Air Pollution and Acute Myocardial Infarction Hospital Admission in Alberta, Canada: A Three-Step Procedure Case-Crossover Study
Bibliographic record
Abstract
Adverse associations between air pollution and myocardial infarction (MI) are widely reported in medical literature. However, inconsistency and sensitivity of the findings are still big concerns. An exploratory investigation was undertaken to examine associations between air pollutants and risk of acute MI (AMI) hospitalization in Alberta, Canada. A time stratified case-crossover design was used to assess the transient effect of five air pollutants (carbon monoxide (CO), nitrogen dioxide (NO2), nitric oxide (NO), ozone (O3) and particulate matter with an aerodynamic diameter ≤2.5 (PM2.5)) on the risk of AMI hospitalization over the period 1999-2009. Subgroups were predefined to see if any susceptible group of individuals existed. A three-step procedure, including univariate analysis, multivariate analysis, and bootstrap model averaging, was used. The multivariate analysis was used in an effort to address adjustment uncertainty; whereas the bootstrap technique was used as a way to account for regression model uncertainty. There were 25,894 AMI hospital admissions during the 11-year period. Estimating health effects that are properly adjusted for all possible confounding factors and accounting for model uncertainty are important for making interpretations of air pollution-health effect associations. The most robust findings included: (1) only 1-day lag NO2 concentrations (6-, 12- or 24-hour average), but not those of CO, NO, O3 or PM2.5, were associated with an elevated risk of AMI hospitalization; (2) evidence was suggested for an effect of elevated risk of hospitalization for NSTEMI (Non-ST Segment Elevation Myocardial Infarction), but not for STEMI (ST segment elevation myocardial infarction); and (3) susceptible subgroups included elders (age ≥65) and elders with hypertension. As this was only an exploratory study there is a need to replicate these findings with other methodologies and datasets.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".