Impact of COVID-19 on healthcare-associated infections in Canadian acute-care hospitals: Interrupted time series (2018–2021)
Bibliographic record
Abstract
Background: Data regarding the effects of the SARS-COV-2 (COVID-19) pandemic on healthcare-associated infections (HAIs) in Canadian acute-care hospitals are limited. We examined the impact of the COVID-19 pandemic on HAIs and antimicrobial resistant organisms in hospitals participating in the Canadian Nosocomial Infection Surveillance Program. Methods: We analyzed 13,406 HAIs including adult mixed intensive care unit (ICU) central-line–associated bloodstream infections (CLABSIs), and healthcare-associated (HA) Clostridioides difficile infection (CDI), methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections (BSI), vancomycin-resistant Enterococcus (VRE) BSI, and carbapenemase-producing Enterobacterales (CPE) infections collected using standardized case definitions and questionnaires from 29–64 hospitals participating in the Canadian Nosocomial Infection Surveillance Program (CNISP) from January 2018 to December 2021. We used a generalized linear mixed model with quasi-Poisson distribution to assess step and slope changes in monthly HAI rates between the pre–COVID-19 pandemic period (January 1, 2018–February 29, 2020; 26 time points) and the COVID-19 pandemic period (March 1, 2020–December 31, 2021; 22 time points). Results were reported as incidence rate ratios (IRRs) with 95% confidence intervals (CIs) and adjusted for seasonality, hospital clustering, and hospital characteristics of interest. Results: In the CNISP network, 7,352 (55%) HAIs were reported in the prepandemic period and 6,054 (45%) in the pandemic period. Median age was significantly younger during the pandemic period compared to the prepandemic period among patients with HA-CDI, HA-MRSA BSI, and adult mixed ICU CLABSIs, and more than half of cases among all reported HAIs were male (range, 52%–65%). The 30-day all-cause in-hospital mortality rate did not significantly change between the prepandemic and pandemic periods for all reported HAIs and was highest among HA-VRE BSIs (34%). Modeling results indicated that the COVID-19 pandemic was associated with an immediate increase in HA-CDI and adult mixed ICU CLABSI rates whereas HA-MRSA BSI, HA-CPE and HA-VRE BSI rates immediately decreased. However, pandemic status did not have a statistically significant lasting impact on monthly rate trends for all reported HAIs after adjusting for seasonality, clustering, and hospital covariates (Fig. 1 and 2). Adjusted IRRs for all HAIs ranged from 1.00 to 1.01 (95% CI, 0.94–0.99 to 1.01–1.05). Conclusions: Although the COVID-19 pandemic placed a significant burden on the Canadian healthcare system, the immediate impact on monthly rates of HAIs in Canadian acute-care hospitals was not sustained over time. Understanding the epidemiological effects of the COVID-19 pandemic in the context of changing patient populations, and clinical and infection control practices, are essential to inform the continued management and prevention of HAIs in Canadian acute-care settings. Disclosures: None
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".