Healthcare-associated infections and antimicrobial resistance in Canadian acute care hospitals, 2019–2023
Bibliographic record
Abstract
Background: Healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) continue to contribute to excess morbidity and mortality among Canadians.Objective: This report describes epidemiologic and laboratory characteristics and trends of HAIs and AMR, 2019-2023, using surveillance and laboratory data submitted by hospitals to the Canadian Nosocomial Infection Surveillance Program (CNISP) and by provincial and territorial laboratories to the National Microbiology Laboratory.Methods: Data was collected from 109 Canadian sentinel acute care hospitals between January 1, 2019 and December 31, 2023, for Clostridioides difficile infections (CDI), methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections (BSIs), vancomycinresistant Enterococcus (VRE) BSIs (specifically Enterococcus faecalis and Enterococcus faecium), carbapenemase-producing Enterobacterales (CPE) and carbapenemase-producing Acinetobacter baumannii (CPA) infections and colonizations and Candida auris (C.auris).Trend analysis for case counts, incidence rates (rates), outcomes, molecular characterization and AMR profiles are presented.Results: Rates remained relatively stable for CDI (range: 4.90-5.35infections per 10,000 patient days) and MRSA BSI (range: 1.00-1.16infections per 10,000 patient days) and increased significantly for VRE BSIs (range: 0.30-0.37infections per 10,000 patient days).Infection rates for CPE remained low compared to other HAIs but doubled non-significantly (rates: 0.08-0.16),CPA counts remained very low (n=4 cases) and C. auris isolates remained low (n=36 isolates). Conclusion:The incidence of MRSA BSIs and CDI remained stable and VRE BSIs and CPE infections increased in the Canadian acute care hospitals participating in CNISP.Few C. auris isolates were identified.Reporting standardized surveillance data to inform the application of infection prevention and control practices in acute care hospitals is 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 | 0.006 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".