Global trends in heart failure from 1990 to 2019: An age‐period‐cohort analysis from the Global Burden of Disease study

Zeye Liu; Ziping Li; Xinqing Li; Yiming Yan; Jinyang Liu; Jing Wang; Jingyuan Guan; Anran Xin; Fengwen Zhang; Wenbin Ouyang; Shouzheng Wang; Ruibing Xia; Yakun Li; Yi Shi; Jing Xie; Yuhui Zhang; Xiangbin Pan

doi:10.1002/ehf2.14915

. 2024 Jun 27;11(5):3264–3278. doi: 10.1002/ehf2.14915

Global trends in heart failure from 1990 to 2019: An age‐period‐cohort analysis from the Global Burden of Disease study

Zeye Liu ^1,^2,^3,^4,⁵, Ziping Li ^2,^3,^4,⁵, Xinqing Li ^5,⁶, Yiming Yan ^2,^3,^4,^5,⁷, Jinyang Liu ^2,^3,^4,⁵, Jing Wang ^5,⁶, Jingyuan Guan ^5,⁶, Anran Xin ^5,⁶, Fengwen Zhang ^2,^3,^4,⁵, Wenbin Ouyang ^2,^3,^4,⁵, Shouzheng Wang ^2,^3,^4,⁵, Ruibing Xia ⁸, Yakun Li ⁹, Yi Shi ^1,^✉, Jing Xie ^10,^✉, Yuhui Zhang ^5,^6,^✉, Xiangbin Pan ^2,^3,^4,^5,^✉

^¹Department of Cardiac Surgery, Peking University People's Hospital, Peking University, Beijing, China

^²Department of Structural Heart Disease, National Center for Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

^³National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing, China

^⁴Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing, China

^⁵National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China

^⁶Heart Failure Center, National Center for Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

^⁷Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, Henan, China

^⁸Department of Medicine, University Hospital Munich, Ludwig‐Maximilians‐University Munich, Munich, Germany

^⁹Laboratory of Experimental Intensive Care and Anesthesiology, Academic Medical Center, Amsterdam, The Netherlands

^¹⁰Department of Pharmacy, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China

Correspondence to: Yi Shi, Department of Cardiac Surgery, Peking University People's Hospital, Peking University, No. 11 Xizhimen South Street, Xicheng District, Beijing, China. Email: shiyi1088@126.com; Jing Xie, Department of Pharmacy, Zhongda Hospital, School of Medicine, Southeast University, No. 87 Dingjiaqiao, Nanjing, Jiangsu Province, China. Email: 3119090250@stu.cpu.edu.cn; Yuhui Zhang, Heart Failure Center, National Center for Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, China. Email: yuhuizhangjoy@126.com; Xiangbin Pan, Department of Structural Heart Disease, National Center for Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, China. Email: panxiangbin@fuwaihospital.org

^✉

Corresponding author.

PMCID: PMC11424301 PMID: 38937863

Abstract

Aims

This study aimed to analyse the global prevalence and disability trends of heart failure (HF) from 1990 to 2019, considering both sexes and country‐specific economic strata.

Methods

This study conducted a secondary analysis employing data from the Global Burden of Disease (GBD) study. The analysis is stratified by sex and Socio‐demographic Index (SDI) levels. Through age‐period‐cohort and Joinpoint regression analyses, we investigated the temporal trends in HF prevalence and years lived with disability (YLDs) during this period.

Results

Between 1990 and 2019, the global prevalence of HF surged by 106.3% (95% uncertainty interval: 99.3% to 114.3%), reaching 56.2 million cases in 2019. While all‐age prevalence and YLDs increased over the 30 year span, age‐standardized rates decreased by 2019. Countries with higher SDI experienced a more pronounced percentage decrease compared with those with lower SDI. Longitudinal analysis revealed an overall improvement in both prevalence and YLDs for HF, albeit with notable disparities between SDI quintiles and sexes. Ischaemic heart disease and hypertensive heart disease emerged as the most rapidly increasing and primarily contributing causes of HF, albeit with variations observed across different countries. The average annual percentage change for prevalence and YLDs over the period was −0.26% and −0.25%, respectively.

Conclusions

This study offers valuable insights into the global burden of HF, considering factors such as population aging, regional disparities, sex differences and aetiological variations. The findings hold significant implications for healthcare planning and resource allocation. Continued assessment of these trends and innovative strategies for HF prevention and management are crucial for addressing this pressing global health concern.

Keywords: age‐period‐cohort analysis, Global Burden of Disease study, heart failure, prevalence, years lived with disability

Introduction

Heart failure (HF) stands as a global epidemic, posing a notable public health challenge. ¹ By 2019, reported cases of HF worldwide had reached approximately 56.19 million. ² With an aging population, advancements in evidence‐based diagnostics and treatments and increased life expectancy, its prevalence is projected to increase further, raising concerns about its impact on global healthcare expenditure. ³ , ⁴ In the United States, projections indicate that healthcare costs for HF patients will surpass US$50 billion by 2030. ⁵

Considerable epidemiological variations in HF are evident at both national and regional levels, influenced by variations in socio‐economic status, healthcare policies and access to medical services. ⁴ Remarkably, the estimated mortality rate for HF in low‐income countries exceeds that of high‐income countries by more than three‐fold and surpasses that of middle‐income countries by over twice. ⁶ Examining these epidemiological trends at a macro level not only lays the groundwork for enhancing public health at a national scale but also facilitates the judicious allocation of medical resources and the formulation of effective health strategies. Nonetheless, most studies have focused on nation‐specific analyses ⁷ , ⁸ or lacked a comprehensive exploration of the effects of age, period, birth cohort and cross‐country comparisons. ² , ⁴

This study aimed to analyse the global prevalence and disability trends of HF from 1990 to 2019, considering both sexes and country‐specific economic strata. We analysed available data on HF, drawing from the Global Burden of Disease (GBD) study spanning from 1990 to 2019. A comprehensive age‐period‐cohort model was employed to evaluate the complex interplay among age, period, birth cohort and the burden of HF. Countries and regions with varying socio‐economic statuses were compared in terms of various factors such as sex and aetiology.

Methods

Data source and definitions

This study draws upon data from the 2019 GBD database. ⁹ The GBD 2019 comprehensively evaluates the burden of 369 diseases, injuries and 87 risk factors across 204 countries or territories. Detailed methodologies for GBD 2019 have been previously documented. ⁹ , ¹⁰ The estimation of GBD involves identifying multiple relevant data sources for each disease or injury by employing 343 sources, including 31 reporting incidences, 192 reporting prevalence and 120 reporting other metrics. ⁹ The Washington University Institutional Review Committee carefully reviewed and approved the waiver of informed consent, predicated on the utilization of de‐identified and aggregated GBD 2019 data. This study strictly adhered to the Reporting Guidelines for Accurate and Transparent Health Estimates (GATHER checklist).

GBD 2019 introduces a comparative risk assessment (CRA) approach, underpinned by a causal framework and a hierarchy of risk factors. Within this framework, HF is categorized as a Level 1 impairment, clinically defined by studies employing structured clinical signs and symptom‐based criteria—the Framingham or European Society of Cardiology (ESC) criteria. The Framingham criteria necessitate patients with HF to satisfy two major criteria or one major criterion and two minor criteria, whereas the ESC criteria mandate typical signs and symptoms caused by structural and/or functional cardiac abnormalities. HF is subdivided into four severity subgroups based on clinical symptoms: treated, mild, moderate and severe HF, ¹¹ with prevalence estimated for American College of Cardiology/American Heart Association (ACC/AHA) stages C and D, representing symptomatic and asymptomatic diagnosed individuals. ⁹

This study aimed to analyse the global prevalence of HF and the associated years lived with disability (YLDs), expressed in absolute counts, all‐age rates and age‐standardized rates (ASRs), considering both sexes collectively as well as disaggregated by gender. Additionally, the percentage change in these metrics between 1990 and 2019 was assessed. YLD provides a measure of the burden endured due to a disease or disability over a specified period. The dataset utilized for this analysis was extracted from the Global Health Data Exchange (GHDx) query tool, which is accessible at https://vizhub.healthdata.org/gbd‐results. All estimates derived from the GBD study were accompanied by 95% uncertainty intervals (UIs), computed from the 25th and 975th ordered values obtained from 1000 samples of the posterior distribution. ⁹ , ¹⁰

Moreover, our investigation incorporated the Socio‐demographic Index (SDI) for each country and region. The SDI serves as a comprehensive indicator of a nation's or region's level of development, encapsulating variables such as average income per capita, mean educational attainment (among individuals aged ≥15 years) and total fertility rate (among individuals aged <25 years). ¹⁰ The SDI scale ranges from 0 to 1, with higher values indicating a more advanced socio‐economic status. In 2019, countries and territories were divided into five distinct SDI categories: high, high‐middle, middle, low‐middle and low SDI.

Statistical analysis

The age‐period‐cohort model was employed to examine the interrelations among age, period and birth cohort concerning the prevalence and disability of HF. ¹² Age effects encapsulate alterations occurring universally across all age groups, while the relative risks associated with period and cohort effects delineate the ASRs relative to the reference group for each respective period and cohort. The age‐period‐cohort model employs two pivotal metrics: the net drift, signifying the overall estimated annual percentage change (APC) in the ASR across time, and the local drift, representing the estimated APC over time specific to each age bracket. ¹³ The age‐period‐cohort model, constructed based on a log‐linear Poisson model, enables the estimation of cumulative effects over a Lexis diagram (Table S1). The specifications and applications of the age‐period‐cohort model have been detailed in previous studies. ¹² , ¹³

In this study, the input data for the age‐period‐cohort model consisted of population, prevalence and YLD data related to HF impairment from various countries and territories between 1990 and 2019. This encompassed 19 age groups, 6 period cohorts and 24 birth cohorts. The fitted APC model calculated overall temporal trends in prevalence/YLDs by combining age‐period‐cohort models, which are presented using net drift, representing the APC obtained after accounting for the aforementioned effects. ¹² , ¹³

Finally, we applied a Joinpoint regression model to scrutinize temporal trends in the prevalence and YLDs of HF from 1990 to 2019. The average annual percentage change (AAPC) served as a comprehensive yet compact measure of trends in HF burden over a fixed interval, calculated as a weighted average of the APC. Significant change points (join points) in APC trends were identified through the Joinpoint regression analysis. ¹⁴ , ¹⁵ , ¹⁶ Extensive descriptions and illustrative examples of both the age‐period‐cohort model and the Joinpoint regression model can be accessed in the Supplemental Methods section provided in the supporting information. Seven countries were selected for analysis: Australia, the United States, the United Kingdom, Brazil, China, India and Ethiopia, representing a spectrum of SDI levels ranging from high to low, respectively.

All statistical tests were conducted with a two‐tailed approach, where statistical significance was defined as P < 0.05. Data analysis was performed using R software Version 4.3.0. The Joinpoint regression model and trend analysis were specifically implemented using Joinpoint Regression Program Version 4.9.1.0, accessible at https://surveillance.cancer.gov/joinpoint/.

Results

Global trends in HF prevalence and YLDs from 1990 to 2019

Between 1990 and 2019, ischaemic heart disease and hypertensive heart disease emerged as the most rapidly escalating and primarily contributing causes of HF, both in terms of prevalence (Figure 1A,B) and YLDs (Figure S1). Notably, they also tended to lead to moderate‐to‐severe cases (Figure 1C).

Causes and outcomes of global heart failure (HF): (A) temporal trends of contributing causes to the prevalence (n × 1 000 000) of HF in both sexes across 204 countries and territories from 1990 to 2019. The solid lines and shaded areas represent the case numbers and their corresponding 95% uncertainty intervals. During this period, the prevalence of ischaemic heart disease increased by 98.7% and hypertensive heart disease by 137.9%. (B) Composition of contributing causes in 1990 and 2019. In 1990, ischaemic heart disease and hypertensive heart disease accounted for 29.0% and 43.6%, respectively, of all contributing causes of HF. By 2019, these proportions had changed to 33.5% and 41.1%, respectively. (C) Alluvial diagram representing the transformation patterns between contributing causes and outcomes of HF in 2019. Ischaemic heart disease and hypertensive heart disease constituted 37.6% and 29.2% of all contributing causes, respectively, while severe HF accounted for 37.7% and 33.2%, respectively.

Over the past three decades, the total global prevalence of HF cases has surged by 106.3% (95% UI: 99.3% to 114.3%), exceeding 50 million in 2019. The overall increase in all‐age prevalence rate reached 726.3 cases per 100 000 population (Table 1). However, the global ASR of HF prevalence in 2019 was 711.9 cases per 100 000 population, indicating a notable decrease of −7.06% (95% UI: −10.1% to −3.60%) compared with the rate three decades prior (766.0 cases per 100 000 population) (Table 1). The YLD rate exhibited a parallel trend. In 2019, the all‐age YLD rate attributable to HF increased by 28.8% (95% UI: 34.0% to 24.2%) compared with 1990, yet the ASR of YLDs decreased by −6.83% (95% UI: −9.88% to −3.38%) (Table 1). These disparities between ASRs and all‐age rates persist across sex disparities (Table S2), highlighting the necessity of considering age composition in assessing HF prevalence and burden.

Table 1.

Trends in HF prevalence and YLDs across SDI quintiles during 1990–2019.

	Global		High SDI		High‐middle SDI		Middle SDI		Low‐middle SDI		Low SDI
	1990	2019	1990	2019	1990	2019	1990	2019	1990	2019	1990	2019
Population
Number, n × 1 000 000	5350 (5239, 5460)	7737 (7483, 7993)	822	1013	1150	1430	1717	2397	1130	1764	528	1128
Percentage of global, %	100	100	15.4	13.1	21.5	18.5	32.1	39.6	21.1	22.8	9.90	14.6
Prevalent cases
Number ^a , n × 1 000 000	27.2 (22.2, 33.4)	56.2 (46.4, 67.8)	9.30 (7.64, 11.2)	14.5 (12.4, 16.9)	7.45 (6.00, 9.19)	15.4 (12.6, 18.8)	6.98 (5.64, 8.68)	17.7 (14.2, 22.0)	2.45 (2.00, 3.03)	6.20 (5.02, 7.72)	1.05 (0.83, 1.32)	2.46 (1.20, 3.05)
Percentage of global, %	100	100	34.1	25.8	27.3	27.3	25.6	31.4	9.00	11.0	3.87	4.37
Percentage change of prevalent cases, 1990–2019, %	106.3 (99.3, 114.3)		55.8 (43.6, 70.4)		106.2 (98.0, 116.0)		153.1 (147.4, 159.0)		152.9 (145.9, 159.9)		133.0 (127.6, 139.2)
All‐age prevalence rate
Rate, per 100 000	509.3 (414.4, 623.5)	726.3 (600.3, 876.2)	1131.4 (929.1, 1364.8)	1430.0 (1225.8, 1664.4)	647.4 (521.2, 798.6)	1073.9 (881.4, 1312.3)	406.4 (328.7, 505.5)	736.8 (594.0, 916.9)	217.2 (176.7, 268.1)	351.6 (284.4, 437.5)	199.6 (158.1, 250.2)	217.6 (175.2, 270.3)
Percentage change of rate, 1990–2019, %	42.6 (37.8, 48.2)		26.4 (16.5, 38.2)		65.9 (59.2, 73.7)		81.3 (77.2, 85.6)		61.9 (57.5, 66.4)		9.03 (6.51, 11.9)
Age‐standardized prevalence rate
Rate, per 100 000	766.0 (626.3, 936.0)	711.9 (591.2, 858.3)	900.4 (746.5, 1079.9)	746.4 (644.8, 863.8)	776.9 (630.1, 951.5)	772.3 (636.8, 936.6)	813.6 (658.6, 1009.1)	787.4 (639.0, 977.1)	502.0 (408.1, 627.0)	513.1 (417.0, 641.5)	534.5 (425.1, 676.7)	546.0 (437.2, 684.6)
Percentage change of rate, 1990–2019, %	−7.06 (−10.1, −3.60)		−17.1 (−22.8, −10.6)		−0.60 (−3.99, 3.07)		−3.22 (−5.06, −1.40)		2.22 (0.46, 3.93)		2.15 (0.27, 4.11)
Net drift of prevalence ^b , % per year	−0.11 (−0.19, −0.02)		−0.21 (−0.33, −0.09)		0.06 (−0.03, 0.14)		0.08 (−0.02, 0.18)		0.07 (−0.01, 0.16)		0.16 (0.06, 0.26)
YLDs
Number ^a , n × 1000	2447.9 (1594.5, 3534.1)	5050.3 (3281.0, 7262.1)	843.8 (542.1, 1227.1)	1311.2 (869.5, 1846.0)	668.2 (431.8, 965.8)	1381.1 (884.8, 1999.4)	623.5 (395.3, 909.1)	1583.8 (1003.5, 2307.3)	217.1 (141.3, 315.0)	551.9 (354.6, 804.1)	94.2 (61.2, 136.8)	220.0 (142.8, 319.7)
Percentage change of YLDs, 1990–2019, %	106.3 (99.2, 114.5)		55.4 (43.4, 69.6)		106.7 (98.3, 117.0)		154.0 (147.7, 160.1)		154.2 (146.6, 161.6)		133.6 (127.7, 140.1)
All‐age YLDs
Rate, per 100 000	45.8 (66.1, 29.8)	65.3 (93.9, 42.4)	102.7 (149.3, 66.0)	129.4 (182.2, 85.8)	58.1 (84.0, 37.5)	96.6 (139.8, 61.9)	17.8 (25.9, 11.6)	19.5 (28.3, 12.7)	19.2 (27.9, 12.5)	31.3 (45.6, 20.1)	36.3 (53.0, 23.0)	66.1 (96.3, 41.9)
Percentage change of rate, 1990–2019, %	28.8 (34.0, 24.2)		8.67 (18.2, 0.48)		44.8 (51.8, 38.8)		17.0 (20.8, 13.6)		52.7 (57.6, 47.8)		52.3 (56.7, 47.7)
Age‐standardized YLDs
Rate, per 100 000	68.6 (44.3, 98.7)	63.9 (41.5, 92.0)	81.7 (52.7, 118.2)	67.7 (45.4, 95.4)	69.5 (44.4, 100.2)	69.4 (44.5, 100.0)	72.2 (46.4, 104.6)	70.4 (45.1, 102.5)	44.1 (28.4, 63.8)	45.5 (29.2, 66.1)	47.3 (30.3, 68.0)	48.6 (30.9, 70.2)
Percentage change of rate, 1990–2019, %	−6.83 (−9.88, −3.38)		−17.1 (−22.6, −10.6)		−0.10 (−3.48, 3.72)		−2.53 (−4.56, −0.57)		3.12 (1.13, 5.04)		2.68 (0.66, 4.89)
Net drift of YLDs ^b , % per year	−0.10 (−0.18, −0.02)		−0.21 (−0.33, −0.09)		0.08 (−0.01, 0.16)		0.09 (−0.01, 0.19)		0.10 (0.02, 0.19)		0.17 (0.07, 0.27)

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Note: Age‐standardized prevalence/YLD rate is computed by direct standardization with the global standard population in GBD 2019.

Abbreviations: GBD, Global Burden of Disease; HF, heart failure; SDI, Socio‐demographic Index; YLDs, years lived with disability.

^{^a}

Parentheses for all GBD health estimates indicate 95% uncertainty intervals; parentheses for net drift indicate 95% confidence intervals.

^{^b}

Net drifts are estimates derived from the age‐period‐cohort model and denote the overall annual percentage change in mortality, which captures the contribution of the effects from calendar time and successive birth cohorts.

Global and regional trends in HF prevalence and YLDs across SDI quintiles from 1990 to 2019

Significant variations in the prevalence and YLDs of HF were observed across different SDI quintiles (Tables 1 and S2 and Figures 2 and S2). While SDI quintiles and ASRs of prevalence and YLDs for HF did not exhibit any notable correlations, the corresponding rates in countries with low and low‐middle SDI were considerably lower than the global level (Table 1). A consistent trend emerged, where countries with higher SDI levels exhibited a greater percentage decrease in ASRs of prevalence and YLDs, whereas those with lower SDI levels demonstrated less improvement (Figures 2 and S2). This consistent pattern held true for both sexes. However, males consistently exhibited higher ASRs of prevalence and YLDs for HF compared with females, regardless of SDI quintile (Figures 2 and S2).

Relationship between Socio‐demographic Index levels and sex‐specific age‐standardized rate (ASR) of prevalence and percentage change of ASR of prevalence for heart failure in 204 countries and territories during 1990–2019. Each coloured dot represents a country, with a solid black line depicting the non‐linear fitting trend. (A) ASR of prevalence and (B) percentage change of ASR of prevalence. ASPR, age‐standardized prevalence rate.

The contributing causes of HF across the SDI quintiles revealed noteworthy trends. Hypertensive heart disease and ischaemic heart disease persist as the primary drivers of HF, irrespective of the SDI level in countries and regions. Excluding these two factors, the proportion of HF prevalence attributed to non‐rheumatic valvular heart disease surged from 0.37% in low SDI to 8.46% in high SDI. Moreover, the proportions of congenital heart disease and rheumatic heart disease increased from −0.44% and −2.10% in high SDI to −3.40% and −5.86% in low SDI, respectively. This pattern is mirrored in the trends observed for YLDs (Figure S3).

Tables S3 and S4 and Figures S4 and S5 present a comprehensive breakdown of HF prevalence and YLDs, encompassing absolute counts, all‐age rates and ASRs, along with their corresponding percentage changes, for both sexes combined in all 204 countries or territories worldwide.

Temporal trends in HF prevalence and YLDs across age groups from 1990 to 2019

Figures S6 and S7 illustrate an inverse relationship between SDI quintiles and the prevalence and YLDs of HF among individuals under 70 years of age. This suggests that populations residing in countries or territories with lower SDI are predisposed to experiencing HF at younger ages. In 2019, the ASRs of prevalence and YLDs among populations under 70 years old were 30.0% and 30.4% in high SDI countries, respectively, contrasting with 52.2% and 52.7% in low SDI countries. Regardless of the SDI quintile, females tend to exhibit HF prevalence and YLDs at a significantly later age than males, while males are more susceptible to experiencing the burden of HF in midlife. Moreover, over the past three decades, there has been a gradual and steady shift in global HF prevalence and YLDs towards the elderly (age 85 and above). This trend is more pronounced in countries with a higher SDI compared with those with a lower SDI.

From 1990 to 2019, across all age groups and irrespective of sex, the prevalence and YLD rates of HF exhibited strong decreasing trends among older age cohorts. This trend was particularly notable in countries or territories characterized by higher SDI quintiles and was considerably more pronounced among older females than older males (Figures S8 and S9).

Age, period and cohort effects on HF prevalence and YLDs

Estimates derived from the age‐period‐cohort model reveal a discernible yet modest decline in the global ASR of HF prevalence {−0.11% [95% confidence interval (CI): −0.19% to −0.02%] per year} and YLDs [−0.10% (95% CI: −0.18% to −0.02%) per year]. This reduction is prominent in countries with high SDI, while lower SDI quintiles exhibit increased trends in both prevalence and YLDs. The global net drift highlights an absolute decline in HF prevalence and YLDs among individuals over 65 years of age, irrespective of sex. High SDI countries demonstrate substantial reductions in HF prevalence and YLDs among individuals over 40 years of age, but a local drift towards increased trends is observed in the 20–35 age group (Table 1 and Figures 3, S10 and S11). Furthermore, disparities in the net drift between males and females are discernible. Globally, ASRs of HF prevalence exhibit slightly smaller reductions among males [−0.08% (95% CI: −0.17% to 0%) per year for males vs. −0.14% (95% CI: −0.22% to −0.06%) per year for females] and YLDs [−0.07% (95% CI: −0.16% to 0.01%) per year for males vs. −0.13% (95% CI: −0.21% to −0.05%) per year for females] compared with females (Table S2 and Figures 3, S10 and S11).

Age‐period‐cohort effects of prevalence rate for heart failure during 1990–2019: (A) local drifts; (B) age effects; (C) period effects; and (D) cohort effects.

Longitudinal analysis of sex‐specific age effects revealed a progressive increase in HF prevalence and YLDs with advancing age, irrespective of SDI, particularly after 50 years of age (Figures 3 and S12).

Period effects demonstrate a global downward trend in both the prevalence and YLDs of HF throughout the study period. During the first half of the period from 1990 to 2019, high SDI countries experienced the most notable decline in period effects, although no notable decrease was observed in the past decade. In countries with high‐middle SDI, a consistent increase was observed during the first half of the 30 year period, followed by a recent but negligible decrease. Conversely, no significant decrease in period effects was observed in low SDI, low‐middle SDI and middle SDI countries over the study period (Figures 3, S10 and S11).

On a global scale, no significant reduction in the risk of HF prevalence and disability has been noted among recent birth cohorts. Substantial decreases were only observed among birth cohorts predating the 1940s, with reductions persisting in high SDI countries until the 1970s, but no significant decreases were seen in subsequent cohorts. Furthermore, no noticeable distinction was observed in low SDI countries. Compared with individuals born in the mid‐20th century, the relative cohort risk of HF prevalence for those born in the most recent cohort (2013–2017) was 1.15 (95% CI: 1.02 to 1.29) for low SDI countries and 1.16 (95% CI: 0.84 to 1.59) for high SDI countries. Similar trends were observed for cohort effects on YLDs (Figure 3, S10 and S11).

Long‐term trends in HF prevalence and YLDs from 1990 to 2019

Figure 4 illustrates the trends over three decades in both the prevalence and YLDs associated with HF. Globally, there is a discernible declining pattern in both HF prevalence and YLDs. The AAPC for prevalence and YLDs over the entire period was −0.26% and −0.25%, respectively. Between 1990 and 2019, the worldwide ASRs of prevalence and YLDs for HF went through six distinct phases, with five of them showing noteworthy shifts. Notably, the most substantial declines in prevalence rates of −0.51% and −0.57% were observed during the periods of 1994–2004 and 2010–2014, respectively. Similarly, YLDs also exhibited the most substantial declines during these periods (−0.50% and −0.57%, respectively).

Joinpoint regression analysis of the age‐standardized rates (ASRs) of prevalence and years lived with disability (YLDs) for heart failure by Socio‐demographic Index (SDI) quintiles in 204 countries and territories during 1990–2019: (A) ASR of prevalence and (B) ASRs of YLDs. AAPC, average annual percentage change; APC, annual percentage change. * indicates statistical significance.

However, the overall decreasing trend in ASRs of prevalence and YLDs was observed exclusively in countries with high (AAPC for prevalence: −0.66%; AAPC for YLDs: −0.66%) and middle SDI countries (AAPC for prevalence: −0.12%; AAPC for YLDs: −0.09%). In contrast, other countries exhibited nearly constant or marginal increases. While the ASRs of prevalence and YLDs in countries with low and low‐middle SDI rankings were relatively modest, the overall trend indicated a gradual increase over the past three decades. Notably, in all SDI quintiles of countries, both the prevalence and disability associated with HF exhibited a consistent upward trend over the last 2–5 years. The most recent period of 2017–2019 witnessed more substantial increases in global prevalence and YLDs at 0.63% and 0.64%, respectively (Figure 4). Sex‐specific Joinpoint regression revealed that the recent surge in the global burden of HF is primarily attributable to females. Between 2015 and 2019, males experienced a smaller positive APC of 0.07% and 0.08% for ASRs of prevalence and YLDs, respectively. In contrast, for females, these figures were markedly significant at 1.11% and 1.13%, respectively, between 2017 and 2019 (Figures S12 and S13).

Temporal trends in HF prevalence and YLDs in exemplary countries

Figures 5 and S14–S21 present data from seven exemplary countries representing various SDI quintiles. Australia, as a high SDI nation, illustrates a net drift of −1.27% (95% CI: −1.41% to −1.12%) per annum in prevalence, with consistently low local drift rates across most age groups. Both period and birth cohort risks exhibit a downward trajectory (Figures 5 and S14). Similarly, the United Kingdom demonstrates favourable age‐period‐cohort effects, exhibiting a notable decline in risk over successive periods and birth cohorts. However, the <40‐year‐old age group does not demonstrate substantial improvements in HF prevalence and YLDs, while the older demographic exhibits a decline (Figures 5 and S14). The overall risk of HF prevalence and disability decreased in these countries, coupled with shifts in age distribution, indicating substantial decreases in the burden among older age groups and no upsurge in risk among recent birth cohorts—a trend typically observed in high SDI countries (Figures S16–S19).

Age‐period‐cohort effects of age‐standardized rates (ASRs) of prevalence for heart failure (HF) from 1990 to 2019 on exemplar countries. Each of the six horizontal graphs represents (1) temporal change in the relative proportion of ASRs of prevalence for HF from 1990 to 2019; (2) temporal change of the prevalence rate for HF from 1990 to 2019; (3) local drifts; (4) age effects; (5) period effects; and (6) cohort effects.

Despite its high SDI classification, the United States ranks the highest in ASRs of prevalence (1198.1 cases per 100 000 population) and YLDs (108.4 cases per 100 000 population) in 2019, with the net drift in these metrics increasing from 1990 to 2019 (Tables S3 and S4). Notably, prevalence and disability exhibit an increasing trend among individuals aged 25–40 years, indicating heightened risk in new birth cohorts after the 1980s (Figures 5 and S14). Additionally, the country does not exhibit a decrease in prevalence and YLDs among the elderly population (Figures S18 and S19).

As a middle SDI country, Brazil has witnessed a notable decrease in HF prevalence and YLDs over the past three decades, with a net drift of −0.33% (95% CI: −0.39% to −0.27%) per annum and −0.23% (95% CI: −0.30% to −0.15%), respectively (Tables S3 and S4), accompanied by an AAPC of −0.31% and −0.25%, respectively (Figures S20 and S21). Additionally, it demonstrates potential for reducing the burden of HF across all age groups, particularly notable in recent births (Figures 5 and S14). Furthermore, the country exhibits characteristics typical of those found in high SDI settings, with a gradual shift in the age distribution of HF prevalence and YLDs towards the older age group (Figures S14–S19). However, Brazil faces a significant threat from Chagas disease as an uncommon cause of HF (Figure S15).

In 2019, approximately one third of the world's HF cases were concentrated in China, where the ASRs of prevalence (1032.8 cases per 100 000 population) and YLDs (92.6 cases per 100 000 population) ranked third globally, indicating a substantial basic burden of HF (Tables S3 and S4). The positive net drift revealed an overall increase in both prevalence and disability risks for HF between 1990 and 2019, particularly among the post‐1940 birth cohorts. A downward trend in prevalence and YLDs was observed only among individuals over 60 years of age (Figures 5 and S14). However, there was a positive ASR of AAPC (Figures S20 and S21). Additionally, China is disproportionately affected by HF caused by hypertensive heart disease (Figure S15). Moreover, as a country with moderate SDI and a large population, India did not exhibit high numbers of HF prevalence cases and YLDs in 2019 compared with the global level (Tables S3 and S4). However, no significant improvement was observed in the age‐period‐cohort effect over the 30 year period, indicating an inactive net drift (Figures 5 and S14) and AAPC (Figures S20 and S21). Rheumatic heart disease plays a significant role in the aetiology of HF in India (Figure S15).

As a low SDI country, Ethiopia has exhibited a notable increase in net drift, indicating a progressive escalation in the relative risk of HF prevalence and YLDs across various periods and birth cohorts (Figures 5 and S14). This notable increase suggests a concerning trend in the development of HF within the Ethiopian population. The aetiology of HF in the country may be closely associated with conditions such as cardiomyopathy, myocarditis and congenital heart disease (Figure S15).

Discussion

This study comprehensively examined global trends in HF prevalence and YLDs from 1990 to 2019, with a particular focus on population aging, regional disparities, sex differentials and aetiological variations. To our knowledge, this study represents the first application of the age‐period‐cohort model to assess temporal trends in HF prevalence and disability on a global scale.

Population aging and HF burden

Between 1990 and 2019, the global population increased by 45%, while the total number of HF cases doubled. However, when considering ASRs, both prevalence and YLDs exhibited a significant decrease. Hence, the aging of the global population is the primary contributor to the escalating burden of HF. ⁵ , ¹⁷ Population aging is a universal phenomenon, notably pronounced in high SDI countries, as indicated by the prevalence of HF across all age groups with lower figures than the ASRs in all SDI quintiles except for those classified as high SDI. Therefore, relying on ASRs to determine trends in HF prevalence in these countries could yield misleading results. Given the higher incidence of HF among the elderly, it is anticipated that this prevalence will continue to increase substantially, propelled by advancements in diagnostic tools and the advent of highly effective therapeutic interventions leading to markedly improved patient outcomes. Consequently, a relatively prolonged disease duration is expected. ⁴ Currently, HF is frequently defined as a cardiovascular syndrome prevalent among older adults, carrying a burden of multimorbidity and frailty that markedly magnifies both individual and societal impacts of HF. ¹⁸ Notably, randomized trials often fail to adequately represent older populations due to exclusion criteria, emphasizing the need for further investigations to improve outcomes within this demographic. ¹⁹ However, the prevailing trend of population aging holds significant implications for healthcare systems worldwide, emphasizing the imperative need for the formulation of comprehensive strategies to manage and prevent HF among older adults.

Regional disparities in HF

Achieving equitable access to primary healthcare is a central objective outlined in the United Nations Sustainable Development Goals. ⁴ This study focuses on assessing HF prevalence across countries stratified by their SDI quintiles. Significant disparities in both period and cohort effects related to HF prevalence were observed across various SDI regions. Previous research has established a link between socio‐economic status and HF incidence. ²⁰ Moreover, studies have demonstrated the importance of integrating socio‐economic status measures into coronary heart disease risk assessment. ²¹ However, our investigation did not reveal a direct correlation between HF prevalence or YLDs and SDI quintiles. On the contrary, countries classified as low and low‐middle SDI exhibit a lower prevalence and disability burden from HF cases, constituting only 13% of global instances. This difference can be attributed to several underlying factors. First, low SDI countries face challenges in accessing medical management, leading to underdiagnosis and delayed HF detection, potentially resulting in an underestimation of its prevalence. Second, prognosis in low SDI countries is hampered by the lack of effective therapeutic interventions, resulting in a shorter disease duration. This observation is significant, given that approximately 80% of the global burden of cardiovascular disease occurs in middle‐ and low‐income countries. ²¹

The evident resource constraints in low SDI countries are underscored by the notable percentage increase observed in the burden of HF over time. Consequently, concerted efforts by governments and communities are imperative to implement effective prevention and management programmes. Encouragingly, there are noteworthy examples. The United Kingdom stands out for its relatively favourable period and cohort effect. The nation's policy advocates for providing multidisciplinary team‐based care for all HF patients, delivered in an integrated manner across healthcare facilities. ²² In Brazil, a middle SDI country with a positive age‐period‐cohort effect, strategic investments, healthcare system reforms and effective legislative and regulatory measures in tobacco control and the promotion of healthy lifestyles have notably influenced the landscape. These efforts have played a pivotal role in mitigating the burden of HF within the country. ²³ Notably, in India, the Indian Council of Medical Research has allocated funding for establishing an HF registry. This initiative aims to collect data from 10 000 patients across 53 hospitals nationwide. The endeavour holds promise in providing valuable insights to support evidence‐based interventions aimed at addressing HF effectively. ²⁴

Sex differences in HF burden

This study highlights consistent disparities between sexes in the prevalence and YLDs associated with HF. Across all SDI quintiles, males consistently exhibit higher ASRs of prevalence and YLDs compared with females. The recent increase in the global risk of HF prevalence and disability over the past 5 years has been primarily driven by females (Figures S12 and S13). Given that sex‐specific mechanistic alterations in HF may act as determinants in its onset, ²⁵ particularly changes related to preserved ejection fraction, we hypothesize that the recent surge in HF prevalence among females is closely linked to novel diagnostic criteria and innovative treatment approaches for HF with preserved ejection fraction (HFpEF). ¹¹ Patients with HFpEF are mostly female, elderly or associated with multiple chronic conditions, unlike patients with a reduced ejection fraction. ²⁶ This shift in paradigm has led to a greater number of HFpEF patients, especially females, receiving accurate diagnosis and effective treatment, contributing to an amplified overall disease burden. ²⁷ Despite the significant impact of HF on morbidity and mortality in females, they are generally underrepresented in randomized trials focusing on HF. ²⁵ This highlights the urgent need for targeted research and interventions aimed at gaining a deeper understanding of sex‐specific factors contributing to the mechanisms, risks and outcomes of HF.

Aetiology differences and implications

The examination of the contributing causes of HF across SDI quintiles yields valuable insights into the diverse factors influencing the HF burden across different countries. Hypertensive heart disease and ischaemic heart disease, prevalent in higher SDI countries, ²⁸ collectively contribute to 40% and 15% of HF cases within the population, respectively. ¹³ Conversely, countries with low SDI contend with nutritional deficiencies, infections and causes linked to congenital abnormalities. For example, the increasing incidence of rheumatic heart disease associated with streptococcal infections, particularly among children and adolescents in low and low‐middle SDI regions, ²⁹ may partly explain the adverse recent cohort effect observed in these areas. Chagas disease, caused by the protozoan Trypanosoma cruzi, is the primary cause of non‐ischaemic cardiomyopathy in South America and represents a distinct aetiological factor for HF in that region. ³⁰ Advances in early screening, surgical interventions and healthcare practices have improved the prognosis for patients with congenital heart conditions, contributing to the rising prevalence. ³¹ This could also contribute to the increased trends in prevalence and disability risks associated with HF observed in recent global birth cohorts. In contrast, countries with middle SDI are undergoing a rapid epidemiological transition, characterized by a shift in disease burden towards degenerative or chronic conditions. ³² , ³³ These findings emphasize the crucial importance of considering regional contexts and aetiological determinants when devising effective interventions and healthcare policies.

Limitations and future research

While this study offers valuable insights into global HF trends, it has certain limitations. First, the reliance on secondary data sources and potential disparities in data quality across countries may lead to bias. The GBD 2019 study, particularly in low SDI countries, primarily depended on covariates associated with HF or trends in neighbouring countries due to limited primary data availability. Second, our focus on prevalence and YLDs excludes other critical aspects, such as mortality and patterns of healthcare utilization. Future research should consider multifaceted drivers of HF trends, encompassing risk factors, healthcare accessibility and therapeutic interventions, to inform targeted policies for mitigating the burden of HF. Third, because of the lack of data, HF severity (e.g., left ventricular ejection fraction, B‐type natriuretic peptide level and New York Heart Association functional classification) could not be analysed comprehensively. A comprehensive evaluation would have provided insights into the types of HF requiring particular attention in each country. Fourth, arrhythmia, especially atrial fibrillation, a notable cause of HF, was not considered due to limitations in GBD data. ³⁴ Fifth, the assumptions made in GBD data for estimating the aetiological causes of HF have inherent limitations, assuming each case of HF has only one cause and relying on input data from individual regions and countries. ⁹ Sixth, this study did not assess socio‐economic status within a country but rather examined HF prevalence by country SDI. We cannot infer any information about HF prevalence or socio‐economic status within a country. Lastly, further investigation is warranted into the impact of emerging diseases and global health crises, such as the COVID‐19 pandemic, on HF trends.

Conclusions

This study elucidates the evolving global burden of HF. The findings emphasize the complex interaction of population aging, regional disparities, sex differences and aetiological variations in influencing the prevalence and disability of HF. These insights hold considerable importance for healthcare policymakers, researchers and practitioners, guiding healthcare planning, resource allocation and the development of focused interventions to mitigate the increasing burden of HF within an aging global population. Future research efforts should continue to monitor these trends and creatively address HF prevention and management.

Funding

This study was financially supported by the CAMS Innovation Fund for Medical Sciences (2021‐I2M‐1‐065), the National Key R&D Program of China (2022YFC2503400, 2023YFC2412705), the National High Level Hospital Clinical Research Funding (2022‐GSP‐GG‐18, 2023‐GSP‐RC‐04, 2023‐GSP‐RC‐17, 2023‐GSP‐QN‐28), the Development Project of National Major Scientific Research Instrument (82327801) and the Sanming Project of Medicine in Shenzhen (SZSM202011013).

Conflict of interest statement

The authors declare no conflicts of interest.

Supporting information

Table S1. The Lexis diagram of GBD 2019 data used for the APC model.

Table S2. Trends in sex‐specific HF prevalence and YLDs across SDI quintiles during 1990–2019.

Table S3. The temporal change of prevalent for HF by SDI quintiles in 204 countries and territories during 1990–2019.

Table S4. The temporal change of YLDs for HF by SDI quintiles in 204 countries and territories during 1990–2019.

Figure S1. Temporal trends of contributing causes for YLDs number (n × 1000) of HF with both sexes combined in 204 countries and territories during 1990–2019.

Figure S2. Relationship between SDI quintiles and the sex‐specific age‐standardized YLDs rate and percent change of age‐standardized YLDs rate for HF in 204 countries and territories during 1990–2019.

Figure S3. Composition and breakdown of contributing causes for HF by SDI quintiles in 2019.

Figure S4. The world map of age‐standardized prevalence rate (per 100,000 persons) and percent change (%) of age‐standardized prevalence rate for HF in 204 countries and territories during 1990–2019.

Figure S5. The world map of age‐standardized YLDs rate (per 100,000 persons) and percent change (%) of age‐standardized YLDs rate for HF in 204 countries and territories during 1990–2019.

Figure S6. The temporal change in the relative proportion of sex‐specific prevalence for HF by SDI quintiles across different age groups in 204 countries and territories during 1990–2019.

Figure S7. The temporal change in the relative proportion of sex‐specific YLDs for HF by SDI quintiles across different age groups in 204 countries and territories during 1990–2019.

Figure S8. The temporal change of the sex‐specific prevalence rate for HF by SDI quintiles in 204 countries and territories during 1990–2019.

Figure S9. The temporal change of the sex‐specific YLDs rate for HF by SDI quintiles in 204 countries and territories during 1990–2019.

Figure S10. Age‐period‐cohort effects of prevalence rate for HF by SDI quintiles in 204 countries and territories during 1990–2019.

Figure S11. Age‐period‐cohort effects of YLDs rate for HF by SDI quintiles in 204 countries and territories during 1990–2019.

Figure S12. The Joinpoint regression analysis of the sex‐specific age‐standardized prevalence rate for HF by SDI quintiles in 204 countries and territories during 1990–2019.

Figure S13. The Joinpoint regression analysis of the sex‐specific age‐standardized YLDs rate for HF by SDI quintiles in 204 countries and territories during 1990–2019.

Figure S14. Age‐period‐cohort effects of age‐standardized YLDs rate for HF from 1990 to 2019 on exemplar countries.

Figure S15. Composition and breakdown of contributing causes for HF in 2019 on exemplar countries.

Figure S16. The temporal change in the relative proportion of sex‐specific prevalence for HF across different age groups from 1990 to 2019 on exemplar countries.

Figure S17. The temporal change in the relative proportion of sex‐specific YLDs for HF across different age groups from 1990 to 2019 on exemplar countries.

Figure S18. The temporal change of the sex‐specific prevalence rate for HF from 1990 to 2019 on exemplar countries.

Figure S19. The temporal change of the sex‐specific YLDs rate for HF from 1990 to 2019 on exemplar countries.

Figure S20. The Joinpoint regression analysis of the sex‐specific age‐standardized prevalence rate for HF from 1990 to 2019 on exemplar countries.

Figure S21. The Joinpoint regression analysis of the sex‐specific age‐standardized YLDs rate for HF from 1990 to 2019 on exemplar countries.

EHF2-11-3264-s001.docx^{(22.6MB, docx)}

Acknowledgements

This study employed high‐quality data from previous studies conducted by collaborators on the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, which advanced cardiovascular medicine.

Liu, Z. , Li, Z. , Li, X. , Yan, Y. , Liu, J. , Wang, J. , Guan, J. , Xin, A. , Zhang, F. , Ouyang, W. , Wang, S. , Xia, R. , Li, Y. , Shi, Y. , Xie, J. , Zhang, Y. , and Pan, X. (2024) Global trends in heart failure from 1990 to 2019: An age‐period‐cohort analysis from the Global Burden of Disease study. ESC Heart Failure, 11: 3264–3278. 10.1002/ehf2.14915.

Zeye Liu, Ziping Li and Xinqing Li contributed equally to this work.

Yi Shi, Jing Xie, Yuhui Zhang and Xiangbin Pan are co‐corresponding authors.

[Correction added on 22 July 2024, after first online publication: Affiliation 5 has been changed to Affiliation 1 and subsequent affiliations have been renumbered in this version.]

Contributor Information

Yi Shi, Email: shiyi1088@126.com.

Jing Xie, Email: 3119090250@stu.cpu.edu.cn.

Yuhui Zhang, Email: yuhuizhangjoy@126.com.

Xiangbin Pan, Email: panxiangbin@fuwaihospital.org.

Data availability statement

This article is part of the GBD Collaborators Network and is based on the GBD protocol (Contact ID: 0034o00001nHH4NAAW). The data employed in this study are publicly available from the Institute for Health Metrics and Evaluation (IHME) at https://vizhub.healthdata.org/gbd‐results.

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