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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...

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Autores principales: White, Zackery, Schlegelmilch, Jeff, Ratner, Jackie, Saxena, Gunjan, Wongsodirdjo, Kevin, Aguilar, Susanna, Kushner, Daniel, Ortega, Jim, Paaso, Aleksi, Bahramirad, Shay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2020
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.
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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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