The association of traffic-related air and noise pollution with maternal blood pressure and hypertensive disorders of pregnancy in the HOME study cohort
Bibliographic record
Abstract
Traffic-related air and noise pollution may increase the risk for cardiovascular disorders, especially among susceptible populations like pregnant women. The objective of this study was to evaluate the association of exposure to traffic-related air pollution and traffic noise with blood pressure in pregnant women. We extracted systolic blood pressure (SBP) and diastolic blood pressure (DBP) at ≥20 weeks gestation, as well as hypertensive disorders of pregnancy from medical records in the HOME Study, a prospective pregnancy and birth cohort from Cincinnati, OH (n = 370). We estimated exposure to elemental carbon attributable to traffic (ECAT),1 a marker of traffic-related air pollution, at women's residences at ~20 weeks gestation using a validated land use regression model and traffic noise using a publicly available transportation noise model. We used linear mixed models and modified Poisson regression adjusted for covariates to examine associations of ECAT and traffic noise with blood pressure and hypertensive disorders of pregnancy risk, respectively. In adjusted models, we found a 1.6 (95% CI = 0.02, 3.3; p = 0.048) mm Hg increase in SBP associated with an interquartile range increase in ECAT concentration; the association was stronger after adjusting for traffic noise (1.9 mm Hg, 95% = 0.1, 3.7; p = 0.035). ECAT concentrations were not significantly associated with DBP or hypertensive disorders of pregnancy, and traffic noise was not associated with SBP, DBP, or hypertensive disorders of pregnancy. There was no evidence of a joint effect of traffic noise and ECAT on any outcome. In this cohort, higher residential traffic-related air pollution exposure at ~20 weeks gestation was associated with higher SBP in late pregnancy. It is important for future studies of traffic-related air or noise pollution to jointly consider both exposures and neighborhood characteristics given their correlation and potential cumulative impact on cardiovascular health.
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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 | 0.000 | 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) | 0.001 | 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".