Antimicrobial use among adult inpatients at hospital sites within the Canadian Nosocomial Infection Surveillance Program: 2009 to 2016
Bibliographic record
Abstract
BACKGROUND: Antimicrobial resistance is a growing threat to the world's ability to prevent and treat infections. Links between quantitative antibiotic use and the emergence of bacterial resistance are well documented. This study presents benchmark antimicrobial use (AMU) rates for inpatient adult populations in acute-care hospitals across Canada. METHODS: In this retrospective surveillance study, acute-care adult hospitals participating in the Canadian Nosocomial Infection Surveillance Program (CNISP) submitted annual AMU data on all systemic antimicrobials from 2009 to 2016. Information specific to intensive care units (ICUs) and non-ICU wards were available for 2014-2016. Data were analyzed using defined daily doses (DDD) per 1000 patient days (DDD/1000pd). RESULTS: Between 2009 and 2016, 16-18 CNISP adult hospitals participated each year and provided their AMU data (22 hospitals participated in ≥1 year of surveillance; 11 in all years). From 2009 to 2016, there was a significant reduction in use (12%) (from 654 to 573 DDD/1000pd, p = 0.03). Fluoroquinolones accounted for the majority of this decrease (47% reduction in combined oral and intravenous use, from 129 to 68 DDD/1000pd, p < 0.002). The top five antimicrobials used in 2016 were cefazolin (78 DDD/1000pd), piperacillin-tazobactam (53 DDD/1000pd), ceftriaxone (49 DDD/1000pd), vancomycin (combined oral and intravenous use was 44 DDD/1000pd; 7% of vancomycin use was oral), and ciprofloxacin (combined oral and intravenous use: 42 DDD/1000pd). Among the top 10 antimicrobials used in 2016, ciprofloxacin and metronidazole use decreased significantly between 2009 and 2016 by 46% (p = 0.002) and 26% (p = 0.002) respectively. Ceftriaxone (85% increase, p = 0.0008) and oral amoxicillin-clavulanate (140% increase, p < 0.0001) use increased significantly but contributed only a small component (8.6 and 5.0%, respectively) of overall use. CONCLUSIONS: This study represents the largest collection of dispensed antimicrobial use data among inpatients in Canada to date. Between 2009 and 2016, there was a significant 12% decrease in AMU, driven primarily by a 47% decrease in fluoroquinolone use. Modest absolute increases in parenteral ceftriaxone and oral amoxicillin-clavulanate use were noted but contributed a small amount of total AMU. Ongoing national surveillance is crucial for establishing benchmarks and antimicrobial stewardship guidelines.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".