The contributions of risk factor trends to cardiometabolic mortality decline in 26 industrialized countries
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease mortality has declined and diabetes mortality has increased in high-income countries. We estimated the potential role of trends in population body mass index, systolic blood pressure, serum total cholesterol and smoking in cardiometabolic mortality decline in 26 industrialized countries. METHODS: Mortality data were from national vital statistics. Body mass index, systolic blood pressure and serum total cholesterol were from a systematic analysis of population-based data. We estimated the associations between change in cardiometabolic mortality and changes in risk factors, adjusted for change in per-capita gross domestic product. We calculated the potential contribution of risk factor trends to mortality decline. RESULTS: Between 1980 and 2009, age-standardized cardiometabolic mortality declined in all 26 countries, with the annual decline between <1% in Mexico to ≈ 5% in Australia. Across the 26 countries together, risk factor trends may have accounted for ≈ 48% (men) and ≈ 40% (women) of cardiometabolic mortality decline. Risk factor trends may have accounted for >60% of decline among men and women in Finland and Switzerland, men in New Zealand and France, and women in Italy; their benefits were smallest in Mexican, Portuguese, and Japanese men and Mexican women. Risk factor trends may have slowed down mortality decline in Chilean men and women and had virtually no effect in Argentinean women. The contributions of risk factors to mortality decline seemed substantially larger among men than among women in the USA, Canada and The Netherlands. CONCLUSIONS: Industrialized countries have varied widely in the extent of risk factor prevention, and its likely benefits for cardiometabolic mortality.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".