Prescription drug monitoring program policy reform: human and veterinary practitioner prescribing in West Virginia, 2008-2020.

No study has examined Prescription Drug Monitoring Program (PDMP) data for West Virginia or among specialty practices, such as veterinary medicine. The objectives of this study were to conduct time series modeling to describe the PDMP policy reform impact on prescribing rates for human and veterinary providers. We obtained data from the WV PDMP for 2008 through 2020 for the number of opioid prescriptions filled and providers. We estimated prescribing rates for human and veterinary providers separately based upon the top five opioids prescribed by veterinarians. We estimated temporal effects using a Bayesian log-normal time series model for humans and veterinarians separately. Throughout the study period prescribing rates increased significantly for veterinarians, and decreased for human providers, particularly during 2018 after implementation of the Opioid Reduction Act. Findings provide novel insight into the differential impact of policy on specialty practices and highlight decreasing human opioid prescribing observed elsewhere.

Identifying high-risk areas for nonfatal opioid overdose: a spatial case-control study using EMS run data.

PURPOSE: The objective of our study was to incorporate stricter probable nonfatal opioid overdose case criteria, and advanced epidemiologic approaches to more reliably detect local clustering in nonfatal opioid overdose activity in EMS runs data. METHODS: Data were obtained using emsCharts for our study area in southwestern Pennsylvania from 2007 to 2018. Cases were identified as emergency medical service (EMS) responses where naloxone was administered, and improvement was noted in patient records between initial and final Glasgow Coma Score. A subsample of all-cause EMS responses sites were used as controls and exact matched to cases on sex and 10-year-age category. Clustering was assessed using difference in Ripley's K function for cases and controls and Kulldorff scan statistics. RESULTS: Difference in K functions indicated no significant difference in probable nonfatal overdose EMS runs across the study area compared to all-cause EMS runs. However, scan statistics did identify significant local clustering of probable nonfatal overdose EMS runs (maximum likelihood = 16.40, P = 0.0003). CONCLUSIONS: Results highlight relevance of EMS data to detect community-level overdose activity and promote reliable use through stricter case definition criteria and advanced methodological approaches. Techniques examined have the potential to improve targeted delivery of neighborhood-level public health response activities using a near real-time data source.