Demographic Predictors of Dropping Out of Treatment (DOT) in Substance Use Disorder Treatment. (27th May 2021)

• Record Type: Journal Article
• Authors: Hanauer, Matthew; Sielbeck-Mathes, Kathryn; Banks, Bre; Mitori, Jessica; Reuveny, Adi
• Is Part Of: Substance use & misuse. Volume 56:Number 8(2021)
• Page Start: 1155
• Page End: 1160
• Publication Date: 2021-05-27
• Subjects: Bayesian analysis -- against medical advice -- substance misuse -- machine learning; Narcotic habit -- Periodicals; Alcoholism -- Periodicals; Substance abuse -- Periodicals; Behavior, Addictive -- Periodicals; Sustance-Related Disorders -- Periodicals; 362.2905
• DOI: 10.1080/10826084.2021.1910708
• Languages: English
• ISSNs: 1082-6084

Abstract

Abstract: Background: Researchers have not studied or used novel methods for identifying potential disparities for sexual minorities, those with criminal pasts, and veterans in (DOT). Methods: We used Bayesian logistic regression to identify factors associated with DOT, tested interaction effects, and used machine learning to classify qualitative responses. Findings: With 2, 772 clients from two inpatient clinics in the Southwest United States, we found sexual minorities and females had 52% and 61%, increases and African Americans had 54% decreases in the odds of DOT. Additionally, those with a criminal past and 34.5 and older were less likely to DOT by 5% relative to clients with no prior involvement in the criminal justice system. Conclusions: This study illustrated the disparities for women and sexual minorities in DOT as well as demonstrated novel methodological approaches to addressing previously unanswered questions.