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1.
Open Forum Infect Dis ; 9(11), 2022.
Article in English | PubMed Central | ID: covidwho-2152126

ABSTRACT

Background: Identifying the source of healthcare personnel (HCP) coronavirus disease 2019 (COVID-19) is important to guide occupational safety efforts. We used a combined whole genome sequencing (WGS) and epidemiologic approach to investigate the source of HCP COVID-19 at a tertiary-care center early in the COVID-19 pandemic. Methods: Remnant nasopharyngeal swab samples from HCP and patients with polymerase chain reaction–proven COVID-19 from a period with complete sample retention (14 March 2020 to 10 April 2020) at Rush University Medical Center in Chicago, Illinois, underwent viral RNA extraction and WGS. Genomes with >90% coverage underwent cluster detection using a 2 single-nucleotide variant genetic distance cutoff. Genomic clusters were evaluated for epidemiologic linkages, with strong linkages defined by evidence of time/location overlap. Results: We analyzed 1031 sequences, identifying 49 clusters that included ≥1 HCP (265 patients, 115 HCP). Most HCP infections were not healthcare associated (88/115 [76.5%]). We did not identify any strong epidemiologic linkages for patient-to-HCP transmission. Thirteen HCP cases (11.3%) were attributed to a potential patient source (weak evidence involving nonclinical staff that lacked location data to prove or disprove contact with patients in same cluster). Fourteen HCP cases (12.2%) were attributed to HCP source (11 with strong evidence). Conclusions: Using genomic and epidemiologic data, we found that most HCP severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections were not healthcare associated. We did not find strong evidence of patient-to-HCP transmission of SARS-CoV-2.

3.
Open Forum Infectious Diseases ; 8(SUPPL 1):S301-S302, 2021.
Article in English | EMBASE | ID: covidwho-1746595

ABSTRACT

Background. Laboratory identification (Lab-ID) of late-onset SARS-CoV-2 positive tests during a hospital stay is a potential public health surveillance approach for hospital-acquired COVID-19. However, prolonged RNA fragment shedding and intermittent detection of SARS-CoV-2 virus via PCR testing among infected patients may hamper interpretation of laboratory-identified events. We aimed to describe the epidemiology of late-onset SARS-CoV-2 laboratory events using clinical criteria, to evaluate the feasibility of a Lab-ID approach to detection of nosocomial SARS-COV-2 infection. Methods. We evaluated all SARS-CoV-2 RT-PCR positive results recovered from patients at two acute-care hospitals in Chicago, IL, during March 1 - November 30, 2020. Each hospital maintained stringent infection control policies through-out the study period. Through chart review (WT & CS), we categorized all initial SARSCoV-2 positive tests collected > Hospital Day 5 (defined as 'late-onset' based on the 5-day mean incubation period for COVID-19) into the following clinical categories: Community Acquired;Unlikely Hospital Acquired;Possible Hospital Acquired;and Probable Hospital Acquired. Categorizations were made using hospital day, symptoms, alternative diagnoses, and clinical notes (Figure 1). Results. Of 2,671 SARS-CoV-2-positive patients, most positive tests (n=2,551;96%) were recovered pre-admit or by Hospital Day 2;first positive tests were uncommon during Hospital Days 6 to 14 (n=40;1.5%);and rare after Hospital Day 14 (n=15;0.6%). By chart review, of the 55 late-onset records reviewed, categorizations in descending order were: Prior positive at outside facility (n=23);Possible Hospital Acquired (n=16);Community Acquired (n=12);Probable Hospital Acquired (n=4). Less than half of the late-onset cases were categorized as a possible or probable hospital acquisition (Figure 2). Conclusion. Hospital-acquired SARS-CoV-2 infection was uncommon. Most late-onset episodes of SARS-CoV-2 were explained by detection at an outside healthcare facility or by delayed diagnosis of patients with symptoms at time of presentation. A Lab-ID approach to nosocomial COVID-19 surveillance would potentially misclassify a substantial number of patients.

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