Observed negative vaccine effectiveness could be the canary in the coal mine for biases in observational COVID-19 studies

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DOI

https://doi.org/10.1016/j.ijid.2023.03.022

Language of the publication
English
Date
2023-06
Type
Article
Author(s)
  • Bodner, Korryn
  • Irvine, Michael A.
  • Kwong, Jeffrey C.
  • Mishra, Sharmistha
Publisher
Elsevier Ltd on behalf of International Society for Infectious Diseases

Abstract

Since the emergence of the SARS-CoV-2 Omicron variant, multiple observational studies have reported negative vaccine effectiveness (VE) against infection, symptomatic infection, and even severity (hospitalization), potentially leading to an interpretation that vaccines were facilitating infection and disease. However, current observations of negative VE likely stem from the presence of various biases (e.g., exposure differences, testing differences). Although negative VE is more likely to arise when true biological efficacy is generally low and biases are large, positive VE measurements can also be subject to the same mechanisms of bias. In this perspective, we first outline the different mechanisms of bias that could lead to false-negative VE measurements and then discuss their ability to potentially influence other protection measurements. We conclude by discussing the use of suspected false-negative VE measurements as a signal to interrogate the estimates (quantitative bias analysis) and to discuss potential biases when communicating real-world immunity research.

Subject

  • Health,
  • Coronavirus diseases,
  • Immunization

Rights

Pagination

111-114

Open access level

Gold

Identifiers

PubMed ID
36990200
ISSN
1878-3511

Article

Journal title
International Journal of Infectious Diseases
Journal volume
131

Sponsors

This work was funded by a Public Health Agency of Canada COVID-19 Immunology Task Force COVID-19 Hot Spots Competition Grant (grant 2021-HQ-000143). SM is supported by a Tier 2 Canada Research Chair in Mathematical Modeling and Program Science.

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Collection(s)

Communicable diseases

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