Estimated County-Level Prevalence of Selected Underlying Medical Conditions Associated with Increased Risk for Severe COVID-19 Illness - United States, 2018.
Identifieur interne : 000207 ( Main/Exploration ); précédent : 000206; suivant : 000208Estimated County-Level Prevalence of Selected Underlying Medical Conditions Associated with Increased Risk for Severe COVID-19 Illness - United States, 2018.
Auteurs : Hilda Razzaghi ; Yan Wang ; Hua Lu ; Katherine E. Marshall ; Nicole F. Dowling ; Gabriela Paz-Bailey ; Evelyn R. Twentyman ; Georgina Peacock ; Kurt J. GreenlundSource :
- MMWR. Morbidity and mortality weekly report [ 1545-861X ] ; 2020.
Descripteurs français
- KwdFr :
- Adulte (MeSH), Appréciation des risques (MeSH), Broncho-pneumopathie chronique obstructive (épidémiologie), Caractéristiques de l'habitat (statistiques et données numériques), Cardiopathies (épidémiologie), Diabète (épidémiologie), Humains (MeSH), Indice de gravité de la maladie (MeSH), Infections à coronavirus (épidémiologie), Insuffisance rénale chronique (épidémiologie), Obésité (épidémiologie), Pandémies (MeSH), Pneumopathie virale (épidémiologie), Population rurale (statistiques et données numériques), Population urbaine (statistiques et données numériques), Prévalence (MeSH), États-Unis (épidémiologie).
- MESH :
- statistiques et données numériques : Caractéristiques de l'habitat, Population rurale, Population urbaine.
- épidémiologie : Broncho-pneumopathie chronique obstructive, Cardiopathies, Diabète, Infections à coronavirus, Insuffisance rénale chronique, Obésité, Pneumopathie virale, États-Unis.
- Adulte, Appréciation des risques, Humains, Indice de gravité de la maladie, Pandémies, Prévalence.
- Wicri :
- geographic : États-Unis.
English descriptors
- KwdEn :
- Adult (MeSH), Coronavirus Infections (epidemiology), Diabetes Mellitus (epidemiology), Heart Diseases (epidemiology), Humans (MeSH), Obesity (epidemiology), Pandemics (MeSH), Pneumonia, Viral (epidemiology), Prevalence (MeSH), Pulmonary Disease, Chronic Obstructive (epidemiology), Renal Insufficiency, Chronic (epidemiology), Residence Characteristics (statistics & numerical data), Risk Assessment (MeSH), Rural Population (statistics & numerical data), Severity of Illness Index (MeSH), United States (epidemiology), Urban Population (statistics & numerical data).
- MESH :
- geographic , epidemiology : United States.
- epidemiology : Coronavirus Infections, Diabetes Mellitus, Heart Diseases, Obesity, Pneumonia, Viral, Pulmonary Disease, Chronic Obstructive, Renal Insufficiency, Chronic.
- statistics & numerical data : Residence Characteristics, Rural Population, Urban Population.
- Adult, Humans, Pandemics, Prevalence, Risk Assessment, Severity of Illness Index.
Abstract
Risk for severe coronavirus disease 2019 (COVID-19)-associated illness (illness requiring hospitalization, intensive care unit [ICU] admission, mechanical ventilation, or resulting in death) increases with increasing age as well as presence of underlying medical conditions that have shown strong and consistent evidence, including chronic obstructive pulmonary disease, cardiovascular disease, diabetes, chronic kidney disease, and obesity (1-4). Identifying and describing the prevalence of these conditions at the local level can help guide decision-making and efforts to prevent or control severe COVID-19-associated illness. Below state-level estimates, there is a lack of standardized publicly available data on underlying medical conditions that increase the risk for severe COVID-19-associated illness. A small area estimation approach was used to estimate county-level prevalence of selected conditions associated with severe COVID-19 disease among U.S. adults aged ≥18 years (5,6) using self-reported data from the 2018 Behavioral Risk Factor Surveillance System (BRFSS) and U.S. Census population data. The median prevalence of any underlying medical condition in residents among 3,142 counties in all 50 states and the District of Columbia (DC) was 47.2% (range = 22.0%-66.2%); counties with the highest prevalence were concentrated in the Southeast and Appalachian region. Whereas the estimated number of persons with any underlying medical condition was higher in population-dense metropolitan areas, overall prevalence was higher in rural nonmetropolitan areas. These data can provide important local-level information about the estimated number and proportion of persons with certain underlying medical conditions to help guide decisions regarding additional resource investment, and mitigation and prevention measures to slow the spread of COVID-19.
DOI: 10.15585/mmwr.mm6929a1
PubMed: 32701937
Affiliations:
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Identifying and describing the prevalence of these conditions at the local level can help guide decision-making and efforts to prevent or control severe COVID-19-associated illness. Below state-level estimates, there is a lack of standardized publicly available data on underlying medical conditions that increase the risk for severe COVID-19-associated illness. A small area estimation approach was used to estimate county-level prevalence of selected conditions associated with severe COVID-19 disease among U.S. adults aged ≥18 years (5,6) using self-reported data from the 2018 Behavioral Risk Factor Surveillance System (BRFSS) and U.S. Census population data. The median prevalence of any underlying medical condition in residents among 3,142 counties in all 50 states and the District of Columbia (DC) was 47.2% (range = 22.0%-66.2%); counties with the highest prevalence were concentrated in the Southeast and Appalachian region. Whereas the estimated number of persons with any underlying medical condition was higher in population-dense metropolitan areas, overall prevalence was higher in rural nonmetropolitan areas. These data can provide important local-level information about the estimated number and proportion of persons with certain underlying medical conditions to help guide decisions regarding additional resource investment, and mitigation and prevention measures to slow the spread of COVID-19.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32701937</PMID><DateCompleted><Year>2020</Year><Month>07</Month><Day>24</Day></DateCompleted><DateRevised><Year>2020</Year><Month>07</Month><Day>24</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1545-861X</ISSN><JournalIssue CitedMedium="Internet"><Volume>69</Volume><Issue>29</Issue><PubDate><Year>2020</Year><Month>Jul</Month><Day>24</Day></PubDate></JournalIssue><Title>MMWR. Morbidity and mortality weekly report</Title><ISOAbbreviation>MMWR Morb. Mortal. Wkly. Rep.</ISOAbbreviation></Journal><ArticleTitle>Estimated County-Level Prevalence of Selected Underlying Medical Conditions Associated with Increased Risk for Severe COVID-19 Illness - United States, 2018.</ArticleTitle><Pagination><MedlinePgn>945-950</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.15585/mmwr.mm6929a1</ELocationID><Abstract><AbstractText>Risk for severe coronavirus disease 2019 (COVID-19)-associated illness (illness requiring hospitalization, intensive care unit [ICU] admission, mechanical ventilation, or resulting in death) increases with increasing age as well as presence of underlying medical conditions that have shown strong and consistent evidence, including chronic obstructive pulmonary disease, cardiovascular disease, diabetes, chronic kidney disease, and obesity (1-4). Identifying and describing the prevalence of these conditions at the local level can help guide decision-making and efforts to prevent or control severe COVID-19-associated illness. Below state-level estimates, there is a lack of standardized publicly available data on underlying medical conditions that increase the risk for severe COVID-19-associated illness. A small area estimation approach was used to estimate county-level prevalence of selected conditions associated with severe COVID-19 disease among U.S. adults aged ≥18 years (5,6) using self-reported data from the 2018 Behavioral Risk Factor Surveillance System (BRFSS) and U.S. Census population data. The median prevalence of any underlying medical condition in residents among 3,142 counties in all 50 states and the District of Columbia (DC) was 47.2% (range = 22.0%-66.2%); counties with the highest prevalence were concentrated in the Southeast and Appalachian region. Whereas the estimated number of persons with any underlying medical condition was higher in population-dense metropolitan areas, overall prevalence was higher in rural nonmetropolitan areas. These data can provide important local-level information about the estimated number and proportion of persons with certain underlying medical conditions to help guide decisions regarding additional resource investment, and mitigation and prevention measures to slow the spread of COVID-19.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Razzaghi</LastName><ForeName>Hilda</ForeName><Initials>H</Initials></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Yan</ForeName><Initials>Y</Initials></Author><Author ValidYN="Y"><LastName>Lu</LastName><ForeName>Hua</ForeName><Initials>H</Initials></Author><Author ValidYN="Y"><LastName>Marshall</LastName><ForeName>Katherine E</ForeName><Initials>KE</Initials></Author><Author ValidYN="Y"><LastName>Dowling</LastName><ForeName>Nicole F</ForeName><Initials>NF</Initials></Author><Author ValidYN="Y"><LastName>Paz-Bailey</LastName><ForeName>Gabriela</ForeName><Initials>G</Initials></Author><Author ValidYN="Y"><LastName>Twentyman</LastName><ForeName>Evelyn R</ForeName><Initials>ER</Initials></Author><Author ValidYN="Y"><LastName>Peacock</LastName><ForeName>Georgina</ForeName><Initials>G</Initials></Author><Author ValidYN="Y"><LastName>Greenlund</LastName><ForeName>Kurt J</ForeName><Initials>KJ</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>07</Month><Day>24</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>MMWR Morb Mortal Wkly Rep</MedlineTA><NlmUniqueID>7802429</NlmUniqueID><ISSNLinking>0149-2195</ISSNLinking></MedlineJournalInfo><SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName></SupplMeshList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003920" MajorTopicYN="N">Diabetes Mellitus</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006331" MajorTopicYN="N">Heart Diseases</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009765" MajorTopicYN="N">Obesity</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">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="D015995" MajorTopicYN="N">Prevalence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D029424" MajorTopicYN="N">Pulmonary Disease, Chronic Obstructive</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D051436" MajorTopicYN="N">Renal Insufficiency, Chronic</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012111" MajorTopicYN="N">Residence Characteristics</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018570" MajorTopicYN="N">Risk Assessment</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012424" MajorTopicYN="N">Rural Population</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012720" MajorTopicYN="N">Severity of Illness Index</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014481" MajorTopicYN="N" Type="Geographic">United States</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014505" MajorTopicYN="N">Urban Population</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading></MeshHeadingList><CoiStatement>All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>7</Month><Day>24</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>7</Month><Day>24</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>7</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32701937</ArticleId><ArticleId IdType="doi">10.15585/mmwr.mm6929a1</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list></list><tree><noCountry><name sortKey="Dowling, Nicole F" sort="Dowling, Nicole F" uniqKey="Dowling N" first="Nicole F" last="Dowling">Nicole F. Dowling</name><name sortKey="Greenlund, Kurt J" sort="Greenlund, Kurt J" uniqKey="Greenlund K" first="Kurt J" last="Greenlund">Kurt J. Greenlund</name><name sortKey="Lu, Hua" sort="Lu, Hua" uniqKey="Lu H" first="Hua" last="Lu">Hua Lu</name><name sortKey="Marshall, Katherine E" sort="Marshall, Katherine E" uniqKey="Marshall K" first="Katherine E" last="Marshall">Katherine E. Marshall</name><name sortKey="Paz Bailey, Gabriela" sort="Paz Bailey, Gabriela" uniqKey="Paz Bailey G" first="Gabriela" last="Paz-Bailey">Gabriela Paz-Bailey</name><name sortKey="Peacock, Georgina" sort="Peacock, Georgina" uniqKey="Peacock G" first="Georgina" last="Peacock">Georgina Peacock</name><name sortKey="Razzaghi, Hilda" sort="Razzaghi, Hilda" uniqKey="Razzaghi H" first="Hilda" last="Razzaghi">Hilda Razzaghi</name><name sortKey="Twentyman, Evelyn R" sort="Twentyman, Evelyn R" uniqKey="Twentyman E" first="Evelyn R" last="Twentyman">Evelyn R. Twentyman</name><name sortKey="Wang, Yan" sort="Wang, Yan" uniqKey="Wang Y" first="Yan" last="Wang">Yan Wang</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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