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Age-Related Disease Burdens, Disparities, and Health Resource Allocation: A Longitudinal Data Analysis of 31 Provinces in Mainland China

23 Pages Posted: 10 Mar 2022

See all articles by Shu Chen

Shu Chen

University of New South Wales (UNSW) - ARC Centre of Excellence in Population Ageing Research (CEPAR)

Yafei Si

University of New South Wales (UNSW)

Katja Hanewald

UNSW Sydney - School of Risk & Actuarial Studies and ARC Centre of Excellence in Population Ageing Research (CEPAR)

Bingqin Li

University of New South Wales (UNSW) - Social Policy Research Centre (SPRC); Social Policy Research Centre, UNSW

Hazel Bateman

UNSW Sydney, CEPAR

Xiaochen Dai

University of Washington - Institute of Health Metrics and Evaluation

Chenkai Wu

Duke Kunshan University

Shenglan Tang

Duke University - Duke Global Health Institute

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Abstract

Background: Measuring chronological age alone does not provide sufficient context for understanding the impact of ageing on societal resource planning. The burden of age-related diseases (ARDs) reflects age-related morbidity and mortality at the population level, which unveils the underlying health burden of ageing. The current study aims to measure the ARD burden and its disparities at subnational level of China, a rapidly ageing country with regional imbalances in economic and health development, and assess the impact of health resource allocation on this burden.

Methods: We used the longitudinal data collected from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 and 2019 to measure the ARD burden in 31 provinces in mainland China, and from China Statistical and Health Statistical Yearbooks for health resources and socio-economic indicators from 2010 to 2016. We first identified the ARDs, defined as diseases with incidence rates that increased quadratically with age, and calculated the burden as the sum of the disability-adjusted life-years (DALYs) of the ARDs. We further compared the disparities in the ARD burden by province, sex, and disease group, based on the ARD burden of non-communicable diseases (NCDs). The ARD burden-adjusted age for each province was also calculated by assuming each province shared the same age-specific burden rate as the national average. Historical changes in burden between 1990 and 2016 were assessed after standardising the age structure. Total health expenditures per capita, total health professional density, licensed doctor density, and licensed nurse density were used as proxy indicators for health resources. Panel data analysis approach was used to assess the impact of these indicators on the burden of ARDs from 2010 to 2016 based on multivariate regression models.

Findings: NCDs accounted for over 90% of China’s total ARD burden in 2019. There were significant regional disparities: the rate of ARD burden was lowest in the south-eastern coast provinces, followed by the central provinces, and trailed by the north-eastern and western provinces. In 2016, the ARD burden-adjusted ages of Shanghai, Beijing, and Zhejiang were the youngest, at 30·86, 30·90 and 36·21 years, respectively. In contrast, the respective ARD burdenadjusted ages of Sichuan, Heilongjiang, and Chongqing were 66·39, 66·14, and 62·98. After standardising the age structure, Tibet, Qinghai, Guizhou, and Xinjiang had the highest burden of ARDs and oldest ARD burden-adjusted age. Males are disproportionately affected by ARDs, with burden rate 70% higher than females. China’s overall age-standardised ARD burden has been decreasing since 1990. The largest decline was observed in the eastern provinces, followed by the central and western provinces. However, the burden rate of neurological disorders has continued to increase, albeit only by a small amount. Panel regression results showed increasing either health expenditures or health workforce density could not significantly lower the ARD burden. However, the existing urban-rural gap in health workforce density was positively associated with a consistent increase in the ARD burden. A 100% increase in the urban-rural ratio in total health professional density, licensed doctor density, and licensed nurse density led to 2·55% (p=0·09; 95% CI: -0·42, 5·53), 2·29% (p=0·07; 95% CI: -0·24, 4·80), and 2.21% (p=0·08; 95% CI: -0·31, 4·73) increases in the ARD burden respectively, ceteris paribus.

Interpretation: Older demographic structure does not necessarily mean higher ageing-related health burden. Resource planning for an ageing society should consider the burden of ARDs. In China, concerted efforts should be made to reduce the ARDs burden and its disparities, especially among western provinces which face greatest health threat due to future ageing. Continued investment in health is useful. Particularly, health workforce supply should be deliberately biased toward rural areas in western provinces.

Funding Information: This study was funded by The Bill & Melinda Gates Foundation; University of New South Wales (UNSW), Australia; ARC Centre of Excellence in Population Ageing Research (CEPAR), Australia; SHARP Fund, UNSW, Australia.

Declaration of Interests: All co-authors declare no conflicts of interest for this study.

Ethics Approval Statement: This study received ethical approval from the University of South Wales (UNSW) Ethics Committee (HC210794).

Keywords: Age-related diseases, Healthy ageing, regional and sex disparities, health resources allocation, China

Suggested Citation

Chen, Shu and Si, Yafei and Hanewald, Katja and Li, Bingqin and Li, Bingqin and Bateman, Hazel and Dai, Xiaochen and Wu, Chenkai and Tang, Shenglan, Age-Related Disease Burdens, Disparities, and Health Resource Allocation: A Longitudinal Data Analysis of 31 Provinces in Mainland China. Available at SSRN: https://ssrn.com/abstract=4054460 or http://dx.doi.org/10.2139/ssrn.4054460

Shu Chen

University of New South Wales (UNSW) - ARC Centre of Excellence in Population Ageing Research (CEPAR) ( email )

Kensington
High St
Sydney, NSW 2052
Australia

Yafei Si

University of New South Wales (UNSW)

Katja Hanewald

UNSW Sydney - School of Risk & Actuarial Studies and ARC Centre of Excellence in Population Ageing Research (CEPAR) ( email )

School of Risk & Actuarial Studies
UNSW Sydney
Sydney, New South Wales NSW 2052
Australia

Bingqin Li

University of New South Wales (UNSW) - Social Policy Research Centre (SPRC) ( email )

Sydney, NSW 2052
Australia

Social Policy Research Centre, UNSW ( email )

Sydney
Australia

Hazel Bateman

UNSW Sydney, CEPAR ( email )

High Street
Sydney, NSW 2052
Australia

Xiaochen Dai

University of Washington - Institute of Health Metrics and Evaluation ( email )

2301 5th Avenue, Suite 600
Seattle, WA 98195
United States

Chenkai Wu

Duke Kunshan University ( email )

No. 8 Duke Avenue
Kunshan, 215316
China

Shenglan Tang (Contact Author)

Duke University - Duke Global Health Institute ( email )

310 Trent Drive
Box 90519
Durham, NC 27710
United States

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