Prediction of the Number of and Care Costs for Disabled Elderly from 2020 to 2050: A Comparison between Urban and Rural Areas in China

Zhang, Liangwen; Fu, Sijia; Fang, Ya

doi:10.3390/su12072598

Cited by 23 publications

(26 citation statements)

References 20 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5 In the future, the demand for the oldest-old care will increase sharply and China's old-age security system will face severe challenges. 6 In the face of the contradiction between supply and demand in the field of aged care in China, on the one hand, informal family care is under enormous pressure due to the miniaturization of family structure and the high rate of women's labor participation. 7,8 On the other hand, formal institutional care also has many problems.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Future Demand of Nursing Staff for the Oldest-Old in China by 2025 Based on Markov Model

Zhang

Shen

Guo

2021

Int J Health Policy Manag

Self Cite

View full text Add to dashboard Cite

Background: An aging population and an increase in the proportion of disabled elderly have brought an unprecedented global challenge, especially in China. Aside lack of professional long-term care facilities, the shortage of human resource for old-age care is also a major threat. Therefore, this study tries to forecast the demand scale of nursing staff for the oldest-old in 2025 in China servicing as a reference for the development plan of human resource for elderly nursing. Methods: Based on CLHLS (Chinese Longitudinal Healthy Longevity Survey) 2011 and 2014, Logit model was used to construct the transition probability matrix of the elderly’s health status (health/mild/moderate/severe disability and death). By using the data of the elderly population aged 65 or over in the 2010 national population census, we projected the number of Chinese oldest-old population in different health status by 2025 through Markov model and projected the scale of the demand of nursing staff combined with the human population ratio method. Results: The forecast shows that the Chinese oldest-old population is about 52.6 million, among which 46.9 million are healthy, 3.7 million are mild, 0.8 million are moderate, and 1.2 million are severely disabled in 2025. Concurrently, the demand scale of nursing staff will be 5.6 million according to the low standard and 11.5 million according to the high standard. Thus, human resource supply of long-term care is worrying. Conclusion: In 2025, the population size of the Chinese oldest-old will be further expanded, and the demand of care will increase accordingly, leading to a vast gap in the nursing staff. Therefore, it is urgent to build a professional nursing staff with excellent comprehensive quality and reasonable quantity, to ensure the sustainable development of China’s elderly care service industry.

show abstract

Section: Introductionmentioning

confidence: 99%

Forecasting Future Demand of Nursing Staff for the Oldest-Old in China by 2025 Based on Markov Model

Zhang

Shen

Guo

2021

Int J Health Policy Manag

Self Cite

View full text Add to dashboard Cite

show abstract

“…Of that total, 69.7% are expected to be urban elderly. By 2050, the population of elderly individuals with mild, moderate, or severe disabilities will be 108%, 104%, and 120%, respectively, as compared to their 2020 totals [ 5 ]. The disability rate of the elderly in China is thus expected to increase rapidly, with a concomitant deepening of the severity of disability.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Influencing Factors on the Preferences of the Elderly for the Combination of Medical Care and Pension in Long-Term Care Facilities Based on the Andersen Model

Wei

Zhang

2020

IJERPH

Self Cite

View full text Add to dashboard Cite

Background: The purpose of this study is to evaluate the status quo and factors that influence the preferences of the elderly for the combination of medical care and pension (CMCP) in long-term care (LTC) facilities and to provide an evidence-based basis for building a multi-tiered, continuous LTC system with CMCP. Methods: Using a multi-stage sampling method, face-to-face questionnaire surveys were conducted on 3260 elderly people aged 60 years or over in 44 communities in 16 sub-districts in six districts in Xiamen. Based on the Andersen model, the chi-square test was used to analyze differences in population distribution, and binary logistic regression analysis was used to analyze the factors affecting the elderly’s preference for CMCP in LTC institutions in terms of the factors of predisposition, enablement, and personal needs. Results: Most elderly people choose traditional home care (82.01%), and only 12.89% choose LTC facilities with CMCP. This choice is influenced by a number of predisposing factors. The elderly who are at the upper end of the age range, have a higher education level, and live in rural areas are more likely to choose CMCP (odds ratio (OR) value greater than 1, p < 0.05). With regard to enabling factors, the elderly who were married, mainly taken care of by spouses, and had better economic status also tended to choose CMCP (OR > 1, p < 0.01). In terms of personal needs, the elderly with worse self-care status tended to choose CMCP (OR > 1, p < 0.01). Enabling factors have the largest contribution to the model, and they have the greatest impact on elder preference for CMCP services. In addition, the elderly with higher age and education level, non-remarried, with better economic status, and with poorer health status have a demand for a wider variety of CMCP services. Compared to those in urban areas, the elderly in rural areas have greater needs, mainly related to personal care, medical care, and psychological counseling. Conclusion: The preference of the elderly for CMCP are lower compared to their preference for home care in Xiamen, China. Preference for CMCP is affected by a range of factors such as age, education level, residence, income, and self-care ability, among which the enabling factors have the greatest impact.

show abstract

“…Furthermore, we used the data from the 7th Chinese Longitudinal Healthy Longevity Survey (CLHLS) in 2014 and referred to the work of Zhang and Fang [16] to attain the proportion of elderly with mild, moderate, and severe disability in the urban population by age groups, as shown in Table 1.…”

Section: Disability Ratiomentioning

confidence: 99%

Application of Smart Healthcare in LTCI, Outpatient Mutual-Aid Guarantee Mechanism, and Sustainability of Medical Insurance Fund for Urban Employees

Ren

Liu

2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

With the aggravation of population aging and the increase of life expectancy, long-term care insurance (LTCI) system has been established to meet the medical and long-term care needs of the increasing elderly population. In China, LTCI system is currently not a stand-alone insurance, but it is attached to the national basic medical insurance fund for urban employees (MIUE). As a result, the expenditure of LTCI is a part of the expenditure of the MIUE, which has an impact on the sustainability of the MIUE. By modeling the income and expenditure of MIUE, especially including the expenditure of LTCI, this study optimized an LTCI system with a higher individual out-of-pocket payment ratio of LTCI and implementation of the outpatient mutual-aid guarantee mechanism (OMAGM), which could improve the sustainability of the MIUE. The study also reveals the following: (i) solely increasing individual out-of-pocket payment ratio of LTCI to 20%–50% can only postpone the deficit on Social Pooling Accounts (SPAs) by 1 or 2 years, and the effect is very limited. (ii) Besides a higher individual out-of-pocket payment ratio, further implementation of a partial OMAGM from 2022 will postpone the deficit on SPAs by 7–9 years, and the implementation of a complete OMAGM from 2022 will postpone the deficit by 14–18 years. Accordingly, China should implement OMAGM as soon as possible to enhance the solvency of MIUE fund, and, in the long run, an independent LTCI scheme should be established to ensure the stability and sustainability of the LTCI fund and the MIUE fund.

show abstract

Prediction of the Number of and Care Costs for Disabled Elderly from 2020 to 2050: A Comparison between Urban and Rural Areas in China

Cited by 23 publications

References 20 publications

Forecasting Future Demand of Nursing Staff for the Oldest-Old in China by 2025 Based on Markov Model

Forecasting Future Demand of Nursing Staff for the Oldest-Old in China by 2025 Based on Markov Model

Analysis of the Influencing Factors on the Preferences of the Elderly for the Combination of Medical Care and Pension in Long-Term Care Facilities Based on the Andersen Model

Application of Smart Healthcare in LTCI, Outpatient Mutual-Aid Guarantee Mechanism, and Sustainability of Medical Insurance Fund for Urban Employees

Contact Info

Product

Resources

About