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Ma QL, Huang FM, Guo W, Feng KY, Huang T, Cai YD. Machine Learning Classification of Time since BNT162b2 COVID-19 Vaccination Based on Array-Measured Antibody Activity. Life (Basel) 2023; 13:1304. [PMID: 37374086 DOI: 10.3390/life13061304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 05/26/2023] [Accepted: 05/29/2023] [Indexed: 06/29/2023] Open
Abstract
Vaccines trigger an immunological response that includes B and T cells, with B cells producing antibodies. SARS-CoV-2 immunity weakens over time after vaccination. Discovering key changes in antigen-reactive antibodies over time after vaccination could help improve vaccine efficiency. In this study, we collected data on blood antibody levels in a cohort of healthcare workers vaccinated for COVID-19 and obtained 73 antigens in samples from four groups according to the duration after vaccination, including 104 unvaccinated healthcare workers, 534 healthcare workers within 60 days after vaccination, 594 healthcare workers between 60 and 180 days after vaccination, and 141 healthcare workers over 180 days after vaccination. Our work was a reanalysis of the data originally collected at Irvine University. This data was obtained in Orange County, California, USA, with the collection process commencing in December 2020. British variant (B.1.1.7), South African variant (B.1.351), and Brazilian/Japanese variant (P.1) were the most prevalent strains during the sampling period. An efficient machine learning based framework containing four feature selection methods (least absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, and maximum relevance minimum redundancy) and four classification algorithms (decision tree, k-nearest neighbor, random forest, and support vector machine) was designed to select essential antibodies against specific antigens. Several efficient classifiers with a weighted F1 value around 0.75 were constructed. The antigen microarray used for identifying antibody levels in the coronavirus features ten distinct SARS-CoV-2 antigens, comprising various segments of both nucleocapsid protein (NP) and spike protein (S). This study revealed that S1 + S2, S1.mFcTag, S1.HisTag, S1, S2, Spike.RBD.His.Bac, Spike.RBD.rFc, and S1.RBD.mFc were most highly ranked among all features, where S1 and S2 are the subunits of Spike, and the suffixes represent the tagging information of different recombinant proteins. Meanwhile, the classification rules were obtained from the optimal decision tree to explain quantitatively the roles of antigens in the classification. This study identified antibodies associated with decreased clinical immunity based on populations with different time spans after vaccination. These antibodies have important implications for maintaining long-term immunity to SARS-CoV-2.
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Affiliation(s)
- Qing-Lan Ma
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Fei-Ming Huang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Wei Guo
- Key Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) & Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai 200030, China
| | - Kai-Yan Feng
- Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou 510507, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai 200444, China
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Schubl SD, Figueroa C, Palma AM, de Assis RR, Jain A, Nakajima R, Jasinskas A, Brabender D, Hosseinian S, Naaseh A, Hernandez Dominguez O, Runge A, Skochko S, Chinn J, Kelsey AJ, Lai KT, Zhao W, Horvath P, Tifrea D, Grigorian A, Gonzales A, Adelsohn S, Zaldivar F, Edwards R, Amin AN, Stamos MJ, Barie PS, Felgner PL, Khan S. Risk factors for SARS-CoV-2 seropositivity in a health care worker population during the early pandemic. BMC Infect Dis 2023; 23:330. [PMID: 37194021 PMCID: PMC10186297 DOI: 10.1186/s12879-023-08284-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 04/27/2023] [Indexed: 05/18/2023] Open
Abstract
BACKGROUND While others have reported severe acute respiratory syndrome-related coronavirus 2(SARS-CoV-2) seroprevalence studies in health care workers (HCWs), we leverage the use of a highly sensitive coronavirus antigen microarray to identify a group of seropositive health care workers who were missed by daily symptom screening that was instituted prior to any epidemiologically significant local outbreak. Given that most health care facilities rely on daily symptom screening as the primary method to identify SARS-CoV-2 among health care workers, here, we aim to determine how demographic, occupational, and clinical variables influence SARS-CoV-2 seropositivity among health care workers. METHODS We designed a cross-sectional survey of HCWs for SARS-CoV-2 seropositivity conducted from May 15th to June 30th 2020 at a 418-bed academic hospital in Orange County, California. From an eligible population of 5,349 HCWs, study participants were recruited in two ways: an open cohort, and a targeted cohort. The open cohort was open to anyone, whereas the targeted cohort that recruited HCWs previously screened for COVID-19 or work in high-risk units. A total of 1,557 HCWs completed the survey and provided specimens, including 1,044 in the open cohort and 513 in the targeted cohort. Demographic, occupational, and clinical variables were surveyed electronically. SARS-CoV-2 seropositivity was assessed using a coronavirus antigen microarray (CoVAM), which measures antibodies against eleven viral antigens to identify prior infection with 98% specificity and 93% sensitivity. RESULTS Among tested HCWs (n = 1,557), SARS-CoV-2 seropositivity was 10.8%, and risk factors included male gender (OR 1.48, 95% CI 1.05-2.06), exposure to COVID-19 outside of work (2.29, 1.14-4.29), working in food or environmental services (4.85, 1.51-14.85), and working in COVID-19 units (ICU: 2.28, 1.29-3.96; ward: 1.59, 1.01-2.48). Amongst 1,103 HCWs not previously screened, seropositivity was 8.0%, and additional risk factors included younger age (1.57, 1.00-2.45) and working in administration (2.69, 1.10-7.10). CONCLUSION SARS-CoV-2 seropositivity is significantly higher than reported case counts even among HCWs who are meticulously screened. Seropositive HCWs missed by screening were more likely to be younger, work outside direct patient care, or have exposure outside of work.
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Affiliation(s)
- Sebastian D Schubl
- Department of Surgery, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Cesar Figueroa
- Department of Surgery, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Anton M Palma
- Institute for Clinical and Translational Sciences, University of California Irvine, Irvine, CA, USA
| | - Rafael R de Assis
- Department of Physiology and Biophysics, University of California Irvine, Irvine, CA, USA
| | - Aarti Jain
- Department of Physiology and Biophysics, University of California Irvine, Irvine, CA, USA
| | - Rie Nakajima
- Department of Physiology and Biophysics, University of California Irvine, Irvine, CA, USA
| | - Algimantas Jasinskas
- Department of Physiology and Biophysics, University of California Irvine, Irvine, CA, USA
| | - Danielle Brabender
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sina Hosseinian
- School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Ariana Naaseh
- School of Medicine, University of California Irvine, Irvine, CA, USA
| | | | - Ava Runge
- School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Shannon Skochko
- School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Justine Chinn
- School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Adam J Kelsey
- Department of Pharmaceutical Sciences, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Kieu T Lai
- Department of Pharmaceutical Sciences, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Weian Zhao
- Department of Pharmaceutical Sciences, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Peter Horvath
- Institute for Clinical and Translational Sciences, University of California Irvine, Irvine, CA, USA
| | - Delia Tifrea
- Department of Pathology, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Areg Grigorian
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Abran Gonzales
- Department of Surgery, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Suzanne Adelsohn
- Department of Pathology, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Frank Zaldivar
- Institute for Clinical and Translational Sciences, University of California Irvine, Irvine, CA, USA
| | - Robert Edwards
- Department of Pathology, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Alpesh N Amin
- Department of Medicine, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Michael J Stamos
- School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Philip S Barie
- Department of Surgery, Weill Cornell Medicine, New York, NY, USA
| | - Philip L Felgner
- Department of Physiology and Biophysics, University of California Irvine, Irvine, CA, USA
| | - Saahir Khan
- Division of Infectious Diseases, Department of Medicine, Keck School of Medicine, University of Southern California, 1520 San Pablo St., Los Angeles, CA, 90033, USA.
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García-Carreras B, Hitchings MDT, Johansson MA, Biggerstaff M, Slayton RB, Healy JM, Lessler J, Quandelacy T, Salje H, Huang AT, Cummings DAT. Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S. Nat Commun 2023; 14:2235. [PMID: 37076502 PMCID: PMC10115837 DOI: 10.1038/s41467-023-37944-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 04/06/2023] [Indexed: 04/21/2023] Open
Abstract
Reconstructing the incidence of SARS-CoV-2 infection is central to understanding the state of the pandemic. Seroprevalence studies are often used to assess cumulative infections as they can identify asymptomatic infection. Since July 2020, commercial laboratories have conducted nationwide serosurveys for the U.S. CDC. They employed three assays, with different sensitivities and specificities, potentially introducing biases in seroprevalence estimates. Using models, we show that accounting for assays explains some of the observed state-to-state variation in seroprevalence, and when integrating case and death surveillance data, we show that when using the Abbott assay, estimates of proportions infected can differ substantially from seroprevalence estimates. We also found that states with higher proportions infected (before or after vaccination) had lower vaccination coverages, a pattern corroborated using a separate dataset. Finally, to understand vaccination rates relative to the increase in cases, we estimated the proportions of the population that received a vaccine prior to infection.
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Affiliation(s)
- Bernardo García-Carreras
- Department of Biology, University of Florida, Gainesville, FL, USA.
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
| | - Matt D T Hitchings
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Michael A Johansson
- COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Matthew Biggerstaff
- COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Rachel B Slayton
- COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jessica M Healy
- COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Justin Lessler
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Carolina Population Center, Chapel Hill, NC, USA
| | | | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Angkana T Huang
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Derek A T Cummings
- Department of Biology, University of Florida, Gainesville, FL, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
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Bayer D, Goldstein IH, Fintzi J, Lumbard K, Ricotta E, Warner S, Busch LM, Strich JR, Chertow DS, Parker DM, Boden-Albala B, Dratch A, Chhuon R, Quick N, Zahn M, Minin VM. Semi-parametric modeling of SARS-CoV-2 transmission using tests, cases, deaths, and seroprevalence data. ARXIV 2023:arXiv:2009.02654v3. [PMID: 32908946 PMCID: PMC7480029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Mechanistic models fit to streaming surveillance data are critical to understanding the transmission dynamics of an outbreak as it unfolds in real-time. However, transmission model parameter estimation can be imprecise, and sometimes even impossible, because surveillance data are noisy and not informative about all aspects of the mechanistic model. To partially overcome this obstacle, Bayesian models have been proposed to integrate multiple surveillance data streams. We devised a modeling framework for integrating SARS-CoV-2 diagnostics test and mortality time series data, as well as seroprevalence data from cross-sectional studies, and tested the importance of individual data streams for both inference and forecasting. Importantly, our model for incidence data accounts for changes in the total number of tests performed. We model the transmission rate, infection-to-fatality ratio, and a parameter controlling a functional relationship between the true case incidence and the fraction of positive tests as time-varying quantities and estimate changes of these parameters nonparametrically. We compare our base model against modified versions which do not use diagnostics test counts or seroprevalence data to demonstrate the utility of including these often unused data streams. We apply our Bayesian data integration method to COVID-19 surveillance data collected in Orange County, California between March 2020 and February 2021 and find that 32-72% of the Orange County residents experienced SARS-CoV-2 infection by mid-January, 2021. Despite this high number of infections, our results suggest that the abrupt end of the winter surge in January 2021 was due to both behavioral changes and a high level of accumulated natural immunity.
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Affiliation(s)
- Damon Bayer
- Department of Statistics, University of California, Irvine, California, U.S.A
| | - Isaac H. Goldstein
- Department of Statistics, University of California, Irvine, California, U.S.A
| | - Jonathan Fintzi
- Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Rockville, Maryland, U.S.A
| | - Keith Lumbard
- Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, Maryland, U.S.A
| | - Emily Ricotta
- Epidemiology Unit, National Institute of Allergy and Infectious Diseases, Bethesda, Maryland, U.S.A
| | - Sarah Warner
- Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, Maryland, U.S.A
| | - Lindsay M. Busch
- Division of Infectious Diseases, Emory University School of Medicine, Atlanta, Georgia, U.S.A
| | - Jeffrey R. Strich
- Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, Maryland, U.S.A
| | - Daniel S. Chertow
- Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, Maryland, U.S.A
| | - Daniel M. Parker
- Susan and Henry Samueli College of Health Sciences, University of California, Irvine, California, U.S.A
| | - Bernadette Boden-Albala
- Susan and Henry Samueli College of Health Sciences, University of California, Irvine, California, U.S.A
| | - Alissa Dratch
- Orange County Health Care Agency, Santa Ana, California, U.S.A
| | - Richard Chhuon
- Orange County Health Care Agency, Santa Ana, California, U.S.A
| | | | - Matthew Zahn
- Orange County Health Care Agency, Santa Ana, California, U.S.A
| | - Volodymyr M. Minin
- Department of Statistics, University of California, Irvine, California, U.S.A
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Irizar P, Pan D, Kapadia D, Bécares L, Sze S, Taylor H, Amele S, Kibuchi E, Divall P, Gray LJ, Nellums LB, Katikireddi SV, Pareek M. Ethnic inequalities in COVID-19 infection, hospitalisation, intensive care admission, and death: a global systematic review and meta-analysis of over 200 million study participants. EClinicalMedicine 2023; 57:101877. [PMID: 36969795 PMCID: PMC9986034 DOI: 10.1016/j.eclinm.2023.101877] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 03/08/2023] Open
Abstract
Background COVID-19 has exacerbated existing ethnic inequalities in health. Little is known about whether inequalities in severe disease and deaths, observed globally among minoritised ethnic groups, relates to greater infection risk, poorer prognosis, or both. We analysed global data on COVID-19 clinical outcomes examining inequalities between people from minoritised ethnic groups compared to the ethnic majority group. Methods Databases (MEDLINE, EMBASE, EMCARE, CINAHL, Cochrane Library) were searched from 1st December 2019 to 3rd October 2022, for studies reporting original clinical data for COVID-19 outcomes disaggregated by ethnicity: infection, hospitalisation, intensive care unit (ICU) admission, and mortality. We assessed inequalities in incidence and prognosis using random-effects meta-analyses, with Grading of Recommendations Assessment, Development, and Evaluation (GRADE) use to assess certainty of findings. Meta-regressions explored the impact of region and time-frame (vaccine roll-out) on heterogeneity. PROSPERO: CRD42021284981. Findings 77 studies comprising over 200,000,000 participants were included. Compared with White majority populations, we observed an increased risk of testing positive for infection for people from Black (adjusted Risk Ratio [aRR]:1.78, 95% CI:1.59-1.99, I2 = 99.1), South Asian (aRR:3.00, 95% CI:1.59-5.66, I2 = 99.1), Mixed (aRR:1.64, 95% CI:1.02-1.67, I2 = 93.2) and Other ethnic groups (aRR:1.36, 95% CI:1.01-1.82, I2 = 85.6). Black, Hispanic, and South Asian people were more likely to be seropositive. Among population-based studies, Black and Hispanic ethnic groups and Indigenous peoples had an increased risk of hospitalisation; Black, Hispanic, South Asian, East Asian and Mixed ethnic groups and Indigenous peoples had an increased risk of ICU admission. Mortality risk was increased for Hispanic, Mixed, and Indigenous groups. Smaller differences were seen for prognosis following infection. Following hospitalisation, South Asian, East Asian, Black and Mixed ethnic groups had an increased risk of ICU admission, and mortality risk was greater in Mixed ethnic groups. Certainty of evidence ranged from very low to moderate. Interpretation Our study suggests that systematic ethnic inequalities in COVID-19 health outcomes exist, with large differences in exposure risk and some differences in prognosis following hospitalisation. Response and recovery interventions must focus on tackling drivers of ethnic inequalities which increase exposure risk and vulnerabilities to severe disease, including structural racism and racial discrimination. Funding ESRC:ES/W000849/1.
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Affiliation(s)
- Patricia Irizar
- School of Social Sciences, University of Manchester, United Kingdom
| | - Daniel Pan
- Department of Respiratory Sciences, University of Leicester, United Kingdom
- Department of Infection and HIV Medicine, University Hospitals Leicester NHS Trust, United Kingdom
- Li Ka Shing Centre for Health Information and Discovery, Oxford Big Data Institute, University of Oxford, United Kingdom
- NIHR Leicester Biomedical Research Centre, United Kingdom
| | - Dharmi Kapadia
- School of Social Sciences, University of Manchester, United Kingdom
| | - Laia Bécares
- Department of Global Health and Social Medicine, King's College London, United Kingdom
| | - Shirley Sze
- Department of Cardiovascular Sciences, University of Leicester, United Kingdom
| | - Harry Taylor
- School of Social Sciences, University of Manchester, United Kingdom
| | - Sarah Amele
- MRC/CSO Social & Public Health Sciences Unit, University of Glasgow, United Kingdom
| | - Eliud Kibuchi
- MRC/CSO Social & Public Health Sciences Unit, University of Glasgow, United Kingdom
| | - Pip Divall
- University Hospitals of Leicester, Education Centre Library, Glenfield Hospital and Leicester Royal Infirmary, United Kingdom
| | - Laura J Gray
- Department of Health Sciences, University of Leicester, United Kingdom
| | - Laura B Nellums
- Division of Epidemiology and Public Health, School of Medicine, University of Nottingham, United Kingdom
| | | | - Manish Pareek
- Department of Respiratory Sciences, University of Leicester, United Kingdom
- Department of Infection and HIV Medicine, University Hospitals Leicester NHS Trust, United Kingdom
- NIHR Leicester Biomedical Research Centre, United Kingdom
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Darling AM, Shephard H, Nestoridi E, Manning SE, Yazdy MM. SARS-CoV-2 infection during pregnancy and preterm birth in Massachusetts from March 2020 through March 2021. Paediatr Perinat Epidemiol 2022; 37:93-103. [PMID: 36512318 PMCID: PMC9877646 DOI: 10.1111/ppe.12944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 11/18/2022] [Accepted: 11/27/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND SARS-CoV-2 infection during pregnancy has been linked to preterm birth, but this association is not well understood. OBJECTIVES To examine the association between SARS-CoV-2 infection and spontaneous and provider-initiated preterm birth (PTB), and how timing of infection, and race/ethnicity as a marker of structural inequality, may modify this association. METHODS We conducted a retrospective cohort study among pregnant people who delivered singleton, liveborn infants (22-44 weeks gestation) from 1 March 2020 to 31 March 2021 (n = 68,288). We used Cox proportional hazards models to compare the hazard of PTB between pregnant people with and without laboratory-confirmed SARS-CoV-2 infection during pregnancy. We evaluated this association according to the trimester of infection, timing from infection to birth, and timing of PTB. We also examined the joint associations of SARS-CoV-2 infection and race/ethnicity with PTB using the relative excess risk due to interaction (RERI). RESULTS Positive SARS-CoV-2 tests were identified for 2195 pregnant people (3.2%). The prevalence of PTB was 7.2% (3.8% spontaneous, 3.6% provider-initiated). SARS-CoV-2 infection during pregnancy was associated with an increased risk of PTB overall (adjusted hazard ratio [HR] 1.53, 95% confidence interval [CI] 1.34, 1.74), and provider-initiated PTB (HR 1.79, 95% CI 1.50, 2.12) but not spontaneous PTB (HR 1.09, 95% CI 0.89, 1.36). Second trimester infections were associated with an increased risk of provider-initiated PTB, and third trimester infections were associated with an increased risk of both PTB subtypes. A joint inverse association between White non-Hispanic race/ethnicity and SARS-CoV-2 infection and spontaneous PTB (HR 0.56, 95% CI 0.34, 0.94; RERI -0.6, 95% CI -1.0, -0.2) was also observed. CONCLUSIONS SARS-CoV-2 infections were primarily associated with an increased risk for provider-initiated PTB in this study. These findings highlight the importance of promoting infection-prevention strategies among pregnant people.
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Affiliation(s)
- Anne Marie Darling
- Massachusetts Center for Birth Defects Research and Prevention, Bureau of Family Health and NutritionMassachusetts Department of Public HealthBostonMassachusettsUSA
| | - Hanna Shephard
- Massachusetts Center for Birth Defects Research and Prevention, Bureau of Family Health and NutritionMassachusetts Department of Public HealthBostonMassachusettsUSA,Council of State and Territorial Epidemiologists Applied Epidemiology FellowshipAtlantaGeorgiaUSA
| | - Eirini Nestoridi
- Massachusetts Center for Birth Defects Research and Prevention, Bureau of Family Health and NutritionMassachusetts Department of Public HealthBostonMassachusettsUSA
| | - Susan E. Manning
- Bureau of Family Health and NutritionMassachusetts Department of Public HealthBostonMassachusettsUSA,COVID‐19 ResponseCenters for Disease Control and PreventionAtlantaGeorgiaUSA
| | - Mahsa M. Yazdy
- Massachusetts Center for Birth Defects Research and Prevention, Bureau of Family Health and NutritionMassachusetts Department of Public HealthBostonMassachusettsUSA
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Bhatia R, Sledge I, Baral S. Missing science: A scoping study of COVID-19 epidemiological data in the United States. PLoS One 2022; 17:e0248793. [PMID: 36223335 PMCID: PMC9555641 DOI: 10.1371/journal.pone.0248793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 09/12/2022] [Indexed: 11/06/2022] Open
Abstract
Systematic approaches to epidemiologic data collection are critical for informing pandemic responses, providing information for the targeting and timing of mitigations, for judging the efficacy and efficiency of alternative response strategies, and for conducting real-world impact assessments. Here, we report on a scoping study to assess the completeness of epidemiological data available for COVID-19 pandemic management in the United States, enumerating authoritative US government estimates of parameters of infectious transmission, infection severity, and disease burden and characterizing the extent and scope of US public health affiliated epidemiological investigations published through November 2021. While we found authoritative estimates for most expected transmission and disease severity parameters, some were lacking, and others had significant uncertainties. Moreover, most transmission parameters were not validated domestically or re-assessed over the course of the pandemic. Publicly available disease surveillance measures did grow appreciably in scope and resolution over time; however, their resolution with regards to specific populations and exposure settings remained limited. We identified 283 published epidemiological reports authored by investigators affiliated with U.S. governmental public health entities. Most reported on descriptive studies. Published analytic studies did not appear to fully respond to knowledge gaps or to provide systematic evidence to support, evaluate or tailor community mitigation strategies. The existence of epidemiological data gaps 18 months after the declaration of the COVID-19 pandemic underscores the need for more timely standardization of data collection practices and for anticipatory research priorities and protocols for emerging infectious disease epidemics.
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Affiliation(s)
- Rajiv Bhatia
- Primary Care and Population Health, Stanford University, Stanford, CA, United States of America
- * E-mail:
| | | | - Stefan Baral
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, United States of America
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8
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Hur S. THE DISTRIBUTIONAL EFFECTS OF COVID-19 AND OPTIMAL MITIGATION POLICIES. INTERNATIONAL ECONOMIC REVIEW 2022; 64:IERE12601. [PMID: 35945921 PMCID: PMC9353370 DOI: 10.1111/iere.12601] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 07/08/2022] [Indexed: 06/14/2023]
Abstract
This paper develops a quantitative heterogeneous agent-life cycle-epidemiological model that is used to study the aggregate and distributional consequences of COVID-19 and mitigation policies. First, a stay-at-home subsidy is preferred to a lockdown because it reduces deaths by more and output by less. Second, Pareto-improving policies can reduce deaths by nearly 45 percent without any reduction in output relative to no public mitigation. Finally, it is possible to simultaneously improve public health and economic outcomes, suggesting that debates regarding a tradeoff between economic and health objectives may be misguided. This article is protected by copyright. All rights reserved.
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9
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Schwandt H, Currie J, von Wachter T, Kowarski J, Chapman D, Woolf SH. Changes in the Relationship Between Income and Life Expectancy Before and During the COVID-19 Pandemic, California, 2015-2021. JAMA 2022; 328:360-366. [PMID: 35797033 PMCID: PMC9264223 DOI: 10.1001/jama.2022.10952] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
IMPORTANCE The COVID-19 pandemic caused a large decrease in US life expectancy in 2020, but whether a similar decrease occurred in 2021 and whether the relationship between income and life expectancy intensified during the pandemic are unclear. OBJECTIVE To measure changes in life expectancy in 2020 and 2021 and the relationship between income and life expectancy by race and ethnicity. DESIGN, SETTING, AND PARTICIPANTS Retrospective ecological analysis of deaths in California in 2015 to 2021 to calculate state- and census tract-level life expectancy. Tracts were grouped by median household income (MHI), obtained from the American Community Survey, and the slope of the life expectancy-income gradient was compared by year and by racial and ethnic composition. EXPOSURES California in 2015 to 2019 (before the COVID-19 pandemic) and 2020 to 2021 (during the COVID-19 pandemic). MAIN OUTCOMES AND MEASURES Life expectancy at birth. RESULTS California experienced 1 988 606 deaths during 2015 to 2021, including 654 887 in 2020 to 2021. State life expectancy declined from 81.40 years in 2019 to 79.20 years in 2020 and 78.37 years in 2021. MHI data were available for 7962 of 8057 census tracts (98.8%; n = 1 899 065 deaths). Mean MHI ranged from $21 279 to $232 261 between the lowest and highest percentiles. The slope of the relationship between life expectancy and MHI increased significantly, from 0.075 (95% CI, 0.07-0.08) years per percentile in 2019 to 0.103 (95% CI, 0.098-0.108; P < .001) years per percentile in 2020 and 0.107 (95% CI, 0.102-0.112; P < .001) years per percentile in 2021. The gap in life expectancy between the richest and poorest percentiles increased from 11.52 years in 2019 to 14.67 years in 2020 and 15.51 years in 2021. Among Hispanic and non-Hispanic Asian, Black, and White populations, life expectancy declined 5.74 years among the Hispanic population, 3.04 years among the non-Hispanic Asian population, 3.84 years among the non-Hispanic Black population, and 1.90 years among the non-Hispanic White population between 2019 and 2021. The income-life expectancy gradient in these groups increased significantly between 2019 and 2020 (0.038 [95% CI, 0.030-0.045; P < .001] years per percentile among Hispanic individuals; 0.024 [95% CI: 0.005-0.044; P = .02] years per percentile among Asian individuals; 0.015 [95% CI, 0.010-0.020; P < .001] years per percentile among Black individuals; and 0.011 [95% CI, 0.007-0.015; P < .001] years per percentile among White individuals) and between 2019 and 2021 (0.033 [95% CI, 0.026-0.040; P < .001] years per percentile among Hispanic individuals; 0.024 [95% CI, 0.010-0.038; P = .002] years among Asian individuals; 0.024 [95% CI, 0.011-0.037; P = .003] years per percentile among Black individuals; and 0.013 [95% CI, 0.008-0.018; P < .001] years per percentile among White individuals). The increase in the gradient was significantly greater among Hispanic vs White populations in 2020 and 2021 (P < .001 in both years) and among Black vs White populations in 2021 (P = .04). CONCLUSIONS AND RELEVANCE This retrospective analysis of census tract-level income and mortality data in California from 2015 to 2021 demonstrated a decrease in life expectancy in both 2020 and 2021 and an increase in the life expectancy gap by income level relative to the prepandemic period that disproportionately affected some racial and ethnic minority populations. Inferences at the individual level are limited by the ecological nature of the study, and the generalizability of the findings outside of California are unknown.
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Affiliation(s)
- Hannes Schwandt
- School of Education and Social Policy, Northwestern University, Evanston, Illinois
- Buehler Center for Health Policy and Economics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
- National Bureau of Economic Research (NBER), Cambridge, Massachusetts
| | - Janet Currie
- National Bureau of Economic Research (NBER), Cambridge, Massachusetts
- Department of Economics, Princeton University, Princeton, New Jersey
| | - Till von Wachter
- National Bureau of Economic Research (NBER), Cambridge, Massachusetts
- Department of Economics, University of California, Los Angeles
- California Policy Lab, University of California, Los Angeles
| | - Jonathan Kowarski
- Department of Economics, University of California, Los Angeles
- California Policy Lab, University of California, Los Angeles
| | - Derek Chapman
- Center on Society and Health, Virginia Commonwealth University School of Medicine, Richmond
| | - Steven H. Woolf
- Center on Society and Health, Virginia Commonwealth University School of Medicine, Richmond
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10
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Serology as a Tool to Assess Infectious Disease Landscapes and Guide Public Health Policy. Pathogens 2022; 11:pathogens11070732. [PMID: 35889978 PMCID: PMC9323579 DOI: 10.3390/pathogens11070732] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 06/10/2022] [Accepted: 06/16/2022] [Indexed: 01/27/2023] Open
Abstract
Understanding the local burden and epidemiology of infectious diseases is crucial to guide public health policy and prioritize interventions. Typically, infectious disease surveillance relies on capturing clinical cases within a healthcare system, classifying cases by etiology and enumerating cases over a period of time. Disease burden is often then extrapolated to the general population. Serology (i.e., examining serum for the presence of pathogen-specific antibodies) has long been used to inform about individuals past exposure and immunity to specific pathogens. However, it has been underutilized as a tool to evaluate the infectious disease burden landscape at the population level and guide public health decisions. In this review, we outline how serology provides a powerful tool to complement case-based surveillance for determining disease burden and epidemiology of infectious diseases, highlighting its benefits and limitations. We describe the current serology-based technologies and illustrate their use with examples from both the pre- and post- COVID-19-pandemic context. In particular, we review the challenges to and opportunities in implementing serological surveillance in low- and middle-income countries (LMICs), which bear the brunt of the global infectious disease burden. Finally, we discuss the relevance of serology data for public health decision-making and describe scenarios in which this data could be used, either independently or in conjunction with case-based surveillance. We conclude that public health systems would greatly benefit from the inclusion of serology to supplement and strengthen existing case-based infectious disease surveillance strategies.
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11
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Tang IW, Vieira VM, Shearer E. Effect of socioeconomic factors during the early COVID-19 pandemic: a spatial analysis. BMC Public Health 2022; 22:1212. [PMID: 35715743 PMCID: PMC9205762 DOI: 10.1186/s12889-022-13618-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/07/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Spatial variability of COVID-19 cases may suggest geographic disparities of social determinants of health. Spatial analyses of population-level data may provide insight on factors that may contribute to COVID-19 transmission, hospitalization, and death. METHODS Generalized additive models were used to map COVID-19 risk from March 2020 to February 2021 in Orange County (OC), California. We geocoded and analyzed 221,843 cases to OC census tracts within a Poisson framework while smoothing over census tract centroids. Location was randomly permuted 1000 times to test for randomness. We also separated the analyses temporally to observe if risk changed over time. COVID-19 cases, hospitalizations, and deaths were mapped across OC while adjusting for population-level demographic data in crude and adjusted models. RESULTS Risk for COVID-19 cases, hospitalizations, and deaths were statistically significant in northern OC. Adjustment for demographic data substantially decreased spatial risk, but areas remained statistically significant. Inclusion of location within our models considerably decreased the magnitude of risk compared to univariate models. However, percent minority (adjusted RR: 1.06, 95%CI: 1.06, 1.07), average household size (aRR: 1.06, 95%CI: 1.05, 1.07), and percent service industry (aRR: 1.05, 95%CI: 1.04, 1.06) remained significantly associated with COVID-19 risk in adjusted spatial models. In addition, areas of risk did not change between surges and risk ratios were similar for hospitalizations and deaths. CONCLUSION Significant risk factors and areas of increased risk were identified in OC in our adjusted models and suggests that social and environmental factors contribute to the spread of COVID-19 within communities. Areas in north OC remained significant despite adjustment, but risk substantially decreased. Additional investigation of risk factors may provide insight on how to protect vulnerable populations in future infectious disease outbreaks.
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Affiliation(s)
- Ian W Tang
- Department of Environmental and Occupational Health, Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, 100 Theory Drive, Irvine, CA, 92617, USA.
- Communicable Disease Control Division, Orange County Health Care Agency, Santa Ana, USA.
| | - Verónica M Vieira
- Department of Environmental and Occupational Health, Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, 100 Theory Drive, Irvine, CA, 92617, USA
| | - Eric Shearer
- Communicable Disease Control Division, Orange County Health Care Agency, Santa Ana, USA
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12
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Colton H, Hodgson D, Hornsby H, Brown R, Mckenzie J, Bradley KL, James C, Lindsey BB, Birch S, Marsh L, Wood S, Bayley M, Dickson G, James DC, Nicklin MJ, Sayers JR, Zafred D, Rowland-Jones SL, Kudesia G, Kucharski A, Darton TC, de Silva TI, Collini PJ. Risk factors for SARS-CoV-2 seroprevalence following the first pandemic wave in UK healthcare workers in a large NHS Foundation Trust. Wellcome Open Res 2022; 6:220. [PMID: 35600250 PMCID: PMC9091808 DOI: 10.12688/wellcomeopenres.17143.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/08/2022] [Indexed: 11/22/2022] Open
Abstract
Background: We aimed to measure SARS-CoV-2 seroprevalence in a cohort of healthcare workers (HCWs) during the first UK wave of the COVID-19 pandemic, explore risk factors associated with infection, and investigate the impact of antibody titres on assay sensitivity. Methods: HCWs at Sheffield Teaching Hospitals NHS Foundation Trust were prospectively enrolled and sampled at two time points. We developed an in-house ELISA for testing participant serum for SARS-CoV-2 IgG and IgA reactivity against Spike and Nucleoprotein. Data were analysed using three statistical models: a seroprevalence model, an antibody kinetics model, and a heterogeneous sensitivity model. Results: Our in-house assay had a sensitivity of 99·47% and specificity of 99·56%. We found that 24·4% (n=311/1275) of HCWs were seropositive as of 12th June 2020. Of these, 39·2% (n=122/311) were asymptomatic. The highest adjusted seroprevalence was measured in HCWs on the Acute Medical Unit (41·1%, 95% CrI 30·0-52·9) and in Physiotherapists and Occupational Therapists (39·2%, 95% CrI 24·4-56·5). Older age groups showed overall higher median antibody titres. Further modelling suggests that, for a serological assay with an overall sensitivity of 80%, antibody titres may be markedly affected by differences in age, with sensitivity estimates of 89% in those over 60 years but 61% in those ≤30 years. Conclusions: HCWs in acute medical units and those working closely with COVID-19 patients were at highest risk of infection, though whether these are infections acquired from patients or other staff is unknown. Current serological assays may underestimate seroprevalence in younger age groups if validated using sera from older and/or more severe COVID-19 cases.
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Affiliation(s)
- Hayley Colton
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - David Hodgson
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Hailey Hornsby
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Rebecca Brown
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Joanne Mckenzie
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Kirsty L. Bradley
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Cameron James
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Benjamin B. Lindsey
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Sarah Birch
- Academic Directorate of Communicable Diseases and Specialised Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
| | - Louise Marsh
- Academic Directorate of Communicable Diseases and Specialised Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
| | - Steven Wood
- Department of Scientific Computing and Informatics, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
| | - Martin Bayley
- Department of Scientific Computing and Informatics, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
| | - Gary Dickson
- Department of Scientific Computing and Informatics, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
| | - David C. James
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, S10 2TN, UK
| | - Martin J. Nicklin
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Jon R. Sayers
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
- Sheffield Institute for Nucleic Acids, University of Sheffield, Sheffield, S10 2TN, UK
| | - Domen Zafred
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Sarah L. Rowland-Jones
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
| | - Goura Kudesia
- Department of Virology, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S5 7AU, UK
| | - Adam Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - CMMID COVID-19 Working Group
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
- Academic Directorate of Communicable Diseases and Specialised Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Scientific Computing and Informatics, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
- Sheffield Institute for Nucleic Acids, University of Sheffield, Sheffield, S10 2TN, UK
- Department of Virology, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S5 7AU, UK
| | - Thomas C. Darton
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
| | - Thushan I. de Silva
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
| | - Paul J. Collini
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
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13
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Riley AR, Kiang MV, Chen YH, Bibbins-Domingo K, Glymour MM. Recent Shifts in Racial/Ethnic Disparities in COVID-19 Mortality in the Vaccination Period in California. J Gen Intern Med 2022; 37:1818-1820. [PMID: 35112284 PMCID: PMC8809629 DOI: 10.1007/s11606-021-07380-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 12/17/2021] [Indexed: 11/30/2022]
Affiliation(s)
- Alicia R Riley
- Department of Sociology, University of California, Santa Cruz, Santa Cruz, California, USA.
| | - Mathew V Kiang
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA
| | - Yea-Hung Chen
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
| | - Kirsten Bibbins-Domingo
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
| | - M Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
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14
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Cardenas VM, Kennedy JL, Williams M, Nembhard WN, Zohoori N, Du R, Jin J, Boothe D, Fischbach LA, Kirkpatrick C, Modi Z, Caid K, Owens S, Forrest JC, James L, Boehme KW, Olgaard E, Gardner SF, Amick BC. State-wide random seroprevalence survey of SARS-CoV-2 past infection in a southern US State, 2020. PLoS One 2022; 17:e0267322. [PMID: 35476717 PMCID: PMC9045671 DOI: 10.1371/journal.pone.0267322] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 04/06/2022] [Indexed: 11/19/2022] Open
Abstract
The purpose of this cross-sectional study was to estimate the proportion of Arkansas residents who were infected with the SARS-CoV-2 virus between May and December 2020 and to assess the determinants of infection. To estimate seroprevalence, a state-wide population-based random-digit dial sample of non-institutionalized adults in Arkansas was surveyed. Exposures were age, sex, race/ethnicity, education, occupation, contact with infected persons, comorbidities, height, and weight. The outcome was past COVID-19 infection measured by serum antibody test. We found a prevalence of 15.1% (95% CI: 11.1%, 20.2%) by December 2020. Seropositivity was significantly elevated among participants who were non-Hispanic Black, Hispanic (prevalence ratio [PRs]:1.4 [95% CI: 0.8, 2.4] and 2.3 [95% CI: 1.3, 4.0], respectively), worked in high-demand essential services (PR: 2.5 [95% CI: 1.5, 4.1]), did not have a college degree (PR: 1.6 [95% CI: 1.0, 2.4]), had an infected household or extra-household contact (PRs: 4.7 [95% CI: 2.1, 10.1] and 2.6 [95% CI: 1.2, 5.7], respectively), and were contacted in November or December (PR: 3.6 [95% CI: 1.9, 6.9]). Our results indicate that by December 2020, one out six persons in Arkansas had a past SARS-CoV-2 infection.
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Affiliation(s)
- Victor M. Cardenas
- Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Joshua L. Kennedy
- Department of Pediatrics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Department of Internal Medicine, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Arkansas Children’s Research Institute, Little Rock, Arkansas, United States of America
| | - Mark Williams
- Department of Health Behavior and Health Education, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Wendy N. Nembhard
- Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Namvar Zohoori
- Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Arkansas Department of Health, Little Rock, Arkansas, United States of America
| | - Ruofei Du
- Department of Biostatistics, College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Jing Jin
- Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Department of Biostatistics, College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Danielle Boothe
- Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Lori A. Fischbach
- Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Los Angeles County Department of Public Health, Outbreak Management Branch, Los Angeles, California, United States of America
| | - Catherine Kirkpatrick
- Arkansas Children’s Research Institute, Little Rock, Arkansas, United States of America
| | - Zeel Modi
- Arkansas Children’s Research Institute, Little Rock, Arkansas, United States of America
| | - Katherine Caid
- Arkansas Children’s Research Institute, Little Rock, Arkansas, United States of America
| | - Shana Owens
- Arkansas Children’s Research Institute, Little Rock, Arkansas, United States of America
| | - J. Craig Forrest
- Department of Microbiology & Immunology, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Laura James
- Department of Pediatrics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Karl W. Boehme
- Department of Microbiology & Immunology, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Centre for Microbial Pathogenesis and Host Inflammatory Responses, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Ericka Olgaard
- Department of Pathology, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Stephanie F. Gardner
- College of Pharmacy, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Benjamin C. Amick
- Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
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15
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Colton H, Hodgson D, Hornsby H, Brown R, Mckenzie J, Bradley KL, James C, Lindsey BB, Birch S, Marsh L, Wood S, Bayley M, Dickson G, James DC, Nicklin MJ, Sayers JR, Zafred D, Rowland-Jones SL, Kudesia G, Kucharski A, Darton TC, de Silva TI, Collini PJ. Risk factors for SARS-CoV-2 seroprevalence following the first pandemic wave in UK healthcare workers in a large NHS Foundation Trust. Wellcome Open Res 2022; 6:220. [PMID: 35600250 PMCID: PMC9091808 DOI: 10.12688/wellcomeopenres.17143.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2022] [Indexed: 11/20/2022] Open
Abstract
Background: We aimed to measure SARS-CoV-2 seroprevalence in a cohort of healthcare workers (HCWs) during the first UK wave of the COVID-19 pandemic, explore risk factors associated with infection, and investigate the impact of antibody titres on assay sensitivity. Methods: HCWs at Sheffield Teaching Hospitals NHS Foundation Trust were prospectively enrolled and sampled at two time points. We developed an in-house ELISA for testing participant serum for SARS-CoV-2 IgG and IgA reactivity against Spike and Nucleoprotein. Data were analysed using three statistical models: a seroprevalence model, an antibody kinetics model, and a heterogeneous sensitivity model. Results: Our in-house assay had a sensitivity of 99·47% and specificity of 99·56%. We found that 24·4% (n=311/1275) of HCWs were seropositive as of 12th June 2020. Of these, 39·2% (n=122/311) were asymptomatic. The highest adjusted seroprevalence was measured in HCWs on the Acute Medical Unit (41·1%, 95% CrI 30·0-52·9) and in Physiotherapists and Occupational Therapists (39·2%, 95% CrI 24·4-56·5). Older age groups showed overall higher median antibody titres. Further modelling suggests that, for a serological assay with an overall sensitivity of 80%, antibody titres may be markedly affected by differences in age, with sensitivity estimates of 89% in those over 60 years but 61% in those ≤30 years. Conclusions: HCWs in acute medical units and those working closely with COVID-19 patients were at highest risk of infection, though whether these are infections acquired from patients or other staff is unknown. Current serological assays may underestimate seroprevalence in younger age groups if validated using sera from older and/or more severe COVID-19 cases.
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Affiliation(s)
- Hayley Colton
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - David Hodgson
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Hailey Hornsby
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Rebecca Brown
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Joanne Mckenzie
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Kirsty L. Bradley
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Cameron James
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Benjamin B. Lindsey
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Sarah Birch
- Academic Directorate of Communicable Diseases and Specialised Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
| | - Louise Marsh
- Academic Directorate of Communicable Diseases and Specialised Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
| | - Steven Wood
- Department of Scientific Computing and Informatics, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
| | - Martin Bayley
- Department of Scientific Computing and Informatics, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
| | - Gary Dickson
- Department of Scientific Computing and Informatics, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
| | - David C. James
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, S10 2TN, UK
| | - Martin J. Nicklin
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Jon R. Sayers
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
- Sheffield Institute for Nucleic Acids, University of Sheffield, Sheffield, S10 2TN, UK
| | - Domen Zafred
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Sarah L. Rowland-Jones
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
| | - Goura Kudesia
- Department of Virology, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S5 7AU, UK
| | - Adam Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - CMMID COVID-19 Working Group
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
- Academic Directorate of Communicable Diseases and Specialised Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Scientific Computing and Informatics, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
- Sheffield Institute for Nucleic Acids, University of Sheffield, Sheffield, S10 2TN, UK
- Department of Virology, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S5 7AU, UK
| | - Thomas C. Darton
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
| | - Thushan I. de Silva
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
| | - Paul J. Collini
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
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16
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Goss CW, Maricque BB, Anwuri VV, Cohen RE, Donaldson K, Johnson KJ, Powderly WG, Schechtman KB, Schmidt S, Thompson JJ, Trolard AM, Wang J, Geng EH. SARS-CoV-2 active infection prevalence and seroprevalence in the adult population of St. Louis County. Ann Epidemiol 2022; 71:31-37. [PMID: 35276338 PMCID: PMC8902054 DOI: 10.1016/j.annepidem.2022.03.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 02/25/2022] [Accepted: 03/02/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND The true prevalence of COVID-19 is difficult to estimate due to the absence of random population-based testing. To estimate current and past COVID-19 infection prevalence in a large urban area, we conducted a population-based survey in St. Louis County, Missouri. METHODS The population-based survey of active infection (PCR) and seroprevalence (IgG antibodies) of adults (≥ 18 years) was conducted through random-digit dialing and targeted sampling of St. Louis County residents with oversampling of Black residents. Infection prevalence of residents was estimated using design-based and raking weighting. RESULTS Between August 17 and October 24, 2020, 1,245 residents completed a survey and underwent PCR testing; 1,073 residents completed a survey and underwent PCR and IgG testing or self-reported results. Weighted prevalence estimates of residents with active infection was 1.9% (95% CI, 0.4% to 3.3%) and 5.6% were ever infected (95% CI, 3.3% to 8.0%). Overall infection hospitalization and fatality ratios were 4.9% and 1.4%, respectively. CONCLUSIONS Through October 2020, the percentage of residents that had ever been infected was relatively low. A markedly higher percentage of Black and other minorities compared to White residents were infected with COVID-19. The St. Louis region remained highly vulnerable to widespread infection in late 2020.
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Affiliation(s)
- Charles W Goss
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO.
| | - Brett B Maricque
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO
| | | | - Rachel E Cohen
- St. Louis County Department of Public Health, Berkeley, MO
| | - Kate Donaldson
- St. Louis County Department of Public Health, Berkeley, MO
| | | | - William G Powderly
- Institute for Public Health, Washington University, St. Louis, MO; Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St. Louis, MO
| | - Kenneth B Schechtman
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO
| | - Spring Schmidt
- St. Louis County Department of Public Health, Berkeley, MO
| | - Jeannette Jackson Thompson
- Health & Behavioral Risk Research Center, University of Missouri School of Medicine Dept. of Health Management & Informatics, Columbia, MO
| | - Anne M Trolard
- Institute for Public Health, Washington University, St. Louis, MO
| | - Jinli Wang
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO
| | - Elvin H Geng
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St. Louis, MO
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17
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Giuliano AR, Pilon-Thomas S, Schell MJ, Abrahamsen M, Islam JY, Isaacs-Soriano K, Kennedy K, Dukes CW, Whiting J, Rathwell J, Hensel JA, Mangual LN, Schonbrunn E, Bikowitz M, Grassie D, Yang Y. SARS-CoV-2 Period Seroprevalence and Related Factors, Hillsborough County, Florida, October 2020-March 2021. Emerg Infect Dis 2022; 28:556-563. [PMID: 35081021 PMCID: PMC8888241 DOI: 10.3201/eid2803.211495] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Estimating the actual extent of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is challenging because virus test positivity data undercount the actual number and proportion of persons infected. SARS-CoV-2 seroprevalence is a marker of past SARS-CoV-2 infection regardless of presence or severity of symptoms and therefore is a robust biomarker of infection period prevalence. We estimated SARS-CoV-2 seroprevalence among residents of Hillsborough County, Florida, USA, to determine factors independently associated with SARS-CoV-2 antibody status overall and among asymptomatic antibody-positive persons. Among 867 participants, SARS-CoV-2 period prevalence (October 2020–March 2021) was 19.5% (asymptomatic seroprevalence was 8%). Seroprevalence was 2-fold higher than reported SARS-CoV-2 virus test positivity. Factors related to social distancing (e.g., essential worker status, not practicing social distancing, contact with a virus-positive person, and length of contact exposure time) were consistently associated with seroprevalence but did not differ by time since suspected or known infection (<6 months vs. >6 months).
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18
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Washburn KJ, LeBrón AM, Reyes AS, Becerra I, Bracho A, Ahn E, Urzúa AS, Foo MA, Zárate S, Tanjasiri SP, Boden-Albala B. Orange County, California COVID-19 Vaccine Equity Best Practices Checklist: A Community-Centered Call to Action for Equitable Vaccination Practices. Health Equity 2022; 6:3-12. [PMID: 35112040 PMCID: PMC8804261 DOI: 10.1089/heq.2021.0048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2022] [Indexed: 11/23/2022] Open
Abstract
Introduction: The coronavirus disease 2019 (COVID-19) pandemic has exacerbated longstanding inequities throughout the United States, disproportionately concentrating adverse social, economic, and health-related outcomes among low-income communities and communities of color. Inequitable distribution, prioritization, and uptake of COVID-19 vaccines due to systemic and organizational barriers add to these disproportionate impacts across the United States. Similar patterns have been observed within Orange County, California (OC). Methods: In response to COVID-19 vaccine inequities unfolding locally, the Orange County Health Equity COVID-19 community-academic partnership generated a tool to guide a more equitable vaccine approach. Contents of the OC vaccine equity best practices checklist emerged through synthesis of community-level knowledge about vaccine inequities, literature regarding equitable vaccination considerations, and practice-based health equity guides. We combined into a memo: the checklist, a written explanation of its goals and origins, and three specific action steps meant to further strengthen the focus on vaccine equity. The memo was endorsed by partnership members and distributed to county officials. Discussion: Since the initial composition of the checklist, the local vaccine distribution approach has shifted, suggesting that equitable pandemic responses require continual re-evaluation of local needs and adjustments to recommendations as new information emerges. To understand and address structural changes needed to reduce racial and socioeconomic inequities exacerbated by the pandemic, authentic partnerships between community, academic, and public health practice partners are necessary. Conclusion: As we face continued COVID-19 vaccine rollout, booster vaccination, and future pandemic challenges, community knowledge and public health literature should be integrated to inform similar equity-driven strategic actions.
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Affiliation(s)
- Kameko J. Washburn
- Department of Health, Society, and Behavior, Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, Irvine, California, USA
| | - Alana M.W. LeBrón
- Department of Health, Society, and Behavior, Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, Irvine, California, USA
- Department of Chicano/Latino Studies, School of Social Sciences, University of California, Irvine, Irvine, California, USA
| | - Abigail S. Reyes
- Community Resilience Projects, University of California, Irvine, Irvine, California, USA
| | - Isabel Becerra
- Coalition of Orange County Community Health Centers, Santa Ana, California, USA
| | | | - Ellen Ahn
- Korean Community Services, Buena Park, California, USA
| | | | - Mary Anne Foo
- Orange County Asian and Pacific Islander Community Alliance, Garden Grove, California, USA
| | - Salvador Zárate
- Department of Anthropology, School of Social Sciences, University of California, Irvine, Irvine, California, USA
| | - Sora Park Tanjasiri
- Department of Epidemiology, Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, Irvine, California, USA
| | - Bernadette Boden-Albala
- Department of Health, Society, and Behavior, Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, Irvine, California, USA
- Department of Epidemiology, Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, Irvine, California, USA
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19
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Jehn M, Pandit U, Sabin S, Tompkins C, White J, Kaleta E, Dale AP, Ross HM, Mac McCullough J, Pepin S, Kenny K, Sanborn H, Heywood N, Schnall AH, Lant T, Sunenshine R. Accuracy of Case-Based Seroprevalence of SARS-CoV-2 Antibodies in Maricopa County, Arizona. Am J Public Health 2022; 112:38-42. [PMID: 34936397 PMCID: PMC8713634 DOI: 10.2105/ajph.2021.306568] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/12/2021] [Indexed: 11/04/2022]
Abstract
We conducted a community seroprevalence survey in Arizona, from September 12 to October 1, 2020, to determine the presence of antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We used the seroprevalence estimate to predict SARS-CoV-2 infections in the jurisdiction by applying the adjusted seroprevalence to the county's population. The estimated community seroprevalence of SARS-CoV-2 infections was 4.3 times greater (95% confidence interval = 2.2, 7.5) than the number of reported cases. Field surveys with representative sampling provide data that may help fill in gaps in traditional public health reporting. (Am J Public Health. 2022;112(1):38-42. https://doi.org/10.2105/AJPH.2021.306568).
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Affiliation(s)
- Megan Jehn
- Megan Jehn and Camila Tompkins are with the School of Human Evolution and Social Change, Arizona State University (ASU), Tempe. Urvashi Pandit, Rebecca Sunenshine, and Jessica White are with the Maricopa County Department of Public Health, Phoenix, AZ. Susanna Sabin is with the Center for Evolution and Medicine, ASU. Erin Kaleta is with the Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ. Ariella P. Dale is with the Centers for Disease Control and Prevention (CDC) assigned to Maricopa County Department of Public Health. Heather M. Ross, Katherine Kenny, Heidi Sanborn, and Natalie Heywood are with the Edson College of Nursing and Health Innovation, ASU. J. Mac McCullough is with the College of Health Solutions, ASU. Susan Pepin is with Knowledge Enterprise, ASU. Amy H. Schnall is with the National Center for Environmental Health, CDC, Atlanta, GA. Timothy Lant is with the Biodesign Institute, ASU
| | - Urvashi Pandit
- Megan Jehn and Camila Tompkins are with the School of Human Evolution and Social Change, Arizona State University (ASU), Tempe. Urvashi Pandit, Rebecca Sunenshine, and Jessica White are with the Maricopa County Department of Public Health, Phoenix, AZ. Susanna Sabin is with the Center for Evolution and Medicine, ASU. Erin Kaleta is with the Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ. Ariella P. Dale is with the Centers for Disease Control and Prevention (CDC) assigned to Maricopa County Department of Public Health. Heather M. Ross, Katherine Kenny, Heidi Sanborn, and Natalie Heywood are with the Edson College of Nursing and Health Innovation, ASU. J. Mac McCullough is with the College of Health Solutions, ASU. Susan Pepin is with Knowledge Enterprise, ASU. Amy H. Schnall is with the National Center for Environmental Health, CDC, Atlanta, GA. Timothy Lant is with the Biodesign Institute, ASU
| | - Susanna Sabin
- Megan Jehn and Camila Tompkins are with the School of Human Evolution and Social Change, Arizona State University (ASU), Tempe. Urvashi Pandit, Rebecca Sunenshine, and Jessica White are with the Maricopa County Department of Public Health, Phoenix, AZ. Susanna Sabin is with the Center for Evolution and Medicine, ASU. Erin Kaleta is with the Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ. Ariella P. Dale is with the Centers for Disease Control and Prevention (CDC) assigned to Maricopa County Department of Public Health. Heather M. Ross, Katherine Kenny, Heidi Sanborn, and Natalie Heywood are with the Edson College of Nursing and Health Innovation, ASU. J. Mac McCullough is with the College of Health Solutions, ASU. Susan Pepin is with Knowledge Enterprise, ASU. Amy H. Schnall is with the National Center for Environmental Health, CDC, Atlanta, GA. Timothy Lant is with the Biodesign Institute, ASU
| | - Camila Tompkins
- Megan Jehn and Camila Tompkins are with the School of Human Evolution and Social Change, Arizona State University (ASU), Tempe. Urvashi Pandit, Rebecca Sunenshine, and Jessica White are with the Maricopa County Department of Public Health, Phoenix, AZ. Susanna Sabin is with the Center for Evolution and Medicine, ASU. Erin Kaleta is with the Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ. Ariella P. Dale is with the Centers for Disease Control and Prevention (CDC) assigned to Maricopa County Department of Public Health. Heather M. Ross, Katherine Kenny, Heidi Sanborn, and Natalie Heywood are with the Edson College of Nursing and Health Innovation, ASU. J. Mac McCullough is with the College of Health Solutions, ASU. Susan Pepin is with Knowledge Enterprise, ASU. Amy H. Schnall is with the National Center for Environmental Health, CDC, Atlanta, GA. Timothy Lant is with the Biodesign Institute, ASU
| | - Jessica White
- Megan Jehn and Camila Tompkins are with the School of Human Evolution and Social Change, Arizona State University (ASU), Tempe. Urvashi Pandit, Rebecca Sunenshine, and Jessica White are with the Maricopa County Department of Public Health, Phoenix, AZ. Susanna Sabin is with the Center for Evolution and Medicine, ASU. Erin Kaleta is with the Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ. Ariella P. Dale is with the Centers for Disease Control and Prevention (CDC) assigned to Maricopa County Department of Public Health. Heather M. Ross, Katherine Kenny, Heidi Sanborn, and Natalie Heywood are with the Edson College of Nursing and Health Innovation, ASU. J. Mac McCullough is with the College of Health Solutions, ASU. Susan Pepin is with Knowledge Enterprise, ASU. Amy H. Schnall is with the National Center for Environmental Health, CDC, Atlanta, GA. Timothy Lant is with the Biodesign Institute, ASU
| | - Erin Kaleta
- Megan Jehn and Camila Tompkins are with the School of Human Evolution and Social Change, Arizona State University (ASU), Tempe. Urvashi Pandit, Rebecca Sunenshine, and Jessica White are with the Maricopa County Department of Public Health, Phoenix, AZ. Susanna Sabin is with the Center for Evolution and Medicine, ASU. Erin Kaleta is with the Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ. Ariella P. Dale is with the Centers for Disease Control and Prevention (CDC) assigned to Maricopa County Department of Public Health. Heather M. Ross, Katherine Kenny, Heidi Sanborn, and Natalie Heywood are with the Edson College of Nursing and Health Innovation, ASU. J. Mac McCullough is with the College of Health Solutions, ASU. Susan Pepin is with Knowledge Enterprise, ASU. Amy H. Schnall is with the National Center for Environmental Health, CDC, Atlanta, GA. Timothy Lant is with the Biodesign Institute, ASU
| | - Ariella P Dale
- Megan Jehn and Camila Tompkins are with the School of Human Evolution and Social Change, Arizona State University (ASU), Tempe. Urvashi Pandit, Rebecca Sunenshine, and Jessica White are with the Maricopa County Department of Public Health, Phoenix, AZ. Susanna Sabin is with the Center for Evolution and Medicine, ASU. Erin Kaleta is with the Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ. Ariella P. Dale is with the Centers for Disease Control and Prevention (CDC) assigned to Maricopa County Department of Public Health. Heather M. Ross, Katherine Kenny, Heidi Sanborn, and Natalie Heywood are with the Edson College of Nursing and Health Innovation, ASU. J. Mac McCullough is with the College of Health Solutions, ASU. Susan Pepin is with Knowledge Enterprise, ASU. Amy H. Schnall is with the National Center for Environmental Health, CDC, Atlanta, GA. Timothy Lant is with the Biodesign Institute, ASU
| | - Heather M Ross
- Megan Jehn and Camila Tompkins are with the School of Human Evolution and Social Change, Arizona State University (ASU), Tempe. Urvashi Pandit, Rebecca Sunenshine, and Jessica White are with the Maricopa County Department of Public Health, Phoenix, AZ. Susanna Sabin is with the Center for Evolution and Medicine, ASU. Erin Kaleta is with the Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ. Ariella P. Dale is with the Centers for Disease Control and Prevention (CDC) assigned to Maricopa County Department of Public Health. Heather M. Ross, Katherine Kenny, Heidi Sanborn, and Natalie Heywood are with the Edson College of Nursing and Health Innovation, ASU. J. Mac McCullough is with the College of Health Solutions, ASU. Susan Pepin is with Knowledge Enterprise, ASU. Amy H. Schnall is with the National Center for Environmental Health, CDC, Atlanta, GA. Timothy Lant is with the Biodesign Institute, ASU
| | - J Mac McCullough
- Megan Jehn and Camila Tompkins are with the School of Human Evolution and Social Change, Arizona State University (ASU), Tempe. Urvashi Pandit, Rebecca Sunenshine, and Jessica White are with the Maricopa County Department of Public Health, Phoenix, AZ. Susanna Sabin is with the Center for Evolution and Medicine, ASU. Erin Kaleta is with the Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ. Ariella P. Dale is with the Centers for Disease Control and Prevention (CDC) assigned to Maricopa County Department of Public Health. Heather M. Ross, Katherine Kenny, Heidi Sanborn, and Natalie Heywood are with the Edson College of Nursing and Health Innovation, ASU. J. Mac McCullough is with the College of Health Solutions, ASU. Susan Pepin is with Knowledge Enterprise, ASU. Amy H. Schnall is with the National Center for Environmental Health, CDC, Atlanta, GA. Timothy Lant is with the Biodesign Institute, ASU
| | - Susan Pepin
- Megan Jehn and Camila Tompkins are with the School of Human Evolution and Social Change, Arizona State University (ASU), Tempe. Urvashi Pandit, Rebecca Sunenshine, and Jessica White are with the Maricopa County Department of Public Health, Phoenix, AZ. Susanna Sabin is with the Center for Evolution and Medicine, ASU. Erin Kaleta is with the Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ. Ariella P. Dale is with the Centers for Disease Control and Prevention (CDC) assigned to Maricopa County Department of Public Health. Heather M. Ross, Katherine Kenny, Heidi Sanborn, and Natalie Heywood are with the Edson College of Nursing and Health Innovation, ASU. J. Mac McCullough is with the College of Health Solutions, ASU. Susan Pepin is with Knowledge Enterprise, ASU. Amy H. Schnall is with the National Center for Environmental Health, CDC, Atlanta, GA. Timothy Lant is with the Biodesign Institute, ASU
| | - Katherine Kenny
- Megan Jehn and Camila Tompkins are with the School of Human Evolution and Social Change, Arizona State University (ASU), Tempe. Urvashi Pandit, Rebecca Sunenshine, and Jessica White are with the Maricopa County Department of Public Health, Phoenix, AZ. Susanna Sabin is with the Center for Evolution and Medicine, ASU. Erin Kaleta is with the Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ. Ariella P. Dale is with the Centers for Disease Control and Prevention (CDC) assigned to Maricopa County Department of Public Health. Heather M. Ross, Katherine Kenny, Heidi Sanborn, and Natalie Heywood are with the Edson College of Nursing and Health Innovation, ASU. J. Mac McCullough is with the College of Health Solutions, ASU. Susan Pepin is with Knowledge Enterprise, ASU. Amy H. Schnall is with the National Center for Environmental Health, CDC, Atlanta, GA. Timothy Lant is with the Biodesign Institute, ASU
| | - Heidi Sanborn
- Megan Jehn and Camila Tompkins are with the School of Human Evolution and Social Change, Arizona State University (ASU), Tempe. Urvashi Pandit, Rebecca Sunenshine, and Jessica White are with the Maricopa County Department of Public Health, Phoenix, AZ. Susanna Sabin is with the Center for Evolution and Medicine, ASU. Erin Kaleta is with the Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ. Ariella P. Dale is with the Centers for Disease Control and Prevention (CDC) assigned to Maricopa County Department of Public Health. Heather M. Ross, Katherine Kenny, Heidi Sanborn, and Natalie Heywood are with the Edson College of Nursing and Health Innovation, ASU. J. Mac McCullough is with the College of Health Solutions, ASU. Susan Pepin is with Knowledge Enterprise, ASU. Amy H. Schnall is with the National Center for Environmental Health, CDC, Atlanta, GA. Timothy Lant is with the Biodesign Institute, ASU
| | - Natalie Heywood
- Megan Jehn and Camila Tompkins are with the School of Human Evolution and Social Change, Arizona State University (ASU), Tempe. Urvashi Pandit, Rebecca Sunenshine, and Jessica White are with the Maricopa County Department of Public Health, Phoenix, AZ. Susanna Sabin is with the Center for Evolution and Medicine, ASU. Erin Kaleta is with the Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ. Ariella P. Dale is with the Centers for Disease Control and Prevention (CDC) assigned to Maricopa County Department of Public Health. Heather M. Ross, Katherine Kenny, Heidi Sanborn, and Natalie Heywood are with the Edson College of Nursing and Health Innovation, ASU. J. Mac McCullough is with the College of Health Solutions, ASU. Susan Pepin is with Knowledge Enterprise, ASU. Amy H. Schnall is with the National Center for Environmental Health, CDC, Atlanta, GA. Timothy Lant is with the Biodesign Institute, ASU
| | - Amy H Schnall
- Megan Jehn and Camila Tompkins are with the School of Human Evolution and Social Change, Arizona State University (ASU), Tempe. Urvashi Pandit, Rebecca Sunenshine, and Jessica White are with the Maricopa County Department of Public Health, Phoenix, AZ. Susanna Sabin is with the Center for Evolution and Medicine, ASU. Erin Kaleta is with the Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ. Ariella P. Dale is with the Centers for Disease Control and Prevention (CDC) assigned to Maricopa County Department of Public Health. Heather M. Ross, Katherine Kenny, Heidi Sanborn, and Natalie Heywood are with the Edson College of Nursing and Health Innovation, ASU. J. Mac McCullough is with the College of Health Solutions, ASU. Susan Pepin is with Knowledge Enterprise, ASU. Amy H. Schnall is with the National Center for Environmental Health, CDC, Atlanta, GA. Timothy Lant is with the Biodesign Institute, ASU
| | - Timothy Lant
- Megan Jehn and Camila Tompkins are with the School of Human Evolution and Social Change, Arizona State University (ASU), Tempe. Urvashi Pandit, Rebecca Sunenshine, and Jessica White are with the Maricopa County Department of Public Health, Phoenix, AZ. Susanna Sabin is with the Center for Evolution and Medicine, ASU. Erin Kaleta is with the Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ. Ariella P. Dale is with the Centers for Disease Control and Prevention (CDC) assigned to Maricopa County Department of Public Health. Heather M. Ross, Katherine Kenny, Heidi Sanborn, and Natalie Heywood are with the Edson College of Nursing and Health Innovation, ASU. J. Mac McCullough is with the College of Health Solutions, ASU. Susan Pepin is with Knowledge Enterprise, ASU. Amy H. Schnall is with the National Center for Environmental Health, CDC, Atlanta, GA. Timothy Lant is with the Biodesign Institute, ASU
| | - Rebecca Sunenshine
- Megan Jehn and Camila Tompkins are with the School of Human Evolution and Social Change, Arizona State University (ASU), Tempe. Urvashi Pandit, Rebecca Sunenshine, and Jessica White are with the Maricopa County Department of Public Health, Phoenix, AZ. Susanna Sabin is with the Center for Evolution and Medicine, ASU. Erin Kaleta is with the Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ. Ariella P. Dale is with the Centers for Disease Control and Prevention (CDC) assigned to Maricopa County Department of Public Health. Heather M. Ross, Katherine Kenny, Heidi Sanborn, and Natalie Heywood are with the Edson College of Nursing and Health Innovation, ASU. J. Mac McCullough is with the College of Health Solutions, ASU. Susan Pepin is with Knowledge Enterprise, ASU. Amy H. Schnall is with the National Center for Environmental Health, CDC, Atlanta, GA. Timothy Lant is with the Biodesign Institute, ASU
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SARS-CoV-2 Seroprevalence in Western Romania, March to June 2021. MEDICINA (KAUNAS, LITHUANIA) 2021; 58:medicina58010035. [PMID: 35056343 PMCID: PMC8781951 DOI: 10.3390/medicina58010035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 12/20/2021] [Accepted: 12/23/2021] [Indexed: 12/11/2022]
Abstract
Background and Objectives: The extent of SARS-CoV-2 infection among a population may be assessed by the presence of serum SARS-CoV-2 antibodies, which indicates previous exposure. The aim of this study was to determine the seroprevalence of SARS-CoV-2 infection in the adult population from Western Romania. Materials and Methods: Samples of 2443 consecutive individuals, referred for routine laboratory investigations, were tested for SARS-CoV-2 antibodies using the Elecsys immunoassay that targets the nucleocapsid protein, for identifying the presence of the total antibodies against SARS-CoV-2. Results: The overall SARS-CoV-2 seroprevalence was 45.60%. SARS-CoV-2 seroprevalence was significantly higher in age group 30-49 years (53.94%) compared to age groups 50-69 years (43.53%) and 70-91 years (30.79%) (p < 0.001, p < 0.001, respectively). No significant difference in seroprevalence was observed between females (44.83%) and males (47.05%). Conclusions: Our data revealed a high seroprevalence of SARS-CoV-2 infection in the adult population from Western Romania and indicate the rapid and significant spread of the virus. The estimated prevalence of 45.60% was 6 times higher than the rate of confirmed COVID-19 cases reported in the study area. This indicates the magnitude of virus transmission in the community.
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21
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Gashi B, Osmani V, Halili R, Hoxha T, Kamberi A, Hoti N, Agahi R, Basha V, Berisha V, Hoxha I. Seroprevalence of Anti-SARS-CoV-2 Antibodies among Municipal Staff in the Municipality of Prishtina. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182312545. [PMID: 34886272 PMCID: PMC8656675 DOI: 10.3390/ijerph182312545] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/22/2021] [Accepted: 11/27/2021] [Indexed: 12/30/2022]
Abstract
BACKGROUND Some studies have assessed the seroprevalence of anti-SARS-CoV-2 antibodies in different populations. Very few studies have explored seroprevalence in municipal workers, an important and potentially high-risk population. This study aims to determine the prevalence of anti-SARS-CoV-2 antibodies in municipal workers, with the additional examination of the association of prevalence with various demographic, health-related, and epidemiological factors. METHODS We surveyed and tested for seroprevalence 418 public servants from the municipality of Prishtina, the capital of Kosovo. The primary prespecified outcome was the seroprevalence of anti-SARS-CoV-2 antibodies, IgG, and IgM. Additional outcomes were crude and adjusted odds ratios of seroprevalence by different factors. RESULTS 21.1% of municipal workers tested positive for either IgM or IgG. Of these, 9.6% were positive for IgM and 19.4% for IgG. Data showed high levels of adherence to protective measures, e.g., social distancing in the office, but calculation of ORs did not show a significant difference between those reporting adherence to such measures and those reporting nonadherence. Of other examined factors, significantly lower odds were observed for smokers (0.52, 95% CI 0.28, 0.97), while municipal workers with infected family members had elevated odds of seropositivity according to both crude (2.19, 95% CI 1.34, 3.59) and adjusted (2.00, 95% CI 1.17, 3.41) ORs. CONCLUSIONS Most answers from public servants demonstrated compliance to social-distancing policies in the workplace, but analysis of crude and adjusted odds ratios did not suggest a significant effect between municipal workers who followed these guidelines and those who did not. Results from this study help Kosovo policy makers in understanding the level of prevalence of COVID-19 in municipal workers and the effect of different factors on such prevalence. Results from the study could inform future decisions on the design and application of protective measures for municipal workers. Our findings should encourage further research to assess the extent of the spread of COVID-19 to other essential workers in Kosovo, including retail workers.
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Affiliation(s)
- Bujar Gashi
- Main Family Medical Centre, Municipality of Prishtina, 10000 Prishtina, Kosovo; (B.G.); (R.H.); (T.H.); (A.K.); (N.H.)
| | - Vesa Osmani
- Evidence Synthesis Group, 10000 Prishtina, Kosovo;
| | - Rrezart Halili
- Main Family Medical Centre, Municipality of Prishtina, 10000 Prishtina, Kosovo; (B.G.); (R.H.); (T.H.); (A.K.); (N.H.)
| | - Teuta Hoxha
- Main Family Medical Centre, Municipality of Prishtina, 10000 Prishtina, Kosovo; (B.G.); (R.H.); (T.H.); (A.K.); (N.H.)
| | - Agron Kamberi
- Main Family Medical Centre, Municipality of Prishtina, 10000 Prishtina, Kosovo; (B.G.); (R.H.); (T.H.); (A.K.); (N.H.)
| | - Nexhmedin Hoti
- Main Family Medical Centre, Municipality of Prishtina, 10000 Prishtina, Kosovo; (B.G.); (R.H.); (T.H.); (A.K.); (N.H.)
| | - Riaz Agahi
- Research Unit, Heimerer College, 10000 Prishtina, Kosovo;
| | - Vlora Basha
- IndexKosova, 10000 Prishtina, Kosovo; (V.B.); (V.B.)
| | - Visar Berisha
- IndexKosova, 10000 Prishtina, Kosovo; (V.B.); (V.B.)
| | - Ilir Hoxha
- Evidence Synthesis Group, 10000 Prishtina, Kosovo;
- Research Unit, Heimerer College, 10000 Prishtina, Kosovo;
- The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH 03766, USA
- Correspondence: ; Tel.: +383-45-588-683
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22
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Yek C, Nam VS, Leang R, Parker DM, Heng S, Souv K, Sovannaroth S, Mayxay M, AbuBakar S, Sasmono RT, Tran ND, Le Nguyen HK, Lon C, Boonnak K, Huy R, Sovann L, Manning JE. The Pandemic Experience in Southeast Asia: Interface Between SARS-CoV-2, Malaria, and Dengue. FRONTIERS IN TROPICAL DISEASES 2021; 2:788590. [PMID: 35373190 PMCID: PMC8975143 DOI: 10.3389/fitd.2021.788590] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Southeast Asia (SEA) emerged relatively unscathed from the first year of the global SARS-CoV-2 pandemic, but as of July 2021 the region is experiencing a surge in case numbers primarily driven by Alpha (B.1.1.7) and subsequently the more transmissible Delta (B.1.617.2) variants. While initial disease burden was mitigated by swift government responses, favorable cultural and societal factors, the more recent rise in cases suggests an under-appreciation of prior prevalence and over-appreciation of possible cross-protective immunity from exposure to endemic viruses, and highlights the effects of vaccine rollout at varying tempos and of variable efficacy. This burgeoning crisis is further complicated by co-existence of malaria and dengue in the region, with implications of serological cross-reactivity on interpretation of SARS-CoV-2 assays and competing resource demands impacting efforts to contain both endemic and pandemic disease.
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Affiliation(s)
- Christina Yek
- Department of Critical Care Medicine, National Institutes of Health Clinical Center, Bethesda, MD, United States
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, United States
| | - Vu Sinh Nam
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
| | | | - Daniel M. Parker
- Department of Population Health and Disease Prevention, University of California, Irvine, Irvine, CA, United States
- Department of Epidemiology, University of California, Irvine, Irvine, CA, United States
| | - Seng Heng
- Ministry of Health, Phnom Penh, Cambodia
| | | | | | - Mayfong Mayxay
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Laos
- Institute of Research and Education Development, University of Health Sciences, Vientiane, Laos
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Sazaly AbuBakar
- WHO Collaborating Center for Arbovirus Reference and Research (Dengue) and Tropical Infectious Diseases Research and Education Center, Universiti Malaya, Kuala Lumpur, Malaysia
| | | | - Nhu Duong Tran
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
| | | | - Chanthap Lon
- International Center of Excellence in Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Phnom Penh, Cambodia
| | - Kobporn Boonnak
- Department of Microbiology and Immunology, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Rekol Huy
- Ministry of Health, Phnom Penh, Cambodia
| | - Ly Sovann
- Ministry of Health, Phnom Penh, Cambodia
| | - Jessica E. Manning
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, United States
- International Center of Excellence in Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Phnom Penh, Cambodia
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23
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Inferring the COVID-19 infection fatality rate in the community-dwelling population: a simple Bayesian evidence synthesis of seroprevalence study data and imprecise mortality data. Epidemiol Infect 2021. [PMCID: PMC8632419 DOI: 10.1017/s0950268821002405] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Abstract
Estimating the coronavirus disease-2019 (COVID-19) infection fatality rate (IFR) has proven to be particularly challenging –and rather controversial– due to the fact that both the data on deaths and the data on the number of individuals infected are subject to many different biases. We consider a Bayesian evidence synthesis approach which, while simple enough for researchers to understand and use, accounts for many important sources of uncertainty inherent in both the seroprevalence and mortality data. With the understanding that the results of one's evidence synthesis analysis may be largely driven by which studies are included and which are excluded, we conduct two separate parallel analyses based on two lists of eligible studies obtained from two different research teams. The results from both analyses are rather similar. With the first analysis, we estimate the COVID-19 IFR to be 0.31% [95% credible interval (CrI) of (0.16%, 0.53%)] for a typical community-dwelling population where 9% of the population is aged over 65 years and where the gross-domestic-product at purchasing-power-parity (GDP at PPP) per capita is $17.8k (the approximate worldwide average). With the second analysis, we obtain 0.32% [95% CrI of (0.19%, 0.47%)]. Our results suggest that, as one might expect, lower IFRs are associated with younger populations (and may also be associated with wealthier populations). For a typical community-dwelling population with the age and wealth of the United States we obtain IFR estimates of 0.43% and 0.41%; and with the age and wealth of the European Union, we obtain IFR estimates of 0.67% and 0.51%.
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24
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Distinct SARS-CoV-2 antibody reactivity patterns elicited by natural infection and mRNA vaccination. NPJ Vaccines 2021; 6:132. [PMID: 34737318 PMCID: PMC8568980 DOI: 10.1038/s41541-021-00396-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 10/08/2021] [Indexed: 01/28/2023] Open
Abstract
We analyzed data from two ongoing COVID-19 longitudinal serological surveys in Orange County, CA., between April 2020 and March 2021. A total of 8476 finger stick blood specimens were collected before and after a vaccination campaign. IgG levels were determined using a multiplex antigen microarray containing antigens from SARS-CoV-2, SARS, MERS, Common CoV, and Influenza. Twenty-six percent of specimens from unvaccinated Orange County residents in December 2020 were SARS-CoV-2 seropositive; out of 852 seropositive individuals 77 had symptoms and 9 sought medical care. The antibody response was predominantly against nucleocapsid (NP), full length, and S2 domain of spike. Anti-receptor binding domain (RBD) reactivity was low and not cross-reactive against SARS S1 or SARS RBD. A vaccination campaign at the University of California Irvine Medical Center (UCIMC) started on December, 2020 and 6724 healthcare workers were vaccinated within 3 weeks. Seroprevalence increased from 13% pre-vaccination to 79% post-vaccination in January, 93% in February, and 99% in March. mRNA vaccination induced higher antibody levels than natural exposure, especially against the RBD domain and cross-reactivity against SARS RBD and S1 was observed. Nucleocapsid protein antibodies can be used to distinguish vaccinees to classify pre-exposure to SARS-CoV-2 Previously infected individuals developed higher antibody titers to the vaccine than non pre-exposed individuals. Hospitalized patients in intensive care with severe disease reach significantly higher antibody levels than mild cases, but lower antibody levels compared to the vaccine. These results indicate that mRNA vaccination rapidly induces a much stronger and broader antibody response than SARS-CoV-2 infection.
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25
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Cooper DM, Zulu MZ, Jankeel A, Ibraim IC, Ardo J, Kasper K, Stephens D, Meyer A, Stehli A, Condon C, Londoño ME, Schreiber CM, Lopez NV, Camplain RL, Weiss M, Golden C, Radom-Aizik S, Boden-Albala B, Chau C, Messaoudi I, Ulloa ER. SARS-CoV-2 acquisition and immune pathogenesis among school-aged learners in four diverse schools. Pediatr Res 2021; 90:1073-1080. [PMID: 34304252 PMCID: PMC8308070 DOI: 10.1038/s41390-021-01660-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/07/2021] [Accepted: 06/11/2021] [Indexed: 01/30/2023]
Abstract
BACKGROUND Understanding SARS-CoV-2 infection in children is necessary to reopen schools safely. METHODS We measured SARS-CoV-2 infection in 320 learners [10.5 ± 2.1 (sd); 7-17 y.o.] at four diverse schools with either remote or on-site learning. Schools A and B served low-income Hispanic learners; school C served many special-needs learners, and all provided predominantly remote instruction. School D served middle- and upper-income learners, with predominantly on-site instruction. Testing occurred in the fall (2020), and 6-8 weeks later during the fall-winter surge (notable for a tenfold increase in COVID-19 cases). Immune responses and mitigation fidelity were also measured. RESULTS We found SARS-CoV-2 infections in 17 learners only during the surge. School A (97% remote learners) had the highest infection (10/70, 14.3%, p < 0.01) and IgG positivity rates (13/66, 19.7%). School D (93% on-site learners) had the lowest infection and IgG positivity rates (1/63, 1.6%). Mitigation compliance [physical distancing (mean 87.4%) and face-covering (91.3%)] was remarkably high at all schools. Documented SARS-CoV-2-infected learners had neutralizing antibodies (94.7%), robust IFN-γ + T cell responses, and reduced monocytes. CONCLUSIONS Schools can implement successful mitigation strategies across a wide range of student diversity. Despite asymptomatic to mild SARS-CoV-2 infection, children generate robust humoral and cellular immune responses. IMPACT Successful COVID-19 mitigation was implemented across a diverse range of schools. School-associated SARS-CoV-2 infections reflect regional rates rather than remote or on-site learning. Seropositive school-aged children with asymptomatic to mild SARS-CoV-2 infections generate robust humoral and cellular immunity.
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Affiliation(s)
- Dan M Cooper
- Institute for Clinical and Translational Science, University of California Irvine, Irvine, CA, USA
- Department of Pediatrics, University of California Irvine School of Medicine, Irvine, CA, USA
| | - Michael Z Zulu
- Center for Virus Research, University of California Irvine, School of Biological Sciences, Irvine, CA, USA
| | - Allen Jankeel
- Center for Virus Research, University of California Irvine, School of Biological Sciences, Irvine, CA, USA
| | - Izabela Coimbra Ibraim
- Center for Virus Research, University of California Irvine, School of Biological Sciences, Irvine, CA, USA
| | - Jessica Ardo
- Children's Hospital of Orange County, Orange, CA, USA
| | | | - Diana Stephens
- Institute for Clinical and Translational Science, University of California Irvine, Irvine, CA, USA
| | - Andria Meyer
- Institute for Clinical and Translational Science, University of California Irvine, Irvine, CA, USA
| | - Annamarie Stehli
- Department of Pediatrics, Pediatric Exercise and Genomics Research Center, University of California Irvine, Irvine, CA, USA
| | - Curt Condon
- Orange County Health Care Agency, Santa Ana, CA, USA
| | - Mary E Londoño
- University of California Irvine School of Medicine, Irvine, CA, USA
| | - Casey M Schreiber
- Children's Hospital of Orange County, Orange, CA, USA
- University of California Irvine Health, Orange, CA, USA
| | - Nanette V Lopez
- Department of Health Sciences, Center for Health Equity Research, Northern Arizona University, Flagstaff, AZ, USA
| | - Ricky L Camplain
- Department of Health Sciences, Center for Health Equity Research, Northern Arizona University, Flagstaff, AZ, USA
| | - Michael Weiss
- Children's Hospital of Orange County, Orange, CA, USA
| | | | - Shlomit Radom-Aizik
- Department of Pediatrics, Pediatric Exercise and Genomics Research Center, University of California Irvine, Irvine, CA, USA
| | | | - Clayton Chau
- Orange County Health Care Agency, Santa Ana, CA, USA
| | - Ilhem Messaoudi
- Center for Virus Research, University of California Irvine, School of Biological Sciences, Irvine, CA, USA
| | - Erlinda R Ulloa
- Department of Pediatrics, University of California Irvine School of Medicine, Irvine, CA, USA.
- Children's Hospital of Orange County, Orange, CA, USA.
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26
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Bruckner T, Das A, Singh P, Boden-Albala B. SARS-CoV-2: An Empirical Investigation of Rose's Population-based Logic. Epidemiology 2021; 32:807-810. [PMID: 34347688 PMCID: PMC8478111 DOI: 10.1097/ede.0000000000001405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 07/19/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Geoffrey Rose's paper "Sick Individuals, Sick Populations" highlights the counterintuitive finding that the largest share of morbidity arises from populations engaging in low- to moderate-risk behavior. Scholars refer to this finding as the prevention paradox. We examine whether this logic applies to SARS-CoV-2 infected persons considered low to moderate risk. METHODS We conducted a population-representative survey and sero-surveillance study for SARS-CoV-2 among adults in Orange County, California. Participants answered questions about health behaviors and provided a finger-pin-prick sample from 10 July to 16 August 2020. RESULTS Of the 2979 adults, those reporting low- and moderate-risk behavior accounted for between 78% and 92% of SARS-CoV-2 infections. Asymptomatic individuals, as well as persons with low and moderate scores for self-reported likelihood of having had SARS-CoV-2, accounted for the majority of infections. CONCLUSIONS Our findings support Rose's logic, which encourages public health measures among persons who self-identify as unlikely to have SARS-CoV-2. See video abstract at, http://links.lww.com/EDE/B860.
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Affiliation(s)
- Tim Bruckner
- From the Program in Public Health, University of California, Irvine, CA
- Center for Population, Inequality, and Policy, University of California, Irvine, CA
| | - Abhery Das
- From the Program in Public Health, University of California, Irvine, CA
| | - Parvati Singh
- Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH
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27
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Parker DM, Bruckner T, Vieira VM, Medina C, Minin VN, Felgner PL, Dratch A, Zahn M, Bartell SM, Boden-Albala B. Predictors of Test Positivity, Mortality, and Seropositivity during the Early Coronavirus Disease Epidemic, Orange County, California, USA. Emerg Infect Dis 2021; 27:2604-2618. [PMID: 34545792 PMCID: PMC8462316 DOI: 10.3201/eid2710.210103] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
We conducted a detailed analysis of coronavirus disease in a large population center in southern California, USA (Orange County, population 3.2 million), to determine heterogeneity in risks for infection, test positivity, and death. We used a combination of datasets, including a population-representative seroprevalence survey, to assess the actual burden of disease and testing intensity, test positivity, and mortality. In the first month of the local epidemic (March 2020), case incidence clustered in high-income areas. This pattern quickly shifted, and cases next clustered in much higher rates in the north-central area of the county, which has a lower socioeconomic status. Beginning in April 2020, a concentration of reported cases, test positivity, testing intensity, and seropositivity in a north-central area persisted. At the individual level, several factors (e.g., age, race or ethnicity, and ZIP codes with low educational attainment) strongly affected risk for seropositivity and death.
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28
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Colton H, Hodgson D, Hornsby H, Brown R, Mckenzie J, Bradley KL, James C, Lindsey BB, Birch S, Marsh L, Wood S, Bayley M, Dickson G, James DC, Nicklin MJ, Sayers JR, Zafred D, Rowland-Jones SL, Kudesia G, Kucharski A, Darton TC, de Silva TI, Collini PJ. Risk factors for SARS-CoV-2 seroprevalence following the first pandemic wave in UK healthcare workers in a large NHS Foundation Trust. Wellcome Open Res 2021; 6:220. [PMID: 35600250 PMCID: PMC9091808 DOI: 10.12688/wellcomeopenres.17143.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/24/2021] [Indexed: 12/23/2022] Open
Abstract
Background: We aimed to measure SARS-CoV-2 seroprevalence in a cohort of healthcare workers (HCWs) during the first UK wave of the COVID-19 pandemic, explore risk factors associated with infection, and investigate the impact of antibody titres on assay sensitivity. Methods: HCWs at Sheffield Teaching Hospitals NHS Foundation Trust were prospectively enrolled and sampled at two time points. SARS-CoV-2 antibodies were tested using an in-house assay for IgG and IgA reactivity against Spike and Nucleoprotein (sensitivity 99·47%, specificity 99·56%). Data were analysed using three statistical models: a seroprevalence model, an antibody kinetics model, and a heterogeneous sensitivity model. Results: As of 12th June 2020, 24·4% (n=311/1275) of HCWs were seropositive. Of these, 39·2% (n=122/311) were asymptomatic. The highest adjusted seroprevalence was measured in HCWs on the Acute Medical Unit (41·1%, 95% CrI 30·0-52·9) and in Physiotherapists and Occupational Therapists (39·2%, 95% CrI 24·4-56·5). Older age groups showed overall higher median antibody titres. Further modelling suggests that, for a serological assay with an overall sensitivity of 80%, antibody titres may be markedly affected by differences in age, with sensitivity estimates of 89% in those over 60 years but 61% in those ≤30 years. Conclusions: HCWs in acute medical units working closely with COVID-19 patients were at highest risk of infection, though whether these are infections acquired from patients or other staff is unknown. Current serological assays may underestimate seroprevalence in younger age groups if validated using sera from older and/or more symptomatic individuals.
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Affiliation(s)
- Hayley Colton
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - David Hodgson
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Hailey Hornsby
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Rebecca Brown
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Joanne Mckenzie
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Kirsty L. Bradley
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Cameron James
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Benjamin B. Lindsey
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Sarah Birch
- Academic Directorate of Communicable Diseases and Specialised Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
| | - Louise Marsh
- Academic Directorate of Communicable Diseases and Specialised Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
| | - Steven Wood
- Department of Scientific Computing and Informatics, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
| | - Martin Bayley
- Department of Scientific Computing and Informatics, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
| | - Gary Dickson
- Department of Scientific Computing and Informatics, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
| | - David C. James
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, S10 2TN, UK
| | - Martin J. Nicklin
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Jon R. Sayers
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
- Sheffield Institute for Nucleic Acids, University of Sheffield, Sheffield, S10 2TN, UK
| | - Domen Zafred
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
| | - Sarah L. Rowland-Jones
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
| | - Goura Kudesia
- Department of Virology, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S5 7AU, UK
| | - Adam Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - CMMID COVID-19 Working Group
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
- Academic Directorate of Communicable Diseases and Specialised Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Scientific Computing and Informatics, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
- Sheffield Institute for Nucleic Acids, University of Sheffield, Sheffield, S10 2TN, UK
- Department of Virology, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S5 7AU, UK
| | - Thomas C. Darton
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
| | - Thushan I. de Silva
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
| | - Paul J. Collini
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals Nhs Foundation Trust, Sheffield, S10 2JF, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, S10 2TN, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
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Vieira V, Tang IW, Bartell S, Zahn M, Fedoruk MJ. SARS-CoV-2 antibody seroprevalence among firefighters in Orange County, California. Occup Environ Med 2021; 78:789-792. [PMID: 34433659 DOI: 10.1136/oemed-2021-107461] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 08/03/2021] [Indexed: 11/04/2022]
Abstract
OBJECTIVES We conducted serological SARS-CoV-2 antibody testing from October to November 2020 to estimate the SARS-CoV-2 seroprevalence among firefighters/paramedics in Orange County (OC), California. METHODS OC firefighters employed at the time of the surveillance activity were invited to participate in a voluntary survey that collected demographic, occupational and previous COVID-19 testing data, and a SARS-CoV-2 immunoglobulin (Ig)G antibody blood test. We collected venous blood samples using mobile phlebotomy teams that travelled to individual fire stations, in coordination with an annual tuberculosis testing campaign for firefighters employed by OC Fire Authority (OCFA), and independently for firefighters employed by cities. We estimated seroprevalence and assessed several potential predictors of seropositivity. RESULTS The seroprevalence was 5.3% among 923 OCFA personnel tested, with 92.2% participating. Among firefighters self-reporting a previous positive COVID-19 antibody or PCR test result, twenty-one (37%) did not have positive IgG tests in the current serosurvey. There were no statistically significant differences in demographic characteristics between cases and non-cases. Work city was a significant predictor of case status (p=0.015). Seroprevalence (4.8%) was similar when aggregated across seven city fire departments (42%-65% participation). In total, 1486°C fire personnel were tested. CONCLUSION Using a strong serosurvey design and large firefighter cohort, we observed a SARS-CoV-2 IgG seroprevalence of 5.3%. The seroprevalence among OC firefighters in October 2020 was lower than the general county population estimated seroprevalence (11.5%) in August. The difference may be due in part to safety measures taken by OC fire departments at the start of the pandemic, as well as differences in antibody test methods and/or duration of antibody response.
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Affiliation(s)
- Verónica Vieira
- Department of Environmental and Occupational Health, University of California Irvine Susan and Henry Samueli College of Health Sciences, Irvine, California, USA .,University of California Irvine Center for Occupational and Environmental Health, Irvine, California, USA
| | - Ian W Tang
- Department of Environmental and Occupational Health, University of California Irvine Susan and Henry Samueli College of Health Sciences, Irvine, California, USA
| | - Scott Bartell
- Department of Environmental and Occupational Health, University of California Irvine Susan and Henry Samueli College of Health Sciences, Irvine, California, USA.,University of California Irvine Center for Occupational and Environmental Health, Irvine, California, USA
| | - Matthew Zahn
- Orange County Health Care Agency, Santa Ana, California, USA
| | - Marion Joseph Fedoruk
- Department of Environmental and Occupational Health, University of California Irvine Susan and Henry Samueli College of Health Sciences, Irvine, California, USA.,University of California Irvine Center for Occupational and Environmental Health, Irvine, California, USA
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Lamba K, Bradley H, Shioda K, Sullivan PS, Luisi N, Hall EW, Mehrotra ML, Lim E, Jain S, Kamali A, Sanchez T, Lopman BA, Fahimi M, Siegler AJ. SARS-CoV-2 Cumulative Incidence and Period Seroprevalence: Results From a Statewide Population-Based Serosurvey in California. Open Forum Infect Dis 2021; 8:ofab379. [PMID: 34377733 PMCID: PMC8339610 DOI: 10.1093/ofid/ofab379] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 07/12/2021] [Indexed: 12/13/2022] Open
Abstract
Background California has reported the largest number of coronavirus disease 2019 (COVID-19) cases of any US state, with more than 3.5 million confirmed as of March 2021. However, the full breadth of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in California is unknown as reported cases only represent a fraction of all infections. Methods We conducted a population-based serosurvey, utilizing mailed, home-based SARS-CoV-2 antibody testing along with a demographic and behavioral survey. We weighted data from a random sample to represent the adult California population and estimated period seroprevalence overall and by participant characteristics. Seroprevalence estimates were adjusted for waning antibodies to produce statewide estimates of cumulative incidence, the infection fatality ratio (IFR), and the reported fraction. Results California's SARS-CoV-2 weighted seroprevalence during August-December 2020 was 4.6% (95% CI, 2.8%-7.4%). Estimated cumulative incidence as of November 2, 2020, was 8.7% (95% CrI, 6.4%-11.5%), indicating that 2 660 441 adults (95% CrI, 1 959 218-3 532 380) had been infected. The estimated IFR was 0.8% (95% CrI, 0.6%-1.0%), and the estimated percentage of infections reported to the California Department of Public Health was 31%. Disparately high risk for infection was observed among persons of Hispanic/Latinx ethnicity and people with no health insurance and who reported working outside the home. Conclusions We present the first statewide SARS-CoV-2 cumulative incidence estimate among adults in California. As of November 2020, ~1 in 3 SARS-CoV-2 infections in California adults had been identified by public health surveillance. When accounting for unreported SARS-CoV-2 infections, disparities by race/ethnicity seen in case-based surveillance persist.
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Affiliation(s)
- Katherine Lamba
- California Department of Public Health, Richmond, California, USA
| | - Heather Bradley
- Department of Population Health Sciences, Georgia State University School of Public Health, Atlanta, Georgia, USA
| | - Kayoko Shioda
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Patrick S Sullivan
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Nicole Luisi
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Eric W Hall
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Megha L Mehrotra
- California Department of Public Health, Richmond, California, USA
| | - Esther Lim
- California Department of Public Health, Richmond, California, USA
| | - Seema Jain
- California Department of Public Health, Richmond, California, USA
| | - Amanda Kamali
- California Department of Public Health, Richmond, California, USA
| | - Travis Sanchez
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Benjamin A Lopman
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | | | - Aaron J Siegler
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
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Hodgson D, Colton H, Hornsby H, Brown R, Mckenzie J, Bradley KL, James C, Lindsey BB, Birch S, Marsh L, Wood S, Bayley M, Dickson G, James DC, Nicklin MJH, Sayers JR, Zafred D, Rowland-Jones SL, Kudesia G, Kucharski A, Darton TC, de Silva TI, Collini PJ. Risk factors for SARS-CoV-2 seroprevalence following the first pandemic wave in UK healthcare workers in a large NHS Foundation Trust. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.07.07.21260151. [PMID: 34268521 PMCID: PMC8282110 DOI: 10.1101/2021.07.07.21260151] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
BACKGROUND We aimed to measure SARS-CoV-2 seroprevalence in a cohort of healthcare workers (HCWs) during the first UK wave of the COVID-19 pandemic, explore risk factors associated with infection, and investigate the impact of antibody titres on assay sensitivity. METHODS HCWs at Sheffield Teaching Hospitals NHS Foundation Trust (STH) were prospectively enrolled and sampled at two time points. SARS-CoV-2 antibodies were tested using an in-house assay for IgG and IgA reactivity against Spike and Nucleoprotein (sensitivity 99·47%, specificity 99·56%). Data were analysed using three statistical models: a seroprevalence model, an antibody kinetics model, and a heterogeneous sensitivity model. FINDINGS As of 12th June 2020, 24·4% (n=311/1275) HCWs were seropositive. Of these, 39·2% (n=122/311) were asymptomatic. The highest adjusted seroprevalence was measured in HCWs on the Acute Medical Unit (41·1%, 95% CrI 30·0-52·9) and in Physiotherapists and Occupational Therapists (39·2%, 95% CrI 24·4-56·5). Older age groups showed overall higher median antibody titres. Further modelling suggests that, for a serological assay with an overall sensitivity of 80%, antibody titres may be markedly affected by differences in age, with sensitivity estimates of 89% in those over 60 years but 61% in those ≤30 years. INTERPRETATION HCWs in acute medical units working closely with COVID-19 patients were at highest risk of infection, though whether these are infections acquired from patients or other staff is unknown. Current serological assays may underestimate seroprevalence in younger age groups if validated using sera from older and/or more symptomatic individuals. RESEARCH IN CONTEXT Evidence before this study: We searched PubMed for studies published up to March 6th 2021, using the terms "COVID", "SARS-CoV-2", "seroprevalence", and "healthcare workers", and in addition for articles of antibody titres in different age groups against coronaviruses using "coronavirus", "SARS-CoV-2, "antibody", "antibody tires", "COVID" and "age". We included studies that used serology to estimate prevalence in healthcare workers. SARS-CoV-2 seroprevalence has been shown to be greater in healthcare workers working on acute medical units or within domestic services. Antibody levels against seasonal coronaviruses, SARS-CoV and SARS-CoV-2 were found to be higher in older adults, and patients who were hospitalised.Added value of this study: In this healthcare worker seroprevalence modelling study at a large NHS foundation trust, we confirm that those working on acute medical units, COVID-19 "Red Zones" and within domestic services are most likely to be seropositive. Furthermore, we show that physiotherapists and occupational therapists have an increased risk of COVID-19 infection. We also confirm that antibody titres are greater in older individuals, even in the context of non-hospitalised cases. Importantly, we demonstrate that this can result in age-specific sensitivity in serological assays, where lower antibody titres in younger individuals results in lower assay sensitivity.Implications of all the available evidence: There are distinct occupational roles and locations in hospitals where the risk of COVID-19 infection to healthcare workers is greatest, and this knowledge should be used to prioritise infection prevention control and other measures to protect healthcare workers. Serological assays may have different sensitivity profiles across different age groups, especially if assay validation was undertaken using samples from older and/or hospitalised patients, who tend to have higher antibody titres. Future seroprevalence studies should consider adjusting for age-specific assay sensitivities to estimate true seroprevalence rates. AUTHOR CONTRIBUTIONS
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Affiliation(s)
- David Hodgson
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, UK
| | - Hayley Colton
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals NHS Foundation Trust, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK
| | - Hailey Hornsby
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK
| | - Rebecca Brown
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK
| | - Joanne Mckenzie
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK
| | - Kirsty L Bradley
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK
| | - Cameron James
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK
| | - Benjamin B Lindsey
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals NHS Foundation Trust, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK
| | - Sarah Birch
- Academic Directorate of Communicable Diseases and Specialised Medicine, Sheffield Teaching Hospitals NHS Foundation Trust, UK
| | - Louise Marsh
- Academic Directorate of Communicable Diseases and Specialised Medicine, Sheffield Teaching Hospitals NHS Foundation Trust, UK
| | - Steven Wood
- Department of Scientific Computing and Informatics, Sheffield Teaching Hospitals NHS Foundation Trust, UK
| | - Martin Bayley
- Department of Scientific Computing and Informatics, Sheffield Teaching Hospitals NHS Foundation Trust, UK
| | - Gary Dickson
- Department of Scientific Computing and Informatics, Sheffield Teaching Hospitals NHS Foundation Trust, UK
| | - David C James
- Department of Chemical and Biological Engineering, University of Sheffield, UK
| | - Martin J H Nicklin
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK
| | - Jon R Sayers
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, UK
- Sheffield Institute for Nucleic Acids, University of Sheffield, UK
| | - Domen Zafred
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK
| | - Sarah L Rowland-Jones
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals NHS Foundation Trust, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, UK
| | - Goura Kudesia
- Department of Virology, Sheffield Teaching Hospitals NHS Foundation Trust, UK
| | - Adam Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, UK
| | - Thomas C Darton
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals NHS Foundation Trust, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, UK
| | - Thushan I de Silva
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals NHS Foundation Trust, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, UK
| | - Paul J Collini
- South Yorkshire Regional Department of Infection and Tropical Medicine, Sheffield Teaching Hospitals NHS Foundation Trust, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK
- The Florey Institute for Host-Pathogen Interactions, University of Sheffield, UK
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Understanding COVID-19 Epidemiology and Implications for Control: The Experience from a Greek Semi-Closed Community. J Clin Med 2021; 10:jcm10132765. [PMID: 34201868 PMCID: PMC8268522 DOI: 10.3390/jcm10132765] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 06/12/2021] [Accepted: 06/18/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND We aimed to estimate the SARS-CoV-2 antigen and antibody seroprevalence in one of the worst-affected by the pandemic semi-closed communities in Greece, Deskati, and evaluate the sociodemographic and clinical correlations of functional antibody responses. METHODS The Ag2019n-CoV V1310/V1330 Rapid Test (Prognosis Biotech, Greece) was used for antigen detection. The Rapid Test 2019-nCoV Total Ig, V1210/V1230 (Prognosis Biotech, Greece), and the SARS-CoV-2 IgG II Quant method (Architect, Abbott, Illinois, USA) were used for antibody testing. RESULTS None of the participants had a positive antigen result. SARS-CoV-2 seropositivity ranged from 13% to 45% in the study population, depending on the method. One-third of the participants with known past infection had a positive antibody test result 77 ± 13 days after infection. Two-fifths of infections determined by serology were asymptomatic. The advancing age and hospitalization predicted seropositivity among patients with past infection. Half of the participants who tested positive for antibodies were not aware of past infection. CONCLUSIONS High-burden contexts in Greece, such as Deskati, are not so far from herd immunity thresholds. We highlighted the value of low-cost serosurveys targeting both symptomatic and asymptomatic populations to evaluate the natural immune response to SARS-CoV-2 in nonvaccinated susceptibles and design evidence-based policies for lifting lockdowns.
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Cooper DM, Zulu MZ, Jankeel A, Ibraim IC, Ardo J, Kasper K, Stephens D, Meyer A, Stehli A, Condon C, Londoño ME, Schreiber CM, Lopez NV, Camplain RL, Weiss M, Golden C, Aizik S, Boden-Albala B, Chau C, Messaoudi I, Ulloa ER. SARS-CoV-2 Acquisition and Immune Pathogenesis Among School-Aged Learners in Four Diverse Schools. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021. [PMID: 33791712 DOI: 10.1101/2021.03.20.21254035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Background Understanding SARS-CoV-2 infection in children is necessary to reopen schools safely. Methods We measured SARS-CoV-2 infection in 320 learners [10.5 ± 2.1(sd); 7-17 y.o.] at four diverse schools with either remote or on-site learning. Schools A and B served low-income Hispanic learners; school C served many special-needs learners; and all provided predominantly remote instruction. School D served middle- and upper-income learners, with predominantly on-site instruction. Testing occurred in the fall (2020), and 6-8 weeks later during the fall-winter surge (notable for a tenfold increase in COVID-19 cases). Immune responses and mitigation fidelity were also measured. Results We found SARS-CoV-2 infections in 17 learners only during the surge. School A (97% remote learners) had the highest infection (10/70, 14.3%, p<0.01) and IgG positivity rates (13/66, 19.7%). School D (93% on-site learners) had the lowest infection and IgG positivity rates (1/63, 1.6%). Mitigation compliance [physical distancing (mean 87.4%) and face covering (91.3%)] was remarkably high at all schools. Documented SARS-CoV-2-infected learners had neutralizing antibodies (94.7%), robust IFN-γ+ T cell responses, and reduced monocytes. Conclusion Schools can implement successful mitigation strategies across a wide range of student diversity. Despite asymptomatic to mild SARS-CoV-2 infection, children generate robust humoral and cellular immune responses. Key Points Successful COVID-19 mitigation was implemented across a diverse range of schools.School-associated SARS-CoV-2 infections reflect regional rates rather than remote or on-site learning.Seropositive school-aged children with asymptomatic to mild SARS-CoV-2 infections generate robust humoral and cellular immunity.
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Allan-Blitz LT, Hertlein F, Klausner JD. Heterogeneity in SARS-CoV-2 Positivity by Ethnicity in Los Angeles. J Racial Ethn Health Disparities 2021; 9:1206-1209. [PMID: 34028706 PMCID: PMC8142866 DOI: 10.1007/s40615-021-01062-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/14/2021] [Accepted: 05/17/2021] [Indexed: 01/17/2023]
Abstract
Recent studies have identified notable disparities in SARS-CoV-2 infection risk among ethnic minorities. We evaluated SARS-CoV-2 test results from individuals presenting for testing in Los Angeles between June and December, 2020. We calculated prevalence ratios for various employment categories. Among 518,914 test results, of which 295,295 (56.9%) were from individuals reporting Hispanic ethnicity, SARS-CoV-2 positivity was 16.5% among Hispanic individuals compared to 5.0% among non-Hispanic individuals (p-value <0.01). The prevalence ratios comparing Hispanic and non-Hispanic individuals were highest for members of the media (PR = 6.7; 95% CI 4.3-10.4), government employees (PR = 4.0; 95% CI 3.3-4.9), and agricultural workers (PR = 4.0; 95% CI 3.2-5.0). Such heterogeneity warrants further investigation in order to develop targeted public health interventions towards specific drivers of SARS-CoV-2 transmission.
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Affiliation(s)
- Lao-Tzu Allan-Blitz
- Department of Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
| | | | - Jeffrey D Klausner
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
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Chen X, Chen Z, Azman AS, Deng X, Sun R, Zhao Z, Zheng N, Chen X, Lu W, Zhuang T, Yang J, Viboud C, Ajelli M, Leung DT, Yu H. Serological evidence of human infection with SARS-CoV-2: a systematic review and meta-analysis. Lancet Glob Health 2021; 9:e598-e609. [PMID: 33705690 PMCID: PMC8049592 DOI: 10.1016/s2214-109x(21)00026-7] [Citation(s) in RCA: 156] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 01/09/2021] [Accepted: 01/13/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND A rapidly increasing number of serological surveys for antibodies to SARS-CoV-2 have been reported worldwide. We aimed to synthesise, combine, and assess this large corpus of data. METHODS In this systematic review and meta-analysis, we searched PubMed, Embase, Web of Science, and five preprint servers for articles published in English between Dec 1, 2019, and Dec 22, 2020. Studies evaluating SARS-CoV-2 seroprevalence in humans after the first identified case in the area were included. Studies that only reported serological responses among patients with COVID-19, those using known infection status samples, or any animal experiments were all excluded. All data used for analysis were extracted from included papers. Study quality was assessed using a standardised scale. We estimated age-specific, sex-specific, and race-specific seroprevalence by WHO regions and subpopulations with different levels of exposures, and the ratio of serology-identified infections to virologically confirmed cases. This study is registered with PROSPERO, CRD42020198253. FINDINGS 16 506 studies were identified in the initial search, 2523 were assessed for eligibility after removal of duplicates and inappropriate titles and abstracts, and 404 serological studies (representing tests in 5 168 360 individuals) were included in the meta-analysis. In the 82 studies of higher quality, close contacts (18·0%, 95% CI 15·7-20·3) and high-risk health-care workers (17·1%, 9·9-24·4) had higher seroprevalence than did low-risk health-care workers (4·2%, 1·5-6·9) and the general population (8·0%, 6·8-9·2). The heterogeneity between included studies was high, with an overall I2 of 99·9% (p<0·0001). Seroprevalence varied greatly across WHO regions, with the lowest seroprevalence of general populations in the Western Pacific region (1·7%, 95% CI 0·0-5·0). The pooled infection-to-case ratio was similar between the region of the Americas (6·9, 95% CI 2·7-17·3) and the European region (8·4, 6·5-10·7), but higher in India (56·5, 28·5-112·0), the only country in the South-East Asia region with data. INTERPRETATION Antibody-mediated herd immunity is far from being reached in most settings. Estimates of the ratio of serologically detected infections per virologically confirmed cases across WHO regions can help provide insights into the true proportion of the population infected from routine confirmation data. FUNDING National Science Fund for Distinguished Young Scholars, Key Emergency Project of Shanghai Science and Technology Committee, Program of Shanghai Academic/Technology Research Leader, National Science and Technology Major project of China, the US National Institutes of Health. TRANSLATION For the Chinese translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Xinhua Chen
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Zhiyuan Chen
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Andrew S Azman
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland; Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Xiaowei Deng
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Ruijia Sun
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Zeyao Zhao
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Nan Zheng
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xinghui Chen
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Wanying Lu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Tingyu Zhuang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Juan Yang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Cecile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Marco Ajelli
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Daniel T Leung
- Division of Infectious Diseases, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China; Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China; Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
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Chvatal-Medina M, Mendez-Cortina Y, Patiño PJ, Velilla PA, Rugeles MT. Antibody Responses in COVID-19: A Review. Front Immunol 2021; 12:633184. [PMID: 33936045 PMCID: PMC8081880 DOI: 10.3389/fimmu.2021.633184] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/25/2021] [Indexed: 01/08/2023] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to spread worldwide as a severe pandemic. Although its seroprevalence is highly variable among territories, it has been reported at around 10%, but higher in health workers. Evidence regarding cross-neutralizing response between SARS-CoV and SARS-CoV-2 is still controversial. However, other previous coronaviruses may interfere with SARS-CoV-2 infection, since they are phylogenetically related and share the same target receptor. Further, the seroconversion of IgM and IgG occurs at around 12 days post onset of symptoms and most patients have neutralizing titers on days 14-20, with great titer variability. Neutralizing antibodies correlate positively with age, male sex, and severity of the disease. Moreover, the use of convalescent plasma has shown controversial results in terms of safety and efficacy, and due to the variable immune response among individuals, measuring antibody titers before transfusion is mostly required. Similarly, cellular immunity seems to be crucial in the resolution of the infection, as SARS-CoV-2-specific CD4+ and CD8+ T cells circulate to some extent in recovered patients. Of note, the duration of the antibody response has not been well established yet.
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Affiliation(s)
- Mateo Chvatal-Medina
- Grupo Inmunovirología, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | | | - Pablo J. Patiño
- Grupo Inmunodeficiencias Primarias, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - Paula A. Velilla
- Grupo Inmunovirología, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - Maria T. Rugeles
- Grupo Inmunovirología, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
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