Construction of a demand and capacity model for intensive care and hospital ward beds, and mortality from COVID-19

Abstract Background This paper describes a model for estimating COVID-19 related excess deaths that are a direct consequence of insufficient hospital ward bed and intensive care unit (ICU) capacity. Methods Compartmental models were used to estimate deaths under different combinations of ICU and war...

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Main Authors: Christopher Martin, Stuart McDonald, Steve Bale, Michiel Luteijn, Rahul Sarkar
Format: Article
Language:English
Published: BMC 2021-04-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Online Access:https://doi.org/10.1186/s12911-021-01504-y
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spelling doaj-f32b857fb236438fa8f4f5ea8f3372e52021-05-02T11:43:08ZengBMCBMC Medical Informatics and Decision Making1472-69472021-04-0121111410.1186/s12911-021-01504-yConstruction of a demand and capacity model for intensive care and hospital ward beds, and mortality from COVID-19Christopher Martin0Stuart McDonald1Steve Bale2Michiel Luteijn3Rahul Sarkar4Director of Modelling At CrystalliseHead of Demographic Assumptions and Methodology at Lloyds Banking GroupSenior Actuary at Munich Re UK Life BranchBiometric Research Data Specialist at Hannover Re UK Life BranchConsultant Physician in Respiratory Medicine and Critical Care at Medway NHS Foundation TrustAbstract Background This paper describes a model for estimating COVID-19 related excess deaths that are a direct consequence of insufficient hospital ward bed and intensive care unit (ICU) capacity. Methods Compartmental models were used to estimate deaths under different combinations of ICU and ward care required and received in England up to late April 2021. Model parameters were sourced from publicly available government information and organisations collating COVID-19 data. A sub-model was used to estimate the mortality scalars that represent increased mortality due to insufficient ICU or general ward bed capacity. Three illustrative scenarios for admissions numbers, ‘Optimistic’, ‘Middling’ and ‘Pessimistic’, were modelled and compared with the subsequent observations to the 3rd February. Results The key output was the demand and capacity model described. There were no excess deaths from a lack of capacity in the ‘Optimistic’ scenario. Several of the ‘Middling’ scenario applications resulted in excess deaths—up to 597 deaths (0.6% increase) with a 20% reduction compared to best estimate ICU capacity. All the ‘Pessimistic’ scenario applications resulted in excess deaths, ranging from 49,178 (17.0% increase) for a 20% increase in ward bed availability, to 103,735 (35.8% increase) for a 20% shortfall in ward bed availability. These scenarios took no account of the emergence of the new, more transmissible, variant of concern (b.1.1.7). Conclusions Mortality is increased when hospital demand exceeds available capacity. No excess deaths from breaching capacity would be expected under the ‘Optimistic’ scenario. The ‘Middling’ scenario could result in some excess deaths—up to a 0.7% increase relative to the total number of deaths. The ‘Pessimistic’ scenario would have resulted in significant excess deaths. Our sensitivity analysis indicated a range between 49,178 (17% increase) and 103,735 (35.8% increase). Given the new variant, the pessimistic scenario appeared increasingly likely and could have resulted in a substantial increase in the number of COVID-19 deaths. In the event, it would appear that capacity was not breached at any stage at a national level with no excess deaths. it will remain unclear if minor local capacity breaches resulted in any small number of excess deaths.https://doi.org/10.1186/s12911-021-01504-yCOVID-19DemandCapacityModelMortalityPublic health
collection DOAJ
language English
format Article
sources DOAJ
author Christopher Martin
Stuart McDonald
Steve Bale
Michiel Luteijn
Rahul Sarkar
spellingShingle Christopher Martin
Stuart McDonald
Steve Bale
Michiel Luteijn
Rahul Sarkar
Construction of a demand and capacity model for intensive care and hospital ward beds, and mortality from COVID-19
BMC Medical Informatics and Decision Making
COVID-19
Demand
Capacity
Model
Mortality
Public health
author_facet Christopher Martin
Stuart McDonald
Steve Bale
Michiel Luteijn
Rahul Sarkar
author_sort Christopher Martin
title Construction of a demand and capacity model for intensive care and hospital ward beds, and mortality from COVID-19
title_short Construction of a demand and capacity model for intensive care and hospital ward beds, and mortality from COVID-19
title_full Construction of a demand and capacity model for intensive care and hospital ward beds, and mortality from COVID-19
title_fullStr Construction of a demand and capacity model for intensive care and hospital ward beds, and mortality from COVID-19
title_full_unstemmed Construction of a demand and capacity model for intensive care and hospital ward beds, and mortality from COVID-19
title_sort construction of a demand and capacity model for intensive care and hospital ward beds, and mortality from covid-19
publisher BMC
series BMC Medical Informatics and Decision Making
issn 1472-6947
publishDate 2021-04-01
description Abstract Background This paper describes a model for estimating COVID-19 related excess deaths that are a direct consequence of insufficient hospital ward bed and intensive care unit (ICU) capacity. Methods Compartmental models were used to estimate deaths under different combinations of ICU and ward care required and received in England up to late April 2021. Model parameters were sourced from publicly available government information and organisations collating COVID-19 data. A sub-model was used to estimate the mortality scalars that represent increased mortality due to insufficient ICU or general ward bed capacity. Three illustrative scenarios for admissions numbers, ‘Optimistic’, ‘Middling’ and ‘Pessimistic’, were modelled and compared with the subsequent observations to the 3rd February. Results The key output was the demand and capacity model described. There were no excess deaths from a lack of capacity in the ‘Optimistic’ scenario. Several of the ‘Middling’ scenario applications resulted in excess deaths—up to 597 deaths (0.6% increase) with a 20% reduction compared to best estimate ICU capacity. All the ‘Pessimistic’ scenario applications resulted in excess deaths, ranging from 49,178 (17.0% increase) for a 20% increase in ward bed availability, to 103,735 (35.8% increase) for a 20% shortfall in ward bed availability. These scenarios took no account of the emergence of the new, more transmissible, variant of concern (b.1.1.7). Conclusions Mortality is increased when hospital demand exceeds available capacity. No excess deaths from breaching capacity would be expected under the ‘Optimistic’ scenario. The ‘Middling’ scenario could result in some excess deaths—up to a 0.7% increase relative to the total number of deaths. The ‘Pessimistic’ scenario would have resulted in significant excess deaths. Our sensitivity analysis indicated a range between 49,178 (17% increase) and 103,735 (35.8% increase). Given the new variant, the pessimistic scenario appeared increasingly likely and could have resulted in a substantial increase in the number of COVID-19 deaths. In the event, it would appear that capacity was not breached at any stage at a national level with no excess deaths. it will remain unclear if minor local capacity breaches resulted in any small number of excess deaths.
topic COVID-19
Demand
Capacity
Model
Mortality
Public health
url https://doi.org/10.1186/s12911-021-01504-y
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