Smoke exposure and childhood atopic eczema and food allergy: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background There is no consensus on the effect of timing and type of smoke exposure on early allergy development. This study aimed to determine the relationship between early eczema or food allergy/hypersensitivity development in children by firstly investigating the effect of smoke exposure across critical development periods and secondly by analyzing effects of parental atcive or passive smoking. Methods Four databases (PubMed, Web of Science, Scopus and Embase) were searched in May 2022 and assessed by two independent reviewers. Case–control, cross‐sectional or cohort studies reporting on smoke exposure from preconception to postnatal periods and atopic eczema, food allergy and/or hypersensitivity outcomes by age 3 years were included. The Newcastle–Ottawa Scale was used to assess study quality. Random effects model was used to estimate the pooled risk ratios. Results From 1689 identified records, 32 studies with nearly 190,000 subjects were included. Parental smoking during preconception, pregnancy and postnatal periods was generally not associated with the risk of eczema, food allergy and food sensitisation development by age 3 years. Maternal active smoking during pregnancy was negatively associated with self‐reported doctor diagnosis of eczema (RR = 0.87, 95% CI 0.77–0.98; I 2 = 50.56) and maternal passive smoking during pregnancy was positively associated with clinician assessment of eczema in one study (RR = 1.38; 95% CI 1.06–1.79). Conclusion Our findings highlighted the importance of in utero programming in early‐life allergy development. Despite the weak evidence, our results suggest pregnant women should minimise their contact with second‐hand smoke to prevent offspring eczema development. There is a need for greater utilisation of objective allergy assessments in future studies.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".