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Risk prediction of covid-19 related death and hospital admission in adults after covid-19 vaccination: national prospective cohort study

BMJ 2021; 374 doi: (Published 17 September 2021) Cite this as: BMJ 2021;374:n2244

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  1. Julia Hippisley-Cox, professor of clinical epidemiology and general practice1,
  2. Carol AC Coupland, senior research fellow and professor of medical statistics in primary care1 2,
  3. Nisha Mehta, clinical director for digital primary care3,
  4. Ruth H Keogh, professor of biostatistics and epidemiology4,
  5. Karla Diaz-Ordaz, associate professor of biostatistics4,
  6. Kamlesh Khunti, professor of primary care diabetes and vascular medicine5,
  7. Ronan A Lyons, professor of public health6,
  8. Frank Kee, professor of public health medicine7,
  9. Aziz Sheikh, professor of primary care research and development8,
  10. Shamim Rahman, deputy director for mental health, disability and shielding analysis9,
  11. Jonathan Valabhji, national clinical director for diabetes and obesity10,
  12. Ewen M Harrison, professor of surgery and data science8,
  13. Peter Sellen, lead analyst for covid-19 clinically extremely vulnerable9,
  14. Nazmus Haq, senior analyst for covid-19 clinically extremely vulnerable9,
  15. Malcolm G Semple, professor of child health and outbreak medicine11,
  16. Peter W M Johnson, national clinical director for cancer10,
  17. Andrew Hayward, professor of infectious disease epidemiology and inclusion health12,
  18. Jonathan S Nguyen-Van-Tam, professor of health protection and deputy chief medical officer2 9
  1. 1Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, UK
  2. 2Division of Primary Care, School of Medicine, University of Nottingham, Nottingham, UK
  3. 3NHS-X, London, UK
  4. 4Department of Medical Statistics and Centre for Statistical Methodology, London School of Hygiene and Tropical Medicine, London, UK
  5. 5Diabetes Research Centre, University of Leicester, Leicester, UK
  6. 6Population Data Science, Swansea University, Swansea, UK
  7. 7Queen’s University, Belfast, UK
  8. 8Usher Institute, University of Edinburgh, Edinburgh, UK
  9. 9Department of Health and Social Care, England, UK
  10. 10NHS England and Improvement, London, UK
  11. 11NIHR Health Protection Research Unit, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK
  12. 12UCL Institute of Epidemiology and Health Care, London, UK
  1. Correspondence to: J Hippisley-Cox julia.hippisley-cox{at} (or @JuliaHCox on Twitter)
  • Accepted 13 September 2021


Objectives To derive and validate risk prediction algorithms to estimate the risk of covid-19 related mortality and hospital admission in UK adults after one or two doses of covid-19 vaccination.

Design Prospective, population based cohort study using the QResearch database linked to data on covid-19 vaccination, SARS-CoV-2 results, hospital admissions, systemic anticancer treatment, radiotherapy, and the national death and cancer registries.

Settings Adults aged 19-100 years with one or two doses of covid-19 vaccination between 8 December 2020 and 15 June 2021.

Main outcome measures Primary outcome was covid-19 related death. Secondary outcome was covid-19 related hospital admission. Outcomes were assessed from 14 days after each vaccination dose. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance was evaluated in a separate validation cohort of general practices.

Results Of 6 952 440 vaccinated patients in the derivation cohort, 5 150 310 (74.1%) had two vaccine doses. Of 2031 covid-19 deaths and 1929 covid-19 hospital admissions, 81 deaths (4.0%) and 71 admissions (3.7%) occurred 14 days or more after the second vaccine dose. The risk algorithms included age, sex, ethnic origin, deprivation, body mass index, a range of comorbidities, and SARS-CoV-2 infection rate. Incidence of covid-19 mortality increased with age and deprivation, male sex, and Indian and Pakistani ethnic origin. Cause specific hazard ratios were highest for patients with Down’s syndrome (12.7-fold increase), kidney transplantation (8.1-fold), sickle cell disease (7.7-fold), care home residency (4.1-fold), chemotherapy (4.3-fold), HIV/AIDS (3.3-fold), liver cirrhosis (3.0-fold), neurological conditions (2.6-fold), recent bone marrow transplantation or a solid organ transplantation ever (2.5-fold), dementia (2.2-fold), and Parkinson’s disease (2.2-fold). Other conditions with increased risk (ranging from 1.2-fold to 2.0-fold increases) included chronic kidney disease, blood cancer, epilepsy, chronic obstructive pulmonary disease, coronary heart disease, stroke, atrial fibrillation, heart failure, thromboembolism, peripheral vascular disease, and type 2 diabetes. A similar pattern of associations was seen for covid-19 related hospital admissions. No evidence indicated that associations differed after the second dose, although absolute risks were reduced. The risk algorithm explained 74.1% (95% confidence interval 71.1% to 77.0%) of the variation in time to covid-19 death in the validation cohort. Discrimination was high, with a D statistic of 3.46 (95% confidence interval 3.19 to 3.73) and C statistic of 92.5. Performance was similar after each vaccine dose. In the top 5% of patients with the highest predicted covid-19 mortality risk, sensitivity for identifying covid-19 deaths within 70 days was 78.7%.

Conclusion This population based risk algorithm performed well showing high levels of discrimination for identifying those patients at highest risk of covid-19 related death and hospital admission after vaccination.


  • Contributors: Study conceptualisation was led by JH-C, CACC, and JSN-V-T. JH-C specified the data, organised data approvals, and data linkage. JH-C, CACC, RHK, and KD-O designed the statistical analysis plan. JH-C and CACC undertook the analyses. JH-C wrote the first draft of the paper and developed the software for the web calculator. The Office of the Chief Medical Officer contributed to the development of the study question and facilitated access to relevant national datasets, contributed to interpretation of data, and contributed to the drafting of the report. All authors contributed to the interpretation of the results and revision of the manuscript and approved the final version of the manuscript. JH-C had full access to all data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. JH-C is the guarantor for the study. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

  • Funding: This study was funded by the National Institute for Health Research (NIHR) following a commission by the Chief Medical Officer for England. The researchers are independent from the NIHR. QResearch was supported by funds from the John Fell Oxford University Press Research Fund, grants from Cancer Research UK (grant C5255/A18085), through the Cancer Research UK Oxford Centre, grants from the Oxford Wellcome Institutional Strategic Support Fund (204826/Z/16/Z), during the conduct of the study. The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

  • Competing interests: All authors have completed the ICMJE uniform disclosure form at and declare: support from the NIHR for the submitted work; JH-C reports grants from NIHR Biomedical Research Centre, Oxford, grants from John Fell Oxford University Press Research Fund, grants from Cancer Research UK, through the Cancer Research UK Oxford Centre, grants from the Oxford Wellcome Institutional Strategic Support Fund and other research councils, during the conduct of the study; JH-C is an unpaid director of QResearch, a not-for-profit organisation that is a partnership between the University of Oxford and EMIS Health, which supplied the QResearch database used for this work; JH-C is a founder and shareholder of ClinRisk and was its medical director until 31 May 2019 (ClinRisk produces open and closed source software to implement clinical risk algorithms (outside this work) into clinical computer systems); JH-C is chair of the NERVTAG risk stratification subgroup and a member of SAGE covid-19 groups and the NHS group advising on prioritisation of use of monoclonal antibodies in covid-19 infection; CACC reports receiving personal fees from ClinRisk outside this work, and is a member of the NERVTAG risk stratification subgroup; KK is supported by the NIHR Applied Research Collaboration-East Midlands and the Leicester BRC and is a member of SAGE; RHK was supported by a UKRI Future Leaders Fellowship (MR/S017968/1); KD-O was supported by a grant from the Alan Turing Institute Health Programme (EP/T001569/1); AS is a member of the Scottish Government Chief Medical Officer’s covid-19 advisory group and a member of AstraZeneca’s thrombotic thrombocytopenic advisory group (both roles are unremunerated); RAL is a member of the Welsh government covid-19 technical advisory group (unrenumerated); MGS reports grants from Department of Health and Social Care NIHR UK, grants from the UK Medical Research Council, grants from Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, during the conduct of the study, and other funds from Integrum Scientific, Greensboro, NC, USA, outside the submitted work; MGS is a member of NERVTAG and attends SAGE covid-19; AH is a member of NERVTAG and the NHS group advising on prioritisation of use of monoclonal antibodies in covid-19 infection; JV is national clinical director for diabetes and obesity at NHS England and Improvement; FK is a member of the Northern Ireland Chief Medical Officer’s pandemic modelling group and strategic intelligence group; JSN-V-T is seconded to the Department of Health and Social Care, England. The views expressed in this manuscript are those of the authors and not necessarily those of Department of Health and Social Care or the UK government.

  • The lead author affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

  • Dissemination to participants and related patient and public communities: Patients will be invited to advise on disseminating the results.

  • Provenance and peer review: Not commissioned; externally peer reviewed.

Data availability statement

To guarantee the confidentiality of personal and health information, only the authors have had access to the data during the study in accordance with the relevant licence agreements. Access to the QResearch data is according to the information on the QResearch website ( The full model, model coefficients, functional form and cumulative incidence function are published on the website.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See:

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