A Comparison of Antibiotic Use in Children Between Canada and Denmark
Bibliographic record
Abstract
BACKGROUND: High rates of antibiotic prescribing in children lead to antibiotic resistance in the community. Surveillance on utilization rates and comparisons with other jurisdictions are methods for benchmarking. Surveillance on antibiotic use is well established in Europe, including Denmark, but until recently, similar data from Canada were lacking. OBJECTIVE: To compare pediatric antibiotic prescribing rates in British Columbia, Canada, with those in Denmark. METHODS: Population-based data on antibiotic prescriptions from British Columbia and Denmark were obtained from 1999 to 2003 for children less than 15 years of age. Annual trends in prescription rates per 1000 children were analyzed by using generalized linear models for all children less than 15 years of age; they were stratified by age group (0-4, 5-9, 10-14 y) for all antibiotics. Class-specific trends were also evaluated for penicillins, cephalosporins, macrolides, sulfonamides and trimethoprim, tetracyclines, and fluoroquinolones. RESULTS: From 1999 to 2003, the overall British Columbia prescription rate was significantly higher than that of Denmark (p < 0.0001) at all age stratifications. In 2003, the British Columbia prescription rate was twice that of Denmark, at 608 versus 385 prescriptions per 1000 children, respectively. In both jurisdictions, the majority of antibiotics used were penicillins (Anatomical Therapeutic Chemical class J01C). However, in British Columbia, most penicillins used were extended-spectrum (83% in 2003); in Denmark, 34% of penicillins used in 2003 were extended-spectrum and 56% were beta-lactamase sensitive. In British Columbia, use of penicillins (-4.5%), cephalosporins (-5.5%), trimethoprim/sulfamethoxazole (-36%), and tetracycline (-1.6%) decreased over time, whereas in Denmark, use of penicillins increased by 11% over time and non-penicillin antibiotics remained stable. A significant increase in macrolide consumption was seen in British Columbia due to use of clarithromycin and azithromycin; in contrast, macrolide consumption declined in Denmark. CONCLUSIONS: Compared with Denmark, the antibiotic prescription rate for children is substantially higher in British Columbia. In addition, there has been a significant increase in the use of macrolides, especially the second-generation agents, in British Columbia compared with the use in Denmark. Further studies are required to delineate reasons for antibiotic prescribing patterns in these 2 jurisdictions.
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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".