Variability in antibiotic use across Ontario acute care hospitals
Bibliographic record
Abstract
BACKGROUND: Antibiotic stewardship is a required organizational practice for Canadian acute care hospitals, yet data are scarce regarding the quantity and composition of antibiotic use across facilities. We sought to examine the variability, and risk-adjusted variability, in antibiotic use across acute care hospitals in Ontario, Canada's most populous province. METHODS: Antibiotic purchasing data from IMS Health, previously demonstrated to correlate strongly with internal antibiotic dispensing data, were acquired for 129 Ontario hospitals from January to December 2014 and linked to patient day (PD) denominator data from administrative datasets. Hospital variation in DDDs/1000 PDs was determined for overall antibiotic use, class-specific use and six practices of clinical or ecological significance. Multivariable risk adjustment for hospital and patient characteristics was used to compare observed versus expected utilization. RESULTS: There was 7.4-fold variability in the quantity of antibiotic use across the 129 acute care hospitals, from 253 to 1873 DDDs/1000 PDs. Variation was evident within hospital subtypes, exceeded that explained by hospital and patient characteristics, and included wide variability in proportion of broad-spectrum antibiotics (IQR 36%-48%), proportion of fluoroquinolones among respiratory antibiotics (IQR 40%-62%), proportion of ciprofloxacin among urinary anti-infectives (IQR 44%-60%), proportion of antibiotics with highest risk for Clostridium difficile (IQR 29%-40%), proportion of 'reserved-use' antibiotics (IQR 0.8%-3.5%) and proportion of anti-pseudomonal antibiotics among antibiotics with Gram-negative coverage (IQR 26%-40%). CONCLUSIONS: There is extensive variability in antibiotic use, and risk-adjusted use, across acute care hospitals. This could motivate, focus and benchmark antibiotic 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".