The aim of this study was to assess the association between alcohol intake and premature mortality (younger than 65 years) and to explore the effect of potential alcohol underreporting by heavy drinkers. We followed-up 20 272 university graduates. Four categories of alcohol intake were considered (abstainer, light, moderate and heavy consumption). Repeated measurements of alcohol intake and updated information on confounders were used in time-dependent Cox models. Potential underreporting of alcohol intake by some heavy drinkers (likely misclassified as light or moderate drinkers) was explicitly addressed in an attempt to correct potential underreporting by using indirect information. During 12·3 years of median follow-up (interquartile range: 6·8–15·0), 226 participants died before their 65th birthday. A higher risk of early mortality was found for the highest category of alcohol intake (≥50 g/d) in comparison with abstention (multivariable-adjusted hazard ratio (HR) = 2·82, 95 % CI 1·38, 5·79). In analyses of alcohol as a continuous variable, the multivariable-adjusted HR was 1·17 (95 % CI 1·08, 1·26), for each 10 g/d of alcohol. This harmful linear association was present both in uncorrected models and in models corrected for potential underreporting. No significant inverse association between light or moderate alcohol intake and premature mortality was observed, even after correcting for potential misclassification. Alcohol intake exhibited a harmful linear dose–response association with premature mortality (<65 years) in this young and highly educated Mediterranean cohort. Our attempts to correct for potential misclassification did not substantially change these results.

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.