Burden and prevalence of prognostic factors for severe COVID-19 in Sweden.
Identifieur interne : 000255 ( Main/Corpus ); précédent : 000254; suivant : 000256Burden and prevalence of prognostic factors for severe COVID-19 in Sweden.
Auteurs : Katalin Gémes ; Mats Talb Ck ; Karin Modig ; Anders Ahlbom ; Anita Berglund ; Maria Feychting ; Anthony A. MatthewsSource :
- European journal of epidemiology [ 1573-7284 ] ; 2020.
English descriptors
- KwdEn :
- Adolescent (MeSH), Adult (MeSH), Age Factors (MeSH), Aged (MeSH), Aged, 80 and over (MeSH), Asthma (epidemiology), Betacoronavirus (MeSH), Cardiovascular Diseases (epidemiology), Child (MeSH), Child, Preschool (MeSH), Coronavirus (MeSH), Coronavirus Infections (epidemiology), Cost of Illness (MeSH), Critical Care (MeSH), Diabetes Mellitus (epidemiology), Humans (MeSH), Infant (MeSH), Middle Aged (MeSH), Neoplasms (epidemiology), Pandemics (MeSH), Pneumonia, Viral (epidemiology), Population Surveillance (methods), Prevalence (MeSH), Prognosis (MeSH), Pulmonary Disease, Chronic Obstructive (epidemiology), Severity of Illness Index (MeSH), Sweden (epidemiology), Young Adult (MeSH).
- MESH :
- geographic , epidemiology : Sweden.
- epidemiology : Asthma, Cardiovascular Diseases, Coronavirus Infections, Diabetes Mellitus, Neoplasms, Pneumonia, Viral, Pulmonary Disease, Chronic Obstructive.
- methods : Population Surveillance.
- Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Betacoronavirus, Child, Child, Preschool, Coronavirus, Cost of Illness, Critical Care, Humans, Infant, Middle Aged, Pandemics, Prevalence, Prognosis, Severity of Illness Index, Young Adult.
Abstract
The World Health Organization and European Centre for Disease Prevention and Control suggest that individuals over the age of 70 years or with underlying cardiovascular disease, cancer, chronic obstructive pulmonary disease, asthma, or diabetes are at increased risk of severe COVID-19. However, the prevalence of these prognostic factors is unknown in many countries. We aimed to describe the burden and prevalence of prognostic factors of severe COVID-19 at national and county level in Sweden. We calculated the burden and prevalence of prognostic factors for severe COVID-19 based on records from the Swedish national health care and population registers for 3 years before 1st January 2016. 9,624,428 individuals were included in the study population. 22.1% had at least one prognostic factor for severe COVID-19 (2,131,319 individuals), and 1.6% had at least three factors (154,746 individuals). The prevalence of underlying medical conditions ranged from 0.8% with chronic obstructive pulmonary disease (78,516 individuals) to 7.4% with cardiovascular disease (708,090 individuals), and the county specific prevalence of at least one prognostic factor ranged from 19.2% in Stockholm (416,988 individuals) to 25.9% in Kalmar (60,005 individuals). We show that one in five individuals in Sweden is at increased risk of severe COVID-19. When compared with the critical care capacity at a local and national level, these results can aid authorities in optimally planning healthcare resources during the current pandemic. Findings can also be applied to underlying assumptions of disease burden in modelling efforts to support COVID-19 planning.
DOI: 10.1007/s10654-020-00646-z
PubMed: 32424571
PubMed Central: PMC7233678
Links to Exploration step
pubmed:32424571Le document en format XML
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Matthews</name><affiliation><nlm:affiliation>Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. anthony.matthews@ki.se.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32424571</idno><idno type="pmid">32424571</idno><idno type="doi">10.1007/s10654-020-00646-z</idno><idno type="pmc">PMC7233678</idno><idno type="wicri:Area/Main/Corpus">000255</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000255</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Burden and prevalence of prognostic factors for severe COVID-19 in Sweden.</title><author><name sortKey="Gemes, Katalin" sort="Gemes, Katalin" uniqKey="Gemes K" first="Katalin" last="Gémes">Katalin Gémes</name><affiliation><nlm:affiliation>Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.</nlm:affiliation></affiliation></author><author><name sortKey="Talb Ck, Mats" sort="Talb Ck, Mats" uniqKey="Talb Ck M" first="Mats" last="Talb Ck">Mats Talb Ck</name><affiliation><nlm:affiliation>Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.</nlm:affiliation></affiliation></author><author><name sortKey="Modig, Karin" sort="Modig, Karin" uniqKey="Modig K" first="Karin" last="Modig">Karin Modig</name><affiliation><nlm:affiliation>Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.</nlm:affiliation></affiliation></author><author><name sortKey="Ahlbom, Anders" sort="Ahlbom, Anders" uniqKey="Ahlbom A" first="Anders" last="Ahlbom">Anders Ahlbom</name><affiliation><nlm:affiliation>Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.</nlm:affiliation></affiliation></author><author><name sortKey="Berglund, Anita" sort="Berglund, Anita" uniqKey="Berglund A" first="Anita" last="Berglund">Anita Berglund</name><affiliation><nlm:affiliation>Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.</nlm:affiliation></affiliation></author><author><name sortKey="Feychting, Maria" sort="Feychting, Maria" uniqKey="Feychting M" first="Maria" last="Feychting">Maria Feychting</name><affiliation><nlm:affiliation>Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.</nlm:affiliation></affiliation></author><author><name sortKey="Matthews, Anthony A" sort="Matthews, Anthony A" uniqKey="Matthews A" first="Anthony A" last="Matthews">Anthony A. Matthews</name><affiliation><nlm:affiliation>Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. anthony.matthews@ki.se.</nlm:affiliation></affiliation></author></analytic><series><title level="j">European journal of epidemiology</title><idno type="eISSN">1573-7284</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent (MeSH)</term><term>Adult (MeSH)</term><term>Age Factors (MeSH)</term><term>Aged (MeSH)</term><term>Aged, 80 and over (MeSH)</term><term>Asthma (epidemiology)</term><term>Betacoronavirus (MeSH)</term><term>Cardiovascular Diseases (epidemiology)</term><term>Child (MeSH)</term><term>Child, Preschool (MeSH)</term><term>Coronavirus (MeSH)</term><term>Coronavirus Infections (epidemiology)</term><term>Cost of Illness (MeSH)</term><term>Critical Care (MeSH)</term><term>Diabetes Mellitus (epidemiology)</term><term>Humans (MeSH)</term><term>Infant (MeSH)</term><term>Middle Aged (MeSH)</term><term>Neoplasms (epidemiology)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (epidemiology)</term><term>Population Surveillance (methods)</term><term>Prevalence (MeSH)</term><term>Prognosis (MeSH)</term><term>Pulmonary Disease, Chronic Obstructive (epidemiology)</term><term>Severity of Illness Index (MeSH)</term><term>Sweden (epidemiology)</term><term>Young Adult (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Sweden</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Asthma</term><term>Cardiovascular Diseases</term><term>Coronavirus Infections</term><term>Diabetes Mellitus</term><term>Neoplasms</term><term>Pneumonia, Viral</term><term>Pulmonary Disease, Chronic Obstructive</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Population Surveillance</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Age Factors</term><term>Aged</term><term>Aged, 80 and over</term><term>Betacoronavirus</term><term>Child</term><term>Child, Preschool</term><term>Coronavirus</term><term>Cost of Illness</term><term>Critical Care</term><term>Humans</term><term>Infant</term><term>Middle Aged</term><term>Pandemics</term><term>Prevalence</term><term>Prognosis</term><term>Severity of Illness Index</term><term>Young Adult</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The World Health Organization and European Centre for Disease Prevention and Control suggest that individuals over the age of 70 years or with underlying cardiovascular disease, cancer, chronic obstructive pulmonary disease, asthma, or diabetes are at increased risk of severe COVID-19. However, the prevalence of these prognostic factors is unknown in many countries. We aimed to describe the burden and prevalence of prognostic factors of severe COVID-19 at national and county level in Sweden. We calculated the burden and prevalence of prognostic factors for severe COVID-19 based on records from the Swedish national health care and population registers for 3 years before 1st January 2016. 9,624,428 individuals were included in the study population. 22.1% had at least one prognostic factor for severe COVID-19 (2,131,319 individuals), and 1.6% had at least three factors (154,746 individuals). The prevalence of underlying medical conditions ranged from 0.8% with chronic obstructive pulmonary disease (78,516 individuals) to 7.4% with cardiovascular disease (708,090 individuals), and the county specific prevalence of at least one prognostic factor ranged from 19.2% in Stockholm (416,988 individuals) to 25.9% in Kalmar (60,005 individuals). We show that one in five individuals in Sweden is at increased risk of severe COVID-19. When compared with the critical care capacity at a local and national level, these results can aid authorities in optimally planning healthcare resources during the current pandemic. Findings can also be applied to underlying assumptions of disease burden in modelling efforts to support COVID-19 planning.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32424571</PMID><DateCompleted><Year>2020</Year><Month>06</Month><Day>01</Day></DateCompleted><DateRevised><Year>2020</Year><Month>06</Month><Day>01</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1573-7284</ISSN><JournalIssue CitedMedium="Internet"><Volume>35</Volume><Issue>5</Issue><PubDate><Year>2020</Year><Month>May</Month></PubDate></JournalIssue><Title>European journal of epidemiology</Title><ISOAbbreviation>Eur. J. Epidemiol.</ISOAbbreviation></Journal><ArticleTitle>Burden and prevalence of prognostic factors for severe COVID-19 in Sweden.</ArticleTitle><Pagination><MedlinePgn>401-409</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1007/s10654-020-00646-z</ELocationID><Abstract><AbstractText>The World Health Organization and European Centre for Disease Prevention and Control suggest that individuals over the age of 70 years or with underlying cardiovascular disease, cancer, chronic obstructive pulmonary disease, asthma, or diabetes are at increased risk of severe COVID-19. However, the prevalence of these prognostic factors is unknown in many countries. We aimed to describe the burden and prevalence of prognostic factors of severe COVID-19 at national and county level in Sweden. We calculated the burden and prevalence of prognostic factors for severe COVID-19 based on records from the Swedish national health care and population registers for 3 years before 1st January 2016. 9,624,428 individuals were included in the study population. 22.1% had at least one prognostic factor for severe COVID-19 (2,131,319 individuals), and 1.6% had at least three factors (154,746 individuals). The prevalence of underlying medical conditions ranged from 0.8% with chronic obstructive pulmonary disease (78,516 individuals) to 7.4% with cardiovascular disease (708,090 individuals), and the county specific prevalence of at least one prognostic factor ranged from 19.2% in Stockholm (416,988 individuals) to 25.9% in Kalmar (60,005 individuals). We show that one in five individuals in Sweden is at increased risk of severe COVID-19. When compared with the critical care capacity at a local and national level, these results can aid authorities in optimally planning healthcare resources during the current pandemic. Findings can also be applied to underlying assumptions of disease burden in modelling efforts to support COVID-19 planning.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Gémes</LastName><ForeName>Katalin</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Talbäck</LastName><ForeName>Mats</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Modig</LastName><ForeName>Karin</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ahlbom</LastName><ForeName>Anders</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Berglund</LastName><ForeName>Anita</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Feychting</LastName><ForeName>Maria</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Matthews</LastName><ForeName>Anthony A</ForeName><Initials>AA</Initials><Identifier Source="ORCID">http://orcid.org/0000-0002-1290-271X</Identifier><AffiliationInfo><Affiliation>Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. anthony.matthews@ki.se.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>05</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Eur J Epidemiol</MedlineTA><NlmUniqueID>8508062</NlmUniqueID><ISSNLinking>0393-2990</ISSNLinking></MedlineJournalInfo><SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName><SupplMeshName Type="Organism" UI="C000656484">severe acute respiratory syndrome coronavirus 2</SupplMeshName></SupplMeshList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000293" MajorTopicYN="N">Adolescent</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000367" MajorTopicYN="N">Age Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000369" MajorTopicYN="N">Aged, 80 and over</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001249" MajorTopicYN="N">Asthma</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="N">Betacoronavirus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002318" MajorTopicYN="N">Cardiovascular Diseases</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002648" MajorTopicYN="N">Child</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002675" MajorTopicYN="N">Child, Preschool</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017934" MajorTopicYN="Y">Coronavirus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017281" MajorTopicYN="Y">Cost of Illness</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003422" MajorTopicYN="N">Critical Care</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003920" MajorTopicYN="N">Diabetes Mellitus</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007223" MajorTopicYN="N">Infant</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009369" MajorTopicYN="N">Neoplasms</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011159" MajorTopicYN="N">Population Surveillance</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015995" MajorTopicYN="N">Prevalence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011379" MajorTopicYN="N">Prognosis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D029424" MajorTopicYN="N">Pulmonary Disease, Chronic Obstructive</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012720" MajorTopicYN="N">Severity of Illness Index</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013548" MajorTopicYN="N" Type="Geographic">Sweden</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Burden of disease</Keyword><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Prevalence</Keyword><Keyword MajorTopicYN="N">Prognostic factors</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>04</Month><Day>18</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>05</Month><Day>05</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>5</Month><Day>20</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>6</Month><Day>2</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>5</Month><Day>20</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32424571</ArticleId><ArticleId IdType="doi">10.1007/s10654-020-00646-z</ArticleId><ArticleId IdType="pii">10.1007/s10654-020-00646-z</ArticleId><ArticleId IdType="pmc">PMC7233678</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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