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Author Notes:

Correspondence: mhaber@emory.edu 1Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 30322 Atlanta, USA

MS, QA and MH developed the model and wrote the manuscript.

WO and KA made significant revisions.

All authors have read the manuscript and agreed to its content.

All the authors have read and approved the manuscript and agreed to be included in the publication.

The authors wish to thank Dr. Ivo Foppa and two reviewers for helpful comment.

The authors declares that they have no competing interests.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


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, and by IPA 1110376-05 with the Centers for Disease Controls and Prevention (CDC).

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the CDC.


  • Case-control study
  • Medically-attended influenza
  • Probability model
  • Symptomatic influenza
  • Test-negative study

A comparison of the test-negative and the traditional case-control study designs for estimation of influenza vaccine effectiveness under nonrandom vaccination.


Journal Title:

BMC Infectious Diseases


Volume 17, Number 1


, Pages 757-757

Type of Work:

Article | Final Publisher PDF


BACKGROUND: As annual influenza vaccination is recommended for all U.S. persons aged 6 months or older, it is unethical to conduct randomized clinical trials to estimate influenza vaccine effectiveness (VE). Observational studies are being increasingly used to estimate VE. We developed a probability model for comparing the bias and the precision of VE estimates from two case-control designs: the traditional case-control (TCC) design and the test-negative (TN) design. In both study designs, acute respiratory illness (ARI) patients seeking medical care testing positive for influenza infection are considered cases. In the TN design, ARI patients seeking medical care who test negative serve as controls, while in the TCC design, controls are randomly selected individuals from the community who did not contract an ARI. METHODS: Our model assigns each study participant a covariate corresponding to the person's health status. The probabilities of vaccination and of contracting influenza and non-influenza ARI depend on health status. Hence, our model allows non-random vaccination and confounding. In addition, the probability of seeking care for ARI may depend on vaccination and health status. We consider two outcomes of interest: symptomatic influenza (SI) and medically-attended influenza (MAI). RESULTS: If vaccination does not affect the probability of non-influenza ARI, then VE estimates from TN studies usually have smaller bias than estimates from TCC studies. We also found that if vaccinated influenza ARI patients are less likely to seek medical care than unvaccinated patients because the vaccine reduces symptoms' severity, then estimates of VE from both types of studies may be severely biased when the outcome of interest is SI. The bias is not present when the outcome of interest is MAI. CONCLUSIONS: The TN design produces valid estimates of VE if (a) vaccination does not affect the probabilities of non-influenza ARI and of seeking care against influenza ARI, and (b) the confounding effects resulting from non-random vaccination are similar for influenza and non-influenza ARI. Since the bias of VE estimates depends on the outcome against which the vaccine is supposed to protect, it is important to specify the outcome of interest when evaluating the bias.

Copyright information:

© The Author(s) 2017

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