Determinants of depressive symptoms in multinational middle-aged and older adults.

Journal: NPJ digital medicine
Published Date: Aug 4, 2025

Abstract

This study harnesses machine learning to dissect the complex socioeconomic determinants of depression risk among older adults across five international cohorts (HRS, ELSA, SHARE, CHARLS, MHAS). Evaluating six predictive algorithms, XGBoost demonstrated superior performance in four cohorts (AUC 0.7677-0.8771), while LightGBM excelled in ELSA (AUC 0.9011). SHAP analyses identified self-rated health as the predominant predictor, though key factors varied notably-gender was especially influential in MHAS. Stratified analyses by income and sex revealed marked heterogeneity: wealth, employment, digital inclusion, and marital status exerted greater influence in lower-income groups, with distinct gender-specific patterns. These findings highlight machine learning's capacity to reveal nuanced, context-dependent risk profiles beyond traditional models, emphasizing the need for tailored interventions that address the diverse vulnerabilities of aging populations, particularly those socioeconomically disadvantaged.

Authors

  • Can Lu
    School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Shenwei Wan
    School of Agricultural Economics and Rural Development, Renmin University of China, Beijing, China.
  • Zhiyong Liu
    State Key Laboratory of Respiratory Disease , Guangzhou Institutes of Biomedicine and Health (GIBH) , Chinese Academy of Sciences (CAS) , Guangzhou-510530 , China . Email: zhang_tianyu@gibh.ac.cn ; ; Tel: (+86)20 3201 5270.

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