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.
Keywords: Bayesian analysis; against medical advice; machine learning; substance misuse.