Examining spatial inequality in COVID-19 positivity rates across New York City ZIP codes

Tse Chuan Yang; Seulki Kim; Yunhan Zhao; Seung won Emily Choi

doi:10.1016/j.healthplace.2021.102574

Examining spatial inequality in COVID-19 positivity rates across New York City ZIP codes

Tse Chuan Yang, Seulki Kim, Yunhan Zhao, Seung won Emily Choi

Research output: Contribution to journal › Article › peer-review

23 Scopus citations

Abstract

We aim to understand the spatial inequality in Coronavirus disease 2019 (COVID-19) positivity rates across New York City (NYC) ZIP codes. Applying Bayesian spatial negative binomial models to a ZIP-code level dataset (N = 177) as of May 31st, 2020, we find that (1) the racial/ethnic minority groups are associated with COVID-19 positivity rates; (2) the percentages of remote workers are negatively associated with positivity rates, whereas older population and household size show a positive association; and (3) while ZIP codes in the Bronx and Queens have higher COVID-19 positivity rates, the strongest spatial effects are clustered in Brooklyn and Manhattan.

Original language	English (US)
Article number	102574
Journal	Health and Place
Volume	69
DOIs	https://doi.org/10.1016/j.healthplace.2021.102574
State	Published - May 2021
Externally published	Yes

Keywords

Bayesian spatial modeling
COVID-19
New York City
Spatial inequality

ASJC Scopus subject areas

Health(social science)
Sociology and Political Science
Life-span and Life-course Studies

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10.1016/j.healthplace.2021.102574

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title = "Examining spatial inequality in COVID-19 positivity rates across New York City ZIP codes",

abstract = "We aim to understand the spatial inequality in Coronavirus disease 2019 (COVID-19) positivity rates across New York City (NYC) ZIP codes. Applying Bayesian spatial negative binomial models to a ZIP-code level dataset (N = 177) as of May 31st, 2020, we find that (1) the racial/ethnic minority groups are associated with COVID-19 positivity rates; (2) the percentages of remote workers are negatively associated with positivity rates, whereas older population and household size show a positive association; and (3) while ZIP codes in the Bronx and Queens have higher COVID-19 positivity rates, the strongest spatial effects are clustered in Brooklyn and Manhattan.",

keywords = "Bayesian spatial modeling, COVID-19, New York City, Spatial inequality",

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T1 - Examining spatial inequality in COVID-19 positivity rates across New York City ZIP codes

AU - Yang, Tse Chuan

AU - Kim, Seulki

AU - Zhao, Yunhan

AU - Choi, Seung won Emily

PY - 2021/5

Y1 - 2021/5

N2 - We aim to understand the spatial inequality in Coronavirus disease 2019 (COVID-19) positivity rates across New York City (NYC) ZIP codes. Applying Bayesian spatial negative binomial models to a ZIP-code level dataset (N = 177) as of May 31st, 2020, we find that (1) the racial/ethnic minority groups are associated with COVID-19 positivity rates; (2) the percentages of remote workers are negatively associated with positivity rates, whereas older population and household size show a positive association; and (3) while ZIP codes in the Bronx and Queens have higher COVID-19 positivity rates, the strongest spatial effects are clustered in Brooklyn and Manhattan.

AB - We aim to understand the spatial inequality in Coronavirus disease 2019 (COVID-19) positivity rates across New York City (NYC) ZIP codes. Applying Bayesian spatial negative binomial models to a ZIP-code level dataset (N = 177) as of May 31st, 2020, we find that (1) the racial/ethnic minority groups are associated with COVID-19 positivity rates; (2) the percentages of remote workers are negatively associated with positivity rates, whereas older population and household size show a positive association; and (3) while ZIP codes in the Bronx and Queens have higher COVID-19 positivity rates, the strongest spatial effects are clustered in Brooklyn and Manhattan.

KW - Bayesian spatial modeling

KW - COVID-19

KW - New York City

KW - Spatial inequality

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Examining spatial inequality in COVID-19 positivity rates across New York City ZIP codes

Abstract

Keywords

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