Burden of cardiovascular disease in Canada.
Bibliographic record
Abstract
BACKGROUND: This report updates the death estimates for cardiovascular disease (CVD) in Canada and introduces a population-based perspective on disease prevalence and health-related quality of life (HRQOL) burden. METHODS: The Canadian Mortality Database was used to estimate the mortality of men and women in different age groups for the 139 Canadian health regions from 1950 to 1999. Heart disease prevalence and its impact on HRQOL were estimated using the 2000-2001 Canadian Community Health Survey (CCHS). Life table techniques were used to estimate the impact of heart disease on life and health expectancy. RESULTS: Although CVD remains the leading cause of death in Canada, between 1950 and 1999 the death rates from CVD dropped from 702 per 100,000 to 288 per 100,000 men, and from 562 per 100,000 to 175 per 100,000 women. Results from the CCHS indicated that 5.4% of men and 4.6% of women reported having heart disease as diagnosed by a medical professional. Of these individuals, 14% of men and 21% of women reported difficulty ambulating - about six times more than people without heart disease. In total, 4.5 years of life expectancy and 2.8 years of health expectancy were lost due to CVD. The study also found large differences in the burden of CVD among men and women and across the 139 Canadian health regions. CONCLUSIONS: CVD is a major disease burden in terms of both mortality and HRQOL and is an important source of health inequalities between populations in Canada. Any attempt to improve the health of Canadians or to reduce health inequalities should include interventions to reduce CVD mortality and morbidity. Given the present impact of CVD on HRQOL, reducing or eliminating heart disease may potentially result in an increase in life expectancy that will be larger than the gains in health expectancy.
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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 | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".