1146. Antibiotic Use in Infants Predicts Asthma Rate in Children 1–4 years at Fine Geographic Scale
Bibliographic record
Abstract
Abstract Background Early-life exposure to antibiotics is associated with childhood asthma. We previously reported that a dramatic drop in infant antibiotic use is correlated with a decline in asthma incidence in children in British Columbia (BC). This study aims to see whether antibiotic exposure predicts asthma at a fine geographic scale after adjustment for known covariates. Methods We used prescribing data from BC PharmaNet, a population-based database capturing all outpatient prescribing for BC population (n = 4.7 million). Prescribing rates for infants <1 year were calculated as prescriptions per 1000 population per year using age and sex-specified denominator estimates. Age-adjusted aggregate asthma incidence data for children 1–4 years were obtained from the BC Ministry of Health Chronic Disease Registry. The disease identification uses a standard case definition making using of diagnostic codes (ICD9-493 and ICD10-J45) in BC’s universal hospital and physician billing databases and relevant asthma-specific drug data from BC PharmaNet. We modeled the association between antibiotic prescribing rate and asthma incidence in 91 Local Health Areas using multivariable Poisson regression employing a generalized linear mixed-effects model adjusting for covariates. Results Between 2000 and 2014, the annual asthma incidence (ages 1–4 years) fell 26% from 27.3 (95% CI: 26.5–28.0) to 20.2 (95% CI: 19.5–20.8) per 1000 population. For children aged 1–4 years in 2000, the average proportion of infants exposed to one or more courses of antibiotics fell from 66.9 to 32.1% over the same interval. Antibiotic was a significant predictor of asthma rate (IRR=1.24 per 10% absolute increase in antibiotic prescribing; 95% CI: 1.19–1.27). Other covariates that remained significant in the model included male sex (IRR=1.56; 95% CI: 1.53–1.58), and atmospheric particulate matter PM 2.5 (IRR=1.08 per interquartile increase; 95% CI: 1.06–1.10). Conclusion Our findings suggest that antibiotic exposure in the first year of life increases the risk of being diagnosed with asthma later in childhood. This is congruent with similar findings at the individual level in a prospective cohort of Canadian children that also points to a pathway through altered gut microbiota. Disclosures All authors: No reported disclosures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".