Associations of Industrial Air Pollutant Mixtures with Preterm Birth and Small for Gestational Age in Alberta, Canada
Why this work is in the frame
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Bibliographic record
Abstract
Introduction: Effects of mixtures of chemicals released to the air by industrial facilities on pregnancy outcomes have been scarcely studied. We conducted a retrospective population-based cohort study to estimate associations of spontaneous preterm birth (sPTB), induced preterm birth (iPTB) and small for gestational age (SGA) with industrial air pollutant mixtures in Alberta, Canada (2006-2012). Methods: We used data from all singletons live births (n=330,957) including maternal data on 21 related risk factors. From the Canadian National Pollutant Released Inventory, we extracted 130 chemicals released into the air by 6,279 industrial facilities. We grouped all chemicals into ten broad classes including gases (e.g., CO), particulate matter (PM), Volatile Organic Compounds (VOCs), Metals, Other-inorganics, and Other-organics. We profiled the mixtures using a novel approach based on the proportional content of the ten chemical classes in the total amounts released by each facility using cluster analysis. Proximity to the facilities emitting mixtures (10-km) from the maternal postal codes at delivery was used as a proxy of exposure. Associations of the mixtures with sPTB, iPTB and SGA were assessed by logistic regression adjusting for relevant maternal risk factors and an area-level socioeconomic status. Results: We profiled eight broad groups of mixtures. Heterogeneous mixtures (including gases, PM and different proportional participation of the other chemical classes) were common (47% of the total emissions) and increased the odds of sPTB by 36% (OR=1.36; CI:1.30-1.63). Scarce mixtures with a high content (>60%) of VOCs increased the odds of SGA by 37% (OR=1.37; CI:1.11-1.69). Mixtures with a high proportion of Metals-, Other-organics- and Other-inorganics increased the odds of iPTB by 17% (OR=1.17; CI:1.05-1.30), 17% (OR=1.17; CI:1.06-1.28) and 24% (OR=1.24; CI:1.09-1.41) respectively. Conclusion: Mixtures showed differential associations with sPTB, iPTB, and SGA.
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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 it