Associations of Pregnancy Outcomes and PM <sub>2.5</sub> in a National Canadian Study
Bibliographic record
Abstract
Abstract Background Numerous studies have examined associations between air pollution and pregnancy outcomes, but most have been restricted to urban populations living near monitors. Objectives We examined the association between pregnancy outcomes and fine particulate matter in a large national study including urban and rural areas. Methods Analyses were based on approximately 3 million singleton live births in Canada between 1999 and 2008. Exposures to PM2.5 (particles of median aerodynamic diameter ≤ 2.5 μm) were assigned by mapping the mother’s postal code to a monthly surface based on a national land use regression model that incorporated observations from fixed-site monitoring stations and satellite-derived estimates of PM2.5. Generalized estimating equations were used to examine the association between PM2.5 and preterm birth (gestational age < 37 weeks), term low birth weight (< 2,500 g), small for gestational age (SGA; < 10th percentile of birth weight for gestational age), and term birth weight, adjusting for individual covariates and neighborhood socioeconomic status (SES). Results In fully adjusted models, a 10-μg/m3 increase in PM2.5 over the entire pregnancy was associated with SGA (odds ratio = 1.04; 95% CI 1.01, 1.07) and reduced term birth weight (–20.5 g; 95% CI –24.7, –16.4). Associations varied across subgroups based on maternal place of birth and period (1999–2003 vs. 2004–2008). Conclusions This study, based on approximately 3 million births across Canada and employing PM2.5 estimates from a national spatiotemporal model, provides further evidence linking PM2.5 and pregnancy outcomes. Citation Stieb DM, Chen L, Beckerman BS, Jerrett M, Crouse DL, Omariba DW, Peters PA, van Donkelaar A, Martin RV, Burnett RT, Gilbert NL, Tjepkema M, Liu S, Dugandzic RM. 2016. Associations of pregnancy outcomes and PM2.5 in a National Canadian Study. Environ Health Perspect 124:243–249; http://dx.doi.org/10.1289/ehp.1408995
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".