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Predictors of Nursing Home Covid-19 Cases: a Community Vulnerability Approach
It is well known that the Covid-19 pandemic has placed considerable burden on nursing homes, including from resident, facility, and community perspectives, among others. This study examined facility and community factors that were related to incident Covid-19 cases in nursing home facilities. N=12,4...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8992189/ http://dx.doi.org/10.1093/geroni/igab046.3635 |
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author | Peterson, Matthew Lawhorne, Larry |
author_facet | Peterson, Matthew Lawhorne, Larry |
author_sort | Peterson, Matthew |
collection | PubMed |
description | It is well known that the Covid-19 pandemic has placed considerable burden on nursing homes, including from resident, facility, and community perspectives, among others. This study examined facility and community factors that were related to incident Covid-19 cases in nursing home facilities. N=12,473 US nursing homes were included in this study. Data from June 2020 - January 2021 from several publicly available sources were combined to create a dataset that included facility name, size, ownership, mortality rate, Covid case rate, personal protective equipment (PPE) and staff shortages, % white residents, and % Medicaid residents. Community factors included core-based statistical area (CBSA) Covid case rates, urban/rural, CBSA death rates, and the CDC’s Social Vulnerability Index (SVI). Zero-inflated Poisson regression models were used to determine predictors of 8-month Covid case counts, normalized by facility size. Results indicated that higher staff shortages, poorer facility rating, for-profit ownership, proportionally more Medicaid and non-white residents were all significantly associated with higher Covid case rates over 8 months (all P < 0.0001). Significant community level predictors of higher cases included urban setting and higher SVI. PPE shortages was not associated with higher case counts. Of all the factors included, SVI was the strongest predictor of Covid case counts. This large US study assists in determining critical facility and community factors that predict increasing Covid burden in nursing homes. Particularly, SVI is an important factor in determining facility and public health policy, and targeting resources in large scale health crises such as the Covid-19 pandemic. |
format | Online Article Text |
id | pubmed-8992189 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-89921892022-04-12 Predictors of Nursing Home Covid-19 Cases: a Community Vulnerability Approach Peterson, Matthew Lawhorne, Larry Innov Aging Abstracts It is well known that the Covid-19 pandemic has placed considerable burden on nursing homes, including from resident, facility, and community perspectives, among others. This study examined facility and community factors that were related to incident Covid-19 cases in nursing home facilities. N=12,473 US nursing homes were included in this study. Data from June 2020 - January 2021 from several publicly available sources were combined to create a dataset that included facility name, size, ownership, mortality rate, Covid case rate, personal protective equipment (PPE) and staff shortages, % white residents, and % Medicaid residents. Community factors included core-based statistical area (CBSA) Covid case rates, urban/rural, CBSA death rates, and the CDC’s Social Vulnerability Index (SVI). Zero-inflated Poisson regression models were used to determine predictors of 8-month Covid case counts, normalized by facility size. Results indicated that higher staff shortages, poorer facility rating, for-profit ownership, proportionally more Medicaid and non-white residents were all significantly associated with higher Covid case rates over 8 months (all P < 0.0001). Significant community level predictors of higher cases included urban setting and higher SVI. PPE shortages was not associated with higher case counts. Of all the factors included, SVI was the strongest predictor of Covid case counts. This large US study assists in determining critical facility and community factors that predict increasing Covid burden in nursing homes. Particularly, SVI is an important factor in determining facility and public health policy, and targeting resources in large scale health crises such as the Covid-19 pandemic. Oxford University Press 2021-12-17 /pmc/articles/PMC8992189/ http://dx.doi.org/10.1093/geroni/igab046.3635 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Abstracts Peterson, Matthew Lawhorne, Larry Predictors of Nursing Home Covid-19 Cases: a Community Vulnerability Approach |
title | Predictors of Nursing Home Covid-19 Cases: a Community Vulnerability Approach |
title_full | Predictors of Nursing Home Covid-19 Cases: a Community Vulnerability Approach |
title_fullStr | Predictors of Nursing Home Covid-19 Cases: a Community Vulnerability Approach |
title_full_unstemmed | Predictors of Nursing Home Covid-19 Cases: a Community Vulnerability Approach |
title_short | Predictors of Nursing Home Covid-19 Cases: a Community Vulnerability Approach |
title_sort | predictors of nursing home covid-19 cases: a community vulnerability approach |
topic | Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8992189/ http://dx.doi.org/10.1093/geroni/igab046.3635 |
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