International Journal of Drug Policy

Background: Drug checking services (DCS) promote drug supply awareness among people who use drugs (PWUD) by detecting adulterants such as fentanyl and xylazine that are associated with overdose morbidity and mortality. However, there is limited research on DCS implementation in Latin America (LA). Methods: We conducted a survey of 38 DCS across LA (n=10) and the US (n=28) and compared program characteristics and barriers between these two regions. We also conducted a focus group discussion (FGD) with staff representing six organizations implementing DCS in LA. FGD themes were mapped to constructs quantitatively assessed in the survey. Results: Compared to US DCS, LA DCS more frequently reported funding gaps as a major implementation barrier (80% vs. 54%), law enforcement confiscating DCS supplies (38% vs. 11%), as well as offering supervised drug consumption (30% vs. 4%) and mental health/counseling (40% vs. 18%), but less frequently reported that DCS equipment was legal (44% vs. 75%). DCS on the Mexico-US border focused on people who inject drugs and offered syringe services, supervised consumption, and rapid sexually transmitted infection testing. DCS in central Mexico, Colombia, Peru, and Chile primarily provided DCS for the nightlife community (e.g., attendees of concerts/raves). Barriers to DCS implementation cited by FGD discussants included inadequate funding, DCS legal ambiguities, lack of government support, and cartel violence. Conclusion: DCS in LA would benefit from increased funding, government support, and a more permissive legal environment, thereby strengthening harm reduction efforts and improving safety for PWUD. Keywords: drug checking services; harm reduction; overdose; people who use drugs; Latin America; fentanyl; tusi

BackgroundRecreational cannabis legalization has expanded rapidly across US states. The regulatory approaches states adopt vary widely, with varying implications for public health. This study aimed to characterize heterogeneity in recreational cannabis laws (RCLs) across US states and to identify state-level characteristics associated with these regulatory models. MethodsWe conducted Latent Class Analysis (LCA) of state-year RCL provisions from 2013 to 2024 (n=612) to identify distinct RCL approaches. Descriptive analyses and exploratory multinomial regression analyses were used to examine correlations between state characteristics and RCL approaches from 2020 to 2024, when sufficient cross-state variation in RCL adoption was available. Eleven recreational cannabis policy provisions spanning governance, potency limits, consumption restrictions, access controls, taxation, marketing regulations, and driving prohibitions are primarily from the Alcohol Policy Information System. State-level characteristics included cannabis use prevalence, market conditions, medical cannabis history, political factors, demographic, and socioeconomic covariates obtained from multiple secondary data sources. ResultsWe identified four latent classes of state-year RCL provisions representing different regulatory approaches: No RCL, Pre-commercial, Full Access, and Dispensary Access. The No RCL corresponded to state-years without RCL. The Pre-commercial class represented state-years in early-stage legalization with a minimal regulated approach in terms of commercial infrastructure. The Full Access class was characterized by permitting on-site retail consumption and home delivery and restricting (but not prohibiting) public use. In contrast, the Dispensary Access class limited retail sales to off-site consumption only, prohibited public use, and imposed stricter market controls. Higher past-month cannabis use prevalence was associated with a greater likelihood of membership in the Full Access class (RRR = 1.78; 95% CI: 1.21-2.62), relative to No RCL. A longer duration since medical cannabis legalization was associated with a higher likelihood of membership in the Dispensary access class (RRR = 1.47; 95% CI: 1.02-2.12). Higher beer excise taxes were associated with a lower likelihood of membership in any RCL class relative to No RCL. ConclusionsFrom 2013 to 2024, US recreational cannabis regulations clustered into four distinct regulatory approaches, with two distinct commercial models: one permitting on-site retail consumption and home delivery, the other restricting sales to off-premises only and prohibiting public use. Higher cannabis use prevalence and longer medical cannabis history were associated with more access-oriented and more restrictive commercial approaches, respectively.

BACKGROUNDThis study evaluates the reach, scalability, and implementation of a large-scale, multi-site tele-buprenorphine program designed to treat opioid use disorder (OUD) within rural carceral settings. Given that individuals transition frequently between jails and the community, these facilities represent a critical window for OUD intervention, yet they often face significant provider shortages and logistical barriers. We conducted a retrospective chart review of 842 unique patients (1,321 treatment episodes) enrolled in the University of Marylands tele-buprenorphine program across six rural county jails between June 2020 and May 2025. Data extracted from jail records and electronic health records were used to analyze patient demographics, prescribing patterns, and program retention. RESULTSThe patient population was primarily male (71.1%) and White (75.7%), with a mean age of 35.4 years. Participants reported high-severity OUD, with an average of 12.6 years of opioid use. Reflecting broad admission criteria, 55.2% of participants were new treatment initiates not receiving MOUD prior to booking. Patients spent a mean of 35.6 days incarcerated before initiation and were retained in the program for an average of 66 days. Buprenorphine doses were titrated from a mean initiation dose of 8.8 mg to 16.2 mg at discharge. The program demonstrated a 99.5% adherence rate among retained patients. Only 3% of the total sample were discharged for medication diversion or hoarding. CONCLUSIONSTelemedicine is a highly feasible and scalable model for delivering evidence-based MOUD in rural jails. By utilizing a "liberal admission policy" that prioritizes both treatment initiation and maintenance, programs can successfully reach high-risk individuals who lack access to community-based care. These findings suggest that tele-buprenorphine can effectively bridge the treatment gap in underserved jurisdictions, potentially reducing the risk of overdose during the high-risk post-release period.

Background: Opioid-related mortality in Texas has escalated dramatically, increasingly driven by illicitly manufactured fentanyl. To address local surges in mortality, the Galveston County Health District deployed the Galveston County Opioid Defense Effort (GCODE) in July 2023, leveraging digitally integrated surveillance data from emergency medical services (EMS) and the Medical Examiner to provide targeted naloxone distribution in identified overdose hot spots. Methods: Using a segmented interrupted time series (ITS) design and Poisson regression with robust standard errors, we evaluated the population-level impact of GCODE on opioid-involved mortality through the end of 2025. Data were sourced from the Galveston Area Ambulance Authority (GAAA) and vital statistics (ICD-10 codes). We assessed mortality trajectory changes, the observed fatality ratio among EMS-detected opioid events (the Survival Gap), and demographic and geographic covariates. Results: The Poisson ITS model included 519 weekly observations (N = 14,827 tract-weeks across 101 census tracts). Pre-intervention, opioid mortality increased by 0.16% weekly (IRR = 1.0016; 95% CI: 1.000-1.003; p = 0.011). Following GCODE deployment, the mortality trajectory reversed to a sustained 0.55% weekly decrease (IRR = 0.9945; 95% CI: 0.990-0.999; p = 0.021). The observed fatality ratio among EMS-detected events declined from 7.59% (preintervention mean; SD = 0.111) to 1.71% (post-intervention; SD = 0.042; Chi^2 = 19.824; p = 0.0001). Opioid decedents were significantly younger than the general mortality population (OR = 0.945 per year of age; p < 0.001), and were descriptively more likely to lack documented race/ethnicity data (41.23% vs. 8.27% Unknown; p < 0.001), limiting equity analysis. Conclusions: The findings are consistent with GCODE having meaningfully reduced opioid mortality by substantially lowering event-level lethality. These results suggest that targeted, digitally coordinated harm reduction can decouple overdose incidence from fatal outcomes, with implications for harm reduction program design in structurally constrained environments.

Pregnant and postpartum people who use drugs in the United States are trying to survive at the intersection of two of the gravest public health crises of the 21st century US: epidemics of (1) maternal mortality and (2) the overdose epidemic. Although extensive evidence documents racial/ethnic disparities in each of these epidemics separately, comparatively little research has characterized racial/ethnic patterns in their collision, that is, in maternal overdose mortality. We analyzed individual-level mortality records from the National Vital Statistics System (NVSS) for 2016-2022 to describe racial/ethnic disparities in pregnancy-associated overdose deaths (PA-OD) and pregnancy-associated substance use disorder-related deaths (PA-SUD). Racial/ethnic-specific mortality rates were calculated per 100,000 live births with exact Poisson confidence intervals. Temporal trends were summarized using annual percent change (APC), and disparities were quantified using rate ratios and differences relative to non-Hispanic White individuals. Overdose-related maternal mortality increased substantially during the study period across multiple racial and ethnic groups. Rates increased nearly threefold among non-Hispanic White individuals and rose more steeply among non-Hispanic Black individuals, producing a Black-White disparity that emerged over time. Rates among Hispanic individuals remained lower but increased rapidly, while estimates among American Indian and Alaska Native individuals were often high but unstable because of small counts. Substance use disorder-related maternal mortality exhibited a pronounced surge during 2019-2021 across several racial and ethnic groups. These findings highlight rapidly evolving racial/ethnic patterns in maternal overdose mortality and underscore the need for targeted prevention and harm-reduction strategies to reduce overdose-related deaths during pregnancy and the postpartum period. FundingWe are grateful to the following NIH grants for supporting this research: U54HD113292 and R01DA059182.

AimsTo characterize decreases in overdose mortality in the United States between 2023 and 2024 by substance involvement, geography, race/ethnicity, demographic, and other key dimensions. DesignPopulation-based study of national death records. SettingUnited States. Participants/casesAll individuals who died from drug overdose between January 1999 and December 2024. MeasurementsAnnual or monthly (annualized) overdose deaths per 100,000 population. Year and month of occurrence of overdose death; substance involvement; census region and division; state; county; race/ethnicity, age, and sex. FindingsAfter over two decades of mostly exponential increases, monthly data show consistent decreases in overdose deaths between June 2023 and December 2024. Decreases reflected declining illicit fentanyl-involved deaths (with and without stimulants); however, increasing trends through 2024 were still seen in deaths involving stimulants without fentanyl, and those involving xylazine. Death rates in the Northeast, South and Midwest fell to 19.5, 19.4 and 17.3 per 100,000, respectively, in December 2024, but remained elevated in the West, compared with other regions, at 27.2 per 100,000. Non-Hispanic Black and African Americans had the largest decrease in death rates in 2023-2024 falling 29.3%, but remained elevated at 36.0 per 100,000, compared to the national average of 23.7 per 100,000. Non-Hispanic American Indian and Alaska Native individuals had the highest overdose mortality rate in 2024, at 50.8 per 100,000. ConclusionsRecent decreases in overdose deaths are encouraging and unprecedented. Racial gaps remained large but shrunk by a modest margin. The geography of the overdose crisis has shifted, with the West now the most affected region, which may have implications for the targeting of funding. The nature of the crisis is also shifting, as stimulants and xylazine continue to represent increasingly important public health challenges, and renewed attention to nonfatal aspects of addiction in the US is needed.

BackgroundCanadas youth are facing mental health crises due to barriers in accessing timely and affordable care. Ontarios OHIP+ pharmacare program, introduced in 2018, provided free prescription drug coverage to individuals under 25. While OHIP+ increased prescription use, its effects on perceived access and well-being among youth with mental health needs remain unclear. MethodsWe used a regression discontinuity design leveraging the OHIP+ age-eligibility cutoff at 25 to estimate its causal impact on unmet health care needs and self-perceived life stress. The sample included Ontario respondents aged 20-29 (n=1,053) from the 2018-2019 Canadian Community Health Survey who reported needing mental health support. Outcomes were self-reported unmet health care needs and a 5-point life stress scale. Models adjusted for sociodemographic factors and used age in months as the running variable. ResultsLoss of OHIP+ eligibility at age 25 was associated with a 19.0 percentage-point increase in the probability of reporting unmet health care needs (95% CI: 0.5 to 37.6 percentage points) and a 1.33-point increase in perceived life stress (95% CI: 0.45 to 2.21). These effects were consistent across subgroups and robust to multiple sensitivity tests. ConclusionOHIP+ improved access and reduced stress among youth with mental health needs while coverage was in place. However, the abrupt loss of eligibility at age 25 increased unmet needs and psychological strain, underscoring the need for continuous, inclusive pharmacare to support youth well-being. What is already known on this topicPublic drug coverage programs such as OHIP+ have been shown to increase prescription drug use among youth in Ontario, Canada. However, little is known about whether such programs improve patient-perceived outcomes. Existing evaluations have largely relied on descriptive data or aggregate prescribing trends, with few studies examining individual-level outcomes using quasi-experimental methods. What this study addsUsing a regression discontinuity design, this study provides the causal evidence that the OHIP+ pharmacare program reduced unmet health care needs and life stress among youth with perceived mental health concerns while they remained eligible for coverage. The findings show that the abrupt loss of eligibility at age 25 was associated with increased unmet needs and heightened stress, suggesting that age-based cutoffs in drug coverage may disrupt care and contribute to psychological strain during a critical life stage. How this study might affect research, practice or policyThis study suggests that universal drug coverage programs should ensure continuity through young adulthood to avoid worsening access gaps. The findings support the need for a national pharmacare framework that promotes equitable and sustained health support.

Large language models (LLMs) are increasingly used for qualitative analysis in substance use research, yet their performance relative to human coders remains underexplored. This study compares ChatGPT-4.0 with human coders in identifying and describing the core innovation of NIH grants focused on reducing opioid overdose. A total of 118 NIH HEAL Initiative grant abstracts were independently coded by ChatGPT and humans to generate innovation descriptions, which were then evaluated by both human raters and ChatGPT for depth/detail and relevance/completeness using 5-point Likert scales. Identical instructions were used across all coding and evaluation stages. ChatGPT-generated descriptions were consistently rated higher than human-generated descriptions on both dimensions. Human evaluators rated ChatGPT outputs at an average of 4.47 for both depth/detail and relevance/completeness, compared to 3.33 and 3.24 for human outputs, respectively (F(1,176)=133.9, p<0.001). These findings suggest that LLMs, when carefully prompted, can enhance the efficiency and quality of qualitative research evaluation.

Introduction. Current best practice is for primary care physicians (PCPs) to screen patients for problematic substance use at checkups. However, this practice is not routine, is done in an unstandardized manner, and contributes to the overburdening of PCPs. Screening practices also target current, potentially problematic use behaviors, thus limiting their capacity to help patients prevent problems before they start. Recent scientific advances in identifying people at high risk for substance use problems as a means of facilitating prevention efforts have not yet been integrated into medical practice. To address these issues, our research team developed a freestanding platform called the Comprehensive Addiction Risk Evaluation System (CARES). CARES provides personalized information about genetic and behavioral/environmental risk for substance use disorder (SUD) and connects individuals to resources based on their risk profile. The present study evaluated the potential for adoption and implementation of CARES within a health care system through qualitative interviews with key stakeholders. Methods. Semi-structured interviews were developed using the Consolidated Framework for Implementation Research (CFIR) and conducted with N=15 interviewees. Transcripts were analyzed using rapid qualitative analysis. Results. Key themes included perceived need for new SUD screening tools, current SUD screening procedures and their pros/cons, openness to new ideas and clinical tools, fit of CARES with organizational goals and priorities, considerations for use of CARES with adolescent populations, anticipated patient response to CARES, barriers to implementation and uptake of CARES, changes required for implementation, and possibility for medical record integration. Interviewees generally expressed need for new screening tools and openness to using new tools, but expressed concern that existing provider burden, lack of SUD knowledge, and discomfort/stigma could stymie efforts to implement CARES. Conclusions. There is a clear need for a low-burden, easy-to-use tool for substance use screening. CARES appears to be an acceptable and feasible approach to fill this gap. These findings will be used to inform pilot implementation of CARES in a clinical care setting.

IntroductionMaternal mental health conditions, comprising maternal suicide and drug overdose, are currently the leading cause of maternal mortality in the United States. However, the relationship between suicidality and drug use behavior in the perinatal period is not well understood. We examined the association between suicidality and drug use behavior among perinatal individuals. Given the racial disparities in both drug use and suicide rates in the U.S., we also examined any differences in suicidality and drug use behavior by race. MethodsParticipants were recruited from a High-Risk Obstetric & Gynecological Clinic in the Midwestern U.S that specializes in providing obstetric care to perinatal individuals who have histories or current use of opioids and other illicit drugs. Participants (N = 66) were a sub-sample of a larger cohort enrolled in an mHealth intervention to support recovery from opioid and stimulant use disorders. We performed chi-square tests and t-tests to examine any significant associations between lifetime suicidality and drug use behavior during the perinatal period. ResultsThe final analytic sample included participants who had responded to the suicidality survey questions (n=43). Nearly 40% (n=16) of our sample endorsed a lifetime history of suicidal thoughts and behaviors (SITB). Of those, 87% (n=15) reported a previous suicide attempt. SITB was significantly associated with cravings for opioids during the perinatal period (p = .01) as well as comorbidities with perinatal anxiety symptoms? ( p < .05), depression symptoms? (p < .05), and bipolar disorder (p < .05). A higher proportion of recent cannabis use was found among mothers with SITB, compared to those without SITB (p=0.04). Mothers with SITB also had a strong positive correlation between preconception and postnatal nicotine use compared to mothers without SITB (p < .01). Finally, while white mothers endorsed more lifetime overdoses (p= 0.01), Black mothers endorsed higher cravings for opioids during pregnancy (p = 0.03). ConclusionsA history of SITB is a distinct risk factor for both illicit and recreational drug use behavior in the perinatal period, and frequently co-occurs with other perinatal mental health conditions. Further research is needed to better understand the directionality of this relationship and the complex interplay between high risk drug use behavior and suicidality.

IntroductionAlthough prior studies suggest e-cigarette use is associated with worse mental health, it remains unclear whether these associations persist independent of diagnosed depression and how tobacco use and depression jointly affect health-related quality of life. Although the long-term health risks of vaping are still unknown, self-reported health is a reliable measure of quality of life. This study provides the first health utility estimates of the independent and combined effects of cigarette use, e-cigarette use, and depression on health-related quality of life. MethodsWe analyzed 2022-2023 Behavioral Risk Factor Surveillance System data on health-related quality of life as measured by self-reported physically or mentally unhealthy days in the past 30 days. The average number of unhealthy days was estimated by age, gender, smoking status (current versus non-smoking), depression status (received a prior diagnosis), and current e-cigarette use status (every day or some day use). We converted the number of overall healthy days into EQ-5D utility scores by age-specific percentile matching of BRFSS and MEPS distributions, a method developed by Jia and Lubetkin. ResultsCigarette use, e-cigarette use, and depression were each associated with worse health-related quality of life. Mentally unhealthy days increased with the accumulation of these conditions. Associations with physically unhealthy days followed a similar pattern, particularly among younger adults, although the magnitude of association was smaller. E-cigarette use alone was associated with 2.0-4.2 (95% CI: 2.0-4.6) additional mentally unhealthy days per month across all age groups. Notably, e-cigarette use was independently associated with poorer mental health among adults aged 18-64 with or without diagnosed depression. After accounting for smoking and depression status, e-cigarette use was associated with disutility scores of 0.011 in men and 0.015 in women among young adults, with the largest losses observed when multiple conditions co-occurred. ConclusionE-cigarette use is associated with poorer health-related quality of life, particularly among younger adults, and these effects are amplified when combined with cigarette use and depression. Quantifying these joint impacts as health utility losses highlights the importance of addressing e-cigarette use within integrated tobacco control and mental health policies, especially for young populations.

BackgroundLong-acting injectable buprenorphine (LAIB) has been positioned as a potentially transformative option for opioid use disorder (OUD), in part because patient experiences reported in qualitative studies emphasize reduced daily burden, increased "freedom," reduced stigma, and fewer pressures related to diversion--while also noting barriers such as insufficient information, early adverse experiences, and concerns about coercion. MethodsWe conducted a cross-sectional online survey of adults recruited from the Behavioral Health Research Panel (BHRP). Eligibility included age [&ge;]18, English literacy, and OUD diagnosis or problematic opioid use within the past 5 years. Survey content assessed buprenorphine experience, knowledge and attitudes toward LAIB, attribute preferences, and open-text feedback. Descriptive statistics were generated; analyses were stratified by buprenorphine experience (experienced vs naive). ResultsAmong 105 participants, 82.9% reported prior buprenorphine use, and 17.1% were buprenorphine-naive. Overall, 53.3% preferred a long-acting injection regimen (weekly/monthly/3-monthly) versus 46.7% preferring a daily oral tablet/film. Convenience and adherence-related themes (e.g., not missing doses, fewer visits) drove LAIB preference, while oral-route preference and concerns about side effects and safety were prominent among those favoring oral formulations. ConclusionsIn this national convenience sample, preferences were nearly evenly split between daily oral and long-acting injectable buprenorphine regimens, with a slight overall preference for LAIB. Findings align with the qualitative literature, emphasizing the practical and psychosocial benefits of LAIB, alongside persistent needs for improved education, shared decision-making, and attention to tolerability, safety perceptions, and cost/coverage barriers.

BackgroundCannabis use is highly prevalent among people who use unregulated drugs. While daily cannabis use has been hypothesized to provide protective effects through substitution or tolerance mechanisms, the relationship between cannabis use frequency and overdose risk remains poorly understood, particularly for infrequent users. MethodsWe conducted a secondary analysis of cross-sectional interview data from people who use unregulated drugs in Vancouver, British Columbia, collected during the fentanyl crisis (November 2019-July 2021; n=657). Binary logistic regression examined associations between self-reported cannabis use frequency (five categories: less than monthly, 1-3 times per month, weekly, more than weekly and daily) and non-fatal overdose in the preceding six months. Daily use served as the reference category. Models adjusted for age, gender, ethnicity, homelessness, mental health, HIV status, incarceration and daily use of alcohol, opioids, fentanyl, cocaine and stimulants. ResultsAmong 657 participants, 95 (14.5%) reported non-fatal overdose in the past six months. In adjusted models with daily cannabis use as the reference, infrequent cannabis use was associated with significantly increased odds of overdose: use 1-3 times per month (aOR=3.17, 95% CI: 1.50-6.69, p=.002) and more than weekly use (aOR=3.13, 95% CI: 1.70-5.76, p<.001) showed approximately three-fold increased odds compared to daily use. Less frequent use showed non-significant trends in the same direction (less than monthly: aOR=1.73, 95% CI: 0.89-3.37, p=.109; weekly: aOR=1.44, 95% CI: 0.59-3.51, p=.421). Sensitivity analysis restricted to participants with daily stimulant or fentanyl use (n=148) revealed even stronger associations. ConclusionsInfrequent cannabis use was associated with substantially increased overdose risk compared to daily use. This frequency-dependent relationship, with infrequent users at highest risk, likely reflects tolerance differences: infrequent users lack tolerance to synergistic cannabis-opioid effects. These findings were completely obscured in preliminary analyses that dichotomized cannabis use as daily versus less-than-daily, demonstrating how analytical choices can mask critical public health insights. Current harm reduction approaches, including cannabis distribution programs, should incorporate frequency-dependent risk communication and develop strategies to protect infrequent users who may be at heightened overdose risk.

Background: Correctional facilities are vital venues for expanding testing and treatment for hepatitis C virus (HCV) infections, essential components of national hepatitis C elimination plans. Objective: This study characterizes HCV testing and treatment outcomes among individuals entering incarceration into California state prisons, overall, by year, and by key individual-level characteristics. Methods: We analyzed individual-level electronic health record data from all adults entering California prisons ('entrants') between July 1, 2016 and June 30, 2023. We quantified the percentages of entrants receiving an HCV antibody test within four weeks of entry, the percentage antibody positive among tested, the percentage RNA positive among antibody positive, and the percentage initiating direct acting antiviral (DAA) treatment within one year among RNA positive. Results: Of entrants, 133,639 (76%) were tested for HCV antibody, 25,455 (19% of tested) were ever HCV-infected, and 16,738 (66% of ever infected) were currently infected. Among individuals currently infected, 7,479 (45%) initiated DAA treatment within one year. Individuals with identified SUD had 3.2 times higher antibody positivity and 1.3 times higher proportions initiating DAA, compared to individuals not having an identified SUD. Discussion: We show that HCV testing and treatment in California prisons, a central component of national hepatitis C elimination efforts, supported effective and equitable increases in access to hepatitis C treatment, particularly for those with SUD.

ObjectivesPeople impacted by the criminal-legal system face significant challenges to securing and sustaining permanent housing. This study was designed to assess housing outcomes of an integrated intervention that offered housing, medical, and behavioral health services to individuals with criminal-legal system involvement. MethodsAfter a baseline needs assessment, participants were linked to services and completed quarterly study visits for up to 12 months. We used descriptive statistics to assess frequency and multivariate logistic regression to assess correlates of being housed at last follow-up. ResultsBetween June 2019 and November 2023, 187 participants were enrolled in Project CHANGE from an area with high incarceration and overdose rates. At baseline, 43% of participants were unstably housed, 37% were homeless, and the remaining resided in a shelter or institution. At the time of last follow-up, 49 participants (26.2%) reported improved housing outcomes, and an additional 121 participants (64.7%) housing situation did not worsen. In multivariate models, individuals who were older (AOR 1.1; 95% CI 1.0-1.1), unstably housed at baseline (AOR 7.2; 95% CI 3.3-16.0), and enrolled in the study for longer (AOR 1.1; 95% CI 1.1-1.3) had higher odds of being housed at last follow-up, whereas those with high severity substance use had lower odds of being housed (AOR 0.3; 95% CI 0.1-0.6.) ConclusionsIn this comprehensive program, integrated housing/health services were time- and cost-intensive to deliver but led to positive housing outcomes. People involved in the criminal-legal system face unique barriers to housing, particularly when compounded by substance use.

Drug overdose deaths in the United States reached record levels during the fentanyl era before recently declining. A plausible hypothesis is that a sudden drop in fentanyl purity beginning in 2023 caused the downturn in overdose mortality. We evaluated this hypothesis by replicating a published analysis with regional overdose data, using models that account for time trends and autocorrelation, and negative control indicators to test for spurious correlation. When fentanyl purity was rising, the national purity series did not track overdose increases in most regions and showed only a modest association in the West. When both purity and mortality later declined, the observed associations were also seen with unrelated macroeconomic indicators that shared the same time pattern. National fentanyl purity alone does not provide a sufficient explanation for recent overdose declines.

ObjectiveTo evaluate the effectiveness of various Large Language Models (LLMs) in identifying reliable predictors of Electronic Nicotine Delivery Systems (ENDS) initiation among adolescents, using solely large-scale survey variable descriptions. MethodsA cohort of 7,943 tobacco-naive adolescents aged 12-16 years from the Population Assessment of Tobacco and Health (PATH) Study was analyzed to predict ENDS use at wave 5. Four instruction-tuned LLMs - GPT-4o, LLaMA 3.1-70B, Qwen 2.5-72B-Instruct, and DeepSeek-V3 - were systematically evaluated for text-based feature selection using only variable descriptions from wave 4.5. Selected features were used to train LightGBM classifiers, with model performance compared to a baseline. ResultsOur findings reveal notable consistency among the four instruction-tuned LLMs, with substantial overlap in the top predictors each model identified. These selected variables spanned critical domains such as peer and household influence, risk perception, and exposure to tobacco-related cues. LightGBM classifiers trained on PATH wave 4.5-5 data using features selected by the LLMs demonstrated strong predictive performance. Notably, Qwen 2.5-72B-Instruct achieved an AUC of 0.791 with 30 predictors, surpassing the baseline AUC of 0.768. DiscussionThe substantial overlap among the top predictors identified by different LLMs suggests a shared reasoning process, despite variations in model architecture and training. LightGBM classifiers trained on these LLM-selected features achieved performance comparable to, or exceeding, models trained on the full set of survey variables, underscoring the high quality of features selected solely from textual descriptions. Moreover, these findings are consistent with previous tobacco regulatory research, further validating the effectiveness of LLM-driven feature selection. ConclusionInstruction-tuned large language models can effectively perform text-based feature selection using survey variable descriptions alone, without accessing raw survey data. This scalable, interpretable, and privacy-preserving framework holds promise for behavioral health research and tobacco use surveillance.

BackgroundAs cigarette smoking continues declining among youth and young adults, smoking rates among older Americans remain unchanged. Historically, cigarette and, more recently, e-cigarette tax policies influenced smoking behavior in younger smokers. Understanding how older smokers respond to tax changes can inform public health strategies. MethodsWe assembled a quarterly panel of state cigarette and e-cigarette tax rates using the CDC STATE System Tobacco (2000-2024) and E-Cigarette Legislation databases (2015-2024), then merged these data to individual-level survey responses from the Behavioral Risk Factor Surveillance System (BRFSS). Our sample included all adults aged [&ge;]65, but our main specification included adults with a history of smoking. We estimated two-way fixed-effects population-weighted linear probability regression models of current smoking and past-year quit attempts. ResultsAmong 3,117,382 adults, 50% had a history of smoking; from which 18% currently smoked. A one-dollar tax increase was associated with current smoking: cigarette tax = -0.61-percentage points (CI = -0.94,-0.28); e-cigarette tax = +0.19-percentage-points (CI = 0.14,0.24). There was no association between cigarette taxes and quit attempts. A one-dollar increase in e-cigarette tax was associated with reduced quit attempts (-0.17-percentage-points; CI = -28,-0.06). Approaching tax parity by one dollar was associated with increased current smoking by 0.23-percentage-points (CI = 0.16,0.29) and reduced quit attempts by -0.17-percentage-points (CI = -0.29,0.05). ConclusionsOlder American smokers appear responsive to cigarette and e-cig tax changes. Policies increasing the relative cost of e-cigarettes may impede cessation and perpetuate smoking rates in older generations at the highest risk of smoking related harm. ImplicationsMany studies have investigated the impact of cigarette and e-cigarette tax changes on smoking behavior in youth or younger adults. This study adds new evidence quantifying how new cigarette and e-cigarette taxes change smoking behavior among older adults, a population yet to realize reductions in smoking despite higher risk of tobacco related harm. Analyzing large, population-based survey data, we show that older smokers change smoking behavior due to cigarette and e-cigarette taxes. Taxation of e-cig products may produce unintended harm among older smokers. Specifically, approaching tax parity may increase cigarette smoking and reduce quit attempts among older adults with a history of smoking.

Objectives: To evaluate the effectiveness and cultural feasibility of family-supervised disulfiram as a first-line treatment for alcohol use disorder (AUD) in Sri Lanka, and to compare its clinical outcomes with standard therapy delivered at a tertiary psychiatric unit. Design: Single-blind Randomized Controlled Trial known as ETAT-RCT (Efficacy of Two Alcohol Treatments) was conducted under routine clinical setup with three parallel groups: family-supervised disulfiram, locally developed psychosocial intervention, and routine treatment. Allocation was independently concealed; assessors were blinded. Analyses followed an intention-to-treat approach using repeated-measures ANOVA (group x time). This paper reports the disulfiram (test) versus routine treatment (control) comparison; the psychosocial intervention will be reported separately. Setting: University Psychiatry Unit, National Hospital of Sri Lanka, Colombo (UPU, NHSLC). Participants: Patients aged [&ge;]14 years with AUD presenting to the unit were recruited consecutively without inducements. Planned allocation ratio was 1:1:1 with 31 participants per arm; key exclusions were lifetime psychotic disorder and current contraindication to disulfiram. Randomisation: Participants were randomised into each treatment arm using an independent concealed paper-based allocation system. Intervention: (1) family-supervised disulfiram, with psychoeducation/support only - DT arm, (2) a locally developed denormalization focused psychosocial programme - PT arm, and (3) standard therapy (motivational/cognitive/behavioural input; naltrexone permitted; no disulfiram/denormalisation) - ST arm. Outcome measures: Primary outcome was Alcohol Use Disorders Identification Test (AUDIT) score at 12 months. Key secondary outcomes were past 30 day alcohol use via Timeline Follow-Back (TLFB); alcohol biomarkers [ALT (alanine aminotransferase), {gamma}-GT (gamma-glutamyl transferase), MCV (mean corpuscular volume)]; locally developed measures of addiction-relevant cognitive, affective, behavioural factors [AARSU (Attitude Assessment Related to Substance Use), BARSU (Behaviour Assessment Related to Substance Use)]; and Quality of Life Enjoyment and Satisfaction Questionnaire Short Form (Q-LES-Q-SF). Outcomes were assessed at baseline, 6, and 12 months. Results: Participants in DT (n=33) and ST (n=38) were comparable at baseline. Both groups showed clinically and statistically significant improvement in AUDIT scores over 12 months (DT: F=39.90, p<0.001; ST: F=49.90, p<0.001), with no group x time interaction (F<0.001, p=0.98). Biomarkers and AARSU, and BARSU and Q-LES-Q-SF to a lesser degree, mirrored the AUDIT pattern. TLFB did not change significantly over time in either arm (p>0.05). In moderator analyses, improvement in AUDIT was not moderated by baseline motivation (F=0.20, p=0.89) but was moderated by baseline AUD severity (F=7.70, p=0.007). No serious adverse events were attributed to disulfiram. Adherence to supervised dosing was generally high during periods of supervision but intermittent overall. Conclusions: In this pilot RCT, family-supervised disulfiram achieved 12-month outcomes comparable to standard therapy in a tertiary Sri Lankan setting. Improvements were independent of baseline motivation and varied by baseline AUD severity. These findings may support family-supervised disulfiram as a culturally feasible first-line option in Sri Lanka; larger, adequately powered multicentre trials are warranted to confirm effectiveness and scalability. Trial registration: SLCTR/2014/021

Background and AimsAlcohol use disorder (AUD) remains a major public health concern, with persistent disparities in access to evidence-based treatment. This study aimed to examine associations between perceived discrimination in healthcare settings (PDHS), patient-clinician communication (PCC), and receipt of treatment for AUD, and compared these with sociodemographic and insurance-related factors. DesignCross-sectional analysis using structural equation modeling (SEM), logistic and multinomial logistic regression, and machine learning approaches including SHapley Additive exPlanations (SHAP). SettingUnited States, using data from the National Institutes of Health All of Us Research Program. ParticipantsA total of 5,287 adults with AUD (mean age 61 years; 57% men), including 71.6% non-Hispanic White, 12.2% Black, and 8.6% Hispanic participants. Insurance coverage included 52% government (Medicaid/Medicare), 37% private, and 21% military with 19% reporting more than one type. MeasurementsPrimary outcomes were receipt of Food and Drug Administration-approved pharmacotherapy and/or psychotherapy for AUD, examined as binary and multinomial outcomes. The primary exposure was PDHS, measured using a 7-item scale (range 7-35), with higher scores indicating more frequent discrimination. PCC, assessed using a 2-item scale (range 2-8) with higher scores indicating poorer communication, was examined as a potential mediator. Models were adjusted for age group, sex at birth, race/ethnicity, insurance type (government, private, military), household income, and Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) scores (range 0-12). FindingsPDHS was associated with poorer PCC ({beta} = 0.209, p < 0.001), although communication was not independently associated with treatment receipt. The indirect pathway from discrimination to treatment via communication was not supported. Military insurance was the strongest predictor of treatment receipt, with 6-7 times higher odds compared with other insurance types. Higher AUDIT-C scores and greater PDHS were also associated with increased likelihood of treatment. In analyses restricted to civilian participants, PDHS showed a stronger association with treatment receipt, while PCC demonstrated more modest effects. Machine learning models identified PDHS, AUDIT-C, and PCC as strong contributors, with the impact of poor communication most pronounced among individuals with lower income. ConclusionsAccess to treatment for alcohol use disorder is most strongly associated with insurance coverage, particularly military insurance. PDHS and PCC also contribute to treatment engagement, with differential effects across socioeconomic groups. These findings highlight the importance of addressing structural and interpersonal barriers to improve equitable access to evidence-based AUD treatment.