Is the long-term decline in cardiovascular-disease mortality in high-income countries over? Evidence from national vital statistics
Bibliographic record
Abstract
BACKGROUND: The substantial decline in cardiovascular-disease (CVD) mortality in high-income countries has underpinned their increasing longevity over the past half-century. However, recent evidence suggests this long-term decline may have stagnated, and even reversed in younger populations. We assess recent CVD-mortality trends in high-income populations and discuss the findings in relation to trends in risk factors. METHODS: We used vital statistics since 2000 for 23 high-income countries published in the World Health Organization Mortality Database. Age-standardized CVD death rates by sex for all ages, and at ages 35-74 years, were calculated and smoothed using LOWESS regression. Findings were contrasted with the Global Burden of Disease (GBD) Study. RESULTS: The rate of decline in CVD mortality has slowed considerably in most countries in recent years for both males and females, particularly at ages 35-74 years. Based on the latest year of data, the decline in the CVD-mortality rate at ages 35-74 years was <2% (about half the annual average since 2000) for at least one sex in more than half the countries. In North America (US males and females, Canada females), the CVD-mortality rate even increased in the most recent year. The GBD Study estimates, after correcting for misdiagnoses, suggest an even more alarming reversal, with CVD death rates rising in seven countries for at least one sex in 2017. The rate of decline and initial level of CVD mortality appear largely unrelated. CONCLUSIONS: A significant slowdown in CVD-mortality decline is now apparent across high-income countries with diverse epidemiological environments. High and increasing obesity levels, limited potential future gains from further reducing already low smoking prevalence, especially in English-speaking countries, and persistent inequalities in mortality risk pose significant challenges for public policy to promote better cardiovascular health.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".