Antibiotic Use in Children Is Associated With Increased Risk of Asthma
Bibliographic record
Abstract
BACKGROUND: Antibiotic exposure in early childhood is a possible contributor to the increasing asthma prevalence in industrialized countries. Although a number of published studies have tested this hypothesis, the results have been conflicting. OBJECTIVE: To explore the association between antibiotic exposure before 1 year of age and development of childhood asthma. METHODS: Using administrative data, birth cohorts from 1997 to 2003 were evaluated (N = 251817). Antibiotic exposure was determined for the first year of life. After the first 24 months of life, the incidence of asthma was determined in both those exposed and not exposed to antibiotics in the first 12 months of life. Cox proportional hazards models were used to adjust for potential confounders and determine the hazard ratios associated with antibiotic exposure for the development of asthma. RESULTS: Antibiotic exposure in the first year of life was associated with a small risk of developing asthma in early childhood after adjusting for gender, socioeconomic status at birth, urban or rural address at birth, birth weight, gestational age, delivery method, frequency of physician visits, hospital visit involving surgery, visits to an allergist, respirologist, or immunologist, congenital anomalies, and presence of otitis media, acute, or chronic bronchitis, and upper and lower respiratory tract infections during the first year of life. As the number of courses of antibiotics increased, this was associated with increased asthma risk, with the highest risk being in children who received >4 courses. All antibiotics were associated with an increased risk of developing asthma, with the exception of sulfonamides. CONCLUSIONS: This study provides evidence that the use of antibiotics in the first year of life is associated with a small risk of developing asthma, and this risk increases with the number of courses of antibiotics prescribed.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".