Drug and alcohol dependence最新文献

{"title":"Opioid misuse detection from cognitive and physiological data with temporal fusion deep learning","authors":"Bhanu Gullapalli ,&nbsp;Yunfei Luo ,&nbsp;Tauhidur Rahman ,&nbsp;Eric L. Garland","doi":"10.1016/j.drugalcdep.2025.112774","DOIUrl":"10.1016/j.drugalcdep.2025.112774","url":null,"abstract":"<div><h3>Introduction</h3><div>Machine learning may enable detection of opioid misuse to prevent opioid-related risks including overdose and opioid use disorder.</div></div><div><h3>Methods</h3><div>Here, we collected 9238 datapoints from on-body sensors and cognitive tasks in a sample of 169 patients who were prescribed opioid analgesics to manage chronic pain. We categorized patients into one of two groups using the Current Opioid Misuse Measure (COMM): those showing signs of opioid misuse (MISUSE+, n = 116) and those without signs of opioid misuse (MISUSE-, n = 53). Heart rate variability and respiration rate were assessed while participants completed a Dot Probe task involving shifting attention towards and away from opioid-related and emotional cues, and a Go/No-Go task involving inhibition of automatic responses. Cross-sectional data (e.g., physiological responses, task reaction times, task accuracy) were analyzed with a temporal fusion transformer machine learning (ML) model to predict COMM opioid misuse status. We employed Leave-One-Group-Out (LOGO) cross-validation with the participants divided into 10 groups. Each cycle, one group was held out for testing, ensuring robust, unbiased model validation across different subsets of participants.</div></div><div><h3>Results</h3><div>The ML model showed good predictive performance for identifying opioid misuse (AUC, 0.81; specificity, 0.78; sensitivity, 0.78). Behavioral responses were stronger predictors of misuse status than physiological signals.</div></div><div><h3>Conclusions</h3><div>ML models using data from cognitive tasks and on-body sensors detected opioid misuse with an accuracy comparable to gold-standard self-reported opioid misuse assessments. Wearable sensors may provide only incremental predictive power over behavioral responses. Our ML model should be benchmarked against objective measures of opioid misuse.</div></div>","PeriodicalId":11322,"journal":{"name":"Drug and alcohol dependence","volume":"274 ","pages":"Article 112774"},"PeriodicalIF":3.9,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Another carfentanil fatal outbreak in Florida?","authors":"Chris Delcher ,&nbsp;Eugene Shin ,&nbsp;Arjun Iyer ,&nbsp;Trokon K. Johnson ,&nbsp;Agnes D. Winokur ,&nbsp;Bruce A. Goldberger","doi":"10.1016/j.drugalcdep.2025.112784","DOIUrl":"10.1016/j.drugalcdep.2025.112784","url":null,"abstract":"<div><h3>Background</h3><div>In 2016 and 2017, carfentanil was implicated in the deadliest fatal outbreak involving a fentanyl analog in the United States with 1190 deaths in Florida alone. Recent surveillance signals suggest that Florida is experiencing a resurgence in carfentanil-involved deaths. The purpose of this paper is to examine carfentanil-involved overdoses using up-to-date medical examiners reports triangulated with carfentanil-related indicators at multiple levels.</div></div><div><h3>Methods</h3><div>Florida’s medical examiner data were analyzed in three periods (2016, 2017, 2018–2023) to examine changes in decedent demographic, geographic, and toxicologic characteristics. We triangulated six additional state and national data sources which included completed death certificates, the National Forensic Laboratory Information System, Reddit mentions, and clinical urine drug tests positive for carfentanil.</div></div><div><h3>Results</h3><div>There were 24 carfentanil-involved deaths in a 2-month period (Dec/Nov 2023). Compared to 2017, recent decedents were significantly older (42.9 years old <em>vs.</em> 37.1 years old, <em>p &lt; 0.0001</em>) with increased exposure to fentanyl (23.4–68.8 %, <em>p &lt; .0001</em>) and methamphetamine (8.5–20.4 %, <em>p = 0.0003</em>). The state’s prior three-county epicenter showed limited involvement (6.5 % vs. 18.9 % total carfentanil deaths) when compared to the prior outbreak. All triangulated national data sources showed strong retrospective concordance. More timely death certificate data suggests that monthly carfentanil deaths were more frequent (<em>mean</em>=4 deaths/mo.) in 2024.</div></div><div><h3>Conclusions</h3><div>The state may have averted a fatal outbreak as of December 2024, but carfentanil deaths have reached new monthly levels with increasingly active signals in other systems. These systems should be monitored regularly to decrease lag time in fatal overdose surveillance for a faster public health response.</div></div>","PeriodicalId":11322,"journal":{"name":"Drug and alcohol dependence","volume":"274 ","pages":"Article 112784"},"PeriodicalIF":3.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extended-release naltrexone versus oral naltrexone for substance use disorders: A systematic review and meta-analysis","authors":"Ahmed Elmosalamy ,&nbsp;Akanksha Sirohi ,&nbsp;Amr Moustafa ,&nbsp;Omar Masoud ,&nbsp;Leslie C. Hassett ,&nbsp;Bhanu P. Kolla ,&nbsp;Balwinder Singh","doi":"10.1016/j.drugalcdep.2025.112789","DOIUrl":"10.1016/j.drugalcdep.2025.112789","url":null,"abstract":"<div><h3>Purpose</h3><div>This systematic review and meta-analysis aimed to compare extended-release naltrexone (XR-NTX) and oral naltrexone (NTX) for treating substance use disorders (SUDs).</div></div><div><h3>Methods</h3><div>A comprehensive search of multiple databases (MEDLINE, Embase, Cochrane, APA PsycInfo, and Scopus) was conducted on October 1, 2024, with no date or language restrictions. Inclusion criteria were adults with SUDs, interventions with XR-NTX, comparisons with oral NTX, and outcomes on treatment persistence, hospitalizations, and emergency department (ED) visits. Randomized controlled trials (RCTs) and observational studies were included. Odds ratios were calculated using the Mantel-Haenszel random-effects model.</div></div><div><h3>Results</h3><div>Of the 1116 studies screened, 27 were selected for full-text review, 15 met the inclusion criteria (3 open-label RCTs [n = 340] and 12 retrospective studies [n = 18,695]) focusing on alcohol use disorder (AUD) and opioid use disorder (OUD), and 7 were included in the meta-analysis. We found no other SUD where the two interventions were compared. Treatment persistence was significantly higher with XR-NTX compared to oral NTX at both 3 months (OR 1.43 [95 % CI: 1.04–1.96], p = 0.03) and 6 months (OR 1.96 [95 % CI: 1.37–2.81], p = 0.0002). No significant differences were observed in healthcare utilization, including inpatient admissions (OR 0.69 [95 % CI: 0.30–1.61], p = 0.39) or ED visits (OR 1.17 [95 % CI: 0.61–2.26], p = 0.63).</div></div><div><h3>Conclusions</h3><div>Patients with AUD and OUD receiving XR-NTX stay in treatment longer than those receiving oral NTX. However, hospitalization and ED visit rates did not differ. Findings are limited due to a small number of RCTs.</div></div>","PeriodicalId":11322,"journal":{"name":"Drug and alcohol dependence","volume":"274 ","pages":"Article 112789"},"PeriodicalIF":3.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detected substances among patients with confirmed bromazolam exposure who present to the emergency department following a suspected opioid and/or stimulant-related non-fatal overdose","authors":"Alyssa M. Falise ,&nbsp;Amanda Sutphin ,&nbsp;Rachel Culbreth ,&nbsp;Kim Aldy ,&nbsp;Keith Burkhart ,&nbsp;Jeffrey Brent ,&nbsp;Sharan L. Campleman ,&nbsp;Hannah Day ,&nbsp;Aprielle Wills ,&nbsp;Alex Krotulski ,&nbsp;Paul Wax ,&nbsp;On Behalf of the ToxIC DOTS Study Group","doi":"10.1016/j.drugalcdep.2025.112776","DOIUrl":"10.1016/j.drugalcdep.2025.112776","url":null,"abstract":"<div><h3>Background</h3><div>Bromazolam, an illegal benzodiazepine, has recently increased in toxicological samples in the United States. Little is known about other substances detected with bromazolam and use intentions. This study investigated bromazolam’s prevalence, co-occurring substances, and self-reported substance use among patients with bromazolam exposure presenting to emergency departments (ED) for opioid/stimulant-related non-fatal overdose.</div></div><div><h3>Methods</h3><div>The Toxicology Investigators Consortium (ToxIC) Drug Overdose Toxico-Surveillance (DOTS) Reporting Program prospectively collected interviews, chart reviews, and toxicological blood analyses among patients presenting to one of 17 EDs following a suspected acute opioid/stimulant overdose. Toxicological analyses were performed qualitatively via liquid chromatography (LC) quadrupole time-of-flight mass spectrometry (MS) and quantitatively with LC tandem quadrupole MS. Sociodemographic characteristics, intended substance use, and detected substances were examined in patients with and without bromazolam exposure (n = 341).</div></div><div><h3>Results</h3><div>Twenty-eight of 341 patients (8.2 %) were bromazolam positive, with an average concentration of 86.4 <!--> <!-->ng/mL (range: 3.5–310.0 <!--> <!-->ng/mL). Fentanyl concentrations were higher among bromazolam-positive patients (positive: mean=11.1 <!--> <!-->ng/mL; negative: mean=8.9 <!--> <!-->ng/mL; <em>p</em> = 0.03), although no qualitative significant differences were noted in fentanyl prevalence when stratified by bromazolam exposure (<em>p</em> = 0.22). Among the 20 bromazolam-positive patients reported using only one substance, none intended to use bromazolam, with fentanyl (n = 7, 35.0%) and heroin (n = 6, 30.0%) being the most reported intended substances. Snorting was the most common administration route (55.0%).</div></div><div><h3>Conclusion</h3><div>Bromazolam exposure was associated with increased fentanyl concentrations compared to those without. Most patients with bromazolam exposure reported intended opioid use prior to their overdose. Future research should investigate bromazolam’s clinical effects and patient outcomes following an overdose.</div></div>","PeriodicalId":11322,"journal":{"name":"Drug and alcohol dependence","volume":"274 ","pages":"Article 112776"},"PeriodicalIF":3.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144605888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of state cigarette and e-cigarette flavors bans on smoking, vaping and dual use in the United States","authors":"John Buckell ,&nbsp;Jamie Tam ,&nbsp;Evelyn Jimenez Mendoza ,&nbsp;Rafael Meza ,&nbsp;Jody Sindelar","doi":"10.1016/j.drugalcdep.2025.112786","DOIUrl":"10.1016/j.drugalcdep.2025.112786","url":null,"abstract":"<div><h3>Background and aims</h3><div>Some states have banned flavors in various tobacco products. This can reduce use of banned products and induce substitution towards non-banned products. The net impact must be determined empirically. The aim of this study is to evaluate the impacts of these bans on both use and substitution.</div></div><div><h3>Setting</h3><div>US tobacco market.</div></div><div><h3>Participants</h3><div>3220 individuals aged 18–41 in the United States who smoked and/or vaped (past 30-day use) completed an online survey.</div></div><div><h3>Measurements</h3><div>Multinomial logistic models regressed changes in tobacco product use between two time periods on: states with and without bans on flavored e-cigarettes and menthol cigarettes, individuals’ characteristics, and other state-level tobacco policies. Estimated models were used to simulate impacts of flavor bans for people who dual use.</div></div><div><h3>Results</h3><div>Policies’ impacts were only observed for those who dual use cigarettes and e-cigarettes. Most who dual use did not change their tobacco product use regardless of state policy. Some significant differences were found by states for those who quit both products. Massachusetts, with bans on both flavored e-cigarettes and menthol cigarettes, had the greatest predicted rate of quitting both products (9 %) compared to states without (3 %). States with e-cigarette flavors bans had higher cessation of e-cigarette use among those who dual use.</div></div><div><h3>Conclusions</h3><div>Flavor bans on cigarettes and e-cigarettes were associated with reduced vaping among those who dual use. Massachusetts saw a higher proportion of quitting all tobacco products, likely because people who smoked in Massachusetts could not substitute with flavored e-cigarettes which had been banned.</div></div>","PeriodicalId":11322,"journal":{"name":"Drug and alcohol dependence","volume":"274 ","pages":"Article 112786"},"PeriodicalIF":3.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A geospatial analysis of naloxone distribution patterns in the Massachusetts overdose education and naloxone distribution program (MA OEND)","authors":"Timothy W. Levengood ,&nbsp;Leah C. Shaw ,&nbsp;Xiao Zang ,&nbsp;Bruce R. Schackman ,&nbsp;Alexander Y. Walley ,&nbsp;Catherine Urquhart ,&nbsp;Traci C. Green ,&nbsp;Avik Chatterjee ,&nbsp;Brandon D.L. Marshall ,&nbsp;Jake R. Morgan","doi":"10.1016/j.drugalcdep.2025.112783","DOIUrl":"10.1016/j.drugalcdep.2025.112783","url":null,"abstract":"<div><h3>Introduction</h3><div>This study describes the distribution and administration of naloxone throughout one state heavily impacted by the opioid overdose crisis. We sought to: 1) assess whether naloxone kits were used in rescue attempts in the same communities where they were distributed, and 2) explore how best to define geographical boundaries for comparing naloxone supply to demand, accounting for naloxone mobility patterns to identify areas of surplus or shortage.</div></div><div><h3>Methods</h3><div>used data from Massachusetts’ Overdose Education and Naloxone Distribution (OEND) programs for 2018–2020, linking participants’ residence, overdose rescue attempt, and refill locations at the ZIP code level. We built a Sankey plot describing the distribution and administration of naloxone between OEND program regions based on refill encounters with a reported rescue attempt. We algorithmically derived “naloxone service areas (NSAs),” defined as collections of municipalities that receive naloxone from high-volume naloxone distribution hubs. For each NSA, we calculated a coverage ratio (naloxone kits dispensed per opioid-related overdose death).</div></div><div><h3>Results</h3><div>From 2018–2020, the MA OEND programs had 88,085 naloxone dispensing encounters among 49,344 people. People reported a prior rescue attempt in 16 % of all observed encounters. There was substantial migration between residence and rescue attempt locations, with much naloxone distributed in Boston. The average naloxone dispensing-to-overdose death ratio was 14 kits per death over the three years, ranging from 8 to 32 kits per death across NSAs.</div></div><div><h3>Conclusions</h3><div>Naloxone kits are often used by people residing in communities other than where kits were distributed, with significant variation in coverage ratios across regions.</div></div>","PeriodicalId":11322,"journal":{"name":"Drug and alcohol dependence","volume":"274 ","pages":"Article 112783"},"PeriodicalIF":3.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing an ICD-10 code approach for estimating xylazine-involved overdose deaths in the United States","authors":"Joseph R. Friedman","doi":"10.1016/j.drugalcdep.2025.112781","DOIUrl":"10.1016/j.drugalcdep.2025.112781","url":null,"abstract":"<div><h3>Introduction</h3><div>The prevalence of the veterinary sedative xylazine in US overdose deaths rose between 2018 and 2021. More updated estimates are limited, partially due to the lack of a dedicated ICD-10 code—a primary mechanism used to specify drugs implicated in overdose deaths in the US, including in the CDC WONDER system, which provides public data for download with a 6-month lag. For other emerging substances lacking dedicated codes, over time umbrella codes have come to <em>de facto</em> represent them, yet it has not been demonstrated if this has occurred for xylazine.</div></div><div><h3>Methods</h3><div>Overdose deaths in CDC WONDER involving T42.7 (“Antiepileptic and sedative-hypnotic drugs, unspecified”) or T46.5 (“Other antihypertensive drugs, not elsewhere classified”) were compared to two more specific, albeit delayed, sources: NVSS describing national trends in 2018–2021 and SUDORS describing state-level trends in 2020–2022. This CDC WONDER approach was also used to estimate trends in xylazine-involved deaths through 2024 Q1 by geography, race/ethnicity, substance co-involvement, and demographic categories.</div></div><div><h3>Results</h3><div>At the national level, concordance between CDC WONDER records and previous NVSS estimates improved after 2019 and became highly similar in 2021 (3480 vs 3468 deaths). Concordance was also high for estimates stratified by race, age, and region. At the state-level, across 49 state-year pairs, correlation between CDC WONDER and SUDORS was 0.97. Estimated xylazine-involved deaths doubled between 2021 and 2024 Q1, and estimated racial inequalities widened.</div></div><div><h3>Discussion</h3><div>T42.7 or T46.5, together, may have become the <em>de facto</em> coding scheme representing xylazine-involved deaths. This approach provides more up-to-date estimates, showing increasing prevalence and worsening racial inequalities in xylazine-involved deaths into 2024.</div></div>","PeriodicalId":11322,"journal":{"name":"Drug and alcohol dependence","volume":"274 ","pages":"Article 112781"},"PeriodicalIF":3.9,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-site study examining the tobacco withdrawal trajectory in people with tobacco and cannabis co-use","authors":"Rachel A. Rabin ,&nbsp;Caryn Lerman ,&nbsp;Robert Schnoll ,&nbsp;Rachel F. Tyndale ,&nbsp;Tony P. George","doi":"10.1016/j.drugalcdep.2025.112778","DOIUrl":"10.1016/j.drugalcdep.2025.112778","url":null,"abstract":"<div><h3>Background</h3><div>Approximately 30 % of people who use tobacco also use cannabis, and rates of co-use are rising. Relative to people who use tobacco alone (TO), individuals who co-use tobacco and cannabis (TC) experience greater difficulty with tobacco cessation, yet mechanisms underlying this phenomenon remain unexplored. Leveraging data from a multi-site, double-blind clinical trial for tobacco cessation, we compared the trajectory of tobacco withdrawal, a strong predictor of relapse, between TC and TO during 11-weeks of tobacco treatment.</div></div><div><h3>Methods</h3><div>People seeking treatment for tobacco were randomized to one of three arms (placebo, nicotine patch or varenicline) and followed for 11-weeks. Participants were parsed according to their cannabis use status determined by a cannabis-positive urine toxicology at screen (N = 1246). We selected participants with end-of-treatment biochemically verified 7-day point prevalence tobacco abstinence (N = 330; TC, n = 55 and TO, n = 275) and examined group differences in tobacco withdrawal severity using the Minnesota Nicotine Withdrawal Scale (MNWS) at baseline, week 1, 4, 8, and week 11 (end-of-treatment).</div></div><div><h3>Results</h3><div>Controlling for age, treatment arm, and site, we found a significant interaction (group x time) effect for withdrawal severity (p &lt; 0.01). Bonferroni-corrected post-hoc comparisons revealed that relative to TO, TC had elevated withdrawal scores at week 1 (TC, M=9.3 ± 5.5; TO, M=7.1 ± 5.6; p &lt; 0.01); no other timepoints showed between-group differences.</div></div><div><h3>Conclusions</h3><div>People who co-use experience greater tobacco withdrawal severity one-week post abstinence compared to people who only use tobacco. Personalized interventions that target immediate tobacco withdrawal and/or cannabis use may help improve tobacco cessation rates for people who co-use both substances.</div></div>","PeriodicalId":11322,"journal":{"name":"Drug and alcohol dependence","volume":"274 ","pages":"Article 112778"},"PeriodicalIF":3.9,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Treatment initiations and opioid overdoses among recently incarcerated people after adoption of the take-home methadone policy","authors":"Benjamin A. Barsky ,&nbsp;Shapei Yan ,&nbsp;Meredith B. Rosenthal","doi":"10.1016/j.drugalcdep.2025.112775","DOIUrl":"10.1016/j.drugalcdep.2025.112775","url":null,"abstract":"<div><div>Opioid overdose is the leading cause of death among recently incarcerated people. Take-home methadone flexibilities adopted at the COVID-19 pandemic’s outset may have facilitated opioid use disorder treatment initiations and prevented opioid overdoses for this population. These flexibilities may have particularly enhanced treatment initiations for rural residents, given relaxed in-person methadone treatment requirements. Leveraging the Massachusetts Department of Public Health’s Public Health Data Warehouse, we assessed whether the Massachusetts take-home methadone policy was associated with changes in post-release initiations of medications for opioid use disorder (MOUD) (i.e., methadone, buprenorphine, and extended-release naltrexone) and opioid overdoses among recently released people, including rural residents. Results show that the monthly initiation rate of any MOUD within 7 days of release did not change after the policy. However, when disaggregating by MOUDs, we find a trend divergence, with increases in methadone offsetting decreases in other MOUDs. After the policy, the monthly rate of methadone initiations increased significantly. By contrast, the monthly rate of buprenorphine initiations decreased, and the monthly rate of extended-release naltrexone initiations remained stable. These patterns generally held among rural residents, who experienced significantly higher methadone initiation rates relative to urban residents after the policy. Furthermore, in contrast to increased opioid overdose rates in Massachusetts and the United States during the pandemic, the monthly adjusted rate of fatal and non-fatal opioid overdoses within 90 days of release remained stable. These findings suggest that take-home methadone flexibilities may facilitate methadone initiations for recently incarcerated individuals, particularly rural residents, and potentially prevent opioid overdoses.</div></div>","PeriodicalId":11322,"journal":{"name":"Drug and alcohol dependence","volume":"274 ","pages":"Article 112775"},"PeriodicalIF":3.9,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changes in health-related quality of life following an exercise intervention for alcohol use disorder: Secondary analyses of a randomized controlled trial (FitForChange)","authors":"André O. Werneck ,&nbsp;Paul Welfordsson ,&nbsp;Mats Hallgren","doi":"10.1016/j.drugalcdep.2025.112773","DOIUrl":"10.1016/j.drugalcdep.2025.112773","url":null,"abstract":"<div><h3>Objective</h3><div>To estimate the effects of aerobic exercise, yoga and usual treatment on health-related quality of life in people with alcohol use disorder.</div></div><div><h3>Methods</h3><div>Secondary analysis from the FitForChange randomized controlled trial investigating effects of exercise on alcohol use in non-treatment seeking adults 18–75 years with alcohol use disorder. Participants were randomized using a parallel, three-group, open-label design (1:1:1), with blinded follow-up assessment. Treatments included 12 weeks of either usual care (telephone counselling), aerobic exercise, or yoga. The secondary outcome of interest was health-related quality of life (12-Item Short-Form Health Survey) at baseline and 12-week follow-up. Primary analyses consisted of intention-to-treat mixed linear models.</div></div><div><h3>Results</h3><div>In total, 140 participants (53.7 ± 11.8 years, 98 women) were recruited. Follow-up was completed for 43/45 participants randomized to TAU, 42/49 to aerobic exercise and 42/46 to yoga. There were time x group interactions for both aerobic exercise and yoga groups (aerobic exercise: baseline: 39.9; 95 %CI: 37.9 – 41.9, follow-up: 47.8; 45.6 – 50.0. Yoga: baseline 40.1; 38.1 – 42.2; follow-up: 47.7; 45.5 – 49.9) for the mental component of health-related quality of life, indicating a higher increase compared with the usual treatment group (baseline: 40.2; 38.1 – 42.3, follow-up: 43.2; 41.0 – 45.3). Item analyses revealed beneficial effects of exercise on physical function, general health, and energy/fatigue.</div></div><div><h3>Conclusions</h3><div>Both aerobic exercise and yoga were effective in increasing mental health-related quality of life among people living with alcohol use disorders.</div></div>","PeriodicalId":11322,"journal":{"name":"Drug and alcohol dependence","volume":"274 ","pages":"Article 112773"},"PeriodicalIF":3.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}