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已发表论文
基于潜在狄利克雷分配（LDA）主题模型方法的中国积极老龄化水平与影响因素评估
Received 24 July 2025
Accepted for publication 1 December 2025
Published 10 December 2025 Volume 2025:18 Pages 3819—3841
DOI https://doi.org/10.2147/RMHP.S556017
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Professor Mecit Can Emre Simsekler
Chunhai Tao,1 Rui Deng1,2
1School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, People’s Republic of China; 2Research Institute of High-Speed Railway and Regional Development, East China Jiaotong University, Nanchang, Jiangxi, People’s Republic of China
Correspondence: Rui Deng, School of Statistics and Data Science, Jiangxi University of Finance and Economics, 169 Shuanggangdong Road, Changbei National Economic and Technological Development Zone, Nanchang, Jiangxi, 330013, People’s Republic of China, Email drui9366@gmail.com
Background: While the concept of active aging has been extensively studied in high-income countries, China faces distinct demographic challenges, including a rapidly growing elderly population, accelerated aging, and aging prior to widespread economic prosperity. These trends highlight the urgent need for a context-specific conceptual and evaluative framework to measure active aging, tailored to China’s socio-cultural and economic realities.
Methods: This study employs the Latent Dirichlet Allocation (LDA) topic model to construct a multidimensional indicator system for measuring active aging among older adults in China. Drawing on five waves of nationally representative panel data from the China Health and Retirement Longitudinal Study (CHARLS), spanning 2011 to 2020, we evaluate individual-level active aging scores using a quantitatively derived framework.
Results: The measurement system consists of six core dimensions and 21 indicators: (1) physical health and functional capacity, (2) psychological well-being and life satisfaction, (3) family caregiving and social security, (4) economic security and intergenerational support, (5) social participation and enabling environments, and (6) lifelong learning and self-management. All scale-based measures demonstrated acceptable internal consistency (Cronbach’s alpha ≥ 0.70). The average active aging score among the full sample was 0.4912± 0.0907.
Conclusion: Active aging levels in China have shown consistent improvements over the observation period, with the most pronounced gains in the eastern region. The central region has seen a narrowing of differences, while the eastern, northeastern, and western parts of the country have seen a widening of differences. Key positive correlates of active aging include educational attainment, urban residence, male gender, alcohol consumption, and being married. Negative associations were found for older age, geographic region, presence of chronic conditions, number of surviving children, and smoking. Among these, education attainment, urban-rural status, age and gender emerged as the most influential factors.
Keywords: active aging, China, LDA, CHARLS, index, influencing factors, regional disparity
1School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, People’s Republic of China; 2Research Institute of High-Speed Railway and Regional Development, East China Jiaotong University, Nanchang, Jiangxi, People’s Republic of China
Correspondence: Rui Deng, School of Statistics and Data Science, Jiangxi University of Finance and Economics, 169 Shuanggangdong Road, Changbei National Economic and Technological Development Zone, Nanchang, Jiangxi, 330013, People’s Republic of China, Email drui9366@gmail.com
Background: While the concept of active aging has been extensively studied in high-income countries, China faces distinct demographic challenges, including a rapidly growing elderly population, accelerated aging, and aging prior to widespread economic prosperity. These trends highlight the urgent need for a context-specific conceptual and evaluative framework to measure active aging, tailored to China’s socio-cultural and economic realities.
Methods: This study employs the Latent Dirichlet Allocation (LDA) topic model to construct a multidimensional indicator system for measuring active aging among older adults in China. Drawing on five waves of nationally representative panel data from the China Health and Retirement Longitudinal Study (CHARLS), spanning 2011 to 2020, we evaluate individual-level active aging scores using a quantitatively derived framework.
Results: The measurement system consists of six core dimensions and 21 indicators: (1) physical health and functional capacity, (2) psychological well-being and life satisfaction, (3) family caregiving and social security, (4) economic security and intergenerational support, (5) social participation and enabling environments, and (6) lifelong learning and self-management. All scale-based measures demonstrated acceptable internal consistency (Cronbach’s alpha ≥ 0.70). The average active aging score among the full sample was 0.4912± 0.0907.
Conclusion: Active aging levels in China have shown consistent improvements over the observation period, with the most pronounced gains in the eastern region. The central region has seen a narrowing of differences, while the eastern, northeastern, and western parts of the country have seen a widening of differences. Key positive correlates of active aging include educational attainment, urban residence, male gender, alcohol consumption, and being married. Negative associations were found for older age, geographic region, presence of chronic conditions, number of surviving children, and smoking. Among these, education attainment, urban-rural status, age and gender emerged as the most influential factors.
Keywords: active aging, China, LDA, CHARLS, index, influencing factors, regional disparity