Abstract
Background and aims
Heart failure (HF) represents a major global public health challenge. This study aims to report the HF prevalence and disability in mainland China from 1990 to 2021, as well as the underlying etiology.
Methods
Data on HF-related prevalence and years lived with disability (YLDs) were obtained from Global Burden of Disease 2021 study for mainland China. Analyses were conducted by age and sex, with the burden rates expressed per 100,000 population. Age-period-cohort models were used to describe the trajectory of HF during 1990–2021. We analyzed the potential etiologies of HF, and performed decomposition analysis to quantify the main drivers of changes in the burden.
Results
In 2021, the prevalence and YLDs rates of HF in mainland China were 920.7 per 100,000 population (95 % uncertainty interval [UI]: 795.7–1080.8) and 90.7 per 100,000 population (95 % UI: 60.9–124.8), respectively. Both the prevalence and YLDs rates were higher in males than in females. From 1990 to 2021, the crude prevalence rate and crude YLDs rate of HF consistently increased, while the age-standardized prevalence rate and age-standardized rate of YLDs exhibited an double-peak trend. By 2021, ischemic heart disease had surpassed hypertensive heart disease as the leading cause of HF prevalence. Population aging was the largest contributor to changes in HF burden.
Conclusion
Our analysis of the GBD 2021 study indicated that the burden of HF is projected to increase substantially with an aging population. Strengthened societal efforts are urgently needed to prevent and manage HF from its causes, with particular attention to older adults.
Keywords: Disease burden, Etiology, Heart failure, Hypertension, Ischemic heart disease
Graphical abstract
Highlights
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Between 1990 and 2021, the crude prevalence and years lived with disability (YLDs) rates of HF in mainland China showed a consistent increase.
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By 2021, ischemic heart disease had surpassed hypertensive heart disease as the leading cause of HF prevalence in mainland China.
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Population aging emerged as the largest contributor to the increasing HF burden in mainland China.
1. Introduction
Heart failure (HF) is a global public health epidemic that poses significant healthcare and socioeconomic challenges [1]. By 2021, approximately 55.5 million cases of HF had been reported worldwide [2]. In China, the nationally representative estimate of HF cases was 13.7 million [3]. However, with advancements in evidence-based medicine, an aging population, and increased life expectancy, the prevalence of HF is rising. Chinese government has integrated cardiovascular diseases management into national strategies like Healthy China 2030. Despite advancements, significant challenges persist in equitable resource distribution and care quality, particularly for HF. One of the United Nations' Sustainable Development Goals (SDGs) for 2030 is to reduce premature prevalence from non-communicable diseases by one-third, a target that can be partially achieved by mitigating the burden of HF [4,5].
The Global Burden of Disease (GBD) study provides a systematic and comprehensive assessment of health losses caused by diseases, injuries, and etiologies across various ages, sexes, and geographic regions [2]. Building on the 2019 framework, GBD 2021 study incorporates a larger dataset and implements further refinements to its computational methodologies, enhancing both rigor and standardization. This investigation has provided detailed analyses of global patterns and regional disparities in HF [2].
We analyzed HF data from GBD 2021 in mainland China from 1990 to 2021. We evaluated the burden and trends of prevalence and disability, identifies the primary etiologies contributing to this burden, and aims to propose more targeted strategies for the prevention and management of HF in China.
2. Methods
2.1. Overview
GBD 2021 database provides comprehensive data on the burden of 371 diseases and injuries across 204 countries and territories and 21 global geographic regions, with detailed regional analyses [2]. The conceptual framework and analytical methods of the GBD study have been extensively described in previous publications [6,7].
In this study, we calculated the HF burden in mainland China in terms of prevalence and years lived with disability (YLDs) across different age groups and sexes. Trends over the past three decades were also analyzed. All rate estimates were expressed per 100,000 population and reported with corresponding 95 % uncertainty intervals (UIs). Age-standardized rates (ASRs) of burden were calculated using the GBD standard population as reference [2].
2.2. Case definition and data sources
In the GBD 2021 study, a structured standard was employed for the clinical diagnosis of HF. Diagnosis was based on the Framingham criteria or the European Society of Cardiology guidelines. Patients meeting the American College of Cardiology (ACC)/American Heart Association (AHA) stages C and D criteria were included, encompassing both those currently symptomatic and those previously diagnosed with HF but asymptomatic at present. The estimation of HF burden and its etiology relied on data extracted from published literature, hospital inpatient records, and claims databases [2]. Detailed information on the original data sources can be found in the GBD 2021 Data Input Sources Tool (http://ghdx.healthdata.org/gbd-2021/data-input-sources) [2].
2.3. Data processing and estimating
The estimation model for HF utilized spatiotemporal Gaussian process regression and the Bayesian mixed-effects meta-regression tool DisMod-MR 2.18. This tool integrates epidemiological data from systematic literature reviews, hospital records, and claims databases to generate internally consistent estimates of epidemiology [8]. Further methodological details have been previously published [7,9].
2.4. Underlying causes
In the GBD 2021 study, impairments are categorized according to etiology and disease hierarchy [2]. The causes of HF investigated in this study are based on specific cardiovascular diseases at the third level of the hierarchy, including atrial fibrillation and flutter, cardiomyopathy and myocarditis, chronic obstructive pulmonary disease, endocarditis, hypertensive heart disease, ischemic heart disease, non-rheumatic valvular heart disease, pulmonary arterial hypertension, rheumatic heart disease, and other cardiovascular and circulatory diseases [2].
2.5. Decomposition analysis
This study analyzed changes in burden and causes of HF from 1990 to 2021, which are essential for guiding policy formulation and intervention strategies. These changes can be attributed to population growth, aging, and epidemiological shifts [10,11]. The decomposition analysis utilized methods developed by Das Gupta in demographic research [12], enabling the identification of complex changes of HF burden. This method decomposes changes in disease burden into contributions from population aging (A), population growth (P), and changes in age-specific rates (M). The decomposition results include both the absolute and relative contributions of each factor to the overall change in disease burden [12].
2.6. Joinpoint regression models
Joinpoint regression models are particularly useful for analyzing long-term disease data with multiple trend segments [13,14]. This approach identifies "joinpoints" that divide time-series data into distinct segments, calculating the annual percent change (APC) for each segment. This allows for a comprehensive analysis of burden trends from 1990 to 2021 [15]. We applied Joinpoint software (version 4.9.1.0, https://surveillance.cancer.gov/joinpoint/), developed by the Division of Cancer Control and Population Sciences at the U.S. National Cancer Institute. The optimal number of joinpoints was selected using the software's default model optimization method, the Monte Carlo permutation test.
2.7. Bayesian Age-Period-Cohort (BAPC) models
To forecast the future prevalence and YLDs of HF over the next 15 years, we employed Bayesian Age-Period-Cohort models [16]. These models offer a robust framework for projections, utilizing integrated nested Laplace approximations (INLA) for full Bayesian inference. We further grouped the data by different age categories and predicted the future burden trends of HF for each group.
2.8. Statistical analysis
A descriptive analysis was performed to assess the burden of HF in China, with a focus on age groups and sex. We compared the prevalence, YLDs, rates, and ASR from 1990 to 2021. All analyses were conducted using R software (version 4.1.2).
3. Results
3.1. Burden of heart failure in mainland China
In 2021, an estimated 13,099,726.6 prevalence cases (95 % [UI]: 11,320,895.1–15,376,466.9) and 1,290,809.7 YLDs (95 % UI: 865,894.0–1,775,731.2) were attributed to HF in mainland China. The crude rate of prevalence and YLDs for HF in 2021 were 920.7 per 100,000 population (95 % UI: 795.7–1080.8) and 90.7 per 100,000 population (95 % UI: 60.9–124.8), respectively (Table 1). Males had higher prevalence and YLDs numbers attributable to HF compared to females, with corresponding rates also being higher in males (Table 1).
Table 1.
Heart failure prevalence, YLDs and rates per 100,000 people in 1990 and 2021 for China.
|
Prevalence |
YLDs |
||||||
|---|---|---|---|---|---|---|---|
|
1990 |
2021 |
1990 |
2021 |
||||
| Number (95 % UI) | Rate (95 % UI) | Number (95 % UI) | Rate (95 % UI) | Number (95 % UI) | Rate (95 % UI) | Number (95 % UI) | Rate (95 % UI) |
| 4683302.6 (4036498.8,5435212.2) | 398.1 (343.1462.0) | 13099726.6 (11320895.1,15376466.9) | 920.7 (795.7,1080.8) | 459519.9 (313552.4,630784.8) | 39.1 (26.7,53.6) | 1290809.7 (865894.0,1775731.2) | 90.7 (60.9124.8) |
| 2446912.9 (2117087.7,2826444.2) | 403.2 (348.9465.8) | 6800802.7 (5851380.9,7956347.8) | 934.0 (803.6,1092.7) | 241029.5 (163609.4,327720.7) | 38.4 (26.0,53.2) | 673358.8 (451327.6,919470.4) | 92.5 (62.0,126.3) |
| 2236389.7 (1921922.3,2610941.8) | 392.6 (337.4458.4) | 6298923.9 (5450492.1,7423301.0) | 906.8 (784.6,1068.7) | 218490.4 (148115.1,302949.2) | 39.7 (27.0,54.0) | 617450.9 (415903.1,862450.9) | 88.9 (59.9124.2) |
YLDs: years lived with disability, UI: uncertainty interval.
The prevalence and YLDs rates of HF in 2021 were more than twice those in 1990. From 1990 to 2021, the overall burden of HF showed a progressive increase, with an estimated annual percentage change (EAPC) of 2.75 for prevalence (95 % CI: 2.68–2.82) and an EAPC of 2.76 for YLDs (95 % CI: 2.70–2.81) (Fig. 1). However, the age-standardized prevalence rate (ASPR) and ASR-YLDs followed an overall double-peak trajectory, with a control of the upward trend after 2015 (Fig. 1). Predictions using the BAPC model suggested that both ASPR and ASR-YLDs would continue to rise over the next 15 years (Fig. S1–S2). ASPR was projected to rise from 687.88 per 100,000 (95 % CI 678.62–697.14) in 2023 to 715.47 (95 % CI 689.76–741.18) by 2035, and ASR-YLDs from 70.16 per 100,000 (95 % CI 66.03–74.28) to 83.07 (95 % CI 71.85–94.29).
Fig. 1.
Trend of heart failure burden by sex in China from 1990 to 2021 ASPR: age-standardized prevalence rate, ASR: age-standardized rate, YLDs: years lived with disability.
3.2. Joinpoint regression analysis
From 1990 to 2021, the Joinpoint regression analysis showed the APC of crude prevalence rates ranged from 1.923 (95 % CI: 1.738–2.108) to 3.136 (95 % CI: 3.104–3.168) (Fig. 2A, Table S1), and 1.347 (95 % CI: 0.913–1.783) to 5.450 (95 % CI: 4.751–6.153) (Fig. 2B–Table S1) of crude YLDs rates. However, the Joinpoint regression analysis of ASR revealed a bimodal pattern, with two significant peaks observed in 2000 and 2012. After 2012, the upward trend of HF burden plateaued, indicating stabilization in ASR (Fig. 2C–D).
Fig. 2.
National APC of heart failure burden from Joinpoint regression analysis APC: annual percent change.
3.3. Sex and age differences
In 1990, the highest prevalence of HF was observed in the 70–74 age group (male: 488,177.1, 95 % UI: 390,565.6–602,673.1; female: 424,023.0, 95 % CI: 335,454.7–526,232.6) (Fig. 3A). Before the age of 75, the prevalence of HF was higher in males than females, but this pattern reversed after 75. After the age of 85, the prevalence in females gradually surpassed that in males (Fig. 3A). The age and sex distribution of YLDs burden in 2021 followed a similar pattern of prevalence (Fig. 3B). By 2021, the highest number of HF cases was found in the 70–74 age group (male: 1,372,222.5, 95 % CI: 1,072,605.7–1,714,437.1; female: 1,121,530.9, 95 % CI: 877,375.9–1,394,878.7) (Fig. 3C). Before the age of 80, the number of HF cases in males was higher than in females, but this trend reversed after 80. Similarly, the prevalence in females surpassed that in males after 85 (Fig. 3C). The distribution of YLDs burden in 2021 also showed a similar trend (Fig. 3D).
Fig. 3.
Heart failure burden by age and sex in 1990 and 2021 Dotted and dashed lines indicate respectively 95 % upper and lower uncertainty intervals.
In 2021, the HF prevalence and YLDs increased across all age groups compared to 1990, with the overall burden shifting towards older age groups. Predictions based on the BAPC model suggested that from 2021 to 2035, both ASPR and ASR-YLDs for HF would continue to rise in most age groups, except for the 90–94 and 95+ age groups, which were projected to show a declining trend (Fig. S3–S4).
3.4. Analysis of etiology to YLDs
Analysis of etiology in 2021 revealed that the greatest HF burdens were attributable to hypertensive heart disease and pulmonary arterial hypertension (both 100 %), followed by cardiomyopathy and myocarditis (74.11 %). The primary etiological contributors to HF were hypertensive heart disease (127.76 per 100,000) and ischemic heart disease (86.17 per 100,000) (Fig. S5). From 1990 to 2021, the burden of various etiologies contributing to HF showed significant growth (Fig. S5). By 2021, a notable shift in the ranking of contributing factors was observed: ischemic heart disease ascended to the leading position (322.42 per 100,000), surpassing hypertensive heart disease, which ranked second (274.97 per 100,000), followed by cardiomyopathy and myocarditis, and rheumatic heart disease (Fig. S5).
3.5. Decomposition analysis
In both 2019 and 2021, changes in HF prevalence were predominantly driven by three independent factors. The most substantial contribution came from shifts in age structure (6,158,669.7 per 100,000, 79.17 %), followed by population growth (1,623,336.4 per 100,000, 19.29 %) (Fig. S6). Similarly, for HF YLDs, the ranking of contributing factors mirrored that of prevalence, with aging being the primary determinant (605,396.82 per 100,000, 72.83 %) and population growth ranking second (159,613.09 per 100,000, 19.2 %) (Fig. S6).
4. Discussion
This study offered the latest and most comprehensive analysis of the burden and trends of HF in mainland China from 1990 to 2021. It emphasized the impact of population growth, aging, sex and age disparities, and the underlying etiologies contributing to the HF burden. The findings revealed that HF remains a significant burden in China, with prevalence and YLD rates more than doubling over the study period and projected to rise further. Population Aging and growth were identified as the predominant drivers, while ischemic heart disease and hypertensive heart disease emerged as the leading etiological contributors.
HF has become a major non-communicable disease, posing substantial challenges to public health and imposing considerable economic and healthcare burdens. Our analysis indicated consistent trends in HF prevalence and YLD rates in China over the past 30 years. Notably, the upward trajectory of ASPR and ASR-YLDs has shown effective control recent years. However, the overall HF burden continued to escalate, primarily driven by population aging. Projections for the next 15 years suggested a continued rise in the HF burden across most age groups.
4.1. Population aging and the burden of HF
Over the past three decades, the crude rate of HF burden has shown a persistent upward trend, whereas the increase in ASR has been effectively controlled since 2012–2015. Age-related conditions such as cardiovascular diseases, neuropsychiatric disorders, and malignancies significantly contribute to the current burden of heart failure. Concurrently, major infectious disease outbreaks during 1990–2021 – particularly SARS and COVID-19 – represent notable risk factors for heart failure development. Decomposition analysis highlighted that population aging is the most significant contributor to the rise in HF prevalence in 2021 compared to 1990, underscoring the profound influence of changes in age structure on the overall HF burden in China. These findings caution against relying solely on ASR to assess HF burden or trends, as it may obscure the true impact of population aging. With the rapid progression of population aging, greater emphasis should be placed on addressing the healthcare needs of older adults, particularly the very elderly [17,18]. This includes overcoming the prevalent exclusion of older adults from clinical research [17,18] and implementing more tailored strategies for disease prevention, treatment, care, and rehabilitation to address the unique health challenges of an aging society.
4.2. Sex differences in HF burden
In 2021, the overall prevalence and YLD rates of HF were higher in males than in females. However, among individuals aged over 85 years, the HF burden in females surpassed that of males. This observation underscores the significant influence of increasing life expectancy and population aging on the future HF burden in females. Despite the growing impact of HF on the prevalence and YLD rates among elderly women, a persistent issue remains in cardiovascular-focused randomized controlled trials, where female representation continues to be insufficient [[19], [20], [21]]. This highlights an pressing need for further research and targeted interventions to better elucidate sex-specific factors in HF prevention and treatment, ensuring more inclusive approaches to address the rising burden in this demographic.
4.3. Etiology of HF
Beyond the influences of sex and age, several modifiable cardiovascular etiologies (rather than risk factors) were strongly associated with the burden of HF. We assessed the contributions of 10 cardiovascular etiologies to HF in the Chinese population. The findings revealed that ischemic heart disease and hypertensive heart disease had the greatest impact on ASR-YLDs of HF in both 1990 and 2021, followed by cardiomyopathy and myocarditis, and rheumatic heart disease. These results align with global-level HF survey analyses from 2019 [22]. Notably, ischemic heart disease has surpassed hypertensive heart disease as the leading cause. This is closely related to improved management of severe hypertension and a significant increase in the number of people with coronary heart disease due to increased risk factors for coronary heart disease over the past three decades. Hypertension, a major risk factor for HF [23,24], contributes to myocardial structural changes through prolonged elevated blood pressure, leading to hypertensive heart disease. Additionally, hypertension can also promote ischemic heart disease—both of which are key contributors to HF development [[23], [24], [25]]. Consequently, a deeper understanding of HF etiology, alongside heightened efforts in prevention, timely treatment, and proactive management of HF-related diseases like effective prevention through Health in All Policies and Health in All Laws [26], will be pivotal in mitigating and improving the growing burden of HF.
5. Limitations
Despite the methodological rigor and statistical robustness of this study, several limitations must be acknowledged. First, there is an inherent delay in data collection. Additionally, the data on HF in mainland China lacks province-specific details. Future studies should focus on gathering more granular data at the provincial level to better understand regional disparities.
6. Conclusions
This study highlights significant progress in the control and management of HF in China. However, the burden of HF in mainland China continues to rise, primarily driven by population growth and aging. Ischemic heart disease and hypertensive heart disease have become the most significant underlying etiologies contributing to the HF burden. These findings underscore the urgent need for targeted healthcare policies and interventions to mitigate the growing national burden of HF.
CRediT authorship contribution statement
Zhang Fang: Writing – original draft, Software, Methodology, Investigation, Formal analysis. Xiaofang Luo: Writing – original draft, Software, Methodology, Investigation, Formal analysis. Yuhui Zhang: Writing – review & editing, Validation, Supervision, Conceptualization. Jian Zhang: Writing – review & editing, Validation, Supervision, Funding acquisition, Conceptualization.
Ethics approval
Ethical approval from an institutional review board and informed consent from patients were not required for this study, as it constitutes a secondary analysis of publicly available and previously published data.
Consent for publication
All the authors have agreed with manuscript well for its submission.
Data availability
All the HF data in this work are publicly accessible at https://vizhub.healthdata.org/gbd-results/.
Funding
This work was supported by Beijing Natural Science Foundation (Grant number: 7222143).
Declaration of competing interst
The authors declare no competing financial interests or personal relationships that could have influenced this work.
Acknowledgments
We thank the Global Burden of Disease Collaboration Network, IHME, the Bill and Melinda Gates Foundation and Beijing Natural Science Foundation. We thank JD_GBDR (version 2.35, Jingding Medical Technology Co., Ltd.) study group for figure drawing and ChatGPT (https://chat.openai.com/) for language editing.
Handling Editor: Dr D Levy
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijcrp.2025.200497.
Contributor Information
Yuhui Zhang, Email: zhangyuhui@fuwai.com.
Jian Zhang, Email: fwzhangjian62@126.com.
ABBREVIATIONS
- AAPC
average annual percentage change
- ASPR
age-standardized prevalence rate
- ASR
age-standardized rate
- ASR-YLDs
age-standardized rate of years lived with disability
- CI
confidence interval
- BAPC
Bayesian Age-Period-Cohort
- GBD
global burden of disease
- HF
heart failure
- UI
uncertainty interval
- YLDs
years lived with disability
Appendix A. Supplementary data
The following are the Supplementary data to this article.
figs1.
figs2.
figs3.
figs4.
figs5.
figs6.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All the HF data in this work are publicly accessible at https://vizhub.healthdata.org/gbd-results/.