Background: Our objective was to examine the temporal relationship between COVID-19 infections among prison staff, incarcerated individuals, and the general population in the county where the prison is located among federal prisons in the United States. Methods: We employed population-standardized regressions with fixed effects for prisons to predict the number of active cases of COVID-19 among incarcerated persons using data from the Federal Bureau of Prisons (BOP) for the months of March to December in 2020 for 63 prisons. Results: There is a significant relationship between the COVID-19 prevalence among staff, and through them, the larger community, and COVID-19 prevalence among incarcerated persons in the US federal prison system. When staff rates are low or at zero, COVID-19 incidence in the larger community continues to have an association with COVID-19 prevalence among incarcerated persons, suggesting possible pre-symptomatic and asymptomatic transmission by staff. Masking policies slightly reduced COVID-19 prevalence among incarcerated persons, though the association between infections among staff, the community, and incarcerated persons remained significant and strong. Conclusion: The relationship between COVID-19 infections among staff and incarcerated persons shows that staff is vital to infection control, and correctional administrators should also focus infection containment efforts on staff, in addition to incarcerated persons.

Background Since the novel coronavirus SARS-COV-2 was first identified to be circulating in the US on January 20, 2020, some of the worst outbreaks have occurred within state and federal prisons. The vulnerability of incarcerated populations, and the additional threats posed to the health of prison staff and the people they contact in surrounding communities underline the need to better understand the dynamics of transmission in the inter-linked incarcerated population/staff/community sub-populations to better inform optimal control of SARS-COV-2. Methods We examined SARS-CoV-2 case data from 101 non-administrative federal prisons between 5/18/2020 to 01/31/2021 and examined the per capita size of outbreaks in staff and the incarcerated population compared to outbreaks in the communities in the counties surrounding the prisons during the summer and winter waves of the SARS-COV-2 pandemic. We also examined the impact of decarceration on per capita rates in the staff/incarcerated/community populations. Results For both the summer and winter waves we found significant inter-correlations between per capita rates in the outbreaks among the incarcerated population, staff, and the community. Over-all during the pandemic, per capita rates were significantly higher in the incarcerated population than in both the staff and community (paired Student’s t-test p = 0.03 and p < 0.001, respectively). Average per capita rates of incarcerated population outbreaks were significantly associated with prison security level, ranked from lowest per capita rate to highest: High, Minimum, Medium, and Low security. Federal prisons decreased the incarcerated population by a relative factor of 96% comparing the winter to summer wave (one SD range [90%,102%]). We found no significant impact of decarceration on per capita rates of SARS-COV-2 infection in the staff community populations, but decarceration was significantly associated with a decrease in incarcerated per capita rates during the winter wave (Negative Binomial regression p = 0.015). Conclusions We found significant evidence of community/staff/incarcerated population inter-linkage of SARS-COV-2 transmission. Further study is warranted to determine which control measures aimed at the incarcerated population and/or staff are most efficacious at preventing or controlling outbreaks.

The views expressed in this report are the author's and do not necessarily reflect those of the Department for Education and Skills.

Public organizations, including institutions in the U.S. criminal justice (CJ) system, have been rapidly releasing information pertaining to COVID-19. Even CJ institutions typically reticent to share information, like private prisons, have released vital COVID-19 information. The boon of available pandemic-related data, however, is not without problems. Unclear conceptualizations, stakeholders’ influence on data collection and release, and a lack of experience creating public dashboards on health data are just a few of the issues plaguing CJ institutions surrounding releasing COVID-19 data. In this article, we detail issues that institutions in each arm of the CJ system face when releasing pandemic-related data. We conclude with a set of recommendations for researchers seeking to use the abundance of publicly available data on the effects of the pandemic.

The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to impact the United States. While age and comorbid health conditions remain primary concerns in the community-based transmission of the virus, empirical evidence continues to suggest that substantial variability exists in the geographic and geodemographic distribution of COVID-19 infection rates. The purpose of this paper is to provide an alternative, spatiotemporal perspective on the pandemic using the state of Wisconsin as a case study. Specifically, in this paper, we explore the geographic nuances of COVID-19 and its spread in Wisconsin using a suite of spatial statistical approaches. We link detected hot spots of COVID-19 to local geodemographic profiles and the presence of high-risk facilities, including federal and state correctional facilities. The results suggest that the virus disproportionately impacts several communities and geodemographic groups and that proximity to risky facilities correlates to increased community infection rates.

Legal and qualitative studies have highlighted that courtroom actors consider multiple aspects of criminal records. However, most quantitative studies on sentencing only included the number of prior convictions or arrests, with little attention to the seriousness trends of those priors. Taking stock from studies on criminal careers, we used group-based trajectory modeling (GBTM) to identify patterns of escalation and de-escalation among a defendant sample in New York State ( n = 56,017), and then examined the role of trajectory groups in four decision points: dismissal, charge reduction, incarceration, and incarceration length. We found that escalation, de-escalation, and a higher stable level of crime seriousness were associated with less favorable outcomes at multiple decision points.

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