Antimicrobial use in Canadian acute-care hospitals: Findings from three national point-prevalence surveys between 2002 and 2017
Bibliographic record
Abstract
Abstract Objectives: The Canadian Nosocomial Infection Surveillance Program conducted point-prevalence surveys in acute-care hospitals in 2002, 2009, and 2017 to identify trends in antimicrobial use. Methods: Eligible inpatients were identified from a 24-hour period in February of each survey year. Patients were eligible (1) if they were admitted for ≥48 hours or (2) if they had been admitted to the hospital within a month. Chart reviews were conducted. We calculated the prevalence of antimicrobial use as follows: patients receiving ≥1 antimicrobial during survey period per number of patients surveyed × 100%. Results: In each survey, 28−47 hospitals participated. In 2002, 2,460 (36.5%; 95% CI, 35.3%−37.6%) of 6,747 surveyed patients received ≥1 antimicrobial. In 2009, 3,566 (40.1%, 95% CI, 39.0%−41.1%) of 8,902 patients received ≥1 antimicrobial. In 2017, 3,936 (39.6%, 95% CI, 38.7%−40.6%) of 9,929 patients received ≥1 antimicrobial. Among patients who received ≥1 antimicrobial, penicillin use increased 36.8% between 2002 and 2017, and third-generation cephalosporin use increased from 13.9% to 18.1% ( P < .0001). Between 2002 and 2017, fluoroquinolone use decreased from 25.7% to 16.3% ( P < .0001) and clindamycin use decreased from 25.7% to 16.3% ( P < .0001) among patients who received ≥1 antimicrobial. Aminoglycoside use decreased from 8.8% to 2.4% ( P < .0001) and metronidazole use decreased from 18.1% to 9.4% ( P < .0001). Carbapenem use increased from 3.9% in 2002 to 6.1% in 2009 ( P < .0001) and increased by 4.8% between 2009 and 2017 ( P = .60). Conclusions: The prevalence of antimicrobial use increased between 2002 and 2009 and then stabilized between 2009 and 2017. These data provide important information for antimicrobial stewardship programs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".