• Medical care · Jan 2010

    Incremental value of using Medicaid claim files to study comorbid conditions and treatments in dually eligible beneficiaries.

    • Cathy J Bradley, Bassam Dahman, Paul M Bataki, and Siran Koroukian.
    • Department of Public Health Management and Policy, Massey Cancer Center, Virginia Commonwealth University, Richmond, VA 23298-0203, USA. cjbradley@vcu.edu
    • Med Care. 2010 Jan 1; 48 (1): 79-84.

    BackgroundAlthough investigations using Medicare claims files are ubiquitous in the health services research literature, Medicaid claims files are used less frequently. Nonetheless, Medicaid is the major payer for healthcare among low-income persons.ObjectiveTo assess the added value of Medicaid claim files for identifying comorbid conditions and cancer treatments in a dually eligible sample.Research DesignData were obtained from linked statewide tumor registries from 2 contiguous Midwestern states (Michigan and Ohio), Medicare and Medicaid enrollment files, and Medicare and Medicaid claims files. We estimated the prevalence of Charlson Comorbidity Index conditions by counting the number of patients with these conditions in the Medicare claims files alone. We then estimate the expected percent increase in the prevalence of comorbid conditions (along with the 95% confidence interval) that could be obtained by using both Medicare and Medicaid claim files. We followed a similar procedure to identify treatments provided to dually eligible patients.ResultsMedicaid claims added very few individuals with comorbid conditions over those identified through Medicare claim files. The increase in the prevalence of comorbid conditions was between 0% and 2.5%. Likewise, Medicaid claims identified few individuals with cancer treatments who were not already identified through Medicare claim files, although variations were noted between the 2 states.ConclusionsThis study suggests that the incremental value of Medicaid inpatient, outpatient, and carrier claims is relatively small over what can be obtained from Medicare claims data.

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