Complex Evolution of Epidemiology of Vascular Diseases, Including Increased Disease Burden: From 2000 to 2015
Bibliographic record
Abstract
BACKGROUND: Vascular diseases, encompassing coronary heart disease (CHD), cerebrovascular disease (CVD), and peripheral artery disease (PAD), are leading causes of the global mortality and morbidity burdens. Our objective was to evaluate the temporal trends in the burden of vascular diseases in the province of Quebec from 2000 to 2015. METHODS: We identified subjects aged ≥ 20 years with vascular diseases in the Quebec Integrated Chronic Disease Surveillance System (a combination of 5 provincial health administrative datasets). We identified Quebecers with CHD, CVD, or PAD by tracking codes identifying vascular diseases (and interventions for CHD) in the hospitalization datasets. We used the 2011 Quebec standard population for age standardization. RESULTS: In 2015, the crude prevalence of vascular diseases was 7.3% (n = 473,305), and the all-cause crude mortality rate was 6.6% (n = 31,320). Age-standardized prevalence of vascular diseases increased relatively by 21.4% between 2000 (5.6%; 99% confidence interval [CI], 5.5-5.6) and 2015 (6.8%; 99% CI, 6.7-6.8), whereas the age-standardized incidence and mortality rates showed relative decreases of 46.2% and 32.6%, respectively. PAD and CVD had lower prevalence and incidence but higher mortality than CHD. Most patients with CHD only had this vascular disease in contrast to patients with PAD who generally had diseases involving more than 1 vascular bed. CHD only and CHD with PAD ranked first and second, respectively, in mortality burdens. CONCLUSIONS: During the last decade, the age-standardized incidence and mortality rate of vascular diseases declined, but their prevalence increased with the overall burden of vascular diseases remaining substantial in Quebec, Canada.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".