1117. A Retrospective Analysis of Paediatric Prescribing in British Columbia from 2013 to 2016
Bibliographic record
Abstract
Abstract Background Antibiotic prescribing in pediatric care is highly prevalent. Often children are prescribed antibiotics for conditions that are commonly self-limiting and viral in etiology such as upper respiratory tract infections. The purpose of this study was to examine the scope of pediatric antibiotic prescribing in British Columbia from 2013 to 2016 and identify potential new provincial antimicrobial stewardship targets. Methods Antibiotic prescription data for children were extracted from a provincial prescription database, and linked to demographic files in order to obtain patient age, sex and geographic location. Prescription rates were then calculated, and trends were examined by major anatomical therapeutic chemical (ATC) classification. Results Our cohort included an average of 271,134 children per year and 1,767,652 antibiotic prescriptions. Over the 4 years, rates of antibiotic prescribing increased 4.5% (from 453 to 474 prescriptions per 1,000 population per year). The greatest increase, across all classes of antibiotics, was seen in children aged 0–2 years of age. By 2016, the greatest increase in prescribing, by class, was observed in J01X (e.g., nitrofurantoin, fosfomycin) with a 1360% increase for children aged 3–9. Across all ages, quinolones (J01M) increased 98%. Remaining classes, including β lactams (J01C), and macrolides (J01F), experienced modest reductions in the older age groups. Conclusion Past studies have illustrated decreasing or static rates of antibiotic prescribing in British Columbia. However, we have identified a paradoxical (4.5%) increase in pediatric antibiotic prescribing since 2013. Although it appears that provincial efforts have been successful in reducing the use of broad-spectrum penicillins (J01C), marked surges in the use of classes like tetracylines (J01A), quinolones (J01M), and other antibacterials (J01X) identify a new potential target for provincial stewardship. 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".