Journal: International Journal of Environmental Research & Public Health, 2023, doi: 10.3390/ijerph20042829
Authors: Christopher J. Rogers, Myriam Forster, Steven Sussman, Jane Steinberg, Jessica L. Barrington-Trimis, Timothy J. Grigsby & Jennifer B. Unger
Abstract:
Adverse childhood experiences (ACE) have a strong association with alcohol and drug use; however, more research is needed to identify protective factors for this association. The present study assesses the longitudinal impact of ACE on problematic alcohol and drug use and the potential moderating effect of perceived social support. Data (n = 1404) are from a sample of Hispanic youth surveyed in high school through young adulthood. Linear growth curve models assessed the effect of ACE and perceived social support over time on problematic alcohol and drug use. Results indicated youth with ACE (vs. those without ACE) report more problematic alcohol and drug use in adolescence and have increased rates into young adulthood. Additionally, findings suggest that social support in high school may moderate the effects of ACE on problematic use over time. Among youth with high levels of support, the association of ACE with problematic alcohol and drug use was diminished. Although ACE can have a persistent impact on problematic alcohol and drug use from adolescence into adulthood, high social support during adolescence may mitigate the negative effects of ACE, lowering early problematic alcohol and drug use, offering the potential for lasting benefits.
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Journal: Preventive Medicine Reports, 2023, doi: 10.1016/j.pmedr.2023.102148
Authors: Nkiruka C. Atuegwu, Eric M. Mortensen, Suchitra Krishnan-Sarin, Reinhard C. Laubenbacher & Mark D. Litt
Abstract:
The use of electronic nicotine delivery systems (ENDS) is increasing among young adults. However, there are few studies regarding predictors of ENDS initiation in tobacco-naive young adults. Identifying the risk and protective factors of ENDS initiation that are specific to tobacco-naive young adults will enable the creation of targeted policies and prevention programs. This study used machine learning (ML) to create predictive models, identify risk and protective factors for ENDS initiation for tobacco-naive young adults, and the relationship between these predictors and the prediction of ENDS initiation. We used nationally representative data of tobacco-naive young adults in the U.S drawn from the Population Assessment of Tobacco and Health (PATH) longitudinal cohort survey. Respondents were young adults (18-24 years) who had never used any tobacco products in Wave 4 and who completed Waves 4 and 5 interviews. ML techniques were used to create models and determine predictors at 1-year follow-up from Wave 4 data. Among the 2,746 tobacco-naive young adults at baseline, 309 initiated ENDS use at 1-year follow-up. The top five prospective predictors of ENDS initiation were susceptibility to ENDS, increased days of physical exercise specifically designed to strengthen muscles, frequency of social media use, marijuana use and susceptibility to cigarettes. This study identified previously unreported and emerging predictors of ENDS initiation that warrant further investigation and provided comprehensive information on the predictors of ENDS initiation. Furthermore, this study showed that ML is a promising technique that can aid ENDS monitoring and prevention programs.
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Journal: Experimental Neurology, 2023, doi: 10.1016/j.expneurol.2023.114318
Authors: Siara Kate Rouzer, Jessica Gutierrez, Kirill V. Larin & Rajesh C. Miranda
Abstract:
Alcohol and marijuana are two of the most consumed psychoactive substances by pregnant people, and independently, both substances have been associated with lifelong impacts on fetal neurodevelopment. Importantly, individuals of child-bearing age are increasingly engaging in simultaneous alcohol and cannabinoid (SAC) use, which amplifies each drug’s pharmacodynamic effects and increases craving for both substances. However, to date, investigations of prenatal polysubstance use are notably limited in both human and non-human populations. In this review paper, we will address what is currently known about combined exposure to these substances, both directly and prenatally, and identify shared prenatal targets from single-exposure paradigms that may highlight susceptible neurobiological mechanisms for future investigation and therapeutic intervention. Finally, we conclude this manuscript by discussing factors that we feel are essential in the consideration and experimental design of future preclinical SAC studies.
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Journal: Addiction Science & Clinical Practice, 2023, doi: 10.1186/s13722-023-00363-4
Authors: Belén Hervera, Grace Seo, Tyler S. Bartholomew, Teresa A. Chueng, Edward Suarez, David W. Forrest, Salma Hernandez, … David P. Serota
Abstract:
Background: Hospitalizations for severe injection drug use-related infections (SIRIs) are characterized by high costs, frequent patient-directed discharge, and high readmission rates. Beyond the health system impacts, these admissions can be traumatizing to people who inject drugs (PWID), who often receive inadequate treatment for their substance use disorders (SUD). The Jackson SIRI team was developed as an integrated infectious disease/SUD treatment intervention for patients hospitalized at a public safety-net hospital in Miami, Florida in 2020. We conducted a qualitative study to identify patient- and clinician-level perceived implementation barriers and facilitators to the SIRI team intervention.
Methods: Participants were patients with history of SIRIs (n = 7) and healthcare clinicians (n = 8) at one implementing hospital (Jackson Memorial Hospital). Semi-structured qualitative interviews were performed with a guide created using the Consolidated Framework for Implementation Research (CFIR). Interviews were transcribed, double coded, and categorized by study team members using CFIR constructs.
Results: Implementation barriers to the SIRI team intervention identified by participants included: (1) complexity of the SIRI team intervention; (2) lack of resources for PWID experiencing homelessness, financial insecurity, and uninsured status; (3) clinician-level stigma and lack of knowledge around addiction and medications for opioid use disorder (OUD); and (4) concerns about underinvestment in the intervention. Implementation facilitators of the intervention included: (1) a non-judgmental, harm reduction-oriented approach; (2) the team’s advocacy for PWID as a means of institutional culture change; (3) provision of close post-hospital follow-up that is often inaccessible for PWID; (4) strong communication with patients and their hospital physicians; and (5) addressing diverse needs such as housing, insurance, and psychological wellbeing.
Conclusion: Integration of infectious disease and SUD treatment is a promising approach to managing patients with SIRIs. Implementation success depends on institutional buy-in, holistic care beyond the medical domain, and an ethos rooted in harm reduction across multilevel (inner and outer) implementation contexts.
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Journal: Biomolecules, 2023, doi: 10.3390/biom13020387
Authors: Kristy A. Carpenter & Russ B. Altman
Abstract:
Drug abuse is a serious problem in the United States, with over 90,000 drug overdose deaths nationally in 2020. A key step in combating drug abuse is detecting, monitoring, and characterizing its trends over time and location, also known as pharmacovigilance. While federal reporting systems accomplish this to a degree, they often have high latency and incomplete coverage. Social-media-based pharmacovigilance has zero latency, is easily accessible and unfiltered, and benefits from drug users being willing to share their experiences online pseudo-anonymously. However, unlike highly structured official data sources, social media text is rife with misspellings and slang, making automated analysis difficult. Generative Pretrained Transformer 3 (GPT-3) is a large autoregressive language model specialized for few-shot learning that was trained on text from the entire internet. We demonstrate that GPT-3 can be used to generate slang and common misspellings of terms for drugs of abuse. We repeatedly queried GPT-3 for synonyms of drugs of abuse and filtered the generated terms using automated Google searches and cross-references to known drug names. When generated terms for alprazolam were manually labeled, we found that our method produced 269 synonyms for alprazolam, 221 of which were new discoveries not included in an existing drug lexicon for social media. We repeated this process for 98 drugs of abuse, of which 22 are widely-discussed drugs of abuse, building a lexicon of colloquial drug synonyms that can be used for pharmacovigilance on social media.
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