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Record W4361302907 · doi:10.1111/ppe.12972

Association between prenatal antibiotic exposure and autism spectrum disorder among term births: A population‐based cohort study

2023· article· en· W4361302907 on OpenAlexafffundabout
Amanda Nitschke, Helena Abreu do Valle, Bruce A. Vallance, Celeste Bickford, Angie Ip, Nancy Lanphear, Bruce P. Lanphear, Whitney Weikum, Tim F. Oberlander, Gillian E. Hanley

Bibliographic record

VenuePaediatric and Perinatal Epidemiology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsSimon Fraser UniversityBC Children's HospitalUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMedicineAutism spectrum disorderCohortPediatricsPopulationAutismTerm (time)Cohort studyDemographyAssociation (psychology)PregnancyObstetricsPsychiatryEnvironmental healthInternal medicineGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Prenatal antibiotic exposure induces changes in the maternal microbiome, which could influence the development of the infant's microbiome-gut-brain axis. OBJECTIVES: We assessed whether prenatal antibiotic exposure is associated with an increased risk of autism spectrum disorder (ASD) in offspring born at term. METHODS: This population-based retrospective cohort study included everyone who delivered a live singleton-term infant in British Columbia, Canada between April 2000 and December 2014. Exposure was defined as filling antibiotic prescriptions during pregnancy. The outcome was an ASD diagnosis from the British Columbia Autism Assessment Network, with a follow-up to December 2016. To examine the association among pregnant individuals treated for the same indication, we studied a sub-cohort diagnosed with urinary tract infections. Cox proportional hazards models were used to estimate unadjusted and adjusted hazard ratios (HR). The analysis was stratified by sex, trimester, cumulative duration of exposure, class of antibiotic, and mode of delivery. We ran a conditional logistic regression of discordant sibling pairs to control for unmeasured environmental and genetic confounding. RESULTS: Of the 569,953 children included in the cohort, 8729 were diagnosed with ASD (1.5%) and 169,922 were exposed to prenatal antibiotics (29.8%). Prenatal antibiotic exposure was associated with an increased risk of ASD (HR 1.10, 95% confidence interval [CI] 1.05, 1.15), particularly for exposure during the first and second trimesters (HR 1.11, 95% CI 1.04, 1.18 and HR 1.09, 95% CI 1.03, 1.16, respectively), and exposure lasting ≥15 days (HR 1.13, 95% CI 1.04, 1.23). No sex differences were observed. The association was attenuated in the sibling analysis (adjusted odds ratio 1.04, 95% CI 0.92, 1.17). CONCLUSIONS: Prenatal antibiotic exposure was associated with a small increase in the risk of ASD in offspring. Given the possibility of residual confounding, these results should not influence clinical decisions regarding antibiotic use during pregnancy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.245

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.279
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2023
Admission routes3
Has abstractyes

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