The association between cannabis use and subjective memory complaints in older adults in the United States.

OBJECTIVE: The U.S. population is aging and increasing numbers of older adults are using cannabis. Cognitive decline is common in older age and subjective memory complaints (SMC) have been associated with increased risk for dementia. While residual cognitive effects of cannabis use at younger ages are well understood, the links between cannabis use and cognition in older adults is less clear. The present study represents the first population-level analysis of cannabis use and SMC in older adults in the U.S. METHOD: We used the National Survey of Drug Use and Health (NSDUH) dataset to evaluate SMC in respondents over age 50 (N = 26,399) according to past-year cannabis use. RESULTS: Results revealed that 13.2% (95%CI: 11.5%-15.0%) of those who reported cannabis use also reported SMC, compared to 6.4% (95%CI: 6.1%-6.8%) among individuals with no cannabis use. Logistic regression revealed a two-fold increase (OR = 2.21, 95%CI: 1.88-2.60) of reporting SMC in respondents who had used cannabis in the past year, which was attenuated (OR = 1.38, 95%CI: 1.10-1.72) when controlling for additional factors. Other covariates, including physical health conditions, misuse of other substances, and mental illness also significantly contributed to SMC outcomes. CONCLUSIONS: Cannabis use represents a modifiable lifestyle factor that has potential for both risk and protective properties that may impact the trajectory of cognitive decline in older age. These hypothesis generating results are important for characterizing and contextualizing population-level trends related to cannabis use and SMC in older adults.

Risky Drinking in Adolescents and Emerging Adults: Differences between Individuals Using Alcohol Only versus Polysubstances.

BACKGROUND: Factors related to risky drinking (e.g., motives, protective behavioral strategies [PBS]) may vary between youth who engage in polysubstance use compared to those who consume alcohol only. We examined differences in factors among youth who consume alcohol only compared to alcohol with other substances (i.e., polysubstance use), and correlates associated with risky drinking between the groups. METHODS: Participants (N = 955; ages 16-24; 54.5% female) who reported recent risky drinking completed measures of alcohol/substance use, alcohol-related consequences, drinking motives, alcohol PBS, mental health symptoms, and emotion dysregulation. Participants were in the polysubstance group if they reported using at least one other substance (e.g., cannabis, stimulants) in addition to alcohol in the past three months. Chi-square and t-tests examined differences between the two groups and multiple regression analyses examined correlates of risky drinking. RESULTS: Most participants (70.4%, n = 672) reported polysubstance use; these individuals engaged in riskier patterns of drinking, experienced more alcohol-related consequences, used fewer PBS, had stronger drinking motives (enhancement, social, coping), endorsed more mental health symptoms, and reported more emotion dysregulation. Regression models showed that emotion dysregulation significantly associated with risky drinking in the alcohol-only group; conformity and coping motives, alcohol PBS, and anxiety symptoms significantly associated with risky drinking in the polysubstance group. CONCLUSIONS: Among risky drinking youth, results indicated youth engaging in polysubstance use have greater comorbidities and individual-level factors associated with risky drinking than youth who consume alcohol only. These findings may inform the tailoring of interventions for individuals who engage in risky drinking and polysubstance use.

Longitudinal within- and between-person associations of substance use, social influences, and loneliness among adolescents and emerging adults who use drugs.

Loneliness is a public health problem causing morbidity and mortality. Individuals with substance use problems are often lonelier than the general population. We evaluate the longitudinal associations between social influences, substance use, and loneliness among adolescents and young adults recruited from an urban Emergency Department (ED). We use secondary data from a natural history study of N=599 youth (ages 14-24) who used drugs at baseline and completed biannual assessments for 24 months; 58% presented to the ED for an assault-related injury and a comparison group comprised 42% presenting for other reasons. Measures assessed cannabis use, alcohol use, and loneliness. Using GEE models, we evaluated the relationships between social influences (peers, parents), substance use, and loneliness via longitudinal data, de-coupling within- and between-person effects. Men reported lower loneliness over time. At the between-person level, individuals with greater alcohol and cannabis use severity and negative peer influences had greater loneliness; positive parental influences were associated with less loneliness. At the within-person level, greater alcohol use severity, negative peer influences, and parental substance use corresponded to increases in loneliness; positive parental influences corresponded to decreases in loneliness. Youth with more severe alcohol and cannabis use had greater loneliness over time. Within individuals, peer and parental social influences were particularly salient markers of loneliness. An ED visit provides an opportunity for linkage to personalized, supportive interventions to curtail negative outcomes of substance use and loneliness.

Patterns of same-day alcohol and cannabis use in adolescents and young adults with risky alcohol use.

Same-day alcohol and cannabis use is relatively common in adolescents and young adults, constituting a higher-risk behavior relative to single-substance use. However, the association between quantity of alcohol and cannabis use on co-use days is understudied. We examined the association between the quantity of alcohol and same-day cannabis use with a multilevel regression analysis in a sample of youth (16-24 years old) with risky alcohol use. Participants reported one or more days of alcohol and cannabis over the past month (N = 468). Quantity of cannabis use was highest on heavy drinking days [M = 0.91 grams, SD = 0.68] followed by moderate drinking days (M = 0.78 grams, SD = 0.63), and lowest on days without alcohol use (M = 0.74 grams, SD = 0.64, p < 0.001). In multilevel modeling analyses, adjusted for clustering within individuals, greater quantity of drinking on a given day was associated with greater cannabis use (estimate = 0.03, p < 0.001). When using alcohol and cannabis on the same day, greater alcohol use was associated with greater cannabis use. Preventing days of heavy use of multiple substances, particularly among at-risk drinkers, may complement interventions addressing co-use generally to prevent substance-related consequences.

Prevalence of cannabis use among individuals with a history of cancer in the United States.

BACKGROUND: Patients with cancer have played a key role in advocating for legal access to cannabis, but little is known about links between cancer and cannabis use or cannabis-related beliefs. The authors used data from a national survey to study these relationships. METHODS: Nationally representative data collected by the National Survey on Drug Use and Health from 2015 to 2019 were acquired. Patterns of cannabis use and cancer history were examined and tested within age group subpopulations via domain analysis using survey weights. RESULTS: Data for 214,505 adults, including 4741 individuals (3.8%) with past (>1 year ago) cancer diagnosis and 1518 individuals (1.2%) with recent (≤1 year ago) cancer diagnosis, were examined. Cannabis use was less common in those with past (8.9%; 95% CI, 8.0%-9.8%) or recent (9.9%; 95% CI, 6.9%-11.1%) cancer diagnosis than in those without a history of cancer (15.9%; 95% CI, 15.7%-16.1%). However, when analyses were stratified by age group, those 18 to 34 years of age were more likely to report past cannabis use, and those 35 to 49 years of age were more likely to report past or recent cannabis use if they had a history of cancer. Younger patients felt that cannabis was more accessible and less risky if they had a history of cancer. CONCLUSIONS: Patients with cancer were less likely to report cannabis use, but there were different cannabis perceptions and use patterns by age. Age should be considered in studies of cannabis and cancer, and policy initiatives may be needed to aid provision of quality information on cannabis risk to those with cancer. LAY SUMMARY: Cannabis (marijuana) use is increasing in the United States, but we do not have much information on the relationship between cannabis use and cancer. We studied information from a representative group of people and found that younger patients generally reported more past and/or recent cannabis use if they had been diagnosed with cancer whereas older individuals did not. Beliefs about cannabis risk and accessibility differed by age. Clinical trials to study cannabis should account for patient age, and accurate information about cannabis should be provided to help patients with cancer make decisions about cannabis use.

Toward a Just-in-Time Adaptive Intervention to Reduce Emerging Adult Alcohol Use: Testing Approaches for Identifying When to Intervene.

ABBREVIATIONS: JITAI: Just-in-time adaptive intervention; ROC: receiver operating characteristic; AUC: area under the curve; MRT: micro-randomized trial.

A Machine-Learning Approach to Predicting Smoking Cessation Treatment Outcomes.

AIMS: Most cigarette smokers want to quit smoking and more than half make an attempt every year, but less than 10% remain abstinent for at least 6 months. Evidence-based tobacco use treatment improves the likelihood of quitting, but more than two-thirds of individuals relapse when provided even the most robust treatments. Identifying for whom treatment is effective will improve the success of our treatments and perhaps identify strategies for improving current approaches. METHODS: Two cohorts (training: N = 90, validation: N = 71) of cigarette smokers enrolled in group cognitive-behavioral therapy (CBT). Generalized estimating equations were used to identify baseline predictors of outcome, as defined by breath carbon monoxide and urine cotinine. Significant measures were entered as candidate variables to predict quit status. The resulting decision trees were used to predict cessation outcomes in a validation cohort. RESULTS: In the training cohort, the decision trees significantly improved on chance classification of smoking status following treatment and at 6-month follow-up. The first split of all decision trees, which was delay discounting, significantly improved on chance classification rates in both the training and validation cohort. Delay discounting emerged as the single best predictor of group CBT treatment response with an average baseline discount rate of ln(k) = -7.1, correctly predicting smoking status of 80% of participants at posttreatment and 81% of participants at follow-up. CONCLUSIONS: This study provides a first step toward personalized care for smoking cessation though future work is needed to identify individuals that are likely to be successful in treatments beyond group CBT. IMPLICATIONS: This study provides a first step toward personalized care for smoking cessation. Using a novel machine-learning approach, baseline measures of clinical and executive functioning are used to predict smoking cessation outcomes following group CBT. A decision point is recommended for the single best predictor of treatment outcomes, delay discounting, to inform future research or clinical practice in an effort to better allocate patients to treatments that are likely to work.

Cannabis expectancies and associations with cannabis use and health functioning among adults with chronic pain.

INTRODUCTION: Cannabis is increasingly available to individuals in the United States, with the majority of states legalizing medical or recreational cannabis use. Cannabis expectancies (e.g., positive expectations from cannabis use) are related to more frequent cannabis use and associated with greater odds of lapse following a cessation attempt. METHODS: To evaluate the relationship between expectancies and cannabis- and health-related outcomes, we surveyed adults who used medical cannabis for chronic pain (n = 633; 52.2 % female; 82.1 % white) over a two-year period. Then, we performed a principal components analysis (PCA) on the Cannabis Expectancies Questionnaire to derive components of cannabis expectancies. We performed k-means clustering to identify subgroups with similar expectancies. The relationship between subgroup assignment and cannabis use outcomes (frequency, quantity), and physical and mental health functioning were evaluated. RESULTS: The PCA identified a three-component solution and k-means clustering identified four subgroups: low expectancies, negative expectancies, amotivation expectancies, and positive expectancies. Participants' cannabis expectancies were dynamic over time. After adjusting for demographics, expectancy subgroup was able to predict six-month cannabis use frequency (χ2(3) = 11.244; p = 0.010) and mental health functioning (χ2(3) = 24.926; p < 0.001), but not quantity (χ2(3) = 2.741, p = 0.433) or physical health functioning (χ2(3) = 5.110, p = 0.164). CONCLUSION: In a large cohort of individuals who use medical cannabis, four subgroups with differing cannabis expectancies emerged: low expectancies, negative expectancies, amotivation expectancies, and positive expectancies. These subgroups predicted subsequent outcomes and may be important for targeted intervention approaches to reduce cannabis use and improve functioning.

Comparison of Treatment Receipt and Hospitalization Among Patients With Stimulant Use Disorder and/or Opioid Use Disorder in the Veterans Health Administration.

OBJECTIVES: Stimulant use is a growing problem, but little is known about service utilization among patients with stimulant use disorder (StUD). In the context of the overdose crisis, much research has focused on patients with opioid use disorder (OUD). It is unclear how the characteristics, treatment receipt, and hospitalization of patients with StUD differ from patients with OUD. METHODS: Electronic health record data were extracted for national Veterans Health Administration patients with a visit from March 1, 2020, to February 28, 2021 with StUD and/or OUD (N = 132,273). We compared patients with StUD without OUD to those with (1) co-occurring StUD + OUD and (2) OUD without StUD. Patient characteristics, substance use disorder treatment, and hospitalizations in the year following patients' first study period visit were descriptively compared. Treatment and hospitalization were also compared in adjusted regression models. RESULTS: Compared with patients with OUD + StUD, those with StUD without OUD were less likely to receive outpatient (adjusted odds ratio [aOR] 0.49, 95% confidence interval [CI] 0.47-0.50) or any treatment (aOR 0.47, 95% CI 0.46-0.49). Compared with patients with OUD without StUD, those with StUD without OUD were less likely to receive outpatient (aOR 0.51, 95% CI 0.49-0.52) or any treatment (aOR 0.56, 95% CI 0.54-0.58) and more likely to receive residential treatment (aOR 2.18, 95% 2.05-2.30) and to be hospitalized (aOR 1.62, 95% 1.56-1.69). CONCLUSIONS: Patients with StUD may be less likely to receive treatment and more likely to be hospitalized than patients with OUD. Efforts focused on mitigating hospitalization and increasing treatment receipt for patients with StUD are needed.

Contingency management for substance use disorders in the U.S. Veterans Health Administration: 2018-2022.

INTRODUCTION: Contingency management (CM) is one of the most effective interventions for substance use disorders (SUDs), including stimulant use disorder. In the United States, the Veterans Health Administration (VHA) led the largest-scale rollout of CM in the US to date, but little is known about characteristics of patients treated and CM clinical practices. METHODS: In this retrospective cohort study, we used VHA electronic health records data to descriptively examine CM treatment course (e.g., number of visits, time between visits, duration of treatment episode) and characteristics of patients receiving CM for SUDs from 2018 to 2022. RESULTS: From January 2018 to September 2022, 2844 patients received CM at 90 VA Health Systems (including 98 VA Medical Center, 7 community-based outpatient clinics, and 15 other sites). The median number of CM visits was 8 (mean = 10.17, SD = 8.12) visits over the course of 1.5 months (median = 45 days, mean = 57.46 days, SD = 62.65). The target substance was stimulants in 86.42 % of visits. Average age of patients was 52.29 years (SD = 12.10), with 55.06 % of patients experiencing homelessness or housing instability, and 97.50 % of patients diagnosed with more than one SUD. Compared to the year prior to the COVID-19 pandemic (03/2019-02/2020; mean = 957.33, SD = 157.71 visits/month), CM visits declined by 83.20 % in the year following the pandemic (03/2020-02/2021; mean = 160.83, SD = 164.14), and have yet to return to pre-pandemic levels. CONCLUSIONS: The CM rollout has been markedly successful in the VHA, with adoption across multiple VHA sites within a complex patient population, indicating the potential for effective, more widespread CM implementation. At the same time, there was a considerable reduction in CM care during the COVID-19 pandemic and CM has not yet returned to pre-pandemic levels. Moreover, only a small minority of VA patients with stimulant use disorder have received CM. Given increasing rates of overdose, including stimulant-involved overdose, it is important to increase CM provision in VHA and non-VHA settings.

A mobile health intervention for emerging adults with regular cannabis use: A micro-randomized pilot trial design protocol.

BACKGROUND: Emerging adult (EA) cannabis use is associated with increased risk for health consequences. Just-in-time adaptive interventions (JITAIs) provide potential for preventing the escalation and consequences of cannabis use. Powered by mobile devices, JITAIs use decision rules that take the person's state and context as input, and output a recommended intervention (e.g., alternative activities, coping strategies). The mHealth literature on JITAIs is nascent, with additional research needed to identify what intervention content to deliver when and to whom. METHODS: Herein we describe the protocol for a pilot study testing the feasibility and acceptability of a micro-randomized trial for optimizing MiWaves mobile intervention app for EAs (ages 18-25; target N = 120) with regular cannabis use (≥3 times per week). Micro-randomizations will be determined by a reinforcement learning algorithm that continually learns and improves the decision rules as participants experience the intervention. MiWaves will prompt participants to complete an in-app twice-daily survey over 30 days and participants will be micro-randomized twice daily to either: no message or a message [1 of 6 types varying in length (short, long) and interaction type (acknowledge message, acknowledge message + click additional resources, acknowledge message + fill in the blank/select an option)]. Participants recruited via social media will download the MiWaves app, and complete screening, baseline, weekly, post-intervention, and 2-month follow-up assessments. Primary outcomes include feasibility and acceptability, with additional exploratory behavioral outcomes. CONCLUSION: This study represents a critical first step in developing an effective mHealth intervention for reducing cannabis use and associated harms in EAs.

Acceptability and feasibility of a mobile behavioral economic health intervention to reduce alcohol use in adults in rural areas.

Background: At-risk alcohol use is associated with increased adverse health consequences, yet is undertreated in healthcare settings. People residing in rural areas need improved access to services; however, few interventions are designed to meet the needs of rural populations. Mobile interventions can provide feasible, low-cost, and scalable means for reaching this population and improving health, and behavioral economic approaches are promising. Methods: We conducted a pilot randomized controlled trial focused on acceptability and feasibility of a mobile behavioral economic intervention for 75 rural-residing adults with at-risk alcohol use. We recruited participants from a large healthcare system and randomized them to one of four virtually-delivered conditions reflecting behavioral economic approaches: episodic future thinking (EFT), volitional choice (VC), both EFT and VC, or enhanced usual care control (EUC). The intervention included a telephone-delivered induction session followed by two weeks of condition-consistent ecological momentary interventions (EMIs; 2x/day) and ecological momentary assessments (EMAs; 1x/day). Participants completed assessments at baseline, post-intervention, and two-month follow-up, and provided intervention feedback. Results: All participants completed the telephone-delivered session and elected to receive EMI messages. Average completion rate of EMAs across conditions was 92.9%. Among participants in active intervention conditions, 89.3% reported the induction session was helpful and 80.0% reported it influenced their future drinking. We also report initial alcohol use outcomes. Discussion: The behavioral economic intervention components and trial procedures evaluated here appear to be feasible and acceptable. Next steps include determination of their efficacy to reduce alcohol use and public health harms.

Impact of COVID-19 on stimulant use disorder treatment: A national cohort study in the Veterans Health Administration.

OBJECTIVE: Rates of stimulant overdose have increased dramatically, which may have been exacerbated by treatment disruptions during the pandemic, but no recent studies have examined use of stimulant use disorder (StUD) treatment. METHODS: In this retrospective cohort study (March 2018 to February 2022) of national Veterans Health Administration patients, we use an interrupted time-series analysis to examine the impact of COVID-19 (starting in March 2020) on treatment use for StUD. RESULTS: The number of patients receiving StUD care was increasing pre-COVID (22,640-23,020, February 2018-February 2020) but dropped post-pandemic to 18,578 in February 2022. The monthly number of patients receiving StUD care increased by 34.6 patients per month (95% CI, 1.1-68.0; P=0.04) before March 2020, decreased by 2803.3 patients (95% CI, -3912.3 to -1694.3; P <0.001) in March 2020, and, accounting for pre-COVID trends, further decreased by 85.85 patients per month (95% CI,-148.9.2 to -23.0; P =0.01) after March 2020. CONCLUSIONS: Care for StUD drastically declined during the COVID-19 pandemic and has yet to show signs of a return to pre-pandemic levels despite surging rates of stimulant-involved overdose deaths and a critical need to engage people with StUD in care.

Perceived risk of harm for different methods of cannabis consumption: A brief report.

BACKGROUND: Emerging adults' (EAs; ages 18-25) perceived risk of cannabis-related harms has decreased in recent decades, potentially contributing to their high prevalence of cannabis consumption. With the changing cannabis policy and product landscape, it is critical to understand perceived risk related to different consumption methods (e.g., smoking, dabbing). We examined differences in cannabis risk perceptions by method and consumption patterns. METHODS: EAs recruited from an emergency department (N=359, 71.3% female, 53.5% Black) completed assessments on individual characteristics, cannabis/other substance use, and perceived risk of cannabis-related harm for four different methods (smoking, vaping, dabbing, ingestion) and two use frequencies (occasional, regular). Analyses examined associations between variables of interest and three mutually exclusive groups: no cannabis use, smoking-only, and multiple/other methods. RESULTS: Forty-two percent of EAs reported no past 3-month cannabis use, 22.8% reported smoking only, and 35.1% reported consumption via multiple/other methods. Among all participants, the methods and frequency with the largest number of EAs endorsing any perceived risk from cannabis were dabbing and vaping cannabis regularly; smoking occasionally had the smallest number of EAs endorsing perceived risk. A greater proportion of EAs in the no use group viewed vaping cannabis regularly as having the most risk (63.6%), whereas the largest proportion of EAs in the smoking-only (64.6%) and multiple/other methods (47.2%) groups perceived dabbing regularly as having the most risk. CONCLUSIONS: This work shows that EAs vary in perceptions of risk across methods of cannabis use and can inform potential directions for public health and policy efforts.

Development and Pilot Testing of an Experimental Cannabis Marketplace: Toward Evaluating the Impact of Cannabis Policy on Consumer Choices.

Introduction: The legal landscape of recreational cannabis production and consumption is rapidly expanding, driving a need to inform empirically supported cannabis regulatory policy. A behavioral economic framework integrating economic constructs (e.g., price, substitutability) with psychology and decision-making sciences, has previously been applied to tobacco regulatory sciences through the use of experimental marketplaces. However, experimental marketplaces have not yet been applied to understand cannabis choice behaviors or study ways to minimize risks from use. Herein, we describe the development and initial feasibility testing of an Experimental Cannabis Marketplace (ECM). The ECM can serve as an experimental platform relevant to evaluating the impact of regulatory policies on cannabis choices and use behavior. Methods: The ECM was designed to resemble an online cannabis dispensary. Adults aged 21 and older with past month cannabis use and past month purchase from a recreational dispensary were recruited online. To test the feasibility and acceptability of the ECM, 62 people completed the ECM shopping task and provided feedback on the ECM prototype. Participants also reported about their typical purchases from real-world dispensaries. Results: Nearly all participants rated the ECM as very (80.65%) or somewhat (16.13%) easy to navigate, and rated task instructions as at least mostly clear (100%). The majority (75.81%) said cannabis products available in the ECM were mostly the same or exactly the same as at their typical dispensary. Participant purchase choices in actual dispensary purchases closely matched ECM purchases, with 88% of product choices in typical real-world cannabis dispensaries matching the ECM products purchased. Discussion: Initial testing of the ECM indicates it is an acceptable and feasible tool for understanding cannabis purchasing and choice behavior. These preliminary findings suggest that the ECM mimics cannabis dispensary settings with people making similar choices to those made in the real world.

Peer- and web-based interventions for risky drinking among US National Guard members: Mission Strong randomized controlled trial.

AIMS: To test separatel the efficacy of a web-based and a peer-based brief intervention (BI), compared with an expanded usual care control (EUC) group, among military reserve component members using alcohoI in a hazardous and harmful manner. DESIGN: In the randomized controlled trial, participants were assigned to: [1] web-based BI with web-based boosters (BI + web), [2] web-based BI with peer-based boosters (BI + peer) or [3] enhanced usual care (EUC). SETTING: Michigan, USA. PARTICIPANTS: A total of 739 Michigan Army National Guard members who reported recent hazardous alcohol use; 84% were male, the mean age was 28 years. INTERVENTION: The BI consisted of an interactive program guided by a personally selected avatar. Boosters were delivered either on the web or personally by a trained veteran peer. A pamphlet, given to all participants, included information on hazardous alcohol use and military-specific community resources and served as the EUC condition. MEASUREMENTS: The primary outcome measure was binge drinking episodes in the past 30 days, measured at 12 months after the BI. FINDINGS: All randomized participants were included in the outcome analyses. In adjusted analyses, BI + peer [beta = -0.43, 95% confidence interval (CI) = -0.56 to -0.31, P < 0.001] and BI + web (beta = -0.34, 95% CI = -0.46 to -0.23, P < 0.001) reduced binge drinking compared with EUC. CONCLUSION: This satudy was a web-based brief intervention for hazardous alcohol use, with either web- or peer-based boosters, reduced binge alcohol use among Army National Guard members.

A systematic review of remotely delivered contingency management treatment for substance use.

BACKGROUND: Substance use and related consequences (e.g., impaired driving, injuries, disease transmission) continue to be major public health concerns. Contingency management (CM) is a highly effective treatment for substance use disorders. Yet CM remains vastly underutilized, in large part due to implementation barriers to in-person delivery. If feasible and effective, remote delivery of CM may reduce barriers at both the clinic- and patient-level, thus increasing reach and access to effective care. Here, we summarize data from a systematic review of studies reporting remote delivery of CM for substance use treatment. METHODS: We conducted a systematic review, reported according to PRISMA guidelines. The study team identified a total of 4358 articles after deduplication. Following title and abstract screening, full-text screening, and reference tracking, 39 studies met the eligibility criteria. We evaluated the methodological quality of the included studies using the Effective Public Health Practice Project Quality tool. RESULTS: Of 39 articles included in the review, most (n = 26) targeted cigarette smoking, with others focusing on alcohol (n = 9) or other substance use or targeting multiple substances (n = 4). Most remotely delivered CM studies focused on abstinence (n = 29), with others targeting substance use reduction (n = 2), intervention engagement (n = 5), and both abstinence and intervention engagement (n = 3). CM was associated with better outcomes (either abstinence, use reduction, or engagement), with increasingly more remotely delivered CM studies published in more recent years. Studies ranged from moderate to strong quality, with the majority (57.5 %) of studies being strong quality. CONCLUSIONS: Consistent with in-person CM, remotely delivered CM focusing on abstinence or use reduction from substances or engagement in substance use treatment services improves outcomes at the end of treatment compared to control conditions. Moreover, remotely delivered CM is feasible across a variety of digital delivery platforms (e.g., web, mobile, and wearable), with acceptability and reduced clinic and patient burden as technological advancements streamline monitoring and reinforcer delivery.

Pilot trial of a telehealth-delivered behavioral economic intervention promoting cannabis-free activities among adults with cannabis use disorder.

BACKGROUND: Cannabis is increasingly consumed and increasingly perceived as harmless. Among those whose use develops into a cannabis use disorder (CUD), <5% initiate and engage in treatment. Thus, novel options for low-barrier, appealing treatments are needed to foster engagement in care. METHODS: We conducted an open trial of a telehealth-delivered multicomponent behavioral economic intervention for non-treatment-engaged adults with CUD. Participants with CUD were recruited from a health system and screened for eligibility. Participants completed behavioral economic indices (cannabis demand, proportionate cannabis-free reinforcement), measures of cannabis use and mental health symptoms, and provided open-ended feedback on the intervention experience. RESULTS: Of the 20 participants who enrolled and engaged in the initial intervention session, 70% (14 out of 20) completed all intervention components. All participants were satisfied/very satisfied with the intervention and 85.7% reported the telehealth delivery made it at least slightly easier/more likely for them to receive substance use care. Baseline to immediate post-treatment, behavioral economic cannabis demand decreased (intensity: Hedges' g = 0.14, maximum total expenditure: Hedges' g = 0.53, maximum expenditure for a single hit: Hedges' g = 0.10) and proportionate cannabis-free reinforcement (Hedges' g = 0.12) increased. Past-month total cannabis use decreased by 8.9% from baseline to post-treatment (Hedges' g = 0.39), along with decreases in recent depression (Hedges' g = 0.50) and anxiety symptoms (Hedges' g = 0.29). DISCUSSION: These preliminary findings suggest that this behavioral economic intervention was highly acceptable and feasible for adults with untreated CUD. Changes in potential mechanisms of behavior change (cannabis demand, proportionate cannabis-free reinforcement) were consistent with reduced frequency of cannabis use and improved mental health outcomes.

Cannabis Use in Patients Seen in an Academic Radiation Oncology Department.

PURPOSE: Cannabis use rates are increasing in the United States. Patients with cancer use cannabis for many reasons, even without high-quality supporting data. This study sought to characterize cannabis use among patients seen in radiation oncology in a state that has legalized adult nonmedical use cannabis and to identify key cannabis-related educational topics. METHODS AND MATERIALS: Cannabis history was documented by providers using a structured template at patient visits in an academic radiation oncology practice October 2020 to November 2021. Cannabis use data, including recency/frequency of use, reason, and mode of administration, were summarized, and logistic regression was used to explore associations between patient and disease characteristics and recent cannabis use. A multivariable model employed stepwise variable selection using the Akaike Information Criterion. RESULTS: Of 3143 patients total, 91 (2.9%) declined to answer cannabis use questions, and 343 (10.9%) endorsed recent use (≤1 month ago), 235 (7.5%) noted nonrecent use (>1 month ago), and 2474 (78.7%) denied history of cannabis use. In multivariable analyses, those ≥50 years old (odds ratio [OR], 0.409; 95% confidence interval [CI], 0.294-0.568; P < .001) or with history of prior courses of radiation (OR, 0.748; 95% CI, 0.572-0.979; P = .034) were less likely, and those with a mental health diagnosis not related to substance use (OR, 1.533; 95% CI, 1.171-2.005; P = .002) or who smoked tobacco (OR, 3.003; 95% CI, 2.098-4.299; P < .001) were more likely to endorse recent cannabis use. Patients reported pain, insomnia, and anxiety as the most common reasons for use. Smoking was the most common mode of administration. CONCLUSIONS: Patients are willing to discuss cannabis use with providers and reported recent cannabis use for a variety of reasons. Younger patients new to oncologic care and those with a history of mental illness or tobacco smoking may benefit most from discussions about cannabis given higher rates of cannabis use in these groups.

Alcohol-involved overdose deaths in US veterans.

BACKGROUND: Alcohol-involved overdose deaths are increasing and often occur with other substances but have been minimally studied compared to other causes of overdose. METHODS: We used national Veterans Health Administration (VHA) records linked to National Death Index data from 2012 to 2018 to examine trends in alcohol-related overdose mortality. Patient characteristics and treatment receipt were compared across categories of alcohol overdose deaths (alcohol-only, alcohol+opioids which may include additional substances, and alcohol+other substances without opioids). RESULTS: From 2012-2018, 2421 Veterans died from an alcohol-involved overdose (alcohol-only: 868, alcohol+opioids: 1269, alcohol+other substances: 284). The alcohol-involved overdose rate increased 57% during this period. Compared to those who died of an alcohol-only overdose, Veterans who died from alcohol+opioids and alcohol+other substances were more likely Black or Hispanic, and to have an opioid use disorder, but less likely to live in rural areas or to be diagnosed with alcohol use disorder (AUD). Only 32.5% of those who died from alcohol-involved overdose received treatment in a substance use disorder clinic in the year preceding death, compared to 65.1% seen in mental health and 85.7% in primary care. Only 9.5% of Veterans who died from alcohol overdose received medication treatment for AUD and 24.8% received psychotherapy for AUD in the year preceding death. CONCLUSIONS: Alcohol overdose is increasing primarily related to overdoses involving opioids and other substances. Most patients did not receive any effective medication or psychotherapy treatments for AUD, suggesting further need to identify those at risk and to target treatment for this vulnerable group in healthcare settings.