Evidence map›Paper›PMID 33634345›Full record

ReviewEuropean journal of epidemiology2021

How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19.

Emma K Accorsi, Xueting Qiu, Eva Rumpler, Lee Kennedy-Shaffer, Rebecca Kahn, Keya Joshi, Edward Goldstein, Mats J Stensrud, Rene Niehus, Muge Cevik and 1 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in European journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
85citing papers in PubMed, 7 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

85 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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  7. The role of asymptomatic and pre-symptomatic infection in SARS-CoV-2 transmission-a living systematic review.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2021
    Pooled it
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25 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Emma K AccorsiCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. eaccorsi@g.harvard.edu.ORCID http://orcid.org/0000-0002-7443-7921
Xueting QiuCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Eva RumplerCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Lee Kennedy-ShafferCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Rebecca KahnCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Keya JoshiCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Edward GoldsteinCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Mats J StensrudDepartment of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Rene NiehusCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Muge CevikDivision of Infection and Global Health Research, School of Medicine, University of St Andrews, St Andrews, UK.
Marc LipsitchCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.

Funding

Understanding and controlling antibiotic resistanceU54GM088558 · NIGMS · HARVARD SCHOOL OF PUBLIC HEALTH · PI COHEN, THEODORE H · 2009 to 2018
$26.4M
Epidemiology of Infectious Diseases and BiodefenseT32AI007535 · NIAID · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI GRAD, YONATAN H, MURRAY, MEGAN B · 1998 to 2025
$4.8M
Casual, Statistical and Mathematical Modeling with Serologic DataU01CA261277 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANAGE, WILLIAM, LIPSITCH, MARC · 2020 to 2023
$2.8M
National Institute of Allergy and Infectious Diseases T32AI007535National Institute of General Medical Sciences (US) U54GM088558NCI NIH HHS U01 CA261277NIAID NIH HHS T32 AI007535NIGMS NIH HHS U54 GM088558
6 · The paper itself

Abstract

In 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.

Indexed as

Research DesignBiasCOVID-19HumansReproducibility of ResultsSARS-CoV-2Seroepidemiologic StudiesCOVID-19Epidemiological biasesMeasurement errorMisclassificationObservational dataSelection bias

Identifiers

PMID33634345
PMCPMC7906244

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.