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OBJECTIVE: Cannabis use disorder diagnoses are increasing among U.S. adults and are more prevalent among people with comorbid psychiatric disorders. Recent changes in cannabis laws, increasing cannabis availability, and higher-potency cannabis may have placed people with cannabis use and psychiatric disorders at disproportionately increasing risk for cannabis use disorder. The authors used Veterans Health Administration (VHA) data to examine whether trends in cannabis use disorder prevalence among VHA patients differ by whether they have psychiatric disorders. METHODS: VHA electronic health records from 2005 to 2019 (N range, 4,332,165-5,657,277) were used to identify overall and age-group-specific (

In the United States, cannabis is increasingly used to manage chronic pain. Veterans Health Administration (VHA) patients are disproportionately affected by pain and may use cannabis for symptom management. Because cannabis use increases the risk of cannabis use disorders (CUDs), we examined time trends in CUD among VHA patients with and without chronic pain, and whether these trends differed by age. From VHA electronic health records from 2005 to 2019 (∼4.3-5.6 million patients yearly), we extracted diagnoses of CUD and chronic pain conditions ( International Classification of Diseases [ ICD ]- 9-CM , 2005-2014; ICD-10-CM , 2016-2019). Differential trends in CUD prevalence overall and age-stratified (

BACKGROUND: Recreational cannabis laws (RCLs) may have spillover effects on binge drinking. Our aims were to investigate binge drinking time trends and the association between RCLs and changes in binge drinking in the United States (U.S.). METHODS: We used restricted National Survey on Drug Use and Health data (2008-2019). We examined trends in the prevalence of past-month binge drinking by age groups (12-20, 21-30, 31-40, 41-50, 51+). Then, we compared model-based prevalences of past-month binge drinking before and after RCL by age group, using multi-level logistic regression with state random intercepts, an RCL by age group interaction term, and controlling for state alcohol policies. RESULTS: Binge drinking declined overall from 2008 to 2019 among people aged 12-20 (17.54% to 11.08%), and those aged 21-30 (43.66% to 40.22%). However, binge drinking increased among people aged 31+ (ages 31-40: 28.11% to 33.34%, ages 41-50: 25.48% to 28.32%, ages 51+: 13.28% to 16.75%). When investigating model-based prevalences after versus before RCL, binge drinking decreased among people aged 12-20 (prevalence difference=-4.8%; adjusted odds ratio (aOR)=0.77, [95% confidence interval (CI) 0.70-0.85]), and increased among participants aged 31-40 (+1.7%; 1.09[1.01-1.26]), 41-50 (+2.5; 1.15[1.05-1.26]) and 51+ (+1.8%; 1.17[1.06-1.30]). No RCL-related changes were noted in respondents ages 21-30. CONCLUSIONS: Implementation of RCLs was associated with increased past-month binge drinking in adults aged 31+ and decreased past-month binge drinking in those aged < 21. As the cannabis legislative landscape continues to change in the U.S., efforts to minimize harms related to binge drinking are critical.

ObjectiveThe Department of Veterans Affairs' (VA) electronic health records (EHR) offer a rich source of big data to study medical and health care questions, but patient eligibility and preferences may limit generalizability of findings. We therefore examined the representativeness of VA veterans by comparing veterans using VA healthcare services to those who do not.MethodsWe analyzed data on 3051 veteran participants age ≥ 18 years in the 2019 National Health Interview Survey. Weighted logistic regression was used to model participant characteristics, health conditions, pain, and self-reported health by past year VA healthcare use and generate predicted marginal prevalences, which were used to calculate Cohen's d of group differences in absolute risk by past-year VA healthcare use.ResultsAmong veterans, 30.4% had past-year VA healthcare use. Veterans with lower income and members of racial/ethnic minority groups were more likely to report past-year VA healthcare use. Health conditions overrepresented in past-year VA healthcare users included chronic medical conditions (80.6% vs. 69.4%, d = 0.36), pain (78.9% vs. 65.9%; d = 0.35), mental distress (11.6% vs. 5.9%; d = 0.47), anxiety (10.8% vs. 4.1%; d = 0.67), and fair/poor self-reported health (27.9% vs. 18.0%; d = 0.40).ConclusionsHeterogeneity in veteran sociodemographic and health characteristics was observed by past-year VA healthcare use. Researchers working with VA EHR data should consider how the patient selection process may relate to the exposures and outcomes under study. Statistical reweighting may be needed to generalize risk estimates from the VA EHR data to the overall veteran population.

Objective: Alcohol use among people living with HIV (PLWH) can reduce adherence and worsen health outcomes. We evaluated the economic cost of an effective smartphone application (HealthCall) to reduce drinking and improve antiretroviral adherence among heavy-drinking PLWH participating in a randomized trial. Method: Participants were randomized to receive a brief drinking-reduction intervention, either (a) the National Institute on Alcohol Abuse and Alcoholism (NIAAA) Clinician's Guide (CG-only, n = 37), (b) CG enhanced by HealthCall to monitor daily alcohol consumption (CG+HealthCall, n = 38), or (c) motivational interviewing delivered by a nonclinician enhanced by HealthCall (MI+HealthCall, n = 39). We used micro-costing techniques to evaluate start-up costs and incremental costs per participant incurred from the health care sector perspective in 2018 U.S. dollars. We also investigated potential cost offsets using participant-reported health care utilization. Results: Participants attended three intervention visits, and each visit cost on average $29 for CG-only, $32 for CG+HealthCall, and $15 for MI+HealthCall. The total intervention cost per participant was $94 for CG-only, $114 for CG+HealthCall, and $57 for MI+HealthCall; the incremental cost of CG+HealthCall compared with CG-only was $20 per participant, and the incremental savings of MI+HealthCall compared with CG-only was $37 per participant. No significant differences in health care utilization occurred among the three groups over 12 months. Conclusions: The cost of enhancing CG with the HealthCall application for heavy-drinking PLWH was modestly higher than using the CG alone, whereas MI enhanced with HealthCall delivered by a nonclinician had a lower cost than CG alone. HealthCall may be a low-cost enhancement to brief interventions addressing alcohol use and antiretroviral adherence among PLWH. [ABSTRACT FROM AUTHOR]

Background Many people who inject drugs in the United States have chronic hepatitis C virus (HCV). On-site treatment in opiate agonist treatment (OAT) programs addresses HCV treatment barriers, but few evidence-based models exist. Methods We evaluated the cost-effectiveness of HCV treatment models for OAT patients using data from a randomized trial conducted in Bronx, New York. We used a decision analytic model to compare self-administered individual treatment (SIT), group treatment (GT), directly observed therapy (DOT), and no intervention for a simulated cohort with the same demographic characteristics of trial participants. We projected long-term outcomes using an established model of HCV disease progression and treatment (hepatitis C cost-effectiveness model: HEP-CE). Incremental cost-effectiveness ratios (ICERs) are reported in 2016 US$/quality-adjusted life years (QALY), discounted 3% annually, from the healthcare sector and societal perspectives. Results For those assigned to SIT, we projected 89% would ever achieve a sustained viral response (SVR), with 7.21 QALYs and a $245 500 lifetime cost, compared to 22% achieving SVR, with 5.49 QALYs and a $161 300 lifetime cost, with no intervention. GT was more efficient than SIT, resulting in 0.33 additional QALYs and a $14 100 lower lifetime cost per person, with an ICER of $34 300/QALY, compared to no intervention. DOT was slightly more effective and costly than GT, with an ICER > $100 000/QALY, compared to GT. In probabilistic sensitivity analyses, GT and DOT were preferred in 91% of simulations at a threshold of <$100 000/QALY; conclusions were similar from the societal perspective. Conclusions All models were associated with high rates of achieving SVR, compared to standard care. GT and DOT treatment models should be considered as cost-effective alternatives to SIT. [ABSTRACT FROM AUTHOR]

Background: Sexual minority populations experience higher rates of substance use and related problems, but little is known about their specific involvement in driving under the influence (DUI) of alcohol (DUIA) and cannabis (DUIC) incidents., Methods: Using data from the 2016 to 2019 National Survey on Drug Use and Health, we used logistic regression models to estimate the interactive effects of sexual identity, race/ethnicity, and gender on past-year DUIA among adults who used alcohol and DUIC among adults who used cannabis, accounting for covariates. Using model estimates and linear combinations, we calculated the predicted probabilities of each outcome and compared sexual identity differences within and across race/ethnicity and gender., Results: With few exceptions, the predicted probabilities of DUIA and DUIC were significantly higher among sexual minority women than heterosexual women of similar race/ethnicity. The results were more variable among men with the probabilities of DUIA and DUIC being significantly higher for some groups of sexual minority men and some groups having probabilities equal to or lower than similar heterosexual men. Some of the largest sexual minority gaps in DUIA and DUIC were observed among Hispanic and Other lesbian women and Black gay men., Conclusions: Sexual minority individuals are more likely to report DUI than their heterosexual counterparts; however, the risk of DUI among sexual minority populations varies by racial/ethnic and gender subgroup. Our findings indicate the importance of applying an intersectional framework when addressing substance-use-related disparities and when designing effective DUI prevention interventions for sexual minority populations., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.