Time-Series Analysis of the Impact of Prescription Drug Monitoring Programs on Heroin Treatment Admissions

Abstract

ABSTRACTBackground: Prescription drug abuse has become a major issue in the United States in recent years. Prescription drug monitoring programs (PDMPs) are designed to help health care providers to prevent such abuses. There may be unintended effects of these programs. Specifically, PDMPs may move prescription opioid users to begin use of heroin. Objectives: This article aims to evaluate the impact of PDMPs on heroin abuse across several different states through use of treatment admissions records obtained from the Treatment Episode Data Set. Methods: Operational dates and other characteristics of state PDMPs were obtained from the Prescription Drug Monitoring Program Training and Technical Assistance Center. Data for the dependent variable were collected from the Treatment Episodes Data Set from 1992 to 2012. Interrupted time-series analyses using autoregressive integrated moving average modeling were used to estimate the effect of presence of an operational PDMP on the number of admissions reporting heroin as their primary drug being used. Results: The relationship between heroin admissions and prescription opioid admissions was significant for the average data (β = 0.41, p = 0.0017) and the 5-year data (β = 0.5, p = 0.036), both showing positive associations between heroin and prescription drug admissions in states in the post PDMP implementation period. Conclusions/Importance: The study found a positive relationship that between heroin and prescription opioid admissions post PDMP implementation. Future research should attempt to identify what this relationship means and how this information can be used to improve opioid policy.