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VA and Medicare Utilization Among Dually Enrolled Veterans with Type 2 Diabetes: A Latent Class Analysis.

Radomski TR, Zhao X, Thorpe CT, Thorpe JM, Good CB, Mor MK, Fine MJ, Gellad WF. VA and Medicare Utilization Among Dually Enrolled Veterans with Type 2 Diabetes: A Latent Class Analysis. Journal of general internal medicine. 2016 May 1; 31(5):524-31.

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BACKGROUND: Many Veterans treated within the VA Healthcare System (VA) are also enrolled in fee-for-service (FFS) Medicare and receive treatment outside the VA. Prior research has not accounted for the multiple ways that Veterans receive services across healthcare systems. OBJECTIVE: We aimed to establish a typology of VA and Medicare utilization among dually enrolled Veterans with type 2 diabetes. DESIGN: This was a retrospective cohort. PARTICIPANTS: 316,775 community-dwelling Veterans age = 65 years with type 2 diabetes who were dually enrolled in the VA and FFS Medicare in 2008-2009. METHODS: Using latent class analysis, we identified classes of Veterans based upon their probability of using VA and Medicare diabetes care services, including patient visits, laboratory tests, glucose test strips, and medications. We compared the amount of healthcare use between classes and identified factors associated with class membership using multinomial regression. KEY RESULTS: We identified four distinct latent classes: class 1 (53.9%) had high probabilities of VA use and low probabilities of Medicare use; classes 2 (17.2%), 3 (21.8%), and 4 (7.0%) had high probabilities of VA and Medicare use, but differed in their Medicare services used. For example, Veterans in class 3 received test strips exclusively through Medicare, while Veterans in class 4 were reliant on Medicare for medications. Living = 40 miles from a VA predicted membership in classes 3 (OR 1.1, CI 1.06-1.15) and 4 (OR 1.11, CI 1.04-1.18), while Medicaid eligibility predicted membership in class 4 (OR 4.30, CI 4.10-4.51). CONCLUSIONS: Veterans with diabetes can be grouped into four distinct classes of dual health system use, representing a novel way to characterize how patients use multiple services across healthcare systems. This classification has applications for identifying patients facing differential risk from care fragmentation.

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