4.4 Article Proceedings Paper

Active Influenza Vaccine Safety Surveillance Potential Within a Healthcare Claims Environment

期刊

MEDICAL CARE
卷 47, 期 12, 页码 1251-1257

出版社

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/MLR.0b013e3181b58b5c

关键词

active vaccine safety surveillance; pandemic influenza; vaccine adverse events; sequential analysis; maxSPRT

资金

  1. PHS HHS [200-2002-00732] Funding Source: Medline

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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 I 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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