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Author Notes:

Correspondence: k.ainslie@imperial.ac.uk

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Subjects:

Research Funding:

This research was supported by the National Institute of Allergies and Infectious Diseases of the National Institutes of Health (NIH) under Award R01AI110474.

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Immunology
  • Medicine, Research & Experimental
  • Research & Experimental Medicine
  • Influenza
  • Test-negative
  • Pandemic
  • Vaccine effectiveness
  • Bias
  • Design
  • Virus

Bias of influenza vaccine effectiveness estimates from test-negative studies conducted during an influenza pandemic

Tools:

Journal Title:

Vaccine

Volume:

Volume 37, Number 14

Publisher:

, Pages 1987-1993

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Test-negative (TN) studies have become the most widely used study design for the estimation of influenza vaccine effectiveness (VE) and are easily incorporated into existing influenza surveillance networks. We seek to determine the bias of TN-based VE estimates during a pandemic using a dynamic probability model. The model is used to evaluate and compare the bias of VE estimates under various sources of bias when vaccination occurs after the beginning of an outbreak, such as during a pandemic. The model includes two covariates (health status and health awareness), which may affect the probabilities of vaccination, developing influenza and non-influenza acute respiratory illness (ARI), and seeking medical care. Specifically, we evaluate the bias of VE estimates when (1) vaccination affects the probability of developing a non-influenza ARI; (2) vaccination affects the probability of seeking medical care; (3) a covariate (e.g. health status) is related to both the probabilities of vaccination and developing an ARI; and (4) a covariate (e.g. health awareness) is related to both the probabilities of vaccination and of seeking medical care. We considered two outcomes against which the vaccine is supposed to protect: symptomatic influenza and medically-attended influenza. When vaccination begins during an outbreak, we found that the effect of delayed onset of vaccination is unpredictable. VE estimates from TN studies were biased regardless of the source of bias present. However, if the core assumption of the TN design is satisfied, that is, if vaccination does not affect the probability of non-influenza ARI, then TN-based VE estimates against medically-attended influenza will only suffer from small (<0.05) to moderate bias (≥0.05 and <0.10). These results suggest that if sources of bias listed above are ruled out, TN studies are a valid study design for the estimation of VE during a pandemic.

Copyright information:

© 2019 Elsevier Ltd.

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/).
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