Disparities in COVID-19 Related Mortality in U.S. Prisons and the General Population

  1. Kathryn M. Nowotny
  2. David Cloud
  3. Alysse G. Wurcel
  4. Lauren Brinkley-Rubinstein

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Abstract

We provide an analysis of COVID-19 mortality data to assess the potential magnitude of COVID-19 among prison residents. Data were pooled from Covid Prison Project and multiple publicly available national and state level sources. Data analyses consisted of standard epidemiologic and demographic estimates. A single case study was included to generate a more in-depth and multi-faceted understanding of COVID-19 mortality in prisons. The increase in crude COVID-19 mortality rates for the prison population has outpaced the rates for the general population. People in prison experienced a significantly higher mortality burden compared to the general population (standardized mortality ratio (SMR) = 2.75; 95% confidence interval = 2.54, 2.96). For a handful of states (n = 5), these disparities were more extreme, with SMRs ranging from 5.55 to 10.56. Four states reported COVID-19 related death counts that are more than 50% of expected deaths from all-causes in a calendar year. The case study suggested there was also variation in mortality among units within prison systems, with geriatric facilities potentially at highest risk. Understanding the dynamic trends in COVID-19 mortality in prisons as they move in and out of “hotspot” status is critical.

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  1. Rapid Reviews Infectious Diseases

    Josiah Rich

    Review 2: "Disparities in COVID-19 Related Mortality in U.S. Prisons and the General Population"

    This study draws much-needed attention to the higher COVID-19 mortality burden among US prison populations, however, reviewers raised several methodological concerns involved in the authors' calculations of the scale of the disparities.

  3. ScreenIT

    SciScore for 10.1101/2020.09.17.20183392: (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

    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.
    • 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.

  4. Version published to 10.1101/2020.09.17.20183392v1 on medRxiv