Elsevier

Drug and Alcohol Dependence

Volume 165, 1 August 2016, Pages 1-8
Drug and Alcohol Dependence

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

https://doi.org/10.1016/j.drugalcdep.2016.04.033Get rights and content

Highlights

  • Impact of PDMPs and pill mill laws on high-risk prescriber in Florida were evaluated.

  • High-risk prescribers accounted for 67% of total opioid volume and 40% of total opioid prescriptions.

  • High-risk prescribers are disproportionately responsive to state policies to reduce opioid abuse.

  • After policy implementation prescribing remains highly concentrated among the same prescribers.

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 (−0.88 mg/month, CI −1.13 to −0.62), and total opioid volume (−3.88 kg/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.

Introduction

Prescription opioid addiction and non-medical use are significant public health problems, responsible for about 44 daily overdose deaths in the United States (Kolodny et al., 2015, United States Department of Health and Human Services, Centers for Disease Control and Prevention, 2015). From 2000 to 2010, large increases in opioid prescription among ambulatory and emergency visits coincided with reductions in use of non-opioid analgesics and an unchanging prevalence of pain among patients (Chang et al., 2014, Daubresse et al., 2013). The burden of opioid-related morbidity has increased markedly over the past decade, with a 153% increase in the rate of opioid-related emergency department visits between 2004 and 2011 (Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality, 2013). Similarly, the age adjusted death rate attributable to prescription opioids quadrupled between 1999 and 2009, surpassing that of stimulants, heroin, and other prescription drugs (Calcaterra et al., 2013). These problems are not limited to the United States; the United Kingdom and other European countries also face increasing use of opioids for non-cancer pain (Stannard, 2013), high number of individuals estimated to be addicted to prescription drugs (Dhalla et al., 2011b), and an increase in drug-related deaths (Dhalla et al., 2011b, Giraudon et al., 2013).

Although there are no magic bullets to address these issues, policy makers play an important role in shaping regulatory, payment and public health policies to reduce opioid-related injuries and deaths (Dhalla et al., 2011b, Franklin et al., 2015, Giraudon et al., 2013, Lyapustina et al., 2016, Stannard, 2013, Stewart and Basler, 2013). Prescriber-oriented interventions, such as updating the guidelines on opioid prescription, have been adopted in many countries, but their penetration is unknown and following the guidelines is not mandatory (Giraudon et al., 2013). Establishing regulatory monitoring of prescription opioids has also been proposed in the United Kingdom (Stewart and Basler, 2013), and implemented at many states in the United States (Florida Office of the Attorney General, 2015, United States Department of Justice, Drug Enforcement Administration, 2011). For example, state policy-makers in the United States have used prescription drug monitoring programs (PDMPs) and “pill mill” laws to address the prescription opioid epidemic. Although these state-sponsored programs are used for a variety of clinical, regulatory and educational purposes, a primary function of PDMPs is to give physicians, pharmacists and other health care providers access to patients’ prescription histories to improve identification and management of individuals at high risk of opioid abuse or diversion (United States Department of Justice and Drug Enforcement Administration, 2011). In contrast, pill mill laws establish state-level regulatory oversight of pain management clinics, including the establishment of prescribing and dispensing requirements, and create penalties for those who do not comply with their requirements (United States Department of Health and Human Services, Centers for Disease Control and Prevention, 2012). While there is growing evidence regarding the effect of these approaches on opioid sales (Haegerich et al., 2014, Rutkow et al., 2015), overdoses (Sauber-Schatz et al., 2013), and deaths (Delcher et al., 2015), less is known about how they affect specific groups of prescribers. This is important, as approximately 20% of U.S. physicians are responsible for prescribing 80% of all opioid analgesics (Blumenschein et al., 2010, Dhalla et al., 2011a, Swedlow et al., 2011).

We previously demonstrated that Florida’s PDMP and pill mill law were associated with modest decreases in opioid prescribing concentrated among providers with higher baseline opioid volume (Rutkow et al., 2015). However, in that analysis, which focused on Florida because of disproportionate levels of opioid-related morbidity and mortality in the state, we used a crude measure to characterize high-volume prescribers and limited our analysis to a select number of prescribing outcomes. In the current analysis, we use a rigorous method of identifying several groups of high-risk prescribers and, in addition to more fully characterizing them, we evaluate the effect of Florida’s policies on their clinical practices, such as their total number of prescription-filling patients with an opioid prescription. Furthermore, we characterize the concentration of opioid volume and prescriptions among this group of prescribers as well as how the policies of interest impact these measures.

Section snippets

Data

Using data from IMS Health’s LifeLink LRx database, we examined anonymized, individual-level prescription claims, which represented approximately 65% of all retail prescription transactions in the United States. The data are automatically transmitted to IMS Health on a weekly basis from pharmacies in retail and food stores, as well as independent and mass merchandiser pharmacies. Claims data include National Drug Code (NDC)-level product information, quantity dispensed, days supply, payment

Characteristics of high-risk prescribers

A total of 1,526 of 38,465 Florida prescribers (3.97%) were identified as high-risk; these prescribers accounted for 66.59% of the opioid volume and 39.99% of the total opioid prescriptions dispensed in Florida during the pre-intervention period. Prescribers with high opioid volumes during any one quarter had a high likelihood of having high opioid volumes during other three calendar quarters (Spearman correlation coefficient: 0.79–0.87). Nearly identical patterns were observed in Georgia, both

Discussion

We quantified the effect of Florida’s PDMP and pill mill law on several populations of high-risk prescribers. The 4% of prescribers who we deemed high-risk accounted for two-thirds of the opioid volume and two-fifths of the total opioid prescriptions dispensed in Florida during the pre-intervention period. Florida’s PDMP and pill mill law were associated with statistically and clinically significantly relative reductions in four of the seven outcomes that we examined among high-risk

Conflict of interest

Dr. Alexander is Chair of the FDA’s Peripheral and Central Nervous System Advisory Committee; serves as a paid consultant to PainNavigator, a mobile startup to improve patients’ pain management; serves as a paid consultant to IMS Health; and serves on an IMS Health scientific advisory board. This arrangement has been reviewed and approved by Johns Hopkins University in accordance with its conflict of interest policies. The statements, findings, conclusions, views, and opinions contained and

Funding

This work was funded by the Robert Wood Johnson Foundation Public Health Law Research Program and the Centers for Disease Control and Prevention under Cooperative Agreement U01CE002499. The funding sources had no role in the design and conduct of the study, analysis, or interpretation of the data; and preparation or final approval of the manuscript prior to publication. The opinions and conclusions expressed are solely of the author(s) and should not be construed as representing the opinions of

Contributors

HC designed the study, managed data, performed analyses, and drafted the manuscript. TT interpreted results and drafted the manuscript. LR designed the study, revised the manuscript and provided critical comments. MD revised the manuscript and provided critical comments. MR revised the manuscript and provided critical comments. MF revised the manuscript and provided critical comments. EAS designed the study, provided critical comments and revised the manuscript. GCA designed the study, secured

Acknowledgements

The statements, findings, conclusions, views, and opinions contained and expressed in this article are based in part on data obtained under license from the following IMS Health Incorporated information service(s): IMS Health LifeLink LRx Database® (2010–2012), IMS Health Incorporated. All Rights Reserved. The statements, findings, conclusions, views, and opinions contained and expressed herein are not necessarily those of IMS Health Incorporated or any of its affiliated or subsidiary entities.

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