Healthcare-associated infections and antimicrobial resistance in Canadian acute care hospitals, 2014–2018
Bibliographic record
Abstract
Background: Healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) pose serious threats to the health of Canadians due to increased morbidity, mortality and healthcare costs.Epidemiologic and laboratory surveillance data, collected through the Canadian Nosocomial Infection Surveillance Program, are used to inform infection prevention and control and antimicrobial stewardship programs and policies.The objective of this study was to describe the epidemiologic and laboratory characteristics and trends of HAIs and AMR from 2014 to 2018 using surveillance data provided by Canadian hospitals participating in the Canadian Nosocomial Infection Surveillance Program.Methods: Data were collected from 70 Canadian sentinel hospitals between January 1, 2014 and December 31, 2018 for Clostridioides difficile infection (CDI), methicillin-resistant Staphylococcus aureus bloodstream infections, vancomycin-resistant Enterococci bloodstream infections and carbapenemase-producing Enterobacteriaceae.Case counts, rates, outcome data, molecular characterization and antimicrobial resistance profiles are presented.Additionally, hospital-level Escherichia coli antibiogram data were collected and are described.Results: Increases in rates per 10,000 patient-days were observed for methicillin-resistant S. aureus bloodstream infections (59%; 0.66-1.05,p=0.023) and vancomycin-resistant Enterococci bloodstream infections (143%; 0.14-0.34,p=0.023).However, CDI rates decreased by 12.5% between 2015 and 2018 (from 6.16-5.39,p=0.042).Carbapenemase-producing Enterobacteriaceae infection rates remained low and stable whereas colonization increased by 375% (0.04-0.19; p=0.014).Conclusion: Ongoing efforts to prevent HAIs and reduce AMR in Canada require consistent, standardized surveillance data from acute care hospitals.Increased collaboration with provincial, territorial and international partners in infection prevention and control, as well as antimicrobial stewardship, will be essential in reducing the burden of observed HAIs (including antimicrobial resistant organisms).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".