Identifying At-Risk Communities and Key Vulnerability Indicators in the COVID-19 Pandemic

  1. Savannah Thais
  2. Shaine Leibowitz
  3. Alejandra Rios Gutierrez
  4. Alexandra Passarelli
  5. Stephanie Santo
  6. Nora Shipp

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Abstract

Throughout the COVID-19 pandemic, certain communities have been disproportionately exposed to detrimental health outcomes and socioeconomic injuries. Quantifying community needs is crucial for identifying testing and service deserts, effectively allocating resources, and informing funding and decision making. We have constructed research-driven metrics measuring the public health and economic impacts of COVID-19 on vulnerable populations. In this work we further examine and validate these indices by training supervised models to predict proxy outcomes and analyzing the feature importances to identify gaps in our original metric design. The indices analyzed in this work are unique among COVID-19 risk assessments due to their robust integration of disparate data sources. Together, they enable more effective responses to COVID-19 driven health inequities.

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    SciScore for 10.1101/2021.09.19.21263805: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.
    • Thank you for including a protocol registration statement.

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

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  2. Version published to 10.1101/2021.09.19.21263805v1 on medRxiv