Journal: Drug and Alcohol Dependence Reports, 2024, doi: 10.1016/j.dadr.2023.100214
Authors: Mehdi Farokhnia, Julia C. Harris, Shannon N. Speed, Lorenzo Leggio, & Renee M. Johnson
Abstract:
Background: Early use of alcohol and cannabis is associated with health and social problems. It is unclear how lifetime use changes for each additional year of age during adolescence, and whether this change varies by sex and race/ethnicity. This study characterized lifetime rates of alcohol and cannabis use by age among 12- to 17-year-old American youth and explored differential patterns by sex and race/ethnicity.
Methods: Data were obtained from the 2019 National Survey on Drug Use and Health. Analyses were restricted to 12-17-year-olds who were non-Hispanic White, non-Hispanic Black, or Hispanic/Latino (n = 11,830). We estimated the increase in lifetime use of alcohol and cannabis by age for the full sample and stratified by sex and race/ethnicity. Slopes of the regression lines were compared to assess differential patterns across groups.
Results: In these cross-sectional analyses, reported lifetime use increased substantially from age 12 to 17 for alcohol (6.4 % to 53.2 %) and cannabis (1.3 % to 35.9 %). The increase in lifetime alcohol use was slightly, but not significantly, steeper among girls than boys (F1,8 = 3.40, p = 0.09). White and Latino youth showed similar rates of increase in lifetime alcohol use, which was significantly flatter among Black youth (F2,12=21.26, p<0.0001). Latino youth had a slightly, but not significantly, steeper increase in lifetime cannabis use than White and Black youth (F2,12=3.17, p = 0.07).
Conclusions: Reports of lifetime alcohol and cannabis use substantially increase from age 12 to 17 and the rates are different according to sex and race/ethnicity, highlighting the need for early and tailored substance use prevention in adolescents.
To read the full text of the article, please visit the publisher’s website.
Journal: Addictive Behaviors, 2024, doi: 10.1016/j.addbeh.2024.107953
Authors: Audrey Hang Hai, Catalina Lopez-Quintero, Amanda Elton, Laura Curran, & Ai Bo
Abstract:
Aim: While the United States is becoming increasingly Multiracial, much is still unknown about the behavioral health of these growing new generations of Multiracial Americans. To narrow this research gap, this study investigated the prevalence/frequency of substance use and major depressive episodes [MDE] among non-Hispanic Multiracial [NHM] adolescents compared to their non-Hispanic White [NHW] counterparts and whether racial differences vary by socioeconomic status.
Methods: We analyzed data from the 2015–2019 National Survey on Drug Use and Health (N = 3,645 NHM and 34,776 NHW adolescents aged 12–17). Average Marginal Effects derived from logistic regression and negative binomial regression were used to examine (1) differences in six outcomes (past-month use of alcohol, cannabis, or drugs other than cannabis [DOTC], past-year MDE, and the frequency of alcohol and cannabis use among past-month users) by Multiracial status; (2) the moderation effect of family income on these associations.
Results: Compared to high-income NHW adolescents, high-income NHM adolescents reported significantly higher prevalence of past-month cannabis and DOTC use, and past-year MDE. No racial differences were observed at other income levels. Furthermore, moderation analyses indicated that the effect of Multiracial status on MDE was larger in the highest income group compared to the lowest income group.
Conclusion: Our findings suggested that NHM adolescents, particularly those from high income families, exhibit increased prevalence of drug use and depression than NHW adolescents. As the US becomes more diverse, there is a need to further examine the social and structural factors driving the identified racial differences.
To read the full text of the article, please visit the publisher’s website.
Journal: Addiction Science & Clinical Practice, 2024, doi: 10.1186/s13722-024-00435-z
Authors: K. Michelle Peavy, Angela Klipsch, Christina S. Soma, Brian Pace, Zac E. Imel, Michael J. Tanana, … David C. Atkins
Abstract:
Background: The opioid epidemic has resulted in expanded substance use treatment services and strained the clinical workforce serving people with opioid use disorder. Focusing on evidence-based counseling practices like motivational interviewing may be of interest to counselors and their supervisors, but time-intensive adherence tasks like recording and feedback are aspirational in busy community-based opioid treatment programs. The need to improve and systematize clinical training and supervision might be addressed by the growing field of machine learning and natural language-based technology, which can promote counseling skill via self- and supervisor-monitoring of counseling session recordings.
Methods: Counselors in an opioid treatment program were provided with an opportunity to use an artificial intelligence based, HIPAA compliant recording and supervision platform (Lyssn.io) to record counseling sessions. We then conducted four focus groups-two with counselors and two with supervisors-to understand the integration of technology with practice and supervision. Questions centered on the acceptability of the clinical supervision software and its potential in an OTP setting; we conducted a thematic coding of the responses.
Results: The clinical supervision software was experienced by counselors and clinical supervisors as beneficial to counselor training, professional development, and clinical supervision. Focus group participants reported that the clinical supervision software could help counselors learn and improve motivational interviewing skills. Counselors said that using the technology highlights the value of counseling encounters (versus paperwork). Clinical supervisors noted that the clinical supervision software could help meet national clinical supervision guidelines and local requirements. Counselors and clinical supervisors alike talked about some of the potential challenges of requiring session recording.
Conclusions: Implementing evidence-based counseling practices can help the population served in OTPs; another benefit of focusing on clinical skills is to emphasize and hold up counselors’ roles as worthy. Machine learning technology can have a positive impact on clinical practices among counselors and clinical supervisors in opioid treatment programs, settings whose clinical workforce continues to be challenged by the opioid epidemic. Using technology to focus on clinical skill building may enhance counselors’ and clinical supervisors’ overall experiences in their places of work.
To read the full text of the article, please visit the publisher’s website.
Journal: Drug and Alcohol Dependence Reports, 2024, doi: 10.1016/j.dadr.2023.100216
Authors: Carol Vidal, Kevin M. Simon, Caroline Brooks, Jacob White, & Jesse D. Hinckley
Abstract:
Given the risks to mental health associated with cannabis use in youth and the increase in cannabis legalization worldwide and in the U.S., there is a need to understand existing evidence-based approaches to integrated management of psychiatric disorders in youth who use cannabis. This systematic review aimed to appraise the current evidence on integrated treatment for adolescents and young adults with common psychiatric disorders who engage in regular cannabis use. A total of 989 studies were screened for inclusion. Study’s titles and abstracts were screened and advanced to full text review for further screening by two independent reviewers. Thirty-five full-text articles were reviewed, with five articles ultimately meeting all criteria for inclusion. Five randomized controlled trials examined the effects of therapeutic interventions in youth with common psychiatric disorders who used cannabis, including two studies on depression, one on bipolar disorder, one on anxiety and one on PTSD were reviewed. No studies were considered high in risk of bias. Overall, there is a paucity of research on the treatment of comorbid adolescent mental health disorders and cannabis use, which limits the ability to draw evidence-based treatment recommendations.
To read the full text of the article, please visit the publisher’s website.
Journal: Translational Psychiatry, 2024, doi: 10.1038/s41398-024-02756-8
Authors: Khushbu Agarwal, Paule V. Joseph, Rui Zhang, Melanie L. Schwandt, Vijay A. Ramchandani, Nancy Diazgranados, … Reza Momenan
Abstract:
Early life stress (ELS) significantly increases susceptibility to alcohol use disorder (AUD) by affecting the interplay between the executive and the salience networks (SNs). The link between AUD and higher body-mass index (BMI) is known, but we lack understanding of how BMI impacts the relationship between ELS and brain connectivity in individuals with AUD. To bridge this gap, we investigated the main and interaction effects of ELS and BMI on brain connectivity in individuals with AUD compared to non-AUD participants (n = 77 sex-matched individuals per group). All participants underwent resting-state functional magnetic resonance imaging, revealing intriguing positive functional connectivity between SN seeds and brain regions involved in somatosensory processing, motor coordination and executive control. Examining the relationship of brain connectivity with ELS and BMI, we observed positive associations with the correlations of SN seeds, right anterior insula (RAIns) and supramarginal gyrus (SMG) with clusters in motor [occipital cortex, supplementary motor cortex]; anterior cingulate cortex (ACC) with clusters in frontal, or executive, control regions (middle frontal gyrus; MFG, precentral gyrus) that reportedly are involved in processing of emotionally salient stimuli (all |β | > 0.001, |p | < 0.05). Interestingly, a negative association of the interaction effect of ELS events and BMI measures with the functional connectivity of SN seeds ACC with decision-making (MFG, precentral gyrus), RAIns and RSMG with visuo-motor control regions (occipital cortex and supplementary motor cortex) (all |β | = -0.001, |p | < 0.05). These findings emphasize the moderating effect of BMI on ELS-associated SN seed brain connectivity in AUD. Understanding the neural mechanisms linking BMI, ELS and AUD can guide targeted interventions for this population.
To read the full text of the article, please visit the publisher’s website.