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

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.