Winter Coronavirus (COVID-19) Infection Study: estimates of infection hospitalisation and fatality risk, 30 May 2024
Updated 11 June 2024
Applies to England and Scotland
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Corrections
This report was corrected on 11 June 2024 after editorial errors were found in the infection hospitalisation risk section of the main points summary for the aged 6 to 17 years age group (third paragraph of the infection hospitalisation risk section of the main points summary). These have now been corrected. The infection hospitalisation risk for the aged 6 to 17 years age group elsewhere in the report was correct and is unchanged.
Report summary
This publication describes the infection hospitalisation risk (IHR) and infection fatality risk (IFR) of the SARS-CoV-2 virus in England. SARS-CoV-2 is a coronavirus that causes COVID-19. The IHR measures the risk of hospital admission given that an individual has been infected with the SARS-CoV-2 virus. The IFR measures the risk of mortality given that an individual has been infected with the SARS-CoV-2 virus.
To ensure that estimates are representative of the wider population, the IHR and IFR estimates from this cohort are adjusted through reweighting. The reweighting approach used by this study aims to provide an estimate of IHR and IFR that are representative of the whole population in terms of age, geography, and sex. The study only includes participants aged over 2 years and therefore all the analyses conducted, and population estimates used in this report do not include individuals aged under 3 years.
In this publication, UKHSA had also planned to include an analysis of those attending work while sick with a self-reported respiratory illness (presenteeism) in England and Scotland over winter 2023 to 2024. However, due to data quality concerns, this analysis has been delayed. UKHSA now plans to release it in autumn 2024.
Main points
Infection hospitalisation risk
In England, between 14 November 2023 and 3 March 2024, the risk of hospitalisation given a SARS-CoV-2 infection was 0.442% (95% Credible Interval (CrI): 0.422% to 0.465%). This corresponds to a 1 in 226 (95% CrI: 1 in 215 to 1 in 237) chance of those infected being hospitalised.
In England, the infection hospitalisation risk was highest in those aged 75 and over. The risk of an individual aged 75 years and over being hospitalised given a SARS-CoV-2 infection was 3.28% (95% CrI: 2.81% to 3.83%). This corresponds to a 1 in 30 (95% CrI: 1 in 26 to 1 in 36) chance of those infected being hospitalised.
In England, the infection hospitalisation risk was lowest in those aged between 6 to 17 years. The risk of an individual aged between 6 to 17 years being hospitalised given a SARS-CoV-2 infection was 0.0226% (95% CrI: 0.0175% to 0.0286%). This corresponds to a 1 in 4,430 (95% CrI: 1 in 3,490 to 1 in 5,700) chance of those infected being hospitalised.
Infection fatality risk
In England, between 14 November 2023 and 3 March 2024, the risk of mortality given a SARS-CoV-2 infection was 0.0831% (95% CrI: 0.0788% to 0.0869%). This corresponds to a 1 in 1200 (95% CrI: 1 in 1,150 to 1 in 1,270) chance of mortality given a SARS-CoV-2 infection.
In England, the infection fatality risk was highest in those aged 75 and over. The risk of mortality given a SARS-CoV-2 infection in an individual aged 75 years and over was 0.725% (95% CrI: 0.600% to 0.876%). This corresponds to a 1 in 138 (95% CrI: 1 in 114 to 1 in 167) chance of mortality given infection with SARS-CoV-2.
In England, the infection fatality risk was lowest in those aged between 3 to 17 years. The risk of mortality given a SARS-CoV-2 infection in an individual aged between 3 to 17 years was 0.0000153% (95% CrI: 0.000000101% to 0.000111%). This corresponds to a 1 in 6,550,000 (95% CrI: 1 in 900,000 to 1 in 993,000,000) chance of mortality given infection with SARS-CoV-2.
Infection hospitalisation risk
The estimated IHR for England from 14 November 2023 to 3 March was 0.442% (95% Credible Interval (CrI): 0.422% to 0.465%). This corresponds to a 1 in 226 (95% CrI: 1 in 215 to 1 in 237) chance of being hospitalised given a SARS-CoV-2 infection.
The IHRs for each age group are provided in Table 1. The IHR estimate ranged from 0.0226% (95% CrI: 0.0175% to 0.0286%) hospitalisation risk given a SARS-CoV-2 infection in the 6 to 17 years age group to 3.28% (95% CrI: 2.81% to 3.83%) hospitalisation risk given a SARS-CoV-2 infection in those aged 75 years and over. This corresponds to a 1 in 4,430 (95% CrI: 1 in 3,490 to 1 in 5,700) chance of being hospitalised given a SARS-CoV-2 infection for those aged 6 to 17 years, compared to a 1 in 30 (95% CrI: 1 in 26 to 1 in 36) chance of being hospitalised given a SARS-CoV-2 infection in those aged 75 years and over.
There are multiple uncertainties that feed into the calculation of an IHR including the incidence itself, the delay distribution from infection to hospitalisation, and hospitalisation definition. Caution should be taken when interpreting and making comparisons to estimates from other studies. The IHR represents an average risk for the average person in a subgroup at a population level, meaning individual risk will vary due to co-morbidities and other factors.
This study has used the definition for SARS-CoV-2 hospitalisations provided by NHS England. This means the IHR was calculated using hospitalisations (new admissions and within hospital diagnosis) of individuals with SARS-CoV-2.
Table 1: For England only, the estimated IHR across age groups
| Date range | Age group | Infection hospitalisation risk |
|---|---|---|
| 14/11/2023 to 03/03/2024 | 6 to 17 years | 0.0226% (95% CrI: 0.0175% to 0.0286%) |
| 14/11/2023 to 03/03/2024 | 18 to 34 years | 0.0317% (95% CrI: 0.0259% to 0.0391%) |
| 14/11/2023 to 03/03/2024 | 35 to 44 years | 0.0400% (95% CrI: 0.0337% to 0.0472%) |
| 14/11/2023 to 03/03/2024 | 45 to 54 years | 0.0763% (95% CrI: 0.0656% to 0.0892%) |
| 14/11/2023 to 03/03/2024 | 55 to 64 years | 0.208% (95% CrI: 0.180% to 0.242%) |
| 14/11/2023 to 03/03/2024 | 65 to 74 years | 0.686% (95% CrI: 0.596% to 0.796%) |
| 14/11/2023 to 03/03/2024 | 75 years and over | 3.28% (95% CrI: 2.81% to 3.83%) |
Infection fatality risk
This study has used the following definition for SARS-CoV-2 death: individuals who had COVID-19 listed as a cause of death on their death certificate or individuals that died within 43 days of a positive SARS-CoV-2 test. The threshold of 43 days was determined as the 95th percentile of the time delay from a positive SARS-CoV-2 test to the date of death (for those individuals with COVID-19 on their death certificate). Other studies may use a different definition for SARS-CoV-2 deaths, therefore direct comparison with other studies should not be attempted. A further discussion of the choices around SARS-CoV-2 death definitions is provided in the quality and methodology information document.
The estimated IFR for England from 14 November 2023 to 3 March was 0.0831% (95% CrI: 0.0788% to 0.0869%). This corresponds to a 1 in 1,200 (95% CrI: 1 in 1,150 to 1 in 1,270) chance of mortality given a SARS-CoV-2 infection.
The IFRs for each age group are provided in Table 2. The IFR estimate ranged from 0.0000153% (95% CrI: 0.000000101% to 0.000111%) mortality risk given a SARS-CoV-2 infection in the 3 to 17 years age group to a 0.725% (95% CrI: 0.600% to 0.876%) mortality risk given a SARS-CoV-2 infection in those aged 75 years and over. This corresponds to a 1 in 6,550,000 (95% CrI: 1 in 900,000 to 1 in 993,000,000) chance of mortality given a SARS-CoV-2 infection for those aged 3 to 17 years, compared to a 1 in 138 (95% CrI: 1 in 114 to 1 in 167) chance of mortality given a SARS-CoV-2 infection in those aged over 75 years.
There are multiple uncertainties that feed into the calculation of an IFR including the incidence itself, the delay distribution from infection to fatality, and fatality definition. Caution should be taken when interpreting, and making comparisons to estimates from other studies. The IFR represents an average risk for the average person in a subgroup at a population level, meaning individual risk will vary due to co-morbidities and other factors.
Table 2: For England only, the estimated IFR across age groups
| Date range | Age group | Infection fatality risk |
|---|---|---|
| 14/11/2023 to 03/03/2024 | 3 to 17 years | 0.000015300% (95% CrI: 0.000000101% to 0.000111000%) |
| 14/11/2023 to 03/03/2024 | 18 to 34 years | 0.000633% (95% CrI: 0.000400% to 0.000982%) |
| 14/11/2023 to 03/03/2024 | 35 to 44 years | 0.00155% (95% CrI: 0.00106% to 0.00224%) |
| 14/11/2023 to 03/03/2024 | 45 to 54 years | 0.00738% (95% CrI: 0.00576% to 0.00941%) |
| 14/11/2023 to 03/03/2024 | 55 to 64 years | 0.0236% (95% CrI: 0.0191% to 0.0287%) |
| 14/11/2023 to 03/03/2024 | 65 to 74 years | 0.1070% (95% CrI: 0.0877% to 0.130%) |
| 14/11/2023 to 03/03/2024 | 75 years and over | 0.725% (95% CrI: 0.600% to 0.876%) |
Methodology
Further information on methodologies can be found in the quality and methodology information document.
To calculate the IHR or IFR, we need to calculate the rate of new SARS-CoV-2 infections that occur each day per 100,000 people in the population, which is referred to as the incidence rate. To calculate the incidence rate, we first need to estimate the proportion of individuals who are infected with SARS-CoV-2 at a given point in time, referred to as the prevalence.
Demographics are over or under-represented in the survey sample, and it is important to account for this to produce estimates of SARS-CoV-2 prevalence and incidence that are representative of population.
A Bayesian multilevel regression and post-stratification (MRP) approach is used to estimate the incidence rate and prevalence for different subgroups (geography, age, and sex). MRP helps to reduce the uncertainty in subgroups estimates that might be under-represented or under-sampled in the original survey. The model partially pools information across subgroups to improve the precision and accuracy of estimates in under-represented groups. Without this approach to pooling information, estimates may not accurately reflect temporal trends in SARS-CoV-2 transmission in the wider population. These model estimates are then re-weighted using the true population size of different subgroups to give a more representative estimate for the target population.
In the survey, once a participant tests positive for SARS-CoV-2, they are asked to take repeat tests every other day until they return 2 negative tests. This repeat testing data is used to estimate the false negative rates of LFDs, over time, for the cohort. This allows us to further model the test sensitivity as it evolves over the epidemic phases. As the study gathers more data the diagnostic performance of the LFD tests will be updated. The repeat testing data is also used to estimate the duration of positivity of SARS-CoV-2 infections, which is further used to calculate incidence and prevalence.
Positivity must be adjusted for the imperfect test sensitivity of LFD tests (causing falsely negative test results) to estimate of prevalence. False negatives occur when an individual is truly infected with SARS-CoV-2 but receives a negative test result. The model estimates the false negative rate we expect to observe in the data, allowing us to adjust for the presence of false negatives when calculating incidence. The estimated false negative rate varies over time, depending on epidemic behaviour, and across different age groups. The model also adjusts for test specificity, however due to the high specificity of LFD tests (very small chance of a falsely positive test), it has a minimal impact on the estimated incidence.
Incidence is calculated by estimating the rate at which new infections of SARS-CoV-2 occur within the subgroups analysed over time, reported as a rate per 100,000 people. Prevalence is a measure of the proportion of the population infected with SARS-CoV-2. An infected individual’s exposure date to SARS-CoV-2 occurs prior to testing positive. Incidence is a measure of new infections by exposure date and therefore, it is reported with a temporal delay relative to prevalence. The prevalence at a given point in time can be expressed in terms of the number of recently infected individuals that have not yet cleared the virus. Using the repeat testing data collected in the survey, we estimate how long someone is likely to test positive for, known as the duration of positivity. The model uses the duration of positivity, obtained from follow up testing, to estimate a time series of incidence rates, by demographic group, that is most credible in generating the observed positivity in the survey cohort. This allows the model to then estimate the prevalence at each point in time from the temporal pattern of incidence rates.
The infection hospitalisation risk (IHR) measures the risk of hospitalisation given that an individual has been infected with the SARS-CoV-2 virus. The infection fatality risk (IFR) measures the risk of mortality given that an individual had been infected with the SARS-CoV-2 virus. The IHR and IFR can be estimated using the incidence rate and the number of new clinical outcomes, such as hospitalisations or deaths, through time. It is modelled by temporally matching the incidence time series to the time series of clinical outcomes, which adjusts for the delay from an individual becoming infected to the outcome occurring, and then estimating the proportion of newly infected individuals for which the outcome occurred.
Daily hospitalisations data come from the NHS England COVID-19 hospital statistics. The time delay was informed using data from the Emergency Care Data Set linked to testing data from the Second Generation Surveillance System (SGSS).
Daily mortality data is obtained from several sources: deaths with a laboratory-confirmed COVID-19 test that were identified from tracing against NHS records, and ONS death registrations which can be linked to a laboratory confirmed COVID-19 test.
Individuals appear to be more likely to test earlier or before the testing window if they are symptomatic. As a result, tests taken earlier in the window are more likely to be positive than tests taken later in the testing window. Additionally, our model incorporates an adjustment for this effect, based upon which day of their testing window an individual took their test. This testing behaviour pattern changed over the winter bank holiday period which has been accounted for in the model.
Note, all the analyses conducted in this report do not include individuals aged under 3 years.
Data sources
Based on responses from the Winter Coronavirus (COVID-19) Infection Study (Winter CIS), commissioned and funded by UK Health Security Agency (UKHSA), to deliver real-time information to help assess the effects of COVID-19 on the lives of individuals and the community, and help understand the potential winter pressures on our health services. The study has been launched jointly by ONS and UKHSA, with data collected via online questionnaire completion and self-reported lateral flow device (LFD) results from previous participants of the COVID-19 Infection Survey (CIS). The ONS 2023 to 2024 population projections will be published alongside the report on positivity. Hospitalisation counts in England are provided by NHS England.
Authors
Alex Glaser – UKHSA
Alexander Phillips – UKHSA, University of Liverpool
Andre Charlett – UKHSA
Christopher Overton – UKHSA, University of Liverpool
Jonathon Mellor – UKHSA
Julie Day – UKHSA
Martyn Fyles – UKHSA
Owen Jones – UKHSA
Robert Paton – UKHSA
Steven Riley – UKHSA, Imperial College London
Thomas Ward – UKHSA
Glossary
Prevalence
The estimated proportion of individuals who are infected with the SARS-CoV-2 virus at a given point in time.
Incidence rate
The incidence rate is an estimate of how many new infections of SARS-CoV-2 occur each day per 100,000 people in the population.
Infection hospitalisation risk (IHR)
Measures the risk of hospital admission given that an individual has been infected with the SARS-CoV-2 virus.
Infection fatality risk (IFR)
Measures the risk of death given that an individual has been infected with the SARS-CoV-2 virus.
Vaccine effectiveness
How effectively vaccinations protect people from health outcomes such as infection, symptomatic disease, hospitalisation, and mortality.