Past work demonstrates that mandated prescription drug monitoring programs (PDMPs) decrease opioid prescribing, but provides limited evidence on mechanisms. We analyze Kentucky's landmark PDMP mandate to disentangle the role of information versus hassle costs. PDMP mandates are meant to affect prescribing through information provision but may also unintentionally affect prescribing through the hassle cost of required record checks. On net, we find that although information clearly affected prescribing, hassle costs explain the majority of the decline in prescribing from this program. Hassle costs, however, did not deter physicians from prescribing opioids to the patients who would benefit the most.

Past work demonstrates that mandated prescription drug monitoring programs (PDMPs) decrease opioid prescribing, but provides limited evidence on mechanisms. We analyze Kentucky's landmark PDMP mandate to disentangle the role of information versus hassle costs. PDMP mandates are meant to affect prescribing through information provision but may also unintentionally affect prescribing through the hassle cost of required record checks. On net, we find that although information clearly affected prescribing, hassle costs explain the majority of the decline in prescribing from this program. Hassle costs, however, did not deter physicians from prescribing opioids to the patients who would benefit the most.

In response to the epidemic of prescription-drug abuse, now 49 US states have passed legislation to establish Prescription Drug Monitoring Programs (PDMPs). These programs track controlled-substance prescribing and usage behavior in an effort to improve patient outcomes and identify and preempt access by drug abusers. We exploit variation in the timing of implementation across states to identify the effectiveness of PDMPs on reducing opioid abuse. In particular, by considering the role of specific program attributes we offer the strongest evidence to date of the potential for PDMP-type policy to decrease opioid-related treatment admissions. We also consider heterogeneity across intensity and tenure of use, which reveals that the largest gains are coming from reductions in the number of less-attached users. Overall, these results have important implications for the effective re-design of PDMP policy.

As states pass regulations to curb opioid abuse, what are the consequences for legitimate pain management? Forty-nine states have adopted prescription drug monitoring programs (PDMP) to detect potentially inappropriate opioid prescribing and use. Several studies have estimated the impact of PDMPs in reducing abuse and deaths. However, no study addresses an equally important question -- do PDMPs generate negative or positive spillover effects for pain management in the millions of Americans suffering from pain? We use 2003-2013 home health agency-level panel data to assess the effect of PDMPs on pain improvements in home health patients. We hypothesize that implementation of a PDMP in the context of home health care may induce agencies to substitute away from opioids to other (potentially more effective) treatments, such as physical therapy. Using a fixed effects, two-stage least squares model, we estimate pain improvements as a result of whether a state has implemented a PDMP and control for endogeneity between state policies and pain management practices. We find that home health agencies in states with PDMPs are on average more successful at improving pain in their patients. However, the effect varies according to how the agencies are reimbursed for services. Our results suggest that home health agencies are adapting their models of care to state opioid policies, and the incentives embedded in reimbursement systems can either promote better or worse pain management.

States, which have the primary legal role in regulating the prescribing and dispensing of prescription medications, have created Prescription Drug Monitoring Programs (PDMP) to try to reduce inappropriate prescribing, dispensing, and related harm. Research assessing whether these interventions are effective has produced inconclusive and contradictory results. Here we examine whether different data sources may have contributed to the varying results. Specifically, we: 1) identify the decisions inherent in creating such a dataset; 2) discuss the public data sources used by researchers in previous work; 3) develop and apply a detailed research protocol to create a novel PDMP law dataset; and 4) to illustrate potential consequences of data choice, apply various data sources to analyze the relationship between PDMP laws and prescribing and dispensing of opioids among disabled Medicare beneficiaries. We find that our dates differ from those in existing datasets, sometimes by many years. The regression analyses generated a twofold difference in point estimates, as well as different signed estimates, depending on the data used. We conclude that the lack of transparency about data assembly in existing datasets, differences among dates by source, and the regression results raise concerns for PDMP researchers and policymakers.