Demographic Predictors of Dropping Out of Treatment (DOT) in Substance Use Disorder Treatment

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.

Keywords:

• Bayesian analysis
• against medical advice
• substance misuse
• machine learning

Declaration of interest

The authors declare that they have no conflict of interest. The authors alone are responsible for the content and writing of the article.

Additional information

Funding

This work was supported by American Addiction Centers.