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A comprehensive county level model to identify factors affecting hospital capacity and predict future hospital demand

The sustained COVID-19 case numbers and the associated hospitalizations have placed a substantial burden on health care ecosystem comprising of hospitals, clinics, doctors and nurses. However, as of today, only a small number of studies have examined detailed hospitalization data from a planning per...

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Autores principales: Bhowmik, Tanmoy, Eluru, Naveen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8630121/
https://www.ncbi.nlm.nih.gov/pubmed/34845301
http://dx.doi.org/10.1038/s41598-021-02376-y
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author Bhowmik, Tanmoy
Eluru, Naveen
author_facet Bhowmik, Tanmoy
Eluru, Naveen
author_sort Bhowmik, Tanmoy
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description The sustained COVID-19 case numbers and the associated hospitalizations have placed a substantial burden on health care ecosystem comprising of hospitals, clinics, doctors and nurses. However, as of today, only a small number of studies have examined detailed hospitalization data from a planning perspective. The current study develops a comprehensive framework for understanding the critical factors associated with county level hospitalization and ICU usage rates across the US employing a host of independent variables. Drawing from the recently released Department of Health and Human Services weekly hospitalization data, we study the overall hospitalization and ICU usage—not only COVID-19 hospitalizations. Developing a framework that examines overall hospitalizations and ICU usage can better reflect the plausible hospital system recovery path to pre-COVID level hospitalization trends. The models are subsequently employed to generate predictions for county level hospitalization and ICU usage rates in the future under several COVID-19 transmission scenarios considering the emergence of new COVID-19 variants and vaccination rates. The exercise allows us to identify vulnerable counties and regions under stress with high hospitalization and ICU rates that can be assisted with remedial measures. Further, the model will allow hospitals to understand evolving displaced non-COVID hospital demand.
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spelling pubmed-86301212021-12-01 A comprehensive county level model to identify factors affecting hospital capacity and predict future hospital demand Bhowmik, Tanmoy Eluru, Naveen Sci Rep Article The sustained COVID-19 case numbers and the associated hospitalizations have placed a substantial burden on health care ecosystem comprising of hospitals, clinics, doctors and nurses. However, as of today, only a small number of studies have examined detailed hospitalization data from a planning perspective. The current study develops a comprehensive framework for understanding the critical factors associated with county level hospitalization and ICU usage rates across the US employing a host of independent variables. Drawing from the recently released Department of Health and Human Services weekly hospitalization data, we study the overall hospitalization and ICU usage—not only COVID-19 hospitalizations. Developing a framework that examines overall hospitalizations and ICU usage can better reflect the plausible hospital system recovery path to pre-COVID level hospitalization trends. The models are subsequently employed to generate predictions for county level hospitalization and ICU usage rates in the future under several COVID-19 transmission scenarios considering the emergence of new COVID-19 variants and vaccination rates. The exercise allows us to identify vulnerable counties and regions under stress with high hospitalization and ICU rates that can be assisted with remedial measures. Further, the model will allow hospitals to understand evolving displaced non-COVID hospital demand. Nature Publishing Group UK 2021-11-29 /pmc/articles/PMC8630121/ /pubmed/34845301 http://dx.doi.org/10.1038/s41598-021-02376-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Bhowmik, Tanmoy
Eluru, Naveen
A comprehensive county level model to identify factors affecting hospital capacity and predict future hospital demand
title A comprehensive county level model to identify factors affecting hospital capacity and predict future hospital demand
title_full A comprehensive county level model to identify factors affecting hospital capacity and predict future hospital demand
title_fullStr A comprehensive county level model to identify factors affecting hospital capacity and predict future hospital demand
title_full_unstemmed A comprehensive county level model to identify factors affecting hospital capacity and predict future hospital demand
title_short A comprehensive county level model to identify factors affecting hospital capacity and predict future hospital demand
title_sort comprehensive county level model to identify factors affecting hospital capacity and predict future hospital demand
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8630121/
https://www.ncbi.nlm.nih.gov/pubmed/34845301
http://dx.doi.org/10.1038/s41598-021-02376-y
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