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BackgroundPaediatric pneumococcal conjugate vaccination (PCV) has reduced adult PCV-serotype disease: PCV7 has greater indirect effects than PCV13. Ongoing surveillance is required to evaluate current vaccine usage and inform future vaccine deployment, particularly with respiratory infection epidemiology changing following SARS-CoV-2 emergence.Methods and FindingsA retrospective cohort study, all adults ≥16 years admitted to three UK hospitals, 2006-2022, with pneumococcal disease. Medical records were reviewed for each clinical episode and serotype data were obtained from the UK Health Security Agency national reference laboratory.We identified 1,501 (40.3%) cases of invasive pneumococcal disease (IPD) with known serotype, 134 (3.6%) IPD cases without serotype data, and 2,084 (56.0%) non-IPD cases, which are typically missed in national surveillance. Disease incidence increased progressively from 2006-2020, followed by a sudden decline after COVID-19 emergence and then a gradual increase to pre-pandemic levels.Paediatric PCV7 introduction reduced adult PCV7 serotype IPD from 29.4% [24.1–35.4] of IPD in 2006-09 to 7.0% [3.7–12.7] in 2021-22. PCV13 introduction also decreased adult vaccine serotype IPD, but considerable residual adult disease remains, causing 34.3% [28.6–40.4] of IPD in 2006-09 and 21.7% [15.5–29.6] 9 in 2021-22, respectively. Serotype replacement diminished the benefits of PCV introduction: PCV20-13 and non-PCV serotypes represented 27.0% [21.9–32.9] and 9.3% [6.3–13.5] of disease in 2006-2009, and 39.5% [31.5–48.2] and 31.8% [24.4–40.2] in 2021-2022, respectively.Serotype shifts have resulted in increasing disease caused by serotype 3 and 8, and the re-emergence of serotype 19F and 19A. These serotype shifts occurred as clinical disease severity changed, and whilst the COVID-19 pandemic disrupted disease severity trends, these have now largely reverted to previous trajectories. Patient age trended upwards and although CURB65 severity decreased there were increased ICU admission rates. Overall, inpatient mortality decreased and hospitalisation duration remained stable.ConclusionsAfter 17 years of PCV use, residual pneumococcal disease due to the vaccine serotypes among hospitalised adults remains. The sharp decline in pneumococcal disease during the COVID-19 pandemic has now reversed, with increasing cases due to vaccine serotypes, especially serotype 3. Around 68.2% of cases in 2022 were potentially covered by the recently licensed 20-valent PCV.

BackgroundUnderstanding the relative vaccine effectiveness (rVE) of new COVID-19 vaccine formulations against SARS-CoV-2 infection is an urgent public health priority. A precise comparison of the rVE of monovalent and bivalent boosters given during the 2022 Spring-Summer and Autumn-Winter campaigns, respectively, in a defined population has not been reported. We therefore assessed rVE against hospitalisation for the Spring-Summer (fourth vs third monovalent mRNA vaccine doses) and Autumn-Winter (fifth BA.1/ancestral bivalent vs fourth monovalent mRNA vaccine dose) boosters.MethodsA prospective single-centre test-negative design case-control study of ≥75 year-olds hospitalised with COVID-19 or other acute respiratory disease. We conducted regression analyses controlling for age, gender, socioeconomic status, patient comorbidities, community SARS-CoV-2 prevalence, vaccine brand and time between baseline dose and hospitalisation.Results682 controls and 182 cases were included in the Spring-Summer booster analysis; 572 controls and 152 cases for the Autumn-Winter booster analysis. A monovalent mRNA COVID-19 vaccine as fourth dose showed rVE 46·9% (95% confidence interval [CI] 14·4-67·3) versus those not boosted. A bivalent mRNA COVID-19 vaccine as fifth dose had rVE 46·4% (95%CI 17·5-65), compared to a fourth monovalent mRNA COVID-19 vaccine dose.InterpretationBoth fourth monovalent and fifth BA.1/ancestral mRNA bivalent COVID-19 vaccine doses demonstrated benefit as a booster in older adults. Bivalent mRNA boosters offer equivalent protection against hospitalisation with Omicron infection to monovalent mRNA boosters given earlier in the year. These findings support the current UK immunisation programme that advises the use of bivalent booster doses.

The serial interval of an infectious disease, commonly interpreted as the time between the onset of symptoms in sequentially infected individuals within a chain of transmission, is a key epidemiological quantity involved in estimating the reproduction number. The serial interval is closely related to other key quantities, including the incubation period, the generation interval (the time between sequential infections), and time delays between infection and the observations associated with monitoring an outbreak such as confirmed cases, hospital admissions, and deaths. Estimates of these quantities are often based on small data sets from early contact tracing and are subject to considerable uncertainty, which is especially true for early coronavirus disease 2019 data. In this paper, we estimate these key quantities in the context of coronavirus disease 2019 for the UK, including a meta-analysis of early estimates of the serial interval. We estimate distributions for the serial interval with a mean of 5.9 (95% CI 5.2; 6.7) and SD 4.1 (95% CI 3.8; 4.7) days (empirical distribution), the generation interval with a mean of 4.9 (95% CI 4.2; 5.5) and SD 2.0 (95% CI 0.5; 3.2) days (fitted gamma distribution), and the incubation period with a mean 5.2 (95% CI 4.9; 5.5) and SD 5.5 (95% CI 5.1; 5.9) days (fitted log-normal distribution). We quantify the impact of the uncertainty surrounding the serial interval, generation interval, incubation period, and time delays, on the subsequent estimation of the reproduction number, when pragmatic and more formal approaches are taken. These estimates place empirical bounds on the estimates of most relevant model parameters and are expected to contribute to modeling coronavirus disease 2019 transmission.

Limited data exist assessing severity of disease in adults hospitalised with Omicron SARS-CoV-2 variant infections, and to what extent patient-factors, including vaccination and pre-existing disease, affect variant-dependent disease severity. This prospective cohort study of all adults (≥18 years of age) hospitalised at acute care hospitals in Bristol, UK assessed disease severity using 3 different measures: FiO2 >28%, World Health Organization (WHO) outcome score >5, and hospital length of stay (LOS) >3 days following admission for Omicron or Delta variant infection. Independent of other variables, including vaccination, Omicron variant infection was associated with a statistically lower severity compared to Delta; risk reductions were 58%, 67%, and 16% for FiO2, WHO score, and LOS, respectively. Younger age and vaccination with two or three doses were also independently associated with lower COVID-19 severity. Despite lower severity relative to Delta, Omicron infection still resulted in substantial patient and public health burden following admission.

BackgroundThere is an urgent public health need to evaluate disease severity in adults hospitalised with Delta and Omicron SARS-CoV-2 variant infections. However, limited data exist assessing severity of disease in adults hospitalised with Omicron SARS-CoV-2 infections, and to what extent patient-factors, including vaccination, age, frailty and pre-existing disease, affect variant-dependent disease severity.MethodsA prospective cohort study of adults (≥18 years of age) hospitalised with acute lower respiratory tract disease at acute care hospitals in Bristol, UK conducted over 10-months. Delta or Omicron SARS-CoV-2 infection was defined by positive SARS-CoV-2 PCR and variant identification or inferred by dominant circulating variant. We constructed adjusted regression analyses to assess disease severity using three different measures: FiO2>28% (fraction inspired oxygen), World Health Organization (WHO) outcome score >5 (assessing need for ventilatory support), and hospital length of stay (LOS) >3 days following admission for Omicron or Delta infection.FindingsIndependent of other variables, including vaccination, Omicron variant infection in hospitalised adults was associated with lower severity than Delta. Risk reductions were 58%, 67%, and 16% for supplementary oxygen with >28% FiO2[Relative Risk (RR)=0·42 (95%CI: 0·34-0·52),P5 [RR=0·33 (95%CI: 0·21-0·50),P3 days [RR=0·84 (95%CI: 0·76-0·92),PInterpretationWe provide reassuring evidence that Omicron infection results in less serious adverse outcomes than Delta in hospitalised patients. Despite lower severity relative to Delta, Omicron infection still resulted in substantial patient and public health burden and an increased admission rate of older patients with Omicron which counteracts some of the benefit arising from less severe disease.FundingAvonCAP is an investigator-led project funded under a collaborative agreement by Pfizer.RESEARCH IN CONTEXTEvidence before this studyThe burden of COVID-19 on hospital services is determined by the prevalence and severity of SARS-CoV-2 variants, and modified by individual factors such as age, frailty and vaccination status. Real world data suggest that vaccine effectiveness is lower and may wane faster over time against symptomatic disease with Omicron (B.1.1.529) than with Delta (B.1.617.2) SARS-CoV-2 variant. However, numbers of hospitalisations as a case proportion during the Omicron wave have been considerably lower than previous waves. Several reports have compared the risk of hospitalisation or severe disease based on SARS-CoV-2 variant, some suggesting that Omicron is probably less severe than Delta in vaccinated and unvaccinated individuals.Added value of this studyThis study provides robust data assessing the relative severity of Delta and Omicron SARS-CoV-2 variants in patients admitted to hospital, including the first analysis assessing risk for any positive pressure ventilatory support, as well as risk of supplementary oxygen requirement and extended hospital admission, that may guide resource planning in hospitals. We found evidence that infection with Omicron was associated with a milder clinical course following hospital admission than that caused by Delta and that vaccination was independently associated with lower in-hospital disease severity using these three separate severity measures. Specifically, compared to Delta, Omicron-related hospitalizations were 58%, 67%, and 16% less likely to require high flow oxygen >28% FiO2, positive pressure ventilatory support or more critical care, and to have a hospital stay lasting more than three days, respectively.This study reports the considerable morbidity resulting from Omicron infection, with 18% of Omicron admissions requiring oxygen supplementation FiO2>28%, 6% requiring positive pressure ventilation, 62% needing hospitalization ≥four days, and 4% in-hospital mortality. In determining the reduced requirement of increased oxygen requirement and total positive pressure requirement, including non-invasive ventilation, this analysis should contribute to future hospital care and service planning assessments.Implications of all the available evidenceThe risk of severe outcomes following SARS-CoV-2 infection is substantially lower for Omicron than for Delta, with greater reductions for more severe disease outcomes. Significant variation in risk occurs with age and vaccination status, with older and unvaccinated individuals remaining at particular risk of adverse outcome. These results highlight the importance of maintaining high levels of vaccine coverage in patient groups at risk of severe disease.The impact of lower severity Omicron-related hospitalization must be balanced against increased transmissibility and overall higher numbers of infections with this variant and there remains a substantial patient and public health burden. The increased admission rate of older patients with Omicron counteracts some of the benefit arising from less severe disease. Despite the risk reduction in high level oxygen supplementation requirement and high dependency care with Omicron compared to earlier variants at the individual level, healthcare systems could still be overwhelmed.

Background Delay or failure to view test results in a hospital setting can lead to delayed diagnosis, risk of patient harm, and represents inefficiency. Factors influencing this were investigated to identify how timeliness and completeness of test review could be improved through an evidence-based redesign of the use of clinical test review software. Methods A cross-section of all abnormal hematology and biochemistry results which were published on a digital test review platform over a 3-year period were investigated. The time it took for clinicians to view these results, and the results that were not viewed within 30 days, were analyzed relative to time of the week, the detailed type of test, and an indicator of patient record data quality. Results The majority of results were viewed within 90 min, and 93.9% of these results viewed on the digital platform within 30 days. There was significant variation in results review throughout the week, shown to be due to an interplay between technical and clinical workflow factors. Routine results were less likely to be reviewed, as were those with patient record data quality issues. Conclusion The evidence suggests that test result review would be improved by stream-lining access to the result platform, differentiating between urgent and routine results, improving handover of responsibility for result review, and improving search for temporary patient records. Altering the timing of phlebotomy rounds and a review of the appropriateness of routine test requests at the weekend may also improve result review rates.

RationaleStreptococcus pneumoniaeepidemiology is changing in response to vaccination and some data suggest empyema incidence is increasing. However, differences exist between UK and USA studies. We describe trends in the clinical phenotype of adult pneumococcal pleural infection, including simple parapneumonic effusions (SPE) in the pneumococcal conjugate vaccination (PCV) era.ObjectivesTo determine whether there were differences in pneumococcal disease presentation and severity associated with pleural infection.MethodsA retrospective cohort study, all adults ≥16 years admitted to three large UK hospitals, 2006-2018 with pneumococcal disease. 2477 invasive pneumococcal cases were identified: 459 SPE and 100 pleural infection cases. Medical records were reviewed for each clinical episode. Serotype data were obtained from the UK Health Security Agency national reference laboratory.ResultsIncidence increased over time, including non-PCV-serotype disease. PCV7-serotype disease declined following paediatric PCV7 introduction, but the effect of PCV13 was less apparent as disease caused by the additional six serotypes plateaued with serotypes 1 and 3 causing such parapneumonic effusions from 2011 onwards.Patients with pleural infection had a median survival 468 days (95% CI 340-590), versus 286 days (95% CI 274-335) in those with SPE. Pleural infection associated with frank pus had lower 90-day mortality than pleural infection without pus (0% versus 29%,PP=0.049).ConclusionsPneumococcal infection continues to cause severe disease despite the introduction of PCVs. The predominance of serotype 1 and 3 in this adult UK cohort is in keeping with previous studies in paediatric and non-UK studies. Rising non-PCV serotype disease and limited impact of PCV13 on cases caused by serotypes 1 and 3 offset the reductions in adult pneumococcal parapneumonic effusion disease burden observed following introduction of the childhood PCV7 programme.KEY MESSAGESWhat is already known on this topicThe epidemiology of pneumococcal infection is changing in both adults and children following pneumococcal conjugate vaccine (PCV) introduction, as a result of direct and indirect vaccine effects. Other studies have reported that serotypes 1 and 3 disproportionately cause pneumococcal pleural disease; however, the clinical phenotype of parapneumonic effusions associated with pneumococcal infection in adults following PCV introduction is not well described.What this study addsIn this study which presents the largest cohort of patients with a single-organism pleural infection, we demonstrate an increasing incidence of parapneumonic effusions related toStreptococcus pneumoniaein adults, attributable to serotype 1 and 3 disease, despite the introduction of PCV13 in the UK childhood vaccination programme. Interestingly, our data suggest that pneumococcal pleural infection is associated with improved survival up to one-year compared to patients with pneumococcal simple parapneumonic effusions.How this study might affect research, practice or policyCareful assessment of the need for specialist respiratory and thoracic surgical intervention in the context of increasing incidence of adult parapneumonic effusions related toStreptococcus pneumoniaewill be required, in addition to ongoing monitoring of the effect on serotype distribution and clinical phenotype of current and future vaccines against pneumococcus.

Since the beginning of the COVID-19 pandemic, the reproduction number [Formula: see text] has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, [Formula: see text] is defined as the average number of secondary infections caused by one primary infected individual. [Formula: see text] seems convenient, because the epidemic is expanding if [Formula: see text] and contracting if [Formula: see text]. The magnitude of [Formula: see text] indicates by how much transmission needs to be reduced to control the epidemic. Using [Formula: see text] in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of [Formula: see text] but many, and the precise definition of [Formula: see text] affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined [Formula: see text], there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate [Formula: see text] vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when [Formula: see text] is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of [Formula: see text], and the data and methods used to estimate it, can make [Formula: see text] a more useful metric for future management of the epidemic.

Funder: NHS Global Digital Exemplar programme, Funder: James S. McDonnell Foundation; FundRef: https://doi.org/10.13039/100000913, Funder: NIHR Health Protection Research Unit in Behavioural Science and Evaluation at the University of Bristol, Funder: Wellcome Trust; FundRef: https://doi.org/10.13039/100004440, Funder: Canadian Institutes for Health Research, Funder: Natural Science and Engineering Research Council of Canada, Since the beginning of the COVID-19 pandemic, the reproduction number [Formula: see text] has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, [Formula: see text] is defined as the average number of secondary infections caused by one primary infected individual. [Formula: see text] seems convenient, because the epidemic is expanding if [Formula: see text] and contracting if [Formula: see text]. The magnitude of [Formula: see text] indicates by how much transmission needs to be reduced to control the epidemic. Using [Formula: see text] in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of [Formula: see text] but many, and the precise definition of [Formula: see text] affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined [Formula: see text], there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate [Formula: see text] vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when [Formula: see text] is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of [Formula: see text], and the data and methods used to estimate it, can make [Formula: see text] a more useful metric for future management of the epidemic.

The rapid emergence of SARS-CoV-2 mutants with new phenotypic properties is a critical challenge to the control of the ongoing pandemic. B.1.1.7 was monitored in the UK through routine testing and S-gene target failures (SGTF), comprising over 90% of cases by March 2021. Now, the reverse is occurring: SGTF cases are being replaced by an S-gene positive variant, which we associate with B.1.617.2. Evidence from the characteristics of S-gene positive cases demonstrates that, following importation, B.1.617.2 is transmitted locally, growing at a rate higher than B.1.1.7 and a doubling time between 5-14 days. S-gene positive cases should be prioritised for sequencing and aggressive control in any countries in which this variant is newly detected.One-Sentence SummaryThe B.1.617.2 variant of SARS-CoV-2 is replacing B.1.1.7 and emerging as the dominant variant in England, evidenced by sustained local transmission.

ObjectivesTo establish whether there is any change in mortality associated with infection of a new variant of SARS-CoV-2, designated a Variant of Concern in December 2020 (VOC-202012/1) compared to that associated with infection with circulating SARS-CoV-2 variants.DesignMatched cohort study. Cases are matched by age, gender, ethnicity, index of multiple deprivation, lower tier local authority region, and sample date of positive specimen, and differing only by detectability of the spike protein gene using the TaqPath assay - a proxy measure of VOC-202012/1 infection.SettingUnited Kingdom, community - based (Pillar 2) COVID-19 testing centres using the TaqPath assay.Participants54,906 pairs of participants testing positive for SARS-CoV-2 in Pillar 2 between 1st October 2020 and 29th January 2021, followed up until the 12th February 2021.Main outcome measuresDeath within 28 days of first positive SARS-CoV-2 test.ResultsThere is a high probability that the risk of mortality is increased by infection with VOC-202012/01 (p ConclusionsIf this finding is generalisable to other populations, VOC-202012/1 infections have the potential to cause substantial additional mortality compared to previously circulating variants. Healthcare capacity planning, national and international control policies are all impacted by this finding, with increased mortality lending weight to the argument that further coordinated and stringent measures are justified to reduce deaths from SARS-CoV-2.

As the number of cases of COVID-19 continues to grow, local health services are at risk of being overwhelmed with patients requiring intensive care. We develop and implement an algorithm to provide optimal re-routing strategies to either transfer patients requiring Intensive Care Units (ICU) or ventilators, constrained by feasibility of transfer. We validate our approach with realistic data from the United Kingdom and Spain. In the UK, we consider the National Health Service at the level of trusts and define a 4-regular geometric graph which indicates the four nearest neighbours of any given trust. In Spain we coarse-grain the healthcare system at the level of autonomous communities, and extract similar contact networks. Through random search optimisation we identify the best load sharing strategy, where the cost function to minimise is based on the total number of ICU units above capacity. Our framework is general and flexible allowing for additional criteria, alternative cost functions, and can be extended to other resources beyond ICU units or ventilators. Assuming a uniform ICU demand, we show that it is possible to enable access to ICU for up to 1000 additional cases in the UK in a single step of the algorithm. Under a more realistic and heterogeneous demand, our method is able to balance about 600 beds per step in the Spanish system only using local sharing, and over 1300 using countrywide sharing, potentially saving a large percentage of these lives that would otherwise not have access to ICU.

This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK'., The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reproduction number has become an essential parameter for monitoring disease transmission across settings and guiding interventions. The UK published weekly estimates of the reproduction number in the UK starting in May 2020 which are formed from multiple independent estimates. In this paper, we describe methods used to estimate the time-varying SARS-CoV-2 reproduction number for the UK. We used multiple data sources and estimated a serial interval distribution from published studies. We describe regional variability and how estimates evolved during the early phases of the outbreak, until the relaxing of social distancing measures began to be introduced in early July. Our analysis is able to guide localized control and provides a longitudinal example of applying these methods over long timescales., Support for R.C. and K.T.A.'s research is provided by the EPSRC via grant no. EP/N014391/1; R.C. is also funded by TSFT as part of the NHS Global Digital Exemplar programme (GDE); there were no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years, no other relationships or activities that could appear to have influenced the submitted work. L.D. and K.T.A. gratefully acknowledge the financial support of The Alan Turing Institute under the EPSRC grant no. EP/N510129/1. L.L. acknowledges the financial support of the EPSRC via Early Career Fellowship EP/P01660X/1. L.D. and E.B.P. are supported by Medical Research Council (MRC) (MC/PC/19067). E.B.P. was partly supported by the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at University of Bristol, in partnership with PHE.

The serial interval of an infectious disease, commonly interpreted as the time between onset of symptoms in sequentially infected individuals within a chain of transmission, is a key epidemiological quantity involved in estimating the reproduction number. The serial interval is closely related to other key quantities, including the incubation period, the generation interval (the time between sequential infections) and time delays between infection and the observations associated with monitoring an outbreak such as confirmed cases, hospital admissions and deaths. Estimates of these quantities are often based on small data sets from early contact tracing and are subject to considerable uncertainty, which is especially true for early COVID-19 data. In this paper we estimate these key quantities in the context of COVID-19 for the UK, including a meta-analysis of early estimates of the serial interval. We estimate distributions for the serial interval with a mean 5.6 (95% CrI 5.1–6.2) and SD 4.2 (95% CrI 3.9–4.6) days (empirical distribution), the generation interval with a mean 4.8 (95% CrI 4.3–5.41) and SD 1.7 (95% CrI 1.0–2.6) days (fitted gamma distribution), and the incubation period with a mean 5.5 (95% CrI 5.1–5.8) and SD 4.9 (95% CrI 4.5–5.3) days (fitted log normal distribution). We quantify the impact of the uncertainty surrounding the serial interval, generation interval, incubation period and time delays, on the subsequent estimation of the reproduction number, when pragmatic and more formal approaches are taken. These estimates place empirical bounds on the estimates of most relevant model parameters and are expected to contribute to modelling COVID-19 transmission.

We describe regional variation in the reproduction number of SARS-CoV-2 infections observed using publicly reported data in the UK, with a view to understanding both if there are clear hot spots in viral spread in the country, or if there are any clear spatial patterns. We estimate that the viral replication number remains slightly above 1 overall but that its trend is to decrease, based on case data up to the 8 April. This suggests the peak of the first wave of COVID-19 patients is imminent. We find that there is significant regional variation in different regions of the UK and that this is changing over time. Within England currently the reproductive ratio is lowest in the Midlands (1.11 95% CI 1.07; 1.14), and highest in the North East of England (1.38 95% CI 1.33-1.42). It remains unclear whether the overall reduction in the reproductive number is a result of social distancing measures, due to the long and variable time delays between infection and positive detection of cases. As we move forwards, if we are to prevent further outbreaks, it is critical that we can both reduce the time taken for detection and improve our ability to predict the regional spread of outbreaks

Objective To establish whether there is any change in mortality from infection with a new variant of SARS-CoV-2, designated a variant of concern (VOC-202012/1) in December 2020, compared with circulating SARS-CoV-2 variants. Design Matched cohort study. Setting Community based (pillar 2) covid-19 testing centres in the UK using the TaqPath assay (a proxy measure of VOC-202012/1 infection). Participants 54 906 matched pairs of participants who tested positive for SARS-CoV-2 in pillar 2 between 1 October 2020 and 29 January 2021, followed-up until 12 February 2021. Participants were matched on age, sex, ethnicity, index of multiple deprivation, lower tier local authority region, and sample date of positive specimens, and differed only by detectability of the spike protein gene using the TaqPath assay. Main outcome measure Death within 28 days of the first positive SARS-CoV-2 test result. Results The mortality hazard ratio associated with infection with VOC-202012/1 compared with infection with previously circulating variants was 1.64 (95% confidence interval 1.32 to 2.04) in patients who tested positive for covid-19 in the community. In this comparatively low risk group, this represents an increase in deaths from 2.5 to 4.1 per 1000 detected cases. Conclusions The probability that the risk of mortality is increased by infection with VOC-202012/01 is high. If this finding is generalisable to other populations, infection with VOC-202012/1 has the potential to cause substantial additional mortality compared with previously circulating variants. Healthcare capacity planning and national and international control policies are all impacted by this finding, with increased mortality lending weight to the argument that further coordinated and stringent measures are justified to reduce deaths from SARS-CoV-2.