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 (...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01905-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849342093468631040 |
|---|---|
| author | Can Lu Shenwei Wan Zhiyong Liu |
| author_facet | Can Lu Shenwei Wan Zhiyong Liu |
| author_sort | Can Lu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9eea841d255f47c7811096bf6bbf1650 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-9eea841d255f47c7811096bf6bbf16502025-08-20T03:43:30ZengNature Portfolionpj Digital Medicine2398-63522025-08-018111210.1038/s41746-025-01905-7Determinants of depressive symptoms in multinational middle-aged and older adultsCan Lu0Shenwei Wan1Zhiyong Liu2School of Medicine and Health Management, Huazhong University of Science and TechnologySchool of Agricultural Economics and Rural Development, Renmin University of ChinaSchool of Medicine and Health Management, Huazhong University of Science and TechnologyAbstract 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.https://doi.org/10.1038/s41746-025-01905-7 |
| spellingShingle | Can Lu Shenwei Wan Zhiyong Liu Determinants of depressive symptoms in multinational middle-aged and older adults npj Digital Medicine |
| title | Determinants of depressive symptoms in multinational middle-aged and older adults |
| title_full | Determinants of depressive symptoms in multinational middle-aged and older adults |
| title_fullStr | Determinants of depressive symptoms in multinational middle-aged and older adults |
| title_full_unstemmed | Determinants of depressive symptoms in multinational middle-aged and older adults |
| title_short | Determinants of depressive symptoms in multinational middle-aged and older adults |
| title_sort | determinants of depressive symptoms in multinational middle aged and older adults |
| url | https://doi.org/10.1038/s41746-025-01905-7 |
| work_keys_str_mv | AT canlu determinantsofdepressivesymptomsinmultinationalmiddleagedandolderadults AT shenweiwan determinantsofdepressivesymptomsinmultinationalmiddleagedandolderadults AT zhiyongliu determinantsofdepressivesymptomsinmultinationalmiddleagedandolderadults |