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Model-based evaluation of policy impacts and the continued COVID-19 risk at long term care facilities
The COVID-19 pandemic severely impacted long-term care facilities resulting in the death of approximately 8% of residents nationwide as of March 2021. As COVID-19 case rates declined and state and county restrictions were lifted in spring 2021, facility managers, local and state health agencies were...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
KeAi Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283126/ https://www.ncbi.nlm.nih.gov/pubmed/35854786 http://dx.doi.org/10.1016/j.idm.2022.07.003 |
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author | Fosdick, Bailey K. Bayham, Jude Dilliott, Jake Ebel, Gregory D. Ehrhart, Nicole |
author_facet | Fosdick, Bailey K. Bayham, Jude Dilliott, Jake Ebel, Gregory D. Ehrhart, Nicole |
author_sort | Fosdick, Bailey K. |
collection | PubMed |
description | The COVID-19 pandemic severely impacted long-term care facilities resulting in the death of approximately 8% of residents nationwide as of March 2021. As COVID-19 case rates declined and state and county restrictions were lifted in spring 2021, facility managers, local and state health agencies were challenged with defining their own policies moving forward to appropriately mitigate disease transmission. The continued emergence of variants of concern and variable vaccine uptake across facilities highlighted the need for a readily available tool that can be employed at the facility-level to determine best practices for mitigation and ensure resident and staff safety. To assist leadership in determining the impact of various infection surveillance and response strategies, we developed an agent-based model and an online dashboard interface that simulates COVID-19 infection within congregate care settings under various mitigation measures. This dashboard quantifies the continued risk for COVID-19 infections within a facility given a designated testing schedule and vaccine requirements. Key findings were that choice of COVID-19 diagnostic (ex. nasal swab qRT-PCR vs rapid antigen) and testing cadence has less impact on attack rate and staff workdays missed than does vaccination rates among staff and residents. Specifically, low vaccine uptake among staff at long-term care facilities puts staff and residents at risk of ongoing COVID-19 outbreaks. Here we present our model and dashboard as an exemplar of a tool for state public health officials and facility directors to gain insights from an infectious disease model that can directly inform policy decisions in the midst of a pandemic. |
format | Online Article Text |
id | pubmed-9283126 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | KeAi Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-92831262022-07-15 Model-based evaluation of policy impacts and the continued COVID-19 risk at long term care facilities Fosdick, Bailey K. Bayham, Jude Dilliott, Jake Ebel, Gregory D. Ehrhart, Nicole Infect Dis Model Original Research Article The COVID-19 pandemic severely impacted long-term care facilities resulting in the death of approximately 8% of residents nationwide as of March 2021. As COVID-19 case rates declined and state and county restrictions were lifted in spring 2021, facility managers, local and state health agencies were challenged with defining their own policies moving forward to appropriately mitigate disease transmission. The continued emergence of variants of concern and variable vaccine uptake across facilities highlighted the need for a readily available tool that can be employed at the facility-level to determine best practices for mitigation and ensure resident and staff safety. To assist leadership in determining the impact of various infection surveillance and response strategies, we developed an agent-based model and an online dashboard interface that simulates COVID-19 infection within congregate care settings under various mitigation measures. This dashboard quantifies the continued risk for COVID-19 infections within a facility given a designated testing schedule and vaccine requirements. Key findings were that choice of COVID-19 diagnostic (ex. nasal swab qRT-PCR vs rapid antigen) and testing cadence has less impact on attack rate and staff workdays missed than does vaccination rates among staff and residents. Specifically, low vaccine uptake among staff at long-term care facilities puts staff and residents at risk of ongoing COVID-19 outbreaks. Here we present our model and dashboard as an exemplar of a tool for state public health officials and facility directors to gain insights from an infectious disease model that can directly inform policy decisions in the midst of a pandemic. KeAi Publishing 2022-07-15 /pmc/articles/PMC9283126/ /pubmed/35854786 http://dx.doi.org/10.1016/j.idm.2022.07.003 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Original Research Article Fosdick, Bailey K. Bayham, Jude Dilliott, Jake Ebel, Gregory D. Ehrhart, Nicole Model-based evaluation of policy impacts and the continued COVID-19 risk at long term care facilities |
title | Model-based evaluation of policy impacts and the continued COVID-19 risk at long term care facilities |
title_full | Model-based evaluation of policy impacts and the continued COVID-19 risk at long term care facilities |
title_fullStr | Model-based evaluation of policy impacts and the continued COVID-19 risk at long term care facilities |
title_full_unstemmed | Model-based evaluation of policy impacts and the continued COVID-19 risk at long term care facilities |
title_short | Model-based evaluation of policy impacts and the continued COVID-19 risk at long term care facilities |
title_sort | model-based evaluation of policy impacts and the continued covid-19 risk at long term care facilities |
topic | Original Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283126/ https://www.ncbi.nlm.nih.gov/pubmed/35854786 http://dx.doi.org/10.1016/j.idm.2022.07.003 |
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