1
|
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.
Collapse
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
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
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.
Collapse
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
| | | |
Collapse
|
4
|
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
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
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.
Collapse
Affiliation(s)
- Sarah Beale
- Institute of Epidemiology and Health Care, University College London, London, UK, WC1E 7HB.
| | | | | | | | | | | | | | | | | | | |
Collapse
|
11
|
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.
Collapse
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
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
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.
Collapse
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
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
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.
Collapse
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.
| |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
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.
Collapse
|
18
|
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.
Collapse
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
| |
Collapse
|
19
|
Looseley A, Wainwright E, Cook T, Bell V, Hoskins S, O'Connor M, Taylor G, Mouton R. Stress, burnout, depression and work satisfaction among
UK
anaesthetic trainees; a quantitative analysis of the Satisfaction and Wellbeing in Anaesthetic Training study. Anaesthesia 2019; 74:1231-1239. [DOI: 10.1111/anae.14681] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/01/2019] [Indexed: 01/16/2023]
Affiliation(s)
| | - E. Wainwright
- Bath Spa University Honorary Research Fellow University of Bath BathUK
| | - T.M. Cook
- Royal United Hospitals Bath NHS Foundation Trust BathUK
- Bristol Medical School University of Bristol UK
| | - V. Bell
- Bristol School of Anaesthesia BristolUK
| | | | - M. O'Connor
- Severn Postgraduate Medical Education Bristol UK
- Swindon and Marlborough NHS Trust UK
| | | | | | | |
Collapse
|
20
|
Nakku J, Kinyanda E, Hoskins S. Prevalence and factors associated with probable HIV dementia in an African population: a cross-sectional study of an HIV/AIDS clinic population. BMC Psychiatry 2013; 13:126. [PMID: 23641703 PMCID: PMC3653745 DOI: 10.1186/1471-244x-13-126] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2012] [Accepted: 04/29/2013] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND The HIV/AIDS infection is common in sub-Saharan Africa and is associated with psychological and neuro- cognitive impairment. These conditions, however, remain largely unrecognized. In this study we aimed to determine the prevalence of probable HIV dementia (PHD) in an HIV clinic population in Uganda and to delineate the factors associated with such impairment in these HIV positive individuals. METHODS Six hundred eighty HIV clinic attendees were surveyed in a cross sectional study. PHD was assessed using the International Dementia Scale (IHDS). Standardized measures were also used to assess clinical, psychological, social and demographic variables. Respondents were aged 18 years and above and did not have severe physical or mental health conditions. Multivariate analysis was conducted to identify associations between PHD and various factors. RESULTS The prevalence of probable HIV dementia was 64.4%. PHD was significantly associated with increasing stress scores and psychosocial impairment but not with age, BMI, CD4 count, use of HAART, or a diagnosis of depression or alcohol dependence. CONCLUSION The prevalence of probable HIV dementia in an ambulatory adult HIV positive population in Uganda was 64.4%. Increasing stress scores and psychosocial impairment were significant contributing factors. Clinicians need to be aware of this and to make efforts to identify neuro-cognitive impairment. Secondly there is need for more studies to better understand the relationship between PHD and stress in HIV populations so as to inform patient care.
Collapse
Affiliation(s)
- Juliet Nakku
- Makerere University College of Health Sciences/Butabika National Referral /Teaching Hospital, P.O.Box 24136 Kampala, Uganda
| | - Eugene Kinyanda
- MRC/UVRI Uganda Research Unit on AIDS & Senior EDCTP Fellowship, Kampala, Uganda
| | | |
Collapse
|
21
|
Kinyanda E, Hoskins S, Nakku J, Nawaz S, Patel V. The prevalence and characteristics of suicidality in HIV/AIDS as seen in an African population in Entebbe district, Uganda. BMC Psychiatry 2012; 12:63. [PMID: 22713589 PMCID: PMC3395571 DOI: 10.1186/1471-244x-12-63] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2011] [Accepted: 06/18/2012] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Suicidality in HIV/AIDS is not only a predictor of future attempted suicide and completed suicide, it is also associated with poor quality of life and poor adherence with antiretroviral therapy. This paper examines the prevalence and correlates of suicidality in HIV/AIDS in the African nation of Uganda. METHODS A cross-sectional study was undertaken among 618 respondents attending two HIV clinics in semi-urban Uganda. A structured questionnaire was used to collect data on demographic, social, psychological and clinical factors. Correlates of suicidality were assessed using mulitvariable logistic regression. RESULTS Prevalence of 'moderate to high risk for suicidality' (MHS) was 7.8 % and that of life-time attempted suicide was 3.9 %. Factors associated with MHS at univariate analysis were: female gender, food insecurity, increasing negative life events, high stress score, negative coping style, past psychiatric history, psychosocial impairment, diagnoses of post-traumatic stress disorder, generalised anxiety disorder and major depressive disorder. Factors independently associated with MHS in multivariate models were female gender, increasing negative life events, a previous psychiatric history, and major depressive disorder. CONCLUSIONS These results are in agreement with the stress-vulnerability model where social and psychological stressors acting on an underlying diathesis (including previous and current psychiatric morbidities) leads to suicidality. These results identify potential targets to mitigate risk through treatment of psychiatric disorders and promoting greater adaptation to living with HIV/AIDS.
Collapse
Affiliation(s)
- Eugene Kinyanda
- MRC/UVRI Uganda Research Unit on AIDS & Senior EDCTP Fellowship, P.O. Box 49, Entebbe, Uganda
| | - Susan Hoskins
- Medical Research Council, Clinical Trials Unit, London, UK
| | - Juliet Nakku
- Butabika National Psychiatric Referral Hospital, Kampala, Uganda
| | - Saira Nawaz
- Dartmouth Institute, Dartmouth College, Hanover, USA
| | - Vikram Patel
- London School of Hygiene & Tropical Medicine, UK & Wellcome Trust Senior Research Fellow in Clinical Science, London, UK
| |
Collapse
|
22
|
Kinyanda E, Hoskins S, Nakku J, Nawaz S, Patel V. Prevalence and risk factors of major depressive disorder in HIV/AIDS as seen in semi-urban Entebbe district, Uganda. BMC Psychiatry 2011; 11:205. [PMID: 22208452 PMCID: PMC3260105 DOI: 10.1186/1471-244x-11-205] [Citation(s) in RCA: 116] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2011] [Accepted: 12/30/2011] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Not much is known about the risk factors of major depressive disorder (MDD) in HIV/AIDS in the African socio-cultural context. Therefore a study was undertaken to examine the prevalence and risk factors of MDD in HIV/AIDS in semi-urban Uganda. METHODS A cross-sectional study was undertaken among 618 respondents attending two HIV clinics in Uganda. RESULTS Prevalence of MDD was 8.1%. Factors associated with MDD at univariate analysis only were female gender, family history of mental illness, negative coping style, alcohol dependency disorder, food insecurity and stress; not associated with MDD were social support, neurocognitive impairment, CD4 counts and BMI. Factors independently associated with MDD were psychosocial impairment, adverse life events, post traumatic stress disorder, generalised anxiety disorder and life-time attempted suicide. CONCLUSION Psychological and social factors were the main risk factors of MDD among ambulatory HIV positive persons with no evidence for the role of the neurotoxic effects of HIV. Treatment approaches for MDD in this patient group should be modeled on those used among non-HIV groups.
Collapse
Affiliation(s)
- Eugene Kinyanda
- Medical Research Council/Uganda Virus Research Institute, Entebbe, Uganda, Africa.
| | - Susan Hoskins
- Medical Research Council, Clinical Trials Unit, 125 Kingsway, London, UK
| | - Juliet Nakku
- Butabika National Psychiatric Referral Hospital, Off Old Port Bell Road, Kampala, Uganda
| | - Saira Nawaz
- Dartmouth Medical School, Dartmouth College, 1 Rope Ferry Road, Hanover, New Hampshire, USA
| | - Vikram Patel
- London School of Hygiene & Tropical Medicine, Keppel Street, London, United Kingdom & Senior Wellcome Trust Fellow in Clinical Science, 215 Euston Road, London, UK
| |
Collapse
|
23
|
Hutchinson E, Parkhurst J, Phiri S, Gibb DM, Chishinga N, Droti B, Hoskins S. National policy development for cotrimoxazole prophylaxis in Malawi, Uganda and Zambia: the relationship between Context, Evidence and Links. Health Res Policy Syst 2011; 9 Suppl 1:S6. [PMID: 21679387 PMCID: PMC3121137 DOI: 10.1186/1478-4505-9-s1-s6] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background Several frameworks have been constructed to analyse the factors which influence and shape the uptake of evidence into policy processes in resource poor settings, yet empirical analyses of health policy making in these settings are relatively rare. National policy making for cotrimoxazole (trimethoprim-sulfamethoxazole) preventive therapy in developing countries offers a pertinent case for the application of a policy analysis lens. The provision of cotrimoxazole as a prophylaxis is an inexpensive and highly efficacious preventative intervention in HIV infected individuals, reducing both morbidity and mortality among adults and children with HIV/AIDS, yet evidence suggests that it has not been quickly or evenly scaled-up in resource poor settings. Methods Comparative analysis was conducted in Malawi, Uganda and Zambia, using the case study approach. We applied the ‘RAPID’ framework developed by the Overseas Development Institute (ODI), and conducted a total of 47 in-depth interviews across the three countries to examine the influence of context (including the influence of donor agencies), evidence (both local and international), and the links between researcher, policy makers and those seeking to influence the policy process. Results Each area of analysis was found to have an influence on the creation of national policy on cotrimoxazole preventive therapy (CPT) in all three countries. In relation to context, the following were found to be influential: government structures and their focus, donor interest and involvement, healthcare infrastructure and other uses of cotrimoxazole and related drugs in the country. In terms of the nature of the evidence, we found that how policy makers perceived the strength of evidence behind international recommendations was crucial (if evidence was considered weak then the recommendations were rejected). Further, local operational research results seem to have been taken up more quickly, while randomised controlled trials (the gold standard of clinical research) was not necessarily translated into policy so swiftly. Finally the links between different research and policy actors were of critical importance, with overlaps between researcher and policy maker networks crucial to facilitate knowledge transfer. Within these networks, in each country the policy development process relied on a powerful policy entrepreneur who helped get cotrimoxazole preventive therapy onto the policy agenda. Conclusions This analysis underscores the importance of considering national level variables in the explanation of the uptake of evidence into national policy settings, and recognising how local policy makers interpret international evidence. Local priorities, the ways in which evidence was interpreted, and the nature of the links between policy makers and researchers could either drive or stall the policy process. Developing the understanding of these processes enables the explanation of the use (or non-use) of evidence in policy making, and potentially may help to shape future strategies to bridge the research-policy gaps and ultimately improve the uptake of evidence in decision making.
Collapse
Affiliation(s)
- Eleanor Hutchinson
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London, UK.
| | | | | | | | | | | | | |
Collapse
|
24
|
Hutchinson E, Droti B, Gibb D, Chishinga N, Hoskins S, Phiri S, Parkhurst J. Translating evidence into policy in low-income countries: lessons from co-trimoxazole preventive therapy. Bull World Health Organ 2011; 89:312-6. [PMID: 21479096 PMCID: PMC3066518 DOI: 10.2471/blt.10.077743] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2010] [Revised: 01/31/2011] [Accepted: 02/01/2011] [Indexed: 11/27/2022] Open
Abstract
In the April 2010 issue of this journal, Date et al. expressed concern over the slow scale-up in low-income settings of two therapies for the prevention of opportunistic infections in people living with the human immunodeficiency virus: co-trimoxazole prophylaxis and isoniazid preventive therapy. This short paper discusses the important ways in which policy analysis can be of use in understanding and explaining how and why certain evidence makes its way into policy and practice and what local factors influence this process. Key lessons about policy development are drawn from the research evidence on co-trimoxazole prophylaxis, as such lessons may prove helpful to those who seek to influence the development of national policy on isoniazid preventive therapy and other treatments. Researchers are encouraged to disseminate their findings in a manner that is clear, but they must also pay attention to how structural, institutional and political factors shape policy development and implementation. Doing so will help them to understand and address the concerns raised by Date et al. and other experts. Mainstreaming policy analysis approaches that explain how local factors shape the uptake of research evidence can provide an additional tool for researchers who feel frustrated because their research findings have not made their way into policy and practice.
Collapse
Affiliation(s)
- Eleanor Hutchinson
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, 15–17 Tavistock Place, London, WC1H 9SH, England.
| | | | | | | | | | | | | |
Collapse
|
25
|
Hoskins S. Book Reviews. Biol J Linn Soc Lond 1999. [DOI: 10.1006/bijl.1999.0397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
26
|
Abstract
During spinal cord development, commissural (C) neurons, located near the dorsal midline, send axons ventrally and across the floor plate (FP). The trajectory of these axons toward the FP is guided in part by netrins. The mechanisms that guide the early phase of C axon extension, however, have not been resolved. We show that the roof plate (RP) expresses a diffusible activity that repels C axons and orients their growth within the dorsal spinal cord. Bone morphogenetic proteins (BMPs) appear to act as RP-derived chemorepellents that guide the early trajectory of the axons of C neurons in the developing spinal cord: BMP7 mimics the RP repellent activity for C axons in vitro, can act directly to collapse C growth cones, and appears to serve an essential function in RP repulsion of C axons.
Collapse
Affiliation(s)
- A Augsburger
- Department of Physiology and Cellular Biophysics, Columbia University, New York, New York 10032, USA
| | | | | | | | | |
Collapse
|
27
|
Abstract
Twenty-eight patients who underwent unilateral total knee arthroplasty and 20 patients who underwent simultaneous bilateral total knee arthroplasties participated in this study and were randomized to have either a fluted or round 10-mm diameter femoral intramedullary alignment rod used during surgery. The intramedullary rods were cannulated and connected with pressure tubing to a monitor which provided measurements of pressure at the tip of each rod. Arterial blood gas measurements on room air were obtained before and on the morning after surgery. An arterial line was placed and an arterial blood gas measurement was obtained at the time of skin incision and again after tourniquet release. Pulmonary shunt was calculated from the arterial blood gas measurements. Intramedullary pressure during rod insertion was significantly higher for the groups of patients having the round compared with the fluted rod. The change in pulmonary shunt during surgery was lowest for the patients in the unilateral group having the fluted rod and highest for the patients in the bilateral group having the round rod. A fluted rather than a round intramedullary alignment rod should be used to minimize intramedullary pressure and pulmonary shunting during unilateral and bilateral total knee arthroplasties.
Collapse
Affiliation(s)
- M D Ries
- Department of Orthopaedic Surgery, University of California, San Francisco Medical Center 94143, USA
| | | | | | | | | | | |
Collapse
|
28
|
Payne D, Hoskins S, Schouten H, van Vleuten H, Tyring S. Increased buffer pH enhances sensitivity and specificity of human papillomavirus detection using consensus primer based PCR. J Virol Methods 1995; 52:105-10. [PMID: 7769023 DOI: 10.1016/0166-0934(94)00148-a] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Polymerase chain reaction (PCR) buffers were optimized for the specific detection of human papillomavirus (HPV) sequences. The effect of pH, potassium chloride concentration and magnesium chloride concentration of three different consensus primers were examined. Several phylogenetically distinct HPVs (HPV1, HPV2, HPV6, HPV8, HPV16, HPV18, and HPV20) were used to determine the optimal buffer components. Genital types were less sensitive to changes in pH than cutaneous types. Higher buffer pH, with a few exceptions, led to increased sensitivity and specificity of HPV detection.
Collapse
Affiliation(s)
- D Payne
- Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston 77555-1029, USA
| | | | | | | | | |
Collapse
|
29
|
Abstract
Urinary catheters, especially in patients with long-term catheter requirements, frequently are a source of infection. Iontophoresis has been proposed as a method to decrease or eliminate such infections. Several types of material were examined for their potential use as electrodes in an iontophoretic catheter system. Silver, copper and nickel electrodes did kill microorganisms but did not show longevity. Carbon and gold electrodes showed longevity and killing of microorganisms. Gold proved to be somewhat better than carbon in killing Klebsiella pneumoniae in a broth. Few organisms survived iontophoresis. Those few that survived (mainly Klebsiella in broth), when rechallenged by iontophoresis, did not show any striking resistance to iontophoresis. Our data support the proposition that inclusion of electrodes, depending on the electrode type, in a catheter probably will decrease or eliminate a bacterial population in urine and, thus, may help prevent catheter-related infections and their sequelae.
Collapse
Affiliation(s)
- C P Davis
- Department of Microbiology, University of Texas Medical Branch, Galveston 77550-2782
| | | | | | | |
Collapse
|
30
|
Abstract
We have studied the orienting behavior of juvenile Rana pipiens in which one eye was rotated at late larval stages and the other eye left intact. Such frogs orient accurately to stimuli falling solely in the visual field of the intact eye and systematically misorient to stimuli falling solely in the field of the rotated eye. Stimuli within the area of visual field overlap elicited two distinct sets of responses, one attributable to the normal and the other to the rotated eye.
Collapse
|