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dc.contributor.authorAccorsi, Emma K
dc.contributor.authorQiu, Xueting
dc.contributor.authorRumpler, Eva
dc.contributor.authorKennedy-Shaffer, Lee
dc.contributor.authorKahn, Rebecca
dc.contributor.authorJoshi, Keya
dc.contributor.authorGoldstein, Edward
dc.contributor.authorStensrud, Mats J
dc.contributor.authorNiehus, Rene
dc.contributor.authorCevik, Muge
dc.contributor.authorLipsitch, Marc
dc.identifier.citationAccorsi , E K , Qiu , X , Rumpler , E , Kennedy-Shaffer , L , Kahn , R , Joshi , K , Goldstein , E , Stensrud , M J , Niehus , R , Cevik , M & Lipsitch , M 2021 , ' How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19 ' , European Journal of Epidemiology .
dc.identifier.otherPURE: 273285060
dc.identifier.otherPURE UUID: 2561a41e-90a3-462d-bdb8-3e83d3d825ba
dc.identifier.otherJisc: 63250406a56f44e19fc9f71d752851ab
dc.identifier.otherPubMed: 33634345
dc.identifier.otherpii: 10.1007/s10654-021-00727-7
dc.identifier.otherpmc: PMC7906244
dc.identifier.otherScopus: 85101752879
dc.identifier.otherWOS: 000621711600001
dc.descriptionFunding: National Institute of Allergy and Infectious Diseases of the National Institutes of Health (award number T32AI007535), the National Institute of General Medical Sciences of the National Institutes of Health (award number U54GM088558), the Morris-Singer Fund, and the National Institutes of Health (cooperative agreement U01 CA261277).en
dc.description.abstractIn response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility.
dc.relation.ispartofEuropean Journal of Epidemiologyen
dc.rightsCopyright © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
dc.subjectEpidemiological biasesen
dc.subjectMeasurement erroren
dc.subjectObservational dataen
dc.subjectSelection biasen
dc.subjectQA Mathematicsen
dc.subjectRA0421 Public health. Hygiene. Preventive Medicineen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.titleHow to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19en
dc.typeJournal itemen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. Arctic Research Centreen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. Infection and Global Health Divisionen
dc.description.statusPeer revieweden

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