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

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 dat...

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Bibliographic Details
Published in:AMIA Summits on Translational Science proceedings 2021, Vol.2021, p.545-554
Main Authors: Boland, Mary Regina, Liu, Jessica, Balocchi, Cecilia, Meeker, Jessica, Bai, Ray, Mellis, Ian, Mowery, Danielle L, Herman, Daniel
Format: Article
Language:English
Subjects:
COVID-19
Humans
Philadelphia - epidemiology
Residence Characteristics
SARS-CoV-2
Spatial Regression
United States - epidemiology
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Summary: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.
ISSN:2153-4063