The test-negative design (TND) has become a standard approach for vaccine effectiveness (VE) studies. However, previous studies suggested that it may be more vulnerable than other designs to misclassification of disease outcome caused by imperfect diagnostic tests. This could be a particular limitation in VE studies where simple tests (e.g. rapid influenza diagnostic tests) are used for logistical convenience. To address this issue, we derived a mathematical representation of the TND with imperfect tests, then developed a bias correction framework for possible misclassification. TND studies usually include multiple covariates other than vaccine history to adjust for potential confounders; our methods can also address multivariate analyses and be easily coupled with existing estimation tools. We validated the performance of these methods using simulations of common scenarios for vaccine efficacy and were able to obtain unbiased estimates in a variety of parameter settings.

Influenza vaccine effectiveness (VE) has to be estimated anew for every season to explore vaccines’ protective effect in the population. We report VE estimates against laboratory-confirmed influenza A(H1N1)pdm09, A(H3N2) and influenza B among children aged 2–17 years, using test-negative design. Pooled data from two German federal states’ surveillance systems for acute respiratory illness from week 40/2012 to 20/2016 was used, yielding a total of 10 627 specimens. Odds ratios and 95% confidence intervals (95% CIs) for the association between laboratory-confirmed influenza and vaccination status were calculated by multivariate logistic regression adjusting for age, sex, illness onset and federal state. VE was estimated as 1-Odds Ratio. Overall adjusted VE was 33% (95% CI: 24·3–40·7). A strong variation of VE between the seasons and subtypes was observed: highest season- and subtype-specific VE of 86·2% (95% CI: 41·3–96·7) was found against A(H1N1)pdm09 in 7–17-year-olds in 2015/16. Low estimates of VE were observed against A(H3N2) in any season, e.g. 1·5% (95% CI: −39·3–30·3) in 2014/15. Estimates showed a tendency to higher VE among 7–17-year-old children, but differences were not statistically significant. Although our findings are common in studies estimating influenza VE, we discussed several explanations for observed low VE.

A hypothetical influenza infection-induced non-specific immunity may reduce the risk of subsequent non-influenza respiratory virus (NIRV) infection and bias the influenza vaccine effectiveness (VE) estimates in test-negative designs (TNDs). We conducted a simulation study using a simple TND model and explored the degree of bias in the VE estimates. The bias was marginal during the usual seasons and most of the time during pandemics; the bias only became large when the influenza infection attack rate increased to pandemic levels (>50%), the true VE was low to moderate, and the non-specific immunity almost completely protected from NIRV infections and lasted at least half the influenza season.