Application of Social Vulnerability Index to Identify High- risk Population of Contracting COVID-19 Infection: a state-level study

  1. Odalys Estefania Lara-Garcia
  2. Violeta Alvarez Retamales
  3. Oswaldo Madrid Suarez
  4. Priyanka Parajuli
  5. Susan Hingle
  6. Robert Robinson

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Social factors that determine a population’s health are known as the social determinants of health. During the past few weeks, as COVID-19 cases grew exponentially, the discrepancy among the number of cases distribution was evident.By applying the social vulnerability index and analyzing data from a total of 102 counties across the state of Illinois, we investigated which factors enhanced the risk of contracting the infection and which were related to a lower risk of infection. Our results showed that social factors such as belonging to a minority group, speaking English less than well, living in a multi-unit structure, and households with individuals of age group of 17 or younger were associated with a higher risk of infection. On the other hand, we found that factors such as living in a mobile home, individuals of age group 65 or older, low income per capita and, older than age 5 with disability were protective. We propose that communities with disproportionate health burdens can be identified by the application of these factors. Future efforts need to focus on decreasing the gap of to decrease the gap of disparity by modifying these social factors.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.08.03.20166983: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analyses were performed using SPSS version 25 (SPSS Inc., Chicago, IL, USA).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.08.03.20166983v1 on medRxiv