Antimicrobial use among paediatric inpatients at hospital sites within the Canadian Nosocomial Infection Surveillance Program, 2017/2018
Bibliographic record
Abstract
BACKGROUND: Antimicrobial resistance threatens the ability to successfully prevent and treat infections. While hospital benchmarks regarding antimicrobial use (AMU) have been well documented among adult populations, there is less information from among paediatric inpatients. This study presents benchmark rates of antimicrobial use (AMU) for paediatric inpatients in nine Canadian acute-care hospitals. METHODS: Acute-care hospitals participating in the Canadian Nosocomial Infection Surveillance Program submitted annual AMU data from paediatric inpatients from 2017 and 2018. All systemic antimicrobials were included. Data were available for neonatal intensive care units (NICUs), pediatric ICUs (PICUs), and non-ICU wards. Data were analyzed using days of therapy (DOT) per 1000 patient days (DOT/1000pd). RESULTS: Nine hospitals provided paediatric AMU data. Data from seven NICU and PICU wards were included. Overall AMU was 481 (95% CI 409-554) DOT/1000pd. There was high variability in AMU between hospitals. AMU was higher on PICU wards (784 DOT/1000pd) than on non-ICU (494 DOT/1000pd) or NICU wards (333 DOT/1000pd). On non-ICU wards, the antimicrobials with the highest use were cefazolin (66 DOT/1000pd), ceftriaxone (59 DOT/1000pd) and piperacillin-tazobactam (48 DOT/1000pd). On PICU wards, the antimicrobials with the highest use were ceftriaxone (115 DOT/1000pd), piperacillin-tazobactam (115 DOT/1000pd), and cefazolin (111 DOT/1000pd). On NICU wards, the antimicrobials with the highest use were ampicillin (102 DOT/1000pd), gentamicin/tobramycin (78 DOT/1000pd), and cefotaxime (38 DOT/1000pd). CONCLUSIONS: This study represents the largest collection of antimicrobial use data among hospitalized paediatric inpatients in Canada to date. In 2017/2018, overall AMU was 481 DOT/1000pd. National surveillance of AMU among paediatric inpatients is necessary for establishing benchmarks and informing antimicrobial stewardship efforts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".