The Association between Prenatal Antibiotic Use and the Risk of Autism Spectrum Disorders among Children: An Updated Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Studies on prenatal antibiotic use and Autism Spectrum Disorder (ASD) risk have yielded inconsistent results. AIM: This study aimed to resolve these discrepancies by conducting a meta-analysis on the relationship between prenatal antibiotic use and ASD in children. METHODS: A comprehensive search was conducted in four main databases: Medline (OVID) PubMed, Scopus, and Web of Science, up to August 1, 2024. The analysis employed random-effect models to estimate effect sizes, including hazard ratios (HR) and odds ratios (OR). Publication bias was assessed using Begg's test and Egger's regression test. Subgroup analyses explored variations in the association based on the trimester of pregnancy. The quality of the included studies was assessed using the Newcastle-Ottawa Scale (NOS). RESULTS: In this meta-analysis, which included twelve studies with a total population of 6,264,831, prenatal antibiotic use was associated with an increased risk of Autism Spectrum Disorder (ASD). The estimated HR for this risk was 1.08 (95% CI: 1.05, 1.12), and the OR was 1.16 (95% CI: 1.09, 1.23), with no detected heterogeneity among studies. The analysis found no publication bias. Significant associations were observed for each trimester: first trimester (HR: 1.11; 95% CI: 1.04, 1.18), second trimester (HR: 1.10; 95% CI: 1.06, 1.14), and third trimester (HR: 1.09; 95% CI: 1.01, 1.18). CONCLUSION: The analysis showed that prenatal antibiotic use is a risk factor for ASD. Prenatal antibiotic use was associated with an increase in the risk of ASD across all trimesters of pregnancy. Future research should focus on elucidating the mechanisms underlying this association by examining the effects of specific antibiotic classes, dosages, and timing during critical developmental periods. Longitudinal studies with comprehensive control for confounding factors are essential for strengthening causal inferences and guiding clinical recommendations regarding antibiotic use during 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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.069 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".