Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
Department of Health Education, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
BMC Public Health. 2023 Jun 15;23(1):1155. doi: 10.1186/s12889-023-15945-9.
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).
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.
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 R 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).
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.
人口老龄化是全球发展的热点问题，增加了社会医疗资源负担。本研究旨在评估中国内地人口老龄化与医疗资源的时空演变及相互作用；评估医疗资源与人口老龄化的匹配水平；并预测老龄化、医疗资源和老龄化资源指标（IAR）的未来趋势。
人口老龄化（EPR）和医疗资源（NHI、NBHI 和 NHTP）数据来源于《中国卫生统计年鉴》和《中国统计年鉴》（2011-2020 年）。采用空间自相关检验时空分布趋势，采用贝叶斯时空效应模型分析时空相互作用。采用改进的评价指标 IAR，通过核密度分析进行可视化，衡量医疗资源与人口老龄化的匹配水平。最后，采用 ETS-DNN 模型预测未来十年人口老龄化、医疗资源及其匹配水平的趋势。
研究发现，中国老龄化人口和医疗资源呈逐年增长趋势，但分布不均。人口老龄化与医疗资源之间存在时空相互作用效应，东部地区两者水平较高，西部地区较低。西北、华北和长三角地区的 IAR 相对较高，但华北和长三角地区的 IAR 呈下降趋势。混合模型（ETS-DNN）的 R 值为 0.9719，31 个地区 2030 年预测中位数 IAR（0.99）高于 2020 年中位数 IAR（0.93）。
本研究分析了人口老龄化与医疗资源的关系，揭示了两者之间的时空相互作用。IAR 评价指标突出了应对人口老龄化挑战和培养有能力的卫生劳动力的必要性。ETS-DNN 预测结果表明，东部地区的医疗资源和老龄化人口更加集中，强调了需要针对特定地区的老龄化安全系统和卫生服务产业。研究结果为应对未来超老龄化社会提供了有价值的政策见解。
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