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 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 | 0.000 | 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".