Urban–rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models
BackgroundFalls among older adults are a significant challenge to global healthy aging. Identifying key factors and differences in fall risks, along with developing predictive models, is essential for differentiated and precise interventions in China’s urban and rural older populations.MethodsThe da...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1597853/full |
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| author | LiHan Lin LiHan Lin XiaoYang Liu CaiHua Cai YiKun Zheng Delong Li GuoPeng Hu |
| author_facet | LiHan Lin LiHan Lin XiaoYang Liu CaiHua Cai YiKun Zheng Delong Li GuoPeng Hu |
| author_sort | LiHan Lin |
| collection | DOAJ |
| description | BackgroundFalls among older adults are a significant challenge to global healthy aging. Identifying key factors and differences in fall risks, along with developing predictive models, is essential for differentiated and precise interventions in China’s urban and rural older populations.MethodsThe data of 5,876 older adults were obtained from the China Health and Retirement Longitudinal Survey (Waves 2015 and 2018). A total of 87 baseline input variables were considered as candidate features. Predictive models for fall risk over the next 3 years among urban and rural older populations were developed using five machine learning algorithms. Logistic regression analysis was employed to identify key factors influencing falls in these populations.ResultsThe fall incidence among older adults was 22.4%, with 23.2% in rural areas and 20.9% in urban areas. Common risk factors across both settings include gender, age, fall history, sleep duration, activities of daily living questionnaire scores, memory status, and chair stand test time. In rural areas, additional risks include being unmarried, having diabetes, heart disease, memory-related medication use, and living in houses built 6–20 years ago. For urban, liver disease, arthritis, physical disabilities, depressive symptoms, weak hand strength, poor relations with children, and digestive medication use are significant risk factors while living in a tidy environment is protective. Random Forest models achieved the highest AUC-ROC and sensitivity in both rural (AUC = 0.732, 95% CI: 0.69–0.78; sensitivity = 0.669) and urban (AUC = 0.734, 95% CI: 0.68–0.79; sensitivity = 0.754) areas. Decision curve analysis confirmed the model’s clinical utility across a range of threshold probabilities. Key predictors included prior experience of falling, gender, and chair stand test performance in rural areas, while in urban areas, experience of falling, gender, and age were the most influential features.ConclusionThe key factors influencing falls among older people differ between urban and rural areas, and the predictive models effectively identify high-risk populations in both settings. This facilitates targeted prevention and precise interventions, supporting healthy aging in China. |
| format | Article |
| id | doaj-art-4b99a9b7800b43e3b3d7d6b5500bcd7e |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Public Health |
| spelling | doaj-art-4b99a9b7800b43e3b3d7d6b5500bcd7e2025-08-20T03:49:32ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-05-011310.3389/fpubh.2025.15978531597853Urban–rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive modelsLiHan Lin0LiHan Lin1XiaoYang Liu2CaiHua Cai3YiKun Zheng4Delong Li5GuoPeng Hu6College of Physical Education, Huaqiao University, Quanzhou, ChinaProvincial University Key Laboratory of Sport and Health Science, School of Physical Education and Sport Science, Fujian Normal University, Fuzhou, ChinaCollege of Physical Education, Huaqiao University, Quanzhou, ChinaCollege of Physical Education, Huaqiao University, Quanzhou, ChinaCollege of Physical Education, Huaqiao University, Quanzhou, ChinaDepartment of Cardiology, Fujian Medical University Affiliated First Quanzhou Hospital, Quanzhou, ChinaCollege of Physical Education, Huaqiao University, Quanzhou, ChinaBackgroundFalls among older adults are a significant challenge to global healthy aging. Identifying key factors and differences in fall risks, along with developing predictive models, is essential for differentiated and precise interventions in China’s urban and rural older populations.MethodsThe data of 5,876 older adults were obtained from the China Health and Retirement Longitudinal Survey (Waves 2015 and 2018). A total of 87 baseline input variables were considered as candidate features. Predictive models for fall risk over the next 3 years among urban and rural older populations were developed using five machine learning algorithms. Logistic regression analysis was employed to identify key factors influencing falls in these populations.ResultsThe fall incidence among older adults was 22.4%, with 23.2% in rural areas and 20.9% in urban areas. Common risk factors across both settings include gender, age, fall history, sleep duration, activities of daily living questionnaire scores, memory status, and chair stand test time. In rural areas, additional risks include being unmarried, having diabetes, heart disease, memory-related medication use, and living in houses built 6–20 years ago. For urban, liver disease, arthritis, physical disabilities, depressive symptoms, weak hand strength, poor relations with children, and digestive medication use are significant risk factors while living in a tidy environment is protective. Random Forest models achieved the highest AUC-ROC and sensitivity in both rural (AUC = 0.732, 95% CI: 0.69–0.78; sensitivity = 0.669) and urban (AUC = 0.734, 95% CI: 0.68–0.79; sensitivity = 0.754) areas. Decision curve analysis confirmed the model’s clinical utility across a range of threshold probabilities. Key predictors included prior experience of falling, gender, and chair stand test performance in rural areas, while in urban areas, experience of falling, gender, and age were the most influential features.ConclusionThe key factors influencing falls among older people differ between urban and rural areas, and the predictive models effectively identify high-risk populations in both settings. This facilitates targeted prevention and precise interventions, supporting healthy aging in China.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1597853/fullolder peoplefall riskmachine learningrural–urban differenceagingpublic health |
| spellingShingle | LiHan Lin LiHan Lin XiaoYang Liu CaiHua Cai YiKun Zheng Delong Li GuoPeng Hu Urban–rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models Frontiers in Public Health older people fall risk machine learning rural–urban difference aging public health |
| title | Urban–rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models |
| title_full | Urban–rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models |
| title_fullStr | Urban–rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models |
| title_full_unstemmed | Urban–rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models |
| title_short | Urban–rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models |
| title_sort | urban rural disparities in fall risk among older chinese adults insights from machine learning based predictive models |
| topic | older people fall risk machine learning rural–urban difference aging public health |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1597853/full |
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