The Impact of COVID-19 Pandemic on Health Care-Associated Infections in Intensive Care Units: Results From the Egypt National Health Care-Associated Infections Surveillance, 2019-2020
Bibliographic record
Abstract
Background The COVID-19 pandemic resulted in the unexpected influx of patients leading to high rates of hospitalization. Focusing resources to mitigate the pandemic unintentionally reduced attention to health care-associated infections (HAIs) prevention programs. Intensive care units (ICUs) have suffered the most burden due to requirement of ventilation. Objective In this paper, we aimed to estimate the national HAI rates at ICUs before and during the COVID-19 pandemic to better identify the pandemic’s impact on HAIs. Methods Egypt’s HAI Surveillance was established in 2016 in 177 governmental ICUs. CDC case definitions and questionnaire were used to collect patients’ data. The types of HAIs targeted included bloodstream infections, pneumonia, and urinary tract infections. Pathogen identification and antimicrobial resistance were performed at the central laboratory. Surveillance data 2019-2020 were obtained, and a descriptive data analysis was performed. HAI rates per 100 patient days and device-associated infections (DAIs) per 1000 device days were compared between 2019 and 2020. Results In 2020, 4028 HAIs were reported, including 777 (19.3%) ICU-acquired reports; however, in 2019, 6242 were reported, including 1084 (17.4%) ICU-acquired ones. Incidence significantly decreased in 2020 compared with 2019 (2.67 vs 2.72, P<.001). The percentages of bloodstream infections, pneumonia, and urinary tract infection in 2020, compared with 2019, were 64.0% versus 61.6%, 10.9% versus 12.1%, and 25.1% versus 23.8%, respectively. DAIs decreased significantly, including CLABSI (2.6 vs 2.5, P<.001), VAP (0.75 vs 0.87, P=.04), and CAUTI (1.5 vs 1.6, P=.02). Klebsiella spp. was the predominant pathogen in both years representing (35.6% and 38.1%), followed by S. aureus (11.2% and 15.4%). The rate of carbapenem-resistant K. pneumoniae insignificantly increased (25% vs 23%, P=0.3), and that of Methicillin-resistant S. aureus decreased (68% vs 70%, P=0.4). Conclusions Egypt’s HAI Surveillance successfully described the impact of COVID-19 pandemic on HAIs. It identified a significant decrease in ICU-acquired HAIs and DAIs at the first pandemic year, which could reflect better the infection control measures. The types of HAIs, causative pathogens, and antimicrobial resistance pattern did not change significantly. Surveillance should be maintained to guide HAIs’ preventive and control measures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".