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Seasonal influenze vaccine effectiveness in the community (SIVE): protocol for a cohort study exploiting a unique national linked data set

Lone, Nazir and Simpson, Colin and Kavanagh, Kimberley and Robertson, Christopher and McMenamin, Jim and Ritchie, Lewis and Sheikh, Aziz (2012) Seasonal influenze vaccine effectiveness in the community (SIVE): protocol for a cohort study exploiting a unique national linked data set. BMJ Open, 2 (2).

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Introduction Seasonal influenza vaccination is recommended for all individuals aged 65 years and over and in individuals younger than 65 years with comorbidities. There is good evidence of vaccine effectiveness (VE) in young healthy individuals but less robust evidence for effectiveness in the populations targeted for influenza vaccination. Undertaking a randomised controlled trial to assess VE is now impractical due to the presence of national vaccination programmes. Quasi-experimental designs offer the potential to advance the evidence base in such scenarios, and the authors have therefore been commissioned to undertake a naturalistic national evaluation of seasonal influenza VE by using data derived from linkage of a number of Scottish health databases. The aim of this study is to examine the effectiveness of the seasonal influenza vaccination in the Scottish population. Methods and analysis A cohort study design will be used pooling data over nine seasons. A primary care database covering 4% of the Scottish population for the period 2000–2009 has been linked to the national database of hospital admissions and the death register and is being linked to the Health Protection Scotland virology database. The primary outcome is VE measured in terms of rate of hospital admissions due to respiratory illness. Multivariable regression will be used to produce estimates of VE adjusted for confounders. The major challenge of this approach is addressing the strong effect of confounding due to vaccinated individuals being systematically different from unvaccinated individuals. Analyses using propensity scores and instrumental variables will be undertaken, and the effect of an unknown confounder will be modelled in a sensitivity analysis to assess the robustness of the estimates.