Collective and Individual Assessment of the Risk of Death from COVID-19 for the Elderly, 2020-2022

Coronavirus disease 2019 (COVID-19) has had profound disruptions worldwide. For a population or individual, it is critical to assess the risk of death for making preventative decisions. In this study, clinical data from approximately 100 million cases were statistically analyzed. A software and an o...

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Published inChina CDC weekly Vol. 5; no. 18; pp. 407 - 412
Main Authors Zhang, Chaobao, Wang, Hongzhi, Wen, Zilu, Bao, Zhijun, Li, Xiangqi
Format Journal Article
LanguageEnglish
Published China Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 05.05.2023
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Abstract Coronavirus disease 2019 (COVID-19) has had profound disruptions worldwide. For a population or individual, it is critical to assess the risk of death for making preventative decisions. In this study, clinical data from approximately 100 million cases were statistically analyzed. A software and an online assessment tool were developed in Python to evaluate the risk of mortality. Our analysis revealed that 76.51% of COVID-19-related fatalities occurred among individuals aged over 65 years, with frailty-associated deaths accounting for more than 80% of these cases. Furthermore, over 80% of the reported deaths involved unvaccinated individuals. A notable overlap was observed between aging and frailty-associated deaths, both of which were connected to underlying health conditions. For those with at least two comorbidities, the proportion of frailty and the proportion of COVID-19-related death were both close to 75 percent. Subsequently, we established a formula to calculate the number of deaths, which was validated using data from twenty countries and regions. Using this formula, we developed and verified an intelligent software designed to predict the death risk for a given population. To facilitate rapid risk screening on an individual level, we also introduced a six-question online assessment tool. This study examined the impact of underlying diseases, frailty, age, and vaccination history on COVID-19-related mortality, resulting in a sophisticated software and a user-friendly online scale to assess mortality risk. These tools offer valuable assistance in informed decision-making.
AbstractList IntroductionCoronavirus disease 2019 (COVID-19) has had profound disruptions worldwide. For a population or individual, it is critical to assess the risk of death for making preventative decisions. MethodsIn this study, clinical data from approximately 100 million cases were statistically analyzed. A software and an online assessment tool were developed in Python to evaluate the risk of mortality. ResultsOur analysis revealed that 76.51% of COVID-19-related fatalities occurred among individuals aged over 65 years, with frailty-associated deaths accounting for more than 80% of these cases. Furthermore, over 80% of the reported deaths involved unvaccinated individuals. A notable overlap was observed between aging and frailty-associated deaths, both of which were connected to underlying health conditions. For those with at least two comorbidities, the proportion of frailty and the proportion of COVID-19-related death were both close to 75 percent. Subsequently, we established a formula to calculate the number of deaths, which was validated using data from twenty countries and regions. Using this formula, we developed and verified an intelligent software designed to predict the death risk for a given population. To facilitate rapid risk screening on an individual level, we also introduced a six-question online assessment tool. ConclusionsThis study examined the impact of underlying diseases, frailty, age, and vaccination history on COVID-19-related mortality, resulting in a sophisticated software and a user-friendly online scale to assess mortality risk. These tools offer valuable assistance in informed decision-making.
Coronavirus disease 2019 (COVID-19) has had profound disruptions worldwide. For a population or individual, it is critical to assess the risk of death for making preventative decisions. In this study, clinical data from approximately 100 million cases were statistically analyzed. A software and an online assessment tool were developed in Python to evaluate the risk of mortality. Our analysis revealed that 76.51% of COVID-19-related fatalities occurred among individuals aged over 65 years, with frailty-associated deaths accounting for more than 80% of these cases. Furthermore, over 80% of the reported deaths involved unvaccinated individuals. A notable overlap was observed between aging and frailty-associated deaths, both of which were connected to underlying health conditions. For those with at least two comorbidities, the proportion of frailty and the proportion of COVID-19-related death were both close to 75 percent. Subsequently, we established a formula to calculate the number of deaths, which was validated using data from twenty countries and regions. Using this formula, we developed and verified an intelligent software designed to predict the death risk for a given population. To facilitate rapid risk screening on an individual level, we also introduced a six-question online assessment tool. This study examined the impact of underlying diseases, frailty, age, and vaccination history on COVID-19-related mortality, resulting in a sophisticated software and a user-friendly online scale to assess mortality risk. These tools offer valuable assistance in informed decision-making.
Author Li, Xiangqi
Wen, Zilu
Zhang, Chaobao
Bao, Zhijun
Wang, Hongzhi
AuthorAffiliation 1 Shanghai Key Laboratory of Clinical Geriatric Medicine; Department of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, China
4 Department of Endocrinology and Metabolism, Gongli Hospital, Naval Medical University, Shanghai, China
3 Department of Scientific Research, Shanghai Public Health Clinical Center, Shanghai, China
2 Shanghai Key Laboratory of Magnetic Resonance; Research Center for Artificial Intelligence in Medical Imaging, East China Normal University, Shanghai, China
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Cites_doi 10.3389/fcimb.2022.836409
10.1093/ageing/afaa184
10.1016/j.jiph.2021.01.002
10.1093/ageing/afaa219
10.1016/S0140-6736(12)62167-9
10.1016/S2666-7568(21)00006-4
10.1016/S0140-6736(19)31785-4
10.1093/cid/ciab493
10.1016/S0140-6736(21)02249-2
10.3389/fpubh.2022.808471
10.1016/j.csbj.2021.04.004
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CorporateAuthor Shanghai Key Laboratory of Magnetic Resonance; Research Center for Artificial Intelligence in Medical Imaging, East China Normal University, Shanghai, China
Department of Scientific Research, Shanghai Public Health Clinical Center, Shanghai, China
Department of Endocrinology and Metabolism, Gongli Hospital, Naval Medical University, Shanghai, China
Shanghai Key Laboratory of Clinical Geriatric Medicine; Department of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, China
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Keywords COVID-19
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References 11
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Snippet Coronavirus disease 2019 (COVID-19) has had profound disruptions worldwide. For a population or individual, it is critical to assess the risk of death for...
IntroductionCoronavirus disease 2019 (COVID-19) has had profound disruptions worldwide. For a population or individual, it is critical to assess the risk of...
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Title Collective and Individual Assessment of the Risk of Death from COVID-19 for the Elderly, 2020-2022
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