Valvular Heart Disease-Related Mortality Between Middle- and High-Income Countries During 2000 to 2019
Bibliographic record
Abstract
Background: Valvular heart disease (VHD) management has evolved rapidly in recent decades, but disparities in health care access persist among countries with varying socioeconomic backgrounds. Objectives: The purpose of this study was to investigate global mortality trends from VHD and assess the difference between middle- and high-income countries. Methods: We obtained mortality data from the World Health Organization Mortality Database for VHD and its subgroups (rheumatic valvular disease [RVD], infective endocarditis [IE], aortic stenosis [AS], and mitral regurgitation [MR]) from 2000 to 2019. Age-specific and age-standardized mortality rates per 100,000 persons in middle- and high-income countries were calculated, and trends were analyzed using joinpoint regression. Results: A total of 93 countries (42 middle-income and 51 high-income) were included in the analysis. Both middle- and high-income countries showed an increasing trend in crude VHD mortality rate. In middle-income countries, the age-standardized VHD-related mortality rate was constant (0.0%/year), with decreasing RVD (-2.7%/year) and increasing IE, AS, and MR (0.8%/year, 2.0%/year, and 2.2%/year, respectively). In high-income countries, the age-standardized VHD-related mortality rate was decreasing (-0.6%/year). However, there was a rapid increase in mortality rate from IE in age ≤39 years after 2009 (7.0%/year). Moreover, there was a decreasing mortality rate from AS after 2015 but an increasing rate from MR after 2013, particularly in age ≥80 years. Conclusions: Our study identified a rising burden of VHD-related mortality worldwide. The distribution and trends of VHD mortality differed between middle- and high-income countries. Further investigation is needed to understand the underlying etiology of these varying mortality trends in VHD and its subgroups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".