Factors Associated with County-Level Variation in the Prescription of Statins

The American College of Cardiology/American Heart Association recommends statins for adults aged 40-75 years with a cardiovascular disease risk factor and a 10-year risk of cardiovascular events of 7.5%-19.9%.

To examine the association of county-level social determinant measures of health and composition of health services with use of statin prescriptions under Medicare Part D.

We used 2013 Medicare Part D prescriber county-level data to construct 2 measures of statin use: (1) statin beneficiaries ÷ total beneficiaries (prevalence [β_PR]) and (2) statin days supplied ÷ (total beneficiaries × 365; adequacy of supply [β_AS]). We used multivariable regression to estimate the association of each measure with county-level demographics and health service measures.

A 1 standard deviation (SD) increase in the proportion of African Americans living in a county is associated with a 0.096 SD decrease in adequacy of supply (β_AS = -0.096; 95% CI = -0.14 to -0.06). The proportion of county residents aged 65+ years who are female was associated with higher prevalence and adequacy of supply (β_PR = 0.06; 95% CI = 0.02 to 0.11; β_AS = 0.09; 95% CI = 0.05 to 0.14). Counties with higher proportions of Medicare Part D prescription expenditures receiving low-income subsidies had lower adequacy of supply (β_AS = -0.28; 95% CI = -0.32 to -0.23). Counties with a higher proportion of Medicare Part D prescribers who are nurse practitioners was associated with lower prevalence and adequacy of supply (β_PR = -0.39; 95% CI = -0.44 to -0.35; β_AS = -0.42; 95% CI = -0.47 to -0.37).

Race and ethnicity, income, and distribution of provider types were significantly associated with county-level variation in statin use, despite being unlikely to measure differences in actual medical need. Such variation more likely reflects predisposing and enabling factors potentially affected by social, economic, and public health policy. Tracking variation in county-level statin use associated with these factors could help policymakers assess progress in reducing health care disparities and better target program resources.

No funding was received for this work. Karpinski reports employment by Aetna, Anthem, and Ingenio-Rx. Vanness reports unrelated consulting fees from CHEORS, Evidera, BMS, Novartis, and Merck. Look has nothing to disclose.