Magnitude, demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries
The Coronavirus Disease 2019 (COVID-19) pandemic has changed many social, economic, environmental and healthcare determinants of health. We applied an ensemble of 16 Bayesian models to vital statistics data to estimate the all-cause mortality effect of the pandemic for 21 industrialized countries. F...
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Veröffentlicht in: | Nature medicine 2020-12, Vol.26 (12), p.1919-1928 |
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creator | Kontis, Vasilis Bennett, James E. Rashid, Theo Parks, Robbie M. Pearson-Stuttard, Jonathan Guillot, Michel Asaria, Perviz Zhou, Bin Battaglini, Marco Corsetti, Gianni McKee, Martin Di Cesare, Mariachiara Mathers, Colin D. Ezzati, Majid |
description | The Coronavirus Disease 2019 (COVID-19) pandemic has changed many social, economic, environmental and healthcare determinants of health. We applied an ensemble of 16 Bayesian models to vital statistics data to estimate the all-cause mortality effect of the pandemic for 21 industrialized countries. From mid-February through May 2020, 206,000 (95% credible interval, 178,100–231,000) more people died in these countries than would have had the pandemic not occurred. The number of excess deaths, excess deaths per 100,000 people and relative increase in deaths were similar between men and women in most countries. England and Wales and Spain experienced the largest effect: ~100 excess deaths per 100,000 people, equivalent to a 37% (30–44%) relative increase in England and Wales and 38% (31–45%) in Spain. Bulgaria, New Zealand, Slovakia, Australia, Czechia, Hungary, Poland, Norway, Denmark and Finland experienced mortality changes that ranged from possible small declines to increases of 5% or less in either sex. The heterogeneous mortality effects of the COVID-19 pandemic reflect differences in how well countries have managed the pandemic and the resilience and preparedness of the health and social care system.
Application of Bayesian models to vital statistics data from 21 industrialized countries shows that approximately 206,000 additional people died than if the COVID-19 pandemic had not occured. The heterogeneous distribution of excess deaths across the countries reflects differences in how the pandemic has been managed as well as the resilience of healthcare systems in these nations. |
doi_str_mv | 10.1038/s41591-020-1112-0 |
format | Article |
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Application of Bayesian models to vital statistics data from 21 industrialized countries shows that approximately 206,000 additional people died than if the COVID-19 pandemic had not occured. The heterogeneous distribution of excess deaths across the countries reflects differences in how the pandemic has been managed as well as the resilience of healthcare systems in these nations.</description><identifier>ISSN: 1078-8956</identifier><identifier>EISSN: 1546-170X</identifier><identifier>DOI: 10.1038/s41591-020-1112-0</identifier><identifier>PMID: 33057181</identifier><language>eng</language><publisher>New York: Nature Publishing Group US</publisher><subject>692/700/478 ; 706/648/453 ; Australia ; Bayesian analysis ; Biomedical and Life Sciences ; Biomedicine ; Cancer Research ; Casualties ; Cause of Death - trends ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; COVID-19 - mortality ; Demographic aspects ; Demography ; Developed Countries - statistics & numerical data ; Epidemics ; Fatalities ; Female ; Geography ; Health aspects ; Health care ; Humans ; Industrial Development - statistics & numerical data ; Industrial nations ; Infectious Diseases ; Male ; Mathematical models ; Men ; Metabolic Diseases ; Molecular Medicine ; Mortality ; Mortality - trends ; Neurosciences ; New Zealand ; Pandemics ; Population Density ; Population Dynamics - statistics & numerical data ; Population Dynamics - trends ; Public Policy ; Resilience ; SARS-CoV-2 - physiology ; Statistical analysis ; Statistics ; Time Factors ; Viral diseases ; Vital statistics</subject><ispartof>Nature medicine, 2020-12, Vol.26 (12), p.1919-1928</ispartof><rights>The Author(s), under exclusive licence to Springer Nature America, Inc. 2020. corrected publication 2021</rights><rights>COPYRIGHT 2020 Nature Publishing Group</rights><rights>The Author(s), under exclusive licence to Springer Nature America, Inc. 2020. corrected publication 2021.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c652t-3855f7ee60182bb3faa97a4c9dbc359ae52af8df64fcddc5656f8196ce6986d33</citedby><cites>FETCH-LOGICAL-c652t-3855f7ee60182bb3faa97a4c9dbc359ae52af8df64fcddc5656f8196ce6986d33</cites><orcidid>0000-0002-1741-8628 ; 0000-0002-9037-0894 ; 0000-0002-0121-9683 ; 0000-0002-2109-8081</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1038/s41591-020-1112-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1038/s41591-020-1112-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33057181$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kontis, Vasilis</creatorcontrib><creatorcontrib>Bennett, James E.</creatorcontrib><creatorcontrib>Rashid, Theo</creatorcontrib><creatorcontrib>Parks, Robbie M.</creatorcontrib><creatorcontrib>Pearson-Stuttard, Jonathan</creatorcontrib><creatorcontrib>Guillot, Michel</creatorcontrib><creatorcontrib>Asaria, Perviz</creatorcontrib><creatorcontrib>Zhou, Bin</creatorcontrib><creatorcontrib>Battaglini, Marco</creatorcontrib><creatorcontrib>Corsetti, Gianni</creatorcontrib><creatorcontrib>McKee, Martin</creatorcontrib><creatorcontrib>Di Cesare, Mariachiara</creatorcontrib><creatorcontrib>Mathers, Colin D.</creatorcontrib><creatorcontrib>Ezzati, Majid</creatorcontrib><title>Magnitude, demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries</title><title>Nature medicine</title><addtitle>Nat Med</addtitle><addtitle>Nat Med</addtitle><description>The Coronavirus Disease 2019 (COVID-19) pandemic has changed many social, economic, environmental and healthcare determinants of health. We applied an ensemble of 16 Bayesian models to vital statistics data to estimate the all-cause mortality effect of the pandemic for 21 industrialized countries. From mid-February through May 2020, 206,000 (95% credible interval, 178,100–231,000) more people died in these countries than would have had the pandemic not occurred. The number of excess deaths, excess deaths per 100,000 people and relative increase in deaths were similar between men and women in most countries. England and Wales and Spain experienced the largest effect: ~100 excess deaths per 100,000 people, equivalent to a 37% (30–44%) relative increase in England and Wales and 38% (31–45%) in Spain. Bulgaria, New Zealand, Slovakia, Australia, Czechia, Hungary, Poland, Norway, Denmark and Finland experienced mortality changes that ranged from possible small declines to increases of 5% or less in either sex. The heterogeneous mortality effects of the COVID-19 pandemic reflect differences in how well countries have managed the pandemic and the resilience and preparedness of the health and social care system.
Application of Bayesian models to vital statistics data from 21 industrialized countries shows that approximately 206,000 additional people died than if the COVID-19 pandemic had not occured. The heterogeneous distribution of excess deaths across the countries reflects differences in how the pandemic has been managed as well as the resilience of healthcare systems in these nations.</description><subject>692/700/478</subject><subject>706/648/453</subject><subject>Australia</subject><subject>Bayesian analysis</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Cancer Research</subject><subject>Casualties</subject><subject>Cause of Death - trends</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>COVID-19 - mortality</subject><subject>Demographic aspects</subject><subject>Demography</subject><subject>Developed Countries - statistics & numerical data</subject><subject>Epidemics</subject><subject>Fatalities</subject><subject>Female</subject><subject>Geography</subject><subject>Health aspects</subject><subject>Health care</subject><subject>Humans</subject><subject>Industrial Development - statistics & numerical data</subject><subject>Industrial nations</subject><subject>Infectious Diseases</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Men</subject><subject>Metabolic Diseases</subject><subject>Molecular Medicine</subject><subject>Mortality</subject><subject>Mortality - trends</subject><subject>Neurosciences</subject><subject>New Zealand</subject><subject>Pandemics</subject><subject>Population Density</subject><subject>Population Dynamics - statistics & numerical data</subject><subject>Population Dynamics - trends</subject><subject>Public Policy</subject><subject>Resilience</subject><subject>SARS-CoV-2 - physiology</subject><subject>Statistical analysis</subject><subject>Statistics</subject><subject>Time Factors</subject><subject>Viral diseases</subject><subject>Vital 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demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries</title><author>Kontis, Vasilis ; Bennett, James E. ; Rashid, Theo ; Parks, Robbie M. ; Pearson-Stuttard, Jonathan ; Guillot, Michel ; Asaria, Perviz ; Zhou, Bin ; Battaglini, Marco ; Corsetti, Gianni ; McKee, Martin ; Di Cesare, Mariachiara ; Mathers, Colin D. ; Ezzati, Majid</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c652t-3855f7ee60182bb3faa97a4c9dbc359ae52af8df64fcddc5656f8196ce6986d33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>692/700/478</topic><topic>706/648/453</topic><topic>Australia</topic><topic>Bayesian analysis</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>Cancer Research</topic><topic>Casualties</topic><topic>Cause of Death - 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Majid</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Magnitude, demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries</atitle><jtitle>Nature medicine</jtitle><stitle>Nat Med</stitle><addtitle>Nat Med</addtitle><date>2020-12-01</date><risdate>2020</risdate><volume>26</volume><issue>12</issue><spage>1919</spage><epage>1928</epage><pages>1919-1928</pages><issn>1078-8956</issn><eissn>1546-170X</eissn><abstract>The Coronavirus Disease 2019 (COVID-19) pandemic has changed many social, economic, environmental and healthcare determinants of health. We applied an ensemble of 16 Bayesian models to vital statistics data to estimate the all-cause mortality effect of the pandemic for 21 industrialized countries. From mid-February through May 2020, 206,000 (95% credible interval, 178,100–231,000) more people died in these countries than would have had the pandemic not occurred. The number of excess deaths, excess deaths per 100,000 people and relative increase in deaths were similar between men and women in most countries. England and Wales and Spain experienced the largest effect: ~100 excess deaths per 100,000 people, equivalent to a 37% (30–44%) relative increase in England and Wales and 38% (31–45%) in Spain. Bulgaria, New Zealand, Slovakia, Australia, Czechia, Hungary, Poland, Norway, Denmark and Finland experienced mortality changes that ranged from possible small declines to increases of 5% or less in either sex. The heterogeneous mortality effects of the COVID-19 pandemic reflect differences in how well countries have managed the pandemic and the resilience and preparedness of the health and social care system.
Application of Bayesian models to vital statistics data from 21 industrialized countries shows that approximately 206,000 additional people died than if the COVID-19 pandemic had not occured. The heterogeneous distribution of excess deaths across the countries reflects differences in how the pandemic has been managed as well as the resilience of healthcare systems in these nations.</abstract><cop>New York</cop><pub>Nature Publishing Group US</pub><pmid>33057181</pmid><doi>10.1038/s41591-020-1112-0</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-1741-8628</orcidid><orcidid>https://orcid.org/0000-0002-9037-0894</orcidid><orcidid>https://orcid.org/0000-0002-0121-9683</orcidid><orcidid>https://orcid.org/0000-0002-2109-8081</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | 692/700/478 706/648/453 Australia Bayesian analysis Biomedical and Life Sciences Biomedicine Cancer Research Casualties Cause of Death - trends Coronaviruses COVID-19 COVID-19 - epidemiology COVID-19 - mortality Demographic aspects Demography Developed Countries - statistics & numerical data Epidemics Fatalities Female Geography Health aspects Health care Humans Industrial Development - statistics & numerical data Industrial nations Infectious Diseases Male Mathematical models Men Metabolic Diseases Molecular Medicine Mortality Mortality - trends Neurosciences New Zealand Pandemics Population Density Population Dynamics - statistics & numerical data Population Dynamics - trends Public Policy Resilience SARS-CoV-2 - physiology Statistical analysis Statistics Time Factors Viral diseases Vital statistics |
title | Magnitude, demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries |
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