Association between antibiotic usage during infancy and asthma incidence among children: a population-level ecological study in British Columbia, Canada
Bibliographic record
Abstract
Background This study follows published associations in BC to 2014 (updated in 2019) to model the predicted incidence of asthma in BC children attributable to antibiotic use within the context of reduced antibiotic use and increased breastfeeding in BC infants from 2000 to 2019. Methods A population-based ecological study was conducted in BC from 2000 to 2019, using outpatient antibiotic prescription data from BC PharmaNet and asthma diagnoses from the Chronic Disease Registry. Breastfeeding estimates were calculated using the Canadian Community Health Survey (CCHS). Population attributable risk (PAR) was calculated using a blended relative risk (RR) of asthma in antibiotic-exposed children who were and were not breastfed. PAR was used to calculate predicted vs. actual asthma incidence in 2019. Negative binomial regression was used to estimate the association between the average antibiotic prescription rate in infants under 1 and asthma incidence in 1–4 year olds, stratified by periods between 2000–2014 and 2015–2019. Results In BC, antibiotic prescribing decreased by 77% in infants under 1 and asthma incidence decreased by 41% in children 1–4 years from 2000 to 2019. BC breastfeeding rates increased from 46% in the 2005 CCHS to 71% in the 2017/18 CCHS. After calculating the PAR using a blended RR, the predicted asthma incidence in 2019 was 18.8/1,000 population. This was comparable to the observed asthma incidence in children 1–4 years of 16.6/1,000 population in 2019. During 2000–2014, adjusted incidence risk ratio (aIRR) for children under Quintile 5 of average antibiotic prescribing was 1.75 (95% CI: 1.63–1.88, P < 0.0001) times higher than that for Quintile 1. However, between 2015 and 2019, this association weakened (as expected because of increasing prevalence of breastfeeding), with the expected asthma incidence for Quintile 5 only 11% (aIRR 1.11, 95% CI: 0.78–1.57) higher than for Quintile 1. Conclusion We identified that over the past 20 years, antibiotic exposure in infants under 1 and asthma incidence in children 1–4 years has decreased significantly. Decreasing antibiotic exposure and increasing breastfeeding (which further mitigates risk associated with antibiotics) are of sufficient scale to explain much of this population trend. Changes in environmental, social and other exposures remain relevant to this complicated etiological pathway.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".