Fine Particulate Air Pollution and Adverse Birth Outcomes: Effect Modification by Regional Nonvolatile Oxidative Potential
Bibliographic record
Abstract
Abstract Background: Prenatal exposure to fine particulate matter air pollution with aerodynamic diameter ≤2.5μm (PM2.5) has been associated with preterm delivery and low birth weight (LBW), but few studies have examined possible effect modification by PM2.5 oxidative potential. Objectives: The aim of this study was to evaluate if regional differences in the oxidative potential of PM2.5 modify the relationship between PM2.5 and adverse birth outcomes. Methods: A retrospective cohort study was conducted using 196,171 singleton births that occurred in 31 cities in the province of Ontario, Canada, from 2006 to 2012. Daily air pollution data were collected from ground monitors, and city-level PM2.5 oxidative potential was measured. We used random-effects meta-analysis to combine the estimates of effect from regression models across cities on preterm birth, term LBW, and term birth weight and used meta-regression to evaluate the modifying effect of PM2.5 oxidative potential. Results: An interquartile increase (2.6 μg/m3) in first-trimester PM2.5 was positively associated with term LBW among women in the highest quartile of glutathione (GSH)-related oxidative potential [odds ratio (OR)=1.28; 95% confidence interval (CI): 1.10, 1.48], but not the lowest quartile (OR=0.99; 95% CI: 0.87, 1.14; p-interaction=0.03). PM2.5 on the day of delivery also was associated with preterm birth among women in the highest quartile of GSH-related oxidative potential [hazard ratio (HR)=1.02; 95% CI: 1.01, 1.04], but not the lowest quartile [HR=0.97; 95% CI: 0.95, 1.00; p-interaction=0.04]. Between-city differences in ascorbate (AA)-related oxidative potential did not significantly modify associations with PM2.5. Conclusions: Between-city differences in GSH-related oxidative potential may modify the impact of PM2.5 on the risk of term LBW and preterm birth. https://doi.org/10.1289/EHP2535
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.011 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".