Associations between smoking and pain in early recovery in residential substance use treatment-seekers.

INTRODUCTION: A growing literature indicates bidirectional associations between pain and tobacco use. Cigarette smokers are at increased risk for chronic pain, and observational and experimental studies indicate that pain increases motivation to smoke. Tobacco use disorder frequently co-occurs with other substance use disorders, which are also associated with chronic pain vulnerability. Despite evidence that pain significantly predicts smoking and relapse, associations between smoking history/trajectory and changes in pain over the course of treatment have not been characterized. The objective of the study was to determine the association between in-treatment smoking trajectory, pack-years (i.e., number of cigarette packs smoked per day multiplied by smoking duration), pain-related interference in daily activities, and pain intensity over the course of residential treatment. METHODS: In this study, 280 adult smokers in a residential SUD treatment center in North Central Florida completed questionnaires assessing cigarette use, pain intensity, and pain interference at treatment entry and discharge (Mean = 80.3 days, SD = 25.6). Most participants were diagnosed with alcohol use disorder (66.1 %). Opioid (27.9 %) and cannabis use disorders (29.6 %) were also common. Participants were grouped by whether their smoking increased (n = 36), decreased (n = 46), or stayed the same (n = 133) from entry to discharge. RESULTS: Analyses indicated a positive association between pack-years and pain intensity at both baseline (r = 0.185, p = 0.018) and discharge (r = 0.184, p = 0.019). Smoking trajectory was associated with pack-years, with those decreasing smoking having greater pack-years than those sustaining or increasing use [F(2,136) = 8.62, p < 0.01, η2p = 0.114]. Mixed general linear models indicated pain intensity [F(1,274) = 44.15, p < 0.0001, η2p = 0.138] and interference in day-to-day activities [F(1,276) = 31.79, p < 0.0001, η2p = 0.103] decreased significantly over time. However, there was no main effect of smoking trajectory on pain intensity [F(2,212) = 2.051, p = 0.131, η2p = 0.019] or of smoking trajectory by time interaction [F(2, 212) = 1.228, p = 0.295, η2p = 0.011]. CONCLUSIONS: Overall, findings provide evidence that smoking behavior influences pain within the context of residential substance use treatment. Given that pain is associated with urge to use substances and risk of return to use, more consistent and rigorous assessment of pain and proactive pain management is likely to enhance substance use treatment outcomes among people who smoke.

Psychiatric and substance use disorders among adults over age 50 who use cannabis: A matched cohort study using electronic health record data.

INTRODUCTION: Adults over age 50 increasingly use cannabis, but few studies have examined co-occurring psychiatric and substance use disorders (SUDs) in this population. The current study utilized electronic health record (EHR) data to compare adults age 50 + with ICD-10 cannabis codes (cases) and matched controls on common psychiatric and SUDs from 2016 to 2020. METHOD: Patients age 50 + from an integrated healthcare system in Hawai'i were identified using ICD-10 codes for cannabis (use, abuse, and dependence) from 2016 to 2018. In a matched cohort design, we selected non-cannabis-using controls (matched on sex and age) from the EHR (n = 275) and compared them to cases (patients with an ICD-10 cannabis code; n = 275) on depressive and anxiety disorders and SUDs (i.e., tobacco, opioid, and alcohol use disorders) over a two-year follow-up period. RESULTS: Participants were 62.8 years (SD = 7.3) old on average; and were White (47.8 %), Asian American (24.4 %), Native Hawaiian or Pacific Islander (19.3 %), or Unknown (8.5 %) race/ethnicity. Conditional multiple logistic regression was used to estimate odds ratios comparing cases vs controls. Participants with an ICD-10 cannabis code had a significantly greater risk of major depressive disorder (OR = 10.68, p < 0.0001) and any anxiety disorder (OR = 6.45, p < 0.0001), as well as specific anxiety or trauma-related disorders (e.g., generalized anxiety disorder, PTSD) and SUDs (ORs 2.72 - 16.00, p < 0.01 for all). CONCLUSIONS: Over a two-year period, diverse adults age 50 + in Hawai'i with ICD-10 cannabis codes experienced higher rates of subsequent psychiatric and SUDs compared to controls. These findings can guide efforts to inform older adults about possible cannabis-related risks.

Treatment of Adolescent Cannabis Use Disorders.

This review summarizes treatments for cannabis use disorder (CUD) in adolescents. The best supported CUD treatments are cognitive behavioral psychotherapies, including family-based models that facilitate environmental changes and youth-focused models that incorporate skills training, motivational interviewing, and contingency management to promote reductions in cannabis use. Some medications show promise in reducing cannabis craving and withdrawal symptoms. Further research is needed on the efficacy and implementation of existing treatments given the changes in cannabis use trends over time and on emerging technologies that may expand access to evidence-based CUD treatments.

Who responds to a multi-component treatment for cannabis use disorder? Using multivariable and machine learning models to classify treatment responders and non-responders.

BACKGROUND AND AIMS: Treatments for cannabis use disorder (CUD) have limited efficacy and little is known about who responds to existing treatments. Accurately predicting who will respond to treatment can improve clinical decision-making by allowing clinicians to offer the most appropriate level and type of care. This study aimed to determine whether multivariable/machine learning models can be used to classify CUD treatment responders versus non-responders. METHODS: This secondary analysis used data from a National Drug Abuse Treatment Clinical Trials Network multi-site outpatient clinical trial in the United States. Adults with CUD (n = 302) received 12 weeks of contingency management, brief cessation counseling and were randomized to receive additionally either (1) N-Acetylcysteine or (2) placebo. Multivariable/machine learning models were used to classify treatment responders (i.e. two consecutive negative urine cannabinoid tests or a 50% reduction in days of use) versus non-responders using baseline demographic, medical, psychiatric and substance use information. RESULTS: Prediction performance for various machine learning and regression prediction models yielded area under the curves (AUCs) >0.70 for four models (0.72-0.77), with support vector machine models having the highest overall accuracy (73%; 95% CI = 68-78%) and AUC (0.77; 95% CI = 0.72, 0.83). Fourteen variables were retained in at least three of four top models, including demographic (ethnicity, education), medical (diastolic/systolic blood pressure, overall health, neurological diagnosis), psychiatric (depressive symptoms, generalized anxiety disorder, antisocial personality disorder) and substance use (tobacco smoker, baseline cannabinoid level, amphetamine use, age of experimentation with other substances, cannabis withdrawal intensity) characteristics. CONCLUSIONS: Multivariable/machine learning models can improve on chance prediction of treatment response to outpatient cannabis use disorder treatment, although further improvements in prediction performance are likely necessary for decisions about clinical care.

Monitoring adherence and abstinence of cannabis use disorder patients: Profile identification and relationship with long-term treatment outcomes.

BACKGROUND: Patients with cannabis use disorder (CUD) show heterogeneous sociodemographic and consumption patterns. Although previous studies, focused on identifying subgroups of CUD patients using input variables, have yielded useful results for planning individualized treatments, no published research has analyzed the profiles of CUD patients according to their therapeutic progress. This study therefore aims to identify subgroups of patients using adherence and abstinence indicators and to explore whether these profiles are associated with sociodemographic characteristics, consumption variables, and long-term therapeutic outcomes. METHODS: This was a retrospective observational study with a multisite sample of 2055 CUD outpatients who were beginning treatment. The study monitored patient data at two-year follow-up. We conducted latent profiles analysis on the appointment attendance ratio and percentage of negative cannabis tests. RESULTS: A three profile solution emerged: i) moderate abstinence/moderate adherence (n = 997); ii) high abstinence/moderate adherence (n = 613); and iii) high abstinence/high adherence (n = 445). The study found the most marked differences at the beginning of treatment for education level (chi2 (8) = 121.70, p < .001), source of referral (chi2 (12) = 203.55, p < .001), and frequency of cannabis use (chi2 (10) = 232.39, p < .001). Eighty percent of patients from the "high abstinence/high adherence" group were relapse-free at two year follow-up. This percentage decreased to 24.3 % in the "moderate abstinence/moderate adherence" group. CONCLUSIONS: Research has shown adherence and abstinence indicators to be useful for identifying subgroups of patients with different prognoses regarding long-term success. Recognizing the sociodemographic and consumption variables associated with these profiles at the beginning of treatment could help to inform the design of more individualized interventions.

Cannabis Use Disorder Treatment Preferences: A Pilot Survey in Current Users of Cannabis.

OBJECTIVES: Highly effective treatments for cannabis use disorder (CUD) are lacking, and patient preferences have not been considered during treatment development. We therefore conducted an exploratory crowdsourced survey of individuals reporting current cannabis use and a willingness to cut down or quit their cannabis use, to determine their interest in various treatment aspects. METHODS: Subjects (n = 63) were queried about their willingness to take medications as a function of type, route, and regimen and to participate in adherence monitoring. Subjects were also asked about their willingness to engage in behavioral/psychosocial interventions as a function of type, setting, and duration. Measures theorized to be associated with treatment preferences were also collected, including cannabis use variables, readiness to change, reduction or cessation goal, perceived cessation barriers, and medication use beliefs and behaviors. RESULTS: Survey responses indicated that efforts to develop CUD medications should focus on nonsynthetic compounds administered orally or by mouth spray no more than once per day to maximize patient acceptance. Remote adherence monitoring and one-on-one outpatient behavioral treatment approaches, especially contingency management, are also anticipated to enhance participation. Most subjects indicated a preference to reduce their cannabis use rather than quit. CONCLUSIONS: These data provide guidance for the development of CUD interventions based on the preferences of individuals interested in treatment for their cannabis use. Additional research is needed to confirm these results in a larger sample and determine if matching CUD patients with their preferred treatments improves success rates.

Correlates of treatment engagement and client outcomes: results of a randomised controlled trial of nabiximols for the treatment of cannabis use disorder.

INTRODUCTION AND AIMS: There is increasing interest and evidence for the use of cannabinoid medications in the treatment of cannabis use disorder, but little examination of the correlates of successful treatment. This paper is a secondary analysis of a randomised placebo-controlled trial of nabiximols for the treatment of cannabis use disorder (CUD), aiming to identify which client and treatment characteristics impact treatment engagement and outcomes. METHOD: Bayesian multiple regression models were used to examine the impact of age, gender, duration of regular cannabis use, daily quantity of cannabis, cannabis use problems, self-efficacy for quitting, sleep, mental health, pain measures, and treatment group upon treatment engagement (retention, medication dose, and counselling participation) and treatment outcomes (achieving end-of-study abstinence, and a 50% or greater reduction in cannabis use days) among the 128 clients participating in the 12-week trial. RESULTS: Among the treatment factors, greater counselling attendance was associated with greater odds of abstinence and ≥ 50% reduction in cannabis use; nabiximols with greater odds of ≥ 50% reduction and attending counselling, and reduced hazard of treatment dropout; and higher dose with lower odds of ≥ 50% reduction. Among the client factors, longer duration of regular use was associated with higher odds of abstinence and 50% reduction, and lower hazard of treatment dropout; greater quantity of cannabis use with reduced hazard of dropout, greater odds of attending counselling, and higher average dose; greater pain at baseline with greater odds of ≥ 50% reduction and higher average dose; and more severe sleep issues with lower odds of ≥ 50% reduction. Males had lower odds of attending counselling. DISCUSSIONS AND CONCLUSIONS: These findings suggest that counselling combined with agonist pharmacotherapy may provide the optimal treatment for cannabis use disorder. Younger clients, male clients, and clients with sleep issues could benefit from extra support from treatment services to improve engagement and outcomes. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ACTRN12616000103460) https://www.anzctr.org.au.

"I got a bunch of weed to help me through the withdrawals": Naturalistic cannabis use reported in online opioid and opioid recovery community discussion forums.

A growing body of research has reported on the potential opioid-sparing effects of cannabis and cannabinoids, but less is known about specific mechanisms. The present research examines cannabis-related posts in two large online communities on the Reddit platform ("subreddits") to compare mentions of naturalistic cannabis use by persons self-identifying as actively using opioids versus persons in recovery. We extracted all posts mentioning cannabis-related keywords (e.g., "weed", "cannabis", "marijuana") from December 2015 through August 2019 from an opioid use subreddit and an opioid recovery subreddit. To investigate how cannabis is discussed at-scale, we identified and compared the most frequent phrases in cannabis-related posts in each subreddit using term-frequency-inverse document frequency (TF-IDF) weighting. To contextualize these findings, we also conducted a qualitative content analysis of 200 random posts (100 from each subreddit). Cannabis-related posts were about twice as prevalent in the recovery subreddit (n = 908; 5.4% of 16,791 posts) than in the active opioid use subreddit (n = 4,224; 2.6% of 159,994 posts, p < .001). The most frequent phrases from the recovery subreddit referred to time without using opioids and the possibility of using cannabis as a "treatment." The most frequent phrases from the opioid subreddit referred to concurrent use of cannabis and opioids. The most common motivations for using cannabis were to manage opioid withdrawal symptoms in the recovery subreddit, often in conjunction with anti-anxiety and GI-distress "comfort meds," and to enhance the "high" when used in combination with opioids in the opioid subreddit. Despite limitations in generalizability from pseudonymous online posts, this examination of reports of naturalistic cannabis use in relation to opioid use identified withdrawal symptom management as a common motivation. Future research is warranted with more structured assessments that examines the role of cannabis and cannabinoids in addressing both somatic and affective symptoms of opioid withdrawal.

Characterizing cannabis use reduction and change in functioning during treatment: Initial steps on the path to new clinical endpoints.

Reduction-based cannabis use endpoints are needed to better evaluate treatments for cannabis use disorder (CUD). This exploratory, secondary analysis aimed to characterize cannabis frequency and quantity reduction patterns and corresponding changes in psychosocial functioning during treatment. We analyzed 16 weeks (4 prerandomization, 12 postrandomization) of data (n = 302) from both arms of a randomized clinical trial assessing pharmacotherapy for CUD. Cannabis consumption pattern classes were extracted with latent profile modeling using self-reported (a) past-week days used (i.e., frequency) and (b) past-week average grams used per using day (i.e., quantity). Changes in mean Marijuana Problem Scale (MPS) and Hospital Anxiety and Depression Scale (HADS) scores were examined among classes. Urine cannabinoid levels were examined in relation to self-reported consumption as a validity check. Two-, three-, four-, and five-class solutions each provided potentially useful conceptualizations of associations between frequency and quantity. Regardless of solution, reductions in MPS scores varied in magnitude across classes and closely tracked class-specific reductions in consumption (e.g., larger MPS reduction corresponded to larger frequency/quantity reductions). Changes in HADS scores were less pronounced and less consistent with consumption patterns. Urine cannabinoid levels closely matched class-specific self-reported consumption frequency. Findings illustrate that frequency and quantity can be used in tandem within mixture model frameworks to summarize heterogeneous cannabis use reduction patterns that may correspond to improved psychosocial functioning. Going forward, similar analytic strategies applied to alternative metrics of cannabis consumption may facilitate construction of useful reduction-based clinical endpoints. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Race, psychosocial characteristics, and treatment outcomes among individuals undergoing treatment for cannabis use disorder: A latent profile analysis based on preferred method of using cannabis.

INTRODUCTION: There are a wide variety of methods for using combustible cannabis which may impact an individual's pattern of use as well as their response to cannabis use disorder (CUD) treatment. Previous research has noted racial/ethnic differences in cannabis users' preferred method of use. METHOD: The current study examined data from a randomized placebo-controlled trial of a pharmacological intervention for adults with CUD. Latent profile analysis classified participants (N = 302) based on their primary method of combustible cannabis use. RESULTS: A four profile solution emerged which identified participants who demonstrated 1) Primarily Joint (n = 50), 2) Primarily Blunt (n = 106), 3) Mixed MoU (n = 30), and 4) Primarily Pipe (i.e., pipe or bong; n = 116) use. Profiles were compared on socio-demographic characteristics and racial differences were found among the four latent profiles as well as differences in their level of use. Cannabis users with a preference for joints were more likely to be White as compared to other racial groups. In contrast, a greater proportion of participants with a preference for blunts were African American. The Primarily Joint profile was found to have the highest cannabis relapse rate at 1-month follow-up (94%) which was significantly greater than the Mixed MoU (74%, x2 = 5.06, p < .05) and Primarily Pipe (78%, x2 = 9.24, p < .01) profiles. Interestingly, there was no difference in 1-Month Follow-up cannabis relapse rates between the Primarily Joint and Blunt profiles (87%, x2 = 9.24, p > .05). CONCLUSIONS: Findings suggest that treatment-seeking individuals who primarily use joints or blunts may face unique challenges that may impact cannabis abstinence. Along with other cannabis-related characteristics, an individual's preferred method of use may represent an important factor to consider in the treatment of CUD.

Momentary coping and marijuana use in treated adults: Exploring mechanisms of treatment.

OBJECTIVE: This study was designed to test the hypothesis that an Individualized Assessment and Treatment Program (IATP) for cannabis use disorder (CUD) that utilized experience sampling (ES) data to individualize treatment would be more effective at eliciting adaptive coping responses in high-risk situations than a more conventional cognitive-behavioral treatment. It was further expected that increases in momentary adaptive coping, positive affect, and self-efficacy expectancies would mediate the effects of treatment on momentary drug use in the hours following a temptation-to-use episode. METHOD: The participants were 198 adults seeking treatment for CUD, randomized to receive either a conventional motivational enhancement + cognitive-behavioral treatment (MET-CBT) with or without contingency management (CM) or an IATP with or without CM. Treatment took place over nine individual sessions, and follow-ups were conducted out to 14 months post-intake. ES data were recorded in all treatments at pretreatment, and at various points during and after treatment. RESULTS: Analyses of ES data indicated that the IATP conditions yielded greater increases in use of adaptive coping skills during temptation episodes than did the MET-CBT conditions. Mediation analyses supported the hypothesis that momentary use of coping skills mediates the effects of IATP on use or non-use of marijuana in the hours following a temptation episode. CONCLUSIONS: Use of an individualized coping treatment approach results in lower use of marijuana in high-risk situations, and actual utilization of adaptive coping appears to be a mechanism of that effect. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Cannabis: A Toxin-Producing Plant with Potential Therapeutic Uses.

For thousands of years, Cannabis sativa has been utilized as a medicine and for recreational and spiritual purposes. Phytocannabinoids are a family of compounds that are found in the cannabis plant, which is known for its psychotogenic and euphoric effects; the main psychotropic constituent of cannabis is Δ9-tetrahydrocannabinol (Δ9-THC). The pharmacological effects of cannabinoids are a result of interactions between those compounds and cannabinoid receptors, CB1 and CB2, located in many parts of the human body. Cannabis is used as a therapeutic agent for treating pain and emesis. Some cannabinoids are clinically applied for treating chronic pain, particularly cancer and multiple sclerosis-associated pain, for appetite stimulation and anti-emesis in HIV/AIDS and cancer patients, and for spasticity treatment in multiple sclerosis and epilepsy patients. Medical cannabis varies from recreational cannabis in the chemical content of THC and cannabidiol (CBD), modes of administration, and safety. Despite the therapeutic effects of cannabis, exposure to high concentrations of THC, the main compound that is responsible for most of the intoxicating effects experienced by users, could lead to psychological events and adverse effects that affect almost all body systems, such as neurological (dizziness, drowsiness, seizures, coma, and others), ophthalmological (mydriasis and conjunctival hyperemia), cardiovascular (tachycardia and arterial hypertension), and gastrointestinal (nausea, vomiting, and thirst), mainly associated with recreational use. Cannabis toxicity in children is more concerning and can cause serious adverse effects such as acute neurological symptoms (stupor), lethargy, seizures, and even coma. More countries are legalizing the commercial production and sale of cannabis for medicinal use, and some for recreational use as well. Liberalization of cannabis laws has led to increased incidence of toxicity, hyperemesis syndrome, lung disease cardiovascular disease, reduced fertility, tolerance, and dependence with chronic prolonged use. This review focuses on the potential therapeutic effects of cannabis and cannabinoids, as well as the acute and chronic toxic effects of cannabis use on various body systems.

Latency to cannabis dependence mediates the relationship between age at cannabis use initiation and cannabis use outcomes during treatment in men but not women.

BACKGROUND: Time from first cannabis use to cannabis dependence (latency) may be an important prognostic indicator of cannabis-related problems and treatment outcomes. Gender differences in latency have been found; however, research in this general area is limited. As cannabis use increases and perceived risk declines, a better understanding of how these factors interact in predicting treatment outcomes is critical. METHODS: A secondary data analysis of a randomized, double-blind, placebo-controlled pharmacotherapy trial for cannabis dependence (N = 302) examined the associations between age of cannabis use onset, time to cannabis dependence (latency), and gender on cannabis use during the trial. Mediation analysis tested whether the association between age of onset and cannabis use during the trial was mediated by latency to cannabis dependence differentially for men and women. RESULTS: Age of use initiation was inversely correlated with latency to dependence prior to treatment [HR(95% CI) = 1.18 (1.06, 1.30); p = .002] and cannabis use during treatment (ß=-1.27; SE = 0.37; p < .001). There was a significant mediation effect between age of onset, latency, and cannabis use that varied by gender. Earlier age of onset predicted longer latency, and subsequently, greater cannabis use during the trial in men (21.4% mediated; p < .05), but not women. Other substance use, race, and past psychiatric diagnosis did not predict latency either independently or in interaction models. CONCLUSION: Findings support existing evidence that early cannabis use onset is associated with worse outcomes and add new knowledge on the differential associations between age of onset, latency to cannabis dependence, and treatment outcomes for men and women.

Measuring individual benefits of psychiatric treatment using longitudinal binary outcomes: Application to antipsychotic benefits in non-cannabis and cannabis users.

We present and evaluate a method for predicting individual treatment benefits based on random effects logistic regression models of binary outcomes that change over time. The method uses empirical Bayes predictors based on patients' characteristics and responses to treatment. It is applicable to both 1-dimentional and 2-dimentional personalized medicine models. Comparisons between predicted and true benefits for simulated new patients using correlations, relative biases and mean-squared errors were used to evaluate prediction performance. The predicted benefits had relatively small biases and relatively high correlations with the true benefits in the simulated new patients. The predictors also captured estimated overall population trends in the evolution of individual benefits. The proposed approach can be used to retrospectively evaluate patients' responses in a clinical trial, or to retrospectively or prospectively predict individual benefits of different treatments for new patients using patients' characteristics and previous responses. The method is used to examine changes in the disorganized dimension of antipsychotic-naïve patients from an antipsychotic randomized clinical trial. Retrospective prediction of individual benefits revealed that more cannabis users had slower and lower responses to antipsychotic treatment as compared to non-cannabis users, revealing cannabis use as a negative prognostic factor for psychotic disorders in the disorganized dimension.

A systematic review of technology-based psychotherapeutic interventions for decreasing cannabis use in patients with psychosis.

Persistent use of cannabis in persons with psychosis is associated with poor symptomatic and functional outcomes and increased healthcare costs. Face-to-face psychological interventions (e.g., Cognitive Behavioral Therapy- [CBT], Motivation Enhancement Therapy- [MET]) are widely used in treating problematic cannabis use. We aimed to comprehensively review the efficacy of technology-based psychological interventions (TBPIs) in decreasing cannabis use, the design of TBPIs, and TBPI-related preferences in individuals with psychosis. For the systematic review, we searched six major databases from their inception to November 27, 2019. We included empirical articles of quantitative and qualitative methodologies related to TBPIs in individuals with psychosis and cannabis misuse and used narrative synthesis to report results. Only eight articles were found showing that technology-based motivational and psycho-education interventions and cognitive enhancement therapy were minimally efficient in achieving cannabis abstinence or decreasing frequency of use. Qualitative exploratory methods and participatory action research were used to elicit patient and clinician preferences and TBPIs were tailored accordingly to improve cannabis use related outcomes. Research on TBPIs in individuals with psychosis and cannabis misuse is in its early phases. A significant research effort is needed for the development of adapted interventions for CUD to capitalize on the potential of web-based applications.

Validation of the Australian Treatment Outcomes Profile for use in clients with cannabis dependence.

INTRODUCTION AND AIMS: The Australian Treatment Outcomes Profile (ATOP) was developed as a clinical tool for monitoring the substance use, health and wellbeing of clients in alcohol and other drug treatment. This is the first psychometric validation of the ATOP in a cannabis-dependent treatment population. DESIGN AND METHODS: A total of 128 individuals with cannabis dependence enrolled in an outpatient randomised controlled trial were administered the ATOP and gold-standard health and wellbeing questionnaires once by clinicians and once by researchers at baseline. Concurrent validity was assessed by testing ATOP Psychological Health, Physical Health and Quality of Life questions against concurrently administered gold-standard questionnaires: the Short Form 36 Health Survey (SF-36), the 21-item Depression, Anxiety and Stress Scale (DASS-21) and the Sheehan Disability Scale (SDS). Interrater reliability was tested by comparing clinician-administered ATOP items at the medical screening interview to the same ATOP items administered by researchers at baseline. RESULTS: ATOP Psychological Health showed moderate to strong correlations with SF-36 Mental Components, SF-36 Mental Health and DASS-21 scores (r = 0.40-0.52) and ATOP Physical Health with SF-36 Physical Components and SF-36 General Health scores (r = 0.36-0.67). The ATOP Quality of Life scale showed moderate agreement with the SDS and six-dimensional health state short form scales (r = 0.38-0.40). ATOP substance use, employment, education and child care items showed good to excellent interrater reliability (Krippendorff's α = 0.62-0.81), and tobacco use, Psychological Health, Physical Health and Quality of Life showed fair to moderate interrater reliability (Krippendorff's α = 0.42-0.53). DISCUSSION AND CONCLUSIONS: The ATOP appears to be valid and reliable when tested in a population with cannabis-dependence, justifying its widespread use in clinical settings.

Adolescent treatment admissions for marijuana following recreational legalization in Colorado and Washington.

INTRODUCTION: There is concern that recreational marijuana legalization (RML) may lead to increased cannabis use disorder (CUD) among youth due to increased marijuana use. This study investigates whether adolescent substance use disorder treatment admissions for marijuana use increased in Colorado and Washington following RML. METHODS: Annual data on 2008-2017 treatment admissions for marijuana use from the SAMHSA TEDS-A dataset for adolescents age 12-17 were used to model state treatment admissions trends. Difference-in-differences models were used to investigate whether treatment admissions increased following RML in Colorado/Washington compared to non-RML states, after adjusting for socioeconomic characteristics and treatment availability. RESULTS: Over all states in the analysis, the rate of adolescent treatment admissions for marijuana use declined significantly over the study period (ß=-3.375, 95 % CI=-4.842, -1.907), with the mean rate falling nearly in half. The decline in admissions rate was greater in Colorado and Washington compared to non-RML states following RML, though this difference was not significant (ß=-7.671, 95 % CI=-38.798, 23.456). CONCLUSION: Adolescent treatment admissions for marijuana use did not increase in Colorado and Washington following RML. This may be because youth marijuana use did not increase, CUD did not increase (even if use did increase), or treatment seeking behaviors changed due to shifts in attitudes and perceptions of risk towards marijuana use.

Altered Effective Connectivity of Central Autonomic Network in Response to Negative Facial Expression in Adults With Cannabis Use Disorder.

BACKGROUND: Cannabis use is associated with an increased risk of stress-related adverse cardiovascular events. Because brain regions of the central autonomic network largely overlap with brain regions related to the neural response to emotion and stress, the central autonomic network may mediate the autonomic response to negative emotional stimuli. We aimed to obtain evidence to determine whether neural connectivity of the central autonomic network is altered in individuals with cannabis use disorder (CUD) when they are exposed to negative emotional stimuli. METHODS: Effective (directional) connectivity (EC) analysis using dynamic causal modeling was applied to functional magnetic resonance imaging data acquired from 23 subjects with CUD and 23 control subjects of the Human Connectome Project while they performed an emotional face-matching task with interleaving periods of negative-face (fearful/angry) and neutral-shape stimuli. The EC difference (modulatory change) was measured during the negative-face trials relative to the neutral-shape trials. RESULTS: The CUD group was similar to the control group in nonimaging measures and brain activations but showed greater modulatory changes in left amygdala to hypothalamus EC (positively associated with Perceived Stress Scale score), right amygdala to bilateral fusiform gyri ECs (positively associated with Perceived Stress Scale score), and left ventrolateral prefrontal cortex to bilateral fusiform gyri ECs (negatively associated with Perceived Stress Scale score). CONCLUSIONS: Left amygdala to hypothalamus EC and right amygdala to bilateral fusiform gyri ECs are possibly part of circuits underlying the risk of individuals with CUD to stress-related disorders. Correspondingly, left ventrolateral prefrontal cortex to bilateral fusiform gyri ECs are possibly part of circuits reflecting a protective mechanism.