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Active Influenza Vaccine Safety Surveillance: Potential Within a Healthcare Claims Environment

Jeffrey S. Brown, Kristen M. Moore, M. Miles Braun, Najat Ziyadeh, K. Arnold Chan, Grace M. Lee, Martin Kulldorff, Alexander M. Walker and Richard Platt
Medical Care
Vol. 47, No. 12 (December 2009), pp. 1251-1257
Stable URL: http://www.jstor.org/stable/27798391
Page Count: 7
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Active Influenza Vaccine Safety Surveillance: Potential Within a Healthcare Claims Environment
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Abstract

Background: Rapid safety assessment of novel vaccines, especially those targeted against pandemic influenza, is a public health priority. Objectives: Assess the feasibility of using healthcare claims data to rapidly detect influenza vaccine adverse events using sequential analytic methods. Research Design: Retrospective pilot study simulating prospective surveillance using 6 cumulative monthly administrative claims data extracts. The first included encounters occurring in October; each subsequent extract included an additional month of encounters. Ten adverse events were evaluated, comparing postvaccination rates during the 2006–2007 influenza season to those expected based on rates observed in the prior season. Subjects: Members of a large, multistate health insurer who had a claim for influenza vaccination during the 2005–2006 or 2006–2007 influenza seasons. Measures: The completeness of monthly claims extracts. Results: Most vaccinations and outcomes were identified early in the 2006–2007 season; about 50% of vaccinations and short latency events were identified in the second monthly data extract, which would typically become available by mid-December, and 80% of vaccinations and events were identified in the third extract. With respect to overall claims lag, approximately 90% of vaccinations and events were identified within 1 to 2 months after vaccination, regardless of vaccination month. Conclusions: This study suggests that administrative claims data might contribute to same season influenza vaccine safety surveillance in large, defined populations, especially during a threat of pandemic influenza. Based on our previous work, we believe this method could be applied to multiple health plans' data to monitor a large portion of the US population.

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