Trends in healthcare-associated infections and antimicrobial-resistant organisms among adults in Canadian acute care hospitals: findings from four point prevalence surveys, 2002 to 2024
Bibliographic record
Abstract
Abstract Objective: To describe trends in the prevalence of healthcare-associated infections (HAIs) and antibiotic-resistant organisms (AROs) in Canadian acute-care hospitals. Design: Repeated point prevalence surveys. Setting: Canadian Nosocomial Infection Surveillance Program (CNISP) hospitals. Methods: Trained infection control professionals reviewed medical records of eligible adult patients and applied standardized definitions to collect demographic data and information on HAIs, AROs, and additional precautions from 39 to 62 hospitals in 2002, 2009, 2017, and 2024. Results: The prevalence of adult patients with at least one HAI increased from 10.4% (95% CI: 9.6%–11.2%) in 2002 to 12.4% (95% CI: 11.7%–13.2%) in 2009, declined to 8.4% (95% CI: 7.8%–9.0%) in 2017, and stabilized in 2024 (8.1%, 95% CI: 7.6%–8.6%) despite 3.1% of HAIs being due to SARS-CoV-2. Between 2017 and 2024, there were increases in bloodstream infections (1.0% to 1.5%, p = 0.002), viral respiratory infections (VRI) (0.3% to 0.6%, p < 0.001), and in the prevalence of patients on additional precautions for carbapenemase-producing organisms (0.1% to 1.7%, p < 0.001) and VRIs (2.1% to 3.6%, p < 0.001). In 2024, AROs were responsible for 6.6% of infections. One-third of HAIs were device-associated, and the prevalence of central line-associated bloodstream infections (CLABSIs) doubled from 0.4% in 2017 to 0.7% in 2024, p = 0.02. Conclusions: A point prevalence survey performed in Canada in 2024 following the COVID-19 pandemic identified a stable prevalence of HAIs and AROs despite the inclusion of SARS-CoV-2. Concerning trends were observed including the increased prevalence of certain HAIs such as CLABSIs and VRIs highlighting the need for ongoing efforts in hospital infection prevention.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".