Traffic-related air pollution and incident asthma in a high-risk birth cohort
Bibliographic record
Abstract
OBJECTIVES: The risk of incident asthma and bronchial hyper-reactivity associated with early life exposure to traffic-related air pollution has not been fully elucidated. We aimed to evaluate the hypothesis that the risk of new onset asthma is positively associated with early exposure to traffic-related air pollution in a well-characterised high-risk birth cohort. METHODS: Infants at high-risk for asthma were recruited for an intervention study. Birth year exposures to NO, NO(2), black carbon and PM(2.5) were estimated by land use regression. At 7 years of age, asthma was assessed by a paediatric allergist and bronchial hyper-reactivity was measured by methacholine challenge. Associations between exposures and outcomes were analysed by stepwise multiple logistic regression, adjusted for potential confounding variables. RESULTS: Exposure estimates were available for 184 children; 23 were diagnosed with asthma and 68 with bronchial hyper-reactivity. The IQR (4.1 μg/m(3)) of birth year PM(2.5) was associated with a significantly increased risk of asthma (OR 3.1, 95% CI 1.3 to 7.4) and with a trend to increased risk of bronchial hyper-reactivity. Similar findings were noted in association with NO and NO(2), while black carbon did not appear to confer increased risk. CONCLUSION: Modest elevations in exposure to some traffic-related air pollutants during the year of birth are associated with new onset asthma assessed at age 7. That significant associations were revealed in spite of a limited sample size emphasises the strengths of a high-risk birth cohort model, along with individual air pollution exposure estimates and well-characterised data on covariates and outcomes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".