AFTER 20 YEARS OF SUSTAINED REDUCTION, RECENT TRENDS IN CARDIOVASCULAR DISEASE BURDEN HAVE STAGNATED OR REVERSED TO INCREASE IN THE REGION OF THE AMERICAS
Bibliographic record
Abstract
Objective: To assess the levels, trends, and socio-demographic inequalities of the Cardiovascular diseases (CVD) burden in countries of the Americas, with the ultimate purpose of providing evidence to guide programmatic interventions and health system responses in the context of the World Health Organization's HEARTS Initiative, and the Resolve To Save Life's Global Cardiovascular Health Program. Design and method: This descriptive population-based study used data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2017. We examined disability-adjusted life-years (DALYs), years lived with disabilities (YLDs), years of life lost (YLLs) due to premature mortality from all-CVDs, and cause-specific CVDs using crude, age-specific, and age-standardized rates per 100,000 population by sex in 37 countries of the Americas. We assessed trends of outcome measures by estimating the average annual percentage change from 1990 to 2017 and quantified cross-country social-demographic inequalities in CVD burden. Results: In the Region of the Americas, age-standardized DALYs due to CVD decreased by -1.9% per year (95% UI -2.0 to -1.7) from 5,263.7 years (95% UI 5,143.1–5,390.0) per 100,000 population in 1990 to 3,159.4 years (3,047.1–3,280.4) in 2017. YLLs accounted for 87% of DALYs. Despite this progress, reduction rates decelerated in 2007–2013 and stagnated to -0.1% per year (95% UI -0.5 to 0.3) in 2013–2017. Ischemic heart disease (IHD) and stroke accounted for 80% of all-CVD DALYs. IHD exhibited similar trend patterns, but stroke showed an increasing trend since 2015. In 2017, CVD burden varied across countries from 1,812 years/100 000 (1,594–2,033) in Peru to 8,662 years/100 000 (7,404–10,029) in Haiti. From 2010 to 2017, reduction rates decelerated in all countries but Peru, trends stagnated in 8 countries and reversed to upward in 10 countries. Conclusions: The deceleration of decreasing trends in CVD burden in 18 countries, and the stagnation and reversed to increasing trends in 18 out of 37 countries of the Americas in recent years, indicates the need for reinvigorated efforts to reduce the CVD burden. The socio-demographic inequalities across countries pose challenges to these efforts. Countries with less favorable progress should be targeted for strengthening their national programs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".