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Importance: Given the high number of opioid overdose deaths in the US and the complex epidemiology of opioid use disorder (OUD), systems models can serve as a tool to identify opportunities for public health interventions., Objective: To estimate the projected 3-year association between public health interventions and opioid overdose-related outcomes among persons with OUD., Design, Setting, and Participants: This decision analytical model used a simulation model of the estimated US population aged 12 years and older with OUD that was developed and analyzed between January 2019 and December 2023. The model was parameterized and calibrated using 2019 to 2020 data and used to estimate the relative change in outcomes associated with simulated public health interventions implemented between 2021 and 2023., Main Outcomes and Measures: Projected OUD and medications for OUD (MOUD) prevalence in 2023 and number of nonfatal and fatal opioid-involved overdoses among persons with OUD between 2021 and 2023., Results: In a baseline scenario assuming parameters calibrated using 2019 to 2020 data remained constant, the model projected more than 16 million persons with OUD not receiving MOUD treatment and nearly 1.7 million persons receiving MOUD treatment in 2023. Additionally, the model projected over 5 million nonfatal and over 145 000 fatal opioid-involved overdoses among persons with OUD between 2021 and 2023. When simulating combinations of interventions that involved reducing overdose rates by 50%, the model projected decreases of up to 35.2% in nonfatal and 36.6% in fatal opioid-involved overdoses among persons with OUD. Interventions specific to persons with OUD not currently receiving MOUD treatment demonstrated the greatest reduction in numbers of nonfatal and fatal overdoses. Combinations of interventions that increased MOUD initiation and decreased OUD recurrence were projected to reduce OUD prevalence by up to 23.4%, increase MOUD prevalence by up to 137.1%, and reduce nonfatal and fatal opioid-involved overdoses among persons with OUD by 6.7% and 3.5%, respectively., Conclusions and Relevance: In this decision analytical model study of persons with OUD, findings suggested that expansion of evidence-based interventions that directly reduce the risk of overdose fatality among persons with OUD, such as through harm reduction efforts, could engender the highest reductions in fatal overdoses in the short-term. Interventions aimed at increasing MOUD initiation and retention of persons in treatment projected considerable improvement in MOUD and OUD prevalence but could require a longer time horizon for substantial reductions in opioid-involved overdoses.

Illicit or 'designer' benzodiazepines are a growing contributor to overdose deaths. We employed natural language processing (NLP) to study benzodiazepine mentions over 10 years on 270 online drug forums (subreddits) on Reddit. Using NLP, we automatically detected mentions of illicit and prescription benzodiazepines, including their misspellings and non-standard names, grouping relative mentions by quarter. On a collection of 17 861 755 posts between 2012 and 2021, we searched for 26 benzodiazepines (8 prescription; 18 illicit), detecting 173 275 mentions. The rate of posts about both prescription and illicit benzodiazepines increased consistently with increases in deaths involving both drug classes, illustrating the utility of surveillance via Reddit., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Public Health Association.)

Background: Despite recent rises in fatal overdoses involving multiple substances, there is a paucity of knowledge about stimulant co-use patterns among people who use opioids (PWUO) or people being treated with medications for opioid use disorder (PTMOUD). A better understanding of the timing and patterns in stimulant co-use among PWUO based on mentions of these substances on social media can help inform prevention programs, policy, and future research directions. This study examines stimulant co-mention trends among PWUO/PTMOUD on social media over multiple years., Methods: We collected publicly available data from 14 forums on Reddit (subreddits) that focused on prescription and illicit opioids, and medications for opioid use disorder (MOUD). Collected data ranged from 2011 to 2020, and we also collected timelines comprising past posts from a sample of Reddit users (Redditors) on these forums. We applied natural language processing to generate lexical variants of all included prescription and illicit opioids and stimulants and detect mentions of them on the chosen subreddits. Finally, we analyzed and described trends and patterns in co-mentions., Results: Posts collected for 13,812 Redditors showed that 12,306 (89.1%) mentioned at least 1 opioid, opioid-related medication, or stimulant. Analyses revealed that the number and proportion of Redditors mentioning both opioids and/or opioid-related medications and stimulants steadily increased over time. Relative rates of co-mentions by the same Redditor of heroin and methamphetamine, the substances most commonly co-mentioned, decreased in recent years, while co-mentions of both fentanyl and MOUD with methamphetamine increased., Conclusion: Our analyses reflect increasing mentions of stimulants, particularly methamphetamine, among PWUO/PTMOUD, which closely resembles the growth in overdose deaths involving both opioids and stimulants. These findings are consistent with recent reports suggesting increasing stimulant use among people receiving treatment for opioid use disorder. These data offer insights on emerging trends in the overdose epidemic and underscore the importance of scaling efforts to address co-occurring opioid and stimulant use including harm reduction and comprehensive healthcare access spanning mental-health services and substance use disorder treatment., (© 2022. The Author(s).)

Background: While reduced exposure to prescription opioids may decrease risks, including overdose and opioid use disorder, abrupt tapering or discontinuation may pose new risks., Objectives: To examine potentially unsafe tapering and discontinuation among dosage changes in opioid prescriptions dispensed to US patients on high-dose long-term opioid therapy., Design: Longitudinal observational study of adults (≥18 years) on stable high-dose (≥50 oral morphine milligram equivalents [MME] daily dosage) long-term opioid therapy during a 180-day baseline and a 360-day follow-up using all-payer pharmaceutical claims data, 2017-2019., Measures: Dosage tapering, increases, and/or stability during follow-up; sustained dosage stability, reductions, or discontinuation at the end of follow-up; and tapering rate. Patients could experience more than one outcome during follow-up., Results: Among 595,078 patients receiving high-dose long-term opioid therapy in the sample, 26.7% experienced sustained dosage reductions and 9.3% experienced discontinuation. Among patients experiencing tapering, 62.0% experienced maximum taper rates between > 10-40% reductions per month and 36.1% experienced monthly rates ≥ 40%. Among patients with mean baseline daily dosages ≥ 150 MME, 47.7% experienced a maximum taper rate ≥ 40% per month. Relative to baseline, 19.7% of patients experiencing tapering had long-term dosage reductions ≥ 40% per month at the end of follow-up., Implications: Dosage changes for patients on high-dose long-term opioid therapy may warrant special attention, particularly over shorter intervals, to understand how potentially sudden tapering and discontinuation can be reduced while emphasizing patient safety and shared decision-making. Rapid discontinuation of opioids can increase risk of adverse outcomes including opioid withdrawal., (Published by Elsevier B.V.)

Introduction: U.S. hospital discharges for opioid overdose increased substantially during the past two decades. This brief report describes 90-day readmissions among patients discharged from inpatient stays for opioid overdose., Methods: In 2018, survey-weighted analysis of hospital stays in the 2016 Healthcare Cost and Utilization Project National Readmissions Database yielded the national estimated proportion of patients with opioid overdose stays that had all-cause readmissions within ≤90 days. A multivariable logistic regression model assessed index stay factors associated with readmission by type (opioid overdose or not). Number of readmissions per patient was assessed., Results: More than 24% (n=14,351/58,850) of patients with non-fatal index stays for opioid overdose had at least one all-cause readmission ≤90 days of index stay discharge and 3% (n=1,658/58,850) of patients had at least one opioid overdose readmission. Less than 0.2% (n=104/58,850) of patients had more than one readmission for opioid overdose. Patient demographic characteristics (e.g., male, older age), comorbidities diagnosed during the index stay (e.g., drug use disorder, chronic pulmonary disease, psychoses), and other index stay factors (Medicare or Medicaid primary payer, discharge against medical advice) were significantly associated with both opioid overdose and non-opioid overdose readmissions. Nearly 30% of index stays for opioid overdose included heroin, which was significantly associated with opioid overdose readmissions., Conclusions: A quarter of opioid overdose patients have ≤90 days all-cause readmissions, although opioid overdose readmission is uncommon. Effective strategies to reduce readmissions will address substance use disorder as well as comorbid physical and mental health conditions., (Published by Elsevier Inc.)