Xi Jun-Yan, Liang Bo-Heng, Zhang Wang-Jian, Yan Bo, Dong Hang, Chen Yuan-Yuan, Lin Xiao, Gu Jing, Hao Yuan-Tao
Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, 74Th Zhongshan 2Nd Rd, Yuexiu District, Guangdong, 510080, China.
Sun Yat-Sen Global Health Institute, Sun Yat-Sen University, Guangdong, 510080, China.
Glob Health Res Policy. 2025 Jan 14;10(1):2. doi: 10.1186/s41256-024-00393-8.
As population aging intensifies, it becomes increasingly important to elucidate the casual relationship between aging and changes in population health. Therefore, our study proposed to develop a systematic attribution framework to comprehensively evaluate the health impacts of population aging.
We used health-adjusted life expectancy (HALE) to measure quality of life and disability-adjusted life years (DALY) to quantify the burden of disease for the population of Guangzhou. The HALE and DALY projections were generated using both the Bayesian age-period-cohort models and the population prediction models. Changes in HALE and DALY between 2010-2020 and 2020-2030 were decomposed to isolate the effects of population aging. Three scenarios were analyzed to examine the relative relationship between disease burden and population aging. In Scenarios 1 and 2, the disease burden rates in 2030 were assumed to either remain at 2020 levels or follow historical trends. In Scenario 3, it was assumed that the absolute numbers of years of life lost (YLL) and years lived with disability (YLD) in 2030 would remain unchanged from the 2020 levels.
Between 2010 and 2020, 56.24% [69.73%] of the increase in male [female, values in brackets] HALE was attributable to the mortality effects in the population aged 60 and over, while - 3.74% [- 9.29%] was attributable to the disability effects. The increase in DALY caused by changes in age structure accounted for 72.01% [46.68%] of the total increase in DALY. From 2020 to 2030, 61.43% [69.05%] of the increase in HALE is projected to result from the mortality effects in the population aged 60 and over, while - 3.88% [4.73%] will be attributable to the disability effects. The increase in DALY due to changes in age structure is expected to account for 102.93% [100.99%] of the total increase in DALY. In Scenario 1, YLL are projected to increase by 45.0% [54.7%], and YLD by 31.8% [33.8%], compared to 2020. In Scenario 2, YLL in 2030 is expected to decrease by - 2.9% [- 1.3%], while YLD will increase by 12.7% [14.7%] compared to 2020. In Scenario 3, the expected YLL rates and YLD rates in 2030 would need to be reduced by 15.3% [15.4%] and 15.4% [15.6%], respectively, compared to 2020.
The disability effects among the elderly population hinder improvements in quality of life, while changes in age structure are the primary driver of disease burden accumulation. To mitigate the excess disease burden caused by population aging, it is essential to achieve a reduction of more than 15% in the disease burden by 2030 compared to 2020. Our proposed attribution framework evaluates the health impacts of population aging across two dimensions: quality of life and disease burden. This framework enables comparisons of these effects over time and across different regions.
随着人口老龄化加剧，阐明老龄化与人口健康变化之间的因果关系变得越发重要。因此，我们的研究旨在构建一个系统归因框架，以全面评估人口老龄化对健康的影响。
我们使用健康调整生命 expectancy（HALE）来衡量生活质量，用伤残调整生命年（DALY）来量化广州人口的疾病负担。HALE 和 DALY 的预测是通过贝叶斯年龄 - 时期 - 队列模型和人口预测模型生成的。分解了 2010 - 2020 年与 2020 - 2030 年期间 HALE 和 DALY 的变化，以分离人口老龄化的影响。分析了三种情景，以研究疾病负担与人口老龄化之间的相对关系。在情景 1 和情景 2 中，假设 2030 年的疾病负担率要么保持在 2020 年的水平要么遵循历史趋势。在情景 3 中，假设 2030 年的生命损失年数（YLL）和残疾生存年数（YLD）的绝对数量与 2020 年水平保持不变。
2010 年至 2020 年期间，男性[女性，括号内为女性数据]HALE 增加的 56.24%[69.73%]归因于 60 岁及以上人群的死亡率影响，而 -3.74%[-9.29%]归因于残疾影响。年龄结构变化导致的 DALY 增加占 DALY 总增加量的 72.01%[46.68%]。从 2020 年到 2030 年，预计 HALE 增加的 61.43%[69.05%]将来自 60 岁及以上人群的死亡率影响，而 -3.88%[4.73%]将归因于残疾影响。年龄结构变化导致的 DALY 增加预计将占 DALY 总增加量的 102.9
