Early-life exposure to traffic-related air pollution and child anthropometry
Bibliographic record
Abstract
BACKGROUND: Early-life exposure to traffic-related air pollution may decrease fetal growth and increase childhood obesity risk. Our objective was to evaluate the relationship of early-life exposure to traffic-related air pollution with birthweight in term newborns and obesity at age 7-8 years in two prospective birth cohorts in Cincinnati, OH (the Health Outcomes and Measures of the Environment (HOME) Study and Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS)). METHODS: We estimated elemental carbon attributable to traffic (ECAT) exposure at residential addresses during pregnancy with a validated land use regression model. We assessed birthweight among term infants using birth records or parent report (HOME Study n= 333 and CCAAPS n=590). We measured children's weight and height at 7-8 years, and calculated age- and sex-specific BMI z-scores (HOME Study n= 198 and CCAAPS n=459). Using multivariable linear regression, we estimated the difference in term birthweight and BMI z-score per interquartile range (IQR) increase in ECAT concentrations in each cohort separately and in the pooled sample. RESULTS: In adjusted models, ECAT exposure was not associated with lower birthweight (pooled sample β: 30g; 95% CI: -6, 66), or with higher BMI z-score (pooled sample β: -0.04; 95% CI: -0.15, 0.08). Infant sex modified the association between ECAT and birthweight (p=0.05). Among male newborns, higher ECAT concentrations were associated with higher birthweight (β: 61g; 95% CI: 9, 113), but we observed no association among female newborns (β: -9g; 95% CI: -58, 41). CONCLUSIONS: In contrast to some prior studies, early-life traffic-related air pollution exposure was not associated with lower birthweight or increased childhood adiposity in these two cohorts.
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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 | 0.000 |
| Research integrity | 0.000 | 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".