Prevalence of Antimicrobial Use in a Network of Canadian Hospitals in 2002 and 2009
Bibliographic record
Abstract
BACKGROUND: Increasing antimicrobial resistance has been identified as an important global health threat. Antimicrobial use is a major driver of resistance, especially in the hospital sector. Understanding the extent and type of antimicrobial use in Canadian hospitals will aid in developing national antimicrobial stewardship priorities. METHODS: In 2002 and 2009, as part of one-day prevalence surveys to quantify hospital-acquired infections in Canadian Nosocomial Infection Surveillance Program hospitals, data were collected on the use of systemic antimicrobial agents in all patients in participating hospitals. Specific agents in use (other than antiviral and antiparasitic agents) on the survey day and patient demographic information were collected. RESULTS: In 2002, 2460 of 6747 patients (36.5%) in 28 hospitals were receiving antimicrobial therapy. In 2009, 3989 of 9953 (40.1%) patients in 44 hospitals were receiving antimicrobial therapy (P<0.001). Significantly increased use was observed in central Canada (37.4% to 40.8%) and western Canada (36.9% to 41.1%) but not in eastern Canada (32.9% to 34.1%). In 2009, antimicrobial use was most common on solid organ transplant units (71.0% of patients), intensive care units (68.3%) and hematology/oncology units (65.9%). Compared with 2002, there was a significant decrease in use of first-and second-generation cephalosporins, and significant increases in use of carbapenems, antifungal agents and vancomycin in 2009. Piperacillin-tazobactam, as a proportion of all penicillins, increased from 20% in 2002 to 42.8% in 2009 (P<0.001). There was a significant increase in simultaneous use of >1 agent, from 12.0% of patients in 2002 to 37.7% in 2009. CONCLUSION: From 2002 to 2009, the prevalence of antimicrobial agent use in Canadian Nosocomial Infection Surveillance Program hospitals significantly increased; additionally, increased use of broad-spectrum agents and a marked increase in simultaneous use of multiple agents were observed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".