Short-Term Exposure to Air Pollution and Incidence of Stroke and Acute Myocardial Infarction in a Japanese Population
Bibliographic record
Abstract
BACKGROUND: Exposure to high levels of air pollution can increase the risk of cardiovascular events. However, there is no clear information in Japan on the effect of pollution on the incidence of stroke and acute myocardial infarction (AMI). Therefore, we investigated the effects of air pollution on the incidence of stroke and AMI in a setting where pollutant levels are rather low. METHODS: Data were obtained from the Takashima Stroke and AMI Registry, which covers a population of approximately 55,000 in Takashima County in central Japan. We applied a time-stratified, bidirectional, case-crossover design to estimate the effects of air pollutants, which included suspended particulate matter (SPM), sulfur dioxide (SO(2)), nitrogen dioxide (NO(2)) and photochemical oxidants (Ox). We used the distributed lag model to estimate the effect of pollutant exposure 0-3 days before the day of event onset and controlled for meteorological covariates in all of the models. RESULTS: There were 2,038 first-ever strokes (1,083 men, 955 women) and 429 first-ever AMI cases (281 men, 148 women) during 1988-2004. The mean pollutant levels were as follows: SPM 26.9 μg/m(3); SO(2) 3.9 ppb; NO(2) 16.0 ppb, and Ox 28.4 ppb. In single-pollutant and two-pollutant models, SO(2) was associated with the risk of cerebral hemorrhage. Other stroke subtypes and AMI were not associated with air pollutant levels. CONCLUSIONS: We observed an association between SO(2) and hemorrhagic stroke; however, we found inconclusive evidence for a short-term effect of air pollution on the incidence of other stroke types and AMI.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".