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Molteni E, Canas LS, Kläser K, Deng J, Bhopal SS, Hughes RC, Chen L, Murray B, Kerfoot E, Antonelli M, Sudre CH, Pujol JC, Polidori L, May A, Hammers PA, Wolf J, Spector PTD, Steves CJ, Ourselin PS, Absoud M, Modat M, Duncan PEL. Post-vaccination infection rates and modification of COVID-19 symptoms in vaccinated UK school-aged children and adolescents: A prospective longitudinal cohort study. Lancet Reg Health Eur 2022; 19:100429. [PMID: 35821715 PMCID: PMC9263281 DOI: 10.1016/j.lanepe.2022.100429] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
BACKGROUND We aimed to explore the effectiveness of one-dose BNT162b2 vaccination upon SARS-CoV-2 infection, its effect on COVID-19 presentation, and post-vaccination symptoms in children and adolescents (CA) in the UK during periods of Delta and Omicron variant predominance. METHODS In this prospective longitudinal cohort study, we analysed data from 115,775 CA aged 12-17 years, proxy-reported through the Covid Symptom Study (CSS) smartphone application. We calculated post-vaccination infection risk after one dose of BNT162b2, and described the illness profile of CA with post-vaccination SARS-CoV-2 infection, compared to unvaccinated CA, and post-vaccination side-effects. FINDINGS Between August 5, 2021 and February 14, 2022, 25,971 UK CA aged 12-17 years received one dose of BNT162b2 vaccine. The probability of testing positive for infection diverged soon after vaccination, and was lower in CA with prior SARS-CoV-2 infection. Vaccination reduced proxy-reported infection risk (-80·4% (95% CI -0·82 -0·78) and -53·7% (95% CI -0·62 -0·43) at 14-30 days with Delta and Omicron variants respectively, and -61·5% (95% CI -0·74 -0·44) and -63·7% (95% CI -0·68 -0.59) after 61-90 days). Vaccinated CA who contracted SARS-CoV-2 during the Delta period had milder disease than unvaccinated CA; during the Omicron period this was only evident in children aged 12-15 years. Overall disease profile was similar in both vaccinated and unvaccinated CA. Post-vaccination local side-effects were common, systemic side-effects were uncommon, and both resolved within few days (3 days in most cases). INTERPRETATION One dose of BNT162b2 vaccine reduced risk of SARS-CoV-2 infection for at least 90 days in CA aged 12-17 years. Vaccine protection varied for SARS-CoV-2 variant type (lower for Omicron than Delta variant), and was enhanced by pre-vaccination SARS-CoV-2 infection. Severity of COVID-19 presentation after vaccination was generally milder, although unvaccinated CA also had generally mild disease. Overall, vaccination was well-tolerated. FUNDING UK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation and Alzheimer's Society, and ZOE Limited.
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Affiliation(s)
- Erika Molteni
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Liane S. Canas
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Kerstin Kläser
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Jie Deng
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Sunil S. Bhopal
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, Tyne and Wear, UK
| | - Robert C. Hughes
- Department of Population Health, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London, UK
| | - Liyuan Chen
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Benjamin Murray
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Eric Kerfoot
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Carole H. Sudre
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- MRC Unit for Lifelong Health and Ageing, Department of Population Health Sciences and Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | | | | | | | | | | | - Prof Tim D. Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Claire J. Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Aging and Health, Guy's and St Thomas’ NHS Foundation Trust, London, UK
| | | | - Michael Absoud
- Children's Neurosciences, Evelina London Children's Hospital, St Thomas’ Hospital, King's Health Partners, Academic Health Science Centre, London, UK
- Department of Women and Children's Health, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Prof Emma L. Duncan
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Endocrinology, Guy's and St Thomas’ NHS Foundation trust, London, UK
- Corresponding author at: Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, WC2R 2LS, Strand, London, UK.
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Canas LS, Österdahl MF, Deng J, Hu C, Selvachandran S, Polidori L, May A, Molteni E, Murray B, Chen L, Kerfoot E, Klaser K, Antonelli M, Hammers A, Spector T, Ourselin S, Steves C, Sudre CH, Modat M, Duncan EL. Disentangling post-vaccination symptoms from early COVID-19. EClinicalMedicine 2021; 42:101212. [PMID: 34873584 PMCID: PMC8635464 DOI: 10.1016/j.eclinm.2021.101212] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 10/28/2021] [Accepted: 11/08/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Identifying and testing individuals likely to have SARS-CoV-2 is critical for infection control, including post-vaccination. Vaccination is a major public health strategy to reduce SARS-CoV-2 infection globally. Some individuals experience systemic symptoms post-vaccination, which overlap with COVID-19 symptoms. This study compared early post-vaccination symptoms in individuals who subsequently tested positive or negative for SARS-CoV-2, using data from the COVID Symptom Study (CSS) app. METHODS We conducted a prospective observational study in 1,072,313 UK CSS participants who were asymptomatic when vaccinated with Pfizer-BioNTech mRNA vaccine (BNT162b2) or Oxford-AstraZeneca adenovirus-vectored vaccine (ChAdOx1 nCoV-19) between 8 December 2020 and 17 May 2021, who subsequently reported symptoms within seven days (N=362,770) (other than local symptoms at injection site) and were tested for SARS-CoV-2 (N=14,842), aiming to differentiate vaccination side-effects per se from superimposed SARS-CoV-2 infection. The post-vaccination symptoms and SARS-CoV-2 test results were contemporaneously logged by participants. Demographic and clinical information (including comorbidities) were recorded. Symptom profiles in individuals testing positive were compared with a 1:1 matched population testing negative, including using machine learning and multiple models considering UK testing criteria. FINDINGS Differentiating post-vaccination side-effects alone from early COVID-19 was challenging, with a sensitivity in identification of individuals testing positive of 0.6 at best. Most of these individuals did not have fever, persistent cough, or anosmia/dysosmia, requisite symptoms for accessing UK testing; and many only had systemic symptoms commonly seen post-vaccination in individuals negative for SARS-CoV-2 (headache, myalgia, and fatigue). INTERPRETATION Post-vaccination symptoms per se cannot be differentiated from COVID-19 with clinical robustness, either using symptom profiles or machine-derived models. Individuals presenting with systemic symptoms post-vaccination should be tested for SARS-CoV-2 or quarantining, to prevent community spread. FUNDING UK Government Department of Health and Social Care, Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK National Institute for Health Research, UK Medical Research Council and British Heart Foundation, Chronic Disease Research Foundation, Zoe Limited.
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Key Words
- AUC, Area under the curve
- BMI, Body mass index
- CI, Confidence interval
- COVID-19 detection
- COVID-19, Coronavirus disease 2019
- CSS, COVID Symptoms Study
- DI, Data invalid
- Early detection
- IQR, inter quartile range
- KCL, King's College London
- LFAT, Lateral flow antigen test
- LR, Logistic Regression
- Mobile technology
- NHS UK, National Health Service of the United Kingdom
- O-AZ, Oxford-AstraZeneca adenovirus-vectored vaccine
- PB, Pfizer-BoiNTech mRNA vaccine
- RF, Random forest
- ROC, Receiver operating curve
- SARS-CoV-2, Severe acute respiratory syndrome-related coronavirus-2
- Self-reported symptoms
- Side-effects
- UK, United Kingdom of Great Britain and Nothern Ireland
- Vaccination
- bMEM, Bayesian mixed-effect model
- rtPCR, Reverse transcription polymerase chain reaction
- severe acute respiratory syndrome‐related coronavirus 2 (SARS-CoV-2)
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Affiliation(s)
- Liane S. Canas
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Marc F. Österdahl
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - Jie Deng
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | | | | | | | - Erika Molteni
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Benjamin Murray
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Liyuan Chen
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Eric Kerfoot
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Kerstin Klaser
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Michela Antonelli
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Alexander Hammers
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- King's College London & Guy's and St Thomas’ PET Centre, London, UK
| | - Tim Spector
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Claire Steves
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - Carole H. Sudre
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Medical Research Council Unit for Lifelong Health and Ageing, Department of Population Science and Experimental Medicine. UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Emma L. Duncan
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
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