The slowdown in the reduction rate of premature mortality from cardiovascular diseases puts the Americas at risk of achieving SDG 3.4: A population trend analysis of 37 countries from 1990 to 2017
Bibliographic record
Abstract
Cardiovascular diseases (CVD) are leading causes of mortality and morbidity in the Americas, resulting in substantial negative economic and social impacts. This study describes the trends and inequalities of CVD burden in the Americas to guide programmatic interventions and health system responses. We examined the CVD burden trends by age, sex, and countries between 1990 and 2017 and quantified social inequalities in CVD burden across countries. In 2017, CVD accounted for 2 million deaths in the Americas, 29% of total deaths. Age-standardized DALY rates caused by CVD declined by -1.9% (95% uncertainty interval, -2.0 to -1.7) annually from 1990 to 2017. This trend varied with a striking decreasing trend over the interval 1994-2003 (annual percent change (APC) -2.4% [-2.5 to 2.2]) and 2003-2007 (APC -2.8% [-3.4 to -2.2]). This was followed by a slowdown in the rate of decline over 2007-2013 (APC -1.83% [-2.1 to -1.6]) and a stagnation during the most recent period 2013-2017 (APC -0.1% [-0.5 to 0.3]). The social inequality in CVD burden along the socio-demographic gradient across countries decreased 2.75-fold. The CVD burden and related social inequality have both substantially decreased in the Americas since 1990, driven by the reduction in premature mortality. This trend occurred in parallel with the improvement in the socioeconomic development and health care of the region. The deceleration and stagnation in the rate of improvement of CVD burden and persistent social inequality pose major challenges to reduce the CVD burden and the achievement of the United Nations' Sustainable Development Goals Target 3.4.
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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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".