Premature Mortality from Cardiovascular Disease in the Americas – Will the Goal of a Decline of “25% by 2025” be Met?
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases (CVD) are the underlying cause 1.6 million deaths per year in the Americas, accounting for 30% of total mortality and 38% of by non-communicable deaths diseases (NCDs). A 25% reduction in premature mortality due four main NCDs was targeted by the 2011 High-level Meeting of the General Assembly on the Prevention and Control of NCDs. While overall CVD mortality fell in the Americas during the past decade, trends in premature CVD mortality during the same period have not been described, particularly in the countries of Latin America and the Caribbean. METHODS: This is a population-based trend-series study based on a total of 6,133,666 deaths to describe the trends and characteristics of premature mortality due to CVD and to estimates of the average annual percentage of change during the period 2000-2010 in the Americas. FINDINGS: Premature mortality due to CVD in the Americas fell by 21% in the period 2000-2010 with a -2.5% average annual rate of change in the last 5 year-a statistically significant reduction of mortality-. Mortality from ischemic diseases, declined by 25% - 24% among men and 26% among women. Cerebrovascular diseases declined by 27% -26% among men and 28% among women. Guyana, Trinidad and Tobago, the Dominican Republic, Bahamas, and Brazil had CVD premature mortality rates over 200 per 100,000 population, while the average for the Region was 132.7. US and Canada will meet the 25% reduction target before 2025. Mexico, Costa Rica, Venezuela, Dominican Republic, Panama, Guyana, and El Salvador did not significantly reduce premature mortality among men and Guyana, the Dominican Republic, and Panama did not achieve the required annual reduction in women. CONCLUSIONS: Trends in premature mortality due to CVD observed in last decade in the Americas would indicate that if these trends continue, the Region as a whole and a majority of its countries will be able to reach the goal of a 25% relative reduction in premature mortality even before 2025.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".