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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».