The Effects of Prescription Drug Monitoring Program Policies on Adverse Health Care Events Involving Opioids

Background and Objective: Between 1999 and 2015, overdose deaths involving opioids have increased nearly six-fold in the U.S. In response to this epidemic, many states implemented policies to optimize prescriber use of Prescription Drug Monitoring Programs (PDMPs). PDMPs are electronic databases that report on filled controlled substances for all patients and, when used by prescribers, may address information asymmetries between prescribers and patients, and in turn, assist prescribers with identifying patients with possible misuse while ensuring legitimate use for pain management. Prominent policies to optimize prescriber use include comprehensive mandates for PDMP use by prescribers, laws allowing delegation of PDMP access to office staff, and interstate PDMP data sharing. A recent publication of the research team found that all three policies were associated with reduction in patterns of opioid prescriptions with high risk for misuse and overdose. In this study, we aim to estimate the effects of these policies on adverse health care events involving opioids.

Population and Data: This study uses the 2011-2015 Health Care Cost Institute (HCCI) claims database, containing claims of over 50 million individuals enrolled in all health plans (employment, market exchange, and Medicare Advantage plans) offered by Aetna, Humana, and UnitedHealthCare. We focus on patients 18-64 with at least one opioid prescription throughout the study years. Data on state policies and effective dates are compiled from several sources such as the National Alliance for Model State Drug Laws.

Methods: Our outcomes are emergency department (ED) visits and inpatient admissions with a diagnosis of opioid overdose, abuse, or dependence. Our unit of analysis is patient-quarter. We examine both the dichotomous outcome of having at least one such event in a quarter and the count of events. We exploit the staggered implementation of PDMP policies across states to estimate difference-in-differences (DD) models, using states that had not implemented the policies as controls. In a secondary analysis, we conduct event study to 1) examine the parallel-trend assumption for the DD estimation, and, 2) to allow the policy effects to differ by time since implementation.

Findings: Analysis is ongoing. We expect to have preliminary findings by Spring 2019.

Implications: All three policies we examine are designed to improve prescriber take-up and effectiveness of PDMPs and are being adopted by an increasing number of states. Our results will inform evolving state policies addressing one of the root causes of the opioid crisis.