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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 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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Aldridge SJ, Agrawal U, Murphy S, Millington T, Akbari A, Almaghrabi F, Anand SN, Bedston S, Goudie R, Griffiths R, Joy M, Lowthian E, de Lusignan S, Patterson L, Robertson C, Rudan I, Bradley DT, Lyons RA, Sheikh A, Owen RK. Uptake of COVID-19 vaccinations amongst 3,433,483 children and young people: meta-analysis of UK prospective cohorts. Nat Commun 2024; 15:2363. [PMID: 38491011 PMCID: PMC10943015 DOI: 10.1038/s41467-024-46451-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 02/27/2024] [Indexed: 03/18/2024] Open
Abstract
SARS-CoV-2 infection in children and young people (CYP) can lead to life-threatening COVID-19, transmission within households and schools, and the development of long COVID. Using linked health and administrative data, we investigated vaccine uptake among 3,433,483 CYP aged 5-17 years across all UK nations between 4th August 2021 and 31st May 2022. We constructed national cohorts and undertook multi-state modelling and meta-analysis to identify associations between demographic variables and vaccine uptake. We found that uptake of the first COVID-19 vaccine among CYP was low across all four nations compared to other age groups and diminished with subsequent doses. Age and vaccination status of adults living in the same household were identified as important risk factors associated with vaccine uptake in CYP. For example, 5-11 year-olds were less likely to receive their first vaccine compared to 16-17 year-olds (adjusted Hazard Ratio [aHR]: 0.10 (95%CI: 0.06-0.19)), and CYP in unvaccinated households were less likely to receive their first vaccine compared to CYP in partially vaccinated households (aHR: 0.19, 95%CI 0.13-0.29).
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Affiliation(s)
- Sarah J Aldridge
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health, and Life Science, Swansea University, Swansea, UK.
| | - Utkarsh Agrawal
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Siobhán Murphy
- Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University, Belfast, UK
| | | | - Ashley Akbari
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health, and Life Science, Swansea University, Swansea, UK
| | | | - Sneha N Anand
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Stuart Bedston
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health, and Life Science, Swansea University, Swansea, UK
| | - Rosalind Goudie
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Rowena Griffiths
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health, and Life Science, Swansea University, Swansea, UK
| | - Mark Joy
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Emily Lowthian
- Department of Education and Childhood Studies, School of Social Sciences, Swansea University, Swansea, UK
| | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Lynsey Patterson
- Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University, Belfast, UK
- Public Health Agency, Belfast, UK
| | - Chris Robertson
- Department of Mathematics and Statistics, Strathclyde University, Glasgow, UK and Public Health Scotland, Glasgow, UK
| | - Igor Rudan
- Centre for Global Health, Usher Institute, the University of Edinburgh, Edinburgh, UK
| | - Declan T Bradley
- Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University, Belfast, UK
- Public Health Agency, Belfast, UK
| | - Ronan A Lyons
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health, and Life Science, Swansea University, Swansea, UK
| | - Aziz Sheikh
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Rhiannon K Owen
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health, and Life Science, Swansea University, Swansea, UK.
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Shi T, Millington T, Robertson C, Jeffrey K, Katikireddi SV, McCowan C, Simpson CR, Woolford L, Daines L, Kerr S, Swallow B, Fagbamigbe A, Vallejos CA, Weatherill D, Jayacodi S, Marsh K, McMenamin J, Rudan I, Ritchie LD, Mueller T, Kurdi A, Sheikh A. Risk of winter hospitalisation and death from acute respiratory infections in Scotland: national retrospective cohort study. J R Soc Med 2024:1410768231223584. [PMID: 38345538 DOI: 10.1177/01410768231223584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024] Open
Abstract
OBJECTIVES We undertook a national analysis to characterise and identify risk factors for acute respiratory infections (ARIs) resulting in hospitalisation during the winter period in Scotland. DESIGN A population-based retrospective cohort analysis. SETTING Scotland. PARTICIPANTS The study involved 5.4 million residents in Scotland. MAIN OUTCOME MEASURES Cox proportional hazard models were used to estimate adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for the association between risk factors and ARI hospitalisation. RESULTS Between 1 September 2022 and 31 January 2023, there were 22,284 (10.9% of 203,549 with any emergency hospitalisation) ARI hospitalisations (1759 in children and 20,525 in adults) in Scotland. Compared with the reference group of children aged 6-17 years, the risk of ARI hospitalisation was higher in children aged 3-5 years (aHR = 4.55; 95% CI: 4.11-5.04). Compared with those aged 25-29 years, the risk of ARI hospitalisation was highest among the oldest adults aged ≥80 years (aHR = 7.86; 95% CI: 7.06-8.76). Adults from more deprived areas (most deprived vs. least deprived, aHR = 1.64; 95% CI: 1.57-1.72), with existing health conditions (≥5 vs. 0 health conditions, aHR = 4.84; 95% CI: 4.53-5.18) or with history of all-cause emergency admissions (≥6 vs. 0 previous emergency admissions, aHR = 7.53; 95% CI: 5.48-10.35) were at a higher risk of ARI hospitalisations. The risk increased by the number of existing health conditions and previous emergency admission. Similar associations were seen in children. CONCLUSIONS Younger children, older adults, those from more deprived backgrounds and individuals with greater numbers of pre-existing conditions and previous emergency admission were at increased risk for winter hospitalisations for ARI.
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Affiliation(s)
- Ting Shi
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, EH8 9AG, Scotland, UK
| | - Tristan Millington
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, EH8 9AG, Scotland, UK
| | - Chris Robertson
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, G1 1XQ, Scotland, UK
- Public Health Scotland, Glasgow, G2 6QE, Scotland, UK
| | - Karen Jeffrey
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, EH8 9AG, Scotland, UK
| | | | - Colin McCowan
- School of Medicine, University of St Andrews, St Andrews, KY16 9AJ, Scotland, UK
| | - Colin R Simpson
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, EH8 9AG, Scotland, UK
- School of Health, Wellington Faculty of Health, Victoria University of Wellington, Wellington, 6140, New Zealand
| | - Lana Woolford
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, EH8 9AG, Scotland, UK
| | - Luke Daines
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, EH8 9AG, Scotland, UK
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, Scotland, UK
| | - Steven Kerr
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, EH8 9AG, Scotland, UK
| | - Ben Swallow
- School of Mathematics and Statistics, University of St Andrews, St Andrews, KY16 9SS, Scotland, UK
| | - Adeniyi Fagbamigbe
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, AB24 2ZD, Scotland, UK
- Department of Epidemiology and Medical Statistics, University of Ibadan, Ibadan 200132, Nigeria
| | - Catalina A Vallejos
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, Scotland, UK
- The Alan Turing Institute, London, NW1 2DB, UK
| | - David Weatherill
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, EH8 9AG, Scotland, UK
| | - Sandra Jayacodi
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, EH8 9AG, Scotland, UK
| | | | - Jim McMenamin
- Public Health Scotland, Glasgow, G2 6QE, Scotland, UK
| | - Igor Rudan
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, EH8 9AG, Scotland, UK
| | - Lewis Duthie Ritchie
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, AB24 2ZD, Scotland, UK
| | - Tanja Mueller
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, G4 0RE, Scotland, UK
| | - Amanj Kurdi
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, G4 0RE, Scotland, UK
- Department of Clinical Pharmacy, College of Pharmacy, Hawler Medical University, Erbil, Iraq
- Division of Public Health Pharmacy and Management, School of Pharmacy, Sefako Makgatho Health Sciences University, Ga-Rankuwa, 0208, South Africa
- Department of Clinical Pharmacy, College of Pharmacy, Al-Kitab University, Kirkuk, Iraq
| | - Aziz Sheikh
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, EH8 9AG, Scotland, UK
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, Scotland, UK
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Millington T, Morrison K, Jeffrey K, Sullivan C, Kurdi A, Fagbamigbe AF, Swallow B, Shi T, Shah SA, Kerr S, Simpson CR, Ritchie LD, Robertson C, Sheikh A, Rudan I. Caveats in reporting of national vaccine uptake. J Glob Health 2024; 14:03006. [PMID: 38330197 PMCID: PMC10852533 DOI: 10.7189/jogh.14.03006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024] Open
Affiliation(s)
| | | | - Karen Jeffrey
- Usher Institute, The University of Edinburgh, Edinburgh, UK
| | | | - Amanj Kurdi
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, United Kingdom
- Department of Clinical Pharmacy, College of Pharmacy, Hawler Medical University, Erbil, Iraq
- College of Pharmacy, Al-Kitab University, Kirkuk, Iraq
- School of Pharmacy, Sefako Makgatho Health Sciences University, Pretoria, South Africa
| | | | - Ben Swallow
- School of Mathematics and Statistics, University of St Andrews, UK
| | - Ting Shi
- Usher Institute, The University of Edinburgh, Edinburgh, UK
| | | | - Steven Kerr
- Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Colin R Simpson
- Usher Institute, The University of Edinburgh, Edinburgh, UK
- School of Health, Wellington Faculty of Health, Victoria University of Wellington, Wellington, NZ
| | - Lewis D Ritchie
- School of Medicine, Medical Sciences & Nutrition, Academic Primary Care, University of Aberdeen, UK
| | - Chris Robertson
- Public Health Scotland, Glasgow, UK
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK
| | - Aziz Sheikh
- Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Igor Rudan
- Usher Institute, The University of Edinburgh, Edinburgh, UK
- Algebra University College, Zagreb, Croatia
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Kerr S, Millington T, Rudan I, McCowan C, Tibble H, Jeffrey K, Fagbamigbe AF, Simpson CR, Robertson C, Hippisley-Cox J, Sheikh A. External validation of the QCovid 2 and 3 risk prediction algorithms for risk of COVID-19 hospitalisation and mortality in adults: a national cohort study in Scotland. BMJ Open 2023; 13:e075958. [PMID: 38151278 PMCID: PMC10753764 DOI: 10.1136/bmjopen-2023-075958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 10/20/2023] [Indexed: 12/29/2023] Open
Abstract
OBJECTIVE The QCovid 2 and 3 algorithms are risk prediction tools developed during the second wave of the COVID-19 pandemic that can be used to predict the risk of COVID-19 hospitalisation and mortality, taking vaccination status into account. In this study, we assess their performance in Scotland. METHODS We used the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 national data platform consisting of individual-level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR virology testing, hospitalisation and mortality data. We assessed the discrimination and calibration of the QCovid 2 and 3 algorithms in predicting COVID-19 hospitalisations and deaths between 8 December 2020 and 15 June 2021. RESULTS Our validation dataset comprised 465 058 individuals, aged 19-100. We found the following performance metrics (95% CIs) for QCovid 2 and 3: Harrell's C 0.84 (0.82 to 0.86) for hospitalisation, and 0.92 (0.90 to 0.94) for death, observed-expected ratio of 0.24 for hospitalisation and 0.26 for death (ie, both the number of hospitalisations and the number of deaths were overestimated), and a Brier score of 0.0009 (0.00084 to 0.00096) for hospitalisation and 0.00036 (0.00032 to 0.0004) for death. CONCLUSIONS We found good discrimination of the QCovid 2 and 3 algorithms in Scotland, although performance was worse in higher age groups. Both the number of hospitalisations and the number of deaths were overestimated.
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Affiliation(s)
- Steven Kerr
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK
| | - Tristan Millington
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK
| | - Igor Rudan
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK
| | - Colin McCowan
- School of Medicine, University of St. Andrews, St Andrews, UK
| | - Holly Tibble
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK
| | - Karen Jeffrey
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK
| | - Adeniyi Francis Fagbamigbe
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
- Department of Epidemiology and Medical Statistics, University of Ibadan, Ibadan, Nigeria
| | - Colin R Simpson
- Faculty of Health, Victoria University of Wellington, Wellington, New Zealand
| | - Chris Robertson
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK
| | - Julia Hippisley-Cox
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Aziz Sheikh
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK
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Kerr S, Greenland S, Jeffrey K, Millington T, Bedston S, Ritchie L, Simpson CR, Fagbamigbe AF, Kurdi A, Robertson C, Sheikh A, Rudan I. Understanding and reporting odds ratios as rate-ratio estimates in case-control studies. J Glob Health 2023; 13:04101. [PMID: 37712381 PMCID: PMC10502767 DOI: 10.7189/jogh.13.04101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023] Open
Abstract
Background We noted that there remains some confusion in the health-science literature on reporting sample odds ratios as estimated rate ratios in case-control studies. Methods We recap historical literature that definitively answered the question of when sample odds ratios (ORs) from a case-control study are consistent estimators for population rate ratios. We use numerical examples to illustrate the magnitude of the disparity between sample ORs in a case-control study and population rate ratios when sufficient conditions for them to be equal are not satisfied. Results We stress that in a case-control study, sampling controls from those still at risk at the time of outcome event of the index case is not sufficient for a sample OR to be a consistent estimator for an intelligible rate ratio. In such studies, constancy of the exposure prevalence together with constancy of the hazard ratio (HR) (i.e., the instantaneous rate ratio) over time is sufficient for this result if sampling time is not controlled; if time is controlled, constancy of the HR will suffice. We present numerical examples to illustrate how failure to satisfy these conditions adds a small systematic error to sample ORs as estimates of population rate ratios. Conclusions We recommend that researchers understand and critically evaluate all conditions used to interpret their estimates as consistent for a population parameter in case-control studies.
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Affiliation(s)
- Steven Kerr
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Sander Greenland
- Department of Epidemiology and Department of Statistics, University of California, Los Angeles, California, USA
| | - Karen Jeffrey
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Tristan Millington
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Stuart Bedston
- Population Data Science, Swansea University Medical School, Swansea, Wales, UK
| | - Lewis Ritchie
- Academic Primary Care, University of Aberdeen School of Medicine and Dentistry, Aberdeen, Scotland, UK
| | - Colin R Simpson
- Wellington Faculty of Health, Victoria University of Wellington, Wellington, NZ
| | - Adeniyi Francis Fagbamigbe
- Institute of Applied Health Sciences, University of Aberdeen School of Medicine and Dentistry, Aberdeen, Scotland, UK
| | - Amanj Kurdi
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, Scotland, UK
- Department of Clinical Pharmacy, College of Pharmacy, Hawler Medical University, Erbil, Iraq
- College of Pharmacy, Al-Kitab University, Kirkuk, Iraq
- School of Pharmacy, Sefako Makgatho Health Sciences University, Pretoria, South Africa
| | - Chris Robertson
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, Scotland
- Public Health Scotland, Glasgow, Scotland, UK
| | - Aziz Sheikh
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Igor Rudan
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
- University College Algebra, Zagreb, Croatia
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Rudan I, Millington T, Antal K, Grange Z, Fenton L, Sullivan C, Buelo A, Wood R, Woolford L, Swann OV, Murray JL, Cullen LA, Moore E, Haider F, Almaghrabi F, McMenamin J, Agrawal U, Shah SA, Kerr S, Simpson CR, Katikireddi SV, Ritchie SLD, Robertson C, Sheikh SA. BNT162b2 COVID-19 vaccination uptake, safety, effectiveness and waning in children and young people aged 12-17 years in Scotland. Lancet Reg Health Eur 2022; 23:100513. [PMID: 36189425 PMCID: PMC9514975 DOI: 10.1016/j.lanepe.2022.100513] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Background The two-dose BNT162b2 (Pfizer-BioNTech) vaccine has demonstrated high efficacy against COVID-19 disease in clinical trials of children and young people (CYP). Consequently, we investigated the uptake, safety, effectiveness and waning of the protective effect of the BNT162b2 against symptomatic COVID-19 in CYP aged 12-17 years in Scotland. Methods The analysis of the vaccine uptake was based on information from the Turas Vaccination Management Tool, inclusive of Mar 1, 2022. Vaccine safety was evaluated using national data on hospital admissions and General Practice (GP) consultations, through a self-controlled case series (SCCS) design, investigating 17 health outcomes of interest. Vaccine effectiveness (VE) against symptomatic COVID-19 disease for Delta and Omicron variants was estimated using a test-negative design (TND) and S-gene status in a prospective cohort study using the Scotland-wide Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) surveillance platform. The waning of the VE following each dose of BNT162b2 was assessed using a matching process followed by conditional logistic regression. Findings Between Aug 6, 2021 and Mar 1, 2022, 75.9% of the 112,609 CYP aged 16-17 years received the first and 49.0% the second COVID-19 vaccine dose. Among 237,681 CYP aged 12-15 years, the uptake was 64.5% and 37.2%, respectively. For 12-17-year-olds, BNT162b2 showed an excellent safety record, with no increase in hospital stays following vaccination for any of the 17 investigated health outcomes. In the 16-17-year-old group, VE against symptomatic COVID-19 during the Delta period was 64.2% (95% confidence interval [CI] 59.2-68.5) at 2-5 weeks after the first dose and 95.6% (77.0-99.1) at 2-5 weeks after the second dose. The respective VEs against symptomatic COVID-19 in the Omicron period were 22.8% (95% CI -6.4-44.0) and 65.5% (95% CI 56.0-73.0). In children aged 12-15 years, VE against symptomatic COVID-19 during the Delta period was 65.4% (95% CI 61.5-68.8) at 2-5 weeks after the first dose, with no observed cases at 2-5 weeks after the second dose. The corresponding VE against symptomatic COVID-19 during the Omicron period were 30.2% (95% CI 18.4-40.3) and 81.2% (95% CI 77.7-84.2). The waning of the protective effect against the symptomatic disease began after five weeks post-first and post-second dose. Interpretation During the study period, uptake of BNT162b2 in Scotland has covered more than two-thirds of CYP aged 12-17 years with the first dose and about 40% with the second dose. We found no increased likelihood of admission to hospital with a range of health outcomes in the period after vaccination. Vaccination with both doses was associated with a substantial reduction in the risk of COVID-19 symptomatic disease during both the Delta and Omicron periods, but this protection began to wane after five weeks. Funding UK Research and Innovation (Medical Research Council); Research and Innovation Industrial Strategy Challenge Fund; Chief Scientist's Office of the Scottish Government; Health Data Research UK; National Core Studies - Data and Connectivity.
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Affiliation(s)
- Igor Rudan
- Usher Institute, The University of Edinburgh, Edinburgh, UK
| | | | | | | | | | | | | | - Rachael Wood
- Usher Institute, The University of Edinburgh, Edinburgh, UK
- Public Health Scotland, Glasgow, UK
| | - Lana Woolford
- Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Olivia V. Swann
- Department of Child Life and Health, University of Edinburgh, Edinburgh, UK
- Royal Hospital for Sick Children, Paediatric Infectious Diseases, Edinburgh, UK
| | | | | | | | - Fasih Haider
- Usher Institute, The University of Edinburgh, Edinburgh, UK
| | | | | | - Utkarsh Agrawal
- School of Medicine, University of St Andrews, St Andrews, UK
| | | | - Steven Kerr
- Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Colin R. Simpson
- School of Health, Wellington Faculty of Health, Victoria University of Wellington, Wellington, New Zealand
| | | | | | - Chris Robertson
- Public Health Scotland, Glasgow, UK
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK
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Florentino PTV, Millington T, Cerqueira-Silva T, Robertson C, de Araújo Oliveira V, Júnior JBS, Alves FJO, Penna GO, Vital Katikireddi S, Boaventura VS, Werneck GL, Pearce N, McCowan C, Sullivan C, Agrawal U, Grange Z, Ritchie LD, Simpson CR, Sheikh A, Barreto ML, Rudan I, Barral-Netto M, Paixão ES. Vaccine effectiveness of two-dose BNT162b2 against symptomatic and severe COVID-19 among adolescents in Brazil and Scotland over time: a test-negative case-control study. Lancet Infect Dis 2022; 22:1577-1586. [PMID: 35952702 PMCID: PMC9359673 DOI: 10.1016/s1473-3099(22)00451-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/22/2022] [Accepted: 06/23/2022] [Indexed: 02/09/2023]
Abstract
BACKGROUND Little is known about vaccine effectiveness over time among adolescents, especially against the SARS-CoV-2 omicron (B.1.1.529) variant. This study assessed the associations between time since two-dose vaccination with BNT162b2 and the occurrence of symptomatic SARS-CoV-2 infection and severe COVID-19 among adolescents in Brazil and Scotland. METHODS We did test-negative, case-control studies in adolescents aged 12-17 years with COVID-19-related symptoms in Brazil and Scotland. We linked records of SARS-CoV-2 RT-PCR and antigen tests to national vaccination and clinical records. We excluded tests from individuals who did not have symptoms, were vaccinated before the start of the national vaccination programme, received vaccines other than BNT162b2 or a SARS-CoV-2 booster dose of any kind, or had an interval between their first and second dose of fewer than 21 days. Additionally, we excluded negative SARS-CoV-2 tests recorded within 14 days of a previous negative test, negative tests recorded within 7 days after a positive test, any test done within 90 days after a positive test, and tests with missing sex and location information. Cases (SARS-CoV-2 test-positive adolescents) and controls (test-negative adolescents) were drawn from a sample of individuals in whom tests were collected within 10 days of symptom onset. We estimated the adjusted odds ratio and vaccine effectiveness against symptomatic COVID-19 for both countries and against severe COVID-19 (hospitalisation or death) for Brazil across fortnightly periods. FINDINGS We analysed 503 776 tests from 2 948 538 adolescents in Brazil between Sept 2, 2021, and April 19, 2022, and 127 168 tests from 404 673 adolescents in Scotland between Aug 6, 2021, and April 19, 2022. Vaccine effectiveness peaked at 14-27 days after the second dose in both countries during both waves, and was significantly lower against symptomatic infection during the omicron-dominant period in Brazil (64·7% [95% CI 63·0-66·3]) and in Scotland (82·6% [80·6-84·5]), than it was in the delta-dominant period (80·7% [95% CI 77·8-83·3] in Brazil and 92·8% [85·7-96·4] in Scotland). Vaccine efficacy started to decline from 27 days after the second dose for both countries, reducing to 5·9% (95% CI 2·2-9·4) in Brazil and 50·6% (42·7-57·4) in Scotland at 98 days or more during the omicron-dominant period. In Brazil, protection against severe disease remained above 80% from 28 days after the second dose and was 82·7% (95% CI 68·8-90·4) at 98 days or more after receiving the second dose. INTERPRETATION We found waning vaccine protection of BNT162b2 against symptomatic COVID-19 infection among adolescents in Brazil and Scotland from 27 days after the second dose. However, protection against severe COVID-19 outcomes remained high at 98 days or more after the second dose in the omicron-dominant period. Booster doses for adolescents need to be considered. FUNDING UK Research and Innovation (Medical Research Council), Scottish Government, Health Data Research UK BREATHE Hub, Fiocruz, Fazer o Bem Faz Bem programme, Brazilian National Research Council, and Wellcome Trust. TRANSLATION For the Portuguese translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Pilar T V Florentino
- Centre of Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Brazil; Biomedical Science Institute, University of São Paulo, São Paulo, Brazil.
| | | | - Thiago Cerqueira-Silva
- LIB and LEITV Laboratories, Instituto Gonçalo Moniz, Oswaldo Cruz Foundation, Salvador, Brazil; Faculty of Medicine, Federal University of Bahia, Salvador, Brazil
| | | | - Vinicius de Araújo Oliveira
- Centre of Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Brazil
| | - Juracy B S Júnior
- Institute of Collective Health, Federal University of Bahia, Salvador, Brazil
| | - Flávia J O Alves
- Centre of Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Brazil
| | - Gerson O Penna
- Tropical Medicine Centre, University of Brasília, Fiocruz School of Government Brasília, Brasília, Brazil
| | | | - Viviane S Boaventura
- LIB and LEITV Laboratories, Instituto Gonçalo Moniz, Oswaldo Cruz Foundation, Salvador, Brazil; Faculty of Medicine, Federal University of Bahia, Salvador, Brazil
| | - Guilherme L Werneck
- Department of Epidemiology, Social Medicine Institute, State University of Rio de Janeiro, Rio de Janeiro, Brazil; Institute of Collective Health Studies, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Neil Pearce
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Colin McCowan
- School of Medicine, University of St Andrews, St Andrews, Scotland, UK
| | | | - Utkarsh Agrawal
- School of Medicine, University of St Andrews, St Andrews, Scotland, UK
| | - Zoe Grange
- Public Health Scotland, Glasgow, Scotland, UK
| | - Lewis D Ritchie
- Academic Primary Care, University of Aberdeen, Aberdeen, Scotland, UK
| | - Colin R Simpson
- School of Health, Wellington Faculty of Health, Victoria University of Wellington, Wellington, New Zealand
| | - Aziz Sheikh
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Mauricio L Barreto
- Centre of Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Brazil
| | - Igor Rudan
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Manoel Barral-Netto
- Centre of Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Brazil; LIB and LEITV Laboratories, Instituto Gonçalo Moniz, Oswaldo Cruz Foundation, Salvador, Brazil
| | - Enny S Paixão
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
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Millington T. An investigation into the effects and effectiveness of correlation network filtration methods with financial returns. PLoS One 2022; 17:e0273830. [PMID: 36070303 PMCID: PMC9451073 DOI: 10.1371/journal.pone.0273830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 08/17/2022] [Indexed: 11/18/2022] Open
Abstract
When studying financial markets, we often look at estimating a correlation matrix from asset returns. These tend to be noisy, with many more dimensions than samples, so often the resulting correlation matrix is filtered. Popular methods to do this include the minimum spanning tree, planar maximally filtered graph and the triangulated maximally filtered graph, which involve using the correlation network as the adjacency matrix of a graph and then using tools from graph theory. These assume the data fits some form of shape. We do not necessarily have a reason to believe that the data does fit into this shape, and there have been few empirical investigations comparing how the methods perform. In this paper we look at how the filtered networks are changed from the original networks using stock returns from the US, UK, German, Indian and Chinese markets, and at how these methods affect our ability to distinguish between datasets created from different correlation matrices using a graph embedding algorithm. We find that the relationship between the full and filtered networks depends on the data and the state of the market, and decreases as we increase the size of networks, and that the filtered networks do not provide an improvement in classification accuracy compared to the full networks.
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Affiliation(s)
- Tristan Millington
- Usher Institute, University of Edinburgh, NINE Bioquarter, Edinburgh, United Kingdom
- * E-mail:
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Abstract
In this paper we construct word co-occurrence networks from transcript data of controls and patients with potential Alzheimer’s disease using the ADReSS challenge dataset of spontaneous speech. We examine measures of the structure of these networks for significant differences, finding that networks from Alzheimer’s patients have a lower heterogeneity and centralization, but a higher edge density. We then use these measures, a network embedding method and some measures from the word frequency distribution to classify the transcripts into control or Alzheimer’s, and to estimate the cognitive test score of a participant based on the transcript. We find it is possible to distinguish between the AD and control networks on structure alone, achieving 66.7% accuracy on the test set, and to predict cognitive scores with a root mean squared error of 5.675. Using the network measures is more successful than using the network embedding method. However, if the networks are shuffled we find relatively few of the measures are different, indicating that word frequency drives many of the network properties. This observation is borne out by the classification experiments, where word frequency measures perform similarly to the network measures.
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Aoyama A, Tonsho M, Ng CY, Lee S, Millington T, Nadazdin O, Wain JC, Cosimi AB, Sachs DH, Smith RN, Colvin RB, Kawai T, Madsen JC, Benichou G, Allan JS. Long-term lung transplantation in nonhuman primates. Am J Transplant 2015; 15:1415-20. [PMID: 25772308 PMCID: PMC4564890 DOI: 10.1111/ajt.13130] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2014] [Revised: 10/27/2014] [Accepted: 11/16/2014] [Indexed: 01/25/2023]
Abstract
Despite advances in surgical technique and clinical care, lung transplantation still remains a short-term solution for the treatment of end-stage lung disease. To date, there has been limited experience in experimental lung transplantation using nonhuman primate models. Therefore, we have endeavored to develop a long-term, nonhuman primate model of orthotopic lung transplantation for the ultimate purpose of designing protocols to induce tolerance of lung grafts. Here, we report our initial results in developing this model and our observation that the nonhuman primate lung is particularly prone to rejection. This propensity toward rejection may be a consequence of 1) upregulated nonspecific inflammation, and 2) a larger number of pre-existing alloreactive memory T cells, leading to augmented deleterious immune responses. Our data show that triple-drug immunosuppression mimicking clinical practice is not sufficient to prevent acute rejection in nonhuman primate lung transplantation. The addition of horse-derived anti-thymocyte globulin and a monoclonal antibody to the IL-6 receptor allowed six out of six lung recipients to be free of rejection for over 120 days.
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Millington T. LindaNielsen. How to Motivate Adolescents—A guide for Parents, Teachers, and Counselors. New Jersey, U.S.A.: Prentice‐Hall. 1982, 194. $6.95. J Adolesc 1983. [DOI: 10.1016/s0140-1971(83)80044-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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