Decarceration and COVID-19 infections in U.S. Immigration and Customs Enforcement detention facilities: a simulation modeling studyResearch in context
Summary: Background: U.S. Immigration and Customs Enforcement (ICE) facilities had high rates of COVID-19 infections and mortality during the global pandemic. We sought to quantify how many COVID-19 infections could have been averted through different decarceration strategies. Methods: We developed...
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| Format: | Article |
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Elsevier
2025-02-01
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| Series: | The Lancet Regional Health. Americas |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667193X24002989 |
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| author | Christopher Weyant Jaimie P. Meyer Daniel Bromberg Chris Beyrer Frederick L. Altice Jeremy D. Goldhaber-Fiebert |
| author_facet | Christopher Weyant Jaimie P. Meyer Daniel Bromberg Chris Beyrer Frederick L. Altice Jeremy D. Goldhaber-Fiebert |
| author_sort | Christopher Weyant |
| collection | DOAJ |
| description | Summary: Background: U.S. Immigration and Customs Enforcement (ICE) facilities had high rates of COVID-19 infections and mortality during the global pandemic. We sought to quantify how many COVID-19 infections could have been averted through different decarceration strategies. Methods: We developed a set of stochastic simulation models of SARS-CoV-2 transmission in ICE facilities. Employing incremental mixture importance sampling (IMIS), we calibrated them to empirical targets derived from publicly available case time series for ICE facilities, and publicly available facility population censuses prior to vaccine availability (May 6, 2020 to December 31, 2020). The models included infection importation from extra-facility sources. We evaluated reduction of the incarcerated population by 10–90%. People who were decarcerated faced background cumulative risks of infection and detection based on a weighted average of county-level estimates from the covidestim model, which is a Bayesian evidence synthesis model. Findings: Without decarceration, the infection rate was 5.05 per 1000 person-days (95% CrI 3.40–6.81) and case rate was 1.53 per 1000 person-days (95% CrI 1.04–2.02). Rates declined linearly when decarceration did not reduce contacts of people remaining in facilities and faster than linearly when it did reduce contacts. At all decarceration levels, rates were substantially higher when contacts were not reduced. Even with 90% decarceration, infection rates for people remaining in facilities were higher than or comparable to otherwise similar free-living people. Interpretation: The decline in COVID-19 infection rates with decarceration was linear or faster than linear depending on how decarceration was implemented. Our findings highlight infection risks associated with incarceration, which compound other health harms of incarceration. Funding: Stanford’s COVID-19 Emergency Response Fund; the National Institute on Drug Abuse; and the National Institute of Mental Health. |
| format | Article |
| id | doaj-art-a15b3823f85743a5adf4abab3da7cc5e |
| institution | Kabale University |
| issn | 2667-193X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | The Lancet Regional Health. Americas |
| spelling | doaj-art-a15b3823f85743a5adf4abab3da7cc5e2024-12-28T05:23:03ZengElsevierThe Lancet Regional Health. Americas2667-193X2025-02-0142100971Decarceration and COVID-19 infections in U.S. Immigration and Customs Enforcement detention facilities: a simulation modeling studyResearch in contextChristopher Weyant0Jaimie P. Meyer1Daniel Bromberg2Chris Beyrer3Frederick L. Altice4Jeremy D. Goldhaber-Fiebert5Department of Health Policy, Stanford School of Medicine, Stanford, CA, USA; Center for Health Policy, Freeman Spogli Institute, Stanford University, Stanford, CA, USASection of Infectious Diseases, Yale School of Medicine, New Haven, CT, USA; Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA; Corresponding author. Yale School of Medicine and Yale School of Public Health, 135 College Street, Suite 323, New Haven, CT 06510, USA.Section of Infectious Diseases, Yale School of Medicine, New Haven, CT, USAGlobal Health Institute, Duke University, Durham, NC, USASection of Infectious Diseases, Yale School of Medicine, New Haven, CT, USA; Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USADepartment of Health Policy, Stanford School of Medicine, Stanford, CA, USA; Center for Health Policy, Freeman Spogli Institute, Stanford University, Stanford, CA, USASummary: Background: U.S. Immigration and Customs Enforcement (ICE) facilities had high rates of COVID-19 infections and mortality during the global pandemic. We sought to quantify how many COVID-19 infections could have been averted through different decarceration strategies. Methods: We developed a set of stochastic simulation models of SARS-CoV-2 transmission in ICE facilities. Employing incremental mixture importance sampling (IMIS), we calibrated them to empirical targets derived from publicly available case time series for ICE facilities, and publicly available facility population censuses prior to vaccine availability (May 6, 2020 to December 31, 2020). The models included infection importation from extra-facility sources. We evaluated reduction of the incarcerated population by 10–90%. People who were decarcerated faced background cumulative risks of infection and detection based on a weighted average of county-level estimates from the covidestim model, which is a Bayesian evidence synthesis model. Findings: Without decarceration, the infection rate was 5.05 per 1000 person-days (95% CrI 3.40–6.81) and case rate was 1.53 per 1000 person-days (95% CrI 1.04–2.02). Rates declined linearly when decarceration did not reduce contacts of people remaining in facilities and faster than linearly when it did reduce contacts. At all decarceration levels, rates were substantially higher when contacts were not reduced. Even with 90% decarceration, infection rates for people remaining in facilities were higher than or comparable to otherwise similar free-living people. Interpretation: The decline in COVID-19 infection rates with decarceration was linear or faster than linear depending on how decarceration was implemented. Our findings highlight infection risks associated with incarceration, which compound other health harms of incarceration. Funding: Stanford’s COVID-19 Emergency Response Fund; the National Institute on Drug Abuse; and the National Institute of Mental Health.http://www.sciencedirect.com/science/article/pii/S2667193X24002989IncarcerationImmigration detentionCOVID-19Simulation modeling |
| spellingShingle | Christopher Weyant Jaimie P. Meyer Daniel Bromberg Chris Beyrer Frederick L. Altice Jeremy D. Goldhaber-Fiebert Decarceration and COVID-19 infections in U.S. Immigration and Customs Enforcement detention facilities: a simulation modeling studyResearch in context The Lancet Regional Health. Americas Incarceration Immigration detention COVID-19 Simulation modeling |
| title | Decarceration and COVID-19 infections in U.S. Immigration and Customs Enforcement detention facilities: a simulation modeling studyResearch in context |
| title_full | Decarceration and COVID-19 infections in U.S. Immigration and Customs Enforcement detention facilities: a simulation modeling studyResearch in context |
| title_fullStr | Decarceration and COVID-19 infections in U.S. Immigration and Customs Enforcement detention facilities: a simulation modeling studyResearch in context |
| title_full_unstemmed | Decarceration and COVID-19 infections in U.S. Immigration and Customs Enforcement detention facilities: a simulation modeling studyResearch in context |
| title_short | Decarceration and COVID-19 infections in U.S. Immigration and Customs Enforcement detention facilities: a simulation modeling studyResearch in context |
| title_sort | decarceration and covid 19 infections in u s immigration and customs enforcement detention facilities a simulation modeling studyresearch in context |
| topic | Incarceration Immigration detention COVID-19 Simulation modeling |
| url | http://www.sciencedirect.com/science/article/pii/S2667193X24002989 |
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