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Changes and trends in mortality, disability-adjusted life years, life expectancy, and healthy life expectancy in China from 1990 to 2021: a secondary analysis of the global burden of disease 2021

Archives of Public Health volume 83, Article number: 93 (2025) Cite this article

Abstract

Background

The aging population in China is increasingly evident, leading to a shift in the patterns of disease burden. This study aims to investigate changes and trends in mortality, disability-adjusted life years (DALYs), life expectancy (LE), and health-adjusted life expectancy (HALE) in China from 1990 to 2021.

Methods

This study presents a secondary analysis of data from the Global Burden of Disease Study 2021, with a focus on mortality, DALYs, LE, and HALE. We examined changes in these indicators in China from 1990 to 2021, comparing them with global averages and across five SDI regions. Using Joinpoint Regression Software, we analyzed trends in the top ten cause-specific DALY rates in 2021. Furthermore, we employed the Bayesian Age-Period-Cohort model to forecast age-standardized rates (ASR) of mortality for the next decade.

Results

China witnessed a decrease in the ASRs of mortality (1198.16/100,000 [1098.61–1294.10] to 644.68/100,000 [555.12–735.51]) and DALYs (43085.42/100,000 [39330.62–47273.39] to 22717.19/100,000 [19748.18–25903.34]) from 1990 to 2021. During the COVID-19 pandemic, the ASRs of mortality and DALY declined in China (23009.47/100,000 [19661.21–26495.58] in 2019), but global rates and those across the five SDI (Socio-demographic Index) regions increased. Projections indicate a continued decline in the ASRs of mortality over the next decade, from 2019 to 2035 and 2021 to 2035. Notably, DALY rates for the top 10 level 2 causes in 2021 decreased over the past three decades, except for musculoskeletal disorders (AAPC% 95%CI, 0.10 [0.07–0.14], men; 0.05 [-0.02–0.13], women) and sense organ diseases (AAPC% 95%CI, 0.38 [0.33–0.43], men; 0.35 [0.30–0.41], women). LE and HALE increased across all age groups in China over the same period, although there was no significant change in the HALE/LE ratio.

Conclusion

Effective policy implementation and technological advancements could play a crucial role in alleviating disease burdens associated with aging in China, thereby reducing the country's all-cause mortality rate and enhancing the quality of life for its residents.

Peer Review reports

Text box 1. Contributions to the literature

1.Effective public health policies in China can help increase life expectancy and reduce the disability-adjusted life years (DALYs) associated with chronic diseases.

2.The mortality rate in China may increase over the next 10 years due to COVID-19.

3.Life expectancy in China increased between 1990 and 2021, but the quality of life associated with this extended longevity did not show a comparable improvement.

Introduction

China, as the world's top two most populous country [1], has undergone profound demographic shifts due to its economic development and advancements in education. The implementation of the one-child family planning policy in the 1970s further accelerated these changes [2], leading to significant alterations in the age structure of Chinese society. As a result, the challenge of an aging population has become increasingly prominent. The number of people aged 65 and over is gradually increasing worldwide, with China now accounting for 13.5% of the elderly population [3]. As the elderly population continues to grow, the prevalence of chronic non-communicable diseases has also increased in China [4], posing a significant threat to the health of older adults. Conditions such as cardiovascular and cerebrovascular diseases, diabetes mellitus, and chronic respiratory diseases, are often closely linked to the lifestyles, dietary habits, and genetic predispositions. While life expectancy in China is expected to continue rising, the impact of these diseases on the quality of life for the elderly still needs further research [5].

While mortality and life expectancy (LE) are essential indicators, they provide only partial understanding of an individual's overall well-being, health, and disease status. Therefore, it is important to explore more comprehensive indicators that account for various factors such as gender, age, disease prevalence, and socio-economic conditions. Metrics like mortality, disability-adjusted life years (DALYs), LE, and health-adjusted life expectancy (HALE) play a crucial role in assessing the health and socio-economic landscape of a population. Among these, DALYs and HALE, are particularly important, as they not only capture mortality but also reflect the loss of function resulting from illness [6, 7]. SDI stands for Socio-demographic Index, which is a composite measure for the position of a country or geographic area within the development spectrum [8].

This study aimed to explore the impact of sex, age, sociodemographic factors, the COVID-19 pandemic, and other specific causes on the shifts and disparities in mortality, DALYs, LE, and HALE in China from 1990 to 2021. The findings have the potential to provide valuable insights to inform policy-making regarding health system planning and resource allocation.

Methods

Data resource

The data used in this study were obtained from the Global Burden of Disease Study 2021 (GBD 2021), which includes information on mortality, DALYs, LE, and HALE. The GBD data were compiled through rigorous process of identification and extraction from multiple sources, including official and international websites, scholarly articles, and key data contributors. Each information was uniquely tagged and recorded in the Global Health Data Exchange (GHDx), creating a precise and reliable system for global health data analysis [9]. This dataset provides both all-age and age-standardized metrics for China, as well as global data and breakdowns across five SDI regions(high SDI, high-middle SDI, middle SDI, low-middle SDI, and low SDI regions). It covers all age groups (0–6 days, 7–27 days, 1–5 months, 6–11 months, 12–23 months, 2–4 years, and every 5-year age group up to 94 years, and 94 years and older). The original dataset can be accessed via the official website: https://www.healthdata.org/.

Variables and measurement

DALYs represent years of healthy life lost, accounting for both mortality and disability. In this study, we analyzed all-cause mortality and all-cause DALYs, and provided a separate analysis focus on cause-specific DALYs (level 2 causes). LE indicates the expected lifespan at a specific age, while HALE reflects the expected years of healthy life at a given age. In this study, LE and HALE within the 0–6 days age group were treated as equivalent to those at birth.

In the GBD study, causes of diseases and injuries are categorized into four levels; they are: three Level 1 causes, 22 Level 2 causes, 174 Level 3 causes, and 301 Level 4 causes [10]. Level 2 causes serve as a mid-level categorization, offering a balance between the broad scope of Level 1 causes and the more specific details of Level 3 and Level 4 causes. This level of categorization provides policymakers and researchers with a practical perspective that is neither too general nor overly detailed.

The SDI serves as a composite measure of development status, closely associated with health outcomes, and ranges from 0 to 1. A score of 0 reflects the lowest per capita income, minimal educational attainment, and the highest total fertility rate, while a score of 1 represents the opposite, indicating higher affluence and development [11].

Statistical analysis

We conducted a comparative analysis of mortality, DALYs, LE, and HALE in China, globally, and across various SDI regions, spanning from 1990 to 2021. The analysis took into account gender, age groups, and specific Level 2 causes. To assess trends in cause-specific DALY rates, we used Joinpoint Regression software (version 4.9.1.0; NCI). Trends were classified as increasing or decreasing only when the average annual percent change (AAPC) was statistically significant (P-value < 0.05); otherwise, they were considered stable. Additionally, we performed the Bayesian age-period-cohort (BAPC) model fitted with integrated nested Laplace approximation (INLA) to project age-standardized mortality rates from 2019 or 2020 to 2035 for China and globally with the R program (version 4.3.1).

Results

Mortality from 1990 to 2021

Over the past three decades, China has witnessed a notable increase in the total number of all-cause mortality, rising from 8,469,131.30 (95% UI 7704601.36–9223988.09) in 1990 to 11,696,257.67 (9,988,541.96–13,439,384.57) in 2021. However, the age-standardized rate (ASR) of all-cause mortality has steadily decreased, from 1,198.16 per 100,000 (1,098.61–1,294.10) in 1990 to 644.68 per 100,000 (555.12–735.51) in 2021. These trends are consistent across both genders.

On a global scale, the total number of all-cause deaths has also experienced a significant increase from 1990 to 2021, rising from 46,097,968.74 (44,939,984.95–47,306,002.08) to 67,871,076.62 (65,111,321.62–70,624,188.63). However, the trajectory of ASRs of mortality has taken a different path. Initially, global ASRs of all-cause mortality exhibited a consistent decline from 1990 (1,107.03 per 100,000, 1,082.61–1,133.36) to 2019 (735.30 per 100,000 [706.59–767.87]). However, from 2019 to 2021 this trend reversed, with ASRs rising to 835.27 per 100,000 (800.83–870.33) in 2021.

The number of all-cause deaths increased from 1990 to 2021 in high SDI, high-middle SDI, middle SDI, and low-middle SDI regions. In contrast, the number of all-cause deaths in low SDI regions increased from 1990 to 2000 and from 2019 to 2021, but decreased from 2000 to 2019. The ASRs of mortality in all five different SDI regions decreased from 1990 to 2019, however, the course was reversed to an increase from 2019 to 2021. Further details are provided in Table 1.

Table 1 Mortality and DALYs in China, the world, and five SDI Regions
Full size table

DALYs from 1990 to 2021

The number of DALYs in China and globally fluctuated for both males and females. The ASRs of DALYs in China decreased for both sexes from 1990 to 2021 (43,085.42 per 100,000 [39,330.62–47,273.39] to 22,717.19 per 100,000 [19,748.18–25,903.34]). Similarly, global ASRs of DALYs declined from 1990 to 2019 (50,765.95 per 100,000 [47,546.03–54,275.24] to 33,759.61 per 100,000 [30,669.84–37,223.12]). However, the global ASRs of DALYs exhibited an annual increase from 2019 to 2021 (33,759.61 per 100,000 [30,669.84–37,223.12] to 36,203.13 [33,062.44–39,613.73]).

The number of DALYs increased from 1990 to 2019 in high-SDI, high-middle SDI, and middle-SDI regions, while it decreased in the low-middle SDI and low-SDI regions. However, the number DALYs increased across all SDI regions from 2019 to 2021. The ASRs of DALYs decreased from 1990 to 2019 in all five different SDI regions and increased from 2019 to 2021. Further details are provided in Table 1.

Changes in mortality and DALYs from 2019 to 2021

Between 2019 and 2021, both all-cause mortality and DALYs experienced an increase in China, across the five SDI regions, and globally. Notably, the ASRs of all-cause mortality and DALYs in China decreased from 2019 to 2021 (23,009.47 per 100,000 [19,661.21–26,495.58] to 22,717.19 per 100,000 [19748.18–25903.34]), while these rates increased in the five SDI regions and worldwide.

Predictions of ASRs of mortality, 2020–2035 and 2022–2035

Based on the global ASRs of mortality from 1990 to 2019, the ASRs of mortality are projected to decrease from 719.71(662.00, 777.43) per 100,000 in 2020 to 560.05(308.34, 811.75) per 100,000 in 2035. However, based on the data from 1990 to 2021, the ASRs of global mortality showed an upward trend, from 841.49 (764.34, 918.64) per 100,00 in 2022 to 1607.53 (158.46, 3059.46) per 100,00. In contrast, the ASRs of mortality in China are expected to show downward trends over the next 10 years. The ASRs of mortality in China are projected to decline from 747.09 [623.79, 870.38] per 100,00 in 2020 to 678.83 [183.55, 1179.73] per 100,00 in 2035, according to the 1990–2019 dataset; and from 663.61 [535.14, 792.07] per 100,00 in 2022 to 518.57 [152.96, 884.41] in 2035, according to the 1990–2021 dataset (Fig. 1).

Fig. 1
figure 1

Projections of China’s and global age-standardized mortality rates, 2019–2035, and 2021 to 2035. A projection of global ASR of mortality, 2019–2035; B projection of China’s ASR of mortality, 2019–2035; C projection of global ASR of mortality, 2021–2035; D projection of China’s ASR of mortality, 2021–2035. ASR, Age-standardized rate

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The top 10 level 2 causes of DALYs rates in 2021

In 2021, the top 10 causes of DALYs rates among Chinese men, at level 2, were cardiovascular diseases (CVD), neoplasms, chronic respiratory disease, unintentional injury, mental disorders, musculoskeletal disorders, transport injuries, neurological disorders, sense organ diseases and diabetes and kidney diseases. Among Chinese women, the top 10 causes were CVD, neoplasms, musculoskeletal disorders, mental disorders, other non-communicable diseases, neurological disorders, chronic respiratory diseases, sense organ diseases, diabetes and kidney diseases, and unintentional injury (as illustrated in Fig. 2).

Fig. 2
figure 2

Trends of age-standardized DALY rates in the world and China. A ASR (/100,000) of DALYs in male, global; B ASR (/100,000) of DALYs in female, global; C ASR (/100,000) of DALYs in male, China; D ASR (/100,000) of DALYs in female, China. DALY, Disability-adjusted life year; ASR, Age-standardized rate

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Trends of the cause-specific DALY rates

The ASRs of DALY for most of the top 10 causes in China decreased from 1990 to 2021 for both men and women. This trend was observed for conditions such as CVD, neoplasms, chronic respiratory disease, unintentional injury, mental disorders, transport injuries, neurological disorders, and diabetes and kidney diseases. However, the ASRs of DALYs for musculoskeletal disorders (AAPC% 95%CI, 0.10 [0.07–0.14] in men; 0.05 [−0.02–0.13] in women) and sense organ diseases (AAPC% 95%CI, 0.38 [0.33–0.43] in men; 0.35 [0.30–0.41] in women) showed either upward or stable trends for both genders during the same period.

Except for musculoskeletal disorders and diabetes and kidney diseases, Global DALY rates for CVD, respiratory infections and tuberculosis, maternal and neonatal disorders, neoplasms, other non-communicable diseases, neurological disorders, and chronic respiratory diseases presented downward trends in both men and women, The global DALY rates for mental disorders remained stable from 1990 to 2019. However, there was a notable increase from 2019 to 2021 (Fig. 2).

The LE and HALE at birth in China and across SDI regions

In 1990, China's LE and HALE were 67.67 and 60.32 years, respectively, both were closest to the figures in the middle SDI region. In 2019, China's LE and HALE reached 77.33 and 68.46 years, respectively, which were closest to the high-middle SDI region. The LEs in 2020 and 2021 were 77.41 and 77.58 years, and both were closest to the high-middle SDI. However, the HALEs in these two years were more in line with those l in the high SDI region (68.46 years in 2020; 68.56 years in 2021). More details can be found in Table 2.

Table 2 LE and HALE at birth in China, globally, and SDI regions in 1990, 2019, 2020, and 2021
Full size table

Changes in LE, HALE, and HALE/LE in China from 1990 to 2021

China’s LE and HALE have steadily increased from 1990 to 2021, particularly among younger age groups (Fig. 3). However, the ratio of HALE to LE has remained stable in younger age groups and even declined in the old age groups. In the 1–5 months and 7–27 days age groups, both LE and HALE were longer than those in the 0–6 days age group. However, the latter group exhibited a higher HALE-to-LE ratio compared to the former two. Over the past three decades, the ratio has remained at approximately 90.0% for men at birth and 87.5% for women at birth. In the same age group, the HALE-to-LE ratio in men was higher than that in women. Additionally, the LE and HALE at birth for women were higher than for men, and both measures increased by SDI (Fig. 4).

Fig. 3
figure 3

Changes in LE, HALE, and HALE/LE% in different age groups in China, 1990- 2021. A change in LE (years) in China, 1990–2021; B change in HALE (years) in China, 1990–2021; C change in HALE/LE (%) in China, 1990–2021, male; D change in HALE/LE (%) in China, 1990–2021, female.LE, life expectancy; HALE, health-adjusted life expectancy

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Fig. 4
figure 4

Changes in LE and HALE in China with SDI, 1990–2021. LE, life expectancy; HALE, health-adjusted life expectancy. SDI, Socio-demographic Index

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Discussion

In this study, we examined the trends in all-cause mortality, Level 2 cause-specific DALYs, LE, and HALE in China from 1990 to 2021. We also conducted comparative analyses among China, global trends, and different SDI regions. Our findings revealed a consistent decline in ASRs of all-cause mortality and all-cause DALYs in China, despite the challenges posed by the COVID-19 pandemic. At the meanwhile, both LE and HALE have shown significant increases. These trends reflect the progress of China's socio-economic development,, advancements in public health, and the effectiveness of relevant policies and interventions.

While LE and HALE have both increased, we did not observe an apparent increase in HALE-to-LE ratios. This suggests that although people are living longer, a considerable portion of the extended lifespan may be spent in an unhealthy state, potentially compromising overall quality of life. Moreover, age-related conditions such as cardiovascular diseases, metabolic disorders, and cancers tend to develop and progress as an individual age [12, 13]. Therefore, actively managing the health of the aging population and slowing down the aging process of the population could effectively alleviate the burden of disease in society [14].

Additionally, as people age, they often experience loss of muscle mass and increased risks of osteoporosis [15, 16]. These contribute significantly to fall-related injuries among the elderly, which can severely impact their quality of life. To address this, it is crucial to strengthen care services for the elderly, in order to mitigate the occurrence of such accidents and improve the overall well-being of this group [17].

Over the past three decades, the ASRs of DALYs for most of the top ten cause-specific diseases have declined, except for musculoskeletal disorders and sensory organ diseases. Musculoskeletal disorders include a spectrum of conditions, such as rheumatoid arthritis, osteoarthritis, low back pain, neck pain, and gout [18]. Notably, musculoskeletal disorders have become the third leading cause of DALYs among young adults globally [19]. Women of childbearing age with MSK disorders are at an increased risk of pregnancy-related complications [20]. Moreover, these disorders strongly associated with certain occupations, particularly in sectors such as manufacturing, construction workers, and nurses [21,22,23], where they are classified as work-related musculoskeletal disorders (WMSDs) [24]. Occupations that involve high physical demands and poor ergonomic practices are particularly susceptible to WMSDs. Nevertheless, the adverse impacts of WMSDs can be mitigated through improved rehabilitative aids and widespread adoption of standardized ergonomic knowledge [25, 26].

Throughout the 30 years spanning from 1990 to 2021, both LE and HALE levels in China have shown a consistent upward trend. Despite the challenges posed by the COVID-19 pandemic, HALE in China has approached levels similar to those observed in high SDI regions, in parallel with a notable increase in China's SDI from 0.46 to 0.71. The diseases that reduce HALE are mainly cancer, cardiovascular diseases, chronic respiratory diseases, and diabetes mellitus [27]. The leading causes of the reduced HALE in Chongqing, China, are cerebrovascular diseases, cancer, and injuries [28]. During the pandemic, China implemented a range of proactive measures, including swift deployment of non-pharmacological interventions, extensive nucleic acid testing, meticulous tracing and isolation of close contacts, and the provision of free vaccination with the novel coronavirus vaccine [29,30,31]. These efforts played an important role in curtailing the mortality rate associated with respiratory infections, with reported new cases accounting for a mere 0.05% of the global total [32]. This success underscored the importance of strategic policy interventions and technological advancements in reducing disease burden. As China’s disease profile gradually shifts from that of a developing country to that of a developed one, it is essential to draw from the successful policies and project experiences of Western countries [33], particularly in areas such as preventive measures, healthcare policy, and elderly care [34, 35]. Given that the impact of a policy may take a long process to demonstrate, it is a future direction for subsequent studies to evaluate and refine these strategies.

Furthermore, the prevalence of mental disorders over the past three decades warrants attention. During the COVID-19 pandemic, the global burden of mental disorders increased significantly. In contrast, the change in the epidemiological burden of mental disorders in China was less pronounced. This discrepancy may be attributable to limited resources for mental health care in China, especially for vulnerable populations such as the elderly, adolescents, and pregnant women [36,37,38]. Physical illnesses often exacerbate mental health conditions as the two being interlinked in a harmful cycle [39]. This underscores the importance of addressing physical health concerns while simultaneously improving mental health care and support for those suffering from mental disorders [40, 41].

This study is subject to several limitations. First, it relies on secondary data from the GBD study 2021, which imposes certain inherent constraints. Second, the absence of detailed data on specific causes affecting HALE limited our ability to fully explore the factors contributing to the decline in HALE in China over the past three decades. U We hope future research, utilizing China's public health data, will provide a more detailed analysis of population aging across different regions and cities. Finally, the COVID-19 pandemic, which altered the global mortality landscape between 2019 and 2021, highlights the needs for a more comprehensive assessment of the current burden of disease in China and the world.

Conclusions

The aging process is associated with an increased prevalence of various health conditions. Effective public health policies and advancements in medical technology are crucial for enhancing the quality of life among the elderly. While drawing lessons from good experiences and advanced technologies of Western countries, it is essential to tailor these strategies to fit the specific conditions of China in order to address the challenges posed by population aging and reduce the associated disease burden.

Data availability

The data were available in the GBD website.

Abbreviations

DALYs:

Disability-adjusted life years

LE:

Life expectancy

HALE:

Health-adjusted life expectancy

SDI:

Socio-demographic Index

ASR:

Age-standardized rates

AAPC:

Average annual percent change

BAPC:

Bayesian age-period-cohort

INLA:

Integrated nested Laplace approximation

CVD:

Cardiovascular diseases

WMSDs:

Work-related musculoskeletal disorders

References

  1. Bank TW. World Bank Open Data. 2023. Available from: https://data.worldbank.org/indicator/SP.POP.TOTL?contextual=default&locations=CN.

  2. Zhang J. The evolution of China’s one-child policy and its effects on family outcomes. J Econ Perspect. 2017;31(1):141–60.

    Article  Google Scholar 

  3. Wang J, Chen C, Zhou J, Ye L, Li Y, Xu L, et al. Healthy lifestyle in late-life, longevity genes, and life expectancy among older adults: a 20-year, population-based, prospective cohort study. Lancet Healthy Longev. 2023;4(10):e535–43.

    Article  PubMed  Google Scholar 

  4. Yang G, Wang Y, Zeng Y, Gao GF, Liang X, Zhou M, et al. Rapid health transition in China, 1990–2010: findings from the Global Burden of Disease Study 2010. Lancet. 2013;381(9882):1987–2015.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Bai R, Liu Y, Zhang L, Dong W, Bai Z, Zhou M. Projections of future life expectancy in China up to 2035: a modelling study. Lancet Public Health. 2023;8(12):e915–22.

    Article  PubMed  Google Scholar 

  6. Kim YE, Jung YS, Ock M, Yoon SJ. A review of the types and characteristics of healthy life expectancy and methodological issues. J Prev Med Public Health. 2022;55(1):1–9.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. Lancet. 2024.

  8. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22.

  9. Huang D, Lai H, Shi X, Jiang J, Zhu Z, Peng J, et al. Global temporal trends and projections of acute hepatitis E incidence among women of childbearing age: age-period-cohort analysis 2021. J Infect. 2024;89(4):106250.

    Article  PubMed  Google Scholar 

  10. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402(10397):203–34.

  11. Lin Y, Jiang B, Cai Y, Luo W, Zhu X, Lin Q, et al. The global burden of glaucoma: findings from the global burden of disease 2019 study and predictions by bayesian age-period-cohort analysis. J Clin Med. 2023;12(5):1828.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Rossiello F, Jurk D, Passos JF, d’Adda di Fagagna F. Telomere dysfunction in ageing and age-related diseases. Nat Cell Biol. 2022;24(2):135–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hou Y, Dan X, Babbar M, Wei Y, Hasselbalch SG, Croteau DL, et al. Ageing as a risk factor for neurodegenerative disease. Nat Rev Neurol. 2019;15(10):565–81.

    Article  PubMed  Google Scholar 

  14. Xia C, Dong X, Li H, Cao M, Sun D, He S, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J (Engl). 2022;135(5):584–90.

    Article  PubMed  Google Scholar 

  15. Larsson L, Degens H, Li M, Salviati L, Lee YI, Thompson W, et al. Sarcopenia: aging-related loss of muscle mass and function. Physiol Rev. 2019;99(1):427–511.

    Article  PubMed  Google Scholar 

  16. Gielen E, Dupont J, Dejaeger M, Laurent MR. Sarcopenia, osteoporosis and frailty. Metabolism. 2023;145:155638.

    Article  CAS  PubMed  Google Scholar 

  17. Phelan EA, Ritchey K. Fall prevention in community-dwelling older adults. Ann Intern Med. 2018;169(11):Itc81-itc96.

    Article  PubMed  Google Scholar 

  18. Wu D, Wong P, Guo C, Tam LS, Gu J. Pattern and trend of five major musculoskeletal disorders in China from 1990 to 2017: findings from the Global Burden of Disease Study 2017. BMC Med. 2021;19(1):34.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Guan SY, Zheng JX, Sam NB, Xu S, Shuai Z, Pan F. Global burden and risk factors of musculoskeletal disorders among adolescents and young adults in 204 countries and territories, 1990–2019. Autoimmun Rev. 2023;22(8):103361.

    Article  PubMed  Google Scholar 

  20. Cao F, Li DP, Wu GC, He YS, Liu YC, Hou JJ, et al. Global, regional and national temporal trends in prevalence for musculoskeletal disorders in women of childbearing age, 1990–2019: an age-period-cohort analysis based on the Global Burden of Disease Study 2019. Ann Rheum Dis. 2024;83(1):121–32.

    Article  PubMed  Google Scholar 

  21. Zhang Y, ElGhaziri M, Nasuti S, Duffy JF. The Comorbidity of musculoskeletal disorders and depression: associations with working conditions among hospital nurses. Workplace Health Saf. 2020;68(7):346–54.

    Article  PubMed  PubMed Central  Google Scholar 

  22. He X, Xiao B, Wu J, Chen C, Li W, Yan M. Prevalence of work-related musculoskeletal disorders among workers in the automobile manufacturing industry in China: a systematic review and meta-analysis. BMC Public Health. 2023;23(1):2042.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Yang F, Di N, Guo WW, Ding WB, Jia N, Zhang H, et al. The prevalence and risk factors of work related musculoskeletal disorders among electronics manufacturing workers: a cross-sectional analytical study in China. BMC Public Health. 2023;23(1):10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hämmig O. Work- and stress-related musculoskeletal and sleep disorders among health professionals: a cross-sectional study in a hospital setting in Switzerland. BMC Musculoskelet Disord. 2020;21(1):319.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Fan LJ, Liu S, Jin T, Gan JG, Wang FY, Wang HT, et al. Ergonomic risk factors and work-related musculoskeletal disorders in clinical physiotherapy. Front Public Health. 2022;10:1083609.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hosseini E, Daneshmandi H, Bashiri A, Sharifian R. Work-related musculoskeletal symptoms among Iranian nurses and their relationship with fatigue: a cross-sectional study. BMC Musculoskelet Disord. 2021;22(1):629.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Xi JY, Zhang WJ, Chen Z, Zhang YT, Chen LC, Zhang YQ, et al. Potential gains in health-adjusted life expectancy by reducing burden of noncommunicable diseases in 188 countries: a population-based study. Value Health. 2023;26(6):802–9.

    Article  PubMed  Google Scholar 

  28. Ruan X, Li Y, Jin X, Deng P, Xu J, Li N, et al. Health-adjusted life expectancy (HALE) in Chongqing, China, 2017: an artificial intelligence and big data method estimating the burden of disease at city level. Lancet Reg Health West Pac. 2021;9:100110.

    PubMed  PubMed Central  Google Scholar 

  29. Tang JL, Li LM. Importance of public health tools in emerging infectious diseases. BMJ. 2021;375:n2374.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Tu H, Hu K, Zhang M, Zhuang Y, Song T. Effectiveness of 14 day quarantine strategy: Chinese experience of prevention and control. BMJ. 2021;375:e066121.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Zhou Y, Jiang H, Wang Q, Yang M, Chen Y, Jiang Q. Use of contact tracing, isolation, and mass testing to control transmission of covid-19 in China. BMJ. 2021;375:n2330.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Tang JL, Abbasi K. What can the world learn from China’s response to covid-19? BMJ. 2021;375:n2806.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Qiu H, Cao S, Xu R. Cancer incidence, mortality, and burden in China: a time-trend analysis and comparison with the United States and United Kingdom based on the global epidemiological data released in 2020. Cancer Commun (Lond). 2021;41(10):1037–48.

    Article  PubMed  Google Scholar 

  34. Wang H, Qin D, Fang L, Liu H, Song P. Addressing healthy aging in China: practices and prospects. Biosci Trends. 2024;18(3):212–8.

    Article  PubMed  Google Scholar 

  35. Feng Z, Wu B. Embracing challenges for population aging in China: building scientific evidence to inform long-term care policymaking and practice. J Aging Soc Policy. 2023;35(5):543–53.

    Article  PubMed  Google Scholar 

  36. Tang X, Lu Z, Hu D, Zhong X. Influencing factors for prenatal Stress, anxiety and depression in early pregnancy among women in Chongqing. China J Affect Disord. 2019;253:292–302.

    Article  PubMed  Google Scholar 

  37. Wang S, Li Q, Lu J, Ran H, Che Y, Fang D, et al. Treatment rates for mental disorders among children and adolescents: a systematic review and meta-analysis. JAMA Netw Open. 2023;6(10):e2338174.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Xiang YT, Ng CH, Yu X, Wang G. Rethinking progress and challenges of mental health care in China. World Psychiatry. 2018;17(2):231–2.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Scott KM, Lim C, Al-Hamzawi A, Alonso J, Bruffaerts R, Caldas-de-Almeida JM, et al. Association of mental disorders with subsequent chronic physical conditions: world mental health surveys from 17 countries. JAMA Psychiat. 2016;73(2):150–8.

    Article  Google Scholar 

  40. Zhang Z, Sum G, Qin VM, Zhao Y, Haregu TN, Oldenburg B, et al. Associations between mental health disorder and management of physical chronic conditions in China: a pooled cross-sectional analysis. Sci Rep. 2021;11(1):5731.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Correa H, Malloy-Diniz LF, da Silva AG. Why psychiatric treatment must not be neglected during the COVID-19 pandemic. Braz J Psychiatry. 2020;42(4):449.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This study was funded by a key discipline project under Shanghai's Three-Year Action Plan for Strengthening the Public Health System (2023-2025) (GWVI-11.1-44).

The content of this paper is solely the responsibility of the authors. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

No financial disclosures were reported by the authors of this paper.

Financial disclosure

No financial disclosures were reported by the authors of this paper.

Funding

This study was funded by the National Key Research and Development Program of China (2022YFC3600901), and a key discipline project under Shanghai's Three-Year Action Plan for Strengthening the Public Health System (2023–2025) (GWVI-11.1–44).

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Author notes

  1. Xuelin Cheng, Wenchang Jia and Jing Zhou contributed to the work equally and should be regarded as co-first authors.

Authors and Affiliations

  1. Department of Health Management Center, Zhongshan Hospital, Fudan University, No. 180 Fenglin Rd., Xuhui, 200032, Shanghai, China

    Xuelin Cheng, Wenchang Jia, Jing Zhou, Yaxin Xu, Jian Zou, Ming Liu, Sunfang Jiang & Xiaopan Li

  2. Department of General Practice, Zhongshan Hospital, Fudan University, No. 180 Fenglin Rd., Xuhui, 200032, Shanghai, China

    Xuelin Cheng & Sunfang Jiang

  3. School of Public Health, Fudan University, Shanghai, 200032, China

    Wenchang Jia

  4. Shanghai Engineering Research Center of AI Technology for Cardiopulmonary Diseases, Zhongshan Hospital, Fudan University, Shanghai, 200032, China

    Ming Liu

Authors
  1. Xuelin Cheng
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Contributions

XC, WJ, JZ, YX, XL, ML, SJ: Conceptualization, Methodology, Software. XC, WJ, JZ: Data curation, Writing- Original draft preparation. XC, WJ, JZ: Visualization, Investigation. XL, ML, SJ: Supervision. XC, WJ, JZ, YX, XL, ML, SJ: Writing- Reviewing and Editing. Sunfang Jiang: Funding acquisition.

Corresponding authors

Correspondence to Ming Liu, Sunfang Jiang or Xiaopan Li.

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Cheng, X., Jia, W., Zhou, J. et al. Changes and trends in mortality, disability-adjusted life years, life expectancy, and healthy life expectancy in China from 1990 to 2021: a secondary analysis of the global burden of disease 2021. Arch Public Health 83, 93 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13690-025-01558-8

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Archives of Public Health

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