Tatyana Lyapustina1, Lainie Rutkow2, Hsien-Yen Chang2, Matthew Daubresse3, Alim F Ramji4, Mark Faul5, Elizabeth A Stuart6, G Caleb Alexander7. 1. Johns Hopkins School of Medicine, 733 N Broadway, Baltimore, MD 21205, United States. Electronic address: tlyapus1@jhmi.edu. 2. Johns Hopkins Bloomberg School of Public Health, Department of Health Policy and Management, 624 N. Broadway St., Baltimore, MD 21205, United States. 3. Johns Hopkins Bloomberg School of Public Health, Department of Epidemiology, 615 N Wolfe St. W6027, Baltimore, MD 21205, United States; Johns Hopkins Bloomberg School of Public Health, Center for Drug Safety and Effectiveness, 615 N Wolfe St. W6035, Baltimore, MD 21205, United States. 4. Johns Hopkins School of Medicine, 733 N Broadway, Baltimore, MD 21205, United States. 5. Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, 4770 Buford Hwy, NE Mail Stop MS F-63 Atlanta, GA 30341-3717, United States. 6. Johns Hopkins Bloomberg School of Public Health, Department of Health Policy and Management, 624 N. Broadway St., Baltimore, MD 21205, United States; Johns Hopkins Bloomberg School of Public Health, Department of Mental Health, Hampton House, 624 N. Broadway, 8th Floor, Baltimore, MD 21205, United States; Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics, 615 N. Wolfe St., Baltimore, MD 21205, United States. 7. Johns Hopkins Bloomberg School of Public Health, Department of Epidemiology, 615 N Wolfe St. W6027, Baltimore, MD 21205, United States; Johns Hopkins Bloomberg School of Public Health, Center for Drug Safety and Effectiveness, 615 N Wolfe St. W6035, Baltimore, MD 21205, United States; Johns Hopkins Medicine, Division of General Internal Medicine, 1800 Orleans St., Baltimore, MD 21287, United States.
Abstract
BACKGROUND: States have attempted to reduce prescription opioid abuse through strengthening the regulation of pain management clinics; however, the effect of such measures remains unclear. We quantified the impact of Texas's September 2010 "pill mill" law on opioid prescribing and utilization. METHODS: We used the IMS Health LRx LifeLink database to examine anonymized, patient-level pharmacy claims for a closed cohort of individuals filling prescription opioids in Texas between September 2009 and August 2011. Our primary outcomes were derived at a monthly level and included: (1) average morphine equivalent dose (MED) per transaction; (2) aggregate opioid volume; (3) number of opioid prescriptions; and (4) quantity of opioid pills dispensed. We compared observed values with the counterfactual, which we estimated from pre-intervention levels and trends. RESULTS: Texas's pill mill law was associated with declines in average MED per transaction (-0.57 mg/month, 95% confidence interval [CI] -1.09, -0.057), monthly opioid volume (-9.99 kg/month, CI -12.86, -7.11), monthly number of opioid prescriptions (-12,200 prescriptions/month, CI -15,300, -9,150) and monthly quantity of opioid pills dispensed (-714,000 pills/month, CI -877,000, -550,000). These reductions reflected decreases of 8.1-24.3% across the outcomes at one year compared with the counterfactual, and they were concentrated among prescribers and patients with the highest opioid prescribing and utilization at baseline. CONCLUSIONS: Following the implementation of Texas's 2010 pill mill law, there were clinically significant reductions in opioid dose, volume, prescriptions and pills dispensed within the state, which were limited to individuals with higher levels of baseline opioid prescribing and utilization.
BACKGROUND: States have attempted to reduce prescription opioid abuse through strengthening the regulation of pain management clinics; however, the effect of such measures remains unclear. We quantified the impact of Texas's September 2010 "pill mill" law on opioid prescribing and utilization. METHODS: We used the IMS Health LRx LifeLink database to examine anonymized, patient-level pharmacy claims for a closed cohort of individuals filling prescription opioids in Texas between September 2009 and August 2011. Our primary outcomes were derived at a monthly level and included: (1) average morphine equivalent dose (MED) per transaction; (2) aggregate opioid volume; (3) number of opioid prescriptions; and (4) quantity of opioid pills dispensed. We compared observed values with the counterfactual, which we estimated from pre-intervention levels and trends. RESULTS: Texas's pill mill law was associated with declines in average MED per transaction (-0.57 mg/month, 95% confidence interval [CI] -1.09, -0.057), monthly opioid volume (-9.99 kg/month, CI -12.86, -7.11), monthly number of opioid prescriptions (-12,200 prescriptions/month, CI -15,300, -9,150) and monthly quantity of opioid pills dispensed (-714,000 pills/month, CI -877,000, -550,000). These reductions reflected decreases of 8.1-24.3% across the outcomes at one year compared with the counterfactual, and they were concentrated among prescribers and patients with the highest opioid prescribing and utilization at baseline. CONCLUSIONS: Following the implementation of Texas's 2010 pill mill law, there were clinically significant reductions in opioid dose, volume, prescriptions and pills dispensed within the state, which were limited to individuals with higher levels of baseline opioid prescribing and utilization.
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