Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Jun 15;23(1):1155.
doi: 10.1186/s12889-023-15945-9.

Spatio-temporal pattern, matching level and prediction of ageing and medical resources in China

Zhenyan Wang #  1 , Wei Ye #  1 , Xicheng Chen #  1 , Yang Li #  1 , Ling Zhang  2 , Fang Li  1 , Ning Yao  1 , Chengcheng Gao  1 , Pengyu Wang  1 , Dong Yi  3 , Yazhou Wu  4
Affiliations

Spatio-temporal pattern, matching level and prediction of ageing and medical resources in China

Zhenyan Wang et al. BMC Public Health. .

Abstract

Objective: Population ageing, as a hot issue in global development, increases the burden of medical resources in society. This study aims to assess the current spatiotemporal evolution and interaction between population ageing and medical resources in mainland China; evaluate the matching level of medical resources to population ageing; and forecast future trends of ageing, medical resources, and the indicator of ageing-resources (IAR).

Methods: Data on ageing (EPR) and medical resources (NHI, NBHI, and NHTP) were obtained from China Health Statistics Yearbook and China Statistical Yearbook (2011-2020). We employed spatial autocorrelation to examine the spatial-temporal distribution trends and analyzed the spatio-temporal interaction using a Bayesian spatio-temporal effect model. The IAR, an improved evaluation indicator, was used to measure the matching level of medical resources to population ageing with kernel density analysis for visualization. Finally, an ETS-DNN model was used to forecast the trends in population ageing, medical resources, and their matching level over the next decade.

Results: The study found that China's ageing population and medical resources are growing annually, yet distribution is uneven across districts. There is a spatio-temporal interaction effect between ageing and medical resources, with higher levels of both in Eastern China and lower levels in Western China. The IAR is relatively high in Northwest, North China, and the Yangtze River Delta, but showed a declining trend in North China and the Yangtze River Delta. The hybrid model (ETS-DNN) gained an R2 of 0.9719, and the predicted median IAR for 2030 (0.99) across 31 regions was higher than the median IAR for 2020 (0.93).

Conclusion: This study analyzes the relationship between population ageing and medical resources, revealing a spatio-temporal interaction between them. The IAR evaluation indicator highlights the need to address ageing population challenges and cultivate a competent health workforce. The ETS-DNN forecasts indicate higher concentrations of both medical resources and ageing populations in eastern China, emphasizing the need for region-specific ageing security systems and health service industries. The findings provide valuable policy insights for addressing a hyper-aged society in the future.

Keywords: Interaction effect; Medical resources; Population ageing; Regression prediction; Spatio-temporal distribution trends.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests. The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views of the organizations listed.

Figures

Fig. 1
Fig. 1
Main technical framework
Fig. 2
Fig. 2
Spatio-temporal distribution trends of EPR and medical resources. Note: Plot (A) shows the EPR LISA plots for 2011, 2014, 2017 and 2020. Plot (B) presents NHTP as an example of spatial–temporal distribution trends in medical resource indicators
Fig. 3
Fig. 3
Spatial distribution and variation of BiLISA between EPR and NHTP. Note: The paper shows the BiLISA plots between EPR and NHTP for 2011, 2014, 2017 and 2020
Fig. 4
Fig. 4
IAR Distribution by Region in China from 2011 to 2020. Note: Plot (A) shows the Annual IAR Box Chart by Province in China; * refers to the median IAR value of each year. Plot (B) presents the kernel density distribution of IAR by region; the maps reflect the hotspot areas of the IAR
Fig. 5
Fig. 5
Distribution of projected EPR, NHTP, and IAR in 2030. Note: Projections of EPR, NHTP and IAR levels in 2030 with ETS-DNN. A EPR; B NHTP; C IAR; D Distribution of IAR by Province (2020 Vs 2030), * refers to the median IAR value of each year
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Cheng X, Yang Y, Schwebel DC, Liu Z, Li L, Cheng P, Ning P, Hu G. Population ageing and mortality during 1990–2017: a global decomposition analysis. PLoS Med. 2020;17(6):e1003138. doi: 10.1371/journal.pmed.1003138. - DOI - PMC - PubMed
    1. Li J, Han X, Zhang X, Wang S. Spatiotemporal evolution of global population ageing from 1960 to 2017. BMC Public Health. 2019;19(1):127. doi: 10.1186/s12889-019-6465-2. - DOI - PMC - PubMed
    1. Fang EF, Xie C, Schenkel JA, Wu C, Long Q, Cui H, et al. A research agenda for ageing in China in the 21st century (2nd edition): focusing on basic and translational research, long-term care, policy and social networks. Ageing Res Rev. 2020;64:101174. - PMC - PubMed
    1. Yue C, Xiang C, Binbin S, Shiyong W. Changes in mortality rates of major chronic diseases among populations aged over 60 years and their contributions to life expectancy increase — China, 2005–2020. China CDC Weekly. 2022;4(39):866–870. doi: 10.46234/ccdcw2022.179. - DOI - PMC - PubMed
    1. Shen X, Zhou S, Zhang X. China’s population aging and regional variation. In: Services for Aging Persons in China. Cham: Springer International Publishing; 2022. p. 19–35.

Publication types