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Jia R, Coupland C, Vinogradova Y, Qureshi N, Turner E, Vedhara K. Mental health conditions and COVID-19 vaccine outcomes: A scoping review. J Psychosom Res 2024; 183:111826. [PMID: 38870550 DOI: 10.1016/j.jpsychores.2024.111826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 04/26/2024] [Accepted: 06/03/2024] [Indexed: 06/15/2024]
Abstract
OBJECTIVE Research shows that people with a history of mental health conditions were at increased risk of COVID-19 infection, hospitalisation, and mortality. However, the relationship between mental health conditions and COVID-19 vaccine outcomes such as vaccine intention, uptake and vaccine breakthrough is not yet well-understood. METHODS We conducted a systematic search on the topics of COVID-19 vaccine intentions, vaccine uptake, and vaccine breakthrough, in relation to mental health conditions (e.g., depression, schizophrenia), in four databases: PubMed, MEDLINE, SCOPUS, and PsychINFO, and the publication lists of Clinical Practice Research Datalink (CPRD), The Health Improvement Network (THIN), OpenSAFELY, and QResearch. Inclusion criteria focussed on studies reporting any of the aforementioned COVID-19 vaccine outcomes among people with mental health conditions. RESULTS Of 251 publications initially identified, 32 met our inclusion criteria. Overall, the evidence is inconclusive regarding the levels of intention to accept COVID-19 vaccines among people with mental health conditions. People with mental health conditions were more likely to have lower uptake of COVID-19 vaccines, compared to people without. Common barriers to COVID-19 vaccine uptake included concerns about the safety, effectiveness, and side effects of the vaccines. Limited evidence also suggests that vaccine breakthrough may be a particular risk for those with substance use disorder. CONCLUSIONS Evidence for the association between COVID-19 vaccine intentions and mental health conditions is mixed. Vaccine uptake might be lower in people with mental health conditions compared to people without, yielding interventions to encourage vaccine uptake in this population. Our understanding of COVID-19 vaccine breakthrough in this population also needs enhancing.
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Affiliation(s)
- Ru Jia
- Nuffield Department of Primary Care Health Sciences, Medical Science Division, University of Oxford, Radcliffe Observatory Quarter, Oxford, OX2 6GG, UK
| | - Carol Coupland
- Nuffield Department of Primary Care Health Sciences, Medical Science Division, University of Oxford, Radcliffe Observatory Quarter, Oxford, OX2 6GG, UK; Centre for Academic Primary Care, Lifespan and Population Health, School of Medicine, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Yana Vinogradova
- Centre for Academic Primary Care, Lifespan and Population Health, School of Medicine, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Nadeem Qureshi
- Centre for Academic Primary Care, Lifespan and Population Health, School of Medicine, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Emma Turner
- Bristol Medical School, University of Bristol, Bristol BS8 1QU, UK
| | - Kavita Vedhara
- School of Psychology, Cardiff University, Tower Building, Cardiff CF10 3AT, UK.
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2
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Ip S, North TL, Torabi F, Li Y, Abbasizanjani H, Akbari A, Horne E, Denholm R, Keene S, Denaxas S, Banerjee A, Khunti K, Sudlow C, Whiteley WN, Sterne JAC, Wood AM, Walker V. Cohort study of cardiovascular safety of different COVID-19 vaccination doses among 46 million adults in England. Nat Commun 2024; 15:6085. [PMID: 39085208 PMCID: PMC11291640 DOI: 10.1038/s41467-024-49634-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 06/11/2024] [Indexed: 08/02/2024] Open
Abstract
The first dose of COVID-19 vaccines led to an overall reduction in cardiovascular events, and in rare cases, cardiovascular complications. There is less information about the effect of second and booster doses on cardiovascular diseases. Using longitudinal health records from 45.7 million adults in England between December 2020 and January 2022, our study compared the incidence of thrombotic and cardiovascular complications up to 26 weeks after first, second and booster doses of brands and combinations of COVID-19 vaccines used during the UK vaccination program with the incidence before or without the corresponding vaccination. The incidence of common arterial thrombotic events (mainly acute myocardial infarction and ischaemic stroke) was generally lower after each vaccine dose, brand and combination. Similarly, the incidence of common venous thrombotic events, (mainly pulmonary embolism and lower limb deep venous thrombosis) was lower after vaccination. There was a higher incidence of previously reported rare harms after vaccination: vaccine-induced thrombotic thrombocytopenia after first ChAdOx1 vaccination, and myocarditis and pericarditis after first, second and transiently after booster mRNA vaccination (BNT-162b2 and mRNA-1273). These findings support the wide uptake of future COVID-19 vaccination programs.
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Affiliation(s)
- Samantha Ip
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
| | - Teri-Louise North
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Fatemeh Torabi
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health, and Life Science, Swansea University, Swansea, Wales, UK
| | - Yangfan Li
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Hoda Abbasizanjani
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health, and Life Science, Swansea University, Swansea, Wales, UK
| | - Ashley Akbari
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health, and Life Science, Swansea University, Swansea, Wales, UK
| | - Elsie Horne
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Rachel Denholm
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Health Data Research UK South-West, Bristol, UK
| | - Spencer Keene
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Spiros Denaxas
- Health Data Research UK, London, UK
- Institute of Health Informatics, University College London, London, UK
- University College London Hospitals Biomedical Research Centre, University College London, London, UK
- BHF Accelerator, London, UK
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - Amitava Banerjee
- Institute of Health Informatics, University College London, London, UK
| | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Cathie Sudlow
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - William N Whiteley
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Jonathan A C Sterne
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Health Data Research UK South-West, Bristol, UK
| | - Angela M Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Cambridge Centre for AI in Medicine, Cambridge, UK
| | - Venexia Walker
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, Bristol, UK
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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3
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Andrews CD, Mathur R, Massey J, Park R, Curtis HJ, Hopcroft L, Mehrkar A, Bacon S, Hickman G, Smith R, Evans D, Ward T, Davy S, Inglesby P, Dillingham I, Maude S, O'Dwyer T, Butler-Cole BFC, Bridges L, Bates C, Parry J, Hester F, Harper S, Cockburn J, Goldacre B, MacKenna B, Tomlinson LA, Walker AJ, Hulme WJ. Consistency, completeness and external validity of ethnicity recording in NHS primary care records: a cohort study in 25 million patients' records at source using OpenSAFELY. BMC Med 2024; 22:288. [PMID: 38987774 PMCID: PMC11234682 DOI: 10.1186/s12916-024-03499-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 06/24/2024] [Indexed: 07/12/2024] Open
Abstract
BACKGROUND Ethnicity is known to be an important correlate of health outcomes, particularly during the COVID-19 pandemic, where some ethnic groups were shown to be at higher risk of infection and adverse outcomes. The recording of patients' ethnic groups in primary care can support research and efforts to achieve equity in service provision and outcomes; however, the coding of ethnicity is known to present complex challenges. We therefore set out to describe ethnicity coding in detail with a view to supporting the use of this data in a wide range of settings, as part of wider efforts to robustly describe and define methods of using administrative data. METHODS We describe the completeness and consistency of primary care ethnicity recording in the OpenSAFELY-TPP database, containing linked primary care and hospital records in > 25 million patients in England. We also compared the ethnic breakdown in OpenSAFELY-TPP with that of the 2021 UK census. RESULTS 78.2% of patients registered in OpenSAFELY-TPP on 1 January 2022 had their ethnicity recorded in primary care records, rising to 92.5% when supplemented with hospital data. The completeness of ethnicity recording was higher for women than for men. The rate of primary care ethnicity recording ranged from 77% in the South East of England to 82.2% in the West Midlands. Ethnicity recording rates were higher in patients with chronic or other serious health conditions. For each of the five broad ethnicity groups, primary care recorded ethnicity was within 2.9 percentage points of the population rate as recorded in the 2021 Census for England as a whole. For patients with multiple ethnicity records, 98.7% of the latest recorded ethnicities matched the most frequently coded ethnicity. Patients whose latest recorded ethnicity was categorised as Other were most likely to have a discordant ethnicity recording (32.2%). CONCLUSIONS Primary care ethnicity data in OpenSAFELY is present for over three quarters of all patients, and combined with data from other sources can achieve a high level of completeness. The overall distribution of ethnicities across all English OpenSAFELY-TPP practices was similar to the 2021 Census, with some regional variation. This report identifies the best available codelist for use in OpenSAFELY and similar electronic health record data.
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Affiliation(s)
- Colm D Andrews
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK.
| | - Rohini Mathur
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Wolfson Institute for Population Health, University of London, London, Queen Mary, E1 2AT, UK
| | - Jon Massey
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Robin Park
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Helen J Curtis
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Lisa Hopcroft
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Amir Mehrkar
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Seb Bacon
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - George Hickman
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Rebecca Smith
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - David Evans
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Tom Ward
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Simon Davy
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Peter Inglesby
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Iain Dillingham
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Steven Maude
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Thomas O'Dwyer
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Ben F C Butler-Cole
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Lucy Bridges
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Chris Bates
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - John Parry
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - Frank Hester
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - Sam Harper
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | | | - Ben Goldacre
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Brian MacKenna
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - Laurie A Tomlinson
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Alex J Walker
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
| | - William J Hulme
- Nuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, Oxford University, Oxford, OX2 6GG, UK
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Pouwels KB, Eyre DW, House T, Aspey B, Matthews PC, Stoesser N, Newton JN, Diamond I, Studley R, Taylor NGH, Bell JI, Farrar J, Kolenchery J, Marsden BD, Hoosdally S, Jones EY, Stuart DI, Crook DW, Peto TEA, Walker AS. Improving the representativeness of UK's national COVID-19 Infection Survey through spatio-temporal regression and post-stratification. Nat Commun 2024; 15:5340. [PMID: 38914564 PMCID: PMC11196632 DOI: 10.1038/s41467-024-49201-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 05/23/2024] [Indexed: 06/26/2024] Open
Abstract
Population-representative estimates of SARS-CoV-2 infection prevalence and antibody levels in specific geographic areas at different time points are needed to optimise policy responses. However, even population-wide surveys are potentially impacted by biases arising from differences in participation rates across key groups. Here, we used spatio-temporal regression and post-stratification models to UK's national COVID-19 Infection Survey (CIS) to obtain representative estimates of PCR positivity (6,496,052 tests) and antibody prevalence (1,941,333 tests) for different regions, ages and ethnicities (7-December-2020 to 4-May-2022). Not accounting for vaccination status through post-stratification led to small underestimation of PCR positivity, but more substantial overestimations of antibody levels in the population (up to 21 percentage points), particularly in groups with low vaccine uptake in the general population. There was marked variation in the relative contribution of different areas and age-groups to each wave. Future analyses of infectious disease surveys should take into account major drivers of outcomes of interest that may also influence participation, with vaccination being an important factor to consider.
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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, Oxford, UK.
| | - 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, UK
- 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 Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK
- IBM Research, Hartree Centre, Sci-Tech, Daresbury, UK
| | - Ben Aspey
- Office for National Statistics, Newport, UK
| | - Philippa C Matthews
- The Francis Crick Institute, London, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Division of infection and immunity, University College London, London, UK
| | - Nicole Stoesser
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the 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 Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, 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
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- Nuffield Department of Medicine, 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
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the 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 Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tim E A Peto
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the 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 Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - A Sarah Walker
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
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5
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Henderson AD, Butler-Cole BFC, Tazare J, Tomlinson LA, Marks M, Jit M, Briggs A, Lin LY, Carlile O, Bates C, Parry J, Bacon SCJ, Dillingham I, Dennison WA, Costello RE, Wei Y, Walker AJ, Hulme W, Goldacre B, Mehrkar A, MacKenna B, Herrett E, Eggo RM. Clinical coding of long COVID in primary care 2020-2023 in a cohort of 19 million adults: an OpenSAFELY analysis. EClinicalMedicine 2024; 72:102638. [PMID: 38800803 PMCID: PMC11127160 DOI: 10.1016/j.eclinm.2024.102638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 04/10/2024] [Accepted: 04/29/2024] [Indexed: 05/29/2024] Open
Abstract
Background Long COVID is the patient-coined term for the persistent symptoms of COVID-19 illness for weeks, months or years following the acute infection. There is a large burden of long COVID globally from self-reported data, but the epidemiology, causes and treatments remain poorly understood. Primary care is used to help identify and treat patients with long COVID and therefore Electronic Health Records (EHRs) of past COVID-19 patients could be used to help fill these knowledge gaps. We aimed to describe the incidence and differences in demographic and clinical characteristics in recorded long COVID in primary care records in England. Methods With the approval of NHS England we used routine clinical data from over 19 million adults in England linked to SARS-COV-2 test result, hospitalisation and vaccination data to describe trends in the recording of 16 clinical codes related to long COVID between November 2020 and January 2023. Using OpenSAFELY, we calculated rates per 100,000 person-years and plotted how these changed over time. We compared crude and adjusted (for age, sex, 9 NHS regions of England, and the dominant variant circulating) rates of recorded long COVID in patient records between different key demographic and vaccination characteristics using negative binomial models. Findings We identified a total of 55,465 people recorded to have long COVID over the study period, which included 20,025 diagnoses codes and 35,440 codes for further assessment. The incidence of new long COVID records increased steadily over 2021, and declined over 2022. The overall rate per 100,000 person-years was 177.5 cases in women (95% CI: 175.5-179) and 100.5 in men (99.5-102). The majority of those with a long COVID record did not have a recorded positive SARS-COV-2 test 12 or more weeks before the long COVID record. Interpretation In this descriptive study, EHR recorded long COVID was very low between 2020 and 2023, and incident records of long COVID declined over 2022. Using EHR diagnostic or referral codes unfortunately has major limitations in identifying and ascertaining true cases and timing of long COVID. Funding This research was supported by the National Institute for Health and Care Research (NIHR) (OpenPROMPT: COV-LT2-0073).
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Affiliation(s)
| | - Ben FC. Butler-Cole
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
| | - John Tazare
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Laurie A. Tomlinson
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Michael Marks
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Mark Jit
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Andrew Briggs
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Liang-Yu Lin
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Oliver Carlile
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Chris Bates
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds LS18 5PX, UK
| | - John Parry
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds LS18 5PX, UK
| | - Sebastian CJ. Bacon
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
| | - Iain Dillingham
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
| | | | - Ruth E. Costello
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Yinghui Wei
- Centre for Mathematical Sciences, School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK
| | - Alex J. Walker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
| | - William Hulme
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
| | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
| | - Amir Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
| | - Brian MacKenna
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
| | - Emily Herrett
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Rosalind M. Eggo
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
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6
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Hoang U, Delanerolle G, Fan X, Aspden C, Byford R, Ashraf M, Haag M, Elson W, Leston M, Anand S, Ferreira F, Joy M, Hobbs R, de Lusignan S. A Profile of Influenza Vaccine Coverage for 2019-2020: Database Study of the English Primary Care Sentinel Cohort. JMIR Public Health Surveill 2024; 10:e39297. [PMID: 38787605 PMCID: PMC11161707 DOI: 10.2196/39297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 12/06/2023] [Accepted: 02/17/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Innovation in seasonal influenza vaccine development has resulted in a wider range of formulations becoming available. Understanding vaccine coverage across populations including the timing of administration is important when evaluating vaccine benefits and risks. OBJECTIVE This study aims to report the representativeness, uptake of influenza vaccines, different formulations of influenza vaccines, and timing of administration within the English Primary Care Sentinel Cohort (PCSC). METHODS We used the PCSC of the Oxford-Royal College of General Practitioners Research and Surveillance Centre. We included patients of all ages registered with PCSC member general practices, reporting influenza vaccine coverage between September 1, 2019, and January 29, 2020. We identified influenza vaccination recipients and characterized them by age, clinical risk groups, and vaccine type. We reported the date of influenza vaccination within the PCSC by International Standard Organization (ISO) week. The representativeness of the PCSC population was compared with population data provided by the Office for National Statistics. PCSC influenza vaccine coverage was compared with published UK Health Security Agency's national data. We used paired t tests to compare populations, reported with 95% CI. RESULTS The PCSC comprised 7,010,627 people from 693 general practices. The study population included a greater proportion of people aged 18-49 years (2,982,390/7,010,627, 42.5%; 95% CI 42.5%-42.6%) compared with the Office for National Statistics 2019 midyear population estimates (23,219,730/56,286,961, 41.3%; 95% CI 4.12%-41.3%; P<.001). People who are more deprived were underrepresented and those in the least deprived quintile were overrepresented. Within the study population, 24.7% (1,731,062/7,010,627; 95% CI 24.7%-24.7%) of people of all ages received an influenza vaccine compared with 24.2% (14,468,665/59,764,928; 95% CI 24.2%-24.2%; P<.001) in national data. The highest coverage was in people aged ≥65 years (913,695/1,264,700, 72.3%; 95% CI 72.2%-72.3%). The proportion of people in risk groups who received an influenza vaccine was also higher; for example, 69.8% (284,280/407,228; 95% CI 69.7%-70%) of people with diabetes in the PCSC received an influenza vaccine compared with 61.2% (983,727/1,607,996; 95% CI 61.1%-61.3%; P<.001) in national data. In the PCSC, vaccine type and brand information were available for 71.8% (358,365/498,923; 95% CI 71.7%-72%) of people aged 16-64 years and 81.9% (748,312/913,695; 95% CI 81.8%-82%) of people aged ≥65 years, compared with 23.6% (696,880/2,900,000) and 17.8% (1,385,888/7,700,000), respectively, of the same age groups in national data. Vaccination commenced during ISO week 35, continued until ISO week 3, and peaked during ISO week 41. The in-week peak in vaccination administration was on Saturdays. CONCLUSIONS The PCSC's sociodemographic profile was similar to the national population and captured more data about risk groups, vaccine brands, and batches. This may reflect higher data quality. Its capabilities included reporting precise dates of administration. The PCSC is suitable for undertaking studies of influenza vaccine coverage.
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Affiliation(s)
- Uy Hoang
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Gayathri Delanerolle
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Xuejuan Fan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Carole Aspden
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rachel Byford
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | | | - William Elson
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Meredith Leston
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Sneha Anand
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Filipa Ferreira
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Mark Joy
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Richard Hobbs
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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7
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Huf SW, Grailey K, Crespo RF, Woldmann L, Chisambi M, Skirrow H, Black K, Hassanpourfard B, Nguyen J, Klaber B, Darzi A. Testing the impact of differing behavioural science informed text message content in COVID-19 vaccination invitations on vaccine uptake: A randomised clinical trial. Vaccine 2024; 42:2919-2926. [PMID: 38553291 DOI: 10.1016/j.vaccine.2024.03.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/16/2024]
Abstract
Behavioural science constructs can be incorporated into messaging strategies to enhance the effectiveness of public health campaigns by increasing the occurrence of desired behaviours. This study investigated the impact of behavioural science-informed text message strategies on COVID-19 vaccination rates in 18-39-year-olds in an area of low uptake in London during the first vaccination offer round in the United Kingdom. This three-armed randomised trial recruited unvaccinated residents of an urban Central London suburb being offered their first vaccination between May and June 2021. Participants were randomised to receive the control (current practice) text message or one of two different behavioural science-informed COVID-19 vaccine invitation strategies. Both intervention strategies contained the phrase "your vaccine is ready and waiting for you", aiming to evoke a sense of ownership, with one strategy also including a pre-alert message. The main outcome measures were vaccination rates at 3 and 8 weeks after message delivery. A total of 88,820 residents were randomly assigned to one of the three trial arms. Each arm had a vaccine uptake rate of 27.2 %, 27.4 % and 27.3 % respectively. The mean age of participants was 28.2 years (SD ± 5.7), the mean index of multiple deprivation was 4.3 (SD ± 2.0) and 50.4 % were women. Vaccine uptake varied by demographics, however there was no significant difference between trial arms (p = 0.872). Delivery was successful for 53.6 % of text messages. Our choice of behavioural science informed messaging strategies did not improve vaccination rates above the rate seen for the current practice message. This likely reflects the wide exposure to public health campaigns during the pandemic, as such text messages nudges were unlikely to alter existing informed decision-making processes. Text message delivery was relatively low, indicating a need for accurate mobile phone number records and multi-modal approaches to reach eligible patients for vaccination. The protocol was registered at clinicaltrials.gov (NCT04895683) on 20/05/2021.
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Affiliation(s)
- Sarah W Huf
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom; Centre for Health Policy, Institute of Global Health Innovation, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Kate Grailey
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom; Centre for Health Policy, Institute of Global Health Innovation, Imperial College London, London, United Kingdom.
| | - Roberto Fernandez Crespo
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom; Centre for Health Policy, Institute of Global Health Innovation, Imperial College London, London, United Kingdom
| | - Lena Woldmann
- Imperial College Health Partners, London, United Kingdom
| | | | - Helen Skirrow
- School of Public Health, Imperial College London, London, United Kingdom
| | - Kirstie Black
- Central London Healthcare CIC, London, United Kingdom
| | | | - Joe Nguyen
- NHS North West London Integrated Care Board (ICB), London, United Kingdom
| | - Bob Klaber
- Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Ara Darzi
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom; Centre for Health Policy, Institute of Global Health Innovation, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, London, United Kingdom
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8
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Gonzalez-Jaramillo N, Abbühl D, Roa-Díaz ZM, Kobler-Betancourt C, Frahsa A. COVID-19 vaccine acceptance in the general population and under-resourced communities from high-income countries: realist review. BMJ Open 2024; 14:e084560. [PMID: 38631831 PMCID: PMC11029206 DOI: 10.1136/bmjopen-2024-084560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/28/2024] [Indexed: 04/19/2024] Open
Abstract
OBJECTIVE To compare vaccination willingness before rollout and 1 year post-rollout uptake among the general population and under-resourced communities in high-income countries. DESIGN A realist review. DATA SOURCES Embase, PubMed, Dimensions ai and Google Scholar. SETTING High-income countries. DEFINITIONS We defined vaccination willingness as the proportion of participants willing or intending to receive vaccines prior to availability. We defined vaccine uptake as the real proportion of the population with complete vaccination as reported by each country until November 2021. RESULTS We included data from 62 studies and 18 high-income countries. For studies conducted among general populations, the proportion of vaccination willingness was 67% (95% CI 62% to 72%). In real-world settings, the overall proportion of vaccine uptake among those countries was 73% (95% CI 69% to 76%). 17 studies reported pre-rollout willingness for under-resourced communities. The summary proportion of vaccination willingness from studies reporting results among people from under-resourced communities was 52% (95% CI 0.46% to 0.57%). Real-world evidence about vaccine uptake after rollout among under-resourced communities was limited. CONCLUSION Our review emphasises the importance of realist reviews for assessing vaccine acceptance. Limited real-world evidence about vaccine uptake among under-resourced communities in high-income countries is a call to context-specific actions and reporting.
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Affiliation(s)
| | - Dominik Abbühl
- ISPM, University of Bern, Bern, Switzerland
- Faculty of Medicine, University of Bern, Bern, Switzerland
| | - Zayne Milena Roa-Díaz
- ISPM, University of Bern, Bern, Switzerland
- Faculty of Medicine, University of Bern, Bern, Switzerland
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9
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Goodfellow L, van Leeuwen E, Eggo RM. COVID-19 inequalities in England: a mathematical modelling study of transmission risk and clinical vulnerability by socioeconomic status. BMC Med 2024; 22:162. [PMID: 38616257 DOI: 10.1186/s12916-024-03387-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 04/10/2024] [Indexed: 04/16/2024] Open
Abstract
BACKGROUND The COVID-19 pandemic resulted in major inequalities in infection and disease burden between areas of varying socioeconomic deprivation in many countries, including England. Areas of higher deprivation tend to have a different population structure-generally younger-which can increase viral transmission due to higher contact rates in school-going children and working-age adults. Higher deprivation is also associated with a higher presence of chronic comorbidities, which were convincingly demonstrated to be risk factors for severe COVID-19 disease. These two major factors need to be combined to better understand and quantify their relative importance in the observed COVID-19 inequalities. METHODS We used UK Census data on health status and demography stratified by decile of the Index of Multiple Deprivation (IMD), which is a measure of socioeconomic deprivation. We calculated epidemiological impact using an age-stratified COVID-19 transmission model, which incorporated different contact patterns and clinical health profiles by decile. To separate the contribution of each factor, we considered a scenario where the clinical health profile of all deciles was at the level of the least deprived. We also considered the effectiveness of school closures and vaccination of over 65-year-olds in each decile. RESULTS In the modelled epidemics in urban areas, the most deprived decile experienced 9% more infections, 13% more clinical cases, and a 97% larger peak clinical size than the least deprived; we found similar inequalities in rural areas. Twenty-one per cent of clinical cases and 16% of deaths in England observed under the model assumptions would not occur if all deciles experienced the clinical health profile of the least deprived decile. We found that more deaths were prevented in more affluent areas during school closures and vaccination rollouts. CONCLUSIONS This study demonstrates that both clinical and demographic factors synergise to generate health inequalities in COVID-19, that improving the clinical health profile of populations would increase health equity, and that some interventions can increase health inequalities.
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Affiliation(s)
- Lucy Goodfellow
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC14 7HT, UK.
| | - Edwin van Leeuwen
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC14 7HT, UK
- Modelling and Economics Unit and NIHR Health Protection Research Unit in Modelling and Health Economics, UK Health Security Agency, London, NW9 5EQ, UK
| | - Rosalind M Eggo
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC14 7HT, UK
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10
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Shuldiner J, Green ME, Kiran T, Khan S, Frymire E, Moineddin R, Kerr M, Tadrous M, Nowak DA, Kwong JC, Hu J, Witteman HO, Hamilton B, Bogoch I, Marshall LJ, Ikura S, Bar-Ziv S, Kaplan D, Ivers N. Characteristics of primary care practices by proportion of patients unvaccinated against SARS-CoV-2: a cross-sectional cohort study. CMAJ 2024; 196:E432-E440. [PMID: 38589026 PMCID: PMC11001391 DOI: 10.1503/cmaj.230816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/26/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Variations in primary care practices may explain some differences in health outcomes during the COVID-19 pandemic. We sought to evaluate the characteristics of primary care practices by the proportion of patients unvaccinated against SARS-CoV-2. METHODS We conducted a population-based, cross-sectional cohort study using linked administrative data sets in Ontario, Canada. We calculated the percentage of patients unvaccinated against SARS-CoV-2 enrolled with each comprehensive-care family physician, ranked physicians according to the proportion of patients unvaccinated, and identified physicians in the top 10% (v. the other 90%). We compared characteristics of family physicians and their patients in these 2 groups using standardized differences. RESULTS We analyzed 9060 family physicians with 10 837 909 enrolled patients. Family physicians with the largest proportion (top 10%) of unvaccinated patients (n = 906) were more likely to be male, to have trained outside of Canada, to be older, and to work in an enhanced fee-for-service model than those in the remaining 90%. Vaccine coverage (≥ 2 doses of SARS-CoV-2 vaccine) was 74% among patients of physicians with the largest proportion of unvaccinated patients, compared with 87% in the remaining patient population. Patients in the top 10% group tended to be younger and live in areas with higher levels of ethnic diversity and immigration and lower incomes. INTERPRETATION Primary care practices with the largest proportion of patients unvaccinated against SARS-CoV-2 served marginalized communities and were less likely to use team-based care models. These findings can guide resource planning and help tailor interventions to integrate public health priorities within primary care practices.
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Affiliation(s)
- Jennifer Shuldiner
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont.
| | - Michael E Green
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - Tara Kiran
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - Shahriar Khan
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - Eliot Frymire
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - Rahim Moineddin
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - Meghan Kerr
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - Mina Tadrous
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - Dominik Alex Nowak
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - Jeffrey C Kwong
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - Jia Hu
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - Holly O Witteman
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - Bryn Hamilton
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - Isaac Bogoch
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - Lydia-Joy Marshall
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - Sophia Ikura
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - Stacey Bar-Ziv
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - David Kaplan
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
| | - Noah Ivers
- Women's College Hospital Institute of Virtual Care and Systems Solutions (Shuldiner, Tadrous, Ivers), Women's College Hospital, Toronto, Ont.; Departments of Family Medicine and Public Health Sciences (Green, Kerr), Queen's University, Kingston, Ont.; ICES (Green, Khan, Moineddin, Tadrous, Kwong, Ivers); Department of Family and Community Medicine (Kiran, Nowak, Kwong), University of Toronto; St. Michael's Hospital (Kiran), Unity Health Toronto; MAP Centre for Urban Health Solutions (Kiran), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; Centre for Health Services and Policy Research (Frymire), Queen's University, Kingston, Ont.; Leslie Dan School of Pharmacy (Tadrous), and Dalla Lana School of Public Health (Nowak), University of Toronto; Women's College Hospital Academic Family Health Team (Nowak), Women's College Hospital; Public Health Ontario (Kwong); University Health Network (Kwong), Toronto, Ont.; Department of Community Health Sciences (Hu), University of Calgary, Calgary, Alta.; VITAM Research Centre for Sustainable Health (Witteman); Department of Family and Emergency Medicine (Witteman), Université Laval, Québec, Que.; Association of Family Health Teams of Ontario (Hamilton); Department of Medicine (Bogoch), University of Toronto; Health Commons Solutions Labs Ontario (Marshall, Ikura); Ontario Health (Bar-Ziv, Kaplan); Institute of Health Policy, Management and Evaluation (Ivers), University of Toronto, Toronto, Ont
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11
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Cezard GI, Denholm RE, Knight R, Wei Y, Teece L, Toms R, Forbes HJ, Walker AJ, Fisher L, Massey J, Hopcroft LEM, Horne EMF, Taylor K, Palmer T, Arab MA, Cuitun Coronado JI, Ip SHY, Davy S, Dillingham I, Bacon S, Mehrkar A, Morton CE, Greaves F, Hyams C, Davey Smith G, Macleod J, Chaturvedi N, Goldacre B, Whiteley WN, Wood AM, Sterne JAC, Walker V. Impact of vaccination on the association of COVID-19 with cardiovascular diseases: An OpenSAFELY cohort study. Nat Commun 2024; 15:2173. [PMID: 38467603 PMCID: PMC10928172 DOI: 10.1038/s41467-024-46497-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 02/29/2024] [Indexed: 03/13/2024] Open
Abstract
Infection with SARS-CoV-2 is associated with an increased risk of arterial and venous thrombotic events, but the implications of vaccination for this increased risk are uncertain. With the approval of NHS England, we quantified associations between COVID-19 diagnosis and cardiovascular diseases in different vaccination and variant eras using linked electronic health records for ~40% of the English population. We defined a 'pre-vaccination' cohort (18,210,937 people) in the wild-type/Alpha variant eras (January 2020-June 2021), and 'vaccinated' and 'unvaccinated' cohorts (13,572,399 and 3,161,485 people respectively) in the Delta variant era (June-December 2021). We showed that the incidence of each arterial thrombotic, venous thrombotic and other cardiovascular outcomes was substantially elevated during weeks 1-4 after COVID-19, compared with before or without COVID-19, but less markedly elevated in time periods beyond week 4. Hazard ratios were higher after hospitalised than non-hospitalised COVID-19 and higher in the pre-vaccination and unvaccinated cohorts than the vaccinated cohort. COVID-19 vaccination reduces the risk of cardiovascular events after COVID-19 infection. People who had COVID-19 before or without being vaccinated are at higher risk of cardiovascular events for at least two years.
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Affiliation(s)
- Genevieve I Cezard
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Rachel E Denholm
- Population Health Sciences, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Health Data Research UK South-West, Bristol, UK
| | - Rochelle Knight
- Population Health Sciences, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- The National Institute for Health and Care Research Applied Research Collaboration West (NIHR ARC West) at University Hospitals Bristol and Weston, Bristol, UK
| | - Yinghui Wei
- Centre for Mathematical Sciences, School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK
| | - Lucy Teece
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Renin Toms
- Population Health Sciences, University of Bristol, Bristol, UK
- Population Wellbeing, School of Health Sciences, Cardiff Metropolitan University, Cardiff, UK
| | - Harriet J Forbes
- Faculty of Epidemiology and Population Health, London School of Hygiene & tropical Medicine, London, UK
| | - Alex J Walker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Louis Fisher
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Jon Massey
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Elsie M F Horne
- Population Health Sciences, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Kurt Taylor
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Tom Palmer
- Population Health Sciences, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Marwa Al Arab
- Population Health Sciences, University of Bristol, Bristol, UK
| | | | - Samantha H Y Ip
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Simon Davy
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Iain Dillingham
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Sebastian Bacon
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Amir Mehrkar
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Caroline E Morton
- Digital Environment Research Institute, Queen Mary University of London, London, UK
| | - Felix Greaves
- National Institute for Health and Care Excellence, London, UK
- Department of Primary Care and Public Health, Imperial College London, London, UK
| | - Catherine Hyams
- Population Health Sciences, University of Bristol, Bristol, UK
| | - George Davey Smith
- Population Health Sciences, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - John Macleod
- Population Health Sciences, University of Bristol, Bristol, UK
- Health Data Research UK South-West, Bristol, UK
- The National Institute for Health and Care Research Applied Research Collaboration West (NIHR ARC West) at University Hospitals Bristol and Weston, Bristol, UK
| | - Nishi Chaturvedi
- MRC Unit for Lifelong Health and Ageing, University College London, London, UK
| | - Ben Goldacre
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - William N Whiteley
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Angela M Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Cambridge Centre of Artificial Intelligence in Medicine, Cambridge, UK
| | - Jonathan A C Sterne
- Population Health Sciences, University of Bristol, Bristol, UK.
- NIHR Bristol Biomedical Research Centre, Bristol, UK.
- Health Data Research UK South-West, Bristol, UK.
| | - Venexia Walker
- Population Health Sciences, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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12
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Gibbs A, Maripuu M, Öhlund L, Widerström M, Nilsson N, Werneke U. COVID-19-associated mortality in individuals with serious mental disorders in Sweden during the first two years of the pandemic- a population-based register study. BMC Psychiatry 2024; 24:189. [PMID: 38454398 PMCID: PMC10921643 DOI: 10.1186/s12888-024-05629-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 02/20/2024] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND Reports at the beginning of the COVID-19 pandemic suggested differences in COVID-19-associated mortality between individuals with serious mental disorders (SMD) and the population at large. AIM To compare the pattern of COVID-19-associated mortality in individuals with and without SMD in Sweden over the two main pandemic years. METHODS We compared the pattern of COVID-19-associated mortality in individuals with and without SMD in Sweden during 2020 and 2021. For SMD, we included psychotic disorder, bipolar disorder, and severe depression. The analysis was based on summary data from the Swedish Board of Health and Welfare covering the entire adult Swedish population. RESULTS The overall relative risk (RR) for experiencing a COVID-19-associated death was 1.66 (CI 1.50-1.83; p < 0.001) for individuals with SMD versus individuals without SMD. The corresponding RRs were 3.25 (CI 2.84-3.71; p < 0.001) for individuals with psychotic disorder, 1.06 (CI 0.88-1.26; p = 0.54) for individuals with bipolar disorder, and 1.03 (CI 0.80-1.32; p = 0.80) for individuals with severe depression. Compared to their respective counterparts in the non-SMD group, in the psychotic disorder and severe depression group, the RR were higher in women than in men. In the bipolar disorder group, the RR was higher in men than in women. The RR of COVID-19-associated death was generally higher in younger individuals with SMD. Individuals with psychosis between 18 and 59 years had the highest RR of COVID-19-associated death with 7.25 (CI 4.54-11.59; p<0.001). CONCLUSIONS Individuals with SMD, and particularly those with psychotic disorders, had a higher risk of COVID-19-associated death than the general population. As this is a pattern also seen with other infections, people with SMD may be similarly vulnerable in future pandemics.
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Affiliation(s)
- Anna Gibbs
- Department of Clinical Sciences, Division of Psychiatry, Sunderby Research Unit, Umeå University, Umeå, Sweden
| | - Martin Maripuu
- Department of Clinical Sciences, Division of Psychiatry, Umeå University, Umeå, Sweden
| | - Louise Öhlund
- Department of Clinical Sciences, Division of Psychiatry, Sunderby Research Unit, Umeå University, Umeå, Sweden
| | | | - Niklas Nilsson
- Department of Clinical Sciences, Division of Psychiatry, Umeå University, Umeå, Sweden
| | - Ursula Werneke
- Department of Clinical Sciences, Division of Psychiatry, Sunderby Research Unit, Umeå University, Umeå, Sweden.
- Department of Psychiatry, Sunderby Hospital, Luleå, 97180, Sweden.
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13
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Jones G, Perry M, Bailey R, Arumugam S, Edwards A, Lench A, Cooper A, Akbari A, Collins B, Harris C, Richardson G, Barry M, Harris P, Fry R, Lyons RA, Cottrell S. Dimensions of equality in uptake of COVID-19 vaccination in Wales, UK: A multivariable linked data population analysis. Vaccine 2023; 41:7333-7341. [PMID: 37932133 DOI: 10.1016/j.vaccine.2023.10.066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 10/12/2023] [Accepted: 10/26/2023] [Indexed: 11/08/2023]
Abstract
Vaccination has proven to be effective at preventing severe outcomes of COVID-19 infection, and uptake in the population has been high in Wales. However, there is a risk that high-level vaccination coverage statistics may mask hidden inequalities in under-served populations, many of whom may be at increased risk of severe outcomes of COVID-19 infection. The study population included 1,436,229 individuals aged 18 years and over, alive and residence in Wales as at 31st July 2022, and excluded immunosuppressed or care home residents. We compared people who had received one or more vaccinations to those with no vaccination using linked data from nine datasets within the Secure Anonymised Information Linkage (SAIL) databank. Multivariable analysis was undertaken to determine the impact of a range of sociodemographic characteristics on vaccination uptake, including ethnicity, country of birth, severe mental illness, homelessness and substance use. We found that overall uptake of first dose of COVID-19 vaccination was high in Wales (92.1 %), with the highest among those aged 80 years and over and females. Those aged under 40 years, household composition (aOR 0.38 95 %CI 0.35-0.41 for 10+ size household compared to two adult household) and being born outside the UK (aOR 0.44 95 %CI 0.43-0.46) had the strongest negative associations with vaccination uptake. This was followed by a history of substance misuse (aOR 0.45 95 %CI 0.44-0.46). Despite high-level population coverage in Wales, significant inequalities remain across several underserved groups. Factors associated with vaccination uptake should not be considered in isolation, to avoid drawing incorrect conclusions. Ensuring equitable access to vaccination is essential to protecting under-served groups from COVID-19 and further work needs to be done to address these gaps in coverage, with focus on tailored vaccination pathways and advocacy, using trusted partners and communities.
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Affiliation(s)
- Gethin Jones
- Vaccine Preventable Disease Programme and Communicable Disease Surveillance Centre, Public Health Wales, 2 Capital Quarter, Tyndall Street, Cardiff CF10 4BZ, Wales, UK.
| | - Malorie Perry
- Vaccine Preventable Disease Programme and Communicable Disease Surveillance Centre, Public Health Wales, 2 Capital Quarter, Tyndall Street, Cardiff CF10 4BZ, Wales, UK; Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University Swansea, SA2 8PP Wales, UK.
| | - Rowena Bailey
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University Swansea, SA2 8PP Wales, UK.
| | - Sudha Arumugam
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University Swansea, SA2 8PP Wales, UK.
| | - Adrian Edwards
- Wales COVID-19 Evidence Centre, PRIME Centre Wales, Division of Population Medicine, School of Medicine, Cardiff University, 8th Floor, Neuadd Meirionnydd, Heath Park, Cardiff CF14 4XN, Wales, UK.
| | - Alex Lench
- Vaccine Preventable Disease Programme and Communicable Disease Surveillance Centre, Public Health Wales, 2 Capital Quarter, Tyndall Street, Cardiff CF10 4BZ, Wales, UK.
| | - Alison Cooper
- Wales COVID-19 Evidence Centre, PRIME Centre Wales, Division of Population Medicine, School of Medicine, Cardiff University, 8th Floor, Neuadd Meirionnydd, Heath Park, Cardiff CF14 4XN, Wales, UK.
| | - Ashley Akbari
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University Swansea, SA2 8PP Wales, UK.
| | - Brendan Collins
- Health and Social Services Group, Health Protection, Welsh Government, Cardiff, UK; Department of Public Health, Policy and Systems, University of Liverpool, UK.
| | - Caroline Harris
- Vaccine Preventable Disease Programme and Communicable Disease Surveillance Centre, Public Health Wales, 2 Capital Quarter, Tyndall Street, Cardiff CF10 4BZ, Wales, UK.
| | - Gill Richardson
- Policy, Research and International Development, Public Health Wales, 2 Capital Quarter, Tyndall Street, Cardiff CF10 4BZ, Wales, UK.
| | - Mai Barry
- Vaccine Preventable Disease Programme and Communicable Disease Surveillance Centre, Public Health Wales, 2 Capital Quarter, Tyndall Street, Cardiff CF10 4BZ, Wales, UK.
| | - Phillippa Harris
- Vaccine Preventable Disease Programme and Communicable Disease Surveillance Centre, Public Health Wales, 2 Capital Quarter, Tyndall Street, Cardiff CF10 4BZ, Wales, UK.
| | - Richard Fry
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University Swansea, SA2 8PP Wales, UK.
| | - Ronan A Lyons
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University Swansea, SA2 8PP Wales, UK.
| | - Simon Cottrell
- Vaccine Preventable Disease Programme and Communicable Disease Surveillance Centre, Public Health Wales, 2 Capital Quarter, Tyndall Street, Cardiff CF10 4BZ, Wales, UK.
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14
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Lin CW, Chang YP, Yen CF. Predictors of Motivation to Receive a COVID-19 Vaccination and the Number of COVID-19 Vaccine Doses Received in Patients with Schizophrenia. Vaccines (Basel) 2023; 11:1781. [PMID: 38140185 PMCID: PMC10747192 DOI: 10.3390/vaccines11121781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/09/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023] Open
Abstract
Individuals with schizophrenia are more likely to be infected with COVID-19 than are members of the general population. No prospective study has examined the associations of multi-dimensional factors with the motivation to receive vaccination against COVID-19. This follow-up study investigated the effects of individual (sociodemographic and illness characteristics, depression, and self-esteem), environmental (perceived social support), and individual-environmental interaction factors (self-stigma and loneliness) on the motivation to receive vaccination against COVID-19 and the number of COVID-19 vaccine doses received one year later among 300 individuals with schizophrenia. The associations of baseline factors with motivation to receive vaccination against COVID-19 and the number of vaccine doses received 1 year later were examined through linear regression analysis. The results indicated that greater loneliness (p < 0.01) and being married or cohabitating (p < 0.05) at baseline were significantly associated with lower motivation to receive vaccination against COVID-19 at follow-up. Disorganization (p < 0.05) at baseline was significantly associated with fewer COVID-19 vaccine doses at follow-up; greater motivation to receive vaccination was significantly associated with more COVID-19 vaccine doses at follow-up (p < 0.001). Health professionals should consider the identified predictors while developing intervention programs aimed at enhancing vaccination against COVID-19 in individuals with schizophrenia.
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Affiliation(s)
- Chien-Wen Lin
- Department of Psychiatry, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
- Department of Psychiatry, School of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Yu-Ping Chang
- School of Nursing, The State University of New York, University at Buffalo, New York, NY 14214-8013, USA
| | - Cheng-Fang Yen
- Department of Psychiatry, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
- Department of Psychiatry, School of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- College of Professional Studies, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
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Mellor J, Overton CE, Fyles M, Chawner L, Baxter J, Baird T, Ward T. Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK. Epidemiol Infect 2023; 151:e172. [PMID: 37664991 PMCID: PMC10600913 DOI: 10.1017/s0950268823001449] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 07/20/2023] [Accepted: 07/25/2023] [Indexed: 09/05/2023] Open
Abstract
Following the end of universal testing in the UK, hospital admissions are a key measure of COVID-19 pandemic pressure. Understanding leading indicators of admissions at the National Health Service (NHS) Trust, regional and national geographies help health services plan for ongoing pressures. We explored the spatio-temporal relationships of leading indicators of hospitalisations across SARS-CoV-2 waves in England. This analysis includes an evaluation of internet search volumes from Google Trends, NHS triage calls and online queries, the NHS COVID-19 app, lateral flow devices (LFDs), and the ZOE app. Data sources were analysed for their feasibility as leading indicators using Granger causality, cross-correlation, and dynamic time warping at fine spatial scales. Google Trends and NHS triages consistently temporally led admissions in most locations, with lead times ranging from 5 to 20 days, whereas an inconsistent relationship was found for the ZOE app, NHS COVID-19 app, and LFD testing, which diminished with spatial resolution, showing cross-correlation of leads between -7 and 7 days. The results indicate that novel surveillance sources can be used effectively to understand the expected healthcare burden within hospital administrative areas though the temporal and spatial heterogeneity of these relationships is a key determinant of their operational public health utility.
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Affiliation(s)
- Jonathon Mellor
- UK Health Security Agency, Data, Analytics and Surveillance, Nobel House, London, UK
| | - Christopher E Overton
- UK Health Security Agency, Data, Analytics and Surveillance, Nobel House, London, UK
- Department of Mathematical Sciences, University of Liverpool, Liverpool, UK
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Martyn Fyles
- UK Health Security Agency, Data, Analytics and Surveillance, Nobel House, London, UK
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Liam Chawner
- UK Health Security Agency, Data, Analytics and Surveillance, Nobel House, London, UK
| | - James Baxter
- UK Health Security Agency, Data, Analytics and Surveillance, Nobel House, London, UK
| | - Tarrion Baird
- UK Health Security Agency, Data, Analytics and Surveillance, Nobel House, London, UK
- Department of Pathology, University of Cambridge, Cambridge, UK
| | - Thomas Ward
- UK Health Security Agency, Data, Analytics and Surveillance, Nobel House, London, UK
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16
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Macdonald O, Green A, Walker A, Curtis H, Croker R, Brown A, Butler-Cole B, Andrews C, Massey J, Inglesby P, Morton C, Fisher L, Morley J, Mehrkar A, Bacon S, Davy S, Evans D, Dillingham I, Ward T, Hulme W, Bates C, Cockburn J, Parry J, Hester F, Harper S, O'Hanlon S, Eavis A, Jarvis R, Avramov D, Parkes N, Wood I, Goldacre B, Mackenna B. Impact of the COVID-19 pandemic on antipsychotic prescribing in individuals with autism, dementia, learning disability, serious mental illness or living in a care home: a federated analysis of 59 million patients' primary care records in situ using OpenSAFELY. BMJ MENTAL HEALTH 2023; 26:e300775. [PMID: 37714668 PMCID: PMC11146375 DOI: 10.1136/bmjment-2023-300775] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/07/2023] [Indexed: 09/17/2023]
Abstract
BACKGROUND The COVID-19 pandemic affected how care was delivered to vulnerable patients, such as those with dementia or learning disability. OBJECTIVE To explore whether this affected antipsychotic prescribing in at-risk populations. METHODS With the approval of NHS England, we completed a retrospective cohort study, using the OpenSAFELY platform to explore primary care data of 59 million patients. We identified patients in five at-risk groups: autism, dementia, learning disability, serious mental illness and care home residents. We calculated the monthly prevalence of antipsychotic prescribing in these groups, as well as the incidence of new prescriptions in each month. FINDINGS The average monthly rate of antipsychotic prescribing increased in dementia from 82.75 patients prescribed an antipsychotic per 1000 patients (95% CI 82.30 to 83.19) in January-March 2019 to 90.1 (95% CI 89.68 to 90.60) in October-December 2021 and from 154.61 (95% CI 153.79 to 155.43) to 166.95 (95% CI 166.23 to 167.67) in care homes. There were notable spikes in the rate of new prescriptions issued to patients with dementia and in care homes. In learning disability and autism groups, the rate of prescribing per 1000 decreased from 122.97 (95% CI 122.29 to 123.66) to 119.29 (95% CI 118.68 to 119.91) and from 54.91 (95% CI 54.52 to 55.29) to 51.04 (95% CI 50.74 to 51.35), respectively. CONCLUSION AND IMPLICATIONS We observed a spike in antipsychotic prescribing in the dementia and care home groups, which correlated with lockdowns and was likely due to prescribing of antipsychotics for palliative care. We observed gradual increases in antipsychotic use in dementia and care home patients and decreases in their use in patients with learning disability or autism.
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Affiliation(s)
- Orla Macdonald
- Pharmacy, Oxford Health NHS Foundation Trust, Oxford, UK
| | - Amelia Green
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Alex Walker
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Helen Curtis
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Richard Croker
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Andrew Brown
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Ben Butler-Cole
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Colm Andrews
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Jon Massey
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Peter Inglesby
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Caroline Morton
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Louis Fisher
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Jessica Morley
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Amir Mehrkar
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Sebastian Bacon
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Simon Davy
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - David Evans
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Iain Dillingham
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Tom Ward
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - William Hulme
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | | | | | | | | | | | | | | | | | | | | | | | - Ben Goldacre
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Brian Mackenna
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
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17
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Fisher L, Curtis HJ, Croker R, Wiedemann M, Speed V, Wood C, Brown A, Hopcroft LEM, Higgins R, Massey J, Inglesby P, Morton CE, Walker AJ, Morley J, Mehrkar A, Bacon S, Hickman G, Macdonald O, Lewis T, Wood M, Myers M, Samuel M, Conibere R, Baqir W, Sood H, Drury C, Collison K, Bates C, Evans D, Dillingham I, Ward T, Davy S, Smith RM, Hulme W, Green A, Parry J, Hester F, Harper S, Cockburn J, O'Hanlon S, Eavis A, Jarvis R, Avramov D, Griffiths P, Fowles A, Parkes N, MacKenna B, Goldacre B. Eleven key measures for monitoring general practice clinical activity during COVID-19: A retrospective cohort study using 48 million adults' primary care records in England through OpenSAFELY. eLife 2023; 12:e84673. [PMID: 37498081 PMCID: PMC10374277 DOI: 10.7554/elife.84673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 07/06/2023] [Indexed: 07/28/2023] Open
Abstract
Background The COVID-19 pandemic has had a significant impact on delivery of NHS care. We have developed the OpenSAFELY Service Restoration Observatory (SRO) to develop key measures of primary care activity and describe the trends in these measures throughout the COVID-19 pandemic. Methods With the approval of NHS England, we developed an open source software framework for data management and analysis to describe trends and variation in clinical activity across primary care electronic health record (EHR) data on 48 million adults.We developed SNOMED-CT codelists for key measures of primary care clinical activity such as blood pressure monitoring and asthma reviews, selected by an expert clinical advisory group and conducted a population cohort-based study to describe trends and variation in these measures January 2019-December 2021, and pragmatically classified their level of recovery one year into the pandemic using the percentage change in the median practice level rate. Results We produced 11 measures reflective of clinical activity in general practice. A substantial drop in activity was observed in all measures at the outset of the COVID-19 pandemic. By April 2021, the median rate had recovered to within 15% of the median rate in April 2019 in six measures. The remaining measures showed a sustained drop, ranging from a 18.5% reduction in medication reviews to a 42.0% reduction in blood pressure monitoring. Three measures continued to show a sustained drop by December 2021. Conclusions The COVID-19 pandemic was associated with a substantial change in primary care activity across the measures we developed, with recovery in most measures. We delivered an open source software framework to describe trends and variation in clinical activity across an unprecedented scale of primary care data. We will continue to expand the set of key measures to be routinely monitored using our publicly available NHS OpenSAFELY SRO dashboards with near real-time data. Funding This research used data assets made available as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20058).The OpenSAFELY Platform is supported by grants from the Wellcome Trust (222097/Z/20/Z); MRC (MR/V015757/1, MC_PC-20059, MR/W016729/1); NIHR (NIHR135559, COV-LT2-0073), and Health Data Research UK (HDRUK2021.000, 2021.0157).
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Affiliation(s)
- Louis Fisher
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Helen J Curtis
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Richard Croker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Milan Wiedemann
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Victoria Speed
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Christopher Wood
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Andrew Brown
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Lisa E M Hopcroft
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rose Higgins
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Jon Massey
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Peter Inglesby
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Caroline E Morton
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Alex J Walker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Jessica Morley
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Amir Mehrkar
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Seb Bacon
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - George Hickman
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Orla Macdonald
- Oxford Health Foundation Trust, Warneford Hospital, Oxford, United Kingdom
| | - Tom Lewis
- Royal Devon University Healthcare NHS Foundation Trust, Barnstaple, United Kingdom
| | | | - Martin Myers
- Lancashire Teaching Hospitals NHS Foundation Trust, Chorley, United Kingdom
| | - Miriam Samuel
- Queen Mary University of London, London, United Kingdom
| | | | | | | | - Charles Drury
- Herefordshire and Worcestershire Health and Care NHS Trust, Worcester, United Kingdom
| | | | | | - David Evans
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Iain Dillingham
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Tom Ward
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Simon Davy
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rebecca M Smith
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - William Hulme
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Amelia Green
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | | | | | | | | | | | | | | | | | | | | | - Brian MacKenna
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
- NHS England, London, United Kingdom
| | - Ben Goldacre
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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18
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Ekezie W, Connor A, Gibson E, Khunti K, Kamal A. A Systematic Review of Behaviour Change Techniques within Interventions to Increase Vaccine Uptake among Ethnic Minority Populations. Vaccines (Basel) 2023; 11:1259. [PMID: 37515074 PMCID: PMC10386142 DOI: 10.3390/vaccines11071259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/11/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023] Open
Abstract
COVID-19 caused significant morbidity and mortality amongst ethnic minority groups, but vaccine uptake remained lower than non-minoritised groups. Interventions to increase vaccine uptake among ethnic minority communities are crucial. This systematic review synthesises and evaluates behaviour change techniques (BCTs) in interventions to increase vaccination uptake in ethnic minority populations. We searched five databases and grey literature sources. From 7637 records identified, 23 studies were included in the review. Interventions were categorised using the Behaviour Change Wheel (BCW) and Behaviour Change Taxonomy v1. Vaccines included influenza, pertussis, tetanus, diphtheria, meningitis and hepatitis. Interventions were primarily delivered in health centres/clinics and community settings. Six BCW intervention functions and policy categories and 26 BCTs were identified. The main intervention functions used were education, persuasion and enablement. Overall, effective interventions had multi-components and were tailored to specific populations. No strong evidence was observed to recommend specific interventions, but raising awareness and involvement of community organisations was associated with positive effects. Several strategies are used to increase vaccine uptake among ethnic minority communities; however, these do not address all issues related to low vaccine acceptance. There is a strong need for an increased understanding of addressing vaccine hesitancy among ethnic minority groups.
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Affiliation(s)
- Winifred Ekezie
- Diabetes Research Centre, University of Leicester, Leicester LE5 4PW, UK
- Centre for Ethnic Health Research, University of Leicester, Leicester LE5 4PW, UK
| | - Aaisha Connor
- School of Social Sciences, Birmingham City University, Birmingham B4 7BD, UK
| | - Emma Gibson
- School of Social Sciences, Birmingham City University, Birmingham B4 7BD, UK
| | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester LE5 4PW, UK
- Centre for Ethnic Health Research, University of Leicester, Leicester LE5 4PW, UK
| | - Atiya Kamal
- School of Social Sciences, Birmingham City University, Birmingham B4 7BD, UK
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19
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Hopcroft LE, Curtis HJ, Brown AD, Hulme WJ, Andrews CD, Morton CE, Inglesby P, Morley J, Mehrkar A, Bacon SC, Eggo RM, Mahalingasivam V, Parker EPK, Tomlinson LA, Bates C, Cockburn J, Parry J, Hester F, Harper S, Goldacre B, Walker AJ, MacKenna B. First dose COVID-19 vaccine coverage amongst adolescents and children in England: an analysis of 3.21 million patients' primary care records in situ using OpenSAFELY. Wellcome Open Res 2023; 8:70. [PMID: 37346822 PMCID: PMC10280033 DOI: 10.12688/wellcomeopenres.18735.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/07/2023] [Indexed: 06/23/2023] Open
Abstract
Background: The coronavirus disease 2019 (COVID-19) vaccination programme in England was extended to include all adolescents and children by April 2022. The aim of this paper is to describe trends and variation in vaccine coverage in different clinical and demographic groups amongst adolescents and children in England by August 2022. Methods: With the approval of NHS England, a cohort study was conducted of 3.21 million children and adolescents' records in general practice in England, in situ and within the infrastructure of the electronic health record software vendor TPP using OpenSAFELY. Vaccine coverage across various demographic (sex, deprivation index and ethnicity) and clinical (risk status) populations is described. Results: Coverage is higher amongst adolescents than it is amongst children, with 53.5% adolescents and 10.8% children having received their first dose of the COVID-19 vaccine. Within those groups, coverage varies by ethnicity, deprivation index and risk status; there is no evidence of variation by sex. Conclusion: First dose COVID-19 vaccine coverage is shown to vary amongst various demographic and clinical groups of children and adolescents.
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Affiliation(s)
- Lisa E. Hopcroft
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Helen J. Curtis
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Andrew D. Brown
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - William J. Hulme
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Colm D. Andrews
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Caroline E. Morton
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Peter Inglesby
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Jessica Morley
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Amir Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Sebastian C. Bacon
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Rosalind M. Eggo
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | | | | | | | | | | | - John Parry
- TPP, TPP House, 129 Low Lane, ,Horsforth, Leeds, LS18 5PX, UK
| | - Frank Hester
- TPP, TPP House, 129 Low Lane, ,Horsforth, Leeds, LS18 5PX, UK
| | - Sam Harper
- TPP, TPP House, 129 Low Lane, ,Horsforth, Leeds, LS18 5PX, UK
| | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Alex J. Walker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Brian MacKenna
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
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20
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Fisher L, Hopcroft LEM, Rodgers S, Barrett J, Oliver K, Avery AJ, Evans D, Curtis H, Croker R, Macdonald O, Morley J, Mehrkar A, Bacon S, Davy S, Dillingham I, Evans D, Hickman G, Inglesby P, Morton CE, Smith B, Ward T, Hulme W, Green A, Massey J, Walker AJ, Bates C, Cockburn J, Parry J, Hester F, Harper S, O’Hanlon S, Eavis A, Jarvis R, Avramov D, Griffiths P, Fowles A, Parkes N, Goldacre B, MacKenna B. Changes in medication safety indicators in England throughout the covid-19 pandemic using OpenSAFELY: population based, retrospective cohort study of 57 million patients using federated analytics. BMJ MEDICINE 2023; 2:e000392. [PMID: 37303488 PMCID: PMC10254692 DOI: 10.1136/bmjmed-2022-000392] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 03/16/2023] [Indexed: 06/13/2023]
Abstract
Objective To implement complex, PINCER (pharmacist led information technology intervention) prescribing indicators, on a national scale with general practice data to describe the impact of the covid-19 pandemic on safe prescribing. Design Population based, retrospective cohort study using federated analytics. Setting Electronic general practice health record data from 56.8 million NHS patients by use of the OpenSAFELY platform, with the approval of the National Health Service (NHS) England. Participants NHS patients (aged 18-120 years) who were alive and registered at a general practice that used TPP or EMIS computer systems and were recorded as at risk of at least one potentially hazardous PINCER indicator. Main outcome measure Between 1 September 2019 and 1 September 2021, monthly trends and between practice variation for compliance with 13 PINCER indicators, as calculated on the first of every month, were reported. Prescriptions that do not adhere to these indicators are potentially hazardous and can cause gastrointestinal bleeds; are cautioned against in specific conditions (specifically heart failure, asthma, and chronic renal failure); or require blood test monitoring. The percentage for each indicator is formed of a numerator of patients deemed to be at risk of a potentially hazardous prescribing event and the denominator is of patients for which assessment of the indicator is clinically meaningful. Higher indicator percentages represent potentially poorer performance on medication safety. Results The PINCER indicators were successfully implemented across general practice data for 56.8 million patient records from 6367 practices in OpenSAFELY. Hazardous prescribing remained largely unchanged during the covid-19 pandemic, with no evidence of increases in indicators of harm as captured by the PINCER indicators. The percentage of patients at risk of potentially hazardous prescribing, as defined by each PINCER indicator, at mean quarter 1 (Q1) 2020 (representing before the pandemic) ranged from 1.11% (age ≥65 years and non-steroidal anti-inflammatory drugs) to 36.20% (amiodarone and no thyroid function test), while Q1 2021 (representing after the pandemic) percentages ranged from 0.75% (age ≥65 years and non-steroidal anti-inflammatory drugs) to 39.23% (amiodarone and no thyroid function test). Transient delays occurred in blood test monitoring for some medications, particularly angiotensin-converting enzyme inhibitors (where blood monitoring worsened from a mean of 5.16% in Q1 2020 to 12.14% in Q1 2021, and began to recover in June 2021). All indicators substantially recovered by September 2021. We identified 1 813 058 patients (3.1%) at risk of at least one potentially hazardous prescribing event. Conclusion NHS data from general practices can be analysed at national scale to generate insights into service delivery. Potentially hazardous prescribing was largely unaffected by the covid-19 pandemic in primary care health records in England.
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Affiliation(s)
- Louis Fisher
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Lisa EM Hopcroft
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Sarah Rodgers
- PRIMIS, School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | - James Barrett
- PRIMIS, School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | - Kerry Oliver
- PRIMIS, School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | - Anthony J Avery
- Centre for Academic Primary Care, School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | - Dai Evans
- PRIMIS, School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | - Helen Curtis
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Richard Croker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Orla Macdonald
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Jessica Morley
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Amir Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Sebastian Bacon
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Simon Davy
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Iain Dillingham
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - David Evans
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - George Hickman
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Peter Inglesby
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Caroline E Morton
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Becky Smith
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Tom Ward
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - William Hulme
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Amelia Green
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Jon Massey
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Alex J Walker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Brian MacKenna
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
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21
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Parker EP, Horne EM, Hulme WJ, Tazare J, Zheng B, Carr EJ, Loud F, Lyon S, Mahalingasivam V, MacKenna B, Mehrkar A, Scanlon M, Santhakumaran S, Steenkamp R, Goldacre B, Sterne JA, Nitsch D, Tomlinson LA. Comparative effectiveness of two- and three-dose COVID-19 vaccination schedules involving AZD1222 and BNT162b2 in people with kidney disease: a linked OpenSAFELY and UK Renal Registry cohort study. THE LANCET REGIONAL HEALTH. EUROPE 2023; 30:100636. [PMID: 37363796 PMCID: PMC10155829 DOI: 10.1016/j.lanepe.2023.100636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/22/2023] [Accepted: 03/28/2023] [Indexed: 06/28/2023]
Abstract
Background Kidney disease is a key risk factor for COVID-19-related mortality and suboptimal vaccine response. Optimising vaccination strategies is essential to reduce the disease burden in this vulnerable population. We therefore compared the effectiveness of two- and three-dose schedules involving AZD1222 (AZ; ChAdOx1-S) and BNT162b2 (BNT) among people with kidney disease in England. Methods With the approval of NHS England, we performed a retrospective cohort study among people with moderate-to-severe kidney disease. Using linked primary care and UK Renal Registry records in the OpenSAFELY-TPP platform, we identified adults with stage 3-5 chronic kidney disease, dialysis recipients, and kidney transplant recipients. We used Cox proportional hazards models to compare COVID-19-related outcomes and non-COVID-19 death after two-dose (AZ-AZ vs BNT-BNT) and three-dose (AZ-AZ-BNT vs BNT-BNT-BNT) schedules. Findings After two doses, incidence during the Delta wave was higher in AZ-AZ (n = 257,580) than BNT-BNT recipients (n = 169,205; adjusted hazard ratios [95% CIs] 1.43 [1.37-1.50], 1.59 [1.43-1.77], 1.44 [1.12-1.85], and 1.09 [1.02-1.17] for SARS-CoV-2 infection, COVID-19-related hospitalisation, COVID-19-related death, and non-COVID-19 death, respectively). Findings were consistent across disease subgroups, including dialysis and transplant recipients. After three doses, there was little evidence of differences between AZ-AZ-BNT (n = 220,330) and BNT-BNT-BNT recipients (n = 157,065) for any outcome during a period of Omicron dominance. Interpretation Among individuals with moderate-to-severe kidney disease, two doses of BNT conferred stronger protection than AZ against SARS-CoV-2 infection and severe disease. A subsequent BNT dose levelled the playing field, emphasising the value of heterologous RNA doses in vulnerable populations. Funding National Core Studies, Wellcome Trust, MRC, and Health Data Research UK.
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Affiliation(s)
- The OpenSAFELY Collaborative
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
- The Francis Crick Institute, London, NW1 1AT, UK
- Kidney Care UK, Alton, UK
- Patient Council, UK Kidney Association, Bristol, UK
- Kidney Research UK, Peterborough, UK
- UK Renal Registry, Bristol, UK
- Health Data Research UK South-West, Bristol, UK
| | - Edward P.K. Parker
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Elsie M.F. Horne
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - William J. Hulme
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
| | - John Tazare
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Bang Zheng
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | | | | | - Susan Lyon
- Patient Council, UK Kidney Association, Bristol, UK
| | | | - Brian MacKenna
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
| | - Amir Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
| | | | | | | | - Ben Goldacre
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Jonathan A.C. Sterne
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Health Data Research UK South-West, Bristol, UK
| | - Dorothea Nitsch
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- UK Renal Registry, Bristol, UK
| | - Laurie A. Tomlinson
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - The LH&W NCS (or CONVALESCENCE) Collaborative
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
- The Francis Crick Institute, London, NW1 1AT, UK
- Kidney Care UK, Alton, UK
- Patient Council, UK Kidney Association, Bristol, UK
- Kidney Research UK, Peterborough, UK
- UK Renal Registry, Bristol, UK
- Health Data Research UK South-West, Bristol, UK
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22
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Nab L, Parker EPK, Andrews CD, Hulme WJ, Fisher L, Morley J, Mehrkar A, MacKenna B, Inglesby P, Morton CE, Bacon SCJ, Hickman G, Evans D, Ward T, Smith RM, Davy S, Dillingham I, Maude S, Butler-Cole BFC, O'Dwyer T, Stables CL, Bridges L, Bates C, Cockburn J, Parry J, Hester F, Harper S, Zheng B, Williamson EJ, Eggo RM, Evans SJW, Goldacre B, Tomlinson LA, Walker AJ. Changes in COVID-19-related mortality across key demographic and clinical subgroups in England from 2020 to 2022: a retrospective cohort study using the OpenSAFELY platform. Lancet Public Health 2023; 8:e364-e377. [PMID: 37120260 PMCID: PMC10139026 DOI: 10.1016/s2468-2667(23)00079-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/01/2023] [Accepted: 03/22/2023] [Indexed: 05/01/2023]
Abstract
BACKGROUND COVID-19 has been shown to differently affect various demographic and clinical population subgroups. We aimed to describe trends in absolute and relative COVID-19-related mortality risks across clinical and demographic population subgroups during successive SARS-CoV-2 pandemic waves. METHODS We did a retrospective cohort study in England using the OpenSAFELY platform with the approval of National Health Service England, covering the first five SARS-CoV-2 pandemic waves (wave one [wild-type] from March 23 to May 30, 2020; wave two [alpha (B.1.1.7)] from Sept 7, 2020, to April 24, 2021; wave three [delta (B.1.617.2)] from May 28 to Dec 14, 2021; wave four [omicron (B.1.1.529)] from Dec 15, 2021, to April 29, 2022; and wave five [omicron] from June 24 to Aug 3, 2022). In each wave, we included people aged 18-110 years who were registered with a general practice on the first day of the wave and who had at least 3 months of continuous general practice registration up to this date. We estimated crude and sex-standardised and age-standardised wave-specific COVID-19-related death rates and relative risks of COVID-19-related death in population subgroups. FINDINGS 18 895 870 adults were included in wave one, 19 014 720 in wave two, 18 932 050 in wave three, 19 097 970 in wave four, and 19 226 475 in wave five. Crude COVID-19-related death rates per 1000 person-years decreased from 4·48 deaths (95% CI 4·41-4·55) in wave one to 2·69 (2·66-2·72) in wave two, 0·64 (0·63-0·66) in wave three, 1·01 (0·99-1·03) in wave four, and 0·67 (0·64-0·71) in wave five. In wave one, the standardised COVID-19-related death rates were highest in people aged 80 years or older, people with chronic kidney disease stage 5 or 4, people receiving dialysis, people with dementia or learning disability, and people who had received a kidney transplant (ranging from 19·85 deaths per 1000 person-years to 44·41 deaths per 1000 person-years, compared with from 0·05 deaths per 1000 person-years to 15·93 deaths per 1000 person-years in other subgroups). In wave two compared with wave one, in a largely unvaccinated population, the decrease in COVID-19-related mortality was evenly distributed across population subgroups. In wave three compared with wave one, larger decreases in COVID-19-related death rates were seen in groups prioritised for primary SARS-CoV-2 vaccination, including people aged 80 years or older and people with neurological disease, learning disability, or severe mental illness (90-91% decrease). Conversely, smaller decreases in COVID-19-related death rates were observed in younger age groups, people who had received organ transplants, and people with chronic kidney disease, haematological malignancies, or immunosuppressive conditions (0-25% decrease). In wave four compared with wave one, the decrease in COVID-19-related death rates was smaller in groups with lower vaccination coverage (including younger age groups) and conditions associated with impaired vaccine response, including people who had received organ transplants and people with immunosuppressive conditions (26-61% decrease). INTERPRETATION There was a substantial decrease in absolute COVID-19-related death rates over time in the overall population, but demographic and clinical relative risk profiles persisted and worsened for people with lower vaccination coverage or impaired immune response. Our findings provide an evidence base to inform UK public health policy for protecting these vulnerable population subgroups. FUNDING UK Research and Innovation, Wellcome Trust, UK Medical Research Council, National Institute for Health and Care Research, and Health Data Research UK.
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Affiliation(s)
- Linda Nab
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Colm D Andrews
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - William J Hulme
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Louis Fisher
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Jessica Morley
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Amir Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Brian MacKenna
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Peter Inglesby
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Caroline E Morton
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Sebastian C J Bacon
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - George Hickman
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - David Evans
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Tom Ward
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Rebecca M Smith
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Simon Davy
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Iain Dillingham
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Steven Maude
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Ben F C Butler-Cole
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Thomas O'Dwyer
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Catherine L Stables
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Lucy Bridges
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | | | | | | | | | - Bang Zheng
- London School of Hygiene & Tropical Medicine, London, UK
| | | | | | | | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Alex J Walker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
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23
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Curtis HJ, MacKenna B, Wiedemann M, Fisher L, Croker R, Morton CE, Inglesby P, Walker AJ, Morley J, Mehrkar A, Bacon SC, Hickman G, Evans D, Ward T, Davy S, Hulme WJ, Macdonald O, Conibere R, Lewis T, Myers M, Wanninayake S, Collison K, Drury C, Samuel M, Sood H, Cipriani A, Fazel S, Sharma M, Baqir W, Bates C, Parry J, Goldacre B. OpenSAFELY NHS Service Restoration Observatory 2: changes in primary care clinical activity in England during the COVID-19 pandemic. Br J Gen Pract 2023; 73:e318-e331. [PMID: 37068964 PMCID: PMC10131234 DOI: 10.3399/bjgp.2022.0301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 10/14/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has disrupted healthcare activity across a broad range of clinical services. The NHS stopped non-urgent work in March 2020, later recommending services be restored to near-normal levels before winter where possible. AIM To describe changes in the volume and variation of coded clinical activity in general practice across six clinical areas: cardiovascular disease, diabetes, mental health, female and reproductive health, screening and related procedures, and processes related to medication. DESIGN AND SETTING With the approval of NHS England, a cohort study was conducted of 23.8 million patient records in general practice, in situ using OpenSAFELY. METHOD Common primary care activities were analysed using Clinical Terms Version 3 codes and keyword searches from January 2019 to December 2020, presenting median and deciles of code usage across practices per month. RESULTS Substantial and widespread changes in clinical activity in primary care were identified since the onset of the COVID-19 pandemic, with generally good recovery by December 2020. A few exceptions showed poor recovery and warrant further investigation, such as mental health (for example, for 'Depression interim review' the median occurrences across practices in December 2020 was down by 41.6% compared with December 2019). CONCLUSION Granular NHS general practice data at population-scale can be used to monitor disruptions to healthcare services and guide the development of mitigation strategies. The authors are now developing real-time monitoring dashboards for the key measures identified in this study, as well as further studies using primary care data to monitor and mitigate the indirect health impacts of COVID-19 on the NHS.
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Affiliation(s)
- Helen J Curtis
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Brian MacKenna
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Milan Wiedemann
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Louis Fisher
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Richard Croker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Caroline E Morton
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Peter Inglesby
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Alex J Walker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Jessica Morley
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Amir Mehrkar
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Sebastian Cj Bacon
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - George Hickman
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - David Evans
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Tom Ward
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Simon Davy
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - William J Hulme
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Orla Macdonald
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | | | - Tom Lewis
- Royal Devon University Healthcare NHS Foundation Trust, Barnstaple
| | - Martin Myers
- Lancashire Teaching Hospitals NHS Foundation Trust, Preston
| | | | | | - Charles Drury
- Herefordshire and Worcestershire Health and Care NHS Trust, Worcester
| | - Miriam Samuel
- Wolfson Institute of Population Health, Queen Mary University of London, London
| | - Harpreet Sood
- University College London Hospitals NHS Foundation Trust, London
| | | | - Seena Fazel
- Department of Psychiatry, University of Oxford, Oxford
| | - Manuj Sharma
- Department of Primary Care and Population Health, University College London, London
| | | | | | | | - Ben Goldacre
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
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24
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Ismail S, Chantler T, Paterson P, Letley L, Bell S, Mounier-Jack S. Adapting SARS-CoV-2 vaccination delivery in England to population needs: a thematic analysis of providers and commissioner's perceptions. BMC Health Serv Res 2023; 23:417. [PMID: 37127638 PMCID: PMC10150662 DOI: 10.1186/s12913-023-09350-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 03/28/2023] [Indexed: 05/03/2023] Open
Abstract
BACKGROUND A national SARS-CoV-2 vaccination programme was implemented in England from 8th December 2020, adopting a series of local level service delivery models to maximise rollout. The evidence base informing service design programme at inception was limited. We examined the real-world implementation of the programme through an assessment of sub-national providers' and commissioners' perspectives on the service delivery models used, to strengthen evidence on the acceptability, effectiveness and efficiency of the service delivery approaches used for SARS-CoV-2 vaccination in England or elsewhere. METHODS Qualitative, cross-sectional analysis based on semi-structured interviews conducted with 87 stakeholders working in SARS-CoV-2 vaccination delivery across four regions in England. Localities were selected according to geography and population socio-economic status. Participants were purposively sampled from health service providers, commissioners and other relevant bodies. Interviews were conducted between February and October 2021, and transcripts were thematically analysed using inductive and deductive approaches. RESULTS Various service delivery models were implemented over the course of the programme, beginning with hospital hubs and mass vaccination sites, before expanding to incorporate primary care-led services, mobile and other outreach services. Each had advantages and drawbacks but primary care-led models, and to some extent pharmacies, were perceived to offer a better combination of efficiency and community reach for equitable delivery. Common factors for success included availability of a motivated workforce, predictability in vaccine supply chains and strong community engagement. However, interviewees noted a lack of coordination between service providers in the vaccination programme, linked to differing financial incentives and fragmentated information systems, among other factors. CONCLUSION A range of delivery models are needed to enable vaccine rollout at pace and scale, and to mitigate effects on routine care provision. However, primary care-led services offer a tried-and-trusted framework for vaccine delivery at scale and pace and should be central to planning for future pandemic responses. Mass vaccination sites can offer delivery at scale but may exacerbate inequalities in vaccination coverage and are unlikely to offer value for money. Policymakers in England should prioritise measures to improve collaboration between service providers, including better alignment of IT systems.
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Affiliation(s)
- Sharif Ismail
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK
| | - Tracey Chantler
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK
| | - Pauline Paterson
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Louise Letley
- UK Health Security Agency, 61 Colindale Avenue, London, NW9 5EQ, UK
| | - Sadie Bell
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK
| | - Sandra Mounier-Jack
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK.
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25
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Hassan L, Sawyer C, Peek N, Lovell K, Carvalho AF, Solmi M, Tilston G, Sperrin M, Firth J. Heightened COVID-19 Mortality in People With Severe Mental Illness Persists After Vaccination: A Cohort Study of Greater Manchester Residents. Schizophr Bull 2023; 49:275-284. [PMID: 36029257 PMCID: PMC9452124 DOI: 10.1093/schbul/sbac118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND HYPOTHESIS Previous studies show that people with severe mental illness (SMI) are at higher risk of COVID-19 mortality, however limited evidence exists regarding risk postvaccination. We investigated COVID-19 mortality among people with schizophrenia and other SMIs before, during and after the UK vaccine roll-out. STUDY DESIGN Using the Greater Manchester (GM) Care Record to access routinely collected health data linked with death records, we plotted COVID-19 mortality rates over time in GM residents with schizophrenia/psychosis, bipolar disorder (BD), and/or recurrent major depressive disorder (MDD) from February 2020 to September 2021. Multivariable logistic regression was used to compare mortality risk (risk ratios; RRs) between people with SMI (N = 193 435) and age-sex matched controls (N = 773 734), adjusted for sociodemographic factors, preexisting comorbidities, and vaccination status. STUDY RESULTS Mortality risks were significantly higher among people with SMI compared with matched controls, particularly among people with schizophrenia/psychosis (RR 3.18, CI 2.94-3.44) and/or BD (RR 2.69, CI 2.16-3.34). In adjusted models, the relative risk of COVID-19 mortality decreased, though remained significantly higher than matched controls for people with schizophrenia (RR 1.61, CI 1.45-1.79) and BD (RR 1.92, CI 1.47-2.50), but not recurrent MDD (RR 1.08, CI 0.99-1.17). People with SMI continued to show higher mortality rate ratios relative to controls throughout 2021, during vaccination roll-out. CONCLUSIONS People with SMI, notably schizophrenia and BD, were at greater risk of COVID-19 mortality compared to matched controls. Despite population vaccination efforts that have prioritized people with SMI, disparities still remain in COVID-19 mortality for people with SMI.
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Affiliation(s)
- Lamiece Hassan
- To whom correspondence should be addressed; Jean McFarlane Building, Division of Psychology and Mental Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK; tel: +44 (0) 161 306 6000, e-mail:
| | - Chelsea Sawyer
- Division of Psychology and Mental Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK
| | - Niels Peek
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, The University of Manchester, M13 9PL, UK
- NIHR Greater Manchester Patient Safety Translational Research Centre, The University of Manchester, Manchester, UK
- Manchester Academic Health Science Centre, National Institute for Health Research Manchester Biomedical Research Centre, The University of Manchester, Manchester, M13 9PL, UK
| | - Karina Lovell
- Division of Nursing, Midwifery and Social Work, University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK
| | - Andre F Carvalho
- IMPACT (Innovation in Mental and Physical Health and Clinical Treatment) Strategic Research Centre, School of Medicine, Barwon Health, Deakin University, Geelong, Victoria, Australia
| | - Marco Solmi
- Psychiatry Department, University of Ottawa, Ottawa, ON, Canada
- The Ottawa Hospital, University of Ottawa, Ottawa, ON, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute (OHRI), University of Ottawa, Ottawa, ON, Canada
| | - George Tilston
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, The University of Manchester, M13 9PL, UK
- Manchester Academic Health Science Centre, National Institute for Health Research Manchester Biomedical Research Centre, The University of Manchester, Manchester, M13 9PL, UK
| | - Matthew Sperrin
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, The University of Manchester, M13 9PL, UK
| | - Joseph Firth
- Division of Psychology and Mental Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK
- Greater Manchester Mental Health NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK
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26
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Hopcroft LE, Curtis HJ, Brown AD, Hulme WJ, Andrews CD, Morton CE, Inglesby P, Morley J, Mehrkar A, Bacon SC, Eggo RM, Mahalingasivam V, Parker EPK, Tomlinson LA, Bates C, Cockburn J, Parry J, Hester F, Harper S, Goldacre B, Walker AJ, MacKenna B. First dose COVID-19 vaccine coverage amongst adolescents and children in England: an analysis of 3.21 million patients' primary care records in situ using OpenSAFELY. Wellcome Open Res 2023. [DOI: 10.12688/wellcomeopenres.18735.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
Abstract
Background: The coronavirus disease 2019 (COVID-19) vaccination programme in England was extended to include all adolescents and children by April 2022. The aim of this paper is to describe trends and variation in vaccine coverage in different clinical and demographic groups amongst adolescents and children in England. Methods: With the approval of NHS England, a cohort study was conducted of 3.21 million children and adolescents’ records in general practice in England, in situ and within the infrastructure of the electronic health record software vendor TPP using OpenSAFELY. Vaccine coverage across various demographic (sex, deprivation index and ethnicity) and clinical (risk status) populations is described. Results: Coverage is higher amongst adolescents than it is amongst children, with 53.5% adolescents and 10.8% children having received their first dose of the COVID-19 vaccine. Within those groups, coverage varies by ethnicity, deprivation index and risk status; there is no evidence of variation by sex. Conclusion: First dose COVID-19 vaccine coverage is shown to vary amongst various demographic and clinical groups of children and adolescents.
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27
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Parker EP, Tazare J, Hulme WJ, Bates C, Carr EJ, Cockburn J, Curtis HJ, Fisher L, Green AC, Harper S, Hester F, Horne EM, Loud F, Lyon S, Mahalingasivam V, Mehrkar A, Nab L, Parry J, Santhakumaran S, Steenkamp R, Sterne JA, Walker AJ, Williamson EJ, Willicombe M, Zheng B, Goldacre B, Nitsch D, Tomlinson LA. Factors associated with COVID-19 vaccine uptake in people with kidney disease: an OpenSAFELY cohort study. BMJ Open 2023; 13:e066164. [PMID: 36720568 PMCID: PMC9890277 DOI: 10.1136/bmjopen-2022-066164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 01/06/2023] [Indexed: 02/02/2023] Open
Abstract
OBJECTIVE To characterise factors associated with COVID-19 vaccine uptake among people with kidney disease in England. DESIGN Retrospective cohort study using the OpenSAFELY-TPP platform, performed with the approval of NHS England. SETTING Individual-level routine clinical data from 24 million people across GPs in England using TPP software. Primary care data were linked directly with COVID-19 vaccine records up to 31 August 2022 and with renal replacement therapy (RRT) status via the UK Renal Registry (UKRR). PARTICIPANTS A cohort of adults with stage 3-5 chronic kidney disease (CKD) or receiving RRT at the start of the COVID-19 vaccine roll-out was identified based on evidence of reduced estimated glomerular filtration rate (eGFR) or inclusion in the UKRR. MAIN OUTCOME MEASURES Dose-specific vaccine coverage over time was determined from 1 December 2020 to 31 August 2022. Individual-level factors associated with receipt of a 3-dose or 4-dose vaccine series were explored via Cox proportional hazards models. RESULTS 992 205 people with stage 3-5 CKD or receiving RRT were included. Cumulative vaccine coverage as of 31 August 2022 was 97.5%, 97.0% and 93.9% for doses 1, 2 and 3, respectively, and 81.9% for dose 4 among individuals with one or more indications for eligibility. Delayed 3-dose vaccine uptake was associated with younger age, minority ethnicity, social deprivation and severe mental illness-associations that were consistent across CKD severity subgroups, dialysis patients and kidney transplant recipients. Similar associations were observed for 4-dose uptake. CONCLUSION Although high primary vaccine and booster dose coverage has been achieved among people with kidney disease in England, key disparities in vaccine uptake remain across clinical and demographic groups and 4-dose coverage is suboptimal. Targeted interventions are needed to identify barriers to vaccine uptake among under-vaccinated subgroups identified in the present study.
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Affiliation(s)
| | - John Tazare
- London School of Hygiene & Tropical Medicine, London, UK
| | - William J Hulme
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | | | | | | | - Helen J Curtis
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Louis Fisher
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Amelia Ca Green
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | | | | | - Elsie Mf Horne
- Population Health Sciences, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | | | | | | | - Amir Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Linda Nab
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | | | | | | | - Jonathan Ac Sterne
- Population Health Sciences, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Health Data Research UK South-West, Bristol, UK
| | - Alex J Walker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | | | - Michelle Willicombe
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, UK
- Imperial College Renal and Transplant Centre, Imperial College Healthcare NHS Trust, London, UK
| | - Bang Zheng
- London School of Hygiene & Tropical Medicine, London, UK
| | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Dorothea Nitsch
- London School of Hygiene & Tropical Medicine, London, UK
- UK Renal Registry, Bristol, UK
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Green ACA, Curtis HJ, Higgins R, Nab L, Mahalingasivam V, Smith RM, Mehrkar A, Inglesby P, Drysdale H, DeVito NJ, Croker R, Rentsch CT, Bhaskaran K, Tazare J, Zheng B, Andrews CD, Bacon SCJ, Davy S, Dillingham I, Evans D, Fisher L, Hickman G, Hopcroft LEM, Hulme WJ, Massey J, MacDonald O, Morley J, Morton CE, Park RY, Walker AJ, Ward T, Wiedemann M, Bates C, Cockburn J, Parry J, Hester F, Harper S, Douglas IJ, Evans SJW, Goldacre B, Tomlinson LA, MacKenna B. Trends, variation, and clinical characteristics of recipients of antiviral drugs and neutralising monoclonal antibodies for covid-19 in community settings: retrospective, descriptive cohort study of 23.4 million people in OpenSAFELY. BMJ MEDICINE 2023; 2:e000276. [PMID: 36936265 PMCID: PMC9951378 DOI: 10.1136/bmjmed-2022-000276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 11/25/2022] [Indexed: 01/15/2023]
Abstract
Objective To ascertain patient eligibility status and describe coverage of antiviral drugs and neutralising monoclonal antibodies (nMAB) as treatment for covid-19 in community settings in England. Design Retrospective, descriptive cohort study, approved by NHS England. Setting Routine clinical data from 23.4 million people linked to data on covid-19 infection and treatment, within the OpenSAFELY-TPP database. Participants Outpatients with covid-19 at high risk of severe outcomes. Interventions Nirmatrelvir/ritonavir (paxlovid), sotrovimab, molnupiravir, casirivimab/imdevimab, or remdesivir, used in the community by covid-19 medicine delivery units. Results 93 870 outpatients with covid-19 were identified between 11 December 2021 and 28 April 2022 to be at high risk of severe outcomes and therefore potentially eligible for antiviral or nMAB treatment (or both). Of these patients, 19 040 (20%) received treatment (sotrovimab, 9660 (51%); molnupiravir, 4620 (24%); paxlovid, 4680 (25%); casirivimab/imdevimab, 50 (<1%); and remdesivir, 30 (<1%)). The proportion of patients treated increased from 9% (190/2220) in the first week of treatment availability to 29% (460/1600) in the latest week. The proportion treated varied by high risk group, being lowest in those with liver disease (16%; 95% confidence interval 15% to 17%); by treatment type, with sotrovimab favoured over molnupiravir and paxlovid in all but three high risk groups (Down's syndrome (35%; 30% to 39%), rare neurological conditions (45%; 43% to 47%), and immune deficiencies (48%; 47% to 50%)); by age, ranging from ≥80 years (13%; 12% to 14%) to 50-59 years (23%; 22% to 23%); by ethnic group, ranging from black (11%; 10% to 12%) to white (21%; 21% to 21%); by NHS region, ranging from 13% (12% to 14%) in Yorkshire and the Humber to 25% (24% to 25%) in the East of England); and by deprivation level, ranging from 15% (14% to 15%) in the most deprived areas to 23% (23% to 24%) in the least deprived areas. Groups that also had lower coverage included unvaccinated patients (7%; 6% to 9%), those with dementia (6%; 5% to 7%), and care home residents (6%; 6% to 7%). Conclusions Using the OpenSAFELY platform, we were able to identify patients with covid-19 at high risk of severe outcomes who were potentially eligible to receive treatment and assess the coverage of these new treatments among these patients. In the context of a rapid deployment of a new service, the NHS analytical code used to determine eligibility could have been over-inclusive and some of the eligibility criteria not fully captured in healthcare data. However targeted activity might be needed to resolve apparent lower treatment coverage observed among certain groups, in particular (at present): different NHS regions, ethnic groups, people aged ≥80 years, those living in socioeconomically deprived areas, and care home residents.
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Affiliation(s)
- Amelia C A Green
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Helen J Curtis
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Rose Higgins
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Linda Nab
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Rebecca M Smith
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Amir Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Peter Inglesby
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Henry Drysdale
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Nicholas J DeVito
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Richard Croker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | | | - John Tazare
- London School of Hygiene and Tropical Medicine, London, UK
| | - Bang Zheng
- London School of Hygiene and Tropical Medicine, London, UK
| | - Colm D Andrews
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Sebastian C J Bacon
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Simon Davy
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Iain Dillingham
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - David Evans
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Louis Fisher
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - George Hickman
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Lisa E M Hopcroft
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - William J Hulme
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Jon Massey
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Jessica Morley
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Caroline E Morton
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Robin Y Park
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Alex J Walker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Tom Ward
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Milan Wiedemann
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | | | | | | | | | - Ian J Douglas
- London School of Hygiene and Tropical Medicine, London, UK
| | | | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Brian MacKenna
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
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Mhereeg M, Jones H, Kennedy J, Seaborne M, Parker M, Kennedy N, Beeson S, Akbari A, Zuccolo L, Davies A, Brophy S. COVID-19 vaccination in pregnancy: views and vaccination uptake rates in pregnancy, a mixed methods analysis from SAIL and the Born-In-Wales Birth Cohort. BMC Infect Dis 2022; 22:932. [PMID: 36503414 PMCID: PMC9742024 DOI: 10.1186/s12879-022-07856-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 11/08/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Vaccine hesitancy amongst pregnant women has been found to be a concern during past epidemics. This study aimed to (1) estimate COVID-19 vaccination rates among pregnant women in Wales and their association with age, ethnicity, and area of deprivation, using electronic health record (EHR) data linkage, and (2) explore pregnant women's views on receiving the COVID-19 vaccine during pregnancy using data from a survey recruiting via social media (Facebook, Twitter), through midwives, and posters in hospitals (Born-In-Wales Cohort). METHODS This was a mixed-methods study utilising routinely collected linked data from the Secure Anonymised Information Linkage (SAIL) Databank (Objective 1) and the Born-In-Wales Birth Cohort participants (Objective 2). Pregnant women were identified from 13th April 2021 to 31st December 2021. Survival analysis was utilised to examine and compare the length of time to vaccination uptake in pregnancy, and variation in uptake by; age, ethnic group, and deprivation area was examined using hazard ratios (HR) from Cox regression. Survey respondents were women who had a baby during the COVID-19 pandemic or were pregnant between 1st November 2021 and 24th March 2022 and participating in Born-In-Wales. Codebook thematic analysis was used to generate themes from an open-ended question on the survey. RESULTS Population-level data linkage (objective 1): Within the population cohort, 8203 (32.7%) received at least one dose of the COVID-19 vaccine during pregnancy, 8572 (34.1%) remained unvaccinated throughout the follow-up period, and 8336 (33.2%) received the vaccine postpartum. Younger women (< 30 years) were less likely to have the vaccine, and those living in areas of high deprivation were also less likely to have the vaccine (HR = 0.88, 95% CI 0.82 to 0.95). Asian and Other ethnic groups were 1.12 and 1.18 times more likely to have the vaccine in pregnancy compared with White women (HR = 1.12, 95% CI 1.00 to 1.25) and (HR = 1.18, 95% CI 1.03 to 1.37) respectively. Survey responses (objective 2): 207 (69%) of participants stated that they would be happy to have the vaccine during pregnancy. The remaining 94 (31%) indicated they would not have the vaccine during pregnancy. Reasons for having the vaccine included protecting self and baby, perceived risk level, and receipt of sufficient evidence and advice. Reasons for vaccine refusal included lack of research about long-term outcomes for the baby, anxiety about vaccines, inconsistent advice/information, and preference to wait until after the pregnancy. CONCLUSION Potentially only 1 in 3 pregnant women would have the COVID-19 vaccine during pregnancy, even though 2 in 3 reported they would have the vaccination, thus it is critical to develop tailored strategies to increase its acceptance rate and decrease vaccine hesitancy. A targeted approach to vaccinations may be required for groups such as younger people and those living in higher deprivation areas.
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Affiliation(s)
- Mohamed Mhereeg
- National Centre for Population Health and Wellbeing Research, Faculty of Medicine, Health and Life Science, Swansea University Medical School, Swansea, Wales, UK.
| | - Hope Jones
- National Centre for Population Health and Wellbeing Research, Faculty of Medicine, Health and Life Science, Swansea University Medical School, Swansea, Wales, UK
| | - Jonathan Kennedy
- National Centre for Population Health and Wellbeing Research, Faculty of Medicine, Health and Life Science, Swansea University Medical School, Swansea, Wales, UK
| | - Mike Seaborne
- National Centre for Population Health and Wellbeing Research, Faculty of Medicine, Health and Life Science, Swansea University Medical School, Swansea, Wales, UK
| | - Michael Parker
- National Centre for Population Health and Wellbeing Research, Faculty of Medicine, Health and Life Science, Swansea University Medical School, Swansea, Wales, UK
| | - Natasha Kennedy
- National Centre for Population Health and Wellbeing Research, Faculty of Medicine, Health and Life Science, Swansea University Medical School, Swansea, Wales, UK
| | - Sarah Beeson
- National Centre for Population Health and Wellbeing Research, Faculty of Medicine, Health and Life Science, Swansea University Medical School, Swansea, Wales, UK
| | - Ashley Akbari
- Population Data Science, Faculty of Medicine, Health and Life Science, Swansea University Medical School, Swansea, Wales, UK
| | - Luisa Zuccolo
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England, UK
| | - Alisha Davies
- Research and Evaluation Division, Public Health Wales, Swansea, UK
| | - Sinead Brophy
- National Centre for Population Health and Wellbeing Research, Faculty of Medicine, Health and Life Science, Swansea University Medical School, Swansea, Wales, UK
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Megatsari H, Kusuma D, Ernawaty E, Putri NK. Geographic and Socioeconomic Inequalities in Delays in COVID-19 Vaccinations: A Cross-Sectional Study in Indonesia. Vaccines (Basel) 2022; 10:1857. [PMID: 36366365 PMCID: PMC9695332 DOI: 10.3390/vaccines10111857] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/21/2022] [Accepted: 11/01/2022] [Indexed: 06/23/2024] Open
Abstract
BACKGROUND Previous studies have provided evidence of inequalities in the coverage of COVID-19 vaccination. However, evidence of such inequalities in delays in vaccinations is lacking. Our study examined the socioeconomic and geographic disparities in terms of days to get the first and second dose of COVID-19 vaccinations in Indonesia. METHODS We conducted a cross-sectional study using the WhatsApp messaging app and social media platforms during December 2021-February 2022. We distributed the questionnaire through our university network to reach all regions. We included 3592 adults aged 15+ years in our analysis. We used two main dependent variables: days to receive the first dose (after national vaccine rollout) and days to receive the second dose (after receiving the first dose). We examined a range of socioeconomic and geographic indicators, including education level, income level, formal employment, working in health facilities, being a health worker, and region. We controlled for sex, age, religion, and urbanicity. We performed multivariate logistic regressions in STATA 15. RESULTS Our findings show considerable delays in getting the first dose among participants (160.7 days or about 5.4 months on average) from Indonesia's national COVID-19 vaccination rollout on 13 January 2021. However, we found a shorter period to receive the second dose after receiving the first dose (41.1 days on average). Moreover, we found significant socioeconomic (i.e., education, income, formal employment, working in health facilities, and being a health worker) and geographic (i.e., in and out of the Java region) inequalities in terms of delays in getting the first dose. However, we did not find significant inequalities in getting the second dose for most inequality indicators, except for working in health facilities. By region, we found that participants living in more deprived areas (out of the Java region) received the second dose 4.9 days earlier. One of the study's key limitations is that there may be an inherent bias with respect to socioeconomics factors since it was conducted online (web-based). CONCLUSIONS While there were considerable delays in getting the first dose, especially among those of a lower socioeconomic status and those in more deprived areas, the waiting time for the second dose was relatively similar for everyone once they were in the system. Effective efforts to address inequalities are essential to ensuring the effectiveness of the national COVID-19 vaccination rollout.
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Affiliation(s)
- Hario Megatsari
- Department of Health Promotion and Behavior Sciences, Faculty of Public Health, Universitas Airlangga, Surabaya 60115, Indonesia
| | - Dian Kusuma
- Department of Health Services Research and Management, School of Health & Psychological Sciences, City University of London, London EC1V 0HB, UK
- Centre for Health Economics & Policy Innovation, Imperial College Business School, South Kensington Campus, Exhibition Rd, London SW7 2AZ, UK
| | - Ernawaty Ernawaty
- Department of Health Policy and Administration, Faculty of Public Health, Universitas Airlangga, Surabaya 60115, Indonesia
- Airlangga Centre for Health Policy (ACeHAP), Universitas Airlangga, Surabaya 60115, Indonesia
| | - Nuzulul K. Putri
- Department of Health Policy and Administration, Faculty of Public Health, Universitas Airlangga, Surabaya 60115, Indonesia
- Airlangga Centre for Health Policy (ACeHAP), Universitas Airlangga, Surabaya 60115, Indonesia
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Roel E, Raventós B, Burn E, Pistillo A, Prieto-Alhambra D, Duarte-Salles T. Socioeconomic Inequalities in COVID-19 Vaccination and Infection in Adults, Catalonia, Spain. Emerg Infect Dis 2022; 28:2243-2252. [PMID: 36220130 PMCID: PMC9622244 DOI: 10.3201/eid2811.220614] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023] Open
Abstract
Evidence on the impact of the COVID-19 vaccine rollout on socioeconomic COVID-19-related inequalities is scarce. We analyzed associations between socioeconomic deprivation index (SDI) and COVID-19 vaccination, infection, and hospitalization before and after vaccine rollout in Catalonia, Spain. We conducted a population-based cohort study during September 2020-June 2021 that comprised 2,297,146 adults >40 years of age. We estimated odds ratio of nonvaccination and hazard ratios (HRs) of infection and hospitalization by SDI quintile relative to the least deprived quintile, Q1. Six months after rollout, vaccination coverage differed by SDI quintile in working-age (40-64 years) persons: 81% for Q1, 71% for Q5. Before rollout, we found a pattern of increased HR of infection and hospitalization with deprivation among working-age and retirement-age (>65 years) persons. After rollout, infection inequalities decreased in both age groups, whereas hospitalization inequalities decreased among retirement-age persons. Our findings suggest that mass vaccination reduced socioeconomic COVID-19-related inequalities.
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Bassal R, Keinan-Boker L, Cohen D, Mendelson E, Lustig Y, Indenbaum V. Estimated Infection and Vaccine Induced SARS-CoV-2 Seroprevalence in Israel among Adults, January 2020-July 2021. Vaccines (Basel) 2022; 10:vaccines10101663. [PMID: 36298527 PMCID: PMC9609359 DOI: 10.3390/vaccines10101663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 10/02/2022] [Accepted: 10/03/2022] [Indexed: 11/06/2022] Open
Abstract
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) emerged in Israel in February 2020 and spread from then. In December 2020, the FDA approved an emergency use authorization of the Pfizer-BioNTech vaccine, and on 20 December, an immunization campaign began among adults in Israel. We characterized seropositivity for IgG anti-spike antibodies against SARS-CoV-2 between January 2020 and July 2021, before and after the introduction of the vaccine in Israel among adults. We tested 9520 serum samples, collected between January 2020 and July 2021. Between January and August 2020, seropositivity rates were lower than 5.0%; this rate increased from September 2020 (6.3%) to April 2021 (84.9%) and reached 79.1% in July 2021. Between January and December 2020, low socio-economic rank was an independent, significant correlate for seropositivity. Between January and July 2021, the 40.00–64.99-year-old age group, Jews and others, and residents of the Northern district were significantly more likely to be seropositive. Our findings indicate a slow, non-significant increase in the seropositivity rate to SARS-CoV-2 between January and December 2020. Following the introduction of the Pfizer-BioNTech vaccine in Israel, a significant increase in seropositivity was observed from January until April 2021, with stable rates thereafter, up to July 2021.
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Affiliation(s)
- Ravit Bassal
- Israel Center for Disease Control, Ministry of Health, Gertner Institute, Chaim Sheba Medical Center, Tel-Hashomer 52621, Israel
- Department of Epidemiology and Preventive Medicine, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel
- Correspondence: ; Tel.: +972-3-7371522
| | - Lital Keinan-Boker
- Israel Center for Disease Control, Ministry of Health, Gertner Institute, Chaim Sheba Medical Center, Tel-Hashomer 52621, Israel
- School of Public Health, University of Haifa, Haifa 3498838, Israel
| | - Dani Cohen
- Department of Epidemiology and Preventive Medicine, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel
| | - Ella Mendelson
- Department of Epidemiology and Preventive Medicine, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel
- Central Virology Laboratory, Public Health Services, Ministry of Health, Chaim Sheba Medical Center, Tel Hashomer 52621, Israel
| | - Yaniv Lustig
- Department of Epidemiology and Preventive Medicine, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel
- Central Virology Laboratory, Public Health Services, Ministry of Health, Chaim Sheba Medical Center, Tel Hashomer 52621, Israel
| | - Victoria Indenbaum
- Central Virology Laboratory, Public Health Services, Ministry of Health, Chaim Sheba Medical Center, Tel Hashomer 52621, Israel
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Brown M, Saund J, Qureshi A, Plowright M, Drury K, Gahir J, Simpson T, Newman T, Adams K, Galloway J, Durairaj K, Elgizouli K, Rampling T, Cole J, Easom N, Goodman AL, Marks M. Demographics and Outcomes of Initial Phase of COVID-19 Medicines Delivery Units Across 4 UK Centers During Peak B1.1.529 Omicron Epidemic: A Service Evaluation. Open Forum Infect Dis 2022; 9:ofac527. [PMID: 36320201 PMCID: PMC9605703 DOI: 10.1093/ofid/ofac527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 10/04/2022] [Indexed: 12/04/2022] Open
Abstract
Background COVID-19 medicines delivery units (CMDU) were established in late December 2021 to deliver early antiviral therapy to patients classified as at risk with the aim of preventing hospitalization. Methods We performed a service evaluation at 4 CMDUs in England. We assessed demographics and triage outcomes of CMDU referral, uptake of antiviral therapy, and the rate of subsequent hospitalizations within 2 weeks of CMDU referral. Results Over a 3-week period, 4788 patients were referred and 3989 were ultimately assessed by a CMDU. Overall, 832 of the patients referred (17%) were judged eligible for treatment and 628 (13%) were ultimately prescribed an antiviral agent. The overall rate of admission within 14 days was 1%. Patients who were admitted were significantly older than those who did not require hospitalization. Of patients prescribed molnupiravir and sotrovimab, 1.8% and 3.2%, respectively, were admitted. Conclusions There was a high volume of referrals to CMDU service during the initial surge of the Omicron wave in the United Kingdom. A minority of patients were judged to be eligible for therapy. In a highly vaccinated population, the overall hospitalization rate was low.
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Affiliation(s)
- Michael Brown
- Division of Infection, University College London Hospitals NHS Foundation Trust, London, United Kingdom
- Clinical Research Department, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Jasjot Saund
- Division of Infection, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Azka Qureshi
- Department of Infection, Guys and St Thomas’s NHS Foundation Trust, London, United Kingdom
- South East London Covid Prevention and Intervention Service, Guys and St Thomas’s NHS Foundation Trust, London, United Kingdom
| | - Megan Plowright
- Department of Infectious Diseases and Tropical Medicine, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Katie Drury
- Infection Research Group, Hull University Teaching Hospitals NHS Foundation Trust, Hull, United Kingdom
| | - Joshua Gahir
- Division of Infection, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Tom Simpson
- Department of Respiratory Medicine, Lewisham Hospital, London, United Kingdom
| | - Thomas Newman
- Department of Infectious Diseases and Tropical Medicine, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Kate Adams
- Infection Research Group, Hull University Teaching Hospitals NHS Foundation Trust, Hull, United Kingdom
| | - James Galloway
- Centre for Rheumatic Disease, Kings College London, London, United Kingdom
| | - Kezia Durairaj
- Department of Infection, Guys and St Thomas’s NHS Foundation Trust, London, United Kingdom
- South East London Covid Prevention and Intervention Service, Guys and St Thomas’s NHS Foundation Trust, London, United Kingdom
| | - Kamla Elgizouli
- Infection Research Group, Hull University Teaching Hospitals NHS Foundation Trust, Hull, United Kingdom
| | - Tommy Rampling
- Division of Infection, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Joby Cole
- Department of Infectious Diseases and Tropical Medicine, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Nicholas Easom
- Infection Research Group, Hull University Teaching Hospitals NHS Foundation Trust, Hull, United Kingdom
| | - Anna L Goodman
- Department of Infection, Guys and St Thomas’s NHS Foundation Trust, London, United Kingdom
- South East London Covid Prevention and Intervention Service, Guys and St Thomas’s NHS Foundation Trust, London, United Kingdom
- Medical Research Council Clinical Trials Unit, University College London, London, United Kingdom
| | - Michael Marks
- Division of Infection, University College London Hospitals NHS Foundation Trust, London, United Kingdom
- Clinical Research Department, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Division of Infection and Immunity, University College London, London, United Kingdom
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Affiliation(s)
- Azeem Majeed
- Department of Primary Care and Public Health, Imperial College London, London W6 8RP, UK
| | - Katrina Pollock
- Department of Infectious Disease, Imperial College London, London W2 1PG, UK
| | | | - Marisa Papaluca
- Department of Primary Care and Public Health, Imperial College London, London W6 8RP, UK
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Nilsson SF, Laursen TM, Osler M, Hjorthøj C, Benros ME, Ethelberg S, Mølbak K, Nordentoft M. Adverse SARS-CoV-2-associated outcomes among people experiencing social marginalisation and psychiatric vulnerability: A population-based cohort study among 4,4 million people. Lancet Reg Health Eur 2022; 20:100421. [PMID: 35789954 PMCID: PMC9242846 DOI: 10.1016/j.lanepe.2022.100421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Background Knowledge of the adverse problems related to SARS-CoV-2 infection in marginalised and deprived groups may help to prioritise more preventive efforts in these groups. We examined adverse outcomes associated with SARS-CoV-2 infection among vulnerable segments of society. Methods Using health and administrative registers, a population-based cohort study of 4.4 million Danes aged at least 15 years from 27 February 2020 to 15 October 2021 was performed. People with 1) low educational level, 2) homelessness, 3) imprisonment, 4) substance abuse, 5) supported psychiatric housing, 6) psychiatric admission, and 7) severe mental illness were main exposure groups. Chronic medical conditions were included for comparison. COVID-19-related outcomes were: 1) hospitalisation, 2) intensive care, 3) 60-day mortality, and 4) overall mortality. PCR-confirmed SARS-CoV-2 infection and PCR-testing were also studied. Poisson regression analysis was used to compute adjusted incidence and mortality rate ratios (IRRs, MRRs). Findings Using health and administrative registers, we performed a population-based cohort study of 4,412,382 individuals (mean age 48 years; 51% females). In all, 257,450 (5·8%) individuals had a PCR-confirmed SARS-CoV-2 infection. After adjustment for age, calendar time, and sex, we found that especially people experiencing homelessness had high risk of hospitalisation (IRR 4·36, 95% CI, 3·09-6·14), intensive care (IRR 3·12, 95% CI 1·29-7·52), and death (MRR 8·17, 95% CI, 3·66-18·25) compared with people without such experiences, but increased risk was found for all studied groups. Furthermore, after full adjustment, including for status of vaccination against SARS-CoV-2 infection, individuals with experiences of homelessness and a PCR-confirmed SARS-CoV-2 infection had 41-times (95% CI, 24·84-68·44) higher risk of all-cause death during the study period compared with individuals without. Supported psychiatric housing was linked to almost 3-times higher risk of hospitalisation and 60-day mortality following SARS-CoV-2 infection compared with the general population with other living circumstances. Interpretation Socially marginalised and psychiatrically vulnerable individuals had substantially elevated risks of adverse health outcomes following SARS-CoV-2 infection. The results highlight that pandemic preparedness should address inequalities in health, including infection prevention and vaccination of vulnerable groups. Funding Novo Nordisk Foundation.
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Markiewicz-Gospodarek A, Górska A, Markiewicz R, Chilimoniuk Z, Czeczelewski M, Baj J, Maciejewski R, Masiak J. The Relationship between Mental Disorders and the COVID-19 Pandemic—Course, Risk Factors, and Potential Consequences. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19159573. [PMID: 35954930 PMCID: PMC9368061 DOI: 10.3390/ijerph19159573] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/11/2022] [Accepted: 08/02/2022] [Indexed: 01/10/2023]
Abstract
In this review the authors discuss that COVID-19 has already had a direct impact on the physical health of many people and that it appears to have put at risk the mental health of large populations. In this review, we also discuss the relationship between mental disorders and the SARS-CoV-2 infection. We convey the disorders’ risk factors and the more serious mental disorder consequences of COVID-19. People with mental health disorders could be more susceptible to the emotional responses brought on by the COVID-19 epidemic. The COVID-19 pandemic may adversely influence the mental health of patients with already diagnosed mental disorders. For the aim of dealing better with the psychological problems of people afflicted by the COVID-19 pandemic, new psychological procedures are required.
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Affiliation(s)
| | - Aleksandra Górska
- Department of Human Anatomy, Medical University of Lublin, 4 Jaczewskiego St., 20-090 Lublin, Poland
| | - Renata Markiewicz
- Department of Psychiatric Nursing, Medical University of Lublin, 18 Szkolna St., 20-124 Lublin, Poland
| | - Zuzanna Chilimoniuk
- Student Scientific Group, Department of Family Medicine, Medical University of Lublin, 6a (SPSK1) Langiewicza St., 20-032 Lublin, Poland
| | - Marcin Czeczelewski
- Department of Forensic Medicine, Medical University of Lublin, 8b Jaczewskiego St., 20-090 Lublin, Poland
| | - Jacek Baj
- Department of Human Anatomy, Medical University of Lublin, 4 Jaczewskiego St., 20-090 Lublin, Poland
| | - Ryszard Maciejewski
- Department of Human Anatomy, Medical University of Lublin, 4 Jaczewskiego St., 20-090 Lublin, Poland
| | - Jolanta Masiak
- II Department of Psychiatry and Psychiatric Rehabilitation, Medical University of Lublin, 1 Głuska (SPSK Nr 1) St., 20-059 Lublin, Poland
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Curtis HJ, Inglesby P, MacKenna B, Croker R, Hulme WJ, Rentsch CT, Bhaskaran K, Mathur R, Morton CE, Bacon SC, Smith RM, Evans D, Mehrkar A, Tomlinson L, Walker AJ, Bates C, Hickman G, Ward T, Morley J, Cockburn J, Davy S, Williamson EJ, Eggo RM, Parry J, Hester F, Harper S, O'Hanlon S, Eavis A, Jarvis R, Avramov D, Griffiths P, Fowles A, Parkes N, Evans SJ, Douglas IJ, Smeeth L, Goldacre B. Recording of 'COVID-19 vaccine declined': a cohort study on 57.9 million National Health Service patients' records in situ using OpenSAFELY, England, 8 December 2020 to 25 May 2021. Euro Surveill 2022; 27:2100885. [PMID: 35983770 PMCID: PMC9389857 DOI: 10.2807/1560-7917.es.2022.27.33.2100885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
BackgroundPriority patients in England were offered COVID-19 vaccination by mid-April 2021. Codes in clinical record systems can denote the vaccine being declined.AimWe describe records of COVID-19 vaccines being declined, according to clinical and demographic factors.MethodsWith the approval of NHS England, we conducted a retrospective cohort study between 8 December 2020 and 25 May 2021 with primary care records for 57.9 million patients using OpenSAFELY, a secure health analytics platform. COVID-19 vaccination priority patients were those aged ≥ 50 years or ≥ 16 years clinically extremely vulnerable (CEV) or 'at risk'. We describe the proportion recorded as declining vaccination for each group and stratified by clinical and demographic subgroups, subsequent vaccination and distribution of clinical code usage across general practices.ResultsOf 24.5 million priority patients, 663,033 (2.7%) had a decline recorded, while 2,155,076 (8.8%) had neither a vaccine nor decline recorded. Those recorded as declining, who were subsequently vaccinated (n = 125,587; 18.9%) were overrepresented in the South Asian population (32.3% vs 22.8% for other ethnicities aged ≥ 65 years). The proportion of declining unvaccinated patients was highest in CEV (3.3%), varied strongly with ethnicity (black 15.3%, South Asian 5.6%, white 1.5% for ≥ 80 years) and correlated positively with increasing deprivation.ConclusionsClinical codes indicative of COVID-19 vaccinations being declined are commonly used in England, but substantially more common among black and South Asian people, and in more deprived areas. Qualitative research is needed to determine typical reasons for recorded declines, including to what extent they reflect patients actively declining.
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Affiliation(s)
- Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - William J Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | | | - Rohini Mathur
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Sebastian Cj Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rebecca M Smith
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Laurie Tomlinson
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | - George Hickman
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Tom Ward
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Jessica Morley
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | - Simon Davy
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | - Rosalind M Eggo
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | | | | | | | | | | | | | | | | | | | - Stephen Jw Evans
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Ian J Douglas
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Liam Smeeth
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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Hulme WJ, Williamson EJ, Green ACA, Bhaskaran K, McDonald HI, Rentsch CT, Schultze A, Tazare J, Curtis HJ, Walker AJ, Tomlinson LA, Palmer T, Horne EMF, MacKenna B, Morton CE, Mehrkar A, Morley J, Fisher L, Bacon SCJ, Evans D, Inglesby P, Hickman G, Davy S, Ward T, Croker R, Eggo RM, Wong AYS, Mathur R, Wing K, Forbes H, Grint DJ, Douglas IJ, Evans SJW, Smeeth L, Bates C, Cockburn J, Parry J, Hester F, Harper S, Sterne JAC, Hernán MA, Goldacre B. Comparative effectiveness of ChAdOx1 versus BNT162b2 covid-19 vaccines in health and social care workers in England: cohort study using OpenSAFELY. BMJ 2022; 378:e068946. [PMID: 35858680 PMCID: PMC9295078 DOI: 10.1136/bmj-2021-068946] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/11/2022] [Indexed: 12/17/2022]
Abstract
OBJECTIVE To compare the effectiveness of the BNT162b2 mRNA (Pfizer-BioNTech) and the ChAdOx1 (Oxford-AstraZeneca) covid-19 vaccines against infection and covid-19 disease in health and social care workers. DESIGN Cohort study, emulating a comparative effectiveness trial, on behalf of NHS England. SETTING Linked primary care, hospital, and covid-19 surveillance records available within the OpenSAFELY-TPP research platform, covering a period when the SARS-CoV-2 Alpha variant was dominant. PARTICIPANTS 317 341 health and social care workers vaccinated between 4 January and 28 February 2021, registered with a general practice using the TPP SystmOne clinical information system in England, and not clinically extremely vulnerable. INTERVENTIONS Vaccination with either BNT162b2 or ChAdOx1 administered as part of the national covid-19 vaccine roll-out. MAIN OUTCOME MEASURES Recorded SARS-CoV-2 positive test, or covid-19 related attendance at an accident and emergency (A&E) department or hospital admission occurring within 20 weeks of receipt of the first vaccine dose. RESULTS Over the duration of 118 771 person-years of follow-up there were 6962 positive SARS-CoV-2 tests, 282 covid-19 related A&E attendances, and 166 covid-19 related hospital admissions. The cumulative incidence of each outcome was similar for both vaccines during the first 20 weeks after vaccination. The cumulative incidence of recorded SARS-CoV-2 infection 20 weeks after first-dose vaccination with BNT162b2 was 21.7 per 1000 people (95% confidence interval 20.9 to 22.4) and with ChAdOx1 was 23.7 (21.8 to 25.6), representing a difference of 2.04 per 1000 people (0.04 to 4.04). The difference in the cumulative incidence per 1000 people of covid-19 related A&E attendance at 20 weeks was 0.06 per 1000 people (95% CI -0.31 to 0.43). For covid-19 related hospital admission, this difference was 0.11 per 1000 people (-0.22 to 0.44). CONCLUSIONS In this cohort of healthcare workers where we would not anticipate vaccine type to be related to health status, we found no substantial differences in the incidence of SARS-CoV-2 infection or covid-19 disease up to 20 weeks after vaccination. Incidence dropped sharply at 3-4 weeks after vaccination, and there were few covid-19 related hospital attendance and admission events after this period. This is in line with expected onset of vaccine induced immunity and suggests strong protection against Alpha variant covid-19 disease for both vaccines in this relatively young and healthy population of healthcare workers.
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Affiliation(s)
- William J Hulme
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | | | - Amelia C A Green
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | | | - Helen I McDonald
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | - Anna Schultze
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - John Tazare
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Helen J Curtis
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Alex J Walker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | | | - Tom Palmer
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | - Elsie M F Horne
- Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
- NIHR Bristol, Biomedical Research Centre, Bristol BS8 2BN, UK
| | - Brian MacKenna
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Caroline E Morton
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Amir Mehrkar
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Jessica Morley
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Louis Fisher
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Sebastian C J Bacon
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - David Evans
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Peter Inglesby
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - George Hickman
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Simon Davy
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Tom Ward
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Richard Croker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Rosalind M Eggo
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Angel Y S Wong
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Rohini Mathur
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Kevin Wing
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Harriet Forbes
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Daniel J Grint
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Ian J Douglas
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | - Liam Smeeth
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Chris Bates
- TPP, TPP House, Horsforth, Leeds LS18 5PX, UK
| | | | - John Parry
- TPP, TPP House, Horsforth, Leeds LS18 5PX, UK
| | | | - Sam Harper
- TPP, TPP House, Horsforth, Leeds LS18 5PX, UK
| | - Jonathan A C Sterne
- Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
- NIHR Bristol, Biomedical Research Centre, Bristol BS8 2BN, UK
- Health Data Research UK South West
| | - Miguel A Hernán
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Ben Goldacre
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
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Andrews C, Schultze A, Curtis H, Hulme W, Tazare J, Evans S, Mehrkar A, Bacon S, Hickman G, Bates C, Parry J, Hester F, Harper S, Cockburn J, Evans D, Ward T, Davy S, Inglesby P, Goldacre B, MacKenna B, Tomlinson L, Walker A. OpenSAFELY: Representativeness of electronic health record platform OpenSAFELY-TPP data compared to the population of England. Wellcome Open Res 2022; 7:191. [PMID: 35966958 PMCID: PMC9346309 DOI: 10.12688/wellcomeopenres.18010.1] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/08/2022] [Indexed: 12/11/2022] Open
Abstract
Background: Since its inception in March 2020, data from the OpenSAFELY-TPP electronic health record platform has been used for more than 20 studies relating to the global COVID-19 emergency. OpenSAFELY-TPP data is derived from practices in England using SystmOne software, and has been used for the majority of these studies. We set out to investigate the representativeness of OpenSAFELY-TPP data by comparing it to national population estimates. Methods: With the approval of NHS England, we describe the age, sex, Index of Multiple Deprivation and ethnicity of the OpenSAFELY-TPP population compared to national estimates from the Office for National Statistics. The five leading causes of death occurring between the 1st January 2020 and the 31st December 2020 were also compared to deaths registered in England during the same period. Results: Despite regional variations, TPP is largely representative of the general population of England in terms of IMD (all within 1.1 percentage points), age, sex (within 0.1 percentage points), ethnicity and causes of death. The proportion of the five leading causes of death is broadly similar to those reported by ONS (all within 1 percentage point). Conclusions: Data made available via OpenSAFELY-TPP is broadly representative of the English population. Users of OpenSAFELY must consider the issues of representativeness, generalisability and external validity associated with using TPP data for health research. Although the coverage of TPP practices varies regionally across England, TPP registered patients are generally representative of the English population as a whole in terms of key demographic characteristics.
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Affiliation(s)
- Colm Andrews
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - Anna Schultze
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Helen Curtis
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - William Hulme
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - John Tazare
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Stephen Evans
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Amir Mehrkar
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - Sebastian Bacon
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - George Hickman
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | | | - John Parry
- TPP, TPP House, Leeds, Yorkshire, LS18 5PX, UK
| | | | - Sam Harper
- TPP, TPP House, Leeds, Yorkshire, LS18 5PX, UK
| | | | - David Evans
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - Tom Ward
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - Simon Davy
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - Peter Inglesby
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - Ben Goldacre
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - Brian MacKenna
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - Laurie Tomlinson
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Alex Walker
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
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Dropkin G. Variation in COVID-19 booster uptake in England: An ecological study. PLoS One 2022; 17:e0270624. [PMID: 35767526 PMCID: PMC9242486 DOI: 10.1371/journal.pone.0270624] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 06/14/2022] [Indexed: 11/19/2022] Open
Abstract
Introduction Variable and low uptake of the COVID-19 booster is a recognised problem, associated with individual characteristics including age, gender, ethnicity, and deprivation. Are there other relevant predictors at area level? Methods Anonymous grouped data was downloaded from the UK Government Coronavirus Dashboard for Middle Super Output Areas (MSOA) in England, along with demographic, employment, and health data from public sources. Mixed models with a random intercept for Upper Tier Local Authority were analysed as quasibinomial Generalized Additive Models. The estimated random effects were then fitted with Bayesian linear mixed models using flu vaccination uptake, change in public health budgets, population proportion of vaccination sites at pharmacies, GP-led, vaccination centres, and hospital hubs, and Region. Results Models for the MSOA-level COVID-19 first and second vaccinations and the Third Injection (including the booster), fit well. Index of Multiple Deprivation, proportion Aged 15-24 and 25-44, and ethnicity groupings Other White, Indian-Pakistani-Bangladeshi, and African-Caribbean-Other Black-Other, are highly significant predictors of lower uptake. The estimated random effects vary widely amongst local authorities, with positive impact of flu vaccine uptake and change in public health budgets, and regional impacts which are positive for London and South East (first and second doses only), and negative for North West and North East. The impact of vaccination sites did not reach 90% credibility, in general. Conclusion COVID-19 vaccination rates at each stage are very well modelled if local authority random effects are included along with non-linear terms for demographic, employment and health data. Deprivation, younger age, and Other White, South Asian, and African-Caribbean-Other ethnicities are associated with lower uptake. The estimated local effects show strong regional variation and are positively associated with flu vaccination and increasing public health budgets. One simple way to improve COVID-19 vaccine uptake in England would be to increase local public health allocations.
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Affiliation(s)
- Greg Dropkin
- Independent Researcher, Liverpool, England
- * E-mail:
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Kokosi T, De Stavola B, Mitra R, Frayling L, Doherty A, Dove I, Sonnenberg P, Harron K. An overview of synthetic administrative data for research. Int J Popul Data Sci 2022; 7:1727. [PMID: 37650026 PMCID: PMC10464868 DOI: 10.23889/ijpds.v7i1.1727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Abstract
Use of administrative data for research and for planning services has increased over recent decades due to the value of the large, rich information available. However, concerns about the release of sensitive or personal data and the associated disclosure risk can lead to lengthy approval processes and restricted data access. This can delay or prevent the production of timely evidence. A promising solution to facilitate more efficient data access is to create synthetic versions of the original datasets which are less likely to hold confidential information and can minimise disclosure risk. Such data may be used as an interim solution, allowing researchers to develop their analysis plans on non-disclosive data, whilst waiting for access to the real data. We aim to provide an overview of the background and uses of synthetic data and describe common methods used to generate synthetic data in the context of UK administrative research. We propose a simplified terminology for categories of synthetic data (univariate, multivariate, and complex modality synthetic data) as well as a more comprehensive description of the terminology used in the existing literature and illustrate challenges and future directions for research.
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Affiliation(s)
- Theodora Kokosi
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Bianca De Stavola
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Robin Mitra
- School of Mathematics, Cardiff University, Cardiff UK
| | | | - Aiden Doherty
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iain Dove
- Office for National Statistics, Titchfield, UK
| | - Pam Sonnenberg
- Department of Infection & Population Health, Institute for Global Health, University College London, London, UK
| | - Katie Harron
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
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Nilsson SF, Laursen TM, Osler M, Hjorthøj C, Benros ME, Ethelberg S, Mølbak K, Nordentoft M. Vaccination against SARS-CoV-2 infection among vulnerable and marginalised population groups in Denmark: A nationwide population-based study. THE LANCET REGIONAL HEALTH. EUROPE 2022; 16:100355. [PMID: 35350631 PMCID: PMC8948003 DOI: 10.1016/j.lanepe.2022.100355] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
BACKGROUND Social deprivation, psychiatric and medical disorders have been associated with increased risk of infection and severe COVID-19-related health problems. We aimed to study the rates of SARS-CoV-2 vaccination in these high-risk groups. METHODS Using health, vaccination, and administrative registers, we performed a population-based cohort study including all Danish residents aged at least 15 years, December 27, 2020, to October 15, 2021. Population groups were people experiencing: (1) homelessness, (2) imprisonment, (3) substance abuse, (4) severe mental illness, (5) supported psychiatric housing, (6) psychiatric admission, and (7) chronic medical condition. The outcome was vaccine uptake of two doses against SARS-CoV-2 infection. We calculated cumulative vaccine uptake and adjusted vaccination incidence rate ratios (IRRs) relative to the general population by sex and population group. FINDINGS The cohort included 4,935,344 individuals, of whom 4,277,380 (86·7%) received two doses of vaccine. Lower cumulative vaccine uptake was found for all socially deprived and psychiatrically vulnerable population groups compared with the general population. Lowest uptake was found for people below 65 years experiencing homelessness (54·6%, 95% confidence interval (CI) 53·4-55·8, p<0·0001). After adjustment for age and calendar time, homelessness was associated with markedly lower rates of vaccine uptake (IRR 0·5, 95% CI 0·5-0·6 in males and 0·4, 0·4-0·5 in females) with similar results for imprisonment. Lower vaccine uptake was also found for most of the psychiatric groups with the lower IRR for substance abuse (IRR 0·7, 0·7-0·7 in males and 0·8, 0·8-0·8 in females). Individuals with new-onset severe mental illness and, especially, those in supported psychiatric housing and with chronic medical conditions had the highest vaccine uptake among the studied population groups. INTERPRETATION Especially, socially deprived population groups, but also individuals with psychiatric vulnerability need higher priority in the implementation of the vaccination strategy to increase equity in immunization uptake. FUNDING Novo Nordisk Foundation.
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Affiliation(s)
- Sandra Feodor Nilsson
- Copenhagen Research Center for Mental Health – CORE, Copenhagen University Hospital – Mental Health Centre CPH, Gentofte Hospitalsvej 15, 4th floor, Hellerup DK-2900, Denmark
- Corresponding author.
| | - Thomas Munk Laursen
- The National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Merete Osler
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospitals, Copenhagen, Denmark
- Section of Epidemiology, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Carsten Hjorthøj
- Copenhagen Research Center for Mental Health – CORE, Copenhagen University Hospital – Mental Health Centre CPH, Gentofte Hospitalsvej 15, 4th floor, Hellerup DK-2900, Denmark
- Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael E. Benros
- Copenhagen Research Center for Mental Health – CORE, Copenhagen University Hospital – Mental Health Centre CPH, Gentofte Hospitalsvej 15, 4th floor, Hellerup DK-2900, Denmark
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Steen Ethelberg
- Statens Serum Institut, Copenhagen, Denmark
- University of Copenhagen, Department of Global Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kåre Mølbak
- Statens Serum Institut, Copenhagen, Denmark
- Department of Veterinary and Animal Science, University of Copenhagen, Copenhagen, Denmark
| | - Merete Nordentoft
- Copenhagen Research Center for Mental Health – CORE, Copenhagen University Hospital – Mental Health Centre CPH, Gentofte Hospitalsvej 15, 4th floor, Hellerup DK-2900, Denmark
- iPSYCH – The Lundbeck Foundation Initiative for Integrated Psychiatric Research, Aarhus, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Hussain B, Latif A, Timmons S, Nkhoma K, Nellums LB. Overcoming COVID-19 vaccine hesitancy among ethnic minorities: A systematic review of UK studies. Vaccine 2022; 40:3413-3432. [PMID: 35534309 PMCID: PMC9046074 DOI: 10.1016/j.vaccine.2022.04.030] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 03/09/2022] [Accepted: 04/06/2022] [Indexed: 12/31/2022]
Abstract
Ethnic minority communities in the UK have been disproportionately affected by the pandemic, with increased risks of infection, severe disease, and death. Hesitancy around the COVID-19 vaccine may be contributing to disparities in vaccine delivery to ethnic minority communities. This systematic review aims to strengthen understanding of COVID-19 vaccine concerns among ethnic minorities in the UK. Five databases were searched in February 2022, yielding 24 peer-reviewed studies reporting on vaccine hesitancy or acceptance in ethnic minority groups. Data were extracted using a standardised form, and quality assessment was carried out using the Standard Quality Criteria. There were three key themes: (1). Prevalence of vaccine hesitancy; (2). Reasons for vaccine hesitancy and acceptance; and (3). Recommendations to address vaccine concerns. Vaccine hesitancy, which was more common among some ethnic minority groups, is a complex phenomenon, driven by misinformation, mistrust, concerns about safety and efficacy, and structural and systemic inequities. Community engagement and tailored communication may help to address vaccine concerns. Robust data disaggregated by ethnicities are needed to better understand barriers and facilitators for COVID-19 vaccine delivery in ethnic minority communities. Strategies to address structural disadvantage need to be inclusive, comprehensive, and behaviorally informed and foster confidence in healthcare systems and governments. Community leaders and health care practitioners may prove to be the most important agents in creating an environment of trust within ethnic minority groups.
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Russell A, de Wildt G, Grut M, Greenfield S, Clarke J. What can general practice learn from primary care nurses' and healthcare assistants' experiences of the COVID-19 pandemic? A qualitative study. BMJ Open 2022; 12:e055955. [PMID: 35292497 PMCID: PMC8927928 DOI: 10.1136/bmjopen-2021-055955] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVES UK general practice has radically altered in response to COVID-19. The general practice nursing team has been central to these changes. To help learn from COVID-19 and maintain a sustainable nursing workforce, general practice should reflect on their support needs and perceptions of organisational strategies. This study aimed to explore primary care nurses' and healthcare assistants' experiences and perceptions of general practice, and the changes made to it, during the pandemic. DESIGN Exploratory qualitative study using semistructured interviews. Interview data were analysed using Braun and Clarke's 'codebook' thematic analysis. SETTING General practices in the Midlands, South East and South West England. Interviews were conducted in February and March 2021, as England began to unlock from its third national lockdown. PARTICIPANTS Practice nurses (n=12), healthcare assistants (n=7), advanced nurse practitioners (n=4) and nursing associates (n=1) recruited using convenience and snowball sampling. RESULTS Three themes were identified. Difficult changes describes dramatic changes made to general practice at the onset of the pandemic, creating confusion and anxiety. Dealing with change characterises how negative emotions were intensified by fear of infection, problematic government guidance, personal protective equipment (PPE) shortages and friction with doctors; but could be mitigated through effective practice communication, peer support and individual coping strategies. An opportunity for improvement highlights certain changes (eg, the increased use of telehealth) that participants believed could be adopted long term to improve efficiency. CONCLUSION General practice should learn from the COVID-19 pandemic to nurture the clinical role and resilience of nurses and healthcare assistants in the postpandemic 'new normal'. Robust PPE provision could enable them to undertake their patient-facing duties safely and confidently. Judicious implementation of telehealth could help preserve the practical and caring nature of nursing. Improving channels of communication and interprofessional collaboration could help realise their potential within the primary care team.
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Affiliation(s)
- Alice Russell
- College of Medical and Dental Sciences, University of Birmingham, Birmingham, West Midlands, UK
| | - Gilles de Wildt
- Institute of Clinical Sciences, University of Birmingham, Birmingham, West Midlands, UK
| | - Minka Grut
- College of Medical and Dental Sciences, University of Birmingham, Birmingham, West Midlands, UK
| | - Sheila Greenfield
- Institute of Applied Health Research, University of Birmingham, Birmingham, West Midlands, UK
| | - Joanne Clarke
- Institute of Applied Health Research, University of Birmingham, Birmingham, West Midlands, UK
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Tessier E, Rai Y, Clarke E, Lakhani A, Tsang C, Makwana A, Heard H, Rickeard T, Lakhani S, Roy P, Edelstein M, Ramsay M, Lopez-Bernal J, White J, Andrews N, Campbell CNJ, Stowe J. Characteristics associated with COVID-19 vaccine uptake among adults aged 50 years and above in England (8 December 2020-17 May 2021): a population-level observational study. BMJ Open 2022; 12:e055278. [PMID: 35232787 PMCID: PMC8889452 DOI: 10.1136/bmjopen-2021-055278] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
OBJECTIVE To determine characteristics associated with COVID-19 vaccine coverage among individuals aged 50 years and above in England since the beginning of the programme. DESIGN Observational cross-sectional study assessed by logistic regression and mean prevalence margins. SETTING COVID-19 vaccinations delivered in England from 8 December 2020 to 17 May 2021. PARTICIPANTS 30 624 257/61 967 781 (49.4%) and 17 360 045/61 967 781 (28.1%) individuals in England were recorded as vaccinated in the National Immunisation Management System with a first dose and a second dose of a COVID-19 vaccine, respectively. INTERVENTIONS Vaccination status with COVID-19 vaccinations. MAIN OUTCOME MEASURES Proportion, adjusted ORs and mean prevalence margins for individuals not vaccinated with dose 1 among those aged 50-69 years and dose 1 and 2 among those aged 70 years and above. RESULTS Of individuals aged 50 years and above, black/African/Caribbean ethnic group was the least likely of all ethnic groups to be vaccinated with dose 1 of the COVID-19 vaccine. However, of those aged 70 years and above, the odds of not having dose 2 was 5.53 (95% CI 5.42 to 5.63) and 5.36 (95% CI 5.29 to 5.43) greater among Pakistani and black/African/Caribbean compared with white British ethnicity, respectively. The odds of not receiving dose 2 was 1.18 (95% CI 1.16 to 1.20) higher among individuals who lived in a care home compared with those who did not. This was the opposite to that observed for dose 1, where the odds of being unvaccinated was significantly higher among those not living in a care home (0.89 (95% CI 0.87 to 0.91)). CONCLUSIONS We found that there are characteristics associated with low COVID-19 vaccine coverage. Inequalities, such as ethnicity are a major contributor to suboptimal coverage and tailored interventions are required to improve coverage and protect the population from SARS-CoV-2.
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Affiliation(s)
- Elise Tessier
- Immunisation and Countermeasures Division, Public Health England, London, UK
| | - Yuma Rai
- Immunisation and Countermeasures Division, Public Health England, London, UK
| | - Eleanor Clarke
- Immunisation and Countermeasures Division, Public Health England, London, UK
| | - Anissa Lakhani
- Immunisation and Countermeasures Division, Public Health England, London, UK
| | - Camille Tsang
- Immunisation and Countermeasures Division, Public Health England, London, UK
| | - Ashley Makwana
- Vaccines and Countermeasures Division, Public Health England, London, UK
| | - Heather Heard
- Health Intelligence Division, Health Improvement Directorate, Public Health England, York, UK
| | - Tim Rickeard
- Vaccines and Countermeasures Division, Public Health England, London, UK
| | - Shreya Lakhani
- Immunisation and Countermeasures Division, Public Health England, London, UK
| | - Partho Roy
- Immunisation and Countermeasures Division, Public Health England, London, UK
| | | | - Mary Ramsay
- Immunisation and Countermeasures Division, Public Health England, London, UK
| | - Jamie Lopez-Bernal
- Immunisation and Countermeasures Division, Public Health England, London, UK
| | - Joanne White
- Immunisation and Countermeasures Division, Public Health England, London, UK
| | - Nick Andrews
- Immunisation and Countermeasures Division, Public Health England, London, UK
| | - Colin N J Campbell
- Immunisation and Countermeasures Division, Public Health England, London, UK
| | - Julia Stowe
- Immunisation and Countermeasures Division, Public Health England, London, UK
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Whiteley WN, Ip S, Cooper JA, Bolton T, Keene S, Walker V, Denholm R, Akbari A, Omigie E, Hollings S, Di Angelantonio E, Denaxas S, Wood A, Sterne JAC, Sudlow C. Association of COVID-19 vaccines ChAdOx1 and BNT162b2 with major venous, arterial, or thrombocytopenic events: A population-based cohort study of 46 million adults in England. PLoS Med 2022; 19:e1003926. [PMID: 35192597 PMCID: PMC8863280 DOI: 10.1371/journal.pmed.1003926] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 01/21/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Thromboses in unusual locations after the Coronavirus Disease 2019 (COVID-19) vaccine ChAdOx1-S have been reported, although their frequency with vaccines of different types is uncertain at a population level. The aim of this study was to estimate the population-level risks of hospitalised thrombocytopenia and major arterial and venous thromboses after COVID-19 vaccination. METHODS AND FINDINGS In this whole-population cohort study, we analysed linked electronic health records from adults living in England, from 8 December 2020 to 18 March 2021. We estimated incidence rates and hazard ratios (HRs) for major arterial, venous, and thrombocytopenic outcomes 1 to 28 and >28 days after first vaccination dose for ChAdOx1-S and BNT162b2 vaccines. Analyses were performed separately for ages <70 and ≥70 years and adjusted for age, age2, sex, ethnicity, and deprivation. We also prespecified adjustment for anticoagulant medication, combined oral contraceptive medication, hormone replacement therapy medication, history of pulmonary embolism or deep vein thrombosis, and history of coronavirus infection in analyses of venous thrombosis; and diabetes, hypertension, smoking, antiplatelet medication, blood pressure lowering medication, lipid lowering medication, anticoagulant medication, history of stroke, and history of myocardial infarction in analyses of arterial thromboses. We selected further covariates with backward selection. Of 46 million adults, 23 million (51%) were women; 39 million (84%) were <70; and 3.7 million (8.1%) Asian or Asian British, 1.6 million (3.5%) Black or Black British, 36 million (79%) White, 0.7 million (1.5%) mixed ethnicity, and 1.5 million (3.2%) were of another ethnicity. Approximately 21 million (46%) adults had their first vaccination between 8 December 2020 and 18 March 2021. The crude incidence rates (per 100,000 person-years) of all venous events were as follows: prevaccination, 140 [95% confidence interval (CI): 138 to 142]; ≤28 days post-ChAdOx1-S, 294 (281 to 307); >28 days post-ChAdOx1-S, 359 (338 to 382), ≤28 days post-BNT162b2-S, 241 (229 to 253); >28 days post-BNT162b2-S 277 (263 to 291). The crude incidence rates (per 100,000 person-years) of all arterial events were as follows: prevaccination, 546 (95% CI: 541 to 555); ≤28 days post-ChAdOx1-S, 1,211 (1,185 to 1,237); >28 days post-ChAdOx1-S, 1678 (1,630 to 1,726), ≤28 days post-BNT162b2-S, 1,242 (1,214 to 1,269); >28 days post-BNT162b2-S, 1,539 (1,507 to 1,572). Adjusted HRs (aHRs) 1 to 28 days after ChAdOx1-S, compared with unvaccinated rates, at ages <70 and ≥70 years, respectively, were 0.97 (95% CI: 0.90 to 1.05) and 0.58 (0.53 to 0.63) for venous thromboses, and 0.90 (0.86 to 0.95) and 0.76 (0.73 to 0.79) for arterial thromboses. Corresponding aHRs for BNT162b2 were 0.81 (0.74 to 0.88) and 0.57 (0.53 to 0.62) for venous thromboses, and 0.94 (0.90 to 0.99) and 0.72 (0.70 to 0.75) for arterial thromboses. aHRs for thrombotic events were higher at younger ages for venous thromboses after ChAdOx1-S, and for arterial thromboses after both vaccines. Rates of intracranial venous thrombosis (ICVT) and of thrombocytopenia in adults aged <70 years were higher 1 to 28 days after ChAdOx1-S (aHRs 2.27, 95% CI: 1.33 to 3.88 and 1.71, 1.35 to 2.16, respectively), but not after BNT162b2 (0.59, 0.24 to 1.45 and 1.00, 0.75 to 1.34) compared with unvaccinated. The corresponding absolute excess risks of ICVT 1 to 28 days after ChAdOx1-S were 0.9 to 3 per million, varying by age and sex. The main limitations of the study are as follows: (i) it relies on the accuracy of coded healthcare data to identify exposures, covariates, and outcomes; (ii) the use of primary reason for hospital admission to measure outcome, which improves the positive predictive value but may lead to an underestimation of incidence; and (iii) potential unmeasured confounding. CONCLUSIONS In this study, we observed increases in rates of ICVT and thrombocytopenia after ChAdOx1-S vaccination in adults aged <70 years that were small compared with its effect in reducing COVID-19 morbidity and mortality, although more precise estimates for adults aged <40 years are needed. For people aged ≥70 years, rates of arterial or venous thrombotic events were generally lower after either vaccine compared with unvaccinated, suggesting that either vaccine is suitable in this age group.
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Affiliation(s)
- William N. Whiteley
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- MRC Population Health Research Unit, Nuffield Department of Population Health University of Oxford, Oxford, United Kingdom
| | - Samantha Ip
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Jennifer A. Cooper
- Department of Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Thomas Bolton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- BHF Data Science Centre, Health Data Research UK, London, United Kingdom
| | - Spencer Keene
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Venexia Walker
- Department of Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Rachel Denholm
- Department of Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Ashley Akbari
- Population Data Science, Health Data Research UK, Swansea University, Swansea, United Kingdom
| | | | | | - Emanuele Di Angelantonio
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom
| | - Spiros Denaxas
- BHF Data Science Centre, Health Data Research UK, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Angela Wood
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom
| | - Jonathan A. C. Sterne
- Department of Population Health Sciences, University of Bristol, Bristol, United Kingdom
- NIHR Bristol Biomedical Research Centre, Bristol, United Kingdom
- HDR UK South West, Bristol, United Kingdom
| | - Cathie Sudlow
- BHF Data Science Centre, Health Data Research UK, London, United Kingdom
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Trends and clinical characteristics of COVID-19 vaccine recipients: a federated analysis of 57.9 million patients’ primary care records in situ using OpenSAFELY. Br J Gen Pract 2021; 72:10. [PMID: 34972809 PMCID: PMC8714525 DOI: 10.3399/bjgp22x718049] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
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Eslava-Schmalbach J, Rosero EB, Garzón-Orjuela N. Global control of COVID-19: good vaccines may not suffice. Rev Panam Salud Publica 2021; 45:e148. [PMID: 34908811 PMCID: PMC8663111 DOI: 10.26633/rpsp.2021.148] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 05/27/2021] [Indexed: 12/17/2022] Open
Abstract
The COVID-19 pandemic has unveiled health and socioeconomic inequities around the globe. Effective epidemic control requires the achievement of herd immunity, where susceptible individuals are conferred indirect protection by being surrounded by immunized individuals. The proportion of people that need to be vaccinated to obtain herd immunity is determined through the herd immunity threshold. However, the number of susceptible individuals and the opportunities for contact between infectious and susceptible individuals influence the progress of an epidemic. Thus, in addition to vaccination, control of a pandemic may be difficult or impossible to achieve without other public health measures, including wearing face masks and social distancing. This article discusses the factors that may contribute to herd immunity and control of COVID-19 through the availability of effective vaccines and describes how vaccine effectiveness in the community may be lower than that expected. It also discusses how pandemic control in some countries and populations may face vaccine accessibility barriers if market forces strongly regulate the new technologies available, according to the inverse care law.
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Affiliation(s)
- Javier Eslava-Schmalbach
- Universidad Nacional de Colombia Bogotá Colombia Universidad Nacional de Colombia, Bogotá, Colombia
| | - Eric B Rosero
- University of Texas Southwestern Medical Center DallasTexas United States of America University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Nathaly Garzón-Orjuela
- Universidad Nacional de Colombia Bogotá Colombia Universidad Nacional de Colombia, Bogotá, Colombia
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Alali WQ, Ali LA, AlSeaidan M, Al-Rashidi M. Effectiveness of BNT162b2 and ChAdOx1 Vaccines against Symptomatic COVID-19 among Healthcare Workers in Kuwait: A Retrospective Cohort Study. Healthcare (Basel) 2021; 9:1692. [PMID: 34946418 PMCID: PMC8701668 DOI: 10.3390/healthcare9121692] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 11/28/2021] [Accepted: 12/03/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Estimating vaccine effectiveness (VE) against severe, acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among healthcare workers (HCWs) is necessary to demonstrate protection from the disease. Between 24 December 2020 and 15 June 2021, we determined the factors associated with vaccine coverage and estimated VE against SARS-CoV-2 infection in HCWs at a secondary hospital in Kuwait. METHODS We extracted sociodemographic, occupational, SARS-CoV-2 infection, and vaccination data for eligible HCWs from the hospital records. Vaccine coverage percentages were cross-tabulated with the HCW factors. Cox regression was used to estimate hazard ratios in vaccinated versus unvaccinated. RESULTS 3246 HCWs were included in the analysis, of which 82.1% received at least one vaccine dose (50.4% only one dose of ChAdOx1, 3.3% only one dose of BNT162b2, and 28.3% two doses of BNT162b2). However, 17.9% of HCWs were unvaccinated. A significantly lower vaccination coverage was reported amongst female HCWs, younger age group (20-30 years), and administrative/executive staff. The adjusted VE of fully vaccinated HCWs was 94.5% (95% CI = 89.4-97.2%), while it was 75.4% (95% CI = 67.2-81.6%) and 91.4% (95% CI = 65.1-97.9%) in partially vaccinated for ChAdOx1 and BNT162b2, respectively. CONCLUSIONS BNT162b2 and ChAdOx1 vaccines prevented most symptomatic infections in HCWs across age groups, nationalities, and occupations.
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Affiliation(s)
- Walid Q. Alali
- Department of Epidemiology & Biostatistics, Faculty of Public Health, Kuwait University, Kuwait City 13060, Kuwait
| | - Lamiaa A. Ali
- Department of Public Health, Faculty of Medicine, Fayoum University, Fayoum 63514, Egypt;
| | - Mohammad AlSeaidan
- Department of Public Health, Ministry of Health, Kuwait City 12009, Kuwait;
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Weinstein O, Krieger I, Cohen AD, Tzur Bitan D. COVID-19 vaccination among individuals with autism spectrum disorder: A population-based study. RESEARCH IN AUTISM SPECTRUM DISORDERS 2021; 89:101865. [PMID: 34548878 PMCID: PMC8445801 DOI: 10.1016/j.rasd.2021.101865] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 09/14/2021] [Accepted: 09/14/2021] [Indexed: 05/08/2023]
Abstract
BACKGROUND Individuals with autistic spectrum disorder (ASD) are more susceptible to COVID-19 morbidity and should therefore be prioritized for vaccination. Although individuals with neurodevelopmental disabilities are given some priority in Israel, it is unclear to what extent individuals with ASD are being vaccinated relative to that of the general population. This study was aimed to assess vaccination prevalence among individuals with ASD. METHOD Individuals with ASD, and age- and sex-matched controls (total n = 11,080), were assessed for prevalence of COVID-19 vaccination by February 2021, approximately a month and a half after the national vaccination distribution plan was launched in Israel. Data were obtained from the database of Clalit Health Services (CHS), the largest healthcare organization in Israel. RESULTS Individuals with ASD were more likely to be vaccinated for COVID-19 (OR = 2.55, 95 %CI 2.35-2.75, p < .001) across both sexes, but only in the 16-20 (OR = 2.04, 95 %CI 1.79-2.32, p < .001) and 21-40 (OR = 3.95, 95 %CI 3.52-4.43, p < .001) age groups. After adjusting for chronic illnesses, ASD remained significant in predicting the uptake of COVID-19 vaccination. CONCLUSIONS Efforts to prioritize ASD patients may improve vaccination prevalence among individuals with ASD, especially among younger individuals. Healthcare providers worldwide should therefore consider prioritization policies so as to increase vaccination rates among this vulnerable population.
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Affiliation(s)
- Orly Weinstein
- Hospitals Division, Clalit Health Services, Tel Aviv, Israel
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Israel Krieger
- Shalvata Mental Health Center, Affiliated With the Sackler School of Medicine, Tel Aviv University, Israel
| | - Arnon Dov Cohen
- Department of Quality Measurements and Research, Clalit Health Services, Tel Aviv, Israel
- Siaal Research Center for Family Medicine and Primary Care, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Dana Tzur Bitan
- Department of Behavioral Sciences, Ariel University, Israel
- Shalvata Mental Health Center, Affiliated With the Sackler School of Medicine, Tel Aviv University, Israel
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