Prenatal exposure to aeroallergens and risk of childhood atopic diseases
Bibliographic record
Abstract
Introduction: Exposure to airborne allergens during the gestational period may increase the risk of developing childhood atopic diseases. This study investigated associations between prenatal exposure to airborne allergens on development of asthma and allergic rhinitis in childhood. We also investigated susceptible windows of exposure and to what extent maternal atopy modified these associations. Methods: Data on 245,499 singleton term births (≥37 weeks) that occurred in six cities in the province of Ontario, Canada from 2004 to 2011 were obtained from a birth registry. Asthma and allergic rhinitis diagnoses up to 6 years of age were identified using administrative databases. Daily concentrations of airborne allergens were obtained from fixed-site monitoring stations in each city. Using Cox regression models combined with distributed lag models (DLMs), we examined weekly average airborne allergen concentrations during pregnancy and the onset of childhood asthma and allergic rhinitis while adjusting for maternal age at delivery, parity, infant’s gender, month/year of birth, maternal atopic status, maternal smoking, maternal intention to breast feed, ambient air pollution and area-level sociodemographic variable. Results: We observed that an interquartile range increase (IQR) (59.6 grains/m3) in pregnancy exposure to total pollen was associated with increased risks of childhood asthma (HR = 1.29, 95% CI: 1.27 to 1.32) and childhood allergic rhinitis (HR = 1.11, 95% CI: 1.07 to 1.17). For both asthma and allergic rhinitis, we identified that the most vulnerable period was over 27 weeks of gestation. Risks were higher among children of mothers with asthma or allergic rhinitis (p for interaction < 0.0013). Conclusions: This study provides evidence that prenatal exposure to airborne allergens increases risk of development of childhood asthma and allergic rhinitis. Presence of maternal asthma and allergic rhinitis appears to enhance susceptibility.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".