Perinatal Exposure to Ambient Air Pollution and Greenness, and the Incidence of Paediatric Diabetes: A Population-Based Cohort Study
Bibliographic record
Abstract
Background: Ambient air pollution exposure during early life has recently been associated with diabetes incidence in children. However, little is known regarding critical exposure windows and if exposure to greenness could modify these associations. This study sought to assess the relationship between selected air pollutants (NO2, PM2.5, O3, and Ox [oxidant capacity]) and the incidence of paediatric diabetes.Methods: Our cohort consisted of 754,698 mother-infant pairs occurring between 2006 and 2012 in the province of Ontario, Canada. Diabetes incidence was ascertained using population-based health administrative data with a validated algorithm. The cohort was followed until 2015. Temporally adjusted exposure to NO2, PM2.5, and O3 was estimated using satellite-based regression, land-use regression, and a fusion-based approach, respectively, and was assigned to subjects’ residential postal codes during pregnancy. Ox was calculated as the redox-weighted average of O3 and NO2. Satellite-derived normalized difference vegetation index (NDVI) was used to represent the amount of green vegetation at a 250m buffer across Ontario. Associations between total pregnancy, trimester specific, and early life exposures to ambient air pollutants and childhood diabetes incidence up to age 6 were estimated using Cox regression models.Results: 1,094 children with diabetes were identified. Each IQR increase in O3 and Ox exposures in the second trimester of pregnancy were associated with hazard ratios of 1.36 (95% CI: 1.07-1.73) and 1.45 (95% CI: 1.05-1.81), respectively. These relationships exhibited linear shapes, and exposure to greenness was found to have a protective modifying effect (p-interaction ≤ 0.04). There were no other positive associations observed for other pollutants and other time periods.Conclusions: Air pollution, especially O3 and Ox, was linked to increased diabetes risk in children. Exposure to greenness during pregnancy appeared to attenuate these associations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".