Healthcare-associated infections and antimicrobial resistance in Canadian acute care hospitals, 2017–2021
Bibliographic record
Abstract
Background: Healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) continue to contribute to excess morbidity and mortality among Canadians.This report describes epidemiologic and laboratory characteristics and trends of HAIs and AMR from 2017 to 2021 (Candida auris 2012-2021) using surveillance and laboratory data submitted by hospitals to the Canadian Nosocomial Infection Surveillance Program (CNISP) and by provincial laboratories to the National Microbiology Laboratory (NML).Methods: Data collected from 88 Canadian sentinel acute care hospitals between January 1, 2017, and December 31, 2021, for Clostridioides difficile infections (CDI, carbapenemaseproducing Enterobacterales (CPE), methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections (BSIs) and vancomycin-resistant Enterococcus (VRE) BSIs.Candida auris (C.auris) surveillance was initiated in 2019 by CNISP and in 2012 by the NML.Case counts, rates, outcomes, molecular characterization and antimicrobial resistance profiles are presented.Results: From 2017 to 2021, increased rates per 10,000 patient days were observed for MRSA BSIs (35%; 0.84-1.13),VRE BSIs (43%; 0.23-0.33)and CPE infections (166%, 0.03-0.08).CDI rates decreased 11% (5.68-5.05).Thirty-one C. auris isolates were identified in Canada from 2012 to 2021, with the majority from Western Canada (68%).Conclusion: From 2017 to 2021, the incidence of MRSA and VRE BSIs, and CPE infections increased in Canadian acute care hospitals participating in a national sentinel network (CNISP) while CDI decreased.Few C. auris isolates were identified from 2012 to 2021.Reporting standardized surveillance data and the consistent application of infection prevention and control practises in acute care hospitals are critical to help decrease the burden of HAIs and AMR in Canada.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".