A COVID-19 Community Vulnerability Index to drive precision policy in the US

  1. Peter Smittenaar
  2. Nicholas Stewart
  3. Staci Sutermaster
  4. Lindsay Coome
  5. Aaron Dibner-Dunlap
  6. Mokshada Jain
  7. Yael Caplan
  8. Christine Campigotto
  9. Sema K. Sgaier

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Background

In April 2020 we released the US COVID-19 Community Vulnerability Index (CCVI) to bring to life vulnerability to health, economic, and social impact of COVID-19 at the state, county, and census tract level. Here we describe the methodology, how vulnerability is distributed across the U.S., and assess the impact on vulnerable communities over the first year of the pandemic.

Methods

The index combines 40 indicators into seven themes, drawing on both public and proprietary data. We associate timeseries of COVID-19 cases, deaths, test site access, and rental arrears with vulnerability.

Results

Although overall COVID-19 vulnerability is concentrated in the South, the seven underlying themes show substantial spatial variability. As of May 13, 2021, the top-third of vulnerable counties have seen 21% more cases and 47% more deaths than the bottom-third of vulnerable counties, despite receiving 27% fewer tests (adjusted for population). Individual vulnerability themes vary over time in their relationship with mortality as the virus swept across the country. Over 20% of households in the top vulnerability tercile have fallen behind on rent. Poorer test site access for rural vulnerable populations early in the pandemic has since been alleviated.

Conclusion

The CCVI captures greater risk of health and economic impact. It has enjoyed widespread use in response planning, and we share lessons learned about developing a data-driven tool in the midst of a fast-moving pandemic. The CCVI and an interactive data explorer are available at precisionforcovid.org/ccvi.

What is already known on this topic

  • Various communities across the United States will experience the adverse effects of public health crises to different degrees of severity.

  • Composite indicators, such as the CDC Social Vulnerability Index, have proven to be valuable to policymakers by turning complex data sets into easily digestible and actionable information. However, the indicators within the Social Vulnerability Index do not fully contextualize the negative impacts spurred by the current pandemic.

What this study adds

  • The U.S. COVID-19 Community Vulnerability Index captures vulnerabilities spanning health, social, and economic dimensions that have been felt by every community in the US differently.

  • Vulnerable populations have experienced more cases and deaths, higher unemployment, and a lack of access to critical support such as testing sites.

  • Precision policies targeting vulnerable populations need to be designed and enacted to decrease the gap in negative consequences experienced in this and future pandemics, and the COVID-19 Community Vulnerability Index is a tool to highlight where and why these inequities occur.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.05.19.21257455: (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

    Software and Algorithms
    SentencesResources
    We performed principal components analysis to understand the shared properties of the CCVI’s underlying themes using the FactoMineR package in R (25), with the corresponding loadings reported in a distance biplot.
    FactoMineR
    suggested: (FactoMineR, RRID:SCR_014602)

    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:
    Limitations: The CCVI assigns one score to each geographic unit, which contains anywhere between a few thousand to millions of individuals (for a select few counties). However, some indicators are available only at the county or state level, such that each census tract is assigned the same score for that indicator. Whilst this is not always an issue - e.g. health system indicators are not sensible at the census tract level - in some cases this hides substantial heterogeneity for an indicator within a geography. A related point is that every indicator is a summary statistic, hiding large individual differences. There are many counties with enormous inequity in health and social indicators, and the plight of vulnerable populations in such communities is easily lost in a population average. The index captures underlying vulnerabilities to COVID-19 that change over months or years, and most indicators are based on pre-pandemic data. This means the index does not reflect the pandemic response, such as state-level policies intended to protect the vulnerable, nor does it reflect the day-to-day changes in cases, deaths, mobility, and so forth. This marks a clear difference to many epidemiological models narrowly focused on infections, hospitalization, and mortality (33, 34). However, in our work with government and other partners, the unchanging nature of the index has proven particularly beneficial, as long as the index was appropriately combined with live data. That is, we consider...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

    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 10.1101/2021.05.19.21257455v1 on medRxiv