A CASE-CROSSOVER STUDY OF AMBIENT AIR POLLUTION AND STROKE: AN EVALUATION OF PATIENT FACTORS THAT MODIFY ASSOCIATIONS
Bibliographic record
Abstract
Background: Several studies have demonstrated positive associations between short-term increases in ambient air pollution and the risk of stroke. While these studies have compared risks across different seasons, type of stroke, and age, the influence of individual level patient characteristics has remained unstudied. Methods: This was a time-stratified case-crossover of 5,945 patients who presented to emergency departments (ED) in Edmonton, Canada between 2003 and 2009 with stroke or a transient ischemic attack (TIA). Chart reviews were conducted to extract information on patient’s disease history, medication use and smoking status. Daily concentrations of ambient pollution (NO2,PM2.5, O3, CO, and SO2) were obtained from fixed-site monitors. Conditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) in relation to an increase in the interquartile range of each pollutant. Stratified analyses were conducted by season, and across variables that captured patient’s disease history, medication use and smoking status. Results: Consistent with a previous ED study conducted during 1992-2002, we observed positive associations between NO2, and PM2.5 between April to September. Specifically, for ischemic stroke the OR for an increase in the interquartile range of the 3-day average of NO2 was 1.57 (95% CI: 1.16-2.11). No statistically significant associations were observed with NO2 or PM2.5 for TIAs, or hemorrhagic strokes. SO2 levels were not associated with any of the stroke types examined. Stratified analysis by patient characteristics for ED visits between April and September revealed stronger associations between NO2 and ischemic stroke for those with a history of stroke (OR=2.43, 95% CI: 1.44-4.08), or heart disease (OR=2.08, 95% CI: 1.25-3.46), and use of insulin or oral hypoglycaemic drugs (OR=2.19, 95% CI: 1.22-3.95). Conclusions: Our results support the hypothesis that individuals with pre-existing comorbid health conditions are at greater risk of experiencing a stroke due to their exposure air pollution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".