Stirrup, Oliver, Hughes, Joseph, Parker, Matthew, Partridge, David G, Shepherd, James G, Blackstone, James, Coll, Francesc, Keeley, Alexander, Lindsey, Benjamin B, Marek, Aleksandra, Peters, Christine, Singer, Joshua B, Tamuri, Asif, de Silva, Thushan I, Thomson, Emma C, Breuer, Judith, The COVID-19 Genomics UK (COG-UK) Consortium, , Bashton, Matthew, Smith, Darren, Nelson, Andrew, Young, Greg and McCann, Clare (2021) Rapid feedback on hospital onset SARS-CoV-2 infections combining epidemiological and sequencing data. eLife, 10. ISSN 2050-084X
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Abstract
Background: Rapid identification and investigation of healthcare-associated infections (HCAIs) is important for suppression of SARS-CoV-2, but the infection source for hospital onset COVID-19 infections (HOCIs) cannot always be readily identified based only on epidemiological data. Viral sequencing data provides additional information regarding potential transmission clusters, but the low mutation rate of SARS-CoV-2 can make interpretation using standard phylogenetic methods difficult. Methods: We developed a novel statistical method and sequence reporting tool (SRT) that combines epidemiological and sequence data in order to provide a rapid assessment of the probability of HCAI among HOCI cases (defined as first positive test >48 hours following admission) and to identify infections that could plausibly constitute outbreak events. The method is designed for prospective use, but was validated using retrospective datasets from hospitals in Glasgow and Sheffield collected February-May 2020. Results: We analysed data from 326 HOCIs. Among HOCIs with time-from-admission ≥8 days the SRT algorithm identified close sequence matches from the same ward for 160/244 (65.6%) and in the remainder 68/84 (81.0%) had at least one similar sequence elsewhere in the hospital, resulting in high estimated probabilities of within-ward and within-hospital transmission. For HOCIs with time-from-admission 3-7 days, the SRT probability of healthcare acquisition was >0.5 in 33/82 (40.2%). Conclusions: The methodology developed can provide rapid feedback on HOCIs that could be useful for infection prevention and control teams, and warrants further prospective evaluation. The integration of epidemiological and sequence data is important given the low mutation rate of SARS-CoV-2 and its variable incubation period.
Item Type: | Article |
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Additional Information: | Funding Information: COG-UK HOCI is funded by the COG-UK consortium, which is supported by funding from the Medical Research Council (MRC) part of UK Research & Innovation (UKRI), the National Institute of Health Research (NIHR) and Genome Research Limited, operating as the Wellcome Sanger Institute. JBr receives funding from the NIHR ULC/UCLH Biomedical Research Centre. FC is funded by Wellcome (grant number: 201344/Z/16/Z). MDP is funded by the NIHR Sheffield Biomedical Research Centre (BRC - IS-BRC-1215-20017). We acknowledge the help of the UCL Comprehensive Clinical Trials Unit. The authors wish to thank the NHS Greater Glasgow and Clyde and Sheffield Teaching Hospitals NHS Foundation Trust infection prevention and control teams for provision of data. The authors thank Michael Chapman for his assistance in the development of this project. Matthew Bashton, Andrew Nelson, Darren Smith, Greg Young are members of the COVID-19 Genomics UK (COG-UK) consortium. |
Uncontrolled Keywords: | COVID-19, Healthcare associated, Hospital onset, Nosocomial, Outbreak, SARS-CoV-2, Whole genome sequencing |
Subjects: | B900 Others in Subjects allied to Medicine C100 Biology C700 Molecular Biology, Biophysics and Biochemistry C900 Others in Biological Sciences |
Department: | Faculties > Health and Life Sciences > Applied Sciences |
Depositing User: | Rachel Branson |
Date Deposited: | 08 Sep 2021 14:20 |
Last Modified: | 08 Sep 2021 14:30 |
URI: | http://nrl.northumbria.ac.uk/id/eprint/47109 |
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