Cardiovascular Mortality Gap Between the United States and Other High Life Expectancy Countries in 2000–2016
Bibliographic record
Abstract
OBJECTIVES: Reductions in U.S. cardiovascular disease (CVD) mortality have stagnated. While other high life expectancy countries (HLCs) have also recently experienced a stall, the stagnation in CVD mortality in the United States appeared earlier and has been more pronounced. The reasons for the stall are unknown. We analyze cross-national variations in mortality trends to quantify the U.S. exceptionality and provide insight into its underlying causes. METHODS: Data are from the World Health Organization (2000-2016). We quantified differences in levels and trends of CVD mortality between the United States and 17 other HLCs. We decomposed differences to identify the individual contributions of major CVD subclassifications (ischemic heart disease [IHD], stroke, other heart diseases). To identify potential behavioral explanations, we compared trends in CVD mortality with trends in other causes of death related to obesity, smoking, alcohol, and drugs. RESULTS: Our study has four central findings: (a) U.S. CVD mortality is consistently higher than the average of other HLCs; (b) the U.S.-HLC gap declined until around 2008 and increased thereafter; (c) the shift from convergence to divergence was mainly driven by slowing IHD and stroke mortality reductions and increasing mortality from other CVD causes; (d) among the potential risk factors, only obesity- and alcohol-related mortality showed age-specific temporal changes that are similar to those observed for cardiovascular mortality. DISCUSSION: The exceptional changes in U.S. CVD mortality are driven by a distinct pattern of slowing reductions in IHD and stroke mortality and deteriorating mortality from other CVD causes. Obesity and alcohol abuse appear to be interrelated factors.
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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 | 0.000 |
| 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".