Evidence map›Paper›PMID 34001753›Full record

ReviewEpidemiology (Cambridge, Mass.)2021

Theoretical Framework for Retrospective Studies of the Effectiveness of SARS-CoV-2 Vaccines.

Joseph A Lewnard, Manish M Patel, Nicholas P Jewell, Jennifer R Verani, Miwako Kobayashi, Mark W Tenforde, Natalie E Dean, Benjamin J Cowling, Benjamin A Lopman

Abstract readReview
In one paragraph

Review in Epidemiology (Cambridge, Mass.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 3 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

73 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Article
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  8. Observational
  9. Article
  10. Improved Methods for Vaccine Effectiveness Studies.The Journal of infectious diseases · 2025
    Article
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13 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

9 authors.

Joseph A LewnardFrom the Division of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, CA.
Manish M PatelCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA.
Nicholas P JewellDepartment of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Jennifer R VeraniCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA.
Miwako KobayashiCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA.
Mark W TenfordeCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA.
Natalie E DeanDepartment of Biostatistics, University of Florida, Gainesville, FL.
Benjamin J CowlingWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, University of Hong Kong, Hong Kong, China.
Benjamin A LopmanDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA.

Funding

Design and Analysis of Vaccine Trials for Emerging Infectious Disease ThreatsR01AI139761 · NIAID · UNIVERSITY OF FLORIDA · PI DEAN, NATALIE EXNER · 2018 to 2022
$3.6M
Emerging methods and applications for test-negative studies of of infectious disease interventionsR01AI148127 · NIAID · UNIVERSITY OF CALIFORNIA BERKELEY · PI JEWELL, NICHOLAS P · 2020 to 2023
$1.8M
NIAID NIH HHS R01 AI139761NIAID NIH HHS R01 AI148127
6 · The paper itself

Abstract

Observational studies of the effectiveness of vaccines to prevent COVID-19 are needed to inform real-world use. Such studies are now underway amid the ongoing rollout of SARS-CoV-2 vaccines globally. Although traditional case-control and test-negative design studies feature prominently among strategies used to assess vaccine effectiveness, such studies may encounter important threats to validity. Here, we review the theoretical basis for estimation of vaccine direct effects under traditional case-control and test-negative design frameworks, addressing specific natural history parameters of SARS-CoV-2 infection and COVID-19 relevant to these designs. Bias may be introduced by misclassification of cases and controls, particularly when clinical case criteria include common, nonspecific indicators of COVID-19. When using diagnostic assays with high analytical sensitivity for SARS-CoV-2 detection, individuals testing positive may be counted as cases even if their symptoms are due to other causes. The traditional case-control design may be particularly prone to confounding due to associations of vaccination with healthcare-seeking behavior or risk of infection. The test-negative design reduces but may not eliminate this confounding, for instance, if individuals who receive vaccination seek care or testing for less-severe illness. These circumstances indicate the two study designs cannot be applied naively to datasets gathered through public health surveillance or administrative sources. We suggest practical strategies to reduce bias in vaccine effectiveness estimates at the study design and analysis stages.

Indexed as

COVID-19VaccinesCOVID-19 VaccinesHumansRetrospective StudiesSARS-CoV-2COVID-19 VaccinesVaccines

Identifiers

PMID34001753
PMCPMC8168935

What OpenQuestion holds

Textmetadata
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.