Characterization of methicillin-resistant <i>Staphylococcus aureus</i> in Canadian hospitals: 17 years of the CANWARD study (2007–23)
Bibliographic record
Abstract
OBJECTIVES: This study reviewed the patient demographic parameters, molecular characteristics and in vitro antimicrobial susceptibility testing results for MRSA isolates infecting inpatients and outpatients presenting for care to tertiary-care Canadian hospitals between 2007 and 2023. METHODS: DNA sequencing was used to generate spa types. Panton-Valentine leukocidin (PVL) genes were detected by PCR. Broth microdilution antimicrobial susceptibility testing (CLSI M7, 12th edition, 2024) was performed with MICs interpreted by CLSI M100 breakpoints (34th edition, 2024) when available. RESULTS: In total, 2697 MRSA were identified among 12734 Staphylococcus aureus isolates submitted to the CANWARD study between 2007 and 2023. The annual proportion of MRSA decreased significantly from 2007 (26.1%) to 2017 (16.0%) and then increased to 24.2% in 2023 (P < 0.0001). From 2007 to 2023, community-associated (CA)-MRSA spa types increased from 20.8% to 75.0% (P < 0.0001), while hospital-associated (HA)-MRSA decreased from 79.2% to 25.0% (P < 0.0001). The predominant MRSA spa types identified among all isolates were t002 (36.3%), an HA-MRSA genotype, and t008 (24.5%), a CA-MRSA genotype. PVL was detected in 36.0% of all MRSA isolates (76.3% of CA-MRSA; 2.7% of HA-MRSA). Percent susceptible values for all MRSA isolates were ≥99% for ceftobiprole, dalbavancin, daptomycin, linezolid, nitrofurantoin and vancomycin. Notable differences in percent susceptible values were identified for clindamycin (HA-MRSA, 38.9%; CA-MRSA, 86.9%) (P < 0.0001) trimethoprim/sulfamethoxazole (HA-MRSA, 91.4%; CA-MRSA, 98.8%) (P < 0.0001) and doxycycline (HA-MRSA, 96.6%; CA-MRSA, 98.8%) (P = 0.0003). CONCLUSIONS: The changing epidemiology of MRSA in Canadian hospitals warrants continued national surveillance efforts as a resource to support therapeutic guidelines and infection control and prevention programmes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".