Dataset Information

Modelling palliative and end-of-life resource requirements during COVID-19: implications for quality care.

ABSTRACT:

Objectives

The WHO estimates that the COVID-19 pandemic has led to more than 1.3 million deaths (1 377 395) globally (as of November 2020). This surge in death necessitates identification of resource needs and relies on modelling resource and understanding anticipated surges in demand. Our aim was to develop a generic computer model that could estimate resources required for end-of-life (EoL) care delivery during the pandemic.

Setting

A discrete event simulation model was developed and used to estimate resourcing needs for a geographical area in the South West of England. While our analysis focused on the UK setting, the model is flexible to changes in demand and setting.

Participants

We used the model to estimate resourcing needs for a population of around 1 million people.

Primary and secondary outcome measures

The model predicts the per-day 'staff' and 'stuff' resourcing required to meet a given level of incoming EoL care activity.

Results

A mean of 11.97 hours of additional community nurse time, up to 33 hours of care assistant time and up to 30 hours additional care from care assistant night sits will be required per day as a result of out of hospital COVID-19 deaths based on the model prediction. Specialist palliative care demand is predicted to increase up to 19 hours per day. An additional 286 anticipatory medicine bundles per month will be necessary to alleviate physical symptoms at the EoL care for patients with COVID-19: an average additional 10.21 bundles of anticipatory medication per day. An average additional 9.35 syringe pumps could be needed to be in use per day.

Conclusions

The analysis for a large region in the South West of England shows the significant additional physical and human resource required to relieve suffering at the EoL as part of a pandemic response.

SUBMITTER: Chalk D

PROVIDER: S-EPMC8154294 | biostudies-literature |

REPOSITORIES: biostudies-literature

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