Risk factors for cardiovascular disease in Canada.
Bibliographic record
Abstract
BACKGROUND: This paper provides an update of the prevalence of important cardiovascular disease (CVD) risk factors in subgroups of the Canadian population. To improve awareness of the impact of CVD risk factor variations on disease burden, smoking-attributable mortality (SAM) has been estimated for the first time for each health region in Canada. METHODS: The 2000/01 Canadian Community Health Survey (CCHS) was used to estimate the prevalence of current smoking, obesity, physical inactivity, low income, diabetes and hypertension. Combining smoking prevalence data from the 2000/01 CCHS, mortality data from the 1995 to 1997 Canadian Mortality Database, and relative risk estimates (relating smoking and smoking-associated deaths) from the American Cancer Society's Cancer Prevention Study II, SAM values were generated using population-attributable risk techniques. RESULTS: Based on self-reported data, the 2000/01 CCHS shows that 26.0% of Canadians currently smoke, 14.9% are obese, 53.5% are physically inactive, 11.3% have low income, 13.0% have hypertension and 4.2% have diabetes. Cardiovascular and all-cause SAM were estimated at 18,209 and 44,271 annual deaths, and contributed to 23% and 22% of total CVD and all-cause mortality in Canada, respectively. There are large variations in the prevalence of CVD risk factors and in SAM estimates between sexes and across age groups and geographic regions. CONCLUSIONS: The high prevalence of potentially modifiable CVD risk factors and the large variation that exists between subgroups of the Canadian population suggest that the burden of CVD could be reduced through risk factor modification. While prevalence data for risk factors in a population give an initial understanding of some of the contributing causes of a disease, the actual burden of disease caused by a risk factor is also modified by the magnitude of the increased risk to mortality and morbidity, and is best represented by its estimated attributable mortality and morbidity.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".