The test-negative design for estimating influenza vaccination effectiveness

How to Cite

Cowling, B. J., & Sullivan, S. G. (2017). The test-negative design for estimating influenza vaccination effectiveness. Online Journal of Public Health Informatics, 9(1).


ObjectiveWe aimed to describe the theoretical basis and the potentialapplications of the test-negative design for estimating influenzavaccination effectiveness in sentinel influenza surveillance.IntroductionThe test-negative design is a variation of the case-control study,in which patients are enrolled in outpatient clinics (and/or hospitals)based on a clinical case definition such as influenza-like illness (ILI).Patients are then tested for influenza virus, and VE is estimated fromthe odds ratio comparing the odds of vaccination among patientstesting positive for influenza versus those testing negative, adjustingfor potential confounding factors. The design leverages existingdisease surveillance networks and as a result, studies using it areincreasingly being reported.MethodsWe sought to examine the theoretical basis for this design usingcausal analysis including directed acyclic graphs. We reviewedstudies that used this design and examined the study populations andsettings, the methodologic choices including analytic approaches, andthe estimates of influenza VE provided. We conducted simulationstudies to examine specific potential biases.ResultsWe show how studies using this design can avoid or minimizebias, and where bias may be introduced with particular study designvariations. A purported advantage of the test-negative designis to minimise selection bias by health-care seeking behaviourand we demonstrate why residual bias may occur. Anotherpurported advantage of the test-negative design is minimization ofmisclassification of the exposure; however we show how this sourceof bias may persist and how exposure misclassification may bea greater cause for concern not dealt with by the study design. Inour review, we found great variation in estimates, but consistencybetween interim and final VE estimates from the same locations,and consistency between VE estimates from inpatient and outpatientstudies in the same locations, age groups and years. One outstandingissue is the potential bias due to non-collapsibility.ConclusionsOur work provides a starting point for further consideration of thevalidity of the test-negative design, which is an efficient approachfor routine monitoring of influenza VE that can be implemented inexisting surveillance systems without substantial additional resources.Harmonization of analytic approaches may improve the potential forpooling VE estimates.
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