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Can Comorbidity Data Explain Cross-State and Cross-National Difference in COVID-19 Death Rates?

Many efforts to predict the impact of COVID-19 on hospitalization, intensive care unit (ICU) utilization, and mortality rely on age and comorbidities. These predictions are foundational to learning, policymaking, and planning for the pandemic, and therefore understanding the relationship between age...

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Autores principales: Cegan, Jeffrey C, Trump, Benjamin D, Cibulsky, Susan M, Collier, Zachary A, Cummings, Christopher L, Greer, Scott L, Jarman, Holly, Klasa, Kasia, Kleinman, Gary, Surette, Melissa A, Wells, Emily, Linkov, Igor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275866/
https://www.ncbi.nlm.nih.gov/pubmed/34267565
http://dx.doi.org/10.2147/RMHP.S313312
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author Cegan, Jeffrey C
Trump, Benjamin D
Cibulsky, Susan M
Collier, Zachary A
Cummings, Christopher L
Greer, Scott L
Jarman, Holly
Klasa, Kasia
Kleinman, Gary
Surette, Melissa A
Wells, Emily
Linkov, Igor
author_facet Cegan, Jeffrey C
Trump, Benjamin D
Cibulsky, Susan M
Collier, Zachary A
Cummings, Christopher L
Greer, Scott L
Jarman, Holly
Klasa, Kasia
Kleinman, Gary
Surette, Melissa A
Wells, Emily
Linkov, Igor
author_sort Cegan, Jeffrey C
collection PubMed
description Many efforts to predict the impact of COVID-19 on hospitalization, intensive care unit (ICU) utilization, and mortality rely on age and comorbidities. These predictions are foundational to learning, policymaking, and planning for the pandemic, and therefore understanding the relationship between age, comorbidities, and health outcomes is critical to assessing and managing public health risks. From a US government database of 1.4 million patient records collected in May 2020, we extracted the relationships between age and number of comorbidities at the individual level to predict the likelihood of hospitalization, admission to intensive care, and death. We then applied the relationships to each US state and a selection of different countries in order to see whether they predicted observed outcome rates. We found that age and comorbidity data within these geographical regions do not explain much of the international or within-country variation in hospitalization, ICU admission, or death. Identifying alternative explanations for the limited predictive power of comorbidities and age at the population level should be considered for future research.
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spelling pubmed-82758662021-07-14 Can Comorbidity Data Explain Cross-State and Cross-National Difference in COVID-19 Death Rates? Cegan, Jeffrey C Trump, Benjamin D Cibulsky, Susan M Collier, Zachary A Cummings, Christopher L Greer, Scott L Jarman, Holly Klasa, Kasia Kleinman, Gary Surette, Melissa A Wells, Emily Linkov, Igor Risk Manag Healthc Policy Commentary Many efforts to predict the impact of COVID-19 on hospitalization, intensive care unit (ICU) utilization, and mortality rely on age and comorbidities. These predictions are foundational to learning, policymaking, and planning for the pandemic, and therefore understanding the relationship between age, comorbidities, and health outcomes is critical to assessing and managing public health risks. From a US government database of 1.4 million patient records collected in May 2020, we extracted the relationships between age and number of comorbidities at the individual level to predict the likelihood of hospitalization, admission to intensive care, and death. We then applied the relationships to each US state and a selection of different countries in order to see whether they predicted observed outcome rates. We found that age and comorbidity data within these geographical regions do not explain much of the international or within-country variation in hospitalization, ICU admission, or death. Identifying alternative explanations for the limited predictive power of comorbidities and age at the population level should be considered for future research. Dove 2021-07-07 /pmc/articles/PMC8275866/ /pubmed/34267565 http://dx.doi.org/10.2147/RMHP.S313312 Text en © 2021 Cegan et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Commentary
Cegan, Jeffrey C
Trump, Benjamin D
Cibulsky, Susan M
Collier, Zachary A
Cummings, Christopher L
Greer, Scott L
Jarman, Holly
Klasa, Kasia
Kleinman, Gary
Surette, Melissa A
Wells, Emily
Linkov, Igor
Can Comorbidity Data Explain Cross-State and Cross-National Difference in COVID-19 Death Rates?
title Can Comorbidity Data Explain Cross-State and Cross-National Difference in COVID-19 Death Rates?
title_full Can Comorbidity Data Explain Cross-State and Cross-National Difference in COVID-19 Death Rates?
title_fullStr Can Comorbidity Data Explain Cross-State and Cross-National Difference in COVID-19 Death Rates?
title_full_unstemmed Can Comorbidity Data Explain Cross-State and Cross-National Difference in COVID-19 Death Rates?
title_short Can Comorbidity Data Explain Cross-State and Cross-National Difference in COVID-19 Death Rates?
title_sort can comorbidity data explain cross-state and cross-national difference in covid-19 death rates?
topic Commentary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275866/
https://www.ncbi.nlm.nih.gov/pubmed/34267565
http://dx.doi.org/10.2147/RMHP.S313312
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