Black/African American Communities are at highest risk of COVID-19: spatial modeling of New York City ZIP Code–level testing results

The population and spatial characteristics of COVID-19 infections are poorly understood, but there is increasing evidence that in addition to individual clinical factors, demographic, socioeconomic, and racial characteristics play an important role. We analyzed positive COVID-19 testing results coun...

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Published inAnnals of epidemiology Vol. 51; pp. 7 - 13
Main Authors DiMaggio, Charles, Klein, Michael, Berry, Cherisse, Frangos, Spiros
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.11.2020
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ISSN1047-2797
1873-2585
1873-2585
DOI10.1016/j.annepidem.2020.08.012

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Abstract The population and spatial characteristics of COVID-19 infections are poorly understood, but there is increasing evidence that in addition to individual clinical factors, demographic, socioeconomic, and racial characteristics play an important role. We analyzed positive COVID-19 testing results counts within New York City ZIP Code Tabulation Areas with Bayesian hierarchical Poisson spatial models using integrated nested Laplace approximations. Spatial clustering accounted for approximately 32% of the variation in the data. There was a nearly five-fold increase in the risk of a positive COVID-19 test (incidence density ratio = 4.8, 95% credible interval 2.4, 9.7) associated with the proportion of black/African American residents. Increases in the proportion of residents older than 65 years, housing density, and the proportion of residents with heart disease were each associated with an approximate doubling of risk. In a multivariable model including estimates for age, chronic obstructive pulmonary disease, heart disease, housing density, and black/African American race, the only variables that remained associated with positive COVID-19 testing with a probability greater than chance were the proportion of black/African American residents and proportion of older persons. Areas with large proportions of black/African American residents are at markedly higher risk that is not fully explained by characteristics of the environment and pre-existing conditions in the population.
AbstractList The population and spatial characteristics of COVID-19 infections are poorly understood, but there is increasing evidence that in addition to individual clinical factors, demographic, socioeconomic, and racial characteristics play an important role. We analyzed positive COVID-19 testing results counts within New York City ZIP Code Tabulation Areas with Bayesian hierarchical Poisson spatial models using integrated nested Laplace approximations. Spatial clustering accounted for approximately 32% of the variation in the data. There was a nearly five-fold increase in the risk of a positive COVID-19 test (incidence density ratio = 4.8, 95% credible interval 2.4, 9.7) associated with the proportion of black/African American residents. Increases in the proportion of residents older than 65 years, housing density, and the proportion of residents with heart disease were each associated with an approximate doubling of risk. In a multivariable model including estimates for age, chronic obstructive pulmonary disease, heart disease, housing density, and black/African American race, the only variables that remained associated with positive COVID-19 testing with a probability greater than chance were the proportion of black/African American residents and proportion of older persons. Areas with large proportions of black/African American residents are at markedly higher risk that is not fully explained by characteristics of the environment and pre-existing conditions in the population.
The population and spatial characteristics of COVID-19 infections are poorly understood, but there is increasing evidence that in addition to individual clinical factors, demographic, socioeconomic, and racial characteristics play an important role.PURPOSEThe population and spatial characteristics of COVID-19 infections are poorly understood, but there is increasing evidence that in addition to individual clinical factors, demographic, socioeconomic, and racial characteristics play an important role.We analyzed positive COVID-19 testing results counts within New York City ZIP Code Tabulation Areas with Bayesian hierarchical Poisson spatial models using integrated nested Laplace approximations.METHODSWe analyzed positive COVID-19 testing results counts within New York City ZIP Code Tabulation Areas with Bayesian hierarchical Poisson spatial models using integrated nested Laplace approximations.Spatial clustering accounted for approximately 32% of the variation in the data. There was a nearly five-fold increase in the risk of a positive COVID-19 test (incidence density ratio = 4.8, 95% credible interval 2.4, 9.7) associated with the proportion of black/African American residents. Increases in the proportion of residents older than 65 years, housing density, and the proportion of residents with heart disease were each associated with an approximate doubling of risk. In a multivariable model including estimates for age, chronic obstructive pulmonary disease, heart disease, housing density, and black/African American race, the only variables that remained associated with positive COVID-19 testing with a probability greater than chance were the proportion of black/African American residents and proportion of older persons.RESULTSSpatial clustering accounted for approximately 32% of the variation in the data. There was a nearly five-fold increase in the risk of a positive COVID-19 test (incidence density ratio = 4.8, 95% credible interval 2.4, 9.7) associated with the proportion of black/African American residents. Increases in the proportion of residents older than 65 years, housing density, and the proportion of residents with heart disease were each associated with an approximate doubling of risk. In a multivariable model including estimates for age, chronic obstructive pulmonary disease, heart disease, housing density, and black/African American race, the only variables that remained associated with positive COVID-19 testing with a probability greater than chance were the proportion of black/African American residents and proportion of older persons.Areas with large proportions of black/African American residents are at markedly higher risk that is not fully explained by characteristics of the environment and pre-existing conditions in the population.CONCLUSIONSAreas with large proportions of black/African American residents are at markedly higher risk that is not fully explained by characteristics of the environment and pre-existing conditions in the population.
Author Frangos, Spiros
DiMaggio, Charles
Berry, Cherisse
Klein, Michael
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Keywords COVID-19
Spatial analysis
Disparity
Language English
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Snippet The population and spatial characteristics of COVID-19 infections are poorly understood, but there is increasing evidence that in addition to individual...
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SubjectTerms Adult
Age Factors
Aged
Aged, 80 and over
Betacoronavirus
Black or African American - statistics & numerical data
Clinical Laboratory Techniques
Coronavirus Infections - diagnosis
Coronavirus Infections - ethnology
COVID-19
COVID-19 Testing
Disparity
Female
Health Status Disparities
Healthcare Disparities - statistics & numerical data
Housing
Humans
Male
Middle Aged
New York City - epidemiology
Original
Pandemics
Pneumonia, Viral - ethnology
Residence Characteristics
SARS-CoV-2
Socioeconomic Factors
Spatial Analysis
Title Black/African American Communities are at highest risk of COVID-19: spatial modeling of New York City ZIP Code–level testing results
URI https://www.clinicalkey.com/#!/content/1-s2.0-S1047279720302933
https://dx.doi.org/10.1016/j.annepidem.2020.08.012
https://www.ncbi.nlm.nih.gov/pubmed/32827672
https://www.proquest.com/docview/2436401323
https://pubmed.ncbi.nlm.nih.gov/PMC7438213
Volume 51
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