Prospective population-based estimation of influenza vaccine effectiveness and burden of disease: Abstract ABSTRACT Public health policy makers need annual estimates of influenza vaccine effectiveness as an ongoing evaluation of the United States influenza vaccination program. These estimates must come from observational epidemiologic studies, which are susceptible to numerous sources of bias in study design and analysis. Furthermore, policy makers need information on influenza incidence and on antigenic match between the vaccine and circulating influenza strains to properly interpret vaccine effectiveness estimates. Among members of Group Health Cooperative (GHC), a managed care organization in western Washington State, we propose: (1) To conduct annual test-negative case-control studies of influenza vaccine effectiveness. This design is effective at reducing confounding caused by differences in healthcare-seeking behavior between vaccinated and unvaccinated subjects. We can further reduce confounding by making use of extensive data on our subjects from GHC electronic medical records and administrative databases. We will verify influenza infection by RT-PCR, and will culture influenza viruses from RT-PCR-positive specimens, to be shared with CDC for antigenic characterization. (2) To estimate the burden of influenza in the same population in which we estimate vaccine effectiveness. For estimating incidence, we take advantage of the fact that our case-control subjects are drawn from a defined, enumerated population of GHC enrollees who have few barriers to same-day care for acute illnesses. Over the course of the five year study period our results will give public health policy makers a greater understanding of how influenza vaccine effectiveness fluctuates from year to year, and how those fluctuations are related to influenza incidence and antigenic match with the vaccine.
We propose to make yearly estimates of influenza vaccine effectiveness and of the burden of influenza among member of Group Health Cooperative (GHC), a large managed care organization in western Washington State. Within this population we will take advantage of existing GHC systems to efficiently enroll patients with medically attended acute respiratory illness (MAARI), to confirm influenza infection by RT-PCR, to isolate influenza viruses to share with CDC, and to collect detailed information on high-risk medical conditions and other important variables. We will use the collected data to estimate vaccine effectiveness using a test-negative case-control design and to estimate the burden of influenza using a probabilistic multiplier model.
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