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...
Saved in:
Published in | China CDC weekly Vol. 5; no. 18; pp. 407 - 412 |
---|---|
Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
China
Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention
05.05.2023
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
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 |
AuthorAffiliation_xml | – name: 4 Department of Endocrinology and Metabolism, Gongli Hospital, Naval Medical University, Shanghai, China – name: 1 Shanghai Key Laboratory of Clinical Geriatric Medicine; Department of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, China – name: 2 Shanghai Key Laboratory of Magnetic Resonance; Research Center for Artificial Intelligence in Medical Imaging, East China Normal University, Shanghai, China – name: 3 Department of Scientific Research, Shanghai Public Health Clinical Center, Shanghai, China |
Author_xml | – sequence: 1 givenname: Chaobao surname: Zhang fullname: Zhang, Chaobao organization: Shanghai Key Laboratory of Clinical Geriatric Medicine; Department of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, China – sequence: 2 givenname: Hongzhi surname: Wang fullname: Wang, Hongzhi organization: Shanghai Key Laboratory of Magnetic Resonance; Research Center for Artificial Intelligence in Medical Imaging, East China Normal University, Shanghai, China – sequence: 3 givenname: Zilu surname: Wen fullname: Wen, Zilu organization: Department of Scientific Research, Shanghai Public Health Clinical Center, Shanghai, China – sequence: 4 givenname: Zhijun surname: Bao fullname: Bao, Zhijun organization: Shanghai Key Laboratory of Clinical Geriatric Medicine; Department of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, China – sequence: 5 givenname: Xiangqi surname: Li fullname: Li, Xiangqi organization: Department of Endocrinology and Metabolism, Gongli Hospital, Naval Medical University, Shanghai, China |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37197177$$D View this record in MEDLINE/PubMed |
BookMark | eNpVkc1P3DAQxa0KVCjl2iPysQeyeGwnjk8ILbRdCQmpanu1bGfcDTgxtbNb8d835WMFl5mR5jdvnvQ-kL0xjUjIJ2AL2XAhz7zv_F_OuFgwpd6RQ850UymmYO_VfECOS7lljHHNOW-b9-RAKNAKlDokbpliRD_1W6R27Ohq7Ppt321spBelYCkDjhNNgU5rpN_7cvd_vkQ7rWnIaaDLm1-rywo0DSk_MlexwxwfTulsi1Vz4R_JfrCx4PFzPyI_v1z9WH6rrm--rpYX15UXGqZKtkE672vvgkPXeAgo65oF5VtUQmvfcunQ1gJ4K61QWjrw0DiHoAHAiyNy_qR7v3EDdn42nm0097kfbH4wyfbm7Wbs1-Z32hpg0Eqp2Kzw-Vkhpz8bLJMZ-uIxRjti2hTDW6i51BLkjC6eUJ9TKRnD7g8w8xiO2YVj5nDmg5PX7nb4SxTiH2ypi10 |
CitedBy_id | crossref_primary_10_1080_22221751_2023_2251589 crossref_primary_10_1186_s12877_024_05177_w crossref_primary_10_51847_VOxl2qREKe crossref_primary_10_3724_abbs_2023249 crossref_primary_10_1007_s00795_024_00381_4 |
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 |
ContentType | Journal Article |
Copyright | Copyright and License information: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2023. Copyright and License information: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2023 2023 |
Copyright_xml | – notice: Copyright and License information: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2023. – notice: Copyright and License information: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2023 2023 |
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 |
CorporateAuthor_xml | – sequence: 0 name: Shanghai Key Laboratory of Magnetic Resonance; Research Center for Artificial Intelligence in Medical Imaging, East China Normal University, Shanghai, China – sequence: 0 name: Shanghai Key Laboratory of Clinical Geriatric Medicine; Department of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, China – sequence: 0 name: Department of Endocrinology and Metabolism, Gongli Hospital, Naval Medical University, Shanghai, China – sequence: 0 name: Department of Scientific Research, Shanghai Public Health Clinical Center, Shanghai, China |
DBID | NPM AAYXX CITATION 7X8 5PM |
DOI | 10.46234/ccdcw2023.077 |
DatabaseName | PubMed CrossRef MEDLINE - Academic PubMed Central (Full Participant titles) |
DatabaseTitle | PubMed CrossRef MEDLINE - Academic |
DatabaseTitleList | MEDLINE - Academic PubMed |
Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
DeliveryMethod | fulltext_linktorsrc |
EISSN | 2096-7071 |
EndPage | 412 |
ExternalDocumentID | 10_46234_ccdcw2023_077 37197177 |
Genre | Journal Article |
GroupedDBID | ALMA_UNASSIGNED_HOLDINGS NPM PGMZT RPM AAYXX CITATION 7X8 5PM |
ID | FETCH-LOGICAL-c391t-48f4bcc5cbfbeb6c1fe4550f7c8e7399c824bea531284a3794b1c16bbe19111c3 |
IEDL.DBID | RPM |
ISSN | 2096-7071 |
IngestDate | Tue Sep 17 21:31:50 EDT 2024 Sat Aug 17 03:42:08 EDT 2024 Fri Dec 06 02:31:57 EST 2024 Sat Sep 28 08:11:47 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 18 |
Keywords | COVID-19 Software Risk evaluation Underlying disease |
Language | English |
License | Copyright and License information: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2023. This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0 |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c391t-48f4bcc5cbfbeb6c1fe4550f7c8e7399c824bea531284a3794b1c16bbe19111c3 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Joint first authors. |
OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10184470/ |
PMID | 37197177 |
PQID | 2815249414 |
PQPubID | 23479 |
PageCount | 6 |
ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_10184470 proquest_miscellaneous_2815249414 crossref_primary_10_46234_ccdcw2023_077 pubmed_primary_37197177 |
PublicationCentury | 2000 |
PublicationDate | 2023-05-05 |
PublicationDateYYYYMMDD | 2023-05-05 |
PublicationDate_xml | – month: 05 year: 2023 text: 2023-05-05 day: 05 |
PublicationDecade | 2020 |
PublicationPlace | China |
PublicationPlace_xml | – name: China – name: Beijing, China |
PublicationTitle | China CDC weekly |
PublicationTitleAlternate | China CDC Wkly |
PublicationYear | 2023 |
Publisher | Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention |
Publisher_xml | – name: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention |
References | 11 1 2 3 4 5 6 7 8 9 10 |
References_xml | – ident: 2 doi: 10.3389/fcimb.2022.836409 – ident: 6 doi: 10.1093/ageing/afaa184 – ident: 3 doi: 10.1016/j.jiph.2021.01.002 – ident: 9 doi: 10.1093/ageing/afaa219 – ident: 8 doi: 10.1016/S0140-6736(12)62167-9 – ident: 10 doi: 10.1016/S2666-7568(21)00006-4 – ident: 11 doi: 10.1016/S0140-6736(19)31785-4 – ident: 4 doi: 10.1093/cid/ciab493 – ident: 5 doi: 10.1016/S0140-6736(21)02249-2 – ident: 1 doi: 10.3389/fpubh.2022.808471 – ident: 7 doi: 10.1016/j.csbj.2021.04.004 |
SSID | ssj0002922286 |
Score | 2.297856 |
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... |
SourceID | pubmedcentral proquest crossref pubmed |
SourceType | Open Access Repository Aggregation Database Index Database |
StartPage | 407 |
SubjectTerms | Methods and Applications |
Title | Collective and Individual Assessment of the Risk of Death from COVID-19 for the Elderly, 2020-2022 |
URI | https://www.ncbi.nlm.nih.gov/pubmed/37197177 https://search.proquest.com/docview/2815249414 https://pubmed.ncbi.nlm.nih.gov/PMC10184470 |
Volume | 5 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1bS8MwFA5zT76I4m3eiCD4YrelTZP2cezCJkxFnOytNGmCw60bbkN88z_4D_0lnqRr2fTNp5bmQvh6kvOd5JwThK5ipcGUFWajimmHCp44IfASJ9Qs8IXkoHLNiW7_jnUH9HboD0uI5bEw1mlfilE1HU-q6ejF-lbOJrKW-4nVHvpNk2WKUl6vbaEt0L9rNrpZf93Q7GqwLEMjBfVOa1Im8t1cFF6tc76pgf7Qyt_ekWvqprOLdlY8ETey8eyhkkr3UWLNfLtC4ThNcK-IpsKNIsMmnmoMrA4_juav5r1lSB42YSS4ef_cazkkxMBUbZ22uaN7_HGDYdT1788veLgHaNBpPzW7zuqaBEd6IVkAypoKKX0ptFCCSaKVCVXWXAaKA_-QgUuFimGygSqKPZiAgkjChFDErHTSO0TldJqqY4SZimOXeQLg1TSs-0LHLJGaE5UIFz5V0HUOXTTLsmFEYEVYkKMC5AhArqDLHNkIBNacQsSpmi7nkRsAZaAhJdDbUYZ00ZfHSQj2JbQONv5BUcEkw94sARmxSbFzmTj5f9NTtG2Gb90Z_TNUXrwt1TlQjoW4sBL2A8142Ss |
link.rule.ids | 230,314,727,780,784,885,27924,27925,53791,53793 |
linkProvider | National Library of Medicine |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV29TsMwELZKGWBBIP7Kr5GQWEhbJ44dj1ULaoECQoDYotixRQWkFW2F2HgH3pAn4eySqIWNKVFiW_aXi-87--6M0GGiDZiy0i5UMeNRyVNPAC_xhGFRKBUHlWt3dLuXrH1Hzx7ChxJieSyMc9pXslfNnl-qWe_R-VYOXlQt9xOrXXebNssUpbxem0PzYcAFmbLS7QzsC7uuwSY5GikoeFpTKlVv9qjwap3zWR30h1j-9o-cUjiny2jphynixqRHK6iks1WUOkPfzVE4yVLcKeKpcKPIsYn7BgOvwze94ZO9b1mah20gCW5e3XdaHhEYuKorc2JP6X5-P8bQ6_rXxydc_DV0d3py22x7PwcleCoQZAQ4GyqVCpU0UkumiNE2WNlwFWkODERFPpU6gd8NlFECgFFJFGFSamLnOhWso3LWz_Qmwkwnic8CCQAbKuqhNAlLleFEp9KHRxV0lEMXDyb5MGKwIxzIcQFyDCBX0EGObAwia_chkkz3x8PYj4A0UEEJtLYxQbpoK-BEgIUJtaOZb1AUsOmwZ9-AlLi02LlUbP2_6j5aaN92L-KLzuX5Nlq0Q3HOjeEOKo9ex3oXCMhI7jlp-wZhq9yA |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1bT8IwFG4UE-OL0XjDa01MfHGMbl27PRIuARUkRgxvy9q1kQiDCMT45n_wH_pLPB2wgL75tGVrm_bbWc932nNOEbqKlAZTVpiFKqYtKnhsBcBLrEAz3xOSg8o1O7rNFqt36G3X6869Ksdzt8pEil4h6Q8KSe8l9a0cDaS98BOz282yyTJFKS_ao1jb62jDc0HKlix1Mws7gVnbYLM8jRSUPLWljOW7OS68UOR8VQ_9IZe_fSSXlE5tB23P2SIuzXq1i9ZUsofi1NhP5ykcJTFuZDFVuJTl2cRDjYHb4cfe-NXcVwzVwyaYBJcfnhsViwQY-GpapmpO6u5_3GDodfH78wsuzj7q1KpP5bo1PyzBkm5AJoC1pkJKTwotlGCSaGUCljWXvuLAQqTvUKEi-OVAIUUAGBVEEiaEIma-k-4ByiXDRB0hzFQUOcwVALKmQdETOmKx1JyoWDjwKI-uF9CFo1lOjBBsiRTkMAM5BJDz6HKBbAhia_YiokQNp-PQ8YE40IASaO1whnTWlstJAFYm1PZXvkFWwKTEXn0DkpKmxl5IxvH_q16gzXalFt43WncnaMuMJPVv9E5RbvI2VWfAQSbiPBW2H41b3ZM |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Collective+and+Individual+Assessment+of+the+Risk+of+Death+from+COVID-19+for+the+Elderly%2C+2020%E2%80%932022&rft.jtitle=China+CDC+weekly&rft.au=Zhang%2C+Chaobao&rft.au=Wang%2C+Hongzhi&rft.au=Wen%2C+Zilu&rft.au=Bao%2C+Zhijun&rft.date=2023-05-05&rft.issn=2096-7071&rft.eissn=2096-7071&rft.volume=5&rft.issue=18&rft.spage=407&rft.epage=412&rft_id=info:doi/10.46234%2Fccdcw2023.077&rft.externalDBID=n%2Fa&rft.externalDocID=10_46234_ccdcw2023_077 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2096-7071&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2096-7071&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2096-7071&client=summon |