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The concoction known as \"lean\" containing codeine and promethazine, holds a prominent cultural presence and is often referenced in mass media platforms (e.g., music and social media). Surprisingly, there's a scarcity of national data characterizing the use of lean. Therefore, the current study investigated the use of lean using national survey data and online forum participant input, and focused on identifying concurrent substance use, exploring co-administration with other substances (e.g., alcohol, cannabis), and determining lean-related experiences. We analyzed data from the National Survey on Drug Use and Health (NSDUH) spanning 2007-2019, identifying persons who used lean (weighted N = 42,275). Additionally, we conducted a Reddit-based study to gather insights about lean consumtion (N = 192). The NSDUH data indicated that lean use was most prevalent among teenagers and young adults (ages 13-21), accounting for 66% of the sample. This trend was more pronounced in male respondents (75%) compared to females. Additionally, the use was predominantly observed among Black/African American (29%), Hispanic (28%), and White (33%) populations, with these groups also reporting higher levels of concurrent alcohol and cannabis use. Similarly, findings from Reddit showed that individuals who used lean were predominantly male (67%) and exhibited elevated concurrent rates of alcohol (83%) and cannabis (46%) use in the past 30 days. Moreover, approximately 66% of respondents met criteria for severe lean use disorder, and 37% acknowledged driving under its influence. The NSDUH data found that mostly young adult males reported consuming lean in the past twelve months, though the racial/ethnic breakdown of persons who used lean was diverse. The Reddit data found that most individuals in the sample met the criteria for a substance use disorder pertaining to their lean consumption. These findings underscore the clinical significance and necessity for further controlled research on lean.

In adolescence, caffeinated beverage consumption is negatively associated with cognitive functioning. The default mode network and dorsal attention network are anticorrelated brain systems that are essentially implicated in attention. Despite the importance of the anticorrelation of default mode network - dorsal attention network on cognitive functioning, no studies have examined the association between this anticorrelation and recent caffeine consumption among youths. This study analyzed baseline data from the Adolescent Brain Cognitive Development℠ Study, the largest longitudinal study examining brain development and adolescent health in the United States, to explore the associations between caffeinated beverage consumption and the strength of anticorrelation between the default mode network - dorsal attention network. A total of N = 4,673 early adolescents (average age 9.9 years, standard deviation = 0.6) had self-report data for two caffeine variables: [a] last 24-hour caffeinated beverage consumption (Yes/No) and [b] weekly caffeinated beverage consumption (continuous). A mixed-effects model was fitted with default mode network - dorsal attention network anticorrelation strength as the outcome. Most of the baseline ABCD sample did not consume a caffeinated beverage in the last 24 hours (n = 3,910; 83.7%). Controlling for covariates (age, attention problems, BMI, family, head motion, MRI scanner, and sex), neither the caffeinated beverage variables nor their interaction were statistically significant. Our study findings identified that approximately 16% of our sample consumed caffeine in the last 24 hours prior to the magnetic resonance imaging scan. We did not find caffeine to impact the default mode network - dorsal attention network anticorrelation strength in this sample. This study may guide the interpretation of functional magnetic resonance imaging results among adolescents who consume caffeinated beverages.

We examined services to facilitate access to entering substance use disorder (SUD) treatment among a national sample of SUD treatment facilities. We analyzed data from the National Survey of Substance Abuse Treatment Services (N-SSATS) 2020. Facilities were included in the sample based on criteria such as SUD treatment provision and being in the U.S. Cluster analysis was conducted using variables including ownership, levels of care, and whether facilities provide services or accept payment options aimed at reducing treatment barriers. National and state-level data on the percentage of facilities in each cluster were presented. Among N = 15,788 SUD treatment facilities four distinct clusters were identified: Cluster 1 consisted of for-profit and government outpatient facilities with high proportions of services to reduce barriers (22.2%). Cluster 2, comprised of non-profit outpatient facilities, offered the most comprehensive array of services to minimize barriers to treatment among all four clusters (25.2%). Cluster 3 included facilities with diverse ownership and care levels and provided a moderate to high degree of services aimed at reducing entry barriers to treatment (26.0%). Cluster 4 was primarily for-profit outpatient facilities with a low proportion of these services (26.6%). This study revealed facility-level groupings with different services to reduce barriers to SUD treatment across various clusters of SUD treatment facilities. While some facilities offered extensive services, others provided fewer. Differences in cluster distributions point to possible facilitators to treatment access for some persons seeking admission to specific treatment facilities. Efforts should be made to ensure that individuals seeking SUD treatment can access these services, and facilities should be adequately equipped to meet their diverse needs.

Background Despite recent financial and policy support for harm reduction in the USA, information on the types of workers within organizations who design, implement, and actualize harm reduction services remains nascent. Little is known about how variability in the harm reduction workforce impacts referrals and linkages to other community supports. This exploratory mixed-methods study asked: (1) Who constitutes the harm reduction workforce? (2) Who provides behavioral health services within harm reduction organizations? (3) Are referral services offered and by whom? (4) Do referrals differ by type of harm reduction worker? Methods Purposive sampling techniques were used to distribute an electronic survey to U.S.-based harm reduction organizations. Descriptive statistics were conducted. Multivariate binary logistic regression models examined the associations (a) between the odds of the referral processes at harm reduction organizations and (b) between the provision of behavioral health services and distinct types of organizational staff. Qualitative data were analyzed using a hybrid approach of inductive and thematic analysis. Results Data from 41 states and Washington, D.C. were collected ( N  = 168; 48% response rate). Four primary types of workers were identified: community health/peer specialists (87%); medical/nursing staff (55%); behavioral health (49%); and others (34%). About 43% of organizations had a formal referral process; among these, only 32% had follow-up protocols. Qualitative findings highlighted the broad spectrum of behavioral health services offered and a broad behavioral health workforce heavily reliant on peers. Unadjusted results from multivariate models found that harm reduction organizations were more than 5 times more likely (95% CI [1.91, 13.38]) to have a formal referral process and 6 times more likely (95% CI [1.74, 21.52]) to have follow-up processes when behavioral health services were offered. Organizations were more than two times more likely (95% CI [1.09, 4.46]) to have a formal referral process and 2.36 (95% CI [1.11, 5.0]) times more likely to have follow-up processes for referrals when behavioral health providers were included. Conclusions The composition of the harm reduction workforce is occupationally diverse. Understanding the types of services offered, as well as the workforce who provides those services, offers valuable insights into staffing and service delivery needs of frontline organizations working to reduce morbidity and mortality among those who use substances. Workforce considerations within U.S.-based harm reduction organizations are increasingly important as harm reduction services continue to expand.

Background Opioid use disorder often co-occurs with other mental health and substance use disorders. Identifying clusters of individuals receiving treatment for opioid use disorder based on co-diagnosed conditions, healthcare plans, and service utilization over a seven-year treatment period provides insight into service needs. Objectives included [1] characterizing the sample [2], examining subtypes of the sample using cluster analysis, and [3] identifying differences in Current Procedural Terminology by subtype to examine service utilization among identified clusters. Methods This study uses secondary data from the electronic medical records of a community health center in a large urban area in the Northeastern United States from 2015 to 2021. The study sample included N  = 705 adults who had an opioid use disorder diagnosis as indicated by the community health center’s electronic medical records. Measures include [1] age [2], race and ethnicity [3], sex [4], healthcare plan(s) [5], co-occurring mental health disorder [6], co-occurring substance use disorder [7], co-occurring mental health disorder or substance use disorder, and [8] Current Procedural Terminology codes for behavioral health service utilization. Cluster analysis was used to examine the sample. These clusters were then analyzed for service utilization with a one-way analysis of variance. Results The cluster analysis identified six clusters with an average silhouette of 0.5, indicating good clustering. These six clusters were operationalized as [1] Medicare/Medicaid healthcare plan with substance use disorder needs [2], Private pay and charity care healthcare plan with cocaine use disorder needs [3], Medicare/Medicaid and other publicly-funded healthcare plans with mood disorder needs [4], Private healthcare plan with low co-occurring disorder needs [5], Other publicly-funded healthcare plan with cannabis use disorder needs [6], Medicare/Medicaid healthcare plan with mental health disorder needs. Service utilization differed between these clusters with cluster mean differences for psychotherapy sessions ( F  = 8.55, p  < 0.001), psychiatric sessions ( F  = 22.72, p  < 0.001), and group therapy sessions ( F  = 10.76, p  < 0.001). Conclusions This study highlights the importance of comprehensive and integrated treatment for substance use disorders and mental health disorders, particularly for those in underserved communities. Healthcare coverage, a socioeconomic factor that impacts access to care, is critical in distinguishing treatment needs and utilization.

Background: When parents or other guardians of children need treatment for a substance use disorder, it presents challenges impacting both them and their children, as a lack of childcare can be a barrier to treatment access. However, some treatment facilities provide childcare services to increase access. Little is known about how local economic factors like childcare costs and income levels are associated with the availability of childcare services in addiction treatment programs in the United States. Objectives: This study’s objectives were twofold: (1) assess whether county-level childcare prices are associated with the availability of childcare services in substance use disorder treatment facilities, and (2) assess whether county-level median household income is associated with the availability of childcare services in substance use disorder treatment facilities. Design: This is a cross-sectional study. Methods: This study examined n = 9003 substance use disorder treatment facilities in the United States. Childcare costs and median income estimates were linked with facilities based on the county in which they were located. Univariable and bivariable statistics were used to examine the facilities. An adjusted logistic regression model was used to evaluate associations between childcare availability and county-level economic indicators, while controlling for facility-level characteristics. Results: Approximately 5.7% (n = 514) of the facilities in the sample had childcare services. The multivariable logistic regression model identified factors associated with facilities having childcare including having outpatient and residential treatment (compared to having only outpatient only), accepting females only (compared to accepting males only), having a pregnant or postpartum program/group, accepting Medicaid, slightly higher county-level toddler center prices, and slightly higher county-level preschool center prices. Conclusion: Local childcare costs, but not median income appears to be slightly associated with the availability of childcare services in substance use disorder treatment settings. Economic investment in family-friendly services may help reduce access barriers for parents seeking treatment.

Background Unlike combustible cigarettes, vaping does not produce smoke, creating ambiguity around indoor vaping policies. Vaping policies in substance use disorder treatment facilities may directly impact how an individual engages with treatment. Objective To examine associated factors with vaping policies within substance use disorder treatment facilities in the United States. Design This cross-sectional study used data from the National Substance Use and Mental Health Services Survey to examine data from substance use disorder treatment facilities. Other data included the state percentage of adults who use e-cigarettes from the Behavioral Risk Factor Surveillance System, and state-level indoor e-cigarette restriction policies from the State Tobacco Activity Tracking and Evaluations System. Methods A total of N = 16 042 substance use disorder treatment facilities in 2022 were included. Factors that were examined include [a] state percentages of adults who vape, [b] state indoor vaping restrictions, [c] tobacco use screening in facilities, [d] smoking/tobacco education and counseling in facilities, [e] availability of nicotine pharmacotherapies in facilities, [f] facility smoking policies, [g] availability of outpatient or non-hospital residential treatment, and [h] availability of a treatment program specifically for adolescents or young adults. Facility vaping policies is the outcome variable in this study with three values: [a] vaping is restricted, [b] vaping in designated outdoor area(s), and [c] permissive vaping policies (anywhere outside, designated indoor areas, anywhere inside, anywhere without restriction). Results Vaping policies across all treatment facilities include 45.9% restricted vaping, 45.9% permitted vaping in designated outdoor area(s), and 8.2% had a permissive vaping policy. State-level percentage of adults who use e-cigarettes, state indoor e-cigarette restrictions, facility smoking policies, and services provided by facilities were associated with different vaping policies, ranging from restrictive to permissive policies. Conclusions Various vaping policies exist in substance use disorder treatment facilities. Different vaping policies may have varied impacts on individuals’ treatment goals.

Tobacco use is associated with morbidity and mortality. Many individuals who present to treatment facilities with substance use disorders (SUDs) other than tobacco use disorder also smoke cigarettes or have a concomitant tobacco use disorder. Despite high rates of smoking among those with an SUD, and numerous demonstrated benefits of comprehensive SUD treatment for tobacco use in addition to co-occurring SUDs, not all facilities address the treatment of comorbid tobacco use disorder. In addition, facilities vary widely in terms of tobacco use policies on campus. This study examined SUD facility smoking policies in a national sample of N = 16,623 SUD treatment providers in the United States in 2021. Most facilities with outpatient treatment (52.1%) and facilities with residential treatment (67.8%) had a smoking policy that permitted smoking in designated outdoor area(s). A multinomial logistic regression model found that among facilities with outpatient treatment (n = 13,778), those located in a state with laws requiring tobacco free grounds at SUD facilities, those with tobacco screening/education/counseling services, and those with nicotine pharmacotherapy were less likely to have an unrestrictive tobacco smoking policy. Among facilities with residential treatment (n = 3449), those with tobacco screening/education/counseling services were less likely to have an unrestrictive tobacco smoking policy. There is variability in smoking policies and tobacco use treatment options in SUD treatment facilities across the United States. Since tobacco use is associated with negative biomedical outcomes, more should be done to ensure that SUD treatment also focuses on reducing the harms of tobacco use.

Lesbian, gay, bisexual, and transgender (LGBT) individuals have a high prevalence of substance use disorders (SUDs) and experience unique barriers to treatment. Little is known about the characteristics of SUD treatment facilities providing LGBT-tailored programs at the outpatient and residential levels of care. The purpose of this study is to examine the availability of LGBT-tailored programs in outpatient and residential SUD treatment facilities. Using the National Survey of Substance Abuse Treatment Services 2020, we conducted logistic regression to examine facility characteristics, including ownership, pay assistance, region, outreach, and telehealth services, associated with having an LGBT-tailored program among SUD treatment facilities. Outpatient facilities that were for-profit, had pay assistance, had community outreach services, and provided telemedicine/telehealth were more likely to have an LGBT-tailored program. Those that were government-owned, in the Midwest, and that accepted Medicaid were less likely to have an LGBT-tailored program. Residential facilities that were in the West, for-profit, and had community outreach services were more likely to have an LGBT-tailored program. This study offers a national examination of the availability of LGBT-tailored programs in SUD treatment facilities. Differences in availability based on ownership, region, pay assistance, and outreach highlight potential gaps in treatment availability.

Background: Alcohol and cannabis use disorders are among the most prevalent substance use disorders in the United States. Both conditions are associated with mental health disorders. Most individuals with co-occurring mental health and substance use disorders who receive treatment encounter mental health treatment services. Objectives: This study examined national mental health treatment facility data over 10 years to describe persons with an alcohol or cannabis use disorder. Design: Annual cross-sectional data were used. Using a descriptive epidemiological approach, this study captured the “who,” “when,” and “where” regarding individuals with alcohol or cannabis use disorders receiving mental health treatment. Methods: Ten years of annual de-identified publicly available administrative data from the Mental Health Client-Level Data, a nationwide dataset based in the United States, were merged from 2013 to 2022. Sample selection criteria included having either an alcohol or cannabis use disorder diagnosis. These 2 samples were examined separately. Examined variables include year, region, treatment setting, age, sex, race and ethnicity, and co-occurring mental health disorder diagnoses. Results: The analytic sample was N = 3 947 802 cases, approximately 6.4% of the merged dataset before sample selection. This includes n = 2 315 686 cases with an alcohol use disorder and n = 1 632 116 cases with a cannabis use disorder. Both samples were majority male at 60%. Numerically, the year with the most alcohol use disorder cases in the dataset was 2020 (n = 278 550), and the year 2018 had the most cannabis use disorder cases in the dataset (n = 199 744). Depressive disorders were the most common co-occurring mental health disorder among the alcohol use disorder sample (n = 821 659; 35.5%) and the cannabis use disorder sample (n = 494 113; 30.3%). Conclusion: This descriptive study highlights the characteristics of individuals with alcohol and cannabis use disorders who received services from mental health treatment facilities during a 10-year period. Findings from this study provide a greater understanding of this population.