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Effectiveness of seasonal influenza vaccination in community-dwelling elderly people: an individual participant data meta-analysis of test-negative design case-control studies.

Background Several aggregate data meta-analyses have provided estimates of the eff ectiveness of influenza vaccination in community-dwelling elderly people. However, these studies ignored the eff ects of patient-level confounders such as sex, age, and chronic diseases that could bias effectiveness estimates. We aimed to assess the confounder-adjusted effectiveness of infl uenza vaccines on laboratory-confirmed influenza among elderly people by conducting a global individual participant data meta-analysis.

Methods In this individual participant data meta-analysis, we considered studies included in a previously conducted aggregate data meta-analysis that included test-negative design case-control studies published up to July 13, 2014. We contacted all authors of the included studies on Dec 1, 2014, to request individual participant data. Patients were excluded if their unique identifi er was missing, their vaccination status was unknown, their outcome status was unknown, or they had had suspected infl uenza infection more than once in the same influenza season. Cases were patients with influenza-like illness symptoms who tested positive for at least one of A H1N1, A H1N1 pdm09, A H3N2, or B viruses; controls were patients with influenza-like illness symptoms who tested negative for these virus types or subtypes. Influenza vaccine eff ectiveness against overall and subtype-specific laboratory-confirmed influenza were the primary and secondary outcomes. We used a generalised linear mixed model to calculate adjusted vaccine effectiveness according to vaccine match to the circulating strains of influenza virus and intensity of the virus activity (epidemic or non-epidemic). Vaccine effectiveness was defined as the relative reduction in risk of laboratory-confirmed influenza in vaccinated patients compared with unvaccinated patients. We did subgroup analyses to estimate vaccine effectiveness according to hemisphere, age category, and health status.

Findings We received 23 of the 53 datasets included in the aggregate data meta-analysis. Furthermore, six additional datasets were provided by data collaborators, which resulted in individual participant data for a total of 5210 participants. A total of 4975 patients had the required data for analysis. Of these, 3146 (63%) were controls and 1829 (37%) were cases. Influenza vaccination was significantly effective during epidemic seasons irrespective of vaccine match status (matched adjusted vaccine effectiveness 44·38%, 95% CI 22·63–60·01; mismatched adjusted vaccine effectiveness 20·00%, 95% CI 3·46–33·68; analyses in the imputed dataset). Seasonal influenza vaccination did not show significant effectiveness during non-epidemic seasons. We found substantial variation in vaccine effectiveness across virus types and subtypes, with the highest estimate for A H1N1 pdm09 (53·19%, 10·25–75·58) and the lowest estimate for B virus types (–1·52%, –39·58 to 26·16). Although we observed no signifi cant diff erences between subgroups in each category (hemisphere, age, and health status), influenza vaccination showed a protective effect among elderly people with cardiovascular disease, lung disease, or aged 75 years and younger.

Interpretation Influenza vaccination is moderately effective against laboratory-confirmed influenza in elderly people during epidemic seasons. More research is needed to investigate factors affecting vaccine protection (eg, brandspecific or type-specific vaccine effectiveness and repeated annual vaccination) in elderly people.

Funding University Medical Center Groningen.

Lancet Respiratory Medicine
Darvishian M, van den Heuvel ER, Bissielo A, Castilla J, Cohen C, Englund H, Gefenaite G, Huang W-Ting, la Bastide-van Germert S, Martinez-Baz I, McArnerney JM, Ntshoe GM, Suzuki M, Turner N and Hak E
8 February 2017
Type of Research 
Menu Category 
Publication Date 
Wednesday, February 8, 2017

Last updated: May 2017