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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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; both teacher heads agree on what is shown here.
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".