Global, regional, and national burdens of heart failure in adolescents and young adults aged 10–24 years from 1990 to 2021: an analysis of data from the Global Burden of Disease Study 2021
Bibliographic record
Abstract
Background: Prior studies suggest prevalence of heart failure (HF) has remained steady or progressively decreased over past 30 years in the general population. Whether this favourable trend occurred in adolescents and young adults aged 10-24 years has yet to be elucidated. We aim to identify the trends in the burden of HF in this young population from 1990 to 2021 to inform areas for targeted intervention and prevention efforts. Methods: We analyzed data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021. The case number and rates per 100,000 population of prevalence and years lived with disability (YLDs) of HF at the global, regional, and national level in the population aged 10-24 years from 1990 to 2021 were reported. In addition, the HF trends by age, sex, and socio-demographic index (SDI) were analyzed. Furthermore, we calculated the average annual percentage changes (AAPC) and identified the year with the most pronounced changes in the trends with the joinpoint regression analysis. In detail, we divided the study population into three age groups: 10-14 years old, 15-19 years old, and 20-24 years old. We also employed the Bayesian age-period-cohort models (BAPC) to predict the future burden of HF up to 2030. Findings: Globally, the prevalence and YLDs rates of HF among adolescents and young adults in 2021 were 148.1 (95% uncertainty interval [UI]: 118.8-185.7) and 14.4 (9.2-21.2) per 100 000 population, increased from 125.5 (100.0-157.7) and 12.2 (7.8-17.8) in 1990 respectively. Noticeable changes in HF prevalence were found in 1994, 2001, 2004, 2010, and 2018. Regionally, East Asia had the most pronounced increase in HF prevalence rate (AAPC = 1.35 [1.28-1.43]) and YLDs rate (AAPC = 1.32 [1.27-1.38]), while the highest HF prevalence rates per 100,000 population were observed in High-income North America (232 [185.4-292]). The prevalence and YLDs of HF increased in most countries except Australia, Canada, and Spain. The largest increase in HF prevalence rate was observed in China (AAPC = 1.39 [1.31-1.48]). By SDI quintile, the middle-SDI quintile countries had the largest increase in prevalence and YLDs rates. By sex, males had a higher prevalence rate per 100,000 population than females (158.0 [95% UI: 126.7-198.9] vs 137.6 [95% UI: 110.0-172.2]) in 2021. Among three age groups, the largest increase in HF prevalence from 1990 to 2021 was found in individuals aged 20-24 years (AAPC = 0.61 [0.6-0.61]). Among all causes of HF, cardiomyopathy and myocarditis accounted for the highest proportion (32.7%) of prevalence cases of HF in 2021, followed by congenital birth defects (27.3%), and rheumatic heart disease (23.8%). The BAPC analysis predicted that the cases of HF prevalence and YLDs would show a rising trend from 2022 to 2030. Interpretation: The burden of HF in adolescents and young adults aged 10-24 years was still increasing globally, which may be obscured by the burden trend of general population. According to different underlying causes of HF, both high-income countries and low- and middle-income countries need to better prevent HF in adolescents and young adults. Funding: National Natural Science Foundation of China (grant 81900452) and Training Fund for Open Projects at Clinical Institutes and Departments of Capital Medical University (grant CCMU2022ZKYXY003).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".