Insights into the protective effects of influenza vaccination: More hospitalizations but lower follow-up mortality during the 2014/15 influenza season in a Swiss cohort

Background Observational studies of influenza vaccination are criticized as flawed due to unmeasured confounding. The goal of this cohort study was to explore the value and role of secondary claims data to inform the effectiveness of influenza vaccination, while systematically trying to reduce potential bias. Methods We iteratively reviewed the components of the PICO approach to refine study design. We analyzed Swiss mandatory health insurance claims of adult patients with chronic diseases, for whom influenza vaccination was recommended in 2014. Analyzed outcomes were all-cause mortality, hospitalization with a respiratory infection or its potential complication, and all-cause mortality after such hospitalization, adjusting for clinical and health care use variables. Cox and multi-state models were applied for time-to-event analysis. Results Of 343,505 included persons, 22.4% were vaccinated. Vaccinated patients were on average older, had more morbidities, higher health care expenditures, and had been more frequently hospitalized. In non-adjusted models, vaccination was associated with increased risk of events. Adding covariates decreased the hazard ratio (HR) both for mortality and hospitalizations. In the full model, the HR [95% confidence interval] for mortality during season was 0.82 [0.77–0.88], and closer to null effect after season. In contrast, HR for hospitalizations was increased during season to 1.28 [1.15–1.42], with estimates closer to null effect after season. HR in multi-state models were similar to those in the single-outcome models, with HR of mortality after hospitalization negative both during and after season. Conclusion In patients with chronic diseases, influenza vaccination was associated with more frequent specific hospitalizations, but decreased risk of mortality overall and after such hospitalization. Our approach of iteratively considering PICO elements helped to consider various sources of bias in the study sequentially. The selection of appropriate, specific outcomes makes the link between intervention and outcome more plausible and can reduce the impact of confounding.