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

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...

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Detalles Bibliográficos
Autores principales: Yang, Tse-Chuan, Kim, Seulki, Zhao, Yunhan, Choi, Seung-won Emily
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8631550/
https://www.ncbi.nlm.nih.gov/pubmed/33895489
http://dx.doi.org/10.1016/j.healthplace.2021.102574
Descripción
Sumario: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.

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