  Test-negative designs are widely used for post-market evaluation of vaccine
effectiveness, particularly in cases where randomization is not feasible.
Differing from classical test-negative designs where only healthcare-seekers
with symptoms are included, recent test-negative designs have involved
individuals with various reasons for testing, especially in an outbreak
setting. While including these data can increase sample size and hence improve
precision, concerns have been raised about whether they introduce bias into the
current framework of test-negative designs, thereby demanding a formal
statistical examination of this modified design. In this article, using
statistical derivations, causal graphs, and numerical simulations, we show that
the standard odds ratio estimator may be biased if various reasons for testing
are not accounted for. To eliminate this bias, we identify three categories of
reasons for testing, including symptoms, disease-unrelated reasons, and case
contact tracing, and characterize associated statistical properties and
estimands. Based on our characterization, we show how to consistently estimate
each estimand via stratification. Furthermore, we describe when these estimands
correspond to the same vaccine effectiveness parameter, and, when appropriate,
propose a stratified estimator that can incorporate multiple reasons for
testing and improve precision. The performance of our proposed method is
demonstrated through simulation studies.
