An analysis of factors influencing dropout in methadone maintenance treatment program in Dehong Prefecture of China based on Cox regression and decision tree modelling

Abstract

Background

The high dropout rate among Methadone Maintenance Treatment (MMT) patients poses a significant challenge to drug dependence treatment programs, especially in regions with prevalent drug use and HIV transmission risks. This study aimed to analyze factors of dropout in MMT clinics over an 18-year period in Dehong Prefecture, Yunnan Province, China.

Methods

A retrospective cohort study was conducted using data from China’s HIV/AIDS Comprehensive Response Information Management System (CRIMS). Participants included individuals who enrolled in MMT between June 2005 and December 2023 and completed baseline surveys. Cox proportional hazards regression identified independent predictors, while decision tree modeling (CART algorithm) captured variable interactions. The decision tree employed Gini impurity minimization, a 70:30 training-test split, and pruning to prioritize factors like treatment duration and urine test results.

Results

The study included 9,435 MMT participants, with a male-to-female ratio of 26:1 (9,086 males and 349 females). The median duration of treatment was 12.2 months (ranging from 2.7 to 43.9 months), with a minimum of 1 day and a maximum of 217 months. From 2005 to 2023, the cumulative dropout rate among MMT patients in Dehong Prefecture reached 89.6% (8,458/9,435), with an incidence rate of 34.75 dropouts per 100 person-years over 24,354.98 person-years of follow-up. The Cox proportional hazards regression identified that participants with occupations as farmers (AHR = 1.52, 95% CI: 1.41–1.62) or positive urine test results (AHR = 2.47, 95% CI: 2.35–2.59) exhibited significantly higher dropout risks. Protective factors included enrollment age > 35 years (AHR = 0.86), being married (AHR = 0.81), higher education levels (AHR = 0.94), good family relationships (AHR = 0.30), and methadone doses > 60 ml/day (AHR = 0.60). The decision tree model prioritized treatment duration as the root node, followed by urine test results, family relationships, education level, and methadone dosage. Patients with ≤ 12 months of treatment and positive urine tests faced the highest dropout probability (98.9%), while those with > 12 months of treatment but poor family relationships and doses ≤ 60 ml showed intermediate risks (82.3%).

Conclusion

Between 2005 and 2023, the dropout rate among MMT patients in Dehong Prefecture was relatively high, driven by modifiable factors (low methadone doses, positive urine tests) and contextual hierarchies (early-phase treatment duration). By integrating Cox regression and decision trees, we advance both epidemiological risk assessment and precision intervention design. Policymakers should prioritize dose optimization and targeted monitoring for high-risk subgroups (e.g., patients ≤ 12 months with concurrent drug use) to improve retention in resource-limited settings.