Applied machine learning analysis: Factors correlated with injection drug use and post-prison medication for opioid use disorder treatment engagement

Abstract

Abstract Objectives This study aimed to classify the factors that were correlated with injection drug use (IDU) and with medications for opioid use disorder (MOUD) treatment engagement among individuals who were recently released from prison. Methods Data for this study were obtained from a Midwestern reentry program for incarcerated individuals with co-occurring opioid use and a mental health disorder between May 1, 2017, and April 30, 2020. CHAID decision tree modeling was utilized to classify IDU and MOUD treatment engagement. Results Those most likely to report IDU were individuals with a Hepatitis C diagnosis and a history of overdose, and those least likely to report IDU were not diagnosed with Hepatitis C, identified as a person of color, and never overdosed on opioids. The subgroup of that were most likely to report MOUD treatment engagement were individuals taking psychiatric medication and who had a history of IDU. The subgroup of participants least likely to report MOUD treatment engagement were individuals prescribed psychiatric medication, without had a history of IDU, and were not participating in substance-use treatment. Conclusion Our findings indicate that, to protect vulnerable populations and to flatten the overdose mortality curve, an increased focus is required within criminal/legal systems to facilitate linkages to care at reentry.