Interprovincial variation in antibiotic use in Canada, 2019: a retrospective cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Geographic trends in antibiotic prescribing show regional variation in antibiotic overuse and antimicrobial resistance, posing a threat to global health care systems. This study's objective was to examine interprovincial variation in outpatient antibiotic dispensing in Canada in 2019. METHODS: We conducted a cross-sectional study of antibiotic prescriptions dispensed in Canadian provinces in 2019, leveraging the IQVIA Geographic Prescription Monitor database. We report annual rates of overall antibiotic dispensing, broad-spectrum antibiotic dispensing and age-specific antibiotic dispensing as prescriptions per 1000 population in each province and nationally. RESULTS: A total of 23 406 640 antibiotic prescriptions were dispensed nationally in 2019, at a rate of 627.3 prescriptions per 1000 population. Overall antibiotic dispensing rates in Newfoundland and Labrador (920.5 prescriptions per 1000 population) and Saskatchewan (713.7 prescriptions per 1000 population) significantly exceeded the national rate, whereas the rate in British Columbia (543.3 prescriptions per 1000 population) was significantly below the national rate. We observed additional variation when provincial rates of antibiotic dispensing were stratified by drug class and age group. INTERPRETATION: We identified interprovincial variation in antibiotic use in Canadian provinces in 2019. These findings highlight the need for provincial targets for antibiotic use to reduce overuse and antimicrobial resistance.
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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 | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".