Device and surgical procedure-related infections in Canadian acute care hospitals, 2019–2023
Bibliographic record
Abstract
Background: Healthcare-associated infections (HAIs) are a significant healthcare burden in Canada.National surveillance of HAIs at sentinel acute care hospitals is conducted by the Canadian Nosocomial Infection Surveillance Program.Objective: This article describes device and surgical procedure-related HAI epidemiology in Canada from 2019 to 2023.Methods: Data were collected from 68 Canadian sentinel acute care hospitals between January 1, 2019, and December 31, 2023, for intensive care unit central line-associated bloodstream infections (ICU-CLABSIs), hip and knee surgical site infections (SSIs), cerebrospinal fluid (CSF) shunt SSIs and paediatric cardiac SSIs.Case counts, rates, patient and hospital characteristics, pathogen distributions and antimicrobial resistance data are presented.Results: Between 2019 and 2023, 2,582 device-related infections and 1,029 surgical procedurerelated infections were reported.Rates of ICU-CLABSIs fluctuated throughout the study period, with an overall increase in all intensive care unit settings except for the neonatal intensive care unit, where a 4% decrease was noted.An increase in SSIs following knee arthroplasty was observed, rising from 0.34 to 0.43 infections per 100 surgeries.Fluctuating trends were also observed in CSF shunt SSIs and paediatric cardiac SSIs over the study period.The most commonly identified pathogens were coagulase-negative staphylococci (23%) in ICU-CLABSIs and Staphylococcus aureus (42%) in SSIs.Conclusion: Epidemiological and microbiological trends among selected device and surgical procedure-related HAIs are essential for benchmarking infection rates nationally and internationally, identifying any changes in infection rates or antimicrobial resistance patterns and helping inform hospital infection prevention and control and antimicrobial stewardship policies and programs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".