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Impact of vaccination by priority group on UK deaths, hospital admissions and intensive care admissions from COVID‐19

Summary National (and global) vaccination provides an opportunity to control the COVID‐19 pandemic, which disease suppression by societal lockdown and individual behavioural changes will not. We modelled how vaccination through the UK’s vaccine priority groups impacts deaths, hospital and ICU admissions from COVID‐19. We used the UK COVID‐19 vaccines delivery plan and publicly available data to estimate UK population by age group and vaccination priority group, including frontline health and social care workers and individuals deemed ‘extreme clinical vulnerable’ or ‘high risk’. Using published data on numbers and distributions of COVID‐19‐related hospital and ICU admissions and deaths, we modelled the impact of vaccination by age group. We then modified the model to account for hospital and ICU admission, and death among health and social care workers and the population with extreme clinical vulnerability and high risk. Our model closely matches the government’s estimates for mortality after vaccination of priority groups 1–4 and groups 1–9. The model shows vaccination will have a much slower impact on hospital and ICU admissions than on deaths. The early prioritisation of healthcare staff and clinically vulnerable patients increases the impact of vaccination on admissions and also protects the healthcare service. An inflection point, when 50% of the adult population has been vaccinated – with deaths reduced by 95% and hospital admissions by 80% – may be a useful point for re‐evaluating vaccine prioritisation. Our model suggests substantial reductions in hospital and ICU admissions will not occur until late March and into April 2021.


Introduction
The SARS-CoV-2 virus that causes COVID-19 continues to drive a pandemic with recent viral mutation increasing both spread and pressure on hospitals and intensive care units (ICU). Efforts to control spread of the virus with personal actions (reduced interpersonal contact, physical distancing, hand washing and mask wearing) or national initiatives (travel restrictions, closure of schools and lockdowns) have suppressed transmission to some extent but do not alter the fundamentals of the pandemic.
Vaccination on a national and global scale provides an opportunity to control the pandemic. Most focus has been on the impact of the vaccination program on reducing deaths, and the priority groups adopted have previously been shown to achieve this aim [1], but its impact on an overstretched healthcare system is also important. At the time of writing, the UK National Health Service (NHS) has had to reduce much elective surgical activity [2] and ICUs are in surge, meaning that they are coping only through expanding beyond their normal location, redeploying staff and decreasing staff-to-patient ratios [3].
In this paper, we have modelled how the vaccination program in the UK is likely to impact on COVID-19-related deaths, hospital admissions and ICU admissions among adults.

Methods
We modelled using data from the Department of Health and Social Care's COVID-19 vaccines delivery plan for the size of population in each vaccination priority group (Table 1) [4].
We based the analysis on data sources from June to July 2020 as this was complete and indicative of the overall impact of the first surge of the pandemic in the UK, enabling valid comparisons across groups. We used population distribution of UK countries from Office for National Statistics (ONS) data [5]. Deaths by age group were from ONS data for June 2020 [6]. The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) study reporting on 20,000 COVID-19 admissions to UK hospitals between 6 February and 19 April 2020 was used for age distribution of patients admitted to hospital [7]. Age distribution of ICU admissions was taken from the Intensive Care National Audit and Research Centre (ICNARC) report of 5 June 2020 [8]. We used publicly available national databases as sources of total numbers of COVID-19-related deaths in England and Wales up until 6 July [6], UK hospital admissions to 6 June UK [9] and ICU admissions in England, Northern Ireland and Wales to 5 June 2020 [8] and these were scaled up to UK populations as necessary. The number of deaths involving COVID-19 among health and social care workers in England and Wales was taken from the ONS publication which reported those registered between 9 March and 20 July 2020 [10]. Age distribution of this group was taken from the database held by Dr Lennane and Professor Cook which underpins several publications on the topic [11,12]. Number of healthcare workers admitted to hospital and ICU was taken from Shah et al.'s analysis of hospital and ICU admission of healthcare staff in Scotland [13]. Age distribution of NHS staff was taken from NHS Employers' data [14]. Age distribution of people with longterm conditions was taken from ONS data [15]. The impact of comorbidities on risk of mortality was estimated from the studies by Williamson et al. and Clift et al. that studied which patient characteristics are associated with COVID-19related death, or hospital admission and death, respectively [16,17]. We made the following assumptions in our model. The vaccination program will be on target to provide a first vaccination dose to all groups 1-4 by 15 February 2021 and will continue at the same rate. Vaccination will broadly be undertaken by group as listed in the vaccine delivery plan [4]. Vaccination will be complete in all groups with 100% take-up. The virus affects the population baseline characteristics in the same manner as in the first wave.
Vaccination is 100% effective in preventing hospitalisation, and this is only consistent with staff in patient-facing roles [11]. Although increasing age is associated with increasing numbers of long-term conditions [18], we assumed allocation to group 4 and 6 is on the basis of any single qualifying comorbidity or condition [19], rather than multiple conditions. The age distribution of patients who are extremely clinically vulnerable and at risk is the same as that of patients with long-term conditions. The proportion of those aged 65-70 with long-term conditions lies at the midpoint of the proportion aged > 65 and those aged 60-65. Data were analysed using Microsoft Excel (Redmond, WA, USA).

Modelling is described in detail in online Supporting
Information Appendix S1 and is described here more briefly. As much of the data available in source material were in 10-year age bands, we combined vaccination priority groups where necessary. Denominators for UK deaths, hospital admissions and ICU admissions were taken, or extrapolated where necessary, from the above data sources.
We used the above data sources to determine deaths and hospital and ICU admissions due to COVID-19 adjusted to UK figures [4][5][6][7][8]. The first model ( Fig. 1) was based only on age bands and ignored adjustments for health and social care workers (group 2), the extremely clinically vulnerable aged < 70 years (group 4) and those at risk aged < 65 years (group 5).
We then adjusted for group 2 (frontline health and social care workers). The overall size of this group was taken from the UK COVID-19 vaccines delivery plan [4]. Excess deaths, hospital and ICU admissions were calculated using the data above [9][10][11][12][13]. Calculated additional deaths in this group [10] were added to group 2. We estimated that data. These additional hospital and ICU admissions were added to group 2. We then removed these additional admissions as follows: we estimated that 20% and 40% would be clinically extremely vulnerable (group 4) or at risk (group 6), respectively, and the remainder were distributed among groups 5-10. We used group size and age distribution of NHS staff to determine distribution of these cases; 10% group 5 and 7, 24% group 8 and 9, 66% group 10. As risk of harm from COVID-19 increases dramatically with age [20], we allocated to these groups in the adjusted distribution 20% group 5 and 7, 30% group 8 and 9, 50% group 10.
We then adjusted for group 4 extremely clinically vulnerable and group 6 at-risk populations. The size of these groups was taken from the UK COVID-19 vaccines delivery plan [4]. We estimated that the extremely clinically vulnerable aged < 70 years (part of group 4) had a four-fold increased risk of hospital admission, ICU admission or death relative to others of their same age and that for those in at risk aged < 65 years population (group 6) the risk increased two-fold. We calculated national mortality, hospital and ICU admission rates by age group and used these to calculate absolute numbers of cases in each group by age. Additional Finally, we analysed the timeline of impact of vaccination on hospitalisations, ICU admissions and deaths, which will differ for several reasons. First, there is a period between vaccination and immunity becoming effective which is likely to be a minimum of 2 weeks [21,22]. Second, a lag between infection and subsequent hospitalisation, which we estimate to be 10 days [23,24]. Finally, there is a lag between hospitalisation and ICU admission which on average is 2-3 days [8,25]. Time from infection to death will vary according to whether the patient is hospitalised and, if so, whether they are admitted to ICUalthough far from all patients are admitted to hospital. We used the estimated time from symptoms to death in the first surge reported by Harrison et al., who estimated 13 days from symptoms to death, equivalent to 17 days from infection to death [26].
Finally, there is a time lag between events and publication of data in the public domain. The anticipated delays between vaccination and impact on reported outcomes are shown in Table 2.
The UK government has targeted completion of vaccination of groups 1-4 by mid-February 2021 [4]. Our assumption is that the vaccination rate required from mid-   and 2864 ICU admissions. These were added to group 6 and the same number removed from groups 7-10 as described above.
Adding these adjustments to those for health and social care workers provides a fully adjusted estimate of the impact of effective vaccination of all those in each priority group on deaths, hospital and ICU admissions ( Fig. 2; Table 3). Figure 3 shows the estimated timeline for impact of vaccinations on the three metrics described. That target appears to be achievable given reported progress over recent weeks [27]. If the current rate of vaccination is maintained, vaccination of the first nine groups may be achieved before material numbers of second doses will become due (in early April, based on the current 12-week dosing interval) which would begin to impact on the rate of further progress. Any increase or decrease in availability of vaccines will of course accelerate or decrease the timescales shown.
The two key assumptions that impact the model are the vaccination take-up rate and vaccine effectiveness. We assume 100% for both, from which any adjustments can then be derived: our estimates can therefore be considered to model a 'best-case scenario'. We have undertaken sensitivity modelling to demonstrate how the model varies if take-up rates are 90% (95% for those over 80), and also if vaccine effectiveness is 90% (Table 4).

Discussion
Our modelling illustrates how vaccination will impact on deaths, predominantly in the elderly population, more rapidly than on hospital and ICU admissions. This has important implications for healthcare policy and in particular for decisions regarding management of national initiatives that seek to maintain control of community viral transmission, most particularly lockdown. The marked variation in impact is highlighted by the notably different median ages of patients: age 83 years for those who die [28], 73 years for hospital admissions [7] and 61 years for ICU admissions [25]. line) will support the very systems that we need to keep hospitals and care services running for the prolonged period that this will be needed [29].
The curves all begin to flatten after priority group 7 has been vaccinated and this raises questions. It may be that vaccination priorities may reasonably be reconsidered, for instance to target high-risk key workers, such as those involved in transport and education [30]  Vaccination is a global imperative [31] and on reaching the flatter part of the curves it may also be rational and moral to consider whether UK stocks of vaccine might also be redistributed to support the global attempts at vaccination in countries with lower access to vaccines [31].
The main limitations to our study are the assumptions we have made in order to create our model. Our assumptions about similarity of mortality rates between UK countries are supported, as calculating total UK deaths by combining numbers from individual countries [32,33] rather than scaling up figures from England and Wales leads to a variation of less than 2%. Our assumptions on vaccination uptake rates are supported by the fact that vaccination acceptance is highest in older patients, high in all groups aged over 50 years and increasing. In a December 2020 report, 78% of respondents were 'very' or 'fairly' likely to accept vaccination, but among those aged > 70 years the rate was 95% and among those aged 50-69 years it was 84% [34]. In January 2021, the acceptance rate had increased to 89% of the whole population, to 98% in those aged > 70 years and to 94%   among those aged 50-69 years [35]. Vaccine effectiveness for the purpose of this paper is its ability to prevent serious illness, rather than symptomatic or asymptomatic infection, which is how the results of the trials of the two vaccines in use to date have been presented. Vaccine efficacy in preventing serious illness is currently expected to be greater than its efficacy in preventing infection or any symptomatic illness [22,23,36]. For current vaccines, the ability to prevent serious illness seems to be high even 2 weeks after a single dose [22,23], and this is likely to exceed 90%. Regarding comorbidity, the average number of comorbidities reported among patients dying from COVID-19 in England and Wales is 2.2, varying little across ages, which supports the contention that priority risk groups are selected on severity of comorbidity rather than number [37].
Our estimates for a four-and two-fold increase in risk of harm among the extremely clinically vulnerable and at-risk