Spatial analysis of COVID-19 clusters and contextual factors in New York City.
Identifieur interne : 000210 ( Main/Exploration ); précédent : 000209; suivant : 000211Spatial analysis of COVID-19 clusters and contextual factors in New York City.
Auteurs : Jack Cordes [États-Unis] ; Marcia C. Castro [États-Unis]Source :
- Spatial and spatio-temporal epidemiology [ 1877-5853 ] ; 2020.
Descripteurs français
- KwdFr :
- Adulte (MeSH), Adulte d'âge moyen (MeSH), Analyse de regroupements (MeSH), Analyse spatiale (MeSH), Appréciation des risques (MeSH), Disparités d'accès aux soins (ethnologie), Disparités d'accès aux soins (économie), Disparités de l'état de santé (MeSH), Femelle (MeSH), Humains (MeSH), Infections à coronavirus (diagnostic), Infections à coronavirus (épidémiologie), Maladies transmissibles émergentes (épidémiologie), Mâle (MeSH), New York (ville) (épidémiologie), Pandémies (statistiques et données numériques), Pneumopathie virale (diagnostic), Pneumopathie virale (épidémiologie), Population urbaine (MeSH), Santé en zone urbaine (ethnologie), Santé en zone urbaine (économie), Sujet âgé (MeSH), Sujet âgé de 80 ans ou plus (MeSH), Techniques de laboratoire clinique (statistiques et données numériques), Épidémies de maladies (statistiques et données numériques).
- MESH :
- diagnostic : Infections à coronavirus, Pneumopathie virale.
- ethnologie : Disparités d'accès aux soins, Santé en zone urbaine.
- statistiques et données numériques : Pandémies, Techniques de laboratoire clinique, Épidémies de maladies.
- économie : Disparités d'accès aux soins, Santé en zone urbaine.
- épidémiologie : Infections à coronavirus, Maladies transmissibles émergentes, New York (ville), Pneumopathie virale.
- Adulte, Adulte d'âge moyen, Analyse de regroupements, Analyse spatiale, Appréciation des risques, Disparités de l'état de santé, Femelle, Humains, Mâle, Population urbaine, Sujet âgé, Sujet âgé de 80 ans ou plus.
English descriptors
- KwdEn :
- Adult (MeSH), Aged (MeSH), Aged, 80 and over (MeSH), Clinical Laboratory Techniques (statistics & numerical data), Cluster Analysis (MeSH), Communicable Diseases, Emerging (epidemiology), Coronavirus Infections (diagnosis), Coronavirus Infections (epidemiology), Disease Outbreaks (statistics & numerical data), Female (MeSH), Health Status Disparities (MeSH), Healthcare Disparities (economics), Healthcare Disparities (ethnology), Humans (MeSH), Male (MeSH), Middle Aged (MeSH), New York City (epidemiology), Pandemics (statistics & numerical data), Pneumonia, Viral (diagnosis), Pneumonia, Viral (epidemiology), Risk Assessment (MeSH), Spatial Analysis (MeSH), Urban Health (economics), Urban Health (ethnology), Urban Population (MeSH).
- MESH :
- geographic , epidemiology : New York City.
- diagnosis : Coronavirus Infections, Pneumonia, Viral.
- economics : Healthcare Disparities, Urban Health.
- epidemiology : Communicable Diseases, Emerging, Coronavirus Infections, Pneumonia, Viral.
- ethnology : Healthcare Disparities, Urban Health.
- statistics & numerical data : Clinical Laboratory Techniques, Disease Outbreaks, Pandemics.
- Adult, Aged, Aged, 80 and over, Cluster Analysis, Female, Health Status Disparities, Humans, Male, Middle Aged, Risk Assessment, Spatial Analysis, Urban Population.
Abstract
Identifying areas with low access to testing and high case burden is necessary to understand risk and allocate resources in the COVID-19 pandemic. Using zip code level data for New York City, we analyzed testing rates, positivity rates, and proportion positive. A spatial scan statistic identified clusters of high and low testing rates, high positivity rates, and high proportion positive. Boxplots and Pearson correlations determined associations between outcomes, clusters, and contextual factors. Clusters with less testing and low proportion positive tests had higher income, education, and white population, whereas clusters with high testing rates and high proportion positive tests were disproportionately black and without health insurance. Correlations showed inverse associations of white race, education, and income with proportion positive tests, and positive associations with black race, Hispanic ethnicity, and poverty. We recommend testing and health care resources be directed to eastern Brooklyn, which has low testing and high proportion positives.
DOI: 10.1016/j.sste.2020.100355
PubMed: 32807400
PubMed Central: PMC7306208
Affiliations:
Links toward previous steps (curation, corpus...)
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Using zip code level data for New York City, we analyzed testing rates, positivity rates, and proportion positive. A spatial scan statistic identified clusters of high and low testing rates, high positivity rates, and high proportion positive. Boxplots and Pearson correlations determined associations between outcomes, clusters, and contextual factors. Clusters with less testing and low proportion positive tests had higher income, education, and white population, whereas clusters with high testing rates and high proportion positive tests were disproportionately black and without health insurance. Correlations showed inverse associations of white race, education, and income with proportion positive tests, and positive associations with black race, Hispanic ethnicity, and poverty. We recommend testing and health care resources be directed to eastern Brooklyn, which has low testing and high proportion positives.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32807400</PMID><DateCompleted><Year>2020</Year><Month>08</Month><Day>31</Day></DateCompleted><DateRevised><Year>2020</Year><Month>08</Month><Day>31</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1877-5853</ISSN><JournalIssue CitedMedium="Internet"><Volume>34</Volume><PubDate><Year>2020</Year><Month>08</Month></PubDate></JournalIssue><Title>Spatial and spatio-temporal epidemiology</Title><ISOAbbreviation>Spat Spatiotemporal Epidemiol</ISOAbbreviation></Journal><ArticleTitle>Spatial analysis of COVID-19 clusters and contextual factors in New York City.</ArticleTitle><Pagination><MedlinePgn>100355</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S1877-5845(20)30033-2</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.sste.2020.100355</ELocationID><Abstract><AbstractText>Identifying areas with low access to testing and high case burden is necessary to understand risk and allocate resources in the COVID-19 pandemic. Using zip code level data for New York City, we analyzed testing rates, positivity rates, and proportion positive. A spatial scan statistic identified clusters of high and low testing rates, high positivity rates, and high proportion positive. Boxplots and Pearson correlations determined associations between outcomes, clusters, and contextual factors. Clusters with less testing and low proportion positive tests had higher income, education, and white population, whereas clusters with high testing rates and high proportion positive tests were disproportionately black and without health insurance. Correlations showed inverse associations of white race, education, and income with proportion positive tests, and positive associations with black race, Hispanic ethnicity, and poverty. We recommend testing and health care resources be directed to eastern Brooklyn, which has low testing and high proportion positives.</AbstractText><CopyrightInformation>Copyright © 2020. Published by Elsevier Ltd.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Cordes</LastName><ForeName>Jack</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston 02115, MA, USA. Electronic address: jcordes@g.harvard.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Castro</LastName><ForeName>Marcia C</ForeName><Initials>MC</Initials><AffiliationInfo><Affiliation>Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston 02115, MA, USA. 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last="Cordes">Jack Cordes</name></region><name sortKey="Castro, Marcia C" sort="Castro, Marcia C" uniqKey="Castro M" first="Marcia C" last="Castro">Marcia C. 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