Journal article

Using Administrative Claims to Identify Children With Chronic Conditions in a Statewide Immunization Registry

Objectives To demonstrate the feasibility and utility of using administrative claims data from commercial health plans to establish a high-risk indicator in a statewide immunization registry for enrollees with chronic conditions. Study Design Retrospective cohort analysis. Methods Administrative data were used to identify children with 1 or more chronic conditions enrolled in 2 commercial health plans during the 2008-2009 and 2009-2010 influenza seasons and matched with a statewide immunization registry. The proportion of cases that successfully matched and historical health services utilization- including influenza vaccinations and missed opportunities- were assessed. Results A total of 93% of children with chronic conditions identified through administrative claims were successfully matched with the statewide registry. Less than one-third of children received the seasonal influenza vaccine in either the 2008-2009 (29%) or 2009-2010 (32%) seasons/ 30% of children received the H1N1 vaccination in 2009-2010. Most children in the 2008-2009 (63%) and 2009-2010 (63%) seasons had at least 1 missed opportunity for seasonal influenza vaccination. Younger children had the highest percentage of missed opportunities while adolescents had the lowest rate of missed opportunities for vaccination. Conclusions It is feasible to identify children with chronic conditions using administrative data and to link them with a statewide immunization registry. Low influenza vaccination rates and high occurrences of missed opportunities among children with chronic conditions suggest the utility of integrating administrative claims data with statewide registries to support various outreach mechanisms- including physician-focused and parent-targeted reminder/recall- based on target age to improve vaccination rates.

Languages

  • English

Journal

American Journal of Managed Care

Volume

5

Type

Journal article

Categories

  • Data

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