Mutual Help Group Participation for Alcohol and Drug Problems: Uncovering Latent Subgroups.
Audrey Hang HaiSehun OhCatalina Lopez-QuinteroChristina S LeeJohn F KellyMichael G VaughnChristopher P Salas-WrightPublished in: Substance use & misuse (2022)
<i>Objective</i>: This report aims to identify US mutual help group (MHG) participants' psycho-socio-behavioral profiles. <i>Method</i>: We used data from the 2015-2018 National Survey on Drug Use and Health and the sample included 1022 adults with past-year substance use disorders (SUD). We conducted a latent class analysis to identify subgroups of MHG participants and estimated multinomial logistic regression models to examine the associations between sociodemographic/intrapersonal characteristics and class membership. <i>Results</i>: Analyses identified three latent classes. Class 1 (<i>Low-Risk group</i>, 54%) reported low risks in all correlates except for serious psychological distress (SPD, 33%). Class 2 (<i>Psychological Distress group</i>, 30%) demonstrated high risks of major depressive episodes (86%) and SPD (93%). Class 3 (<i>Criminal Justice System Involvement group</i>, 16%) showed high involvement in arrests (100%) and drug-related arrests (67%) and moderate risks for SPD (54%) and behavioral problems, e.g., drug selling (46%) and theft (35%). Compared to Class 1, Class 2 was more likely to be female, out of the labor force, and to show high risk propensity, and Class 3 was more likely to have lower education and drug use disorders. Class 3 was also less likely to be older, belong to the "other" racial/ethnic category, have lower English proficiency, and report alcohol use disorder. <i>Conclusions</i>: The three subgroups of the US MHG participant population illustrate the complex and heterogeneous psycho-social-behavioral profiles of MHG participants with SUD. MHG referral's effectiveness may be augmented by tailoring it to the patient/client's specific psycho-socio-behavioral profile.
Keyphrases
- mental health
- healthcare
- randomized controlled trial
- human health
- systematic review
- emergency department
- public health
- risk assessment
- case report
- machine learning
- bipolar disorder
- single molecule
- big data
- quality improvement
- artificial intelligence
- adverse drug
- deep learning
- climate change
- middle aged
- cardiopulmonary resuscitation