Regional variability in outpatient antibiotic use in Ontario, Canada: a retrospective cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Regional variability in antibiotic use is associated with both antibiotic overuse and antimicrobial resistance. Our objectives were to benchmark outpatient antibiotic use and to evaluate geographic variability among health regions in the province of Ontario, Canada. METHODS: This was a cross-sectional study of antibiotics dispensed from outpatient retail pharmacies in Ontario between March 2016 and February 2017. We analyzed variability in the number of antibiotic prescriptions dispensed per 1000 population among Ontario's 14 health regions with crude and adjusted Poisson regression models. Adjusted models controlled for rurality, 4 physician characteristics and 6 population characteristics. RESULTS: There were 8 352 578 antibiotics dispensed during the 1-year study period or 621 per 1000 population. The most commonly prescribed antibiotic classes were narrow-spectrum penicillins, macrolides, first-generation cephalosporins and second-generation fluoroquinolones, with adult women receiving the highest rate of prescriptions: 985 antibiotic prescriptions per 1000 population. There was geographic variability in total and class-specific antibiotic use. In the health region with the highest use 778 antibiotics were dispensed per 1000 population whereas in the health region with the lowest use 534 antibiotics were dispensed per 1000 population. The adjusted marginal standardized antibiotic prescription rates for the health regions with the highest and lowest use were 787 (95% confidence interval [CI] 658-934) and 546 (95% CI 494-606) antibiotic prescriptions per 1000 population, respectively. INTERPRETATION: We described baseline antibiotic usage in Ontario over a 12-month period, noting variability among some health regions. Our findings highlight the need for interventions to optimize antibiotic use and slow the emergence of antimicrobial resistance.
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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 | 0.001 | 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) | 0.001 | 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".