Trends in health care–associated infections in acute care hospitals in Canada: an analysis of repeated point-prevalence surveys
Bibliographic record
Abstract
BACKGROUND: Health care–associated infections are a common cause of patient morbidity and mortality. We sought to describe the trends in these infections in acute care hospitals, using data from 3 national point-prevalence surveys. METHODS: The Canadian Nosocomial Infection Surveillance Program (CNISP) conducted descriptive point-prevalence surveys to assess the burden of health care–associated infections on a single day in February of 2002, 2009 and 2017. Surveyed infections included urinary tract infection, pneumonia, Clostridioides difficile infection, infection at surgical sites and bloodstream infections. We compared the prevalence of infection across the survey years and considered the contribution of antimicrobial-resistant organisms as a cause of these infections. RESULTS: We surveyed 28 of 33 (response rate 84.8%) CNISP hospitals (6747 patients) in 2002, 39 of 55 (response rate 71.0%) hospitals (8902 patients) in 2009 and 47 of 66 (response rate 71.2%) hospitals (9929 patients) in 2017. The prevalence of patients with at least 1 health care–associated infection increased from 9.9% in 2002 (95% confidence interval [CI] 8.4%–11.5%) to 11.3% in 2009 (95% CI 9.4%–13.5%), and then declined to 7.9% in 2017 (95% CI 6.8%–9.0%). In 2017, device-associated infections accounted for 35.6% of all health care–associated infections. Methicillin-resistant Staphylococcus aureus (MRSA) accounted for 3.9% of all organisms identified from 2002 to 2017; other antibiotic-resistant organisms were uncommon causes of infection for all survey years. INTERPRETATION: In CNISP hospitals, there was a decline in the prevalence of health care–associated infection in 2017 compared with previous surveys. However, strategies to prevent infections associated with medical devices should be developed. Apart from MRSA, few infections were caused by antibiotic-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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| 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".