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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Bibliographic Details
Published in:Spatial and spatio-temporal epidemiology Vol. 34; p. 100355
Main Authors: Cordes, Jack, Castro, Marcia C.
Format: Journal Article
Language:English
Published: Netherlands Elsevier Ltd 01.08.2020
Subjects:
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
Online Access:Get full text
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Summary:•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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:1877-5845
1877-5853
1877-5853
DOI:10.1016/j.sste.2020.100355