The world is facing the complex challenge of population aging [1]. Life expectancy at birth has risen markedly worldwide since 1990, although with different temporal patterns of this trend [2]. The new demography of low fertility and low mortality produces population aging. Between 2020 and 2050, the worldwide population of people aged 60 and over […]

Background: 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. Methods: 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. Results: 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. Conclusions: 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.

Background: To understand the magnitude and spatial–temporal distribution of the regional burden attributable to severe mental disorders is of great essential and high policy relevance. The study aimed to address the burden of severe mental disorders by evaluating the years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) in Guangdong, China. Methods: We undertook a longitudinal study based on a multicenter database established by the Health Commission of Guangdong, involving a total of 21 prefectures and four economic regions in the Guangdong province. A total of 520,731 medical records from patients with severe mental disorders were collected for 2010–2020. Data were analyzed via an integrated evaluation framework by synthesizing prevalence estimates, epidemiological adjustment as well as comorbidity assessment to develop internally consistent estimates of DALY. DALY changes during 2010–2020 were decomposed by population growth and aging and further grouped by Socio-demographic Index (SDI). DALYs were projected to 2030 by the weighted median annualized rate of change in 2010–2020. Results: In 2010–2020, the average DALYs for severe mental disorders reached 798,474 (95% uncertainty interval [UI]: 536,280–1,270,465) person-years (52.2% for males, and 47.8% for females). Severe mental disorders led to a great amount of disease burden, especially in Guangzhou, Shenzhen, and Foshan cities. Schizophrenia and mental retardation with mental disorders were the two leading sources of the burden ascribed to severe mental disorders. Population growth and aging could be accountable for the increasing burden of severe mental disorders. Economic regions with higher SDI carried a greater burden but had lower annualized rates of change in DALYs. The overall burden of severe mental disorders is projected to rise modestly over the next decade. Conclusions: The findings urge prioritization of initiatives focused on public mental health, prevention strategies, health resources reallocation, and active involvement of authorities to effectively address the anticipated needs.

Study objective: Investing in health to improve healthy life expectancy (HLE) is fundamental to create a demographic dividend. However, how dramatic population ageing affects HLE remains unknown. This study aims to quantify and project the major diseases and injuries attributed to changes in population size and age structure that contributed to substantial losses in HLE.Methods: Using data from 188 countries in the Global Burden of Disease Study 2021 and World Population Prospects 2024, we assessed the correlation between HLE and total dependency ratios. Furthermore, we decomposed the mortality and disability burden attributable to changes in population size as well as age structure for 22 disease and injury categories and then quantified the loss of HLE due to the attributable burden. Additionally, we projected the loss of HLE due to priority diseases in 2030, while considering the impact of population ageing.Results: From 2010 to 2019, globally, the mortality and disability burden attributable to age structure caused 0.40 years and 0.71 years of HLE loss, while for population size, these two estimates were 1.18 years and 1.00 years. By 2030, the mortality and disability burden attributable to age structure may lead to 0.76 years and 0.89 years of HLE loss, while for population size, these two predictions will be 1.21 years and 1.17 years.Discussion: Population size growth is a consistent and crucial contributor to HLE losses. Reaping the second demographic dividend requires eliminating the double burden of premature death caused by infectious and chronic diseases, whereas gaining the sustainable third demographic dividend requires investments in healthy and successful ageing.

Objective: In the context of aging, Chinese families consisting of more than three generations (grandparents, parents, children) are the norm. The second generation (parents) and other family members may establish a downward (contact only with children) or two-way multi-generational relationship (contact with children and grandparents). These multi-generational relationships may have the potential effect on multimorbidity burden and healthy life expectancy in the second generation, but less is known about the direction and intensity of this effect. This study aims to explore this potential effect. Methods: We obtained longitudinal data from the China Health and Retirement Longitudinal Study from 2011 to 2018, which included 6,768 people. Cox proportional hazards regression was used to assess the association between multi-generational relationships and the number of multimorbidity. The Markov multi-state transition model was used to analyze the relationship between multi-generational relationships and the severity of multimorbidity. The multistate life table was used to calculate healthy life expectancy for different multi-generational relationships. Results: The risk of multimorbidity in two-way multi-generational relationship was 0.830 (95% CIs: 0.715, 0.963) times higher than that in downward multi-generational relationship. For mild multimorbidity burden, downward and two-way multi-generational relationship may prevent aggravation of burden. For severe multimorbidity burden, two-way multi-generational relationship may aggravate the burden. Compared with two-way multi-generational relationship, the second generations with downward multi-generational relationship has a higher healthy life expectancy at all ages. Conclusion: In Chinese families with more than three generations, the second generations with severe multimorbidity burden may aggravate the condition by providing support to elderly grandparents, and the support provided by offspring to the second generations plays a vital positive role in improving the quality of life and narrowing the gap between healthy life expectancy and life expectancy.