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 journalArticlepeer-review

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 languageEnglish
Article number102574
JournalHealth and Place
Volume69
DOIs
StatePublished - May 2021

Keywords

  • Bayesian spatial modeling
  • COVID-19
  • New York City
  • Spatial inequality

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