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Fong WLE, Nguyen VG, Burns R, Boukari Y, Beale S, Braithwaite I, Byrne TE, Geismar C, Fragaszy E, Hoskins S, Kovar J, Navaratnam AMD, Oskrochi Y, Patel P, Tweed S, Yavlinsky A, Hayward AC, Aldridge RW. The incidence of COVID-19-related hospitalisation in migrants in the UK: Findings from the Virus Watch prospective community cohort study. J Migr Health 2024; 9:100218. [PMID: 38559897 PMCID: PMC10978526 DOI: 10.1016/j.jmh.2024.100218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 08/11/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Background Migrants in the United Kingdom (UK) may be at higher risk of SARS-CoV-2 exposure; however, little is known about their risk of COVID-19-related hospitalisation during waves 1-3 of the pandemic. Methods We analysed secondary care data linked to Virus Watch study data for adults and estimated COVID-19-related hospitalisation incidence rates by migration status. To estimate the total effect of migration status on COVID-19 hospitalisation rates, we ran mixed-effect Poisson regression for wave 1 (01/03/2020-31/08/2020; wildtype), and mixed-effect negative binomial regressions for waves 2 (01/09/2020-31/05/2021; Alpha) and 3 (01/06/2020-31/11/2021; Delta). Results of all models were then meta-analysed. Results Of 30,276 adults in the analyses, 26,492 (87.5 %) were UK-born and 3,784 (12.5 %) were migrants. COVID-19-related hospitalisation incidence rates for UK-born and migrant individuals across waves 1-3 were 2.7 [95 % CI 2.2-3.2], and 4.6 [3.1-6.7] per 1,000 person-years, respectively. Pooled incidence rate ratios across waves suggested increased rate of COVID-19-related hospitalisation in migrants compared to UK-born individuals in unadjusted 1.68 [1.08-2.60] and adjusted analyses 1.35 [0.71-2.60]. Conclusion Our findings suggest migration populations in the UK have excess risk of COVID-19-related hospitalisations and underscore the need for more equitable interventions particularly aimed at COVID-19 vaccination uptake among migrants.
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Affiliation(s)
- Wing Lam Erica Fong
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Vincent G Nguyen
- Institute of Health Informatics, University College London, London NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
| | - Rachel Burns
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Yamina Boukari
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Sarah Beale
- Institute of Health Informatics, University College London, London NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Isobel Braithwaite
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Thomas E Byrne
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Cyril Geismar
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Ellen Fragaszy
- Institute of Health Informatics, University College London, London NW1 2DA, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Susan Hoskins
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Jana Kovar
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Annalan MD Navaratnam
- Institute of Health Informatics, University College London, London NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Youssof Oskrochi
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Parth Patel
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Sam Tweed
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Alexei Yavlinsky
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, London NW1 2DA, UK
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2
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Navaratnam AMD, O'Callaghan C, Beale S, Nguyen V, Aryee A, Braithwaite I, Byrne TE, Fong WLE, Fragaszy E, Geismar C, Hoskins S, Kovar J, Patel P, Shrotri M, Weber S, Yavlinsky A, Aldridge RW, Hayward AC. Eyeglasses and risk of COVID-19 transmission-analysis of the Virus Watch Community Cohort study. Int J Infect Dis 2024; 139:28-33. [PMID: 38008351 DOI: 10.1016/j.ijid.2023.10.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 10/20/2023] [Accepted: 10/27/2023] [Indexed: 11/28/2023] Open
Abstract
OBJECTIVES The importance of SARS-CoV-2 transmission via the eyes is unknown, with previous studies mainly focusing on protective eyewear in healthcare settings. This study aimed to test the hypothesis that wearing eyeglasses is associated with a lower risk of COVID-19. METHODS Participants from the Virus Watch prospective community cohort study responded to a questionnaire on the use of eyeglasses and contact lenses. Infection was confirmed through data linkage, self-reported positive results, and, for a subgroup, monthly capillary antibody testing. Multivariable logistic regression models, controlling for age, sex, income, and occupation, were used to identify the odds of infection depending on frequency and purpose of eyeglasses or contact lenses use. RESULTS A total of 19,166 participants responded to the questionnaire, with 13,681 (71.3%, CI 70.7-72.0) reporting they wore eyeglasses. Multivariable logistic regression model showed a 15% lower odds of infection for those who reported using eyeglasses always for general use (odds ratio [OR] 0.85, 95% 0.77-0.95, P = 0.002) compared to those who never wore eyeglasses. The protective effect was reduced for those who said wearing eyeglasses interfered with mask-wearing and was absent for contact lens wearers. CONCLUSIONS People who wear eyeglasses have a moderate reduction in risk of COVID-19 infection, highlighting that eye protection may make a valuable contribution to the reduction of transmission in community and healthcare settings.
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Affiliation(s)
| | - Christopher O'Callaghan
- Infection, Immunity & Inflammation Department, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Sarah Beale
- Institute of Health Informatics, University College London, London, UK; Institute of Epidemiology and Health Care, University College London, London, UK
| | - Vincent Nguyen
- Institute of Health Informatics, University College London, London, UK
| | - Anna Aryee
- Institute of Health Informatics, University College London, London, UK
| | | | - Thomas E Byrne
- Institute of Health Informatics, University College London, London, UK
| | | | - Ellen Fragaszy
- Institute of Health Informatics, University College London, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Cyril Geismar
- Institute of Health Informatics, University College London, London, UK
| | - Susan Hoskins
- Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Parth Patel
- Institute of Health Informatics, University College London, London, UK
| | - Madhumita Shrotri
- Institute of Health Informatics, University College London, London, UK
| | - Sophie Weber
- Institute of Health Informatics, University College London, London, UK
| | - Alexei Yavlinsky
- Institute of Health Informatics, University College London, London, UK
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, London, UK
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
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3
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Patel P, Beale S, Nguyen V, Braithwaite I, Byrne TE, Erica Fong WL, Fragaszy E, Geismar C, Hoskins S, Navaratnam AMD, Shrotri M, Kovar J, Aryee A, Hayward AC, Aldridge RW. Inequalities in access to paid sick leave among workers in England and Wales. Int J Health Plann Manage 2023; 38:1864-1876. [PMID: 37549127 PMCID: PMC10946983 DOI: 10.1002/hpm.3697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 07/20/2023] [Indexed: 08/09/2023] Open
Abstract
BACKGROUND It is poorly understood which workers lack access to sick pay in England and Wales. This evidence gap has been of particular interest in the context of the Covid-19 pandemic given the relationship between presenteeism and infectious disease transmission. METHOD This cross-sectional analysis (n = 8874) was nested within a large community cohort study based across England and Wales (Virus Watch). An online survey in February 2021 asked participants in work if they had access to paid sick leave. We used logistic regression to examine sociodemographic factors associated with lacking access to sick pay. RESULTS Only 66% (n = 5864) of participants reported access to sick pay. South Asian workers (adjusted odds ratio [OR] 1.40, 95% confidence interval [CI] 1.06-1.83) and those from Other minority ethnic backgrounds (OR 2.93, 95% CI 1.54-5.59) were more likely to lack access to sick pay compared to White British workers. Older workers (OR range 1.72 [1.53-1.93]-5.26 [4.42-6.26]), workers in low-income households (OR 2.53, 95% CI 2.15-2.98) and those in transport, trade, and service occupations (OR range 2.03 [1.58-2.61]-5.29 [3.67-7.72]) were also more likely to lack access to sick pay compared respectively to workers aged 25-44, those in high income households and managerial occupations. DISCUSSION Unwarranted age and ethnic inequalities in sick pay access are suggestive of labour market discrimination. Occupational differences are also cause for concern. Policymakers should consider expanding access to sick pay to mitigate transmission of Covid-19 and other endemic respiratory infections in the community, and in the context of pandemic preparation.
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Affiliation(s)
- Parth Patel
- Institute of Health InformaticsUniversity College LondonLondonUK
| | - Sarah Beale
- Institute of Health InformaticsUniversity College LondonLondonUK
- Institute of Epidemiology and Health CareUniversity College LondonLondonUK
| | - Vincent Nguyen
- Institute of Health InformaticsUniversity College LondonLondonUK
| | | | - Thomas E. Byrne
- Institute of Health InformaticsUniversity College LondonLondonUK
| | | | - Ellen Fragaszy
- Institute of Health InformaticsUniversity College LondonLondonUK
- Department of Infectious Disease EpidemiologyLondon School of Hygiene and Tropical MedicineLondonUK
| | - Cyril Geismar
- Institute of Health InformaticsUniversity College LondonLondonUK
| | - Susan Hoskins
- Institute of Epidemiology and Health CareUniversity College LondonLondonUK
| | | | | | - Jana Kovar
- Institute of Epidemiology and Health CareUniversity College LondonLondonUK
| | - Anna Aryee
- Institute of Health InformaticsUniversity College LondonLondonUK
| | - Andrew C. Hayward
- Institute of Epidemiology and Health CareUniversity College LondonLondonUK
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4
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Byrne T, Kovar J, Beale S, Braithwaite I, Fragaszy E, Fong WLE, Geismar C, Hoskins S, Navaratnam AMD, Nguyen V, Patel P, Shrotri M, Yavlinsky A, Hardelid P, Wijlaars L, Nastouli E, Spyer M, Aryee A, Cox I, Lampos V, Mckendry RA, Cheng T, Johnson AM, Michie S, Gibbs J, Gilson R, Rodger A, Abubakar I, Hayward A, Aldridge RW. Cohort Profile: Virus Watch-understanding community incidence, symptom profiles and transmission of COVID-19 in relation to population movement and behaviour. Int J Epidemiol 2023; 52:e263-e272. [PMID: 37349899 PMCID: PMC10555858 DOI: 10.1093/ije/dyad087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/31/2023] [Indexed: 06/24/2023] Open
Affiliation(s)
- Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Ellen Fragaszy
- Institute of Epidemiology and Health Care, University College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Susan Hoskins
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Pia Hardelid
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Linda Wijlaars
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Eleni Nastouli
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
- Francis Crick Institute, London, UK
- University College London Hospital, London, UK
| | | | - Anna Aryee
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Ingemar Cox
- Department of Computer Science, University College London, London, UK
| | - Vasileios Lampos
- Department of Computer Science, University College London, London, UK
| | - Rachel A Mckendry
- London Centre for Nanotechnology and Division of Medicine, University College London, London, UK
| | - Tao Cheng
- SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, University College London, London, UK
| | - Anne M Johnson
- Centre for Population Research in Sexual Health and HIV, Institute for Global Health, London, UK
| | - Susan Michie
- Centre for Behaviour Change, University College London, London, UK
| | - Jo Gibbs
- Institute for Global Health, University College London, London, UK
| | - Richard Gilson
- Institute for Global Health, University College London, London, UK
| | - Alison Rodger
- Institute for Global Health, University College London, London, UK
- Royal Free London NHS Foundation Trust, London, UK
| | - Ibrahim Abubakar
- Institute for Global Health, University College London, London, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
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5
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Boukari Y, Beale S, Nguyen V, Fong WLE, Burns R, Yavlinsky A, Hoskins S, Lewis K, Geismar C, Navaratnam AM, Braithwaite I, Byrne TE, Oskrochi Y, Tweed S, Kovar J, Patel P, Hayward A, Aldridge R. SARS-CoV-2 infections in migrants and the role of household overcrowding: a causal mediation analysis of Virus Watch data. J Epidemiol Community Health 2023; 77:649-655. [PMID: 37463770 PMCID: PMC10511992 DOI: 10.1136/jech-2022-220251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 07/07/2023] [Indexed: 07/20/2023]
Abstract
BACKGROUND Migrants are over-represented in SARS-CoV-2 infections globally; however, evidence is limited for migrants in England and Wales. Household overcrowding is a risk factor for SARS-CoV-2 infection, with migrants more likely to live in overcrowded households than UK-born individuals. We aimed to estimate the total effect of migration status on SARS-CoV-2 infection and to what extent household overcrowding mediated this effect. METHODS We included a subcohort of individuals from the Virus Watch prospective cohort study during the second SARS-CoV-2 wave (1 September 2020-30 April 2021) who were aged ≥18 years, self-reported the number of rooms in their household and had no evidence of SARS-CoV-2 infection pre-September 2020. We estimated total, indirect and direct effects using Buis' logistic decomposition regression controlling for age, sex, ethnicity, clinical vulnerability, occupation, income and whether they lived with children. RESULTS In total, 23 478 individuals were included. 9.07% (187/2062) of migrants had evidence of infection during the study period vs 6.27% (1342/21 416) of UK-born individuals. Migrants had 22% higher odds of infection during the second wave (total effect; OR 1.22, 95% CI 1.01 to 1.47). Household overcrowding accounted for approximately 36% (95% CI -4% to 77%) of these increased odds (indirect effect, OR 1.07, 95% CI 1.03 to 1.12; proportion accounted for: indirect effect on log odds scale/total effect on log odds scale=0.36). CONCLUSION Migrants had higher odds of SARS-CoV-2 infection during the second wave compared with UK-born individuals and household overcrowding explained 36% of these increased odds. Policy interventions to reduce household overcrowding for migrants are needed as part of efforts to tackle health inequalities during the pandemic and beyond.
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Affiliation(s)
- Yamina Boukari
- Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Vincent Nguyen
- Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Rachel Burns
- Institute of Health Informatics, University College London, London, UK
| | - Alexei Yavlinsky
- Institute of Health Informatics, University College London, London, UK
| | - Susan Hoskins
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Kate Lewis
- Population, Policy and Practice Department, University College London Great Ormond Street Institute of Child Health, London, UK
| | - Cyril Geismar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Annalan Md Navaratnam
- Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | | | - Thomas E Byrne
- Institute of Health Informatics, University College London, London, UK
| | - Youssof Oskrochi
- Institute of Health Informatics, University College London, London, UK
| | - Sam Tweed
- Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Health Informatics, University College London, London, UK
| | - Parth Patel
- Institute of Health Informatics, University College London, London, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Robert Aldridge
- Institute of Health Informatics, University College London, London, UK
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6
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Hoskins S, Beale S, Nguyen V, Boukari Y, Yavlinsky A, Kovar J, Byrne T, Fong WLE, Geismar C, Patel P, Johnson AM, Aldridge RW, Hayward A. Deprivation, essential and non-essential activities and SARS-CoV-2 infection following the lifting of national public health restrictions in England and Wales. NIHR Open Res 2023; 3:46. [PMID: 37994319 PMCID: PMC10663878 DOI: 10.3310/nihropenres.13445.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/25/2023] [Indexed: 11/24/2023]
Abstract
Background Individuals living in deprived areas in England and Wales undertook essential activities more frequently and experienced higher rates of SARS-CoV-2 infection than less deprived communities during periods of restrictions aimed at controlling the Alpha (B.1.1.7) variant. We aimed to understand whether these deprivation-related differences changed once restrictions were lifted. Methods Among 11,231 adult Virus Watch Community Cohort Study participants multivariable logistic regressions were used to estimate the relationships between deprivation and self-reported activities and deprivation and infection (self-reported lateral flow or PCR tests and linkage to National Testing data and Second Generation Surveillance System (SGSS)) between August - December 2021, following the lifting of national public health restrictions. Results Those living in areas of greatest deprivation were more likely to undertake essential activities (leaving home for work (aOR 1.56 (1.33 - 1.83)), using public transport (aOR 1.33 (1.13 - 1.57)) but less likely to undertake non-essential activities (indoor hospitality (aOR 0.82 (0.70 - 0.96)), outdoor hospitality (aOR 0.56 (0.48 - 0.66)), indoor leisure (aOR 0.63 (0.54 - 0.74)), outdoor leisure (aOR 0.64 (0.46 - 0.88)), or visit a hairdresser (aOR 0.72 (0.61 - 0.85))). No statistical association was observed between deprivation and infection (P=0.5745), with those living in areas of greatest deprivation no more likely to become infected with SARS-CoV-2 (aOR 1.25 (0.87 - 1.79). Conclusion The lack of association between deprivation and infection is likely due to the increased engagement in non-essential activities among the least deprived balancing the increased work-related exposure among the most deprived. The differences in activities highlight stark disparities in an individuals' ability to choose how to limit infection exposure.
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Affiliation(s)
- Susan Hoskins
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
| | - Sarah Beale
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, England, WC1E 7HB, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, England, WC1E 7HB, UK
| | - Yamina Boukari
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, England, WC1E 7HB, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, England, WC1E 7HB, UK
| | - Parth Patel
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
| | - Anne M. Johnson
- Institute for Global Health, University College London, London, England, WC1N 1EH, UK
| | - Robert W. Aldridge
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, England, WC1E 7HB, UK
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7
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Geismar C, Nguyen V, Fragaszy E, Shrotri M, Navaratnam AMD, Beale S, Byrne TE, Fong WLE, Yavlinsky A, Kovar J, Hoskins S, Braithwaite I, Aldridge RW, Hayward AC, White PJ, Jombart T, Cori A. Bayesian reconstruction of SARS-CoV-2 transmissions highlights substantial proportion of negative serial intervals. Epidemics 2023; 44:100713. [PMID: 37579586 DOI: 10.1016/j.epidem.2023.100713] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 07/25/2023] [Accepted: 07/31/2023] [Indexed: 08/16/2023] Open
Abstract
BACKGROUND The serial interval is a key epidemiological measure that quantifies the time between the onset of symptoms in an infector-infectee pair. It indicates how quickly new generations of cases appear, thus informing on the speed of an epidemic. Estimating the serial interval requires to identify pairs of infectors and infectees. Yet, most studies fail to assess the direction of transmission between cases and assume that the order of infections - and thus transmissions - strictly follows the order of symptom onsets, thereby imposing serial intervals to be positive. Because of the long and highly variable incubation period of SARS-CoV-2, this may not always be true (i.e an infectee may show symptoms before their infector) and negative serial intervals may occur. This study aims to estimate the serial interval of different SARS-CoV-2 variants whilst accounting for negative serial intervals. METHODS This analysis included 5 842 symptomatic individuals with confirmed SARS-CoV-2 infection amongst 2 579 households from September 2020 to August 2022 across England & Wales. We used a Bayesian framework to infer who infected whom by exploring all transmission trees compatible with the observed dates of symptoms, based on a wide range of incubation period and generation time distributions compatible with estimates reported in the literature. Serial intervals were derived from the reconstructed transmission pairs, stratified by variants. RESULTS We estimated that 22% (95% credible interval (CrI) 8-32%) of serial interval values are negative across all VOC. The mean serial interval was shortest for Omicron BA5 (2.02 days, 1.26-2.84) and longest for Alpha (3.37 days, 2.52-4.04). CONCLUSIONS This study highlights the large proportion of negative serial intervals across SARS-CoV-2 variants. Because the serial interval is widely used to estimate transmissibility and forecast cases, these results may have critical implications for epidemic control.
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Affiliation(s)
- Cyril Geismar
- MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK; Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Ellen Fragaszy
- Institute of Epidemiology and Health Care, University College London, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK; Institute of Epidemiology and Health Care, University College London, London, UK
| | - Thomas E Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Susan Hoskins
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Peter J White
- MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Thibaut Jombart
- MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
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Geismar C, Nguyen V, Fragaszy E, Shrotri M, Navaratnam AMD, Beale S, Byrne TE, Fong WLE, Yavlinsky A, Kovar J, Hoskins S, Braithwaite I, Aldridge RW, Hayward AC. Symptom profiles of community cases infected by influenza, RSV, rhinovirus, seasonal coronavirus, and SARS-CoV-2 variants of concern. Sci Rep 2023; 13:12511. [PMID: 37532756 PMCID: PMC10397315 DOI: 10.1038/s41598-023-38869-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 07/16/2023] [Indexed: 08/04/2023] Open
Abstract
Respiratory viruses that were suppressed through previous lockdowns during the COVID-19 pandemic have recently started to co-circulate with SARS-CoV-2. Understanding the clinical characteristics and symptomatology of different respiratory viral infections can help address the challenges related to the identification of cases and the understanding of SARS-CoV-2 variants' evolutionary patterns. Flu Watch (2006-2011) and Virus Watch (2020-2022) are household community cohort studies monitoring the epidemiology of influenza, respiratory syncytial virus, rhinovirus, seasonal coronavirus, and SARS-CoV-2, in England and Wales. This study describes and compares the proportion of symptoms reported during illnesses infected by common respiratory viruses. The SARS-CoV-2 symptom profile increasingly resembles that of other respiratory viruses as new strains emerge. Increased cough, sore throat, runny nose, and sneezing are associated with the emergence of the Omicron strains. As SARS-CoV-2 becomes endemic, monitoring the evolution of its symptomatology associated with new variants will be critical for clinical surveillance.
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Affiliation(s)
- Cyril Geismar
- MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Ellen Fragaszy
- Institute of Epidemiology and Health Care, University College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Thomas E Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Susan Hoskins
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
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9
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Nguyen VG, Yavlinsky A, Beale S, Hoskins S, Byrne TE, Lampos V, Braithwaite I, Fong WLE, Fragaszy E, Geismar C, Kovar J, Navaratnam AMD, Patel P, Shrotri M, Weber S, Hayward AC, Aldridge RW. Comparative effectiveness of different primary vaccination courses on mRNA-based booster vaccines against SARs-COV-2 infections: a time-varying cohort analysis using trial emulation in the Virus Watch community cohort. Int J Epidemiol 2023; 52:342-354. [PMID: 36655537 PMCID: PMC10114109 DOI: 10.1093/ije/dyad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 01/13/2023] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND The Omicron B.1.1.529 variant increased severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in doubly vaccinated individuals, particularly in the Oxford-AstraZeneca COVID-19 vaccine (ChAdOx1) recipients. To tackle infections, the UK's booster vaccination programmes used messenger ribonucleic acid (mRNA) vaccines irrespective of an individual's primary course vaccine type, and prioritized the clinically vulnerable. These mRNA vaccines included the Pfizer-BioNTech COVID-19 vaccine (BNT162b2) the Moderna COVID-19 vaccine (mRNA-1273). There is limited understanding of the effectiveness of different primary vaccination courses on mRNA booster vaccines against SARs-COV-2 infections and how time-varying confounders affect these evaluations. METHODS Trial emulation was applied to a prospective community observational cohort in England and Wales to reduce time-varying confounding-by-indication driven by prioritizing vaccination based upon age, vulnerability and exposure. Trial emulation was conducted by meta-analysing eight adult cohort results whose booster vaccinations were staggered between 16 September 2021 and 05 January 2022 and followed until 23 January 2022. Time from booster vaccination until SARS-CoV-2 infection, loss of follow-up or end of study was modelled using Cox proportional hazard models and adjusted for age, sex, minority ethnic status, clinically vulnerability and deprivation. RESULTS A total of 19 159 participants were analysed, with 11 709 ChAdOx1 primary courses and 7450 BNT162b2 primary courses. Median age, clinical vulnerability status and infection rates fluctuate through time. In mRNA-boosted adults, 7.4% (n = 863) of boosted adults with a ChAdOx1 primary course experienced a SARS-CoV-2 infection compared with 7.7% (n = 571) of those who had BNT162b2 as a primary course. The pooled adjusted hazard ratio (aHR) was 1.01 with a 95% confidence interval (CI) of: 0.90 to 1.13. CONCLUSION After an mRNA booster dose, we found no difference in protection comparing those with a primary course of BNT162b2 with those with a ChAdOx1 primary course. This contrasts with pre-booster findings where previous research shows greater effectiveness of BNT162b2 than ChAdOx1 in preventing infection.
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Affiliation(s)
- Vincent Grigori Nguyen
- Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Alexei Yavlinsky
- Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Susan Hoskins
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Thomas E Byrne
- Institute of Health Informatics, University College London, London, UK
| | - Vasileios Lampos
- Department of Computer Science, University College London, London, UK
| | | | | | - Ellen Fragaszy
- Institute of Health Informatics, University College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Cyril Geismar
- Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | | | - Parth Patel
- Institute of Health Informatics, University College London, London, UK
| | - Madhumita Shrotri
- Institute of Health Informatics, University College London, London, UK
| | - Sophie Weber
- Institute of Health Informatics, University College London, London, UK
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, London, UK
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10
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Beale S, Yavlinsky A, Hoskins S, Nguyen V, Byrne T, Fong WLE, Kovar J, Van Tongeren M, Aldridge RW, Hayward A. Between-occupation differences in work-related COVID-19 mitigation strategies over time: Analysis of the Virus Watch Cohort in England and Wales. Scand J Work Environ Health 2023:4092. [PMID: 37066842 DOI: 10.5271/sjweh.4092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2023] Open
Abstract
OBJECTIVES COVID-19 mitigations have had a profound impact on workplaces, however, multisectoral comparisons of how work-related mitigations were applied are limited. This study aimed to investigate (i) occupational differences in the usage of key work-related mitigations over time and (ii) workers' perceptions of these mitigations. METHODS Employed/self-employed Virus Watch study participants (N=6279) responded to a mitigation-related online survey covering the periods of December 2020-February 2022. Logistic regression was used to investigate occupation- and time-related differences in the usage of work-related mitigation methods. Participants' perceptions of mitigation methods were investigated descriptively using proportions. RESULTS Usage of work-related mitigation methods differed between occupations and over time, likely reflecting variation in job roles, workplace environments, legislation and guidance. Healthcare workers had the highest predicted probabilities for several mitigations, including reporting frequent hand hygiene [predicted probability across all survey periods 0.61 (95% CI 0.56-0.66)] and always wearing face coverings [predicted probability range 0.71 (95% CI 0.66-0.75) - 0.80 (95% CI 0.76-0.84) across survey periods]. There were significant cross-occupational trends towards reduced mitigations during periods of less stringent national restrictions. The majority of participants across occupations (55-88%) agreed that most mitigations were reasonable and worthwhile even after the relaxation of national restrictions; agreement was lower for physical distancing (39-44%). CONCLUSIONS While usage of work-related mitigations appeared to vary alongside stringency of national restrictions, agreement that most mitigations were reasonable and worthwhile remained substantial. Further investigation into the factors underlying between-occupational differences could assist pandemic planning and prevention of workplace COVID-19 transmission.
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Affiliation(s)
- Sarah Beale
- Institute of Epidemiology and Health Care, University College London, London, UK, WC1E 7HB.
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11
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Beale S, Hoskins S, Byrne T, Fong WLE, Fragaszy E, Geismar C, Kovar J, Navaratnam AMD, Nguyen V, Patel P, Yavlinsky A, Johnson AM, Van Tongeren M, Aldridge RW, Hayward A. Differential Risk of SARS-CoV-2 Infection by Occupation: Evidence from the Virus Watch prospective cohort study in England and Wales. J Occup Med Toxicol 2023; 18:5. [PMID: 37013634 PMCID: PMC10068189 DOI: 10.1186/s12995-023-00371-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 03/21/2023] [Indexed: 04/05/2023] Open
Abstract
BACKGROUND Workers across different occupations vary in their risk of SARS-CoV-2 infection, but the direct contribution of occupation to this relationship is unclear. This study aimed to investigate how infection risk differed across occupational groups in England and Wales up to April 2022, after adjustment for potential confounding and stratification by pandemic phase. METHODS Data from 15,190 employed/self-employed participants in the Virus Watch prospective cohort study were used to generate risk ratios for virologically- or serologically-confirmed SARS-CoV-2 infection using robust Poisson regression, adjusting for socio-demographic and health-related factors and non-work public activities. We calculated attributable fractions (AF) amongst the exposed for belonging to each occupational group based on adjusted risk ratios (aRR). RESULTS Increased risk was seen in nurses (aRR = 1.44, 1.25-1.65; AF = 30%, 20-39%), doctors (aRR = 1.33, 1.08-1.65; AF = 25%, 7-39%), carers (1.45, 1.19-1.76; AF = 31%, 16-43%), primary school teachers (aRR = 1.67, 1.42- 1.96; AF = 40%, 30-49%), secondary school teachers (aRR = 1.48, 1.26-1.72; AF = 32%, 21-42%), and teaching support occupations (aRR = 1.42, 1.23-1.64; AF = 29%, 18-39%) compared to office-based professional occupations. Differential risk was apparent in the earlier phases (Feb 2020-May 2021) and attenuated later (June-October 2021) for most groups, although teachers and teaching support workers demonstrated persistently elevated risk across waves. CONCLUSIONS Occupational differences in SARS-CoV-2 infection risk vary over time and are robust to adjustment for socio-demographic, health-related, and non-workplace activity-related potential confounders. Direct investigation into workplace factors underlying elevated risk and how these change over time is needed to inform occupational health interventions.
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Affiliation(s)
- Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK.
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK.
| | - Susan Hoskins
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Department of Infectious Disease Epidemiology, London, School of Hygiene and Tropical Medicine , Keppel Street, London, WC1E 7HT, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Anne M Johnson
- Institute for Global Health, University College London, London, WC1N 1EH, UK
| | - Martie Van Tongeren
- Division of Population Health, Health Services Research & Primary Care, University of Manchester, Manchester, M13 9NT, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
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12
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Nguyen V, Liu Y, Mumford R, Flanagan B, Patel P, Braithwaite I, Shrotri M, Byrne T, Beale S, Aryee A, Fong WLE, Fragaszy E, Geismar C, Navaratnam AMD, Hardelid P, Kovar J, Pope A, Cheng T, Hayward A, Aldridge R. Tracking Changes in Mobility Before and After the First SARS-CoV-2 Vaccination Using Global Positioning System Data in England and Wales (Virus Watch): Prospective Observational Community Cohort Study. JMIR Public Health Surveill 2023; 9:e38072. [PMID: 36884272 PMCID: PMC9997704 DOI: 10.2196/38072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 08/05/2022] [Accepted: 09/29/2022] [Indexed: 03/09/2023] Open
Abstract
BACKGROUND Evidence suggests that individuals may change adherence to public health policies aimed at reducing the contact, transmission, and spread of the SARS-CoV-2 virus after they receive their first SARS-CoV-2 vaccination when they are not fully vaccinated. OBJECTIVE We aimed to estimate changes in median daily travel distance of our cohort from their registered addresses before and after receiving a SARS-CoV-2 vaccine. METHODS Participants were recruited into Virus Watch starting in June 2020. Weekly surveys were sent out to participants, and vaccination status was collected from January 2021 onward. Between September 2020 and February 2021, we invited 13,120 adult Virus Watch participants to contribute toward our tracker subcohort, which uses the GPS via a smartphone app to collect data on movement. We used segmented linear regression to estimate the median daily travel distance before and after the first self-reported SARS-CoV-2 vaccine dose. RESULTS We analyzed the daily travel distance of 249 vaccinated adults. From 157 days prior to vaccination until the day before vaccination, the median daily travel distance was 9.05 (IQR 8.06-10.09) km. From the day of vaccination to 105 days after vaccination, the median daily travel distance was 10.08 (IQR 8.60-12.42) km. From 157 days prior to vaccination until the vaccination date, there was a daily median decrease in mobility of 40.09 m (95% CI -50.08 to -31.10; P<.001). After vaccination, there was a median daily increase in movement of 60.60 m (95% CI 20.90-100; P<.001). Restricting the analysis to the third national lockdown (January 4, 2021, to April 5, 2021), we found a median daily movement increase of 18.30 m (95% CI -19.20 to 55.80; P=.57) in the 30 days prior to vaccination and a median daily movement increase of 9.36 m (95% CI 38.6-149.00; P=.69) in the 30 days after vaccination. CONCLUSIONS Our study demonstrates the feasibility of collecting high-volume geolocation data as part of research projects and the utility of these data for understanding public health issues. Our various analyses produced results that ranged from no change in movement after vaccination (during the third national lock down) to an increase in movement after vaccination (considering all periods, up to 105 days after vaccination), suggesting that, among Virus Watch participants, any changes in movement distances after vaccination are small. Our findings may be attributable to public health measures in place at the time such as movement restrictions and home working that applied to the Virus Watch cohort participants during the study period.
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Affiliation(s)
- Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, United Kingdom
- Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Yunzhe Liu
- SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, University College London, London, United Kingdom
| | - Richard Mumford
- Technical Research Department, Esri, Edinburgh, United Kingdom
| | | | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, United Kingdom
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, United Kingdom
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, United Kingdom
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, United Kingdom
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, United Kingdom
- Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Anna Aryee
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, United Kingdom
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, United Kingdom
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, United Kingdom
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, United Kingdom
- Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, United Kingdom
- Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Pia Hardelid
- Department of Population, Policy and Practice, University College London Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Addy Pope
- Technical Research Department, Esri, Edinburgh, United Kingdom
| | - Tao Cheng
- SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, University College London, London, United Kingdom
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Robert Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, United Kingdom
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13
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Serisier A, Beale S, Boukari Y, Hoskins S, Nguyen V, Byrne T, Fong WLE, Fragaszy E, Geismar C, Kovar J, Yavlinsky A, Hayward A, Aldridge RW. A case-crossover study of the effect of vaccination on SARS-CoV-2 transmission relevant behaviours during a period of national lockdown in England and Wales. Vaccine 2023; 41:511-518. [PMID: 36496282 PMCID: PMC9721283 DOI: 10.1016/j.vaccine.2022.11.073] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/28/2022] [Accepted: 11/29/2022] [Indexed: 12/11/2022]
Abstract
BACKGROUND Studies of COVID-19 vaccine effectiveness show increases in COVID-19 cases within 14 days of a first dose, potentially reflecting post-vaccination behaviour changes associated with SARS-CoV-2 transmission before vaccine protection. However, direct evidence for a relationship between vaccination and behaviour is lacking. We aimed to examine the association between vaccination status and self-reported non-household contacts and non-essential activities during a national lockdown in England and Wales. METHODS Participants (n = 1154) who had received the first dose of a COVID-19 vaccine reported non-household contacts and non-essential activities from February to March 2021 in monthly surveys during a national lockdown in England and Wales. We used a case-crossover study design and conditional logistic regression to examine the association between vaccination status (pre-vaccination vs 14 days post-vaccination) and self-reported contacts and activities within individuals. Stratified subgroup analyses examined potential effect heterogeneity by sociodemographic characteristics such as sex, household income or age group. RESULTS 457/1154 (39.60 %) participants reported non-household contacts post-vaccination compared with 371/1154 (32.15 %) participants pre-vaccination. 100/1154 (8.67 %) participants reported use of non-essential shops or services post-vaccination compared with 74/1154 (6.41 %) participants pre-vaccination. Post-vaccination status was associated with increased odds of reporting non-household contacts (OR 1.65, 95 % CI 1.31-2.06, p < 0.001) and use of non-essential shops or services (OR 1.50, 95 % CI 1.03-2.17, p = 0.032). This effect varied between men and women and different age groups. CONCLUSION Participants had higher odds of reporting non-household contacts and use of non-essential shops or services within 14 days of their first COVID-19 vaccine compared to pre-vaccination. Public health emphasis on maintaining protective behaviours during this post-vaccination time period when individuals have yet to develop full protection from vaccination could reduce risk of SARS-CoV-2 infection.
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Affiliation(s)
- Aimee Serisier
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Sarah Beale
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK; Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK.
| | - Yamina Boukari
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Susan Hoskins
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Vincent Nguyen
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK; Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Cyril Geismar
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK; Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Robert W Aldridge
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
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14
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Beale S, Burns R, Braithwaite I, Byrne T, Lam Erica Fong W, Fragaszy E, Geismar C, Hoskins S, Kovar J, Navaratnam AMD, Nguyen V, Patel P, Yavlinsky A, Van Tongeren M, Aldridge RW, Hayward A. Occupation, Worker Vulnerability, and COVID-19 Vaccination Uptake: Analysis of the Virus Watch prospective cohort study. Vaccine 2022; 40:7646-7652. [PMID: 36372668 PMCID: PMC9637514 DOI: 10.1016/j.vaccine.2022.10.080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 10/28/2022] [Accepted: 10/29/2022] [Indexed: 11/09/2022]
Abstract
BACKGROUND Occupational disparities in COVID-19 vaccine uptake can impact the effectiveness of vaccination programmes and introduce particular risk for vulnerable workers and those with high workplace exposure. This study aimed to investigate COVID-19 vaccine uptake by occupation, including for vulnerable groups and by occupational exposure status. METHODS We used data from employed or self-employed adults who provided occupational information as part of the Virus Watch prospective cohort study (n = 19,595) and linked this to study-obtained information about vulnerability-relevant characteristics (age, medical conditions, obesity status) and work-related COVID-19 exposure based on the Job Exposure Matrix. Participant vaccination status for the first, second, and third dose of any COVID-19 vaccine was obtained based on linkage to national records and study records. We calculated proportions and Sison-Glaz multinomial 95% confidence intervals for vaccine uptake by occupation overall, by vulnerability-relevant characteristics, and by job exposure. FINDINGS Vaccination uptake across occupations ranged from 89-96% for the first dose, 87-94% for the second dose, and 75-86% for the third dose, with transport, trade, service and sales workers persistently demonstrating the lowest uptake. Vulnerable workers tended to demonstrate fewer between-occupational differences in uptake than non-vulnerable workers, although clinically vulnerable transport workers (76%-89% across doses) had lower uptake than several other occupational groups (maximum across doses 86%-96%). Workers with low SARS-CoV-2 exposure risk had higher vaccine uptake (86%-96% across doses) than those with elevated or high risk (81-94% across doses). INTERPRETATION Differential vaccination uptake by occupation, particularly amongst vulnerable and highly-exposed workers, is likely to worsen occupational and related socioeconomic inequalities in infection outcomes. Further investigation into occupational and non-occupational factors influencing differential uptake is required to inform relevant interventions for future COVID-19 booster rollouts and similar vaccination programmes.
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Affiliation(s)
- Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK; Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK.
| | - Rachel Burns
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK; Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Susan Hoskins
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK; Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK; Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK
| | - Martie Van Tongeren
- Centre for Occupational and Environmental Health, University of Manchester, Manchester M13 9PL, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
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15
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Hoskins S, Beale S, Nguyen V, Boukari Y, Yavlinsky A, Kovar J, Byrne T, Fragaszy E, Fong WLE, Geismar C, Patel P, Navaratnam AMD, van Tongeren M, Johnson AM, Aldridge RW, Hayward A. Relative contribution of essential and non-essential activities to SARS-CoV-2 transmission following the lifting of public health restrictions in England and Wales. Epidemiol Infect 2022; 151:e3. [PMID: 36475452 PMCID: PMC9990391 DOI: 10.1017/s0950268822001832] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 10/24/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022] Open
Abstract
PURPOSE We aimed to understand which non-household activities increased infection odds and contributed greatest to SARS-CoV-2 infections following the lifting of public health restrictions in England and Wales. PROCEDURES We undertook multivariable logistic regressions assessing the contribution to infections of activities reported by adult Virus Watch Community Cohort Study participants. We calculated adjusted weighted population attributable fractions (aPAF) estimating which activity contributed greatest to infections. FINDINGS Among 11 413 participants (493 infections), infection was associated with: leaving home for work (aOR 1.35 (1.11-1.64), aPAF 17%), public transport (aOR 1.27 (1.04-1.57), aPAF 12%), shopping once (aOR 1.83 (1.36-2.45)) vs. more than three times a week, indoor leisure (aOR 1.24 (1.02-1.51), aPAF 10%) and indoor hospitality (aOR 1.21 (0.98-1.48), aPAF 7%). We found no association for outdoor hospitality (1.14 (0.94-1.39), aPAF 5%) or outdoor leisure (1.14 (0.82-1.59), aPAF 1%). CONCLUSION Essential activities (work and public transport) carried the greatest risk and were the dominant contributors to infections. Non-essential indoor activities (hospitality and leisure) increased risk but contributed less. Outdoor activities carried no statistical risk and contributed to fewer infections. As countries aim to 'live with COVID', mitigating transmission in essential and indoor venues becomes increasingly relevant.
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Affiliation(s)
- Susan Hoskins
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Yamina Boukari
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Annalan M. D. Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Martie van Tongeren
- Centre for Occupational and Environmental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, Greater Manchester, UK
| | - Anne M. Johnson
- Institute for Global Health, University College London, London, WC1N 1EH, UK
| | - Robert W. Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
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16
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Geismar C, Nguyen V, Fragaszy E, Shrotri M, Navaratnam AMD, Beale S, Byrne TE, Fong WLE, Yavlinsky A, Kovar J, Braithwaite I, Aldridge RW, Hayward AC, White P, Jombart T, Cori A. Bayesian reconstruction of household transmissions to infer the serial interval of COVID-19 by variants of concern: analysis from a prospective community cohort study (Virus Watch). Lancet 2022; 400 Suppl 1:S40. [PMID: 36929985 PMCID: PMC9691060 DOI: 10.1016/s0140-6736(22)02250-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
BACKGROUND The serial interval is a key epidemiological measure that quantifies the time between an infector's and an infectee's onset of symptoms. This measure helps investigate epidemiological links between cases, and is an important parameter in transmission models used to estimate transmissibility and inform control strategies. The emergence of multiple variants of concern (VOC) during the SARS-CoV-2 pandemic has led to uncertainties about potential changes in the serial interval of COVID-19. We estimated the household serial interval of multiple VOC using data collected by the Virus Watch study. This online, prospective, community cohort study followed-up entire households in England and Wales since mid-June 2020. METHODS This analysis included 5842 symptomatic individuals with confirmed SARS-CoV-2 infection among 2579 households from Sept 1, 2020, to Aug 10, 2022. SARS-CoV-2 variant designation was based upon national surveillance data of variant prevalence by date and geographical region. We used a Bayesian framework to infer who infected whom by exploring all transmission trees compatible with the observed dates of symptoms, given assumptions on the incubation period and generation time distributions using the R package outbreaker2. FINDINGS We characterised the serial interval of COVID-19 by VOC. The mean serial interval was shortest for omicron BA5 (2·02 days; 95% credible interval [CrI] 1·26-2·84) and longest for alpha (3·37 days; 2·52-4·04). The mean serial interval before alpha (wild-type) was 2·29 days (95% CrI 1·39-2·94), 3·11 days (2·28-3·90) for delta, 2·72 days (2·01-3·47) for omicron BA1, and 2·67 days (1·90-3·46) for omicron BA2. We estimated that 17% (95% CrI 5-26) of serial interval values are negative across all variants. INTERPRETATION Most methods estimating the reproduction number from incidence time series do not allow for a negative serial interval by construction. Further research is needed to extend these methods and assess biases introduced by not accounting for negative serial intervals. To our knowledge, this study is the first to use a Bayesian framework to estimate the serial interval of all major SARS-CoV-2 VOC from thousands of confirmed household cases. FUNDING UK Medical Research Council and Wellcome Trust.
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Affiliation(s)
- Cyril Geismar
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Ellen Fragaszy
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK; Institute of Epidemiology and Health Care, University College London, London, UK
| | - Thomas E Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Peter White
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Thibaut Jombart
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
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17
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Shrotri M, Fragaszy E, Nguyen V, Navaratnam AMD, Geismar C, Beale S, Kovar J, Byrne TE, Fong WLE, Patel P, Aryee A, Braithwaite I, Johnson AM, Rodger A, Hayward AC, Aldridge RW. Spike-antibody responses to COVID-19 vaccination by demographic and clinical factors in a prospective community cohort study. Nat Commun 2022; 13:5780. [PMID: 36184633 PMCID: PMC9526787 DOI: 10.1038/s41467-022-33550-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 09/22/2022] [Indexed: 12/04/2022] Open
Abstract
Vaccination constitutes the best long-term solution against Coronavirus Disease-2019; however, vaccine-derived immunity may not protect all groups equally, and the durability of protective antibodies may be short. We evaluate Spike-antibody responses following BNT162b2 or ChAdOx1-S vaccination amongst SARS-CoV2-naive adults across England and Wales enrolled in a prospective cohort study (Virus Watch). Here we show BNT162b2 recipients achieved higher peak antibody levels after two doses; however, both groups experience substantial antibody waning over time. In 8356 individuals submitting a sample ≥28 days after Dose 2, we observe significantly reduced Spike-antibody levels following two doses amongst individuals reporting conditions and therapies that cause immunosuppression. After adjusting for these, several common chronic conditions also appear to attenuate the antibody response. These findings suggest the need to continue prioritising vulnerable groups, who have been vaccinated earliest and have the most attenuated antibody responses, for future boosters.
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Affiliation(s)
- Madhumita Shrotri
- Institute of Health Informatics, University College London, London, UK
| | - Ellen Fragaszy
- Institute of Health Informatics, University College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Vincent Nguyen
- Institute of Health Informatics, University College London, London, UK
| | | | - Cyril Geismar
- Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Thomas E Byrne
- Institute of Health Informatics, University College London, London, UK
| | | | - Parth Patel
- Institute of Health Informatics, University College London, London, UK
| | - Anna Aryee
- Institute of Health Informatics, University College London, London, UK
| | | | - Anne M Johnson
- Institute for Global Health, University College London, London, UK
| | - Alison Rodger
- Institute for Global Health, University College London, London, UK
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, London, UK.
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18
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Navaratnam AMD, Shrotri M, Nguyen V, Braithwaite I, Beale S, Byrne TE, Fong WLE, Fragaszy E, Geismar C, Hoskins S, Kovar J, Patel P, Yavlinsky A, Aryee A, Rodger A, Hayward AC, Aldridge RW. Nucleocapsid and spike antibody responses following virologically confirmed SARS-CoV-2 infection: an observational analysis in the Virus Watch community cohort. Int J Infect Dis 2022; 123:104-111. [PMID: 35987470 PMCID: PMC9385348 DOI: 10.1016/j.ijid.2022.07.053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES Seroprevalence studies can provide a measure of SARS-CoV-2 cumulative incidence, but a better understanding of spike and nucleocapsid (anti-N) antibody dynamics following infection is needed to assess the longevity of detectability. METHODS Adults aged ≥18 years, from households enrolled in the Virus Watch prospective community cohort study in England and Wales, provided monthly capillary blood samples, which were tested for spike antibody and anti-N. Participants self-reported vaccination dates and past medical history. Previous polymerase chain reaction (PCR) swabs were obtained through Second Generation Surveillance System linkage data. The primary outcome variables were seropositivity and total anti-N and spike antibody levels after PCR-confirmed infection. RESULTS A total of 13,802 eligible individuals provided 58,770 capillary blood samples. A total of 537 of these had a previous positive PCR-confirmed SARS-CoV-2 infection within 0-269 days of antibody sample date, among them 432 (80.45%) having a positive anti-N result. Median anti-N levels peaked between days 90 and 119 after PCR results and then began to decline. There is evidence of anti-N waning from 120 days onwards, with earlier waning for females and younger age categories. CONCLUSION Our findings suggest that anti-N has around 80% sensitivity for identifying previous COVID-19 infection, and the duration of detectability is affected by sex and age.
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Affiliation(s)
| | - Madhumita Shrotri
- Institute of Health Informatics, University College London, United Kingdom
| | - Vincent Nguyen
- Institute of Health Informatics, University College London, United Kingdom
| | - Isobel Braithwaite
- Institute of Health Informatics, University College London, United Kingdom
| | - Sarah Beale
- Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Thomas E Byrne
- Institute of Health Informatics, University College London, United Kingdom
| | | | - Ellen Fragaszy
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, United Kingdom
| | - Cyril Geismar
- Institute of Health Informatics, University College London, United Kingdom
| | - Susan Hoskins
- Institute of Health Informatics, University College London, United Kingdom
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Parth Patel
- Institute of Health Informatics, University College London, United Kingdom
| | - Alexei Yavlinsky
- Institute of Health Informatics, University College London, United Kingdom
| | - Anna Aryee
- Institute of Health Informatics, University College London, United Kingdom
| | - Alison Rodger
- Institute for Global Health, University College London, London, United Kingdom
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, United Kingdom.
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19
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Aldridge RW, Yavlinsky A, Nguyen V, Eyre MT, Shrotri M, Navaratnam AMD, Beale S, Braithwaite I, Byrne T, Kovar J, Fragaszy E, Fong WLE, Geismar C, Patel P, Rodger A, Johnson AM, Hayward A. SARS-CoV-2 antibodies and breakthrough infections in the Virus Watch cohort. Nat Commun 2022; 13:4869. [PMID: 35982056 PMCID: PMC9387883 DOI: 10.1038/s41467-022-32265-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 07/22/2022] [Indexed: 12/27/2022] Open
Abstract
A range of studies globally demonstrate that the effectiveness of SARS-CoV-2 vaccines wane over time, but the total effect of anti-S antibody levels on risk of SARS-CoV-2 infection and whether this varies by vaccine type is not well understood. Here we show that anti-S levels peak three to four weeks following the second dose of vaccine and the geometric mean of the samples is nine fold higher for BNT162b2 than ChAdOx1. Increasing anti-S levels are associated with a reduced risk of SARS-CoV-2 infection (Hazard Ratio 0.85; 95%CIs: 0.79-0.92). We do not find evidence that this antibody relationship with risk of infection varies by second dose vaccine type (BNT162b2 vs. ChAdOx1). In keeping with our anti-S antibody data, we find that people vaccinated with ChAdOx1 had 1.64 times the odds (95% confidence interval 1.45-1.85) of a breakthrough infection compared to BNT162b2. We anticipate our findings to be useful in the estimation of the protective effect of anti-S levels on risk of infection due to Delta. Our findings provide evidence about the relationship between antibody levels and protection for different vaccines and will support decisions on optimising the timing of booster vaccinations and identifying individuals who should be prioritised for booster vaccination, including those who are older, clinically extremely vulnerable, or received ChAdOx1 as their primary course. Our finding that risk of infection by anti-S level does not interact with vaccine type, but that individuals vaccinated with ChAdOx1 were at higher risk of infection, provides additional support for the use of using anti-S levels for estimating vaccine efficacy.
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Affiliation(s)
- Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Max T Eyre
- Centre of Health Informatics, Computing and Statistics, Lancaster Medical School, Lancaster University, Lancaster, UK
- Liverpool School of Tropical Medicine, Liverpool, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Alison Rodger
- Institute for Global Health, University College London, London, UK
| | - Anne M Johnson
- Institute for Global Health, University College London, London, UK
| | - Andrew Hayward
- Centre of Health Informatics, Computing and Statistics, Lancaster Medical School, Lancaster University, Lancaster, UK
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20
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Hoskins S, Beale S, Nguyen V, Fragaszy E, Navaratnam AM, Smith C, French C, Kovar J, Byrne T, Fong WLE, Geismar C, Patel P, Yavlinksy A, Johnson AM, Aldridge RW, Hayward A. Settings for non-household transmission of SARS-CoV-2 during the second lockdown in England and Wales - analysis of the Virus Watch household community cohort study. Wellcome Open Res 2022; 7:199. [PMID: 36874571 PMCID: PMC9975411 DOI: 10.12688/wellcomeopenres.17981.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/22/2022] [Indexed: 11/20/2022] Open
Abstract
Background: "Lockdowns" to control serious respiratory virus pandemics were widely used during the coronavirus disease 2019 (COVID-19) pandemic. However, there is limited information to understand the settings in which most transmission occurs during lockdowns, to support refinement of similar policies for future pandemics. Methods: Among Virus Watch household cohort participants we identified those infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outside the household. Using survey activity data, we undertook multivariable logistic regressions assessing the contribution of activities on non-household infection risk. We calculated adjusted population attributable fractions (APAF) to estimate which activity accounted for the greatest proportion of non-household infections during the pandemic's second wave. Results: Among 10,858 adults, 18% of cases were likely due to household transmission. Among 10,475 participants (household-acquired cases excluded), including 874 non-household-acquired infections, infection was associated with: leaving home for work or education (AOR 1.20 (1.02 - 1.42), APAF 6.9%); public transport (more than once per week AOR 1.82 (1.49 - 2.23), public transport APAF 12.42%); and shopping (more than once per week AOR 1.69 (1.29 - 2.21), shopping APAF 34.56%). Other non-household activities were rare and not significantly associated with infection. Conclusions: During lockdown, going to work and using public or shared transport independently increased infection risk, however only a minority did these activities. Most participants visited shops, accounting for one-third of non-household transmission. Transmission in restricted hospitality and leisure settings was minimal suggesting these restrictions were effective. If future respiratory infection pandemics emerge these findings highlight the value of working from home, using forms of transport that minimise exposure to others, minimising exposure to shops and restricting non-essential activities.
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Affiliation(s)
- Susan Hoskins
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, Greater London, WC1E 7HT, UK
| | - Annalan M.D. Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Colette Smith
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Clare French
- NIHR Health Protection Research Unit in Behavioural Science and Evaluation, Uinversity of Bristol, Bristol, BS8 2BN, UK
| | - Jana Kovar
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Alexei Yavlinksy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Anne M. Johnson
- Institute for Global Health, University College London, London, WC1N 1EH, UK
| | - Robert W. Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Andrew Hayward
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Virus Watch Collaborative
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, Greater London, WC1E 7HT, UK
- NIHR Health Protection Research Unit in Behavioural Science and Evaluation, Uinversity of Bristol, Bristol, BS8 2BN, UK
- Institute for Global Health, University College London, London, WC1N 1EH, UK
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21
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Yavlinsky A, Beale S, Nguyen V, Shrotri M, Byrne T, Geismar C, Fragaszy E, Hoskins S, Fong WLE, Navaratnam AMD, Braithwaite I, Patel P, Kovar J, Hayward A, Aldridge RW. Anti-spike antibody trajectories in individuals previously immunised with BNT162b2 or ChAdOx1 following a BNT162b2 booster dose. Wellcome Open Res 2022. [DOI: 10.12688/wellcomeopenres.17914.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: The two most common SARS-CoV-2 vaccines in the UK, BNT162b2 (Pfizer-BioNTech) and ChAdOx1 nCoV-19 (Oxford-AstraZeneca), employ different immunogenic mechanisms. Compared to BNT162b2, two-dose immunisation with ChAdOx1 induces substantially lower peak anti-spike antibody (anti-S) levels and is associated with a higher risk of breakthrough infections. To provide preliminary indication of how a third booster BNT162b2 dose impacts anti-S levels, we performed a cross-sectional analysis using capillary blood samples from vaccinated adults participating in Virus Watch, a prospective community cohort study in England and Wales. Methods: Blood samples were analysed using Roche Elecsys Anti-SARS-CoV-2 S immunoassay. We analysed anti-S levels by week since the third dose for vaccines administered on or after 1 September 2021 and stratified the results by second-dose vaccine type (ChAdOx1 or BNT162b2), age, sex and clinical vulnerability. Results: Anti-S levels peaked at two weeks post-booster for BNT162b2 (22,185 U/mL; 95%CI: 21,406-22,990) and ChAdOx1 second-dose recipients (19,203 U/mL; 95%CI: 18,094-20,377). These were higher than the corresponding peak antibody levels post-second dose for BNT162b2 (12,386 U/mL; 95%CI: 9,801-15,653, week 2) and ChAdOx1 (1,192 U/mL; 95%CI: 818-1735, week 3). No differences emerged by second dose vaccine type, age, sex or clinical vulnerability. Anti-S levels declined post-booster for BNT162b2 (half-life=44 days) and ChAdOx1 second dose recipients (half-life=40 days). These rates of decline were steeper than those post-second dose for BNT162b2 (half-life=54 days) and ChAdOx1 (half-life=80 days). Conclusions: Our findings suggest that peak anti-S levels are higher post-booster than post-second dose, but levels are projected to be similar after six months for BNT162b2 recipients. Higher peak anti-S levels post-booster may partially explain the increased effectiveness of booster vaccination compared to two-dose vaccination against symptomatic infection with the Omicron variant. Faster waning trajectories post-third dose may have implications for the timing of future booster campaigns or four-dose vaccination regimens for the clinically vulnerable.
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22
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Beale S, Hoskins S, Byrne T, Fong WLE, Fragaszy E, Geismar C, Kovar J, Navaratnam AM, Nguyen V, Patel P, Yavlinsky A, Johnson AM, Van Tongeren M, Aldridge RW, Hayward A. Workplace contact patterns in England during the COVID-19 pandemic: Analysis of the Virus Watch prospective cohort study. Lancet Reg Health Eur 2022; 16:100352. [PMID: 35475035 PMCID: PMC9023315 DOI: 10.1016/j.lanepe.2022.100352] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Background Workplaces are an important potential source of SARS-CoV-2 exposure; however, investigation into workplace contact patterns is lacking. This study aimed to investigate how workplace attendance and features of contact varied between occupations across the COVID-19 pandemic in England. Methods Data were obtained from electronic contact diaries (November 2020-November 2021) submitted by employed/self-employed prospective cohort study participants (n=4,616). We used mixed models to investigate the effects of occupation and time for: workplace attendance, number of people sharing workspace, time spent sharing workspace, number of close contacts, and usage of face coverings. Findings Workplace attendance and contact patterns varied across occupations and time. The predicted probability of intense space sharing during the day was highest for healthcare (78% [95% CI: 75-81%]) and education workers (64% [59%-69%]), who also had the highest probabilities for larger numbers of close contacts (36% [32%-40%] and 38% [33%-43%] respectively). Education workers also demonstrated relatively low predicted probability (51% [44%-57%]) of wearing a face covering during close contact. Across all occupational groups, workspace sharing and close contact increased and usage of face coverings decreased during phases of less stringent restrictions. Interpretation Major variations in workplace contact patterns and mask use likely contribute to differential COVID-19 risk. Patterns of variation by occupation and restriction phase may inform interventions for future waves of COVID-19 or other respiratory epidemics. Across occupations, increasing workplace contact and reduced face covering usage is concerning given ongoing high levels of community transmission and emergence of variants. Funding Medical Research Council; HM Government; Wellcome Trust.
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Affiliation(s)
- Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Susan Hoskins
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Annalan M.D. Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Anne M. Johnson
- Institute for Global Health, University College London, London WC1N 1EH, UK
| | - Martie Van Tongeren
- Centre for Occupational and Environmental Health, University of Manchester, Manchester M13 9PL, UK
| | - Robert W. Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Virus Watch Collaborative
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
- Institute for Global Health, University College London, London WC1N 1EH, UK
- Centre for Occupational and Environmental Health, University of Manchester, Manchester M13 9PL, UK
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23
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Beale S, Patel P, Rodger A, Braithwaite I, Byrne T, Fong WLE, Fragaszy E, Geismar C, Kovar J, Navaratnam A, Nguyen V, Shrotri M, Aryee A, Aldridge R, Hayward A. Occupation, work-related contact and SARS-CoV-2 anti-nucleocapsid serological status: findings from the Virus Watch prospective cohort study. Occup Environ Med 2022; 79:oemed-2021-107920. [PMID: 35450951 PMCID: PMC9072780 DOI: 10.1136/oemed-2021-107920] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [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] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 03/28/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Risk of SARS-CoV-2 infection varies across occupations; however, investigation into factors underlying differential risk is limited. We aimed to estimate the total effect of occupation on SARS-CoV-2 serological status, whether this is mediated by workplace close contact, and how exposure to poorly ventilated workplaces varied across occupations. METHODS We used data from a subcohort (n=3775) of adults in the UK-based Virus Watch cohort study who were tested for SARS-CoV-2 anti-nucleocapsid antibodies (indicating natural infection). We used logistic decomposition to investigate the relationship between occupation, contact and seropositivity, and logistic regression to investigate exposure to poorly ventilated workplaces. RESULTS Seropositivity was 17.1% among workers with daily close contact vs 10.0% for those with no work-related close contact. Compared with other professional occupations, healthcare, indoor trade/process/plant, leisure/personal service, and transport/mobile machine workers had elevated adjusted total odds of seropositivity (1.80 (1.03 to 3.14) - 2.46 (1.82 to 3.33)). Work-related contact accounted for a variable part of increased odds across occupations (1.04 (1.01 to 1.08) - 1.23 (1.09 to 1.40)). Occupations with raised odds of infection after accounting for work-related contact also had greater exposure to poorly ventilated workplaces. CONCLUSIONS Work-related close contact appears to contribute to occupational variation in seropositivity. Reducing contact in workplaces is an important COVID-19 control measure.
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Affiliation(s)
- Sarah Beale
- UCL Institute of Epidemiology and Health Care, University College London, London, UK
| | - Parth Patel
- UCL Institute of Health Informatics, University College London, London, UK
| | - Alison Rodger
- UCL Institute of Health Informatics, University College London, London, UK
| | - Isobel Braithwaite
- Extreme Events and Health Protection Team, Centre for Radiation, Chemicals and Environmental Hazards, Public Health England, London, UK
| | - Thomas Byrne
- UCL Institute of Health Informatics, University College London, London, UK
| | | | - Ellen Fragaszy
- UCL Institute of Health Informatics, University College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Cyril Geismar
- UCL Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- UCL Institute of Epidemiology and Health Care, University College London, London, UK
- UCL Institute of Health Informatics, University College London, London, UK
| | - Annalan Navaratnam
- UCL Institute of Health Informatics, University College London, London, UK
| | - Vincent Nguyen
- UCL Institute of Health Informatics, University College London, London, UK
| | - Madhumita Shrotri
- UCL Institute of Health Informatics, University College London, London, UK
| | - Anna Aryee
- UCL Institute of Health Informatics, University College London, London, UK
| | - Robert Aldridge
- UCL Institute of Health Informatics, University College London, London, UK
| | - Andrew Hayward
- UCL Institute of Epidemiology and Health Care, University College London, London, UK
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24
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Fragaszy E, Shrotri M, Geismar C, Aryee A, Beale S, Braithwaite I, Byrne T, Eyre MT, Fong WLE, Gibbs J, Hardelid P, Kovar J, Lampos V, Nastouli E, Navaratnam AM, Nguyen V, Patel P, Aldridge RW, Hayward A. Symptom profiles and accuracy of clinical case definitions for COVID-19 in a community cohort: results from the Virus Watch study. Wellcome Open Res 2022; 7:84. [PMID: 37745779 PMCID: PMC10514573 DOI: 10.12688/wellcomeopenres.17479.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/31/2022] [Indexed: 09/23/2023] Open
Abstract
Background: Understanding symptomatology and accuracy of clinical case definitions for community COVID-19 cases is important for Test, Trace and Isolate (TTI) and future targeting of early antiviral treatment. Methods: Community cohort participants prospectively recorded daily symptoms and swab results (mainly undertaken through the UK TTI system). We compared symptom frequency, severity, timing, and duration in test positive and negative illnesses. We compared the test performance of the current UK TTI case definition (cough, high temperature, or loss of or altered sense of smell or taste) with a wider definition adding muscle aches, chills, headache, or loss of appetite. Results: Among 9706 swabbed illnesses, including 973 SARS-CoV-2 positives, symptoms were more common, severe and longer lasting in swab positive than negative illnesses. Cough, headache, fatigue, and muscle aches were the most common symptoms in positive illnesses but also common in negative illnesses. Conversely, high temperature, loss or altered sense of smell or taste and loss of appetite were less frequent in positive illnesses, but comparatively even less frequent in negative illnesses. The current UK definition had 81% sensitivity and 47% specificity versus 93% and 27% respectively for the broader definition. 1.7-fold more illnesses met the broader case definition than the current definition. Conclusions: Symptoms alone cannot reliably distinguish COVID-19 from other respiratory illnesses. Adding additional symptoms to case definitions could identify more infections, but with a large increase in the number needing testing and the number of unwell individuals and contacts self-isolating whilst awaiting results.
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Affiliation(s)
- Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Anna Aryee
- Centre of Health Informatics, Computing and Statistics, Lancaster University, Lancaster, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Max T. Eyre
- Centre of Health Informatics, Computing and Statistics, Lancaster University, Lancaster, UK
- Liverpool School of Tropical Medicine, Liverpool, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jo Gibbs
- Institute for Global Health, University College London, London, UK
| | - Pia Hardelid
- Population, Policy and Practice Research and Teaching Department, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Vasileios Lampos
- Department of Computer Science, University College London, London, UK
| | - Eleni Nastouli
- Francis Crick Institute, London, UK
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Annalan M.D. Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Robert W. Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Virus Watch Collaborative
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
- Centre of Health Informatics, Computing and Statistics, Lancaster University, Lancaster, UK
- Liverpool School of Tropical Medicine, Liverpool, UK
- Institute for Global Health, University College London, London, UK
- Population, Policy and Practice Research and Teaching Department, University College London, London, UK
- Department of Computer Science, University College London, London, UK
- Francis Crick Institute, London, UK
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
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25
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Geismar C, Fragaszy E, Nguyen V, Fong WLE, Shrotri M, Beale S, Rodger A, Lampos V, Byrne T, Kovar J, Navaratnam AMD, Patel P, Aldridge RW, Hayward A. Household serial interval of COVID-19 and the effect of Variant B.1.1.7: analyses from prospective community cohort study (Virus Watch). Wellcome Open Res 2022; 6:224. [PMID: 34796276 PMCID: PMC8564743 DOI: 10.12688/wellcomeopenres.16974.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/01/2021] [Indexed: 01/21/2023] Open
Abstract
Introduction: Increased transmissibility of B.1.1.7 variant of concern (VOC) in the UK may explain its rapid emergence and global spread. We analysed data from putative household infector - infectee pairs in the Virus Watch Community cohort study to assess the serial interval of COVID-19 and whether this was affected by emergence of the B.1.1.7 variant. Methods: The Virus Watch study is an online, prospective, community cohort study following up entire households in England and Wales during the COVID-19 pandemic. Putative household infector-infectee pairs were identified where more than one person in the household had a positive swab matched to an illness episode. Data on whether or not individual infections were caused by the B.1.1.7 variant were not available. We therefore developed a classification system based on the percentage of cases estimated to be due to B.1.1.7 in national surveillance data for different English regions and study weeks. Results: Out of 24,887 illnesses reported, 915 tested positive for SARS-CoV-2 and 186 likely ‘infector-infectee’ pairs in 186 households amongst 372 individuals were identified. The mean COVID-19 serial interval was 3.18 (95%CI: 2.55-3.81, sd=4.36) days. There was no significant difference (p=0.267) between the mean serial interval for VOC hotspots (mean = 3.64 days, (95%CI: 2.55 – 4.73)) days and non-VOC hotspots, (mean = 2.72 days, (95%CI: 1.48 – 3.96)). Conclusions: Our estimates of the average serial interval of COVID-19 are broadly similar to estimates from previous studies and we find no evidence that B.1.1.7 is associated with a change in serial intervals. Alternative explanations such as increased viral load, longer period of viral shedding or improved receptor binding may instead explain the increased transmissibility and rapid spread and should undergo further investigation.
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Affiliation(s)
- Cyril Geismar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.,Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Vincent Nguyen
- Institute of Epidemiology and Health Care, University College London, London, UK.,Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Institute of Epidemiology and Health Care, University College London, London, UK.,Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Alison Rodger
- Institute for Global Health, University College London, London, UK
| | - Vasileios Lampos
- Department of Computer Science, University College London, London, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Annalan M D Navaratnam
- Institute of Epidemiology and Health Care, University College London, London, UK.,Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
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26
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Aldridge RW, Pineo H, Fragaszy E, Eyre MT, Kovar J, Nguyen V, Beale S, Byrne T, Aryee A, Smith C, Devakumar D, Taylor J, Katikireddi SV, Fong WLE, Geismar C, Patel P, Shrotri M, Braithwaite I, Patni N, Navaratnam AMD, Johnson A, Hayward A. Household overcrowding and risk of SARS-CoV-2: analysis of the Virus Watch prospective community cohort study in England and Wales. Wellcome Open Res 2021. [DOI: 10.12688/wellcomeopenres.17308.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: Household overcrowding is associated with increased risk of infectious diseases across contexts and countries. Limited data exist linking household overcrowding and risk of COVID-19. We used data collected from the Virus Watch cohort to examine the association between overcrowded households and SARS-CoV-2. Methods: The Virus Watch study is a household community cohort of acute respiratory infections in England and Wales. We calculated overcrowding using the measure of persons per room for each household. We considered two primary outcomes: PCR-confirmed positive SARS-CoV-2 antigen tests and laboratory-confirmed SARS-CoV-2 antibodies. We used mixed-effects logistic regression models that accounted for household structure to estimate the association between household overcrowding and SARS-CoV-2 infection. Results: 26,367 participants were included in our analyses. The proportion of participants with a positive SARS-CoV-2 PCR result was highest in the overcrowded group (9.0%; 99/1,100) and lowest in the under-occupied group (4.2%; 980/23,196). In a mixed-effects logistic regression model, we found strong evidence of an increased odds of a positive PCR SARS-CoV-2 antigen result (odds ratio 2.45; 95% CI:1.43–4.19; p-value=0.001) and increased odds of a positive SARS-CoV-2 antibody result in individuals living in overcrowded houses (3.32; 95% CI:1.54–7.15; p-value<0.001) compared with people living in under-occupied houses. Conclusion: Public health interventions to prevent and stop the spread of SARS-CoV-2 should consider the risk of infection for people living in overcrowded households and pay greater attention to reducing household transmission.
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27
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Byrne T, Patel P, Shrotri M, Beale S, Michie S, Butt J, Hawkins N, Hardelid P, Rodger A, Aryee A, Braithwaite I, Fong WLE, Fragaszy E, Geismar C, Kovar J, Navaratnam AMD, Nguyen V, Hayward A, Aldridge RW. Trends, patterns and psychological influences on COVID-19 vaccination intention: Findings from a large prospective community cohort study in England and Wales (Virus Watch). Vaccine 2021; 39:7108-7116. [PMID: 34728095 PMCID: PMC8498741 DOI: 10.1016/j.vaccine.2021.09.066] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 12/27/2022]
Abstract
BACKGROUND Vaccination intention is key to the success of any vaccination programme, alongside vaccine availability and access. Public intention to take a COVID-19 vaccine is high in England and Wales compared to other countries, but vaccination rate disparities between ethnic, social and age groups has led to concern. METHODS Online survey of prospective household community cohort study participants across England and Wales (Virus Watch). Vaccination intention was measured by individual participant responses to 'Would you accept a COVID-19 vaccine if offered?', collected in December 2020 and February 2021. Responses to a 13-item questionnaire collected in January 2021 were analysed using factor analysis to investigate psychological influences on vaccination intention. RESULTS Survey response rate was 56% (20,785/36,998) in December 2020 and 53% (20,590/38,727) in February 2021, with 14,880 adults reporting across both time points. In December 2020, 1,469 (10%) participants responded 'No' or 'Unsure'. Of these people, 1,266 (86%) changed their mind and responded 'Yes' or 'Already had a COVID-19 vaccine' by February 2021. Vaccination intention increased across all ethnic groups and levels of social deprivation. Age was most strongly associated with vaccination intention, with 16-24-year-olds more likely to respond "Unsure" or "No" versus "Yes" than 65-74-year-olds in December 2020 (OR: 4.63, 95 %CI: 3.42, 6.27 & OR 7.17 95 %CI: 4.26, 12.07 respectively) and February 2021 (OR: 27.92 95 %CI: 13.79, 56.51 & OR 17.16 95 %CI: 4.12, 71.55). The association between ethnicity and vaccination intention weakened, but did not disappear, over time. Both vaccine- and illness-related psychological factors were shown to influence vaccination intention. CONCLUSIONS Four in five adults (86%) who were reluctant or intending to refuse a COVID-19 vaccine in December 2020 had changed their mind in February 2021 and planned to accept, or had already accepted, a vaccine.
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Affiliation(s)
- Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK.
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK.
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK; Institute of Epidemiology and Health Care, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
| | - Susan Michie
- Centre for Behaviour Change, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
| | - Jabeer Butt
- Race Equality Foundation, 27 Greenwood Pl, London NW5 1LB, UK
| | | | - Pia Hardelid
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, 30 Guilford St, London WC1N 1EH, UK
| | - Alison Rodger
- Institute for Global Health, University College London, 30 Guilford St, London WC1N 1EH, UK; Royal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK
| | - Anna Aryee
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel St, London WC1E 7HT, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK; Institute of Epidemiology and Health Care, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK; Institute of Epidemiology and Health Care, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK; Institute of Epidemiology and Health Care, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK.
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Geismar C, Fragaszy E, Nguyen V, Fong WLE, Shrotri M, Beale S, Rodger A, Lampos V, Byrne T, Kovar J, Navaratnam AMD, Patel P, Aldridge RW, Hayward A. Serial interval of COVID-19 and the effect of Variant B.1.1.7: analyses from prospective community cohort study (Virus Watch). Wellcome Open Res 2021; 6:224. [PMID: 34796276 DOI: 10.12688/wellcomeopenres.16974.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2021] [Indexed: 01/21/2023] Open
Abstract
Introduction: Increased transmissibility of B.1.1.7 variant of concern (VOC) in the UK may explain its rapid emergence and global spread. We analysed data from putative household infector - infectee pairs in the Virus Watch Community cohort study to assess the serial interval of COVID-19 and whether this was affected by emergence of the B.1.1.7 variant. Methods: The Virus Watch study is an online, prospective, community cohort study following up entire households in England and Wales during the COVID-19 pandemic. Putative household infector-infectee pairs were identified where more than one person in the household had a positive swab matched to an illness episode. Data on whether or not individual infections were caused by the B.1.1.7 variant were not available. We therefore developed a classification system based on the percentage of cases estimated to be due to B.1.1.7 in national surveillance data for different English regions and study weeks. Results: Out of 24,887 illnesses reported, 915 tested positive for SARS-CoV-2 and 186 likely 'infector-infectee' pairs in 186 households amongst 372 individuals were identified. The mean COVID-19 serial interval was 3.18 (95%CI: 2.55 - 3.81) days. There was no significant difference (p=0.267) between the mean serial interval for VOC hotspots (mean = 3.64 days, (95%CI: 2.55 - 4.73)) days and non-VOC hotspots, (mean = 2.72 days, (95%CI: 1.48 - 3.96)). Conclusions: Our estimates of the average serial interval of COVID-19 are broadly similar to estimates from previous studies and we find no evidence that B.1.1.7 is associated with a change in serial intervals. Alternative explanations such as increased viral load, longer period of viral shedding or improved receptor binding may instead explain the increased transmissibility and rapid spread and should undergo further investigation.
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Affiliation(s)
- Cyril Geismar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.,Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Vincent Nguyen
- Institute of Epidemiology and Health Care, University College London, London, UK.,Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Institute of Epidemiology and Health Care, University College London, London, UK.,Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Alison Rodger
- Institute for Global Health, University College London, London, UK
| | - Vasileios Lampos
- Department of Computer Science, University College London, London, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Annalan M D Navaratnam
- Institute of Epidemiology and Health Care, University College London, London, UK.,Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
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Beale S, Byrne T, Fragaszy E, Kovar J, Nguyen V, Aryee A, Fong WLE, Geismar C, Patel P, Shrotri M, Patni N, Braithwaite I, Navaratnam A, Johnson AM, Aldridge RW, Hayward AC. Reported exposure to SARS-CoV-2 and relative perceived importance of different settings for SARS-CoV-2 acquisition in England and Wales: Analysis of the Virus Watch Community Cohort. Wellcome Open Res 2021. [DOI: 10.12688/wellcomeopenres.17067.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
We aimed to assess the relative importance of different settings for SARS-CoV-2 transmission in a large community cohort based on perceived location of infection for self-reported confirmed SARS-COV-2 cases. We demonstrate the importance of home, work and education as perceived venues for transmission. In children, education was most important and in older adults essential shopping was of high importance. Our findings support public health messaging about infection control at home, advice on working from home and restrictions in different venues.
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Shrotri M, Navaratnam AMD, Nguyen V, Byrne T, Geismar C, Fragaszy E, Beale S, Fong WLE, Patel P, Kovar J, Hayward AC, Aldridge RW. Spike-antibody waning after second dose of BNT162b2 or ChAdOx1. Lancet 2021; 398:385-387. [PMID: 34274038 PMCID: PMC8285117 DOI: 10.1016/s0140-6736(21)01642-1] [Citation(s) in RCA: 278] [Impact Index Per Article: 92.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 07/07/2021] [Accepted: 07/08/2021] [Indexed: 01/10/2023]
Affiliation(s)
- Madhumita Shrotri
- Institute of Health Informatics, University College London, WC1E 6BT London, UK
| | | | - Vincent Nguyen
- Institute of Health Informatics, University College London, WC1E 6BT London, UK
| | - Thomas Byrne
- Institute of Health Informatics, University College London, WC1E 6BT London, UK
| | - Cyril Geismar
- Institute of Health Informatics, University College London, WC1E 6BT London, UK
| | - Ellen Fragaszy
- Institute of Health Informatics, University College London, WC1E 6BT London, UK
| | - Sarah Beale
- Institute of Health Informatics, University College London, WC1E 6BT London, UK
| | - Wing Lam Erica Fong
- Institute of Health Informatics, University College London, WC1E 6BT London, UK
| | - Parth Patel
- Institute of Health Informatics, University College London, WC1E 6BT London, UK
| | - Jana Kovar
- Institute of Health Informatics, University College London, WC1E 6BT London, UK
| | - Andrew C Hayward
- Institute of Health Informatics, University College London, WC1E 6BT London, UK
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, WC1E 6BT London, UK.
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Hayward A, Fragaszy E, Kovar J, Nguyen V, Beale S, Byrne T, Aryee A, Hardelid P, Wijlaars L, Fong WLE, Geismar C, Patel P, Shrotri M, Navaratnam AMD, Nastouli E, Spyer M, Killingley B, Cox I, Lampos V, McKendry RA, Liu Y, Cheng T, Johnson AM, Michie S, Gibbs J, Gilson R, Rodger A, Aldridge RW. Risk factors, symptom reporting, healthcare-seeking behaviour and adherence to public health guidance: protocol for Virus Watch, a prospective community cohort study. BMJ Open 2021; 11:e048042. [PMID: 34162651 PMCID: PMC8230990 DOI: 10.1136/bmjopen-2020-048042] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 03/29/2021] [Accepted: 04/13/2021] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION The coronavirus (COVID-19) pandemic has caused significant global mortality and impacted lives around the world. Virus Watch aims to provide evidence on which public health approaches are most likely to be effective in reducing transmission and impact of the virus, and will investigate community incidence, symptom profiles and transmission of COVID-19 in relation to population movement and behaviours. METHODS AND ANALYSIS Virus Watch is a household community cohort study of acute respiratory infections in England and Wales and will run from June 2020 to August 2021. The study aims to recruit 50 000 people, including 12 500 from minority ethnic backgrounds, for an online survey cohort and monthly antibody testing using home fingerprick test kits. Nested within this larger study will be a subcohort of 10 000 individuals, including 3000 people from minority ethnic backgrounds. This cohort of 10 000 people will have full blood serology taken between October 2020 and January 2021 and repeat serology between May 2021 and August 2021. Participants will also post self-administered nasal swabs for PCR assays of SARS-CoV-2 and will follow one of three different PCR testing schedules based on symptoms. ETHICS AND DISSEMINATION This study has been approved by the Hampstead National Health Service (NHS) Health Research Authority Ethics Committee (ethics approval number 20/HRA/2320). We are monitoring participant queries and using these to refine methodology where necessary, and are providing summaries and policy briefings of our preliminary findings to inform public health action by working through our partnerships with our study advisory group, Public Health England, NHS and government scientific advisory panels.
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Affiliation(s)
- Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Department of Infectious Disease Epidemiology, LSHTM, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Vincent Nguyen
- Institute of Epidemiology and Health Care, University College London, London, UK
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Institute of Epidemiology and Health Care, University College London, London, UK
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Anna Aryee
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Pia Hardelid
- Centre for Paediatric Epidemiology and Biostatistics, Institute of Child Health, University College London, London, UK
| | - Linda Wijlaars
- Population, Policy and Practice, University College London, London, UK
- Primary Care and Population Health, University College London, London, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Cyril Geismar
- Institute of Epidemiology and Health Care, University College London, London, UK
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Annalan M D Navaratnam
- Institute of Epidemiology and Health Care, University College London, London, UK
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Eleni Nastouli
- Population, Policy and Practice, University College London, London, UK
- Francis Crick Institute, London, UK
| | - Moira Spyer
- Population, Policy and Practice, University College London, London, UK
- Francis Crick Institute, London, UK
| | - Ben Killingley
- University of Nottingham School of Medicine, Nottingham, UK
- University College London Hospital, London, UK
| | - Ingemar Cox
- Department of Computer Science, University College London, London, UK
| | - Vasileios Lampos
- Department of Computer Science, University College London, London, UK
| | - Rachel A McKendry
- London Centre for Nanotechnology and Division of Medicine, University College London, London, UK
| | - Yunzhe Liu
- SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, University College London, London, UK
| | - Tao Cheng
- SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, University College London, London, UK
| | - Anne M Johnson
- Institute for Global Health, University College London, London, UK
| | - Susan Michie
- Centre for Behaviour Change, University College London, London, UK
| | - Jo Gibbs
- Institute for Global Health, University College London, London, UK
| | - Richard Gilson
- Institute for Global Health, University College London, London, UK
| | - Alison Rodger
- Institute for Global Health, University College London, London, UK
- Royal Free London NHS Foundation Trust, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
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