Prenatal Exposure to Specific PM<sub>2.5</sub> Chemical Constituents and Preterm Birth in China: A Nationwide Cohort Study
Bibliographic record
Abstract
Exposure to fine particulate matter (PM2.5) during pregnancy has been associated with preterm birth (PTB). However, the existing evidence is inconsistent, and the roles of specific PM2.5 chemical constituents remain unclear. Based on the China Labor and Delivery Survey, we included birth data from 89 hospitals in 25 provinces in mainland China, and conducted a national multicenter cohort study to examine the associations of PM2.5 and its chemical constituents with PTB risk in China. We applied satellite-based models to predict prenatal PM2.5 mass and six main component exposure. Multilevel logistic regression analysis was used to examine the associations, controlling for sociodemographic characteristics, seasonality, and spatial variation. We observe an increased PTB risk with an increase in PM2.5 mass and the most significant association is found during the third trimester when the adjusted odds ratio (OR) per interquartile range increases in PM2.5 total mass is 1.12 (95% confidence Interval, CI: 1.05–1.20). Infants conceived by assisted reproductive technology (ART) show greater PTB risk associated with PM2.5 exposure (OR = 1.33, 95% CI: 1.05–1.69) than those conceived naturally (OR = 1.11, 95% CI: 1.03–1.19). We also find black carbon, sulfate, ammonium and nitrate, often linked to fossil combustion, have comparable or larger estimates of the effect (OR = 1.07–1.14) than PM2.5. Our findings provide evidence that components mainly from fossil fuel combustion may have a perceptible influence on increased PTB risk associated with PM2.5 exposure in China. Additionally, compared to natural conception, conception through ART may be more susceptible to PM2.5 exposure.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".