A County-Level Susceptibility Index and Coronavirus Disease 2019 Mortality in the United States: A Socioecological Study

  1. Sadiya S. Khan
  2. Megan E. McCabe
  3. Amy E. Krefman
  4. Lucia C. Petito
  5. Xiaoyun Yang
  6. Kiarri N. Kershaw
  7. Lindsay Pool
  8. Norrina B. Allen

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

As of June 2020, the United States (US) has experienced the highest number of deaths related to coronavirus disease 2019 (Covid-19) in the world, but significant geographic heterogeneity exists at the county-level. Therefore, we sought to classify counties in the United States across multiple domains utilizing a socioecological framework and examine the association between these county-level groups and Covid-19 mortality. We harmonized and linked county-level sociodemographic, health, and environmental metrics associated with increased susceptibility for Covid-19 mortality. Latent class analysis defined a county-level susceptibility index (CSI) based on these metrics (n=2701 counties). Next, we used linear regression models to estimate the associations of the CSI and Covid-19 deaths per capita and initial mortality doubling time (as of 6/2/20), adjusted for days since first Covid-19 case. We identified 4 groups classified by the CSI with distinct sociodemographic, health, and environmental profiles and widespread geographic dispersion. Covid-19 deaths per capita were significantly higher in the group consisting of rural, vulnerable counties (55.8 [95% CI 50.3-61.2] deaths per 100,000) compared with the group with diverse, urban counties (32.2 [27.3-37.0]) at similar points in the outbreak (76 days since first case). Our findings can inform equitable resource allocation for Covid-19 to allow targeted public health preparedness and response in vulnerable counties.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.07.04.20146084: (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:
    Limitations of our analysis include potential misclassification of Covid-19 mortality data due to under-ascertainment of Covid-19 cases due to variable testing availability and resources across the US. Further, we were not able to adjust for testing per capita at the county level as this information is only available at the state-level. However, our analysis relied on the available comprehensive estimates of cumulative Covid-19 mortality at the county-level and incorporated novel methodologic approaches. Policy changes during the study period (e.g. social distancing, shelter-in-place) were heterogeneous across counties and states and therefore were not adjusted for, but may have biased our results towards the null. Finally, our analysis does not account for individual-level characteristics or predictors of disease severity. However, the current study is ecological. It is not meant to infer causation; rather it is meant to inform large-scale comparisons at the population-level to communicate with local health departments and leaders to inform decisions at the county-level.32 The findings from this current study provide unique insights into county-level characteristics that may inform the lack of reserve or resilience at a population-level when challenged by an unexpected stress of a highly infectious communicable disease that carries a significant risk of morbidity and mortality, such as Covid-19.17 The CSI classification scheme expands upon prior analyses by developing a simp...

    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 10.1101/2020.07.04.20146084v1 on medRxiv