International journal of nursing studies · 2026
Forecasting long-term care demands for dementia in China (2020-2040): A Markov model analysis of urban-rural disparities.
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Zhu Peijia, Yang Qifan, Chen Jie, Zhang Liangwen, Fang Ya
Аннотация
BACKGROUND: Since no cure for dementia has been found, providing accessible long-term care has become a major global public health challenge. The reliance on informal care has become a significant issue in global healthcare systems, especially in regions with limited formal care resources. The impact of dementia care demands at different stages on resource allocation in China remains unclear.
OBJECTIVE: This study projects dementia care costs and workforce gaps from 2020 to 2040 using a nationwide Markov model to analyze long-term care demands under urban-rural disparities.
DESIGN: A micro-simulation study.
METHODS: We developed a Markov model with five health states (healthy, mild, moderate, severe dementia, and death) based on a Chinese aging cohort, using a 1-year cycle. The model integrated multi-source data (Chinese Longitudinal Healthy Longevity Survey, United Nations projections, official statistics, and surveys) to project age, gender, and urban-rural specific changes in dementia care demands among the population aged 65 and above. Scenario analyses (low and high standards) were conducted to predict care costs and workforce demands from both formal and informal care perspectives over 20 years.
RESULTS: By 2040, China's dementia population is forecast to reach 29.83 million, with mild dementia accounting for 60.32 % of cases. Informal care will remain dominant, with workforce gaps ranging from 3.37 million (low standard) to 5.78 million (high standard). The burden is heaviest among rural females aged 65-69, but overall, urban areas face higher burdens than rural ones. Cumulative long-term care costs over 20 years are estimated to range from US$387.74 million (low standard, 0.0026 % of 2020 GDP) to US$937.52 million (high standard, 0.0064 % of 2020 GDP), driven primarily by increasing new dementia cases due to population aging.
CONCLUSIONS: Our study highlights the urgent need for enhanced informal care support, early screening for mild dementia, and an accessible long-term care system to address growing challenges. The findings provide a foundation for developing a visualization tool to support real-time policy decision-making in dementia care resource planning.
OBJECTIVE: This study projects dementia care costs and workforce gaps from 2020 to 2040 using a nationwide Markov model to analyze long-term care demands under urban-rural disparities.
DESIGN: A micro-simulation study.
METHODS: We developed a Markov model with five health states (healthy, mild, moderate, severe dementia, and death) based on a Chinese aging cohort, using a 1-year cycle. The model integrated multi-source data (Chinese Longitudinal Healthy Longevity Survey, United Nations projections, official statistics, and surveys) to project age, gender, and urban-rural specific changes in dementia care demands among the population aged 65 and above. Scenario analyses (low and high standards) were conducted to predict care costs and workforce demands from both formal and informal care perspectives over 20 years.
RESULTS: By 2040, China's dementia population is forecast to reach 29.83 million, with mild dementia accounting for 60.32 % of cases. Informal care will remain dominant, with workforce gaps ranging from 3.37 million (low standard) to 5.78 million (high standard). The burden is heaviest among rural females aged 65-69, but overall, urban areas face higher burdens than rural ones. Cumulative long-term care costs over 20 years are estimated to range from US$387.74 million (low standard, 0.0026 % of 2020 GDP) to US$937.52 million (high standard, 0.0064 % of 2020 GDP), driven primarily by increasing new dementia cases due to population aging.
CONCLUSIONS: Our study highlights the urgent need for enhanced informal care support, early screening for mild dementia, and an accessible long-term care system to address growing challenges. The findings provide a foundation for developing a visualization tool to support real-time policy decision-making in dementia care resource planning.