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Maust DT, Lin LA, Blow FC, Marcus SC. County and Physician Variation in Benzodiazepine Prescribing to Medicare Beneficiaries by Primary Care Physicians in the USA. Journal of general internal medicine. 2018 Dec 1; 33(12):2180-2188.
BACKGROUND: Physicians widely prescribe benzodiazepines (BZD) despite well-recognized harms. OBJECTIVE: To determine county and provider characteristics that predict high-intensity BZD prescribing by primary care physicians (PCPs) to Medicare beneficiaries. DESIGN: Cross-sectional analysis of the 2015 Medicare Part D Public Use Files (PUF). SUBJECTS: n? = 122,054 PCPs who prescribed 37.3 billion medication days. MAIN MEASURES: Primary outcome was intensity of BZD prescribing (days prescribed/total medication days) at the county- and physician levels. PCP and county characteristics were derived from the Part D PUF, Area Health Resources Files, and County Health Rankings. Logistic regression determined the characteristics associated with high-intensity (top quartile) BZD prescribing. KEY RESULTS: Beneficiaries were prescribed over 1.2 billion days of BZD in 2015, accounting for 2.3% of all medication days prescribed in Part D. Top quartile counties had 3.1 times higher BZD prescribing than the lowest (3.4% vs. 1.1%; F? = 3293.8, df? = 3, p? < 0.001). Adjusting for county-level demographics and health care system characteristics (including supply of mental health providers), counties with more adults with at least some college had lower odds of high-intensity prescribing (per 5% increase, adjusted odds ratio [AOR] 0.80, 99% confidence interval (CI) 0.73-0.87, p? < 0.001), as did higher income counties (per US$1000 increase, AOR 0.93, CI 0.91-0.95, p? < 0.001). Top quartile PCPs prescribed at 6.5 times the rate of the bottom (3.9% vs. 0.6%; F? = 63,910.2, df? = 3, p? < 0.001). High-intensity opioid prescribing (AOR 4.18, CI 3.90-4.48, p? < 0.001) was the characteristic most strongly associated with BZD prescribing. CONCLUSIONS: BZD prescribing appears to vary across counties and providers and is related to non-patient characteristics. Further work is needed to understand how such non-clinical factors drive variation.