Evidence mapPaperPMID 39810282Full record

ArticleGlobal health research and policy2025

Effects of population aging on quality of life and disease burden: a population-based study.

Jun-Yan Xi, Bo-Heng Liang, Wang-Jian Zhang, Bo Yan, Hang Dong, Yuan-Yuan Chen, Xiao Lin, Jing Gu, Yuan-Tao Hao

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Article in Global health research and policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

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34citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed.

  1. Trial
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  18. Physiological responses to mask-associated COFrontiers in public health · 2026
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jun-Yan Xi *Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, 74Th Zhongshan 2Nd Rd, Yuexiu District, Guangdong, 510080, China.
Bo-Heng Liang *Department of Chronic Non-Communicable Disease Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangdong, 510440, China.
Wang-Jian ZhangDepartment of Medical Statistics, School of Public Health, Sun Yat-Sen University, 74Th Zhongshan 2Nd Rd, Yuexiu District, Guangdong, 510080, China.
Bo YanSchool of Health Sciences, Guangzhou Xinhua University, Guangdong, 510520, China.
Hang DongDepartment of Chronic Non-Communicable Disease Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangdong, 510440, China.
Yuan-Yuan ChenDepartment of Chronic Non-Communicable Disease Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangdong, 510440, China.
Xiao LinDepartment of Medical Statistics, School of Public Health, Sun Yat-Sen University, 74Th Zhongshan 2Nd Rd, Yuexiu District, Guangdong, 510080, China. linx87@mail.sysu.edu.cn.
Jing GuDepartment of Medical Statistics, School of Public Health, Sun Yat-Sen University, 74Th Zhongshan 2Nd Rd, Yuexiu District, Guangdong, 510080, China. gujing5@mail.sysu.edu.cn.
Yuan-Tao HaoCenter for Public Health and Epidemic Preparedness and Response, Peking University, Haidian District, 38Th Xueyuan Road, Beijing, 100191, China. haoyt@bjmu.edu.cn.ORCID http://orcid.org/0000-0003-4146-9262

Funding

China Postdoctoral Science Foundation 2021M693594Fundamental Research Funds for the Central Universities, Sun Yat-sen University 51000-31610048Guangdong Basic and Applied Basic Research Foundation 2020A1515110230Guangdong Basic and Applied Basic Research Foundation 2021A1515011765National Key R & D Program of China 2022YFC3600804National Natural Science Foundation of China 82204154National Natural Science Foundation of China 82373684
6 · The paper itself

Abstract

backgroundAs 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.

methodsWe 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.

resultsBetween 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.

conclusionsThe 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.

Indexed as

AgingCost of IllnessDisability-Adjusted Life YearsQuality of LifeAdolescentAdultAgedAged, 80 and overBayes TheoremChinaFemaleHumansLife ExpectancyMaleMiddle AgedPersons with DisabilitiesAttribution analysisBurden of diseaseDifferential decompositionPopulation agingPredictionQuality of life

Identifiers

PMID39810282
PMCPMC11731452

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.