Assessing the multi-dimensional effects of air pollution on maternal complications and birth outcomes: A structural equation modeling approach
Bibliographic record
Abstract
• This study identified a dust-related metal mixture in air in South Florida and untangled the complex associations between the identified mixture, pregnancy complications, and birth outcomes. • The study emphasizes the importance of considering time windows of exposure. • Preeclampsia and gestational diabetes are mediators to the associations between metal mixture exposure and birth outcomes. This cross-sectional study aims to investigate the direct and indirect relationships between exposure to a metal mixture in air and adverse pregnancy outcomes across gestational stages. With 46,829 births in 2021 in two Florida counties and Air Quality System data, structure equation modeling was used to construct latent metal mixtures in PM 2.5 and unravel their effects on pregnancy complications (preeclampsia and gestational diabetes) and birth outcomes (low birth weight and preterm birth risks). A latent variable featuring seven metals (Aluminum, Calcium, Iron, Magnesium, Manganese, Silicon, Vanadium) was identified through the measurement model. The latent metal mixture exposure had direct effects on gestational diabetes and preterm birth (1 st trimester, 2 nd trimester), low birth weight (1 st trimester), and preeclampsia (2 nd trimester). When considering total effects, the effects on low birth weight in the 1 st trimester and on preeclampsia in 2 nd trimester were masked, and the latent metal mixture increased the low-birth-weight risk in 2 nd trimester by 2% (OR = 1.02, 95%CI = [1.00, 1.03]). This study reveals time-dependent associations between a metal mixture in PM 2.5 exposure and adverse pregnancy outcomes, highlights the need to address dust in PM2.5, and provides additional evidence for understanding the pathway of the pollution effects on fetal health.
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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 | 0.000 | 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) | 0.000 | 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".