Genetic epidemiology of SARS-CoV-2 transmission in renal dialysis units – A high risk community-hospital interface

Li, Kathy K., Woo, Y. Mun, Stirrup, Oliver, Hughes, Joseph, Ho, Antonia, Filipe, Ana Da Silva, Johnson, Natasha, Smollett, Katherine, Mair, Daniel, Carmichael, Stephen, Tong, Lily, Nichols, Jenna, Aranday-Cortes, Elihu, Brunker, Kirstyn, Parr, Yasmin A., Nomikou, Kyriaki, McDonald, Sarah E., Niebel, Marc, Asamaphan, Patawee, Sreenu, Vattipally B., Robertson, David L., Taggart, Aislynn, Jesudason, Natasha, Shah, Rajiv, Shepherd, James, Singer, Josh, Taylor, Alison H.M., Cousland, Zoe, Price, Jonathan, Lees, Jennifer S., Jones, Timothy P.W., Lopez, Carlos Varon, MacLean, Alasdair, Starinskij, Igor, Gunson, Rory, Morris, Scott T.W., Thomson, Peter C., Geddes, Colin C., Traynor, Jamie P., Breuer, Judith, Thomson, Emma C., Mark, Patrick B., Bashton, Matthew, Nelson, Andrew, Smith, Darren, Young, Greg and The COVID-19 Genomics UK (COG-UK) consortium, (2021) Genetic epidemiology of SARS-CoV-2 transmission in renal dialysis units – A high risk community-hospital interface. Journal of Infection, 83 (1). pp. 96-103. ISSN 0163-4453

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Official URL: https://doi.org/10.1016/j.jinf.2021.04.020

Abstract

Objectives: Patients requiring haemodialysis are at increased risk of serious illness with SARS-CoV-2 infection. To improve the understanding of transmission risks in six Scottish renal dialysis units, we utilised the rapid whole-genome sequencing data generated by the COG-UK consortium. Methods: We combined geographical, temporal and genomic sequence data from the community and hospital to estimate the probability of infection originating from within the dialysis unit, the hospital or the community using Bayesian statistical modelling and compared these results to the details of epidemiological investigations. Results: Of 671 patients, 60 (8.9%) became infected with SARS-CoV-2, of whom 16 (27%) died. Within-unit and community transmission were both evident and an instance of transmission from the wider hospital setting was also demonstrated. Conclusions: Near-real-time SARS-CoV-2 sequencing data can facilitate tailored infection prevention and control measures, which can be targeted at reducing risk in these settings.

Item Type: Article
Additional Information: Funding Information: COG-UK 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. Also MRC (MC UU 1201412), UKRI/Wellcome (COG-UK), Wellcome Trust Collaborator Award (206298/Z/17/Z – ARTIC Network; TCW Wellcome Trust Award 204802/Z/16/Z. Matthew Bashton, Andrew Nelson, Darren Smith, Greg Young are members of the COVID-19 Genomics UK (COG-UK) consortium.
Uncontrolled Keywords: SARS-CoV-2, COVID-19, Haemodialysis, Renal dialysis unit, Infection control, Rapid sequencing, Outbreak, Nosocomial
Subjects: B100 Anatomy, Physiology and Pathology
B200 Pharmacology, Toxicology and Pharmacy
C700 Molecular Biology, Biophysics and Biochemistry
Department: Faculties > Health and Life Sciences > Applied Sciences
Depositing User: Elena Carlaw
Date Deposited: 15 Jun 2021 10:51
Last Modified: 02 Jul 2021 10:15
URI: http://nrl.northumbria.ac.uk/id/eprint/46445

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