Recent Trends in Hospitalizations for Cardiovascular Disease, Stroke, and Vascular Cognitive Impairment in Canada
Bibliographic record
Abstract
BACKGROUND: We analyzed hospitalization rates for a broad set of cardiovascular diseases, stroke, and vascular cognitive impairment (VCI) between 2007 and 2016 in Canada to characterize population-level trends and demographic and provincial/territorial variation in inpatient health care utilization. METHODS: Record-level administrative hospitalization data from April 1, 2007 to March 31, 2017 for individuals aged 0-105 years were obtained from the Canadian Institute for Health Information Discharge Abstract Database. Data were available for all provinces and territories, except Quebec. Using the International Classification of Diseases (10th Revision, Canada) diagnostic coding standards, we identified disease categories related to cardiovascular disease, stroke, or VCI. Hospitalizations, crude and standardized, for age and sex (direct method) were calculated using the 2011 Census as the standard population. RESULTS: Between 2007 and 2016, percent decreases in standardized hospitalization rates were relatively small for heart failure and stroke (-2.4% and -4.7%, respectively), whereas those for coronary artery and vascular disease and heart rhythm disorders were moderate (-27.4% and -16.8%, respectively). Percent increases were relatively small for congenital heart disease (+7.2%) and moderate for acquired valvular heart disease (+31.1%) and VCI (+23.4%). There were notable age- and sex-specific differences along with provincial/territorial variation. CONCLUSIONS: Between 2007 and 2016, there was an overall decrease in standardized hospitalization rates for coronary artery and vascular disease, heart failure, heart rhythm disorders, and stroke, and an increase in hospitalization rates for structural heart disease (congenital heart disease and acquired valvular heart disease) and VCI in 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".