Spatial analysis of COVID-19 clusters and contextual factors in New York City

•Proportion positive tests were positively associated with marginalized statuses.•Low testing and high positivity were associated with public transportation use.•We recommend testing and health care resources be directed to eastern Brooklyn. Identifying areas with low access to testing and high case...

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Vydáno v:	Spatial and spatio-temporal epidemiology Ročník 34; s. 100355
Hlavní autoři:	Cordes, Jack, Castro, Marcia C.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Netherlands Elsevier Ltd 01.08.2020
Témata:	Adult Aged Aged, 80 and over Clinical Laboratory Techniques - statistics & numerical data Cluster Analysis Communicable Diseases, Emerging - epidemiology Coronavirus Infections - diagnosis Coronavirus Infections - epidemiology COVID-19 COVID-19 Testing Disease Outbreaks - statistics & numerical data Female Health inequalities Health Status Disparities Healthcare Disparities - economics Healthcare Disparities - ethnology Humans Infectious disease Male Middle Aged New York City - epidemiology Pandemics - statistics & numerical data Pneumonia, Viral - diagnosis Pneumonia, Viral - epidemiology Risk Assessment Spatial Analysis Urban health Urban Health - economics Urban Health - ethnology Urban Population New York City Cluster analysis Infectious disease Urban health Health inequalities
ISSN:	1877-5845, 1877-5853, 1877-5853
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Abstract	•Proportion positive tests were positively associated with marginalized statuses.•Low testing and high positivity were associated with public transportation use.•We recommend testing and health care resources be directed to eastern Brooklyn. 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.
AbstractList	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. • Proportion positive tests were positively associated with marginalized statuses. • Low testing and high positivity were associated with public transportation use. • We recommend testing and health care resources be directed to eastern Brooklyn. 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. 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.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. •Proportion positive tests were positively associated with marginalized statuses.•Low testing and high positivity were associated with public transportation use.•We recommend testing and health care resources be directed to eastern Brooklyn. 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.
ArticleNumber	100355
Author	Cordes, Jack Castro, Marcia C.
Author_xml	– sequence: 1 givenname: Jack surname: Cordes fullname: Cordes, Jack email: jcordes@g.harvard.edu organization: Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston 02115, MA, USA – sequence: 2 givenname: Marcia C. surname: Castro fullname: Castro, Marcia C. email: mcastro@hsph.harvard.edu organization: Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston 02115, MA, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32807400$$D View this record in MEDLINE/PubMed
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Snippet	•Proportion positive tests were positively associated with marginalized statuses.•Low testing and high positivity were associated with public transportation... 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... • Proportion positive tests were positively associated with marginalized statuses. • Low testing and high positivity were associated with public transportation...
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SubjectTerms	Adult Aged Aged, 80 and over Clinical Laboratory Techniques - statistics & numerical data Cluster Analysis Communicable Diseases, Emerging - epidemiology Coronavirus Infections - diagnosis Coronavirus Infections - epidemiology COVID-19 COVID-19 Testing Disease Outbreaks - statistics & numerical data Female Health inequalities Health Status Disparities Healthcare Disparities - economics Healthcare Disparities - ethnology Humans Infectious disease Male Middle Aged New York City - epidemiology Pandemics - statistics & numerical data Pneumonia, Viral - diagnosis Pneumonia, Viral - epidemiology Risk Assessment Spatial Analysis Urban health Urban Health - economics Urban Health - ethnology Urban Population
Title	Spatial analysis of COVID-19 clusters and contextual factors in New York City
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