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Social Vulnerability Index and COVID-19

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thesis
posted on 01.05.2021, 00:00 by Haewon Oh
Although the COVID-19 crisis has had global impacts, COVID-19 has disproportionately affected poor, segregated racial/ethnic populations on the local level across the United States. To understand the effects of COVID-19 on such populations, various indexes have been developed to help identify the communities most likely to be impacted by the disease. These indexes include the Social Vulnerability Index and the COVID-19 Community Vulnerability Index. To determine the efficacy of the Social Vulnerability Index and COVID-19 Community Vulnerability Index, this study (1) uses Poisson regression analysis to compare the ability of Social Vulnerability Index and COVID-19 Community Vulnerability Index themes to explain actual Illinois county infection rates and case fatality rates, (2) examines the relative contributions of the four Social Vulnerability Index themes and six COVID-19 Community Vulnerability Index themes to the composite score using the Principal Component Analysis, Random Forest model, and relative importance metrics in a linear regression model, and (3) applies the three methods to explain case fatality rates. The study revealed that the Social Vulnerability Index better explains COVID-19 infection and case fatality rates compared to the COVID-19 Community Vulnerability Index, with the Minority Status and Language theme of both indexes appearing to be related to infection rates. In addition, using Principal Component Analysis, Random Forest, and relative importance metrics in linear regression analysis to rank the importance of Social Vulnerability Index and COVID-19 Community Vulnerability Index themes, the Minority Status and Language theme was found to make the least contribution to the composite score at the county level. The Minority Status and Language theme was the most important COVID-19 Community Vulnerability Index factor in explaining the infection rate, but it appeared to be the least important to the composite score. This would explain why the COVID-19 Community Vulnerability Index composite score showed no significant relationship to the infection rate. When the three analysis methods were applied to explain the case fatality rate, the Random Forest model and Principal Component Analysis revealed that the Socioeconomic Status theme of the Social Vulnerability Index made the greatest contribution to that rate. Thus, I conclude that the case fatality rate is strongly related to Socioeconomic Status factors such as income, as the severity of COVID-19 is dependent on the ability to access medical treatment, and that the infection rate is related to the Minority Status and Language theme. For the COVID-19 Community Vulnerability Index, all three methods produced very different results. On the whole, I conclude that the Social Vulnerability Index is a more appropriate index than the COVID-19 Community Vulnerability Index for explaining overall COVID-19 infection and case fatality rates. One issue with the COVID-19 Community Vulnerability Index is that the variables used for the Epidemiological Factors and Healthcare System Factors themes are measured at the state or county level. Thus, I argue that indexes such as the COVID-19 Community Vulnerability Index need to be constructed at the community and census tract levels.

History

Advisor

Kim, Sage

Chair

Kim, Sage

Department

Public Health Sciences_Health Policy and Administration

Degree Grantor

University of Illinois at Chicago

Degree Level

Masters

Degree name

MS, Master of Science

Committee Member

Peterson, Caryn E Sun, Jiehuan Konda, Sreenivas

Submitted date

May 2021

Thesis type

application/pdf

Language

en

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