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Forecasting Informal care needs of the urban-rural older adults in China based on microsimulation model
Zhang, Liangwen1,2; Shen, Shuyuan2,3; Zhang, Wenzheng1,2; Fang, Ya1,2
2024-12-01
Source PublicationBMC Public Health
ISSN1471-2458
Volume24Issue:1
Abstract

Background: Forecasting the intensity, source, and cost of informal care for older adults in China is essential to establish and enhance policy support systems for informal care within the context of East Asian traditional culture that emphasizes filial piety. This study aims to analyze the current situation and influencing factors for the informal care needs and predict the trends of informal care needs for older adults in China from 2020 to 2040. Methods: Using the CHARLS database from 2015 to 2018, this study first combined a two-part model and a multinomial logit to analyze the influencing factors for the informal care needs of urban-rural older adults in China. Secondly, a multi-state Markov model was constructed to forecast the number of urban-rural older populations in each health state from 2020 to 2040. Finally, based on a microsimulation model, this study predicted the trends of informal care intensity, source, and cost for older adults in urban and rural areas from 2020 to 2040. Results: In 2040, the size of the disabled older population in China will expand further. In rural areas, the total number of disabled people in 2040 (39.77 million) is 1.50 times higher than that in 2020; In urban areas, the total number of disabled people in 2040 (56.01 million) is 2.51 times higher than that in 2020. Compared with 2020, older adults population with mild, moderate and severe disability in 2040 would increase by 87.60%, 101.70%, and 115.08%, respectively. In 2040, the number of older adults receiving low-, medium-, and high-intensity care in China will be 38.60 million, 22.89 million, and 41.69 million, respectively, and older people will still rely on informal care provided by spouses and children (from spouses only: 39.26 million, from children only: 36.74 million, from spouses and children only: 16.79 million, other: 10.39 million). The total cost of informal care in 2040 will be 1,086.65 billion yuan, 2.22 times that of 2020 (490.31 billion yuan), which grows faster than the economic growth rate. Conclusion: From 2020 to 2040, the informal care needs of older people in rural areas will increase first and then decrease due to the demographic structure and rapid urbanization. In contrast, the informal care needs of older people in urban areas will continuously increase from 2020 to 2040, with the growth rate gradually slowing down. This study provides an evidence-based rationale for scientifically measuring the economic value of informal care and reasonably allocating care resources.

KeywordInformal Care Markov Model Needs Forecast Older Adults
DOI10.1186/s12889-024-19747-5
URLView the original
Language英語English
PublisherBioMed Central Ltd
Scopus ID2-s2.0-85202835854
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Document TypeJournal article
CollectionDEPARTMENT OF PUBLIC HEALTH AND MEDICINAL ADMINISTRATION
Affiliation1.State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Xiang An Nan Road, Xiang An District, Fujian Province, China
2.Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
3.Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, SAR, Macao
Recommended Citation
GB/T 7714
Zhang, Liangwen,Shen, Shuyuan,Zhang, Wenzheng,et al. Forecasting Informal care needs of the urban-rural older adults in China based on microsimulation model[J]. BMC Public Health, 2024, 24(1).
APA Zhang, Liangwen., Shen, Shuyuan., Zhang, Wenzheng., & Fang, Ya (2024). Forecasting Informal care needs of the urban-rural older adults in China based on microsimulation model. BMC Public Health, 24(1).
MLA Zhang, Liangwen,et al."Forecasting Informal care needs of the urban-rural older adults in China based on microsimulation model".BMC Public Health 24.1(2024).
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