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Theoretical Framework for Retrospective Studies of the Effectiveness of SARS-CoV-2 Vaccines.
Lewnard, Joseph A; Patel, Manish M; Jewell, Nicholas P; Verani, Jennifer R; Kobayashi, Miwako; Tenforde, Mark W; Dean, Natalie E; Cowling, Benjamin J; Lopman, Benjamin A.
  • Lewnard JA; From the Division of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, CA.
  • Patel MM; Division of Infectious Diseases & Vaccinology, School of Public Health, University of California, Berkeley, Berkeley, CA.
  • Jewell NP; Center for Computational Biology, College of Engineering, University of California, Berkeley, Berkeley, CA.
  • Verani JR; COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA.
  • Kobayashi M; Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom.
  • Tenforde MW; Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA.
  • Dean NE; COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA.
  • Cowling BJ; COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA.
  • Lopman BA; COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA.
Epidemiology ; 32(4): 508-517, 2021 07 01.
Article in English | MEDLINE | ID: covidwho-1232231
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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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Vaccines / COVID-19 Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Topics: Vaccines Limits: Humans Language: English Journal: Epidemiology Journal subject: Epidemiology Year: 2021 Document Type: Article Affiliation country: EDE.0000000000001366

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Vaccines / COVID-19 Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Topics: Vaccines Limits: Humans Language: English Journal: Epidemiology Journal subject: Epidemiology Year: 2021 Document Type: Article Affiliation country: EDE.0000000000001366