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Visualizing and assessing US county-level COVID19 vulnerability - 25/05/21

Doi : 10.1016/j.ajic.2020.12.009 
Gina Cahill, MPH a, Carleigh Kutac, MPH b, Nicholas L. Rider, DO a,
a Baylor College of Medicine and Texas Children's Hospital, Section of Immunology Allergy and Retrovirology, Houston, TX 
b Baylor Saint Luke's Medical Center, Houston, TX 

Address correspondence to Nicholas L. Rider, DO, Baylor College of Medicine and Texas Children's Hospital, Section of Immunology Allergy and Retrovirology, Houston, TX.Baylor College of Medicine and Texas Children's HospitalSection of Immunology Allergy and RetrovirologyHoustonTX

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Highlights

Unique effects of the COVID19 pandemic are felt at the county level.
Combining information visualization techniques and publicly available datasets can yield insights about pandemic spread and community vulnerability.
Determinants of COVID19 county-level vulnerability are incompletely understood and additional study is warranted.

El texto completo de este artículo está disponible en PDF.

Resumen

Background

Like most of the world, the United States’ public health and economy are impacted by the COVID19 pandemic. However, discrete pandemic effects may not be fully realized on the macro-scale. With this perspective, our goal is to visualize spread of the pandemic and measure county-level features which may portend vulnerability.

Methods

We accessed the New York Times GitHub repository COVID19 data and 2018 United States Census data for all United States Counties. The disparate datasets were merged and filtered to allow for visualization and assessments about case fatality rate (CFR%) and associated demographic, ethnic and economic features.

Results

Our results suggest that county-level COVID19 fatality rates are related to advanced population age (P < .001) and less diversity as evidenced by higher proportion of Caucasians in High CFR% counties (P < .001). Also, lower CFR% counties had a greater proportion of the population reporting has having 2 or more races (P < .001). We noted no significant differences between High and Low CFR% counties with respect to mean income or poverty rate.

Conclusions

Unique COVID19 impacts are realized at the county level. Use of public datasets, data science skills and information visualization can yield helpful insights to drive understanding about community-level vulnerability.

El texto completo de este artículo está disponible en PDF.

Key Words : Novel Coronavirus, Population-health, Data visualization, SARS-CoV-2


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 Conflicts of interest: GC and CK have nothing to disclose. NLR received consulting fees for scientific advisory activities with Takeda Pharmaceuticals, Horizon Therapeutics and CSL Behring. He also receives royalties from Wolters Kluwer for topic contribution to UpToDate.


© 2020  Association for Professionals in Infection Control and Epidemiology, Inc.. Publicado por Elsevier Masson SAS. Todos los derechos reservados.
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Vol 49 - N° 6

P. 678-684 - juin 2021 Regresar al número
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