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Gu Q, Wei J, Yoon CH, Yuan K, Jones N, Brent A, Llewelyn M, Peto TEA, Pouwels KB, Eyre DW, Walker AS. Distinct patterns of vital sign and inflammatory marker responses in adults with suspected bloodstream infection. J Infect 2024; 88:106156. [PMID: 38599549 DOI: 10.1016/j.jinf.2024.106156] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 04/01/2024] [Accepted: 04/04/2024] [Indexed: 04/12/2024]
Abstract
OBJECTIVES To identify patterns in inflammatory marker and vital sign responses in adult with suspected bloodstream infection (BSI) and define expected trends in normal recovery. METHODS We included patients ≥16 y from Oxford University Hospitals with a blood culture taken between 1-January-2016 and 28-June-2021. We used linear and latent class mixed models to estimate trajectories in C-reactive protein (CRP), white blood count, heart rate, respiratory rate and temperature and identify CRP response subgroups. Centile charts for expected CRP responses were constructed via the lambda-mu-sigma method. RESULTS In 88,348 suspected BSI episodes; 6908 (7.8%) were culture-positive with a probable pathogen, 4309 (4.9%) contained potential contaminants, and 77,131(87.3%) were culture-negative. CRP levels generally peaked 1-2 days after blood culture collection, with varying responses for different pathogens and infection sources (p < 0.0001). We identified five CRP trajectory subgroups: peak on day 1 (36,091; 46.3%) or 2 (4529; 5.8%), slow recovery (10,666; 13.7%), peak on day 6 (743; 1.0%), and low response (25,928; 33.3%). Centile reference charts tracking normal responses were constructed from those peaking on day 1/2. CONCLUSIONS CRP and other infection response markers rise and recover differently depending on clinical syndrome and pathogen involved. However, centile reference charts, that account for these differences, can be used to track if patients are recovering line as expected and to help personalise infection.
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Affiliation(s)
- Qingze Gu
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Chang Ho Yoon
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Kevin Yuan
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nicola Jones
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Andrew Brent
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK; Oxford University Hospitals NHS Foundation Trust, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Oxford University Hospitals NHS Foundation Trust, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.
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Antony Oliver MC, Graham M, Gass KM, Medley GF, Clark J, Davis EL, Reimer LJ, King JD, Pouwels KB, Hollingsworth TD. Reducing the Antigen Prevalence Target Threshold for Stopping and Restarting Mass Drug Administration for Lymphatic Filariasis Elimination: A Model-Based Cost-effectiveness Simulation in Tanzania, India and Haiti. Clin Infect Dis 2024; 78:S160-S168. [PMID: 38662697 PMCID: PMC11045020 DOI: 10.1093/cid/ciae108] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND The Global Programme to Eliminate Lymphatic Filariasis (GPELF) aims to reduce and maintain infection levels through mass drug administration (MDA), but there is evidence of ongoing transmission after MDA in areas where Culex mosquitoes are the main transmission vector, suggesting that a more stringent criterion is required for MDA decision making in these settings. METHODS We use a transmission model to investigate how a lower prevalence threshold (<1% antigenemia [Ag] prevalence compared with <2% Ag prevalence) for MDA decision making would affect the probability of local elimination, health outcomes, the number of MDA rounds, including restarts, and program costs associated with MDA and surveys across different scenarios. To determine the cost-effectiveness of switching to a lower threshold, we simulated 65% and 80% MDA coverage of the total population for different willingness to pay per disability-adjusted life-year averted for India ($446.07), Tanzania ($389.83), and Haiti ($219.84). RESULTS Our results suggest that with a lower Ag threshold, there is a small proportion of simulations where extra rounds are required to reach the target, but this also reduces the need to restart MDA later in the program. For 80% coverage, the lower threshold is cost-effective across all baseline prevalences for India, Tanzania, and Haiti. For 65% MDA coverage, the lower threshold is not cost-effective due to additional MDA rounds, although it increases the probability of local elimination. Valuing the benefits of elimination to align with the GPELF goals, we find that a willingness to pay per capita government expenditure of approximately $1000-$4000 for 1% increase in the probability of local elimination would be required to make a lower threshold cost-effective. CONCLUSIONS Lower Ag thresholds for stopping MDAs generally mean a higher probability of local elimination, reducing long-term costs and health impacts. However, they may also lead to an increased number of MDA rounds required to reach the lower threshold and, therefore, increased short-term costs. Collectively, our analyses highlight that lower target Ag thresholds have the potential to assist programs in achieving lymphatic filariasis goals.
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Affiliation(s)
- Mary Chriselda Antony Oliver
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Matthew Graham
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Katherine M Gass
- Neglected Tropical Diseases Support Centre, The Task Force for Global Health, Decatur, Georgia, USA
| | - Graham F Medley
- Centre for Mathematical Modelling of Infectious Disease and Department of Global Health and Development, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Jessica Clark
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Emma L Davis
- Mathematics Institute and the Zeeman Institute for Systems Biology and Infectious Disease Epidemiological Research, University of Warwick, Coventry, United Kingdom
| | - Lisa J Reimer
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
| | - Jonathan D King
- Department of Control of Neglected Tropical Diseases, World Health Organization, Geneva, Switzerland
| | - Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
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Kingston R, Vella V, Pouwels KB, Schmidt JE, Abdelatif El-Abasiri RA, Reyna-Villasmil E, Hassoun-Kheir N, Harbarth S, Rodríguez-Baño J, Tacconelli E, Arieti F, Gladstone BP, de Kraker MEA, Naylor NR, Robotham JV. Excess resource use and cost of drug-resistant infections for six key pathogens in Europe: a systematic review and Bayesian meta-analysis. Clin Microbiol Infect 2024; 30 Suppl 1:S26-S36. [PMID: 38128781 DOI: 10.1016/j.cmi.2023.12.013] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 12/05/2023] [Accepted: 12/11/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Quantifying the resource use and cost of antimicrobial resistance establishes the magnitude of the problem and drives action. OBJECTIVES Assessment of resource use and cost associated with infections with six key drug-resistant pathogens in Europe. METHODS A systematic review and Bayesian meta-analysis. DATA SOURCES MEDLINE (Ovid), Embase (Ovid), Econlit databases, and grey literature for the period 1 January 1990, to 21 June 2022. STUDY ELIGIBILITY CRITERIA Resource use and cost outcomes (including excess length of stay, overall costs, and other excess in or outpatient costs) were compared between patients with defined antibiotic-resistant infections caused by carbapenem-resistant (CR) Pseudomonas aeruginosa and Acinetobacter baumannii, CR or third-generation cephalosporin Escherichia coli (3GCREC) and Klebsiella pneumoniae, methicillin-resistant Staphylococcus aureus, and vancomycin-resistant Enterococcus faecium, and patients with drug-susceptible or no infection. PARTICIPANTS All patients diagnosed with drug-resistant bloodstream infections (BSIs). INTERVENTIONS NA. ASSESSMENT OF RISK OF BIAS An adapted version of the Joanna Briggs Institute assessment tool, incorporating case-control, cohort, and economic assessment frameworks. METHODS OF DATA SYNTHESIS Hierarchical Bayesian meta-analyses were used to assess pathogen-specific resource use estimates. RESULTS Of 5969 screened publications, 37 were included in the review. Data were sparse and heterogeneous. Most studies estimated the attributable burden by, comparing resistant and susceptible pathogens (32/37). Four studies analysed the excess cost of hospitalization attributable to 3GCREC BSIs, ranging from -€ 2465.50 to € 6402.81. Eight studies presented adjusted excess length of hospital stay estimates for methicillin-resistant S. aureus and 3GCREC BSIs (4 each) allowing for Bayesian hierarchical analysis, estimating means of 1.26 (95% credible interval [CrI], -0.72 to 4.17) and 1.78 (95% CrI, -0.02 to 3.38) days, respectively. CONCLUSIONS Evidence on most cost and resource use outcomes and across most pathogen-resistance combinations was severely lacking. Given the importance of this evidence for rational policymaking, further research is urgently needed.
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Affiliation(s)
- Rhys Kingston
- Field Service Data Science Team, UK Health Security Agency, London, UK
| | | | - Koen B Pouwels
- Nuffield Department of Population Health, Health Economics Research Centre, University of Oxford, Oxford, UK
| | | | | | - Eduardo Reyna-Villasmil
- Infectious Diseases and Microbiology Division, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen Macarena, Department of Medicine, University of Sevilla/CSIC, Sevilla, Spain
| | - Nasreen Hassoun-Kheir
- Infection Control Program, Geneva University Hospitals and Faculty of Medicine, WHO Collaborating Center, Geneva, Switzerland
| | - Stephan Harbarth
- Infection Control Program, Geneva University Hospitals and Faculty of Medicine, WHO Collaborating Center, Geneva, Switzerland
| | - Jesús Rodríguez-Baño
- Infectious Diseases and Microbiology Division, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen Macarena, Department of Medicine, University of Sevilla/CSIC, Sevilla, Spain
| | - Evelina Tacconelli
- Infectious Diseases, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Fabiana Arieti
- Infectious Diseases, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Beryl Primrose Gladstone
- Department of Internal Medicine, DZIF-Clinical Research Unit, Infectious Diseases, University Hospital Tübingen, Tübingen, Germany
| | - Marlieke E A de Kraker
- Infection Control Program, Geneva University Hospitals and Faculty of Medicine, WHO Collaborating Center, Geneva, Switzerland
| | - Nichola R Naylor
- HCAI, Fungal, AMR, AMU, & Sepsis Division, UK Health Security Agency, London, UK
| | - Julie V Robotham
- HCAI, Fungal, AMR, AMU, & Sepsis Division, UK Health Security Agency, London, UK.
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Hassoun-Kheir N, Guedes M, Ngo Nsoga MT, Argante L, Arieti F, Gladstone BP, Kingston R, Naylor NR, Pezzani MD, Pouwels KB, Robotham JV, Rodríguez-Baño J, Tacconelli E, Vella V, Harbarth S, de Kraker MEA. A systematic review on the excess health risk of antibiotic-resistant bloodstream infections for six key pathogens in Europe. Clin Microbiol Infect 2024; 30 Suppl 1:S14-S25. [PMID: 37802750 DOI: 10.1016/j.cmi.2023.09.001] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/01/2023] [Accepted: 09/03/2023] [Indexed: 10/08/2023]
Abstract
BACKGROUND Antimicrobial resistance is a global threat, which requires novel intervention strategies, for which priority pathogens and settings need to be determined. OBJECTIVES We evaluated pathogen-specific excess health burden of drug-resistant bloodstream infections (BSIs) in Europe. METHODS A systematic review and meta-analysis. DATA SOURCES MEDLINE, Embase, and grey literature for the period January 1990 to May 2022. STUDY ELIGIBILITY CRITERIA Studies that reported burden data for six key drug-resistant pathogens: carbapenem-resistant (CR) Pseudomonas aeruginosa and Acinetobacter baumannii, third-generation cephalosporin or CR Escherichia coli and Klebsiella pneumoniae, methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecium. Excess health outcomes compared with drug-susceptible BSIs or uninfected patients. For MRSA and third-generation cephalosporin E. coli and K. pneumoniae BSIs, five or more European studies were identified. For all others, the search was extended to high-income countries. PARTICIPANTS Paediatric and adult patients diagnosed with drug-resistant BSI. INTERVENTIONS Not applicable. ASSESSMENT OF RISK OF BIAS An adapted version of the Joanna-Briggs Institute assessment tool. METHODS OF DATA SYNTHESIS Random-effect models were used to pool pathogen-specific burden estimates. RESULTS We screened 7154 titles, 1078 full-texts and found 56 studies on BSIs. Most studies compared outcomes of drug-resistant to drug-susceptible BSIs (46/56, 82.1%), and reported mortality (55/56 studies, 98.6%). The pooled crude estimate for excess all-cause mortality of drug-resistant versus drug-susceptible BSIs ranged from OR 1.31 (95% CI 1.03-1.68) for CR P. aeruginosa to OR 3.44 (95% CI 1.62-7.32) for CR K. pneumoniae. Pooled crude estimates comparing mortality to uninfected patients were available for vancomycin-resistant Enterococcus and MRSA BSIs (OR of 11.19 [95% CI 6.92-18.09] and OR 6.18 [95% CI 2.10-18.17], respectively). CONCLUSIONS Drug-resistant BSIs are associated with increased mortality, with the magnitude of the effect influenced by pathogen type and comparator. Future research should address crucial knowledge gaps in pathogen- and infection-specific burdens to guide development of novel interventions.
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Affiliation(s)
- Nasreen Hassoun-Kheir
- Infection Control Program, Geneva University Hospitals and Faculty of Medicine, World Health Organization Collaborating Center, Geneva, Switzerland
| | - Mariana Guedes
- Department of Medicine, University of Sevilla/Instituto de Biomedicina de Sevilla (IBiS)/Consejo Superior de Investigaciones Científicas (CSIC), Sevilla, Spain; Infectious Diseases and Microbiology Division, Hospital Universitario Virgen Macarena, Sevilla, Spain; Infection and Antimicrobial Resistance Control and Prevention Unit, Hospital Epidemiology Centre, Centro Hospitalar Universitário São João, Porto, Portugal
| | - Marie-Therese Ngo Nsoga
- Infection Control Program, Geneva University Hospitals and Faculty of Medicine, World Health Organization Collaborating Center, Geneva, Switzerland
| | - Lorenzo Argante
- Department of Vaccine Clinical Statistics, GSK, Siena, Italy
| | - Fabiana Arieti
- Infectious Diseases, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Beryl P Gladstone
- The German Center for Infection Research (DZIF)-Clinical Research Unit, Infectious Diseases, Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Rhys Kingston
- Healthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage & Sepsis Division, United Kingdon Health Security Agency, London, UK
| | - Nichola R Naylor
- Healthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage & Sepsis Division, United Kingdon Health Security Agency, London, UK
| | - Maria D Pezzani
- Infectious Diseases, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Julie V Robotham
- Healthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage & Sepsis Division, United Kingdon Health Security Agency, London, UK
| | - Jesús Rodríguez-Baño
- Department of Medicine, University of Sevilla/Instituto de Biomedicina de Sevilla (IBiS)/Consejo Superior de Investigaciones Científicas (CSIC), Sevilla, Spain; Infectious Diseases and Microbiology Division, Hospital Universitario Virgen Macarena, Sevilla, Spain; CIBERINFEC, Instituto de Salud Carlos III, Madrid, Spain
| | - Evelina Tacconelli
- Infectious Diseases, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Venanzio Vella
- Department of Bacterial Vaccine Epidemiology, GSK, Siena, Italy
| | - Stephan Harbarth
- Infection Control Program, Geneva University Hospitals and Faculty of Medicine, World Health Organization Collaborating Center, Geneva, Switzerland
| | - Marlieke E A de Kraker
- Infection Control Program, Geneva University Hospitals and Faculty of Medicine, World Health Organization Collaborating Center, Geneva, Switzerland.
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Ayoubkhani D, Zaccardi F, Pouwels KB, Walker AS, Houston D, Alwan NA, Martin J, Khunti K, Nafilyan V. Employment outcomes of people with Long Covid symptoms: community-based cohort study. Eur J Public Health 2024:ckae034. [PMID: 38423541 DOI: 10.1093/eurpub/ckae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Evidence on the long-term employment consequences of SARS-CoV-2 infection is lacking. We used data from a large, community-based sample in the UK to estimate associations between Long Covid and employment outcomes. METHODS This was an observational, longitudinal study using a pre-post design. We included survey participants from 3 February 2021 to 30 September 2022 when they were aged 16-64 years and not in education. Using conditional logit modelling, we explored the time-varying relationship between Long Covid status ≥12 weeks after a first test-confirmed SARS-CoV-2 infection (reference: pre-infection) and labour market inactivity (neither working nor looking for work) or workplace absence lasting ≥4 weeks. RESULTS Of 206 299 participants (mean age 45 years, 54% female, 92% white), 15% were ever labour market inactive and 10% were ever long-term absent during follow-up. Compared with pre-infection, inactivity was higher in participants reporting Long Covid 30 to <40 weeks [adjusted odds ratio (aOR): 1.45; 95% CI: 1.17-1.81] or 40 to <52 weeks (aOR: 1.34; 95% CI: 1.05-1.72) post-infection. Combining with official statistics on Long Covid prevalence, and assuming a correct statistical model, our estimates translate to 27 000 (95% CI: 6000-47 000) working-age adults in the UK being inactive because of Long Covid in July 2022. CONCLUSIONS Long Covid is likely to have contributed to reduced participation in the UK labour market, though it is unlikely to be the sole driver. Further research is required to quantify the contribution of other factors, such as indirect health effects of the pandemic.
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Affiliation(s)
- Daniel Ayoubkhani
- Leicester Real World Evidence Unit, Leicester Diabetes Centre, Department of Population Health Sciences, University of Leicester, Leicester, UK
- Data and Analysis for Social Care and Health Division, Office for National Statistics, Newport, UK
| | - Francesco Zaccardi
- Leicester Real World Evidence Unit, Leicester Diabetes Centre, Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Koen B Pouwels
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - A Sarah Walker
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Donald Houston
- City-Regional Economic Development Institute, Birmingham Business School, University of Birmingham, Birmingham, UK
| | - Nisreen A Alwan
- School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
- NIHR Applied Research Collaboration (ARC) Wessex, Southampton, UK
| | | | - Kamlesh Khunti
- Leicester Real World Evidence Unit, Leicester Diabetes Centre, Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Vahé Nafilyan
- Data and Analysis for Social Care and Health Division, Office for National Statistics, Newport, UK
- Department of Medical Statistics, Faculty of Epidemiology and Population Health, Environment and Society, London School of Hygiene & Tropical Medicine, London, UK
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Allel K, Hernández-Leal MJ, Naylor NR, Undurraga EA, Abou Jaoude GJ, Bhandari P, Flanagan E, Haghparast-Bidgoli H, Pouwels KB, Yakob L. Costs-effectiveness and cost components of pharmaceutical and non-pharmaceutical interventions affecting antibiotic resistance outcomes in hospital patients: a systematic literature review. BMJ Glob Health 2024; 9:e013205. [PMID: 38423548 PMCID: PMC10910705 DOI: 10.1136/bmjgh-2023-013205] [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: 06/22/2023] [Accepted: 01/26/2024] [Indexed: 03/02/2024] Open
Abstract
INTRODUCTION Limited information on costs and the cost-effectiveness of hospital interventions to reduce antibiotic resistance (ABR) hinder efficient resource allocation. METHODS We conducted a systematic literature review for studies evaluating the costs and cost-effectiveness of pharmaceutical and non-pharmaceutical interventions aimed at reducing, monitoring and controlling ABR in patients. Articles published until 12 December 2023 were explored using EconLit, EMBASE and PubMed. We focused on critical or high-priority bacteria, as defined by the WHO, and intervention costs and incremental cost-effectiveness ratio (ICER). Following Preferred Reporting Items for Systematic review and Meta-Analysis guidelines, we extracted unit costs, ICERs and essential study information including country, intervention, bacteria-drug combination, discount rates, type of model and outcomes. Costs were reported in 2022 US dollars ($), adopting the healthcare system perspective. Country willingness-to-pay (WTP) thresholds from Woods et al 2016 guided cost-effectiveness assessments. We assessed the studies reporting checklist using Drummond's method. RESULTS Among 20 958 articles, 59 (32 pharmaceutical and 27 non-pharmaceutical interventions) met the inclusion criteria. Non-pharmaceutical interventions, such as hygiene measures, had unit costs as low as $1 per patient, contrasting with generally higher pharmaceutical intervention costs. Several studies found that linezolid-based treatments for methicillin-resistant Staphylococcus aureus were cost-effective compared with vancomycin (ICER up to $21 488 per treatment success, all 16 studies' ICERs CONCLUSION Robust information on ABR interventions is critical for efficient resource allocation. We highlight cost-effective strategies for mitigating ABR in hospitals, emphasising substantial knowledge gaps, especially in low-income and middle-income countries. Our study serves as a resource for guiding future cost-effectiveness study design and analyses.PROSPERO registration number CRD42020341827 and CRD42022340064.
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Affiliation(s)
- Kasim Allel
- Disease Control Department, London School of Hygiene & Tropical Medicine, London, UK
- Institute for Global Health, University College London, London, UK
- Department of Health and Community Sciences, University of Exeter, Exeter, UK
| | - María José Hernández-Leal
- Department of Community, Maternity and Paediatric Nursing, University of Navarra, Pamplona, Spain
- Millennium Nucleus on Sociomedicine, Santiago, Chile
| | - Nichola R Naylor
- Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK
- HCAI, Fungal, AMR, AMU & Sepsis Division, UK Health Security Agency, London, UK
| | - Eduardo A Undurraga
- Escuela de Gobierno, Pontificia Universidad Catolica de Chile, Santiago, Chile
- CIFAR Azrieli Global Scholars program, Canadian Institute for Advanced Research, Toronto, Ontario, Canada
| | | | - Priyanka Bhandari
- Disease Control Department, London School of Hygiene & Tropical Medicine, London, UK
| | - Ellen Flanagan
- Disease Control Department, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Koen B Pouwels
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - Laith Yakob
- Disease Control Department, London School of Hygiene & Tropical Medicine, London, UK
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Wei J, Stoesser N, Matthews PC, Khera T, Gethings O, Diamond I, Studley R, Taylor N, Peto TEA, Walker AS, Pouwels KB, Eyre DW. Risk of SARS-CoV-2 reinfection during multiple Omicron variant waves in the UK general population. Nat Commun 2024; 15:1008. [PMID: 38307854 PMCID: PMC10837445 DOI: 10.1038/s41467-024-44973-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 01/10/2024] [Indexed: 02/04/2024] Open
Abstract
SARS-CoV-2 reinfections increased substantially after Omicron variants emerged. Large-scale community-based comparisons across multiple Omicron waves of reinfection characteristics, risk factors, and protection afforded by previous infection and vaccination, are limited. Here we studied ~45,000 reinfections from the UK's national COVID-19 Infection Survey and quantified the risk of reinfection in multiple waves, including those driven by BA.1, BA.2, BA.4/5, and BQ.1/CH.1.1/XBB.1.5 variants. Reinfections were associated with lower viral load and lower percentages of self-reporting symptoms compared with first infections. Across multiple Omicron waves, estimated protection against reinfection was significantly higher in those previously infected with more recent than earlier variants, even at the same time from previous infection. Estimated protection against Omicron reinfections decreased over time from the most recent infection if this was the previous or penultimate variant (generally within the preceding year). Those 14-180 days after receiving their most recent vaccination had a lower risk of reinfection than those >180 days from their most recent vaccination. Reinfection risk was independently higher in those aged 30-45 years, and with either low or high viral load in their most recent previous infection. Overall, the risk of Omicron reinfection is high, but with lower severity than first infections; both viral evolution and waning immunity are independently associated with reinfection.
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Affiliation(s)
- Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Francis Crick Institute, 1 Midland Road, London, UK
- Division of infection and immunity, University College London, London, UK
| | | | | | | | | | | | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - Koen B Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
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Campbell A, Borek AJ, McLeod M, Tonkin-Crine S, Pouwels KB, Roope LS, Hayhoe BW, Majeed A, Walker AS, Holmes A. Impact of the COVID-19 pandemic on antimicrobial stewardship support for general practices in England: a qualitative interview study. BJGP Open 2023; 7:BJGPO.2022.0193. [PMID: 37290780 PMCID: PMC10646204 DOI: 10.3399/bjgpo.2022.0193] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 04/13/2023] [Accepted: 05/02/2023] [Indexed: 06/10/2023] Open
Abstract
BACKGROUND In England, clinical commissioning group (CCG; now replaced by Integrated Care Systems [ICSs]) and primary care network (PCN) professionals support primary care prescribers to optimise antimicrobial stewardship (AMS). AIM To explore views and experiences of CCG and PCN staff in supporting AMS, and the impact of COVID-19 on this support. DESIGN & SETTING Qualitative interview study in primary care in England. METHOD Semi-structured interviews with staff from CCG and PCNs responsible for AMS were conducted at two timepoints via telephone. These were audio-recorded, transcribed, and analysed thematically. RESULTS Twenty-seven interviews were conducted with 14 participants (nine CCG, five PCN) in December 2020-January 2021 and February-May 2021. The study found that AMS support was (1) deprioritised in order to keep general practice operational and deliver COVID-19 vaccines; (2) disrupted as social distancing made it harder to build relationships, conduct routine AMS activities, and challenge prescribing decisions; and (3) adapted, with opportunities identified for greater use of technology and changing patient and public perceptions of viruses and self-care. It was also found that resources to support AMS were valued if they were both novel, to counter AMS 'fatigue', and sufficiently familiar to fit with existing and/or future AMS. CONCLUSION AMS needs to be reprioritised in general practice in the post-pandemic era and within the new ICSs in England. This should include interventions and strategies that combine novel elements with already familiar strategies to refresh prescribers' motivation and opportunities for AMS. Behaviour change interventions should be aimed at improving the culture and processes for how PCN pharmacists voice concerns about AMS to prescribers in general practice and take advantage of the changed patient and public perceptions of viruses and self-care.
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Affiliation(s)
- Anne Campbell
- National Institute for Health Research (NIHR), Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - Aleksandra J Borek
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Monsey McLeod
- National Institute for Health Research (NIHR), Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
- Centre for Medication Safety and Service Quality, Pharmacy Department, Imperial College Healthcare NHS Trust, London, UK
- NIHR Imperial Patient Safety Translational Research Centre, Imperial College London, London, UK
| | - Sarah Tonkin-Crine
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - Koen B Pouwels
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Laurence Sj Roope
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Azeem Majeed
- Primary Care and Public Health, Imperial College London, London, UK
| | - A Sarah Walker
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Alison Holmes
- National Institute for Health Research (NIHR), Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
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9
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Lai X, Zhang H, Pouwels KB, Patenaude B, Jit M, Fang H. Estimating global and regional between-country inequality in routine childhood vaccine coverage in 195 countries and territories from 2019 to 2021: a longitudinal study. EClinicalMedicine 2023; 60:102042. [PMID: 37304497 PMCID: PMC10249397 DOI: 10.1016/j.eclinm.2023.102042] [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] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/26/2023] [Accepted: 05/26/2023] [Indexed: 06/13/2023] Open
Abstract
Background Global routine childhood vaccine coverage has plateaued in recent years, and the COVID-19 pandemic further disrupted immunisation services. We estimated global and regional inequality of routine childhood vaccine coverage from 2019 to 2021, particularly assessing the impacts of the COVID-19 pandemic. Methods We used longitudinal data for 11 routine childhood vaccines from the WHO-UNICEF Estimates of National Immunization Coverage (WUENIC), including 195 countries and territories in 2019-2021. The slope index of inequality (SII) and relative index of inequality (RII) of each vaccine were calculated through linear regression to express the difference in coverage between the top and bottom 20% of countries at the global and regional levels. We also explored inequalities of routine childhood vaccine coverage by WHO regions and unvaccinated children by income groups. Findings Globally between January 1, 2019 and December 31, 2021, most childhood vaccines showed a declining trend in coverage, and therefore an increasing number of unvaccinated children, especially in low-income and lower-middle-income countries. Between-country inequalities existed for all 11 routine childhood vaccine coverage indicators. The SII for the third dose of diphtheria-tetanus-pertussis-containing vaccine (DTP3) coverage was 20.1 percentage points (95% confidence interval: 13.7, 26.5) in 2019, and rose to 23.6 (17.5, 30.0) in 2020 and 26.9 (20.0, 33.8) in 2021. Similar patterns were found for RII results and in other routine vaccines. In 2021, the second dose of measles-containing vaccine (MCV2) coverage had the highest global absolute inequality (31.2, [21.5-40.8]), and completed rotavirus vaccine (RotaC) coverage had the lowest (7.8, [-3.9, 19.5]). Among six WHO regions, the European Region consistently had the lowest inequalities, and the Western Pacific Region had the largest inequalities for many indicators, although both increased from 2019 to 2021. Interpretation Global and regional inequalities of routine childhood vaccine coverage persisted and substantially increased from 2019 to 2021. These findings reveal economic-related inequalities by vaccines, regions, and countries, and underscore the importance of reducing such inequalities. These inequalities were widened during the COVID-19 pandemic, resulting in even lower coverage and more unvaccinated children in low-income countries. Funding Bill & Melinda Gates Foundation.
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Affiliation(s)
- Xiaozhen Lai
- Department of Health Policy and Management, School of Public Health, Peking University, Beijing, China
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Haijun Zhang
- Department of Health Policy and Management, School of Public Health, Peking University, Beijing, China
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
- International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
| | - Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Bryan Patenaude
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
- International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
| | - Mark Jit
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- School of Public Health, University of Hong Kong, Hong Kong SAR, China
| | - Hai Fang
- China Center for Health Development Studies, Peking University, Beijing, China
- Peking University Health Science Center-Chinese Center for Disease Control and Prevention Joint Research Center for Vaccine Economics, Peking University, Beijing, China
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10
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Buckell J, Jones J, Matthews PC, Diamond SI, Rourke E, Studley R, Cook D, Walker AS, Pouwels KB. COVID-19 vaccination, risk-compensatory behaviours, and contacts in the UK. Sci Rep 2023; 13:8441. [PMID: 37231004 PMCID: PMC10209557 DOI: 10.1038/s41598-023-34244-2] [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: 05/26/2022] [Accepted: 04/26/2023] [Indexed: 05/27/2023] Open
Abstract
The physiological effects of vaccination against SARS-CoV-2 (COVID-19) are well documented, yet the behavioural effects not well known. Risk compensation suggests that gains in personal safety, as a result of vaccination, are offset by increases in risky behaviour, such as socialising, commuting and working outside the home. This is potentially important because transmission of SARS-CoV-2 is driven by contacts, which could be amplified by vaccine-related risk compensation. Here, we show that behaviours were overall unrelated to personal vaccination, but-adjusting for variation in mitigation policies-were responsive to the level of vaccination in the wider population: individuals in the UK were risk compensating when rates of vaccination were rising. This effect was observed across four nations of the UK, each of which varied policies autonomously.
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Affiliation(s)
- John Buckell
- Health Economics Research Centre, Richard Doll Building, Old Road Campus, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK.
- Health Behaviours, Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, UK.
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
| | - Joel Jones
- Office for National Statistics, Newport, UK
| | - Philippa C Matthews
- The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
- Division of Infection and Immunity, University College London, Gower St, London, WC1E 6BT, UK
- Department of Infection, University College London Hospitals, 235 Euston Rd, London, NW1 2BU, UK
| | | | | | | | | | - Ann Sarah Walker
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - Koen B Pouwels
- Health Economics Research Centre, Richard Doll Building, Old Road Campus, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
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11
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Rozenbaum MH, Begier E, Kurosky SK, Whelan J, Bem D, Pouwels KB, Postma M, Bont L. Incidence of Respiratory Syncytial Virus Infection in Older Adults: Limitations of Current Data. Infect Dis Ther 2023:10.1007/s40121-023-00802-4. [PMID: 37310617 DOI: 10.1007/s40121-023-00802-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/30/2023] [Indexed: 06/14/2023] Open
Abstract
INTRODUCTION Respiratory syncytial virus (RSV) is an important cause of severe respiratory illness in older adults and adults with respiratory or cardiovascular comorbidities. Published estimates of its incidence and prevalence in adult groups vary widely. This article reviews the potential limitations affecting RSV epidemiology studies and suggests points to consider when evaluating or designing them. METHODS Studies reporting the incidence or prevalence of RSV infection in adults in high-income Western countries from 2000 onwards were identified via a rapid literature review. Author-reported limitations were recorded, together with presence of other potential limitations. Data were synthesized narratively, with a focus on factors affecting incidence estimates for symptomatic infection in older adults. RESULTS A total of 71 studies met the inclusion criteria, most in populations with medically attended acute respiratory illness (ARI). Only a minority used case definitions and sampling periods tailored specifically to RSV; many used influenza-based or other criteria that are likely to result in RSV cases being missed. The great majority relied solely on polymerase chain reaction (PCR) testing of upper respiratory tract samples, which is likely to miss RSV cases compared with dual site sampling and/or addition of serology. Other common limitations were studying a single season, which has potential for bias due to seasonal variability; failure to stratify results by age, which underestimates the burden of severe disease in older adults; limited generalizability beyond a limited study setting; and absence of measures of uncertainty in the reporting of results. CONCLUSIONS A significant proportion of studies are likely to underestimate the incidence of RSV infection in older adults, although the effect size is unclear and there is also potential for overestimation. Well-designed studies, together with increased testing for RSV in patients with ARI in clinical practice, are required to accurately capture both the burden of RSV and the potential public health impact of vaccines.
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Affiliation(s)
| | | | | | | | | | | | | | - Louis Bont
- University Medical Center Utrecht, Utrecht, The Netherlands
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12
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Wei J, Matthews PC, Stoesser N, Newton JN, Diamond I, Studley R, Taylor N, Bell JI, Farrar J, Kolenchery J, Marsden BD, Hoosdally S, Jones EY, Stuart DI, Crook DW, Peto TEA, Walker AS, Pouwels KB, Eyre DW. Protection against SARS-CoV-2 Omicron BA.4/5 variant following booster vaccination or breakthrough infection in the UK. Nat Commun 2023; 14:2799. [PMID: 37193713 PMCID: PMC10187514 DOI: 10.1038/s41467-023-38275-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [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: 01/20/2023] [Accepted: 04/21/2023] [Indexed: 05/18/2023] Open
Abstract
Following primary SARS-CoV-2 vaccination, whether boosters or breakthrough infections provide greater protection against SARS-CoV-2 infection is incompletely understood. Here we investigated SARS-CoV-2 antibody correlates of protection against new Omicron BA.4/5 (re-)infections and anti-spike IgG antibody trajectories after a third/booster vaccination or breakthrough infection following second vaccination in 154,149 adults ≥18 y from the United Kingdom general population. Higher antibody levels were associated with increased protection against Omicron BA.4/5 infection and breakthrough infections were associated with higher levels of protection at any given antibody level than boosters. Breakthrough infections generated similar antibody levels to boosters, and the subsequent antibody declines were slightly slower than after boosters. Together our findings show breakthrough infection provides longer-lasting protection against further infections than booster vaccinations. Our findings, considered alongside the risks of severe infection and long-term consequences of infection, have important implications for vaccine policy.
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Affiliation(s)
- Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Francis Crick Institute, 1 Midland Road, London, UK
- Division of infection and immunity, University College London, London, UK
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - John N Newton
- European Centre for Environment and Human Health, University of Exeter, Truro, UK
| | | | | | | | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | | | - Jaison Kolenchery
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - Koen B Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
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13
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Harris J, Pouwels KB, Johnson T, Sterne J, Pithara C, Mahadevan K, Reeves B, Benedetto U, Loke Y, Lasserson D, Doble B, Hopewell-Kelly N, Redwood S, Wordsworth S, Mumford A, Rogers C, Pufulete M. Bleeding risk in patients prescribed dual antiplatelet therapy and triple therapy after coronary interventions: the ADAPTT retrospective population-based cohort studies. Health Technol Assess 2023; 27:1-257. [PMID: 37435838 PMCID: PMC10363958 DOI: 10.3310/mnjy9014] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2023] Open
Abstract
Background Bleeding among populations undergoing percutaneous coronary intervention or coronary artery bypass grafting and among conservatively managed patients with acute coronary syndrome exposed to different dual antiplatelet therapy and triple therapy (i.e. dual antiplatelet therapy plus an anticoagulant) has not been previously quantified. Objectives The objectives were to estimate hazard ratios for bleeding for different antiplatelet and triple therapy regimens, estimate resources and the associated costs of treating bleeding events, and to extend existing economic models of the cost-effectiveness of dual antiplatelet therapy. Design The study was designed as three retrospective population-based cohort studies emulating target randomised controlled trials. Setting The study was set in primary and secondary care in England from 2010 to 2017. Participants Participants were patients aged ≥ 18 years undergoing coronary artery bypass grafting or emergency percutaneous coronary intervention (for acute coronary syndrome), or conservatively managed patients with acute coronary syndrome. Data sources Data were sourced from linked Clinical Practice Research Datalink and Hospital Episode Statistics. Interventions Coronary artery bypass grafting and conservatively managed acute coronary syndrome: aspirin (reference) compared with aspirin and clopidogrel. Percutaneous coronary intervention: aspirin and clopidogrel (reference) compared with aspirin and prasugrel (ST elevation myocardial infarction only) or aspirin and ticagrelor. Main outcome measures Primary outcome: any bleeding events up to 12 months after the index event. Secondary outcomes: major or minor bleeding, all-cause and cardiovascular mortality, mortality from bleeding, myocardial infarction, stroke, additional coronary intervention and major adverse cardiovascular events. Results The incidence of any bleeding was 5% among coronary artery bypass graft patients, 10% among conservatively managed acute coronary syndrome patients and 9% among emergency percutaneous coronary intervention patients, compared with 18% among patients prescribed triple therapy. Among coronary artery bypass grafting and conservatively managed acute coronary syndrome patients, dual antiplatelet therapy, compared with aspirin, increased the hazards of any bleeding (coronary artery bypass grafting: hazard ratio 1.43, 95% confidence interval 1.21 to 1.69; conservatively-managed acute coronary syndrome: hazard ratio 1.72, 95% confidence interval 1.15 to 2.57) and major adverse cardiovascular events (coronary artery bypass grafting: hazard ratio 2.06, 95% confidence interval 1.23 to 3.46; conservatively-managed acute coronary syndrome: hazard ratio 1.57, 95% confidence interval 1.38 to 1.78). Among emergency percutaneous coronary intervention patients, dual antiplatelet therapy with ticagrelor, compared with dual antiplatelet therapy with clopidogrel, increased the hazard of any bleeding (hazard ratio 1.47, 95% confidence interval 1.19 to 1.82), but did not reduce the incidence of major adverse cardiovascular events (hazard ratio 1.06, 95% confidence interval 0.89 to 1.27). Among ST elevation myocardial infarction percutaneous coronary intervention patients, dual antiplatelet therapy with prasugrel, compared with dual antiplatelet therapy with clopidogrel, increased the hazard of any bleeding (hazard ratio 1.48, 95% confidence interval 1.02 to 2.12), but did not reduce the incidence of major adverse cardiovascular events (hazard ratio 1.10, 95% confidence interval 0.80 to 1.51). Health-care costs in the first year did not differ between dual antiplatelet therapy with clopidogrel and aspirin monotherapy among either coronary artery bypass grafting patients (mean difference £94, 95% confidence interval -£155 to £763) or conservatively managed acute coronary syndrome patients (mean difference £610, 95% confidence interval -£626 to £1516), but among emergency percutaneous coronary intervention patients were higher for those receiving dual antiplatelet therapy with ticagrelor than for those receiving dual antiplatelet therapy with clopidogrel, although for only patients on concurrent proton pump inhibitors (mean difference £1145, 95% confidence interval £269 to £2195). Conclusions This study suggests that more potent dual antiplatelet therapy may increase the risk of bleeding without reducing the incidence of major adverse cardiovascular events. These results should be carefully considered by clinicians and decision-makers alongside randomised controlled trial evidence when making recommendations about dual antiplatelet therapy. Limitations The estimates for bleeding and major adverse cardiovascular events may be biased from unmeasured confounding and the exclusion of an eligible subgroup of patients who could not be assigned an intervention. Because of these limitations, a formal cost-effectiveness analysis could not be conducted. Future work Future work should explore the feasibility of using other UK data sets of routinely collected data, less susceptible to bias, to estimate the benefit and harm of antiplatelet interventions. Trial registration This trial is registered as ISRCTN76607611. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 27, No. 8. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- Jessica Harris
- Bristol Trials Centre, University of Bristol, Bristol, UK
| | - Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Thomas Johnson
- Department of Cardiology, Bristol Heart Institute, Bristol, UK
| | - Jonathan Sterne
- National Institute for Health Research Biomedical Research Centre, Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - Christalla Pithara
- National Institute for Health Research Applied Research Collaboration West (NIHR ARC West), Bristol, UK
| | | | - Barney Reeves
- Bristol Trials Centre, University of Bristol, Bristol, UK
| | | | - Yoon Loke
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Daniel Lasserson
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Brett Doble
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Sabi Redwood
- National Institute for Health Research Applied Research Collaboration West (NIHR ARC West), Bristol, UK
| | - Sarah Wordsworth
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Andrew Mumford
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Chris Rogers
- Bristol Trials Centre, University of Bristol, Bristol, UK
| | - Maria Pufulete
- Bristol Trials Centre, University of Bristol, Bristol, UK
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14
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Ayoubkhani D, Bosworth ML, King S, Pouwels KB, Glickman M, Nafilyan V, Zaccardi F, Khunti K, Alwan NA, Walker AS. Risk of Long Covid in people infected with SARS-CoV-2 after two doses of a COVID-19 vaccine: community-based, matched cohort study. Open Forum Infect Dis 2022; 9:ofac464. [PMID: 36168555 PMCID: PMC9494414 DOI: 10.1093/ofid/ofac464] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [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] [Received: 05/20/2022] [Accepted: 09/08/2022] [Indexed: 11/25/2022] Open
Abstract
We investigated long COVID incidence by vaccination status in a random sample of UK adults from April 2020 to November 2021. Persistent symptoms were reported by 9.5% of 3090 breakthrough severe acute respiratory syndrome coronavirus 2 infections and 14.6% of unvaccinated controls (adjusted odds ratio, 0.59 [95% confidence interval, .50–.69]), emphasizing the need for public health initiatives to increase population-level vaccine uptake.
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Affiliation(s)
- Daniel Ayoubkhani
- Health Analysis and Life Events Division, Office for National Statistics , Newport , UK
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester , Leicester , UK
| | - Matthew L Bosworth
- Health Analysis and Life Events Division, Office for National Statistics , Newport , UK
| | - Sasha King
- Methodology and Quality Directorate, Office for National Statistics , London , UK
| | - Koen B Pouwels
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford , Oxford , UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford , Oxford , UK
| | - Myer Glickman
- Health Analysis and Life Events Division, Office for National Statistics , Newport , UK
| | - Vahé Nafilyan
- Health Analysis and Life Events Division, Office for National Statistics , Newport , UK
- Faculty of Public Health, Environment and Society, London School of Hygiene & Tropical Medicine , London , UK
| | - Francesco Zaccardi
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester , Leicester , UK
| | - Kamlesh Khunti
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester , Leicester , UK
| | - Nisreen A Alwan
- School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton , Southampton , UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust , Southampton , UK
- NIHR Applied Research Collaboration (ARC) Wessex , Southampton , UK
| | - A Sarah Walker
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford , Oxford , UK
- Nuffield Department of Medicine, University of Oxford , Oxford , UK
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15
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Ayoubkhani D, Bosworth ML, King S, Pouwels KB, Glickman M, Nafilyan V, Zaccardi F, Khunti K, Alwan NA, Walker AS. Risk of Long COVID in People Infected With Severe Acute Respiratory Syndrome Coronavirus 2 After 2 Doses of a Coronavirus Disease 2019 Vaccine: Community-Based, Matched Cohort Study. Open Forum Infect Dis 2022; 9:ofac464. [PMID: 36168555 DOI: 10.1101/2022.02.23.22271388] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 09/08/2022] [Indexed: 05/22/2023] Open
Abstract
We investigated long COVID incidence by vaccination status in a random sample of UK adults from April 2020 to November 2021. Persistent symptoms were reported by 9.5% of 3090 breakthrough severe acute respiratory syndrome coronavirus 2 infections and 14.6% of unvaccinated controls (adjusted odds ratio, 0.59 [95% confidence interval, .50-.69]), emphasizing the need for public health initiatives to increase population-level vaccine uptake.
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Affiliation(s)
- Daniel Ayoubkhani
- Health Analysis and Life Events Division, Office for National Statistics, Newport, United Kingdom
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, United Kingdom
| | - Matthew L Bosworth
- Health Analysis and Life Events Division, Office for National Statistics, Newport, United Kingdom
| | - Sasha King
- Methodology and Quality Directorate, Office for National Statistics, London, United Kingdom
| | - Koen B Pouwels
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Myer Glickman
- Health Analysis and Life Events Division, Office for National Statistics, Newport, United Kingdom
| | - Vahé Nafilyan
- Health Analysis and Life Events Division, Office for National Statistics, Newport, United Kingdom
- Faculty of Public Health, Environment and Society, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Francesco Zaccardi
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, United Kingdom
| | - Kamlesh Khunti
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, United Kingdom
| | - Nisreen A Alwan
- School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- National Institute for Health Research Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
- National Institute for Health Research Applied Research Collaboration Wessex, Southampton, United Kingdom
| | - A Sarah Walker
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
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Candio P, Pouwels KB, Meads D, Hill AJ, Bojke L, Williams C. Modelling decay in effectiveness for evaluation of behaviour change interventions: a tutorial for public health economists. Eur J Health Econ 2022; 23:1151-1157. [PMID: 34914010 PMCID: PMC9395462 DOI: 10.1007/s10198-021-01417-7] [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] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 11/22/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND PURPOSE Recent methodological reviews of evaluations of behaviour change interventions in public health have highlighted that the decay in effectiveness over time has been mostly overlooked, potentially leading to suboptimal decision-making. While, in principle, discrete-time Markov chains-the most commonly used modelling approach-can be adapted to account for decay in effectiveness, this framework inherently lends itself to strong model simplifications. The application of formal and more appropriate modelling approaches has been supported, but limited progress has been made to date. The purpose of this paper is to encourage this shift by offering a practical guide on how to model decay in effectiveness using a continuous-time Markov chain (CTMC)-based approach. METHODS A CTMC approach is demonstrated, with a contextualized tutorial being presented to facilitate learning and uptake. A worked example based on the stylized case study in physical activity promotion is illustrated with accompanying R code. DISCUSSION The proposed framework presents a relatively small incremental change from the current modelling practice. CTMC represents a technical solution which, in absence of relevant data, allows for formally testing the sensitivity of results to assumptions regarding the long-term sustainability of intervention effects and improving model transparency. CONCLUSIONS The use of CTMC should be considered in evaluations where decay in effectiveness is likely to be a key factor to consider. This would enable more robust model-based evaluations of population-level programmes to promote behaviour change and reduce the uncertainty surrounding the decision to invest in these public health interventions.
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Affiliation(s)
- Paolo Candio
- Centre for Economics of Obesity, Institute of Applied Health Research, University of Birmingham, Birmingham, B15 2TT, UK.
- Health Economics Research Centre, University of Oxford, Oxford, UK.
| | - Koen B Pouwels
- Health Economics Research Centre, University of Oxford, Oxford, UK
| | - David Meads
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Andrew J Hill
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Laura Bojke
- Centre for Health Economics, University of York, York, UK
| | - Claire Williams
- Health Economics Research Centre, University of Oxford, Oxford, UK
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House T, Riley H, Pellis L, Pouwels KB, Bacon S, Eidukas A, Jahanshahi K, Eggo RM, Sarah Walker A. Inferring risks of coronavirus transmission from community household data. Stat Methods Med Res 2022; 31:1738-1756. [PMID: 36112916 PMCID: PMC9465559 DOI: 10.1177/09622802211055853] [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] [Indexed: 01/02/2023]
Abstract
The response of many governments to the COVID-19 pandemic has involved measures to control within- and between-household transmission, providing motivation to improve understanding of the absolute and relative risks in these contexts. Here, we perform exploratory, residual-based, and transmission-dynamic household analysis of the Office for National Statistics COVID-19 Infection Survey data from 26 April 2020 to 15 July 2021 in England. This provides evidence for: (i) temporally varying rates of introduction of infection into households broadly following the trajectory of the overall epidemic and vaccination programme; (ii) susceptible-Infectious transmission probabilities of within-household transmission in the 15-35% range; (iii) the emergence of the Alpha and Delta variants, with the former being around 50% more infectious than wildtype and 35% less infectious than Delta within households; (iv) significantly (in the range of 25-300%) more risk of bringing infection into the household for workers in patient-facing roles pre-vaccine; (v) increased risk for secondary school-age children of bringing the infection into the household when schools are open; (vi) increased risk for primary school-age children of bringing the infection into the household when schools were open since the emergence of new variants.
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Affiliation(s)
- Thomas House
- Department of Mathematics, 5292University of Manchester, Manchester UK
- IBM Research, Hartree Centre, Daresbury UK
- The Alan Turing Institute for Data Science and Artificial Intelligence, London UK
| | - Heather Riley
- Department of Mathematics, 5292University of Manchester, Manchester UK
| | - Lorenzo Pellis
- Department of Mathematics, 5292University of Manchester, Manchester UK
- The Alan Turing Institute for Data Science and Artificial Intelligence, London UK
| | - Koen B Pouwels
- 105596Nuffield Department of Medicine, University of Oxford, Oxford UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, , Oxford UK
| | - Sebastian Bacon
- The DataLab, 12205Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford UK
| | | | | | - Rosalind M Eggo
- Centre for Mathematical Modelling of Infectious Diseases, 4906London School of Hygiene and Tropical Medicine, London UK
| | - A Sarah Walker
- 105596Nuffield Department of Medicine, University of Oxford, Oxford UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford UK
- MRC Clinical Trials Unit at UCL, UCL, London UK
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Vihta KD, Pouwels KB, Peto TEA, Pritchard E, Eyre DW, House T, Gethings O, Studley R, Rourke E, Cook D, Diamond I, Crook D, Matthews PC, Stoesser N, Walker AS. Symptoms and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Positivity in the General Population in the United Kingdom. Clin Infect Dis 2022; 75:e329-e337. [PMID: 34748629 PMCID: PMC8767848 DOI: 10.1093/cid/ciab945] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [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: 08/19/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND "Classic" symptoms (cough, fever, loss of taste/smell) prompt severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) polymerase chain reaction (PCR) testing in the United Kingdom. Studies have assessed the ability of different symptoms to identify infection, but few have compared symptoms over time (reflecting variants) and by vaccination status. METHODS Using the COVID-19 Infection Survey, sampling households across the United Kingdom, we compared symptoms in PCR-positives vs PCR-negatives, evaluating sensitivity of combinations of 12 symptoms (percentage symptomatic PCR-positives reporting specific symptoms) and tests per case (TPC) (PCR-positives or PCR-negatives reporting specific symptoms/ PCR-positives reporting specific symptoms). RESULTS Between April 2020 and August 2021, 27 869 SARS-CoV-2 PCR-positive episodes occurred in 27 692 participants (median 42 years), of whom 13 427 (48%) self-reported symptoms ("symptomatic PCR-positives"). The comparator comprised 3 806 692 test-negative visits (457 215 participants); 130 612 (3%) self-reported symptoms ("symptomatic PCR-negatives"). Symptom reporting in PCR-positives varied by age, sex, and ethnicity, and over time, reflecting changes in prevalence of viral variants, incidental changes (eg, seasonal pathogens (with sore throat increasing in PCR-positives and PCR-negatives from April 2021), schools reopening) and vaccination rollout. After May 2021 when Delta emerged, headache and fever substantially increased in PCR-positives, but not PCR-negatives. Sensitivity of symptom-based detection increased from 74% using "classic" symptoms, to 81% adding fatigue/weakness, and 90% including all 8 additional symptoms. However, this increased TPC from 4.6 to 5.3 to 8.7. CONCLUSIONS Expanded symptom combinations may provide modest benefits for sensitivity of PCR-based case detection, but this will vary between settings and over time, and increases tests/case. Large-scale changes to targeted PCR-testing approaches require careful evaluation given substantial resource and infrastructure implications.
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Affiliation(s)
- Karina Doris Vihta
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Department of Engineering, University of Oxford, Oxford, United Kingdom
| | - Koen B Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Emma Pritchard
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
| | - David W Eyre
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
- IBM Research, Hartree Centre, Sci-Tech Daresbury, United Kingdom
| | - Owen Gethings
- Office for National Statistics, Newport, United Kingdom
| | - Ruth Studley
- Office for National Statistics, Newport, United Kingdom
| | - Emma Rourke
- Office for National Statistics, Newport, United Kingdom
| | - Duncan Cook
- Office for National Statistics, Newport, United Kingdom
| | - Ian Diamond
- Office for National Statistics, Newport, United Kingdom
| | - Derrick Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Ann Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
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19
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Naylor NR, Evans S, Pouwels KB, Troughton R, Lamagni T, Muller-Pebody B, Knight GM, Atun R, Robotham JV. Quantifying the primary and secondary effects of antimicrobial resistance on surgery patients: Methods and data sources for empirical estimation in England. Front Public Health 2022; 10:803943. [PMID: 36033764 PMCID: PMC9413182 DOI: 10.3389/fpubh.2022.803943] [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: 10/28/2021] [Accepted: 07/04/2022] [Indexed: 01/21/2023] Open
Abstract
Antimicrobial resistance (AMR) may negatively impact surgery patients through reducing the efficacy of treatment of surgical site infections, also known as the "primary effects" of AMR. Previous estimates of the burden of AMR have largely ignored the potential "secondary effects," such as changes in surgical care pathways due to AMR, such as different infection prevention procedures or reduced access to surgical procedures altogether, with literature providing limited quantifications of this potential burden. Former conceptual models and approaches for quantifying such impacts are available, though they are often high-level and difficult to utilize in practice. We therefore expand on this earlier work to incorporate heterogeneity in antimicrobial usage, AMR, and causative organisms, providing a detailed decision-tree-Markov-hybrid conceptual model to estimate the burden of AMR on surgery patients. We collate available data sources in England and describe how routinely collected data could be used to parameterise such a model, providing a useful repository of data systems for future health economic evaluations. The wealth of national-level data available for England provides a case study in describing how current surveillance and administrative data capture systems could be used in the estimation of transition probability and cost parameters. However, it is recommended that such data are utilized in combination with expert opinion (for scope and scenario definitions) to robustly estimate both the primary and secondary effects of AMR over time. Though we focus on England, this discussion is useful in other settings with established and/or developing infectious diseases surveillance systems that feed into AMR National Action Plans.
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Affiliation(s)
- Nichola R. Naylor
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London, London, United Kingdom,Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, Antimicrobial Resistance (AMR) Centre, London School of Hygiene and Tropical Medicine, London, United Kingdom,Healthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage and Sepsis division, United Kingdom Health Security Agency, London, United Kingdom,*Correspondence: Nichola R. Naylor
| | - Stephanie Evans
- Healthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage and Sepsis division, United Kingdom Health Security Agency, London, United Kingdom
| | - Koen B. Pouwels
- Nuffield Department of Population Health, Health Economics Research Centre, University of Oxford, Oxford, United Kingdom,The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
| | - Rachael Troughton
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London, London, United Kingdom
| | - Theresa Lamagni
- Healthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage and Sepsis division, United Kingdom Health Security Agency, London, United Kingdom
| | - Berit Muller-Pebody
- Healthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage and Sepsis division, United Kingdom Health Security Agency, London, United Kingdom
| | - Gwenan M. Knight
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, Antimicrobial Resistance (AMR) Centre, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Rifat Atun
- Department of Global Health and Population, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, United States,Department of Global Health and Social Medicine, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Julie V. Robotham
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London, London, United Kingdom,Healthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage and Sepsis division, United Kingdom Health Security Agency, London, United Kingdom
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20
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Naylor NR, Evans S, Pouwels KB, Troughton R, Lamagni T, Muller-Pebody B, Knight GM, Atun R, Robotham JV. Quantifying the primary and secondary effects of antimicrobial resistance on surgery patients: Methods and data sources for empirical estimation in England. Front Public Health 2022. [DOI: 10.5210.3389/fpubh.2022.803943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Antimicrobial resistance (AMR) may negatively impact surgery patients through reducing the efficacy of treatment of surgical site infections, also known as the “primary effects” of AMR. Previous estimates of the burden of AMR have largely ignored the potential “secondary effects,” such as changes in surgical care pathways due to AMR, such as different infection prevention procedures or reduced access to surgical procedures altogether, with literature providing limited quantifications of this potential burden. Former conceptual models and approaches for quantifying such impacts are available, though they are often high-level and difficult to utilize in practice. We therefore expand on this earlier work to incorporate heterogeneity in antimicrobial usage, AMR, and causative organisms, providing a detailed decision-tree-Markov-hybrid conceptual model to estimate the burden of AMR on surgery patients. We collate available data sources in England and describe how routinely collected data could be used to parameterise such a model, providing a useful repository of data systems for future health economic evaluations. The wealth of national-level data available for England provides a case study in describing how current surveillance and administrative data capture systems could be used in the estimation of transition probability and cost parameters. However, it is recommended that such data are utilized in combination with expert opinion (for scope and scenario definitions) to robustly estimate both the primary and secondary effects of AMR over time. Though we focus on England, this discussion is useful in other settings with established and/or developing infectious diseases surveillance systems that feed into AMR National Action Plans.
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21
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Vihta KD, Pouwels KB, Peto TEA, Pritchard E, House T, Studley R, Rourke E, Cook D, Diamond I, Crook D, Clifton DA, Matthews PC, Stoesser N, Eyre DW, Walker AS. Omicron-associated changes in SARS-CoV-2 symptoms in the United Kingdom. Clin Infect Dis 2022; 76:ciac613. [PMID: 35917440 PMCID: PMC9384604 DOI: 10.1093/cid/ciac613] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 07/14/2022] [Accepted: 07/22/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND The SARS-CoV-2 Delta variant has been replaced by the highly transmissible Omicron BA.1 variant, and subsequently by Omicron BA.2. It is important to understand how these changes in dominant variants affect reported symptoms, while also accounting for symptoms arising from other co-circulating respiratory viruses. METHODS In a nationally representative UK community study, the COVID-19 Infection Survey, we investigated symptoms in PCR-positive infection episodes vs. PCR-negative study visits over calendar time, by age and vaccination status, comparing periods when the Delta, Omicron BA.1 and BA.2 variants were dominant. RESULTS Between October-2020 and April-2022, 120,995 SARS-CoV-2 PCR-positive episodes occurred in 115,886 participants, with 70,683 (58%) reporting symptoms. The comparator comprised 4,766,366 PCR-negative study visits (483,894 participants); 203,422 (4%) reporting symptoms. Symptom reporting in PCR-positives varied over time, with a marked reduction in loss of taste/smell as Omicron BA.1 dominated, maintained with BA.2 (44%/45% 17 October 2021, 16%/13% 2 January 2022, 15%/12% 27 March 2022). Cough, fever, shortness of breath, myalgia, fatigue/weakness and headache also decreased after Omicron BA.1 dominated, but sore throat increased, the latter to a greater degree than concurrent increases in PCR-negatives. Fatigue/weakness increased again after BA.2 dominated, although to a similar degree to concurrent increases in PCR-negatives. Symptoms were consistently more common in adults aged 18-65 years than in children or older adults. CONCLUSIONS Increases in sore throat (also common in the general community), and a marked reduction in loss of taste/smell, make Omicron harder to detect with symptom-based testing algorithms, with implications for institutional and national testing policies.
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Affiliation(s)
- Karina-Doris Vihta
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Department of Engineering, University of Oxford, Oxford, United Kingdom
| | - Koen B Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Emma Pritchard
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
- IBM Research, Hartree Centre, Sci-Tech Daresbury, Daresbury, United Kingdom
| | - Ruth Studley
- Office for National Statistics, Newport, United Kingdom
| | - Emma Rourke
- Office for National Statistics, Newport, United Kingdom
| | - Duncan Cook
- Office for National Statistics, Newport, United Kingdom
| | - Ian Diamond
- Office for National Statistics, Newport, United Kingdom
| | - Derrick Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - David A Clifton
- Department of Engineering, University of Oxford, Oxford, United Kingdom
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Francis Crick Institute, London, United Kingdom
- Division of Infection and Immunity, University College London, London, United Kingdom
- Department of Infection, University College London Hospitals, London, United Kingdom
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - David W Eyre
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ann Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
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Ward IL, Bermingham C, Ayoubkhani D, Gethings OJ, Pouwels KB, Yates T, Khunti K, Hippisley-Cox J, Banerjee A, Walker AS, Nafilyan V. Risk of covid-19 related deaths for SARS-CoV-2 omicron (B.1.1.529) compared with delta (B.1.617.2): retrospective cohort study. BMJ 2022; 378:e070695. [PMID: 35918098 PMCID: PMC9344192 DOI: 10.1136/bmj-2022-070695] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
OBJECTIVE To assess the risk of covid-19 death after infection with omicron BA.1 compared with delta (B.1.617.2). DESIGN Retrospective cohort study. SETTING England, United Kingdom, from 1 December 2021 to 30 December 2021. PARTICIPANTS 1 035 149 people aged 18-100 years who tested positive for SARS-CoV-2 under the national surveillance programme and had an infection identified as omicron BA.1 or delta compatible. MAIN OUTCOME MEASURES The main outcome measure was covid-19 death as identified from death certification records. The exposure of interest was the SARS-CoV-2 variant identified from NHS Test and Trace PCR positive tests taken in the community (pillar 2) and analysed by Lighthouse laboratories. Cause specific Cox proportional hazard regression models (censoring non-covid-19 deaths) were adjusted for sex, age, vaccination status, previous infection, calendar time, ethnicity, index of multiple deprivation rank, household deprivation, university degree, keyworker status, country of birth, main language, region, disability, and comorbidities. Interactions between variant and sex, age, vaccination status, and comorbidities were also investigated. RESULTS The risk of covid-19 death was 66% lower (95% confidence interval 54% to 75%) for omicron BA.1 compared with delta after adjusting for a wide range of potential confounders. The reduction in the risk of covid-19 death for omicron compared with delta was more pronounced in people aged 18-59 years (number of deaths: delta=46, omicron=11; hazard ratio 0.14, 95% confidence interval 0.07 to 0.27) than in those aged ≥70 years (number of deaths: delta=113, omicron=135; hazard ratio 0.44, 95% confidence interval 0.32 to 0.61, P<0.0001). No evidence of a difference in risk was found between variant and number of comorbidities. CONCLUSIONS The results support earlier studies showing a reduction in severity of infection with omicron BA.1 compared with delta in terms of hospital admission. This study extends the research to also show a reduction in the risk of covid-19 death for the omicron variant compared with the delta variant.
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Affiliation(s)
| | | | | | | | - Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Thomas Yates
- Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK
- National Institute for Health Research (NIHR) Leicester Biomedical Research Centre (BRC), Leicester General Hospital, Leicester, UK
| | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK
- National Institute for Health Research (NIHR) Leicester Biomedical Research Centre (BRC), Leicester General Hospital, Leicester, UK
| | - Julia Hippisley-Cox
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Amitava Banerjee
- Institute of Health Informatics, University College London, London, UK
- Department of Cardiology, Barts Health NHS Trust, London, UK
| | - Ann Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
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Wei J, Matthews PC, Stoesser N, Diamond I, Studley R, Rourke E, Cook D, Bell JI, Newton JN, Farrar J, Howarth A, Marsden BD, Hoosdally S, Jones EY, Stuart DI, Crook DW, Peto TEA, Walker AS, Eyre DW, Pouwels KB. SARS-CoV-2 antibody trajectories after a single COVID-19 vaccination with and without prior infection. Nat Commun 2022; 13:3748. [PMID: 35768431 PMCID: PMC9243074 DOI: 10.1038/s41467-022-31495-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 06/17/2022] [Indexed: 11/10/2022] Open
Abstract
Given high SARS-CoV-2 incidence, coupled with slow and inequitable vaccine roll-out in many settings, there is a need for evidence to underpin optimum vaccine deployment, aiming to maximise global population immunity. We evaluate whether a single vaccination in individuals who have already been infected with SARS-CoV-2 generates similar initial and subsequent antibody responses to two vaccinations in those without prior infection. We compared anti-spike IgG antibody responses after a single vaccination with ChAdOx1, BNT162b2, or mRNA-1273 SARS-CoV-2 vaccines in the COVID-19 Infection Survey in the UK general population. In 100,849 adults median (50 (IQR: 37-63) years) receiving at least one vaccination, 13,404 (13.3%) had serological/PCR evidence of prior infection. Prior infection significantly boosted antibody responses, producing higher peak levels and/or longer half-lives after one dose of all three vaccines than those without prior infection receiving one or two vaccinations. In those with prior infection, the median time above the positivity threshold was >1 year after the first vaccination. Single-dose vaccination targeted to those previously infected may provide at least as good protection to two-dose vaccination among those without previous infection.
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Affiliation(s)
- Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Francis Crick Institute, 1 Midland Road, London, UK
- Division of infection and immunity, University College London, London, UK
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | | | | | | | | | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- European Centre for Environment and Human Health, University of Exeter, Truro, UK
| | | | - Alison Howarth
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Koen B Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
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Yoon CH, Bartlett S, Stoesser N, Pouwels KB, Jones N, Crook DW, Peto TEA, Walker AS, Eyre DW. Mortality risks associated with empirical antibiotic activity in Escherichia coli bacteraemia: an analysis of electronic health records. J Antimicrob Chemother 2022; 77:2536-2545. [PMID: 35723965 PMCID: PMC9410673 DOI: 10.1093/jac/dkac189] [Citation(s) in RCA: 0] [Impact Index Per Article: 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] [Received: 01/27/2022] [Accepted: 05/17/2022] [Indexed: 11/14/2022] Open
Abstract
Background Reported bacteraemia outcomes following inactive empirical antibiotics (based on in vitro testing) are conflicting, potentially reflecting heterogeneity in causative species, MIC breakpoints defining resistance/susceptibility, and times to rescue therapy. Methods We investigated adult inpatients with Escherichia coli bacteraemia at Oxford University Hospitals, UK, from 4 February 2014 to 30 June 2021 who were receiving empirical amoxicillin/clavulanate with/without other antibiotics. We used Cox regression to analyse 30 day all-cause mortality by in vitro amoxicillin/clavulanate susceptibility (activity) using the EUCAST resistance breakpoint (>8/2 mg/L), categorical MIC, and a higher resistance breakpoint (>32/2 mg/L), adjusting for other antibiotic activity and confounders including comorbidities, vital signs and blood tests. Results A total of 1720 E. coli bacteraemias (1626 patients) were treated with empirical amoxicillin/clavulanate. Thirty-day mortality was 193/1400 (14%) for any active baseline therapy and 52/320 (16%) for inactive baseline therapy (P = 0.17). With EUCAST breakpoints, there was no evidence that mortality differed for inactive versus active amoxicillin/clavulanate [adjusted HR (aHR) = 1.27 (95% CI 0.83–1.93); P = 0.28], nor of an association with active aminoglycoside (P = 0.93) or other active antibiotics (P = 0.18). Considering categorical amoxicillin/clavulanate MIC, MICs > 32/2 mg/L were associated with mortality [aHR = 1.85 versus MIC = 2/2 mg/L (95% CI 0.99–3.73); P = 0.054]. A higher resistance breakpoint (>32/2 mg/L) was independently associated with higher mortality [aHR = 1.82 (95% CI 1.07–3.10); P = 0.027], as were MICs > 32/2 mg/L with active empirical aminoglycosides [aHR = 2.34 (95% CI 1.40–3.89); P = 0.001], but not MICs > 32/2 mg/L with active non-aminoglycoside antibiotic(s) [aHR = 0.87 (95% CI 0.40–1.89); P = 0.72]. Conclusions We found no evidence that EUCAST-defined amoxicillin/clavulanate resistance was associated with increased mortality, but a higher resistance breakpoint (MIC > 32/2 mg/L) was. Additional active baseline non-aminoglycoside antibiotics attenuated amoxicillin/clavulanate resistance-associated mortality, but aminoglycosides did not. Granular phenotyping and comparison with clinical outcomes may improve AMR breakpoints.
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Affiliation(s)
- Chang Ho Yoon
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, UK.,Nuffield Department of Medicine, University of Oxford, UK
| | - Sean Bartlett
- Nuffield Department of Medicine, University of Oxford, UK
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, UK
| | - Koen B Pouwels
- Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, UK.,Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nicola Jones
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, UK.,The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, UK
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25
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Ayoubkhani D, Bermingham C, Pouwels KB, Glickman M, Nafilyan V, Zaccardi F, Khunti K, Alwan NA, Walker AS. Trajectory of long covid symptoms after covid-19 vaccination: community based cohort study. BMJ 2022; 377:e069676. [PMID: 35584816 PMCID: PMC9115603 DOI: 10.1136/bmj-2021-069676] [Citation(s) in RCA: 167] [Impact Index Per Article: 83.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/12/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE To estimate associations between covid-19 vaccination and long covid symptoms in adults with SARS-CoV-2 infection before vaccination. DESIGN Observational cohort study. SETTING Community dwelling population, UK. PARTICIPANTS 28 356 participants in the Office for National Statistics COVID-19 Infection Survey aged 18-69 years who received at least one dose of an adenovirus vector or mRNA covid-19 vaccine after testing positive for SARS-CoV-2 infection. MAIN OUTCOME MEASURE Presence of long covid symptoms at least 12 weeks after infection over the follow-up period 3 February to 5 September 2021. RESULTS Mean age of participants was 46 years, 55.6% (n=15 760) were women, and 88.7% (n=25 141) were of white ethnicity. Median follow-up was 141 days from first vaccination (among all participants) and 67 days from second vaccination (83.8% of participants). 6729 participants (23.7%) reported long covid symptoms of any severity at least once during follow-up. A first vaccine dose was associated with an initial 12.8% decrease (95% confidence interval -18.6% to -6.6%, P<0.001) in the odds of long covid, with subsequent data compatible with both increases and decreases in the trajectory (0.3% per week, 95% confidence interval -0.6% to 1.2% per week, P=0.51). A second dose was associated with an initial 8.8% decrease (95% confidence interval -14.1% to -3.1%, P=0.003) in the odds of long covid, with a subsequent decrease by 0.8% per week (-1.2% to -0.4% per week, P<0.001). Heterogeneity was not found in associations between vaccination and long covid by sociodemographic characteristics, health status, hospital admission with acute covid-19, vaccine type (adenovirus vector or mRNA), or duration from SARS-CoV-2 infection to vaccination. CONCLUSIONS The likelihood of long covid symptoms was observed to decrease after covid-19 vaccination and evidence suggested sustained improvement after a second dose, at least over the median follow-up of 67 days. Vaccination may contribute to a reduction in the population health burden of long covid, although longer follow-up is needed.
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Affiliation(s)
- Daniel Ayoubkhani
- Health Analysis and Life Events Division, Office for National Statistics, Newport, UK
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Charlotte Bermingham
- Health Analysis and Life Events Division, Office for National Statistics, Newport, UK
| | - Koen B Pouwels
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Myer Glickman
- Health Analysis and Life Events Division, Office for National Statistics, Newport, UK
| | - Vahé Nafilyan
- Health Analysis and Life Events Division, Office for National Statistics, Newport, UK
- Faculty of Public Health, Environment, and Society, London School of Hygiene and Tropical Medicine, London, UK
| | - Francesco Zaccardi
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Kamlesh Khunti
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Nisreen A Alwan
- School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
- NIHR Applied Research Collaboration (ARC) Wessex, Southampton, UK
| | - A Sarah Walker
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
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26
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Wei J, Pouwels KB, Stoesser N, Matthews PC, Diamond I, Studley R, Rourke E, Cook D, Bell JI, Newton JN, Farrar J, Howarth A, Marsden BD, Hoosdally S, Jones EY, Stuart DI, Crook DW, Peto TEA, Walker AS, Eyre DW. Antibody responses and correlates of protection in the general population after two doses of the ChAdOx1 or BNT162b2 vaccines. Nat Med 2022; 28:1072-1082. [PMID: 35165453 PMCID: PMC9117148 DOI: 10.1038/s41591-022-01721-6] [Citation(s) in RCA: 109] [Impact Index Per Article: 54.5] [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: 11/14/2021] [Accepted: 01/27/2022] [Indexed: 12/25/2022]
Abstract
Antibody responses are an important part of immunity after Coronavirus Disease 2019 (COVID-19) vaccination. However, antibody trajectories and the associated duration of protection after a second vaccine dose remain unclear. In this study, we investigated anti-spike IgG antibody responses and correlates of protection after second doses of ChAdOx1 or BNT162b2 vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the United Kingdom general population. In 222,493 individuals, we found significant boosting of anti-spike IgG by the second doses of both vaccines in all ages and using different dosing intervals, including the 3-week interval for BNT162b2. After second vaccination, BNT162b2 generated higher peak levels than ChAdOX1. Older individuals and males had lower peak levels with BNT162b2 but not ChAdOx1, whereas declines were similar across ages and sexes with ChAdOX1 or BNT162b2. Prior infection significantly increased antibody peak level and half-life with both vaccines. Anti-spike IgG levels were associated with protection from infection after vaccination and, to an even greater degree, after prior infection. At least 67% protection against infection was estimated to last for 2-3 months after two ChAdOx1 doses, for 5-8 months after two BNT162b2 doses in those without prior infection and for 1-2 years for those unvaccinated after natural infection. A third booster dose might be needed, prioritized to ChAdOx1 recipients and those more clinically vulnerable.
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Affiliation(s)
- Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Koen B Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | | | | | | | | | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- Health Improvement Directorate, Public Health England, London, UK
| | | | - Alison Howarth
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, University College London, London, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.
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27
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Lumley SF, Rodger G, Constantinides B, Sanderson N, Chau KK, Street TL, O’Donnell D, Howarth A, Hatch SB, Marsden BD, Cox S, James T, Warren F, Peck LJ, Ritter TG, de Toledo Z, Warren L, Axten D, Cornall RJ, Jones EY, Stuart DI, Screaton G, Ebner D, Hoosdally S, Chand M, Crook DW, O’Donnell AM, Conlon CP, Pouwels KB, Walker AS, Peto TEA, Hopkins S, Walker TM, Stoesser NE, Matthews PC, Jeffery K, Eyre DW. An Observational Cohort Study on the Incidence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection and B.1.1.7 Variant Infection in Healthcare Workers by Antibody and Vaccination Status. Clin Infect Dis 2022; 74:1208-1219. [PMID: 34216472 PMCID: PMC8994591 DOI: 10.1093/cid/ciab608] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [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: 03/09/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Natural and vaccine-induced immunity will play a key role in controlling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. SARS-CoV-2 variants have the potential to evade natural and vaccine-induced immunity. METHODS In a longitudinal cohort study of healthcare workers (HCWs) in Oxfordshire, United Kingdom, we investigated the protection from symptomatic and asymptomatic polymerase chain reaction (PCR)-confirmed SARS-CoV-2 infection conferred by vaccination (Pfizer-BioNTech BNT162b2, Oxford-AstraZeneca ChAdOx1 nCOV-19) and prior infection (determined using anti-spike antibody status), using Poisson regression adjusted for age, sex, temporal changes in incidence and role. We estimated protection conferred after 1 versus 2 vaccinations and from infections with the B.1.1.7 variant identified using whole genome sequencing. RESULTS In total, 13 109 HCWs participated; 8285 received the Pfizer-BioNTech vaccine (1407 two doses), and 2738 the Oxford-AstraZeneca vaccine (49 two doses). Compared to unvaccinated seronegative HCWs, natural immunity and 2 vaccination doses provided similar protection against symptomatic infection: no HCW vaccinated twice had symptomatic infection, and incidence was 98% lower in seropositive HCWs (adjusted incidence rate ratio 0.02 [95% confidence interval {CI} < .01-.18]). Two vaccine doses or seropositivity reduced the incidence of any PCR-positive result with or without symptoms by 90% (0.10 [95% CI .02-.38]) and 85% (0.15 [95% CI .08-.26]), respectively. Single-dose vaccination reduced the incidence of symptomatic infection by 67% (0.33 [95% CI .21-.52]) and any PCR-positive result by 64% (0.36 [95% CI .26-.50]). There was no evidence of differences in immunity induced by natural infection and vaccination for infections with S-gene target failure and B.1.1.7. CONCLUSIONS Natural infection resulting in detectable anti-spike antibodies and 2 vaccine doses both provide robust protection against SARS-CoV-2 infection, including against the B.1.1.7 variant.
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Affiliation(s)
- Sheila F Lumley
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Gillian Rodger
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Bede Constantinides
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Nicholas Sanderson
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
| | - Kevin K Chau
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Teresa L Street
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
| | - Denise O’Donnell
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Alison Howarth
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Stephanie B Hatch
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Kennedy Institute of Rheumatology Research, University of Oxford, United Kingdom
| | - Stuart Cox
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Tim James
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Fiona Warren
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Liam J Peck
- Medical School, University of Oxford, Oxford, United Kingdom
| | - Thomas G Ritter
- Medical School, University of Oxford, Oxford, United Kingdom
| | - Zoe de Toledo
- Medical School, University of Oxford, Oxford, United Kingdom
| | - Laura Warren
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - David Axten
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Richard J Cornall
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Gavin Screaton
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Daniel Ebner
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Target Discovery Institute, University of Oxford, Oxford, United Kingdom
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Meera Chand
- National Infection Service, Public Health England Colindale, United Kingdom
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Anne-Marie O’Donnell
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | | | - Koen B Pouwels
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Susan Hopkins
- National Infection Service, Public Health England Colindale, United Kingdom
| | - Timothy M Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Nicole E Stoesser
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Philippa C Matthews
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Katie Jeffery
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - David W Eyre
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Correspondence: D. Eyre, Microbiology Department, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK ()
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Abstract
BACKGROUND Before the emergence of the B.1.617.2 (delta) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), vaccination reduced transmission of SARS-CoV-2 from vaccinated persons who became infected, potentially by reducing viral loads. Although vaccination still lowers the risk of infection, similar viral loads in vaccinated and unvaccinated persons who are infected with the delta variant call into question the degree to which vaccination prevents transmission. METHODS We used contact-testing data from England to perform a retrospective observational cohort study involving adult contacts of SARS-CoV-2-infected adult index patients. We used multivariable Poisson regression to investigate associations between transmission and the vaccination status of index patients and contacts and to determine how these associations varied with the B.1.1.7 (alpha) and delta variants and time since the second vaccination. RESULTS Among 146,243 tested contacts of 108,498 index patients, 54,667 (37%) had positive SARS-CoV-2 polymerase-chain-reaction (PCR) tests. In index patients who became infected with the alpha variant, two vaccinations with either BNT162b2 or ChAdOx1 nCoV-19 (also known as AZD1222), as compared with no vaccination, were independently associated with reduced PCR positivity in contacts (adjusted rate ratio with BNT162b2, 0.32; 95% confidence interval [CI], 0.21 to 0.48; and with ChAdOx1 nCoV-19, 0.48; 95% CI, 0.30 to 0.78). Vaccine-associated reductions in transmission of the delta variant were smaller than those with the alpha variant, and reductions in transmission of the delta variant after two BNT162b2 vaccinations were greater (adjusted rate ratio for the comparison with no vaccination, 0.50; 95% CI, 0.39 to 0.65) than after two ChAdOx1 nCoV-19 vaccinations (adjusted rate ratio, 0.76; 95% CI, 0.70 to 0.82). Variation in cycle-threshold (Ct) values (indicative of viral load) in index patients explained 7 to 23% of vaccine-associated reductions in transmission of the two variants. The reductions in transmission of the delta variant declined over time after the second vaccination, reaching levels that were similar to those in unvaccinated persons by 12 weeks in index patients who had received ChAdOx1 nCoV-19 and attenuating substantially in those who had received BNT162b2. Protection in contacts also declined in the 3-month period after the second vaccination. CONCLUSIONS Vaccination was associated with a smaller reduction in transmission of the delta variant than of the alpha variant, and the effects of vaccination decreased over time. PCR Ct values at diagnosis of the index patient only partially explained decreased transmission. (Funded by the U.K. Government Department of Health and Social Care and others.).
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Affiliation(s)
- David W Eyre
- From the Big Data Institute (D.W.E.) and the Health Economics Research Centre (K.B.P.), the Nuffield Department of Population Health, National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (D.W.E., K.B.P., A.S.W., T.E.A.P.), and the Nuffield Department of Medicine (A.S.W., T.E.A.P.), University of Oxford, Oxford, and the Department of Health and Social Care, National Health Service Test and Trace (D.T., M.P., T.F.), Deloitte MCS (D.C.), and William Harvey Research Institute, Queen Mary University of London (T.F.), London - all in the United Kingdom
| | - Donald Taylor
- From the Big Data Institute (D.W.E.) and the Health Economics Research Centre (K.B.P.), the Nuffield Department of Population Health, National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (D.W.E., K.B.P., A.S.W., T.E.A.P.), and the Nuffield Department of Medicine (A.S.W., T.E.A.P.), University of Oxford, Oxford, and the Department of Health and Social Care, National Health Service Test and Trace (D.T., M.P., T.F.), Deloitte MCS (D.C.), and William Harvey Research Institute, Queen Mary University of London (T.F.), London - all in the United Kingdom
| | - Mark Purver
- From the Big Data Institute (D.W.E.) and the Health Economics Research Centre (K.B.P.), the Nuffield Department of Population Health, National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (D.W.E., K.B.P., A.S.W., T.E.A.P.), and the Nuffield Department of Medicine (A.S.W., T.E.A.P.), University of Oxford, Oxford, and the Department of Health and Social Care, National Health Service Test and Trace (D.T., M.P., T.F.), Deloitte MCS (D.C.), and William Harvey Research Institute, Queen Mary University of London (T.F.), London - all in the United Kingdom
| | - David Chapman
- From the Big Data Institute (D.W.E.) and the Health Economics Research Centre (K.B.P.), the Nuffield Department of Population Health, National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (D.W.E., K.B.P., A.S.W., T.E.A.P.), and the Nuffield Department of Medicine (A.S.W., T.E.A.P.), University of Oxford, Oxford, and the Department of Health and Social Care, National Health Service Test and Trace (D.T., M.P., T.F.), Deloitte MCS (D.C.), and William Harvey Research Institute, Queen Mary University of London (T.F.), London - all in the United Kingdom
| | - Tom Fowler
- From the Big Data Institute (D.W.E.) and the Health Economics Research Centre (K.B.P.), the Nuffield Department of Population Health, National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (D.W.E., K.B.P., A.S.W., T.E.A.P.), and the Nuffield Department of Medicine (A.S.W., T.E.A.P.), University of Oxford, Oxford, and the Department of Health and Social Care, National Health Service Test and Trace (D.T., M.P., T.F.), Deloitte MCS (D.C.), and William Harvey Research Institute, Queen Mary University of London (T.F.), London - all in the United Kingdom
| | - Koen B Pouwels
- From the Big Data Institute (D.W.E.) and the Health Economics Research Centre (K.B.P.), the Nuffield Department of Population Health, National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (D.W.E., K.B.P., A.S.W., T.E.A.P.), and the Nuffield Department of Medicine (A.S.W., T.E.A.P.), University of Oxford, Oxford, and the Department of Health and Social Care, National Health Service Test and Trace (D.T., M.P., T.F.), Deloitte MCS (D.C.), and William Harvey Research Institute, Queen Mary University of London (T.F.), London - all in the United Kingdom
| | - A Sarah Walker
- From the Big Data Institute (D.W.E.) and the Health Economics Research Centre (K.B.P.), the Nuffield Department of Population Health, National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (D.W.E., K.B.P., A.S.W., T.E.A.P.), and the Nuffield Department of Medicine (A.S.W., T.E.A.P.), University of Oxford, Oxford, and the Department of Health and Social Care, National Health Service Test and Trace (D.T., M.P., T.F.), Deloitte MCS (D.C.), and William Harvey Research Institute, Queen Mary University of London (T.F.), London - all in the United Kingdom
| | - Tim E A Peto
- From the Big Data Institute (D.W.E.) and the Health Economics Research Centre (K.B.P.), the Nuffield Department of Population Health, National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (D.W.E., K.B.P., A.S.W., T.E.A.P.), and the Nuffield Department of Medicine (A.S.W., T.E.A.P.), University of Oxford, Oxford, and the Department of Health and Social Care, National Health Service Test and Trace (D.T., M.P., T.F.), Deloitte MCS (D.C.), and William Harvey Research Institute, Queen Mary University of London (T.F.), London - all in the United Kingdom
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29
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Pritchard E, Jones J, Vihta KD, Stoesser N, Matthews PPC, Eyre DW, House T, Bell JI, Newton PJN, Farrar J, Crook PD, Hopkins S, Cook D, Rourke E, Studley R, Diamond PI, Peto PT, Pouwels KB, Walker PAS. Monitoring populations at increased risk for SARS-CoV-2 infection in the community using population-level demographic and behavioural surveillance. Lancet Reg Health Eur 2022; 13:100282. [PMID: 34927119 PMCID: PMC8665900 DOI: 10.1016/j.lanepe.2021.100282] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
BACKGROUND The COVID-19 pandemic is rapidly evolving, with emerging variants and fluctuating control policies. Real-time population screening and identification of groups in whom positivity is highest could help monitor spread and inform public health messaging and strategy. METHODS To develop a real-time screening process, we included results from nose and throat swabs and questionnaires taken 19 July 2020-17 July 2021 in the UK's national COVID-19 Infection Survey. Fortnightly, associations between SARS-CoV-2 positivity and 60 demographic and behavioural characteristics were estimated using logistic regression models adjusted for potential confounders, considering multiple testing, collinearity, and reverse causality. FINDINGS Of 4,091,537 RT-PCR results from 482,677 individuals, 29,903 (0·73%) were positive. As positivity rose September-November 2020, rates were independently higher in younger ages, and those living in Northern England, major urban conurbations, more deprived areas, and larger households. Rates were also higher in those returning from abroad, and working in healthcare or outside of home. When positivity peaked December 2020-January 2021 (Alpha), high positivity shifted to southern geographical regions. With national vaccine roll-out from December 2020, positivity reduced in vaccinated individuals. Associations attenuated as rates decreased between February-May 2021. Rising positivity rates in June-July 2021 (Delta) were independently higher in younger, male, and unvaccinated groups. Few factors were consistently associated with positivity. 25/45 (56%) confirmed associations would have been detected later using 28-day rather than 14-day periods. INTERPRETATION Population-level demographic and behavioural surveillance can be a valuable tool in identifying the varying characteristics driving current SARS-CoV-2 positivity, allowing monitoring to inform public health policy. FUNDING Department of Health and Social Care (UK), Welsh Government, Department of Health (on behalf of the Northern Ireland Government), Scottish Government, National Institute for Health Research.
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Affiliation(s)
- Emma Pritchard
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Joel Jones
- Office for National Statistics, Newport, UK
| | - Karina-Doris Vihta
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Department of Engineering, University of Oxford, Oxford, UK
| | - Nicole Stoesser
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Prof Philippa C. Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - David W. Eyre
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Department of Engineering, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK
- IBM Research, Hartree Centre, Sci-Tech Daresbury, UK
| | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | | | | | - Prof Derrick Crook
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Susan Hopkins
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Healthcare-Associated Infection and Antimicrobial Resistance Division, Public Health England, London, UK
- National Institute for Health Research, Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | | | | | | | | | - Prof Tim Peto
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Koen B. Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Prof A. Sarah Walker
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - COVID-19 Infection Survey Team
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Office for National Statistics, Newport, UK
- Department of Engineering, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Mathematics, University of Manchester, Manchester, UK
- IBM Research, Hartree Centre, Sci-Tech Daresbury, UK
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
- Health Improvement Directorate, Public Health England, London, UK
- Wellcome Trust, London, UK
- Healthcare-Associated Infection and Antimicrobial Resistance Division, Public Health England, London, UK
- National Institute for Health Research, Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
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30
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Nicholson G, Lehmann B, Padellini T, Pouwels KB, Jersakova R, Lomax J, King RE, Mallon AM, Diggle PJ, Richardson S, Blangiardo M, Holmes C. Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework. Nat Microbiol 2022; 7:97-107. [PMID: 34972825 PMCID: PMC8727294 DOI: 10.1038/s41564-021-01029-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 11/18/2021] [Indexed: 12/23/2022]
Abstract
Global and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, Rt, which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and Rt. We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of Rt are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and Rt that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease.
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Affiliation(s)
- George Nicholson
- University of Oxford, Oxford, UK.
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK.
| | - Brieuc Lehmann
- University of Oxford, Oxford, UK.
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK.
| | - Tullia Padellini
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, University of Oxford, Oxford, UK
| | - Radka Jersakova
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK
- The Alan Turing Institute, London, UK
| | - James Lomax
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK
- The Alan Turing Institute, London, UK
| | - Ruairidh E King
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK
- MRC Harwell Institute, Harwell, UK
| | - Ann-Marie Mallon
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK
- MRC Harwell Institute, Harwell, UK
| | - Peter J Diggle
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, UK
| | - Sylvia Richardson
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Marta Blangiardo
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Chris Holmes
- University of Oxford, Oxford, UK.
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK.
- The Alan Turing Institute, London, UK.
- MRC Harwell Institute, Harwell, UK.
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31
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Walker AS, Vihta KD, Gethings O, Pritchard E, Jones J, House T, Bell I, Bell JI, Newton JN, Farrar J, Diamond I, Studley R, Rourke E, Hay J, Hopkins S, Crook D, Peto T, Matthews PC, Eyre DW, Stoesser N, Pouwels KB. Tracking the Emergence of SARS-CoV-2 Alpha Variant in the United Kingdom. N Engl J Med 2021; 385:2582-2585. [PMID: 34879193 PMCID: PMC8693687 DOI: 10.1056/nejmc2103227] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
| | | | - Owen Gethings
- Office for National Statistics, Newport, United Kingdom
| | | | - Joel Jones
- Office for National Statistics, Newport, United Kingdom
| | - Thomas House
- University of Manchester, Manchester, United Kingdom
| | - Iain Bell
- Office for National Statistics, Newport, United Kingdom
| | - John I Bell
- University of Oxford, Oxford, United Kingdom
| | - John N Newton
- Office for Health Improvement and Disparities, London, United Kingdom
| | | | - Ian Diamond
- Office for National Statistics, Newport, United Kingdom
| | - Ruth Studley
- Office for National Statistics, Newport, United Kingdom
| | - Emma Rourke
- Office for National Statistics, Newport, United Kingdom
| | - Jodie Hay
- University of Glasgow, Glasgow, United Kingdom
| | | | | | - Tim Peto
- University of Oxford, Oxford, United Kingdom
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32
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Pouwels KB, Pritchard E, Matthews PC, Stoesser N, Eyre DW, Vihta KD, House T, Hay J, Bell JI, Newton JN, Farrar J, Crook D, Cook D, Rourke E, Studley R, Peto TEA, Diamond I, Walker AS. Effect of Delta variant on viral burden and vaccine effectiveness against new SARS-CoV-2 infections in the UK. Nat Med 2021; 27:2127-2135. [PMID: 34650248 PMCID: PMC8674129 DOI: 10.1038/s41591-021-01548-7] [Citation(s) in RCA: 319] [Impact Index Per Article: 106.3] [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: 08/18/2021] [Accepted: 09/21/2021] [Indexed: 12/13/2022]
Abstract
The effectiveness of the BNT162b2 and ChAdOx1 vaccines against new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections requires continuous re-evaluation, given the increasingly dominant B.1.617.2 (Delta) variant. In this study, we investigated the effectiveness of these vaccines in a large, community-based survey of randomly selected households across the United Kingdom. We found that the effectiveness of BNT162b2 and ChAdOx1 against infections (new polymerase chain reaction (PCR)-positive cases) with symptoms or high viral burden is reduced with the B.1.617.2 variant (absolute difference of 10-13% for BNT162b2 and 16% for ChAdOx1) compared to the B.1.1.7 (Alpha) variant. The effectiveness of two doses remains at least as great as protection afforded by prior natural infection. The dynamics of immunity after second doses differed significantly between BNT162b2 and ChAdOx1, with greater initial effectiveness against new PCR-positive cases but faster declines in protection against high viral burden and symptomatic infection with BNT162b2. There was no evidence that effectiveness varied by dosing interval, but protection was higher in vaccinated individuals after a prior infection and in younger adults. With B.1.617.2, infections occurring after two vaccinations had similar peak viral burden as those in unvaccinated individuals. SARS-CoV-2 vaccination still reduces new infections, but effectiveness and attenuation of peak viral burden are reduced with B.1.617.2.
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Affiliation(s)
- Koen B Pouwels
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Emma Pritchard
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Nicole Stoesser
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - David W Eyre
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Karina-Doris Vihta
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Department of Engineering, University of Oxford, Oxford, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK
- IBM Research, Hartree Centre, Sci-Tech Daresbury, UK
| | - Jodie Hay
- Glasgow Lighthouse Laboratory, Glasgow, UK
- University of Glasgow, Glasgow, UK
| | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- Health Improvement Directorate, Public Health England, London, UK
| | | | - Derrick Crook
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | | | | | | | - Tim E A Peto
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | | | - A Sarah Walker
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- MRC Clinical Trials Unit at UCL, University College London, London, UK
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33
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Wei J, Matthews PC, Stoesser N, Maddox T, Lorenzi L, Studley R, Bell JI, Newton JN, Farrar J, Diamond I, Rourke E, Howarth A, Marsden BD, Hoosdally S, Jones EY, Stuart DI, Crook DW, Peto TEA, Pouwels KB, Walker AS, Eyre DW. Anti-spike antibody response to natural SARS-CoV-2 infection in the general population. Nat Commun 2021; 12:6250. [PMID: 34716320 PMCID: PMC8556331 DOI: 10.1038/s41467-021-26479-2] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [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: 07/02/2021] [Accepted: 10/06/2021] [Indexed: 01/08/2023] Open
Abstract
Understanding the trajectory, duration, and determinants of antibody responses after SARS-CoV-2 infection can inform subsequent protection and risk of reinfection, however large-scale representative studies are limited. Here we estimated antibody response after SARS-CoV-2 infection in the general population using representative data from 7,256 United Kingdom COVID-19 infection survey participants who had positive swab SARS-CoV-2 PCR tests from 26-April-2020 to 14-June-2021. A latent class model classified 24% of participants as 'non-responders' not developing anti-spike antibodies, who were older, had higher SARS-CoV-2 cycle threshold values during infection (i.e. lower viral burden), and less frequently reported any symptoms. Among those who seroconverted, using Bayesian linear mixed models, the estimated anti-spike IgG peak level was 7.3-fold higher than the level previously associated with 50% protection against reinfection, with higher peak levels in older participants and those of non-white ethnicity. The estimated anti-spike IgG half-life was 184 days, being longer in females and those of white ethnicity. We estimated antibody levels associated with protection against reinfection likely last 1.5-2 years on average, with levels associated with protection from severe infection present for several years. These estimates could inform planning for vaccination booster strategies.
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Affiliation(s)
- Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | | | | | | | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- Health Improvement Directorate, Public Health England, London, UK
| | | | | | | | - Alison Howarth
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Koen B Pouwels
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
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Wu T, Pouwels KB, Welbourn R, Wordsworth S, Kent S, Wong CKH. Does bariatric surgery reduce future hospital costs? A propensity score-matched analysis using UK Biobank Study data. Int J Obes (Lond) 2021; 45:2205-2213. [PMID: 34211116 DOI: 10.1038/s41366-021-00887-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 05/20/2021] [Accepted: 06/22/2021] [Indexed: 02/06/2023]
Abstract
OBJECTIVES To estimate the hospital costs among persons with obesity undergoing bariatric surgery compared with those without bariatric surgery. METHODS We analysed the UK Biobank Cohort study linked to Hospital Episode Statistics, for all adults with obesity undergoing bariatric surgery at National Health Service hospitals in England, Scotland, or Wales from 2006 to 2017. Surgery patients were matched with controls who did not have bariatric surgery using propensity scores approach with a ratio of up to 1-to-5 by year. Inverse probability of censoring weighting was used to correct for potential informative censoring. Annual and cumulative hospital costs were assessed for the surgery and control groups. RESULTS We identified 348 surgical patients (198 gastric bypass, 73 sleeve gastrectomy, 77 gastric banding) during the study period. In total, 324 surgical patients and 1506 matched control participants were included after propensity score matching. Mean 5-year cumulative hospital costs were €11,659 for 348 surgical patients. Compared with controls, surgical patients (n = 324) had significantly higher inpatient expenditures in the surgery year (€7289 vs. €2635, P < 0.001), but lower costs in the subsequent 4 years. The 5-year cumulative costs were €11,176 for surgical patients and €8759 for controls (P = 0.001). CONCLUSIONS Bariatric surgery significantly increased the inpatient costs in the surgery year, but was associated with decreased costs in the subsequent 4 years. However, any cost savings made up to 4 years were not enough to compensate for the initial surgical expenditure.
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Affiliation(s)
- Tingting Wu
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Richard Welbourn
- Department of Bariatric Surgery, Musgrove Park Hospital, Taunton, United Kingdom
| | - Sarah Wordsworth
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.,Oxford National Institute for Health Research Biomedical Research Centre, Oxford, United Kingdom
| | - Seamus Kent
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.,Science Policy and Research, National Institute for Health and Care Excellence, London, United Kingdom
| | - Carlos K H Wong
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China. .,Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China.
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35
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Eyre DW, Lumley SF, Wei J, Cox S, James T, Justice A, Jesuthasan G, O'Donnell D, Howarth A, Hatch SB, Marsden BD, Jones EY, Stuart DI, Ebner D, Hoosdally S, Crook DW, Peto TEA, Walker TM, Stoesser NE, Matthews PC, Pouwels KB, Walker AS, Jeffery K. Quantitative SARS-CoV-2 anti-spike responses to Pfizer-BioNTech and Oxford-AstraZeneca vaccines by previous infection status. Clin Microbiol Infect 2021; 27:1516.e7-1516.e14. [PMID: 34111577 PMCID: PMC8180449 DOI: 10.1016/j.cmi.2021.05.041] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [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: 03/21/2021] [Revised: 05/23/2021] [Accepted: 05/25/2021] [Indexed: 10/31/2022]
Abstract
OBJECTIVES We investigated determinants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) anti-spike IgG responses in healthcare workers (HCWs) following one or two doses of Pfizer-BioNTech or Oxford-AstraZeneca vaccines. METHODS HCWs participating in regular SARS-CoV-2 PCR and antibody testing were invited for serological testing prior to first and second vaccination, and 4 weeks post-vaccination if receiving a 12-week dosing interval. Quantitative post-vaccination anti-spike antibody responses were measured using the Abbott SARS-CoV-2 IgG II Quant assay (detection threshold: ≥50 AU/mL). We used multivariable logistic regression to identify predictors of seropositivity and generalized additive models to track antibody responses over time. RESULTS 3570/3610 HCWs (98.9%) were seropositive >14 days post first vaccination and prior to second vaccination: 2706/2720 (99.5%) were seropositive after the Pfizer-BioNTech and 864/890 (97.1%) following the Oxford-AstraZeneca vaccines. Previously infected and younger HCWs were more likely to test seropositive post first vaccination, with no evidence of differences by sex or ethnicity. All 470 HCWs tested >14 days after the second vaccination were seropositive. Quantitative antibody responses were higher after previous infection: median (IQR) >21 days post first Pfizer-BioNTech 14 604 (7644-22 291) AU/mL versus 1028 (564-1985) AU/mL without prior infection (p < 0.001). Oxford-AstraZeneca vaccine recipients had lower readings post first dose than Pfizer-BioNTech recipients, with and without previous infection, 10 095 (5354-17 096) and 435 (203-962) AU/mL respectively (both p < 0.001 versus Pfizer-BioNTech). Antibody responses >21 days post second Pfizer vaccination in those not previously infected, 10 058 (6408-15 582) AU/mL, were similar to those after prior infection followed by one vaccine dose. CONCLUSIONS SARS-CoV-2 vaccination leads to detectable anti-spike antibodies in nearly all adult HCWs. Whether differences in response impact vaccine efficacy needs further study.
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Affiliation(s)
- David W Eyre
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Nuffield Department of Population Health, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK.
| | - Sheila F Lumley
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Stuart Cox
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Tim James
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Anita Justice
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | - Denise O'Donnell
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Alison Howarth
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, UK; Kennedy Institute of Rheumatology Research, University of Oxford, UK
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Daniel Ebner
- Nuffield Department of Medicine, University of Oxford, Oxford, UK; Target Discovery Institute, University of Oxford, Oxford, UK
| | - Sarah Hoosdally
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Derrick W Crook
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tim E A Peto
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Timothy M Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK; Oxford University Clinical Research Unit, Ho Chi Minh City, Viet nam
| | - Nicole E Stoesser
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Koen B Pouwels
- Nuffield Department of Population Health, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK
| | - A Sarah Walker
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Katie Jeffery
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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Allerton F, Pouwels KB, Bazelle J, Caddy S, Cauvin A, De Risio L, Swann J, Warland J, Kent A. Prospective trial of different antimicrobial treatment durations for presumptive canine urinary tract infections. BMC Vet Res 2021; 17:299. [PMID: 34488771 PMCID: PMC8422737 DOI: 10.1186/s12917-021-02974-y] [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: 02/02/2021] [Accepted: 07/20/2021] [Indexed: 12/03/2022] Open
Abstract
Background Avoidance of unnecessary antimicrobial administration is a key tenet of antimicrobial stewardship; knowing the optimal duration of therapy obviates over-treatment. However, little research has been performed to establish course lengths for common canine infections. In clinical practice, antimicrobial therapy is frequently prescribed in dogs presenting lower urinary tract signs (haematuria, pollakiuria and dysuria/stranguria). The proposed length of treatment in International Consensus guidelines has decreased with each iteration, but these recommendations remain arbitrary and largely extrapolated from experience in people. Methods The objective of this prospective, multi-centre study is to find the shortest course duration that is non-inferior to the standard duration of 7 days of amoxicillin/clavulanate in terms of clinical outcomes for female dogs with lower urinary tract signs consistent with a urinary tract infection. An electronic data capture platform will be used by participating veterinarians working in clinical practice in the United Kingdom. Eligible dogs must be female, aged between 6 months and 10 years and have lower urinary tract signs of up to seven days’ duration. Enrolment will be offered in cases where the case clinician intends to prescribe antimicrobial therapy. Automatic pseudo-randomisation to treatment group will be based on the day of presentation (Monday-Friday); all antimicrobial courses will be completed on the Sunday after presentation generating different treatment durations. Follow-up data will be collected 1, 8 and 22–26 days after completion of the antimicrobial course to ensure effective safety netting, and to monitor short-term outcome and recurrence rates. Informed owner consent will be obtained in all cases. The study is approved by the Ethical Review Board of the University of Nottingham and has an Animal Test Certificate from the Veterinary Medicine’s Directorate. Discussion This study has been designed to mirror current standards of clinical management; conclusions should therefore, be widely applicable and guide practising veterinarians in their antimicrobial decision-making process. A duration-response curve will be created allowing determination of the optimal treatment duration for the management of female dogs with lower urinary tract signs. It is hoped that these results will contribute valuable information to improve future antimicrobial stewardship as part of a wider one-health perspective. Supplementary Information The online version contains supplementary material available at 10.1186/s12917-021-02974-y.
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Affiliation(s)
- Fergus Allerton
- Willows Veterinary Centre and Referral Service; part of Linnaeus Veterinary Limited, Highlands Road, Shirley, Solihull, UK.
| | - Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.,NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial, Oxford, UK.,Resistance at University of Oxford in partnership with Public Health England, Oxford, UK
| | - Julien Bazelle
- Davies Veterinary Specialists; part of Linnaeus Veterinary Limited, Manor Farm Business Park, Higham Gobion, Hitchin, UK
| | - Sarah Caddy
- Cambridge Institute for Therapeutic Immunology and Infectious Disease, Jeffery Cheah Biomedical Centre, Puddicomb Way, Cambridge Biomedical Campus, Cambridge, UK
| | | | - Luisa De Risio
- Linnaeus Veterinary Limited, Friars gate, Shirley, Solihull, UK
| | - James Swann
- Columbia Stem Cell Initiative, Columbia University, 650 West 168th Street, NY, 10032, New York, USA
| | - James Warland
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Puddicombe Way, Cambridge, UK
| | - Andrew Kent
- Willows Veterinary Centre and Referral Service; part of Linnaeus Veterinary Limited, Highlands Road, Shirley, Solihull, UK
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Wei J, Stoesser N, Matthews PC, Ayoubkhani D, Studley R, Bell I, Bell JI, Newton JN, Farrar J, Diamond I, Rourke E, Howarth A, Marsden BD, Hoosdally S, Jones EY, Stuart DI, Crook DW, Peto TEA, Pouwels KB, Eyre DW, Walker AS. Antibody responses to SARS-CoV-2 vaccines in 45,965 adults from the general population of the United Kingdom. Nat Microbiol 2021; 6:1140-1149. [PMID: 34290390 PMCID: PMC8294260 DOI: 10.1038/s41564-021-00947-3] [Citation(s) in RCA: 196] [Impact Index Per Article: 65.3] [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: 06/02/2021] [Accepted: 07/01/2021] [Indexed: 02/08/2023]
Abstract
We report that in a cohort of 45,965 adults, who were receiving either the ChAdOx1 or the BNT162b2 SARS-CoV-2 vaccines, in those who had no prior infection with SARS-CoV-2, seroconversion rates and quantitative antibody levels after a single dose were lower in older individuals, especially in those aged >60 years. Two vaccine doses achieved high responses across all ages. Antibody levels increased more slowly and to lower levels with a single dose of ChAdOx1 compared with a single dose of BNT162b2, but waned following a single dose of BNT162b2 in older individuals. In descriptive latent class models, we identified four responder subgroups, including a 'low responder' group that more commonly consisted of people aged >75 years, males and individuals with long-term health conditions. Given our findings, we propose that available vaccines should be prioritized for those not previously infected and that second doses should be prioritized for individuals aged >60 years. Further data are needed to better understand the extent to which quantitative antibody responses are associated with vaccine-mediated protection.
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Affiliation(s)
- Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | | | | | - Iain Bell
- Office for National Statistics, Newport, UK
| | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- Health Improvement Directorate, Public Health England, London, UK
| | | | | | | | - Alison Howarth
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Koen B Pouwels
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
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38
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Lumley SF, Wei J, O'Donnell D, Stoesser NE, Matthews PC, Howarth A, Hatch SB, Marsden BD, Cox S, James T, Peck LJ, Ritter TG, de Toledo Z, Cornall RJ, Jones EY, Stuart DI, Screaton G, Ebner D, Hoosdally S, Crook DW, Conlon CP, Pouwels KB, Walker AS, Peto TEA, Walker TM, Jeffery K, Eyre DW. The Duration, Dynamics, and Determinants of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibody Responses in Individual Healthcare Workers. Clin Infect Dis 2021; 73:e699-e709. [PMID: 33400782 DOI: 10.1101/2020.11.02.20224824] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [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: 01/04/2021] [Indexed: 05/20/2023] Open
Abstract
BACKGROUND Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG) antibody measurements can be used to estimate the proportion of a population exposed or infected and may be informative about the risk of future infection. Previous estimates of the duration of antibody responses vary. METHODS We present 6 months of data from a longitudinal seroprevalence study of 3276 UK healthcare workers (HCWs). Serial measurements of SARS-CoV-2 anti-nucleocapsid and anti-spike IgG were obtained. Interval censored survival analysis was used to investigate the duration of detectable responses. Additionally, Bayesian mixed linear models were used to investigate anti-nucleocapsid waning. RESULTS Anti-spike IgG levels remained stably detected after a positive result, for example, in 94% (95% credibility interval [CrI] 91-96%) of HCWs at 180 days. Anti-nucleocapsid IgG levels rose to a peak at 24 (95% CrI 19-31) days post first polymerase chain reaction (PCR)-positive test, before beginning to fall. Considering 452 anti-nucleocapsid seropositive HCWs over a median of 121 days from their maximum positive IgG titer, the mean estimated antibody half-life was 85 (95% CrI 81-90) days. Higher maximum observed anti-nucleocapsid titers were associated with longer estimated antibody half-lives. Increasing age, Asian ethnicity, and prior self-reported symptoms were independently associated with higher maximum anti-nucleocapsid levels and increasing age and a positive PCR test undertaken for symptoms with longer anti-nucleocapsid half-lives. CONCLUSIONS SARS-CoV-2 anti-nucleocapsid antibodies wane within months and fall faster in younger adults and those without symptoms. However, anti-spike IgG remains stably detected. Ongoing longitudinal studies are required to track the long-term duration of antibody levels and their association with immunity to SARS-CoV-2 reinfection.
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Affiliation(s)
- Sheila F Lumley
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Jia Wei
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Denise O'Donnell
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Nicole E Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Alison Howarth
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Stephanie B Hatch
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Kennedy Institute of Rheumatology Research, University of Oxford, United Kingdom
| | - Stuart Cox
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Tim James
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Liam J Peck
- Medical School, University of Oxford, Oxford, United Kingdom
| | - Thomas G Ritter
- Medical School, University of Oxford, Oxford, United Kingdom
| | - Zoe de Toledo
- Medical School, University of Oxford, Oxford, United Kingdom
| | - Richard J Cornall
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Gavin Screaton
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Daniel Ebner
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Target Discovery Institute, University of Oxford, Oxford, United Kingdom
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | | | - Koen B Pouwels
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Timothy M Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Katie Jeffery
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - David W Eyre
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
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Lumley SF, Wei J, O’Donnell D, Stoesser NE, Matthews PC, Howarth A, Hatch SB, Marsden BD, Cox S, James T, Peck LJ, Ritter TG, de Toledo Z, Cornall RJ, Jones EY, Stuart DI, Screaton G, Ebner D, Hoosdally S, Crook DW, Conlon CP, Pouwels KB, Walker AS, Peto TEA, Walker TM, Jeffery K, Eyre DW. The Duration, Dynamics, and Determinants of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibody Responses in Individual Healthcare Workers. Clin Infect Dis 2021; 73:e699-e709. [PMID: 33400782 PMCID: PMC7929225 DOI: 10.1093/cid/ciab004] [Citation(s) in RCA: 176] [Impact Index Per Article: 58.7] [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: 01/04/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG) antibody measurements can be used to estimate the proportion of a population exposed or infected and may be informative about the risk of future infection. Previous estimates of the duration of antibody responses vary. METHODS We present 6 months of data from a longitudinal seroprevalence study of 3276 UK healthcare workers (HCWs). Serial measurements of SARS-CoV-2 anti-nucleocapsid and anti-spike IgG were obtained. Interval censored survival analysis was used to investigate the duration of detectable responses. Additionally, Bayesian mixed linear models were used to investigate anti-nucleocapsid waning. RESULTS Anti-spike IgG levels remained stably detected after a positive result, for example, in 94% (95% credibility interval [CrI] 91-96%) of HCWs at 180 days. Anti-nucleocapsid IgG levels rose to a peak at 24 (95% CrI 19-31) days post first polymerase chain reaction (PCR)-positive test, before beginning to fall. Considering 452 anti-nucleocapsid seropositive HCWs over a median of 121 days from their maximum positive IgG titer, the mean estimated antibody half-life was 85 (95% CrI 81-90) days. Higher maximum observed anti-nucleocapsid titers were associated with longer estimated antibody half-lives. Increasing age, Asian ethnicity, and prior self-reported symptoms were independently associated with higher maximum anti-nucleocapsid levels and increasing age and a positive PCR test undertaken for symptoms with longer anti-nucleocapsid half-lives. CONCLUSIONS SARS-CoV-2 anti-nucleocapsid antibodies wane within months and fall faster in younger adults and those without symptoms. However, anti-spike IgG remains stably detected. Ongoing longitudinal studies are required to track the long-term duration of antibody levels and their association with immunity to SARS-CoV-2 reinfection.
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Affiliation(s)
- Sheila F Lumley
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Jia Wei
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Denise O’Donnell
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Nicole E Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Alison Howarth
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Stephanie B Hatch
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Kennedy Institute of Rheumatology Research, University of Oxford, United Kingdom
| | - Stuart Cox
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Tim James
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Liam J Peck
- Medical School, University of Oxford, Oxford, United Kingdom
| | - Thomas G Ritter
- Medical School, University of Oxford, Oxford, United Kingdom
| | - Zoe de Toledo
- Medical School, University of Oxford, Oxford, United Kingdom
| | - Richard J Cornall
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Gavin Screaton
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Daniel Ebner
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Target Discovery Institute, University of Oxford, Oxford, United Kingdom
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | | | - Koen B Pouwels
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Timothy M Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Katie Jeffery
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - David W Eyre
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
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Morrell L, Buchanan J, Roope LSJ, Pouwels KB, Butler CC, Hayhoe B, Tonkin-Crine S, McLeod M, Robotham JV, Holmes A, Walker AS, Wordsworth S. Public preferences for delayed or immediate antibiotic prescriptions in UK primary care: A choice experiment. PLoS Med 2021; 18:e1003737. [PMID: 34460825 PMCID: PMC8439451 DOI: 10.1371/journal.pmed.1003737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Revised: 09/14/2021] [Accepted: 07/15/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Delayed (or "backup") antibiotic prescription, where the patient is given a prescription but advised to delay initiating antibiotics, has been shown to be effective in reducing antibiotic use in primary care. However, this strategy is not widely used in the United Kingdom. This study aimed to identify factors influencing preferences among the UK public for delayed prescription, and understand their relative importance, to help increase appropriate use of this prescribing option. METHODS AND FINDINGS We conducted an online choice experiment in 2 UK general population samples: adults and parents of children under 18 years. Respondents were presented with 12 scenarios in which they, or their child, might need antibiotics for a respiratory tract infection (RTI) and asked to choose either an immediate or a delayed prescription. Scenarios were described by 7 attributes. Data were collected between November 2018 and February 2019. Respondent preferences were modelled using mixed-effects logistic regression. The survey was completed by 802 adults and 801 parents (75% of those who opened the survey). The samples reflected the UK population in age, sex, ethnicity, and country of residence. The most important determinant of respondent choice was symptom severity, especially for cough-related symptoms. In the adult sample, the probability of choosing delayed prescription was 0.53 (95% confidence interval (CI) 0.50 to 0.56, p < 0.001) for a chesty cough and runny nose compared to 0.30 (0.28 to 0.33, p < 0.001) for a chesty cough with fever, 0.47 (0.44 to 0.50, p < 0.001) for sore throat with swollen glands, and 0.37 (0.34 to 0.39, p < 0.001) for sore throat, swollen glands, and fever. Respondents were less likely to choose delayed prescription with increasing duration of illness (odds ratio (OR) 0.94 (0.92 to 0.96, p < 0.001)). Probabilities of choosing delayed prescription were similar for parents considering treatment for a child (44% of choices versus 42% for adults, p = 0.04). However, parents differed from the adult sample in showing a more marked reduction in choice of the delayed prescription with increasing duration of illness (OR 0.83 (0.80 to 0.87) versus 0.94 (0.92 to 0.96) for adults, p for heterogeneity p < 0.001) and a smaller effect of disruption of usual activities (OR 0.96 (0.95 to 0.97) versus 0.93 (0.92 to 0.94) for adults, p for heterogeneity p < 0.001). Females were more likely to choose a delayed prescription than males for minor symptoms, particularly minor cough (probability 0.62 (0.58 to 0.66, p < 0.001) for females and 0.45 (0.41 to 0.48, p < 0.001) for males). Older people, those with a good understanding of antibiotics, and those who had not used antibiotics recently showed similar patterns of preferences. Study limitations include its hypothetical nature, which may not reflect real-life behaviour; the absence of a "no prescription" option; and the possibility that study respondents may not represent the views of population groups who are typically underrepresented in online surveys. CONCLUSIONS This study found that delayed prescription appears to be an acceptable approach to reducing antibiotic consumption. Certain groups appear to be more amenable to delayed prescription, suggesting particular opportunities for increased use of this strategy. Prescribing choices for sore throat may need additional explanation to ensure patient acceptance, and parents in particular may benefit from reassurance about the usual duration of these illnesses.
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Affiliation(s)
- Liz Morrell
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- * E-mail:
| | - James Buchanan
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
- NIHR Biomedical Research Centre Oxford, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Laurence S. J. Roope
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
- NIHR Biomedical Research Centre Oxford, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Koen B. Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
| | - Christopher C. Butler
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Primary Care Sciences, University of Oxford, Oxford, United Kingdom
| | - Benedict Hayhoe
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Sarah Tonkin-Crine
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Primary Care Sciences, University of Oxford, Oxford, United Kingdom
| | - Monsey McLeod
- NIHR Health Protection Research Unit in Healthcare-Associated Infections and Antimicrobial Resistance, Imperial College London, London, United Kingdom
- Centre for Medication Safety and Service Quality, Pharmacy Department, Imperial College Healthcare NHS Trust, London, United Kingdom
- NIHR Imperial Patient Safety Translational Research Centre, Imperial College London, London, United Kingdom
| | - Julie V. Robotham
- Modelling and Economics Unit, National Infection Service, Public Health England, London, United Kingdom
| | - Alison Holmes
- NIHR Health Protection Research Unit in Healthcare-Associated Infections and Antimicrobial Resistance, Imperial College London, London, United Kingdom
| | - A. Sarah Walker
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
- NIHR Biomedical Research Centre Oxford, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Sarah Wordsworth
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
- NIHR Biomedical Research Centre Oxford, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
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Walker AS, Pritchard E, House T, Robotham JV, Birrell PJ, Bell I, Bell JI, Newton JN, Farrar J, Diamond I, Studley R, Hay J, Vihta KD, Peto TEA, Stoesser N, Matthews PC, Eyre DW, Pouwels KB. Ct threshold values, a proxy for viral load in community SARS-CoV-2 cases, demonstrate wide variation across populations and over time. eLife 2021; 10:e64683. [PMID: 34250907 PMCID: PMC8282332 DOI: 10.7554/elife.64683] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 07/06/2021] [Indexed: 12/22/2022] Open
Abstract
Background Information on SARS-CoV-2 in representative community surveillance is limited, particularly cycle threshold (Ct) values (a proxy for viral load). Methods We included all positive nose and throat swabs 26 April 2020 to 13 March 2021 from the UK's national COVID-19 Infection Survey, tested by RT-PCR for the N, S, and ORF1ab genes. We investigated predictors of median Ct value using quantile regression. Results Of 3,312,159 nose and throat swabs, 27,902 (0.83%) were RT-PCR-positive, 10,317 (37%), 11,012 (40%), and 6550 (23%) for 3, 2, or 1 of the N, S, and ORF1ab genes, respectively, with median Ct = 29.2 (~215 copies/ml; IQR Ct = 21.9-32.8, 14-56,400 copies/ml). Independent predictors of lower Cts (i.e. higher viral load) included self-reported symptoms and more genes detected, with at most small effects of sex, ethnicity, and age. Single-gene positives almost invariably had Ct > 30, but Cts varied widely in triple-gene positives, including without symptoms. Population-level Cts changed over time, with declining Ct preceding increasing SARS-CoV-2 positivity. Of 6189 participants with IgG S-antibody tests post-first RT-PCR-positive, 4808 (78%) were ever antibody-positive; Cts were significantly higher in those remaining antibody negative. Conclusions Marked variation in community SARS-CoV-2 Ct values suggests that they could be a useful epidemiological early-warning indicator. Funding Department of Health and Social Care, National Institutes of Health Research, Huo Family Foundation, Medical Research Council UK; Wellcome Trust.
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Affiliation(s)
- A Sarah Walker
- Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of OxfordOxfordUnited Kingdom
- MRC Clinical Trials Unit at UCL, UCLLondonUnited Kingdom
| | - Emma Pritchard
- Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of OxfordOxfordUnited Kingdom
| | - Thomas House
- Department of Mathematics, University of ManchesterManchesterUnited Kingdom
- IBM Research, Hartree CentreSci-Tech DaresburyUnited Kingdom
| | - Julie V Robotham
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of OxfordOxfordUnited Kingdom
- National Infection Service, Public Health EnglandLondonUnited Kingdom
| | - Paul J Birrell
- National Infection Service, Public Health EnglandLondonUnited Kingdom
- MRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public HealthCambridgeUnited Kingdom
| | - Iain Bell
- Office for National StatisticsNewportUnited Kingdom
| | - John I Bell
- Office of the Regius Professor of Medicine, University of OxfordOxfordUnited Kingdom
| | - John N Newton
- Health Improvement Directorate, Public Health EnglandLondonUnited Kingdom
| | | | - Ian Diamond
- Office for National StatisticsNewportUnited Kingdom
| | - Ruth Studley
- Office for National StatisticsNewportUnited Kingdom
| | - Jodie Hay
- University of GlasgowGlasgowUnited Kingdom
- Lighthouse Laboratory in Glasgow, Queen Elizabeth University HospitalGlasgowUnited Kingdom
| | - Karina-Doris Vihta
- Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of OxfordOxfordUnited Kingdom
| | - Timothy EA Peto
- Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of OxfordOxfordUnited Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe HospitalOxfordUnited Kingdom
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of OxfordOxfordUnited Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe HospitalOxfordUnited Kingdom
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe HospitalOxfordUnited Kingdom
| | - David W Eyre
- Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of OxfordOxfordUnited Kingdom
- Lighthouse Laboratory in Glasgow, Queen Elizabeth University HospitalGlasgowUnited Kingdom
- Big Data Institute, Nuffield Department of Population Health, University of OxfordOxfordUnited Kingdom
| | - Koen B Pouwels
- Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of OxfordOxfordUnited Kingdom
- Health Economics Research Centre, Nuffield Department of Population Health, University of OxfordOxfordUnited Kingdom
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Buchanan J, Roope LSJ, Morrell L, Pouwels KB, Robotham JV, Abel L, Crook DW, Peto T, Butler CC, Walker AS, Wordsworth S. Preferences for Medical Consultations from Online Providers: Evidence from a Discrete Choice Experiment in the United Kingdom. Appl Health Econ Health Policy 2021; 19:521-535. [PMID: 33682065 PMCID: PMC7937442 DOI: 10.1007/s40258-021-00642-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/23/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND In the UK, consultations for prescription medicines are available via private providers such as online pharmacies. However, these providers may have lower thresholds for prescribing certain drugs. This is a particular concern for antibiotics, given the increasing burden of antimicrobial resistance. Public preferences for consultations with online providers are unknown, hence the impact of increased availability of online consultations on antibiotic use and population health is unclear. OBJECTIVE To conduct a discrete choice experiment survey to understand UK public preferences for seeking online consultations, and the factors that influence these preferences, in the context of having symptoms for which antibiotics may be appropriate. METHODS In a survey conducted between July and August 2018, general population respondents completed 16 questions in which they chose a primary care consultation via either their local medical centre or an online provider. Consultations were described in terms of five attributes, including cost and similarity to traditional 'face-to-face' appointments. Choices were modelled using regression analysis. RESULTS Respondents (n = 734) placed a high value on having a consultation via their local medical centre rather than an online provider, and a low value on consultations by phone or video. However, respondents characterised as 'busy young professionals' showed a lower strength of preference for traditional consultations, with a higher concern for convenience. CONCLUSION Before COVID-19, the UK public had limited appetite for consultations with online providers, or for consultations that were not face-to-face. Nevertheless, prescriptions from online providers should be monitored going forward, particularly for antibiotics, and in key patient groups.
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Affiliation(s)
- James Buchanan
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 9NS, UK.
- National Institute for Health Research Health Research Protection Unit in Healthcare Associated Infections and Antimicrobial Resistance, Oxford, OX3 9DU, UK.
| | - Laurence S J Roope
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 9NS, UK
- National Institute for Health Research Health Research Protection Unit in Healthcare Associated Infections and Antimicrobial Resistance, Oxford, OX3 9DU, UK
- National Institute for Health Research Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU, UK
| | - Liz Morrell
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 9NS, UK
| | - Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 9NS, UK
- National Institute for Health Research Health Research Protection Unit in Healthcare Associated Infections and Antimicrobial Resistance, Oxford, OX3 9DU, UK
- Modelling and Economics Unit, National Infection Service, Public Health England, Wellington House, 133-155 Waterloo Road, London, SE1 8UG, UK
| | - Julie V Robotham
- National Institute for Health Research Health Research Protection Unit in Healthcare Associated Infections and Antimicrobial Resistance, Oxford, OX3 9DU, UK
- Modelling and Economics Unit, National Infection Service, Public Health England, Wellington House, 133-155 Waterloo Road, London, SE1 8UG, UK
| | - Lucy Abel
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK
| | - Derrick W Crook
- National Institute for Health Research Health Research Protection Unit in Healthcare Associated Infections and Antimicrobial Resistance, Oxford, OX3 9DU, UK
- National Institute for Health Research Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU, UK
- Nuffield Department of Medicine, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7BN, UK
| | - Tim Peto
- National Institute for Health Research Health Research Protection Unit in Healthcare Associated Infections and Antimicrobial Resistance, Oxford, OX3 9DU, UK
- National Institute for Health Research Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU, UK
- Nuffield Department of Medicine, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7BN, UK
| | - Christopher C Butler
- National Institute for Health Research Health Research Protection Unit in Healthcare Associated Infections and Antimicrobial Resistance, Oxford, OX3 9DU, UK
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK
| | - A Sarah Walker
- National Institute for Health Research Health Research Protection Unit in Healthcare Associated Infections and Antimicrobial Resistance, Oxford, OX3 9DU, UK
- National Institute for Health Research Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU, UK
- Nuffield Department of Medicine, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7BN, UK
| | - Sarah Wordsworth
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 9NS, UK
- National Institute for Health Research Health Research Protection Unit in Healthcare Associated Infections and Antimicrobial Resistance, Oxford, OX3 9DU, UK
- National Institute for Health Research Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU, UK
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Pritchard E, Matthews PC, Stoesser N, Eyre DW, Gethings O, Vihta KD, Jones J, House T, VanSteenHouse H, Bell I, Bell JI, Newton JN, Farrar J, Diamond I, Rourke E, Studley R, Crook D, Peto TEA, Walker AS, Pouwels KB. Impact of vaccination on new SARS-CoV-2 infections in the United Kingdom. Nat Med 2021; 27:1370-1378. [PMID: 34108716 PMCID: PMC8363500 DOI: 10.1038/s41591-021-01410-w] [Citation(s) in RCA: 183] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 05/26/2021] [Indexed: 12/20/2022]
Abstract
The effectiveness of COVID-19 vaccination in preventing new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in the general community is still unclear. Here, we used the Office for National Statistics COVID-19 Infection Survey—a large community-based survey of individuals living in randomly selected private households across the United Kingdom—to assess the effectiveness of the BNT162b2 (Pfizer–BioNTech) and ChAdOx1 nCoV-19 (Oxford–AstraZeneca; ChAdOx1) vaccines against any new SARS-CoV-2 PCR-positive tests, split according to self-reported symptoms, cycle threshold value (<30 versus ≥30; as a surrogate for viral load) and gene positivity pattern (compatible with B.1.1.7 or not). Using 1,945,071 real-time PCR results from nose and throat swabs taken from 383,812 participants between 1 December 2020 and 8 May 2021, we found that vaccination with the ChAdOx1 or BNT162b2 vaccines already reduced SARS-CoV-2 infections ≥21 d after the first dose (61% (95% confidence interval (CI) = 54–68%) versus 66% (95% CI = 60–71%), respectively), with greater reductions observed after a second dose (79% (95% CI = 65–88%) versus 80% (95% CI = 73–85%), respectively). The largest reductions were observed for symptomatic infections and/or infections with a higher viral burden. Overall, COVID-19 vaccination reduced the number of new SARS-CoV-2 infections, with the largest benefit received after two vaccinations and against symptomatic and high viral burden infections, and with no evidence of a difference between the BNT162b2 and ChAdOx1 vaccines. Results from the Office of National Statistics COVID-19 Infection Survey in the United Kingdom demonstrate that the ChAdOx1 nCoV-19 and BNT162b2 vaccines reduce the incidence of new SARS-CoV-2 infections by up to 65% with a single dose and up to 80% after two doses, with no significant differences in efficacy observed between the two vaccines.
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Affiliation(s)
- Emma Pritchard
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford, University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford, University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - David W Eyre
- National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford, University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Karina-Doris Vihta
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - Joel Jones
- Office for National Statistics, Newport, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK.,IBM Research, Hartree Centre, Daresbury, UK
| | | | - Iain Bell
- Office for National Statistics, Newport, UK
| | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- Health Improvement Directorate, Public Health England, London, UK
| | | | | | | | | | - Derrick Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford, University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford, University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,MRC Clinical Trials Unit at UCL, University College London, London, UK
| | - Koen B Pouwels
- National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK. .,Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK. .,Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
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Lumley SF, O'Donnell D, Stoesser NE, Matthews PC, Howarth A, Hatch SB, Marsden BD, Cox S, James T, Warren F, Peck LJ, Ritter TG, de Toledo Z, Warren L, Axten D, Cornall RJ, Jones EY, Stuart DI, Screaton G, Ebner D, Hoosdally S, Chand M, Crook DW, O'Donnell AM, Conlon CP, Pouwels KB, Walker AS, Peto TEA, Hopkins S, Walker TM, Jeffery K, Eyre DW. Antibody Status and Incidence of SARS-CoV-2 Infection in Health Care Workers. N Engl J Med 2021; 384:533-540. [PMID: 33369366 PMCID: PMC7781098 DOI: 10.1056/nejmoa2034545] [Citation(s) in RCA: 597] [Impact Index Per Article: 199.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND The relationship between the presence of antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the risk of subsequent reinfection remains unclear. METHODS We investigated the incidence of SARS-CoV-2 infection confirmed by polymerase chain reaction (PCR) in seropositive and seronegative health care workers attending testing of asymptomatic and symptomatic staff at Oxford University Hospitals in the United Kingdom. Baseline antibody status was determined by anti-spike (primary analysis) and anti-nucleocapsid IgG assays, and staff members were followed for up to 31 weeks. We estimated the relative incidence of PCR-positive test results and new symptomatic infection according to antibody status, adjusting for age, participant-reported gender, and changes in incidence over time. RESULTS A total of 12,541 health care workers participated and had anti-spike IgG measured; 11,364 were followed up after negative antibody results and 1265 after positive results, including 88 in whom seroconversion occurred during follow-up. A total of 223 anti-spike-seronegative health care workers had a positive PCR test (1.09 per 10,000 days at risk), 100 during screening while they were asymptomatic and 123 while symptomatic, whereas 2 anti-spike-seropositive health care workers had a positive PCR test (0.13 per 10,000 days at risk), and both workers were asymptomatic when tested (adjusted incidence rate ratio, 0.11; 95% confidence interval, 0.03 to 0.44; P = 0.002). There were no symptomatic infections in workers with anti-spike antibodies. Rate ratios were similar when the anti-nucleocapsid IgG assay was used alone or in combination with the anti-spike IgG assay to determine baseline status. CONCLUSIONS The presence of anti-spike or anti-nucleocapsid IgG antibodies was associated with a substantially reduced risk of SARS-CoV-2 reinfection in the ensuing 6 months. (Funded by the U.K. Government Department of Health and Social Care and others.).
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Affiliation(s)
- Sheila F Lumley
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Denise O'Donnell
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Nicole E Stoesser
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Philippa C Matthews
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Alison Howarth
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Stephanie B Hatch
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Brian D Marsden
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Stuart Cox
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Tim James
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Fiona Warren
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Liam J Peck
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Thomas G Ritter
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Zoe de Toledo
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Laura Warren
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - David Axten
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Richard J Cornall
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - E Yvonne Jones
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - David I Stuart
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Gavin Screaton
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Daniel Ebner
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Sarah Hoosdally
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Meera Chand
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Derrick W Crook
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Anne-Marie O'Donnell
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Christopher P Conlon
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Koen B Pouwels
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - A Sarah Walker
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Tim E A Peto
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Susan Hopkins
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Timothy M Walker
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - Katie Jeffery
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
| | - David W Eyre
- From Oxford University Hospitals NHS Foundation Trust (S.F.L., N.E.S., P.C.M., S.C., T.J., F.W., L.W., D.A., A.-M.O., K.J.), Nuffield Department of Medicine (S.F.L., D.O., N.E.S., P.C.M., A.H., S.B.H., B.D.M., R.J.C., E.Y.J., D.I.S., G.S., D.E., S. Hoosdally, D.W.C., C.P.C., A.S.W., T.E.A.P., T.M.W.), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (N.E.S., P.C.M., S. Hoosdally, D.W.C., A.S.W., T.E.A.P., D.W.E.), the Kennedy Institute of Rheumatology Research (B.D.M.), the Medical School, University of Oxford (L.J.P., T.G.R., Z.T.), Target Discovery Institute (D.E.), Nuffield Department of Population Health (A.-M.O., K.B.P., D.W.E.), and the Big Data Institute (D.W.E.), University of Oxford, and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (N.E.S., P.C.M., S. Hoosdally, D.W.C., K.B.P., A.S.W., T.E.A.P., D.W.E.), Oxford, and the National Infection Service, Public Health England at Colindale, London (M.C., S. Hopkins) - all in the United Kingdom; and the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam (T.M.W.)
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Pouwels KB, House T, Pritchard E, Robotham JV, Birrell PJ, Gelman A, Vihta KD, Bowers N, Boreham I, Thomas H, Lewis J, Bell I, Bell JI, Newton JN, Farrar J, Diamond I, Benton P, Walker AS. Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey. Lancet Public Health 2021; 6:e30-e38. [PMID: 33308423 PMCID: PMC7786000 DOI: 10.1016/s2468-2667(20)30282-6] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 11/16/2020] [Accepted: 11/19/2020] [Indexed: 01/19/2023]
Abstract
BACKGROUND Decisions about the continued need for control measures to contain the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on accurate and up-to-date information about the number of people testing positive for SARS-CoV-2 and risk factors for testing positive. Existing surveillance systems are generally not based on population samples and are not longitudinal in design. METHODS Samples were collected from individuals aged 2 years and older living in private households in England that were randomly selected from address lists and previous Office for National Statistics surveys in repeated cross-sectional household surveys with additional serial sampling and longitudinal follow-up. Participants completed a questionnaire and did nose and throat self-swabs. The percentage of individuals testing positive for SARS-CoV-2 RNA was estimated over time by use of dynamic multilevel regression and poststratification, to account for potential residual non-representativeness. Potential changes in risk factors for testing positive over time were also assessed. The study is registered with the ISRCTN Registry, ISRCTN21086382. FINDINGS Between April 26 and Nov 1, 2020, results were available from 1 191 170 samples from 280 327 individuals; 5231 samples were positive overall, from 3923 individuals. The percentage of people testing positive for SARS-CoV-2 changed substantially over time, with an initial decrease between April 26 and June 28, 2020, from 0·40% (95% credible interval 0·29-0·54) to 0·06% (0·04-0·07), followed by low levels during July and August, 2020, before substantial increases at the end of August, 2020, with percentages testing positive above 1% from the end of October, 2020. Having a patient-facing role and working outside your home were important risk factors for testing positive for SARS-CoV-2 at the end of the first wave (April 26 to June 28, 2020), but not in the second wave (from the end of August to Nov 1, 2020). Age (young adults, particularly those aged 17-24 years) was an important initial driver of increased positivity rates in the second wave. For example, the estimated percentage of individuals testing positive was more than six times higher in those aged 17-24 years than in those aged 70 years or older at the end of September, 2020. A substantial proportion of infections were in individuals not reporting symptoms around their positive test (45-68%, dependent on calendar time. INTERPRETATION Important risk factors for testing positive for SARS-CoV-2 varied substantially between the part of the first wave that was captured by the study (April to June, 2020) and the first part of the second wave of increased positivity rates (end of August to Nov 1, 2020), and a substantial proportion of infections were in individuals not reporting symptoms, indicating that continued monitoring for SARS-CoV-2 in the community will be important for managing the COVID-19 pandemic moving forwards. FUNDING Department of Health and Social Care.
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Affiliation(s)
- Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK; The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, University of Oxford, Oxford, UK.
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK; IBM Research, Hartree Centre, Sci-Tech, Daresbury, UK
| | - Emma Pritchard
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, University of Oxford, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Paul J Birrell
- National Infection Service, Public Health England, London, UK; Medical Research Council (MRC) Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Cambridge, UK
| | - Andrew Gelman
- Department of Statistics, Columbia University, New York, NY, USA
| | - Karina-Doris Vihta
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, University of Oxford, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | | | | | | | - Iain Bell
- Office for National Statistics, Newport, UK
| | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- Health Improvement Directorate, Public Health England, London, UK
| | | | | | | | - Ann Sarah Walker
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, University of Oxford, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK; The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; MRC Clinical Trials Unit at University College London, London, UK
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46
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Smith DRM, Duval A, Pouwels KB, Guillemot D, Fernandes J, Huynh BT, Temime L, Opatowski L. Optimizing COVID-19 surveillance in long-term care facilities: a modelling study. BMC Med 2020; 18:386. [PMID: 33287821 PMCID: PMC7721547 DOI: 10.1186/s12916-020-01866-6] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 11/23/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Long-term care facilities (LTCFs) are vulnerable to outbreaks of coronavirus disease 2019 (COVID-19). Timely epidemiological surveillance is essential for outbreak response, but is complicated by a high proportion of silent (non-symptomatic) infections and limited testing resources. METHODS We used a stochastic, individual-based model to simulate transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) along detailed inter-individual contact networks describing patient-staff interactions in a real LTCF setting. We simulated distribution of nasopharyngeal swabs and reverse transcriptase polymerase chain reaction (RT-PCR) tests using clinical and demographic indications and evaluated the efficacy and resource-efficiency of a range of surveillance strategies, including group testing (sample pooling) and testing cascades, which couple (i) testing for multiple indications (symptoms, admission) with (ii) random daily testing. RESULTS In the baseline scenario, randomly introducing a silent SARS-CoV-2 infection into a 170-bed LTCF led to large outbreaks, with a cumulative 86 (95% uncertainty interval 6-224) infections after 3 weeks of unmitigated transmission. Efficacy of symptom-based screening was limited by lags to symptom onset and silent asymptomatic and pre-symptomatic transmission. Across scenarios, testing upon admission detected just 34-66% of patients infected upon LTCF entry, and also missed potential introductions from staff. Random daily testing was more effective when targeting patients than staff, but was overall an inefficient use of limited resources. At high testing capacity (> 10 tests/100 beds/day), cascades were most effective, with a 19-36% probability of detecting outbreaks prior to any nosocomial transmission, and 26-46% prior to first onset of COVID-19 symptoms. Conversely, at low capacity (< 2 tests/100 beds/day), group testing strategies detected outbreaks earliest. Pooling randomly selected patients in a daily group test was most likely to detect outbreaks prior to first symptom onset (16-27%), while pooling patients and staff expressing any COVID-like symptoms was the most efficient means to improve surveillance given resource limitations, compared to the reference requiring only 6-9 additional tests and 11-28 additional swabs to detect outbreaks 1-6 days earlier, prior to an additional 11-22 infections. CONCLUSIONS COVID-19 surveillance is challenged by delayed or absent clinical symptoms and imperfect diagnostic sensitivity of standard RT-PCR tests. In our analysis, group testing was the most effective and efficient COVID-19 surveillance strategy for resource-limited LTCFs. Testing cascades were even more effective given ample testing resources. Increasing testing capacity and updating surveillance protocols accordingly could facilitate earlier detection of emerging outbreaks, informing a need for urgent intervention in settings with ongoing nosocomial transmission.
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Affiliation(s)
- David R M Smith
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France.
- Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology team, Montigny-Le-Bretonneux, France.
- Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire national des arts et métiers, Paris, France.
| | - Audrey Duval
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France
- Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology team, Montigny-Le-Bretonneux, France
| | - Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - Didier Guillemot
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France
- Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology team, Montigny-Le-Bretonneux, France
- AP-HP, Paris Saclay, Public Health, Medical Information, Clinical Research, Le Kremlin-Bicêtre, France
| | - Jérôme Fernandes
- Clinique de soins de suite et réadaptation, Choisy-Le-Roi, France
| | - Bich-Tram Huynh
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France
- Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology team, Montigny-Le-Bretonneux, France
| | - Laura Temime
- Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire national des arts et métiers, Paris, France
- PACRI unit, Institut Pasteur, Conservatoire national des arts et métiers, Paris, France
| | - Lulla Opatowski
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France
- Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology team, Montigny-Le-Bretonneux, France
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Pouwels KB, Vansteelandt S, Batra R, Edgeworth J, Wordsworth S, Robotham JV. Estimating the Effect of Healthcare-Associated Infections on Excess Length of Hospital Stay Using Inverse Probability-Weighted Survival Curves. Clin Infect Dis 2020; 71:e415-e420. [PMID: 32047916 PMCID: PMC7713691 DOI: 10.1093/cid/ciaa136] [Citation(s) in RCA: 4] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 02/07/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Studies estimating excess length of stay (LOS) attributable to nosocomial infections have failed to address time-varying confounding, likely leading to overestimation of their impact. We present a methodology based on inverse probability-weighted survival curves to address this limitation. METHODS A case study focusing on intensive care unit-acquired bacteremia using data from 2 general intensive care units (ICUs) from 2 London teaching hospitals were used to illustrate the methodology. The area under the curve of a conventional Kaplan-Meier curve applied to the observed data was compared with that of an inverse probability-weighted Kaplan-Meier curve applied after treating bacteremia as censoring events. Weights were based on the daily probability of acquiring bacteremia. The difference between the observed average LOS and the average LOS that would be observed if all bacteremia cases could be prevented was multiplied by the number of admitted patients to obtain the total excess LOS. RESULTS The estimated total number of extra ICU days caused by 666 bacteremia cases was estimated at 2453 (95% confidence interval [CI], 1803-3103) days. The excess number of days was overestimated when ignoring time-varying confounding (2845 [95% CI, 2276-3415]) or when completely ignoring confounding (2838 [95% CI, 2101-3575]). CONCLUSIONS ICU-acquired bacteremia was associated with a substantial excess LOS. Wider adoption of inverse probability-weighted survival curves or alternative techniques that address time-varying confounding could lead to better informed decision making around nosocomial infections and other time-dependent exposures.
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Affiliation(s)
- Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
| | - Stijn Vansteelandt
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Rahul Batra
- Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, King’s College London and Guy’s and St Thomas’ National Health Services Foundation Trust, London, United Kingdom
| | - Jonathan Edgeworth
- Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, King’s College London and Guy’s and St Thomas’ National Health Services Foundation Trust, London, United Kingdom
| | - Sarah Wordsworth
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Julie V Robotham
- Modelling and Economics Unit, National Infection Service, Public Health England, London, United Kingdom
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Miller L, Costelloe CE, Robotham JV, Pouwels KB. Overuse of antibiotics: Can viral vaccinations help stem the tide? Br J Clin Pharmacol 2020; 87:87-89. [PMID: 33207008 PMCID: PMC7753246 DOI: 10.1111/bcp.14651] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 10/16/2020] [Accepted: 11/03/2020] [Indexed: 02/05/2023] Open
Affiliation(s)
- Lucy Miller
- Global Digital Health Unit, Department of Primary Care and Public Health, Imperial College London, London, UK
| | - Ceire E Costelloe
- Global Digital Health Unit, Department of Primary Care and Public Health, Imperial College London, London, UK
| | - Julie V Robotham
- HCAI and AMR Division, National Infection Service, Public Health England, London, UK
| | - Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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49
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Morrell L, Buchanan J, Roope LSJ, Pouwels KB, Butler CC, Hayhoe B, Moore MV, Tonkin-Crine S, McLeod M, Robotham JV, Walker AS, Wordsworth S. Delayed Antibiotic Prescription by General Practitioners in the UK: A Stated-Choice Study. Antibiotics (Basel) 2020; 9:antibiotics9090608. [PMID: 32947965 PMCID: PMC7558347 DOI: 10.3390/antibiotics9090608] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 09/10/2020] [Accepted: 09/14/2020] [Indexed: 12/15/2022] Open
Abstract
Delayed antibiotic prescription in primary care has been shown to reduce antibiotic consumption, without increasing risk of complications, yet is not widely used in the UK. We sought to quantify the relative importance of factors affecting the decision to give a delayed prescription, using a stated-choice survey among UK general practitioners. Respondents were asked whether they would provide a delayed or immediate prescription in fifteen hypothetical consultations, described by eight attributes. They were also asked if they would prefer not to prescribe antibiotics. The most important determinants of choice between immediate and delayed prescription were symptoms, duration of illness, and the presence of multiple comorbidities. Respondents were more likely to choose a delayed prescription if the patient preferred not to have antibiotics, but consultation length had little effect. When given the option, respondents chose not to prescribe antibiotics in 51% of cases, with delayed prescription chosen in 21%. Clinical features remained important. Patient preference did not affect the decision to give no antibiotics. We suggest that broader dissemination of the clinical evidence supporting use of delayed prescription for specific presentations may help increase appropriate use. Establishing patient preferences regarding antibiotics may help to overcome concerns about patient acceptance. Increasing consultation length appears unlikely to affect the use of delayed prescription.
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Affiliation(s)
- Liz Morrell
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; (J.B.); (L.S.J.R.); (K.B.P.); (S.W.)
- Correspondence:
| | - James Buchanan
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; (J.B.); (L.S.J.R.); (K.B.P.); (S.W.)
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford OX2 6GG, UK; (C.C.B.); (S.T.-C.); (A.S.W.)
- NIHR Biomedical Research Centre Oxford, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Laurence S. J. Roope
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; (J.B.); (L.S.J.R.); (K.B.P.); (S.W.)
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford OX2 6GG, UK; (C.C.B.); (S.T.-C.); (A.S.W.)
- NIHR Biomedical Research Centre Oxford, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Koen B. Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; (J.B.); (L.S.J.R.); (K.B.P.); (S.W.)
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford OX2 6GG, UK; (C.C.B.); (S.T.-C.); (A.S.W.)
| | - Christopher C. Butler
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford OX2 6GG, UK; (C.C.B.); (S.T.-C.); (A.S.W.)
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Benedict Hayhoe
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, London W2 1PG, UK;
| | - Michael V. Moore
- Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton SO17 1BJ, UK;
| | - Sarah Tonkin-Crine
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford OX2 6GG, UK; (C.C.B.); (S.T.-C.); (A.S.W.)
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Monsey McLeod
- NIHR Health Protection Research Unit in Healthcare-Associated Infections and Antimicrobial Resistance, Imperial College London, London SW7 2AZ, UK;
- Centre for Medication Safety and Service Quality, Pharmacy Department, Imperial College Healthcare NHS Trust, London W2 1NY, UK
- NIHR Imperial Patient Safety Translational Research Centre, Imperial College London, London SW7 2AZ, UK
| | - Julie V. Robotham
- Modelling and Economics Unit, National Infection Service, Public Health England, London SE1 8UG, UK;
| | - A. Sarah Walker
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford OX2 6GG, UK; (C.C.B.); (S.T.-C.); (A.S.W.)
- NIHR Biomedical Research Centre Oxford, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
- Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Sarah Wordsworth
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; (J.B.); (L.S.J.R.); (K.B.P.); (S.W.)
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford OX2 6GG, UK; (C.C.B.); (S.T.-C.); (A.S.W.)
- NIHR Biomedical Research Centre Oxford, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
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50
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Roope LSJ, Buchanan J, Morrell L, Pouwels KB, Sivyer K, Mowbray F, Abel L, Cross ELA, Yardley L, Peto T, Walker AS, Llewelyn MJ, Wordsworth S. Why do hospital prescribers continue antibiotics when it is safe to stop? Results of a choice experiment survey. BMC Med 2020; 18:196. [PMID: 32727604 PMCID: PMC7391515 DOI: 10.1186/s12916-020-01660-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 06/08/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Deciding whether to discontinue antibiotics at early review is a cornerstone of hospital antimicrobial stewardship practice worldwide. In England, this approach is described in government guidance ('Start Smart then Focus'). However, < 10% of hospital antibiotic prescriptions are discontinued at review, despite evidence that 20-30% could be discontinued safely. We aimed to quantify the relative importance of factors influencing prescriber decision-making at review. METHODS We conducted an online choice experiment, a survey method to elicit preferences. Acute/general hospital prescribers in England were asked if they would continue or discontinue antibiotic treatment in 15 hypothetical scenarios. Scenarios were described according to six attributes, including patients' presenting symptoms and whether discontinuation would conflict with local prescribing guidelines. Respondents' choices were analysed using conditional logistic regression. RESULTS One hundred respondents completed the survey. Respondents were more likely to continue antibiotics when discontinuation would 'strongly conflict' with local guidelines (average marginal effect (AME) on the probability of continuing + 0.194 (p < 0.001)), when presenting symptoms more clearly indicated antibiotics (AME of urinary tract infection symptoms + 0.173 (p < 0.001) versus unclear symptoms) and when patients had severe frailty/comorbidities (AME = + 0.101 (p < 0.001)). Respondents were less likely to continue antibiotics when under no external pressure to continue (AME = - 0.101 (p < 0.001)). Decisions were also influenced by the risks to patient health of continuing/discontinuing antibiotic treatment. CONCLUSIONS Guidelines that conflict with antibiotic discontinuation (e.g. pre-specify fixed durations) may discourage safe discontinuation at review. In contrast, guidelines conditional on patient factors/treatment response could help hospital prescribers discontinue antibiotics if diagnostic information suggesting they are no longer needed is available.
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Affiliation(s)
- Laurence S J Roope
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK. .,NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, University of Oxford, Oxford, UK. .,NIHR Health Protection Research Unit (HPRU) in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford in partnership with Public Health England (PHE), Oxford, UK.
| | - James Buchanan
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK.,NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, University of Oxford, Oxford, UK.,NIHR Health Protection Research Unit (HPRU) in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford in partnership with Public Health England (PHE), Oxford, UK
| | - Liz Morrell
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
| | - Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK.,NIHR Health Protection Research Unit (HPRU) in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford in partnership with Public Health England (PHE), Oxford, UK
| | - Katy Sivyer
- Centre for Clinical and Community Applications of Health Psychology, University of Southampton, Southampton, UK
| | - Fiona Mowbray
- Centre for Clinical and Community Applications of Health Psychology, University of Southampton, Southampton, UK
| | - Lucy Abel
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Elizabeth L A Cross
- Department of Microbiology and Infection, Brighton and Sussex University Hospitals NHS Trust, Eastern Road, Brighton, UK
| | - Lucy Yardley
- Centre for Clinical and Community Applications of Health Psychology, University of Southampton, Southampton, UK.,School of Psychological Science, University of Bristol, Clifton, UK
| | - Tim Peto
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, University of Oxford, Oxford, UK.,Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK.,Oxford University Hospitals NHS Trust, Oxford, UK
| | - A Sarah Walker
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, University of Oxford, Oxford, UK.,NIHR Health Protection Research Unit (HPRU) in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford in partnership with Public Health England (PHE), Oxford, UK.,Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Martin J Llewelyn
- Department of Microbiology and Infection, Brighton and Sussex University Hospitals NHS Trust, Eastern Road, Brighton, UK.,Brighton and Sussex Medical School, Brighton, UK
| | - Sarah Wordsworth
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK.,NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, University of Oxford, Oxford, UK
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