TY - JOUR
T1 - Neighborhood-level disparities and subway utilization during the COVID-19 pandemic in New York City
AU - Carrión, Daniel
AU - Colicino, Elena
AU - Pedretti, Nicolo Foppa
AU - Arfer, Kodi B.
AU - Rush, Johnathan
AU - DeFelice, Nicholas
AU - Just, Allan C.
N1 - Funding Information:
This work was supported by NIH grants UL1TR001433 and P30ES023515. DC was funded by NIH T32HD049311. Thanks to Sebastian Rowland for his thoughtful comments on a draft. We gratefully acknowledge the OpenStreetMap contributors, who provided the data for the water mask used in Fig. 3 and supplemental Figs. 5 and 10.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12/1
Y1 - 2021/12/1
N2 - The COVID-19 pandemic has yielded disproportionate impacts on communities of color in New York City (NYC). Researchers have noted that social disadvantage may result in limited capacity to socially distance, and consequent disparities. We investigate the association between neighborhood social disadvantage and the ability to socially distance, infections, and mortality in Spring 2020. We combine Census Bureau and NYC open data with SARS-CoV-2 testing data using supervised dimensionality-reduction with Bayesian Weighted Quantile Sums regression. The result is a ZIP code-level index with weighted social factors associated with infection risk. We find a positive association between neighborhood social disadvantage and infections, adjusting for the number of tests administered. Neighborhood disadvantage is also associated with a proxy of the capacity to socially isolate, NYC subway usage data. Finally, our index is associated with COVID-19-related mortality.
AB - The COVID-19 pandemic has yielded disproportionate impacts on communities of color in New York City (NYC). Researchers have noted that social disadvantage may result in limited capacity to socially distance, and consequent disparities. We investigate the association between neighborhood social disadvantage and the ability to socially distance, infections, and mortality in Spring 2020. We combine Census Bureau and NYC open data with SARS-CoV-2 testing data using supervised dimensionality-reduction with Bayesian Weighted Quantile Sums regression. The result is a ZIP code-level index with weighted social factors associated with infection risk. We find a positive association between neighborhood social disadvantage and infections, adjusting for the number of tests administered. Neighborhood disadvantage is also associated with a proxy of the capacity to socially isolate, NYC subway usage data. Finally, our index is associated with COVID-19-related mortality.
UR - http://www.scopus.com/inward/record.url?scp=85108156774&partnerID=8YFLogxK
U2 - 10.1038/s41467-021-24088-7
DO - 10.1038/s41467-021-24088-7
M3 - Article
C2 - 34140520
AN - SCOPUS:85108156774
SN - 2041-1723
VL - 12
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 3692
ER -