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Current Data Gaps in Modeling Essential Worker Absenteeism Due to COVID-19
With the uncertain physical and mental health implications of COVID-19 infection, companies have taken a myriad of actions that aim to reduce the risk of employees contracting the virus, with most grounded in reducing or eliminating in-person interactions. Our preliminary analysis indicates that whi...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591754/ https://www.ncbi.nlm.nih.gov/pubmed/32907678 http://dx.doi.org/10.1017/dmp.2020.353 |
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author | White, Zackery Schlegelmilch, Jeff Ratner, Jackie Saxena, Gunjan Wongsodirdjo, Kevin Aguilar, Susanna Kushner, Daniel Ortega, Jim Paaso, Aleksi Bahramirad, Shay |
author_facet | White, Zackery Schlegelmilch, Jeff Ratner, Jackie Saxena, Gunjan Wongsodirdjo, Kevin Aguilar, Susanna Kushner, Daniel Ortega, Jim Paaso, Aleksi Bahramirad, Shay |
author_sort | White, Zackery |
collection | PubMed |
description | With the uncertain physical and mental health implications of COVID-19 infection, companies have taken a myriad of actions that aim to reduce the risk of employees contracting the virus, with most grounded in reducing or eliminating in-person interactions. Our preliminary analysis indicates that while there is some data to support modelling absenteeism, there are gaps in the available evidence, requiring the use of assumptions that limit precision and efficacy for decision support. Improved data on time-to-recovery after hospitalization, absenteeism due to family or other household member illness, and mental health’s impact on returning to work will support the development of more robust absenteeism models and analytical approaches. |
format | Online Article Text |
id | pubmed-7591754 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-75917542020-10-28 Current Data Gaps in Modeling Essential Worker Absenteeism Due to COVID-19 White, Zackery Schlegelmilch, Jeff Ratner, Jackie Saxena, Gunjan Wongsodirdjo, Kevin Aguilar, Susanna Kushner, Daniel Ortega, Jim Paaso, Aleksi Bahramirad, Shay Disaster Med Public Health Prep Letter to the Editor With the uncertain physical and mental health implications of COVID-19 infection, companies have taken a myriad of actions that aim to reduce the risk of employees contracting the virus, with most grounded in reducing or eliminating in-person interactions. Our preliminary analysis indicates that while there is some data to support modelling absenteeism, there are gaps in the available evidence, requiring the use of assumptions that limit precision and efficacy for decision support. Improved data on time-to-recovery after hospitalization, absenteeism due to family or other household member illness, and mental health’s impact on returning to work will support the development of more robust absenteeism models and analytical approaches. Cambridge University Press 2020-09-10 /pmc/articles/PMC7591754/ /pubmed/32907678 http://dx.doi.org/10.1017/dmp.2020.353 Text en © Society for Disaster Medicine and Public Health, Inc. 2020 http://creativecommons.org/licenses/by/4.0/ This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Letter to the Editor White, Zackery Schlegelmilch, Jeff Ratner, Jackie Saxena, Gunjan Wongsodirdjo, Kevin Aguilar, Susanna Kushner, Daniel Ortega, Jim Paaso, Aleksi Bahramirad, Shay Current Data Gaps in Modeling Essential Worker Absenteeism Due to COVID-19 |
title | Current Data Gaps in Modeling Essential Worker Absenteeism Due to COVID-19 |
title_full | Current Data Gaps in Modeling Essential Worker Absenteeism Due to COVID-19 |
title_fullStr | Current Data Gaps in Modeling Essential Worker Absenteeism Due to COVID-19 |
title_full_unstemmed | Current Data Gaps in Modeling Essential Worker Absenteeism Due to COVID-19 |
title_short | Current Data Gaps in Modeling Essential Worker Absenteeism Due to COVID-19 |
title_sort | current data gaps in modeling essential worker absenteeism due to covid-19 |
topic | Letter to the Editor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591754/ https://www.ncbi.nlm.nih.gov/pubmed/32907678 http://dx.doi.org/10.1017/dmp.2020.353 |
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