The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020

ISARIC4C Investigators; CMMID COVID-19 Working Group; Gwenan M. Knight; Thi Mui Pham; James Stimson; Sebastian Funk; Yalda Jafari; Diane Pople; Stephanie Evans; Mo Yin; Colin S. Brown; Alex Bhattacharya; Russell Hope; Malcolm G. Semple; Julie V. Robotham; Jonathan M. Read; Ben S. Cooper

doi:10.1186/s12879-022-07490-4

The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020

ISARIC4C Investigators, CMMID COVID-19 Working Group, Gwenan M. Knight^*, Thi Mui Pham, James Stimson, Sebastian Funk, Yalda Jafari, Diane Pople, Stephanie Evans, Mo Yin, Colin S. Brown, Alex Bhattacharya, Russell Hope, Malcolm G. Semple, Julie V. Robotham, Jonathan M. Read, Ben S. Cooper

^*Corresponding author for this work

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

23 Citations (Scopus)

29 Downloads (Pure)

Abstract

Background: SARS-CoV-2 is known to transmit in hospital settings, but the contribution of infections acquired in hospitals to the epidemic at a national scale is unknown.

Methods: We used comprehensive national English datasets to determine the number of COVID-19 patients with identified hospital-acquired infections (with symptom onset > 7 days after admission and before discharge) in acute English hospitals up to August 2020. As patients may leave the hospital prior to detection of infection or have rapid symptom onset, we combined measures of the length of stay and the incubation period distribution to estimate how many hospital-acquired infections may have been missed. We used simulations to estimate the total number (identified and unidentified) of symptomatic hospital-acquired infections, as well as infections due to onward community transmission from missed hospital-acquired infections, to 31st July 2020.

Results: In our dataset of hospitalised COVID-19 patients in acute English hospitals with a recorded symptom onset date (n = 65,028), 7% were classified as hospital-acquired. We estimated that only 30% (range across weeks and 200 simulations: 20–41%) of symptomatic hospital-acquired infections would be identified, with up to 15% (mean, 95% range over 200 simulations: 14.1–15.8%) of cases currently classified as community-acquired COVID-19 potentially linked to hospital transmission. We estimated that 26,600 (25,900 to 27,700) individuals acquired a symptomatic SARS-CoV-2 infection in an acute Trust in England before 31st July 2020, resulting in 15,900 (15,200–16,400) or 20.1% (19.2–20.7%) of all identified hospitalised COVID-19 cases.

Conclusions: Transmission of SARS-CoV-2 to hospitalised patients likely caused approximately a fifth of identified cases of hospitalised COVID-19 in the “first wave” in England, but less than 1% of all infections in England. Using time to symptom onset from admission for inpatients as a detection method likely misses a substantial proportion (> 60%) of hospital-acquired infections.

Original language	English
Article number	556
Journal	BMC Infectious Diseases
Volume	22
Issue number	1
DOIs	https://doi.org/10.1186/s12879-022-07490-4
Publication status	Published - 18 Jun 2022

Bibliographical note

Funding Information: This work was supported by a UK Medical Research Council Skills Development Fellowship (Grant Number MR/P014658/1 to GMK) and the Society for Laboratory Automation and Screening (Grant Number: SLAS_VS2020 to TMP). Any opinions, findings, and conclusions or recommendations expressed in
this material are those of the author(s) and do not necessarily reflect those of the Society for Laboratory Automation and Screening. This work was also supported by a joint grant from UKRI and NIHR (Grant Number: COV0357/MR/V028456/1 to GMK, supporting YJ, JMR, BC and JVR, grant number MR/V038613/1 for JMR). Support was also received from the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infections and Antimicrobial Resistance at Oxford University in partnership with Public Health England (PHE) (Grant Number: NIHR200915 supporting BC and JVR). Further support was provided by a Senior Research Fellowship from the Wellcome Trust (Grant Number: 210758/Z/18/Z to SF) and a Singapore National Medical Research Council Research Fellowship (Grant Number: NMRC/Fellowship/0051/2017 to MY). MGS and ISARIC4C: This work uses data provided by patients and collected by the NHS as part of their care and support #DataSavesLives. The COVID-19 Clinical Information Network (CO-CIN) data was collated by ISARIC4C Investigators. Data provision was supported by grants from: the National Institute for Health Research (NIHR; award CO-CIN-01), the Medical Research Council (MRC; grant MC_PC_19059), and by the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE), (award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927), Liverpool Experimental Cancer Medicine Centre (Grant C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (award IS-BRC-1215-20013), and NIHR Clinical Research Network providing infrastructure support.

The following funding sources are acknowledged as providing funding for the CMMID working group authors. This research was partly funded by the Bill & Melinda Gates Foundation (INV-001754: MQ; INV-003174: KP, MJ, YL; INV-016832: SRP, KA; NTD Modelling Consortium OPP1184344: CABP, GFM; OPP1191821: KO’R; OPP1157270: KA; OPP1139859: BJQ). CADDE MR/S0195/1 &
FAPESP 18/14389-0 (PM). EDCTP2 (RIA2020EF-2983-CSIGN: HPG). ERC Starting
Grant (#757699: MQ). ERC (SG 757688: CJVA, KEA). This project has received
funding from the European Union’s Horizon 2020 research and innovation
programme—project EpiPose (101003688: AG, KLM, KP, MJ, RCB, YL;
101003688: WJE). FCDO/Wellcome Trust (Epidemic Preparedness Coronavirus
research programme 221303/Z/20/Z: CABP). This research was partly funded by the Global Challenges Research Fund (GCRF) project ’RECAP’ managed through RCUK and ESRC (ES/P010873/1: CIJ). HDR UK (MR/S003975/1: RME). HPRU (This research was partly funded by the National Institute for Health Research (NIHR) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care200908: NIB). MRC (MR/N013638/1: EF; MR/V027956/1: WW). Nakajima Foundation (AE). NIHR (16/136/46: BJQ; 16/137/109: BJQ; PR-OD-1017-20002: WJE; 16/137/109: FYS, MJ, YL; 1R01AI141534-01A1: DH; NIHR200908: AJK, LACC, RME; NIHR200929: CVM, FGS, MJ, NGD; PR-OD-1017-20002: AR). Royal Society (Dorothy Hodgkin Fellowship: RL). Singapore Ministry of Health (RP). UK DHSC/UK Aid/NIHR (PR-OD-1017-20001: HPG). UK MRC (MC_PC_19065—Covid 19: understanding the dynamics and drivers of the COVID-19 epidemic using real-time outbreak analytics: SC, WJE, NGD, RME, YL). Wellcome Trust (206250/Z/17/Z: AJK; 206471/Z/17/Z: OJB; 210758/Z/18/Z: JDM, JH, KS, SA, SRM; 221303/Z/20/Z: MK; 206250/Z/17/Z: TWR; 208812/Z/17/Z: SC, SFlasche). No funding (DCT, SH).

Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Publisher Copyright: © The Author(s) 2022.

Citation: Knight, G.M., Pham, T.M., Stimson, J. et al. The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020. BMC Infect Dis 22, 556 (2022).

DOI: https://doi.org/10.1186/s12879-022-07490-4

Keywords

COVID-19
Mathematical modelling
Nosocomial transmission
SARS-CoV-2

UN SDGs

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

Access to Document

10.1186/s12879-022-07490-4Licence: CC BY

Knight2022TheContributionOfHospital-AcquiredInfectionsToTheCOVID-19EpidemicInEnglandBMCInfectDisFinal published version, 3.01 MBLicence: CC BY

Cite this

ISARIC4C Investigators, CMMID COVID-19 Working Group, Knight, G. M., Pham, T. M., Stimson, J., Funk, S., Jafari, Y., Pople, D., Evans, S., Yin, M., Brown, C. S., Bhattacharya, A., Hope, R., Semple, M. G., Robotham, J. V., Read, J. M., & Cooper, B. S. (2022). The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020. BMC Infectious Diseases, 22(1), Article 556. https://doi.org/10.1186/s12879-022-07490-4

@article{0abf298fc32e48caae789b3bc1f1785f,

title = "The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020",

abstract = "Background: SARS-CoV-2 is known to transmit in hospital settings, but the contribution of infections acquired in hospitals to the epidemic at a national scale is unknown. Methods: We used comprehensive national English datasets to determine the number of COVID-19 patients with identified hospital-acquired infections (with symptom onset > 7 days after admission and before discharge) in acute English hospitals up to August 2020. As patients may leave the hospital prior to detection of infection or have rapid symptom onset, we combined measures of the length of stay and the incubation period distribution to estimate how many hospital-acquired infections may have been missed. We used simulations to estimate the total number (identified and unidentified) of symptomatic hospital-acquired infections, as well as infections due to onward community transmission from missed hospital-acquired infections, to 31st July 2020. Results: In our dataset of hospitalised COVID-19 patients in acute English hospitals with a recorded symptom onset date (n = 65,028), 7% were classified as hospital-acquired. We estimated that only 30% (range across weeks and 200 simulations: 20–41%) of symptomatic hospital-acquired infections would be identified, with up to 15% (mean, 95% range over 200 simulations: 14.1–15.8%) of cases currently classified as community-acquired COVID-19 potentially linked to hospital transmission. We estimated that 26,600 (25,900 to 27,700) individuals acquired a symptomatic SARS-CoV-2 infection in an acute Trust in England before 31st July 2020, resulting in 15,900 (15,200–16,400) or 20.1% (19.2–20.7%) of all identified hospitalised COVID-19 cases. Conclusions: Transmission of SARS-CoV-2 to hospitalised patients likely caused approximately a fifth of identified cases of hospitalised COVID-19 in the “first wave” in England, but less than 1% of all infections in England. Using time to symptom onset from admission for inpatients as a detection method likely misses a substantial proportion (> 60%) of hospital-acquired infections.",

keywords = "COVID-19, Mathematical modelling, Nosocomial transmission, SARS-CoV-2",

author = "{ISARIC4C Investigators} and {CMMID COVID-19 Working Group} and Knight, {Gwenan M.} and Pham, {Thi Mui} and James Stimson and Sebastian Funk and Yalda Jafari and Diane Pople and Stephanie Evans and Mo Yin and Brown, {Colin S.} and Alex Bhattacharya and Russell Hope and Semple, {Malcolm G.} and Sam Abbott and Amy Gimma and Gibbs, {Hamish P.} and Kaja Abbas and Barnard, {Rosanna C.} and Sandmann, {Frank G.} and Bosse, {Nikos I.} and Paul Mee and McCarthy, {Ciara V.} and Matthew Quaife and Kucharski, {Adam J.} and Jarvis, {Christopher I.} and Joel Hellewell and Emilie Finch and Alicia Rosello and Mark Jit and Rachael Pung and Eggo, {Rosalind M.} and Akira Endo and Graham Medley and Tully, {Damien C.} and Wong, {Kerry L.M.} and Yang Liu and Katharine Sherratt and Munday, {James D.} and Chapman, {Lloyd A.C.} and St{\'e}phane Hu{\'e} and Kathleen O{\textquoteright}Reilly and Davies, {Nicholas G.} and Meakin, {Sophie R.} and Sun, {Fiona Yueqian} and Oliver Brady and Villabona-Arenas, {C. Julian} and Atkins, {Katherine E.} and Kiesha Prem and David Hodgson and Mihaly Koltai and Robotham, {Julie V.} and Read, {Jonathan M.} and Cooper, {Ben S.}",

note = "Funding Information: This work was supported by a UK Medical Research Council Skills Development Fellowship (Grant Number MR/P014658/1 to GMK) and the Society for Laboratory Automation and Screening (Grant Number: SLAS_VS2020 to TMP). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect those of the Society for Laboratory Automation and Screening. This work was also supported by a joint grant from UKRI and NIHR (Grant Number: COV0357/MR/V028456/1 to GMK, supporting YJ, JMR, BC and JVR, grant number MR/V038613/1 for JMR). Support was also received from the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infections and Antimicrobial Resistance at Oxford University in partnership with Public Health England (PHE) (Grant Number: NIHR200915 supporting BC and JVR). Further support was provided by a Senior Research Fellowship from the Wellcome Trust (Grant Number: 210758/Z/18/Z to SF) and a Singapore National Medical Research Council Research Fellowship (Grant Number: NMRC/Fellowship/0051/2017 to MY). MGS and ISARIC4C: This work uses data provided by patients and collected by the NHS as part of their care and support #DataSavesLives. The COVID-19 Clinical Information Network (CO-CIN) data was collated by ISARIC4C Investigators. Data provision was supported by grants from: the National Institute for Health Research (NIHR; award CO-CIN-01), the Medical Research Council (MRC; grant MC_PC_19059), and by the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE), (award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927), Liverpool Experimental Cancer Medicine Centre (Grant C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (award IS-BRC-1215-20013), and NIHR Clinical Research Network providing infrastructure support. The following funding sources are acknowledged as providing funding for the CMMID working group authors. This research was partly funded by the Bill & Melinda Gates Foundation (INV-001754: MQ; INV-003174: KP, MJ, YL; INV-016832: SRP, KA; NTD Modelling Consortium OPP1184344: CABP, GFM; OPP1191821: KO{\textquoteright}R; OPP1157270: KA; OPP1139859: BJQ). CADDE MR/S0195/1 & FAPESP 18/14389-0 (PM). EDCTP2 (RIA2020EF-2983-CSIGN: HPG). ERC Starting Grant (#757699: MQ). ERC (SG 757688: CJVA, KEA). This project has received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme—project EpiPose (101003688: AG, KLM, KP, MJ, RCB, YL; 101003688: WJE). FCDO/Wellcome Trust (Epidemic Preparedness Coronavirus research programme 221303/Z/20/Z: CABP). This research was partly funded by the Global Challenges Research Fund (GCRF) project {\textquoteright}RECAP{\textquoteright} managed through RCUK and ESRC (ES/P010873/1: CIJ). HDR UK (MR/S003975/1: RME). HPRU (This research was partly funded by the National Institute for Health Research (NIHR) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care200908: NIB). MRC (MR/N013638/1: EF; MR/V027956/1: WW). Nakajima Foundation (AE). NIHR (16/136/46: BJQ; 16/137/109: BJQ; PR-OD-1017-20002: WJE; 16/137/109: FYS, MJ, YL; 1R01AI141534-01A1: DH; NIHR200908: AJK, LACC, RME; NIHR200929: CVM, FGS, MJ, NGD; PR-OD-1017-20002: AR). Royal Society (Dorothy Hodgkin Fellowship: RL). Singapore Ministry of Health (RP). UK DHSC/UK Aid/NIHR (PR-OD-1017-20001: HPG). UK MRC (MC_PC_19065—Covid 19: understanding the dynamics and drivers of the COVID-19 epidemic using real-time outbreak analytics: SC, WJE, NGD, RME, YL). Wellcome Trust (206250/Z/17/Z: AJK; 206471/Z/17/Z: OJB; 210758/Z/18/Z: JDM, JH, KS, SA, SRM; 221303/Z/20/Z: MK; 206250/Z/17/Z: TWR; 208812/Z/17/Z: SC, SFlasche). No funding (DCT, SH). Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Publisher Copyright: {\textcopyright} The Author(s) 2022. Citation: Knight, G.M., Pham, T.M., Stimson, J. et al. The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020. BMC Infect Dis 22, 556 (2022). DOI: https://doi.org/10.1186/s12879-022-07490-4",

year = "2022",

month = jun,

day = "18",

doi = "10.1186/s12879-022-07490-4",

language = "English",

volume = "22",

journal = "BMC Infectious Diseases",

issn = "1471-2334",

publisher = "BioMed Central",

number = "1",

}

ISARIC4C Investigators, CMMID COVID-19 Working Group, Knight, GM, Pham, TM, Stimson, J, Funk, S, Jafari, Y, Pople, D, Evans, S, Yin, M, Brown, CS , Bhattacharya, A , Hope, R, Semple, MG, Robotham, JV, Read, JM & Cooper, BS 2022, 'The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020', BMC Infectious Diseases, vol. 22, no. 1, 556. https://doi.org/10.1186/s12879-022-07490-4

TY - JOUR

T1 - The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020

AU - ISARIC4C Investigators

AU - CMMID COVID-19 Working Group

AU - Knight, Gwenan M.

AU - Pham, Thi Mui

AU - Stimson, James

AU - Funk, Sebastian

AU - Jafari, Yalda

AU - Pople, Diane

AU - Evans, Stephanie

AU - Yin, Mo

AU - Brown, Colin S.

AU - Bhattacharya, Alex

AU - Hope, Russell

AU - Semple, Malcolm G.

AU - Abbott, Sam

AU - Gimma, Amy

AU - Gibbs, Hamish P.

AU - Abbas, Kaja

AU - Barnard, Rosanna C.

AU - Sandmann, Frank G.

AU - Bosse, Nikos I.

AU - Mee, Paul

AU - McCarthy, Ciara V.

AU - Quaife, Matthew

AU - Kucharski, Adam J.

AU - Jarvis, Christopher I.

AU - Hellewell, Joel

AU - Finch, Emilie

AU - Rosello, Alicia

AU - Jit, Mark

AU - Pung, Rachael

AU - Eggo, Rosalind M.

AU - Endo, Akira

AU - Medley, Graham

AU - Tully, Damien C.

AU - Wong, Kerry L.M.

AU - Liu, Yang

AU - Sherratt, Katharine

AU - Munday, James D.

AU - Chapman, Lloyd A.C.

AU - Hué, Stéphane

AU - O’Reilly, Kathleen

AU - Davies, Nicholas G.

AU - Meakin, Sophie R.

AU - Sun, Fiona Yueqian

AU - Brady, Oliver

AU - Villabona-Arenas, C. Julian

AU - Atkins, Katherine E.

AU - Prem, Kiesha

AU - Hodgson, David

AU - Koltai, Mihaly

AU - Robotham, Julie V.

AU - Read, Jonathan M.

AU - Cooper, Ben S.

N1 - Funding Information: This work was supported by a UK Medical Research Council Skills Development Fellowship (Grant Number MR/P014658/1 to GMK) and the Society for Laboratory Automation and Screening (Grant Number: SLAS_VS2020 to TMP). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect those of the Society for Laboratory Automation and Screening. This work was also supported by a joint grant from UKRI and NIHR (Grant Number: COV0357/MR/V028456/1 to GMK, supporting YJ, JMR, BC and JVR, grant number MR/V038613/1 for JMR). Support was also received from the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infections and Antimicrobial Resistance at Oxford University in partnership with Public Health England (PHE) (Grant Number: NIHR200915 supporting BC and JVR). Further support was provided by a Senior Research Fellowship from the Wellcome Trust (Grant Number: 210758/Z/18/Z to SF) and a Singapore National Medical Research Council Research Fellowship (Grant Number: NMRC/Fellowship/0051/2017 to MY). MGS and ISARIC4C: This work uses data provided by patients and collected by the NHS as part of their care and support #DataSavesLives. The COVID-19 Clinical Information Network (CO-CIN) data was collated by ISARIC4C Investigators. Data provision was supported by grants from: the National Institute for Health Research (NIHR; award CO-CIN-01), the Medical Research Council (MRC; grant MC_PC_19059), and by the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE), (award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927), Liverpool Experimental Cancer Medicine Centre (Grant C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (award IS-BRC-1215-20013), and NIHR Clinical Research Network providing infrastructure support. The following funding sources are acknowledged as providing funding for the CMMID working group authors. This research was partly funded by the Bill & Melinda Gates Foundation (INV-001754: MQ; INV-003174: KP, MJ, YL; INV-016832: SRP, KA; NTD Modelling Consortium OPP1184344: CABP, GFM; OPP1191821: KO’R; OPP1157270: KA; OPP1139859: BJQ). CADDE MR/S0195/1 & FAPESP 18/14389-0 (PM). EDCTP2 (RIA2020EF-2983-CSIGN: HPG). ERC Starting Grant (#757699: MQ). ERC (SG 757688: CJVA, KEA). This project has received funding from the European Union’s Horizon 2020 research and innovation programme—project EpiPose (101003688: AG, KLM, KP, MJ, RCB, YL; 101003688: WJE). FCDO/Wellcome Trust (Epidemic Preparedness Coronavirus research programme 221303/Z/20/Z: CABP). This research was partly funded by the Global Challenges Research Fund (GCRF) project ’RECAP’ managed through RCUK and ESRC (ES/P010873/1: CIJ). HDR UK (MR/S003975/1: RME). HPRU (This research was partly funded by the National Institute for Health Research (NIHR) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care200908: NIB). MRC (MR/N013638/1: EF; MR/V027956/1: WW). Nakajima Foundation (AE). NIHR (16/136/46: BJQ; 16/137/109: BJQ; PR-OD-1017-20002: WJE; 16/137/109: FYS, MJ, YL; 1R01AI141534-01A1: DH; NIHR200908: AJK, LACC, RME; NIHR200929: CVM, FGS, MJ, NGD; PR-OD-1017-20002: AR). Royal Society (Dorothy Hodgkin Fellowship: RL). Singapore Ministry of Health (RP). UK DHSC/UK Aid/NIHR (PR-OD-1017-20001: HPG). UK MRC (MC_PC_19065—Covid 19: understanding the dynamics and drivers of the COVID-19 epidemic using real-time outbreak analytics: SC, WJE, NGD, RME, YL). Wellcome Trust (206250/Z/17/Z: AJK; 206471/Z/17/Z: OJB; 210758/Z/18/Z: JDM, JH, KS, SA, SRM; 221303/Z/20/Z: MK; 206250/Z/17/Z: TWR; 208812/Z/17/Z: SC, SFlasche). No funding (DCT, SH). Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Publisher Copyright: © The Author(s) 2022. Citation: Knight, G.M., Pham, T.M., Stimson, J. et al. The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020. BMC Infect Dis 22, 556 (2022). DOI: https://doi.org/10.1186/s12879-022-07490-4

PY - 2022/6/18

Y1 - 2022/6/18

N2 - Background: SARS-CoV-2 is known to transmit in hospital settings, but the contribution of infections acquired in hospitals to the epidemic at a national scale is unknown. Methods: We used comprehensive national English datasets to determine the number of COVID-19 patients with identified hospital-acquired infections (with symptom onset > 7 days after admission and before discharge) in acute English hospitals up to August 2020. As patients may leave the hospital prior to detection of infection or have rapid symptom onset, we combined measures of the length of stay and the incubation period distribution to estimate how many hospital-acquired infections may have been missed. We used simulations to estimate the total number (identified and unidentified) of symptomatic hospital-acquired infections, as well as infections due to onward community transmission from missed hospital-acquired infections, to 31st July 2020. Results: In our dataset of hospitalised COVID-19 patients in acute English hospitals with a recorded symptom onset date (n = 65,028), 7% were classified as hospital-acquired. We estimated that only 30% (range across weeks and 200 simulations: 20–41%) of symptomatic hospital-acquired infections would be identified, with up to 15% (mean, 95% range over 200 simulations: 14.1–15.8%) of cases currently classified as community-acquired COVID-19 potentially linked to hospital transmission. We estimated that 26,600 (25,900 to 27,700) individuals acquired a symptomatic SARS-CoV-2 infection in an acute Trust in England before 31st July 2020, resulting in 15,900 (15,200–16,400) or 20.1% (19.2–20.7%) of all identified hospitalised COVID-19 cases. Conclusions: Transmission of SARS-CoV-2 to hospitalised patients likely caused approximately a fifth of identified cases of hospitalised COVID-19 in the “first wave” in England, but less than 1% of all infections in England. Using time to symptom onset from admission for inpatients as a detection method likely misses a substantial proportion (> 60%) of hospital-acquired infections.

AB - Background: SARS-CoV-2 is known to transmit in hospital settings, but the contribution of infections acquired in hospitals to the epidemic at a national scale is unknown. Methods: We used comprehensive national English datasets to determine the number of COVID-19 patients with identified hospital-acquired infections (with symptom onset > 7 days after admission and before discharge) in acute English hospitals up to August 2020. As patients may leave the hospital prior to detection of infection or have rapid symptom onset, we combined measures of the length of stay and the incubation period distribution to estimate how many hospital-acquired infections may have been missed. We used simulations to estimate the total number (identified and unidentified) of symptomatic hospital-acquired infections, as well as infections due to onward community transmission from missed hospital-acquired infections, to 31st July 2020. Results: In our dataset of hospitalised COVID-19 patients in acute English hospitals with a recorded symptom onset date (n = 65,028), 7% were classified as hospital-acquired. We estimated that only 30% (range across weeks and 200 simulations: 20–41%) of symptomatic hospital-acquired infections would be identified, with up to 15% (mean, 95% range over 200 simulations: 14.1–15.8%) of cases currently classified as community-acquired COVID-19 potentially linked to hospital transmission. We estimated that 26,600 (25,900 to 27,700) individuals acquired a symptomatic SARS-CoV-2 infection in an acute Trust in England before 31st July 2020, resulting in 15,900 (15,200–16,400) or 20.1% (19.2–20.7%) of all identified hospitalised COVID-19 cases. Conclusions: Transmission of SARS-CoV-2 to hospitalised patients likely caused approximately a fifth of identified cases of hospitalised COVID-19 in the “first wave” in England, but less than 1% of all infections in England. Using time to symptom onset from admission for inpatients as a detection method likely misses a substantial proportion (> 60%) of hospital-acquired infections.

KW - COVID-19

KW - Mathematical modelling

KW - Nosocomial transmission

KW - SARS-CoV-2

UR - http://www.scopus.com/inward/record.url?scp=85132455783&partnerID=8YFLogxK

U2 - 10.1186/s12879-022-07490-4

DO - 10.1186/s12879-022-07490-4

M3 - Article

C2 - 35717168

AN - SCOPUS:85132455783

SN - 1471-2334

VL - 22

JO - BMC Infectious Diseases

JF - BMC Infectious Diseases

IS - 1

M1 - 556

ER -

The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020

Abstract

Bibliographical note

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this