The Association Between Maternal Asthma and Child Autism: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Maternal asthma has been linked to child autism. In this study, we systematically reviewed observational studies published between July 2001 and February 2024 that assessed maternal asthma during pregnancy (exposure) and child autism (outcome). Databases searched included MEDLINE, CINAHL, EMBASE, and PsycINFO. Of the 350 potential studies, 19 met the inclusion criteria (2,530,716 participants; 73,065 autistic participants). Quality was assessed with the Newcastle–Ottawa Scale. Meta‐analyses using proportions and odds ratios were conducted using the Mantel–Haenszel method with a random‐effects model. Compared to women without asthma, there was an increased odds of child autism with any history of maternal asthma (OR = 1.32; 95% CI = 1.21, 1.44; I 2 = 61%, n = 14), with current asthma during pregnancy (OR = 1.23; 95% CI = 1.12, 1.35; I 2 = 35%, n = 10) and with medication use during pregnancy (OR = 1.48; 95% CI = 1.30, 1.68; I 2 = 0%, n = 3). However, when women with asthma who used asthma medication were compared to those with asthma who did not use medication, there were no increased odds for child autism (OR = 1.07; 95% CI = 0.89, 1.27; I 2 = 34%, n = 2). Maternal asthma is associated with an increased odds of child autism. Future studies should consider neurodivergence in the parents, the severity of asthma, and the effectiveness of prescribed medication in managing the mother's asthma to improve our understanding of this association. Trial Registration: PROSPERO registration: CRD42021265060
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".