Associations between cannabis policies and state-level specialty cannabis use disorder treatment in the United States, 2004-2019.

BACKGROUND: Cannabis use disorder (CUD) treatment prevalence decreased in the US between 2002 and 2019, yet structural mechanisms for this decrease are poorly understood. We tested associations between cannabis laws becoming effective and self-reported CUD treatment. METHODS: Restricted-use 2004-2019 National Surveys on Drug Use and Health included people ages 12+ classified as needing CUD treatment (i.e., past-year DSM-5-proxy CUD or last/current specialty treatment for cannabis). Time-varying indicators of medical cannabis laws (MCL) with/without cannabis dispensary provisions differentiated state-years before/after laws using effective dates. Multi-level logistic regressions with random state intercepts estimated individual- and state-adjusted CUD treatment odds by MCLs and model-based changes in specialty CUD treatment state-level prevalence. Secondary analyses tested associations between CUD treatment and MCL or recreational cannabis laws (RCL). RESULTS: Using a broad treatment need sample definition in 2004-2014, specialty CUD treatment prevalence decreased by 1.35 (95 % CI = -2.51, -0.18) points after MCL without dispensaries and by 2.15 points (95 % CI = -3.29, -1.00) after MCL with dispensaries provisions became effective, compared to before MCL. Among people with CUD in 2004-2014, specialty treatment decreased only in MCL states with dispensary provisions (aPD = -0.91, 95 % CI = -1.68, -0.13). MCL were not associated with CUD treatment use in 2015-2019. RCL were associated with lower CUD treatment among people classified as needing CUD treatment, but not among people with past-year CUD. CONCLUSIONS: Policy-related reductions in specialty CUD treatment were concentrated in states with cannabis dispensary provisions in 2004-2014, but not 2015-2019, and partly driven by reductions among people without past-year CUD. Other mechanisms (e.g., CUD symptom identification, criminal-legal referrals) could contribute to decreasing treatment trends.

Trends in cannabis or cocaine-related dependence and alcohol/drug treatment in Argentina, Chile, and Uruguay.

BACKGROUND: In the context of changing cannabis and other drug policy and regulation, concerns may arise regarding drug treatment access and use. We assessed cannabis/cocaine-related dependence and treatment in Argentina, Chile, and Uruguay. METHODS: Nationally representative cross-sectional household surveys of people ages 15-64 in Argentina (4 surveys, 2006-2017), Chile (7 surveys, 2006-2018), and Uruguay (4 surveys, 2006-2018) were harmonized. We estimated weighted prevalences of cannabis or cocaine-related (cocaine or cocaine paste) dependence, based on meeting 3+ past-year ICD-10 dependence criteria. We estimated weighted prevalences of past-year alcohol/drug treatment use (Argentina, Chile) or use/seeking (Uruguay) among people with past-year cannabis/cocaine-related dependence. We tested model-based prevalence trends over time and described individual-level treatment correlates by country. RESULTS: Cannabis/cocaine dependence prevalence increased in the region starting in 2010-2011, driven by cannabis dependence. Adjusted cannabis dependence prevalence increased from 0.7% in 2010 to 1.5% in 2017 in Argentina (aPD=0.8, 95% CI= 0.3, 1.2), from 0.8% in 2010 to 2.8% in 2018 in Chile (aPD=2.0, 95% CI= 1.4, 2.6), and from 1.4% in 2011 to 2.4% in 2018 in Uruguay (aPD=0.9, 95% CI= 0.2, 1.6). Cocaine-related dependence increased in Uruguay, decreased in Argentina, and remained stable in Chile. Among people with past-year cannabis/cocaine dependence, average alcohol/drug treatment use prevalence was 15.3% in Argentina and 6.0% in Chile, while treatment use/seeking was 14.7% in Uruguay. Alcohol/drug treatment prevalence was lower among people with cannabis dependence than cocaine-related dependence. Treatment correlates included older ages in all countries and male sex in Argentina only. CONCLUSION: Alcohol/drug treatment use among people with cannabis/cocaine-related dependence remained low, signaling an ongoing treatment gap in the context of growing cannabis dependence prevalence in the region. Additional resources may be needed to increase treatment access and uptake. Future studies should assess contributors of low treatment use, including perceived need, stigma, and service availability.

Non-pharmacological Treatment for Chronic Pain in US Veterans Treated Within the Veterans Health Administration: Implications for Expansion in US Healthcare Systems.

BACKGROUND: Consensus guidelines recommend multimodal chronic pain treatment with increased use of non-pharmacological treatment modalities (NPM), including as first-line therapies. However, with many barriers to NPM uptake in US healthcare systems, NPM use may vary across medical care settings. Military veterans are disproportionately affected by chronic pain. Many veterans receive treatment through the Veterans Health Administration (VHA), an integrated healthcare system in which specific policies promote NPM use. OBJECTIVE: To examine whether veterans with chronic pain who utilize VHA healthcare were more likely to use NPM than veterans who do not utilize VHA healthcare. DESIGN: Cross-sectional nationally representative study. PARTICIPANTS: US military veterans (N = 2,836). MAIN MEASURES: In the 2019 National Health Interview Survey, veterans were assessed for VHA treatment, chronic pain (i.e., past 3-month daily or almost daily pain), symptoms of depression and anxiety, substance use, and NPM (i.e., physical therapy, chiropractic/spinal manipulation, massage, psychotherapy, educational class/workshop, peer support groups, or yoga/tai chi). KEY RESULTS: Chronic pain (45.2% vs. 26.8%) and NPM use (49.8% vs. 39.4%) were more prevalent among VHA patients than non-VHA veterans. After adjusting for sociodemographic characteristics, psychiatric symptoms, physical health indicators, and use of cigarettes or prescription opioids, VHA patients were more likely than non-VHA veterans to use any NPM (adjusted odds ratio [aOR] = 1.52, 95% CI: 1.07-2.16) and multimodal NPM (aOR = 1.80, 95% CI: 1.12-2.87) than no NPM. Among veterans with chronic pain, VHA patients were more likely to use chiropractic care (aOR = 1.90, 95% CI = 1.12-3.22), educational class/workshop (aOR = 3.02, 95% CI = 1.35-6.73), or psychotherapy (aOR = 4.28, 95% CI = 1.69-10.87). CONCLUSIONS: Among veterans with chronic pain, past-year VHA use was associated with greater likelihood of receiving NPM. These findings may suggest that the VHA is an important resource and possible facilitator of NPM. VHA policies may offer guidance for expanding use of NPM in other integrated US healthcare systems.

State-level marijuana policies and marijuana use and marijuana use disorder among a nationally representative sample of adults in the United States, 2015-2017: Sexual identity and gender matter.

BACKGROUND: Research demonstrates an association between state-level medical marijuana laws (MMLs) and increased marijuana use (MU) and MU disorder (MUD) among adults, but has yet to explore this association among lesbian, gay and bisexual (LGB) individuals, including gender differences. METHODS: We pooled the 2015-2017 National Survey on Drug Use and Health data for adults (n = 126,463) and used gender-stratified adjusted multivariable logistic regression to model the odds of past-year MU, past-year medical MU, daily/near-daily MU, and MUD; we also tested the interaction between MML state residence and sexual identity. RESULTS: Bisexual women had higher past-year MU (40% versus 10.3%; aOR = 2.9[2.4-3.4]), daily/near-daily MU (9.8% versus 1.5%; aOR = 4.6[3.3-6.2]), and medical MU ((5.5% versus 1.2%) aOR = 5.5[3.8-8.1]) than heterosexual women. Gay/lesbian women also had higher past-year MU (26.1% versus 10.3%; aOR = 2.8[2.2-3.7]), daily/near-daily MU (5.6% versus 1.5%; aOR = 2.9[1.8-4.6]), and medical MU (4.7% versus 1.2%; aOR = 3.0(1.4-6.6]) than heterosexual women. Bisexual women in MML states had higher past-year MU ((44.4% vs. 34.1%); aOR = 1.8[1.5-2.1]) and medical use (7.1% vs. 3.3% (aOR = 2.5[1.5-3.9]) than bisexual women in non-MML states. The odds of any past-year medical MU for bisexual versus heterosexual women was different in MML versus non-MML states (Exponentiated ß = 0.53, p = 0.01). Gay men in MML states had higher past year MU (31.2% versus 25.7%; aOR = 1.6[1.1-2.5] and medical MU (6.4% vs 1.7%; aOR = 5.0[4.2-6.1]) than gay men in non-MML states. CONCLUSIONS: Results suggest that MMLs may differentially impact MU for sexual minority individuals-particularly bisexual women. Findings demonstrate the need for states enacting MMLs to consider potential differential impacts on LGB populations.

Association of US Medical Marijuana Laws With Nonmedical Prescription Opioid Use and Prescription Opioid Use Disorder.

Importance: Between 1997 and 2017, the United States saw increases in nonmedical prescription opioid use and its consequences, as well as changes in marijuana policies. Ecological-level research hypothesized that medical marijuana legalization may reduce prescription opioid use by allowing medical marijuana as an alternative. Objectives: To investigate the association of state-level medical marijuana law enactment with individual-level nonmedical prescription opioid use and prescription opioid use disorder among prescription opioid users and to determine whether these outcomes varied by age and racial/ethnic groups. Design, Setting, and Participants: This cross-sectional study used restricted data on 627 000 individuals aged 12 years and older from the 2004 to 2014 National Survey on Drug Use and Health, a population-based survey representative of the civilian population of the United States. Analyses were completed from March 2018 to May 2018. Exposures: Time-varying indicator of state-level medical marijuana law enactment (0 = never law enactment, 1 = before law enactment, and 2 = after law enactment). Main Outcomes and Measures: Past-year nonmedical prescription opioid use and prescription opioid use disorder among prescription opioid users. Odds ratios of nonmedical prescription opioid use and prescription opioid use disorder comparing the period before and after law enactment were presented overall, by age and racial/ethnic group, and adjusted for individual- and state-level confounders. Results: The study sample included 627 000 participants (51.51% female; 9.88% aged 12-17 years, 13.30% aged 18-25 years, 14.30% aged 26-34 years, 25.02% aged 35-49 years, and 37.50% aged ≥50 years; the racial/ethnic distribution was 66.97% non-Hispanic white, 11.83% non-Hispanic black, 14.47% Hispanic, and 6.73% other). Screening and interview response rates were 82% to 91% and 71% to 77%, respectively. Overall, there were small changes in nonmedical prescription opioid use prevalence after medical marijuana law enactment (4.32% to 4.86%; adjusted odds ratio, 1.13; 95% CI, 1.06-1.20). Prescription opioid use disorder prevalence among prescription opioid users decreased slightly after law enactment, but the change was not statistically significant (15.41% to 14.76%; adjusted odds ratio, 0.95; 95% CI, 0.81-1.11). Outcomes were similar when stratified by age and race/ethnicity. Conclusions and Relevance: This study found little evidence of an association between medical marijuana law enactment and nonmedical prescription opioid use or prescription opioid use disorder among prescription opioid users. Further research should disentangle the potential mechanisms through which medical marijuana laws may reduce opioid-related harm.

Impact of Medical Marijuana Laws on State-Level Marijuana Use by Age and Gender, 2004-2013.

In states that have passed medical marijuana laws (MMLs), marijuana use (MU) increased after MML enactment among people ages 26 and older, but not among ages 12-25. We examined whether the age-specific impact of MMLs on MU varied by gender. Data were obtained from the 2004-2013 restricted-use National Survey on Drug Use and Health, aggregated at the state level. The exposure was a time-varying indicator of state-level MML (0 = No Law, 1 = Before Law, 2 = After Law). Outcomes included past-month MU prevalence, daily MU prevalence among past-year users (i.e., 300+ days/year), and past-year marijuana use disorder (MUD) prevalence. Linear models tested the state-level MML effect on outcomes by age (12-17, 18-25, 26+) and gender. Models included a state-level random intercept and controlled for time- and state-level covariates. Past-month MU did not increase after enactment of MML in men or women ages 12-25. Among people 26+, past-month MU increased for men from 7.0% before to 8.7% after enactment (+ 1.7%, p < 0.001) and for women from 3.1% before to 4.3% after enactment (+ 1.1%, p = 0.013). Among users 26+, daily MU also increased after enactment in both genders (men 16.3 to 19.1%, + 2.8%, p = 0.014; women 9.2 to 12.7%, + 3.4%, p = 0.003). There were no statistically significant increases in past-year MUD prevalence for any age or gender group after MML enactment. Given the statistically significant increase in daily use among past-year users aged 26+ following enactment, education campaigns should focus on informing the public of the risks associated with regular marijuana use.

"Those edibles hit hard": Exploration of Twitter data on cannabis edibles in the U.S.

AIMS: Several states in the U.S. have legalized cannabis for recreational or medical uses. In this context, cannabis edibles have drawn considerable attention after adverse effects were reported. This paper investigates Twitter users' perceptions concerning edibles and evaluates the association edibles-related tweeting activity and local cannabis legislation. METHODS: Tweets were collected between May 1 and July 31, 2015, using Twitter API and filtered through the eDrugTrends/Twitris platform. A random sample of geolocated tweets was manually coded to evaluate Twitter users' perceptions regarding edibles. Raw state proportions of Twitter users mentioning edibles were ajusted relative to the total number of Twitter users per state. Differences in adjusted proportions of Twitter users mentioning edibles between states with different cannabis legislation status were assesed via a permutation test. RESULTS: We collected 100,182 tweets mentioning cannabis edibles with 26.9% (n=26,975) containing state-level geolocation. Adjusted percentages of geolocated Twitter users posting about edibles were significantly greater in states that allow recreational and/or medical use of cannabis. The differences were statistically significant. Overall, cannabis edibles were generally positively perceived among Twitter users despite some negative tweets expressing the unreliability of edible consumption linked to variability in effect intensity and duration. CONCLUSION: Our findings suggest that Twitter data analysis is an important tool for epidemiological monitoring of emerging drug use practices and trends. Results tend to indicate greater tweeting activity about cannabis edibles in states where medical THC and/or recreational use are legal. Although the majority of tweets conveyed positive attitudes about cannabis edibles, analysis of experiences expressed in negative tweets confirms the potential adverse effects of edibles and calls for educating edibles-naïve users, improving edibles labeling, and testing their THC content.

Older, Less Regulated Medical Marijuana Programs Have Much Greater Enrollment Rates Than Newer 'Medicalized' Programs.

Twenty-three states and the District of Columbia have passed laws implementing medical marijuana programs. The nineteen programs that were in operation as of October 2014 collectively had over one million participants. All states (including D.C.) with medical marijuana laws require physicians directly or indirectly to authorize the use of marijuana at their discretion, yet little is known about how medical marijuana programs vary regarding adherence to basic principles of medical practice and associated rates of enrollment. To explore this, we analyzed marijuana programs according to seven components of traditional medical care and pharmaceutical regulation. We then examined enrollment rates, while controlling for potentially confounding state characteristics. We found that fourteen of the twenty-four programs were nonmedical and collectively enrolled 99.4 percent of participants nationwide, with enrollment rates twenty times greater than programs deemed to be "medicalized." Policy makers implementing or amending medical marijuana programs should consider the powerful relationship between less regulation and greater enrollment. Researchers should consider variations across programs when assessing programs' population-level effects.