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COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care

Author

Listed:
  • Richard M Wood

    (UK National Health Service (BNSSG CCG)
    University of Bath)

  • Christopher J McWilliams

    (University of Bristol)

  • Matthew J Thomas

    (University of Bristol)

  • Christopher P Bourdeaux

    (University of Bristol)

  • Christos Vasilakis

    (University of Bath)

Abstract

Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome. In appreciating these ‘capacity-dependent’ deaths, this paper reports on the clinically-led development of a stochastic discrete event simulation model designed to capture the key dynamics of the intensive care admissions process for COVID-19 patients. With application to a large public hospital in England during an early stage of the pandemic, the purpose of this study was to estimate the extent to which such capacity-dependent deaths can be mitigated through demand-side initiatives involving non-pharmaceutical interventions and supply-side measures to increase surge capacity. Based on information available at the time, results suggest that total capacity-dependent deaths can be reduced by 75% through a combination of increasing capacity from 45 to 100 beds, reducing length of stay by 25%, and flattening the peak demand to 26 admissions per day. Accounting for the additional ‘capacity-independent’ deaths, which occur even when appropriate care is available within the intensive care setting, yields an aggregate reduction in total deaths of 30%. The modelling tool, which is freely available and open source, has since been used to support COVID-19 response planning at a number of healthcare systems within the UK National Health Service.

Suggested Citation

  • Richard M Wood & Christopher J McWilliams & Matthew J Thomas & Christopher P Bourdeaux & Christos Vasilakis, 2020. "COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care," Health Care Management Science, Springer, vol. 23(3), pages 315-324, September.
    • Handle: RePEc:kap:hcarem:v:23:y:2020:i:3:d:10.1007_s10729-020-09511-7
    DOI: 10.1007/s10729-020-09511-7
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    References listed on IDEAS

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    1. Eren Demir & Christos Vasilakis & Reda Lebcir & David Southern, 2015. "A simulation-based decision support tool for informing the management of patients with Parkinson’s disease," International Journal of Production Research, Taylor & Francis Journals, vol. 53(24), pages 7238-7251, December.
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    5. Griffiths, J.D. & Williams, J.E. & Wood, R.M., 2013. "Modelling activities at a neurological rehabilitation unit," European Journal of Operational Research, Elsevier, vol. 226(2), pages 301-312.
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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Allocation and rationing

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    Cited by:

    1. G.J. Melman & A.K. Parlikad & E.A.B. Cameron, 2021. "Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation," Health Care Management Science, Springer, vol. 24(2), pages 356-374, June.
    2. Matthias Klumpp & Dominic Loske & Silvio Bicciato, 2022. "COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(8), pages 1263-1285, November.
    3. Richard M. Wood & Adrian C. Pratt & Charlie Kenward & Christopher J. McWilliams & Ross D. Booton & Matthew J. Thomas & Christopher P. Bourdeaux & Christos Vasilakis, 2021. "The Value of Triage during Periods of Intense COVID-19 Demand: Simulation Modeling Study," Medical Decision Making, , vol. 41(4), pages 393-407, May.
    4. Linying Yang & Teng Zhang & Peter Glynn & David Scheinker, 2021. "The development and deployment of a model for hospital-level COVID-19 associated patient demand intervals from consistent estimators (DICE)," Health Care Management Science, Springer, vol. 24(2), pages 375-401, June.
    5. Akira Watanabe & Hiroyuki Matsuda, 2023. "Effectiveness of feedback control and the trade-off between death by COVID-19 and costs of countermeasures," Health Care Management Science, Springer, vol. 26(1), pages 46-61, March.
    6. Lidia Betcheva & Feryal Erhun & Antoine Feylessoufi & Peter Fryers & Paulo Gonçalves & Houyuan Jiang & Paul Kattuman & Tom Pape & Anees Pari & Stefan Scholtes & Carina Tyrrell, 2024. "An Adaptive Research Approach to COVID-19 Forecasting for Regional Health Systems in England," Interfaces, INFORMS, vol. 54(6), pages 500-516, November.
    7. Costase Ndayishimiye & Christoph Sowada & Patrycja Dyjach & Agnieszka Stasiak & John Middleton & Henrique Lopes & Katarzyna Dubas-Jakóbczyk, 2022. "Associations between the COVID-19 Pandemic and Hospital Infrastructure Adaptation and Planning—A Scoping Review," IJERPH, MDPI, vol. 19(13), pages 1-22, July.
    8. Samantha L. Zimmerman & Alexander R. Rutherford & Alexa Waall & Monica Norena & Peter Dodek, 2023. "A queuing model for ventilator capacity management during the COVID-19 pandemic," Health Care Management Science, Springer, vol. 26(2), pages 200-216, June.
    9. Berta, P. & Bratti, M. & Fiorio, C.V. & Pisoni, E. & Verzillo, S., 2021. "Administrative border effects in Covid-19 related mortality," Health, Econometrics and Data Group (HEDG) Working Papers 21/21, HEDG, c/o Department of Economics, University of York.
    10. Ortiz-Barrios, Miguel & Arias-Fonseca, Sebastián & Ishizaka, Alessio & Barbati, Maria & Avendaño-Collante, Betty & Navarro-Jiménez, Eduardo, 2023. "Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study," Journal of Business Research, Elsevier, vol. 160(C).
    11. Eugenio F. Sánchez-Úbeda & Pedro Sánchez-Martín & Macarena Torrego-Ellacuría & Ángel Del Rey-Mejías & Manuel F. Morales-Contreras & José-Luis Puerta, 2021. "Flexibility and Bed Margins of the Community of Madrid’s Hospitals during the First Wave of the SARS-CoV-2 Pandemic," IJERPH, MDPI, vol. 18(7), pages 1-22, March.

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