SMU Data Science Review


Addiction and substance abuse disorder is a significant problem in the United States. Over the past two decades, the United States has faced a boom in substance abuse, which has resulted in an increase in death and disruption of families across the nation. The State of Ohio has been particularly hard hit by the crisis, with overdose rates nearly doubling the national average. Established in the mid 1970’s Sober Living Housing is an alcohol and substance use recovery model emphasizing personal responsibility, sober living, and community support. This model has been adopted by the Ohio Recovery Housing organization, which seeks to provide a safe and communal environment to assist in recovering individuals facing substance abuse disorder. As a result of the organization’s efforts, residents in the Ohio Recovery Housing program have seen increased rates of Recovery Capital scores and decreased occurrences of relapse and return to the criminal justice system. A key predictor of positive Ohio Recovery Housing resident outcomes is the length of the program stay. It is crucial that recovery housing residents continue with the program for a minimum of six months, as this time period is associated with reduced rates of recidivism, relapse, and interaction with the criminal justice system. This research explores the use of clustering techniques and predictive modeling to help inform potential risks and factors of treatment disruption before the critical six-month mark of recovery housing support.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License