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

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.

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