Racial and ethnic disparities in SARS-CoV-2 pandemic: analysis of a COVID-19 observational registry for a diverse US metropolitan population.
Identifieur interne : 000295 ( Main/Exploration ); précédent : 000294; suivant : 000296Racial and ethnic disparities in SARS-CoV-2 pandemic: analysis of a COVID-19 observational registry for a diverse US metropolitan population.
Auteurs : Farhaan S. Vahidy [Sao Tomé-et-Principe, États-Unis] ; Juan Carlos Nicolas [États-Unis] ; Jennifer R. Meeks [États-Unis] ; Osman Khan [États-Unis] ; Alan Pan [États-Unis] ; Stephen L. Jones [États-Unis] ; Faisal Masud [États-Unis] ; H Dirk Sostman [États-Unis] ; Robert Phillips [États-Unis] ; Julia D. Andrieni [États-Unis] ; Bita A. Kash [États-Unis] ; Khurram Nasir [États-Unis]Source :
- BMJ open [ 2044-6055 ] ; 2020.
Descripteurs français
- KwdFr :
- Adulte (MeSH), Adulte d'âge moyen (MeSH), Afro-Américains (statistiques et données numériques), Betacoronavirus (MeSH), Comorbidité (MeSH), Densité de population (MeSH), Disparités de l'état de santé (MeSH), Enregistrements (MeSH), Facteurs raciaux (MeSH), Facteurs socioéconomiques (MeSH), Femelle (MeSH), Hispano-américain (statistiques et données numériques), Humains (MeSH), Infections à coronavirus (ethnologie), Modèles logistiques (MeSH), Mâle (MeSH), Pandémies (MeSH), Pneumopathie virale (ethnologie), Population d'origine européenne (statistiques et données numériques), Sujet âgé (MeSH), Surveillance de la population (MeSH), Texas (épidémiologie), Études transversales (MeSH).
- MESH :
- ethnologie : Infections à coronavirus, Pneumopathie virale.
- statistiques et données numériques : Afro-Américains, Hispano-américain, Population d'origine européenne.
- épidémiologie : Texas.
- Adulte, Adulte d'âge moyen, Betacoronavirus, Comorbidité, Densité de population, Disparités de l'état de santé, Enregistrements, Facteurs raciaux, Facteurs socioéconomiques, Femelle, Humains, Modèles logistiques, Mâle, Pandémies, Sujet âgé, Surveillance de la population, Études transversales.
English descriptors
- KwdEn :
- Adult (MeSH), African Americans (statistics & numerical data), Aged (MeSH), Betacoronavirus (MeSH), Comorbidity (MeSH), Coronavirus Infections (ethnology), Cross-Sectional Studies (MeSH), European Continental Ancestry Group (statistics & numerical data), Female (MeSH), Health Status Disparities (MeSH), Hispanic Americans (statistics & numerical data), Humans (MeSH), Logistic Models (MeSH), Male (MeSH), Middle Aged (MeSH), Pandemics (MeSH), Pneumonia, Viral (ethnology), Population Density (MeSH), Population Surveillance (MeSH), Race Factors (MeSH), Registries (MeSH), Socioeconomic Factors (MeSH), Texas (epidemiology).
- MESH :
- geographic , epidemiology : Texas.
- ethnology : Coronavirus Infections, Pneumonia, Viral.
- statistics & numerical data : African Americans, European Continental Ancestry Group, Hispanic Americans.
- Adult, Aged, Betacoronavirus, Comorbidity, Cross-Sectional Studies, Female, Health Status Disparities, Humans, Logistic Models, Male, Middle Aged, Pandemics, Population Density, Population Surveillance, Race Factors, Registries, Socioeconomic Factors.
Abstract
INTRODUCTION
Data on race and ethnic disparities for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are limited. We analysed sociodemographic factors associated with higher likelihood of SARS-CoV-2 infection and explore mediating pathways for race and ethnic disparities in the SARS-CoV-2 pandemic.
METHODS
This is a cross-sectional analysis of the COVID-19 Surveillance and Outcomes Registry, which captures data for a large healthcare system, comprising one central tertiary care hospital, seven large community hospitals and an expansive ambulatory/emergency care network in the Greater Houston area. Nasopharyngeal samples for individuals inclusive of all ages, races, ethnicities and sex were tested for SARS-CoV-2. We analysed sociodemographic (age, sex, race, ethnicity, household income, residence population density) and comorbidity (Charlson Comorbidity Index, hypertension, diabetes, obesity) factors. Multivariable logistic regression models were fitted to provide adjusted OR (aOR) and 95% CI for likelihood of a positive SARS-CoV-2 test. Structural equation modelling (SEM) framework was used to explore three mediation pathways (low income, high population density, high comorbidity burden) for the association between non-Hispanic black (NHB) race, Hispanic ethnicity and SARS-CoV-2 infection.
RESULTS
Among 20 228 tested individuals, 1551 (7.7%) tested positive. The overall mean (SD) age was 51.1 (19.0) years, 62% were females, 22% were black and 18% were Hispanic. NHB and Hispanic ethnicity were associated with lower socioeconomic status and higher population density residence. In the fully adjusted model, NHB (vs non-Hispanic white; aOR, 2.23, CI 1.90 to 2.60) and Hispanic ethnicity (vs non-Hispanic; aOR, 1.95, CI 1.72 to 2.20) had a higher likelihood of infection. Older individuals and males were also at higher risk of infection. The SEM framework demonstrated a significant indirect effect of NHB and Hispanic ethnicity on SARS-CoV-2 infection mediated via a pathway including residence in densely populated zip code.
CONCLUSIONS
There is strong evidence of race and ethnic disparities in the SARS-CoV-2 pandemic that are potentially mediated through unique social determinants of health.
DOI: 10.1136/bmjopen-2020-039849
PubMed: 32784264
PubMed Central: PMC7418666
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Racial and ethnic disparities in SARS-CoV-2 pandemic: analysis of a COVID-19 observational registry for a diverse US metropolitan population.</title>
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<author><name sortKey="Nicolas, Juan Carlos" sort="Nicolas, Juan Carlos" uniqKey="Nicolas J" first="Juan Carlos" last="Nicolas">Juan Carlos Nicolas</name>
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<author><name sortKey="Khan, Osman" sort="Khan, Osman" uniqKey="Khan O" first="Osman" last="Khan">Osman Khan</name>
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<author><name sortKey="Pan, Alan" sort="Pan, Alan" uniqKey="Pan A" first="Alan" last="Pan">Alan Pan</name>
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<author><name sortKey="Jones, Stephen L" sort="Jones, Stephen L" uniqKey="Jones S" first="Stephen L" last="Jones">Stephen L. Jones</name>
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</author>
<author><name sortKey="Kash, Bita A" sort="Kash, Bita A" uniqKey="Kash B" first="Bita A" last="Kash">Bita A. Kash</name>
<affiliation wicri:level="2"><nlm:affiliation>Center for Outcomes Research, Houston Methodist Research Institute, Houston, Texas, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Center for Outcomes Research, Houston Methodist Research Institute, Houston, Texas</wicri:regionArea>
<placeName><region type="state">Texas</region>
</placeName>
</affiliation>
<affiliation wicri:level="2"><nlm:affiliation>Weill Cornell Medicine, New York, New York, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Weill Cornell Medicine, New York, New York</wicri:regionArea>
<placeName><region type="state">État de New York</region>
</placeName>
</affiliation>
<affiliation wicri:level="2"><nlm:affiliation>Texas A&M University School of Rural Public Health, College Station, Texas, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Texas A&M University School of Rural Public Health, College Station, Texas</wicri:regionArea>
<placeName><region type="state">Texas</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Nasir, Khurram" sort="Nasir, Khurram" uniqKey="Nasir K" first="Khurram" last="Nasir">Khurram Nasir</name>
<affiliation wicri:level="2"><nlm:affiliation>Center for Outcomes Research, Houston Methodist Research Institute, Houston, Texas, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Center for Outcomes Research, Houston Methodist Research Institute, Houston, Texas</wicri:regionArea>
<placeName><region type="state">Texas</region>
</placeName>
</affiliation>
<affiliation wicri:level="2"><nlm:affiliation>Department of Cardiology, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX, United States.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Cardiology, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX</wicri:regionArea>
<placeName><region type="state">Texas</region>
</placeName>
</affiliation>
</author>
</analytic>
<series><title level="j">BMJ open</title>
<idno type="eISSN">2044-6055</idno>
<imprint><date when="2020" type="published">2020</date>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult (MeSH)</term>
<term>African Americans (statistics & numerical data)</term>
<term>Aged (MeSH)</term>
<term>Betacoronavirus (MeSH)</term>
<term>Comorbidity (MeSH)</term>
<term>Coronavirus Infections (ethnology)</term>
<term>Cross-Sectional Studies (MeSH)</term>
<term>European Continental Ancestry Group (statistics & numerical data)</term>
<term>Female (MeSH)</term>
<term>Health Status Disparities (MeSH)</term>
<term>Hispanic Americans (statistics & numerical data)</term>
<term>Humans (MeSH)</term>
<term>Logistic Models (MeSH)</term>
<term>Male (MeSH)</term>
<term>Middle Aged (MeSH)</term>
<term>Pandemics (MeSH)</term>
<term>Pneumonia, Viral (ethnology)</term>
<term>Population Density (MeSH)</term>
<term>Population Surveillance (MeSH)</term>
<term>Race Factors (MeSH)</term>
<term>Registries (MeSH)</term>
<term>Socioeconomic Factors (MeSH)</term>
<term>Texas (epidemiology)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Adulte (MeSH)</term>
<term>Adulte d'âge moyen (MeSH)</term>
<term>Afro-Américains (statistiques et données numériques)</term>
<term>Betacoronavirus (MeSH)</term>
<term>Comorbidité (MeSH)</term>
<term>Densité de population (MeSH)</term>
<term>Disparités de l'état de santé (MeSH)</term>
<term>Enregistrements (MeSH)</term>
<term>Facteurs raciaux (MeSH)</term>
<term>Facteurs socioéconomiques (MeSH)</term>
<term>Femelle (MeSH)</term>
<term>Hispano-américain (statistiques et données numériques)</term>
<term>Humains (MeSH)</term>
<term>Infections à coronavirus (ethnologie)</term>
<term>Modèles logistiques (MeSH)</term>
<term>Mâle (MeSH)</term>
<term>Pandémies (MeSH)</term>
<term>Pneumopathie virale (ethnologie)</term>
<term>Population d'origine européenne (statistiques et données numériques)</term>
<term>Sujet âgé (MeSH)</term>
<term>Surveillance de la population (MeSH)</term>
<term>Texas (épidémiologie)</term>
<term>Études transversales (MeSH)</term>
</keywords>
<keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Texas</term>
</keywords>
<keywords scheme="MESH" qualifier="ethnologie" xml:lang="fr"><term>Infections à coronavirus</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" qualifier="ethnology" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>African Americans</term>
<term>European Continental Ancestry Group</term>
<term>Hispanic Americans</term>
</keywords>
<keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Afro-Américains</term>
<term>Hispano-américain</term>
<term>Population d'origine européenne</term>
</keywords>
<keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Texas</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Adult</term>
<term>Aged</term>
<term>Betacoronavirus</term>
<term>Comorbidity</term>
<term>Cross-Sectional Studies</term>
<term>Female</term>
<term>Health Status Disparities</term>
<term>Humans</term>
<term>Logistic Models</term>
<term>Male</term>
<term>Middle Aged</term>
<term>Pandemics</term>
<term>Population Density</term>
<term>Population Surveillance</term>
<term>Race Factors</term>
<term>Registries</term>
<term>Socioeconomic Factors</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Adulte</term>
<term>Adulte d'âge moyen</term>
<term>Betacoronavirus</term>
<term>Comorbidité</term>
<term>Densité de population</term>
<term>Disparités de l'état de santé</term>
<term>Enregistrements</term>
<term>Facteurs raciaux</term>
<term>Facteurs socioéconomiques</term>
<term>Femelle</term>
<term>Humains</term>
<term>Modèles logistiques</term>
<term>Mâle</term>
<term>Pandémies</term>
<term>Sujet âgé</term>
<term>Surveillance de la population</term>
<term>Études transversales</term>
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<front><div type="abstract" xml:lang="en"><p><b>INTRODUCTION</b>
</p>
<p>Data on race and ethnic disparities for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are limited. We analysed sociodemographic factors associated with higher likelihood of SARS-CoV-2 infection and explore mediating pathways for race and ethnic disparities in the SARS-CoV-2 pandemic.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>METHODS</b>
</p>
<p>This is a cross-sectional analysis of the COVID-19 Surveillance and Outcomes Registry, which captures data for a large healthcare system, comprising one central tertiary care hospital, seven large community hospitals and an expansive ambulatory/emergency care network in the Greater Houston area. Nasopharyngeal samples for individuals inclusive of all ages, races, ethnicities and sex were tested for SARS-CoV-2. We analysed sociodemographic (age, sex, race, ethnicity, household income, residence population density) and comorbidity (Charlson Comorbidity Index, hypertension, diabetes, obesity) factors. Multivariable logistic regression models were fitted to provide adjusted OR (aOR) and 95% CI for likelihood of a positive SARS-CoV-2 test. Structural equation modelling (SEM) framework was used to explore three mediation pathways (low income, high population density, high comorbidity burden) for the association between non-Hispanic black (NHB) race, Hispanic ethnicity and SARS-CoV-2 infection.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>RESULTS</b>
</p>
<p>Among 20 228 tested individuals, 1551 (7.7%) tested positive. The overall mean (SD) age was 51.1 (19.0) years, 62% were females, 22% were black and 18% were Hispanic. NHB and Hispanic ethnicity were associated with lower socioeconomic status and higher population density residence. In the fully adjusted model, NHB (vs non-Hispanic white; aOR, 2.23, CI 1.90 to 2.60) and Hispanic ethnicity (vs non-Hispanic; aOR, 1.95, CI 1.72 to 2.20) had a higher likelihood of infection. Older individuals and males were also at higher risk of infection. The SEM framework demonstrated a significant indirect effect of NHB and Hispanic ethnicity on SARS-CoV-2 infection mediated via a pathway including residence in densely populated zip code.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b>
</p>
<p>There is strong evidence of race and ethnic disparities in the SARS-CoV-2 pandemic that are potentially mediated through unique social determinants of health.</p>
</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32784264</PMID>
<DateCompleted><Year>2020</Year>
<Month>09</Month>
<Day>08</Day>
</DateCompleted>
<DateRevised><Year>2020</Year>
<Month>09</Month>
<Day>08</Day>
</DateRevised>
<Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2044-6055</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>10</Volume>
<Issue>8</Issue>
<PubDate><Year>2020</Year>
<Month>08</Month>
<Day>11</Day>
</PubDate>
</JournalIssue>
<Title>BMJ open</Title>
<ISOAbbreviation>BMJ Open</ISOAbbreviation>
</Journal>
<ArticleTitle>Racial and ethnic disparities in SARS-CoV-2 pandemic: analysis of a COVID-19 observational registry for a diverse US metropolitan population.</ArticleTitle>
<Pagination><MedlinePgn>e039849</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1136/bmjopen-2020-039849</ELocationID>
<Abstract><AbstractText Label="INTRODUCTION">Data on race and ethnic disparities for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are limited. We analysed sociodemographic factors associated with higher likelihood of SARS-CoV-2 infection and explore mediating pathways for race and ethnic disparities in the SARS-CoV-2 pandemic.</AbstractText>
<AbstractText Label="METHODS">This is a cross-sectional analysis of the COVID-19 Surveillance and Outcomes Registry, which captures data for a large healthcare system, comprising one central tertiary care hospital, seven large community hospitals and an expansive ambulatory/emergency care network in the Greater Houston area. Nasopharyngeal samples for individuals inclusive of all ages, races, ethnicities and sex were tested for SARS-CoV-2. We analysed sociodemographic (age, sex, race, ethnicity, household income, residence population density) and comorbidity (Charlson Comorbidity Index, hypertension, diabetes, obesity) factors. Multivariable logistic regression models were fitted to provide adjusted OR (aOR) and 95% CI for likelihood of a positive SARS-CoV-2 test. Structural equation modelling (SEM) framework was used to explore three mediation pathways (low income, high population density, high comorbidity burden) for the association between non-Hispanic black (NHB) race, Hispanic ethnicity and SARS-CoV-2 infection.</AbstractText>
<AbstractText Label="RESULTS">Among 20 228 tested individuals, 1551 (7.7%) tested positive. The overall mean (SD) age was 51.1 (19.0) years, 62% were females, 22% were black and 18% were Hispanic. NHB and Hispanic ethnicity were associated with lower socioeconomic status and higher population density residence. In the fully adjusted model, NHB (vs non-Hispanic white; aOR, 2.23, CI 1.90 to 2.60) and Hispanic ethnicity (vs non-Hispanic; aOR, 1.95, CI 1.72 to 2.20) had a higher likelihood of infection. Older individuals and males were also at higher risk of infection. The SEM framework demonstrated a significant indirect effect of NHB and Hispanic ethnicity on SARS-CoV-2 infection mediated via a pathway including residence in densely populated zip code.</AbstractText>
<AbstractText Label="CONCLUSIONS">There is strong evidence of race and ethnic disparities in the SARS-CoV-2 pandemic that are potentially mediated through unique social determinants of health.</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>Vahidy</LastName>
<ForeName>Farhaan S</ForeName>
<Initials>FS</Initials>
<Identifier Source="ORCID">0000-0002-3464-2111</Identifier>
<AffiliationInfo><Affiliation>Center for Outcomes Research, Houston Methodist Research Institute, Houston, Texas, USA fvahidy@houstonmethodist.org.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Houston Methodist Neurological Institute, Houston, Texas, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Nicolas</LastName>
<ForeName>Juan Carlos</ForeName>
<Initials>JC</Initials>
<AffiliationInfo><Affiliation>Center for Outcomes Research, Houston Methodist Research Institute, Houston, Texas, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Meeks</LastName>
<ForeName>Jennifer R</ForeName>
<Initials>JR</Initials>
<AffiliationInfo><Affiliation>Center for Outcomes Research, Houston Methodist Research Institute, Houston, Texas, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Khan</LastName>
<ForeName>Osman</ForeName>
<Initials>O</Initials>
<AffiliationInfo><Affiliation>Center for Outcomes Research, Houston Methodist Research Institute, Houston, Texas, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Pan</LastName>
<ForeName>Alan</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Center for Outcomes Research, Houston Methodist Research Institute, Houston, Texas, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Jones</LastName>
<ForeName>Stephen L</ForeName>
<Initials>SL</Initials>
<AffiliationInfo><Affiliation>Center for Outcomes Research, Houston Methodist Research Institute, Houston, Texas, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Weill Cornell Medicine, New York, New York, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Surgery, Houston Methodist Hospital, Houston, TX, United States.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Masud</LastName>
<ForeName>Faisal</ForeName>
<Initials>F</Initials>
<AffiliationInfo><Affiliation>Weill Cornell Medicine, New York, New York, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Anesthesiology and Critical Care, Houston Methodist Hospital, Houston, TX, United States.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Sostman</LastName>
<ForeName>H Dirk</ForeName>
<Initials>HD</Initials>
<AffiliationInfo><Affiliation>Weill Cornell Medicine, New York, New York, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Houston Methodist Research Institute, Houston, Texas, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Houston Methodist Academic Institute, Houston, TX, United States.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Phillips</LastName>
<ForeName>Robert</ForeName>
<Initials>R</Initials>
<AffiliationInfo><Affiliation>Center for Outcomes Research, Houston Methodist Research Institute, Houston, Texas, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Weill Cornell Medicine, New York, New York, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Cardiology, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX, United States.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Andrieni</LastName>
<ForeName>Julia D</ForeName>
<Initials>JD</Initials>
<AffiliationInfo><Affiliation>Weill Cornell Medicine, New York, New York, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Medicine, Houston Methodist Hospital, Houston, TX, United States.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Kash</LastName>
<ForeName>Bita A</ForeName>
<Initials>BA</Initials>
<AffiliationInfo><Affiliation>Center for Outcomes Research, Houston Methodist Research Institute, Houston, Texas, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Weill Cornell Medicine, New York, New York, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Texas A&M University School of Rural Public Health, College Station, Texas, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Nasir</LastName>
<ForeName>Khurram</ForeName>
<Initials>K</Initials>
<AffiliationInfo><Affiliation>Center for Outcomes Research, Houston Methodist Research Institute, Houston, Texas, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Cardiology, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX, United States.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
<PublicationType UI="D064888">Observational Study</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2020</Year>
<Month>08</Month>
<Day>11</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>England</Country>
<MedlineTA>BMJ Open</MedlineTA>
<NlmUniqueID>101552874</NlmUniqueID>
<ISSNLinking>2044-6055</ISSNLinking>
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</MeshHeading>
<MeshHeading><DescriptorName UI="D012959" MajorTopicYN="N">Socioeconomic Factors</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D013781" MajorTopicYN="N" Type="Geographic">Texas</DescriptorName>
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<Keyword MajorTopicYN="Y">infectious diseases</Keyword>
<Keyword MajorTopicYN="Y">public health</Keyword>
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<CoiStatement>Competing interests: None declared.</CoiStatement>
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<affiliations><list><country><li>Sao Tomé-et-Principe</li>
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<name sortKey="Jones, Stephen L" sort="Jones, Stephen L" uniqKey="Jones S" first="Stephen L" last="Jones">Stephen L. Jones</name>
<name sortKey="Jones, Stephen L" sort="Jones, Stephen L" uniqKey="Jones S" first="Stephen L" last="Jones">Stephen L. Jones</name>
<name sortKey="Jones, Stephen L" sort="Jones, Stephen L" uniqKey="Jones S" first="Stephen L" last="Jones">Stephen L. Jones</name>
<name sortKey="Kash, Bita A" sort="Kash, Bita A" uniqKey="Kash B" first="Bita A" last="Kash">Bita A. Kash</name>
<name sortKey="Kash, Bita A" sort="Kash, Bita A" uniqKey="Kash B" first="Bita A" last="Kash">Bita A. Kash</name>
<name sortKey="Kash, Bita A" sort="Kash, Bita A" uniqKey="Kash B" first="Bita A" last="Kash">Bita A. Kash</name>
<name sortKey="Khan, Osman" sort="Khan, Osman" uniqKey="Khan O" first="Osman" last="Khan">Osman Khan</name>
<name sortKey="Masud, Faisal" sort="Masud, Faisal" uniqKey="Masud F" first="Faisal" last="Masud">Faisal Masud</name>
<name sortKey="Masud, Faisal" sort="Masud, Faisal" uniqKey="Masud F" first="Faisal" last="Masud">Faisal Masud</name>
<name sortKey="Meeks, Jennifer R" sort="Meeks, Jennifer R" uniqKey="Meeks J" first="Jennifer R" last="Meeks">Jennifer R. Meeks</name>
<name sortKey="Nasir, Khurram" sort="Nasir, Khurram" uniqKey="Nasir K" first="Khurram" last="Nasir">Khurram Nasir</name>
<name sortKey="Nasir, Khurram" sort="Nasir, Khurram" uniqKey="Nasir K" first="Khurram" last="Nasir">Khurram Nasir</name>
<name sortKey="Nicolas, Juan Carlos" sort="Nicolas, Juan Carlos" uniqKey="Nicolas J" first="Juan Carlos" last="Nicolas">Juan Carlos Nicolas</name>
<name sortKey="Pan, Alan" sort="Pan, Alan" uniqKey="Pan A" first="Alan" last="Pan">Alan Pan</name>
<name sortKey="Phillips, Robert" sort="Phillips, Robert" uniqKey="Phillips R" first="Robert" last="Phillips">Robert Phillips</name>
<name sortKey="Phillips, Robert" sort="Phillips, Robert" uniqKey="Phillips R" first="Robert" last="Phillips">Robert Phillips</name>
<name sortKey="Phillips, Robert" sort="Phillips, Robert" uniqKey="Phillips R" first="Robert" last="Phillips">Robert Phillips</name>
<name sortKey="Sostman, H Dirk" sort="Sostman, H Dirk" uniqKey="Sostman H" first="H Dirk" last="Sostman">H Dirk Sostman</name>
<name sortKey="Sostman, H Dirk" sort="Sostman, H Dirk" uniqKey="Sostman H" first="H Dirk" last="Sostman">H Dirk Sostman</name>
<name sortKey="Sostman, H Dirk" sort="Sostman, H Dirk" uniqKey="Sostman H" first="H Dirk" last="Sostman">H Dirk Sostman</name>
</country>
</tree>
</affiliations>
</record>
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