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. 2022 May 18;22(1):1003.
doi: 10.1186/s12889-022-13219-4.

The impact of COVID-19 vaccination in prisons in England and Wales: a metapopulation model

Collaborators, Affiliations

The impact of COVID-19 vaccination in prisons in England and Wales: a metapopulation model

Ciara V McCarthy et al. BMC Public Health. .

Abstract

Background: High incidence of cases and deaths due to coronavirus disease 2019 (COVID-19) have been reported in prisons worldwide. This study aimed to evaluate the impact of different COVID-19 vaccination strategies in epidemiologically semi-enclosed settings such as prisons, where staff interact regularly with those incarcerated and the wider community.

Methods: We used a metapopulation transmission-dynamic model of a local prison in England and Wales. Two-dose vaccination strategies included no vaccination, vaccination of all individuals who are incarcerated and/or staff, and an age-based approach. Outcomes were quantified in terms of COVID-19-related symptomatic cases, losses in quality-adjusted life-years (QALYs), and deaths.

Results: Compared to no vaccination, vaccinating all people living and working in prison reduced cases, QALY loss and deaths over a one-year period by 41%, 32% and 36% respectively. However, if vaccine introduction was delayed until the start of an outbreak, the impact was negligible. Vaccinating individuals who are incarcerated and staff over 50 years old averted one death for every 104 vaccination courses administered. All-staff-only strategies reduced cases by up to 5%. Increasing coverage from 30 to 90% among those who are incarcerated reduced cases by around 30 percentage points.

Conclusions: The impact of vaccination in prison settings was highly dependent on early and rapid vaccine delivery. If administered to both those living and working in prison prior to an outbreak occurring, vaccines could substantially reduce COVID-19-related morbidity and mortality in prison settings.

Keywords: COVID-19; Mathematical model; Prisons; Public health; Vaccination.

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Conflict of interest statement

O.O’M. is employed by Her Majesty's Prison and Probation Service. All other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
A-C Cases, QALY loss and deaths over one year in an average local male prison, under each vaccination scenario per sub-population and in total. Staff Group 1 are prison staff without contact with those are incarcerated. Staff Group 2 are staff with contact with those who are incarcerated. D-F Vaccination course per case, QALY lost, and death averted over one year under each vaccination scenario. The axes match those used in Fig. 4 for ease of comparison
Fig. 2
Fig. 2
Incidence of new clinical cases over time under each vaccination scenario, including uncertainty captured using probabilistic sensitivity analysis
Fig. 3
Fig. 3
Cases, QALY loss and deaths over one year under differing assumptions about mixing between sub-groups. Scenario (1): homogenous mixing amongst staff; no contact with those who are incarcerated. Scenario (2): homogenous mixing amongst Staff Group 2 and those who are incarcerated; no contact with Staff Group 1. The axes match those of Fig. 4 for ease of comparison
Fig. 4
Fig. 4
Incidence of new clinical cases over one year in an average local male prison, under seven different vaccination scenarios. Assuming 10%, 30% and 50% of the population have prior natural immunity
Fig. 5
Fig. 5
Incidence of new clinical cases over time under each vaccination scenario and under different values of R0
Fig. 6
Fig. 6
A-C Cases, QALY loss and deaths over one year under each vaccination scenario, when assuming a higher IFR in those who are incarcerated. D-F Vaccination courses per case, QALY loss and death averted, when assuming higher IFR in those who are incarcerated
Fig. 7
Fig. 7
Sensitivity of estimated total cases averted under each vaccination scenario to variation in parameters. Bars show PRCCs corresponding to each parameter varied in probabilistic sensitivity analysis

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