On the bias of estimates of influenza vaccine effectiveness from test-negative studies

• Type of Material: Article
• Authors: Kylie E. C. Ainslie, Emory University; Meng Shi, Emory University; Michael Haber, Emory University; Walter Orenstein, Emory University
• Language: English
• Date: 2017-12-19
• Publisher: Elsevier: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2017 Elsevier Ltd
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.vaccine.2017.10.107
• Title of Journal or Parent Work: Vaccine
• ISSN: 0264-410X
• Volume: 35
• Issue: 52
• Start Page: 7297
• End Page: 7301
• Grant/Funding Information: The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the CDC.; 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).
• Keywords: Vaccine effectiveness; Life Sciences & Biomedicine; SPAIN; NETWORK; Test-negative design; Research & Experimental Medicine; DESIGN; Science & Technology; SEASON; Medicine, Research & Experimental; LABORATORY-CONFIRMED INFLUENZA; Immunology; Influenza; Bias; AUSTRALIA
• Research Categories: Biology, Biostatistics; Health Sciences, Immunology

Abstract

Estimates of the effectiveness of influenza vaccines are commonly obtained from a test-negative design (TND) study, where cases and controls are patients seeking care for an acute respiratory illness who test positive and negative, respectively, for influenza infection. Vaccine effectiveness (VE) estimates from TND studies are usually interpreted as vaccine effectiveness against medically-attended influenza (MAI). However, it is also important to estimate VE against any influenza illness (symptomatic influenza (SI)) as individuals with SI are still a public health burden even if they do not seek medical care. We present a numerical method to evaluate the bias of TND-based estimates of influenza VE with respect to MAI and SI. We consider two sources of bias: (a) confounding bias due to a (possibly unobserved) covariate that is associated with both vaccination and the probability of the outcome of interest and (b) bias resulting from the effect of vaccination on the probability of seeking care. Our results indicate that (a) VE estimates may suffer from substantial confounding bias when a confounder has a different effect on the probabilities of influenza and non-influenza ARI, and (b) when vaccination reduces the probability of seeking care against influenza ARI, then estimates of VE against MAI may be unbiased while estimates of VE against SI may be have a substantial positive bias.