Regional variations in cardiovascular mortality in Canada.
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death in Canada with wide, unexplained regional variations in heart disease mortality. However, no studies to date have explored the relationship between a number of health region characteristics and regional variation in heart disease mortality rates across Canada. INTRODUCTION: We studied the contribution of various traditional cardiac risk factors, social determinants of health and other community characteristics to regional variations in heart disease mortality rates across Canada. METHODS: Cardiovascular disease and ischemic heart disease (IHD) age-standardized mortality rates were obtained from Statistics Canada for three years - 1995 to 1997. Health region characteristics were taken from the 2000/2001 Canadian Community Health Survey, and the 1996 Canadian Census and the Labour Force Survey. Linear regression analyses and analyses of variance were employed to identify relationships between these health region characteristics and CVD and IHD mortality rates. RESULTS: Significant regional variations in CVD mortality rates per 100,000 population were observed. Newfoundland and Labrador had the highest CVD and IHD mortality rates, while Nunavut and the Northwest Territories had the lowest CVD and IHD mortality rates. Health region smoking and unemployment rates were identified as the most important factors associated with CVD and IHD mortality at the health region level. CONCLUSIONS: Significant regional variations in age-standardized CVD and IHD mortality were noted both at the provincial/territorial level and the health region level. Efforts to reduce CVD and IHD mortality in Canada require attention to both traditional risk factors (eg, smoking) and broader determinants of health (eg, unemployment rates).
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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 | 0.002 | 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) | 0.003 | 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".