergent variants have greatly improved since the onset of COVID-19 pandemics (31). Before COVID-19, influenza virus sequences are archived in GISAID. Quickly mutating pathogens such as influenza virus and coronavirus should be monitored because they have pandemic potential. As we showed in this study, assays targeting these pathogens need to keep up with their evolution, and the developed methodology facilitates genomic surveillance of any quickly mutating pathogen.
In this study, we developed a PRIMES algorithm that calculates the sensitivity and specificity of SARS-CoV-2 variant-specific PCR assays in silico for prespecified geographical regions. Using PRIMES, we designed two PCR assays for detecting the Alpha and Delta variants. We verified those variant-specific PCR assays with in vitro experiments using synthetic RNA controls. We also showed that these assays could detect the dominant variants in actual sewage samples, and these PCR results were confirmed by NGS analysis of the spike gene amplicons. The PRIMES-designed PCR assays can also be applied to assign dominant variants in human specimens. Because RNA levels in human specimens are higher than those in sewage in general, the false positives from the variant-specific qPCR assays (i.e., allele-specific qPCR assays) may result in measurements above the LOD, thereby affecting RNA quantification. However, we confirmed that these errors account for less than 0.1% of RNA quantification (Fig. 6), so the errors are not expected to impact the assignment of the dominant variant. In summary, the PRIMES-designed PCR assays will contribute to improving the capacity for SARS-CoV-2 variant surveillance. This tool will be especially helpful for underserved regions, because PCR assays are more accessible and scalable tools than sequencing-based SARS-CoV-2 variant surveillance.
MATERIALS AND METHODS
Analysis and design of PCR assays using PRIMES.
The most widely used computational tool for assigning lineages to SARS-CoV-2 genomes is the Phylogenetic Assignment of Named Global Outbreak Lineages (Pangolin; https://pangolin.cog-uk.io/). Pangolin is a lineage designation pipeline that takes a FASTA file as input, containing one or more query sequences. Each query sequence is first aligned to the SARS-CoV-2 reference genome (Wuhan-Hu-1; GenBank accession no. NC_045512.2) using minimap2 v2.17 (32). After trimming of the noncoding regions at the 5′ and 3′ ends of the aligned sequences, the sequences are assigned to the most likely lineage out of all currently designated lineages by use of an underlying machine learning model referred to as PangoLEARN. The current version of PangoLEARN is a decision tree trained on data from GISAID that were manually curated with lineages.
By considering the lineage designation of Pangolin as ground truth, we performed an in silico analysis of the efficacy of PCR assays using PRIMES (available at https://github.com/elkebir-group/primes). Specifically, we searched for an exact match of the target sequence (containing a mutation targeted by the PCR assay) in each GISAID sequence and then estimated the overall specificity and sensitivity of the PCR assay defined as follows: (1) Sensitivity=True positivesTrue positives + False negatives
(2) Specificity=True negativesTrue negatives + False positives
where true positives is the number of virus sequences that include the target sequence and also belong to the lineage of interest according to Pangolin. False negatives is the number of virus sequences that do not include the target sequence but belong to the lineage of interest. True negatives is the number of virus sequences that do not include the target sequence and do not belong to the lineage of interest as well. Finally, false positives is the number of virus sequences that include the target sequence but do not belong to the lineage of interest. Note that our analysis assumes that the PCR assays do not tolerate mismatches in the target sequence.
While the in silico estimates of the sensitivity and specificity are valuable in their own right, they can also be used to design effective variant-specific PCR assays. Specifically, for a lineage of interest and a set of characteristic mutations, we use PRIMES to identify the set of mutations that should be targeted by PCR assays with high specificity and sensitivity. We employed this approach to design PCR assays to detect the presence SARS-CoV-2 of both Alpha (e.g., B.1.1.7) and Delta (e.g., B.1.617.2) variants in sewage samples.
Sewage sample processing.
We followed the guidelines for minimum information for publication of quantitative real-time PCR experiments (MIQE) to ensure the credibility and reproducibility of our data (33). Detailed information on the MIQE is summarized in Table S3 in the supplemental material. Also, detailed information from sample collection to data analysis is described in Table S4. Briefly, we used ISCO automatic samplers (catalog no. 6712; Teledyne ISCO, USA) to collect 3-day composite sewage samples (about 2 L) from the sewer distribution system across Champaign County, IL, USA. MgCl2 was added to the sewage samples at a final concentration of 50 mM to facilitate the coagulation of viruses and sewage sludge. We kept sewage samples on ice while moving them to our laboratory in 2 h. We gently removed the supernatant upon arrival and added 200 μL of bovine coronavirus (BCoV) to the remaining solution (about 50 mL) to determine virus recovery efficiency. The recovery efficiency of BCoV ranged from 0.58% to 2.37%, which is similar to those previously reported (34). After 5 min of incubation at room temperature, we centrifuged the mixture at 10,000 rpm (13,900 × g) for 30 min (Sorvall Legend RT Plus; Thermo Fisher Scientific, USA). The supernatant was discarded again, and the sludge (about 1 g) was taken to harvest viruses. Then, we extracted viral RNA from the sludge using a viral RNA extraction minikit (Qiagen, Germany) by following the manufacturer’s procedure. The RNA extracts were purified using an RNA purification kit (RNeasy MinElute cleanup kit; Qiagen, German) to reduce the PCR inhibition. It took less than 9 h from sample collection to RNA extraction. The RNA samples were stored at −80°C until downstream analyses were ready. The same sample preparation processes were applied to drainages discharged from a food processing industry whenever we processed sewage samples. There are no sources of human feces that merged with these drainages, which were therefore used for negative controls. Indeed, we did not detect any SARS-CoV-2 from these negative controls. Therefore, we are confident that there were no false positives for SARS-CoV-2 in our sewage samples. With the concentrations of RNA extracts, we used equations 3 to 5 to determine the virus concentrations (C) in sewage samples: (3) Csewage=CRNA extract×concentration factorrecovery efficiency
(4) Concentration factor=volume of RNA extractvolume of initial sewage
(5) Recovery efficiency =number of BCoV in RNA extractnumber of BCoV in initial sewage
Determination of LODs and LOQs.
We first determined the limit of detection (LOD) and the limit of quantification (LOQ) for Alpha and Delta variants with serial dilutions of the synthetic RNA controls. We prepared 10-fold serial dilutions of synthetic RNA controls and determined a positive sample fraction at each concentration. The number of replicates for concentrations near the LOD was 20, while the number of samples for the higher concentration was 4. We used a sigmoidal function (equation 6) to determine the trend lines for fraction-positive samples with different concentrations and calculated the LODs (35): (6) Y=11+e−a−b×log(X)
where X is gene copy (gc/μL), Y is positive rate, and both a and b are constants. The LOQ was defined as the lowest concentration with a coefficient of variation (CV) of less than 35% (35). We calculated the CV using equation 7: (7) CV=(1 + E)(SD)2×ln(1+E)−1
where E is qPCR efficiency and SD is standard deviation of Cq values.
PCR assays for SARS-CoV-2 variant detection in synthetic RNA control.
We applied the RT-qPCR assays to 10-fold serial dilutions of synthetic RNA controls to determine LOQs and LODs. For example, we applied the RT-qPCR assay for the Alpha variant to 10-fold serial dilutions of Alpha variant RNA controls. LOQ was defined as the lowest concentration with a coefficient of variation (CV) of less than 35% (35). LOD was defined as the concentration at which RNA samples test positive (i.e., Cq < 40) with 95% probability.
We applied each RT-qPCR assay for the Alpha or Delta variant to the synthetic controls of its target variant and the WT to determine the cross-reactivity. This process is important, because our assays for the Alpha and Delta variants were designed to detect only a SNP of target variants among other lineages that do not have the same SNP. In this experiment, we mixed synthetic RNA controls of the WT with the Alpha variant or the Alpha and Delta variants because these two mixtures represented the transitions where one dominant variant was replaced by the other one in our community. For instance, the “others” (mainly B.1.2) were dominant until February 2021, and the Alpha variant raced to be the dominant variant around March 2021. Also, the Alpha variant was dominant in April and May in 2021, but the Delta variant competed with the Alpha variant around June 2021 (Fig. 4b). The total SARS-CoV-2 concentrations (i.e., N gene concentrations) of the mixtures were 104 and 101 gc/μL, which is a reasonable concentration range of SARS-CoV-2 in local sewage samples (20). Also, we mixed the two different RNA controls at four different ratios (i.e., 9:1, 7:3, 3:7, and 1:9) to mimic different scenarios of variant dynamics.
PCR assays for SARS-CoV-2 variant detection in sewage samples.
We conducted six different RT-qPCR assays to analyze sewage samples. Three assays targeted different loci of the SARS-CoV-2 genome for virus quantification and dominant variant detection. The other three assays were applied to measure bovine coronavirus (BCoV), pepper mild mottle virus (PMMoV), and Tulane virus (TV), which were used for calculation of virus recovery efficiency, normalization of SARS-CoV-2 to human feces, and inhibition tests, respectively. BCoV was added to the sludge as described previously. PMMoV was used as an internal control to represent the presence of human feces. We detected more than 100-fold-higher concentrations of PMMoV than SARS-CoV-2 N gene in the sewage samples (>108 PMMoV gc/g sludge); therefore we concluded that our sewage samples contained human feces from local residents living in the sewersheds. The RNA extracts were diluted 2-fold in molecular biology-grade water (Millipore Sigma, USA) before the quantification. We spiked 10 μL of RNA extract or 10 μL of molecular biology-grade water with 1 μL TV RNA, followed by analysis of the TV RNA in those two types of samples. We found differences in Cq values between the RNA extract and the negative controls (i.e., molecular biology-grade water) that were less than ±1, which indicated a negligible impact of PCR inhibitors on our samples (36). We used TaqMan-based RT-qPCR for the N1 gene detection, as suggested by the CDC, and SYBR-based RT-qPCR for the other five assays (Table 3). The SYBR-based RT-qPCR started with mixing 3 μL of viral genome with 5 μL of 2 × iTaq universal SYBR green reaction mix, 0.125 μL of iScript reverse transcriptase from the iTaq universal SYBR green reaction mix (Bio-Rad Laboratories, USA), 0.3 μL of 10 μM forward primer for each virus, 0.3 μL of 10 μM reverse primer for each virus, and 1.275 μL of molecular biology-grade water (Corning, NY, USA). The PCR cocktail for the one-step RT-qPCR was placed in 96-well plates (catalog no. 4306737; Applied Biosystems, USA) and analyzed by a qPCR system (QuantStudio 3; Thermo Fisher Scientific, USA). The thermocycle began with 10 min at 50°C and 1 min at 90°C, followed by 40 cycles of 30 s at 60°C and 1 min at 90°C. The annealing temperature was determined based on the optimal temperature of antibody-mediated hot-start iTaq DNA polymerase (iTaq universal SYBR green one-step kit; Bio-Rad Laboratories, USA). We analyzed melting curves and found no primer-dimers from our RT-qPCR analyses. The TaqMan-based RT-qPCR was initiated by mixing 5 μL of viral genome with 5 μL of TaqMan fast virus 1-step master mix (catalog no. 4444432; Applied Biosystems, USA), 1.5 μL of primer/probe mixture for the N1 gene (2019-nCoV RUO kit; Integrated DNA Technologies, USA), and 8.5 μL of water. The 20 μL of mixture was analyzed by the same qPCR system used for the SYBR-based RT-qPCR, except for a different thermal cycle (5 min at 50°C and 20 s at 95°C, followed by 45 cycles of 3 s at 95°C and 30 s at 55°C). We used synthetic RNA controls to get standard curves for the WT, Alpha variant, and Delta variant (TWIST Bioscience, USA; part numbers 102024, 103907, and 104533, respectively). The PCR standard curves were obtained for every RT-qPCR analysis with 10-fold serial dilutions of synthetic RNA controls, and PCR efficiencies for RT-qPCR were higher than 85% (R2 > 0.99). The SYBR signal was normalized to the ROX reference dye. The cycle of quantification (Cq) values were determined automatically by QuantStudio Design & Analysis Software (v1.5.1). Based on the melting curves, the primers were specifically bound to the target genome. The numbers of technical replicates were 4 for synthetic RNA controls and 3 for sewage samples except for LOD and LOQ determination, for which 20 replicates were analyzed.
TABLE 3 Information for RT-qPCR assays applied to sewage samples
Target species Target gene or mutation Primer name Sequence (5′–3′) GC content (%) Tm (°C) Amplicon size (bp) (location) Purpose
SARS-CoV-2 Na CDC_N1_Forward GACCCCAAAATCAGCGAAAT 45.0 61.1 73 (28287–28358) Total SARS-CoV-2
CDC_N1_Reverse TCTGGTTACTGCCAGTTGAATCTG 45.8 64.5
CDC_N1_Probe ACCCCGCATTACGTTTGGTGGACC 58.3 70.3
S:A570Db Alpha_Forward ACAATTTGGCAGAGACATCGA 42.9 62.3 85 (23251–23335) Alpha variant
Alpha_Reverse AGAACATGGTGTAATGTCAAGAATC 36.0 61.7
S:P681Rf Delta_Forward ATCAGACTCAGACTAATTCACG 40.9 59.6 87 (23583–23669) Delta variant
Delta_Reverse TTTCTGCACCAAGTGACATA 40.0 59.7
PMMOVc PMMOV_Forward GAGTGGTTTGACCTTAACGTTTGA 41.7 63.4 68 (1878–1945) Normalization to feces
PMMOV_Reverse TTGTCGGTTGCAATGCAAGT 45.0 63.6
BCoVd BCoV_Forward CTAGTAACCAGGCTGATGTCAATACC 46.2 64.2 88 (29799–29886) Recovery efficiency
BCoV_Reverse GGCGGAAACCTAGTCGGAATA 52.4 63.5
TVe TV_Forward GTGCGCATCCTTGAGACAAT 50.0 63.0 133 (879–1011) Inhibition test
TV_Reverse TTGGAGCCGGGTAGAAACAT 50.0 63.5
a Taqman-based RT-qPCR was used.
b Lee et al., 2021 (13).
c Haramoto et al., 2013 (37); coding sequences for replicase protein were targeted (GenBank accession no. MN496154.1).
d Cho et al., 2013 (38); N gene is targeted (GenBank accession no. LC494177.1).
e Fuzawa et al., 2020 (39); FLA45_gp1 gene is targeted (GenBank accession no. NC_043512.1).
f The specificity of the primer pair targeting this mutation was checked by the primer-blast tool (National Center of Biotechnology Information). We confirmed that our primers do not target any sequences of their host cells (Homo sapiens; taxonomy ID 9606).
Next-generation sequencing to assign SARS-CoV-2 lineages.
The PCR results were confirmed by sequencing the spike gene of three controls (wild type, Alpha variant, Delta variant) and two sewage samples (samples 5 and 6) on the Illumina MiSeq platform. A set of three pairs of in-house-designed primers were used to amplify the spike of RNA samples using the SuperScript III one-step RT-PCR system with Platinum Taq high-fidelity DNA polymerase (ThermoFisher). Amplicons were purified using a QIAquick PCR purification kit (Qiagen), quantified using a Qubit fluorometer, and subject to library preparation using a Nextera XT kit and sequencing on MiSeq.
Data availability.
All the sequence data analyzed in this study are publicly available at GISAID (https://www.gisaid.org/). The analyzed and processed real data results are available at https://github.com/elkebir-group/primes-data.
Code availability.
The code has been deposited on Github at https://github.com/elkebir-group/primes.
ACKNOWLEDGMENTS
We acknowledge funding from the Grainger College of Engineering and the Jump ARCHES program of OSF Healthcare in conjunction with the University of Illinois. Sequencing was funded in part by the Food and Drug Administration Veterinary Laboratory Investigation and Response Network (FOA PAR-17-141) under grant no. 1U18FD006673-01. M.E.-K. acknowledges the National Science Foundation (grant no. CCF-2027669 and CCF-2046488).
We also acknowledge Bill Brown for sampling site selection, Hayden Wennerdahl, Kip Stevenson, Laura Keefer, and Art Schmidt for sampling deployment, and Yuqing Mao, Matthew Robert Loula, Aashna Patra, Kristin Joy Anderson, Mikayla Diedrick, Hubert Lyu, Hamza Elmahi Mohamed, Jad R. Karajeh, Runsen Ning, Rui Fu, Kate O’Brien, and Kyukyoung Kim for sewage sampling and processing.
Supplemental material is available online only.
10.1128/aem.02289-21.1 Supplemental file 1 Fig. S1 to S4 and Tables S1 to S4. Download aem.02289-21-s0001.pdf, PDF file, 1.5 MB
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PMC009xxxxxx/PMC9004406.txt |
==== Front
J Surg Case Rep
J Surg Case Rep
jscr
Journal of Surgical Case Reports
2042-8812
Oxford University Press
10.1093/jscr/rjac076
rjac076
Case Report
AcademicSubjects/MED00910
jscrep/030
Bronchopleural fistula due to cavitary pneumonia after SARS-CoV-2 infection treated with open thoracostomy
Belyayev Leonid A Department of Surgery, Veterans Affairs Medical Center, West Roxbury, MA 02132, USA
Department of Surgery, Brigham and Women’s Hospital, Boston, MA 02115, USA
Harvard Medical School, Boston, MA 02115, USA
https://orcid.org/0000-0002-5036-3868
Foroushani Sophia M Department of Surgery, Veterans Affairs Medical Center, West Roxbury, MA 02132, USA
Department of Surgery, Boston Medical Center, Boston University School of Medicine, Boston, MA 02118, USA
Wiener Daniel C Department of Surgery, Veterans Affairs Medical Center, West Roxbury, MA 02132, USA
Department of Surgery, Brigham and Women’s Hospital, Boston, MA 02115, USA
Harvard Medical School, Boston, MA 02115, USA
Branch-Elliman Westyn Department of Surgery, Veterans Affairs Medical Center, West Roxbury, MA 02132, USA
Harvard Medical School, Boston, MA 02115, USA
Marshall M Blair Department of Surgery, Brigham and Women’s Hospital, Boston, MA 02115, USA
Harvard Medical School, Boston, MA 02115, USA
Khalil Hassan A Department of Surgery, Veterans Affairs Medical Center, West Roxbury, MA 02132, USA
Department of Surgery, Brigham and Women’s Hospital, Boston, MA 02115, USA
Harvard Medical School, Boston, MA 02115, USA
Correspondence address. Brigham and Women’s Hospital, 75 Francis St Boston, MA 02115, USA. Tel: +1-516-508-8202; E-mail: leobelyayev@gmail.com
4 2022
11 4 2022
11 4 2022
2022 4 rjac07630 1 2022
15 2 2022
This work is written by US Government employees and is in the public domain in the US.
2022
This work is written by US Government employees and is in the public domain in the US.
Abstract
Severe coronavirus disease of 2019 (COVID-19) disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes substantial parenchymal damage in some patients. There is a paucity of literature describing the surgical management COVID-19 associated bronchopleural fistula after failure of medical therapy. We present the case of a 59-year-old woman with SARS-CoV-2 pneumonia, secondary bacterial pneumonia with bronchopleural fistula and radiographic and clinical evidence of disease refractory to medical therapy. After a course of culture-driven antimicrobial therapy and failure to improve following drainage with tube thoracostomy, she was treated successfully with Clagett open thoracostomy. After resolution of the bronchopleural fistula, the thoracostomy was closed and she was discharged home. In cases of severe COVID-19 complicated by bronchopleural fistula with parenchymal destruction, a tailored approach involving surgical management when indicated can lead to acceptable outcomes without significant morbidity.
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pmcINTRODUCTION
Severe coronavirus disease of 2019 (COVID-19) disease is associated with direct lung injury and secondary lung infections. Bacterial pneumonia complicates ~25% of mechanically ventilated patients with COVID-19 [1–4]. Staphylococcus aureus is a common cause of ventilator-associated pneumonia that can lead to a severe fibrinopurulent reaction and empyema, parenchymal destruction and air leak. Despite several reports of lung necrosis necessitating salvage operations, there are no reports on open thoracostomy drainage for the treatment of COVID-19 associated bronchopleural fistula after failed conservative management [5].
CASE REPORT
We present a case of a 59-year-old woman with no smoking history and past history notable for hyperlipidemia and migraines who presented to a community hospital with symptoms of fatigue, malaise and shortness of breath. She was not vaccinated for COVID-19. She tested positive for COVID-19 by polymerase chain reaction. She was treated with casirivimab and imdevimab monoclonal antibodies in the outpatient setting but continued to have fever, persistent cough and shortness of breath. Given these symptoms, she was treated with a course of amoxicillin-clavulanic acid and prednisone in the ambulatory setting for presumed bronchitis.
Three weeks following her initial COVID-19 diagnosis, she presented to the hospital with ongoing symptoms and was found to have a right hydropneumothorax and cavitary lesion with parenchymal destruction (Fig. 1). She was treated with broad-spectrum antibiotics and percutaneous chest tube drainage, which showed a continuous one- to two-column air leak. Her pleural fluid grew methicillin-sensitive S. aureus (MSSA). Bronchoscopy revealed mucosal edema and erythema, particularly in the right upper lobe. Despite treatment, she had a persistent air leak, and she was transferred to our facility for further management.
At the time of transfer, the patient was afebrile and hemodynamically normal, and she was not requiring supplemental oxygen. Her C-reactive protein level was 14.86 mg/l (reference <10) and white blood cell count was 8800 per mm3 (reference 4.5–10). Prior to transfer, her C-reactive protein level had been within normal limits. Computed tomography (CT) of the chest showed a residual pneumothorax (Fig. 2) and peripheral bronchopleural fistula (BPF). We treated her with open thoracostomy drainage (Clagett procedure), removing a segment of the second rib and suturing skin flaps to the thickened parietal pleura (Fig. 3). Due to institutional policy, no intra-operative images were obtained. On direct visualization, the visceral pleura overlying the upper lobe was thickened, but no frank purulence was noted. There was fibrinous exudate and air leak at two sites on the surface of the lung. A sample of the fibrinous tissue was sent to microbiology for clinical culture, which subsequently grew MSSA.
Figure 1 (A) Plain film of chest showing apical hydropneumothorax. (B) Computed tomography axial image demonstrating a complex hydropneumothorax with significant right sided parenchymal infiltrate.
Figure 2 Axial image demonstrating right upper lobe consolidation and residual pneumothorax despite tube thoracostomy.
Figure 3 (A) Location of incision for Clagett open thoracostomy, and (B) maturation of thoracostomy with skin flaps.
The wound was managed with −75 mmHg negative pressure wound therapy and bedside dressing changes twice per week. Within 4 days, the air leak resolved, and we considered a latissimus dorsi muscle flap for closure of the defect. We repeated a CT for operative planning. This showed a small space and no residual fluid collections. Ten days following the Clagett procedure, we explored the cavity in the operating room and found healthy granulation tissue with a limited residual space and decided that a muscle flap was not needed. The Clagett window was closed in layers over a 19Fr channel drain. The drain had minimal serous output and was removed 3 days later.
As the patient lived several hours away from the closest hospital, we obtained a second post-operative chest CT to ensure that she had no additional collections and to establish a new baseline. This demonstrated a 4 cm collection in the subcutaneous space. We aspirated the fluid, which showed no growth. The patient was discharged on a course of oral moxifloxacin. A repeat chest CT 4 weeks after Clagett closure was obtained and showed improved aeration of the right upper lobe (Fig. 4).
Figure 4 Axial CT image 4 weeks after Clagett closure.
DISCUSSION
Secondary pulmonary bacterial infection is a known cause of significant morbidity for patients recovering from severe acute respiratory syndrome coronavirus 2 pneumonia [3]. Staphylococcal infections are a well-recognized source of empyema and lung necrosis with abscess formation. However, the natural history of these infections in the setting of COVID-19 is not well known. Reports of complications of necrotizing or cavitary infections, such as bronchopleural fistula formation, are also sparse in the literature. Although nonoperative options including tube thoracostomy can be successful, open thoracostomy drainage remains a critical element in the thoracic surgeons repertoire for the management of significant lung parenchymal destruction and empyema when the lung fails to expand, especially with persistent air leak from a bronchopleural fistula [5, 6]. Key tenets of open thoracostomy include control of sepsis through wide drainage to create an aperture facilitating frequent dressing changes either in the form of wet-to-dry gauze or negative pressure wound therapy. This approach also allows for removal of fibrinous deposits on the lung promote formation of granulation tissue and spontaneous closure of bronchopleural fistulae.
Closure of the Clagett space can be facilitated in a variety of ways to include primary closure over drains, pedicled flap closure (omental, myofascial) or thoracoplasty with collapse of the chest wall [7]. The fundamental principle is obliteration of empty space after surgical sterilization of the cavity. Use of endobronchial valves has been described in the treatment of bronchopleural fistulae, though in our opinion would need to be done judiciously and in combination with a strategy to manage the infected pleural space [8, 9]. Despite the majority of super-infections occurring in critically ill patients with COVID-19, a minority may present similarly to our patient with progressively worsening focal parenchymal disease. A tailored approach with aggressive management of sepsis, nutritional support and characterization of the underlying pathology can lead to acceptable outcomes with minimal morbidity.
CONFLICT OF INTEREST STATEMENT
None declared.
FUNDING
None.
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References
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2. Adelman MW, Bhamidipati DR, Hernandez-Romieu AC, Babiker A, Woodworth MH, Robichaux C, et al. Secondary bacterial pneumonias and bloodstream infections in patients hospitalized with COVID-19. Ann Am Thorac Soc 2021;18 :1584–7.
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7. Dal Agnol G, Vieira A, Oliveira R, Ugalde Figueroa PA. Surgical approaches for bronchopleural fistula. Shanghai Chest 2017;1 :14–4.
8. Talon A, Arif MZ, Mohamed H, Khokar A, Saeed AI. Bronchopleural fistula as a complication in a COVID-19 patient managed with endobronchial valves. J Investig Med High Impact Case Reports 2021;9 :0–2.
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PMC009xxxxxx/PMC9004449.txt |
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Nat Rev Nephrol
Nat Rev Nephrol
Nature Reviews. Nephrology
1759-5061
1759-507X
Nature Publishing Group UK London
35414006
571
10.1038/s41581-022-00571-2
Comment
COVID-19 vaccine hesitancy
Dubé Eve eve.dube@crchudequebec.ulaval.ca
1
MacDonald Noni E. 2
1 grid.23856.3a 0000 0004 1936 8390 Department of Anthropology, Laval University, Quebec City, Quebec Canada
2 grid.55602.34 0000 0004 1936 8200 Department of Pediatrics, Dalhousie University, Halifax, Nova Scotia Canada
12 4 2022
2022
18 7 409410
© Springer Nature Limited 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.
The COVID-19 pandemic has highlighted the vital role of vaccination in preventing life-threatening diseases and improving global health. Understanding and addressing the concerns of vaccine-hesitant individuals, including those with chronic diseases, is key to increasing vaccine acceptance and uptake.
Subject terms
Social sciences
Disease prevention
Public health
issue-copyright-statement© Springer Nature Limited 2022
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pmcVaccines are among the most effective tools to protect individuals against vaccine-preventable diseases (VPDs) — including COVID-19 — and their associated morbidity and mortality1. For people with chronic medical conditions, vaccines reduce the risk of further deterioration of health and death owing to VPDs2. Vaccination directly protects vaccinated individuals and indirectly protects those who cannot be immunized or who do not respond robustly to vaccination, through the development of community immunity. Patients with kidney disease are particularly vulnerable to COVID-19 and those with impaired immune systems do not mount good antibody responses to the primary series of COVID-19 vaccination. Protection against COVID-19 for patients on dialysis can be increased through administration of a third dose in the primary vaccine series and personalized strategies for additional booster doses3.
The vaccine acceptance continuum
Despite strong recommendations, COVID-19 vaccine acceptance varies widely between countries and between groups with different sociodemographic characteristics4. COVID-19 vaccine uptake is suboptimal even among people with chronic medical conditions who are at increased risk of complications associated with SARS-CoV-2 infection4. The term ‘vaccine hesitant’ is commonly used to describe those who are unsure about or unwilling to receive one, some or all recommended vaccines, despite the availability of vaccination services. Vaccine acceptance exists on a continuum, ranging from a minority who stridently oppose all vaccinations to the majority who are willing to accept all recommended vaccines. Vaccine-hesitant individuals are a heterogeneous group with varying levels of indecision and concerns in the middle of this continuum5. This group is of particular interest to public health services, as many vaccine-hesitant individuals may be amenable to changing their vaccination attitudes and behaviours if their concerns are adequately addressed and systemic barriers in access to health services are removed (for example, discrimination, stigmatization, racism and gender barriers). By contrast, individuals who are vocal vaccine refusers are unlikely to change their decision not to be vaccinated.
COVID-19 vaccine uptake is suboptimal even among people with chronic medical conditions
Factors that influence vaccine uptake
At the individual level, various sociodemographic characteristics (for example, age, gender, socioeconomic status and geographical location) and many other factors (for example, low perceived risk of VPD, concerns regarding vaccine safety and/or effectiveness, belief in alternative prevention measures for VPD and/or negative past experiences with health services) are associated with suboptimal vaccine uptake5. For people living with chronic medical conditions, additional barriers to vaccine acceptance include real and perceived contraindications, risks of individual vaccines with respect to specific medical conditions and a lack of awareness of vaccine recommendations among specialists caring for these patients2.
many vaccine-hesitant individuals may be amenable to changing their vaccination attitudes and behaviours
Acceptance and refusal of vaccines is also highly context-dependent and should not be reduced to individual factors. Social, cultural, economic, organizational, historical and political factors (for example, cultural values, social norms, ease of access to health services, recommendations by health-care providers, social networks and the vaccine communication environment) influence how people perceive and make decisions about vaccination6.
The COVID-19 pandemic has highlighted additional issues with regards to vaccine acceptance and uptake. First, the scale of the vaccination campaign is exceptional; the aim is to offer the primary series of COVID vaccines to the entire adult population worldwide. In many settings, no established adult immunization programmes exist. This lack of infrastructure makes delivering a primary series of vaccines difficult, even when doses are available. Moreover, in countries where adult immunization programmes are already in place (for example for influenza), uptake of these routine vaccines has often been low. Second, the COVID ‘infodemic’ — an overabundance of information, some of which is true and some false or misleading — complicates how people search for and access reliable information about vaccination. Misinformation and disinformation can substantially reduce vaccine acceptance7 and might influence the vaccine advice of health-care providers5. Political decisions such as policy responses to the pandemic have also been influenced by the infodemic, and by partisan ideas about the risk of COVID-19 and the effectiveness of preventive measures8. Third, the long duration of the pandemic has generated ‘pandemic fatigue’, resulting in demotivation to follow public health recommendations (including for vaccination).
To reflect increased understanding of the complexity of vaccine acceptance and uptake, the Royal Society of Canada Working Group on COVID-19 Vaccine Acceptance has developed a framework of factors that influence vaccine acceptance with four major domains: people and communities; health-care workers; health-care and public health systems; and immunization knowledge (Supplementary Fig. 1). The people and communities domain embraces the goal of the World Health Organization Immunization Agenda 2030, which emphasizes ‘leaving no one behind’ and ensuring immunization across the life course1. The health-care workers domain reflects the important role of the recommendations of health-care providers in influencing vaccine acceptance. The health-care and public health systems domain highlights the role of immunization programmes and health policies. Legislation, regulations and political decisions that may or may not support public health recommendation also have an important influence on vaccine acceptance. The immunization knowledge domain highlights the importance of reliable information regarding vaccination (that is, easily accessible, accurate and up-to-date information tailored for targeted subgroups of the general population and of health-care providers). Each individual domain influences the other domains and all domains are influenced by the broader context (for example, the extent of collaborations and communications about COVID-19 vaccination)5. Although developed for Canada, the principles are broadly applicable to other countries worldwide.
Approaches to increase vaccine uptake
The factors that lead to vaccine acceptance, hesitancy or refusal are complex and a good understanding of local barriers to and concerns about vaccination is critical to develop tailored interventions. Simply giving people more information about vaccine risks and benefits is often unsuccessful because this approach does not account for the myriad ways that knowledge is mediated in diverse communities. Education-based interventions are often not designed to address the specific issues faced by groups that are affected by inequitable distribution of power and resources.
All interventions should be tailored to the different positions held along the vaccine acceptance continuum within local communities. For example, the public health intervention goal for those who are vocal vaccine refusers should not be to convince them to accept vaccination, but rather to minimize the effect that their critical discourses could have on others (for example, by monitoring and addressing online misinformation about vaccines, implementing policies to limit public advertising against vaccines and teaching the public about the techniques used to promote false information about vaccines). For people who are willing to accept vaccination, the goal is to maintain vaccine confidence with transparent and honest communication while ensuring ease of access to vaccination services. Interventions for vaccine-hesitant individuals should aim to ensure that they make informed vaccination decisions that align with their own values. Listening to and addressing concerns using motivational interviewing techniques is very effective at increasing vaccine acceptance within this group9.
Conclusions
The COVID-19 pandemic clearly demonstrates that infectious diseases can disproportionately affect certain populations as a result of imbalances in power and resources, and that inequities in vaccine uptake persist worldwide. These disparities result from intertwined factors including vaccine hesitancy, discrimination and stigmatization at individual, institutional and population levels. Although most people are vaccine confident, this attitude does not automatically translate into vaccine uptake as other barriers to vaccination exist. Understanding which individuals are vaccine hesitant and why, what barriers to accessing vaccination services exist and how to cultivate vaccine confidence is essential to inform the development of tailored strategies to increase vaccine uptake.
Supplementary information
Supplementary Information
Supplementary information
The online version contains supplementary material available at 10.1038/s41581-022-00571-2.
Competing interests
The authors declare no competing interests.
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References
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