Quantifying biases in test-negative studies of vaccine effectiveness
Test-negative designs have become commonplace in assessments of seasonal influenza vaccine effectiveness. Vaccine effectiveness is measured from the exposure odds ratio (OR) of vaccination among individuals seeking treatment for acute respiratory illness and receiving a laboratory test for influenza infection. This approach is widely believed to correct for differential healthcare-seeking behavior among vaccinated and unvaccinated persons. However, the relation of the measured OR to true vaccine effectiveness is poorly understood. We derive the OR under circumstances of real-world test-negative studies. The OR recovers the true vaccine direct effect when two conditions are met: (1) that individuals vaccination decisions are uncorrelated with exposure or susceptibility to infection, and (2) that vaccination confers \"all-or-nothing\" protection (whereby certain individuals have no protection while others are perfectly protected). Biased effect size estimates arise if either condition is unmet. Such bias may suggest misleading associations of the OR with time since vaccination or the force of infection of influenza. The test-negative design may also fail to correct for differential healthcare-seeking behavior among vaccinated and unvaccinated persons without stringent criteria for enrollment and testing. Our findings demonstrate a need to reassess how data from test-negative studies are interpreted for policy decisions conventionally based on causal inferences.