Prenatal exposure to air pollution and the risk of eczema in childhood: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract An increasing number of studies investigated the association between air pollution during pregnancy and the risk of eczema in offspring. However, no meta-analysis has confirmed the existence and size of their association to date. We systematically searched PubMed, Web of Science, Cochrane Library, and Embase databases to select the observational controlled studies published from the inception date to October 16, 2021. Quality evaluation was guided by the Newcastle-Ottawa Scale (NOS). Sensitivity analysis was applied to assess the impact of each included study on the combined effects, and publication bias was examined by the Begg’s tests and Egger’s tests. A total of 12 articles involving 69,374 participants met our eligibility criteria. The significant association between the maternal exposure to NO2 (per 10 μg/m3 increased) and childhood eczema was observed, with a pooled risk estimate of 1.13 (95% CI: 1.06-1.19), but no association was observed between exposure to PM10, PM2.5, and SO2 and the risk of eczema in offspring. Besides, the effect of maternal NO2 exposure on childhood eczema was significant in the first and second trimesters, but not in the third trimester. There was notable variability in geographic location (p = 0.037) and air pollutant concentration (p = 0.031) based on meta-regression. Our findings indicated that prenatal exposure to NO2 was a risk factor for elevated risk of eczema in childhood, especially in the first and second trimesters. Further studies with larger sample sizes considering different constituents of air pollution and various exposure windows are needed to validate 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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".