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 and decision tree models were used to identify factors influencing treatment dropout. 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. The results showed that the dropout rate among MMT patients in Dehong Prefecture, Yunnan Province, from 2005 to 2023 was 89.6% (8,458/9,435). Cox regression analysis indicated that factors significantly associated with higher dropout risk included being a farmer (AHR = 1.52) and a positive urine test (AHR = 2.47), while lower dropout risks were associated with enrollment age over 35 years, being married, a higher education level, relatively good family relationships, and a daily methadone dose > 60 ml. In the decision tree model, the treatment duration was the root node, followed by recent urine test results, family relationships, education level, and methadone dosage. Conclusion Between 2005 and 2023, the dropout rate among MMT patients in Dehong Prefecture, Yunnan Province, was relatively high. Traditional risk factors (including age, education level, being marital stutas, and occupation) and potentially modifiable risk factors (including the most recent urine test, relationship with family, and average daily dose) have a significant impact on the increased risk of dropout in MMT. Targeted interventions, such as increasing methadone dosage, enhancing support for patients with positive urine tests, and involving family members in treatment, could improve MMT retention. The use of decision tree models allows for personalized strategies, facilitating better management of dropout risks in high-risk MMT populations.