Global, Regional, and National Burden of Valvular Heart Disease, 1990 to 2021
Bibliographic record
Abstract
BACKGROUND: Valvular heart disease poses an escalating global health challenge with an increasing impact on mortality and disability. This study aims to comprehensively analyze the global burden of valvular heart disease. METHODS AND RESULTS: Using the Global Burden of Disease 2021 data, we analyzed the prevalence and disability-adjusted life years, examining implications across demographics and geographic regions. In 2021, an estimated 54.8 million (95% uncertainty interval [UI], 43.3-67.6) cases of rheumatic heart disease, 13.3 million (95% UI, 11.4-15.2) cases of nonrheumatic calcific aortic valve disease (CAVD), and 15.5 million (95% UI, 14.5-16.7) cases of nonrheumatic degenerative mitral valve disease (DMVD) were reported globally. Despite the rising prevalence, disability-adjusted life years declined between 1991 and 2021. Among individuals aged 70 years or older, the age-standardized prevalences were 1803.6 per 100 000 (95% UI, 1535.5-2055.7) for CAVD and 2148.9 per 100 000 (95% UI, 2001.4-2310.1) for DMVD. Sub-Saharan Africa had the highest age-standardized prevalence for rheumatic heart disease; Conversely, high-income regions led in CAVD and DMVD prevalence. Rheumatic heart disease had the highest age-standardized prevalence of 1184.2 per 100 000 (95% UI, 932.4-1478.2) in low Socio-Demographic Index (SDI) regions, whereas CAVD peaked at 349.8 per 100 000 (95% UI, 303.6-395.8) in high SDI regions. The most substantial increases in age-standardized prevalences of CAVD from 1990 to 2021 occurred in the middle SDI and low-middle SDI regions. A parallel trend was noted for DMVD. CONCLUSIONS: Rheumatic heart disease remains a significant burden in low SDI regions, whereas CAVD and DMVD pose challenges in high SDI regions with aging populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".