Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England

ISARIC4C Investigators, CMMID COVID-19 Working Group, Quentin J. Leclerc*, Naomi M. Fuller, Ruth H. Keogh, Karla Diaz-Ordaz, Richard Sekula, Malcolm G. Semple, Meera Chand-Kumar, Jake Dunning, Samreen Ijaz, Richard Tedder, Maria Zambon, William Edmunds, Stefan Flasche, Frank Sandmann, Mark Jit, Katherine E. Atkins, Simon R. Procter, Gwenan M. Knight

*Corresponding author for this work

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Abstract

Background: Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient’s “bed pathway” - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy.

Methods: We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020.

Results: In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: “Ward, CC, Ward”, “Ward, CC”, “CC” and “CC, Ward”. Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities.

Conclusions: We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19.

Trial registration: The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.

Original languageEnglish
Article number566
JournalBMC Health Services Research
Volume21
Issue number1
Early online date9 Jun 2021
DOIs
Publication statusE-pub ahead of print - 9 Jun 2021

Bibliographical note

Funding Information: The authors declare the following funding sources. QJL: MRC London Interdisciplinary Doctoral Programme (MR/N013638/1), NMF: BBSRC London Interdisciplinary Doctoral Programme (BB/M009513/1), RHK: UK Research and Innovation Future Leaders Fellowship (MR/S017968/1), KDO: Wellcome Trust - Royal Society Sir Henry Dale Fellowship (218554/Z/19/Z), SRP: Bill & Melinda Gates Foundation (OPP1180644, INV-016832), GMK: MRC (MR/P014658/1).
The ISARIC4C Investigators were 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), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927).
The following funding sources are acknowledged as providing funding for the CMMID COVID-19 Working Group authors. Alan Turing Institute (AE). BBSRC LIDP (BB/M009513/1: DS). This research was partly funded by the Bill & Melinda Gates Foundation (INV-001754: MQ; INV-003174: KP, MJ, YL; NTD Modelling Consortium OPP1184344: CABP, GFM; OPP1183986: ESN; OPP1191821: MA). BMGF (OPP1157270: KA). DFID/Wellcome Trust (Epidemic Preparedness Coronavirus research programme 221303/Z/20/Z: CABP, KvZ). Elrha R2HC/UK DFID/Wellcome Trust/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 Care (KvZ). ERC Starting Grant (#757699: MQ; 757688: CJVA). This project has received funding from the European Union’s Horizon 2020 research and innovation programme - project EpiPose (101003688: KP, MJ, PK, RCB, WJE, YL). This research was partly funded by the Global Challenges Research Fund (GCRF) project ‘RECAP’ managed through RCUK and ESRC (ES/P010873/1: AG, CIJ, TJ). HDR UK (MR/S003975/1: RME). MRC (MR/N013638/1: NRW). Nakajima Foundation (AE). NIHR (16/136/46: BJQ; 16/137/109: BJQ, FYS, MJ, YL; Health Protection Research Unit for Immunisation NIHR200929: NGD; Health Protection Research Unit for Modelling Methodology HPRU-2012-10096: TJ; NIHR200908: RME; NIHR200929: FGS, MJ; PR-OD-1017-20002: AR, WJE). Royal Society (Dorothy Hodgkin Fellowship: RL; RP\EA\180004: PK). UK DHSC/UK Aid/NIHR (ITCRZ 03010: HPG). UK MRC (LID DTP MR/N013638/1: GRGL; MC_PC_19065: AG, NGD, RME, SC, TJ, WJE, YL). Authors of this research receive funding from UK Public Health Rapid Support Team funded by the United Kingdom Department of Health and Social Care (TJ). Wellcome Trust (206250/Z/17/Z: AJK, TWR; 206471/Z/17/Z: OJB; 208812/Z/17/Z: SC, SFlasche; 210758/Z/18/Z: JDM, JH, KS, NIB, SA, SFunk, SRM). No funding (AMF, AS, DCT, JW, YWDC).
The views expressed are those of the authors and not necessarily those of any of the funders named above. Funders were not involved in the study and collection, analysis, and interpretation of data and in writing the manuscript.
Open access funding provided by Karolinska Institute.

MGS reports grants from DHSC NIHR UK, MRC UK, and HPRU in Emerging and Zoonotic Infections during the conduct of the study; minority ownership of Integrum Scientific LLC (Greensboro, NC, USA) outside the submitted work.

QJL, NMF, RHK, KDO, RS, KEA, SRP, GMK declare that they have no competing interests to disclose.

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: © 2021, The Author(s).

Citation: Leclerc, Q.J., Fuller, N.M., Keogh, R.H. et al. Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England. BMC Health Serv Res 21, 566 (2021).

DOI: https://doi.org/10.1186/s12913-021-06509-x

Keywords

  • Bed occupancy
  • Bed pathway
  • COVID-19
  • Hospitalisation
  • Length of stay
  • SARS-CoV-2

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