Effects of imperfect test sensitivity and specificity on observational studies of influenza vaccine effectiveness

Background: The recently developed test-negative design is now standard for observational studies of influenza vaccine effectiveness (VE). It is unclear how influenza test misclassification biases test-negative VE estimates relative to VE estimates from traditional cohort or case-control studies. Methods: We simulated populations whose members may develop acute respiratory illness (ARI) due to influenza and to non-influenza pathogens. In these simulations, vaccination reduces the risk of influenza but not of non-influenza ARI. Influenza test sensitivity and specificity, risks of influenza and non-influenza ARI, and VE were varied across the simulations. In each simulation, we estimated influenza VE using a cohort design, a case-control design, and a test-negative design. Results: In the absence of influenza test misclassification, all three designs accurately estimated influenza VE. In the presence of misclassification, all three designs underestimated VE. Bias in VE estimates was slightly greater in the test-negative design than in cohort or case-control designs. Assuming the use of highly sensitive and specific reverse-transcriptase polymerase chain reaction tests for influenza, bias in the test-negative studies was trivial across a wide range of realistic values for VE. Discussion: Although influenza test misclassification causes more bias in test-negative studies than in traditional cohort or case-control studies, the difference is trivial for realistic combinations of attack rates, test sensitivity/specificity, and VE. (C) 2015 Elsevier Ltd. All rights reserved.