The economic benefits of reducing cardiovascular disease mortality in Quebec, Canada
Bibliographic record
Abstract
OBJECTIVES: We assess how different scenarios of cardiovascular disease (CVD) prevention, aimed at meeting targets set by the World Health Organization (WHO) for 2025), may impact healthcare spending in Quebec, Canada over the 2050 horizon. METHODS: We provide long-term forecasts of healthcare use and costs at the Quebec population level using a novel dynamic microsimulation model. Using both survey and administrative data, we simulate the evolution of the Quebec population's health status until death, through a series of dynamic transitions that accounts for social and demographic characteristics associated with CVD risk factors. RESULTS: A 25% reduction in CVD mortality between 2012 and 2025 achieved through decreased incidence could contain the pace of healthcare cost growth towards 2050 by nearly 7 percentage points for consultations with a physician, and by almost 9 percentage points for hospitalizations. Over the 2012-2050 period, the present value of cost savings is projected to amount to C$13.1 billion in 2012 dollars. The years of life saved due to improved life expectancy could be worth another C$38.2 billion. Addressing CVD mortality directly instead would bring about higher healthcare costs, but would generate more value in terms of years of life saved, at C$69.6 billion. CONCLUSIONS: Potential savings associated with plausible reductions in CVD, aimed at reaching a World Health Organization target over a 12-year period, are sizeable and may help address challenges associated with an aging population.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".