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

The COVID-19 Genomics UK (COG-UK) Consortium; Kathy K. Li; Y. Mun Woo; Oliver Stirrup; Joseph Hughes; Antonia Ho; Ana Da Silva Filipe; Natasha Johnson; Katherine Smollett; Daniel Mair; Stephen Carmichael; Lily Tong; Jenna Nichols; Elihu Aranday-Cortes; Kirstyn Brunker; Yasmin A. Parr; Kyriaki Nomikou; Sarah E. McDonald; Marc Niebel; Patawee Asamaphan; Vattipally B. Sreenu; David L. Robertson; Aislynn Taggart; Natasha Jesudason; Rajiv Shah; James Shepherd; Josh Singer; Alison H.M. Taylor; Zoe Cousland; Jonathan Price; Jennifer S. Lees; Timothy P.W. Jones; Carlos Varon Lopez; Alasdair MacLean; Igor Starinskij; Rory Gunson; Scott T.W. Morris; Peter C. Thomson; Colin C. Geddes; Jamie P. Traynor; Judith Breuer; Emma C. Thomson; Patrick B. Mark; Matthew Bashton; Andrew Nelson; Darren Smith; Greg Young

doi:10.1016/j.jinf.2021.04.020

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

The COVID-19 Genomics UK (COG-UK) Consortium, Kathy K. Li, Y. Mun Woo, Oliver Stirrup, Joseph Hughes, Antonia Ho, Ana Da Silva Filipe, Natasha Johnson, Katherine Smollett, Daniel Mair, Stephen Carmichael, Lily Tong, Jenna Nichols, Elihu Aranday-Cortes, Kirstyn Brunker, Yasmin A. Parr, Kyriaki Nomikou, Sarah E. McDonald, Marc Niebel, Patawee AsamaphanVattipally B. Sreenu, David L. Robertson, Aislynn Taggart, Natasha Jesudason, Rajiv Shah, James Shepherd, Josh Singer, Alison H.M. Taylor, Zoe Cousland, Jonathan Price, Jennifer S. Lees, Timothy P.W. Jones, Carlos Varon Lopez, Alasdair MacLean, Igor Starinskij, Rory Gunson, Scott T.W. Morris, Peter C. Thomson, Colin C. Geddes, Jamie P. Traynor, Judith Breuer, Emma C. Thomson, Patrick B. Mark^*, Matthew Bashton, Andrew Nelson, Darren Smith, Greg Young

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

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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.

Original language	English
Pages (from-to)	96-103
Number of pages	8
Journal	Journal of Infection
Volume	83
Issue number	1
Early online date	22 Apr 2021
DOIs	https://doi.org/10.1016/j.jinf.2021.04.020
Publication status	Published - 1 Jul 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

COVID-19
Haemodialysis
Infection control
Nosocomial
Outbreak
Rapid sequencing
Renal dialysis unit
SARS-CoV-2
Humans
Hospitals
Molecular Epidemiology
Bayes Theorem
Renal Dialysis/adverse effects

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Final published versionFinal published version, 1.14 MBLicence: CC BY
Advance online versionFinal published version, 1.12 MBLicence: CC BY

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The COVID-19 Genomics UK (COG-UK) Consortium, Li, K. K., Woo, Y. M., Stirrup, O., Hughes, J., Ho, A., Filipe, A. D. S., Johnson, N., Smollett, K., Mair, D., Carmichael, S., Tong, L., Nichols, J., Aranday-Cortes, E., Brunker, K., Parr, Y. A., Nomikou, K., McDonald, S. E., Niebel, M., ... Young, G. (2021). Genetic epidemiology of SARS-CoV-2 transmission in renal dialysis units – A high risk community-hospital interface. Journal of Infection, 83(1), 96-103. https://doi.org/10.1016/j.jinf.2021.04.020

@article{6a8907b3d7fc4d81b881a39e297eec5e,

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

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.",

keywords = "COVID-19, Haemodialysis, Infection control, Nosocomial, Outbreak, Rapid sequencing, Renal dialysis unit, SARS-CoV-2, Humans, Hospitals, Molecular Epidemiology, Bayes Theorem, Renal Dialysis/adverse effects",

author = "\{The COVID-19 Genomics UK (COG-UK) Consortium\} and Li, \{Kathy K.\} and Woo, \{Y. Mun\} and Oliver Stirrup and Joseph Hughes and Antonia Ho and Filipe, \{Ana Da Silva\} and Natasha Johnson and Katherine Smollett and Daniel Mair and Stephen Carmichael and Lily Tong and Jenna Nichols and Elihu Aranday-Cortes and Kirstyn Brunker and Parr, \{Yasmin A.\} and Kyriaki Nomikou and McDonald, \{Sarah E.\} and Marc Niebel and Patawee Asamaphan and Sreenu, \{Vattipally B.\} and Robertson, \{David L.\} and Aislynn Taggart and Natasha Jesudason and Rajiv Shah and James Shepherd and Josh Singer and Taylor, \{Alison H.M.\} and Zoe Cousland and Jonathan Price and Lees, \{Jennifer S.\} and Jones, \{Timothy P.W.\} and Lopez, \{Carlos Varon\} and Alasdair MacLean and Igor Starinskij and Rory Gunson and Morris, \{Scott T.W.\} and Thomson, \{Peter C.\} and Geddes, \{Colin C.\} and Traynor, \{Jamie P.\} and Judith Breuer and Thomson, \{Emma C.\} and Mark, \{Patrick B.\} and Matthew Bashton and Andrew Nelson and Darren Smith and Greg Young",

note = "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.",

year = "2021",

month = jul,

day = "1",

doi = "10.1016/j.jinf.2021.04.020",

language = "English",

volume = "83",

pages = "96--103",

journal = "Journal of Infection",

issn = "0163-4453",

publisher = "W.B. Saunders Ltd",

number = "1",

}

The COVID-19 Genomics UK (COG-UK) Consortium, Li, KK, Woo, YM, Stirrup, O, Hughes, J, Ho, A, Filipe, ADS, Johnson, N, Smollett, K, Mair, D, Carmichael, S, Tong, L, Nichols, J, Aranday-Cortes, E, Brunker, K, Parr, YA, Nomikou, K, McDonald, SE, Niebel, M, Asamaphan, P, Sreenu, VB, Robertson, DL, Taggart, A, Jesudason, N, Shah, R, Shepherd, J, Singer, J, Taylor, AHM, Cousland, Z, Price, J, Lees, JS, Jones, TPW, Lopez, CV, MacLean, A, Starinskij, I, Gunson, R, Morris, STW, Thomson, PC, Geddes, CC, Traynor, JP, Breuer, J, Thomson, EC, Mark, PB, Bashton, M , Nelson, A , Smith, D & Young, G 2021, 'Genetic epidemiology of SARS-CoV-2 transmission in renal dialysis units – A high risk community-hospital interface', Journal of Infection, vol. 83, no. 1, pp. 96-103. https://doi.org/10.1016/j.jinf.2021.04.020

TY - JOUR

T1 - Genetic epidemiology of SARS-CoV-2 transmission in renal dialysis units – A high risk community-hospital interface

AU - The COVID-19 Genomics UK (COG-UK) Consortium

AU - Li, Kathy K.

AU - Woo, Y. Mun

AU - Stirrup, Oliver

AU - Hughes, Joseph

AU - Ho, Antonia

AU - Filipe, Ana Da Silva

AU - Johnson, Natasha

AU - Smollett, Katherine

AU - Mair, Daniel

AU - Carmichael, Stephen

AU - Tong, Lily

AU - Nichols, Jenna

AU - Aranday-Cortes, Elihu

AU - Brunker, Kirstyn

AU - Parr, Yasmin A.

AU - Nomikou, Kyriaki

AU - McDonald, Sarah E.

AU - Niebel, Marc

AU - Asamaphan, Patawee

AU - Sreenu, Vattipally B.

AU - Robertson, David L.

AU - Taggart, Aislynn

AU - Jesudason, Natasha

AU - Shah, Rajiv

AU - Shepherd, James

AU - Singer, Josh

AU - Taylor, Alison H.M.

AU - Cousland, Zoe

AU - Price, Jonathan

AU - Lees, Jennifer S.

AU - Jones, Timothy P.W.

AU - Lopez, Carlos Varon

AU - MacLean, Alasdair

AU - Starinskij, Igor

AU - Gunson, Rory

AU - Morris, Scott T.W.

AU - Thomson, Peter C.

AU - Geddes, Colin C.

AU - Traynor, Jamie P.

AU - Breuer, Judith

AU - Thomson, Emma C.

AU - Mark, Patrick B.

AU - Bashton, Matthew

AU - Nelson, Andrew

AU - Smith, Darren

AU - Young, Greg

N1 - 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.

PY - 2021/7/1

Y1 - 2021/7/1

N2 - 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.

AB - 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.

KW - COVID-19

KW - Haemodialysis

KW - Infection control

KW - Nosocomial

KW - Outbreak

KW - Rapid sequencing

KW - Renal dialysis unit

KW - SARS-CoV-2

KW - Humans

KW - Hospitals

KW - Molecular Epidemiology

KW - Bayes Theorem

KW - Renal Dialysis/adverse effects

UR - https://www.scopus.com/pages/publications/85105091183

U2 - 10.1016/j.jinf.2021.04.020

DO - 10.1016/j.jinf.2021.04.020

M3 - Article

C2 - 33895226

AN - SCOPUS:85105091183

SN - 0163-4453

VL - 83

SP - 96

EP - 103

JO - Journal of Infection

JF - Journal of Infection

IS - 1

ER -

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

Abstract

UN SDGs

Keywords

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