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At our hospital, universal severe acute respiratory coronavirus virus 2 (SARS-CoV-2) polymerase chain reaction (PCR) testing was performed upon admission and again after 2 inpatient days. As community-wide prevalence, admission, and vaccination rates varied, the number needed to benefit fluctuated between 16 and 769 and the cost per additional detection fluctuated between $800 and $29,400. These 2 metrics were negatively associated with new hospital admissions. No other community indicator was associated with the number needed to benefit and cost per additional detection.

A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.1.345 variant carrying the E484K mutation was detected in 4 patients with no apparent epidemiological association from a hospital network in upstate New York. Subsequent analysis identified an additional 11 B.1.1.345 variants from this region between December 2020 and February 2021., (Published by Oxford University Press for the Infectious Diseases Society of America 2021.)

Prospective serial sampling of 70 patients revealed clinically relevant cycle thresholds (Ct) occurring 9, 26, and 36 days after symptom onset. Race, gender, and corticosteroids apparently did not influence RNA positivity. In a retrospective analysis of 180 patients, initial Ct did not correlate with requirements for admission or intensive care., (© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Background: Cleanliness of hospital surfaces helps prevent healthcare-associated infections, but comparative evaluations of various cleaning strategies during COVID-19 pandemic surges and worker shortages are scarce. Purpose and methods: To evaluate the effectiveness of daily, enhanced terminal, and contingency-based cleaning strategies in an acute care hospital (ACH) and a long-term care facility (LTCF), using SARS-CoV-2 RT-PCR and adenosine triphosphate (ATP) assays. Daily cleaning involved light dusting and removal of visible debris while a patient is in the room. Enhanced terminal cleaning involved wet moping and surface wiping with disinfectants after a patient is permanently moved out of a room followed by ultraviolet light (UV-C), electrostatic spraying, or room fogging. Contingency-based strategies, performed only at the LTCF, involved cleaning by a commercial environmental remediation company with proprietary chemicals and room fogging. Ambient surface contamination was also assessed randomly, without regard to cleaning times. Near-patient or high-touch stationary and non-stationary environmental surfaces were sampled with pre-moistened swabs in viral transport media. Results: At the ACH, SARS-CoV-2 RNA was detected on 66% of surfaces before cleaning and on 23% of those surfaces immediately after terminal cleaning, for a 65% post-cleaning reduction (p = 0.001). UV-C enhancement resulted in an 83% reduction (p = 0.023), while enhancement with electrostatic bleach application resulted in a 50% reduction (p = 0.010). ATP levels on RNA positive surfaces were not significantly different from those of RNA negative surfaces. LTCF contamination rates differed between the dementia, rehabilitation, and residential units (p = 0.005). 67% of surfaces had RNA after room fogging without terminal-style wiping. Fogging with wiping led to a -11% change in the proportion of positive surfaces. At the LTCF, mean ATP levels were lower after terminal cleaning (p = 0.016). Conclusion: Ambient surface contamination varied by type of unit and outbreak conditions, but not facility type. Removal of SARS-CoV-2 RNA varied according to cleaning strategy. Implications: Previous reports have shown time spent cleaning by hospital employed environmental services staff did not correlate with cleaning thoroughness. However, time spent cleaning by a commercial remediation company in this study was associated with cleaning effectiveness. These findings may be useful for optimizing allocation of cleaning resources during staffing shortages. [ABSTRACT FROM AUTHOR]

Background: The availability of accurate, reliable, and timely clinical data is crucial for clinicians, researchers, and policymakers so that they can respond effectively to emerging public health threats. This was typified by the recent SARS-CoV-2 pandemic and the critical knowledge and data gaps associated with novel Coronavirus 2019 disease (COVID-19). We sought to create an adaptive, living data mart containing detailed clinical, epidemiologic, and outcome data from COVID-19 patients in our healthcare system. If successful, the approach could then be used for any future outbreak or disease. Methods: From 3/13/2020 onward, demographics, comorbidities, outpatient medications, along with 75 laboratory, 2 imaging, 19 therapeutic, and 4 outcome-related parameters, were manually extracted from the electronic medical record (EMR) of SARS-CoV-2 positive patients. These parameters were entered on a registry featuring calculation, graphing tools, pivot tables, and a macro programming language. Initially, two internal medicine residents populated the database, then professional data abstractors populated the registry. Clinical parameters were developed with input from infectious diseases and critical care physicians and using a modified COVID-19 worksheet from the U.S. Centers for Disease Control and Prevention (CDC). Registry contents were migrated to a browser-based, metadata-driven electronic data capture software platform. Eventually, we developed queries and used various business intelligence tools which enabled us to semi-automate data ingestion of 147 clinical and outcome parameters from the EMR, via a large U.S. hospital-based, service-level, all-payer database. Statistics were performed in R and Minitab. Results: From March 13, 2020 to May 17, 2021, 549,691 SARS-CoV-2 test results on 236,144 distinct patients, along with location, admission status, and other epidemiologic details are stored on the cloud-based BI platform. From March 2020 until May 2021, extraction of clinical-epidemiologic parameter had to be performed manually. Of those, 543 have had >/=75 parameters fully entered in the registry. Ten clinical characteristics were significantly associated with the need for hospital admission. Only one characteristic was associated with a need for ICU admission. Use of supplemental oxygen, vasopressors and outpatient statin were associated with increased mortality. Initially, 0.5hrs -1.5 hours per patient chart (approximately 450-575 person hours) were required to manually extract the parameters and populate the registry. As of May 17, 2021, semi-automated data ingestion from the U.S. hospital all-payer database, employing user-defined queries, was implemented. That process can ingest and populate the registry with 147 clinical, epidemiologic, and outcome parameters at a rate of 2 hours per 100 patient charts. Conclusion: A living COVID-19 registryrepresents a mechanism to facilitate optimal sharing of data between providers, consumers, health information networks, and health plans through technology-enabled, secure-access electronic health information. Our approach also involves a diversity of new roles in the field, such as using residents, staff, and the quality department, in addition to professional data extractors and the health informatics team. Initially, due to the overwhelming number of infections that continues to accelerate, and the labor/time intense nature of the project, only a small fraction of all patients with COVID-19 had all parameters entered in the registry. Therefore, this report also offers lessons learned and discusses sustainability issues, should others wish to establish a registry. It also highlights the registry's local and broader public health significance. Beginning in June 2021, whole-genome sequencing results such as harboring important viral mutations, or variants of concern will be linked to the clinical metadata. [ABSTRACT FROM AUTHOR]