Determinants of COVID-19 Case Fatality Rate in the US: Spatial Analysis Over One Year of the Pandemic

  1. Niranjan Kathe
  2. Rajvi Wani

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Abstract

Background: The United States continues to account for the highest proportion of the global Coronavirus Disease-2019 (COVID-19) cases and deaths. Currently, it is important to contextualize COVID-19 fatality to guide mitigation efforts. Objectives: The objective of this study was to assess the ecological factors (policy, health behaviors, socio-economic, physical environment, and clinical care) associated with COVID-19 case fatality rate (CFR) in the United States. Methods: Data from the New York Times’ COVID-19 repository and the Centers for Disease Control and Prevention Data (01/21/2020 - 02/27/2021) were used. County-level CFR was modeled using the Spatial Durbin model (SDM). The SDM estimates were decomposed into direct and indirect impacts. Results: The study found percent positive for COVID-19 (0.057% point), stringency index (0.014% point), percent diabetic (0.011% point), long-term care beds (log) (0.010% point), premature age-adjusted mortality (log) (0.702 % point), income inequality ratio (0.078% point), social association rate (log) (0.014% point), percent 65 years old and over (0.055% point), and percent African Americans (0.016% point) in a given county were positively associated with its COVID-19 CFR. The study also found food insecurity, long-term beds (log), mental health-care provider (log), workforce in construction, social association rate (log), and percent diabetic of a given county as well as neighboring county were associated with given county’s COVID-19 CFR, indicating significant externalities. Conclusion: The spatial models identified percent positive for COVID-19, stringency index, elderly, college education, race/ethnicity, residential segregation, premature mortality, income inequality, workforce composition, and rurality as important ecological determinants of the geographic disparities in COVID-19 CFR.

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  1. Version published to 10.36469/jheor.2021.22978
  2. ScreenIT

    SciScore for 10.1101/2020.12.02.20242685: (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
    Covariates: The covariates selected to predict the county-level incidence and mortality were based on the County Health Ranking framework.
    Covariates
    suggested: None

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The study has important limitations. First, log transforming the outcomes i.e. cumulative incidence and mortality rates as a linear dependent variable may mask the variations across counties. To our understanding, there is no software package currently available that runs a spatial lag model with a dependent variable with Poisson or Binomial distribution and thus, this study transformed the outcomes in their logarithmic form. Given the cross-sectional nature of the study, no causal inferences can be made. There are considerable differences in the testing rates across regions and counties and can influence the observed incidence rate. The list of variables is by no means comprehensive and does not include several other factors such as mobility, local restriction policies (county or city-level), compliance with local and federal prevention guidelines. The current analysis is ecological in nature and no direct inferences can be drawn at the individual level.

    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.

  3. Version published to 10.1101/2020.12.02.20242685 on medRxiv