Journal of general internal medicine · 2018
County and Physician Variation in Benzodiazepine Prescribing to Medicare Beneficiaries by Primary Care Physicians in the USA.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Maust Donovan T, Lin Lewei A, Blow Frederic C, Marcus Steven C
Аннотация
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.
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.