Carceral Amplification of COVID-19: Impacts for Community, Corrections Officer, and Incarcerated Population Risks

  1. Eric T. Lofgren
  2. Kristian Lum
  3. Aaron Horowitz
  4. Brooke Mabubuonwu
  5. Kellen Meyers
  6. Nina H. Fefferman

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Abstract

COVID-19 is challenging many societal institutions, including our criminal justice systems. Some have proposed or enacted (e.g., the State of New Jersey) reductions in the jail and/or prison populations. We present a mathematical model to explore the epidemiologic impact of such interventions in jails and contrast them with the consequences of maintaining unaltered practices. We consider infection risk and likely in-custody deaths, and estimate how within-jail dynamics lead to spill-over risks, not only affecting incarcerated people but increasing exposure, infection, and death rates for both corrections officers and the broader community beyond the justice system. We show that, given a typical jail-community dynamic, operating in a business-as-usual way results in substantial, rapid, and ongoing loss of life. Our results are consistent with the hypothesis that large-scale reductions in arrest and speeding of releases are likely to save the lives of incarcerated people, jail staff, and the wider community.

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  1. Version published to 10.1097/ede.0000000000001476
  2. Version published to 10.1101/2020.04.08.20058842v3 on medRxiv
  3. NCRC

    Our take

    Authors sought to describe the dynamics of COVID-19 within the Allegheny County, PA jail system across multiple age groups and personnel and three distinct transmission populations (community, processing and court, and jail) in this study, available as a preprint and thus not yet peer reviewed. Results indicated that interventions that reduced population mixing within the jail system markedly delayed the COVID-19 outbreak and reduced the magnitude of the epidemic curve. Incarcerated populations are more vulnerable to COVID-19 infection and severe outcomes, as these populations experience high rates of turnover, less access to healthcare resources, and limited ability to practice social distancing. This study highlights the importance of considering reform policies that would impact society’s most vulnerable.

    Study design

    modeling-simulation

    Study population and setting

    Authors used a Susceptible-Exposed-Infected-Medically treated-Removed model to describe the dynamics of COVID-19 transmission within the Allegheny County, PA corrections system, among incarcerated persons and their contacts. Authors segmented the model into five sub-populations (persons <18 years, low-risk adults, high-risk adults, elderly persons >65 years, and corrections personnel) and three transmission systems (the community, processing and court, and jail). Persons <18 years were only included in the community system, but could interact with sub-populations that were able to move within systems (e.g., a low risk adult moving from the community to processing and court). The model assumed that approximately 100 persons were arrested per day. Authors also included several interventions/policy changes (shelter-in-place orders, reductions in arrests, increases in rates of release, and changes to within-jail conditions) to estimate the impact these adaptations had on transmission dynamics within the three transmission systems.

    Summary of main findings

    The model estimated that an unmitigated outbreak would result in 926,108 infections, 51,497 hospitalizations, and 12,133 deaths in the community over the course of 180 days. The model estimated a more severe outbreak among incarcerated individuals: the 2,500 person-capacity jail was 0.2% the size of the greater community and individuals in this system experienced 4,949 infections, 264 hospitalizations, and 79 deaths. The peak was also much earlier in the jail system than the community system, occurring 31 days after the first infective case compared to 88 days after. Assuming shelter-in-place orders, infections, hospitalizations, and deaths in the community were considerably delayed and reduced (e.g., infections were reduced to 450,621). Conversely, within incarcerated populations, infections during the first half of the simulation (days 0-90) were similar to the unmitigated simulation, and the second half (days 91-180) were notably worse, resulting in a total of 7,421 infections over the course of the entire simulation. Reform within the jail system had greater impacts on incarcerated individuals, and to a lesser extent, jail personnel and the community. For example, discontinuing the arrests of bail-eligible individuals results in 22.9% and 3.9% reductions in infections among incarcerated individuals and the community, respectively. Discontinuing all low-level offense arrests resulted in 74.0%, 10.2%, and 19.7% reductions in infections among incarcerated individuals, jail personnel, and the community, respectively.

    Study strengths

    The model accounted for movement of populations between the different systems, and did not restrict movement to one direction. Authors calibrated the model based on the size and mixing patterns of each transmission system’s population.

    Limitations

    Authors assumed that once patients were hospitalized, they did not spread COVID-19 any further. Although PPE worn by hospital staff limits ongoing transmission of COVID-19 in such settings, this may not always be true. Authors also assumed that all individuals leaving jail went straight back to the community, and did not account for other post-jail destinations, such as prison. However, authors argued that this is a small percentage of the population leaving jail and would not have a significant impact on results. The model used parameters specific to Allegheny County, PA (e.g., daily arrests, number of corrections personnel, size of jail population), so results may not be generalizable to locations with different population makeups.

    Value added

    Despite the United States having one of the highest incarceration rates in the world, most public models have excluded jails and other correctional facilities from their projections, potentially vastly underestimating the impact of COVID-19. This study is among the first to estimate the impact of COVID-19 on incarcerated populations, as well as on those with whom incarcerated individuals interact.

  4. ScreenIT

    SciScore for 10.1101/2020.04.08.20058842: (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

    Software and Algorithms
    SentencesResources
    The model was implemented in R 3.6.3 using the de-Solve package, with the visualization of results primarily using ggplot2.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

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

    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.

  5. Version published to 10.1101/2020.04.08.20058842v2 on medRxiv
  6. Version published to 10.1101/2020.04.08.20058842v1 on medRxiv