| Study on medication adherence factors among patients with severe mental disorders in Zhuhai city based on XGBoost model
Ye Zhongshu1,2, Teng Yongyong1,2, Quan Jingju1,2, Sun Yajun1,2, Huang Jiaju1,2,
Wu Yixuan1,2, Han Changlin1,2, Zhang Guangchuan1,2*
(1.The Third People′s Hospital of Zhuhai, Zhuhai 519000, China;
2.Zhuhai?Engineering?Technology?Research?Center?of?Occupational and Mental Health, Zhuhai 519000, China
*Corresponding author: Zhang Guangchuan, E-mail: jamie_chuan@126.com)
【Abstract】 Background Low medication adherence in patients with severe mental disorders (SMDs) increases the disease burden on patients' families and society. Medication adherence is influenced by numerous factors, and traditional methods (such as Logistic regression) struggle to quantify the importance of these factors. Extreme gradient boosting (XGBoost) combined with Shapley's additivity explanation (SHAP) is less commonly used in this field.. Objective To explore the factors influencing medication adherence among SMDs patients in Zhuhai and provide a reference for optimizing patient management strategies. Methods A cross-sectional study was conducted using data from SMD patients managed by the Zhuhai Mental Health System Platform between January 1, 2023, and March 31, 2025. A total of 9,329 patients diagnosed with six types of SMDs (including schizophrenia and bipolar disorder) were included. Influencing factors were screened using univariate analysis and multivariate logistic regression analysis,, and an XGBoost model combined with the SHAP algorithm was constructed to quantify the importance of each influencing factor. Results Among 9,329 patients, 8,446 demonstrated medication adherence, yielding an adherence rate of 90.53%. Multivariable analysis identified several risk factors significantly associated with medication non-adherence: being unmarried (OR=1.237, 95% CI: 1.019~1.502) or divorced (OR=1.389, 95% CI: 1.038~1.832); a diagnosis of mental retardation with psychiatric disorders (OR=3.025, 95% CI: 2.402~3.796) or paranoid psychosis (OR=5.117, 95% CI: 3.086~8.299); a disease duration of 2~4 years (OR=1.355, 95% CI: 1.085~1.696), 4~6 years (OR=2.143, 95% CI: 1.671~2.747), or >6 years (OR=1.681, 95% CI: 1.365~2.079); lack of guardian subsidies (OR=1.412, 95% CI: 1.099~1.801); absence of a disability certificate (OR=1.900, 95% CI: 1.588~2.282); not being enrolled in care and support groups (OR=1.384, 95% CI: 1.183~1.617) or community services (OR=1.313, 95% CI: 1.042~1.645); and not cohabiting with a guardian (OR=1.257, 95% CI: 1.048~1.501). Conversely, the enrollment in special outpatient disease programs (OR=0.716, 95% CI: 0.609~0.842) and a family history of mental illness (OR=0.713, 95% CI: 0.503~0.982) were identified as protective factors.The XGBoost model exhibited robust predictive performance, with a sensitivity of 0.433, specificity of 0.944, accuracy of 0.891, an Area Under the Curve (AUC) of 0.837, and an F1 score of 0.449. Feature importance ranking indicated that the three most influential predictors were disease duration, clinical diagnosis, and the acquisition of disability certificates. Conclusion Policy-based support (acquisition of disability certificates, special outpatient disease enrollment) and clinical disease characteristics (disease duration, diagnosis type) are key factors affecting medication adherence among patients with severe mental disorders in Zhuhai City. [Funded by Zhuhai Medical Research Project (number, 2220009000281)] |