Rural-Urban Differences in Stroke Risk Factors, Incidence, and Mortality in People With and Without Prior Stroke
Bibliographic record
Abstract
Background Rural residence is associated with stroke incidence and mortality, but little is known about potential rural/urban differences in ambulatory stroke care. Methods and Results We used the CANHEART (Cardiovascular Health in Ambulatory Care Research Team) cohort, created from linked administrative databases from the province of Ontario, Canada, and divided into primary (N=6 207 032) and secondary (N=75 823) prevention cohorts based on the absence or presence of prior stroke. We defined rural communities as those with a population size of ≤10 000 and within each of the primary and secondary prevention cohorts, compared cardiovascular risk factors and care between rural and urban areas. We then calculated sex-/age-standardized rates of stroke incidence and mortality per 1000 person-years between January 1, 2008 and December 31, 2012 and used cause-specific hazard models to compare outcomes in rural versus urban areas adjusting for age, sex, income, ethnicity, smoking, physical activity and comorbid conditions, and accounting for the competing risk of death in the model for the occurrence of stroke incidence. In the primary prevention cohort, rural residents were less likely than urban ones to be screened for diabetes mellitus (70.9% versus 81.3%) and hyperlipidemia (66.2% versus 78.4%) and less likely to achieve diabetes mellitus control (hemoglobin A1c ≤7% in 51.3% versus 54.3%; P<0.001 for all comparisons). In the secondary prevention cohort, the prevalence and treatment of risk factors were similar in rural and urban residents. After adjustment for sociodemographic and comorbid conditions, rural residence was associated with higher rates of stroke and all-cause mortality in both the primary prevention (adjusted hazard ratio [aHR] for stroke, 1.06; 95% CI, 1.04-1.09; aHR for mortality, 1.09; 95% CI, 1.08-1.10) and the secondary prevention cohort (aHR for stroke, 1.11; 95% CI, 1.02-1.19; aHR for mortality, 1.07; 95% CI, 1.03-1.11). Conclusions In this population-based study of over 6 million people with universal access to physician and hospital services, risk factors were more prevalent but less likely to be controlled in rural than in urban residents without prior stroke, whereas in those with prior stroke, risk factor prevalence and treatment were similar. Rural residence was associated with the rate of stroke and death even after adjustment for risk factors. Future efforts should focus not only on control of known vascular risk factors but also on addressing other determinants of health in rural communities.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".