Genetic epidemiology of SARS-CoV-2 transmission in renal dialysis units – A high risk community-hospital interface - 19/06/21

Doi : 10.1016/j.jinf.2021.04.020

Kathy K. Li ^a,^{^{2
equal contribution.}}, Y. Mun Woo ^b,^{^{2
equal contribution.}}, Oliver Stirrup ^c, Joseph Hughes ^a, Antonia Ho ^a, Ana Da Silva Filipe ^a, Natasha Johnson ^a, Katherine Smollett ^a, Daniel Mair ^a, Stephen Carmichael ^a, Lily Tong ^a, Jenna Nichols ^a, Elihu Aranday-Cortes ^a, Kirstyn Brunker ^a, Yasmin A. Parr ^a, Kyriaki Nomikou ^a, Sarah E. McDonald ^a, Marc Niebel ^a, Patawee Asamaphan ^a, Vattipally B Sreenu ^a, David L. Robertson ^a, Aislynn Taggart ^a, Natasha Jesudason ^a, Rajiv Shah ^a, James Shepherd ^a, Josh Singer ^a, Alison H.M. Taylor ^d, Zoe Cousland ^d, Jonathan Price ^d, Jennifer S. Lees ^d,^e, Timothy P.W. Jones ^f, Carlos Varon Lopez ^g, Alasdair MacLean ^h, Igor Starinskij ^h, Rory Gunson ^h, Scott T.W. Morris ^b, Peter C. Thomson ^b, Colin C. Geddes ^b, Jamie P. Traynor ^b, Judith Breuer ⁱ, Emma C. Thomson ^a,^k,^⁎ , Patrick B. Mark ^a,^e,^{^{1
joint senior and corresponding authors.},}^⁎
The COVID-19 Genomics UK (COG-UK) consortium^j,^³
Full list of consortium names and affiliations are listed in the appendix.
^a MRC-University of Glasgow Centre for Virus Research, Sir Michael Stoker building, 464 Bearsden Road, Glasgow, G61 1QH, UK
^b The Glasgow Renal & Transplant Unit, Queen Elizabeth University Hospital University Hospital, 1345 Govan Road, Glasgow, G51 4TF, UK
^c Institute for Global Health, University College London, London, UK
^d Renal Unit, University Hospital Monklands, Monkscourt Ave, Airdrie ML6 0JS, Canada
^e Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow G12 8TA, UK
^f Department of Infectious Diseases, University Hospital Monklands, Monkscourt Ave, Airdrie ML60JS, Canada
^g Department of Microbiology, University Hospital Monklands, Monkscourt Ave, Airdrie ML6 0JS, Canada
^h West of Scotland Specialist Virology Centre, Glasgow Royal Infirmary, UK
ⁱ Institute of Child Health University College London, Cruciform Building, Gower Street, London, WC1E 6BT, UK
^j https://www.cogconsortium.uk, UK
^k Department of Clinical Research, London School of Hygiene and Tropical Medicine, UK

^⁎Corresponding author at: The Glasgow Renal & Transplant Unit, Queen Elizabeth University Hospital, 1345 Govan Road, Glasgow G51 4TF, UKThe Glasgow Renal & Transplant UnitQueen Elizabeth University Hospital1345 Govan RoadGlasgowG51 4TFUK^⁎⁎Corresponding author at: University of Glasgow Centre for Virus Research, Sir Michael Stoker building, 464 Bearsden Road, Glasgow G61 1QH, UKUniversity of Glasgow Centre for Virus ResearchSir Michael Stoker building, 464 Bearsden RoadGlasgowG61 1QHUK

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Highlights

•	Haemodialysis patients are at increased risk of severe disease and mortality from COVID-19 disease.
•	Phylogenetic analysis using whole genome sequencing of SARS-CoV-2 provides limited information on transmission events.
•	Addition of Bayesian statistical reporting tool can help to resolve transmission and introduction events in renal dialysis units for targeted infection control measures.
•	Transferability to other community-hospital interface for targeting infection control measures.

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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.

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Keywords : SARS-CoV-2, COVID-19, Haemodialysis, Renal dialysis unit, Infection control, Rapid sequencing, Outbreak, Nosocomial

Plan

Introduction

Methods

Study design and participants

Laboratory diagnosis

Sequencing

Phylogenetic and probabilistic analysis

Survival statistics

Results

Description of cases, treatments and outcomes in patients requiring haemodialysis with COVID-19

Genomic and epidemiological investigation

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Vol 83 - N° 1

P. 96-103 - juillet 2021 Retour au numéro

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Genetic epidemiology of SARS-CoV-2 transmission in renal dialysis units – A high risk community-hospital interface - 19/06/21

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