Regional variation in self-reported heart disease prevalence in Canada.
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease is the leading cause of death in Canada. OBJECTIVE: To provide an analysis of the self-reported prevalence of heart disease and three specific cardiac conditions--myocardial infarction (MI), angina and congestive heart failure (CHF)--in subgroups of the Canadian population. METHODS: Data from the Public Use Microdata File from Statistics Canada's 2000/2001 Canadian Community Health Survey (CCHS) were used to estimate the crude self-reported prevalence of heart disease, MI, angina and CHF in Canada. The data are reported by age and sex groups, as well as by province or territory and health region. RESULTS: Based on the 2000/2001 CCHS data, it was estimated that among Canadians 12 years of age and older, 5.0% (n=1,286,000) have heart disease, 2.1% (n=537,000) have had a heart attack, 1.9% (n=483,000) have angina and 1.0% (n=264,000) have CHF. Marked variation in the prevalence of heart disease and the other specific cardiac conditions exists across age and sex groups, and across geographical regions. The prevalence of heart disease is low among those younger than 50 years; thereafter, the prevalence of heart disease increases and is more common among men than among women. By 70 years of age, at least one in four men and one in five women report having heart disease. Large differences in the burden of heart disease were observed across provinces, territories and health regions. Comparison of the highest and lowest prevalence rates among provinces and territories revealed a 1.9-fold difference for heart disease, a 2.8-fold difference for MI, a 2.3-fold difference for angina and a 3.3-fold difference for CHF. CONCLUSIONS: Large regional differences in the prevalence of heart disease and other specific cardiac conditions were observed across Canada. These data may assist health system planners to identify those regions and population subgroups most affected by heart disease, and to support the development of heart disease prevention and treatment programs.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".