The acute lag effects of elevated ambient air pollution on stillbirth risk in Ulaanbaatar, Mongolia
Bibliographic record
Abstract
Abstract Ulaanbaatar city (UB), the capital and the home to half of Mongolia’s total population, has experienced extreme seasonal air pollution in the past two decades with levels of fine particulate matter with an aerodynamic diameter less than 2.5 micrometers (PM 2.5 ) exceeding 500 μ g/m 3 during winter. Based on monitoring data, (PM 2.5 ), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and carbon monoxide (CO) exposures were estimated for residential areas across UB using Random Forest models. We collected individual-level data on 1093 stillbirths from UB hospital records (2010-2013) and a surveillance database (2014-2018). Using a time-stratified case-crossover design, we investigated whether short-term increases in daily ambient air pollutants with different exposure lags (2 to 6 days) before delivery were associated with stillbirth. We estimated associations using conditional logistic regression and examined individual-level characteristics for effect modification. During the cold season (Oct-Mar) we observed significantly elevated relative odds of stillbirth per interquartile range increase in mean concentrations of PM 2.5 (odds ratio [OR]=1.35, 95% confidence interval [CI]=1.07-1.71), SO 2 (OR=1.71, 95% CI=1.06-2.77), NO 2 (OR=1.30, 95% CI=0.99-1.72), and CO (OR=1.44, 95% CI=1.17-1.77) 6 days before delivery after adjusting for apparent temperature with a natural cubic spline. The associations of pollutant concentrations with stillbirth were significantly stronger among those younger than 25, nulliparous, and without comorbidities or pregnancy complications during stratified analyses. There was a clear pattern of increased risk for women living in areas of lower socioeconomic status. We conclude that acute exposure to ambient air pollution before delivery may trigger stillbirth, and this risk is higher for certain subsets of women.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".