Background Quantitative attribution of the burden of disease due to population aging is an important part of setting meaningful global health priorities. This study comprehensively examines the burden of disease attributable to population aging in 188 countries from 1990 to 2019, incorporates a comprehensive range of diseases, and projects the burden of disease due to population aging till 2050. Methods We extracted data from 1990 to 2019 for 188 countries from the Global Burden of Disease Study 2019. We decomposed the change in disease burden into the contribution of the age structure of the population, population size, and age-specific disability-adjusted life years (DALYs) rates due to all other reasons. We used the Bayesian age-period-cohort model to evaluate the effects of age on temporal trends, and then to predict the possible disease burden in 2050. Results At the global level, the change in total DALYs associated with age structure, population size, and all other reasons is 27.4%, 16.8%, and 89.4% (absolute level of DALYs attributable to age structure: -15.20 million, 9.32 million, and -49.58 million) of the absolute level of DALYs gap between 2019 and 1990. The absolute level of DALYs changes attributable to age structure for communicable, maternal, neonatal, and nutritional diseases were negative in all income groups from 1990 to 2019. For non-communicable diseases, the contribution was positive except in the low-income group. For injuries, the contribution was positive in lower-middle-income groups and low-income groups. By 2050, DALY rates decreased in all income groups, if compared to 2019. However, a total of 132 countries may see a gradual increase of all-cause DALYs attributable to population aging. Conclusions The direction and intensity of the effects of population aging on the burden of disease vary by region and disease, with huge implications for global health in the future.
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.
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.
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