Abstract WP180: Rural-Urban Differences in Stroke Incidence, Mortality and Risk Factor Prevalence in Ontario
Bibliographic record
Abstract
Introduction: Little is known about stroke incidence, mortality and risk factor prevalence in rural versus urban regions. We used linked population-based administrative databases to compare cardiovascular risk factors, stroke incidence and stroke-related death in residents of rural and urban Ontario, Canada. Methods: We used a sub-population of the Cardiovascular Health in Ambulatory Care Research Team (CANHEART) cohort, consisting of individuals aged 40 to 105 in Ontario with no known hospitalization for stroke in the 20 years prior to January 1, 2008. We defined rural regions as those with a population size ≤ 30, 000. We compared the age- and sex- standardized prevalence of risk factors, as well as 5-year stroke incidence and stroke-related mortality rates, in people residing in rural versus urban areas. Results: The study sample consisted of 6,207,032 individuals. Compared to residents of urban regions, rural residents were more likely to smoke (25.3% versus 19.3%), be obese (25.1% versus 19.3%) and live in a low-income area (43.5% versus 38.3%) (P<0.001 for all comparisons). However, there were no differences in the proportion of residents with hypertension, diabetes and atrial fibrillation. Age- and sex-standardized stroke incidence was higher in rural compared to urban areas (2.18 versus 1.99 events/1000 person-years; p<0.001), as was stroke-related mortality (0.79 versus 0.65 events/1000 person-years; p<0.001). Conclusions: Certain cardiovascular risk factors have increased prevalence in rural areas compared to urban areas. Furthermore, stroke incidence and mortality rates were greater in rural areas in Ontario. Future efforts should focus on reducing regional discrepancies in social determinants of health and addressing risk factor prevalence, particularly smoking and obesity, in rural regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".