Injustices in pandemic vulnerability: A spatial-statistical analysis of the CDC Social Vulnerability Index and COVID-19 outcomes in the U.S.

  1. Jamie Song
  2. Eugenia South
  3. Sara Solomon
  4. Douglas Wiebe

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Abstract

Background

The COVID-19 pandemic has exacerbated health injustices in the U.S. driven by racism and other forms of structural violence. Research has shown the disproportionate impacts of COVID-19 morbidity and mortality in the most marginalized communities.

Objectives

We examined the associations between COVID-19 cumulative incidence (CI) and case-fatality risk (CFR) and the CDC’s Social Vulnerability Index (SVI), a composite score assessing historical marginalization and thus vulnerability to disaster events.

Methods

Using county-level data from national databases, we used population density, Gini index, percent uninsured, and average annual temperature as covariates, and employed negative binomial regression to evaluate relationships between SVI and COVID-19 outcomes. Optimized hot spot analysis identified hot spots of COVID-19 CI and CFR, which were compared in terms of SVI using logistic regression.

Results

As of 2/3/21, 26,452,031 cases of and 448,786 deaths from COVID-19 had been reported in the U.S. Negative binomial regression showed that counties in the top SVI quintile reported 13.7% higher CI (p<0.001) than those in the bottom SVI quintile. Additionally, each unit increase in a county’s SVI score was associated with a 0.2% increase in CFR (p<0.001). Logistic regression analysis showed that counties in the lowest SVI quintile had significantly greater odds of being in a CI hot spot than all other counties, yet counties in the highest SVI quintile had 63% greater odds (p=0.008) of being in a CFR hot spot than counties in the lowest SVI quintile.

Conclusion

We demonstrated a significant relationship between SVI and CFR, but the relationship between SVI and CI is complex and warrants further investigation. SVI may help elucidate unequal impacts of COVID-19 and guide prioritization of vaccines to communities most impacted by structural injustices.

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  1. ScreenIT

    SciScore for 10.1101/2021.05.27.21257889: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    ArcMap 10.7 was used for geospatial analysis.
    ArcMap
    suggested: None
    11 The SVI has been applied to research on COVID-19 in the U.S.,12,13,14 and has been adapted into other metrics such as Snyder and Parks’ socio-ecological vulnerability index,15 and the Pandemic Vulnerability Index developed for the NIH by Marvel and colleagues.16 In this study, we used the SVI and its component “themes” as proxies for the various forms of social, economic, and political marginalization that are historically embedded and spatially heterogeneous across the U.S.
    Marvel
    suggested: (Marvel , RRID:SCR_017621)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: We identified several limitations of our study. First, we relied on national data reported by thousands of localities, which inevitably introduced bias. Many cases and deaths were geographically non-specific, or specific to geographies that were not classified under the U.S. county system. For example, the majority of the state of Utah was not represented in this study because the reported data were not specific to county-level geographies. Furthermore, our results are likely biased by underreporting of cases by localities and states; in addition, we were unable to account for disparities in testing that would differentially deflate incidence estimates. We likely also underestimated mortality, based on studies showing significant underreporting of COVID-19 mortality in the U.S.26 Secondly, our county-level analysis is subject to ecological bias; observations of county-level CI and CFR are unable to fully capture individual or even community-level disease transmission phenomena. This bias could have been reduced with data at the ZIP code or census tract levels, which were not publicly available because of ethical and welfare concerns with COVID-19 data privacy. Third, the variable sizes of U.S. counties may have negatively impacted the validity of Getis-Ord Gi* hot spot analysis,27 though the optimization algorithms performed in OHSA may have helped to account for this problem. Fourth, SVI is a composite metric, and it does have advantages in assessing many differ...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.05.27.21257889v1 on medRxiv