The Association Between High and Unnecessary Antibiotic Prescribing: A Cohort Study Using Family Physician Electronic Medical Records
Bibliographic record
Abstract
BACKGROUND: Approximately 25% of outpatient antibiotic prescriptions are unnecessary among family physicians in Canada. Minimizing unnecessary antibiotics is key for community antibiotic stewardship. However, unnecessary antibiotic prescribing is much harder to measure than total antibiotic prescribing. We investigated the association between total and unnecessary antibiotic use by family physicians and evaluated inter-physician variability in unnecessary antibiotic prescribing. METHODS: This was a cohort study based on electronic medical records of family physicians in Ontario, Canada, between April 2011 and March 2016. We used predefined expected antibiotic prescribing rates for 23 common primary care conditions to calculate unnecessary antibiotic prescribing rates. We used multilevel Poisson regression models to evaluate the association between total antibiotic volume (number of antibiotic prescriptions per patient visit), adjusted for multiple practice- and physician-level covariates, and unnecessary antibiotic prescribing. RESULTS: There were 499 570 physician-patient encounters resulting in 152 853 antibiotic prescriptions from 341 physicians. Substantial inter-physician variability was observed. In the fully adjusted model, we observed a significant association between total antibiotic volume and unnecessary prescribing rate (adjusted rate ratio 2.11 per 10% increase in total use; 95% CI 2.05-2.17), and none of the practice- and physician-level variables were associated with unnecessary prescribing rate. CONCLUSIONS: We demonstrated substantial inter-physician variability in unnecessary antibiotic prescribing in this cohort of family physicians. Total antibiotic use was strongly correlated with unnecessary antibiotic prescribing. Total antibiotic volume is a reasonable surrogate for unnecessary antibiotic use. These results can inform community antimicrobial stewardship efforts.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".