1
|
Nab L, Schaffer AL, Hulme W, DeVito NJ, Dillingham I, Wiedemann M, Andrews CD, Curtis H, Fisher L, Green A, Massey J, Walters CE, Higgins R, Cunningham C, Morley J, Mehrkar A, Hart L, Davy S, Evans D, Hickman G, Inglesby P, Morton CE, Smith RM, Ward T, O'Dwyer T, Maude S, Bridges L, Butler-Cole BFC, Stables CL, Stokes P, Bates C, Cockburn J, Hester F, Parry J, Bhaskaran K, Schultze A, Rentsch CT, Mathur R, Tomlinson LA, Williamson EJ, Smeeth L, Walker A, Bacon S, MacKenna B, Goldacre B. OpenSAFELY: A platform for analysing electronic health records designed for reproducible research. Pharmacoepidemiol Drug Saf 2024; 33:e5815. [PMID: 38783412 DOI: 10.1002/pds.5815] [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: 01/31/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024]
Abstract
Electronic health records (EHRs) and other administrative health data are increasingly used in research to generate evidence on the effectiveness, safety, and utilisation of medical products and services, and to inform public health guidance and policy. Reproducibility is a fundamental step for research credibility and promotes trust in evidence generated from EHRs. At present, ensuring research using EHRs is reproducible can be challenging for researchers. Research software platforms can provide technical solutions to enhance the reproducibility of research conducted using EHRs. In response to the COVID-19 pandemic, we developed the secure, transparent, analytic open-source software platform OpenSAFELY designed with reproducible research in mind. OpenSAFELY mitigates common barriers to reproducible research by: standardising key workflows around data preparation; removing barriers to code-sharing in secure analysis environments; enforcing public sharing of programming code and codelists; ensuring the same computational environment is used everywhere; integrating new and existing tools that encourage and enable the use of reproducible working practices; and providing an audit trail for all code that is run against the real data to increase transparency. This paper describes OpenSAFELY's reproducibility-by-design approach in detail.
Collapse
Affiliation(s)
- Linda Nab
- 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
| | - William Hulme
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Nicholas J DeVito
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Iain Dillingham
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Milan Wiedemann
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Colm D Andrews
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Helen Curtis
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Louis Fisher
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Amelia Green
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Jon Massey
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Caroline E Walters
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Rose Higgins
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Christine Cunningham
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Jessica Morley
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Amir Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Liam Hart
- 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
| | - David Evans
- 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
| | - Peter Inglesby
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Caroline E Morton
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Rebecca M Smith
- 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
| | - 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
| | - Lucy Bridges
- 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
| | - Catherine L Stables
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Pete Stokes
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | | | | | | | | | - Anna Schultze
- London School of Hygiene and Tropical Medicine, London, UK
| | | | - Rohini Mathur
- London School of Hygiene and Tropical Medicine, London, UK
| | | | | | - Liam Smeeth
- London School of Hygiene and Tropical Medicine, London, UK
| | - Alex Walker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Sebastian Bacon
- 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
| | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| |
Collapse
|
2
|
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). Lancet Reg Health Eur 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] [What about the content of this article? (0)] [Affiliation(s)] [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).
Collapse
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
| |
Collapse
|
3
|
Brown AD, Fisher L, Curtis HJ, Wiedemann M, Hulme WJ, Speed V, Hopcroft LEM, Cunningham C, Costello RE, Galloway JB, Russell MD, Bechman K, Kurt Z, Croker R, Wood C, Walker AJ, Schaffer AL, Bacon SCJ, Mehrkar A, Hickman G, Bates C, Cockburn J, Parry J, Hester F, Harper S, Goldacre B, MacKenna B. OpenSAFELY: The impact of COVID-19 on azathioprine, leflunomide and methotrexate monitoring, and factors associated with change in monitoring rate. Br J Clin Pharmacol 2024. [PMID: 38589944 DOI: 10.1111/bcp.16062] [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] [Received: 12/12/2023] [Revised: 03/04/2024] [Accepted: 03/05/2024] [Indexed: 04/10/2024] Open
Abstract
AIMS The COVID-19 pandemic created unprecedented pressure on healthcare services. This study investigates whether disease-modifying antirheumatic drug (DMARD) safety monitoring was affected during the COVID-19 pandemic. METHODS A population-based cohort study was conducted using the OpenSAFELY platform to access electronic health record data from 24.2 million patients registered at general practices using TPP's SystmOne software. Patients were included for further analysis if prescribed azathioprine, leflunomide or methotrexate between November 2019 and July 2022. Outcomes were assessed as monthly trends and variation between various sociodemographic and clinical groups for adherence with standard safety monitoring recommendations. RESULTS An acute increase in the rate of missed monitoring occurred across the study population (+12.4 percentage points) when lockdown measures were implemented in March 2020. This increase was more pronounced for some patient groups (70-79 year-olds: +13.7 percentage points; females: +12.8 percentage points), regions (North West: +17.0 percentage points), medications (leflunomide: +20.7 percentage points) and monitoring tests (blood pressure: +24.5 percentage points). Missed monitoring rates decreased substantially for all groups by July 2022. Consistent differences were observed in overall missed monitoring rates between several groups throughout the study. CONCLUSION DMARD monitoring rates temporarily deteriorated during the COVID-19 pandemic. Deterioration coincided with the onset of lockdown measures, with monitoring rates recovering rapidly as lockdown measures were eased. Differences observed in monitoring rates between medications, tests, regions and patient groups highlight opportunities to tackle potential inequalities in the provision or uptake of monitoring services. Further research should evaluate the causes of the differences identified between groups.
Collapse
Affiliation(s)
- Andrew D Brown
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - Louis Fisher
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - Helen J Curtis
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - Milan Wiedemann
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - William J Hulme
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - Victoria Speed
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - Lisa E M Hopcroft
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - Christine Cunningham
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | | | | | - Mark D Russell
- Centre for Rheumatic Diseases, King's College London, UK
| | - Katie Bechman
- Centre for Rheumatic Diseases, King's College London, UK
| | - Zeyneb Kurt
- Northumbria University, Newcastle upon Tyne, UK
| | - Richard Croker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - Chris Wood
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - Alex J Walker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - Andrea L Schaffer
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - Seb C J Bacon
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - Amir Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - George Hickman
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | | | | | | | | | | | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| | - Brian MacKenna
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
| |
Collapse
|
4
|
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. [PMID: 38531661 DOI: 10.1111/bcp.16030] [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] [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.
Collapse
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
| |
Collapse
|
5
|
Ainsworth B, Horwood J, Walter SR, Miller S, Chalder M, De Vocht F, Denison-Day J, Elwenspoek MMC, Curtis HJ, Bates C, Mehrkar A, Bacon S, Goldacre B, Craggs P, Amlôt R, Francis N, Little P, Macleod J, Moore M, Morton K, Rice C, Sterne J, Stuart B, Towler L, Willcox ML, Yardley L. Implementing Germ Defence digital behaviour change intervention via all primary care practices in England to reduce respiratory infections during the COVID-19 pandemic: an efficient cluster randomised controlled trial using the OpenSAFELY platform. Implement Sci 2023; 18:67. [PMID: 38049846 PMCID: PMC10694966 DOI: 10.1186/s13012-023-01321-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 11/15/2023] [Indexed: 12/06/2023] Open
Abstract
BACKGROUND Germ Defence ( www.germdefence.org ) is an evidence-based interactive website that promotes behaviour change for infection control within households. To maximise the potential of Germ Defence to effectively reduce the spread of COVID-19, the intervention needed to be implemented at scale rapidly. METHODS With NHS England approval, we conducted an efficient two-arm (1:1 ratio) cluster randomised controlled trial (RCT) to examine the effectiveness of randomising implementation of Germ Defence via general practitioner (GP) practices across England, UK, compared with usual care to disseminate Germ Defence to patients. GP practices randomised to the intervention arm (n = 3292) were emailed and asked to disseminate Germ Defence to all adult patients via mobile phone text, email or social media. Usual care arm GP practices (n = 3287) maintained standard management for the 4-month trial period and then asked to share Germ Defence with their adult patients. The primary outcome was the rate of GP presentations for respiratory tract infections (RTI) per patient. Secondary outcomes comprised rates of acute RTIs, confirmed COVID-19 diagnoses and suspected COVID-19 diagnoses, COVID-19 symptoms, gastrointestinal infection diagnoses, antibiotic usage and hospital admissions. The impact of the intervention on outcome rates was assessed using negative binomial regression modelling within the OpenSAFELY platform. The uptake of the intervention by GP practice and by patients was measured via website analytics. RESULTS Germ Defence was used 310,731 times. The average website satisfaction score was 7.52 (0-10 not at all to very satisfied, N = 9933). There was no evidence of a difference in the rate of RTIs between intervention and control practices (rate ratio (RR) 1.01, 95% CI 0.96, 1.06, p = 0.70). This was similar to all other eight health outcomes. Patient engagement within intervention arm practices ranged from 0 to 48% of a practice list. CONCLUSIONS While the RCT did not demonstrate a difference in health outcomes, we demonstrated that rapid large-scale implementation of a digital behavioural intervention is possible and can be evaluated with a novel efficient prospective RCT methodology analysing routinely collected patient data entirely within a trusted research environment. TRIAL REGISTRATION This trial was registered in the ISRCTN registry (14602359) on 12 August 2020.
Collapse
Affiliation(s)
- Ben Ainsworth
- School of Psychology, University of Southampton, Southampton, UK
- Department of Psychology, University of Bath, Bath, UK
| | - Jeremy Horwood
- NIHR Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK.
- Centre for Academic Primary Care (CAPC), Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK.
- NIHR Health Protection Research Unit (HPRU) in Behavioural Science and Evaluation at University of Bristol, Bristol, UK.
| | - Scott R Walter
- NIHR Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Sascha Miller
- School of Psychology, University of Southampton, Southampton, UK
| | - Melanie Chalder
- Centre for Academic Primary Care (CAPC), Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Frank De Vocht
- NIHR Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | | | - Martha M C Elwenspoek
- NIHR Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Helen J Curtis
- Nuffield Department of Primary Care Health Sciences, The Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK
| | | | - Amir Mehrkar
- Nuffield Department of Primary Care Health Sciences, The Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK
| | - Seb Bacon
- Nuffield Department of Primary Care Health Sciences, The Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK
| | - Ben Goldacre
- Nuffield Department of Primary Care Health Sciences, The Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK
| | - Pippa Craggs
- NIHR Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Richard Amlôt
- NIHR Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
- Behavioural Science and Insights Unit, UK Health Security Agency, London, UK
| | - Nick Francis
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Paul Little
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - John Macleod
- NIHR Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
- Centre for Academic Primary Care (CAPC), Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Michael Moore
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Kate Morton
- School of Psychology, University of Southampton, Southampton, UK
| | | | - Jonathan Sterne
- Centre for Academic Primary Care (CAPC), Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Beth Stuart
- Faculty of Medicine and Dentistry, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Lauren Towler
- School of Psychology, University of Southampton, Southampton, UK
| | | | - Lucy Yardley
- School of Psychology, University of Southampton, Southampton, UK
- NIHR Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
- Centre for Academic Primary Care (CAPC), Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- NIHR Health Protection Research Unit (HPRU) in Behavioural Science and Evaluation at University of Bristol, Bristol, UK
- School of Psychological Science, University of Bristol, Bristol, UK
| |
Collapse
|
6
|
Bardsley M, Loveridge P, Bednarska NG, Smith S, Morbey RA, Amirthalingam G, Elson WH, Bates C, de Lusignan S, Todkill D, Elliot AJ. The Epidemiology of Chickenpox in England, 2016-2022: An Observational Study Using General Practitioner Consultations. Viruses 2023; 15:2163. [PMID: 38005841 PMCID: PMC10674747 DOI: 10.3390/v15112163] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 11/26/2023] Open
Abstract
Chickenpox is a common childhood disease caused by varicella-zoster virus (VZV). VZV vaccination is not part of the UK childhood immunisation programme, but its potential inclusion is regularly assessed. It is therefore important to understand the ongoing burden of VZV in the community to inform vaccine policy decisions. General practitioner (GP) chickenpox consultations were studied from 1 September 2016 to 9 December 2022. Over the study period, the mean weekly chickenpox consultation rate per 100,000 population in England was 3.4, with a regular peak occurring between weeks 13 and 15. Overall, rates decreased over time, from a mean weekly rate of 5.5 in 2017 to 4.2 in 2019. The highest mean weekly rates were among children aged 1-4 years. There was no typical epidemic peak during the COVID-19 pandemic, but in 2022, rates were proportionally higher among children aged < 1 year old compared to pre-pandemic years. Chickenpox GP consultation rates decreased in England, continuing a longer-term decline in the community. The COVID-19 pandemic impacted rates, likely caused by the introduction of non-pharmaceutical interventions to prevent SARS-CoV-2 transmission. The lasting impact of the interruption of typical disease transmission remains to be seen, but it is important to monitor the chickenpox burden to inform decisions on vaccine programmes.
Collapse
Affiliation(s)
- Megan Bardsley
- Real-Time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham B2 4BH, UK; (M.B.); (P.L.); (N.G.B.); (S.S.); (R.A.M.); (D.T.)
| | - Paul Loveridge
- Real-Time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham B2 4BH, UK; (M.B.); (P.L.); (N.G.B.); (S.S.); (R.A.M.); (D.T.)
| | - Natalia G. Bednarska
- Real-Time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham B2 4BH, UK; (M.B.); (P.L.); (N.G.B.); (S.S.); (R.A.M.); (D.T.)
| | - Sue Smith
- Real-Time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham B2 4BH, UK; (M.B.); (P.L.); (N.G.B.); (S.S.); (R.A.M.); (D.T.)
| | - Roger A. Morbey
- Real-Time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham B2 4BH, UK; (M.B.); (P.L.); (N.G.B.); (S.S.); (R.A.M.); (D.T.)
| | - Gayatri Amirthalingam
- Immunisation and Vaccine Preventable Diseases Division, UK Health Security Agency, London NW9 5EQ, UK;
| | - William H. Elson
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6ED, UK; (W.H.E.); (S.d.L.)
| | | | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6ED, UK; (W.H.E.); (S.d.L.)
| | - Daniel Todkill
- Real-Time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham B2 4BH, UK; (M.B.); (P.L.); (N.G.B.); (S.S.); (R.A.M.); (D.T.)
| | - Alex J. Elliot
- Real-Time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham B2 4BH, UK; (M.B.); (P.L.); (N.G.B.); (S.S.); (R.A.M.); (D.T.)
| |
Collapse
|
7
|
Macdonald O, Green A, Walker A, Curtis H, Croker R, Brown A, Butler-Cole B, Andrews C, Massey J, Inglesby P, Morton C, Fisher L, Morley J, Mehrkar A, Bacon S, Davy S, Evans D, Dillingham I, Ward T, Hulme W, Bates C, Cockburn J, Parry J, Hester F, Harper S, O'Hanlon S, Eavis A, Jarvis R, Avramov D, Parkes N, Wood I, Goldacre B, Mackenna B. Impact of the COVID-19 pandemic on antipsychotic prescribing in individuals with autism, dementia, learning disability, serious mental illness or living in a care home: a federated analysis of 59 million patients' primary care records in situ using OpenSAFELY. BMJ Ment Health 2023; 26:e300775. [PMID: 37714668 DOI: 10.1136/bmjment-2023-300775] [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] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/07/2023] [Indexed: 09/17/2023]
Abstract
BACKGROUND The COVID-19 pandemic affected how care was delivered to vulnerable patients, such as those with dementia or learning disability. OBJECTIVE To explore whether this affected antipsychotic prescribing in at-risk populations. METHODS With the approval of NHS England, we completed a retrospective cohort study, using the OpenSAFELY platform to explore primary care data of 59 million patients. We identified patients in five at-risk groups: autism, dementia, learning disability, serious mental illness and care home residents. We calculated the monthly prevalence of antipsychotic prescribing in these groups, as well as the incidence of new prescriptions in each month. FINDINGS The average monthly rate of antipsychotic prescribing increased in dementia from 82.75 patients prescribed an antipsychotic per 1000 patients (95% CI 82.30 to 83.19) in January-March 2019 to 90.1 (95% CI 89.68 to 90.60) in October-December 2021 and from 154.61 (95% CI 153.79 to 155.43) to 166.95 (95% CI 166.23 to 167.67) in care homes. There were notable spikes in the rate of new prescriptions issued to patients with dementia and in care homes. In learning disability and autism groups, the rate of prescribing per 1000 decreased from 122.97 (95% CI 122.29 to 123.66) to 119.29 (95% CI 118.68 to 119.91) and from 54.91 (95% CI 54.52 to 55.29) to 51.04 (95% CI 50.74 to 51.35), respectively. CONCLUSION AND IMPLICATIONS We observed a spike in antipsychotic prescribing in the dementia and care home groups, which correlated with lockdowns and was likely due to prescribing of antipsychotics for palliative care. We observed gradual increases in antipsychotic use in dementia and care home patients and decreases in their use in patients with learning disability or autism.
Collapse
Affiliation(s)
- Orla Macdonald
- Pharmacy, Oxford Health NHS Foundation Trust, Oxford, UK
| | - Amelia Green
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Alex Walker
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Helen Curtis
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Richard Croker
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Andrew Brown
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Ben Butler-Cole
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Colm Andrews
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Jon Massey
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Peter Inglesby
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Caroline Morton
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Louis Fisher
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Jessica Morley
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Amir Mehrkar
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Sebastian Bacon
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Simon Davy
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - David Evans
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Iain Dillingham
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Tom Ward
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - William Hulme
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | | | | | | | | | | | | | | | | | | | | | | | - Ben Goldacre
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Brian Mackenna
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| |
Collapse
|
8
|
Fisher L, Curtis HJ, Croker R, Wiedemann M, Speed V, Wood C, Brown A, Hopcroft LEM, Higgins R, Massey J, Inglesby P, Morton CE, Walker AJ, Morley J, Mehrkar A, Bacon S, Hickman G, Macdonald O, Lewis T, Wood M, Myers M, Samuel M, Conibere R, Baqir W, Sood H, Drury C, Collison K, Bates C, Evans D, Dillingham I, Ward T, Davy S, Smith RM, Hulme W, Green A, Parry J, Hester F, Harper S, Cockburn J, O'Hanlon S, Eavis A, Jarvis R, Avramov D, Griffiths P, Fowles A, Parkes N, MacKenna B, Goldacre B. Eleven key measures for monitoring general practice clinical activity during COVID-19: A retrospective cohort study using 48 million adults' primary care records in England through OpenSAFELY. eLife 2023; 12:e84673. [PMID: 37498081 PMCID: PMC10374277 DOI: 10.7554/elife.84673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 07/06/2023] [Indexed: 07/28/2023] Open
Abstract
Background The COVID-19 pandemic has had a significant impact on delivery of NHS care. We have developed the OpenSAFELY Service Restoration Observatory (SRO) to develop key measures of primary care activity and describe the trends in these measures throughout the COVID-19 pandemic. Methods With the approval of NHS England, we developed an open source software framework for data management and analysis to describe trends and variation in clinical activity across primary care electronic health record (EHR) data on 48 million adults.We developed SNOMED-CT codelists for key measures of primary care clinical activity such as blood pressure monitoring and asthma reviews, selected by an expert clinical advisory group and conducted a population cohort-based study to describe trends and variation in these measures January 2019-December 2021, and pragmatically classified their level of recovery one year into the pandemic using the percentage change in the median practice level rate. Results We produced 11 measures reflective of clinical activity in general practice. A substantial drop in activity was observed in all measures at the outset of the COVID-19 pandemic. By April 2021, the median rate had recovered to within 15% of the median rate in April 2019 in six measures. The remaining measures showed a sustained drop, ranging from a 18.5% reduction in medication reviews to a 42.0% reduction in blood pressure monitoring. Three measures continued to show a sustained drop by December 2021. Conclusions The COVID-19 pandemic was associated with a substantial change in primary care activity across the measures we developed, with recovery in most measures. We delivered an open source software framework to describe trends and variation in clinical activity across an unprecedented scale of primary care data. We will continue to expand the set of key measures to be routinely monitored using our publicly available NHS OpenSAFELY SRO dashboards with near real-time data. Funding This research used data assets made available as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20058).The OpenSAFELY Platform is supported by grants from the Wellcome Trust (222097/Z/20/Z); MRC (MR/V015757/1, MC_PC-20059, MR/W016729/1); NIHR (NIHR135559, COV-LT2-0073), and Health Data Research UK (HDRUK2021.000, 2021.0157).
Collapse
Affiliation(s)
- Louis Fisher
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Helen J Curtis
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Richard Croker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Milan Wiedemann
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Victoria Speed
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Christopher Wood
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Andrew Brown
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Lisa E M Hopcroft
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rose Higgins
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Jon Massey
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Peter Inglesby
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Caroline E Morton
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Alex J Walker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Jessica Morley
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Amir Mehrkar
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Seb Bacon
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - George Hickman
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Orla Macdonald
- Oxford Health Foundation Trust, Warneford Hospital, Oxford, United Kingdom
| | - Tom Lewis
- Royal Devon University Healthcare NHS Foundation Trust, Barnstaple, United Kingdom
| | | | - Martin Myers
- Lancashire Teaching Hospitals NHS Foundation Trust, Chorley, United Kingdom
| | - Miriam Samuel
- Queen Mary University of London, London, United Kingdom
| | | | | | | | - Charles Drury
- Herefordshire and Worcestershire Health and Care NHS Trust, Worcester, United Kingdom
| | | | | | - David Evans
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Iain Dillingham
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Tom Ward
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Simon Davy
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rebecca M Smith
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - William Hulme
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Amelia Green
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | | | | | | | | | | | | | | | | | | | | | - Brian MacKenna
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
- NHS England, London, United Kingdom
| | - Ben Goldacre
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
9
|
Nadarajah R, Wu J, Hogg D, Raveendra K, Nakao YM, Nakao K, Arbel R, Haim M, Zahger D, Parry J, Bates C, Cowan C, Gale CP. Prediction of short-term atrial fibrillation risk using primary care electronic health records. Heart 2023; 109:1072-1079. [PMID: 36759177 PMCID: PMC10359547 DOI: 10.1136/heartjnl-2022-322076] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [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: 11/01/2022] [Accepted: 01/26/2023] [Indexed: 02/11/2023] Open
Abstract
OBJECTIVE Atrial fibrillation (AF) screening by age achieves a low yield and misses younger individuals. We aimed to develop an algorithm in nationwide routinely collected primary care data to predict the risk of incident AF within 6 months (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)). METHODS We used primary care electronic health record data from individuals aged ≥30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between 2 January 1998 and 30 November 2018, randomly divided into training (80%) and testing (20%) datasets. We trained a random forest classifier using age, sex, ethnicity and comorbidities. Prediction performance was evaluated in the testing dataset with internal bootstrap validation with 200 samples, and compared against the CHA2DS2-VASc (Congestive heart failure, Hypertension, Age >75 (2 points), Stroke/transient ischaemic attack/thromboembolism (2 points), Vascular disease, Age 65-74, Sex category) and C2HEST (Coronary artery disease/Chronic obstructive pulmonary disease (1 point each), Hypertension, Elderly (age ≥75, 2 points), Systolic heart failure, Thyroid disease (hyperthyroidism)) scores. Cox proportional hazard models with competing risk of death were fit for incident longer-term AF between higher and lower FIND-AF-predicted risk. RESULTS Of 2 081 139 individuals in the cohort, 7386 developed AF within 6 months. FIND-AF could be applied to all records. In the testing dataset (n=416 228), discrimination performance was strongest for FIND-AF (area under the receiver operating characteristic curve 0.824, 95% CI 0.814 to 0.834) compared with CHA2DS2-VASc (0.784, 0.773 to 0.794) and C2HEST (0.757, 0.744 to 0.770), and robust by sex and ethnic group. The higher predicted risk cohort, compared with lower predicted risk, had a 20-fold higher 6-month incidence rate for AF and higher long-term hazard for AF (HR 8.75, 95% CI 8.44 to 9.06). CONCLUSIONS FIND-AF, a machine learning algorithm applicable at scale in routinely collected primary care data, identifies people at higher risk of short-term AF.
Collapse
Affiliation(s)
- Ramesh Nadarajah
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Jianhua Wu
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Department of Dentistry, University of Leeds, Leeds, UK
| | - David Hogg
- School of Computing, University of Leeds, Leeds, UK
| | | | - Yoko M Nakao
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Kazuhiro Nakao
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Ronen Arbel
- Maximizing Health Outcomes Research Lab, Sapir College, Hof Ashkelon, Israel
- Community Medical Services Division, Clalit Health Services, Tel Aviv, Israel
| | - Moti Haim
- Department of Cardiology, Soroka University Medical Center, Beer Sheva, Israel
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Doron Zahger
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
- Cardiology, Soroka Medical Center, Beer Sheva, Israel
| | | | | | | | - Chris P Gale
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Cardiology, Leeds General Infirmary, Leeds, UK
| |
Collapse
|
10
|
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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [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.
Collapse
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
| |
Collapse
|
11
|
Hyatt A, Chan B, Moodie R, Varlow M, Bates C, Foliaki S, Palafox N, Burich S, Aranda S. Strengthening cancer control in the South Pacific through coalition-building: a co-design framework. Lancet Reg Health West Pac 2023; 33:100681. [PMID: 37181526 PMCID: PMC10166990 DOI: 10.1016/j.lanwpc.2022.100681] [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] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 12/12/2022] [Accepted: 12/20/2022] [Indexed: 01/15/2023]
Abstract
Background Cancer is a significant problem for the South Pacific region due to a range of complex health challenges. Currently gaps in diagnosis, treatment and palliative care are significant, and while governmental commitment is strong, economic constrains limit health system strengthening. Alliances have been successful in strengthening non-communicable disease and cancer control policy and services in resource constrained settings. A regional coalition approach has therefore been recommended as an effective solution to addressing many of the challenges for cancer control in the South Pacific. However, evidence regarding the effective mechanisms for development of alliances or coalitions is scarce. This study aimed to 1) create a Coalition Development Framework; 2) assess the use of the Framework in practice to co-design a South Pacific Coalition. Methods Creation of the Coalition Development Framework commenced with a scoping review and content analysis of existing literature. Synthesis of key elements formed an evidence-informed step-by-step guide for coalition-building. Application of the Framework comprised consultation and iterative discussions with key South Pacific cancer control stakeholders in Fiji, New Caledonia, Papua New Guinea, Samoa and Tonga. Concurrent evaluation of the Framework utilising Theory of Change (ToC) and qualitative analysis of stakeholder consultations was undertaken. Findings The finalised Coalition Development Framework comprised four phases with associated actions and deliverables: engagement, discovery, unification, action and monitoring. Application of the Framework in the South Pacific identified overwhelming support for a Cancer Control Coalition through 35 stakeholder consultations. Framework phases enabled stakeholders to confirm coalition design and purpose, strategic imperatives, structure, local foundations, barriers and facilitators, and priorities for action. ToC and thematic consultation analysis confirmed the Framework to be an effective mechanism to drive engagement, unification and action in alliance-building. Interpretation A Coalition to drive cancer control has significant support among key Pacific stakeholders, and establishment can now be commenced. Importantly results confirm the effective application of the Coalition Development Framework in an applied setting. If momentum is continued, and a regional South Pacific Coalition established, the benefits in reducing the burden of cancer within the region will be substantial. Funding This work was completed for a Masters of Public Health project. Cancer Council Australia provided project funding.
Collapse
Affiliation(s)
- Amelia Hyatt
- Cancer Control Policy, Cancer Council Australia, Sydney, NSW, Australia
- Department of Health Services Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | | | - Rob Moodie
- School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Megan Varlow
- Cancer Control Policy, Cancer Council Australia, Sydney, NSW, Australia
| | - Chris Bates
- Nossal Institute for Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Sunia Foliaki
- Research Centre for Hauora and Health, Massey University-Wellington Campus, Wellington, New Zealand
| | - Neal Palafox
- John A. Burns School of Medicine, University of Hawaii, University of Hawaii Cancer Center, Honolulu, United States
| | | | - Sanchia Aranda
- Department of Health Services Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Department of Nursing, University of Melbourne, Melbourne, VIC, Australia
| |
Collapse
|
12
|
Sohal K, Mason D, Birkinshaw J, West J, McEachan RR, Elshehaly M, Cooper D, Shore R, McCooe M, Lawton T, Mon-Williams M, Sheldon T, Bates C, Wood M, Wright J. Connected Bradford: a Whole System Data Linkage Accelerator. Wellcome Open Res 2022; 7:26. [PMID: 36466951 PMCID: PMC9682213 DOI: 10.12688/wellcomeopenres.17526.2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/26/2022] [Indexed: 11/09/2022] Open
Abstract
The richness of linked population data provides exciting opportunities to understand local health needs, identify and predict those in most need of support and evaluate health interventions. There has been extensive investment to unlock the potential of clinical data for health research in the UK. However, most of the determinants of our health are social, economic, education, environmental, housing, food systems and are influenced by local authorities. The Connected Bradford Whole System Data Linkage Accelerator was set up to link health, education, social care, environmental and other local government data to drive learning health systems, prevention and population health management. Data spanning a period of over forty years has been linked for 800,000 individuals using the pseudonymised NHS number and other data variables. This prospective data collection captures near real time activity. This paper describes the dataset and our Connected Bradford Whole System Data Accelerator Framework that covers public engagement; practitioner and policy integration; legal and ethical approvals; information governance; technicalities of data linkage; data curation and guardianship; data validity and visualisation.
Collapse
Affiliation(s)
- Kuldeep Sohal
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - John Birkinshaw
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - Jane West
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - Rosemary R.C. McEachan
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - Mai Elshehaly
- Department of Computer Science, University of Bradford, Bradford, BD7 1DP, UK
| | - Duncan Cooper
- Public Health, Bradford Metropolitan District Council, Bradford, BD1 1HX, UK
| | - Rob Shore
- Public Health, Bradford Metropolitan District Council, Bradford, BD1 1HX, UK
| | - Michael McCooe
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - Tom Lawton
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | | | - Trevor Sheldon
- Institute of Population Health Sciences, Queen Mary University of London, London, E1 4NS, UK
| | - Chris Bates
- The Phoenix Partnership, Leeds, LS18 5PX, UK
| | - Megan Wood
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| |
Collapse
|
13
|
De B, Andres G, Bates C, Staren E, Kutscher E, Brooks D, Buscema J, Gin R, Thaker N. Total Cost of Cancer Care: Evaluating Health System Expenditures through Eight Performance Periods of the Oncology Care Model. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.524] [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]
|
14
|
Grint DJ, Wing K, Houlihan C, Gibbs HP, Evans SJW, Williamson E, McDonald HI, Bhaskaran K, Evans D, Walker AJ, Hickman G, Nightingale E, Schultze A, Rentsch CT, Bates C, Cockburn J, Curtis HJ, Morton CE, Bacon S, Davy S, Wong AYS, Mehrkar A, Tomlinson L, Douglas IJ, Mathur R, MacKenna B, Ingelsby P, Croker R, Parry J, Hester F, Harper S, DeVito NJ, Hulme W, Tazare J, Smeeth L, Goldacre B, Eggo RM. Severity of Severe Acute Respiratory System Coronavirus 2 (SARS-CoV-2) Alpha Variant (B.1.1.7) in England. Clin Infect Dis 2022; 75:e1120-e1127. [PMID: 34487522 PMCID: PMC8522415 DOI: 10.1093/cid/ciab754] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) alpha variant (B.1.1.7) is associated with higher transmissibility than wild-type virus, becoming the dominant variant in England by January 2021. We aimed to describe the severity of the alpha variant in terms of the pathway of disease from testing positive to hospital admission and death. METHODS With the approval of NHS England, we linked individual-level data from primary care with SARS-CoV-2 community testing, hospital admission, and Office for National Statistics all-cause death data. We used testing data with S-gene target failure as a proxy for distinguishing alpha and wild-type cases, and stratified Cox proportional hazards regression to compare the relative severity of alpha cases with wild-type diagnosed from 16 November 2020 to 11 January 2021. RESULTS Using data from 185 234 people who tested positive for SARS-CoV-2 in the community (alpha = 93 153; wild-type = 92 081), in fully adjusted analysis accounting for individual-level demographics and comorbidities as well as regional variation in infection incidence, we found alpha associated with 73% higher hazards of all-cause death (adjusted hazard ratio [aHR]: 1.73; 95% confidence interval [CI]: 1.41-2.13; P < .0001) and 62% higher hazards of hospital admission (1.62; 1.48-1.78; P < .0001) compared with wild-type virus. Among patients already admitted to the intensive care unit, the association between alpha and increased all-cause mortality was smaller and the CI included the null (aHR: 1.20; 95% CI: .74-1.95; P = .45). CONCLUSIONS The SARS-CoV-2 alpha variant is associated with an increased risk of both hospitalization and mortality than wild-type virus.
Collapse
Affiliation(s)
- Daniel J Grint
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Kevin Wing
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Catherine Houlihan
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Hamish P Gibbs
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Stephen J W Evans
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Elizabeth Williamson
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Helen I McDonald
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Krishnan Bhaskaran
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - George Hickman
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Emily Nightingale
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Anna Schultze
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Christopher T Rentsch
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | | | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Sebastian Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Simon Davy
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Angel Y S Wong
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Laurie Tomlinson
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Ian J Douglas
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Rohini Mathur
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Peter Ingelsby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | | | | | - Nicholas J DeVito
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Will Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - John Tazare
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Liam Smeeth
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rosalind M Eggo
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| |
Collapse
|
15
|
Hulme WJ, Williamson EJ, Green ACA, Bhaskaran K, McDonald HI, Rentsch CT, Schultze A, Tazare J, Curtis HJ, Walker AJ, Tomlinson LA, Palmer T, Horne EMF, MacKenna B, Morton CE, Mehrkar A, Morley J, Fisher L, Bacon SCJ, Evans D, Inglesby P, Hickman G, Davy S, Ward T, Croker R, Eggo RM, Wong AYS, Mathur R, Wing K, Forbes H, Grint DJ, Douglas IJ, Evans SJW, Smeeth L, Bates C, Cockburn J, Parry J, Hester F, Harper S, Sterne JAC, Hernán MA, Goldacre B. Comparative effectiveness of ChAdOx1 versus BNT162b2 covid-19 vaccines in health and social care workers in England: cohort study using OpenSAFELY. BMJ 2022; 378:e068946. [PMID: 35858680 PMCID: PMC9295078 DOI: 10.1136/bmj-2021-068946] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/11/2022] [Indexed: 12/17/2022]
Abstract
OBJECTIVE To compare the effectiveness of the BNT162b2 mRNA (Pfizer-BioNTech) and the ChAdOx1 (Oxford-AstraZeneca) covid-19 vaccines against infection and covid-19 disease in health and social care workers. DESIGN Cohort study, emulating a comparative effectiveness trial, on behalf of NHS England. SETTING Linked primary care, hospital, and covid-19 surveillance records available within the OpenSAFELY-TPP research platform, covering a period when the SARS-CoV-2 Alpha variant was dominant. PARTICIPANTS 317 341 health and social care workers vaccinated between 4 January and 28 February 2021, registered with a general practice using the TPP SystmOne clinical information system in England, and not clinically extremely vulnerable. INTERVENTIONS Vaccination with either BNT162b2 or ChAdOx1 administered as part of the national covid-19 vaccine roll-out. MAIN OUTCOME MEASURES Recorded SARS-CoV-2 positive test, or covid-19 related attendance at an accident and emergency (A&E) department or hospital admission occurring within 20 weeks of receipt of the first vaccine dose. RESULTS Over the duration of 118 771 person-years of follow-up there were 6962 positive SARS-CoV-2 tests, 282 covid-19 related A&E attendances, and 166 covid-19 related hospital admissions. The cumulative incidence of each outcome was similar for both vaccines during the first 20 weeks after vaccination. The cumulative incidence of recorded SARS-CoV-2 infection 20 weeks after first-dose vaccination with BNT162b2 was 21.7 per 1000 people (95% confidence interval 20.9 to 22.4) and with ChAdOx1 was 23.7 (21.8 to 25.6), representing a difference of 2.04 per 1000 people (0.04 to 4.04). The difference in the cumulative incidence per 1000 people of covid-19 related A&E attendance at 20 weeks was 0.06 per 1000 people (95% CI -0.31 to 0.43). For covid-19 related hospital admission, this difference was 0.11 per 1000 people (-0.22 to 0.44). CONCLUSIONS In this cohort of healthcare workers where we would not anticipate vaccine type to be related to health status, we found no substantial differences in the incidence of SARS-CoV-2 infection or covid-19 disease up to 20 weeks after vaccination. Incidence dropped sharply at 3-4 weeks after vaccination, and there were few covid-19 related hospital attendance and admission events after this period. This is in line with expected onset of vaccine induced immunity and suggests strong protection against Alpha variant covid-19 disease for both vaccines in this relatively young and healthy population of healthcare workers.
Collapse
Affiliation(s)
- William J Hulme
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | | | - Amelia C A Green
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | | | - Helen I McDonald
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | - Anna Schultze
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - John Tazare
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Helen J Curtis
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Alex J Walker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | | | - Tom Palmer
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | - Elsie M F Horne
- Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
- NIHR Bristol, Biomedical Research Centre, Bristol BS8 2BN, UK
| | - Brian MacKenna
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Caroline E Morton
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Amir Mehrkar
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Jessica Morley
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Louis Fisher
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Sebastian C J Bacon
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - David Evans
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Peter Inglesby
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - George Hickman
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Simon Davy
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Tom Ward
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Richard Croker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Rosalind M Eggo
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Angel Y S Wong
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Rohini Mathur
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Kevin Wing
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Harriet Forbes
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Daniel J Grint
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Ian J Douglas
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | - Liam Smeeth
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Chris Bates
- TPP, TPP House, Horsforth, Leeds LS18 5PX, UK
| | | | - John Parry
- TPP, TPP House, Horsforth, Leeds LS18 5PX, UK
| | | | - Sam Harper
- TPP, TPP House, Horsforth, Leeds LS18 5PX, UK
| | - Jonathan A C Sterne
- Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
- NIHR Bristol, Biomedical Research Centre, Bristol BS8 2BN, UK
- Health Data Research UK South West
| | - Miguel A Hernán
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Ben Goldacre
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| |
Collapse
|
16
|
Green A, Curtis H, Hulme W, Williamson E, McDonald H, Bhaskaran K, Rentsch C, Schultze A, MacKenna B, Mahalingasivam V, Tomlinson L, Walker A, Fisher L, Massey J, Andrews C, Hopcroft L, Morton C, Croker R, Morley J, Mehrkar A, Bacon S, Evans D, Inglesby P, Hickman G, Ward T, Davy S, Mathur R, Tazare J, Eggo R, Wing K, Wong A, Forbes H, Bates C, Cockburn J, Parry J, Hester F, Harper S, Douglas I, Evans S, Smeeth L, Goldacre B. Describing the population experiencing COVID-19 vaccine breakthrough following second vaccination in England: a cohort study from OpenSAFELY. BMC Med 2022; 20:243. [PMID: 35791013 PMCID: PMC9255436 DOI: 10.1186/s12916-022-02422-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 05/30/2022] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND While the vaccines against COVID-19 are highly effective, COVID-19 vaccine breakthrough is possible despite being fully vaccinated. With SARS-CoV-2 variants still circulating, describing the characteristics of individuals who have experienced COVID-19 vaccine breakthroughs could be hugely important in helping to determine who may be at greatest risk. METHODS With the approval of NHS England, we conducted a retrospective cohort study using routine clinical data from the OpenSAFELY-TPP database of fully vaccinated individuals, linked to secondary care and death registry data and described the characteristics of those experiencing COVID-19 vaccine breakthroughs. RESULTS As of 1st November 2021, a total of 15,501,550 individuals were identified as being fully vaccinated against COVID-19, with a median follow-up time of 149 days (IQR: 107-179). From within this population, a total of 579,780 (<4%) individuals reported a positive SARS-CoV-2 test. For every 1000 years of patient follow-up time, the corresponding incidence rate (IR) was 98.06 (95% CI 97.93-98.19). There were 28,580 COVID-19-related hospital admissions, 1980 COVID-19-related critical care admissions and 6435 COVID-19-related deaths; corresponding IRs 4.77 (95% CI 4.74-4.80), 0.33 (95% CI 0.32-0.34) and 1.07 (95% CI 1.06-1.09), respectively. The highest rates of breakthrough COVID-19 were seen in those in care homes and in patients with chronic kidney disease, dialysis, transplant, haematological malignancy or who were immunocompromised. CONCLUSIONS While the majority of COVID-19 vaccine breakthrough cases in England were mild, some differences in rates of breakthrough cases have been identified in several clinical groups. While it is important to note that these findings are simply descriptive and cannot be used to answer why certain groups have higher rates of COVID-19 breakthrough than others, the emergence of the Omicron variant of COVID-19 coupled with the number of positive SARS-CoV-2 tests still occurring is concerning and as numbers of fully vaccinated (and boosted) individuals increases and as follow-up time lengthens, so too will the number of COVID-19 breakthrough cases. Additional analyses, to assess vaccine waning and rates of breakthrough COVID-19 between different variants, aimed at identifying individuals at higher risk, are needed.
Collapse
Affiliation(s)
- Amelia Green
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Helen Curtis
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - William Hulme
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Elizabeth Williamson
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Helen McDonald
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Krishnan Bhaskaran
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Christopher Rentsch
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Anna Schultze
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Brian MacKenna
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | | | - Laurie Tomlinson
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Alex Walker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Louis Fisher
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Jon Massey
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Colm Andrews
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Lisa Hopcroft
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Caroline Morton
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Richard Croker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Jessica Morley
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Amir Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Seb Bacon
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - David Evans
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Peter Inglesby
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - George Hickman
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Tom Ward
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Simon Davy
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Rohini Mathur
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - John Tazare
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Rosalind Eggo
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Kevin Wing
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Angel Wong
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Harriet Forbes
- 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
| | - Frank Hester
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - Sam Harper
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - Ian Douglas
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Stephen Evans
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Liam Smeeth
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK.
| |
Collapse
|
17
|
Wong AY, Tomlinson L, Brown JP, Elson W, Walker AJ, Schultze A, Morton CE, Evans D, Inglesby P, MacKenna B, Bhaskaran K, Rentsch CT, Powell E, Williamson E, Croker R, Bacon S, Hulme W, Bates C, Curtis HJ, Mehrkar A, Cockburn J, McDonald HI, Mathur R, Wing K, Forbes H, Eggo RM, Evans SJ, Smeeth L, Goldacre B, Douglas IJ. Association between oral anticoagulants and COVID-19-related outcomes: a population-based cohort study. Br J Gen Pract 2022; 72:e456-e463. [PMID: 35440465 PMCID: PMC9037187 DOI: 10.3399/bjgp.2021.0689] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 02/06/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Early evidence has shown that anticoagulant reduces the risk of thrombotic events in those infected with COVID-19. However, evidence of the role of routinely prescribed oral anticoagulants (OACs) in COVID-19 outcomes is limited. AIM To investigate the association between OACs and COVID-19 outcomes in those with atrial fibrillation and a CHA2DS2-VASc score of 2. DESIGN AND SETTING On behalf of NHS England, a population-based cohort study was conducted. METHOD The study used primary care data and pseudonymously-linked SARS-CoV-2 antigen testing data, hospital admissions, and death records from England. Cox regression was used to estimate hazard ratios (HRs) for COVID-19 outcomes comparing people with current OAC use versus non-use, accounting for age, sex, comorbidities, other medications, deprivation, and general practice. RESULTS Of 71 103 people with atrial fibrillation and a CHA2DS2-VASc score of 2, there were 52 832 current OAC users and 18 271 non-users. No difference in risk of being tested for SARS-CoV-2 was associated with current use (adjusted HR [aHR] 0.99, 95% confidence interval [CI] = 0.95 to 1.04) versus non-use. A lower risk of testing positive for SARS-CoV-2 (aHR 0.77, 95% CI = 0.63 to 0.95) and a marginally lower risk of COVID-19-related death (aHR, 0.74, 95% CI = 0.53 to 1.04) were associated with current use versus non-use. CONCLUSION Among those at low baseline stroke risk, people receiving OACs had a lower risk of testing positive for SARS-CoV-2 and severe COVID-19 outcomes than non-users; this might be explained by a causal effect of OACs in preventing severe COVID-19 outcomes or unmeasured confounding, including more cautious behaviours leading to reduced infection risk.
Collapse
Affiliation(s)
- Angel Ys Wong
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Laurie Tomlinson
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Jeremy P Brown
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - William Elson
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Anna Schultze
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Peter Inglesby
- 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
| | - Krishnan Bhaskaran
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Christopher T Rentsch
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Emma Powell
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Elizabeth Williamson
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Seb Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - William Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | | | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | | | - Helen I McDonald
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London and NIHR Health Protection Research Unit (HPRU) in Immunisation, London School of Hygiene and Tropical Medicine, London
| | - Rohini Mathur
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Kevin Wing
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | | | - Rosalind M Eggo
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Stephen Jw Evans
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Liam Smeeth
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London and NIHR Health Protection Research Unit (HPRU) in Immunisation, London School of Hygiene and Tropical Medicine, London
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Ian J Douglas
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| |
Collapse
|
18
|
MacKenna B, Kennedy NA, Mehrkar A, Rowan A, Galloway J, Matthewman J, Mansfield KE, Bechman K, Yates M, Brown J, Schultze A, Norton S, Walker AJ, Morton CE, Harrison D, Bhaskaran K, Rentsch CT, Williamson E, Croker R, Bacon S, Hickman G, Ward T, Davy S, Green A, Fisher L, Hulme W, Bates C, Curtis HJ, Tazare J, Eggo RM, Evans D, Inglesby P, Cockburn J, McDonald HI, Tomlinson LA, Mathur R, Wong AYS, Forbes H, Parry J, Hester F, Harper S, Douglas IJ, Smeeth L, Lees CW, Evans SJW, Goldacre B, Smith CH, Langan SM. Risk of severe COVID-19 outcomes associated with immune-mediated inflammatory diseases and immune-modifying therapies: a nationwide cohort study in the OpenSAFELY platform. Lancet Rheumatol 2022; 4:e490-e506. [PMID: 35698725 PMCID: PMC9179144 DOI: 10.1016/s2665-9913(22)00098-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Background The risk of severe COVID-19 outcomes in people with immune-mediated inflammatory diseases and on immune-modifying drugs might not be fully mediated by comorbidities and might vary by factors such as ethnicity. We aimed to assess the risk of severe COVID-19 in adults with immune-mediated inflammatory diseases and in those on immune-modifying therapies. Methods We did a cohort study, using OpenSAFELY (an analytics platform for electronic health records) and TPP (a software provider for general practitioners), analysing routinely collected primary care data linked to hospital admission, death, and previously unavailable hospital prescription data. We included people aged 18 years or older on March 1, 2020, who were registered with TPP practices with at least 12 months of primary care records before March, 2020. We used Cox regression (adjusting for confounders and mediators) to estimate hazard ratios (HRs) comparing the risk of COVID-19-related death, critical care admission or death, and hospital admission (from March 1 to Sept 30, 2020) in people with immune-mediated inflammatory diseases compared with the general population, and in people with immune-mediated inflammatory diseases on targeted immune-modifying drugs (eg, biologics) compared with those on standard systemic treatment (eg, methotrexate). Findings We identified 17 672 065 adults; 1 163 438 adults (640 164 [55·0%] women and 523 274 [45·0%] men, and 827 457 [71·1%] of White ethnicity) had immune-mediated inflammatory diseases, and 16 508 627 people (8 215 020 [49·8%] women and 8 293 607 [50·2%] men, and 10 614 096 [64·3%] of White ethnicity) were included as the general population. Of 1 163 438 adults with immune-mediated inflammatory diseases, 19 119 (1·6%) received targeted immune-modifying therapy and 181 694 (15·6%) received standard systemic therapy. Compared with the general population, adults with immune-mediated inflammatory diseases had an increased risk of COVID-19-related death after adjusting for confounders (age, sex, deprivation, and smoking status; HR 1·23, 95% CI 1·20-1·27) and further adjusting for mediators (body-mass index [BMI], cardiovascular disease, diabetes, and current glucocorticoid use; 1·15, 1·11-1·18). Adults with immune-mediated inflammatory diseases also had an increased risk of COVID-19-related critical care admission or death (confounder-adjusted HR 1·24, 95% CI 1·21-1·28; mediator-adjusted 1·16, 1·12-1·19) and hospital admission (confounder-adjusted 1·32, 1·29-1·35; mediator-adjusted 1·20, 1·17-1·23). In post-hoc analyses, the risk of severe COVID-19 outcomes in people with immune-mediated inflammatory diseases was higher in non-White ethnic groups than in White ethnic groups (as it was in the general population). We saw no evidence of increased COVID-19-related death in adults on targeted, compared with those on standard systemic, therapy after adjusting for confounders (age, sex, deprivation, BMI, immune-mediated inflammatory diseases [bowel, joint, and skin], cardiovascular disease, cancer [excluding non-melanoma skin cancer], stroke, and diabetes (HR 1·03, 95% CI 0·80-1·33), and after additionally adjusting for current glucocorticoid use (1·01, 0·78-1·30). There was no evidence of increased COVID-19-related death in adults prescribed tumour necrosis factor inhibitors, interleukin (IL)-12/IL‑23 inhibitors, IL-17 inhibitors, IL-6 inhibitors, or Janus kinase inhibitors compared with those on standard systemic therapy. Rituximab was associated with increased COVID-19-related death (HR 1·68, 95% CI 1·11-2·56), with some attenuation after excluding people with haematological malignancies or organ transplants (1·54, 0·95-2·49). Interpretation COVID-19 deaths and hospital admissions were higher in people with immune-mediated inflammatory diseases. We saw no increased risk of adverse COVID-19 outcomes in those on most targeted immune-modifying drugs for immune-mediated inflammatory diseases compared with those on standard systemic therapy. Funding UK Medical Research Council, NIHR Biomedical Research Centre at King's College London and Guy's and St Thomas' NHS Foundation Trust, and Wellcome Trust.
Collapse
Affiliation(s)
- Brian MacKenna
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Nicholas A Kennedy
- Department of Gastroenterology, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
- IBD Research Group, University of Exeter, Exeter, UK
| | - Amir Mehrkar
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Anna Rowan
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - James Galloway
- Centre of Rheumatic Diseases, King's College London, London, UK
| | - Julian Matthewman
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Kathryn E Mansfield
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Katie Bechman
- Centre of Rheumatic Diseases, King's College London, London, UK
| | - Mark Yates
- Centre of Rheumatic Diseases, King's College London, London, UK
| | - Jeremy Brown
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Anna Schultze
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Sam Norton
- Centre of Rheumatic Diseases, King's College London, London, UK
| | - Alex J Walker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Caroline E Morton
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - David Harrison
- Intensive Care National Audit and Research Centre, London, UK
| | - Krishnan Bhaskaran
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Christopher T Rentsch
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Elizabeth Williamson
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Richard Croker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Seb Bacon
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - George Hickman
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Tom Ward
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Simon Davy
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Amelia Green
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Louis Fisher
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - William Hulme
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Helen J Curtis
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - John Tazare
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Rosalind M Eggo
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - David Evans
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Peter Inglesby
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Helen I McDonald
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Laurie A Tomlinson
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Rohini Mathur
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Angel Y S Wong
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Harriet Forbes
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | | | | | | | - Ian J Douglas
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Liam Smeeth
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Charlie W Lees
- Centre for Genomics and Experimental Medicine, University of Edinburgh Western General Hospital, Edinburgh, UK
| | - Stephen J W Evans
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Ben Goldacre
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Catherine H Smith
- St John's Institute of Dermatology, King's College London, London, UK
- St John's Institute of Dermatology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Sinéad M Langan
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- St John's Institute of Dermatology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| |
Collapse
|
19
|
Tazare J, Walker AJ, Tomlinson LA, Hickman G, Rentsch CT, Williamson EJ, Bhaskaran K, Evans D, Wing K, Mathur R, Wong AYS, Schultze A, Bacon S, Bates C, Morton CE, Curtis HJ, Nightingale E, McDonald HI, Mehrkar A, Inglesby P, Davy S, MacKenna B, Cockburn J, Hulme WJ, Warren-Gash C, Bhate K, Nitsch D, Powell E, Mulick A, Forbes H, Minassian C, Croker R, Parry J, Hester F, Harper S, Eggo RM, Evans SJW, Smeeth L, Douglas IJ, Goldacre B. Rates of serious clinical outcomes in survivors of hospitalisation with COVID-19 in England: a descriptive cohort study within the OpenSAFELY platform. Wellcome Open Res 2022; 7:142. [PMID: 37362009 PMCID: PMC10285340 DOI: 10.12688/wellcomeopenres.17735.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/25/2022] [Indexed: 03/07/2024] Open
Abstract
Background: Patients surviving hospitalisation for COVID-19 are thought to be at high risk of cardiometabolic and pulmonary complications, but quantification of that risk is limited. We aimed to describe the overall burden of these complications in people after discharge from hospital with COVID-19. Methods: Working on behalf of NHS England, we used linked primary care records, death certificate and hospital data from the OpenSAFELY platform. We constructed three cohorts: patients discharged following hospitalisation with COVID-19, patients discharged following pre-pandemic hospitalisation with pneumonia, and a frequency-matched cohort from the general population in 2019. We studied seven outcomes: deep vein thrombosis (DVT), pulmonary embolism (PE), ischaemic stroke, myocardial infarction (MI), heart failure, AKI and new type 2 diabetes mellitus (T2DM) diagnosis. Absolute rates were measured in each cohort and Fine and Gray models were used to estimate age/sex adjusted subdistribution hazard ratios comparing outcome risk between discharged COVID-19 patients and the two comparator cohorts. Results: Amongst the population of 77,347 patients discharged following hospitalisation with COVID-19, rates for the majority of outcomes peaked in the first month post-discharge, then declined over the following four months. Patients in the COVID-19 population had markedly higher risk of all outcomes compared to matched controls from the 2019 general population. Across the whole study period, the risk of outcomes was more similar when comparing patients discharged with COVID-19 to those discharged with pneumonia in 2019, although COVID-19 patients had higher risk of T2DM (15.2 versus 37.2 [rate per 1,000-person-years for COVID-19 versus pneumonia, respectively]; SHR, 1.46 [95% CI: 1.31 - 1.63]). Conclusions: Risk of cardiometabolic and pulmonary adverse outcomes is markedly raised following discharge from hospitalisation with COVID-19 compared to the general population. However, excess risks were similar to those seen following discharge post-pneumonia. Overall, this suggests a large additional burden on healthcare resources.
Collapse
Affiliation(s)
- The OpenSAFELY Collaborative
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
- NIHR Health Protection Research Unit (HPRU) in Immunisation, London, UK
| | - John Tazare
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Alex J. Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | | | - George Hickman
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | | | | | | | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - Kevin Wing
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Rohini Mathur
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Angel YS. Wong
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Anna Schultze
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Seb Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - Chris Bates
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | | | - Helen J. Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
- NIHR Health Protection Research Unit (HPRU) in Immunisation, London, UK
| | | | | | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - Simon Davy
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | | | - William J. Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | | | - Ketaki Bhate
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Dorothea Nitsch
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Emma Powell
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Amy Mulick
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Harriet Forbes
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | | | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - John Parry
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - Frank Hester
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - Sam Harper
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - Rosalind M. Eggo
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | | | - Liam Smeeth
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Ian J Douglas
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| |
Collapse
|
20
|
Schultze A, Nightingale E, Evans D, Hulme W, Rosello A, Bates C, Cockburn J, MacKenna B, Curtis HJ, Morton CE, Croker R, Bacon S, McDonald HI, Rentsch CT, Bhaskaran K, Mathur R, Tomlinson LA, Williamson EJ, Forbes H, Tazare J, Grint D, Walker AJ, Inglesby P, DeVito NJ, Mehrkar A, Hickman G, Davy S, Ward T, Fisher L, Green ACA, Wing K, Wong AYS, McManus R, Parry J, Hester F, Harper S, Evans SJW, Douglas IJ, Smeeth L, Eggo RM, Goldacre B, Leon DA. Mortality among Care Home Residents in England during the first and second waves of the COVID-19 pandemic: an observational study of 4.3 million adults over the age of 65. Lancet Reg Health Eur 2022; 14:100295. [PMID: 35036983 PMCID: PMC8743167 DOI: 10.1016/j.lanepe.2021.100295] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [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: 11/28/2022]
Abstract
BACKGROUND Residents in care homes have been severely impacted by COVID-19. We describe trends in the mortality risk among residents of care homes compared to private homes. METHODS On behalf of NHS England we used OpenSAFELY-TPP to calculate monthly age-standardised risks of death due to all causes and COVID-19 among adults aged >=65 years between 1/2/2019 and 31/03/2021. Care home residents were identified using linkage to Care and Quality Commission data. FINDINGS We included 4,340,648 people aged 65 years or older on the 1st of February 2019, 2.2% of whom were classified as residing in a care or nursing home. Age-standardised mortality risks were approximately 10 times higher among care home residents compared to those in private housing in February 2019: comparative mortality figure (CMF) = 10.59 (95%CI = 9.51, 11.81) among women, and 10.87 (9.93, 11.90) among men. By April 2020 these relative differences had increased to more than 17 times with CMFs of 17.57 (16.43, 18.79) among women and 18.17 (17.22, 19.17) among men. CMFs did not increase during the second wave, despite a rise in the absolute age-standardised COVID-19 mortality risks. INTERPRETATION COVID-19 has had a disproportionate impact on the mortality of care home residents in England compared to older residents of private homes, but only in the first wave. This may be explained by a degree of acquired immunity, improved protective measures or changes in the underlying frailty of the populations. The care home population should be prioritised for measures aimed at controlling COVID-19. FUNDING Medical Research Council MR/V015737/1.
Collapse
Affiliation(s)
- Anna Schultze
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
| | - Emily Nightingale
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - William Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - Alicia Rosello
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
| | - Chris Bates
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX
| | | | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - Seb Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - Helen I McDonald
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
| | | | - Krishnan Bhaskaran
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
| | - Rohini Mathur
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
| | - Laurie A Tomlinson
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
| | | | - Harriet Forbes
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
| | - John Tazare
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
| | - Daniel Grint
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - Nicholas J DeVito
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - George Hickman
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - Simon Davy
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - Tom Ward
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - Louis Fisher
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - Amelia CA Green
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - Kevin Wing
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
| | - Angel YS Wong
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
| | - Robert McManus
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX
| | - John Parry
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX
| | - Frank Hester
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX
| | - Sam Harper
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX
| | - Stephen JW Evans
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
| | - Ian J Douglas
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
| | - Liam Smeeth
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
| | - Rosalind M Eggo
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
| | - David A Leon
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
- International Laboratory For Population and Health, National Research University Higher School of Economics, Moscow, Russia
| |
Collapse
|
21
|
Fisher L, Speed V, Curtis HJ, Rentsch CT, Wong AYS, Schultze A, Massey J, Inglesby P, Morton CE, Wood M, Walker AJ, Morley J, Mehrkar A, Bacon S, Hickman G, Bates C, Croker R, Evans D, Ward T, Cockburn J, Davy S, Bhaskaran K, Smith B, Williamson E, Hulme W, Green A, Eggo RM, Forbes H, Tazare J, Parry J, Hester F, Harper S, Meadows J, O'Hanlon S, Eavis A, Jarvis R, Avramov D, Griffiths P, Fowles A, Parkes N, Douglas IJ, Evans SJW, Smeeth L, MacKenna B, Tomlinson L, Goldacre B. Potentially inappropriate prescribing of DOACs to people with mechanical heart valves: A federated analysis of 57.9 million patients' primary care records in situ using OpenSAFELY. Thromb Res 2022; 211:150-153. [PMID: 35168181 DOI: 10.1016/j.thromres.2022.01.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 01/14/2022] [Accepted: 01/24/2022] [Indexed: 11/23/2022]
Affiliation(s)
- Louis Fisher
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - Victoria Speed
- King's Thrombosis Centre, Department of Haematological Medicine, King's College Hospital, London SE5 9RS, United Kingdom of Great Britain and Northern Ireland
| | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - Christopher T Rentsch
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom of Great Britain and Northern Ireland
| | - Angel Y S Wong
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom of Great Britain and Northern Ireland
| | - Anna Schultze
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom of Great Britain and Northern Ireland
| | - Jon Massey
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - Marion Wood
- NHS England and NHS Improvement, Skipton House, 80 London Road, London SE1 6LH, United Kingdom of Great Britain and Northern Ireland
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - Jessica Morley
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - Seb Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - George Hickman
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - Chris Bates
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds LS18 5PX, United Kingdom of Great Britain and Northern Ireland
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - Tom Ward
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - Jonathan Cockburn
- Faculty of Health Sciences, Bristol Medical School, Bristol BS8 1UD, United Kingdom of Great Britain and Northern Ireland
| | - Simon Davy
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - Krishnan Bhaskaran
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom of Great Britain and Northern Ireland
| | - Becky Smith
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - Elizabeth Williamson
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom of Great Britain and Northern Ireland
| | - William Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - Amelia Green
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland
| | - Rosalind M Eggo
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom of Great Britain and Northern Ireland
| | - Harriet Forbes
- Faculty of Health Sciences, Bristol Medical School, Bristol BS8 1UD, United Kingdom of Great Britain and Northern Ireland
| | - John Tazare
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom of Great Britain and Northern Ireland
| | - John Parry
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds LS18 5PX, United Kingdom of Great Britain and Northern Ireland
| | - Frank Hester
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds LS18 5PX, United Kingdom of Great Britain and Northern Ireland
| | - Sam Harper
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds LS18 5PX, United Kingdom of Great Britain and Northern Ireland
| | - Jonathan Meadows
- EMIS Health, Fulford Grange, Micklefield Lane, Rawdon, Leeds LS19 6BA, United Kingdom of Great Britain and Northern Ireland
| | - Shaun O'Hanlon
- EMIS Health, Fulford Grange, Micklefield Lane, Rawdon, Leeds LS19 6BA, United Kingdom of Great Britain and Northern Ireland
| | - Alex Eavis
- EMIS Health, Fulford Grange, Micklefield Lane, Rawdon, Leeds LS19 6BA, United Kingdom of Great Britain and Northern Ireland
| | - Richard Jarvis
- EMIS Health, Fulford Grange, Micklefield Lane, Rawdon, Leeds LS19 6BA, United Kingdom of Great Britain and Northern Ireland
| | - Dima Avramov
- EMIS Health, Fulford Grange, Micklefield Lane, Rawdon, Leeds LS19 6BA, United Kingdom of Great Britain and Northern Ireland
| | - Paul Griffiths
- EMIS Health, Fulford Grange, Micklefield Lane, Rawdon, Leeds LS19 6BA, United Kingdom of Great Britain and Northern Ireland
| | - Aaron Fowles
- EMIS Health, Fulford Grange, Micklefield Lane, Rawdon, Leeds LS19 6BA, United Kingdom of Great Britain and Northern Ireland
| | - Nasreen Parkes
- EMIS Health, Fulford Grange, Micklefield Lane, Rawdon, Leeds LS19 6BA, United Kingdom of Great Britain and Northern Ireland
| | - Ian J Douglas
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom of Great Britain and Northern Ireland
| | - Stephen J W Evans
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom of Great Britain and Northern Ireland
| | - Liam Smeeth
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom of Great Britain and Northern Ireland
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland; NHS England and NHS Improvement, Skipton House, 80 London Road, London SE1 6LH, United Kingdom of Great Britain and Northern Ireland
| | - Laurie Tomlinson
- London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom of Great Britain and Northern Ireland
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, United Kingdom of Great Britain and Northern Ireland.
| |
Collapse
|
22
|
Williamson EJ, Tazare J, Bhaskaran K, McDonald HI, Walker AJ, Tomlinson L, Wing K, Bacon S, Bates C, Curtis HJ, Forbes HJ, Minassian C, Morton CE, Nightingale E, Mehrkar A, Evans D, Nicholson BD, Leon DA, Inglesby P, MacKenna B, Davies NG, DeVito NJ, Drysdale H, Cockburn J, Hulme WJ, Morley J, Douglas I, Rentsch CT, Mathur R, Wong A, Schultze A, Croker R, Parry J, Hester F, Harper S, Grieve R, Harrison DA, Steyerberg EW, Eggo RM, Diaz-Ordaz K, Keogh R, Evans SJW, Smeeth L, Goldacre B. Comparison of methods for predicting COVID-19-related death in the general population using the OpenSAFELY platform. Diagn Progn Res 2022; 6:6. [PMID: 35197114 PMCID: PMC8865947 DOI: 10.1186/s41512-022-00120-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 01/04/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Obtaining accurate estimates of the risk of COVID-19-related death in the general population is challenging in the context of changing levels of circulating infection. METHODS We propose a modelling approach to predict 28-day COVID-19-related death which explicitly accounts for COVID-19 infection prevalence using a series of sub-studies from new landmark times incorporating time-updating proxy measures of COVID-19 infection prevalence. This was compared with an approach ignoring infection prevalence. The target population was adults registered at a general practice in England in March 2020. The outcome was 28-day COVID-19-related death. Predictors included demographic characteristics and comorbidities. Three proxies of local infection prevalence were used: model-based estimates, rate of COVID-19-related attendances in emergency care, and rate of suspected COVID-19 cases in primary care. We used data within the TPP SystmOne electronic health record system linked to Office for National Statistics mortality data, using the OpenSAFELY platform, working on behalf of NHS England. Prediction models were developed in case-cohort samples with a 100-day follow-up. Validation was undertaken in 28-day cohorts from the target population. We considered predictive performance (discrimination and calibration) in geographical and temporal subsets of data not used in developing the risk prediction models. Simple models were contrasted to models including a full range of predictors. RESULTS Prediction models were developed on 11,972,947 individuals, of whom 7999 experienced COVID-19-related death. All models discriminated well between individuals who did and did not experience the outcome, including simple models adjusting only for basic demographics and number of comorbidities: C-statistics 0.92-0.94. However, absolute risk estimates were substantially miscalibrated when infection prevalence was not explicitly modelled. CONCLUSIONS Our proposed models allow absolute risk estimation in the context of changing infection prevalence but predictive performance is sensitive to the proxy for infection prevalence. Simple models can provide excellent discrimination and may simplify implementation of risk prediction tools.
Collapse
Affiliation(s)
- Elizabeth J Williamson
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
| | - John Tazare
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Krishnan Bhaskaran
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Helen I McDonald
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
- NIHR Health Protection Research Unit (HPRU) in Immunisation, London, UK
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - Laurie Tomlinson
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Kevin Wing
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Sebastian Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - Chris Bates
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - Harriet J Forbes
- University of Bristol, Beacon House, Queens Road, Bristol, BS8 1QU, UK
| | - Caroline Minassian
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - Emily Nightingale
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - Brian D Nicholson
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - David A Leon
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - Nicholas G Davies
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Nicholas J DeVito
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - Henry Drysdale
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | | | - William J Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - Jessica Morley
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - Ian Douglas
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Christopher T Rentsch
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Rohini Mathur
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Angel Wong
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Anna Schultze
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| | - John Parry
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - Frank Hester
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - Sam Harper
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - Richard Grieve
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - David A Harrison
- Intensive Care National Audit & Research Centre (ICNARC), 24 High Holborn, Holborn, London, WC1V 6AZ, UK
| | | | - Rosalind M Eggo
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Karla Diaz-Ordaz
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Ruth Keogh
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Stephen J W Evans
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Liam Smeeth
- London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
| |
Collapse
|
23
|
Sohal K, Mason D, Birkinshaw J, West J, McEachan RR, Elshehaly M, Cooper D, Shore R, McCooe M, Lawton T, Mon-Williams M, Sheldon T, Bates C, Wood M, Wright J. Connected Bradford: a Whole System Data Linkage Accelerator. Wellcome Open Res 2022; 7:26. [PMID: 36466951 PMCID: PMC9682213 DOI: 10.12688/wellcomeopenres.17526.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/22/2021] [Indexed: 11/20/2022] Open
Abstract
The richness of linked population data provides exciting opportunities to understand local health needs, identify and predict those in most need of support and evaluate health interventions. There has been extensive investment to unlock the potential of clinical data for health research in the UK. However, most of the determinants of our health are social, economic, education, environmental, housing, food systems and are influenced by local authorities. The Connected Bradford Whole System Data Linkage Accelerator was set up to link health, education, social care, environmental and other local government data to drive learning health systems, prevention and population health management. Data spanning a period of over forty years has been linked for 800,000 individuals using the pseudonymised NHS number and other data variables. This prospective data collection captures near real time activity. This paper describes the dataset and our Connected Bradford Whole System Data Accelerator Framework that covers public engagement; practitioner and policy integration; legal and ethical approvals; information governance; technicalities of data linkage; data curation and guardianship; data validity and visualisation.
Collapse
Affiliation(s)
- Kuldeep Sohal
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - John Birkinshaw
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - Jane West
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - Rosemary R.C. McEachan
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - Mai Elshehaly
- Department of Computer Science, University of Bradford, Bradford, BD7 1DP, UK
| | - Duncan Cooper
- Public Health, Bradford Metropolitan District Council, Bradford, BD1 1HX, UK
| | - Rob Shore
- Public Health, Bradford Metropolitan District Council, Bradford, BD1 1HX, UK
| | - Michael McCooe
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - Tom Lawton
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | | | - Trevor Sheldon
- Institute of Population Health Sciences, Queen Mary University of London, London, E1 4NS, UK
| | - Chris Bates
- The Phoenix Partnership, Leeds, LS18 5PX, UK
| | - Megan Wood
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| |
Collapse
|
24
|
Rowan A, Bates C, Hulme W, Evans D, Davy S, A Kennedy N, Galloway J, E Mansfield K, Bechman K, Matthewman J, Yates M, Brown J, Schultze A, Norton S, J. Walker A, E. Morton C, Bhaskaran K, T. Rentsch C, Williamson E, Croker R, Bacon S, Hickman G, Ward T, Green A, Fisher L, J Curtis H, Tazare J, M. Eggo R, Inglesby P, Cockburn J, I. McDonald H, Mathur R, YS Wong A, Forbes H, Parry J, Hester F, Harper S, J Douglas I, Smeeth L, A Tomlinson L, W Lees C, Evans S, Smith C, M. Langan S, Mehkar A, MacKenna B, Goldacre B. A comprehensive high cost drugs dataset from the NHS in England - An OpenSAFELY-TPP Short Data Report. Wellcome Open Res 2021; 6:360. [PMID: 35634533 PMCID: PMC9120928 DOI: 10.12688/wellcomeopenres.17360.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/10/2021] [Indexed: 11/20/2022] Open
Abstract
Background: At the outset of the COVID-19 pandemic, there was no routine comprehensive hospital medicines data from the UK available to researchers. These records can be important for many analyses including the effect of certain medicines on the risk of severe COVID-19 outcomes. With the approval of NHS England, we set out to obtain data on one specific group of medicines, "high-cost drugs" (HCD) which are typically specialist medicines for the management of long-term conditions, prescribed by hospitals to patients. Additionally, we aimed to make these data available to all approved researchers in OpenSAFELY-TPP. This report is intended to support all studies carried out in OpenSAFELY-TPP, and those elsewhere, working with this dataset or similar data. Methods: Working with the North East Commissioning Support Unit and NHS Digital, we arranged for collation of a single national HCD dataset to help inform responses to the COVID-19 pandemic. The dataset was developed from payment submissions from hospitals to commissioners. Results: In the financial year (FY) 2018/19 there were 2.8 million submissions for 1.1 million unique patient IDs recorded in the HCD. The average number of submissions per patient over the year was 2.6. In FY 2019/20 there were 4.0 million submissions for 1.3 million unique patient IDs. The average number of submissions per patient over the year was 3.1. Of the 21 variables in the dataset, three are now available for analysis in OpenSafely-TPP: Financial year and month of drug being dispensed; drug name; and a description of the drug dispensed. Conclusions: We have described the process for sourcing a national HCD dataset, making these data available for COVID-19-related analysis through OpenSAFELY-TPP and provided information on the variables included in the dataset, data coverage and an initial descriptive analysis.
Collapse
Affiliation(s)
- Anna Rowan
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Chris Bates
- TPP, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - William Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Simon Davy
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Nicholas A Kennedy
- Department of Gastroenterology, Royal Devon & Exeter NHS Foundation Trust, Exeter, UK
- IBD Research Group, University of Exeter, Exeter, UK
| | - James Galloway
- Centre of Rheumatic Diseases, King's College London, London, UK
| | - Kathryn E Mansfield
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Katie Bechman
- Centre of Rheumatic Diseases, King's College London, London, UK
| | - Julian Matthewman
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Mark Yates
- Centre of Rheumatic Diseases, King's College London, London, UK
| | - Jeremy Brown
- Centre of Rheumatic Diseases, King's College London, London, UK
| | - Anna Schultze
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Sam Norton
- TPP, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - Alex J. Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Caroline E. Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Krishnan Bhaskaran
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Christopher T. Rentsch
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Elizabeth Williamson
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Seb Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - George Hickman
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Tom Ward
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Amelia Green
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Louis Fisher
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - John Tazare
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Rosalind M. Eggo
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | | | - Helen I. McDonald
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Rohini Mathur
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Angel YS Wong
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Harriet Forbes
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - John Parry
- TPP, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - Frank Hester
- TPP, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - Sam Harper
- TPP, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
| | - Ian J Douglas
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Liam Smeeth
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Laurie A Tomlinson
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Charlie W Lees
- Centre for Genomics and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | - Stephen Evans
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Catherine Smith
- St John's Institute of Dermatology, Guy's and St Thomas' NHS Foundation Trust, London, SE1 9RT, UK
| | - Sinéad M. Langan
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
- St John's Institute of Dermatology, Guy's and St Thomas' NHS Foundation Trust, London, SE1 9RT, UK
| | - Amir Mehkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| |
Collapse
|
25
|
Curtis HJ, MacKenna B, Walker AJ, Croker R, Mehrkar A, Morton C, Bacon S, Hickman G, Inglesby P, Bates C, Evans D, Ward T, Cockburn J, Davy S, Bhaskaran K, Schultze A, Rentsch CT, Williamson E, Hulme W, Tomlinson L, Mathur R, Drysdale H, Eggo RM, Wong AY, Forbes H, Parry J, Hester F, Harper S, Douglas I, Smeeth L, Goldacre B. OpenSAFELY: impact of national guidance on switching anticoagulant therapy during COVID-19 pandemic. Open Heart 2021; 8:e001784. [PMID: 34785588 PMCID: PMC8595296 DOI: 10.1136/openhrt-2021-001784] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 10/08/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Early in the COVID-19 pandemic, the National Health Service (NHS) recommended that appropriate patients anticoagulated with warfarin should be switched to direct-acting oral anticoagulants (DOACs), requiring less frequent blood testing. Subsequently, a national safety alert was issued regarding patients being inappropriately coprescribed two anticoagulants following a medication change and associated monitoring. OBJECTIVE To describe which people were switched from warfarin to DOACs; identify potentially unsafe coprescribing of anticoagulants; and assess whether abnormal clotting results have become more frequent during the pandemic. METHODS With the approval of NHS England, we conducted a cohort study using routine clinical data from 24 million NHS patients in England. RESULTS 20 000 of 164 000 warfarin patients (12.2%) switched to DOACs between March and May 2020, most commonly to edoxaban and apixaban. Factors associated with switching included: older age, recent renal function test, higher number of recent INR tests recorded, atrial fibrillation diagnosis and care home residency. There was a sharp rise in coprescribing of warfarin and DOACs from typically 50-100 per month to 246 in April 2020, 0.06% of all people receiving a DOAC or warfarin. International normalised ratio (INR) testing fell by 14% to 506.8 patients tested per 1000 warfarin patients each month. We observed a very small increase in elevated INRs (n=470) during April compared with January (n=420). CONCLUSIONS Increased switching of anticoagulants from warfarin to DOACs was observed at the outset of the COVID-19 pandemic in England following national guidance. There was a small but substantial number of people coprescribed warfarin and DOACs during this period. Despite a national safety alert on the issue, a widespread rise in elevated INR test results was not found. Primary care has responded rapidly to changes in patient care during the COVID-19 pandemic.
Collapse
Affiliation(s)
- Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Caroline Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Seb Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - George Hickman
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Tom Ward
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Simon Davy
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Krishnan Bhaskaran
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Anna Schultze
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Christopher T Rentsch
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Elizabeth Williamson
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - William Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Laurie Tomlinson
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Rohini Mathur
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Henry Drysdale
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Rosalind M Eggo
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Angel Yun Wong
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Harriet Forbes
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | | | | | - Ian Douglas
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Liam Smeeth
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| |
Collapse
|
26
|
Walker AJ, MacKenna B, Inglesby P, Tomlinson L, Rentsch CT, Curtis HJ, Morton CE, Morley J, Mehrkar A, Bacon S, Hickman G, Bates C, Croker R, Evans D, Ward T, Cockburn J, Davy S, Bhaskaran K, Schultze A, Williamson EJ, Hulme WJ, McDonald HI, Mathur R, Eggo RM, Wing K, Wong AY, Forbes H, Tazare J, Parry J, Hester F, Harper S, O'Hanlon S, Eavis A, Jarvis R, Avramov D, Griffiths P, Fowles A, Parkes N, Douglas IJ, Evans SJ. 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:e806-e814. [PMID: 34340970 PMCID: PMC8340730 DOI: 10.3399/bjgp.2021.0301] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 06/18/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Long COVID describes new or persistent symptoms at least 4 weeks after onset of acute COVID-19. Clinical codes to describe this phenomenon were recently created. AIM To describe the use of long-COVID codes, and variation of use by general practice, demographic variables, and over time. DESIGN AND SETTING Population-based cohort study in English primary care. METHOD Working on behalf of NHS England, OpenSAFELY data were used encompassing 96% of the English population between 1 February 2020 and 25 May 2021. The proportion of people with a recorded code for long COVID was measured overall and by demographic factors, electronic health record software system (EMIS or TPP), and week. RESULTS Long COVID was recorded for 23 273 people. Coding was unevenly distributed among practices, with 26.7% of practices having never used the codes. Regional variation ranged between 20.3 per 100 000 people for East of England (95% confidence interval [CI] = 19.3 to 21.4) and 55.6 per 100 000 people in London (95% CI = 54.1 to 57.1). Coding was higher among females (52.1, 95% CI = 51.3 to 52.9) than males (28.1, 95% CI = 27.5 to 28.7), and higher among practices using EMIS (53.7, 95% CI = 52.9 to 54.4) than those using TPP (20.9, 95% CI = 20.3 to 21.4). CONCLUSION Current recording of long COVID in primary care is very low, and variable between practices. This may reflect patients not presenting; clinicians and patients holding different diagnostic thresholds; or challenges with the design and communication of diagnostic codes. Increased awareness of diagnostic codes is recommended to facilitate research and planning of services, and also surveys with qualitative work to better evaluate clinicians' understanding of the diagnosis.
Collapse
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
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Laurie Tomlinson
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Christopher T Rentsch
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Jessica Morley
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Seb Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - George Hickman
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | | | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Tom Ward
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | | | - Simon Davy
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Krishnan Bhaskaran
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Anna Schultze
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Elizabeth J Williamson
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - William J Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Helen I McDonald
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Rohini Mathur
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Rosalind M Eggo
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Kevin Wing
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Angel Ys Wong
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Harriet Forbes
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - John Tazare
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | | | | | | | | | | | | | | | | | | | | | - Ian J Douglas
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| | - Stephen Jw Evans
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London
| |
Collapse
|
27
|
Wong AYS, Tomlinson LA, Brown JP, Elson W, Walker AJ, Schultze A, Morton CE, Evans D, Inglesby P, MacKenna B, Bhaskaran K, Rentsch CT, Powell E, Williamson E, Croker R, Bacon S, Hulme W, Bates C, Curtis HJ, Mehrkar A, Cockburn J, McDonald HI, Mathur R, Wing K, Forbes H, Eggo RM, Evans SJW, Smeeth L, Goldacre B, Douglas IJ. Association between warfarin and COVID-19-related outcomes compared with direct oral anticoagulants: population-based cohort study. J Hematol Oncol 2021; 14:172. [PMID: 34666811 PMCID: PMC8525065 DOI: 10.1186/s13045-021-01185-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 10/04/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Thromboembolism has been reported as a consequence of severe COVID-19. Although warfarin is a commonly used anticoagulant, it acts by antagonising vitamin K, which is low in patients with severe COVID-19. To date, the clinical evidence on the impact of regular use of warfarin on COVID-19-related thromboembolism is lacking. METHODS On behalf of NHS England, we conducted a population-based cohort study investigating the association between warfarin and COVID-19 outcomes compared with direct oral anticoagulants (DOACs). We used the OpenSAFELY platform to analyse primary care data and pseudonymously linked SARS-CoV-2 antigen testing data, hospital admissions and death records from England. We used Cox regression to estimate hazard ratios (HRs) for COVID-19-related outcomes comparing warfarin with DOACs in people with non-valvular atrial fibrillation. We also conducted negative control outcome analyses (being tested for SARS-CoV-2 and non-COVID-19 death) to assess the potential impact of confounding. RESULTS A total of 92,339 warfarin users and 280,407 DOAC users were included. We observed a lower risk of all outcomes associated with warfarin versus DOACs [testing positive for SARS-CoV-2, HR 0.73 (95% CI 0.68-0.79); COVID-19-related hospital admission, HR 0.75 (95% CI 0.68-0.83); COVID-19-related deaths, HR 0.74 (95% CI 0.66-0.83)]. A lower risk of negative control outcomes associated with warfarin versus DOACs was also observed [being tested for SARS-CoV-2, HR 0.80 (95% CI 0.79-0.81); non-COVID-19 deaths, HR 0.79 (95% CI 0.76-0.83)]. CONCLUSIONS Overall, this study shows no evidence of harmful effects of warfarin on severe COVID-19 disease.
Collapse
Affiliation(s)
| | - Angel Y S Wong
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
| | - Laurie A Tomlinson
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Jeremy P Brown
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - William Elson
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Anna Schultze
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Krishnan Bhaskaran
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Christopher T Rentsch
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Emma Powell
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Elizabeth Williamson
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Seb Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - William Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Helen I McDonald
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.,NIHR Health Protection Research Unit (HPRU) in Immunisation, London, UK
| | - Rohini Mathur
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Kevin Wing
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Harriet Forbes
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Rosalind M Eggo
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Stephen J W Evans
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Liam Smeeth
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.,NIHR Health Protection Research Unit (HPRU) in Immunisation, London, UK
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Ian J Douglas
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| |
Collapse
|
28
|
Williamson EJ, McDonald HI, Bhaskaran K, Walker AJ, Bacon S, Davy S, Schultze A, Tomlinson L, Bates C, Ramsay M, Curtis HJ, Forbes H, Wing K, Minassian C, Tazare J, Morton CE, Nightingale E, Mehrkar A, Evans D, Inglesby P, MacKenna B, Cockburn J, Rentsch CT, Mathur R, Wong AYS, Eggo RM, Hulme W, Croker R, Parry J, Hester F, Harper S, Douglas IJ, Evans SJW, Smeeth L, Goldacre B, Kuper H. Risks of covid-19 hospital admission and death for people with learning disability: population based cohort study using the OpenSAFELY platform. BMJ 2021; 374:n1592. [PMID: 34261639 PMCID: PMC8278652 DOI: 10.1136/bmj.n1592] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/09/2021] [Indexed: 02/01/2023]
Abstract
OBJECTIVE To assess the association between learning disability and risk of hospital admission and death from covid-19 in England among adults and children. DESIGN Population based cohort study on behalf of NHS England using the OpenSAFELY platform. SETTING Patient level data were obtained for more than 17 million people registered with a general practice in England that uses TPP software. Electronic health records were linked with death data from the Office for National Statistics and hospital admission data from NHS Secondary Uses Service. PARTICIPANTS Adults (aged 16-105 years) and children (<16 years) from two cohorts: wave 1 (registered with a TPP practice as of 1 March 2020 and followed until 31 August 2020); and wave 2 (registered 1 September 2020 and followed until 8 February 2021). The main exposure group consisted of people on a general practice learning disability register; a subgroup was defined as those having profound or severe learning disability. People with Down's syndrome and cerebral palsy were identified (whether or not they were on the learning disability register). MAIN OUTCOME MEASURE Covid-19 related hospital admission and covid-19 related death. Non-covid-19 deaths were also explored. RESULTS For wave 1, 14 312 023 adults aged ≥16 years were included, and 90 307 (0.63%) were on the learning disability register. Among adults on the register, 538 (0.6%) had a covid-19 related hospital admission; there were 222 (0.25%) covid-19 related deaths and 602 (0.7%) non-covid deaths. Among adults not on the register, 29 781 (0.2%) had a covid-19 related hospital admission; there were 13 737 (0.1%) covid-19 related deaths and 69 837 (0.5%) non-covid deaths. Wave 1 hazard ratios for adults on the learning disability register (adjusted for age, sex, ethnicity, and geographical location) were 5.3 (95% confidence interval 4.9 to 5.8) for covid-19 related hospital admission and 8.2 (7.2 to 9.4) for covid-19 related death. Wave 2 produced similar estimates. Associations were stronger among those classified as having severe to profound learning disability, and among those in residential care. For both waves, Down's syndrome and cerebral palsy were associated with increased hazards for both events; Down's syndrome to a greater extent. Hazard ratios for non-covid deaths followed similar patterns with weaker associations. Similar patterns of increased relative risk were seen for children, but covid-19 related deaths and hospital admissions were rare, reflecting low event rates among children. CONCLUSIONS People with learning disability have markedly increased risks of hospital admission and death from covid-19, over and above the risks observed for non-covid causes of death. Prompt access to covid-19 testing and healthcare is warranted for this vulnerable group, and prioritisation for covid-19 vaccination and other targeted preventive measures should be considered.
Collapse
Affiliation(s)
| | - Helen I McDonald
- London School of Hygiene and Tropical Medicine, London, UK
- National Institute for Health Research (NIHR) Health Protection Research Unit in Vaccines and Immunisation, London, UK
| | | | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Sebastian Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Simon Davy
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Anna Schultze
- London School of Hygiene and Tropical Medicine, London, UK
| | | | | | - Mary Ramsay
- National Institute for Health Research (NIHR) Health Protection Research Unit in Vaccines and Immunisation, London, UK
- Public Health England, London, UK
| | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Kevin Wing
- London School of Hygiene and Tropical Medicine, London, UK
| | | | - John Tazare
- London School of Hygiene and Tropical Medicine, London, UK
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Dave Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | | | - Rohini Mathur
- London School of Hygiene and Tropical Medicine, London, UK
| | - Angel Y S Wong
- London School of Hygiene and Tropical Medicine, London, UK
| | | | - William Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | | | | | - Ian J Douglas
- London School of Hygiene and Tropical Medicine, London, UK
| | | | - Liam Smeeth
- London School of Hygiene and Tropical Medicine, London, UK
| | - Ben Goldacre
- National Institute for Health Research (NIHR) Health Protection Research Unit in Vaccines and Immunisation, London, UK
| | - Hannah Kuper
- London School of Hygiene and Tropical Medicine, London, UK
| |
Collapse
|
29
|
Wong AY, MacKenna B, Morton CE, Schultze A, Walker AJ, Bhaskaran K, Brown JP, Rentsch CT, Williamson E, Drysdale H, Croker R, Bacon S, Hulme W, Bates C, Curtis HJ, Mehrkar A, Evans D, Inglesby P, Cockburn J, McDonald HI, Tomlinson L, Mathur R, Wing K, Forbes H, Eggo RM, Parry J, Hester F, Harper S, Evans SJ, Smeeth L, Douglas IJ, Goldacre B. Use of non-steroidal anti-inflammatory drugs and risk of death from COVID-19: an OpenSAFELY cohort analysis based on two cohorts. Ann Rheum Dis 2021; 80:943-951. [PMID: 33478953 PMCID: PMC7823433 DOI: 10.1136/annrheumdis-2020-219517] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/08/2021] [Accepted: 01/08/2021] [Indexed: 12/22/2022]
Abstract
OBJECTIVES To assess the association between routinely prescribed non-steroidal anti-inflammatory drugs (NSAIDs) and deaths from COVID-19 using OpenSAFELY, a secure analytical platform. METHODS We conducted two cohort studies from 1 March to 14 June 2020. Working on behalf of National Health Service England, we used routine clinical data in England linked to death data. In study 1, we identified people with an NSAID prescription in the last 3 years from the general population. In study 2, we identified people with rheumatoid arthritis/osteoarthritis. We defined exposure as current NSAID prescription within the 4 months before 1 March 2020. We used Cox regression to estimate HRs for COVID-19 related death in people currently prescribed NSAIDs, compared with those not currently prescribed NSAIDs, accounting for age, sex, comorbidities, other medications and geographical region. RESULTS In study 1, we included 536 423 current NSAID users and 1 927 284 non-users in the general population. We observed no evidence of difference in risk of COVID-19 related death associated with current use (HR 0.96, 95% CI 0.80 to 1.14) in the multivariable-adjusted model. In study 2, we included 1 708 781 people with rheumatoid arthritis/osteoarthritis, of whom 175 495 (10%) were current NSAID users. In the multivariable-adjusted model, we observed a lower risk of COVID-19 related death (HR 0.78, 95% CI 0.64 to 0.94) associated with current use of NSAID versus non-use. CONCLUSIONS We found no evidence of a harmful effect of routinely prescribed NSAIDs on COVID-19 related deaths. Risks of COVID-19 do not need to influence decisions about the routine therapeutic use of NSAIDs.
Collapse
Affiliation(s)
- Angel Ys Wong
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Anna Schultze
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Krishnan Bhaskaran
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Jeremy P Brown
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Christopher T Rentsch
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Elizabeth Williamson
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Henry Drysdale
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Seb Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - William Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | | | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | | | - Helen I McDonald
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Laurie Tomlinson
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Rohini Mathur
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Kevin Wing
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Harriet Forbes
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Rosalind M Eggo
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | | | | | | | - Stephen Jw Evans
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Liam Smeeth
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Ian J Douglas
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| |
Collapse
|
30
|
Mathur R, Rentsch CT, Morton CE, Hulme WJ, Schultze A, MacKenna B, Eggo RM, Bhaskaran K, Wong AYS, Williamson EJ, Forbes H, Wing K, McDonald HI, Bates C, Bacon S, Walker AJ, Evans D, Inglesby P, Mehrkar A, Curtis HJ, DeVito NJ, Croker R, Drysdale H, Cockburn J, Parry J, Hester F, Harper S, Douglas IJ, Tomlinson L, Evans SJW, Grieve R, Harrison D, Rowan K, Khunti K, Chaturvedi N, Smeeth L, Goldacre B. Ethnic differences in SARS-CoV-2 infection and COVID-19-related hospitalisation, intensive care unit admission, and death in 17 million adults in England: an observational cohort study using the OpenSAFELY platform. Lancet 2021; 397:1711-1724. [PMID: 33939953 PMCID: PMC8087292 DOI: 10.1016/s0140-6736(21)00634-6] [Citation(s) in RCA: 236] [Impact Index Per Article: 78.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 02/26/2021] [Accepted: 03/09/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND COVID-19 has disproportionately affected minority ethnic populations in the UK. Our aim was to quantify ethnic differences in SARS-CoV-2 infection and COVID-19 outcomes during the first and second waves of the COVID-19 pandemic in England. METHODS We conducted an observational cohort study of adults (aged ≥18 years) registered with primary care practices in England for whom electronic health records were available through the OpenSAFELY platform, and who had at least 1 year of continuous registration at the start of each study period (Feb 1 to Aug 3, 2020 [wave 1], and Sept 1 to Dec 31, 2020 [wave 2]). Individual-level primary care data were linked to data from other sources on the outcomes of interest: SARS-CoV-2 testing and positive test results and COVID-19-related hospital admissions, intensive care unit (ICU) admissions, and death. The exposure was self-reported ethnicity as captured on the primary care record, grouped into five high-level census categories (White, South Asian, Black, other, and mixed) and 16 subcategories across these five categories, as well as an unknown ethnicity category. We used multivariable Cox regression to examine ethnic differences in the outcomes of interest. Models were adjusted for age, sex, deprivation, clinical factors and comorbidities, and household size, with stratification by geographical region. FINDINGS Of 17 288 532 adults included in the study (excluding care home residents), 10 877 978 (62·9%) were White, 1 025 319 (5·9%) were South Asian, 340 912 (2·0%) were Black, 170 484 (1·0%) were of mixed ethnicity, 320 788 (1·9%) were of other ethnicity, and 4 553 051 (26·3%) were of unknown ethnicity. In wave 1, the likelihood of being tested for SARS-CoV-2 infection was slightly higher in the South Asian group (adjusted hazard ratio 1·08 [95% CI 1·07-1·09]), Black group (1·08 [1·06-1·09]), and mixed ethnicity group (1·04 [1·02-1·05]) and was decreased in the other ethnicity group (0·77 [0·76-0·78]) relative to the White group. The risk of testing positive for SARS-CoV-2 infection was higher in the South Asian group (1·99 [1·94-2·04]), Black group (1·69 [1·62-1·77]), mixed ethnicity group (1·49 [1·39-1·59]), and other ethnicity group (1·20 [1·14-1·28]). Compared with the White group, the four remaining high-level ethnic groups had an increased risk of COVID-19-related hospitalisation (South Asian group 1·48 [1·41-1·55], Black group 1·78 [1·67-1·90], mixed ethnicity group 1·63 [1·45-1·83], other ethnicity group 1·54 [1·41-1·69]), COVID-19-related ICU admission (2·18 [1·92-2·48], 3·12 [2·65-3·67], 2·96 [2·26-3·87], 3·18 [2·58-3·93]), and death (1·26 [1·15-1·37], 1·51 [1·31-1·71], 1·41 [1·11-1·81], 1·22 [1·00-1·48]). In wave 2, the risks of hospitalisation, ICU admission, and death relative to the White group were increased in the South Asian group but attenuated for the Black group compared with these risks in wave 1. Disaggregation into 16 ethnicity groups showed important heterogeneity within the five broader categories. INTERPRETATION Some minority ethnic populations in England have excess risks of testing positive for SARS-CoV-2 and of adverse COVID-19 outcomes compared with the White population, even after accounting for differences in sociodemographic, clinical, and household characteristics. Causes are likely to be multifactorial, and delineating the exact mechanisms is crucial. Tackling ethnic inequalities will require action across many fronts, including reducing structural inequalities, addressing barriers to equitable care, and improving uptake of testing and vaccination. FUNDING Medical Research Council.
Collapse
Affiliation(s)
- Rohini Mathur
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
| | - Christopher T Rentsch
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - William J Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Anna Schultze
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Rosalind M Eggo
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Krishnan Bhaskaran
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Angel Y S Wong
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Elizabeth J Williamson
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Harriet Forbes
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Kevin Wing
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Helen I McDonald
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; National Institute for Health Research Health Protection Research Unit in Immunisation, London, UK
| | | | - Seb Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Nicholas J DeVito
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Henry Drysdale
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | | | | | | | - Ian J Douglas
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Laurie Tomlinson
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Stephen J W Evans
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Richard Grieve
- Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK
| | - David Harrison
- Intensive Care National Audit and Research Centre, London, UK
| | - Kathy Rowan
- Intensive Care National Audit and Research Centre, London, UK
| | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Nishi Chaturvedi
- Medical Research Council Unit for Lifelong Health and Ageing, University College London, London, UK
| | - Liam Smeeth
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| |
Collapse
|
31
|
Schultze A, Bates C, Cockburn J, MacKenna B, Nightingale E, Curtis HJ, Hulme WJ, Morton CE, Croker R, Bacon S, McDonald HI, Rentsch CT, Bhaskaran K, Mathur R, Tomlinson LA, Williamson EJ, Forbes H, Tazare J, Grint DJ, Walker AJ, Inglesby P, DeVito NJ, Mehrkar A, Hickman G, Davy S, Ward T, Fisher L, Evans D, Wing K, Wong AYS, McManus R, Parry J, Hester F, Harper S, Evans SJW, Douglas IJ, Smeeth L, Eggo RM, Goldacre B. Identifying Care Home Residents in Electronic Health Records - An OpenSAFELY Short Data Report. Wellcome Open Res 2021; 6:90. [PMID: 34471703 PMCID: PMC8374378 DOI: 10.12688/wellcomeopenres.16737.1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/14/2021] [Indexed: 11/23/2022] Open
Abstract
Background: Care home residents have been severely affected by the COVID-19 pandemic. Electronic Health Records (EHR) hold significant potential for studying the healthcare needs of this vulnerable population; however, identifying care home residents in EHR is not straightforward. We describe and compare three different methods for identifying care home residents in the newly created OpenSAFELY-TPP data analytics platform. Methods: Working on behalf of NHS England, we identified individuals aged 65 years or older potentially living in a care home on the 1st of February 2020 using (1) a complex address linkage, in which cleaned GP registered addresses were matched to old age care home addresses using data from the Care and Quality Commission (CQC); (2) coded events in the EHR; (3) household identifiers, age and household size to identify households with more than 3 individuals aged 65 years or older as potential care home residents. Raw addresses were not available to the investigators. Results: Of 4,437,286 individuals aged 65 years or older, 2.27% were identified as potential care home residents using the complex address linkage, 1.96% using coded events, 3.13% using household size and age and 3.74% using either of these methods. 53,210 individuals (32.0% of all potential care home residents) were classified as care home residents using all three methods. Address linkage had the largest overlap with the other methods; 93.3% of individuals identified as care home residents using the address linkage were also identified as such using either coded events or household age and size. Conclusion: We have described the partial overlap between three methods for identifying care home residents in EHR, and provide detailed instructions for how to implement these in OpenSAFELY-TPP to support research into the impact of the COVID-19 pandemic on care home residents.
Collapse
Affiliation(s)
- Anna Schultze
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Chris Bates
- The Phoenix Partnership, Leeds, LS18 5PX, UK
| | | | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Emily Nightingale
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, Select, WC1E 7HT, UK
| | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - William J Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Seb Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Helen I McDonald
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Christopher T Rentsch
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Krishnan Bhaskaran
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Rohini Mathur
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Laurie A Tomlinson
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Elizabeth J Williamson
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Harriet Forbes
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - John Tazare
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Daniel J Grint
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Nicholas J DeVito
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - George Hickman
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Simon Davy
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Tom Ward
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Louis Fisher
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Kevin Wing
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Angel YS Wong
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | | | - John Parry
- The Phoenix Partnership, Leeds, LS18 5PX, UK
| | | | - Sam Harper
- The Phoenix Partnership, Leeds, LS18 5PX, UK
| | - Stephen JW Evans
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Ian J Douglas
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Liam Smeeth
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Rosalind M Eggo
- 1 Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, Select, WC1E 7HT, UK
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| |
Collapse
|
32
|
Forbes H, Morton CE, Bacon S, McDonald HI, Minassian C, Brown JP, Rentsch CT, Mathur R, Schultze A, DeVito NJ, MacKenna B, Hulme WJ, Croker R, Walker AJ, Williamson EJ, Bates C, Mehrkar A, Curtis HJ, Evans D, Wing K, Inglesby P, Drysdale H, Wong AYS, Cockburn J, McManus R, Parry J, Hester F, Harper S, Douglas IJ, Smeeth L, Evans SJW, Bhaskaran K, Eggo RM, Goldacre B, Tomlinson LA. Association between living with children and outcomes from covid-19: OpenSAFELY cohort study of 12 million adults in England. BMJ 2021; 372:n628. [PMID: 33737413 PMCID: PMC7970340 DOI: 10.1136/bmj.n628] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/07/2021] [Indexed: 02/06/2023]
Abstract
OBJECTIVE To investigate whether risk of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and outcomes of coronavirus disease 2019 (covid-19) differed between adults living with and without children during the first two waves of the UK pandemic. DESIGN Population based cohort study, on behalf of NHS England. SETTING Primary care data and pseudonymously linked hospital and intensive care admissions and death records from England, during wave 1 (1 February to 31 August 2020) and wave 2 (1 September to 18 December 2020). PARTICIPANTS Two cohorts of adults (18 years and over) registered at a general practice on 1 February 2020 and 1 September 2020. MAIN OUTCOME MEASURES Adjusted hazard ratios for SARS-CoV-2 infection, covid-19 related admission to hospital or intensive care, or death from covid-19, by presence of children in the household. RESULTS Among 9 334 392adults aged 65 years and under, during wave 1, living with children was not associated with materially increased risks of recorded SARS-CoV-2 infection, covid-19 related hospital or intensive care admission, or death from covid-19. In wave 2, among adults aged 65 years and under, living with children of any age was associated with an increased risk of recorded SARS-CoV-2 infection (hazard ratio 1.06 (95% confidence interval 1.05 to 1.08) for living with children aged 0-11 years; 1.22 (1.20 to 1.24) for living with children aged 12-18 years) and covid-19 related hospital admission (1.18 (1.06 to 1.31) for living with children aged 0-11; 1.26 (1.12 to 1.40) for living with children aged 12-18). Living with children aged 0-11 was associated with reduced risk of death from both covid-19 and non-covid-19 causes in both waves; living with children of any age was also associated with lower risk of dying from non-covid-19 causes. For adults 65 years and under during wave 2, living with children aged 0-11 years was associated with an increased absolute risk of having SARS-CoV-2 infection recorded of 40-60 per 10 000 people, from 810 to between 850 and 870, and an increase in the number of hospital admissions of 1-5 per 10 000 people, from 160 to between 161 and 165. Living with children aged 12-18 years was associated with an increase of 160-190 per 10 000 in the number of SARS-CoV-2 infections and an increase of 2-6 per 10 000 in the number of hospital admissions. CONCLUSIONS In contrast to wave 1, evidence existed of increased risk of reported SARS-CoV-2 infection and covid-19 outcomes among adults living with children during wave 2. However, this did not translate into a materially increased risk of covid-19 mortality, and absolute increases in risk were small.
Collapse
Affiliation(s)
- Harriet Forbes
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Seb Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Helen I McDonald
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Caroline Minassian
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Jeremy P Brown
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Christopher T Rentsch
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Rohini Mathur
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Anna Schultze
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Nicholas J DeVito
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - William J Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Elizabeth J Williamson
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Chris Bates
- The Phoenix Partnership, 129 Low Lane, Horsforth, Leeds, UK
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Kevin Wing
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Henry Drysdale
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Angel Y S Wong
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Robert McManus
- The Phoenix Partnership, 129 Low Lane, Horsforth, Leeds, UK
| | - John Parry
- The Phoenix Partnership, 129 Low Lane, Horsforth, Leeds, UK
| | - Frank Hester
- The Phoenix Partnership, 129 Low Lane, Horsforth, Leeds, UK
| | - Sam Harper
- The Phoenix Partnership, 129 Low Lane, Horsforth, Leeds, UK
| | - Ian J Douglas
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Liam Smeeth
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Stephen J W Evans
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Krishnan Bhaskaran
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Rosalind M Eggo
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Laurie A Tomlinson
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| |
Collapse
|
33
|
Rentsch CT, DeVito NJ, MacKenna B, Morton CE, Bhaskaran K, Brown JP, Schultze A, Hulme WJ, Croker R, Walker AJ, Williamson EJ, Bates C, Bacon S, Mehrkar A, Curtis HJ, Evans D, Wing K, Inglesby P, Mathur R, Drysdale H, Wong AYS, McDonald HI, Cockburn J, Forbes H, Parry J, Hester F, Harper S, Smeeth L, Douglas IJ, Dixon WG, Evans SJW, Tomlinson L, Goldacre B. Hydroxychloroquine treatment does not reduce COVID-19 mortality; underdosing to the wrong patients? - Authors' reply. Lancet Rheumatol 2021; 3:e172-e173. [PMID: 33655224 PMCID: PMC7906669 DOI: 10.1016/s2665-9913(21)00030-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Christopher T Rentsch
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Nicholas J DeVito
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Krishnan Bhaskaran
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Jeremy P Brown
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Anna Schultze
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - William J Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Elizabeth J Williamson
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Chris Bates
- The Phoenix Partnership, Horsforth, Leeds, UK
| | - Seb Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Kevin Wing
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Rohini Mathur
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Henry Drysdale
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Angel Y S Wong
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Helen I McDonald
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | | | - Harriet Forbes
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - John Parry
- The Phoenix Partnership, Horsforth, Leeds, UK
| | | | - Sam Harper
- The Phoenix Partnership, Horsforth, Leeds, UK
| | - Liam Smeeth
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Ian J Douglas
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - William G Dixon
- Centre for Epidemiology Versus Arthritis, The University of Manchester, Manchester, UK
| | - Stephen J W Evans
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Laurie Tomlinson
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| |
Collapse
|
34
|
Grint DJ, Wing K, Williamson E, McDonald HI, Bhaskaran K, Evans D, Evans SJ, Walker AJ, Hickman G, Nightingale E, Schultze A, Rentsch CT, Bates C, Cockburn J, Curtis HJ, Morton CE, Bacon S, Davy S, Wong AY, Mehrkar A, Tomlinson L, Douglas IJ, Mathur R, Blomquist P, MacKenna B, Ingelsby P, Croker R, Parry J, Hester F, Harper S, DeVito NJ, Hulme W, Tazare J, Goldacre B, Smeeth L, Eggo RM. Case fatality risk of the SARS-CoV-2 variant of concern B.1.1.7 in England, 16 November to 5 February. Euro Surveill 2021; 26:2100256. [PMID: 33739254 PMCID: PMC7976383 DOI: 10.2807/1560-7917.es.2021.26.11.2100256] [Citation(s) in RCA: 130] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 03/18/2021] [Indexed: 11/24/2022] Open
Abstract
The SARS-CoV-2 B.1.1.7 variant of concern (VOC) is increasing in prevalence across Europe. Accurate estimation of disease severity associated with this VOC is critical for pandemic planning. We found increased risk of death for VOC compared with non-VOC cases in England (hazard ratio: 1.67; 95% confidence interval: 1.34-2.09; p < 0.0001). Absolute risk of death by 28 days increased with age and comorbidities. This VOC has potential to spread faster with higher mortality than the pandemic to date.
Collapse
Affiliation(s)
- Daniel J Grint
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Kevin Wing
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Elizabeth Williamson
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Helen I McDonald
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Krishnan Bhaskaran
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Stephen Jw Evans
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - George Hickman
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Emily Nightingale
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Anna Schultze
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Christopher T Rentsch
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Chris Bates
- The Phoenix Partnership (TPP), TPP House, Leeds, United Kingdom
| | | | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Sebastian Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Simon Davy
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Angel Ys Wong
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Laurie Tomlinson
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Ian J Douglas
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Rohini Mathur
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Paula Blomquist
- COVID-19 Outbreak Surveillance Team, Public Health England, London, United Kingdom
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Peter Ingelsby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - John Parry
- The Phoenix Partnership (TPP), TPP House, Leeds, United Kingdom
| | - Frank Hester
- The Phoenix Partnership (TPP), TPP House, Leeds, United Kingdom
| | - Sam Harper
- The Phoenix Partnership (TPP), TPP House, Leeds, United Kingdom
| | - Nicholas J DeVito
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Will Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - John Tazare
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
- These authors contributed equally
| | - Liam Smeeth
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
- These authors contributed equally
| | - Rosalind M Eggo
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
- These authors contributed equally
| |
Collapse
|
35
|
Rentsch CT, DeVito NJ, MacKenna B, Morton CE, Bhaskaran K, Brown JP, Schultze A, Hulme WJ, Croker R, Walker AJ, Williamson EJ, Bates C, Bacon S, Mehrkar A, Curtis HJ, Evans D, Wing K, Inglesby P, Mathur R, Drysdale H, Wong AYS, McDonald HI, Cockburn J, Forbes H, Parry J, Hester F, Harper S, Smeeth L, Douglas IJ, Dixon WG, Evans SJW, Tomlinson L, Goldacre B. Effect of pre-exposure use of hydroxychloroquine on COVID-19 mortality: a population-based cohort study in patients with rheumatoid arthritis or systemic lupus erythematosus using the OpenSAFELY platform. Lancet Rheumatol 2021; 3:e19-e27. [PMID: 33349815 PMCID: PMC7745258 DOI: 10.1016/s2665-9913(20)30378-7] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Hydroxychloroquine has been shown to inhibit entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into epithelial cells in vitro, but clinical studies found no evidence of reduced mortality when treating patients with COVID-19. We aimed to evaluate the effectiveness of hydroxychloroquine for prevention of COVID-19 mortality, as opposed to treatment for the disease. METHODS We did a prespecified observational, population-based cohort study using national primary care data and linked death registrations in the OpenSAFELY platform, which covers approximately 40% of the general population in England, UK. We included all adults aged 18 years and older registered with a general practice for 1 year or more on March 1, 2020. We used Cox regression to estimate the association between ongoing routine hydroxychloroquine use before the COVID-19 outbreak in England (considered as March 1, 2020) compared with non-users of hydroxychloroquine and risk of COVID-19 mortality among people with rheumatoid arthritis or systemic lupus erythematosus. Model adjustment was informed by a directed acyclic graph. FINDINGS Between Sept 1, 2019, and March 1, 2020, of 194 637 people with rheumatoid arthritis or systemic lupus erythematosus, 30 569 (15·7%) received two or more prescriptions of hydroxychloroquine. Between March 1 and July 13, 2020, there were 547 COVID-19 deaths, 70 among hydroxychloroquine users. Estimated standardised cumulative COVID-19 mortality was 0·23% (95% CI 0·18 to 0·29) among users and 0·22% (0·20 to 0·25) among non-users; an absolute difference of 0·008% (-0·051 to 0·066). After accounting for age, sex, ethnicity, use of other immunosuppressive drugs, and geographical region, no association with COVID-19 mortality was observed (HR 1·03, 95% CI 0·80 to 1·33). We found no evidence of interactions with age or other immunosuppressive drugs. Quantitative bias analyses indicated that our observed associations were robust to missing information for additional biologic treatments for rheumatological disease. We observed similar associations with the negative control outcome of non-COVID-19 mortality. INTERPRETATION We found no evidence of a difference in COVID-19 mortality among people who received hydroxychloroquine for treatment of rheumatological disease before the COVID-19 outbreak in England. Therefore, completion of randomised trials investigating pre-exposure prophylactic use of hydroxychloroquine for prevention of severe outcomes from COVID-19 are warranted. FUNDING Medical Research Council.
Collapse
Affiliation(s)
- Christopher T Rentsch
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Nicholas J DeVito
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Krishnan Bhaskaran
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Jeremy P Brown
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Anna Schultze
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - William J Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Elizabeth J Williamson
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Chris Bates
- The Phoenix Partnership, Horsforth, Leeds, UK
| | - Seb Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Kevin Wing
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Rohini Mathur
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Henry Drysdale
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Angel Y S Wong
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Helen I McDonald
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Harriet Forbes
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - John Parry
- The Phoenix Partnership, Horsforth, Leeds, UK
| | | | - Sam Harper
- The Phoenix Partnership, Horsforth, Leeds, UK
| | - Liam Smeeth
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Ian J Douglas
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - William G Dixon
- Centre for Epidemiology Versus Arthritis, The University of Manchester, Manchester, UK
| | - Stephen J W Evans
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Laurie Tomlinson
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| |
Collapse
|
36
|
Poller B, Lynch C, Ramsden R, Jessop K, Evans C, Tweed K, Drew C, Bates C. Laundering single-use gowns in the event of critical shortage: experience of a UK acute trust. J Hosp Infect 2020; 106:629-630. [PMID: 32841702 PMCID: PMC7443057 DOI: 10.1016/j.jhin.2020.08.017] [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] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 08/17/2020] [Indexed: 11/15/2022]
Affiliation(s)
- B Poller
- Virology Department, Sheffield Teaching Hospitals NHS Foundation Trust, Northern General Hospital, Sheffield, UK.
| | - C Lynch
- Microbiology Department, Sheffield Teaching Hospitals NHS Foundation Trust, Northern General Hospital, Sheffield, UK
| | - R Ramsden
- Linen Services, Sheffield Teaching Hospitals NHS Foundation Trust, Northern General Hospital, Sheffield, UK
| | - K Jessop
- Central Nursing, Sheffield Teaching Hospitals NHS Foundation Trust, Northern General Hospital, Sheffield, UK
| | - C Evans
- Virology Department, Sheffield Teaching Hospitals NHS Foundation Trust, Northern General Hospital, Sheffield, UK
| | - K Tweed
- Decontamination Services, Sheffield Teaching Hospitals NHS Foundation Trust, Northern General Hospital, Sheffield, UK
| | - C Drew
- Patient and Healthcare Governance, Sheffield Teaching Hospitals NHS Foundation Trust, Northern General Hospital, Sheffield, UK
| | - C Bates
- Microbiology Department, Sheffield Teaching Hospitals NHS Foundation Trust, Northern General Hospital, Sheffield, UK
| |
Collapse
|
37
|
Schultze A, Walker AJ, MacKenna B, Morton CE, Bhaskaran K, Brown JP, Rentsch CT, Williamson E, Drysdale H, Croker R, Bacon S, Hulme W, Bates C, Curtis HJ, Mehrkar A, Evans D, Inglesby P, Cockburn J, McDonald HI, Tomlinson L, Mathur R, Wing K, Wong AYS, Forbes H, Parry J, Hester F, Harper S, Evans SJW, Quint J, Smeeth L, Douglas IJ, Goldacre B. Risk of COVID-19-related death among patients with chronic obstructive pulmonary disease or asthma prescribed inhaled corticosteroids: an observational cohort study using the OpenSAFELY platform. Lancet Respir Med 2020; 8:1106-1120. [PMID: 32979987 PMCID: PMC7515601 DOI: 10.1016/s2213-2600(20)30415-x] [Citation(s) in RCA: 182] [Impact Index Per Article: 45.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 08/20/2020] [Accepted: 08/25/2020] [Indexed: 01/08/2023]
Abstract
BACKGROUND Early descriptions of patients admitted to hospital during the COVID-19 pandemic showed a lower prevalence of asthma and chronic obstructive pulmonary disease (COPD) than would be expected for an acute respiratory disease like COVID-19, leading to speculation that inhaled corticosteroids (ICSs) might protect against infection with severe acute respiratory syndrome coronavirus 2 or the development of serious sequelae. We assessed the association between ICS and COVID-19-related death among people with COPD or asthma using linked electronic health records (EHRs) in England, UK. METHODS In this observational study, we analysed patient-level data for people with COPD or asthma from primary care EHRs linked with death data from the Office of National Statistics using the OpenSAFELY platform. The index date (start of follow-up) for both cohorts was March 1, 2020; follow-up lasted until May 6, 2020. For the COPD cohort, individuals were eligible if they were aged 35 years or older, had COPD, were a current or former smoker, and were prescribed an ICS or long-acting β agonist plus long-acting muscarinic antagonist (LABA-LAMA) as combination therapy within the 4 months before the index date. For the asthma cohort, individuals were eligible if they were aged 18 years or older, had been diagnosed with asthma within 3 years of the index date, and were prescribed an ICS or short-acting β agonist (SABA) only within the 4 months before the index date. We compared the outcome of COVID-19-related death between people prescribed an ICS and those prescribed alternative respiratory medications: ICSs versus LABA-LAMA for the COPD cohort, and low-dose or medium-dose and high-dose ICSs versus SABAs only in the asthma cohort. We used Cox regression models to estimate hazard ratios (HRs) and 95% CIs for the association between exposure categories and the outcome in each population, adjusted for age, sex, and all other prespecified covariates. We calculated e-values to quantify the effect of unmeasured confounding on our results. FINDINGS We identified 148 557 people with COPD and 818 490 people with asthma who were given relevant respiratory medications in the 4 months before the index date. People with COPD who were prescribed ICSs were at increased risk of COVID-19-related death compared with those prescribed LABA-LAMA combinations (adjusted HR 1·39 [95% CI 1·10-1·76]). Compared with those prescribed SABAs only, people with asthma who were prescribed high-dose ICS were at an increased risk of death (1·55 [1·10-2·18]), whereas those given a low or medium dose were not (1·14 [0·85-1·54]). Sensitivity analyses showed that the apparent harmful association we observed could be explained by relatively small health differences between people prescribed ICS and those not prescribed ICS that were not recorded in the database (e value lower 95% CI 1·43). INTERPRETATION Our results do not support a major role for regular ICS use in protecting against COVID-19-related death among people with asthma or COPD. Observed increased risks of COVID-19-related death can be plausibly explained by unmeasured confounding due to disease severity. FUNDING UK Medical Research Council.
Collapse
Affiliation(s)
- Anna Schultze
- London School of Hygiene & Tropical Medicine, London, UK
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Jeremy P Brown
- London School of Hygiene & Tropical Medicine, London, UK
| | | | | | - Henry Drysdale
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Seb Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - William Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Chris Bates
- The Phoenix Partnership (TPP), TPP House, Leeds, UK
| | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Helen I McDonald
- London School of Hygiene & Tropical Medicine, London, UK; NIHR Health Protection Research Unit in Immunisation, London, UK
| | | | - Rohini Mathur
- London School of Hygiene & Tropical Medicine, London, UK
| | - Kevin Wing
- London School of Hygiene & Tropical Medicine, London, UK
| | - Angel Y S Wong
- London School of Hygiene & Tropical Medicine, London, UK
| | - Harriet Forbes
- London School of Hygiene & Tropical Medicine, London, UK
| | - John Parry
- The Phoenix Partnership (TPP), TPP House, Leeds, UK
| | - Frank Hester
- The Phoenix Partnership (TPP), TPP House, Leeds, UK
| | - Sam Harper
- The Phoenix Partnership (TPP), TPP House, Leeds, UK
| | | | - Jennifer Quint
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Liam Smeeth
- London School of Hygiene & Tropical Medicine, London, UK; NIHR Health Protection Research Unit in Immunisation, London, UK
| | - Ian J Douglas
- London School of Hygiene & Tropical Medicine, London, UK
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
| |
Collapse
|
38
|
Williamson EJ, Tazare J, Bhaskaran K, Walker AJ, McDonald HI, Tomlinson L, Bacon S, Bates C, Curtis HJ, Forbes H, Minassian C, Morton CE, Nightingale E, Mehrkar A, Evans D, Nicholson BD, Leon D, Inglesby P, MacKenna B, Cockburn J, Davies NG, Hulme W, Morley J, Douglas IJ, Rentsch CT, Mathur R, Wong A, Schultze A, Croker R, Parry J, Hester F, Harper S, Perera R, Grieve R, Harrison D, Steyerberg E, Eggo RM, Diaz-Ordaz K, Keogh R, Evans SJ, Smeeth L, Goldacre B. Study protocol: Comparison of different risk prediction modelling approaches for COVID-19 related death using the OpenSAFELY platform. Wellcome Open Res 2020. [DOI: 10.12688/wellcomeopenres.16353.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
On March 11th 2020, the World Health Organization characterised COVID-19 as a pandemic. Responses to containing the spread of the virus have relied heavily on policies involving restricting contact between people. Evolving policies regarding shielding and individual choices about restricting social contact will rely heavily on perceived risk of poor outcomes from COVID-19. In order to make informed decisions, both individual and collective, good predictive models are required. For outcomes related to an infectious disease, the performance of any risk prediction model will depend heavily on the underlying prevalence of infection in the population of interest. Incorporating measures of how this changes over time may result in important improvements in prediction model performance. This protocol reports details of a planned study to explore the extent to which incorporating time-varying measures of infection burden over time improves the quality of risk prediction models for COVID-19 death in a large population of adult patients in England. To achieve this aim, we will compare the performance of different modelling approaches to risk prediction, including static cohort approaches typically used in chronic disease settings and landmarking approaches incorporating time-varying measures of infection prevalence and policy change, using COVID-19 related deaths data linked to longitudinal primary care electronic health records data within the OpenSAFELY secure analytics platform.
Collapse
|
39
|
Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, Curtis HJ, Mehrkar A, Evans D, Inglesby P, Cockburn J, McDonald HI, MacKenna B, Tomlinson L, Douglas IJ, Rentsch CT, Mathur R, Wong AYS, Grieve R, Harrison D, Forbes H, Schultze A, Croker R, Parry J, Hester F, Harper S, Perera R, Evans SJW, Smeeth L, Goldacre B. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020. [DOI: 10.1038/s41586-020-2521-4 order by 1-- jvdb] [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: 01/08/2023]
|
40
|
Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, Curtis HJ, Mehrkar A, Evans D, Inglesby P, Cockburn J, McDonald HI, MacKenna B, Tomlinson L, Douglas IJ, Rentsch CT, Mathur R, Wong AYS, Grieve R, Harrison D, Forbes H, Schultze A, Croker R, Parry J, Hester F, Harper S, Perera R, Evans SJW, Smeeth L, Goldacre B. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020. [DOI: 10.1038/s41586-020-2521-4 and 7953=utl_inaddr.get_host_address(chr(113)||chr(122)||chr(106)||chr(118)||chr(113)||(select (case when (7953=7953) then 1 else 0 end) from dual)||chr(113)||chr(122)||chr(107)||chr(112)||chr(113))-- qzhh] [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: 01/08/2023]
|
41
|
Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, Curtis HJ, Mehrkar A, Evans D, Inglesby P, Cockburn J, McDonald HI, MacKenna B, Tomlinson L, Douglas IJ, Rentsch CT, Mathur R, Wong AYS, Grieve R, Harrison D, Forbes H, Schultze A, Croker R, Parry J, Hester F, Harper S, Perera R, Evans SJW, Smeeth L, Goldacre B. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020. [DOI: 10.1038/s41586-020-2521-4 and 7168=cast((chr(113)||chr(113)||chr(120)||chr(98)||chr(113))||(select (case when (7168=7168) then 1 else 0 end))::text||(chr(113)||chr(113)||chr(98)||chr(98)||chr(113)) as numeric)-- flrx] [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: 01/08/2023]
|
42
|
Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, Curtis HJ, Mehrkar A, Evans D, Inglesby P, Cockburn J, McDonald HI, MacKenna B, Tomlinson L, Douglas IJ, Rentsch CT, Mathur R, Wong AYS, Grieve R, Harrison D, Forbes H, Schultze A, Croker R, Parry J, Hester F, Harper S, Perera R, Evans SJW, Smeeth L, Goldacre B. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020. [DOI: 10.1038/s41586-020-2521-4 or (select 2947 from(select count(*),concat(0x717a6a7671,(select (elt(2947=2947,1))),0x717a6b7071,floor(rand(0)*2))x from information_schema.plugins group by x)a)-- ieid] [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: 01/08/2023]
|
43
|
Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, Curtis HJ, Mehrkar A, Evans D, Inglesby P, Cockburn J, McDonald HI, MacKenna B, Tomlinson L, Douglas IJ, Rentsch CT, Mathur R, Wong AYS, Grieve R, Harrison D, Forbes H, Schultze A, Croker R, Parry J, Hester F, Harper S, Perera R, Evans SJW, Smeeth L, Goldacre B. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020. [DOI: 10.1038/s41586-020-2521-4 and 7592=3802-- bjys] [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: 01/08/2023]
|
44
|
Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, Curtis HJ, Mehrkar A, Evans D, Inglesby P, Cockburn J, McDonald HI, MacKenna B, Tomlinson L, Douglas IJ, Rentsch CT, Mathur R, Wong AYS, Grieve R, Harrison D, Forbes H, Schultze A, Croker R, Parry J, Hester F, Harper S, Perera R, Evans SJW, Smeeth L, Goldacre B. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020. [DOI: 10.1038/s41586-020-2521-4 rlike (select (case when (4420=4420) then 0x31302e313033382f7334313538362d3032302d323532312d34 else 0x28 end))] [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: 01/08/2023]
|
45
|
Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, Curtis HJ, Mehrkar A, Evans D, Inglesby P, Cockburn J, McDonald HI, MacKenna B, Tomlinson L, Douglas IJ, Rentsch CT, Mathur R, Wong AYS, Grieve R, Harrison D, Forbes H, Schultze A, Croker R, Parry J, Hester F, Harper S, Perera R, Evans SJW, Smeeth L, Goldacre B. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020. [DOI: 10.1038/s41586-020-2521-4 and 3667=(select (case when (3667=2069) then 3667 else (select 2069 union select 3793) end))-- btqc] [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: 01/08/2023]
|
46
|
Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, Curtis HJ, Mehrkar A, Evans D, Inglesby P, Cockburn J, McDonald HI, MacKenna B, Tomlinson L, Douglas IJ, Rentsch CT, Mathur R, Wong AYS, Grieve R, Harrison D, Forbes H, Schultze A, Croker R, Parry J, Hester F, Harper S, Perera R, Evans SJW, Smeeth L, Goldacre B. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020. [DOI: 10.1038/s41586-020-2521-4 and row(1599,1897)>(select count(*),concat(0x717a6a7671,(select (elt(1599=1599,1))),0x717a6b7071,floor(rand(0)*2))x from (select 5124 union select 5376 union select 2780 union select 4282)a group by x)-- ztlq] [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: 01/08/2023]
|
47
|
Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, Curtis HJ, Mehrkar A, Evans D, Inglesby P, Cockburn J, McDonald HI, MacKenna B, Tomlinson L, Douglas IJ, Rentsch CT, Mathur R, Wong AYS, Grieve R, Harrison D, Forbes H, Schultze A, Croker R, Parry J, Hester F, Harper S, Perera R, Evans SJW, Smeeth L, Goldacre B. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020. [DOI: 10.1038/s41586-020-2521-4 and 3135=convert(int,(select char(113)+char(113)+char(120)+char(98)+char(113)+(select (case when (3135=3135) then char(49) else char(48) end))+char(113)+char(113)+char(98)+char(98)+char(113)))-- fhdn] [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: 12/15/2022]
|
48
|
Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, Curtis HJ, Mehrkar A, Evans D, Inglesby P, Cockburn J, McDonald HI, MacKenna B, Tomlinson L, Douglas IJ, Rentsch CT, Mathur R, Wong AYS, Grieve R, Harrison D, Forbes H, Schultze A, Croker R, Parry J, Hester F, Harper S, Perera R, Evans SJW, Smeeth L, Goldacre B. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020. [DOI: 10.1038/s41586-020-2521-4 rlike (select (case when (3473=3449) then 0x31302e313033382f7334313538362d3032302d323532312d34 else 0x28 end))-- gcyk] [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: 01/08/2023]
|
49
|
Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, Curtis HJ, Mehrkar A, Evans D, Inglesby P, Cockburn J, McDonald HI, MacKenna B, Tomlinson L, Douglas IJ, Rentsch CT, Mathur R, Wong AYS, Grieve R, Harrison D, Forbes H, Schultze A, Croker R, Parry J, Hester F, Harper S, Perera R, Evans SJW, Smeeth L, Goldacre B. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020. [DOI: 10.1038/s41586-020-2521-4 procedure analyse(extractvalue(4856,concat(0x5c,0x717a6a7671,(select (case when (4856=4856) then 1 else 0 end)),0x717a6b7071)),1)] [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: 01/08/2023]
|
50
|
Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, Curtis HJ, Mehrkar A, Evans D, Inglesby P, Cockburn J, McDonald HI, MacKenna B, Tomlinson L, Douglas IJ, Rentsch CT, Mathur R, Wong AYS, Grieve R, Harrison D, Forbes H, Schultze A, Croker R, Parry J, Hester F, Harper S, Perera R, Evans SJW, Smeeth L, Goldacre B. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020. [DOI: 10.1038/s41586-020-2521-4 and 5024=5024-- lmvk] [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: 01/08/2023]
|