Background: Identifying the most effective state laws and provisions to reduce opioid overdose deaths remains critical.
Methods: Using expert ratings of opioid laws, we developed annual state scores for three domains: opioid prescribing restrictions, harm reduction, and Medicaid treatment coverage. We modeled associations of state opioid policy domain scores with opioid-involved overdose death counts in 3133 counties, and among racial/ethnic subgroups in 1485 counties (2013-2020). We modeled a second set of domain scores based solely on experts' highest 20 ranked provisions to compare with the all-provisions model.
Results: From 2013 to 2020, moving from non- to full enactment of harm reduction domain laws (i.e., 0 to 1 in domain score) was associated with reduced county-level relative risk (RR) of opioid overdose death in the subsequent year (adjusted RR = 0.84, 95 % credible interval (CrI): 0.77, 0.92). Moving from non- to full enactment of opioid prescribing restrictions and Medicaid treatment coverage domains was associated with higher overdose in 2013-2016 (aRR 1.69 (1.35, 2.11) and aRR 1.20 (1.11, 1.29) respectively); both shifted to the null in 2017-2020. Effect sizes and direction were similar across racial/ethnic groups. Results for experts' highest 20 ranked provisions did not differ from the all-provision model.
Conclusions: More robust state harm reduction policy scores were associated with reduced overdose risk, adjusting for other policy domains. Harmful associations with opioid prescribing restrictions in 2013-2016 may reflect early unintended consequences of these laws. Medicaid coverage domain findings did not align with experts' perceptions, though data limitations precluded inclusion of several highly ranked Medicaid policies.
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http://dx.doi.org/10.1016/j.drugpo.2025.104713 | DOI Listing |
J Neural Transm (Vienna)
January 2025
Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany.
The majority of patients with cannabis use disorder (CUD) regularly take medication. Cannabinoids influence metabolism of some commonly prescribed drugs. However, little is known about the characteristics and frequency of potential cannabis-drug (CDIs) and drug-drug interactions (DDIs) in patients with CUD.
View Article and Find Full Text PDFInt J Drug Policy
January 2025
Center for Opioid Epidemiology and Policy, Department of Population Health, NYU Grossman School of Medicine, New York University, New York City, NY, USA. Electronic address:
Background: Identifying the most effective state laws and provisions to reduce opioid overdose deaths remains critical.
Methods: Using expert ratings of opioid laws, we developed annual state scores for three domains: opioid prescribing restrictions, harm reduction, and Medicaid treatment coverage. We modeled associations of state opioid policy domain scores with opioid-involved overdose death counts in 3133 counties, and among racial/ethnic subgroups in 1485 counties (2013-2020).
Pain
January 2025
Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States.
Rapid declines in opioid analgesics dispensed in American communities since 2011 raise concerns about inadequate access to effective pain management among patients for whom opioid therapies are appropriate, especially for those living in racial/ethnic minority and socioeconomically deprived communities. Using 2011 to 2021 national data from the Automated Reports and Consolidated Ordering System and generalized linear models, this study examined quarterly per capita distribution of oxycodone, hydrocodone, and morphine (in oral morphine milligram equivalents [MMEs]) by communities' racial/ethnic and socioeconomic profiles. Communities (defined by 3-digit-zip codes areas) were classified as "majority White" (≥50% self-reported non-Hispanic White population) vs "majority non-White.
View Article and Find Full Text PDFCurr Pain Headache Rep
January 2025
Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA.
Purpose Of Review: Artificial intelligence (AI) offers a new frontier for aiding in the management of both acute and chronic pain, which may potentially transform opioid prescribing practices and addiction prevention strategies. In this review paper, not only do we discuss some of the current literature around predicting various opioid-related outcomes, but we also briefly point out the next steps to improve trustworthiness of these AI models prior to real-time use in clinical workflow.
Recent Findings: Machine learning-based predictive models for identifying risk for persistent postoperative opioid use have been reported for spine surgery, knee arthroplasty, hip arthroplasty, arthroscopic joint surgery, outpatient surgery, and mixed surgical populations.
J Addict Med
January 2025
From the Division of General Internal Medicine, San Francisco General Hospital, Department of Medicine, University of California San Francisco, San Francisco, CA (LWS); San Francisco Department of Public Health, San Francisco, CA (POC); Vital Strategies, New York, NY (KB, DC); Network for Public Health Law, Edina, MN (CSD); and New York University Grossman School of Medicine, New York, NY (CSD).
Stimulant use disorder (StUD) is a rapidly growing concern in the United States, with escalating rates of death attributed to amphetamines and cocaine. No medications are currently approved for StUD treatment, leaving clinicians to navigate off-label medication options. Recent studies suggest that controlled prescription psychostimulants such as dextroamphetamine, methylphenidate, and modafinil are associated with reductions in self-reported stimulant use, craving, and depressive symptoms.
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