Quantifying the Gap between Expected and Actual Rates of Antibiotic Prescribing in British Columbia, Canada
Bibliographic record
Abstract
Despite decades of stewardship efforts to combat antimicrobial resistance and quantify changes in use, the quality of antibiotic use in British Columbia (BC) remains unknown. As the overuse and misuse of antibiotics drives antibiotic resistance, it is imperative to expand surveillance efforts to examine the quality of antibiotic prescriptions. In late 2019, Canadian expected rates of antibiotic prescribing were developed for common infections. These rates were utilized to quantify the gap between the observed rates of prescribing and Canadian expected rates for antibiotic use for the province of BC. The prescribing data were extracted and matched to physician billing systems using anonymized patient identifiers from 1 January 2000 to 31 December 2018. Outpatient prescribing was further subdivided into community and emergency department settings and stratified by the following age groups: <2 years, 2-18 years, and ≥19 years. The proportions of physician visits that received antibiotic prescription were compared against the Canadian expected rates to quantify the unnecessary use for 18 common indications. Respiratory tract infections (RTI), including acute bronchitis, acute sinusitis, and acute pharyngitis, reported significant levels of overprescribing. Across all ages and health care settings, prescribing for RTI indications occurred at rates 2-8 times higher than the expected rates recommended by a group of expert Canadian physicians. Understanding the magnitude of unnecessary prescribing is a first step in delineating the provincial prescribing quality. The quantification of antibiotic overuse offers concrete targets for provincial stewardship efforts to reduce unnecessary prescribing by an average of 30% across both outpatient and emergency care settings.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".