Data-Driven Development of a Small-Area COVID-19 Vulnerability Index for the United States

  1. Ofer Amram
  2. Solmaz Amiri
  3. Emily L. Thorn
  4. Jacob J. Mansfield
  5. Pranav Mellacheruvu
  6. Pablo Monsivais

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Abstract

As the COVID-19 pandemic continues to surge in the United States, it has become clear that infection risk is higher in certain populations, particularly socially and economically marginalized groups. Social risk factors, together with other demographic and community characteristics, may reveal local variations and inequities in COVID risk that could be useful for targeting testing and interventions. Yet to date, rates of infection and estimations of COVID risk are typically reported at the county and state level. In this study we develop a small area vulnerability index based on publicly-available sociodemographic data and 668,428 COVID diagnoses reported in 4,803 ZIP codes in the United States (15% of all ZIP codes). The outcome was COVID-19 diagnosis rates per 100,000 people by ZIP code. Explanatory variables included sociodemographic characteristics obtained from the 2018 American Community Survey 5-year estimates. Bayesian multivariable techniques were used to capture complexities of spatial data and spatial autocorrelation and identify individual risk factors and derive their respective weights in the index. COVID-19 diagnosis rates varied from zero to 29,508 per 100,000 people. The final vulnerability index showed that higher population density, higher percentage of noninsured, nonwhite race and Hispanic ethnicity were positively associated with COVID-19 diagnosis rates. Our findings indicate disproportionate risk of COVID-19 infection among some populations and validate and expand understanding of these inequities, integrating several risk factors into a summary index reflecting composite vulnerability to infection. This index can provide local public health and other agencies with evidence-based metrics of COVID risk at a geographical scale that has not been previously available to most US communities.

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    SciScore for 10.1101/2020.08.17.20176248: (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

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Methodological Considerations and Limitations: Our sample included ZIP code level data in rural and urban settings and across 19 states. Nonetheless, our sample is limited by the states and municipalities for which data was available at the time of analysis and resource-limited settings may thus be underrepresented. Additionally, differences in the local criteria used by public health agencies to confirm positive cases may have reduced our power to detect associations. Our final analysis included data from 4,803 ZIP codes, but future studies with a wider range of ZIP codes may improve reliability of the associations we report. Conclusions: A dynamic evidence base indicates disproportionate risk of COVID-19 infection among some socio-demographic populations.2,7,14,15,24,25,27 Our study helps to validate and expand understanding of these inequities, integrating several risk factors into an index reflecting composite vulnerability to infection. Our findings reinforce the urgent need for increased testing accessibility for vulnerable communities at higher risk of infection. Importantly, these results also reinforce the concurrent need to address structural racism and its numerous adverse impacts on the health and healthcare of Black Americans and other communities of color.

    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.

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  2. Version published to 10.1101/2020.08.17.20176248v1 on medRxiv