Assessing the impact of antibiotic stewardship program elements on antibiotic use across acute-care hospitals: an observational study
Bibliographic record
Abstract
OBJECTIVES: Antibiotic use varies widely between hospitals, but the influence of antimicrobial stewardship programs (ASPs) on this variability is not known. We aimed to determine the key structural and strategic aspects of ASPs associated with differences in risk-adjusted antibiotic utilization across facilities. DESIGN: Observational study of acute-care hospitals in Ontario, Canada METHODS: A survey was sent to hospitals asking about both structural (8 elements) and strategic (32 elements) components of their ASP. Antibiotic use from hospital purchasing data was acquired for January 1 to December 31, 2014. Crude and adjusted defined daily doses per 1,000 patient days, accounting for hospital and aggregate patient characteristics, were calculated across facilities. Rate ratios (RR) of defined daily doses per 1,000 patient days were compared for hospitals with and without each antimicrobial stewardship element of interest. RESULTS: Of 127 eligible hospitals, 73 (57%) participated in the study. There was a 7-fold range in antibiotic use across these facilities (min, 253 defined daily doses per 1,000 patient days; max, 1,872 defined daily doses per 1,000 patient days). The presence of designated funding or resources for the ASP (RRadjusted, 0·87; 95% CI, 0·75-0·99), prospective audit and feedback (RRadjusted, 0·80; 95% CI, 0·67-0·96), and intravenous-to-oral conversion policies (RRadjusted, 0·79; 95% CI, 0·64-0·99) were associated with lower risk-adjusted antibiotic use. CONCLUSIONS: Wide variability in antibiotic use across hospitals may be partially explained by both structural and strategic ASP elements. The presence of funding and resources, prospective audit and feedback, and intravenous-to-oral conversion should be considered priority elements of a robust ASP.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 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".