Temporal and Age-Specific Trends in Acute Stroke Incidence: A 15-Year Population-Based Study of Administrative Data in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Contemporary data on temporal trends in acute stroke incidence, specific to stroke type and age, are lacking. We sought to evaluate temporal trends in incidence of ischemic stroke and intracerebral hemorrhage over 15 years in a large population. METHODS: We used linked administrative data to identify all emergency department visits and hospital admissions for first-ever ischemic stroke or intracerebral hemorrhage in Ontario, Canada from 2003-2017. We evaluated annual age-/sex-standardized incidence per 100,000 person-years for ischemic stroke and intracerebral hemorrhage across the study period. We used negative binomial regression to determine incidence rate ratios for each year compared to 2003, with assessment of modification by age, sex, or stroke type. RESULTS: Our cohort had 163,574 people with stroke (88% ischemic stroke). For ischemic stroke and intracerebral hemorrhage combined, age-/sex-standardized incidence decreased between 2003 and 2011 (standardized rate 109.4 to 85.8 per 100,000; 22%), then increased until 2017 (standardized rate 96.8 per 100,000; 13%). The pattern of change was similar for ischemic stroke and intracerebral hemorrhage, and for men and women, but was modified by age. For those aged 60 and above, adjusted incidence rate ratios decreased from 2003 to 2011 then subsequently increased, whereas for those aged <60 years incidence rate ratios increased throughout the entire study time period, particularly after 2011. CONCLUSIONS: Acute stroke incidence decreased from 2003 to 2011 but subsequently increased until 2017. Among those aged <60, incidence increased continuously from 2003 to 2017 but especially after 2011. The underlying reasons for these changes should be determined.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".