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Title: Association of Neighborhood-Level Factors and COVID-19 Infection Patterns in Philadelphia Using Spatial Regression.

Authors: Boland, Mary Regina; Liu, Jessica; Balocchi, Cecilia; Meeker, Jessica; Bai, Ray; Mellis, Ian; Mowery, Danielle L; Herman, Daniel

Published In AMIA Jt Summits Transl Sci Proc, (2021)

Abstract: As of August 2020, there were ~6 million COVID-19 cases in the United States of America, resulting in ~200,000 deaths. Informatics approaches are needed to better understand the role of individual and community risk factors for COVID-19. We developed an informatics method to integrate SARS-CoV-2 data with multiple neighborhood-level factors from the American Community Survey and opendataphilly.org. We assessed the spatial association between neighborhood-level factors and the frequency of SARS-CoV-2 positivity, separately across all patients and across asymptomatic patients. We found that neighborhoods with higher proportions of individuals with a high-school degree and/or who were identified as Hispanic/Latinx were more likely to have higher SARS-CoV-2 positivity rates, after adjusting for other neighborhood covariates. Patients from neighborhoods with higher proportions of individuals receiving public assistance and/or identified as White were less likely to test positive for SARS-CoV-2. Our approach and its findings could inform future public health efforts.

PubMed ID: 34457170 Exiting the NIEHS site

MeSH Terms: COVID-19*; Humans; Philadelphia/epidemiology; Residence Characteristics; SARS-CoV-2; Spatial Regression; United States/epidemiology

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