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Wang HI, Doran T, Crooks MG, Khunti K, Heightman M, Gonzalez-Izquierdo A, Qummer Ul Arfeen M, Loveless A, Banerjee A, Van Der Feltz-Cornelis C. Prevalence, risk factors and characterisation of individuals with long COVID using Electronic Health Records in over 1.5 million COVID cases in England. J Infect 2024; 89:106235. [PMID: 39121972 PMCID: PMC11409608 DOI: 10.1016/j.jinf.2024.106235] [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: 10/03/2023] [Revised: 07/24/2024] [Accepted: 07/27/2024] [Indexed: 08/12/2024]
Abstract
OBJECTIVES This study examines clinically confirmed long-COVID symptoms and diagnosis among individuals with COVID in England, aiming to understand prevalence and associated risk factors using electronic health records. To further understand long COVID, the study also explored differences in risks and symptom profiles in three subgroups: hospitalised, non-hospitalised, and untreated COVID cases. METHODS A population-based longitudinal cohort study was conducted using data from 1,554,040 individuals with confirmed SARS-CoV-2 infection via Clinical Practice Research Datalink. Descriptive statistics explored the prevalence of long COVID symptoms 12 weeks post-infection, and Cox regression models analysed the associated risk factors. Sensitivity analysis was conducted to test the impact of right-censoring data. RESULTS During an average 400-day follow-up, 7.4% of individuals with COVID had at least one long-COVID symptom after acute phase, yet only 0.5% had long-COVID diagnostic codes. The most common long-COVID symptoms included cough (17.7%), back pain (15.2%), stomach-ache (11.2%), headache (11.1%), and sore throat (10.0%). The same trend was observed in all three subgroups. Risk factors associated with long-COVID symptoms were female sex, non-white ethnicity, obesity, and pre-existing medical conditions like anxiety, depression, type II diabetes, and somatic symptom disorders. CONCLUSIONS This study is the first to investigate the prevalence and risk factors of clinically confirmed long-COVID in the general population. The findings could help clinicians identify higher risk individuals for timely intervention and allow decision-makers to more efficiently allocate resources for managing long-COVID.
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Affiliation(s)
- Han-I Wang
- Department of Health Sciences, University of York, York, UK; Institute of Health Informatics, University College of London, London, UK.
| | - Tim Doran
- Department of Health Sciences, University of York, York, UK
| | | | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Melissa Heightman
- University College London Hospitals NHS Foundation Trust, London, UK
| | | | | | - Antony Loveless
- Patient and Public Involvement (PPI) member for STIMULATE-ICP Consortium, Institute of Health Informatics, University College of London, London, UK
| | - Amitava Banerjee
- Institute of Health Informatics, University College of London, London, UK
| | - Christina Van Der Feltz-Cornelis
- Department of Health Sciences, University of York, York, UK; Hull York Medical School, York, UK; Institute of Health Informatics, University College of London, London, UK
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Dashtban A, Mizani MA, Pasea L, Tomlinson C, Mu Y, Islam N, Rafferty S, Warren-Gash C, Denaxas S, Horstmanshof K, Kontopantelis E, Petersen S, Sudlow C, Khunti K, Banerjee A. Vaccinations, cardiovascular drugs, hospitalization, and mortality in COVID-19 and Long COVID. Int J Infect Dis 2024; 146:107155. [PMID: 38942167 DOI: 10.1016/j.ijid.2024.107155] [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: 03/04/2024] [Revised: 06/06/2024] [Accepted: 06/23/2024] [Indexed: 06/30/2024] Open
Abstract
OBJECTIVE To identify highest-risk subgroups for COVID-19 and Long COVID(LC), particularly in contexts of influenza and cardiovascular disease(CVD). METHODS Using national, linked electronic health records for England (NHS England Secure Data Environment via CVD-COVID-UK/COVID-IMPACT Consortium), we studied individuals (of all ages) with COVID-19 and LC (2020-2023). We compared all-cause hospitalization and mortality by prior CVD, high CV risk, vaccination status (COVID-19/influenza), and CVD drugs, investigating impact of vaccination and CVD prevention using population preventable fractions. RESULTS Hospitalization and mortality were 15.3% and 2.0% among 17,373,850 individuals with COVID-19 (LC rate 1.3%), and 16.8% and 1.4% among 301,115 with LC. Adjusted risk of mortality and hospitalization were reduced with COVID-19 vaccination ≥ 2 doses(COVID-19:HR 0.36 and 0.69; LC:0.44 and 0.90). With influenza vaccination, mortality was reduced, but not hospitalization (COVID-19:0.86 and 1.01, and LC:0.72 and 1.05). Mortality and hospitalization were reduced by CVD prevention in those with CVD, e.g., anticoagulants- COVID:19:0.69 and 0.92; LC:0.59 and 0.88; lipid lowering- COVID-19:0.69 and 0.86; LC:0.68 and 0.90. COVID-19 vaccination averted 245044 of 321383 and 7586 of 8738 preventable deaths after COVID-19 and LC, respectively. INTERPRETATION Prior CVD and high CV risk are associated with increased hospitalization and mortality in COVID-19 and LC. Targeted COVID-19 vaccination and CVD prevention are priority interventions. FUNDING NIHR. HDR UK.
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Affiliation(s)
- Ashkan Dashtban
- Institute of Health Informatics, University College London, London, UK
| | - Mehrdad A Mizani
- Institute of Health Informatics, University College London, London, UK; British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - Laura Pasea
- Institute of Health Informatics, University College London, London, UK
| | | | - Yi Mu
- Institute of Health Informatics, University College London, London, UK
| | - Nazrul Islam
- Primary Care Research Centre, Faculty of Medicine, University of Southampton, Southampton, UK
| | | | - Charlotte Warren-Gash
- Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
| | - Kim Horstmanshof
- Institute of Health Informatics, University College London, London, UK
| | | | | | - Cathie Sudlow
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK; Health Data Research UK, University College London, London, UK
| | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Amitava Banerjee
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK; Barts Health NHS Trust, London, UK; University College London Hospitals NHS Trust, London, UK.
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3
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Schaffer AL, Park RY, Tazare J, Bhaskaran K, MacKenna B, Denaxas S, Dillingham I, Bacon SCJ, Mehrkar A, Bates C, Goldacre B, Greaves F, Macleod J, Tomlinson LA, Walker A. Changes in sick notes associated with COVID-19 from 2020 to 2022: a cohort study in 24 million primary care patients in OpenSAFELY-TPP. BMJ Open 2024; 14:e080600. [PMID: 38960458 PMCID: PMC11227761 DOI: 10.1136/bmjopen-2023-080600] [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: 10/05/2023] [Accepted: 06/05/2024] [Indexed: 07/05/2024] Open
Abstract
OBJECTIVES Long-term sickness absence from employment has negative consequences for the economy and can lead to widened health inequalities. Sick notes (also called 'fit notes') are issued by general practitioners when a person cannot work for health reasons for more than 7 days. We quantified the sick note rate in people with evidence of COVID-19 in 2020, 2021 and 2022, as an indication of the burden for people recovering from COVID-19. DESIGN Cohort study. SETTING With National Health Service (NHS) England approval, we used routine clinical data (primary care, hospital and COVID-19 testing records) within the OpenSAFELY-TPP database. PARTICIPANTS People 18-64 years with a recorded positive test or diagnosis of COVID-19 in 2020 (n=365 421), 2021 (n=1 206 555) or 2022 (n=1 321 313); general population matched in age, sex and region in 2019 (n=3 140 326), 2020 (n=3 439 534), 2021 (n=4 571 469) and 2022 (n=4 818 870); people hospitalised with pneumonia in 2019 (n=29 673). PRIMARY OUTCOME MEASURE Receipt of a sick note in primary care. RESULTS Among people with a positive SARS-CoV-2 test or COVID-19 diagnosis, the sick note rate was 4.88 per 100 person-months (95% CI 4.83 to 4.93) in 2020, 2.66 (95% CI 2.64 to 2.67) in 2021 and 1.73 (95% CI 1.72 to 1.73) in 2022. Compared with the age, sex and region-matched general population, the adjusted HR for receipt of a sick note over the entire follow-up period (up to 10 months) was 4.07 (95% CI 4.02 to 4.12) in 2020 decreasing to 1.57 (95% CI 1.56 to 1.58) in 2022. The HR was highest in the first 30 days postdiagnosis in all years. Among people hospitalised with COVID-19, after adjustment, the sick note rate was lower than in people hospitalised with pneumonia. CONCLUSIONS Given the under-recording of postacute COVID-19-related symptoms, these findings contribute a valuable perspective on the long-term effects of COVID-19. Despite likely underestimation of the sick note rate, sick notes were issued more frequently to people with COVID-19 compared with those without, even in an era when most people are vaccinated. Most sick notes occurred in the first 30 days postdiagnosis, but the increased risk several months postdiagnosis may provide further evidence of the long-term impact.
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Affiliation(s)
- Andrea L Schaffer
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Unviersity of Oxford, Oxford, UK
| | - Robin Y Park
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Unviersity of Oxford, Oxford, UK
| | - John Tazare
- London School of Hygiene and Tropical Medicine, London, UK
| | | | - Brian MacKenna
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Unviersity of Oxford, Oxford, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- University College London Hospitals Biomedical Research Centre, London, UK
- BHF Data Science Centre, Health Data Research UK, London, UK
| | - Iain Dillingham
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Unviersity of Oxford, Oxford, UK
| | - Sebastian C J Bacon
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Unviersity of Oxford, Oxford, UK
| | - Amir Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Unviersity of Oxford, Oxford, UK
| | | | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Unviersity of Oxford, Oxford, UK
| | - Felix Greaves
- National Institute for Health and Care Excellence, London, UK
- Department of Primary Care and Public Health, Imperial College London, London, UK
| | - John Macleod
- NIHR Applied Research Collaboration West, Bristol, UK
| | | | - Alex Walker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Unviersity of Oxford, Oxford, UK
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4
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Wood C, Speed V, Fisher L, Curtis HJ, Schaffer AL, Walker AJ, Croker R, Brown AD, Cunningham C, Hulme WJ, Andrews CD, Butler-Cole BFC, Evans D, Inglesby P, Dillingham I, Bacon SCJ, Davy S, Ward T, Hickman G, Bridges L, O'Dwyer T, Maude S, Smith RM, Mehrkar A, Bates C, Cockburn J, Parry J, Hester F, Harper S, Goldacre B, MacKenna B. The impact of COVID-19 on medication reviews in English primary care. An OpenSAFELY-TPP analysis of 20 million adult electronic health records. Br J Clin Pharmacol 2024; 90:1600-1614. [PMID: 38531661 PMCID: PMC7616229 DOI: 10.1111/bcp.16030] [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: 10/09/2023] [Revised: 01/22/2024] [Accepted: 01/29/2024] [Indexed: 03/28/2024] Open
Abstract
AIMS The COVID-19 pandemic caused significant disruption to routine activity in primary care. Medication reviews are an important primary care activity ensuring safety and appropriateness of prescribing. A disruption could have significant negative implications for patient care. Using routinely collected data, our aim was first to describe codes used to record medication review activity and then to report the impact of COVID-19 on the rates of medication reviews. METHODS With the approval of NHS England, we conducted a cohort study of 20 million adult patient records in general practice, in-situ using the OpenSAFELY platform. For each month, between April 2019 and March 2022, we report the percentage of patients with a medication review coded monthly and in the previous 12 months with breakdowns by regional, clinical and demographic subgroups and those prescribed high-risk medications. RESULTS In April 2019, 32.3% of patients had a medication review coded in the previous 12 months. During the first COVID-19 lockdown, monthly activity decreased (-21.1% April 2020), but the 12-month rate was not substantially impacted (-10.5% March 2021). The rate of structured medication review in the last 12 months reached 2.9% by March 2022, with higher percentages in high-risk groups (care home residents 34.1%, age 90+ years 13.1%, high-risk medications 10.2%). The most used medication review code was Medication review done 314530002 (59.5%). CONCLUSIONS There was a substantial reduction in the monthly rate of medication reviews during the pandemic but rates recovered by the end of the study period. Structured medication reviews were prioritized for high-risk patients.
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Affiliation(s)
- Christopher Wood
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Victoria Speed
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
- Department of Thrombosis and Haemostasis, King's College Hospital, London, UK
| | - Louis Fisher
- 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
| | - Andrea L Schaffer
- 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
| | - Richard Croker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Andrew D Brown
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Christine Cunningham
- 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
| | - Colm D Andrews
- 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
| | - David Evans
- 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
| | - Iain Dillingham
- 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
| | - Tom Ward
- 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
| | - Lucy Bridges
- 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
| | - Steven Maude
- 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
| | | | | | | | | | | | - 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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5
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Ayoubkhani D, Zaccardi F, Pouwels KB, Walker AS, Houston D, Alwan NA, Martin J, Khunti K, Nafilyan V. Employment outcomes of people with Long Covid symptoms: community-based cohort study. Eur J Public Health 2024; 34:489-496. [PMID: 38423541 PMCID: PMC11161149 DOI: 10.1093/eurpub/ckae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Evidence on the long-term employment consequences of SARS-CoV-2 infection is lacking. We used data from a large, community-based sample in the UK to estimate associations between Long Covid and employment outcomes. METHODS This was an observational, longitudinal study using a pre-post design. We included survey participants from 3 February 2021 to 30 September 2022 when they were aged 16-64 years and not in education. Using conditional logit modelling, we explored the time-varying relationship between Long Covid status ≥12 weeks after a first test-confirmed SARS-CoV-2 infection (reference: pre-infection) and labour market inactivity (neither working nor looking for work) or workplace absence lasting ≥4 weeks. RESULTS Of 206 299 participants (mean age 45 years, 54% female, 92% white), 15% were ever labour market inactive and 10% were ever long-term absent during follow-up. Compared with pre-infection, inactivity was higher in participants reporting Long Covid 30 to <40 weeks [adjusted odds ratio (aOR): 1.45; 95% CI: 1.17-1.81] or 40 to <52 weeks (aOR: 1.34; 95% CI: 1.05-1.72) post-infection. Combining with official statistics on Long Covid prevalence, and assuming a correct statistical model, our estimates translate to 27 000 (95% CI: 6000-47 000) working-age adults in the UK being inactive because of Long Covid in July 2022. CONCLUSIONS Long Covid is likely to have contributed to reduced participation in the UK labour market, though it is unlikely to be the sole driver. Further research is required to quantify the contribution of other factors, such as indirect health effects of the pandemic.
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Affiliation(s)
- Daniel Ayoubkhani
- Leicester Real World Evidence Unit, Leicester Diabetes Centre, Department of Population Health Sciences, University of Leicester, Leicester, UK
- Data and Analysis for Social Care and Health Division, Office for National Statistics, Newport, UK
| | - Francesco Zaccardi
- Leicester Real World Evidence Unit, Leicester Diabetes Centre, Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Koen B Pouwels
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - A Sarah Walker
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Donald Houston
- City-Regional Economic Development Institute, Birmingham Business School, University of Birmingham, Birmingham, UK
| | - Nisreen A Alwan
- School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
- NIHR Applied Research Collaboration (ARC) Wessex, Southampton, UK
| | | | - Kamlesh Khunti
- Leicester Real World Evidence Unit, Leicester Diabetes Centre, Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Vahé Nafilyan
- Data and Analysis for Social Care and Health Division, Office for National Statistics, Newport, UK
- Department of Medical Statistics, Faculty of Epidemiology and Population Health, Environment and Society, London School of Hygiene & Tropical Medicine, London, UK
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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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7
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Knuppel A, Boyd A, Macleod J, Chaturvedi N, Williams DM. The long COVID evidence gap in England. Lancet 2024; 403:1981-1982. [PMID: 38729195 DOI: 10.1016/s0140-6736(24)00744-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 01/31/2024] [Accepted: 04/08/2024] [Indexed: 05/12/2024]
Affiliation(s)
| | - Andy Boyd
- Institute of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - John Macleod
- Institute of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; NIHR Applied Research Collaboration (ARC) West, Bristol, UK
| | - Nishi Chaturvedi
- MRC Unit of Lifelong Health and Ageing at UCL, University College London, London W1E 7HB, UK
| | - Dylan M Williams
- MRC Unit of Lifelong Health and Ageing at UCL, University College London, London W1E 7HB, UK.
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8
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Heald AH, Williams R, Jenkins DA, Stewart S, Bakerly ND, Mccay K, Ollier W. The prevalence of long COVID in people with diabetes mellitus-evidence from a UK cohort. EClinicalMedicine 2024; 71:102607. [PMID: 38813442 PMCID: PMC11133790 DOI: 10.1016/j.eclinm.2024.102607] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 03/28/2024] [Accepted: 04/05/2024] [Indexed: 05/31/2024] Open
Abstract
Background It was apparent from the early phase of the SARS-CoV-2 virus (COVID-19) pandemic that a multi-system syndrome can develop in the weeks following a COVID-19 infection, now referred to as Long COVID. Given that people living with diabetes are at increased risk of hospital admission/poor outcomes following COVID-19 infection we hypothesised that they may also be more susceptible to developing Long COVID. We describe here the prevalence of Long COVID in people living with diabetes when compared to matched controls in a Northwest UK population. Methods This was a retrospective cohort study of people who had a recorded diagnosis of type 1 diabetes (T1D) or type 2 diabetes (T2D) who were alive on 1st January 2020 and who had a proven COVID-19 infection. We used electronic health record data from the Greater Manchester Care Record collected from 1st January 2020 to 16th September 2023, we determined the prevalence of Long COVID in people with T1D and T2D vs matched individuals without diabetes (non-DM). Findings There were 3087 T1D individuals with 14,077 non-diabetes controls and 3087 T2D individuals with 14,077 non-diabetes controls and 29,700 T2D individuals vs 119,951 controls. For T1D, there was a lower proportion of Long COVID diagnosis and/or referral to a Long COVID service at 0.33% vs 0.48% for matched controls. The prevalence of Long COVID In T2D individuals was 0.53% vs 1:3 matched controls 0.54%. For T2D, there were differences by sex in the prevalence of Long COVID in comparison with 1:3 matched controls. For Long COVID between males with T2D and their matched controls, the prevalence was lower in matched controls at 0.46%.vs 0.54% (0.008). When considering the prevalence of LC between females with T2D and their matched controls, the prevalence was higher in matched controls at 0.61% vs 0.53% (0.007). The prevalence of Long COVID in males with T2D vs females was not different. T2D patients at older vs younger age were at reduced risk of developing Long COVID (OR 0.994 [95% CI) [0.989, 0.999]). For females there was a minor increase of risk (OR 1.179, 95% CI [1.002, 1.387]). Presence of a higher body mass index (BMI) was also associated an increased risk of developing Long COVID (OR 1.013, 95% CI [1.001, 1.026]). The estimated general population prevalence of Long COVID based on general practice coding (not self-reported) of this diagnosis was 0.5% of people with a prior acute COVID-19 diagnosis. Interpretation Recorded Long COVID was more prevalent in men with T2D than in matched non-T2D controls with the opposite seen for T2D women, with recorded Long COVID rates being similar for T2D men and women. Younger age, female sex and higher BMI were all associated with a greater likelihood of developing Long COVID when taken as individual variables. There remains an imperative for continuing awareness of Long COVID as a differential diagnosis for multi-system symptomatic presentation in the context of a previous acute COVID-19 infection. Funding The time of co-author RW was supported by the NIHR Applied Research Collaboration Greater Manchester (NIHR200174) and the NIHR Manchester Biomedical Research Centre (NIHR203308).
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Affiliation(s)
- Adrian H. Heald
- The School of Medicine and Manchester Academic Health Sciences Centre, The University of Manchester, Manchester, UK
- Department of Diabetes and Endocrinology, Salford Royal Hospital, Salford, UK
| | - Richard Williams
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Applied Research Collaboration Greater Manchester, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - David A. Jenkins
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Applied Research Collaboration Greater Manchester, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Stuart Stewart
- Centre for Primary Care & Health Services Research, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Donal O’Donoghue Renal Research Centre, Northern Care Alliance Research & Innovation, Salford Royal NHS Foundation Trust, Salford, UK
| | - Nawar Diar Bakerly
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Department of Respiratory Medicine, Salford Royal Hospital, Salford, UK
- School of Biological Sciences, Manchester Metropolitan University, Manchester, UK
| | - Kevin Mccay
- Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, UK
| | - William Ollier
- Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, UK
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9
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Carlile O, Briggs A, Henderson AD, Butler-Cole BF, Tazare J, Tomlinson LA, Marks M, Jit M, Lin LY, Bates C, Parry J, Bacon SC, Dillingham I, Dennison WA, Costello RE, Walker AJ, Hulme W, Goldacre B, Mehrkar A, MacKenna B, Herrett E, Eggo RM. Impact of long COVID on health-related quality-of-life: an OpenSAFELY population cohort study using patient-reported outcome measures (OpenPROMPT). THE LANCET REGIONAL HEALTH. EUROPE 2024; 40:100908. [PMID: 38689605 PMCID: PMC11059448 DOI: 10.1016/j.lanepe.2024.100908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/22/2024] [Accepted: 04/04/2024] [Indexed: 05/02/2024]
Abstract
Background Long COVID is a major problem affecting patient health, the health service, and the workforce. To optimise the design of future interventions against COVID-19, and to better plan and allocate health resources, it is critical to quantify the health and economic burden of this novel condition. We aimed to evaluate and estimate the differences in health impacts of long COVID across sociodemographic categories and quantify this in Quality-Adjusted Life-Years (QALYs), widely used measures across health systems. Methods With the approval of NHS England, we utilised OpenPROMPT, a UK cohort study measuring the impact of long COVID on health-related quality-of-life (HRQoL). OpenPROMPT invited responses to Patient Reported Outcome Measures (PROMs) using a smartphone application and recruited between November 2022 and October 2023. We used the validated EuroQol EQ-5D questionnaire with the UK Value Set to develop disutility scores (1-utility) for respondents with and without Long COVID using linear mixed models, and we calculated subsequent Quality-Adjusted Life-Months (QALMs) for long COVID. Findings The total OpenPROMPT cohort consisted of 7575 individuals who consented to data collection, with which we used data from 6070 participants who completed a baseline research questionnaire where 24.6% self-reported long COVID. In multivariable regressions, long COVID had a consistent impact on HRQoL, showing a higher likelihood or odds of reporting loss in quality-of-life (Odds Ratio (OR): 4.7, 95% CI: 3.72-5.93) compared with people who did not report long COVID. Reporting a disability was the largest predictor of losses of HRQoL (OR: 17.7, 95% CI: 10.37-30.33) across survey responses. Self-reported long COVID was associated with an 0.37 QALM loss. Interpretation We found substantial impacts on quality-of-life due to long COVID, representing a major burden on patients and the health service. We highlight the need for continued support and research for long COVID, as HRQoL scores compared unfavourably to patients with conditions such as multiple sclerosis, heart failure, and renal disease. 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)
- Oliver Carlile
- 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
| | | | - Ben F.C. 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
- Hospital for Tropical Diseases, University College London Hospital, London, WC1E 6JD, UK
- Division of Infection and Immunity, University College London, London, WC1E 6BT, UK
| | - Mark Jit
- 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
| | - 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 C.J. 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
| | - 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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10
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Jeffrey K, Woolford L, Maini R, Basetti S, Batchelor A, Weatherill D, White C, Hammersley V, Millington T, Macdonald C, Quint JK, Kerr R, Kerr S, Shah SA, Rudan I, Fagbamigbe AF, Simpson CR, Katikireddi SV, Robertson C, Ritchie L, Sheikh A, Daines L. Prevalence and risk factors for long COVID among adults in Scotland using electronic health records: a national, retrospective, observational cohort study. EClinicalMedicine 2024; 71:102590. [PMID: 38623399 PMCID: PMC11016856 DOI: 10.1016/j.eclinm.2024.102590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 03/07/2024] [Accepted: 03/21/2024] [Indexed: 04/17/2024] Open
Abstract
Background Long COVID is a debilitating multisystem condition. The objective of this study was to estimate the prevalence of long COVID in the adult population of Scotland, and to identify risk factors associated with its development. Methods In this national, retrospective, observational cohort study, we analysed electronic health records (EHRs) for all adults (≥18 years) registered with a general medical practice and resident in Scotland between March 1, 2020, and October 26, 2022 (98-99% of the population). We linked data from primary care, secondary care, laboratory testing and prescribing. Four outcome measures were used to identify long COVID: clinical codes, free text in primary care records, free text on sick notes, and a novel operational definition. The operational definition was developed using Poisson regression to identify clinical encounters indicative of long COVID from a sample of negative and positive COVID-19 cases matched on time-varying propensity to test positive for SARS-CoV-2. Possible risk factors for long COVID were identified by stratifying descriptive statistics by long COVID status. Findings Of 4,676,390 participants, 81,219 (1.7%) were identified as having long COVID. Clinical codes identified the fewest cases (n = 1,092, 0.02%), followed by free text (n = 8,368, 0.2%), sick notes (n = 14,469, 0.3%), and the operational definition (n = 64,193, 1.4%). There was limited overlap in cases identified by the measures; however, temporal trends and patient characteristics were consistent across measures. Compared with the general population, a higher proportion of people with long COVID were female (65.1% versus 50.4%), aged 38-67 (63.7% versus 48.9%), overweight or obese (45.7% versus 29.4%), had one or more comorbidities (52.7% versus 36.0%), were immunosuppressed (6.9% versus 3.2%), shielding (7.9% versus 3.4%), or hospitalised within 28 days of testing positive (8.8% versus 3.3%%), and had tested positive before Omicron became the dominant variant (44.9% versus 35.9%). The operational definition identified long COVID cases with combinations of clinical encounters (from four symptoms, six investigation types, and seven management strategies) recorded in EHRs within 4-26 weeks of a positive SARS-CoV-2 test. These combinations were significantly (p < 0.0001) more prevalent in positive COVID-19 patients than in matched negative controls. In a case-crossover analysis, 16.4% of those identified by the operational definition had similar healthcare patterns recorded before testing positive. Interpretation The prevalence of long COVID presenting in general practice was estimated to be 0.02-1.7%, depending on the measure used. Due to challenges in diagnosing long COVID and inconsistent recording of information in EHRs, the true prevalence of long COVID is likely to be higher. The operational definition provided a novel approach but relied on a restricted set of symptoms and may misclassify individuals with pre-existing health conditions. Further research is needed to refine and validate this approach. Funding Chief Scientist Office (Scotland), Medical Research Council, and BREATHE.
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Affiliation(s)
- Karen Jeffrey
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Lana Woolford
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Rishma Maini
- Public Health Scotland, Glasgow and Edinburgh, UK
| | | | - Ashleigh Batchelor
- Patient and Public Contributors, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - David Weatherill
- Patient and Public Contributors, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Chris White
- Patient and Public Contributors, Usher Institute, University of Edinburgh, Edinburgh, UK
| | | | | | | | - Jennifer K. Quint
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Robin Kerr
- NHS Borders, Melrose, UK
- NHS Dumfries & Galloway, Dumfries, UK
| | - Steven Kerr
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | | | - Igor Rudan
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | | | - Colin R. Simpson
- Usher Institute, University of Edinburgh, Edinburgh, UK
- School of Health, Wellington Faculty of Health, Victoria University of Wellington, Wellington, NZ
| | - Srinivasa Vittal Katikireddi
- Public Health Scotland, Glasgow and Edinburgh, UK
- MRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, UK
| | - Chris Robertson
- Public Health Scotland, Glasgow and Edinburgh, UK
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK
| | - Lewis Ritchie
- Academic Primary Care, University of Aberdeen, Aberdeen, UK
- Institute of Applied Health Sciences, University of Aberdeen, UK
| | - Aziz Sheikh
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Luke Daines
- Usher Institute, University of Edinburgh, Edinburgh, UK
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11
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Haag L, Richardson J, Haig C, Cunningham Y, Fraser H, Brosnahan N, Ibbotson T, Ormerod J, White C, McIntosh E, O'Donnell K, Sattar N, McConnachie A, Lean M, Blane D, Combet E. Baseline Characteristics in the Remote Diet Intervention to REduce long-COVID Symptoms Trial (ReDIRECT). NIHR OPEN RESEARCH 2024; 4:7. [PMID: 39145102 PMCID: PMC11320183 DOI: 10.3310/nihropenres.13522.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/19/2024] [Indexed: 08/16/2024]
Abstract
Background The persistence of symptoms for ≥12 weeks after a COVID-19 infection is known as Long COVID (LC), a condition with unclear pathophysiology and no proven treatments to date. Living with obesity is a risk factor for LC and has symptoms which may overlap with and aggravate LC. Methods ReDIRECT is a remotely delivered trial assessing whether weight management can reduce LC symptoms. We recruited people with LC and BMI >27kg/m 2. The intervention was delivered remotely by dietitians, with online data collection (medical and dietary history, COVID-19 infection and vaccination, body composition, LC history/symptoms, blood pressure, quality of life, sociodemographic data). Participants self-selected the dominant LC symptoms they most wanted to improve from the intervention. Results Participants (n=234) in England (64%) and Scotland (30%) were mainly women (85%) of white ethnicity (90%), with 13% living in the 20% most deprived areas, a mean age of 46 (SD10) years, and median BMI of 35kg/m 2 (IQR 32-40). Before starting the study, 30% reported more than one COVID-19 infection (82% confirmed with one or more positive tests). LC Diagnosis was mainly by GPs (71%), other healthcare professionals (9%), or self-diagnosed (21%). The median total number of symptoms was 6 (IQR 4-8). Self-selected dominant LC symptoms included fatigue (54%), breathlessness (16%), pain (12%), anxiety/depression (1%) and "other" (17%). At baseline, 82% were taking medication, 57% reported 1+ other medical conditions. Quality of life was poor; 20% were on long-term sick leave or reduced working hours. Most (92%) reported having gained weight since contracting COVID-19 (median weight change +11.5 kg, range -11.5 to +45.3 kg). Conclusions Symptoms linked to LC and overweight are diverse and complex. Remote trial delivery enabled rapid recruitment across the UK yet certain groups (e.g. men and those from ethnic minority groups) were under-represented. Trial registration ISRCTN registry ( ISRCTN12595520, 25/11/2021).
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Affiliation(s)
- Laura Haag
- Human Nutrition, School of Medicine, Dentistry & Nursing, University of Glasgow, Glasgow, Scotland, G31 2ER, UK
| | - Janice Richardson
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, Scotland, G12 8TA, UK
| | - Caroline Haig
- Robertson Centre for Biostatistics, School of Health and Wellbeing, University of Glasgow, Glasgow, Scotland, G12 8TA, UK
| | - Yvonne Cunningham
- General Practice & Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, Scotland, G12 8TA, UK
| | - Heather Fraser
- Health Economics and Health Technology Assessment, School of Health and Wellbeing, University of Glasgow, Glasgow, Scotland, G12 8TA, UK
| | | | - Tracy Ibbotson
- General Practice & Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, Scotland, G12 8TA, UK
| | | | | | - Emma McIntosh
- Health Economics and Health Technology Assessment, School of Health and Wellbeing, University of Glasgow, Glasgow, Scotland, G12 8TA, UK
| | - Kate O'Donnell
- General Practice & Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, Scotland, G12 8TA, UK
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, Scotland, G12 8TA, UK
| | - Alex McConnachie
- Robertson Centre for Biostatistics, School of Health and Wellbeing, University of Glasgow, Glasgow, Scotland, G12 8TA, UK
| | - Mike Lean
- Human Nutrition, School of Medicine, Dentistry & Nursing, University of Glasgow, Glasgow, Scotland, G31 2ER, UK
| | - David Blane
- General Practice & Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, Scotland, G12 8TA, UK
| | - Emilie Combet
- Human Nutrition, School of Medicine, Dentistry & Nursing, University of Glasgow, Glasgow, Scotland, G31 2ER, UK
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12
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Català M, Mercadé-Besora N, Kolde R, Trinh NTH, Roel E, Burn E, Rathod-Mistry T, Kostka K, Man WY, Delmestri A, Nordeng HME, Uusküla A, Duarte-Salles T, Prieto-Alhambra D, Jödicke AM. The effectiveness of COVID-19 vaccines to prevent long COVID symptoms: staggered cohort study of data from the UK, Spain, and Estonia. THE LANCET. RESPIRATORY MEDICINE 2024; 12:225-236. [PMID: 38219763 DOI: 10.1016/s2213-2600(23)00414-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/13/2023] [Accepted: 10/30/2023] [Indexed: 01/16/2024]
Abstract
BACKGROUND Although vaccines have proved effective to prevent severe COVID-19, their effect on preventing long-term symptoms is not yet fully understood. We aimed to evaluate the overall effect of vaccination to prevent long COVID symptoms and assess comparative effectiveness of the most used vaccines (ChAdOx1 and BNT162b2). METHODS We conducted a staggered cohort study using primary care records from the UK (Clinical Practice Research Datalink [CPRD] GOLD and AURUM), Catalonia, Spain (Information System for Research in Primary Care [SIDIAP]), and national health insurance claims from Estonia (CORIVA database). All adults who were registered for at least 180 days as of Jan 4, 2021 (the UK), Feb 20, 2021 (Spain), and Jan 28, 2021 (Estonia) comprised the source population. Vaccination status was used as a time-varying exposure, staggered by vaccine rollout period. Vaccinated people were further classified by vaccine brand according to their first dose received. The primary outcome definition of long COVID was defined as having at least one of 25 WHO-listed symptoms between 90 and 365 days after the date of a PCR-positive test or clinical diagnosis of COVID-19, with no history of that symptom 180 days before SARS-Cov-2 infection. Propensity score overlap weighting was applied separately for each cohort to minimise confounding. Sub-distribution hazard ratios (sHRs) were calculated to estimate vaccine effectiveness against long COVID, and empirically calibrated using negative control outcomes. Random effects meta-analyses across staggered cohorts were conducted to pool overall effect estimates. FINDINGS A total of 1 618 395 (CPRD GOLD), 5 729 800 (CPRD AURUM), 2 744 821 (SIDIAP), and 77 603 (CORIVA) vaccinated people and 1 640 371 (CPRD GOLD), 5 860 564 (CPRD AURUM), 2 588 518 (SIDIAP), and 302 267 (CORIVA) unvaccinated people were included. Compared with unvaccinated people, overall HRs for long COVID symptoms in people vaccinated with a first dose of any COVID-19 vaccine were 0·54 (95% CI 0·44-0·67) in CPRD GOLD, 0·48 (0·34-0·68) in CPRD AURUM, 0·71 (0·55-0·91) in SIDIAP, and 0·59 (0·40-0·87) in CORIVA. A slightly stronger preventative effect was seen for the first dose of BNT162b2 than for ChAdOx1 (sHR 0·85 [0·60-1·20] in CPRD GOLD and 0·84 [0·74-0·94] in CPRD AURUM). INTERPRETATION Vaccination against COVID-19 consistently reduced the risk of long COVID symptoms, which highlights the importance of vaccination to prevent persistent COVID-19 symptoms, particularly in adults. FUNDING National Institute for Health and Care Research.
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Affiliation(s)
- Martí Català
- Pharmaco- and Device Epidemiology Group, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Núria Mercadé-Besora
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain
| | - Raivo Kolde
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Nhung T H Trinh
- Pharmacoepidemiology and Drug Safety Research Group, Department of Pharmacy, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - Elena Roel
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain
| | - Edward Burn
- Pharmaco- and Device Epidemiology Group, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Trishna Rathod-Mistry
- Pharmaco- and Device Epidemiology Group, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Kristin Kostka
- Pharmaco- and Device Epidemiology Group, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Wai Yi Man
- Pharmaco- and Device Epidemiology Group, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Antonella Delmestri
- Pharmaco- and Device Epidemiology Group, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Hedvig M E Nordeng
- Pharmacoepidemiology and Drug Safety Research Group, Department of Pharmacy, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway; Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - Anneli Uusküla
- Department of Family Medicine and Public Health, University of Tartu, Tartu, Estonia
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Daniel Prieto-Alhambra
- Pharmaco- and Device Epidemiology Group, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK; Oxford National Institute for Health and Care Research Biomedical Research Centre, University of Oxford, Oxford, UK; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
| | - Annika M Jödicke
- Pharmaco- and Device Epidemiology Group, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
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13
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Torrance N, MacIver E, Adams NN, Skåtun D, Scott N, Kennedy C, Douglas F, Hernandez-Santiago V, Grant A. Lived experience of work and long COVID in healthcare staff. Occup Med (Lond) 2024; 74:78-85. [PMID: 38071754 PMCID: PMC10875925 DOI: 10.1093/occmed/kqad117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2024] Open
Abstract
BACKGROUND Healthcare workers (HCWs) had a greater occupational risk of exposure to coronavirus disease 2019 (COVID-19) and reported higher rates of long COVID (LC). This has implications for the provision of health care in already stretched health services. AIMS This study explored the impact of LC on a range of UK National Health Service (NHS) HCWs, their health and well-being, the effect on work patterns, and occupational support received. METHODS Mixed-methods study, online survey and qualitative interviews. Participants self-reporting LC symptoms were recruited through social media and NHS channels. Interviews used maximum variation sampling of 50 HCWs including healthcare professionals, ancillary and administration staff. Thematic analysis was conducted using NVivo software. RESULTS A total of 471 HCWs completed the online survey. Multiple LC symptoms were reported, revealing activity limitations for 90%. Two-thirds had taken sick leave, 18% were off-work and 33% reported changes in work duties. There were few differences in work practices by occupational group. Most participants were working but managing complex and dynamic symptoms, with periods of improvement and exacerbation. They engaged in a range of strategies: rest, pacing, planning and prioritizing, with work prioritized over other aspects of life. Symptom improvements were often linked to occupational medicine, managerial, colleague support and flexible workplace adjustments. CONCLUSIONS LC has a significant impact on the lives of HCWs suffering prolonged symptoms. Due to the variability and dynamic nature of symptoms, workplace support and flexible policies are needed to help retain staff.
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Affiliation(s)
- N Torrance
- School of Nursing, Midwifery & Paramedic Practice, Robert Gordon University, Aberdeen AB10 7QE, UK
| | - E MacIver
- School of Nursing, Midwifery & Paramedic Practice, Robert Gordon University, Aberdeen AB10 7QE, UK
| | - N N Adams
- School of Nursing, Midwifery & Paramedic Practice, Robert Gordon University, Aberdeen AB10 7QE, UK
| | - D Skåtun
- Health Economics Research Unit, University of Aberdeen, Aberdeen AB25 2ZD, UK
| | - N Scott
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK
| | - C Kennedy
- School of Nursing, Midwifery & Paramedic Practice, Robert Gordon University, Aberdeen AB10 7QE, UK
| | - F Douglas
- School of Nursing, Midwifery & Paramedic Practice, Robert Gordon University, Aberdeen AB10 7QE, UK
| | | | - A Grant
- School of Nursing, Midwifery & Paramedic Practice, Robert Gordon University, Aberdeen AB10 7QE, UK
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14
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Ghafari M, Hall M, Golubchik T, Ayoubkhani D, House T, MacIntyre-Cockett G, Fryer HR, Thomson L, Nurtay A, Kemp SA, Ferretti L, Buck D, Green A, Trebes A, Piazza P, Lonie LJ, Studley R, Rourke E, Smith DL, Bashton M, Nelson A, Crown M, McCann C, Young GR, Santos RAND, Richards Z, Tariq MA, Cahuantzi R, Barrett J, Fraser C, Bonsall D, Walker AS, Lythgoe K. Prevalence of persistent SARS-CoV-2 in a large community surveillance study. Nature 2024; 626:1094-1101. [PMID: 38383783 PMCID: PMC10901734 DOI: 10.1038/s41586-024-07029-4] [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: 01/29/2023] [Accepted: 01/04/2024] [Indexed: 02/23/2024]
Abstract
Persistent SARS-CoV-2 infections may act as viral reservoirs that could seed future outbreaks1-5, give rise to highly divergent lineages6-8 and contribute to cases with post-acute COVID-19 sequelae (long COVID)9,10. However, the population prevalence of persistent infections, their viral load kinetics and evolutionary dynamics over the course of infections remain largely unknown. Here, using viral sequence data collected as part of a national infection survey, we identified 381 individuals with SARS-CoV-2 RNA at high titre persisting for at least 30 days, of which 54 had viral RNA persisting at least 60 days. We refer to these as 'persistent infections' as available evidence suggests that they represent ongoing viral replication, although the persistence of non-replicating RNA cannot be ruled out in all. Individuals with persistent infection had more than 50% higher odds of self-reporting long COVID than individuals with non-persistent infection. We estimate that 0.1-0.5% of infections may become persistent with typically rebounding high viral loads and last for at least 60 days. In some individuals, we identified many viral amino acid substitutions, indicating periods of strong positive selection, whereas others had no consensus change in the sequences for prolonged periods, consistent with weak selection. Substitutions included mutations that are lineage defining for SARS-CoV-2 variants, at target sites for monoclonal antibodies and/or are commonly found in immunocompromised people11-14. This work has profound implications for understanding and characterizing SARS-CoV-2 infection, epidemiology and evolution.
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Affiliation(s)
- Mahan Ghafari
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Department of Biology, University of Oxford, Oxford, UK.
- Pandemic Science Institute, University of Oxford, Oxford, UK.
| | - Matthew Hall
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Pandemic Science Institute, University of Oxford, Oxford, UK
| | - Tanya Golubchik
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Sydney Infectious Diseases Institute (Sydney ID), School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Daniel Ayoubkhani
- Office for National Statistics, Newport, UK
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK
| | - George MacIntyre-Cockett
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Helen R Fryer
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Laura Thomson
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Pandemic Science Institute, University of Oxford, Oxford, UK
| | - Anel Nurtay
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Steven A Kemp
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Biology, University of Oxford, Oxford, UK
- Pandemic Science Institute, University of Oxford, Oxford, UK
| | - Luca Ferretti
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Pandemic Science Institute, University of Oxford, Oxford, UK
| | - David Buck
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Angie Green
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Amy Trebes
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Paolo Piazza
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Lorne J Lonie
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | | | | | - Darren L Smith
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Matthew Bashton
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Andrew Nelson
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Matthew Crown
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Clare McCann
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Gregory R Young
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Rui Andre Nunes Dos Santos
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Zack Richards
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Mohammad Adnan Tariq
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | | | | | - Christophe Fraser
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Pandemic Science Institute, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
- Wellcome Sanger Institute, Cambridge, UK
| | - David Bonsall
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Pandemic Science Institute, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford, UK
| | - Ann Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - Katrina Lythgoe
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Department of Biology, University of Oxford, Oxford, UK.
- Pandemic Science Institute, University of Oxford, Oxford, UK.
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15
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Barclay M, Renzi C, Antoniou A, Denaxas S, Harrison H, Ip S, Pashayan N, Torralbo A, Usher-Smith J, Wood A, Lyratzopoulos G. Phenotypes and rates of cancer-relevant symptoms and tests in the year before cancer diagnosis in UK Biobank and CPRD Gold. PLOS DIGITAL HEALTH 2023; 2:e0000383. [PMID: 38100737 PMCID: PMC10723831 DOI: 10.1371/journal.pdig.0000383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 10/05/2023] [Indexed: 12/17/2023]
Abstract
Early diagnosis of cancer relies on accurate assessment of cancer risk in patients presenting with symptoms, when screening is not appropriate. But recorded symptoms in cancer patients pre-diagnosis may vary between different sources of electronic health records (EHRs), either genuinely or due to differential completeness of symptom recording. To assess possible differences, we analysed primary care EHRs in the year pre-diagnosis of cancer in UK Biobank and Clinical Practice Research Datalink (CPRD) populations linked to cancer registry data. We developed harmonised phenotypes in Read v2 and CTV3 coding systems for 21 symptoms and eight blood tests relevant to cancer diagnosis. Among 22,601 CPRD and 11,594 UK Biobank cancer patients, 54% and 36%, respectively, had at least one consultation for possible cancer symptoms recorded in the year before their diagnosis. Adjusted comparisons between datasets were made using multivariable Poisson models, comparing rates of symptoms/tests in CPRD against expected rates if cancer site-age-sex-deprivation associations were the same as in UK Biobank. UK Biobank cancer patients compared with those in CPRD had lower rates of consultation for possible cancer symptoms [RR: 0.61 (0.59-0.63)], and lower rates for any primary care consultation [RR: 0.86 (95%CI 0.85-0.87)]. Differences were larger for 'non-alarm' symptoms [RR: 0.54 (0.52-0.56)], and smaller for 'alarm' symptoms [RR: 0.80 (0.76-0.84)] and blood tests [RR: 0.93 (0.90-0.95)]. In the CPRD cohort, approximately representative of the UK population, half of cancer patients had recorded symptoms in the year before diagnosis. The frequency of non-specific presenting symptoms recorded in the year pre-diagnosis of cancer was substantially lower among UK Biobank participants. The degree to which results based on highly selected biobank cohorts are generalisable needs to be examined in disease-specific contexts.
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Affiliation(s)
- Matthew Barclay
- Department of Behavioural Science and Health, Institute of Epidemiology and Healthcare, University College London, London, United Kingdom
| | - Cristina Renzi
- Department of Behavioural Science and Health, Institute of Epidemiology and Healthcare, University College London, London, United Kingdom
- Faculty of Medicine, University Vita-Salute San Raffaele, Milan, Italy
| | - Antonis Antoniou
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Hannah Harrison
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Samantha Ip
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
| | - Nora Pashayan
- Department of Applied Health Research, Institute of Epidemiology and Healthcare, University College London, London, United Kingdom
| | - Ana Torralbo
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Juliet Usher-Smith
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Angela Wood
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
- Cambridge Centre for Artificial Intelligence in Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Georgios Lyratzopoulos
- Department of Behavioural Science and Health, Institute of Epidemiology and Healthcare, University College London, London, United Kingdom
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16
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Tufts J, Guan N, Zemedikun DT, Subramanian A, Gokhale K, Myles P, Williams T, Marshall T, Calvert M, Matthews K, Nirantharakumar K, Jackson LJ, Haroon S. The cost of primary care consultations associated with long COVID in non-hospitalised adults: a retrospective cohort study using UK primary care data. BMC PRIMARY CARE 2023; 24:245. [PMID: 37986044 PMCID: PMC10662438 DOI: 10.1186/s12875-023-02196-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 10/28/2023] [Indexed: 11/22/2023]
Abstract
BACKGROUND The economic impact of managing long COVID in primary care is unknown. We estimated the costs of primary care consultations associated with long COVID and explored the relationship between risk factors and costs. METHODS Data were obtained on non-hospitalised adults from the Clinical Practice Research Datalink Aurum primary care database. We used propensity score matching with an incremental cost method to estimate additional primary care consultation costs associated with long COVID (12 weeks after COVID-19) at an individual and UK national level. We applied multivariable regression models to estimate the association between risk factors and consultations costs beyond 12 weeks from acute COVID-19. RESULTS Based on an analysis of 472,173 patients with COVID-19 and 472,173 unexposed individuals, the annual incremental cost of primary care consultations associated with long COVID was £2.44 per patient and £23,382,452 at the national level. Among patients with COVID-19, a long COVID diagnosis and reporting of longer-term symptoms were associated with a 43% and 44% increase in primary care consultation costs respectively, compared to patients without long COVID symptoms. Older age, female sex, obesity, being from a white ethnic group, comorbidities and prior consultation frequency were all associated with increased primary care consultation costs. CONCLUSIONS The costs of primary care consultations associated with long COVID in non-hospitalised adults are substantial. Costs are significantly higher among those diagnosed with long COVID, those with long COVID symptoms, older adults, females, and those with obesity and comorbidities.
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Affiliation(s)
- Jake Tufts
- University Hospitals of Morecambe Bay NHS Foundation Trust, Lancashire, LA9 7RG, UK
| | - Naijie Guan
- Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Dawit T Zemedikun
- School of Population and Global Health (M431), The University of Western Australia, 35 Stirling Highway, Perth, WA, 6009, Australia
| | - Anuradhaa Subramanian
- Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Krishna Gokhale
- Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Puja Myles
- Clinical Practice Research Datalink, Medicines and Healthcare Products Regulatory Agency, London, E14 4PU, UK
| | - Tim Williams
- Clinical Practice Research Datalink, Medicines and Healthcare Products Regulatory Agency, London, E14 4PU, UK
| | - Tom Marshall
- Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Melanie Calvert
- Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, B15 2TT, UK
- Applied Research Collaboration (ARC) West Midlands, National Institute for Health Research (NIHR), Birmingham, CV4 7AJ, UK
- NIHR Birmingham Biomedical Research Centre, University Hospital Birmingham and University of Birmingham, Birmingham, B15 2TH, UK
- NIHR Birmingham-Oxford Blood and Transplant Research Unit (BTRU) in Precision Transplant and Cellular Therapeutics, University of Birmingham, Birmingham, B15 2TT, UK
| | - Karen Matthews
- Long Covid SOS, Charity Registered in England & Wales, 11A Westland Road, Faringdon, SN7 7EX, Oxfordshire, UK
| | | | - Louise J Jackson
- Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Shamil Haroon
- Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
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17
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Bosworth ML, Shenhuy B, Walker AS, Nafilyan V, Alwan NA, O’Hara ME, Ayoubkhani D. Risk of New-Onset Long COVID Following Reinfection With Severe Acute Respiratory Syndrome Coronavirus 2: A Community-Based Cohort Study. Open Forum Infect Dis 2023; 10:ofad493. [PMID: 37953820 PMCID: PMC10633780 DOI: 10.1093/ofid/ofad493] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 10/02/2023] [Indexed: 11/14/2023] Open
Abstract
Background Little is known about the risk of long COVID following reinfection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We estimated the likelihood of new-onset, self-reported long COVID after a second SARS-CoV-2 infection, compared to a first infection. Methods We included UK COVID-19 Infection Survey participants who tested positive for SARS-CoV-2 between 1 November 2021 and 8 October 2022. The primary outcome was self-reported long COVID 12-20 weeks after each infection. Separate analyses were performed for those <16 years and ≥16 years. We estimated adjusted odds ratios (aORs) for new-onset long COVID using logistic regression, comparing second to first infections, controlling for sociodemographic characteristics and calendar date of infection, plus vaccination status in participants ≥16 years of age. Results Overall, long COVID was reported by those ≥16 years after 4.0% and 2.4% of first and second infections, respectively; the corresponding estimates among those aged <16 years were 1.0% and 0.6%. The aOR for long COVID after second compared to first infections was 0.72 (95% confidence interval [CI], .63-.81) for those ≥16 years and 0.93 (95% CI, .57-1.53) for those <16 years. Conclusions The risk of new-onset long COVID after a second SARS-CoV-2 infection is lower than that after a first infection for persons aged ≥16 years, though there is no evidence of a difference in risk for those <16 years. However, there remains some risk of new-onset long COVID after a second infection, with around 1 in 40 of those aged ≥16 years and 1 in 165 of those <16 years reporting long COVID after a second infection.
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Affiliation(s)
- Matthew L Bosworth
- Data and Analysis for Social Care and Health Division, Office for National Statistics, Newport, United Kingdom
| | - Boran Shenhuy
- Methodology and Quality Directorate, Office for National Statistics, Newport, United Kingdom
| | - A Sarah Walker
- National Institute for Health Research Health Protection Research Unit in Healthcare-Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Vahé Nafilyan
- Data and Analysis for Social Care and Health Division, Office for National Statistics, Newport, United Kingdom
- Faculty of Public Health, Environment and Society, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Nisreen A Alwan
- School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- National Institute for Health Research Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton National Health Service Foundation Trust, Southampton, United Kingdom
| | | | - Daniel Ayoubkhani
- Data and Analysis for Social Care and Health Division, Office for National Statistics, Newport, United Kingdom
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, United Kingdom
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18
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Vivaldi G, Pfeffer PE, Talaei M, Basera TJ, Shaheen SO, Martineau AR. Long-term symptom profiles after COVID-19 vs other acute respiratory infections: an analysis of data from the COVIDENCE UK study. EClinicalMedicine 2023; 65:102251. [PMID: 38106559 PMCID: PMC10721552 DOI: 10.1016/j.eclinm.2023.102251] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 09/04/2023] [Accepted: 09/15/2023] [Indexed: 12/19/2023] Open
Abstract
Background Long COVID is a well recognised, if heterogeneous, entity. Acute respiratory infections (ARIs) due to other pathogens may cause long-term symptoms, but few studies compare post-acute sequelae between SARS-CoV-2 and other ARIs. We aimed to compare symptom profiles between people with previous SARS-CoV-2 infection, people with previous non-COVID-19 ARIs, and contemporaneous controls, and to identify clusters of long-term symptoms. Methods COVIDENCE UK is a prospective, population-based UK study of ARIs in adults. We analysed data for 16 potential long COVID symptoms and health-related quality of life (HRQoL), reported between January 21 and February 15, 2021, by participants unvaccinated against SARS-CoV-2. We classified participants as having previous SARS-CoV-2 infection or previous non-COVID-19 ARI (≥4 weeks prior) or no reported ARI. We compared symptoms by infection status using logistic and fractional regression, and identified symptom clusters using latent class analysis (LCA). This study is registered with ClinicalTrials.gov, NCT04330599. Findings We included 10,171 participants (1311 [12.9%] with SARS-CoV-2 infection, 472 [4.6%] with non-COVID-19 ARI). Both types of infection were associated with increased prevalence/severity of most symptoms and decreased HRQoL compared with no infection. Participants with SARS-CoV-2 infection had increased odds of problems with taste/smell (odds ratio 19.74, 95% CI 10.53-37.00) and lightheadedness or dizziness (1.74, 1.18-2.56) compared with participants with non-COVID-19 ARIs. Separate LCA models identified three symptom severity groups for each infection type. In the most severe groups (representing 22% of participants for both SARS-CoV-2 and non-COVID-19 ARI), SARS-CoV-2 infection presented with a higher probability of problems with taste/smell (probability 0.41 vs 0.04), hair loss (0.25 vs 0.16), unusual sweating (0.38 vs 0.25), unusual racing of the heart (0.43 vs 0.33), and memory problems (0.70 vs 0.55) than non-COVID-19 ARI. Interpretation Both SARS-CoV-2 and non-COVID-19 ARIs are associated with a wide range of symptoms more than 4 weeks after the acute infection. Research on post-acute sequelae of ARIs should extend from SARS-CoV-2 to include other pathogens. Funding Barts Charity.
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Affiliation(s)
- Giulia Vivaldi
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Paul E. Pfeffer
- Barts Health NHS Trust, London, UK
- Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Mohammad Talaei
- Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Tariro Jayson Basera
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Seif O. Shaheen
- Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Adrian R. Martineau
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Asthma UK Centre for Applied Research, Queen Mary University of London, London, UK
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19
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Hetlevik Ø, Wensaas KA, Baste V, Emberland KE, Özgümüs T, Håberg SE, Rortveit G. Prevalence and predictors of post-COVID-19 symptoms in general practice - a registry-based nationwide study. BMC Infect Dis 2023; 23:721. [PMID: 37880583 PMCID: PMC10599052 DOI: 10.1186/s12879-023-08727-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 10/18/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND With Norwegian national registry data, we assessed the prevalence of post-COVID-19 symptoms at least 3 months after confirmed infection, and whether sociodemographic factors and pre-pandemic health problems were risk factors for these symptoms. METHODS All persons with a positive SARS-CoV-2 PCR test from February 2020 to February 2021 (exposed) were compared to a group without a positive test (unexposed) matched on age, sex, and country of origin. We used Cox regression to estimate hazard ratios (HR) for 18 outcome symptoms commonly described as post-COVID-19 related, registered by GPs. We compared relative risks (RR) for fatigue, memory disturbance, or shortness of breath among exposed and unexposed using Poisson regression models, assessing sex, age, education, country of origin, and pre-pandemic presence of the same symptom and comorbidity as possible risk factors, with additional analyses to assess hospitalisation for COVID-19 as a risk factor among exposed. RESULTS The exposed group (N = 53 846) had a higher prevalence of most outcome symptoms compared to the unexposed (N = 485 757), with the highest risk for shortness of breath (HR 2.75; 95%CI 2.59-2.93), fatigue (2.08; 2.00-2.16) and memory disturbance (1.41;1.26-1.59). High HRs were also found for disturbance of smell/taste and hair loss, but frequencies were low. Concerning risk factors, sociodemographic factors were at large similarly associated with outcome symptoms in both groups. Registration of the outcome symptom before the pandemic increased the risk for fatigue, memory disturbance and shortness of breath after COVID-19, but these associations were weaker among exposed. Comorbidity was not associated with fatigue and shortness of breath in the COVID-19 group. For memory disturbance, the RR was slightly increased with the higher comorbidity score both among exposed and unexposed. CONCLUSION COVID-19 was associated with a range of symptoms lasting more than three months after the infection.
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Affiliation(s)
- Øystein Hetlevik
- Department of Global Public Health and Primary Care, University of Bergen, Postbox 7804, Bergen, NO-5020, Norway.
| | - Knut-Arne Wensaas
- Research Unit for General Practice, NORCE Norwegian Research Centre, Bergen, Norway
| | - Valborg Baste
- National Centre for Emergency Primary Health Care, NORCE Norwegian Research Centre, Bergen, Norway
| | - Knut Erik Emberland
- Department of Global Public Health and Primary Care, University of Bergen, Postbox 7804, Bergen, NO-5020, Norway
| | - Türküler Özgümüs
- Department of Global Public Health and Primary Care, University of Bergen, Postbox 7804, Bergen, NO-5020, Norway
| | - Siri Eldevik Håberg
- Centre for Fertility and Health, The Norwegian Institute of Public Health, Oslo, Norway
| | - Guri Rortveit
- Department of Global Public Health and Primary Care, University of Bergen, Postbox 7804, Bergen, NO-5020, Norway
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20
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Edwards L, Pickett J, Ashcroft DM, Dambha-Miller H, Majeed A, Mallen C, Petersen I, Qureshi N, van Staa T, Abel G, Carvalho C, Denholm R, Kontopantelis E, Macaulay A, Macleod J. UK research data resources based on primary care electronic health records: review and summary for potential users. BJGP Open 2023; 7:BJGPO.2023.0057. [PMID: 37429634 PMCID: PMC10646196 DOI: 10.3399/bjgpo.2023.0057] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 06/12/2023] [Accepted: 07/07/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUND The range and scope of electronic health record (EHR) data assets in the UK has recently increased, which has been mainly in response to the COVID-19 pandemic. Summarising and comparing the large primary care resources will help researchers to choose the data resources most suited to their needs. AIM To describe the current landscape of UK EHR databases and considerations of access and use of these resources relevant to researchers. DESIGN & SETTING Narrative review of EHR databases in the UK. METHOD Information was collected from the Health Data Research Innovation Gateway, publicly available websites and other published data, and from key informants. The eligibility criteria were population-based open-access databases sampling EHRs across the whole population of one or more countries in the UK. Published database characteristics were extracted and summarised, and these were corroborated with resource providers. Results were synthesised narratively. RESULTS Nine large national primary care EHR data resources were identified and summarised. These resources are enhanced by linkage to other administrative data to a varying extent. Resources are mainly intended to support observational research, although some can support experimental studies. There is considerable overlap of populations covered. While all resources are accessible to bona fide researchers, access mechanisms, costs, timescales, and other considerations vary across databases. CONCLUSION Researchers are currently able to access primary care EHR data from several sources. Choice of data resource is likely to be driven by project needs and access considerations. The landscape of data resources based on primary care EHRs in the UK continues to evolve.
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Affiliation(s)
| | | | - Darren M Ashcroft
- Centre for Pharmacoepidemiology and Drug Safety, NIHR Greater Manchester Patient Safety Translational Research Centre, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | | | - Azeem Majeed
- Primary Care and Public Health, Imperial College London, London, UK
| | | | - Irene Petersen
- Department of Primary Care & Population Health, Institute of Epidemiology & Health, University College London, London, UK
| | - Nadeem Qureshi
- Centre for Academic Primary Care, University of Nottingham, Nottingham, UK
| | - Tjeerd van Staa
- Health eResearch Centre, University of Manchester, Manchester, UK
| | - Gary Abel
- Department of Health and Community Sciences (Medical School), Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Chris Carvalho
- Clinical Effectiveness Group, Queen Mary University of London, London, UK
| | - Rachel Denholm
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Centre for Academic Primary Care, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Health Data Research UK South-West, Bristol, UK
- NIHR Applied Research Collaboration (ARC) West, Bristol, UK
| | - Evangelos Kontopantelis
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK
| | | | - John Macleod
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Applied Research Collaboration (ARC) West, Bristol, UK
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21
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Cheetham NJ, Penfold R, Giunchiglia V, Bowyer V, Sudre CH, Canas LS, Deng J, Murray B, Kerfoot E, Antonelli M, Rjoob K, Molteni E, Österdahl MF, Harvey NR, Trender WR, Malim MH, Doores KJ, Hellyer PJ, Modat M, Hammers A, Ourselin S, Duncan EL, Hampshire A, Steves CJ. The effects of COVID-19 on cognitive performance in a community-based cohort: a COVID symptom study biobank prospective cohort study. EClinicalMedicine 2023; 62:102086. [PMID: 37654669 PMCID: PMC10466229 DOI: 10.1016/j.eclinm.2023.102086] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/16/2023] [Accepted: 06/21/2023] [Indexed: 09/02/2023] Open
Abstract
Background Cognitive impairment has been reported after many types of infection, including SARS-CoV-2. Whether deficits following SARS-CoV-2 improve over time is unclear. Studies to date have focused on hospitalised individuals with up to a year follow-up. The presence, magnitude, persistence and correlations of effects in community-based cases remain relatively unexplored. Methods Cognitive performance (working memory, attention, reasoning, motor control) was assessed in a prospective cohort study of participants from the United Kingdom COVID Symptom Study Biobank between July 12, 2021 and August 27, 2021 (Round 1), and between April 28, 2022 and June 21, 2022 (Round 2). Participants, recruited from the COVID Symptom Study smartphone app, comprised individuals with and without SARS-CoV-2 infection and varying symptom duration. Effects of COVID-19 exposures on cognitive accuracy and reaction time scores were estimated using multivariable ordinary least squares linear regression models weighted for inverse probability of participation, adjusting for potential confounders and mediators. The role of ongoing symptoms after COVID-19 infection was examined stratifying for self-perceived recovery. Longitudinal analysis assessed change in cognitive performance between rounds. Findings 3335 individuals completed Round 1, of whom 1768 also completed Round 2. At Round 1, individuals with previous positive SARS-CoV-2 tests had lower cognitive accuracy (N = 1737, β = -0.14 standard deviations, SDs, 95% confidence intervals, CI: -0.21, -0.07) than negative controls. Deficits were largest for positive individuals with ≥12 weeks of symptoms (N = 495, β = -0.22 SDs, 95% CI: -0.35, -0.09). Effects were comparable to hospital presentation during illness (N = 281, β = -0.31 SDs, 95% CI: -0.44, -0.18), and 10 years age difference (60-70 years vs. 50-60 years, β = -0.21 SDs, 95% CI: -0.30, -0.13) in the whole study population. Stratification by self-reported recovery revealed that deficits were only detectable in SARS-CoV-2 positive individuals who did not feel recovered from COVID-19, whereas individuals who reported full recovery showed no deficits. Longitudinal analysis showed no evidence of cognitive change over time, suggesting that cognitive deficits for affected individuals persisted at almost 2 years since initial infection. Interpretation Cognitive deficits following SARS-CoV-2 infection were detectable nearly two years post infection, and largest for individuals with longer symptom durations, ongoing symptoms, and/or more severe infection. However, no such deficits were detected in individuals who reported full recovery from COVID-19. Further work is needed to monitor and develop understanding of recovery mechanisms for those with ongoing symptoms. Funding Chronic Disease Research Foundation, Wellcome Trust, National Institute for Health and Care Research, Medical Research Council, British Heart Foundation, Alzheimer's Society, European Union, COVID-19 Driver Relief Fund, French National Research Agency.
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Affiliation(s)
- Nathan J. Cheetham
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Rose Penfold
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
- Edinburgh Delirium Research Group, Ageing and Health, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Vicky Bowyer
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Carole H. Sudre
- MRC Unit for Lifelong Health and Ageing, Department of Population Health Sciences, University College London, London, United Kingdom
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Liane S. Canas
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jie Deng
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Benjamin Murray
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Eric Kerfoot
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Khaled Rjoob
- MRC Unit for Lifelong Health and Ageing, Department of Population Health Sciences, University College London, London, United Kingdom
| | - Erika Molteni
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Marc F. Österdahl
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Nicholas R. Harvey
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | | | - Michael H. Malim
- Department of Infectious Diseases, King's College London, London, United Kingdom
| | - Katie J. Doores
- Department of Infectious Diseases, King's College London, London, United Kingdom
| | - Peter J. Hellyer
- Centre for Neuroimaging Sciences, King's College London, London, United Kingdom
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Alexander Hammers
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
- King's College London & Guy's and St Thomas' PET Centre, King's College London, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Emma L. Duncan
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
- Guy's & St Thomas's NHS Foundation Trust, London, United Kingdom
| | - Adam Hampshire
- Department of Brain Sciences, Imperial College London, United Kingdom
| | - Claire J. Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
- Guy's & St Thomas's NHS Foundation Trust, London, United Kingdom
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22
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McInerney CD, Kotzé A, Bacon S, Cutting JE, Fisher L, Goldacre B, Johnson OA, Kua J, McGuckin D, Mehrkar A, Moonesinghe SR. Postoperative mortality and complications in patients with and without pre-operative SARS-CoV-2 infection: a service evaluation of 24 million linked records using OpenSAFELY. Anaesthesia 2023; 78:692-700. [PMID: 36958018 PMCID: PMC7616145 DOI: 10.1111/anae.16001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/20/2023] [Indexed: 03/25/2023]
Abstract
Surgical decision-making after SARS-CoV-2 infection is influenced by the presence of comorbidity, infection severity and whether the surgical problem is time-sensitive. Contemporary surgical policy to delay surgery is informed by highly heterogeneous country-specific guidance. We evaluated surgical provision in England during the COVID-19 pandemic to assess real-world practice and whether deferral remains necessary. Using the OpenSAFELY platform, we adapted the COVIDSurg protocol for a service evaluation of surgical procedures that took place within the English NHS from 17 March 2018 to 17 March 2022. We assessed whether hospitals adhered to guidance not to operate on patients within 7 weeks of an indication of SARS-CoV-2 infection. Additional outcomes were postoperative all-cause mortality (30 days, 6 months) and complications (pulmonary, cardiac, cerebrovascular). The exposure was the interval between the most recent indication of SARS-CoV-2 infection and subsequent surgery. In any 6-month window, < 3% of surgical procedures were conducted within 7 weeks of an indication of SARS-CoV-2 infection. Mortality for surgery conducted within 2 weeks of a positive test in the era since widespread SARS-CoV-2 vaccine availability was 1.1%, declining to 0.3% by 4 weeks. Compared with the COVIDSurg study cohort, outcomes for patients in the English NHS cohort were better during the COVIDSurg data collection period and the pandemic era before vaccines became available. Clinicians within the English NHS followed national guidance by operating on very few patients within 7 weeks of a positive indication of SARS-CoV-2 infection. In England, surgical patients' overall risk following an indication of SARS-CoV-2 infection is lower than previously thought.
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Affiliation(s)
- C D McInerney
- Academic Unit of Primary Medical Care, University of Sheffield, UK
- School of Computing, University of Leeds, UK
- National Institute for Health Research Yorkshire and Humber Patient Safety Translational Research Centre, Bradford, UK
| | - A Kotzé
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
- School of Medicine, University of Leeds, UK
| | - S Bacon
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - J E Cutting
- Gloucestershire Royal Hospitals NHS Foundation Trust, Gloucester, UK
| | - L Fisher
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - B Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - O A Johnson
- School of Computing, University of Leeds, UK
- National Institute for Health Research Yorkshire and Humber Patient Safety Translational Research Centre, Bradford, UK
| | - J Kua
- Division of Surgery and Interventional Science, Department of Targeted Intervention, Centre for Peri-operative Medicine, University College London, UK
| | - D McGuckin
- Division of Surgery and Interventional Science, Department of Targeted Intervention, Centre for Peri-operative Medicine, University College London, UK
| | - A Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - S R Moonesinghe
- Division of Surgery and Interventional Science, Department of Targeted Intervention, Centre for Peri-operative Medicine, University College London, 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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Fung KW, Baye F, Baik SH, Zheng Z, McDonald CJ. Prevalence and characteristics of long COVID in elderly patients: An observational cohort study of over 2 million adults in the US. PLoS Med 2023; 20:e1004194. [PMID: 37068113 PMCID: PMC10150975 DOI: 10.1371/journal.pmed.1004194] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 05/01/2023] [Accepted: 03/14/2023] [Indexed: 04/18/2023] Open
Abstract
BACKGROUND Incidence of long COVID in the elderly is difficult to estimate and can be underreported. While long COVID is sometimes considered a novel disease, many viral or bacterial infections have been known to cause prolonged illnesses. We postulate that some influenza patients might develop residual symptoms that would satisfy the diagnostic criteria for long COVID, a condition we call "long Flu." In this study, we estimate the incidence of long COVID and long Flu among Medicare patients using the World Health Organization (WHO) consensus definition. We compare the incidence, symptomatology, and healthcare utilization between long COVID and long Flu patients. METHODS AND FINDINGS This is a cohort study of Medicare (the US federal health insurance program) beneficiaries over 65. ICD-10-CM codes were used to capture COVID-19, influenza, and residual symptoms. Long COVID was identified by (a) the designated long COVID code B94.8 (code-based definition), or (b) any of 11 symptoms identified in the WHO definition (symptom-based definition), from 1 to 3 months post-infection. A symptom would be excluded if it occurred in the year prior to infection. Long Flu was identified in influenza patients from the combined 2018 and 2019 Flu seasons by the same symptom-based definition for long COVID. Long COVID and long Flu were compared in 4 outcome measures: (a) hospitalization (any cause); (b) hospitalization (for long COVID symptom); (c) emergency department (ED) visit (for long COVID symptom); and (d) number of outpatient encounters (for long COVID symptom), adjusted for age, sex, race, region, Medicare-Medicaid dual eligibility status, prior-year hospitalization, and chronic comorbidities. Among 2,071,532 COVID-19 patients diagnosed between April 2020 and June 2021, symptom-based definition identified long COVID in 16.6% (246,154/1,479,183) and 29.2% (61,631/210,765) of outpatients and inpatients, respectively. The designated code gave much lower estimates (outpatients 0.49% (7,213/1,479,183), inpatients 2.6% (5,521/210,765)). Among 933,877 influenza patients, 17.0% (138,951/817,336) of outpatients and 24.6% (18,824/76,390) of inpatients fit the long Flu definition. Long COVID patients had higher incidence of dyspnea, fatigue, palpitations, loss of taste/smell, and neurocognitive symptoms compared to long Flu. Long COVID outpatients were more likely to have any-cause hospitalization (31.9% (74,854/234,688) versus 26.8% (33,140/123,736), odds ratio 1.06 (95% CI 1.05 to 1.08, p < 0.001)), and more outpatient visits than long Flu outpatients (mean 2.9(SD 3.4) versus 2.5(SD 2.7) visits, incidence rate ratio 1.09 (95% CI 1.08 to 1.10, p < 0.001)). There were less ED visits in long COVID patients, probably because of reduction in ED usage during the pandemic. The main limitation of our study is that the diagnosis of long COVID in is not independently verified. CONCLUSIONS Relying on specific long COVID diagnostic codes results in significant underreporting. We observed that about 30% of hospitalized COVID-19 patients developed long COVID. In a similar proportion of patients, long COVID-like symptoms (long Flu) can be observed after influenza, but there are notable differences in symptomatology between long COVID and long Flu. The impact of long COVID on healthcare utilization is higher than long Flu.
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Affiliation(s)
- Kin Wah Fung
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, United States of America
| | - Fitsum Baye
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, United States of America
| | - Seo H. Baik
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, United States of America
| | - Zhaonian Zheng
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, United States of America
| | - Clement J. McDonald
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, United States of America
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25
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Shemtob L, Beaney T, Norton J, Majeed A. How can we improve the quality of data collected in general practice? BMJ 2023; 380:e071950. [PMID: 36921932 DOI: 10.1136/bmj-2022-071950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Affiliation(s)
- Lara Shemtob
- Department of Primary Care and Public Health, Imperial College London, London, UK
- National Institute for Health Research Applied Research Collaboration Northwest London, Imperial College London, UK
| | - Thomas Beaney
- Department of Primary Care and Public Health, Imperial College London, London, UK
- National Institute for Health Research Applied Research Collaboration Northwest London, Imperial College London, UK
| | - John Norton
- National Institute for Health Research Applied Research Collaboration Northwest London, Imperial College London, UK
| | - Azeem Majeed
- Department of Primary Care and Public Health, Imperial College London, London, UK
- National Institute for Health Research Applied Research Collaboration Northwest London, Imperial College London, UK
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26
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O'Mahoney LL, Routen A, Gillies C, Ekezie W, Welford A, Zhang A, Karamchandani U, Simms-Williams N, Cassambai S, Ardavani A, Wilkinson TJ, Hawthorne G, Curtis F, Kingsnorth AP, Almaqhawi A, Ward T, Ayoubkhani D, Banerjee A, Calvert M, Shafran R, Stephenson T, Sterne J, Ward H, Evans RA, Zaccardi F, Wright S, Khunti K. The prevalence and long-term health effects of Long Covid among hospitalised and non-hospitalised populations: A systematic review and meta-analysis. EClinicalMedicine 2023; 55:101762. [PMID: 36474804 PMCID: PMC9714474 DOI: 10.1016/j.eclinm.2022.101762] [Citation(s) in RCA: 254] [Impact Index Per Article: 254.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 11/08/2022] [Accepted: 11/09/2022] [Indexed: 12/03/2022] Open
Abstract
Background The aim of this study was to systematically synthesise the global evidence on the prevalence of persistent symptoms in a general post COVID-19 population. Methods A systematic literature search was conducted using multiple electronic databases (MEDLINE and The Cochrane Library, Scopus, CINAHL, and medRxiv) until January 2022. Studies with at least 100 people with confirmed or self-reported COVID-19 symptoms at ≥28 days following infection onset were included. Patient-reported outcome measures and clinical investigations were both assessed. Results were analysed descriptively, and meta-analyses were conducted to derive prevalence estimates. This study was pre-registered (PROSPERO-ID: CRD42021238247). Findings 194 studies totalling 735,006 participants were included, with five studies conducted in those <18 years of age. Most studies were conducted in Europe (n = 106) or Asia (n = 49), and the time to follow-up ranged from ≥28 days to 387 days. 122 studies reported data on hospitalised patients, 18 on non-hospitalised, and 54 on hospitalised and non-hospitalised combined (mixed). On average, at least 45% of COVID-19 survivors, regardless of hospitalisation status, went on to experience at least one unresolved symptom (mean follow-up 126 days). Fatigue was frequently reported across hospitalised (28.4%; 95% CI 24.7%-32.5%), non-hospitalised (34.8%; 95% CI 17.6%-57.2%), and mixed (25.2%; 95% CI 17.7%-34.6%) cohorts. Amongst the hospitalised cohort, abnormal CT patterns/x-rays were frequently reported (45.3%; 95% CI 35.3%-55.7%), alongside ground glass opacification (41.1%; 95% CI 25.7%-58.5%), and impaired diffusion capacity for carbon monoxide (31.7%; 95% CI 25.8%-3.2%). Interpretation Our work shows that 45% of COVID-19 survivors, regardless of hospitalisation status, were experiencing a range of unresolved symptoms at ∼ 4 months. Current understanding is limited by heterogeneous study design, follow-up durations, and measurement methods. Definition of subtypes of Long Covid is unclear, subsequently hampering effective treatment/management strategies. Funding No funding.
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Affiliation(s)
| | - Ash Routen
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Clare Gillies
- Diabetes Research Centre, University of Leicester, Leicester, UK
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Winifred Ekezie
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Anneka Welford
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Alexa Zhang
- Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Urvi Karamchandani
- Faculty of Medicine, School of Public Health, Imperial College London, London, UK
| | | | | | - Ashkon Ardavani
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | | | - Grace Hawthorne
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Ffion Curtis
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | | | - Abdullah Almaqhawi
- Department of Family and Community Medicine, College of Medicine, King Faisal University, Al Ahsa, Saudi Arabia
| | - Thomas Ward
- Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - Daniel Ayoubkhani
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, UK
- Office for National Statistics, Government Buildings, Newport, UK
| | - Amitava Banerjee
- Faculty of Population Health Sciences, Institute of Health Informatics, University College London, London, UK
- Department of Population Science and Experimental Medicine, University College London, London, UK
| | - Melanie Calvert
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre and NIHR Applied Research Collaboration West Midlands, University Hospital Birmingham and University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation and Centre for Patient Reported Outcomes Research, University of Birmingham, Birmingham, UK
| | - Roz Shafran
- Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Terence Stephenson
- Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Jonathan Sterne
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Helen Ward
- Faculty of Medicine, School of Public Health, Imperial College London, London, UK
| | - Rachael A. Evans
- Department of Respiratory Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Respiratory Department, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Francesco Zaccardi
- Diabetes Research Centre, University of Leicester, Leicester, UK
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, UK
| | | | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester, UK
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27
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Astin R, Banerjee A, Baker MR, Dani M, Ford E, Hull JH, Lim PB, McNarry M, Morten K, O'Sullivan O, Pretorius E, Raman B, Soteropoulos DS, Taquet M, Hall CN. Long COVID: mechanisms, risk factors and recovery. Exp Physiol 2023; 108:12-27. [PMID: 36412084 PMCID: PMC10103775 DOI: 10.1113/ep090802] [Citation(s) in RCA: 83] [Impact Index Per Article: 83.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/24/2022] [Indexed: 11/23/2022]
Abstract
NEW FINDINGS What is the topic of this review? The emerging condition of long COVID, its epidemiology, pathophysiological impacts on patients of different backgrounds, physiological mechanisms emerging as explanations of the condition, and treatment strategies being trialled. The review leads from a Physiological Society online conference on this topic. What advances does it highlight? Progress in understanding the pathophysiology and cellular mechanisms underlying Long COVID and potential therapeutic and management strategies. ABSTRACT Long COVID, the prolonged illness and fatigue suffered by a small proportion of those infected with SARS-CoV-2, is placing an increasing burden on individuals and society. A Physiological Society virtual meeting in February 2022 brought clinicians and researchers together to discuss the current understanding of long COVID mechanisms, risk factors and recovery. This review highlights the themes arising from that meeting. It considers the nature of long COVID, exploring its links with other post-viral illnesses such as myalgic encephalomyelitis/chronic fatigue syndrome, and highlights how long COVID research can help us better support those suffering from all post-viral syndromes. Long COVID research started particularly swiftly in populations routinely monitoring their physical performance - namely the military and elite athletes. The review highlights how the high degree of diagnosis, intervention and monitoring of success in these active populations can suggest management strategies for the wider population. We then consider how a key component of performance monitoring in active populations, cardiopulmonary exercise training, has revealed long COVID-related changes in physiology - including alterations in peripheral muscle function, ventilatory inefficiency and autonomic dysfunction. The nature and impact of dysautonomia are further discussed in relation to postural orthostatic tachycardia syndrome, fatigue and treatment strategies that aim to combat sympathetic overactivation by stimulating the vagus nerve. We then interrogate the mechanisms that underlie long COVID symptoms, with a focus on impaired oxygen delivery due to micro-clotting and disruption of cellular energy metabolism, before considering treatment strategies that indirectly or directly tackle these mechanisms. These include remote inspiratory muscle training and integrated care pathways that combine rehabilitation and drug interventions with research into long COVID healthcare access across different populations. Overall, this review showcases how physiological research reveals the changes that occur in long COVID and how different therapeutic strategies are being developed and tested to combat this condition.
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Affiliation(s)
- Rónan Astin
- Department of Respiratory MedicineUniversity College London Hospitals NHS Foundation TrustLondonUK
- Centre for Human Health and PerformanceInstitute for Sport Exercise and HealthUniversity College LondonLondonUK
| | - Amitava Banerjee
- Institute of Health InformaticsUniversity College LondonLondonUK
- Department of CardiologyBarts Health NHS TrustLondonUK
| | - Mark R. Baker
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Melanie Dani
- Imperial Syncope UnitImperial College Healthcare NHS TrustLondonUK
| | | | - James H. Hull
- Institute of SportExercise and Health (ISEH)Division of Surgery and Interventional ScienceUniversity College LondonLondonUK
- Royal Brompton HospitalLondonUK
| | - Phang Boon Lim
- Imperial Syncope UnitImperial College Healthcare NHS TrustLondonUK
| | - Melitta McNarry
- Applied Sports, Technology, Exercise and Medicine Research CentreSwansea UniversitySwanseaUK
| | - Karl Morten
- Applied Sports, Technology, Exercise and Medicine Research CentreSwansea UniversitySwanseaUK
- Nuffield Department of Women's and Reproductive HealthUniversity of OxfordOxfordUK
| | - Oliver O'Sullivan
- Academic Department of Military RehabilitationDefence Medical Rehabilitation Centre Stanford HallLoughboroughUK
- School of MedicineUniversity of NottinghamNottinghamUK
| | - Etheresia Pretorius
- Department of Physiological SciencesFaculty of ScienceStellenbosch UniversityStellenboschSouth Africa
- Department of Biochemistry and Systems BiologyInstitute of SystemsMolecular and Integrative BiologyFaculty of Health and Life SciencesUniversity of LiverpoolLiverpoolUK
| | - Betty Raman
- Radcliffe Department of MedicineDivision of Cardiovascular MedicineUniversity of OxfordOxfordUK
- Radcliffe Department of MedicineDivision of Cardiovascular MedicineOxford University Hospitals NHS Foundation TrustOxfordUK
| | | | - Maxime Taquet
- Department of PsychiatryUniversity of OxfordOxfordUK
- Oxford Health NHS Foundation TrustOxfordUK
| | - Catherine N. Hall
- School of Psychology and Sussex NeuroscienceUniversity of SussexFalmerUK
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Bygdell M, Leach S, Lundberg L, Gyll D, Martikainen J, Santosa A, Li H, Gisslén M, Nyberg F. A comprehensive characterization of patients diagnosed with post-COVID-19 condition in Sweden 16 months after the introduction of the International Classification of Diseases Tenth Revision diagnosis code (U09.9): a population-based cohort study. Int J Infect Dis 2023; 126:104-113. [PMID: 36410693 PMCID: PMC9678230 DOI: 10.1016/j.ijid.2022.11.021] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/26/2022] [Accepted: 11/14/2022] [Indexed: 11/22/2022] Open
Abstract
OBJECTIVES The objective of this study was to provide a comprehensive characterization of patients diagnosed with post-COVID-19 condition (PCC) during the first 16 months of use of the International Classification of Diseases revision 10 (ICD-10) diagnosis code U09.9 in Sweden. METHODS We used data from national registers and primary health care databases for all adult inhabitants of the two largest regions in Sweden, comprising 4.1 million inhabitants (approximately 40% of the Swedish population). We present the cumulative incidence and incidence rate of PCC overall and among subgroups and describe patients with COVID-19 with or without PCC regarding sociodemographic characteristics, comorbidities, subsequent diseases, COVID-19 severity, and virus variants. RESULTS Of all registered COVID-19 cases available for PCC diagnosis (n = 506,107), 2.0% (n = 10,196) had been diagnosed with PCC using ICD-10 code U09.9 as of February 15, 2022 in the two largest regions in Sweden. The cumulative incidence was higher among women than men (2.3% vs 1.6%, P <0.001). The majority of PCC cases (n = 7162, 70.2%) had not been hospitalized for COVID-19. This group was more commonly female (69.9% vs 52.9%, P <0.001), had a tertiary education (51.0% vs 44.1%, P <0.001), and was older (median age difference 5.7 years, P <0.001) than non-hospitalized patients with COVID-19 without PCC. CONCLUSION This characterization furthers the understanding of patients diagnosed with PCC and could support policy makers with appropriate societal and health care resource allocation.
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Affiliation(s)
- Maria Bygdell
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; School of Public Health and Community Medicine, Institute of Medicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
| | - Susannah Leach
- Department of Microbiology and Immunology, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Department of Clinical Pharmacology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Lisa Lundberg
- Department of Microbiology and Immunology, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Department of Clinical Pharmacology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - David Gyll
- Region Uppsala, Svartbäcken Primary Care, Uppsala, Sweden
| | - Jari Martikainen
- Bioinformatics and Data Centre, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Ailiana Santosa
- School of Public Health and Community Medicine, Institute of Medicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Huiqi Li
- School of Public Health and Community Medicine, Institute of Medicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Magnus Gisslén
- Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Department of Infectious Diseases, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Mukherjee S, Kshirsagar M, Becker N, Xu Y, Weeks WB, Patel S, Ferres JL, Jackson ML. Identifying long-term effects of SARS-CoV-2 and their association with social determinants of health in a cohort of over one million COVID-19 survivors. BMC Public Health 2022; 22:2394. [PMID: 36539760 PMCID: PMC9765366 DOI: 10.1186/s12889-022-14806-1] [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] [Received: 10/29/2021] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Despite an abundance of information on the risk factors of SARS-CoV-2, there have been few US-wide studies of long-term effects. In this paper we analyzed a large medical claims database of US based individuals to identify common long-term effects as well as their associations with various social and medical risk factors. METHODS The medical claims database was obtained from a prominent US based claims data processing company, namely Change Healthcare. In addition to the claims data, the dataset also consisted of various social determinants of health such as race, income, education level and veteran status of the individuals. A self-controlled cohort design (SCCD) observational study was performed to identify ICD-10 codes whose proportion was significantly increased in the outcome period compared to the control period to identify significant long-term effects. A logistic regression-based association analysis was then performed between identified long-term effects and social determinants of health. RESULTS Among the over 1.37 million COVID patients in our datasets we found 36 out of 1724 3-digit ICD-10 codes to be statistically significantly increased in the post-COVID period (p-value < 0.05). We also found one combination of ICD-10 codes, corresponding to 'other anemias' and 'hypertension', that was statistically significantly increased in the post-COVID period (p-value < 0.05). Our logistic regression-based association analysis with social determinants of health variables, after adjusting for comorbidities and prior conditions, showed that age and gender were significantly associated with the multiple long-term effects. Race was only associated with 'other sepsis', income was only associated with 'Alopecia areata' (autoimmune disease causing hair loss), while education level was only associated with 'Maternal infectious and parasitic diseases' (p-value < 0.05). CONCLUSION We identified several long-term effects of SARS-CoV-2 through a self-controlled study on a cohort of over one million patients. Furthermore, we found that while age and gender are commonly associated with the long-term effects, other social determinants of health such as race, income and education levels have rare or no significant associations.
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Affiliation(s)
- Sumit Mukherjee
- Insitro Labs, work done while at Microsoft, South San Francisco, USA
| | - Meghana Kshirsagar
- grid.419815.00000 0001 2181 3404AI for Good Research Lab, Microsoft Corporation, 1 Microsoft Way, WA 98052 Redmond, USA
| | - Nicholas Becker
- grid.419815.00000 0001 2181 3404AI for Good Research Lab, Microsoft Corporation, 1 Microsoft Way, WA 98052 Redmond, USA ,grid.34477.330000000122986657University of Washington, Seattle, USA
| | - Yixi Xu
- grid.419815.00000 0001 2181 3404AI for Good Research Lab, Microsoft Corporation, 1 Microsoft Way, WA 98052 Redmond, USA
| | - William B. Weeks
- grid.419815.00000 0001 2181 3404AI for Good Research Lab, Microsoft Corporation, 1 Microsoft Way, WA 98052 Redmond, USA
| | - Shwetak Patel
- grid.34477.330000000122986657University of Washington, Seattle, USA
| | - Juan Lavista Ferres
- grid.419815.00000 0001 2181 3404AI for Good Research Lab, Microsoft Corporation, 1 Microsoft Way, WA 98052 Redmond, USA
| | - Michael L. Jackson
- grid.488833.c0000 0004 0615 7519Kaiser Permanente Washington, Seattle, USA
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Abstract
PURPOSE OF REVIEW To describe the burden of post-COVID respiratory sequelae in posthospital and nonhospitalized COVID-19 survivors and to describe the priorities of clinical management. RECENT FINDINGS Due to varying definitions of 'Long COVID' or 'Post-COVID', the prevalence of post-COVID sequelae or persisting symptoms is challenging to estimate but ranges from 2.3 to 51%. Risk factors for persistent post-COVID symptoms include age, female sex, deprivation, presence of comorbidities; and in posthospital COVID-19 survivors, the severity of acute infection. Common post-COVID respiratory symptoms include breathlessness, cough and chest pain and many individuals also experience exercise intolerance. The most common pulmonary function test abnormality is impaired diffusing capacity for carbon monoxide. In posthospital COVID-19 survivors, the prevalence of interstitial lung damage is 5-11%. Disordered breathing is common in all post-COVID patients and respiratory physiotherapy is helpful. SUMMARY The vast numbers of COVID-19 infections globally implies that a large number of people will be affected by post-COVID sequelae even with conservative estimates. A significant number of people are affected for several months and up to years following acute infection. Post-COVID sequelae have a detrimental impact on quality of life and ability to work.
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Debski M, Tsampasian V, Haney S, Blakely K, Weston S, Ntatsaki E, Lim M, Madden S, Perperoglou A, Vassiliou VS. Post-COVID-19 syndrome risk factors and further use of health services in East England. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0001188. [PMID: 36962824 PMCID: PMC10022108 DOI: 10.1371/journal.pgph.0001188] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 10/24/2022] [Indexed: 12/05/2022]
Abstract
Post-COVID syndrome, defined as symptoms persisting for more than twelve weeks after the diagnosis of COVID-19, has been recognised as a new clinical entity in the context of SARS-CoV-2 infection. This study was conducted to characterise the burden and predictors for post-COVID-19 syndrome in the local population. It was a community-based web-survey study conducted in Norfolk, East England, UK. We sent the survey to patients with confirmed COVID-19 infection by real-time polymerase chain reaction by December 6th, 2020. Questions related to the pre-COVID and post-COVID level of symptoms and further healthcare use. Baseline characteristics were collected from the primary care records. Logistic regression analysis was conducted to establish predictors for post-COVID-19 syndrome and further healthcare utilisation. Of 6,318 patients, survey responses were obtained from 1,487 participants (23.5%). Post-COVID-19 syndrome symptoms were experienced by 774 (52.1%) respondents. Male sex compared to female sex was a factor protective of post-COVID symptoms; relative risk (RR) 0.748, 95% confidence interval (CI), 0.605-0.924. Body mass index was associated with a greater risk of developing post-COVID-19 symptoms (RR 1.031, 95% CI, 1.016-1.047, for 1 kg/m2). A total of 378 (25.4%) people used further health services after their index COVID-19 infection, of whom 277 (73.2%) had post-COVID symptoms. Male sex was negatively associated with the use of further health services (RR 0.618, 95% CI, 0.464-0.818) whereas BMI was positively associated (RR 1.027, 95% CI, 1.009-1.046). Overall, post-COVID-19 symptoms increased the probability of using health services with RR 3.280, 95% CI, 2.540-4.262. This survey of a large number of people previously diagnosed with COVID-19 across East England shows a high prevalence of self-reported post-COVID-19 syndrome. Female sex and BMI were associated with an increased risk of post-COVID-19 syndrome and further utilisation of healthcare.
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Affiliation(s)
- Maciej Debski
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom
- Cardiology Department, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, United Kingdom
| | - Vasiliki Tsampasian
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom
- Cardiology Department, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, United Kingdom
| | - Shawn Haney
- Norfolk and Waveney Integrated Care Board, Norwich, United Kingdom
| | - Katy Blakely
- Cardiology Department, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, United Kingdom
- Norfolk and Waveney Integrated Care Board, Norwich, United Kingdom
| | - Samantha Weston
- Norfolk and Waveney Integrated Care Board, Norwich, United Kingdom
| | - Eleana Ntatsaki
- Rheumatology Department, East Suffolk and North Essex Foundation NHS Trust, Ipswich Hospital, Ipswich, United Kingdom
- Centre for Rheumatology, University College London, London, United Kingdom
| | - Mark Lim
- Norfolk and Waveney Integrated Care Board, Norwich, United Kingdom
| | - Susan Madden
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom
| | - Aris Perperoglou
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Vassilios S. Vassiliou
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom
- Cardiology Department, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, United Kingdom
- Institute of Continuing Education, University of Cambridge, Cambridge, United Kingdom
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Pantelic M, Ziauddeen N, Boyes M, O’Hara ME, Hastie C, Alwan NA. Long Covid stigma: Estimating burden and validating scale in a UK-based sample. PLoS One 2022; 17:e0277317. [PMID: 36417364 PMCID: PMC9683629 DOI: 10.1371/journal.pone.0277317] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 10/24/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Stigma can be experienced as perceived or actual disqualification from social and institutional acceptance on the basis of one or more physical, behavioural or other attributes deemed to be undesirable. Long Covid is a predominantly multisystem condition that occurs in people with a history of SARSCoV2 infection, often resulting in functional disability. This study aimed to develop and validate a Long Covid Stigma Scale (LCSS); and to quantify the burden of Long Covid stigma. METHODS Data from the follow-up of a co-produced community-based Long Covid online survey using convenience non-probability sampling was used. Thirteen questions on stigma were designed to develop the LCSS capturing three domains-enacted (overt experiences of discrimination), internalised (internalising negative associations with Long Covid and accepting them as self-applicable) and anticipated (expectation of bias/poor treatment by others) stigma. Confirmatory factor analysis tested whether LCSS consisted of the three hypothesised domains. Model fit was assessed and prevalence was calculated. RESULTS 966 UK-based participants responded (888 for stigma questions), with mean age 48 years (SD: 10.7) and 85% female. Factor loadings for enacted stigma were 0.70-0.86, internalised 0.75-0.84, anticipated 0.58-0.87, and model fit was good. The prevalence of experiencing stigma at least 'sometimes' and 'often/always' was 95% and 76% respectively. Anticipated and internalised stigma were more frequently experienced than enacted stigma. Those who reported having a clinical diagnosis of Long Covid had higher stigma prevalence than those without. CONCLUSION This study establishes a scale to measure Long Covid stigma and highlights common experiences of stigma in people living with Long Covid.
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Affiliation(s)
- Marija Pantelic
- Brighton and Sussex Medical School, University of Sussex, Falmer, United Kingdom
- Department of Social Policy and Intervention, University of Oxford, Oxford, United Kingdom
| | - Nida Ziauddeen
- School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- NIHR Applied Research Collaboration Wessex, Southampton, United Kingdom
| | - Mark Boyes
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia
- Curtin enAble Institute, Faculty of Health Sciences, Curtin University, Perth, Australia
| | | | - Claire Hastie
- Patient contributor, Long Covid Support https://www.longcovid.org
| | - Nisreen A. Alwan
- School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- NIHR Applied Research Collaboration Wessex, Southampton, United Kingdom
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
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O’Hare AM, Vig EK, Iwashyna TJ, Fox A, Taylor JS, Viglianti EM, Butler CR, Vranas KC, Helfand M, Tuepker A, Nugent SM, Winchell KA, Laundry RJ, Bowling CB, Hynes DM, Maciejewski ML, Bohnert ASB, Locke ER, Boyko EJ, Ioannou GN. Complexity and Challenges of the Clinical Diagnosis and Management of Long COVID. JAMA Netw Open 2022; 5:e2240332. [PMID: 36326761 PMCID: PMC9634500 DOI: 10.1001/jamanetworkopen.2022.40332] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 09/21/2022] [Indexed: 11/06/2022] Open
Abstract
Importance There is increasing recognition of the long-term health effects of SARS-CoV-2 infection (sometimes called long COVID). However, little is yet known about the clinical diagnosis and management of long COVID within health systems. Objective To describe dominant themes pertaining to the clinical diagnosis and management of long COVID in the electronic health records (EHRs) of patients with a diagnostic code for this condition (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10] code U09.9). Design, Setting, and Participants This qualitative analysis used data from EHRs of a national random sample of 200 patients receiving care in the Department of Veterans Affairs (VA) with documentation of a positive result on a polymerase chain reaction (PCR) test for SARS-CoV-2 between February 27, 2020, and December 31, 2021, and an ICD-10 diagnostic code for long COVID between October 1, 2021, when the code was implemented, and March 1, 2022. Data were analyzed from February 5 to May 31, 2022. Main Outcomes and Measures A text word search and qualitative analysis of patients' VA-wide EHRs was performed to identify dominant themes pertaining to the clinical diagnosis and management of long COVID. Results In this qualitative analysis of documentation in the VA-wide EHR, the mean (SD) age of the 200 sampled patients at the time of their first positive PCR test result for SARS-CoV-2 in VA records was 60 (14.5) years. The sample included 173 (86.5%) men; 45 individuals (22.5%) were identified as Black and 136 individuals (68.0%) were identified as White. In qualitative analysis of documentation pertaining to long COVID in patients' EHRs 2 dominant themes were identified: (1) clinical uncertainty, in that it was often unclear whether particular symptoms could be attributed to long COVID, given the medical complexity and functional limitations of many patients and absence of specific markers for this condition, which could lead to ongoing monitoring, diagnostic testing, and specialist referral; and (2) care fragmentation, describing how post-COVID-19 care processes were often siloed from and poorly coordinated with other aspects of care and could be burdensome to patients. Conclusions and Relevance This qualitative study of documentation in the VA EHR highlights the complexity of diagnosing long COVID in clinical settings and the challenges of caring for patients who have or are suspected of having this condition.
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Affiliation(s)
- Ann M. O’Hare
- Health Services Research & Development Center of Innovation for Veteran-Centered and Value-Driven Care, VA Puget Sound Health Care System, Seattle, Washington
- Hospital and Specialty Medicine and Geriatrics and Extended Care Services, VA Puget Sound Health Care System, Seattle, Washington
- Department of Medicine, University of Washington, Seattle
| | - Elizabeth K. Vig
- Hospital and Specialty Medicine and Geriatrics and Extended Care Services, VA Puget Sound Health Care System, Seattle, Washington
- Department of Medicine, University of Washington, Seattle
| | - Theodore J. Iwashyna
- Pulmonary and Critical Care Medicine, Department of Health Policy & Management, School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Alexandra Fox
- Seattle Epidemiologic Research and Information Center, VA Puget Sound, Seattle, Washington
| | | | - Elizabeth M. Viglianti
- Department of Internal Medicine Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor
| | - Catherine R. Butler
- Health Services Research & Development Center of Innovation for Veteran-Centered and Value-Driven Care, VA Puget Sound Health Care System, Seattle, Washington
- Hospital and Specialty Medicine and Geriatrics and Extended Care Services, VA Puget Sound Health Care System, Seattle, Washington
- Department of Medicine, University of Washington, Seattle
| | - Kelly C. Vranas
- Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland, Oregon
- Oregon Health & Science University, Portland
| | - Mark Helfand
- Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland, Oregon
- Oregon Health & Science University, Portland
| | - Anaïs Tuepker
- Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland, Oregon
- Oregon Health & Science University, Portland
| | - Shannon M. Nugent
- Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland, Oregon
- Oregon Health & Science University, Portland
| | - Kara A. Winchell
- Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland, Oregon
| | - Ryan J. Laundry
- Health Services Research & Development Center of Innovation for Veteran-Centered and Value-Driven Care, VA Puget Sound Health Care System, Seattle, Washington
| | - C. Barrett Bowling
- Geriatric Research Education and Clinical Center, Durham VA Medical Center, Durham, North Carolina
- Department of Medicine, Duke University, Durham, North Carolina
| | - Denise M. Hynes
- Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland, Oregon
- Oregon Health & Science University, Portland
- College of Public Health and Human Sciences and Center for Quantitative Life Sciences, Oregon State University, Corvallis
| | - Matthew L. Maciejewski
- Center of Innovation to Accelerate Discovery and Practice Transformation, Durham VA Medical Center, Durham, North Carolina
- Department of Population Health Sciences, Duke University, Durham, North Carolina
- Division of General Internal Medicine, Department of Medicine, Duke University, Durham, North Carolina
| | - Amy S. B. Bohnert
- VA Center for Clinical Management Research, Ann Arbor VA, Ann Arbor, Michigan
- Departments of Anesthesiology and Psychiatry, University of Michigan Medical School, Ann Arbor
| | - Emily R. Locke
- Health Services Research & Development Center of Innovation for Veteran-Centered and Value-Driven Care, VA Puget Sound Health Care System, Seattle, Washington
- Seattle Epidemiologic Research and Information Center, VA Puget Sound, Seattle, Washington
| | - Edward J. Boyko
- Hospital and Specialty Medicine and Geriatrics and Extended Care Services, VA Puget Sound Health Care System, Seattle, Washington
- Department of Medicine, University of Washington, Seattle
- Seattle Epidemiologic Research and Information Center, VA Puget Sound, Seattle, Washington
| | - George N. Ioannou
- Health Services Research & Development Center of Innovation for Veteran-Centered and Value-Driven Care, VA Puget Sound Health Care System, Seattle, Washington
- Hospital and Specialty Medicine and Geriatrics and Extended Care Services, VA Puget Sound Health Care System, Seattle, Washington
- Department of Medicine, University of Washington, Seattle
- Seattle Epidemiologic Research and Information Center, VA Puget Sound, Seattle, Washington
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Al-Husinat L, Nusir M, Al-Gharaibeh H, Alomari AA, Smadi MM, Battaglini D, Pelosi P. Post-COVID-19 syndrome symptoms after mild and moderate SARS-CoV-2 infection. Front Med (Lausanne) 2022; 9:1017257. [PMID: 36262270 PMCID: PMC9573938 DOI: 10.3389/fmed.2022.1017257] [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: 08/11/2022] [Accepted: 09/06/2022] [Indexed: 11/23/2022] Open
Abstract
Background Post-COVID-19 Syndrome (PCS) is characterized by residual symptoms following the initial recovery from severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. The prevalence of PCS is known to be the highest among severe and critical forms of the disease. However, the occurrence and risk factors for PCS after mild or moderate SARS-CoV-2 infection has not been extensively investigated. Methods Online and offline via both paper or mailed questionnaires distributed among Jordan collected between 1st and 21st August 2021, including a total number of 800 respondents, of whom 495 had previous mild to moderate COVID-19 infection. The Newcastle post-COVID syndrome Follow-up Screening Questionnaire was modified, translated, and used as a standard instrument for data collection regarding psychological, medical, and socio-economic symptoms post-infection. The primary outcome was the prevalence of PCS after mild to moderate COVID-19 in Jordan. Secondary outcome was the identification of PCS risk factors. Results The most common PCS symptom was mood disturbance followed by fatigue, anxiety, and myalgia. Female gender significantly increased the risk for multiple PCS symptoms. Age < 30 years was found to be an independent risk factor for myalgia (p = 0.001). Conclusion PCS is highly prevalent among COVID-19 survivors in Jordan, especially in females and patients with comorbidities. Planning physical and mental rehabilitation services is recommended for those patients with PCS symptoms after mild to moderate COVID-19 infection.
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Affiliation(s)
- Lou'i Al-Husinat
- Department of Clinical Medical Sciences, Faculty of Medicine, Yarmouk University, Irbid, Jordan
| | - Mokeem Nusir
- Department of Clinical Medical Sciences, Faculty of Medicine, Yarmouk University, Irbid, Jordan
| | - Haitham Al-Gharaibeh
- Department of Clinical Medical Sciences, Faculty of Medicine, Yarmouk University, Irbid, Jordan
| | - Amer A Alomari
- Department of Neurosurgery, San Filippo Neri Hospital/ASLRoma1, Rome, Italy
| | - Mahmoud M Smadi
- Department of Mathematics and Statistics, Jordan University of Science and Technology, Irbid, Jordan
| | - Denise Battaglini
- Anesthesia and Intensive Care, San Martino Policlinico Hospital, IRCCS for Oncology and Neuroscience, Genoa, Italy
| | - Paolo Pelosi
- Anesthesia and Intensive Care, San Martino Policlinico Hospital, IRCCS for Oncology and Neuroscience, Genoa, Italy.,Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
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35
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Affiliation(s)
- Trisha Greenhalgh
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Manoj Sivan
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK
| | - Brendan Delaney
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Rachael Evans
- Institute for Lung Health, Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - Ruairidh Milne
- School of Healthcare Enterprise and Innovation, University of Southampton, Southampton, UK
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Donnachie E, Hapfelmeier A, Linde K, Tauscher M, Gerlach R, Greissel A, Schneider A. Incidence of post-COVID syndrome and associated symptoms in outpatient care in Bavaria, Germany: a retrospective cohort study using routinely collected claims data. BMJ Open 2022; 12:e064979. [PMID: 36137635 PMCID: PMC9511014 DOI: 10.1136/bmjopen-2022-064979] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVES To estimate the treatment incidence of post-COVID syndrome (postinfectious sequelae present at least 12 weeks following infection) in the context of ambulatory care in Bavaria, Germany, and to establish whether related diagnoses occur more frequently than in patients with no known history of COVID-19. DESIGN Retrospective cohort analysis of routinely collected claims data. SETTING Ambulatory care in Bavaria, Germany, observed from January 2020 to March 2022 (data accessed May 2022). PARTICIPANTS 391 990 patients with confirmed COVID-19 diagnosis, 62 659 patients with other respiratory infection and a control group of 659 579 patients with no confirmed or suspected diagnosis of COVID-19. PRIMARY AND SECONDARY OUTCOME MEASURES Primary outcome is diagnosis of post-COVID syndrome documented in ambulatory care. Secondary outcomes are: chronic fatigue syndrome, psychological disorder, fatigue, mild cognitive impairment, disturbances of taste and smell, dyspnoea, pulmonary embolism and myalgia. RESULTS Among all patients with confirmed COVID-19, 14.2% (95% CI 14.0% to 14.5%) received a diagnosis of a post-COVID syndrome, and 6.7% (95% CI 6.5% to 6.9%) received the diagnosis in at least two quarterly periods during a 2-year follow-up. Compared with patients with other respiratory infections and with controls, patients with COVID-19 more frequently received a variety of diagnoses including chronic fatigue syndrome (1.6% vs 0.6% and 0.3%, respectively), fatigue (13.3% vs 9.2% and 6.0%), dyspnoea (9.9% vs 5.1% and 3.2%) and disturbances of taste and smell (3.2% vs 1.2% and 0.5%). The treatment incidence of post-COVID syndrome was highest among adults aged 40-59 (19.0%) and lowest among children aged below 12 years (2.6%). CONCLUSIONS Our results demonstrate a moderately high incidence of post-COVID syndrome 2 years after COVID-19 diagnosis. There is an urgent need to find efficient and effective solutions to help patients with dyspnoea, fatigue, cognitive impairment and loss of smell. Guidelines and treatment algorithms, including referral criteria, and occupational and physical therapy, require prompt and coherent implementation.
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Affiliation(s)
- Ewan Donnachie
- Bavarian Association of Statutory Health Insurance Physicians, München, Germany
| | - Alexander Hapfelmeier
- Institute of General Practice and Health Services Research, TUM School of Medicine, Technical University Munich, Munich, Germany
- Institute of AI and Informatics in Medicine, TUM School of Medicine, Technical University Munich, München, Germany
| | - Klaus Linde
- Institute of General Practice and Health Services Research, TUM School of Medicine, Technical University Munich, Munich, Germany
| | - Martin Tauscher
- Bavarian Association of Statutory Health Insurance Physicians, München, Germany
| | - Roman Gerlach
- Bavarian Association of Statutory Health Insurance Physicians, München, Germany
| | - Anna Greissel
- Institute of General Practice and Health Services Research, TUM School of Medicine, Technical University Munich, Munich, Germany
| | - Antonius Schneider
- Institute of General Practice and Health Services Research, TUM School of Medicine, Technical University Munich, Munich, Germany
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Ayoubkhani D, Bosworth ML, King S, Pouwels KB, Glickman M, Nafilyan V, Zaccardi F, Khunti K, Alwan NA, Walker AS. Risk of Long COVID in People Infected With Severe Acute Respiratory Syndrome Coronavirus 2 After 2 Doses of a Coronavirus Disease 2019 Vaccine: Community-Based, Matched Cohort Study. Open Forum Infect Dis 2022; 9:ofac464. [PMID: 36168555 DOI: 10.1101/2022.02.23.22271388] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 09/08/2022] [Indexed: 05/22/2023] Open
Abstract
We investigated long COVID incidence by vaccination status in a random sample of UK adults from April 2020 to November 2021. Persistent symptoms were reported by 9.5% of 3090 breakthrough severe acute respiratory syndrome coronavirus 2 infections and 14.6% of unvaccinated controls (adjusted odds ratio, 0.59 [95% confidence interval, .50-.69]), emphasizing the need for public health initiatives to increase population-level vaccine uptake.
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Affiliation(s)
- Daniel Ayoubkhani
- Health Analysis and Life Events Division, Office for National Statistics, Newport, United Kingdom
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, United Kingdom
| | - Matthew L Bosworth
- Health Analysis and Life Events Division, Office for National Statistics, Newport, United Kingdom
| | - Sasha King
- Methodology and Quality Directorate, Office for National Statistics, London, United Kingdom
| | - Koen B Pouwels
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Myer Glickman
- Health Analysis and Life Events Division, Office for National Statistics, Newport, United Kingdom
| | - Vahé Nafilyan
- Health Analysis and Life Events Division, Office for National Statistics, Newport, United Kingdom
- Faculty of Public Health, Environment and Society, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Francesco Zaccardi
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, United Kingdom
| | - Kamlesh Khunti
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, United Kingdom
| | - Nisreen A Alwan
- School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- National Institute for Health Research Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
- National Institute for Health Research Applied Research Collaboration Wessex, Southampton, United Kingdom
| | - A Sarah Walker
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
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38
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Ayoubkhani D, Bosworth ML, King S, Pouwels KB, Glickman M, Nafilyan V, Zaccardi F, Khunti K, Alwan NA, Walker AS. Risk of Long COVID in People Infected With Severe Acute Respiratory Syndrome Coronavirus 2 After 2 Doses of a Coronavirus Disease 2019 Vaccine: Community-Based, Matched Cohort Study. Open Forum Infect Dis 2022; 9:ofac464. [PMID: 36168555 PMCID: PMC9494414 DOI: 10.1093/ofid/ofac464] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 09/08/2022] [Indexed: 11/25/2022] Open
Abstract
We investigated long COVID incidence by vaccination status in a random sample of UK adults from April 2020 to November 2021. Persistent symptoms were reported by 9.5% of 3090 breakthrough severe acute respiratory syndrome coronavirus 2 infections and 14.6% of unvaccinated controls (adjusted odds ratio, 0.59 [95% confidence interval, .50-.69]), emphasizing the need for public health initiatives to increase population-level vaccine uptake.
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Affiliation(s)
- Daniel Ayoubkhani
- Health Analysis and Life Events Division, Office for National Statistics, Newport, United Kingdom
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, United Kingdom
| | - Matthew L Bosworth
- Health Analysis and Life Events Division, Office for National Statistics, Newport, United Kingdom
| | - Sasha King
- Methodology and Quality Directorate, Office for National Statistics, London, United Kingdom
| | - Koen B Pouwels
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Myer Glickman
- Health Analysis and Life Events Division, Office for National Statistics, Newport, United Kingdom
| | - Vahé Nafilyan
- Health Analysis and Life Events Division, Office for National Statistics, Newport, United Kingdom
- Faculty of Public Health, Environment and Society, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Francesco Zaccardi
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, United Kingdom
| | - Kamlesh Khunti
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, United Kingdom
| | - Nisreen A Alwan
- School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- National Institute for Health Research Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
- National Institute for Health Research Applied Research Collaboration Wessex, Southampton, United Kingdom
| | - A Sarah Walker
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
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Meza-Torres B, Delanerolle G, Okusi C, Mayor N, Anand S, Macartney J, Gatenby P, Glampson B, Chapman M, Curcin V, Mayer E, Joy M, Greenhalgh T, Delaney B, de Lusignan S. Differences in Clinical Presentation With Long COVID After Community and Hospital Infection and Associations With All-Cause Mortality: English Sentinel Network Database Study. JMIR Public Health Surveill 2022; 8:e37668. [PMID: 35605170 PMCID: PMC9384859 DOI: 10.2196/37668] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/06/2022] [Accepted: 05/17/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Most studies of long COVID (symptoms of COVID-19 infection beyond 4 weeks) have focused on people hospitalized in their initial illness. Long COVID is thought to be underrecorded in UK primary care electronic records. OBJECTIVE We sought to determine which symptoms people present to primary care after COVID-19 infection and whether presentation differs in people who were not hospitalized, as well as post-long COVID mortality rates. METHODS We used routine data from the nationally representative primary care sentinel cohort of the Oxford-Royal College of General Practitioners Research and Surveillance Centre (N=7,396,702), applying a predefined long COVID phenotype and grouped by whether the index infection occurred in hospital or in the community. We included COVID-19 infection cases from March 1, 2020, to April 1, 2021. We conducted a before-and-after analysis of long COVID symptoms prespecified by the Office of National Statistics, comparing symptoms presented between 1 and 6 months after the index infection matched with the same months 1 year previously. We conducted logistic regression analysis, quoting odds ratios (ORs) with 95% CIs. RESULTS In total, 5.63% (416,505/7,396,702) and 1.83% (7623/416,505) of the patients had received a coded diagnosis of COVID-19 infection and diagnosis of, or referral for, long COVID, respectively. People with diagnosis or referral of long COVID had higher odds of presenting the prespecified symptoms after versus before COVID-19 infection (OR 2.66, 95% CI 2.46-2.88, for those with index community infection and OR 2.42, 95% CI 2.03-2.89, for those hospitalized). After an index community infection, patients were more likely to present with nonspecific symptoms (OR 3.44, 95% CI 3.00-3.95; P<.001) compared with after a hospital admission (OR 2.09, 95% CI 1.56-2.80; P<.001). Mental health sequelae were more strongly associated with index hospital infections (OR 2.21, 95% CI 1.64-2.96) than with index community infections (OR 1.36, 95% CI 1.21-1.53; P<.001). People presenting to primary care after hospital infection were more likely to be men (OR 1.43, 95% CI 1.25-1.64; P<.001), more socioeconomically deprived (OR 1.42, 95% CI 1.24-1.63; P<.001), and with higher multimorbidity scores (OR 1.41, 95% CI 1.26-1.57; P<.001) than those presenting after an index community infection. All-cause mortality in people with long COVID was associated with increasing age, male sex (OR 3.32, 95% CI 1.34-9.24; P=.01), and higher multimorbidity score (OR 2.11, 95% CI 1.34-3.29; P<.001). Vaccination was associated with reduced odds of mortality (OR 0.10, 95% CI 0.03-0.35; P<.001). CONCLUSIONS The low percentage of people recorded as having long COVID after COVID-19 infection reflects either low prevalence or underrecording. The characteristics and comorbidities of those presenting with long COVID after a community infection are different from those hospitalized. This study provides insights into the presentation of long COVID in primary care and implications for workload.
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Affiliation(s)
- Bernardo Meza-Torres
- 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
| | - Cecilia Okusi
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Nikhil Mayor
- Royal Surrey NHS Foundation Trust, Guildford, United Kingdom
| | - Sneha Anand
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Jack Macartney
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Piers Gatenby
- Royal Surrey NHS Foundation Trust, Guildford, United Kingdom
| | - Ben Glampson
- Imperial College Healthcare NHS Trust, Imperial Clinical Analytics, Research & Evaluation (iCARE), London, United Kingdom
| | - Martin Chapman
- King's College London, Population Health Sciences, London, United Kingdom
| | - Vasa Curcin
- King's College London, Population Health Sciences, London, United Kingdom
| | - Erik Mayer
- Imperial College Healthcare NHS Trust, Imperial Clinical Analytics, Research & Evaluation (iCARE), London, United Kingdom
| | - Mark Joy
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Trisha Greenhalgh
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Brendan Delaney
- Department of Surgery & Cancer, Institute of Global Health Innovation, Imperial College London, London, United Kingdom
| | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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40
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Mayor N, Meza-Torres B, Okusi C, Delanerolle G, Chapman M, Wang W, Anand S, Feher M, Macartney J, Byford R, Joy M, Gatenby P, Curcin V, Greenhalgh T, Delaney B, de Lusignan S. Developing a Long COVID Phenotype for Postacute COVID-19 in a National Primary Care Sentinel Cohort: Observational Retrospective Database Analysis. JMIR Public Health Surveill 2022; 8:e36989. [PMID: 35861678 PMCID: PMC9374163 DOI: 10.2196/36989] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 05/16/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Following COVID-19, up to 40% of people have ongoing health problems, referred to as postacute COVID-19 or long COVID (LC). LC varies from a single persisting symptom to a complex multisystem disease. Research has flagged that this condition is underrecorded in primary care records, and seeks to better define its clinical characteristics and management. Phenotypes provide a standard method for case definition and identification from routine data and are usually machine-processable. An LC phenotype can underpin research into this condition. OBJECTIVE This study aims to develop a phenotype for LC to inform the epidemiology and future research into this condition. We compared clinical symptoms in people with LC before and after their index infection, recorded from March 1, 2020, to April 1, 2021. We also compared people recorded as having acute infection with those with LC who were hospitalized and those who were not. METHODS We used data from the Primary Care Sentinel Cohort (PCSC) of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) database. This network was recruited to be nationally representative of the English population. We developed an LC phenotype using our established 3-step ontological method: (1) ontological step (defining the reasoning process underpinning the phenotype, (2) coding step (exploring what clinical terms are available, and (3) logical extract model (testing performance). We created a version of this phenotype using Protégé in the ontology web language for BioPortal and using PhenoFlow. Next, we used the phenotype to compare people with LC (1) with regard to their symptoms in the year prior to acquiring COVID-19 and (2) with people with acute COVID-19. We also compared hospitalized people with LC with those not hospitalized. We compared sociodemographic details, comorbidities, and Office of National Statistics-defined LC symptoms between groups. We used descriptive statistics and logistic regression. RESULTS The long-COVID phenotype differentiated people hospitalized with LC from people who were not and where no index infection was identified. The PCSC (N=7.4 million) includes 428,479 patients with acute COVID-19 diagnosis confirmed by a laboratory test and 10,772 patients with clinically diagnosed COVID-19. A total of 7471 (1.74%, 95% CI 1.70-1.78) people were coded as having LC, 1009 (13.5%, 95% CI 12.7-14.3) had a hospital admission related to acute COVID-19, and 6462 (86.5%, 95% CI 85.7-87.3) were not hospitalized, of whom 2728 (42.2%) had no COVID-19 index date recorded. In addition, 1009 (13.5%, 95% CI 12.73-14.28) people with LC were hospitalized compared to 17,993 (4.5%, 95% CI 4.48-4.61; P<.001) with uncomplicated COVID-19. CONCLUSIONS Our LC phenotype enables the identification of individuals with the condition in routine data sets, facilitating their comparison with unaffected people through retrospective research. This phenotype and study protocol to explore its face validity contributes to a better understanding of LC.
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Affiliation(s)
- Nikhil Mayor
- Royal Surrey NHS Foundation Trust, Guildford, United Kingdom
| | - Bernardo Meza-Torres
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
- Department of Clinical and Experimental Medicine, University of Surrey, Guildford, United Kingdom
| | - Cecilia Okusi
- Department of Surgery & Cancer, Institute of Global Health Innovation, Imperial College London, London, United Kingdom
| | - Gayathri Delanerolle
- Department of Surgery & Cancer, Institute of Global Health Innovation, Imperial College London, London, United Kingdom
| | - Martin Chapman
- Population Health Sciences, Kings College London, London, United Kingdom
| | - Wenjuan Wang
- Population Health Sciences, Kings College London, London, United Kingdom
| | - Sneha Anand
- Department of Surgery & Cancer, Institute of Global Health Innovation, Imperial College London, London, United Kingdom
| | - Michael Feher
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Jack Macartney
- 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
| | - Mark Joy
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Piers Gatenby
- Royal Surrey NHS Foundation Trust, Guildford, United Kingdom
| | - Vasa Curcin
- Population Health Sciences, Kings College London, London, United Kingdom
| | - Trisha Greenhalgh
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Brendan Delaney
- Department of Surgery & Cancer, Institute of Global Health Innovation, Imperial College London, London, United Kingdom
| | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
- Royal College of General Practitioners Research and Surveillance Centre, London, United Kingdom
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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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42
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Thompson EJ, Williams DM, Walker AJ, Mitchell RE, Niedzwiedz CL, Yang TC, Huggins CF, Kwong ASF, Silverwood RJ, Di Gessa G, Bowyer RCE, Northstone K, Hou B, Green MJ, Dodgeon B, Doores KJ, Duncan EL, Williams FMK, Steptoe A, Porteous DJ, McEachan RRC, Tomlinson L, Goldacre B, Patalay P, Ploubidis GB, Katikireddi SV, Tilling K, Rentsch CT, Timpson NJ, Chaturvedi N, Steves CJ. Long COVID burden and risk factors in 10 UK longitudinal studies and electronic health records. Nat Commun 2022; 13:3528. [PMID: 35764621 PMCID: PMC9240035 DOI: 10.1038/s41467-022-30836-0] [Citation(s) in RCA: 217] [Impact Index Per Article: 108.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 05/19/2022] [Indexed: 12/14/2022] Open
Abstract
The frequency of, and risk factors for, long COVID are unclear among community-based individuals with a history of COVID-19. To elucidate the burden and possible causes of long COVID in the community, we coordinated analyses of survey data from 6907 individuals with self-reported COVID-19 from 10 UK longitudinal study (LS) samples and 1.1 million individuals with COVID-19 diagnostic codes in electronic healthcare records (EHR) collected by spring 2021. Proportions of presumed COVID-19 cases in LS reporting any symptoms for 12+ weeks ranged from 7.8% and 17% (with 1.2 to 4.8% reporting debilitating symptoms). Increasing age, female sex, white ethnicity, poor pre-pandemic general and mental health, overweight/obesity, and asthma were associated with prolonged symptoms in both LS and EHR data, but findings for other factors, such as cardio-metabolic parameters, were inconclusive.
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Affiliation(s)
- Ellen J Thompson
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK.
| | - Dylan M Williams
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK.
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Alex J Walker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxfort, UK
| | - Ruth E Mitchell
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Tiffany C Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, BD9 6RJ, UK
| | - Charlotte F Huggins
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Alex S F Kwong
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Richard J Silverwood
- Centre for Longitudinal Studies, UCL Social Research Institute, University College London, London, UK
| | - Giorgio Di Gessa
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Ruth C E Bowyer
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Kate Northstone
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Bo Hou
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, BD9 6RJ, UK
| | - Michael J Green
- MRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, UK
| | - Brian Dodgeon
- Centre for Longitudinal Studies, UCL Social Research Institute, University College London, London, UK
| | - Katie J Doores
- School of Immunology & Microbial Sciences, King's College London, London, UK
| | - Emma L Duncan
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Andrew Steptoe
- Department of Epidemiology and Public Health, University College London, London, UK
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Rosemary R C McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, BD9 6RJ, UK
| | - Laurie Tomlinson
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, 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, Oxfort, UK
| | - Praveetha Patalay
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
- Centre for Longitudinal Studies, UCL Social Research Institute, University College London, London, UK
| | - George B Ploubidis
- Centre for Longitudinal Studies, UCL Social Research Institute, University College London, London, UK
| | | | - Kate Tilling
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Christopher T Rentsch
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Nishi Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK.
- Department of Ageing and Health, Guys and St Thomas's NHS Foundation Trust, London, UK.
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Ayoubkhani D, Bermingham C, Pouwels KB, Glickman M, Nafilyan V, Zaccardi F, Khunti K, Alwan NA, Walker AS. Trajectory of long covid symptoms after covid-19 vaccination: community based cohort study. BMJ 2022; 377:e069676. [PMID: 35584816 PMCID: PMC9115603 DOI: 10.1136/bmj-2021-069676] [Citation(s) in RCA: 170] [Impact Index Per Article: 85.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/12/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE To estimate associations between covid-19 vaccination and long covid symptoms in adults with SARS-CoV-2 infection before vaccination. DESIGN Observational cohort study. SETTING Community dwelling population, UK. PARTICIPANTS 28 356 participants in the Office for National Statistics COVID-19 Infection Survey aged 18-69 years who received at least one dose of an adenovirus vector or mRNA covid-19 vaccine after testing positive for SARS-CoV-2 infection. MAIN OUTCOME MEASURE Presence of long covid symptoms at least 12 weeks after infection over the follow-up period 3 February to 5 September 2021. RESULTS Mean age of participants was 46 years, 55.6% (n=15 760) were women, and 88.7% (n=25 141) were of white ethnicity. Median follow-up was 141 days from first vaccination (among all participants) and 67 days from second vaccination (83.8% of participants). 6729 participants (23.7%) reported long covid symptoms of any severity at least once during follow-up. A first vaccine dose was associated with an initial 12.8% decrease (95% confidence interval -18.6% to -6.6%, P<0.001) in the odds of long covid, with subsequent data compatible with both increases and decreases in the trajectory (0.3% per week, 95% confidence interval -0.6% to 1.2% per week, P=0.51). A second dose was associated with an initial 8.8% decrease (95% confidence interval -14.1% to -3.1%, P=0.003) in the odds of long covid, with a subsequent decrease by 0.8% per week (-1.2% to -0.4% per week, P<0.001). Heterogeneity was not found in associations between vaccination and long covid by sociodemographic characteristics, health status, hospital admission with acute covid-19, vaccine type (adenovirus vector or mRNA), or duration from SARS-CoV-2 infection to vaccination. CONCLUSIONS The likelihood of long covid symptoms was observed to decrease after covid-19 vaccination and evidence suggested sustained improvement after a second dose, at least over the median follow-up of 67 days. Vaccination may contribute to a reduction in the population health burden of long covid, although longer follow-up is needed.
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Affiliation(s)
- Daniel Ayoubkhani
- Health Analysis and Life Events Division, Office for National Statistics, Newport, UK
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Charlotte Bermingham
- Health Analysis and Life Events Division, Office for National Statistics, Newport, UK
| | - Koen B Pouwels
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Myer Glickman
- Health Analysis and Life Events Division, Office for National Statistics, Newport, UK
| | - Vahé Nafilyan
- Health Analysis and Life Events Division, Office for National Statistics, Newport, UK
- Faculty of Public Health, Environment, and Society, London School of Hygiene and Tropical Medicine, London, UK
| | - Francesco Zaccardi
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Kamlesh Khunti
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Nisreen A Alwan
- School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
- NIHR Applied Research Collaboration (ARC) Wessex, Southampton, UK
| | - A Sarah Walker
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
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Sivan M, Greenhalgh T, Darbyshire JL, Mir G, O'Connor RJ, Dawes H, Greenwood D, O'Connor D, Horton M, Petrou S, de Lusignan S, Curcin V, Mayer E, Casson A, Milne R, Rayner C, Smith N, Parkin A, Preston N, Delaney B. LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS (LOCOMOTION): protocol for a mixed-methods study in the UK. BMJ Open 2022; 12:e063505. [PMID: 35580970 PMCID: PMC9114312 DOI: 10.1136/bmjopen-2022-063505] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 04/29/2022] [Indexed: 12/26/2022] Open
Abstract
INTRODUCTION Long COVID, a new condition whose origins and natural history are not yet fully established, currently affects 1.5 million people in the UK. Most do not have access to specialist long COVID services. We seek to optimise long COVID care both within and outside specialist clinics, including improving access, reducing inequalities, helping self-management and providing guidance and decision support for primary care. We aim to establish a 'gold standard' of care by systematically analysing current practices, iteratively improving pathways and systems of care. METHODS AND ANALYSIS This mixed-methods, multisite study is informed by the principles of applied health services research, quality improvement, co-design, outcome measurement and learning health systems. It was developed in close partnership with patients (whose stated priorities are prompt clinical assessment; evidence-based advice and treatment and help with returning to work and other roles) and with front-line clinicians. Workstreams and tasks to optimise assessment, treatment and monitoring are based in three contrasting settings: workstream 1 (qualitative research, up to 100 participants), specialist management in 10 long COVID clinics across the UK, via a quality improvement collaborative, experience-based co-design and targeted efforts to reduce inequalities of access, return to work and peer support; workstream 2 (quantitative research, up to 5000 participants), patient self-management at home, technology-supported monitoring and validation of condition-specific outcome measures and workstream 3 (quantitative research, up to 5000 participants), generalist management in primary care, harnessing electronic record data to study population phenotypes and develop evidence-based decision support, referral pathways and analysis of costs. Study governance includes an active patient advisory group. ETHICS AND DISSEMINATION LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS study is sponsored by the University of Leeds and approved by Yorkshire & The Humber-Bradford Leeds Research Ethics Committee (ref: 21/YH/0276). Participants will provide informed consent. Dissemination plans include academic and lay publications, and partnerships with national and regional policymakers. TRIAL REGISTRATION NUMBER NCT05057260, ISRCTN15022307.
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Affiliation(s)
- Manoj Sivan
- Academic Department of Rehabilitation Medicine, University of Leeds, Leeds, UK
| | - Trisha Greenhalgh
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Ghazala Mir
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Rory J O'Connor
- Academic Department of Rehabilitation Medicine, University of Leeds, Leeds, UK
| | - Helen Dawes
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - Darren Greenwood
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
| | | | - Mike Horton
- Academic Department of Rehabilitation Medicine, University of Leeds, Leeds, UK
| | - Stavros Petrou
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
- Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK
| | - Vasa Curcin
- Department of Primary Care and Public Health Sciences, King's College London, London, UK
| | - Erik Mayer
- Department of Biosurgery and Surgical Technology, Imperial College London, London, UK
| | - Alexander Casson
- Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK
| | - Ruairidh Milne
- Public Health, Wessex Institute, University of Southampton, Southampton, UK
| | | | | | - Amy Parkin
- Department of Occupational Therapy, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Nick Preston
- Academic Department of Rehabilitation Medicine, University of Leeds, Leeds, UK
| | - Brendan Delaney
- Department of Surgery and Cancer, Imperial College London, London, UK
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45
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Neculicioiu VS, Colosi IA, Costache C, Sevastre-Berghian A, Clichici S. Time to Sleep?-A Review of the Impact of the COVID-19 Pandemic on Sleep and Mental Health. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:3497. [PMID: 35329184 PMCID: PMC8954484 DOI: 10.3390/ijerph19063497] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/10/2022] [Accepted: 03/14/2022] [Indexed: 02/06/2023]
Abstract
Sleep is intrinsically tied to mental and overall health. Short sleep duration accompanies the modern lifestyle, possibly reaching epidemic proportions. The pandemic and subsequent lockdowns determined a fundamental shift in the modern lifestyle and had profound effects on sleep and mental health. This paper aims to provide an overview of the relationship between sleep, mental health and COVID-19. Contrasting outcomes on sleep health have been highlighted by most reports during the pandemic in the general population. Consequently, while longer sleep durations have been reported, this change was accompanied by decreases in sleep quality and altered sleep timing. Furthermore, an increased impact of sleep deficiencies and mental health burden was generally reported in health care workers as compared with the adult general population. Although not among the most frequent symptoms during the acute or persistent phase, an increased prevalence of sleep deficiencies has been reported in patients with acute and long COVID. The importance of sleep in immune regulation is well known. Consequently, sleep deficiencies may influence multiple aspects of COVID-19, such as the risk, severity, and prognosis of the infection and even vaccine response.
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Affiliation(s)
- Vlad Sever Neculicioiu
- Department of Microbiology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (I.A.C.); (C.C.)
| | - Ioana Alina Colosi
- Department of Microbiology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (I.A.C.); (C.C.)
| | - Carmen Costache
- Department of Microbiology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (I.A.C.); (C.C.)
| | - Alexandra Sevastre-Berghian
- Department of Physiology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (A.S.-B.); (S.C.)
| | - Simona Clichici
- Department of Physiology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (A.S.-B.); (S.C.)
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Abstract
In this Comment article, Nisreen Alwan discusses what her experience as both a public health academic and a person living with Long COVID has taught her about the importance of including those with lived experience of a condition in setting the research agenda.
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47
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Ward H, Flower B, Garcia PJ, Ong SWX, Altmann DM, Delaney B, Smith N, Elliott P, Cooke G. Global surveillance, research, and collaboration needed to improve understanding and management of long COVID. Lancet 2021; 398:2057-2059. [PMID: 34774190 PMCID: PMC8580495 DOI: 10.1016/s0140-6736(21)02444-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 11/03/2021] [Indexed: 01/19/2023]
Affiliation(s)
- Helen Ward
- School of Public Health, Imperial College London, London W2 1PG, UK.
| | - Barnaby Flower
- Department of Infectious Disease, Imperial College London, London W2 1PG, UK
| | - Patricia J Garcia
- School of Public Health, Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | - Daniel M Altmann
- Department of Immunology and Inflammation, Faculty of Medicine, Hammersmith Hospital, Imperial College London, London, UK
| | - Brendan Delaney
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Nikki Smith
- Patient Advisory Panel, REACT-Long Covid Study, Imperial College London, London, UK
| | - Paul Elliott
- School of Public Health, Imperial College London, London W2 1PG, UK
| | - Graham Cooke
- Department of Infectious Disease, Imperial College London, London W2 1PG, UK
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48
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Affiliation(s)
- Amitava Banerjee
- Institute of Health Informatics, University College London, London
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49
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Buttery S, Philip KEJ, Williams P, Fallas A, West B, Cumella A, Cheung C, Walker S, Quint JK, Polkey MI, Hopkinson NS. Patient symptoms and experience following COVID-19: results from a UK-wide survey. BMJ Open Respir Res 2021; 8:8/1/e001075. [PMID: 34732518 PMCID: PMC8572361 DOI: 10.1136/bmjresp-2021-001075] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 10/19/2021] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVES To investigate the experience of people who continue to be unwell after acute COVID-19, often referred to as 'long COVID', both in terms of their symptoms and their interactions with healthcare. DESIGN We conducted a mixed-methods analysis of responses to a survey accessed through a UK online post-COVID-19 support and information hub, between April and December 2020, about people's experiences after having acute COVID-19. PARTICIPANTS 3290 respondents, 78% female, 92.1% white ethnicity and median age range 45-54 years; 12.7% had been hospitalised. 494(16.5%) completed the survey between 4 and 8 weeks of the onset of their symptoms, 641(21.4%) between 8 and 12 weeks and 1865 (62.1%) >12 weeks after. RESULTS The ongoing symptoms most frequently reported were: breathing problems (92.1%), fatigue (83.3%), muscle weakness or joint stiffness (50.6%), sleep disturbances (46.2%), problems with mental abilities (45.9%), changes in mood, including anxiety and depression (43.1%) and cough (42.3%). Symptoms did not appear to be related to the severity of the acute illness or to the presence of pre-existing medical conditions. Analysis of free-text responses revealed three main themes: (1) experience of living with COVID-19: physical and psychological symptoms that fluctuate unpredictably; (2) interactions with healthcare that were unsatisfactory; (3) implications for the future: their own condition, society and the healthcare system, and the need for research CONCLUSION: Consideration of patient perspectives and experiences will assist in the planning of services to address problems persisting in people who remain symptomatic after the acute phase of COVID-19.
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Affiliation(s)
- Sara Buttery
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Keir E J Philip
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Parris Williams
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Andrea Fallas
- Asthma UK and British Lung Foundation Partnership, London, UK
| | - Brigitte West
- Asthma UK and British Lung Foundation Partnership, London, UK
| | - Andrew Cumella
- Asthma UK and British Lung Foundation Partnership, London, UK
| | - Cheryl Cheung
- Asthma UK and British Lung Foundation Partnership, London, UK
| | - Samantha Walker
- Asthma UK and British Lung Foundation Partnership, London, UK
| | - Jennifer K Quint
- Department of Respiratory Epidemiology, Occupational Medicine and Public Health, Imperial College London, London, UK
| | - Michael I Polkey
- National Heart and Lung Institute, Imperial College London, London, UK.,Respiratory Medicine, Royal Brompton and Harefield NHS Foundation Trust, London, UK
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50
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Walker AJ, MacKenna B, Goldacre B. Clinical coding of long COVID in English primary care: a federated analysis of 58 million patient records in situ using OpenSAFELY. Br J Gen Pract 2021; 71:495. [PMID: 34711574 PMCID: PMC8544137 DOI: 10.3399/bjgp21x717449] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Affiliation(s)
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford.
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford,
Oxford
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford,
Oxford
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