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. 2025 Feb 15;26(1):56.
doi: 10.1186/s12931-025-03135-7.

Long-term trends in the burden of asthma in China: a joinpoint regression and age-period-cohort analysis based on the GBD 2021

Na Li  1 , Yuhan Xu  2 , Xinru Xiao  1 , Ziqi Ding  1 , Chuang Sun  1 , Qian Zhang  3   4
Affiliations

Long-term trends in the burden of asthma in China: a joinpoint regression and age-period-cohort analysis based on the GBD 2021

Na Li et al. Respir Res. .

Abstract

Background: To develop effective strategies for controlling asthma, a thorough assessment of its disease burden is essential. In this study, we examined long-term trends in the asthma burden in China over the past three decades and analyzed its epidemiological features.

Methods: We assessed the burden of asthma in China via the global burden of disease (GBD) 2021 database, focusing on prevalence, incidence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs). Additionally, we employed joinpoint analysis and age-period-cohort (apc) methods to interpret the epidemiological characteristics of asthma. Finally, we analyzed the attributable burden of asthma to gain a comprehensive understanding of its impact.

Results: The age-standardized incidence rate (ASIR) and mortality rate (ASMR) for both sexes in China shifted from 524.81 (95% UI: 421.31, 672.76) to 314.17 (95% UI: 283.22, 494.10) and from 5.82 (95% UI: 4.46, 8.50) to 1.47 (95% UI: 1.15, 1.79) per 100,000 population between 1990 and 2021. According to joinpoint analysis, the average annual percentage change (AAPC) in the age-standardized incidence rate was - 1.2 (95% CI: - 1.4, - 1.1), indicating a gradual but fluctuating decline (with significant turning points in 2005 and 2014). The apc fitting results suggest that the prevalence is now lower than it was in the past and that the relative prevalence risk is high among adolescents and middle-aged to elderly individuals, possibly due to different pathophysiological mechanisms. In 2021, the primary asthma-related burdens were metabolic risks, especially obesity.

Conclusions: In conclusion, we found that the disease burden of asthma in China has significantly decreased. However, it remains a major concern among adolescents and elderly individuals. Metabolic risk factors, particularly obesity, are the main contributors to the asthma burden. It is essential to address specific risk factors and develop targeted public health strategies for different age groups.

Keywords: Age-period-cohort analysis; Asthma; Disease burden; Epidemiological study; GBD 2021; Joinpoint regression.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: All the statistical analyses in our study were based on publicly available summary data. This study does not contain any studies with human or animal subjects performed by any of the authors, therefore, no ethical approval was needed. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Trends in asthma burden across different years for various indicators. The Y-axis represents the percentage of the total disease burden attributable to each indicator, whereas the X-axis represents the years. The red line and shaded area indicate the predicted curve and confidence interval for females, whereas the blue line represents males. The six graphs illustrate the proportions of different indicators within the overall disease burden
Fig. 2
Fig. 2
Joinpoint regression analysis of age-standardized asthma incidence rates in China from 1990 to 2021. The joinpoint regression model is used to identify significant changes in the trend (joinpoints) and to calculate the annual percentage change for each trend segment. This figure illustrates the trend segments and joinpoints, which typically coincide with significant public health developments or events
Fig. 3
Fig. 3
Age-period-cohort (apc) model analysis of asthma prevalence in China. A Age-specific changes in prevalence. B Prevalence changes by cohort. C Baseline prevalence changes across different periods. D Diagram of age-period-cohort effect estimates. To account for multiple dimensions of influencing factors in disease progression, we used the apc model to decompose asthma prevalence into age, period, and cohort effects. This analysis helps reveal the true impact of asthma by representing each age group through different cohorts across various periods. The age distribution for each period is influenced by both age and period effects
Fig. 4
Fig. 4
Attributable risk factors for DALYs in China in 1990 and 2021 among different age groups
See this image and copyright information in PMC

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