Predictors of treatment attrition among individuals in substance use disorder treatment: A machine learning approach

IF 3.7 2区医学 Q1 PSYCHOLOGY, CLINICAL

Addictive behaviors Pub Date : 2025-01-25 DOI:10.1016/j.addbeh.2025.108265

Jill A. Rabinowitz , Jonathan L. Wells , Geoffrey Kahn , Jennifer D. Ellis , Justin C. Strickland , Martin Hochheimer , Andrew S. Huhn

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引用次数: 0

Abstract

Background

Early treatment discontinuation in substance use disorder treatment settings is common and often difficult to predict. We leveraged a machine learning approach (i.e., random forest) to identify individuals at risk for treatment attrition, and specific factors associated with treatment discontinuation.

Method

Participants (N = 29,809) were individuals ≥ 18 years who attended substance use disorder treatment facilities in the United States. Using random forest, we aimed to predict three outcomes (1) leaving against medical advice (AMA), (2) discharging involuntarily, and (3) discharging early for any reason. Predictors included participant demographics, substance use the month before and at intake, indices of mental and physical health, as well as treatment center and program type.

Findings

We observed low to moderate area under the curve (range = 0.631–0.671), high negative predictive values (range = 0.853–0.965), and low positive predictive values (0.088–0.336) across the three treatment attrition outcomes. The most robust predictors of the three outcomes included treatment center, treatment type, and participant age. Additional predictors of the three outcomes included employment status; reason for treatment; primary drug at intake and frequency of use; prescription opioid, benzodiazepine, or heroin use at intake; living status at intake; and driving under the influence prior to treatment.

Conclusions

Our models were able to accurately identify individuals who remained in treatment, but not those who left treatment prematurely. The most robust predictors of treatment discontinuation were treatment center and program type, suggesting that targeting treatment facility features may have a significant impact on reducing treatment attrition and improving long-term recovery.

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来源期刊

Addictive behaviors 医学-药物滥用

CiteScore

8.40

自引率

4.50%

发文量

283

审稿时长

46 days

期刊介绍： Addictive Behaviors is an international peer-reviewed journal publishing high quality human research on addictive behaviors and disorders since 1975. The journal accepts submissions of full-length papers and short communications on substance-related addictions such as the abuse of alcohol, drugs and nicotine, and behavioral addictions involving gambling and technology. We primarily publish behavioral and psychosocial research but our articles span the fields of psychology, sociology, psychiatry, epidemiology, social policy, medicine, pharmacology and neuroscience. While theoretical orientations are diverse, the emphasis of the journal is primarily empirical. That is, sound experimental design combined with valid, reliable assessment and evaluation procedures are a requisite for acceptance. However, innovative and empirically oriented case studies that might encourage new lines of inquiry are accepted as well. Studies that clearly contribute to current knowledge of etiology, prevention, social policy or treatment are given priority. Scholarly commentaries on topical issues, systematic reviews, and mini reviews are encouraged. We especially welcome multimedia papers that incorporate video or audio components to better display methodology or findings. Studies can also be submitted to Addictive Behaviors? companion title, the open access journal Addictive Behaviors Reports, which has a particular interest in ''non-traditional'', innovative and empirically-oriented research such as negative/null data papers, replication studies, case reports on novel treatments, and cross-cultural research.