Exploring predictors of substance use disorder treatment engagement with machine learning: The impact of social determinants of health in the therapeutic landscape.

Autor: Eddie D; Recovery Research Institute, Center for Addiction Medicine, Massachusetts General Hospital, USA; Department of Psychiatry, Harvard Medical School, USA. Electronic address: deddie@mgh.harvard.edu., Prindle J; Suzanne Dworak-Peck School of Social Work, University of Southern California, USA., Somodi P; Viterbi School of Engineering, Computer Science, University of Southern California, USA., Gerstmann I; Viterbi School of Engineering, Computer Science, University of Southern California, USA., Dilkina B; Viterbi School of Engineering, Computer Science, University of Southern California, USA., Saba SK; Suzanne Dworak-Peck School of Social Work, University of Southern California, USA., DiGuiseppi G; Suzanne Dworak-Peck School of Social Work, University of Southern California, USA., Dennis M; Lighthouse Institute, Chestnut Health Systems, Normal, IL, USA., Davis JP; RAND Corporation, USA.
Jazyk: angličtina
Zdroj: Journal of substance use and addiction treatment [J Subst Use Addict Treat] 2024 Sep; Vol. 164, pp. 209435. Date of Electronic Publication: 2024 Jun 08.
DOI: 10.1016/j.josat.2024.209435
Abstrakt: Background: Improved knowledge of factors that influence treatment engagement could help treatment providers and systems better engage patients. The present study used machine learning to explore associations between individual- and neighborhood-level factors, and SUD treatment engagement.
Methods: This was a secondary analysis of the Global Appraisal of Individual Needs (GAIN) dataset and United States Census Bureau data utilizing random forest machine learning and generalized linear mixed modelling. Our sample (N = 15,873) included all people entering SUD treatment at GAIN sites from 2006 to 2012. Predictors included an array of demographic, psychosocial, treatment-specific, and clinical measures, as well as environment-level measures for the neighborhood in which patients received treatment.
Results: Greater odds of treatment engagement were predicted by adolescent age and psychiatric comorbidity, and at the neighborhood-level, by low unemployment and high population density. Lower odds of treatment engagement were predicted by Black/African American race, and at the neighborhood-level by high rate of public assistance and high income inequality. Regardless of the degree of treatment engagement, individuals receiving treatment in areas with high unemployment, alcohol sale outlet concentration, and poverty had greater substance use and related problems at baseline. Although these differences reduced with treatment and over time, disparities remained.
Conclusions: Neighborhood-level factors appear to play an important role in SUD treatment engagement. Regardless of whether individuals engage with treatment, greater loading on social determinants of health such as unemployment, alcohol sale outlet density, and poverty in the therapeutic landscape are associated with worse SUD treatment outcomes.
Competing Interests: Declaration of competing interest David Eddie is on the scientific advisory boards of mental-healthcare companies ViviHealth and Innerworld and is a partner in Peer Recovery Consultants. The remaining authors declare that they have no known potential competing financial or personal interests.
(Copyright © 2024 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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