September 10, 2025npj Digital MedicineOpen Access

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

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Authors

CLCan LuHuazhong University of Science and Technology SWShenwei Wan XLXiaogang LiJiangsu University

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Implication

Machine learning reveals varied socioeconomic factors influencing depressive symptoms in multinational older adult cohorts, indicating tailored solutions are vital.

Key Points

XGBoost and LightGBM effectively predicted depression risk in various older adult cohorts, showing AUC scores of up to 0.9011.
Self-rated health emerged as a primary predictor, with variations influenced by gender, notably in the MHAS cohort.
Stratified analyses highlighted heterogeneity in socioeconomic factors like wealth and employment affecting depression risk in lower-income groups.
These findings emphasize the potential of machine learning to identify specific vulnerabilities, supporting tailored interventions for socioeconomically disadvantaged aging populations.

Cite This Study

Lu et al. (2025) studied this question.

synapsesocial.com/papers/68c240deb210217d6479cba4 https://doi.org/10.1038/s41746-025-01905-7

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Implication

Machine learning reveals varied socioeconomic factors influencing depressive symptoms in multinational older adult cohorts, indicating tailored solutions are vital.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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Also Consider