Prenatal antibiotic exposure and childhood asthma: a population-based study
Bibliographic record
Abstract
Antibiotic use during infancy alters gut microbiota and immune development and is associated with an increased risk of childhood asthma. The impact of prenatal antibiotic exposure is unclear. We sought to characterise the association between prenatal antibiotic exposure and childhood asthma.We performed a population-based cohort study using prescription records, hospitalisation records and physician billing claims from 213 661 mother-child dyads born in Manitoba, Canada between 1996 and 2012. Associations were determined using Cox regression, adjusting for maternal asthma, postnatal antibiotics and other potential confounders. Sensitivity analyses evaluated maternal antibiotic use before and after pregnancy.36.8% of children were exposed prenatally to antibiotics and 10.1% developed asthma. Prenatal antibiotic exposure was associated with an increased risk of asthma (adjusted hazard ratio (aHR) 1.23, 95% CI 1.20-1.27). There was an apparent dose response (aHR 1.15, 95% CI 1.11-1.18 for one course; aHR 1.26, 95% CI 1.21-1.32 for two courses; and aHR 1.51, 95% CI 1.44-1.59 for three or more courses). Maternal antibiotic use during 9 months before pregnancy (aHR 1.27, 95% CI 1.24-1.31) and 9 months postpartum (aHR 1.32, 95% CI 1.28-1.36) were similarly associated with asthma.Prenatal antibiotic exposure was associated with a dose-dependent increase in asthma risk. However, similar associations were observed for maternal antibiotic use before and after pregnancy, suggesting the association is either not directly causal, or not specific to pregnancy.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".