Perinatal Exposure to Traffic-Related Air Pollution and Atopy at 1 Year of Age in a Multi-Center Canadian Birth Cohort Study
Bibliographic record
Abstract
BACKGROUND: The role of traffic-related air pollution (TRAP) exposure in the development of allergic sensitization in children is unclear, and few birth cohort studies have incorporated spatiotemporal exposure assessment. OBJECTIVES: We aimed to examine the association between TRAP and atopy in 1-year-old children from an ongoing national birth cohort study in four Canadian cities. METHODS: We identified 2,477 children of approximately 1 year of age with assessment of atopy for inhalant (Alternaria, Der p, Der f, cat, dog, cockroach) and food-related (milk, eggs, peanuts, soy) allergens. Exposure to nitrogen dioxide (NO2) was estimated from city-specific land use regression models accounting for residential mobility and temporal variability in ambient concentrations. We used mixed models to examine associations between atopy and exposure during pregnancy and the first year of life, including adjustment for covariates (maternal atopy, socioeconomic status, pets, mold, nutrition). We also conducted analyses stratified by time-location patterns, daycare attendance, and modeled home ventilation. RESULTS: Following spatiotemporal adjustment, TRAP exposure after birth increased the risk for development of atopy to any allergens [adjusted odds ratio (aOR) per 10 μg/m3 NO2 = 1.16; 95% CI: 1.00, 1.41], but not during pregnancy (aOR = 1.02; 95% CI: 0.86, 1.22). This association was stronger among children not attending daycare (aOR = 1.61; 95% CI: 1.28, 2.01) compared with daycare attendees (aOR = 1.05; 95% CI: 0.81, 1.28). Trends to increased risk were also found for food (aOR = 1.17; 95% CI: 0.95, 1.47) and inhalant allergens (aOR = 1.28; 95% CI: 0.93, 1.76). CONCLUSION: Using refined exposure estimates that incorporated temporal variability and residential mobility, we found that traffic-related air pollution during the first year of life was associated with atopy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".