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Drug Alcohol Depend. 2016 Aug 1;165:1-8. doi: 10.1016/j.drugalcdep.2016.04.033. Epub 2016 Jun 2.

Impact of prescription drug monitoring programs and pill mill laws on high-risk opioid prescribers: A comparative interrupted time series analysis.

Author information

1
Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway St., Baltimore, MD 21205, United States; Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St. W6035, Baltimore, MD 21205, United States. Electronic address: hchang24@jhmi.edu.
2
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St. W6035, Baltimore, MD 21205, United States. Electronic address: tlyapus1@jhmi.edu.
3
Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway St., Baltimore, MD 21205, United States. Electronic address: lrutkow@jhu.edu.
4
Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St. W6035, Baltimore, MD 21205, United States; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St. W6035, Baltimore, MD 21205, United States. Electronic address: mdaubre1@jhu.edu.
5
Department of Mathematics, Statistics, and Computer Science, St. Olaf College, Regents Hall of Science 302, Northfield, MN 55057, United States. Electronic address: richeym@stolaf.edu.
6
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, United States. Electronic address: mgf7@cdc.gov.
7
Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway St., Baltimore, MD 21205, United States; Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St. W6035, Baltimore, MD 21205, United States; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, 8th Floor, Baltimore, MD 21205, United States; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, 8th Floor, Baltimore, MD 21205, United States. Electronic address: estuart@jhsph.edu.
8
Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St. W6035, Baltimore, MD 21205, United States; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St. W6035, Baltimore, MD 21205, United States; Division of General Internal Medicine, Johns Hopkins Medicine, 615 N Wolfe St., Baltimore, MD 21205, United States. Electronic address: galexand@jhsph.edu.

Abstract

BACKGROUND:

Prescription drug monitoring programs (PDMPs) and pill mill laws were implemented to reduce opioid-related injuries/deaths. We evaluated their effects on high-risk prescribers in Florida.

METHODS:

We used IMS Health's LRx Lifelink database between July 2010 and September 2012 to identify opioid-prescribing prescribers in Florida (intervention state, N: 38,465) and Georgia (control state, N: 18,566). The pre-intervention, intervention, and post-intervention periods were: July 2010-June 2011, July 2011-September 2011, and October 2011-September 2012. High-risk prescribers were those in the top 5th percentile of opioid volume during four consecutive calendar quarters. We applied comparative interrupted time series models to evaluate policy effects on clinical practices and monthly prescribing measures for low-risk/high-risk prescribers.

RESULTS:

We identified 1526 (4.0%) high-risk prescribers in Florida, accounting for 67% of total opioid volume and 40% of total opioid prescriptions. Relative to their lower-risk counterparts, they wrote sixteen times more monthly opioid prescriptions (79 vs. 5, p<0.01), and had more prescription-filling patients receiving opioids (47% vs. 19%, p<0.01). Following policy implementation, Florida's high-risk providers experienced large relative reductions in opioid patients and opioid prescriptions (-536 patients/month, 95% confidence intervals [CI] -829 to -243; -847 prescriptions/month, CI -1498 to -197), morphine equivalent dose (-0.88mg/month, CI -1.13 to -0.62), and total opioid volume (-3.88kg/month, CI -5.14 to -2.62). Low-risk providers did not experience statistically significantly relative reductions, nor did policy implementation affect the status of being high- vs. low- risk prescribers.

CONCLUSIONS:

High-risk prescribers are disproportionately responsive to state policies. However, opioids-prescribing remains highly concentrated among high-risk providers.

KEYWORDS:

High-risk opioid prescribing; Pill mill law; Prescription drug abuse; Prescription drug monitoring program; Time series analysis

PMID:
27264166
PMCID:
PMC4985620
DOI:
10.1016/j.drugalcdep.2016.04.033
[Indexed for MEDLINE]
Free PMC Article

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