Hospital antimicrobial stewardship funding and resourcing impact on broad-spectrum antibiotic use: a cross-sectional study
Bibliographic record
Abstract
Abstract Background: Antimicrobial stewardship programs (ASPs) aim to mitigate antimicrobial resistance (AMR) by optimizing antibiotic use including reducing unnecessary broad-spectrum therapy. This study evaluates the impact of ASP funding and resources on the use of broad-spectrum antibiotics in Ontario hospitals. Methods: We conducted a cross-sectional study of antimicrobial use (AMU) across 63 Ontario hospitals from April 2020 to March 2023. The Ontario ASP Landscape Survey provided data on ASP resourcing and antibiotic utilization. The main outcome was the proportion of all antibiotics that were broad-spectrum, defined as: fluoroquinolones; third-generation cephalosporins; beta-lactam/beta-lactamase inhibitors; carbapenems; clindamycin; and parenteral vancomycin. Secondary outcomes included the proportions of individual antibiotic classes listed above and anti-pseudomonal agents. Statistical analysis involved logistic regression to determine the odds ratio (OR) of the association between ASP funding/resourcing and broad-spectrum antibiotic use. Results: Among 63 hospitals, 48 reported designated ASP funding/resources. Median broad-spectrum antibiotic use was 52.5%. ASP funding/resources was not associated with overall broad-spectrum antibiotic use (0.97, 95% CI: 0.75–1.25, P = 0.79). However, funding was associated with lower use of fluoroquinolones (OR 0.67, 95% CI: 0.46–0.96, P = 0.03), clindamycin (OR 0.69, 95% CI: 0.47–1.00, P = 0.05), and anti-pseudomonal agents (OR 0.76, 95% CI: 0.59–0.98, P = 0.03). Conclusion: The presence of designated funding and resources for hospital ASPs is linked to reduced use of specific broad-spectrum antibiotics but not overall broad-spectrum antibiotic use. Enhancing ASP resourcing may be an important factor in limiting targeted antibiotic use, thereby increasing the effectiveness of efforts to mitigate AMR.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".