US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysis.
Identifieur interne : 000180 ( Main/Exploration ); précédent : 000179; suivant : 000181US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysis.
Auteurs : Taylor Chin [États-Unis] ; Rebecca Kahn [États-Unis] ; Ruoran Li [États-Unis] ; Jarvis T. Chen [États-Unis] ; Nancy Krieger [États-Unis] ; Caroline O. Buckee [États-Unis] ; Satchit Balsari [États-Unis] ; Mathew V. Kiang [États-Unis]Source :
- BMJ open [ 2044-6055 ] ; 2020.
Descripteurs français
- KwdFr :
- Adulte (MeSH), Analyse de regroupements (MeSH), Analyse de survie (MeSH), Appréciation des risques (méthodes), Appréciation des risques (statistiques et données numériques), Betacoronavirus (MeSH), Caractéristiques familiales (MeSH), Ethnies (statistiques et données numériques), Facteurs de risque (MeSH), Facteurs âges (MeSH), Femelle (MeSH), Humains (MeSH), Indice de gravité de la maladie (MeSH), Infections à coronavirus (diagnostic), Infections à coronavirus (épidémiologie), Mâle (MeSH), Pandémies (MeSH), Pauvreté (statistiques et données numériques), Pneumopathie virale (diagnostic), Pneumopathie virale (épidémiologie), Prévalence (MeSH), Santé publique (méthodes), Santé publique (statistiques et données numériques), Sujet âgé (MeSH), États-Unis (MeSH), Études transversales (MeSH).
- MESH :
- diagnostic : Infections à coronavirus, Pneumopathie virale.
- méthodes : Appréciation des risques, Santé publique.
- statistiques et données numériques : Appréciation des risques, Ethnies, Pauvreté, Santé publique.
- épidémiologie : Infections à coronavirus, Pneumopathie virale.
- Adulte, Analyse de regroupements, Analyse de survie, Betacoronavirus, Caractéristiques familiales, Facteurs de risque, Facteurs âges, Femelle, Humains, Indice de gravité de la maladie, Mâle, Pandémies, Prévalence, Sujet âgé, États-Unis, Études transversales.
- Wicri :
- geographic : États-Unis.
English descriptors
- KwdEn :
- Adult (MeSH), Age Factors (MeSH), Aged (MeSH), Betacoronavirus (MeSH), COVID-19 (MeSH), Cluster Analysis (MeSH), Coronavirus Infections (diagnosis), Coronavirus Infections (epidemiology), Cross-Sectional Studies (MeSH), Ethnic Groups (statistics & numerical data), Family Characteristics (MeSH), Female (MeSH), Humans (MeSH), Male (MeSH), Pandemics (MeSH), Pneumonia, Viral (diagnosis), Pneumonia, Viral (epidemiology), Poverty (statistics & numerical data), Prevalence (MeSH), Public Health (methods), Public Health (statistics & numerical data), Risk Assessment (methods), Risk Assessment (statistics & numerical data), Risk Factors (MeSH), SARS-CoV-2 (MeSH), Severity of Illness Index (MeSH), Survival Analysis (MeSH), United States (MeSH).
- MESH :
- geographic : United States.
- diagnosis : Coronavirus Infections, Pneumonia, Viral.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- methods : Public Health, Risk Assessment.
- statistics & numerical data : Ethnic Groups, Poverty, Public Health, Risk Assessment.
- Adult, Age Factors, Aged, Betacoronavirus, COVID-19, Cluster Analysis, Cross-Sectional Studies, Family Characteristics, Female, Humans, Male, Pandemics, Prevalence, Risk Factors, SARS-CoV-2, Severity of Illness Index, Survival Analysis.
Abstract
OBJECTIVES
To illustrate the intersections of, and intercounty variation in, individual, household and community factors that influence the impact of COVID-19 on US counties and their ability to respond.
DESIGN
We identified key individual, household and community characteristics influencing COVID-19 risks of infection and survival, guided by international experiences and consideration of epidemiological parameters of importance. Using publicly available data, we developed an open-access online tool that allows county-specific querying and mapping of risk factors. As an illustrative example, we assess the pairwise intersections of age (individual level), poverty (household level) and prevalence of group homes (community-level) in US counties. We also examine how these factors intersect with the proportion of the population that is people of colour (ie, not non-Hispanic white), a metric that reflects histories of US race relations. We defined 'high' risk counties as those above the 75th percentile. This threshold can be changed using the online tool.
SETTING
US counties.
PARTICIPANTS
Analyses are based on publicly available county-level data from the Area Health Resources Files, American Community Survey, Centers for Disease Control and Prevention Atlas file, National Center for Health Statistic and RWJF Community Health Rankings.
RESULTS
Our findings demonstrate significant intercounty variation in the distribution of individual, household and community characteristics that affect risks of infection, severe disease or mortality from COVID-19. About 9% of counties, affecting 10 million residents, are in higher risk categories for both age and group quarters. About 14% of counties, affecting 31 million residents, have both high levels of poverty and a high proportion of people of colour.
CONCLUSION
Federal and state governments will benefit from recognising high intrastate, intercounty variation in population risks and response capacity. Equitable responses to the pandemic require strategies to protect those in counties at highest risk of adverse COVID-19 outcomes and their social and economic impacts.
DOI: 10.1136/bmjopen-2020-039886
PubMed: 32873684
PubMed Central: PMC7467554
Affiliations:
- États-Unis
- Californie, Massachusetts
- Cambridge (Massachusetts), Stanford (Californie)
- Université Harvard, Université Stanford
Links toward previous steps (curation, corpus...)
Le document en format XML
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Chen</name><affiliation wicri:level="2"><nlm:affiliation>Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Boston, MA, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Boston, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author><author><name sortKey="Krieger, Nancy" sort="Krieger, Nancy" uniqKey="Krieger N" first="Nancy" last="Krieger">Nancy Krieger</name><affiliation wicri:level="2"><nlm:affiliation>Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Boston, MA, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Boston, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author><author><name sortKey="Buckee, Caroline O" sort="Buckee, Caroline O" uniqKey="Buckee C" first="Caroline O" last="Buckee">Caroline O. Buckee</name><affiliation wicri:level="2"><nlm:affiliation>Epidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Epidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author><author><name sortKey="Balsari, Satchit" sort="Balsari, Satchit" uniqKey="Balsari S" first="Satchit" last="Balsari">Satchit Balsari</name><affiliation wicri:level="2"><nlm:affiliation>Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation><affiliation wicri:level="4"><nlm:affiliation>FXB Center for Health and Human Rights, Harvard University, Cambridge, Massachusetts, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>FXB Center for Health and Human Rights, Harvard University, Cambridge, Massachusetts</wicri:regionArea><placeName><region type="state">Massachusetts</region><settlement type="city">Cambridge (Massachusetts)</settlement></placeName><orgName type="university">Université Harvard</orgName></affiliation></author><author><name sortKey="Kiang, Mathew V" sort="Kiang, Mathew V" uniqKey="Kiang M" first="Mathew V" last="Kiang">Mathew V. Kiang</name><affiliation wicri:level="4"><nlm:affiliation>FXB Center for Health and Human Rights, Harvard University, Cambridge, Massachusetts, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>FXB Center for Health and Human Rights, Harvard University, Cambridge, Massachusetts</wicri:regionArea><placeName><region type="state">Massachusetts</region><settlement type="city">Cambridge (Massachusetts)</settlement></placeName><orgName type="university">Université Harvard</orgName></affiliation><affiliation wicri:level="4"><nlm:affiliation>Center for Population Health Sciences, Stanford University, Palo Alto, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Population Health Sciences, Stanford University, Palo Alto, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation><affiliation wicri:level="4"><nlm:affiliation>Epidemiology and Population Health, Stanford University, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Epidemiology and Population Health, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author></analytic><series><title level="j">BMJ open</title><idno type="eISSN">2044-6055</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult (MeSH)</term><term>Age Factors (MeSH)</term><term>Aged (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>COVID-19 (MeSH)</term><term>Cluster Analysis (MeSH)</term><term>Coronavirus Infections (diagnosis)</term><term>Coronavirus Infections (epidemiology)</term><term>Cross-Sectional Studies (MeSH)</term><term>Ethnic Groups (statistics & numerical data)</term><term>Family Characteristics (MeSH)</term><term>Female (MeSH)</term><term>Humans (MeSH)</term><term>Male (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (diagnosis)</term><term>Pneumonia, Viral (epidemiology)</term><term>Poverty (statistics & numerical data)</term><term>Prevalence (MeSH)</term><term>Public Health (methods)</term><term>Public Health (statistics & numerical data)</term><term>Risk Assessment (methods)</term><term>Risk Assessment (statistics & numerical data)</term><term>Risk Factors (MeSH)</term><term>SARS-CoV-2 (MeSH)</term><term>Severity of Illness Index (MeSH)</term><term>Survival Analysis (MeSH)</term><term>United States (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte (MeSH)</term><term>Analyse de regroupements (MeSH)</term><term>Analyse de survie (MeSH)</term><term>Appréciation des risques (méthodes)</term><term>Appréciation des risques (statistiques et données numériques)</term><term>Betacoronavirus (MeSH)</term><term>Caractéristiques familiales (MeSH)</term><term>Ethnies (statistiques et données numériques)</term><term>Facteurs de risque (MeSH)</term><term>Facteurs âges (MeSH)</term><term>Femelle (MeSH)</term><term>Humains (MeSH)</term><term>Indice de gravité de la maladie (MeSH)</term><term>Infections à coronavirus (diagnostic)</term><term>Infections à coronavirus (épidémiologie)</term><term>Mâle (MeSH)</term><term>Pandémies (MeSH)</term><term>Pauvreté (statistiques et données numériques)</term><term>Pneumopathie virale (diagnostic)</term><term>Pneumopathie virale (épidémiologie)</term><term>Prévalence (MeSH)</term><term>Santé publique (méthodes)</term><term>Santé publique (statistiques et données numériques)</term><term>Sujet âgé (MeSH)</term><term>États-Unis (MeSH)</term><term>Études transversales (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>United States</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Public Health</term><term>Risk Assessment</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Appréciation des risques</term><term>Santé publique</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Ethnic Groups</term><term>Poverty</term><term>Public Health</term><term>Risk Assessment</term></keywords><keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Appréciation des risques</term><term>Ethnies</term><term>Pauvreté</term><term>Santé publique</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Age Factors</term><term>Aged</term><term>Betacoronavirus</term><term>COVID-19</term><term>Cluster Analysis</term><term>Cross-Sectional Studies</term><term>Family Characteristics</term><term>Female</term><term>Humans</term><term>Male</term><term>Pandemics</term><term>Prevalence</term><term>Risk Factors</term><term>SARS-CoV-2</term><term>Severity of Illness Index</term><term>Survival Analysis</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte</term><term>Analyse de regroupements</term><term>Analyse de survie</term><term>Betacoronavirus</term><term>Caractéristiques familiales</term><term>Facteurs de risque</term><term>Facteurs âges</term><term>Femelle</term><term>Humains</term><term>Indice de gravité de la maladie</term><term>Mâle</term><term>Pandémies</term><term>Prévalence</term><term>Sujet âgé</term><term>États-Unis</term><term>Études transversales</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>États-Unis</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>OBJECTIVES</b></p><p>To illustrate the intersections of, and intercounty variation in, individual, household and community factors that influence the impact of COVID-19 on US counties and their ability to respond.</p></div><div type="abstract" xml:lang="en"><p><b>DESIGN</b></p><p>We identified key individual, household and community characteristics influencing COVID-19 risks of infection and survival, guided by international experiences and consideration of epidemiological parameters of importance. Using publicly available data, we developed an open-access online tool that allows county-specific querying and mapping of risk factors. As an illustrative example, we assess the pairwise intersections of age (individual level), poverty (household level) and prevalence of group homes (community-level) in US counties. We also examine how these factors intersect with the proportion of the population that is people of colour (ie, not non-Hispanic white), a metric that reflects histories of US race relations. We defined 'high' risk counties as those above the 75th percentile. This threshold can be changed using the online tool.</p></div><div type="abstract" xml:lang="en"><p><b>SETTING</b></p><p>US counties.</p></div><div type="abstract" xml:lang="en"><p><b>PARTICIPANTS</b></p><p>Analyses are based on publicly available county-level data from the Area Health Resources Files, American Community Survey, Centers for Disease Control and Prevention Atlas file, National Center for Health Statistic and RWJF Community Health Rankings.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>Our findings demonstrate significant intercounty variation in the distribution of individual, household and community characteristics that affect risks of infection, severe disease or mortality from COVID-19. About 9% of counties, affecting 10 million residents, are in higher risk categories for both age and group quarters. About 14% of counties, affecting 31 million residents, have both high levels of poverty and a high proportion of people of colour.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSION</b></p><p>Federal and state governments will benefit from recognising high intrastate, intercounty variation in population risks and response capacity. Equitable responses to the pandemic require strategies to protect those in counties at highest risk of adverse COVID-19 outcomes and their social and economic impacts.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32873684</PMID><DateCompleted><Year>2020</Year><Month>09</Month><Day>16</Day></DateCompleted><DateRevised><Year>2020</Year><Month>12</Month><Day>18</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2044-6055</ISSN><JournalIssue CitedMedium="Internet"><Volume>10</Volume><Issue>9</Issue><PubDate><Year>2020</Year><Month>Sep</Month><Day>01</Day></PubDate></JournalIssue><Title>BMJ open</Title><ISOAbbreviation>BMJ Open</ISOAbbreviation></Journal><ArticleTitle>US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysis.</ArticleTitle><Pagination><MedlinePgn>e039886</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1136/bmjopen-2020-039886</ELocationID><Abstract><AbstractText Label="OBJECTIVES" NlmCategory="OBJECTIVE">To illustrate the intersections of, and intercounty variation in, individual, household and community factors that influence the impact of COVID-19 on US counties and their ability to respond.</AbstractText><AbstractText Label="DESIGN" NlmCategory="METHODS">We identified key individual, household and community characteristics influencing COVID-19 risks of infection and survival, guided by international experiences and consideration of epidemiological parameters of importance. Using publicly available data, we developed an open-access online tool that allows county-specific querying and mapping of risk factors. As an illustrative example, we assess the pairwise intersections of age (individual level), poverty (household level) and prevalence of group homes (community-level) in US counties. We also examine how these factors intersect with the proportion of the population that is people of colour (ie, not non-Hispanic white), a metric that reflects histories of US race relations. We defined 'high' risk counties as those above the 75th percentile. This threshold can be changed using the online tool.</AbstractText><AbstractText Label="SETTING" NlmCategory="METHODS">US counties.</AbstractText><AbstractText Label="PARTICIPANTS" NlmCategory="METHODS">Analyses are based on publicly available county-level data from the Area Health Resources Files, American Community Survey, Centers for Disease Control and Prevention Atlas file, National Center for Health Statistic and RWJF Community Health Rankings.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Our findings demonstrate significant intercounty variation in the distribution of individual, household and community characteristics that affect risks of infection, severe disease or mortality from COVID-19. About 9% of counties, affecting 10 million residents, are in higher risk categories for both age and group quarters. About 14% of counties, affecting 31 million residents, have both high levels of poverty and a high proportion of people of colour.</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">Federal and state governments will benefit from recognising high intrastate, intercounty variation in population risks and response capacity. Equitable responses to the pandemic require strategies to protect those in counties at highest risk of adverse COVID-19 outcomes and their social and economic impacts.</AbstractText><CopyrightInformation>© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Chin</LastName><ForeName>Taylor</ForeName><Initials>T</Initials><Identifier Source="ORCID">http://orcid.org/0000-0001-6852-1169</Identifier><AffiliationInfo><Affiliation>Epidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA taylorchin@g.harvard.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kahn</LastName><ForeName>Rebecca</ForeName><Initials>R</Initials><Identifier Source="ORCID">http://orcid.org/0000-0001-9511-6142</Identifier><AffiliationInfo><Affiliation>Epidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Ruoran</ForeName><Initials>R</Initials><Identifier Source="ORCID">http://orcid.org/0000-0001-7575-2758</Identifier><AffiliationInfo><Affiliation>Epidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chen</LastName><ForeName>Jarvis T</ForeName><Initials>JT</Initials><Identifier Source="ORCID">http://orcid.org/0000-0002-7412-1783</Identifier><AffiliationInfo><Affiliation>Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Boston, MA, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Krieger</LastName><ForeName>Nancy</ForeName><Initials>N</Initials><Identifier Source="ORCID">http://orcid.org/0000-0002-4815-5947</Identifier><AffiliationInfo><Affiliation>Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Boston, MA, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Buckee</LastName><ForeName>Caroline O</ForeName><Initials>CO</Initials><AffiliationInfo><Affiliation>Epidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Balsari</LastName><ForeName>Satchit</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>FXB Center for Health and Human Rights, Harvard University, Cambridge, Massachusetts, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kiang</LastName><ForeName>Mathew V</ForeName><Initials>MV</Initials><Identifier Source="ORCID">http://orcid.org/0000-0001-9198-150X</Identifier><AffiliationInfo><Affiliation>FXB Center for Health and Human Rights, Harvard University, Cambridge, Massachusetts, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Population Health Sciences, Stanford University, Palo Alto, California, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Epidemiology and Population Health, Stanford University, Stanford, California, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>09</Month><Day>01</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMJ Open</MedlineTA><NlmUniqueID>101552874</NlmUniqueID><ISSNLinking>2044-6055</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000367" MajorTopicYN="Y">Age Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="N">Betacoronavirus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="N">COVID-19</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016000" MajorTopicYN="N">Cluster Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="Y">Coronavirus Infections</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="N">diagnosis</QualifierName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003430" MajorTopicYN="N">Cross-Sectional Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005006" MajorTopicYN="N">Ethnic Groups</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005191" MajorTopicYN="Y">Family Characteristics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="Y">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="Y">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="N">diagnosis</QualifierName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011203" MajorTopicYN="N">Poverty</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015995" MajorTopicYN="N">Prevalence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011634" MajorTopicYN="Y">Public Health</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018570" MajorTopicYN="N">Risk Assessment</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012307" MajorTopicYN="N">Risk Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086402" MajorTopicYN="N">SARS-CoV-2</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012720" MajorTopicYN="N">Severity of Illness Index</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016019" MajorTopicYN="Y">Survival Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014481" MajorTopicYN="N" Type="Geographic">United States</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">epidemiology</Keyword><Keyword MajorTopicYN="N">health policy</Keyword><Keyword MajorTopicYN="N">public health</Keyword></KeywordList><CoiStatement>Competing interests: None declared.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>9</Month><Day>3</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>9</Month><Day>3</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>9</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32873684</ArticleId><ArticleId IdType="pii">bmjopen-2020-039886</ArticleId><ArticleId IdType="doi">10.1136/bmjopen-2020-039886</ArticleId><ArticleId 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