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Hospital-acquired infection (HAI) rates were negatively affected by the the coronavirus disease 2019 (COVID-19) pandemic. We describe the incidence of HAIs, main pathogens, and multidrug-resistant organisms (MDROs) isolated in cancer patients before and during the pandemic.
This retrospective, comparative study included patients with HAIs. We compared 2 periods: the prepandemic period (2018, 2019, and the first 3 months of 2020) with the pandemic period (April–December 2020 and all of 2021).
Instituto Nacional de Cancerología, a tertiary-care oncology public hospital in Mexico City, Mexico.
Patients with the following HAIs were included: nosocomial pneumonia, ventilator-associated pneumonia (VAP), secondary bloodstream infection (BSI), central-line–associated bloodstream infection (CLBSI), and Clostridioides difficile infection (CDI). Demographic data, clinical characteristics, pathogens isolated, and MDRO data were included.
We identified 639 HAIs: 381 (7.95 per 100 hospital discharges) in the prepandemic period and 258 (7.17 per 100 hospital discharges) in the pandemic period. Hematologic malignancy was documented in 263 (44.3%) patients; 251 (39.2%) were in cancer progression or relapse. Nosocomial pneumonia was more frequent during the pandemic period (40.3% vs 32.3%; P = .04). Total episodes of VAP were not different between the 2 periods (28.1% vs 22.1%; P = .08), but during the pandemic period, the VAP rate was higher among COVID-19 patients than non–COVID-19 patients (72.2% vs 8.8%; P < .001). Escherichia coli, Stenotrophomonas maltophilia, and Staphylococcus aureus bacteremia cases were more frequent in the pandemic period. Extended-spectrum β-lactamases (ESBL)–E. coli was the only MDRO that occurred more frequently during the pandemic period.
In cancer patients, nosocomial pneumonia was more frequent during the pandemic period. We did not observe a significant impact on other HAIs. MDROs did not significantly increase during the pandemic.
Starting from a Markov chain with a finite or a countable infinite alphabet, we consider the chain obtained when all but one symbol are indistinguishable for the practitioner. We study conditions on the transition matrix of the Markov chain ensuring that the image chain has continuous or discontinuous transition probabilities with respect to the past.
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