A multilevel analysis of changing telehealth availability in opioid use disorder treatment settings: Conditional effects of rurality, the number and types of medication for opioid use disorder available, and time, US, 2016-2023.

PURPOSE: The opioid overdose crisis requires strengthening treatment systems with innovative technologies. How people use telehealth for opioid use disorder (OUD) is evolving and differs in rural versus urban areas, as telehealth is emerging as a local resource and complementary option to in-person treatment. We assessed changing trends in telehealth and medication for OUD (MOUD) and pinpoint locations of low telehealth and MOUD access. METHODS: We used national data from the Mental health and Addiction Treatment Tracking Repository (2016-2023) to identify specialty outpatient SUD treatment facilities in the United States (N = 83,988). We modeled the availability of telehealth using multilevel multivariable logistic regression, adjusting for covariates. We included a 3-way interaction to test for conditional effects of rurality, the number of MOUD medication types dispensed, and year. We included two random effects to account for clustering within counties and states. FINDINGS: We identified 495 facilities that offered both telehealth and all three MOUD medication types (methadone, buprenorphine, naltrexone) in 2023, clustered in the eastern United States. We identified a statistically significant 3-way interaction (p < 0.0001), indicating that telehealth in facilities that did not offer MOUD shifted from more telehealth in rural facilities in earlier years to more telehealth in urban facilities in later years. CONCLUSIONS: Treatment facilities that offer both telehealth and all three MOUD medication types may improve access for hard-to-reach populations. We stress the importance of continued health system strengthening and technological resources in vulnerable rural communities, while acknowledging a changing landscape of increased OUD incidence and MOUD demand in urban communities.

Cigarette smoking and prostate cancer aggressiveness among African and European American men.

PURPOSE: Smoking is a modifiable lifestyle factor that has not been established as a prostate cancer risk factor, nor emphasized in prostate cancer prevention. Studies have shown that African American (AA) smokers have a poorer cancer prognosis than European Americans (EAs), while having a lower prevalence of heavy smoking. We examined the relationship between cigarette smoking and prostate cancer aggressiveness and assessed racial differences in smoking habits on the probability of high-aggressive prostate cancer. METHODS: Using data from the North Carolina-Louisiana Prostate Cancer Project (n = 1,279), prostate cancer aggressiveness was defined as high or low based on Gleason scores, serum prostate-specific antigen levels, and tumor stage. Cigarette smoking was categorized as current, former, or never smokers. Multivariable logistic regression was used to estimate adjusted odds ratios (OR) and 95% confidence intervals (CI). RESULTS: Self-reported current (OR = 1.99; 95% CI 1.30-3.06) smoking was associated with high-aggressive prostate cancer relative to never smokers. When stratified by self-reported race, the odds of having high-aggressive cancer increased among AA current (OR = 3.58; 95% CI 2.04-6.28) and former smokers (OR = 2.21; 95% CI 1.38-3.53) compared to AA never smokers, but the odds were diminished among the EA stratum (Pself-reported race x smoking status = 0.003). CONCLUSION: Cigarette smoking is associated with prostate cancer aggressiveness, a relationship modulated by self-reported race. Future research is needed to investigate types of cigarettes smoked and metabolic differences that may be contributing to the racial disparities observed.

Temporal Trends in Telehealth Availability in Mental Health Treatment Settings: Differences in Growth by State Rurality, 2015-2020.

We sought to investigate temporal trends in telehealth availability among outpatient mental health treatment facilities and differences in the pace of telehealth growth by state urbanicity and rurality. We used the National Mental Health Services Survey (2015-2020) to identify outpatient mental health treatment facilities in the US (N = 28,989 facilities; 2015 n = 5,018; 2020 n = 4,889). We used logistic regression to model telehealth, predicted by time, state rurality (1 to 10% rural, 10 to < 20%, 20 to < 30%, or [Formula: see text] 30%), and their interaction, and adjusted for relevant covariates. We estimated the predicted probability of telehealth based on our model. We estimated effects with and without data from 2020 to assess whether the rapid and widespread adoption of telehealth during the COVID-19 pandemic changed the rural/urban trajectories of telehealth availability. We found that telehealth grew fastest in more urban states (year*rurality interaction p < 0.0001). Between 2015 and 2020, the predicted probability of telehealth in more urban states increased by 51 percentage points (from 9 to 61%), whereas telehealth in more rural states increased by 38 percentage points (from 23 to 61%). Predicted telehealth also varied widely by state, ranging from more than 75% of facilities (RI, OR) to below 20% (VT, KY). Health systems and new technological innovations must consider the unique challenges faced by urban populations and how best practices may be adapted to meet the growing urban demand. We framed our findings around the need for policies that minimize barriers to telehealth.

Blunt and non-blunt cannabis use associated with cigarette, e-cigarette, and cigar initiation: Findings from the population assessment of tobacco and health (PATH) study.

BACKGROUND: Smoking cannabis using a tobacco-derived cigar shell or wrap, called blunt smoking, exposes individuals to non-trivial amounts of nicotine. The extent smoking blunts impact the risk of initiating other tobacco products is not well understood. We investigated if past-year blunt smoking is related to the risk of initiating cigarettes, e-cigarettes, and cigars. METHODS: We obtained data on a nationally representative, non-institutionalized, civilian cohort of US residents aged 12 years and older who had never used cigarettes, e-cigarettes, or any cigar at baseline and surveyed annually for three years from the Population Assessment of Tobacco and Health Study. We estimated the proportional hazard (odds) of initiating these tobacco products associated with past-year blunt smoking, non-blunt cannabis use, or neither using discrete-time survival analyses. RESULTS: Smoking blunts increased the risk of starting cigarettes (OR = 4.5), e-cigarettes (OR = 3.7), and cigars (OR = 6.7) compared to using neither blunts nor cannabis. Non-blunt cannabis use also increased the risk of starting cigarettes (OR = 4.0) but moderately for e-cigarettes (OR = 2.8) and any cigar (OR = 2.2). Blunt use was strongly related to starting combustible tobacco (cigarettes or cigars; OR = 9.0) and any three tobacco products (OR = 10.9). Exploratory findings showed that cigarillos drove cigar results and effect modification by age, race/ethnicity, and sex. CONCLUSIONS: People who smoke blunts risk starting cigarettes, e-cigarettes, and cigars more than those who abstain from cannabis. Blunts may contribute to tobacco initiation above cannabis alone.

Nicotine Dependence among Current Cigarette Smokers Who Use E-Cigarettes and Cannabis.

BACKGROUND: Co-use of tobacco and cannabis and dual use of cigarettes and e-cigarettes are very common among young adults. However, it is unclear whether co-use of cigarettes, e-cigarettes, and/or cannabis is associated with higher levels of nicotine dependence than cigarette-only use. We investigated the relationship between cigarette/nicotine dependence and co-use of tobacco and cannabis among 4 groups of cigarette smokers aged 18-35: cigarette-only smokers, cigarette-e-cigarette (CIG-ECIG) co-users, cigarette-cannabis (CIG-CAN) co-users, and cigarette-e-cigarette-cannabis (CIG-ECIG-CAN) co-users. METHODS: Data were from a 2018 cross-sectional survey based on a national convenience sample of smokers aged 18-35 (n = 315). Cigarette/nicotine dependence was measured by the Fagerstrom Test of Nicotine Dependence (FTND) and e-cigarette dependence was measured by the Penn State E-cigarette Dependence Index. Bivariate analyses examined sociodemographic and tobacco/other substance use characteristics by co-use status and multivariable linear regression assessed the relationship between co-use and nicotine dependence. RESULTS: In the sample, 27.6% were cigarette-only smokers, 24.8% were CIG-ECIG, 27.6% were CIG-CAN, and 20.0% were CIG-ECIG-CAN co-users. Significant differences were observed in sociodemographic and tobacco/other substance use characteristics by co-use status. E-cigarette co-users had low e-cigarette dependence, but moderate FTND scores. In adjusted analyses, only CIG-ECIG co-use was associated with higher FTND scores compared to cigarette-only smoking. However, CIG-ECIG and CIG-ECIG-CAN co-use were associated with higher FTND scores compared to CIG-CAN co-use. CONCLUSIONS: Co-use of cigarettes and e-cigarettes was associated with greater nicotine dependence among smokers aged 18-35. Additional research is needed to understand the underlying mechanisms of these relationships and inform prevention efforts.

Impact of COVID-19 on the characteristics of opioid overdose deaths in Arkansas.

BACKGROUND: In the US, spikes in drug overdose deaths overlapping with the COVID-19 pandemic create concern that persons who use drugs are especially vulnerable. This study aimed to compare the trends in opioid overdose deaths and characterize opioid overdose deaths by drug subtype and person characteristics pre-COVID (2017-2019) and one-year post-COVID-19 emergence (2020). METHODS: We obtained death certificates on drug overdose deaths in Arkansas from January 1, 2017, through December 31, 2020. Our analyses consisted of an interrupted time-series and segmented regression analysis to assess the impact of COVID-19 on the number of opioid overdose deaths. RESULTS: The proportion of opioid overdose deaths increased by 36% post-COVID emergence (95% CI: 14%, 59%). The trend in overdose deaths involving synthetic narcotics other than methadone, such as fentanyl and tramadol, has increased since 2018 (74 in 2018 vs 79 in 2019; p=0.02 and 79 in 2019 versus 158 in 2020; p = 0.03). Opioid overdose deaths involving methamphetamine have more than doubled (36 in 2019 vs 82 in 2020; p = 0.06) despite remaining steady from 2018 to 2019. Synthetic narcotics have surpassed methamphetamine (71% vs. 37%) as the leading cause of opioid overdose deaths in Arkansas during the pandemic. This study found that synthetic narcotics are the significant drivers of the increase in opioid overdose deaths in Arkansas during the pandemic. CONCLUSIONS: The co-occurrence of the COVID-19 pandemic and the drug abuse epidemic further highlights the increased need for expanding awareness and availability of resources for treating substance use disorders.

Resilience Factors in the Association Between Depressive Symptoms and Suicidality.

PURPOSE: To investigate whether life satisfaction and optimism might reduce the risk of suicidal thoughts and behaviors among adolescents with depressive symptoms. METHODS: Participants were 1,904 youth from the NEXT Generation Health Study, a national sample of U.S. adolescents, followed over 7 years from 2009/2010 to 2015/2016. Longitudinal latent profile analysis and logistic regressions were conducted. RESULTS: We identified three subgroups of adolescents with different patterns of depressive symptoms across the first six waves: "Low" (40%), "Mild" (42%), and "Moderate to Severe" (18%). The Moderate to Severe (OR = 14.47, 95% CI [6.61, 31.66]) and Mild (OR = 3.90, 95% CI [2.22, 6.86]) depression profiles had significantly higher odds of developing suicidality than the Low depression profile. Both life satisfaction and optimism moderated the association between depressive symptom profile and suicidality. The difference in suicidality risk between the Mild and Low depression profiles was significantly attenuated at high versus low levels of life satisfaction, with a difference of -.08, 95% CI [-.14, -.03]. In addition, the difference in suicidality risk between the Moderate to Severe and Low depression profiles was attenuated at high versus low levels of optimism, with a difference of -.11, 95% CI [-.21, -.01]. CONCLUSIONS: For adolescents transitioning to young adulthood, resilience factors such as life satisfaction and optimism may buffer against suicidality risk in the face of mild or moderate to severe depressive symptoms.

Neighbourhood context and binge drinking from adolescence into early adulthood in a US national cohort.

BACKGROUND: Underage binge drinking is a serious health concern that is likely influenced by the neighbourhood environment. However, longitudinal evidence has been limited and few studies have examined time-varying neighbourhood factors and demographic subgroup variation. METHODS: We investigated neighbourhood influences and binge drinking in a national cohort of US 10th grade students at four times (2010-2014; n = 2745). We estimated odds ratios (OR) for past 30-day binge drinking associated with neighbourhood disadvantage, personal and property crime (quartiles), and number of liquor, beer and wine stores within 5 km, and then evaluated whether neighbourhood associations differ by age, sex and race/ethnicity. RESULTS: Neighbourhood disadvantage was associated with binge drinking before 18 [OR = 1.54; 95% confidence interval (1.14, 2.08)], but not after 18 years of age. Property crime in neighbourhoods was associated with a higher odds of binge drinking [OR = 1.54 (0.96, 2.45)], an association that was stronger in early adulthood [4th vs 1st quartile: OR = 1.77 (1.04, 3.03)] and among Whites [4th vs 1st quartile: OR = 2.46 (1.03, 5.90)]. Higher density of liquor stores predicted binge drinking among Blacks [1-10 stores vs none: OR = 4.31 (1.50, 12.36)] whereas higher density of beer/wine stores predicted binge drinking among Whites [one vs none for beer: OR = 2.21 (1.06, 4.60); for wine: OR = 2.04 (1.04, 4.03)]. CONCLUSIONS: Neighbourhood conditions, particularly those related to economic circumstances, crime and alcohol outlet density, were related to binge drinking among young adults, but associations varied across age and individual characteristics.

Neighbourhood disadvantage and depressive symptoms among adolescents followed into emerging adulthood.

BACKGROUND: Residents of disadvantaged neighbourhoods report higher levels of depressive symptoms; however, few studies have employed prospective designs during adolescence, when depression tends to emerge. We examined associations of neighbourhood social fragmentation, income inequality and median household income with depressive symptoms in a nationally representative survey of adolescents. METHODS: The NEXT Generation Health Study enrolled 10th-grade students from 81 US high schools in the 2009-2010 school year. Depressive symptoms were assessed with the Modified Depression Scale (wave 1) and the paediatric Patient-Reported Outcome Measurement Information System (waves 2-6). Neighbourhood characteristics at waves 1, 3, 4, and 5 were measured at the census tract level using geolinked data from the American Community Survey 5-year estimates. We used linear mixed models to relate neighbourhood disadvantage to depressive symptoms controlling for neighbourhood and individual sociodemographic factors. RESULTS: None of the models demonstrated evidence for associations of social fragmentation, income inequality or median household income with depressive symptoms. CONCLUSION: Despite the prospective design, repeated measures and nationally representative sample, we detected no association between neighbourhood disadvantage and depressive symptoms. This association may not exist or may be too small to detect in a geographically dispersed sample. Given the public health significance of neighbourhood effects, future research should examine the developmental timing of neighbourhood effects across a wider range of ages than in the current sample, consider both objective and subjective measures of neighbourhood conditions, and use spatially informative techniques that account for conditions of nearby neighbourhoods.

State alcohol policies, taxes, and availability as predictors of adolescent binge drinking trajectories into early adulthood.

BACKGROUND AND AIMS: A number of alcohol policies in the United States have been presumed to reduce underage youth drinking. This study characterized underage youth binge-drinking trajectories into early adulthood and tested associations with the strength of the alcohol policy environment, beer excise taxes and number of liquor stores. DESIGN: Longitudinal cohort study. SETTING: United States. PARTICIPANTS: A national cohort of 10th graders in 2010 (n = 2753), assessed annually from 2010 to 2015. MEASUREMENTS: Participants reported on their 30-day binge drinking [defined as consuming five or more+ (for boys) or four or more (for girls) drinks within 2 hours]. We scored the strength of 19 state-level policies at baseline and summarized them into an overall score and two subdomain scores. We also assessed state beer excise taxes (dollars/gallon) and linked the number of liquor stores in 1 km to the participants' geocoded address. FINDINGS: We identified five binge-drinking trajectories: low-risk (32.9%), escalating (26.1%), late-onset (13.8%), chronic (15.1%) and decreasing (12.0%). Lower overall alcohol policy strength was associated with increased risk of being in the escalating versus low-risk binge-drinking class [relative risk ratio (RRR) = 1.44 per 1 standard deviation (SD) in policy score; 95% confidence interval (CI) = 1.17, 1.77)]. Higher beer excise taxes were associated with a reduced risk of being in the escalating class (RRR = 0.22 per 1-dollar increase; 95% CI = 0.09, 0.50). The number of liquor stores was not significantly associated with any binge-drinking trajectory. CONCLUSIONS: In the United States, stronger state alcohol policies and higher beer excise taxes appear to be associated with lower risk of escalating alcohol consumption trajectories among underage youth.

Correction to: When Marijuana Is Used before Cigarettes or Alcohol: Demographic Predictors and Associations with Heavy Use, Cannabis Use Disorder, and Other Drug-related Outcomes.

The original version of this article contained a mistake: The first sentence in the Results section of the Abstract should read as follows: "The proportion who had initiated marijuana before other substances increased from 4.4% to 8.0%".

When Marijuana Is Used before Cigarettes or Alcohol: Demographic Predictors and Associations with Heavy Use, Cannabis Use Disorder, and Other Drug-related Outcomes.

Recent loosening of state and federal policy restrictions on marijuana, along with changes in social norms regarding marijuana use and decreases in prevalence of other types of substance use, may lead to increases in youth initiating marijuana before other types of substances such as alcohol and tobacco. We investigated predictors and potential consequences of initiating marijuana before other drugs for youth aged 12-21-years in the USA. Nationally representative, cross-sectional survey data from the US National Survey on Drug Use and Health supplied self-reported age of first marijuana, cigarettes, alcohol, other tobacco, and other illegal drug use among 12-21-year-old samples from 2004 to 2014 (n = 275,559). We first examined the degree to which initiating marijuana use first was associated with sex, age, race/ethnicity, and survey year. Then, we examined whether using marijuana first predicted heavy marijuana use, cannabis use disorder (CUD), alcohol use disorder (AUD), nicotine dependence (ND), or lifetime use of other illegal drugs. Among all survey youth (substance users and non-users), the proportion using marijuana first increased from 4.8 to 8.8% from 2004 to 2014. Those using marijuana first (vs. alcohol or cigarettes first) were more likely to be male and older and Black, American Indian/Alaskan Native, multiracial, or Hispanic than White or Asian. Among substance users and adjusting for age of onset and the number of substances used, using marijuana first was associated higher odds of heavy current marijuana use and CUD. In recent years, youth have been increasingly likely to use marijuana as their first drug and sequence of initiation is associated with race/ethnicity, gender, and age. Using marijuana first might increase the chance of heavy use and CUD.

Does Starting to Smoke Cigars Trigger Onset of Cannabis Blunt Smoking?

Introduction: Among United States teens during the 1990s, increasing cigar use coincided with increasing use of tobacco cigar shells filled with cannabis, called "blunts." Cigar smokers are more likely to use cannabis, and we hypothesized that starting to smoke cigars might be a probabilistic "trigger" of blunt smoking. We turned to the case-crossover approach to evaluate this hypothesis. Methods: Within US National Surveys on Drug Use and Health, 2009-2013, we identified a nationally representative sample of newly incident blunt smokers aged 12- to 21-years-old (n = 4868) and compared month-of-onsets for smoking of cigars and blunts. Using the subjects-as-their-own-controls case-crossover design, we specified the first month prior to blunt use as a "hazard interval" and the second month prior to blunt use as a "control interval." We used Mantel-Haenszel (MH) estimators to estimate the matched-pairs odds ratio (OR). Results: The MH OR estimate was 1.7 (95% CI = 1.3, 2.3), with excess odds of cigar onsets during the hazard interval relative to the control interval. Two alternative control interval specifications yielded congruent estimates (OR = 2.7 and 2.9, respectively). Conclusions: A short interval right after starting to smoke cigars may be one of increased risk of starting to smoke blunts. We discuss cigar, cigarillo, and "blunt wraps" control approaches that might reduce both tobacco and cannabis-related harms. Implications: If this evidence is correct, increased market-targeting to promote youthful cigar and cigarillo smoking might be followed by increased rates of blunt smoking in a vulnerable population. As noted by others, enhanced risk of smoking-attributable harms might be a consequence of mixed tobacco-cannabis formulations.

Polygenic Score × Intervention Moderation: an Application of Discrete-Time Survival Analysis to Model the Timing of First Marijuana Use Among Urban Youth.

The present study examines the interaction between a polygenic score and an elementary school-based universal preventive intervention trial and its effects on a discrete-time survival analysis of time to first smoking marijuana. Research has suggested that initiation of substances is both genetically and environmentally driven (Rhee et al., Archives of general psychiatry 60:1256-1264, 2003; Verweij et al., Addiction 105:417-430, 2010). A previous work has found a significant interaction between the polygenic score and the same elementary school-based intervention with tobacco smoking (Musci et al., in press). The polygenic score reflects the contribution of multiple genes and has been shown in prior research to be predictive of smoking cessation, tobacco use, and marijuana use (Uhl et al., Molecular Psychiatry 19:50-54, 2014). Using data from a longitudinal preventive intervention study (N = 678), we examined age of first marijuana use from sixth grade to age 18. Genetic data were collected during emerging adulthood and were genotyped using the Affymetrix 6.0 microarray (N = 545). The polygenic score was computed using these data. Discrete-time survival analysis was employed to test for intervention main and interaction effects with the polygenic score. We found main effect of the polygenic score approaching significance, with the participants with higher polygenic scores reporting their first smoking marijuana at an age significantly later than controls (p = .050). We also found a significant intervention × polygenic score interaction effect at p = .003, with participants at the higher end of the polygenic score benefiting the most from the intervention in terms of delayed age of first use. These results suggest that genetics may play an important role in the age of first use of marijuana and that differences in genetics may account for the differential effectiveness of classroom-based interventions in delaying substance use experimentation.

Costs of substance use disorders from claims data for Medicare recipients from a population-based sample.

Medicare spending is projected to increase over the next decade, including for substance use disorders (SUD). Our objective was to determine whether SUDs are associated with higher six-year Medicare costs (1999-2004) among participants in the Baltimore Epidemiologic Catchment Area (ECA) Study. Medicare claims data for the years 1999-2004 from the Centers for Medicare and Medicaid Services were linked to four waves of data from the Baltimore ECA cohort collected between 1981 and 2005 (n=566). A generalized linear model with a log link and gamma distribution was used to examine direct Medicare costs associated with SUD status. Medicare recipients with no history of SUD had mean six-year costs of $42,576. Those with a history of SUD based on both Baltimore ECA and Medicare data, or based on Medicare claims data alone, had significantly higher costs ($98,754 and $64,876, respectively). A history of SUD based solely on Baltimore ECA data alone had lower average costs ($25,491). Findings indicate that Medicare costs differ by source of SUD diagnosis when comparing treatment versus survey data. This may have future implications for projecting Medicare costs among SUD individuals as healthcare coverage expands under the Affordable Care Act.

Differences in alcohol use patterns between adolescent Asian American ethnic groups: Representative estimates from the National Survey on Drug Use and Health 2002-2013.

OBJECTIVE: Studies have suggested that alcohol use prevalence is increasing among Asian American adolescents and there may be significant differences between specific adolescent Asian American ethnicities. METHOD: Data from the National Survey on Drug Use and Health (2002-2013) were used to estimate prevalence of alcohol use (lifetime, past-month, past-year) and problem (binge drinking, alcohol use disorder [AUD], and early initiation of use) outcomes among adolescent Asian American ethnicities. RESULTS: Filipino Americans had the highest prevalence of lifetime (29.3%) and past-month (10.3%) use; Korean Americans had the highest prevalence of past-year use (22.7%). Asian Indian Americans had the lowest prevalence of all three use indicators: 14.6%, 11.9%, and 4.9% for lifetime, past-year, and past-month, respectively. Korean Americans had the highest prevalence of binge drinking (5.4%), Filipino Americans had the highest prevalence of AUD (3.5%), and Vietnamese Americans had the highest prevalence of early initiation of use (13.5%). Asian Indian Americans had the lowest prevalence for all three alcohol problem indicators: 2.6%, 1.0%, and 4.9% for binge drinking, AUD, and early initiation of use, respectively. CONCLUSIONS: Prevalence estimates of alcohol outcomes among Korean, Japanese, and Filipino American adolescents were high and similar to other racial groups that are often considered higher risk racial groups. Estimates among large subgroups with low alcohol use prevalence, Chinese and Asian Indian Americans, may mask high rates among other Asian ethnicities when alcohol use estimates are presented among Asians overall. When feasible, researchers should present alcohol use estimates disaggregated by specific Asian American ethnicities and investigate differences in risk factors across groups.

Usual Modes of Marijuana Consumption Among High School Students in Colorado.

OBJECTIVE: The purpose of this study was to assess the prevalence of modes of marijuana consumption among Colorado youth and explore variation by demographics, access, substance use, and risk perceptions. METHOD: Data are from a 2013 survey of Colorado high school students (N = 25,197; 50.5% female). The outcome variable was usual mode of marijuana consumption (i.e., smoking, vaporizing, ingesting edibles, or other) among those reporting past 30-day marijuana use. Classification variables included sex, grade level, race/ethnicity, sexual orientation, current alcohol and cigarette use, frequent marijuana use, early marijuana use (<13 years), perceived harmfulness, and perceived wrongfulness. We calculated prevalence estimates overall and by the variables listed above, and also conducted multinomial logistic regression models. RESULTS: Findings indicate that 15% of Colorado high school students who use marijuana report that they usually use a mode of consumption other than smoking. Among students reporting past 30-day marijuana use, 85% said smoking was their usual mode of consumption. The remainder reported that their usual mode of consumption was vaporizing (6%), ingesting edibles (5%), or another method (4%). Boys, Whites, Asians, and 12th graders were the most likely to report vaporizing. High perceived harmfulness was associated with vaporizing or ingesting edibles. CONCLUSIONS: The majority of Colorado youth who use marijuana usually smoke it. Youth may be using vaporizers and ingesting edibles as a way to reduce the harm associated with inhaling combusted smoke.

Trends in registered medical marijuana participation across 13 US states and District of Columbia.

BACKGROUND: Medical marijuana (MMJ) laws and policies have evolved rapidly over the past decade in the United States. Public health concerns over the impact of these laws might be influenced by the degree of MMJ participation, but little is known about changes to this population over time. Trends in registered MMJ participation are described for 13 states in the US and District of Columbia (DC) since 2001. METHODS: Numbers of MMJ participants were obtained from state MMJ patient registries. A subset of states reported differences by sex and age. Prevalence of MMJ participation per 1000 was calculated. Data from California and Washington were not available. RESULTS: MMJ participation was relatively low and flat from 2001 to 2008 (i.e., less than 5 per 1000 adults). Participation rose sharply in Colorado, Montana, and Michigan in 2009-2010, but not for other states. High rates can currently be found in Colorado, Oregon, and Montana (i.e., 15-30 per 1000) with the national average around 7.6 per 1000 adults. Two-thirds of participants are male, but sex differences may be decreasing over time. Less than 1% of MMJ registrants are under 18, but this segment is growing in Colorado and Oregon. Participants tend to be older (50s), but Colorado and Arizona have larger proportions of young adult (21-30) registrants. CONCLUSIONS: Participation in MMJ programs varies considerably by state and within states over time. Trends are discussed within the context of federal and state policies, and the availability of marijuana via dispensaries.

Past 15-year trends in adolescent marijuana use: Differences by race/ethnicity and sex.

BACKGROUND: The potential for increases in adolescent marijuana use is an important concern given recent changes in marijuana policy. The purpose of this study was to estimate trends in marijuana use from 1999 to 2013 among a national sample of US high school students. We examine changes over time by race/ethnicity and sex. METHODS: Data are from the National Youth Risk Behavior Survey (YRBS), which involves biennial, school-based surveys that generate nationally representative data about 9th-12th grade students in the United States. Students self-reported sex, race/ethnicity, and marijuana use (i.e., lifetime use, past 30-day use, any use before age 13). We generated national estimates of the prevalence of marijuana use for the time period, and also tested for linear and quadratic trends (n=115,379). RESULTS: The prevalence of lifetime marijuana use decreased modestly from 1999 to 2009 (44% to 37%), and has increased slightly since 2009 (41%). Other marijuana use variables (e.g., past 30-day use) followed a similar pattern over time. The prevalence of past 30-day use from 1999 to 2013 for all groups and both sexes was 22.5%, and it was lowest among Asians and highest among American Indian/Alaska Natives. Although boys have historically had a higher prevalence of marijuana use, results indicate that male-female differences in marijuana use decreased over time. CONCLUSION: Despite considerable changes in state marijuana policies over the past 15 years, marijuana use among high school students has largely declined. Continued surveillance is needed to assess the impact of policy changes on adolescent marijuana use.

Cannabis problem experiences among users of the tobacco-cannabis combination known as blunts.

BACKGROUND: In most of the world, cannabis smokers mix loose tobacco inside a joint, pipe, spliff, or cone. More recently, a 'blunt' formulation combines these two drugs by inserting cannabis into a hollowed-out cigar. Epidemiological research linking simultaneous use of these two drugs and the development of cannabis use disorders (CUD) remains unclear. This study estimates associations linking blunt smoking with levels and subtypes of cannabis problems. METHODS: Cross-sectional data on 27,767 past-year cannabis users were analyzed from the US National Survey on Drug Use and Health (NSDUH) conducted from 2009 to 2012. Ten self-reported items of DSM-IV CUD features elicited a single latent trait of cannabis problem (CP) severity, which was then regressed on past-year blunt smoking and past-month blunt frequency measures within the context of a conceptual model. Differential item functioning (DIF) analysis evaluated potential bias in CP feature response by blunt smoking history. RESULTS: Past-year blunt smoking was associated with higher CP severity compared to cannabis users who did not smoke blunts. Days of blunt smoking in the past month also predicted higher CP severity than less frequent blunt use. Those smoking blunts experienced more subjectively felt tolerance and having spent more time obtaining or using cannabis, but were less likely to experience other problems, even at the same level of CP severity. CONCLUSIONS: These findings suggest smoking blunts might promote the development of problematic cannabis use. Responses to cannabis problems differed by history of blunt smoking, possibly implicating an influence of tobacco on measurement of cannabis use disorders.