The modifying effect of socioeconomic status on the relationship between traffic, air pollution and respiratory health in elementary schoolchildren
Bibliographic record
Abstract
The volume and type of traffic and exposure to air pollution have been found to be associated with respiratory health, but few studies have considered the interaction with socioeconomic status at the household level. We investigated the relationships of respiratory health related to traffic type, traffic volume, and air pollution, stratifying by socioeconomic status, based on household income and education, in 3591 schoolchildren in Windsor, Canada. Interquartile range changes in traffic exposure and pollutant levels were linked to respiratory symptoms and objective measures of lung function using generalised linear models for three levels of income and education. In 95% of the relationships among all cases, the odds ratios for reported respiratory symptoms (a decrease in measured lung function), based on an interquartile range change in traffic exposure or pollutant, were greater in the lower income/education groups than the higher, although the odds ratios were in most cases not significant. However, in up to 62% of the cases, the differences between high and low socioeconomic groups were statistically significant, thus indicating socioeconomic status (SES) as a significant effect modifier. Our findings indicate that children from lower socioeconomic households have a higher risk of specific respiratory health problems (chest congestion, wheezing) due to traffic volume and air pollution exposure.
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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 | 0.001 |
| 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".