The COVID-19 crisis has had global impacts. Across the United States racial/ethnic minority communities have been disproportionately affected by the pandemic. To help policy makers identify the most vulnerable communities to COVID-19, several indices have been utilized, including the Surgo Institute’s COVID-19 Community Vulnerability Index (CCVI), adding two additional domains to the CDC’s Social Vulnerability Index (SVI). However, the utility and effectiveness of these indices have not been fully explored.

Objectives

This analysis aimed to examine whether the two additional domains, epidemiologic and healthcare utilization factors, of the CCVI improved the ability to explain COVID-19 infection rates and case fatality rates.

Methods

To determine the efficacy of the CCVI in relation to SVI, this study: 1) used Poisson regression to compare how well CCVI and SVI explain Illinois county level infection and case fatality rates; 2) examined the relative contributions of the four SVI and six CCVI domains to the composite index score using the Random Forest model and relative importance metrics in a linear regression model; and 3) applied these methods to explain case fatality rates.

Results

The findings indicated that the SVI better explained COVID-19 infection and case fatality rates compared to the CCVI. The minority status and language domain was the most important CCVI factor in explaining the infection rate, but it appeared to be the least important to the composite score. Whereas epidemiological factor was not related to the COVID-19 outcomes, it contributed the most to the composite score.

Conclusion

SVI is a more appropriate index than the CCVI for explaining overall COVID-19 outcomes. One potential reason is that the epidemiologic and healthcare utilization domains used in CCVI are primarily measured at the state or county level, which limits the utilization of the CCVI at the local level. We conclude that hyper-local data are essential in building indices to be able to accurately reflect local conditions.

Public health implications

These indices could help policy makers provide more adequate services in higher vulnerable communities specific to each disaster. Local specific data are critical in improving the capacity to respond to disaster events such as COVID-19.