Abstract
Background
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. The aim of this study is 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 or reduce lengths of stay.
Methods
A stochastic discrete event simulation model is developed to represent the key dynamics of the intensive care admissions process for COVID-19 patients. Model inputs are aligned to the levers available to planners in responding to the pandemic. Key outputs include the duration of time at maximum capacity (to inform workforce requirements), peak daily deaths (for mortuary planning), and total deaths over the course of the pandemic (as an ultimate marker of intervention efficacy). The model is applied to the COVID-19 response at a large hospital in England for which the effect of a number of possible interventions are simulated. Packaged as an open source tool, the model is made freely available on github for wider use.
Results
Capacity-dependent deaths are closely associated with both the nature and effectiveness of non-pharmaceutical interventions and the availability of intensive care beds. For the hospital under consideration, results suggest that capacity-dependent deaths can be reduced five-fold through a combination of social isolation policies, a doubling in bed capacity, and 25% reductions to length of stay.
Conclusions
Without improved treatment or vaccination, there is little that can be done to reduce deaths occurring when the patient has otherwise been treated in the most appropriate hospital setting. Healthcare planners should therefore focus on taking measures to keep to a minimum the capacity-dependent deaths that are within their influence.
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. The aim of this study is 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 or reduce lengths of stay.
Methods
A stochastic discrete event simulation model is developed to represent the key dynamics of the intensive care admissions process for COVID-19 patients. Model inputs are aligned to the levers available to planners in responding to the pandemic. Key outputs include the duration of time at maximum capacity (to inform workforce requirements), peak daily deaths (for mortuary planning), and total deaths over the course of the pandemic (as an ultimate marker of intervention efficacy). The model is applied to the COVID-19 response at a large hospital in England for which the effect of a number of possible interventions are simulated. Packaged as an open source tool, the model is made freely available on github for wider use.
Results
Capacity-dependent deaths are closely associated with both the nature and effectiveness of non-pharmaceutical interventions and the availability of intensive care beds. For the hospital under consideration, results suggest that capacity-dependent deaths can be reduced five-fold through a combination of social isolation policies, a doubling in bed capacity, and 25% reductions to length of stay.
Conclusions
Without improved treatment or vaccination, there is little that can be done to reduce deaths occurring when the patient has otherwise been treated in the most appropriate hospital setting. Healthcare planners should therefore focus on taking measures to keep to a minimum the capacity-dependent deaths that are within their influence.
| Original language | English |
|---|---|
| Publisher | medRxiv |
| Number of pages | 14 |
| DOIs | |
| Publication status | Published - 6 Apr 2020 |
