Maternal exposure to ambient air pollution and risk of early childhood cancers: A population-based study in Ontario, Canada
Bibliographic record
Abstract
Background There are increasing concerns regarding the role of exposure to ambient air pollution during pregnancy in the development of early childhood cancers. Objective This population based study examined whether prenatal and early life (< 1 year of age) exposures to ambient air pollutants, including nitrogen dioxide (NO 2 ) and particulate matter with aerodynamic diameters ≤ 2.5 μm (PM 2.5 ), were associated with selected common early childhood cancers in Canada. Methods 2,350,898 singleton live births occurring between 1988 and 2012 were identified in the province of Ontario, Canada. We assigned temporally varying satellite-derived estimates of PM 2.5 and land-use regression model estimates of NO 2 to maternal residences during pregnancy. Incident cases of 13 subtypes of pediatric cancers among children up to age 6 until 2013 were ascertained through administrative health data linkages. Associations of trimester-specific, overall pregnancy and first year of life exposures were evaluated using Cox proportional hazards models, adjusting for potential confounders. Results A total of 2044 childhood cancers were identified. Exposure to PM 2.5 , per interquartile range increase, over the entire pregnancy, and during the first trimester was associated with an increased risk of astrocytoma (hazard ratio (HR) per 3.9 μg/m3 = 1.38 (95% CI: 1.01, 1.88) and, HR per 4.0 μg/m 3 = 1.40 (95% CI: 1.05–1.86), respectively). We also found a positive association between first trimester NO 2 and acute lymphoblastic leukemia (ALL) (HR = 1.20 (95% CI: 1.02–1.41) per IQR (13.3 ppb)). Conclusions In this population-based study in the largest province of Canada, results suggest an association between exposure to ambient air pollution during pregnancy, especially in the first trimester and an increased risk of astrocytoma and ALL. Further studies are required to replicate the findings of this study with adjustment for important individual-level confounders.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".