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<p>Depression poses a significant challenge to global healthy aging, with factors predicting depressive symptoms among the elderly evolving alongside societal and economic development. This study used data from the China Health and Retirement Longitudinal Study (2011, 2015, 2020), employing a Random Forest Model to assess the importance of factors in predicting depressive symptoms and their changes over time, and logistic regression to estimate the direction and magnitude of these associations. Based on these analyses, findings revealed that (1) the prevalence of depressive symptoms remained relatively stable over time, with no significant trends observed between 2011 (31.5%), 2015 (28.3%), and 2020 (30.4%). (2) The importance of demographic (-0.0280), economic (-0.0304), and objective health (-0.1911) factors on depressive symptoms declined over the study period, whereas the contributions of cognitive (+ 0.0571), subjective health (+ 0.0661), lifestyle (+ 0.0947), and family support (+ 0.0398) factors increased significantly. (3) Pain, sleep quality, activities of daily living, marital status, child contact, and residence remained enduring key factors, while cognitive scores, self-rated health, social activities, internet access, and physical activity emerged as increasingly important factors. These findings highlight the evolving nature of key factors predicting depressive symptoms in the elderly. Future policies should prioritize subjective health, lifestyle, and family factors, along with enduring and increasing key factors like self-rated health, marital status, physical activity, and internet access, to optimize public health resources and support healthy aging.</p>

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The ten-year evolution of factors predicting depressive symptoms among the Chinese elderly: an analysis based on the random forest model

  • 期刊论文

  • 发表日期：

    2026年1月6日

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  • Jun Wang,
  • Shufeng Chen,
  • Yiwen Wang,
  • Guoxiao Sun

摘要

Depression poses a significant challenge to global healthy aging, with factors predicting depressive symptoms among the elderly evolving alongside societal and economic development. This study used data from the China Health and Retirement Longitudinal Study (2011, 2015, 2020), employing a Random Forest Model to assess the importance of factors in predicting depressive symptoms and their changes over time, and logistic regression to estimate the direction and magnitude of these associations. Based on these analyses, findings revealed that (1) the prevalence of depressive symptoms remained relatively stable over time, with no significant trends observed between 2011 (31.5%), 2015 (28.3%), and 2020 (30.4%). (2) The importance of demographic (-0.0280), economic (-0.0304), and objective health (-0.1911) factors on depressive symptoms declined over the study period, whereas the contributions of cognitive (+ 0.0571), subjective health (+ 0.0661), lifestyle (+ 0.0947), and family support (+ 0.0398) factors increased significantly. (3) Pain, sleep quality, activities of daily living, marital status, child contact, and residence remained enduring key factors, while cognitive scores, self-rated health, social activities, internet access, and physical activity emerged as increasingly important factors. These findings highlight the evolving nature of key factors predicting depressive symptoms in the elderly. Future policies should prioritize subjective health, lifestyle, and family factors, along with enduring and increasing key factors like self-rated health, marital status, physical activity, and internet access, to optimize public health resources and support healthy aging.