Demographic, Social, and Behavioral Predictors of Substance Use During Medication-Assisted Treatment
Jack Samuel Womack, Manik Ahuja, Praveen FernandopulleIntroduction
Opioid Use Disorder (OUD) remains a critical public health issue in the United States. While Medications for Opioid Use Disorder (MOUD) are an effective and growing treatment option for OUD, use and integration into care remains low.
Objectives
This paper examines the effect of understudied demographic, social, and behavioral predictors on MOUD success using decrease in substance use level during MOUD as outcome, to better inform OUD policy and treatment matching.
Design
Cross-sectional study.
Methods
Using cross-sectional data from the nationally-representative 2023 Treatment Episode Data Set—Discharges (TEDS-D), we extracted all cases including MOUD use and indicating primary substance use level at both admission and discharge (n=173,992). Logistic regression analyses were run to test the association between decrease in primary substance use level from admission to discharge (outcome) and predictors organized in domains based on a modified Andersen-Gelberg Behavioral Model for Vulnerable Populations.
Results
This study found that older adults (AOR=1.61, 95% CI=1.54-1.67), veterans (AOR=2.02, 95% CI=1.80-2.25), and those with criminal justice involvement (AOR=1.84, 95% CI=1.72-1.98) have a significantly higher likelihood of substance use decrease during the course of MOUD. Those experiencing homelessness (AOR=0.79, 95% CI=0.76-0.83), those with co-occurring mental health diagnoses (AOR=0.79, 95% CI=0.77-0.81), and IV drug users (AOR=0.89, 95% CI=0.86-0.91) are significantly at-risk for poor MOUD outcomes.
Conclusion
Health systems and policymakers can use these predictors to develop targeted screening protocols, expand MOUD access in criminal justice and VA settings, and allocate intensive services to those at-risk for poor MAT outcomes. Future quality improvement initiatives and studies should validate these predictors to ensure quality of care.