Silvia S Martins1, William Ponicki2, Nathan Smith3, Ariadne Rivera-Aguirre4, Corey S Davis5, David S Fink6, Alvaro Castillo-Carniglia7, Stephen G Henry8, Brandon D L Marshall9, Paul Gruenewald2, Magdalena Cerdá10. 1. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, United States. Electronic address: ssm2183@cumc.columbia.edu. 2. Prevention Research Center, Pacific Institute for Research and Evaluation, Berkeley, CA, United States. 3. Violence Prevention Research Program, Department of Emergency Medicine, UC Davis School of Medicine, CA, United States. 4. Violence Prevention Research Program, Department of Emergency Medicine, UC Davis School of Medicine, CA, United States; Department of Population Health, NYU School of Medicine, New York, NY, United States. 5. Network for Public Health Law, Los Angeles, CA, United States. 6. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, United States. 7. Society and Health Research Center, Facultad de Humanidades, Universidad Mayor, Chile. 8. Department of Internal Medicine, University of California Davis, Sacramento, CA, United States. 9. Department of Epidemiology, Brown University School of Public Health, Providence, RI, United States. 10. Department of Population Health, NYU School of Medicine, New York, NY, United States.
Abstract
BACKGROUND: Prescription drug monitoring programs (PDMP), by reducing access to prescribed opioids (POs), may contribute to a policy environment in which some people with opioid dependence are at increased risk for transitioning from POs to heroin/other illegal opioids. This study examines how PDMP adoption and changes in the characteristics of PDMPs over time contribute to changes in fatal heroin poisoning in counties within states from 2002 to 2016. METHODS: Latent transition analysis to classify PDMPs into latent classes (Cooperative, Proactive, and Weak) for each state and year, across three intervals (1999-2004, 2005-2009, 2010-2016). We examined the association between probability of PDMP latent class membership and the rate of county-level heroin poisoning death. RESULTS: After adjustment for potential county-level confounders and co-occurring policy changes, adoption of a PDMP was significantly associated with increased heroin poisoning rates (22% increase by third year post-adoption). Findings varied by PDMP type. From 2010-2016, states with Cooperative PDMPs (those more likely to share data with other states, to require more frequent reporting, and include more drug schedules) had 19% higher heroin poisoning rates than states with Weak PDMPs (adjusted rate ratio [ARR] = 1.19; 95% CI = 1.14, 1.25). States with Proactive PDMPs (those more likely to report outlying prescribing and dispensing and provide broader access to law enforcement) had 6% lower heroin poisoning rates than states with No/Weak PDMPs (ARR = 0.94; 95% CI = 0.90, 0.98). CONCLUSION: There is a consistent, positive association between state PDMP adoption and heroin poisoning mortality. However, this varies by PDMP type, with Proactive PDMPs associated with a small reduction in heroin poisoning deaths. This raises questions about the potential for PDMPs to support efforts to decrease heroin overdose risk, particularly by using proactive alerts to identify patients in need of treatment for opioid use disorder. Future research on mechanisms explaining the reduction in heroin poisonings after enactment of Proactive PDMPs is merited.
BACKGROUND: Prescription drug monitoring programs (PDMP), by reducing access to prescribed opioids (POs), may contribute to a policy environment in which some people with opioid dependence are at increased risk for transitioning from POs to heroin/other illegal opioids. This study examines how PDMP adoption and changes in the characteristics of PDMPs over time contribute to changes in fatal heroinpoisoning in counties within states from 2002 to 2016. METHODS: Latent transition analysis to classify PDMPs into latent classes (Cooperative, Proactive, and Weak) for each state and year, across three intervals (1999-2004, 2005-2009, 2010-2016). We examined the association between probability of PDMP latent class membership and the rate of county-level heroinpoisoning death. RESULTS: After adjustment for potential county-level confounders and co-occurring policy changes, adoption of a PDMP was significantly associated with increased heroinpoisoning rates (22% increase by third year post-adoption). Findings varied by PDMP type. From 2010-2016, states with Cooperative PDMPs (those more likely to share data with other states, to require more frequent reporting, and include more drug schedules) had 19% higher heroinpoisoning rates than states with Weak PDMPs (adjusted rate ratio [ARR] = 1.19; 95% CI = 1.14, 1.25). States with Proactive PDMPs (those more likely to report outlying prescribing and dispensing and provide broader access to law enforcement) had 6% lower heroinpoisoning rates than states with No/Weak PDMPs (ARR = 0.94; 95% CI = 0.90, 0.98). CONCLUSION: There is a consistent, positive association between state PDMP adoption and heroinpoisoning mortality. However, this varies by PDMP type, with Proactive PDMPs associated with a small reduction in heroinpoisoning deaths. This raises questions about the potential for PDMPs to support efforts to decrease heroinoverdose risk, particularly by using proactive alerts to identify patients in need of treatment for opioid use disorder. Future research on mechanisms explaining the reduction in heroin poisonings after enactment of Proactive PDMPs is merited.
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