Time trends in mortality from heart failure and atherosclerotic cardiovascular disease in people with and without diabetes: a multi-national population-based study
Bibliographic record
Abstract
BACKGROUND: Contemporary trends in cardiovascular disease (CVD) cause-specific mortality by diabetes status are inadequately described. We examined trends by diabetes status in coronary heart disease (CHD), cerebrovascular disease, and heart failure mortality, and mortality rate ratios (people with diabetes versus those without diabetes) across nine high-income jurisdictions. METHODS: We assembled CVD cause-specific mortality data from nine administrative datasets (Europe [n=5], Australia [n=1], Canada [n=2], and South Korea [n=1]), spanning 2000-23. Using Poisson regression, we estimated mortality rates by diabetes status and mortality rate ratios. FINDINGS: There were 2·92 million CVD deaths over 1·30 billion person-years of follow-up. In all jurisdictions and in both people with and without diabetes, the total CVD and CHD mortality rates fell across the observed time period. The 5-year percent changes in CHD mortality ranged from -11·5% to -32·3%. Reductions in heart failure mortality were smaller than those for CHD mortality (except in Scotland) and smaller than those for cerebrovascular mortality (except in Scotland and Denmark). Heart failure mortality increased in Ontario, Canada. The excess CHD mortality associated with diabetes (mortality rate ratio ~2·0) fell in three of nine jurisdictions and was stable or uncertain in the remainder. No jurisdiction had a fall in excess heart failure mortality associated with diabetes. INTERPRETATION: Declines in heart failure mortality in both people with and without diabetes were less marked than were declines in CHD and cerebrovascular disease mortality in most jurisdictions. Heart failure mortality rate ratios have not decreased. A greater focus on reducing heart failure mortality in people with and without diabetes might be required. FUNDING: US Centers for Disease Control and Prevention, Diabetes Australia Research Program, Victoria State Government Operational Infrastructure Support Program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".